7,186 Matching Annotations
  1. Apr 2023
    1. Bacon's dictum regarding the proneness of the mind, in explanation, towards unity and simplicity, at no matter what sacrifice of material, has found no more striking exemplification than that offered in the fortunes of psychology. The least developed of the sciences, for a hundred years it has borne in its presentations the air of the one most completely finished. The infinite detail and complexity of the simplest psychical life, its interweavings with the physical organism, with the life of others in the social organism,-- created no special difficulty; and in a book like James Mill's Analysis we find every mental phenomenon not only explained, but explained by reference to one principle. That rich and colored experience, never the same in two nations, in two individuals, in two moments of the same life,-- whose thoughts, desires, fears, and hopes have furnished the material for the ever-developing literature of the ages, for a Homer and a Chaucer, a Sophocles and a Shakespeare, for the unwritten tragedies and comedies of daily life,-- was neatly and carefully dissected, its parts labeled and stowed away in their proper pigeon-holes, the inventory taken, and the whole stamped with the stamp of un fait accompli. Schematism was supreme, and the air of finality was over all. We know better now. We know that that life of man whose unfolding furnishes psychology its material is the most difficult and complicated subject which man can investigate. We have some consciousness of its ramifications and of its connections. We see that man is somewhat more than a neatly dovetailed psychical machine who may be taken as an isolated individual, laid on the dissecting table of analysis and duly anatomized. We know that his life is bound up with the life of society, of the nation in the ethos and nomos; we know that he is closely connected with all the past by the lines of education, tradition, and heredity; we know that man is indeed the microcosm who has gathered into himself the riches of the world, both of space and of time, the world physical and the world psychical. We know also of the complexities of the individual life. We know that our mental life is not a syllogistic sorites, but an enthymeme most of whose members are suppressed; that large tracts never come into consciousness; that those which do get into consciousness, are vague and transitory, with a meaning hard to catch and read; are infinitely complex, involving traces of the entire life history of the individual, or are vicarious, having significance only in that for which they stand; that psychical life is a continuance, having no breaks into "distinct ideas which are separate existences"; that analysis is but a process of abstraction, leaving us with a parcel of parts from which the "geistige Band" is absent; that our distinctions, however necessary, are unreal and largely arbitrary; that mind is no compartment box nor bureau of departmental powers; in short, that we know almost nothing about the actual activities and processes of the soul. We know that the old psychology gave descriptions of that which has for the most part no existence, and which at the best it but described and did not explain. I do not say this to depreciate the work of the earlier psychologists. There is no need to cast stones at those who, having a work to do, did that work well and departed. With Sir William Hamilton and J. Stuart Mill the school passed away. It is true that many psychologists still use their language and follow their respective fashions. Their influence, no doubt, is yet everywhere felt. But changed conditions are upon us, and thought, no more than revolution, goes backward. Psychology can live no better in the past than physiology or physics; but there is no more need for us to revile Hume and Reid for not giving birth to a full and complete science, than there is for complaining that Newton did not anticipate the physical knowledge of to-day, or Harvey the physiological. The work of the earlier psychologists bore a definite and necessary relation both to the scientific conditions and the times in which it was done. If they had recognized the complexity of the subject and attempted to deal with it, the science would never have been begun. The very condition of its existence was the neglect of the largest part of the material, the seizing of a few schematic ideas and principles, and their use for universal explanation. Very mechanical and very abstract to us, no doubt, seems their division of the mind into faculties, the classification of mental phenomena into the regular, graded, clear-cut series of sensation, image, concept, etc.; but let one take a look into the actual processes of his own mind, the actual course of the mental life there revealed, and he will realize how utterly impossible were the description, much more the explanation, of what goes on there, unless the larger part of it were utterly neglected, and a few broad schematic rubrics seized by which to reduce this swimming chaos to some semblance of order. Again, the history of all science demonstrates that much of its progress consists in bringing to light problems. Lack of consciousness of problems, even more than lack of ability to solve them, is the characteristic of the non-scientific mind. Problems cannot be solved till they are seen and stated, and the work of the earlier psychologists consisted largely in this sort of work. Further, they were filled with the Zeitgeist of their age, the age of the eighteenth century and the Aufklärung, which found nothing difficult, which hated mystery and complexity, which believed with all its heart in principles, the simpler and more abstract the better, and which had the passion of completion. By this spirit, the psychologists as well as the other thinkers of the day were mastered, and under its influence they thought and wrote. Thus their work was conditioned by the nature of science itself, and by the age in which they lived. This work they did, and left to us a heritage of problems, of terminology, and of principles which we are to solve, reject, or employ as best we may. And the best we can do is to thank them, and then go about our own work; the worst is to make them the dividing lines of schools, or settle in hostile camps according to their banners. We are not called upon to defend them, for their work is in the past; we are not called upon to attack them, for our work is in the future. It will be of more use briefly to notice some of the movements and tendencies which have brought about the change of attitude, and created what may be called the "New Psychology." Not the slightest of these movements has been, of course, the reaction of the present century, from the abstract, if clear, principles of the eighteenth, towards concrete detail, even though it be confused. The general failure of the eighteenth century in all but destructive accomplishment forced the recognition of the fact that the universe is not so simple and easy a matter to deal with, after all; that there are many things in earth, to say nothing of heaven, which were not dreamed of in the philosophy of clearness and abstraction, whether that philosophy had been applied along the lines of the state, society, religion, or science. The world was sated with system and longed for fact. The age became realistic. That the movement has been accompanied with at least temporary loss in many directions, with the perishing of ideals, forgetfulness of higher purpose, decay of enthusiasm, absorption in the petty, a hard contentedness in the present, or a cynical pessimism as to both present and future, there can be no doubt. But neither may it be doubted that the movement was a necessity to bring the Antæaus of humanity back to the mother soil of experience, whence it derives its strength and very life, and to prevent it from losing itself in a substanceless vapor where its ideals and purposes become as thin and watery as the clouds towards which it aspires. Out of this movement and as one of its best aspects came that organized, systematic, tireless study into the secrets of nature, which, counting nothing common or unclean, thought no drudgery beneath it, or rather thought nothing drudgery,-- that movement which with its results had been the great revelation given to the nineteenth century to make. In this movement psychology took its place, and in the growth of physiology which accompanied it I find the first if not the greatest occasion of the development of the New Psychology. It is a matter in every one's knowledge that, with the increase of knowledge regarding the structure and functions of the nervous system, there has arisen a department of science known as physiological psychology, which has already thrown great light upon psychical matters. But unless I entirely misapprehend the popular opinion regarding the matter, there is very great confusion and error in this opinion, regarding the relations of this science to psychology. This opinion, if I rightly gather it, is, that physiological psychology is a science which does, or at least claims to, explain all psychical life by reference to the nature of the nervous system. To illustrate: very many professed popularizers of the results of scientific inquiry, as well as laymen, seem to think that the entire psychology of vision is explained when we have a complete knowledge of the anatomy of the retina, of its nervous connection with the brain, and of the centre in the latter which serves for visual functions; or that we know all about memory if we can discover that certain brain cells store up nervous impressions, and certain fibres serve to connect these cells,-- the latter producing the association of ideas, while the former occasion their reproduction. In short, the commonest view of physiological psychology seems to be that it is a science which shows that some or all of the events of our mental life are physically conditioned upon certain nerve-structures, and thereby explains these events. Nothing could be further from the truth. So far as I know, all the leading investigators clearly realize that explanations of psychical events, in order to explain, must themselves be psychical and not physiological. However important such knowledge as that of which we have just been speaking may be for physiology, it has of itself no value for psychology. It tells simply what and how physiological elements serve as a basis for psychical acts; what the latter are, or how they are to be explained, it tells us not at all. Physiology can no more, of itself, give us the what, why, and how of psychical life, than the physical geography of a country can enable us to construct or explain the history of the nation that has dwelt within that country. However important, however indispensable the land with all its qualities is as a basis for that history, that history itself can be ascertained and explained only through historical records and historic conditions. And so psychical events can be observed only through psychical means, and interpreted and explained by psychical conditions and facts. What can be meant, then, by saying that the rise of this physiological psychology has produced a revolution in psychology? This: that it has given a new instrument, introduced a new method,-- that of experiment, which has supplemented and corrected the old method of introspection. Psychical facts still remain psychical, and are to be explained through psychical conditions; but our means of ascertaining what these facts are and how they are conditioned have been indefinitely widened. Two of the chief elements of the method of experiment are variation of conditions at the will and under the control of the experimenter, and the use of quantitative measurement. Neither of these elements can be applied through any introspective process. Both may be through physiological psychology. This starts from the well-grounded facts that the psychical events known as sensations arise through bodily stimuli, and that the psychical events known as volitions result in bodily movements; and it finds in these facts the possibility of the application of the method of experimentation. The bodily stimuli and movements may be directly controlled and measured, and thereby, indirectly, the psychical states which they excite or express. There is no need at this day to dwell upon the advantages derived in any science from the application of experiment. We know well that it aids observation by indefinitely increasing the power of analysis and by permitting exact measurement, and that it equally aids explanation by enabling us so to vary the constituent elements of the case investigated as to select the indispensable. Nor is there need to call attention to the especial importance of experiment in a science where introspection is the only direct means of observation. We are sufficiently aware of the defects of introspection. We know that it is limited, defective, and often illusory as a means of observation, and can in no way directly explain. To explain is to mediate; to connect the given fact with an unseen principle; to refer the phenomenon to an antecedent condition,-- while introspection can deal only with the immediate present, with the given now. This is not the place to detail the specific results accomplished through this application of experiment to the psychological sphere; but two illustrations may perhaps be permitted: one from the realm of sensation, showing how it has enabled us to analyze states of consciousness which were otherwise indecomposable; and the other from that of perception, showing how it has revealed processes which could be reached through no introspective method. It is now well known that no sensation as it exists in consciousness is simple or ultimate. Every color sensation, for example, is made up by at least three fundamental sensory quales, probably those of red, green, and violet; while there is every reason to suppose that each of these qualities, far from being simple, is compounded of an indefinite number of homogeneous units. Thus the simplest musical sensation has also been experimentally proved to be in reality not simple, but doubly compound. First, there is the number of qualitatively like units constituting it which occasion the pitch of the note, according to the relations of time in which they stand to each other; and second, there is the relation which one order of these units bears to other secondary orders, which gives rise to the peculiar timbre or tone-color of the sound; while in a succession of notes these relations are still further complicated by those which produce melody and harmony. And all this complexity occurs, be it remembered, in a state of consciousness which, to introspection, is homogeneous and ultimate. In these respects physiology has been to psychology what the microscope is to biology, or analysis to chemistry. But the experimental method has done more than reveal hidden parts, or analyze into simpler elements. It has aided explanation, as well as observation, by showing the processes which condition a psychical event. This is nowhere better illustrated than in visual perception. It is already almost a commonplace of knowledge that, for example, the most complex landscape which we can have before our eyes, is, psychologically speaking, not a simple ultimate fact, nor an impression stamped upon us from without, but is built up from color and muscular sensations, with, perhaps, unlocalized feelings of extension, by means of the psychical laws of interest, attention, and interpretation. It is, in short, a complex judgment involving within itself emotional, volitional, and intellectual elements. The knowledge of the nature of these elements, and of the laws which govern their combination into the complex visual scene, we owe to physiological psychology, through the new means of research with which it has endowed us. The importance of such a discovery can hardly be overestimated. In fact, this doctrine that our perceptions are not immediate facts, but are mediated psychical processes, has been called by Helmholtz the most important psychological result yet reached. But besides the debt we owe Physiology for the method of experiment, is that which is due her for an indirect means of investigation which she has put within our hands; and it is this aspect of the case which has led, probably, to such misconceptions of the relations of the two sciences as exist. For while no direct conclusions regarding the nature of mental activities or their causes can be drawn from the character of nervous structure or function, it is possible to reason indirectly from one to the other, to draw analogies and seek confirmation. That is to say, if a certain nervous arrangement can be made out to exist, there is always a strong presumption that there is a psychical process corresponding to it; or if the connection between two physiological nerve processes can be shown to be of a certain nature, one may surmise that the relation between corresponding psychical activities is somewhat analogous. In this way, by purely physiological discoveries, the mind may be led to suspect the existence of some mental activity hitherto overlooked, and attention directed to its workings, or light may be thrown on points hitherto obscure. Thus it was, no doubt, the physiological discovery of the time occupied in transmission of a nervous impulse that led the German psychologists to their epoch-making investigations regarding the time occupied in various mental activities; thus, too, the present psychological theories regarding the relation of the intellectual and volitional tracts of minds were undoubtedly suggested and largely developed in analogy with Bell's discovery of the distinct nature of the sensory and motor nerves. Again, the present theory that memory is not a chamber hall for storing up ideas and their traces or relies, but is lines of activity along which the mind habitually works, was certainly suggested from the growing physiological belief that the brain cells which form the physical basis of memory do not in any way store up past impressions or their traces, but have, by these impressions, their structure so modified as to give rise to a certain functional mode of activity. Thus many important generalizations might be mentioned which were suggested and developed in  analogy with physiological discoveries. The influence of biological science in general upon psychology has been very great. Every important development in science contributes to the popular consciousness, and indeed to philosophy, some new conception which serves for a time as a most valuable category of classification and explanation. To biology is due the conception of organism. Traces of the notion are found long before the great rise of biological science, and, in particular, Kant has given a complete and careful exposition of it; but the great rôle which the "organic" conception has played of late is doubtless due in largest measure to the growth of biology. In psychology this conception has led to the recognition of mental life as an organic unitary process developing according to the laws of all life, and not a theatre for the exhibition of independent autonomous faculties, or a rendezvous in which isolated, atomic sensations and ideas may gather, hold external converse, and then forever part. Along with this recognition of the solidarity of mental life has come that of the relation in which it stands to other lives organized in society. The idea of environment is a necessity to the idea of organism, and with the conception of environment comes the impossibility of considering psychical life as an individual, isolated thing developing in a vacuum. This idea of the organic relation of the individual to that organized social life into which he is born, from which he draws his mental and spiritual sustenance, and in which he must perform his proper function or become a mental and moral wreck, forms the transition to the other great influence which I find to have been at work in developing the New Psychology. I refer to the growth of those vast and as yet undefined topics of inquiry which may be vaguely designated as the social and historical sciences,-- the sciences of the origin and development of the various spheres of man's activity. With the development of these sciences has come the general feeling that the scope of psychology has been cabined and cramped till it has lost all real vitality, and there is now the recognition of the fact that all these sciences possess their psychological sides, present psychological material, and demand treatment and explanation at the hands of psychology. Thus the material for the latter, as well as its scope, have been indefinitely extended. Take the matter of language. What a wealth of material and of problems it offers. How did it originate; was it contemporaneous with that of thought, or did it succeed it; how have they acted and reacted upon each other; what psychological laws have been at the basis of the development and differentiation of languages, of the development of their structure and syntax, of the meaning of words, of all the rhetorical devices of language. Any one at all acquainted with modern discussions of language will recognize at a glance that the psychological presentation and discussion of such problems is almost enough of itself to revolutionize the old method of treating psychology. In the languages themselves, moreover, we have a mine of resources, which, as a record of the development of intelligence, can be compared only to the importance of the paleontological record to the student of animal and vegetable life. But this is only one aspect, and not comparatively a large one, of the whole field. Folk-lore and primitive culture, ethnology and anthropology, all render their contributions of matter, and press upon us the necessity of explanation. The origin and development of myth, with all which it includes, the relation to the nationality, to language, to ethical ideas, to social customs, to government and the state, is itself a psychological field wider than any known to the previous century. Closely connected with this is the growth of ethical ideas, their relations to the consciousness and activities of the nation in which they originate, to practical morality, and to art. Thus I could go through the various spheres of human activity, and point out how thoroughly they are permeated with psychological questions and material. But it suffices to say that history in its broadest aspect is itself a psychological problem, offering the richest resources of matter. Closely connected with this, and also influential in the development of the New Psychology, is that movement which may be described as the commonest thoughts of everyday life in all its forms, whether normal or abnormal. The cradle and the asylum are becoming the laboratory of the psychologist of the latter half of the nineteenth century. The study of children's minds, the discovery of their actual thoughts and feelings from babyhood up, the order and nature of the development of their mental life and the laws governing it, promises to be a mine of greatest value. When it was recognized that insanities are neither supernatural interruptions nor utterly inexplicable "visitations," it gradually became evident that they were but exaggerations of certain of the normal workings of the mind, or lack of proper harmony and co-ordination among these workings; and thus another department of inquiries, of psychical experiments performed by nature, was opened to us, which has already yielded valuable results. Even the prison and the penitentiary have made their contributions. If there be any need of generalizing the foregoing, we may say that the development of the New Psychology has been due to the growth, on the one hand, of the science of physiology, giving us the method of experiment, and, on the other, of the sciences of humanity in general, giving us the method of objective observation, both of which indefinitely supplement and correct the old method of subjective introspection. So much for the occasioning causes and method of the New Psychology. Are its results asked for? It will be gathered, from what has already been said, that its results cannot be put down in black and white like those of a mathematical theory. It is a movement, no system. But as a movement it has certain general features. The chief characteristic distinguishing it from the old psychology is undoubtedly the rejection of a formal logic as its model and test. The old psychologists almost without exception held to a nominalistic logic. This of itself were a matter of no great importance, were it not for the inevitable tendency and attempt to make living concrete facts of experience square with the supposed norms of an abstract, lifeless thought, and to interpret them in accordance with its formal conceptions. This tendency has nowhere been stronger than in those who proclaimed that "experience" was the sole source of all knowledge. They emasculated experience till their logical conceptions could deal with it; they sheared it down till it would fit their logical boxes; they pruned it till it presented a trimmed tameness which would shock none of their laws; they preyed upon its vitality till it would go into the coffin of their abstractions. And neither so-called "school" was free from this tendency. The two legacies of fundamental principles which Hume left, were: that every distinct idea is a separate existence, and that every idea must be definitely determined in quantity and quality. By the first he destroyed all relation but accident; by the second he denied all universality. But these principles are framed after purely logical models; they are rather the abstract logical principles of difference and identity, of A is A and A is not B, put in the guise of a psychological expression. And the logic of concrete experience, of growth and development, repudiates such abstractions. The logic of life transcends the logic of nominalistic thought. The reaction against Hume fell back on certain ultimate, indecomposable, necessary first truths immediately known through some mysterious simple faculty of the mind. Here again the logical model manifests itself. Such intuitions are not psychological; they are conceptions bodily imported from the logical sphere. Their origin, tests, and character are all logical. But the New Psychology would not have necessary truths about principles; it would have the touch of reality in the life of the soul. It rejects the formalistic intuitionalism for one which has been well termed dynamic. It believes that truth, that reality, not necessary beliefs about reality, is given in the living experience of the soul's development. Experience is realistic, not abstract. Psychical life is the fullest, deepest, and richest manifestation of this experience. The New Psychology is content to get its logic from this experience, and not do violence to the sanctity and integrity of the latter by forcing it to conform to certain preconceived abstract ideas. It wants the logic of fact, of process, of life. It has within its departments of knowledge no psycho-statics, for it can nowhere find spiritual life at rest. For this reason, it abandons all legal fiction of logical and mathematical analogies and rules; and is willing to throw itself upon experience, believing that the mother which has borne it will not betray it. But it makes no attempts to dictate to this experience, and tell it what it must be in order to square with a scholastic logic. Thus the New Psychology bears the realistic stamp of contact with life. From this general characteristic result most of its features. It has already been noticed that it insists upon the unity and solidarity of psychical life against abstract theories which would break it up into atomic elements or independent powers. It lays large stress upon the will; not as an abstract power of unmotivated choice, nor as an executive power to obey the behests of the understanding, the legislative branch of the psychical government, but as a living bond connecting and conditioning all mental activity. It emphasizes the teleological element, not in any mechanical or external sense, but regarding life as an organism in which immanent ideas or purposes are realizing themselves through the development of experience. Thus modern psychology is intensely ethical in its tendencies. As it refuses to hypostatize abstractions into self-subsistent individuals, and as it insists upon the automatic spontaneous elements in man's life, it is making possible for the first time an adequate psychology of man's religious nature and experience. As it goes into the depths of man's nature it finds, as stone of its foundation, blood of its life, the instinctive tendencies of devotion, sacrifice, faith, and idealism which are the eternal substructure of all the struggles of the nations upon the altar stairs which slope up to God. It finds no insuperable problems in the relations of faith and reason, for it can discover in its investigations no reason which is not based upon faith, and no faith which is not rational in its origin and tendency. But to attempt to give any detailed account of these features of the New Psychology would be to go over much of the recent discussions of ethics and theology. We can conclude only by saying that, following the logic of life, it attempts to comprehend life.

      The thing I derived of this article is that we must understand the past to progress without developing what has been established for us we will fail as a society if there is not a evaluation of the past before doing trying to further psychological breakthrough.

    1. In Russian, by the word krasota (beauty) we mean only that which pleases the sight. And though latterly people have begun to speak of “an ugly deed,” or of “beautiful music,” it is not good Russian. A Russian of the common folk, not knowing foreign languages, will not understand you if you tell him that a man who has given his last coat to another, or done anything similar, has acted “beautifully,” that a man who has cheated another has done an “ugly” action, or that a song is “beautiful.” In Russian a deed may be kind and good, or unkind and bad. Music may be pleasant and good, or unpleasant and bad; but there can be no such thing as “beautiful” or “ugly” music.

      What do you think about this, Meliora students? How much do you think language influences our perception of "beauty" or "art?" Which came first? Find an example of a linguist's interpretation and summarize it.

    1. Diversity as a term stands for the differences that exist among all individuals in a society and then workplaces and other smaller settings. These differences are based on race, religion, ethnicity, age, nationality, political perspectives, and religious commitments. They also include distinct views, values, and ideas. A diverse workspace includes people with varying characteristics and beliefs in an equal and respective manner. Many leaders tend to think that implementing diversity in their company is a challenging task. However, they fail to understand that the benefits are well worth the effort.

      Diversity celebrates uniqueness, stories, perspectives, histories, etc...

      Equity recognizes that every person has different starting point- some of us start with advantages, others may have started with disadvantages

      Inclusiveness... is a result of Diversity + Equity. When the effort is made to celebrate our special sauce AND make room at the tables we sit at, THEN we've got an inclusive workplace

      Either end of the spectrum is either tokenization or erasure.

    1. Author Response

      Reviewer #1 (Public Review):

      The authors of this manuscript aimed to systematically evaluate the pleiotropic effects of MCR-1-mediated colistin resistance. They evaluated the effect of MCR-1 and MCR-3 carried on different plasmids on antimicrobial peptides (AMPs) and assessed their ultimate effect on virulence. The authors find that MCR-1-mediated colistin resistance correlates with increased resistance against some host AMPs, but also increased sensitivity to others. The authors also find that MCR-1 alone is associated with resistance to human serum and to elements of the complement system. This highlights a potential selective advantage for MCR-1-mediated resistance to host immune factors and a potential for enhanced virulence.

      The methods have been well established before and adequately support their main findings. While determining the role of MCR-1 in a single genetic background is important to better understand its potential pleiotropic effects against a diversity of AMPs and in a variety of scenarios, the impact and significance of the results are partially ameliorated because different genetic backgrounds, particularly those most relevant to a clinical (or agricultural) context were not considered. The results depicted here are still a necessary and important step towards a more comprehensive understanding of the pleiotropic effects of MCR-1. But, interactions between plasmids and host genomes and their co-evolution can have important effects more generally. The authors do mention this in the discussion and suggest it to be an important avenue for future work. However, given the objective of the study and the clinical and agricultural context in which the authors have framed their work, it seems more relevant to include those distinct genetic backgrounds already here.

      The conclusions stemming from the results found in Figure 3, and Figures 4c and d seem too overreaching to me. The associated resistance to AMPs from pigs seems to be only strong enough against one of the five tested AMPs and hence concluding that these impose a strong selective pressure in the pig's gut seems unsubstantiated. Similarly, the difference in survival probability within their in vivo system, though statistically significant, seems to be very ild between their MCR-1 and empty vector control.

      Thank you for the comment. We agree on the effect of MCR-MOR on AMP susceptibility and have edited the paragraph by removing the lines on strong selective pressure in the pig gut. As regards the 4c and 4d results (4e and 4f in the revised version), it is interesting and statistically convincing that MCR increases bacterial virulence despite the cost of MCR expression. And importantly, this effect is even stronger in the case of LPS treatment where the immune system is stimulated, expressing diverse host AMPs (PMID: 19897755). This shows MCR-mediated advantages to bacteria in the complex host environment.

      Reviewer #2 (Public Review):

      Jangir et al test the hypothesis that resistance to the antimicrobial peptide (AMP) colistin can simultaneously increase resistance to other AMPS with related modes of action. Because AMPS comprise part of innate immunity, their central concern is that colistin resistance may compromise host defenses and thereby increase bacterial virulence. Their results show that MCR-1, whether expressed from naturally circulating or synthetic plasmids, can increase the MIC to AMPS from humans, pigs, and chickens, and impart fitness benefits at sub-MIC concentrations. In addition, they find that MCR-1-containing strains have increased survival in human plasma and are more lethal in an insect infection model.

      The conclusions of the paper are generally well supported by the results, but some aspects could be clearer and better defended with a few small additional experiments.

      Strengths:

      Using both synthetic and natural plasmids makes it possible to cleanly separate the effects of MCR-1 from the effects of other plasmid-borne genes or plasmid copy numbers. This helps confirm the causal role of MCR-1 on altered AMP susceptibility.

      Testing the survival of transformed isolates in human serum and in insects points to relevance in the more immunologically complex host environment where cells are exposed to a suite of factors that reduce bacterial survival.

      Thank you!

      Weaknesses/suggestions:

      Although increases in MIC are evident for different AMPS, the effects are generally modest. To address this, it might be helpful to use pairwise competition assays, as in Figure 1, to establish that even small changes to MIC are associated with clear selective benefits.

      Thank you for the suggestion. We agree that in some cases the change in MIC is modest, however, we would like to highlight that small-level changes in resistance have important clinical implications. For example, resistance mutations conferring a small change in MIC can ensure the survival of pathogenic bacteria in antibiotic-treated hosts (PMID: 30131514). Additionally, a comparison between competition assays (Fig 1) and MICs (Fig 2) clearly shows that small changes in MIC are associated with substantial fitness benefits. For example, for pSEVA:MCR-1, the fold change in MIC of CATH2 (chicken), PMAP23 (pig), and LL37 (human) ranges between 1.05 and 1.5, however, the competitive fitness ranges from 10% to 17%. This issue is discussed in the revised manuscript (lines 306-317, page 13)

      ….This would be especially helpful in assays with human serum and in Galleria where the concentrations of AMPS or other immune components are unknown.

      It is clear that MCR-1 increases resistance to serum and virulence (Figure 4). However, we agree with the reviewer that the selective benefits of MCR-1 in complex host environments are not known (i.e., serum or Galleria). We have revised the final paragraph of the discussion to reflect this limitation of our study (lines 370-382, page 15).

      Assays using human serum are interesting but challenging to interpret given the diverse causes of bacterial killing, including complement. Although this was partly addressed in Supplementary Figure 6, I found the predictions of these experiments unclear. First, I think these experiments are too central to be relegated to the supplemental materials; they belong in the main text. Secondly, it is important to explicitly spell out the expectations of using heat-killed serum (which will degrade any heat-labile components) or complement-deficient serum. It should be clearer under which conditions MCR-1-containing strains are predicted to do better or worse than controls.

      We have addressed this in the revised version. We have moved Supplementary Fig 6 to the main text, and have edited the text, clarifying the model prediction (lines 245-257, page 10).

      Galleria is a useful infection model for virulence, but it is unclear what drives differences between strains. First, bacterial numbers aren't measured in this assay, so it isn't known if increased virulence is due to increased bacterial growth or decreased bacterial clearance. As above, I think these assays would be stronger using the competition-based approach in Figure 1. This would indicate bacterial numbers through time and directly show the selective benefit associated with MCR-1. Second, it would be useful to elaborate on why MCR-1 increases virulence, especially any known similarities between Galleria AMPS and those tested in Figures 1 and 2. Overall, it would help if Galleria were less of a black box.

      We agree that the mechanism underlying increased virulence remains to be explored and thus, we have already discussed this in the discussion as a limitation (lines, 370-382, page 15). However, elucidating the mechanisms by which MCR-1 increases virulence would clearly be an interesting line of research moving forward.

    1. Author Response

      Reviewer #1 (Public Review):

      In this manuscript, Sampaio et al. tackle the role of fluid flow during left-right axis symmetry breaking. The left-right axis is broken in the left-right organiser (LRO) where cilia motility generates a directional flow that permit to dictate the left from the right embryonic side. By manipulating the fluid moved by cilia in zebrafish, the authors conclude that key symmetry breaking event occurs within 1 hour through a mechanosensory process.

      Overall, while the study undeniably represents a huge amount of work, the conclusions are not sufficiently backed up by the experiments. Furthermore, the results provided present a limited advance to the field: the transient activity of the LRO is well established, and narrowing down this activity to 1 hour (even though unclear from the presented data that it is a valid conclusion) does not help to understand better the mechanism of symmetry breaking.

      We thank the reviewer1 for acknowledging the hard experimental set up. However, we must argue that knowing the exact timing that is more sensitive to fluid flow manipulations is a very important advance we provide here. The reason is because this type of experiment is giving us the physiological timing in a WT embryo. It is one thing to know the system can respond to optical tweezers earlier than 5 ss and later than 5 ss, as Yuan lab did recently, but quite another to constrain the physiological timing at which the process occurs in an unperturbed manner (as much as possible). Our aim was the latter. Our rationale is that knowing the physiological time is important to provide clues, for example we had these types of questions at the time: is the physiological time before or after cell rearrangements occur? is it falling in a directional or non-directional flow regime? Is it governed by a mild flow or stronger one? Is it before or after dand5 becomes asymmetric? Some of these questions that we think we all know the answers for, could be challenged by our experiments… so it is indeed very important to not assume we know the answer, and ask the question again in an unbiased way with every new technique available! We wanted to be unbiased, and we think that is the beauty of our time-window experiment. Indeed, it shows the physiological time-window peaks at 5 ss which is later than Yuan’s lab calcium transient recording and before dand5 asymmetric expression. In our opinion this is compatible and makes perfect sense because although the system already shows calcium transients before and can respond to lack of Pkd2 or optical tweezer cilia manipulations at 1 ss – 3 ss, it is from 4 to 6 ss, peaking at 5 ss, that it is most responsive physiologically to the fluid extraction and therefore both mechanical and chemical perturbations.

      We have made additional experiments and used smFISH on WT embryos for detecting dand5 expression with cellular resolution, and we have quantified asymmetries in dand5 number of transcripts as early as 6 ss (new Figure 7 and new author: Catarina Bota) that further support our time-window claim. Degradation of dand5 mRNA has been the mechanism suggested to be at the base of the asymmetric dand5 expression, which is usually a very fast mechanism. This new piece of evidence supports that the physiological breaking of symmetry is stronger around 5 ss. (see new discussion on this subject on page 27).

      Regarding the symmetry breaking. The fact that anterior angular velocity was the major difference between embryos that recovered without LR defects versus those that did not, reveals that angular velocity must be tightly regulated by cilia motility and CFTR activity to bring back fluid and flow directionality, which together confer the robustness of flow. This is now better explained in the manuscript. We agree that the novelty regarding angular velocity may seem incremental compared to our work from 2014, where we only analyzed speed (Sampaio et al, 2014). However, here we provided more resolution and detailed parameters of angular velocity per sections of the LRO as well as tangential and radial velocities, the components of angular velocity. The Radial component shows a trend towards left anterior that is now discussed in the text as evidence for a left difference. The present work shows that anterior angular velocity has a major role in the successful recovery of the symmetry breaking process, which was not claimed before. Here we challenged the embryo to bring to light the most important parameters.

      Importantly, the authors do not provide any convincing experiments to back up the mechanosensory hypothesis because the fluid extraction experiments affect both the chemical and physical features of the LRO, so it is impossible to disentangle the two with this approach.

      We agree the first extraction experiment (Figures 1-3 and Table 1) affects both mechanisms and does not disentangle them, and that was, in fact, our goal for the first experiment - the finding of the exact time-window for symmetry breaking. However, in the second part of the work (Figures 4-5 and Table 2) we provide a 20,000 times dilution experiment, this dilution experiment is very different than the extraction one. We apologize if this was not clear and hope to have made it clear this time.

      We must agree with the reviewer that chemosensing is not excluded, in fact we had provided a paragraph in the discussion about EV secretion rates to tone down our claim and did acknowledge that secretion could still overcome the dilution we are causing. We think we had already addressed this problem in the previous eLife manuscript but now we have discussed the possibilities and the experimental evidence that supports each of them (see page 28, last paragraph). The key experiment that does not fit with secretion is pointed out in the end, and we ask the reviewer to read it in the context of wildtype animals. We agree both scenarios must be discussed and leave space for future data on mmp21 and CIROP. However, so far, in zebrafish we cannot favor chemosensing as much as mechanosensing, we can only wait for more discoveries and be open.

    1. Author Response

      Reviewer #1 (Public Review):

      The model put forward by the authors in this manuscript is a simple and exciting one, explaining the function of AGS3 as a negative regulator of LGN, acting as a 'dominant-negative' version of LGN. Overall, the results support the model very well, and the results shown in Fig 6, which clearly reveal the functional relevance of AGS3, add strength to the paper.

      We thank the reviewer for their enthusiasm regarding our finding that AGS3 acts as an endogenous dominant-negative to inhibit LGN. We appreciate their assertion that the results support the model and that the functional relevance to epidermal stratification is a strength.

      In Figures 3A and B, the authors claim that AGS3 overexpression leads to depolarization of LGN in epidermal stem cells. However, in the example provided in Figure 3A, the LGN signal appears to be stronger than the control, with more LGN still on the apical side (many would categorize this as 'apically polarized'). In the scoring shown in Figure 3B, I am not sure if 'eyeballing' is the right way to decide whether it is polarized/depolarized/absent. The authors should come up with a bit more quantitative method to quantify the localization/amount of LGN and explain the method well in the manuscript. A similar concern regarding the determination of the LGN localization pattern applies to the rest of figure 3 as well.

      We agree with this important critique about the methodology used to assess LGN expression patterns. While we have historically included categorical analyses like those used in Fig. 3A,B in past publications (Williams et al, NCB 2014; Lough et al eLife, 2019), we have also now performed additional, unbiased, quantitative measures of LGN fluorescent intensity, as described in greater detail above. We added these new data in Fig. 4C-J, while the data previously in Fig. 3A,B have now been redistributed between Fig. 3E,F (overexpression) and Fig. 4A,B (knockdown).

      Reviewer #2 (Public Review):

      To date, only a handful of studies have addressed the importance of AGS3, a paralog of the relatively well-characterized spindle orientation factor LGN. The authors now show that AGS3 acts as a negative regulator of LGN and propose that this activity could work through competition for binding partner(s). Remarkably, regulation is temporally restricted in such a way that the conserved role played by LGN in metaphase spindle orientation is unaffected. Instead, AGS3 regulates a post-metaphase function for LGN, namely Telophase Correction. The article is well-written, the experiments are performed at a high level, and the claims are generally supported by the data. Two main points of confusion are raised in the current version. 1) The authors show that AGS3 regulates cortical localization of LGN, but would need to clarify how LGN is being affected. 2) The authors propose in the discussion that AGS3 might exert its regulatory effect through competition for NuMA, an important binding partner for LGN, but would need to clarify how and why NuMA would be involved in Telophase Correction.

      We thank the reviewer for appreciating the novelty of our findings regarding the understudied LGN/pins paralog AGS3. In regards to the first point, as described earlier, we have added additional quantitative analyses of how AGS3 affects cortical LGN fluorescent intensity in Fig. 4C-J. We now show that AGS3 loss leads to broader and higher expression levels throughout mitosis, and therefore we have amended our model to soften the claim that AGS3 primarily operates during telophase correction. This renders the second point somewhat moot, but we nonetheless have expanded our Discussion to note that NuMA can be cortically recruited to the anaphase cortex independent of LGN (lines 531-542). We also contextualize our findings with the Reviewer’s own recent study which proposes a “threshold model” of cortical Insc as a determinant of spindle orientation (Neville et al, 2023), and speculate that a similar model could apply in our system, perhaps with AGS3 binding and sequesting Insc rather than NuMA (lines 543-556).

      Reviewer #3 (Public Review):

      This paper examines the mechanisms that control division orientation in the basal layers of the epidermis. Previous work established LGN as a key promoter of divisions where one of the siblings populates the differentiated layers (perpendicular). This work addresses two important, related issues - the mechanisms that determine whether a particular division is planar vs perpendicular, and the function of AGS3, and LGN paralog that has been enigmatic. A central finding is that AGS3 is required for the normal distribution of planar and perpendicular divisions (roughly equal) such that in its absence the distribution is skewed towards the perpendicular. Interestingly, however, the authors find that AGS3 has no detectable effect on orientation if the orientation is measured at anaphase. This timing aspect builds upon previous work from this group demonstrating a phenomenon they term "telophase correction" in which the orientation changes at the latest phases of division (and possibly post division?). Thus AGS3 seems to exert its effect using these later mechanisms and this is supported by further analysis by the authors. Importantly, the authors show that AGS3 acts through LGN, based on localization data and an epistasis analysis. The function of AGS3 has been highly enigmatic so resolving this issue while providing a useful step towards understanding how the division orientation decision is made, makes for exciting progress towards an important problem. I found the overall narrative and presentation to be quite good and especially appreciated the thoughtful discussion section that did an excellent job of putting the results in context and speculating how unknown aspects of the mechanism might work based on current clues. With that said, I think there are some important issues that should be resolved.

      We thank the Reviewer for this excellent summary of our findings and appreciation of the significance of the issues that our study addresses.

      Regarding the orientation measurements, the authors should specify how the midbody marker was used to mark sibling cells, especially given the midbody can move following division. For example, how can the authors be confident that the siblings in the middle panel of 1A are correct and not an adjacent cell? Regarding quantification, it would be useful for the authors to comment on how the following would influence their measurements: 1) movements along the z-axis, and 2) movement of the nucleus within the cell

      We have used this methodology for over a decade, and while it is not flawless, we have included several safeguards to ensure that sibling cells are correctly identified. We have added additional details to the Methods section (lines 867-869, 873-879).

      A similar question is how much telophase correction really happens in telophase. How confident are the authors that the process actually occurs during division and not subsequent to it? What is drawn in their previous paper and in Figure 7A implies that post-division movements may be important. It would be useful for the authors to comment on whether they can make the distinction and whether or not it might be important.

      Our intent in coining the term “telophase correction” was to imply that this process initiates, rather than completes, during telophase. We apologize for this confusion and have clarified this in the text (lines 80-82). Since most mammalian cells complete M phase in ~1h, with the longest time spent in prophase, in the absence of direct evidence to the contrary, it may be prudent to assume that telophase, like metaphase and anaphase, is relatively short, on the order of minutes. Since we cannot directly observe reformation of the nuclear membrane in our movies, we cannot be sure when telophase ends. Likewise, we do not currently have a suitable marker of the spindle midbody for live-imaging, so cannot be sure when cytokinesis completes. That said, we feel confident that most of the reorientation is occurring prior to cytokinesis, because we have previously reported that the greatest changes in daughter cell positioning occur within the first 10-15 minutes of anaphase onset, when a gap in membrane-GFP/TdTomato is still visible (Lough et al, eLife, 2019). However, while we feel that there are many interesting questions that our work raises about the timing or reorientation relative to specific mitotic stages—e.g. is the midbody asymmetrically positioned, inherited, or ejected?—these questions are beyond the scope of the present study.

      Does the division angle in the AGS3 OE experiment (Figure 1D) correlate with AGS3 levels within the cell?

      This is an interesting question, and indeed, we our hypothesis would predict that it would. However, it is not straightforward to quantify AGS3 or mRFP1 levels, and as we explain in a new section of the Results (lines 212-237), we have some concerns that N-terminally tagged AGS3 may not be fully functional. We have added new data with C-terminally tagged AGS3-mKate2, which we feel provides even stronger evidence that mKate2+ cells show a planar shift compared to mKate2- cells (Fig. 3C,D). In the future, we could test this hypothesis at the population level by comparing division orientation profiles for AGS3-mKate2+ cells carrying either a non-targeting scramble or Gpsm11147 shRNA. We would predict that knocking down endogenous AGS3 while overexpressing AGS3-mKate2 should give an intermediate phenotype.

      I found the localization data to be the weakest part of the paper and feel that some reconsideration and reanalysis are warranted. First, the quantifications in Figures 2C, 3B, and 3F are unnecessarily vague scoring-based metrics. In 2C, "Localization pattern" should be replaced with membrane/cytoplasm ratio or an equivalent quantification. In 3B "LGN localization" should be replaced with apical/cytoplasmic and apical/basal ratios or equivalents. In 3F, "Polarized LGN frequency" should be replaced with apical/basal ratio or equivalent. It seems to me that non-AI processed data would be most appropriate for these quantifications unless such processing can be justified.

      This issue was raised by the previous two Reviewers and has been addressed by new data added to Figure 4.

      Second, it is important to note that the cytoplasmic localization of AGS3 does not allow one to conclude that AGS3 is not on the membrane. Unfortunately, high cytoplasmic signal can preclude the determination of membrane-bound signal.

      We agree with the Reviewer and have softened our language throughout the text.

      Finally, I had difficulty reconciling the images of LGN shown in Figure 3 with the conclusions made by the authors.

      We have added additional, representative images of LGN expression in control and AGS3 KD cells in Figure 4C-E.

      The challenge of the localization data is troubling because an important conclusion of the paper is that AGS3 acts via LGN. The localization data provided one leg of support for this conclusion and the other is provided by an epistasis analysis. Unfortunately, this data seems to be right on the edge because it is based on the difference between the solid and dashed blue lines in Figure 5B not being significant. However, we can see how close this is by comparing the solid and dashed red lines in the adjacent 5C, which are significantly different. Between the localization data, which doesn't seem clear cut, and the epistasis experiment, which is on the razor's edge, I'm concerned that the conclusion that AGS3 acts through LGN may be going beyond what the data allows.

      We appreciate the Reviewer’s comments about the importance of these two lines of experimentation: 1) AGS3’s effect on LGN localization, and 2) epistasis experiments between AGS3/Gpsm1 and LGN/Gpsm2. We feel we have significantly strengthened this first pillar with the additional data presented in Fig. 4C-J. Regarding the second point, we would like to emphasize that we present three lines of evidence for the existence of an epistatic relationship between LGN and AGS3: 1) the static division orientation data comparing LGN single KOs to both LGN KO + AGS3 KD and AGS3+LGN dKOs (Fig. 6B); 2) live imaging division orientation/telophase correction comparing LGN KOs to AGS3+LGN dKOs (Fig. 6C-E); 3) lineage tracing data comparing LGN KOs to AGS3+LGN dKOs (Fig. 7H,I). Further, we think the reviewer may have misconstrued the data presented in Fig. 5C (now Fig. 6C). The dashed lines indicate orientation at anaphase and solid lines 1h after anaphase, so the shift between dashed and solid lines indicates telophase correction, which occurs to similar (and statiscially significant) degrees in both LGN single mutants and AGS3+LGN dKOs. Comparisons between the single and double mutant would be between red and magenta solid lines or red and magenta dashed lines, and neither of these are statistically significant. We realize that our use of dashed lines in Fig. 5B (now Fig. 6B), which we normally only use to refer to anaphase entry in live imaging data, may have caused this confusion. Therefore, we have changed all plots to solid lines¬ in Fig. 6B, and use light and dark magenta, respectively, to differentiate between LGN KO + AGS3 KD and AGS3+LGN dKOs.

    2. Reviewer #3 (Public Review):

      This paper examines the mechanisms that control division orientation in the basal layers of the epidermis. Previous work established LGN as a key promoter of divisions where one of the siblings populates the differentiated layers (perpendicular). This work addresses two important, related issues - the mechanisms that determine whether a particular division is planar vs perpendicular, and the function of AGS3, and LGN paralog that has been enigmatic. A central finding is that AGS3 is required for the normal distribution of planar and perpendicular divisions (roughly equal) such that in its absence the distribution is skewed towards the perpendicular. Interestingly, however, the authors find that AGS3 has no detectable effect on orientation if the orientation is measured at anaphase. This timing aspect builds upon previous work from this group demonstrating a phenomenon they term "telophase correction" in which the orientation changes at the latest phases of division (and possibly post division?). Thus AGS3 seems to exert its effect using these later mechanisms and this is supported by further analysis by the authors. Importantly, the authors show that AGS3 acts through LGN, based on localization data and an epistasis analysis. The function of AGS3 has been highly enigmatic so resolving this issue while providing a useful step towards understanding how the division orientation decision is made, makes for exciting progress towards an important problem. I found the overall narrative and presentation to be quite good and especially appreciated the thoughtful discussion section that did an excellent job of putting the results in context and speculating how unknown aspects of the mechanism might work based on current clues. With that said, I think there are some important issues that should be resolved.

      Regarding the orientation measurements, the authors should specify how the midbody marker was used to mark sibling cells, especially given the midbody can move following division. For example, how can the authors be confident that the siblings in the middle panel of 1A are correct and not an adjacent cell?

      Regarding quantification, it would be useful for the authors to comment on how the following would influence their measurements: 1) movements along the z-axis, and 2) movement of the nucleus within the cell.

      A similar question is how much telophase correction really happens in telophase. How confident are the authors that the process actually occurs during division and not subsequent to it? What is drawn in their previous paper and in Figure 7A implies that post-division movements may be important. It would be useful for the authors to comment on whether they can make the distinction and whether or not it might be important.

      Does the division angle in the AGS3 OE experiment (Figure 1D) correlate with AGS3 levels within the cell?

      I found the localization data to be the weakest part of the paper and feel that some reconsideration and reanalysis are warranted.

      First, the quantifications in Figures 2C, 3B, and 3F are unnecessarily vague scoring-based metrics. In 2C, "Localization pattern" should be replaced with membrane/cytoplasm ratio or an equivalent quantification. In 3B "LGN localization" should be replaced with apical/cytoplasmic and apical/basal ratios or equivalents. In 3F, "Polarized LGN frequency" should be replaced with apical/basal ratio or equivalent. It seems to me that non-AI processed data would be most appropriate for these quantifications unless such processing can be justified.

      Second, it is important to note that the cytoplasmic localization of AGS3 does not allow one to conclude that AGS3 is not on the membrane. Unfortunately, high cytoplasmic signal can preclude the determination of membrane-bound signal.

      Finally, I had difficulty reconciling the images of LGN shown in Figure 3 with the conclusions made by the authors.

      The challenge of the localization data is troubling because an important conclusion of the paper is that AGS3 acts via LGS. The localization data provided one leg of support for this conclusion and the other is provided by an epistasis analysis. Unfortunately, this data seems to be right on the edge because it is based on the difference between the solid and dashed blue lines in Figure 5B not being significant. However, we can see how close this is by comparing the solid and dashed red lines in the adjacent 5C, which are significantly different. Between the localization data, which doesn't seem clear cut, and the epistasis experiment, which is on the razor's edge, I'm concerned that the conclusion that AGS3 acts through LGN may be going beyond what the data allows.

    1. Lateral reading is a strategy that enables people to emulate how professional fact checkers establish the credibility of online information. It involves opening up new browser tabs to search for information about the organisation or individual behind a site before diving into its contents. Only after consulting the open web do skilled searchers gauge whether expending attention is worth it. Before critical thinking can begin, the first step is to ignore the lure of the site and check out what others say about its alleged factual reports.

      I've always heard about lateral reading but never reallly used it. I think it wasn't until a couple years ago did I really start to implement lateral reading into my own learning. I would ever go further and say that this may because of the growth of the digital world. What I mean by this is that there has definitely been an influx in "fake news" the last decade or so, and so more and more people who at least try to be media literate may go out of their way to read and decipher the truth more carefully.

      I am actually a firm advocate for lateral reading because I know it can work. It has helped me evaluate credibility of articles online and it can probably help others as well.

    1. can you really have privacy?

      First thing that comes to mind is no of course. I think of how much privacy I have on the media. Even though technology is such a great advantage and that many of us use everyday, it's over course not as protected as we may think.

    1. Antagonistic bots can also be used as a form of political pushback that may be ethically justifiable.

      I think this is a very interesting point to bring up. Could bots fall under free speech if they are making a political statement? Bots in general raise many questions about how they can be used and the actions of bots/ the people behind them can be morally gray. I think that points to more consideration towards regulation of some type, but how can we do that without infringing upon rights?

    2. Fake Bots

      I wonder to what extent can we find out if something is a bot vs something is not. I think this can be very interesting as there are many reasons as to why someone may want to pretend to be a bot, for example, they do not need to face the same consequences as a bot is treated usually not to the same ethical framework as humans are.

    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Manuscript number: RC-2022-01771

      Corresponding author(s): Franck Pichaud and Rhian Walther

      1. General Statements [optional]

      We are grateful for the reviewers’ comments and suggestions. Both reviewers agree that our work addresses a poorly understood questions in biology and medicine, and that it will be of interest to the community of cell and developmental biologists.

      We note that most of the comments/suggestions, especially from Rev#2, are concerned with the text. These include suggested references to be added, a need to expand on the Method description and suggested points of discussion. We have addressed all these issues in the revised manuscript.

      Our work aims to understand which pathways control the basal geometry of epithelial cells, and how cells coordinate remodeling of their basal geometry to organize a tissue in 3D, from apical (top) to basal (bottom). This is a relatively understudied area, especially when compared to the breadth of work related to the pathways that control the apical geometry of epithelial cells.

      The apical geometry of an epithelial cell is a direct function of the number of adherens junctions the cell shares with their neighbors. Suppression or extension of adherens junctions underpins apical geometry remodeling. Basally, this same cell will be attached to the basement membrane though integrin receptors. We use the fly retina, where cells adopt stereotyped basal geometry, to investigate whether and how integrin adhesion might induce cell basal geometry remodeling in morphogenesis.

      The novel finding we report that a temporal sequence of event seems to underpin cell basal geometry remodeling in the retina, whereby i) laminin accumulates at specific location within the basement membrane, which is ii) accompanied by a concomitant accumulation of Dystroglycan (DG), and subsequently iii) integrin receptors are recruited to these sites of high Laminin-DG. This, along with our genetic experiments, suggests that a Laminin-DG-Integrin axis controls the basal geometry of retinal cells. In this axis, we envisage patterning of the basement membrane through Laminin-DG directs integrin recruitment, which in turn induces cell basal geometry remodeling. To our knowledge, this pathway in epithelial morphogenesis, spanning from ECM regulation to integrin polarization, has not been reported before. As the function of these components in basal adhesion is conserved across phyla, we anticipate our findings will be broadly relevant for our understanding of epithelial morphogenesis.

      2. Description of the planned revisions

      The main suggestion, common to both our reviewers, is that we should provide further re-assurance that the RNAi strains we use to target basement membrane components and the DG and integrin pathways are specific, and that these strains do not come with off-target effects.

      We will follow this recommendation by i) including referencing when a line that we have used has been validated elsewhere, ii) by using at least two independent RNAi strains to target a gene of interest, iii) by making use of the deGrad-FP system (Caussinus et al., 2013) to target proteins instead of genes, iv) by making use of available mutant strains. This is all relatively straightforward, and I will detail the proposed experiments as part of the following point-by-point rebuttal and revision plan.

      REVIEWER #1

      Commenting on the need to provide further controls related to some of our RNAi experiments

      1)* All the genetics experiments are based on RNAi induced knock-down approach. Although such an approach is easy to justify for genes associated with lethality when mutated, it becomes less relevant for non-lethal ones as Dystroglycan complex components (Dg, Dys, Sgc) for which null and viable mutants are published and available. The phenotype of such mutants should be provided. *

      AND

      *There is no data explaining how these RNAi lines were validated. The fact that it gives the phenotype expected by the authors is obviously not sufficient. This point is essential to exclude off-target effects and to be able to compare the different genotypes (see #2). For instance, the strong effect of sarcoglycan could be questioned. Is it really specific? If yes, is the difference with other Dystroglycan complex members only due to RNAi efficiency or does it have a specific function? *

      AND

      Line 255, "These perturbations led to a failure of bPS/Mys to accumulate at the grommet". Dg mutants are viable (PMID: 18093579); do they show consistent phenotypes?

      __RE: __Our main methodology has been to use available RNAi strains to perturb composition of the basement membrane and to inhibit the expression of components of the DG and Integrin pathways. As pointed out by the reviewer, this approach allows us to assess the function of genes that might be embryonic lethal and allows us to specifically target the basal geometry remodeling step without perturbing earlier steps of retinal morphogenesis. This is important for the basement membrane and integrins, which are required although retinal tissue development. See for example: (Fernandes et al., 2014, Thuveson, 2019 #3787).

      We are aware that mutant alleles are available for dg, dys and sgc allow for recovering adult homozygous (or trans-heterozygous) animals. However, based on our previous experience using mutants for which only very few flies make it to adulthood, we feel it is best not to examine those animals. Compensatory pathways might be at play that could mask a phenotype (Please see our recent work on the viable roughest null allele in cell intercalation (Blackie et al., 2021).

      Therefore, we propose to induce mutant clones for dg, dys and sgc using the Flp/FRT system, using the strongest alleles that are available to us. Of note, in our experience stable proteins might not show a phenotype in small clones, but will develop a phenotype in larger ones, as the protein becomes further diluted upon multiple rounds of cell division. Bearing this in mind, we will generate animals where the whole retina is mutant for these genes. This will be done using the GMR-hid system (Stowers and Schwarz, 1999).

      Specifically, we will target Dg, Dys and Sgc using:

      Dystroglycan:

      • The dg nonsense mutations, leading to expression of truncated proteins: DgO86 (stop codon at the R87 residue) and dgO43 (stop codon at the W462 residue) (Christoforou et al., 2008). While previous studies have suggested that these alleles are homozygous viable (Christoforou et al., 2008; Zhan et al., 2010), we have obtained this strain from the Bloomington Stock Centre, and note that no homozygous flies make it to adult. In preliminary work, we also note that clones mutant for the dgO86 allele generated with the flp-FRT system are very small, comprised of only one or two cells. This suggests that DG is required for cell proliferation or viability. These dg alleles are available on the G13 FRT which is not compatible with any FRT system designed to eliminate the wild type cells. To use the GMR-hid system, we will have to first recombine these dg alleles onto the appropriate FRT chromosome. Dystrophin:

      • The dys3397 allele, which is semi-lethal P-element insertion in the dys Very few adult flies homozygous for this allele flies are recovered (Christoforou et al., 2008). We will have to recombine this allele onto an FRT chromosome to generate whole mutant retinas.

      • The deficiency Df(3R)Exel6184, which removes the dys coding frame (Christoforou et al., 2008).
      • We will also use dysE17, because it has been used before (Catalani et al., 2021; Cerqueira Campos et al., 2020; Mirouse et al., 2009). This lesion is a Q2807 Stop codon in the C-terminal region common to all 6 dys The Df(3R)Exel6184 and dysE17 alleles have been recombined onto FRT82B, which will allow us to make use of the GMR-hid system to generate whole mutant retinas. Sarcoglycan:

      • Sgc (three subunits in Drosophila) using the deletion allele dscg169 (Allikian et al., 2007). We will have to recombine this mutation onto an FRT chromosome to generate whole mutant retinas. In addition, we will reproduce our RNAi phenotypes using additional available RNAi lines from stock centers and from previous studies, targeting different regions of dg, dys and scg. For dys we will use a validated RNAi line. For dg we will use a second RNAi line previously used in (Cerqueira Campos et al., 2020; Villedieu et al., 2023) For dys, we will use a second line previously used in (Cerqueira Campos et al., 2020). For Sarcoglycans, we will complement our work targeting scgd by also targeting scga.

      Moreover, since a functional endogenously GFP-tagged Dg strain is now available (Villedieu et al., 2023) along with the Dys::GFP strain we have already used, we will target these proteins using the DeGrad-FP system (Caussinus et al., 2013). The main advantage with this system is that, as with RNAi, we can target a specific time window without affecting earlier steps in retinal morphogenesis. In addition, these experiments will address the possibility that DG and Dys might be stable in cells – inhibiting genes expression in flp-FRT induced clones does not always correlate with inhibiting protein function. We think that the well-established deGrad-GFP will be useful here to address the reviewer’s comment.

      We trust these complementary approaches will more than address the reviewers’ comment by further ascertaining that the RNAi phenotypes we report here for Laminin, and the DG and integrin pathway, are specific.

      Please note that we show in Fig.3 that the basal geometry phenotype we report for the talin RNAi, using an RNAi line reported in several previous studies (Lemke et al., 2019; Perkins et al., 2010; Xie and Auld, 2011; Xie et al., 2014), is comparable the phenotype we observed using the Flp-FRT system to induce mys1 mutant clones. So, we are confident this RNAi line is specific of talin. Nevertheless, we will also show results using second RNAi line targeting *talin. *

      *- Authors claimed that laminin RNAi (or MMPs overexpression) affects cell geometry but why it is not analyzed by PCA? It is not consistent with the other figures. *

      __RE: __To address this comment, we will provide the PCA analysis for the Laminin and MMP phenotypes.

      __REVIEWER #2 __

      • Line 208, "we found that LanB2 RNAi leads to defects in bPS/Mys Integrin localization". Here, because the authors use only single RNAi, there remains the possibility that the observed phenotype was caused by an off-target effect. The authors should exclude this possibility by using another RNAi or mutants. In case of LanB2, however, showing that one RNAi against LanB1 shows the same phenotype would be enough, because LanB1 is another single subunit of fly Laminin __RE: __We have now included loss-of-function mutant clones for LanB1, using the LanB1KG003456 allele, showing defects in integrin localization resembling the LanB2 RNAi (please refer to section 3: revision already done, Section). We trust that this is good validation of the LanB2 RNAi strain. These new results have been added to Figure 6 (6E-6F).

      RE:This is the same for all the RNAi experiments”. Please refer to our response to Reviewer 1, above.

      2) *As the authors write "Laminin-rich domains", I suppose that they assume that LanA/B1 accumulates in a restricted region of the BM. However, it has been reported that the majority of Laminin in the fly embryo is soluble and floating in the haemolymph (fly's 'blood' or body fluid) (PMID: 29129537). Therefore, the LanA/B1 observed in the figures might be just floating in the intercellular space and doing nothing on the BM. The authors should exclude this possibility to support their idea that Laminin localised in a specific region of the BM recruits Integrin. For example, does secreted GFP (PMID: 12062063) not behave in the same way as LanA/B1? Can the authors show that the LanA/B1 is indeed incorporated in the BM by FRAP or any methods? *

      RE: While formally possible, our data suggest that it is unlikely that “LanA/B1 is just floating in the intercellular space and doing nothing on the BM”. For instance, our results show that the DG pathway component Scgd is required for accumulation of LanA::GFP (Fig.7E-F). The most likely explanation for this requirement is DG binding to Laminin fibers.

      Nevertheless, we will follow up on the reviewer’s comment and perform FRAP on LanA::GFP, as this is relatively straightforward. We will also try the GFP secretion experiment using the suggested GFPsecr transgene generated by the Vincent lab in 2000.

      3) Line 240. "RNAi against dSarcoglycan led to a decrease in LanA::GFP expression at the presumptive grommet at 20h APF (Figure 7F)". As to this result, the authors seem to interpret that Laminin is not recruited to the "specific BM domain" in grommet in the absence of Dg signalling. However, other possibilities exist, e.g., that the global expression level of Laminin was reduced, or that the intercellular space into which soluble Laminin (see the issue 4 above) flows was narrowed down. The authors should show the data that exclude (or at least reduce) these possibilities.

      __RE: __Addressing Rev2 point (1) will rule out that Laminin is in soluble form. To address the comment that the global expression level of Laminin might be decreased, we will quantify the amount of LanA::GFP that is not at the grommet and compare wild type animals with the scgd ones.

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      __REVIEWER #1 __

      • Line 208, "we found that LanB2 RNAi leads to defects in bPS/Mys Integrin localization". Here, because the authors use only single RNAi, there remains the possibility that the observed phenotype was caused by an off-target effect. The authors should exclude this possibility by using another RNAi or mutants. In case of LanB2, however, showing that one RNAi against LanB1 shows the same phenotype would be enough, because LanB1 is another single subunit of fly Laminin __RE: __We have included new results – LanB2 loss of function – showing the role of Laminin in being required for Integrin localization in the secondary and tertiary pigment cells (revised Figure 6 – panels E-F)

      Line 237: For this, we used both RNAi against LanB2 and a loss-of-function allele of LanB1. Consistent with our model, we found that in both cases bPS/Mys Integrin localization was affected. bPS/Mys failed to accumulate at the grommet, and instead was distributed at the basal plasma membrane into punctate domains (Figure 6A-F). In addition, these perturbation experiments affected cell basal geometry remodeling (Figure 6A, 6C, 6E).

      2)* Methods section describing genetic conditions is really sketchy. The genotype corresponding to each figure is not provided and I guess that GMR-Gal4 has been used in all experiments using the Gal4 system but it is never clearly stated. *

      __RE: __We have revisited the Methods section and Figure Legends to ensure all appropriate information is readily accessible to the reader. The reviewer is correct that the retinal GMR-Gal4 driver was used to express the RNAi used in this study.

      3) PCA analysis. - In the WT situation it would be really informative to know which variable(s) is/are really discriminant between the two cell populations and then maybe to focus a bit more on these parameters. For instance, a PCA correlation circle plotting both cells and variables would be very helpful.

      __RE: __We have followed the reviewer’s advice and amended the Methods section accordingly. We now provide the PCA correlation circle plotting both cells and variables in Suppl. Fig. 3, for talin RNAi and MysDN, and Suppl. Fig. 10 for DG and Scgd RNAi

      *Methods: *

      Line 522 : Principle component analysis

      Principal component analysis (PCA) was carried out using the Scikit-learn library in Python. The Standard scaler package was used to standardize the data across all metrics before calculating the principal components. The PCA package was then used to perform the PCA. Metrics included in the PCA were as follows: extent, major axis length, minor axis length, eccentricity, roundness, circularity, area, cell shape index, perimeter.

      The cell types (secondary and tertiary pigment cells) were assigned by following the cells in 3D to the apical surface where the cell types could be identified. Cells that could not be clearly assigned as either secondary or tertiary pigment cells were excluded from the PCA.

      Extent is the area of an object divided by the area or the smallest rectangle (bounding box) that can fit around the object.

      Major axis length is the longest line that can be drawn through an object.

      Minor axis length is the line that can be drawn through an object which is perpendicular to the major axis.

      __Eccentricity __is the ratio of the length of the short (minor) axis to the length of the long

      (major) axis.

      Roundness is a comparison of an object to the best fit circle of an object. The closer the object is to a perfect circle, the more round it will be.

      Circularity is a measure of the smoothness of an object.

      Cell shape index is a dimensionless parameter to describe cell shape. When cells have smaller contacts with their neighbours the cell shape index is small.

      Correlation circle plots were generated using the mlxtend plotting package in python using the plot PCA correlation graph function.

      • Please also see the graphs we now provide in Suppl. Fig.4*. *

      We are also commenting on these results.

      Line 174: To understand which parameters explained most of the variance in the PCA analysis we generated correlation circle plots (Supplementary Figure 4). For wildtype cells, perimeter and circularity contribute most to the variance between secondary and tertiary pigment cells along the PC1 axis. Eccentricity and minor axis length contribute most to variance along the PC2 axis (Supplementary Figure 4A). For talin RNAi and MysDN cells, the correlation circle plots are remarkably similar (Supplementary Figure 4B-C), indicating that these genetic perturbations have similar effects on cell basal geometry. To confirm this result, we performed PCA comparing secondary and tertiary pigment cells for these two genotypes. In both genotypes, cells fail to form discrete clusters (Supplementary Figure 4D-E). For the secondary pigment cells, expressing talin RNAi or MysDN leads to an increase in cell roundness. For the tertiary pigment cell, these genotypes lead to an increase in circularity (Supplementary Figure 4D-E). Examining the original segmentation data confirmed that, relative to wildtype cells, either genetic perturbation has a similar effect on key cell shape parameters (Supplementary Figure 4F-G).

      *- In loss of function conditions, when the tissue is strongly affected, how do the authors recognize the two cell populations if PCA cannot? *

      __RE: __In these genotypes, each cell type is identified based on their apical position and geometry. When a cell cannot be identified it is not included in the analysis. This allowed us to track the cells from apical to basal. We now make this clear in the Methods section.

      Line 529: The cell types (secondary and tertiary pigment cells) were assigned by following the cells in 3D to the apical surface where the cell types could be identified. Cells that could not be clearly assigned as either secondary or tertiary pigment cells were excluded from the PCA.

      - On the opposite, based on the provided image, Dys RNAi seems to have a mild effect and it seems that my eyes can easily recognize those two cell populations based on their shape. So why PCA cannot?

      __RE: __We respectfully disagree with this comment. In the Dys RNAi, one cannot tell which is a secondary and which is a tertiary by visual inspection of the basal surface only. This is consistent with the PCA analysis, now described more thoroughly in Supplemental Figure 4. The Dys RNAi cells tend to remain elongated and they do not round up as much as the Scgd RNAi cells, which gives the false impression that the phenotype is closer to that of the wild type.

      - Based on the proposed images, some phenotypes look clearly different depending on the genotype, e.g. Talin and Mys (figure 3) or Dys and Sgc (Figure 8). In other words, the fact that PCA cannot separate the cell pollutions in these different genotypes does not necessarily mean that their effect is identical. Could authors perform PCA analysis between mutants? If they are different, again it might be very interesting to identify the discriminating parameters.

      RE: We did not claim the defect was identical__. __

      The basal geometries look somewhat different depending on the genotype, and we envisage this is due to differences in RNAi strength and perhaps differences in protein stability. This is the case for Dys and Scgd, as outlined in the preceding point. With respect to talin and mys, none of the authors can distinguish by eye the talin RNAi from mys1 phenotypes. We have informally asked our institutional colleagues, and they were also unable to distinguish these genotypes.

      Nevertheless, we have expanded our PCA analysis between phenotypes, considering one cell type at a time. This analysis shows that these phenotypes show partial overlap, outside of the wildtype range. While there are similarities, it does not reveal, however, any specific relationship between genes of interest (see previous).

      Line 178: For talin RNAi and MysDN cells, the correlation circle plots are remarkably similar (Supplementary Figure 4B-C), indicating that these genetic perturbations have similar effects on cell basal geometry. To confirm this result, we performed PCA comparing secondary and tertiary pigment cells for these two genotypes. In both genotypes, cells fail to form discrete clusters (Supplementary Figure 4D-E). For the secondary pigment cells, expressing talin RNAi or MysDN leads to an increase in cell roundness. For the tertiary pigment cell, these genotypes lead to an increase in circularity (Supplementary Figure 4D-E). Examining the original segmentation data confirmed that, relative to wildtype cells, either genetic perturbation has a similar effect on key cell shape parameters (Supplementary Figure 4F-G).

      *- From what I can understand, each PCA analysis has been done on a single retina. If true, more replicates should be included. If not true, the number of independent retinas should be mentioned. *

      __RE: __All PCA analyses have been done using multiple retinas from different animals. We have clarified this in the figure legends.

      4) Minor comments: - Globally, the article suffers from a lack of details, especially in the methods section and/or in figure legends.

      RE: please see what we have done to address this comment, in section (2) above.

      *- Also, several points could be advantageously discussed. For instance, why MMPs have different effects according to their specificity? Also, what could be the meaning of the nice differential pattern between integrin alpha subunits? *

      __RE: __We were concerned this would be seen as too speculative by our reviewers. Following the reviewer’s advice, we are happy to share our current working model and speculations on this.

      Results:

      Line 242: Moreover, and consistent with basement membrane regulation being important for cell basal geometry remodeling, we found that degrading the basement membrane by expressing Matrix Metalloproteases MMP1 or MMP2 in retinal cells leads to a failure in bPS/Mys localization at the grommet and prevented cell basal geometry remodeling (Figure 6G-J). While recombinant Drosophila MMP1 and 2 can degrade Col-IV, only MMP2 can degrade Laminin (Wen et al., 2020). The MMP2 phenotype we observed in basal surface organization is stronger than that of the MMP1 overexpression. Our results, therefore, suggest that both Col-IV and Laminin play a role in controlling the basal geometry of retinal cells. This suggestion is consistent with our finding that both these basement membrane proteins are enriched at the grommet once cells have acquired their basal geometry.

      Discussion:

      Line 386: Integrins can bind to Col-IV and to Laminin (Hynes, 2002). Our experiments show that MMP2 overexpression leads to a stronger phenotype than MMP1. In addition to catalyzing Collagen-IV proteolysis, MMP2 can degrade Laminin, which is something MMP1 does not seem to be able to do (Wen et al., 2020). Therefore, our results suggest that both Col-IV and Laminin are required for cell basal geometry remodeling.

      Line 408*: *

      The cone cells express two Integrin receptors, ____a____PS1/Mew-____b____PS/Mys and ____a____PS2/if-____b____PS/Mys

      We found that while the interommatidial cells express aPS1/Mew-bPS/Mys, the cone cells express both aPS1/Mew-bPS/Mys and aPS2/if-bPS/Mys. Thus, different cell types express different aPS subunits. It is not clear why the cone cells express two a-subunits. In the developing follicular epithelium of the fly oocyte, cells switch from expressing aPS1/Mew-bPS/Mys, to expressing aPS2/if-bPS/Mys (Delon and Brown, 2009). In this tissue, the developmental switch between aPS1 and aPS2 expression was shown to correlate with a change in stress fiber orientation. In addition, aPS1-bPS/Mys was also shown to be required to control F-actin levels basally. aPS1 mutant cells presented elevated levels of F-actin, a phenotype not seen in aPS2 mutant cells. Remarkably, in this tissue, aPS2-bPS/Mys, but not aPS1/Mew-bPS/Mys was able to recruit the integrin adapter Tensin. The authors envisaged that the aPS2 Tensin interaction might confer robustness in basal surface remodeling. With analogy to the follicular epithelium, we speculate that in the cone cells, aPS1-bPS/Mys and aPS2/Mew-bPS/Mys synergize in mediating robust attachment to the basement membrane, to ensure these cells do not detach as the retina lengthens along the apical-basal axis (Longley and Ready, 1995). We also note that in retinal development, the cone cells form new adherens and septate junctions at their basal feet (Banerjee et al., 2008). These cells, therefore, present two sets of adherens and Septate junctions. It is also possible that the atypical situation seen with the cone cells expressing two a subunits, is linked to the formation of these new junctions at the basal pole of these cells. It will be interesting to examine these possibilities, and to establish the role these two a-subunits play in cone cell morphogenesis. Further, the presence of two distinct integrin subunits within the cone cells may have implications when considering Integrin signaling during cone cell morphogenesis.

      *- In Methods, a list of metrics is given for the PCA analysis but some look very similar and it would be helpful to define them briefly. *

      RE: Please refer to what we have done to address this comment in section (2) above.

      *- Figures are not always color-blind adjusted (e.g. dots on PCA graphs). *

      __RE: __We have rectified this oversight.

      __REVIEWER #2 __

      1)* Line 169, "From these experiments, we conclude that Integrin adhesion is required for cell basal geometry remodeling during retinal morphogenesis". It has been long known that integrin is necessary for the gross morphogenesis of the eye (e.g., Zusman et al. 1993, PMID: 8076515). The authors need to cite these preceding researches and should clarify what new findings this new work adds to the previous knowledge. *

      __RE: __Following the reviewer’s suggestion, we have added this reference which precedes (Longley and Ready, 1995)mentioned in the paper. Both references show that integrins are required for eye integrity and attribute this function to the contraction phase of retinal development. Notably, contraction occurs after cells have remodelled their basal geometry, which we have focused on in this study.

      Line 128: The Integrin bPS subunit (Myspheroid, Mys) is required to maintain surface integrity late in retinal development, as the tissue surface undergoes basal contraction (Longley and Ready, 1995; Zusman et al., 1993).

      4) Line 180, "Using available functional GFP protein traps [49, 50]", the authors investigate the behaviour of Laminin subunits LanA and LanB1. First, ref [50] is not relevant here and should be removed. Moreover, the Laminin-GFPs the authors used are not protein traps, but transgenic strains harbouring genes and most of their regulatory information, with the ORFs tagged with GFP [49]. Furthermore, while the ref [49] reported the functionality of LanB1-GFP, this reference did not fully address the functionality of LanA-GFP. The authors need another reference on it (PMID: 29129537), which demonstrated that LanA-GFP rescues LanA mutants.

      5) Related to the issue above, in addition to LanA and LanB1, the authors examine the localisation of the following BM proteins using GFP-fusion: Perlecan/Trol, Collagen IV/Viking, Nidogen, and SPARC. The authors do not explicitly describe the nature of these GFP fusions, but I am afraid that the authors think all of them are "functional protein traps". However, in fact while Perlecan and Collagen IV are protein traps, Nidogen and SPARC are transgenics including regulatory sequences made in the ref [49]. This must be clarified. Moreover, to rely on the data obtained using these GFP fusions, their functionality must be confirmed by appropriate references or/and the authors' own data. For information, ref [62] showed the functionality of Perlecan-GFP and Collagen IV-GFP protein traps (they are both homozygous viable), and the Nidogen-GFP transgene rescues the BM deficiency of Ndg mutants (PMID: 30260959). These reports must be explained in the text, and I would like the authors collect and show more information.

      __RE: __We have deleted ref 50. We thank the reviewer for flagging the issue with our referencing. We have now amended this section.

      Line 204: To this end, we examined the localization and requirement of the Laminin A and B1 subunits (Laminina, LanA and Lamininb, LanB1), Perlecan/Trol, Collagen-IV/Viking (Col-IV), the glycoprotein Nidogen (Entactin/Ndg), and the secreted glycoprotein protein-acidic-cysteine-rich (Sparc), which are all components of the basement membrane (Walma and Yamada, 2020). For Laminin, Ndg and SPARC, we used strains generated from a fosmid library, and expressing a functional GFP-tagged transgene under the control of their own respective promoter (Dai et al., 2018; Matsubayashi et al., 2017; Sarov et al., 2016). For Col-IV and Perlecan, we used functional GFP exon-trap strains (Morin et al., 2001).

      6) Line 200, "These specific patterns of expression for LamininA/B1, Collagen IV, Perlecan, Nidogen and Sparc". I have several comments here: - 5A. These patterns are discussed only using single optical sections. To highlight the difference in their localisation patterns more objectively, multiple sections and/or 3D images should be shown.

      RE: (a) These are all projections of 3 to 5 confocal sections, and we have amended the manuscript to make this point clearer. (b) Following the reviewer’s advice, we now provide sagittal sections so the reader can better appreciate what is detected above and below the grommet. Please see new Fig. 5.

      5B. Can the authors discuss, hypothesise, or speculate the biological meaning of the difference? * AND*

      *5C. It has been reported that in the mammalian skin BM, different components show distinct localisation patterns (PMID: 33972551). It would be interesting to cite this paper and discuss the generality of the non-uniform distribution of BM components. *

      __RE: __The revised manuscript offers a short discussion in this topic.

      Line: 367 The idea that different cell types in a tissue can express different ECM components, and thus induce localized specialization of a basement membrane is well-supported by recent work in the mouse hair follicle. In this sensory organ, the architecture and composition of the basement membrane is highly specialized depending on the cell-cell and cell-tissue interface considered (Cheng et al., 2018; Fujiwara et al., 2011; Joost et al., 2016). Moreover, different cell populations – epithelial stem cells and fibroblasts, express different ECM components in the hair follicle (Tsutsui et al., 2021), supporting the notion that specific basement membrane organization contributes to cell-cell communication and overall 3D tissue architecture.

      7) Line 215, "However, inhibiting the expression of Collagen IV, Ndg, Perlecan and Sparc individually, by expressing RNAi against these genes in all retinal cells, did not lead to defects in bPS/Mys localization". To conclude so, the authors must demonstrate that the used RNAis efficiently removed its target proteins.

      __RE: __We have removed this section referring to Collagen IV, Ndg, Perlecan and Sparc.

      Instead, we now focus solely on Laminin. Because Laminin accumulation at the presumptive grommet precedes that of the other ECM factors examined in our study, we favor a model in which Laminin plays a key role in promoting integrin localization.

      8)* Line 222, "DG is required to organize the ECM in several experimental settings [42, 43, 45, 51]". Here, the authors must mention to a preceding paper that reported the eye deficiency of Dg mutant flies (PMID: 20463973), and discuss what new findings authors can add to the previous report. *

      __RE: __We have followed this recommendation.

      Line 441: We also note that a previous study showed that early in retinal development, DG localizes at the apical membrane of the photoreceptors. This study proposed that DG promotes elongation of these sensory neurons, independently to any potential role this surface receptor might play in basement membrane organization (Zhan et al., 2010). This conclusion was based on Df(2R)Dg248 mutant clones and trans-heterozygous retinas, where DG function was impaired not only in photoreceptors, but in all interommatidial cell types. Moreover, the basement membrane was not examined in this study. Our work, and the fact the bulk of retinal cell elongation occurs late in retinal development(Longley and Ready, 1995), is consistent with DG playing a role in retinal cell elongation and overall tissue thickening.

      Under “Advance”:

      *The 3D imaging of ommatidia development is beautiful and of good descriptive value. ** However, as mentioned in the major comments 1, 2, 3, and 8 above, I am afraid that the search of preceding literature seems insufficient, and it is often unclear what this manuscript add to existing knowledge. *

      __RE: __The logic of how the reviewer links points 2, and 3 they raise as part of their review, to their assessment of how our work advances the field, is unclear to me. Their Points 2 and 3 have to do with making sure we better explain how the functional ECM transgenes were generated and by whom. The importance the reviewer places on points 2, 3 when considering the Advance our work provides to the field does not appear justified to me.

      Point 1 refers to a previous study by Zusman et al., published in 1993. Using partial loss of function alleles and heat-shock inducible rescue constructs they show that bPS/Mys plays a role in eye development. They note that in adult eyes, retinal cells are not attached to their basement membrane. They show this is accompanied by a failure for the retina to elongate along the apical-basal axis. These phenotypes are consistent with a role for integrins in mediating attachment of epithelial cells to the basement membrane, and we are now referring to this work in the revised manuscript. A much more relevant reference to our work however, is (Longley and Ready, 1995), which we have used repeatedly in our manuscript to stress what was novel about our work.

      Point 8 refers to a previous report implicating DG in photoreceptor elongation, which is a developmental phase that mostly occurs after the process we are studying here (please see Fig.3 of (Longley and Ready, 1995) for quantification using sections). The photoreceptors do no contribute basal profiles at the basal surface of the retina. The DysGFP signal we detect at this tissue surface, in the presumptive and established grommet, is clearly coming from the pigment cells, not from the photoreceptor axons which are found at this basal location. We now discuss this previous report, to make what is clearer what is novel about our own work.

      .

      Minor comments: - Line 85, "This is the case in the follicular epithelium for example". Here, the text would be more reader-friendly if the authors could clarify this is the follicular epithelium of the fly ovary.

      __RE: __We have modified the text to address this comment.

      - Line 203-, regarding all the experiments involving the Gal4-UAS system. Not all the readers are familiar with the system. A brief explanation on it should be added in the main text. Moreover, in the Results section, not in the Methods, the authors should show what Gal4 they used, and where is the Gal4 expressed.

      __RE: __We have amended the manuscript accordingly.

      *- Line 239, "We found that inhibiting the expression of the DG cofactor, dSarcoglycan [53] was most effective in inhibiting this pathway in retinal cells". Here, the authors should show the data. *

      __RE: __This statement is based on the results shown in Fig.8 and Suppl. Fig.9, which make use of a PCA representation to quantify the Dg, Dys and dScg RNAi phenotypes in cell basal geometry. We have re-phrased this statement to make it clear that we are referring to the RNAi-based perturbation of these genes’ expression.

      4. Description of analyses that authors prefer not to carry out

      We will address all the reviewer comments as they will consolidate our findings.

      Our further validation of the few RNAi lines used in our study that have not been used before in publications will also be valuable to the community.

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      Wen, D., Chen, Z., Zhang, Z., and Jia, Q. (2020). The expression, purification, and substrate analysis of matrix metalloproteinases in Drosophila melanogaster. Protein Expr Purif 171, 105629.

      Xie, X., and Auld, V.J. (2011). Integrins are necessary for the development and maintenance of the glial layers in the Drosophila peripheral nerve. Development 138, 3813-3822.

      Xie, X., Gilbert, M., Petley-Ragan, L., and Auld, V.J. (2014). Loss of focal adhesions in glia disrupts both glial and photoreceptor axon migration in the Drosophila visual system. Development 141, 3072-3083.

      Zhan, Y., Melian, N.Y., Pantoja, M., Haines, N., Ruohola-Baker, H., Bourque, C.W., Rao, Y., and Carbonetto, S. (2010). Dystroglycan and mitochondrial ribosomal protein L34 regulate differentiation in the Drosophila eye. PLoS One 5, e10488.

      Zusman, S., Grinblat, Y., Yee, G., Kafatos, F.C., and Hynes, R.O. (1993). Analyses of PS integrin functions during Drosophila development. Development 118, 737-750.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      Cell shape remodelling is essential for tissue morphogenesis. To model this event, the fruit fly Drosophila melanogaster has been widely used. In the pupal retina, ommatidial cells change their structure to form the photo-sensing machinery in the compound eye. Previous studies investigating this event mainly focused on the cell shape change at the apical plane. However, the cell shape at the basal side and the three-dimensional (3D) structure of the cells have been little studied.

      In this manuscript, the authors address this issue by combining state-of-art 3D imaging and fly genetics. They report that at the initial stage of eye development, a basement membrane (BM) component Laminin accumulates at the basal side of the ommatidial cells in a manner dependent on the BM-receptor molecule dystroglycan (Dg). The authors propose that this Dg-dependent Laminin accumulation induces the polarisation of integrin at the basal surface, which is essential for proper ommatidia morphogenesis.

      Major comments:

      The beautiful images presented here provide interesting descriptions of the events occurring during eye development. Also, the authors propose an attractive and simple hypothesis that the Dg-dependent recruitment of Laminin leads to integrin polarisation and tissue morphogenesis. However, I'm afraid that this hypothesis is not supported enough by the presented data. In addition, the novelty of some conclusions and the reliability of a number of reagents used are unclear. Specific concerns are described below:

      1. Line 169, "From these experiments, we conclude that Integrin adhesion is required for cell basal geometry remodeling during retinal morphogenesis". It has been long known that integrin is necessary for the gross morphogenesis of the eye (e.g., Zusman et al. 1993, PMID: 8076515). The authors need to cite these preceding researches and should clarify what new findings this new work adds to the previous knowledge.
      2. Line 180, "Using available functional GFP protein traps [49, 50]", the authors investigate the behaviour of Laminin subunits LanA and LanB1. First, ref [50] is not relevant here and should be removed. Moreover, the Laminin-GFPs the authors used are not protein traps, but transgenic strains harbouring genes and most of their regulatory information, with the ORFs tagged with GFP [49]. Furthermore, while the ref [49] reported the functionality of LanB1-GFP, this reference did not fully address the functionality of LanA-GFP. The authors need another reference on it (PMID: 29129537), which demonstrated that LanA-GFP rescues LanA mutants.
      3. Related to the issue above, in addition to LanA and LanB1, the authors examine the localisation of the following BM proteins using GFP-fusion: Perlecan/Trol, Collagen IV/Viking, Nidogen, and SPARC. The authors do not explicitly describe the nature of these GFP fusions, but I am afraid that the authors think all of them are "functional protein traps". However, in fact while Perlecan and Collagen IV are protein traps, Nidogen and SPARC are transgenics including regulatory sequences made in the ref [49]. This must be clarified. Moreover, to rely on the data obtained using these GFP fusions, their functionality must be confirmed by appropriate references or/and the authors' own data. For information, ref [62] showed the functionality of Perlecan-GFP and Collagen IV-GFP protein traps (they are both homozygous viable), and the Nidogen-GFP transgene rescues the BM deficiency of Ndg mutants (PMID: 30260959). These reports must be explained in the text, and I would like the authors collect and show more information.
      4. Line 182-, LanA and LanB1 "accumulate at the center of the ommatidium, in a pattern resembling the grommet structure (Figure 4A and Supplementary Figure 4)"... "LamininA/B1 accumulation at the presumptive grommet precedes Integrin accumulation at this location. It suggests that localized Laminin might control Integrin localization in the interommatidial cells". Based on these results, the authors discuss that "generating specific polygonal geometries at the basal surface of cells starts with organizing the ECM to establish a pattern of Laminin-rich domains, distributed across the tissue basal surface" (Line 267).

      As the authors write "Laminin-rich domains", I suppose that they assume that LanA/B1 accumulates in a restricted region of the BM. However, it has been reported that the majority of Laminin in the fly embryo is soluble and floating in the haemolymph (fly's 'blood' or body fluid) (PMID: 29129537). Therefore, the LanA/B1 observed in the figures might be just floating in the intercellular space and doing nothing on the BM. The authors should exclude this possibility to support their idea that Laminin localised in a specific region of the BM recruits Integrin. For example, does secreted GFP (PMID: 12062063) not behave in the same way as LanA/B1? Can the authors show that the LanA/B1 is indeed incorporated in the BM by FRAP or any methods? 5. Line 200, "These specific patterns of expression for LamininA/B1, Collagen IV, Perlecan, Nidogen and Sparc". I have several comments here: 5A. These patterns are discussed only using single optical sections. To highlight the difference in their localisation patterns more objectively, multiple sections and/or 3D images should be shown. 5B. Can the authors discuss, hypothesise, or speculate the biological meaning of the difference? 5C. It has been reported that in the mammalian skin BM, different components show distinct localisation patterns (PMID: 33972551). It would be interesting to cite this paper and discuss the generality of the non-uniform distribution of BM components. 6. Line 208, "we found that LanB2 RNAi leads to defects in bPS/Mys Integrin localization". Here, because the authors use only single RNAi, there remains the possibility that the observed phenotype was caused by an off-target effect. The authors should exclude this possibility by using another RNAi or mutants. This is the same for all the RNAi experiments. In case of LanB2, however, showing that one RNAi against LanB1 shows the same phenotype would be enough, because LanB1 is another single subunit of fly Laminin. 7. Line 215, "However, inhibiting the expression of Collagen IV, Ndg, Perlecan and Sparc individually, by expressing RNAi against these genes in all retinal cells, did not lead to defects in bPS/Mys localization". To conclude so, the authors must demonstrate that the used RNAis efficiently removed its target proteins. 8. Line 222, "DG is required to organize the ECM in several experimental settings [42, 43, 45, 51]". Here, the authors must mention to a preceding paper that reported the eye deficiency of Dg mutant flies (PMID: 20463973), and discuss what new findings authors can add to the previous report. 9. Line 240. "RNAi against dSarcoglycan led to a decrease in LanA::GFP expression at the presumptive grommet at 20h APF (Figure 7F)". As to this result, the authors seem to interpret that Laminin is not recruited to the "specific BM domain" in grommet in the absence of Dg signalling. However, other possibilities exist, e.g., that the global expression level of Laminin was reduced, or that the intercellular space into which soluble Laminin (see the issue 4 above) flows was narrowed down. The authors should show the data that exclude (or at least reduce) these possibilities. 10. Line 255, "These perturbations led to a failure of bPS/Mys to accumulate at the grommet". Dg mutants are viable (PMID: 18093579); do they show consistent phenotypes?

      Minor comments:

      1. Line 85, "This is the case in the follicular epithelium for example". Here, the text would be more reader-friendly if the authors could clarify this is the follicular epithelium of the fly ovary.
      2. Line 203-, regarding all the experiments involving the Gal4-UAS system. Not all the readers are familiar with the system. A brief explanation on it should be added in the main text. Moreover, in the Results section, not in the Methods, the authors should show what Gal4 they used, and where is the Gal4 expressed.
      3. Line 239, "We found that inhibiting the expression of the DG cofactor, dSarcoglycan [53] was most effective in inhibiting this pathway in retinal cells". Here, the authors should show the data.

      Referee cross-commenting

      This session includes comments from both reviewers

      Reviewer 2: I almost totally agree with Reviewer 1, who is also mainly concerned about the functional analyses part of the paper while being impressed by the authors' beautiful imaging. One issue that Reviewer 1 and I apparently disagree with is the Estimated time to Complete Revisions: while they say 1-3 months, I say 3-6. However, actually I don't think this is a serious discrepancy. Thinking of the time to obtain flies and carry out their crosses necessary for the requested experiments, I'm afraid that the revision cannot be done in 1 month. However, if the authors are fortunate, they may finish the revision in 2-3 months. As I still think that the authors may struggle, I would say the time 2-6 months. I'd be glad if the comments of Reviewer 1 and me could complement with each other to help the revision of the manuscript.

      Reviewer 1:As Reviewer #2 mentioned, there is a strong convergence of our opinions on this article, which should make the work of the authors easier. In fact, I hesitated between 1-3 or 3-6 months for the estimated revision time.

      Reviewer2: Thank you Reviewer #1 for your response. I guess we (Reviewers #1 and #2) have reached an agreement now, haven't we?

      Significance

      General assessment:

      The beautiful images presented here provide interesting descriptions of the events occurring during eye development. Also, the authors' hypothesis on the Dg-dependent recruitment of Laminin leading to integrin polarisation and tissue morphogenesis is simple and attractive. However, I'm afraid that this hypothesis is not supported enough by the presented data. In addition, the novelty of some conclusions and the reliability of a number of reagents used are unclear. Therefore, I cannot say that the conclusions of this manuscript are solid.

      Advance:

      The 3D imaging of ommatidia development is beautiful and of good descriptive value. However, as mentioned in the major comments 1, 2, 3, and 8 above, I am afraid that the search of preceding literature seems insufficient, and it is often unclear what this manuscript add to existing knowledge.

      Audience:

      If the issues mentioned above have been solved, this manuscript would be of general interest to researchers in various fields in cell and developmental biology. Would not be restricted to those using Drosophila.

    1. Men who write openly as gay men have also often been excluded from the consensus of the traditional canon and may operate more forcefully now within a specifically gay /lesbian canon

      I think this is an interesting point to bring up. Maybe the "queer media" we consume and have consumed could have been more widely accepted if homophobia wasn't so prevalent.

    1. Reviewer #2 (Public Review):

      I believe the authors succeeded in finding neural evidence of reactivation during REM sleep. This is their main claim, and I applaud them for that. I also applaud their efforts to explore their data beyond this claim, and I think they included appropriate controls in their experimental design. However, I found other aspects of the paper to be unclear or lacking in support. I include major and medium-level comments:

      Major comments, grouped by theme with specifics below:<br /> Theta.<br /> Overall assessment: the theta effects are either over-emphasized or unclear. Please either remove the high/low theta effects or provide a better justification for why they are insightful.

      Lines ~ 115-121: Please include the statistics for low-theta power trials. Also, without a significant difference between high- and low-theta power trials, it is unclear why this analysis is being featured. Does theta actually matter for classification accuracy?

      Lines 123-128: What ARE the important bands for classification? I understand the point about it overlapping in time with the classification window without being discriminative between the conditions, but it still is not clear why theta is being featured given the non-significant differences between high/low theta and the lack of its involvement in classification. REM sleep is high in theta, but other than that, I do not understand the focus given this lack of empirical support for its relevance.

      Line 232-233: "8). In our data, trials with higher theta power show greater evidence of memory reactivation." Please do not use this language without a difference between high and low theta trials. You can say there was significance using high theta power and not with low theta power, but without the contrast, you cannot say this.

      Physiology / Figure 2.<br /> Overall assessment: It would be helpful to include more physiological data.

      It would be nice, either in Figure 2 or in the supplement, to see the raw EEG traces in these conditions. These would be especially instructive because, with NREM TMR, the ERPs seem to take a stereotypical pattern that begins with a clear influence of slow oscillations (e.g., in Cairney et al., 2018), and it would be helpful to show the contrast here in REM. Also, please expand the classification window beyond 1 s for wake and 1.4 s for sleep. It seems the wake axis stops at 1 s and it would be instructive to know how long that lasts beyond 1 s. The sleep signal should also go longer. I suggest plotting it for at least 5 seconds, considering prior investigations (Cairney et al., 2018; Schreiner et al., 2018; Wang et al., 2019) found evidence of reactivation lasting beyond 1.4 s.

      Temporal compression/dilation.<br /> Overall assessment: This could be cut from the paper. If the authors disagree, I am curious how they think it adds novel insight.

      Line 179 section: In my opinion, this does not show evidence for compression or dilation. If anything, it argues that reactivation unfolds on a similar scale, as the numbers are clustered around 1. I suggest the authors scrap this analysis, as I do not believe it supports any main point of their paper. If they do decide to keep it, they should expand the window of dilation beyond 1.4 in Figure 3B (why cut off the graph at a data point that is still significant?). And they should later emphasize that the main conclusion, if any, is that the scales are similar.

      Line 207 section on the temporal structure of reactivation, 1st paragraph: Once again, in my opinion, this whole concept is not worth mentioning here, as there is not really any relevant data in the paper that speaks to this concept.

      Behavioral effects.<br /> Overall assessment: Please provide additional analyses and discussion.

      Lines 171-178: Nice correlation! Was there any correlation between reactivation evidence and pre-sleep performance? If so, could the authors show those data, and also test whether this relationship holds while covarying our pre-sleep performance? The logic is that intact reactivation may rely on intact pre-sleep performance; conversely, there could be an inverse relationship if sleep reactivation is greater for initially weaker traces, as some have argued (e.g., Schapiro et al., 2018). This analysis will either strengthen their conclusion or change it -- either outcome is good.

      Unlike Schönauer et al. (2017), they found a strong correspondence between REM reactivation and memory improvement across sleep; however, there was no benefit of TMR cues overall. These two results in tandem are puzzling. Could the authors discuss this more? What does it mean to have the correlation without the overall effect? Or else, is there anything else that may drive the individual differences they allude to in the Discussion?

      Medium-level comments<br /> Lines 63-65: "We used two sequences and replayed only one of them in sleep. For control, we also included an adaptation night in which participants slept in the lab, and the same tones that would later be played during the experimental night were played."

      I believe the authors could make a stronger point here: their design allowed them to show that they are not simply decoding SOUNDS but actual memories. The null finding on the adaptation night is definitely helpful in ruling this possibility out.

      Lines 129-141: Does reactivation evidence go down (like in their prior study, Belal et al., 2018)? All they report is theta activity rather than classification evidence. Also, I am unclear why the Wilcoxon comparison was performed rather than a simple correlation in theta activity across TMR cues (though again, it makes more sense to me to investigate reactivation evidence across TMR cues instead).

      Line 201: It seems unclear whether they should call this "wake-like activity" when the classifier involved training on sleep first and then showing it could decode wake rather than vice versa. I agree with the author's logic that wake signals that are specific to wake will be unhelpful during sleep, but I am not sure "wake-like" fits here. I'm not going to belabor this point, but I do encourage the authors to think deeply about whether this is truly the term that fits.

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      Reply to the reviewers

      We would truly like to thank all 3 reviewers for insightful, helpful and thus constructive comments.

      Reviewer #1

      Summary

      In this manuscript, Lockyer et al. provide novel insights into the mechanism by which Toxoplasma gondii avoids parasite restriction in IFNγ-activated human cells. To identify potentially secreted proteins supporting parasite survival in IFNγ-activated human foreskin fibroblasts (HFF), the authors designed a CRISPR screen of Toxoplasma secretome candidates based on hyperLOPIT protein localization data. By this approach, they identified novel secreted proteins supporting parasite growth in IFNγ-activated cells. Among the gene identified, they found MYR3 a known component of the putative translocon in charge of protein export through the parasitophorous vacuole membrane. Therefore, the authors focused their investigations on GRA57, a dense granule protein of unknown function, which affects parasite survival to a lesser extent than the MYR component. The resistance phenotype conferred by GRA57 was confirmed by fluorescence microscopy. Importantly, the authors provide evidence that the protective function of GRA57 is not as well conserved in murine cells of the same type (MEF) as in HFF. To further explore the mechanism by which GRA57 protect the parasites in IFNγ-activated cells, the authors searched for protein partners by biochemistry. By immunoprecipitation and tandem mass spectrometry, they identified two other putative dense granule proteins, GRA70 and GRA71, which co-purified with GRA57-HA tagged protein. Noteworthy, both proteins were also found in the CRISPR screens with significant score conferring resistance. High-content imaging analysis confirmed the protective effect conferred by GRA57, GRA70, and GRA71 individually at similar levels. After ruling out an effect of tryptophan deprivation in parasite clearance, or a role of GRA57 in protein export normally mediated by the MYR translocon, and a role on host cell gene expression by RNA-Seq, the authors investigated the ubiquitination of the parasitophorous vacuole membrane, a marker previously thought to initiate parasite clearance. A reduction in ubiquitin labeling around the vacuole of mutant parasites is observed, which is quite surprising given the correlated increase in parasite clearance. The authors concluded that ubiquitin recruitment may not be directly linked to the parasite clearance mechanism.

      Major comments

      • Figure 2C. In this figure, the restriction effect of IFNγ is about 60% (or 40% survival) for RHdeltaUPRT parasites grown in HFFs, which is quite different from the 85% mentioned earlier in the results section. How was actually done the first assay? Settings with 60% restriction sounds reasonable and indicates that a substantial fraction of the parasite population evades the restrictive effect of IFNγ, which provides a clear rationale for the main objective of this study, namely the identification of effectors supporting parasite development in human cells in the presence of IFNγ.

      This discrepancy in restriction likely arises from the differences in the parasites used in these assays and the measurements of restriction. The 85%/90% restriction initially mentioned is from the pooled CRISPR screens using the effector knockout pool. This restriction level was assessed by counting of parasites retrieved following infection of IFNg-stimulated HFFs. The 60% restriction of wildtype parasites seen in Figure 2 is a separate assay. This percentage was calculated by measuring total mCherry fluorescence area within infected HFFs. We expect the restriction of the pooled CRISPR population to be higher than in restriction assays performed with either wild type parasites or single genetic knockouts. We included the 85%/90% numbers to highlight that the HFFs were highly restrictive in the screen, but we have now removed references to these numbers in the results section to avoid confusion with later results that use more accurate measures of survival. We refer to this restriction level instead in the discussion section.

      Optional comment: GRA70 and GRA71 were both copurified with GRA57, but what about GRA71 expression and localization? Is there a reason why this protein partner has not been studied further just like GRA70?

      Tagging of GRA71 was attempted but was not successful in a first attempt. We have not re-attempted this tagging as Krishnamurthy et al 2023 (PMID: 36916910) recently tagged and localised GRA71, demonstrating it is also an intravacuolar dense granule protein with similar localisation to GRA57 and GRA70- we feel there is minimal value in us repeating this.

      *Is there any change in GRA57, GRA70, and GRA71 localization and/or amount when cells were pretreated with IFNγ? *

      Thank you for this suggestion, we have now conducted further investigation to address this. We checked the localisation of GRA57-HA and GRA70-V5 in IFNg-stimulated HFFs and found no change to their localisation. This data has been added in Supplementary Figure S4 in our revised manuscript. Alignment of our RNA-Seq data to the Toxoplasma genome, now included as Supplementary Data 4, also shows there is no significant up or downregulation in expression of any of the three proteins when HFFs are pretreated with IFNg.

      Do they still form a complex in the absence of IFNγ?

      We did not investigate this in this manuscript, however in Krishnamurthy et al 2023 (PMID: 36916910) CoIPs using GRA57 and GRA70 in the absence of IFNγ also identified these three proteins as interaction partners, so formation of the complex is likely IFNg-independent.

      • In the absence of GRA70 or GRA71 is GRA57 expression and/or localization affected?*

      We did not investigate this possibility in this manuscript, however doing so would require the generation of epitope tagged lines in knockout backgrounds. We believe this represents a significant body of work and would therefore be suitable for a future study focused on the further characterisation of this complex. The RNA-Seq data shows that GRA70 and GRA71 expression levels are not significantly different in the RH∆GRA57 strain (Supplementary Data 4) which we have now included as a statement in the results section.

      • *Page 13, result section. To determine whether GRA57 has any direct or indirect effect on host cell gene expression, the authors performed RNA-Seq analysis of HFF cells pretreated or not with IFNγ. First, as for proteomic data, were the data deposited on GEO or another repository database? *

      Second, were any effect detected on parasite gene expression? Reads alignment could be done using the T. gondii reference genome to determine whether IFNg or gra57 KO has any effect on parasite genes. Possibly, other secreted proteins not necessarily expressed at the tachyzoite stage and therefore not captured in the hyperLOPIT protein analysis are specifically expressed in these conditions.

      We will deposit the RNA-Seq data on GEO prior to final publication. We did perform read alignment using the Toxoplasma gondii reference genome, and we agree it would be useful to include this analysis. We have now provided this data in Supplementary Data 4. Comparison of parasite gene expression between RH∆Ku80 and RH∆GRA57 revealed very few major changes (L2FC 2) that were also rescued in the RH∆GRA57::GRA57 line, irrespective of IFNg stimulation. Of the few genes that were up or downregulated in the RH∆GRA57 parasites, these were all uncharacterised. Collectively this data did not provide any mechanistic insight into the function of GRA57, and we think it unlikely the GRA57 phenotype is related to major changes in host or parasite gene expression. We have amended the manuscript to highlight this.

      Optional comment: RNA-Seq analysis points to a clear induction of GBPs upon IFNγ treatment in HFF. Given the clear function of GBP in parasite clearance, have the authors ever hypothesized that GRA57 could be involved in preventing GBP binding to the PVM?

      We have not tested if GBP recruitment is influenced by GRA57, however GBPs have previously been shown to be dispensable for restriction of Toxoplasma growth in HFFs (Niedelman et al 2013, PMID: 24042117) despite being robustly induced by IFNg stimulation (Kim et al 2007, PMID: 17404298). We have modified the manuscript to highlight this.

      Minor comments

      • Page 4, introduction, 8th paragraph. Regarding the role of IST, it might be less prone to controversy to state: 'a condition that may only be met in the early stages of infection.'

      We agree and have changed this.

      • Page 4, end of introduction. Changing '... indicating that the three proteins function in a complex'. Changing to '... indicating that the three proteins function in the same pathway.' might be more appropriate for the conclusion.

      We agree and have changed this.

      • Page 4, result section, first paragraph. 'strain specific and independent effectors'. Are the authors talking about strain-specific and non-strain-specific factors?

      Yes- we have changed the text to reflect this.

      - Page 6, result section. 'GRA25, an essential virulence factor in mice'. It is not clear to the reviewer how a virulence factor is essential since both parasite and mouse survival is achieved in the GRA25 mutant. I suggest to replace 'essential' by 'major'.

      We agree and have changed this.

      - Page 7. 'showing that GRA57 resides in the intravacuolar network (IVN) (Figure 2A)'. From the image shown, GRA57 clearly localizes into the PV, but it is hard to tell whether GRA57 is associated with the intravacuolar network. Colocalization assay or electron microscopy would be necessary to draw such conclusions.

      We agree and have changed all references to this localisation as ‘intravacuolar’ instead of specifically the IVN.

      - 'uprt locus'. Lower case letters and italic are generally preferred to designate mutants, whereas upper case letters are generally used for wild type alleles. (Sibley et al., Parasitology Today, 1991. Proposal for a uniform genetic nomenclature in Toxoplasma gondii).

      We agree and have changed this.

      - The authors mentioned in the introduction that ROP1 contributes to T. gondii resistance to IFNγ in murine and human macrophages. However, they did not comment on whether ROP1 was found important in the screen performed here in human HFF cells. It may be useful to reference ROP1 in Figure 1 as GRA15, GRA25, etc.

      ROP1 was not found to be important in the HFF screens (+IFNg L2FCs in RH: -0.1, PRU: -0.46). As ROP1 was characterised as an IFNg resistance effector in macrophages, this discrepancy may therefore represent a cell type-specific difference, so we feel it is not relevant to highlight for the purposes of the screens presented here.

      - Figure 2D. The authors compared the restriction effect of IFNγ on parasites grown in HFF and MEF host cells. However, as represented - % + IFNγ/- IFNγ - it cannot be estimated whether the parasites grew similarly in the two host cell types in the absence of IFN. Please indicate whether or not the growth was similar in both cell types.

      As these restriction assays were not carried out concurrently and were designed to measure IFNg survival, we feel it would be inaccurate to compare parasite growth between the two cell types using this data. The focus of these experiments was to investigate the restrictive effect of IFNg across parasite strains, using the -IFNg condition to control for differences in growth rate or MOI. Therefore we feel it is appropriate for the focus of our manuscript to represent the data in this way.

      - pUPRT plasmid. Any reference or vector map would be appreciated.

      We have added the reference for this plasmid.

      - Page 9, figure 3A, mass spectrometry analysis. I did not find the MS data in supplementals. Were the data deposited in on PRIDE database or another data repository?

      The table was included as Supplementary Data 2, however this was not referred to in the main text. We have now amended the text to include this. The data will be deposited on PRIDE prior to final publication.

      - Figures 3E and 3F. It might be worth mentioning, at least in the figure legend, that GRA3 localizes at PV membrane and is exposed to the host cell cytoplasm (to mediate interactions with host Golgi). The signal for GRA3 following saponin treatment is here an excellent control that should be highlighted, indicating that saponin effectively permeabilized the host cell membrane.

      We agree and have updated the figure legend and the main text. We have also added a reference to Cygan et al 2021__ (__PMID: 34749525) in support of this data, which found GRA57, but not GRA70 or GRA71, enriched at the PVM.

      • Page 11, section title. I think that the authors meant 'GRA57, GRA70 and GRA71 confer resistance to vacuole clearance in IFNγ-activated HFFs.'

      We agree and have changed this.

      • Page 11, in the result section comparing the effect of GRA57 mutant with MYR component KO, the authors are referring to host pathways that are counteracted by MYR-dependent effectors released into the host cell. It is not clear which pathways the authors are referring to.

      It is not known exactly which host pathways mediate vacuole clearance or parasite growth restriction, or which MYR-dependent parasite effectors specifically resist these defences, therefore we have removed this statement from the text for clarity.

      • Page 16, discussion, end of 4th paragraph. '... to promote parasite survival in IFNγ activated cells' sounds better.

      We agree and have changed this.

      • Page 22-23, Methods section, c-Myc nuclear translocation assays and elsewhere. Please indicate how many events were actually analyzed. For example, in this assay, to determine the median nuclear c-Myc signal, how many infected cells were analyzed for each biological replicate?

      We have updated the methods section for the c-Myc nuclear translocation and ubiquitin-recruitment assays to include details on how many events were analysed.

      **Referees cross-commenting**

      Overall, I agree with most of the co-reviewers' remarks. I agree with reviewer #2 that this manuscript reports interesting data for the field of parasitology, but that the broad interest for immunologists is somewhat limited by the lack of a description of the mechanism by which these effectors oppose IFNgamma-inducible cell-autonomous defenses. I also agree with the other reviewers' comments regarding the GRA57, 70, and 71 heterotrimeric complex, which would require further description. In its present form, the manuscript undoubtedly represents an interesting starting point for further investigations and any additional data regarding the mode of interaction of the identified effectors and their function related or not to ubiquitylation would bring a significant added value.

      Reviewer #1 (Significance (Required)):

      Despite the fact that humans are accidental intermediate hosts for Toxoplasma gondii, the parasite may develop a persistent infection, demonstrating that it has effectively avoided host defenses. While Toxoplasma gondii has been extensively studied in mice, much less is known about the mechanisms by which the parasite establishes a chronic infection in humans. In this context, this article described very interesting data about the way this parasite counteracts human cell-autonomous innate immune system. This is a fascinating and important topic lying at the interface between parasitology and immunology. Indeed, the highly specialized secretory organelles characteristics of apicomplexan parasites are key to govern host-cell and parasite interactions ranging from host cell transcriptome modification to counteracting immune defense mechanisms. Overall, this article presents a significant contribution to the field of parasitology by identifying novel players involved in Toxoplasma gondii's evasion of human cell-autonomous immunity. Most conclusions are generally well supported by cutting-edge approaches and state of the art methods. Despite being a highly competitive field, this article stands out as the first screen designed specifically to identify virulence factors for human cells and extends our understanding of the secreted dense granule proteins resident of the parasitophorous vacuole. Importantly, the authors provide evidence that these players are active in different strain backgrounds and act in a way that is independent of the export machinery in charge of delivering effector proteins directly into the host cell. However, substantial further research is needed to fully understand the mechanism by which these novel players confer resistance to the parasite in IFNγ activated human cells and how their mode of action differs from that mediated by the translocation machinery (MYR complex). As a microbiologist and biochemist, I find this work of a particular interest to a broad audience, especially to parasitologists and immunologists, as it may unveil unexpected aspects of human innate immunity involved in parasite clearance with proteins unique to Apicomplexa phylum.

      Reviewer #2

      This paper reports high-quality genetic screening data identifying three novel Toxoplasma virulence factors (Gra57,70, and 71) that promote survival of two distinct Toxoplasma strains (type I RH and type II Pru) inside IFN-gamma primed human fibroblasts. Follow-up studies, exclusively focused on type I RH Toxoplasma, confirm the screening data. Gra57 IP Mass-Spec data suggest that Gra57, 70, and 71 may form a protein complex, a model supported by comparable IF staining patterns

      Major:

      - It is unclear what statistical metric was used to define screen hits as strain-dependent vs strain-independent. A standard approach would be to use a specific z-score value (often a z-score of 2) above or below best fit linear relationship between L2FCU for RH vs Pru as depicted in Fig.1D. Gra25 and Gra35 appear to be specific for Pru but it would be helpful to approach this type of categorization statistically. Also, such an analysis may reveal that only Pru-specific but not RH-specific hits were identified. Could the authors speculate why that would be?

      We did not use a specific statistical metric to define screen hits as strain-dependent vs strain-independent, but GRA57 was selected as a strain-independent hit based on having a L2FC of RH specific: TGME49_309600 (GRA71) & CST9

      PRU specific: GRA35, GRA25, ROP17, GRA23 & GRA45

      Strain-independent: MYR3, GRA57, TGME49_249990 (GRA70) & MYR1

      This agrees with our selection of strain-independent hits. However, we feel that using either L2FC or Z-score cut-offs is equally arbitrary, and we would therefore prefer to leave the data displayed without these cut-offs. It is indeed interesting that there appear to be more strain-specific hits in the PRU screen, but we cannot speculate as to why this may be as we did not explore this further here.

      *- The paper proposes that Gra57, 70, and 71 form a heterotrimeric complex. This is based on the Mass-spec data from the original Gra57 pulldown, similar IF staining patterns, and comparable phenotypic presentation of the individual KO strains. However, only the MS data provide somewhat direct evidence for the formation a trimeric complex, and these data are by no means definitive. As this is a key finding of the MS, it should be further supported by additional biochemical data. Ideally, the authors should reconstitute the trimeric complex in vitro using recombinant proteins. Admittedly, this could be quite an undertaking with various potential caveats. Alternatively, reciprocal pulldowns of the 3 components could be performed. Super-resolution microscopy of the 3 Gra proteins might present another avenue to obtain more compelling evidence in support of the central claim of this work, *

      We attempted a reciprocal pulldown using our GRA70-V5 line which unfortunately failed to verify the MS data, but we believe this is primarily due to differences in the affinity matrix that we used for this pulldown (anti-V5 vs anti-HA) and would require further optimisation or generation of a GRA70-HA line. However, while these revisions were being performed, another group published data demonstrating through pulldown of GRA57 and GRA70 that these proteins interact with each other, GRA71, and GRA32__ (__Krishnamurthy et al 2023, PMID: 36916910). We also identified GRA32 as enriched in our MS data, but to a less significant degree than GRA70 and GRA71. Together we believe that this independent data set is a robust validation of our findings, and strongly justifies the conclusion that these proteins form a complex.

      We agree with the reviewer that further biochemical characterisation of the complex will be an interesting avenue for future research, but we feel it would require a substantial amount of further work. As suggested, super-resolution microscopy of the 3 proteins would require the generation of either double or triple tagged Toxoplasma lines, or antibodies against one or more of the complex members. Again, we feel this would represent a substantial body of further work. Reconstitution of the complex in vitro would require recombinant expression and purification of multiple large proteins that are all multidomain and possibly membrane associated/integrated. Assuming a 1:1:1 stoichiometric assembly this complex would be 446kDa. Purification of such proteins and reconstitution of the complex in vitro is therefore likely to represent many challenges and we do not feel this would be trivial to accomplish.

      - The ubiquitin observations made in this paper are a bit preliminary and the authors' interpretation of their data is vague. The authors may want to re-consider that ubiquitylated delta Gra57 PVs are being destroyed with much faster kinetics than ubiquitylated WT PVs. The reduced number of ubiquitylated delta Gra57 PVs compared to ubiquitylated WT PVs across three timepoints (as shown by the authors in Fi. S8) does not disprove the 'fast kinetics model.' To test the fast kinetics ubiquitin-dependent null hypothesis, video microscopy could be used to measure the time from PV ubiquitylation onset to PV destruction

      We agree with the reviewer that the possibility remains that GRA57 knockouts are cleared within the first hour of infection, and we have amended our text to reflect this. However, we think this is unlikely given that GRA57 knockouts are also less ubiquitinated in unstimulated cells, yet do not show any growth differences in unstimulated HFFs. Also considering the new data we have provided showing reduced recognition of GRA57 knockouts by the E3 ligase RNF213 (Figure 5D), we expect that the observed reduction in ubiquitination is highly likely to be unlinked to the increased susceptibility of GRA57 knockouts to IFNg. We have amended the discussion to state this conclusion more strongly.

      The recently published manuscript that also identified GRA57/GRA70/GRA71 as effectors in HFFs showed that deletion of these effectors leads to premature egress from IFNg-activated HFFs__ (__Krishnamurthy et al 2023, PMID: 36916910). In light of this new data, we hypothesised that early egress could be causing the apparent reduction in ubiquitination. We have now provided data that disproves this hypothesis (Figure S10), as inhibition of egress did not rescue the ubiquitination phenotype. We also did not observe enhanced restriction of GRA57 knockout parasites at 3 hours post-infection (Figure S10B), suggesting clearance, or egress, happens after this time point.

      We agree with the reviewer that determining the kinetics of IFNg restriction of these knockouts in HFFs would be interesting, however we feel this is more suited to future work. Imaging ubiquitin recruitment in live cells would also require the generation of new reporter host cell lines which would require a substantial amount of further work.

      - Related to the point above. We know that different ubiquitin species are found at the PVM in IFNgamma-primed cells but to what degree each Ub species exerts an anti-parasitic effect is not well established. The paper only monitors total Ub at the PVM. Could it be that delta Gra57 PVs are enriched for a specific Ub species but depleted for another? The authors touch on this in the Discussion but these are easy experiments to perform and well within the scope of the study. At least the previously implicated ubiquitin species M1, K48, and K63 should be monitored and their colocalization with Toxo PVMs quantified

      We agree that these experiments are within the scope of this study. We have now investigated the ubiquitin phenotype further by assessing the recruitment of M1, K48 and K63 ubiquitin linkages to the vacuoles of GRA57 knockouts. We observed depletion of both M1 and K63 linked ubiquitin. This data is now included in Figure 5 and Figure S8.

      The E3 ligase RNF213 has recently been shown to facilitate recruitment of M1 and K63-linked ubiquitin to Toxoplasma vacuoles in HFFs (Hernandez et al 2022, PMID: 36154443 & Matta et al 2022, DOI: https://doi.org/10.1101/2022.10.21.513197 ). We therefore additionally assessed the recruitment of RNF213 to GRA57 knockouts, and found RNF213 recruitment was also reduced. Given that a reduction in RNF213 recruitment should correlate with a decrease in restriction, this data further supports our conclusion that the ubiquitin and restriction phenotypes are not causally linked. The observation that GRA57 knockouts are less susceptible to recognition by RNF213 also opens an exciting avenue for further research into the host recognition of Toxoplasma vacuoles by RNF213, for which currently the target is unknown.

      Minor:

      - For readers not familiar with Toxo genetics, the authors should include a sentence or two in the results section explaining the selection of HXGPRT deletion strains for the generation of Toxo libraries

      We agree and have added this in.

      - the highest scoring hits from the Pru screen (Gra35 &25) weren't investigated further. These hits appear to be specific for Pru. Some discussion as to why there are Pru-specific factors (but maybe not RH-specific factors) seems warranted

      As mentioned above, we agree that it is indeed interesting that there appear to be more strain-specific hits in the PRU screen, but we cannot speculate as to why this may be as we did not explore the reasons for this further in this manuscript. Without substantial further investigation it cannot be determined whether these represent true strain-specific differences or reflect technical variability between the independent screens. We therefore feel it is sufficient to highlight effectors with the strongest phenotypes in each screen, without drawing strong conclusions regarding strain-specificity.

      **Referees cross-commenting**

      My reading of the comments is that there's consensus that this is a high quality study revealing novel Toxo effectors that undermine human cell-autonomous immunity and an important study in the field of parasitology. I might be the outlier that doesn't see much of an advance for the field of immunology since we don't really know what these effectors are doing, and the preliminary studies addressing this point are not well developed, with some confusing results.

      My major comment #2 and rev#1's major comment #2 are, I think, essentially asking for the same thing, namely some more robust data on substantiating the formation of a trimeric complex.

      My co-reviewers made great comments all across and I don't see any real discrepancies between the reviewers' comments - just some variation in what we, the reviewers, focused on

      Reviewer #2 (Significance (Required)):

      The discovery of a novel set of secreted Gra proteins critical for enhanced Toxoplasma survival specifically in IFNgamma primed human fibroblasts (but not mouse fibroblasts) is an important discovery for the Toxoplasma field. However, the study is somewhat limited in its scope as it fails to determine which, if any, specific IFNgamma-inducible cell-autonomous immune pathway is antagonized by Gra57 &Co. Instead, the paper reports that parasitophorous vacuoles (PVs) formed by Gra57 deletion mutants acquire less host ubiquitin than PVs formed by the parental WT strain. Because host-driven PV ubiquitylation is generally considered anti-parasitic, this observation is counterintuitive, and no compelling model is presented to explain these unexpected findings. Overall, this is a well conducted Toxoplasma research study with a few technical shortcomings that need to be addressed. However, in its current form, the study provides only limited insights into possible mechanisms by which Toxoplasma undermines human immunity. This study certainly provides an exciting starting point for further explorations.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Summary:

      Toxoplasma gondii virulence and immune responsed upon infection in mice are well described. In contrast, little is known about human responses, particularly upon IFNγ-activation. However, host ubiquitination of the parasitophorous vacuole has been shown to be associated with parasite clearence in human cells.

      Targeted CRISPR screens were used in the type I RH and type II Pru strain of Toxoplasma gondii to identify dense granule and rhoptry proteins. Human foreskin fibroblasts (HFFs) stimulated with IFNγ were used for infection of the knock-out parasites to identify guide RNAs and thus their corresponding genes to identify genes conferring growth benefits. Beside components of the MYR translocon, gra57 was identified. This gene was then knock-out or epitope-tagged in RH. The tagged line confirmed GRA57 localisation in the intravacuolar network confirming previously published work from another lab. Knock-out of gra57 lead to a moderate decrease in survival in HFFs, but not in mouse cells. Co-immunoprecipitation experiments with GRA57 identified 2 dense granule proteins that also display IFNγ-specific phenotypes with similar localisation as GRA57, and all are resistance factors in IFNγ-activated HFFs. Knock-out of GRA57 does not impact tryptophan metabolism, effector export of gene expression of the host cells. However, deletion of GRA57 or its interaction partners reduces ubiquitination of the parasitophorous vacuole.

      Major comments:

      This is a well executed study with informative, novel data. Here a few comments and questions:

      - LFC cut-off of the CRISPR screen should be clearly stated.

      We have amended this in the text.

      - What is the rationale for using Prugniaud as the type II strain of choice and not ME49?

      Both ME49 and PRU strains are widely used in the field, but as the PRU strain was used previously by our group for in vivo screens of Toxoplasma effectors (Young et al 2019 PMID: 31481656, Butterworth et al 2022 PMID: 36476844) ,using PRU here allows for direct comparison of our screening datasets.

      - Figure 4A does not list all the significant genes that are then mentioned in the text below. This should be amended.

      It is unclear what the reviewer is referring to here (Figure 4A displays restriction assay data).

      *- RNA-Seq data is inadequately presented. Although, the actual genes regulated may be of secondary importance in this study, it would still be good to have a few key genes mentioned as a quality control statement. *

      This was also raised by reviewer 1. We have now modified the manuscript to highlight that we observed robust induction of interferon-stimulated genes in our IFNg-treated conditions, but minimal differential gene expression between HFFs infected with the different parasite strains.

      *- It is stated that "...GRA57 is not as important for survival in MEFs as in HFFS". With no significant change observed, it should be re-phrased to something like ""...indicatin that GRA57 is s important for survival in MEFs as in HFFS." *

      We have re-phrased this statement.

      *- Optional: GRA57 was described by the Bradley lab to be in the PV in tachyzoites and in the cyst wall in bradyzoites. Although it tissue cysts are not the focus of this paper and the knock-out is created also in a cyst-forming strain, it would have been useful to look for a phenotype of the knockout in cysts, in vitro at least, better both in in vitro and in vivo. In future, this could also be useful for the authors bringing in more citations. *

      We agree with the reviewer that the impact of GRA57 on cyst formation would be an interesting topic for further exploration, however the focus of our study is on the role of secreted Toxoplasma effectors during the acute stages of infection.

      Minor comments:

      - Line numbers would be useful for an efficient review process.

      We have added these to the revised manuscript.

      - Strictly speaking, we have to talk about the sexual development taking place in felid and not feline hosts (Introduction; Felidae versus Felinae).

      We have amended this in the text.

      - Please insert spaces between numbers and units.

      We have corrected this.

      - Domain structures are presented, but maybe the AlphaFold 3D predictions could be added in a supplemental figure?

      For GRA70 and GRA71 the AlphaFold 3D predictions are readily available on ToxoDB, whereas for GRA57 the prediction is not available due its size. We therefore independently analysed GRA57 using the full implementation of AlphaFold 2 (not ColabFold). We attempted submissions of putative discrete domains as well as the full-length protein, however both approaches yielded predictions with low confidence and low structural content, except for a ~100aa region of helical residues. We chose not to include the AlphaFold 3D predictions for all three proteins as the confidence for these predictions is low with pLDDT scores of commonly *- To improve the confidence of the co-immunoprecipitation, it would be necessary to use another tagged protein GRA70 or 71) and see if the same complex can be pulled down. Like this, one could also address what happens in a GRA57KO line? Do GRA70 and 71 stay together in the absence of GR57 forming a dimer? *

      Reviewer 2 raised a similar point regarding the reciprocal pulldown, please see above for our detailed response to this. As suggested, we attempted a reciprocal pulldown using our GRA70-V5 line which unfortunately did not reconstitute the complex, but we believe this was due to technical differences in the epitope tag (V5 vs HA) and affinity matrix used. Overall, we believe that more detailed study of the assembly and biochemistry of this complex will require substantially more work and the generation of further cell lines, which would be beyond the scope of this study.

      Reviewer #3 (Significance (Required)):

      Significance:

      This study endeavours to start closing an important knowledge gab of host defence in non-rodent hosts, especially humans. The data is solid using two different strains and yields novel insights into players of host cell resistance in humans against T. gondii. Using a targeted screening approach of rhoptry and dense granule proteins, they focused their interest on a subcategory of secreted proteins. The authors have not limited themselves to the screening and localisation study, but also investigated effect on host cells and host cell response. The identification of GRA57 being an important resistance factor and forming a heterodimer with GRA70 and GRA71 is novel. This study is of interest to cell biologists in the field of cyst-forming Coccidia, especially T. gondii and researchers interested in host resistance, parasite clearance by the host and parasite virulence.

      I am a cell biologist working in Toxoplasma gondii and other Coccidians.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #2

      Evidence, reproducibility and clarity

      This paper reports high-quality genetic screening data identifying three novel Toxoplasma virulence factors (Gra57,70, and 71) that promote survival of two distinct Toxoplasma strains (type I RH and type II Pru) inside IFN-gamma primed human fibroblasts. Follow-up studies, exclusively focused on type I RH Toxoplasma, confirm the screening data. Gra57 IP Mass-Spec data suggest that Gra57, 70, and 71 may form a protein complex, a model supported by comparable IF staining patterns

      Specific criticisms

      Major:

      • It is unclear what statistical metric was used to define screen hits as strain-dependent vs strain-independent. A standard approach would be to use a specific z-score value (often a z-score of 2) above or below best fit linear relationship between L2FCU for RH vs Pru as depicted in Fig.1D. Gra25 and Gra35 appear to be specific for Pru but it would be helpful to approach this type of categorization statistically. Also, such an analysis may reveal that only Pru-specific but not RH-specific hits were identified. Could the authors speculate why that would be?
      • The paper proposes that Gra57, 70, and 71 form a heterotrimeric complex. This is based on the Mass-spec data from the original Gra57 pulldown, similar IF staining patterns, and comparable phenotypic presentation of the individual KO strains. However, only the MS data provide somewhat direct evidence for the formation a trimeric complex, and these data are by no means definitive. As this is a key finding of the MS, it should be further supported by additional biochemical data. Ideally, the authors should reconstitute the trimeric complex in vitro using recombinant proteins. Admittedly, this could be quite an undertaking with various potential caveats. Alternatively, reciprocal pulldowns of the 3 components could be performed. Super-resolution microscopy of the 3 Gra proteins might present another avenue to obtain more compelling evidence in support of the central claim of this work
      • The ubiquitin observations made in this paper are a bit preliminary and the authors' interpretation of their data is vague. The authors may want to re-consider that ubiquitylated delta Gra57 PVs are being destroyed with much faster kinetics than ubiquitylated WT PVs. The reduced number of ubiquitylated delta Gra57 PVs compared to ubiquitylated WT PVs across three timepoints (as shown by the authors in Fi. S8) does not disprove the 'fast kinetics model.' To test the fast kinetics ubiquitin-dependent null hypothesis, video microscopy could be used to measure the time from PV ubiquitylation onset to PV destruction
      • Related to the point above. We know that different ubiquitin species are found at the PVM in IFNgamma-primed cells but to what degree each Ub species exerts an anti-parasitic effect is not well established. The paper only monitors total Ub at the PVM. Could it be that delta Gra57 PVs are enriched for a specific Ub species but depleted for another? The authors touch on this in the Discussion but these are easy experiments to perform and well within the scope of the study. At least the previously implicated ubiquitin species M1, K48, and K63 should be monitored and their colocalization with Toxo PVMs quantified

      Minor:

      • For readers not familiar with Toxo genetics, the authors should include a sentence or two in the results section explaining the selection of HXGPRT deletion strains for the generation of Toxo libraries
      • the highest scoring hits from the Pru screen (Gra35 &25) weren't investigated further. These hits appear to be specific for Pru. Some discussion as to why there are Pru-specific factors (but maybe not RH-specific factors) seems warranted

      Referees cross-commenting

      My reading of the comments is that there's consensus that this is a high quality study revealing novel Toxo effectors that undermine human cell-autonomous immunity and an important study in the field of parasitology. I might be the outlier that doesn't see much of an advance for the field of immunology since we don't really know what these effectors are doing, and the preliminary studies addressing this point are not well developed, with some confusing results.

      My major comment #2 and rev#1's major comment #2 are, I think, essentially asking for the same thing, namely some more robust data on substantiating the formation of a trimeric complex.

      My co-reviewers made great comments all across and I don't see any real discrepancies between the reviewers' comments - just some variation in what we, the reviewers, focused on

      Significance

      The discovery of a novel set of secreted Gra proteins critical for enhanced Toxoplasma survival specifically in IFNgamma primed human fibroblasts (but not mouse fibroblasts) is an important discovery for the Toxoplasma field. However, the study is somewhat limited in its scope as it fails to determine which, if any, specific IFNgamma-inducible cell-autonomous immune pathway is antagonized by Gra57 &Co. Instead, the paper reports that parasitophorous vacuoles (PVs) formed by Gra57 deletion mutants acquire less host ubiquitin than PVs formed by the parental WT strain. Because host-driven PV ubiquitylation is generally considered anti-parasitic, this observation is counterintuitive, and no compelling model is presented to explain these unexpected findings. Overall, this is a well conducted Toxoplasma research study with a few technical shortcomings that need to be addressed. However, in its current form, the study provides only limited insights into possible mechanisms by which Toxoplasma undermines human immunity. This study certainly provides an exciting starting point for further explorations.

    1. Be this as it may, the peculiar relations between the United States and the Indians occupying our territory are such, that we should feel much difficulty in considering them as designated by the term foreign state, were there no other part of the constitution which might shed light on the meaning of these words. But we think that in construing them, considerable aid is furnished by that clause in the eighth section of the third article; which empowers congress to ‘regulate commerce with foreign nations, and among the several states, and with the Indian tribes.’ In this clause they are as clearly contradistinguished by a name appropriate to themselves, from foreign nations, as from the several states composing the union. They are designated by a distinct appellation; and as this appellation can be applied to neither of the others, neither can the appellation distinguishing either of the others be in fair construction applied to them. The objects, to which the power of regulating commerce might be directed, are divided into three distinct classes-foreign nations, the several states, and Indian tribes.

      This is important information as this is what is going to be used as the body and closing arguments, to strip the Cherokee nation of any help from the Federal government, making it so they have it continue to listen to the laws that Georgia's people set.

    1. Author Response

      Reviewer #2 (Public Review):

      The authors use data from 3 cross-sectional age-stratified serosurveys on Enterovirus D68 from England between 2006 and 2017 to examine the transmission dynamics of this pathogen in this setting. A key public health challenge on EV-D68 has been its implication in outbreaks of acute flaccid myelitis over the past decade, and past circulation patterns and population immunity to this pathogen are not yet well-understood. Towards this end, the authors develop and compare a suite of catalytic models as fitted to this dataset and incorporate different assumptions on how the force of infection varies over time and age. They find high overall EV-D68 seroprevalence as measured by neutralizing antibodies, and detect increased transmission during this time period as measured by the annual probability of infection and basic reproduction number. Interestingly, their data indicate very high seroprevalence in the youngest children (1 year-olds), and to accommodate this observation, the authors separate the force of infection in this age class from the other groups. They then reconstruct the historical patterns of EV-D68 circulation using their models and conclude that, while the serologic data suggest that transmissibility has increased between serosurvey rounds, additional factors not accounted for here (e.g., changes in pathogenicity) are likely necessary to explain the recent emergence of AFM outbreaks, particularly given the broader age-profile of reported AFM cases. The Discussion mentions important current unknowns on the biological interpretation of EV-D68 neutralizing antibody titers for protection against infection and disease. The analysis is rigorous and the conclusions are well-supported, but a few aspects of the work need to be clarified and extended, detailed below:

      1) Due to the lack of a clear single cut-point for seropositivity on this assay, the authors sensibly present results for two cut-points in the main text (1:16 and 1:64). While some differences that stem from using different cut-points are fully expected (i.e., seroprevalence being higher using the less stringent cut-point), differences that are less expected should be further discussed. For instance, it was not clear in Figure 2 why the annual probability of infection decreased after 2010 using the 1:64 cut-point, while it continued to increase using the 1:16 cut-point. It would also be helpful to explain why overall seroprevalence and R0 continue to increase over this time period using the 1:64 cut-point. Lastly, it would be useful to see the x-axis in Figure 4 extended to the start of the time period that FOI is estimated, with accompanying credible intervals.

      For the discussion on differences between the two cut-offs, please see response to essential comment 1.

      Extending the x-axis before 2006 in Figure 4 is not possible. Estimates of the overall seroprevalence at a year y require FOI estimates up until y-40. This implies the first estimates we can provide are for 2006.

      Credible intervals have been added to Figure 4.

      2) Additional context of EV-D68 in the study setting of England would be useful. While the Introduction does mention AFM cases "in the UK and elsewhere in Europe" (line 53), a summary of reported data on EV-D68/AFM in England prior to this study would provide important context. The Methods refers to "whether transmission had increased over time (before the first reported big outbreak of EV-D68 in the US in 2014)" (lines 133-134), rather than in this setting. It would be useful to summarize the viral genomic data from the region for additional context - particularly since the emergence of a viral clade is highlighted as a co-occurrence with the increased transmissibility detected in this analysis.

      We have added a figure (new Figure 1 – figure supplement 1) showing the annual number of EV-D68 detections reported by Public Health England from 2004 to 2020.

      We have also added the following text to the introduction: “Similarly, in the UK, reported EV-D68 virus detections also show a biennial pattern between 2014 and 2018 (Figure 1 – figure supplement 1).”

      We have also amended the sentence in the Methods.

      Finally, below is a screenshot of the nexstrain tree for EV-D68 based on the VP1 region and with tips representing sequences from the UK (light blue) and European countries in colour. There is a lot of mixing between sequences from different regions, indicating widespread transmission and small regional clustering. We have added the following text to the Discussion: “Reported EV-D68 outbreaks in 2014 and 2016 were due to clade B viruses, while the 2018 outbreaks were reported to be linked to both B3 and A2 clade viruses in the UK (10), France (32) and elsewhere.”

      Reviewer #3 (Public Review):

      In the proposed manuscript, the authors use cross-sectional seroprevalence data from blood samples that were tested for evidence of antibodies against D68 for the UK. Samples were collected at 3 time points from individuals of all ages. The authors then fit a suite of serocatalytic models to explain the changing level of seropositivity by age. From each model they estimate the force of infection and assess whether there have been changes in transmissibility over the study period. D68 is an important pathogen, especially due to its links with acute flaccid myelitis, and its transmission intensity remains poorly understood.

      Serocatalytic models appear to be appropriate here. I have a few comments.

      The biggest challenge to this project is the difficulty in assigning individuals as seronegative or seropositive. There is no clear bimodal distribution in titers that would allow obvious discrimination and apparently no good validation data with controls with known serostatus. The authors tackle this problem by presenting results to four different cut-points (1:16 to 1:128) - resulting in seropositivity ranging from around 50% to around 80%. They then run the serocatalytic models with two of these (1:16 and 1:64) - leading to a range of FoI values of 0.25-0.90 for the 1 year olds and 0.05-0.25 for older age groups (depending on model and cutpoint). This represents a substantial amount of variability. While I certainly see the benefit of attacking this uncertainty head on, it does ultimately limit the inferences that can be made about the underlying risk of infection in UK communities, except that it's very uncertain and possibly quite high.

      I find the force of infection in 1 year olds very high (with a suggestion that up to 75% get infected within a year) and difficult to believe, especially as the force of infection is assumed much lower for all other ages.

      The authors exclude all <1s due to maternal antibodies, which seems sensible, however, does this mean that it is impossible for <1s to become infected in the model? We know for other pathogens (e.g., dengue virus) with protection from maternal antibodies that the protection from infection is gone after a few months. Maybe allowing for infections in the first year of life too would reduce the very large, and difficult to believe, difference in risk between 1 year olds and older age groups. I suspect you wouldn't need to rely on <1 serodata - just allow for infections in this time period.

      Relatedly, would it be possible to break the age data into months rather than years in these infants to help tease apart what happens in the critical early stages of life.

      Yes. We have added two figures (new Figures 1C and 1D) showing the prevalence of antibodies in children <1 yo. We show these data for the three serosurveys combined, because the number of individuals per month of age is very small.

      One of the major findings of the paper is that there is a steadily increasing R0. This again is difficult to understand. It would suggest there are either year on year increases in inherent transmissibility of the virus through fitness changes, or year on year increases in the mixing of the population. It would be useful for the authors to discuss potential explanations for an inferred gradual increase in R0.

      We have removed the estimates of R0 from the manuscript.

      On a similar note, I struggle to reconcile evidence of a stable or even small drop in FoI in the 1:64 models 4 and 5 from 2010/11 (Figure 3) with steadily increasing R0 in this period (Figure 4). Is this due to changes in the susceptibility proportion. It would be good to understand if there are important assumptions in the Farrington approach that may also contribute to this discrepancy.

      We have removed the estimates of R0 from the manuscript and only present the reconstruction of the annual number of new infections per age class and year (new Figure 5). We think this measure is more adapted to the discussion of the results.

      In addition, when using the classical expression R{0t}=1/(1-S(t)), with S(t) the annual proportion seropositive, the high seroprevalence estimates (new Figure 4) result in extremely high estimates of the basic reproduction number (median ranges: 11.6 – 29.7 for 1:16 and 3.3 – 7.6 for 1:64 during the period 2006 to 2017).

      We had previously used the Farrington approach as it is adapted to cases when the force of infections is different for different age classes.

      The R0 estimates (Figure 4) should also be presented with uncertainty.

      R0 no longer presented, but estimates of overall seroprevalence now presented with uncertainty.

      Finally, given the substantial uncertainty in the assay, it seems optimistic to attempt to fit annual force of infections in the 30 year period prior to the start of the sampling periods. I would be tempted to include a constant lambda prior to the dates of the first study across the models considered.

      We thank the reviewers for the suggestion.

      We implemented this change (constant FOI before 2006) in the previous models without maternal antibodies and the result for the random-walk-based models was that the variance of the random walk was estimated over a very short period, thus resulting in a rather non- smoothed FOI.

      Implementing this change with the new models with maternal antibodies and random-walk on the FOI was technically a bit complex. We therefore kept the simple random-walk over the whole period and added the following paragraph to the Discussion:

      “It is important to interpret well the results for the estimates of the FOI over time from our analysis under the assumptions of the models. First, as the best model uses a random walk on the FOI, the change in transmission that we infer happens continuously over several years. In reality, this may have occurred differently (e.g. in a shorter period of time). Our ability to recover more complex changes in transmission is limited by the data available. It would not be surprising if EV-D68 has exhibited biennial (or longer) cycles of transmission in England over the last few years, as it has been shown in the US (7) and is common for other enteroviruses (30). However, it is difficult to recover changes at this finer time scale with serology data unless sampling is very frequent (at least annual). Therefore, our study can only reveal broader long-term secular changes. Second, interpretation of the results before 2006 must be avoided for two resasons. On the one hand, as we go backwards in time, there is more uncertaintly about the time of seroconversion of the individuals informing the estimates of the FOI. On the other hand, because age and time are confounded in cross-sectional seroprevalence measurements, the random walk on time may account for possible differences in the FOI through age (possibly higher in the youngest age classes, and lowest in the oldest), which are note explicitly accounted for here. This may explain the decline in FOI when going backwards in time before the first cross-sectional study in 2006.”

    1. How do you interpret the term mental model and why do you think that it is important for learning?

      Having learned the concept of 'schema' in my high school's psychology course, I think the mental models should mean the same thing as schema: we either assimilate new knowledge to pre-existing mental structures or we accommodate new knowledge to form new structures. These mental structures play essential roles in increasing the speed of memory encoding and recalling. Even though sometimes schema may cause memory distortions since it's based on pre-existing notions, with enough rehearsal, this could be avoided.

  2. Mar 2023
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      Reply to the reviewers

      Response to reviewers

      We thank all reviewers for their comments and suggestions. In line, below, are our responses, marked in Bold. Textural changes in the manuscript are also marked in Bold.

      Reviewer #1__ (__Evidence, reproducibility and clarity (Required)):

      **Summary:**

      Anuculeate red blood cell (RBC) is one of the interesting biological models that indicate the presence of eukaryotic circadian system independent of transcription-translation feedback. In this manuscript, the authors set up a new method for quantifying the circadian rhythmicity in RBC. The method called "Bloody Blotting" was developed through the careful and insightful investigation of "non-specific band" observed in the western blotting of peroxiredoxin, which has been used for the circadian monitoring of RBC. The authors characterized that the "non-specific' circadian-fluctuating signals, which can be observed by ECL imaging without any antibodies(-HRP), were attributed to ferrous-haem, but not ferric-haem, cross-linked to Hb upon cell lysis. Through the Bloody Blotting, this study suggests that the circadian fluctuation of ferrous-/ferric-haem exist in human and mouse RBC, and the period of rhythmicity is not affected by the canonical clock genes.

      **Major comments:**

      1)Although the authors conducted a careful biochemical evaluation of the "Bloody Blotting" signal, it is still unclear whether the changes in the Hb* (or Hb2*) signal corresponds to the changes in the ferrous-haem level in vivo. A direct perturbation on the level of in vivo ferrous-/ferric-haem is required. For example, is the Hb* (or Hb2*) signal decreased by the administration of amyl nitrite (in mice)?

      __Thank you for the suggestion. We have addressed this and the second reviewer’s comment in a new Figures 4 & S4 and section titled “Effect of rhythms in metHb on vascular flow and body temperature”. __

      For clarity, we have relabelled the schematic in A to “rest phase” and “activity phase” to consolidate data from humans and mice which both feature in the manuscript. We performed two experiments to test the model in Fig 4A and perturb metHb in vivo. The first is a direct perturbation of metHb levels in vivo with sodium nitrite, an oxidising agent that causes methaemoglobinemia. Reflecting our results ex vivo, RBC from differentially entrained mice sampled at the same external time, but 12h apart in terms of the light:dark cycle, contained significantly different metHb levels, with more metHb in the rest phase (revised Figure 4B, C). Whereas, RBCs from mice also given nitrite in their active phase contained more metHb (and thus lower Hb2* activity) than control (revised Figure 4B and S4). The second experiment tests the effect of sodium nitrite on core body temperature. Our hypothesis predicts that nitrite should accentuate the daytime drop in core body temperature, via the increased metHb-mediated production of NO to stimulate increased vasodilation (Ignacio et al 1981 and Cosby et al 2003). Revised figures 4D and E show that the effect of nitrite on body temperature (which has a large active vs inactive difference) is indeed daytime-specific.

      Methods for these experiments have been added to Experimental Procedures.

      2)The authors speculated that the higher PRX-SO2/3 signal during the first 24 hrs in mice is due to the sapling time at the resting phase (line ~235). The effect of sampling time should be easily tested by maintaining the mice group in 12-hr shifted L/D cycles and sampling the blood in the same o'clock (i.e., now the active phase). This type of experiment is also critical for the evaluation of Bloody Blotting because the level of Hb*/Hb2* signals may be affected by not only the circadian timing of mice but also the daily environmental fluctuation of a biochemistry laboratory (this is particularly important for the Bloody Blotting because some of the critical steps including the cross-linking between haem and Hb are supposed to occur in a test tube). If the signal of Bloody Blotting reflects the in vivo circadian rhythmicity, the 12-hr shifted L/D mice RBCs should have 12-hr shifted Bloody Blotting fluctuation pattern.

      __We acknowledge this possibility. To test this, we sampled RBCs from mice kept under DL and LD conditions, as detailed in the new sections in the Experimental Procedures, harvesting blood at the same clock time. This gave us blood from mice in the “active” phase and “rest” phase – labels as per Figure 4B. Figure 4B shows that Hb2* signal significantly differs between mice in active and rest phases, even though these samples were collected and processed at the same external time. __

      Separately from Hb2* activity, upon further reading of the literature we suspect that the higher PRX-SO2/3 signal detected in mouse RBCs (Fig 2) compared with human may be due to blood acidification during animal sacrifice by CO2. Additional text has been added to Supplementary Figure S3 to remark upon this, as follows:

      "Interestingly, compared with human RBC time courses (Henslee et al., 2017; O’Neill and Reddy, 2011), we observed that murine PRX-SO2/3 immunoreactivity was extremely high during the first 24 hours of each 72-hour time course (Figure S3A). We attribute this to the different conditions under which blood was collected: blood was collected from mice culled by CO2 asphyxiation during their habitual rest phase by cardiac puncture and exposed immediately to atmospheric oxygen levels, whereas human blood was collected from subjects during their habitual active phase through venous collection into a vacuum-sealed collection vial. Thus, the initial high PRX-SO2/3 signal in mice may be related to CO2-acidification of the blood during culling, which affects PRX-SO2/3 but does not affect Hb oxidation status____."

      3)Do the casein kinase inhibitors (ref: Beale, JBR 2019) affect the period of Bloody Blotting signals?

      We have not experimentally addressed this as we consider it beyond the scope of the current study, which has instead focused on the in vivo relevance of the rhythms in metHb. Nevertheless, given the identical periodicity of PRX rhythms and Hb* rhythms (this paper), and the periodicity of PRX rhythms and rhythms in membrane conductance (Henslee et al, Nat Commun, 2018), we see no reason why the period lengthening of rhythms in membrane conductance reported in Beale et al, JBR, 2019 would not also been seen in PRX or Hb* rhythms.

      **Minor comments:**

      4)The authors quantify the dimer of Hb (Hb2*). This is important information but only explained in the supplementary figure legend. It should be explained in the main text. In addition, it is difficult to evaluate the fluctuation of Hb* (not Hb2*) because, as the authors stated, most of the Hb* signals are saturated. The saturation problem should be easily solved by reducing the sample loading volume. Quantification of Hb* is important at least experiments shown in figure 1A-G because the dimerization of Hb can be also affected by factors other than the in vivo ferrous-/ferric-haem conversion.

      Thank you for pointing this out. Indeed the data throughout the original manuscript is Hb2*. We have brought this explanation into Figure 1 legend and labelled all figures consistently with Hb2*. We include quantification of Hb* and Hb2* of the in vivo metHb perturbation experiment (Figure 4) in the uncropped membranes shown in Supplementary Figure 4. The quantification of Hb* (Supplementary Figure 4D) gives the same result as the quantification of Hb2* (Figure 4B).

      5)In the quantification of Hb2* (Figure 1A, 2E, 3C), were the signals normalized to Total Hb?

      In the quantification of Hb2* throughout, signals were normalised to total protein through coomassie stain, apart from Figure 4B which used SYPRO Ruby. Each figure presents the Hb band of coomassie or SYPRO Ruby for simplicity, but the full gels are included in Supplementary Figures 1, 3 and 4.

      6)The explanation and interpretation of the experiment shown in figure 3D should be more careful. The pulse-oximetry was conducted in normal working day conditions (real world setting) and thus should be affected by environmental and social daily signals.

      __We have changed the section to the following (edits in bold): __

      "Remarkably, in contrast to total Hb (SpHb) that displayed no significant 24h variation, the proportion of metHb (SpMet) in the blood exhibited a striking daily variation that rose during the evening and peaked during the night (Figure 3D). These subjects were in a real-world setting, and thus affected by environmental and social cues from a normal working day. However, the evening rise and night-time peak is consistent with ____the reduction in Hb2* activity at the end of the waking period in laboratory conditions (Figure 3B)____."

      7)Typos at figure indicators in supplementary figure legends. Sup figure 1A legend refers to main figure "2" (should be 1), and figure S3 legend refers to main figure 1 (should be 3).

      Thank you for pointing this out. We have corrected these legends.

      Reviewer #1 (Significance (Required)):

      The detection of circadian oscillation in RBC has been not easy because the experiment requires careful sample preparation and specific antibodies (Milev Methods Enzymol 2016) or a specific instrument for dielectrophesis (Henslee). The Bloody Blotting technic developed in this study will overcome this technical problem because Bloody Blotting does not rely on specific antibody and only requires conventional tools for western blotting. Because circadian biology of RBC is particularly important in the field of circadian research to evaluate the presence of eukaryotic circadian oscillator without transcription-translation feedback loops, this study will be interested a wide community of circadian clock researchers. This reviewer has expertise in the field of circadian genomics, biochemistry, animal experiments in mice as well as human.

      Thank you for taking the time to read and constructively comment on our work

      Reviewer #2 (Evidence, reproducibility and clarity (Required)): **Summary**

      The study aims to provide a new tool for detecting the hemoglobin oxidative status named "Bloody blotting". It is based on redox- sensitive covalent linkage between the haem and the haemoglobin. This linkage is a consequence of an artifactual reaction provoked by the protein extraction, due to the lysis buffer's properties. In addition, using an in vitro (red blood cells) or in vivo (patients' blood) model the authors provide insight in the oscillating nature in the oxygen-carrying and nitrite reductase capacity of the blood, which is unaffected by the mutation of CK1εtau/tau and Fbxl3aafh/fh

      **Major comments:**

      In my honest opinion, the work does not provide interesting addition to what it is known in literature. The conclusions are summarized into a model (Fig.4) t, which is too speculative related to the amount and quality of results showed in the paper.

      __We are disappointed by the reviewer's response. The physiological basis for daily rhythms in body temperature cooling is not currently understood, this work provides a testable basis for understanding it. Whilst we understand that the reviewer might not find immediate value in the biochemical mechanisms that initially informed our investigation, the recent publication of our investigation of human brain temperature rhythms (Rzechorzek et al., Brain, 2022) demonstrates that daily biological temperature rhythms are of broad interest (Altmetric score >2000). Daily temperature rhythms have almost exclusively been assumed to result from daily rhythms in heat production, yet the evidence for a contribution via daily rhythms of cooling is equally strong yet has received scant attention. __

      __The speculative model that the reviewer refers to was a hypothesis that drew together multiple lines of published evidence for future experimental testing, not a conclusion, and was labelled as such in the original manuscript. To accommodate the reviewer's critique, however, we tested the model with new experiments, that are included in the revised Figure 4 and section titled “Effect of rhythms in metHb on vascular flow and body temperature”. __

      For clarity, we have relabelled the schematic in A to “rest phase” and “active phase” to consolidate data from humans and mice which both feature in the manuscript, and described it as a hypothesis to avoid confusion. We performed two experiments to test this model. The first is a direct perturbation of metHb levels in vivo with sodium nitrite, an oxidising agent that causes methaemoglobinemia. RBCs from mice given nitrite in their active phase contain more metHb (and thus lower Hb2* activity) than control (Figure 4B). Reflecting our results ex vivo, RBC from mice sampled 12h apart contain significantly different metHb levels, with more metHB in the rest phase (Figure 4B, C). The second experiment tests the effect of sodium nitrite on core body temperature. Our model predicts that nitrite should further reduce core body temperature in the daytime, via the increased production of metHb (Figure 4C) and vasodilation (Ignacio et al 1981 and Cosby et al 2003). Figure 4D and E show that body temperature (which has a large active vs inactive difference) is further lowered upon nitrite treatment, and that this effect is restricted to the daytime, consistent with our hypothesis.

      __Methods for these experiments have been added to Experimental Procedures. __

      The title is misleading. The authors did not use any mutant for clock factors, but they used a kinase (CK1εtau/tau) and a ubiquitin ligase (Fbxl3aafh/afh) mutant, which are important in the regulation of proteins belonging to the clock machinery.

      We respectfully disagree that the title was misleading. Mice and cultured cells/tissues that are mutant for CK1 and FBXL3 demonstrably show altered clock gene activity (See Godhino et al, Science, 2007, also Meng et al, Neuron, 2008, also Fig 2). Moreover, CK1 and FBXL3 are generally regarded as key components of the circadian clock due to their critical function in the regulation of clock proteins (e.g., Hirano et al, Nat. Struct. Mol. Biol., 2016). Being anucleate, RBCs lack the capacity for changes in clock gene activity and the period of oscillation is not affected by mutations that affect the activity and period of clock gene-oscillations in nucleated cells and whole mice. Since the rhythms of Hb oxidation persist in isolated RBCs, they cannot be dependent on clock gene activity and so must be considered to function independent of clock genes.

      In light of the new data on mouse body temperature presented in revised Fig 4D/E, however, we have changed the title to better communicate the revised scope of the manuscript, as follows:

      "Mechanisms and physiological function of daily haemoglobin oxidation rhythms in red blood cells"

      Speaking of the specific points described in the paper, there are aspects that are not convincing. First, the bloody blotting is a consequence of a specific reagent contained in the lysis buffer used for the protein extraction, which reacts with the haemoglobin beta and alpha (as shown by Mass Spec). The peroxidase reaction is an artifact coming from this reaction, which simply follows the rhythmicity of peroxiding accumulation in the red blood cells, whose rhythmicity is known to be circadian. I do not really understand the utility of this technique, which anyway is limited to the specific lysis buffer, but for scientific reasons, researchers need often a different kind of lysis buffer. This means that the approach shows strong limitation to the chemical environment of the lysis buffer. I do not see in it a useful tool that can replace antibodies.

      Apologies, we have not been clear enough. The bloody blotting is indeed a consequence of lysis, since that lysis condition fixes the cellular state at the time of lysis. In this case, the variation in Hb oxidation status is fixed at the time of lysis. The peroxidase activity we report is indeed revealed on membranes by the covalent interaction of the haem and Hb, which occurs at the point of lysis, and reports the oxidation state of the haem at the point of lysis. As we detail, haem exhibits peroxidase activity, so the signal we observe at molecular weights corresponding to Hb and Hb2 is peroroxidase activity due to covalently bound haem, where the peroxidase activity varies with the oxidation state of the haem. We have reorganised text associated with Figure 1, including changes to the final paragraph of the section to make explicitly clear that that the rhythm is due to a fixing of the redox state of Hb at the time of lysis – that a true underlying rhythm is revealed.

      This technique is indeed limited to the observation of haem-peroxidase activity in RBCs on membranes. But as we explain in the manuscript, this is a far quicker and simpler method of observing RBC circadian rhythms than other methods, including immunoblotting for peroxiredoxins. Furthermore, it is common to change lysis buffer according to the downstream purpose.

      Second, the oscillation in the peroxidase activity of PRX-SO2/3 is well known to be circadian (Edgar et al., 2012. doi:10.1038/nature11088.).

      Many apologies, we do not understand the point. It is indeed correct that PRX-SO2/3 abundance oscillations have been reported in RBCs and other cells and organisms. Here we report another rhythm, separate to PRX: the rhythm in Hb:metHb. The PRX-SO2/3 blots serve as a positive control for rhythmicity.

      Finally the circadian rhythms of red blood cells is already described and the corresponding author already published different papers about. The info provided in this paper do not add any new piece to the puzzle.

      Respectfully, we report a novel rhythm in RBCs and demonstrate its functional relevance in vivo in humans (Figure 3) and mice (Figure 4), i.e., it is the identity of the rhythmic species that is novel, not that there are rhythms. What we further add with this study is that rhythms are not influenced by the cellular/organismal environment during RBC development (Figure 2), occur in vivo, in freely moving people (Figure 3) and metHb has a functionally significant role in body temperature rhythms (Figure 4). Furthermore, we report a novel technique for uncovering this rhythm in RBCs.

      At this stage I do not consider the paper suitable for a publication. Other observations. Authors should describe how cells were synchronized.

      RBCs in vitro were not synchronised by external cues. As reported in the Methods section, they were maintained at constant temperature after isolation. Fibroblasts were synchronised by temperature cycles as detailed and employed previously.

      In experiments performed in vitro should be used the SD instead of the SEM.

      We respectfully disagree. The SEM quantifies how precisely you know the true mean of the population - in each case we use it, we also present replicates’ data from which the mean is calculated (e.g. Fig 1A, Fig 2E, Fig 3B and Fig 4B). This gives the real scatter of the data, as a SD would.

      **Minor comments:**

      There are many English mistakes in the article, also errors in naming figures in the figure legends.

      We have carefully re-examined the manuscript to find and fix these errors.

      Figure 1B needs an appropriate loading control.

      We have added the coomassie loading control to revised Figure 1B, with uncropped membranes shown in revised Supplementary Figure 1B

      In experiments performed in vitro should be used the SD instead of the SEM.

      SD vs SEM, see reply above.

      Reviewer #2 (Significance (Required)):

      Nature and significance of the advance At this stage I do not see any significance or advance in the field.

      Compared to existing published knowledge. The The bloody blotting seems to be an original approach although full of limitation and based on artifactual reactions. PRX-SO2/3 is well known to be circadian (Edgar et al., 2012. doi:10.1038/nature11088.), therefore the paper does not add any new insight. The clock mutation do not affect the circadian rhythm in RBC is also known (O' Neill and Reddy, 2011). Therefore the results showed in the figure 3 support already published observations but do not add any particular insight.

      It is unclear to us how the reviewer has misunderstood the scope and focus of the manuscript to such an extent. All previous work in this area by our own and other labs has been appropriately acknowledged. To reiterate the novel elements of this work:

      - A daily rhythm of Hb redox state in mouse and human red blood cells, in vitro and in vivo. This was speculated about in O'Neill & Reddy (Nature, 2011) but never directly tested until now.

      - That clock gene mutations that post-translationally regulate circadian period in nucleated mammalian cells do not affect circadian period in anucleate mammalian cells. O'Neill & Reddy (Nature, 2011) did not show this, rather we looked at (nucleated) fibroblasts that were deficient for Cry1/2 (a transcriptional repressor).

      - A novel assay for measuring mammalian RBC rhythms - nowhere is it proposed that the assay would be useful in any other context, as the reviewer seems to imply.

      - A mechanistic basis for understanding how daily rhythms in cooling of body temperature might arise, a poorly studied aspect of mammalian physiology.

      __The elements in this work that are not completely novel are included as controls, they are not the focus of the manuscript e.g. PRX-SO2/3 rhythms have not previously been shown under these conditions in mouse RBCs, only human, so these blots are included as a control for rhythmicity in Fig2. Similarly, the period of oscillation of a genetically-encoded Cry1:Luc reporter in mouse fibroblasts would be predicted to be longer and shorter in Fbxl3 and Ck1 mutants, respectively, but nowhere this been published so we have included it as a control. __

      Audience Chronobiologists, and medical science.

      Fild of expertises (reviewer) Chronobiology, molecular biology, medical science.

      **Referees cross-commenting**

      I read your comment and they were very detailed. From my point of view I am very skeptical, as I discussed about the utility of the Bloody Blotting. Also the results showed in the paper are not very innovative fro my point of view. I would like to know what do you think about.

      The rhythmicity is given by the elements present in the protein extraction. The reaction is given by the specific lysis buffer used in that experiment. Using another lysis buffer would not allow anybody to see some signal without a proper antobody. The authors claim that bloody blotting is useful because a researcher does not need to buy an antibody, but what if you don't work with a total total extract of proteins? In that case, you need to change the lysis buffer, and, therefore, the bloody blotting is not useful anymore. However, If you believe in that way, and you are two people agreeing in that, I will not oppose myself although I do not agree.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)): **Summary:** Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

      The manuscript "Clock gene-independent daily regulation of haemoglobin oxidation in red blood cells" describes a new assay for quantification of haemoglobin oxidation status (bloody blotting) in anucleate red blood cells". This study furthers our understanding of the role of a post-translational oscillator (PTO) in generating circadian rhythms in biology. The authors first describe how earlier work demonstrated 24h rhythms in the intensity of chemiluminescent bands on membranes blotted with protein from red blood cells (RBCs) in the absence of antibodies after exposure to ECL. They go on to address what these bands represent (through various approaches including the use of chemical inhibitors and mass spectrometry) and conclude that they are observing haemoglobin oxidation status. It is proposed that this assay represents a novel manner (complementary to earlier work) in which to report circadian rhythms in RBCs. The manuscript goes on to demonstrate the persistence of 24h rhythms in haemoglobin oxidation status in murine RBCs, including cells isolated from two clock mutant mice. Finally, the study utilises RBCs collected from human volunteers maintained under controlled conditions and demonstrate robust rhythms via "blood blotting", this data is presented alongside pulse co-oximetry data to examine physiological relevance of these rhythms.

      **Major comments:** -Are the key conclusions convincing?

      The key conclusions are well supported by the data. The discussion does become quite speculative, and this needs to be addressed.

      -Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

      The discussion around the physiological relevance of daily regulation of haemoglobin redox status is extensive (lines 366-403) as is the discussion on RBCs and the TTFL-less clock mechanisms (lines 405-429). Whilst interesting and well thought out, and well supported by the literature, these sections are very speculative and in my opinion should be toned down.

      Thank you to the reviewer for both the compliment and suggestion. Indeed, these discussion sections were too long. We have reorganised the physiological relevance section to reduce its length and better accommodate the new data presented in the new experiments in Figure 4.

      We have cut the TTFL-less section text by more than half.

      -Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

      Further experiments would be required to support the discussion about the role of daily rhythms in haemoglobin oxidation status in regulating oxygen carrying capacity of the blood, vascular tone, body temperature and sleep-wake cycle. As the authors state, these experiments are beyond the scope of this study, but are of course of major interest. It would be more appropriate to limit the discussion to what has been demonstrated directly by the data presented, with just a few sentences speculating on physiological relevance.

      __As above, we acknowledge that we were speculative in that section and we have curtailed the discussion as suggested. __

      -Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

      If the focus of the discussion is shifted as suggested, there is no need to pursue any further experiments. -Are the data and the methods presented in such a way that they can be reproduced? Yes. The methods are complete, and data presented very well. -Are the experiments adequately replicated and statistical analysis adequate?

      Yes

      **Minor comments:**

      -Specific experimental issues that are easily addressable.

      1.In the murine fibroblasts/RBC experiments in Figure 2 - what genotype were the wildtype controls? The main text suggests PER2::luc (line 226) but methods suggest Cry1:luc - could the authors clarify this?

      __Thank you for pointing out this mistake, corrected text to Cry1:luciferase __

      2.In figure 2B and 2D the blots show two samples for each time point (except for 72h where there is just one) are these technical repeats? This should be clarified.

      Apologies, the labelling of this figure was not clear – for space reasons we only labelled every 2nd timepoint – the time course was 3-hourly. We have corrected the figure to label each timepoint.

      3.The controls for the bloody blots are referred to as coomassie in Figure 1. In Figure 2, the controls for PRX-SO2/3 are referred to as "loading" but are coomassie stained gels - could this be standardised? Also Figure 2D - no controls? In Figure 3B controls are referred to as 'Total Hb from coomassie staining - I wasn't clear what this was.

      Thank you. Throughout we have now labelled loading controls by their method (coomassie or SYPRO Ruby). Figure 2D is taken from the same gel as Figure 2B and so the same coomassie gel stain is used as a loading control. We have altered the figure legend to reflect this. Each figure presents the Hb band of coomassie or SYPRO Ruby for simplicity, but the full gels are included in Supplemetary Figures 1, 3 and 4. We have changed each figure legend to reflect: “coomassie stained gels were used as loading controls; the Hb band from the coomassie stained gel is shown”.

      4.Figure 3A "S1" and "S2" stated in legend but only "S" used in the schematic

      Many thanks for pointing this out. We have corrected the schematic to S1 and S2.

      -Are prior studies referenced appropriately? Yes absolutely. -Are the text and figures clear and accurate? Mostly, few comments above.

      -Do you have suggestions that would help the authors improve the presentation of their data and conclusions? No

      Reviewer #3 (Significance (Required)): -Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

      The study describes a rapid and relatively simple assay for observing 24h rhythms in RBC function. On a technical basis - this will likely be of significant use to others in the field. Further work examining rhythms in haemoglobin oxidation in RBCs in clock mutant mice confirms independence from the transcriptional-translational feedback loop, which further supports earlier work in this field. Finally, studies in humans (bloody blotting in combination with pulse co-oximetry) provide a glimpse into the functional relevance of these daily oscillations

      -Place the work in the context of the existing literature (provide references, where appropriate).

      The authors have done an excellent job of reviewing the literature in the field and contextualising their data. This current data is a significant advance in the field.

      -State what audience might be interested in and influenced by the reported findings.

      This work will be of interest to circadian biologists and adds weight to the relatively new concept of a post-translational oscillator (PTO). Further work showing the relevance of this PTO on physiological function will be of great interest.

      -Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

      Circadian, Clock genes, mouse models,

      I do not have a background in biochemistry and do not feel overly qualified to comment constructively on approaches taken to address what is driving the observed rhythmic peroxidase activity in RBCs (e.g NiNTA affinity chromatography, use of reductants to reduce thioester bonds and use of NEM to alkylate Hb cysteine residues).

      **Referees cross-commenting**

      In terms of the utility, as my review indicated, I do feel that this manuscript advances the field, providing a rapid and relatively simple way to measure rhythms in RBCs. Reviewer 1 explained this nicely in their significance summary.

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      Reply to the reviewers

      Manuscript number: RC- 2023-01819

      Corresponding author(s): Gernot Längst and Harald Wodrich

      Full revision of the manuscript

      1. General Statements [optional]

      This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

      2. Point-by-point description of the revisions

      Dear Reviewers, thank you very much for your appreciation of our study and your input. In this point-to-point response, we amended our text marked in blue colour.

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      The authors have addressed the nucleoprotein structure of human adenovirus during the very early stages of infection, and its relationship to onset of expression of viral genes, using a combination of RNA-seq, MNase-seq, ChIP-seq and single genome imaging. They show that in the virion and the newly-infecting DNA, protein VII is precisely position at specific sites on the viral DNA, with greater accessibility at early gene promoters compared to other regions. Nucleosomes containing H3.3 replace specific protein VII at distinct positions at the transcription start sites of genes, which are then acetylated. Association with histones and nucleosomes occurs prior to transcription. These studies confirm and greatly expand on results already in the literature, and also elucidate a novel role for protein VII in orchestrating positioning of nucleosomes prior to initiation of transcription.

      The authors provide excellent data in support of their conclusions and, in many instances, use alternative experiments (i.e. two different approaches) to support their claims. The details of methods are adequate (with small exceptions outlined below) and statistical methods appropriate.

      Minor comments:

      Line 561 "Protein VII molecules were exchanged for positioned nucleosomes at the +1 site of actively transcribed genes". This statement seems to suggest that the +1 position almost acts as a nucleating site, where replacement of a single, specific protein VII molecule at +1 is an initiating event, which then spreads from that site and into the rest of the gene. Data shown in Figure 6G and 6H shows that H3.3 appears to be found equally along the full length of E1A as early as 1 hr post infection (with no real "enhancement" at the +1 position), and that the overall levels simply increase over the next 4 hrs.

      As the reviewer pointed out, the histone ChIP-seq peaks are broader than the +1 nucleosome region, extending into the transcribed regions of the gene. This is expected, as the mean length of the immunoprecipitated DNA is about 400bp long. Still, ChIP-seq peaks are in proximity to the transcription start site and overlap with the position of the +1 nucleosome. As we do not have the required resolution, we toned done our statement. The text now reads as follows: “Protein VII molecules were exchanged for nucleosomes downstream of the transcription start site, overlapping the +1 nucleosome site, of actively transcribed genes“ (line 568 ff).

      Curiously, the authors chose not to use a wildtype virus for their studies - the virus contains a deletion in the E3 region. For clarity, I suggest that the authors should preferentially use an alternative designation for their virus rather than HAd-C5. Perhaps HAd-C5delE3 to differentiate this work from studies that truly use wildtype virus.

      As requested by the reviewer we have updated the nomenclature to HAd-C5dE3 throughout the text and the figures.

      The obvious limitation of the studies using the fluorescent TAF1-beta to label Ad genomes is that as protein VII is replaced by nucleosomes, the genomes would have declining detection by this method. Genomes devoid of protein VII would be "invisible".

      Our MNase data show that within the first 4h only a fraction of pVII is removed from the viral genome e.g. at early genes, while most of the genome remains bound by protein VII. This should provide enough binding sites for TAF1-beta to label Ad genomes without a significant drop in the signal. Furthermore, our recent work (PMID:29997215, Fig. 1D) compared the TAF1-beta labelling system with a second in vivo detection system (AnchOR3) that directly labels the viral DNA independently of protein VII in the same cells. This direct comparison of two technically non-related methods to detect individual incoming adenoviral genomes in living cells showed the equivalence of both methods, at least for the first hours of infection showing that partial removal of protein VII does not affect the fluorescent TAF1-beta staining.

      Line 275 "Interestingly, a central region of the viral genome (Late3) and a region between the E3 and E4 genes exhibited almost no peaks" for protein VII. The virus utilized in this study lacked at least part of the E3 region. Did this deletion "cause" this region to be devoid of protein VII? Is the same absence of protein VII peaks observed in a fully wildtype virus? Also, can the authors provide any speculation as to why the Late3 region also lacks protein VII?

      We confirm the reviewer's observation. The region marked as Late3 and the region between E3 and E4 is present in the genome and is, as the reviewer observed, not chromatinized in our analysis. At this point, we can only speculate. We have two not mutually exclusive hypotheses. First, both regions could be involved in the proper packaging of the viral genome into the capsid. Physical constraints during packaging may preclude this region from being packaged into pVII. Second, as we observed that pVII positioning correlates with distinct DNA sequence patterns (revised Fig.4 D and E, see response to reviewer 3 for details), it might be that the sequence composition at the pVII depleted regions disfavour pVII assembly to keep those regions available for cellular factors that drive processes post genome delivery, such as transcription. Our time-resolved MNase analysis shows that indeed post genome delivery, this site in the Late3 region becomes protected (Fig. 5C), suggesting the binding of one or more cellular factors. As shown in Figure S6 we find conserved binding sites for several transcription factors at this MNase protected site.

      Whether the chromatinization devoid regions would shift in position, remain in place or be chromatinized in a wildtype virus has to be addressed in the future and cannot be answered at this point. To address the comment, we have expanded the discussion (line 620 ff)

      Line 569 "Reasons could be that the few genomes undergoing nucleosome assembly and active transcription produce the replication enzymes, whereas the bulk of genomes enters replication without activation as an elegant way to avoid repeated chromatinization." This argument may make sense in the context of a high MOI infection, but would certainly limit virus function during normal, pathogenic infection where the MOI is likely extremely low. Essentially, the authors data predicts that 80% of normal, low MOI infections don't progress to gene expression (at least during the first 4 hrs analyzed in this study).

      We follow the argument of the reviewer. The high MOI in our study was necessary to perform the combined ‘omics’ approach to arrive at meaningful data within reasonable sequencing depth. To have equivalence we also used high MOI for the imaging approach. A detailed analysis for the effect of low MOI as well as positioning effects (see reviewer comment below) on transcriptional activation is an important question and will be addressed in future studies that require different techniques in addition. To address this comment, we have updated the discussion to emphasize the importance of MOI and positioning effects (line 587 ff).

      Line 576 "This observation is in agreement with recent pVII-ChIP experiments showing transcription and replication independent pVII removal in early infection (Giberson et al., 2018; Komatsu and Nagata, 2012; Komatsu et al., 2011)." The authors can also state that histone and nucleosome deposition is also independent of transcription and replication, as has been alluded to in the same cited studies but proven more directly in this study.

      We have changed the text accordingly (line 576 and 598).

      Line 672 - the authors should be more definitive in the MOI that are used in all of their experiments. Line 672 states that an MOI of 3000 physical particles are applied per cell. There can be great variation between cell lines in how much virus binds to (and enters) a cell based on the surface levels of Ad receptors on different cell types. However, in general, 3000 is very high. Work by Wang et al. (PMID:24139403) showed that at an MOI of 200 or below most Ad will traffic correctly to the nucleus, whereas at an MOI above 200 there is a significant defect in Ad trafficking within the cell. How is this expected to affect all of the results in this study?

      We agree with this and the other reviewer that this is an important issue. The actual dose of virus that enters a given cell is dependent on the concentration of virus particles in the inoculum and the time and temperature this inoculum is in contact with the cells and the cells respective susceptibility to the virus. We applied an infection dose of 3000 physical particles per cell in a defined volume (1ml) at 37˚C for 30min followed by inoculum removal. We prefer this description because with these infection conditions, we find on average well below 100 virus particles that enter the cell (=> This is e.g., reflected in the number of accumulating genomes shown in figure 2A). In contrast, this permits to have enough viruses inside the cell to perform the different “omics” techniques applied in our study to obtain meaningful results at reasonable sequencing depths. This experimental setting was carefully chosen in full awareness of the work by Wang et al., cited by the reviewer, to avoid e.g., overloading the nuclear import rate. Thus, our experimental conditions do not exceed the “MOI of >200” that would affect nuclear import rates. The number (>200) in the Wang et al. study refers to the number of virus particles inside the cell, the infection condition used in the Wang study was an MOI of 30 bound to Hela cells in the cold for 30min and warmed for 150min which is significantly more virus than we have used in our study. We have expanded the information on the MOI used in the material and methods section to clarify this point (line 685 ff).

      Figure 5 is of low resolution and was difficult to read.

      We thank the reviewer for spotting it. It seems that the Figure quality was compromised during the PDF conversion. We updated the Figures and checked the resolution after PDF conversion.

      Figure S3 is missing a box from the top set of images indicating the region that is expanded in the detail picture.

      We updated Figure S3

      While I realize it is supplemental data, the difference in quality between the agarose gels shown in Figure S4A and S5A is shocking.

      The nature of the experiments is very different and therefore the expected MNase digestion profiles on agarose gels look different. In Figure S5 viral particles were digested with MNase, resulting in a smeary decrease in DNA size. This looks very different from the regular MNase pattern of whole cells that is dominated by the regularly spaced nucleosomes in the heterochromatic regions of the genome. As pVII protects only about 70bp of DNA and its spacing is not as homogenous as the nucleosomal spacing, the pictures shown in Figure S5A were expected as they are.

      Figure S7 is of low resolution.

      We updated the Figures and checked the resolution after PDF conversion.

      Reviewer #1 (Significance (Required)):

      At least in the field of adenovirus research, this is a very important study. There has been considerable debate in the field regarding the timing and degree of protein VII removal and histone deposition, and the necessity of active transcription for these two events. The data provided in this manuscript clearly shows that some protein VII is removed from early active genes and replaced by nucleosomes, and that these events occur prior to initiation of transcription. The authors speculate that the specific placement of protein VII, a protamine-like protein, on the Ad genome prescribes where nucleosomes are placed. This finding should be of interest to a broad general audience, as it provides novel information on chromatin assembly within mammalian cell. Key words for this reviewer: adenovirus research, HAdV nucleoprotein structure

      Reviewer #2 (Evidence, reproducibility and clarity (Required))

      The submitted manuscript presents a detailed and comprehensive analysis of the adenoviral nucleoprotein complexes as infection progresses, starting with the "adenosome" assembled with pVII which are then progressively replaced with H3.3.-containing nucleosomes as the infection progresses. The submission presents a combination of in situ and populational analyses of the viral DNA accessibility and complexes through infection. I brief, the infecting viral genomes are assembled in some 250 adenosomes with pVII, which become progressively replaced as infection progresses with nucleosomes containing H3.3 and acetylated H3K17, starting at the active promoters of the E genes. Chromatin remodeling precedes transcription, and the accessibility differs for genes of different kinetic classes at differ times after infection, although there is no correlation between accessibility and H3.3. or acetylation content. Only about 20% of the genomes become transcriptionally active, though, which somewhat complicates the analyses of the populational studies of accessibility and occupancy. Overall, the study is well conceived, performed and presented. A few issues that deserve further analyses and discussion, as described below.

      Major issues.

      As figure 2 nicely shows, only about 20% of the intranuclear genomes become transcriptionally active. However, MNase and ChIP analyses cannot differentiate these genomes from the 80% that are transcriptionally inactive. The interpretation of the positioning of pVII (figure 4) or the changes in compaction of the adenoviral chromatin at different loci (figure 5) does not appear to consider this heterogeneity other than for a brief comment about the stringent MNase digestion in page 11. The authors favor a model in which the changes in compaction shown in figure 5, at mild MNase digestions, directly correlate with transcription of the respective genes. This could well be correct, and in fact the correlation may be underestimated as 80% of the genomes may not undergo any changes, but it may also be incorrect. The analyses presented cannot differentiate whether the changes in chromatin compaction occur in only a subset of genomes or in all the genomes, regardless of whether they are transcribed or not, or even only in the non-transcribed genomes (which appears extremely unlikely). This intrinsic limitation to the methods used (and I know of no better alternative) should be acknowledged and discussed for the benefit of the reader. This limitation also impacts the analyses of the lack of correlation between H3.3 and acetylated H3K27 occupancy and compaction.

      A discussion is amended and located starting from line 571 in the text. “The heterogeneity of 80% inactive genomes and 20% activated genomes complicates the analysis of the MNase-seq data. High MNase concentrations do not differentiate between both states, and we suggest that low MNase conditions capture the dynamic viral proportion, changing and preparing its genome for gene activation. The data nicely suggest such a scenario, but there is the caveat that we catch an effect of the mixed population that we cannot differentiate.”

      The analysis of the histone ChIP is discussed below.

      Perhaps out of necessity to reach the required sensitivity, a high multiplicity of infection was used (although the actual moi is not stated, there are about 25-30 pVII foci/ per nuclei). The presentation, analyses and discussion of the results should emphasize this context. For example, one would presume that at low moi, when only one genome enters each cell, the percentage of transcriptionally active genomes in a given cell will be either 0 or 100%, but the "system" becomes saturated as more and more genomes enter the nucleus at higher moi resulting in only a subset of them being transcriptionally active. Along this line of reasoning, it is intriguing that the percentage of genomes estimated to be in nucleosomes at 4 hpi (14%) approaches the percentage of transcribed genomes.

      This issue was also raised by reviewer 1 (see detailed comment above). The reviewer is correct that we chose to use a higher MOI to reach the required sensitivity in our different “Omics” assays. The imaging approach was adapted to reach the morphological equivalence to fit this analysis. We agree that it would be interesting to also study the MOI effect on transcriptional activation (as well as positioning effects, see comment below) but this requires different approaches and will be addressed in a future study. To address this comment (and others in this review) we revised the text in the discussion to emphasize the importance of MOI and possible other effects such as positioning (line 587 ff).

      The changes in chromatin compaction presented in figure 5 are in some respect puzzling. The compaction of most of the late genes increases as infection progresses, at least for the first four hours, as the authors discuss. However, the L genes appear to be at least as accessible as the E ones at the early times, when only the E are transcribed to high levels. This appears counterintuitive, and may not be consistent with the main conclusion that increase accessibility to a given gen directly correlates to its transcriptional activity level. The data presented in Figure 5C deserves a more nuanced analysis and discussion, parsing out the changes in accessibility to each given gene at different times from the different accessibility to the different genes at any given time. The later does not appear to support the main conclusion reached by the authors that accessibility to each individual gen correlates with its transcriptional level.

      We thank the reviewer for raising this point. While the viral genomes enter the nucleus, the viral chromatin structure is tightly condensed. Therefore, it is unlikely that after nuclear entry the viral chromatin undergoes further compaction. With our analysis, we expect to detect only decompaction of genomic sites relative to 0 hpi, when the virus has not entered the nucleus yet. At some sites and particularly at the Late genes the signal is decreasing, most likely due to normalization to sequencing depth and the variation in the number of viral genomes but not due to changes in compaction. We realized that the negative accessibility scores we used in the study are misleading and give a false impression. Therefore, we changed the analysis in that way, that negative values were not permitted and converted to zeros.

      Additionally, we raised the temporal resolution of the analysis and compared the accessibility at all available timepoints against 0 hpi as suggested by the reviewer. Now, we clearly observe, that most accessibility changes are accomplished rapidly after nuclear import, already at 1 hpi and do not change much after, until 4 hpi. Regions of decompaction coincide with early expressed genes and occur before transcription, underscoring the conclusions made in the study. Nevertheless, while most genomic regions covering late genes do not show decompaction, we observed some local sites showing a high accessibility score. As transcription at those sites appears later in the life cycle of the virus, we can only speculate about the function e.g. as enhancer elements.

      The Text and Figures were changed accordingly (line 347 ff).

      New legend:

      __C) __Profile illustrates HAd-C5dE3 genome coverage by low MNase-seq fragments. The average of two replicates is shown, except at timepoint 0 hpi where only one replicate was available. The accessibility score was calculated as the log(fold-change) between the indicated timepoint and 0 hpi. The score was assessed for each pVII peak (orange bars) and negative scores were set to 0. A new accessibility peak arising during infection in the Late3 region is marked by an asterisk. __D) __Boxplot showing the accessibility score distribution in each domain at each tested timepoint after infection.

      Minor comments

      The authors may wish to highlight in the discussion that the analyses are so far limited to a single adenovirus.

      We have taken up the suggestion of the reviewer and included it in the discussion part, starting at line 607:

      “The structural analysis is still limited to a single adenovirus genotype and it will be interesting to test whether these dynamic changes are conserved among other adenoviruses. Furthermore, reproducing such organization in adenoviral vectors could result in efficient and sustained transgene expression.”

      The y-axes in the transcriptome figures (figure 1 B, S2) could be presented in Log(2) scale, such that transcript levels at all times can be appreciated in the same graph (the earlier times are just not visible in a linear scale)

      As requested by the reviewer we changed the data to log2 scale. As there is no qualitative difference to the log10 scale, presented in the original version, we would like to keep the figure as it is. To highlight changes at early time points we generated the average expression of early genes in Fig1C.

      As an information for the reviewer, we provide here the data plotted as log2 scale.

      The (lack of) phenotype of the 24xMS2 binding site recombinant adenovirus used should be shown.

      We observed no difference in phenotype between the parental and the MS2 modified virus. We updated Figure S3 and included a gel analysis and specific infectivity data to show this absence of difference.

      The kymograph analyses presented in figure 3B appear to show that there are some sites of transcript accumulation sites which do not harbor viral genomes (i.e., green only tracks). Moreover, the interpretation of the TAF1beta-mCherry signal is complicated by the (fully expected) significant "background" signal. Although these results are consistent with those obtained by RNAscope/pVII staining, there appears to be intrinsic limitations to the system, which preclude reaching strong conclusions from it. These confirmatory analyses should probably be moved to the supplementary information section and removed from the main text and figures. The longer evaluation data mentioned as not shown in page 8 is critical to the conclusions and should be shown.

      Here we disagree with the reviewer and prefer to keep the data as main figure. All (immobile) transcript accumulation sites are identified by the kymograph analysis and coincide with a genome while free transcripts show a high mobility that is not picked up in the kymograph analysis. This is independently verified in the provided supplemental movies. Depending on the positioning of the genome inside the living cell, accumulating transcripts can appear adjacent to or on top of a genome. This explains the slight shift between RNA and DNA signal for some genomes in the merged image of the kymograph. This is expected as only fully transcribed transcripts and not nascent transcripts are marked by MS2 (the MS2 loops are positioned in the 3’UTR). Also, all genomes (transcribing and non-transcribing) can be identified in the kymograph above background level. To clarify the representation, we have added labels to the kymograph to show which signal is DNA and RNA and a merge respectively. We are convinced that this data set is in strong support of our study, as it is the only technique that permits the discrimination of transcribing and non-transcribing genomes in living cells at real time.

      As requested, we have also added two additional examples for a longer observation period (10min) into the supplemental data Fig. S3C.

      Although the plot of cleavage frequency presented in figure S5 is clear, it would be beneficial to the reader if the actual peaks were also presented to compare their distribution (if any) in gDNA and virus particle.

      In Figure S5 we wanted to test whether the regions lacking pVII peaks are resulting from the absence of pVII, protecting the DNA, and therefore being fully hydrolyzed by MNase, or whether this region is tightly packed by pVII thereby protecting DNA from MNase digestion. To test both possibilities we used a very limited MNase digestion approach, where even free DNA is not fully hydrolyzed, allowing the capture of DNA fragments. Therefore, the sequenced fragments comprise a mixture of protected and un-protected fragments. In this assay, the pVII protected fragments are not fully digested to the monomeric state, but a mix of mono-, di- and other multimers are present. As reflected by the fragment size distribution with the peak between 100-200 bp (Fig S5B), pVII dimers are predominantly enriched when compared to the high MNase digestion used to map pVII positions (compare to Fig4 B). Therefore, the peaks in the S5 data set have a low resolution and do not provide exact pVII positions (see below). Therefore, we would like to keep S5 as it is. We clarified this point in the text (line 279 ff)

      Legend:

      Fragment coverage plot of MNase digestions of gDNA (black) or Ad chromatin in virus particles (purple).

      The mRNA analyses of selected transcription factors provides little information, as there is no context, there is variability between experiments, and in most cases the changes appear modest. As these results are not critical to the conclusions or analyses, perhaps the authors may wish to remove them from the manuscript. Alternatively, more in-depth analyses would be required.

      We agree with the reviewer, that more information for the reader is needed. Therefore, we performed a statistical analysis of expression changes between 0 hpi and 4 hpi of the shown transcription factors using DESeq2. We added the corresponding log2(fold-change) and p-values to the figure. And adapted the text (line 471) and figure accordingly.

      Legend:

      Gene expression changes of transcription factors over the infection time course. P-values and log2(fold-changes) from differential gene expression analysis between 4 hpi and 0 hpi using DESeq2 are indicated. ns = not significant

      It is unclear why the even distribution of H3.1-flag signal across the genome is considered indicative of no specific recruitment. The results presented are equally consistent with equal incorporation across the genome. Perhaps the authors have some additional information, such as an irrelevant antibody, input DNA, or the like, to support the conclusion. If so, that evidence should be presented and discussed. If not, the interpretation should be revisited. As an added complexity, endogenous H3.1 is normally expressed during S-phase. It is possible that Adenovirus infection may induce higher levels of expression of (untagged)endogenous H3.1, which would outcompete the tagged ectopically expressed histone. These analyses deserve a more nuanced and in-depth analysis.

      We have taken several measures in the study to address the concern of the reviewer. We consider timepoint 0 hpi as background control as the viral genome has not entered the nucleus yet. Consistently, we observe very few reads mapped to the Ad genome regardless of antibody and construct used (Fig 6B). Additionally, all samples at 0 hpi cluster together in PCA (Fig 6C) and correlation analysis (Fig S7D)

      H3.1 Flag tagged samples show at later timepoints (1 - 4hpi) slightly higher percentages of mapped reads to Ad, but plateau already at 1 hpi (Fig 6B) and cluster together in PCA (Fig 6C) and correlation analysis (Fig S7D) with 0 hpi samples. The low background signal starting at 1 hpi for H3.1 might arise due to the change of Ad genome location to the nucleus.

      Even though, the number of Ad mapped reads at later timepoints was low in H3.1 Flag tagged samples, it could still be that they accumulate at few sites on the Ad genome indicating a specific deposition. We tested this by plotting the signal across the whole Ad genome (Fig S7E) and zooming into the data (compare scale of H3.3 and H3.1 plot), but we could not detect any reproducible local enrichments. To enable the reader a better comparison between the levels of H3.1 incorporation with H3.3 we put now both on the same scale (Fig 6D and Fig S7D) clearly showing that we cannot detect H3.1 incorporation at Ad genomes in the first 4 hours of infection. The H3.1 signal corresponds to the background noise. We think for two reasons, that it is very unlikely that endogenous H3.1 outcompetes the tagged H3.1:

      • The time scale for the cells to transition into S-Phase and upregulate endogenous H3.1 would be only 1-2 hours in our timeseries experiments and therefore too short. To also show these experimentally we amended an experiment for the reviewer that is not included in the manuscript. The Western blots below show that the protein amount of H3 does not increase in the first 4hours of infection. Cells were infected and whole cell extracts were prepared 4hpi.
      • As most cells are not in S-phase in our experiments, the expression levels of H3.3 variant is higher than H3.1. With the Flag ChIPs we can clearly show that the tagged H3.3 are not outcompeted by endogenous H3.3. As there is a high sequence similarity between H3.3 and H3.1 it is very unlikely that they behave in that regard differently.

        It is highly unlikely that the somewhat higher H3K27ac signal observed in the H3.3 than in the H3.1 expressing cells may result from higher H3.3. occupancy in the viral genome as speculated in page 13. The total levels of H3.3. are unlikely to increase by the ectopically expressed one, and even if they did it is not likely that the occupancy of the viral genome would be limited by the levels of H3.3. This speculation should be removed.

      We removed the speculation.

      Materials and methods are too concise. A longer more detailed version, as supplementary information, would be highly desirable.

      We extended the materials and methods part.

      Reviewer #2 (Significance (Required)):

      The major strengths of this manuscript lie on its comprehensiveness, using several in situ and populational approaches to address biologically critical questions regarding the regulation of viral replication by chromatin and epigenetics. Experiments appears very well designed and performed and are mostly clearly presented. The interpretation analyses and discussion of the results may benefit from a more nuanced analysis of the issues posed by the existence of different populations of viral genomes in the cells infected at high moi and the accessibility across different genes at any given time versus the levels of transcription of the different genes, which appears not to be fully consistent with one of the main conclusions reached.

      This study makes a very significant contribution, describing the dynamic changes in the adenoviral nucleoprotein complexes at the early times of infection and providing a full description of both the adenosomes and the nucleosomes in more and less transcribed loci. The results are properly analyzed in context of what is known about the regulation of viral gene transcription by chromatin dynamics in other systems, including similarities and differences. This study is likely to be of high interest to a wide audience, ranging from virologist to epigeneticists, to those working in gene therapy and vectored vaccines.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      The manuscript "Adenoviral chromatin organization primes for early gene activation" combines RNA-seq, MNase-seq, ChIP-seq, and single genome and transcript imaging (immunofluorescence, RNA-scope, and live cell techniques) during early Adenovirus infection in vitro to characterise the spatiotemporal dynamics of viral chromatin organisation and association with gene transcription. The manuscript is an interesting read and the authors have combined multiple complimentary techniques to make a substantial contribution to understanding the early events occurring after nuclear import of viral genomes. Adenoviruses are important causes of human and animal pathology, are a useful model of non-integrating extra-chromosomal DNA virus infection in mammalian cells, and are useful vectors for vaccination and the discoveries may influence gene therapy DNA vector design. The chromatin organisation in adenovirus infection is distinct from other DNA viruses, and is relatively poorly understood compared to, for example, SV40 or herpesviruses. The manuscript describes an early transition from purely viral chromatin with Adenovirus protein pVII packaging the virus in virions, to a viral-human hybrid chromatin pattern with apparently strategically positioned H3.3 nucleosomes and viral pVII "Adenosomes" in the early hours after nuclear import of the viral genome. The data shows that packaged Adenoviruses are in a transcriptionally accessible form and gene expression occurs rapidly after infection, the combination of the MNase-seq data with ChIP-seq data is particularly interesting demonstrating and average ~238 adenosomes positioned by specific DNA code protecting 60-70bp of DNA, and that the genome is accessible at loci that also decondense on infection, with adenosomes being replaced by cellular H3.3 containing nucleosomes at distinct sites. Particularly they show that +1 H3K27 acetylated nucleosomes are acquired at the TSS of key early genes. The authors argue that their spatiotemporal data imply that this chromatin transition "primes" for early gene transcription. The manuscript is well written, uncovers important viral chromatin biology by combining multiple experimental techniques, and the data is generally very clearly presented. A few comments follow. Major concerns: • Abstract and Title: o the abstract and title suggest that because the chromatin changes are observed coincidentally or before transcriptional changes, and that this means that these chromatin changes "prime" (title) or are "required" and play a "central role" (abstract) in early gene expression. The temporal relationship would be consistent with chromatin changes being required for transcriptional changes, but do not imply necessity. Experiments to demonstrate the necessity of these changes for early gene transcription are lacking, and I recommend amending the text or additional experiments to provide this evidence directly.

      We observe a clear timing of events, with chromatin opening, nucleosome assembly at the 5’ end of the gene followed by transcriptional activation, suggesting that these structural changes are essential for gene activation. Still, we cannot prove the direct dependency. Therefore we toned down the title of our manuscript and formulate the findings more conservatively.

      The title now reads: “Changes in adenoviral chromatin organization precede early gene activation”

      Results:o The IF data in Fig S1 is convincing, showing viral particles are accessible quickly in the nucleus. Although no statistics are provided for S1B and C, pVII foci appear at 0.5hpi and appear to mostly accumulate between 0.5hpi and 1hpi with further import between 1hpi and 4hpi. Can the authors be sure that a single pVII IF focus represents a single genome? If genomes tend to aggregate as they accumulate the number of foci per nucleus may not increase linearly with the number of genomes imported. Have the authors considered analysing the intensity of the individual pVII foci over the time points? A related question is whether the authors assume that all packaged virions contain intact complete viral genomes? Many viruses comprise some mixture of complete and incomplete packaged genomes, and the subsequent analyses determine the proportion of transcriptionally active copies with RNA-Scope to a single transcript E1A which lies at one end of the viral genome. Please comment explicitly on whether this is assumed and whether this assumption is realistic in light of known Adenovirus biology.

      We appreciate the reviewer's concern. Several studies in the adenovirus field have shown equivalence between protein VII nuclear foci and individual genomes, including our own (PMID: 26332038). Probably the most accurate study was performed by Daniel Engels lab PMID: 19406166, who used nuclear protein VII foci to titrate viral as well as vector genomes. In contrast, a different study from Patrick Hearings lab PMID: 21345950 showed that past 4hpi, the number of nuclear protein VII foci gradually declines. Based on our experience and because our study is limited to 4 hpi we are confident that protein VII foci accurately reflect individual viral genomes.

      Concerning genome packaging, adenovirus particles contain a single viral genome that is protected at each end by a covalently attached protein preventing its degradation. The packaging of adenoviruses is extremely efficient and only complete genomes are packaged into fully assembled particles. All viruses used in this study have been purified by double CsCl gradient purification. This density gradient based purification protocol removes all particles that are either empty or damaged or would contain partial genomes.

      o The RNA-Seq data in Fig 1 and Fig S2 and Table S1 demonstrates transcription of early genes is barely observable at 1hpi but is observable by 2hpi and is clearly much increased by 4hpi. Fig 2C, visualising pVII foci directly within single cells, suggests that approximately 80% of foci are observed by 1hpi and a further 20% between 1hpi and 2hpi and little thereafter. These data convincingly demonstrate that nuclear import is rapid, typically occurring in the first hour. The E1A RNA-Scope data in figure 2, visualising individual mRNA transcripts of E1A, is more sensitive than the bulk RNA-Seq, and shows transcripts at 1hpi with clearly discernible transcription by 2hpi (2A&D) which suggests that transcription occurs early, by 2hpi. Thus transcription lags nuclear genome import by approximately one hour by these methods. However, the conclusions of the subsequent analyses depend on the chromatin changes clearly preceding, rather than being approximately coincident with transcription, therefore transcription being evident by 2hpi is relevant as figure 6A and D suggest that the chromatin remodelling is subtle before 2hpi on the bulk sequencing analyses. The authors should comment on this given the importance to their argument.

      As stated by the reviewer we observe a clear lag between nuclear import and transcriptional activation. And we do also observe the largest changes in nucleosome occupancy (ChIP-seq data) between 1 and 2 hpi (Fig6A and D). Compared to 0hpi, we observe the strongest increase of nucleosome occupancy between 1hpi and 2hpi (4-8fold effect), whereas depending on the area a 2-3fold increase in occupancy can be observed from 2hpi to 4hpi (Fig6D). An effect that one would expect with chromatin structure preceding gene activation. Furthermore, the timing of nucleosome assembly perfectly matches the increase of MNase accessibility at 1 hpi, supporting our conclusions.

      o The validation of the E1A probe specificity in Fig 2B looks convincing, but there are no data presented for multiple cells to reassure that this image is representative. The equivalent figure for 2D for the Ad5-GFP control would address this.

      We include a large field overview with multiple cells for virus and vector control as new supplemental figure S2B showing that the RNAscope detection of the E1A transcript is highly specific.

      o Figure 2E is presented as a colocalization analysis but appears to be a ratio of mRNA foci to pVII foci per cell. If this is an incorrect interpretation then some clarification in the figure legend would be helpful. If this interpretation of these data is correct, then it is not truly a colocalization analysis, as a single genome may give rise to multiple transcripts and so a ratio We apologize that this figure was not clear. The data are based on real colocalizations and represent the number of pVII dots positive for E1A normalized with the total number of nuclear pVII. We have clarified the figure legend accordingly.

      o The live cell imaging experiments are elegant and convincing, but the agreement in Fig 3D of the % colocalization in MS2-BP data with the RNA-scope data is potentially misleading for the reasons outlined in the prior comment. Is the data in Fig 2E the same as the data in the right hand panel of Fig 3D. If so please comment on the n discrepancy (n=30 in 2E vs n=22 in 3D). The observation that 20% of genomes are transcriptionally active, via bursting or otherwise, is interesting, and would be consistent with the Suomalainen et al reference. The authors discuss two hypotheses to explain these findings: transcriptional bursting or a subset ~20% of genomes being transcriptionally active. This is an interesting and begs the question as to why this may occur. Assuming all imported genomes are intact (previous comment), it appears from the presented images that the foci at the radial periphery of the nucleus may be more frequently transcriptionally active, despite the nuclear periphery being enriched for heterochromatin. The authors might consider analysing the radial position of their TAF1B-mCherry genomes (active and inactive) as this might support position effect variegation rather than bursting as an explanation and they appear to already have the data to perform such analyses.

      o In the presented images (Fig 3A and Fig S3) it appears a higher proportion of genomes than 20% appear to be transcriptionally active, particularly in the low MOI experiment. The authors may wish to comment on this and quantify whether the proportion of transcribing genomes was affected by the input MOI.

      This and the previous comment concerning the influence of MOI, transcriptional bursting and the positioning effect of the genome on the transcriptional activity have also been in part raised above. As stated in our response to reviewer 1 we have used a high MOI in our experiments to have equivalence between all experimental approaches. We agree with the reviewers that all aspects (dose, bursting and positioning) merit a detailed investigation, which we plan in future studies. To be consistent and comparable in our comprehensive approach we decided to not include such studies here as they would address a different question. Nevertheless, to address this (and the above) comments we now mention positioning effects in the results (line 214) and enlarged the discussion (line 587 ff) where we especially raised awareness that such pertinent questions can be addressed with the tools presented in our study.

      We also decided to visually separate the comparison of MS2 and RNAscope data to avoid misleading the reader. Furthermore, the RNAscope data have been replaced. The RNAscope data are indeed from Fig. 2. The difference in n was due to our mistake showing two different normalized data sets. Data were either normalized using total amount of nuclear protein VII (Fig. 2E) or the total amount of nuclear E1A signals (Fig 3D), which due to the more heterogenous signal did not include all cells. In the updated version both figures display data normalized by total amount of nuclear protein VII

      o Fig 4C suggests that there is a large GC preference (or bias) in the pVII occupied regions. The authors may wish to comment on this and present a track with Adenovirus GC composition in Fig 4D.

      We thank the reviewer for raising this point. As suggested by the reviewer we analysed the GC content under pVII peaks and in the linker DNA. Indeed, pVII occupied regions have a significant higher GC content indicating that pVII preferentially positions at GC rich regions. We included this analysis as an additional Figure 4E (line 302 f).

      Legend:

      Boxplot showing GC content of pVII occupied (pVII) or free (linker) regions. Two biological replicates are shown side by side and the p-value of a students t-test of the corresponding pairs is indicated above.

      o Figure 6 presents convincing data showing H3.3. nucleosome positioning and acetylation at E1A and the data is nicely presented showing these changes occur early being observable by 2 and 4 hpi. Again, these changes are not convincingly prior to early gene activation but are certainly occurring early, and may occur prior to early gene activation at the level of individual foci, however, this is not demonstrated definitively.

      This question belongs to the same context addressed by the reviewer above. Please refer to the answer given above.

      Minor comments:

      Introduction: o Paragraph 1 - Introduction for DNA viruses in general, but the authors appear to be talking about Adenoviruses specifically, "little is known about the structural organization of the genome" and "nuclear viral genomes could undergo different parallel fates", arguably these statements are not accurate for other DNA viruses (e.g. Epstein Barr Virus) suggest amending the wording for clarity.

      The manuscript text was updated as suggested.

      o paragraph 2 - Why do the authors say that Adenoviruses are prototypic DNA viruses?

      We removed the term prototypic.

      o Paragraph 3 - A recent study is referenced but multiple references are given.

      The references were updated

      o "Protein VII stays associated with the viral genome imported to the nucleus, while pV dissociates from the viral DNA following ubiquitylation (Puntener et al., 2011). The fate of the μ-peptide is not known". - The reference suggests that pV dissociates on entry to the cytoplasm and during capsid disassembly at the nuclear pore. I find this sentence confusing as it doesn't make it clear that pV is lost before nuclear entry which is important for interpreting the data.

      We clarified this in the manuscript text

      Results:

      o Figure 5 is almost unreadable due to low resolution.

      We updated the Figures and checked the resolution after PDF conversion.

      o Reference to Fig 4C in text comes after Fig4D.

      The order of Figure panels was changed accordingly.

      Reviewer #3 (Significance (Required)):

      The manuscript is well written, uncovers important viral chromatin biology by combining multiple experimental techniques, and the data is generally very clearly presented

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      Reply to the reviewers

      *Summary: This paper illustrates the role of GDF15 in ganglionic eminence featuring the influence on neurogenesis and progenitor proliferation. Using the conventional knockout GDF-15 mouse lines, the authors provide a series of counting data indicating that GDF15 controls neurogenesis in the late embryonic or adult neural stem cells in the ventral forebrain. *

      We would like to thank the reviewer to reading our manuscript and providing comments. As the reviewer highlights, we have used a conventional GDF15 knock-in knock-out model since the growth factor is expressed not only at tissue levels but also at a systemic level. Therefore, targeted ablation of GDF15 would be complicated and the results difficult to interpret. Moreover, GDF15 and its receptor GFRAL in normal conditions are expressed at very low levels in adult mice and mutant mice do not display any obvious developmental phenotype. However, GDF15 is characteristically expressed during development and our previous data have shown that its expression is particularly increased at later developmental ages and particularly in neural precursors, which are both the focus of our analysis. Although these previous analyses have long highlighted that GDF15 is particularly expressed in the V-SVZ and in the choroid plexus, its physiological role in this system remains to the best of our knowledge unknown. It is important to investigate this issue because, as we mention below, GDF15 expression is increased, including in NSCs, upon brain injury and aging. As the reviewer rightly mentions, using this conventional approach and straightforward quantitative analyses, instruments available and normally used for the investigation of biological phenomena, we have discovered that GDF15 directly affects the number of ependymal cells and neural stem cells thereby providing a first function for the expression of the growth factor in this region.

      Major comments:

      The entire story in this manuscript seems similar to the previous findings where the authors demonstrated the role of GDF15 in the hippocampal neural stem cells in relation to EGF and CXCR4.

      As the reviewer rightly mentions we have in a previous paper investigated the effect of GDF15 on the stem cells of the hippocampus. However, we would like respectfully to disagree with the reviewer that our current manuscript describes similar findings. Whereas we have previously shown that the effect of GDF15 in hippocampal stem cells and neurogenesis is a transient inhibition of proliferation due to reduced EGFR expression, we here found that absence of GDF15 leads instead to increased proliferation and a permanent increase of stem cell number, besides of ependymal cells. As the reviewer rightly mentions, on the basis of our previous observations, also in this study we have analyzed EGFR expression and signalling. However, although our data clearly show that EGFR expression and more importantly signalling are altered also in this niche, we could show that the effect of GDF15 is more complex than altering EGFR signalling. Since we show for the first time in this study, that besides GDF15, neural progenitors also express GFRAL, our data point at a selective effect of GDF15 in the development of neural stem cell in the GE and in the deriving adult niche, which include change in EGFR signalling.

      *The data and the conclusion presented here sound reasonable to me. The current manuscript, however, gave me the impression that the story is less impactful and rather descriptive. To improve the quality of the current draft, the authors may wish to clearly highlight the novelty of the findings, not simply apply the previous strategy to the other anatomical brain regions. Alternatively, the draft could emphasize the similarity of utilising the same biological strategies to control the number of adult stem cells in the distinct stem cell niche. *

      We would like to thank again the reviewer for the positive consideration of our quantitative analyses, although we cannot agree with the referee’s conclusion about the impact of our current study. In particular, we would like to focus here not only on the specific findings of the two studies that, as we mentioned above, reach different conclusions, but also on the general meaning of our new study in the context of understanding the role of GDF15. Besides during development and aging, GDF15 expression is promptly upregulated in several pathologies including, cancer, injury and neurodegeneration. Indeed, a growing body of evidence has highlighted the possible role of GDF15 as stress hormone and mitokine, which could be part of a conserved mechanism of the body to signal and respond to stress. Within this context, it would be very important to understand the effect of GDF15 on stem cells, as it may prompt not only understanding of the physiological role of GDF15 but also of the mechanism underlying the response and contribution of stem cell to stress and injury. Supporting this view, it was recently shown that GDF15 is upregulated in quiescent neural stem cells following brain injury (Llorens-Bobadilla et al., 2015). However, the main problem in investigating the physiological role of GDF15 is that the expression of its recently discovered receptor is very limited in the brain. Therefore, our data here are important not only because of the novel effect that they describe for GDF15 in NSC development, but also because they show that GDF15 directly affects stem cell behavior. We have now followed the suggestion of the reviewer and re-edited several parts of the manuscript including editorial changes to highlight the relevance of our findings and the figures to better illustrate them. In particular, we have added also additional analyses to support our conclusions. These include data illustrated in Fig.1B, C; Fig. 4B, G and Fig. 5G.

      *I also have two concerns which may help to improve the current draft. Firstly, I would suggest considering the data presentation as many of the counting data are not accompanied by representative images or a detailed description of the methods, which could impede the credibility of the data. For instance, it is not clear to me how the authors judged the apical vs subapical progenitor counting as neither the pictures nor the methods clearly specified how these are distinguished. Same to the Figures 3 and 8. *

      We thank the reviewer for these suggestions, which we have now implemented in our revised manuscript. These changes include addition of representative images to Figs. 1, 2, 3, 4, 5, 6, as well as an illustration of how apical vs subapical cells were counted in Fig. 2A, an illustration showing how ependymal and single-ciliated cells were determined in Fig. 8A, and more detailed descriptions in the materials and methods section.

      *I would also suggest the authors may wish to carefully review the text as many abbreviations are not properly stated (for example, what are P+ progenitors?), or not properly explained why the particular gene expression is analysed (EGF, Sox2, CXCR4). This is also applied to the anatomical jargon (like apical, subapical etc), which can be specified in the figure by introducing the cartoons or point-on images. *

      We thank the reviewer for pointing these shortcomings out. We have now done a careful proofreading of the text and implemented changes in the relative figures.

      The changes include: Explanation of Prominin-1-expressing (P+) progenitors (p. 10, line 35) and apical and subapical progenitors (p.3, line 11-23), as well as more detailed reasoning for the analysis of gene expression for EGFR, Sox2 and CXCR4 (p. 9, line 5-7; p.12, line 44 and following)

      *Last not least, I should point out that the author uses less commonly used terminology. To my knowledge, the SVZ progenitors in the GE are now called basal progenitors (Bandler et al 2017 for example) and the word intermediate progenitors is used for the Tbr2+ IP cells in the developing cortex. MASH-1 is an old gene name as it is revised as Ascl1 (please refer to any recent papers and web databases such as Mouse Genome Informatics, and NCBI, for instance). The use of prevailed wording will help the readers to understand the presented story. *

      We have now revised the terminology according to the reviewer’s suggestion.

      Minor comments:

      Abstract Line 12-15 is confusing: What does "genotype" means?

      We used the term to refer to the genetic differences between WT and GDF15 mutant mice with respect to GDF15 expression. We apologize for the lack of clarity. We have now modified the paragraph in an effort to improve its clarity.

      Introduction Despite the focus of this paper being on the proliferation in GE, the introduction mixed up the references describing the dorsal telencephalon. It's better to cite the ventral GE as some progenitor behaviours are different from the ones in the dorsal. Maybe it's better to dedicate more to describing the lineage trajectory in the ventral GE and the molecular players (such as EGF), which makes it harder to understand the rationales of the several experiments.

      We would like to thank the reviewer for making this helpful comment. In the introduction we have tried to make two points: firstly, to clarify how the different progenitor types in the VZ can be distinguished based on the localization of their site of mitosis and secondly the importance of studying GDF15 in the context of NSCs in the subependymal zone of the lateral ventricle. For the first point we have several studies referring to dividing dynamics of radial glia in the developing cortex. This reflect the fact that many papers have studied both apical and subapical radial glia within the context of the developing cortex, unlike subapical progenitors, which were first discovered in the developing ganglionic eminence. A similar problem applies to the analysis of EGFR expression in the context of VZ progenitors, although we agree with the reviewer that it should introduced for a better understanding of our analyses. Therefore, we have now introduced several changes in our introduction to eliminate the shortcomings and to offset the imbalance in terms of citations.

      Results

      Fig1: V/SVZ -> VZ? I think V means ventricles while VZ is for the ventricular zone. Single-channel images should be presented to demonstrate the positive or negative cells for each antigen. Only a subset of progenitors in the adult SVZ is GDF15 positive although this is not described in the text.

      We have now replaced V/SVZ with V-SVZ, meaning ventricular-subventricular zone, throughout the manuscript and added single channel images to all figures where it is relevant.

      *Why the GDP15 staining was performed only in the adult sections, but not in E18 while the GFRAL is shown in both stages? The text claims "GDF15 is particularly expressed in the germinal region of the GE" but I did not find the data shown in this draft. *

      The fact that GDF15 is expressed in the choroid plexus and in the subependymal region of the lateral ventricle was first observed in the neonatal rat brain and prompted us to investigate the hypothesis that GDF15 may affect NSCs. Moreover, in our previous manuscript we have confirmed that GDF15 is expressed in neural progenitors of the embryonic murine GE (Carrillo-Garcia et al., 2014). In this new manuscript, we have complemented these data by adding the missing information concerning the expression of the protein in the adult V-SVZ. Notably, we also investigate for the first time the expression of the receptor in this area. This is key issue in the field, since the expression of GFRAL has been reported only in few regions of the brain, which is in apparent contrast with the growing list of effects in which GDF15 has been involved. For completeness of information and to further strengthen our conclusions we have now added new set of images in figure 1B, C showing co-expression of GFRAL and EGFR.

      *Line 24-25: I did not understand this statement. *

      The sentence refers to the results published in our previous paper, as mentioned in our reply above, which illustrate expression of GDF15 in the GE at different ages of development and in the adult V-SVZ. In an effort to improve its clarity, we have now modified the sentence into: “Consistent with these observations, we have previously reported that in the GE, Gdf15 transcripts increase at late developmental age and remain high in the adult V-SVZ.”

      Fig. 2 Line 33: what are apical P+ progenitors?

      We apologize for this shortcoming. P+ is the abbreviation for Prominin-1 immunopositive progenitors. This information has been now added to the text.

      *Fig. 2A: The total analysed cells are not described in M&M. *

      We have now added this information in the relevant section of the manuscript (p. 7, lines 36-43).

      *Fig 2 C and D. While the counting of apical or subapical progenitors has been done respectively, the representative images of which regions are judged as apical or subapical are not shown. This comment also applies to Line 41: I did not get the logic of how this analysis will be able to distinguish apical or subapical cell division. *

      Mitotic apical and subapical progenitors have been detected on the basis of the position of their nuclei. Namely, mitosis was considered apical if the nucleus of the dividing cell was within two nuclei distance (~ 10 µm) of the apical surface, and considered subapical if the nuclei of the dividing cells was at a greater distance from the apical surface. Besides adding this information to the manuscript, see “Image analysis” in the “Materials and Methods” section, we have now illustrated our approach in the new Fig. 2B.

      *Fig. 2 E and F: I am not sure why the proliferation was assessed in vitro whole mount cultures. IP injection in vivo animals would be more convincing. *

      We have used the same whole mount preparation to determine changes in proliferation upon acute fixation of the tissue. We have then determined the effect of growth factors and pharmacological modulators in whole-mount explants preparation as this would allow us to test their effect in standardized conditions. For the sake of consistency, we have then used the same whole mount explant setting to investigate proliferation by means of IdU incorporation. We selected this mean of analysis because changes in proliferation were already detected upon tissue fixation, and direct exposure of the tissue to the pharmacological modulators allowed us to investigate the direct effect of the drugs on proliferation behavior. Using this setting, we have obtained data that are compatible and consistent with our analysis on acutely fixed preparations. We agree with the reviewer that these experiments could be also repeated by injecting the IdU in vivo, however this would be against the current animal 3R guidelines that prompt to minimize the use of animal in vivo experiments and only when they cannot be replaced by alternative approaches in vitro or ex vivo.

      *Fig. 3 I am not sure the mitotic spindle orientation analysis is very informative to stand out as one independent figure. In some contexts (Noctor et al 2008), it does not correlate to the asymmetric or symmetric division modes. *

      We would like to like to respectfully disagree on this issue. The reason why we think this data set is important is twofold. Firstly, previous papers pointing at changes in the number of NSCs in the GE, have established that this was caused by a change in the spindle orientation leading to the generation of extra SNP (Falk et al., 2017), indicating a role for the orientation of the mitotic spindle in this context. Since we observed that GDF15 promotes not only progenitor proliferation but also apical divisions, it is important to show that this effect does not reflect a change in spindle orientation. Secondly, these data set highlights an age-dependent effect on the orientation of the mitotic spindle that is fully consistent with previously published data supporting the solidity of our findings. However, since we did not see any significant differences between the WT and Gdf15-/- animals, we have decided to move this data to a supplementary figure (new supplementary figure S3).

      *Fig. 4 I am not convinced by this data since how the fluorescent intensity is measured is not described. If the internal controls to adjust the staining variation among samples are not used, the data is not convincing to me. The representative pictures are not convincing either to claim the substantial differences. Perhaps immunoblotting is better to be employed to quantify the protein expression difference. *

      We have now added additional pictures with higher magnification to show the difference in EGFR intensity, including a calibration bar (Fig. 4). Quantitative analysis showed a trend decrease at E18 which is strongly significant in the adult V-SVZ. We now also show analysis of phEGFR and modified extensively the relative result section (see also our reply to the comment on Evidence, reproducibility and clarity of reviewer 2). Furthermore, we have added the following paragraph to the methods section:

      “For fluorescence intensity measurements of EGFR, slices stained at the same time with the same antibody solutions, and imaged on the same day with constant confocal microscope settings (laser intensity, gain, pixel dwell time), were measured using Fiji/ImageJ. Raw immunofluorescence intensity was normalized by subtracting background fluorescence levels, i.e. fluorescence in cells considered negative for EGFR. To rule out any unspecific secondary antibody binding, fluorescence was compared to slices incubated with secondary, but not primary antibodies (2nd only control); no difference was found between 2nd only control and cells considered negative in EGFR-labelled samples, or between 2nd only controls of different genotypes.”

      *Fig. 5 This is very busy figure composed of mainly counting graphs of different experiments. I think at least it is better to separate the data in vivo or culture. *

      We have now rearranged the figure according to the reviewer’s suggestion. We have moved, also according to Reviewer 2’s suggestions, some less relevant data to supplementary figure S4 (that is, previous panels G-J) and added confocal images to illustrate the results of previous panels C-E. We hope that this improved the focus and clarity of this figure.

      *Fig. 6. Pictures of Day 2 and Day 7 should be presented to highlight the difference between them. *

      We have now added pictures showing the cell culture at DIV2 and DIV7, as well as the different treatments, in Fig. 6A.

      *Fig. 7 Mash-1 should be rephrased as Ascl1. *

      We have now changed the name of the gene throughout the manuscript.

      Fig. 8 A: I am not sure why these pictures are B&W even though the two antigens are stained. The main text needs more description since no explanation of FOP, b-catenin etc. The picture of GFD15 KO looks having massive numbers of FOP+ cells, which is not correlated to the counting, I guess?

      We apologize for the lack of clarity. We have now added additional panels (Fig. 8A) to illustrate the rationale behind the analysis and to demonstrate how ependymal and single-ciliated cells were counted. We have added the following sections to the manuscript text:

      Materials and methods:

      “Both the β-catenin and fibroblast growth factor receptor 1 oncogene partner (FOP) primary antibodies are mouse monoclonal antibodies of the same immunoglobulin class. Therefore, for this double immunostaining both antigens were revealed using the same secondary antibody and each was distinguished based on the localization and morphology of the labelling, which is lining the cell boundaries or at the basal body of the cilia for β-catenin and FOP, respectively.”

      Results:

      “We here used β-catenin to label cell-cell contacts, thereby visualizing cell boundaries, and fibroblast growth factor receptor 1 oncogene partner (FOP), a centrosomal protein, to visualize the basal body of the cilia. As both β-catenin and FOP-antibodies where derived from the same host species, the antigens were labelled in a single fluorescent channel and differentiated based on label localisation and intensity (Fig. 8A). Cells with a single centrosome or centrosome pair (one to two FOP+ dots) were counted as single-ciliated (SC), whereas cells with more than two centrosomes, i.e. multiciliated cells, were counted as ependymal (Epen; Fig. 8A).”

      We have also added the following paragraph to the figure legend:

      “(A) Schematic showing counting of ependymal (Epen) and single-ciliated (SC) cells using FOP and β-catenin as markers. (a’) Closeup of WT image in (B), showing β-catenin, indicating cell-cell-contacts, and FOP, indicating ciliary basal bodies/centrosomes, in a single channel. Scale bar = 10 µm. (a’’) β-catenin and FOP labels are distinguished by location, morphology and label intensity, with FOP being single dots that are more intense than β-catenin and located within the cell boundaries. (a’’’) Cells containing one or two centrosomes were considered SC cells (red), while cells with more than two centrosomes were considered multiciliated and therefore Epen (blue).”

      We would also like to point out to the reviewer that since FOP was used as a label for the ciliary base, the “massive numbers of FOP+ cells” (i.e., multiciliated cells) were indeed quantified in Fig. 8B (now 8C) as ependymal cells (Epen).

      ** Referees cross-commenting**

      I agree with Reviewer #2's comment that despite the amount of data presented, they are not presented in a coherent manner. I would suggest revising carefully before submitting to any journals. As detailed above the manuscript has been revised to improve clarity and coherence according the reviewer’s suggestions.

      Reviewer #1 (Significance (Required)):

      *The presented finding of the role of GDF15 in the ventral progenitors are evident and a new finding has not been reported. Since the same effects and signalling pathways involved in adult hippocampus neurogenesis are previously published by the same authors, the impact of the current manuscript is limited. I think heightening the role of GDF15 in the biological context of ventral progenitors, or alternatively, making a comparison to the previous finding would greatly improve the quality of the draft. In my opinion, this work would be appealing to the community of neural stem cells but maybe not to the broad audience. My expertise is neurodevelopmental biology focusing on neuronal lineages and neurogenesis. *

      We have already clarified that the effect that we report here of GDF15 on NSCs is not only novel, but is also very different from what we have previously observed in the hippocampus (see also our reply above to the comment on evidence, reproducibility and clarity of reviewer 1). Although many environmental signals and growth factors have been implicated in the regulation of NSC proliferation and self-renewal, GDF15 is, to our knowledge, one of the few factors directly regulating the number of apical NSCs. Following the suggestion of the reviewer, we have now revised our manuscript in order to highlight the difference between the two studies. Besides being important within the field of NSCs, we believe that our data are also important for understanding the physiological role of GDF15, whose expression is increased during development and in the response to a growing list of stressors. For such an understanding, it is essential to identify target cell populations which can directly respond to the growth factor. Our finding that the GDF15 receptor is expressed in NSCs provide first evidence that GDF15 can directly modulate stem cell development, providing a first function for the increase in its expression. Moreover, our observation that GFRAL continues to be expressed in adult NSCs opens up to the possibility that the increase in the expression of the growth factor in the stress response is to recruit/modulate stem cell behavior. Consistent with this scenario, it was recently observed that brain injury promoted and increase of GDF15 expression in NSCs (Llorens-Bobadilla et al., 2015).

      • Reviewer #2 (Evidence, reproducibility and clarity (Required)): *

      * Here the authors explore the role of GDF15 during development of the adult neural stem cell niche at the lateral wall of the lateral ventricle using GDF15 knock-out mice. They find increased progenitor proliferation at neonatal stages and at 8weeks, compensated by neuronal death. Further they report that EGFR+ cells are arranged differently in the GDF15 mutants (in clusters rather than columns) with also lower levels of EGFR. This is surprising to me, as the authors observe an increase in proliferation. They then report that addition of EGF leads to an increase in prominin+ progenitors in the GDF15KO, but not the WT, but there is lower levels of EGFR in the KOs. They then block CXCR4, which is allegedly required for GDF15 to modulate EGFR expression, and they find that this blocking reduces proliferation mostly in WT cells. As can be seen from this summary, to me, the model of how GDF15 loss is supposed to increase proliferation is not clear. Even less so, when in adult SVZ, EGFR+ progenitors were increased, while EGFR was reduced at postnatal stages. Beyond this, the authors show convincingly that ependymal cells are increased at adult stages, while I see no data supporting their claim of NSCs to be increased (at least not reaching levels of significance).*

      • Taken together, this manuscript contains a lot of data, but to me no coherent picture emerges. If the picture is that the higher proliferation rate of apical progenitors at E18 generates more ependymal cells, then this should be shown (by including analysis e.g. at P5 when ependymal cells emerge). How GDF15 would affect proliferation in general is also not clear to me - maybe an unbiased analysis by RNA-seq could help to separate the main effects from diving into known candidates that seem not to explain the main aspects.*

      The reviewer mentions multiple aspects of our study that we would like to clarify. Therefore, we apologize for our lengthy reply.

      Firstly, the apparent contradiction between the increase in progenitor proliferation despite the concomitant decrease in EGFR levels. It is long known activation of EGFR regulates multiple aspects of progenitor behavior including proliferation, migration and differentiation. It is also known that responsiveness of NSCs to EGF increases during development, a process that is paralleled by an increase in expression of EGFR expression increase with developmental age, peaking around the perinatal age. However, since NSCs start to slow their cell cycle and enter quiescence exactly at the same time in which the increase in responsiveness to EGF and in EGFR expression in NSCs during this same period, it is clear that there is not a linear correlation between NSC proliferation and EGFR expression. A possible explanation for this apparently counterintuitive observation is that the increase in EGFR expression is paralleled by an increase in the expression of Lrig1, a developmental negative regulator of EGFR (Jeong et al., 2020). Moreover, in our study we report that lack of GDF15 leads to a decrease in the expression of EGFR protein and not in the levels of EGFR mRNA. In light of the well-known feedback mechanism by which in the presence of high EGFR activation the receptor transits to late endosome for degradation, a decrease in the protein levels could actually represent a higher level of activation EGFR signalling in the mutant progenitors than in the wild type counterpart. This is consistent with the characteristics of punctuate EGFR immunostaining we see in the mutant tissue and our analysis of EGFR activation. Our data show that despite difference in activation kinetics, wild type and mutant progenitors similarly respond to exogenous stimulation with EGF. Moreover, there is no difference between the two genotypes in the expression of EGFR in mitotic cells, and blockade of EGFR more dramatically affects the proliferation of mutant than WT progenitors. Finally, exposure to exogenous EGF promotes the proliferation of WT but not mutant progenitors. Taken together, these observations suggest that endogenous activation of EGFR driving proliferation is higher in mutant than WT progenitors. Consistent with this hypothesis, our new data illustrated in Fig. 5A-G of the revised manuscript, show that EGFR is similarly phosphorylated in mutant and in WT progenitors and that levels of Phospho-EGFR are observed in regions with low levels of EGFR expression, especially in the postnatal V-SVZ.

      With respect to the effect of CXCR4, we have investigated its effect with respect to the ability of GDF15 to promote EGFR expression at the cell surface and secondly with respect to affect proliferation in vivo and in vitro. Both experiments reveal a permissive role of the receptor, whose activity is necessary for GDF15 to promote EGFR expression at the cell surface and for cell to undergo proliferation. While these observations confirm in part our previously published data, the molecular mechanisms underlying these effects remain unclear. However, since AMD on its own does not affect EGFR expression in either WT or mutant progenitors, the two effects are not related. Despite the absence of a clear mechanism by which CXCR4 affects proliferation, our data indicate that the permissive effect of CXCR4 is more important for the proliferation of TAPs rather than for NSCs. Therefore, the different effect of CXCR4 inhibition between WT and mutant progenitors likely reflect the fact that the latter are enriched in NSCs.

      Finally, the evidence that NSCs are significantly increased in the mutant V-SVZ is reported in Fig. 8. In this figure, it is clearly reported that compared to the WT counterpart at early postnatal stages, only the multiciliated ependymal cells are significantly increased in the mutant niche, whereas uniciliated progenitors display only a trend increase (Panel C). However, the total number of apical cells is increased in the mutant V-SVZ, indicating that the number of both cell types are likely increased. Consistent with this hypothesis, in panel G of the same figure we show that in the adult V-SVZ, when the ependymal cells are fully differentiated, also apical GFAP+ NSCs are significantly increased. Notably, in this figure we show that GFAP+ NSCs also display a primary cilium, an elongated morphology and lack multiple cilia, and therefore are not atypical ependymal cells. Finally, in supplementary table S6, we show no difference in terms of % of clone forming cells between dissociated cell preparations of the WT and mutant V-SVZ. These observations and our finding of increased Ki67 apical expression in the adult V-SVZ, illustrated in supplementary figure S2B, clearly show that apical NSCs are increased.

      We have now introduced multiple modification in text and figures to clarify the mechanisms underlying the effect of GDF15 ablation on EGFR expression and activation and the differential effect of CXCR4 on WT and mutant progenitors. The new data set illustrating phosphoEGFR are illustrated in figure 4B, G. We have also modified figure 8 in an effort to illustrate more clearly the effect of lack of GDF15 on NSC number.

      *Major comments: *

      *1) Inconsistencies start already in Figure 1: The authors show expression of the receptor at neonatal stages (much higher) and adult stages, but GDF15 is shown only in adult stages and the citations of their previous work suggests that indeed it may not be present at this early stage in the GE VZ (p.8, line 10). If it is, please show. If it isn't, could it be that it is in the CSF and signals only to apical cells? *

      As the reviewer rightly mentions, GDF15 is present in the CSF and signals mainly to apical cells, as it is known to be secreted by the choroid plexus (Böttner et al., 1999; Schober et al., 2001). However, we would like to respectfully disagree with the reviewer. In our previous work, we clearly showed that GDF15 expression increases at E16 and is highest at E18 in the GE, which is the reason we specifically chose this timepoint for analysis (compare Carrillo-Garcia et al. (2014), Fig. 1A). In this manuscript we have also shown that EGFR expressing progenitors in the GE express GDF15. As the expression of GDF15 at embryonic and neonatal ages has already been investigated by us and others for two decades (see also Schober et al. (2001)), we refrained from showing expression of GDF15 at these ages again. However, we have now modified the relevant result section to clearly highlight the existence of this previous findings.

      *2) An overview of the KO phenotype by lower power pictures would be helpful. For example an overview over the GE and PH3 immunostaining WT and KO at comparable section levels. *

      Our analysis is based on whole-mount preparation of the whole GE. To standardize our analysis the same number of pictures were taken at similar locations to obtain a quantification representative of the apical surface of the whole GE. Therefore, the areas of interest were not selected on the basis of the number of mitotic cells and the differences observed do not reflect a positional effect. Lower power pictures illustrating the whole GE, are unlikely to be helpful, because they would not show the nuclear immunostaining. However, we have now modified the relevant Material and Methods section as follows to describe the standardization of our quantitative analysis: “Whole mounts were imaged using a Leica TCS SP8 confocal microscope with a 40x or 63x oil immersion objective and LASX software (Leica). For the quantification, an average of three different regions of interest were chosen at fixed rostral, dorsal and ventral position of the GE or V-SVZ and averaged for the collection of a single data set.”

      * 3) Figure 2B- where is the apical surface, where are we in the GE? Where was quantification done? *

      We have now added images detailing the localization of apical and subapical cells in new Fig. 2A, as well as further clarifications of the imaging and quantification in the materials and methods section.

      * 4) Clarify the part with EGF signaling and/or take a more comprehensive view by a proteomic or transcriptomic approach, as EGFR and CXCR4 which were already investigated previously, may not explain the phenotype. *

      We agree with the reviewer that our data should prompt a more comprehensive approach. However, this is surely a work worthy of a separate manuscript, since we agree with the reviewer that changes in EGFR and CXCR4 do not fully explain the effect of GDF15 on proliferation. We have now clarified our conclusions about EGF signalling, modifying the relevant part in the result section. We have also modified the abstract as follows:

      “From a mechanistic point of view, we show that active EGFR is essential to maintain proliferation in the developing GE and that GDF15 affects EGFR trafficking and signal transduction. Consistent with a direct involvement of GDF15, exposure of the GE to the growth factor normalized proliferation and EGFR expression and it decreased the number of apical progenitors. A similar decrease in the number of apical progenitors was also observed upon exposure to exogenous EGF. However, this effect was not associated with reduced proliferation, illustrating the complexity of the effect of GDF15.”

      *5) Do the authors actually think that the effects on EGFR are in the cells expressing the GDF15 receptor? Then please show co-localization. *

      As both EGFR and GFRAL are widely expressed in the embryonic GE (see Figs 1B and 4B), making overlap inevitable, we did previously not assume the need to show co-localization. We have now added images showing co-localization of EGFR and GFRAL in E18 and adult brain sections in Fig. 1B.

      *6) Figure 5D shows virtually no apical mitosis in WT, but indeed there are apical mitosis in WT E18 GE as one can also see in panel 5A. *

      We apologize for the confusion. In the manuscript, we use Ki67 and analysis of nuclear morphology to determine the number of cells undergoing cell division, i.e. in meta-, ana- or telophase and immunostaining with antibody with phH3+, which stains additionally cells also at late G2 and early mitotic stages. Consistent with this, the number of mitotic cells scored with Ki67 and quantified in Fig. 5D is smaller than the number of phH3+ cells that is illustrated in Fig. 5A. Throughout the manuscript, cells labeled by phH3 immunoreactivity are named “phH3+ cells”, as quantified in Fig. 5B, whereas with “dividing cells” we refer to cells with Ki67 labeling that show nuclear morphology of meta-, ana- or telophase. We have, also according to the suggestions of reviewer 1, added images of the whole mounts analyzed for Fig. 5D, as well as the following text in the materials and methods section: “Cells were considered dividing if the nuclei were labelled with Ki67 and the nuclear morphology showed signs of division, i.e. meta-, ana- or telophase, in DAPI and Ki67-channels. For the sake of clarity, “dividing cells” only refers to this way of detection, while cells positive for phH3 are termed “phH3+ cells”, as phH3 also labels cells in interphase and prophase, as well as late G2 phase.”

      *7) For the effect on ependymal cell generation it could be good to include an intermediate age, such as P5-7, when ependymal cells differentiate, staining e.g. for Lynkeas or Mcidas, known fate determinants regulating ependymal cell differentiation at that time. *

      Most of our research was performed in either E18 or adult animals, where ependymal cells are either not yet present or already fully differentiated. Since ependymal cell differentiation starts at birth, we used P2 animals to look at ependymal cell differentiation. As shown in Fig. 8B, C this age is appropriate to study early ependymal differentiation, as a lot of multiciliated ependymal cells are already present at this age, and the difference between WT and Gdf15-/- animals is clearly visible and significant. While another age or additional markers might be interesting, we argue that it would not add to the conclusion or significance of this paper, as we can see this phenotype already at age P2 and it can still be detected it in adult animals.

      *Minor comments: *

      *-) p. 8, subapical progenitors are mentioned in line 42 without explaining how they are defined. *

      We have now added more detailed definitions of apical and subapical progenitors to the introduction.

      *-) p.8, line 44: the word increased in mentioned 2x *

      We have removed the additional word.

      *-) In the description of Figure 8 C and D seem to have been mixed up. *

      We have changed the description of Fig. 8.

      * ** Referees cross-commenting***

      *I also fully agree with the point that this manuscript is very difficult to read. I think that anyhow the results have to be reorganized to focus on the most important data, so rewriting will have to be done for clarity either way. *

      We apologize for the lack of clarity we have now extensively modified and re-edited the manuscript in an effort to improve its clarity.

      * Reviewer #2 (Significance (Required)): *

      * Exploring signmalling factors important for the stem cell niche is important, and the GDF15 indeed seems to have an effect there. The problem is, that much has been done with this factor already, but of course a mechanistic understanding of whats going on is important and could be the strength of this manuscript. However, it is really not clear, which mechanisms causes what. What is clear, is that the increased proliferation of neuronal progenitors is counterbalanced by death. Its also clear that ependymal cells are increased, which is an interesting effect. But how and why is not clear and may be the best to focus in this paper. *

      As the reviewer mentions, several publications focus on GDF15. However, there is only one publication investigating the effect of GDF15 on neural stem cells and this focuses on the hippocampus. Therefore, we would like to respectively disagree with the conclusion of the reviewer “that much has been done with this factor”. Moreover, a serious problem with previous studies investigating GDF15 is the fact that its receptor is scarcely expressed and therefore it is not clear if these studies investigate direct or indirect effects of the growth factor. Since we here for the first time show that neural stem cells in the GE and V-SVZ express GDF15-receptor GFRAL, our study for the first time show a direct involvement of GDF15 on proliferation, number of ependymal cells and, as detailed in our reply above, apical NSCs. This knowledge is not only relevant to the field of normal and cancer stem cells, but also within the context of the role of GDF15 as mitokine and as stress hormone (see also our reply to major comments 2 of reviewer 1). Therefore, although we agree with the reviewer that the molecular mechanisms underlying the effect of GDG15 need further investigation, our data are novel and of relevance to the general scientific community.

      References:

      Böttner, M., Suter-Crazzolara, C., Schober, A., Unsicker, K., 1999. Expression of a novel member of the TGF-beta superfamily, growth/differentiation factor-15/macrophage-inhibiting cytokine-1 (GDF-15/MIC-1) in adult rat tissues. Cell Tissue Res 297, 103-110.

      Carrillo-Garcia, C., Prochnow, S., Simeonova, I.K., Strelau, J., Hölzl-Wenig, G., Mandl, C., Unsicker, K., von Bohlen Und Halbach, O., Ciccolini, F., 2014. Growth/differentiation factor 15 promotes EGFR signalling, and regulates proliferation and migration in the hippocampus of neonatal and young adult mice. Development 141, 773-783.

      Falk, S., Bugeon, S., Ninkovic, J., Pilz, G.A., Postiglione, M.P., Cremer, H., Knoblich, J.A., Gotz, M., 2017. Time-Specific Effects of Spindle Positioning on Embryonic Progenitor Pool Composition and Adult Neural Stem Cell Seeding. Neuron 93, 777-791 e773.

      Jeong, D., Lozano Casasbuenas, D., Gengatharan, A., Edwards, K., Saghatelyan, A., Kaplan, D.R., Miller, F.D., Yuzwa, S.A., 2020. LRIG1-Mediated Inhibition of EGF Receptor Signaling Regulates Neural Precursor Cell Proliferation in the Neocortex. Cell Rep 33, 108257.

      Llorens-Bobadilla, E., Zhao, S., Baser, A., Saiz-Castro, G., Zwadlo, K., Martin-Villalba, A., 2015. Single-Cell Transcriptomics Reveals a Population of Dormant Neural Stem Cells that Become Activated upon Brain Injury. Cell Stem Cell 17, 329-340.

      Schober, A., Böttner, M., Strelau, J., Kinscherf, R., Bonaterra, G.A., Barth, M., Schilling, L., Fairlie, W.D., Breit, S.N., Unsicker, K., 2001. Expression of growth differentiation factor-15/ macrophage inhibitory cytokine-1 (GDF-15/MIC-1) in the perinatal, adult, and injured rat brain. J Comp Neurol 439, 32-45.

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      Referee #2

      Evidence, reproducibility and clarity

      Here the authors explore the role of GDF15 during development of the adult neural stem cell niche at the lateral wall of the lateral ventricle using GDF15 knock-out mice. They find increased progenitor proliferation at neonatal stages and at 8weeks, compensated by neuronal death. Further they report that EGFR+ cells are arranged differently in the GDF15 mutants (in clusters rather than columns) with also lower levels of EGFR. This is surprising to me, as the authors observe an increase in proliferation. They then report that addition of EGF leads to an increase in prominin+ progenitors in the GDF15KO, but not the WT, but there is lower levels of EGFR in the KOs. They then block CXCR4, which is allegedly required for GDF15 to modulate EGFR expression, and they find that this blocking reduces proliferation mostly in WT cells. As can be seen from this summary, to me, the model of how GDF15 loss is supposed to increase proliferation is not clear. Even less so, when in adult SVZ, EGFR+ progenitors were increased, while EGFR was reduced at postnatal stages. Beyond this, the authors show convincingly that ependymal cells are increased at adult stages, while I see no data supporting their claim of NSCs to be increased (at least not reaching levels of significance). Taken together, this manuscript contains a lot of data, but to me no coherent picture emerges. If the picture is that the higher proliferation rate of apical progenitors at E18 generates more ependymal cells, then this should be shown (by including analysis e.g. at P5 when ependymal cells emerge). How GDF15 would affect proliferation in general is also not clear to me - maybe an unbiased analysis by RNA-seq could help to separate the main effects from diving into known candidates that seem not to explain the main aspects.

      Major comments:

      1. Inconsistencies start already in Figure 1: The authors show expression of the receptor at neonatal stages (much higher) and adult stages, but GDF15 is shown only in adult stages and the citations of their previous work suggests that indeed it may not be present at this early stage in the GE VZ (p.8, line 10). If it is, please show. If it isn't, could it be that it is in the CSF and signals only to apical cells?
      2. An overview of the KO phenotype by lower power pictures would be helpful. For example an overview over the GE and PH3 immunostaining WT and KO at comparable section levels.
      3. Figure 2B- where is the apical surface, where are we in the GE? Where was quantification done?
      4. Clarify the part with EGF signaling and/or take a more comprehensive view by a proteomic or transcriptomic approach, as EGFR and CXCR4 which were already investigated previously, may not explain the phenotype.
      5. Do the authors actually think that the effects on EGFR are in the cells expressing the GDF15 receptor? Then please show co-localization.
      6. Figure 5D shows virtually no apical mitosis in WT, but indeed there are apical mitosis in WT E18 GE as one can also see in panel 5A.
      7. For the effect on ependymal cell generation it could be good to include an intermediate age, such as P5-7, when ependymal cells differentiate, staining e.g. for Lynkeas or Mcidas, known fate determinants regulating ependymal cell differentiation at that time.

      Minor comments:

      • p. 8, subapical progenitors are mentioned in line 42 without explaining how they are defined.
      • p.8, line 44: the word increased in mentioned 2x
      • In the description of Figure 8 C and D seem to have been mixed up.

      ** Referees cross-commenting**

      I also fully agree with the point that this manuscript is very difficult to read. I think that anyhow the results have to be reorganized to focus on the most important data, so rewriting will have to be done for clarity either way.

      Significance

      Exploring signmalling factors important for the stem cell niche is important, and the GDF15 indeed seems to have an effect there. The problem is, that much has been done with this factor already, but of course a mechanistic understanding of whats going on is important and could be the strength of this manuscript. However, it is really not clear, which mechanisms causes what. What is clear, is that the increased proliferation of neuronal progenitors is counterbalanced by death. Its also clear that ependymal cells are increased, which is an interesting effect. But how and why is not clear and may be the best to focus in this paper.

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      Reply to the reviewers

      1. General Statements

      We thank all the reviewers for their time and effort in the peer-review process. We appreciate the positive reflections on the study and the feedback comments which were well thought-out and articulated. Considering these comments has led us to deeper reflections on the conceptualization of the mechanisms at play, and we hope that our responses here and revisions of the manuscript have improved the presentation of the data and our interpretation of these complex matters. As a result, we have now incorporated a new supplementary figure 5 and present a new model figure with the corresponding comments in the text.

      1. Point-by-point description of the revisions

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      In this manuscript Sanchez-Martinez et al investigated the function of ntc, a Drosophila homologue of FBXO7. The mechanisms by which mutations in this protein cause autosomal recessive PD are poorly understood. The protein has previously been implicated in PINK1/Parkin mitophagy however the mechanistic detail is lacking. The data presented here provide an important insight into the molecular functions of ntc as well as mitophagy in vivo in general. Ntc was found to promote ubiquitination of mitochondrial proteins which is counteracted by USP30. The basal ubiquitination regulated by these two enzymes is proposed to act as a permissive factor for the initiation of Pink1/Parkin mitophagy. The conclusions are based on strong data in vivo and there is a lot to like about this paper. The analyses are done rigorously, conclusions are balanced and well supported and there is a lot of conceptual novelty in the dataset. At the same time the paper raises some questions with regard to the role of mitophagy, at least in Drosophila. Not all of these could be answered during revisions but it would be useful to address the points outlined below.

      1. The functional measurements such as climbing, flight and lifespan are used to complement the data on mitophagy and mitochondrial health. However, it is clear that these do not correlate. Ntc KOs and Pink1/Parkin flies have reduced climbing and flight ability, however ntc KO does not affect mitochondrial function. In case of Pink1/Parkin the assumption is that impaired fly functionality is due to damaged mitochondria. This is clearly not the case with Ntc. How relevant is climbing/flight/lifespan to the role of Ntc in mitophagy?
      • The reviewer raises a very good point, and we agree that there isn’t a strict, linear connection between the cellular process of mitophagy and the presentation of organismal neuromuscular phenotypes such as motor behaviours and lifespan. Considering this point further, starts to highlight the complexity of the situation at hand: it is becoming clear that there are many different forms of mitophagy, and these perform different functions in cellular remodelling and homeostasis. And, of course, there are many ways to interfere with neuromuscular function (as well as lifespan). So, it follows that some forms of mitophagy may dramatically impact neuromuscular homeostasis when disrupted, while others may not. We and others have described that basal mitophagy is minimally by loss of Pink1/parkin in vivo, so the organismal phenotypes clearly do not relate to this biology. But it is currently unclear how the phenotypes may relate to physiologically relevant stress-induced mitophagy as the precise nature of this, as well as the methods to experimentally manipulate it, are lacking.

      Here, we are initially documenting new phenotypes for ntc, with no bias for the mechanistic cause, all of which are worthy of description to gain a holistic view of the overall contribution of this gene function to organismal integrity. It is clear from the literature that ntc/FBXO7 has multiple functions, for instance, regulating proteasome function and caspase activation, so it follows that genetic loss is likely to impinge on multiple cellular functions causing pleiotropic effects.

      We have always been careful not to consider (or claim) that the organismal phenotypes, such as motor function or lifespan, are specifically due to defective mitophagy but are an overall readout of the health and functioning of the neuromuscular system. Nevertheless, these phenotypes are useful in investigating manipulations that improve or worsen the effect of Pink1/parkin (or ntc) mutants, which, a priori, may or may not also modulate mitophagy. While we have documented these new organismal phenotypes of ntc mutants and analysed the impact on basal and stress- induced mitophagy, we have not drawn a cause-effect link between the two but a correlation at least. Nevertheless, this issue raises some important considerations for the field in terms of the different kinds of mitophagy, and when they may be needed, and the impact on cells, systems or whole-organisms when they are defective. This issue is explored more in answer to Q3 below.

      1. Fig 3 is somewhat patchy, mitophagy is shown for USP30 and ntc KO epistasis but climbing index used in OE setting. These data do not match, and it feels like experiments that are shown are the ones that worked. The relevance of climbing index to mitophagy is unclear as mentioned above. Does KO/OE of ntc and USP30 affect levels of mitochondria, e.g. Marf used as a maker for mitochondria in Fig. 1? And if not why not, considering that ntc/USP30 but not PINK/Parkin control basal mitophagy?
      • The main purpose of the data in Figure 3 was to document the impact of ntc manipulation on basal mitophagy, and by extension to link this to the known mitophagy regulator, USP30, whose loss of function has been documented to promote stress-induced mitophagy. Here, we successfully demonstrated USP30 RNAi and ntc OE cause an increase in mitophagy, and established their genetic relationship. However, it is very important to our modus operandi that we have orthogonal evidence for this relationship and understand the impact at an organismal level. As the reviewer indicates, the obvious choice that would align with the mitophagy data would be to assess whether loss of ntc prevents a USP30 RNAi phenotype. However, in our hands USP30 knockdown using the same RNAi line had no discernible impact on viability or behaviour in adult flies, precluding this experiment. Of note, we did observe a detrimental impact of USP30 knockdown on adult viability using a different RNAi line (KK) but this has 2 known off-targets so this result is unreliable. An alternative approach to genetically test the antagonistic relationship between USP30 and ntc, and equally valid in our view, is to assess whether ntc OE can counteract a USP30 OE phenotype. Here, we were fortunate that USP30 OE does indeed provoke an organismal phenotype, and this was suppressed by ntc OE consistent with the mitophagy data. It is unfortunate that the more obvious option was not workable on this occasion, but we hold that the genetic relationship was nevertheless substantiated as expected, albeit with alternative manipulations. Importantly, we established the validity of this approach by demonstrating the known genetic interaction between USP30 and parkin, whereby USP30 OE locomotor phenotype is suppressed by parkin OE (now, Fig. S3D). To substantiate this approach more clearly, we have now added to the text (lines 200-201) and figure (Fig. S3C) the lack of observable effect by USP30 knockdown as noted above.

      As to the second point, assessing whether levels of mitochondria are changed by ntc/USP30 manipulations; according to the immunoblot presented in new Supplementary Figure 5A (and replicates), the levels of ATP5a are not notably changed by ntc O/E or USP30 RNAi. Marf levels are also unchanged though this is not shown. This is in line with our expectations since, as discussed above, ntc/USP30 are only one set of regulators of one type of mitophagy and several others exist. The reviewer will likely be aware that the levels of mitochondria are tightly regulated and fine-tuned for the specific need in different tissue types, and that substantial changes to mitochondrial content can be catastrophic for cell and tissue viability. While this is relatively straightforward to achieve in cultured cells, substantial reductions in mitochondrial content are non-viable in an in vivo context. Of course, in physiological conditions, rates of degradation are kept in fine-balance with biogenesis and proliferation so non-catastrophic alterations in mitochondrial content are usually counteracted by compensatory changes in proliferation or degradation.

      1. What is the role of mitophagy in the maintenance of mitochondrial function in Drosophila in general? Pink1/Parkin KO assumed to result in dysfunctional mitochondria due to impairment of damage-induced mitophagy which is a minor contributor to mitophagy as has previously been published by the authors and confirmed in this dataset using mitophagy reporter. At the same time ntc is clearly required for mitophagy, but mitochondria remains structurally and functionally intact in ntc KO. The most straightforward interpretation of these data is that Pink1/Parkin contribute little to mitophagy in flies and their effect on mitochondria and fly function is independent of mitophagy. Instead ntc (and USP30) strongly regulate mitophagy but mitophagy is not important for the maintenance of mitochondrial function. The effect of ntc on fly function is also independent of its role in mitophagy/mitochondria. Unless there is an alternative explanation the entire dataset would need to be reinterpreted and discussed differently.
      • We agree that this is an important point raised by the study findings and needs to be clearly articulated in the text, but we don’t think it is as simple as whether ‘mitophagy’ contributes to mitochondrial and organismal integrity. First, as mentioned above, it is becoming apparent that it is crucial for the field to clearly and consciously distinguish between basal and induced forms of mitophagy. Basal mitophagy is likely, though not yet proven, to be an important component of mitochondrial quality control in metazoans and largely act in a house-keeping manner providing continual surveillance of mitochondrial quality and quantity. As such, like many other critical biological processes, it is likely to be supported in a ‘belt-and-braces’ manner by several mechanisms working in parallel with a degree of functional redundancy. In contrast, induced mitophagy is presumed to be quiescent until stimulated into action at specific times for specific purposes. For instance, it is assumed, though not yet proven, that PINK1/Parkin stress-induced mitophagy is stimulated in response to some kind of physiological stress or damage to mitochondria that may be catastrophic if left unchecked.

      We and others have shown before that PINK1/Parkin are minimally involved in basal mitophagy in vivo but they are well-established to promote stress-induced mitophagy. In contrast, we have found that ntc regulates basal mitophagy and, we posit, facilitates PINK1/Parkin mitophagy by providing the initiating ubiquitination. How does this map onto the mitochondrial/organismal phenotypes? There are clear disruptions to energy-intensive, mitochondria-rich tissues inPink1/parkin mutants which are not grossly affected in ntc mutants. On the other hand, ntc mutants show a dramatically short lifespan, much shorter than Pink1/parkin mutants, while other measures of mitochondrial integrity are fine.

      The Pink1/parkin phenotypes are consistent with a catastrophic loss of tissue integrity caused by the lack of a crucial protective measure (induced mitophagy) for a specific circumstance (we think, mitochondrial ‘damage’ arising from a huge metabolic burst). In contrast, while loss of ntc causes a partial (but not complete) loss of basal mitophagy, these same tissues appear to be able to cope with this impact on house-keeping QC but importantly are also able to mount a stress-induced response via Pink1/parkin still being present. On the other hand, it should be remembered that ntc is known to perform other important cellular functions, such as regulating proteasome function and caspase activation, and it is perhaps loss of these functions that causes the dramatic loss of vitality.

      Importantly, although Pink1/parkin do not contribute to basal (steady-state) mitophagy, we think it is not appropriate to think of Pink1/parkin mitophagy as a ‘minor’ contribution. Since, under the particular triggering conditions of damage or stress that stimulate Pink/parkin mitophagy, apparently only Pink1/parkin can perform this role in certain Drosophila tissues, and this stress- induced mitophagy is crucial to tissue integrity, as exemplify by the fact that increasing basal mitophagy via ntc O/E still is not sufficient to rescue Pink1 mutants. In this specific context, this is a major mitophagy pathway.

      In summary, the connection between mitochondrial autophagic degradation and mitochondrial/organismal health is not a simple one and we would avoid conflating different aspects of mitochondrial QC with the expectation that the consequences of their dysfunction would be the same. Nevertheless, these well-considered feedback comments have crystallised the need to elaborate these ideas in the Discussion where we have added a new section (lines 359-387).

      Reviewer #1 (Significance (Required)):

      Very strong genetic data presented; novel functions for human Park15 homologue in Drosophila; mechanistic insight into the ubiquitination of mitochondria by two opposing enzymes. Overall very interesting paper but interpretation is less clear which needs to be addressed.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      In this ms. Sanchez-Martinez and colleagues study the role of the ub ligase FBXO7, in regulating mitophagy - highlighting that mutations in FBXO7 associate with Parkinson's disease and defects in mitochondrial homeostasis. Using the fly as model, they carry out a series of expts. investigating ntc (Drosophila ortholog of FBX07) demonstrating that it can functionally rescue Parkin but not PINK1 deficiency. Expanding on this, they propose a model whereby ntc/FBX07 regulates basal mitophagy and also acts as a priming Ub-ligase for Parkin mediated mitophagy, finding that the dub USP30 counteracts these ntc function. Overall the data are robust and support the authors' conclusions and model, the manuscript is well written and I think can be accepted as is.

      • We thank the reviewer for their appreciation of the work and the time taken to provide supportive feedback.

      While outside the scope of this study to understand why, I find it very interesting that ntc cannot rescue the PINK1 deficient phenotype, argues that PINK1 may be having additional effects beyond regulating mitochondrial ubiquitylation.

      • We entirely agree with the reviewer, this is a very intriguing finding. Indeed, there are several examples in the literature showing that PINK1 performs additional functions than just triggering mitophagy. But in the current context we interpret these data as further support for a clear mechanistic distinction between basal mitophagy and stress-induced mitophagy as discussed at length to the other reviewers’ comments.

      Reviewer #2 (Significance (Required)):

      Importance for understanding the role of FBX07 function - relevant for Parkinson's disease, also demonstrates a role for it in priming for PINK1/Parkin dependent mitophagy.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      In this manuscript, Sanchez-Martinez et al characterise the role of nutracker (ntc), the presumed Drosophila orthologue of human FBXO7 (whose gene is mutated in autosomal recessive PD), in mitophagy and phenotypes associated with neurodegeneration in flies (climbing index, dopaminergic neuron loss, rough eye phenotype, and others). FBXO7 (human) has been previously shown to restore parkin (not Pink1) phenotypes and mitochondrial morphology in Drosophila and implicated in Pink1-parkin mitophagy, however the role of ntc in basal mitophagy and its genetic interaction with USP30 has not been previously reported. Key findings include: evidence for functional homology between ntc and FBXO7, and that Ntc/FBXO7 is required for basal mitophagy (and reverses USP30 function) in a Pink1-parkin independent manner.

      FBXO7/ntc is clearly an important regulator of mitophagy and its overexpression can suppress Parkin phenotypes, however FBX07/ntc has not been studied as intensively as Parkin and Pink1, therefore this work represents important insight into mitophagy regulators (broad interest to many overlapping fields).

      However, in addition to minor points and controls requested below, some further characterisation of the signals on the mitochondria induced by ntc/FBXO7 would improve the novelty of the study and the mechanistic insight provided. For example, the authors look at total ubiquitin and pS65- Ub, whereas if they looked at specific substrates that they mention in the discussion (e.g. OMM and translocon proteins) it would allow a less speculative discussion.

      Figure 1: The authors show that overexpression of ntc can rescue Parkin null phenotypes but not Pink1 phenotypes. In very similar experiments, overexpression of FBXO7 (human) has been shown to rescue Parkin phenotypes but not pink1 phenotypes (Burchell), appropriately mentioned by the authors.

      The western blots are not terribly clear and would benefit from quantification (particularly H).

      • We had previously performed the quantification on replicate experiments but had considered that the result was clear enough without quantification, and that including a quantification may make the figure too crowded. However, we have now added the quantification to the figure to support these results.

      Specific ubiquitylated substrates like translocon proteins would be very interesting (alternatively, this could be provided in figure 7).

      • We agree that this would be a very interesting aspect to investigate, but we feel that since the emphasis of the current study is clearly on the regulation of mitophagy, and not on specific substrates as has been published elsewhere (Phu et al., Mol Cell, 2020 (Ref. 42); Ordureau et al., Mol Cell, 2020 (Ref. 39)), investigating the impact of ntc and USP30 on the ubiquitination of the translocon would be a distraction to the focus of the study.

      If the rough eye phenotype is highly homogenous, state in text otherwise, a relative roughness quantification would be more informative.

      • The rough eye phenotype described here is indeed highly stereotyped and homogeneous. We have added this comment to the text for clarity (lines 124-126).

      Figure 2: Although mainly in agreement with Burchell et al findings (that there is no disruption of mito morphology or dopaminergic neuron loss caused by ntc loss), the loss flight ability in the ntc mutant is partially discrepant with Burchell et al (results not shown in Burchell et al). Can the authors explain the discrepancy? It is important finding that the ntc functionally orthogue of FBXO7 and differs from the Burchell et al conclusions.

      • The reviewer raises a good point regarding discrepant interpretations with earlier preliminary work that we didn’t specifically elaborate in the current manuscript. For the Burchell et al study we performed a series of non-exhaustive analyses with reagents that were most readily available at the time. The flight data described in Burchell et al (as ‘data not shown’) were done with what we now know to be a hypomorphic allele, which did not give a strong flight defect that we were expecting to see as a phenocopy of parkin mutants. Moreover, experiments aimed at testing the functional homology sought to rescue the only reported ntc phenotype at that time – male sterility – which did not work. It is worth noting that GAL4/UAS-mediated expression is known to be very inefficient in the male germline, so we originally interpreted the lack of rescue with this caveat in mind. It is also worth adding that, subsequent to the Burchell et al study, we have seen that expression of FBXO7 can rescue the caspase-3 activation in ntc mutant spermatocytes, supporting their functional homology. Importantly, during the Burchell et al study we did not have reagents to test the effects of ntc overexpression, obtained subsequently, which have provided compelling data that support a functional homology between ntc and FBXO7. At the time of writing the current manuscript we did not specifically revisit the Burchell et al text to note this strongly stated conclusion. We realise that this requires unequivocal clarification and thank the reviewer for pointing this out. We have amended the text to clarify this important point (lines 285-293).

      Figure 7: A,B. It is not clear that mitochondria have been enriched - can the authors show on mitochondria or show the fractionation quality?

      • Mitochondrial enrichment is a standard procedure in our lab, with consistently acceptable results, so we apologize for omitting a demonstration of this. We have now added these data to a new supplementary figure S5A. The corresponding information has also been added to the text (line 253). We have also extended this analysis to now show that total ubiquitination is not changed in ntc OE or USP30 RNAi, highlighting the specificity for accumulated ubiquitination on the mitochondria. This has been added to supplementary figure S5Band text lines 253-254.

      C/D. The text that accompanies these figures needs further explanation and clarification and I found this result hard to understand without referring to the discussion. I think the authors are concluding that pS65 is ubiquitylated by FBXO7? I think this should be re-written in the results section. If it is a major point that the authors want to make, a complementary approach would be advised - possibly human cells/mass spectrometry.

      • We apologise that this was confusing and have simplified the text accordingly to improve the clarity (lines 260-262). While this specific analysis is not a major point of the study, it provides a useful additional measure of how ntc/USP30 contributes to mitochondrial ubiquitination which *is* a key focus of the study so we have revised the Discussion to better highlight this point (lines359- 387).

      As for Figure 1, specific ubiquitylated substrates at the OMM such as the translocon subunits would be informative.

      • As discussed above, the role of USP30, at least, on ubiquitination of protein import in the translocon has been documented elsewhere and further specific analysis on this here would be a distraction from the main focus of the study.

      Minor points

      Figure 8 model and discussion: Nice discussion. However, unless protein import/ubiquitylation of translocon factors/localisation of FBXO7 to the translocon is shown in the manuscript, I would recommend more clarity in the figure legend to emphasise what is speculation based on other papers and what are new findings from the paper.

      • This is a fair point and we agree that it is good to be clear about which aspects of the working model are reflections of the data presented here and which are extrapolation/speculation from the literature. We have modified the figure and the figure legend accordingly.

      Reviewer #3 (Significance (Required)):

      FBXO7/ntc is clearly an important regulator of mitophagy however its mechanism of action has not been studied as intensively as Parkin and Pink1, therefore this work contains important insight into mitophagy regulators.

      It will be of broad interest to many overlapping fields, and has translational impact in that mitophagy is disrupted in many diseases and FBXO7 itself is mutated in Parkinson's disease.

    1. Author Response

      Reviewer #1 (Public Review):

      This study focuses on the role of polo like kinase 1 (PLK-1) during oocyte meiosis. In mammalian oocytes, Plk1 localizes to chromosomes and spindle poles, and there is evidence that it is required for nuclear envelope breakdown, spindle formation, chromosome segregation, and polar body extrusion. However, how Plk1 is targeted to its various locations and how it performs these functions is not well understood. This study uses C. elegans oocytes as a model to explore PLK-1 function during meiosis. They take advantage of an analogue-sensitive allele of plk-1, which enabled them to bypass nuclear envelope breakdown defects that occur following PLK-1 RNAi. This allowed them to dissect later roles of PLK-1 in oocytes, demonstrating that depletion causes defects in spindle organization, chromosome congression, segregation, and polar body extrusion. Moreover, the authors defined mechanisms by which PLK-1 is targeted to chromosomes, showing that CENP-C (HCP-4) is required for localization to chromosome arms and that BUB-1 is required for targeting to the midbivalent region. Finally, they demonstrate that upon removal of PLK-1 from both domains, there are severe meiotic defects. These findings are interesting. However, there is a need for additional analysis to better support some of their conclusions, and to aid in interpretation of particular phenotypes. Specific comments are below.

      • For many important claims of the paper, a single representative image is shown but the n is not noted. This is an issue throughout the paper for much of the localization analysis (e.g. Figure 1B, 1C, 1D, 2A, 2B, 3A, 3B, 3C, etc.); in cases like this, numbers should be included to increase the rigor of the presented data. How many images or movies were analyzed that looked like the one shown? For linescans, were they done only on one image? How many independent experiments were done, etc?

      We had initially chosen a representative image. Localisation was the same in all images that allowed ‘proper’ assessment of PLK-1 localisation. In our case, this means that we can only analyse bivalents that are perpendicular to the light path to distinguish between bivalent, chromosome arms, and kinetochore. We now report the number of oocytes (N) and bivalents (n) analysed for each condition. The line scans were done in one representative image.

      • In the abstract, it is stated that PLK-1 plays a role in spindle assembly/stability (this is also stated elsewhere, e.g. line 101). This phrasing implies that the authors have demonstrated roles in both spindle assembly and stability. However, to distinguish between these roles, they would have to show that removal of PLK-1 before spindle assembly causes defects, and also that removal of PLK-1 from pre-formed spindles causes collapse. I don't think it is necessary to do this, as the spindle roles of PLK-1 are not a focus of the paper. However, the language should be altered so that it does not imply that the paper has demonstrated roles in both. A good place to do this would be in the section from lines 144-147, where they first discuss the spindle defects. It would be straightforward to explain that their approach does not distinguish between spindle assembly and stability, and that PLK-1 could have a role in either or both.

      We fully agree with this comment. We cannot distinguish between spindle assembly and stability, and it is also not the focus of our current work. We have changed the text accordingly.

      • It is stated that there is kinetochore localization of PLK-1 (and I do see some dim cup-like localization in images after PLK-1 is removed from the chromosome arms via HCP-4 RNAi). However, this cup-like localization is not clear in most wild-type images (e.g. Figure 1B, 1D, 2A, 3A, etc.). Although I recognize that the chromatin staining might be obscuring kinetochore localization, if PLK-1 was truly a kinetochore protein I would also expect it to localize to filaments within the spindle (as many other kinetochore proteins do), especially since the authors state that BUB-1 targets PLK-1 to the kinetochore (and BUB-1 is in the filaments). In fact, the only images where it looks like PLK-1 may be localized to filaments are in Figure 4C and 6A, when HCP-4 has been depleted (though I don't know if this generally true across all HCP-4 RNAi images). For me, this calls into question the conclusion that PLK-1 truly is on the kinetochore in wild type conditions - could it be that PLK-1 only localizes to the kinetochore (and to the filaments) when HCP-4 is depleted? The authors need to resolve this issue and provide better evidence that PLK-1 normally localizes to the kinetochore, if they want to make this claim. Additionally, the observation that PLK-1 is not on the kinetochore filaments (in wild type conditions) should be addressed in the text somewhere - do the authors think that this is a special type of kinetochore protein that does not localize to the filaments?

      While our initial claim of PLK-1 kinetochore localisation was based on its cup-like localisation, we have now performed additional analysis and experiments to confirm this claim. First, we corroborated that PLK-1 cup-like pattern co-localises with the Mis12 complex component KNL-3 (New Figure 5-figure supplement 1). Second, we show that PLK-1 is present in the so called ‘linear elements’ (filaments) both within the spindle and in the cortex. Since PLK-1 presence in these filaments is seen in wild type as well as hcp-4 mutant oocytes, we conclude that PLK-1 likely localises in kinetochore in normal conditions.

      • The authors should provide a control experiment, treating wild-type worms with 10uM 3-IB-PP1. This would be important to ensure that the spindle defects seen at this concentration in the plk-1as strain are not non-specific effects of the inhibitor. There is a control in Figure 1 - figure supplement 3 using 1uM 3-IB-PP1 but didn't see a control for 10uM (the concentration at which spindle defects are observed).

      This control has now been included in Figure 1-figure supplement 3.

      • In Figure 2F, the gels for BUB-1+PLK-1 look different in the presence and absence of phosphorylation by Cdk1 - for these data, I agree with the authors that it looks as if the complex elutes at a higher volume if BUB-1 is not phosphorylated (lines 200-204). However, Figure 2G has a repeat of the condition with phosphorylated BUB-1, and in this panel, the complex appears to elute at a higher volume than it did on the gel in panel F. The gel in panel G looks much more similar to the unphosphorylated condition in panel F. The authors need to explain this discrepancy (i.e., Is there a reason why the gels cannot be compared between panels? How reproducible are these data?). Ideally, the authors would include a repeat of the unphosphorylated BUB-1 + PLK-1 condition in panel G, done at the same time as the conditions shown in that panel, to avoid the impression that their results may not be reproducible.

      The specific elution volume cannot be compared in different experiments as the column has proven to “drift” over time – with proteins eluting at a later volume than they did previously despite extensive washing. What is reproducible under the experimental conditions is that the unphosphorylated wild type proteins, or the phosphorylated T527A/T163A mutant proteins A) elute at a later volume than the phosphorylated wild type proteins and B) bind to a lower proportion of the MBP-PLK1PBD (as you can see in the relative absorbance profiles and Coomassie gels).

      • The authors would need to provide convincing evidence that co-depletion of BUB-1 and HCP-4 delocalizes PLK-1 from the chromosomes entirely, and that this co-depletion condition is more severe than either single depletion alone.

      We now provide a quantitation on the total PLK-1 levels to go along the images (New Figure 8-figure supplement 1).

      Additionally, the bub-1T527A and hcp-4T163A alleles are nice tools to, in theory, more specifically delocalize PLK-1 from the midbivalent and chromosome arms, respectively, to explore the functions of chromosome-associated PLK-1. However, I think the authors cannot rule out the possibility that other proteins are also being depleted from the midbivalent and/or chromosome arms in their conditions, and that this delocalization may contribute to the phenotypes observed. For example, hcp-4 depletion was recently shown to delocalize KLP-19 from the chromosome arms (Horton et.al. 2022), so in the experiment shown in Figure 6E (HCP-4 RNAi in the bub-1 mutant), PLK-1 was likely not the only protein missing from the chromosome arms. Therefore, understanding if other proteins are absent from these domains (in the bub-1T527A and hcp-4T16A3 mutants) would help the reader understand and interpret the presented phenotypes (and how specific they are to PLK-1 loss). Consequently, I think that to better understand the co-depletion analysis presented in Figure 6 (and Figure 6 supplement 1), the authors should analyze other midbivalent and chromosome arm proteins, to determine if any are also delocalized (e.g. SUMO, KLP-19, MCAK, etc.).

      As stated above, this paper focuses on identifying the specific meiotic events PLK-1 plays a role in and characterising its targeting mechanism. We are following on this work to understand what proteins are regulated by PLK-1 in different chromosome domains and how this relates to the observed phenotypes.

      For the current, we should emphasise that mutating a single Thr residue within an STP motif in a largely disordered region is far more specific than depleting HCP-4 or BUB-1, making it likely that the observed effects are mediated through PLK-1 targeting. It should be noted that the finding presented in Horton et.al. 2022 is in contradiction with another study in which hcp-4 depletion did not impact KLP-19 localisation (Hattersley et al 2022).

      Additionally, instead of performing a combination of mutant and RNAi analysis (i.e. HCP-4 RNAi in the bub-1 mutant (Figure 6) and BUB-1 RNAi in the hcp-4 mutant (Figure 6 figure supplement 1)), it would be more powerful to generate a double mutant - this has a higher chance of being a more specific depletion condition.

      We have performed these experiments, which are now presented in Figure 9.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):


      Summary:

      In this manuscript, Roberts et al. hypothesised that the 5:2 diet (a popular form of IF, a dietary strategy within the Intermittent fasting that is thought to increase adult hippocampal neurogenesis - AHN) would enhance AHN in a ghrelin-dependent manner. To do this, the Authors used immunohistochemistry to quantify new adult-born neurons and new neural stem cells in the hippocampal dentate gyrus of adolescent and adult wild-type mice and mice lacking the ghrelin receptor, following six weeks on a 5:2 diet. They report an age-related decline in neurogenic processes and identify a novel role for ghrelin-receptor in regulating the formation of new adult

      born neural stem cells in an age-dependent manner. However, the 5:2 diet did not affect new neuron or neural stem cell formation in the dentate gyrus, nor did alter performance on a spatial learning and memory task. They conclude that the 5:2 diet used in their study does not increase AHN or improve associated spatial memory function.

      Major comments:

      One criticism might be the fact that many aspects are addressed at the same time. For instance it is not fully clear the role of ghrelin with respect to testing the DR effects on AHN. Although the link between ghrelin, CR and AHN is explained by citing several previous studies, it is difficult to identify the main focus of the study. Maybe this is due to the fact that the Authors analyse and comment throughout the paper the different experimental approaches used by different

      Authors to study effect of DR to AHN. This is not bad in principle, since I think the Authors have a deep knowledge of this complex matter, but all this results in a difficulty to follow the flow of the rationale in the manuscript.

      We appreciate the reviewer’s critique regarding the rationale of the studies presented in the manuscript.

      The role of ghrelin in the regulation of AHN by dietary interventions such as CR and IF is a major interest of our lab and is the main focus of the study. We, and others, have shown that ghrelin mediates the beneficial effects of CR on AHN. It is often assumed that ghrelin will elicit similar effects in other DR paradigms. We selected the 5:2 diet since it is widely practiced by humans, but it has not been well tested experimentally.

      We sought to empirically test how the neurogenic response to 5:2 differed between mice with functional and impaired ghrelin signaling.

      Given that plasma ghrelin levels and AHN are reduced during ageing, we also wanted to determine if 5:2 diet could slow or even prevent neurogenic decline in ageing mice.

      We will re-write the manuscript to ensure that our primary aim is clearly presented. We will also reanalyze the data, with genotype and 5:2 diet as key variables. To help maintain focus, the variable of age will be analyzed separately. This amendment will, we hope, help the reader follow the narrative of our manuscript.

      Another major point: the Discussion is too long. The Authors analyse all the possible reasons why different studies obtained different results concerning the effectiveness of DR in stimulating adult neurogenesis. Thus, the Discussion seems more as a review article dealing with different methods/experimental approaches to evaluate DR effects. We know that sometimes different results are due to different experimental approaches, yet, when an effect is strong and clear, it occurs in different situations. Thus, I think that the Authors must be less shy in expressing their conclusions, also reducing the methodological considerations. It is also well known that sometimes different results can be due to a study not well performed, or to biases from the Authors.

      In our discussion, we felt that it was particularly important to be as rigorous as possible in contextualizing our findings with other published data, whilst highlighting methodological differences. Our aim was to be as precise as possible when comparing findings across studies, however, this resulted in the narrative drifting from the key objectives of our study – namely, to determine the effect of 5:2 diet on neurogenesis and whether or not ghrelin-signalling regulated the process. We will amend the text of the discussion to ensure that the key points of our study are only compared and contrasted with relevant studies in the field. We thank the reviewer for their candid comment.


      Minor comments:

      • This sentence: "There is an age-related decline in adult hippocampal neurogenesis" cannot be put in the HIGHLIGHTS, since is a well known aspect of adult hippocampal neurogenesis

      The reviewer is correct to state this. Our study replicates this interesting age-related phenomenon. However, we will remove it from the ‘Highlights’ section.

      • Images in Figure 5 are not good quality.

      We apologise for this oversight. We will review each figure and panel to ensure that high-resolution images, that are appropriately annotated, are used throughout the manuscript.

      • In general, there are not a lot of images referring to microscopic/confocal photographs across the entire manuscript.

      We structured the manuscript with a limited number of figures and associated microscope captured panels, with the aim of presenting representative images to illustrate the nature and quality of the IHC protocols. However, we will amend the figures for the revised manuscript to provide representative microscopy images, with each group included and clearly annotated.

      • The last sentence of the Discussion "These findings suggest that distinct DR regimens differentially regulate neurogenesis in the adult hippocampus and that further studies are required to identify optimal protocols to support cognition during ageing" is meaningless in the context of the study, and in contrast with the main results. Honestly, my impression is that the Authors do not want to disappoint the conclusions of the previous studies; an alternative is that other Reviewers asked for this previously.

      We do not believe that this statement is contradictory to our findings, as distinct DR paradigms do appear to regulate AHN in different ways. However, we agree that we can be more explicit with regards to our own study findings and will prioritize the conclusions of our study over those of the entire field during revision.

      Reviewer #1 (Significance (Required)):

      value the significance of publishing studies that will advance the field.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):


      In this manuscript, Roberts et al. investigate the effect of the 5:2 diet on adult hippocampal neurogenesis (AHN) in mice via the ghrelin receptor. Many studies have reported benefits of dietary restriction (DR) on the brain that include increasing neurogenesis and enhancing cognitive function. However, neither the mechanisms underlying the effects of the 5:2 diet, nor potential benefits on the brain, are well understood. The authors hypothesize that the 5:2 diet enhances AHN and cognitive function via ghrelin-receptor signaling. To test this, they placedadolescent and adult ghrelin receptor knockout or wild type mice on either the 5:2 or ad libitum (AL) diet for 6 weeks, followed by spatial memory testing using an object in place (OIP) task. The authors also assessed changes in AHN via IHC using multiple markers for cell proliferation and neural stem cells. The authors observed a decrease in AHN due to age (from adolescent to adult), but not due to diet or ghrelin-receptor signaling. While loss of the ghrelin-receptor impaired spatial memory, the 5:2 diet did not affect cognitive function. The authors conclude that the 5:2 diet does not enhance AHN or spatial memory.

      We thank the reviewer for this summary. We note that there was a significant reduction in new neurones (BrdU+/NeuN+) cells in GHS-R null animals, regardless of age or diet (3 way ANOVA of age, genotype and diet (sexes pooled): Genotype P = 0.0290). These data suggest that the loss of ghrelin receptor signalling does impair AHN. However, we will re-analyse our data in light of reviewer 1 comments to remove ‘age’ as a variable. The new analyses and associated discussion will be presented in our revised manuscript.

      The authors use a 5:2 diet but fail to provide a basic characterization of this dietary intervention. For example, was the food intake assessed? In addition to the time restriction of the feeding, does this intervention also represent an overall caloric restriction or not? According to the provided results, the 5:2 diet does not appear to regulate adult hippocampal neurogenesis contrary to the authors' original hypothesis. Did the authors measure the effects of the 5:2 diet on any other organ system? Do they have any evidence that the intervention itself resulted in any well documented benefits in other cell types? Such data would provide a critical positive control for their intervention.

      This is an important point raised by the reviewer. Currently, we carefully quantified weight change across the duration of the study. However, we do not know whether the 5:2 diet reduced overall food intake or whether it impacted the timing of feeding events. To overcome this limitation, we will now test what impact the 5:2 dietary regime has on food intake and the timing of feeding. This study will allow us to correlate any changes with 5:2 diet. In addition, we have collected tibiae to quantify skeletal growth and have collected both liver and plasma (end point) samples which will be used to assess changes in the GH-IGF-1 axis. These additional studies will allow us to characterise the effects of the 5:2 paradigm on key indicators of physiological growth. These new data will be incorporated into the revised manuscript.

      Based on the effects of ghrelin in other dietary interventions, the authors speculate that the effect of the 5:2 diet is similarly mediated through ghrelin. However, the authors do not provide any basic characterization of ghrelin signaling to warrant this strong focus on the GSH-R mice. While the GSH-R mice display changes in NSC homeostasis and neurogenesis, none of these effects appear to be modified by the 5:2 diet. Thus, the inclusion of the GSH-R mice does not seem warranted and detracts from the main 5:2 diet focus of the manuscript.

      The role of ghrelin signalling via its receptor, GHSR, is a central tenet of our hypothesis. The loxTB-GHS-R null mouse is a well validated model of impaired ghrelin signalling, in which insertion of a transcriptional blocking cassette prevents expression of the ghrelin receptor (ZIgman et al.2005 JCI). We have previously shown that this mouse model is insensitive to calorie restriction (CR) mediated stimulation of AHN, in contrast to WT mice (Hornsby et al. 2016), justifying its suitability as a model for assessing the role of ghrelin signalling in response to DR interventions, such as the 5:2 paradigm. Whilst our findings do not support a role for ghrelin signalling in the context of the 5:2 diet studied, we did follow the scientific method to empirically test the stated hypothesis. While critiques of experimental design are welcome, the removal of these data may perpetuate publication bias in favour of positive outcomes and is something we wish to avoid.

      Neurogenesis is highly sensitive to stress. The 5:2 diet may be associated with stress which could counteract any benefits on neurogenesis in this experimental paradigm. Did the authors assess any measures of stress in their cohorts? Were the mice group housed or single housed?

      We thank the reviewer for raising this point. We have open-field recordings that will now be analysed to assess general locomotor activity, anxiety and exploration behaviour. Additionally, we will assess levels of the stress hormone, ACTH, in end point plasma samples. These datasets will be incorporated into the revised manuscript.

      The authors state that the 5:2 diet led to a greater reduction in body weight (31%) in adolescent males compared to other groups. However, it appears that the cohorts were not evenly balanced and the adolescent 5:2 male mice started out with a significantly higher starting weight (Supplementary Figure 1). The difference in starting weight at such a young age is significantly confounding the conclusion that the 5:2 diet is more effective at limiting weight gain specifically in this group.

      We thank the reviewer for highlighting this limitation. In the revision we will re-focus our discussion around the Δ Body weight repeated measures data, which compares the daily body weight of each group to its baseline value - thereby normalising any intergroup differences in starting weight. Furthermore, we will restructure figures 1 and S1 so that figure 1 presents only the repeated measure Δ Body weight data, while data for body weight both at baseline and on the final day of the study will be presented in figure S1.

      The authors count NSCs as Sox2+S100b- cells. However, the representative S100b staining does not look very convincing. Instead, it would be more appropriate to count Sox2+GFAP+ cells with a single vertical GFAP+ projection. Alternatively, the authors could also count Nestin-positive cells. Additionally, the authors label BrdU+ Sox2+ S100B- cells as "new NSCs". However, it appears that the BrdU labeling was performed approximately 6 weeks before the tissue was collected (Figure 1A). Thus, these BrdU-positive NSCs most likely represent label retaining/quiescent NSCs that divided during the labeling 6 weeks prior but have not proliferated since. As such, the term "new NSC" is misleading and would suggest an NSC that was actively dividing at the time of tissue collection.

      We apologise for presenting low-resolution images – these will be replaced by high-resolution images in the revised manuscript. In this study we have quantified the actively dividing BrdU+/Sox2+/S100B- cells that represent type II NSCs (rather than GFAP+ or Nestin+ type I NSCs) that have incorporated BrdU within the time period of the 6-week intervention. We appreciate the reviewer’s comments concerning the “new NSCs” terminology. We agree that we should be more specific in clarifying that the NSCs identified are those labelled during the 1st week of the 6-week intervention. We will amend this throughout the revised manuscript by re-naming these cells as 6-week old NSCs.

      Overall, this manuscript lacks a clear focus and narrative. Due to a lack of an affect by the 5:2 diet on hippocampal neurogenesis, the authors mostly highlight already well-known effects of aging and Grehlin/GSH-R on neurogenesis. Moreover, the authors repeatedly use age-related decline and morbidities as a rational for their study. However, they assess the effects of the 5:2 diet on neurogenesis only in adolescent and young mature but not aged mice.

      To provide greater clarity, and in accordance with reviewer 1’s comments, we will amend the text throughout to provide a focus on the data obtained. The objective of the changes will be to re-enforce the original study narrative. In relation to the use of the term ‘age-related decline’ or ‘age-related changes’, we think that these are appropriate to our study. Physiological ageing doesn’t begin at a specific point of chronological time, but is a process that is continuously ongoing. Indeed, our data is in agreement with previous studies reporting an age-related reduction in AHN at 6 months of age (e.g Kuhn et al.1996).

      Minor Points

      The authors combine the data from both male and female mice for most bar graphs. While this does not appear to matter for neurogenesis or behavioral readouts, there are very significant sexually dimorphic differences with respect to body size and weight. As such, male and female mice in Figure 1D,F should not be plotted in the same bar graph.

      We agree that sexual dimorphism exists with respect to body size and weight. We used distinct male and female symbols for each individual animal on these bar graphs, but do agree with the reviewer that sexual dimorphic differences should be emphasized. To achieve this, we will include additional supplementary graphs presenting the sex differences in starting weight, final weight, and weight change versus starting weight.

      The Figure legends are very brief and should be expanded to include basic information of the experimental design, statistical analyses etc.

      We thank the reviewer for this comment. We will provide specific experimental detaisl in the revised figure legends.

      Many figures include a representative image. However, it is often unclear if that is a representative image of a WT or mutant mouse, or a 5:2 or control group (Figure 2A, 3A, 4A, 5A).

      We structured the manuscript with a limited number of figures and associated microscope captured panels, with the aim of presenting representative images to illustrate the nature and quality of the IHC protocols. However, we will amend the figures for the revised manuscript to provide representative microscopy images, with each group included and clearly annotated.

      It would be helpful to provide representative images of DCX-positive cells in Figure 3A-F. Additionally, the authors should include a more extensive description of how this quantification was performed in the method section.

      We will revise the manuscript to provide representative high-resolution Dcx+ images displaying cells of each category. The method will also be revised to include a detailed description of how the quantification and classification was performed.

      The authors state "the hippocampal rostro-caudal axis (also known as the dorsoventral[] axis". However, the rostral-caudal and dorsal-central axis are usually considered perpendicular to one another.

      We agree that the dorso-ventral and rostral-caudal axes are anatomically distinct. The terms are often used interchangeably in the literature, which can lead misinterpretations (e.g the caudal portion of dorsal hippocampus is often mislabelled as ventral hippocampus). To avoid ambiguity, mislabelling or misidentification, we will include a supplementary figure detailing our anatomical definitions of the rostral and caudal poles of the hippocampus, alongside representative images and the bregma coordinates.

      Reviewer #2 (Significance (Required)):


      Understanding the mechanisms of a popular form of intermittent fasting (5:2 diet) that is not well understood is an interesting topic. Moreover, examining the effect of this form of intermittent fasting on the brain is timely. Notwithstanding, while the authors use multiple markers to validate the effect of the 5:2 diet on adult hippocampal neurogenesis, concerns regarding experimental design, validation, and data analysis weaken the conclusions being drawn.

      We thank reviewer 2 for this significance statement. We will revise the manuscript, as mentioned above, to clarify the experimental design, improve presentation of the data, and re-focus the narrative of the primary aims of the study.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):


      Summary


      In this study, Roberts and colleagues used a specific paradigm of intermitted fasting, the 5:2 diet, meaning 5 days ad libitum food and 2 non-consecutive days of fasting. They exposed adolescent and adult wild-type mice and ghrelin receptor knockout mice (GHS-R-/-) for 6 weeks to this paradigm, followed by 1 week ad libitum food. They further used the "object in place task" (OIP) to assess spatial memory performance. At the end of the dietary regime, the authors quantified newborn neurons and neural stem cells (NSCs) by immunohistochemistry. Roberts

      et al. show that the 5:2 diet does not change the proliferation of cells in the hippocampus, but report an increased number of immature neurons (based on DCX) in all the mice exposed to the 5:2 diet. This change however did not result in an increased number of mature adult-born neurons, as assessed by a BrdU birthdating paradigm. The authors further show diet-independent effects of the ghrelin receptor knockout, leading to less adult born neurons, but more NSCs in the adolescent mice and a lower performance in the OIP task.

      Major comments:

      The main conclusion of this study is that a specific type of intermitted fasting (5:2 diet) has no effects on NSC proliferation and neurogenesis. As there are several studies showing beneficial effects of intermitted fasting on adult neurogenesis, while other studies found no effects, it is important to better understand the effects of such a dietary paradigm.

      The experimental approaches used in this manuscript are mostly well explained, but it is overall rather difficult to follow the results part, as the authors always show the 4 experimental groups together (adolescent vs adult and wt vs GHS-R-/-). They highlight the main effects comparing all the groups, which most of the time is the factor "age". Age is a well-known and thus not surprising negative influencer of adult neurogenesis. Instead of focusing on the main tested factor, namely the difference in diet, the authors show example images of the two age classes

      (adolescent vs adult), which does not underly the major point they are making. Most of the time, they do not provide a post hoc analysis, so it is difficult to judge if the results with a significant main effect would be significant in a direct 1 to 1 comparison of the corresponding groups. The authors point out themselves that previous rodent studies did not use such a 5:2 feeding pattern, so having diet, age and genotype as factors at the same time makes the assessment of the diet effect more difficult.

      The manuscript would improve if the authors restructure their data to compare first the diet groups (adolescent wt AL vs 5:2 and in a separate comparison adult wt AL vs 5:2) and only in a later part of the results check if the Ghrelin receptor plays a role or not in this paradigm.

      We thank the reviewer for these comments. In line with comments from the other reviewers we will re-formulate the presentation of our datasets. We will remove ‘age’ as a key variable as age related changes are to be expected. For the revision, we will separate the adolescent and adult mouse data sets, plotting individual graphs for both. This should provide a clearer focus on 5:2 responses in both assessed genotypes.

      This re-configuration will impact the data being analysed and, therefore, the statistical analysis presented. In our original manuscript post hoc analyses were performed, however, only significant post hoc comparisons were highlighted (e.g figure 5). Non-significant post hoc comparisons have not been presented. In the method section of the revised manuscript, we will clarify that we’ll report post hoc differences when they are observed.

      During our study design, we decided to assess diet and genotype in parallel - as part of the same analysis. This seemed to us to be the most appropriate statistical method, so that we assessed dietary responses in both WT and GHS-R null mice.

      As this 5:2 is a very specific paradigm, it is furthermore difficult to compare these results to other studies and the conclusions are only valid for this specific pattern and timing of the intervention (6 weeks). It remains unclear why the authors have not first tried to establish a study with wildtype mice and a similar duration as in previous studies observing beneficial effects of intermitted fasting on neurogenesis. Like this, it would have been possible to make a statement if the 5:2 per se does not increase neurogenesis or if the 6 weeks exposure were just too short.

      The reviewer raises this relevant point which we considered during the study design period. Given that we had previously reported significant modulation of AHN with a relatively short period of 30% CR (14 days followed by 14 days AL refeeding (Hornsby et al.2016)), we predicted that a 6 week course on the 5:2 paradigm (totalling 12 days of complete food restriction over the 6 week period) would provide a similar dietary challenge. The fact that we did not observe similar changes in AHN with this 5:2 paradigm is notable.

      The graphical representation of the data could also be improved. Below are a few

      examples listed:

      1.) Figure 1 B and C, the same symbol and colours are used for the adolescent and adult animals, which makes the graphs hard to read. One colour and symbol per group throughout the manuscript would be better.

      We thank the reviewer for this comment. We will amend the presentation of the graphs throughout the manuscript to ensure that they are easier to interpret.

      2.) The authors found no differences in the total number of Ki67 positive cells in the DG. However, Ki67 staining does not allow to conclude the type of cell which is proliferating. It would thus strengthen the findings if this analysis was combined with different markers, such as Sox2, GFAP and DCX.

      Double labelling of Ki67 positive cells would allow for further insight into the identity of distinct proliferating cell populations. However, quantifying Ki67 immunopositive cells within the sub-granular zone of the GCL, as a single marker, is commonly used in studies of AHN. Given that studies of intermittent fasting, calorie restriction and treatment with exogenous acyl-ghrelin report no effect on NPC cell division, we decided not to pursue this line of inquiry.

      3.) In Figure 3, the authors say that the diet increases the number of DCX in adolescent and adult mice, which is not clear when looking at the graph in 3B. Are there any significant differences when directly comparing the corresponding groups, for instance the WT AL vs the WT 5:2? It is further not clear how the authors distinguished the different types of DCX morphology-wise. The quantification in C and D would need to be illustrated by example images. Furthermore, the colour-code used in these graphs is not explained and remains unclear

      While the 3 way ANOVA does yield a significant overall effect for diet, we agree that it is indeed difficult to see a difference on the graph, although the mean values of the adolescent 5:2 animals are more prominent than the AL counterparts. Mean +/- SEM will be provided in the supplementary section of the revised manuscript. Furthermore, we will clarify the method used to identify distinct DCX+ morphologies, include representative high-resolution images of each DCX+ cell category, and amend the colour coding to avoid misinterpretation.

      4) In Figure 5, the authors show that the number of new NSCs is significantly increased in the adolescent GHS-R-/- mice, independent of the diet, but this increase does not persist in the adult mice. They conclude that "the removal of GHS-R has a detrimental effect on the regulation of new NSC number..." this claim is not substantiated and needs to be reformulated. As the GHS-R-/- mice have a transcriptional blockage of Ghrs since start of its expression, would such an effect on NSC regulation not result in an overall difference in brain development, as ghrelin is also important during embryonic development?

      This is an interesting point. However, we disagree that the statement "the removal of GHS-R has a detrimental effect on the regulation of new NSC number..." is unsubstantiated, since it does not exclude any developmental deficits in these mice that may account for the differences observed. Nonetheless, we will rephrase the sentence to clarify our intended point and remove any ambiguity.

      5.) In Figure 6, the authors asses spatial memory performance with a single behavioral test, the OIP. As these kind of tests are influenced by the animal's motivation to explore, it's anxiety levels, physical parameters (movement) etc., the interpretation of such a test without any additional measured parameters can be problematic. The authors claim that the loss of GHS-R expression impairs spatial memory performance. As the discrimination ratio was calculated, it is not possible to see if there is an overall difference in exploration time between genotypes. This would be a good additional information to display.

      We thank the reviewer for this insight. We have open-field recordings that will now be analysed to assess general locomotor activity, anxiety and exploration behaviour. These data, alongside exploratory time of the mice during the OIP task will be incorporated into the revised manuscript.

      Besides these points listed above, the methods are presented in such a way that they can be reproduced. The experiments contained 10-15 mice per group, which is a large enough group to perform statistical analyses. As mentioned above, the statistical analysis over all 4 groups with p-values for the main effects should be followed by post hoc multiple comparison tests to allow the direct comparison of the corresponding groups.

      Reviewer #3 (Significance (Required)):

      In the last years, growing evidences suggested that IF might have positive effect on health in general and also for neurogenesis. However, a few recent studies report no effects on neurogenesis, using different IF paradigms. This study adds another proof that not all IF paradigms influence neurogenesis and shows that more work needs to be done to better understand when and how IF can have beneficial effects. This is an important finding for the neurogenesis field, but the results are only valid for this specific paradigm used here, which limits its significance. The reporting of such negative findings is however still important, as it shows that IF is not just a universal way to increase neurogenesis. In the end, such findings might have the potential to bring the field together to come up with a more standardized dietary intervention paradigm, which would be robust enough to give similar results across laboratories and mouse strains, and would allow to test the effect of genetic mutations on dietary influences of neurogenesis.

      We thank the reviewer for their insightful and thorough feedback.

      1. Description of the revisions that have already been incorporated in the transferred manuscript

      Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

      The manuscript has not been revised at this stage.

      2. Description of analyses that authors prefer not to carry out

      Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

      • *

      We have included in our replies to the reviewers a description of the amendments that we will make to our manuscript. Two requested revisions stand out as being unnecessary or cannot be provided within the scope of a revision.

      The first was the request to perform the 5:2 study in older mice. This an interesting suggestion, however, the expense and time needed to maintain mice into old age (e.g >18 months) cannot be provided within the scope of our revision. In addition, given that we report no effect of the 5:2 paradigm on AHN in adolescent (7 week old) and adult (7 month old) mice, there is less justification for such a study in older mice.

      The second request, that we disagree with, was to remove data relating to the GHS-R null mice (see reviewer 2, point 2). The role of ghrelin signalling via its receptor, GHS-R, is a central tenet of our hypothesis. Whilst our findings do not support a role for ghrelin signalling in the context of the 5:2 diet studied, we followed the scientific method to empirically test the stated hypothesis. While critiques of experimental design are welcome, the removal of such data may perpetuate publication bias in favour of positive outcomes and is something we wish to avoid.

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      Referee #2

      Evidence, reproducibility and clarity

      The manuscript by Veen and colleagues assesses two transcription factors, and makes the novel conclusion that they regulate each other in a manner that is required for photoreceptor regeneration in zebrafish. The work is potentially exciting, because similar findings from zebrafish have found traction in translation to mammals, where regeneration of photoreceptors has surprising promise to treat blindness.

      The authors have been ambitious in there approach to the problem by disrupting these genes in the adult retina, which is the appropriate context required to assess photoreceptor regeneration. Because technologies for conditional gene ablation are not very available in zebrafish, these authors use electroporation of morpholinos to accomplish their goals. Where most researchers have abandoned this very challenging approach, it seems these authors have found some success.

      Together the technical feat and intriguing conclusions combine, in my opinion, to make this paper worthy of serious consideration for publication. I would hate to see it not be made available for public consumption. Its' merits are strong, but some shortcomings in communication and interpretation nevertheless should be addressed. I suggest Major and minor concerns below.

      I suspect that doing further experiments would be asking a lot of the authors at this point, but I point out some possible experiments that would improve the manuscript if my suspicion is wrong. Without further experiments, I suggest much of the writing needs to be carefully qualified and less deterministic.

      Major concerns:

      1. The authors need to quantify impacts of MO without MTZ (or with MTZ on wildtype fish without the nfsb Tg). Alternatively, the interpretation needs to be softened considerably. Observations made include increased proliferation and more PR, but these are not clearly connected by the data in a way that allows you to claim "more regeneration". A plausible alternative is that the MO was protective, and the MTZ did not kill as many PR cells when the genes were knocked down. Moreover, Figure 3 shows that only one of two proliferation markers is increased (how to explain?) and only at one timepoint, so this may be a fluke. I suggest softening the conclusions to state that gene knockdown increased proliferation and led to increased PR abundance, thus implying improved regeneration (and provide the alternative interpretation). E.g. the punchy titles of Figures 3, 5, 7 are not supported by the data; neither is text in Discussion bottom of page 15.
      2. Fig 5 quantification of proliferation is needed if the interpretation is about regeneration (see comment 1). Instead, the conclusions could be reworked to match the data.
      3. I'm unclear on why these experiments couldn't have been completed in mutant zebrafish. Are they not viable?
      4. Sequence and chemistry of the MO knockdown reagents must be provided. If they are similar to previously published MO reagents (several for both gene targets have been published) then this might be used to improve confidence of MO efficacy. Were the MOs modified to facilitate electroporation? The gene targets also must be listed with less ambiguity, e.g. when "prox1" is mentioned, do you mean prox1a? Without these details, the experiments fail to provide enough info to permit replication.
      5. A suggestion to improve the text [no need for new experiments]: The Discussion should address assumptions about MO knockdowns in regards to: a) efficacy, and b) specificity. E.g. (a) future experiments might challenge the efficacy by measuring the abundance of genes that are regulated by prox1 and her6. E.g. (b) future experiments should challenge the specificity of the MO reagents by testing to see if the same result is attained with disparate MO oligos, by phenocopy with CRISPR, performing the work in mutants (I assume rescuing the knockdown by replacing the target gene is not feasible by electroporation, but that would be ideal).
      6. The claim that Prox1 is in PR (Figure 4 title) is not convincing. Does the scRNA-Seq confirm this, and why not invoke this data to clarify more concretely? Figure 4A shows a lot of green prox1 signal, but that is very inconsistent with what is shown in Figure 4G, where no prox1 signal is observed in the PR. On page 12, which relates to this Figure, the authors instead say that Prox1 is detected in PR after injury (a big difference compared to title of Fig4!). Fig4I' shows some signal in the area of the PR, but the overlap of the signals is not convincing and it looks to mostly be adjacent to the zpr1 signal; maybe it is Muller glia or some other cone type, or rod cells. If it is Muller glia or rods, then the interpretation needs to be adjusted. Regardless, it is unclear if this is in LWS cones, which is presumably what regenerates after LWS cone ablation(?)
      7. Figures showing prox1 or Hes1 IHC (Fig 2, 4, 6, 7 & Supps) - how many replicates were evaluated (how many individual fish were assessed) to determine that these IHCs are representative.
      8. Some of the data, i.e. some photomicrographs of IHC, are used repeatedly in separate Figures. I cannot find a comment in the manuscript acknowledging this. Panel F is identical in Figures 3 and 5, and panel 7E is identical to Supp panel S5E. My opinion here is mixed: I think re-using these Figures is marginally ok if it is explicitly and repeatedly described (e.g. in Methods, Results, and Fig Legends), but I also think that if the authors have replicated the experiments sufficiently, then they will surely have some other micrographs to use. My opinion is tipped into grumpy and worried about good data integrity, because in both cases the lines that indicate retinal layers are drawn in different places between the replicated panels; that could happen out of sloppy-ness or instead could be a ploy to help hide the Figure recycling. I prefer to assume the authors are of good intent and have made an error (indeed the panels are all meant to represent the same control treatments) but I would not want the manuscript published without explicitly rectifying this issue. Minimally the replicate micrographs should be explicitly acknowledged. My search for other duplicated panels was not exhaustive.

      Minor points:

      • a) Page numbers and line numbers would make it less work to prepare a constructive critique of this paper. Similarly, the Figures need Figure numbers.
      • b) On the histograms, does each dot represent an individual fish? (i.e. an independent biological replicate).
      • c) It would be lovely to learn that left vs. right eyes were used as internal controls in each case, and then the authors could plot the difference between control & treatment within each individual. Perhaps this would allow normalizations or more powerful statistical tests, and then the PCNA data would be more aligned with the conclusions, for example.
      • d) Figure 5: expected to see quantification of PH3 here, akin to Figure 3.
      • e) P. 6 secondary antibodies probably did not come from ZIRC
      • f) More should be done to acknowledge past papers examining Her6, Hes1 and Prox1 in vertebrate retina.
      • g) I do not see how the final section of the manuscript (beginning with "Insulinoma" to the end of the Discussion is relevant to the paper. A very odd ending to this manuscript. Some sentences (especially beginning the section with a topic sentence) would be need to be added if this writing is to remain.
      • h) The final two sentences of the Abstract were interesting - these ideas are unfortunately not Discussed again later in the manuscript.
      • i) What is the source of the transgenic zebrafish line Tg(lws2:nfsb-mCherry) ? Is it maybe from Wang...Yan 2020 PLOS BIOL (PMID: 32168317)? If yes, it would be ideal to provide an allele number. If no, construction of this line should be described.
      • j) Bottom page 4 says "Two transgenic lines used were crossed" but only one line is mentioned.
      • k) Then on page 7, the text says "Zebrafish line Tg(her4.1:dRFP/gfap:GFP/lws2:nfsb-mCherry) for red cone ablation, ..." which muddies the waters even further.
      • l) When the antibody zpr1 is described, it is mentioned as a "zinc finger" (many instances throughout). This is incorrect, and the words "zinc finger" can be removed.
      • m) It would be useful to state in Methods, and at first occurrence in figure legends, that the antibody ZPR1 labels double cones (the red & green cones), and these make up about half of the cone photoreceptor population. (i.e. not all cones are evaluated in this work).
      • n) Figure 2 desperately needs a panel describing methodological timeline, similar to Fig 1D. It is really hard to figure what happened when (e.g. when did ablation occur? When was the MO delivered?). This also should be described more explicitly in the Methods, which seem quite vague on this point: Electroporated fish went straight into MTZ?
      • o) Throughout the authors refer to injury, e.g. hpi = hours post injury. I don't think this represents the methods very well at all, because they have ablated the cells, not injured them. Injured cells don't regenerate (because they are not dead). This miswording contributes to confusion interpreting the Figures, which are not decipherable as stand-alone items.
      • p) There is a really weird yellow dotted line that spans between and ACROSS adjacent panels in Figure 2. It covers the white line separating panels F' & G', and then again in F" and G".
      • q) Fig 2, it is evident that Hes1 protein is not eliminated so you cannot claim it is "not expressed". It is perhaps reduced in abundance, but signal is still obviously present.
      • r) Title of Figure 6 needs to rewritten: LLPS may be occurring, but until you manipulate both LLPS and Prox1 together, you cannot claim that they act through one another.
      • s) Figure 7 title needs to be rewritten: PR are not quantified here.
      • t) Figure 6: I am deeply incredulous that applying any chemical to zebrafish for only two minutes can alter cell differentiation, except perhaps via toxicity. Perhaps examples of similar impacts can be provided from the literature to make it seem more credible that the mechanism here is LLPS in retinal cells.

      The following minor comments are all captured under the notion that the Figure Legends all need to be re-written by a senior colleague. Figures+legends should be interpretable as stand-alone items. All these Figures fail this minimal standard. Below are some issues, but really I'd suggest starting with a blank slate.<br /> - u) Figure 1 must mention Drosophila. So very very confusing to read this believing it is about zebrafish.<br /> - v) Figure 1 what is "deadpan"?<br /> - w) Fig 2 title, how do you know these progenitors are MG-derived?<br /> - x) Fig 2, define abbreviation MG<br /> - y) Fig 2 title, Hes1 is less abundant, but that might be from alternative mechanisms other than "reduced expression" (e.g. altered PTMs, increased clearance, LLPS, etc)<br /> - z) Fig 3 legend is a jumble of oddity. At least three distinct signals are supposedly labelled in pink(?). separately, What about the mCherry - is it also in pink?<br /> - aa) Most Figures: we know they are micrographs, so you don't need to lead the Description saying "micrographs of...". Instead, describe the logic of the experiment and the overall interpretations.<br /> - bb) All Figures: it is really odd to list the data (averages & variances, including implausible significant digits on each) for every treatment - that is what the histograms are meant to convey.<br /> - cc) Figure 4 should send the reader to the Supplemental so they know that the no-primary control experiment is available.<br /> - dd) Fig 6B legend - explain what the chemicals are meant to do (e.g. "block LLPS").<br /> - ee) Fig 6 define "2m" = 2 minutes?<br /> - ff) Several more abbreviations are not defined: hpi, ONL, etc...

      Significance

      The manuscript by Veen and colleagues assesses two transcription factors, and makes the novel conclusion that they regulate each other in a manner that is required for photoreceptor regeneration in zebrafish. The work is potentially exciting, because similar findings from zebrafish have found traction in translation to mammals, where regeneration of photoreceptors has surprising promise to treat blindness.

      Together the technical feat and intriguing conclusions combine, in my opinion, to make this paper worthy of serious consideration for publication. I would hate to see it not be made available for public consumption. Its' merits are strong, but some shortcomings in communication and interpretation nevertheless should be addressed

    1. Might utilizing scientific methods to collect and analyze nutritional information — while guided by social-scientific frameworks and research practices that explain how power and inequity operate in society — result in new insights on the ways in which nutritional disparities exist within communities? What if we then drew on our knowledge of qualitative methodologies, such as interviews and focus groups, to bring in the voices and lived experiences of people working in these fields or encountering these issues?

      I think this was a great example of how the integration of different perspectives may add up to a unique and specific solution to a problem with the help of its respective expertise. I would also want to incorporate my management information systems degree with public health and a humanist course to help people in different parts of the world to create efficient health systems while also working with companies for their goodwill projects and actually implementing them for the good for equitable or even free healthcare.

    1. Since this book is also about ethics, we should mention that the first thing these women were asked to program on the ENIAC was some calculations to help build thermonuclear bombs. How do you think they might have felt about being asked to do this? The building of those bombs involved many scientists and other professionals along the way, several of whom were not on board with the idea of what their calculations were being used for. This has raised questions about moral responsibility: were the women made complicit in whatever moral wrongs may have come about using calculations they performed using the ENIAC?

      This note added to my understanding including the pictures here and the description of the history of computer language as well as the code entered by the women to generate a series of thoughts. It made me aware of the tremendous advances in computer language and technology today.

    1. Perspectivas en el uso de la ciencia

      "As We May Think" es un ensayo escrito por Vannevar Bush en 1945, en el que se presenta su visión de cómo la tecnología de la información podría mejorar la capacidad de los seres humanos para almacenar, acceder y procesar información. Por ejemplo, este autor describe una máquina de almacenamiento y recuperación de información, a la que llama "Memex", cuyo propósito sería que los usuarios pudiesen tener acceso a grandes cantidades de información de manera rápida y fácil. Me parece importante en Bush que destaca la importancia de la colaboración y el intercambio de información entre expertos en diferentes campos y también argumenta que la tecnología de la información tiene el potencial de ayudar a los expertos en distintos campos a compartir sus conocimientos y trabajar juntos para resolver problemas complejos.

    2. Durante años, los inventos han ampliado los poderes físicos de las personas en lugar de los poderes de su mente. Argumenta que están a la mano los instrumentos que, si se desarrollan adecuadamente, darán a la sociedad acceso y dominio sobre el conocimiento heredado de las épocas. La perfección de estos instrumentos pacíficos, sugiere, debería ser el primer objetivo de nuestros científicos.

      Esto es buenísimo para la innovación de nuevos inventos que pueden beneficiar la humanidad por medio de la imaginación del ser humano pero creo se debe ser limitado debido a la gran imaginación que contiene el ser humano pero dicha imaginación se puede crear ideas buenas, malas y desechables.

    1. Egoism

      I found this part controversial and attractive to me. Altruism seems morally correct. However, Egoism is naturally born in human beings so people tend to make decisions that benefit themselves. While they may defect the society. It is morally wrong to affect society while benefiting yourself. I think there's no right or wrong action when comparing Egoism and Altruism. People would have different ambitions. It is morally wise if people tend to pursue more benefits for society. Also, we are not able to judge people who takes own profits as the most important thing.

    1. Abstract

      This work has been published in GigaByte Journal under a CC-BY 4.0 license (https://doi.org/10.46471/gigabyte.79), and has published the reviews under the same license. These are as follows.

      **Reviewer 1. Xuanmin Guang **

      Han et al. had carried out genome assembly of Aplectana chamaeleonis, analysised the genome’s repeat content and annotated the genome. They descripted the geneset’s function and done a PSMC analysis. The genome is a key source for research, but there are so many mistakes in the manuscript, I suggest the author to revies the manuscript carefully and the grama and content should be re-organized. Some suggestions have been listed below:

      1. In the context part, the first two sentence lacks continuity in logic, please change them.
      2. The author didn’t mention which sequence platform they had used in the context, I think this should be added.
      3. The average sequence length in the table is 496kbp, but the author it as 496Mbp , this is a mistake.
      4. In table 1, why there aren’t any gaps in the scaffold genome?
      5. The author said that “This suggests that the significant expansion of repeating elements is an important manifestation of species differences”. Its unreasonable to get this conclusion only based your genome repeat analysis.
      6. In the text they claim that 12887 function gene had annotated, I want to know how much gene they have annotated? Please add this in the manuscript.
      7. Too many decimal places have been used in the Table2.

      Re-review: The author revised the paper as I concerned in the report and the paper could be accepted now.

      Reviewer 2. Jianbin Wang

      In this manuscript, Hou et al. present a genome assembly for Aplectana chamaeleonis, a parasitic nematode that infects amphibians. They report a genome of ~1 Gb, most of which is composed of repetitive elements. This genome draft is significant as it is the first assembled for this or any Cosmocercidae species. It may provide insights into the evolution of the nematodes – if it is thoroughly compared to other nematode genomes. It may also allow for better species identification than previous morphological methods. While the conclusions on genome size and composition described in the paper appear sound, there are many questions that go unanswered. The reasoning behind why this research was undertaken is not clear. What is the ecological or agricultural and economic impact of the species? How would the genome provide a better understanding of this species? More specific information is also needed to better understand the genome. How many chromosomes does this species have? Is there any cytology to help answer this question? Any notion of sex chromosome vs. autosome? This genome is much bigger than most of the assembled parasitic nematodes. The author did not make any efforts to explain what might contribute to this. Could the big size due to contamination in the samples used? Judging from the images, it does not look very convincing to me how clean the sample was for the genomic DNA extraction. Overall, there is a lack of in-depth data analysis and comparison between this genome and many other available nematode genomes. About the overall presentation and organization of the manuscript, the context is often lacking from results. How do these results compare to related species? How does figure 4/the demographic history fit in to this story? A round of general proofreading needs to be done for grammar, punctuation, capitalization, italics, etc – see below for some specific examples. In the Abstract, the repeat content in the Ascaris genome is 72.45%, and the total length is more than 742 Mb. The math does not add up (1.1 Gb x 72.45% = 797 Mb). Or do you mean the Aplectana genome? Should say total length of repeats. Why is this “Ascaris” genome? Ascaris is a parasite that infects pigs and human. Some sentences need addressing/clarification: Page 1. “and their diversity is also very high, many of which are above the national second-level protected animals” – what is the significance of this/how are these ideas related? Page 2. “Through the characteristics of the genome sequence, it shows that the genome is a highly continuous genome” – need to be more specific with metric and data. Page 4. “In addition, the enrichment of A. chamaeleonis genes in all metabolic pathways was found in twelve metabolic pathways.” – not sure what you are trying to say about the all or 12 pathways. Figure 1. - Images need scalebars. In A, what is the mat of material? For A, crop out area around the worm and enlarge the worm image. In B the worm is dark/shows little contrast or detail. In C, label which image is the head and which is the tail (or specify left vs. right in the legend text). The images in B and C look like they were taken using a cell phone pointed at a computer monitor – are there higher quality images? Table 1. – Why is the data in all four columns the exact same? What is the difference between each column? This appear to be a mistake when preparing the table. Very sloppy and unfortunate! Table 2 – Significant figures on the %s?. Is the “other” category needed (same for Fig2C)? Table 3 – Check text spacing (e.g. % in genome). Figure 3 – Recommend to redo the spacing of figures, increase size of text in each part of this figure. Need to refer to parts of figures in the body/text (Fig 3a vs. 3b vs. 3c). Can 3b be sorted from most number of genes to least? Figure 4 is not referenced in the body text. Consider merging Fig 4 with Fig 3. Figure 4 is lacking a description in the legend – what are the grey lines, definition of LGM? The x-axis scale and orientation are unintuitive – is the present on the left and the past on the right? Past should be on the left. Methods Genomic DNA was purification for Long-reads libraries preparation – should say purified What is the meaning of “The generation we used was 0.17” – what generation is this? and “the mutation rate was 9×10-9” needs units. The sentence “we used the pairwise sequentially Markovian coalescent (PSMC) model to estimate the effective population size of A. chamaeleonis within last million years.” should be moved to the section immediately after its current location.

      Re-review: Overall, the writing has been improved in several places and is somewhat clearer than in the previous draft. These changes are mostly related to the minor concerns raised. However, many questions related to the broader impact of this research and how the new genome compares to other nematode species remain unanswered. The following comments were largely ignored. 1. The reasoning behind why this research was undertaken is not clear. 2. What is the ecological or agricultural and economic impact of the species? How would the genome provide a better understanding of this species? 3. More specific information is also needed to better understand the genome. How many chromosomes does this species have? Is there any cytology to help answer this question? Any notion of sex chromosome vs. autosome? 4. This genome is much bigger than most of the assembled parasitic nematodes. The author did not make an effort to explain what might contribute to this. 5. Overall, there is a lack of in-depth data analysis and comparison between this genome and many other available nematode genomes. How do these results compare to related species? 6. About the overall presentation and organization of the manuscript, the context is often lacking from results. Another round of general proofreading needs to be done for grammar, punctuation, capitalization, italics, etc. – see below for additional specific examples. The authors, not the reviewers, need to make a concerted effort to read and proofread their own manuscript.

      In addition to the big picture points raised above, several other issues that were either brought up last time or are new and need to be addressed: 1. Not sure Table 1 is present the right way. The columns and rows should be reversed, I think. If so, there will be only one column - do you still need a table? 2. “Through the characteristics of the genome sequence, it shows that the genome is a highly continuous genome.” Unclear. The authors mentioned that they have fixed this in their response to the reviewers, but no change was seen in the updated manuscript. 3. “The generation we used was 0.17, and the mutation rate was 9×10-9 [8].” These numbers need units after them. Again, this was addressed in the response but not written out or clarified in the revised text. 4. “In addition, the enrichment of A. chamaeleonis genes in all metabolic pathways was found in twelve metabolic pathways.” Not sure what the authors were trying to say about the all or 12 pathways. Still confusing. 5. Photographs of the worms are still lacking scale bars. 6. Make sure that all genus and species names are italicized (in body text and in Fig.3). 7. Make section heading format is consistent (check capitalization). 8. “The results showed that 91 % of the sequences were compared to Arthropoda (1898/2088) and 7 % were compared to Arthropoda (122/2088).” Both of these say Arthropoda - is that a mistake? Also "compared to" is not the correct word, maybe "similar to"? 9. LGM acronym is defined after the second use of "last glacial period", should appear after the first use. Also, LGM stands for last glacial maximum, not period. This should be corrected.

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      Reply to the reviewers

      Thank you for the rapid and favorable reviews of our manuscript entitled “Long-Read Genome Assembly and Gene Model Annotations for the Rodent Malaria Parasite Plasmodium yoelii 17XNL.” We particularly appreciated that both reviewers had substantial, detailed expertise with the sequencing and assembly of Plasmodium genomes, and valued their questions and suggestions to ensure high rigor of our work. We have addressed all of the reviewers’ comments in the revised manuscript, and have provided a point-by-point response to each below.

      Response to Reviewers

      Note: Point-by-point responses are provided in italics below each reviewer comment below. Line numbers referenced in our responses refer to their final line position in the Track Changes version of the manuscript.


      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      The manuscript entitled "Long-Read Genome Assembly and Gene Model annotation for the Rodent Malaria Parasite P. yoelii 17XNL" is a well-written manuscript providing updates and important observations about the genome assembly and annotation of this specific non-lethal isolate. The group overall did a great job showing how the application of newer technologies such as long-read DNA and direct RNA sequencing to generate top-quality genomes to be used as a reference for the community. Here are some comments about the work presented:

      Response: Thank you for your positive feedback and suggestions on how to clarify these findings. We have improved the revised manuscript based on your feedback and suggestions below.

      Major comments: - The authors added several result information across the methods section. Making the text repetitive, since the same is also presented in the results section. Please revise the method section to remove results from this section.

      Response: We agree and have streamlined both the Results and Methods sections to remove redundancy in these descriptions.

      • Some methods are also redundant in the Result section. For example, in line 141-142, the group describe which DNA extraction kit they used (again this is correctly mentioned in the methods section).

      Response*: We agree and have removed minutiae such as these from the Results section. These details remain in the Methods section to ensure reproducibility. *

      • Besides important, the group added several information about method comparison between base call accuracy and sequencing methods. I agree that having this information in the supplemental material is great, but I would be careful to not focus too much on those, since most of the observations are already well-known by the community and focus more in the biological relevance of what is being generated with the newly updated genome.

      Response: The advances in base calling algorithms do make substantial improvements to the Nanopore reads. We have only included a short description of this in the main manuscript and feel this is an appropriate amount of context for the typical reader. Those that love these details and want to dig further can find this content in our supplemental information.

      • The group did a great job generating two versions of the genome, and an updated gene annotation set using long-read sequencing. But the major question is, how about alternative splicing? They mention the use of it (line 350) but I don't see any result about how many alternative transcripts were observed, and if they were differentially detected in different life stages of the sets used for the RNA sequencing. This is a very important result to be added since one of the key pieces of information that long-read RNA sequencing brings for Genome annotation.

      Response: We have now expanded this description in the manuscript to note that 866 genes are predicted to have multiple transcript isoforms (Lines 240-241). Moreover, we have now generated a Supplemental Table 4 that lists these isoforms in the revised manuscript. As we have not conducted further validation of this large number of transcript isoforms, we have left the description at this level.

      • Same observation as above for potential long ncRNAs.

      Response: We agree that lncRNAs are a fascinating aspect of the biology of the parasite, but a proper analysis of this class of RNA is far outside of the scope of this current study. Automatic identification approaches with Nanopore data will likely yield high numbers of false positives, which require manual curation for rigorous annotation. We hope others can use these data to accelerate such studies as well.

      • From what I understand the Hifi run was able to generate a gapless genome assembly and the ONT run did not. What was the final coverage for each? From my experience with P. falciparum genomes, ONT even with the rapid kit was able to generate chromosomal level assemblies if the coverage was >100x (but again, this is not a rule). Add those valuable observations about the depth so the reader can check if other variables in the comparison should be made.

      Response: This is a particularly interesting aspect of not only our datasets, but of other Plasmodium genomes as well. This issue occurs at least in part due to the presence of many repeated elements in the subtelomeric regions. It is important to note that these repeated elements do not resolve into a single haplotype in an assembly due to conflicting information, not due to lack of coverage. For instance, regions may differ by only a few nucleotides that each have significant read support. We are particularly interested in a recent preprint that concludes that P. falciparum harbors extrachromosomal plasmids with these var sequences present (doi.org/10.1101/2023.02.02.526885). *If this observation is supported via peer review, this interpretation could also begin to explain our results with P. yoelii 17XNL as well. *

      • Also be sure that the structural comparisons between the genomes are not the ones used after running ragtag.py. If so, there is a high chance of structural bias in the scaffolded contigs.

      Response: We apologize for the confusion. We did not use ragtag for the PacBio assembly, and all structural and variant comparisons were done using the PacBio assembly. However, we did use ragtag for the Nanopore assembly that is included in this study as an additional resource to our community. These data were not used for variant calling though.

      • How Prokka differed from Braker2 for the Mitochondria/API annotation? This needs to be very well described since prokka is made for prokaryotic organisms and not for eukaryotic ones. And Braker2 uses a custom build dataset for training, which I believe contains known information about MIT/API for Plasmodium species.

      Response: We first applied Braker2 to the organellar genomes and identified only 6 genes in the apicoplast genome and only 2 genes in the mitochondrial genome. Due to their prokaryotic origin, we then tested if Prokka could alleviate this issue. To do so, we applied Prokka to the 17X reference genome and found that it detected all of its annotated organellar genes. Therefore, we also applied Prokka to our Py17XNL genome to annotate the genes found on the apicoplast and mitochondrial genomes. As a final validation check, the gene annotations on these two organellar genomes are effectively identical between 17X and 17XNL. This is consistent with the sequencing results and assemblies that show that the apicoplast genome is identical and the mitochondrial genome differs in a single, notable deletion in 17XNL.

      • Figure 5B, what is the peak observed in the mitochondria? What genes? Repeats?

      Response: What appears to be an inward pointed trough actually reflects the deletion of bases in 17XNL compared to the 17X assembly. We have clarified this in the manuscript on Lines 296-297 and in the legend of Figure 5.

      Minor comments: - For Oxford nanopore sequencing using the ligation kit, did the group check for potential chimeric reads generated by the protocol?

      Response*: We did. We used the adapter trimming software, Porechop, to identify and bin chimeric reads that were eliminated from the dataset. This method is described in the Makefile associated with the manuscript. *

      • Check if all species are italicized (for example, line 187 P. yoelii is not)

      Response: We have italicized this instance of P. yoelii and have reviewed the document to search for any other words that should be italicized.

      • In methods add the parameters for minimap2 for the direct RNA alignment

      Response*: We would encourage readers to view our MakeFile that has all of the commands and parameters used for the bioinformatic work reported here. *

      • For variant calling, I would use a minimum of 10x coverage to make a variant call instead of 5x. Besides looking well reproducible between all checks, I would be careful mainly with the single bp deletions with a such low threshold.

      Response: Read counts for the called variants were generally greater than 20. Moreover, we took these validations a step further and manually curated these variants using the data from multiple sequencing platforms used in this study to ensure high rigor in making these variant calls. We have further clarified this in the revised manuscript.

      • In some parts of the methods, the authors mentioned slight modifications in some protocols (for example, lines 443 and 454), besides well described in the text, could you highlight what were the modifications in the text? This will facilitate many other researchers to understand why those modifications were needed.

      Response: We have clarified these modifications in the revised manuscript. In short, these modifications consisted of: 1) For the HMW gDNA prep kit, an agitation speed of 1500 rpm was used as opposed to the recommended 2000 rpm due to limitations of our instruments. 2) A slow end over end mixing by hand was preferred over using a vertical rotating mixer as yield was consistently greater with this change. 3) For the RNeasy kit, the lysate was passed through a 20-gauge needle for homogenization of the sample. Instead of an on-column DNaseI treatment, the RNA was treated with DNaseI off of the column to promote complete DNA digestion. 4) A second elution from the RNeasy column was performed in order to improve yield.

      • As mentioned in the major, the data analysis method section needs rework to remove results from the text.

      Response: We have revised the manuscript accordingly.

      • The group mentioned that small contigs not mapping to Py17X were discarded. What are those? Repeats? Contamination?

      Response: These contigs were of mouse origin, as P. yoelii was grown in Swiss webster mice in this work. We have clarified this in the revised manuscript on Lines 183-184.

      Reviewer #1 (Significance (Required)):

      This work generated a strong method and resource for a better genome assessment of P. yoelii for the community. As I mentioned in my comments, some more details about the findings such as alternative splicing and lncRNAs may strengthen them even more the publication. I know that comparative analysis between Py17X and XNL is not in the scope here, but more information about it, such as a synteny plot would be great for the community to understand that they can rely on this new reference genome. I've been working with eukaryotic and prokaryotic genomes for more than a decade and I have a lot of experience with all the methods presented. I believe that potentially the depth generated for the ONT data may be one of the factors for not reaching the chromosomal level of this isolate, since HiFI was. The group did a great job on the method description, and I believe that the community will be very happy to incorporate this genome as one of the references for this organism.

      Response: We are thrilled that you value the data and the rigor of our approaches. We also believed that a direct comparison between 17X and 17XNL strains is critical. Because of this, we provided details of this comparison in Figures 5 and 6, as well as in supplemental files. Because our colleagues often use these strains interchangeably, it is important for our community to know what differences are present between the parental 17X and the cloned 17XNL line. While substantial identity exists between the 17X and 17XNL strains, there are many variants between them, including many that affect genes that are known to have essential functions for the parasite. For this reason and more, we believe the true 17XNL genome assembly will be a preferred reference once it is fully integrated into PlasmoDB.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      The paper has three distinct parts, 1. Assembly of the P. yoelii yoelii 17XNL 2 Annotation of the genome and adding UTR regions 3. Comparing the sequence of 17XNL with 17X .

      Assembly: The authors present a novel assembly for the P. yoelii yoelii 17XNL genome. They used two different approaches, comparing Oxford Nanopore (ONT) long reads + Illumina DNA with PacBio Hifi. None of the approaches generated a telomer to telomer assembly so sequences from the 17X reference was used to fill in the mssing sequence.

      Response: Please also see the comment from Reviewer 1 and our response. The presence of many repeated elements in the subtelomeric regions leads to the challenges noted here about a telomere-to-telomere assembly, as well. The presence of these elements means that the sequences do not resolve into a single haplotype in an assembly due to conflicting information, not due to lack of coverage. Because of this, we have chosen to harmonize the selected haplotype at these subtelomeric regions with that of 17X, while still acknowledging and providing the complex data associated with the subtelomeric regions.

      Annotation Next, they generated long reads (ONT)and Illumina RNA-Seq to improve the annotation. Although, their annotation is not better than the current P. yoelii 17X reference genome in PlasmoDB, they could predict the UTR regions and alternative splice sites due to the 3' capturing approach and long reads. Having the UTR annotated and potentially having alternative splice sides is useful for the field.

      Response: We agree that the additional gene model annotations for both UTRs and alternative transcript isoforms is a valuable resource to our community. We are working with PlasmoDB currently to make these data readily accessible.

      17XNL - 17X comparison The author compared the 17XNL with the 17X reference. Both genomes were done with Pacbio, and it should be noted that P. yoelii has a GC content of probably ~23% with several homopolymer tracks. Further, the 17XNL genotype was obtained from a 17X culture, so the genomes are expected to be very similar as the author noted in the introduction. The authors found ~2000 differences; some are in genes, but many are indels, which very well could be sequencing errors. Finally, the authors claim that this genome could become relevant for the community as new reference to perform analysis. As their genome is so similar to 17X and they have to show that their annotation is at least as good as the current 17X reference genome (manual curated) and the difference are not due to sequence error in 17X or 17XNL.

      Response: As we describe below, we have taken multiple steps to inspect the quality of the 17X genome assembly (it is very robust), to call variants between strains, and to validate them using our data across multiple sequencing platforms and via manual curation. Because of this, we view these as true variants between the 17X and 17XNL genomes

      Major comments Overall I struggle to see the need for a "NEW" P. yoelii reference. It would be good to state how similar these genomes are - they are basically identical. As the 17XNL is curated manually, it would have made more sense to me to start from that one and then generate the UTR annotation and include splice sides. This could be easily loaded into an alternative Web-apollo track and then merged to the current annotation to be useful to the community.

      Response*: We chose to generate a new reference assembly for 17XNL because the current one is from 2002, remains in >5000 contigs, has gene identifiers that do not align with other current Plasmodium gene models (e.g., PY00204 vs. PY17X_0502200), and historically has had problematic gene models attributed to individual genes. This clean start ensures that users can know the provenance of the underlying data that created the genome assembly and gene models. *

      I wonder if many of the differences the authors found between 17X and the 17XNL reference are true. The authors are correct that some differences between 17X and 17XNL are true. I could not find any evidence of genome polishing with tools like Pilon or ICORN to correct sequencing errors, I wonder if these differences are sequencing errors.

      Response: The PacBio-based assembly received no error correction or polishing. It should be noted that all variants that were called automatically were also manually verified using data from multiple sequencing platforms generated in this study. Moreover, for coding sequences, we imposed a threshold that 80% of all reads at the variant’s location needed to support the variant in order to be considered true. Through these strict thresholds, we eliminated many potential variants that only had support from one sequencing platform. We highlight several variants that were confirmed through multiple datasets in Table 2.

      Did the authors look into the reads of the NCBI - GCA_900002385.2 - assembly? Maybe they could use the underlying Illumina reads if theirs don't have enough coverage. Also, the differences between 17X and 17XNL could be that the reference is wrong. How many pseudo genes did they obtain? Are there more or less than in the current reference?

      To confirm the calls, could you also map the 17XNL reads against the 17X reference and see if they are still true. As the same time, map the 17X illumina reads to see if the reference is correct at this state. When looking at the alignments, it can be seen that many different are in low complexity/repetitive regions.

      Response: We analyzed both their raw and assembled data to compare them with our results, and we determined that the 17X data and assembly were robust and that these difference likely reflect true variance between the strains. The 17X reference has 57 pseudogenes that are annotated as pir, fam-a/c, or others. Overall, there were 1057 pir genes annotated in the 17X genome, whereas we annotated 1048 for our Py17XNL genome. There were 302 fam-a/b genes annotated in the 17X genome, whereas we annotated 301 for our Py17XNL genome. As noted above, we confirmed variant calls using data from multiple sequencing platforms in this study as well as through manual curation.

      The authors sequence their genome with a HiFi Pacbio run and also ONG + DNASeq... but why did they not get 16 chomromes out? For example the current P. yoelii reference was assembled directly into far less pieces than theirs [P. chabaudi assembles into 16 pieces]. Could it be a different read depth or is it the fragment length? Could the authors please comment on that. Also, if there were contigs, why did they fill the sequence with 17X sequence, rather than keeping gaps? So in the end, their sequence is a hybrid, of 17X and 17XNL, right?

      Response: Please see our responses above to both Reviewer 1 and 2 regarding the heterogeneity of the subtelomeric regions that indicate that a single haplotype is not readily called. This is not due to insufficient read depth, but rather we believe it reflects something fascinating about Plasmodium genomes in these regions. A recent preprint (doi.org/10.1101/2023.02.02.526885) provides one possible interpretation for this observation.

      Why do you think you had less coverage of CCS read around the telomer ends? Do you think it is a systematic issue of the PacBio Hifi? Did you see any evidence of Illumina or ONT reads - or could it be that while culturing the telomer ends dropped off?

      Response: See our response above about the challenging nature of the subtelomeric regions of Plasmodium genomes. As above, this is not an issue of coverage per se, but rather of heterogeneous related sequences that are not readily resolved into a single haplotype. In order to minimize the risk of sequencing a genome of a mixture of heterogeneous parasites, we sequenced “Pass 0” parasites received directly from BEI Resources to ensure this genome reflects the established P. yoelii 17XNL clone.

      I realised that the authors used a lot of primary tools. I wonder why they chose that path, as there are several tools to do automatic finishing for long read assemblies: Assemblosis, ARAMIS, MpGAP or ILRA. Especially the last one focuses on Plasmodium genomes. Please comment.

      Response: We initially started our bioinformatic analyses using established tools such as these. Specifically, we first tried Companion and ILRA, but the results were not superior to those we achieved with the workflow we describe in this manuscript, which also provided greater parameter control.

      Also, for the annotation, could it not be better to transfer the manually curated genome annotation with LIFT off or RATT? All these tools are widely used in the generation of reference genomes in the parasitology field. I annotated their sequence with Companion, and although their gene models are good and some of the Companion calls might need improvement, overall, the Companion results look more exact to me.

      Response: Companion was the original tool we used for the generation of gene models. While we found that for a pre-package software platform it performed excellently, we found it to be insufficiently customizable and the results were not sufficiently accurate from our assessment. Additionally, lifting over information always raises the risk of imposing a different perspective on what is truly present. We believe that a high quality, de novo assembly is always preferable, and therefore chose this workflow.

      The code is very well organised, and it was easy to follow. Are you planning to put it on a GitHub repository?

      Response: We appreciate this recognition. We believe clear reporting of the bioinformatics work is critical for rigor and reproducibility. Yes, all of this will also be provided in GitHub to benefit the wider community.

      For the annotation in the attachment, there were two files. I had a look at them and they were quite different. As 17X and this genome are basically identical (Response: The two gff files represent either a Nanopore only or hybrid Nanopore+Illumina-based model. The latter produced a more comprehensive annotation of gene models, which is what we have proceeded with. However, we provided both in case end users find value in the Nanopore only annotation which has a 3’ bias due to the mechanism of how sequencing occurs via this approach.

      We have found meaningful variations in genome sequence that potentially impact biological function (see Discussion). Therefore, we maintain that these genomes are not basically identical and are useful to the malaria research community for these reasons and more.

      It is excellent that the genome is submitted to NCBI. Why are there 18k proteins? Are these the alternative spliced forms?

      Response*: We are not certain how this interpretation might have arisen, as we only have reported 7047 potential transcript isoforms to NCBI based upon our data. *

      Minor The current Py 17X genome in PlasmoDB is a Pacbio assembly (https://plasmodb.org/plasmo/app/record/dataset/TMPTX_pyoeyoelii17X), but not part of the 2014 paper. It was submitted later to NCBI than the paper the authors cite. Also, the current P. berghei Pacbio genome is from Fougère et al. PLoS Pathog 2016;12(11):e1005917.

      Response: We have now made a detailed note about the Py17X PacBio dataset in our revised manuscript on Lines 186-187. Mentions of the current P. berghei genome assembly had already cited the Foug’ere et al. publication.

      I tried to open the supplemental tables, but they were all in pdf rather than excel and split over several pages. Two had missing information, e.g. UTR per gene. From the name of the tables, I had an idea of what they should contain, but for a re-submission, it would be good to have them in the correct format.

      Response: We agree that provision of the PDFs of the supplemental files is not the ideal way to review these analyses. The complete data was also provided in the Excel files provided to Review Commons. We will ensure that the affiliate journal receives the Excel files for completion’s sake.

      To me, the beginning of the results reads a bit like an introduction (the part which sequencing technology to use)

      Response: We agree, and as noted to Reviewer 1 above, we have streamlined this section of the revised manuscript.

      Could you add to the tables: Sequence Coverage of the three technology, how many contigs you had before ordering the contigs and the number of pseudogenes in the annotation?

      Response: This information is now provided in Supplemental Table 3 in the revised manuscript.

      I struggle with the section header line 229-230 that the new sequence is more complete as it is a hybrid assembly with 17X. Alternatively, please explain how the consensus was built.

      Response: We agree and have revised this section header for accuracy.

      The authors correctly state that ONG is great, lines 333ff, but why does it not generate telomer-to-telomer chromosomes in this case? Please discusss.

      Response: Please see our response to this above for remarks made by both Reviewer 1 and 2. We have also added clarifying text in our revised manuscript discussing why this may have occurred.

      Reviewer #2 (Significance (Required)):

      General assessment As mentioned above, I struggle to see this as a strong leap for the malaria community to use this genome, as it is so similar to the current 17X genome, which is manually curated in plasmodb. Response: We agree that it is important to know how similar the genomes of 17X and the cloned 17XNL strain are. It is perhaps even more important to know what the key differences are as well. In this study, we have asked and answered these questions, and identified 2000+ variants between the strains. We have manually curated several of the variants that impact the expression of essential/important genes, and found that biologically meaningful differences exist (see Discussion). Finally, we have also provided additional information on the gene models of 17XNL, including an experimental definition of UTRs and transcript isoforms. Together, we hold that these data will not only match those currently available for 17X, but will exceed them. We are currently working with PlasmoDB to make these data readily accessible to our community.

      Advance The authors should make the comparison of ONT and PacBio HiFi clearer and discuss why the technologies still don't generate telomer-to-telomer sequences. From the biological side, none of the found differences were related to the different phenotype between 17X and 17XNL.

      Response: We have provided these comparisons and all related data to the reader in this manuscript, as well as through public depositories. Please see above for our responses as to why a true telomere-to-telomere assembly is challenging with Plasmodium parasites, and for a recent preprint that might provide an explanation for this. Finally, the phenotypic differences between 17X and 17XNL are variable, which might reflect differences in individual parasite stocks as has been historically seen in the spontaneous development of lethality in multiple laboratories. While we do not find any particular genetic difference correlates with a specific phenotype, these data using the cloned 17XNL parasite available from BEI provides a robust reference with a defined parasite stock.

      Audience: I do agree that adding the UTR sequence will be useful for those working with P. yoelii as a model, or who want to do comparative UTR analysis across species.

      Response: We agree that this additional gene model information will be valuable. We are working with PlasmoDB to make this information readily available and are already integrating it into our ongoing studies.

    1. Author Response

      Reviewer 1 (Public Review):

      Fox, Birman, and Gardner use a previously proposed convolutional neural network of the ventral visual pathway to test the behavioral and physiological impact of an attentional gain spotlight operating on the inputs to the network. They show that a gain modulation that matches the behavioral benefit of attentional cueing in a matching behavioral task, induces changes in the receptive fields (RFs) of the model units, which are consistent with previous neurophysiological reports: RF scaling, RF shift towards the attentional focus, and RF shrinkage around the focus of attention. Ingenious simulations then allow them to isolate the specific impact of these RF modulations in achieving performance improvements. The simulations show that RF scaling is primarily responsible for the improvement in performance in this computational model, whereas RF shift does not induce any significant change in decoding performance. This is significant because many previous studies have hypothesized a leading role of RF shifts in attentional selection. With their elegant approach, the authors show in this manuscript that this is questionable and argue that changes in the shape of RFs are epiphenomena of the truly relevant modulation, which is the multiplicative scaling of neural responses.

      Strengths:

      The use of a multi-layer network that accomplishes visual processing, with an approximate correspondence with the visual system, is a strength of this manuscript that allows it to address in a principled way the behavioral advantage contributed by various attentional neural modulations.

      The simulations designed to isolate the contributions of the various RF modulations are very ingenious and convincingly demonstrate a superior role of gain modulation over RF shifts in improving detection performance in the model.

      We thank the reviewer for these supportive comments.

      Weaknesses:

      There is no mention of a possible specificity of the manuscript conclusions in relation to the type of task to be performed. It is conceivable that mechanisms that are not important for detection tasks are instead crucial for a reproduction task, as in Vo et al. (2017).

      We agree that other behavioral tasks may rely on different attentional mechanisms then the ones we have studied here for detection and discrimination and now specifically point this out in the discussion [379-395].

      The manuscript puts emphasis on the biological plausibility of the model, and some quantitative agreements. But at some important points these comparisons do not appear very consistent:

      1) It is unclear what output of the model at each cortical area is to be compared with neurophysiological data. On the one hand, the manuscript argues that a 1.25 attentional factor is consistent with single-neuron results, but here this factor is applied to the inputs into V1 units. When this modulation goes through normalization in area V1, the output of V1 has a 2x gain. Intuitively, one would think that recordings in V1 neurons would correspond to layer V1 outputs in the model, but this is not the approach taken in the manuscript. This needs clarification. Also, note that the 20-40% gain reported in line 287 corresponds to high-order visual areas (V4 or MT), but not to V1, in the cited references. The quantitative correspondence between gain factors at various processing steps in the model and in the data is confusing and should be clearer.

      We agree that making a one-to-one mapping of gain effects measured in neurophysiology and different layers of the CNN is problematic. We therefore have clarified that the introduction of gain at the earliest stages of processing is meant to study how gain propagates through a complex CNN and has downstream effects [49-52 and 410-447] and we have also also clarified the various uncertainties in making one-to-one mapping from the CNN to neurophysiological measurements of gain [410-447].

      2) The model assumes a gain modulation in the inputs to V1. This would correspond to an attentional gain modulation in LGN unit outputs. There is little evidence of such strong modulation of LGN activity by attention. Also in V1 attentional modulation is small. As stated in Discussion (line 295), there is no reason to favor the current model as opposed to a model where the attentional gain is imposed later on in the visual hierarchy (for example V4). If anything, neurophysiology would be more consistent with this last scenario, given the evidence for direct V4 gain control from frontal eye fields (Moore and Armstrong, Nature 2003). The rationale for focusing on a model that incorporates the attentional spotlight on the inputs to V1 should be disclosed.

      We agree that measurements of gain changes with attention appear larger in later stages of visual processing and do not wish to explicitly link the gain changes imposed at the earliest stages of processing in our CNN observer model with changes in input from LGN as we agree this would be unrealistic. Instead, our goal was to examine how gain changes can propagate through complex neural networks and cause downstream effects on spatial tuning properties and the efficacy of readout. We have substantially re-written the manuscript, in particular the introduction [24-38, 49-52] and discussion [441-447] to better describe this rationale. We also now explicitly discuss how our propagated gain test demonstrates exactly the reviewer’s point - that gain can be injected late in the system, rather than at the earliest stages [274-276, 441-447].

      3) The model chosen is the CORnet-z model, but this model does not include recurrent dynamics within each layer. Recurrent dynamics is a prominent feature in the cortex, and there is evidence indicating that attentional modulations operate differently in feedforward and in recurrent architectures (Compte and Wang, Cerebral Cortex 2006). A specific feature of recurrent models is that the attentional spotlight need not be a multiplicative factor (which is biologically complicated) but an additive term before the ReLU non-linearity, which achieves the expected RF modulations (Compte and Wang, 2006). A model with recurrence thus represents another architecture that links gain and shift in a way that has not been explored in this manuscript, and this may limit the generalization of the conclusions (line 205).

      We appreciate the reviewer pointing us toward the Compte paper and we’ve added a discussion of recurrence as an alternate model [410-423].

      Reviewer 2 (Public Review):

      This manuscript by Fox, Birman, and Gardner combines human behavioral experiments with spatial attention manipulation and computational modeling (image-computable convolutional neural network models) to investigate the computational mechanisms that may underlie improvements in behavioral performance when deploying spatial attention.

      Strengths:

      • The manuscript is clear and the analyses, modeling, and exposition are executed well.

      • The behavioral experiments are carefully conducted and of high quality.

      • The manuscript takes a creative approach to constructing a ”neural network observer model”, that is, coupling an image-computable model to a potential readout mechanism that specifies how the representations might be used for the purposes of behavior. The focused analyses of the model innards (architecture, parameters) provide insight into how different model components lead to the final behavior of the model.

      Thank you for these supportive comments.

      Weaknesses:

      • The overall conclusions and insights gained seem heavily dependent on particular choices and design decisions made in this specific model. In particular, the readout mechanism lacks some critical descriptive details, and it is not clear whether the readout mechanism (512-dimensional representation that reflects summing over visual space) is a reasonable choice. As such, while the computational analyses and results may be correct for this model, it is not clear whether the strong general conclusions are justified. Thus, the results in their current form feel more like exploratory work showing proof of concept of how the issue of attention and underlying computational mechanisms can be studied in a rigorous and concrete computational modeling context, rather than definitive results concerning how attention operates in the visual system.

      Please see below for our response to the issue with readout and conclusions.

      Overall, the work is solidly constructed, but the overall generality and strength of the conclusions require substantial dampening.

    1. These perceptions of too much newsroom attention going towards topics like politics and Coronavirus also reflect younger audiences’ broader desire for diverse news agendas, voices, and perspectives. As we discuss throughout this report, young people – particularly 18–24s – have different attitudes toward how the news is practised: they are more likely than older groups to believe media organisations should take a stand on issues like climate change and to think journalists should be free to express their personal views on social media.

      I think the difference between YOUNG and PAST generations of consumers is that young people grew up on platforms to where you can reach millions of views, and millions of people can consume your beliefs and reporting. I think why past generations may think news is super conservative, is because they have simply grown up being told what to believe, or being relied on newspapers and programs on the television, instead of so many different perspectives we see today. I think 18-24 is the most progressive timeline of adults that this modern world has ever seen, with great perspectives, great focus on real issues, and that is simply because all of them put the effort to organize and share ideas on platforms like twitter, facebook, youtube, reddit, you name it. You see now more than ever for younger groups of activists that they are not just trying to be heard but they are trying to TEACH! And I believe that is the gap between now and then, trying to show people political beliefs and reports, and now it is to teach and help form people on their own opinion or teach people how to analyze certain struggles we face today.

    2. Three years later, we now turn our attention to how young people’s news habits and attitudes have changed amid rising concerns about news distrust and avoidance, increasing public attention to social issues such as climate change and social justice, and the growth of newer platforms such as TikTok and Telegram.

      Honestly, when looking at a perspective like this, even though it is very early in the article, I take a step back and try to remind myself what an app like TikTok is really about. This app is not about "climate change, social justice", though it may be full of videos with tons of views and videos on such topics, the app is simply made for short attention spans, made for constant clicks, its based solely around an ALGORITHM, not serious social change, even though it is a personal anecdote, I do not believe TikTok inspires real change, or real news consumption, I think the algorithm promotes topics that keep you engaged for a solid 10-90 seconds, and you move on to the next one. I am an avid social media user, since the age 12, so from a decade of experience, the wave of attention span is completely forgot about and the idea that real news is consumed solely on incoming apps like tiktok feels very lazy. Just my thoughts, but my personal belief are real political commentators on YouTube, shows on streaming platforms, or daily news cycles on television, is what real habits that are truly being formed deeply in the conscience of this young generation.

    1. Companies use the data they collect in a variety of ways, including tailoring advertisements (ads) to you, marketing, developing or improving services offered within the app, and sharing or selling the data to third-party companies.

      This has happened to me many times. At first, it was a little bit spooky and I would hear people making jokes about how their phone is "listening". But that actually was the reality. I don't even need to vocalize something that I am thinking about for all of the apps I am using to know the content I want to see, when, where, and how. I often think about this in terms of tik tok's famous "for you" page, which is a constant stream of video content specifically tailored "for you" to watch. It truly is scary, and also makes me think about how much bias and closed-mindedness that can create. Though some may think that it opens up the mind to new forms of media that they may not "otherwise watch" because they are being fed the media without having to choose what they're watching, it can actually create more bias. For example, if we think about political opinions, you are likely only being fed information in favor of your party and against your opposing party. But we are never being shown the media in support of the opposing party, or their versions of negativity about the party we associate with. It further deepens the support for our own party and the lack thereof of the opposing party. This argument can go for anything, as we fall deeper into the rabbit holes of our interests.

    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity):

      The study "Mesenchymal stem cell models reveal critical role of Myc as early molecular event in osteosarcomagenesis" by Akkawi et al shows that BM-MSCs are a foundational tool for study of osteosarcoma. And authors identified Myc and its targets as early molecular events in osteosarcoma formation, using BM-MSCs knocked out of both p53 and Wwox. Although the quality and validity of the data presented in this manuscript are not in question, this reviewer has doubts about the novelty of the findings in this current version of manuscript. It does not clearly indicate what is different from previous findings.

      Response: We thank the reviewer for the kind words regarding our manuscript. Our study revealed that combined deletion of WWOX and p53, two known tumor suppressors in OS, results in early transformation of BM-MSCs that mediates upregulation of Myc. We believe that this notion is original and has not been described before. In addition, we provide evidence that this genetic manipulation has an advantage over p53 deletion alone. More details are provided below.

      1. It has already been reported that p53-/- BM-MSCs are the cells of origin in osteosarcoma development (ref. 21).

      Response: We agree with the reviewer about this notion/fact and we indeed cited this reference. It should be noted that in this study the cell of origin of osteosarcoma was proposed to be BM-MSCs-derived osteogenic progenitors when p53 and Rb are co-deleted. Interestingly, this study did not show whether p53-/- BM-MSCs-derived osteogenic progenitors are able to form osteosarcoma or any other type of sarcomas. In our model BM-MSCs p53-KO alone were not able to develop osteosarcoma tumors as well, even when injected intratibially. Moreover, we showed that co-deletion of Wwox and p53 in BM-MSCs-derived osteogenic progenitors have the ability to form OS at earlier stages at the time point when deletion of p53 alone is still not enough to induce osteosarcomagenesis. Based on our findings and those in the literature, we believe that this combined genetic perturbation is critical for initiation of OS.

      1. Indispensability of Myc in osteosarcomagenesis was revealed (PMID: 12098700).

      Response: It is well known that Myc is a potent oncogene and overexpressed in osteosarcoma (ref. 12, 13, 28 and ref. 29) as well as in many other human malignancies. The referred study (PMID: 12098700; ref 32) which expressed Myc in lymphocytes based their analysis of Myc contribution at the tumor stage (at the time of tumor detection). Our study, on the contrary, shows that upregulation of Myc is an early event in osteogenic-committed BM-MSCs deficient for Wwox and p53 in tumor-free mice. This notion indicates that combined deletion of two important tumor suppressors, WWOX and p53, promotes osteosarcoma through early changes in Myc levels. Our data further show that p53 deletion alone is dispensable for this change to happen at early stages further highlighting the significance of our findings. The referred paper was cited and discussed in the discussion section (ref 32).

      Our data further shed light on WWOX as a key determinant of Myc function placing it as an upstream regulator. To further proof this, we propose in our revision plan to knockout WWOX in yBM SKO cells and/or restore WWOX in yBM DKO cells and determine consequences on Myc levels and activity as well as on tumorigenesis. In addition, we shall perform a ChIP-seq assay for Myc in yBM DKO and compare it to yBM SKO cells to show whether WWOX deletion is indeed results in enhanced chromatin accessibility of Myc.

      1. Osteosarcoma formation of p53flox-OsxCre mice was rescued by Myc-depletion and BM-MSCs from p53flox-OsxCre was shown to upregulate Myc (PMID: 34803166).

      Response: This is a very relevant new study that we regrettably missed to cite so we are grateful for the reviewer to bring this up. In our revised version, we will be citing this important reference and discussing it (ref 33). Although this article shows that p53 deficiency promotes osteosarcomagenesis mediated through oncogenic Myc, which we also showed in our study (figure 6C, D), but the authors did not validate the tumorigenic ability of these cells at this earlier stage of osteosarcomagenesis. The later was showed in our study by injecting yBM-MSCs deficient for p53 intratibially into immunocompromised mice and showed that they lack tumorigenic ability although they display a mild upregulation of Myc (figure S5). On the other hand, co-deletion of Wwox and p53 at this earlier stage resulted in even higher levels of Myc and inducing osteosarcomagenesis at this earlier stage. Therefore, our study provides unforeseen effect of genetic perturbation that promotes OS initiation.

      First of all, the authors need to cite the above papers and compare their findings with them to better clarify the value of their findings.

      Response: Thank you for this comment, we will cite and discuss these valuable studies in our revised version.

      The only one clear finding would be that Wwox functions as a tumor suppressor by repressing Myc function in the absence of p53, but unfortunately, no data have been presented on the mechanism Some additional analysis would be needed to mention it

      Response: As addressed above, our revision plan will include:

      1. Depleting WWOX in yBM SKO and/or restoring WWOX in yBM DKO cells to further prove the tumor suppressor function of WWOX.
      2. Performing ChIP-seq assay for Myc in yBM DKO and compare it to yBM SKO cells to show whether WWOX deletion is indeed results in enhanced chromatin accessibility of Myc.

      In the first place, Myc is upregulated in the absence of both p53 and Wwox, compared in only p53-null situation? Western blotting would be better to show it

      Response: In our revised version we will add a western blotting showing the upregulation of Myc in DKO (WWOX, p53) compared to SKO (p53) yBM cells; This is already added in new Figure 5S C.

      1. In Figure 1D, should separate each panel so that it is clearly visible. What is the blue-colored fluorescence, DAPI? If so, why don't tdTomato positive cells overlap with blue (Figs, 1D, 2C, 4C, 4E?

      Response: In our revised version we added more precise images in all Figures indicating the overlap between DAPI (blue) and tdTomato (Red).

      1. Why was MCM7 chosen among the Myc targets (S Figure 3)? What is the rationale for this?

      Response: Thank you for this important notion. MCM7 is part of the MCMs protein family, that plays and essential role as a helicase and organizing center in DNA replication initiation. Moreover, several studies show the upregulation of MCM7 in several types of cancers among which is osteosarcoma, as cited in our manuscript (Ref. 14, 15, 16). MCM7 is also a direct target of Myc and has been shown to be a druggable target, by SVA as has been presented in Fig 7. Altogether, these facts and observations made us exploring its significance in our mode. In our revision plan, we will also explore other Myc targets through performing ChIP-seq on DKO cells.

      In Figure 5 legend, what does "yBM cells (1.5, 4-months) (n=6)" mean? yBM cells (1.5-months) (n=3) and yBM cells (4-months) (n=3)?

      Response: Thank you for this notion. yBM, at age of 1.5 months or 4-months were collected from tumor free mice and analyzed. In our revised paper, we updated and clarified this in the Figure 5 legend.

      1. In Figure 7B, is there a correlation between MCM7 and Myc protein expression levels?

      Response: Thank you for this comment. In our revised version we added a western blot analysis showing the upregulation of both Myc and MCM7 in yBM-DKO compared to SKO cells; new Figure S5 C. (did the mean 7B upper panne, if so, we have to add this in the updated version).

      Also, do MCM7 and Myc immunopositivites overlap in Figure 7G?

      Response: In our revised version we will perform Myc IHC on same tumor sections. In the meanwhile, we added a western blot analysis showing the inhibitory effect of Simvastatin on both MCM7 and Myc in vitro (new Fig 7B, lower panel, re-blotted for Myc).

      In S Figure 4C, what is 'PC'? What sample was loaded?

      Response: PC refers to positive control for p53 that was used which was in this case HEPG2 cells treated with Nutlin to stabilize p53. p53 antibody used in this plot (IC12-Cell signaling) detects both human and mouse p53. A note was added to Figure legend.

      1. In S Figure 2A, what does 'US' (BM-US) mean? In S Figure 4F, what does 'US' and 'S' (Direct US and Direct S) mean?

      Response: Thank you for this notion. We apologize for not clearly defined these symbols. In our revised version we added clarifications in the legends of these figures. The symbols are as follow: US: unstained BM-control, S: stained BM, Direct: directly collected BM and checked with FACS before culturing.

      1. Overall, this manuscript, there are too many symbols and it is cumbersome. Ex, in S Figure 3, yBM_DKO, Tum_DKO, DKOT, DKO-BMT, etc. All figures should be consistent with the same notation.

      Response: We apologize for this in consistency in using too many symbols. In our revised version we will provide a table with all the symbols that should be consistent all over the manuscript

      Reviewer #1 (Significance):

      Although the quality and validity of the data presented in this manuscript are not in question, this reviewer has doubts about the novelty of the findings in this current version of manuscript. It does not clearly indicate what is different from previous findings, such as;

      1. It has already been reported that p53-/- BM-MSCs are the cells of origin in osteosarcoma development (ref.21).
      2. Indispensability of Myc in osteosarcomagenesis was revealed (PMID: 12098700).
      3. Osteosarcoma formation of p53flox-OsxCre mice was rescued by Myc-depletion and BM-MSCs from p53flox-OsxCre was shown to upregulate Myc (PMID: 34803166).

      First of all, the authors need to cite the above papers and compare their findings with them to better clarify the value of their findings.

      Response: Thank you for your valuable comments, and as we mentioned these important studies were and will be cited and discussed properly in our revised version.

      The only one clear finding would be that Wwox functions as a tumor suppressor by repressing Myc function in the absence of p53, but unfortunately, no data have been presented on the mechanism.

      Response: As stated in our response above, we argue that our observations showing very early transformation of BM-MSCs in combined genetic perturbation of WWOX and p53 is novel. In our revision plan we propose to perform additional ex vivo experiments to prove this notion by performing WWOX deletion in SKO-yBM cells and WWOX restoration in DKO-yBM cells and test consequences on Myc levels/activity and tumorigenicity. To further shed light on the mechanistic outcome of WWOX action in this context, we shall perform Myc ChIP and ChIP seq assays in yBM DKO and compare it to yBM SKO cells to show whether WWOX deletion is indeed results in enhanced chromatin accessibility of Myc [follow up of Fig-I shown above]. These experiments should further strengthen our findings.

      Reviewer #2 (Evidence, reproducibility and clarity):

      In current study, the authors established a mouse model with tdTomato expression under the OSX-controlled double deletions of Wwox and Trp53. Such mouse strain gives a great platform to study the OS development and therapeutic potential. Experiments are clear and convincing. Results are well presented.

      Response: We thank the reviewer for the kind words regarding our manuscript and acknowledging its clarity and validity.

      To better improve this study, few minor suggestions regarding the data are as following:

      1. Some of the legends on figures are too small to read, or in low quality. please change these labels.

      Response: We thank the reviewer for this notion. We apologize for the low quality and inconvenience. In our revised version, we shall provide an improved resolution of legends.

      1. For Figure 7C and D, from 7C, the control WT BM showed clear resistance to the SVA treatment, but in 7D, there is almost no cells in the WT BM group. Data of this group might be missed?

      Response: Thank you for this comment. Cre+WT BM cells (shown in 7D) were unable to form colonies as shown previously in figures 2B and 4B. Fig 7C refers to sensitivity to SVA using MTT assay.

      1. For figure 7G, the difference among MCM7 IHC staining of two groups didn't show as much as the statistical analysis in the right panel. Authors may consider using MCM7 western blot to check its levels after SVA treat.

      Response: Thank you for this notion. We updated the Figure showing a more representative image (new Figure 7G).

      Reviewer #2 (Significance):

      This study uses a transgenic mouse model with tdTomato expressed in combination of loss of p53 and Wwox under the OSX lineage to study early initiation of OS. They found only DKO bone marrow cells can form OS in a subsequent orthotopic mouse model, but not the p53 single KO cells. After compare the RNA-seq from these different cell population, they identified the Myc pathway is the key player to promote OS development, especially the MCM7. Moreover, they tested SVA in treating these BM cells and reveal a therapeutic potential. This animal model is a good platform to study OS, especially at the early stage. Most results are clear and convincing. With the identification of Myc pathway, they further tested the SVA effects on treating these DKO BM. This is an important study and provided meaningful information to the OS, even broad cancer research community.

      Response: We thank the reviewer for his/her supporting comments, and acknowledging the importance of our study.

      However, the significance, or novelty of this work is not sufficient. For instance, SKO BM won't form tumor in the IT injection assays compare to the DKO BM groups, therefore, the involvement of Wwox during the OS tumorigenesis is clear. However, authors didn't explore any potential mechanisms of Wwox function or related signaling behind this observation

      Response: We thank the reviewer for this very important comment. As mentioned previously, response to reviewer 1, our results indicate that combined deletion of two important known tumor suppressors, WWOX and p53, promotes osteosarcoma through early changes in Myc levels. Our data further show that p53 deletion alone is dispensable for this change to happen at early stages further highlighting the significance of our findings. In our revision plan we will do knockout of WWOX in yBM SKO and restore WWOX in yBM DKO cells. In addition, we are currently working to perform ChIP assay for Myc in yBM DKO and compare it to yBM SKO cells to show whether WWOX deletion is indeed results in enhanced chromatin accessibility of Myc.

      And the RNA-seq analysis mostly focus on c-Myc pathway and its downstream targets. Given the well-known relationship of p53, c-myc even RB in the OS, it will be more interesting and attractive to see a clear mechanism of Wwox in this context.

      Response: We thank the reviewer for this suggestion. Indeed, combined deletion of WWOX and p53 resulted in alteration of key cellular pathways involved in OS development. Due to the capacity of the work, we focused here on this important notion showing very early upregulation of Myc in BM-MSCs isolated from DKO cells, but not from SKO cells. Future work can expand use of this model to address relationship with other key pathways and genes.

      Second, since authors took effort to generate this Tomato-DKO mice, it could be clearer if they isolate tdTomato positive cells instead of a mixture of BM, culture them, differentiate them, and perform more assays using these cells. In this way, it will give better clean background for all assays, and may be able to find novel effectors during this OS progression process.

      Response: Thank you for this important suggestion. BM-MSCs cells collected directly from the mouse (Tom+, Sca1+, CD45-) represents a very small and minute population and was used to be cultured for enrichment as was done in previous studies (ref. 33 and 34). So direct collecting this small population and injecting directly to immunocompromised mice is not feasible. Moreover, further validation of the cultured cells used in our study confirmed their mesenchymal identity. We however, propose to try performing in vitro tumorigenic assays on these sorted cells. In our revision plan we suggest performing colony formation assays and soft agar assays to address tumorigenicity of these cells.

      Third, within the text, authors tried to use OB differentiation and some other assays to discuss the OS origin cells, MSC or OB; but didn't get a preferred conclusion. It could be possible to better understand this process with the single cell RNA-seq using these BM from different mice or at different ages

      Response: Thank you for this important point. According to our results we can conclude that BM-MSCs committed to the osteoblast lineage are supposed to be the cell of origin for OS and will be clearly emphasized in our revised version. Preforming a single cell RNA-seq is beyond the scope of this study and can be explored in future studies.

      In general, this is a clean, straightforward study, and they established a very useful model to study OS. But the mechanisms merit is somewhere short

      Response: Thank you for the kind words, as proposed previously our plan to further investigate the mechanism of WWOX regulatory effect on Myc will be addressed using the in vitro assays of WWOX deletion/restoration to SKO/DKO-yBM cells respectively. Moreover, ChIP-seq assay for Myc in yBM DKO and compare it to yBM SKO cells to show whether WWOX deletion is indeed results in enhanced chromatin accessibility of Myc.

      Reviewer #3 (Evidence, reproducibility and clarity):

      Summary: The work describes analysis of an Osx1-Cre p53fl/fl Wwoxfl/fl mouse model compared to an Osx1-Cre p53fl/fl model. The authors have assessed the osteosarcoma inducing potential of cells within the bone marrow by including a tdTom reporter of Cre expression. They conclude that bone marrow mesenchymal cells can give rise to osteosarcoma when transplanted. Based on transcriptomics they contend that upregulation of Myc is important and its target MCM7 can be targeted with simvastatin.

      Major comments: -

      Are the key conclusions convincing?

      The authors can generate tumors in immunocompromised mice upon injecting cells derived from bone marrow flushed. This is not surprising given the data available now about expression of Osx1-Cre

      Response: Thank you for this notion. In our study we showed that yBM cells harboring the deletion of two known important tumor suppressor genes WWOX and p53 collected from tumor free mice are tumorigenic compared to SKO p53 at this earlier stage. This is a novel notion that has not been presented previously. In our revised version, we propose to further study the mechanism of WWOX action on Myc accessibility.

      The analysis of MSCs lacks detail and needs significant more improvement and assessment by FACS using the well-defined criteria for mesenchymal cells that have been developed

      Response: Thank you for this comment. In our revised version we will add another marker (CD11b), in addition to the used markers (CD45/Sca-1 and CD29) which are all well known to define MSCs as mentioned in Ref. 21, 33, 34.

      It is not clear to me from the available information if the authors have used bone marrow cells that were flushed and immediately transplanted or if all cells transplanted have been placed in culture first, adherent cells expanded in culture media favoring survival and proliferation of non-hematopoietic cells and then transplanted- this is important to clarify explicitly as it is important to the significance of the study. If these cells are all used after culture, then the novelty of these studies are questionable as it was demonstrated previously that similar types of cells give rise to OS when transplanted (PMID: 18697945).

      Response: Thank you for this important point. In the referred reference (PMID: 18697945, ref 34) authors used p53/Rb Cre- stromal cells that were cultured in vitro then infected with Ad-Cre and then injected subcutaneously in immunocompromised mice. In our study, we provide evidence that genetically manipulated young BM-MSCs (for Wwox/p53) are tumorigenic when injected intratibially, a more relevant niche for these cells, and this involved upregulation of Myc. In our revised version, we shall provide more mechanistic insights on the functional relationship of WWOX and Myc. Using BM-MSCs cells that are directly collected from the mouse (Tom+, Sca1+, CD45-) is not feasible due to very low percentage of cells which has been also previously reported by many groups (Ref 34). In our revision plan, we also propose to try performing in vitro tumorigenic assays on sorted cells.

      Moreover, in our study we validated that the deletion of p53 alone at this earlier stage is not enough to induce osteosarcomagenesis (which was not shown previously) suggesting additional hits are required for OS formation. Importantly, co-deletion of Wwox and p53 using the same Cre line resulted in the upregulation/higher levels of Myc that promotes osteosarcomagenesis at this earlier stage.

      • Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

      I think overall the authors are appropriately cautious in interpretation. The points raised above regarding the nature of the cells would need clarification and then the claims reassessed however.

      Response: Thank you for this acknowledgment. According to our results we can conclude that BM-MSCs committed to the osteoblast lineage could be the cell of origin for OS and this will be clearly emphasized in the discussion of our revised version.

      Claims re "metastatic potential" should be significantly reconsidered - the authors present (motility assays) which should be referred to as motility assays. The injection of cells intravenously is a lodgment assay of cells in the venous circulation and does not equate to the process a cancer cell must undergo and survive to metastasis from a primary tumor in an immune competent environment. The claims around these assays should be significantly reconsidered.

      Response: Thank you for this important comment. In our revision plan we will check the lungs of IT injected mice for the presence of lung metastatic nodules (tdTomato positive cells in the lungs).

      • Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

      It is not explained or justified what the control cohort in Fig 7F is significantly smaller than the treated cohort. This will affect the statistical analysis and interpretation. There is no statement regarding blinding/randomization (or not) in the in vivo simvastatin experiment - this needs to be added.

      Response: In our revised version we clarified in the materials and methods section clearly the randomization of the mice selection for each group and number of mice in each group.

      The authors should include discussion that these are relatively long latency OS models compared to p53/pRb compound mutants and contrast with previous data from these models where in vitro cultured cells did give rise to OS in vivo after Cre treatment.

      Response: Thank you for this suggestion. In our revised paper, we will include the latency of OS in our model and compare them to p53/Rb, and emphasize that our model tested the tumorigenic ability of SKO yBM cells and showed that they are unable to form osteosarcoma tumors at this early stage compared to DKO yBM cells.

      • Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

      If a patient could to be treated based on these data, then the extra experiments to provide a robust preclinical dataset should be provided otherwise significant caution should be stated.

      Response: Our study provides a proof-of-concept showing that our model can be used to screen for drugs that could inhibit OS development. The inhibitory potential of SVA affecting the progression of OS for clinical assessment would certainly need further investigation that goes beyond the scope of this paper.

      • Are the data and the methods presented in such a way that they can be reproduced?

      See comments regarding cells used for injection and blinding. Need to more clearly describe the cells being injected and what was done to them from isolation to injection.

      Response: Thank you for this comment. In our revised version we will add clearly in the materials and methods section the protocol of cell isolation and injection, randomization of the mice selection for each group and number of mice in each group.

      • Are the experiments adequately replicated and statistical analysis adequate? Unclear if appropriate experiment completed in Fig 7F as no justification of different sample sizes is provided nor a statement reblinding/randomization.

      Response: Thank you for this point. In our revised version we will add clearly in the materials and methods section the randomization of the mice selection for each group and number of mice in each group.

      Minor comments:

      • Specific experimental issues that are easily addressable.

      The Osx1-Cre expresses a Cre:GFP fusion - the authors should correlate the GFP and tomato signal.

      Response: We thank the reviewer for this point. We shall perform validation analysis in our revised plan.

      Fig 2A- if 50% of the bone marrow is tdTom positive this is not evident in the image in Fig 1D. Quantify the images in Fig 1D. The tumor images in Fig 2C appear to have only sporadic tdTom positive cells - the authors should explain this further.

      Response: Thank you for this notion, Figure 2A represents BM collected from a tumor bearing mouse which has a higher percentage of tomato positive BM cells compared to tumor free mouse (Fig 1D). Fig 2C is updated

      Need statistics added to all GSEA plots.

      Response: Thank you for this comment, statistics will be added in our revised version.

      4C is very different than 2C - 4C is more consistent with the levels of tomato stated

      Response: Thank you for this notion, the images were updated in our revised version

      • Are prior studies referenced appropriately?

      This seems appropriate

      Response: Thank you for this comment.

      • Are the text and figures clear and accurate?

      Figures are not ideal and could be improved in terms of >clarity and text size

      Response: We apologize for the low quality. In our revised version, we shall provide an improved resolution and accuracy.

      Several sections of text are either contradictory or questionably accurate:

      page 3: Molecular studies of OS are significantly hindered by its genetic complexity and chromosomal instability, which precludes the identification of a single recurrent event associated with OS. Contrasts with the following text: Page 18 - p53 has been extensively shown to play a central role in OS development in both human and mouse models. The data from sequencing of human OS and mouse models and canine data all point to p53 loss as being a central event in OS.

      Response: We apologize for this inaccurate statement. In our revised paper, we revised this statement to reflect the common event of p53 deletion in OS and its significance in osteosarcomagenesis.

      Page 18: High genetic heterogeneity and chromosomal instability limit the early diagnosis of OS and lead to lung metastases and a worse prognosis. I don't understand this statement given that intratumor characteristics are not a determinant of early diagnosis - the patient being aware they have an issue and the clinical follow up determine the rate of diagnosis (and access to healthcare).

      Response: We shall revise this statement to reflect the genetic heterogeneity of OS tumors.

      Page 21: Consistent with their roles as tumor suppressor genes, the combined deletion of WWOX and p53 in Osx1-positive progenitor cells resulted in their transformation and growth advantage. Didn't the authors reach this conclusion from their previous work published in 2016? –

      Response: Thank you for this notion. In our previous paper, we provided evidence that WWOX and p53 loss contributes to more aggressive OS tumor formation. In the current study, we provide evidence about early events contributes to osteosarcomagenesis using our Wwox/p53 model.

      Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      improve figure clarity and text sizes

      Response: We thank the reviewer for this notion; in our revised version, we shall provide an improved resolution, accuracy and clarity.

      Reviewer #3 (Significance):

      • Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

      I think this is largely an incremental study which largely confirms previous studies.

      Response: Although our results are consistent with some previously noted observations, we clearly provide unforeseen evidence that links Wwox/p53 in early osteosarcomagenesis and suggest that p53 deletion alone is not enough at this stage; other hits are required as in Wwox/p53 or Rb/p53. The mechanism of how DKO cells (Wwox/p53) results in Myc upregulation is also novel and will be further tested in our revised submission.

      • Place the work in the context of the existing literature (provide references, where appropriate).

      The study is a modest advance over the existing literature.

      Response: We believe that with our revision plan, our paper will provide a significant advancement in the research in osteosarcomagenesis.

      • State what audience might be interested in and influenced by the reported findings.

      This would be relevant to basic sarcoma researchers.

      • Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

      Generated multiple murine models of OS and characterized and applied them for biological understanding and preclinical studies.

    1. Author Response

      Reviewer #1 (Public Review):

      Rosas et al studied the mechanism/s that enabled carbapenems resistance of a Klebsiella isolate, FK688, which was isolated from an infected patient. To identify and characterize this mechanism, they used a combination of multiple methods. They started by sequencing the genome of this strain by a combination of short and long read sequencing. They show that Klebsiella FK688 does not encode a carbapenemase, and thus looked for other mechanisms that can explain this resistance. They discover that both DHA-1 (located on the mega-plasmid) and an inactivation of the porin OmpK36, are required for carbapenem resistance in this strain. By using experimental evolution, it was shown that resistance is lost rapidly in the absence of antibiotics selection, by a deletion in pNAR1 that removed blaDHA-1. Moreover, their results suggested that it is likely that exposure to other antibiotics selected for the acquisition of the mega-plasmid that carries DHA-1, which then enabled this strain to gain resistance to carbapenemase by a single deletion.

      The major strength of this study is the use of various approaches, to tackle an important and interesting problem.

      The conclusions of this paper are mostly well supported by data, but one aspect is not clear enough. The description of the evolutionary experiment is not clear. I could not find a clear description of the names of the evolved populations. However, the authors describe strains B3 and A2, but their source is not clear. The legends of the relevant figure (Figure 5) are confusing. For example, the text describing panel B is not related to the image shown in this panel. Moreover, it is shown in panel C (and written in the main text) that the OmpK36+ evolved populations had only translucent colonies, so what is the source of B3(o)?

      We appreciate the point and in response have added a panel to Figure 5 (in the revised paper this is now Fig. 5A) to illustrate the evolutionary experiment and specify that there are two lineages (A and B) with 20 replicates each that, after 200 generations of evolution, give rise to populations of which A2 and B3 are the exemplars characterized.

      We have corrected the legends in Figure 5.

      We now explain (sentence starting on Line 197) that the B3 (o) is the single isolate of an opaque colony from lineage B3, it is the only colony that we identified from out of 595 colonies observed in the B3 population. B3(o) was sequenced and analysed as a comparator and has some value in that regard, despite being an anomaly.

      Reviewer #2 (Public Review):

      The authors sequenced a clinical pathogen, Klebsiella FK688, and definitively establish the genetic basis of the carbapenem-resistance phenotype of this strain. They also show that the causal mutations confer reduced fitness under laboratory conditions, and that carbapenem sensitivity readily re-evolves in the lab due to the fitness costs associated with the resistance mutations in the clinical isolate. They also establish that subinhibitory concentrations of ceftazidime select for the otherwise deleterious blaDHA-1 gene. Based on this finding the authors speculate that prior beta-lactam selection faced by the ancestors of Klebsiella FK688 potentiated the evolution of the carbapenem-resistance phenotype of this strain. If this hypothesis is true, then prior history of beta-lactam exposure may generally potentiate the evolution of carbapenem resistance.

      Strengths:

      From a technical perspective, the findings in this paper are solid. In addition, the authors establish a simple genetic basis for carbapenem resistance in a clinical strain, which is a valuable and non-trivial finding (i.e. they show that the CRE phenotype in this strain is not an omnigenic trait distributed over hundreds of loci).

      Weaknesses:

      The main weakness of this paper is that the authors draw overly broad conclusions of a conceptual nature from narrow experimental findings. This could be addressed by drawing more modest and narrow implications from the findings.

      1) The title of this paper is "Treatment history shapes the evolution of complex carbapenem-resistant phenotypes in Klebsiella spp." But they provide no data on the treatment history of the patient from whom this strain was isolated from. Therefore, the authors have no evidence to support their central claim. Indeed, it is completely possible that this strain never faced beta-lactam selection in the past, or that the patient's hypothetical history of betalactamase was irrelevant for the evolution of FK688. First, it is completely possible that this is a hospital-acquired infection, such that the history of this strain is due to selection in other contexts in the hospital that have little to do with the patient's treatment history. Second, it is completely possible that this strain (the chromosome anyway) has no prior history of beta-lactamase selection, and that it acquired the megaplasmid containing blaDHA-1 via conjugation from some other strain. In this second hypothetical scenario, it is possible that the fitness cost of the blaDHA-1 gene is not particularly high in a different source strain, but that it has some cost in the FK688 strain that it was isolated from. And of course, fitness costs in the human host could be very different than fitness costs in the laboratory, where strains are evolving under strong selection for fast growth. And given the benefit of resistance, it's clear that this strain clearly has a strong fitness advantage over faster-growing sensitive strains in the context of the source patient under antibiotic treatment.

      My general point here is that the broad claims made about patient history or prior history shaping the evolution of this strain are largely indefensible because there is no data here to make solid inferences about how prior history shaped the evolution of this strain.

      We appreciate the point and have changed our title and scaled back the strength of our conclusions regarding patient treatment history.

      2) Historical contingency. The authors claim that their work shows how historical contingency shapes the evolution of resistance. One problem with this claim is that it is trivial- this is only a significant claim if the reader believes that prior history is not important in the evolution of antibiotic resistance, which is a straw-man null hypothesis, to mix a couple metaphors. To be more concrete, clearly strain background (prior history) matters-eliminating the plasmid with the resistance gene eliminates resistance. But that is not particularly surprising, given the past 50 years of evolutionary microbiology literature on plasmids and resistance. By contrast to this work, the major contribution of papers that examine the role of historical contingency in evolution (i.e. various Lenski papers) is that those works quantitatively measure the role of history in comparison to other factors (chance, adaptation). Since this work is a deep dive into a single clinical isolate, the data presented here do not and cannot shed light on the role of historical contingency in the emergence of this strain. The authors' claims about the prior history that led to the CRE phenotype are reasonable- but are fundamentally speculative. I have nothing against speculation, as long as it is clear what claims are speculative, and what are concrete implications. But the authors frame these speculative claims as concrete implications of their findings.

      This is a fair point. We have reframed the study to not focus on historical contingency.

      As the reviewer points out, any discussion about historical contingency in the context of evolution is trivial in one sense. One of the reasons that the studies of Lenski and Blount provide new insights into the role of historical evolution because they knew the history of their populations (at, least for the number of generations since the LTEE began), and had a high degree of control and understanding of the growth conditions where the trait evolved. As such, they could go back to time points before the trait evolved, and then repeat the evolution experiment many times, in the exact same environment where the trait originally evolved, and then count how often they observed the evolution of that trait.

      Here we study a clinical isolate, and have less understanding of the evolutionary history of our strain. While we cannot re-evolve carbapenem resistant in the exact same environment experienced by the FK688 strain, we did test the capacity for the wild type, and two possible intermediate genotypes genotypes, to evolve carbapenem resistance in growth media with carbapenem.

      Altogether- we have comprehensive evidence for the genetic cause of carbapenem resistance: the BLA1 plasmid + OmpK36. We showed, by experiment, that it is much more likely for carbapenem resistance to evolve in a FK688 strain that carries the BLA1 plasmid, than in an FK688 strain that did not carry the plasmid even if it had acquired the OmpK36 mutation. We think this not trivial because a significant proportion of all of the carbapenem resistant Klebsiella that have been isolated are non-carbapenemase CRE. Our reconstruction provides a plausible explanation for why non-carbapenemase CRE evolve – because they are evolving from strains that have already been treated with a non-carbapenem beta-lactam drug and have thereby selected for the presence of a beta-lactamase (that is not a carbapenemase).

      So, while we have scaled back the strength of our claims, we do think that our results can provide some insight into how the evolutionary history of a pathogen can shape the molecular path to antibiotic resistance.

      3) The authors claim that "[This work] suggests that the strategic combinations of antibiotics could direct the evolution of low-fitness, drug-resistant genotypes". I suppose this is true, but I also think this is a stretch of an implication given these findings. To be blunt, while I suppose it's better to have costly resistance variants that re-evolve sensitivity than to have low-cost high-resistance strains circulating, I think the patient's family would probably disagree that the evolution of a low-fitness drug-resistant genotype was good or strategic in the clinical context, even if better from a public health perspective. Low-fitness drug-resistant strains are just as lethal under clinical antibiotic concentrations!

      Thank you for the comment, we see how this sentence could be seen as too strong a conclusion and have rewritten the last sentence of the DISCUSSION (line 351):

      “These results show how an individual’s treatment history might shape the evolution of AMR, and should be taken into consideration in order to explain the evolution of non-carbapenemase CRE”

      The authors do show the plausibility of their hypothesis/model that prior beta-lactam selection is sufficient to potentiate the evolution of carbapenem-resistance (by the additional ompK loss-of-function mutation). I think those findings are very nice. But the authors undermine their results by extrapolating too far from their data. Hence, I think narrowing the scope of the implications would improve this paper.

      In addition to narrowing the scope of the implications as written, I also would like to add that there may be other ways of framing this paper (other than historical contingency) that may make the significance of this work more apparent to a broader audience. This may be worth considering during the revision process.

      We have taken these suggestions on board and have re-framed the final sentences of the ABSTRACT, INTRODUCTION and DISCUSSION accordingly. Specifically, we have removed reference to historical contingency and instead have reframed our experiments as providing a genetic and evolutionary explanation for an interesting and concerning cause of antibiotic resistance – non-carbapenemase CRE.

    1. But why do the feeble-minded tend so strongly to become delinquent? The answer may be stated in simple terms. Morality depends upon two things: (a) the ability to foresee and to weigh the possible consequences for self and others of different kinds of behavior; and (b) upon the willingness and capacity to exercise self-restraint. That there are many intelligent criminals is due to the fact that (a) may exist without (b). On the other hand, (b) presupposes (a). In other words, not all criminals are feeble-minded, but all feeble-minded are at least potential criminals. That every feeble-minded woman is a potential prostitute would hardly be disputed by any one. Moral judgment, like business judgment, social judgment, or any other kind of higher thought process, is a function of intelligence. Morality cannot flower and fruit if intelligence remains infantile.

      I like this explanation because it gives some insight as to why feeble-minded people become delinquents. I like that it stated not all criminals are feeble-minded because there are some criminals that are very intelligent. The unabomber for example, Ted Kaczynski was a mathematics professor, and retired to later became a bomb maker. He wasn't caught for a good while which shows that he was intelligent. I think this is important to the history of psychology because we now have developed knowledge that feeble-minded people aren't always criminals, but have some potential of becoming a criminal.

    2. Instead of wasting energy in the vain attempt to hold mentally slow and defective children up to a level of progress which is normal to the average child, it will be wiser to take account of the inequalities of children in original endowment and to differentiate the course of study in such a way that each child will be allowed to progress at the rate which is normal to him, whether that rate be rapid or slow.

      I think this would be the best approach to help children that are slower than "normal" children. I stated earlier that the best way to help children learn is to see how they learn such as being hands on, visual, or auditory learning. Everyone doesn't learn stuff at the exact same time, it may take some people longer to understand the concept than other people. Everyone learns differently. This is important to the history of psychology because it helps us understand that children who are slower than other children can develop intelligence at their own speed. I think another reason this is important to the history of psychology is because we now understand the grade of intelligence is different for everyone, but people can build intelligence at their own progressive rate and that we can't force someone who is slower at learning to be at normal speed.

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      Reply to the reviewers

      1. General Statements [optional]

      We thank the reviewers for their comments and very helpful suggestions to improve the manuscript. All the reviewers address that further confirmation of the causality of activity-induced AMPK activation and AMPK-induced mitochondrial fission and mitophagy regulating dendritic outgrowth in immature neurons would strengthen the significance of this study. We believe that this is the first study demonstrating that AMPK mediates activity-dependent dendritic outgrowth of immature neurons, and that regulation of mitophagy is critical for dendrite development.

      We can perform most of the experimentations and corrections requested by the reviewers. We have already made several revisions and are currently working on additional experiments. All experiments will be finished in several weeks and we expect to submit a full revision by the due date.

      2. Description of the planned revisions

      Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

      Reviewer #1-1.- MMP alone is not a good indicator of mitochondrial health. For instance, ATPase inhibitor causes increase in MMP and complex I inhibition diminish MMP and in both cases mitochondrial function is impaired. On the other hand, authors use increased flickering and mitochondrial ROS production as an indicator of enhanced respiration but they could also be used as indicators of mitochondrial dysfunction. Other assays, such as oxygen consumption, are needed to assess the mitochondrial function.

      *Related comments by Reviewer #2-C. In figure 6 it is unclear what is the significance of the TMRM "flickering" parameter quantified and the difference between the control and knockdown condition is small on average. *

      Increase in TMRM flickering and mitochondrial ROS production, which we used as indicators of enhanced mitochondrial respiration, can certainly also be caused by mitochondrial dysfunction. We think it difficult to adopt an oxygen consumption assay in our system, as the transfection efficiency in the primary hippocampal culture is low (~10%). Instead, we plan to assess the mitochondrial function in control and AMPK deficient cells by using an ATP FRET sensor targeted to mitochondria (Mito-ATeam, Imamura et al., PNAS, 2009; Yoshida et al., Methods Mol Biol 2017). Mito-ATeam will be transfected in neurons to compare mitochondrial ATP synthesis in control and AMPK deficient neurons.

      *Reviewer #1-2.- It would be interesting to show a better characterization of the mitophagy flux and to test whether pharmacological or genetic stimulation of mitophagy could revert the effect of AMPK KD on dendritic outgrowth, ultimately linking AMPK, mitophagy and dendritic outgrowth. The latter experiments may be challenging but not impossible, for example see (PMID: 27760312). *

      We understand that it is important to demonstrate more strongly the correlation of the AMPK-induced peripheral fission and subsequent mitophagy of fragmented mitochondria with dendritic outgrowth. We will attempt the suggested experiment to see if induction of autophagy could revert the dendritic hypoplasia by AMPK KD. However, because AMPK deficiency generates elongated mitochondria defective in fission rather than fragmented mitochondria that are failed to undergo mitophagy, we doubt that activating mitophagy will properly remove damaged mitochondria.

      In parallel to the above experiments, we currently analyze if inhibition of mitochondrial fission or mitophagy would phenocopy the hypoplastic dendrites of AMPK-deficient neurons, and if the activation of fission would rescue the phenotypes of AMPK KD, to strengthen the causality of AMPK-dependent fission, autophagy and dendrite outgrowth. So far we have observed that inhibition of mitochondrial fission by MFF knockdown or inhibition of autophagy by bafilomycin treatment strongly suppress dendrite outgrowth. MFF knockdown also leads to the elongation of mitochondria with decreased association of p62-puncta, strikingly reminiscent of AMPK-deficient neurons. Please see attached figures. Completed analyses will be included in the full revision.

      *Reviewer #1-4.- Results clearly indicate that AMPK enhances mitochondrial fission, and that AMPK is necessary for proper dendritic outgrowth. However, as indicated, the role of AMPK-dependent mitochondrial fission in promoting dendritic growth is not well demonstrated. A possible, and not very difficult experiment, would be the expression of non-phosphorylable MFF S155/172 mutant (perhaps is also needed to knock down the endogenous MFF). Use of this mutant would abolish AMPK-dependent mitochondrial fission while preserving its other functions. *

      Related comments by Reviewer #3-3. The authors could further confirm the claim by examining how mutations in Mff and ULK2 which cannot be phosphorylated by AMPK can rescue defects in mitochondrial fission and spine density.

      We will examine if the expression of non-phosphorylable MFF S155/172 mutant would cause defective autophagy and dendritic arbor growth similarly to AMPK KD neurons. In addition, we will test whether MFF S155/172 mutant would inhibit activity-induced mitochondrial fission to strengthen the link between activity-AMPK-MFF-autophagy axis and dendritic outgrowth.

      *Reviewer #1-Minor 2.- It is intriguing that as shown in Fig. 2A, rather than an increase in pAMPK/AMPK at DIV5 seems there is less phosphorylation despite FRET analysis indicate more AMPK activation. On the other hand, most of the blots in Fig. 6 seem to be overexposed. *

      The exposure time of WB in Fig. 2A was adjusted so that all lanes can be compared. We will fix the exposure time.

      Reviewer#2-A. Most of the evidence on the role of AMPKa2 relies on a shRNA-based strategy. The authors have performed this approach with the best practice, including selecting 2 shRNA plasmids for each gene, and performing a rescue experiment with shRNA -resistant cDNA. Yet, it is critical to provide stronger evidence with all the tools available to demonstrate the role of AMPKa2 in dendritic development. This is especially important because the effect reported by the authors is a transient effect: indeed, dendritic development appears abnormal in very young neurons (P5) but largely normal afterwards (P10). Hence one cannot discard a non specific effect on cell viability or sampling effect. The number of neurons counted is fairly low (about 30 neurons per condition) and it is not clear if they come from several independent cultures. It is known that plasmid preparation can impact cell viability and performing the experiment with only one batch of plasmid prep could explain why one plasmid would produce a short-lived effect on cell morphology. Two shRNA constructs are presented in figure S2A but only one is used for morphological experiments quantified in S2D-E with again a very low N number. The specific experiments I would recommend would be to increase the N: at least 25-30 neurons counted per culture, 3 independent cultures, and presenting the results of the two shRNA plasmids for both AMPKa1 and AMPKa2. Furthermore, the immunofluorescence validation of knockdown provided in figure S2B is not really convincing, a nuclear marker is lacking to determine where cells are (it seems that many cells are present in the image, maybe some of them with low AMPKa2 expression as well). A quantification should be provided as well as evidence for shRNA #1 and #2. *

      *

      We thank the reviewer for valuable suggestions to improve our manuscript. All the knockdown analyses were done from three independent experiments using different mouse litters and multiple batches of plasmid prep. N number was low because of a low transfection efficiency in the primary culture. We will repeat experiments and increase the sample number. We will also present results of the two shRNA constructs. We will redo the immunofluorescence for validation of shRNA knockdown and replace Extended Data Fig. 2B which was pointed out as not being clear.

      *Reviewer #2-C. The observation, in vivo, that dendritic development is normal at P10 is intriguing but this reconciles the observation of altered dendritic development with previous studies demonstrating that AMPK knockout has little effect on brain development, as well as previous studies (Mairet-Coello et al. Neuron 2013, Lee et al. Nat commun 2022) targeting AMPKa2 in the hippocampus of AD mouse models by in utero electroporation. This is a critical aspect of the paper and as stated in the discussion, the previous studies only looked at the end product (neuronal morphology appears normal after development) but not the process of neuronal development and maturation. The in vitro experiment offer the possibility to study dendritic development over time in the same population of neurons, either through selected time points, or through time lapse imaging. This would strengthen one of the most original aspect of this work. *

      We thank the reviewer for an important suggestion. We will analyze if dendritic morphology and mitochondria would recover in later stages in culture. However, the dendritic growth defects in AMPK KD neurons are apparently more severe in culture and our preliminary results have shown that dendritic growth defects and mitochondrial elongation persist until 10DIV. We anticipate that AMPK deficiency is complemented by certain compensation mechanisms in vivo that are not present in culture, such as chemical signals or synaptic inputs from correct afferents. We will confirm the recovery of dendritic outgrowth in vivo using an AMPK alpha2 knockout mouse. We will include the results in vivo and in vitro in the revised manuscript.

      * The authors use a FRET probe to witness AMPK activity, and this part raises a lot of questions. A lot of the signal matches the regularly spaced activity peaks suggesting that FRET response is a coincidence detector of calcium waves. Hence, is the FRET signal influenced by intracellular calcium concentration, or changes in pH? To address this question, the proper control would be to use a FRET biosensor with a mutated AMPK phosphorylation site and demonstrating the absence of response to calcium waves. *

      We think it unlikely that the FRET probe detects calcium concentration or pH change, as its kinetics and timing are different from calcium spikes. For confirmation, we will examine a FRET probe lacking phosphorylation sites to negate that calcium waves directly activate the FRET probe.

      * Also, the parameter used for quantification is a so-called "number of FRET peaks over 3 minutes" for which the biological significance is unknown. On average there are 1-2 such "peaks" in control conditions (figure 4). These peaks have low amplitude, sometimes around 0.05-0.1 of the YFP/CFP ratio, which is about what is expected even in AMPKa2 knockdown cells (figure S4C). Are there changes in the baseline of FRET signal? *

      We monitor FRET at 3-5 sec intervals and is set to 3 minutes due to gradual photobleaching. Although the event frequency is 0-4 times per 3 minutes observation, it is nearly absent in AMPK KD (1 small peak in 3 cells out of 40 cells) or activity deprivation, which we consider a significant difference. We have replaced Figure 4B, 4D, 4I, 4J andExtended Data Fig.4E. The basal FRET signal is lowered in AMPK KD cells, but also varies depending on the expression level of the probe. For comparision of the results shown in Figure 4 and Extended Data Fig.4, we have changed the y-axis to the normalized FRET signal {FRET/FRETbaseline} and jRGECO signals (DF/F0) in Fig. 4F, Extended Data Fig.4C, 4D.

      *

      *

      *Finally, given that calcium peaks and AMPK activity peaks overlap, one key observation is the continued presence of calcium peaks upon AMPKa2 knockdown in figure S4D. Yet, the scale for jRGECO1 intensity in figure S4D differs from the scale in figure 4, making it difficult to interpret. It seems that on average the delta (peak-baseline) is 2000 in wild-type cells (figure 4), compared to 500 in AMPKa2 knockdown cells, which suggests a strong reduction in calcium signal amplitude upon knockdown of AMPK. This should be clarified to demonstrate that the FRET probe peaks are really due to AMPK activity. Also, the effect of STO-609 should be added to this figure. *

      We think that the presence of calcium transients in AMPK KD cells supports our conclusion that AMPK is downstream of calcium signaling. The amplitude of calcium spikes was actually lowered in AMPK KD cells. We think it is due to the reduction of the cell size and complexity in KD cells. To negate that AMPK inhibition affects calcium influx, we will examine if acute inhibition by an AMPK inhibitor will suppress only FRET signals but not calcium waves. In addition, we will monitor calcium waves and FRET signals in neurons treated with STO-609 or AICAR. STO-609 and AICAR should decrease and increase FRET signals without affecting calcium influx.

      • Other comments by Reviewer #2*
      • Similarly, the number of events in figure 5F-G is really low. Is a difference between 0.02 in the control group and 0.01 in the knockdown group physiologically relevant?*

      Since p62 puncta contact only a small mitochondrial region, the overlap area of mitochondria with p62 in the total mitochondrial area is small. We will analyze the number of p62 puncta associated with mitochondria per unit dendritic area.

        • Lines 339-350, the authors discuss about a putative regulatory loop involving AMPK dephosphorylation. Since this part of the discussion is based on the FRET signal, the authors should consider if an alternative explanation could be the kinetics of the biosensor dephosphorylation.* We will revise Discussion to argue about alternative possibilities of dynamic oscillation of the FRET signal when we get data from the above experiments.

      *In terms of significance, I would have two major criticisms. The first is that it appears that many of the findings by the authors are redundant with observations of the roles of CAMKK2-AMPK-MFF-ULK1 in AD model mice, see for example the work by Polleux (Mairet-Coello et al. Neuron 2013, Lee et al., Nat commun 2022). As said above, my opinion is that the paper should put more emphasis on the transient effect of AMPK, which would be a novel observation and, as the authors rightfully discuss, a phenotype potentially overlooked in previous studies of AMPK KO mice. The second is that many points in the discussion seem to be over reached and are not entirely supported by the data. As an example lines 298-299 "leading to mitochondrial dysfunction with low respiratory activity" (not addressed in this manuscript), lines 312-313 "multiple signatures of mitochondrial dysfunction such as reduced delta-Psi-m and ROS production" (biological significance of these parameters?), lines 332-334 "AMPK phosphorylation dynamically oscillates in dendrites, depending on Ca2+ influx and CAMKK2 activity, while it is independent of LKB1" (the authors don't study AMPK phosphorylation, and the experimental data has many limitations that need addressing), etc. *

      We thank reviewer’s guidance. We think this is the first study showing AMPK function in dendritic arbor growth in immature neurons before synaptogenesis. We will rewrite the manuscript to emphasize that neuronal activity in immature neurons regulates dendrite formation via AMPK in a short time window during brain development. Discussion will be revised according to the data of the ongoing additional experiments.

      Reviewer#3-1. All these studies are done in invitro neuronal culture modal with transfection of ShRNAs to Knockdown AMPK. An alternative possibility is that authors could use an AMPK Conditional Knockout mouse models Conditional deletion of (AMPKα1/α2 (AMPKα1−/−; AMPKα2F/F; Emx1-Cre) derived neurons for this study.

      We showed the effect of AMPK knockdown in hippocampal neurons in culture and in vivo (Fig. 2). For validation, we also examined CRISPR interference (Extended data Fig.2). We will examine in vivo phenotypes in pyramidal neurons in AMPK alpha2 knockout mice to further validate our observation.

      Description of the revisions that have already been incorporated in the transferred manuscript

      Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

      *Reviewer #1-1.- Authors use mitochondrial membrane potential (MMP), MMP flickering and mitochondrial ROS production as indicators of mitochondrial function, but this is not convincing. To analyze MMP, authors use TMRM fluorescence normalized by mitochondria area. This is not correct, using this strategy would mean that a symmetric fission would instantly double MMP and fusion would half MMP. The analysis must be made by tracing ROIs of the same surface in different mitochondria and determining TMRM fluorescence in these ROIs. *

      We have reanalyzed TMRM fluorescence using the method indicated. As a result, TMRM fluorescence show a slight but significant decrease (p=0.0071) in AMPK KD cells. Extended Data Fig. 5C has been replaced accordingly. We thank the Reviewer for kind guidance!

      Reviewer #1-3.- The authors treat neurons with glutamate to support the view that synaptic activity activates AMPK and promotes mitochondrial fission. However, the concentration used (100 mM) may be excitotoxic. Synaptic activity can be induced by electric field stimulation, although this require equipment that may not be available in the authors' lab. Another alternative is network disinhibition with bicuculine or to use lower concentrations of glutamate. In any case, since neurons are immature and may respond differently from mature neurons, it would be worth to verify synaptic activity by analyzing Ca2+ transients.

      *Reviewer #1-Minor 3.- It is necessary more explanation about spontaneous Ca2+ transients in immature cultures. What percentage of neuros experience it? Is it synchronized? *

      *Related comments by Reviewer#2-D. It is well established and thus not surprising that AMPK activity increases in response to synaptic activity. It is more surprising to witness such an effect of activity in very immature neurons, where presumably synapses are sparse and not well developed. For example dendritic segments in Figure 1E and 3A don't have dendritic spines. Western-blot and/or immunofluorescence of synaptic markers with comparison to fully mature neurons would complete figure 1 and make the case whether the reported effects are marginal or a strong driver for dendritic development and AMPK regulation. *

      We thank the reviewers’ point that we failed to emphasize in the original manuscript.

      We focus on AMPK function during activity-dependent dendritic outgrowth in immature neurons before the onset of synaptogenesis. It has been shown that synaptogenesis occurs in dissociated hippocampal cultures between 7-12 DIV (eg, Renger et al., Neuron 2001) and that developing dendrites at 5 DIV are activated by ambient glutamate which is spontaneously released from nearby immature axon terminals and undergo spontaneous Ca2+ transients, and this non-synaptic activity is important for dendritic outgrowth (Andreae and Burrone, Cell Rep 2015). We have observed that Ca2+ transients in individual neurons are variable in frequency and magnitude and are not synchronized in consistent with previous studies. We have performed immunofluorescence with a synaptic marker PSD95 and confirmed that dendritic spines are not yet differentiated and PSD95 is sparsely distributed along the dendritic shaft in DIV5 hippocampal neurons. We describe the nature of Ca2+ transients in the Results more clearly and provide high magnified images and immunofluorescence with a synaptic marker PSD95 of the neurons at DIV5 and DIV13 as a new Fig. 1A. We believe that this is the first indication of AMPK function in non-synaptic neuronal activity during dendritogenesis.

      We have observed induction of mitochondrial fission in neurons treated with 1 µM glutamate. Extended Data Fig. 1E has been replaced accordingly. Since GABA is known to induce depolarization in immature neurons (Soriano et al., PNAS 2008), we would like to exclude bicuculine treatment from this analysis.

      *Reviewer #1-5.- The statistical analysis seems appropriate, but it is confusing that sometimes non-parametric and sometimes parametric tests are used. It is not indicated which test is used to determine normality since the methods section lacks a statistical analysis section.

      *

      We have revised Methods and have described statistical analysis in detail.

      *Reviewer #1-Minor 1.- Authors should double check the analysis shown in Fig. 1A. As it is shown, Ca2+ transients are 2-3% higher than basal, when the video shown in video 1 seem to indicate much more. *

      Thank you for pointing this out. In the original version, the percentile change was erroneously measured across the entire visual field, including areas without neurons. We have replaced Fig. 1B (original Fig. 1A) with reanalyzed data in the proximal region of the apical dendrite.

      *Reviewer #1-Minor 4.- It is interesting that AMPK KD in vivo impairs dendritic architecture at P5, however at P10 the defect seem to be somehow compensated. This result apparently detracts from the relevance of the findings, however last year was published a paper in which in an animal model of Huntington's disease dendritic architecture is delayed during the first week but normalizes thereafter. Despite later normalization in dendritic architecture, this early defect in maturation has effects in adulthood as pharmacological restoration of arborization during the neonatal period suppresses some phenotypes observed in adulthood (PMID: 36137051). I believe that discussing this paper would help the reader to recognize the potential relevance of the findings. *

      *Related comments by Reviewer #2: Nonetheless let aside the technical concern, if their findings hold true, this is an intriguing mechanism. There are interesting parallels to be made with observations of altered morphology and excitability of neurons in Huntington's disease model mice during the first postnatal week. These changes spontaneously reverts and are undetectable in the second week (Braz et al. Science 2022). Thus, precedent suggests that indeed dendritic development can take a slow course, and this study also suggests that this is important later since normalization of abnormal excitability during the first week in HTT mice prevents some of the phenotypes later in life. Here again, an interesting parallel could be made with the known role of AMPK in synaptic loss in AD models. *

      We thank the reviewers for the supportive comments. We will refer this paper and discuss about potential significance of the transient defects in early dendrite morphology in AMPK deficient neurons.

      *Reviewer#2-B. The Crispr method lacks validation which should be provided somehow. The drug-based experiment relies on compound C, a notoriously non specific AMPK inhibitor (see for example Bain et al. Biochem J 2007, or Vogt et al. Cell Signal 2011). Data obtained with Compound C is hard to interpret given the number of kinases that are affected by the drug and should be removed from the manuscript. *

      We have added immunofluorescence images for validation of AMPK deletion by CRISPRi (Extended Data Fig. 2F).

      We think the results of Compound C treatment support our conclusion in combination with KD and CRISPRi, but will delete the results in accordance with this comment.

      • Other comments by Reviewer#2*
      • Figure 5A-C relies on the quantification of fission events that appear very rare (0.4 event per 20 minutes). The difference between the two groups is between 0.1 and 0.2 events on average. Since this was quantified on a fairly low number of cells (N=14), it is hard to appreciate exactly how many events have been observed and the actual physiological relevance. Furthermore individual datapoints should be added to the figure to estimate variability.*

      The number of fission events was counted in mitochondria in a unit length of dendrite of similar diameter, and normalized by the number of mitochondria. The values were thus small as they represent average number of events in one mitochondrion in 20 minutes. We have replaced the Fig. 1K, 3F, 5B and 5C to show the number of fission events in mitochondria included in a unit length of dendrites of similar diameter. Individual data points have been included.

      Reviewer #3-4. Authors showed activity-dependent calcium signaling controls mitochondrial homeostasis and dendritic outgrowth via AMP-activated protein kinase (AMPK) in developing hippocampal neurons do the cortical mitochondria respond the same way as the hippocampal neurons?

      Thank you for the comment. As pyramidal neurons in the cerebral cortex and hippocampus are basically the same origin, it is likely that they share the same signaling. We use hippocampal neurons in this study to perform quantitative analysis of dendritic morphology in the same type of neurons. Primary cultures of cortical neurons contain multiple different cell types, making it difficult to analyze the same cell type.

      4. Description of analyses that authors prefer not to carry out

      Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

      *Reviewer #1: If activity is observed in only a portion of the neurons, taking advantage of the stablished long-term live imaging protocol in the authors' lab, it would be interesting to study in the same culture whether neurons that experience spontaneous activity develop more than those that do not. *

      We prefer not to carry out this analysis, as activity-dependent dendritic growth has already been well described in previous papers. It will take considerable time to observe the number of neurons for analysis of correlation between Ca2+ transients and dendrite morphology. We would like to focus our effort to demonstrate AMPK signaling during activity-dependent dendritic growth.

      Reviewer#3-2. Another technical issue here, most of the experiments are carried out on Neurobasal media, which has a lot of glucose plus substitution of glutamax might be not the perfect conditions for AMPK. Authors could not obtain evidence supporting the regulation of mitochondria biogenesis by PGC1α phosphorylation and expression. This surprise me, if you could reduce the glucose concentration if might change.

      We observed little or no changes in phosphorylation of PGC1alpha by enhancing or suppressing neuronal activity or AMPK activity. As mitochondrial biogenesis is very active in growing neurons, we surmise that PGC1alpha and mitochondrial biogenesis is regulated by multiple mechanisms during neuronal differentiation and AMPK activation/inhibition might not induce visible changes. We agree the reviewer that there is room to seek the conditions under which changes in PGC1alpha can be detected, but we do not see why Neurobasal plus glutamax is not suitable for this study. Multiple papers studying AMPK function in cultured neurons use similar culture media (Sample et al., Mol. Cell. Biol., 2015; Muraleedharan et al., Cell. Rep., 2020; Lee et al., Nat. Commun., 2022). We might see PGC1 phosphorylation by glucose deprivation, as it decreases glycolysis-derived ATP and thereby activates AMPK. Since we focus on AMPK activation by calcium signals, we are afraid that it would be difficult to distinguish AMPK activation by ATP deficiency or calcium signaling in glucose deficient condition. In addition, glucose deprivation would affect neuronal activity (which consumes large amount of ATP) and neuronal differentiation including dendritic outgrowth.

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      Reply to the reviewers

      Point-by-point response to reviewer comments

      General statement

      Several studies have previously demonstrated functional links between the death receptors (DR) TRAIL-R1/2 and the Unfolded protein response (UPR). In this manuscript, we describe the previously unrecognized IRE1-dependent dual regulation of the expression of another DR, CD95, and CD95L-induced cell death. Our work therefore adds to the current knowledge on the functional links existing between UPR and DR signaling and provides novel mechanistic insights on a dual regulation involving both transcriptional and post-transcriptional control of the expression of CD95 mRNA expression by IRE1. To demonstrate this, we have used both genetic (overexpression of XBP1s or dominant-negative forms of IRE1) and pharmacologic (IRE1 RNase inhibitor) approaches and cellular models of glioblastoma (GB) and triple-negative breast cancer (TNBC). We show that IRE1 RNase activity promotes CD95 expression and CD95-mediated cell death via the transcription factor XBP1s whilst IRE1 RNase limits CD95 expression and cell death via its ability to cleave RNAs (through RIDD, for Regulated IRE1-dependent decay of RNAs, activity). Furthermore, we report that IRE1-mediated control of CD95 expression is active in vivo, using a model of CD95-mediated fulminant hepatitis in mice. Lastly, we correlate these results to the pathology by showing that CD95 expression is decreased in RIDD high or XBP1s low human GB and TNBC tumors.

      We thank the reviewers for their fair assessment of our manuscript and for their insightful comments. Below, we describe the experiments we plan to carry out to address the reviewers’ comments.

      Reviewer #1 (Evidence, reproducibility and clarity (Required)): Summary: Here the authors argue that IRE1 activation has opposite effects on Fas/CD95 expression/stability in a number of contexts, via either RIDD-dependent degradation of Fas mRNA or XBP-1-mediated induction of Fas expression, which led to either increased or decreased sensitivity to Fas-induced apoptosis in a number of settings. Major issues: The study is somewhat preliminary and inconclusive in that it is not clear why the RIDD function of XBP-1 appears to predominate in vitro in the cell lines examined, leading to modest increases in Fas expression levels (Figure 1) when IRE1 DN versus IRE1 WT constructs are overexpressed, which is at odds with the latter part of the paper which suggests that inhibition of RIDD led to reduced Fas expression levels. However, this could be due to supraphysiological levels of IRE1 being expressed under overexpression conditions, leading to confounding results. Similarly, when XBP-1s is overexpressed in vitro (Figure 5) the modest increases in CD95/Fas expression and sensitization to Fas-induced cell death may not be fully representative of what would be observed at physiological levels of XBP-1s activation. The in vivo results obtained using an IRE1 RNase inhibitor (MKC8866) contradict the earlier part of the study (as Fas levels decreased and there was protection from Fas-induced liver toxicity) and this could be due to a multitude of reasons. There is no doubt that impacting on IRE1 activity has interesting effects on CD95/Fas expression, which can be up- or -down-regulated, with consequences for cell death induced via engagement of the latter receptor, however, the manuscript does not offer a lot of clarity on which outcome is the predominant one in the context of engagement of the UPR. I have the following suggestions for improvement.

      We thank the reviewer for this overall positive assessment.

      1. The authors should induce ER stress using Thaps, Brefeldin A and Tunicamycin, and explore the effects of doing this on Fas expression levels in the context of silencing endogenous IRE1, XBP-1 and PERK.

      We do agree with this reviewer that the proposed experiments might further highlight which of the IRE1-dependent control of CD95 expression dominates upon ER stress induction. Therefore, we will perform the requested experiment in the various cell lines already used in the manuscript.

      We propose to evaluate the expression of CD95 (at the mRNA and total protein levels) under ER stress induction (by different ER stressors) upon knock-down of IRE1 or XBP1. Other DRs (TRAIL-R1 and 2) have been shown to be induced by PERK activation and it is also demonstrated that PERK and IRE1 signaling pathways coregulate each other. As such, we also propose to assess whether PERK could also control CD95 expression in this setting.

      1. The authors should explore the effects of silencing of IRE1, XBP-1 and PERK on constitutive Fas expression and the outcome of Fas/CD95-induced apoptosis in cells not experiencing an overt activation of the UPR (i.e. in the absence of Thaps, Brefeldin A or other UPR inducer).

      We thank the reviewer for their suggestion and will perform the requested experiments as proposed.

      1. The specificity of MKC8866 at the concentration used (30 uM) is unclear. What effect does MKCC have on sensitivity towards Fas-induced apoptosis, similar to the type of experimental set up presented in Figure 5A, 5B?

      Regarding the specificity of MKC8866, this drug has been optimized and refined from a family of IRE1-specific endoribonuclease inhibitors initially obtained from a chemical library screen [1-3]. This salicylaldehyde analog has already shown to be effective in multiple cancer models including breast [4, 5] and prostate [2] cancers. We have recently demonstrated its efficacy in a GB mouse model [6]. It is therefore a widely used IRE1 inhibitor, including in the dose range 10-30 mM used in this study (e.g [4, 5]). We therefore do not think it is in the scope of this manuscript to re-assess it specificity. However, we will aim at testing an additional IRE1 inhibitor to assess whether similar effects can be observed on CD95 expression in cells. To do so, we propose to use a novel IRE1 kinase inhibitor developed in the laboratory (DOI: 10.26434/chemrxiv-2022-2ld35 – Accepted iScience) and shown to efficiently blunt IRE1 activity in GB. As also suggested by the reviewer, we will assess whether the use of MKC-8866 can affect CD95L-induced cell death in cell lines.

      1. Similarly, what effects does MKC8866 (at 30 uM) have on key Fas pathway determinants, such as Fas, FLIPL, FLIPs, Caspase-8, FADD, RIPK1, A20, CYLD, cIAP-1, cIAP-2 and Bid? There are many points at which MKC8866 could influence the outcome of Fas receptor engagement beyond the receptor itself.

      In the present manuscript, we have shown that MKC-8866 reduces CD95 expression in mouse liver (IHC depicted in Figure 4B and S3B) in vivo and that, when used at 30 mM in vitro, it prevents the loss of CD95 expression induced by tunicamycin or thapsigargin in U87 cells (Fig 1C-F). We do agree with the reviewer that IRE1 may impact CD95-induced cell death beyond modulating CD95 expression, as also already discussed in the present manuscript. Therefore, and as suggested, we will assess whether MKC-8866, used at 30 mM, also impacts on the basal cellular expression of the various components of CD95 signaling mentioned by this reviewer.

      Minor issues:

      1. For the Fas mRNA cleavage experiments presented in Figure 2, there are no irrelevant control mRNAs to allow the reader to judge whether the effects presented are specific to Fas mRNA or are commonly observed for many mRNAs at these amounts of IRE1 (1 ug, 0.5 ug, which appear high).

      The expression of Fas mRNA was already normalized to GAPDH (which does not seem to vary upon incubation with IRE1). We nevertheless will test the expression of additional “irrelevant” RNAs as suggested by the reviewer.

      Reviewer #1 (Significance (Required)): General assessment: this is an interesting study, as there is little knowledge currently concerning how the UPR influences Fas expression or Fas-dependent outcomes. However, the impact of this work is limited by the overexpression approaches used, which could produce artifactual results, as well as the contradictory message of the study.

      Although we think that the message of the manuscript is indeed complex, the work presented herein does not rely exclusively on overexpression approaches as our genetic-based results are also comforted by the use of pharmacologic inhibitors of IRE1.

      Advance: the advance reported here is relatively modest and limited in scope due to the inconclusive nature of the data presented.

      Audience: this study will be of interests to specialists in the UPR and cell death communities.

      We thank the reviewer for acknowledging the overall novelty of our work. We do hope that the experiments proposed will address her/his remaining concerns.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      The authors address here for the first time the connection between CD95, which is known as Fas, and ER stress. The role of another DR, TRAIL-R2 has been already reported, but this is the first study uncovering the link between Cd95 system and ER stress. The study is performed on the high level and supported by all necessary controls. They find the connection between IRE1 and CD95 and show that it might play a role in Cd95 signaling and attenuate CD95-mediated cell death.

      Further, the correlation between CD95 expression and IRE is found in tumors. Importantly the authors find out the connection between XBP1 and CD95 expression, which was not reported to date. Hence, it is a very important and highly essential piece of research.

      We thank the reviewer for these very positive comments and the acknowledging of the novelty and importance of our study.

      However, I would like to clarify the several issues:

      1: Figure 1. Tunicamycin obviously leads to deglycosylation of CD95, which is indicated by the appearance of 35 Kda band. This should be highlighted and commented.

      We agree, this will be commented on in the text.

      1. Figure 2c, d. The piece of mRNA structure, which is synthesized, might have the different secondary structure and might be not cleaved by IRE, accordingly. More detailed comments have to be provided in this regard.

      The model depicted in Figure 2B is a predicted computational secondary structure of CD95 mRNA. In the experiments performed in Figure 2A, C and D the mRNA was extracted from U87 cells prior to incubation with recombinant IRE1 and the resulting products analyzed using RT-qPCR with primers flanking different portions of the CD95 mRNA sequence. For Figures 2C and D, the primers used flank the two sites which were predicted to be cleaved by IRE1 based on previous work from our lab [7]. Even though we cannot exclude that additional sites can be targeted beyond these two, the fact that the amplification of CD95 sequence is reduced in samples pre-incubated with recombinant IRE1 strongly suggests that IRE1 is indeed able to cleave CD95 mRNA in these regions in vitro. We will modify the main text to further explain this point.

      1. Figure 3. Caspase-8-3 western blots show beautiful effects but did authors see some effects further downstream, eg on PARP1 cleavage? Was cell death (not viability) measured as well? Can you comment on this?

      This is absolutely right, we will test PARP-1 cleavage in this setting as suggested. Given the morphology of the cells we observed in the viability experiments, we would expect a similar trend using cell death assays. However, we do agree with the reviewer that this should be proven experimentally, so we will perform these experiments again using cell death assays as a read out.

      1. Did the authors looked at the DISC assembly? Did they detect some differences there?

      No, we did not. We would expect some difference given the impact we have observed on CD95 expression, caspase-8 activation and cell death of expressing dominant negative forms of IRE1, but this of course needs to be actually tested. We are in the process of optimizing CD95 DISC experiments in our lab and we therefore hope to be able to address this reviewer’s comment in a revised version of the manuscript.

      Reviewer #2 (Significance (Required)):

      This is an excellent study. The authors address here for the first time the connection between CD95, which is known as Fas, and ER stress. The role of another DR, TRAIL-R2 has been already reported, but this is the first study uncovering the link between Cd95 system and ER stress. The study is performed on the high level and supported by all necessary controls. This is an important advance for the death receptor field.

      Thank you again for these very positive comments and your insightful appreciation of our work.

      References

      1. Volkmann, K., Lucas, J. L., Vuga, D., Wang, X., Brumm, D., Stiles, C., Kriebel, D., Der-Sarkissian, A., Krishnan, K., Schweitzer, C., Liu, Z., Malyankar, U. M., Chiovitti, D., Canny, M., Durocher, D., Sicheri, F. & Patterson, J. B. (2011) Potent and selective inhibitors of the inositol-requiring enzyme 1 endoribonuclease, J Biol Chem. 286, 12743-55.
      2. Sheng, X., Nenseth, H. Z., Qu, S., Kuzu, O. F., Frahnow, T., Simon, L., Greene, S., Zeng, Q., Fazli, L., Rennie, P. S., Mills, I. G., Danielsen, H., Theis, F., Patterson, J. B., Jin, Y. & Saatcioglu, F. (2019) IRE1α-XBP1s pathway promotes prostate cancer by activating c-MYC signaling, Nat Commun. 10, 323.
      3. Langlais, T., Pelizzari-Raymundo, D., Mahdizadeh, S. J., Gouault, N., Carreaux, F., Chevet, E., Eriksson, L. A. & Guillory, X. (2021) Structural and molecular bases to IRE1 activity modulation, Biochem J. 478, 2953-2975.
      4. Logue, S. E., McGrath, E. P., Cleary, P., Greene, S., Mnich, K., Almanza, A., Chevet, E., Dwyer, R. M., Oommen, A., Legembre, P., Godey, F., Madden, E. C., Leuzzi, B., Obacz, J., Zeng, Q., Patterson, J. B., Jager, R., Gorman, A. M. & Samali, A. (2018) Inhibition of IRE1 RNase activity modulates the tumor cell secretome and enhances response to chemotherapy, Nat Commun. 9, 3267.
      5. Almanza, A., Mnich, K., Blomme, A., Robinson, C. M., Rodriguez-Blanco, G., Kierszniowska, S., McGrath, E. P., Le Gallo, M., Pilalis, E., Swinnen, J. V., Chatziioannou, A., Chevet, E., Gorman, A. M. & Samali, A. (2022) Regulated IRE1α-dependent decay (RIDD)-mediated reprograming of lipid metabolism in cancer, Nat Commun. 13, 2493.
      6. Le Reste, P. J., Pineau, R., Voutetakis, K., Samal, J., Jégou, G., Lhomond, S., Gorman, A. M., Samali, A., Patterson, J. B., Zeng, Q., Pandit, A., Aubry, M., Soriano, N., Etcheverry, A., Chatziioannou, A., Mosser, J., Avril, T. & Chevet, E. (2020) Local intracerebral Inhibition of IRE1 by MKC8866 sensitizes glioblastoma to irradiation/chemotherapy in vivo, 841296.
      7. Voutetakis, K. D., D.; Vlachavas, E-I., Leonidas, DD.; Chevet, E.; Chatzioannou, A. (In preparation) RNA sequence motif and structure in IRE1-mediated cleavage.
    1. Reviewer #2 (Public Review):

      The authors' manuscript has several strengths. First, the authors consider multiple relevant levels of biology including genomics, transcriptomics, structural and functional neuroimaging, cognitive neuroscience, and psychological/environmental factors. Such an approach is often necessary to deconvolute the complexities of psychiatric phenotypes. The authors have taken careful steps to think about potential confounds (e.g., ancestry for PRS) and to try to define their phenotypes (e.g., psychological resilience and biological aging) as best as they can, given the data they have access to from the ABCD study. The manuscript is well written overall.

      My main concerns relate to core assumptions and techniques that underlie the premise of the study. First, while there is comorbidity between AD and MDD, a causal relationship between the two (in either direction) is not established. Though MDD often predates AD, this is to be expected given MDD's high lifetime prevalence (15-20% of the general population) and typical age of onset before age 65. Because AD typically presents late in life (>65 years of age), MDD will, by definition, usually predate AD. While new onset, late life MDD is often the first presenting symptom of AD/Parkinson's disease and other neurodegenerative conditions, it is also not clear that this is the same disorder as idiopathic MDD.

      To this point, two genetic tools can help us determine the biological relationship between MDD/AD, genetic correlation and Mendelian Randomization. Using the data from the MDD PRS used in this analysis, the Supplementary Table 3 from the Howard et al. 2019 paper (https://doi.org/10.1038/s41593-018-0326-7) reveals a genetic correlation of -0.041 between the two. This indicates essentially no strong relationship between the MDD/AD (perhaps even a slightly inverse relationship). Mendelian Randomization studies in addition to the Howard et al paper (https://doi.org/10.1212/WNL.0000000000010463) find no causal role for MDD towards AD and vice versa. Thus, their comorbidity is likely mediated by additional factors. Additionally, while stress contributes to AD pathophysiology, AD is strongly genetic and, given its late onset, it is unclear how genetic risk for AD would meaningfully impact the psychological resilience of a 9 to 10-year-old.

      My second concern is regarding the statement "adolescents at genetic risk for AD/MDD" when describing the sample. Per Howard et al 2019 out-of-sample prediction testing, the MDD PRS used by the authors explains between 1.5-3.2% of the phenotypic variance in MDD when used on a sample such as ABCD. MDD PRS is in its infancy and cannot reliably be used to identify individuals at high risk of MDD given that even individuals in the top 10th percentile of MDD PRS have an odds ratio for depression of only ~2.4. We would expect 90 or so individuals in this cohort to fall into this group leaving significant concerns about statistical power and the potential for false positive discoveries. While the AD PRS is significantly further along compared to MDD because of AD's simpler genetic architecture, the same concerns apply as, outside of APOE, the AD PRS does not capture the majority of phenotypic variance in AD.

      The authors state that they wish to examine the effects of perinatal adversity directly/indirectly on biological aging and then assess the potential effects of biological aging on resilience. The authors use of pubertal age as a measure of accelerated aging is understandable given the data available, though not ideal. There are well validated measures of biological age such as Horvath's epigenetic clock. While advanced pubertal age is technically a form of accelerated aging, the majority of pubertal age as a phenotype is not likely to be explained by perinatal adversity. Rather, a combination of unmeasured variables including genetic variation, dietary factors, environmental exposures (endocrine disrupting chemicals), and obesity that play a substantial role in determining pubertal age. Childhood stress has been shown to have relatively small effects on pubertal age (d = -0.1) (10.1037/bul0000270).

      Lastly, the authors employ the use of an as of yet unpublished technique to map neurotransmitters density to structural data from neuroimaging studies. While this technique is certainly interesting, its face validity is not clear given that many of the receptor-disease associations reported in the original preprint do not line up with what we know about the biology of these disorders from strong human genetics data or current FDA approved treatments. Moreover, the authors mention "Excitation/Inhibition" imbalance but the technique used appears to only include glutamate data from one receptor type, mGluR5. This may not be an adequate measure of E/I imbalance, despite there being a statistically significant finding.

      Measuring both transcriptional output from GWAS loci and gene expression correlates from MRI data is a noisy and challenging prospect. Indeed, recent research has shown poor correlation between gene expression and neurotransmitter receptor density.(https://doi.org/10.1016/j.neuroimage.2022.119671).

      Thus, fundamental aspects of this manuscript including the use of MDD PRS to identify "at risk" individuals, the unclear link between AD and adolescent psychological resilience, the use of prepubertal age as a measure of biological age, and the limited conclusions that can be drawn from the gene expression and receptor density technique limits confidence in the results as presented.

    1. Author Response:

      What is novel here is that we calculated the time-varying retinal motion patterns generated during the gait cycle using a 3D reconstruction of the terrain. This allows calculation of the actual statistics of retinal motion experienced by walkers over a broad range of normal experience. We certainly do not mean to claim that stabilizing gaze is novel, and agree that the general patterns follow directly from the geometry as worked out very elegantly by Koenderink and others.  We spend time describing the terrain-linked gaze behavior because it is essential for understanding the paper. We do not claim that the basic saccade/stabilize/saccade behavior is novel and now make this clearer.

      The other novel aspect is that the motion patterns vary with gaze location which in turn varies with terrain in a way that depends on behavioral goals. So while some aspects of the general patterns are not unexpected, the quantitative values depend on the statistics of the behavior.  The actual statistics require these in situ measurements, and this has not previously been done, as stated in the abstract.

      The measured statistics provide a well-defined set of hypotheses about the pattern of direction and speed tuning across the visual field in humans. Points of comparison in the existing literature are hard to find because the stimuli have not been closely matched to actual retinal flow patterns, and the statistics will vary with the species in question. However, recent advances allow for neurophysiological measurements and eye tracking during experiments with head-fixed running, head-free, and freely moving animals. These emerging paradigms will allow the study of retinal optic flow processing in contexts that do not require simulated locomotion. While the exact the relation between the retinal motion statistics we have measured and the response properties of motion-sensitive cells remains unresolved, the emerging tools in neurophysiology and computation make similar approaches with different species more feasible.

      A more detailed description of the methods including the photogrammetry and the reference frames for the measurements has been added primarily to the Methods section.

      Reviewer #1 (Public Review):

      Much experimental work on understanding how the visual system processes optic flow during navigation has involved the use of artificial visual stimuli that do not recapitulate the complexity of optic flow patterns generated by actual walking through a natural environment. The paper by Muller and colleagues aims to carefully document "retinal" optic flow patterns generated by human participants walking a straight path in real terrains that differ in "smoothness". By doing so, they gain unique insights into an aspect of natural behavior that should move the field forward and allow for the development of new, more principled, computational models that may better explain the visual processing taking place during walking in humans.

      Strengths:

      Appropriate, state-of-the-art technology was used to obtain a simultaneous assessment of eye movements, head movements, and gait, together with an analysis of the scene, so as to estimate retinal motion maps across the central 90 deg of the visual field. This allowed the team to show that walkers stabilize gaze, causing low velocities to be concentrated around the fovea and faster velocities at the visual periphery (albeit more the periphery of the camera used than the actual visual field). The study concluded that the pattern of optic flow observed around the visual field was most likely related to the translation of the eye and body in space, and the rotations and counter-rotations this entailed to maintain stability. The authors were able to specify what aspects of the retinal motion flow pattern were impacted by terrain roughness, and why (concentration of gaze closer to the body, to control foot placement), and to differentiate this from the impact of lateral eye movements. They were also able to identify generalizable aspects of the pattern of retinal flow across terrains by subsampling identical behaviors in different conditions.

      Weaknesses:

      While the study has much to commend, it could benefit from additional methodological information about the computations performed to generate the data shown. In addition, an estimation of inter-individual variability, and the role of sex, age, and optical correction would increase our understanding of factors that could impact these results, thus providing a clearer estimate of how generalizable they are outside the confines of the present experiments.

      Properties of gait depend on the passive dynamics of the body and factors such as leg length and subject specific cost functions which are influenced by image quality and therefore by optical correction. In this experiment all subjects were normal acuity or corrected to normal (with no information regarding their uncorrected vision). This is now noted in the Methods. The goal of the present work was to calculate average statistics over a range of observers and conditions in order to constrain the experience-dependent properties one might see in neurophysiology. We have added between-subjects error bars to Figure 2 and added gaze angle distributions as a function of terrain for individual observers in the Supplementary materials. Figure 4 b and d now show standard errors across subjects. Individual subject plots are shown in the Supplementary materials. For Figure 2, most variability between subjects occurs in the Flat and Bark terrains where one might expect individual choices of energetic costs versus speed and stability etc might come into play. This is supported by our subsequent unpublished work on factors influencing foothold choice. We have also found that leg length determines path choices and thus will influence the retinal motion. Differences between observers are now noted in the text. These individual subject differences should indicate the range of variability that might be expected in the underlying neural properties and perhaps in behavioral sensitivity. Because of the size of our dataset (n=11) it is not feasible to make comparisons of sex or age. There were equal numbers of males and females and age ranged from 24 to 54. Now noted in the Methods section.

      Reviewer #2 (Public Review):

      The goal of this study was to provide in situ measurements of how combined eye and body movements interact with real 3D environments to shape the statistics of retinal motion signals. To achieve this, they had human walkers navigate different natural terrains while they measured information about eyes, body, and the 3D environment. They found average flow fields that resemble the Gibsonian view of optic flow, an asymmetry between upper and lower visual fields, low velocities at the fovea, a compression of directions near the horizontal meridian, and a preponderance of vertical directions modulated by lateral gaze positions.

      Strengths of the work include the methodological rigor with which the measurements were obtained. The 3D capture and motion capture systems, which have been tested and published before, are state-of-the-art. In addition, the authors used computer vision to reconstruct the 3D terrain structure from the recorded video.

      Together this setup makes for an exciting rig that should enable state-of-the-art measurements of eye and body movements during locomotion. The results are presented clearly and convincingly and reveal a number of interesting statistical properties (summarized above) that are a direct result of human walking behavior.

      A weakness of the article concerns tying the behavioral results and statistical descriptions to insights about neural organization. Although the authors relate their findings about the statistics of retinal motion to previous literature, the implications of their findings for neural organization remain somewhat speculative and inconclusive. An efficient coding theory of visual motion would indeed suggest that some of the statistics of retinal motion patterns should be reflected in the tuning of neural populations in the visual cortex, but as is the present findings could not be convincingly tied to known findings about the neural code of vision. Thus, the behavioral results remain strong, but the link to neural organization principles appears somewhat weak.

      We agree, but we think that strengthening the neural links requires future studies. As mentioned above, it is very difficult to relate the measured statistics to existing neurophysiological literature and we have tried to make this clearer in the Discussion (p14, 15, 16). This is because the stimuli chosen are typically arbitrary and not chosen to be realistic examples of patterns consistent with natural motion across a ground plane. Other stimuli are simply inconsistent with self-motion together with gaze stabilization (eg not zero velocity at the fovea). It has also been technically difficult to map cell properties across the visual field. We have made the comparisons we thought were useful. The point of the paper is to provide a hypothesis about the pattern of direction and speed tuning across the visual field. So the challenge for neurophysiology is to show how the observed cell properties vary across the visual field. Note also that the motion patterns will be influenced by the body motion of the animal in question, and because of this we are now collaborating with a group who are attempting to record from monkey MT/MST during locomotion while tracking eyes and body. Similarly we are training neural networks to learn the patterns generated by human gait to develop more specific hypotheses about receptive field properties.

      Reviewer #3 (Public Review):

      Gaze-stabilizing motor coordination and the resulting patterns of retinal image flow are computed from empirically recorded eye movement and motion capture data. These patterns are assessed in terms of the information that would be potentially useful for guiding locomotion that the retinal signals actually yield. (As opposed to the "ecological" information in the optic array, defined as independent of a particular sensor and sampling strategy).

      While the question posed is fundamental, and the concept of the methodology shows promise, there are some methodological details to resolve. Also, some terminological ambiguities remain, which are the legacy of the field not having settled on a standardized meaning for several technical terms that would be consistent across laboratory setups and field experiments.

      Technical limits and potential error sources should be discussed more. Additional ideas about how to extend/scale up the approach to tasks with more complex scenes, higher speed or other additional task demands and what that might reveal beyond the present results could be discussed.

      This issue is addressed in more detail in the Discussion, second paragraph, and also the second last paragraph.

    1. Author Response

      Reviewer #1 (Public Review):

      This work presents a unification model (of sorts) for explaining how the flow of evidence through networks can be controlled during decision-making. The authors combine two general frameworks previously used as neural models of cortical decision-making, dynamic normalization (that implement value encoding via firing activity) and recurrent network models (which capture winner-take-all selection processes) into a unified model called the local disinhibition-based decision model (LDDM). The simple motif of the LDDM allows for the disinhibition of excitatory cells that represent the engagement of individual actions that happens through a recurrent inhibitory loop (i.e., a leaky competing accumulator). The authors show how the LDDM works effectively well at explaining both decision dynamics and the properties of cortical cells during perceptual decision-making tasks.

      All in all, I thought this was an interesting study with an ambitious goal. But like any good study, there are some open issues worth noting and correcting.

      MAJOR CONCERNS

      1. Big picture

      This was a comprehensive and extremely well-vetted set of theoretical experiments. However, the scope and complexity also made the take-home message hard to discern. The abstract and most of the introduction focus on the framing of LDDM as a hybrid of dynamic normalization models (DNM) and recurrent network models (RNMs). This is sold as a unification of value normalization and selection into a novel unified framework. Then the focus shifts to the role of disinhibition in decision-making. Then in the Discussion, the goal is stated as to determine whether the LDDM generates persistent activity and does this activity differ from RNMs. As a reader, it seems like the paper jumps between two high- level goals: 1) the unification of DNM and RNM architectures, and 2) the role of disinhibition. This constant changing makes it hard to focus as the reader goes on. So what is the big picture goal specifically?

      Also, the framing of value normalization and WTA as a novel computational goal is a bit odd as this is a major focus of the field of reinforcement learning (both abstractly at the computational level and more concretely in models of the circuits that regulate it). I know that the authors do not think they are the first to unify value judgements with selection criteria. The writing just comes across that way and should be clarified.

      We thank the Reviewer for their thoughtful consideration of the overall framing of the big picture goals of the paper. Upon reflection, we agree that the paper really centers on the importance of incorporating disinhibition into computational circuit-based models of decision-making. Thus, we have significantly revised the Introduction and Discussion to focus on the theoretical and empirical importance of incorporating disinhibition into computational models of decision-making, and use the integration of value normalization and WTA selection as an example of how disinhibition increases the richness of circuit decision models. Please see the response to recommendations below for more detail on the changes.

      1. Link to other models

      The LDDM is described as a novel unification of value normalization and winner-take-all (WTA) selection, combining value processing and selection. While the authors do an excellent job of referencing a significant chunk of the decision neuroscience literature (160 references!) the motif they end up designing has a highly similar structure to a well-known neural circuit linked to decision-making: the cortico-basal ganglia pathways. Extensive work over the past 20+ years has highlighted how cortical-basal ganglia loops work via disinhibition of cortical decision units in a similar way as the LDDM (see the work by Michael Frank, Wei Wei, Jonathan Rubin, Fred Hamker, Rafal Bogacz, and many others). It was surprising to not see this link brought up in the paper as most of the framing was on the possibility of the LDDM representing cortical motifs, yet as far as I know, there does not exist evidence for such architectures in the cortex, but there is in these cortical-basal ganglia systems.

      We thank the Reviewer for the suggestion to link the LDDM to disinhibition in CBG models; this is indeed an important body of empirical and computational work that we overlooked in the original manuscript. We have now added text to the Discussion to highlight the link between LDDM and these CBL disinhibition models, focusing on how they are conceptually similar and how they differ. Please see our response to recommendations below for a more detailed discussion of the revisions.

      1. Model evaluations

      The authors do a great job of extensively probing the LDDM under different conditions and against some empirical data. However, most of the time there is no "control" model or current state-of-the-art model that the LDDM is being compared against. In a few of the simulation experiments, the LDDM is compared against the DNM and RNM alone, so as to show how the two components of the LDDM motif compare against the holistic model itself. But this component model comparison is inconsistently used across simulation experiments.

      Also, it is worth asking whether the DNM and RNM are appropriate comparison models to vet the LDDM against for two reasons. First, these are the components of the full LDDM. So these tests show us how the two underlying architectural systems that go into LDDM perform independently, but not necessarily how the LDDM compares against other architectures without these features. Second, as pointed out in my previous comment, the LDDM is a more complex model, with more parameters, than either the DNM or RNM. The field of decision neuroscience is awash in competing decision models (including probabilistic attractor models, non-recurrent integrators, etc.). If we really want to understand the utility of the LDDM, it would be good to know how it performs against similarly complex models, as opposed to its two underlying component models.

      We greatly appreciate the Reviewer’s comments on the point of model comparison, which points out that our original manuscript failed to clearly convey a very important difference between the LDDM and the existing RNM(s). In the revision, we now make it clearer that the fundamental difference between the LDDM and the RNMs is the architecture of disinhibition (see the revised Introduction, especially p. 8 lines 164-168). The LDDM is not simply a combination of the DNM model with RNM architecture (a point we may have mistakenly conveyed in the original manuscript): the introduction of disinhibition separates LDDM inhibition into option-selective subpopulations, as opposed to the single pooled inhibition of RNM models. Given this fact, the LDDM predicts unique selectiveinhibition dynamics shown in recent optogenetic and calcium imaging results, a finding inconsistent with the common-pooled and non-selective inhibition assumed in the existing RNMs and many of its variants. Thus, we believe that a comparison between the LDDM and the RNM, which share similar level of complexity and numbers of parameters, is important.

      We also appreciated the Reviewer’s concern about testing the LDDM against alternative models. In order to better connect to the existing literature, we now compare the LDDM to another standard circuit model of decision-making - the leaky competing accumulator (LCA) model. The LCA is a circuit model that captures many of the aspects of perceptual decision-making seen in the mathematical drift diffusion model (DDM), but with a construction that allows for fitting to behavioral data and comparison of underlying unit activities. Please see our response to recommendations below for further detail.

      1. Comparison to physiological data

      I quite enjoyed the comparisons of the excitatory cell activity to empirical data from the Shadlen lab experiments. However, these were largely qualitative in nature. In conjunction with my prior point on the models that the LDDM is being compared against, it would be ideal to have a direct measure of model fits that can be used to compare the performance of different competing "control" models. These measures would have to account for differences in model complexity (e.g., AIC or BIC), but such an analysis would help the reader understand the utility of the LDDM in connecting with empirical data much better.

      We agree with the Reviewer that a quantitative comparison of the match between model neural predictions and empirical neurophysiological data is important. First, we wish to clarify that the model neural predictions are simulated from models fit to the behavioral (choice and RT data), not from fits to the neural activity traces – a point we now clarify in the text. While directly fitting dynamic models (LDDM, RNM, or LCA) to the neurophysiological data is appealing, there are currently several obstacles to this approach. The first problem is the complexity of the dynamic neural traces. Despite the long history of the random-dot motion paradigm, detailed features of the dynamics are still not understood. For example, the stereotyped initial dip after stimulus onset may reflect a reset of the network state to improve signal to noise ratio (Conen and Padoa-Schioppa, 2015) or simply reflect a surround suppression-like lateral inhibition in visual processing. A second problem is that the primary difference between the models is the activity of inhibitory (and disinhibitory) neurons, which are typically not recorded in neurophysiological experiments; thus, there is a lack of empirical data to which to fit the models. In the revision, we clarified that the model fitting to the Roitman & Shadlen data is for behavioral data only, and model unit activity traces are derived from models fit to behavioral data.

      That being said, we agree that a quantitative comparison of model activity predictions is helpful. Because the models are fit not to the neural data but to the behavioral data, rather than using likelihood-based measures like AIC and BIC we used a simple RMSE measure to compare the match between predicted and neural activity patterns (revised Fig. 6E, Fig 6-S4E, Fig 6-S5E). Please see response to recommendations below for details.

      Reviewer #2 (Public Review):

      The aim of this article was to create a biologically plausible model of decision-making that can both represent a choice's value and reproduce winner-take-all ramping behavior that determines the choice, two fundamental components of value- based decision-making. Both of these aspects have been studied and modeled independently but empirical studies have found that single neurons can switch between both of the aspects (i.e., from representing value to winner-take-all ramping behavior) in ways that are not well described by current biological plausible models of decision making.

      The current article provides a thorough investigation of a new model (the local disinhibition decision model; LDDM) that has the goal of combining value representations and winner-takes-all ramping dynamics related to choice. Their model uses biologically plausible disinhibition to control the levels of inhibition in a local network of simulated neurons. Through a careful series of simulation experiments, they demonstrate that their network can first represent the value of different options, then switch to winner-takes-all ramping dynamics when a choice needs to be made. They further demonstrate that their single model reproduces key components of value-based and winner-takes-all dynamics found in both neural and behavioral data. They additionally conduct simulation studies to demonstrate that recurrent excitatory properties in their network produce value-persistence behavior that could be related to memory. They end by conducting a careful simulation study of the influence of GABA agonists that provide clear and testable predictions of their proposed role of inhibition in the neural processes that underlie decision-making. This last piece is especially important as it provides a clear set of predictions and experiments to help support or falsify their model.

      There are overall many strengths to this paper. As the authors note, current network models do not explain both value- based and ramping-like decision-making properties. Their thorough simulation studies and their validation against empirical neural and behavioral data will be of strong interest to neuroscientists and psychologists interested in value- based decision-making. The simulations related to persistence and the GABA-agonist experiments they propose also provide very clear guidelines for future research that would help advance the field of decision-making research.

      Although the methods and model were generally clear, there was a fair amount of emphasis on the role of recurrence in the LDDM, but very little evidence that recurrence was important or necessary for any of the empirical data examined. The authors do demonstrate the importance of recurrence in some of their simulation studies (particularly in their studies of persistence), but these would need to be compared against empirical data to be validated. Nevertheless, the model and thorough simulation investigations will likely help develop more precise theories of value-based decision-making.

      We appreciate the Reviewer’s thoughtful comments. These comments - especially about anatomic recurrence and its relationship to the parameter 𝛼 - inspired us to think more about the uniqueness of the current circuit to others, especially the implications related to the parameters 𝛼 (i.e., self-excitation) and 𝛽 (i.e., local disinhibition). Recurrence is required to drive winner-take-all competition in the standard RNM of decision-making. However, we show here with both analytical and numerical approaches that recurrence helps WTA competition but is not necessary in our model. Instead, the key feature of the LDDM is to utilize disinhibition in conjunction with lateral inhibition to realize winner-take-all competition. That leads to many different predictions of the current model from the existing models, such as selective inhibition and flexible control of dynamics.

      In response to the Reviewer’s points and after careful consideration of the differential equations, we realized that in our model fitting, the 𝛼 parameter fitting to zero does not necessarily mean recurrence should be zero. The 𝛼 parameter shares a lot of similarity to the baseline gain control (parameter BG in our revision), and thus is unidentifiable in the current dataset. In the interest of parsimony, we did not include the parameter BG in the original manuscript, but now include it because it reveals the difficulty of interpreting fit 𝛼 values as simply the level of recurrence.

      Overall, disinhibition (𝛽) in the LDDM is required for WTA activity while recurrence (𝛼) can contribute but is not necessary; however, 𝛼 is theoretically important for generating persistent activity, with the caveat that in the current framework there is an unclear relationship between fit 𝛼 and recurrence. Regardless, we agree that the contribution of 𝛼 to the LDDM framework is worth further testing and examining with future empirical data.

      Reviewer #3 (Public Review):

      Shen et al. attempt to reconcile two distinct features of neural responses in frontoparietal areas during perceptual and value-guided decision-making into a single biologically realistic circuit model. First, previous work has demonstrated that value coding in the parietal cortex is relative (dependent on the value of all available choice options) and that this feature can be explained by divisive normalization, implemented using adaptive gain control in a recurrently connected circuit model (Louie et al, 2011). Second, a wealth of previous studies on perceptual decision-making (Gold & Shadlen 2007) have provided strong evidence that competitive winner-take-all dynamics implemented through recurrent dynamics characterized by mutual inhibition (Wang 2008) can account for categorical choice coding. The authors propose a circuit model whose key feature is the flexible gating of 'disinhibition', which captures both types of computation - divisive normalization and winner-take-all competition. The model is qualitatively able to explain the 'early' transients in parietal neural responses, which show signatures of divisive normalization indicating a relative value code, persistent activity during delay periods, and 'late' accumulation-to-bound type categorical responses prior to the report of choice/action onset.

      The attempt to integrate these two sets of findings by a unified circuit model is certainly interesting and would be useful to those who seek a tighter link between biologically realistic recurrent neural network models and neural recordings. I also appreciate the effort undertaken by the authors in using analytical tools to gain an understanding of the underlying dynamical mechanism of the proposed model. However, I have two major concerns. First, the manuscript in its current form lacks sufficient clarity, specifically in how some of the key parameters of the model are supposed to be interpreted (see point 1 below). Second, the authors overlook important previous work that is closely related to the ideas that are being presented in this paper (see point 2 below).

      1) The behavior of the proposed model is critically dependent on a single parameter 'beta' whose value, the authors claim, controls the switch from value-coding to choice-coding. However, the precise definition/interpretation of 'beta' seems inconsistent in different parts of the text. I elaborate on this issue in sub-points (1a-b) below:

      1a). For instance, in the equations of the main text (Equations 1-3), 'beta' is used to denote the coupling from the excitatory units (R) to the disinhibitory units (D) in Equations 1-3. However, in the main figures (Fig 2) and in the methods (Equation 5-8), 'beta' is instead used to refer to the coupling between the disinhibitory (D) and the inhibitory gain control units (G). Based on my reading of the text (and the predominant definition used by the authors themselves in the main figures and the methods), it seems that 'beta' should be the coupling between the D and G units.

      1b). A more general and critical issue is the failure to clearly specify whether this coupling of D-G units (parameterized by 'beta') should be interpreted as a 'functional' one, or an 'anatomical' one. A straightforward interpretation of the model equations (Equations 5-8) suggests that 'beta' is the synaptic weight (anatomical coupling) between the D and G units/populations. However, significant portions of the text seem to indicate otherwise (i.e a 'functional' coupling). I elaborate on this in subpoints (i-iii) below:

      (1b-i). One of the main claims of the paper is that the value of 'beta' is under 'external' top-down control (Figure 2 caption, lines 124-126). When 'beta' equals zero, the model is consistent with the previous DNM model (dynamic normalization, Louie et al 2011), but for moderate/large non-zero values of 'beta', the network exhibits WTA dynamics. If 'beta' is indeed the anatomical coupling between D and G (as suggested by the equations of the model), then, are we to interpret that the synaptic weight between D-G is changed by the top-down control signal within a trial? My understanding of the text suggests that this is not in fact the case. Instead, the authors seem to want to convey that top-down input "functionally" gates the activity of D units. When the top-down control signal is "off", the disinhibitory units (D) are "effectively absent" (i.e their activity is clamped at zero as in the schematic in Fig 2B), and therefore do not drive the G units. This would in- turn be equivalent to there being no "anatomical coupling" between D and G. However when the top-down signal is "on", D units have non-zero activity (schematic in Fig 2B), and therefore drive the G units, ultimately resulting in WTA-like dynamics.

      (1b-ii). Therefore, it seems like when the authors say that beta equals zero during the value coding phase they are almost certainly referring to a functional coupling from D to G, or else it would be inconsistent with their other claim that the proposed model flexibly reconfigures dynamics only through a single topdown input but without a change to the circuit architecture (reiterated in lines 398-399, 442-444, 544-546, 557-558, 579-590). However, such a 'functional' definition of 'beta' would seem inconsistent with how it should actually be interpreted based on the model equations, and also somewhat misleading considering the claim that the proposed network is a biologically realistic circuit model.

      (1b-iii). The only way to reconcile the results with an 'anatomical' interpretation of 'beta' is if there is a way to clamp the values of the 'D' units to zero when the top-down control signal is 'off'. Considering that the D units also integrate feed- forward inputs from the excitatory R units (Fig 2, Equations 1-3 or 5-8), this can be achieved either via a non-linearity, or if the top-down control input multiplicatively gates the synapse (consistent with the argument made in lines 115-116 and 585-586 that this top-down control signal is 'neuromodulatory' in nature). Neither of these two scenarios seems to be consistent with the basic definition of the model (Equations 1-3), which therefore confirms my suspicion that the interpretation of 'beta' being used in the text is more consistent with a 'functional' coupling from D to G.

      We thank the reviewer for pointing out this confusion. We apologize that the original illustrations (Fig. 2A) and the differential equations in Methods (Eqs. 5-8) did not convey very well our ideas. 𝛽 is intended to reference the coupling from R to D, not a change in the weights between D and G units. We realize there was some confusion on this part due to inconsistency between our original figures, text, and supplementary material.

      Given the lack of clarity in the previous version as well as the Reviewer’s questions, we now emphasize that 𝛽 represents a functional coupling between the R and D neurons. The biological assumption of the disinhibitory architecture is built based on recent findings that VIP neurons in the cortex always inhibit other neighboring inhibitory cells, such as SST and PV neurons, and consequently disinhibit the neighboring primary neurons (e.g., Fu et al., 2014; Karnani et al., 2014, 2016). We did not see evidence in the literature of fast-changing (anatomic) connections between VIP and SST/PV. However, there is evidence that the responsiveness of VIP neurons to excitatory neurons can be modulated by changing the concentrations of neuromodulators, such as acetylcholine and serotonin (Prönneke et al., 2020). While the stereotype of neuromodulator action is slow dynamics, recent findings show that for example basal forebrain cholinergic neurons respond to reward and punishment with surprising speed and precision (18 ± 3ms) (Hangya et al., 2015) to modulate arousal, attention, and learning in the neocortex. Given the large number of studies that identify long-term projections and neuromodulatory inputs to VIP neurons (e.g., Pfeffer et al., 2013; Pi et al., 2013; Alitto & Dan, 2013; Tremblay et al., 2016), we believe that it will be more plausible to assume the connection weights between R and D in our case is quickly modulated within a trial.

      To clarify this issue in the revised manuscript, we made the following corrections:

      1. We repositioned the 𝛽 parameter in Fig. 2A between the connection from R to D, to align the description of 𝛽 modulating R to D in the main text.

      2. We modified the differential equations 5-8 (now numbered as Eqs. 28-32) in Methods (pp. 61) to include the disinhibitory unit D as an independent control from the inhibitory unit I, in order to be consistent with the disinhibitory D units in LDDM. Such a change makes tiny differences in the model predictions (please see dynamics simulated after the change in Fig. 2-figure supplement 1B).

      3. We updated the neural circuit motif in Fig. 2 -figure supplement 1A accordingly.

      2) The main contribution of the manuscript is to integrate the characteristics of the dynamic normalization model (Louie et al, 2011) and the winner-take-all behavior of recurrent circuit models that employ mutual inhibition (Wang, 2008), into a circuit motif that can flexibly switch between these two computations. The main ingredient for achieving this seems to be the dynamical 'gating' of the disinhibition, which produces a switch in the dynamics, from point-attractor-like 'stable' dynamics during value coding to saddle-point-like 'unstable' dynamics during categorical choice coding. While the specific use of disinhibition to switch between these two computations is new, the authors fail to cite previous work that has explored similar ideas that are closely related to the results being presented in their study. It would be very useful if the authors can elaborate on the relationship between their work and some of these previous studies. I elaborate on this point in (a-b) below:

      2a) While the authors may be correct in claiming that RNM models based on mutual inhibition are incapable of relative value coding, it has already been shown previously that RNM models characterized by mutual inhibition can be flexibly reconfigured to produce dynamical regimes other than those that just support WTA competition (Machens, Romo & Brody, 2005). Similar to the behavior of the proposed model (Fig 9), the model by Machens and colleagues can flexibly switch between point-attractor dynamics (during stimulus encoding), line-attractor dynamics (during working memory), and saddle-point dynamics (during categorical choice) depending on the task epoch. It achieves this via a flexible reconfiguration of the external inputs to the RNM. Therefore, the authors should acknowledge that the mechanism they propose may just be one of many potential ways in which a single circuit motif is reconfigured to produce different task dynamics. This also brings into question their claim that the type of persistent activity produced by the model is "novel", which I don't believe it is (see Machens et al 2005 for the same line-attractor-based mechanism for working memory)

      We thank the Reviewer for pointing out the conceptual similarities between the LDDM and the Machens Romo Brody model, and now include a discussion of the link between the two early in the revised Discussion (p. 38, lines 826-837). Please see response to recommendations below for a more detailed discussion of this point.

      2b) The authors also fail to cite or describe their work in relation to previous work that has used disinhibition-based circuit motifs to achieve all 3 proposed functions of their model - (i) divisive normalization (Litwin-Kumar et al, 2016), (ii) flexible gating/decision making (Yang et al, 2016), and working memory maintenance (Kim & Sejnowski,2021)

      The Reviewer notes several relevant papers, and we have now discussed them and their relationship to the LDDM in a revised Discussion section (pp. 35-36). Please see response to recommendations below for a more details.

    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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      Reply to the reviewers

      We would like to thank the reviewers for their extensive review of our manuscript and constructive criticism. We have attempted to address the points raised in the reviewer's comments and have performed additional experiments and have edited the text of the manuscript to explain these points. Please see below, our point-by-point response to the reviewer’s comments. In the submitted revised manuscript, some figure numbers have changed from the prior reviewed version.

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      In this MS, Mrj - a member of the JDP family of Hsp70 co-chaperones was identified as a regulator of the conversion of Orb2A (the Dm ortholog of CPEB) to its prion-like form.

      In drosophila, Mrj deletion does not cause any gross neurodevelopmental defect nor leads to detectable alterations in protein homeostasis. Loss of Mrj, however, does lead to altered Orb2 oligomerization. Consistent with a role of prion-like characteristics of Orb2 in memory consolidation, loss of Mrj results in a deficit in long-term memory.

      Aside from the fact that there are some unclarities related to the physicochemical properties of Orb2 and how Mrj affects this precisely, the finding that a chaperone could be important for memory is an interesting observation, albeit not entirely novel.

      In addition, there are several minor technical concerns and questions I have that I feel the authors should address, including a major one related to the actual approach used to demonstrate memory deficits upon loss of Mrj.

      Reviewer #1 (Significance (Required)):

      Figure 1 (plus related Supplemental figures): • There seem to be two isoforms of Mrj (like what has been found for human DNAJB6). I find it striking to see that only (preferentially?) the shorter isoform interacts with Orb2. For DNAJB6, the long isoform is mainly related to an NLS and the presumed substrate binding is identical for both isoforms. If this is true for Dm-Mrj too, the authors could actually use this to demonstrate the specificity of their IPs where Orb2 is exclusively non-nuclear?

      According to Flybase, Mrj has 8 predicted isoforms of which four are of 259 amino acids (PA, PB, PC, and PD), 3 are of 346 amino acids (PE, PG, and PH) and one is of 208 amino acids (PF) length (Supplementary data 1). We isolated RNA from flyheads and used this in RT-PCR experiments to check which Mrj isoforms express in the brain. Since both the 346 amino acid (1038 nucleotide long) and 259 amino acids (777 nucleotides long) form, which we refer to as the long and middle isoform, has the same N and C terminal sequences we used the same primer pair for this, but on RT-PCR the only amplicon we got corresponds to the 259 amino acid form. For the 208 amino acids (624 nucleotides long) form we designed a separate forward primer and attempted to amplify this using RT-PCR but were unable to detect this isoform also. This data is now presented in Supplemental Figure 4B. Since the only isoform detected from fly head cDNA corresponded to the 259 amino acid form, we think this is the predominant isoform of Mrj expressing in Drosophila and this is what is in our DnaJ library and what we have used in all our experiments here. This is also the same isoform described in previous papers on Drosophila Mrj (Fayazi et al, 2006; Li et al, 2016b). For this 259 amino acid Mrj isoform, we see its expression in both the nucleus and cytoplasm (Supplemental Figure 4C). As the long 346 AA fragment was undetectable in the brain, it was not feasible to address the reviewer’s point of using the long and short forms of Mrj for IP with Orb2. However, we have performed IP of human CPEB2 (hCPEB2) with the long and short isoforms of human DnaJB6 and have detected interaction of hCPEB2 with both the long and short isoforms of DnaJB6 (Supplemental Figure 6E).

      • I would be interested to know a bit more about the other 5 JDPs that are interactors with Orb2: are the human orthologs of those known? It is striking that these other 5 JDPs interact with Orb2 in Dm (in IPs) but have no impact on Sup35 prion behavior. Importantly, this does not imply they may not have impact on the prion-like behavior of other Dm substrates, including Dm-Orb2.

      We have performed BlastP analysis of CG4164, CG9828, CG7130, DroJ2, and Tpr2 protein sequences against Human proteins. Based on this we have listed the highest-ranking candidate identified here for each of these genes.

      Drosophila Gene

      Human gene

      Query cover

      Percent identity

      E value

      CG4164

      dnaJ homolog subfamily B member 11 isoform 1

      98 %

      62.96%

      2e-150

      CG9828

      dnaJ homolog subfamily A member 2

      92%

      39.41%

      3e-84

      CG7130

      dnaJ homolog subfamily B member 4 isoform d

      56%

      69.44%

      2e-30

      Tpr2

      dnaJ homolog subfamily C member 7 isoform 1

      93%

      46.22%

      6e-139

      DroJ2

      dnaJ homolog subfamily A member 4 isoform 2

      98%

      60.60%

      2e-169

      In the context of the chimeric Sup35-based assay where Orb2A’s Prion-like domain (PrD) is coupled with the C-terminal domain of Sup35, the only protein which could convert Orb2A PrD-Sup35 C from its non-prion state to prion state was Mrj. Within the limitations of this heterologous-system based assay, the other 5 DnaJ domain proteins as well as the Hsp70’s were unable to convert the Orb2A PrD from its non-prion to prion-like state. What these other 5 interacting JDP proteins are doing through their interaction with Orb2A and if they are even expressing in the Orb2 relevant neurons will need to be tested separately and will be the subject of our future studies.

      • The data in panels H, I indeed suggest that Mrj1 alters the (size of) the oligomers. It would be important to know what is the actual physicochemical change that is occurring here. The observed species are insoluble in 0.1 % TX100 but soluble in 0.1% SDS, which suggest they could be gels, but not real amyloids such as formed by the polyQ proteins that require much higher SDS concentrations (~2%) to be solubilized. This is relevant as Mrj1 reduces polyQ amyloidogenesis whereas is here is shown to enhance Orb2A oligomerization/gelidification. In the same context, it is striking to see that without Mrj the amount of Orb2A seems drastically reduced and I wonder whether this might be due to the fact that in the absence of Mrj a part of Orb2A is not recovered/solubilized due to its conversion for a gel to a solid/amyloid state? In other words: Mrj1 may not promote the prion state, but prevents that state to become an irreversible, non-functional amyloid?

      On the reviewer’s point to address what is the actual physicochemical change occurring here, we will need to develop methods to purify the Orb2 oligomers in significant quantities to examine and distinguish if they are of gel or real amyloid-like nature. Currently, within the limitations of our ongoing work, this has not been possible for us to do and we can attempt to address this in our future work. Cryo-EM derived structure of endogenous Orb2 oligomers purified from a fly head extract from 3 million fly heads, made in the TritonX-100 and NP-40 containing buffer, the same buffer as what we have used here for the first soluble fraction, showed these oligomers as amyloids (Hervas et al, 2020). If the oligomers extracted using 0.1% and 2% SDS are structurally and physicochemically different, within the limitations of our current work, had not been possible to address.

      The other point raised by the reviewer is, if in the absence of Mrj (in the context of Figure 4 of our previously submitted manuscript), a part of Orb2 is not solubilized due to us using a lower 0.1% SDS for extraction. To address this, we attempted to see how much of leftover Orb2 is remaining in the pellet after extraction with 0.1 % SDS. Towards this, according to the reviewers’ suggestion, we used a higher 2% SDS containing buffer to resuspend the leftover pellet after 0.1% SDS extraction, and post solubilisation ran all the fractions in SDD-AGE. We did this experiment with both wild-type and Mrj knockout fly heads. Under these different extractions, we first observed while there is more Orb2 in the soluble fraction (Triton X-100 extracted) of Mrj knockout, this amount is reduced in both the 0.1% SDS solubilized and 2% SDS solubilized fractions. So, even though there is leftover Orb2 after 0.1% SDS extraction, which can be extracted using 2% SDS, this amount is reduced in Mrj knockout. The other observation here is the Orb2 extracted using 2% SDS shows a longer smear in comparison to the 0.1% SDS extracted form suggesting a possibility of more and higher-sized oligomers present in this fraction. Since we do not have the exact physicochemical characterization of these oligomers detected with 0.1% and 2% SDS-containing buffer, we are not differentiating them by using the terms gels and real amyloids, but refer to them as 0.1% SDS soluble Orb2 oligomers and 2% SDS soluble Orb2 oligomers. Overall, our observations here suggest in absence of Mrj, both of these kinds of Orb2 oligomers are decreased and so Mrj is most likely promoting the formation of Orb2 oligomers. It is possible that the 0.1% SDS soluble Orb2 oligomers gradually accumulate and undergo a further transition to the 2% SDS soluble Orb2 oligomers, so if in absence of Mrj, the formation of the 0.1% SDS soluble Orb2 oligomers is decreased, the next step of formation of 2% SDS soluble Orb2 oligomers also be decreased. This data is now presented in Figure 5H, I and J).

      On the other possibility raised by the reviewer that Mrj can prevent the oligomeric state of Orb2 to become an irreversible non-functional amyloid, we think it is still possible for Mrj to do this but this could not be tested under the present conditions.

      • It may be good for clarity to refer to the human Mrj as DNAJB6 according to the HUGO nomenclature. Also, the first evidence for its oligomerization was by Hageman et al 2010.

      We have now changed mentions of human Mrj to DNAJB6. We apologize for missing the Hageman et al 2010 reference and have now cited this reference in the context of Mrj oligomerization.

      • It is striking to see that Mrj co-Ips with Hsp70AA, Hsp70-4 but not Hsp70Cb. The fact that interactions were detected without using crosslinking is also striking given the reported transient nature of J-domain-Hsp70 interactions Together, this may even suggest that Mrj-1 is recognized as a Hsp70 substrate (for Hsp70AA, Hsp70-4 but not Hsp70Cb) rather than as a co-chaperone. In fact, a variant of Mrj-1 with a mutation in the HPD motif should be used to exclude this option.

      In IP experiments we notice Mrj interacts with Hsp70Aa and Hsc70-4 but not with Hsc70-1 and Hsc70Cb. In our previously submitted manuscript, we realized we made a typo on the figure, where we referred to Hsp70Aa as Hsc70Aa. We have corrected this in the current revised manuscript. On the crosslinking point raised by the reviewer, we reviewed the published literature for studies of immunoprecipitation experiments which showed an interaction between DnaJB6 and Hsp70. We noted while one of the papers (Kakkar et al, 2016) report the use of a crosslinker in the experiment which showed an interaction between GFP-Hsp70 and V5-DnaJB6, in another two papers the interaction between endogenous Mrj and endogenous Hsp/c70 (Izawa et al, 2000) and Flag-Hsp70 and GFP-DnaJB6 (Bengoechea et al, 2020) could be detected without using any crosslinker. Our observations of detecting the interaction of Mrj with Hsp70Aa and Hsc70-4 in the absence of a crosslinker are thus similar to the observations reported by (Izawa et al, 2000; Bengoechea et al, 2020).

      On the point of if Mrj is a substrate for Hsp70aa and Hsc70-4 and not a co-chaperone, we feel in the context of this manuscript, since we are focussing on the role of Mrj in the regulation of oligomerization of Orb2 and in memory, the experiment with HPD motif mutant is probably not necessary here. However, if the reviewers suggest this experiment to be essential, we can attempt this experiment by making this HPD motif mutant.

      • The rest of these data reconfirm nicely that Mrj/DNAJB6 can suppress polyQ-Htt aggregation. Yet note that in this case the oligomers that enter the agarose gel are smaller, not bigger. This argues that Mrj is not an enhancer of oligomerization, but rather an inhibitor of the conversion of oligomers to a more amyloid like state.

      Figure 2 and Supplemental Figure 4 discuss the effect of Mrj on Htt aggregation. We have used 2 different Htt constructs here. For Figure 2I, we used only Exon1 of Htt with the poly Q repeats. Here in SDD-AGE, for the control lane, we see the Htt oligomers as a smear for the control. For Mrj, we see only a small band at the bottom which can be interpreted most likely as either a monomer or as small oligomers since we do not observe any smear here. However, for the 588 amino acid fragment of HttQ138 in the SDD-AGE we do not see a difference in the length of the smear but in the intensity of the smear of the Htt oligomers (Supplemental Figure 4E). Based on this we are suggesting in presence of Mrj, there are lesser Htt oligomers. On the point of Mrj is not an enhancer of oligomerization, but rather an inhibitor of the conversion of oligomers to a more amyloid-like state, our experiments with the Mrj knockout show reduced Orb2 oligomers (both for 0.1% and 2% SDS soluble forms), suggesting Mrj plays a role in the conversion of Orb2 to the oligomeric state. If Mrj inhibits the conversion of oligomers to a more amyloid-like state, this is possible but we couldn’t test this hypothesis here. However, for Htt amyloid aggregates, previous works done by other labs with DnaJB6 as well as our experiments with Mrj suggest this as a likely possibility.

      Figure 3: • The finding that knockout of DNAJB6 in mice is embryonic lethal is related to a problem with placental development and not embryonic development (Hunter et al, 1999; Watson et al, 2007, 2009, 2011) as well recognized by the authors. Therefore, the finding that deletion of Dm-Mrj has no developmental phenotype in Drosophila may not be that surprising.

      We agree with the reviewer’s point that DNAJB6 mutant mice have a problem with placental development. However, one of the papers cited here (Watson et al, 2009) suggests DNAJB6 also plays a crucial role in the development of the embryo independent of the placenta development defect. The mammalian DNAJB6 mutant embryos generated using the tetraploid complementation method show severe neural defects including exencephaly, defect in neural tube closure, reduced neural tube size, and thinner neuroepithelium. Due to these features seen in the mice knockout, and the lack of such developmental defects in the Drosophila knockout, we interpreted our findings in Drosophila as significantly different from the mammals.

      • It is a bit more surprising that Mrj knockout flies showed no aggregation phenotype or muscle phenotype, especially knowing that DNAJB6 mutations are linked to human diseases associated with aggregation (again well recognized by the authors). However, most of these diseases are late-onset and the phenotype may require stress to be revealed. So, while important to this MS in terms of not being a confounder for the memory test, I would like to ask the authors to add a note of caution that their data do not exclude that loss of Mrj activity still may cause a protein aggregation-related disease phenotype. Yet, I also do think that for the main message of this MS, it is not required to further test this experimentally.

      We agree with the reviewer and have added this suggestion in the discussion that loss of Mrj may still result in a protein aggregation-related disease phenotype, probably under a sensitized condition of certain stresses which is not tested in this manuscript.

      Figure 4:

      • IPs were done with Orb2A as bite and clearly pulled down substantial amounts of GFP-tagged Mrj. For interactions with Orb2B, a V5-tagged Mrj was use and only a minor fraction was pulled down. Why were two different Mrj constructs used for Arb2A and Orb2b?

      In the previously submitted manuscript, we have used HA-tagged Mrj (not V5) for checking the interaction with full-length Orb2B tagged with GFP. This was done with the goal of using the same Mrj-HA construct as that used in the initial Orb2A immunoprecipitation experiment. Since this has raised some concern as in the IPs to check for interaction between truncated Orb2A constructs (Orb2A325-GFP and Orb2AD162-GFP) and Mrj (Mrj-RFP), we used a different GFP and RFP tag combination. To address this, we have now added the same tag combinations for the IPs (Mrj-RFP with Orb2A-GFP and Orb2B-GFP). In these immunoprecipitation experiments where Mrj-RFP was pulled down using RFP Trap beads, we were able to detect positive interaction with GFP-tagged Orb2A and Orb2B. This data is now added in Figure 4F and 4I. We also have added the IP data for interaction between Mrj-HA and untagged Orb2B in Figure 4K, similar to the combination of initial experiment between Mrj-HA and untagged Orb2A.

      • In addition, I think it would be important what one would see when pulling on Mrj1, especially under non-denaturing conditions and what is the status of the Orb2 that is than found to be associated with Mrj (monomeric, oligomeric and what size).

      We have now performed IP from wild-type fly heads using anti Mrj antibody and ran the immunoprecipitate in SDS-PAGE and SDD-AGE followed by immunoblotting them with anti-Orb2 antibody. Our experiments suggest that immunoprecipitating endogenous Mrj brings down both the monomeric and oligomeric forms of Orb2. This data is now added in Figure 4L, M and N.

      • This also relates to my remark at figure 1 and the subsequent fractionation experiments they show here in which there is a slight (not very convincing) increase in the ratio of TX100-soluble and insoluble (0.1% SDS soluble) material. My question would be if there is a remaining fraction of 0.1% insoluble (2% soluble) Orb2 and how Mrj affects that? As stated before, this is (only) mechanistically relevant to understanding why there is less oligomers of Orb2 in terms of Mrj either promoting it or by preventing it to transfer from a gel to a solid state. The link to the memory data remains intriguing, irrespective of what is going on (but also on those data I do have several comments: see below).

      We have addressed this in response to the reviewer’s comments on Figure 1. We find in both wild type and Mrj knockout fly heads, there are Orb2 oligomers that can be detected using 0.1% SDS extraction and with further extraction with 2% SDS. The 2% SDS soluble Orb2 oligomers were previously insoluble during 0.1% SDS-based extraction. However, the amounts of both of these oligomers are reduced in Mrj knockout fly heads. Since we do not have the physicochemical characterization of both of these kinds of oligomers, we are not using the terms gel or solid state here but referring to these oligomers as 0.1% SDS soluble Orb2 oligomers and 2% SDS soluble Orb2 oligomers. We speculate that the 0.1% SDS soluble Orb2 oligomers over time transition to the 2% SDS soluble Orb2 oligomers. As in the absence of Mrj in the knockout flies, both the 0.1% SDS soluble and 2% SDS soluble Orb2 oligomers are decreased, this suggests Mrj is promoting the formation of Orb2 oligomers. On the reviewer’s point, if Mrj can prevent the transition from 0.1% SDS soluble to 2% SDS soluble Orb2 oligomers, we think it is possible for Mrj to both promote oligomerization of Orb2 as well as prevent it from forming bigger non-functional oligomers, but the second point is not tested here. The relevant data is now presented in Figure 5H, I and J.

      • I also find the sentence that "Mrj is probably regulating the oligomerization of endogenous Orb2 in the brain" somewhat an overstatement. I would rather prefer to say that the data show that Mrj1 affects the oligomeric behavior/status of Orb2.

      Based on the reviewer’s suggestion we have now changed the sentence to Mrj is probably regulating the oligomeric status of Orb2

      Figure 5:

      • To my knowledge, the Elav driver regulates expression in all neurons, but not in glial cells that comprise a significant part of the fly heads/brain. The complete absence of Mrj in the fly-heads when using this driver is therefore somewhat surprising. Or do we need to conclude from this that glial cells normally already lack Mrj expression?

      On driving Mrj RNAi with Elav Gal4, we did not detect any Mrj in the western. We attempted to address the glial contribution towards Mrj’s expression we used a Glia-specific driver Repo Gal4 line to drive the control and Mrj RNAi line and performed a western blot using fly head lysate with anti-Mrj antibody. In this experiment, we did not observe any difference in Mrj levels between the two sets. As the Mrj antibody raised by us works in western blots but not in immunostainings, we could not do a colocalization analysis with a glial marker. However, we used the Mrj knockout Gal4 line to drive NLS-GFP and performed immunostainings of these flies with a glial marker anti-Repo antibody. Here we see two kinds of cells in the brain, one which have GFP but no Repo and the other where both are present together. This suggest that while Glial cells have Mrj but probably majority of Mrj in the brain comes from the neurons. We also found a reference where it was shown that Elav protein as well as Elav Gal4 at earlier stages of development expresses in neuroblasts and in all Glia (Berger et al, 2007). So, another possibility is when we are driving Mrj RNAi using Elav Gal4, this knocks down Mrj in both the neurons as well as in the glia. This coupled with the catalytic nature of RNAi probably creates an effective knockdown of Mrj as seen in the western blot. This data is now added in Supplementary Figure 5G and H.

      • Why not use these lines also for the memory test for confirmation? I understand the concerns of putative confounding effects of a full knockdown (which were however not reported), but now data rely only on the mushroom body-specific knockdown for the 201Y Gal4 line, for which the knockdown efficiency is not provided. But even more so, here a temperature shift (22oC-30oC) was required to activate the expression of the siRNA. For the effects of this shift alone no controls were provided. The functional memory data are nice and consistent with the hypothesis, but can it be excluded that the temperature shift (rather than the Mrj) knockdown has caused the memory defects? I think it is crucial to include the proper controls or use a different knockdown approach that does not require temperature shifts or even use the knockout flies.

      We have now performed the memory experiments with Mrj knockout flies. Our experiments show at 16 and 24-hour time points Mrj knockout flies have significantly reduced memory in comparison to the control wildtype. This data is now added in Figure 6B.

      Figure 6:

      The finding of a co-IP between Rpl18 and Mrj (one-directional only) by no means suffices to conclude that Mrj may interact with nascent Orb2 chains here (which would be the relevant finding here). The fact that Mrj is a self-oligomerising protein (also in vitro, so irrespective of ribosomal associations!), and hence is found in all fractions in a sucrose gradient, also is not a very strong case for its specific interaction with polysomes. The finding that there is just more self-oligomerizing Orb2A co-sedimenting with polysomes in sucrose gradients neither is evidence for a direct effect of Mrj enhancing association of Orb2A with the translating ribosomes even though it would fit the hypothesis. So all in all, I think the data in this figure and non-conclusive and the related conclusions should be deleted.

      We have now performed the reverse co-IP between Rpl18-Flag and Mrj-HA using anti-HA antibody and could detect an interaction between the two. This data is now added in Supplementary Figure 6A.

      To address if Mrj is a self-oligomerizing protein that can migrate to heavier polysome fractions due to its size, we have loaded recombinant Mrj on an identical sucrose gradient as we use for polysome analysis. Post ultra-centrifugation we fractionated the gradients and checked if Mrj can be detected in the fraction numbers where polysomes are present. In this experiment, we could not detect recombinant Mrj in the heavier polysome fractions (data presented in Supplementary Figure 6B). Overall, our observations of Mrj-Rpl18 IPs, the presence of cellularly expressed Mrj in polysome fractions, and the absence of recombinant Mrj from these fractions, suggest a likelihood of Mrj’s association with the translating ribosomes.

      On the reviewer’s point of us concluding Mrj may interact with nascent Orb2 chains, we have not mentioned this possibility in the manuscript as we don’t have any evidence to suggest this. We have also added a sentence: This indicates that in presence of Mrj, the association of Orb2A with the translating ribosomes is enhanced, however, if this is a consequence of increased Orb2A oligomers due to Mrj or caused by interaction between polysome-associated Orb2A and Mrj will need to be tested in future.

      Based on these above-mentioned points, we hope we can keep the data and conclusions of this section.

      Overall, provided that proper controls/additional data can be provided for the key experiments of memory consolidation, I find this an intriguing study that would point towards a role of a molecular chaperone in controlling memory functions via regulating the oligomeric status of a prion-like protein and that is worthwhile publishing in a good journal.

      However, in terms of mechanistical interpretations, several points have to be reconsidered (see remarks on figure 1,4); this pertains especially to what is discussed on page 13. In addition, I'd like the authors to put their data into the perspective of the findings that in differentiated neurons DNAJB6 levels actually decline, not incline (Thiruvalluvan et al, 2020), which would be counterintuitive if these proteins are playing a role as suggested here in memory consolidation.

      We have addressed the comments on Figures 1 and 4 earlier. We have also added new memory experiment’s data with the Mrj knockout in Figure 6.

      We have attempted to put the observations with Drosophila Mrj in perspective to observations in Thiruvalluvan et al, on human DnaJB6 in the discussions as follows:

      Can our observation in Drosophila also be relevant for higher mammals? We tested this with human DnaJB6 and CPEB2. In mice CPEB2 knockout exhibited impaired hippocampus-dependent memory (Lu et al, 2017), so like Drosophila Orb2, its mammalian homolog CPEB2 is also a regulator of long-term memory. In immunoprecipitation assay we could detect an interaction between human CPEB2 and human DnaJB6, suggesting the feasibility for DnaJB6 to play a similar role to Drosophila Mrj in mammals. However, as the human DnaJB6 level was observed to undergo a reduction in transitioning from ES cells to neurons, (Thiruvalluvan et al, 2020) how this can be reconciled with its possible role in the regulation of memory. We speculate, such a reduction if is happening in the brain will occur in a highly regulatable manner to allow precise control over CPEB2 oligomerization only in specific neurons where it is needed and the reduced levels of DnaJB6 is probably sufficient to aid CPEB oligomerization Alternatively, there may be additional chaperones that may function in a stage-specific manner and be able to compensate for the decline in levels of DNAJB6.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary: The manuscript describes the role of the Hsp40 family protein Mrj in the prion-like oligomerization of Orb2. The authors demonstrate that Mrj promotes the oligomerization of Orb2, while a loss in Mrj diminishes the extent of Orb2 oligomerization. They observe that while Mrj is not an essential gene, a loss in Mrj causes deficiencies in the consolidation of long-term memory. Further, they demonstrate that Mrj associates with polysomes and increases the association of Orb2 with polysomes.

      Major comments: None

      Minor comments:

      1. In the section describing the chaperone properties of Mrj in clearing Htt aggregates (Fig 2), the legend describes that "Mrj-HA constructs are more efficient in decreasing Htt aggregation compared to Mrj-RFP". It would be helpful to add Mrj-RFP to the quantification in Fig 2G to know exactly the difference in efficiency. Is there an explanation for why the 2 constructs behave differently?

      We have added the quantitation of Htt aggregates in presence of Mrj-RFP in the revised version (Data presented in Figure 2G). While the efficiency of Mrj-RFP to decrease Htt aggregates is significantly less in comparison to Mrj-HA, it is still significantly better in comparison to the control CG7133-HA construct. It is possible, due to the tagging of Mrj with a larger tag (RFP), this reduces its ability to decrease the Htt aggregates in comparison to the construct where Mrj is tagged with a much smaller HA tag.

      Figs A, B, C, G need to have quantification of the percentage of colocalization with details about the number of cells quantified for each experiment.

      We have now added the intensity profile images and colocalization quantitation (pearson’s coefficient) in the Supplemental Figure 5A and B. This quantitation is done from multiple ROI’s taken from at 4-6 cells.

      In Fig 6 B, C, F, G it would be helpful to label the 40S, 60S and 80S peaks in the A 254 trace.

      We have now labeled the 80S, and polysome peaks in the Figure 7B, C, F and G. We could not separate the 40S and 60S peaks in the A254 trace.

      It's interesting that Mrj has opposing functions with regard to aggregation when comparing huntingtin with Orb2. From the literature presented in the discussion, it appears as though chaperones including Mrj have an anti-aggregation role for prions. It would be helpful to have more discussion around why, in the case of Orb2, this is different. The discussion states that "The only Hsp40 chaperone which was found similar to Mrj in increasing Orb2's oligomerization is the yeast Jjj2 protein" - this point needs elaboration, as well as a reference.

      In the discussions section we have now added the following speculations on this:

      One question here is why Mrj behaves differently with Orb2 in comparison to other amyloids. Orb2 differs from other pathogenic amyloids in its extremely transient existence in the toxic intermediate form (Hervás et al, 2016). For the pathogenic amyloids, since they exist in the toxic intermediate form for longer, Mrj probably gets more time to act and prevent or delay them from forming larger aggregates. For Orb2, Mrj may help to quickly transition it from the toxic intermediate state, thereby helping this state to be transient instead of longer. An alternate possibility is post-transition from the toxic intermediate state, Mrj stabilizes these Orb2 oligomers and prevents them from forming larger aggregates. This can be through Mrj interacting with Orb2 oligomers and blocking its surface thereby preventing more Orb2 from assembling over it. Another difference between the Orb2 oligomeric amyloids and the pathogenic amyloids is in the nature of their amyloid core. For the pathogenic amyloids, this core is hydrophobic devoid of any water molecules, however for Orb2, the core is hydrophilic. This raises another possibility that if the Orb2 oligomers go beyond a certain critical size, Mrj can destabilize these larger Orb2 aggregates by targeting its hydrophilic core.

      On the Jjj2 point raised by the reviewer, we have added the (Li et al, 2016a) reference now and elaborated as:

      The only Hsp40 chaperone which was found similar to Mrj in increasing Orb2’s oligomerization is the yeast Jjj2 protein. In Jjj2 knockout yeast strain, Orb2A mainly exists in the non-prion state, whereas on Jjj2 overexpression the non-prion state could be converted to a prion-like state. In S2 cells coexpression of Jjj2 with Orb2A lead to an increase in Orb2 oligomerization (Li et al, 2016a). However, Jjj2 differs from Mrj, as when it is expressed in S2 cells, we do not detect it to be present in the polysome fractions.

      The Jjj2 polysome data is now presented in Supplementary Figure 6C.

      Reviewer #2 (Significance (Required)):

      General assessment:

      Overall, the work is clearly described and the manuscript is very well-written. The motivation behind the study and its importance are well-explained. I only have minor comments and suggestions to improve the clarity of the work. The study newly describes the interaction between the chaperone Mrj and the translation regulator Orb2. The experiments that the screen for proteins that interact with Orb2 and promote its oligomerization are very thorough. The experiments that delve into the role of Mrj in protein synthesis are a good start, and need to be explored further, but that is beyond the scope of this study.

      Advance: The study describes a new interaction between the chaperone Mrj and the translation regulator Orb2. The study is helpful in expanding our knowledge of prion regulators as well factors that affect memory acquisition and consolidation.

      Audience: This paper will be of most interest to basic researchers.

      My expertise is in Drosophila genetics and neuronal injury.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      The manuscript submitted by Desai et al. identifies a chaperone of the Hsp40 family (Mrj) that binds Orb2 and modulates its oligomerization, which is critical for Orb2 function in learning and memory in Drosophila. Orb2 are proteins with prion-like properties whose oligomerization is critical for their function in the storage of memories. The main contribution of the article is the screen of Hsp40 and Hsp70-family proteins that bind Orb2. The authors show IP results for all the candidates tested, including those that bind Fig. 1) and those that don't (Supp Fig 3). There is also a figure devoted to examining the interaction of Mrj with polyglutamine models (Htt). They also generate a KO mutant that is viable and shows no gross defects or protein aggregation. Lastly, they show that the silencing of Mrj in the mushroom body gamma neurons results in weaker memories in a courtship paradigm. Although the data is consistent and generally supportive of the hypothesis, key details are missing in several areas, including controls. Additionally, the interpretation of some results leaves room for debate. Overall, this is an ambitious article that needs additional work before publication.

      Specific comments:

      1. General concern over the interpretation of IP experiments and colocalization. These experiments don't necessarily reflect direct interactions. They are consistent with direct interaction but not the only explanation for a positive IP or colocalization.

      This paper is centred on the interaction between Orb2A and Mrj, which we have detected using immunoprecipitation. The reviewer’s concern here is, this experiment is not able to distinguish if this can be a direct protein-protein interaction or if the two proteins are part of a complex.

      To address this concern we have used purified recombinant protein-based pulldowns. Our experiments with purified protein pulldowns (GST tagged Mrj from E.coli with Orb2A from E.coli or Orb2A-GFP from Sf9 cells) suggest Orb2A and Mrj can directly interact amongst themselves. This data is now presented in Figure 1J and K.

      The Huntingtin section has a few concerns. The IF doesn't show all controls and the quantification is not well done in terms of what is relevant. A major problem is the interpretation of Fig 2F. The idea is that Mrj prevents the aggregation of Htt, which is the opposite of what is observed with Orb2. The panel actually shows a large Htt aggregate instead of multiple small aggregates. This has been reported before in Drosophila and other systems with different polyQ models. Mrj and other Hsp40 and Hsp70 proteins modify Htt aggregation, but in an unexpected way. This affects the model shown in Fig. 6H. Lastly, Fig 2H and 2I show very different level of total Htt.

      In Figure 2F of the previously submitted manuscript, we have shown representative images of HttQ103-GFP cells coexpressing with a control DnaJ protein CG7133-HA and Mrj-HA. In Figure 2G we quantitated the number of cells showing aggregates within the population of doubly transfected cells. On the reviewer’s point of figure 2F showing large Htt aggregates instead of multiple small aggregates, we do not see a large Htt aggregate in presence of Mrj in this figure, the pattern looks diffused here and very different from the control CG7133 where the aggregates are seen. We have performed the same experiment with a different Htt construct (588 amino acids long fragment) tagged with RFP, and here also we notice in presence of Mrj, the aggregates are decreased and the expression pattern looks diffused (Supplementary Figure 4E, 4F).

      If the comment on large Htt aggregates in presence of Mrj is concerning figure 2E, here we show Mrj-RFP to colocalize with the Htt aggregates. Here, even though Mrj-RFP colocalizes with Htt aggregates, it rescues the Htt aggregation phenotype as in comparison to the control CG7133, the number of cells with Htt aggregates is still significantly less here. We have added this quantitation of rescue by Mrj-RFP in the revised manuscript now. The observation of colocalization of Mrj-RFP with Htt aggregates is similar to previous reports of chaperones rescuing Htt aggregation and yet showing colocalization with the aggregates. Both Hdj-2 and Hsc70 suppress Htt aggregation and yet were observed to colocalize with Htt aggregates in the cell line model as well as in nuclear inclusions in the brain (Jana et al, 2000). In a nematode model of Htt aggregation, DNJ-13 (DnaJB-1), HSP-1 (Hsc70), and HSP-11 (Apg-2) were shown to colocalize with Htt aggregates and yet decrease the Htt aggregation (Scior et al, 2018). Hsp70 was also found to colocalize with Htt aggregates in Hela cells (Kim et al, 2002).

      Regarding Figures 2H and 2I, while figure 2H is of an SDS-PAGE to show no difference in the levels of monomeric HttQ103 (marked with *) in presence of Mrj and the control CG7133, figure 2I is for the same samples ran in an SDD-AGE where reduced amount of Htt oligomers as seen with the absence of a smear in presence of Mrj. The apparent difference in Htt levels between 2H and 2I is due to the detection of Htt aggregates/oligomers in the SDD-AGE which are unable to enter the SDS-PAGE and hence undetected. In Supplementary Figure 4E, similar experiments were done with the longer Htt588 fragment and here we notice in the SDD-AGE reduced intensity of the smear made up of Htt oligomers, again suggesting a reduction in Htt aggregates. Thus our results are not in contradiction to previous studies where Mrj was found to rescue Htt aggregate-associated toxicity.

      Endogenous expression of Mrj using Gal4 line: where else is it expressed in the brain / head and in muscle. Fig 3G shows no muscle abnormalities but no evidence is shown for muscle expression. It is nice that Fig 3E and F show no abnormal aggregates in the Mrj mutant, but this would be maybe more interesting if flies were subjected to some form of stress.

      We have now added images of the brain and muscles to show the expression pattern of Mrj. Using Mrj Gal4 line and UAS- CD8GFP, we noticed enriched expression in the optic lobes, mushroom body, and olfactory lobes. We also noticed GFP expression in the larval muscles and neuromuscular junction synaptic boutons. This data is now presented in Supplementary Figure 5C, D, E and F.

      On the reviewer’s point of subjecting the Mrj KO flies to some form of stress, we have not performed this. We have added in the discussions a note of caution, that loss of Mrj may still result in a protein aggregation-related disease phenotype, probably under a sensitized condition of certain stresses which is not tested in this manuscript.

      Fig. 5B shows no Mrj detectable from head homogenates in flies silencing Mrj in neurons with Elav-Gal4. It would be nice if they could show that ONLY neurons express Mrj in the head. Also noted, Elav-Gal4 is a weak driver, so it is surprising that it can generate such robust loss of Mrj protein

      We have used an X chromosome Elav Gal4 driver to drive the UAS-Mrj RNAi line and here we could not detect Mrj in the western. To address the reviewer’s point on the glial contribution towards expression of Mrj, we used a Glial driver Repo Gal4 to drive Mrj RNAi. In this experiment, we did not detect any difference in Mrj levels between the control and the Mrj RNAi line (presented now in Supplementary Figure 5G). We also used the Mrj knockout Gal4 line to drive NLS-GFP and immunostained these using a glial marker anti-Repo antibody. Here, we were able to detect cells colabelled by GFP as well as Repo, suggesting Mrj is likely to be present in the glial cells (presented now in Supplementary Figure 5H). We also looked in the literature and found a reference where it was shown that Elav protein as well as Elav Gal4 at earlier stages of development expresses in neuroblasts and in all Glia (Berger et al, 2007). So, another possibility is when we are driving Mrj RNAi using Elav Gal4, this knocks down Mrj in both the neurons as well as in the glia.

      Fig 4-Colocalization of Orb2 with Mrj lacks controls. The quantification could describe other phenomena because the colocalization is robust but the numbers shown describe something else.

      We have now added the intensity profile and colocalization quantitation (pearson’s coefficient) in Supplemental Figure 5A and B. This quantitation is done from multiple ROI’s taken from 4-6 cells. Also, to suggest the interaction of Orb2 isoforms with Mrj, we are not depending on colocalization alone and have used immunoprecipitation experiments to support our observations.

      Fly behavior. The results shown for Mrj RNAi alleles is fine but it would be more robust if this was validated with the KO line AND rescued with Mrj overexpression.

      We have now performed memory assays with the Mrj knockout. Our experiments showed Mrj knockouts to show significantly decreased memory in comparison to wild-type flies at 16 and 24-hour time points (presented in Figure 6B). We have not been able to make an Mrj Knockout-UAS Mrj recombinant fly, most likely due to the closeness of the two with respect to their genomic location in second chromosome.

      Minor comments:

      Please, revise minor errors, there are several examples of words together without a space.

      We have identified the words without space and have corrected them now.

      Intro: describe the use of functional prions. Starting the paragraph with this sentence and then explaining what prion diseases are is a little confusing. Also "prion proteins" can be confusing because the term refers to PrP, the protein found in prions.

      We have now altered the introduction and have described functional prions.

      Results, second subtitle in page 5. This sentence is quite confusing using prion-like twice

      We have now changed the heading to “Drosophila Mrj converts Orb2A from non-prion to a prion-like state.”

      Page 6: "conversion from non-prion to prion-like form...". This can be presented differently. Prion-like properties are intrinsic, proteins don't change from non-prion to prion-like. They may be oligomeric or monomeric or highly aggregated but the prion-like property doesn't change

      We agree with the reviewer's point of Prion-like properties are intrinsic, but the protein might or might not exist in the prion-like state or confirmation. When we are using the term conversion from non-prion to prion-like form we mean to suggest a conformational conversion leading to the eventual formation of the oligomeric species. We also noted the terminology of non-prion to prion-like state change is used in several papers (Satpute-Krishnan & Serio, 2005; Sw & Yo, 2012; Uptain et al, 2001).

      Scale bars and text are too small in several figures

      We have now mentioned in the figure legends the size of the scale bars. For several images we have made the scale bars also larger.

      Not sure why Fig 4C is supplemental, seems like an important piece of data.

      We have kept this data in the supplemental data as we performed this experiment with recombinant protein which is tagged with 6X His and we are not sure if this high degree of oligomerization/aggregation of recombinant Mrj and further precipitation over time, happens inside the cells/ brain.

      Intro to Mrj KO in page 7 is too long. Most of it belongs in the discussion

      We have now moved the portions on mammalian DNAJB6 which were earlier in the results section to the discussions section.

      Change red panels in IF to other color to make it easier for colorblind readers.

      We have now changed the red panels to magenta. We apologize for our figures not being colorblind friendly earlier.

      The discussion is a little diffuse by trying to compare Orb2 with mammalian prions and amyloids and yeast prions.

      We looked into the functional prion data and couldn’t find much on chaperone mediated regulation of these. Also, we felt comparing with the amyloids and yeast prions brings out the contrast with respect to the Mrj mediated regulatory differences between the two.

      Reviewer #3 (Significance (Required)):

      This is a paper with a broad scope and approaches. The paper describes the role of Mrj in the oligomerization of Orb2 by protein biochemistry techniques and determine the role of loss of Mrj in the mushroom bodies in fly behavior.

      The audience for this content is basic research and specialized. The role of Mrj in Orb2 aggregation and function sheds new light on the mechanisms regulating the function of this protein involved in a novel mechanism of learning and memory.

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    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #1

      Evidence, reproducibility and clarity

      In this MS, Mrj - a member of the JDP family of Hsp70 co-chaperones was identified as a regulator of the conversion of Orb2A (the Dm ortholog of CPEB) to its prion-like form.

      In drosophila, Mrj deletion does not cause any gross neurodevelopmental defect nor leads to detectable alterations in protein homeostasis. Loss of Mrj, however, does lead to altered Orb2 oligomerization. Consistent with a role of prion-like characteristics of Orb2 in memory consolidation, loss of Mrj results in a deficit in long-term memory.

      Aside from the fact that there are some unclarities related to the physicochemical properties of Orb2 and how Mrj affects this precisely, the finding that a chaperone could be important for memory is an interesting observation, albeit not entirely novel.

      In addition, there are several minor technical concerns and questions I have that I feel the authors should address, including a major one related to the actual approach used to demonstrate memory deficits upon loss of Mrj.

      Significance

      Figure 1 (plus related Supplemental figures):

      • There seem to be two isoforms of Mrj (like what has been found for human DNAJB6). I find it striking to see that only (preferentially?) the shorter isoform interacts with Orb2. For DNAJB6, the long isoform is mainly related to an NLS and the presumed substrate binding is identical for both isoforms. If this is true for Dm-Mrj too, the authors could actually use this to demonstrate the specificity of their IPs where Orb2 is exclusively non-nuclear?
      • I would be interested to know a bit more about the other 5 JDPs that are interactors with Orb2: are the human orthologs of those known? It is striking that these other 5 JDPs interact with Orb2 in Dm (in IPs) but have no impact on Sup35 prion behavior. Importantly, this does not imply they may not have impact on the prion-like behavior of other Dm substrates, including Dm-Orb2.
      • The data in panels H,I indeed suggest that Mrj1 alters the (size of) the oligomers. It would be important to know what is the actual physicochemical change that is occurring here. The observed species are insoluble in 0.1 % TX100 but soluble in 0.1% SDS, which suggest they could be gels, but not real amyloids such as formed by the polyQ proteins that require much higher SDS concentrations (~2%) to be solubilized. This is relevant as Mrj1 reduces polyQ amyloidogenesis whereas is here is shown to enhance Orb2A oligomerization/gelidification. In the same context, it is striking to see that without Mrj the amount of Orb2A seems drastically reduced and I wonder whether this might be due to the fact that in the absence of Mrj a part of Orb2A is not recovered/solubilized due to its conversion for a gel to a solid/amyloid state? In other words: Mrj1 may not promote the prion state, but prevents that state to become an irreversible, non-functional amyloid?

      Figure 2 (plus related Supplemental figures):

      • It may be good for clarity to refer to the human Mrj as DNAJB6 according to the HUGO nomenclature. Also, the first evidence for its oligomerization was by Hageman et al 2010.
      • It is striking to see that Mrj co-IPs with Hsp70AA, Hsp70-4 but not Hsp70Cb. The fact that interactions were detected without using crosslinking is also striking given the reported transient nature of J-domain-Hsp70 interactions Together, this may even suggest that Mrj-1 is recognized as a Hsp70 substrate (for Hsp70AA, Hsp70-4 but not Hsp70Cb) rather than as a co-chaperone. In fact, a variant of Mrj-1 with a mutation in the HPD motif should be used to exclude this option.
      • The rest of these data reconfirm nicely that Mrj/DNAJB6 can suppress polyQ-Htt aggregation. Yet note that in this case the oligomers that enter the agarose gel are smaller, not bigger. This argues that Mrj is not an enhancer of oligomerization, but rather an inhibitor of the conversion of oligomers to a more amyloid like state.

      Figure 3:

      • The finding that knockout of DNAJB6 in mice is embryonic lethal is related to a problem with placental development and not embryonic development (Hunter et al, 1999; Watson et al, 2007, 2009, 2011) as well recognized by the authors. Therefore, the finding that deletion of Dm-Mrj has no developmental phenotype in Drosophila may not be that surprising.
      • It is a bit more surprising that Mrj knockout flies showed no aggregation phenotype or muscle phenotype, especially knowing that DNAJB6 mutations are linked to human diseases associated with aggregation (again well recognized by the authors). However, most of these diseases are late-onset and the phenotype may require stress to be revealed. So, while important to this MS in terms of not being a confounder for the memory test, I would like to ask the authors to add a note of caution that their data do not exclude that loss of Mrj activity still may cause a protein aggregation-related disease phenotype. Yet, I also do think that for the main message of this MS, it is not required to further test this experimentally.

      Figure 4:

      • IPs were done with Orb2A as bite and clearly pulled down substantial amounts of GFP-tagged Mrj. For interactions with Orb2B, a V5-tagged Mrj was use and only a minor fraction was pulled down. Why were two different Mrj constructs used for Arb2A and Orb2b?
      • In addition, I think it would be important what one would see when pulling on Mrj1, especially under non-denaturing conditions and what is the status of the Orb2 that is than found to be associated with Mrj (monomeric, oligomeric and what size).
      • This also relates to my remark at figure 1 and the subsequent fractionation experiments they show here in which there is a slight (not very convincing) increase in the ratio of TX100-soluble and insoluble (0.1% SDS soluble) material. My question would be if there is a remaining fraction of 0.1% insoluble (2% soluble) Orb2 and how Mrj affects that? As stated before, this is (only) mechanistically relevant to understanding why there is less oligomers of Orb2 in terms of Mrj either promoting it or by preventing it to transfer from a gel to a solid state. The link to the memory data remains intriguing, irrespective of what is going on (but also on those data I do have several comments: see below).
      • I also find the sentence that "Mrj is probably regulating the oligomerization of endogenous Orb2 in the brain" somewhat an overstatement. I would rather prefer to say that the data show that Mrj1 affects the oligomeric behavior/status of Orb2.

      Figure 5:

      • To my knowledge, the Elav driver regulates expression in all neurons, but not in glial cells that comprise a significant part of the fly heads/brain. The complete absence of Mrj in the fly-heads when using this driver is therefore somewhat surprising. Or do we need to conclude from this that glial cells normally already lack Mrj expression?
      • Why not use these lines also for the memory test for confirmation? I understand the concerns of putative confounding effects of a full knockdown (which were however not reported), but now data rely only on the mushroom body-specific knockdown for the 201Y Gal4 line, for which the knockdown efficiency is not provided. But even more so, here a temperature shift (22oC-30oC) was required to activate the expression of the siRNA. For the effects of this shift alone no controls were provided. The functional memory data are nice and consistent with the hypothesis, but can it be excluded that the temperature shift (rather than the Mrj) knockdown has caused the memory defects? I think it is crucial to include the proper controls or use a different knockdown approach that does not require temperature shifts or even use the knockout flies.

      Figure 6:

      The finding of a co-IP between Rpl18 and Mrj (one-directional only) by no means suffices to conclude that Mrj may interact with nascent Orb2 chains here (which would be the relevant finding here). The fact that Mrj is a self-oligomerising protein (also in vitro, so irrespective of ribosomal associations!), and hence is found in all fractions in a sucrose gradient, also is not a very strong case for its specific interaction with polysomes. The finding that there is just more self-oligomerizing Orb2A co-sedimenting with polysomes in sucrose gradients neither is evidence for a direct effect of Mrj enhancing association of Orb2A with the translating ribosomes even though it would fit the hypothesis. So all in all, I think the data in this figure and non-conclusive and the related conclusions should be deleted.

      Overall, provided that proper controls/additional data can be provided for the key experiments of memory consolidation, I find this an intriguing study that would point towards a role of a molecular chaperone in controlling memory functions via regulating the oligomeric status of a prion-like protein and that is worthwhile publishing in a good journal.

      However, in terms of mechanistical interpretations, several points have to be reconsidered (see remarks on figure 1,4); this pertains especially to what is discussed on page 13. In addition, I'd like the authors to put their data into the perspective of the findings that in differentiated neurons DNAJB6 levels actually decline, not incline (Thiruvalluvan et al, 2020), which would be counterintuitive if these proteins are playing a role as suggested here in memory consolidation.

    1. Reviewer #3 (Public Review):

      In empirical data, the dependence of microbial diversity on environmental temperature can take multiple different functional forms, while the previous theory has not established a clear understanding of when the temperature-dependence of diversity should take a particular form, and why. The authors seek to understand what forms are possible, and when they will occur, via analysis of the feasibility (i.e. positivity) of Lotka-Volterra equation solutions. This is combined with an assumption for the way that species' growth rates depend on temperature, along with an assumption for the way species interaction rates depend on temperature. Together, this completely specifies the form of the Lotka-Volterra equations, and whether all species in the model can coexist indefinitely at a given temperature, or whether only a lower-diversity subset can persist.

      The overall goal is valuable, and the overall approach of using this classic model of species interactions is justifiable. My main question marks relate to the way the conditions on feasibility (i.e. when all species will have positive equilibria), whether and when we need to consider the stability of these feasible solutions, and finally how general the way in which model parameters are specified to depend on temperature. I will expand on these three issues below. A more minor issue is that the authors set up this problem with extensive reference to the interaction of consumers and resources, referencing previous approaches that explicitly model these. Since resources are not explicitly present in the Lotka-Volterra formalism, it would be helpful to have a clearer justification for the authors' rationale in choosing this kind of model.

      (1) Conditions on growth and interaction rates for feasibility and stability. The authors approach this using a mean field approximation, and it is important to note that there is no particular temperature dependence assumed here: as far as it goes, this analysis is completely general for arbitrary Lotka-Volterra interactions.

      However, the starting point for the authors' mean field analysis is the statement that "it is not possible to meaningfully link the structure of species interactions to the exact closed-form analytical solution for [equilibria] 𝑥^*_𝑖 in the Lotka-Volterra model.

      I may be misunderstanding, but I don't agree with this statement. The time-independent equilibrium solution with all species present (i.e. at non-zero abundances) takes the form

      x^* = A^{-1}r

      where A is the inverse of the community matrix, and r is the vector of growth rates. The exceptions to this would be when one or more species has abundance = 0, or A is not invertible. I don't think the authors intended to tackle either of these cases, but maybe I am misunderstanding that.

      So to me, the difficulty here is not in writing a closed-form solution for the equilibrium x^*, it is in writing the inverse matrix as a nice function of the entries of the matrix A itself, which is where the authors want to get to. In this light, it looks to me like the condition for feasibility (i.e. that all x^* are positive, which is necessary for an ecologically-interpretable solution) is maybe an approximation for the inverse of A---perhaps valid when off-diagonal entries are small. A weakness then for me was in understanding the range of validity of this approximation, and whether it still holds when off-diagonal entries of A (i.e. inter-specific interactions) are arbitrarily large. I could not tell from the simulation runs whether this full range of off-diagonal values was tested.

      As a secondary issue here, it would have been helpful to understand whether the authors' feasible solutions are always stable to small perturbations. In general, I would expect this to be an additional criterion needed to understand diversity, though as the authors point out there are certain broad classes of solutions where feasibility implies stability.

      (2) I did not follow the precise rationale for selecting the temperature dependence of growth rate and interaction rates, or how the latter could be tested with empirical data, though I do think that in principle this could be a valuable way to understand the role of temperature dependence in the Lotka-Volterra equations.

      First, as the authors note, "the temperature dependence of resource supply will undoubtedly be an important factor in microbial communities"

      Even though resources aren't explicitly modeled here, this suggests to me that at some temperatures, resource supply will be sufficiently low for some species that their growth rates will become negative. For example, if temperature dependence is such that the limiting resource for a given species becomes too low to balance its maintenance costs (and hence mortality rate), it seems that the net growth rate will be negative. The alternative would be that temperature affects resource availability, but never such that a limiting resource leads to a negative growth rate when a taxon is rare.

      On the other hand, the functional form for the distribution of growth rates (eq 3) seems to imply that growth rates are always positive. I could imagine that this is a good description of microbial populations in a setting where the resource supply rate is controlled independently of temperature, but it wasn't clear how generally this would hold.

      Secondly, while I understand that the growth rate in the exponential phase for a single population can be measured to high precision in the lab as a function of temperature, the assumption for the form of the interaction rates' dependence on temperature seems very hard to test using empirical data. In the section starting L193, the authors seem to fit the model parameters using growth rate dependence on temperature, but then assume that it is reasonable to "use the same thermal response for growth rates and interactions". I did not follow this, and I think a weakness here is in not providing clear evidence that the functional form assumed in Equation (4) actually holds.

    1. Dynamic Scoping Modern programming languages implement global and/or module scope and/or lexical scope. A name defined globally is available everywhere in the code. A name given module scope is only directly accessible within that module (and may be available outside if qualified with the module name). A name defined with lexical scope is available inside the current lexical block and (typically) the blocks it encloses. All three of these are statically defined: the meaning of a variable name can be determined at compilation time. In the past, languages such as Perl also offered dynamic scope. This looks a little like lexical scope, except the names defined in a block are available not just in that block but also in all the functions invoked by that block, and functions invoked below them, and so on. The scope is only determined at runtime: the name exists for the duration of the block that defines it, and it exists in all functions executed during that time. As you can imagine, this was both powerful and widely abused: it’s hard to know just what a name means when its definition depends on the execution flow. This is one reason we don’t often see dynamic scoping in current languages. Unison’s abilities are a form of dynamic scoping. However, they overcome many of the issues with previous kinds of dynamic scoping because they are fully type safe. You cannot accidentally use a name injected freom a higher context, and you always know where every name comes from.

      This is really fascinating! In some ways this makes me think of React's context which enables passing data deeply down a component tree.

    1. . They were for the most partcut from extensive typescripts of his, other copies of which stillexist. Some few were cut from typescripts which we have notbeen able to trace and which it is likely that he destroyed but forthe bits that he put in the box.

      In Zettel, the editors indicate that many of Wittgenstein's zettels "were for the most part cut from extensive typescripts of his, other copies of which still exist." Perhaps not knowing of the commonplace book or zettelkasten traditions, they may have mistook the notes in his zettelkasten as having originated in his typescripts rather than them having originated as notes which then later made it into his typescripts!

      What in particular about the originals may have made them think it was typescript to zettel?

    1. Author Response

      Reviewer #1 (Public Review):

      Part 1: Type 2 deiodinase

      Table I is supposed to clarify and summarize the results but brings confusion. The text says that table I supports the claim that "in the cerebellum, Luc-mRNA was lower in the Ala92-Dio2 mice" whereas figure 1G does not show any difference. It is unclear whether Table I and figure 1 report the same data, and what the statistical tests are actually addressing (effect of genotype vs effect of treatment, whereas what matters here is only the interaction between genotype and treatment). Overall, it is not acceptable to present quantitative data without giving numbers, standard deviation, p-value, etc. as in Table I.

      Thank you. We agree with the reviewer. We intended to minimize the amount of data presented, which was already very large, and therefore only presented the ratios of thr/alaDio2 and which created confusion. This part was removed from the new version of the MS.

      Also, evaluating T3 signaling by only looking at the luc reporter and the Hprt housekeeping gene is not always sufficient (many T3 responsive genes can be found in the literature and more than one housekeeping gene should be used as a reference).

      Thank you. The advantage of using the THAI mouse is that the Luciferase reporter gene is driven by a promoter that is only sensitive to T3, which is not the case for any other T3-responsive responsive gene. The Hprt housekeeping signal was stable among the samples, and the differences observed were not caused by differences in the housekeeping gene expression. This part was removed from the new version of the MS.

      Another important weakness is that the wild-type mice have a proline at position 92. Why not include them? In absence of structural prediction, one wonders whether the mouse models are relevant to the human situation and whether the absence of the proline reduces the enzymatic activity when substituted for an Ala or Thr. This might have been addressed in previous work, but the authors should explain.

      The position 92 in DIO2 is occupied by Thr in humans. Its Km(T4) is indistinguishable from mouse Dio2 which has a Pro in the position 92 (4nM vs. 3.1nM) [PMID 8754756; PMID: 10655523]. Humans also carry an Ala in position 92. Comparing the two human alleles is the purpose of the study.

      Experiment 2: Ala92-Dio2 Astrocytes Have Limited Ability to Activate T4 to T3

      Here, the authors use primary cell cultures from different areas of the brain to measure the in vitro conversion of T4 to T3 by Dio2. They find that hippocampus astrocytes are less active, notably if they come from Ala92-Dio2 mice.

      This part has the following weaknesses:

      • This result correlates with the results from Fig 1F however the difference between Ala92-Dio2 and Thr92-Dio2 is significant in vitro, but not in vivo.

      From a deiodinase perspective, TH signaling in vivo depends on the presence of D2 (expressed in glial cells) and D3 (expressed in neurons), whereas in vitro it only depends on D2. In fact, D2 and D3 are known for a reciprocal regulation to preserve TH signaling [PMID: 33123655]. Thus, it is conceivable that the differences observed between the two models are explained by the intrinsic differences in the models.

      What matters is not the activity/astrocytes, but the total activity of the brain area, which depends on the number of astrocytes x individual activity. This is not measured.

      We respectfully disagree with the reviewer. The total D2 activity in a brain area depends fundamentally on the number of astrocytes in that area and on the intrinsic activity of the enzyme. The reviewer is suggesting that having an area denser in astrocytes expressing a catalytically less active D2 preserves a normal local T3 production. This is unlikely to be the case because we have no evidence that the density of astrocytes is different in Ala-DIo2 mice. Please keep in mind that the intimate relationship between astrocytes and neurons is what defines the microenvironment that surrounds the neuron. By separating astrocytes from neurons we are able to measure T3 production that is occurring in the neuronal microenvironment and show that cells obtained from AlaDio2 mouse produce less T3.

      • What the authors called 'primary astrocytes' is an undefined mixed population of glial cells, (including radial glial cells, stem cells, ependymal cells, progenitor cells, etc...) that proliferated differentially for more than a week in culture, among which an unknown ratio expresses Dio2. The cellular model is thus poorly characterized, and the interpretation must be prudent.

      • Again, wild-type mice are not included.

      Thank you. We now include a reference to illustrate the types and percentages of cells present in our cultures. Given that the study is to compare the Thr92 and the Ala92 alleles, which are both present in humans, we did not believe it was necessary to include them here. Please note (as explained above) the Km(T4) for Thr92 and Pro92-Dio2 is indistinguishable.

      Part 2: Neuronal response to T3 Involves MCT8 and Retrograde TH transport

      The authors next move to primary neuronal cultures, prepared from the fetal cortex which they grow in the microfluidic chamber to study axonal transport. This is a surprising move: the focus is not on Dio2 anymore, but on the MCT8 transporter, which is known in humans to play an important role to transfer TH into the brain. It is expressed mainly in glia, but also in neurons. They study the influence of endosomes and type 3 deiodinase on the trafficking and metabolism of TH.

      Thank you.

      It would be useful to perform an experiment, in which radioactive T3 is introduced in the "wrong" side of the chamber, in an attempt to detect a possible anterograde transport. This would address the possibility that Mct8 also promotes efflux and control so that the chamber is not leaking.

      Thank you. To satisfy the reviewer, we have conducted three new experiments adding 125IT3 in the MC-CS. The first experiment verified that the T3 transport in the cortical neurons also occurs anterogradely. The second experiment showed that the anterograde transport depends on mct8. The third experiment shows that D3 activity in the neuronal soma is limiting the amount of T3 transported along axons. We have included a new paragraph in the results section describing these experiments (Line 154 to 167), and a new supplementary figure (Figure 3—figure supplement 3). We have also discussed these new findings. Line 383 to 386. In every experiment, we have controlled for the possibility of leaking using one device without neurons that received radioactive T3. After 24 and 72h samples from the opposite side were obtained but did not contain any radioactive T3. We refer the reviewer to figure 1, where this is explained.

      The authors use sylichristin as an inhibitor of Mct8, to demonstrate that transport is Mct8 dependent. They do not provide indications or references that would clearly indicate that this drug is a fully selective antagonist of Mct8 (but not of Oatp1c1, Mct10, Lat1, Lat2, etc., the other TH transporters). A good alternative would be to use Mct8 KO mice as controls.

      Thank you. We refer the reviewer to reference 27 [J. Johannes et al., Silychristin, a Flavonolignan Derived from the Milk Thistle, Is a Potent Inhibitor of the Thyroid Hormone Transporter MCT8. Endocrinology 157, 1694-1701 (2016)] clearly indicating that Silychristin has a remarkable specificity toward MCT8. While using mct8 KO is interesting, it would have prevented us from testing some of our hypotheses. Being able to selectively inhibit Mct8 either in the MC-CS or in the MC-AS was a clear advantage. For example, pls see the experiment in which we add T3 in the MC-AS and the silychristin in the MC-CS (Fig. 3F). Here, we discovered new roles of mct8, such as its involvement in the release of T3 from the endosomes (line 228 to 231).

      The B27 used in primary neuronal culture might contain TH. This is not easy to know, but at least some batches do.

      Thank you. While the neurons were cultured in B27, all experiments were performed in cells incubated with neurobasal only (B27 was removed 24 earlier). This was not clear in the initial version, where there was only a vague reference in the legend of figure 3F. Now, this has been explained in the footnote of figure 3 and in line 207.

      The presence of astrocytes, probably expressing Mct8 and Dio2 is inevitable in primary neuronal cultures, and is not mentioned, but might interfere with TH metabolism.

      Thank you. We were aware that, under normal conditions, primary neuronal culture contains 25% of astrocytes. This was however minimized/eliminated by 2-day culture with the anti-mitotic cytosine arabinoside, which restricts astrocytes and microglia to <0.01 in this type of culture. This was explained in the initial version of the manuscript in the material and methods section (lines x to x) and supported with reference 53 (reference 57 in the previous version).

      Part 3: T3 Transport Triggers Localized TH Signaling in the Mouse Brain

      The authors return to in vivo experiments, implanting T3 crystals, labeled or not with radioactive iodine. They do so in the hypothalamus, where they address the retrograde transport of TH in TRH neurons, and in the cortex, looking for contralateral transport. These data are the most difficult to interpret. - First, T3 is hydrosoluble and would probably migrate without active transport.

      Thank you. Please note that at no point we characterized the T3 transport “active transport”, which by definition is an ATP-dependent process. Please note that to address the issue raised by the reviewer “migrate without active transport”, in both experimental approaches, we included controls to assess the random diffusion of T3.

      In hypothalamic studies, we used the (i) cerebral cortex and (ii) the lateral hypothalamus, a region that is immediately adjacent to the PVN. Neither region exhibit an axonal connection to the median emminence. The results, in both cases, show that the presence of radioactive T3 in the control areas was minimal when compared to the PVN (Fig. 5C).

      In the cerebral cortical studies, we included ipsi- and contra-lateral hypothalamic measurements that served as controls given the absence of a connection between the cortex and the hypothalamus. Accordingly, T3 signaling was not detected in any of the control regions (Fig. 6C previous version; now figure 5). Thus, these controls indicate that it is unlikely that the results could be explained by “migrate without active transport” of T3.

      • The authors do not demonstrate that these specific neuronal populations contain Mct8, and that these observations are connected to the previous in vitro observation (which used cortical neurons prepared from the fetus).

      Thank you. In the previous version, we did not make it abundantly clear that the EM pictures in Fig. 3D-G (previous version; now figure 2 D-G) were from neurons in the mouse motor cortex (this information is now explained in lines 149 to 151), which is where we inserted the T3 crystals. In addition, we have done more histological work on the brain M1 (cortex) of adult mice and found that many neurons in the M1 express D3 and Mct8—lines 433-434 and Figure 5 G-K (along with histological studies showing the specificity of the ab against D3 Fig S6).

      The possibility that astrocytes are involved, as reported in the literature, is not considered.

      • Here again, using Mct8KO mice would greatly help to interpret the data. In particular, the experiments with cold T3 involve a 48h delay which is very long in comparison to the 30 minutes required for long-distance transfer of radioactive T3.

      Thank you. We are unsure about the question posed by the reviewer. We are wondering how would astrocytes play a role in inter-hemispheric transport of T3? Given that astrocytes are not known to project across long distances, we have not considered this possibility. We agree that using the Mct8KO mouse could have provided supporting evidence of the role played by Mct8 in this process, but please keep in mind that the Mct8KO mouse does not have or exhibits a very mild brain phenotype, indicating that during development compensatory mechanisms have occurred that obviate the function of the transporter. This compensatory mechanism most likely involved Oatp1c1, given that only the double Mct8 and Oatp1c1 KO mouse develops a significant phenotype. This consideration directed us to the utilization of sylycristin, the highly selective Mct8 inhibitor, which disrupts the Mct8 pathway in a mouse that developed normally.

      The two approaches used to demonstrate neuronal T3 transport in vivo are fundamentally different. The hypothalamus experiments employed radioactive T3, whereas T3 crystals were used in the cerebral cortex. The first approach studied T3 transport whereas the second studied downstream T3 effects, logically requiring more time. The solid T3 implant requires time to release T3 and activate gene expression. In the original paper that utilized T3 implants in the rodent brain, samples were processed after 4 days. (Dyess et al. 1988 Endo; PMID 3139393)

      Discussion

      Considering the diversity of questions that are addressed in the study, it is not surprising that the discussion is not covering all aspects. The authors implicitly consider that their conclusions can be extended to all neurons, while they use in their experiments a variety of different populations coming from either the fetal cortex, hippocampus, adult cortex, or hypothalamus. The claim that they discovered a mechanism applying to all neurons is not supported by the data.

      Thank you. We agree with the reviewer: the high number of neuronal subtypes might include different mechanisms in T3 transport. Our studies involved cortical (central) and dorsal root ganglia (peripheral) neurons in vitro and cortical and hypothalamic neurons in vivo. Thus we think that the described mechanism is not confined to specific neuronal subtypes. The discussion has been modified accordingly (lines 402 to 411).

      Moreover, we have done immunofluorescence studies to characterize the neurons present in the MC-CS better. We have found that all the neurons residing in the MC-CS are excitatory, expressing the vesicular glutamate transporter 1 (Vglut1). But no neurons were expressing GAD67, a marker for inhibitory neurons Figure 5—figure supplement 5). This is supported by the fact that during the mouse's brain development, the embryonic days 14.5 to 17.5 is the birth date of layer 4 and 2/3 excitatory neurons (PMID: 34163074). These neurons are migrating and have not extended their cellular processes, making them more likely to survive the isolation protocol from the cortex. On the other hand, the neurons (mostly excitatory) already residing in the cortex may have expanded their processes and changed their morphology, making them less capable of surviving the isolation process.

      Some highly relevant literature is not cited. In particular:

      • Mct8 KO mice do not have marked brain hypothyroidism (PMID: 24691440) which at least suggests that the pathway discovered by the authors can be efficiently compensated by alternative pathways.

      We agree with the reviewer. As mentioned above, a compensatory mechanism triggered during development “compensates” for the inactivation of Mct8. That, however, does not mean that mct8 is not critically important. We have added that limitation to the discussion (lines 342); ref 46.

      • Dio3 KO only increases T3 signaling in a few brain areas and only in the long term (PMID: 20719855).

      Thank you. That is now included in the ms; ref 25.

      • Anterograde transport of T3 has been reported for some brainstem neurons (PMID: 10473259).

      Thank you. This was our mistake, indeed. We had worked on several versions of the manuscript that included references to her seminal work but unfortunately deleted it from the final version. This is now included in refs 48 and 49.

      Reviewer #2 (Public Review):

      Salas-Lucia et al. investigated two main questions: whether the Thr92Ala-DIO2 mutation impairs brain responsiveness to T4 therapy under hypothyroidism induction and the mechanisms of neuronal retrograde transport of T3. They find that the Thr92Ala-DIO2 mutation reduces T4-initiated T3 signaling in the hippocampus, but not in other brain regions. Using neurons cultured in microfluidic chambers, they further describe a novel mechanism for retrograde transport of T3 that depends on MCT8 and endosomal loading (possibly protecting T3 from D3-mediated cytosolic degradation) and microtubule retrotransport. Finally, they present evidence of retrograde transport of T3 through hypothalamic projections and interhemispheric connections in vivo. The main novelty of this study is the delineation of the mechanism of T3 retrograde transport in neurons. This is interesting from the cell biology perspective. The notion of impaired hippocampal T3 signaling is relevant for the cognitive outcomes of hypothyroidism and its associated therapy.

      Thank you.

      Although the data are exciting and relevant for the community, some issues need to be addressed so that conclusions are more clearly justified by data:

      1) The title and the abstract mean that dissecting this novel mechanism of T3 retrograde transport may help improve cognition or brain responsiveness in patients taking T4 or L-T3 therapy. However, how initial results (Figs 1 and 2) connect to later data is not essentially clear. For example, do Thr92Ala-DIO2 mice present altered retrograde transport of T3? Would stimulation of retrograde transport in Thr92Ala-DIO2 mice rescue neurological phenotypes? Can the authors address this experimentally?

      Thank you. These are all interesting points raised by the reviewer. However, the three reviewers felt that a connection between the studies in astrocytes and the studies in neurons was missing, and complained about the disjoint nature of the manuscript. To satisfy the reviewers we removed from the MS the experiments with astrocytes and DIO2 polymorphism, and focused on the neuronal transport of T3.

      2) Although the authors present in vivo evidence of retrograde T3 transport in the hypothalamus and motor cortex, given the select susceptibility of the hippocampus to hypothyroidism, it would be especially interesting to test whether this mechanism also happens in a hippocampal circuit (CA3-CA1 Schaffer collaterals, mossy fibers or perforant pathway).

      Thank you. We agree that this would be interesting, but technically challenging. Nonetheless, we intend to study this in the future.

      3) Table 1 should present the raw values for Ala92-DIO2 mice and treatments instead of only displaying the direction of change and statistical significance. From Panels 1E-J, it is unclear if Thr92Ala-DIO2 mice or treatments caused any real change in brain regions other than the hippocampus.

      Thank you. These experiments were removed from the new version of the MS.

      4) The authors put forward the notion that a rapid nondegradative endosome/lysosome incorporation protects T3 from D3 degradation in the cytosol. Their experiments with pharmacological modulation of MCT8, lysosomes, and microtubules are in this direction. However, they do not represent an unequivocal demonstration of this mechanism. Therefore, the authors should be more cautious in their interpretation and discuss the limitations of their approaches.

      Thank you. The manuscript was edited to reflect these important points.

      Reviewer #3 (Public Review):

      Initially, Salas-Lucia et al examined the effect of deiodinase polymorphism on thyroid hormone-medicated transcription using a transgenic animal model and found that the hippocampus may be the region responsible for altered behavior. Then, by changing to topic completely, they examined T3 transport through the axon using a compartmentalized microfluid device. By using various techniques including an electron microscope, they identified that T3 is uptaken into clathrin-dependent, endosomal/non-degradative lysosomes (NDLs), transported in the axon to reach the nucleus and activate thyroid hormone receptor-mediated transcription.

      Although both topics are interesting, it may not be appropriate to deal with two completely different topics in one paper. By deleting the topic shown in Table 1, Figure 1, and Figure 2, the scope of the manuscript can be more clear.

      Thank you. We did as suggested by the reviewer. These studies were removed from the present version of the ms.

      Their finding showing that triiodothyronine is retrogradely transported through axon without degradation by type 3 deiodinase provides a novel pathway of thyroid hormone transport to the cell nucleus and thus can contribute greatly to increasing our understanding of the mechanisms of thyroid hormone action in the brain.

      Thank you.

    1. Author Response

      Reviewer #2 (Public Review):

      In their study the authors aimed to investigate the dissemination of Enterobacterales plasmids between geographically and temporally restricted isolates recovered from different niches, such as human blood stream infections, livestock, and wastewater treatment works. By using a very strict similarity threshold (Mash distance < 0.0001) the authors identified so-called groups of near-identical plasmids in which plasmids from different genera, species, and clonal background co-clustered. Also, 8% of these groups contained plasmids from different niches (e.g., human BSI and livestock) while in 35% of these cross-niche groups plasmids carried antimicrobial resistance (AMR) genes suggesting recent transfer of AMR plasmids between these ecological niches.

      Next, the authors set-out to examine the wider plasmid population structure by clustering plasmids based on 21-mer distributions capturing both coding and non-coding plasmid regions and using a data-driven threshold to build plasmid networks and the Louvain algorithm to detect the plasmid clusters. This yielded 247 clusters of which almost half of the clusters contained BSI plasmids and plasmids from at least one other niche, while 21% contained plasmids carrying AMR genes. To further assess cross-niche plasmids similarities, the authors performed an additional plasmid pangenome-like analysis. This highlighted patterns of gain and loss of accessory plasmid functions in the background of a conserved plasmid backbone.

      By comparing plasmid core gene or plasmid backbone phylogenies with chromosome core gene phylogenies, the authors assessed in more detail the dissemination of plasmids between humans and livestock. This indicated that, at least for E. coli, AMR dissemination between human and livestock-associated niches is most likely not the result of clonal spread but that plasmid movement plays an important role in cross-niche dissemination of AMR.

      Based on these data the authors conclude that in Enterobacterales plasmid spread between different ecological niches could be relatively common, even might be occurring at greater rates than estimated, as signatures of near-identity could be transient once plasmids occupy and adept to a different niche. After such a host jump, subsequent acquisition, and loss of parts of the accessory plasmid gene content, as a result of plasmid evolution after inter-host transfer, may obscure this near-identity signature. As stated by the authors, this will raise challenges for future One Health-based genomic studies.

      Strengths

      The article is well written with a clear structure. The authors have used for their analysis a comprehensive collection of more than 1500 whole genome sequenced and fully assembled isolates, yielding a dataset of more than 3600 fully assembled plasmids across different bacterial genera, species, clonal backgrounds, and ecological niches. A strong asset of the collection, especially when analyzing dissemination of AMR contained on plasmids, is that isolates were geographically and temporally restricted. Bioinformatic analyses used to discern plasmid similarity are beyond state-of-the-art. The conclusions about dissemination of plasmids between genera, species, clonal background and across ecological niches are well supported by the data. Although conclusions about inter-host plasmid dissemination patterns may have been drawn before, this is to my knowledge the first time that patterns of dissemination of plasmids have been studied at such a high-level of detail in such a well selected dataset using so many fully assembled genomes.

      Weaknesses

      One conclusion that is not entirely supported by the data is the general statement in the discussion that "cross-niche plasmid in not driven by clonal lineages". From the tanglegram, displaying the low congruence between the plasmid and chromosome core gene phylogeny in E. coli, this conclusion is probably valid for E. coli, but this not necessarily means that this is also the case for the other Enterobacterales genera and species included in this study. For these other genera, the data supporting this conclusion are not given, probably because total number of isolates for certain genera were low, or because certain niches were clearly underrepresented in certain genera.

      Thank you for reviewing our manuscript.

      We agree that this statement in the conclusion was too general, and have adapted it (lines 407-409):

      “By examining plasmid relatedness compared to bacterial host relatedness in E. coli, we demonstrated that plasmids seen across different niches are not necessarily associated with clonal lineages”

      In the limitations section of the Discussion, we have also referenced this specifically as a limitation (lines 422-424):

      “Although we evaluated four bacterial genera, 72% (1,044/1,458) of our sequenced isolates were E. coli, and so our analyses and findings are particularly focused on this species.”

      Furthermore, the BSI as well as the livestock niches were analyzed as single niches while the BSI niche included both nosocomial and community-derived BSI isolates and the Livestock niche included samples from different livestock-related hosts. Given the fact that a substantial number of plasmids were available from cattle, sheep, pigs, and poultry, it would be interesting to see whether particular livestock hosts were more frequently found in the cross-niche plasmid clusters than other livestock hosts and whether the BSI plasmids in these cross-niche clusters were predominantly of community or nosocomial origin.

      We agree that analyses which distinguish between nosocomial/community acquired BSI isolates would be interesting further work, but are beyond the scope of this study. Our analysis of the BSI/livestock cross-niche near-identical plasmid groups details the livestock hosts involved (lines 144-154). Briefly, of the n=8 BSI/livestock cross-niche groups, these involved

      • pig/poultry (1/8)

      • poultry (1/8)

      • pig (2/8)

      • sheep (3/8)

      • cattle/pig/poultry (1/8)

      We have added a note of explanation in the methods to explain how the distance threshold we use for near-identical clustering is maximally conservative at small plasmid sizes (a single SNP produces a new plasmid cluster) but remains highly conservative (tens of SNPs) at large plasmid sizes.

      We have carefully considered the point about whether particular hosts were more frequently found in cross-niche plasmid clusters. However, we do not think it is easy to infer whether a particular livestock host is represented more frequently in these cross-niche events than would be expected from chance, given the low density of the sampling.

      We have reorganised the paragraph in lines 144-154 to provide more clarity on the groups’ niches.

      “Sharing between BSI and livestock-associated isolates was supported by 8/17 cross-niche groups (n=45 plasmids). Of these, n=3/8 groups contained BSI/sheep plasmids: one group contained mobilisable Col-type plasmids, the remaining two groups contained conjugative FIB-type plasmids. Of these, one group contained plasmids carrying the AMR genes aph(3'')-Ib, aph(6)-Id, blaTEM-1, dfrA5, sul2, and the other group contained plasmids carrying the MDR efflux pump protein robA (see Materials and Methods). A further n=2/8 groups contained BSI/pig mobilisable Col-type plasmids, of which one group other carried the AMR genes aph(3'')-Ib, aph(6)-Id, dfrA14, and sul2. Lastly, n=1/8 groups contained BSI/poultry non-mobilisable Col-type plasmids, n=1/8 contained BSI/pig/poultry/influent non-mobilisable Col-type plasmids, and n=1/8 contained BSI/cattle/pig/poultry/influent mobilisable Col-type plasmids.”

      We have also added this as a limitation in the discussion (lines 424-426):

      “Additionally, we did not sample livestock-associated niches densely enough to explore individual livestock types (cattle/pigs/poultry/sheep) sharing plasmids with BSI isolates (see Appendix 1 Fig. 9).”

      We have already recognised that our culture methods may have affected our sensitivity to detect Klebsiella spp. isolates in the livestock/environmental samples – we have expanded on this to explicitly highlight that this may have affected our capacity to evaluate Klebsiella-associated plasmids (lines 443-444):

      “This limited our ability to study the epidemiology of livestock Klebsiella plasmids.”

    1. Author Response

      Reviewer #1 (Public Review):

      We would like to thank reviewer #1 for her helpful comments and would like to respond to these as follows:

      1) “Editing efficiencies were variable (99% to 0%) depending on the species, being worst for L. major.”

      It is true that the editing efficiency was different in each species and worst for L. major. However, it is important to note that these efficiencies varied not only for each species but also amongst genes and especially chosen sgRNA sequences. Variations in efficiency across sgRNAs targeting the same gene and locus is a common problem in any CRISPR approach. We made this clearer in our revised manuscript (line 670 – 673).

      2) “The use of premature termination codons also clearly raises issues for false positives and negatives, especially as there is no evidence for nonsense-mediated mRNA decay in Leishmania.”

      We have now included in our revised manuscript that it is currently unclear whether a classical nonsense-mediated decay pathway is present in Leishmania or not. If such a pathway would be present, mutant mRNAs in which a termination codon is present within the normal open reading frame would be removed (Clayton, Open Biology 2019; Delhi et al., PLoS One 2011). But if not, remaining N-terminal protein parts could be functional and may lead to false positive and negative results. However, as reviewer #2 pointed out, this may also provide extra information about functional domains of the targeted protein and highlights that our tool can not only be used to create functional null mutants by inserting premature STOP codons but also to pursue targeted mutagenesis screens (line 674 - 683).

      3) “There are already two genome-wide screening options for Leishmania, so the advantages and disadvantages of the method proposed here need to be discussed in a much more detailed and balanced way.”

      We have revised our manuscript to include in our introduction (line 36 - 73) and discussion (line 658 - 697) a better comparison of all potential tools for genome-wide screening in Leishmania, including RNAi, bar-seq and base editing screening. We highlight why we think that base editing has unique advantages.

      4) “In the "LeishGEM" project (http://www.leishgem.org) all Leishmania mexicana genes will be knocked out and each KO will be bar-coded. At the end, 170 pooled populations of 48 bar-coded mutants will be publicly available. The only real reason the authors of the current paper give for not using this approach is that it is labour-intensive. However, LeishGEM is funded and underway, with several centres involved, so that argument is weak.”

      In our original manuscript we gave multiple reasons why we think that the LeishGEdit method, which is being used for the LeishGEM screen and has been developed by the lead author of our here presented study, has clear disadvantages compared to base editing.

      As written in our original manuscript (line 709 – 716): “However, for a bar-seq screen, each barcoded mutant needs to be created individually by replacing target genes with drug selectable marker cassettes (20,21), making them extremely labour intensive and most likely “one-offs” on a genome-wide scale. Furthermore, aneuploidy in some Leishmania species can be a major challenge for gene replacement strategies as multiple rounds of transfection or isolation of clones may be required to target genes on multi-copy chromosomes. Using gene replacement approaches it is also not feasible to study multi-copy genes that have copies on multiple chromosomes. These are major disadvantages of bar-seq screening.”

      Therefore, we still think that the main disadvantage of bar-seq screening is that it is labour-intensive as each mutant needs to be created individually. The fact that LeishGEM requires five years and several research centres to knockout all genes in just one Leishmania species is proof for this argument.

      However, to clarify our position about this further, we have listed other disadvantages of the LeishGEM screen, including difficulties of sharing mutant pools between labs, possible problems in expanding mutant pools without losing uniformity, no ability to change the composition of generated pools and limited ability to distinguish between technical failures and essentiality. If any of these problems would occur, it would require a de novo generation of barcoded mutants and therefore this is an extremely labour-intensive method for large-scale screening. We also added that bar-seq screens are not feasible in Leishmania species that display extreme cases of aneuploidy, such as L. donovani (line 59 – 73).

      Despite all these disadvantages of the LeishGEdit approach for the LeishGEM project, there are of course also clear advantages, which we also point out in our introduction (line 52 – 55).

      5) “There is also a preprint describing RNAi for functional analysis in Leishmania braziliensis.”

      Although our original manuscript included the pre-print about RNAi screening in Leishmania braziliensis already (line 706-709), we understand that this deserves a stronger discussion. We have therefore highlighted now RNAi as a possible tool for genome-wide screening in selected Leishmania species in our revised introduction (line 36 - 43). However, we also argue that RNAi approaches are at the moment only available to Leishmania of the Viannia subgenus and that RNAi activity greatly varies between the species (line 36 – 43 and 665 - 669). In addition, we discuss that the use of RNAi genome-wide screens is much less specific, as usually randomly sheared genomic DNA is used to generate RNAi libraries (line 687 - 689). Since the pre-print is now published, we have replaced the pre-print publication with the peer-reviewed one.

      Reviewer #2 (Public Review):

      We would like to thank reviewer #2 for helpful comments and would like to respond to those as follows:

      1) “Line 482 - the authors wrote 'As expected, the proportion of cells showing a motility phenotype in the IFT88 targeted L. infantum population decreased further' Why is this result expected? Presumably, this is due to the fact that cells without a functional IFT system lack flagella and grow slower so can be outcompeted by faster-growing mutants. This speaks to the major caveat highlighted by the authors in the discussion and the final small-scale screen. In a population of cells, those with deleterious mutations in an essential gene or one whose disruption results in slower growth will be outcompeted by cells in which a non-deleterious mutation has occurred, which feeds into the issue of timing.”

      As the reviewer highlighted himself, deleterious mutations that result in slower growth will be outcompeted by cells in which a non-deleterious mutation has occurred. We have stated that the complete deletion of IFT88 in Leishmania mexicana has been shown to have reduced doubling time (Beneke et al., PLoS Pathogens 2019) and are therefore most likely outcompeted from the pool (line 529 – 532 and 767 - 769).

      2) “The authors show with CRK3 this process of non-deleterious mutants outcompeting deleterious mutants does result in a detectable drop in the number of parasites with specific CRK3 guides but not in those with IFT88. Is this due to the fact that the outgrowth of the non-deleterious IFT88 mutants occurs rapidly or that the mutation of the targets in IFT88 was ineffective? The data presented in Figure 5 shows that for some species at least a mutation of the IFT88 gene was possible. This might mean that for certain genes the outgrowth occurs within the first 12 days after transfections so will not be seen using this approach, without a wider study, which is beyond the scope of this manuscript it will be difficult to know.”

      As we stated in our discussion, we did not test IFT88 guides individually in L. mexicana. Therefore, the editing rate observed for the IFT88 guides in L. major and L. infantum (Fig. 5) may differ from the editing rate in L. mexicana, which is the species we used for the pooled transfection screen. It is therefore difficult to conclude why IFT88 was not depleted from the pool. This may be due to lower guide activity in L. mexicana or rapid selection of non-deleterious mutations (line 769 - 774). We are therefore planning to further optimize our system by streamlining the editing efficiency and eliminating species-specifics effects (line 735 - 745). As the reviewer highlighted, this is beyond the scope of this study.

      However, the reviewer raises a fair point about the exact timing of isolating DNA from pools, which might influence when exactly parasites with a deleterious mutation are depleted from the pool. This may differ between guides and may even be gene specific. We have added this point to our discussion (776 - 780).

      3) “The authors highlight that this base editing approach will leave potentially functional regions of the NT of proteins, which is true and may mean genes are missed. However, this may also provide extra information about the protein's function/domain structure if STOP codons in certain positions showed an effect on function whereas those in others don't.”

      We thank reviewer #2 for pointing out that functional parts of truncated proteins following base editing may actually allow to draw additional conclusions. We have included this in the manuscript (681 - 683).

    1. Author Response

      Reviewer #1 (Public Review):

      This umbrella review aims to synthesize the results of systematic reviews of the impact of the COVID-19 pandemic on various dimensions of cancer care from prevention to treatment. This is a challenging endeavor given the diversity of outcomes that can be assessed in cancer care.

      Search and review methods are good and are in line with recommendations for umbrella reviews. Perhaps one weakness of the search strategy was that only one database (Pubmed) was searched. The search strategy appears adequate, though perhaps some more search terms related to reviews and cancer could have been included. It is therefore possible that some reviews may have been missed by the search strategy.

      It is challenging to perform a good umbrella review that yields novel insights, as it is difficult to combine results from different reviews which themselves combine results from different studies with different methodologies. However, I think perhaps one of the main weaknesses of this study is that it is not clear to me what is the core objective of the umbrella review, and how analyses relate to that core objective. In other words, I do not understand based on the introduction what new information the authors are hoping to learn from their umbrella review that could not be learned from reading the individual systematic reviews, beyond a vague objective of "synthesizing" the literature. Because of this, it is not very clear to me how the data extracted and the analysis fits into the larger objectives, and what the new knowledge generated by this review is. Based on the reported results, it would appear that one of the main goals is to assess the quality of systematic reviews and of the underlying studies in the reviews, but it is hard to tell. I think there are potentially important insights this review could tell us, but the message and implications of current evidence remain for me a little confused in the current manuscript.

      We thank the reviewer for the encouraging remarks on our work, and for the useful feedback. We have now addressed all concerns as outline below.

      Reviewer #2 (Public Review):

      This umbrella review summarizes the results of systematic reviews about the impact of the COVID-19 pandemic on cancer care. PRISMA checklist is used for reporting. The literature search was performed in PubMed and systematic reviews published until November 29th, 2022 were included. The quality of included systematic reviews was appraised using the AMSTAR-2 tool and data were reported descriptively due to the high heterogeneity of 45 included studies. Based on the results of this paper, regardless of the low quality of included evidence, COVID-19 affected cancer care in many ways including delay and postponement of cancer screening, diagnosis, and treatment. Also, patients with cancer had been affected psychologically, socially, and financially during the COVID-19 pandemic.

      The main limitation of the current study is that the authors have searched only one database, which might have missed some relevant systematic reviews. Also, most of the included reviews in this paper had low and medium methodological quality.

      We thank the reviewer for this excellent remark. Guideline on umbrella reviews suggest PubMed, reference screening and an additional bibliographic database for an optimal database combination for searching systematic reviews (Goossen K et al. 2020). To follow the guidelines, and considering the specialized focused on COVID-19, in addition to Pubmed and reference screening, we also performed a search in the WHO COVID-19 Database. Furthermore, we revised the search strategy in Pubmed to include mesh terms. The search was performed by a specialized librarian with experiences in systematic review searches. Overall, we retrieve 485 new references, and found 6 new studies that met out inclusion criteria to be included in final analysis. We have now revised the manuscript to reflect the above changes, and also highlighted this as a strength of our work. In addition, we added the new detailed search strategy in the supplemental material.

    1. Author Response

      Reviewer #2 (Public Review):

      In this manuscript, the authors use an embedding of human olfactory perceptual data within a graph neural network (which they term principal odor map, or POM). This embedding is a better predictor of a diverse set of olfactory neural and behavior data than methods that use chemical features as a starting point to create embeddings. The embedding is also seen to be better for comparison of pairwise similarities (distances of various sorts) - the claim is that proximity of pairs of odors in the POM is predictive of their similarity in neural data from olfactory receptor neurons.

      A major strength of the paper is the conceptualization of the problem. The authors have previously described a graph neural net (GNN) to predict verbal odor descriptors from molecular features (here, a 2019 preprint is cited, but a newer related one in 2022 describing the POM is not cited). They now use the embedding created by that GNN to predict similarities in large and diverse datasets in olfactory neuroscience (which the authors have curated from published work). They show that predictions from POM are better than just generic chemical features. The authors also present an interesting hypothesis that the underlying latent structure discovered by the GNN relates to metabolic pathway proximity, which they claim accounts for the success in the prediction of a wide range of data (insect sensory neuron responses to human behavior). In addition to the creativity of the project, the technical aspects, are sound and thorough.

      There are some questions about the ideas, and the size of the effects observed.

      1) The authors frame the manuscript by invoking an analogy to other senses, and how naturalstatistics affect what's represented (and how similarity is defined). However, in vision or audition, the part of the world that different animals "look at" can be very different (different wavelengths, different textures and spatial frequencies, etc). It is still unresolved why any given animal has the particular range of reception it has. Each animal is presumably adapted for its ecological niche, which can have different salient sensory features. In vision, different animals pick different sound bandwidths or EM spectra. Therefore, it is puzzling to think that all animals will somehow treat chemicals the same way.

      Our assumption (an assumption of the broader interpretation, not of the analyses themselves) that all terrestrial animals have a correlated odor environment is certainly only true for some values of “correlated”. One could imagine, for example, that some animals are able to exploit food energy sources that humans cannot (for example, plants with high cellulose content), and that they might therefore be adapted to smell metabolic signatures of such plants, whereas humans would not be so adapted. This seems quite reasonable and there are probably many such examples. In future work they might be used to test the theory directly: representations might be more likely to differ across species on tasks when the relevant ecological niches are non-overlapping. We have updated the discussion to propose such future tests. However, it is also apparent that the odor environment overall is nonetheless highly correlated across species. Recent work (Mayhew et al, PNAS) showed that nearly all molecules that pass simple mass transport requirements (that should apply to all mammals, at the least) are likely to have an odor to humans, so it seems unlikely that the “olfactory blind spots” are intrinsically large.

      2) The performance index could be made clearer, and perhaps raw numbers shown beforeshowing the differences from the benchmark (Mordred molecular descriptor). For example, can we get a sense of how much variance in the data does it explain, what percent of the hold-out tests does it fit well, etc.?

      The performance index in Figure 1 is required to compare across different types of tasks, which are in turn dictated by the nature of the data (e.g. continuous vs categorical). Regression tasks yields an R2 value and categorical tasks yield an AUROC. We normalized and placed these on a single scale in order to show all of the tasks clearly together. We have added a table to the shared code (from link in Methods section, go to predictive_performance/data/dataset_performance_index_raw.csv) that shows the original (non-normalized) values, for both the POM and the benchmark(s) across multiple seeds and various metrics with the model hyper-parameters that generate the best performance.

      3) The "fitting" and predictions are in line with how ML is used for classification and regression inlots of applications. The end result is a better fit (prediction), but it's not actually clear whether there are any fundamental regularities or orders identified. The metabolic angle is very intriguing, but it looks like Mordred descriptor does a very good job as well (extended figure 5 [now Figure 2-figure supplement 5]). Is it possible to show the relation between metabolic distance and Mordred distance in Figure 2c? In fact, even there, cFP distance looks very well correlated with metabolic distance (we are talking about r= 0.9 vs r = 0.8). This could simply be due to a slightly nonlinear mapping between chemical similarity and perceptual similarity (which was used to get POM distance).

      We show additional “showdown” comparisons between metabolic distance, POM distance, and alternative distance metrics in the new Figure 2-figure supplement 3 and Figure 2-figure supplement 4. Indeed, the Mordred descriptors perform well; after all, metabolic reactants and products must be at least somewhat structurally related. But POM (derived only from human perceptual data) outperforms it significantly. Visual inspection of Figure 2c also reveals that the dispersion of structural distances (at a given metabolic distance) is just much higher than the dispersion of POM distances. This won’t change if one uses a non-linear curve fit, as it is a property of the data itself.

      It’s also worth noting while r=0.8 and r=0.9 might seem close, in terms of variance unexplained (1 - r2) they are approximately two-fold different. Reducing the unexplained variance by half seems like a meaningful difference. Alternatively, if one simulates scatter plots with correlation r=0.8 vs r=0.9, it is apparent that the latter is simply a much tighter relationship.

      4) How frequent are such examples shown in Fig 2d? Pentenal and pentenol are actually verysimilar in many ways, and it may be that Tanimoto distance is not a great descriptor of chemical similarity. cFP edit distance is quite small, just like metabolic distance. The thiol example on the right is much better. Also, even in Fig 2C POM vs metabolic distance, the lowest metabolic distances have large variations in the POM values - so there too, metabolic reactions that create very different molecules in 1 step can vary widely in POM distance as well.

      We agree that Tanimoto distance is not perfect. We were unable to find a measure of structural distance that agreed with human intuitions about “structural distance” in all cases; indeed that intuition is often generated by an understanding of odor/flavor characteristics of function in metabolic networks, which would beg the question! To answer the question about the frequency of examples like the ones shown in Figure 2d, we created a new density map (Figure 2-figure supplement 4) showing the number of one-step metabolite pairs for a given range of POM vs cFP edit/Tanimoto distance. We found >25 pairs of metabolites in the same “small POM distance” and “large structural distance” quadrant from which we found the original examples shown in Figure 2d..

      5) A major worry is that Mordred descriptors are doing fine, and POM offers only a smallimprovement (but statistically significant of course). Another way to ask this question is this: if you plot pairwise correlation/distance of pairs of odors from POM against that for Mordred, how correlated does this look? My suspicion is that it will be highly correlated.

      It will look highly correlated (as shown in the new Figure 2-figure supplement 3). The reason is that metabolic reactions cannot make arbitrary transformations to molecules (the reactants must have some structural relationship to the products) or similarly that olfactory receptors (in any species) cannot have arbitrary tuning – at the end of the day receptors mostly bind to similar-looking classes of molecules. As stated above, we believe that the improvement here is not just statistically significant but meaningful – a 2-fold drop in unexplained variance is large – and that it is important to identify principles by which the nervous system can be tuned, above and beyond the physical constraints imposed by basic rules of chemistry.

      Also, the metabolic distances that we constructed from available data are themselves noisy, since not all metabolic pathways and the compounds that compose them are known, which places an upper bound on the correlation that we could have obtained. Despite that, we still found a correlation of r>0.9.

      6) The co-occurrence in mixtures and close POM distance may arise from the way theembedding was done - with perceptual descriptors used as a key variable. Humans may just classify molecules that occur in a mixture as similar just from experiencing them together. Can the authors show that these same molecules in Fig 4d,e have very similar representations in neural data from insects or mice?

      We have added a new Figure 4-figure supplement 1 to show this. One constraint is that the neural datasets must contain molecules that are also in the natural substance datasets used in Figure 4. In all cases where the data is sufficient to be powered to test the hypothesis (i.e. more than five co-occuring pairs of molecules in essential oil), we observe an effect in the predicted direction.

    2. Reviewer #2 (Public Review):

      In this manuscript, the authors use an embedding of human olfactory perceptual data within a graph neural network (which they term principal odor map, or POM). This embedding is a better predictor of a diverse set of olfactory neural and behavior data than methods that use chemical features as a starting point to create embeddings. The embedding is also seen to be better for comparison of pairwise similarities (distances of various sorts) - the claim is that proximity of pairs of odors in the POM is predictive of their similarity in neural data from olfactory receptor neurons.

      A major strength of the paper is the conceptualization of the problem. The authors have previously described a graph neural net (GNN) to predict verbal odor descriptors from molecular features (here, a 2019 preprint is cited, but a newer related one in 2022 describing the POM is not cited). They now use the embedding created by that GNN to predict similarities in large and diverse datasets in olfactory neuroscience (which the authors have curated from published work). They show that predictions from POM are better than just generic chemical features. The authors also present an interesting hypothesis that the underlying latent structure discovered by the GNN relates to metabolic pathway proximity, which they claim accounts for the success in the prediction of a wide range of data (insect sensory neuron responses to human behavior). In addition to the creativity of the project, the technical aspects, are sound and thorough.

      There are some questions about the ideas, and the size of the effects observed.

      1. The authors frame the manuscript by invoking an analogy to other senses, and how natural statistics affect what's represented (and how similarity is defined). However, in vision or audition, the part of the world that different animals "look at" can be very different (different wavelengths, different textures and spatial frequencies, etc). It is still unresolved why any given animal has the particular range of reception it has. Each animal is presumably adapted for its ecological niche, which can have different salient sensory features. In vision, different animals pick different sound bandwidths or EM spectra. Therefore, it is puzzling to think that all animals will somehow treat chemicals the same way.

      2. The performance index could be made clearer, and perhaps raw numbers shown before showing the differences from the benchmark (Mordred molecular descriptor). For example, can we get a sense of how much variance in the data does it explain, what percent of the hold-out tests does it fit well, etc.?

      3. The "fitting" and predictions are in line with how ML is used for classification and regression in lots of applications. The end result is a better fit (prediction), but it's not actually clear whether there are any fundamental regularities or orders identified. The metabolic angle is very intriguing, but it looks like Mordred descriptor does a very good job as well (extended figure 5). Is it possible to show the relation between metabolic distance and Mordred distance in Figure 2c? In fact, even there, cFP distance looks very well correlated with metabolic distance (we are talking about r= 0.9 vs r = 0.8). This could simply be due to a slightly nonlinear mapping between chemical similarity and perceptual similarity (which was used to get POM distance).

      4. How frequent are such examples shown in Fig 2d? Pentenal and pentenol are actually very similar in many ways, and it may be that Tanimoto distance is not a great descriptor of chemical similarity. cFFP edit distance is quite small, just like metabolic distance. The thiol example on the right is much better. Also, even in Fig 2C POM vs metabolic distance, the lowest metabolic distances have large variations in the POM values - so there too, metabolic reactions that create very different molecules in 1 step can vary widely in POM distance as well.

      5. A major worry is that Mordred descriptors are doing fine, and POM offers only a small improvement (but statistically significant of course). Another way to ask this question is this: if you plot pairwise correlation/distance of pairs of odors from POM against that for Mordred, how correlated does this look? My suspicion is that it will be highly correlated.

      6. The co-occurrence in mixtures and close POM distance may arise from the way the embedding was done - with perceptual descriptors used as a key variable. Humans may just classify molecules that occur in a mixture as similar just from experiencing them together. Can the authors show that these same molecules in Fig 4d,e have very similar representations in neural data from insects or mice?

    1. Author Response

      Reviewer #1 (Public Review):

      Collins et al use mesoscopic two-photon imaging to simultaneously record activity from basal forebrain cholinergic or noradrenergic axons in several distant regions of the dorsal cortex during spontaneous behavior in head-fixed awake mice. They find that activity in axons from both neuromodulatory systems is closely correlated with measures of behavioral state, such as whisking, locomotion and face movements. While axons were globally correlated with these behavioral state-related metrics across the dorsal cortex, they also find evidence of behavioral state independent heterogenous signals.

      The use of simultaneous multiarea optical recordings across a large extent of dorsal cortex with single axon resolution for studying the coherence of neuromodulatory afferents across cortical areas is novel and addresses important questions regarding neuromodulation in the neocortex. The manuscript is clearly written, the data is well presented and, for the most part, carefully analyzed. Parts of the manuscript confirm previous results on the influence of behavioral state on norepinephrine and acetylcholine cortical afferents. However, the observation that these modulations are globally broadcasted to the dorsal cortex while behavioral state independent heterogenous signals are also present in these axons is novel and important for the field.

      While the evidence for a behavioral state driven global modulation of activity in both neuromodulatory systems is quite clear, I have concerns that the apparent heterogeneity in axonal responses might be driven by movement-induced artifacts. Moreover, even in the case that the heterogeneity in calcium activity across axons is confirmed, it might not be driven by differences in spiking activity across neuromodulatory axons as concluded, but by other mechanisms that are not explicitly discussed or considered.

      1) Motion artifacts are always a concern when imaging from small structures in behaving animals. This issue is addressed in the manuscript in Fig 2A-C by comparing axonal responses to "autofluorescent blebs that did not have calcium-dependent activity" (line 1011). Still, as calcium-dependent activity and motion artifacts can both be locked to behavioral variables the "bleb" selection criterion seems biased and flawed with a circular logic. "Blebs" presenting motion-induced changes in fluorescence that may pass as neural activity will be wrongly excluded when from the "bleb" control group using this criterion. This will result in an underestimation of the extent of the contamination of the GCaMP signals by movement-induced artifacts. This potential confound might generate apparent heterogeneity across axons and regions as some axons and some cortical areas might be more prone to movements artifacts than others.

      Thank you for the suggestion. We agree that motion artifacts are a reasonable concern. We rigorously addressed this concern by introducing non-calcium-dependent mCherry into cholinergic cortical axons and demonstrating that motion cannot explain our results (see Fig. 2F, Fig. 4H,L,P, Fig. 4 - figure supplement 1G, Video 3, and response above). These axons were chosen for analysis based solely on their ability to be imaged, in a manner identical to that of GCaMP6s containing axons.

      We agree that the observed evidence of heterogeneity is not as clear as the evidence of a common signal. We now carefully present our evidence. Heterogeneity may arise from variations in activity between single axons that is not explained by a common signal such as behavioral state. Heterogeneity could also be signaled by variations in correlated activity between axons. We now address these two possibilities in our manuscript. Our new analysis reveals that the correlated activity between axons is as expected for axons that are variably correlated to a common signal, such as behavioral state. Although we do find some evidence of correlation outside this common signal, we are not able to discern if this is related to imaging axon segments that are part of the same axon, or if it truly represents an independent signal. This is now stated in the text. On the other hand, strong variations in axonal activity from trial to trial that appear to be separate from the common signal is also prevalent. We now point out this variation as a possible source of heterogeneity. Since we do not know the source or meaning of this heterogeneous activity, we discuss only the possibility that it may hold behaviorally relevant information in these modulatory systems.

      2) In the case that the heterogeneity is indeed due to differences in calcium activity, it might be not due to modularity in spiking activity within the LC or the BF as interpreted and discussed in the manuscript. As calcium signaling in axons not only relates to spiking activity but can also reflect presynaptic modulations, the observed heterogeneity might be due to local action of presynaptic modulators in a context of global identical broadcasted activity. The current dataset does not allow distinguishing which of the two different mechanisms underlies the observed signal heterogeneity.

      It is true that our data set is unable to determine whether presynaptic modulations contribute to any observed heterogeneity. We have adjusted our interpretation of heterogeneity throughout the manuscript and have specifically addressed this comment in the discussion by presenting the possibility that a global signal could be locally modulated.

      Reviewer #3 (Public Review):

      Acetylcholine and Norepinephrine are two of the most powerful neuromodulators in the CNS. Recently developments of new methods allow monitoring of the dynamic changes in the activity of these agents in the brain in vivo. Here the authors explore the relationship between the dynamic changes in behavioral states and those of ACh and NE in the cortex. Since neuromodulatory systems cover most of the cortical tissue, it is essential to be able to monitor the activity of these systems in many cortical areas simultaneously. This is a daunting task because the axons releasing NE and ACh are very thin. To my knowledge, this study is the first to use mesoscopic imaging over a wide range of the cortex at the single axon resolution in awake animals. They find that almost any observable change in behavioral state is accompanied by a transient change in the activity of cortical ACh and NE axonal segments. Whisking is significantly correlated with ACh and NE. The authors also explore the spatial pattern of activity of ACh and NE axons over the dorsal cortex and find that most of the dynamics is synchronous over a wide spatial scale. They look for deviation from this pattern (which I will discuss later). Lastly, the authors monitor the activity of cortical interneurons capable of releasing ACh.

      Comments:

      1) On a broad overview, I find the discussion of behavioral states, brain states, and neuromodulation states quite confusing. To begin with, I am not convinced by the statement that "brain states or behavioral states change on a moment-to-moment basis." I find that the division of brain activity into microstates (e.g., microarousal) is counterproductive. After all, at the extreme, going along this path, we might eventually have an extremely high dimensional space of all neuronal activity, and any change in any neuron would define a new brain state. Similarly, mice can walk without whisking, can whisk without walking, can walk and whisk, are all these different behavioral states? And if so, are they all associated with different brain states? And if so, are they all associated with different brain states? Most importantly, in the context of this manuscript, one would expect that different states (brain, behavior) would be associated with at least four potential states of the ACh x NE system (high ACh and High NE, High ACh and Low NE, etc.). However, the reported findings indicate that the two systems are highly synchronized (or at least correlated), and both transiently go on with any change from a passive state to an active state. Therefore, the manuscript describes a rather confined relationship of the neuromodulation systems with the rather rich potential of brain and behavioral states. Of course, this is only my viewpoint, and the authors are not obliged to accept it, but they should recognize that the viewpoint they take for granted is not shared by all and consider acknowledging it in the manuscript.

      We thank this reviewer for this thoughtful comment. While it is clear that animals do in fact exhibit distinct and clear brain and behavioral states (e.g. sleep, waking, grooming, still, walking, etc.), it is beyond the scope of the present manuscript to attempt to tackle this complex field - rather, we refer the reader to a recent review that we have published on this important topic (McCormick, Nestvogel, and He 2020). We agree that properly delineating brain and behavioral states is of great importance, as it could significantly impact experimental design and interpretation of results. Since all of the relevant substates that a mouse may exhibit have not yet been determined, we decided to use changes in whisking and walking behaviors to differentiate between distinct behavioral states owing to: 1) historical use of these measures in behavioral and neural states in head-fixed mice, 2) relative ease of measurement of these variables, 3) a clearly observable relationship with cholinergic and noradrenergic activity with these measures of behavior, and, arguably most importantly, 4) assumed relevance to the animal (Musall et al. 2019; Reimer et al. 2016; Salkoff et al. 2020; Stringer et al. 2019).

      Our manuscript seeks to simply relate the activity of cholinergic and noradrenergic axons across the dorsal surface of the cortex in comparison to these commonly used measures of spontaneous behavior in head-fixed mice to discern to what relative degree there are common, global signals in these two modulatory systems and how they relate to changes in the measured behaviors. Somewhat surprisingly, previous studies have found that neural activity throughout the dorsal cortex of mice is strongly related to movements of the face and body as well as behavioral arousal (Stringer et al. 2019; Musall et al. 2019; Salkoff et al. 2020). Here we determine to what degree these commonly used measures of “state” are already reflected in the GCaMP6s activity of cholinergic and noradrenergic axons (and local cortical interneurons).

      We agree with the interpretation that our results suggest a confined relationship between spontaneous cholinergic and noradrenergic activity in the cortex within the spontaneous behaviors that we observe. We, by no means, mean to suggest that this confined relationship is the only relationship cholinergic and noradrenergic systems exhibit to each other or to behavior. It seems very likely that in the wide variety of behavior exhibited by freely moving mice in their lifetime, there are times in which the activity of cholinergic and noradrenergic systems exhibit a radically different relationship to each other and to behavior. We simply cannot know this without experimental examination. We now mention this possibility in the discussion and give a few appropriate references.

      2) Most of the manuscript (bar one case) reports nearly identical dynamics of ACh and NE. Is that a principle? What makes these systems behave so similarly? Why have two systems that act nearly the same? Still, if there is a difference, it is the time scale of the ACh compared to the NE. Can the authors explain this difference or speculate what drives it?

      Perhaps one of the most striking findings in recent years from examination of mouse brain activity is the prominence and prevalence of a general signal in nearly all neural systems that relates to movement and arousal of the animal (Stringer et al. 2019; Salkoff et al. 2020). Here we report that this signal is also strongly present within the cholinergic and noradrenergic systems. Perhaps this is unsurprising, since everywhere one looks, one finds this global signal. However, we feel that understanding the presence and nature of this large signal is critical to deciphering behavior-related signals in these systems in the future. We discuss this point in the discussion. The one difference we did find is in the more transient nature of NE axonal activity versus both behavior and cholinergic axon activity. We now speculate on this difference in the discussion.

      3) Whisker activity explains most strongly the neuromodulators dynamics, but pupil dilation almost does not (in contrast to many previous reports including reports of the same authors). If I am not mistaken, this was nearly ignored in the presentation of the results and the discussion section. Could the author elaborate more on what is the reason for this discrepancy?

      We apologize for the misleading presentation of our results. In Fig. 3C and D it is clear that pupil diameter is highly coherent with both cholinergic and noradrenergic axon activity, as published previously. In the present study, this coherence peaks at 0.4 to 0.5 for both. In our previous study (Reimer et al. 2016), the cholinergic activity also peaked in coherence at low frequencies at around 0.4 to 0.5 (Reimer et al., Fig. 1H) while the noradrenergic activity coherence peaked at 0.6 to 0.7. The present study was not optimized for pupil diameter examination, since we kept the light levels as low as possible (resulting in low dynamic range of pupil dilations since they were nearly always enlarged to near maximum) in order to increase the S/N of cortical axon activity. We now mention these similarities and differences and caveats in the manuscript. An additional important point is that the kinetics of pupil diameter changes are slow in comparison to whisker movements, reducing the ability of pupil dilation to accurately track changes in axonal activity at frequencies greater than approximately 0.2 Hz (Fig. 2 - figure supplement 2). This is now mentioned in the text.

      4) I find the question of homogenous vs. heterogenous signaling of both the ACh and NE systems quite important. It is one thing if the two systems just broadcast "one bit" information to the whole brain or if there are neuromodulation signals that are confined in space and are uncorrelated with the global signal. However, the way the analysis of this question is presented in the manuscript is very difficult to follow, and eventually, the take-home message is unclear. The discussion section indicates that the results support that beyond a global synchronized signal, there is a significant amount of heterogeneous activity. I think this question could benefit from further analysis. I suggest trying to demonstrate more specific examples of axonal ROIs where their activity is decorrelated with the global signal, test how consistent this property is (for those ROIs), and find a behavioral parameter that it predicts.

      Also, in the discussion part, I am missing a discussion of the potential mechanism that allows this heterogeneity. On the one hand, an area may receive NE/ACh innervation from different BF/LC neurons, which are not completely synchronized. But those neurons also innervate other areas, so what is the expected eventual pattern? Also, do the results support neuromodulation control by local interneuron circuits targeting the axons (as is the case with dopaminergic axons in the Basal Ganglia)?

      Our results clearly demonstrate a robust global signal that is common across cholinergic and noradrenergic axons which is related to behavioral state. We have less strong, but still present, evidence for a heterogeneous signal in addition to this global signal. This evidence is based largely upon the large variation in activities in different axon segments during behavioral events that appear similar. This result suggests that the axon segments we monitored do not all act as if they are members of the same axon. We now discuss the strong evidence for the global signal present in our data, and leave open the possibility of a heterogeneous signal whose mechanisms and importance remains to be determined.

      5) The axonal signal seems to be very similar across the cortex. I am not sure this is technically possible, but given that NE axons are thin and non-myelinated and taking advantage of the mesoscopic scale, could the author find any clue for the propagation of the signal on the rostral to caudal axis?

      We were unable to detect propagation across the cortical sheet and believe this is beyond the scope of the present study.

      6) While the section about local VCIN is consistent with the story, it is somehow a sidetrack and ends the manuscript on the wrong note. I leave it to the authors to decide but recommend them to reconsider if and where to include it. Unfortunately, the figure attached was on a very poor resolution, and I could not look into the details, so I am afraid that I could not review this section properly.

      We believe this adds to the manuscript and therefore have decided to include this data.

    2. Reviewer #3 (Public Review):

      Acetylcholine and Norepinephrine are two of the most powerful neuromodulators in the CNS. Recently developments of new methods allow monitoring of the dynamic changes in the activity of these agents in the brain in vivo. Here the authors explore the relationship between the dynamic changes in behavioral states and those of ACh and NE in the cortex. Since neuromodulatory systems cover most of the cortical tissue, it is essential to be able to monitor the activity of these systems in many cortical areas simultaneously. This is a daunting task because the axons releasing NE and ACh are very thin. To my knowledge, this study is the first to use mesoscopic imaging over a wide range of the cortex at the single axon resolution in awake animals. They find that almost any observable change in behavioral state is accompanied by a transient change in the activity of cortical ACh and NE axonal segments. Whisking is significantly correlated with ACh and NE. The authors also explore the spatial pattern of activity of ACh and NE axons over the dorsal cortex and find that most of the dynamics is synchronous over a wide spatial scale. They look for deviation from this pattern (which I will discuss later). Lastly, the authors monitor the activity of cortical interneurons capable of releasing ACh.

      Comments:<br /> 1. On a broad overview, I find the discussion of behavioral states, brain states, and neuromodulation states quite confusing. To begin with, I am not convinced by the statement that "brain states or behavioral states change on a moment-to-moment basis." I find that the division of brain activity into microstates (e.g., microarousal) is counterproductive. After all, at the extreme, going along this path, we might eventually have an extremely high dimensional space of all neuronal activity, and any change in any neuron would define a new brain state. Similarly, mice can walk without whisking, can whisk without walking, can walk and whisk, are all these different behavioral states? And if so, are they all associated with different brain states? Most importantly, in the context of this manuscript, one would expect that different states (brain, behavior) would be associated with at least four potential states of the ACh x NE system (high ACh and High NE, High ACh and Low NE, etc.). However, the reported findings indicate that the two systems are highly synchronized (or at least correlated), and both transiently go on with any change from a passive state to an active state. Therefore, the manuscript describes a rather confined relationship of the neuromodulation systems with the rather rich potential of brain and behavioral states. Of course, this is only my viewpoint, and the authors are not obliged to accept it, but they should recognize that the viewpoint they take for granted is not shared by all and consider acknowledging it in the manuscript.<br /> 2. Most of the manuscript (bar one case) reports nearly identical dynamics of ACh and NE. Is that a principle? What makes these systems behave so similarly? Why have two systems that act nearly the same? Still, if there is a difference, it is the time scale of the ACh compared to the NE. Can the authors explain this difference or speculate what drives it?<br /> 3. Whisker activity explains most strongly the neuromodulators dynamics, but pupil dilation almost does not (in contrast to many previous reports including reports of the same authors). If I am not mistaken, this was nearly ignored in the presentation of the results and the discussion section. Could the author elaborate more on what is the reason for this discrepancy?<br /> 4. I find the question of homogenous vs. heterogenous signaling of both the ACh and NE systems quite important. It is one thing if the two systems just broadcast "one bit" information to the whole brain or if there are neuromodulation signals that are confined in space and are uncorrelated with the global signal. However, the way the analysis of this question is presented in the manuscript is very difficult to follow, and eventually, the take-home message is unclear. The discussion section indicates that the results support that beyond a global synchronized signal, there is a significant amount of heterogeneous activity. I think this question could benefit from further analysis. I suggest trying to demonstrate more specific examples of axonal ROIs where their activity is decorrelated with the global signal, test how consistent this property is (for those ROIs), and find a behavioral parameter that it predicts. Also, in the discussion part, I am missing a discussion of the potential mechanism that allows this heterogeneity. On the one hand, an area may receive NE/ACh innervation from different BF/LC neurons, which are not completely synchronized. But those neurons also innervate other areas, so what is the expected eventual pattern? Also, do the results support neuromodulation control by local interneuron circuits targeting the axons (as is the case with dopaminergic axons in the Basal Ganglia)?<br /> 5. The axonal signal seems to be very similar across the cortex. I am not sure this is technically possible, but given that NE axons are thin and non-myelinated and taking advantage of the mesoscopic scale, could the author find any clue for the propagation of the signal on the rostral to caudal axis?<br /> 6. While the section about local VCIN is consistent with the story, it is somehow a sidetrack and ends the manuscript on the wrong note. I leave it to the authors to decide but recommend them to reconsider if and where to include it. Unfortunately, the figure attached was on a very poor resolution, and I could not look into the details, so I am afraid that I could not review this section properly.

    1. Author Response

      Reviewer #1 (Public Review):

      In this study, the authors aim to identify the cell state dynamics and molecular mechanisms underlying melanocyte regeneration in zebrafish. By analyzing thousands of single-cell transcriptomes over regeneration in both wild-type and Kit mutant animals, they provide thorough and convincing evidence of (1) two paths to melanocyte regeneration and (2) that Kit signaling, via the RAS/MAPK pathway, is a key regulator of this process. Finally, the authors suggest that another proliferative subpopulation cells, expressing markers of a separate pigment cell type, constitute an additional population of progenitors with the ability to contribute to melanocytes. The data supporting this claim are not as convincing, and the authors failed to show that these cells did indeed differentiate into melanocytes. Despite the challenges of describing this third cell state, this study offers compelling new findings on the mechanisms of melanocyte regeneration and provides paths forward to understanding why some animals lack this capacity.

      The majority of the main conclusions are well supported by the data, but one claim, in particular, should be revisited by the authors.

      (1) Provided evidence that the aox5(hi)mitfa(lo) population of cells contributes to melanocyte regeneration is inconclusive and somewhat circumstantial. First, the transcriptional profiles of these cells are much more consistent with the xanthophore lineage. Indeed, xanthophores have been shown to express mitfa (in embryos in Parichy, et al. 2003 (PMID: 10862741), and in post-embryonic cells in Saunders, et al. 2019). Second, while the authors address this possibility in Supplemental figure 7 by showing that interstripe xanthophores fail to divide following melanocyte ablation, they fail to account for the stripe-resident xanthophores/xanthoblasts. The presence and dynamics of aox5+ stripe-resident xanthophores/xanthoblasts are detailed in McMenamin, et al., 2014 (PMID: 25170046) and Eom, et al., 2015 (PMID: 26701906). Without direct evidence that the symmetrically-dividing, aox5+ cells measured in this study do indeed differentiate into melanocytes, it is more likely that these cells are a dividing population of xanthophores/xanthoblasts. The authors should revise their claims accordingly.

      We agree with the editor and reviewers that the identities of the mitfa+aox5hi cells and the interplay between these cells and the mitfa+aox5lo cells is a fascinating, and originally unexpected, aspect of this manuscript. The issue, as we see it, is whether mitfa+aox5hi cells that arise via cell division during regeneration are multipotent pigment cell progenitors or ‘cryptic’ xanthophores. The experiments we have performed to address this ambiguity have not worked for technical reasons, so we have tempered text in the relevant Results and Discussion sections to leave both options open. We have backed off from calling these cells progenitors but have included additional data showing that they (i.e. the mitfa+aox5hi subpopulation of cells that we believe are daughters of mitfa+aox5hi cycling cells) express multiple markers associated with multipotent pigment cell progenitors that have been characterized in developing zebrafish. Our expanded Discussion is as follows:

      “Heterogeneity may also be evident by the additional mitfa+aox5hi G2/M adj subpopulation that likely arises via cell divisions during regeneration. There are reasons to think that this could be a progenitor subpopulation. Firstly, these cells arose in response to specific ablation of melanocytes. Secondly, this subpopulation expresses markers that are associated with multipotent pigment progenitors cells found during development (Budi, et al., 2011; Saunders, et al., 2019). Thirdly, although this subpopulation expresses aox5 and some other markers associated with xanthophores, we showed that differentiated xanthophores are not ablated by the melanocyte-ablating drug neocuproine and this mitfa+aox5hi subpopulation does not make new pigmented xanthophores following neocuproine treatment. However, current observations cannot definitively determine the potency and fates adopted by these cells. One possibility is that these cells are indeed progenitors that arise through cell divisions, are in an as yet undefined way lineally related to MP-0 and MP-1 subpopulations, and ultimately give rise to new melanocytes during additional rounds of regeneration. Given their expression of markers associated with multipotent pigment cell progenitors, these cells could be multipotent but fated toward the melanocyte lineage following melanocyte-specific ablation. However, we cannot exclude the possibility that these cells are another cell type. For example, there is a type of partially differentiated xanthophores that populate adult melanocyte stripes (McMenamin, et al., 2014). At least some of these cells arise from embryonic xanthophores that transitioned through a cryptic and proliferative state (McMenamin, et al., 2014). That the descendants remain partially differentiated could indicate that they are in more of a xanthoblast state and maintain proliferative capacity (Eom, et al., 2015). It is possible that some or all of the cells in question are melanocyte stripe-resident, partially-differentiated xanthophores that arise: a) from cell divisions that are triggered by loss of interactions with melanocytes or, b) simply to fill space that is vacated due to melanocyte death. Such causes for partially-differentiated xanthophore divisions have not been documented, but nonetheless this possibility must be considered given the mitfa and aox5 expression and proliferative potential of these cells. Transcriptional profiles of ‘cryptic’ xanthophores are not available to help clarify the nature of these cells. Lastly, the relationship between adult progenitor populations – MP-0, MP-1 and, potentially, mitfa+aox5hi G2/M adj – and other progenitors present at earlier developmental stages is unclear and could be defined through additional long-term lineage tracing studies. In particular, previous examinations of pigment cell progenitors in developing zebrafish have identified dorsal root ganglion-associated pigment cell progenitors in larvae that contribute to adult pigmentation patterns (Singh, et al., 2016; Dooley, et al., 2013; Budi, et al., 2011). It is possible that these cells give rise to the adult progenitors we have identified. The further alignment of cell types that have been observed in vivo and cell subpopulations defined through expression profiling is a necessary route for understanding the complex relationship between stem and progenitor cells in development, homeostasis, and regeneration.”

      (1) At line 140, it is noted that Xanthophores are pteridine-producing, but they also get their yellow color from carotenoids (especially in adults). This should be noted as well, especially since the authors display the xanthophore marker, scarb1, which plays a key role in xanthophore carotenoid coloration.

      [Mapping expression levels onto UMAP space for scarb1 and perhaps other markers of xan, irid, or proliferation would be helpful as a supplement to the dot plot in Fig 1 and could help to clarify the transcriptomic signature of mitfa+ aox5-hi cells and plausibility of the model that they are an McSC population. -Parichy]

      We thank the reviewer for the suggestion, and we have changed the text to include the carotenoid coloration facts of xanthophores as follows:

      “aox5 is expressed in differentiated xanthophores, a pteridine- and carotenoid-producing pigment cell type of zebrafish, and in some undifferentiated pigment progenitor cells”

      Additionally, we have also added a new Figure Supplement to Figure 1 (Figure 1 – figure supplement 3) with feature plots demonstrating the expression of xanthophore markers scarb1 and bco2b, iridophore markers lypc and cdh11, and proliferation markers pcna and mki67. As noted above, there is some heterogeneity within the large grouping of mitfa+aox5hi cells. Whereas some markers associated with xanthophores are broadly expressed in this grouping (e.g. scarb1), others have more restricted expression (e.g. bco2b). The heterogeneity could reflect multiple differentiation states of xanthophores, multiple types of differentiated xanthophores, xanthophore progenitors and/or less fate-restricted pigment cell progenitors that cluster in this grouping.

      (2) The authors should provide the list of genes that comprise their cluster signatures (line 252) as part of the supplementary tables.

      We have now included a table of genes in the cluster signatures. The Supplementary Table is called “Supplementary File 2.”

      (3) The authors should more clearly describe how they performed lineage tracing (line 339). Additionally, for the corresponding figure 4E, the authors should list the number of cells traced. The source data only contains calculated percentages rather than counts for each type of differentiation. My understanding is that the number listed in the figure legend is the number of fish (i.e. n = 4), but this should be clarified as well.

      [A supplementary figure of labeled cells is important here with enough context to show that cells can be re-identified unambiguously. Additionally note that "lineage tracing" will typically be assumed to mean single-cell labeling and tracking, so if that is not the case for these experiments it would be preferable to use an alternative descriptor. -Parichy]

      We have included additional detail in our revised manuscript. In Figure 4E we now include the number of cells imaged and have included a breakdown of the raw numbers in the Source Data. We have also included Supplementary Animations as examples of the single-cell tracing that we perform through serial imaging.

      Additionally, the point about using ‘lineage tracing’ is well taken. We have replaced this with ‘serial imaging’ through the text.

      (4) Line 321, the authors list the mean regeneration percentages for the kita and kitlga(lf) mutants, but these differences are not significantly different according to Figure 4B. By listing the means (which should be noted), the authors seem to be highlighting the differences but then do not comment on them. The description and integration of this result into the main text should be clarified.

      We have changed the wording in the text to clarify that the mean percentage is being listed. We have also reworded the text to de-emphasize the mean percentage difference between kita(lf) and kitlga(lf) mutants, instead highlighting that their defects are similar. In the figure legend we have clarified that the mean percentage regeneration is being shown.

      (5) In Figure 6E, the RNA-velocity result is not particularly consistent with the authors' claims. Visually, the arrows seem fairly randomly directed. The data in 6B, showing gene expression associated with the S phase and G2/M phase much more clearly convey the directionality of the loop (S phase, followed by G2/M). I suggest that the authors weaken their claim about the RNA-velocity result or remove it altogether and focus on the cell cycle-related gene expression signatures.

      We thank the reviewer for their careful eye here. We have decided to remove the RNA-velocity result previously displayed in Figure 6E. As the reviewer points out the results are more clearly demonstrated by Figure 6B.

    1. Author Responses

      Reviewer #1 (Public Review):

      This work aimed at investigating how a BMI decoding performance is impacted by changing the conditions under which a motor task is performed. They recorded motor cortical activity using multielectrode arrays in two monkeys executing a finger flexion and extension task in four conditions: normal (no load, neutral wrist position), loaded (manipulandum attached to springs or rubber bands to resist flexion), wrist (no load, flexed wrist position) or both (loaded and flexed wrist). They found, as expected, that BMI decoders trained and tested on data sets collected during the same conditions performed better at predicting kinematics and muscle activity than others trained and tested across conditions. They also report that the performance of monkeys a BMI task involving the online control of a virtual hand was almost unaffected by changing either the actual manipulandum conditions as above or switching between decoders trained from data collected under different conditions. As for the neuronal activity, they found a mix of changes across task contexts. Interestingly, a principal component analysis revealed that activity in each context falls within well-aligned manifolds, and that the context-dependent variance in neuronal activity strongly correlated to the amplitude of muscle activity.

      Strengths

      The current study expands on previous findings about BMI decoders generalizability and contributes scientifically in at least three important ways.

      First, their results are obtained from monkeys performing a fine finger control task with up to two degrees of freedom. This provides a powerful setting to investigate fine motor control of the hand in primates. The authors use the accuracy of BMI decoders between data sets as a measure of stationarity in the neuronsto-fingers mapping, which provides a reliable assessment. They show that changes in wrist angle or finger load affect the relationship between cortical neurons and otherwise identical movements. Interestingly, this result holds up for both kinematics and muscle activity predictions, albeit being stronger for the latter.

      Second, their results confirming that neuronal activity recorded during different task conditions lies effectively within a common manifold is interesting. It supports prior observations, but in the specific context of finger movements.

      Third, the dPCA results provide interesting and perhaps unexpected information about the fact that amplitude of muscle activity (or force) is clearly present in the motor cortical activity. This is possibly one of the most interesting findings because extracting a component from neural activity that can related robustly to muscle activity across context would provide great benefits to the development of BMIs for functional electrical stimulation.

      Overall, the analyses are well designed and the interpretation of the results is sound.

      Weaknesses

      I found the discussion about the possible reasons why offline decoders are more sensitive to context than online decoders very interesting. Nonetheless, as the authors recognize, the possibility that the BMI itself causes a change in context, "in the plant", limits their interpretation. It could mean for the monkeys to switch from one suboptimal decoder to another, causing a ceiling effect occluding generalization errors.

      Overall, several new and original results were obtained through these experiments and analyses. Nonetheless, I found it difficult to extract a clear unique and strong take-home message. The study comes short of proposing a new way to improve BMIs generalizability or precisely identifying factors that influence decoders generalizability.

      We thank the reviewer for the positive comments. Relating these results to BMI design and interpreting the adaptation to contexts during online trials comprised a bulk of the essential revisions from the eLife editorial staff. More details can be found in common response #2 and essential revisions #1-3. To summarize, we added an analysis of neural activity during online trials to provide insight into how the monkeys were adapting. We have expanded the discussion of online adaptation, as detailed in essential revision #2. We also expanded discussion of how both the online and offline results might affect BMI design, as detailed in essential revision #3.

      Reviewer #2 (Public Review):

      The authors motivate this study by the medical need to develop brain-machine interfaces (BMIs) to restore lost arm and hand function, for example through functional electrical stimulation. More specifically, they are interested in developing BMI decoding algorithms that work across a variety of "contexts" that a BMI user would encounter out in the real world, for example having their hand in different postures and manipulating a variety of objects. They note that in different contexts, the motor cortex neural activity patterns that produce the desired muscle outputs may change (including neurons' specific relationship to different muscles' activations), which could render a static decoder trained in a different context inaccurate.

      To test whether this potential challenge is indeed the case, this study tested BMI control of virtual (onscreen) fingers by two rhesus macaques trained to perform 1 or 2 degree-of-freedom non-grasping tasks either by moving their fingers, or just controlling the virtual finger kinematics with neural activity. The key experimental manipulations were context shifts in the form of springs on the fingers or flexion of the wrist (or both). BMI performance was then evaluated when these context changes were present, which builds on this group's previous demonstration of accurate finger BMI without any context shifts.

      The study convincingly shows the aforementioned context shifts do cause large changes in measured firing rates. When neural decoding accuracy (for both muscle and position/velocity) is evaluated across these context changes, reconstruction accuracy is substantially impaired. The headline finding, however, is that that despite this, BMI performance is, on aggregate, not substantially reduced. Although: it is noteworthy that in a second experiment paradigm where the decoder was trained on the spring or wrist-manipulated context and tested in a normal context, there were quite large performance reductions in several datasets as quantified by multiple performance measures; this asymmetry in the results is not really explored much further. The changes in neural activity due to context shifts appear to be relatively modest in magnitude and can be fit well as simple linear shifts (in the neural state space), and the authors posit that this would make it feasible (in future work) to find context-invariant neural readouts that would result in more robust muscle activity decoders.

      An additional novel contribution of this study is showing that these motor cortical signals support quite accurately decode muscle activations during non-prehensile finger movements (and also that the EMG decoding was more negatively affected by context shifts than kinematics decoding); previous work decoded finger kinematics but not these kinetics. Note that this was demonstrated with just one of the two monkeys (the second did not have muscle recordings).

      This is a rigorous study, its main results are well-supported, and it does not make major claims beyond what the data support.

      One of its limitations is that while the eventual motivating goal is to show that decoders are robust across a variety of tasks of daily living, only two specific types of context shifts are tested here, and they are relatively simple and potentially do not result in as strong a neural change as could be encountered in realworld context shifts. This is by no means a major flaw (simplifying experimental preparations are a standard and prudent way to make progress). But the study could point this out a bit more prominently that their results do not preclude that more challenging context shifts will be encountered by BMI users, and this study in its current form does not indicate how strong a perturbation the tested context shifts are relative to the full possible range of hand movement context shifts that would be encountered during human daily living activities.

      A second limitation is that while the discrepancy between large offline decoding performance reduction and small online performance reduction are attributed to rapid sensorimotor adaptation, this process is not directly examined in any detail.

      Third, the assessment of how neural dynamics change in a way that preserves the overall shape of the dynamics is rather qualitative rather than quantitative, and that this implementation of a more contextagnostic finger BMI is left for future work.

      We thank the reviewer for the positive comments. We agree that the paper could discuss how this work impacts a wider range of movements and we now include more discussion to that point as detailed in the responses to feedback below. We also acknowledge that the paper did not directly examine online adaptation and we have now included an analysis aimed at answering how the monkeys adapted to the context changes during online tasks.

      Reviewer #3 (Public Review):

      In this manuscript the authors ask whether finger movements in non-human primates can be predicted from neural activity recorded from the primary motor cortex. This question is driven by an ultimate goal of using neural decoding to create brain-computer interfaces that can restore upper limb function using prosthetics or functional electrical stimulation systems. More specifically, since functional use of the hand (real or prosthetic) will ultimately require generating very different grasp forces for different objects, these experiments use a constant set of finger kinematics, but introduce different force requirements for the finger muscles using several different techniques. Under these different conditions (contexts), the study examines how population neural activity changed and uses decoder analyses to look at how these different contexts affect offline predictions of muscle forces and finger kinematics, as well as the animals' ability to use different decoders to control 1 or 2-DOF online. In general, the study found that when linear models were trained on one context from offline data, they did not generalize well to the other context. However, when performance was tested online (monkeys controlling a virtual hand in real time using neural activity related to movement of their own hands) with a ReFIT Kalman filter, the animals were able to complete the task effectively, even with a decoder trained without the springs or wrist perturbation. The authors show data to support the idea that neural activity was constrained to the same manifold in the different contexts, which enabled the animals to rapidly change their behavior to achieve the task goals, compared to the more complex requirement of having to learn entirely new patterns of neural activity. This work takes studies that have been conducted for upper-limb movements and extends them to include hand grasp, which is important for creating decoders for brain-computer interfaces. Finally, the authors show using dPCA can extract features during changes in context that may be related to the activity of specific muscles that would allow for improved decoders.

      Strengths

      The issue of hand control, and how it compares to arm control, is an important question to tackle in sensorimotor control and in the development of brain-computer interfaces. Interestingly, the experiments use two very different ways of changing the muscle force requirements for achieving the same finger movements; springs attached to a manipulandum and changes in wrist posture. Using both paradigms the decoder analysis clearly shows that linear models trained without any manipulation do not predict muscle forces or finger kinematics well, clearly illustrating the limitations of common linear decoders to generalize to scenarios that might encompass real grasping activities that require forceful interactions. Using a welldescribed real-time decoder (ReFIT Kalman Filter), the authors show that this performance decrease observed offline is easily overcome in online testing. The metrics used to make these claims are welldescribed, and the likely explanations for these findings are described well. A particular strength of this manuscript is that, at least for these relatively simple movements and contexts, a component of neural activity (identified using dPCA) is identified that is significantly modulated by the task context in a way that sensibly represents the changes in muscle activity that would be required to complete the task in the new contexts. We thank the reviewer for the positive comments.

      Weaknesses

      The differences between exemplar data sets and comprehensively tested contexts was difficult to follow. There are many references to how many datasets or trials were used for a particular experiment, but overall, this is fragmented across the manuscript. As a result, it is difficult to assess how generalizable the results of the manuscript were across time or animal, or whether day-to-day variations, or the different data collection schedules had an effect.

      Thank you for the comment, we have added in the number of sessions in results in multiple places throughout the paper. For example, starting line 274 in the results:

      "During these 10 sessions the context changes were tested 15 times: four times for the wrist context, seven times for the spring context, and four times for the combined wrist and spring context."

      The introduction allocates a lot of space to discussing the concepts of generating (computing) movements as opposed to representing movements and relates this to ideas of neural dynamics. The distinction between these as described in the introduction is not very clear, nor is it clear what specific hypothesis this leads to for these experiments. Further, this line of thinking is not returned to in the discussion, so the contribution of these experiments to ideas raised in the introduction are unclear.

      Thank you for the comment, we have written a new paragraph relating these results to the concept of generating movement. Starting line 452 of the discussion:

      "During the offline tasks, many channels changed neural activity with context, with 20.9% to 61.7% of tuned SBP channels modulating activity with context (Table I). The magnitude of these shifts were relatively small, especially when compared to the large changes in required muscle activation (Figure 2D-E), with weak trends to require greater activation for resisted flexion and lesser for assisted extension (Figure 7B-C). Additionally, the neural manifolds underlying movements in each context were well-aligned (Figure 7D). Using dPCA we found that while a large proportion of neural variance was explained by dPCA components that did not change with context, a significant proportion of the neural variance is associated with components that are context-dependent (Figure 8B). Visually, the context components are shifting the trajectories without changing the overall shape and the shift in neural activity is strongly correlated with muscle activations in new contexts (Figure 8C). This agrees with other studies which found lower variance activity may be related to the actual motor commands (Gallego et al., 2018; Russo et al., 2018; Saxena et al., 2022)."

      The complexity of the control that was possible in this task (1 or 2 DOF finger flexion/extension) was low. Further, the manipulations that were used to control context were simple and static. Both these factors likely contribute to the finding that there was little change in the principal angles of the high-variance principal components. While this is not a criticism of the specific results presented here, the simplicity of the task and contexts, contrasted with the complexity of hand control more generally, especially for even moderately dexterous movements, makes it unclear how well the finding of stable manifolds will scale. On a related point, it is unclear whether the feature, identified using dPCA, that could account for changes in muscle activity, could be robustly captured in more realistic behaviors. It is stated that future work is needed, but at this point, the value of identifying this feature is highly speculative.

      Thank you for the comment, we have included more discussion to relate these results to decoder development in general as described in essential revision #3 from the editor.

      The maintained control in online BMI trials could also be explained by another factor, which I don't think was explicitly described by either of the two suggestions. Prism goggle experiments introduce a visual shift can be learned quickly, and some BCI experiments have introduced simple rotations in the decoder output (e.g. Chase et. al. 2012, J Neurophys). This latter case is likely similar in concept to in-manifold perturbations. Regardless, the performance can be rapidly rescued by simply re-aiming, which is a simple behavioral adaptation. In a 1DOF or 2DOF control case like used in these experiments, with constant visual feedback on performance, the change in context could likely be rapidly learned by the animals, maybe even within a single trial. In other words, the high performance in the online case may be a consequence of the relatively simple task demands, and the simple biomechanical solution to this problem (push harder). What is the expectation that the results seen in these experiments would be relevant to more realistic situations that require grasp and interaction?

      Thank you for the suggestion, we agree that the quick adaptation is likely related to re-aiming. To this end, we have included a re-aiming analysis, as described in essential revisions #1 and #2 from the editor and common response #2, to look into the quick adjustment.

      Some of the figures were difficult to read and the captions contained some minor incorrect information. The primary purpose of some of the figures was not immediately clear from the caption. For example, the bar plots in Figures 5 and 6 were very small and difficult to read. This also made distinguishing the data from the two different animals challenging.

      Thank you for the comments, multiple figures have been edited to increase legibility and a review of text has been done to fix errors and improve interpretability.

      There is no specific quantification of the data in Figures 4D and 5D. In Figure 4D it seems apparent that the vast majority of the points are below the unity line. But, it remains unclear, particularly in Figure 5D whether the correlations between the two contexts truly are different or not in a way that would allow conclusive statements.

      Thank you for the comments, Figure 4D has been moved to the supplement and 5D has now been replaced by figures analyzing the neural activity patterns during the online task.

    1. Think about looking at a piece of abstract art or a computer’s circuit board for the first time. With little knowledge, you may find it difficult to explain what you’re seeing — which would also make it difficult for you to answer the question, “What’s your perspective on this artwork or circuit board?” You have a perspective, you just don’t have the tools to talk about it meaningfully. With a little help, however (e.g., acquired knowledge from a teacher, friend, YouTube, and so on), you can learn to identify and talk about certain brushstrokes and the color palette (or reflow and silkscreen layering). Consequently, your perspective will be enriched, and you’ll be more articulate when you share that perspective with others.

      Reading this example of how our perspectives change as we gain knowledge enlightens many things for me. It helped me reflect on everything that changes our perspectives as we learn more about it. For example looking at a math problem that seems difficult at first but once you learn how to solve it, it's not so difficult with the knowledge you learned and eventually your perspective on the math problem has changed to viewing it as an easy problem.

    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      I summarise the major findings of the work below. In my opinion the range and application of approaches has provided a broad evidence base that, in general, supports the authors conclusions. However, there are, in my opinion, particular failures to utilise and communicate this evidence. The manuscript may be much improved with attention in the following areas. In each case I will give general criticism with a few examples, but the principals of my comments could be applied throughout the work.

      1) Insufficient quantification. The investigation combines various sources of qualitative data (EM, fluorescence microscopy, western blotting) to generate a reasonably strong evidence base. However, the work is over-reliant on representative images and should include more quantification from repeat experiments. When there are multiple fluorescence micrographs with intensity changes (not necessarily just representative images) (e.g. Figure 1 or 2) the authors should consider making measurements of these. Also the VLP production assays, which are assessed by western blotting would particularly benefit from a quantitative assessment (either by densitometry or, if samples remain, ELISA/similar approach).

      We have performed quantification of immunofluoresence, western blotting and VLP experiments from existing data. These quantification are presented in our revised manuscript. An overview of new quantification is shown below:

      Data shown

      Quantification now shown in

      Method

      Analysis

      Figure 1A

      Supp F1C

      IF

      HAE (-/+ SARS-CoV-2)

      • Tetherin total fluorescence intensity

      Figure 1D

      Supp F1E

      IF

      HeLa+ACE2 (-/+ SARS-CoV-2 )

      • Tetherin total fluorescence intensity

      Figure 2C

      Supp F2B

      IF

      A549+ACE2 (-/+ SARS-CoV-2)

      • Tetherin total fluorescence intensity

      Figure 2G

      Supp F2D

      IF

      T84 (-/+ SARS-CoV-2)

      • Tetherin total fluorescence intensity

      Supp F4A

      Supp F4B

      IF

      HeLa + ss-HA-Spike transients (-/+ HA stained cells) - Tetherin total fluorescence intensity

      Figure 4D

      Supp F4E

      IF

      HeLa + TetOne ss-HA-Spike stables (-/+ Dox)

      • Tetherin total fluorescence intensity

      Figure 4F

      Supp F4G

      W blot

      HeLa + TetOne ss-HA-Spike stables (-/+ Dox)

      – Tetherin abundance

      Figure 4G

      Supp F4I

      W blot – lysates

      Spike VLP experiments

      – tetherin abundance

      Figure 4G

      Supp F4J

      W blot - VLPs

      Spike VLP experiments

      • N-FLAG abundance

      Figure 6A

      Supp F7A

      W blot – lysates

      ORF3a VLP experiments

      – tetherin abundance

      Figure 6A

      Supp F7B

      W blot - VLPs

      ORF3a VLP experiments

      • N-FLAG

      For immunofluoresence anaysis, the mean, standard deviation, number of cells analysed and number of independent experiments are shown in the updated figure legends. Statistical analysis is also detailed in figure legends. Methods for the quantificaiton of fluoresence intensity is included in the Methods section.

      Densitometry was performed on western blots and VLP experiments as suggested. The mean, standard devisation and number of independent expreiments analysed are expressed in figure legends. Methods for densityometry quantification is now included.

      2) Insufficient explanation. I found some of the images and legends contained insufficient annotation and/or description for a non-expert reader to appreciate the result(s). Particularly if the authors want to draw attention to features in micrographs they should consider using more enlarged/inset images and annotations (e.g. arrows) to point out structures (e.g DMVs etc.). This short coming exacerbates the lack of quantification.

      Additional detail has been provided to the figure legends, and we have updated several figures to draw attention to features in micrographs. Black arrowheads have been added to Figures 1E, 2D, 2H to highlight plasma membrane-associated virions, and asterisks to highlight DMVs in Figures 1E, 2D and Supplemental Figures 2C, 2E. Similarly, typical Golgi cisternae are highlighted by white arrowheads micrographs in Figure 2E. These figure legends have also been modified to highlight these additions.

      3) Insufficient exploration of the data. I had a sense that some aspects of the data seem unconsidered or ignored, and the discussion lacks depth and reflection. For example the tetherin down-regulation apparent in Figures 1 and 2 is not really explained by the spike/ORF3a antagonism described later on, but this is not explicitly addressed.

      We have made changes throughout the manuscript, but the discussion especially has been modified. We now discuss the ORF3a data in more depth, discuss possible mechanisms by which ORF3a alone enhances VLP release, and discuss our ORF7a data in context to previous reports.

      The discussion has been updated to now include a better description of our data, and additional writing putting our work in to context with previously published work. See discussion section of revised manuscript.

      Also, Figure 6 suggests that ORF3a results in high levels of incorporation of tetherin in to VLPs, but I don't think this is even described(?). The discussion should also include more comparison with previous studies on the relationship between SARS-2 and tetherin.

      We have added a section to discuss how ORF3a may enhance VLP release,

      ‘We found that the expression of ORF3a enhanced VLP independently of its ability to relocalise tetherin (Figure 6A). This may be due to either the ability of ORF3a to induce Golgi fragmentation [38] which facilitates viral trafficking [39], or due to enhanced lysosomal exocytosis [37]. Tetherin was also found in VLPs upon co-expression with ORF3a (Figure 6A) which may also indicate to enhanced release via lysosomal exocytosis [37].

      The secretion of lysosomal hydrolases has been reported upon expression of ORF3a [31] and whilst this may in-part be due to enhanced lysosome-plasma membrane fusion, our data highlights that ORF3a impairs the retrograde trafficking of CIMPR (Supplemental Figures 6B, 6F, 6G), which may similarly increase hydrolase secretion.’ – (Line 625-654).

      The discussion has been developed to compare the relationship between SARS-CoV-2 and tetherin in previous studies,

      ‘SARS-CoV-1 ORF7a is reported to inhibit tetherin glycosylation and localise to the plasma membrane in the presence of tetherin [18]. We did not observe any difference in total tetherin levels, tetherin glycosylation, ability to form dimers, or surface tetherin upon expression of either SARS-CoV-1 or SARS-CoV-2 ORF7a (Figures 4A, 4B, 4C).

      Others groups have demonstrated a role for ORF7a in sarbecovirus infection and both SARS-CoV-1 and SARS-CoV-2 virus lacking ORF7a show impaired virus replication in the presence of tetherin [18,41]. A direct interaction between SARS-CoV-1 ORF7a and SARS-CoV-2 ORF7a and tetherin have been described [18,41], although the precise mechanism(s) by which ORF7a antagonises tetherin remains enigmatic. We cannot exclude that ORF7a requires other viral proteins to antagonise tetherin, or that ORF7a antagonises tetherin via another mechanism. For example, ORF7a can potently antagonise IFN signalling [42] which would impair tetherin induction in many cell types.’ – (Line 667-704).

      I have no minor comments on this draft of the manuscript.

      Reviewer #1 (Significance (Required)):

      Tetherin, encoded by the BST2 gene, is an antiviral restriction factor that inhibits the release of enveloped viruses by creating tethers between viral and host membranes. It also has a capacity for sensing and signalling viral infection. It is most widely understood in the context of HIV-1, however, there is evidence of restriction in a wide variety of enveloped viruses, many of which have evolved strategies for antagonising tetherin. This knowledge informs on viral interactions with the innate immune system, with implications for basic virology and translational research.

      This study investigates tetherin in the context of SARS-CoV-2. The authors use a powerful collection of tools (live virus, gene knock out cells, recombinant viral and host expression systems) and a variety of approaches (microscopy, western blotting, infection assays), which is, itself, a strength. The study provides evidence to support a series of conclusions: I) BST2/tetherin restricts SARS-CoV-2 II) SARS-CoV-2 ablates tetherin expression III) spike protein can modestly down-regulate tetherin IV) ORF3A dysregulates tetherin localisation by altering retrograde trafficking. These conclusions are broadly supported by the data and this study make significant contributions to our understanding of SARS-CoV-2/tetherin interactions.

      My enthusiasm is reduced by, in my opinion, a failure of the authors to fully quantify, explain and explore their data. I expect the manuscript could be significantly improved without further experimentation by strengthening these aspects.

      This manuscript will be of interest to investigators in virology and/or cellular intrinsic immunity. Given the focus on SARS-CoV-2 it is possible/likely that it will find a slightly broader readership.

      I have highly appropriate skills for evaluating this work being experienced in virology, SARS-CoV-2, cell biology and microscopy.

      We wish to thank Reviewer #1 for their comments which have helped us to improve the quality of our revised manuscript.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      BST2/tetherin can restrict the release and transmission of many enveloped viruses, including coronaviruses. In many cases, restricted viruses have developed mechanisms to abrogate tetherin-restriction by expressing proteins that antagonize tetherin; HIV-1 Vpu-mediated antagonism of tetherin restriction is a particularly well studied example. In this paper, Stewart et al. report their studies of the mechanism(s) underlying SARS-CoV-2 antagonism of tetherin restriction. They conclude that Orf3a is the primary virally encoded protein involved and that Orf3a manipulates endo-lysosomal trafficking to decrease tetherin cycling and divert the protein away from putative assembly sites.

      Major comments:- In my view some of the claims made by the authors are not fully supported by the data. For example, the bystander effect discussed in line 162 may suggest that infected cells can produce IFN but does not 'indicate' that they do

      This text has now been edited,

      ‘The levels of tetherin in uninfected HAE cells is lower than observed in uninfected neighbours in infected wells demonstrating that infected HAE cells are able to generate IFN to act upon uninfected neighbouring cells, enhancing tetherin expression.’ - (Lines 163-172).

      Most of the EM images show part of a cell profile, so statements such as (line 192) 'virus containing tubulovesicular organelles were often polarised towards sites of significant surface-associated virus' should be backed up with appropriate images, or indicated as 'not shown', or removed (the observation is not so important for this story). Line 196, DMVs can't be seen in these micrographs.

      The statement 'virus containing tubulovesicular organelles were often polarised towards sites of significant surface-associated virus' has been removed. The micrographs in Figure 1E have been re-cropped, and image iii replaced with an image showing DMVs and budding virions. Plasma membrane-associated virions are highlighted by black arrowheads, DMVs by black asterisks, and intracellular virion by a white arrow.

      Line 391, I can't see much change in CD63 distribution.

      CD63 reproducibly appears clustered towards the nuclei in ORF3a expressing cells, whilst CD63 positive puncta are abundant in the periphery of mock cells. CD63 puncta are also larger, and the staining of CIMPR and VPS35 also appears to be associated with larger organelles. We have amended the text to now read,

      ‘Expression of ORF3a also disrupted the distribution of numerous endosome-related markers including CIMPR, VPS35, CD63, which all localised to larger and less peripheral puncta (Supplemental Figure 6B), and the mixing of early and late endosomal markers’ - (Line 469).

      Quantification of the diameter of CD63 puncta indicate that they are larger in ORF3a expressing cells than in mock cells. Mock cells - 0.71μm (SD; 0.19), ORF3a - 1.15μm (SD;0.35). At least 75 organelles per sample, from 10 different cells. We have not included this data as we do not wish to labor this point but are happy to include this quantification if required to do so.

      Line 321, the authors show that ORF7a does not affect tetherin localization, abundance, glycosylation or dimer formation, but they don't show that it doesn't restrict SARS-CoV-2. Can they be sure that epitope tagging this molecule does not abrogate function (or the functions of any of the other tagged proteins for that matter), or that ORF7a works in conjunction with one of the other viral proteins?

      We are careful in the manuscript not to claim that ORF7a has no effect on tetherin. Our data indicate that ‘ORF7a does not directly influence tetherin localisation, abundance, glycosylation or dimer formation’ - (Line 361-362).

      We were unable to reproduce an effect of ORF7a on tetherin glycosylation. Our data conflicts with that presented by Taylor et al, 2015, where ORF7a impaired tetherin glycosylation and ORF7a localised to the plasma membrane in tetherin expressing cells. The experiments performed by Taylor et al used HEK293 cells and ectopically expressed tagged tetherin. The differences in results may be attributed to the differences between cell lines or due to differences between endogenous or ectopic / tagged tetherin.

      The study by Taylor et al uses SARS-CoV-1 ORF7a-HA from Kopecky-Bromberg et al., 2007 (DOI: 1128/JVI.01782-06), where the -HA tag is positioned at the C-terminus. Our ORF7a-FLAG constructs have a C-terminal epitope tag. While we cannot exclude the possibility that tagged proteins may act differently from untagged ones, the differences between our findings and previous work appear unlikely to be due to epitope tags.

      Our manuscript states that although we cannot find any effect of ORF7a on tetherin localisation, abundance, glycosylation, or dimer formation, we cannot exclude that ORF7a impacts tetherin by another mechanism. For example, ORF7a has been found to antagonise interferon responses. Tetherin is abundantly expressed in HeLa cells and expression does not require induction through interferon. None of our experiments above would be impacted by interferon antagonism yet this could impact other cell types besides infection in vivo. These possibilities may explain the reported differential impact of ORF7a by different labs. An addition comment has been added to the discussion to reflect this,

      ’We cannot exclude that ORF7a requires other viral proteins to antagonise tetherin, or that ORF7a antagonises tetherin via another mechanism. For example, ORF7a potently antagonises IFN signalling [38], which would impair tetherin induction in many cell types. - (Line 701-704).

      Note - Reference 38 has been added to the manuscript – Xia et al., Cell Reports DOI: 10.1016/j.celrep.2020.108234

      In the ORF screen, a number of the constructs are expressed at low level, is it possible they [the authors] are missing something?

      Some of the ORFs expressed in the miniscreen appear poorly expressed. We accept that in the use of epitope tagged constructs expression levels of individual viral proteins may impact upon a successful screen. However, this screen was performed to identify any potential changes in tetherin abundance or localisation, and the screen did successfully identify ORF3a, which we were able to follow-up and verify.

      Line 376, the authors refer to ORF3a being a viroporin. A recent eLife paper (doi: 10.7554/eLife.84477; initially published in BioRxiv) refutes this claim and builds on other evidence that ORF3a interacts with the HOPS complex. The authors should at least mention this work, especially in the discussion, as it would seem to provide a molecular mechanism to support their conclusions.

      This paper had not been peer reviewed at the time of our initial submission. We have now included the following text,

      ‘SARS-CoV-2 ORF3a is an accessory protein that localises to and perturbs endosomes and lysosomes [29]. It may do so by acting either as a viroporin [30] or by interacting with, and possibly interfering with the function of VPS 39, a component of the HOPS complex which facilitates tethering of late endosomes or autophagosomes with lysosomes [29,31]. Given ORF3a likely impairs lysosome function, the observed increased….’ - (Lines 444-449).

      Fig 3, the growth curves illustrated in Fig3 C and D do not have errors bars; how many times were these experiments repeated?

      These experiments require more repeats to include error bars. Infection and plaque assay (Figure 3C, 3D) are currently ongoing and we plan to complete them in the next 6-8 weeks and include them in the finalised manuscript.

      In the new experiments, infections will additionally be performed at MOI 0.01, in addition to the previous MOIs (1 and 5).

      Line 396, the authors show increased co-localization with LAMP1. As LAMP1 is found in late endosomes as well as lysosomes, they cannot claim the redistributed tetherin is specifically in lysosomes.

      We have altered the text to now say:

      ‘The ORF3a-mediated increase in tetherin abundance within endolysosomes could be due to defective lysosomal degradation.’ - (Line 475).

      There seems to be a marked difference in the anti-rb555 signal in the 'mock' cells in panels 5H and Suppl 6E. Is there a good reason for this, or does this indicate variability between experiments?

      Antibody uptake experiments in Figure 5H and Supp Figure 6E were performed and acquired on different days. Relatively low levels of signal are available in these antibody uptake experiments, and the disperse labelling seen in the mocks does not aid this.

      Fig 6a, why is there negligible VLP release from cells lacking BST2 and ORF3a-strep? How many times were these experiments performed? Is this a representative image? I think it confusing to refer to the same protein by two different names in the same figure (i.e. BST2 and tetherin). Do the authors know how the levels of ORF3a expressed in cells in these experiments compares to those seen in infected cells?

      We have changed the blot in Figure 6A for one with clearer FLAG bands. Three independent experiments were performed for Figure 6A. Quantification of VLPs is now included in Supplemental Figure 7B.

      We have changed ‘Bst2’ to ‘tetherin’ in all previous figures relating to protein; Figure 4G, Figure 6A, B, C.

      We have no current information to compare ORF3a levels in these experiments versus in infected cells. We can investigate quantifying this if necessary.

      My final point is, perhaps, the trickiest to answer, but nevertheless needs to be considered. As far as we know, SARS-CoV-2 and at least some other coronaviruses, bud into organelles of the early secretory pathway, often considered to be ERGIC. In the experiments shown here the authors provide evidence that ORF3a can influence tetherin recycling, but the main way of showing this is through its increased association with endocytic organelles. Do the authors have any evidence that Orf3a reduces tetherin levels in the ERGIC or whether the tetherin cycling pathway(s) involve the ERGIC?

      This is an interesting point, and as the reviewer concedes, this is tricky to answer. Expression of ORF3a causes the redistribution or remodeling of various organelles (Figures 1E, 2D, 2F, Supp Figures 2C, 2E, 3E, 6B, 6C, 6D). We have been unable to test the direct involvement of ERGIC, despite attempts with a number of commercial antibodies. Given the huge rearrangements of organelles during SARS-CoV-2 infection, it is unclear exactly what will happen to the distribution of ERGIC.

      Minor comments: Line 53, delete 'shell' its redundant and confusing when the authors have said coronaviruses have a membrane.

      Deleted.

      Line 61, delete 'the'

      Deleted.

      Line 72, delete 'enveloped'; coronaviruses already described as enveloped viruses (line 53)

      Deleted.

      Lines 93 - 100, lop-sided discussion of the viral life cycle; this paragraph is mostly about entry, which is not relevant to this paper, and does not really deal with the synthesis and assembly side of the cycle.

      We have now added the following text,

      ‘….liberating the viral nucleocapsid to the cytosol of the cell. Upon uncoating, the RNA genome is released into the host cytosol and replication-transcription complexes assemble to drive the replication of the viral genome and the expression of viral proteins. Coronaviruses modify host organelles to generate viral replication factories - so-called DMVs (double-membrane vesicles) that act as hubs for viral RNA synthesis [10]. SARS-CoV-2 viral budding occurs at ER-to-Golgi intermediate compartments (ERGIC) and newly formed viral particles traffic through secretory vesicles to the plasma membrane where they are released to the extracellular space.’ - (Lines 95-104).

      Line 103, why are the neighbouring cells 'naive'?

      ‘naïve’ removed.

      Line 112 - 113, delete last phrase; tetherin is described as an IFN stimulated gene in line 111; to be accurate, the beginning of the sentence should be 'Tetherin is expressed from a type 1 Interferon stimulated gene ...'

      Amended.

      Line 118 - 119, should say 'For tetherin-restricted enveloped viruses' as not all enveloped viruses are restricted by tetherin.

      Amended.

      Line 131, coronaviruses are not the only family of tetherin-restricted viruses that assemble on intracellular membranes, e.g. bunyaviruses.

      This has been modified and now reads,

      ‘In order for tetherin to tether coronaviruses, tetherin must be incorporated in the virus envelope during budding which occurs in intracellular organelles.’ - (Lines 133-135).

      Line 192, there is no EM data in Supplemental Fig 1C.

      This has now been removed.

      Line 251, 'a synchronous infection event' should be 'synchronous infection' as there will be multiple infection events.

      This has been changed.

      Page 13 (and elsewhere), unlike Southern, 'Western' should not have a capital letter, except at the start of a sentence.

      These have been updated throughout the manuscript (Lines 183, 341, 3549, 356, 392, 509, 763, 1330, 1399).

      Lines 330 and 352, can the authors quantitate S protein-induced reduction in cell surface tetherin rather than using the somewhat subjective 'mild'?

      These are now changed to,

      ‘Transient transfection of cells with ss-HA-Spike caused a 32% decrease in tetherin as observed by immunofluorescence (Supplemental Figure 4A, 4B), with…’ – (Line 370).

      ‘To explore whether the Spike-induced tetherin downregulation altered virus release, we performed experiments with virus like particles (VLPs) in HEK293T …’ – (Line 399).

      Line 379, OFR, should be ORF.

      Yes, changed.

      Line 448, 'Tetherin retains the ability' - did it ever loose it?

      This has been rephrased to,

      ‘Tetherin has the ability to restrict a number of different enveloped viruses that bud at distinct organelles.’ - (Line 547).

      Line 451, 'luminal' is confusing in this context.

      This has been modified to,

      ‘Tetherin forms homodimers between opposing membranes (e.g., plasma membrane and viral envelope) that are linked via disulphide bonds.’ - (Line 549).

      Line 453, the process of virus envelopment is likely to be more than a 'single step'

      This now reads,

      ‘…virus during viral budding, which occurs in modified ERGIC organelles.’ - (Line 552).

      Line 457, in my view the notion that Vpu abrogation of tetherin restriction is just due to redistribution of tetherin to the TGN is somewhat simplistic and disregards a lot of other work.

      We have removed mention of mechanisms of tetherin antagonism by other viruses. The key point we wish to make here is that tetherin is lost from the budding compartment. This now reads,

      ‘Many enveloped viruses antagonise tetherin by altering its localisation and removing it from the respective site of virus budding.’ – (Line 552-553).

      Line 472, what is meant by 'resting states'?

      This should have been ‘in the absence of stimulation’ and have now been re-written,

      ‘Tetherin is an IFN-stimulated gene (ISG) [13], and many cell types express low levels of tetherin in the absence of stimulation.’ - (Line 577).

      Line 1204, how were 'mock infected cells .......... infected'?

      This has now been re-written,

      ‘Differentiated nasal primary human airway epithelial (HAE) cells were embedded to OCT….’ - (Line 1385).

      Reviewer #2 (Significance (Required)):

      This study builds on published work supporting the notion that SARS-Cov-2 ORF3a is an antagonist for the restriction factor tetherin. Importantly, it provides insights to the the mechanism of ORF3a mediated tetherin antagonism, specifically to ORF3a inhibits tetherin cycling, diverting the protein to lysosomes and away from compartment(s) where virions assemble. Overall, the authors provide good supporting evidence for these conclusions, however there are issues that the authors need to address.

      We wish to thank Reviewer #2 for their insightful comments and suggestions for improving this work.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Restriction factors are major barriers against viral infections. A prime example is Tetherin (aka BST2), which is able to physically tether budding virions to the plasma membrane preventing release of the infectious particles. Of note, tetherin has broad anti-viral activity and has been established as a crucial innate immune defense factor against HIV, IAV, SARS-CoV-2 and other important human pathogens. However, successful viruses like SARS-CoV-2 evolved strategies to counteract restriction factors and promote their replication. Important restriction factors, such as tetherin, may often be targeted by multiple viral strategies to ensure complete suppression of their anti-viral activities by the pathogen. Of note, it was previously published that the accessory protein ORF7a of SARS-CoV-2 binds to (Petrosino et al, Chemistry Europe, 2021) and antagonizes it (Martin-Sancho et al, Molecular Cell, 2021). Previous data on SARS-CoV also revealed that ORF7a promotes cleavage of tetherin (Taylor et al, 2015, J Virol). In this manuscript, the authors show that tetherin restricts SARS-CoV-2 by tethering virions to the plasma membrane and propose that tetherin is targeted by two proteins of SARS-CoV-2. Whereas the Spike protein promotes degradation of tetherin, the accessory protein ORF3a redirects tetherin away from newly forming SARS-CoV-2 virions. While the overall findings that both S and ORF3a are additionally targeting tetherin is both novel and intriguing, additional evidence is needed to support this. In addition, the authors show that in their experimental setups ORF7a does not induce cleavage of tetherin. This is in direct contrast to previously published data both on SARS-CoV(-1) and -2 (Taylor et al, 2015, J Virol; Petrosino et al, Chemistry Europe, 2021; Martin-Sancho et al, Molecular Cell, 2021). From my point of view that needs further experimental confirmation. While the authors state that the impact of Spike on tethrin is mild, the experiments should still allow the conclusion whether there is a (mild) effect or not. The mechanism of ORF3a is fortunately more robustly assessed and provides some novel insights. Unfortunately, the whole manuscript suffers from a striking lack of quantifications. In addition, it is not clear whether and how many times experiments were repeated to the same results. Overall, the data in this manuscript seem very speculative and preliminary and thus do not support the authors conclusions.

      Major:

      Much of the data seems like it was only done once. As I am sure that this is a writing issue, please clearly state how many times the individual assays were repeated, provide the quantification graphs and appropriate statistics. Some experiments may need additional quantification and confirmation by other methods to be convincing.

      Quantification is provided throughout the revised manuscript. Figure legends have also been updated to provide information on quantification and statistical analysis.

      For example, Figure 1A, C and D: Please quantify the levels of tetherin and use an alternative readout, e.g. Western blotting of infected cells.

      Quantification has been performed and included in our revised manuscript in Supplemental Figures 1C, 1E. Tetherin is not shown in Figure 1C.

      A table is provided (above) to highlight the additional quantification.

      Figure 2A: Please quantify.

      We are not sure we understand this point. The western blot shown in Figure 2A demonstrates the ectopic expression of ACE2 in our A549 cell line. A549 cells have been used by many labs to study SARS-CoV-2 infection, but express negligible ACE2.

      Fig 3A: Please show and confirm successful tetherin KO in the cell lines that are used not only in microscopy.

      A new blot is now shown in Figure 3A, including a blot demonstrating tetherin loss in both KO lines.

      Figure 4C: Please quantify

      Currently flow cytometry experiments have been performed twice each and this is now detailed in the figure legends. The data shown in each panel is representative and the data has been explored using analogous approaches. For example, Figure 4C is complemented by Figures 4A and 4B, Figures 4E is complemented by 4D and 4F. We do not feel that repeating these flow cytometry analysis will significantly improve the manuscript.

      Figure 4D: Please quantify the effects are not obvious from the images provided.

      Quantification is now provided in Supplemental Figure 4E.

      Figure 4E, F Please provide a quantification of multiple independent repeats, the claimed differences are neither striking nor obvious.

      Quantification of 4F is now provided in Supplemental Figure 4G. Tetherin levels were quantified to be reduced by 25% (SD: 8%) by addition of Doxycycline and induction of ss-HA-Spike. Information for quantification is provided in figure legends.

      Figure 5A: Please quantify

      These experiments have currently been performed twice and this is now described in the figure legends. Data shown is representative. We can perform one more repeat of these experiments to quantify if neccessary, but do not feel it will significantly alter the manuscript.

      Figure 3C and D: At timepoint 0 the infection input levels are different. The initial infection levels have to be the same to draw the conclusion that tetherin KO affects virion release and not the initial infection efficiency. Can the authors either normalize or ensure that the initial infection is the same in all conditions and that variations in the initial infection efficiency do not correlated with the impact of tetherin on replication/release ? How often were those experiments repeated? Are the marginal differences in infectious titre significant? Overall the impact of tetherin on SARS-CoV-2 is very underwhelming but that may be due to efficient viral tetherin-counteraction strategies. Why is the phenotype inverted at 72 h?

      Equal amounts of virus, as measured by plaque-forming units (PFU), were used for both HeLa cell lines and thus at 0 hpi the variation seen is within the parameters of the assay used. It remains possible that tetherin affects virus entry but this is unlikely and this assay was not designed to investigate that effect.

      Growth curve assays are currently being repeated using an MOI of 0.01, 1 and 5. We are removing the 72 hpi sample from future experiments. At this time point, we find that the extensive cell death caused by viral replication (especially at higher MOIs) makes it difficult to accurately separate the released from intracellular fractions and conclusions cannot be accurately drawn from the data.

      Additional repeats of these experiments are in progress and will be included in the finalised manuscript.

      Figure 4B and C: Can the authors provide an explanation why SARS-CoV ORF7a is not inducing cleavage/removes glycosylation of tetherin. To show that the assays work, an independent positive control needs to be included. The FACS data in C is unfortunately not quantified.

      See above comments (Reviewer #2) regarding discussion on ORF7a. Additional text has been included to discuss ORF7a data,

      ‘SARS-CoV-1 ORF7a is reported to inhibit tetherin glycosylation and localise to the plasma membrane in the presence of tetherin [18]. We did not observe any difference in total tetherin levels, tetherin glycosylation, ability to form dimers, or surface tetherin upon expression of either SARS-CoV-1 or SARS-CoV-2 ORF7a (Figures 4A, 4B, 4C).

      Others groups have demonstrated a role for ORF7a in sarbecovirus infection and both SARS-CoV-1 and SARS-CoV-2 virus lacking ORF7a show impaired virus replication in the presence of tetherin [18,41]. A direct interaction between SARS-CoV-1 ORF7a and SARS-CoV-2 ORF7a and tetherin have been described [18,41], although the precise mechanism(s) by which ORF7a antagonises tetherin remains enigmatic. We cannot exclude that ORF7a requires other viral proteins to antagonise tetherin, or that ORF7a antagonises tetherin via another mechanism. For example, ORF7a can potently antagonise IFN signalling [42] which would impair tetherin induction in many cell types.’ – (Line 667-704).

      Fig 4G: The rationale and result of this experiment are not clear.

      The rationale for Spike VLP experiments is explained at Line 403. Given that Spike caused a reproducible decrease in cellular tetherin, we examined whether this downregulation was sufficient to antagonise tetherin and increase VLP yield.

      Fig 6: What is the benefit of doing the VLP assays as opposed to genuine virus experiments? To me it rather seems to be making the data unnecessarily complex. Again, no quantifications or repeats are provided.

      VLPs are used to separate the budding and release process from the replication process of RNA viruses. VLPs have been used in a number of SARS-CoV (DOI: 1002/jmv.25518) and HIV-1 (DOI: https://doi.org/10.1186/1742-4690-7-51) studies to analyse the impact of tetherin (and tetherin mutants) on release.

      VLP experiment quantification are now included throughout.

      Minor: Fig 1D: How do the authors explain the mainly intracellular Spike staining?

      We do not understand this point. Spike staining is intracellular, whether expressed alone or in the context of infected cells.

      Please add statistical analyses on the data e.g. Fig. 3 C and D

      Additional repeats of these experiments are in progress and will be included in the finalised manuscript.

      Fig. 4B and F: Why do the annotated sizes of tetherin differ between the blots?

      Figures 4B and 4F are run in non-reduced and reduced conditions respectively. In order to best show the dimer deficient C3A-Tetherin, blots are typically run in non-reduced conditions to exemplify dimer formation and to highlight any defects in dimer formation. The rest of the blots in the manuscript are run in denaturing conditions to aid blotting of other proteins. (Lines 957-958) and now (Lines 1356-1357).

      Fig. 5A: What is ORF6a? Do the authors mean ORF6?

      Yes, this has been changed.

      An MOI of 1 is NOT considered a low or relevant MOI. Can the authors either rephrase or repeat experiments with an actual low or relevant MOI i.e. 0.01 ?

      We are currently repeating these experiments and are including MOIs of 0.01, 1 and 5.

      Why were the cell models switched between Figure 1 and 2 and essentially the same experiments repeated?

      HeLa cells express high levels of tetherin at steady state, whilst A549 cells require IFN stimulation. HeLa cells demonstrate that tetherin downregulation occurs via an IFN-independent manner. A549 and T84 cells are more physiologically relevant cell types for SARS-CoV-2 infection. These points are stated in Lines 230 and 261.

      The manuscript may benefit a lot from streamlining and removing unessential deviations from the main message (e.g. discussions why multistep/single step growth curves are used/not relevant; why are they shown if the authors conclude that a single step is not relevant?). The discussion is extremely lengthy and does not provide sufficient discussion of the presented data.

      The multistep/single step growth curve text will be adapted, but it will be re-written after additional infection experiments.

      We have removed from the Discussion a small section discussing ORF7a mutants, given that the emphasis of our manuscript is not on ORF7a.

      We have also removed a small section describing the rearrangements of intracellular organelles by SARS-CoV-2 as it does not directly relate to the central message of our manuscript.

      According to my opinion, the current manuscript does not provide significant advancement for the field. While the intention was to update and expand our existing knowledge about tetherin restriction by SARS-CoV-2, the experiments do not support this yet. However, the general premise and approach/concept of the manuscript would be appealing to a broader audience. I especially like the notion that multiple proteins of SARS-CoV-2 could synergistically counteract an important innate immune defense factor, tetherin. My expertise is on SARS-CoV-2 and the interplay between the virus and host cell restriction factors.

      Reviewer #3 (Significance (Required)):

      According to my opinion, the current manuscript does not provide significant advancement for the field. While the intention was to update and expand our existing knowledge about tetherin restriction by SARS-CoV-2, the experiments do not support this yet. However, the general premise and approach/concept of the manuscript would be appealing to a broader audience. I especially like the notion that multiple proteins of SARS-CoV-2 could synergistically counteract an important innate immune defense factor, tetherin. My expertise is on SARS-CoV-2 and the interplay between the virus and host cell restriction factors.

      We thank Reviewer#3 for their comments and suggestions for improving this work.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      BST2/tetherin can restrict the release and transmission of many enveloped viruses, including coronaviruses. In many cases, restricted viruses have developed mechanisms to abrogate tetherin-restriction by expressing proteins that antagonize tetherin; HIV-1 Vpu-mediated antagonism of tetherin restriction is a particularly well studied example. In this paper, Stewart et al. report their studies of the mechanism(s) underlying SARS-CoV-2 antagonism of tetherin restriction. They conclude that Orf3a is the primary virally encoded protein involved and that Orf3a manipulates endo-lysosomal trafficking to decrease tetherin cycling and divert the protein away from putative assembly sites.

      Major comments:

      In my view some of the claims made by the authors are not fully supported by the data. For example, the bystander effect discussed in line 162 may suggest that infected cells can produce IFN but does not 'indicate' that they do. Most of the EM images show part of a cell profile, so statements such as (line 192) 'virus containing tubulovesicular organelles were often polarised towards sites of significant surface-associated virus' should be backed up with appropriate images, or indicated as 'not shown', or removed (the observation is not so important for this story). Line 196, DMVs can't be seen in these micrographs. Line 391, I can't see much change in CD63 distribution.

      Line 321, the authors show that ORF7a does not affect tetherin localization, abundance, glycosylation or dimer formation, but they don't show that it doesn't restrict SARS-CoV-2. Can they be sure that epitope tagging this molecule does not abrogate function (or the functions of any of the other tagged proteins for that matter), or that ORF7a works in conjunction with one of the other viral proteins? In the ORF screen, a number of the constructs are expressed at low level, is it possible they are missing something?

      Line 376, the authors refer to ORF3a being a viroporin. A recent eLife paper (doi: 10.7554/eLife.84477; initially published in BioRxiv) refutes this claim and builds on other evidence that ORF3a interacts with the HOPS complex. The authors should at least mention this work, especially in the discussion, as it would seem to provide a molecular mechanism to support their conclusions.

      Fig 3, the growth curves illustrated in Fig3 C and D do not have errors bars; how many times were these experiments repeated?

      Line 396, the authors show increased co-localization with LAMP1. As LAMP1 is found in late endosomes as well as lysosomes, they cannot claim the redistributed tetherin is specifically in lysosomes.

      There seems to be a marked difference in the anti-rb555 signal in the 'mock' cells in panels 5H and Suppl 6E. Is there a good reason for this, or does this indicate variability between experiments?

      Fig 6a, why is there negligible VLP release from cells lacking BST2 and ORF3a-strep? How many times were these experiments performed? Is this a representative image? I think it confusing to refer to the same protein by two different names in the same figure (i.e. BST2 and tetherin). Do the authors know how the levels of ORF3a expressed in cells in these experiments compares to those seen in infected cells?

      My final point is, perhaps, the trickiest to answer, but nevertheless needs to be considered. As far as we know, SARS-CoV-2 and at least some other coronaviruses, bud into organelles of the early secretory pathway, often considered to be ERGIC. In the experiments shown here the authors provide evidence that ORF3a can influence tetherin recycling, but the main way of showing this is through its increased association with endocytic organelles. Do the authors have any evidence that Orf3a reduces tetherin levels in the ERGIC or whether the tetherin cycling pathway(s) involve the ERGIC?

      Minor comments:

      Overall, the manuscript should be carefully edited to ensure the text reads clearly. A few examples of thing that need to be fixed are:-

      Line 53, delete 'shell' its redundant and confusing when the authors have said coronaviruses have a membrane.

      Line 61, delete 'the'

      Line 72, delete 'enveloped'; coronaviruses already described as enveloped viruses (line 53)

      Lines 93 - 100, lop-sided discussion of the viral life cycle; this paragraph is mostly about entry, which is not relevant to this paper, and does not really deal with the synthesis and assembly side of the cycle.

      Line 103, why are the neighbouring cells 'naive'?

      Line 112 - 113, delete last phrase; tetherin is described as an IFN stimulated gene in line 111; to be accurate, the beginning of the sentence should be 'Tetherin is expressed from a type 1 Interferon stimulated gene ...'

      Line 118 - 119, should say 'For tetherin-restricted enveloped viruses' as not all enveloped viruses are restricted by tetherin.

      Line 131, coronaviruses are not the only family of tetherin-restricted viruses that assemble on intracellular membranes, e.g. bunyaviruses.

      Line 192, there is no EM data in Supplemental Fig 1C.

      Line 251, 'a synchronous infection event' should be 'synchronous infection' as there will be multiple infection events

      Page 13 (and elsewhere), unlike Southern, 'Western' should not have a capital letter, except at the start of a sentence.

      Lines 330 and 352, can the authors quantitate S protein-induced reduction in cell surface tetherin rather than using the somewhat subjective 'mild'?

      Line 379, OFR, should be ORF.

      Line 448, 'Tetherin retains the ability' - did it ever loose it?

      Line 451, 'luminal' is confusing in this context.

      Line 453, the process of virus envelopment is likely to be more than a 'single step'

      Line 457, in my view the notion that Vpu abrogation of tetherin restriction is just due to redistribution of tetherin to the TGN is somewhat simplistic and disregards a lot of other work.

      Line 472, what is meant by 'resting states'?

      Line 1204, how were 'mock infected cells .......... infected'?

      Significance

      This study builds on published work supporting the notion that SARS-Cov-2 ORF3a is an antagonist for the restriction factor tetherin. Importantly, it provides insights to the the mechanism of ORF3a mediated tetherin antagonism, specifically to ORF3a inhibits tetherin cycling, diverting the protein to lysosomes and away from compartment(s) where virions assemble. Overall, the authors provide good supporting evidence for these conclusions, however there are issues that the authors need to address.

    1. Reviewer #2 (Public Review):

      In this manuscript, Roberts et al. present XTABLE, a tool to integrate, visualise and extract new insights from published datasets in the field of preinvasive lung cancer lesions. This approach is critical and to be highly commended; whilst the Cancer Genome Atlas provided many insights into cancer biology it was the development of accessible visualisation tools such as cbioportal that democratised this knowledge and allowed researchers around the world to interrogate their genes and pathways of interest. XTABLE is trying to do this in the preinvasive space and should certainly be commended as such. We are also very impressed by the transparency of the approach; it is quite simple to download and run XTABLE from their Gitlab account, in which all data acquisition and analysis code can be easily interrogated.

      We would however strongly advocate deploying XTABLE to a web-accessible server so that researchers without experience in R and git can utilise it. We found it a little buggy running locally and cannot be sure whether this is due to my setup or the code itself. Some issues clearly need development; Progeny analysis brings up a warning "Not working for GSE109743 on the server and not sure why". GSEA analysis does not seem to work at all, raising an error "Length information for genome hg38 and gene ID ensGene is not available". In such relatively complex software, some such errors can be overlooked, as long as the authors have a clear process for responding to them, for example using Gitlab issue reporting. Some acknowledgement that this is an ongoing development would be helpful.

      The authors discuss some very important differences between the datasets in the text. Most notably they differ in endpoints and in the presence of laser capture. We would advocate including some warning text within the XTABLE application to explain these. For example, the "persistent/progressive" endpoint used in Beane et al (next biopsy is the same or higher grade) is not the same as the "progressive" endpoint in Teixeira et al (next biopsy is cancer); samples defined as "persistent/progressive" may never progress to cancer. This may not be immediately obvious to a user of XTABLE who wishes to compare progressive and regressive lesions. Similarly, the use of laser capture is important; the authors state that not using laser capture has the advantage of capturing microenvironment signals, but differentiating between intra-lesional and stromal signals is important, as shown in the Mascaux and Pennycuick papers. The authors cannot do much about the different study designs, but as the goal is to make these data more accessible We think some brief description of these issues within the app would help to prevent non-expert users from drawing incorrect conclusions.

      The authors themselves illustrate this clearly in their analysis of CIN signatures in progression potential. They observe that there is a much clearer progressive/regressive signal in GSE108124 compared to GSE114489 and GSE109743. This does not seem at all surprising, since the first study used a much stricter definition of progression - these samples are all about to become cancer whereas "progressive" samples in GSE109743 may never become cancer - and are much enriched for CIN signals due to laser capture. Their discussion states "CIN scores as a predictor of progression might be limited to microdissected samples and CIS lesions"; you cannot really claim this when "progression" in the two cohorts has such a different meaning. To their credit, the authors do explain these issues but they really should be clearly spelled out within the app.

      We are not sure we agree with their analysis of CDK4/Cyclin-D1 and E2F expression in early lesions. The authors claim these are inhibited by CDKN2A and therefore are markers of CDKN2A loss of function. But these genes are markers of proliferation and can be driven by a range of proliferative processes. Histologically, low-grade metaplasias and dysplasias all represent proliferative epithelium when compared to normal control, but most never become cancer. It is too much of a leap to say that these are influenced by CDKN2A because that gene is inactivated in LUSC; do the authors have any evidence that this gene is altered at the genomic level in low-grade lesions?

      Overall this tool is an important step forwards in the field. Whilst we are a little unconvinced by some of their biological interpretations, and the tool itself has a few bugs, this effort to make complex data more accessible will be greatly enabling for researchers and so should be commended. In the future, we would like to see additional molecular data integrated into this app, for example, the whole genome and methylation data mentioned in line 153. However, we think this is an excellent start to combining these datasets.

    2. Author Response:

      Reviewer #1 (Public Review):<br /> <br /> Roberts et al have developed a tool called "XTABLE" for the analysis of publicly available transcriptomic datasets of premalignant lesions (PML) of lung squamous cell carcinoma (LUSC). Detection of PMLs has clinical implications and can aid in the prevention of deaths by LUSC. Hence efforts such as this will be of benefit to the scientific community in better understanding the biology of PMLs.

      The authors have curated four studies that have profiled the transcriptomes of PMLs at different stages. While three of them are microarray-based studies, one study has profiled the transcriptome with RNA-seq. XTABLE fetches these datasets and performs analysis in an R shiny app (a graphical user interface). The tool has multiple functionalities to cover a wide range of transcriptomic analyses, including differential expression, signature identification, and immune cell type deconvolution.

      The authors have also included three chromosomal instability (CIN) signatures from literature based on gene expression profiles. They showed one of the CIN signatures as a good predictor of progression. However, this signature performed well only in one study. The authors have further utilised the tool XTABLE to identify the signalling pathways in LUSC important for its developmental stages. They found the activation of squamous differentiation and PI3K/Akt pathways to play a role in the transition from low to high-grade PMLs

      The authors have developed user-friendly software to analyse publicly available gene expression data from premalignant lesions of lung cancer. This would help researchers to quickly analyse the data and improve our understanding of such lesions. This would pave the way to improve early detection of PMLs to prevent lung cancer.

      Strengths:

      1. XTABLE is a nicely packaged application that can be used by researchers with very little computational knowledge.<br /> 2. The tool is easy to download and execute. The documentation is extensive both in the article and on the GitLab page.<br /> 3. The tool is user-friendly, and the tabs are intuitively designed for successive steps of analysis of the transcriptome data.<br /> 4. The authors have properly elaborated on the biological interest in investigating PMLs and their clinical significance.

      Weaknesses:

      The article is focused on the development and the utility of the tool XTABLE. While the tool is nicely developed, the need for a tool focussing only on the investigation of PMLs is not justified. Several shiny apps and online tools exist to perform transcriptomic analysis of published datasets. To list a few examples - i) http://ge-lab.org/idep/ ; ii) http://www.uusmb.unam.mx/ideamex/ ; iii) RNfuzzyApp (Haering et al., 2021); iv) DEGenR (https://doi.org/10.5281/zenodo.4815134); v) TCC-GUI (Su et al., 2019). While some of these are specific to RNA-seq, there are plenty of such shiny apps to perform both RNA-seq and microarray data analysis. Any of these tools could also be used easily for the analysis of the four curated datasets presented in this article. The authors could have elaborated on the availability of other tools for such analysis and provided an explanation of the necessity of XTABLE. Since 3 of the 4 datasets they curated are from microarray technology, another good example of a user-friendly tool is NCBI GEO2R. This is integrated with the NCBI GEO database, and the user doesn't need to download the data or run any tools. iDEP-READS (http://bioinformatics.sdstate.edu/reads/) provide an online user-friendly tool to download and analyse data from publicly available datasets. Another such example is GEO2Enrichr (https://maayanlab.cloud/g2e/). These tools have been designed for non-bioinformatic researchers that don't involve downloading datasets or installing/running other tools.

      Two of these tools (IDEP and TCC-GUI) were reviewed in a literature review covering 20 Shiny apps performed two years ago prior to work on XTABLE starting. Three of the suggested tools (IDEP, RNFuzzyApp, TCC-GUI) are for processing only RNA-seq datasets. IDEAMEX appears to be for RNA-seq data only and is severely limited in its downstream analysis capabilities. DEGenR appears to handle microarray datasets and features an option to retrieve data directly from GEO. However, it appears to be based on GEO2R (with additional downstream analyses) where it automatically logtransforms already log-transformed data and unlike GEO2R, you do not have the option to not apply a log-transformation. A refreshed literature search focusing on microarray datasets highlighted three additional tools. iGEAK which hasn’t been updated in three years and seems to have compatibility issues running on new Windows and Mac machines. sMAP, an upcoming Shiny app for microarray data published in bioRxiv on 29 May 2022. MAAP which has the same issue of log-transforming already log-transformed data. iDEP-READS does not list the datasets used in XTABLE. GEO2Enrichr appears to require the counts table and experimental design in one file, performs a “characteristic direction” DEG test and outputs enriched pathways. These apps require not just downloading of datasets but reformatting and renaming of expression data files and creation of additional files for setting up the DEG analysis which is not practical for the number of samples we have (122, 63, 33, 448) even if these apps handled microarray data. XTABLE also incorporates AUC metrics, which is appropriate given the number of samples in each dataset and tool known for adequately controlling FDR, which is not seen in other apps as well as emphasis on individual gene results and interrogation.

      A new paragraph on the discussion section (lines 361-370) of the discussion addresses the potential use of existing applications instead of XTABLE

      Secondly, XTABLE doesn't provide a solution to integrate the four datasets incorporated in the tool. One can only analyse one dataset at a time with XTABLE. The differences in terms of methodology and study design within these four datasets have been elaborated on in the article. However, attempts to integrate them were lacking.

      We repeatedly considered different strategies of integrating the analysis of the four datasets and we always reached the conclusion that it was hardly going to offer any advantage, or that it might be counterproductive.

      Integration can occur at multiple levels. One possibility is to carry out the same analysis (e.g. expression of a given gene in two groups of samples) in all datasets. Since the design and methodologies of the four studies differ substantially (different stages, different definitions of progression status, etc), a unique stratification for all datasets is not possible. Moreover, interrogating the four datasets simultaneously would slow the analysis, with no significant advantage in terms of speed. Another possibility is the integration of results in the same output. For instance, obtain a single chart with the expression of a given gene in multiple subgroups of the four datasets. We think that the results from each cohort should be kept separately and then compared with a similar analysis from other datasets due to differences in design. Scientifically, this is the best way to proceed as it avoids confusions.

      Nevertheless, XTABLE allows the export of data for further analysis. The user can use this option to integrate data using other applications or statistical packages.

      We do understand the attractiveness of integration between the four datasets is and we seriously considered it. But there is a fine balance between user-friendliness, flexibility, and scientific rigour. We think that XTABLE achieves this balance. Increasing integration of datasets might lead to error and wrong conclusions due to biological and methodological differences between studies. We believe that comparing analyses obtained independently from the four cohorts is the most sensible way to proceed.

      We propose to discuss these aspects accordingly.

      The integrative analysis of two or more datasets has been discussed in a new paragraph (382-391)

      The tool also lacks the flexibility for users to add more datasets. This would be helpful when there are more datasets of PMLs available publicly.

      This was also a permanent topic for discussion while designing XTABLE. Creating a tool that could be used to analyse other cohorts of precancerous lesions, while maintaining the ease of use was certainly a challenge. We had to adapt XTABLE to the characteristics of each one of the four databases: specific stratification criteria, different nomenclatures for the different sample types, etc. Designing a shiny app that can be adapted to other present or future datasets without the need of changing the code is simply not practical.

      The flexibility that these other Shiny apps incorporate to analyse any RNA-seq dataset requires the contrasts used for the differentially expressed gene analysis be manually defined. IDEP requires an experimental design file where sample names in the counts file must match exactly the sample names in this experimental design file and pre-processing visualisation is limited to the first 100 samples. RNFuzzyApp is similar but we could not format the experimental design file in a way that did not result in the app crashing upon upload. TCC-GUI requires all the sample names to be renamed to the contrast group with the addition of the replicate number. Apps that allow datasets to be uploaded do not have a practical or easy way to set up the DEG analysis of more than a couple dozen samples.

      Future versions of XTABLE can be updated to include additional curated PML datasets that would enhance hypothesis generation upon request. Importantly, the code is freely available and can be modified by other scientists to add their cohorts of interest, although we agree that a high level of expertise in coding will be needed. We propose to add these considerations to the text.

      The possibilities of expansion of XTABLE to new databases are discussed in lines 392-398

      Understanding the biology of PML progression would require a multi-omics approach. XTABLE analyses transcriptome data and lacks integration of other omics data. The authors mention the availability of data from whole exome, methylation, etc from the four studies they have selected. However, apart from the CIN scores, they haven't integrated any of the other layers of omics data available.

      Only one dataset (GSE108104) contains whole-exome sequencing and methylation data. We considered that a multi-omics approach in XTABLE would result in an overcomplicated application. As far as early detection and biomarker discovery is concerned, transcriptomic data is the most interesting parameter.

      Also discussed in lines 382-391

      Lastly, the authors could have elaborated on the limitations of the tool and their analysis in the discussion.

      We propose to raise these limitations accordingly in the discussion.

      See above.

      Reviewer #2 (Public Review):

      In this manuscript, Roberts et al. present XTABLE, a tool to integrate, visualise and extract new insights from published datasets in the field of preinvasive lung cancer lesions. This approach is critical and to be highly commended; whilst the Cancer Genome Atlas provided many insights into cancer biology it was the development of accessible visualisation tools such as cbioportal that democratised this knowledge and allowed researchers around the world to interrogate their genes and pathways of interest. XTABLE is trying to do this in the preinvasive space and should certainly be commended as such. We are also very impressed by the transparency of the approach; it is quite simple to download and run XTABLE from their Gitlab account, in which all data acquisition and analysis code can be easily interrogated.

      We would however strongly advocate deploying XTABLE to a web-accessible server so that researchers without experience in R and git can utilise it. We found it a little buggy running locally and cannot be sure whether this is due to my setup or the code itself. Some issues clearly need development; Progeny analysis brings up a warning "Not working for GSE109743 on the server and not sure why". GSEA analysis does not seem to work at all, raising an error "Length information for genome hg38 and gene ID ensGene is not available". In such relatively complex software, some such errors can be overlooked, as long as the authors have a clear process for responding to them, for example using Gitlab issue reporting. Some acknowledgement that this is an ongoing development would be helpful.

      We thank the reviewer for these comments. We will inspect the code to address those warnings, implement a system for issue reporting, and add the acknowledgements suggested by the reviewer. Regarding the deployment of XTABLE to a web-accessible server, this could present a challenge in the long term as computing resources need to be allocated for years and the economic cost involved.

      The code has been inspected to remove the warning and errors pointed out by the reviewer.

      The authors discuss some very important differences between the datasets in the text. Most notably they differ in endpoints and in the presence of laser capture. We would advocate including some warning text within the XTABLE application to explain these. For example, the "persistent/progressive" endpoint used in Beane et al (next biopsy is the same or higher grade) is not the same as the "progressive" endpoint in Teixeira et al (next biopsy is cancer); samples defined as "persistent/progressive" may never progress to cancer. This may not be immediately obvious to a user of XTABLE who wishes to compare progressive and regressive lesions. Similarly, the use of laser capture is important; the authors state that not using laser capture has the advantage of capturing microenvironment signals, but differentiating between intra-lesional and stromal signals is important, as shown in the Mascaux and Pennycuick papers. The authors cannot do much about the different study designs, but as the goal is to make these data more accessible We think some brief description of these issues within the app would help to prevent non-expert users from drawing incorrect conclusions.

      The authors themselves illustrate this clearly in their analysis of CIN signatures in progression potential. They observe that there is a much clearer progressive/regressive signal in GSE108124 compared to GSE114489 and GSE109743. This does not seem at all surprising, since the first study used a much stricter definition of progression - these samples are all about to become cancer whereas "progressive" samples in GSE109743 may never become cancer - and are much enriched for CIN signals due to laser capture. Their discussion states "CIN scores as a predictor of progression might be limited to microdissected samples and CIS lesions"; you cannot really claim this when "progression" in the two cohorts has such a different meaning. To their credit, the authors do explain these issues but they really should be clearly spelled out within the app.

      This is a very good point. We will add the warning text about the differences between studies regarding the definition of progression potential and the differences and sample processing (LCM or o not) so that the user is permanently aware of the differences between cohorts.

      A new tab (Dataset) has been added table with the methodologies used in each of each study, and the differences in progression status definitions. Additionally, we emphasized these differences in the main text of the manuscript (lines 296-300 and 403-409).

      We are not sure we agree with their analysis of CDK4/Cyclin-D1 and E2F expression in early lesions. The authors claim these are inhibited by CDKN2A and therefore are markers of CDKN2A loss of function. But these genes are markers of proliferation and can be driven by a range of proliferative processes. Histologically, low-grade metaplasias and dysplasias all represent proliferative epithelium when compared to normal control, but most never become cancer. It is too much of a leap to say that these are influenced by CDKN2A because that gene is inactivated in LUSC; do the authors have any evidence that this gene is altered at the genomic level in low-grade lesions?

      We are grateful for this comment. There is currently not evidence that CDKN2A mutations occur in low-grade lesions and therefore, we cannot argue that the of CDK4/Cyclin-D1 and E2F expression signature are the result of CDKN2A inactivation in low-grade lesions. We propose to modify the text to introduce these caveats to our conclusion an make our interpretations more accurate.

      We have modified the discussion (lines 443-454) to address the interpretation of our results regarding the connection between CDKN2A inactivation and the CDK4/cyclin-D1 and E2F signatures. We now focus our conclusions on the pathway itself and we mention Cyclin-D1 and CDKN2A alterations as a potential modulator of the changes in the pathway, but leaving the discussion open to other drivers.

      Overall this tool is an important step forwards in the field. Whilst we are a little unconvinced by some of their biological interpretations, and the tool itself has a few bugs, this effort to make complex data more accessible will be greatly enabling for researchers and so should be commended. In the future, we would like to see additional molecular data integrated into this app, for example, the whole genome and methylation data mentioned in line 153. However, we think this is an excellent start to combining these datasets.

    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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      Reply to the reviewers

      We thank the reviewers for their enthusiasm for our work and constructive feedback.

      Below please find our point-by-point response to the comments:

      Reviewer #1 -Key conclusions that were less convincing: -RhoA and NMII are in the title as mechanistic downstream regulators of CaM, but the results in Fig 8 call into question the role of RhoA. Why does RhoA activation not influence cell size and circularity? Can you overexpress MRLC-GFP and inhibit Rho and restore the wt phenotype? The role of NMII is also not clear - why does overexpressing MLCK-CA not have a phenotype but overexpressing MLCK downstream target MRLC show the phenotype? Are there any alternative pathways to regulate MRLC? It's not being discussed or described in 6D's schematic.

      Response: Rho activation usually leads to the formation of more stress fibers and therefore does not lead to decreased cell size and increased circularity observed in MFN2 KO. The phenotype is restored by either ROCK or MLCK knockdown. We have discussed in the main text that the formation of PAB requires both RhoA and NMII activation under restricted spatiotemporal control.

      MRLC has three major regulators (Ikebe & Hartshorne, 1985; Isotani et al., 2004). As we discussed, MLCK and ROCK phosphorylate MRLC at either Ser19 or Ser19 and Thr18. MRLC is dephosphorylated and inactivated by its phosphatase MLCP. We tried to knock down MLCP in wt MEF cells but failed to see any cell morphology changes (data not shown).

      We were also surprised to see MRLC-GFP overexpression with Rho Activator can phenocopy PAB, but “MLCK-CA + Rho Activator” failed to. We believe it is because MLCK-CA constitutively over-activates a broad range of downstream effectors while overexpressing MRLC mimics endogenous activation or NMII alone. Also, only a proportion of cells acquired PAB structure under Rho Activator and MRLC overexpression, which indicates PAB formation also requires specific spatiotemporal controls.

      *Rewrite for clarity -The role of ER/mito contacts in the system was unclear (since ER/mito contacts were not observed nor evaluated directly). *

      __Response: __We have included additional data to measure ER/mito contacts in MEFs. Our result is consistent with numerous previous reports that MFN2 regulates ER/mito contacts. The data is now included in Fig. S3.

      * -What role does focal adhesions have on PAB formation or any part of the model - There were results showing larger focal adhesions in the MFN2-/- cells, but not sure how this fits in with the bigger picture of contractility and PABs, and focal adhesions were not in the model in Figure 5.*

      __Response: __Focal adhesion and actomyosin are tightly coupled, and our work focuses on the actin network. Our model did not include FAs since FA is not a significant focus in this study.

      * -Whether regulating calcium impacts PAB formation*

      __Response: __Calcium likely regulates PAB formation. We have shown PAB cell percentage decreases in mfn2-/- with ER-mito tethering contrast in Fig. S3.

      -The role of PABs in migration is also unclear - can you affect PAB formation or get rid of PABs and quantify cell migration?

      __Response: __Our data suggest that PAB formation and cell migration are inversely correlated. Since PAB results from a contractile actin band on the cell periphery, its role in defective cell spreading and migration is expected. We demonstrated that MLCK and ROCK knockdown reduced PABs and rescued cell spreading.

      -It was hard to understand the correlation between the membrane tension of MFN2-/- cells and their ability to spread on softer substrates. How does this result fit in with the overarching model?

      __Response: __Reduced membrane tension is presumably associated with decreased cell spreading. Softer substrates attenuate the mechanical force on focal adhesion proteins and the actin cytoskeleton (Burridge & Chrzanowska-Wodnicka, 2003; Pelham & Wang, 1997; Wong et al., 2015), which is required for focal adhesion maturation. As a result, softer substrates can reduce the over-contraction in the MFN2 KO cells. The results support the model that MFN2 KO cells have enhanced cell contraction on the substrates dependent on substrate interaction and force transduction on focal adhesions. Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

      __Response: __We have removed the MFN2-related disease from the introduction to focus the paper more on cell biology in vitro.

      *-There were a number of findings that did not seem to fit in with the paper, or they were included, but were not robustly described nor quantified. As an example, Paxillin-positive focal adhesions were evaluated in MFN2-/- and with various pharmacological approaches, but there is no quantification with respect to size, number, or distribution of focal adhesions, despite language in the main text that there are differences between conditions. *

      __Response: __We have quantified the focal adhesions in the KO cells, and the data is now included in Fig. 5. We used the actin distribution to quantify the “PAB”; therefore, FAs are not a significant focus of this study.

      *Also, the model was presented in figure 5, and then there were several pieces of data presented afterward, some that are included in the model (myosin regulation), and some that are not in the model (membrane tension, TFM, substrate stiffness, etc). * __Response: __Membrane tension and substrate stiffness dependence are physical properties of the cell. The model focuses on the molecular mechanism that leads to PAB formation.

      The stiffness/tension figure was not clear to me, and it was difficult to make sense of the data since one would predict that an increase in actomyosin contractility at the cortex would lead to higher membrane tension, not lower, and then how membrane tension relates to spreading on soft matrices is also unclear.

      __Response: __The result was surprising to us initially. However, the MFN2 KO cells have increased actomyosin contractility only at the cell-substrate interface but not throughout the entire cell cortex. A less spread cell would have a more relaxed membrane and display a lower membrane tension, consistent with our observation. Softer matrices reduce cell contractility at the cell-substrate interface, which allows MFN2 KO cells to relax and spread better. We have emphasized in the discussion of our manuscript that MFN2 KO cells have an increase in actomyosin contractility only at the cell-substrate interface.

      The manuscript seems like an amalgamation of different pieces of data that do not necessarily fit together into a cohesive story, so the authors are encouraged to either remove these data, or shore them up and weave them into the narrative.

      __Response: __We respectfully disagree with the reviewer since the cell morphology, actin structure, substrate interaction, and cell mechanics are tightly correlative and provide a complete picture of the role of MFN2 in regulating cell behavior.

      * Request additional experiments 1. -The imaging used for a lot of the quantification (migration, circularity) is difficult to resolve. The cells often look like they are not imaged in the correct imaging plane. It would be helpful to have better representative images such that it is clear how the cells were tracked and how cell periphery regions of interest were manually drawn. Focal adhesions should also be shown without thresholding.*

      __Response: __We used a TIRF microscope and imaged a Z stack. Imaris was used to combine the Z-stack images. The images in the manuscripts are now of the lowest stack with background subtraction.

      2. -For most of the quantification, it appears that the experiment was only performed once and that a handful of cells were quantified. The figure legend indicates the number of cells (often reported as >12 cells or >25 cells), but the methods indicate that high content imaging was performed, and so the interpretation is that these experiments were only performed once. Biological replicates are required. If the data do represent at least 3 biological replicates, then more cell quantification is required (12 or 25 cells in total would mean quantifying a small number of cells per replicate).

      Response: __We quantified more cells and indicated the number of cells quantified in the figure legends. The experiments are with three biological replicates.__

      * 3. -Mitochondrial morphology and quantification should be performed in the MFN2 knockdown and rescue lines.*

      __Response: __Mitochondrial morphology is well characterized in the Mfn2 KO and rescue MEF cells (Chen et al., 2003; Naon et al., 2016; Samanas et al., 2020). We observed a similar phenotype using mito-RFP to label mitochondrial structure (Fig. S1).

      4.-Many of the comparisons throughout the figures is between MFN2 knockdown and MFN2 knockdown plus rescue or genetic/pharmacological approaches, but a comparison that is rarely made is between wildtype and experimental. These comparisons could be useful in comparing partial rescues and potential redundancies with the other mitofusin.

      Response: We have included the WT in our assays (Fig. 2-6). We also confirmed that MFN1 could not rescue the MFN2 defects (Fig. 2). We observed partial and complete rescue in different assays. It would be difficult to conclude whether the phenotype is due to the redundancies with the other mitofusin because not all cells are rescued at the endogenous level.

      * 5. -For the mito/ER tethering experiments, it is important to show that ER/mito contacts are formed and not formed in the various conditions with imaging approaches.*

      __Response: __We adopted a previously established method to quantify ER-mitochondria contacts with the probe SPLICSL (Cieri et al., 2017; Vallese et al., 2020). Our results align with previous reports that Mfn2-null MEFs displayed significantly decreased ER-mitochondria contacts. MFN2 re-expression or ER-mitochondria tethering structure restored the contacts. (Fig. S3).

      * 6. -For some of the pharmacological perturbations, it would be helpful to show that the perturbation actually led to the expected phenotype - as an example, in cells treated with different concentrations of A23187, what are the intracellular calcium levels and how do these treatments influence PAB formation? This aspect should be generally applied across the study - when a modification is made, that particular phenotype should first be evaluated, before dissecting how the perturbation affects downstream phenotypes.*

      __Response: __We selected a collection of well-characterized inhibitors broadly used in the literature for pharmacological perturbations. For example, numerous studies used A23187 treatment to raise intracellular calcium to examine related actin cytoskeleton changes (Carson et al., 1994; Goldfine et al., 1981; Shao et al., 2015). We titrated the drugs in WT in preliminary experiments and observed similar phenotypes. (data not shown). We then use the same concentration to treat the MFN2 KO cells. Overall, we use pharmacological perturbations as supporting evidence. We use genetics (knockdown or overexpression) to validate our results.

      7. -In Figures 4 and 5, the thresholding approaches in the images make the focal adhesions difficult to resolve, and therefore it is difficult to determine the size. As described above, these metrics should be defined and quantified.

      __Response: __We used a TIRF microscope and imaged a Z stack. Imaris was used to combine the Z-stack images. The images in the manuscripts are now of the lowest stack with background subtraction.

      8. -What is a PAB? How is it defined? What metrics make a structure a PAB versus regular cortical actin - are there quantifiable metrics? In figure 8, there are some structures that are labelled as a PAB, but some aren't (as an example, the left panel in 8b is a PAB, but the right panel in 8A is not, but they look the same), so a PAB should be defined with quantifiable measures, and then applied to the entire study.

      __Response: __We developed an algorism to quantify PAB cells. We first used the ImageJ plugin FiloQuant (Jacquemet et al., 2019) to identify the cell border and cytoskeleton, then used our custom algorism to quantify the percentage of actin in the cell border area. The cellular circularity is also calculated at the same time. If the cell contains more than 50% actin in the cell border area, and the circularity is higher than 0.6, we then count it as a “PAB” cell (Fig. S2).

      -As described above, why does RhoA activation not influence cell size and circularity? Can you overexpress MRLC-GFP and inhibit Rho and restore the wildtype phenotype?

      __Response: __Rho activation usually leads to the formation of more stress fibers and therefore does not lead to decreased cell size and increased circularity observed in MFN2 KO.

      We are sorry that we don’t understand the rationale of this experiment proposed by the reviewer. ROCK inhibition restored the wildtype phenotype in MFN2 KO cells (Fig.7). Figure 8 is to create the MFN2 KO phenotype in WT cells, which requires both Rho and MRLC overactivation.

      * 10 Are the data and the methods presented in such a way that they can be reproduced? -We appreciate that the authors quantified many parameters, although some quantifications were missing. There are some missing methods - how was directionality quantified, was migration quantified by selecting the approximate center of cells using MTrackJ or were centroids quantified by outlining cells, for instance. Also, given that some of the phenotypes were somewhat arbitrarily assigned (ie. what constitutes a PAB?), it may be difficult to reproduce these approaches and interpret data appropriately.*

      __Response: __We have clarified directionality quantification methods and other details. We used MTrackJ to track cell migration. And as we mentioned above, we came up with a customized algorithm to quantify PAB cells, which shows the critical effectors in a more quantifiable way.

      * 11. Are the experiments adequately replicated and statistical analysis adequate? -Unfortunately, while the approximate number of cells was reported, the number of biological replicates were not reported, and therefore, the experimental information and statistical analyses are not adequate.*

      __Response: __We have quantified more cells and indicated the number of repeats and cells quantified in the figure legend. Minor comments: Specific experimental issues that are easily addressable.

      * 12. - for some of the graphs - mostly about calcium levels - fold change is shown, but raw values should also be included to determine whether the basal levels of calcium are different across the conditions.*

      __Response: __Delta F/F0 is the standard method to normalize dye loading in cells for calcium concentration measurements (Kijlstra et al., 2015; Zhou et al., 2021).

      13. - scale bars in every panel should also help make the points clearer.

      __Response: __We have added scale bars in all panels.

      * Reviewer #2 (Evidence, reproducibility and clarity (Required)): 1. Fig. 1A: The Mfn1 Western Blot is not of publication quality. Moreover, quantitation is necessary.*

      __Response: __We performed additional western blots, changed the representative images, and quantified the level of knockdown and overexpression (Fig.2 and 7). We did not quantify the WB in Fig.1A since it was to confirm that the Mfn1-/- or Mfn2-/- were knock-out cell lines.

      2. Fig. 1B (as well as Fig. 2G and others): the date do not reflect cellular size but instead spread cellular area.

      __Response: __We thank the reviewer for this suggestion. We have changed all similar descriptions to “Spread Area” in the main text and figures.

      3. Fig. 1C, D: Mfn1-null MEFs appear to be more spindle-shaped than wt cells, yet their circularity tends to be elevated. Do the authors have an explanation?

      __Response: __The circularity of Mfn1-/- MEFs has a slight increase but is not significant compared to the wt cells. As we observed, Mfn1-/- MEFs have fewer protrusions than wt, which may contribute to the slight increase in its circularity (Fig. 5C). However, this is not the focus of this study.

      * 4. Fig. 2A: The Mfn1 levels in Mfn2-/- + Mfn2 are lower than Mfn2-/-? Does this imply a crosstalk between Mfn1 and Mfn2 expression.*

      __Response: __We agree with the reviewer that a compensatory change in MFN1 expression might happen in Mfn2-/- + MFN2 MEFs. Previous research also indicated crosstalk between MFN1 and MFN2 expression (Sidarala et al., 2022).

      * 5. Fig. 2H: The authors should provide co-staining of mitochondria and Mfn2 as well.*

      __Response: __Co-staining of mitochondria and MFN2 in Mfn2-/- MEFs or rescue lines has been done in numerous previous studies (Chen et al., 2003; Naon et al., 2016; Samanas et al., 2020). In this work, we transfected our cells with mito-RFP and showed mitochondria changes in Mfn2-/- and rescue MEF cells (Fig. S1G).

      6. Fig. 4D-E: Western blots are not of publication quality. Looking at the blots provided in Fig. 4D, the reviewer is not convinced with the quantitative data shown in Fig. 4E. For instance, the intensity of pCaMKII band for "vec" does not look 3x higher than that of "+MFN2", whereas that of "+MFN2" looks much higher than that of WT.

      __Response: __We have performed additional western blots and changed the representative images.

      * 7. Fig. 5C: The authors should stain for vinculin, which are present in mature FAs only, rather than paxillin which are present in all FAs. This would strengthen the authors' conclusions. Also, FA size should be quantified.*

      __Response: __We have quantified FA size in Figure 5. The maturity of FAs is not a major focus of this study. It is likely that most FAs here are mature since they are connected with stress fibers.

      * 8. Fig. 6C - Why does the background have a grid and appear grey in color? Also, the cell interior appears in different colors in the different images. The authors should take a z-stack of images and provide the raw image files.*

      __Response: __We used a TIRF microscope and imaged a Z stack. Imaris was used to combine the Z-stack images. The images in the manuscripts are now of the lowest stack with background subtraction.

      9. Fig 7C: The MLCII Western blot is not of publication quality, and may affect the quantification provided in Fig. 7D.

      __Response: __We have performed additional western blots and changed the representative images.

      * 10. Fig 8: Do cell treatments with Rho Activator and MLCK-CA also impair migration velocity similar to Mfn2-null cells?*

      __Response: __Our data indicated that Rho Activator and MRLC induced the “PAB” structure seen in MFN2 KO cells. It is likely that cell migration is impaired here. Spatiotemporal regulation of Rho Activation is important to cell migration, it is known that Rho overactivation can significantly inhibit cell migration (Nobes & Hall, 1999). Showing Rho Activator and MLCK-CA will reduce cell migration will not add new knowledge to the cell migration field. However not all cell migration defects are associated with the PAB. We, therefore, focused on PAB quantification in this figure.

      11. Fig. 9A: The authors claim that wt cells have actin bundles that protrude against the membrane while Mfn2-null cells do not. This does not look convincing as the Mfn2-null actin bundle seem to be pushing against the membrane at the bottom of the image. No quantification is provided. It is unclear what conclusion can be drawn from the super-resolution images.

      __Response: __We used super-resolution imaging to demonstrate the details of the peripheral actin band (PAB) structure. We have used two boxes to enlarge the regions where membrane parallel actin structures are predominant. The quantification of PAB is provided in other figures.

      12. Suppl. Fig. 5C: The authors should take images using a confocal microscope for cells with Flipper-TR construct, eliminate the background and the cell center to only consider the cell periphery. Nikon TE2000 does not seem to be a confocal microscope.

      Response: __The amount of Flipper-TR that MEF cells can take in was limited. With the current signal-to-noise ratio, complete background elimination is not feasible. A confocal microscope is not necessary for Flipper-TR FLIM imaging (García-Calvo et al., 2022). __* Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      General Comments (Major) 1. Data Presentation and analysis: The data analysis would benefit from using a method such as Super-Plots to show the data from separate biological repeats and to use N numbers that represent the number of biological repeats rather than the number of cells analysed. Please see the following reference for a suggestion on how to analyse the data: Lord SJ, Velle KB, Mullins RD, Fritz-Laylin LK. SuperPlots: Communicating reproducibility and variability in cell biology. J Cell Biol. 2020 Jun 1;219(6):e202001064. doi: 10.1083/jcb.202001064. PMID: 32346721; PMCID: PMC7265319.*

      __Response: __We have changed the dot colors to show data from separate biological repeats.

      • Another general comment is that many of the experiments show analysis of very few numbers of cells or maybe only one field of view in a microscopy quantification experiment. This seems unusually low - for example, in Figure 1E only 6 cells have been analysed. It seems like more could have been done and if statistical analysis like we suggest in 1 above is used, this might reveal that some of the differences are less significant than the authors think/report. This is important, as cells are noisy and it is unusual to have such high significance for experiments like cell migration and other parameters unless a lot of measurements are made. In Figure 1G, it appears that only one field of view has been used to quantify the data. We routinely use 3-5 fields of view to get a representative sample of what the cells are doing.*

      __Response: __We have quantified more cells and indicated the number of repeats and cells quantified in the figure legend.

      • Some of the micrographs appear to be missing scale bars- e.g. Fig. 2H, Fig. 8*

      __Response: __We have included scale bars in the lower right corner of all panels.

      * 4. In the cell tracking experiments, only some of the cells in the images appear to have been tracked. How were the tracked cells chosen? Normally, we would track every cell to avoid bias in selection.*

      __Response: __We tracked all the cells in the view at the beginning of the experiments.

      5. The western blot images do not show the molecular size of the bands. Show ladder position

      __Response: __We have added bands to show molecular weights.

      6. Mostly the graphs show individual data points, which is good, but in some cases only a bar is shown- it would be nice to have individual points overlaid on the bars- e.g. Figure 1I, 4E, 5B, 5E, 7B, 7D

      __Response: __We have updated the graph to show individual points.

      7. Many of the confocal images look very processed- they have no background and have a hazy black halo around the cell. I am not familiar with the type of processing that was done and I worry that the images are only showing a masked and processed version of the actual data. The authors need to explain what processing they have done and probably also to provide the unprocessed images in a supplementary figure or dataset for readers to see. The methods description is inadequate as it only says Image J was used to process the data.

      __Response: __We used a TIRF microscope and imaged a Z stack. Imaris was used to combine the Z-stack images. The images in the manuscripts are now of the lowest stack with background subtraction.

      * Individual comments on Figures: Figure 1: See general comments above- consider to use Superplots, more cells and more fields of view in quantifications. Show experimental points in bar graph.*

      __Response: __We have quantified more cells and used super plots to display the data. The number of repeats and cells quantified are indicated in the figure legend.

      Figure 2: In 2E, the colours are very similar for two of the experiments so it is difficult to distinguish them- e.g. the MFN1 vs MFN2 rescue data both appear dark blue. Response: We have changed the color for MFN1 rescue to distinguish the two samples better.

      In 2H are the magnifications really the same for the WT as the +DOX and -DOX? The cell in the WT looks huge. Is this representative? Also, the phalloidin stain looks very spotty on the WT. This seems unusual.

      __Response: __The images are of the same scale. The Mfn2-/- MEFs are smaller, and DOX-induced MFN2 expression can only partially rescue the cell size.

      * Figure 3: Not many cells were analysed in 3B, especially the zero time point.*

      __Response: __We have quantified more cells.

      * Please define +T, we assume it is the tether construct, but it is not defined*

      __Response: __We defined T as a tether in the main text and the figure legend. In 3F, how were the tracked cells chosen?

      __Response: __We tracked all the cells in the view at the beginning of the experiments.

      * Figure 4:* 4B: Why have they not tested FK506 and STO609 on the WT cells?

      __Response: __We focused on understanding the MFN2 KO phenotype. Since neither FK506 nor STO609 altered the MFN2 KO phenotype, we did not include them in the WT group.

      4C: How were the tracked cells chosen?

      __Response: __We tracked all the cells in the view at the start of the experiments.

      4D-E: The blot doesn't look representative of the quantification- were the numbers normalised to vinculin? The difference between WT and vector looks too large to be real if the amounts were normalised to the vinculin, as vinculin is increased in vector. Likewise, the pCAMKII looks to be substantially decreased from the +MFN2, but this is not what the quantification shows.

      __Response: __We have performed additional western blots and changed the representative images.

      Figure 5: 5B- please clarify which ratio is shown here. I assume it is the ratio of RhoA-GTP vs RhoA between the zero and 4 minute time points.

      __Response: __Yes, we have clarified this point in the figure legend.

      5C- These images appear to have a mask around the cell. It is hard to tell where the edge of the cell really is- what sort of processing was used? Especially for the paxillin staining, why is there no cytoplasm shown? Is this because the image is in TIRF?

      __Response: __We used a TIRF microscope and imaged a Z stack. Imaris was used to combine the Z-stack images. The images in the manuscripts are now of the lowest stack with background subtraction.

      Figure 6: Figure 6C- the blebbistatin treated cell looks very large- is this representative?

      __Response: __Yes, Blebbistatin-treated cells are larger (Fig. 6A).

      Figure 7: Fig 7C- The lanes for MLCII are all run together- is this from a different gel? Is this quantification accurate?

      __Response: __We have performed additional western blots and changed the representative images.

      Fig. 7F- What is the % level of knockdown achieved?

      __Response: __The level of knockdown is labeled on the figure.

      Figure 8: Fig 8A,B- does the scale bar represent all of the images in these two panels?

      __Response: __Yes, the figure legend is updated to clarify this point.

      Fig 8C,D- Superplots would be helpful here.

      __Response: __We have used super plots to display the data.

      Supplementary Data: The OCR data do not add much and are not discussed much in the manuscript. Perhaps they could be omitted.

      __Response: __Our OCR data ruled out the possibility of metabolic regulation. Since MFN2 is a mitochondria protein with its typical functions in metabolic pathways, we cannot omit its influence on metabolism here. As we observed, shMLCK enhanced OCR, shROCK reduced OCR, and both knock-down rescued cell morphology and motility. We believe that PAB formation is independent of MFN2’s function in metabolic regulation.

      Figure S3- The figure label doesn't match the manuscript test- was fibrinogen or collagen used?

      __Response: __We tried cover glass alone, collagen, and fibronectin-coated glass. The PAB formation is independent of these extracellular substrates. We did not try fibrinogen because MEF cell is reported to prefer fibronectin (Lehtimäki et al., 2021).

      Reference

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      Chen, H., Detmer, S. A., Ewald, A. J., Griffin, E. E., Fraser, S. E., & Chan, D. C. (2003). Mitofusins Mfn1 and Mfn2 coordinately regulate mitochondrial fusion and are essential for embryonic development. The Journal of Cell Biology, 160(2), 189. https://doi.org/10.1083/JCB.200211046

      Cieri, D., Vicario, M., Giacomello, M., Vallese, F., Filadi, R., Wagner, T., Pozzan, T., Pizzo, P., Scorrano, L., Brini, M., & Calì, T. (2017). SPLICS: a split green fluorescent protein-based contact site sensor for narrow and wide heterotypic organelle juxtaposition. Cell Death & Differentiation 2018 25:6, 25(6), 1131–1145. https://doi.org/10.1038/s41418-017-0033-z

      García-Calvo, J., López-Andarias, J., Maillard, J., Mercier, V., Roffay, C., Roux, A., Fürstenberg, A., Sakai, N., & Matile, S. (2022). HydroFlipper membrane tension probes: imaging membrane hydration and mechanical compression simultaneously in living cells. Chemical Science, 13(7), 2086–2093. https://doi.org/10.1039/D1SC05208J

      Goldfine, S. M., Schroter, E. H., & Izzard, C. S. (1981). Calcium-dependent shortening of fibroblasts induced by the ionophore, A23187. Journal of Cell Science, 50(1), 391–405. https://doi.org/10.1242/JCS.50.1.391

      Ikebe, M., & Hartshorne, D. J. (1985). Phosphorylation of Smooth Muscle Myosin at Two Distinct Sites by Myosin Light Chain Kinase*. Journal of Biological Chemistry, 260, 10027–10031. https://doi.org/10.1016/S0021-9258(17)39206-2

      Isotani, E., Zhi, G., Lau, K. S., Huang, J., Mizuno, Y., Persechini, A., Geguchadze, R., Kamm, K. E., & Stull, J. T. (2004). Real-time evaluation of myosin light chain kinase activation in smooth muscle tissues from a transgenic calmodulin-biosensor mouse. Proceedings of the National Academy of Sciences of the United States of America, 101(16), 6279–6284. https://doi.org/10.1073/PNAS.0308742101

      Kijlstra, J. D., Hu, D., Mittal, N., Kausel, E., van der Meer, P., Garakani, A., & Domian, I. J. (2015). Integrated Analysis of Contractile Kinetics, Force Generation, and Electrical Activity in Single Human Stem Cell-Derived Cardiomyocytes. Stem Cell Reports, 5(6), 1226. https://doi.org/10.1016/J.STEMCR.2015.10.017

      Lehtimäki, J. I., Rajakylä, E. K., Tojkander, S., & Lappalainen, P. (2021). Generation of stress fibers through myosin-driven reorganization of the actin cortex. ELife, 10, 1–43. https://doi.org/10.7554/ELIFE.60710

      Naon, D., Zaninello, M., Giacomello, M., Varanita, T., Grespi, F., Lakshminaranayan, S., Serafini, A., Semenzato, M., Herkenne, S., Hernández-Alvarez, M. I., Zorzano, A., De Stefani, D., Dorn, G. W., & Scorrano, L. (2016). Critical reappraisal confirms that Mitofusin 2 is an endoplasmic reticulum-mitochondria tether. Proceedings of the National Academy of Sciences of the United States of America, 113(40), 11249–11254. https://doi.org/10.1073/PNAS.1606786113/SUPPL_FILE/PNAS.201606786SI.PDF

      Nobes, C. D., & Hall, A. (1999). Rho GTPases Control Polarity, Protrusion, and Adhesion during Cell Movement. The Journal of Cell Biology, 144(6), 1235. https://doi.org/10.1083/JCB.144.6.1235

      Pelham, R. J., & Wang, Y. L. (1997). Cell locomotion and focal adhesions are regulated by substrate flexibility. Proceedings of the National Academy of Sciences of the United States of America, 94(25), 13661. https://doi.org/10.1073/PNAS.94.25.13661

      Samanas, N. B., Engelhart, E. A., & Hoppins, S. (2020). Defective nucleotide-dependent assembly and membrane fusion in Mfn2 CMT2A variants improved by Bax. Life Science Alliance, 3(5). https://doi.org/10.26508/LSA.201900527

      Shao, X., Li, Q., Mogilner, A., Bershadsky, A. D., & Shivashankar, G. V. (2015). Mechanical stimulation induces formin-dependent assembly of a perinuclear actin rim. Proceedings of the National Academy of Sciences of the United States of America, 112(20), E2595–E2601. https://doi.org/10.1073/PNAS.1504837112/SUPPL_FILE/PNAS.1504837112.SM03.AVI

      Sidarala, V., Zhu, J., Levi-D’Ancona, E., Pearson, G. L., Reck, E. C., Walker, E. M., Kaufman, B. A., & Soleimanpour, S. A. (2022). Mitofusin 1 and 2 regulation of mitochondrial DNA content is a critical determinant of glucose homeostasis. Nature Communications 2022 13:1, 13(1), 1–16. https://doi.org/10.1038/s41467-022-29945-7

      Vallese, F., Catoni, C., Cieri, D., Barazzuol, L., Ramirez, O., Calore, V., Bonora, M., Giamogante, F., Pinton, P., Brini, M., & Calì, T. (2020). An expanded palette of improved SPLICS reporters detects multiple organelle contacts in vitro and in vivo. Nature Communications, 11(1). https://doi.org/10.1038/S41467-020-19892-6

      Wong, S. Y., Ulrich, T. A., Deleyrolle, L. P., MacKay, J. L., Lin, J. M. G., Martuscello, R. T., Jundi, M. A., Reynolds, B. A., & Kumar, S. (2015). Constitutive activation of myosin-dependent contractility sensitizes glioma tumor-initiating cells to mechanical inputs and reduces tissue invasion. Cancer Research, 75(6), 1113–1122. https://doi.org/10.1158/0008-5472.CAN-13-3426

      Zhou, W., Hsu, A. Y., Wang, Y., Syahirah, R., Wang, T., Jeffries, J., Wang, X., Mohammad, H., Seleem, M. N., Umulis, D., & Deng, Q. (2021). Mitofusin 2 regulates neutrophil adhesive migration and the actin cytoskeleton. Journal of Cell Science, 133(17). https://doi.org/10.1242/JCS.248880/VIDEO-11

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      Referee #3

      Evidence, reproducibility and clarity

      This study explores the importance of MFN2, a known endoplasmic reticulum-mitochondria linker protein, in cell motility and actin-myosin organization. It is known that the mitofusin proteins MFN1 and MFN2 can tether the mitochondria to the ER and are connected with calcium regulation in muscle and non-muscle cell types. Calcium flux mediated by mitofusins has previously been implicated in apoptosis and ER stress. In this study, the authors show that loss or depletion of MFN2 (but not MFN1) can lead to aberrant calcium increase in the cytoplasm and trigger actin-myosin reorganisation. They show that the actin and myosin changes are linked with activation of RhoA and also that they can be suppressed by inhibiting myosin light chain phosphorylation/ activation. They also show that cells with reduced MFN2 are softer (using atomic force microscopy), which agrees with activation of RhoA and contractility. If cells are plated on softer substrata, it partially compensates for the over-activation of RhoA.

      In many places, the data support the claims made, but in several places the experiments could be made more convincing or be more clearly presented. Some of the experiments appear to have been repeated only 1-2 times and very few cells or fields of view have been analysed.

      General Comments (Major)

      1. Data Presentation and analysis: The data analysis would benefit from using a method such as Super-Plots to show the data from separate biological repeats and to use N numbers that represent the number of biological repeats rather than the number of cells analysed. Please see the following reference for a suggestion on how to analyse the data: Lord SJ, Velle KB, Mullins RD, Fritz-Laylin LK. SuperPlots: Communicating reproducibility and variability in cell biology. J Cell Biol. 2020 Jun 1;219(6):e202001064. doi: 10.1083/jcb.202001064. PMID: 32346721; PMCID: PMC7265319.
      2. Another general comment is that many of the experiments show analysis of very few numbers of cells or maybe only one field of view in a microscopy quantification experiment. This seems unusually low - for example, in Figure 1E only 6 cells have been analysed. It seems like more could have been done and if statistical analysis like we suggest in 1 above is used, this might reveal that some of the differences are less significant than the authors think/report. This is important, as cells are noisy and it is unusual to have such high significance for experiments like cell migration and other parameters unless a lot of measurements are made. In Figure 1G, it appears that only one field of view has been used to quantify the data. We routinely use 3-5 fields of view to get a representative sample of what the cells are doing.
      3. Some of the micrographs appear to be missing scale bars- e.g. Fig. 2H, Fig. 8
      4. In the cell tracking experiments, only some of the cells in the images appear to have been tracked. How were the tracked cells chosen? Normally, we would track every cell to avoid bias in selection.
      5. The western blot images do not show the molecular size of the bands.
      6. Mostly the graphs show individual data points, which is good, but in some cases only a bar is shown- it would be nice to have individual points overlaid on the bars- e.g. Figure 1I, 4E, 5B, 5E, 7B, 7D
      7. Many of the confocal images look very processed- they have no background and have a hazy black halo around the cell. I am not familiar with the type of processing that was done and I worry that the images are only showing a masked and processed version of the actual data. The authors need to explain what processing they have done and probably also to provide the unprocessed images in a supplementary figure or dataset for readers to see. The methods description is inadequate as it only says Image J was used to process the data.

      Individual comments on Figures:

      Figure 1: See general comments above- consider to use Superplots, more cells and more fields of view in quantifications. Show experimental points in bar graph.

      Figure 2: In 2E, the colours are very similar for two of the experiments so it is difficult to distinguish them- e.g. the MFN1 vs MFN2 rescue data both appear dark blue. In 2H are the magnifications really the same for the WT as the +DOX and -DOX? The cell in the WT looks huge. Is this representative? Also, the phalloidin stain looks very spotty on the WT. This seems unusual.

      Figure 3: Not many cells were analysed in 3B, especially the zero time point. Please define +T, we assume it is the tether construct, but it is not defined In 3F, how were the tracked cells chosen?

      Figure 4: 4B: Why have they not tested FK506 and STO609 on the WT cells? 4C: How were the tracked cells chosen? 4D-E: The blot doesn't look representative of the quantification- were the numbers normalised to vinculin? The difference between WT and vector looks too large to be real if the amounts were normalised to the vinculin, as vinculin is increased in vector. Likewise, the pCAMKII looks to be substantially decreased from the +MFN2, but this is not what the quantification shows.

      Figure 5: 5B- please clarify which ratio is shown here. I assume it is the ratio of RhoA-GTP vs RhoA between the zero and 4 minute time points. 5C- These images appear to have a mask around the cell. It is hard to tell where the edge of the cell really is- what sort of processing was used? Especially for the paxillin staining, why is there no cytoplasm shown? Is this because the image is in TIRF?

      Figure 6: Figure 6C- the blebbistatin treated cell looks very large- is this representative?

      Figure 7: Fig 7C- The lanes for MLCII are all run together- is this from a different gel? Is this quantification accurate? Fig. 7F- What is the % level of knockdown achieved?

      Figure 8: Fig 8A,B- does the scale bar represent all of the images in these two panels? Fig 8C,D- Superplots would be helpful here.

      Supplementary Data:

      The OCR data do not add much and are not discussed much in the manuscript. Perhaps they could be omitted. Figure S3- The figure label doesn't match the manuscript test- was fibrinogen or collagen used?

      Significance

      The main novelty here appears to be the connection between excess cytoplasmic calcium, MFN2 loss and RhoA/myosin activation. This is interesting and a useful addition to the literature. It is unclear what the significance is perhaps, as increased cytoplasmic calcium is likely to cause multiple effects in addition to these. So, this effect may be a side-effect of uncoupling the ER and the mitochondria. Nonetheless, it is important to know about this and it will likely inform future studies on the mitofusins.

      This will be of interest to basic researchers studying mitochondria function and the connections between signaling and mitochondria function.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      Wang et al use a combination of cell biological and biochemical approaches to show that Mitofusin2 (MFN2) - a protein typically known to regulate mitochondrial morphology - regulates cell morphology by regulating calcium levels and downstream cell contractility players. They show that cells deficient in MFN2 exhibit increased intracellular calcium levels, and overactivation of myosin II, leading to increased cell contractility. Furthermore, MFN2-deficient cells exhibit an F-actin ring around the cell periphery (which they call PABs). The study takes advantage of both pharmacological and genetic perturbations, as well a variety of assays to support many of their findings; however, it is unclear how these findings are related to each other. Furthermore, MFN2-related disease was raised a few times in the abstract and throughout the manuscript, but it's unclear how the findings in the paper relate to disease states, both in terms of the cell biology, as well as the model that was used (MEFs). This reviewer applauds the authors for exploring MFN2 function outside of its conventional role in mitochondria; but it was difficult to parse through the findings to resolve a mechanistic explanation for how MFN2 affect cell behavior, and what role, if any, PABs have on biological function. While it is important to dissect mitochondrial-independent functions for MFN2, given the whole scale changes in cells in MFN2-deficient cells, and the fact that there is a metabolic phenotype, it is difficult to know how many of the observed phenotypes are downstream of perturbed mitochondrial function versus on cytoskeletal dynamics directly.

      Major comments:

      Are the key conclusions convincing?

      • Key conclusions that were convincing:
        • MFN2-/- cells exhibit decreased cell velocity, decreased cell size, increased cell circularity, and increased intracellular calcium
        • modifying the levels of calcium has an effect on cell circulariy.
        • MFN2-/- cells exhibit increased activation of contractility players
      • Key conclusions that were less convincing:
        • RhoA and NMII are in the title as mechanistic downstream regulators of CaM, but the results in Fig 8 call into question the role of RhoA. Why does RhoA activation not influence cell size and circularity? Can you overexpress MRLC-GFP and inhibit Rho and restore the wt phenotype? The role of NMII is also not clear - why does overexpressing MLCK-CA not have a phenotype but overexpressing MLCK downstream target MRLC show the phenotype? Are there any alternative pathways to regulate MRLC? It's not being discussed or described in 6D's schematic.
        • The role of ER/mito contacts in the system was unclear (since ER/mito contacts were not observed nor evaluated directly).
        • What role does focal adhesions have on PAB formation or any part of the model - There were results showing larger focal adhesions in the MFN2-/- cells, but not sure how this fits in with the bigger picture of contractility and PABs, and focal adhesions were not in the model in Figure 5.
        • Whether regulating calcium impacts PAB formation
        • The role of PABs in migration is also unclear - can you affect PAB formation or get rid of PABs and quantify cell migration?
        • It was hard to understand the correlation between the membrane tension of MFN2-/- cells and its ability to spread on softer substrates. How does this result fit in with the overarching model?

      Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

      • There were a number of findings that did not seem to fit in with the paper, or they were included, but were not robustly described nor quantified. As an example, Paxillin-positive focal adhesions were evaluated in MFN2-/- and with various pharmacological approaches, but there is no quantification with respect to size, number, or distribution of focal adhesions, despite language in the main text that there are differences between conditions. Also, the model was presented in figure 5, and then there were several pieces of data presented afterward, some that are included in the model (myosin regulation), and some that are not in the model (membrane tension, TFM, substrate stiffness, etc). The stiffness/tension figure was not clear to me, and it was difficult to make sense of the data since one would predict that an increase in actomyosin contractility at the cortex would lead to higher membrane tension, not lower, and then how membrane tension relates to spreading on soft matrices is also unclear. The manuscript seems like an amalgamation of different pieces of data that do not necessarily fit together into a cohesive story, so the authors are encouraged to either remove these data, or shore them up and weave them into the narrative.

      Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

      • The imaging used for a lot of the quantification (migration, circularity) is difficult to resolve. The cells often look like they are not imaged in the correct imaging plane. It would be helpful to have better representative images such that it is clear how the cells were tracked and how cell periphery regions of interest were manually drawn. Focal adhesions should also be shown without thresholding.
      • For most of the quantification, it appears that the experiment was only performed once and that a handful of cells were quantified. The figure legend indicates the number of cells (often reported as >12 cells or >25 cells), but the methods indicate that high content imaging was performed, and so the interpretation is that these experiments were only performed once. Biological replicates are required. If the data do represent at least 3 biological replicates, then more cell quantification is required (12 or 25 cells in total would mean quantifying a small number of cells per replicate).
      • Mitochondrial morphology and quantification should be performed in the MFN2 knockdown and rescue lines.
      • Many of the comparisons throughout the figures is between MFN2 knockdown and MFN2 knockdown plus rescue or genetic/pharmacological approaches, but a comparison that is rarely made is between wildtype and experimental. These comparisons could be useful in comparing partial rescues and potential redundancies with the other mitofusin.
      • For the mito/ER tethering experiments, it is important to show that ER/mito contacts are formed and not formed in the various conditions with imaging approaches
      • For some of the pharmacological perturbations, it would be helpful to show that the perturbation actually led to the expected phenotype - as an example, in cells treated with different concentrations of A23187, what are the intracellular calcium levels and how do these treatments influence PAB formation? This aspect should be generally applied across the study - when a modification is made, that particular phenotype should first be evaluated, before dissecting how the perturbation affects downstream phenotypes.
      • In Figures 4 and 5, the thresholding approaches in the images make the focal adhesions difficult to resolve, and therefore it is difficult to determine the size. As described above, these metrics should be defined and quantified.
      • What is a PAB? How is it defined? What metrics make a structure a PAB versus regular cortical actin - are there quantifiable metrics? In figure 8, there are some structures that are labelled as a PAB, but some aren't (as an example, the left panel in 8b is a PAB, but the right panel in 8A is not, but they look the same), so a PAB should be defined with quantifiable measures, and then applied to the entire study.
      • As described above, why does RhoA activation not influence cell size and circularity? Can you overexpress MRLC-GFP and inhibit Rho and restore the wildtype phenotype?

      Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

      • These suggested experiments described above will take a substantial amount of time and money, as it appears that some experiments were only performed once, and therefore many of these experiments might need to be performed 2-3 more times. Also, experiments showing that addition of drugs lead to expected outcomes prior to analyzing downstream phenotypes will also require a significant amount of time. It is hard to predict how long it will take, but we would guess, 6-8 months?

      Are the data and the methods presented in such a way that they can be reproduced?

      • We appreciate that the authors quantified many parameters, although some quantifications were missing. There are some missing methods - how was directionality quantified, was migration quantified by selecting the approximate center of cells using MTrackJ or were centroids quantified by outlining cells, for instance. Also, given that some of the phenotypes were somewhat arbitrarily assigned (ie. what constitutes a PAB?), it may be difficult to reproduce these approaches and interpret data appropriately.

      Are the experiments adequately replicated and statistical analysis adequate?

      • Unfortunately, while the approximate number of cells was reported, the number of biological replicates were not reported, and therefore, the experimental information and statistical analyses are not adequate.

      Minor comments:

      Specific experimental issues that are easily addressable. - for some of the graphs - mostly about calcium levels - fold change is shown, but raw values should also be included to determine whether the basal levels of calcium are different across the conditions. - scale bars in every panel should also help make the points clearer.

      Are prior studies referenced appropriately?

      • Yes

      Are the text and figures clear and accurate?

      • Some of the data in the figures were unclear - see above for more info.

      Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      • See above for more info.

      Significance

      Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

      • There is a growing field of mitochondrial biology and how it relates to cell migration. This paper examines the function of a key mitochondrial morphology regulator, MFN2, and dissects a role for MFN2 in migration at the level of cytoskeletal regulation. We think that this is interesting, and that it's clear that MFN2 has multiple functions in the cell, but the phenotypes are so pleiotropic that it's difficult to parse out mechanistic understanding. The authors also describe a new actin architecture - a structure that they refer to as PABs - but there is no indication that PABs form in other cell types or tissues, or in other contexts, so it is unclear whether PABs are an important structure or an artifact of the system. Furthermore, part of the motivation of the work seems to be to understand MFN-related pathologies, but using a MEF system does not necessarily allow for that. One way to strengthen this part of the manuscript is to potentially use disease-relevant MFN2 mutations and determine downstream effects on cell morphology and migration.

      State what audience might be interested in and influenced by the reported findings.

      • This work would appeal to card-carrying cell biologists.

      Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

      • cell migration; actin; mitochondria
    1. What is the relationship between protocols and agency? Do protocols assume or require a set of participating agents with autonomy or free-will?

      Initial thoughts — review later I mean, if I had to pull in some Bandura, it's bi-drectional determinism? Right? So it's influencing behaviour as an environmental factor that could also be done by thinking?

      If I think about Csikszentmihalyi in Good Business on culture as a game, perhaps rules are to games what protocols are to culture? If culture is a set of norms that keep you from anomie / entropy and make spaces for alienation, then the agency of the individual may be developed over time (control over consciousness) that may allow for greater expression over agency to follow or not follow protocols. In this sense, protocols would be the default, and intentionally not following protocols (probabilistically not by chance) might require agency? That is if we are following the definition that good protocols have the Schelling point or become default and are almost invisible untill they break.

      Bureaucracy may be an example of a deeply frustrating protocol?

    1. Author Response

      Reviewer #1 (Public Review):

      Demographic inference is a notoriously difficult problem in population genetics, especially for non-model systems in which key population genetic parameters are often unknown and where the reality is always a lot more complex than the model. In this study, Rose et al. provided an elegant solution to these challenges in their analysis of the evolutionary history of human specialization in Ae. aegypti mosquitoes. They first applied state-of-the-art statistical phasing methods to obtain haplotype information in previously published mosquito sequences. Using this phased data, they conducted cross-coalescent and isolation-with-migration analyses, and they innovatively took advantage of a known historical event, i.e., the spread of Ae. aegypti to South America, to infer the key model parameters of generation time and mutation rate. With these parameters, they were able to confirm a previous hypothesis, which suggests that human specialists evolved at the end of the African Humid Period around 5,000 years ago when Ae. aegypti mosquitoes in the Sahel region had to adapt to human-derived water storage as their breeding sites during intense dry seasons. The authors further carried out an ancestry tract length analysis, showing that human specialists have recently introgressed into Ae. aegypti population in West African cities in the past 20-40 years, likely driven by rapid urbanization in these cities.

      Given all the complexities and uncertainties in the system, the authors have done outstanding jobs coming up with well-informed research questions and hypotheses, carrying out analyses that are most appropriate to their questions, and presenting their findings in a clear and compelling fashion. Their results reveal the deep connections between mosquito evolution and past climate change as well as human history and demonstrate that future mosquito control strategies should take these important interactions into account, especially in the face of ongoing climate change and urbanization. Methodologically, the analytical approach presented in this paper will be of broad interest to population geneticists working on demographic inference in a diversity of non-model organisms.

      In my opinion, the only major aspect that this paper can still benefit from is more explicit and in-depth communication and discussion about the assumptions made in the analyses and the uncertainties of the results. There is currently one short paragraph on this in the discussion section, but I think several other assumptions and sources of uncertainties could be included, and a few of them may benefit from some quantitative sensitivity analyses. To be clear, I don't think that most of these will have a huge impact on the main results, but some explicit clarification from the authors would be useful.

      Below are some examples:

      Thank you very much for your kind words and your feedback! We have expanded our discussion of assumptions and uncertainties – we have responded to each point below:

      1) Phasing accuracy: statistical phasing is a relatively new tool for non-model species, and it is unclear from the manuscript how accurate it is given the sample size, sequencing depth, population structure, genetic diversity, and levels of linkage disequilibrium in the study system. If authors would like to inspire broader adoption of this workflow, it would be very helpful if they could also briefly discuss the key characteristics of a study system that could make phasing successful/difficult, and how sensitive cross-coalescent analyses are to phasing accuracy.

      We agree that this is an important topic to expand on. We have clarified as follows:

      Results, Page 4, last paragraph: “Over 95% of prephase calls had maximal HAPCUT2 phred-scaled quality scores of 100 and prephase blocks (i.e. local haplotypes) were 728bp long on average (interquartile range 199-1009bp). We then used SHAPEIT4.2 to assemble the prephase blocks into chromosome-level haplotypes, using statistical linkage patterns present across our panel of 389 individuals (25).”

      Discussion, Page 8, last paragraph: “Overall linkage disequilibrium is relatively low in Ae. aegypti, dropping off quickly over a few kilobases and reaching half its maximum value within about 50kb (37); this is likely sufficient for assembling shorter, high-confidence prephase blocks into longer haplotypes in many cases. However, phase-switch errors may be common across longer distances – potentially affecting inferences in the most recent time windows. Nevertheless, the similar results we obtain using different proxy populations (and thus different input haplotype structures) for human-specialist and generalist lineages (see Figure S1) suggest that our results are robust to potential mistakes in long-range haplotype phasing.”

      Discussion, Page 9, paragraph 2: “Here, we take advantage of a continent-wide set of genomes, combined with read-based prephasing and population-wide statistical phasing to develop a phasing panel that should enable future studies in Ae. aegypti with a lower barrier to entry. The same approach may work for other study organisms with similar population genomic properties; high levels of diversity are helpful for prephasing and at least moderate levels of linkage disequilibrium are important for the assembly of prephase blocks.”

      2) Estimation of mutation rate and generation time: the estimation of these importantparameters is made based on the assumption that they should maximize the overlap between the distribution of estimated migration rate and the number of enslaved people crossing the Atlantic, but how reasonable is this assumption, and how much would the violation of this assumption affect the main result? Particularly, in the MSMC-IM paper (Wang et al. 2020, Fig 2A), even with a simulated clean split scenario, the estimated migration rate would have a wide distribution with a lot of uncertainty on both sides, so I believe that the exact meaning and limitations of such estimated migration rate over time should be clarified. This discussion would also be very helpful to readers who are thinking about using similar methods in their studies. Furthermore, the authors have taken 15 generations per year as their chosen generation time and based their mutation rate estimates on this assumption, but how much will the violation of this assumption affect the result?

      This is a great point. We have expanded our discussion of how this assumption affects our conclusions (see Discussion page 9, first paragraph): “Furthermore, we chose a scaling factor that maximized overlap between the peak of estimated Ae. aegypti migration and the peak of the Atlantic Slave Trade (Fig. 2B). If we instead consider alternative scenarios where peak migration occurred at the very beginning of the slave trade era, around 1500, then our inferred mutation rate would be lower (about 2.4e-9, assuming 15 generations per year), pushing back the split of human-specialist lineages to about 10,000 years before present. This scenario seems less plausible, in part because our isolation-with-migration analyses suggest a gradual onset of migration between continents rather than a single, early-pulse model. It would also make it harder to explain the timing of the bottleneck we see in invasive populations; the first signs of this bottleneck occur at the beginning of the slave trade (~500 years ago) with our current calibration (Fig. S1A), but would be pushed to a pre-trade date in this alternative scenario. We can also consider a scenario in which peak Ae. aegypti migration occurred more recently, perhaps around 1850, corresponding to increased global shipping traffic outside the slave trade alone. In this case, our inferred mutation rate would be higher (or generation time lower), and the split of human-specialist lineages would be placed at about 3,000 years ago. Overall, the best match between the existing literature and our data corresponds to our main estimates, but alternative scenarios could gain support if future research finds evidence for a different time course of invasion than is suggested by the epidemiological literature.”

      We have slightly expanded our description of calibration in Results, page 5, last paragraph: “The fact that we see good overlap between the two distributions (yellow–white color) across a wide range of reasonable mutation rates and generation times for Ae. aegypti is consistent with our understanding of the species’ recent history and supports our approach. For example, if we take the common literature value of 15 generations per year (0.067 years per generation) (17, 20), the de novo mutation rate that maximizes correspondence between the two datasets is 4.85x10-9 (black dot in Figure 2A, used in Figure 2B), which is on the order of values documented in other insects. We chose to carry forward this calibrated scaling factor (corresponding to any combination of mutation rate and generation time found along the line in Figure 2A) into subsequent analyses.”

      We have also expanded on the uncertainty of our analyses (see Discussion page 8, last paragraph): “First, the temporal resolution of our inferences is relatively low, and both previously published simulations (39) and our own bootstrap replicates (Figure 2B–D, grey lines) suggest relatively wide bounds for the precise timing of events.”

      3) The effect of selection: all analyses in this paper assume that no selection is at play,and the authors have excluded loci previously found to be under selection from these analyses, but how effective is this? In the ancestry tract length analysis, in particular, the authors have found that the human-specialist ancestry tends to concentrate in key genomic regions and suggested that selection could explain this, but doesn't this mean that excluding known loci under selection was insufficient? If the selection has indeed played an important role at a genome-wide level, how would it affect the main results (qualitatively)?

      We have clarified that we excluded those loci from our timing estimates for both MSMC and ancestry tract analyses, but then re-ran the ancestry tract analysis with all regions included to visualize and assess how tracts were distributed along chromosomes. See Methods, page 12, paragraph 2: “Since selection associated with adaptation to urban habitats could shape lengths of admixture tracts, we masked regions previously identified as under selection between human-specialists and generalists when estimating admixture timing—namely, the outlier regions in (2). However, we used an unmasked analysis to determine and visualize the genome-wide distribution of ancestries (Fig. 3).”

      We have also added additional discussion of the expected effects of selection on our analyses (see Discussion, page 9, last paragraph): “Positive selection during adaptive introgression can increase tract lengths and make admixture appear to be more recent than it actually is. For this reason, we masked regions of the genome thought to underlie adaptation to human habitats before running our analysis. Nevertheless, if selection has acted outside these regions, admixture may be somewhat older than we estimate.”

    1. This week, we looked at the Nara period, where Japanese rulers began to settle in one consistent capitol and embraced Buddhism. One of the interesting features of the Nara period was that the rulers continued to stay in one city; the most interesting part about that for me is why they had not done that in the first place. I am aware that the most obvious reason is the religious/superstitious issue about not wanting to be where the ghost of the last ruler was, but I wonder if there was any more pragmatic reason to keep moving. It strikes me that moving capitols would necessarily be an expensive process, and would mean that temples and palaces could not be improved over many decades. With these apparent drawbacks, why bother moving the capitol so much? I do not think that I have the information to suggest any other educated answers than the one of getting away from the death of the past ruler, but I would be interested in finding out if any (credible) theories do exist. The only possible (and extremely superficial) reason that I would suggest is that it does effectively convey the wealth of the rulers. Spending all of the funds that it would take to build the new capitol, along with raising all of the corvee labor that would also be required could be a good way to present one’s power to one’s subjects.

      I like the question that you raise about why Emperors/Leaders did not have a consistent location for the Empire. With this in mind, I would like to consider if back then could be considered more tumultuous than today or if each Emperor wanted a sense of individual rule that they may have changed their empire’s location? I liked the questions that you raised about what it meant for the Emperor’s to have fully embraced Buddhism, whether it meant that it affected their ruling style or if they became Monks. This reminds of the same way that Roman/Greeks (I forget, I think Romans) took on the ideas of Stoicism as they ruled. One example of this was Marcus Aurelius and how he was both a Stoic and an Emperor. I believe that the Emperor’s can have multiple identities at once, and embody those multiple identities through not only leading their country but through other aspects of their life, such as how they act.

    1. Thinking back to what we have already learned, I think it is really cool how gender roles were very much included in the basis of Japanese religion, yet they internalize it much differently than how the U.S internalizes our founding ideas. It is crazy to me that a female writer could get this much momentum and have such a long lasting impact on culture, while the U.S still seems to struggle with the idea of women being culture creators

      I first like the comparisons that you drew between the U.S and Japan when it comes to the approach of female writers as it seemed reminiscent of how the U.S only created the 19th Amendment in 1920 which seems to reflect the judgments mentioned about female writers. Also, the comparison between religion is an interesting one as yes, both the U.S and Japan have religious undertones the way that they deal with those undertones may be shaped by the religion itself along with the individuals who convince the public using religion as a basis for being correct. About the idea of the male’s ideal woman, the part that I was most curious about was whether these ideas would trickle down to the non-court men by accident? By exposing the court men for their idealistic and unrealistic ideas of who and what a woman should be I wonder if this idea was already permeating through Japan or if the Tale Of Genji led for other men to model themselves out of these fictional characters as though I can’t think of an example as of this moment, doesn’t things that start up in “high society” trickle down (not like the failed trickle down economy, but what I mean is that before Cars were for everyone, first the rich (equivalent to the court of Heian life) and then to everyone. I am essentially wondering if these ideas were widespread among Japanese men before the Tale of Genji or if the Tale of Genji gave Men an inspiration of Court Life, and in order to act more important/high and mighty if they too, would have copied the Tale of Genji men to be more like court members. I wonder if the court members would have enjoyed the Tale of Genji as to me it reminds me that it would almost be like fictional tabloids which often had psychological effects on celebrities and royalty like Princess Diana and Britney Spears – would they have enjoyed being exposed in this way? I imagined the commoners would be fascinated with this story the same way that we humans buy tabloids or read gossip about the rich/famous instead of the rich/famous reading inserted characters about themselves?

    1. Some may question how a society so intertwined with Bhuddist ideals could ever rationalize the fighting that would ensue. To that I would answer that just as we might have noticed court jealousy and superficiality in our own lives, desire for power is equally a characteristic of human society in of itself. That is to say I think perhaps such strife was inevitable. Perhaps because of these Bhuddist ideals there was less infighting than otherwise would have occurred, but nonetheless so long as there are vacuums or weaknesses in power, some will seek to fill that void.

      I thought this analysis was interesting. In other words, you think that without the influence of Buddhist ideals, there would have been more conflict? And that in any society - no matter how much value is placed on harmony and peacefulness - conflict is inevitable?

    1. consider whether the information the students need tolearn is invariable, since behaviorism stems from the idea thatknowledge is objective and there is one right answer (Keramida,2015). Behaviorism would be a useful approach to helping studentsmemorize and recall terms and facts

      The behaviorist approach is a staple in math, so that even this book included the example of student's receiving immediate feedback for solving a math equation. Indeed there is a time and place for that since many times we are asking students for the right answer to a problem.

      But with the new formats for key exams such as STAAR, it may be time to look at other ways of asking questions. We need to help students to form connections and thinking critically. At first I was quite discouraged when we stopped assigning Quizizz due to how easily students cheat on it. It's the same with other gamified websites such as Kahoot and Blooket. For Quizizz, I used to enjoy the premium features and question types as well as counting with all my customized assignments I had prepared over the years.

      So that has forced me to restructure my assignments and face the reality that I must look beyond behaviorism. It's a shame about the cheating, but all I can do is direct my students to think, struggle, and practice math while they are in the classroom.

    1. It's not a ZK furniture though. Index cards were not used to store atomic notes, or have alphanumeric indexes. :)

      Oh, but it is ZK furniture in every sense! The narrow definition of zettelkasten in common use (in this subreddit and in many other locations on the internet) to describe only card indexes/digital software which have the numbering scheme and form of Niklas Luhmann's only works for his and a number of imitators from roughly 2007/2013 to the present. Prior to this it is a much more generic term in Germany and elsewhere known in English as a card index or card file, but academics and others have been using practices broadly similar to Luhmann's for centuries in a variety of forms.

      You're likely right that this particular piece of furniture had a business-specific market use case for the majority of its users, but I'm sure there was a subset of customers, particularly those in academia, which may have used it primarily as a note storage or personal knowledge management tool in a way highly similar to Luhmann's. Because it was in America, it was unlikely to have been called by the German name zettelkasten, though there were many German-Americans (Gotthard Deutsch and S. D. Goitein come to mind) who had this practice and may have done so, though I've seen no direct evidence of this at present in their writings. Not all card indexes were used for business or library purposes. In addition to academic researchers, we know a variety of mid-century comedians used their card indexes for collation and storage of jokes over their careers.

      The quality of the advertisement is hard to make out, but on close examination it appears to have four drawers and the scale leads me to think that this would likely have accommodated 3 x 5" index cards. Some upcoming research work may uncover the manufacturing specifics and I'll share them as I find them.

      As for Harrison and Placcius they're definitely there and people talk about them occasionally, though few seem as interested in the historical aspects despite the fact that they have a lot to demonstrate about the pros/cons of various practices. I remember adding them both to the English wikipedia page in July 2021. Certainly they could stand to be more widely known for their work, as could Leibniz. More on both can be found mentioned in the following: - Cevolini, Alberto. “Where Does Niklas Luhmann’s Card Index Come From?” Erudition and the Republic of Letters 3, no. 4 (October 24, 2018): 390–420. https://doi.org/10.1163/24055069-00304002. - Blair, Ann M. Too Much to Know: Managing Scholarly Information before the Modern Age. Yale University Press, 2010. https://yalebooks.yale.edu/book/9780300165395/too-much-know. - Blei, Daniela. “How the Index Card Cataloged the World.” The Atlantic, December 1, 2017. https://www.theatlantic.com/technology/archive/2017/12/how-the-index-card-catalogued-the-world/547271/. - Vincentius Placcius. De arte excerpendi. Vom Gelahrten Buchhalten Liber singularis, quo genera et praecepta excerpendi... Gottfried Liebezeit, 1689. http://archive.org/details/bub_gb_IgMVAAAAQAAJ.

      There's also a bit on Placcius in: - Krajewski, Markus. Paper Machines: About Cards & Catalogs, 1548-1929. Translated by Peter Krapp. History and Foundations of Information Science. MIT Press, 2011. https://mitpress.mit.edu/books/paper-machines.

      The bigger hero, in my opinion, is Konrad Gessner and his work from 1548 which outlined much of the common "rules" note takers, practitioners of ars excerpendi, zettelers, and card indexers have been using ever since, including an early idea which many would now call "atomic notes". Much of his work, however was transferring ideas of commonplace book practices of his day into the form of paper slips which were heavily used until mass manufacture of index cards in the 20th century made them cheap and plentiful. Within the note taking space online the community also broadly ignores influential figures like Agricola, Erasmus, and Melanchthon who make some big strides in popularizing a variety of methods in the 1400-1500s.

    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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      Reply to the reviewers

      We would like to thank all the reviewers for their positive evaluations of our work and constructive comments, in particular for highlighting that our work “provides new insight into cancer metabolism knowledge”, is “conceptually interesting and experimentally well performed” and “the findings presented here will be very interesting to a broad range of researchers, including the cancer, metabolism and wider cell biology communities”.

      Reviewer #1 (Evidence, reproducibility and clarity (Required)): Nazemi et al. show that the extracellular matrix (ECM) has a crucial role in sustaining the growth of invasive breast and pancreatic cells during nutrient deprivation. In particular, under amino acid starvation, cancer cells internalize ECM by macropinocytosis and activate phenylalanine and tyrosine catabolism, which in turn support cell growth in nutrient stress conditions. The paper is well written and the results shown are very interesting. The experimental plan is well designed to assess the hypothesis and the description of the methods is sufficiently detailed to reproduce the analyses, which are also characterized by appropriate internal controls. Finally, the data provided sustain the conclusions proposed by the authors.

      * Major comment:*

      Since the authors performed their experiments on invasive breast and pancreatic cancers and it has been noted that stress conditions could promote the escape of cancer cells from the site of origin (e.g., Jimenez and Goding, Cell Metabolism 2018; Manzano et al, EMBO Reports 2020), it would be interesting to evaluate how ECM internalization could have a role in sustaining the invasive abilities of cancer cells under amino acid starvation. Which is the impact of the inhibition of macropinocytosis and tyrosine catabolism on cell invasion? The authors could evaluate this aspect by in vitro 2D and 3D analysis.

      This is a very important point, and we are planning to investigate this by using:

      • 2D single cell migration assays on cell-derived matrices (we have extensively used this system to characterise invasive cell migration; Rainero et al., 2015; Rainero et al., 2012)
      • 3D spheroids assays, to assess collective/3D cell invasion through collagen I and matrigel mixtures. Both experiments will be performed under amino acid starvation, in the presence of pharmacological inhibitors and siRNAs targeting macropinocytosis (FRAX597, PAK1) and tyrosine catabolism (Nitisinone, HPDL). Preliminary data suggest that both FRAX597 and Nitisinone reduce cell invasiveness.

      In addition, to strengthen the paper and give a stronger significance in terms of clinical translatability, it could be useful to implement the analysis of breast and pancreatic patients by publicly dataset evaluating for example free survival, disease free survival, overall survival and metastasis free survival.

      We have now included in the manuscript new data in figure 6 O-R showing that high HPDL expression correlates with reduces overall survival, distant metastasis-free survival, relapse-free survival and palliative performance scale in breast cancer patients. In response to other reviewers’ comments, we have removed the pancreatic cancer data from our manuscript.

      Minor Comment:

      The text and the figures are clear and accurate. The references cited support the hypothesis, rightly introduce the results and are appropriate for the discussion. However, the paragraph relative to figure 4 is a little confusing. Changing the order of the description of the results could be useful.

      We apologise for the lack of clarity in this section. We have now re-organised the data both in the figure and in the result section, to describe the findings in a more logical way.

      Reviewer #1 (Significance (Required)): Based on my metabolic background in tumour aetiology and progression, I think that this study provides new insights into cancer metabolism knowledge, in particular on how the stroma may drive metabolic reprogramming of cancer cells sustaining cell growth in nutrient stress conditions. Together with other similar studies on the stromal non-cellular components, the data here shown can contribute to expand the knowledge on the factors that promote cancer metabolic plasticity, which is exploited from cancer cells to obtain advantages in terms of growth, survival and progression. In conclusion, I think that the results shown are new and the manuscript is well presented. Following the short revision process suggested, it will be eligible for a final publication in a medium-high impact factor journal.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      • Please find enclosed my reviewing comments on the manuscript entitled "The extracellular matrix supports cancer cell growth under amino acid starvation by promoting tyrosine catabolism" by Nazemi et al.*

      In this manuscript the group of Elena Romero and colleagues provides evidence that breast cancer cells, and pancreatic cancer cell, use matrix proteins degradation to feed their proliferative metabolic needs under amino acid starvation. Under this drastic condition, cancer cells use micropinocytosis to uptake matrix proteins, a process that requires mTORC1 activation and PAK1. Furthermore, a metabolomic study demonstrates that ECM-dependent cancer cell growth relies on tyrosine catabolism. Altogether, I found this study conceptually interesting and experimentally well performed. Experiments are well controlled and state-of-the-art technologies used in this manuscript make it a good candidate for publication. However, some aspects of the work need to be strengthen to reinforce the overall good quality of the manuscript, therefore, please find below some experimental propositions. 1. Despite the reviewer proposition, I believe that the additional experiments using the PDAC cancer cell does not improve the quality of the manuscript. Instead, it brings confusion to me, since the relative addition is minor compare to what is demonstrated using breast cancer cells.

      We have decided to remove the pancreatic cancer cell data from the manuscript.

      To importantly improve the potential impact of this manuscript, I suggest to add in vivo data using either syngenic mice model of breast cancer or xenografted human breast cancer cells in nude mice. What would be the impact of micropinocytosis and tyrosine catabolism inhibition on cancer growth, in vivo, should be demonstrated? If possible, it may be interesting to demonstrate that this micropinocytosis may interfere with cancer evolution toward a metastatic phenotype using, for example, the PyMT-MMTV mice model of breast cancer development?

      We will perform orthotopic mammary fat pad injections in immunocompetent mice, to monitor primary tumour growth and localised invasion in the presence of Nitisinone or vehicle control. PyMT-driven breast cancer cells have been generated in the Blyth lab, from FVB-pure MMTV-PyMT mice and we have preliminary data indicating that these cells are able to internalise ECM and grow under starvation in an ECM-dependent manner. Prior to performing any in vivo work, we will perform further in vitro experiment to confirm the role of tyrosine catabolism in these cells. Nitisinone is an FDA-approved drug that has already been used in mouse models. Blood tyrosine levels can be measured to assess tyrosine catabolism inhibition by Nitisinone. These experiments will be conducted in collaboration with the Blyth lab at the CRUK Beatson Institute in Glasgow.

      Data obtained using cancer cells with different metastatic property suggest that the ability to use ECM to compensate for soluble nutrient starvation is acquired during cancer progression. To further demonstrate that it is the case, would it be possible that non metastatic breast cancer cells are not able to perform micropinocytosis? Is PAK1 overexpressed with increase cancer cells metastatic ability, without affecting invasive capacity in 3D spheroids?

      To address these points, we have started to measure PAK1 expression across the MCF10 series of cell lines, where MCF10A are non-transformed mammary epithelial cells, MCF10A-DCIS are ductal carcinoma in situ cells and MCF10CA1 are metastatic breast cancer cells. Our preliminary data show that there is no upregulation of PAK1 expression in the metastatic cells compared to non-transformed or non-invasive cancer cells. This suggest that the over-expression of PAK1 might not be a valuable strategy to address this point.

      In addition, we found that collagen I uptake was upregulated in MCF10CA1 compared to MCF10A and MCF10A-DCIS. We will corroborate our preliminary data by quantifying collagen I and cell-derived matrices internalisation across the 3 cell lines.

      What would be the efficacy to promote the ECM-dependent growth under starvation following a mTORC1 in non-invasive cancer cells?

      We will measure the growth of MCF10A and MCF10A-DCIS on ECM under starvation in the presence of the mTOR activator MHY1485. Western blot analysis of downstream targets of mTORC1 (p-S6 and p-4EBP1) will confirm the extent of mTOR activation.

      The discrepancy of cancer cells proliferation under starvation condition between plastic and ECM-based supports could be explained by the massive difference of support rigidity. This is also probably the case between CDM made by normal fibroblast and CAF. It brings the question of studying the role of matrix stiffness in regard to micropinocytosis-dependent cancer cells growth. It would also explain why this process is link to aggressive cancer cell behaviour, as ECM goes stiffer with time in cancer development. It may not be the case, but the demonstration that mechanical cues from the ECM could regulate the micropinocytosis-dependent cancer cells growth under amino acid starvation could bring additional value to the manuscript.

      We will use 2 experimental approaches to address the effect of different stiffness in ECM-dependent cell growth:

      1. Polyacrylamide hydrogels coated with different ECM components.
      2. Collagen I gels in which the stiffness is modified by Ribose treatment (this approach has been published by the Parson’s lab). Our preliminary data confirmed that ribose cross-linking increased YAP nuclear localisation and collagen I can still be internalised under these conditions. We will assess ECM endocytosis and cell growth under starvation conditions (using EdU incorporation in conjunction with A and high throughput imaging with B)

      Along with this, it has been demonstrated that matrix rigidity regulates glutaminolysis in breast cancer, resulting in aspartate production and cancer cells proliferation. Is asparate production increase by micropinocytosis? Could you rescue cancer cells growth by aspartate supplementation?

      Our metabolomics experiments were performed under amino acid starvation; therefore glutamine was not present in the media. Nor glutaminolysis intermediates nor aspartate were upregulated on ECM compared to plastic in our datasets, suggesting that aspartate might not be involved in this system. We added this point in the discussion. However, glutamine, glutamate and aspartate were found to be upregulated on collagen I compared to plastic in complete media, where the most enriched pathway was alanine, aspartate and glutamate metabolism. Future work will address the role of the ECM in supporting cancer cell metabolism in the absence of nutrient starvation.

      Data presented in Fig 1 and SF1 show that breast cancer cell lines growth in a comparable manner either they are cultured on plastic or on 3D ECM substrates in complete media. Again, on thick 3D substrates, in which the stiffness is lower compared to plastic, I would have thought that cancer cells would have grown slower. Could you please discuss this finding in regard to the literature?

      Our experiments in full media were performed in the presence of dialysed serum, to represent a better control for the starvation conditions, which were in the presence of dialysed serum. This is consistent with a vast body of literature assessing nutrient starvation conditions in the presence of dialysed serum. This could explain the discrepancy between ours and published results. We have addressed this point in the discussion.

      If you have the capacity to do so in your lab or in collaboration, would it be possible to measure the exact stiffness of the different matrix you use in this manuscript? Or using hydrogel, would it be possible to study the role of matrix stiffness in the ECM-dependent cancer cells growth under AA starvation? I would understand that this may be out of the scope of the present manuscript, but I again believe that such finding would reinforce the manuscript.

      We don’t have the capacity to measure the stiffness in our lab, however NF-CDM and CAF-CDM, generated by the same cells and using the same protocol, have been previously measured at ~0.4kPa and ~0.8 kPa, respectively (Hernandez-Fernaud et al., 2017). We have now included this in the paper. As mentioned in response to point 4, we will use hydrogels to directly test the effect of matrix stiffness on ECM-dependent cell growth under nutrient starvation.

      In SF 3A-C, it is shown that ECM does not affect caspase-dependent cell death under AA starvation. Did you considered a non-caspase dependent cell death that may be triggered by AA starvation?

      We will complement the caspase 3/7 data by performing PI staining, to detect all forms of cell death. Preliminary data indicate that, consistent with our cas3/7 data, amino acid starvation promotes cell death, but the presence of the ECM doesn’t affect the percentage of PI positive cells, corroborating our conclusions that the ECM modulates cell proliferation and not cell death. We will complete these experiments in both MDA-MB-231 and MCF10CA1 cells and will include them in figure S3.In fig 5, it is shown that inhibition of Focal Adhesion Kinase (FAK) does not impair the ECM-dependent rescue of cancer cell growth under starvation. To further decipher the concept of adhesion dependent signalling, maybe the authors could also inhibit the Src kinase or ITG-beta1 activation?

      Integrin b1 is also required for ECM internalisation (our unpublished data), therefore interfering with integrin function would make the interpretation of the data quite complex. As suggested by the reviewer, we will use the Src inhibitor PP2, which has been extensively used in the literature in MDA-MB-231 cells. Preliminary data indicate that, despite significantly reducing cell proliferation in complete media, Src inhibition does not affect cell growth on collagen I under amino acid starvation, consistent with our FAK inhibitor data. We will complete these experiments on both collagen I and cell-derived matrices and will include them in figure 5.

      Minor comment, in F1B, it is written "AA free starvation" while in every others legend, it is written "AA starvation". I believe the "free" should be removed.

      We apologise for this mistake; we have now removed “free” from the legend.

      Reviewer #2 (Significance (Required)): Altogether, I found this study conceptually interesting and experimentally well performed. Experiments are well controlled and state-of-the-art technologies used in this manuscript make it a good candidate for publication. However, some aspects of the work need to be strengthen to reinforce the overall good quality of the manuscript.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)): In this manuscript the authors explore the mechanisms that metastatic cancer cells use to adapt their metabolism. The authors show that the growth of cancer cell lines is supported by uptake of ECM components in nutrient-starved conditions. The authors propose a very interesting mechanism in which the cells adapt their metabolism to ECM uptake as nutrient source via a PAK1-dependent macropinocytosis pathway which in turn increases tyrosine catabolism. Several key aspects of the authors complex hypothesis require further controls to fully support the authors ideas. As a disclaimer we do not feel qualified enough to comment on the metabolite experiments. Please find our detailed comments below.

      * Major -The ECM mediated increase of cell growth under amino acid (AA) starvation is nicely shown In Fig.1 but the authors should include the full medium data from figure S1 in the graphs of Fig. 1 to enable the reader to evaluate the magnitude of rescue effect of the ECM components. The values should also be included in the results text*.

      We have now moved all the complete media data into the main figure and highlighted the extent of the rescue in the result section.

      Also the authors only glutamine starve in Fig1&2 and then don't mention it again can the authors please include a sentence to explain why this experiment was dropped.

      As now highlighted in the result section, we focused on the amino acid starvation as it resulted in the strongest difference between normal and cancer. On the one hand, also non-invasive breast cancer cells can use ECM (namely matrigel) to grow under glutamine starvation, while this is not the case under amino acid starvation. On the other hand, only CAF-CDM, but not normal-CDM, could rescue cell growth under amino acid starvation. We reasoned that this condition was more likely to identify cancer-specific phenotypes.

      - The evaluation of uptake pathways is very interesting. The focus on macropinocytosis is not entirely justified in our opinion looking at FigS4A. Caveolin1/2 and DNM1/3 seem to have strongest effect on uptake of Matrigel and not PAK1? Statements like "Since our data indicate that macropinocytosis is the main pathway controlling ECM endocytosis..." are not justified nor are they really needed in our opinion. Several pathways can be implicated in passive uptake.

      We have now removed the statement, as suggested by the reviewer. In addition, we will assess CDM uptake upon caveolin 1/2 and DNM 2/3 knock-down, to test whether the effects are matrigel specific.

      - The authors use FAK inhibition to evaluate the effect of focal adhesion signalling on their phenotypes and conclude that there is no connection between the observed increase of cell proliferation in presence of ECM and adhesion signalling. To make this assessment the authors need at the very least to show that their FAK inhibitor treatment at the used concentration results in changes in focal adhesions and the associated force transduction.

      In the result section, we are including a western blot analysis showing that the concentration of FAK inhibitor used in sufficient so significantly reduced FAK auto-phosphorylation. Based on published evidence (Horton et al., 2016), FAK inhibition does not affect focal adhesion formation, but only the phosphorylation events. Therefore, we don’t think that we will be able to detected changes in focal adhesions regardless of the concentration of the inhibitor we use. To strengthen the observation that ECM-dependent cell growth in independent from adhesion signalling, as suggested by reviewer #2, we will use the Src inhibitor PP2, which has been extensively used in the literature in MDA-MB-231 cells. Preliminary data indicate that, despite significantly reducing cell proliferation in complete media, Src inhibition does not affect cell growth on collagen I under amino acid starvation, consistent with our FAK inhibitor data. We will complete these experiments on both collagen I and cell-derived matrices and will include them in figure 5.

      -The pancreatic cancer data currently feels a bit like an afterthought. We suggest to remove this data from the manuscript. If this data is included we suggest the authors should expand this section and repeat key experiments of earlier figures.

      We have now removed these data from the manuscript, as this was also the suggestion of reviewer #2.

      -Was the fetal bovine serum (FBS) and Horse Serum (HS) the authors use in their experiments tested for ECM components? The authors mention that the FBS for MDA231 cells was dialysed but not the HS.

      HS was used at a much lower concentration that FBS in our cell proliferation experiments (2.5% compared to 10%). We will characterise both sera components by mass spectrometry analysis, in collaboration with Dr Collins, biOMICS Facility, University of Sheffield.

      Minor comments:

      -Please can the authors provide experimental data directly comparing NF-CAM versus CAF-CDM on the same graph (Figure 1D-E).

      In the experiments included in the manuscript, the two matrices were generated independently, and we don’t feel it is appropriate to combine the results in the same graph. We are now repeating these experiments by generating both matrices in the same plates, so that we can present the data in the same graph. -Please can the authors give more insight to the use of 25% Plasmax to mimic starved tumor microenvironment. Is there previous research that suggests the nutrient values are representative of TME?

      Apologies for not clarifying this in the initial submission, the rationale behind this choice is based on the observation that, in pancreatic cancers, nutrients were shown to be depleted between 50-75% (Kamphorst et al., 2015). We have now stated this in the result section.

      -Fig3E Can the authors please include example images of the pS6 staining in the supplementary figures and explain "mTOR endosomal index" in figure legend.

      We have now included the representative images (new figure 3E) and we have described how the mTOR endosomal index was calculated both in the figure legend and in the method section. -Can the authors include a negative control for the mTORC1 localisation in Fig.3 (such as use of rapamycin/Torin)?

      Amino acid starvation is the gold-standard control for mTORC1 lysosomal targeting, as described in a variety of publications, including Manifava et al., 2016; Meng et al., 2021; Averous et al., 2014. In addition, Torin 1 treatment has been shown to result in a significant accumulation of mTOR on lysosomes compared with untreated cells (Settembre et al., 2012). Consistent with this, we looked at mTOR localisation in the presence of Rapamycin and we did not detect any reduction in lysosomal targeting.

      - The PAK1 expression level blots in the knockdown experiments should be quantified from N=3.

      We have not included the quantification of the western blots in the new supplementary figure 5.

      -What is the FA index in Fig.5, explain how it is calculated. Why not use FA size alone?

      We have now defined this is the method section. We haven’t used FA size alone, as this measure can be affected by cell size. If a cell is bigger, the overall FA size will be bigger, but this doesn’t necessarily reflect a change in adhesions.

      -Can the authors please use paragraphs on page 9 to improve readability. We apologise for overlooking this, we have now used paragraph in this section.

      Reviewer #3 (Significance (Required)): The findings presented here will be very interesting to a broad range of researchers including the cancer, metabolism and wider cell biology communities. The Rainero lab has progressed the idea that ECM uptake supports cancer progression and the data presented here has the potential to significantly advance our understanding of the underlying cellular mechanisms.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      This study by Viedor et al. examines the role of TIS7 (IFRD1) and its ortholog SKMc15 (IFRD2) in the regulation of adipogenesis via their ability to modulate the levels of DLK1 (Pref-1), a well-known inhibitor of adipogenesis. They generate SKMc15 KO mice and cross them to previously published TIS7 KO mice. All 3 mutant strains show decreased fat mass, with the effect being most pronounced in double KO mice (dKO). Using mouse embryonic fibroblasts (MEFs) from mutant mice, they authors ascribe a defect in adipogenic differentiation of mutant cells to an upregulation of DLK-1. In the case of TIS7, they propose that this is due to its known inhibition of Wnt signaling, which regulates DLK-1 expression. In the case of SKMc15, they suggest a new mechanism linked to its ability to suppress translation. Overall, the work is of interest, with the finding, that SKMc15 regulates adipocyte differentiation being its novelty, and generally well done, though multiple aspects need to be improved to bolster the conclusions put forth.

      **Major concerns:**

      1)The main mechanism put forth by the authors to explain the inability of dKO cells to differentiate into adipocytes is the upregulation of DLK-1 levels. However, this notion is never directly tested. Authors should test if knockdown of DLK-1 in dKO cells is sufficient to correct the defect in differentiation, or if additional factors are involved.

      Response: In response to the reviewer’s concerns, we have generated two stable cell lines expressing short hairpin RNAs directed against DLK1 in the TIS7 SKMc15 dKO MEFs. With these two and the parental dKO MEF cell line, we have performed adipogenesis differentiation experiments as explained in the manuscript before. Figure EV2C (left and right panels) shows that knockdown of DLK1 with two different DLK1 shRNA constructs (targeting DLK1 with or without the extracellular cleavage site) significantly (P2)There are multiple instances were the authors refer to "data not shown", such as when discussing the body length of dKO mice. Please show the data in all cases (Supplementary Info is fine) or remove any discussion of data that is not shown and cannot be evaluated.

      Response: Following three results were in the initial version of our manuscript mentioned as “data not shown”:

      • line 137: “body length, including the tail did not significantly differ between WT and dKO mice”
      • line 307: “higher concentrations of free fatty acids in the feces of dKO mice”
      • line 331: “effects of ectopic expression of TIS7, SKMc15 and their co-expression on DLK-1 levels” In the current version of the manuscript, we provide these results as:

      • Figure EV1A shows no significant difference in body length.

      • The significantly elevated levels of free fatty acids and energy determined by bomb calorimetry in the feces of dKO animals fed HFD are shown in Figures 6A and B, respectively.
      • The significant inhibitory effect of ectopic expression of TIS7 and SKMc15 on DLK1 levels was identified by both qPCR and WB analyses, which are shown in Figure 3B. 3)Indirect calorimetry data shown in Fig. S1 should include an entire 24 hr cycle and plots of VO2, activity and other measured parameters shown (only RER and food intake are shown), not just alluded to in the legend.

      Response: Based on the reviewer’s suggestion, we present here a table containing all parameters measured in the indirect calorimetry experiment.

      Metabolic phenotyping presented in Figure EV1B containing 21 hours measurement was performed exactly according to the standardized protocol previously published by Rozman J. et al. [1]. All phenotyping tests were performed following the International Mouse Phenotyping Resource of Standardized Screens (IMPReSS) pipeline routines.

      4)It is surprising that the dKO mice weight so much less than WT even though their food consumption and activity levels are similar, and their RER does not indicate a switch in fuel preference. An explanation could be altered lipid absorption. The authors indicate that feces were collected. An analysis of fat content in feces (NEFAs, TG) needs to be performed to examine this possibility. The discussion alludes to it, but no data is shown.

      __Response: __We thank the reviewer for bringing up this important point that prompted us to present data clarifying this aspect of the metabolic phenotype of dKO mice. As shown in Figures 6A,B, while fed with HFD, dKO mice had higher concentrations of free fatty acids in the feces (109 ± 10.4 µmol/g) when compared to the WT animals (78 ± 6.5 µmol/g) and a consequent increase in feces energy content (WT: 14.442 ± 0.433 kJ/g dry mass compared to dKO: 15.497 ± 0.482 kJ/g dry mass). Thus, lack of TIS7 and SKMc15 reduced efficient free fatty acid uptake in the intestines of mice.

      5)It would be important to know if increased MEK/ERK signaling and SOX9 expression are seen in fat pads of mutant mice, not just on the MEF system. Similarly, what are the expression levels of PPARg and C/EBPa in WAT depots of mutant mice?

      Response: To address this point, we have now performed the MEK/ERK activity measurement for the revised version of the manuscript in gonadal WAT tissue (GWAT). As noted in samples from several mice, there was an increase in p42 and p44 MAPK phosphorylation in G WAT isolated from dKO mice compared with the G WAT from WT control mice (Figure 4G).).

      The mRNA expression levels of PPARg and C/EBPa were significantly downregulated in GWAT samples isolated from dKO mice compared with levels from WT control animals (Figure 4H). However, we did not find any significant difference in SOX9 expression in fat pads. Total amounts of Sox9 mRNA in terminally differentiated adipocytes were very low and not within the reliable detection range, and the variation between animals within the same group was too great. Therefore, we provide these data only for the reviewer’s information here and do not present them in the manuscript.

      6)Analysis of Wnt signaling in Fig. 3c should also include a FOPflash control reporter vector, to demonstrate specificity. Also, data from transfection studies should be shown as mean plus/minus STD and not SEM. This also applies to all other cell-based studies (e.g., Fig. 6b,c).

      Response: To address the reviewer’s concerns, we performed FOPflash control reporter measurements in MEFs of all four genotypes. As expected, in every tested cell line the luciferase activity of the FOPflash reporter was substantially lower than that of TOPflash, confirming the specificity of this reporter system.

      We also thank the reviewer for this important reference to our statistical analyses. We have revised the original data and found that the abbreviation SEM was inadvertently used in the legends instead of STD. STD was always used in the original analyses and therefore we have corrected all legends accordingly in the new version of the manuscript.

      7)It is unclear why the authors used the MEF model rather than adipocyte precursors derived from the stromal vascular fraction (SVF) of fat pads from mutant mice. If they did generate data from SVF progenitors, they should include it.

      __Response: __We agree with this comment, although performing the experiments was challenging enough for us. Therefore, we isolated inguinal fat pads and obtained SVF cells from mice of all four genotypes (WT, TIS7, SKMc15 single and double KOs) and have repeated crucial experiments, i.e. adipocyte differentiation, DLK1, PPARg and C/EBPa mRNA and protein analyses in these cells. Novel data gained in this cell system fully confirmed our previous observations in MEFs. Therefore, in the current version of the manuscript we have replaced figures describing the effects of lacking TIS7 and SKMc15 in MEFs by adipose tissues samples (Figures 2D,E, 4G,H,I and 6C) or SVF cells from inguinal WAT (Figures 2A,B,F,G,H, 3C,D,E and F). In addition to the results obtained from SVF cells of inguinal WAT, we also obtained comparable data from SVF cells isolated from fat pads of gonadal WAT. We provide the results from gonadal WAT hereafter for the reviewers' information only.

      amido black

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      undifferentiated G WAT cells

      The only experiments where we have still used data obtained in MEFs are those where the ectopic expression or effects of shRNA were necessary (e.g. Figures 2C, 3B,H,I, 5F,G EV2B,C and EV3 A-F).

      8)Given that the authors' proposed mechanism involves both, transcriptional and post-transcriptional regulation of DLK-1 by TIS7 and SKMc15, Fig. 4d should be a Western blot capturing both of these events, and not just quantitation of mRNA levels.

      Response: As requested by the reviewer, we have added in Figure 3B the Western blot analysis of DLK1 expression. Secondly, this experiment was entirely redone and we now show the effects of ectopic expression of SKMc15, TIS7 alone and their combination side by side with the control GFP. We present here the effects of stable expression of ectopic TIS7 and SKMc15 in dKO MEFs following the viral delivery of expression constructs, antibiotic selection and 8 days of adipocyte differentiation.

      9)There is no mention of the impact on brown adipose tissue (BAT) differentiation of KO of TIS7, SKMc15, or the combination. Given the role of BAT in systemic metabolism beyond energy expenditure, the authors need to comment on this issue.

      Response: We thank the reviewer for bringing up this important point that prompted us to better describe the phenotype of TIS7, SKMc15 and double knockout mice. We measured DLK1 protein levels in BAT isolated from WT, TIS7, and SKMc15 mice with single and double knockout and detected a significant increase in DLK1 protein levels in all three knockout genotypes. Five mice per genotype were analyzed, and the statistical analysis in Figure 4I represents the mean ± STD. The p-values are based on the results of the Student's t-test and one-way Anova analysis (p-value = 0.0241).

      **Minor comments:**

      10)The y axis in Fig. 2c is labeled as gain of body weight (g). Is it really the case that WT mice gained 30 g of body weight after just 3 weeks of HFD? This rate of increase seems extraordinary, and somewhat unlikely. Please re-check the accuracy of this panel.

      Response: We thank the reviewer for drawing our attention to the apparent mislabeling of the y-axis. The correct labeling is: "Increase in body weight in %" and Figure 1F has been corrected accordingly.

      11)The Methods indicates all statistical analysis was performed using t tests, but this is at odds with some figure legends that indicate additional tests (e.g., ANCOVA).

      Response: This inaccurate information in the manuscript was corrected.

      12)Please specify in all cases the WAT depot used for the analysis shown (e.g., Fig. 3d is just labeled as WAT, as are Fig. 4a,e, etc.).

      Response: This information was added at all appropriate places of the manuscript.

      13)Fig. 5d is missing error bars, giving the impression that this experiment was performed only once (Fig. 5c). The legend has no details. Please amend.

      __Response: __We thank the reviewer for this important point regarding the statistical analyses. In the new version of the manuscript, we have included a graph (now Figure 4D) depicting results of three independent experiments including the results of the statistical analysis performed. Statistical analysis was performed using One-Way ANOVA (P=0.0016).

      Reviewer #1 (Significance (Required)):

      The role of TIS7 in adipocyte differentiation is well established. The only truly novel finding in this work is the observation that SKMc15 also plays a role in adipogenesis. The molecular mechanisms proposed (modulation of DLK-1 levels) are not novel, but make sense. However, they need to be bolstered by additional data.

      **Referees cross-commenting**

      I think we are all in agreement that the findings in this work are of interest, but that significant additional work is required to discern the mechanisms involved. In my view, a direct and specific link between SKMc15 and translation of DLK-1 needs to be established and its significance for adipogenesis in cells derived from the SVF of fat pads determined. Reviewer 2 has suggested some concrete ways to provide evidence of a direct link.

      __Response: __We agree with the reviewer's comment and have also noted that this point will be crucial in assessing the novelty value of our manuscript, as was also expressed in the referees cross-commenting. Therefore, we have now additionally performed a polysomal RNA analysis, which has of course been included in the current version of the manuscript.

      We analyzed the differences in DLK-1 translation between wild-type control cells and SKMc15 knockout cells in the gradient-purified ribosomal fractions by DLK-1 qPCR. Our analysis identified significantly (pSimilarly, as proposed by the reviewer, we have established stromal vascular fraction cell cultures from inguinal fat pads. In SVF cells of TIS7 and SKMc15 single and double knockout mice, we found increased DLK1 mRNA and protein levels (Figures 2F,G and H) as well as decreased PPARg and C/EBPa levels (Figures 3C,D,E and F). Specifically, we found that the ability of knockout SVF cells to differentiate into adipocytes was significantly downregulated (Figures 2A and B), fully confirming our original findings in TIS7 and SKMc15 knockout MEFs.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      **Summary:**

      In the current study, Vietor et al. aimed to explore the regulation of Delta-like homolog 1 (DLK-1), an inhibitor of adipogenesis, and demonstrated a role for TIS7 and its orthologue SKMc15 in the regulation of adipogenesis by controlling the level of DLK-1. Using mouse models with whole body deficiency of TIS7 (TIS7 KO) or SKMc15 (SKMc15KO) and double KO (TIS7 and SKMc15 dKO) mice, the authors used a combination of in-vivo experiments and cell culture experiments with mouse embryonic fibroblasts derived from the KO animals, to show that the concurrent depletion of TIS7 and SKMc15 dramatically reduced the amount of adipose tissues and protected against diet-induced obesity in mice, which was associated with defective adipogenesis in vitro.

      **Major Comments:**

      Overall, this study presents convincing evidence that TIOS7 and SKMc15 are necessary for optimal adipogenesis, and proposes a novel mechanism for the control of DLK1 abundance via coordinated regulation of DLK-1 transcription and translation. However, a number of questions remain largely unanswered. In particular, the direct ability of SKMc15 to regulate the translation of DLK-1 is lacking, and this claim remains speculative. SKMc15 being a general inhibitor of translation, SKMc15 may have an effect on adipogenesis independently of its regulation of DLK-1. Thus, addressing the following comments would further improve the quality of the manuscript:

      Response:

      We have been very attentive to these comments to improve the novelty and quality of our manuscript and have tried to address them experimentally. Therefore, this thorough revision of our manuscript took a longer time. First, we identified polysomal enrichment of DLK-1 RNA in SKMc15 KO MEFs, demonstrating that SKMc15 translationally affects DLK-1 levels (Figure 3I). Second, treatment with a recombinant DLK-1 protein as well as its ectopic expression quite clearly blocked adipocyte differentiation of WT MEFs (Figures EV3B,C). In addition, two different shRNA constructs targeting DLK-1 significantly induced adipocyte differentiation of TIS7 SKMc15 dKO MEFs (Figure EV2C, left and right panels). We believe that these results, taken together, sufficiently support our proposed mechanism, namely that TIS7 and SKMc15 control adipocyte differentiation through DLK-1 regulation.

      • The experimental evidence supporting that SKMc15 controls DLK-1 protein levels comes primarily from the observations that DLK-1 abundance is further increased in SKMc15 KO and dKO WAT than in TIS7KO WAT (Fig 3d), and that translation is generally increased in SKMc15 KO and dKO cells (Fig 5a). However, since the rescue experiment is performed in dKO cells, by restoring both TIS7 and SKMc15 together, it is impossible to disentangle the effects on DLK-1 transcription, DLK-1 translation and on adipogenesis. A more detailed description of the TIS7 and SKM15c single KO cells, with or without re-expression of TIS7 and SKMc15 individually, at the level of DLK-1 mRNA expression and DLK-1 protein abundance would be necessary. In addition, polyribosome fractioning followed by qPCR for DLK-1 in each fraction, and by comparison with DLK-1 global expression in control and SKMc15 KO cells, would reveal the efficiency of translation for DLK-1 specifically, and directly prove a translational control of DLK-1 by SKMc15. Alternatively, showing that DLK-1 is among the proteins newly translated in SKMc15 KO cells (Fig. 5a) would be helpful. Response: As suggested by the reviewer we used single TIS7 and SKMc15 knockout cells and demonstrated that both, TIS7 and SKMc15, affect Dlk-1 mRNA levels. We identified a highly significant effect on total DLK-1 mRNA levels in SKMc15 knockout MEFs as presented in Figure 3H. We also show that DLK-1 mRNA is specifically enriched in polysomal fractions obtained from proliferating SKMc15 knockout MEFs when compared to WT MEFs. However, the strong accumulation of DLK-1 mRNA in polysomes cannot be explained by transcriptional upregulation of DLK-1 alone, suggesting that regulation also occurs at the translational level. We took up this suggestion and ectopically expressed TIS7 and SKMc15 separately or together. For this purpose, we used not only MEF cell lines with double knockout but also with single knockout. Our recent data showed that stable ectopic expression of SKMc15 significantly increased adipocyte differentiation in both, single and double TIS7 and SKMc15 knockout MEF cell lines (Figures EV1C,D and EV2A). Ectopic expression of TIS7 significantly induced the adipocyte differentiation in TIS7 single knockout MEFs (Figure EV1C). In addition, both genes down regulated DLK-1 mRNA expression in dKO MEFs (Figure EV2A, bar chart on the right). We fully agree with the opinion of both reviewers and as already explained above we identified by qPCR in the polysomes that SKMc15 directly regulates DLK-1 translation (Figure 3I).

      • While the scope of the study focuses on the molecular control of adipogenesis by TIS7 and SKMc15 via the regulation of DLK-1, basic elements of the metabolic characterization of the KO animals providing the basis for this study would be useful. Since the difference in body weight between WT and dKO animals is already apparent 1 week after birth (Fig 1a), it would be interesting to determine whether the fat mass is decreased at an earlier age than 6 months (Fig 1b). The dKO mice are leaner despite identical food intake, activity and RER (Sup Fig 1). It remains unclear whether defective fat mass expansion is a result or consequence of this phenotype. Is the excess energy stored ectopically? The authors mention defective lipid absorption, however, these data are not presented in the manuscript. It would be interesting to investigate the relative contribution of calorie intake and adipose lipid storage capacity in the resistance to diet-induced obesity. In addition, data reported in Fig 1c seem to indicate a preferential defect in visceral fat development, as compared to subcutaneous fat. It would be relevant if the authors could quantify it and comment on it. Are TIS7 and SKMc15 differentially expressed in various adipose depots? The authors used embryonic fibroblasts as a paradigm to study adipogenesis. It would be important to investigate, especially in light of the former comment, whether pre-adipocytes from subcutaneous and visceral stroma-vascular fractions present similar defects in adipogenesis. Response: We addressed the issue of lipid storage capacity raised by the reviewer using two experimental methods. First, we have analyzed feces of mice fed with high fat diet. The free fatty acids content in dKO mice feces was significantly (PConcerning the question of younger animals, we have repeated microCT fat measurements on a group of 1-2 months old WT and dKO male mice (n=4 per group). The total amount of abdominal fat was in WT mice significantly higher than in dKO mice (P=0.019; Student’s T-test). We provide these data only for the reviewer’s information here and do not present them in the manuscript.

      We have also followed the reviewer’s advice and revisited our microCT measurements of abdominal fat and anylyzed the possible differences between subcutaneous and visceral fat. In all three types of abdominal fat mass measurement (total, subcutaneous and visceral) there was always significantly (ANOVA P=0.034 subcutaneous, P=0.002 total and P=0.002 visceral fat) less fat in the dKO group (n=8) of mice when compared to WT (n=12) mice. However, the difference was more prominent in visceral (P=0.001; Student’s T-test) than in subcutaneous fat (P=0.027; Student’s T-test). We provide these data only for the reviewer’s information here and do not present them in the manuscript.

      In addition, we have analyzed the expression of TIS7 and SKMc15 mRNA expression in both, inguinal and gonadal WAT. Our qPCR result showed that both genes are expressed in different types of WAT. The qPCR analysis was performed on RNA isolated from undifferentiated SVF cells isolated from several animals. The expression of TIS7 and SKMc15 was normalized on GAPDH. Data represent mean and standard deviation of technical replicates from several mice as labeled in the graph. We provide these data only for the reviewer’s information here and do not present them in the manuscript.

      Topics of a) stromal vascular fraction as a source of pre-adipocytes and b) comparison of TIS7 and SKMc15 roles in gonadal vs. inguinal fat pads we answered in response to the Reviewer #1, point 7. The results are presented in Figures 2, 3 and 4 and in this document.

      Both data and methods are explained clearly. The experiments are, for the most part, adequately replicated. However, whenever multiple groups are compared, ANOVA should be employed instead of t-test for statistical analysis.

      Response: Thank you for pointing this out. Wherever it was applicable, we used ANOVA for the statistical analysis of data.

      **Minor comments:**

      • Figure 4 d. The appropriate control would be WT with empty vector Response: this experiment was entirely replaced by the new Figure 3B where stably transfected MEF cells expressing TIS7 or SKMc15 were used.

      • Figure 7c/d. The appropriate control would be WT with empty vector Response: We have now generated new, confirmatory data in MEF cells stably expressing TIS7 or SKMc15 following lentiviral expression.

      • Figure 5C. An additional control would be WT with WT medium __Response: __We agree with your suggestion and therefore we have incorporated this control in all experimental repeats presented in the new Figure 4C.

      • Figure 2: In the legends, the "x" is missing for the dKO regression formula __Response: __Thank you, we have corrected this mistake. In the current version of the manuscript it is Figure 1D.

      • Since the role of SKMc15 in adipogenesis has never been described, the authors could consider describing the single SKMc15 KO in addition to the dKO, or explain the rationale for focusing the study on dKO. __Response: __The original reason for focusing on dKO mice and cells was the obvious and dominant phenotype in this animal model. However, we have sought to address the reviewer's concerns and have now also examined DLK-1 mRNA levels in proliferating SKMc15 knockout MEFs (Figure 3H). In addition to this experiment, we measured DLK-1 mRNA levels also during the process of adipocyte differentiation of single knockout cells. In WT MEFs we observed a transient increase of DLK-1 mRNA only on day 1. In contrast, significantly elevated DLK-1 mRNA levels were found in TIS7 single-knockout MEFs throughout the differentiation process, with the highest level reached at day 8. Interestingly, in SKMc15 single knockout MEFs we found an upregulation of DLK-1 mRNA level in proliferating cells but not a further increase during the differentiation. This supported our idea that SKMc15 acts mainly via translational regulation of DLK-1. We provide these data only for the reviewer’s information here and do not present them in the manuscript.

      To emphasize this point, we revised the entire manuscript accordingly and added data on SKMc15 knockout mice. In particular, experiments presenting data characterizing SKMc15 single knockout mice are presented in: Figures 1C,D,E and F, Figures 2A,B,C and D, Figures 3E,F,H and I, Figures 4A and I and in Figure EV1D.

      Reviewer #2 (Significance (Required)):

      While the effects of DLK-1 on adipogenesis have been widely documented, the factors controlling DLK-1 expression and function remain poorly understood. Here the authors propose a novel mechanism for the regulation of DLK-1, and how it affects adipocyte differentiation. This study should therefore be of interest for researchers interested in the molecular control of adipogenesis and cell differentiation in general. Furthermore, the characterization of the function of SKMc15 in the control of translation may be of interest to a broader readership.

      **Referees cross-commenting**

      I agree with all the comments raised by the other reviewers. Addressing the often overlapping but also complementary questions would help to clarify the molecular mechanisms by which TIS7 and SKMc15 control adipogenesis, and support the conclusions raised by the authors.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      In the article, "The negative regulator DLK1 is transcriptionally regulated by TIS7 (IFRD1) and translationally by its orthologue SKMc15 (IFRD2)", the authors performed a double knockout (dKO) of TIS7 and its orthologue SKMc15 in mice and could show that those dKO mice had less adipose tissue compared to wild-type (WT) mice and were resistant to a high fat-diet induced obesity. The study takes advantage of number of different methods and approaches and combines both in vivo and in vitro work. However, some more detailed analysis and clarifications would be needed to fully justify some of the statements. Including the role of TIS7 as a transcriptional regulator of DLK1, SKMc15 as translational regulator of DLK1 and overall contribution of DLK1 in the observed differentiation defects. The observed results could still be explained by many indirect effects caused by the knock-outs and more direct functional connections between the studied molecules would be needed. Moreover, some assays appear to be missing biological replicates and statistical analysis. Please see below for more detailed comments:

      **Major comments:**

      -Are the key conclusions convincing? Yes.

      -Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? No.

      -Would additional experiments be essential to support the claims of the paper? Yes. Please see my comments.

      -Are the suggested experiments realistic in terms of time and resources? Recombinant DLK1 10 μg - Tetu-bio - 112€ ; 8 days of adipocyte differentiation in 3 biological replicate ~ 1 month.

      __Response: __We followed the advice of the individual reviewers as expressed in “Referees cross-commenting” and tested this idea experimentally. Since the manufacturer couldn’t suppy information on biological activities of recombinant DLK-1 proteins, we analyzed in vivo the effects of two different ones, namely RPL437Mu01 and RPL437Mu02. The 8-day adipocyte differentiation protocol showed that the RPL437Mu02 protein was cytotoxic to WT MEF cells and therefore could not be used for analysis. On the other hand, treatment with the Mu01 recombinant DLK-1 protein did not result in a substantial cell death. According to oil red O staining, incubation with 3.3 mg/ml (final concentration) RPL437Mu01 led to 75% inhibition of adipocyte differentiation when compared to not treated WT MEFs (Figure EV3B and C).

      -Are the data and the methods presented in such a way that they can be reproduced? Yes.

      -Are the experiments adequately replicated and statistical analysis adequate?

      Adequately reproduced yes. Please see my comments concerning the statistical analysis.

      1)Fig1a: In the method section it is written that an unpaired 2-tailed Student's t test was used for all statistical comparisons. However, here something like Multivariate analysis of variance (MANOVA) should rather be used to assess statistical significance between the mice. Moreover, the details of this should be clearly stated in the corresponding Figure legend.

      __Response: __Based on this suggestion, we have revised all of our statistical analyses. In several cases, (Figures 1F, 2B and C) we have replaced the statistical analysis using Student’s T test with Anova. However, based on the definition “the difference between ANOVA and MANOVA is merely the number of dependent variables fit. If there is one dependent variable then the procedure ANOVA is used”, in case of Figure 1A we used ANOVA.

      2)Fig2a: please use an appropriate title for Fig2a instead of "Abdominal fat vs. body mass".

      Response: Title of the Figure 1D (formerly Figure 2a) we changed to “Effect of TIS7 and SKMc15 on the abdominal fat mass”.

      3)Fig2c: in the method section it is written that an unpaired 2-tailed Student's t test was used for all statistical comparisons. However, in Fig2c 4 groups are compared (WT, TIS7 KO, SKMc15 KO and dKO) and thus something like Multivariate analysis of variance (MANOVA) should rather be used to assess statistical significance.

      Response: For Figure 1F (formerly Figure 2c), in the revised version of the manuscript, we applied the ordinary one-way ANOVA with Holm-Šidák's multiple comparison test. This analysis gave us statistically even more significant results concerning the difference between WT and dKO mice than previously found by Student's T test. The results in detail were as follows:

      Holm-Šidák's multiple comparisons test Summary Adjusted P Value

      WT vs. TIS7 KO ** 0,0096

      WT vs. SKMc15 KO * 0,0308

      WT vs. dKO **** 4)Fig2 conclusion: Additive or just showing stronger effect?

      Response: We have re-phrased the concluding summary for Figure 1F (formerly Figure 2c). We agree that the precise description of differences found between the weight of single and double knockout animals should be described as “stronger” and not additive effect of knockout of both genes.

      5)Fig3a: the microscope picture for SKMc15 KO shows that cells might have died. Please state the percentage of cell death.

      Response: We would like to comment on these concerns of the reviewer as follows: In the image in Figure 3 of the original manuscript, the density of SKMc15 KO MEF cells after the adipocyte differentiation protocol was lower than in the WT control. Regarding the possible cell death, the cells stained with Oil Red O were adherent and alive. The adipocyte differentiation protocol consists of 3 days proliferation and further 5 days of differentiation including three changes of media during which dead cells are washed away and their vitality cannot be checked. However, in the meantime, we have repeated this protocol and the density of SKMc15 knockout MEFs was now not substantially lower than those of controls. Despite the comparable cell density, we have seen a substantial negative effect of the SKMc15 knockout on the adipogenic differentiation ability of these cells. Several examples are shown here:

      TIS7 +/+ SKMc15 +/+ MEFs

      TIS7 +/+ SKMc15 -/- MEFs

      oil red O staining; 8d differentiated cells

      Importantly, in the current version of our manuscript we replaced MEFs (shown in the former Figure 3a) by SVF cells (Figure 2A in the current manuscript). In these cells we did not see any significant difference in their density after 8 days of the adipocyte differentiation protocol.

      6)Fig3b: It would be informative to additionally observe some of marker genes for adipogenesis and whether all of them are affected.

      Response: In our newly established SVF cell lines, derived from inguinal WAT we have confirmed data previously identified in MEFs. As shown in the new Figure 3, PPARg and C/EBPa mRNA levels were downregulated in all knockout SVF cell lines, both undifferentiated (Figures 3C and D) and adipocyte differentiated (Figures 3E and F). On the other hand, DLK-1 mRNA and protein levels, both in undifferentiated (Figures 2F and G) and adipocyte differentiated (Figure 2H) SVF cells were significantly upregulated in dKO cells when compared to WT cells.

      7)Fig3b: instead of using an unpaired 2-tailed Student's t test with proportion, an one-way ANOVA would be more appropriate.

      __Response: __On the recommendation of the reviewer, we applied a simple ANOVA to our new data from SVF cells using the Holm-Šidák test for multiple comparisons. The Anova summary using GraphPad Prism Ver. 9.2 identified statistically highly significant (P value 8)Fig3c: Same comment as for Fig3b.

      __Response: __Also, in this experiment (now Figure 2C) we used ordinary one-way ANOVA with Holm-Šidák's multiple comparisons test. The ANOVA summary identified statistically highly significant (P value 9)Fig3d: A representative Western blot for 3 independent experiments is shown. Please add the other two as supplementary materials.

      __Response: __Here we provide examples of the requested two additional, independent experiments. These refer now to the Figure 2D in the revised version of the manuscript:

      31 07 2020

      = manuscript

      b____-catenin

      22 07 2020

      WT

      TIS7 KO

      dKO

      SKMc15 KO

      WT

      TIS7 KO

      dKO

      SKMc15 KO

      actin

      b____-catenin

      30 07 2020

      actin

      b____-catenin

      WT

      TIS7 KO

      dKO

      SKMc15 KO

      WT

      TIS7 KO

      dKO

      SKMc15 KO

      actin

      10)Fig3d:Is this distinguishing between the active and inactive catenin?

      __Response: __No, the b-catenin antibody, that we used is not discriminating between active and inactive b-catenin forms.

      11)Fig4a: Please perform qPCR for measuring DLK-1 mRNA levels in TIS7 KO and SKMc15 KO samples to check whether there is a correlation between mRNA and protein level as the statement of the authors is that "DLK1 is transcriptionally regulated by TIS7 (IFRD1) and translationally by its orthologue SKMc15".

      Response: Similar questions were raised by Reviewer 2 on p. 11 “Since the role of SKMc15 in adipogenesis has never been described, the authors could consider describing the single SKMc15 KO in addition to the dKO, or explain the rationale for focusing the study on dKO.” Please see our reply to his comment.

      12)Fig4c: please add the other two western blots as supplementary materials.

      __Response: __Here we provide data from two additional, independent experiments.

      13)Fig4d: The effects in MEFs appear quite modest. What about a rescue with TIS7 or SKMc15 alone?

      __Response: __As mentioned already in response to the question 2 of Reviewer #1, in our newly performed experiments we found significant inhibitory effects of ectopic TIS7 and SKMc15 expression on DLK1 levels, identified both by qPCR and WB analyses (Figure 3B).

      14)Page 12, row 207: I would not call histones transcription factors.

      __Response: __We re-phrased this sentence accordingly.

      15)Fig4e: Would be good to see a schematic overview of the locations of the ChIP primers in relation to the known binding sites and the gene (TSS, gene body). Moreover, the results include an enrichment for only one region while in the text two different regions are discussed. Importantly, to confirm the specificity of the observed enrichment, a primer pair targeting an unspecific control region not bound by the proteins should be included.

      __Response: __The selection of oligonucleotide sequences used for ChIP analyses of the binding of b-catenin, TIS7 and SKMc15 to the Dlk-1 promoter was, based on the following reference, as mentioned in Methods section of our original manuscript on p.21, line 494: Paul C, Sardet C, Fabbrizio E. “The Wnt-target gene Dlk-1 is regulated by the Prmt5-associated factor Copr5 during adipogenic conversion”. Biol Open. 2015 Feb 13;4(3):312-6. doi: 10.1242/bio.201411247.

      We used two regions of the Dlk-1 promoter: a proximal one, encompassing the TCF binding site 2 (TCFbs2) and a more distal one, annotated as “A”:

      Oligonucleotide sequences used for ChIP PCR:

      Dlk-1 TCFbs2 5'f CATTTGACGGTGAACATATTGG

      5'r GCCCAGACCCCAAATCTGTC

      Dlk-1 region A (-2263/-2143) 5'f TTGTCTAACCACCCTACCTCAAA

      5’r CTCTGAGAAAAGATGTTGGGATTT

      We observed specific binding at the proximal site.

      16)Fig5a: Has this experiment been replicated? That is no mention about the reproducibility or quantification of this result. This is the main experiment regarding the role of SKMc15 as a translational regulator of DLK1, also mentioned in the title of the manuscript.

      __Response: __This relates to the Figure 4A in the revised manuscript. Yes, we repeated this experiment several times. Here we provide images and quantifications of three independent experiments.

      17)Fig5b: Showing another unaffected secreted protein would be an appropriate control here.

      Response: As recommended by the reviewer, we have performed an additional WB with a recombinant anti-Collagen I antibody [Abcam, [EPR22209-75] ab255809]. Medium from 8 days adipocyte differentiated WT and dKO MEFs was concentrated using Centriprep 30K and resolved on 10% SDS-PAGE gel. Western blot presented in the new Fig 4 B shows even slightly higher amounts of Collagen-1 protein in medium from WT than in dKO MEFs. Mw of the detected band was approximately 35 kDa, which corresponded to the manufacturer’s information.

      18)Fig5c: I would recommend to perform additional experiments to prove that DLK-1 secreted in the medium can contribute and is responsible for the inhibition of the differentiation. Indeed, a time course of adipocyte differentiation followed by the addition of soluble DLK-1 would confirm that DLK-1 can inhibit adipocyte differentiation in this experimental setup. Moreover, silencing (for example RNAi) of DLK1 in the dKO cells before harvesting the conditioned media would allow to estimate the contribution of DLK1 to the observed inhibition of differentiation by the media. This is important because many other molecules could also be mediating this inhibition.

      __Response: __We agree with this reviewer’s concern, which are shared by other reviewers. Similarly, as in response to Reviewer #2 and as already mentioned above, in response to “major comments” of Reviewer #3, in our novel experiments we found that treatment with recombinant DLK-1 protein as well as ectopic expression of DLK-1 blocked adipocyte differentiation of WT MEFs (Figures EV3B,C,D and E) as well as medium from dKO shDLK-1 391 cells (Figure EV3F).

      19)Fig5c: The details and the timeline of the experiment with conditioned media are not provided in the figure or in the methods. At what time point was conditioned media changed? How long were the cells kept in conditioned media? How does this compare to the regular media change intervals? Could the lower differentiation capacity relate to turnover of the differentiation inducing compounds in the media due to longer period between media change? Moreover, is the result statistically significant after replication?

      __Response: __Based on the reviewer`s comment we have added technical information concerning the experimental protocol of the treatment with conditioned media. In general, the treatment for adipocyte differentiation was identical with the previous experiments. The only difference was that after three days in proliferation medium, we used either fresh differentiation medium or 2-day-old differentiation medium from dKO control or dKO-shDLK-1 391 cell cultures then for wild-type cells, as shown in the figure (Figure EV3F). Cells were incubated additional five days with the differentiation medium with two changes of media, every second day. The adipocyte differentiation of medium “donor” cells and the DLK-1 protein levels in these cells were monitored by oil red O staining and Western blot analysis, respectively.

      Additionally, we show now in Figure 4C representative images from three independent biological repeats and in Figure 4D the statistical analysis confirming a significant decrease in adipocyte differentiation ability of WT MEFs following their incubation with a conditioned differentiation medium from dKO MEFs.

      20)Fig5d: please add a statistical analysis of the oil-red-o quantification.

      __Response: __As requested, we included statistical analysis of at least three independent experiments. In Figure 4D we present the statistical analysis confirming a significant decrease in adipocyte differentiation of WT MEFs following their incubation with the differentiation medium from dKO cells. Additionally, Figure 4C shows representative images of oil red O staining from several independent experiments.

      21)Fig7c-d: Does overexpression also rescue the PPARg and CEBPa induction during differentiation. The importance of their induction in undifferentiated MEFs is a little difficult to judge.

      __Response: __We have focused our attention primarily on the ability of TIS7 and SKMc15 to “rescue” the adipocyte differentiation phenotype of dKO MEFs. dKO MEFs stably expressing SKMc15, TIS7 or both genes were differentiated into adipocytes for 8 days and afterwards stained with oil red O. There was a statistically significant increase in oil red O staining following the individual ectopic expression of SKMc15 (p=5.7E-03), a negative effect of TIS7 ectopic expression and a significant (p=9.3E-03), positive effect of co-expression of both genes (Figure EV2A). We found a significant decrease in Dlk-1 mRNA expression following the ectopic expression of TIS7 and/or SKMc15 (Figure EV2A, very right panel). However, C/EBPa mRNA levels were only partially rescued in 8 days differentiated MEFs by TIS7 and/or SKMc15 ectopic expression, and PPARg mRNA levels were not significantly altered.

      22)Fig8: it is not surprising that PPARg targets are not induced in the absence of PPARg. What is the upstream event explaining this defect? Is DLK1 alone enough to explain the results? Could there be additional mediators of the differences? How big are transcriptome-wide differences between WT MEFs and dKO MEFs?

      __Response: __We agree with the reviewer that the lean phenotype of dKO mice most likely cannot be explained by simple transcriptional regulation of PPARg. Although we showed that in undifferentiated MEFs, the levels of PPARg and C/EBPa are controlled (or upregulated) by both TIS7 and SKMc15, we also expected differences in the expression of genes regulating fat uptake. To determine changes in expression of lipid processing and transporting molecules, we performed transcriptome analyses of total RNA samples isolated from the small intestines of HFD-fed WT type and dKO animals. Cluster analyses of lipid transport-related gene transcripts revealed differences between WT type and dKO animals in the expression of adipogenesis regulators. Those included among other genes the following, mentioned as examples:

      • peroxisome proliferator-activated receptors γ (PPARγ) and d [2], fatty acid binding proteins 1 and 2 (FABP1, 2) [3],
      • cytoplasmic fatty acid chaperones expressed in adipocytes,
      • acyl-coenzyme A synthetases 1 and 4 (ACSL1,4) found to be associated with histone acetylation in adipocytes, lipid loading and insulin sensitivity [4],
      • SLC27a1, a2 fatty acid transport proteins, critical mediators of fatty acid metabolism [5],
      • angiotensin-converting enzyme (ACE) playing a regulatory role in adipogenesis and insulin resistance [6],
      • CROT, a carnitine acyltransferase important for the oxidation of fatty acids, a critical step in their metabolism [7],
      • phospholipase PLA2G5 robustly induced in adipocytes of obese mice [8]; [9]. Parts of the following text are embedded in the manuscript.

      We decided to study in more detail the regulation of CD36 that encodes a very long chain fatty acids (VLCFA) transporter because CD36 is an important fatty acid transporter that facilitates fatty acids (FA) uptake by heart, skeletal muscle, and also adipose tissues [10]. PPARγ induces CD36 expression in adipose tissue, where it functions as a fatty acid transporter, and therefore, its regulation by PPARγ contributes to the control of blood lipids. Diacylglycerol acyltransferase 1 (DGAT1), a protein associated with the enterocytic triglyceride absorption and intracellular lipid processing is besides CD36 another target gene of adipogenesis master regulator PPARγ [11]. DGAT1 mRNA levels are strongly up regulated during adipocyte differentiation [12], its promoter region contains a PPARγ binding site and DGAT1 is also negatively regulated by the MEK/ERK pathway. DGAT1 expression was shown to be increased in TIS7 transgenic mice [13] and its expression was decreased in the gut of high fat diet-fed TIS7 KO mice [14]. Importantly, DGAT1 expression in adipocytes and WAT is up regulated by PPARγ activation [11].

      Heatmap of hierarchical cluster analysis of intestinal gene expression involved in lipid transport altered in TIS7 SKMc15 dKO mice fed a high-fat diet for 3 weeks.

      What is the upstream event explaining this defect?

      Wnt pathway causes epigenetic repression of the master adipogenic gene PPARγ. There are three epigenetic signatures implicated in repression of PPARγ: increased recruitment of MeCP2 (methyl CpG binding protein 2) and HP-1α co-repressor to PPARγ promoter and enhanced H3K27 dimethylation at the exon 5 locus in a manner dependent on suppressed canonical Wnt. These epigenetic effects are reproduced by antagonism of canonical Wnt signaling with Dikkopf-1.

      Zhu et al. showed that Dlk1 knockdown causes suppression of Wnt and thereby epigenetic de-repression of PPARγ [15]. Dlk1 levels positively correlate with Wnt signaling activity and negatively with epigenetic repression of PPARγ [16]. Activation of the Wnt pathway caused by DLK1 reprograms lipid metabolism via MeCP2-mediated epigenetic repression of PPARγ [17]. Blocking the Wnt signaling pathway abrogates epigenetic repressions and restores the PPARγ gene expression and differentiation [18].

      **Minor comments:**

      1)Please use the same font in the main text for the references.

      Response: We thank the reviewer for the remark. This issue was corrected.

      Reviewer #3 (Significance (Required)):

      The study provides interesting insights into the role of these factors in adipocyte differentiation that would be relevant especially to researchers working on adipogenesis and cellular differentiation in general. The authors find the studied factors to have additive contribution to the differentiation efficiency. However, the exact nature of the roles and whether they are strictly speaking additive or synergistic is not clear. More detailed analysis of their contribution and molecular interplay would add to the broader interest of the study on molecular networks controlling cellular differentiation.

      **Referees cross-commenting**

      I very much agree on the different points raised by the other reviewers, some of which are also matching my own already raised concerns. And therefore it makes sense to request these modifications from the authors.

      References

      1. Rozman, J., M. Klingenspor, and M. Hrabe de Angelis, A review of standardized metabolic phenotyping of animal models. Mamm Genome, 2014. 25(9-10): p. 497-507.
      2. Lefterova, M.I., et al., PPARgamma and the global map of adipogenesis and beyond. Trends Endocrinol Metab, 2014. 25(6): p. 293-302.
      3. Garin-Shkolnik, T., et al., FABP4 attenuates PPARgamma and adipogenesis and is inversely correlated with PPARgamma in adipose tissues. Diabetes, 2014. 63(3): p. 900-11.
      4. Joseph, R., et al., ACSL1 Is Associated With Fetal Programming of Insulin Sensitivity and Cellular Lipid Content. Mol Endocrinol, 2015. 29(6): p. 909-20.
      5. Anderson, C.M. and A. Stahl, SLC27 fatty acid transport proteins. Mol Aspects Med, 2013. 34(2-3): p. 516-28.
      6. Riedel, J., et al., Characterization of key genes of the renin-angiotensin system in mature feline adipocytes and during in vitro adipogenesis. J Anim Physiol Anim Nutr (Berl), 2016. 100(6): p. 1139-1148.
      7. Zhou, S., et al., Increased missense mutation burden of Fatty Acid metabolism related genes in nunavik inuit population. PLoS One, 2015. 10(5): p. e0128255.
      8. Wootton, P.T., et al., Tagging SNP haplotype analysis of the secretory PLA2-V gene, PLA2G5, shows strong association with LDL and oxLDL levels, suggesting functional distinction from sPLA2-IIA: results from the UDACS study. Hum Mol Genet, 2007. 16(12): p. 1437-44.
      9. Sergouniotis, P.I., et al., Biallelic mutations in PLA2G5, encoding group V phospholipase A2, cause benign fleck retina. Am J Hum Genet, 2011. 89(6): p. 782-91.
      10. Coburn, C.T., et al., Defective uptake and utilization of long chain fatty acids in muscle and adipose tissues of CD36 knockout mice. J Biol Chem, 2000. 275(42): p. 32523-9.
      11. Koliwad, S.K., et al., DGAT1-dependent triacylglycerol storage by macrophages protects mice from diet-induced insulin resistance and inflammation. J Clin Invest, 2010. 120(3): p. 756-67.
      12. Cases, S., et al., Identification of a gene encoding an acyl CoA:diacylglycerol acyltransferase, a key enzyme in triacylglycerol synthesis. Proc Natl Acad Sci U S A, 1998. 95(22): p. 13018-23.
      13. Wang, Y., et al., Targeted intestinal overexpression of the immediate early gene tis7 in transgenic mice increases triglyceride absorption and adiposity. J Biol Chem, 2005. 280(41): p. 34764-75.
      14. Yu, C., et al., Deletion of Tis7 protects mice from high-fat diet-induced weight gain and blunts the intestinal adaptive response postresection. J Nutr, 2010. 140(11): p. 1907-14.
      15. Zhu, N.L., et al., Hepatic stellate cell-derived delta-like homolog 1 (DLK1) protein in liver regeneration. J Biol Chem, 2012. 287(13): p. 10355-10367.
      16. Zhu, N.L., J. Wang, and H. Tsukamoto, The Necdin-Wnt pathway causes epigenetic peroxisome proliferator-activated receptor gamma repression in hepatic stellate cells. J Biol Chem, 2010. 285(40): p. 30463-71.
      17. Tsukamoto, H., Metabolic reprogramming and cell fate regulation in alcoholic liver disease. Pancreatology, 2015. 15(4 Suppl): p. S61-5.
      18. Miao, C.G., et al., Wnt signaling in liver fibrosis: progress, challenges and potential directions. Biochimie, 2013. 95(12): p. 2326-35.
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      Referee #3

      Evidence, reproducibility and clarity

      Summary: The manuscript analyzes how the constriction of a tissue by an enveloping basement membrane alters the migration of cells migrating through that tissue. The tissue analyzed is the Drosophila egg chamber, an important model for basement membrane studies in vivo, and the cells migrating through it are the border cells. The border cells migrate through the center of the egg chamber, moving as a cluster between the nurse cells, which are in turn surrounded by follicle cells, which secrete the basement membrane on the outside of the egg chamber. The authors decrease and increase the basement membrane stiffness with various genetic perturbations, and they find that the border cells move more rapidly when the stiffness is reduced. They then investigate how basement membrane stiffness is communicated to the border cells several cell layers inside, by measuring cortical tension with laser-recoil. They found that external basement membrane stiffness alters the cortical tension of the nurse cells and the follicle cells, such that reduced matrix stiffness causes reduced cortical tension; further, reducing cortical tension directly within the cells also results in increased border cell migration rates. They conclude that basement membrane stiffness can alter cell migration in a new way, by altering constriction and cortical tension, with an inverse relationship between stiffness and migration rate. This is a strong manuscript and I would request very few changes.

      The authors are commended on the rigor and completeness of their study. Several independent methods are used to alter basement membrane stiffness (loss of laminin, knock-down of laminin, knock-down of collagen IV, over-production of collagen IV - all of which end up changing collagen IV levels) and all show the same result. Further, they are extremely rigorous about testing and excluding an attractive alternative hypothesis, that the basement membrane of the border cell cluster itself controls its migration rate. The use of mirror-Gal4 is very elegant and convincing, as it expressed only in the central part of the egg chamber, and they found border cells responded differently only in that region. Moreover, the authors were exceptionally thorough in reproducing the basement membrane mechanical data in their own hands using the bursting assay. Overall, the experimental data support the claims of the paper. There is only one more control I would like to see, for the knockdown of laminin in the border cell cluster with a triple-Gal4 combination. Presumably using all three Gal4 lines was necessary to get complete knockdown, and it would be nice to see anti-laminin for the border cell cluster under these knockdown conditions.

      Despite the rigor, because all of the manipulations to the basement membrane alter the levels of collagen IV, the authors cannot formally exclude the possibility that collagen IV in the basement membrane has another function besides stiffness, perhaps sequestering a signaling ligand, and that this other function somehow alters the cortical tension of the egg chamber. In the paper by Crest et al, externally applied collagenase served as a control for this possibility, but collagenase will not work for the authors because this study is in vivo. I suggest the authors bring up this caveat in the discussion. If they wanted to extend the study (optional), they could knock down the crosslinking enzyme peroxidasin in the egg chamber, which ought to reduce basement membrane stiffness without changing the collagen content. The problem here is that it hasn't already been shown to work that way in the egg chamber, and so both stiffness and collagen levels would need to be measured. Testing the stiffness directly would be difficult, since the bursting assay is not actually a measurement of stiffness (more on that below). Rather than go this route, I suggest just acknowledging the formal possibility, which seems to me unlikely anyway.

      In terms of clarity, the manuscript absolutely needs a schematic at the beginning to introduce the egg chamber and border cell migration, labeling the cell types, showing the route and direction of border cell migration, and labeling the A/P axis. Without this the non-expert reader cannot readily understand the study.

      Finally, in terms of clarity, the authors repeatedly use statements such as "stiffness influences migration rate". Influences how? These results are not intuitive to me, and it would help enormously if the authors would make statements like, decreasing stiffness increases migration (as I tried to in my summary). Here are two examples of statements to refine: • Line 189 - "We found that reducing laminin levels affected the migration speed of both phases (Fig.1F, G)." Please say increased, not affected. • Line 245 -"Altogether, these results demonstrate that the stiffness of the follicle BM influences dynamics and mode of BC migration." Again, be specific about how. There are many such statements, from the abstract to the results to the discussion, where it would help the clarity to be more precise about what kind of influence.

      Minor comments: • The movies are beautiful! • All the quantitative data are shown in bar charts with means and errors. It is much better to show the individual data points, superimposing the means and distributions on top of the individual points. • The bursting assay does not actually measure basement membrane stiffness; rather, it measures failure after elastic expansion. These are related, as was found by Crest et al and the authors say that at one point, but stiffness and failure are not the same thing. Please change the language discussing this assay to "mechanical properties" rather than stiffness. • The laser-recoil assays are done well and are convincing. Throughout the results section, the authors describe these as measuring "cortical tension", which is correct. However, in the figure legends the language changes to "membrane tension" which is only one component of cortical tension. Change them all to cortical tension. • In the Discussion, it would be nice to include something on the two different modes of migration (tumbling and not tumbling). • I suggest changing the title to remove the word "forces", because forces are never directly measured from basement membrane. • Although Dai et al (Science 2020) is discussed near the end, I suggest bringing this reference up to the introduction, so the reader can have the background on the mechanical aspects of border cell migration at the start of this study. • Two typos (there may be more): At the bottom of Fig. 2, text turns strangely white that should probably be black; and in line 260, you mean Fig. S5 not S4 (laser ablation).

      Significance

      Mechanobiology, and mechanobiology of the basement membrane, is a vibrant area of study now, arising from the intersection of biophysics/engineering and genetics. There is general interest in how the basement membrane alters forces within the tissue, and this study is the first to my knowledge to relate basement membrane mechanics to migration via constriction and cortical tension. The authors do a great job of discussing the broader significance of their work in the Discussion. To greatly broaden the scope of this work in the future, the authors could collaborate with a mouse team to look for similar responses in a mammalian tissue, as they discuss. It is worth noting that there is a lot of work on matrix stiffness and migration showing that stiffness promotes migration speed; in these cases, matrix is a substrate, not a compression mechanism. But the opposite nature of the result in interesting and makes this work non-intutive and perhaps hard for some readers to grasp.<br /> As the paper is written now, I think the audience for this work would mostly be oogenesis, border cell migration, and/or basement membrane researchers in the Drosophila community, of which there are many (I am in this camp). With some rewriting to make it more accessible to other audiences, I think it would be interesting to a larger developmental biology audience. The content is not like any other paper I know, but it may be similar in scope and subject matter to the papers detailing how follicle cells and basement membrane interact during follicle rotation.

    1. The first occasion of our love to hear, Like one I speak that cannot tears restrain. As we for pastime one day reading were How Lancelot by love was fettered fast— All by ourselves and without any fear— Moved by the tale our eyes we often cast On one another, and our colour fled; But one word was it, vanquished us at last. When how the smile, long wearied for, we read Was kissed by him who loved like none before, This one, who henceforth never leaves me, laid A kiss on my mouth, trembling the while all o’er.

      Francesca di Rimini was forced to marry Giovanni (Gianciotto) Malatesta an older crippled man but falls in love with his younger brother Paolo. Francesca and Paolo are in second circle of hell and forever trapped in a whirlwind because they gave into their desires for each other and committed adultery. In this passage Francesca is recalling how they were reading the story of Lancelot and Guinevere. Francesca romanticizes the kiss and gives Paolo chivalric virtues. This is significant because “If Francesca sees in her lover the peer of such a worthy as Lancelot, she may also see in herself the equal of his Queen: and she may even think that she had a right to betray Gianciotto…” (Poggioli 337). This serves to highlight Dante’s theme: the perfection of Gods Justice.

      Poggioli, Renato. “Tragedy or Romance? A Reading of the Paolo and Francesca Episode in Dante’s Inferno.” PMLA, vol. 72, no. 3, 1957, pp. 313–58. JSTOR, https://doi.org/10.2307/460460. Accessed 10 Mar. 2023.

    1. There's some interesting comparison to the ideas here and the long term state-of-the-art in information management, particularly in business and library settings which Bush wholly ignores.

      Most fascinatingly Bush "coins" memex here, but prior art for the Memindex as a similar product in the office/business productivity space easily goes back to 1906 and was popular to and through at least the early 1950s.

      For details on this, see:

      https://boffosocko.com/2023/03/09/the-memindex-method-an-early-precursor-of-the-memex-hipster-pda-43-folders-gtd-basb-and-bullet-journal-systems/

    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Manuscript number: RC-2022-01776

      Corresponding author(s): David Bryant

      1. General Statements [optional]

      We describe an ARF6 GTPase module that controls integrin recycling to drive invasion in PTEN-null Ovarian Cancer (OC). We used high-throughput, time-lapse imaging and machine learning to characterise spheroid behaviours from a series of cell lines modelling common genetic lesions in OC patients. We identified that PTEN loss was associated with increased invasion, the formation of invasive protrusions enriched for the PTEN substrate PI(3,4,5)P3, and enhanced recycling of integrins in an ARF6-dependent matter. We utilised Mass Spectrometry proteomics and unbiased labelling to investigate the interactome of ARF6, identifying a single ARF GAP (AGAP1) and a single ARF GEF (CYTH2). Importantly, this ARF6-AGAP1-CYTH2 modality was associated with poor clinical outcome in patients.

      We thank all Reviewers for their highly complementary assessment of our manuscript, describing our paper as a "very impressive study, very well done and controlled with rigorous statistical analyses that uses sophisticated methods", a study that is "stunning in its thoroughness and depth and breadth of its molecular analysis", with "experiments are properly designed, and the data are well presented. The conclusions are appropriate and supported by the data". Finally, we would like to thank the reviewers for appreciating that our results are "of significance for both scientific discovery and clinical application, which will interest the broad audience in both basic and clinical research".

      2. Description of the planned revisions

      Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

      Reviewer comments in bold. Our response in non-bold.

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      This paper by Konstantinou et al aims at deciphering the mechanisms by which PTEN loss could be driving poorer prognosis in patients. The authors use their great high-throughput 3D screening method coupled to an unbiased proteomic method and a CRISPR screen to uncover a new pro-invasive axis driving collective invasion of high-grade serous ovarian carcinoma (HGSOC) cells. Overall, this is a very impressive study, very well done and controlled with rigorous statistical analyses that uses sophisticated methods to convincingly show that the CYTH2-ARF6-AGAP1-ITGA6/ITGB1 module is required for the pro-invasive effect of PTEN depletion and discriminates patients with poorest prognosis.

      __

      MAJOR COMMENTS __

      Below are listed all the claims that, in my opinion, are not adequately supported by the data.

      1) Choice of the cell line: More justification on the use of the ID8 cell line and on the p53 deletion is needed. The authors need to clearly state that most p53 mutations in ovarian cancer are missense mutations that lead to a strong accumulation of a p53 protein devoid of transcriptional activity. Nevertheless, it seems that p53 mutations are not associated to differences in patient survival. Hence the choice of studying PTEN loss in the complete absence of p53, a situation that does not mirror the clinical situation, needs to be explained. Moreover, the in vivo experiments already performed in the literature mentioned in the discussion should be mentioned in the introduction to provide more context and physiological relevance to this study (especially regarding the special focus on the p53 null/ dKO cells throughout the study).

      We will update the manuscript with a detailed explanation of the cell line of choice. Briefly, while indeed Tp53 is found mutated in HGSOC, approximately 30-35 % of these are classified as null mutations (PMID: 21552211), making models with null Trp53 representative of the clinical situation. Further, there is no difference in patient outcome in HGSOC by Tp53 mutation type (PMID: 20229506), while gene expression data from TCGA suggest that HGSC is marked by loss of wild-type P53 signalling regardless of Tp53 mutation type (PMID 25109877). Thus, we believe our choice of model can faithfully mirror the clinical situation.

      2) "Therefore, PTEN loss in ovarian cancer, particularly at the protein level, occurs in the tumour epithelium and is associated with upregulated AKT signalling and poor overall survival". This claim is an over-interpretation and over-generalisation of the data presented. I appreciate the honesty of the authors in showing all the ovarian datasets that are available and highlight the discrepancies in expression of the proteins they study in stroma and epithelium. I think the way to present these data in the text without over-interpreting and generalizing would be to show that there is a clear epithelial-specific downregulation of PTEN at the mRNA level. Most likely due to the contribution to other cell types in the stroma, only 3 out of 5 bulk tumour mRNA datasets show a tumour specific downregulation of PTEN and no association with survival based on a median split of PTEN mRNA expression. Nevertheless, although there is no direct correlation between PTEN mRNA and protein levels, patients with low PTEN protein levels have poorer survival that is associated to an upregulation of Akt signalling. This allows to have a clearer conclusion, based solely on the protein data presented and no over-generalisation using the mRNA data. This, to me, makes a stronger case for studying PTEN loss in ovarian cancer and is fully supported by the data presented.

      We will incorporate this reviewer suggestion into the modified manuscript.

      3) PTEN loss induces modest effects in 2D culture. The authors make claims regarding the fact that some of the phenotypes they look at happen after PTEN depletion alone or in combination with p53 loss and are more prominent in 3D vs 2D. Many of these are insufficiently backed up by data. A few key experiments are also only performed in 2D and should be done in 3D. Finally, some clarifications if the role of PTEN is most prominent on either collective, ECM-induced or 3D-dependent invasion.

      some clarifications if the role of PTEN is most prominent on either collective, ECM-induced or 3D-dependent invasion

      We believe that the reviewer may be confused. Both of our models, either spheroids or invading monolayers, are events occurring inside gels of ECM. Therefore, these are all are 3D, ECM-induced, collective invasion. We have not performed 2D migration assays. We apologise that the this was not clearer in the first submission. We will correct this in the updated manuscript.

      First, the authors claim that PTEN loss alone (i.e. without p53 deletion) leads to changes in Akt signalling. Supp fig 1H clearly shows that there is no significant increase in Akt activation, although there seems to be one in the Western Blot (WB) presented in supp fig 1G. There is a clear, significant increase in the Akt activation in all the PTEN KO clones when in association with p53 loss though. This claim is hence not backed up by data and the conclusion seems to be that the effect on Akt signalling requires both deletion of p53 and PTEN.

      The reviewer is correct: that the increase to pAKT levels upon PTEN KO is more robust with co-KO of TP53, thereby indicating synergy with p53. We will update the manuscript to note this, accordingly.

      It will be interesting to see a quantification of the pS473-Akt staining (supp fig S1J), as it seems from these images that pAkt is preferentially found on rounded cells. It should also be performed in 3D conditions to see if there is an enrichment at invasive tips and back-up the invasion data.

      This observation made us realise that the images we had included were giving the wrong impression (that pAkt levels would be highest in round cells). Based on the quantitation in Fig. S1M, PTEN KO cells (which have elevated pAkt levels), show a marked depletion of rounded cells. Therefore, pAkt elevated is not associated with being enriched in rounded cells. We will replace this image with cells mirroring the phenotypes quantified in Fig S1M.

      We used 2D for quantitation of pAKT staining, as we perform a like for like comparison. We cannot compare pAkt in 3D protrusions accurately between genotypes because of the frequency of protrusions: in p53 KO protrusion are rare. In 3D, therefore, it is not a situation where protrusions are present in both genotypes and we compare enrichment or depletion in a stable structure. Rather, what we can provide is whether when protrusions form, there is clear pAkt labelling in a protrusion. We will include for the revision a representative image of each phenotype in 3D, including a 3D Trp53-/-;Pten-/- spheroid stained for pAKT S473.

      Arf6 is recruited to the invasive tips of cells invading a 2D wound (fig4D). How do the authors reconcile the fact that all the machinery required for 3D invasion is present but that PTEN loss has a modest effect on cells in 2D? If the wound assay was done on glass, it should be done again on ECM coated glass to see if it recapitulates the effects seen in 3D. This experiment will help deconvolute if the effect of PTEN loss is more linked to collective behaviour than 3D organization or presence of ECM.

      We again apologise for not being clearer in our description. Both the wound assays and the IF of invading monolayer were performed with cell monolayers invading into Matrigel. Monolayers are grown on top of Matrigel, wounded, and then overlayed with Matrigel. Therefore, this is orthogonal to our spheroid assay, and completely 3D. We will address this comment by changing the text in the results section to highlight the 3D nature of the method.

      The recycling assays are all done in 2D, condition under which the authors claim that the PTEN phenotype is weakest. Although I understand that it is not possible to do this assay in 3D, its contribution to elucidating the mechanism by which integrins participate in the PTEN loss invasive phenotype is not clear. The requirement of integrins relies on the data showing that ITGB1 KO results in no collagen4-positive basement membrane of the cysts and greatly impaired invasion. Experiments looking at the integrin localisation would be helpful: can an enrichment at the invasive tips can be seen? Are ITGA6 and/or ITGB1 repartitions homogeneous between the cysts membranes and the invasive tips? In my opinion the Src/FAK data is not enough to draw the conclusions of fig7I schematic.

      We will endeavour to include images of 3D spheroids of Trp53-/-;Pten-/- cells and stained for β1 integrin (total and active) and α5 integrin to interrogate localisation at the tips.

      4) Expression of AGAP1 isoforms do not alter ARF6 levels. Data in fig 6C, D show a significant downregulation of Arf6 and Akt signalling after expression of AGAP1S. Can the authors clarify what they mean?

      We thank the reviewer for picking up that discrepancy between the results and the text. We will change the relevant text to highlight that expression of AGAP1S is associated with a statistically significant reduction of roughly 30% in ARF6 levels and 10% in p:t AKT. We do not know why AGAP1s may enact such an effect.

      5) Arf6 is not modulated in the different cell lines: data in fig4B (far right graph) and supp fig 4B, J seem to indicate otherwise. Can the authors clarify what they mean?

      It is not clear exactly what the reviewer is referring to here. If the reviewer is referring to Supplementary Figure 4B, this is an experiment examining the levels of ARF5 or ARF6 upon knockdown, so levels would be expected to vary. Fig S4B does not correspond to the experiment performed in S4J. Our interpretation is that loss of p53 alone or in combination with Pten does not seem to be consistently be accompanied with an increase in either the levels of total or bulk GTP-bound ARF6 that could explain the dependency of Trp53-/-;Pten-/- on the GTPase for the invasive phenotype. We will make our interpretation clearer in the text

      6) Immunofluorescence panels without quantifications: Quantifications for the different stainings shown in fig3A; 4D, E; 5H; 7B and supp fig S1L, J; S3 need to be included to fully back the conclusions of the authors. Indeed, these images are used to draw conclusions and not only as illustrations.

      It is not possible to do a direct comparison between protrusion vs no protrusion (see our response above). We will include a line scan to show clear enrichment at the end of the tip for image shown. Quantitation for Figure S1L is already included (S1K and M), quantitation for Figure S1J is presented in Fig S1I and for Fig 5H quantitation of the phenotype is present in Fig 5I.

      7) Quantifications of invasion show that WT cysts become hyper-protrusive at around the half experiment mark (around 30-40hrs). Nevertheless, all movies or galleries show spherical cysts, which does not seem representative. Can the authors change this or explain why these images/movies were chosen?

      We present the fold change at each time point because that is intuitively easier to understand rather than the raw number. The quantitation does not show that the cysts necessarily become hyper-protrusive at the specific timepoint, but rather that the proportion of hyper-protrusive cysts observed in this genotype peaks at the specific timepoint. This phenotype may still be in the minority of behaviours. As an example, something that occurs 5% of the time in the control, with a two-fold increase in behaviours, might still only be 10% of the population. Therefore, adding in a picture that may be representative of a small proportion of the population may not be a realistic depiction of what is happening across the entire population. We will provide the reviewer with the exact percentage of spheroids that are classified as hyper-protrusive at the specific cell line across timepoints, to make this clearer.

      8) Since it seems that the main effect of PTEN is to drive the localisation and intensity of recycling of Arf6 cargoes, it will be helpful to confirm that all the proteins involved in the Arf6 module be shown to be accumulated/present at the pro-invasive tips. Immunofluorescence stainings showing the presence of AGAP1 (could be done with the AGAP1S isoform that is mNeon-tagged), pS473-Akt, ITGB1 (active integrin if possible, otherwise total integrin), ITGA5, PI3K should be included if possible. A quantification comparing signal in the cysts and in the invasive tips should also be included to see if there is an accumulation to PIP3-enriched areas.

      We will endeavour to include the requested images.

      9) Data in fig5I convincingly show that PTEN loss induces a fragmented collagen4-positive basement membrane. The authors use this data to claim that this is one of the ways that PTEN could be driving invasion but no correlation between these structures and the hyper-protrusive phenotype is made. This experiment needs to be done to support this claim.

      This comment made us realise that in an attempt to make images simpler (displayed nuclei and COL4 only), we omitted a staining for where protrusions were moving through gaps in the ECM. We will update these times to demonstrate such events.

      __

      MINOR COMMENTS __

      1) Data visualization: I think that the heatmap representation is overkill when only 2 or 3 conditions are presented. A graph showing the evolution of area or spherical/Hyper-protrusive phenotype proportions across time would be easier to read and more impactful: each genotype could be presented with a colour and the spherical/hyper-protrusive phenotypes as either plain or dashed lanes across time. I understand that this representation allows for the stats to be done at each time points but they are generally pretty clear (especially for the PTEN KO or dKO phenotypes) and do not need to be done for each time point in my opinion. These heatmaps could be put in supplementary figures if the authors feel strongly about putting stats for each time points.

      We thank their reviewer for their suggestion. We believe that our approach, while complex, is the best visualisation to reflect both the changes across time but also between conditions while allowing appreciation of the statistical significance. This visualisation has been optimised by our lab over years of working with this type of data and we would prefer that they remain consistent with the accepted standard of our other publications. We are, however, happy to expand the explanation in the text on how to interpret the bubble heatmaps.

      Fig supp S1M, fig 5I should be presented as a stacked histogram to improve readability and merged with fig supp S1K.

      We will merge Figures S1M and S1K. We believe that Figure 5I is easier to read as is.

      Displaying fold change as antilog rather than log values would be easier for the reader to realise the magnitude of the differences.

      We disagree with the reviewer.

      A bar graph would be easier to read than the matrix representation for fig 6B.

      We disagree with the reviewer as we feel it makes it easier to directly compare each lipid between the two cell lines.

      The way Area data is presented throughout to me makes it very difficult to understand what is going on. Could the authors at least give some explanations in figure legends. A curve graph displaying the evolution of the area across time would be easier to read and see the differences between conditions.

      Please see our response to Minor point 1

      2) It is confusing that, in fig supp S1M, there is a significant decrease of the rounded phenotype after PTEN loss that is not associated to a significant change in another of the categories. Could the authors explain how?

      This can be simply explained from our data: while the rounded phenotype was reduced in a consistent way across replicate experiments (therefore resulting in significance), the effect on the other two phenotypes was not consistent (not set in magnitude and directionality). This therefore does not lead to a significant (i.e. consistent) effect on the latter two phenotypes. PTEN loss therefore seems to allow cells to undergo – at the expense of being round - a range of shape changes, rather than a set phenotype.

      3) One of the big differences of the PTEN KO cells seems their ability to invade through the matrigel bed and migration on the glass below (supp movie S2). From what I gather, these cysts would be considered out of focus and excluded from the analysis. Would it be possible that this would minimize some of the results? Would it be possible to include a quantification of this particular phenotype to confirm it is specific to PTEN KO cells?

      In the same spirit, could the authors provide the percentage of non-classified cysts, to make sure that the same proportion of cysts is quantified across all different genotypes.

      Indeed, we cannot exclude that we under-estimate the magnitude of the effect on the PTEN null. We will include this point in the discussion. We can include a reviewer-only figure showing the proportion of cysts and levels of the ‘OutOfFocus’ objects across cell lines.

      __

      4) Can the authors clarify how a 0 fold change (in log value) in fig 2D can be highly significant? __

      We believe that the reviewer is equating statistical significance with something being biologically meaningful. Statistical analysis does not indicate a priori whether something is biologically meaningful. Rather, it assesses the likelihood that an observed result is occurring by chance (or not). For instance, if a small change (e.g 0.04 in a log2 fold change) occurred repeatedly across experimental replicates this is unlikely to be a result of chance, and therefore could be statistically significant. Yet, such a small magnitude of effect is probably biologically minor. This is why our heatmaps provide both statistical significance, fold change, and consistency in magnitude of effect.

      5) Delta isoform of PI3K seems to have an effect on area in the middle of the experiment, but has no effect at all on invasion. Could the authors comment? Are these smaller cysts still as invasive? There might be an interesting uncoupling between proliferation and invasion there.

      The cysts are actually slightly larger with PI3Kδ inhibition and there is no change in invasion. We will expand our comments in text as well to account for this observation.

      6) ITGB1 depletion seems to induce a downregulation of Akt protein. Is that right? Does it change Akt localisation? Is there a dose effect whereby there is not enough Akt protein to mediate invasion?

      The p:t AKT ratio does not change consistently across all gRNAs (Figure 5C) but we can look at Akt (total) protein levels and include this information if needed.

      __

      7) Stats should be added directly on the graphs for the recycling assays, doing a pairwise comparison of the different genotypes for each time points. Can the authors clarify what the t-32min quantification graphs adds (fig7E, supp fig S8G-I)? I would advise to remove them, as this data is already presented in the recycling assay graphs. __

      We don't include these because although they are technical replicates, they are demonstrative of a single experiment. What we include instead is the quantitation across independent biological experiments (which each have their own internal multiple technical replicates), where it is appropriate to include statistical analysis.

      8) There is a substantial amount of typos and erroneous references to figures. I listed below the ones that I spotted and I encourage the authors to carefully check.

      1. there are some mistakes in referencing the number of cysts in supp table 1. There is for example no cysts experiments in Figure 1 but yet there are some references to figure 1 in supp table 1. Please correct it. I think it will be easier for the reader if the number of cysts quantified for each conditions was also indicated in the figure legends. Supp table 1 can still be included for readers that want additional details.
      2. comma missing page 3
      3. page 3 and 4: PI(3,4)2 means PI(3,4)P2? Can be shorten to PIP2 for ease of read and specify if it is another PIP2 specie otherwise
      4. define CYTH abbreviation: I suppose this is for cytohesin?
      5. fig1F-I: don't understand why TCGA.OV is specified on some but not all the graphs. It seems to me that all the data are from TCGA.OV? Makes it seems it is nit the case
      6. legend of fig1H, I: y axis is -Log10 values in 1I, not Log10 values
      7. page 6: dKO abbreviation is already specified above and should be used to avoid repetition and for ease of read
      8. supp fig S1D: missing legend for the second bar (after Wild Type)
      9. supp fig S1N: legend of the X-axis should be below the axis
      10. supp fig S1O: the numerotation of the X-axis needs to be below the line of the axis for ease of read, not above it
      11. legend of S2A: clones 1.12 and 1.15 are p53-/-;PTEN-/- and not PTEN-/-
      12. supp figS2C can the authors specify the different stages of matrigel (liquid or gel) that are used for the invasion assay, to make it easier for the non-specialist to understand what is going on. Please confirm that the 50% GFR matrigel makes a gel on top of the cells and fill in the wound to produce the 3D invasion assay setup.
      13. page 7: no parental cells are used in S3A, B only p53 null and p53 null and dKO. Please also specify what cells are being compared in the text
      14. description of arrow heads and colours need to be moved to figure legends and not in main text (page 7)
      15. fig 2D: the signification of the dot in the circles needs to be in the legends (since it is its first apparition in the manuscript). It only appears later on, in supp2A legend. Additional description of the matrices is necessary, as they contain a lot of information to digest to understand fully what is going on
      16. legend of fig3: error in figure reference: area data is D and not E, protrusive phenotypes are E and not F
      17. arrow missing in fig3B
      18. fig 3D,E, G, H: please indicate the cell line studied
      19. fig 3I: the different genotypes need to be stated on the galleries for clarity
      20. page 8: define Arf6-mNG in the text
      21. __ page 9: "We thank the reviewer for their careful examination of the manuscript. We will go through all above points and make the corresponding careful adjustments to the manuscript.

      OPTIONAL SUGGESTIONS

      1) Choice of cell line: There is a high number of patients (around 9% according to (Cole et al. 2016)) that present the R248Q gain-of-function mutation. A recent study has shown that this mutant p53 protein is associated to an activation of Akt signalling and an increase of the intercellular trafficking of EGFR (Lai et al. 2021). Given that EGFR was also a hit in this screen, that is seems to have a central role in Arf6 cargoes (fig 4G), I think it would be a great addition to this study. It could hence cooperate with PTEN loss to drive strong, robust invasion.

      This is an excellent observation and one we will likely follow-up in an independent study.

      2) Are MAPK involved in the PTEN KO pro-invasive phenotype? In particular Erk1/2, since EGFR is one of the PTEN loss induced Arf6 cargoes.

      This is an excellent observation and one we will likely follow-up in an independent study.

      __

      REFERENCE Cole, Alexander J., Trisha Dwight, Anthony J. Gill, Kristie-Ann Dickson, Ying Zhu, Adele Clarkson, Gregory B. Gard, et al. 2016. « Assessing Mutant P53 in Primary High-Grade Serous Ovarian Cancer Using Immunohistochemistry and Massively Parallel Sequencing ». Scientific Reports 6 (1): 26191. _https://doi.org/10.1038/srep26191_.

      Lai, Zih-Yin, Kai-Yun Tsai, Shing-Jyh Chang, et Yung-Jen Chuang. 2021. « Gain-of-Function Mutant TP53 R248Q Overexpressed in Epithelial Ovarian Carcinoma Alters AKT-Dependent Regulation of Intercellular Trafficking in Responses to EGFR/MDM2 Inhibitor ». International Journal of Molecular Sciences 22 (16): 8784. _https://doi.org/10.3390/ijms22168784_. __

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      The authors have conducted a study of the molecular requirements for cancer invasion that is stunning in its thoroughness and depth and breadth of its molecular analysis. The writing is exceptionally precise though also very dense (see below). The molecular model proposed is that PTEN loss (in a p53 null background) leads to reliance upon ARF6 for invasion, with regulation through interactions with AGAP1 and beta1-integrin and it is convincingly demonstrated. They focus on interpreting the consequences of genetic and pharmacologic manipulations in a cell line, using a series of 2D and 3D assays. The phenotypes are more prominent in 3D assays.

      Concerns and Suggestions:

      • There is a disconnect between the essentially complete loss of protrusions and invasion in 3D (e.g. 4A) and the reduction in magnitude of protrusive invasion but the continued presence of elongated cells with protrusions in 2D (e.g. S4C). This discrepancy is present in a couple of comparisons and is glossed over in quick callouts to many figure panels.

        We thank the reviewer for mentioning this as this comment was very helpful in determining that we needed to clarify our description of the role of ARF6 to protrusion formation vs maturation. In the Trp53-/- genotype, protrusions can form, but they rapidly retract, failing to mature into structures that drive invasion through ECM (e.g. Figure S2E). This protrusion maturation occurs upon PTEN KO. When ARF6 depleted, PTEN-null cells can form protrusions, but now again lack the ability to mature into invasion-inducing structures.

      This concept of needing ARF6 for protrusion maturation and maintenance is underpinned by our model of ARF6 regulating recycling of active integrin back to the protrusion front. Indeed, we have observed ARF6 being required not for protrusion initiation, but rather ensuring protrusions are not retracted in other contexts (i.e. upon loss of the ARF6 GEF protein IQSEC1 in invading 3D culture of PC3 cells; PMID: 33712589).

      We also note that, as responded to Reviewer 1, the assay is a 3D invasion rather than 2D migration assay, with cells sandwiched between Matrigel.

      We will update the relevant sections of the results and discussion with the point above.

      Once a journal has been identified, it would be wise for the editor to allow some flexibility in word limit to enable some very dense sections to be expanded slightly to guide the reader through the experiments and results more clearly. For example, in the section "ARF6 regulates active integrin pools...", there are callouts like (Fig. 7C,E; S8A-C; G-I) and then (Fig. 7D,E; Fig. S8E-F, H-I). It takes a lot of time to unpack these different experimental claims based on a single sentence.

      We greatly appreciate the refreshing comments of this reviewer to advocate for actions to improve clarity in our reporting. We would take glad advantage of such a possibility.

      The patient data on CYTH2 and its relationship to survival is modestly convincing.

      In Ovarian Cancer, effects on survival are often minor. This is not a disease where one often sees large shifts in survival, which is why we are so excited about the large shifts that we do see with the ARF GTPase module we identified. However, we concede that the effects on CYTH2, although significant, are not vast changes. We will point this out and tone down our language.

      Very minor- search on %- there are a few inconsistencies in terms of spaces and commas vs. periods. The Methods also have some inconsistencies in terms of spaces between numbers and units or numbers and degrees Celsius. References are also in a different font. Overall it was extremely carefully written though (just dense).

      We thank the reviewer for their careful inspection of our manuscript. We will carefully go over the sections flagged before resubmission

      Reviewer #2 (Significance (Required)):

      One limitation of the experimental design is that the depth of molecular analysis in vitro comes at the expense of any in vivo validation, which the authors acknowledge in the Discussion. They attempt to make similar points using analysis of patient survival data from public databases but these analyses generally yielded small magnitude differences. The main audience for this study is likely to be cell biologists interested in cell migration, cell-ECM adhesion, cancer invasion, and GTPases. I don't see any need for new experiments- what can be done has been done and then some. I do think that it would benefit readers if the text could be made less dense.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Summary: Using a murine HGSOC 3D cell model, in combination with analysis of human ovarian cancer datasets, the authors uncover a CYTH2-ARF6-AGAP1 signaling module regulated by PTEN and identify a biomarker for tumor invasion and targeted therapy.

      Major comments:

      __The findings of this study are significant as they reveal a critical signaling module that controls tumor invasion by mediating tumor cell interaction with the extracellular matrix. The experiments are properly designed, and the data are well presented. The conclusions are appropriate and supported by the data. The limitation of the study has also been discussed properly.

      One suggestion regarding the survival analysis in Fig. 6 and 7. __

      The authors noted that the CYTH2-ARF6-AGAP1 module is not specifically or only induced in Pten-null contexts, but rather that Pten-null cells become more dependent on the module for enacting the invasive phenotype. Based on this, it would be interesting to evaluate how the PTEN status impacts the survival difference by integrating the PTEN genomic status (WT versus mutation) or its expression level (protein or mRNA) into the survival analysis of patient cohorts in Fig. 6 and Fig. 7.

      We thank the reviewer for this excellent point. We will include such analysis, where possible. One consideration will be that extensive division of patients based on these molecular characteristics may results in patient numbers too low to draw conclusions of significance.

      **Referees cross-commenting**

      Gene deletion and mutation may elicit different functional outcomes. I therefore agree with Reviewer #1 that "the choice of studying PTEN loss in the complete absence of p53, a situation that does not mirror the clinical situation, needs to be explained".

      We will make our reasons for this choice clear in the text before submission. Please refer to response to Reviewer 1, Major comment 1.

      Reviewer #3 (Significance (Required)):

      The model used and data presented in this study are of significance for both scientific discovery and clinical application, which will interest the broad audience in both basic and clinical research.

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      4. Description of analyses that authors prefer not to carry out

    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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      Reply to the reviewers

      1. General Statements [optional]

      We are grateful to the reviewers for highlighting the value and power of our 3D chimeric dataset to explore cancer/stellate interactions in pancreatic cancer invasion. We also appreciate their support of our findings identifying divergent roles for the two related enzymes ADAMTS2 and ADAMTS14. We thank the reviewers for their detailed comments, which have allowed us to prepare a significantly stronger and clearer manuscript.

      Following the reviewers comments we have made three major changes to the manuscript, which we will outline here in addition to the point-by-point rebuttal.

      1. i) Revised manuscript structure. We have modified the structure of the manuscript, which we hope improves the clarity and accessibility of the work.

      Figure 1 remains the description of our 3D invasion model and our approach to identify stellate cell and cancer cell transcriptomic information from this context.

      Figure 2 describes our focus on proteases and now includes concordance of our data with clinical data sets. This is also now where we describe the strikingly opposing roles for ADAMTS2 and ADAMTS14 in regulating invasion.

      Figure 3 is now the figure demonstrating that ADAMTS2 and ADAMTS14 have an equal contribution to collagen processing from stellate cells. This is an important experiment given that the main physiological roles for these enzymes are in the processing of collagen, and the importance of collagen for cancer progression. It was therefore reasonable to hypothesise that the effect of these enzymes on invasion could be due to differences in their collagen processing in this context. The finding that both have an equal effect on collagen processing points towards a wider, and more diverse, role for these enzymes in regulating biology.

      Figure 4 describes the divergent roles of these two enzymes on myofibroblast differentiation, and by extension TGFβ bioavailability. In this figure we now include experiments with TGFβ reporter constructs, which demonstrate an increase in active TGFβ following loss of ADAMTS14 and a reduction in TGFβ activity following loss of ADAMTS2.

      Figure 5 is our matrisomic experiment to identify enriched enzyme-specific substrates following knockdown of either ADAMTS2 or ADAMTS14.

      Figure 6 details our investigation into the substrate responsible for the reduction in invasion following loss of ADAMTS2. As the previous matrisomic experiment identified only two enriched ADAMTS2 substrates, we investigated both in our 3D assays, identifying SERPINE2 as the responsible substrate. Further analysis identified a reduction in plasmin activity in ADAMTS2 deficient cells. This was rescued with co-knockdown of SERPINE2, implicating this pathway as being crucial for mediating the effect of ADAMTS2. Additionally, we now include experiments demonstrating that concomitant knockdown of SERPINE2 alongside ADAMTS2 rescues the reduction in TGFβ activity observed with ADAMTS2 loss alone.

      Figure 7 describes our analysis of ADAMTS14 substrates. As the matrisomics identified a large change in proteins following ADAMTS14 knockdown, we performed an siRNA screen of candidates to identify those responsible for ADAMTS14 phenotype. This, followed by further validation in our 3D invasive assay, revealed Fibulin2 as the responsible substrate. Fibulin2 has a well-established role in regulating TGFβ release from the matrix. In accordance with this we present new data using TGFβ reporter constructs, which demonstrate that the increase in active TGFβ following ADAMTS14 knockdown can be reversed with co-knockdown of Fibulin2.

      1. ii) Improvement of the clinical significance of our chimeric data set and ADAMTS proteins. Ideally, we would like to present IHC images of ADAMTS2 and ADAMTS14 expression in PDAC tissue samples to corroborate our in vitro findings. However as these enzymes are secreted, this precludes antibody based imaging, as it would not provide cell type specific information. RNA scope presents an alternative, however we have experienced technical issues with this technique due to RNA degradation in PDAC tissue and unavailability of ADAMTS2/14 specific probes. In place of this we have used a range of publically available resources.

      We have compared our chimeric data set with human clinical data using the resource published by Maurer and colleagues (PMID: 30658994). This paper presents transcriptomic data from PDAC tumour and stromal compartments using laser microdissection of clinical tissue. In accordance with our data set, the majority of metzincins, including ADAMTS2 and ADAMTS14, are expressed in the stromal compartment. These data are presented in updated figure 2.

      We have also examined ADAMTS2 and ADAMTS14 expression in PDAC and CAF subtypes using publically available data sets. Using the TCGA dataset, we identified that ADAMTS2 and ADAMTS14 are highly expressed in PDAC tumours compared to normal counterparts. As the majority of PDAC is comprised of stroma, the bulk transcriptomic data from TCGA, combined with the results from the Maurer publication, lead us to conclude that this expression reflects the stromal origin of these proteases. In addition, using publically available single cell RNA sequencing data published by Luo and colleagues (PMID: 36333338), we identified ADAMTS2 and ADAMTS14 expression in the prominent PDAC CAF subtypes, inflammatory and myofibroblastic CAFs. Together these data demonstrate that these enzymes are enriched in clinical disease, which when combined with our mechanistic 3D studies implies a greater role for these enzymes in disease progression than previously appreciated.

      iii) Improved mechanistic link between ADAMTS2 and ADAMTS14 with TGFβ bioavailability

      To strengthen the association between ADAMTS2 and ADAMTS14 function, their substrates SERPINE2 and Fibulin2, and TGFβ bioavailability, we have performed the following experiments using TGFβ reporter constructs:

      We have taken conditioned media from stellate cells lacking either ADAMTS2 or ADAMTS14, along with co-knockdown of their substrate, and stimulated a recipient cell line expressing a SMAD Luciferase reporter. These cells express luciferase in response to TGFβ stimulation. In accordance with a role for ADAMTS14 and Fibulin2 in regulating TGFβ, we demonstrate that following ADAMTS14 knockdown there is a strong increase in active TGFβ in the media (Figure 4I), which is abrogated with co-knockdown of Fibulin2 (Figure 7F).

      We have also obtained a fluorescent reporter, CAGA-eGFP, which expresses GFP in response to TGFβ stimulation in order to examine TGFβ activity in 3D cultures. Stellate cells expressing this construct were embedded in collagen: Matrigel hydrogels following knockdown of either ADAMTS2 or ADAMTS14 and CAGA fluorescence recorded after 72 hours of culture. In accordance with our data, stellate cells deficient in ADAMTS14 showed increased fluorescence in 3D, indicative of increased TGFβ activity, which was abrogated with co-knockdown of Fibulin2 (Figure 4J, K and 7G, H). Equally, loss of ADAMTS2 reduced TGFβ activity in 3D culture, which was rescued with co-knockdown of SERPINE2 (Figure 4J, K and 6 D, E).

      These experiments confirm a link between the ADAMTS enzyme, its relevant substrate, and TGFβ bioavailability. Together with extensive published work linking SERPINE2 and Fibulin2 with TGFβ release we are confident in our proposed mechanism for the dichotomic relationship of ADAMTS2 and ADAMTS14 in regulating TGFβ and thus myofibroblast action.

      2. Point-by-point description of the revisions

      This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

      • *

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      • *

      This study aims to explain the opposing contributions of stromal stellate cells/CAFs to PDAC. By first identifying stroma-specific proteases, followed by a process of candidate selection and elimination, the authors find that two specific metalloproteases that share enzymatic activity against collagen in fact have differential activity on TGFb availability. This could be interpreted as a way of shaping the CAF population and tumor-promotin or -restricting properties of the stroma.

      There are several flaws that the authors could address to improve the manuscript:

      1. In the flow of experiments and analyses, there is a strange mix of fully unbiased discovery phases followed by functional experiments that do not consider all possible candidates to test, and vice versa. For instance, from the mixed-species transcript analysis, ADAMTS2 and -14 are chosen based on their shared collagenase activity based on literature. However, the authors then perform again a proteomics analysis to identify things from the entire matrisome that are cleaved by these enzymes? Then, for ADAMTS2 a co-silencing approach is done on one selected candidate (Serpine2), but for ADAMTS14 an siRNA screen is performed? The problem of this approach is that the rationale for some studied enzymes is very strong, where as for others it is not.

      We thank the reviewer for their comment and trust the revised manuscript provides more clarity for the rationale of our approach. We performed the chimera sequencing as a discovery experiment to reveal the communication between cancer and stellate cells in a 3D, invasive context. We present the chimera experiment and data here as a resource for the community, with our analysis of ADAMTS2 and ADAMTS14 function serving as a first example of the biological insight this data set can reveal. Other insights revealed from this dataset are active avenues of research in our group.

      Our finding that ADAMTS2 and ADAMTS14 have dramatically opposing roles in regulating invasion was especially striking given their equal contribution to collagen processing in this context. This led us to conclude that the divergent nature of these enzymes must be due to enzyme-specific substrates. A substrate repertoire for these enzymes has been previously published (PMID: 26740262) and we reasoned that the responsible substrate would be enriched following knockdown of the relevant enzyme. Thus we preformed matrisomics on cells lacking either of these enzymes, which did indeed reveal enrichment of known, enzyme-specific substrates that we could use for further analysis.

      The matrisome following ADAMTS2 knockdown was minimally changed and only presented enrichment of two ADAMTS2 substrates. As there was only a minimal cellular phenotype in 2D following loss of ADAMTS2, we decided to concentrate our studies on the two identified substrates in our 3D assay. Conversely as the matrisome following ADAMTS14 knockdown was dramatically different from control cells, and ADAMTS14 knockdown presented a clear phenotype in αSMA expression, we decided to perform a screen of all matrisome hits. This highlighted the role of IL-1β in mediating myofibroblast differentiation, which has been reported elsewhere and validated our approach. Further, this refined the number of enriched ADAMTS14 substrates to two, MMP1 and Fibulin2, with Fibulin2 being identified as the responsible candidate in our 3D assays.

      The ECM is more than just collagen. Choosing these two metalloproteases based on their shared collagen substrate is an approach that perhaps oversimplifies the ECM a bit, and again, does not provide the strongest rationale that these metalloproteases are most likely to explain counteracting stromal activities on tumor growth and progression.

      We fully agree with the reviewers comment and feel our work acutely demonstrates this point. Loss of either ADAMTS2 or ADAMTS14 had similar effects on collagen processing; implicating their divergent roles on invasion was independent of their effects on collagen regulation. This work therefore showcases the incredible complexity of ECM regulation in tumour progression. As discussed in the manuscript, collagen along with other elements of the ECM can regulate tumour progression and we believe our work adds an additional facet to this.

      Related to the above: How were the stellate cells used for the matrisome analysis grown? In the suspension setup or adherent? This will have a large impact on the outcome. Is there for instance hyaluronic acid in this matrix?

      The matrisome analysis was conducted on cells cultured in 2D. Vitamin C was added to the media to promote matrix production. We agree that this is not truly reflective of the in vivo situation but as a discovery tool this led us to identify the ADAMTS2 and ADAMTS14 substrates responsible for the function observed in 3D.

      1. Performing the species-specific transcript analysis both ways is a neat approach, but why did the authors ignore the opportunity to formally overlay/compare the two stromal gene sets to define likely candidates based on statistics?

      We primarily used this approach as a discovery tool to identify key differences between cancer and stellate cell compartments. Comparing the two species data sets is problematic as the murine cancer cells express many elements found in the stellate cells, while the human data set presents a cleaner comparison. This is evident from comparing metzincin expression in the two data sets. The human data set (Figure 2A) shows clear separation between cancer and stellate compartments, which is less evident in the murine data set (Supp figure 2A). As noted in supplementary figure 1A, unlike the human cancer cells used in this study, the murine cancer cells are capable of invading without stellate support (although when cultured with stellate cells invasive projections are always stellate led). Nevertheless the murine data set matches the human, although with less clarity.

      Minor comments: The bioinformatics Methods need more details on how reads were mapped to the different genomes. How many mismatches were allowed and was the mapping done separately or using for instance Xenofilter?

      We have improved the methodology section to include more detail for this separation. Using STAR aligner, reads were mapped to host species using a combined human and mouse genome. Ambiguous reads were subsequently discarded from the analysis. While there are bioinformatic packages that seek to match ambiguous reads to parent species we did not use these for our analysis.

      The authors use the knowledge on the activities of both ADAMTS2 and -14 on collagen as a rationale to choose these two. Is there really a need for the paragraph (and associated figures) from line 102 on?

      Given the prominent role collagen has been shown to have in regulating PDAC progression and the primary role for ADAMTS2 and ADAMTS14 being collagen processing, we initially hypothesised that the divergent role for these enzymes on invasion could be due to differences in collagen processing in this context. The fact that both equally contribute to collagen processing is surprising and adds to the novelty of our findings that these enzymes have a more complex role in regulating stromal biology.

      We have altered the structure of the manuscript to emphasise this point. The divergent roles of ADAMTS2 and ADAMTS14 on invasion are now presented in Figure 2, with their equal role in collagen processing now presented after in Figure 3. Figure 4 onwards now details the opposing roles of these enzymes in myofibroblast differentiation and our investigation into the enzyme-specific substrates responsible for this.

      Abstract, line 21; some words are missing?

      We thank the reviewer for bringing this to our attention and have now amended the abstract.

      Were the siRNA screen hits validated?


      Yes, hits relevant for our further investigations, MMP1 and Fibulin2, are presented in the manuscript.

      What is the genotype of the mouse cancer cells? KPC-derived?

      DT6066 are KPC derived while R254 are derived from KPF mice. This has been added to the methods with relevant reference.

      Reviewer #1 (Significance (Required)):

      The trick of dissecting tumor from stromal signals in spheroid cocultures by RNA-Seq is a cool trick, but not new and the authors should probably cite some prior work.

      We have included reference to other work where researchers have used species deconvolution to explore heterocellular interactions (Lines 68-72). However, we believe our work is one of the first to use this approach to explore cellular interactions in an in vitro, 3D, invasive context.

      What this all means for patients (or in vivo tumors even) remains unclear. There is some debate on whether highly activated CAFs (ACTA2/aSMA+ cells, some call them myCAFs) are indeed tumor-restrictive or whether they promote invasion. The authors appear to argue the latter (which I can agree with) but without any translational work to show what the net outcome of this mechanism is, the study remains descriptive and perhaps of limited interest.

      We contend that our 3D invasion model is a powerful tool to understand the role of stellate cells in leading invasion. We have shown the utility of this model in several studies to dissect the biology of this cell type, revealing the importance of the nuclear translocation of FGFR1 in stellate invasion (PMID: 36357571), the role of the kinase PKN2 in regulating stellate heterogeneity (PMID: 35081338) and the influence of cancer cell-derived exosomes on stellate invasion (PMID: 33592190).

      CAFs within PDAC stroma are highly plastic and can adopt multiple functions depending on distinct environmental cues. Thus, identifying how they are regulated is of paramount importance if they are to be therapeutically targeted. We contend that our mechanistic studies using heterocellular 3D models can aid in the dissection of the biology of these cells with more granularity than offered by clinical or in vivo studies, particularly in the context of secreted proteases. To add clinical relevance for our findings we have compared our chimera data set with previously published laser microdissected tumour and stroma PDAC tissue (Figure 2B), and identified ADAMTS2 and ADAMTS14 expression in prominent CAF subtypes (inflammatory and myofibroblastic) from published single cell RNA seq data taken from tumours (Supp figure 2C). As these enzymes are produced in multiple CAF subtypes, genetically targeting them in vivo appears prohibitive. The generation of ADAMTS2 and ADAMTS14 specific inhibitors would be required to assess their roles in vivo.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      The manuscript by Carter and colleagues examines that role of cancer-associated fibroblasts (CAFs) in regulation of invasion in a 3D co-culture assay with epithelial cells. The authors propose that invasive chains of cancer cells are led by fibroblasts. The authors utilise a system of co-culture to create chimeric human-mouse fibroblast-cancer cell spheroids (both directions utilised, to eliminate species bias) to allow for in situ sequencing of the co-operating transcriptional programmes of each cell type during 3D invasion. From this powerful approach, this allowed the authors to identify two key two collagen-processing enzymes, ADAMTS2 and ADAMTS14, as contributing to CAF function in their system. The authors identify that these two enzymes have opposing roles in invasion, and map some of their key substrates in invasion, which extend beyond collagen-processing. The authors propose that one key function is to control the processing of TGFbeta, the latter of which is a regulator of the myofibroblast subpopulation of CAF. Overall, the findings of the manuscript are interesting, but need some further proof-of-principal demonstrations to extend their findings to support the claims within.

      • The authors demonstrate a clear role of ADAMTS2 and ADAMTS14 in stellate function during differentiation and invasion. Is there any evidence of such changes in patient materials? Could the authors query publicly available databases of micro-dissected stroma vs epithelium to validate the translational relevance of their findings?

      We thank the reviewer for their suggestion; we have now explored clinical relevance of ADAMTS2 and ADAMTS14 expression in two ways. We have used previously published work by Maurer and colleagues (PMID: 30658994), which descibes transcriptomic analysis of laser microdissected tumour and stroma from pancreatic cancer tissue. In accordance with our chimeric data set the majority of metzincins, including ADAMTS2 and ADAMTS14, are expressed in the sromal compartment (Figure 2B). We have also used publically available scRNA seq data to examine ADAMTS2 and ADAMTS14 expression in distinct CAF subtypes (Supp Figure 2C). Both ADAMTS2 and ADAMTS14 are expressed in inflammatory and myofibroblastic CAFs, with ADAMTS14 expression lower than that of ADAMTS2. Given the complexity of CAF heterogeneity it is possible that ADAMTS2/14 secretion by one population regulates the resulting phenotype of surrounding CAFs, however this hypothesis if beyond the scope of our current work.

      Major comments: - Page no. 4, Line 71, The authors conclude that the invasion in the chimeric spheroids is "led by" stellate cells. This is a key concept in the manuscript. How do the authors define the "led by" phenomena? What is the frequency that this occurs?

      In our experience all invasive projections are stellate led, defined as a stellate-labelled nucleus present at the tip of invasive projections. Indeed the human cancer cells used in this study are incapable of invading in the absence of stellate cells (Supp figure 1 A). We have previously reported this model where we demonstrated FGFR1 activity in the stellate cells is crucial for invasion (PMID: 36357571). Others have demonstrated the general importance for fibroblasts in leading invasion (PMID: 18037882, 28218910). Interestingly in our study, mouse cancer cells were capable of invading in the absence of stellate cells. However, when cultured with stellate cells, projections were predominantly stellate led.

      • For Figure 2A and S2A, the text suggests that the heatmap represents the stellate vs cancer cell expression (as shown in Figure 1B and S1B) in the respective species but the labelling below the heatmap suggests they are all cancer cells (Mia, Pan, R2 and DT). Is this a typo? Could the authors clarify this?

      We use Mia, Pan, R2 and DT to define the sphere combination from which the data originated. We have improved the clarity of the heatmaps by colour coding the different cell types within each sphere, and matching it with the cell type data presented in the heat map. We hope this improved labelling makes the heatmaps more accessible.

      • The text and the figures are lacking information about the cell line names used in the experiment, e.g, Figure 2C, 2D, 2SB, 2SC and 2SD does not indicate what cell line was used in the study. This is the same with other figures as well. Please indicate in all instances exactly which samples are queried.

      We have now included reference to the cell type and stellate cell species used in each experiment in relevant figure legends. Key 3D invasive experiments were conducted with both human and mouse stellate cells.

      • It's mentioned in the text that the authors have used the cancer and stellate cells in a 1:2 ratio but the numbers of stellate cells look different between different spheroids confocal images. e.g. The numbers look very different between the Miapaca2:PS1 vs Miapaca2:mPSC spheroids. Is this simply the representative images, or are their bona fide differences. This, in turn, would impact on claims of cells being 'led' by stellate cells. Can the authors clarify?

      This is a consequence of the method by which the stellate cells were immortalised. Human PS1 stellate cells were immortalised with hTERT, while mouse stellate cells were immortalised with SV40. A consequence of this is that the mouse stellate cells proliferate faster in 3D than the human stellate cells, with both proliferating slower than the cancer cell compartment. So while spheroids start at 1000 cells (666 stellate, 333 cancer) with stellate cells as the prominent component they are quickly overtaken by the cancer cells. Despite this difference in proliferation we find no difference in the invasive capacity of the stellate cells, with invasive projections always stellate led irrespective of whether they are human or mouse.

      • While for most of the experiments the authors generated the chimeric spheroids first and then performed the respective experiments, it appears that for the invasion assay simply co-culture of Cancer cells and stellate cells was done. Is this correct? Have the authors tried performing the assay with the chimeric spheroids to see if the stellate cells still invade?

      The Boyden chamber migration assay was conducted by seeding a co-culture of stellate and cancer cells in the apical compartment then imaging their migration to the basolateral side. This provided a second method to predominantly showcase the enhanced migration of cells lacking ADAMTS14 in a manner that could be quantified over time. We have not tried placing spheroids in the apical compartment and imaging invasion through the pores.

      • The authors claim that ADAMTS2 and ADAMTS14 regulate the bioavailability of TGFB, and this is a key reason that these regulate CAF differentiation. However, there is no direct demonstration of this concept, which is conspicuous by absence. Could the authors either directly demonstrate this, or remove such notions from the results, and explicitly state that this is an untested speculation in discussion? Examples of this are:

      o Line 173, authors state "ADAMTS2 facilitates TGFβ release through degradation of the plasmin inhibitor, SERPINE2 (Figure 5D)"

      o Line 196 authors conclude "Together these data implicate 197 ADAMTS14 as a key regulator of TGFβ bioavailability (Figure 6F)."

      o Line 240 states "This reduces the activation of Plasmin, preventing the release of TGFβ (Figure 5C)." Since this is just a model without detailed experiments, It will be better to propose rather than conclude.

      We appreciate the reviewer’s concern and have now added additional experiments to strengthen the association of ADAMTS enzymes and TGFβ bioavailability.

      Using a TGFβ-responsive luciferase reporter we demonstrate that the media from stellate cells lacking ADAMTS14 has greatly increased amounts of active TGFβ (Figure 4), which is abrogated when Fibulin2 is knocked down alongside (Figure 7). This links ADAMTS14 and Fibulin2 to TGFβ activity. Given the extensive literature detailing a role for Fibulin2 in regulating matrix TGFβ release through interactions with fibrillin (e.g, PMID: 19349279, 12598898, 12429738) we believe this is how ADAMTS14 is regulating myofibroblast differentiation. As we do not directly examine the association of Fibulin2 with fibrillin in this manuscript we have amended the associated statements to reflect this.

      We have also used a TGFβ-responsive fluorescent reporter to examine TGFβ activity of stellate cells in 3D. Consistent with our results, loss of ADAMTS2 reduces, while loss of ADAMTS14 enhances, TGFβ activity (Figure 4), which can be reversed with concomitant knockdown of their respective substrates SERPINE2 (Figure 6) and Fibulin2 (Figure 7).

      • Figure S5C shows a less invasive phenotype in the NTCsi + ADAMTS14si spheroids compared to the NTCsi + NTCsi control. However, there appears no appreciable difference between NTCsi + ADAMTS14si and NTCsi + NTCsi spheroids' brightfield images in Figure 5SD.

      Could the authors comment on this?

      We thank the reviewer for bringing this to our attention and apologise for our mistake. The images were positioned erroneously. This has now been corrected and the images reflect the quantification that demonstrates a clear increase in invasion following loss of ADAMTS14, which is abrogated with co-knockdown of Fibulin2.

      Minor Comments: - Page no. 2, Line 20 has an incomplete sentence "Crosstalk between cancer and stellate cells is pivotal in pancreatic cancer, resulting in differentiation 21 of stellate cells into myofibroblasts that drive."

      Apologies for the error. This has been rectified.

      • Figure 2C; Figure S2C and Figure S5E lack quantification for the western blots.

      We have now included densitometry for all western blots, presenting values relative to the respective loading control and normalised to the experimental control. Values are averages taken from all biological repeats with significance indicated where relevant.

      • Why did the authors choose to investigate the Metzincin family? Could the authors provide their reasoning to investigate these proteins, to the exclusion of other candidates?

      We focused on the metzincin family, as they are best known for their involvement in cancer invasion. A goal for this manuscript is to present our chimera data set as a discovery tool for the community. While this initial manuscript focuses on protease activity, we have further projects on-going that have used this data set to identify important elements of cancer/stellate communication.

      • Info about the number of fields imaged per sample for the microscopy data is missing in the figure legends (e.g. Figure 2F and 2I, Figure 5SF).

      We have now included a statement in each relevant figure legend to indicate that quantification was performed on at least five fields of view per biological repeat.

      • Any particular reason why the ADAMTS2 expression was not checked through Western blotting like ADAMTS14 in Figure S2B.

      We attempted to examine ADAMTS2 by western blotting but were unable to find an antibody that produced consistent results with our samples, and corroborated consistent knockdown by PCR.

      • The legends for Figure 3SC and 5SF mention that "Images are representative of at least two biological replicates". How many technical replicates were used? It would be useful if the relative intensity of the images is measured and plotted in a graph.

      We have now moved these images to the main figure alongside quantification of αSMA intensity. Images are collected from two biological repeats with quantification obtained from at least five fields of view per image. Together these data strongly demonstrate that loss of ADAMTS14 increases αSMA fibre intensity, which is blocked by either an inhibitor of TGFβ signalling (Figure 4), or co-knockdown of Fibulin2 (Figure 7).

      Reviewer #2 (Significance (Required)):

      This work provides an examination of the cross talk between fibroblasts and cancer cells in a 3-Dimensional culture model of pancreatic tumour cell invasion. By using chimeric human-mouse spheroids, the authors are able to identify cell-type specific transcripts by bulk RNA sequencing in situ. This advance is not to be underestimated as a number of existing approaches for cell type-specific profiling (eg. single-cell sequencing) relies upon dissociation of cell communities prior to sequencing. It is very likely that transcriptional programmes change during this isolation process. This approach allows the authors to identify transcriptional co-operating programmes in situ. This data provides a resource to understand this key co-operation of these two cell types during tumourigenesis, and will be of interest to the pancreatic cancer field. In addition, the mapping of the key substrate of these enzymes provides further insights that may be useful in understanding the expanded target repertoire of these enzymes beyond collagen processing.

      We thank the reviewer for their strong support of our chimeric spheroid approach and resulting investigation into the dichotomic roles of ADAMTS2 and ADAMTS14.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      AMADTS2 and ADAMTS14 belong to the disintegrin and metalloproteinase with thrombospondin motif protein family, mainly produced by pancreatic stellate cells (PSC) and are related to cancer cell invasion. This study reveals that ADAMTS2 and ADAMTS14 have opposite roles in myofibroblast differentiation based on experiments testing HSC driven cancer cell invasion, variant expression of HSC activation makers and the related downstream targets analysed upon RNA sequencing analyses. The authors (TA) established PSC/cancer cell chimeric spheroids for investigating the crosstalk between these cell types in 3D in vitro. Based on their findings, they claim that ADAMTS2 and ADAMTS14 have different functions regarding PSC and TGF-β activation. However, their conclusions mainly rely on quantitatve data of invasion and mechanistic details are completely lacking.

      Comments: Typos, even in the abstract, e.g. first sentence incomplete

      We apologise for the error in the abstract and have rectified this in the revised manuscript.

      Introduction is rather sparce with one third of the text repeating the results of the study

      Our manuscript details a discovery experiment using chimeric spheroids to identify cancer cell and stellate cell transcriptomes in a 3D invasive context. We then showcase the power of this data set by using it to identify and then describe divergent roles for ADAMTS2 and ADAMTS14 in shaping stellate cell biology. Given this two-tiered approach we incorporated text that would normally be placed in the introduction into the results section (e.g. our description of the importance of collagen processing in PDAC, presented as a prelude to the results from figure 3). We feel this improves the flow of the manuscript, rather than having information that isn’t necessarily relevant to the reader at the outset.

      Some citations do not at all fit with the position where they are placed; needs approval

      We have examined this in detail and are confident in our use of appropriate references throughout.

      In this study, it is said that TS2 (AMADTS2) and TS14 (ADAMTS14) have opposite functions on myofibroblast differentiation, with individual depletion leading to distinct matrisomal phenotypes in PSC. However, both similarly contribute to collagen processing. As we know, collagen is increased in response to TGFβ signaling, since TS2 depletion (knock down, kd) inhibits and TS14 kd is suggested to promote TGFβ activation, it is expected that this has impact on the available collagen levels. How do the authors explain that nevertheless the kd effect on collagen is very similar?

      The primary effects of these enzymes are on the processing of pro-collagen to its mature form, rather than on the production of collagen. This is evidenced in figure 3B where collagen expression in the whole cell lysate is the same following ADAMTS2 knockdown, and slightly reduced with loss of ADAMTS14, but the mature form is lost in the cell culture supernatant.

      While myofibroblast differentiation is associated with increased collagen production, it is possible that this is perturbed in a situation where the cell is surrounded by collagen that is incompletely processed (e.g. through biomechanical feedback). Given that our results clearly indicated that the effect of ADAMTS2 and ADAMTS14 on invasion is independent of their roles in collagen processing, this avenue is beyond the scope of the current manuscript.

      The authors claim that TS2 facilitates TGFβ release and TS14 is a key regulator of TGFβ bioavailability. However, throughout the whole data, there is no experimental evidence for this conclusion. TGFβ activation, LAP concentration and downstream effects should be provided.

      Most of the conclusions in the manuscript are based on effects to invasion and the estimated quantification histograms. "Black boxes in between the treatment, e.g. knockdown and readout, that relate to the signals and mechanisms remain black boxes throughout. For example, the impact of the treatments on stellate cell activation markers, the cancer cells invasion signaling, the SERPINE2- and Fibulin2-dependent myofibroblast differentiation pathways should be mechanistically investigated.

      We disagree with this comment. Our invasive model shows a clear role for ADAMTS2 and ADAMTS14 in regulating invasion, which is mitigated by disrupting their substrates SERPINE2 and Fibulin2.

      ADAMTS2 loss is associated with a reduction in plasmin activity, which again is mitigated with concurrent loss of SERPINE2. Equally, inhibition of plasmin activity with Aprotinin matches the loss of invasion observed with loss of ADAMTS2. Plasmin has a well-established role in mediating TGFβ release from the matrix. We have now included additional experiments using a TGFβ fluorescent reporter in 3D culture. This demonstrates that loss of ADAMTS2 reduces TGFβ activity, which can be rescued with co-knockdown of SERPINE2 (Figure 6). Our data therefore support a mechanism where ADAMTS2 blocks TGFβ release from the matrix, and therefore myofibroblast differentiation, through its regulation of SERPINE2 activity.

      We have strengthened our proposed mechanism for ADAMTS14 regulation of TGFβ through Fibulin2 with the use of both luciferase and fluorescent TGFβ reporter constructs. Using these reporters, we demonstrate that stellate cells lacking ADAMTS14 exhibit increased TGFβ activity (Figure 4), which is mitigated with co-knockdown of Fibulin2 (Figure 7). Combined with the effects on αSMA expression and 3D invasion, our data fit with a model where ADAMTS14 regulates TGFβ bioavailability through Fibulin2.

      The authors investigate one cell line each for their conclusions; we know that different cell lines behave differently; can they confirm that the finding they present is of general validity or a finding that is specific for the tested cancer/PSC cell lines. Can the principle findings also be proven in primary cells. More importantly, the authors should proof their findings in PaCa tissue of patients as follows: Expression of the proteases in the tissue, related variation of matrisome signatures, e.g. by snRNASeq, to confirm relevance of the finding.

      All our key 3D invasive experiments are repeated with both human and mouse stellate cells, adding strength to our proposed association with ADAMTS2 and SERPINE2, and ADAMTS14 and Fibulin2, on the invasive capacity of stellate cells. As detailed above we have explored the clinical relevance of our findings by examining laser dissected tumour and stromal data from PDAC tissue, and scRNA fibroblast data. These data confirm that ADAMTS2 and ADAMTS14 are predominantly expressed in the stromal compartment of the tumour and are associated with key CAF subtypes present in the PDAC environment, inflammatory and myofibroblastic CAFs.

      Details related to the figures: Figure 1: Are the numbers of PSC and PaCa cells integrated in the spheres related to the numbers found in patients?

      The 2:1 ratio of stellate to cancer cells used to produce spheres is a technical requirement and reflects the numbers in patients (PMID: 23359139). Cancer cells will proliferate substantially faster than the stellate cells so at the end of the experiment (day 3) the spheres are predominantly cancer cells. Nevertheless the stellate cells are able to drive invasion of the cancer cells, which can be quantitatively assessed in this model.

      B, it seems that the PSC in the spheroid are not equally distributed but instead are all located in close vicinity to eachother in a cloud; is that the representative situation for the spheres and is this similar in the PaCa cancer tissue? Does this have influence on the results?

      We have replaced this image with a more representative image that shows mouse stellate cells dispersed throughout the sphere.

      Figure 2: It is interesting to hear that BMP1, which is actually a ligand for BMP signaling is a protease for Collagen. How does this work?

      While the BMP family generally belong to the TGFβ superfamily, BMP1 is the exception in that it is a C-terminal collagenase. Please refer to reference 21 in the manuscript (PMID: 33879793), which details the role of BMP1 on collagen processing and the resulting effect on PDAC progression.

      C, Quantification of all blots should be presented.

      We have now included densitometry for all western blots, presenting values relative to the loading control and normalised to the experimental control. Values are averages taken from all biological repeats with significance indicated by stars.

      Figure S2: TS2 kd and TS14 kd should be confirmed and provided by both qrt PCR and WB data.

      We were unable to assess ADAMTS2 knockdown by western blot due to the quality of available antibodies. We are confident that either western or PCR confirmation of knockdown is sufficient, especially given the strong phenotype observed with the resulting knockdown.

      Figure 3: F; this result is arguing against the conclusion that TGFb bioavailability is a function of the ADAMs, since the kd impacts on the treatment result with exogenous TGFb. This suggests an effect downstream of ligand activation by proteasomal cleavage, e.g. receptor activation or signal transduction; this needs clarification. H, I: TGFβR inhibitor reduces TS14kd enhanced αSMA expression. How is unclear and needs clarification, since from F we know that already activated TGFβ needs TS2 to fully induce αSMA expression.

      SupplFig.3: B, C, as above!

      αSMA expression in stellate cells requires continuous exposure to TGFβ over 48 hours. Active TGFβ has an incredibly short half-life (minutes) and so requires positive feedback to maintain signalling. We propose that following ADAMTS2 knockdown the cells are incapable of releasing further TGFβ to maintain the phenotype. Equally following ADAMTS14 knockdown the cells are able to release more TGFβ, which is incapable of initiating signalling when the receptor is blocked.

      Figure 4: TIMP1 is a canonical TGFb signaling target gene in fibrosis. How the authors explain that TIMP1 is upregulated in both knockdowns, when they claim that TS2 and 14 have opposing functions on TGFb activation. This result as well puts their conclusions as regards TGFb and also the myofibroblast phenotype into question. Especially, since TIMP1 signifies stellate cell activation not only in the pancreas, but also in the liver and kidney. C, D, E should be explained in more detail and all details of the results should be presented.

      TIMP1 is a substrate for both ADAMTS2 and ADAMTS14, so its enrichment following knockdown of either is unsurprising, reflective of reduced cleavage of TIMP1. Both our 3D invasive assessment in Figure 6 and αSMA imaging in supplementary figure 5 demonstrate that TIMP1 is not responsible for the effect observed as a consequence from loss of either ADAMTS2 or ADAMTS14.

      This holds also for the different myofibroblast phenotypes. All data should be included. From recent scRNASeq investigations, several myofibroblast populations were described and compared, e.g my-stellate cells vs i-stellate cells. To which of these phenotypes the identified populations belong?

      As mentioned above, we have interrogated publically available data sets and identified ADAMTS2 and ADAMTS14 expression in multiple CAF subtypes. As these proteases are secreted it is probable that one CAF subtype can control the phenotype of surrounding CAFs through ADAMTS2 and ADAMTS14 production. While intriguing, this hypotheses is beyond the scope of the current work.

      Figure 5: C, Only brightfield images are provided, confocal images are suggested for comparison of +/- Aprotinin treatment.

      We do not think the addition of confocal images will add to the comparison. Aprotinin clearly reduces invasion, which coupled with the action of stellate-derived SERPINE2 on invasion, and reduced plasmin activity following ADAMTS2 knockdown, suggests that plasmin is important for regulating the effects of ADAMTS2 on invasion.

      The efficiency of TS2 and Serpine2 kd should be provided by qrt PCR and WB.

      TS2 kd promoted SERPINE2 expression should also be presented by qrt PCR and WB.

      We are confident that either western or PCR confirmation of knockdown is sufficient. Of note is that following ADAMTS2 knockdown, SERPINE2 expression is unchanged (sup figure 4C). This would indicate that the enrichment of SERPINE2 observed in the matrisome following loss of ADAMTS2 is reflective of reduced cleavage, rather than a change in expression.

      Figure 6: A, why ta use aSMA and not invasive activity as a readout here?

      Increased αSMA expression following ADAMTS14 knockdown provides a strong, clear, 2D phenotype to act as a readout for an siRNA screen with high-content imaging. Performing such a screen with our 3D invasive model is currently impractical.

      There are many parameters leading to decreased aSMA expression upon kd; (1) why only MMP1 and Fibulin were selected as candidates?

      From our αSMA screen, MMP1 and Fibulin2 knockdown were the only candidates that were able to both prevent an increase in αSMA seen with ADAMTS14 loss alone, and are known ADAMTS14 substrates. Further validation in our 3D invasive model demonstrated that Fibulin2 and not MMP1 was responsible for the effect of ADAMTS14 loss on invasion.

      (2) the single kd control of the screen candidates is missing!

      We feel this control is not needed, as the goal of the experiment was to establish which candidate was responsible for mediating the effects brought about by ADAMTS14 knockdown. Increased αSMA expression with IL-1β loss validates our approach, as this is a known negative regulator of TGFβ signalling.

      (3) Can it be expected that all these matrisomal proteins are involved in aSMA expression regulation? I have doubts.

      We agree with the reviewers comment, from the siRNA screen (sup figure 5B) it is clear that the majority of the identified matrisome proteins have a minimal effect on αSMA expression following loss of ADAMTS14.

      C, D, E, why MMP1 was not also tested in these assays?

      Our spheroid assay clearly demonstrated that invasion was enhanced following ADAMTS14 knockdown even with co-knockdown of MMP1. Given the strong rescue observed with co-knockdown of Fibulin2 we proceeded to further analyse this candidate over MMP1.

      F, Fibrillin is shown in the figure but not described in the text. It would be quite interesting to see whether Fibrillin kd has the same effect as TS14 kd on LTGF-β activation (which of course need to be shown experimentally).

      The association of fibrillin with TGFβ release is well established as it underpins the biology behind Marfan syndrome. Loss of fibrillin, or mutations to its TGFβ binding sites results in a phenotype consistent with super active TGFβ signalling.

      E, what is the meaning of αSMA intensity quantification? By IF staining of αSMA? PSC αSMA expression should be quantified by qrt PCR and WB.

      We have now incorporated the confocal images analysing αSMA expression into the main figure and labelled the quantification accordingly. We feel this improves the clarity of the figures. Every western blot is now presented with quantification.

      Also here, kd efficiency of TS14 and Fibulin2 should be provided by qrt PCR and WB.

      Figure S5E should be part of figure 6, qrt PCR of Fibulin2 should be added.

      We have moved this western blot to the main figure (Fig 7C). We feel additional PCR validation of Fibulin 2 knockdown is not necessary.

      Figure 5/6 and throughout: It is claimed that ADAMTS2 and ADAMTS14 regulate TGFβ bioavailability through SREPINE2-Plasmin and Fibulin2. As mentioned above, TGFβ activation is only mentioned in the schemes, but no experimental evidence is given. In addition, according to previous studies, ADAMTSs can activate latent TGFβ directly by interaction with the LAP of latent TGFβ. .

      We have now included extra experimental evidence to support an association of ADAMTS proteins with TGFβ bioavailability. Using a TGFβ luciferase reporter construct, we demonstrate that active TGFβ is increased following loss of ADAMTS14, which is abrogated with concomitant loss of Fibulin2. This provides further evidence that ADAMTS14 is mediating its effects on myofibroblast differentiation / invasion through TGFβ release.

      Figure 3B, C, and 6D: We are confused from the migration/invasion assays. Invasion should be based on migration of tumor cells, whereas in the migration assays only stellate cells seem to be active? Can you explain this to us? According to Figure 3B, stellate and cancer cells are cocultured in the chamber. Is this the same condition as for the experiment presented as figure 6D?

      In our migration assay, stellate and cancer cells are co-cultured in the apical chamber and cell migration imaged over time. We pooled data of both cancer and stellate cell migration following stellate specific knockdown of either ADAMTS2 or ADAMTS14, which showed an increase in cell migration following loss of ADAMTS14. In figure 7, we again use this assay to demonstrate that Fibulin2 expression accounts for the phenotype observed from loss of ADAMTS14.

      In summary, this study for the first time found that ADAMTS2 and ADAMTS14 have opposite roles on myofibroblast differentiation, which is shown by using chimeric spheroids of stellate and pancreatic cancer cells. The authors claim a therapeutic potential for pancreatic cancer by regulating ADAMTS2/14-mediated stellate cell activation, which should avoid cancer cell invasion. The approach is interesting and there is preliminary evidence, however the study has many gaps and requires substantive workload.

      We thank the reviewer for their support of our findings. We hope the additional data, combined with the known role for these substrates in the regulation of TGFβ, strengthens the clarity of our manuscript.

    1. Author Response

      Reviewer #1 (Public Review):

      • The statistical procedures used are not completely described and may not be appropriate.

      We revised the text in Methods and Results sections to give more details about the methods used.

      -As only two levels of delay were tested, it is not possible to directly test whether the subjective discounting function is hyperbolic or exponential and hence whether the delay is encoded subjectively or objectively.

      We agree with the reviewer. A higher number of task parameters may offer a better resolution to evaluate the discounting functions. Fortunately, this does not affect our main results.

      • The task has several variable interval lengths (hold in: 1.2-2.8 s, short delay: 1.8-2.3 s, long delay: 3.5-4s) that frustrate interpretation. The distribution of these delays is not described, for example as it reads it seems possible that some long delay rewards are delivered with shorter latency between cue and reward than some short delay rewards (1.2 + 3.5 = 4.7s vs. 2.8+2.3 = 5.1 s).

      We revised the text to address that ambiguity. In the new version of the manuscript, we describe short versus long delays considering the total delay intervals between instruction cue onset and reward delivery [short delay (3.5-5.6s) and long delay (5.2-7.3s)]. Within each delay category, individual delays were distributed in a gaussian fashion such that the two delay ranges overlapped for 9% of trials. These details are now described in the revised Methods section (pg. 22).

      -The authors have not considered that if the delay value is encoding, then the value, both objectively and subjectively, may be changing as the delay elapses. The variation of these task intervals may have an effect on the value of delay.

      In the present study, we report a dynamic integration between the desirability of the expected reward and the imposed delay to reward delivery across the waiting period. Our results (e.g. see Fig. 6) do not fit with simple linear (or logarithmic) effects corresponding to continuous regular changes as the delay elapses. We found different types of interactions (Discounting± and Compounding±) at different periods of the hold period and in different single units. We did not find a way to model all these types of interactions with this type of approach.

      Reviewer #2 (Public Review):

      • Plots of "rejection rate" (trials where the monkeys failed to wait until the rewards) as a function of delay and reward size seem to indicate that the monkeys understood the visual cue. The rejection rates were very low (less than 4% for almost all conditions) which indicates that the monkeys did not have a hard time inhibiting their behavior. It also meant that the authors could not compare trials where the monkeys successfully waited with trials where they failed to wait. This missing comparison weakens the link between the neurophysiological observations and the conclusions the authors made about the signals they observed.

      Here, our main goal was to describe the dynamic STN signals engaged during the waiting period without studying action-related activities. In the discussion (pg. 20), we clearly wrote ‘Further research is needed to determine whether the neural signals identified here causally drive animals’ behavior or rather just participate to reflect or evaluate the current situation.’ Consequently, our conclusions were already tempered by that point.

      In addition, we address the same limitation by writing (pg. 20): “An important avenue for future research will be to determine how STN signals, such as those described here, change when animals run out of patience and finally decide to stop waiting. To do this, however, smaller reward sizes and longer delays might be used to promote more escape behaviors during the delay interval.”

      • The authors examined the STN activity aligned to the start of the delay and also aligned to the reward. Most of the "delay encoding" in the STN activity was observed near the end of the waiting period. The trouble with the analysis is that a neuron that responded with exactly the same response on short and long trials could appear to be modulated by delay. This is easiest to see with a diagram, but it should be easy to imagine a neural response that quickly rose at the time of instruction and then decayed slowly over the course of 2 seconds. For long trials, the neuron's activity would have returned to baseline, but for short trials, the activity would still be above baseline. As such, it is not clear how much the STN neurons were truly modulated by delay.

      We agree with the reviewers. Our original analyses using two-time windows had the potential to introduce biases in the detection of neuronal activities modulated by the delay. To overcome this issue, we modified the time frame of all of our analyses (neuronal activity, eye position, EMG). Now, the revised version of the manuscript only reports activities across one-time window aligned to the time of instruction cue delivery (i.e., -1 to 3.5s relative to instruction cue onset). This time frame corresponds to the minimum possible interval between instruction cues and reward delivery. We have revised all of the figures and we re-calculated all of the statistics using that one analysis window. Despite these major modifications, our key findings were not changed substantially. We found the same pattern in STN activities, with a strong encoding of reward (48% of neurons) preceding a late encoding of delay (39% of neurons). We also updated the text in Methods and Results sections to reflect the revised analyses.

      • Another concern is the presence of eye movement variables in the regressions that determine whether a neuron is reward or delay encoding. If the task variables modulated eye movements (which would not be surprising) and if the STN activity also modulated eye movements, then, even if task variables did not directly modulate STN activity, the regression would indicate that it did. This is commonly known as "collider bias". This is, unfortunately, a common flaw in neuroscience papers.

      Because the presence of eye variables did not influence how neurons were selected by the GLM, we do not think it likely that our analysis was susceptible to “collider bias”. Nonetheless, to control for that possibility directly, we have now repeated the GLM analyses with eye movement variables excluded. Results are shown in a new figure (Fig.4 – supplementary 1). Exclusion of eye parameters produced results that are very similar to those from the GLM that included eye parameters (differences <3 degrees). We have added text to the manuscript describing this added control analysis.

    1. Hale, with a tasty love of intellectual pursuit: Here is the invisible world, caught, defined, and calculated. In these books the Devil stands stripped of all his brute disguises. Have no fear now – I mean to crush him utterly if he has shown his face! He starts for the bed. Rebecca: Will it hurt the child, sir? Hale: I cannot tell. If she is truly in the Devil’s grip we may have to rip and tear to get her free. Rebecca: I think I’ll go, then. I am too old for this. I pray to God for you, sir. She rises.

      Hale seems eager to flex his authority here and to whet his intellectual appetite and as he takes out a book about witchcraft and prepares to examine Betty further, Rebecca departs. She clearly dismisses all this fuss as foolishness.

      And like that, Proctor and Rebecca, two voices of reason, leave before the investigations begin. Those who cannot stop hysteria from growing often do not take it seriously until it is too late.

    1. We manage to find time for our students, for phone calls home, for report writing and programming all the while letting our engagement with learning slip down the list of things to do. Ensuring your personal learning is a priority is essential

      I like this emphasis on the perspective that teachers also need to always be learning as well. To be able to teach effectively, you have to be constantly learning. I think this perspective is especially important in modern times. In today's society, the world changes rapidly and frequently. For instance, there are always ongoing discussions about social issues and these discussions bring to light problems that not everyone may have been previously aware of. We are constantly learning new things about the language we use, people's identities, and implicit biases we may have. It is vital to stay up to date on these conversations and constantly learn, so that you can take such ideas into account when addressing your students.

    1. I think you have to have a high tolerance for ambiguity, in the initial stages of the project, because a lot of times when you’re working with clients, they may not know what they want, and they may have just a vague idea, and you kind of got to be willing and able to go with that and sort of explore the outcomes that you’re trying to achieve as you move forward

      We need to detangle, to get to what people really seem to want.

    1. The orb-web

      This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giad002), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

      Reviewer 1: Jonathan Coddington

      This paper presents the first uloborid spider genome--and it is a chromosome level assembly. Genomes of this family are important because the orb web is supposedly independently and convergently evolved in this group. Although my expertise is not in the technology and informatics of genome sequencing, it appears to be well done.

      Figure 1 A. geniculate -- spelling N. clavipes = T. clavipes Table S1 Number of Componenet Sequences-- typo Text single exon We found a -- typo can be ascribed by -- can be inferred by? an Araneid orb-weaver-- araneid usually not capitalized ♂X1X2/♀X1X1X2X2.[48] should be ♂X1X2/♀X1X1X2X2 [48]. You might want to be careful about citing Purcell & Pruitt, see https://purcelllab.ucr.edu/blog6.html and other questions about Pruitt's work.

      Re methods, it would be of interest to know what HMW DNA fragment sizes were (expressed as kb, or mb), although Tape Stations are not very accurate. For people who collect spiders with the intent to yield HMW DNA, such data are important. Data are scarce, so any facts are significant.

      Any homologs of the Pyriform spidroin (PySp) in Acanthoscurria? Piriform silk attachment points are a synapomorphy of araneomorph or "true" spiders. Liphistiomorph and mygalomorph spiders do not (cannot?) make point attachments, and the inability to make point attachments either to substrate or silk-silk point attachments probably constrains/ed the evolution of web architectures in non-araneomorph spiders. Therefore finding homologs to PySp spidroins in non-araneomorph spiders is of great interest to explain araneomorph web architecture diversity.

      Likewise, tubuliform spidroin (TuSp) is probably a synapomorphy of entelegyne spiders, with derived female genitalia--a "flow-though" sperm management system. Eggsacs occur widely in non-entelegyne spiders, so it is a mystery why entelegynes have specialized spigots, glands, and spidroins for the same purpose. Indeed, the particular function of tubuliform silk is not clear. Any thoughts on this? E.g.

      It is good to see attention paid to the mitochondrial genome, as many whole genome studies ignore it. In spiders, early work claimed that tRNA's appeared to be peculiar. Masta and Boore. 2004. The Complete Mitochondrial Genome Sequence of the Spider Habronattus oregonensis Reveals Rearranged and Extremely Truncated tRNAs. Molecular Biology and Evolution, Volume 21, Issue 5, May 2004, Pages 893-902. Any comments on U. diversus tRNAs from that point of view?

      Finally, any comments on evidence for or against the convergent evolution of the orb web? Homology between the pseudoflagelliform and flagelliform spidroins would be pertinent. The intro does raise expectations that some of the macro / larger evolutionary questions will be addressed in the paper, but many, see above, are only cursory or not too much. Perhaps include a sentence in intro acknowledging this, but saying that this paper intends to present the genome and address sex chromosomes, but other topics? For example the sections on some of the spidroins do not extensively discuss comparisons with other spider genomes.

      Reviewer 2: Hui Xiang

      In this study, the authors generated huge genome sequencing data and RNA-seq data and provided a genome assembly with rather complicated merging approach, of a spider with novel phylogenetic position. The genome undoubtedly added novel and important resources for deep understanding of spider evolution. However, there are still severe issues that need to be addressed. 1. There are huge sequencing data from different samples. However, I don't think that marge of different assemblies is good for a final qualified genome. Given high heterozygosity, that illumina data and ONT data from different individuals is quite difficult to use for assembling a clean genome. As shown in Table 2, assembly by Hify approach is not obviously inferior compared with the merged one, but obviously much better in avoiding redundancy. I strongly suggest that the author adopt the genome assembly of Hify data from one individual, instead of merging two sets of assemblies. Illumina and Nanopore assembly may be helpful in fully deciphering silk proteins. 2. Proportion of repeats are somewhat affected by the quality of assembly. The high heterozygous genome assembly is complicated merged by diverse batch of data, so the real quality might be not as good as the author described. The quality of repeat is especially hard to evaluate. Hence the statements on genome size (Line 193-200) are not convictive. 3. About the assembly of RNA-seq data. The authors get huge amounts of data. However, it is not so helpful to obtain novel transcripts if the data is saturated. More importantly, assembly of short reads is even not so useful to obtain long transcripts. 4. As to whole genome duplication. The authors did not provided solid evidence supporting that WGD occurred in U. diversus genome. They only demonstrated two hox clusters therein. The synteny analysis was quite confusing which is not helpful in confirmation of WGD. They need to provide more solid genome-wide evidence, or otherwise totally downplay the statements. 5. The identification of the sex chromosome is still vague. The statements are not well organized. The statements and the results are so vague and not convictive. "While 8 of the 10 pseudochromsomes had a median read depth of 40 ± 2, pseudochromosomes 3 and 10 were outliers, with read depths of 36 and 33, respectively." The difference in sequencing depth is rather convictive. As I know the authors sequenced female and male samples. So why they didn't clearly compare the depth of the two sex chromosomes between them and make more evidence? Other: 1. The information of chromosome-level spider genome are not Incomplete. As I know, there is a black widow genome with chromosome-level. The authors need to added this one. 2. The authors need to release the sequences of the spidroins the identified and described.

      Reviewer 3: Zhisheng Zhang, Ph.D

      The manuscript GIGA-D-22-00169 presents a chromosome-level genome of the cribellate orb-weaving spider Uloborus diversus. The assembly reinforces evidence of an ancient arachnid genome duplication and identifies complete open reading frames for every class of spidroin gene. And the authors identified the two X chromosomes for U. diversus and identify candidate sex-determining genes.

      The methods of work are well fited to the aims of the study, clearly described, and well written.

      Minor comments:

      1. In the Figure 1B, I noticed that it noted the estimated divergence times of the Araneae, I think there should be add the reference, or detail describe how to do.

      2. There is something wrong with the table format, such as Table1, 2, 5 and Table 6.

      3. Line 70: "chromosome- scale" changes to "chromosome-scale".

      4. Line 147 to lines 148: Line breaks error.

      5. Line 458: "[48]" in the wrong location.

      6. Line 511-512: In the genome of spider Uloborus diversus, which chromosome the genes of "sex lethal (sxl)" and "doublesex (dsx)" located at?

      7. Line 515-516: "The 534 shared sex-linked genes in these three species, 14 are predicted to be DNA/RNA-binding", if these sex-linked genes have difference on RNA level between male and female?

      8. Line 685: "Dovetail Chicago and Dovetail Hi-C Sequencing" should be bold.

      9. Line 764: "We then used the Trinity assembler43 v.2.12.0", the number of 43 may be redundancy.

      10. Some softwares lack the number of RRID, such as line 223 "BRAKER2", line 245 of "NOVOplasty", line 790 of "tRNAscan-SE", line 773 of "RepeatModeler", line 774 of "RepeatMasker", line 797 of "EMBOSS", and so on.

      11. Lines 780 "using the BRAKER 2 pipeline" changes to "using the BRAKER2 pipeline".

      12. Lines 950: "Literature Cited" changes to "Reference".

      13. Lines 952-953: wrong cite. The World Spider Catalog is a web online, the version and the data you accessed from should also added, and the author's name should change to World Spider Catalog.

    1. That which may perhaps makesuch equality incredible is but a vain conceit of one’s own wisdom, which almostall men think they have in a greater degree than the vulgar, that is, than all menbut themselves, and a few others whom by fame or for concurring withthemselves they approve.

      That's interesting, overall humans are extremelt selfish, and because of this we fail to view each other as equals, increasing the amount of social issues that we have to deal with. The last part of this about men only seeing others as equal if they approve of that other person sounds alot like the basis of the US's racial history.

    2. In such condition there is no place for industry, because thefruit thereof is uncertain, and consequently no culture of the earth, no navigationnor use of the commodities that may be imported by sea, no commodiousbuilding, no instruments of moving and removing such things as require muchforce, no knowledge of the face of the earth; no account of time, no arts, noletters, no society, and, which is worst of all, continual fear and danger of violentdeath, and the life of man solitary, poor, nasty, brutish, and short.

      I think right here Hobbes is telling us what he thinks life would be like with no government. We wouldn't live very long and in a state of constant fear.

    1. Author Response

      Reviewer #2 (Public Review):

      1) Mechanistic details of how FCA regulates FLC have been extensively studied, and both transcriptional and co-transcriptional regulations occur. I understand that FCA affects the 3'end processing of antisense COOLAIR RNAs, which regulate FLC. FCA also physically interacts with COOLAIR RNAs and other proteins, including chromatin-modifying complexes, which establish epigenetic repression of FLC regardless of vernalisation. In addition, FCA appears to function to resolve R-loop at the 3' end FLC, and FLC preferentially interacts with m6A-modified COOLAIR by forming liquid condensates. FCA is also alternatively spliced in an autoregulatory manner, and fca-1 mutant was reported to be a null allele as fca-1 cannot produce the functional form of FCA transcripts (r-form).

      However, I could not find any information on the fca-3 allele, which was reported to exhibit a weaker phenotype in terms of flowering time (Koornneef et al., 1991). In this manuscript, the authors showed that the level of FLC expression is lower than fca-1 and higher than Ler WT, but I could not find any other relevant information on the nature of the fca-3 allele. Given the known details on the function of FCA, the authors should explain how fca-3 shows an "intermediate" phenotype, which is highly relevant to the argument for an "analog" mode of regulation in fca-3. Therefore, the nature of the fca-3 mutant should be described in detail.

      We thank the reviewers for pointing out this omission. We have added much more information on the genotypes in the methods of the manuscript. We emphasise, however, that the rationale for selecting fca-3 as an intermediate mutant was empirical: namely, it generates an intermediate level of FLC expression (Fig. 1C and Fig. 1S1).

      2) The authors used a transgene (FLC-venus) in which an FLC fragment from ColFRI was used. Both fca-1 and fca-3 is Ler background where FLC sequence variations are known. I understand that the authors introgressed the transgenic in Ler background to avoid the transgene effect, but it is not known whether fca-1 or fca-3 mutations have the same impact on Col- FLC.

      We tested the expression of both endogenous (Ler) and FLC-Venus (Col-FLC) copies in these mutants by qPCR and found similar results (Fig. 1S1C,D), indicating that the fca-1 and fca-3 mutations have similar effects in both cases.

      3) Fig. 3A: I understand that Fig 3A is the qRT-PCR data using whole seedlings, and the gradual reduction of FLC from 7 DAG to 21 DAG was used to test the "analog" vs. "digital" mode of gene regulation in fca-1 and fca-3. I am not sure whether this is biologically relevant.

      Indeed, Ler is the only line that has transitioned to flowering during the experiment, with both fca lines being late flowering mutants. We totally agree that for Ler, later timepoints may be biologically irrelevant. It is used in this case as a negative control for the imaging, since FLC in Ler was already mostly OFF from the first timepoint and no biological conclusions are drawn from the later times. We have added a comment to this effect in the results section, also clarifying in the discussion that our focus is on the early regulation of FLC. Therefore, by looking at the young seedling in wildtype Ler, as we and others have previously, we are already looking too late to capture the switching of FLC to OFF. However, we expect that this combination of analog and digital regulation will be highly

      relevant to FLC regulation in wild-type plants in different accessions, partly leading to the differences in autumn FLC levels that were shown to be so important in the wild (Hepworth et al. 2020).

      3-a) The authors wrote that "This experiment revealed a decreasing trend in fca-3 and Ler (Fig. 3A)". But, I do also see a "decreasing trend" in fca-1 as well (although I understand that they may not be statistically significant). I also noticed that the level of FLC in fca-1 at 7 day has a greater variation. Is there any explanation?

      The level of FLC in fca-1 at 7 days is indeed more variable in these experiments. However, in a new second experiment, this is not the case (Fig. 3S2). In addition, a similar effect has not been observed in the ColFRI genotype (Fig. S9F of Antoniou-Kourounioti et al. 2018). Therefore, we believe this greater variation in one data set may simply be due to random fluctuations.

      For the decreasing trend in fca-1 in Fig. 3A, as the reviewer says, this is not significant. However, in the second experiment, we again see a decrease, which is now slow but significant. The decrease could be due to a subset of fca-1 ON cells switching off (in tissue that we have not imaged) and we comment on this slow decrease in the text.

      3-b) The decreasing trend observed in Ler (although the expression of FLC is already relatively low in Ler) may be the basis for the biological relevance. But Fig. 3D shows that the FLC-venus intensity in Ler root is not "decreasing". The authors interpreted that "root tip cells in Ler could switch off early, while ON cells still remain at the whole plant level that continue to switch off, thereby explaining the decrease in the qPCR experiment." Does this mean that the root tip system with FLC-venus cannot recapitulate other parts of plants (especially at the shoot tip where FLC function is more relevant)?

      The authors utilize the root system with transgenes in mutant backgrounds to observe and model the gene repression (transgene repression, to be exact). If the root tip cells behave differently from other parts of plants, how could the authors use data obtained from the root tip system?

      We now show that FLC-Venus in Ler, fca-3, fca-1 in young leaves have similar expression patterns to roots, thus validating the root system as an appropriate one to study the switching dynamics, see response to Essential comment 3. Nevertheless, in Fig. 3A, we show that FLC expression declines even in Ler. However, the levels here are low, so if it is indeed a subfraction of late-switching cells that are responsible, these cells cannot form a large proportion of the plant. We now make this clear in the text.

      4) I do see both fca-1 and fca-3 can express FCA at a comparable level (Fig. 3B); thus, I guess that the authors are measuring total FCA transcripts and that fca-3 may result in different levels of "functional form" of FCA. But this is not clearly discussed.

      We have now added yellow boxes in Fig. 2S3 to show additional examples of short files of ON cells in fca-3 and fca-4. To further improve the interpretation of this image (and all others in the manuscript) we have changed the presentation of the imaging using a different colourmap to enhance clarity.

      5) Quantification based on image intensity needs to be carefully controlled. Ideally, a threshold to call "ON" or "OFF" state should be based on the comparison to internal control and it is not clear to me how the authors determined which cells are ON or OFF based on image intensity (especially in fca-3).

      For the wild-type and fca-1 situations there is no switching in the model, and hence no dynamical changes in the FLC protein levels. As the FLC levels in the ON or OFF states are simply fit to the data using log-normal distributions, this would simply be a fitting exercise for fca-1 and Ler, and little would be learnt. Hence, we have not pursued this line of analysis.

      6) In many parts, I had to guess how the experiments were performed with what kind of tissues/samples. The methods section can benefit from a more thorough description.

      We have now gone through and added the missing information.

      Related to Public review #2. What is the phenotype (flowering time) of FLC-venus in fca-1 and fca-3? In addition, how many independent lines were used? Do they behave similarly?

      It was observed that with the additional FLC gene (in the form of the FLC-Venus), flowering is delayed as expected. However, this was not quantified in this work. Instead, we validated that the expression of the transgene was equivalent to endogeneous between genotypes, as shown in Fig. 1S1, supporting that this is an appropriate readout for FLC expression. One line for each genotype was selected and used in this work. In addition, we also now use fca-4, which has similar expression to fca-3, and where FLC-Venus also behaves similarly to the fca-3 case (Fig. 1S1, 2S3).

      Reviewer #3 (Public Review):

      1) The way the authors define ON and OFF cells sounds a bit arbitrary to me and, in my understanding, can affect a lot the outcomes and derived conclusions. The authors define ON cells to those cells having more than one transcript, or when they are above the value of 0.5 of the Venus intensity measure - what would it happen if the thresholds are slightly above these levels? And why such thresholds should be the same for the studied lines Ler, fca-3 and fca-1? By looking at the distributions of mRNAs and Venus intensities in Ler and fca-3 plants, one could argue that all cells are in an OFF, 'silent' state, and that what is measured is some 'leakage', noise or simply cell heterogeneity in the expression levels. If there is a digital regulation, I would expect to see this bimodality more clearly at some point, as it was captured in Berry et al (2015) - perhaps cells in fca-1 show at a certain level of bimodality? When seeing bimodality, one could separate ON and OFF states by unmixing gaussians, or something in these lines that makes the definition less arbitrary and more robust.

      As explained in Essential comment 5, we have removed arbitrary thresholding from the manuscript and only used absolute thresholds from smFISH (now changed to >3, and shown that our results are robust to varying these thresholds, Fig. 2S2). If all cells are in the OFF state and fca-3 just has higher noise/heterogeneity, then this does not explain the reduction in expression over time. Nor can such heterogeneity explain the short files of ON cells and longer files of OFF cells in Fig. 2S3: the cells should just be a random mix of varying FLC levels. Our results are much more compatible with switching into a heritable silenced state. Finally, with bimodality, this is difficult to see as clearly as before due to the wide levels of expression in fca-3, but we believe it is present: a well-defined OFF state together with a broad ON state. This broadness makes extracting the ON cells quite difficult as a completely rigorous unmixing of the two states is just not possible.

      2) The authors use means in all their plots for histograms and data, and perform tests that rely on these means. However, many of these plots are skewed right distributions, meaning that mean is not a good measure of center. I think using median would be more appropriate, and statistical tests should be rather done on medians instead of means. If tests using medians were performed, I believe that some of the pointed results will be less significant, and this will affect the conclusions of this work.

      Highly expressing FLC lines and mutants, such as ColFRI and fca-9, often used for vernalization studies, are late flowering, but do eventually flower even with no decrease in FLC levels (and so no switching). This is not an artifact of using roots versus shoots, and presumably arises from there being multiple inputs into the flowering decision which can allow the FLC-mediated flowering inhibition to eventually be overcome.

      3) Some data might require more repeats, together with its quantification. For instance, the expression levels for fca-1 in Fig 2E and Fig 3D at 7 days after sowing look qualitatively different to me - not just the mean looks different, but also the distribution; fca-1 in Fig 3D looks more monomodal, while in Fig 2E it looks it shows more a bimodal distribution. Having these two different behaviours in these two repeats indicates that, more ideally, three repeats might be needed, together with their quantification. Fig. 2C would also need some repeats. In Fig 1S1 C and D, it would be good to clarify in which cases there are 2 or more repeats -3 repeats might be needed for those cases in Fig 1S1 C-D that have large error bars.

      The data in Figs. 2C and 2E are both based on two independent experiments, with the results combined. The data in Fig. 3D is almost entirely based on three independent experiments. We have now stated this in the legend. The Venus imaging was performed on separate microscopes for Fig. 2 and Fig. 3 and this possibly accounts some of the observed differences. However, we do not think that the data in Fig 2E for fca-1 supports a bimodal distribution: the slight peak at higher levels is, we believe, much more likely to be a statistical fluctuation. For Fig. 1S1 C and D, we now clarify in the legend that n=2 biological replicates for fca-3 and n=3 for others.

      Also, when doing the time courses, I find it would be very beneficial to capture an earlier time point for all the lines, to see whether it is easier to capture the digital nature of the regulation. Note that the authors have already pointed that 7 days after sowing might be too late for Ler line to capture the switch.

      We agree that capturing earlier time points for Ler in particular is interesting and important. However, we have found that this requires specialist imaging in the embryo and we feel that this is really beyond the scope of this manuscript and will instead form the basis of a future publication.

    1. Author Response

      Reviewer #1 (Public Review):

      The authors use what is potentially a novel method for bootstrapping sequence data to evaluate the extent to which SARS-CoV-2 transmissions occurred between regions of the world, between France and other European countries, and between some distinct regions within France. Data from the first two waves of SARS-CoV-2 in Europe were considered, from 2020 into January 2021. The paper provides more detail about the specific spread of the virus around Europe, specifically within France, than other work in this area of which I am aware.

      First of all, we would like to thank reviewer #1 for their evaluation and their various comments which, in our opinion, have allowed us to considerably improve the manuscript.

      An interesting facet of the methodology used is the downsampling of sequence data, generating multiple bootstraps each of around 500-1000 sequences and conducting analysis on each one. This has the strength of sampling, in total, a large number of sequences, while reducing the overall computational cost of analysis on a database that contains in total several hundred thousand sequences. A question I had about the results concerns the extent of downsampling versus the rate of viral migration: If between-country movements are rapid, a reduced sample could be misleading, for example characterising a transmission path from A to B to C as being from A to C by virtue of missing data. I acknowledge that this would be a problem with any phylogeographic analysis relying on limited data. However, in this case, how does the rate of migration between locations compare to the length of time between samples in the reduced trees? Along these lines, I was unclear to what extent the reported proportions of intra- versus inter-regional transmissions (e.g. line 223) would be vulnerable to sampling effects.

      This question is indeed a very important one. Between-country movement rate can be high but the contagious period for a SARS-CoV-2-infected individual is short (a bit less than two weeks in average). In our subsamples, the dated trees have a median branch length around 20 days. To ensure that our subsamples did not introduce errors in estimating the exchange events between locations, we conducted a simulation. Briefly, we generated a tree of 1,000,000 tips with a five-states discrete trait. We then took 100 subsampled 1000-leaves trees, reconstructed the ancestry for the discrete trait and assess transitions between states. The error rate is less than 3% on average: it comprises the missing data, as you pointed out, and the errors in reconstructing the ancestry for the trait deeper in the tree.

      We think that overall, less than 3% is a satisfying error rate.

      The results of this specific simulation were added to the paper (lines 150-157) and as Figure 2—figure supplement 1.

      A further question around the methodology was the use of an artificially high fixed clock rate in the phylogenetic analysis so as to date the tree in an unbiased way. Although I understood that the stated action led to the required results, given the time available for review I was unable to figure out why this should be so. Is this an artefact of under-sampling, or of approximations made in the phylogenetic inference? Is this a well-known phenomenon in phylogenetic inference?

      We thank reviewer #1, who was, as reviewer #2 and the editor, disturbed by the use of an artificially fast and fixed molecular clock. It was an artifact to correct a mistake in our code that has been fixed. See the answer to point (3) of the editor.

      The value of this kind of research is highlighted in the paper, in that genomic data can be used to assess and guide public health measures (line 64). This work elucidates several facts about the geographical spread of SARS-CoV-2 within France and between European countries. The more clearly these facts can be translated into improved or more considered public health action, through the evaluation of previous policy actions, or through the explication of how future actions could lead to improved outcomes, the more this work will have a profound and ongoing impact.

      This is a very interesting point to emphasize indeed. We are currently discussing with public health specialists in our institution on how to assess past public health actions using phylodynamics data in a statistically valid manner.

      Reviewer #2 (Public Review):

      This study represents an important contribution to our understanding of SARS-CoV-2 transmission dynamics in France, Europe and globally during the early pandemic in 2020 and the authors should be congratulated for tackling this important question. Through evaluation of the contributions of intra- and inter-regional transmission at global, continental, and domestic levels, the authors provided compelling, although as of yet correlative and incomplete, evidence towards how international travel restrictions reduced inter-regional transmission while permitting increased transmission intra-regionally. Unfortunately, however this work suffers from a number of serious analytical shortcomings, all of which can be overcome in a major revision and re-analysis.

      We would like to thank the reviewer #2 for their evaluation and their various comments. We want to point that reviewer #2 was contacted for advice on strategy for the molecular clock since she performed a study on a similar topic describing SARS-CoV-2 epidemics in Canada during 2020. We strongly believe that all reviewer #2 comments drastically contributed to improve the quality of this work.

      With this genomic epidemiology analysis, the authors disentangled the relative contributions of different geographic levels to transmission events in France and in Europe in the first two COVID-19 waves of 2020. By partitioning the analysis into three complementary, but distinct, geographic levels, the migration flows in and out of continents, countries in Europe, and regions in France were inferred using maximum likelihood ancestral state reconstruction. The major strengths of this paper were the inclusion of multiple geographic levels, the comparison of different rate symmetries in the ancestral character estimation, and the comprehensive qualitative descriptions of comparisons over time and geographies. However, there were also major weaknesses that need to be addressed and are described in more detail below. They include summing across replicates that were drawn with replacement and were not independent; inadequate justification for excluding underrepresented geographies; the assertion that positive correlation between intra-regional transmission and deaths validates the accuracy of the analysis; considering the framework the authors have chosen for this analysis the analysis would accommodate and benefit strongly from increasing the size of the sequence sets selected for analysis in each replicate; and the sparsity of quantitative (over qualitative or exploratory) comparisons and statistics in the reporting of results. In particular, it would greatly strengthen the paper if the authors could better evaluate the effect of travel restrictions on importations and exportations by testing hypotheses, quantifying changes in the presence of restrictions, or estimating inflection points in importation rates.

      We are grateful for this comprehensive listing of the strengths and weaknesses of our study. Regarding the limitations of this study, these will be detailed specifically for each dedicated remark of the reviewer. We would like to emphasize that all the remarks and limitations reported here by reviewer #2 are in our opinion fully justified. We hence have tried to bring additional analyses (study of the Pango lineages, averaging of the subsamples, simulation study to justify the size of the sampling), a modification of the methodology (in particular concerning the molecular clock) and a thorough rewriting of the “Results” section.

      General comments on the Background: Need to elaborate on how this study fits into the big picture in the first paragraph. Should discuss how phylodynamics contributes to understanding of viral outbreaks, SARS-CoV-2 epidemiology and viral evolution.

      We have added in the “Introduction” section some elements to better understand why phylodynamics is an important field in the epidemiology of SARS-CoV-2 and its evolution.

      The authors should consider a hypothesis driven framework for their analyses, for example considering the geographically central position of France what hypotheses stem from this considering sources of viral importations and destinations of exportations from/to Europe vs other international? Or other a priori expectations.

      We agree with reviewer #2 about this remark. Indeed, given the central position of France, we can hypothesize that it has strongly participated in the dissemination of the virus within Europe. This hypothesis has been included in the "Introduction" section of the revised version (lines 102-105).

      To address the computational limits of phylogenetic reconstruction, 100 replicates of fewer than 1000 sequences each were sampled for each epidemic wave at each level. The inter- and intra-regional transmissions were averaged and then summed across replicates in order to compare the relative roles played by each geography towards transmission. While we see the logic in using the sum across replicates, this is highly likely to bias results, especially since in the methods, this is described as sampling with replacement between replicates (LX). The validity of summing replicates needs to be discussed and are likely most appropriately presented as mean or median. Also, these samples are quite small considering the computational capacity of the maximum likelihood tools being used. We recommend repeating the analysis with a substantially larger number of sequences per sample.

      We thank reviewer #2 for this relevant remark. We initially summed the subsamples, a strategy that may possibly bias the results. In the new version of the manuscript, we averaged the subsamples by region and by week as recommended (and stated in the methods, line 536-537).

      About the size of our subsamples, it made no difference to use 1,000, 2,000 or 5,000 genomes in each subsample. To get a more definitive and scientifically sound answer, we performed a simulation assay that has been included in the manuscript and is shown is what is now figure 2 (and figure 2—figure supplement 1). These simulations show that our subsampling strategy allows for an accurate estimate of transition rates for a discrete parameter (lines 107-160).

    1. Author Response

      Reviewer #1 (Public Review):

      The paper addresses an interesting question - how genetic changes in Y. pestis have led to phenotypic divergence from Y. pseudotuberculosis - and provides strong evidence that the frameshift mutation in rcsD is involved. Overall, I found the data to be clearly presented, and most of the conclusions well supported by the data. The authors convincingly show that (i) the frameshift mutation in rcsD alters the regulation of biofilm formation, (ii) this effect depends upon expression of a small protein that corresponds to the C-terminal portion of RcsD, and (iii) the frameshift mutation in rcsD prevents loss of the pgm locus. I felt that the discussion/conclusions about what phosphorylates/dephosphorylates RcsB and how this impacts biofilm formation are overstated, as there are no experiments that directly address this question. I also felt that the authors' model for what phosphorylates/dephosphorylates RcsB in Y. pestis should be more clearly articulated, even if it is only presented as speculation. Lastly, the authors propose that full-length RcsD is made in Y. pestis and contributes to phosphorylation of RcsB, but the evidence for this is weak (faint band in Figure 2d). It may be that the N-terminal domain of RcsD is functional. I recommend either softening this conclusion or testing this hypothesis further, e.g., by introducing an in-frame stop codon early in rcsD after the frame-shift.

      Thanks for your comments. We have provided a model and revised the discussion about phosphorylation/dephosphorylation of RcsB and how this impacts biofilm formation (Figure 8 and Supplementary Figure 4). In addition, we have introduced an in-frame stop codon in rcsD before the frameshift and showed that full-length RcsD is only made in wildtype Y. pestis but not in the rcsDpe-stop mutant (Supplementary Figure 1g).

      Reviewer #2 (Public Review):

      Guo et al. have investigated the consequences of a frameshift mutation in the rcsD gene in the Yersinia pseudotuberculosis progenitor that is conserved in modern Y. pestis strains. Interestingly, they identify a start codon with a ribosome binding site that enables production of an Hpt-domain protein from the C-terminus in Y. pestis. Targeted deletion of this Hpt-domain increased biofilm production in Y. pestis. They find that the ancestral RcsDpstb (full length) is a positive regulator of biofilm in Y. pestis while the Hpt-domain version (RcsDYP) represses biofilm in vitro. When fleas were infected with Y. pestis expressing the ancestral RcsDPSTB protein, there was no difference in bacterial survival or rate of proventricular blockage. This strain also killed mice the same rate (in a different Y. pestis strain background). However, replacing RcsDYP with RcsYPTB dramatically increases the frequency of pgm locus deletion (containing Hms ECM and yersiniabactin genes) during flea infection. The authors predict that this would reduce the invasiveness of the bacteria in mammals and/or flea blockage in subsequent flea-rodent-flea transmission cycles. They also measured global gene expression differences between RcsDPSTB compared to the wild-type strain. They argue that the frameshift of RcsD maintaining the Hpt-domain (RcsDYP) was needed to regulate biofilm while limiting loss of the pgm locus.

      Loss of the pgm locus was not tested in the Y. pestis rcsD mutant strain (lacking the entire gene or just the C-terminal Hpt domain). Therefore, the claim that maintaining the Hpt-domain protein was important lacks convincing evidence. Additionally, it is possible that the population of rcsDpe::rcsDpstb after in vitro growth for 6 days would still be proficient at infecting and blocking fleas, even though many of the bacteria would have lost the pgm locus. Production of Hms polysaccharide by pgm+ could trans-complement those that are pgm-. The nature of the pgm locus loss is assumed to be due to recombination between IS elements. This is certainly the likeliest explanation but not the only one. The authors checked for pgm loss by phenotype (CR binding) and by two sets of primers, one targeting the hmsS gene and another set that is unspecified. Loss of the entire pgm (especially yersiniabactin genes) should be clarified.

      Thanks for your comments. We have now provided the data to show that deletion of RcsD-Hpt resulted in increased loss of the pgm locus (Figure 5d) to strengthen the claim that maintenance of the Hpt-domain is significant for retention of the pgm locus. We also agree that 6-day old cultures of a mixture of pgm+ and pgm- rcsDpe::rcsDpstb will still be capable of infecting and blocking fleas. However, these strains will be less efficient at causing disease in the vertebrate host in the absence of the pgm locus. We agree that recombination between IS elements might not be the only cause of loss of the pgm locus. To verify the loss of the pgm locus, we have used two sets of primers. One set targets the hmsS gene and another set targets the upstream and downstream sequences of the pgm locus (Supplementary Table 3). We have clarified this in the revised manuscript (Line 610-613).

      Reviewer #3 (Public Review):

      The Rcs phosphorelay plays an important role in regulating gene expression in bacteria; most of the current knowledge about the Rcs proteins is from E. coli. Yersinia pestis, carrying mutations in two central components of the Rcs machinery, provides an interesting example of how evolution has shaped this system to fit the life cycle of this bacteria. In bacteria other than Y. pestis, most Rcs activating signals are sensed via the outer membrane lipoprotein RcsF; from there, signalling depends on inner membrane protein IgaA, a negative regulator of RcsD. Histidine kinase RcsC is the source of the phosphorylation cascade that goes from the histidine kinase domain of RcsC to the response regulator domain of RcsC, from there to the histidine phosphotransfer (Hpt) domain of RcsD, and finally to the response regulator RcsB. RcsB, alone or with other proteins, regulates transcription of many genes, both positively and negatively. These authors have previously shown that RcsA, a co-regulator that acts with RcsB at some promoters, is functional in Y. pseudotuberculosis but mutant in Y. pestis, and that this leads to increased biofilm in the flea. The authors also noted that rcsD in Y. pestis contains a frameshift after codon 642 in this 897 aa protein; in theory that should eliminate the Hpt domain from the expressed protein. However, they found evidence that the frame-shifted gene had a role in regulation. This paper investigates this in more depth, providing clear evidence for expression of the Hpt domain (without the N-terminal domain), and demonstrating a critical role for this domain in repressing biofilm formation. The Y. pseudotuberculosis RcsD does not express a detectable amount of the Hpt domain nor does it repress biofilm formation. The ability of the Hpt domain protein to keep biofilm formation low explains most of what is observed for the full-length frame-shifted protein.

      1) The authors provide a substantial amount of data supporting the expression of the C-terminus of RcsD is sufficient and necessary for low biofilm levels, and that this is dependent upon the active site His in the RcsD Hpt domain (H844A) as well as other components of the basic phosphorelay (RcsC and RcsB). However, it is only possible to see this protein by Western blot in 100-fold "Enriched" lysates (Figure 2). No small protein was detected in the RcsDpstb strain, although the enriched lysate was not shown for this. Without that experiment, it is not possible to evaluate whether the small protein is also made from the rcsDpstb gene. Either answer would be interesting, and would allow other conclusions to be drawn. Is the RBS and start codon the same for the HPT region of this rcsD gene (it could be added to Supplementary Table 6). If the small protein is made, is its ability to function blocked by the excess full length protein in terms of interactions with RcsC? Or is the expression of the small protein dependent upon loss of overlapping translation from the upstream start?

      The small Hpt protein may be produced from expression of the epitope tagged rcsDpstb gene as it can be detected in an enriched isolation of this sample (Supplementary Figure 1f). Because only a small amount of the RcsD-Hpt is produced from the rcsDpstb substitution, it might only function at low levels in the presence of large amounts of RcsDpstb. The RBS and start codon are the same for the RcsD-Hpt in Y. pestis and Y. pseudotuberculosis, we have added them in the Supplementary Table 6. In addition, we have provided a model to show the function and regulation of RcsD and Hpt (Supplementary Figure 4).

      2) In many phosphorelays, the protein kinase also acts as a phosphatase, and which direction P flows is critical for regulation. It is often difficult to follow what the model for this is in this paper, and that is important to understand for evaluating the results. Most of this paper uses two assays, biofilm formation and crystal violet staining (also related to biofilm formation) to assess the functioning of the Rcs phosphorelay. Based on the behavior of the rcsB mutant, it would seem that functional Yersinia pestis Rcs (RcsDpe) represses this behavior, and this correlates with RcsB phosphorylation (Figure4). What is the basis (Line 443-44) for saying that RcsD phosphorylates RcsB while RcsDHpt dephosphorylates? Yersinia pseudotuberculosis RcsD(pstb) shows no difference with the rcsB mutant. Doesn't that suggest that RcsDpstb is no longer repressing (phosphorylating)? In the presence of the RcsDpstb as well as multicopy RcsF, an activating signal in other organisms, RcsDpstb seems able to phosphorylate. This all suggests that the full-length protein, like the Hpt domain, is capable of phosphorylating, but that it may be doing nothing in the absence of signal (or dephosphorylating). Given these results, saying that RcsDpstb is positively regulating biofilm formation (Fig.1 title, and elsewhere) is somewhat misleading. What it presumably does is prevent the Hpt domain, expressed from the chromosomal locus in Figure1b, from signalling to RcsB. By itself, it is not clear it is doing anything. Understanding this clearly is important for interpreting this system and the tested mutants. A clear model and how phosphate is flowing in the various situations would help a lot. Currently Supplementary Figure3 seems to reflect the appropriate directional arrows, but the text does not. Moving the rcsB data earlier in the paper (after Figure1, 2, or maybe earlier, before Figure3) would certainly help.

      RcsD dephosphorylates RcsB while RcsD-Hpt phosphorylates RcsB. Expression of RcsDpstb in the wild type strain and the N-term deletion mutant resulted in increased biofilm, indicating RcsB is less phosphorylated (Figure 1b and 1c). While over-expression of RcsD-Hpt resulted in decreased biofilm formation, indicating RcsB is more phosphorylated. In addition, the Phos-tag experiments showed that the RcsDpstb strain has a lower level of phosphorylated RcsB (Figure 4b). Expression of RcsDpstb in the wild type strain showed similar results as a rcsB mutant indicating a lower level of phosphorylated RcsB in the presence of RcsDpstb.

      It is possible that the RcsDpstb interferes with the ability for RcsD-Hpt to phosphorylate RcsB. However, plasmid expression of the rcsDpstb-H844A mutant in the Y. pestis rcsDN-term deletion mutant formed significantly less biofilm than wild type rcsDpstb indicating H844 might be important for RcsD to dephosphorylate RcsB (Supplementary Figure 2b and Line 180-183). In addition, it is known that RcsD plays a dual role in phosphorylation and dephosphorylation of RcsB in other organisms (Majdalani N, et al., 2005, J. Bacteriol. https://doi.org/10.1128/JB.187.19.6770-6778.2005; Wall EA, et al., 2020, Plos Genetics, https://doi.org/10.1371/journal.pgen.1008610; Takeda S., et al., 2001, Mol. Microbiol., https://doi: 10.1046/j.1365-2958.2001.02393.x). We therefore think it is safe to say that the full length RcsD might function to dephosphorylate RcsB. We have modified the model in the revised manuscript (Supplementary Figure 4 and Figure 8). Regulation of RcsB has been investigated previously. The main finding of our manuscript is regulation of RcsB by the mutated RcsD (RcsD-Hpt). Thus, we have moved the known rcsB deletion mutant data to Figure 1 in the revised manuscript as suggested. We kept the rest of data in Figure 4 the same. We think it might be better to first show the mutation of rcsD alters Rcs signaling and then show how this occurs (by affecting RcsB phosphorylation).

      3) The authors show (in their pull-down) that there is a bit of full-length RcsD even in the frame-shifted protein. Is there any clear evidence this does anything here? Does the N-terminus (truncated after the frame-shift) have a function?

      We have introduced a stop codon in rcsDpe and showed that full-length RcsD is made by rcsDpe but not by rcsDpe with the stop codon (Supplementary Figure 1g). RcsDN-term seems do not have a function in our tested condition (Figure 1e).

      4) While the RNA seq data is useful addition here, it is difficult to interpret without a bit more data on the strain used for the RNA seq, including the biofilm phenotypes of the WT and mutant derivatives, as well as the relevant rcsD sequences, and maybe expression of a few genes or proteins (Hms or hmsT). Are these similar in the parallel strains used earlier in the paper and the one for RNA seq, in WT, rcsB- and the RcsDpstb derivative? It would appear that rcsB- and rcsDpstb have opposite effects, at least at 25{degree sign}C, while in Figure4, these two derivatives have similar effects on biofilm. Is this due to temperature, strains, or biofilm genes that are not shown here? It is certainly possible that the ability of the full-length RcsD changes its kinase/phosphatase balance as a function of temperature, or dependent on other differences in these Y. pestis strains.

      The strain used for RNA seq is a derivative of the biovar Microtus strain 201 which has a similar in vitro phenotype as the strain KIM6+ (Line 297-298). We used this strain for RNA seq because it has the virulence plasmid pCD1 and we wanted to analyze the gene expression of this plasmid, which is required for virulence, as well. RNAseq data showed that rcsB- and rcsDpstb have opposite effects on mRNA level of some genes. However, no significant change in expression of biofilm genes was noted in the RNAseq data set. In fact, our previous data has shown that the biofilm related (hmsT and hmsD) genes are only moderately (Less than 2-fold change between wild type and rcsB mutant) regulated by RcsB based on RT-PCR and β-gal analysis (Sun YC, et al., 2012, J. Bacteriol. https:// doi: 10.1128/JB.06243-11and Guo XP, et al., 2015, Sci. Rep. https://doi: 10.1038/srep08412 and Figure 4c).

    1. ogical record. As in the earlier analmedical science, once we know something of the disease and its causes, we may codify thtoms to permit accurate diagnosis. Similarly, in the archaeological world when we undsomething of the relationship between the character of cultural systems and the chartheir by-products, we may codify these derivatives to permit the accurate diagnosis fchaeological traces of the kind of cultural system that stood behin

      Binford's analogy here, to me at least, feels like an oversimplification of both disciplines. Which I think may be valid if he did not write in such a meandering and complex way. I don't know, maybe that's just me

    1. is:

      17

      Shrinkage factor questions:

      (Preethi) Can you explain what a shrinkage factor is?

      **Response-Yes, let’s talk about this in class with the dice roll example. Given what we know (or think we know) about the variance of the individual error term and the level-2 error term, it is a way to provide a Bayesian estimate of the level 2 effect. This actually matches the intuition of what we do naturally in these situations (that’s the goal of the example).

      (Meredith) Is there an ideal value for Rhat (the “shrinkage factor”) that we’d want to reach? I know the closer to 1 the better, but that seems hard to reach. Is Rhat of like 0.7 good?

      Response--if you have a decent sample size within level 2 units, then the reliability will be pretty high. Having a “low” R isn’t bad…it just means that you are hedging your bet a bit about what the effect for that level 2 unit is. I.e., the empirical bayes estimates are the way we make use of level 2 estimates despite the fact that some may have small sample sizes.

      (Anna) Just to clarify if you have a shrinkage (R) of 1 then your estimated between effect would be what you get for Empirical Bayes? And if so, does that mean that the school itself was just in line with your estimates or that your data as a whole is ‘reliable’?

      Response--As noted above, reliable/unreliable in the context of estimating the shrinkage factor doesn’t mean that there is anything wrong with your data (i.e., it isn’t corrupted or damaged), it just refers to the sample size within a level 2 unit (i.e., a school) and how much confidence we place on the estimate taken from that sample. Smaller samples means less confidence, and we hedge a little towards the overall population mean.

  3. tandfbis.s3.amazonaws.com tandfbis.s3.amazonaws.com
    1. Over 2.5 million people have purchased the Power Balance Wristband,which claims to improve energy, flexibility, and balance (DiSalvo, 2011).More specifically, the Power Balance (2010) company claims that “optimalhealth and peak performance occur when your body maintains ionic bal-ance (the exchange between negative and positive charges) and free flow-ing energy pathways (harmony) at the optimum frequency” (EnergyBalance & Systemic Harmony Are the Keys, para. 1). These tiny siliconwristbands retail for $29.95 or more, and in 2010, the company sold over2.5 million bracelets. Several famous athletes such as Shaquille O’Neal andDavid Beckham endorsed the product and CNBC even declared the wrist-band Sports Product of the Year in 2010 (DiSalvo, 2011). In 2010, afterdemands from the Australian government to produce evidence in supportof their amazing claims, Power Balance LLC admitted that there was “nocredible scientific evidence” to support their claims, and they offered a fullrefund to customers (Power Balance, 2010). This is just one example se-lected from a countless number in which millions of people spent billionsof dollars on a worthless product.“Just go to www.criticalthinking.com” by Kirk Anderson. Used with permission.

      I understand what you want to talk about here, but I didn't find the bracelet sample she gave very appropriate. This is because we do not know for what purpose the people who bought the bracelet purchased it. Of course, there may be those who think it is useful, but he may have taken it just because he liked the shape. He may have also bought this bracelet because he saw it on the famous athlete he loves.

    2. A racist-hate website may look like a reliable newssource; bogus health information is sold as though it really was “doctor-recommended,” and information about international conflicts can provideone-sided accounts that appear to be fair and unbiased.

      While doing resource research on the internet, we should pay attention to certain issues. For example, the first thing that matters is whether the source is reliable or not. Since the Internet is an unlimited area with access to everyone, its reliability is just as difficult and troublesome. We should approach everything we read or see with skepticism and think critically so that undesirable consequences do not occur.

    1. The

      This work has been published in GigaScience Journal under a CC-BY 4.0 license (https://doi.org/10.1093/gigascience/giac076) and has published the reviews under the same license.

      Reviewer 1 Satoshi Hiraoka

      In this manuscript, the authors developed a new tool, DeePVP, for predicting Phage Virion Proteins (PVPs) using the Deep learning approach. The purpose of this study is meaningful. As the authors described in the Introduction section, currently it is difficult to annotate functions of viral genes precisely because of its huge sequence diversity and existence of many unknown functions, and there are still many rooms to improve the performance of in silico annotation of phage genes including PVPs. Although I'm not an expert in machine learning, the newly proposed method based on Deep learning seems to be appropriate. The proposed tool showed clear outperformance compared with the other previously proposed tools, and thus, the tool might be valuable for further deep analysis of many viral genomes. Indeed, the authors conducted two case studies using real phage genomes and reported novel findings that may have insight into the genomics of the phages. Overall, the manuscript is well written, and I feel the tool has a good potential to contribute to the wide fields of viral genomics. Unfortunately, I have concerns including the source cord openness. Also, I have some suggestions that would increase the clarity and impact of this manuscript if addressed.

      Major: I did not find DeePVP source cord on the GitHub page. Is the tool not open source? I strongly recommend the author disclose all scripts of the tool for further validation and secondary usage by other scientists. Or, at least, clearly state why the source cords need to hold private. Also, I was much confused about the GitHub page because the uploaded files are not well structured. Scripts and data used for performance evaluation were included in 'data.zip' file, which should be renamed to be an appropriate one. 'Source code' button in the Releases page strangely links to the 'Supporting_data.zip' files which only contained installing manual but not source cord file. The authors should prepare the GitHub page appropriately that, for example, upload all source cords to the 'main' branch rather than include them in zip file, and 'source code' file in Releases should contain actual source code files rather than manual PDF. According to the Material and method section, 1) using the Deep learning approach, and 2) using th large dataset retrieved from PhANNs as teacher dataset, are two of the important improvement from the other studies in the PVP identification task. Someone may suspect the better performance of DeePVP was mostly contributed by the increased teaching dataset rather than the used classification method. Is there a possibility that the previously proposed tools (especially the tools except for PhANNs) with re-training using the large PhANNs dataset could reach better performances than DeePVP? The naming of 'Reliability index' (L249) is inaccurate. The score did not support the prediction 'reliability' (i.e., whether the predicted genes are truly PVP or not) but just reflects the fact that the gene is predicted as PVP by many tools without considering whether it is correct or incorrect. The sentence 'A higher n indicates that this protein is predicted as PVP by more tools at the same time, and therefore, the prediction may be more reliable.' in L252 is not logical. I dose not fully agree with the discussion that the tool will facilitate viral host prediction as mentioned in L294-302. It is very natural that if the phages are phylogenetically close and possess similar genomic structures including PVP-enriched regions, those will infect the same microbial lineage as a host. However, this is not evaluated systematically in wide phage lineages. In general, almost all phage-host relations are unknown in nature except few numbers of specific viruses such as E. Coli phages. Further detailed studies should be needed on whether and how degree the conservation of PVP-enriched region could be a potentially good feature to predict phage-host relationship. I think the phage-host prediction is beyond the scope of this tool, and thus the analysis could be deleted in this manuscript or just briefly mention in the Discussion section as a future perspective.

      Minor: The URL of the GitHub page is better to describe in the last of the Abstract or inside of the main text in addition to the 'Availability of supporting source code and requirements' section. This will make it easy for many readers to access the homepage and use the tool. Fig 2 and 3. I think it is better to change the labels of the x-axis like 0 kb, 20 kb, 40 kb, ..., and 180 kb. This will make it easy for understanding that the horizontal bar represented the viral genome.

      Re-review:

      I read the revised manuscript and acknowledge that the authors made efforts to take reviewers' comments into account. My previous points have been addressed and I feel the manuscript was improved. I think the word 'incomplete proteins' in L391-396 would be rephrased like 'partial genes' because here we should consider protein-encoding genes (or protein sequences), not proteins themselves, and the word 'incomplete' is a bit ambiguous.

    1. As harmful as discrimination, conscious or unconscious, may be on shaping group outcomes

      In Egypt we unconsciously judge homosexual and I think within especially with males they can tend to get physically aggressive, not only verbally, when people have a different point of view from theirs

    1. Abstract

      This work has been published in GigaScience Journal under a CC-BY 4.0 license (https://doi.org/10.1093/gigascience/giac073 and has published the reviews under the same license.

      Reviewer 1. Siyuan Ma

      Reviewer Comments to Author: In Kang, Chong, and Ning, the authors present Meta-Prism 2, a microbial community analysis framework, which calculates sample-sample dissimilarities and queries microbial profiles similar to those of user-provided targets. Meta-Prism 2 adopts efficient algorithms to achieve the time and memory efficiency required for modern microbiome "big data" application scenarios. The authors evaluated Meta-Prism 2's performance, both in terms of separating different biomes' microbial profiles and time/memory usage, on a variety of real-world studies. I find the application target of Meta-Prism appealing: achieving efficient dissimilarity profiling is increasingly relevant for modern microbiome applications. However, I'm afraid the manuscript appears to be in poor state, with insufficient details for crucial methods and results components. Some display items are either missing or mis-referenced. As such, I cannot recommend for its acceptance, unless major improvements are made. My comments are detailed below.

      Major 1. The authors claim that from its previous iteration, the biggest improvements are: (1) removal of redundant nodes in 1-against-N sample comparisons. (2) functionality for similarity matrix calculation (3) exhaustive search among all available samples.

      a. (1) seems the most crucial for the method's improved efficiency. However, the details on why these nodes can be eliminated, and how dissimilarity calculation is achieved post-elimination are not sufficient. The caption for Figure 1C, and relevant Methods texts (lines 173-188) should be expanded, to at least explain i) why it is valid to calculate (dis)similarity postelimination based on aggregation, ii) how aggregation is achieved for the target samples. b. I may not have understood the authors on (2), but this improvement seems trivial? Is it simply that Meta-Prism 2 has a new function to calculate all pair-wise dissimilarities on a collection of microbial profiles? c. For (3), it should be made clearer that Meta-Prism 1 does not do this. I needed to read the authors' previous paper to understand the comment about better flexibility in customized datasets. I assume that this improvement is enabled because Meta-Prism 2 is vastly faster compared to 1? If so, it might be helpful to point this out explicitly.

      1. I am lost on the accuracy evaluation results for predicting different biomes (Figure 2). a. How are biomes predicted for each microbial sample? b. What is the varying classification threshold that generates different sensitivities and specificities? c. Does "cross-validation" refer to e.g. selection of tuning parameters during model training, or for evaluation model performances? d. What are the "Fecal", "Human", and "Combined" biomes for the Feast cohort? Such details were not provided in Shenhav et al.

      Moderate 1. I understand that this was previously published, but could the authors comment on the intuitions behind their dissimilarity measure, and how it compares to similar measures such as the weighted UniFrac? a. Does Meta-Storm and Meta-Prism share the same similarity definition? If so, why would they differ in terms of prediction accuracies? 2. There seems to be some mis-referencing on the panels of Figure 1. a. Panel B was not explained at all in the figure caption. b. Line 185 references Figure 1E, which does not exist.

      Minor 1. The Meta-Prism 1 publication was referenced with duplicates (#16 and 24) 2. There are minor language issues throughout the manuscript, but for they do not affect understanding of the materials. Examples: a. Line 94: analysis -> analyze b. Line 193: We also obtained a dataset that consists of ...

      Re-review:

      I find most of my questions addressed. My only remaining issue is still that the three biomes from FEAST (Fecal, Human, and Mixed) are still not clearly defined. The only definition I could find is line 206-208 "We also obtained a dataset that consists of 10,270 samples belonging to three biomes: Fecal, Human, and Mixed, which have been used in the FEAST study, defined as the FEAST dataset". Are "Fecal" simply stool samples, and "Human" samples biopsies from the human gut? What is "Mixed"? As a main utility of Meta-Prism is source tracking, it is important for the reader to understand what these biomes are, to understand the resolution of the source tracking results. If this can be resolved, I'll be happy to recommend the manuscript's acceptance.

      Reviewer 2. Yoann Dufresne

      In this article the authors present Meta-Prism 2, a software to compute distances between metagenomic samples and also query a specific sample against a pool of samples. They call "sample" a precomputed file with abundance of multiple taxa. In the article they first succinctly present multiple aspects on the underlying algorithms. Then they provide an extensive analysis on the precision, ram and time consumption of the software. Finally, they show 3 applications of Meta-Prism 2.

      I will start to say that the execution time of the tool looks very good compared to all other tools. But I have multiple concerns about these numbers. - First, I like to reproduce the results of a paper before approving it. But I had a few problems doing so. * The tool do not compile as it is on git. I had to modify a line of code to compile it. This is nothing very bad but authors of tools should be sure that their main code branch is always compiling. See the end of the review for bug and fix. * The analysis are done using samples from MGnify. I found related OTU tsv files linked in the supplementary but no explanation on how to transform such files in pdata files that the software is processing. * The only way to directly reproduce the results is to trust the pdata files present on the github of the authors. I would like to make my own experiments and compare the time to transform OTU files into pdata with the actual run time of MP2. - The authors evaluated the accuracy of their method (which is nice) but did not gave access on the scripts that were used for that. I would like to see the code and try to reproduce the figure by myself on my own data. - The 2nd and 3rd applications are explained in plain text but there is no script related neither any table of graphics to reproduce or explain the results. The only way for me to evaluate this part is to trust the word of the authors. I would like the authors to show me clear and indisputable evidences.

      For the methods part it is similar. We have hints on what the authors did, but not a full explanation: - For the similarity function, I would like to know where it comes from. The cited papers [14] and [24] do not help on the comprehension of the formula. If the function is from another paper, I ask the authors to add a clear reference (paper + section in the paper) ; if not, I would like the authors to explain in details why this particular function, how they constructed it and how it behaves. - The authors refer multiple times to "sparse format" applied to disk & cache but never defined what they mean by that. I would like to see in this section which exact datastructure is used. - In the Fast 1-N sample comparison, the authors write about "current methods" but without citing them. I would like the authors to refer to precise methods/software, succinctly describe them and then compare their methods on top of that. Also in this part, the authors point at figure 1E that is not present in the manuscript. - The figure 1 is not fully understandable without further details in the text. For example, what is Figure 1C4 ?

      I want to point that the paper is not correctly balanced in term of content. 1.5 page for time execution analysis is too much compared to the 2 pages of methods and less than 1 page of real data applications.

      Finally, the authors are presenting a software but are not following the development standards. They should provide unit and functional tests of their software. I also strongly recommend them to create a continuous integration page with the git. With such a tool the compilation problem would not exist.

      To conclude, I think that the authors very well engineered the software but did not present it the right way. I suggest the authors to rewrite the paper with strong improvements of the "methods" and "Real data application" sections. Also, to provide a long term useful software, they have to add guaranties to the code as tests and CI.

      For all these reasons, I recommend to reject this paper.

      --- Bug & Fix ---

      make mkdir -p build g++ -std=c++14 -O3 -m64 -march=native -pthread -c -o build/loader.o src/loader.cpp g++ -std=c++14 -O3 -m64 -march=native -pthread -c -o build/newickParser.o src/newickParser.cpp g++ -std=c++14 -O3 -m64 -march=native -pthread -c -o build/simCalc.o src/simCalc.cpp g++ -std=c++14 -O3 -m64 -march=native -pthread -c -o build/structure.o src/structure.cpp g++ -std=c++14 -O3 -m64 -march=native -pthread -c -o build/main.o src/main.cpp src/main.cpp: In function 'int main(int, const char)': src/main.cpp:128:31: error: 'class std::ios_base' has no member named 'clear' 128 | buf.ios_base::clear(); | ^~~~~ make: * [makefile:7: build/main.o] Error 1

      To fix the bug: src/main.cpp:128 => buf.ios.clear();

  4. Feb 2023
    1. Moderation and Violence# You might remember from Chapter 14 that social contracts, whether literal or metaphorical, involve groups of people all accepting limits to their freedoms. Because of this, some philosophers say that a state or nation is, fundamentally, violent. Violence in this case refers to the way that individual Natural Rights and freedoms are violated by external social constraints. This kind of violence is considered to be legitimated by the agreement to the social contract. This might be easier to understand if you imagine a medical scenario. Say you have broken a bone and you are in pain. A doctor might say that the bone needs to be set; this will be painful, and kind of a forceful, “violent” action in which someone is interfering with your body in a painful way. So the doctor asks if you agree to let her set the bone. You agree, and so the doctor’s action is construed as being a legitimate interference with your body and your freedom. If someone random just walked up to you and started pulling at the injured limb, this unagreed violence would not be considered legitimate. Likewise, when medical practitioners interfere with a patient’s body in a way that is non-consensual or not what the patient agreed to, then the violence is considered illegitimate, or morally bad. We tend to think of violence as being another “normatively loaded” word, like authenticity. But where authenticity is usually loaded with a positive connotation–on the whole, people often value authenticity as a good thing–violence is loaded with a negative connotation. Yes, the doctor setting the bone is violent and invasive, but we don’t usually call this “violence” because it is considered to be a legitimate exercise of violence. Instead, we reserve the term “violence” mostly for describing forms of interference that we consider to be morally bad.

      When it comes to moderation, violence can be an issue because the act of moderating, or enforcing social contracts, can be seen as a form of interference or constraint on individual freedoms. This is why some people may view moderation as inherently violent, even if it is carried out in a peaceful manner.

    1. Editorial Note (David Reinstein)

      We are grateful to the authors of this paper for agreeing to participate and engage with the Unjournal’s evaluation of this paper, and for following through with this. (Although this was an NBER working paper this was selected before we began the “Unjournal Direct track”.)

      In our current phase, The Unjournal is mainly targeting empirical papers (and papers with quantitative simulations, impact evaluations, direct policy recommendations, etc.) This paper would instead mainly be considered ‘applied macroeconomic/growth theory’. Nonetheless, we saw this work as particularly important and influential for reasons mentioned here (considering tradeoffs between positive and negative consequences of AI; explicit economic modeling of 'singularities; and the paper appears in ‘economics of effective altruism and longermism’ syllabi; and its nearly 500 citations).

      We are also grateful for the extremely diligent work of the evaluators. My impression (from my own experience, from discussions, and given the incentives we have in place) is that we rarely see referees and colleagues actually reading and the checking math and proofs in their peers’ papers. Here Phil Trammel did so and spotted an error in a proof of one of the central results of the paper (the ‘singularity’ in Example 3). Thankfully, he was able to communicate with the authors, and work out a corrected proof of the same result (see philiptrammell.com “Growth given Cobb-Douglas Automation”) currently linked here.

      The authors have acknowledged and this error (and a few smaller bugs), confirmed the revised proof, and link a marked up version on their page. This is ‘self-correcting research’, and it’s great!

      Even though the same result was preserved, I believe this provides a valuable service.

      1. Readers of the paper who saw the incorrect proof (particularly students) might be deeply confused. They might think ‘Can I trust this papers’ other statements?’ ‘Am I deeply misunderstanding something here? Am I not suited for this work?’ This happened to me a lot in graduate school; at least some of the time it may have been because of errors and typos in the paper.
      2. I suspect many math-driven paper also contain flaws which are never spotted, and these sometimes may affect the substantive results (unlike in this case).

      Again, I’m grateful for the present authors for being willing to put their work through this public checking, and acknowledging and correcting the errors. I now have more confidence that the paper’s results are valid, and that the authors have confidence in their work. This makes their research output more credible overall, and it sets a great example for the field.

      Evaluators were asked to follow the general guidelines available here . For this paper we did not give specific suggestions on ‘which aspects to evaluate’. In addition to written evaluations (similar to journal peer review), we ask evaluators to provide quantitative metrics on several aspects of each article. These are put together below.

    1. Life is not a series of gig lamps symmetrically arranged; life is a luminous halo, a semi-transparent envelope surrounding us from the beginning of consciousness to the end. Is it not the task of the novelist to convey this varying, this unknown and uncircumscribed spirit, whatever aberration or complexity it may display, with as little mixture of the alien and external as possible? We are not pleading merely for courage and sincerity; we are suggesting that the proper stuff of fiction is a little other than custom would have us believe it. It is, at any rate, in some such fashion as this that we seek to define the quality which distinguishes the work of several young writers, among whom Mr. James Joyce is the most notable, from that of their predecessors. They attempt to come closer to life, and to preserve more sincerely and exactly what interests and moves them, even if to do so they must discard most of the conventions which are commonly observed by the novelist. Let us record the atoms as they fall upon the mind in the order in which they fall, let us trace the pattern, however disconnected and incoherent in appearance, which each sight or incident scores upon the consciousness. Let us not take it for granted that life exists more fully in what is commonly thought big than in what is commonly thought small.

      I think what Woolf is saying is that: life's luminous halo, or semi-transparent envelope surrounding us from the beginning of consciousness to the end is not the custom in which the writers of the time were subscribing to but that the novelist was to convey, " unknown and uncircumscribed spirit, whatever aberration or complexity it may display, with as little mixture of the alien and external as possible" Woolf goes on the say: "They attempt to come closer to life, and to preserve more sincerely and exactly what interests and moves them," And this is how she describes Mr. James Joyce.

    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Thank you for conducting the peer-review of our manuscript. We really appreciate the constructive criticism of the reviewers, and we are happy to note the positive appreciation of our core findings regarding the role of the decapping machinery during apical hook and lateral root formation and the identification of ASL9 as a target of the decapping machinery. However, both reviewers note we over-interpretate about the function of ASL9 in cytokinin and auxin responses which is not always supported by our data. Based on their feedback, we have toned down our claims and performed additional experiments and analyses and addressed all the comments raised by both reviewers. We hope this substantially revised and improved version of our manuscript will be better accepted.

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      In this manuscript, the authors describe the role of the mRNA decay machinery in apical hook formation during germination in darkness in A.thaliana. As reported, this machinery is predominantly described in literature in stress response processes, whereas little is known about its involvement during developmental processes. In detail, the authors demonstrated, via RNA immunoprecipitation (RIP) and genetic experiments, the direct regulation of the LATERAL ORGAN BOUNDARIES DOMAIN 3 (LBD3)/ASYMMETRIC LEAVES 2-19 LIKE 9 (ASL9) mRNA stability by the mRNA decapping machinery subunits DCPs. According to the manuscript, ASL9 controls apical hooking, LR development and primary root growth is regulating cytokinin signalling and hence its regulation helps to maintain a correct balance of auxin/cytokinin. Indeed, they showed an impair apical hooking and LR defects both in mRNA decapping mutants, where they observed more capped ASL9 compared to WT, and in ASL9 over-expressor lines. Moreover, they reported a largely restoration of over-expressor lines phenotype in the arr10-5arr12-1 double mutants. This work present simple but interesting data that corroborate the authors hypothesis.

      Our response: We thank the reviewer for acknowledging the significance of our findings although we wonder what it´s meant by “simple data”. Through a combination of (complicated) genetics, phenotyping, cell imaging and molecular biology, we have provided mechanistic evidence on the function of the decapping machinery during 2 different post embryonic developmental events. Please see our detailed answers to the reviewer’s comments in the following.

      Nonetheless, I have both major comments and minor comments to improve the manuscript: MAJOR COMMENTS: 1. I am a bit concerned by the fact that cytokinin, auxin, LBD3, ARR12 and ARR10 have been largely involved in vasculature development and that the obtained results might be due to their role in vasculature development more than in LBD3 mRNA decapping process. Authors should provide evidence that their results are independent from vasculature defects present in those backgrounds or in case discuss this possibility.

      __Our response: __We are a bit puzzled on how vasculature development could explain the apical hook phenotype observed in the decapping mutant. Data like the rapid assembly of P-bodies upon IAA (Fig. 3C) treatments and the overall decreased DR5 signal in dcp mutants (Fig.S5&6) are all consistent with a process precluding vasculature formation. However, we still discuss the possibility that the developmental defects observed in mRNA decapping mutants and ASL9 overexpressor might be related to the vasculature development in these plants (Line 239-244).

      The interaction between the described players and auxin is not clear. From the reported experiments it is difficult to understand what authors wants to report as in S4 and S5 are reported experiments not fully described in the text (authors report about introgression of DR5::GFP in dcp1 and 2 mutants, but SD4 reports ACC treatments of DR5::GFP,dcp2 mutants and SD5 of 7 dpg root meristems of this strain ). Please describe and discuss better the experiment. Also, to this reviewer it is difficult to understand whether the absence of auxin activity in the dcp2 mutants hypocotyl is merely an effect of the lack of the hook formation in this background or a cause. Please clarify this point including new experiments (axr1 or axr3 mutants might help in understand this point).

      __Our response: __We follow the reviewer’s suggestions and trust we now describe and discuss Fig S5&6 (old Fig S4&S5) clear in Line 188-193. As axr1 has been published with apical hook and lateral root defect (old Line 42, new Line 39&169), we did not repeat it in new experiments but emphasize it in Line 169.

      Authors conclude that mRNA decapping is also involved in root growth. However, they do not report direct evidences regarding root growth but mostly regarding the mere root lenght at a precise developmental stage. Please eliminate this point or provide new experiments (e.g., root length and root meristem activity over time)

      __Our response: __We follow the reviewer’s suggestions and eliminate the data regarding to primary root growth (Fig. 3-6 &S2)

      Regarding root growth defects, these might be due to defect in the vasculature development, please analyse this point or report new experiments (e.g., vasculature analysis of dcp1,2 mutants or tissue specific expression of DCP2).

      __Our response: __We largely agree with the reviewer, all the decapping components DCP1, DCP2, DCP5 and PAT1 exhibit high expression in xylem cells and low expression in procambium cells (Brady et al., 2007) indicating functions of decapping components in vasculature development. However, we did not include this knowledge in our manuscript since we decided to eliminate the primary root growth data (Fig.3-6&S2).

      For consistency the last paragraph of result section: "ASL9 directly contributes to apical hooking, LR formation and primary root growth" should be part of the result section entitled "Accumulation of ASL9 suppresses LR formation and primary root growth". Authors should move this result in the paragraph before "Interference of a cytokinin pathway and/or exogenous auxin restores developmental defects of ASL9 over-expressor and mRNA decay deficient mutants".

      __Our response: __We agree thus we reorganize the result sections and move "ASL9 directly contributes to apical hooking and LR formation" before "Interference of a cytokinin pathway and/or exogenous auxin restores developmental defects of ASL9 over-expressor and mRNA decay deficient mutants" (Line 152).

      I suggest being consistent in the description of the statistical analysis. In particular: - I suggest reporting the meaning of ANOVA letters and the P-value in each figure as sometimes these information are missing, especially in Fig.2.

      __Our response: __We used ANOVA letters when comparing among genotypes and treatments, for example Fig 2A; and we used stars when comparing to controls, for example old Fig 2F. For consistency, we use letters for all the statistical analysis now and we report the meaning of the letters clearly in the figure legends (Fig. 1-6, S1-5&7). However, we think that putting the P-values in each figure would not be reader-friendly, and thus we have not done this.

      • in Fig.S3 please report the statistical significance on bars and the statistical analysis performed.

      __Our response: __We thank the reviewer for pointing it out, we report the statistical analysis now in new Fig. S2 (old Fig. S3).

      MINOR COMMENTS: L31- please replace "normal" with "proper"

      __Our response: __We thank the reviewer for the suggestion, now we replace "normal" with "proper"(Line 30)

      L42-please report the acronym of axr1

      __Our response: __The acronym of axr1 is correctly reported (Line 40).

      L57, L59-please include the entire name of DCP2 and XRN

      __Our response: __The entire name of DCP2 and XRN are correctly included (Line 55 &57).

      -Please report how many plants were analysed in legend or in methods section

      __Our response: __The numbers of plants in analysis are now reported in figure legends (Fig. 1-6, S1,2&7).

      -Please report how many transgenic independent lines were obtained in methods section

      __Our response: __The numbers of transgenic independent lines are now reported in methods (Line 292)

      • Please, try to change the colours of the graph in Fig.S2A-B, as it quite difficult to distinguish light grey shades.

      __Our response: __We thank the reviewer’s suggestions, the colours of new Fig.S3&4 (old Fig.S2) are changed.

      • In Fig. 5A and S5A scale bars are missing.

      __Our response: __We thank the reviewer for pointing this out, scale bars are correctly added in new Fig 4 &S6 (old Fig 5 &S5).

      Reviewer #1 (Significance (Required)): The manuscript is interesting and analyse important and overlooked aspects of the role of mRNA decapping in development. Nonetheless experiments reported are not particularly innovative and not always sound. Also authors analysis are a bit superficial probably because they decide to utilize too many systems in their research (root development, hook development and lateral root development).

      Our response: We thank the reviewer again for acknowledging the significance of our findings and hope we satisfied the reviewer with our answers above. However, we would like to ask what is the purpose of writing “experiments are not particularly innovative”? We admit we used established and robust experiments which we found sufficient to answer the overlooked aspects of the role of mRNA decpping in apical hook and lateral root development as also noted by the reviewer, but maybe we simply don't understand the comment.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Major Comments 1. My main concern is about the authors' conclusions on the role of mRNA decay and ASL9/LBD3 in the control over cytokinin and auxin responses. I don't think that based on the data presented the authors may do the conclusions stated on lines 184-185, see also the points below.

      __Our response: __We agree thus we tone down our conclusion in our new manuscript (Line 197-199), see answers below for detail.

      The conclusion about the role of ASL9 and its direct involvement in the apical hook formation and lateral root development/main root growth is a bit exaggerated, based on rather tiny effects mediated by the introduction of asl9-1 into the dcp5-1. Rather, the data suggest that misregulation of other transcripts in the mRNA decay-deficient lines might be responsible for the observed defects. That is also apparent from slightly different phenotypes seen in dcp5-1/pat triple compared to oxASL9 (Fig. 3A). The strong dependency of oxASL9 phenotype on the presence of functional ARR10 and ARR12 implies cytokinin signaling-dependent mechanism of ASL9/LBD3 action (see also point 3 below). Considering the aforementioned phenotype differences between the dcp5-1/pat triple and oxASL9, it would be interesting to see the possible dependence of the mRNA decay-deficient line phenotypes on the cytokinin signaling, too.

      __Our response: __We note restoration of dcp5-1 developmental defects in asl9 backgrounds is partial, indicating other ASLs or non-ASLs also contributing to apical hook and lateral root development (old Line 224-225, new Line 229-230 &234-235). We also note that partial suppression is a common phenomenon when studying discrete developmental traits. Two such examples could include the knockout of TPXL5 which partially suppressed the increase of LR density in the hy5 mutant and the introduction of a point mutation in SnRK2.6 in the gsnor1-3/ost1-3 double-mutant partially suppressed the effect of gsnor1-3 on ABA-induced stomatal closure (Qian et al., 2022 The Plant Cell doi.org/10.1093/plcell/koac358; Wang et al., 2015 PNAS 112, 613). In addition to such discrete developmental traits, more dramatic phenotypes like autoimmunity may also only be partially suppressed (Zhang et al., 2012 CH&M 11, 253). However, we agree that it’s interesting to check the dependence of cytokinin signaling of the developmental defects in mRNA decay-deficient mutants. Unfortunately, we were only able to cross arr10 arr12 into dcp5-1. This line showed similar partial restoration of dcp5-1 developmental defects as seen for dcp5-1asl9-1. Overall, these data indicates that contribution of mRNA decapping targeting ASL9 transcripts during apical hook and LR formation depends on ARR10 and ARR12 (Fig. 4&6, Line 180-186).

      Also the hypothesis on the upregulation of cytokinin signaling in the mRNA decay mutants and Col-0/oxASL9 is very indirect and should be tested using e.g. TCSn:GFP. The type A ARRs (RRAs) are not only the negative regulators of cytokinin signaling, but also the cytokinin primary response genes. Thus, the downregulation of RRAs could mean the downregulation of the cytokinin signaling pathway in the mRNA decay mutants and/or Col-0/oxASL9. The latter seems to be the case as shown recently (Ye et al., 2021).

      __Our response: __We thank the reviewer for suggesting a different annotation of our result regarding to type-A ARRs. Ye et al reported accumulation of ASL9/LBD3 induced downregulation of cytokinin pathway based on weaker ARR5 and TCSn-GFP signal(Ye et al., 2021). However, the fact that knocking out cytokinin signaling activator genes ARR10 and ARR12 largely restored developmental defects in ASL9 over-expressors lead to the hypothesis of upregulated cytokinin signaling in ASL9 over-expressors (Fig 5). Therefore, we substitute “upregulation” with “misregulation” for cytokinin signaling to compromise in our new manuscript (Line 174).

      The hypothesis on the causal link between the observed auxin-related defects and upregulated cytokinin signaling (Discussion, lines 214-216) is more than speculation. This could be tested by introducing arr10 arr12 into the dcp2-1/DR5-GFP and/or dcp5-1/DR5-GFP.

      __Our response: __We thank the reviewer for the suggestions, due to time and funds management, we decided to check auxin related gene expression in dcp5-1arr10-5arr12-1 mutants instead of making transgenic plants in triple mutant. The repressed expression of SAUR23 and TAR2 in dcp5-1 is partially restored (Fig. S4), indicating possible repression of auxin signaling caused by upregulated cytokinin signaling. However, for consistency in cytokinin signaling description, we tone down the hypothesis on the link between auxin-related defects and cytokinin signaling (Line 218-220).

      Compared to the text/quantification of the effect of asl9-1 mutant on the hook formation (Fig. S1D), I see exaggerated hook formation both in the presence and absence of ACC in asl9-1, at least on the figures shown in Fig. S1C. Are the shown seedlings not representative?

      __Our response: __We thank the reviewer for pointing our mistakes out, the shown seedlings are representative but mislabeled and the mistakes are corrected now in our new manuscript (Fig. S1C).

      Minor Comments 1. Syntax problem in the sentence on lines 45-46 (?).

      __Our response: __We thank the reviewer for pointing it out, syntax problem of this sentence is solved now in new manuscript (Line 41-44).

      The sentence on lines 48-49 should be rephrased. It implies the cytokinins regulate the amount of RRBs, which is not correct (cytokinins control phosphorylation of RRBs, not their abundance, RRAs are not TFs).

      __Our response: __We now rephrase the sentence in a correct way (Line 46)

      In the FL for Fig. 2F there is mentioned that MYC-YFP was used as a control compared to the main text mentioning YFP-WAVE (?).

      __Our response: __We thank the reviewer for pointing this out, the YFP-WAVE line we used is MYC-YFP transgenic plants, we now include this information in our manuscript (Line 136) and for consistency we changed MYC-YFP to YFP-WAVE in Fig. 2F.

      Naito et al. (2007) suggest ASL9 as a target of cytokinin signaling, but I don't think they imply the involvement of ASL9 in the cytokinin signaling as mentioned e.g. on line 166 (?)

      __Our response: __We largely agree with the reviewer thus we also cite Ye’s paper here in our new manuscript (Line 165)

      References Ye L, Wang X, Lyu M, Siligato R, Eswaran G, Vainio L, Blomster T, Zhang J, Mahonen AP. 2021. Cytokinins initiate secondary growth in the Arabidopsis root through a set of LBD genes. Curr Biol 31(15): 3365-3373 e3367.

      Reviewer #2 (Significance (Required)):

      The authors provide interesting data suggesting possible role of mRNA decay machinery in the hook and lateral root formation and main root growth via decapping-mediated control over ASL9/LBD3 transcript abundance. Based on the observed interaction of the observed phenotypes with hormonal regulations, the authors' conclude mechanistic link between the mRNA decay/ASL9 and cytokinin and auxin responses.

      Our response: We thank the reviewer for acknowledging the significance of our findings.

    1. level 2A_Dull_SignificanceOp · 2 hr. agoYes! When I run across a comment on a book I haven’t read yet but seems interesting I make a little card with the comment and book title2ReplyGive AwardShareReportSaveFollowlevel 2taurusnoises · 2 hr. agoObsidianSo, you keep the titles of books you want to read organized in folgezettel (you give them an alphanumeric ID?) among your ZK notes? That's really interesting!

      I've done something like this when I think a particular reference(s) can answer a question related to a train of thought. But I keep cards of unread sources at the front of my sources section so that it's easier to pull it out frequently to prioritize and decide what I should be reading or working on next. These will then have links to the open questions I've noted, so that I can go back to those sections either as I'm reading/writing or to add those ideas into the appropriate folgezettel. These sorts of small amounts of work documented briefly can add up quickly over time. Source cards with indications of multiple open questions that might be answered is sometimes a good measure of desire to read, though other factors can also be at play.

      That to-read pile of bibliographic source notes (a mini antilibrary) is akin to walking into a party and surveying a room. I may be aware of some of the people I haven't met yet and the conversations we might have, but if there are interesting questions I know I want to ask of specific ones or conversations I already know I want to have, it can be more productive to visit those first.

      This sort of practice has been particularly helpful for times when I want to double check someone's sources or an original context, but don't have the time to do it immediately, don't want to break another extended train of thought, have to wait on materials, or may have to make a trip to consult physical materials that are singular or rare. For quick consultative reading, this can be a boon when I know I don't want or need to read an entire work, but skimming a chapter or a few pages for a close reading of a particular passage. I'll often keep a pile of these sorts of sources at hand so that I can make a short trip to a library, pick them up, find what I need and move on without having to recreate large portions of context to get the thing done because I've already laid most of the groundwork.

    1. Conversation Hey, JB, I played a pickup game at the Rec today. At first, the older guys laughed and wouldn’t let me in unless I could hit from half-court . . . Of course, I did. All net. I wait for JB to say something, but he just smiles, his eyes all moony. I showed them guys how the Bells ball. I scored fourteen points. They told me I should try out for junior varsity next year ’cause I got hops . . . JB, are you listening? JB nods, his fingers tapping away on the computer, chatting probably with Miss Sweet Tea. I told the big guys about you, too. They said we could come back and run with them anytime. What do you think about that? HELLO—Earth to JB? Even though I know he hears me, the only thing JB is listening to is the sound of his heart bouncing on the court of love.

      Conversation Dad, this girl is making Jordan act weird. He’s here, but he’s not. He’s always smiling. His eyes get all spacey whenever she’s around, and sometimes when she’s not. He wears your cologne. He’s always texting her. He even wore loafers to school. Dad, you gotta do something. Dad does something. He laughs. Filthy, talking to your brother right now would be like pushing water uphill with a rake, son. This isn’t funny, Dad. Say something to him. Please. Filthy, if some girl done locked up JB, he’s going to jail. Now let’s go get some doughnuts.

      Basketball Rule #5 When you stop playing your game you’ve already lost.

      Showoff UP by sixteen with six seconds showing, JB smiles, then STRUTS side steps stutters Spins, and SI NKS a sick SLICK SLIDING SWeeeeeeeeeeT SEVEN-foot shot. What a showoff.

      Out of Control Are you kidding me? Come on. Ref, open your eyes. Ray Charles could have seen that kid walked. CALL THE TRAVELING VIOLATION! You guys are TERRIBLE! Mom wasn’t at the game tonight, which meant that all night Dad was free to yell at the officials, which he did.

      Mom calls me into the kitchen after we get home from beating St. Francis. Normally she wants me to sample the macaroni and cheese to make sure it’s cheesy enough, or the oven-baked fried chicken to make sure it’s not greasy and stuff, but today on the table is some gross-looking orange creamy dip with brown specks in it. A tray of pita-bread triangles is beside it. Maybe Mom is having one of her book club meetings. Sit down, she says. I sit as far away from the dip as possible. Maybe the chicken is in the oven. Where is your brother? she asks. Probably on the phone with that girl. She hands me a pita. No thanks, I say, then stand up to leave, but she gives me a look that tells me she’s not finished with me. Maybe the mac is in the oven. We’ve talked to you two about your grandfather, she says. He was a good man. I’m sorry you never got to meet him, Josh. Me too, he looked cool in his uniforms. That man was way past cool. Dad said he used to curse a lot and talk about the war. Mom’s laugh is short, then she’s serious again. I know we told you Grandpop died after a fall, but the truth is he fell because he had a stroke. He had a heart disease. Too many years of bad eating and not taking care of himself and so— What does this have to do with anything? I ask, even though I think I already know. Well, our family has a history of heart problems, she says, so we’re going to start eating better. Especially Dad. And we’re going to start tonight with some hummus and pita bread. FOR MY VICTORY DINNER? Josh, we’re going to try to lay off the fried foods and Golden Dragon. And when your dad takes you to the recreation center, no Pollard’s or Krispy Kreme afterward, understand? And I understand more than she thinks I do. But is hummus really the answer?

      35–18 is the final score of game six. A local reporter asks JB and I how we got so good. Dad screams from behind us, They learned from Da Man! The crowd of parents and students behind us laughs. On the way home Dad asks if we should stop at Pollard’s. I tell him I’m not hungry, plus I have a lot of homework, even though I skipped lunch today and finished my homework during halftime.

      Too Good Lately, I’ve been feeling like everything in my life is going right: I beat JB in Madden. Our team is undefeated. I scored an A+ on the vocabulary test. Plus, Mom’s away at a conference, which means so is the Assistant Principal. I am a little worried, though, because, as Coach likes to say, you can get used to things going well, but you’re never prepared for something going wrong.

      I’m on Free Throw Number Twenty-Seven We take turns, switching every time we miss. JB has hit forty-one, the last twelve in a row. Filthy, keep up, man, keep up, he says. Dad laughs loud, and says, Filthy, your brother is putting on a free-throw clinic. You better— And suddenly he bowls over, a look of horror on his face, and starts coughing while clutching his chest, only no sound comes. I freeze. JB runs over to him. Dad, you okay? he asks. I still can’t move. There is a stream of sweat on Dad’s face. Maybe he’s overheating, I say. His mouth is curled up like a little tunnel. JB grabs the water hose, turns the faucet on full blast, and sprays Dad. Some of it goes in Dad’s mouth. Then I hear the sound of coughing, and Dad is no longer leaning against the car, now he’s moving toward the hose, and laughing. So is JB. Then Dad grabs the hose and sprays both of us. Now I’m laughing too, but only on the outside.

      He probably just got something stuck in his throat, JB says when I ask him if he thought Dad was sick and shouldn’t we tell Mom what happened. So, when the phone rings, it’s ironic that after saying hello, he throws the phone to me, because, even though his lips are moving, JB is speechless, like he’s got something stuck in his throat.

      i·ron·ic [AY-RON-IK] adjective Having a curious or humorous unexpected sequence of events marked by coincidence. As in: The fact that Vondie hates astronomy and his mom works for NASA is ironic. As in: It’s not ironic that Grandpop died in a hospital and Dad doesn’t like doctors. As in: Isn’t it ironic that showoff JB, with all his swagger, is too shy to talk to Miss Sweet Tea, so he gives me the phone?

      This Is Alexis—May I Please Speak to Jordan? Identical twins are no different from everyone else, except we look and sometimes sound exactly alike.

      Phone Conversation (I Sub for JB) Was that your brother? Yep, that was Josh. I’m JB. I know who you are, silly—I called you. Uh, right. You have any siblings, Alexis? Two sisters. I’m the youngest. And the prettiest. You haven’t seen them. I don’t need to. That’s sweet. Sweet as pomegranate. Okay, that was random. That’s me. Jordan, can I ask you something? Yep. Did you get my text? Uh, yeah. So, what’s your answer? Uh, my answer. I don’t know. Stop being silly, Jordan. I’m not. Then tell me your answer. Are y’all rich? I don’t know. Didn’t your dad play in the NBA? No, he played in Italy. But still, he made a lot of money, right? It’s not like we’re opulent. Who says “opulent”? I do. You never use big words like that at school . . . I have a reputation to uphold. Is he cool? Who? Your dad. Very. So, when are you gonna introduce me? Introduce you? To your parents. I’m waiting for the right moment. Which is when? Uh— So, am I your girlfriend or not? Uh, can you hold on for a second? Sure, she says. Cover the mouthpiece, JB mouths to me. I do, then whisper to him: She wants to know are you her boyfriend. And when are you gonna introduce her to Mom and Dad. What should I tell her, JB? Tell her yeah, I guess, I mean, I don’t know. I gotta pee, JB says, running out of the room, leaving me still in his shoes. Okay, I’m back, Alexis. So, what’s the verdict, Jordan? Do you want to be my girlfriend? Are you asking me to be your girl? Uh, I think so. You think so? Well, I have to go now. Yes. Yes, what? I like you. A lot. I like you, too . . . Precious. So, now I’m Precious? Everyone calls you JB. Then I guess it’s official. Text me later. Good night, Miss Sweet— What did you call me? Uh, good night, my sweetness. Good night, Precious. JB comes running out of the bathroom. What’d she say, Josh? Come on, tell me. She said she likes me a lot, I tell him. You mean she likes me a lot? he asks. Yeah . . . that’s what I meant.

      JB and I eat lunch together every day, taking bites of Mom’s tuna salad on wheat between arguments: Who’s the better dunker, Blake or LeBron? Which is superior, Nike or Converse? Only today I wait at our table in the back for twenty-five minutes, texting Vondie (home sick), eating a fruit cup (alone), before I see JB strut into the cafeteria with Miss Sweet Tea holding his precious hand.

      Boy walks into a room with a girl. They come over. He says, Hey, Filthy McNasty like he’s said forever, but it sounds different this time, and when he snickers, she does too, like it’s some inside joke, and my nickname, some dirty punch line.

      At practice Coach says we need to work on our mental game. If we think we can beat Independence Junior High— the defending champions, the number one seed, the only other undefeated team— then we will. But instead of drills and sprints, we sit on our butts, make weird sounds— Ohmmmmmmmm Ohmmmmmmmm— and meditate. Suddenly I get this vision of JB in a hospital. I quickly open my eyes, turn around, and see him looking dead at me like he’s just seen a ghost.

      Second-Person After practice, you walk home alone. This feels strange to you, because as long as you can remember there has always been a second person. On today’s long, hot mile, you bounce your basketball, but your mind is on something else. Not whether you will make the playoffs. Not homework. Not even what’s for dinner. You wonder what JB and his pink Reebok–wearing girlfriend are doing. You do not want to go to the library. But you go. Because your report on The Giver is due tomorrow. And JB has your copy. But he’s with her. Not here with you. Which is unfair. Because he doesn’t argue with you about who’s the greatest, Michael Jordan or Bill Russell, like he used to. Because JB will not eat lunch with you tomorrow or the next day, or next week. Because you are walking home by yourself and your brother owns the world.

      Third Wheel You walk into the library, glance over at the music section. You look through the magazines. You even sit at a desk and pretend to study. You ask the librarian where you can find The Giver. She says something odd: Did you find your friend? Then she points upstairs. On the second floor, you pass by the computers. Kids checking their Facebook. More kids in line waiting to check their Facebook. In the Biography section you see an old man reading The Tipping Point. You walk down the last aisle, Teen Fiction, and come to the reason you’re here. You remove the book from the shelf. And there, behind the last row of books, you find the “friend” the librarian was talking about. Only she’s not your friend and she’s kissing your brother.

      tip·ping point [TIH-PING POYNT] noun The point when an object shifts from one position into a new, entirely different one. As in: My dad says the tipping point of our country’s economy was housing gamblers and greedy bankers. As in: If we get one C on our report cards, I’m afraid Mom will reach her tipping point and that will be the end of basketball. As in: Today at the library, I went upstairs, walked down an aisle, pulled The Giver off the shelf, and found my tipping point.

      The main reason I can’t sleep is not because of the game tomorrow tonight, is not because the stubble on my head feels like bugs are break dancing on it, is not even because I’m worried about Dad. The main reason I can’t sleep tonight is because Jordan is on the phone with Miss Sweet Tea and between the giggling and the breathing he tells her how much she’s the apple of his eye and that he wants to peel her and get under her skin and give me a break. I’m still hungry and right about now I wish I had an apple of my own.

      Surprised I have it all planned out. When we walk to the game I will talk to JB man to man about how he’s spending way more time with Alexis than with me and Dad. Except when I hear the horn, I look outside my window and it’s raining and JB is jumping into a car with Miss Sweet Tea and her dad, ruining my plan.

      Conversation In the car I ask Dad if going to the doctor will kill him. He tells me he doesn’t trust doctors, that my grandfather did and look where it got him: six feet under at forty-five. But Mom says your dad was really sick, I tell him, and Dad just rolls his eyes, so I try something different. I tell him that just because your teammate gets fouled on a lay-up doesn’t mean you shouldn’t ever drive to the lane again. He looks at me and laughs so loud, we almost don’t hear the flashing blues behind us.

      Game Time: 6:00 p.m. At 5:28 p.m. a cop pulls us over because Dad has a broken taillight. At 5:30 the officer approaches our car and asks Dad for his driver’s license and registration. At 5:32 the team leaves the locker room and pregame warm-ups begin without me. At 5:34 Dad explains to the officer that his license is in his wallet, which is in his jacket at home. At 5:37 Dad says, Look, sir, my name is Chuck Bell, and I’m just trying to get my boy to his basketball game. At 5:47 while Coach leads the Wildcats in team prayer, I pray Dad won’t get arrested. At 5:48 the cop smiles after verifying Dad’s identity on Google, and says, You “Da Man”! At 5:50 Dad autographs a Krispy Kreme napkin for the officer and gets a warning for his broken taillight. At 6:01 we arrive at the game but on my sprint into the gym I slip and fall in the mud.

      This is my second year playing for the Reggie Lewis Wildcats and I’ve started every game until tonight, when Coach tells me to go get cleaned up then find a seat on the bench. When I try to tell him it wasn’t my fault, he doesn’t want to hear about sirens and broken taillights. Josh, better an hour too soon than a minute too late, he says, turning his attention back to JB and the guys on the court, all of whom are pointing and laughing at me.

      Basketball Rule #6 A great team has a good scorer with a teammate who’s on point and ready to assist.

      Josh’s Play-by-Play At the beginning of the second half we’re up twenty-three to twelve. I enter the game for the first time. I’m just happy to be back on the floor. When my brother and I are on the court together this team is unstoppable, unfadeable. And, yes, undefeated. JB brings the ball up the court. Passes the ball to Vondie. He shoots it back to JB. I call for the ball. JB finds me in the corner. I know y’all think it’s time for the pick-and-roll, but I got something else in mind. I get the ball on the left side. JB is setting the pick. Here it comes— I roll to his right. The double-team is on me, leaving JB free. He’s got his hands in the air, looking for the dish from me. Dad likes to say, When Jordan Bell is open you can take his three to the bank, cash it in, ’cause it’s all money. Tonight, I’m going for broke. I see JB’s still wide open. McDonald’s drive-thru open. But I got my own plans. The double-team is still on me like feathers on a bird. Ever seen an eagle soar? So high, so fly. Me and my wings are— and that’s when I remember: MY. WINGS. ARE. GONE. Coach Hawkins is out of his seat. Dad is on his feet, screaming. JB’s screaming. The crowd’s screaming, FILTHY, PASS THE BALL! The shot clock is at 5. I dribble out of the double-team. 4 Everything comes to a head. 3I see Jordan. 2 You want it that bad? HERE YA GO! 1 . . .

      Before Today, I walk into the gym covered in more dirt than a chimney. When JB screams FILTHY’S McNasty, the whole team laughs. Even Coach. Then I get benched for the entire first half. For being late. Today, I watch as we take a big lead, and JB makes four threes in a row. I hear the crowd cheer for JB, especially Dad and Mom. Then I see JB wink at Miss Sweet Tea after he hits a stupid free throw. Today, I finally get into the game at the start of the second half. JB sets a wicked pick for me just like Coach showed us in practice, And I get double-teamed on the roll just like we expect. Today, I watch JB get open and wave for me to pass. Instead I dribble, trying to get out of the trap, and watch as Coach and Dad scream for me to pass. Today, I plan on passing the ball to JB, but when I hear him say “FILTHY, give me the ball,” I dribble over to my brother and fire a pass so hard, it levels him, the blood from his nose still shooting long after the shotclock buzzer goes off.

    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity):

      This manuscript describes studies that indicate roles for the ALK and LTK receptors in neuronal polarity, cortical patterning and behavior in mice. I really liked the study and overall think that it deserves publication in a high-ranking journal. It reports important and novel results and benefits from a comprehensive analysis at multiple levels, including cell biological, biochemical and behavior. The points raised below are suggestions for consideration at the discretion of the authors.

      We thank the reviewer for the positive and enthusiastic comments on our study and especially for noting that it is appropriate for publication in a high-ranking journal. We greatly appreciate the valuable suggestions, the majority of which we have incorporated into the revised manuscript.

      1. The term "DKO" appears in the Introduction without explanation. I assume this means double KO mice lacking both receptors from birth. It should be indicated here, just in case.

      We have added text at the first appearance of DKO (ie results section) to indicate that this refers to double knockout mice that lack both Ltk and Alk from birth.

      1. The last paragraph of the Introduction is redundant with the Abstract. This is a stylistic question, which is up to the authors. Nevertheless, as a suggestion, they could take the opportunity here to explain the rationale of the study and why they did what they did._

      We have made some modifications to provide an indication of the rationale for the studies.

      1. Is "single cell in situ mRNA analysis" standard in situ hybridization or something else? Why is it called "single-cell"? It could be misleading.

      This was a typographical error and has been corrected to single molecule in situ.

      1. In Fig. S1B, could the authors please include expression patterns of LTK in adult brain? It'd seem that is the most relevant place to look given the analysis that follows in the paper.

      We have replaced the previous panels with new plots (now Fig. S1G) showing the relative expression of Ltk, Alk and their ligand, Alkal2 in embryos (E15.5), newborn (P0) and post-natal Day 2 (P2) and Day 7 (P7) and in adults both in the cortex and whole brain. The results confirm that Alk and Ltk are both expressed in the cortex and brain but in varying patterns with Alk expression decreasing with age and Ltk increasing, particularly in the cortex. In contrast, Alkal2 expression is relatively constant throughout.

      Related comments #5, #7, #8 and #9.

      1. I have an issue in general in the first part of the manuscript with regards to the labeling of cortical layers. How were CP, IZ and SVZ/VZ defined? Specific markers should be used to identify their actual boundaries. Guesswork from the DAPI pattern (if that is what was used) is not really appropriate.

      2. In Fig. 1F, again, how were the boundaries between the cortical areas (dotted lines) determined? This is particularly important for the mutant sections....

      3. In Fig. S3C-F, the all-critical quantification of Ctip2 cells at P2 seems to be missing in this figure. It would important to provide this in light of the comments above. Again, the same problem with the layer boundaries is clear here.....

      4. In Figure 2A and B, % positive cells is plotted but we are not told what is the reference (100%) level. ... Also, the idea of drawing a little rectangle in the IZ and CP and counting only there is flawed. ...Finally, again, we are not told how the boundaries of the different cortex areas were established. ...

      Response to related comments #5, #7, #8 and #9.

      As exemplified in the related comments above, the reviewer indicated that they “__have an issue in general in the first part of the manuscript with regards to the labeling of cortical layers.”

      We thank the reviewer for this insightful comment. Development of the mouse cortex follows a stereotypical pattern, thus we used a combination of DAPI ( ie nuclear density is characteristic of some layers), and layer specific markers (Satb2, Ctip2, Pax6, Sox2, Tbr2) to label the cortical layers. While this is generally acceptable for wild type mice, we agree with the reviewer’s comment that this may not be appropriate in mutant mice. Accordingly, we have now taken a more unbiased approach and repeated all of the quantitation after creating equally sized bins that span the entire cortical length and have plotted the quantitation by bin location. The general location of layers in WT mice has been marked on the images for reference. Our conclusions that there are defects in early patterning that are resolving by ~P7 is unchanged.

      With this re-quantitation, some of the previous reviewer comments within #5, 7, 8 and 9 no longer apply (ie a missing plot, box placement being subjective, etc) and so have not been responded to. With regards to the question of what is the reference (ie 100%) for the plots showing the y-axis as % positive; this was determined based on the total number of DAPI+ cells counted in each region. This information has been added to the legends and methods along with details of the new quantitation method.

      1. Comparing Fig. 1 and Fig. S2, there would seem to be little or no additive nor synergistic effects of the double mutation, as the phenotype in the DKO appears to be completely attributable to the Ltk KO. What does this mean? Providing the expression patterns of the two receptors at the ages used here (i.e., P2 and P7) would also be helpful.

      The relative contribution of Alk or Ltk in comparison to the DKO, varies as a function of age (E15.5, P2, P7) that generally correlates with their level of expression, as per the Reviewer’s suggestion. For example, at E15.5, a reduction in the number of Sox2+ or Tbr2+ cells is observed for either Alk or Ltk knockouts alone, with a more prominent reduction in the case of Alk alone, and with the DKOs showing the greatest reduction. In contrast, when examining Ctip2 levels at P2, the loss of Ltk alone yields a stronger effect. In agreement with these observations, analysis of mRNA expression levels show that Alk levels are highest in the embryonic cortex and brain and steadily decline until adulthood, while Ltk expression increases with maximal levels occurring post-natally. As indicated for our reply to comment #4, we have now added plots showing the relative level of expression of Alk and Ltk at various ages from embryos to adults (Fig S1G).

      1. At the end of page 8, it is concluded that Alk/Ltk promote neuronal migration. Is this a cell-autonomous effect? Given the very sparse expression of these receptors (Fig S1), cell-autonomy (which is being implied by the authors) is not at all clear. Is the migration of Alk+ cells affected in the Ltk mutant? Vice-versa?

      In our analysis of mRNA expression using RNAscope we originally included a widefield image that depicts the entire cortex where it is difficult to see expression at the cell level. We now also provide a magnified image of the E15.5 SVZ/VZ that shows that most cells do express the receptors (Fig. S1B). Thus, the results are consistent with the idea that the defect in migration is a cell autonomous effect.

      1. In Fig. S4A, as every cell in these panels bears probe signal, it'd be important to present a negative control, perhaps from KO cultures or wild type cells lacking receptor expression in the same field as expressing cells. At a 75%, 1 in 4 cells in any field should be receptor-negative.

      As requested, we now provide images with a wider field of view that includes negative cells.

      1. Figure S4B is difficult to interpret in the absence of Tau and MAP2 markers, as GFP does not discriminate between axons and dendrites.

      In the original submission we quantitated Tau-1 and MAP2 co-stained neurons in many experiments to demonstrate that Ltk/Alk act on axons, but in some cases, we used Tuj1 to more easily visualize and quantitate neurites. Nevertheless, as requested by the reviewers, in the revised manuscript we have repeated and replaced most of the results with Tuj1 or phalloidin staining with experiments using Tau-1 and MAP2 antibodies, including Fig. 5B-D and Fig. 6A-D and G as well as for Fig. S4B. The new data is consistent with our results using Tuj1 staining and further support our conclusions that Ltk/Alk act via Igf1-r to regulate neuronal polarity.

      In general, the authors are recommended to show more than one cell per condition in their figures. Readers need to be convinced that these are robust phenotypes easily observed on many cells in the same field.

      Due to space constraints, we included only a single representative image for each condition and then provided quantitation to support our conclusions. We have numerous images for all of the presented data and could provide a collage for all panels if considered appropriate. In the meantime, we have added additional images for several experiments in the Main Figures (Fig. 5A-D, Fig. 6A, C) and in Suppl. Figure S4A, B, C where sufficient space was readily available.

      1. In Fig. S4C and D, do the KO neurons become bipolar? I don't see examples of multipolar neurons in the images provided.

      Upon siRNA mediated knockdown of Ltk and/or Alk, we observe about 50% of the neurons are bipolar (ie display the typical wild type single axon phenotype) while roughly 40% display the multiple axon phenotype. With the exception of the control (siCTL), the images provided were selected to show neurons with multiple axons. However, in some of the images, the arrowheads pointing to the axons were inadvertently omitted. These have now been added.

      1. Is there a way to quantify the effects shown in Fig. 3E?

      We attempted to quantitate the number and direction of neurites in the brain sections but because this is a dense tissue, even with Golgi staining, we found it impossible to trace individual neurites back to the cell body and thus were unable to quantitate the effects. As an alternative, we have provided additional images (Fig. S3B) from distinct mice to support our observations of aberrant horizontal neurites in the adult cortex.

      1. The DKO display a dramatically different behavior phenotype compared to single Kos. How can this result be explained given that DKOs are indistinguishable from single KOs in all other parameters studied?

      The reviewer is correct, that the single KO mice do not manifest noticeable behavioural defects except when older and challenged with the most demanding task, the Puzzle box, which measures complex executive functions. We speculate that alternative cortical re-wiring in the single knockouts is sufficient to maintain normal circuitry that cannot be compensated when both Ltk and Alk receptors are deleted. It is also possible that Ltk/Alk regulated signalling events, besides Igf-1r/PI3K could contribute to the behavioural defects observed in the DKO mice, such as the ALK-LIMK-cofilin pathway which regulates synaptic scaling mentioned by the reviewer (Zhou et al., Cell Rep. 2021). Nevertheless, the strong phenotype of the DKOs confirms that Ltk/Alk are important for proper brain function, thus our preference is to retain the behavioural data in the manuscript but to discuss that alternative Ltk/Alk pathways could contribute to the phenotype (which we have now incorporated into the text).

      1. At the end of the behavior section, the authors attribute the phenotypes observed to defects in neuronal polarization. Given that polarization was only studied in vitro, it may be a premature to conclude that neurons fail to polarize in vivo in the absence of direct evidence showing this.

      We agree and have modified the text to remove this inaccurate assertation.

      1. Regarding P-AKT studies, it would be interesting to assess the effects of the ALK7LTK ligands (e.g., from conditioned medium) on the levels of P-AKT in WT neurons.

      We agree that this would be interesting and we had attempted this experiment, but found that treatment of WT cortical neurons with medium conditioned with the ALKAL2 ligand did not change the levels of pAKT under our experimental conditions (namely 20-30 min treatment with ACM). Because the data is negative, it makes it difficult to make a firm conclusion, but if true, it is possible that other pathways might be involved when WT cortical neurons are stimulated with ligand.

      1. In the mid part of page 14, the sentence "Treatment of WT cortical neurons with AG1024 at a dose (1 μM) at which only IGF-1R but not InsR was inhibited restored the single axon phenotype in DKO neurons" is confusing. Treatment performed in WT neurons but assessed in DKO neurons? This must be a typo.

      Thank you for pointing out this typo. It has been corrected.

      1. For completion, it would be informative to test whether IGF-1 antagonizes the effects of ALK and LTK ligands in axon formation.

      As suggested, we performed the requested experiment (with 3 independent repeats). In brief, four hours post-plating neurons were treated with control or ALKAL2-conditioned media and Igf-1 was added after 1 hour. Neurons were fixed at 36 hours, stained for MAP2 and Tau-1 and axons (Tau-1+) quantitated. Consistent with our previous findings, Igf-1 promotes the formation of multiple axons while ligand inhibits axon formation. In the ligand-treated neurons, addition of Igf-1 did not result in a statistically-significant change in the number of axons. These findings are consistent with our model that activation of Ltk/Alk promotes a decrease in cell-surface Igf1-r. This data has been added to the manuscript (Fig. 7J).

      1. The quality of the blot provided to illustrate levels of activated Igf-1r in Fig. 7A is clearly suboptimal. It is not apparent from that blot that phosphorylation of Igf1r is increased in the mutant neurons as the band intensities are indistinguishable. Was this performed in cortex extracts or cultured neurons? Is it affected by treatment with ALK/LTK ligands?

      We apologize for a labelling error that has caused confusion for both reviewers. We have replaced the blots and corrected the labels. We have noted in the legend that the experiments were performed using cultured cortical neurons.

      1. Given the physical interaction between ALK/LTK and IGF-R1, these receptors are presumably co-internalized upon ligand treatment, or? Does treatment with IGF1 induces internalization of ALK or LTK?

      This is a very interesting question. Unfortunately, due to the lack of suitable antibodies for the mouse versions of Alk or Ltk, we are not able to perform these experiments in cortical neurons with endogenous receptor expression. However, our co-immunoprecipitation experiments and in vitro kinase assays, indicate that only versions of LTK and/or ALK with active kinase domains can interact with IGF-1R and that the activated LTK/ALK receptors then phosphorylate IGF-1R and trigger IGF-1R internalization (Fig. 7 and Fig. 8 model). Thus, we would expect that treatment with IGF-1 in the absence of LTK/ALK activation will not affect LTK/ALK internalization but will trigger IGF-1R endocytosis.

      1. The last paragraph in the Results section may be more appropriate for Discussion to avoid repetition. But it is of course up to the authors to decide on stylistic issues.

      We prefer to include a summary of the experimental findings and the model figure at the end of the results.

      1. There is a discussion of possible redundancies between ALK and LTK in the Discussion section which appears to contradict itself. It is first stated (end of p. 18) that the two receptors are not redundant but both required for function. But in p. 19, the significant behavioral phenotypes observed in DKO mice, but not in single KO mice, are attributed to redundancy and compensation between the receptors. This needs some clarification. It's difficult to understand how there can be redundancy for behavior but not for structure or function.

      We have clarified in the discussion, that both receptors are required in the context of neuronal polarity and migration whereas in the case of behaviour, compensatory mechanisms in neural circuitry or perhaps non-redundant Igf-1r independent pathways result in a strong phenotype only in DKO and can compensate for single but not double knockouts.

      Reviewer #1 (Significance):

      see above

      Reviewer #2 (Evidence, reproducibility and clarity):

      Christova et al. analyzed single and double knockout mice for Alk and Ltk to investigate their function in the nervous system and describe defects in cortical development and behavioral deficits. The defects in the formation of cortical layers suggest a delay in radial migration. In culture, 40% of cortical neurons from knockout embryos extend multiple axons. The mechanism responsible for this phenotype is explored in some detail. The authors conclude that Alk and Ltk function non-redundantly to regulate the Igf-1 receptor (Igf-1r). Inactivation of Alk or Ltk increases surface expression and activity of Igf-1r, which induces the formation of multiple axons. The authors propose that Alk and Ltk interact with Igf-1r and promote its endocytosis after activation by their ligand Alkal2, thereby preventing the formation of additional axons. However, the defects in neurogenesis, migration and behavior may have a different cause and should not be attributed only to Igf-1r.

      We would like to thank the reviewer for all the insightful comments and suggestions which we feel have strengthened our study.

      We appreciate the reviewer’s acknowledgement that we have shown that Igf-1r is in involved in Alk/Ltk-mediated regulation of axon outgrowth. To provide evidence that Igf-1r is also important for Ltk/Alk regulated migration in vivo, we explored the effect of the Igf-1r inhibitor, PPP on the migration of neurons in WT and DKO mice by BrdU labelling. Excitingly, this analysis revealed that PPP administration resulted in a partial rescue of the migration defect in Ltk/Alk DKO mice, with BrdU+ neurons being localized to the most superficial layers in P2 mice (Fig. 6F). Thus, these data are consistent with our model that loss of Ltk/Alk can disrupt both neuronal polarity and migration via IGF-1r. We do agree with the reviewer that we have not directly shown that the behavioural defects can be attributed to Igf-1r and it is certainly possible that other pathways or mechanisms may be involved in the complex phenotype. We have updated the manuscript and discuss the potential involvement of other pathways in the discussion.

      Major comments<br /> 1) The role of Alk/Ltk in suppressing the formation of multiple axons is demonstrated by culturing neurons from knockout mice, suppression with siRNAs and treatment with inhibitors. These experiments consistently show that about 40% of cultured neurons extend more than one axon when Alk, Ltk or both are inactivated. Single and double knockout mice are largely normal with the exception of a delay in the formation of distinct cortical layers. The phenotypes of the knockout lines indicate a function in cortical development but Alk and Ltk are not "indispensable" as suggested (p. 18)._

      We will modify the wording to remove the statement that Alk and Ltk are “indispensable” for cortical patterning and rather will indicate that the receptors ‘contribute’ to the timing of cortical patterning.

      The morphology of cortical neurons was analyzed by Golgi staining. A few potential axons (Fig. 3E) were identified only by an absence of dendritic spines and their aberrant trajectory. These results indicate that there are ectopic extensions in the cortex but do not demonstrate that neurons extend multiple axons also in vivo. It has to be confirmed that these extensions are positive for axon-specific markers and that several axons originate from one soma to demonstrate a multiple axon phenotype in vivo. A quantification of the number of neurons with multiple axons would be required to conclude that this phenotype occurs at a similar frequency in vivo.

      As indicated in response to reviewer #1, we attempted to quantitate the Golgi stained images but found it impossible to trace individual neurites to the cell body and thus could not unambiguously identify and quantitate axons. Accordingly, and as suggested by the reviewer, we have modified our conclusion to simply state there are aberrant extensions in the cortex in vivo. Although we were unable to do quantitation, to further support our conclusions, we have provided additional Golgi stained images of WT and DKO mice from an independent experiment (Fig. S3B).

      2) According to the model presented in Fig. 7, Alkal2 activates Alk and Ltk, which stimulate the endocytosis of Igf-1r and thereby prevents the formation of additional axons. A quantification of Igf-1r surface levels by the biotinylation of surface proteins and Western blot shows an increase in knockout neurons. The authors suggest that Alk/Ltk activation stimulates Igf-1r endocytosis but do not demonstrate this directly. An increase in surface expression could also result from a stimulation of exocytosis or recycling.

      We showed that ligand-induced activation of Ltk/Alk in WT neurons resulted in a loss of biotin-labelled cell-surface Igf-1r, which is strongly indicative of increased internalization and cannot be explained by exocytosis. However, the reviewer is correct, that we cannot exclude the possibility that changes in exocytosis or recycling might also occur and that in the unstimulated DKO neurons, the increase in surface expression of Igf-1r could also result from a stimulation of exocytosis or recycling. Indeed, several papers (Laurino et al, 2005, PMID: 16046480; Oksdath et al, 2017, PMID: 27699600; Quiroga et al, 2018, PMID: 29090510) have reported that exocytosis mediated transport of IGF-1R and activation of IGF-1R/PI3K pathway is essential for the regulation of membrane expansion during axon formation. Accordingly, we have modified the discussion text to incorporate this possibility.

      3) The localization of Alk, Ltk and Alkal2 was determined by in situ hybridization. The signals are weak and it is not clear if they are specific because a negative control is missing. An analysis by immunofluorescence staining would be more informative.

      RNAscope is designed so that a single molecule of RNA is visualized as a punctuate signal dot with high specificity. In lower magnification images, such as those we showed to provide an overall view of expression in the cortex, it is difficult to discern the individual ‘dots’, particularly for genes with low expression, giving the impression that the signal is weak. However, at high magnification (63X) the signals are readily visible as seen in a new panel in Fig. S1B). We also neglected to mention that positive probes with all 3 labels (POLR2A: Channel C1, PPIB: Channel C2, UBC:Channel C3) as well as a negative probe (Bacterial dap gene) supplied by the manufacturer were used on our samples to validate specificity. We have corrected the oversight and have now added this information to the methods section.

      Regarding immunofluorescence, we have rigorously tested numerous commercially-available antibodies and have undertaken repeated attempts to produce our own antibodies that recognize mouse Ltk or Alk, and are appropriate for immunofluorescence, but have had no success. The high specificity enabled by the RNAscope technology is thus currently the most reliable way we can examine expression, with the added advantage that we can simultaneously assess expression of both receptors and the ligand in an individual cell within a section.

      Alk appears to be expressed mainly in the ventricular zone (VZ) while Ltk shows a low expression in the SVZ and the cortical plate (CP). This expression pattern is not consistent with a function in regulating axon formation in multipolar neurons, which extend axons in the lower intermediate zone (IZ) (Namba et al., Neuron 2014) and not in the VZ or SVZ (p. 18).

      It is well described that multipolar neurons can be found in the SVZ, while bipolar neurons are preferentially in the IZ. Neurons expressing Ltk, Alk and their ligand, Alkal2 can be found in both compartments (albeit levels appear higher in the SVZ), thus we feel our results are consistent with a role for the receptors in regulating neuronal polarization.

      It is also essential to analyze the subcellular localization of Alk and Ltk at least in cultured neurons. Ltk has been reported as an ER-resident protein that regulates the export from the ER (Centonze et al., 2019), which would not be consistent with the model.

      Unfortunately, the lack of antibodies with mouse reactivity prevents us from analyzing the subcellular localization of Alk and Ltk in cultured neurons. As mentioned by the reviewer, LTK has been reported as an ER-resident protein (in cancer cells) and similarly, many other tyrosine kinase receptors including IGF1R, have been reported to be localized to diverse intracellular compartments like Golgi, nucleus or mitochondria (reviewed in Rieger and O’Connor, 2021, Front Endocrinol:PMID: 33584548). However, since extracellular ligands for LTK and ALK are known, we feel it is a reasonable expectation that they will have a role as cell-surface receptors. Understanding the functions of RTK receptors and the interplay between the various compartments would nevertheless be an interesting area for future research.

      4) The results convincingly show that an increased activity of Igf-1r is responsible for the formation of additional axons by cultured knockout neurons. The model in Fig. 7 explains how Alk/Ltk suppress the formation of multiple axons in culture but a key question remains to be addressed: why does Igf-1r remain active in the future axon? Are Alk/Ltk restricted to or selectively activated in dendrites? It is important to determine if Alk and Ltk are absent from the future axon before or after neuronal polarity is established.

      We thank the reviewer for acknowledging that we have provided convincing data that increased activity of Igf-1r is responsible for the formation of multiple axons. Addressing why Igf-1r remains active in the future axon and if and how Ltk/Alk are selectively activated in dendrites and axons are all excellent questions, which we plan to pursue in future work, particularly when antibodies for Alk and Ltk become available.

      Which cells produce Alkal2 in neuronal cultures and in vivo?_ _These points can be easily addressed and should be investigated.

      We have confirmed that Alkal2 is expressed in the isolated cortical neurons, consistent with our demonstration that siRNA-mediated abrogation of Alkal2 expression in cultured neurons regulates polarity and that ligand levels do not change in Ltk/Alk double knock out mice (Fig. S1G and S6A). Whether other non-neuronal cell types also express Alkal2 would be an interesting future direction.

      Why does an increase of Igf-1r surface expression in knockout neurons result in a stimulation of Igf-1r autophosphorylation? Neurons are cultured in a defined medium without Igf-1 and increased surface levels by themselves should not lead to an increased activity.

      We have not mechanistically determined why/how Igf-1r displays enhanced autophosphorylation in DKO neurons. Thus, we can only speculate about possibilities. Perhaps there are low levels of Igf-1 in the cortical cell extracts, or is produced by the cortical neurons; there may be compensatory mechanisms engaged when Ltk/Alk are lost to ensure neuronal survival, or perhaps the increase in cell-surface Igf-1r promotes ligand-independent activation of receptors in the absence of ligand.

      The results presented in this manuscript are consistent with a role of Igf-1r in the formation of multiple axons in the absence of Alk/Ltk. However, inhibition of Igf-1r by various means does not prevent axon formation in controls. Igf-1 has been implicated in axon formation (Sosa at al., 2006) but a knockout of Igf-1r does not result in a loss of axons but a reduction of axon length in cultured neurons (Jin et al., PLoS One 2019). Axon-specific markers are used only for some experiments but not in Figs. 3D, 5B-D and 6 where the neuronal marker Tuj1 does not allow the unambiguous identification of axons. Staining with an axonal marker and a quantification of axon length are required to distinguish between a block in axon formation and a reduction in axon growth in Figs. 3A, 5 and 6.

      In the original submission we quantitated Tau-1 and MAP2 co-stained neurons in many experiments to demonstrate that Ltk/Alk act on axons, but in some cases we used Tuj1 to more easily visualize and quantitate neurites. Nevertheless, as requested by the reviewers, in the revised manuscript we have repeated and replaced most of the results with Tuj1 or phalloidin staining with experiments using Tau-1 and MAP2 antibodies, including Fig. 5B-D and Fig. 6A-D and G, as well as for Fig. S4B requested by reviewer #1). The new data is consistent with our results using Tuj1 staining and further support our conclusions that Ltk/Alk act via Igf1-r to regulate neuronal polarity. With regards to Fig. 3D, we have been experiencing ongoing technical issues in generating human stem cell derived cortical neurons and have been unable to undertake Tau1/MAP2 staining of the human cortical neurons. Given that the point being made is minor, we have removed this panel from the paper.

      With regards to the comment on that inhibition of Igf1-r did not prevent basal axon formation: in our prior quantitation of WT neurons in which Igf1-r was inhibited using either siIgf1-r or PPP, we noticed a trend towards an increase in the number of neurons with no axons, but this was not statistically significant. Upon the repeat of experiments and re-quantitation with Tau-1/MAP2 co-staining, we do see a statistically-significant increase in the number of WT neurons without axons. This is in agreement with several prior studies (including one cited by the reviewer) indicating Igf1-r is important for neuronal polarity (Sosa, 2006; PMID:16845384, Neito Guil 2017 PMID:28794445). The text has been modified accordingly.

      5) The analysis with layer specific markers and BrdU labeling reveals defects in the formation of cortical layers that suggest a delay in neuronal migration. The number of Sox2+ and Tbr2+ cells is lower in knockout neurons indicating a possible reduction in the number of proliferating progenitors and a defect in neurogenesis (Fig. 1). The number of neurons positive for layer-specific markers or BrdU was quantified as the percent of DAPI-positive cells. This does not allow distinguishing between a change in the distribution and a reduction in the number of neurons due to defects in neurogenesis. It would be more informative to quantify the total number Ctip+, Satb2+ or BrdU+ cells in the VZ, SVZ, IZ and CP._

      In the in vivo BrdU labelling experiment, we did not co-stain sections with DAPI. However, in the immunofluorescence analysis in mice of the same ages, we did determine the total number of cells (ie by DAPI) that is shown in the plots in Fig. 1A and Fig. S2A/B. These results show that there are a similar number of cells in WT and mutant SVZ/VZ, consistent with the notion that there is a change in distribution rather than in reduction in the number of neurons due to defective neurogenesis. We neglected to mention this important point in the results and have now modified the text accordingly.

      6) The deficits observed in behavioral tests do not correlate with the defects in neuronal development. While the single knockouts show defects in cortical development only the double knockout displays behavioral deficits. The behavioral phenotype could be completely independent of Igf-1r. Alk has been implicated in regulating retrograde transport (Fellows et al., EMBO Rep. 2020) and synaptic scaling (Zhou et al., Cell Rep. 2021). Since there is no clear correlation between structural and behavioral changes these data are not obviously linked to the other results.

      The reviewer is correct, that the single KO mice do not manifest noticeable behavioural defects except when older and challenged with the most demanding task, the Puzzle box, which measures complex executive functions. We speculate that alternative cortical re-wiring in the single knockouts is sufficient to maintain normal circuitry that cannot be compensated when both Ltk and Alk receptors are deleted. However, we do agree that Ltk/Alk regulated signalling events, besides Igf-1r/PI3K could contribute to the behavioural defects observed in the DKO mice, such as the ALK-LIMK-cofilin pathway which regulates synaptic scaling as cited by the reviewer (Zhou et al., Cell Rep. 2021). Nevertheless, the strong phenotype of the DKOs confirms that Ltk/Alk are important for proper brain function, thus our preference is to retain the behavioural data in the manuscript but to discuss that alternative Ltk/Alk pathways could contribute to the phenotype (which we have now incorporated into the text).

      It should be noted that the study by Fellows et al in EMBO Rep 2020 shows Igf1-r, not ALK regulates retrograde transport so we have not included this study in the updated text.

      Minor comments

      1) Fig. 3 shows defects in the corpus callosum where axons are restricted to the upper half in the wild type but not the knockout. These results could indicate a guidance defect but do not show a "failure in axon migration through the corpus callosum" (p. 17). It is also not demonstrated "that the aberrant axon tracts may be the result of effects on neuronal morphology" (p. 19). Without additional experiments to trace axonal projections e.g. by DiI labeling it is not possible to determine the actual cause for the observation shown in Fig. 3F._

      We agree with the reviewer and have modified the concluding sentence so that the defects are described without attributing the cause to the defects on neuronal morphology.

      2) Active kinases from SignalChem are used for the in vitro kinase assays. The increased phosphorylation of Igf-1r could also result from a stimulation of auto-phosphorylation and not a direct phosphorylation by Ltk. Previous results indicate that phosphorylation of Y1250/1251 leads to increased internalization and degradation (Rieger et al., Sci. Signal. 2020), which would be an alternative explanation how Alk/Ltk regulate surface expression. Antibodies that are specific for Igf-1r phosphorylation at Y1135/1136 or Y1250/1251 could address this possibility (Rieger at al., Sci. Signal. 2020).

      It is rather surprising that for the Igf-1r, which is such a well-studied receptor, the mechanisms that regulate trafficking, exocytosis recycling, etc are so poorly understood and that this topic is currently an active area of investigation. The focus of our study was on understanding the role of Ltk/Alk in the brain and as part of this effort we demonstrated that Ltk/Alk can control neuronal polarity through Igf-1r phosphorylation. We believe that shedding light on the detailed mechanism of how enhanced Igf-1r phosphorylation induced by Ltk/Alk activation regulates Igf-1r trafficking is an exciting project for future work, but we feel that to thoroughly investigate this question is beyond the scope of the current study. We have, nevertheless, highlighted these points with additional references in the discussion.

      3) The specificity of the siRNAs has to be verified in neurons by rescue experiments and the suppression of the targeted proteins confirmed by immunofluorescence staining.

      We agree that rescue experiments are the gold standard, and we attempted to do this. However, we found that nucleofection of both siRNAs and cDNAs encoding either EGFP alone or Ltk/Alk was highly toxic to neurons with few surviving the treatment. As an alternative we used a pool of siRNAs, to minimize off-target effects and used genetic KOs or chemical inhibitors to verify the observations.

      4) The position of molecular weight markers is missing for most Western blots.

      We added the position of molecular weight markers for all the western blots in the revised manuscript.

      5) It is not indicated which conditions show a significant difference in Fig. 6.

      We thank the reviewer for pointing this out. We added the significant differences to all figures, including Fig. 6.

      6) Why does the Western blot in Fig. 7A show a double band with the anti-phospho-Igf-1r antibody in the knockout? Which of the bands was used for the quantification?

      We apologize for a labelling error that has caused confusion for both reviewers. We have replaced the blots and corrected the labels.

      7) Details of the plasmids used and information (catalog number) for recombinant GST-Ltk and His-Igf-1r should be included in Materials and Methods.

      The additional information and catalog numbers have been added to the Materials and Methods.

      Reviewer #2 (Significance):

      The receptor tyrosine kinase Alk has been studied mainly for its involvement in several types of cancer but the physiological functions of Alk and its close relative Ltk remain poorly understood. The regulation of Igf-1r is an interesting and important result to understand the physiological function of Alk and Ltk. However, several points have to be addressed before the manuscript would be suitable for publication.

      We thank the reviewer for indicating that this is interesting and important study. We trust that the additional data and clarifications provided, have addressed the reviewers concerns.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #1

      Evidence, reproducibility and clarity

      This manuscript describes studies that indicate roles for the ALK and LTK receptors in neuronal polarity, cortical patterning and behavior in mice. I really liked the study and overall think that it deserves publication in a high-ranking journal. It reports important and novel results and benefits from a comprehensive analysis at multiple levels, including cell biological, biochemical and behavior. The points raised below are suggestions for consideration at the discretion of the authors.

      1. The term "DKO" appears in the Introduction without explanation. I assume this means double KO mice lacking both receptors from birth. It should be indicated here, just in case.
      2. The last paragraph of the Introduction is redundant with the Abstract. This is a stylistic question, which is up to the authors. Nevertheless, as a suggestion, they could take the opportunity here to explain the rationale of the study and why they did what they did.
      3. Is "single cell in situ mRNA analysis" standard in situ hybridization or something else? Why is it called "single-cell"? It could be misleading.
      4. In Fig. S1B, could the authors please include expression patterns of LTK in adult brain? It'd seem that is the most relevant place to look given the analysis that follows in the paper.
      5. I have an issue in general in the first part of the manuscript with regards to the labeling of cortical layers. How were CP, IZ and SVZ/VZ defined? Specific markers should be used to identify their actual boundaries. Guesswork from the DAPI pattern (if that is what was used) is not really appropriate.
      6. Comparing Fig. 1 and Fig. S2, there would seem to be little or no additive nor synergistic effects of the double mutation, as the phenotype in the DKO appears to be completely attributable to the Ltk KO. What does this mean? Providing the expression patterns of the two receptors at the ages used here (i.e., P2 and P7) would also be helpful.
      7. In Fig. 1F, again, how were the boundaries between the cortical areas (dotted lines) determined? This is particularly important for the mutant sections, as apparent cortical thickness would be easily be affected by the plane of the section. Simply assuming that the CP is of equal thickness than the one in the WT may be incorrect. I feel the authors cannot just place dotted lines in the figure without explaining the criteria that was used to determine their location. Also, there is a significant (many fold) increase in Ctip2 cells in the IZb of the mutant (1F) that it's not explained in the text. The quantification of Ctip2 cells in the CP and IZa of the mutant is missing in the histogram. It should be indicated, even if very low. Again, the key point here is the criteria used for the<br /> boundaries between areas. May be what it's marked as IZa in the mutant is still part of the CP, in which case the number of Ctip2 cells would be increased there, not decreased, as claimed in the text.
      8. In Fig. S3C-F, the all-critical quantification of Ctip2 cells at P2 seems to be missing in this figure. It would important to provide this in light of the comments above. Again, the same problem with the layer boundaries is clear here. The Ltk KO would have normal levels of Ctip2 cells if the CP thickness were to be larger (due to e.g., the plane of the section not being perfectly perpendicular to the brain surface).
      9. In Figure 2A and B, % positive cells is plotted but we are not told what is the reference (100%) level. Was it the total number of cells in the entire cortex (including SVZ and VZ)? That cannot be the case, since CP+IZ in WT alone reaches almost 100%. What is 100% here please? Also, the idea of drawing a little rectangle in the IZ and CP and counting only there is flawed. The values would change drastically depending on where the rectangle is placed. They need to count the whole field of view, as it was done in the previous figures. Finally, again, we are not told how the boundaries of the different cortex areas were established. As explained earlier, distance from the surface (or from<br /> the bottom) of the cortex would be greatly affected by the plane of the section. This problem will need a more satisfying solution for the data to be interpreted in the way it has been done.
      10. At the end of page 8, it is concluded that Alk/Ltk promote neuronal migration. Is this a cell-autonomous effect? Given the very sparse expression of these receptors (Fig S1), cell-autonomy (which is being implied by the authors) is not at all clear. Is the migration of Alk+ cells affected in the Ltk mutant? Vice-versa?
      11. In Fig. S4A, as every cell in these panels bears probe signal, it'd be important to present a negative control, perhaps from KO cultures or wild type cells lacking receptor expression in the same field as expressing cells. At a 75%, 1 in 4 cells in any field should be receptor-negative.
      12. Figure S4B is difficult to interpret in the absence of Tau and MAP2 markers, as GFP does not discriminate between axons and dendrites. In general, the authors are recommended to show more than one cell per condition in their figures. Readers need to be convinced that these are robust phenotypes easily observed on many cells in the same field.
      13. In Fig. S4C and D, do the KO neurons become bipolar? I don't see examples of multipolar neurons in the images provided.
      14. Is there a way to quantify the effects shown in Fig. 3E?
      15. The DKO display a dramatically different behavior phenotype compared to single Kos. How can this result be explained given that DKOs are indistinguishable from single KOs in all other parameters studied?
      16. At the end of the behavior section, the authors attribute the phenotypes observed to defects in neuronal polarization. Given that polarization was only studied in vitro, it may be a premature to conclude that neurons fail to polarize in vivo in the absence of direct evidence showing this.
      17. Regarding P-AKT studies, it would be interesting to assess the effects of the ALK7LTK ligands (e.g., from conditioned medium) on the levels of P-AKT in WT neurons.
      18. In the mid part of page 14, the sentence "Treatment of WT cortical neurons with AG1024 at a dose (1 μM) at which only IGF-1R but not InsR was inhibited restored the single axon phenotype in DKO neurons" is confusing. Treatment performed in WT neurons but assessed in DKO neurons? This must be a typo.
      19. For completion, it would be informative to test whether IGF-1 antagonizes the effects of ALK and LTK ligands in axon formation.
      20. The quality of the blot provided to illustrate levels of activated Igf-1r in Fig. 7A is clearly suboptimal. It is not apparent from that blot that phosphorylation of Igf1r is increased in the mutant neurons as the band intensities are indistinguishable. Was this performed in cortex extracts or cultured neurons? Is it affected by treatment with ALK/LTK ligands?
      21. Given the physical interaction between ALK/LTK and IGF-R1, these receptors are presumably co-internalized upon ligand treatment, or? Does treatment with IGF1 induces internalization of ALK or LTK?
      22. The last paragraph in the Results section may be more appropriate for Discussion to avoid repetition. But it is of course up to the authors to decide on stylistic issues.
      23. There is a discussion of possible redundancies between ALK and LTK in the Discussion section which appears to contradict itself. It is first stated (end of p. 18) that the two receptors are not redundant but both required for function. But in p. 19, the significant behavioral phenotypes observed in DKO mice, but not in single KO mice, are attributed to redundancy and compensation between the receptors. This needs some clarification. It's difficult to understand how there can be redundancy for behavior but not for structure or function.

      Significance

      see above

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity):

      The manuscript from Li et al. describes the authors' attempt to redirect the exocytic Rab Sec4 to endocytic vesicles by fusing the GEF-domain of Sec2 to the CUE domain of the endosomal GEF Vps9, which binds to ubiquitin. The authors show that the localization of the Sec2GEF-GFP-CUE construct is slightly shifted from polarized towards non-polarized sites. Sec2GFP-CUE positive structures acquire Sec4 and Sec4 effectors like exocytic vesicles but are less motile and show delayed plasma membrane fusion. Expression of Sec2GEF-GFP-CUE was enhanced if expressed in a subset of secretory and endocytic mutants and cause delayed Mup1 uptake from the plasma membrane. As Vps9, Sec2GEF-GFP-CUE accumulated on Class E compartments in vps4Δ strains.<br /> The authors ask here whether vesicular identity is largely predetermined by the correct localization of the specific GEFs of small GTPases and thus localization of the Rab. Although this an interesting hypothesis, the authors observed that endocytic traffic was not reversed by relocating Sec4 to these vesicles. This seems to be due to the strong affinity of the Sec2 GEF-domain for Sec4 but probably also due to the rather weak relocalization via the CUE domain. Thus, only a portion of Sec2 was displaced from its native site. Since the efficiency of this rewiring was not defined, it remains unclear whether the observed mild effects indeed speak against the assumed dominant role of the GEFs and small GTPases in shaping organelle identity or whether they are rather due to an inefficient relocalization.

      Our data demonstrate a dramatic relocalization of Sec2-GEF-GFP-CUE relative to Sec2-GEF-GFP. In the case of Sec2-GEF-GFP or Sec2-GEF-GFP-CUE M419D the cytoplasmic pool is predominant and only 30% of cells exhibit a detectable concentration, while in the case of Sec2-GEF-GFP-CUE 80% of cells show bright puncta and there is little or no detectable cytoplasmic pool (Fig 1A). Clearly the CUE domain can function as a localization domain that relies upon ubiquitin binding. Furthermore, half of the Sec2-GEF-GFP-CUE puncta colocalize with Vps9 (Fig S1). The high cytoplasmic background of Vps9 could mask additional colocalization, therefore we reexamined colocalization in a vps4__D_ _mutant in which the Vps9 cytoplasmic pool is reduced due to increased association with the expanded Class E late endosomes. In this situation we observe about 80% colocalization with Vps9 as well as substantial colocalization with Ypt51 and Vps8 (Fig 2). We now also show significant colocalization with PI(3)P (Fig S3D). Thus, our data demonstrate that addition of the CUE domain does indeed relocalize Sec2GEF to endocytic membranes. The Sec2 GEF activity then leads to the recruitment of Sec4 and Sec4 effectors, including Myo2 which in turn leads to their delivery to polarized sites. We now show by EM that the bright Sec2-GEF-GFP-CUE puncta correlate with clusters of 80 nm vesicles (Fig 5B). Our data argues that these are hybrid compartments carrying both endocytic and exocytic markers. We have restructured our paper to help clarify and emphasize this key point.

      Specific comments:<br /> 1. The authors state decidedly that the recruitment of Vps9 occurs ubiquitin-dependent via the CUE-domain. While the CUE-domain is the only known and a likely localization determinant of Vps9, it was not a strong localization determinant. Apart from being present in some puncta, Vps9 localized strongly to the cytosol (Paulsel et al. 2013, Nagano et al. 2019). Shideler et al. also showed that ubiquitin-binding is not required for Vps9 function in vivo, which indicates that other localizing mechanisms may play a role e. g. by positive feedback of GEF-domain-Rab5 interactions which might be initiated by the other Rab5-GEF Muk1 or as suggested by transport from the Golgi (Nagano et al. 2019). These observations indicate that the CUE-domain is a rather weak recruitment domain, which was not discussed in this manuscript. The localization of the Sec2GEF-GFP-control to the polarized sites in 30% of the cells furthermore suggests that the used Sec2GEF-GFP-CUE retains some native localization via the GEF-domain. Since the relocation efficiency of Sec2GEF-GFP-CUE was not defined, the obtained phenotypic effects allow for only vague conclusions. Although the mild endo- and exocytosis defects as well as the accumulation of Sec2GEF-GFP-CUE at Class E compartments indicate that the CUE-domain indeed conferred some relocation to endosomes, this was not shown for the sec2Δ strain e. g. by looking at colocalizations with endocytic versus exocytic markers and comparing their relative abundance at the Sec2GEF-GFP-CUE-positive structures. While some of the Sec2GEF-GFP-CUE-positive structures colocalized with Mup1 in the Mup1-uptake assay, it would be important to clarify how many endosomal properties are retained and how many exocytic properties are gained by these chimeric vesicles e. g. by looking for the presence of specific phosphoinositides, or Rab5 and Rab5 effectors. A competition between endosomal and the acquired exocytic factors could also be another possible explanation for the immobility of the Sec2GEF-GFP-CUE structures and less efficient recruitment of Sec4 effectors in addition to the proposed lack of PI4P.

      As summarized above, we observed dramatic relocalization of Sec2GEF that was strongly dependent upon the ability of the CUE domain to bind to ubiquitin. We also observed colocalization with Ypt51 and Vps8 as well as transient colocalization with internalized Mup1. We now also show significant colocalization with PI(3)P (Fig S3D). Full length Vps9 is probably subject to additional levels of regulation, perhaps autoinhibitory in nature, however our construct contains only the CUE domain which can clearly function as an efficient localization domain on its own. The high cytoplasmic pool of Vps9 reflects the rapid turnover of its ubiquitin binding sites, since it is efficiently recruited to membranes in vps4__D_ cells. The relocalized Sec2GEF domain was quite effective in recruiting Sec4 as well as most known Sec4 effectors. The recruitment of Myo2 leads to localization to sites of polarized growth. All of our studies were done in a sec2__D _background except for the analysis of dominant growth effects, as now explicitly stated at the beginning of the Results section.

      1. While the colocalization of the Sec2GEF-GFP-CUE-signal with Sec4 indicates that this GEF-construct is generally active, it remains unclear whether the activity of the tagged constructs differ from that of the wild type Sec2 protein. This could be analyzed in vitro via a MANT-GDP GEF-activity assay (Nordmann et al., 2010). Again, it remains unclear how much of the Sec2GEF-Sec4 colocalization represents the retained native localization versus synthetic localization at chimeric endo-exocytic vesicles.

      The structure and nucleotide exchange mechanism of the Sec2 GEF domain have been thoroughly analyzed in prior studies and are well understood. There is no reason to think that the constructs we generated here would alter the exchange activity as the fusions are far removed from the Sec4 binding site and our analysis here confirms that they are active in vivo. We do not feel that there would be much to be gained by doing in vitro exchange assays and it would entail a great deal of work.

      1. The authors mention that tagging with GFP increases the stability of the expressed constructs. However, it remains unclear whether this is also the case for the other tags (NeonGreen, mCherry) used in the other experiments. Are the constructs expressed at similar levels?

      We have compared the levels of the various tagged constructs and they appear to be similar (Fig S5A).

      1. In Figure 5: The incomplete colocalization of Sec2GEF-GFP-CUE with Vps9 is explained by the short-timed accessibility of ubiquitin moieties. Apart from the likely retained native localization or weak CUE-domain-function, this observation could also be due to competition between Vps9 and Sec2GEF-GFP-CUE for the available ubiquitin target structures.

      As previously shown, Vps9 normally displays a prominent cytoplasmic pool. Deletion of Vps4 leads to recruitment of this pool to expanded endosomes through an increase in the lifetime of the ubiquitin binding sites. The high cytoplasmic background in VPS4 cells could obscure some colocalization with Sec2GEF-GFP-CUE and indeed we observe increased colocalization in vps4__D_ _cells in which the cytoplasmic pool of Vps9 has been recruited to endosomes. Expression of Sec2GEF-GFP-CUE does not appear to significantly alter the localization of Vps9.

      Minor remarks:<br /> 1. Fig. 3C do not contain the arrowheads as indicated in the legend, making it harder to interpret.

      These have been added.

      1. The image chosen for Sec2-GFP in Fig. 4B suggests less colocalization between Sec2-GFP and Sec8 than between Sec2GEF-GFP-CUE and Sec8. They rather look next to each other.

      The images initially chosen were not representative. We have replaced them with better images from the same experiment.

      1. Figure 5: While resolution limits are possibly reached regarding endosomes, it might be interesting to check by thin section electron microscopy whether and how class E compartment formation is affected by Sec2GEF-GFP-CUE expression.

      We have now done EM using permanganate fixation of both VPS4 and vps4__D_ cells (Fig 5B and below). In both backgrounds Sec2GEF-GFP-CUE expression leads to the formation of clusters of 80 nm vesicles that appear to correlate with the fluorescent puncta visible by light microscopy. The vps4__D _cells have in addition curved linear membrane structures that represent class E endosomes (see images at end of this file). The class E endosomes appear similar in cells expressing Sec2GEF-GFP-CUE, Sec2-GFP or Sec2. We did not observe any obvious spatial relationship between the class E structures and the vesicle clusters.

      1. Discussion: "Furthermore, delivery of Mup1-GFP to the vacuole was slowed in Sec2GEF-GFP-CUE cells..." - The authors studied "the clearance of Mup1-GFP from the plasma membrane" and not vacuolar delivery. They did not show much vacuolar localization.

      We now include quantitation of Mup1-GFP at both the plasma membrane and vacuole (Fig 6 and Fig S8). This shows a reduced rate of depletion from the plasma membrane and a delayed appearance in the vacuole.

      Literature:<br /> Nagano, M., Toshima, J. Y., Siekhaus, D. E., & Toshima, J. (2019): Rab5-mediated endosome formation is regulated at the trans-Golgi network. Nature Communications Biology, 2 (1), 1-12.<br /> Nordmann, M., Cabrera, M., Perz, A., Bröcker, C., Ostrowicz, C., Engelbrecht-Vandré, S., & Ungermann, C. (2010): The Mon1-Ccz1 complex is the GEF of the late endosomal Rab7 homolog Ypt7. Current Biology, 20(18), 1654-1659.<br /> Paulsel, A. L., Merz, A. J., & Nickerson, D. P. (2013): Vps9 family protein Muk1 is the second Rab5 guanosine nucleotide exchange factor in budding yeast. Journal of Biological Chemistry, 288 (25), 18162-18171.<br /> Shideler, T., Nickerson, D. P., Merz, A. J., & Odorizzi, G. (2015): Ubiquitin binding by the CUE domain promotes endosomal localization of the Rab5 GEF Vps9. Molecular Biology of the Cell, 26 (7), 1345-1356.

      Reviewer #1 (Significance):

      • see above
      • has some deficits in interpretation as the Rab relocalization was not complete and thus conclusions are limiting

      Reviewer #2 (Evidence, reproducibility and clarity):

      This paper tries to address a fundamental question in cell biology, namely, what machinery is sufficient to tell a vesicle know where to go and what to do when it gets there. Several groups have shown that localization of some Rab/Ypt GEFs to an orthogonal compartment can lead to redirecting a Rab/Ypt to that membrane, where they can bind their partners abnormally. This story tries to explore what happens next.

      Here, Novick and colleagues took a part of the SEC2 GEF for secretory vesicle SEC4 Rab/Ypt and anchored it to endocytic structures to ask whether that was enough to relocalize those structures and drive inappropriate trafficking events. A challenge and advantage in the study is the fact that not all of the GEF relocalized-and that enables the cells to survive as SEC4p is needed for cell growth and membrane delivery--but this incomplete relocalization complicates phenotypic analysis--some SEC4 is on secretory vesicles and some is relocalized apparently to endocytic structures. Another challenge is that the two compartments both show "polarized" distributions so it is hard to know what compartment the reader is looking at, in a given figure. This makes the story very challenging to digest for a non-yeast expert trying to understand the conclusions.

      The authors show that the CUE domain can serve to partially localize SEC2GEF-GFP-CUE and this function relies on its ability to interact with ubiquitin. The localization is distinct from that of full length Sec2, nonetheless "many structures bearing Sec2GEF-GFP-CUE localize close to the normal sites of cell surface growth despite their abnormal appearance". The authors conclude that SEC4p and its effectors were recruited to these puncta with variable efficiency and the puncta were static; normal secretion was not blocked. This is not really a surprise as some SEC4p is still directed to secretory granules and cells do not show a vesicle accumulation phenotype by EM. Missing seems to be a clear-cut visual assay for exocytosis of secretory granules or endocytic structures despite attempts to include live cell imaging.

      We now show that the bright Sec2GEF-GFP-CUE_ puncta correspond to clusters of 80nm vesicles (Fig 5B). Our FRAP analysis demonstrates that Sec2GEF-GFP-CUE _is able to enter into pre-existing, bleached puncta (Fig 1E). One interpretation is that the vesicle cluster remains static, while individual vesicles enter and exit the cluster.

      The authors showed that SEC2-GFP-CUE structures fail to acquire Sro7 and do not seem to be able to assemble a complex with the tSNARE SEC9. Is this because Sro7 is being retained on the remaining secretory vesicles that also have SEC4 and other effectors that may be recruited to those structures by coordinate recognition?

      We demonstrate that at least half of the Sec2GEF-GFP-CUE puncta colocalize with Vps9 and this becomes even more evident in a vps4__D_ _mutant (Fig 2A). There is also substantial colocalization with the Rab5 homolog Ypt51, the endocytic marker Vps8 and PI(3)P (Fig 2 and Fig S3D). Nearly all of these puncta also colocalize with Sec4 and most of its downstream effectors. Thus, it seems that we have generated a hybrid compartment, as we intended. The surprise is how well the cells can cope with this situation. One possible explanation is offered in the Discussion: In yeast the TGN is thought to play the role of the early endosome and may be the site of Vps9 membrane recruitment. Thus Sec2GEF-GFP-CUE might be initially recruited to the TGN and the hybrid vesicles formed from this compartment might function to bring secretory cargo from the TGN to the cell surface just like normal secretory vesicles, with the caveat that the presence of endocytic machinery is somewhat inhibitory to Sro7 function, slowing fusion.

      There seem to be no issues with data as presented; a diagram of the SEC2-GFP-CUE would help the reader as would use of terms "secretory vesicle" and "endocytic vesicle" and how they were always distinguished rather than "polarized structure" which cannot distinguish these compartments.

      We have tried to be careful in our use of terms. We refer to the Sec2-GFP-CUE puncta using the unbiased terms “structures” or “puncta” until we show EM demonstrating that these puncta represent clusters of 80 nm vesicles.

      CROSS-CONSULTATION COMMENTS<br /> The two assessments come to the same conclusion--I agree that better definition of the precise phenotypes could be valuable but the limitation of incomplete relocalization will be hard to overcome in the absence of enormous effort.

      Reviewer #2 (Significance):

      This story represents a valiant effort and presents clean data but the impact and significance of the findings are limited due to the difficult phenotypic starting points (SEC4 in two places), and lack powerful exo- or endocytosis assays and better compartment-specific markers.

      The work will be of interest to yeast cell biologists studying the secretory and endocytic pathways. My expertise is mammalian cell biology of the secretory and endocytic pathways.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity):

      This manuscript by Gouignard et al., reports that a matrix metalloproteinase MMP28 regulates neural crest EMT and migration by transcriptional control rather than matrix remodeling. The manuscript is clearly written and provides sufficient evidence and control experiments to demonstrate that the MMP28 can translocate into nucleus of non-producing cells and that nuclear localization and catalytic activity are essential for the activity of MMP28 to regulate gene transcription. ChIP-PCR analysis also suggests that MMP28 can bind to the proximal promoters of Twist and others. However, since weak binding is also detected between MMP14 and the promoters, a more direct evidence that such binding can indeed promote Twist expression will be more appreciated.

      Thank you for this comment. First, to represent the data from our ChIP assays we normalized all intensities to the GFP condition such that all levels are expressed fold change to GFP and we performed statistical comparisons. This shows that the enrichment of promoter regions by MMP28 and MMP14 are not equivalent.

      Second, to substantiate our previous ChIP data, we performed a new set of ChIP experiments, by performing three independent chromatin immunoprecipitations (biological replicates), and used primers targeting three new domains in the proximal promoter of Twist and primers against two domains in the proximal promoter of E-cadherin and one domain 1kb away from transcription start of E-cadh. We found that pull down with MMP28 significantly enriches the three tested domains within the proximal promoter of Twist but not those of the E-cadherin promoter, compared to GFP pull down. These data were added to Figure 7.

      However, we do not propose that MMP28 might act as a transcription factor and be able to promote Twist expression on its own. We apologize if some of the initial description of our data were too blunt and might have misled the reviewers. First, the protein sequence of MMP28, like those of all other MMPs, does not contain any typical DNA binding sites. In addition, ectopic overexpression of MMP28 is not sufficient to promote ectopic Twist expression (as shown in supplementary Figure 4) whereas, by contrast, Twist is able to promote ectopic expression of Cadherin-11 (see new Supplementary Figure 11). This indicates that MMP28 has an effect on Twist expression in the context of neural crest only and is not capable of activating Twist expression by itself.

      Also, it should be added that enrichments of promoter domains by MMP28 pull-down are very modest in comparison to enrichments obtain with Twist pull-downs. Therefore, a more plausible role for MMP28 is to be part of a regulatory cascade with other factors involved in regulating the expression of the target genes important for EMT. Other MMPs such as MMP14 and MMP3 have been shown to interact with chromatin with some transcriptional downstream effects but multiple domains of these proteins seem to equally mediate such interactions. None of the data published in these studies rules out a relay via cofactors. We extensively modified the text describing our data and provided additional context.

      Identifying the putative partners and their functional relationship with MMP28 is a project on its own and beyond the scope of this study.

      While the nuclear translocation and transcription regulation activity of MMP28 is clearly the focus of the study, there are some minor issues that should be further clarified in the functional studies in the earlier part of the manuscript.

      First, the effect of the splicing MO is somewhat unexpected. I would think that the splicing MO would lead to the retention of intron one and therefore premature termination or frameshift of the protein product, but RT-PCR or RT-qPCR suggest that there is no retention of intron 1, but a reduction in the full-length transcript, exon 1, or exon 7-8. Why is that?

      Thank you for this comment. This is presumably due to nonsense mediated RNA decay. We have not explored the biochemistry of MMP28 RNA following injection with MOspl. Splicing MOs can have multiple effects. As explained on the GeneTools website splicing MOs disturb the normal processing of pre-mRNA and cells have various ways to deal with this and there are multiple possible outcomes. The PCR with E1-I1 suggests that intron 1 is not retained. Therefore, a putative concern would be that MOspl led to exon-skipping and to the generation of a truncated form of MMP28. However, we have checked that it is not the case. The fact that the PCR using E7-E8 primers indicates a reduction as well suggests an overall degradation of the mRNA for MMP28. Importantly, the effect of MOspl can be rescued using MMP28 mRNA indicating that the knockdown is specific.

      Second, the effect of the splicing MO and ATG MO in NC explant spreading seems to be somewhat different, with ATG MO strongly repressed explant spreading, cell protrusion, and cell dispersion, while splicing MO does not affect cell dispersion, but affects the formation of cell protrusions. Does this reflects different severity of the phenotype or does the product of splicing MO display some activity?

      Thank you for this comment. However, we think that there may be a confusion. Data on Fig2 (MOatg) and Fig3 (MOspl) both show a decrease of neural crest migration in vivo (Figure 2a-b) and of neural crest dispersion ex vivo (Fig2c, Fig3i-k). Along the course of the project we have never observed a difference in penetrance or intensity of the phenotypes between the two MOs.

      Also, the switch between ATG MO and splicing MO is a bit confusing, maybe it is better to keep splicing MO only in the main text and move results involving ATG MO to supplementary studies.

      The reason is purely historical. We had an effect with MOatg that can be rescued but there is no available anti-Xenopus MMP28 to assess its efficiency. So we turned to MOspl to have an internal control of efficiency by PCR. This provides an independent knockdown method reinforcing the findings. Both MOs have been controlled for specificity by rescue with MMP28 and display similar effect on NC migration/dispersion. We see no harm in keeping both in the main figures but if the reviewer feels strongly about this we could perform the suggested redistribution of data between main and supp figures.

      Lastly, in Figure 3C and 3J, it says that the distance of migration or explant areas were normalized to CMO, while normalization against the contralateral uninjected side, or explant area at time 0 makes more sense.

      Thank you for this comment as it will allow us to explain better these quantifications. Regarding in vivo measurements (Figure 3c), it is indeed the ratio between injected and non-injected sides that is performed in all conditions and then the ratios are normalized to CMO. We have now clarified this point on all instances throughout the figures.

      Regarding ex vivo measurements (Figure 3j), NC explants are placed onto fibronectin and left to adhere for 1 hour before time-lapse imaging starts. NC cells extracted from MMP28 morphant embryos are not as efficient at adhering and spreading as control NC cells. Therefore, normalizing to t0 would erase that initial difference between control and MMP28 conditions. By normalizing to CMO at t_final we can visualize the initial defect of adhesion and spreading as well as the overall defects since CMO at t_final represents the 100% dispersion possible over the time course of the movie.

      Referee Cross-commenting

      I agree with comments from both Reviewers 2 and 3, especially that whether MMP28 regulates placode development (through Six1 expression) should be addressed.

      Reviewer #1 (Significance):

      This work provides novel insights of how a metalloprotease that is normally considered to function extracellularly can transfer into the nucleus of neighboring cells and regulate transcription. This would be of interest to researchers studying EMT, cell migration, and the functions of extracellular proteins in general. My expertise is in neural crest EMT and migration, and cytoskeletal regulation of cell behavioral changes. I do not have enough background on biochemical analysis.

      Reviewer #2 (Evidence, reproducibility and clarity):

      Summary:

      In this study, Gouignard et al. beautifully use the Xenopus neural crest as a model system to examine the role of the matrix metalloproteinase MMP28 during EMT. The authors show that mmp28 is expressed by the placodes adjacent to the neural crest. Using in vivo and in vitro perturbation experiments, they show that the catalytic function of MMP28 is necessary for the expression of several neural crest markers, as well as neural crest migration and adhesion. Next, the authors use grafting, confocal imaging, and biochemistry to convincingly demonstrate that MMP28 is translocated into the nucleus of neural crest cells from the adjacent placodes. Finally, nuclear localization of MMP28-GFP is necessary to rescue twist and sox10 expression in MMP28 morphants, and ChIP-PCR experiments suggest direct interactions between MMPs and the proximal promoters of several neural crest genes. These results have significant implications on the field of EMT and highlight an underappreciated role for MMPs as direct regulators of gene expression.

      Major comments:

      Overall, the experiments presented in this study are thoroughly controlled and the results are clearly quantitated and rigorously analyzed. Most claims are well supported by multiple lines of experimental evidence; however, there are a few experiments or observations that this reviewer thinks should be reconsidered for more clarity and accuracy.

      1. Supplementary Figure 1 shows the effect of MMP28-MOspl on additional ectodermal markers and shows that there is a significant loss of six1 expression from the placodal domain following MMP28 knockdown. The authors note this as a "slight reduction" on line 95, but since this shows a larger reduction in gene expression than some of the neural crest markers (snai2, sox8, foxd3), this reviewer thinks these results warrant a more significant discussion in this study.

      Thank you for this comment. We apologize for the poor choice of word regarding the description of the effect on Six1 expression. We corrected the associated paragraph.

      Although we do observe a reduction of Six1 expression upon MMP28 knockdown, this cannot explain the observed downregulation of some neural crest genes in our MMP28 experiments. There are noticeable differences between the effects of Six1 loss of function that have been reported in the literature and the MMP28 knockdown phenotypes we describe. As suggested by the reviewer, we added a paragraph in the discussion.

      Does MMP28 localize to the nucleus of placodal cells as it does with neural crest? If so, is it through interaction with the six1 proximal promoter? If MMP28 does not localize to the nucleus, that would suggest MMP28 function with a different mechanism between epithelial cells distinct from role in EMT. These questions could be addressed by analysis of the placode cells in the images in Figure 5 and use of primers against the six1 proximal promoter on any remaining samples from the ChIP experiment.

      Thank you for this comment. To address whether nuclear entry is specific to the neural crest-placodes interaction, we performed new grafts:

      • 1/ we replaced neural crest cells from embryos expressing MMP28-GFP by placodal cells injected with Rhodamine-dextran. This generates grafted embryos with control placodes next to placodes overexpressing MMP28-GFP. There, we can analyze entry of MMP28-GFP in placodal cells that do not overexpress it. We detected MMP28 in the cytoplasm and in the nucleus of these placodal cells. However, the rate of nuclear entry was lower than in NC cells.

      • 2/ To assess the importance of the cell type producing MMP28, we grafted NC cells injected with Rhodamine-dextran next to caudal ectoderm expressing MMP28-GFP. MMP28 was detected in cytoplasm and the nucleus of the NC cells but with a lower efficiency than when NC are grafted next to placodes expressing MMP28-GFP.

      • 3/ We made animal caps sandwiches with animal caps injected with Rhodamine-dextran and animal caps expressing MMP28-GFP. In this case MMP28-GFP is detected in the cytoplasm but fails to reach the nucleus.

      Collectively, these data indicate that placodes can import MMP28 produced by placodes and that NC can import MMP28 produced by other cells than placodes. However, in both cases the rate of nuclear entry was lower than in the NC-placode situation. Finally, the animal cap sandwiches indicate that entry into the cells does not predict entry into the nucleus. All these data were added to Supp Figure 7. Statistical comparisons of the proportion of cells with cytoplasmic and nuclear MMP28-GFP in all grafts were added to Figure 5.

      The Six1 promoter analysis suggested is beyond the scope of this study as our focus is primarily on the role of MMP28 in neural crest development.

      1. In Figure 2c, the authors rescue MMP28-MOatg with injection of MMP28wt mRNA. Does the MOatg bind to the exogenous mRNA? If so, this may just reflect titration of the MOatg. If this is the case, this experiment should be repeated with MOspl instead of MOatg.

      Thank you for this comment. MOatg is designed upstream of the ATG and thus the binding site is not included in the expression construct. We added this important technical information in the methods. Of note, we already have the suggested equivalent of Fig2C with the MOspl on figure 3.

      1. Is there a missing data point in Figure 2d corresponding to the upper bounds of the whisker in the 6 hour time point for the MMP28-MOatg dataset?

      Thank you for pointing this out. The top data point was indeed missing from the graph, and we apologize for this oversight. We have now updated the figure with the correct graph.

      1. The authors present ChIP-PCR results in Figure 7 as the major evidence to support the mechanism of nuclear MMP28 in regulating neural crest EMT through physical interaction with target gene promoters. However, the experimental design and presentation in Figure 7 are somewhat unconventional, and as such, difficult to interpret. First, instead of displaying the band brightness across the gel, the authors should normalize their bands to their negative GFP control, thus allowing for interpretation as a "fold enrichment over GFP control". It would be most clear to present these results in the form of a plot similar to Shimizu-Hirota et al., 2012, Figure 6D. Using qPCR instead of gel-based quantitation would further increase reproducibility by removing any bias in image analysis.

      Thank you for this comment. For each band the value of the adjacent local background was subtracted. We have now normalized to GFP to provide graphs showing the fold change to GFP enrichment as requested.

      However, we would like to point out that we do not propose that MMP28 might act as a transcription factor and be able to promote Twist expression on its own. First, the protein sequence of MMP28 does not contain any typical DNA binding sites, as is the case for any other MMPs. In addition, ectopic overexpression of MMP28 is not sufficient to promote ectopic Twist expression (see sup figure 4) contrary to Twist that can ectopically induce Cadherin-11 for instance (see sup figure 11). Further, enrichments of promoter domains by MMP28 pull downs are very modest in comparison of the enrichments promoted by Twist pull downs.

      A more plausible role for MMP28 is that it is recruited via an interaction with other factors involved in regulating the expression of the target genes related to EMT. Identifying the partners and their functional relationship with MMP28 is a project on its own, and beyond the scope of this study.

      Second, a proximal promoter sequence represents only ~250 bp upstream from the transcriptional start site. What is the rationale for testing multiple loci up to 3 kb upstream?

      Thank you for pointing this out. The use of the term “proximal” was indeed misleading we have now corrected this part in the text. Regulatory sequences can be located anywhere so we initially had a broader approach to test for interactions. Following on this reviewer’s comment, we removed the data points corresponding to the very distal sites. In addition, we performed three new independent ChIP-PCR assays with primers in the proximal portion of Twist and E-cadherin promoters and found enrichment in ChIP with MMP28-GFP compared to GFP for Twist but not for E-cadherin (whose expression was not affected by MMP28 knockdown). These data were added to Figure 7.

      It is surprising to see that most of these proteins do not show significant enrichment to a particular locus across this ~3 kb territory, while this reviewer would expect to see enrichment close to the TSS that quickly is lost as you move further upstream. Can you explain why MMP28, MMP14, and often Twist, show similar enrichment across this long genomic region?

      Thank you for this comment. Our initial choice of representation did not allow to compare profiles properly. Fold-enrichment to GFP, as suggested by this reviewer, now shows that Twist, MMP28 and MMP14 do not display the same pattern of enrichment across the various loci and that MMP28 pull downs leads to significant enrichments of some of the domains tested in Cad11 and Twist promoters.

      Third, the authors should include additional genomic loci to act as negative controls. For example, E-cadherin was unaffected by MMP28-MOspl, thus there may be no physical interaction between the E-cadherin locus and MMP28. It would be ideal to display results from at least one neural crest-related and one non-neural crest-related gene. Finally, this experiment requires statistical analyses to increase confidence in these interactions.

      Thank you for this comment. We tested binding to E-cadherin promoter for GFP and MMP28-GFP and found no enrichment with MMP28. We also performed statistics as requested. These data were added to Figure 7.

      Minor comments:

      1. The authors should expand their abstract to more explicitly describe the experiments and results presented within this study.

      Done

      1. In the introduction, line 57 is unclear. "MMP28 is the latest member..." Is this chronologically? Evolutionarily? After this, the authors' statement that the roles of MMP28 are "poorly described" (lines 59-60) seems contradicting with their next sentences citing several studies that document the roles of MMP28 in diverse systems.

      Thank you for this comment. The term “poorly described” was meant with respect to other MMPs with more extensive literature. We have now rephrased this part. Regarding the “latest member” we meant the last to be identified. We have now rephrased this part.

      1. To increase clarity, the authors should define which cell types are labeled by in situ hybridization for sox10 and foxi4.1 in Figure 1e.

      Thank you, we performed the requested clarifications and expanded the change to add the cell types labelled by the other genes used on the figure (see figure legend).

      1. The PCR analysis for mmp28 splicing shown in Figure 1g is very clear and well demonstrates the efficacy of the MMP28-MOspl. However, the authors should note in the figure legend what the "ODC" row represents as this is unclear.

      We added the definition of ODC in the figure legends and in the methods.

      1. On line 118 the authors first reference "MOatg" but should explicitly define this reagent and its mechanism of action for clarity.

      We performed the requested clarification.

      Referee Cross-commenting

      As with Reviewer #1, I was surprised that the RT-PCR analysis presented in support of the splicing MO lacked retention of intron one. I reasoned this might be due to reduced transcript abundance through a mechanism such as nonsense-mediated decay, but I agree that this data raises questions that the authors should address.

      Thank you for this comment. Indeed, this is presumably due to nonsense mediated RNA decay. We have not explored the biochemistry of MMP28 RNA following injection with MOspl. Splicing MOs can have multiple effects. As explained on the GeneTools website splicing MOs disturb the normal processing of pre-mRNA and cells have various ways to deal with this and there are multiple possible outcomes. The PCR with E1-I1 suggests that intron 1 is not retained. Therefore, a putative concern would be that MOspl led to exon-skipping and to the generation of a truncated form of MMP28. However, we have checked that it is not the case. The fact that the PCR using E7-E8 primers indicates a reduction as well suggest an overall degradation of the mRNA for MMP28. Importantly, the effect of MOspl can be rescued using MMP28 mRNA indicating that the knockdown is specific.

      I also agree with the other comments from Reviewers 1 and 3.

      Reviewer #2 (Significance):

      This study by Gouignard et al. provides compelling evidence for the role of MMP28 during neural crest EMT. As neural crest cells share similar EMT and migration mechanisms with cancer progression, they represent a powerful system in which to study these biological processes in vivo. Previous work on MMP function has focused primarily on extracellular matrix remodeling and the effect on cell migration, with less attention given to the role of MMPs during EMT. More recent reports in other systems have begun to elucidate a role for MMP translocation into the nucleus, indicating a surprising and novel mechanism for these proteins. This work would be of particular interest to audiences interested in cancer, cell, and developmental biology, as it highlights the importance of the non-canonical function of metalloproteinases during EMT and migration.

      Reviewer #3 (Evidence, reproducibility and clarity):

      Summary

      This study by Gouignard and colleagues explores the mechanisms involving the matrix-metalloprotease MMP28 in the epithelial-to-mesenchymal transition (EMT) of neural crest cells. Interestingly and provocatively, they focus not only on the extracellular functions of this protease but also on the roles of MMP28 in the nucleus. This in non-conventional sub-cellular localization is shared with other MMPs, but its significance remains poorly understood. Here, the authors show that the nuclear function of MMP28 impacts the expression of key EMT regulators in neural crest cells in vivo.

      Using Xenopus laevis as a powerful animal model to explore the early development, the authors show that mmp28 expression is found in the ectodermal placodal tissue adjacent to the neural crest prior and after EMT.<br /> In the first part of the study, the authors show that MMP28 depletion affects a subset of neural crest marker gene expression (snai2, twi, sox10) but not others (sox9, snai1), suggesting a specific role on a subset of the genes important for neural crest EMT. The MMP28 depletion phenotype is restored by coinjecting MMP28 MO and MMP28 mRNA, provided that the catalytic activity of the encoded protein is maintained. Next, epistasis (rescue) experiments show that Twist1 can compensate MMP28 depletion.<br /> The second part of the study elegantly shows that MMP28 produced by host adjacent tissues can translocate into the nucleus of neural crest cells grafted from a donor embryo (devoid of MMP28-GFP expression). It also shows that MMP28 nuclear localization as well as its catalytic activity are both required for activating the neural crest gene twist1 and sox10; and that MMP28 is found bound on the chromatin of twist1, cad11 and sox10.<br /> Altogether, these experiments strongly support a model for the nuclear role of MMP28 in the activation (or maintenance) of key genes of the EMT program in vertebrate neural crest cells.

      Major comments

      The key conclusions are:

      Conclusion 1: MMP28, expressed and secreted by placodes, is important for complete neural crest patterning prior to EMT, including activation of twist1 and EMT effector cadherin 11 genes. MMP28 is important for neural crest EMT and migration in vivo and in explant assay in vitro.

      However, this conclusion omits potential indirect effect of interfering with placode formation itself, as indicated by the strong decrease in six1 expression in morphant embryos. The effect of MMP28MO on the expression of six1 is as strong as for neural crest markers snai2, twi, for example. Line 95, "slight reduction" should be modified.

      Thank you or this comment. We have now modified the associated text.

      What this may mean for placodal development itself, as well as for indirect effects on neural crest cells need to be discussed.

      Following this comment, we added a paragraph in the discussion about Six1.

      Conclusion 2: Gain of Twist 1 (but not Cadherin 11) rescues MMP28 morphant phenotype, allowing EMT to occur and restoring several parameters of cell migration in vivo and in explant assay

      Conclusion 3: When secreted from adjacent cells, MMP28 is translocated into the nucleus of neural crest cells and displays a nuclear function important for the activation of twist1 expression.

      Both conclusions 2 and 3 are supported by multiple elegant and convincing experimental data. These conclusions do not depend on mmp28 exclusive expression by the placodal ectoderm, and would still be important if there was a minor expression in the neural crest cells themselves (and thus an autocrine effect).

      Additional experiments to strengthen the conclusions<br /> Related to Conclusion 1:

      • line 102-106: In the rescue experiment, is six1 expression rescued too?

      Thank you for this comment. As detailed in the newly added discussion paragraph about the effects of Six1 loss of function that have been described in the literature, it is very unlikely that our NC phenotypes stem from the observed reduction of Six1 expression.

      Nonetheless, following this comment we checked for Six1 expression in the placodal domain following MMP28 knockdown and rescue condition. In the rescue condition, only 25% of the embryos had recovered Six1 expression in placodes while 75% of the embryos recovered Sox10 expression in neural crest cells. These data further confirm that rescue of placodal genes is not a pre-requisite for the rescue of neural crest genes and were added in Supp Figure 5.

      Although MMP28 is likely to have a role in placodes as well, the expansion of Sox2 and Pax3 expression domain and the loss of Eya1 expression typically associated with Six1 knockdown did not occur in MMP28 knockdown. Our story being focused on neural crest cells, we did not investigate further how the MMP28-dependent effect on Six1 might impair placode development.

      • Figure 2g: qPCR analysis suggests that mmp28 is expressed in the neural crest explants themselves, levels being lowered by the MO injection. The levels of this potential expression in the neural crest itself should be compared to the levels in the placodal ectoderm. How do the authors exclude an effect of the MO within the neural crest tissue, independently of roles from the placodal tissue?

      Thank you for this comment. There is a very small subpopulation of NC cells called the medial crest that expresses MMP28. They are along a thin line along the edge of the neural folds. We previously described this in Gouignard et al Phil Trans Royal Soc B 2020. It is useful for us as an internal control for MO efficiency but the expression in placodes is much stronger and involves many more cells. However, this expression called our attention at the onset of the project and we performed some experiments to assess whether some of the observed effects were due to a NC-autonomous effect, as suggested by this reviewer. To test for this we performed targeted injected of the MO such that the medial crest would receive the MO but not the placodes. Targeting the medial crest with MMP28-MO had no effect on Sox10 expression. These data were added to new supp Figure 1.

      The cost and time for these additional experiments is limited (about 3 weeks), and uses reagents already available to the authors.

      Data and Methods are described with details including all necessary information to replicate the study. Replication is carefully done and statistical analysis seems convincing.

      Minor comments

      Experimental suggestions to further strengthen the conclusions.<br /> Related to Conclusion 1: - Figure 1e, frontal histological sections would help distinguishing between placodal tissue and neural crest mesenchyme.

      Thank you for this comment. We previously published a detailed expression pattern with such sections (Gouignard et al Phil Trans Royal Soc B, 2020). We rephrased the text to better refer to this previous publication.

      Related to Conclusion 2: - Figure 3: in explants co-injected with twist1 mRNA, is cad11 expression restored? Could this indicate if cad11 is (or is not) part of the program controlled by Twist1 (as suggested by the last main figure)?

      Thank you for this comment. We checked for Cadherin-11 expression in control MO, MMP28-MOspl and MOspl+Twist mRNA and Twist is indeed capable of inducing Cadherin-11 and even leads to ectopic activation of Cad11 on the injected side. These data were added to new Supp Figure 11.

      Related to Conclusion 3: is MMP28 translocation seen in any cell context? Could the authors repeat experiments in Figure 6a with animal cap ectoderm? And with sandwich animal cap ectoderm, one expressing MMP28-GFP versions (wt, deltaSPNLS) and the other Rhodamine Dextran only? This would allow to generalize the mechanism or on the contrary to show a neural crest specificity.

      Thank you for this comment. Following this suggestion and comments from the other reviewers, we performed new grafting experiments.

      • 1/ we replaced neural crest cells from embryos expressing MMP28-GFP by placodal cells injected with Rhodamine-dextran. This generates grafted embryos with control placodes next to placodes overexpressing MMP28-GFP. There, we can analyze entry of MMP28-GFP in placodal cells that do not overexpress it. We detected MMP28 in the cytoplasm and in the nucleus of these placodal cells. However, the rate of nuclear entry was lower than in NC cells.
      • 2/ To assess the importance of the cell type producing MMP28 we grafted NC cells injected with Rhodamine-dextran next to caudal ectoderm expressing MMP28-GFP. MMP28 was detected in cytoplasm and the nucleus of the NC cells but with a lower efficiency than when NC are grafted next to placodes expressing MMP28-GFP.
      • 3/ We made animal caps sandwiches with animal caps injected with Rhodamine-dextran and animal caps expressing MMP28-GFP. In this case MMP28-GFP is detected in the cytoplasm but fails to reach the nucleus. These data indicate that placodes can import MMP28 produced by placodes and that NC can import MMP28 produced by other cells than placodes. However, in both cases the rate of nuclear entry was lower than in the NC-placode situation. Finally the animal cap sandwiches indicate that entry into the cells does not predict entry into the nucleus. All these data were added to new Supp Figure 7 and quantifications of import of MMP28-GFP in the cytoplasm and the nucleus all conditions added to Figure 5.

      In supplementary figure 4a, the grey (RDx) is not visible in the zoom in images.

      As the grey channel interferes with visualizing the green channel, we only show the grey channel on the first low magnification image so that the position of grafted cells can be seen. We found it better to omit it from the zoomed in images to avoid masking the GFP signal.

      In figure 7a,b MMP14 is green, GFP is grey (mentioned wrongly in line 276)

      Thank you for pointing this out. We have extensively modified Figure 7 and such issues are now resolved.

      Bibliographical references are accurate. Clarity of the text and figures is excellent, except maybe Figure 7, where a qPCR analysis would be easier to visualize, especially with low-level or fuzzy bands on the gel.

      Thank you. We have now modified Figure 7, including normalization to GFP to show fold-change enrichment and have added new data from three independent ChIP assays for proximal Twist and E-cadherin promoters that we hope further substantiate our initial observations.

      Reviewer #3 (Significance):

      Place of the work in the field's context:

      In cancer, the MMP proteins are widely described in multiple tumor contexts and promote cell invasion. In development, several studies have focused on their functions in the extracellular space. The nuclear localization of MMP family proteins has been described previously but remained poorly understood so far. This work is thus a pioneer study aiming to understand MMP28 nuclear function.

      Advance:

      This study makes a significant advance in the field, by unraveling the importance of the MMP28 activity in the cell nucleus for the expression of key EMT regulators. Moreover, the study suggests that extracellular MMP28 secreted by adjacent cells or tissues can be internalized and transported to cell nucleus into cells located several cell diameters away. This study thus supports a novel facet of MMP proteins activity, complementary to their previously described role on the extracellular matrix, and further favoring cell invasion, in development and potentially in cancer too.

      The target audience goes without doubt beyond developmental biologists (the primary interest) and also includes cell and cancer biologists, and any biologist interested by MMPs or cell invasion mechanisms in vivo.

      My field of expertise is developmental biology focused on neural and neural crest early development, mainly using animal models in vivo and some cell culture experiments. I also focus on some aspects of cancer cell migration.

    1. For years inventions have extended man's physical powers rather than the powers of his mind.
    2. warfare
      • Comment
      • Observation
        • it is known that warfare is a significant source of technological innovation
        • this can be explained by evolutionary biology
        • our instruct for survival is strongest in ( inter-species) conflict
        • such is the deep irony of human progress
        • now, in the Anthropocene, humanity is waging another war for survival, caused by our war against nature
      • we can characterized this war as a war against past ignorance
    3. “Consider a future device …  in which an individual stores all his books, records, and communications, and which is mechanized so that it may be consulted with exceeding speed and flexibility. It is an enlarged intimate supplement to his memory.”
      • The explorations of a system that could
      • record our learning trail in life
        • personal individual synthesis
        • new knowledge gained by social learning:
          • from direct, synchronous, real-time interaction with another live other human being
          • from indirect, asynchronous, non-real-time interaction with cultural artefacts produced by another
      • Bush famously named cc this the "memex"
    4. Even if utterly new recording procedures do not appear, these present ones are certainly in the process of modification and extension.

      Inventions are not made in a vacuum, requiring cultural influences

    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Response to reviewers' comments

      We thank the reviewers for their constructive evaluation of our manuscript. In the following point-by-point response, we explain how we will implement the suggested modifications.

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Summary:

      The formation of meiotic double-stranded DNA breaks is the starting point of meiotic recombination. DNA breaks are made by the topoisomerase-like SPO11, which interacts with a number of regulatory factors including REC114, MEI4 and IHO1. Despite the key role this process has in the continuation, and genetic variation, or eukaryotic life, there is very little known about how this process is regulated. Laroussi et al make use of biochemical, biophysical and structural biological approaches to extensively characterise the REC114-MEI4-IHO1 complex.

      This is an outstanding biochemical paper. The experiments are well planned and beautifully executed. The protein purifications used are very clean, and the figures well presented. Importantly Laroussi et. al describe, and carefully characterise through point mutational analysis, the direct physical interaction between IHO1 and REC114-MEI4. This is an interaction that has, at least in yeast, previously been suggested to be driven by liquid-liquid separation. The careful and convincing work presented here represents an important paradigm-shift for the field.

      I am fully supportive of publication of this excellent and important study.

      We thank the reviewer for his/her positive comments, appreciation of the importance of our study and suggested modifications.

      Major comments:

      Point 1:

      My only major concern is regarding Figure 4, and specifically the AF2 model of the coiled-coil tetramer of IHO1. Given the ease with which MSAs of coiled-coils can become "contaminated" with non-orthologous sequences, I would urge some caution with this model. This is especially since the yeast ortholog of IHO1, Mer2, has been previously reported to be an anti-parallel tetramer (albeit, not very well supported by the data). The authors have several choices here. 1) They could simply reduce the visibility of the IHO1 tetramer model, and indicate caution in the parallel tetramer model. 2) They could consider using a structure prediction algorithm that doesn't use an MSA (e.g. ESMFold). 3) They could try to obtain experimental evidence for a parallel coiled-coil tetramer, e.g. through EM, SAXS or FRET approaches. I would like to make it crystal clear, however, that I would be *very* supportive of approach 1) or 2). An experimental approach is *not* necessary.

      Assuming the authors don't take a wet-lab approach, this shouldn't take more than a couple of weeks.

      This is a very good suggestion. We are aware of the previously reported anti-parallel architecture of the yeast IHO1 ortholog Mer2 (Claeys Bouuaert et al., Nature 2021). It should be noted, that in the recent preprint, posted by the Claeys Bouuaert lab (BioRxiv, https://doi.org/10.1101/2022.12.16.520760), a high confidence model of yeast Mer2 (and for human) parallel tetrameric coliled-coil is presented, apparently consistent with their previous XL-MS results (Claeys Bouuaert et al., Nature 2021).

      To clarify this issue we will follow the suggestions of Reviewer 1 and 2.

      1. As suggested also by Reviewer 2, we will produce a tethered dimer of IHO1125-260, connected by a short linker and determine its MW by SEC-MALLS (and SAXS).
      2. In the meantime we followed the suggestion of Reviewer 1 and modelled the IHO1130-281 by the ESMfold, which is another recent powerful AI-based program that does not use multiple sequence alignments. Remarkably, the predicted structure is very similar to the one predicted by AlphaFold, also predicting the parallel arrangement of IHO1. This model will be included as a supplementary figure.
      3. We will also point out in the text that these models, despite being very convincing, remain models.

        Minor comments:

      Point 2:

      The observation that REC114 and MEI4 can also form a 4:2 complex is very interesting and potentially important. Did the authors also try to model this higher order complex in AF2?

      Yes, we did this with the hope that we could identify residues whose mutation could limit the fast exchange between the 2:1 and 4:2 states. Unfortunately, no convincing additional contacts are modelled by AlphaFold. This PAE plot will be included as a supplementary figure.

      Point 3:

      Similarly to above, what does the prediction of the full-length REC114:MEI4 2:1 complex look like? Presumably the predicted interaction regions align well with experimental data, but it would be interesting to see (and easy to run).

      The AlphaFold modelling of the FL REC114:MEI4 (2:1) complex will be included as supplementary figure. It is consistent with the model comprising only the interacting regions. No additional convincing contacts are predicted.

      Point 4:

      Did the authors carry out SEC-MALS experiments on any IHO1 fragment lacking the coiled-coil domain? It was previously reported for Mer2 that the C-terminal region can form dimers, for example (OPTIONAL).

      We can easily do that. We have the N- and C- terminal regions lacking the coiled-coil expressed as MBP fusions and they will be analysed by SEC-MALLS.

      Point 5:

      Given that full-length REC114 is used for the IHO1 interaction studies, do the authors have any data as to the stoichiometry of the REC114FL-MEI41-127 complex? (OPTIONAL)

      We have repeatedly analysed the REC114-MEI4-IHO1 complex sample by SEC-MALLS and native mass spectrometry, but in both cases the sample is too complex to be interpreted. This is like due to the fast exchange between REC114-MEI4 2:1 and 4:2 complexes and low binding affinity of IHO1 for REC114.

      Point 6:

      Did the authors try AF2 modelling of the REC114-IHO1 interaction using orthologs from other species?

      Yes, but not extensively. We will repeat this modelling again.

      **Referees cross commenting**

      I will add cross-comments to the comments of Reviewer #2

      Firstly, the comments made by Reviewer #2 are technically correct. Firstly, reviewer #2 points out that the oligomerization states that the authors report could, in part, be artifactual the based on the his-tag purification method. This is indeed correct. However, given that none of the oligomerization states reported are per se unusual, given what is already known (including pre-prints from the Keeney and Claeys Bouuaert laboratories), I think the authors could forego this step.

      Secondly, the use of an experimental structural method, such as SAXS, would certainly add value to the paper. Also Reviewer #2 is correct in pointing out the availability of the ESRF beamlines to the authors. However, while SAXS is a useful method, I personally consider the use of mutants to validate the interactions, an even stronger piece of evidence that the AlphaFold2 interactions are correct. I must disagree somewhat with Reviewer #2 with their argument that SAXS would validate the fold. Certainly if one of the AF2 predicted structures is radically wrong, then SAXS would produce scattering data, and a subsequent distance distribution plot that is incompatible with the AF2 model. However, a partly correct AF2 model, of roughly the right shape, might still fit into a SAXS envelope.

      Reviewer #2 shares my concern on the parallel coiled-coil of IHO1, and their suggested solution is very elegant.

      In my view, due to the time constraints imposed by the partially competing work from the Keeney and Claeys Bouuaert laboratories (recently on biorxiv). I would support the authors if they chose the quickest route to publication.

      Reviewer #1 (Significance (Required)):

      General assessment: The strengths of the paper are as follows:

      1) Quality of experiments - The proteins used have been properly purified (SEC) and properly handled. The experiments are carefully carried out and controlled.

      2) Detail - The authors carry out the considerable effort of characterising protein interactions. through separation-of-function mutants. This adds to the quality of the paper, and renders the AF2 models as convincing as experimentally determined structures

      3) Conceptual advances - The most important conceptual advance is the direct binding of the N-term of IHO1 to REC114. That this is the same region as used by both TOPOVIBL and ANKRD31 points to a complex regulation.

      4) Integrity - the authors have taken great care not to "oversell" the results. The data are presented, and analysed, without hyperbole.

      Limitations - The only limitation of the paper is the lack of in vivo experiments to test their findings. However given the time and effort required, and the pressing need to publish this exciting study, this is not a problem.

      Advance: The paper provides advances from a number of directions, both conceptual and mechanistic. Mechanistically the description of the REC114-MEI14 complex is important, and in particular the observation that it can also form a higher order 4:2 structure. Likewise, while IHO1 was inferred to be a tetramer (based on work on Mer2) this was never proven formally. Most importantly, is the physical linkage between IHO1 and REC114, and that this is an interaction that is incompatible with TOPOVIBL and ANKRD31.

      Audience:

      Given the central role of meiotic recombination in eukaryotic life, any studies that shed additional light on the regulation of meiosis are suitable for a broad audience. However, this subject paper will be more specifically of interest to the meiosis community. The elegant methodology will also be of interest to structural biologists and protein biochemists.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      This manuscript addresses the structure of the REC114-MEI4-IHO1 complex, which controls the essential process of programmed DSB induction by SPO11/TOPOVIBL in meiosis.

      The manuscript carefully combines biochemistry, biophysics and modelling in an integrative manner to report the architecture of the full REC114-MEI4-IHO1 complex that is not itself amenable to direct structure elucidation such as by X-ray crystallography. These are important results that will be of interest to the recombination and meiosis fields. The data are generally convincing and interpretations appear correct, so the manuscript is certainly suitable for publication. I have included some suggestions below that I believe would strengthen the manuscript and enhance our confidence in the findings. Whilst the manuscript is publishable in its current format, I believe the suggestions given below would make it into a much stronger paper.

      We thank the reviewer for his/her positive comments on our study and the suggestions below.

      I have two general suggestions:

      Point 1:

      Analyses have been performed on fusion proteins (His, His-MBP etc). we have previously observed that bulky tags such as MBP can interfere with oligomeric state through steric hindrance, and that His-tags can mediated formation of larger oligomers, seemingly through coordination of metals leached from IMAC purification. This latter point has also been observed by others

      https://www.sciencedirect.com/science/article/pii/S1047847722000946.

      Where possible, I would repeat SEC-MALS experiments using untagged proteins, or at least following incubation with EDTA to mitigate the potential for His-mediated oligomerization.

      We agree with this reviewer’s comment that expression tags can have unexpected impact of the protein behaviour.

      1. For REC114-MEI4 complex the stoichiometry is assessed by several techniques. Figure 1f,g shows analytical ultracentrifugation, which was performed on the minimal REC114226-254-MEI41-43 complex that contains no fusion tag showing that this stoichiometry is independent of fusion tags. We will nevertheless repeat the SEC-MALLS on REC114-MEI41-127 after removing the His-tag of MEI4 as suggested.
      2. For the REC114 dimer, we cannot remove the His-MBP tag since this short fragment of REC114226-254 is no stable without MBP. The dimerization of Rec114 was already reported in (Claeys Bouuaert et al., Nature 2021). The dimerization is sensitive to specific point mutations within REC114. We will however, repeat the SEC-MALLS experiment following incubation with EDTA to mitigate the potential for His-mediated oligomerization.
      3. The presented SEC-MALLS on IHO1 fragments (Figure 4b) was done on proteins without fusion tags. Reviewer 1 and 2 also agreed that additional repeats of the experiments without fusion tags are not necessary.

      The authors have relied upon mutagenesis to validate Alphafold2 models. Their results are convincing but only confirm that contacts involved in structures rather than the specific fold per se. Their finding would be greatly strengthen by collecting SEC-SAXS data and fitting models directly to the scattering data. This is extremely sensitive, so a close fit provides the best possible evidence of accuracy of the model. SAXS is affected by unstructured regions and tags, so would have to be performed using structural cores of untagged proteins rather than full-length constructs. Given the local availability of world-class SAXS beamlines at the ESRF, which is next door to the leading author's institute, it seems that the collection of SAXS data would be practical for them.

      The usage of SAXS is discussed in the specific points below. We will attempt to do SEC-SAXS on the REC114-MEI4 complex. Due to instability of REC114226-254 without MBP, SAXS cannot be done. We will also do SAXS on the IHO1 tetramer.

      My specific comments are below:

      Point 2:

      Figure 1d

      The SEC-MALS shows multiple species, with 2:1 and 4:2 representing a minority of total species present. What are the larger oligomers? Could these be an artefactual consequence of the His-tags (as described above)?

      This SEC-MALLS will be repeated without the His-tag on MEI4.

      Point 3:

      Figure 1f,g

      The AUC changes over concentration and pH are intriguing - have they tried MALS in these conditions? This would be much more informative as it would reveal the range of species present. Low concentrations could be analysed by peak position even if scattering is insufficient to provide interpretable MW fits. I would advise doing this without his tag or adding EDTA (as described above).

      We will perform this experiment as suggested.

      Point 4:

      Figure 2

      I would like to see the models validated by SAXS using minimum core untagged constructs. This could be sued to test the validity of the 2:1 model directly, and to model the 4:2 complex by multiphase analysis and/or docking together of 2:1 complexes.

      The hydrophobic LALALAII region of MEI4 is interesting and the mutagenesis data do agree with the model. However, it is important to point out that any decent model would place this hydrophobic helix in the core of the complex, and so would be disrupted by mutagenesis. Hence, the mutagenesis results confirm that the hydrophobic helix is critical for the interaction, but does not confirm that the specific alphafold model is more valid than any other model in which the helix is similarly in a core position.

      We will attempt to perform the SEC-SAXS measurements. The challenge here will be obtaining a sample that is monodisperse in solution being required for SAXS. We showed the fast exchange between the 2:1 and 4:2 oligomeric state. The AUC data indicates that the sample has a predominantly 2:1 stoichiometry at 0.2 mg/ml, pH 4.5 and 500mM NaCl. Given the small size of the complex, the signal at 0.2 mg/ml is likely to be noisy.

      Point 5:

      Figure 3

      This would also benefit from SAXS validation of the structural core. The mutagenesis here provides convincing evidence in favour of the structure as specific hydrophobics ether disrupt or have no effect, exactly as predicted. Hence, their data strongly support the dimer model. As this provides the framework for the 2:1 complex, these data make me far more confident of the previous 2:1 model in figure 2. I am wondering whether it would be better to present these data first such that the reader can see the 2:1 model being built upon these strong foundations?

      We agree with this suggestion and will present the REC114 dimerization data before the REC114-MEI4 complex. However, REC114226-254 is not stable without the MBP tag so is not suitable for SAXS analysis.

      Point 6:

      Figure 4

      The MALS data convincingly show formation of a tetramer. How do we know that it is parallel? The truncation supports this but coiled-coils do sometimes form alternative structures when truncated (e.g. anti-parallel can become parallel when sequence is removed), and alphafold seems to have a tendency of predicting parallel coiled-coils even when the true structure of anti-parallel (informal observation by us and others). A simple test would be to make a tethered dimer of 110-240, with a short flexible linker between two copies of the same sequence - if parallel it should form a tetramer of double the length, if anti-parallel it should form a dimer of the same length - determinable by MALS (and SAXS).

      To address this point we will perform this experiment as suggested by Reviewer 2. We will produce a tethered dimer of IHO1 110-240, connected by a short linker and determine its MW by MALS (and possibly SAXS). We also performed ESMfold modelling (Reviewer 1, Point 1), resulting in the same model. As the IHO1 tetramer is likely suitable for SAXS analysis, we will also perform SAXS on it.

      Point 7:

      Figures 5/6

      The interaction is clear albeit low affinity (but within the biologically interesting range). It would be nice to obtain MALS (using superose 6) data showing the stoichiometry of the complex - are the data too noisy to be interpretable owing to dissociation? The alpahfold model and mutagenesis data strongly support the conclusion that the IHO1 N-term binds to the PH domain, as presented.

      We have repeatedly analysed the REC114-MEI4-IHO1 complex sample by SEC-MALLS (on Superose 6) and native mass spectrometry, but in both cases the sample is too complex to be interpreted. This is likely due to the fast exchange between REC114-MEI4 2:1 and 4:2 complexes and low binding affinity of IHO1 for REC114.

      **Referees cross commenting**

      Just to clarify a couple of points regarding consultation comments from reviewer 1:

      The suggestion regarding tags was mostly directed to the cases in which MALS data are noisy, with multiple oligomeric species (such as figure 1d). In these cases, i wondered whether the large MW species may be artefactual and could be resolved by removal of the tags. In cases where oligomers agree with those reported by other labs, I agree that there's no need to explore these further.

      In terms of SAXS, I agree that fitting models into envelopes will not distinguish between similar folds. However, fitting models directly to raw scattering data is extremely sensitive and I have never seen good fits with low chi2 values for incorrect models (even when very similar in overall shape to the correct structure).

      Reviewer #2 (Significance (Required)):

      The manuscript carefully combines biochemistry, biophysics and modelling in an integrative manner to report the architecture of the full REC114-MEI4-IHO1 complex that is not itself amenable to direct structure elucidation such as by X-ray crystallography. These are important results that will be of interest to the recombination and meiosis fields. The data are generally convincing and interpretations appear correct, so the manuscript is certainly suitable for publication. I have included some suggestions below that I believe would strengthen the manuscript and enhance our confidence in the findings. Whilst the manuscript is publishable in its current format, I believe the suggestions given below would make it into a much stronger paper.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Laroussi et al used Alphafold models to predict the assembly of REC114-MEI4-IHO1 complex, and verified the structure using different biochemical experiments. Both Alphafold predictions and experiment data are convincing for the overall protein complex assembly. Importantly, they identified a motif on IHO1 that share the same binding site on REC114 with TOPOVIBL and ANKRD31, suggesting that REC114 acts as a regulatory base coordinating different binding partners during meiosis progression. Overall, I believe this is a nice biochemistry paper, but lacks insights into the biology (I believe those in vivo data is beyond the scope of this paper), at least more discussions are needed in terms of these findings.

      We thank the reviewer for the supportive comments on our manuscript and its evaluation. We agree with the reviewer, that including in vivo data, that might provide further biological insights, would be useful. However, there is currently no good cellular model for meiotic recombination in mouse and thus our structure-based mutations will need to be tested in transgenic mice. Such data will take a long time to obtain and would delay the publication these in-vitro results that already provide novel insight into the REC114-MEI4-IHO1 complex architecture. We will, nevertheless, as suggested, strengthen the discussion of the biological implications of our findings.

      Some minor points:

      Point 1:

      Any data showing MEI4 forms a dimer on its own?

      As mentioned in the manuscript, full-length MEI4 is difficult to produce in bacteria or insect cells. Thus, we worked with the N-terminal fragment which in absence of REC114 is nor very stable. We will perform SEC-MALLS to assess its oligomeric state. Alphafold suggests dimeric arrangement of MEI4, but only with low confidence.

      Point 2:

      In Fig2 and Sup Fig4, HisMBP-MEI4, you see more MBP than the fusion protein, especially more obvious in the mutants. What's your explanation?

      The N-terminus of MEI4 is well produced when co-expressed with REC114. For the pull-down experiments in Figure 2 we expressed it as His-MBP fusion in absence of REC114. In this situation, there is a degradation between MBP and MEI4. We find this very often for proteins that not very stable, which is the case of MEI4 without REC114. This is the best way we could produce at least some MEI4 in absence of REC114. The MBP protein could probably be removed by other chromatography techniques, but we think that for the purpose of the pull-down its presence is not interfering with the REC114-MEI4 binding.

      Point 3:

      TOPOVIBL and ANKRD31, I am curious if you have looked at the conserved residues on these motifs.

      We show a strong conservation of the IHO1 among vertebrates (Fig. 6c). We will further analyse the sequence conservation in more distant species.

      Point 4:

      Reference needed when stating that IHO1 was not interacting with REC114 in previous biochemical assay in the discussion part.

      This will be corrected

      Point 5:

      Also, have you run AlphaFold that gives multiple models? Sometimes, with short motifs, 1 or 2 models of several models give good confidence for the interaction.

      Using in-house Alphafold installation producing 25 models did not reveal better models.

      Reviewer #3 (Significance (Required)):

      While most of the interactions between REC114 and MEI4 or IHO1 were established with Y2H or other biochemical assays before. This paper used the AlphaFold, and finally verified the findings with biochemical experiments, which helps to establish the exact motifs/residues involved in the interaction. For example, the MEI4-REC114 interfaces are novel, more interestingly, the IHO1 shares the same interface with ANKRD31 and TOPOVIBL. Thus, this finding of REC114-MEI4-IHO1 complex assembly would be interesting to people with different working areas. I would like to see more studies on the coordination IHO1 with ANKRD31 or TOPOVIBL in the future.

    1. Author Response

      Reviewer #1 (Public Review):

      This manuscript describes a relatively novel approach to discovering combinations of herbal medications that may help modulate immune responses, and in turn help treat diseases such as cancer. The authors use breast plasma call mastitis as a disease in which they present results from a non-blinded clinical trial with modest results. The main shortcomings are a lack of rigor around standardizing the control group given steroids versus the treatment group given the combinations of herbal medications. There needs to be a detailed statistical analysis of the comparison in tumor size, stage, invasiveness, etc. as well as consideration of confounding disease states (autoimmune disease, prior cancers, diabetes, etc.). While the results are interesting in that the use of herbal medications is often overlooked in Western medicine, the manuscript needs great detail in the clinical comparison in order to provide convincing evidence for an effect.

      Many thanks for your very kind words about our work. We are excited to hear that you think our manuscript is relatively novel with considerable translational impact to the field of herbal medications. We are grateful for your valuable time and efforts you have spent to provide your very insightful comments, which are of great help for our revision.

      Reviewer #2 (Public Review):

      The work is rather interesting and novel because for the first time, the authors employed knowledge graph, a cutting-edge technique in the domain of artificial intelligence, to identify a novel herbal drug combination for the treatment of PCM. The results of the clinical trial study clearly demonstrated that the drug combination is effective to ameliorate the symptoms of PCM patients and improve the general health status of the patients. Overall, the strategy of this manuscript may provide a paradigm for the design of drug combination towards many other human disorders.

      We are truly grateful for your very kind words about our work. It is very encouraging to know that you think our work is novel and of significance for the field. We sincerely appreciate the valuable time and kind efforts that you have spent on the thorough review of our manuscript.

      Reviewer #3 (Public Review):

      The major merit of the manuscript is that the authors introduced the concept of knowledge graph into the domain of herbal drugs or TCM. Namely, the authors designed a knowledge graph towards systematic immunity or immunotherapy based on massive data mining techniques. The authors successfully identified an herbal drug combination for PCM with the help of a scoring system. Moreover, the authors conducted a clinical trial study and the clinical data showed that the herbal drug combination holds great promise as an effective treatment for PCM. The weakness of the manuscript is that some details for the herbal drug combination and the clinical trial study are missing.

      Many thanks for your very kind words about our work. We are excited to hear that you think our work is relatively novel and holds great promise as an effective remedy for PCM. We are truly thankful for your valuable time and efforts you have spent to provide your very insightful comments, which are of great help for our revision.

    1. Author Response

      Reviewer #1 (Public Review):

      The authors developed a new concept: Skeletal age, which is chronological age + years lost due to suffering a low-energy fracture. There seem to be conceptual problems with this concept: It is not known if the years lost are lost due to the fracture or co-morbidities.

      The Reviewer raises an important point, and we are happy to discuss it as follows. While it is not possible to show the causal relationship between a fragility fracture and excess mortality, it has been shown repeatedly that a fracture is associated with an increased risk of pre-mature mortality after accounting for comorbidities and frailty. Indeed, we and others have found that comorbidities contribute little to the increased risk10,11. Moreover, in a previous study using the ‘relative survival analysis’ technique12, we have shown that hip and proximal fractures were associated with reduced life expectancy after accounting for time-related changes in background mortality in the population, suggesting that hip and proximal fractures are an independent clinical risk factor for mortality.

      In this study, we used a multivariable Cox’s proportional hazards model to adjust for confounding effects of age and severity of comorbidities, and our result clearly indicated that a fracture is associated with years of life lost. Moreover, comorbidities were considered a factor in an individual's risk profile for estimating skeletal age. As a result, skeletal age reflects the common real-world scenario that the combination of comorbidities and proximal or lower leg fractures compounded post-fracture excess mortality, much greater than each alone13.

      Technically, there are two steps to individualise skeletal age for each individual with a specific risk profile. First, we used the statistical approach recommended for the individualisation of survival time prediction using statistical models14 to individualise specific mortality risk for each participant with a specific risk profile. Specifically, we calculated the prognostic risk index as a single-number summary of the combined effects of his/her specific risk profile of a specific fracture site and the severity of comorbidity. His/her individualised fracture-mortality association was then computed as the difference between his/her prognostic index and the mean prognostic index of “typical” people in the general population. In the second step, we used the Gompertz law of mortality and the Danish national lifetable data to transform the individualised association into life expectancy loss as a result of a fracture15.

      We have modified part of the description of the methodology as follows:

      “For the second aim, we determined skeletal age for individual based on the individual’s specific risk profile. First, we calculated the prognostic risk index as a single-number summary of the combined effects of his/her specific fracture site and the severity of comorbidity51. The prognostic index is a linear combination of the risk factors with weights derived from the regression coefficients. The individualised fracture-mortality association for an individual with a specific risk profile is then the difference between the individual's prognostic index and the mean prognostic index of 'typical' people in the general population51. In the second step, we used the Gompertz law of mortality and the Danish national lifetable data to transform the excess mortality into life expectancy loss as a result of a fracture49.”.

      In addition, with the possible exception of zoledronate after hip fracture, we have no evidence that this increased risk of mortality can be changed with interventions.

      We agree that there is a lack of strong evidence from randomised controlled trials supporting the benefit of anti-resorptive therapy on post-fracture survival. As mentioned above, the mention of zoledronic acid was simply for illustrating the use of skeletal age to convey a treatment benefit. We have decided to remove the section related to the benefit of pharmacological treatment on post-fracture mortality.

      Furthermore, it is not clear why the authors think that patients and doctors will better understand the implications of older "skeletal age", on future fracture risk and the need for prevention, for example, the 10-year risk of MOF? Knowing that my bones are older than me, could make a patient feel even more fragile and afraid of being physically active. The treatment will reduce the risk of future fractures, but this study provides no information about the effect on mortality of preventing the subsequent fracture or the risk of mortality associated with recurrent fractures.

      The risk of fracture is typically conveyed to patients and the public in terms of absolute risk metric (e.g., probability) or relative risk metrics (e.g., risk ratio). However, patients and doctors often struggle to comprehend probabilistic statements such as 'Your risk of death over the next 10 years is 5% if you have suffered from a bone fracture'. The underappreciation of post-fracture mortality's gravity has caused patients to be hesitant towards treatment and prevention, contributing to the current crisis of osteoporosis treatment.

      We consider that skeletal age will make doctor-patient risk communication more intuitive and probably more effective. For example, for the same 2-fold increased mortality risk of hip fracture, telling a 60-year man with a hip fracture that his skeletal age would be 66 years old, equivalent to a 6-year loss of life is much more intuitive. The patient might be thus more likely to accept the recommended pharmacological treatment, ultimately improving health benefits. However, we have not had RCT evidence for the effectiveness of skeletal age, and this will be one of our future research focus. We would like to point out that there is RCT evidence that effective age (such as 'Heart Age', 'Lung Age') could improve the uptake of preventive actions. For example, informing patients about their heart age, as shown by Lopez-Gonzalez et al16 was found to better improve their cardiovascular risk compared to informing the Framingham probabilistic risk score.

      Introduction:

      The statement that treatment reduces the risk of dying, needs modification as the majority of clinical trials have not demonstrated reduced mortality with treatment.

      We have modified the statement as follows: “In randomised controlled trials, treating high-risk individuals with bisphosphonates or denosumab reduces the risk of fracture4, though whether the reduction translates into reduced mortality risk remains contentious5, 6.”

      It is not clear how the skeletal age captures the risk of a future fracture. The other difference between the idea of "skeletal age" and for example "heart age" is that there are treatments available for heart disease that reduce the risk of mortality, as mentioned above this has not been shown consistently in clinical trials in osteoporosis.

      We take the Reviewer's point, but we would like to point out that there are at least two RCTs on zoledronic acid showing that treating patients with a fragility fracture reduces their risk of mortality17,18.

      Because the risk profile that is associated with a post-fracture mortality is also associated with the risk of fracture, skeletal age can be seen as a measure of the decline of the skeleton due to a fracture or exposure to risk factors that raise the risk of fracture. Thus, a 60-year-old with a skeletal age of 66 is in the same risk category as a 66-year-old with 'favourable risk factors' or at least the ones that are potentially modifiable. Hence, an older skeletal age means a greater risk of fracture.

      Neither the “Skeletal Age” nor the “Heart Age”16,19,20 has the treatment intervention incorporated into its calculator. We have added details to explain how the assessment of skeletal age would provide the conceptual risk of both fracture and post-fracture mortality as follows:

      “Unlike the current fracture risk assessment tools17 which estimate the probability of fracture over a period of time using probability-based metrics, such as relative risk and absolute risk, skeletal age quantifies the consequence of a fracture using a natural frequency metric. A natural frequency metric has been consistently shown to be easier and more friendly to doctors and patients than the probability-based metrics9 11 30. It is not straightforward to appreciate the importance of the two-fold increased risk of death (i.e., relative risk = 2.0) without knowing the background risk (i.e., 2 folds of 1% would remarkably differ from 2 folds of 10%). By contrast, for the same 2-fold mortality risk of hip fracture, telling a 60-year man with a hip fracture that his skeletal age would be 66 years old, equivalent to a 6-year loss of life, is more intuitive. The skeletal age can also be interpreted as the individual being in the same risk category as a 66-year-old with 'favorable risk factors' or at least the ones that are potentially modifiable. Hence, an older skeletal age means a greater risk of fracture.”.

      Discussion:

      The prevalent comorbidities; cardiovascular diseases, cancer, and diabetes, suggest that fracture patients die from their comorbidities and not their fractures.

      Please refer to the above response for more detail. Briefly, the multivariable Cox’s proportional hazards regression adjusted for the confounding effect of age and the severity of comorbidities, indicating the association between fracture and mortality was independent of aging and comorbidity severity. On the other hand, skeletal age is a measure of excess mortality related to either fracture or co-morbidities or both.

      The discussion should be more balanced as there is a number of clinical trials demonstrating reductions in vertebral and non-vertebral fractures without effect on mortality. There may be specific effects of zoledronate on mortality, but that has not been shown for the vast majority of treatments.

      Please refer to the above response for more detail. Specifically, as the study primarily aimed at introducing skeletal age as a new metric for risk communication, we have decided to omit the paragraph discussing the potential benefit of zoledronic acid on post-fracture mortality risk in order to maintain the clarity and focus of the study.

      It is not correct that FRAX does not take mortality into account? It does not tell you specifically how high the risk of dying and how high the risk of a fracture is but integrates the two. "Skeletal age" does not provide either information, it just tells you that your skeleton is older than your chronological age - most patients and doctors will not associate that with an increased risk of dying - only of frailty.

      Although it is commonly believed that FRAX accounts for competing risk of death, it does not provide the risk of post-fracture mortality. Indeed, none of the current fracture risk assessment tools was designed to provide post-fracture mortality risk5. Skeletal age fills the gap by providing the excess mortality following a fracture for an individual with specific risk profile.

      The statement that zoledronate reduces the "skeletal age" by 3 years, has not been demonstrated and it is not clear how this can be demonstrated by the analysis reported here. As the reduced mortality has only been shown for the Horizon RFT, this cannot be inferred for other treatments and other fracture types. The information provided by the "skeletal age" is only that the fracture you already had took x years of your remaining lifetime. With the exception of perhaps zoledronate after hip fracture, we have no indication from clinical trials that the treatment of osteoporosis will change this.

      The current study was not designed to examine the effectiveness of an intervention. The statement related to the survival benefit of zoledronate is used to illustrate how skeletal age is used to convey the treatment benefit in real-world doctor-patient risk communication. Given the hazard ratio of 0.72 for zoledronate-mortality association17, a patient might find the statement “Zoledronic acid treatment helps a patient with a hip fracture gain (back) 3 years of life” much easier to understand and probably more persuasive than the traditional statement of “Zoledronic acid treatment reduced the risk of death by 28%”.

      Reviewer #2 (Public Review):

      The paper of Tran et al. introduces the concept of 'skeletal age' as a means of conveying the combined risk of fracture and fracture-associated mortality for an individual. Skeletal age is defined as the sum of chronological age and the number of years of life lost associated with a fracture. Using the very comprehensive Danish national registry and employing Cox's proportional hazards model they estimated the hazard of mortality associated with a fracture. Skeletal age was estimated for each age and fracture site stratified by gender. The authors propose to replace the fracture probability with skeletal age for individualized fracture risk assessment.

      Strengths of the study lie in the novelty of the concept of 'skeletal age' as an informative metric to internalize the combined risks of fracture and mortality, the very large and well-described Danish National Hospital Discharge Registry, the sophisticated statistical analysis and the clear messages presented in the manuscript. The limitations of the study are acknowledged by the authors.

      We appreciate your positive remark that captures the essence of our work.

      References:

      1. Lujic S, Simpson JM, Zwar N, Hosseinzadeh H, Jorm L. Multimorbidity in Australia: Comparing estimates derived using administrative data sources and survey data. PloS one 2017; 12(8): e0183817.
      2. Andersen TF, Madsen M, Jorgensen J, Mellemkjoer L, Olsen JH. The Danish National Hospital Register. A valuable source of data for modern health sciences. Dan Med Bull 1999; 46(3): 263-8.
      3. Vestergaard P, Mosekilde L. Fracture risk in patients with celiac Disease, Crohn's disease, and ulcerative colitis: a nationwide follow-up study of 16,416 patients in Denmark. Am J Epidemiol 2002; 156(1): 1-10.
      4. Hundrup YA, Hoidrup S, Obel EB, Rasmussen NK. The validity of self-reported fractures among Danish female nurses: comparison with fractures registered in the Danish National Hospital Register. Scand J Public Health 2004; 32(2): 136-43.
      5. Beaudoin C, Moore L, Gagne M, et al. Performance of predictive tools to identify individuals at risk of non-traumatic fracture: a systematic review, meta-analysis, and meta-regression. Osteoporos Int 2019; 30(4): 721-40.
      6. Spiegelhalter D. How old are you, really? Communicating chronic risk through 'effective age' of your body and organs. BMC Med Inform Decis Mak 2016; 16: 104.
      7. Vestergaard P, Rejnmark L, Mosekilde L. Osteoporosis is markedly underdiagnosed: a nationwide study from Denmark. Osteoporos Int 2005; 16(2): 134-41.
      8. Roerholt C, Eiken P, Abrahamsen B. Initiation of anti-osteoporotic therapy in patients with recent fractures: a nationwide analysis of prescription rates and persistence. Osteoporos Int 2009; 20(2): 299-307.
      9. Cummings SR, Lui LY, Eastell R, Allen IE. Association Between Drug Treatments for Patients With Osteoporosis and Overall Mortality Rates: A Meta-analysis. JAMA Int Med 2019; 179(11): 1491-500.
      10. Chen W, Simpson JM, March LM, et al. Comorbidities Only Account for a Small Proportion of Excess Mortality After Fracture: A Record Linkage Study of Individual Fracture Types. J Bone Miner Res 2018; 33(5):795-802
      11. Vestergaard P, Rejnmark L, Mosekilde L. Increased mortality in patients with a hip fracture-effect of pre-morbid conditions and post-fracture complications. Osteoporos Int 2007; 18(12): 1583-93.
      12. Tran T, Bliuc D, Hansen L, et al. Persistence of Excess Mortality Following Individual Nonhip Fractures: A Relative Survival Analysis. J Clin Endocrinol Metab 2018; 103(9): 3205-14.
      13. Tran T, Bliuc D, Ho-Le T, et al. Association of Multimorbidity and Excess Mortality After Fractures Among Danish Adults. JAMA Netw Open 2022; 5(10): e2235856.
      14. Henderson R, Keiding N. Individual survival time prediction using statistical models. J Med Ethics 2005; 31(12): 703-6.
      15. Kulinskaya E, Gitsels LA, Bakbergenuly I, Wright N. Calculation of changes in life expectancy based on proportional hazards model of an intervention. Insur Math Econ 2020; 93: 27-35. 16 Lopez-Gonzalez AA, Aguilo A, Frontera M, et al. Effectiveness of the Heart Age tool for improving modifiable cardiovascular risk factors in a Southern European population: a randomized trial. Eur J Prev Cardiol 2015; 22(3): 389-96.
      16. Lyles KW, Colon-Emeric CS, Magaziner JS, et al. Zoledronic acid and clinical fractures and mortality after hip fracture. N Engl J Med 2007; 357(18): 1799-809.
      17. Reid IR, Horne AM, Mihov B, et al. Fracture Prevention with Zoledronate in Older Women with Osteopenia. N Engl J Med 2018; 379(25): 2407-16.
      18. Bonner C, Batcup C, Cornell S, et al. Interventions Using Heart Age for Cardiovascular Disease Risk Communication: Systematic Review of Psychological, Behavioral, and Clinical Effects. JMIR Cardio 2021; 5(2): e31056.
      19. Svendsen K, Jacobs DR, Morch-Reiersen LT, et al. Evaluating the use of the heart age tool in community pharmacies: a 4-week cluster-randomized controlled trial. Eur J Public Health 2020; 30(6): 1139-45.
      20. Suissa S. Immortal time bias in pharmaco-epidemiology. Am J Epidemiol 2008; 167(4): 492-9.
    2. Reviewer #1 (Public Review):

      The authors developed a new concept: Skeletal age, which is chronological age + years lost due to suffering a low-energy fracture.<br /> There seem to be conceptual problems with this concept: It is not known if the years lost are lost due to the fracture or co-morbidities. In addition, with the possible exception of zoledronate after hip fracture, we have no evidence that this increased risk of mortality can be changed with interventions. Furthermore, it is not clear why the authors think that patients and doctors will better understand the implications of older "skeletal age", on future fracture risk and the need for prevention, for example, the 10-year risk of MOF? Knowing that my bones are older than me, could make a patient feel even more fragile and afraid of being physically active. The treatment will reduce the risk of future fractures, but this study provides no information about the effect on mortality of preventing the subsequent fracture or the risk of mortality associated with recurrent fractures.

      Introduction:<br /> The statement that treatment reduces the risk of dying, needs modification as the majority of clinical trials have not demonstrated reduced mortality with treatment.<br /> It is not clear how the skeletal age captures the risk of a future fracture. The other difference between the idea of "skeletal age" and for example "heart age" is that there are treatments available for heart disease that reduce the risk of mortality, as mentioned above this has not been shown consistently in clinical trials in osteoporosis.

      Discussion:<br /> The prevalent comorbidities; cardiovascular diseases, cancer, and diabetes, suggest that fracture patients die from their comorbidities and not their fractures.<br /> The discussion should be more balanced as there is a number of clinical trials demonstrating reductions in vertebral and non-vertebral fractures without effect on mortality. There may be specific effects of zoledronate on mortality, but that has not been shown for the vast majority of treatments.<br /> It is not correct that FRAX does not take mortality into account? It does not tell you specifically how high the risk of dying and how high the risk of a fracture is but integrates the two. "Skeletal age" does not provide either information, it just tells you that your skeleton is older than your chronological age - most patients and doctors will not associate that with an increased risk of dying - only of frailty.<br /> The statement that zoledronate reduces the "skeletal age" by 3 years, has not been demonstrated and it is not clear how this can be demonstrated by the analysis reported here. As the reduced mortality has only been shown for the Horizon RFT, this cannot be inferred for other treatments and other fracture types.<br /> The information provided by the "skeletal age" is only that the fracture you already had took x years of your remaining lifetime. With the exception of perhaps zoledronate after hip fracture, we have no indication from clinical trials that the treatment of osteoporosis will change this.

    1. Author Response

      Reviewer #1 (Public Review):

      The article "Identification of a weight loss-associated causal eQTL in MTIF3 and the effects of MTIF3 deficiency on human adipocyte function" explored the functional roles of MTIF3 during adipocyte differentiation. In persons living with obesity, genetic variation at the MTIF3 locus associates with body mass index and responses to weight loss interventions. MTIF3 regulates mitochondrial protein expression and gene knockouts cause cardiomyopathy in mice. This paper provides insight into the impacts of MTIF3 knockout on adipocyte differentiation and the expression effects of the eQTL on MTIF3 levels. The authors implement a CRISPR/Cas9 gene editing approach coupled with an in vitro platform to detect influences of MTIF3 on adipocyte glucose metabolism and gene expression. This method may serve as a platform to explore knockouts in human cell lines, so it may allow the discovery of new gene x environment influences on in vitro outcomes related to differentiation, growth, and metabolism.

      The conclusions of this paper are mostly well supported by data, but some experimental conditions and data analysis needs to be clarified and extended.

      1) The authors use CRISPR/Cas9 to generate the rs1885988 variant in the human white adipocyte cell line and performed a comprehensive validation analysis of gene editing (Figure 1). qPCR analysis showed reduced MTIF3 expression during human adipocyte differentiation (Figure 1E, F). To expand the importance of the rs1885988 variant, the authors should have provided target gene measurements to verify the canonical differentiation profile (e.g., FABP4, ADIPOQ) and help readers understand the overall impact of gene editing at the MTIF3 locus.

      Thank you for your suggestions. As you requested, we have quantified several adipocyte differentiation markers in the allele-edited cells after 12 days of adipogenic differentiation. The data (Figure 1-figure supplement 1) shows no significant difference between cells with the different genotypes. We have added more information about this in lines 100-101, and also in another context in lines 105-116.

      Notably, the intra-group variation of the marker gene expression is large (Figure 1-figure supplement 1), which makes it difficult to clearly state how much the allele editing, as opposed to random variation resulting from single cell cloning, contributes to the differentiation outcome. However, if we also consider MTIF3 knockout cells (that do not need to be single-cell cloned), their differentiation marker expression also appears unaffected (Figure 3-figure supplement 1). Taken together then, it is unlikely the allele editing with the consequent effect on MTIF3 expression affects adipogenic differentiation in our experiments. We mention the absence of effect of MTIF3 knockout on differentiation in the paragraph starting on line 137.

      2) The direct mechanistic influences of MTIF3 on adipocyte function remain unclear. MTIF3 regulates the translation initiation of mitochondrial protein synthesis. Western blots of OXPHOS proteins do not per se underscore supercomplex formation, which is also a process mediated by MTIF3. Blue native gel electrophoresis may prove a better method to establish the effects of MTIF3 loss-of-function on supercomplex formation.

      As suggested, we have run blue native gel electrophoresis to detect the formation of OXPHOS respiration complexes. In the revised manuscript (lines: 158-168 and Figure 4 E,F), we show how MTIF3 knockout indeed interferes with the complex formation, with lower abundance of complexes V/III2+IV1, III2/IV2 and IV1. Additionally, although the blot signal for complex I+III2+IVn is diffuse, it appears higher in scrambled control cells than in MTIF3 knockout cells. Interestingly, complex II content is slightly higher in MTIF3 knockouts, which may result from a compensatory regulation mechanism, as none of the subunits of complex II is encoded by mitochondrial DNA. We also found several faster-migrating (“undefined bands” in the figure) in the MTIF3 knockout samples, although it is hard to determine whether those are single chain proteins, or degradation or mistranslation products. Overall though, the native gel blots show impaired OXPHOS complex assembly in MTIF3 knockout samples.

      In addition, we performed western blots for other mitochondrial proteins, including COX II (subunit of OXPHOS complex IV), ND2 (subunit of OXPHOS complex I), ATP8 (subunit of OXPHOS complex V), and CYTB (subunit of OXPHOS complex III). The data (Figure 4 A,B), show decreased ND2 and COX II, trending decrease of CYTB, and unaffected ATP8 content in MTIF3 knockout adipocytes.

      The methods (paragraph starting at line 479), results (paragraph starting at line 145), and discussion (lines: 261-263, 274-277) were incorporated in the revised manuscript.

      3) Based on the findings, the authors argue that MTIF3 knockout alters the function of adipocytes. However, many of the experiments show fairly small effect sizes (Figure 5A, Figure 6A). How does the MTIF3 knockout explicitly perform functions related to body weight regulation? Gene editing in vivo would have helped to substantiate the authors' conclusions.

      In the paper we are looking at the consequences of MTIF3 deficiency in one cell type, over short time, in vitro. The outcome of body weight regulation, e.g. during weight loss, would result from long-term effects of MTIF3-altered metabolism in more than one tissue. We envisage that small changes in energy metabolism in not only fat, but also in e.g. muscle, would make a substantial difference over time in vivo (this, we cannot capture in in vitro models). We have added this discussion to lines 294-311.

      As for in vivo genomic editing, the alleles of interest are specific to the human genome. Ideally, a genotype-based recall study in humans would be appropriate, but due to time and resource limitation, we are not able to conduct such a study at the moment (although we certainly hope to perform such a study in the future). As for modeling the MTIF3 deficiency in mice – the MTIF3 knockout mice are not viable [1], and certainly other options (e.g. overexpression, tissue-specific knockouts) are possible and tempting to investigate. This, however, would require considerable additional work which we could only perform in a future project.

      4) In several instances, the authors refer to 'feeding' cells with glucose (line 206, line 171). Feeding experiments often imply complex nutrient interventions in animal models and people, which cannot be easily recapitulated in cell culture. The in vitro experiments simply alter levels of glucose and more precise language would state the specific challenges accurately.

      In the revised manuscript, we have substituted “feeding” for exact glucose concentration, or “glucose concentration” where appropriate. (paragraph starting at line 215, and lines 577-578, 597, 873-879)

      Reviewer #2 (Public Review):

      Huang Mi, et al. investigated the role of MTIF3, the mitochondrial translation initiation factor 3, in the function of adipocytes. They first detected the expression of the obesity-related MTIF3 variants based on the GTEx database and found two variants lead to an increase in MTIF3 expression. Then they knockout MTIF3 in differentiated hWAs adipocytes and characterized the mitochondrial function. They found loss of MTIF3 decrease mitochondrial respiration and fatty acid oxidation. They further treated cells with low glucose medium to mimic weight loss intervention and found MTIF3 knockout adipocytes lose fewer triglycerides than control adipocytes. This paper provides new information about MTIF3 in adipocytes and the potential functional role of MTIF3 in mitochondrial function.

      1) The authors provided sufficient data to show those two genetic variants increase MTIF3 expression. Their CRISPR/Cas9 knockin cell line is also convincing. But they didn't show if the genetic variants affect adipogenesis. Adipogenesis is an important process for weight gain and fat deposition. In lines 103-107, the authors mentioned that the "allele-edited cells have some problem in differentiated state, e.g. triglyceride or mitochondrial content", so they used an inducible Cas9 system. However, the issue of differentiated allele-edited cells may be the functional effect of MTIF3 genetic variants, such as interrupting adipogenesis, decreasing triglyceride, or affecting mitochondrial number. The authors should provide that information.

      Thank you for all your suggestions. We think we were not clear regarding this issue. We did not mean that the allele-edited cells have problem in differentiated state, which then definitely could be (as you point out) due to the functional effect of MTIF3 genetic variants. The problem relates to the process of single-cell cloning itself, which inherently introduces random variation. As a consequence, the data on adipogenic differentiation in allele-edited cells has relatively high intra-group variation. We have added more clarifying text in lines 104-116.

      To provide the data on this, per your request, in the revised manuscript we include the results for the rs67785913-edited cells in Figure 1-figure supplement 1. As shown, we observed no differences in the expression of adipogenic markers (ADIPOQ, PPARG, CEBPA, SREBF1 and FABP4) or in mitochondrial content between the two rs67785913 genotypes. Since the intra-group variation is often high, it is hard to conclude how much the rs67785913 eQTL affects the quantified variables. Much of the variation could instead be ascribed to the effects of single cell cloning.

      The cloning per se introduces random variation, but is required to obtain homozygous allele-edited cells. Because of this dilemma, and to clarify how much MTIF3 expression can actually influence adipogenic differentiation, we have, during the revision, also used the hWAs-iCas9 cells to generate MTIF3 knockouts at the preadipocyte stage and then tested their differentiation capacity. As we show in Figure 3-figure supplement 1, we found no apparent differences in adipogenic marker gene expression between scrambled control and MTIF3 knockout cells (we mention that in lines 137-144). Taken together, our results may indicate that the rs67785913 genotype, through affecting MTIF3 expression, is unlikely to regulate adipogenic differentiation.

      2) In Figure 4, the author mentioned that MTIF3 knockout does not affect the expression of adipogenic differentiation markers. They need to provide more evidence to prove their point. Oil-red O staining is a clearer way to quantify adipocyte differentiation in cell culture. In addition, in Fig. 4B western blot, the author should include MTIF3 as a control to show the knockout efficiency. It is not clear the meaning of plus and minus in that panel. The author should also compare the total triglyceride levels in MTIF3 knockout cells and control cells.

      We have now included Oil-red O staining results and total triglyceride levels (Figure 3 F,G), which show no apparent differences between scrambled control and MTIF3 knockout cells (method: lines 427-431; results: lines 137-144). We also added the MTIF3 blots to figure 4A as a control, showing high and consistent MTIF3 knockout efficiency in independent experiments. In the original manuscript, the plus and minus referred to control and knockout, respectively. To clarify that, we have changed the expression to SC and KO in the revised manuscript.

      With regards to Oil-red O vs. quantification of adipogenic markers, we actually prefer the latter method, as it gives more accurate and less variable results than Oil-red O (at least in the cell line we use). We have, however, performed Oil-red O as well to address your question.

      3) MTIF3 is a translation initiation factor in mitochondria and is involved in the protein synthesis of mitochondrial DNA-encoding genes. The authors should check protein levels rather than the mRNA levels of mitochondrial DNA-encoding genes (Fig. 6E). It's interesting to see the increase of mRNA levels of ND1 and ND2, which might be feedback of lower translation. Since ND1 and ND2 are in OXPHOS complex I, the expression levels of complex I in MTIF3 KO cells would be worth checking. Additionally, the author should also check the mitochondria copy number.

      As suggested, we have detected several mitochondrial encoding proteins which are subunits of each mitochondrial OXPHOS complex. As shown in figure 4A, ND2 (subunit of OXPHOS complex I) and COX II (subunit of OXPHOS complex IV) expression were significantly reduced, CYTB (subunit of OXPHOS complex V) expression tended to decrease, and ATP8 expression was not affected in the MTIF3 knockout adipocytes. We also detected the formation of the OXPHOS respiration complex in extracted mitochondrial proteins and found MTIF3 perturbation affect mitochondrial complex assembly. The detailed methods (lines: 479-490), results (lines: 145-169) and discussion (lines: 260-262, 274-277) were incorporated in the revised manuscript.

      We have also added the mitochondrial copy number data (Figure 3A), showing that MTIF3 knockout has lower mitochondrial content (methods: lines 491-500; results: 156-157)

      4) MTIF3 knockout adipocytes retain more triglycerides under glucose restriction is interesting. It may link to the previous result of lower fatty acid oxidation in MTIF3 knockout adipocytes. However, the authors then showed there is no difference in lipolysis. The author should discuss those results in the manuscript.The authors could also check lipolysis in glucose restriction conditions. It's also necessary to include the triglyceride levels of KO cell lines at full medium

      We have now examined the glycerol release in glucose restriction condition, and found no differences between control and MTIF3 knockouts (Figure 6-figure supplement 1). Interestingly, in 1 mM glucose, both genotypes released less glycerol than at 25 mM glucose, and this has been observed before in SGBS cell line [2] According to your suggestion, we have added the total triglyceride content at 25 mM glucose condition (Figure 6C), which also was not different between control and MTIF3 knockout cells. We speculate the higher retention of triglycerides in the knockouts could be due to higher re-esterification of lipolytically released fatty acids, since, as we observed, fatty acid oxidation is impaired in the knockouts. In the revised manuscript, we added that to the discussion (lines: 289-293).

      References

      1. Rudler, D.L., et al., Fidelity of translation initiation is required for coordinated respiratory complex assembly. Sci Adv, 2019. 5(12): p. eaay2118.
      2. Renes, J., et al., Calorie restriction-induced changes in the secretome of human adipocytes, comparison with resveratrol-induced secretome effects. Biochim Biophys Acta, 2014. 1844(9): p. 1511-22.
    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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      Reply to the reviewers

      We thank the reviewer for their comments. We are encouraged that the reviewers found our research “important study that addresses the interplay between two major Rho-type small GTPases involved in cell division” and “of interest to those interested in the cell biology of mitotic exit”. We agree with the comments raised by the reviewers and have provided new data as per their recommendation. We have also made changes to the text and format of the paper. We feel that with these changes the manuscript is stronger and we thank the reviewers for their suggestions. Below we provide a detailed response to the reviewers’ comments.

      Reviewer #1

      *This manuscript focuses on the role of Cdc42 in Rho1 activation during fission yeast cytokinesis. The primary finding is that active Cdc42 and its downstream effector Pak1 prevents accumulation of active Rho1 and the synthesis of cell wall material, at early stages of cytokinesis and despite the local recruitment of the Rho1 GEF Rgf1. The data supporting these conclusions are reasonably sound. *

      *Additional experiments are presented to suggest that Cdc42 and Pak1 negatively regulate Rgf1, this conclusion is not as strongly supported (though it may be true) *

      *These study relies on a newly described probe for active Rho1. However this probe is not sufficiently well validated. *

      *Overall the manuscript was not assembled with sufficient care and rigor, these deficits could be readily corrected. *

      The major point of the paper is that Cdc42 and Pak1 negatively regulate Rho1 activation. However, during late cytokinesis, active Cdc42 and active Rho1 co-exist at the division site. Thus, Cdc42 activation induces a delay in Rho1 activation, but how this delay is overcome is not investigated or even discussed. Indeed, while the delay is shown the transience of this inhibition is not explicitly mentioned. At a minimum, the authors should highlight this point for the readers.

      We are encouraged by the fact that the reviewer found our data “reasonably sound”. We agree that this manuscript does not provide the molecular details of how Cdc42 inhibits Rho1 activation. Our genetic data suggests that this is likely mediated by multiple pathways possibly involving the regulation of the Rho regulators Rgf1, Rgf2 and Rga5. We are currently investigating the molecular details of this regulation and hope to report it in another manuscript.

      Our data shows that while Cdc42 inhibits Rho1, the SIN pathway is essential for Rho1 activation regardless of the presence of Cdc42. While Cdc42 is activated at the division site as the ring completes assembly, the SIN pathway is activated immediately prior to ring constriction similar to that of Rho1 activation. It is possible that once the SIN is activated at the division site, it overcomes Cdc42-mediated Rho1 inhibition. We have highlighted this in the discussion section of this manuscript and are currently investigating the molecular details of this regulation.

      *Specific points 1 - RBD probe This probe is central to this manuscript. However, there is insufficient validation of its target. Figure 1 shows the localization and its independence of Rho2. The authors should provide direct evidence that it recognizes Rho1 (for example using a repressible promotor or an anchor away approach).

      *

      We thank the reviewers for their comments on the RBD probe. We have now provided validation for the RBD-probe. We have used rho1 temperature-sensitive and switch-off mutants to show loss of RBD-probe localization in these mutants. This data is provided I the revised manuscript in Fig1 and Supplementary fig. S1.

      At various places in the manuscript the authors refer to this probe as "Rho-probe", RBD-probe, RBD, RBD-(mNG or tdTomato). On page 11 the authors state, "As per our observations, we refer to the Rho-probe signal at the division site as active Rho1 from here onwards." Yet, in the very next paragraph they refer to the localization of the "Rho probe". * This is also an issue with the figures. For example, in figures 4B,C ; 5B,C; 6B; 7B,C the figures are titled either "Rho1 activation at division site", "Rho1-probe at division site"; "Rho1-probe appearance at division site" ; "Rho1-probe in non-constricting rings". *

      We agree that these multiple terms to describe the probe is confusing. We have restricted the terms to either “RBD-mNG” or “RBD-tdTomato” when reporting the data and use “Rho-probe” for descriptive purposes.

      In fig 3, RBD-nNG is quantified in a graph entitled "localizaton [sic] of Rho1-GEFs at division site"

      We thank our reviewers for identifying this error in our labeling of the graph in Fig. 3E. This figure now reads “Localization of Rgf1, Rgf3, active Rho1 at the division site”

      In all figures but two, 5c and 7c, the authors quantify Rho1 activation by the presence or absence of the probe, rather than a quantitative measure or the extent of recruitment of the probe. This could be analyzed my quantitatively.

      We appreciate this comment and provide this response in order to clarify our reasoning for presenting this data. We quantified the intensities of RBD-mNG or RBD-tdTomato where ever relevant to the question we are addressing for each experiment performed.

      Where we look at Rho1 activation at the division site with respect to SPB distances, we are reporting the differences in the timing of Rho activation with respect to mitotic progression. However, in Figures 5c and 7c, and now also Fig 1 of the revised manuscript, we quantified the intensities of the probe as this indicated the changes in overall active Rho1 levels under our experimental conditions. We have added in the text for earlier experiments where we do not report intensity measurements for the active Rho probe that we do not observe any differences in the intensity levels.

      *2 - Regulation of Rgf1 by Cdc42 and Pak1. The results shown in figure 8 show that "early Rho1 activation in gef1 mutants is not Rgf3-dependent". Figure 9 establishes "loss of rgf1 prevents premature Rho1 activation in gef1Δ cells restoring it to normal in late anaphase (Fig.9A, B)." This finding indicates that Rgf1, but not Rgf3, is required for Rho1 premature activation. This finding doesn't rule out the possibility that Cdc42 and Pak1 might be required to turn off RhoGAPs to allow active Rho1 to accumulate. This analysis concludes with this unclear and ungrammatical sentence, "While we were unable to assess the Rho-probe in the rgf1Δ rgf2Δ double mutants due to its lethality [sic; is the Rho probe lethal?], our observations suggest that apart from Rgf1 early Rho1 activation in gef1Δ cells is either due to activation of Rgf2 or due to inhibition of Rga5." *

      We thank you for your insight and agree with these remarks. We could not investigate Rho1 activation in rgf1Δ rgf2Δ double mutants since the double mutants are inviable. We have re-worded the sentence to reflect our findings appropriately.

      *The conclusion that this regulation is due to control of Rgf1 should be toned down. E.g. from the abstract: "We provide functional and genetic evidence which indicates that Pak1 regulates Rho1 activation likely via the regulation of its GEF Rgf1." *

      We have now removed this statement from the abstract. We have also clarified in the discussion that the molecular details of how Cdc42 inhibits Rho1 is not known and needs to be investigated. While our data suggests that the regulator Rgf1 and Rga5 may be involved in the process the details are unclear and we are currently investigating this regulation.

      *SECTION B - Significance ======================== This manuscript ties together several recent papers from the author's lab on the control of Cdc42 activation during cytokinesis and older papers on the role of Rho1 in Bgs1 activation. It provides missing information into the temporal regulation of septum assembly.

      The authors make a point of the similarities of fission yeast cytokinesis to animal cell cytokinesis. Indeed the second sentence reads, "The fission yeast model system divides via an actomyosin-based contractile ring, which is assembled in the medial region of the cell, as in animal cells (Balasubramanian et al., 2004; Pollard, 2010).". However, the authors fail to point out the many differences between yeast and animal cell cytokinesis until the last paragraph of the discussion. If the authors want to include the similarities in the introduction, they should also include the differences. For example, ring assembly is independent of Rho1 activation in fission yeast, but dependent on RhoA activation in animal cells. *

      We thank the reviewer for pointing out this deficiency in our writing. We have now amended the introduction to highlight the differences between Rho1 activity in fission yeast and animal cells during cytokinesis. We have added the following text to the Introduction section.

      “The animal Rho1 homolog RhoA is required for ring formation and is essential for cytokinesis (Basant and Glotzer, 2018). While in yeast, Rho1 is essential for septum formation, the current literature suggests that it is dispensable for ring formation (Onishi et al., 2013; Yoshida, 2009). In fission yeast where both the actomyosin ring and the septum have important roles in the proper coordination of cytokinesis, Rho1 has no reported roles in ring formation but is essential for septation (Balasubramanian et al., 2004).”

      *This work will be of interest to biologists working on yeast cell division. To a lesser extent it will be of interest to biologists interested in cytokinesis and coordination of distinct GTPase pathways.

      Additional points*

      1 - The text is overly wordy and needs extensive revision. Many of the experiments could be explained more clearly and with somewhat less genetic jargon. The introduction has quite a bit of extraneous information and lacks relevant facts, such as the function of Bgs1, which is central to the results.

      We have now modified the text to remove unnecessary genetic jargon. We have also provided additional text to describe the role of Bgs1 in the Introduction.

      2 - page 4 "GEFs promote GTP binding, thus keeping the GTPase active while the GAPs increase GTP hydrolysis, thus promoting GTPase inactivation." GEFs promote GTP binding, but they do not keep the GTPase active (an inhibitor of a RhoGAP would do that), they activate the GTPases.

      We thank the reviewers for highlighting this error. We have corrected this sentence, which now reads “GEFs promote GTP exchange to activate the GTPase, while the GAPs increase GTP hydrolysis to promote GTPase inactivation.”

      *3 - The current literature on animal cell cytokinesis indicates little direct role in cytokinesis, rather than the author's statement, "In larger eukaryotes, the role of Cdc42 activation has been reported mostly in meiotic division events such as polar body extrusion in oocytes, but not much is known about its role in cytokinesis in somatic cell division (Drechsel et al., 1997; Na and Zernicka-Goetz, 2006)." See for example, PMID 10898977, 10871280 which indicate Cdc42 does not play a major role during cytokinesis in at least a few systems where it has been analyzed. *

      We thank our reviewer for this observation and agree that this statement can be expanded to further explain the role of Cdc42 in animal cytokinesis. The paragraph has been re-written as follows-

      Pg5 - “In animal cells, the direct role of Cdc42 in cytokinesis remains indefinite. In Xenopus embryos and mouse fibroblasts for example, constitutively active Cdc42 impairs cytokinesis completion (Drechsel et al., 1997). However, in other cases such as in mouse embryonic stem cells, Cdc42 was only critical for development but not cytokinesis (Chen et al., 2000). RNA interference in animal cells demonstrate that that while RhoA is required for cytokinesis, Cdc42 is not required for this process (Jantsch-Plunger et al., 2000). Cdc42 also promotes spindle positioning and polar body extrusion in mouse oocytes, but it is not known whether its localization at these spindles affects RhoA (Na and Zernicka-Goetz, 2006). Thus, the role of Cdc42 in the cytokinetic process may be cell-type specific, and these data highlight the importance for more investigation to elucidate Cdc42 regulation in dividing cells (Jordan and Canman, 2012).”

      Reviewer #2

      *In many fungal cells, including fission yeast, the deposition of a new cell wall (a septum) between daughter cells is essential for cytokinesis. Cell wall synthases are trafficked to and activated at the division site, and dysregulated trafficking and/or synthase activation can lead to cytokinetic defects. In this study, the authors use fluorescent probes for Cdc42 and Rho1 activity and live-cell imaging to investigate the timing and regulation of Rho1 activity in fission yeast, and specifically, the role of Cdc42 in regulating Rho1. Summary of the proposed model: Gef1 -> active Cdc42 -> Pak1 --| Rgf1 -> active Rho1 -> septum formation

      Major comments

      (1) As far as I can gather from the authors' description in the manuscript and quick literature search, this will be the first publication in S. pombe utilizing the HR1-C2 domain of Pkc2p as fluorescent probe for active Rho1 (RBD-mNG). While a comparable domain of S. cerevisiae Pkc1p (not "pck2" as referenced by the authors in Page 25) has been used for similar purposes, given the importance of this probe and the precedent it sets in the S. pombe literature, it is imperative that proper tests are performed to validate that its localization reflects activity of Rho1 and nothing else (such as membrane binding of the C2 domain or transcriptional regulation of the pkc2 promoter). Such tests should also be independent of the hypotheses central to the current study (i.e., effects of Gef1, Pak1, Rgf1/2 on the timing of RBD-mNG localization). Can the authors provide data to address this point? Examples include, but not limited to, rho1 mutants, expression of constitutively active Rho1, or temporary expression of dominant-negative Rho1.*

      We agree with the reviewer and now provide data to show loss of the localization of the Rho-probe RBD-mNG in rho1 mutants. Using temperature-sensitive and switch-off mutants we show that under mutant conditions the RBD-mNG localization is lost at the division site and also from the cell ends. This provides strong evidence that the probe detects active Rho1 in the cells.

      *(2) Related, M&M does not provide sufficient details about the amino-acid positions corresponding to the "RBD" domain of Pkc2, thus precluding readers from reproducing the experiments. This needs to be clarified. *

      We now provide in the materials and methods the details of how this probe was generated including the base pairs of the budding yeast PKC1 and the fission yeast pck2 promoter.

      (3) In Figure 1B, RBD1-mNG localizes clearly to the medial region of rho2∆ cells when the Rlc1-tdTomato ring has not formed. Does this mean that Rho2 has a major role in forming the contractile ring that is independent of Rho1 activation? On this other hand, however, data in Fig. S2BC suggest that RBD-mNG does not localize to the medial region in rho2∆ cells until Rlc1-tdTomato ring forms (the timing of which seems normal). This discrepancy needs to be addressed.

      In response to the issue raised here, we do not see active Rho1 at the division site of cells without rings. However, after cytokinesis, while cells are in septation, although the ring has disappeared, active Rho1 lingers at the division site. The cell shown in the panel is a septated cell after ring constriction completes. We have included DIC panels of these cells to show that active Rho1 lingers in septating cells.

      *(4) Given the nature of RBD-mNG localization, it seems unavoidable to have some level of arbitrariness in measuring the onset of its localization at the division site. It would be advisable for the authors to be specific in M&M about how they defined the onset of localization, i.e., whether it was based on universal threshold in signal intensity, ratio, etc. or on manual curation (ideally double-blind).

      *

      We have updated the methods to describe that “onset of localization” was performed via double-blind visual observations.

      Minor comments (1) Throughout the manuscript, there are quite a few places where inconsistencies in genetic nomenclature can cause confusion to readers. Below are some examples. Figs. 6B, 7B, 10B: pak1(-ts), shk1, and orb2-34 (including faint labels under category marks in 6B). Fig. 9B (gef1+ rgf1∆) vs 9C (rgf1∆). Wild-type alleles are implicit in some figures, while explicit in others.

      We have corrected these inconsistencies.

      *(2) The first hypothesis (Fig. 1C) is that the AMR might regulate Rho1 activation. The ring is disrupted with LatA, but Rho remains active. They cite this as evidence that the AMR does not activate Rho1, but were the cells treated before or after the rings formed? If before, then the experiment demonstrates what the authors claim, but if after, it only shows that the AMR is not essential to maintain Rho activity. *

      We agree with the reviewer that this is an important distinction. We have modified this statement to “These results indicate that while at the division site the actin cytoskeleton is not required for maintaining Rho1 activation, it is necessary at the growth sites of interphase cells.”

      *(3) Page 8: "Time-lapse imaging of cells simultaneously expressing CRIB-3xGFP and RBD-tdTomato [...] while Rho1 is activated ~20 minutes after SPB duplication (Fig. 2B)." This appears to refer to Fig. 2C. *

      We thank the reviewers for catching this error in the text. We have now corrected it, showing timelapse as Fig. 2C, and an Image of cells simultaneously expressing CRIB and RBD as Fig. 2B.

      *(4) Page 9: "[...] Rgf1 and Rgf3 localize as early as the time of ring assembly at an average SPB distance of 4-5 µm (Fig. 3D)." This sentence is confusing. How was the average calculated over the earliest ring assembly in non-time-lapse data? Fig. 3DE show distances between SPBs as short as 2.5 µm, not 4-5 µm, and average of ~8 µm for all cells at different stages of mitosis. This confusion needs to be clarified. *

      We thank the reviewer for observing this mistake in our writing and interpretation. We agree that the text does not reflect the accurate interpretations of the data collected and have now fixed these errors. The current sentence reading “In an asynchronous population of cells, we find that Rgf1 and Rgf3 localize as early as the time of ring assembly at an average SPB distance of 4-5µm.” has now been replaced with the description shown below-

      “Using the distance between SPBs of anaphase cells as a proxy for timing of cytokinesis, we find that in most anaphase cells, Rgf1-GFP and Rgf3-eGFP was localized at the division site at very early stages in anaphase (Fig. 3D, E). This can be observed by the short distance between the SPBs of ~2µm (Fig. 3D). We also measured the distance for which active Rho1 appeared at the division site, and find that at the distance between SPBs of ~10µm, active Rho1 was present at the division site in ~50% of the population of control cells (Fig. 3E).”

      *(5) Fig. 5. Both the intensity and onset of RBD-mNG localization were affected by cdc42g12v expression. These two may form a causative relationship: reduced overall RBD signal may cause failed detection of early RBD localization. Can the authors compare cells with similar mean RBD-mNG signal intensities (Fig. 5B) and confirm that the timing of appearance at the division site is still delayed in gef1+ cdc42g12v relative to gef1+ empty? *

      We thank the reviewers for pointing this out and appreciate the opportunity to further clarify our observations. While there is clear decrease in Rho-probe intensity at the division site of on cells expressing cdc42G12V, we did see some variation in the extent of the decrease likely due to the variation in the expression levels of cdc42g12V. To provide a more accurate analysis of our observation we have shown the changes in the timing and intensities of Rho-probe localization. However, due to the noisy nature of the data we cannot compare the intensities in individual cells at specific spindle pole body distance between cells. As observed cdc42G12V significantly reduces Rho1 activity globally, not just at the division site. To cherry-pick cdc42G12V cells with similar active rho1 intensity to assess time of Rho1 activation may lead to subconscious data manipulation and will not address how early Rho1 activation is regulated.

      *Reviewer #3

      Onwubiko et al., present a clear and well written manuscript detailing the mechanistic understanding of how Rho1 is activated in a timely manner to ensure cytokinesis occurs in a scheduled manner at the end of telophase. Using fission yeast as a model system, and with the development of a novel Rho1 biosensor, they implicate a series of GTPases, exchange factors, GTPase activating proteins and kinases acting downstream of Cdc42 in the timely activation of Rho1. Specifically, they find that Cdc42 prevents premature Rho1 activation in early anaphase in a manner requiring the kinase Pak1. They observe that the Rho1 activators Rgf1 and Rgf3 localise to the division site in early anaphase, but Rho1 doesn't get activated until late anaphase, suggesting that control mechanisms ensure that these GEFs are held inactive, or that RhoGAP activity is able to balance this activation in early anaphase. This suppression of Rho1 activity in early anaphase requires Cdc42 and Pak1 and implicate (by omission) Rgf1, rather than Rgf3, is the relevant GEF.

      I liked this manuscript, it was clearly written the experimental progression was logical and the data were easy to interpret from the figures. The conclusions were precise, believable and not overstated. The manuscript provides novel observations and through good use of a series of rescues/mutants, illuminates a pathway that is held in check by Cdc42 to ensure timely Rho1 activation. The novel Rho1 probe is exciting and shows well differently regulated pools of active Rho1 at the division site and the growing tips. I thought the co-imaging/measurement of ring placement and SBP duplication allowed a really clear understanding of the kinetics during this rapid phase of the cell cycle. A critique of the study is that the the mechanism by which Cdc42 controls Pak1, and by which Pak1 controls Rgf1/Rgf2 is left unclear. I guess there could always be a molecular expansion of these points (e.g., how does Cdc42 control Pak1; how does Pak1 control Rgf1; how is Rgf activity restricted when localised), but I think that would only enhance, rather than change, the level of detail of the paper's message. I think the paper's current conclusions stand on their own, the data is clear and believable, the experiments are well performed. There are a number of observations in the paper that are left open for future studies, and I think this is a positive (e.g., any separable role of Rgf1/Rgf2 and how Rga5 integrates into this pathway. As such, I am tempted to recommend accept with only minor amendments as outlined below.

      1. P8 P15: should the call out be to Fig 2C, rather than 2B. *

      We thank reviewer for their highlighting this error in our text. We have now fixed it.

      *P14 L17: should it be 'gef1+ rgf3-', not 'gef1+, rgf3+' *

      We have fixed this error and further clarified the terms for easy understanding.

      Structure wise, I thought the section on Rga5 didn't really fit well on P16; it seemed sandwiched between two sections on GEFs. Is there a more appropriate place to place these data - perhaps between the paragraph breaks on P17? Related to this data, the conclusion on P16 suggests 'other' regulators of RhoGAP activity act to repress Rho1 function. Would 'additional' regulators of RhoGAP activity be more appropriate as there is some function contributed by Rga5?

      We have now moved this section to the end of the section on Rho1 regulators after we discuss the Rho1 GEFs. We have also modified the text to clarify that multiple regulators are likely involved in the regulation of Cdc42-mediated Rho1 inhibition.

      *In Fig 10b, you haven't defined orb2-34. Is it the rgf1-delete?

      *

      The mutant orb2-34 is a temperature sensitive allele of the pak1 kinase. To avoid confusion, we have replaced the allele name with pak1-ts in figure 10 and in the text.

      • I find the sentence at the top of P18: 'Rho1 activation in pak1+ rgf1+....at 25oC and 35.5oc occurred at longer and similar SBP distances' quite hard to interpret. Could you perhaps expand it to make your message clearer? *

      We thank the reviewer for pointing this out. These statements have now been re-written for clarity. 'Rho1 activation in pak1+ rgf1+....at 25ºC and 35.5ºC” has been changed, and now reads as follows:

      “The timing of RBD-mNG localization at the division site occurs late in cytokinesis during late anaphase as depicted by longer SPB distances in pak1+ rgf1+, pak1-ts rgf1+, and pak1+ rgf1Δ cells at 25ºC (Fig.10B). As previously shown, RBD-mNG localizes to the division site in early anaphase in pak1-ts rgf1+ cells at the restrictive temperature (35.5ºC, Fig. 7A, B). In agreement with our reasoning, early RBD-mNG localization in pak1-ts mutants at 35.5ºC was rescued in the absence of rgf1 (Fig. 10A, B).”

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      This manuscript focuses on the role of Cdc42 in Rho1 activation during fission yeast cytokinesis. The primary finding is that active Cdc42 and its downstream effector Pak1 prevents accumulation of active Rho1 and the synthesis of cell wall material, at early stages of cytokinesis and despite the local recruitment of the Rho1 GEF Rgf1. The data supporting these conclusions are reasonably sound.

      Additional experiments are presented to suggest that Cdc42 and Pak1 negatively regulate Rgf1, this conclusion is not as strongly supported (though it may be true)

      These study relies on a newly described probe for active Rho1. However this probe is not sufficiently well validated.

      Overall the manuscript was not assembled with sufficient care and rigor, these deficits could be readily corrected.

      The major point of the paper is that Cdc42 and Pak1 negatively regulate Rho1 activation. However, during late cytokinesis, active Cdc42 and active Rho1 co-exist at the division site. Thus, Cdc42 activation induces a delay in Rho1 activation, but how this delay is overcome is not investigated or even discussed. Indeed, while the delay is shown the transience of this inhibition is not explicitly mentioned. At a minimum, the authors should highlight this point for the readers.

      Specific points

        • RBD probe This probe is central to this manuscript. However, there is insufficient validation of its target. Figure 1 shows the localization and its independence of Rho2. The authors should provide direct evidence that it recognizes Rho1 (for example using a repressible promotor or an anchor away approach).

      At various places in the manuscript the authors refer to this probe as "Rho-probe", RBD-probe, RBD, RBD-(mNG or tdTomato). On page 11 the authors state, "As per our observations, we refer to the Rho-probe signal at the division site as active Rho1 from here onwards." Yet, in the very next paragraph they refer to the localization of the "Rho probe". This is also an issue with the figures. For example, in figures 4B,C ; 5B,C; 6B; 7B,C the figures are titled either "Rho1 activation at division site", "Rho1-probe at division site"; "Rho1-probe appearance at division site" ; "Rho1-probe in non-constricting rings". In fig 3, RBD-nNG is quantified in a graph entitled "localizaton [sic] of Rho1-GEFs at division site"

      In all figures but two, 5c and 7c, the authors quantify Rho1 activation by the presence or absence of the probe, rather than a quantitative measure or the extent of recruitment of the probe. This could be analyzed my quantitatively. 2. - Regulation of Rgf1 by Cdc42 and Pak1. The results shown in figure 8 show that "early Rho1 activation in gef1 mutants is not Rgf3-dependent". Figure 9 establishes "loss of rgf1 prevents premature Rho1 activation in gef1Δ cells restoring it to normal in late anaphase (Fig.9A, B)." This finding indicates that Rgf1, but not Rgf3, is required for Rho1 premature activation. This finding doesn't rule out the possibility that Cdc42 and Pak1 might be required to turn off RhoGAPs to allow active Rho1 to accumulate. This analysis concludes with this unclear and ungrammatical sentence, "While we were unable to assess the Rho-probe in the rgf1Δ rgf2Δ double mutants due to its lethality [sic; is the Rho probe lethal?], our observations suggest that apart from Rgf1 early Rho1 activation in gef1Δ cells is either due to activation of Rgf2 or due to inhibition of Rga5." The conclusion that this regulation is due to control of Rgf1 should be toned down. E.g. from the abstract: "We provide functional and genetic evidence which indicates that Pak1 regulates Rho1 activation likely via the regulation of its GEF Rgf1."

      Referees cross-commenting

      I think reviews are appropriate and speak for themselves.

      Significance

      This manuscript ties together several recent papers from the author's lab on the control of Cdc42 activation during cytokinesis and older papers on the role of Rho1 in Bgs1 activation. It provides missing information into the temporal regulation of septum assembly.

      The authors make a point of the similarities of fission yeast cytokinesis to animal cell cytokinesis. Indeed the second sentence reads, "The fission yeast model system divides via an actomyosin-based contractile ring, which is assembled in the medial region of the cell, as in animal cells (Balasubramanian et al., 2004; Pollard, 2010).". However, the authors fail to point out the many differences between yeast and animal cell cytokinesis until the last paragraph of the discussion. If the authors want to include the similarities in the introduction, they should also include the differences. For example, ring assembly is independent of Rho1 activation in fission yeast, but dependent on RhoA activation in animal cells.

      This work will be of interest to biologists working on yeast cell division. To a lesser extent it will be of interest to biologists interested in cytokinesis and coordination of distinct GTPase pathways.

      Additional points

        • The text is overly wordy and needs extensive revision. Many of the experiments could be explained more clearly and with somewhat less genetic jargon. The introduction has quite a bit of extraneous information and lacks relevant facts, such as the function of Bgs1, which is central to the results.
        • page 4 "GEFs promote GTP binding, thus keeping the GTPase active while the GAPs increase GTP hydrolysis, thus promoting GTPase inactivation." GEFs promote GTP binding, but they do not keep the GTPase active (an inhibitor of a RhoGAP would do that), they activate the GTPases.
        • The current literature on animal cell cytokinesis indicates little direct role in cytokinesis, rather than the author's statement, "In larger eukaryotes, the role of Cdc42 activation has been reported mostly in meiotic division events such as polar body extrusion in oocytes, but not much is known about its role in cytokinesis in somatic cell division (Drechsel et al., 1997; Na and Zernicka-Goetz, 2006)." See for example, PMID 10898977, 10871280 which indicate Cdc42 does not play a major role during cytokinesis in at least a few systems where it has been analyzed.
    1. Background

      This work has been peer reviewed in GigaScience ( see https://doi.org/10.1093/gigascience/giac097 ), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

      Reviewer name: Giulia De Riso

      In this study, a workflow is presented to generate classification models from DNA methylation data. Methods to deal with harmonization and missing data imputation are presented and the benefit of adopting them for classification tasks is tested on case-control datasets of schizophrenia and Parkinson disease. The authors support this workflow with source code. Although mostly based on already known methodologies, the present study may help orient studies aimed at building and applying DNA methylation based models. However, some major concerns can be raised:

      Majors: In different points of the manuscript, the authors refer to their approach as a pipeline. Indeed, this approach should be composed of sequential modules, in which the output of a module becomes the input of the next one. Although the modules are clearly distinguishable, their organization in the pipeline is less straightforward (also considering that modules can be adopted both to build a model and to use it on new data). The authors could think to draw a scheme of the pipeline, or to adopt a different term to refer to the presented approach. From the model performance perspective, the ML models poorly perform for schizophrenia. The authors point to inner characteristics of the disease as a possible reason for this. However, this point should be better commented in the Discussion section.

      Besides this, the impact of the smaller number of samples included in the training set and the higher proportion of imputed features compared to Parkinson disease on the classification accuracy should be discussed. In addition, since the authors provided the code, is there a way to select samples to include in training/test sets based on random choice (classical 70-30% splitting) instead of source dataset? "For machine learning models, we used only those CpG sites that have the same distribution of methylation levels in different datasets in the control group (methylation levels in the case group typically have greater variability because of disease heterogeneity).": is this filtering performed only on the datasets included in the training set, or also on the test set? It seems the former, but the authors should clearly state this point. Accuracy with weighted averaging should be defined with a formula in the methods section Regarding the ML models, the authors chose different types of decision-trees ensemble, along with a deep learning one. They should contextualize this choice (why different models from the same family?).

      In addition, ML models built on DNA methylation are often based on elastic net or Support-Vector Machines, which are not accounted for in this work. The authors should comment on this aspect in limitations, and state whether the code they provided for their approach could be customized to adopt different models from the ones they presented.

      Regarding the Imputation Method column in Table 2, the meaning is not clear. Are the different imputation methods described in the Imputation of missing values section paired with the ML models presented in Table 2? If yes, some of the methods (like KNN) are missing. In the harmonization section, Models for case-control classification are trained on different numbers and sets of CpGs. To assess the effect of harmonization alone, the number of CpGs should be instead fixed. This is especially critical for schizophrenia, when the number of features for the non-harmonized data is 35145 whereas the one for harmonized data is 110,137. Dimensionality reduction section: are the models from imputed and not-imputed data trained only on harmonized data? And how the set of 50911CpG sites for Parkinson and 110137 CpG sites for schizophrenia is selected?

      Imputation of missing values section: it is not clear on which CpGs and on which samples imputation is performed. Also, it is not clear whether the imputation has been tested on the best-performing model.

      Minors: Page 1, line 2: "DNA methylation is associated with epigenetic modification". DNA methylation is an epigenetic mark itself. Do the authors mean histone marks?

      Page 1, from line 7: "DNA methylation consists of binding a methyl group to cytosine in the cytosineguanine dinucleotides (CpG sites). Hypermethylation of CpG sites near the gene promoter is known to repress transcription, while hypermethylation in the gene body appears to have an opposite, also less pronounced effect.": references should be added

      Page 2, from line 2 : "Current epigenome-wide association studies (EWAS) test DNAm associations with human phenotypes, health conditions and diseases.": references should be added

      Page 3: "In most cases, an increase in dimensionality does not provide significant benefits, since lower dimensionality data may contain more relevant information". This point could be presented in a reverse way (higher dimensionality data may contain redundant information), introducing the collinearity issue. In addition, this issue could be introduced before the missing values and imputation section.

      Page 3: references for "Modern machine-l earning-based artificial intelligence systems are powerful and promising tools" could be more specific for the field of epigenetics and DNA methylation.

    1. Abstract

      This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giac094 ), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows: Reviewer name: George Taiaroa

      The authors provide a potentially useful dataset relating to transcripts from cultured SARS-CoV-2 material in a commonly used cell line (Vero). Relevant sequence data are publicly available and descriptions on the preparation of these data are for the most part detailed and adequate, although this is lacking at times.

      Although the authors state that this dataset overcomes the limitations of available transcriptomic datasets, I do not believe this to be an accurate statement; based on comparable published work in this cell line, transcriptional activity is expected to peak at approximately one day post infection (Chang et al. 2021, Transcriptional and epi-transcriptional dynamics of SARS-CoV-2 during cellular infection), with the 96 hour period of infection described likely representing overlapping cellular infections of different stages.

      Secondly, many in the field have moved to use more appropriate cell lines in place of the Vero African Monkey kidney cell line, to better reflect changes in transcription during the course of infection in human and/or lung epithelial cells (See Finkel et al. 2020, The coding capacity of SARS-CoV-2). Lastly, the study would ideally be performed with a publicly available SARS-CoV-2 strain, as has been the case for earlier studies of this nature to allow for reproducibility and extension of the work presented by others.

      That said, the data are publicly available and could be of use. Primary comments I think that a statement detailing the ethics approval for this work would be essential, given materials used were collected from posthumously from a patient. Similarly, were these studies performed under appropriate containment, given classifications of SARS-CoV-2 at the time of the study? I do not know what the authors mean in reference to a 'mixed time point sample' for the one direct RNA sample in this study; could this please be clarified? Secondary comments I believe the authors may over-simplify discontinuous extension of minus strands in saying that

      'The gRNA and the sgRNAs have common 3'-termini since the RdRP synthesizes the positive sense RNAs from this end of the genome'. Each of the 5' and 3' sequence of gRNAs/sgRNAs are shared through this process of replication. 'Infections are typically carried out using fresh, rapidly growing cells, and fresh cultures are also used as mock-infected cells.However, gene expression profiles may undergo alterations non-infected cells during the propagation therefore, we cannot decide whether the transcriptional changes in infected are due to the effect of the virus or to the time factor of culturing. This phenomenon is practically never tested in the experiments.' I do not follow what these sentences are referring to. 'Altogether, we generated almost 64 million long-reads, from which more than 1.8 million reads mapped to the SARS-CoV-2 and almost 48 million to the host reference genome, respectively (Table 1).

      The obtained read count resulted in a very high coverage across the viral genome (Figure 1). Detailed data on the read counts, quality of reads including read lengths (Figure 2), insertions, deletions, as well as mismatches are summarized Supplementary Tables.' Could this perhaps be more appropriately placed in the data analysis section, rather than background?

    1. Author Response

      Reviewer #1 (Public Review):

      The authors conducted a thorough analysis of the correlation between height and measures of cognitive abilities (what are essentially IQ test components) across four cohorts of children and adolescents in the UK measured between 1957 and 2018. The authors find the strength of the association between height and cognitive measures declined over this time frame--for example, among 10- and 11-year-olds born in 1958, height explained roughly 3% of the variation in verbal reasoning scores; this dropped to approximately 0.6% among those born in 2001. These associations were further attenuated after accounting for proxy measures of social class.

      The authors' analyses were performed carefully and their observations regarding declining height / cognitive measure associations are likely to be robust if we interpret their results with an important caveat: these results reflect measurements aimed at assessing cognition rather than cognition itself. The importance of this distinction is evidenced by the changing correlation structure of the cognitive measures over time. For example, age 11 verbal / math scores were correlated at >= 0.75 at the first two time points but dropped to 0.33 at the most recent time point. Similar patterns are present for the other cognitive measures and time points. The authors' conclude that such changes are unlikely to impact their primary findings, but I'm less certain. For example, one interpretation of this finding is that older cognitive measures were simply worse at indexing distinct cognitive domains and instead reflected a combination of cognitive ability together with non-specific factors relating to opportunity, health, class, etc. Further, height was historically a stronger proxy for class and economic status than it is today (e.g., by capturing adequate nutritional intake, risk for childhood disease, etc.). Together, then, previously high height / cognitive measure correlations might reflect the fact that both phenotypes previously indexed socio-economic factors to a greater extent than they might today (which is still non-negligible).

      We agree, it is possible that our results could in principle be explained by changes to the measures. We have provided further analysis to attempt to inform the likelihood of this suggestion and have expanded our discussion of this issue (Discussion, explanation of findings section; copied below).

      First, we conducted additional sensitivity analysis repeating our main analysis using cognition measures in which the number of response options was set to be the same for each test (the lowest common denominator across all cohorts). This was tested in two separate approaches: 1) by reducing the number of categories to the same number in each cohort; and 2) or by picking a random sample of question items for each category. Our main findings were unchanged: described in “Additional and sensitivity analyses” section, Figs S20-S21.

      Regarding the suggestion that “high height / cognitive measure correlations might reflect the fact that both phenotypes previously indexed socio-economic factors to a greater extent than they might today” – we sought to account for this by adjustment for measured indicators of socioeconomic position, and found the trend remained after adjustment (Fig 1 panel 2). As in other observational studies we cannot fully rule out the possibility of residual confounding however (Discussion, Explanation of findings paragraph 2).

      “The multi-purpose and multidisciplinary cohorts used cognition tests which differed slightly in each cohort. It is therefore possible that differences in testing could have either: 1) entirely generated the pattern of results we observed, such that if identical tests were used the association between cognition and height would otherwise have been identical in each cohort; in contrast to previous findings which reported using identical tests20; or 2) biased our results, such that if identical tests were used the decline in association between cognition and height would have been less marked than we reported. While we cannot directly falsify this alternative hypothesis given our reliance on historical data sources, a number of lines of reasoning suggest that the first scenario is unlikely. First, our results were similar when using 4 different cognitive tests (spanning mathematical and verbal reasoning); any bias which generated the results we observed should be similarly present across all 4 tests. Other things being equal, one would expect that more discriminatory tests (i.e., those with a greater number of responses) would have higher accuracy and thus better index cognition. Our results were similar when the youngest cohort had similar numbers of unique scores in cognitive tests compared with the oldest cohort (Verbal @ 11 years: n=41 in 1946c, n=40 in 2001c) and fewer unique scores (Maths @ 7/11: n=51 in 1946c, n=21 in 2001c). Our results were also similar in sensitivity analyses in which the number of response options were set to be the same in each cohort. Higher random measurement error in the independent variable (cognition) would lead to weakened observed associations with the outcome (height),52 yet we do not a-priori anticipate that this such error was higher in younger across all tests in such a manner that would have led to the correlation we observed. Ensuring comparability of exposure is a major challenge across such large timespans. Reassuringly, our results are consistent with those from a previous study which reported consistent tests being used (from 1939-1967).20 However, even seemingly identical require modification across time (e.g., for verbal reasoning/vocabulary there is typically a need to adapt question items due to societal and cultural changes over time in vocabulary and numerical use); further, changes to education such as increases in testing may have led to increasing preparedness and familiarity with testing than in the past even where identical tests are used.

      Interestingly, we observed a marked reduction in the correlation between cognitive tests across time (e.g., between verbal and maths scores). This trend has been reported in previous studies53 54 and warrants future investigation; it is consistent with evidence that IQ gains across time seemingly differ by cognitive domain,45 potentially capturing differences across time in cognitive skill use and development in the population. Previous studies using three (1958-2001c) of the included cohorts have also reported changing associations between cognition (verbal test scores at 10/11 years) and other traits: a declining negative association with birth weight19 and a change in direction of association with maternal age (from negative to positive);55 each finding has plausible explanations based on changes across time in relevant societal phenomena (improved medical conditions19 and changes in parental characteristics,55 respectfully), yet also cannot conclusively falsify the notion that differences in tests used influences the results obtained. In this paper, we used multiple tests and sensitivity analyses to attempt to address this.”

      Additionally, their findings add an interesting data point to a collection of recent results suggesting that the relationship between cognitive and anthropometric measures is complex and difficult to interpret. For example, studies using genetic markers to examine shared genetic bases have virtually all relied on methods assuming mating is random, which is not the case empirically. Howe et al. (doi.org/10.1038/s41588-022-01062-7) recently reported that the ostensible genetic correlation of -.32 between years of education and BMI attenuates to -.05 when using direct-effect estimates, which should theoretically be immune to the effects of non-random mating and other confounding variables. Likewise, Keller et al. (doi.org/10.1371/journal.pgen.1003451) and Border et al. (doi.org/10.1101/2022.03.21.485215) used very different approaches to arrive at the same conclusion that ~50% of the nominal genetic correlation between IQ and height could be attributed to bivariate assortative mating rather than shared causal biological factors. Given that assortative mating on both IQ measures and height involves many other traits (not just two as assumed in such bivariate models), the true extent to which height / IQ correlations reflect causal factors is plausibly even lower than these estimates suggest. For these reasons, I do not entirely agree with the authors' review of previous findings in the introduction, where they write "recent studies have suggested that links between higher cognition and taller height can be largely explained by genetic factors", though it is certainly true that this claim has been made.

      We have revised our introduction to better reflect the complexity of previous findings and to note that this claim.

      Reviewer #2 (Public Review):

      The authors use birth cohorts with extensive cognitive assessments and height measurements along with data on parental height and socioeconomic status. The authors estimate that the correlation between height and cognitive ability has approximately halved in the last 60 years.

      Quantile regression results suggest that this is due to a stronger association between low cognitive ability and short stature in older cohorts, potentially due to environmental factors that cause both and that have been removed by improvements in the environment in the last 60 years.

      While this is a plausible hypothesis, the evidence presented in the manuscript is unable to rule out alternative hypotheses, such as changes in assortative mating.

      The results in the manuscript will be of interest to researchers investigating how genetics and environment lead to correlations between cognitive and physical/health traits, and to researchers interested in the relationship between social and health inequalities.

      While my sense of the evidence presented is that there is fairly solid statistical evidence for a trend where the correlation between cognitive ability and height declines over time, there is no formal quantification of this trend nor measurement of the uncertainty in the trend.

      We now include additional statistical tests to compare estimates in each cohort (Fig S6). We have opted to include this in supplemental material given the large number of tests included already.

      Similarly, the quantile regression plots in Figure 2 appear to show a trend across the height deciles for the two oldest cohorts, but no quantification of how strong this is nor what uncertainty exists is calculated. Furthermore, if the apparent trend in the quantile regression plots is true, wouldn't this imply a non-linear association between height and cognitive ability for the older cohorts? Can this be seen in the scatterplots or in a non-linear regression?

      We included 95% confidence intervals in our quantile regression analyses which provide an indication of uncertainty. We believe that given the substantial amount of analyses (across 4 historical cohorts and 4 cognition tests; 23 supplemental results) further work would be best placed to undertake additional statistical exploration of both quantile regression and non-linear associations. We would be happy to reconsider this if requested.

      I think the authors could have done more with their data to investigate the contribution of assortative mating to the observed trend. Looking at Figure S4, it looks like the correlation between mother's education and father's height in the 2001 cohort is substantially lower than for previous cohorts. While cognitive ability may not be available for parents, one could look at, for example, father's education and mother's height across the cohorts and see if there is a downward trend in correlation.

      We now include in Figure S5 cross-cohort investigation of the correlation between parental height and maternal education. We find that the correlation is similar across 1946c, 1958c, and 1970c, yet is weaker in 2001c (Fig S5). We comment on this in the paper (see revised discussion, explanation of findings section). Interpretation of these results is complicated by measurement error in parental education (typically reported for both parents by mothers). Further, interpretation may be further complicated by reductions in the socioeconomic patterning of height across time (see https://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(18)30045-8/fulltext). Future would which focuses on assortative mating could investigate these issues.

      Reviewer #3 (Public Review):

      A difficulty with the paper is the different cognitive tests used in the different cohorts; the authors address this at some length in the discussion. However, I am afraid that this matter makes the results hard or impossible to interpret along the lines of their research question. One would need to know that, if these cognitive tests were administered in a single cohort at one time, they would have the same correlation with height.

      Please see our responses to Reviewer 1 and our revised Discussion. We are reliant upon imperfect historical data to make inferences on long-run trends, in the absence of ideal data for this paper (eg, the same tests used in all cohorts born in 1946, 1958, 1970 and millennium; though even in this instance some changes would be required (eg, to the words chosen in verbal reasoning tasks; see Discussion, explanation of findings section)).

      I judge that the main limitation of the method is the fact that different cognitive tests are used in the different cohorts. The tests in themselves are valid tests of cognitive functions. However, given that the focus of the study is on the change in correlations across time, then it is a worry that the tests are different; that is, the authors have the burden of proving to us that, if the environmental/social changes had NOT been operative across time, then the height-cognitive test correlations would be the same. What can the authors do to prove to us that if, say, all of these different-cohort verbal tests had been given to a single cohort on a single occasion, then they would have the same correlations with height? The same goes for the mathematics based tests. I note the tests' somewhat different distributions in Figure 1, but that is not the only thing that could lead to different correlations with, say, height. I am aware that all cognitive tests tend to correlate positively and that they all have loadings on general intelligence; however, different tests will not necessarily have the same correlations with outside variables (e.g. height). This will depend on things such as their content, their reliability/internal consistency etc.

      In the Results the authors state: "Cognitive test scores were strongly-moderately positively correlated with each other, with the size of the correlation weakening across time." That's true, but perhaps, also a major concern for this study. One possible reason for the decline in verbal-maths test correlations across cohorts (old to recent) is that the nature of these tests has changed across time, either/both in terms of content (what capabilities are assessed) or something such as reliability/internal consistency/ceiling-or-floor effects (how well the capabilities are assessed). That is, given that the height-cognitive test correlations show a similarly declining pattern of correlations over cohorts, it could be that the tests' contents (of the different tests) is partly or wholly responsible. I raise that as a possibility only, and I appreciate that it might be correct, as the authors prefer, that there is an inherent lowering of intelligence-height correlations over time, but I do not think that one can rule out-with the present study's design-that it might have been due to the change in tests. For example, a reading-math correlation of 0.74 in 1946 lowered to a correlation of .32 in 2001, in the face of different tests. To show that this is not due to the different tests being used would require more information. If this is a true result, it is big news.

      Please see our responses to Reviewer 1. This includes additional analysis and an expanded discussion of this possible cause of bias. We hope our manuscript now provides further evidence and discussion to inform the likelihood of this possibility.

      I have a suggestion: if the authors wish to rule out the possibility that the lowering intelligence-height correlations across cohorts are due to different cognitive tests being used, they should take all the cognitive tests used here and apply them cross-sectionally to single-year-born samples (of 11- and 16-year olds) that have also been measured for height. If the cognitive tests all correlate at the same level with height within each of these two samples (they needn't do so across the 11- and 16-year olds), then one could proceed more safely with between-cohorts (1946, 1958, 1970, 2001) comparisons of the correlations.

      We thank the reviewer for this suggestion. However we are unsure that we understood the suggested analysis or whether it was tractable given our data—the cohorts we used were born in either 1946, 1958, 1970, or around 2000. We do not have cross-sectional samples of 11 and 16 year olds at the same time.

    1. We further evaluated the pipeline with a genome containing simulated HGT regions. Since our78HGT identification pipeline has two main steps, sequence composition-based filtering step and79genome comparison step. The evaluation was done for the two steps (Figure S3, Table S1). While80top 1% fragments were input to the pipeline, 20.6% correct results would be identified after81sequence composition-based filtering and 14.3% correct results identified after genome comparison.82When the percentage of fragments input was up to 50%, 83.4% and 77.7% correct results were83identified after two steps respectively. It can be seen that the precision of prediction was higher than8460% for all cases. This indicated that we may have underestimated the number of HGTs (low recall85rate) but majority of the identified HGTs were highly reliable.

      This paragraph was a bit confusing to follow but I think I got the gist of it after a few passes through! I'm curious if you thought about controlling for natural variation in 4mer frequency throughout the genome, as some other methods have found that this helps reduce off target predictions (reviewed in https://doi.org/10.1371/journal.pcbi.1004095). It may not be necessary since you do a second step after the initial screen, but I was just curious if that was something you thought about putting in place, and if so, why you decided against it

  5. drive.google.com drive.google.com
    1. Role Card

      <span style="color: blue;">Policy Consideration: Role Cards</span>

      For more information about the Role Cards system see the annotation on page 7.

      As an alternative example, Civic Square structures their role relationships as follows:

      We think about relationships within the ecology of our team as first, second and third order connections. We are moving towards becoming teams of teams, so your everyday may involve a smaller focused team; week-to-week some others, and further month-to-month connections with the wider team. In this particular role we foresee you working with these key people initially:

      (a) Primary Connections [Primary connections]<br/> (b) Secondary Connections [Secondary connections]

      The job description is co-developed over an initial period, which looks to define responsibilities individually and collectively, with the understanding that it is not a static process, and they hope to revisit this together regularly.

    1. Author Response

      Reviewer #2 (Public Review):

      The manuscript "Optimal Cancer Evasion in a Dynamic Immune Microenvironment Generates Diverse Post-Escape Tumor Antigenicity Profiles" by George and Levine describes TEAL - a mathematical model for the dynamics of cancer evolution in response to immune recognition. The authors consider a process in which tumor cells from one clone are characterized by a set of neoantigens that may be recognized by the immune system with a certain probability. In response to the recognition, the tumor may adapt to evade immune recognition, by effective removal of recognizable neoantigens. The authors characterize the statistics of this adaptive process, considering, in particular, the evasion probability parameter, and a possibility of an adaptive strategy when this parameter is optimized in each step of the evolution. The dynamics of the latter process are solved with a dynamic programming approach. In the optimal case, the model captures the tradeoff between a cancer population's need for adaptability in hostile immune microenvironments and the cost of such adaptability to that population. Additionally, immune recognition of neoantigens is incorporated. These two factors, antitumor vs pro-tumor IME as quantified by the Beta penalty term, and the level of immune recognition as quantified by the rate q, form the basis of a characterization of tumors as 'hot' or 'cold'.

      I think this framework is a valuable attempt to formally characterize the processes and conditions that result in immunologically hot vs cold tumors. The model and the analytical work are sound and potentially interesting to a major audience. However, certain points require clarification for evaluation of the relevance of the model:

      1) Tumor clonality

      My main concern is about the lack of representation of the evolutionary process in the model and that the heterogeneity of the tumor is just glossed over.

      The single mention of the problem occurs in Section 2, p2: "Our focus is on a clonal population, recognizing that subclonal TAA distributions in this model may be studied by considering independent processes in parallel for each clone."

      I don't think this assumption resolves the impact of tumor heterogeneity on the immune evasion process. Furthermore, I would claim that the process depicted in Fig 1A is very rare and that cancers rarely lose recognizable neoantigens - typically it would be realized via subclonal evolution, with an already present cancer clone without the neoantigens picking up. Similarly, the adaptation of a tumor clone is an evolutionary process - supposedly the subclones that manage to escape recognition via genetic or epigenetic changes are the ones that persist. It is not clear what the authors assume about the heterogeneity of the adapting/adapted population between different generations, n->(n+1). Is the implicit assumption that the n+1 generation is again clonal, i.e. that the fitness advantage of the resulting subclone was such that the remaining clones were eliminated? Or does the model just focuses on the fittest subclone? A discussion on whether these considerations are relevant to the result would clarify the relevance of the result.

      We thank the reviewer for these helpful clarifying points. Empirical evidence in lung cancer exists for genomic changes manifesting as lost neoantigens in treatment-resistant clones (and Anagnostou et al. Cancer Discovery 2017) showed that those lost antigens were also shown to generate functional immune responses). Similar results for melanoma have also been shown (Verdegaal et al. Nature 2016), with loss of neoantigens associated with reactivity in TILs. Recent observations (Jaeger et al. Clinical Cancer Research 2020) even show that mutated peptides may be hid by protein stabilization, in addition to reduced expression patterns. We however do wish to clarify that our model implicitly equates antigen loss and the progression of a subpopulation currently adapted to evade immune targeting – either by direct pruning of the fittest subclone or by stochastic emergence and subsequent growth of a new one lacking the targeted antigens – as equivalent.

      Because we for foundational understanding studied the case where a single clonal signature was tracked in time, we under-explained the implementation of such a model in more complicated cases. As mentioned previously, the next most complicated scenario involves a heterogeneous population of cancer cells with disjoint neoantigen profiles. In this case, a parallel process can be studied wherein the effects of recognition in one environment are decoupled from the other (relevant to, for example, spatially distinct sub-populations). This description however misses the case where such disparate populations evolve to express shared antigens, or in the case where there are both clonal and subclonal antigen targets. Here, our model can still be applied in parallel to study distinct clones but requires additional structure. Namely, in this case we would need to incorporate non-trivial coupling between the possible recognition/selection against certain antigens shared across clones. For example, control of a population with clonal antigens {a,b} but having unique subclones having either antigens {w,x} or {y,z} could be considered by studying the process in parallel, and control in the next periods would require recognition/selection against either 1) at least one of {w,x} and at least one of {y,z}, or 2) at least one of {a,b}. In this more general framework, the arrival of new subclones with distinct features from the parent clone in question could also be incorporated and studied across time periods. This strategy of subdividing more complicated evolutionary structures has now been further elaborated on in the Methods section, and we have expounded these points in the discussion (see additions given under Editor Comment 2).

      2) Time scales

      Section 2, p2: "We assume henceforth that the recognition-evasion pair consists of the T cell repertoire of the adaptive immune system and a cancer cell population, recognizable by a minimal collection of s_n TAAs present on the surface of cancer cells in sufficient abundance for recognition to occur over some time interval n.".

      How do the results depend on the duration of interval n? The duration should be long enough to allow for recognition and, up to some limiting duration, proportional to the TAA recognition probability q. However, it should not be so long that the state of the system can change significantly. A clarification on this point is needed.

      We agree with the reviewer that these points should be elaborated upon when discussing the time interval. Very briefly, we opted for a discrete-time model tracking a cancer population under selective immune pressure. In order for 𝒒 to represent the total recognition probability of an immune system against a particular TAA, the time interval 𝚫𝒏 in question is a coarse-grained feature representing the time between the earliest chance that the adaptive immune system may identify a cancer clone and the latest point after which such a recognition event would no longer be able to prevent cancer escape. This time period may vary substantially across cancer subtypes and depends on the cancer per-cell division rate, for example (George, Levine. Can Res 2020). As the reviewer pointed out, in implementing such a model there is an asymmetric risk to considering 𝚫𝒏 too large, as the future state of the system may not be well-reflected by the simple loss and addition of new TAAs. On the other hand, considering small time intervals 𝚫𝒏, while possible, would require the incorporation of additional intermediate states ending in neither cancer elimination nor cancer escape.

      We have clarified the points that the reviewer has brought up by adding them to the discussion section: In this discrete-time evolutionary model, the intertemporal period considered represents the time period between the earliest moment that the adaptive immune system may identify a cancer clone and the latest point after which such a recognition event would no longer be able to prevent cancer escape (George, Levine. Can Res 2020). This effectively gives 𝒒 a probabilistic representation for the total rate of opportunity to recognize a given TAA during cancer progression. Implementing this model in cancer subtype-specific contexts thus requires a consideration of the per-cell division rates, for example.

      Reviewer #3 (Public Review):

      Cancer cell populations co-evolve under the pressure exerted by the recognition of tumor-associated antigens by the adaptive immune system. Here, George and Levine analyze how cancers could dynamically adapt the rate of tumor-associated antigen loss to optimize their probability of escape. This is an interesting hypothesis that if confirmed experimentally could potentially inform treatments. The authors analyze mathematically how such optimally adapting tumors gain and lose tumorassociated antigens over time. By simplifying the complex interplay of immune recognition and tumor evolution in a toy model, the authors are able to study questions of practical interest analytically or through stochastic simulations. They show how different model parameters relating to the tumor microenvironment and immune surveillance lead to different dynamics of tumor immunogenicity, and more immunologically hot or cold tumors.

      Simple models are important because they allow an exhaustive study of dynamical regimes for different parameters, such as has been done elegantly in this study. However, in this quest for simplification, the authors have not considered biological features that are likely to be of importance for understanding the process of cancer immune co-evolution in generality: tumor heterogeneity and immune recognition that only stochastically results in cancer elimination. In this sense, this paper might be seen as the opening act in a series of more sophisticated models, and the authors discuss avenues towards such further developments.

      We share the reviewer’s credence in foundational modeling for comprehensive predictions on available dynamical behavior for the important problem at hand. The reviewer also correctly points out that that future model refinement will be needed to further develop the foundational model developed in this work. In an attempt to illustrate one of the more reasonable generalizations, which is to include nontrivial sub-clonal heterogeneity in tumor antigens, we now describe how one would go about enhancing the existing model to address this, which has been added to the Methods and Discussion sections (see additions given under Editor Comment 2).

    2. Reviewer #2 (Public Review):

      The manuscript "Optimal Cancer Evasion in a Dynamic Immune Microenvironment Generates Diverse Post-Escape Tumor Antigenicity Profiles" by George and Levine describes TEAL - a mathematical model for the dynamics of cancer evolution in response to immune recognition. The authors consider a process in which tumor cells from one clone are characterized by a set of neoantigens that may be recognized by the immune system with a certain probability. In response to the recognition, the tumor may adapt to evade immune recognition, by effective removal of recognizable neoantigens. The authors characterize the statistics of this adaptive process, considering, in particular, the evasion probability parameter, and a possibility of an adaptive strategy when this parameter is optimized in each step of the evolution. The dynamics of the latter process are solved with a dynamic programming approach. In the optimal case, the model captures the tradeoff between a cancer population's need for adaptability in hostile immune microenvironments and the cost of such adaptability to that population. Additionally, immune recognition of neoantigens is incorporated. These two factors, anti-tumor vs pro-tumor IME as quantified by the Beta penalty term, and the level of immune recognition as quantified by the rate q, form the basis of a characterization of tumors as 'hot' or 'cold'.

      I think this framework is a valuable attempt to formally characterize the processes and conditions that result in immunologically hot vs cold tumors. The model and the analytical work are sound and potentially interesting to a major audience. However, certain points require clarification for evaluation of the relevance of the model:

      1) Tumor clonality

      My main concern is about the lack of representation of the evolutionary process in the model and that the heterogeneity of the tumor is just glossed over.

      The single mention of the problem occurs in Section 2, p2: "Our focus is on a clonal population, recognizing that subclonal TAA distributions in this model may be studied by considering independent processes in parallel for each clone."

      I don't think this assumption resolves the impact of tumor heterogeneity on the immune evasion process. Furthermore, I would claim that the process depicted in Fig 1A is very rare and that cancers rarely lose recognizable neoantigens - typically it would be realized via subclonal evolution, with an already present cancer clone without the neoantigens picking up. Similarly, the adaptation of a tumor clone is an evolutionary process - supposedly the subclones that manage to escape recognition via genetic or epigenetic changes are the ones that persist. It is not clear what the authors assume about the heterogeneity of the adapting/adapted population between different generations, n->(n+1). Is the implicit assumption that the n+1 generation is again clonal, i.e. that the fitness advantage of the resulting subclone was such that the remaining clones were eliminated? Or does the model just focuses on the fittest subclone? A discussion on whether these considerations are relevant to the result would clarify the relevance of the result.

      2) Time scales

      Section 2, p2: "We assume henceforth that the recognition-evasion pair consists of the T cell repertoire of the adaptive immune system and a cancer cell population, recognizable by a minimal collection of s_n TAAs present on the surface of cancer cells in sufficient abundance for recognition to occur over some time interval n.".

      How do the results depend on the duration of interval n? The duration should be long enough to allow for recognition and, up to some limiting duration, proportional to the TAA recognition probability q. However, it should not be so long that the state of the system can change significantly. A clarification on this point is needed.

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      Reply to the reviewers

      1. General Statements We thank the reviewers for their thoughtful comments and suggestions, which have improved the manuscript. We are particularly gratified by their positive comments about the significance of the findings. Our point-by-point responses to the reviewer comments and suggestions are summarized below. Line numbers have been added to the revised manuscript to make it easier to locate the changes.

      Point-by-point description of the revisions

      Reviewer #1__ __

      *1) In the study there is a lack of consideration of other targets. In and of itself this is not a problem, but once the author's identified the T130A mutation as being a key for protection it would have been good to Sanger sequence the other T. gondii myosins - a quick alignment of the TgMyo's A, C, H (class XIV), along with D and E suggests that the motif is highly conserved. This raises the currently unexplored (and exciting!) prospect of a pan-myosin inhibitor, and that there might have been mutations at an equivalent position in the other four KNX002 resistant clones - for example, MyoC has been proposed to provide some level of functional redundancy in the absence of MyoA. *

      Because the goal of this work was to evaluate the druggability of TgMyoA, we specifically designed our experiments to identify resistance-conferring mutations in TgMyoA or its light chains, as described on lines 366-372. This strategy yielded the TgMyoA T130A mutation, which enabled us to rigorously determine that inhibiting TgMyoA, and TgMyoA only, was sufficient to slow the progression of disease in vivo. Because we took this targeted approach, our results did not address: (a) the basis of resistance in the 4 resistant clones that did not contain a mutation in TgMyoA or its light chains and (b) whether KNX-002 inhibits any of the other ten parasite myosins.

      The most informative way to address (a) would be to do whole genome sequencing on each of the mutants, since resistance might have nothing to do with the other parasite myosins or their light chains. Any potential resistance-conferring mutations identified would need to be regenerated in a non-mutagenized background and functionally characterized, as we have done here for the T130A mutation, to be certain that this particular mutation was responsible for resistance. The most direct way to address (b) would be to individually express each of the other ten parasite myosins together with its specific associated light chains and the myosin co-chaperone protein TgUNC, purify the motor and determine the effect of the compound on motor activity (as we have done for TgMyoA; Fig. 1). These are both major undertakings that are beyond the goals and scope of the current manuscript. Critically, the absence of such data does not impact the conclusions of our phenotypic studies, which used the CRISPR-engineered T130A parasite line.

      We nevertheless agree with the reviewer that these are both interesting questions that should be studied further, and we now discuss them on lines 416-422 and 451-453.

      2) The fact that T130 is not thought to be the binding site of KNX002 is only introduced quite late on - this also relates to the next point - is the binding pocket conserved??? It is intriguing that the residue and proximal amino acid environment are highly conserved with vertebrates, but that KNX002 does not have an effect on their activity in their screen assay. It would be useful to know if the differences in the structures of the myosins can provide an explanation for this - along the same lines, given that the crystal structure of TgMyoA is available (PMID: 30348763), it would be useful for the authors to provide a molecular model for the binding of the inhibitor to the proposed point of engagement.

      These are excellent questions that we unfortunately cannot yet answer. Docking simulations of KNX-002 to the published structure of TgMyoA in its pre-powerstroke state have thus far not yielded any promising results. The site of KNX-002 binding to P. falciparum MyoA was determined by X-ray crystallography (ref. 54); however, the PfMyoA in that study was in the post-rigor state and the coordinates of the co-crystal structure have not yet been made available in the PDB database for homology modeling.

      The lack of effect of KNX-002 on the vertebrate muscle myosins may not be surprising. Although the 3D structures of myosins are rather conserved, their primary structures are quite different, which likely contributes to the different effects of KNX-002 on the different myosins. TgMyoA and PfMyoA are more similar to each other than they are to the vertebrate muscle myosins, which may enable the specific targeting of MyoA in apicomplexan parasites (lines 308-310).

      *If this is an allosteric site it is possible that the mutation functions indirectly upon binding of KNX002 to the orthosteric site, but this would be useful to help the reader to understand this (and there are bioinformatic prediction tools that will score allostery - which would be interesting to include). This is explained somewhat in the discussion - but this should be introduced much earlier to clarify. What is known about allosteric regulation of Myo function? Is this a known site of regulation? *

      Allosteric modulation of human cardiac myosin by small molecules such as omecamtiv is well established, and allosteric effects of the T130A mutation are certainly a possibility. As molecular motors, myosins depend on a complex and highly interconnected network of allosteric interactions to perform their function (for example, see ref. 59). This complexity, combined with the fact that the data on the PfMyoA-KNX-002 structure have not yet been released, makes it very difficult to generate any sort of model that would support meaningful conclusions. A statement to this effect has been added to the Discussion (line 375-382), and the likelihood that T130 is not part of the actual binding site for the compound is now mentioned at the very beginning of the paragraph that discusses the potential mechanism of action of the T130A mutation (lines 375-376).

      *3) Introduction: 'Nearly one third of the world's population is infected with the apicomplexan parasite' - given that these data are extrapolated from serology, this should be reworded - it's fairer to say that they 'are or have been infected...' *

      Done – line 65.

      *4) Page 4: figure 1A - can you provide some explanation for incomplete inhibition in the screen - there seems to be a residual amount (15-20%) of activity that is not inhibited. *

      The compound inhibits 80-90% of the motor’s activity at 40uM. We did not test higher concentrations in the ATPase assay; presumably we would see incrementally more inhibition as we increase compound concentration further, but the concentrations used enabled us to construct a reproducible IC50 curve without adding potentially confounding amounts of DMSO (carrier) to the assay.

      *5) The authors demonstrate a general effect on growth over 7 days. It would be good to use a replication assay (e.g. parasites/vacuole over a single lytic cycle) to confirm that KNX002 does not affect cellular division. This would further strengthen the argument that the phenotypic effect is primarily via impacts on motility. *

      A figure showing the lack of effect of KNX-002 on replication has now been added (new Supplemental Figure 2) and a paragraph describing these data and their implications added to the Results section (lines 137-143).

      *6) Page 5: 'selected for parasites resistant to KNX-002 by growth in 40 μM KNX-002.' - could the authors add text to explain why that concentration was chosen. *

      40 μM is close to the compound’s IC90 of 37.6 μM and, although we tried a number of different compound concentrations and selection schemes, 40 μM yielded parasites with the greatest shift in IC50. We now include this rationale on lines 606-607, as well as a new figure showing the shift in IC50 curves for all 5 resistant lines (Suppl. Figure 8).

      *7) Page 6: 'suggesting that the effects of the T130A mutation on motor function are due to more subtle structural changes' - it's fair to say that there are not gross structural changes based on the data presented, but that does not mean it is therefore a 'more subtle structural change' - surely the mutation could prevent KNX002 binding without effecting TgMyoA structure? *

      Based on the residues within P. falciparum MyoA that participate in binding to KNX-002 (ref. 54), it is unlikely that T130 of TgMyoA participates directly in compound binding. Mutation of T130 to alanine therefore seems most likely to impact compound binding through a change in protein structure, discussed more fully now on lines 375-382.

      *8) Page 6: 'the proportion of filaments moving' - in the figure it's referred to as the 'fraction of filaments', which makes more sense for the data presented. Please correct to 'fraction' throughout the manuscript (discussion, page 9 - possibly other instances!). Along the same lines, in figure 6 it would be good to change the y axes on the '% moving' to be 'fraction moving' and change the numbers - this would make it easier for the reader to understand the index values presented in the lower panels - if you do the calculation with the % values presented the numbers don't make sense (as fractions they do). The axes for motility also go up to 125% - please correct - based on the data presented there is no need for this to be above 100% (or 1 - see above). *

      We thank the reviewer for this suggestion; “percent moving” and “proportion moving” have been changed throughout the manuscript and figures to “fraction moving”. The y-axis labels on the motility and IC50 curves have also been modified as suggested.

      *9) Page 7: 'tested whether KNX-002 (20mg/kg, administered intraperitoneally on the day of infection and two days later' - please provide some rationale for the concentration used. *

      A preliminary dose tolerance study was conducted prior to the infection experiments, with doses ranging from 5-20 mg/kg. The study showed that two doses of 20 mg/kg, administered two days apart, resulted in minor hepatoxicity without signs of pain or distress. 20mg/kg was therefore considered the maximal tolerated dose. This rationale is now included on lines 715-721.

      *10) Page 10: 'the T130A mutation is likely to have long range structural impact that could alter the KNX-002 binding pocket' - this is particularly interesting, and should be addressed with a model - do the authors think that the T130 region be a conserved site of allosteric regulation? This would be good to expand upon in the discussion - mutation of an allosteric site as a mechanism of resistance is unusual, and typically described as being unlikely - and used as justification for the targeted drugging of allosteric sites. *

      See response to comment #2 above and the new text on lines 375-382.

      Reviewer #2

      *1) Considering (i) the moderate effect of KNX-002 on the acute infection process in CBA mice that received tachyzoites intraperitoneally, (ii) the fact that the drug application cannot be envisaged outside of the context of reactivation of cystogenic strains (in particular with respect to cerebral toxoplasmosis as emphasized in the introductive section), which implies the drug would have to be delivered and active in the brain parenchyma, a condition not analyzed here, it would be appropriate to modify the current title. It would be more relevant to highlight the solid body of data on the identification and functional characterization of the compound and derivatives in vitro and in the host mouse model. Apart from the title, the discussion should also recontextualize the in vivo assays and the information these assays bring on the slight delay of the "mortality" of some but not all mice. *

      We agree that the major clinical application of any new anti-Toxoplasma chemotherapy would be treatment of a reactivated infection, particularly in the brain (although there could also be a role for treatment of pregnant women), and that the data we present with this compound do not speak directly to clinical efficacy in this context. That said, reactivation leads to an active infection whose pathogenesis requires TgMyoA-dependent motility, invasion and egress, like the active infections analyzed here. The KNX-002 scaffold would likely need to be modified to enable it to cross the blood-brain barrier and access parasites in the brain, but that would be a normal step in any campaign to develop new drugs for toxoplasmosis (which is well beyond the scope of this study; see response to comment #11).

      Given these considerations, we gave much thought to how to accurately describe the results from the animal experiments – and we therefore appreciate the reviewer’s comment. For the title, we arrived the word “druggable”, because it has the very specific meaning described on lines 100-101: a protein whose activity is amenable to inhibition by small molecules. In our experiments with mice infected with wild-type parasites, nine of the ten compound-treated animals survived longer than the untreated controls, and 40% of the treated mice were still alive at the end of the experiment. Nevertheless, we stayed away from terms like “therapeutic” or “treatment”, for exactly the reasons the reviewer raises. We believe that the current title is an accurate summary of what we found, since we have indeed shown that MyoA is amenable to inhibition by a small molecule in a well-established animal model of infection (CBA mice infected intraperitoneally). Showing for the first time that the MyoA is druggable, in vivo, provides the rationale for identifying more potent compounds that can access the brain and serve as bona fide leads for drug development.

      To the reviewer’s point, we also reviewed all sections of the text where we described the animal experiments, and in the revised manuscript we replaced all instances in the text of “ameliorate disease”, “prevent disease” and “decrease the susceptibility of mice to a lethal infection” with the more circumspect phrases “alter disease progression” or “slow disease progression” (lines 46, 56, 110, 297, 315, Figure 9 legend). We also changed the title of the Results section describing these data from “KNX-002 treatment decreases the susceptibility of mice to lethal infection with T. gondii” to “KNX-002 treatment slows disease progression in mice infected with a lethal dose of T. gondii” (line 284).

      *2) Motility analysis: This comment concerns the Figure 7. It seems to the reviewers that the major hypothesis to test in data presented in panel B is that the wild type and the T130A mutant tachyzoite respond differently under similar drug conditions rather than the two populations without drug. These statistics could be added easily, hence it would validate that the proportion of motile mutant parasites is not affected by the drug when compared to vehicle. *

      These statistical comparisons have now been added to revised figure 7, as suggested. Since this comparison was between different parasite lines, it required the use of unpaired t-tests (vs. the paired t-tests used for different compound treatments of the same parasite line). We have therefore revised all 3D motility figures (Figures 4 and 7, Suppl. Figures 7 and 12) and their legends to clearly indicate which samples are being compared to which and whether paired or unpaired statistical tests are being used.

      However, the statistics shown panel C rather suggest that the drug does impact on the speed of the moving parasites, including when these carry the "resistance" T130 A mutation. It is not clear what we can gain in terms of messages with the motility index except to "slightly reverse" the analysis on panel B and to favor a no-effect of KNX-002 on the mutant parasite motile skills, on which the author might give more explanation. When comparing these quantitative tests with the panel presented above (panel A) it seems that the mutant parasite is still impacted by the MyoA inhibitor. Although there is no doubt for the reviewers that the T130A mutant emerging from the selected T. gondii resistant clones is a valuable probe for assessing drug selectivity: indeed the assays validate KNX-002 as a direct TgMyoA ATPase inhibitor, it might be good to rephrase some sentences and to have a harmonized definition of the parasite motility index throughout the text (Figure 7 legend, result and discussion sections).

      The reviewer is correct that there is a decrease in the speed of compound-treated T130A parasites, as the p-values on Figure 7C indicate. This is why we state in the text that “the mutant parasites retain some sensitivity to the compound” (line 263). We were careful throughout the manuscript to refer to the resistance provided by the mutation as “partial”, or to describe it as a “reduced sensitivity”. Partial resistance is still sufficient to establish compound specificity, as noted by the reviewer in this comment.

      We present the motility index not to try to “reverse” the effect of the compound on the mutant’s speed, but because the compound has two simultaneous effects on motility -- a decrease in the fraction moving and a decrease in speed of those that do move. Combining these two effects into one value (while still showing each component individually, as we have) enables comparison to the analogous actin filament motility index from the in vitro motility assays, and provides a more complete picture of the impact of compound treatment on parasite motility. This is a similar approach to that used in studies of e.g., phagocytosis, where the widely reported “phagocytic index” corresponds to the fraction of cells that have internalized at least one particle multiplied by the average number of beads internalized. The motility index of the mutant parasites is significantly less impacted by KNX-002 than the motility index of wild-type parasites (Figure 7D).

      We have further clarified the definition and rationale for using the parasite motility index throughout, as suggested (lines 233-235, 264-267, 345-348).

      *This reviewer's concern was accentuated by the comparison between the actin filament sliding index and the parasite motility index which appears as such far stretch; Aside from the "far stretched claims" easy to re-address in a revised version, the readers have appreciated the writing quality and most figure illustration. The discussion nicely synthetizes the whole dataset, including those related to the 4 T. gondii clones that resisted to KNX-002 but not through mutations targeting any of the myosinA chains. *

      We have added additional text to the discussion listing possible reasons for the differential effects of the mutation on the filament and parasite motility indices (lines 403-406).

      4) Ab*stract: the concept of "ameliorate disease" in this framework is odd and the objective of the work can be rephrased in a simple way (see below) *

      See response to Comment #1; “ameliorate” has been replaced with “alter disease progression” (line 46).

      *5) Introduction section: we think that the references on the impairment of invasiveness for the KoMyoA should be included (Bichet et al., BMC Biology 2016) as it has provided proof of an alternative and suboptimal mode of entry in many different cell types, thereby arguing that in absence of MyoA function, parasite invasiveness is not fully abolished and this without considering any MyoC-driven MyoA compensation. *

      We thank the reviewer for catching this oversight; the Bichet citation has been added (line 93).

      6) Introduction, third paragraph: in the sentence "Because the parasite can compensate for the loss or reduced expression of proteins important to its life cycle [29-31], small-molecule inhibitors of TgMyoA would serve as valuable complementary tools for determining how different aspects of motor function contribute to parasite motility and the role played by TgMyoA in parasite dissemination and virulence ». We definitively agree with this view but saying that, we think it would be worth evaluating (or simply discussing) the potency of the KNX-002 against MyoC, which compensatory contribution has been debated and remains questionable (at least to the reviewers) with respect to cell invasiveness restoration (related to the comment above).

      We have included a discussion of a potential compensatory role for MyoC and the value of determining in future studies whether KNX-002 (or its more potent downstream analogs) inhibit any of the other parasite myosins (lines 419-423). Whether or not MyoC can functionally compensate for a lack of MyoA – we agree this is a controversial question – it is important to note (as we do on line 440-442) that “T. gondii engineered to express low levels of TgMyoA … are completely avirulent [28], arguing that sufficiently strong inhibition of TgMyoA is likely, on its own, to be therapeutically useful”.

      *7) If we are correct, the screen and the characterization study have been performed with two different products (CK2140597 and KNX-002 the compound library and the re-synthetized one, respectively). Could we make sure that the two have the same potency? *

      The source of compound used in each of the assays is now explicitly described on lines 481-490). Commercially obtained compound yielded an IC50 in growth assays of 16.2 and 14.9 μM (Figures 2 and 5, respectively), and compound synthesized by us yielded an IC50 value of 19.7 μM (Figure 3). The 95% confidence intervals of these three independent IC50 determinations with two different sources of compound overlap (lines 484-486).

      8) We understood how the authors came to the conclusion that the KNX-002 impact on growth of the parasite and they stated "growth in culture" in the subsection title but then refers to parasite growth. Therefore, it looks a bit confusing for the reader since intracellular growth per se is probably not modified but this feature was not looked at it in this study (we would expect no impact based on published data on MyoA- genetically deficient tachyzoites, except if the drug impacts host cell metabolism for instances). Instead, it is the overall expansion of the parasite population that is analyzed here and clearly shown to be impacted. This decrease in population expansion on a cell monolayer likely results from impaired MyoA-dependent egress and invasiveness upon chemical inactivation of MyoA. Accordingly, it appears difficult to understand what is an IC50 for the "overall" growth in the context of this study. The authors should rephrase for better accuracy when necessary, including in the graph Fig2 legend axis.

      While assays that measure parasite expansion in culture are by convention called “growth” assays (e.g., see Gubbels et al, High-Throughput Growth Assay for Toxoplasma gondii Using Yellow Fluorescent Protein AAC 47 (2003) 309, the paper on which our assay was based), we take the reviewer’s point that a reader may incorrectly ascribe the inhibition to some other aspect of the lytic cycle (e.g., intracellular replication), rather than a myosin-dependent motility-based process. We have therefore now: (a) more clearly defined the growth assay as one that measures parasite expansion in culture (lines 132-138); (b) described the myosin-dependent and -independent steps of the lytic cycle (lines 137-140); and (c) added a new figure (new Suppl. Figure 2, lines 140-143) showing that the compound has no effect on intracellular replication.

      *9) The authors should clarify for the reader (i) why they use in some case myofibrils and other muscle F actin when measuring the Myosin ATPase activity, (ii) what does mean XX% calcium activation and (iii) why using 75% in these assays which is 3 times higher from the original assays. (iv) Why they did not include non muscle actins in their study since Myosins also extensively work on non muscle actins. *

      (i) For both striated and smooth muscle myosins, the assays used here are well established and have identified compounds that have translated into animal models of disease. To assay the activity of myosins from striated muscle types, particularly to determine compound selectivity, myofibril assays are preferred as they recapitulate more of the biology as a more "native", membrane-free preparation and respond cooperatively to calcium activation. For cardiac, fast and slow skeletal muscle it is possible to derive high quality myofibril preparations that can be activated by calcium. A reference describing the value of using myofibrils in assays of striated muscle myosin ATPase activity has been added (ref. 71, line 517).

      Smooth muscle, a non-striated tissue, is regulated differently and calcium exerts an effect not through binding to troponin as in the striated muscle but through g-protein signaling, with phosphorylation as an end result, making the contraction slower and also much slower to reverse - in line with the physiological role of the muscle. The only way to reliably reconstitute smooth muscle ATPase activity has been through purification and reconstitution of a more reductionist system. The SMM S1 needs to be crosslinked to the actin to achieve high enough local concentrations to generate robust ATPase activity. A reference describing the use of this assay to identify small molecule inhibitors of SMM is now included (ref. 73, line 522).

      (ii, iii) Striated muscle myofibrils are responsive to calcium, as muscle contraction is mediated in vivo through calcium release from the sarcoplasmic reticulum. Titrating calcium can activate the myofibril ATPase activity up to the plateau (100%) and provide optimal signal to noise and sensitivity for the particular activity being assayed. For counterscreening to determine selectivity, we adjusted the assay conditions to a high basal ATPase activity (75% calcium) to provide high sensitivity for detecting inhibition. A sentence explaining this rationale has been added on lines 519-520.

      (iv) We used skeletal muscle actin in all of our in vitro assays since we have shown skeletal muscle actin to be a good substrate for TgMyoA (ref 33, cited on line 536) and skeletal muscle actin can be purified in larger quantities than native actin from parasites or functional recombinant protein from insect cells. Others have also shown that the closely related MyoA from P. falciparum moves skeletal muscle actin at the same speeds as recombinant P. falciparum actin (Bookwalter et al [2017] JBC 292:19290).

      *10) The protocol of image analysis of the 3D motility assay was increased to 80 seconds for the test of KNX-002 selectivity using wild type and mutant parasites (Fig 7) when compared to the test of KXN-002 concentration effect on wild type tachyzoites (60 sec in the result section, in Fig 4 legend and in the Methods' section). Is there any specific reason? *

      The data for Figure 4 were captured earlier in the project than those of Figure 7 and Suppl. Figure 7. In the intervening time we upgraded our Nikon Elements software from v.3.20 to v.5.11 (as already described on lines 583 and 588). With the upgrade to v.5.11, we also began using Nikon’s Illumination Sequence (IS) module, a graphical user interface that provides greater time resolution through a more efficient approach to building the z-stacks and saving the data. With the addition of v.5.11 and the IS module we were able to capture twice the number of image volumes in 80 sec than we were in 60 sec using v.3.20, and that became our standard operating procedure. Other than the improved time resolution, the 60s and 80s assays give indistinguishable relative results. We have now clarified in the methods (line 588-589) that we used the IS module to acquire the data in Figure 7 and Suppl. Figure 7.

      *11) In the mouse infection experimental design (Method section), it seems that they were no biological replicates in the case of the drug-treated (parasites + mice) which is not the case for the comparison of virulence between MyoA wild type and T130 mutants. If true, and considering what the authors wish to emphasize as a main message, it is fairly complicated to convincingly conclude about the KNX-002 effectiveness in vivo. Maybe the authors could explain their limitations. *

      Since we did not know how the compound and parasites would interact in mice – and in keeping with animal welfare standards – we decided that rather than doing multiple replicates with smaller numbers of infected mice we would do a single experiment with a large enough number of mice per treatment condition to ensure that if any animals died unexpectedly or had to be euthanized prematurely we would still have sufficient numbers for robust statistical comparison. Single experiments with ten treated and ten untreated mice are a generally accepted approach in early studies of drug effectiveness (e.g., Ferrreira et al Parasite 2002, 9:261; Rutaganira et al, J. Med. Chem. 2017, 60: 9976; Zhang et al IJP Drugs and Drug Resistance 2019, 9:27), and power analysis shows that if mortality is 100% in untreated mice and 50% in treated mice, 10 mice per group will provide an 80% probability of detecting the difference with a p value<br /> *We are also not sure why the compound has been injected only twice, at the time of parasite injection and two days after whereas the mice succumbed after 8 to 9 days even without MyoA inhibitors. Although quite difficult to measure, do the authors have any knowledge (based on the chemistry for example) of the compound stability and lipophilicity in blood and tissues? Because the IC50 on free tachyzoites appears significantly higher (5.3 uM, Fig4) than the in vitro molecular assay, when assessed in motility tests, and is increased for intracellular growth (Fig 8), it is somehow expected that the current compound would not work that great in vivo. Did the author try to provide the inhibitor intravenously every day? *

      IP injection is a standard method of administration for early drug treatment studies, and two considerations contributed to our decision to inject on days 0 and 2 post-infection: (a) the preliminary dose-tolerance studies, which were done with two IP doses of compound two days apart, showed evidence of mild toxicity so we were hesitant to inject more frequently, inject IV, or use more compound/injection; (ii) we expect the compound to work primarily on egressing and extracellular parasites, and since the parasite’s lytic cycle takes approximately 48 hours, this two-day injection schedule was chosen to maximize exposure of the extracellular parasites to freshly injected compound early in establishment of the infection. This rationale has now been added to the Methods section (lines 715-721).

      In terms of the doing systematic studies of dosing, stability, PK/PD, drug partitioning etc., it is important to restate that the primary goal of this work was to test whether inhibiting TgMyoA activity in vivo alters the course of infection. The data reported in the manuscript demonstrated this to be the case. As we state on lines 454-457, “While KNX-002 provided the means to rigorously test the druggability of TgMyoA, it caused weight loss and histological evidence of liver damage in the treated infected mice. Before further animal work, it will therefore be necessary to develop more potent and less toxic analogs that retain specificity for parasite myosin.” Our colleagues at Kainomyx have in fact initiated a drug development campaign based on the KNX-002 scaffold and have already identified a derivative named KNX-115, that is 20-fold more potent against recombinant P. falciparum MyoA (described on lines 356-361). Given Kainomyx’s ongoing efforts we do not believe it makes sense to do any further animal experiments at this time with KNX-002. It will be more informative and ethical to undertake, e.g., dosing, PK and PD studies with the more potent and less toxic derivatives that emerge from the Kainomyx drug development program, once these compounds become publicly available. This does not diminish the importance of the proof-of-principle experiments reported here, which as the reviewer stated, “provide a strong rationale for developing new therapeutic strategies based on targeting MyoA”; rather, it makes it hard to justify doing additional animal studies with a compound that we know will soon be replaced with more potent and less toxic derivatives.

      12) Figure 4: 2D and 3D Motility- the authors should comment on the fact that in 2D conditions with 10 uM of KNX-002, circular trajectories (one complete circle so at least 2 parasite lengths but sometimes more) largely dominate over others, whereas in absence of KNX-002 these circular trajectories are barely detectable and helical trajectories predominate. What could that mean as regard to the MyoA functional contribution to either process?

      This is an interesting question that we cannot currently answer. Perhaps helical 2D gliding requires more myosin-generated force than circular 2D gliding, but this is pure speculation at this point. Whatever the explanation, the observation is striking and we believe should be reported as it shows a clear effect of the compound on motility in the widely-used 2D trail deposition assay.

      *13) Figure 7: Besides the major point raised above for panel C, the information carried by the Figure could be stronger if an additional panel is introduced regarding the interesting assay on the preserved structural stability of the MyoA mutant over the WT MyoA (currently in SupFig7) *

      Former Suppl. Figure 7 (now Suppl. Figure 9) addresses one particular explanation for the differential effects of the mutation in the in vitro motility assay (Figure 6) and the parasite 3D motility assay (Figure 7). The data in Suppl. Figures 14A and 14B address two other possibilities. For consistency with the other figures and clarity of the narrative, we would prefer to leave the data in Suppl. Figure 9 as a supplemental figure.

      14) Material and Methods - Parasite motility assays: remove the duplicated [16] reference.

      Done.

      *15) The discussion starts with the ongoing debate on mechanisms underlying zoite motility; We found that the work of Pavlou et al. (ACS nano, 2020) should be part of the references listed there, as it brings evidence that a specific traction polar force is required probably in concert with microtubule storage energy at the focal point, a result that questions the prevailing model. *

      This was another oversight; the citation has been added (line 307).

      *16) Concerning the C3-20 and C3-21 compounds, the sentence "they have no effect on the activity of the recombinant TgMyoA (AK and GEW, unpublished data)" in the paragraph starting by "There have been only two previously..." should be refrained unless showing the results. *

      We have removed reference to this unpublished work, as suggested (lines 338-340).

      17) If possible, the authors should expand more on the effect of KNX-002 on Plasmodium falciparum and its homolog PfMyoA.

      We have expanded our discussion of these preprint data from others on lines 356-361.

      Reviewer #3

      *1) The T130A IC50 was done on the mutagenized clone 5. The authors currently don't have data showing IC50 on the independently generated T130A mutant, to see if the IC50s are similar to one another, or if there were additional resistance mutations present in clone 5. *

      Because we did not insert the T130A mutation into a fluorescent parasite background, we cannot directly compare its IC50 in the fluorescence-based growth assay to that of the line generated by chemical mutagenesis. Plaque assays do not require fluorescent parasites but, in our hands, these assays lack the sensitivity to reproducibly detect the expected subtle (50. While we agree that it would be interesting to know if the mutant generated by chemical mutagenesis contains any additional resistance-conferring mutations, not having this information does not alter the conclusion that the T130A mutation alone reduces the sensitivity of the motor to KNX-002 (Figures 6-9). See also response to Reviewer 1, comment 1; a discussion of the value of determining what other resistance mechanisms are available to the parasite for this class of compounds is now included on lines 416-422.

      *2) For 3D motility assays, it is currently unclear from the data and text what the expected maximal inhibition of motility would be; e.g., would parasites depleted of MyoA display 0% motility. Understanding the dynamic range of this assay could help clarify whether this residual 5% motility explains why parasites treated with 20 uM KNX-002 can still form small plaques. This could be achieved by referencing previous work that assesses 3D motility after depletion of a critical motility factor. *

      A small fraction of TgMyoA knockout parasites are still capable of motility in 3D (13% when normalized to wildtype for displacements > 2 μm [ref. 46]), so the dynamic range of the 3D assay for TgMyoA-deficient parasites compared to wild-type parasites is 0.13-1.0. The 13% residual motility of the TgMyoA parasites is now referred to on lines 419-420. Treatment of wild-type parasites with 20μM KNX-002 results in a fractional motility of ~0.24 compared to untreated controls (Figures 4 and 7). This less than complete inhibition compared to the knockout is not surprising, since motor activity is not completely inhibited at 20 μM compound (Figures 1 and 6) and parasite growth as assayed either by the fluorescence-based method (Figure 2A) or plaquing (Figure 8) shows greater inhibition at 40 and 80 μM compound than at 20 μM.

      *3) It would be informative for the authors to discuss the rationale for the selected treatment regime. Since many drug-treatments involve daily dosing, was the two-dose regime based on poor tolerance of the compound in mice or other considerations? *

      See response to reviewer 2, comment 11; the rationale for this dosing regimen has now been added to the Methods (line 715-721).

      *4) Track length is not considered as a parameter in the filament sliding assays (Fig. 6) or the 3D motility assays (Fig. 7). These may be valuable parameters for the authors to examine; however, the time frames analyzed might be insufficient to capture track lengths. Could the authors include analyses of track lengths or discuss the technical limitations of their assays? *

      In the in vitro motility assays, almost all of the actin filaments move for the entire 60s of video recording so trajectory length is directly proportional to speed and therefore does not provide any additional information. For the parasite 3D motility assay, we have added a new figure (Suppl. Figure 12) showing the effect of the compound on the displacement of wild-type and T130A parasites, along with new text describing these data (lines 269-273).

      *5) When discussing the minor discrepancies between the results with recombinant protein and parasite motility, the authors could consider the relative concentration of motors in the pellicle; i.e. it might be necessary to inhibit a greater % of all the motors to truly block motility, perhaps consistent with the higher compound concentrations needed to affect parasite motility. *

      This possible explanation has been added to the Discussion (lines 403-406).

      6) The authors should include the IC50 data for all 5 KNX-002 resistant clones in the supplementary data. While the 5/26 clones showed >2.5-fold increase in IC50 for KNX-002, it's unclear how the IC50 of the single clone harboring the T130A MyoA mutation compared to the other resistant clones.

      A figure has been added showing these data (new Suppl. Figure 8).

      *7) For plaque assays, the authors should indicate how much DMSO was used for 0 KNX-002 conditions. It should presumably be the corresponding concentration at 80 µM drug and if not, that control should be performed to account for effects of DMSO at higher concentrations at all drug concentrations tested. *

      In all experiments involving treatment with compound, the compound was serially diluted in DMSO to the appropriate range of concentrations prior to dissolving it in aqueous buffer for the experiment itself, enabling an equivalent amount of DMSO to be added to all samples in that experiment, including the DMSO only vehicle controls. This clarifying statement and the final range of DMSO concentrations in each of the different types of experiments has been added to the Methods section (lines 486-491). * *

      *8) Authors should indicate the origins of their hexokinase for counter screens. *

      The hexokinase used was from Millipore Sigma (#H6380); the supplier has been added on line 507.

      *9) Authors should indicate µM on graphs. *

      The μM label has been added to the graphs where it was missing (Figures 2 and 5, Suppl. Figures 4, 6, 8).

      *10) In Figures 2A, 5A, and 5B, the use of colored lines (e.g., of different hues) could make the graphs more legible. *

      We have experimented with color as a way to discriminate between the different doses on these graphs, but found the use of 8 different colors to be more distracting than helpful. The color-coding approach would be even less useful for readers who have color vision deficiency (including one of our authors). Symbol groupings have been added to the right of all growth curves to improve the legibility of the graphs.

      *11) In Figure 2C, it isn't clear which cell line was treated with sodium azide to generate the positive control. *

      It was the HFF cells that were treated with azide as a positive control; Figure 2C has been modified to make this clear.

      *12) In the discussion, "a" is missing in the phrase "...mutation is likely to have long range structural impact..." *

      Done. * *

      *13) The abbreviation of species (spp.) should be followed by a period. *

      Done.

      Other

      Further SAR analyses using an optimized actin-dependent myosin ATPase assay resulted in minor changes to Suppl. Figure 3 and Figure 3, with no significant changes to the conclusions. The text has been modified accordingly (lines 155-161, 178-180).

      All other changes to the manuscript not noted above were editorial in nature, made to either improve clarity or correct minor errors in the previous version.

    1. Reviewer #2 (Public Review):

      The authors aim to make a reliable plate-based system for imposing drought stress (which for experiments like this would be better referred to as low water potential stress). This is an admirable goal as a reliable experimental system is key to conducting successful low-water potential experiments and some of the experimental systems in use have problems. They compare several treatments but seem to be unaware that such comparisons need to be based on the measurement of water potential as the fundamental measure of how severe the level of water limitation is. Only by comparing things at the same water potential can one determine if the methods used to impose the low water potential are introducing confounding factors. In this manuscript, they compare several agar-plate-based treatments to what they view as a baseline experiment of plants subjected to soil drying. However, that baseline soil drying (vermiculite drying, to be precise) experiment illustrates many of the problems present in the molecular drought literature in that they give no information on plant or soil water potential or water content. Thus, there is no way to know how severe the drought stress was in that experiment and no way for any other lab to reproduce it. It is directly akin to doing a heat stress experiment and not reporting the actual temperature.

      They compare transcriptome data from this soil drying experiment to transcriptome data from agar plates with PEG, mannitol or salt added. However, this comparison is problematic, because none of the treatments being compared are at the same water potential (as mentioned above). Also, the PEG-infused agar plates have limitations in that no buffer is added and it is not clear that anything is done to check or control the pH. Adding PEG to the solution will reduce the pH. Thus, in their unbuffered PEG plates, the plants are almost certainly exposed to low pH stress and this can explain the supposed difference they observe between PEG and other treatments, especially since the plants are left on such stressful pH conditions for a relatively long period. It is also problematic that the comparison between soil drying and plate-based treatments is at different times (5 vs 14 days). They also show an over-reliance on the GO annotations of drought-induced gene expression. This GO annotation is based on experiments using very severe stress for a short time period. It is notorious for not accurately reflecting what happens on longer-term exposure to more moderate levels of low water potential stress. Thus, for example, we would not expect many of the canonical drought regulation genes (RD29A and similar genes) to be upregulated in the longer-term treatments as its expression is induced rapidly but also rapidly declines back to near baseline at the plant acclimates to the low water potential stress.

      The authors have not always considered literature that would be relevant to their topic. For example, there is a number of studies that have reported (and deposited in the public database) transcriptome analysis of plants on PEG-plates or plants exposed to well-controlled, moderate severity soil drying assays (for the latter, check the paper of Des Marais et al. and others, for the former, Verslues and colleagues have published a series of studies using PEG-agar plates). They also overlook studies that have recorded growth responses of wild type and a range of mutants on properly prepared PEG plates and found that those results agree well with results when plants are exposed to a controlled, partial soil drying to impose a similar low water potential stress. In short, the authors need to make such comparisons to other data and think more about what may be wrong with their own experimental designs before making any sweeping conclusions about what is suitable or not suitable for imposing low water potential stress.

      To solve the problem of using these other systems to impose low water potential stress, the authors propose the seemingly logical (but overly simplistic) idea of adding less water to the same mix of nutrients and agar. Because the increased agar concentration does not substantially influence water potential (the agar polymerizes and thus is not osmotically active), what they are essentially doing is using a concentrated solution of macronutrients in the growth media to impose stress. This is a rediscovery of an old proposal that concentrated macronutrient solutions could be used to study the osmotic component of salt stress (see older papers of Rana Munns). There are also effects of using very hard agar that is of unclear relationship to actual drought stress and low water potential. Thus, I see no reason to think that this would be a better method to impose low water potential.

    2. Author Response:

      eLife assessment

      This work is an attempt to establish conditions that accurately and efficiently mimic a drought response in Arabidopsis grown on defined agar-solidified media - an admirable goal as a reliable experimental system is key to conducting successful low water potential experiments and would enable high-throughput genetic screening (and GWAS) to assess the impacts of environmental perturbations on various genetic backgrounds. The authors compare transcriptome patterns of plant subjected to water limitation imposed using different experimental systems. The work is valuable in that it lays out the challenges of such an endeavor and points out shortcomings of previous attempts. However, a lack of water relations measurements, incomplete experimental design, and lack of critical evaluation of these methods in light of previous results render the proposed new methodology inadequate.

      We thank eLife for the initial assessment and comments to our work. In our revised manuscript we plan to address the main concerns raised by reviewers. Specifically, we plan to perform water relations measurements for all our treatment assays, as well as explore the separate effects agar hardening and nutrient concentration have in our low-water agar assay. We will also provide a more in depth critical review of our results compared to previously published results.

      Reviewer #1 (Public Review):

      High-throughput genetic screening is a powerful approach to elucidate genes and gene networks involved in a variety of biological events. Such screens are well established in single-celled organisms (i.e. CRISPR-based K/O in tissue culture or unicellular organisms; screens of natural variants in response to drugs). It is desirable to extend such methodology, for example to Arabidopsis where more than 1000 ecotypes from around the Northern hemisphere are available for study. These ecotypes may be locally adapted and are fully sequenced, so the system is set up for powerful exploration of GxE. But to do so, establishing consistent "in vitro" conditions that mimic ecologically relevant conditions like drought is essential. 

      The authors note that previous attempts to mimic drought response have shortcomings, many of which are revealed by 'omics type analysis. For example, three treatments thought to induce osmotic stress; the addition of PEG, mannitol, or NaCl, fail to elicit a transcriptional response that is comparable to that of bonafide drought. As an alternative, the authors suggest using a low water-agar assay, which in the things they measure, does a better job of mimicking osmotic stress responses. The major issues with this assay are, however, that it introduces another set of issues, for example, changing agar concentration can lead to mechanical effects, as illustrated nicely in the work of Olivier Hamant's group.

      We thank the reviewer for their comments. We hypothesize that our low-water agar assay is able to replicate drought gene expression patterns through a combination of hardened agar and higher nutrient concentration. However, we did not explore the separate effects each of these factors may play in eliciting such responses. Thus, in our revised manuscript, we will explore what role the mechanical effects of changing agar concentration has on root gene expression. However, we suspect that the mechanical effects introduced by hard agar does not introduce another issue per se, but in fact may help with replicating the transcriptional effects seen under drought.

      Reviewer #2 (Public Review):

      […] The authors have not always considered literature that would be relevant to their topic. For example, there is a number of studies that have reported (and deposited in the public database) transcriptome analysis of plants on PEG-plates or plants exposed to well-controlled, moderate severity soil drying assays (for the latter, check the paper of Des Marais et al. and others, for the former, Verslues and colleagues have published a series of studies using PEG-agar plates). They also overlook studies that have recorded growth responses of wild type and a range of mutants on properly prepared PEG plates and found that those results agree well with results when plants are exposed to a controlled, partial soil drying to impose a similar low water potential stress. In short, the authors need to make such comparisons to other data and think more about what may be wrong with their own experimental designs before making any sweeping conclusions about what is suitable or not suitable for imposing low water potential stress. 

      To solve the problem of using these other systems to impose low water potential stress, the authors propose the seemingly logical (but overly simplistic) idea of adding less water to the same mix of nutrients and agar. Because the increased agar concentration does not substantially influence water potential (the agar polymerizes and thus is not osmotically active), what they are essentially doing is using a concentrated solution of macronutrients in the growth media to impose stress. This is a rediscovery of an old proposal that concentrated macronutrient solutions could be used to study the osmotic component of salt stress (see older papers of Rana Munns). There are also effects of using very hard agar that is of unclear relationship to actual drought stress and low water potential. Thus, I see no reason to think that this would be a better method to impose low water potential. 

      We thank the reviewer for their comments. In our revised manuscript, we will address points regarding plant and soil water potential; similar concerns were also raised by Reviewer 1 and 3. We note that we report vermiculite water content in Supplementary Table 4.

      We would like to clarify that both the PEG media and overlay solution were buffered - we did not include this within the written description in the methods, but will do in our revised manuscript.

      We agree with the reviewer’s concern that it may be problematic to compare the transcriptomic profiles of seedling and mature plants. In light of this, we plan to explore what effects our treatment media has on mature rosettes.

      We note that we do not claim that PEG is unable to produce low-water potential responses similar to partial soil drying. Indeed, we indicate that it is a good technique for eliciting phenotypes comparable to drought at the physiological level (line 48). Rather, we claim that PEG is unable to produce gene expression responses that are sufficiently similar to partial vermiculite drying.

      Reviewer #3 (Public Review):

      […] The authors observed that gene expression responses of roots in their 'low-water agar' assay resembled more closely the water deficit in pots compared to the PEG, mannitol, and salt treatments (all at the highest dose). In particular, 28 % of PEG led to the down-regulation of many genes that were up-regulated under drought in pots. Through GO term analysis, it was pointed out that this may be due to the negative effect of PEG on oxygen solubility since downregulated genes were over-represented in oxygen-related categories. The data also shows that the treatment with abscisic acid on plates was very good at simulating drought in roots. Gene expression changes in shoots showed generally a high concordance between all treatments at the highest dose and water deficit in pots, with mannitol being the closest match. This is surprising, since plants grow in plates under non-transpiring conditions, while a mismatch between water loss by transpiration on water supply via the roots leads to drought symptoms such as wilting in pot and field-grown plants. The authors concluded that their 'low-water agar' assay provides a better alternative to simulate drought on plates. 

      Strengths: 

      The development of a more robust assay to simulate drought on plates to allow for high-throughput screening is certainly an important goal since many phenotypes that are discovered on plates cannot be recapitulated on the soil. Adding less water to the media mix and thereby increasing agar strength and nutrient concentration appears to be a good approach since nutrients are also concentrated in soils during water deficit, as pointed out by the authors. To my knowledge, this approach has not specifically been used to simulate drought on plates previously. Comparing their new 'low-water agar' assay to popular treatments with PEG, mannitol, salt, and abscisic acid, as well as plants grown in pots on vermiculite led to a comprehensive overview of how these treatments affect gene expression changes that surpass previous studies. It is promising that the impact of 'low-water agar' on the shoot size of 20 diverse Arabidopsis accessions shows some association with plant fitness under drought in the field. Their methodology could be powerful in identifying a better substitute for plate-based high-throughput drought assays that have an emphasis on gene expression changes. 

      Weaknesses: 

      While the authors use a good methodological framework to compare the different drought treatments, gene expression changes were only compared between the highest dose of each stress assay (Fig. 2B, 3B). From Fig. 1F it appears that gene expression changes depend significantly on the level of stress that is imposed. Therefore, their conclusion that the 'low-water agar' assay is better at simulating drought is only valid when comparing the highest dose of each treatment and only for gene expression changes in roots. Considering how comparable different levels of stress were in this study leads to another weakness. The authors correctly point out that PEG, mannitol, and salt are used due to their ability to lower the water potential through an increase in osmotic strength (L. 45/46). In soils, water deficit leads to lower water potential, due to the concentration of nutrients (as pointed out in L. 171), as well as higher adhesion forces of water molecules to soil particles and a decline in soil hydraulic conductivity for water, which causes an imbalance between supply and demand (see Juenger and Verslues, The Plant Cell 2022 for a recent review). While the authors selected three different doses for each treatment that are commonly used in the literature, these are not necessarily comparable on a physiological level. For example, 200 mM mannitol has an approximate osmotic potential of around -5 bar (Michel et al. Plant Physiol. 1983) whereas 28 % PEG has an osmotic potential closer to -10 bar (Michel et al. Plant Physiol. 1973). It also remains unclear how the increase in agar concentration versus the increase in nutrient concentration in the 'low-water agar' affect water potentials. For these reasons it cannot be known whether a better match of the 'low-water agar' at the 28% dose to water deficit in pots for roots in comparison to the other treatments is due to a good match in stress levels with the 'low-water agar' or adverse side-effect of PEG, mannitol, or and salt on gene regulation. Lastly, since only two biological replicates for RNA sequencing were collected per treatment, it is not possible to know how much variance exists and if this variance is greater than the treatments themselves. 

      We thank the reviewer for their comments. In our statistical analyses, we found that dose-responsive genes (as fit by a linear model) were very similar to those genes found differentially expressed at the highest dose. Thus, for clarity, we decided to simply present the genes differentially expressed at the highest dose. We see now that this might have been an oversimplification. In our revised manuscript, we will present genes that are dose responsive across the range of treatment doses, thus providing more evidence that lower doses of low-water agar are also capable of simulating drought (as is suggested by overlap analysis of Figure 2A).

      Additionally, we will also explore the osmotic potential of each of our different assays to provide a better benchmark of how comparable each of our treatments are (as similarly requested by Reviewer 1 and 2). Lastly, to address concerns regarding the size of variance in gene expression, we will sequence a 3rd replicate of RNA.

    1. Plyometric Training

      Numerous books and articles have been written about plyometric training for athletes. However very few offer detailed progressive programs that take into account the need for a system of training that can be applied to a broad range of athletes. Instead you get a smorgasbord of exercises and opinions. Although the works of Chu, Radcliffe and Gambetta were outstanding at the time of their writing, very little has been written in the last ten years that connects our current knowledge of functional training with how to design and implement a system of plyometric exercises. In order to fully understand plyometrics, we must look at basics like terminology, volume and frequency.

      Terminology

      The first area that needs to be addressed in the area of plyometric training is terminology. The language of plyometrics must be universal so that any coach or athlete can view the program of any other coach or athlete and understand the exercises ideally without photos or video. The discrepancies in terminology were first brought to my attention by Mike Clark of the National Academy of SportsMedicine. Clark pointed out in a 2000 lecture that many coaches currently used names to describe plyometric exercises that were not properly descriptive of the movement.

      Skip- single leg takeoff with two foot contacts

      Although many might view these descriptions as simple and common sense, I realized that I inadvertently had misclassified exercises. We had always referred to two legged jumps over hurdles as hurdle hops. I believe that this was and still is a common error among many strength and conditioning and track coaches. Clark made the facetious point that "bunnies don't hop, they jump".

      Many might view this as a minor discrepancy but, a call from a coach in California made me realize the cost of "minor discrepancies".

      The coach in question called me and said "Boy, are your guys great athletes, I can't get one guy on my team to do those thirty inch hurdle hops you guys do." I quickly realized that my "minor discrepancy" had caused this coach to try to perform an exercise with one leg that we had been doing with two. He had his athletes hurdle hopping as the program indicated while I had mine hurdle jumping.

      A small detail? Maybe.

      The reality is that an athlete could have been badly injured because of my incorrect use of descriptive terminology.

      Categories of Exercises

      After looking at terminology, the next area to examine is the categories of the different types of jumps, hops and bounds. I believe that this is the major failing of the most popular commercially available ACL injury prevention programs.

      The two most popular, The Santa Monica PEP program and the Sportsmetrics program focus almost exclusively on jumps with no emphasis on bounds or hops. The reality is that the mechanism of the ACL tear is most frequently in a single leg hop (actually a redundancy as the term hop denotes single leg) or bound scenario, not a double leg jump.

      A sound plyometric program must include a balance of exercises from each terminology category. Athletes must perform a balance of jumps, hops and bounds. In addition, hops must be done both forward, at 45 degree angles and potentially side to side. It should be noted that hopping medially and laterally are entirely different in both the muscles stressed and the injury prevention potential.

      Medial hops ( hops toward the midline) are more difficult and provide much needed stress to the hip stabilizers.

      Volume

      One question that begs to be answered revolves around the volume of jumps. Volume is measured by the number of jumps per session and has most frequently been measured by the number of foot contacts. Recently we have seen lots of recommendations for what are being referred to as extensive plyometrics. The concept basically advocates a high volume of "little jumps" to build up to more intense plyometrics.My feeling is that the term extensive plyometrics is a bit of an oxymoron. The whole idea of plyometrics is facilitate explosive contractions with the eventual goal of reduced ground contact time

      One of the major failings of many plyometric programs is too high a number of foot contacts. Extensive plyometrics not only doesnt solve this problem but, more than likely exacerbates it. We also have to distinguish if some of the "extensive plyometrics" recommended are valuable, necessary or even really plyometrics ? Although in a technical sense all movements involve strech shortening I'm not sure that jump rope or line hops prepares the tissue properly for the more intense activity to follow?

      We try to keep the number of jumps, hops and bounds at roughly 25 per day and 100 per week and never use extensive plyometrics in a preparatory phase.

      Intensity

      Instead, we use a realtively constant volume of drills that progress in intensity. The intensity of plyometric training is difficult to measure and really involves understanding the difference between a program of controlled jump training and a true plyometric program. Many exercises that we consider to be plyometric in nature are actually simply jumping exercises. A box jump is really just a jump. In order to be "truly" plyometric there needs to be a reactive component. However, our program is probably better described as a "progression to plyometrics" program.

      Controlling the intensity of plyometric exercises is actually based on controlling how gravity is allowed to enter the picture and to act on the body. Jumps up to a box or hops up to a box are the lowest intensity as they involve a strong concentric contraction but minimize eccentric stress by not allowing the body to "in effect" come down. With box jumps and box hops, what goes up does not really come down. The body is accelerated up to a height but not allowed to travel back down. The athlete jumps up and steps down, thereby effectively negating the effect of gravity as an accelerating force.

      What we do know is that mistakes in plyometric progressions will manifest themselves primarily as patella femorall issues. This could be due to tendon loading issues or to overstress of the patella femoral joint but in either case the issue is too much jumping ( or hopping)and or drills that are not properly progressed in intensity. Volume is frequently the enemy, particularly in atheltes that already experience a high volume of foot contacts in practice or training. Professor Jill Cook points out that peak tendon stress is at the point of switching from eccentric to concentric contraction. The goal of our progression is prepare for that point in a more controlled and thoughtful manner.

      Chu's early work classified intensity of jumps based on whether the jumps were done in place or, covered horizontal distance. Although this early quantification system of in-place, short, and long was state of the art in the eighties, our increased analysis of the effects of physics on the body leads us to a system that I believe better describes the effect of jumps. I prefer classifying jumps as gravity reduced or gravity enhanced and then move to semi-elastic ( bounce) and elastic (rebound or continuous). Early plyometric descriptions left no room for jumps that were actually not plyometric in nature.

      The following videos illustrate our progression

      Frequency-

      One of the first questions when discussing frequency and plyometrics relates to the NSCA position statement. I find it intriguing that the NSCA once published such a short sighted piece. In the initial position statement the NSCA took the position that plyometrics should only be done twice per week. This has since been amended to read that the same joints should not be worked on consecutive days. The NSCA takes no position on intensity or volume other than to indicate that depths jumps may be too intense for larger athletes. My feeling is that plyometrics can be performed up to four times per week but, must be divided into linear and multi-directional days. Linear plyometrics involve pure sagittal plane jumps and hops, while multi-directional plyometrics work in the frontal and transverse planes.

      Transverse Plane Plyometrics

      I believe that athletes must do decelerative work in the transverse plane but, think that transverse plane jumps and hops must be approached with great care. It must be noted that in many cases the transverse plane exercises recommended look very much like the injury mechanisms we are trying to avoid.

      Age/ Level of Experience

      Another interesting point in the NSCA statement relates to the development of a proper strength base for plyometrics. No one has defined what proper is. Previously foolish, short-sighted recommendations were made relative to strength base. Some writers recommended a certain number of weeks of strength training prior to beginning a plyometric program, others recommended a certain strength level prior to undertaking a plyometric program. It is my feeling that strength training and plyometric training can be done concurrently providing common sense is used.

      The reality is that young athletes begin intense plyometric programs without a strength training base or a required strength level every day. Both gymnastics and figure skating involve intense plyometric type activity from very young ages. The key is to manage the effect of gravity on the body. The keys to a plyometric program are simple:

      Good plyometrics are quiet. Failure to land quietly indicates that the athlete lacks eccentric strength and that the exercise is inappropriate. All that may be necessary is to decrease the height of the obstacle involved. Athletes should only jump onto boxes that they can land on quietly.

      PS- Athletes should always jump from and land in from the same position.

    1. Reviewer #3 (Public Review):

      This manuscript reveals opioid suppression of breathing could occur via multiple mechanisms and at multiple sites in the pontomedullary respiratory network. The authors show that opioids inhibit an excitatory pontomedullary respiratory circuit via three mechanisms: 1) postsynaptic MOR-mediated hyperpolarization of KF neurons that project to the ventrolateral medulla, 2) presynaptic MOR mediated inhibition of glutamate release from dorsolateral pontine terminals onto excitatory preBötC and rVRG neurons, and 3) postsynaptic MOR-mediated hyperpolarization of the preBötC and rVRG neurons that receive pontine glutamatergic input.

      This manuscript describes in detail a useful method for dissecting the relationship between the dorsolateral pons and the rostral medulla, which will be useful for various researchers. It's also great to see how many different methods have been applied to improve the accuracy of the results.

      1. Relationship between the dorsolateral pons and rostral ventrolateral medulla.

      The method of this paper is a good paper to show a very precise relationship between the presence of opioid receptors and the dorsolateral pons and rostral ventrolateral medulla, and for opioid receptors, based on the expression of Oprm1, the use of genetically modified mice with anterograde or retrograde viruses with additional fluorescent colors showed both anterograde and retrograde projections, revealing a relationship between the dorsolateral pons and rostral ventrolateral medulla.

      For example, to visualize dorsal pontine neurons expressing Oprm1, Oprm1Cre/Cre mice were crossed with Ai9tdTomato Cre reporter mice to generate Ai9tdT/+ oprm1Cre/+ mice (Oprm1Cre/tdT mice) expressing tdTomato on neurons that also express MOR at any point during development, and the retrograde virus encoding Cre-dependent expression of GFP (retrograde AAV-hSIN-DIO-eGFP was injected into the respiratory center of Oprm1Cre/+ mice and into the ventral respiratory neuron group, showing that KF neurons expressing Oprm1 project to the respiration-related nucleus of the ventrolateral medulla.

      However, although the authors have also corrected it, the virus may spread to other places as well as where they thought it would be injected, and it is important to note that it is injected accordingly to mark the injection site with an anterograde virus encoding a different fluorescent color mCherry, and the extent of the injection is quantified, which is excellent as a control experiment.

      In addition, the respiratory center seems to be related not only to preBötC but also to pFRG recently, so if the relation with it is described, it is important from the viewpoint of the effect on the respiratory center and the effect on the rhythm.

      2. Electrophysiological approaches and useful methods for target neurons

      Oprm1Cre/+ mice), the authors found abundant Oprm1 + projections in the preBötC region of the medulla oblongata (respiratory center) and sought to determine whether presynaptic opioid receptors inhibit glutamate release from KF terminals to excitatory preBötC and rVRG neurons, since KF neurons in the dorsolateral pons projecting to the ventrolateral medulla oblongata had been shown to be glutamatergic and to have opioid receptors. The authors injected a channelrhodopsin-2-encoding virus (AAV2-hSin-hChR2 (H134R) -EYFP-WPRE-PA) into the dorsolateral pontine KF of vglu2Cre / tdT mice and performed whole-cell voltage-clamp recordings from td tomato-expressing, excitatory vglu2-expressing preBötC and rVRG neurons, contained in acute brain slices. Moreover, both opioid-sensitive and opioid-insensitive KF neurons that project to preBötC and rVRG were visible and recorded using FluoSpheres which are much more visible in acute brain sections than retrograde tracers of viruses.

      1) Optogenetic stimulation of the KF terminus was blocked by the AMPA-type glutamate receptor antagonist DNQX. In excitatory pre-BötC and rVRG neurons, the terminals from the dorsal pontine KF were activated by optogenetic stimulation, and the KF synapses to the medullary respiratory neurons were found to be monosynaptic because oEPSCs(optical stimulated EPSCs) were removed by TTX but were subsequently restored by the application of K-channel blocker 4AP. Thus, KF neurons have been shown to send monosynaptic glutamatergic projections to excitatory ventrolateral medullary neurons using terminal optogenetic stimulation and receptor and channel inhibitors.

      2) To determine whether opioids inhibit glutamate release from KF terminals to medullary respiratory neurons, we recorded a pair of oEPSCs (50 ms stimulus interval) from excitatory preBötC and rVRG neurons and applied an endogenous opioid agonist, [Met5] enkephalin (ME), to the perfusion solution. ME is preBötC and rVRG neurons, indicating inhibition of glutamate release by presynaptic MOR PPR. Thus, presynaptic opioid receptors have been shown electrophysiologically to inhibit glutamate release from KF terminals to excitatory pre-BötC and rVRG neurons.

      3) Whether excitatory pre-BötC or rVRG neurons themselves receiving opioid-sensitive glutamatergic synaptic inputs from KF are hyperpolarized by opioids can be determined by monitoring their retention currents.

      4) Since FluoSpheres are much more visible in acute brain sections than retrograde tracers of viruses and do not spread to injection sites, they chose to record from retrogradely labeled KF neurons with FluoSpheres injected into preBötC or rVRG in wild-type mice, allowing us to label KF neurons regardless of Oprm1 expression status and determine the projection patterns of both Oprm1 + and Oprm1- neurons. Whole-cell voltage-clamp recordings from fluorescent KF neurons contained in acute brain slices show that the presence of ME-mediated outward current can identify KF neurons that express functional MORs and are opioid-sensitive compared to neurons that lack ME-mediated outward current (insensitive). This suggests that both opioid-sensitive and opioid-insensitive KF neurons project to preBötC and rVRG.

      Although much has been written about the relationship between KF neurons and medulla oblongata neurons and their being glutaminergic neurons, detailed descriptions of the recorded neuronal firing patterns are lacking. You should describe what firing pattern the recorded neurons had. If we don't do that, we won't be able to tell whether it's a respiratory neuron or another tonic firing neuron, so I don't think we can discuss whether it's involved in the respiratory rhythm.

      3. Compare the distribution of neurons

      To examine the distribution of Oprm1 + and Oprm1- dorsolateral pontine neurons projecting to the ventrolateral medulla, we injected retrograde AAV-hSin-DIO-eGFP and retrograde AAV-hSin-mCherry into preBötC and rVRG of Oprm1Cre/+ mice and found a neuronal distribution in which Oprm1-expressing projection neurons expressed GFP and mCherry, but not Oprm1-expressing projection neurons expressed only mCherry.

      In addition, rostral glutamatergic KF neurons express FoxP2, while MOR-expressing glutamatergic neurons in the lateral parabrachial region that project to the forebrain express the CGRP-encoding gene, Calca. In view of this, the authors performed immunohistochemistry for FoxP2 and CGRP on Oprm1 + KF neurons projecting to the ventrolateral medulla, and Oprm1 + medulla oblongata projecting KF neurons expressed FoxP2 but not CGRP. The expression of CGRP was not observed in rostral KF and medullary projection Oprm1 + neurons and neurites but was strong in lateral parabrachial neurons and their axonal fiber projections. Can you describe the relationship between CGRP and FoxP2 and recorded neurons?

    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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      Reply to the reviewers

      We thank both reviewers for their constructive criticism and the insightful comments on our manuscript. Reviewer 1 states that:

      „The strength of this manuscript lies in its comprehensive analysis of Bim1 function, the quality of the results and that the experiments are generally well controlled and interpreted. „

      And „the findings of this comprehensive analysis are of great value to the microtubule field, especially for people working in budding yeast. „

      • *

      While Reviewer 2 adds:

      „The current study is indeed rich with new insights into the mechanisms by which these molecules function, and will no doubt prove valuable to a number of people in the microtubule/motor/yeast mitosis fields. As someone who is interested in and studies mitosis in budding yeast, I found the study to be interesting.

      • *

      Both reviewers conclude that:

      “…there are useful data in the manuscript that make this an important contribution and that it should definitely be published”

      • *

      • *

      Both reviewers raised two major areas of concern: 1. A confusing overall structure makes the study hard to follow. 2. A clearer distinction needs to made between what has already been reported in the literature, and what are new insights provided in this study. In this regard, the appropriate citations need to be made at various positions throughout the manuscript.

      In this full revision, we have addressed these major points of criticism of the reviewers as follows:

      We have re-organized and re-focused the manuscript to make it more accessible and easier to follow for the reader. We have followed a suggestion from reviewer 1 and now present all experiments characterizing mitotic spindle phenotypes and how they can be suppressed consecutively in Figures 2-5 and then finish the manuscript with the characterization of the spindle orientation phenotype. This way of ordering by biological pathway allows for a better flow of the manuscript.

      Throughout the text, we have added citations to better indicate the previous state of knowledge and how the presented experiments either confirm or extend the previous findings in the field. This helps to put our current study better into and overall perspective.

      In addition, we have addressed the specific points raised by both reviewers in full. Please see below our point-by-point answer.

      Reviewer1

      There is already a huge body of published information on mitotic spindle positioning via the Kar9 and dynein pathways that grew since the late 1990s. The genetic relationships and molecular interactions between the components of these 2

      pathways are well studied (many studies, including Liakopoulos et al. 2003, are not cited by the authors). The authors

      should make sure to cite and compare to the relevant primary literature when they report findings that have been

      described before. This will help to distinguish novel findings from validation of previous results.

      We have added relevant citations throughout the manuscript, please see below.

      "The strict dependence of Kar9 and Cik1-Kar3 on the presence of Bim1, as well as the different effects of bim1Δ on

      nuclear and cytoplasmic Bik1, may reflect the formation of stable complexes between Bim1 and these binding partners in

      cells." I believe this has already been shown (Kumar et al., 2021 and Manatschal et al., 2016). There are several other

      instances as well where additional literature should be cited, for example Gardner et al., 2008 and Gardner et al. 2014.

      We have now cited the Manatschal and Kumar papers in this section of the revised manuscript. We have also cited the mentioned Gardner papers later in the manuscript.

      The selection of targets to study in figure 1 doesn't seem to follow the listed criteria. Many proteins included in the

      study were not found by IP-MS, but some perfect targets according to the listed criteria like Duo1 were not included in the

      study. In addition, there are more sophisticated ways of finding Bim1 binding motifs in the literature

      (https://doi.org/10.1016/j.cub.2012.07.047). I suggest, the authors declare that they rationally chose to study 21 proteins

      of interest but remove the claim that their approach was systematic.

      We have changed the wording accordingly and removed the claim of systematic target selection.

      Much of the microscopy data was acquired after release from alpha factor arrest. What is the reason for this

      perturbation? An exponentially growing culture should mostly consist of mitotic cells anyway. Since this treatment affects

      cell size and potentially protein levels/concentrations, testing its influence on spindle position as well as levels on MTs for

      the most relevant proteins of interest would be important to exclude introduction of artifacts.

      In principle that’s correct, but using synchronized cultures has the great advantage that mitotic timing and all the parameters associated related to it (spindle length etc.) can be quantified much better and we obtain larger N and thus get better statistics using this approach. In a typical log culture only one third of the cells are in mitosis and this entails very different states of mitosis. Observations times are limited due to fluorescent bleaching and low signal intensity. We therefore feel the benefits of alpha-factor release outweigh the problems and we compare all mutants under the same conditions.

      Some of the results obtained from bim1Δ cells are a challenge to interpret due to the wide range of processes that

      involve Bim1 and therefor the potential for many off-target effects- including a global change in microtubule dynamical

      behavior in both the cytoplasm and the nucleus that will influence the length distributions and microtubule lifetime (and

      thus number). The authors must carefully consider these caveats.

      We agree in principle and have therefore not only characterized the bim1 deletion, but also more specific bim1 mutants. We also show that some aspects of the bim1 delta phenotypes, but not others, can be rescued by different strategies.

      The results section on page 12 refers to phenotypes of kar9 delete cells with respect to Bim1-GFP on cytoplasmic

      microtubules. In the figure 3D,F I only found data for Kar9-AID, though. The authors should refer to supplementary figure

      5A or even better include quantification similar to figure 3F.

      We have corrected this in the revised text. We refer to the Kar9-AID, for which we have the quantification.

      The observation that cytoplasmic Bim1 localization depends on interaction with its cargo Kar9 (figure 3 + 7) fits into the

      model that Kumar et al (https://doi.org/10.1016/j.str.2021.06.012) proposed in which Kar9 oligomerization is required for

      its Bim1 dependent localization to microtubules. It would be valuable to point that out.

      We have now included a sentence that our findings support this model and added the respective citation.

      I don't fully understand the model proposed in Figure 5H and discussion page 26. Based on figure 5E, it does not look

      like there is a higher concentration of Bik1 along the lattice in bim1 delete. So how would Bik1 increase Kip2 processivity

      if its levels are only increased due to a MT length change? If Kip2 was not fully processive, you would rather expect to

      see less of it at the tip of a longer microtubule in bim1 delete. The model suggested by Chen et al

      (https://doi.org/10.7554/eLife.48627.001) suggests that Kip2 only gets loaded at the minus-end and processively walks

      towards the +end without falling off. Are the authors suggesting that bim1 deletion changes this behavior?

      We have rephrased this section in results and discussion and more clearly state that there is no increase in Bik1 per MT length unit in the bim1 deletion. We have amended the discussion and grant that we currently cannot explain by which molecular mechanisms Bik1 may contribute to the observed increase in Kip2 plus-end localization under conditions of a bim1 deletion.

      I don't see evidence for independent pools of Bik1 in the cytoplasm and nucleus as claimed on top of page 21. Total

      Bik1 levels on cytoplasmic microtubules seem to be well explained by their length. Please explain better or remove the

      statement.

      We have removed the respective statement from the revised manuscript.

      The experiments in supplementary figure 7B are difficult to interpret. The localization on cytoplasmic microtubules is

      different, but probably explained by the formation of Bim1 heterodimers. Therefore this experiment is difficult to interpret

      and should be removed.

      As requested, we have removed this experiment from the revised manuscript.

      top of page 24: Kar9 localization in metaphase depends exclusively on SxIP, not on LxxPTPh (Manatschal 2016). The

      paragraph should be removed as it is not supported by published data or sufficiently by the authors to merit the

      conclusion.

      We have reformulated this to avoid a misunderstanding. We merely show that in the context of the artificial GCN4 construct a fragment just including the LxxPTPh motif is sufficient for Bim1-dependent localization to microtubules in nucleus and cytoplasm. This makes no statement about localization determinants of the authentic Kar9 protein.

      Top of page 26: The genetic interactions between the Kar9 pathway and the dynein pathway were already well known

      before this work. Please reformulate accordingly.

      We have re-written this section and introduce the two pathways with the respective citations in the very beginning of the section before describing the experiments.

      page 27 second paragraph: There is no selective pressure to evolve compensation mechanisms for gene deletions. I

      suggest the authors consider that Kar9 and dynein partially redundant, with Kar9 acting to position the spindle prior to

      metaphase and dynein to maintain spindle position in the mother and bud compartments in late metaphase and

      anaphase. The authors should consider the quantitative analysis of Kar9 and dynein dependent spindle positioning

      reported in Shulist et al. 2017 and the method for analysis of spindle length and position in 3D in Meziane et al. 2021.

      We have rephrased the section on the partially redundant Kar9 and Dynein pathways. See below our answer for measuring spindle length.

      In addition, it is not clear to me which results suggest that the relocalization of Bik1 is required in the bim1 delete. Why

      would wild type levels not be sufficient for dynein pathway function? The authors have not conclusively shown that

      nuclear migration relies on upregulating the dynein pathway in bim1Δ cells. If there is no supporting data, the paragraph

      should be removed.

      In this revised manuscript we have phrased our observations more carefully and acknowledge the limitations regarding molecular insights. We present indications for increased levels of Dynein-Dynactin pathway components at plus-ends in the bim1 deletion cells, but it is indeed unclear, whether an increased Bik1 level in the cytoplasm is required to achieve this.

      Please provide more details about intensity quantification on page 35. Were these measured on sum or max

      projected stacks? What was the method of background subtraction?

      Analysed images are optical axis integration scans over 3 μm taken on a Deltavision microscope. This procedure gives a sum projection. Local background was determined for every cell by drawing a line under a signal curve derived by line scan. The background line connects regions that are still within the cell but are outside of spindle (or microtubule). We added a sentence in the materials and methods section under point 2.

      Are the spindle lengths in Figure 2E measured in 2D or 3D? Bim1 deletion might lead to more misalignment of the

      spindles in z due to inactivation of the Kar9 pathway and thus partially explain the shorter spindles. The measurements

      should therefore be performed in 3D.

      As we have used optical axis integration (OAIs) on the Deltavision microscope and obtained a sum projection of this virtual stack, the spindles were measured in 2D and we don’t have the information to measure in 3D (this would require a regular stack). We show that there are different ways to restore different aspects of spindle length with alternative strategies. These are unlikely to influence just spindle orientation. In addition, we see that Bim1 deletion has an effect on the size of a nascent bipolar spindle when spindle orientation is similar to wild-type cells. We agree that z-misalignment may affect absolute values of spindle size of Bim1 deletion in late metaphase and it would be better to measure in 3D. However, we think in this case it is unlikely to affect our conclusions in this study.

      The authors should try to shorten the text. There is a lot of redundancy between results and discussion sections.

      We have to shortened the text to avoid redundancy (before >43000 characters, now around 41000 characters, and we have decreased the number of main figures from 9 to 8.

      Data is shown that leads to conclusions that are already supported by the literature should be moved to the

      supplementary material.

      In the course of re-organizing the manuscript we have tried to do this.

      Reviewer 2:

      "Robustness of Ndc80 loading might be achieved by the coexistence of multiple kinetochore assembly pathways or

      alternatively determined by intrinsic Ndc80 properties." Wouldn't Ndc80 levels be determined by Ndc80 kinetochore

      loading, and not by end-binding proteins? This seems to be the more likely means to regulate Ndc80 levels.

      We have removed this statement from the revised manuscript.

      "Upon analyzing the associations in the cytoplasm, we found that Kar9-3xGFP foci on bud-directed cytoplasmic

      microtubules were abolished in the bim1Δ strain, consistent with earlier reports." It would be helpful if the authors

      commented on the how the localization of some of these proteins are affected by bim1Δ on the mother-directed plus

      ends. Although I understand the need to account for one class of plus end for the sake of consistency (and the distinct

      behaviors of the mother vs bud-directed plus end), the text as written leaves me wondering about the other plus end.

      We have noticed that the bim1 deletion led to the loss of asymmetric distribution on cytoplasmic microtubules for a number of components. Most prominent are Bik1, Kip2 and proteins of dynein-dynactin complex. We felt that further analysing this phenotype was beyond the scope of this study.

      "The CAP-Gly domain construct, expressed from a BIM1 promoter, almost exclusively localized to the spindle of yeast

      cells." For clarity, the authors should explicitly state that the CAP-Gly domain in question is from Bik1. Although this can

      be deduced, this was not abundantly clear.

      We have clarified this in the text and in the figure.

      "In addition to Ase1, we followed the kinetochore proteins Ndc80-GFP and Sgo1-GFP which specifically marks

      kinetochores that lack tension." This sentence should add "the latter of which..." to clarify that SgoI, but not Ndc80

      exhibits this behavior.

      We have added the phrase “the latter of which” to clarify this point.

      "We observed that bim1Δ cells had mispositioned kinetochores with a bright Sgo1-GFP signal that was much stronger

      than in wild-type cells." I don't see the mispositioned kinetochores described here. Are the authors referring to the fact

      that Sgo1 is brighter, which suggests tension-free KTs? If so, this should be clearly stated as such, since the authors are

      not explicitly assessed kinetochore "positioning".

      We have rephrased the sentence to clarify. We refer to a lack of bi-lobed Ndc80 signal and a bright Sgo1-GFP signal as two aspects of the phenotype.

      "We speculate that Bim1-Bik1 in a complex with its cargo Cik1-Kar3 is active after bi-polar spindle formation but before

      late metaphase and Ase1 can partially substitute for nuclear Bim1 functions." I struggled to grasp the reasoning for these

      conclusions. I assume the former point (the timing for Bim1-Bik1-Cik-Kar3) is due to the localization dynamics of Bim1

      and Bik1, while the latter (Ase1 can substitute for Bim1) is due to the synthetic interaction between Bim1 and Ase1 (I

      needed to look this latter point up myself). Or is this latter point due to the brighter spindle Ase1-GFP intensity? In either

      case, the authors should more clearly state their reasoning.

      We have clarified this statement in the revised discussion.

      The error bars in Figures 3A and 6E (shown as 95% CI) and elsewhere seem very small for the parameters that are

      being plotted. Spindle length values as shown in Figure 2E cover a broad range (as would be expected for a biological

      process), and it would be more accurate if the error bars in Fig 3A and 6E reflect this, even if it means they start

      overlapping each other. I find the error as shown to be misleading to your readers, and unless the authors have very

      good reason to use 95% CI (which is not as meaningful as standard deviation), then I would encourage them to use

      standard deviation.

      We prefer to use CI for the spindle length plots over time for consistency reason and to avoid overlap, which would make the graphs difficult to read. We have changed the text to provide the standard deviation instead of the standard error of the mean for spindle length and metaphase duration, see point below.

      The same is true for the values stated throughout the text (e.g., for mitotic timing "47{plus minus}2 min" for metaphase

      duration; for distance between SPB and bud neck {plus minus} 0.1 μm, etc). I am highly skeptical that metaphase

      duration (for example) ranged from only 46-48 minutes. Please use standard deviation to describe a more accurate

      description of the range of values for these parameters.

      In the revised manuscript, we now give the mean values plus/minus standard deviation, instead of the standard error of the mean, as requested. In addition, the range of values is directly visible from the individual data points in the plots.

      "Unexpectedly, the kar9 deletion mutant displayed a slightly accelerated metaphase progression relative to wild-type

      cells (26{plus minus}1 min) (Figure 3C). This could be attributed to an increased level of Bim1 on the metaphase spindle

      of kar9Δ (or Kar9-AID) cells." The authors should give us more rationale to explain the "attributing the increased levels of

      Bim1" point here. Do they think that the levels of spindle-associated Bim1 impact metaphase duration somehow? If so,

      how?

      We have added a sentence, speculating about how this could be accomplished.

      "Overall, our cell biology data suggested that major nuclear Bim1 functions are conducted in a complex with Cik1-

      Kar3, while Bik1 and Kar9 have a smaller impact, probably affecting the nuclear- cytoplasmic distribution of Bim1."

      Although I understand and agree with the former conclusion (that Bim1 functions are conducted via Cik1-Kar3"), the latter

      was confusing to me. Did the authors mean that "Bim1 impacts Bik1 and Kar9 to a lesser extent", rather than vice versa?

      The authors are discussing Bim1 functioning via Cik1, but then switch to discussing how Bik1 and Kar9 affect Bim1.

      We have removed the second part of the sentence from the revised manuscript.

      "Next, we compared the comparing genetic interaction profile of a bim1 deletion to that of various other factors by reanalyzing the synthetic genetic interaction data..." Remove "comparing".

      Thanks for pointing out this typo, we have removed it in the revised manuscript.

      As someone who is unfamiliar with the analysis shown in Figure 3H, I think it would be useful to list a Pearson

      correlation value for two genes that are not functionally related. This would help define a lower limit for this analysis.

      For functionally unrelated genes the Pearson correlation between genetic interaction (GI) profiles is very close to zero. The graph below depicts Pearson correlation between GI profile of Bim1 and GIs of every yeast gene (data used for graph is taken from thecellmap.org).

      The axes for the plots in Figure 5E and 5I are very confusing to me. I don't understand what I'm looking at. Why does

      it go from 0 to 1, and then back to 0-1 again? I don't see how this can account for MTs of different lengths. Normalizing all MT length values to 1 would do this, no?

      We have clarified the labelling in the revised manuscript. The x-axis gives the relative position from either the plus-end, or the Spindle pole body (both set to position 0) in micrometres. This allowed us to compare fluorescent intensities on cytoplasmic microtubules of different lengths in wild-type and bim1 delete.

      "These observations are consistent with the idea that Bik1 acts as a processivity factor for Kip2: If more Bik1 is

      present on the lattice, then more Kip2 molecules are able to reach plus-ends without detachment." Perhaps I'm

      misunderstanding the plot shown in Figure 5E, but it seems to indicate that the levels of lattice-bound Bik1 are the same

      in BIM1 and bim1Δ cells (higher SPB-localized levels, though). There are also lower levels of Bik1 at the plus ends in

      bim1Δ cells. So, if Bik1 were a processivity factor for Kip2, this would suggest that they would remain bound at plus ends

      as well, which these data suggest is not the case…

      We have added a section to the discussion that deals with this point and we speculate about the reasons why Kip2 is increased at plus-ends, while Bik1 is not.

      "The data on the CH-Cik1 fusion is very compelling, and indeed supports their hypothesis that Bim1's main role in the

      nucleus is to target Cik1 to the spindle MT plus ends. That being said, it would be a simple, but important task to ensure

      that this fusion behaves as suggested (restores Cik1 plus end binding in cells). Otherwise, it can't' be ruled out that this

      fusion is rescuing bim1Δ functions by some other means. However, as stated above, it's unclear how much was already

      known about this fusion from the lab's previous work.

      In our previous study (Kornakov et al., 2020) we have shown that the CH-Cik1Delta74 fusion indeed is sufficient to enrich Kar3 at plus ends. We expect the same to be true for this slightly different fusion construct. We have added a respective sentence to the results section.

      Regarding the p1-p6 promoter data: p6 is missing from Figure S6A, in spite of it being referenced in the text and the

      figure.

      Thanks for pointing this out, we have corrected that in the revised manuscript and do not refer to p6 anymore.

      "Exogenously expressed Ase1 displayed a similar level and kinetics of localization compared to the endogenous

      protein, indicating that binding sites for microtubule crosslinkers are not a limiting factor on the budding yeast spindle."

      Specifically, the authors show that binding sites for Ase1 may not be limiting (the overlapping 95% CI bars if Fig S6B

      suggest this is not significant), not all crosslinkers. The authors should not use such broad language to describe results

      from one experiment with one crosslinker.

      We have rephrased to make clear that our statement only refers to Ase1.

      "We found that all bim1 mutants were less well recruited to the metaphase spindle compared to the wild-type protein,

      indicating that Bim1-interacting proteins strongly contribute to Bim1 localization." Can the authors rule out the defects in

      localization of these mutants is not compromised MT binding by the Bim1 mutants? Also, regarding this statement: "To

      test that the observed recruitment defects of bim1 mutants are not a result of a compromised spindle or microtubule

      structure, we examined their localization in a situation when GFP-tagged mutants were covered with the unlabeled wildtype

      allele. Indeed, in this situation, the Bim1 mutants displayed very similar localization profiles (Supplementary Figure

      7B)." I wasn't sure what these results were similar to: the wild-type protein, or the mutant without the presence of WT

      Bim1? The lack of quantitation made this difficult to determine.

      At the request of reviewer 1, we have removed the analysis of Bim1-GFP mutants over an unlabelled Bim1 wild-type from the manuscript.

      The zoom crops for many of the images (Fig 1F and C, 3D, 5J, etc) are not labeled. I realize the legends indicated

      what was what, but it would be much easier for the reader if these panels were labeled in the figure.

      We have indicated the channel by a respective frame around the zoom throughout the manuscript. We think this makes orientation easier.

      "While in vitro reconstitution experiments have suggested that Bim1 is required to fully reconstitute the Kip2-

      dependent loading of the Dynein-Dynactin complex to microtubule-plus ends in vitro (Roberts et al., 2014), our

      experiments indicate that it may contribute relatively little to this pathway in cells." Work from other labs have also shown

      Bim1 is dispensable for dynein function in cells. This should be noted by the authors, and the appropriate work cited (see

      work from Lee and Pellman labs. In fact work from the Lee lab showed that Kip2 is dispensable for plus end binding of

      dynein).

      We have re-written this section and now also refer to the Markus 2009 paper (Wei-Lih Lee lab).

      References are missing throughout the text. I have listed a few examples below:

      "We have previously shown that the phenotype of Bim1-binding deficient Cik1 mutants can be rescued by fusing the

      CH-domain to this Cik1 mutant (cik1-Δ74)."

      We have listed the citation of our 2020 paper (Kornakov et al.)

      "We constructed a series of strains expressing an extra copy of Ase1-GFP under different constitutive promoters of

      increasing strength (p1 to p6)"; where did these promoters come from?

      They were selected based on a systematic analysis of promoter strength in Shaw et al., 2019, DOI: 10.1016/j.cell.2019.02.023 . We have added that citation to the methods section.

      "double point mutation exchanging two conserved residues in the EBH domain (bim1 Y220A E228A) is predicted to

      eliminate all EBH-dependent cargo interactions, but does not affect protein dimerization."

      We have cited the Honnapa 2009 paper here.

      "A deletion of the terminal five amino acids is predicted to prevent binding of the CAP-Gly domain of Bik1 to Bim1. The

      combination of both mutations is expected to simultaneously prevent both types of interaction."

      We have cited the Stangier 2018 paper here.

      "Spindle positioning in budding yeast is achieved via two pathways, one relying on the protein Kar9 which interacts

      with the actin-based motor Myo2." Yin et al 2000 should be added (in addition to Hwang et al).

      We have now included the Yin et al. 2000 citation.

      "For nuclear migration to occur efficiently, the Dynein-Dynactin complex must be enriched at the plus-ends of

      cytoplasmic microtubules..." Should cite work from the Lee lab here.

      We now cite Markus and Lee, 2011 as an example.

      "These long microtubules can interact with the bud cortex and initiate pulling events to move the nucleus (Omer et al.,

      2018)." Many papers pre-dating the Omer study found this to the case, including work from the Cooper lab (see Adames

      et al). These studies should be cited either in place of the Omer study, or in addition.

      We have cited additional studies besides the Omer paper.

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      Referee #2

      Evidence, reproducibility and clarity

      The authors of the study performed a systematic assessment of the role of Bim1 in the MT-binding activity and function of a large number of nuclear and cytoplasmic MT-associated proteins (MAPs), as well as their role during mitosis and spindle positioning. For example, they find that the reliance of MT-binding activity of several MAPs varies from complete reliance on Bim1, to almost no role (in some cases, loss of Bim1 even increases MAP-MT binding). The density and quality of the data, and the large number of players analyzed by the study, are certainly impressive, and there is no doubt a lot of valuable information contained within that will be of use to many people in the MAP/mitosis/yeast cell biology community. However, I feel the manuscript can be greatly improved following some significant revisions. In particular, although some of their findings are indeed interesting and useful, and can be used to reliably draw conclusions, it is difficult to parse out what is novel, and what is a rehashing of old data. For instance, the role of Bim1 in Bik1/Kip2 targeting was described years (Carvalho et al), and I was surprised to see that the CH-Cik1 fusion was previously described by the authors' lab a couple years ago (see note below regarding lack of appropriate citations and lack of description of previous knowledge). Also, how much did we already know about the Bim1 truncations shown in Figure 7 and S7, and how they might disrupt binding to partners? Finally, regarding this statement in the Discussion: "Our analysis indicates that Bim1 contributes to both of these processes as part of two key protein complexes (Figure 9A): Bim1-Kar9-Myo2 in the cytoplasm and Bim1- Bik1-Cik1-Kar3 in the nucleus." As far as I know, these things have been known for many years; their work might help to support these findings, but the statement as written misleads the readers in to believing the present work proves these old concepts.

      One of the main issues with reading a manuscript with so much data about so many different players and pathways is that this leads to a situation in which each story is only superficially covered, with only minimal depth or detail. This made the paper somewhat difficult for me to follow (and I am a fan of budding yeast mitosis!), especially given the frequent switching from one pathway to another (e.g., the Cik1 section started on page 12 appears to be continued on page.17, only after talking about the spindle orientation story in between the two Cik1 sections). I'm not sure what to suggest, but the manuscript can be improved if the authors try to refocus some of the sections to make it easier to follow one story at a time, for a particular molecule (e.g., Cik1) or pathway (spindle orientation). In addition to explicitly describing what is already known about a particular molecule/pathway, the writing can be greatly improved by introducing their reasoning for the experiments in question. Some of the sections lack sufficient rationale for me to understand the justification for their experiments (e.g., why try to overexpress Ase1 to rescue bim1∆ phenotypes, as described on page 19?).

      Although there is likely much to learn from this study, I felt that some conclusions were a little bold (see below), while alternative hypotheses were not addressed (perhaps Bim1 simply competes for MT binding with some of these factors, thus accounting for them increasing their spindle-binding behavior?). For example, the authors make a point that loss of Bim1 enhances dynein-dynactin function. However, it is important to note that mutations in tubulin (tub2-430∆) and other MAPs (Kar9 or Ase1, the latter of which the authors point out) also lead to increased dynein activity (see work by Yeh et al., 2000, and work from the Moore lab). It is unknown whether mutations to these genes affect dynein targeting in cells similar to what the authors describe here. Thus, a direct causal relationship between their bim1∆ phenotypes and enhanced dynein activity is unclear, and at best is speculative. It's also worth noting that overexpression of Bik1 has been shown to actually reduce Dhc1 localization to plus ends in cells (see Markus et al 2011), which would argues against a simple mechanism of increasing Bik1 correlating with increasing dynein localization and activity.

      Below are some specific points.

      1. "Robustness of Ndc80 loading might be achieved by the coexistence of multiple kinetochore assembly pathways or alternatively determined by intrinsic Ndc80 properties." Wouldn't Ndc80 levels be determined by Ndc80 kinetochore loading, and not by end-binding proteins? This seems to be the more likely means to regulate Ndc80 levels.
      2. "Upon analyzing the associations in the cytoplasm, we found that Kar9-3xGFP foci on bud-directed cytoplasmic microtubules were abolished in the bim1Δ strain, consistent with earlier reports." It would be helpful if the authors commented on the how the localization of some of these proteins are affected by bim1∆ on the mother-directed plus ends. Although I understand the need to account for one class of plus end for the sake of consistency (and the distinct behaviors of the mother vs bud-directed plus end), the text as written leaves me wondering about the other plus end.
      3. "The CAP-Gly domain construct, expressed from a BIM1 promoter, almost exclusively localized to the spindle of yeast cells." For clarity, the authors should explicitly state that the CAP-Gly domain in question is from Bik1. Although this can be deduced, this was not abundantly clear.
      4. "In addition to Ase1, we followed the kinetochore proteins Ndc80-GFP and Sgo1-GFP which specifically marks kinetochores that lack tension." This sentence should add "the latter of which..." to clarify that SgoI, but not Ndc80 exhibits this behavior.
      5. "We observed that bim1Δ cells had mispositioned kinetochores with a bright Sgo1-GFP signal that was much stronger than in wild-type cells." I don't see the mispositioned kinetochores described here. Are the authors referring to the fact that Sgo1 is brighter, which suggests tension-free KTs? If so, this should be clearly stated as such, since the authors are not explicitly assessed kinetochore "positioning".
      6. "We speculate that Bim1-Bik1 in a complex with its cargo Cik1-Kar3 is active after bi-polar spindle formation but before late metaphase and Ase1 can partially substitute for nuclear Bim1 functions." I struggled to grasp the reasoning for these conclusions. I assume the former point (the timing for Bim1-Bik1-Cik-Kar3) is due to the localization dynamics of Bim1 and Bik1, while the latter (Ase1 can substitute for Bim1) is due to the synthetic interaction between Bim1 and Ase1 (I needed to look this latter point up myself). Or is this latter point due to the brighter spindle Ase1-GFP intensity? In either case, the authors should more clearly state their reasoning.
      7. The error bars in Figures 3A and 6E (shown as 95% CI) and elsewhere seem very small for the parameters that are being plotted. Spindle length values as shown in Figure 2E cover a broad range (as would be expected for a biological process), and it would be more accurate if the error bars in Fig 3A and 6E reflect this, even if it means they start overlapping each other. I find the error as shown to be misleading to your readers, and unless the authors have very good reason to use 95% CI (which is not as meaningful as standard deviation), then I would encourage them to use standard deviation.
      8. The same is true for the values stated throughout the text (e.g., for mitotic timing "47{plus minus}2 min" for metaphase duration; for distance between SPB and bud neck {plus minus} 0.1 µm, etc). I am highly skeptical that metaphase duration (for example) ranged from only 46-48 minutes. Please use standard deviation to describe a more accurate description of the range of values for these parameters.
      9. "Unexpectedly, the kar9 deletion mutant displayed a slightly accelerated metaphase progression relative to wild-type cells (26{plus minus}1 min) (Figure 3C). This could be attributed to an increased level of Bim1 on the metaphase spindle of kar9Δ (or Kar9-AID) cells." The authors should give us more rationale to explain the "attributing the increased levels of Bim1" point here. Do they think that the levels of spindle-associated Bim1 impact metaphase duration somehow? If so, how?
      10. "Overall, our cell biology data suggested that major nuclear Bim1 functions are conducted in a complex with Cik1- Kar3, while Bik1 and Kar9 have a smaller impact, probably affecting the nuclear- cytoplasmic distribution of Bim1." Although I understand and agree with the former conclusion (that Bim1 functions are conducted via Cik1-Kar3"), the latter was confusing to me. Did the authors mean that "Bim1 impacts Bik1 and Kar9 to a lesser extent", rather than vice versa? The authors are discussing Bim1 functioning via Cik1, but then switch to discussing how Bik1 and Kar9 affect Bim1.
      11. "Next, we compared the comparing genetic interaction profile of a bim1 deletion to that of various other factors by re-analyzing the synthetic genetic interaction data..." Remove "comparing".
      12. As someone who is unfamiliar with the analysis shown in Figure 3H, I think it would be useful to list a Pearson correlation value for two genes that are not functionally related. This would help define a lower limit for this analysis.
      13. The axes for the plots in Figure 5E and 5I are very confusing to me. I don't understand what I'm looking at. Why does it go from 0 to 1, and then back to 0-1 again? I don't see how this can account for MTs of different lengths. Normalizing all MT length values to 1 would do this, no?
      14. "These observations are consistent with the idea that Bik1 acts as a processivity factor for Kip2: If more Bik1 is present on the lattice, then more Kip2 molecules are able to reach plus-ends without detachment." Perhaps I'm misunderstanding the plot shown in Figure 5E, but it seems to indicate that the levels of lattice-bound Bik1 are the same in BIM1 and bim1∆ cells (higher SPB-localized levels, though). There are also lower levels of Bik1 at the plus ends in bim1∆ cells. So, if Bik1 were a processivity factor for Kip2, this would suggest that they would remain bound at plus ends as well, which these data suggest is not the case.
      15. "The data on the CH-Cik1 fusion is very compelling, and indeed supports their hypothesis that Bim1's main role in the nucleus is to target Cik1 to the spindle MT plus ends. That being said, it would be a simple, but important task to ensure that this fusion behaves as suggested (restores Cik1 plus end binding in cells). Otherwise, it can't' be ruled out that this fusion is rescuing bim1∆ functions by some other means. However, as stated above, it's unclear how much was already known about this fusion from the lab's previous work.
      16. Regarding the p1-p6 promoter data: p6 is missing from Figure S6A, in spite of it being referenced in the text and the figure.
      17. "Exogenously expressed Ase1 displayed a similar level and kinetics of localization compared to the endogenous protein, indicating that binding sites for microtubule crosslinkers are not a limiting factor on the budding yeast spindle." Specifically, the authors show that binding sites for Ase1 may not be limiting (the overlapping 95% CI bars if Fig S6B suggest this is not significant), not all crosslinkers. The authors should not use such broad language to describe results from one experiment with one crosslinker.
      18. "We found that all bim1 mutants were less well recruited to the metaphase spindle compared to the wild-type protein, indicating that Bim1-interacting proteins strongly contribute to Bim1 localization." Can the authors rule out the defects in localization of these mutants is not compromised MT binding by the Bim1 mutants? Also, regarding this statement: "To test that the observed recruitment defects of bim1 mutants are not a result of a compromised spindle or microtubule structure, we examined their localization in a situation when GFP-tagged mutants were covered with the unlabeled wild-type allele. Indeed, in this situation, the Bim1 mutants displayed very similar localization profiles (Supplementary Figure 7B)." I wasn't sure what these results were similar to: the wild-type protein, or the mutant without the presence of WT Bim1? The lack of quantitation made this difficult to determine.
      19. The zoom crops for many of the images (Fig 1F and C, 3D, 5J, etc) are not labeled. I realize the legends indicated what was what, but it would be much easier for the reader if these panels were labeled in the figure.
      20. "While in vitro reconstitution experiments have suggested that Bim1 is required to fully reconstitute the Kip2- dependent loading of the Dynein-Dynactin complex to microtubule-plus ends in vitro (Roberts et al., 2014), our experiments indicate that it may contribute relatively little to this pathway in cells." Work from other labs have also shown Bim1 is dispensable for dynein function in cells. This should be noted by the authors, and the appropriate work cited (see work from Lee and Pellman labs. In fact work from the Lee lab showed that Kip2 is dispensable for plus end binding of dynein).
      21. References are missing throughout the text. I have listed a few examples below:
        • a. "We have previously shown that the phenotype of Bim1-binding deficient Cik1 mutants can be rescued by fusing the CH-domain to this Cik1 mutant (cik1-Δ74)."
        • b. "We constructed a series of strains expressing an extra copy of Ase1-GFP under different constitutive promoters of increasing strength (p1 to p6)"; where did these promoters come from?
        • c. "double point mutation exchanging two conserved residues in the EBH domain (bim1 Y220A E228A) is predicted to eliminate all EBH-dependent cargo interactions, but does not affect protein dimerization."
        • d. "A deletion of the terminal five amino acids is predicted to prevent binding of the CAP-Gly domain of Bik1 to Bim1. The combination of both mutations is expected to simultaneously prevent both types of interaction."
        • e. "Spindle positioning in budding yeast is achieved via two pathways, one relying on the protein Kar9 which interacts with the actin-based motor Myo2." Yin et al 2000 should be added (in addition to Hwang et al).
        • f. "For nuclear migration to occur efficiently, the Dynein-Dynactin complex must be enriched at the plus-ends of cytoplasmic microtubules..." Should cite work from the Lee lab here.
        • g. "These long microtubules can interact with the bud cortex and initiate pulling events to move the nucleus (Omer et al., 2018)." Many papers pre-dating the Omer study found this to the case, including work from the Cooper lab (see Adames et al). These studies should be cited either in place of the Omer study, or in addition.

      Referees cross-commenting

      It seems that one of my major concerns is reflected in Reviewer #1's review: that a lot of the findings described in the manuscript have been published elsewhere, and are not novel. In spite of this, I do think there are useful data in this manuscript that make this an important contribution, and that it should definitely be published. However, this would first require a significant re-writing with appropriate description of known vs unknown, and additional citations.

      Significance

      The current study aims to clarify the role of Bim1 (EB1 homolog in budding yeast) in the various pathways in which it has been implicated. To achieve this aim, the authors assess the localization of numerous other microtubule-associated proteins in cells with and without Bim1. In addition to high quality localization data (e.g., intensity values), the authors perform a number of cell biological assessments (e.g., mitotic spindle length values before, during and after anaphase), genetic assessments (synthetic interaction assays), and in vitro binding assays. The current study is indeed rich with new insights into the mechanisms by which these molecules function, and will no doubt prove valuable to a number of people in the microtubule/motor/yeast mitosis fields. As someone who is interested in and studies mitosis in budding yeast, I found the study to be interesting.

    1. we cannot see why the regulations made by ourselves shouldnot, on the contrary, be a protection and a benefit for every one of us. And yet, when we consider howunsuccessful we have been in precisely this field of prevention of suffering, a suspicion dawns on us thathere, too, a piece of unconquerable nature may lie behind -this time a piece of our own psychicalconstitution.

      I surprisingly agree with that statement. I think he's saying that we may try to create rules and boundaries for ourselves and our relationships as a protection mechanism, however we still end up suffering because of it.

    2. And yet, when we consider howunsuccessful we have been in precisely this field of prevention of suffering, a suspicion dawns on us thathere, too, a piece of unconquerable nature may lie behind -this time a piece of our own psychicalconstitution.

      Freud introduces the first two sources of suffering as something physical and uncontrollable. I think here he is also saying human nature and behavior is also something that can't easily be controlled.

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      Reply to the reviewers

      We would like to thank the reviewers for their thorough and positive assessment of our work. We also thank them for their careful review of our manuscript. Our responses to their specific comments are provided in the lines below.

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Summary:

      The manuscript entitled „Metastatic potential in clonal melanoma cells is driven by a rare, early-invading subpopulation" by Kaur and colleagues provides a phenotypical analysis of the invasive potential of established melanoma cell lines on single cell level. The aim of the study was to answer the question if even homologous tumor cells bear the intrinsic potential to give rise to cells with high invasive (and therefore potentially metastatic) capacity in absence of selection pressure from the tumor microenvironment.

      The authors used clones from two different melanoma cell lines (to prevent the accumulation of random (epi)genetic changes during cultivation) and performed invasion assays with Matrigel-coated transwell inlays to differentiate between cells that were able to invade early (up to 8 h, approx. 1% of the total cell population) or late (8-24 h; approx. 3% of the total cell population) after plating. Comparative RNA sequencing of early invaders and non-invaders populations revealed a high expression of SEMA3C in early invaders, which was then established as marker in the used cell lines. Interestingly, in vivo models using NSG mice injected with a mixture of early and late invading melanoma cells revealed that both contributed similarly to the primary tumor, while metastatic cells in the lung consisted almost exclusively of early invaders. Subsequent ATAC sequencing revealed an increase of binding sites for the transcription factor NKX2.2 in the early invaders. Functional analyses revealed that a knockout of NKX2.2. led to an increase in both invasion and proliferation. Finally, the authors showed with different sorted early and late invaders as well as SEMA3Chigh and SEMA3Clow expressers that pro-invasive features go along with reduced proliferation potential in accordance to previously published data. However, they decrease with time, thus demonstrating a reversion of the phenotype and high plasticity.

      Major comments:

      In general, the paper contains novel and interesting data, is concisely written and supported by replicates. The key conclusion, the presence of a small proportion of highly invasive cells in a seemingly homologous cell population and their striking requirement for lung metastasis, is very convincing. In vitro, SEMA3C was confirmed as a marker for the early invaders in two independent cell lines. However, a few questions remain open, as detailed below:

      We thank the reviewer for their positive assessment of our work. We also thank them for their careful review of our manuscript. Our responses to their specific comments are provided in the lines below.

      1) The relevance of NKX2.2 in the early invaders is currently unclear to me.

      The ATAC sequencing data revealed a high enrichment of accessible NKX2.2 binding sites in early invaders, and data were tested by comparative RNA sequencing of control cells and cells with NKX2.2 ko (Figure 2). The Figure legend of Figure 2 says: "NKX2.2 is a transcription factor that promotes the invasive subpopulation", but the data don`t support this (ko leads to reduced invasion). Accordingly, the authors also state in the Results part "... the direction of the effect is the opposite of what one might have expected".

      To set the role of NKX2.2 into context, it would be useful to confirm the actual involvement of NFX2.2 in the invasive phenotype and clarify if NFX2.2. might probably even suppress some pro-invasive genes. I would advise to investigate the protein levels and/or protein localization of NFX2.2 and probably perform ChIp experiments on selected pro-invasive genes that play a role in the early invaders.

      The reviewer has raised some excellent points about our studies of NKX2.2 and its role in invasion. Indeed, we were also surprised by the fact that NKX2.2 had the opposite effect as expected (its peaks are enriched for accessibility in the early invaders in FS4, but knockout leads to increased invasion). We elected to include the results because it was a hypothesis we tested, so in the interest of full disclosure of results, we chose to leave the result in.

      The reviewer has also made some nice suggestions about how to further explore the role of NKX2.2 in regulation (e.g. ChIP-seq). Owing to the complexity of validating and performing this assay, we felt these experiments were beyond the scope of the current manuscript; we hope to explore these possibilities more fully in the future.

      Another excellent suggestion the reviewer made was to look at the regulatory capacity of NKX2.2 to directly demonstrate the link between NKX2.2 regulation and expression differences between early- and late-invading cells. In order to establish this connection, we used a gene set from molecular signatures database (MSigDB: https://www.gsea-msigdb.org/gsea/msigdb/human/geneset/NKX2_2_TARGET_GENES.html) consisting of genes with an NKX2.2 binding site within their promoter (TSS -1000 bp to TSS +100 bp) identified by the gene transcription regulation database (GTRD–paper here: https://pubmed.ncbi.nlm.nih.gov/33231677/). We used the Fisher’s exact test to see if the overlap between these genes regulated by NKX2.2 and genes that are differentially expressed between early-invading cells versus their respective parental population in both cell lines had more overlap than one would expect by chance. Indeed, the p-values using this approach were 3.937e-16 and 0.037 for the FS4 and 1205Lu cell lines, respectively. These results, combined with the motif analysis with our ATAC-seq data, demonstrated that the activity of NKX2.2 is relevant in the early-invading state. We thank the reviewer for the suggestion and feel this additional analysis has improved our conclusions about NKX2.2.

      Also, we further checked whether NKX2.2 levels correlated in early versus late invading cells across a panel of cell lines (Fig. 2C). We found that in 4/6 of these lines, NKX2.2 expression was higher in the early invaders. These results further support the case that NKX2.2 is an important positive regulator of invasion in multiple contexts.

      “In order to establish the generality of our results, we measured NKX2.2 expression levels across multiple cell lines by single molecule mRNA FISH. We found that the early invaders had higher levels of NKX2.2 expression in four out of the 6 lines tested (Fig. 2C), demonstrating the generality of our results and strengthening the case that NKX2.2 is a potential regulator of early invasiveness. The role of NKX2.2 as a regulator of early invasiveness was further established through comparative analysis between genes with NKX2.2 promoter region binding sites (-1000 bp to +100 bp relative to the transcription start site (TSS) as annotated by the Gene Transcription Regulation Database (GTRD)) and genes differentially expressed in early-invading and parental cells. Analysis using Fisher's exact test revealed a significant overlap between GTRD annotated genes regulated by NKX2.2 and genes expressed in FS4 (****p=3.937e-16) and 1205Lu (*p=0.037) early-invading cells. These results, in complement with our results from ATAC-sequencing motif analysis, further supported the relevance of NKX2.2 regulation in the early-invading state.”

      2) The sequencing data are currently accessible via a Dropbox link. They should be deposited instead in a data repository.

      We thank the reviewer for noting this problem. We have uploaded all data to the SRA/GEO at the following links:

      https://urldefense.com/v3/https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE224772;!!IBzWLUs!SEr5DTViPf08-IBQnv0ml-CoLX3cbaiNlCz-DJbpIKm7UcVXlL9-OD9reVQJs5pm_gzeqJYC_dM-MV8DonwX4c4$

      https://urldefense.com/v3/https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE224769;!!IBzWLUs!SEr5DTViPf08-IBQnv0ml-CoLX3cbaiNlCz-DJbpIKm7UcVXlL9-OD9reVQJs5pm_gzeqJYC_dM-MV8DtY6ZB3A$

      https://urldefense.com/v3/https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE224771;!!IBzWLUs!SEr5DTViPf08-IBQnv0ml-CoLX3cbaiNlCz-DJbpIKm7UcVXlL9-OD9reVQJs5pm_gzeqJYC_dM-MV8Dq_3ghAU$

      Minor comments:

      1) The cell line used for Supplementary Figure 4 should be named in the figure legend.

      We thank the reviewer for the suggestion. We have included the name of the cell line in the figure legend for Supplementary Figure 4. The text reads as follows:

      “A. FS4 melanoma cells were sorted based on SEMA3C expression. Cells were live-imaged for ~10 days every hour and single cells were tracked manually for cell position, cell division and lineage. Lineages were traced manually from single cells. Cell speed was calculated for each cell using the average distance traveled over time.”

      2) In Figures 4H-M and Supplementary Figure 4D-I, the authors describe data performed in "sister" and "cousin" cells. It would be useful to provide a definition for both in the main text or figure legend.

      This is a very good point. We have provided the following definitions in the main text, and have changed the wording from “sister” to “sibling” to avoid gendered terminology:

      “(sibling cells are defined as those that share a common parent cell, and cousin cells are defined as those that share a common grandparent.)”

      3) Discussion: "This lack of permanence may reflect the fact that the invasive cells are not subjected to stress-in our case, cells merely pass through a transwell, which may be the reason for the "burning in" of the phenotype in the case of resistance."

      This sentence is misleading - please clarify.

      We apologize for the confusion caused by this sentence. We have now changed it to the following:

      “It is interesting that the early-invading cells eventually revert to the population average even after going through the transwell. Such a result contrasts with our previous work (Shaffer et al., 2017b), in which a rare subpopulation became permanently therapy resistant and did not revert even after several weeks off-treatment. One possibility is that the stress of undergoing therapy treatment induces a transcriptional rewiring, and this rewiring is not induced by the migration through transwells. Further studies will be required to test these hypotheses.”

      Furthermore, there are some errors in the reference to the Figures throughout the paper. These which should be corrected:

      We thank the reviewer for their detailed reading and finding these issues. We have now fixed them all in our revised manuscript.

      4) Results, section "NKX2.2 is a transcription factor that promotes the invasive subpopulation".

      Here the authors write: "...we performed RNA sequencing on the NKX2.2 knockout cells and compared the effects on gene expression to the gene expression differences between early vs. non- invaders across the two cell lines." This sentence should contain the reference to Supplementary Figure 3B-D (which is otherwise not referred to).

      We thank the reviewer for their detailed reading and noticing this issue. We have now referenced Supplementary Figure 3B-D in the text cited above.

      5) Results: "Overexpression of SEMA3C in FS4 cells revealed no changes in invasiveness, suggesting that SEMA3C is a marker with no functional relevance to invasiveness per se; Fig. 1D, Fig. 2A-B)"

      The correct reference should be: Suppl. Fig. 1D, Fig. 2A-B. Also, in the current manuscript version the authors jump from Figures 1 to Figure 2 A,B, before coming back to Figure 1. To avoid this, I would advise to shift the current Figure 2A, B to Figure 1 or the supplementary information.

      We thank the reviewer for pointing out this error in the reference to these figures. Figure 2A-B is now referenced as “Supp. Fig. 1 E-F”. The figure legend has also been updated.

      6) Results: "We then sampled lungs from mice at various times post-injection to look for metastatic cells (Fig.1F, Suppl. Fig. 2B,C)."

      As Supplementary Figure 2B, C does not show metastasis, but rather primary tumor growth, I would advise the following wording: "We then sampled lungs from mice at various times post-injection to look for metastatic cells (Fig.1F) and overall tumor growth (Suppl. Fig. 2B,C)."

      We thank the reviewer for their advice to reword the sentence cited above. We have now edited the text to read as suggested by the reviewer. In addition, Supp. Fig. 2B,C is not referenced as Supp. Fig. 2C,D.

      "We then sampled lungs from mice at various times post-injection to look for metastatic cells (Fig.1F) and overall tumor growth (Supp. Fig. 2C,D)."

      7) Results: "Interestingly, NKX2.2 knockout cells showed markedly increased invasion and proliferation (Fig. 2A,B), suggesting a change in regulation of both processes. "

      The correct reference is Fig. 2C, D.

      The reviewer is right that we only have results in one cell line, and fully agree that the results in FS4 are only correlative. We have now weakened the language in the abstract and the results to emphasize that this result held in 1205Lu cells only.

      • Given the robust literature regarding phenotypic switching in melanoma, the NKX2.2 knockout increasing both invasiveness and proliferation (figures 2C, 2D) suggests it may not be involved in phenotype switching. Perhaps NKX2.2 is a negative regulator of cell activity/metabolism. We thank the reviewer for highlighting the possible connections with metabolism. To explore this possibility , we performed metabolic assays on NKX2.2 knockout and AAVS control cells and observed no significant changes in Extracellular acidification rate (B). We did observe some differences in oxygen consumption rate in the cells (A), but the differences do not seem to be large enough or systematic enough to be meaningful given the variation within the controls. We have now included these results in Supp. Fig. 3E-F.

      Note, the data previously referenced as Figure 2C,D is now in Figure 2A,B.

      “NKX2.2 is a transcriptional repressor and activator essential for the differentiation of pancreatic endocrine cells (Habener et al., 2005). In mice, deletion of NKX2.2 prevents the specification of pancreatic islet cells resulting in the replacement of insulin-expressing β cells and glucagon-expressing α cells with ghrelin-expressing cells; This lack of specification resulted in mortality of newborn mice due to hyperglycemia (Sussel et al. 1998; Prado et al. 2004). Given the link of NKX2.2 with glucose metabolism, we wondered whether NKX2.2 had an effect on metabolic activity prompting us to test the NKX2.2 knockout lines for metabolic differences in the oxygen consumption rate (OCR; an indicator of oxidative phosphorylation) and the extracellular acidification rate (ECAR; an indicator of glycolysis) of the cells. Seahorse assay analysis revealed no systematic differences in metabolic activity (Supp. Fig. 3E,F).”

      We thank the reviewer for the correction. The reference has now been corrected in the main text.

      Reviewer #1 (Significance (Required)):

      Nature and significance of the advance/ literature context:

      In their manuscript, the authors provide interesting biological data about the presence of intrinsically and reversibly pro-invasive / pro-metastatic melanoma cells in a seemingly homogenous subpopulation. With SEMA3C, they also provide a marker for early invading cells, which might be useful in future studies to identify therapeutic vulnerabilities for this subgroup. This study sheds further light on the functional effects of phenotypic plasticity, which was previously described particularly in the context of therapy resistance, as mentioned by the authors.

      We thank the reviewer for their kind assessment of the impact of our work.

      Audience:

      The study is interesting for scientists from the melanoma field as well as the cancer metastasis field in general.

      Own expertise:

      Melanoma, phenotypic switch, metabolism, signal transduction, stress response

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Metastatic potential in clonal melanoma cells is driven by a rare, early-invading subpopulation

      Kaur et al.

      In this manuscript the authors highlight a small subpopulation of "early-invading" melanoma cells and functionally characterize the nuances of these early cells compared to their slowly invading counterparts. A cell surface marker, SEMA3C and the transcription factor NKX2.2 were associated with differences in the invasive rates. Importantly, the group demonstrates that existence of the invasive subpopulation is not reliant on genetic changes, and thus exhibits plasticity. While the underlying concept surrounding this paper (phenotypic plasticity) is not novel, highlighting a surface marker and transcription factor that may, at least in part, be associated with phenotype plasticity is interesting. However, the current study seems underdeveloped. Specific points of concern are listed:

      Major

      • Only two cell lines are used throughout this study. We thank the reviewer for pointing out the need for more cell lines. We have now added two new cell lines to our study, WM793 and WM1799, both of which recapitulate the fundamental phenomenology in question. Although we did not show it in our initial submission, we had originally queried a panel of melanoma cell lines in order to determine their suitability for our study (from which we settled on 1205Lu and FS4). This panel has multiple melanoma cell lines obtained from a variety of melanoma tumor samples from Radial Growth Phase (RGP), Vertical Growth Phase (VGP), and metastatic tissues. We now have included these data in our revised manuscript, since they further support our point.

      “We tested a panel of different melanoma cell lines from Radial Growth Phase (RGP), Vertical Growth Phase (VGP), and metastatic tumor types for the existence of fast invading subpopulations. We used four patient-derived melanoma cell lines, FS4, 1205Lu, WM1799, WM793, all of which have BRAF mutations (V600K for FS4, V600E for 1205Lu, WM1799, and WM793) and are known to be highly invasive in vitro and in vivo (27). Out of the 11 melanoma cell lines tested, the FS4 (not shown) and 1205lu cell lines displayed the highest levels of fast invading subpopulations (Supp. Fig. 1A).”

      First, we showed that they all have an invasive subpopulation, with 1205Lu and FS4 (not shown) having the most invasive cells. Second, validating a central claim of the manuscript, we showed that many of these cell lines, including WM1799 and WM793, showed much higher levels of both SEMA3C (4/6) and NKX2.2 (4/6) expression in the early invading population as compared to the late invading population.

      Together, these data make a strong case that our findings generalize across multiple cell lines, including RGP and VGP models. We have incorporated new text that reads as follows:

      “In order to establish the generality of our results, we measured expression of the surface marker SEMA3C across the early and late invading subpopulations of a panel of melanoma cell lines. We found that SEMA3C levels were higher in the early invading subpopulation in 4 of the 6 lines tested (Supp. Fig. 1H). Thus, these results held across a variety of cell lines and, thus, were not a unique feature of a particular patient sample.”

      • The in vivo metastasis assay in figure 1 is difficult to interpret and presents a number of concerns. 1) Only ~50% of early invading cells were labeled with GFP, this confounds many aspects of the experiment. The authors comment that in the primary tumor, as expected "...a roughly equal mix of human melanoma cells that were GFP positive and negative." If there was an expectation of equal proliferative rates in the primary tumor of early and late invading cells, given that only 1/2 of the early cells were GFP+, wouldn't we expect only 25% of the human cells to be GFP+?

      The reviewer has raised a very important quantitative question about our experiments, which we have now addressed with a more thorough set of analyses. Initially, we quantified GFP positivity post -transduction by looking at fluorescent protein levels, for which the threshold was fairly arbitrary, and potentially could have miscounted many GFP positive cells as GFP negative due to low but non-zero levels of expression. We hence recalculated our positivity rate based on single molecule RNA FISH for GFP and mCherry, given that the technique is sensitive down to even veryl ow levels of expression.

      As can be seen in Supp. Fig. 2B, the vast majority of transduced cells did indeed get the transgene and had some level of expression of GFP/mCherry. At a threshold of 5/10 molecules (GFP/mCherry, respectively), we obtained 88% and 96.15% positivity rates for GFP and mCherry, respectively. At these rates of positivity, we would expect much closer to 50% of the cells being GFP positive in the tumors, as observed. We thank the reviewer for noticing this discrepancy, and feel that our new analysis clears up the confusion and strengthens our results. These results are described in the main text as follows:

      “We labeled the cells with sufficient virus so that 88% of the early invaders were labeled with GFP and 96.15% of the late invaders were labeled with mCherry (Supp. Fig. 2B). We then sampled lungs from mice at various times post-injection to look for metastatic cells (Fig.1F) and overall tumor growth (Supp. Fig. 2C,D).”

      2) The authors note technical difficulties in detecting mCherry in sections. It seems as though this forced them to use a RNA FISH probe to identify human vs. mouse and by extension/negative selection the human FISH positive, GPF negative cell represented a mCherry stained late-invading cell. This is not ideal and seems over complicated. If the population of interest was engineered to express mCherry, why not directly probe for mCherry?

      The reviewer has raised an important point about our experimental design. Indeed, we attempted multiple times and in multiple ways to detect mCherry protein directly. We tried multiple times with multiple antibodies, but the signal was simply not detectable. Hence, we arrived at the experimental design we outlined. We felt that a fully transparent disclosure of the issues was preferable, even if it did make the design sound overly complex. We will note that our primary result—that the vast majority of the metastatic cells are GFP positive and hence derived from fast invaders—is robust to any detection issues for mCherry.

      3) Given the poor initial labeling/transduction of the early invaders, how can the authors be confident that all human cells without GFP signal are late invaders?

      The reviewer raises a great point that is addressed by our GFP and mCherry RNA FISH analysis above, showing that the transduction efficiency was actually quite a bit higher than initially thought due to low but non-zero GFP signal being counted as GFP negative. With the much higher transduction efficiencies we have now validated, we believe that the vast majority of human cells with no GFP signal should be late invaders.

      • The authors may have missed an opportunity to study FS4 clone F6 and 1205 clone E11. What is the SEMA3C and NKX2.2 status of these clones? Are they able to revert expressions? The reviewer has pointed out an interesting opportunity for further exploration. Unfortunately, because they were identified as part of an initial screening study, those particular clones were not kept for subsequent analysis. However, in our revised manuscript, we have now worked up multiple additional cell lines (WM1799 and WM793), both of which had high expression levels of both SEMA3C (Supp. Fig. 1H, shown above) and NKX2.2 (Fig. 2C) in the early invading subpopulation. Currently, we do not have data on reversion experiments for these two cell lines, but we would expect them to behave similarly to the other cell lines we examined in this study.

      • The lack of statistical analysis/comparisons throughout the paper needs to be addressed. We thank the reviewer for pointing out these deficiencies. We have now added statistical comparisons throughout.

      • In figures 1E and 3B, why do the parental (homogenous) cells demonstrate less invasiveness than the selected for the SEMA3C low or "late-invaders" respectively? This is an important point that the reviewer has raised. The finding did occur in every replicate, so we assume it is biologically and not statistical. We have now included the following language in the discussion noting the issue and some possible explanations.

      “It is worth noting that, while the SEMA3C-high (early-invading) subpopulation drove the highly invasive phenotype, the SEMA3C-low (late-invading) subpopulation also displayed a somewhat more invasive phenotype than the parental population. It is unclear what the underlying cause of this difference in invasive behavior is between the SEMA3C-low and parental populations. One possibility is that paracrine signaling between cells in the parental population confers them with less invasive potential than when the cells are isolated into early- and late-invading subpopulations. Another possibility is that technical factors associated with the sorting of SEMA3C-low cells from the parental population alter their invasive properties, thus making them distinct from the parental population.”

      • Conclusions that NKX2.2 knockout increases invasiveness and proliferation are based on 1 cell line. The comparisons done with FS4 early and late invading cells in Figure 1F may be supportive but is correlative in nature. The reviewer is right that we only have results in one cell line, and fully agree that the results in FS4 are only correlative. We have now weakened the language in the abstract and the results to emphasize that this result held in 1205Lu cells only.

      • Given the robust literature regarding phenotypic switching in melanoma, the NKX2.2 knockout increasing both invasiveness and proliferation (figures 2C, 2D) suggests it may not be involved in phenotype switching. Perhaps NKX2.2 is a negative regulator of cell activity/metabolism. We thank the reviewer for highlighting the possible connections with metabolism. To explore this possibility , we performed metabolic assays on NKX2.2 knockout and AAVS control cells and observed no significant changes in Extracellular acidification rate (B). We did observe some differences in oxygen consumption rate in the cells (A), but the differences do not seem to be large enough or systematic enough to be meaningful given the variation within the controls. We have now included these results in Supp. Fig. 3E-F.

      Note, the data previously referenced as Figure 2C,D is now in Figure 2A,B.

      “NKX2.2 is a transcriptional repressor and activator essential for the differentiation of pancreatic endocrine cells (Habener et al., 2005). In mice, deletion of NKX2.2 prevents the specification of pancreatic islet cells resulting in the replacement of insulin-expressing β cells and glucagon-expressing α cells with ghrelin-expressing cells; This lack of specification resulted in mortality of newborn mice due to hyperglycemia (Sussel et al. 1998; Prado et al. 2004). Given the link of NKX2.2 with glucose metabolism, we wondered whether NKX2.2 had an effect on metabolic activity prompting us to test the NKX2.2 knockout lines for metabolic differences in the oxygen consumption rate (OCR; an indicator of oxidative phosphorylation) and the extracellular acidification rate (ECAR; an indicator of glycolysis) of the cells. Seahorse assay analysis revealed no systematic differences in metabolic activity (Supp. Fig. 3E,F).”

      • Given that sorted SEMA3C high levels did not revert to parental FS4 levels, yet the invasive phenotype reverted to parental-like behavior undermines the usefulness of SEMA3C as a marker of invasiveness. The reviewer has brought up an important point. We were able to show that 1205Lu cells had SEMA3C levels revert to those of the parental. The reviewer is right that FS4 did not, which may be because it takes longer for FS4 to revert. It is true that the phenotypic behavior did revert. We have seen similar things in our therapy resistance work (Shaffer et al. 2017, etc.). One possible reason is that the phenotype is governed by multiple factors, and so the phenotype can revert before the expression of SEMA3C. We still think that SEMA3C is a good marker, just perhaps context dependent. We have added text to the discussion to make these important points.

      “We note that SEMA3C levels in FS4-SEMA3C-high cells did not revert to the parental levels within two weeks. This incomplete reversion may be because SEMA3C takes longer to revert than the tested time period. Interestingly, the invasive phenotype did revert in this time period, suggesting that there may be multiple factors associated with the phenotype beyond SEMA3C. It may thus be that SEMA3C is a marker of the early-invading population, but only in certain contexts.”

      Minor

      • How does SEMA3C and/or NKX2.2 expression (here 1.5% of FS4 cells were noted as "SEMA3C high") of metastatic cell lines (FS4 and 1205) compare to RGP and VGP cell lines? The reviewer has asked a great question about radial and vertical growth phase cells. We have tested several other cell lines to determine cell lines that were suitable for transwell assays. We have now included two figures (Supp. Fig. 1H and Fig. 2C) showing the SEMA3C and NKX2.2 status of each of these cell lines (parental cells) and their different subpopulations (early invaders and late invaders)—see also Reviewer #2, Major point 1. We found that the same pattern of SEMA3C-high cells held for both RGP and VGP cell lines.

      • There were a number of instances throughout the manuscript that were not clear, colloquial, or simply unnecessary - i.e. description of transwell assay. The reviewer has raised a good point about our language. We have gone through and tried to improve the clarity and precision. As for descriptions of the various assays, we have found that some readers of our papers are unfamiliar with these assays, so we elected to keep those descriptions in. We hope the reviewer does not object too strenuously.

      • The authors only analyze/mention lung metastases. Were metastases observed at other sites? The reviewer has posed a very good question about whether metastasis occurred at other locations. We stained additional tissues (liver and kidney) that were collected from the same mice and stained as per our lung invasion assays. As shown in our new Supplemental Fig. 2E, we found a similar pattern with the vast majority of metastatic cells being GFP positive; i.e., early-invaders, just as was the case for lung. We thank the reviewer for this helpful suggestion.

      “In the lung, however, we saw predominantly GFP-positive cells, showing that the vast majority of cells that migrated from the primary tumor site were initially early invading cells (Fig. 1I,J). The number of GFP cells in the lung was variable, but generally increased with time. The liver and kidney also showed an enrichment of GFP-positive cells (early invaders), suggesting that the metastatic potential of these cells is not limited to any one particular metastatic location (Supp. Fig. 2E). Thus, we established that the highly invasive subpopulation was able to drive metastasis in vivo.”

      • What is PE indicating in Figure 1D? Apologies, PE refers to the channel we used for the sorting on the FACS machine and stands for “Phycoerythrin”. To avoid any confusion, we have omitted the “PE” text on the y-axis of Fig. 1D.

      • The number of invaded cells seems to vary quite a bit between experiments - Parental 1205 cells in Fig 2C = ~200, yet 1205 clone F6 and the non-clonal 1205 cell line demonstrate ~10,000. Similar differences observed with Fs4 cells - Parental Fig 1E vs. Empty control Figure 2A. The reviewer has a good eye—indeed, there is a wide variability in the amount of invading cells. We have now remarked on this variability in the results section:

      “We note that the number of invading cells varied significantly between experiments. This variability is due to the fact that we employed transwell dishes with different growth areas, ranging from 0.33 cm2 to 4.67 cm2, leading us to collect different cell numbers for individual experiments. The cell density per cm2, however, was kept constant between experiments.”

      Note that Figure 2C and Figure 2A are now referenced as Figure 2A and Supplemental Figure 1F, respectively .

      Reviewer #2 (Significance (Required)):

      This work contributes to the growing fields of phenotypic plasticity and intratumoral heterogeneity. The authors claim to have identified a surface marker SEMA3C and a transcription factor NKX2.2 that may play a role in driving invasive proclivity. Importantly, the group demonstrates that changes in these proteins are not genetic, and therefore represent "intrinsic differences" that are a property of the tumor. Furthermore, the authors indicate how the present observations of early invading cells parallels drug resistance phenomena as their previous works highlights intrinsically resistant subpopulations (Shaffer et al., Nature 2017, Torre et al., Nature Genetics 2021 and others.). Taken together, the current and previous work underscores the importance of cell to cell non-genetic variability in disease progression and response to therapy.

      We thank the reviewer for their kind comments on the significance of our manuscript.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      In this study, Kaur et al. intended to use similar strategy that the same group had developed (https://www.nature.com/articles/nature22794) to identify the subpopulation in melanoma responsible for metastasis. In brief, the melanoma cell population was subjected to the selection of a specific phenotype (transwell migration dubbed as "invasiveness" behavior). By comparing the early and late invaders, a cell maker was identified to allow distinguishing the high-invasive subpopulation. A series of experiments were devised to validate the metastatic function of the high-invasive cells and delineate the signaling that drove this phenotype. The authors concluded that this rare subpopulation was originated from transcriptional fluctuation, and invasiveness is a trade-off of cell growth. Therefore, as the cells growing, overtime the phenotype was reverted to low invasiveness.

      Consistency is the most important factor for evaluating observation over temporal and spatial range. Therefore, several controls need to be clarified before further investigation in mechanisms:

      1) If the rare invader cells are arising from gene expression fluctuation, the SEMA3C-low population of parental line should generate SEMA3C-high invader subpopulation over time. This should be addressed.

      The reviewer has made an excellent point. Indeed, it is the case that the SEMA3C-low population starts to regenerate the high invader subpopulation over time. We have re-graphed Figure 3D to demonstrate this fact more clearly (See Supplemental Fig. 5A,B), showing that the SEMA3C low population regenerates many more SEMA-3C high cells after 14 days.

      2) Both early and late invader cells exhibited higher invasiveness than the parental line (Fig. 3B). Therefore, the in vivo metastatic potential of the three lines should be compared to validate the role of the invader cells in the metastatic function.

      We thank the reviewer for their comment about testing all three populations in the in vivo context. It is an excellent suggestion, but in order to fully control the experiment, we would need to add all three populations in three separate colors. Given the difficulties we had with getting even the two colors to work together, we think it is beyond the scope of our current efforts to attempt this complex experiment. We have added the following caveat to the text:

      “For unknown reasons, the parental population consistently showed lower invasiveness than the early- and late-invading subpopulations. Given that we did not test the parental population for invasiveness in vivo, future studies may address the sources and mechanisms by which the parental population differs and how those differences manifest in vivo.”

      3) To evaluate the possible intervention of cellular function by fluorescent proteins (https://doi.org/10.1016/j.ccell.2022.01.015), admix of GFP- and mCherry-labeled populations of early invader cells should be used as a control in Fig. 1F. Noticeably, the labeling ratio of the two populations was not even in Fig. 1F.

      The reviewer has brought up an important point about the potential differences brought about by the fluorescent proteins themselves. At this point, it is difficult to redo these complex in vivo experiments, but we can appeal to the fact that the admixture is maintained throughout time as the primary tumor site still has a roughly equal ratio of GFP and mCherry cells in it (Fig. 1I and Supp. Fig. 2E).

      4) When the invader cells were expanded and passed, their invasiveness will revert to the level similar to parental line in 14 days (Fig. 3B). The isolated cells were expanded for further testing and manipulation in Fig. 1C and 1F, respectively. How long did was the period for cell expansion in these experiments?

      We thank the reviewer for bringing up an important question about the details of cell expansion. For the RNA-seq, the cells were directly processed upon going through the transwell, so there was no expansion period. We have made sure to outline this more carefully in our methods section (see below).

      “RNA sequencing and analysis:

      RNA collection and library prep: Each treatment/sample was tested in 3 separate biological replicates. Upon passing through the transwell, cells were immediately collected and processed for RNA sequencing. Total RNA isolation was performed using the phenol-chloroform extraction followed by RNA cleanup using RNAeasy Micro (Qiagen 74004) kit. For transwell assays, library preparation was performed using Nebnext single-cell/low input RNA library prep kit (E6420L, NEB). For NKX2.2 CRISPR experiments, library preparation was done using NEBNext Poly(A) mRNA Magnetic Isolation Module (NEB E7490L) integrated with NEBNext Ultra II RNA Library Prep Kit for Illumina (NEB E7770L).

      Mouse tumor implantation and growth:

      All mouse experiments were conducted in collaboration with Dr. Meenhard Herlyn at The Wistar Institute, Philadelphia, PA. NSG mice (NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ) were bred in-house at The Wistar Institute Animal Facility. All experiments were performed under approval from the Wistar Institute Care and Use Committee (protocol 201174). As in the case of RNA sequencing experiments, cells were not expanded prior to injection into the mouse, but were collected and implanted right after passing through the transwell. 50,000 melanoma cells were suspended in DMEM with 10% FBS and injected subcutaneously in the left flank of the mouse.”

      5) If invasiveness and growth are trade-off, why did the mCherry-labeled cells not dominate the population of primary tumors in Fig. 1J?

      Note that Figure 1J is now referenced as Figure 1I. The reviewer brings up a good point. For potential explanations, first, the difference in growth rate is not large, so we would not necessarily expect mCherry cells to dominate on this timescale. Also, we believe that in vivo, the tradeoff may be mitigated by other factors and cell-cell interactions that are not present in vitro. We have added a note on this point to the results.

      “(Note that these numbers were similar despite the slightly increased growth rate of the late-invading subpopulation; we assume this is due to the relatively small difference and cell-cell interactions that could prevent one population from dominating the other.)”

      6) In Fig. 1G, why RNA FISH was not used to detect mCherry-labeled cells?

      Another excellent point. RNA FISH in tissue sections can often be rather challenging due to various reasons including RNA degradation, and mCherry RNA signal was hard to definitively show in these sections. Hence, we opted for MALAT1, which is very heavily expressed and hence provided a strong and reliable signal.

      “For technical reasons, the mCherry cells were not detectable due to the fluorescence of the mCherry protein not being visible in the mouse sections. Nevertheless, we were able to detect late invaders in the population by using a human-specific MALAT1 RNA FISH probe that binds only to human MALAT1 RNA and not mouse MALAT1 RNA (28).”

      7) In vivo cycling (harvesting the cells from metastatic site and implanting them to the primary site in mouse models) has been employed to select metastatic sublines from a parental line. Could in vivo cycling make the early invader phenotype fixed?

      The reviewer has raised a very interesting point about cycling and selection. Indeed, the 1205Lu cells were derived from repeated cycling of invasive lung cells. That is probably the reason that these cells were useful for our assay, because the percentage of early-invading cells was higher. Nevertheless, the cells still have a significant proportion of late invaders, suggesting that the phenotype has not yet been fixed in the population. Perhaps with further cycling, such a fixation could be achieved. We have now noted this possibility in our discussion.

      “It is also possible that repeated cycles of selection, even of non-genetic phenotypes, could lead to an increased fraction of invasive cells. Indeed, 1205Lu cells were derived by exactly such repeated cycles, which presumably are the reason they have a higher percentage of invasive cells; however, despite these repeated rounds of selection, most cells are still not highly invasive, suggesting that it is difficult for this property to fully fix in the population.”

      **Referees cross-commenting**

      Both reviewers' questions are important for adequate controls.

      Reviewer #3 (Significance (Required)):

      There are several studies trying to identify subpopulation responsible for the metastasis of melanoma and other types of cancer, and a few mechanisms have been revealed. However, the significance depends on if the results can be validated on clinical data. It is lacking in this study.

      We thank the reviewer for their statement of interest in the problem. We agree that it is helpful to link these results to clinical data. We did perform TCGA analyses of several different genes, including SEMA3C, that emerged from our data, and there were no systematic relationships to phenotype. Of course, the relationship to clinical data is complex and many important factors are not obvious from the TCGA data, so we do not think that necessarily diminishes our results. Rather, we think our results raise a conceptual point that there can be rare cells with non-genetic differences that can drive metastasis. Further work will be required to translate these results to the clinic.

      We have added the following to the main text:

      “We found that the SEMA3C-high cells were far more invasive, intrinsically, than SEMA3C-low cells and the population overall, thus demonstrating that cells vary intrinsically in their invasiveness, and the very invasive subpopulation is marked by the expression of SEMA3C (Fig. 1E). Note, overexpression of SEMA3C in FS4 single cell clones revealed no changes in invasiveness, suggesting that SEMA3C is a marker with no functional relevance to invasiveness per se (Fig. 1D; Supp. Fig. 1E-G). We verified the expression levels of the genes identified in our RNA sequencing study in the The Cancer Genome Atlas (TCGA) data. We combined the list of differentially expressed genes in early invaders with the gene set enrichment analysis (GSEA) “Hallmarks of cancer epithelial-mesenchymal transition” and compared expression in primary vs. metastatic TCGA samples, finding no appreciable difference (Fig. 5A-B). These data suggest that these markers do not have obvious clinical correlates. Moreover, Kaplan Meier analysis comparing the survival time (days to death) between patient cohorts with either high or low SEMA3C expression levels revealed that SEMA3C does not predict survival time post-diagnosis, as both survival curves (p=0.898) follow comparable trends between the two cohorts (Fig. 5C). However, conceptually, our results raise the possibility that a rare, non-genetically defined subpopulation of cells may drive metastasis due to its increased degree of invasiveness, which further data collection efforts in patient samples may help validate.”

    1. Author Response

      Reviewer #1 (Public Review):

      1) I was confused about the nature of the short-term plasticity mechanism being modeled. In the Introduction, the contrast drawn is between synaptic rewiring and various plasticity mechanisms at existing synapses, including long-term potentiation/depression, and shorter-term facilitation and depression. And the synaptic modulation mechanism introduced is modeled on STDP (which is a natural fit for an associative/Hebbian rule, especially given that short-term plasticity mechanisms are more often non-Hebbian).

      Indeed, because of its associative nature, the modulation mechanism was envisioned to be STDP-like, i.e. on faster time scales than the complete rewiring of the network (via backpropagation) but slower time scales than things like STSP which, as the reviewer points out, are usually not considered associative. One thing we do want to highlight is that backpropagation and the modulation mechanism are certainly not independent of one another. During training, the network’s weights that are being adjusted by backpropagation are experiencing modulations, and said modulations certainly factor into the gradient calculation.

      We have edited the abstract and introduction to try to make the distinction of what we are trying to model clearer.

      1) cont: On the other hand, in the network models the weights being altered by backpropagation are changes in strength (since the network layers are all-to-all), corresponding more closely to LTP/LTD. And in general, standard supervised artificial neural network training more closely resembles LTP/LTD than changing which neurons are connected to which (and even if there is rewiring, these networks primarily rely on persistent weight changes at existing synapses).

      Although we did not highlight this particular biological mechanism because we wanted to keep the updates as general as possible, one could view the early versus late LTP. We have added an additional discussion of how the associative modulation mechanisms and backpropagation might biologically map into this mechanism in the discussion section.

      1) cont: Moreover, given the timescales of typical systems neuroscience tasks with input coming in on the 100s of ms timescale, the need for multiple repetitions to induce long-term plasticity, and the transient nature/short decay times of the synaptic modulations in the SM matrix, the SM matrix seems to be changing on a timescale faster than LTP/LTD and closer to STP mechanisms like facilitation/depression. So it was not clear to me what mechanism this was supposed to correspond to.

      We note that although the structure of the tasks certainly resembles known neuroscience experiments that happen on shorter time scales (and with the introduction of the 19 new NeuroGym tasks, even more so), we did not have a particular time scale for task effects in mind. So each piece of “evidence” in the integration tasks may indeed occur over significantly slower time scales and could abstractly represent multiple repetitions in order to induce (say) early phase LTP.

      Given that the separation between the two plasticity mechanisms may be clearer for STSP, and indeed many of the tasks we investigate may more naturally be mapped to tasks that occur on time scales more relevant to STSP, we have introduced a second modulation rule that is only dependent upon the presynaptic firing rates. See our response to the Essential Revisions above for additional details on these new results.

      2) A number of studies have explored using short-term plasticity mechanisms to store information over time and have found that these mechanisms are useful for general information integration over time. While many of these are briefly cited, I think they need to be further discussed and the current work situated in the context of these prior studies. In particular, it was not clear to me when and how the authors' assumptions differed from those in previous studies, which specific conclusions were novel to this study, and which conclusions are true for this specific mechanism as opposed to being generally true when using STP mechanisms for integration tasks.

      We have added additional works to the related works sections and expanded the introduction to try to better convey the differences with our work and previous studies. Briefly, mostly our assumptions differed from previous studies in that we considered a network that relied only on synaptic modulations to do computations, rather than a network with both recurrence and synaptic modulations. This allowed us to isolate the computational power and behavior of computing using synaptic modulations alone.

      It is hard to say which of the conclusions are generally true when using STP mechanisms for integration tasks without a comprehensive comparison of the various models of STP on the same tasks we investigated here. That being said, we believe we have presented in this work conclusions that are not present in other works (as far as we are aware) including: (1) a demonstration of the strength of computing with synaptic connection on a large variety of sequential tasks, (2) an investigation into the dynamics of such computations how they might manifest in neuronal recordings, and (3) a brief look at how these different dynamics might be computational beneficial in neuroscience-relevant areas. We also note that one reason for the simplicity of our mechanism is that we believe it captures many effects of synaptic modulations (e.g. gradual increase/decrease of synaptic strength that eventually saturates) with a relatively simple expression, and so we believe other STP mechanisms would yield qualitatively similar results. We have edited the text to try to clarify when conclusions are novel to this study and when we are referencing results from other works.

      Reviewer #2 (Public Review):

      On the other hand, the general principle appears (perhaps naively) very general: any stimulus-dependent, sufficiently long-lived change in neuronal/synaptic properties is a potential memory buffer. For instance, one might wonder whether some non-associative form of synaptic plasticity (unlike the Hebbian-like form studied in the paper), such as short-term synaptic plasticity which depends only on the pre-synaptic activity (and is better motivated experimentally), would be equally effective. Or, for that matter, one might wonder whether just neuronal adaptation, in the hidden layer, for instance, would be sufficient. In this sense, a weakness of this work is that there is little attempt at understanding when and how the proposed mechanism fails.

      We have tried to address if the simplicity of the tasks considered in this work may be a reason for the MPN’s success by training it on 19 additional neuroscience tasks (see response to Essential Revisions above). Across all these additional tasks, we found the MPN performs comparable to its RNN counterparts.

      To address whether associativity is necessary in our setup we have introduced a version of the MPN that has modulation updates that are only presynaptic dependent. We call this the “MPNpre” and have added several results across the paper addressing its computational abilities (again, additional details are provided above in Essential Revisions). We find the MPNpre has dynamics that are qualitatively the same as its MPN counterpart and has very comparable computational capabilities.

      Certainly, some of the tasks we consider may also be solvable by introducing other forms of computation such as neuronal adaptation. Indeed, we believe the ability of the brain to solve tasks in so many different ways is one of the things that makes it so difficult to study. Our work here has attempted to highlight one particular way of doing computations (via synapse dynamics) and compared it to one particular other form (recurrent connections). Extending this work to even more forms of computation, including neuronal dynamics, would be very interesting and further help distinguish these different computational methods from one another.

      Reviewer #3 (Public Review):

      Because the MPN is essentially a low-pass filter of the activity, and the activity is the input - it seems that integration is almost automatically satisfied by the dynamics. Are these networks able to perform non-integration tasks? Decision-making (which involves saddle points), for instance, is often studied with RNNs.

      We have tested the MPN on 19 additional supervised learning tasks found in the NeuroGym package (Molano-Mazon et. al., 2022), which consists of several decision-making-based tasks and added these results to the main text (see response to Essential Revisions above, and also Figs. 7i & 7j). Across all tasks we investigated, we found the MPN performs at comparable levels to its RNN counterparts.

      Manuel Molano-Mazon, Joao Barbosa, Jordi Pastor-Ciurana, Marta Fradera, Ru-Yuan Zhang, Jeremy Forest, Jorge del Pozo Lerida, Li Ji-An, Christopher J Cueva, Jaime de la Rocha, et al. “NeuroGym: An open resource for developing and sharing neuroscience tasks”. (2022).

      The current work has some resemblance to reservoir computing models. Because the M matrix decays to zero eventually, this is reminiscent of the fading memory property of reservoir models. Specifically, the dynamic variables encode a decaying memory of the input, and - given large enough networks - almost any function of the input can be simply read out. Within this context, there were works that studied how introducing different time scales changes performance (e.g., Schrauwen et al 2007).

      Thank you for pointing out this resemblance and work. In our setup, the fact that lamba is the same for the entire network means all elements of M decrease uniformly (though the learned modulation updates may allow for the growth of M to be non-uniform). One modification that we think would be very interesting to explore is the effects on the dynamics of non-uniform learning rates or decays across synapses. In this setting, the M matrix could have significantly different time scales and may even further resemble reservoir computing setups. We have added a sentence to the discussion section discussing this possibility.

      Another point is the interaction of the proposed plasticity rule with hidden-unit dynamics. What will happen for RNNs with these plasticity rules? I see why introducing short-term plasticity in a "clean" setting can help understand it, but it would be nice to see that nothing breaks when moving to a complete setting. Here, too, there are existing works that tackle this issue (e.g., Orhan & Ma, Ballintyn et al, Rodriguez et al).

      Thank you for pointing out these additional works, they are indeed very relevant and we have added them all to the text where relevant.

      Here we believe we have shown that either recurrent connections or synaptic dynamics alone can be used to solve a wide variety of neuroscience tasks. We don’t believe a hybrid setting with both synaptic dynamics and recurrence (e.g. a Vanilla RNN with synaptic dynamics) would “break” any part of this setup. Since each of the computational mechanisms could be learned to be suppressed the network could simply solve the task by relying on only one of the two mechanisms. For example, it could use a strictly non-synaptic solution by driving eta (the learning rate of the modulations) to zero or it could use a non-recurrent solution by driving the influence of recurrent connections to be very small. Orhan & Ma mention they have a hard time training a Vanilla RNN with Hebbian modulations on the recurrent weights for any modulation effect that goes back more than one time step, but unlike our work they rely on a fixed modulation strength.

      Indeed, we think how networks with multiple computational mechanisms will solve tasks is a very interesting question to be further investigated, and a hybrid solution may be likely. We believe our work is valuable in that it illuminates one end of the spectrum that is relatively unexplored: how such tasks could be solved using just synaptic dynamics. However, what type of solution a complete setup ultimately lands on is likely largely dependent upon both the initialization and the training procedure, so we felt exploring the dynamics of such networks was outside the scope of this work.

      One point regarding biological plausibility - although the model is abstract, the fact that the MPN increases without bounds are hard to reconcile with physical processes.

      Note although the MPN expression does not have explicit bounds, in practice the exponential decay eventually does balance with the SM matrix updates, and so we observe a saturation in its size (Fig. 4c, except for the case of lamba=1.0, which is not considered elsewhere in the text). However, we explicitly added modulation bounds to the M matrix update expression and did not find it significantly changed the results (see comments on Essential Revisions above for details).

    1. Compiled ratings, author response, and editorial comment


      Ratings and predictions

      Ratings (1-100)

      <table> <tr> <td> </td> <td>Evaluator 1 </td> <td> </td> <td>Evaluator 2 </td> <td> </td> <td>Evaluator 3 </td> <td> </td> </tr> <tr> <td>Rating category </td> <td>Rating (0-100) </td> <td>90% CI </td> <td>Rating (0-100) </td> <td>90% CI </td> <td>Rating (0-100) </td> <td>Confidence </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>Overall assessment </td> <td>40 </td> <td>20-60 </td> <td>80 </td> <td>60-90 </td> <td>65 </td> <td>Medium </td> </tr> <tr> <td>Advancing knowledge and practice </td> <td>30 </td> <td>20-60 </td> <td>80 </td> <td>70-90 </td> <td>70 </td> <td>Medium </td> </tr> <tr> <td>Methods: Justification, reasonableness, validity, robustness </td> <td>50 </td> <td>40-60 </td> <td>70 </td> <td>50-90 </td> <td>Not qualified </td> <td> </td> </tr> <tr> <td>Logic & communication </td> <td>60 </td> <td>40-75 </td> <td>85 </td> <td>65-95 </td> <td>80 </td> <td>Medium-to-high </td> </tr> <tr> <td>Open, collaborative, replicable </td> <td>70 </td> <td>40-75 </td> <td>73 </td> <td>50-95 </td> <td>Not qualified </td> <td> </td> </tr> <tr> <td>Relevance to global priorities </td> <td>90 </td> <td>60-95 </td> <td>85 </td> <td>70-90 </td> <td>80 </td> <td>High </td> </tr> </table>

      Journal predictions (1-5)

      <table> <tr> <td> </td> <td>Evaluator 1 </td> <td> </td> <td>Evaluator 2 </td> <td> </td> <td>Evaluator 3 </td> <td> </td> </tr> <tr> <td>Prediction metric </td> <td>Rating (0-5) </td> <td>90% CI </td> <td>Rating (0-5) </td> <td>90% CI </td> <td>Rating (0-5) </td> <td>Confidence </td> </tr> <tr> <td>What ‘quality journal’ do you expect this work will be published in? </td> <td>2 </td> <td>1-2 </td> <td>3.5 </td> <td>3-5 </td> <td>3.5 </td> <td>Medium </td> </tr> <tr> <td>On a ‘scale of journals’, what tier journal should this be published in? </td> <td>2 </td> <td>1-2 </td> <td>4 </td> <td>3-5 </td> <td>3.5 </td> <td>High </td> </tr> </table>

      Author response

      To start with we would like to commend the format and reviewer comments which were of extremely high quality. The evaluations provided well thought out and constructively critical analysis of the work, pointing out several assumptions which could impact findings of the paper while also recognizing the value of the work in spite of some of these assumptions. Research in this space is difficult due to the highly interdisciplinary nature of the questions being asked, and the major uncertainties that need to be addressed. We value good epistemics and understand that it takes many people critically looking at a problem to achieve this, which is what motivated our participation in the Unjournal pilot. A format which allows work to be published and reviewed in an open nuanced manner can reduce the friction of working on such questions and speed up communal sense making on important questions. We are excited to have participated and look forward to seeing how Unjournal progresses. We hope that future work highlighted by the reviewers that addresses assumptions and issues of the paper will be undertaken, by external parties who are better equipped to critically analyse this area of research improving epistemics in relation to nuclear risk, resilient foods, AGI safety and the greater the existential risk space.

      To clarify, the intention of the comparison of resilient foods to AGI safety was chosen as AGI safety is considered the greatest x-risk by many. Consequently, comparison of cost-effectiveness of resilient foods to AGI safety was intended to highlight the merit of resilient foods to motivate further investment, as opposed to motivating redirecting funding from AGI safety to resilient foods.

      We have included responses to aspects of the evaluations below.

      Evaluation 1

      Structure of cost-effectiveness argument

      • The biggest issue with interpretability this causes is that I struggle to understand what features of the analysis are making resilient food appear cost-effective because of some feature of resilient food, and which are making resilient food appear cost-effective because of some feature of AI. The methods used by the authors mean that a mediocre case for resilient food could be made to look highly cost-effective with an exceptionally poor case for AI, since their central result is the multiplier of value on a marginally invested dollar for resilient food vs AI. This is important, because the authors’ argument is that resilient food should be funded because it is more effective than AI Risk management, but this is motivated by AI Risk proponents agreeing AI Risk is important – in scenarios where AI Risk is not worth investing in then this assumption is broken and cost effectiveness analysis against a ’do nothing’ alternative is required. For example, the authors do not investigate scenarios where the benefit of the intervention in the future is negative because “negative impacts would be possible for both resilient foods and AGI safety and there is no obvious reason why either would be more affected”. While this is potentially reasonable on a mathematical level, it does mean that it would be perfectly possible for resilient foods to be net harmful and the paper not correctly identify that funding them is a bad idea – simply because funding AI Risk reduction is an even worse idea, and this is the only given alternative. If the authors want to compare AGI risk mitigation and resilient foods against each other without a ‘do nothing’ common comparator (which I do not think is a good idea), they must at the very least do more to establish that the results of their AI Risk model map closely to the results which cause the AI Risk community to fund AI Risk mitigation so much. As this is not done in the paper, a major issue of interpretability is generated.

      We could have compared to the Open Philanthropy last dollar if that had been available at the time of publishing ($200 trillion per world saved or 0.05 basis points of existential risk per $billion): https://forum.effectivealtruism.org/posts/NbWeRmEsBEknNHqZP/longterm-cost-effectiveness-of-founders-pledge-s-climate. Our median for spending $100 million is ~2x10^-10 far future potential increase per dollar, or 500 basis points per $billion, or ~10,000 times as cost-effective. Ours is about 500 times as cost effective as the upper bound on that page.

      • More generally, this causes the authors to have to write up their results in a non-natural fashion. As an example of the sort of issues this causes, conclusions are expressed in entirely non-natural units in places (“Ratio of resilient foods mean cost effectiveness to AGI safety mean cost effectiveness” given $100m spend), rather than units which would be more natural (“Cost-effectiveness of funding resilient food development”). I cannot find expressed anywhere in the paper a simple table with the average costs and benefits of the two interventions, although a reference is made to Denkenberger & Pearce (2016) where these values were presented for near-term investment in resilient food. This makes it extremely hard for a reader to draw sensible policy conclusions from the paper unless they are already an expert in AGI risk and so have an intuitive sense of what an intervention which is ‘3-6 times more cost-effective than AGI risk reduction’ looks like. The paper might be improved by the authors communicating summary statistics in a more straightforward fashion.

      Figure 5 is Far future potential increase per $, which is an absolute value. That said, we acknowledge that the presentation of findings throughout could have been made more straightforward for non-expert readers and will aim to communicate summary statistics in a more accessible way in future work.

      Continuing on from this point, I don’t understand the conceptual framework that has the authors consider the value of invested dollars in resilient food at the margin. The authors’ model of the value of an invested dollar is an assumption that it is distributed logarithmically. Since the entire premise of the paper hinges on the reasonability of this argument, it is very surprising there is no sensitivity analysis considering different distributions of the relationship between intervention funding and value. Nevertheless, I am also confused as to the model even on the terms the authors describe; the authors’ model appears to be that there is some sort of ‘invention’ step where the resilient food is created and discovered (this is mostly consistent with Denkenberger & Pearce (2016), and is the only interpretation consistent with the question asked in the survey). In which case, the marginal value of the first invested dollar is zero because the ’invention’ of the food is almost a discrete and binary step. The marginal value per dollar continues to be zero until the 86 millionth dollar, where the marginal value is the entire value of the resilient food in its entirety. There seems to be no reason to consider the marginal dollar value of investment when a structural assumption made by the authors is that there is a specific level of funding which entirely saturates the field, and this would make presenting results significantly more straightforward – it is highly nonstandard to use marginal dollars as the unit of cost in a cost-effectiveness analysis, and indeed is so nonstandard I’m not certain fundamental assumptions of cost-effectiveness analysis still hold.

      In the survey, we ask about the job of spending $100 million, but then we refer to the cost per life saved paper which discusses separate interventions of research, planning, and piloting, some of these interventions such as early stage research don't cost very much money and increase the probability of success, which is why we argue marginal thinking makes sense. For instance, significant progress in the last year in prioritizing the most cost-effective resilient foods that also feed a lot of people has been achieved, this could lead to development and deployment of much more effective food production methods for such scenarios.

      Methods

      The presentation of the sensitivity analysis as ‘number of parameters needed to flip’ is nonstandard, but a clever way to intuitively express the level of confidence the authors have in their conclusions. Although clever, I am uncertain if the approach is appropriately implemented; the authors limit themselves to the 95% CI for their definition of an ‘unfavourable’ parameter, and I think this approach hides massive structural uncertainty with the model. For example, in Table 5 the authors suggest their results would only change if the probability of nuclear war per year was 4.8x10^-5 (plus some other variables changing) rather than their estimated of 7x10^-3 (incidentally, I think the values for S model and E model are switched in Table 5 – the value for pr(nuclear war) in the table’s S model column corresponds to the probability given in the E model).

      This appears to be a coincidence, the lowest 5th percentile value of all nuclear war probabilities was used, which was given by the furthest year into the future with no nuclear war. For S model this is 49 years into the future and has a value of 4.8x10^-5 and for E model this is 149 years into the future and has a value of 1.8X10^-4 (see inserted screen shots).

      S model 5th percentile of nuclear war probability per year after x years of no nuclear war (lowest probability of nuclear war per year after 49 years no nuclear war): Figure link

      image1

      E model 5th percentile of nuclear war probability per year after x years of no nuclear war (lowest probability of nuclear war per year after 149 years no nuclear war): Figure link

      image2

      Third, the authors could have done more to make it clear that the ‘Expert Model’ was effectively just another survey with an n of 1. Professor Sandburg, who populated the Expert Model, is also an author on this paper and so it is unclear what if any validation of the Expert Model could reasonably have been undertaken – the E model is therefore likely to suffer from the same drawbacks as the S model. It is also unclear if Professor Sandburg knew the results of the S Model before parameterising his E Model – although this seems highly likely given that 25% of the survey’s respondents were Professor Sandburg’s co-authors. This could be a major source of bias, since presumably the authors would prefer the two models to agree and the expert parameterising the model is a co-author.

      Professor Sandberg was not shown the S model parameters to avoid introducing bias. That said, we acknowledge that the small size of the existential risk field, and influence of several highly cited early works such as the FHI TECHNICAL REPORT Global Catastrophic Risks Survey have the potential to introduce anchoring bias.

      Parameter estimates

      Notwithstanding my concerns about the use of the survey instrument, I have some object level concerns with specific parameters described in the model.

      • The discount rate for both costs and benefits appears to be zero, which is very nonstandard in economic evaluation. Although the authors make reference to “long termism, the view that the future should have a near zero discount rate”, the reference for this position leads to a claim that a zero rate of pure time preference is common, and a footnote observing that “the consensus against discounting future well-being is not universal”. To be clear, pure time preference is only one component of a well-constructed discount rate and therefore a discount rate should still be applied for costs, and probably for future benefits too. Even notwithstanding that I think this is an error of understanding, it is a limitation of the paper that discount rates were not explored, given they seem very likely to have a major impact on conclusions.

      Thank you for highlighting this point, this is an important consideration that would make valuable future work.

      • A second concern I have relating to parameterisation is the conceptual model leading to the authors’ proposed costing for the intervention. The authors explain their conceptual model linking nuclear war risk to agricultural decline commendably clearly, and this expands on the already strong argument in Denkenberger & Pearce (2016). However, I am less clear on their conceptual model linking approximately $86m of research to the widescale post-nuclear deployment of resilient foods. The assumption seems to be (and I stress this is my assumption based on Denkenberger & Pearce (2016) – it would help if the authors could make it explicit) that $86m purchases the ‘invention’ of the resilient food, and once the food is ‘invented’ then it can be deployed when needed with only a little bit of ongoing training (covered by the $86m). This seems to me to be an optimistic assumption; there seems to be no cost associated with disseminating the knowledge, or any raw materials necessary to culture the resilient food. Moreover, the model seems to structurally assume that distribution chains survive the nuclear exchange with 100% certainty (or that the materials are disseminated to every household which would increase costs), and that an existing resilient food pipeline exists at the moment of nuclear exchange which can smoothly take over from the non-resilient food pipeline.

      Denkenberger & Pearce (2016) does not include costs post GCR and only considers R&D, and response and preparedness planning, and related costs pre disaster. This work pre disaster would likely result in expenditure post disaster being significantly lower than stored food.

      I have extremely serious reservations about these points. I think it is fair to say that an economics paper which projected benefits as far into the future as the authors do here without an exploration of discount rates would be automatically rejected by most editors, and it is not clear why the standard should be so different for existential risk analysis. A cost of $86m to mitigate approximately 40% of the impact of a full-scale nuclear war between the US and a peer country seems prima facie absurd, and the level of exploration of such an important parameter is simply not in line with best practice in a cost-effectiveness analysis (especially since this is the parameter on which we might expect the authors to be least expert). I wouldn’t want my reservations about these two points to detract from the very good and careful scholarship elsewhere in the paper, but neither do I want to give the impression that these are just minor technical details – these issues could potentially reverse the authors’ conclusions, and should have been substantially defended in the text.

      We agree that this estimate from the published work is likely low and have since updated our view on cost upwards. The nuclear war probability utilized does not include other sources of nuclear risk such as accidental detonation of nuclear weapons leading to escalation, intentional attack, or dyads involving China.

      Evaluation 2

      The Methods section is well organised and documented, but once in a while it lacks clarity and it uses terminology that may or may not be appropriate. Here’s a list of things Ii found a bit confusing:

      • Terminology
        • The submodels for food and AGI are said to be “independent”; is this meant in a probabilistic way? Are there no hidden/not modelled variables that influence both?

      In reality we anticipate that there are a myriad of ways in which nuclear risk and AGI would interact with one another. Are AI systems implemented in nuclear command and control? If so when and how does this change nuclear war probability? What will data sets used to train AI systems post nuclear exchange look like compared to present? Post nuclear exchange will there be greater pressure to utilize autonomous systems? How many/which chip fabs will be destroyed during a nuclear exchange?

      Capturing such interactions in the model in a rigorous way would have required a considerable section within the paper, which was beyond the scope of what could be included. We raised that the submodels are independent to make people aware of this simplifying assumption.

      We believe that investigating the interdependence of x-risks is an important open question that would make valuable future work.

      • The “expert” model was quite confusing for me, maybe because “Sandberg” and the reference number after “Sandberg” don’t match, or maybe because I was expecting a survey vs. expert judgement quantification of uncertainty. As I said (structured) expert judgement is one of my interests: https://link.springer.com/book/10.1007/978-3-030-46474-5

      There is an error in the referencing, this should have linked to the following guesstimate model: Denkenberger, D., Sandberg, A., Cotton-Barrat, O., Dewey, D., & Li, S. (2019b). Food without the sun and AI X risk cost effectiveness general far future impact publication. Guesstimate. https://www.getguesstimate.com/models/11691

      • In the caption of fig 2, “index nodes” and “variable nodes” are introduced. Index nodes are later described, but I don't think I understood what was meant by “variable” nodes. Aren’t all probabilistic nodes variable?

      This language comes from analytica taxonomies of the different types of nodes, this is simply describing what the nodes are in the analytica implementation. See this link for more information: https://docs.analytica.com/index.php/Create_and_edit_nodes

      • Underlying assumptions/definitions
      • The structure of the models is not discussed. How did you decide that this is a robust structure (no sensitivity to structure performed as far as I understood)

      An earlier model only considered collapse and nonrecovery of civilization as the route to far future impact. The current structure developed the structure further and is more inclusive.

      • What is meant by “the data from surveys was used directly instead of constructing continuous distributions”?

      Instead of sampling from a distribution created from the survey data, the model randomly draws a survey response value from the index of values for each of the 32000 model runs.

      It is great that the models are available upon request, but it would be even better if they would be public so the computational reproducibility could be evaluated as well.

      Links to the models are available at the following links.

      S-model: https://www.getguesstimate.com/models/13082

      E-model: https://www.getguesstimate.com/models/11691


      Editorial note

      Evaluators were asked to follow the general guidelines available here. They were also provided with this document with additional resources specific to the paper, rationale for its selection, and an ‘editorial’ first pass of aspects of the paper to consider.

      Note that this evaluation was organized during the Unjournal pilot phase and was managed manually using several Google Docs. The format may differ from future evaluations that will be managed with the Kotahi Platform.

      The evaluations were conducted on the version of the article published in the International Journal of Disaster Risk Reduction, however, we have posted the evaluations on the preprint for accessibility.

    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      RC-2022-01661

      Response to reviewers:

      Review Commons questions and Reviewers’ comments verbatim in plain text.

      Authors’ responses in bold text. Line numbers refers to numbers in the marked-up manuscript. In text citations in this document – see bibliography at bottom of this document.

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Summary: Cells within multicellular organisms are mutually dependent on each other - cells of one type or in one location provide signals that can regulate the health and differentiation of the target cells that receive those signals. Such signalling can operate bi-directionally, emphasizing the co-dependence of cells upon each other. The ovarian follicle provides an excellent model system to study intercellular signaling and its consequences, in this case between the oocyte and the somatic granulosa cells that surround it. Oocytes secrete members of the TGFbeta growth factor family that are required for normal differentiation of the granulosa cells, which in turn is necessary for normal development of the oocyte. Here the autohors show that adding TGFB-type growth factors (cumulin or BMP15) to the cuture medium during in vitro maturation increases the fraction of oocytes that can reach the blastocyst stage (improved developmental competence) and alters the pattern of protein landscape in both the (cumulus) granulosa cells and the oocyte. Changes in the mitochondria and parameters relevant to energy metabolism are also altered. They conclude that these changes underpin the acquisition of developmental competence by the oocytes.

      Major issues: The authors are world leaders in this field and therefore exceptionally well-qualified to carry out the proposed work. There are a number of issues, however, that limit the confidence with which conclusions may be drawn.

      First, the experimental strategy makes drawing inferences about the role of cumulin and BMP15 challenging. Maturing oocytes express GDF9 and BMP15 (the components of cumulin). Thus, the experiments are not comparing presence vs absence of cumulin and BMP15, but rather comparing oocytes and cumulus cells exposed to supra-physiological levels of these factors to controls that are exposed to physiological levels. In other words, the experimental setup detects changes that occur in response to higher than normal levels of the factors. Ideally, one would have complementary experiments where GDF9 and BMP15 were deleted from the system, to illustrate the effects of their absence. This would be a massive additional undertaking, however. Yet, without such experiments, relying on the results of the overexpression approach to understand the functions of cumulin and BMP15 at physiological levels is risky. RESPONSE #1 We appreciate these insightful perspectives. We apologise for not making it clear that the model used is not in fact an overexpression model. This is because, by removing the cumulus-oocyte complex from the follicle and studying it in vitro (oocyte IVM), secretion of these growth factors by the oocyte is notably compromised, so the controls are not exposed to normal physiological levels as suggested by the reviewer. This loss of normal secretion ex vivo is evidenced by: 1) in Mester B. et al _[1]_; Figure 2, we showed the mouse oocytes matured in vitro (i.e. as per the current study) are essentially devoid of the mature domain BMP15 protein, which will therefore be likewise for cumulin as cumulin contains one subunit of BMP15, and 2) mammalian cumulus-oocyte complexes explanted and cultured in vitro by IVM benefit (in terms of developmental competence) from the addition of exogenous oocyte-secreted factors such as BMP15, GDF9 and cumulin, demonstrating that they are rate-limiting under IVM conditions. We were the first to demonstrate this in 2006 _[2]_ which has been subsequently verified in many papers, including in the current paper for cumulin. The exact extent to which the controls are deficient in BMP15 and cumulin is unclear, as there are not yet reliable mouse ELISAs for these, but the model is an add-back model rather than an overexpression model. We have now added text at lines 150-152 and in the Fig 1 legend, to make this point clearer.

      Re using complimentary deletion, knock-out or antagonist-type experiments: we agree this would be ideal. However, this is likely impossible as cumulin is a non-covalent heterodimer of BMP15 and GDF9 (as first named and characterised by us: Mottershead DG et al ____[3]____). Hence, to knockout cumulin one needs to knockout either or both of BMP15 and GDF9, making it impossible to discriminate the actions of the heterodimer from the homodimers. In support of this, reviewer #3 made exactly this point, and stated “Such functional analysis cannot be done using gene knockout mouse lines…… only functional work as the one presented in this manuscript can find the mechanisms of action of these hormones”. This issue is further complicated by the fact that BMP15 and GDF9 are thought to exist as homodimers, as well as monomers, including in equilibrium in heterodimeric form as cumulin (also noted by Reviewer #3). Furthermore, there is no cumulin-specific antagonist, e.g. a cumulin-specific neutralizing antibody. Small molecule signaling inhibitors (e.g. Smad2/3 or Smad1/5/8 antagonists) certainly block cumulin actions, but therefore simultaneously also block GDF9 or BMP15 actions. Collectively, these unique (with the TGFβ superfamily) structural peculiarities of cumulin make it complex to interrogate its mechanisms of action, to the extent that others have largely focused on BMP15 or GDF9 homodimer actions only, when in reality, cumulin is likely the key natural protagonist responsible for oocyte paracrine signalling. We have added a paragraph to this effect to the discussion, at lines 417-423, including acknowledging the experimental limitations of the study dictated by having to deal with a noncovalent heterodimer.

      Second, the granulosa cells and oocytes interact throughout the prolonged period of growth, and this is the time when the beneficial effects of the granulosa cells on the oocyte have been most clearly documented. Yet the experiments focus on the much shorter period of meiotic maturation. This is when oocyte-granulosa cell interaction is being down-regulated, even if not entirely disrupted. RESPONSE #2: Indeed, oocyte-granulosa interaction is absolutely essential during oocyte growth, development and meiotic maturation, for healthy oocyte function, including the orchestrated down-regulation of oocyte-granulosa interactions during the latter phase. As pioneered by John Eppig and others, including ourselves ____[4]____ (ref has 673 citations), the master conductor of this dynamic oocyte-granulosa interaction during oocyte meiotic maturation are the oocyte-secreted factors. Hence, these factors are critical at this stage, and we maintain that this is a very important phase of oocyte development to study.

      Third, the data reported illustrate associations or correlations, but no experiments test the function of the changes in the proteome or of the changes in the morphology of the mitochondria or ER. Which if any of these is linked to the improved development of the oocytes after fertilization is unknown. Moreover, no experiments address how the growth factors cause the observed changes, which occur over a period of a few hours. RESPONSE #3 This is true. The study is already very large and has many functional experiments (e.g. oocyte respiration, oocyte MS, etc), that follow-up the findings from the proteomic analysis. Hence, the study has taken a global cellular metabolism approach, e.g. we show that cumulin downregulates oxidative phosphorylation globally, c.f. pathways within OXPHOS. We found an abundance of individual proteins altered in this period (see figure 4) and to follow up on the actions and consequences of individual proteins would: 1) at best show small incremental effects, as metabolism of such a cellular syncytium is vastly complex and inter-connected, 2) further increase the size of what is already a large study, and 3) detract from the more important wholistic effects on cumulus-oocyte complex metabolism, which must act as whole, interacting entity, to support the complexities of supporting early life post-fertilization.

      __Taken together, these issues unfortunately limit the potential impact of the work. But the amount of work required to address them would be substantial and not really feasible for this manuscript. The best route may be to present the work as an overexpression study that has identified associations, with a discussion that acknowledges the limitations of this approach. __RESPONSE #4 This is not an over-expression study – see RESPONSE #1 above. We have added text in the discussion at lines 417-423, that acknowledges the limitations of the study by the impossibility to conduct a killer knockout experiment of cumulin.

      Minor issues: The text of the manuscript should be revised in a number of places. 32: We characterized the molecular mechanisms by which two model OSFs, cumulin and BMP15, regulate oocyte maturation and cumulus-oocyte cooperativity. --Mechanistic studies were not performed. RESPONSE #5 The scope of this work was to; (a) identify global changes to protein expression, and (b) to use this data to implement follow-up experiments on some of the lead indicators, such as metabolism (respiration, small molecule metabolic markers) and cellular morphology. This work provides the groundwork, insight and rationale for future additional studies of specific mechanisms of COC interactions. As discussed at RESPONSE# 1, these studies are as close as anyone can probably get currently to mechanistic studies of a NOVEL noncovalent heterodimer, when the noncovalent homodimers are also in play, as also noted by reviewer #3 who specifically references mechanisms: “…… only functional work as the one presented in this manuscript can find the mechanisms of action of these hormones”.

      In some instances, in the interests of brevity, we made remarks based on our data, but without specifying details in the text. To redress this, we have now added specific details which illustrate and justify our statements based on the data collected (see RESPONSES #6, #7, #9 below). For greater clarity, we have also restructured our supplementary data set to cover the analysis progression from full raw proteomic data to differentially expressed proteins, to use of differentially expressed proteins in network analysis. The supplementary data set now includes the full proteomics lists for both cells and treatments (Supplementary Tables S1, S2, S3, S4), protein sequences confidently identified by both proteomic software platforms (Supplementary Tables S5, S6), differentially expressed proteomics lists for both cells and treatments (Supplementary Tables S7, S8, S9, S10), differentially expressed protein list used for the network analysis (supplementary Table S11). The Table S11 lists are intended to facilitate use by readers to perform their own analyses, if they so wish, since they can simply copy and paste the list to the on-line STRING platform. Finally, the reanalysed network analysis output, based on the differentially expressed proteins shown in supplementary Table S11, are shown in supplementary Tables S12 and S13.

      __40: Collectively, these data demonstrate that OSFs remodel cumulus cell metabolism during oocyte maturation in preparation for ensuing fertilization and embryonic development. --No mechanistic studies demonstrate this. __RESPONSE #6 There is no mention of mechanism in this sentence at line 40 and we have provided exhaustive evidence that cumulus cell metabolism is remodelled as stated (Figures 4B and 4C). For example, of the 59 upregulated proteins in the cumulus cells of cumulin treated COC (Figure 4C and supplementary Table S11), 38 (i.e. 64%) are involved in primary metabolic processes (supplementary Table S12), including amino acid metabolism (GOT2, SHMT1, CTH, MAT2B), lipid and steroid metabolism (CERS5, DHCR7, HSD17b4), aldehydes metabolism (RDH11), nucleotides biosynthesis (RRM1, GMPR2), glycans biosynthesis and protein glycosylation (UGDH, GFPT2, GALNT2), respiratory chain (mt-ND1). The cellular macromolecule metabolic process is also a significantly enriched network, involving 26 out of the 59 upregulated proteins (i.e. 44%, Figure 4C and supplementary Table S11) and includes processes such as protein complex assembly (TM9sF4, DHX30, AP2M1), RNA metabolism and mRNA processing (DDX17, DDX5, DDX39bPRPF19, PRPF6, HNRNPF, CPSF6). To help clarify the specificity of our findings, we have added this text to the revised manuscript (lines 465-474).

      __46: Oocyte-secreted factors downregulate protein catabolic processes, and upregulate DNA binding, translation, and ribosome assembly in oocytes. --No direct evidence is provided. __RESPONSE #7 The proteomic data provides direct evidence that these processes are involved. Sentence modified at lines 47-48 to be more specific re processes. Additional text has been included (revised manuscript lines 434-443) to provide specific details of the differentially expressed proteins involved in each of these processes.

      48: Oocyte-secreted factors alter mitochondrial number... --Need to establish that the MitoTracker is a suitable tool to measure the number of mitochondria. RESPONSE #8____ We recognise that total mitochondrial uptake of the MitoTracker Orange dye could be a reflection of either mitochondrial function (polarity) and/or mitochondrial number, given the manufacturer’s (Thermo Fischer) statement that “MitoTracker™ Orange CMTMRos is an orange-fluorescent dye that stains mitochondria in live cells and its accumulation is dependent upon membrane potential”, as we specified in several places in the original manuscript (Lines 354-355, 366-367 and 235 of the marked up manuscript version) . However, we agree that in several places in the manuscript we also indicated that MitoTracker was being used as a measure of mitochondrial number. To avoid this ambiguity, we have made some clarifications in the text (revised manuscript lines 235, 351-352, 377, 481-482, and in Figure 5B legend). Given the extensive and diverse metabolic changes indicated by the proteomic data, our aim was to explore the potential role of mitochondria in response to cumulin and BMP15 treatment of COCs, which we did by use of EM morphology studies (figure 5A), mitochondrial respiration (figures 6B and 6C), quantification of energy metabolites, such as ATP, NAD and related compounds, by mass spectrometry (figure 6D), metabolites identified in multispectral unmixing studies (figure 7) and mitochondrial function using MitoTracker (figure 5B). Collectively this data suggested a modest downturn of energy metabolism, particularly in cumulin treated COCs. This downturn did not cause a change in net energy charge in COCs (figure 6D) despite a reduction in redox ratio in both cells (figure 7A and 7B) and respiration in COCs (Figure 6B and 6C), and could reflect adaptive changes in response to cumulin and BMP15, reflecting metabolic plasticity/Warburg effect, as explained in the discussion (revised manuscript lines 453-551).

      79: ...for maintaining genomic stability and integrity of the oocyte... 83: ...minimizing secondary production of potentially DNA damaging free radicals. --Please provide supporting references from the literature. RESPONSE #9 References have been added (lines 82 and 85 of the revised manuscript)

      373: This study provides a detailed exploration of the mechanisms by which oocyte-secreted factors... --No mechanistic studies were performed. RESPONSE #10 We respectfully disagree. One of many mechanisms we have studied here is OXPHOS. We have shown this is how OSFs change metabolism – that is a mechanism. As discussed at RESPONSE #1, these studies are as close as anyone can probably get currently to mechanistic studies of a noncovalent heterodimer, when the noncovalent homodimers are also in play, as also noted by reviewer #3 who specifically references mechanisms: “…… only functional work as the one presented in this manuscript can find the mechanisms of action of these hormones”. Please also refer to the comments in RESPONSE #5.

      383: Collectively, these data demonstrate that oocyte paracrine signaling remodels COC metabolism in preparation for ensuing fertilization and embryonic development. --Studies do not show that the differences observed between control and treatment groups are related to fertilizability or embryonic development. RESPONSE #11 The data in Fig 2C, 2D show exactly that; that the difference between control and treatment (cumulin) is an increase in embryonic development. It does not show fertilizability, so we removed that at lines 41 and 415.

      396: suggesting that cumulin affects meiosis in the oocyte and may increase meiotic fidelity... --This statement is highly speculative. RESPONSE #12 We accept this critique - reference to meiosis and meiotic fidelity removed, line 435 (revised manuscript).

      409: ...lacks the machinery for amino acid uptake... --Is the oocyte unable to take up any amino acids or only certain amino acids? RESPONSE #13 Thank you for noting this as this sentence is too absolute. Oocytes have a very poor capacity to take up most or even all AAs, which are instead supplied to the oocyte via cumulus cells. Sentence modified at lines 455-456 to be less absolute.

      In general, the manuscript is written clearly. However, in several places, technical terms or jargon will make tough going for readers who are not already familiar with the techniques being used. These should be explained using language that will be understood by journal readers who are unfamiliar with the details of the techniques. Examples include:

      51: define metabolic workload using scientific terms.

      RESPONSE #14____ “metabolic workload” rephrased to “metabolic processes”. Lines 52-53.

      67: metabolically 'inept' requires more precision. RESPONSE #15 “metabolically inept” rephrased to “metabolically dependent on surrounding granulosa cells” ____[5]____. Line 69

      262: explain 'multispectral analysis' RESPONSE #16 A citation has been added, which explains the technique (ref ____[6]____ at the end of this response letter, which is the same paper as citation [34] in the revised manuscript; lines 111 and 217; revised manuscript). A detailed explanation of this technique has also been added in the supplementary information, under the section “Multispectral microscopy”.

      268: how is 'limited' overlap defined. RESPONSE #17 The phrase “distinct profiles, with limited overlap between…” has been rephrased to “distinct profiles, between…” (line 279 of the revised manuscript), as the main point is that the patterns/profiles across treatments are different, and we did not quantify the extent of overlap.

      318: define higher workload RESPONSE #18 the phrase “…implying a higher workload for both organelles” has been replaced with a more specific explanation; “We suggest that such changes in morphology may be related to the remarkable increase in a diversity of metabolic processes which we observed (Figure 4C and supplementary Table S12), since ER morphology and architecture is known to be highly dynamic in response to environmental and developmental factors which affect cells” ____[7]____ (Lines 342-345).

      324: provide documentation or citations to support the assertion that the intensity of MitoTracker staining is an accurate proxy for the number of mitochondria.

      RESPONSE #19____ Please refer to explanation under RESPONSE #8

      358: Multispectral discrimination modelling utilised cellular image features from the autofluorescent profiles of oocytes and cumulus cells. --Please clarify this merthodology and provide support for its utility.

      RESPONSE #20____ The supplementary information section (Multispectral microscopy, lines 239-258) has been expanded and clarifications provided as to the wavelengths of the channels, the features used and the unsupervised nature of algorithms.

      360: intersection of union of 5-22%

      RESPONSE #21____ This is a measure of the extent of overlap of data distribution for each class (treatment), i.e. of how different they are. The ellipse (Fig 3D) represents one standard deviation around the central mean value for that data set. The overlap of these ellipses is quantified by their intersection over union (IoU) value, which is the ratio of the area of the two-ellipse intersections, divided by the area of their union (the shape created by their overlap being treated as creating one continuous object). IoU values range from 0 to 100% for fully separated and fully overlapping, respectively. Hence, a 5% IoU represents a low level of overlap of data distribution between treatments. Brief explanatory text has now been added at line 387-388.

      Comments on Figures. Fig. 3A, B. The total number of proteins and the number of differentially expressed proteins among the treatment groups don't match between A and B. For example, A (Mascot-Sheffield) indicates that 17 proteins were differentially expressed between untreated and cumulin-treated oocytes. B shows (138 + 74) expressed un the untreated but not cumulin-treated and (156 + 87) expressed in the cumulin-treated but not untreated. Please account for this difference. RESPONSE #22 The panels in Fig 3A and Fig 3B each contain different representations of the information contained within the proteomics dataset, and explain different aspects of the data. The Venn diagram panels in Figure 3B display the level of overlap of specific proteins identified in each cell, treatment and software subgroup. The degree of overlap in each cluster is high (i.e., 76 – 78% for Mascot/scaffold and 95 - 97 % for PD2.4) as would be expected within the same cell type and analysis approach, where the main variable is cell treatment. We agree that the total numbers in the Venn diagrams did not exactly match the total numbers in Figure 3A, which likely resulted from using slightly different parameters during data processing. We have now used exactly the same data set in panels A and B (the full PD2.4 and Mascot/scaffold datasets are shown in the supplementary proteomics summary Excel spreadsheet), so that total numbers are now identical, and will hopefully avoid any confusion in comparing across panels. However, the main conclusion to be drawn from Fig 3B remains unchanged, in that it shows that by far the majority of identified proteins overlap between treatments (control, BMP, cumulin), regardless of cell type or data analysis approach. However, it should be noted that Figure 3B has no information about protein fold change/differential expression, and only represents numbers of proteins confidently identified, and the level of overlap of identified proteins between treatments. Only panel 3A shows differential protein expression relative to the respective control groups.

      Fig. 3D. What do the circles represent and how were their parameters (size, position) established? RESPONSE #23 The separation of data distributions for each class is shown by an ellipse for each cluster, which encompasses one standard deviation around the central mean values. This text has now been added to the Fig 3 legend.

      Reviewer #1 (Significance (Required)): These studies identify changes in cumulus cells and oocytes that occur in response to addition of cumulin or BMP15 to the culture medium during in vitro maturation. While the data are new, the significance of the advance is limited by (i) the fact that the control group were exposed to physiological levels of GDF9 and BMP15, so this is essentially an over-epxression study and (ii) no mechanistic studies experimentally tested how the observed changes (eg, in quantity of a specific protein) affect the developmental potential of the oocytes or cumulus cells. RESPONSE #24 We thank the reviewer for their perspectives however we respectfully disagree on all accounts. We have rebutted these 2 concerns: point (i) at RESPONSE #1, and point (ii) at RESPONSE #5 above.

      Reviewer expertise: growth and meiotic maturation of the mammalian oocyte

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary: The report by Richani et al, presents a research carried out in mice, in which they treated cumulus-oocyte complexes with either BMP15 and cumulin. Upon treatment they evaluated a series of biologically relevant parameters in oocytes and cumulus cells. Their findings indicate that the treatment with these molecules alter the molecular composition of oocytes and cumulus cells (proteome and metabolome), mitochondrial morphology in cumulus cells and overall oxygen consumption in COCs.

      Major comments: - Are the key conclusions convincing? - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? * part of the discussion related to metabolic pathways being up regulated due to the treatments need to the revised because For instance, It is hard for me to grasp how a pathway with 2 proteins achieved FDR significance below 0.01, as I see in figure 4c

      RESPONSE #25____ Network enrichment was performed using the open access software STRING ( ____https://string-db.org/ [8]____), and we have now provided additional information on how we utilised STRING in the supplementary information section, under “Gene Ontology Network Enrichment Analysis” (lines 176-217). STRING utilises information available in the Gene Ontology (GO) database ( ____http://geneontology.org/docs/ontology-documentation/____ ) to determine; (a) how many of the differentially expressed proteins identified in the proteomics experimental data fall into specific networks, (b) how much enrichment this represents relative to a random network of the same size, and (c) whether the enrichment is statistically significant based on the FDR statistic. The size of each GO network within the background set (whole genome or other) will therefore be a major determinant of whether the number of proteins identified in the proteomics experiment represents significant enrichment of a particular network. A few proteins identified within a small background network will represent greater enrichment (and lower FDR score) than the same number of proteins in a much larger network. In fact the “count in network” is often approximately the inverse of the enrichment strength (see supplementary Table S12, within the supplementary dataset Excel spreadsheet). Note that only significantly differentially expressed proteins were used for the network analysis presented in this paper, so even in the case where just 2 proteins are significantly enriched in a network (e.g., “Farnesyl diphosphate metabolic process” identified in the GO biological process section of BMP15 treated cumulus cells) they represent two upregulated proteins in a small network, so the functional/biological significance of this is likely quite high.

      In revision of the manuscript we noticed that we had likely originally used the full lists of differentially expressed proteins for network analysis, rather than separating up and downregulated proteins as intended. Furthermore an updated version of STRING is now available, with improvements in the method of correction for multiple testing within the FDR output (STRING version 11.5, current since August 12, 2021). We have therefore revised the STRING network analyses, and have provided a list of the STRING input proteins (supplementary Table S11), STRINGv11.5 gene ontology (GO) functional enrichments for up and downregulated proteins in BMP and cumulin treated cumulus cells and oocytes respectively (supplementary Tables S12 and S13), and replaced the very large Figure 4C and D heatmaps (submitted version) with a summary (new Figure 4C; revised version). The updated heat maps can still be viewed in supplementary Tables S12 and S13 (the heatmaps now being the updated ones, deriving from our review response).

      * In the discussion the authors use the term "oocyte secreted factors" a lot (one example lanes 490, 515, 516, 517), but they should specify BMP15 and cumulin, because these were their treatments. *Including in the title, you did not evaluate all oocyte paracrine factors, just BMP15 and cumulin RESPONSE #26 “Oocyte secreted factors (OSFs)” replaced with BMP15 and cumulin throughout the manuscript where we refer specifically to our treatments, results or discussion of results, except where we refer to “these OSFs” (eg line 34), and not where we refer to the principal of OSF signalling more generically. Re the latter, hence we wish to retain the title as is, as BMP15 and cumulin are prototypical oocyte secreted factors, as the title refers to the principal more generally.

      - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. NA

      • Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. NA

      • Are the data and the methods presented in such a way that they can be reproduced? *no, in some instances, the methods are not described, see my comment below about enrichment analysis. RESPONSE #27 Addressed next below

      • Are the experiments adequately replicated and statistical analysis adequate? *I was not able to access enrichment analysis.

      RESPONSE #28____ The method of Network Enrichment is now described in more detail in the supplementary methods section. See previous explanation under RESPONSE #25 above.

      *lines 241-242: "MitoTracker staining and data from metabolite analysis by mass spectrometry were analysed by one-way ANOVA with Tukey's (parametric data) or Kruskal-Wallis (non- parametric data) post-hoc tests. " Specify which test was used for which data RESPONSE #29 Post-hoc test for MitroTracker data was Tukey’s, as already stated in Figure 5 legend. Post-hoc test for metabolite analyses was Kruskal-Wallis – text now added to Figure 6 legend.

      Minor comments: - Specific experimental issues that are easily addressable. NA

      • Are prior studies referenced appropriately? Yes

      • Are the text and figures clear and accurate? *lines 178-180: "expressed proteins list was further analyzed using STRING software to explore clustering and enrichment of specific molecular functions, and biological pathways. Detailed methodology and rationale for this approach is provided in the supplementary methods." I did not read text in the supplementary materials indicating how enrichment analysis was carried out.

      RESPONSE #30____ Our apologies for this oversight. We have now provided additional information on how we utilised STRING in the supplementary information, in a new section titled “Gene Ontology Network Enrichment Analysis” (lines 176-217).

      * What was the concentration of treatment for the samples used for proteome and mascot/scaffold experiments?

      RESPONSE #31____ The two bioinformatic analyses were conducted on common biological samples, so naturally the treatment concentrations were also the same. Text modified at line 175 to make this clearer.

      * lanes 263 and 264: "Cell types and treatment conditions can be clearly distinguished based on these orthogonal global approaches." I did not see what is the basis for this statement

      RESPONSE #32____ The sentences immediately following this (i.e. lines 274-281) elaborated the basis for this statement, particularly where it is explicitly stated “____Proteomic heat maps (Fig. 3C) and multispectral analysis plots (Fig. 3D) both show distinct profiles, between controls, BMP15 and cumulin treated COCs, in both cell types.____”, at lines 277-281.

      The data for the two global approaches are shown in Figure 3C (heat maps generated by PD2.4 comparing differences in protein abundance across treatments, shown separately for cumulus cells and oocytes), and Figure 3D (linear discriminant analysis comparing differences in multispectral imaging data across treatments, shown separately for cumulus cells and oocytes). Both of these global analyses show clear differences in distribution pattern between controls (untreated) and treated samples (BMP15 and cumulin), in both oocytes and cumulus cells. The approaches are (a) global, since each relates to analysis of the complete cell extracts (as opposed to targeting a specific component/analyte), and (b) orthogonal because different and unrelated measurement techniques are used i.e., proteomics (mass spectrometry) and multispectral imaging (spectroscopy).____ *I did not understand the discrepancy between the numbers observed in Figure 3A and Figure 3B.

      RESPONSE #33____ Refer to RESPONSE #22 above. We have checked the data, and revised the Venn diagrams (Figure 3B) with data analysed using identical parameters, for both Figures 3A and 3B, to avoid confusion over protein numbers. We also noticed and corrected a discrepancy with regard to the number of differentially expressed oocyte proteins under the merged data column of Figure 3A.____ *I could not make sense of the shades of green or red that were used in 4C and 4D. Is the reader only supposed to make those comparisons within column? RESPONSE #34 Note: Figures 4C and 4D are now Supplementary Tables S12 and S13. The red shades represent network enrichment analysis of upregulated proteins, while the green shades represent network enrichment analysis of downregulated proteins. The colour gradients in each case follow the numerical values for “count in network”, enrichment strength, and lower FDR, with greater colour intensity for higher numbers (and lower FDR). However, we agree that the original four panels (A, B, C and D) comprising figure 4, made for a very large and potentially overwhelming figure. To simplify the data presentation we have reprocessed the data in STRING (see details under RESPONSE #25 above) and have moved the now considerably shorter network lists (originally displayed as Figures 4C and 4D) to supplementary Tables S12 and S13, and the new Figure 4C provides a network enrichment summary instead. This is likely easier to comprehend, with the marked contrast in networks identified between oocytes and cumulus cells easier to see. The numbers of up and downregulated proteins on which the network analysis is based are also shown in Figure 4C, while the specific proteins used and networks identified are shown in supplementary tables S11, S12 and S13 (original colour coding retained, and also explained within each table). - Do you have suggestions that would help the authors improve the presentation of their data and conclusions? *Figure 4 is really hard to process. At least in my pdf it spanned 4 pages.

      RESPONSE #35____ Indeed Figure 4 was large and has now been shortened. We made considerable effort to attempt to present in Fig 4 the vast amount of proteomic data in a summarized, hopefully comprehensible fashion. We have now moved Figs 4C and 4D to the supplementary, and replaced it with the simplified new Fig 4C (tabular format). Pease also see comments under RESPONSES#25 and #34 re this. *I did not understand why put networks that are not significant as up-regulated or down-regulated. Besides, as mentioned above, I do not know how significance was assessed.. RESPONSE #36 Network analysis was performed using only those proteins which were significantly differentially expressed and had a consistent direction of fold change in both mascot/scaffold spectral counting and PD2.4 peak intensity proteomics quantitative approaches. Proteins with no significant expression change (i.e., the majority of proteins, which represented proteins with __Reviewer #2 (Significance (Required)):

      • Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. - Place the work in the context of the existing literature (provide references, where appropriate). *This paper is significant because it provided a variety of measurements following the treatment of cumulus cells with BMP15 and cumulin. The authors show that these two oocyte factors can impact the molecular structure, physiology and structure of organelles in cumulus cells. The work is well contextualized with the current literature. RESPONSE #37 We thank the reviewer for these positive remarks.

      • State what audience might be interested in and influenced by the reported findings. *Researchers in the field of developmental biology would be most interested in this report.

      • Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. * I do not have expertise in hyperspectral analysis. I have been working with cumulus-oocyte complexes for over a decade, mixing technologies in cell biology, microscopy, high-throughput genome, and proteome analysis. We do all our bioinformatics work in-house.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      The work is interesting. Cumulin is a heterodimer hormone formed of GDF9 and BMP15. It is the main oocyte secreted factor. Being an heterodimer, gene knockout provides very little information about its mechanism of action. The team has a unique form of cumulin that is stable. This is why I think this work is important. However, I found two technical issues: one regarding mitochondrial count using MitoTracker and the other about comparing gene lists between the two cell types when protein input submitted to mass spectrometry differ between the two cell types. It is expected to find more with more input material. The text would need to be adjusted accordingly. Also, there is a lot of free statements and a lack of precision that is annoying. In my opinion, there are many overstatements that are not supported by the data because the work was not designed to test what is stated. The Discussion is very circular as the same statements come back on the next pages. RESPONSE #38 See specific responses below

      Detailed review:

      The manuscript entitled "Oocyte and cumulus cell cooperativity and metabolic plasticity under the direction of oocyte paracrine factors" reports an in depth analysis of the exposure of cumulus oocyte complexes to either BMP15 or cumulin, the GDF9-BMP15 heterodimer. Impact assessment was done by determining developmental competence of the exposed oocytes, comparative profiling of the proteomes and spectral emissions as well as testing a potential impact at the ultrastructure level by electron microscopy imagery. Mitochondrial respiration as well as abundance of related metabolites was contrasted between the two treatments.

      Overall, the work is interesting. It is very difficult to study hormonal heterodimers because they originate from two different genes and they can naturally be found in a monomeric as well as a dimeric state. Such functional analysis cannot be done using gene knockout mouse lines. Genetic disruption provided the background that GDF9 and BMP15 are key oocyte secreted factors however only functional work as the one presented in this manuscript can find the mechanisms of action of these hormones. RESPONSE #39 We thank the reviewer for these positive comments, especially in relation to the difficulty of getting to the mechanism of actions of a non-covalent heterodimer, and hence the importance of functional experiments in providing mechanistic insights.

      Comments:

      I really appreciated the reference to auto-symbiosis. We often see the reference to a cellular syncytium but this one is interesting. RESPONSE #40 Thank you.

      Although I appreciated the work, two important technical issues (between cell types comparisons and mitochondrial count) have been raised and there is a bit of unnecessary overselling throughout the manuscript. Sticking to the results would keep the value of the work high and wouldn't give that impression of overstatement. RESPONSE #41 Technical issues – see responses below, as well as responses to other reviewers. We have provided additional methodological information for greater clarity, and added specific observations from our data, to support all statements, to avoid the impression of unsubstantiated overstatements.

      Technical issues:

      While the gene/protein enrichment analysis can be influenced by the input material submitted to mass spectrometry, the gene network analysis is influenced by the number of gene/proteins available for the enrichment analysis. It is thus difficult to compare both cell types. RESPONSE #42 We agree that shorter protein lists might be expected to result in fewer networks. However, it is interesting to consider the possible reasons for the shorter list:

      (1) In our case the amount of protein extracted from oocytes (2-3____m____g) was much less than from cumulus cells (15-17____m____g) as explained in the “Mass Spectrometry for proteomic analysis” section in the Supplementary Information. This is because COCs have many more cumulus cells than oocytes by number as well as total mass. Consequently it was possible to load a larger ____m____g amount of total peptides from cumulus cells onto the nanoLCMSMS system, but it should be noted that on-column loading is not only determined by the total amount of material injected, but also by the limits in capacity of the C18 peptide capture cartridge upstream from the column (which is 1 – 1.5 ug estimated from the binding capacity of C18 with a bed volume of 0.35____m____L, since the trap cartridges have dimensions of 300____m____m ID and 5mm length; ____http://tools.thermofisher.com/content/sfs/manuals/Man-M5001-LC-Nano-Capillary-Micro-Columns-ManM5001-EN.pdf____ and ____https://www.optimizetech.com/opti-pak-trap-columns/____ ). Consequently, the different initial loading of oocyte vs cumulus cell proteins/peptides are likely to have made little if any contribution to proteome coverage, since 2-17____m____g all exceed the trap cartridge binding capacity, and consequently 1 - 1.5____m____g was captured and transferred to the nano-column, while the excess was transferred to waste. Based on the capacity limits of the capture cartridge, there was likely enough peptides/proteins in both oocyte and cumulus cell extracts to reach the saturation point, and therefore much more consistent on-column loadings than the initial ____m____g loadings would imply. We have added some additional information re this to the method section (see the section “Mass Spectrometry for proteomic analysis” in the Supplementary Information).

      (2) The expressed proteomes of different cell types may be expected to differ not only in specific proteins expressed but also in the number of different proteins. In a recent study by Marei et al ____[9]____, equal amounts of total protein (9.5ug) from bovine oocytes and matching cumulus cells were prepared for their proteomics comparisons, and interestingly these authors also report about half as many proteins identified in oocytes as compared with cumulus cells, despite equal amounts of total protein used; “A total of 1703 and 1185 proteins were identified in CCs and oocytes, respectively, 679 of which were common.” Furthermore, a transcriptomic study of bovine oocytes and cumulus cells by Moorey et al ____[10]____, showed 69 and 128 differentially expressed genes (DEGs) in oocytes and cumulus cells respectively (comparing small vs large cells in each case), pointing to about double the differential gene expression in cumulus cells than oocytes, again implying a larger cumulus cell vs oocyte transcriptome. Our data support these observations, which collectively suggest a real difference in proteome size between oocytes and cumulus cells. If the difference in proteome size is real, then differences in network enrichment are also likely to have biological relevance, despite differences in size of the differentially expressed proteins lists.

      (3) Even if initial protein loading was a contributing factor to the size of the oocyte vs cumulus cell proteomes, it is of note that we observed approximately 2 fold fewer total proteins identified in oocytes as in cumulus cells (Figure 3A, 3B and new Figure 4C), yet the difference between number of identified networks is multiple-fold (a cumulative total of 2 networks identified in BMP15 and cumulin treated oocytes vs 143 networks identified in BMP15 and cumulin treated cumulus cells – see new Figure 4C). Furthermore, there does not seem to be a strictly linear relationship between the number of proteins submitted for network analysis and the numbers of networks identified. For example, 34 upregulated proteins in cumulin treated oocytes identified a single enriched network, while a similar number of 38 upregulated proteins in BMP15 treated cumulus cells, identified a total of 42 networks (new Figure 4C), and similarly cumulin treated cumulus cells had 59 upregulated proteins and 58 downregulated proteins, which resulted in 57 and 23 enriched networks respectively.

      Also, when performing GO terms analysis, the level of "branching" can explain the results. In other words, GO terms are organized in a tree like structure where general elements (e.g. nucleus) are delineated in finer elements (e.g. nuclear function) leading to finer ones (e.g. DNA binding)... to finer ones (e.g. DNA repair)... etc. The number of genes/proteins available in the initial list directly dictates to which level of precision the analysis can reach. In the present work, the number of identified network may simply reflect the number of elements available in the initial lists. With more info on the cumulus cells side, it is logical to be able to reach finer branches that contain only a few genes. I have looked in the supplemental data files but could not find more info about the background used. Was it all known proteins? Was it all identified proteins where the differentially expressed proteins are compared to the detected proteins? Using the list of detected proteins as background for the analysis could help. Proteome Discoverer generated much less differentially expressed proteins between treatments than Mascot/Scaffold (2-17 vs. 74-390). Maybe use the Mascot/Scaffold data using the same number of top genes (e.g. 87) between both cell types. Then it would be much more comparable. RESPONSE #43 Please also refer to the explanations under RESPONSE #34 and #42 above. We have added an additional explanation of how we performed the enrichment analysis, in the supplementary information section under the heading “Gene Ontology Network Enrichment Analysis”. In the data presented here we used the whole mouse genome as our background set. The number of total proteins identified by Mascot/Scaffold and ProteomeDiscoverer were similar, but actually considerably more differentially expressed proteins were identified using ProteomeDiscoverer (Fig 3A), as expected using peak intensity vs spectral counting ____[11]____. The spectral counting approaches usually identify fewer differentially expressed proteins, but also with a lower quantitative false positive rate, while peak intensity approaches tend to identify more differentially expressed proteins, but with a higher quantitative false positive rate ____[11]____. Our reasoning was therefore to combine proteins which vary in common across both platforms, to maximise the differentially expressed proteins list while simultaneously minimising the quantitative false positive rate. We thank the reviewer for the suggestion of using our full protein list as the background set. Initially we revised our network enrichment analysis (see comments under RESPONSE #25) still using the mouse whole genome, resulting in fewer overall networks, but improved contrast between oocytes and cumulus cells (see summary in new Figure 4C, and network analysis details in supplementary Tables S12 and S13). We then repeated the network analyses using our full protein list (4450 proteins identified in both oocytes and cumulus cells; see background list in Supplementary Table S11) as the background set. With this we similarly found no enriched GO networks for BMP15 and cumulin treated oocytes, and only 6 and 1 enriched network in BMP15 and cumulin treated cumulus cells. We suggest that detecting network enrichment against a cell specific background list may not give us the same level of “contrast” as can be achieved when comparing against the whole mouse genome.

      Line 226 and 324-328 and line 350: I have never seen the use of MitoTracker Orange to count mitochondria. According to the manufacturer: "MitoTracker Orange CMTMRos is an orange-fluorescent dye that stains mitochondria in live cells and its accumulation is dependent upon membrane potential. The dye is well-retained after aldehyde fixation." It is indicative of mitochondrial potential but it is not a method to count the number of mitochondria within a cell. I do not agree that more fluorescence means more mitochondria. RESPONSE #44 We agree and in places we used ambiguous language re mitochondrial function vs mitochondrial number. We have now clarified and corrected this - please refer to detailed comments and manuscript changes under RESPONSE #8.

      I understand that the MitoTracker data is counterintuitive to the oxygen consumption rate and stable levels of energetic metabolites. However, as the authors mention, mitochondria are known to be capable of switching from aerobic to anaerobic energy production. In some cases, heterogeneity in the mitochondrial population (such as the one in the oocyte) could mean that a mosaic respiratory potential exists where some mitochondria are more aerobic than others... To change the number of mitochondria, either fission or mitophagy must occur. Although mitochondrial DNA replication is done in approximatively 2 h and fission/division can occur over 1 h, and protein ubiquitination is done over 12 h-18 h during mitophagy, TEM micrographs (figure 5) do not show elongated mitochondria in the process of division. To detect active mitophagy, protein markers and association with lysosome would be needed. A shift in mitochondrial number may not be the suitable interpretation of the data. RESPONSE #45 Please refer to comments under RESPONSE #8

      For the spectral data analysis (Figure 3D), how did the three replicates perform? The figure does not show the replication variance relative to the treatment variance. RESPONSE #46 A version of Figure 3D but with the replicates colour-coded has been added to Supplementary Material (Supplementary Figure 2) and the manuscript text has been revised with the information that data from the three replicates are shown, added to the caption to Figure 3D.

      Wording/interpretation issues

      Lines 114-116: "This intercellular cooperativity facilitates oocyte maturation while simultaneously protecting germ-line genomic integrity, in a manner which could not be achieved by a single cell." This is an overstatement because genomic integrity was not assessed. Why consider that the nuclear function found in the proteome contrast is necessarily associated with genomic integrity. Miosis requires in dept chromatin handling. What evidence provided from the results is associated with cellular numbers. The presence of cumulus cells is known to support meiosis but it doesn't mean that some of the cellular processes have been imparted to the surrounding somatic cells. The work done for this manuscript does not test any of this claim. RESPONSE #47 We accept this point and agree that, especially the claim re germ-line genomic integrity, is an overstatement. This has been removed. We maintain however that there is ample evidence in our results that there is clear inter-cellular metabolic cooperativity between oocyte and cumulus cells and that this ultimately leads to an oocyte with improved developmental competence. The sentence has been modified to reflect this, line 117-118.

      On numerous occasions, the statements are imprecise. For example: Line 274: "More than double the number..." Since doubling a minute value does not mean the same thing as doubling a large value, values, measurements with units and ideally with SEM should be added. RESPONSE #48 Has been rephrased (see line 284 of the revised manuscript)

      Line 287: "... and almost a third more significant networks..." Please add values. RESPONSE #49 Section has been deleted (line 291-300 of the revised manuscript)

      On the same statement, since sample input material to the mass spectrometry is vastly different between cumulus cells and oocytes, is it truly comparable? Could these differences between the two cell types be associated with the amounts of proteins in the extracted samples? Typically, more variable results are obtained with the low input. It sometimes lead to apparently more difference between treatments simply because of low count numbers. On line 292, authors mentioned that protein loading was considered. How was that done? Low input cannot be compensated or normalized. The following statement on line 293 indicate that more proteins were identified in cumulus cells. This is probably due to more input material submitted to mass spectrometry. It is not necessarily a difference in protein diversity between cumulus cells and oocytes. RESPONSE #50 Please refer to detailed explanations under RESPONSES #42 and #43

      Line 293: "... resulted in the identification of about double the number..." Please add values. RESPONSE #51 Values added at lines 305-306, and additional detail has been added to this section of the manuscript (lines 305-317 revised manuscript). Line 294: "However, there were 4-5 times as many differentially expressed proteins..." Please add values. RESPONSE #52 Values added and additional detail added to this section (new lines 309-312 of the revised manuscript).

      Line 298: "...difference was quite marked..." More factual info should be added. RESPONSE #53 Values added and additional detail has been provided (lines 314-317 of the revised manuscript), as follows; “____Cumulin appeared to have a greater impact on proteomic differential expression in both cell types than BMP15 did, with 59 vs 38 and 34 vs 27 upregulated proteins in cumulin vs BMP15 treated cumulus cells and oocytes respectively, and similarly 14 vs 6 downregulated proteins in cumulin vs BMP15 treated cumulus cells and oocytes respectively (Figure 4C)”.

      Line 305: Again, the whole comparison between cell types could be argued from the standpoint of input material subjected to the analysis. Given the point is to state that cumulin has a profound impact on cumulus cells, maybe it is not necessary to compare with the oocyte data. It is logical that an oocyte secreted factor targets the neighbouring cells. The point can be made without raising the question about the potential issue of input material. RESPONSE #54 We agree with the reviewers point that it is logical that OSFs should target cumulus cells, with lesser impact on the oocyte. Nonetheless the treatments were performed on COCs, and even though the OSFs are targeting the cumulus cells, however ultimately the cumulus cells response is expected to impact oocytes. Therefore, it is relevant to look at proteomic changes to both cell types and also the related network analysis. We have however rephrased this section, to be more specific as to which data we are reporting, and have included additional citations (lines 325-334 of the revised manuscript).

      __Line 317-317: "... exhibited more rounded and swollen mitochondria..." How was that determined? In the periphery of the oolemma, mitochondria aggregates in clusters which can be quite different from one another. Maybe proportions of different shapes of mitochondria could be provided if enough mitochondria are counted from the EM micrographs. __RESPONSE #55 These are subjective observations of the typical morphological features seen in response to the different treatments. This is the typical application of TEM. Quantitative features of mitochondria are better assessed using confocal than TEM, which is the complimentary approach we took using MitroTracker in the companion figure 5B, the text for which immediately follows the TEM results. We altered the text at the sentence in question to note that these are subjective observations (line 340).

      Line 169: What do you mean by "The results were merged based on consistency..."? This seems to be a trivial way to analyse the data. RESPONSE #56 The majority of published papers reporting data dependent analysis (DDA) proteomics results utilise just a single quantitative method (i.e., either spectral counting or peak intensity). This certainly simplifies reporting, and avoids confronting uncomfortable discrepancies between different analytical approaches. However, we reasoned that robust expression change data would maintain consistency, despite the orthogonal quantitative methods. We consider it a notable strength of the approach used here that we have utilised a differentially expressed proteins list which includes only those proteins with consistent direction of fold-change in both the spectral counting and peak intensity workflows. Please also refer to comments under RESPONSE #43, re spectral counting vs peak intensity quantitative methods in data dependent analysis (DDA) proteomics.

      Line 170: "A further requirement was that at least one, if not both methods..." Again, when did you decide to use one method or to use both? Why not use the common ground from both methods? RESPONSE #57 Refer also to RESPONSE #43. In fact the main question being asked in many/most proteomics experiments is whether there is a real expression change between treatment groups. Therefore fold-change is the most pertinent common ground across disparate quantitative methodology, and indeed commonality of fold-change was the basis for merging the datasets. Since integrating peak areas is a very different approach to counting the number of spectra, then this difference in approach can make a big difference to the p-values, and is the reason why spectral counting is less sensitive to detect differential expression. For similar reasons the fold-change ratio may differ somewhat between these quantitative methodologies. However direction of fold-change is a minimum requirement for demonstrating consistent trends, hence we used this as the common ground for merging the datasets.

      Line 384: Is the paracrine signaling remodeling COC metabolism or is it enhancing the rate at which it is done? I believe this switch in metabolism occurs in untreated COCs. RESPONSE #58 We see the reviewers point in this subtle difference in wording. We agree that there is a switch in metabolism in untreated COCs during maturation – our point is that that process of changing metabolism is further remodelled by oocyte paracrine signals, to the overall betterment of the oocyte in terms of competence. We have edited this sentence to make this point clearer (line 413-415). Our data on energy charge, respiration, energy metabolite levels (Figure 6), redox potential (Figure 7) and mitotracker intensity (Figure 5) are all presented in comparison with “untreated” cells, and our conclusion that there is remodelling of metabolism is therefore relative to “untreated” COCs.

      __ __The Discussion is somewhat circular. Section will need to be adjusted if the Mitotracker-based mitochondrial count and between cell types gene/protein lists comparisons are removed.

      Accounts for mitochondrial counts: (lines 387-393) (lines 424-427) (line 463).

      RESPONSE #59 All reference to Mitotracker in the context of mitochondrial counts only have been altered to Mitotracker being an indicator of mitochondrial function/polarity and/or counts. Accounts for comparisons of gene lists length between cell types: Lines 389-391 and 475-477 and 496-499). RESPONSE #60 Please see comments under RESPONSE # 53 and the new Figure 4C.

      Line 395: "... a substantial number of oocyte upregulated proteins... Please provide number. RESPONSE #61 Additional specific proteins have been listed to support our claims of effects on specific processes (see lines 435-443 of the revised manuscript). Also see comments under RESPONSE # 7.

      Line 397: The data was not designed to test the potential of cumulin to preserve meiotic fidelity. This is an overstatement since DNA binding is part of the normal course of even during meiosis. Again, cumulin could accelerate the kinetic of meiosis. RESPONSE #62 Reference to meiosis and meiotic fidelity removed, line 435.

      __ __Line 402-405: the work was not designed to determine if cumulin would shift work allocation between the oocyte and the cumulus cells. Showing that cumulin drives meosis is interesting by itself.

      __RESPONSE #63____ Not clear that any change is requested or needed. This sentence is interpreting the significance of the results, as required in a Discussion.


      __Line 453-455: the link with the epigenome is an overstatement. RNA and DNA processing pathways are general cellular processes.

      RESPONSE #64____ The link to the epigenome was a reference to some published work. However it was linked to observations in the current data, and additional information has now been added to the updated manuscript to explain this further, as follows (currently lines 509-516);

      "These included significantly enriched networks of RNA binding, helicase activity, ribonucleoprotein complex biogenesis, and mRNA processing (supplementary Tables S11 and S12; upregulated proteins RNF20, SHMT1, DHX30, DDX17, DDX5, PRPF19, RPS4X, NOP58, DDX39b, HNRNPF, RPS271, NOP56, PRPF6, POLR2b, CPSF6, OOEP), as well as upregulation of key epigenetic regulators (HDAC2 and UHRF1; see supplementary Table S11), histone modifying protein MTA2, and significant network enrichment of the spliceosomal complex (supplementary Table S12; proteins DDX5, PRPF19, HNRNPF, PRPF6, POLR2B), which has been linked to epigenetic regulation ____[12]____.

      Minor details Line 36: I suggest to be more precise on the "nuclear" function that is affected in the oocyte. Given that oocytes are transcriptionaly quiescent at this stage, some might argue that it is a vague statement.

      RESPONSE #65____ Information relating to specific oocyte upregulated proteins and their cellular roles has been added to the updated manuscript (currently lines 434-443).

      DNA binding and ribosomal constituents (Fig. 4A, 4C),

      In vitro should be in italic because it is Latin. RESPONSE #6____6 corrected throughout

      __Lines 125-126: are the batch numbers relevant to anything? __RESPONSE #6____7 We would assume so – for the historical record. These are in-house produced proteins, cumulin is complex to produce and only a few labs worldwide have made it.

      __Line 168: Mascor = Mascot __RESPONSE #6____8 Corrected

      __Line 168: a reference for the software? __RESPONSE #6____9 URL and published references added (lines 172-175 revised manuscript)

      Line 178: need a reference for the software? RESPONSE #70 URL and published references added (line 185)

      __Line 187: Need a complete source for "Procure, 812" __RESPONSE #71 Added

      Line 188: Need a complete source for "Diatome" RESPONSE #72 Added

      Line 197: Need a complete source for "Cell-Tak" RESPONSE #73 Added

      Line 232: though = through RESPONSE #74 Corrected

      Line 243: define OCR RESPONSE #75 Added

      Line 268: If I am not mistaking, it is not a multispectral analysis. The multispectral values were analysed through a principal component analysis. RESPONSE #7____6 Data was analysed through linear discriminative analysis (LDA). This information has been added in Line 278.

      Line 363: What is the "behaviour" of an oocyte and cumulus cells? RESPONSE #77 replaced with “function”

      Line 512-513: Maybe add more on the fact that most clinics use ovulated eggs and do not perform IVM. However, IVM is needed is specific contexts such as PCOS. RESPONSE #78 Edited accordingly; lines 575-577.

      Reviewer #3 (Significance (Required)):

      Cumulin is the most potent oocyte secreted factor. Its mecanism of action is still unknown.

      I have been working on the mammalian oocyte for the past 25 years.

      References

      1. Mester, B., et al., Oocyte expression, secretion and somatic cell interaction of mouse bone morphogenetic protein 15 during the peri-ovulatory period. Reprod Fertil Dev, 2015. 27(5): p. 801-11.
      2. Hussein, T.S., J.G. Thompson, and R.B. Gilchrist, Oocyte-secreted factors enhance oocyte developmental competence. Dev Biol, 2006. 296(2): p. 514-21.
      3. Mottershead, D.G., et al., Cumulin, an Oocyte-secreted Heterodimer of the Transforming Growth Factor-beta Family, Is a Potent Activator of Granulosa Cells and Improves Oocyte Quality. J Biol Chem, 2015. 290(39): p. 24007-20.
      4. Gilchrist, R.B., M. Lane, and J.G. Thompson, Oocyte-secreted factors: regulators of cumulus cell function and oocyte quality. Hum Reprod Update, 2008. 14(2): p. 159-77.
      5. Sugiura, K., F.L. Pendola, and J.J. Eppig, Oocyte control of metabolic cooperativity between oocytes and companion granulosa cells: energy metabolism. Dev Biol, 2005. 279(1): p. 20-30.
      6. Campbell, J.M., et al., Multispectral autofluorescence characteristics of reproductive aging in old and young mouse oocytes. Biogerontology, 2022. 23(2): p. 237-249.
      7. Schwarz, D.S. and M.D. Blower, The endoplasmic reticulum: structure, function and response to cellular signaling. Cell Mol Life Sci, 2016. 73(1): p. 79-94.
      8. Szklarczyk, D., et al., STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res, 2019. 47(D1): p. D607-D613.
      9. Marei, W.F.A., et al., Proteomic changes in oocytes after in vitro maturation in lipotoxic conditions are different from those in cumulus cells. Sci Rep, 2019. 9(1): p. 3673.
      10. Moorey, S.E., et al., Differential Transcript Profiles in Cumulus-Oocyte Complexes Originating from Pre-Ovulatory Follicles of Varied Physiological Maturity in Beef Cows. Genes (Basel), 2021. 12(6).
      11. Ramus, C., et al., Benchmarking quantitative label-free LC-MS data processing workflows using a complex spiked proteomic standard dataset. J Proteomics, 2016. 132: p. 51-62.
      12. Luco, R.F., et al., Epigenetics in alternative pre-mRNA splicing. Cell, 2011. 144(1): p. 16-26.
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      Referee #3

      Evidence, reproducibility and clarity

      The work is interesting. Cumulin is a heterodimer hormone formed of GDF9 and BMP15. It is the main oocyte secreted factor. Being an heterodimer, gene knockout provides very little information about its mechanism of action. The team has a unique form of cumulin that is stable. This is why I think this work is important. However, I found two technical issues: one regarding mitochondrial count using MitoTracker and the other about comparing gene lists between the two cell types when protein input submitted to mass spectrometry differ between the two cell types. It is expected to find more with more input material. The text would need to be adjusted accordingly. Also, there is a lot of free statements and a lack of precision that is annoying. In my opinion, there are many overstatements that are not supported by the data because the work was not designed to test what is stated. The Discussion is very circular as the same statements come back on the next pages.

      Detailed review:

      The manuscript entitled "Oocyte and cumulus cell cooperativity and metabolic plasticity under the direction of oocyte paracrine factors" reports an in depth analysis of the exposure of cumulus oocyte complexes to either BMP15 or cumulin, the GDF9-BMP15 heterodimer. Impact assessment was done by determining developmental competence of the exposed oocytes, comparative profiling of the proteomes and spectral emissions as well as testing a potential impact at the ultrastructure level by electron microscopy imagery. Mitochondrial respiration as well as abundance of related metabolites was contrasted between the two treatments.

      Overall, the work is interesting. It is very difficult to study hormonal heterodimers because they originate from two different genes and they can naturally be found in a monomeric as well as a dimeric state. Such functional analysis cannot be done using gene knockout mouse lines. Genetic disruption provided the background that GDF9 and BMP15 are key oocyte secreted factors however only functional work as the one presented in this manuscript can find the mechanisms of action of these hormones.

      Comments:

      I really appreciated the reference to auto-symbiosis. We often see the reference to a cellular syncytium but this one is interesting.

      Although I appreciated the work, two important technical issues (between cell types comparisons and mitochondrial count) have been raised and there is a bit of unnecessary overselling throughout the manuscript. Sticking to the results would keep the value of the work high and wouldn't give that impression of overstatement.

      Technical issues:

      While the gene/protein enrichment analysis can be influenced by the input material submitted to mass spectrometry, the gene network analysis is influenced by the number of gene/proteins available for the enrichment analysis. It is thus difficult to compare both cell types.

      Also, when performing GO terms analysis, the level of "branching" can explain the results. In other words, GO terms are organized in a tree like structure where general elements (e.g. nucleus) are delineated in finer elements (e.g. nuclear function) leading to finer ones (e.g. DNA binding)... to finer ones (e.g. DNA repair)... etc. The number of genes/proteins available in the initial list directly dictates to which level of precision the analysis can reach. In the present work, the number of identified network may simply reflect the number of elements available in the initial lists. With more info on the cumulus cells side, it is logical to be able to reach finer branches that contain only a few genes. I have looked in the supplemental data files but could not find more info about the background used. Was it all known proteins? Was it all identified proteins where the differentially expressed proteins are compared to the detected proteins? Using the list of detected proteins as background for the analysis could help. Proteome Discoverer generated much less differentially expressed proteins between treatments than Mascot/Scaffold (2-17 vs. 74-390). Maybe use the Mascot/Scaffold data using the same number of top genes (e.g. 87) between both cell types. Then it would be much more comparable.

      Line 226 and 324-328 and line 350: I have never seen the use of MitoTracker Orange to count mitochondria. According to the manufacturer: "MitoTracker{trade mark, serif} Orange CMTMRos is an orange-fluorescent dye that stains mitochondria in live cells and its accumulation is dependent upon membrane potential. The dye is well-retained after aldehyde fixation." It is indicative of mitochondrial potential but it is not a method to count the number of mitochondria within a cell. I do not agree that more fluorescence means more mitochondria.

      I understand that the MitoTracker data is counterintuitive to the oxygen consumption rate and stable levels of energetic metabolites. However, as the authors mention, mitochondria are known to be capable of switching from aerobic to anaerobic energy production. In some cases, heterogeneity in the mitochondrial population (such as the one in the oocyte) could mean that a mosaic respiratory potential exists where some mitochondria are more aerobic than others... To change the number of mitochondria, either fission or mitophagy must occur. Although mitochondrial DNA replication is done in approximatively 2 h and fission/division can occur over 1 h, and protein ubiquitination is done over 12 h-18 h during mitophagy, TEM micrographs (figure 5) do not show elongated mitochondria in the process of division. To detect active mitophagy, protein markers and association with lysosome would be needed. A shift in mitochondrial number may not be the suitable interpretation of the data.

      For the spectral data analysis (Figure 3D), how did the three replicates perform? The figure does not show the replication variance relative to the treatment variance.

      Wording/interpretation issues

      Lines 114-116: "This intercellular cooperativity facilitates oocyte maturation while simultaneously protecting germ-line genomic integrity, in a manner which could not be achieved by a single cell." This is an overstatement because genomic integrity was not assessed. Why consider that the nuclear function found in the proteome contrast is necessarily associated with genomic integrity. Miosis requires in dept chromatin handling. What evidence provided from the results is associated with cellular numbers. The presence of cumulus cells is known to support meiosis but it doesn't mean that some of the cellular processes have been imparted to the surrounding somatic cells. The work done for this manuscript does not test any of this claim.

      On numerous occasions, the statements are imprecise. For example: Line 274: "More than double the number..." Since doubling a minute value does not mean the same thing as doubling a large value, values, measurements with units and ideally with SEM should be added.

      Line 287: "... and almost a third more significant networks..." Please add values.

      On the same statement, since sample input material to the mass spectrometry is vastly different between cumulus cells and oocytes, is it truly comparable? Could these differences between the two cell types be associated with the amounts of proteins in the extracted samples? Typically, more variable results are obtained with the low input. It sometimes lead to apparently more difference between treatments simply because of low count numbers. On line 292, authors mentioned that protein loading was considered. How was that done? Low input cannot be compensated or normalized. The following statement on line 293 indicate that more proteins were identified in cumulus cells. This is probably due to more input material submitted to mass spectrometry. It is not necessarily a difference in protein diversity between cumulus cells and oocytes.

      Line 293: "... resulted in the identification of about double the number..." Please add values.

      Line 294: "However, there were 4-5 times as many differentially expressed proteins..." Please add values.

      Line 298: "...difference was quite marked..." More factual info should be added.

      Line 305: Again, the whole comparison between cell types could be argued from the standpoint of input material subjected to the analysis. Given the point is to state that cumulin has a profound impact on cumulus cells, maybe it is not necessary to compare with the oocyte data. It is logical that an oocyte secreted factor targets the neighbouring cells. The point can be made without raising the question about the potential issue of input material.

      Line 317-317: "... exhibited more rounded and swollen mitochondria..." How was that determined? In the periphery of the oolemma, mitochondria aggregates in clusters which can be quite different from one another. Maybe proportions of different shapes of mitochondria could be provided if enough mitochondria are counted from the EM micrographs.

      Line 169: What do you mean by "The results were merged based on consistency..."? This seems to be a trivial way to analyse the data.

      Line 170: "A further requirement was that at least one, if not both methods..." Again, when did you decide to use one method or to use both? Why not use the common ground from both methods?

      Line 384: Is the paracrine signaling remodeling COC metabolism or is it enhancing the rate at which it is done? I believe this switch in metabolism occurs in untreated COCs.

      The Discussion is somewhat circular. Section will need to be adjusted if the Mitotracker-based mitochondrial count and between cell types gene/protein lists comparisons are removed.

      Accounts for mitochondrial counts: (lines 387-393) (lines 424-427) (line 463).

      Accounts for comparisons of gene lists length between cell types: Lines 389-391 and 475-477 and 496-499).

      Line 395: "... a substantial number of oocyte upregulated proteins... Please provide number.

      Line 397: The data was not designed to test the potential of cumulin to preserve meiotic fidelity. This is an overstatement since DNA binding is part of the normal course of even during meiosis. Again, cumulin could accelerate the kinetic of meiosis.

      Line 402-405: the work was not designed to determine if cumulin would shift work allocation between the oocyte and the cumulus cells. Showing that cumulin drives meosis is interesting by itself.

      Line 453-455: the link with the epigenome is an overstatement. RNA and DNA processing pathways are general cellular processes.

      Minor details

      Line 36: I suggest to be more precise on the "nuclear" function that is affected in the oocyte. Given that oocytes are transcriptionaly quiescent at this stage, some might argue that it is a vague statement.

      In vitro should be in italic because it is Latin.

      Lines 125-126: are the batch numbers relevant to anything?

      Line 168: Mascor = Mascot

      Line 168: a reference for the software?

      Line 178: need a reference for the software?

      Line 187: Need a complete source for "Procure, 812"

      Line 188: Need a complete source for "Diatome"

      Line 197: Need a complete source for "Cell-Tak"

      Line 232: though = through

      Line 243: define OCR

      Line 268: If I am not mistaking, it is not a multispectral analysis. The multispectral values were analysed through a principal component analysis.

      Line 363: What is the "behaviour" of an oocyte and cumulus cells?

      Line 512-513: Maybe add more on the fact that most clinics use ovulated eggs and do not perform IVM. However, IVM is needed is specific contexts such as PCOS.

      Significance

      Cumulin is the most potent oocyte secreted factor. Its mecanism of action is still unknown.

      I have been working on the mammalian oocyte for the past 25 years.

    1. Author Response

      Reviewer #1 (Public Review):

      Reviewer 1 confirmed the view that your paper provides new insight into YTHDC1 function in regulating SC activation/proliferation but added that some of the data could be improved to fully support the conclusions. Specifically:

      The title "Nuclear m6A Reader YTHDC1 Promotes Muscle Stem Cell Activation/Proliferation by Regulating mRNA Splicing and Nuclear Export" seems a bit overstated. Their data are not sufficient to show YTHDC1 regulating nuclear export. From figure 6 we could see some mRNAs export was inhibited upon YTHDC1 loss but intron retention also occurs on these mRNAs, for example, Dnajc14. Since intron retention could lead to mRNA nuclear retention, the mRNA export inhibition may be caused by splicing deficiency. From the data they provided we could not draw the conclusion that YTHDC1 directly affects mRNA export. I think they could not emphasize this point in the title.

      Thanks for the suggestion. It is true that in our initial submission, we had more data to support YTHDC1 regulation of mRNA splicing but not enough on nuclear export. It will take substantial amount of time and efforts to have thorough dissection on both mechanisms. Nevertheless, we argue that our data does provide evidence on YTHDC1 regulation of nuclear export. For example, in Figures 6 C, H, and M, only ~20% of the target mRNAs (such as Dnaj14) showed alteration in both splicing and export upon YTHDC1 loss while the majority of the export targets showed no splicing deficiency. For example, Btbd7 and Tiparp in Figure 6 N showed no intron retention. In addition, we have now performed Co-IP experiments to validate the interaction between YTHDC1 and THOC7 (new result added in Figure 7L), which provides extra evidence to support YTHDC1 function in regulating mRNA nuclear export. We thus would like to keep the original title in order to reflect the multifaceted function of YTHDC1 in muscle stem cells.

      The mechanism of YTHDC1 promoting muscle stem cell activation/proliferation is not solidified. The authors could strengthen their evidence through bioinformatics analysis or give more discussion. Besides, the previous work done by Zhao and colleagues (Zhao et al,. Nature 542, 475-478 (2017).) reported another m6A reader Ythdf2 promotes m6A-dependent maternal mRNA clearance to facilitate zebrafish maternal-to-zygotic transition. Does YTHDC1 regulate mRNA clearance during SC activation/proliferation? The authors should explore this possibility by deep-seq data analysis and give some discussion.

      Thanks for the critical comment. For the first concern, we think YTHDC1 promotes muscle stem cell activation/proliferation through the multi-level gene regulatory capabilities of YTHDC1 on both transcriptional and post-transcriptional processes and the myriads of targets regulated by YTHDC1. In addition, with the newly added data, we believe that YTHDC1’s function is largely dependent on its synergism with hnRNPG (Figure 7 K). We have added the discussion in lines 421-427 of the revised text. For the second question, our data showed that YTHDC1 predominantly localizes in the nucleus of SCs and myoblasts (Figure 1 F&G), thus it may not have a role in regulating mRNA clearance in the cytoplasm like YTHDF2. Nevertheless, there are a few existing reports1, 2 suggesting its possible role in mRNA degradation and stability which may arise from its transient shuttling to cytoplasm of cells. We have now added this point in lines 469-472 of the revised text.

      Reviewer #2 (Public Review):

      Reviewer 2 was similarly positive stating that several tour-de-force techniques were used to examine m6A and the biological consequence in satellite cells and that there was a large amount of data supporting the conclusions with only a few minor weaknesses.

      General points: The main body is lengthy, and some content can be reduced or condensed. For example, RNA-seq was used to determine gene expression in WT and cKO cells, but the purpose of this is not well justified given that YTHDC1 mainly functions to regulate splicing and nuclear expert of mRNA rather than controlling their expression levels. Does the RNA-seq data suggest that YTHDC1 may also regulate gene expression independent of m6A reader function?

      Thanks for the comment. We have now revised the entire text to condense the content. Nevertheless, we must point out that the purpose of the RNA-seq is to provide extra evidence for the proliferation defect of the YTHDC1 KO cells but not to search for the underlying mechanism. We have now revised in lines 159-160 to clarify this.

      Reference:

      1. Shima, H., Matsumoto, M., Ishigami, Y., Ebina, M., Muto, A., Sato, Y., Kumagai, S., Ochiai, K., Suzuki, T. & Igarashi, K. S-Adenosylmethionine Synthesis Is Regulated by Selective N(6)-Adenosine Methylation and mRNA Degradation Involving METTL16 and YTHDC1. Cell Rep 21, 3354-3363 (2017).
      2. Zhang, Z., Wang, Q., Zhao, X., Shao, L., Liu, G., Zheng, X., Xie, L., Zhang, Y., Sun, C. & Xu, R. YTHDC1 mitigates ischemic stroke by promoting Akt phosphorylation through destabilizing PTEN mRNA. Cell Death Dis 11, 977 (2020).
      3. He, P.C. & He, C. m(6) A RNA methylation: from mechanisms to therapeutic potential. EMBO J 40, e105977 (2021).
      4. Widagdo, J., Anggono, V. & Wong, J.J. The multifaceted effects of YTHDC1-mediated nuclear m(6)A recognition. Trends Genet 38, 325-332 (2022).
      5. Sheng, Y., Wei, J., Yu, F., Xu, H., Yu, C., Wu, Q., Liu, Y., Li, L., Cui, X.L., Gu, X., Shen, B., Li, W., Huang, Y., Bhaduri-Mcintosh, S., He, C. & Qian, Z. A Critical Role of Nuclear m6A Reader YTHDC1 in Leukemogenesis by Regulating MCM Complex-Mediated DNA Replication. Blood (2021).
      6. Cheng, Y., Xie, W., Pickering, B.F., Chu, K.L., Savino, A.M., Yang, X., Luo, H., Nguyen, D.T., Mo, S., Barin, E., Velleca, A., Rohwetter, T.M., Patel, D.J., Jaffrey, S.R. & Kharas, M.G. N(6)-Methyladenosine on mRNA facilitates a phase-separated nuclear body that suppresses myeloid leukemic differentiation. Cancer Cell 39, 958-972 e958 (2021).
      7. Chen, C., Liu, W., Guo, J., Liu, Y., Liu, X., Liu, J., Dou, X., Le, R., Huang, Y., Li, C., Yang, L., Kou, X., Zhao, Y., Wu, Y., Chen, J., Wang, H., Shen, B., Gao, Y. & Gao, S. Nuclear m(6)A reader YTHDC1 regulates the scaffold function of LINE1 RNA in mouse ESCs and early embryos. Protein Cell 12, 455-474 (2021).
      8. Xiao, W., Adhikari, S., Dahal, U., Chen, Y.S., Hao, Y.J., Sun, B.F., Sun, H.Y., Li, A., Ping, X.L., Lai, W.Y., Wang, X., Ma, H.L., Huang, C.M., Yang, Y., Huang, N., Jiang, G.B., Wang, H.L., Zhou, Q., Wang, X.J., Zhao, Y.L. & Yang, Y.G. Nuclear m(6)A Reader YTHDC1 Regulates mRNA Splicing. Mol Cell 61, 507-519 (2016).
      9. Webster, M.T., Manor, U., Lippincott-Schwartz, J. & Fan, C.M. Intravital Imaging Reveals Ghost Fibers as Architectural Units Guiding Myogenic Progenitors during Regeneration. Cell Stem Cell 18, 243-252 (2016).
      10. Yankova, E., Blackaby, W., Albertella, M., Rak, J., De Braekeleer, E., Tsagkogeorga, G., Pilka, E.S., Aspris, D., Leggate, D., Hendrick, A.G., Webster, N.A., Andrews, B., Fosbeary, R., Guest, P., Irigoyen, N., Eleftheriou, M., Gozdecka, M., Dias, J.M.L., Bannister, A.J., Vick, B., Jeremias, I., Vassiliou, G.S., Rausch, O., Tzelepis, K. & Kouzarides, T. Small-molecule inhibition of METTL3 as a strategy against myeloid leukaemia. Nature 593, 597-601 (2021).
      11. Otto, A., Schmidt, C., Luke, G., Allen, S., Valasek, P., Muntoni, F., Lawrence-Watt, D. & Patel, K. Canonical Wnt signalling induces satellite-cell proliferation during adult skeletal muscle regeneration. J Cell Sci 121, 2939-2950 (2008).
      12. Liu, J., Gao, M., He, J., Wu, K., Lin, S., Jin, L., Chen, Y., Liu, H., Shi, J., Wang, X., Chang, L., Lin, Y., Zhao, Y.L., Zhang, X., Zhang, M., Luo, G.Z., Wu, G., Pei, D., Wang, J., Bao, X. & Chen, J. The RNA m(6)A reader YTHDC1 silences retrotransposons and guards ES cell identity. Nature 591, 322-326 (2021).
      13. Xu, W., Li, J., He, C., Wen, J., Ma, H., Rong, B., Diao, J., Wang, L., Wang, J., Wu, F., Tan, L., Shi, Y.G., Shi, Y. & Shen, H. METTL3 regulates heterochromatin in mouse embryonic stem cells. Nature 591, 317-321 (2021).
      14. Roberson, P.A., Romero, M.A., Osburn, S.C., Mumford, P.W., Vann, C.G., Fox, C.D., McCullough, D.J., Brown, M.D. & Roberts, M.D. Skeletal muscle LINE-1 ORF1 mRNA is higher in older humans but decreases with endurance exercise and is negatively associated with higher physical activity. J Appl Physiol (1985) 127, 895-904 (2019).
      15. Mumford, P.W., Romero, M.A., Osburn, S.C., Roberson, P.A., Vann, C.G., Mobley, C.B., Brown, M.D., Kavazis, A.N., Young, K.C. & Roberts, M.D. Skeletal muscle LINE-1 retrotransposon activity is upregulated in older versus younger rats. Am J Physiol Regul Integr Comp Physiol 317, R397-R406 (2019).
    1. Author Response

      Reviewer #1 (Public Review):

      Laurent et al. generate genotyping data from 259 individuals from Cabo Verde to investigate the histories and patterns of admixture in the set of islands that make up Cabo Verde. The authors had previously studied admixture in an earlier study but in a smaller set of individuals from two cities on one island (from Santiago) in Cabo Verde. Here, the authors sample from all the islands of Cabo Verde to study admixture in these islands and reveal that there is a varied picture of admixture in that the demographic histories are distinct amongst this set of islands.

      I found the article interesting and clearly written, and I like that it highlights that admixture is a dynamic process that has manifested differently in distinct geographical regions, which will be of broad interest. It also highlights how genetic ancestry patterns are correlated with the populations that were in power/enslaved during colonial times and proposes that certain social practices (e.g. legally enforced segregation) might have affected the distribution/length of runs of homozygosity.

      We thank the reviewer for this positive and encouraging appreciation of our work.

      My main suggestion is that the authors provide a set of hypotheses regarding admixture that may explain their observations, and it would be nice to see if at least one of these has some support using simulations. Could the authors run simulations under their proposed demographic model for populations in Cabo Verde vs what we would expect in a pseudo-panmictic population with two sources of admixture? The authors probably already have simulations they could use. And then see how pre/post admixture founding events change patterns of ancestry.

      As suggested by the reviewer, in the revised version of the manuscript, we conducted the same MetHis-ABC scenario-choice and posterior parameter inference considering the 225 Cabo Verde-born individuals as a single random-mating population, in addition to our main results considering each island of birth separately. Most interestingly, we find that our ABC inferences fail to accurately reconstruct the detailed admixture history of Cabo Verde when considered as a whole instead of per each island of birth separately. This is due to admixture histories substantially differing across islands of birth of individuals, also consistent with the significantly differentiated genetic patterns within Cabo Verde obtained from ADMIXTURE, local-ancestry inferences, ROH, and isolation-by-distance analyses. These results are now implemented throughout the revised version of the manuscript and in supplementary figures and tables. See in particular Results L758-769, and Appendix1-figures and tables, Figure7-figure supplement 1-3, and Appendix 5-table 10.

      Reviewer #2 (Public Review):

      In this article, the authors leveraged patterns on the empirical genomic data and the power of simulations and statistical inferences and aimed to address a few biologically and culturally relevant questions about Cabo Verde population's admixture history during the TAST era. Specifically, the authors provided evidence on which specific African and European populations contributed to the population per island if the genetic admixture history parallels language evolution, and the best-fitting admixture scenario that answers questions on when and which continental populations admixed on which island, and how that influenced the island population dynamics since then.

      Strengths

      1) This study sets a great example of studying population history through the lens of genetics and linguistics, jointly. Historically most of the genetic studies of population history either ignored the sociocultural aspects of the evidence or poorly (or wrongly) correlated that with genetic inference. This study identified components in language that are informative about cultural mixture (strictly African-origin words versus shared European-African words), and carefully examined the statistical correlation between genetic and linguistic variation that occurred through admixture, providing a complete picture of genetic and sociocultural transformation in the Cabo Verde islands during TAST.

      We thank the reviewer for this very enthusiastic and encouraging comment on our work.

      2) The statistical analyses are carefully designed and rigorously done. I especially appreciate the careful goodness-of-fit checking and parameter error rates estimation in the ABC part, making the inference results more convincing.

      Again, we thank the reviewer for this positive comment.

      Weaknesses

      1) Most of the methods in the main analyses here were previously developed (eg. MDS, MetHis, RF/NN-ABC). However, when being introduced and applied here, the authors didn't reinstate the necessary background (strength and weakness, limitations and usage) of these methods to make them justifiable over other methods. For example, why ADS-MDS is used here to examine the genetic relationship between Cabo Verde populations and other worldwide populations, rather than classic PCA and F-statistics?

      As mentioned in the answer to the general comments, we extensively modified our manuscript in both Results and Material and Methods, to clarify and justify our reasoning for each one of the analyses conducted, and to discuss pros and cons of the methods used. We warmly thank the reviewers for this request, as we believe it allowed us to strongly improve the accessibility of our work in particular for the less specialized audience, as well as equally crucially improve replicability of our work for specialists. See in particular Results L185-193, L245-250, L368-371, L380-386, L495-511, L567-571, L606-621, and the corresponding Material and Methods sections.

      For the particular example of PCA raised by the reviewer: see Results L185-193.

      For that of F-statistics, see Results L368-386. Note that we added the F-stat analysis suggested by the reviewer to the revised version of our manuscript (see detailed answers below), Figure 3-figure supplement 2.

      We believe that these changes strongly strengthen our manuscript and enlarged its potential readership, and we thank, again, the reviewer for this request.

      2) The senior author of this paper has an earlier published article (Verdu et al. 2017 Current Biology) on the same population, using a similar set of methods and drew similar conclusions on the source of genetic and linguistic variation in Cabo Verde. Although additional samples on island levels are added here and additional analyses on admixture history were performed, half of the main messages from this paper don't seem to provide new knowledge than what we already learned from the 2017 paper.

      We substantially modified the text of the revised version of the manuscript to address the concern raised by the reviewer in numerous locations of the Abstract, Introduction and Results and Discussion sections, thus hoping to highlight better what we think is the profound novelty brought by this study. In particular, see Introduction L128-153.

      3) Furthermore, there are a few essential factors that could confound different aspects of the major analyses in this article that I believe should be taken into account and discussed. Such factors include the demographic history of source populations prior to admixture, different scenarios of the recipient population size changes, differences in recombination rates across the genome and between African and European populations, etc.

      We thank the reviewer for these comments which allowed us to improve the clarity of our manuscript and rise very interesting discussion points that we had overlooked. As indicated in part in the general answer to reviewers above:

      1) We clarified our methods’ design and discussed extensively its limitations with respect to ancestral populations’ sizes mis-specifications. Indeed, ancestral source population sizes are not modelized in our MetHis-ABC approach. Instead, we consider that the observed proxy source populations from Africa and Europe are at the drift-mutation equilibrium and are large since the initial and recent founding of Cabo Verde in the 1460’s, and thus use observed genetic variation patterns in these populations to build virtual gamete reservoirs for the admixture history of Cabo Verde with the MetHis-ABC framework. Therefore, while we cannot evaluate explicitly the influence of ancestral source population sizes differences on our inferences in Cabo Verde, as we now state in the revised version of our manuscript: “we nevertheless implicitly take the real demographic histories of these source populations into account in our simulations, as we use observed genetic patterns themselves the product of this demographic history to create the virtual source populations at the root of the admixture history of each Cabo Verdean island.”. We then discuss the outcome of such an approach which mimics satisfactorily the real data for ABC inference. See in particular the revised versions of the Material and Methods L1454-1491 novel section “Simulating the admixed population from source-populations for 60,000 independent SNPs with MetHis”, and Results L637-649.

      2) Concerning the possibilities for population-size changes in the admixed population in our simulations and ABC inferences, we clarified our Material and Methods and explanations of our Results to better show that we readily consider various possible scenarios (for each island separately). Indeed, with our MetHis simulation design, given values of model-parameters correspond either to a constant, a linearly increasing, or a hyperbolic increase in reproductive size in the admixed population over time. We further clarified our Results and Discussion pointing out that we find, a posteriori, indeed, different demographic regimes among islands.

      Nevertheless, reviewers are right that we did not test the possibility for bottlenecks. We thus substantially expanded the Results and Discussion sections in multiple locations to highlight this limitation and the challenges involved in overcoming it in future work. See in particular Material and Methods L1386-1404 section “Hyperbolic increase, linear increase, or constant reproductive population size in the admixed population”, Results L739-742, and Discussion L934-941, and Perspectives.

      3) Finally, concerning recombination rate, we considered only independent SNPs in our simulation and inference process, as is now clarified in multiple locations throughout the text. Otherwise, we further discuss matters of recombination concern regarding specifically our ROH analyses, as suggested in the detailed reviewer’s comments. In brief, we note that in Figure 8 Pemberton 2012 (AJHG 91:275-292) shows that occurrence of long ROH at the same genomic location across individuals is correlated with low recombination rates, although the effect is relatively weak unless in extreme recombination cold spots. Unless there were many extreme recombination cold spots that were different among the islands or ancestral populations, we anticipate fine-scale recombination rate differences not to matter very much for total ROH levels in these data. Similarly, we do not expect large genome-wide differences in mutation rate, and therefore we don’t anticipate minor local variation in mutation rates to make a systematic difference in total ROH levels. We now refer to these important points in the revised version of our Results L414-415.

      Overall, the paper is of interest to the field of human evolutionary genetics - that not only does it tell the story of a historically important population, but also the methodology behind this paper sets a great example for future research to study genetic and sociocultural transformations under the same framework.

      We would like to thank the reviewer for this very encouraging conclusion and for the detailed revision of our work which, we believe, helped us to substantially improve our manuscript.

    1. Author Response

      Reviewer #1 (Public Review):

      This work describes a new method, Proteinfer, which uses dilated neural networks to predict protein function, using EC terms and GO terms. The software is fast and the server-side performance is fast and reliable. The method is very clearly described. However, it is hard to judge the accuracy of this method based on the current manuscript, and some more work is needed to do so.

      I would like to address the following statement by the authors: (p3, left column): "We focus on Swiss Prot to ensure that our models learn from human-curated labels, rather than labels generated by electronic annotation".

      There is a subtle but important point to be made here: while SwissProt (SP) entries are human-curated, they might still have their function annotated ("labeled") electronically only. The SP entry comprises the sequence, source organism, paper(s) (if any), annotations, cross-references, etc. A validated entry does not mean that the annotation was necessarily validated manually: but rather that there is a paper backing the veracity of the sequence itself, and that it is not an automatic generation from a genome project.

      Example: 009L_FRG3G is a reviewed entry, and has four function annotations, all generated by BLAST, with an IEA (inferred by electronic annotation) evidence code. Most GO annotations in SwissProt are generated that way: a reviewed Swissprot entry, unlike what the authors imply, does not guarantee that the function annotation was made by non-electronic means. If the authors would like to use non-electronic annotations for functional labels, they should use those that are annotated with the GO experimental evidence codes (or, at the very least, not exclusively annotated with IEA). Therefore, most of the annotations in the authors' gold standard protein annotations are simply generated by BLAST and not reviewed by a person. Essentially the authors are comparing predictions with predictions, or at least not taking care not to do so. This is an important point that the authors need to address since there is no apparent gold standard they are using.

      The above statement is relevant to GO. But since EC is mapped 1:1 to GO molecular function ontology (as a subset, there are many terms in GO MFO that are not enzymes of course), the authors can easily apply this to EC-based entries as well.

      This may explain why, in Figure S8(b), BLAST retains such a high and even plateau of the precision-recall curve: BLAST hits are used throughout as gold-standard, and therefore BLAST performs so well. This is in contrast, say to CAFA assessments which use as a gold standard only those proteins which have experimental GO evidence codes, and therefore BLAST performs much poorer upon assessment.

      We thank the reviewer for this point. We regret if we gave the impression that our training data derives exclusively, or even primarily, from direct experiments on the amino acid sequences in question. We had attempted to address this point in the discussion with this section:

      "On the other hand, many entries come from experts applying existing computational methods, including BLAST and HMM-based approaches, to identify protein function. Therefore, the data may be enriched for sequences with functions that are easily ascribable using these techniques which could limit the ability to estimate the added value of using an alternative alignment-free tool. An idealised dataset would involved training only on those sequences that have themselves been experimentally characterized, but at present too little data exists than would be needed for a fully supervised deep-learning approach."

      We have now added a sentence in the early sentence of of the manuscript reinforcing this point:

      "Despite its curated nature, SwissProt contains many proteins annotated only on the basis of electronic tools."

      We have also removed the phrase "rather than labels generated by a computational annotation pipeline" because we acknowledge that this could be read to imply that computational approaches are not used at all for SwissProt which would not be correct.

      While we agree that SwissProt contains many entries inferred via electronic means, we nevertheless think its curated nature makes an important difference. Curators as far as possible reconcile all known data for a protein, often looking for the presence of key residues in the active sites. There are proteins where electronic annotation would suggest functions in direct contradiction to experimental data, which are avoided due to this curation process. As one example, UniProt entry Q76NQ1 contains a rhomboid-like domain typically found in rhomboid proteases (IPR022764) and therefore inputting it into InterProScan results in a prediction of peptidase activity (GO:0004252). However this is in fact an inactive protein, as discovered by experiment, and so is not annotated with this activity in SwissProt. ProteInfer successfully avoids predicting peptidase activity as a result of this curated training data. (For transparency, ProteInfer is by no means perfect on this point: there are also cases in which UniProt curators have annotated single proteins as inactive but ProteInfer has not learnt this relationship, due to similar sequences which remain active).

      We had also attempted to address this point by comparing with phenotypes seen in a specific high-throughput experimental assay ("Comparison to experimental data" section).

      We have now added a new analysis in which we assess the recall of GO terms while excluding IEA annotation codes. We find that at the threshold that maximises F1 score in the full analysis, our approach is able to recall 60-75% (depending on ontology) of annotations. Inferring precision is challenging due to the fact that only a very small proportion of the possible function*gene combinations have in fact been tested, making it difficult to distinguish a true negative from a false negative.

      "We also tested how well our trained model was able to recall the subset of GO term annotations which are not associated with the "inferred from electronic annotation" (IEA) evidence code, indicating either experimental work or more intensely-curated evidence. We found that at the threshold that maximised F1 score for overall prediction, 75% of molecular function annotations could be successfully recalled, 61% of cellular component annotations, and 60% of biological process annotations."

      Pooling GO DAGs together: It is unclear how the authors generate performance data over GO as a whole. GO is really 3 disjoint DAGs (molecular function ontology or MFO, Biological Process or BPO, Cellular component or CCO). Any assessment of performance should be over each DAG separately, to make biological sense. Pooling together the three GO DAGs which describe completely different aspects of the function is not informative. Interestingly enough, in the browser applications, the GO DAG results are distinctly separated into the respective DAGs.

      Thank you for this suggestion. To answer the question of how we were previously generating performance data: this was simply by treating all terms equivalently, regardless of their ontology.

      We agree that it would be helpful to the reader to split out results by ontology type, especially given clear differences in performance.

      We now provide PR-curve graphs split by ontology type.

      We have also added the following text:

      "The same trends for the relative performance of different approaches were seen for each of the direct-acyclic graphs that make up the GO ontology (biological process, cellular component and molecular function), but there were substantial differences in absolute performance (Fig S10). Performance was highest for molecular function (max F1: 0.94), followed by biological process (max F1:0.86) and then cellular component (max F1:0.84)."

      Figure 3 and lack of baseline methods: the text refers to Figures 3A and 3B, but I could only see one figure with no panels. Is there an error here? It is not possible at this point to talk about the results in this figure as described. It looks like Figure 3A is missing, with Fmax scores. In any case, Figure 3(b?) has precision-recall curves showing the performance of predictions is the highest on Isomerases and lowest in hydrolases. It is hard to tell the Fmax values, but they seem reasonably high. However, there is no comparison with a baseline method such as BLAST or Naive, and those should be inserted. It is important to compare Proteinfer with these baseline methods to answer the following questions: (1) Does Proteinfer perform better than the go-to method of choice for most biologists? (2) does it perform better than what is expected given the frequency of these terms in the dataset? For an explanation of the Naive method which answers the latter question, see: ( https://www.nature.com/articles/nmeth.2340 )

      We apologise for the errors in figure referencing in the text here. This emerged in part from the two versions of text required to support an interactive and legacy PDF version. We had provided baseline comparisons with BLAST in Fig. 5 of the interactive version (correctly referenced in the interactive version) and in Fig. S7 of the PDF version (incorrectly referenced as Fig 3B).

      We have now moved the key panel of Fig S7 to the main-text of the PDF version (new Fig 3B), as suggested also by the editor, and updated the figure referencing appropriately. We have also added a Naive frequency-count based baseline. This baseline would not appear in Fig 3B due to axis truncation, but is shown in a supplemental figure, new Fig S9. We thank the reviewer and the editor for raising these points.

      Reviewer #2 (Public Review):

      In this paper, Sanderson et al. describe a convolutional neural network that predicts protein domains directly from amino acid sequences. They train this model with manually curated sequences from the Swiss-Prot database to predict Enzyme Commission (EC) numbers and Gene Ontology (GO) terms. This paper builds on previous work by this group, where they trained a separate neural network to recognize each known protein domain. Here, they train one convolutional neural network to identify enzymatic functions or GO terms. They discuss how this change can deal with protein domains that frequently co-occur and more efficiently handle proteins of different lengths. The tool, ProteInfer, adds a useful new tool for computational analysis of proteins that complements existing methods like BLAST and Pfam.

      The authors make three claims:

      1) "ProteInfer models reproduce curator decisions for a variety of functional properties across sequences distant from the training data"

      This claim is well supported by the data presented in the paper. The authors compare the precision-recall curves of four model variations. The authors focus their training on the maximum F1 statistic of the precision-recall curve. Using precision-recall curves is appropriate for this kind of problem.

      2) "Attribution analysis shows that the predictions are driven by relevant regions of each protein sequence".

      This claim is very well supported by the data and particularly well illustrated by Figure 4. The examples on the interactive website are also very nice. This section is a substantial innovation of this method. It shows the value of scanning for multiple functions at the same time and the value of being able to scan proteins of any length.

      3) "ProteInfer models create a generalised mapping between sequence space and the space of protein functions, which is useful for tasks other than those for which the models were trained."

      This claim is also well supported. The print version of the figure is really clear, and the interactive version is even better. It is a clever use of UMAP representations to look at the abstract last layer of the network. It was very nice how each sub-functional class clustered.

      The interactive website was very easy to use with a good user interface. I expect will be accessible to experimental and computational biologists.

      The manuscript has many strengths. The main text is clearly written, with high-level descriptions of the modeling. I initially printed and read the static PDF version of the paper. The interactive form is much more fun to read because of the ability to analyze my favorite proteins and zoom in on their figures (e.g. Figure 8). The new Figure 1 motivates the work nicely. The website has an excellent interactive graphic showing how the number of layers in the network and the kernel size change how data is pooled across residues. I will use this tool in my teaching.

      We are grateful for these comments. We are excited that the reviewer hopes to use this figure for teaching, which is exactly the sort of impact we hoped for this interactive manuscript. We agree that the interactive manuscript is by far the most compelling version of this work.

      The manuscript has only minor weaknesses. It was not clear if the interactive model on the website was the Single CNN model or the Ensemble CNN model.

      We thank the reviewer for pointing out the ambiguity here. The model shown on the website is a Single CNN model, and is chosen with hyperparameters that achieve good performance whilst being readily downloadable to the user's machine for this demonstration without use of excessive bandwidth. We have added additional sentences to address this better in the manuscript.

      " When the user loads the tool, lightweight EC (5MB) and GO model (7MB) prediction models are downloaded and all predictions are then performed locally, with query sequences never leaving the user's computer. We selected the hyperparameters for these lightweight models by performing a tuning study in which we filtered results by the size of the model's parameters and then selected the best performing models. This approach uses a single neural network, rather than an ensemble. Inference in the browser for a 1500 amino-acid sequence takes < 1.5 seconds for both models "

      Overall, ProteInfer will be a very useful resource for a broad user base. The analysis of the 171 new proteins in Figure 7 was particularly compelling and serves as a great example of the utility and power of ProteInfer. It completes leading tools in a very valuable way. I anticipate adding it to my standard analysis workflows. The data and code are publicly available.

      Reviewer #3 (Public Review):

      In this work, the authors employ a deep convolutional neural network approach to map protein sequence to function. The rationales are that (i) once trained, the neural network would offer fast predictions for new sequences, facilitating exploration and discovery without the need for extensive computational resources, (ii) that the embedding of protein sequences in a fixed-dimensional space would allow potential analyses and interpretation of sequence-function relationships across proteins, and (iii) predicting protein function in a way that is different from alignment-based approaches could lead to new insights or superior performance, at least in certain regimes, thereby complementing existing approaches. I believe the authors demonstrate i and iii convincingly, whereas ii was left open-ended.

      A strength of the work is showing that the trained CNNs perform generally on par with existing alignment based-methods such as BLASTp, with a precision-recall tradeoff that differs from BLASTp. Because the method is more precise at lower recall values, whereas BLASTp has higher recall at lower precision values, it is indeed a good complement to BLASTp, as demonstrated by the top performance of the ensemble approach containing both methods.

      Another strength of the work is its emphasis on usability and interpretability, as demonstrated in the graphical interface, use of class activation mapping for sub-sequence attribution, and the analysis of hierarchical functional clustering when projecting the high-dimensional embedding into UMAP projections.

      We thank the reviewer for highlighting these points.

      However, a main weakness is the premise that this approach is new. For example, the authors claim that existing deep learning "models cannot infer functional annotation for full-length protein sequences." However, as the proposed method is a straightforward deep neural network implementation, there have been other very similar approaches published for protein function prediction. For example, Cai, Wang, and Deng, Frontiers in Bioengineering and Biotechnology (2020), the latter also being a CNN approach. As such, it is difficult to assess how this approach differs from or builds on previous work.

      We agree that there has been a great deal of exciting work looking at the application of deep learning to protein sequences. Our core code has been publicly available on GitHub since April 2019 , and our preprint has now been available for more than a year. We regret the time taken to release a manuscript and for it to reach review: this was in part due to the SARS-CoV-2 pandemic, which the first author was heavily involved in the scientific response to. Nevertheless, we believe that our work has a number of important features that distinguish it from much other work in this space.

      ● We train across the entire GO ontology. In the paper referenced by the reviewer, training is with 491 BP terms, 321 MF terms, and 240 CC terms. In contrast, we train with a vocabulary of 32,102 GO labels, and the majority of these are predicted at least once in our test set. ● We use a dilated convolutional approach. In the referenced paper the network used is instead of fixed dimensions. Such an approach means there is an upper limit on how large a protein can be input into the model, and also means that this maximum length defines the computational resources used for every protein, including much smaller ones. In contrast, our dilated network scales to any size of protein, but when used with smaller input sequences it performs only the calculations needed for this size of sequence.

      ● We use class-activation mapping to determine regions of a protein responsible for predictions, and therefore potentially involved in specific functions.

      ● We provide a TensorFlow.JS implementation of our approach that allows lightweight models to be tested without any downloads

      ● We provide a command-line tool that provides easy access to full models.

      We have made some changes to bring out these points more clearly in the text:

      "Since natural protein sequences can vary in length by at least three orders of magnitude, this pooling is advantageous because it allows our model to accommodate sequences of arbitrary length without imposing restrictive modeling assumptions or computational burdens that scale with sequence length. In contrast, many previous approaches operate on fixed sequence lengths: these techniques are unable to make predictions for proteins larger than this sequence length, and use unnecessary resources when employed on smaller proteins."

      We have added a table that sets out the vocabulary sizes used in our work (5,134 for EC and 32,109 for GO):

      "Gene Ontology (GO) terms describe important protein functional properties, with 32,109 such terms in Swiss-Pr ot (Table S6) that cov er the molecular functions of proteins (e.g. DNA-binding, amylase activity), the biological processes they are involved in (e.g. DNA replication, meiosis), and the cellular components to which they localise (e.g. mitochondrion, cytosol)."

      A second weakness is that it was not clear what new insights the UMAP projections of the sequence embedding could offer. For example, the authors mention that "a generalized mapping between sequence space and the space of protein functions...is useful for tasks other than those for which the models were trained." However, such tasks were not explicitly explained. The hierarchical clustering of enzymatic proteins shown in Fig. 5 and the clustering of non-enzymatic proteins in Fig. 6 are consistent with the expectation of separability in the high-dimensional embedding space that would be necessary for good CNN performance (although the sub-groups are sometimes not well-separated. For example, only the second level and leaf level are well-separated in the enzyme classification UMAP hierarchy). Therefore, the value-added of the UMAP representation should be something like using these plots to gain insight into a family or sub-family of enzymes.

      We thank the reviewer for highlighting this point. There are two types of embedding which we discuss in the paper. The first is the high-dimensional representation of the protein that the neural network constructs as part of the prediction process. This is the embedding we feel is most useful for downstream applications, and we discuss a specific example of training the EC-number network to recognise membrane proteins (a property on which it was not trained): "To quantitatively measure whether these embeddings capture the function of non-enzyme proteins, we trained a simple random forest classification model that used these embeddings to predict whether a protein was annotated with the intrinsic component of membrane GO term. We trained on a small set of non-enzymes containing 518 membrane proteins, and evaluated on the rest of the examples. This simple model achieved a precision of 97% and recall of 60% for an F1 score of 0.74. Model training and data-labelling took around 15 seconds. This demonstrates the power of embeddings to simplify other studies with limited labeled data, as has been observed in recent work (43, 72)."

      As the reviewer points out, there is a second embedding created by compressing this high-dimensional down to two dimensions using UMAP. This embedding can also be useful for understanding the properties seen by the network, for example the GO term s highlighted in Fig. 7 , but in general it will contain less information than the higher-dimensional embedding.

      The clear presentation, ease of use, and computationally accessible downstream analytics of this work make it of broad utility to the field.

    1. Author Responses

      Reviewer #1 (Public Review):

      The authors present a very detailed short report on a previously undocumented behaviour where flying squirrels are believed to have created grooves in various species of nuts to aid their secure storage in the crotch or forks of twigs. The behaviour is suggested to have evolved as an adaptive strategy in this population of flying squirrels because of the challenges for nut caching in a rainforest environment.

      Thanks

      Using detailed photographs, GPS locations, measurements and camera trap videos, the authors describe the behaviour in great depth providing a useful base for comparative and future studies. However, the weakest point of this study is that the authors did not detect any squirrels making the grooves and only monitored nuts once they were cached. Therefore more research needs to be done to ascertain who, how and where the grooves are produced in the first place.

      Three new videos are attached to show that two squirrel species are rotate and carving the nuts to create the grooves. By the new videos, we can also observe that squirrels re-fixed the nuts between the twigs by carving the nuts. These direct observations can support the claim better. See Supplementary Media files 6-8.

      This work will be of great interest to scholars of animal behaviour and cognition and draws attention to a novel behaviour that warrants further study in similar species.

      Yes, it is. Thanks

      Reviewer #2 (Public Review):

      The authors describe observations of an innovative food caching behavior attributed to two species of flying squirrels and likened the behavior to architectural joints used by humans. The discovery of nuts stored in the crook of shrub branches, facilitated by indented rings seemingly carved by squirrels, possibly represents an interesting food handling innovation that may function to prevent spoilage in a damp tropical ecosystem.

      Thanks!

      I applaud the efforts to survey the area multiple times after the initial discovery, and the use of trail cameras to try capture evidence of animal associations. For what is in essence a natural history note, the authors did a great job of trying to gather a variety of supporting evidence. The videos capturing squirrels visiting and retrieving the cached nuts were compelling, and the shaking of the shrubs demonstrating the difficulty in dislodging the nuts helps build the case that the nuts are cached effectively.

      Thanks!

      The most glaring gap in the evidence is that there is no direct observation of the squirrels actually performing this nut carving behavior, only associating with the nuts after they have been cached.There must be more documentation provided to explicitly link the causality between squirrels and this caching innovation.

      We have included three additional videos to demonstrate that squirrels of both species rotate and carve the nuts to create the grooves. These new videos also show that squirrels can fit the nuts between twigs by carving the nuts. We think that these direct observations clearly support our claim, but agree that it was oversight not to included them in the first draft. See Supplementary Media files 6-8.

      The second major weakness is more to do with writing style and could be addressed with significant revisions to phrasing and development of ideas. This is namely to do with the claim that this is somehow an evolved behavior, without providing evidence that 1) it is indeed the squirrels performing this behavior, 2) that is confers some kind of fitness benefit, and 3) hard evidence that this caching method does indeed prevent decomposition/germination in comparison to the more traditional caching methods of these species. Given the limited geographic range of the observations, I wonder how much of this is actually attributable to learning and/or innovation by these individuals. These ideas are not developed fully, and sometimes the writing wanders among learning and evolution without exploring the deep links among the two concepts.

      1) As above, three new videos establish that the squirrels do, in fact, carve the nuts. See Supplementary Media files 6-8.

      2) We added more description to suggest how this behavior likely confers fitness benefit in the discussion. At this point, however, it is correct to say that we have no hard evidence to demonstrate this, and thus, we’ve attempted to ‘tighten up’ the discussion accordingly so that our arguments (and its limitations) are more understandable.

      3) We revised the statistics about the proportion of nuts that were fresh during each of the surveys, and added some references about how long is required for the nuts to germinate in natural conditions. L163-172.

      Third, the connection to architecture is attention-grabbing, but I'd like to see this fleshed out a bit more with more text description (and a visual here would help immensely).

      We added more description about how the grooving, caching and checking processes were performed by squirrels and how the principles of this suspension are similar to the mortise-tenon joint as employed by humans. L186-202. As above, three new videos are attached.

      Ultimately this work stands to potentially contribute a fascinating piece of evidence into the growing literature on animal cognition, spatial awareness, caching behavior, innovation, and adaptation, but currently, the claims are unsupported by the evidence presented.

      Thank you for your comments about the potential importance of our work on this interesting system. In this version we try to focus more tightly on the aspects for which we have new information to interpret.

      Reviewer #3 (Public Review):

      The authors were trying to describe and document the grooving behaviour of nuts in two species of flying squirrels (Hylopetes Phayrei electilis and H. alboniger) as well as related such behaviour to tool use or that the squirrels are smart. To achieve these objectives, the authors conducted three field surveys. They also set out a camera later to capture animal species that interacted with these nuts. They found that these nuts with grooves are fixed between twigs and can be found in different small plant species. Both species of squirrels made grooves a nut. More shallow grooves are found in nuts that are fixed on alive than dead trees. Ellipsoid nuts have deeper grooves than oblate nuts. They concluded that these nut grooving behaviours are evolved or learned in those flying squirrel populations, and related these behaviours to tool use as well as that the squirrels are smart.

      Thanks!

      One strength of this work is that the data were collected in the field, which may provide hard evidence with video footage showing the two flying squirrel populations made grooves on nuts as well as fixing them between twigs. This evidence will induce new interests to understand the causes and consequences of such nut grooving behaviour. It may be bold to claim that such behaviour involves advance cognition or cognitive process without proper, systematic, experiments. Accordingly, whether the squirrels are 'smart' remains unclear. The authors did well in describing and documenting the nut grooving behaviours of the two species of flying squirrels, which has achieved their first aim. However, as mentioned above, whether such behaviour is 'smart' will need more systematic investigations.

      We have removed the description about cognition or cognitive process in the paper, and the paper is focused on the grooving behavious. “Smart” is also removed, with other words used instead.

    1. Author Response

      Reviewer #2 (Public Review):

      By now, the public is aware of the peculiarities underlying the omicron variants emergence and dissemination globally. This study investigates the mutational biography underlying how mutation effects and epistasis manifest in binding to therapeutic receptors.

      The study highlights how epistasis and other mutation effect measurements manifest in phenotypes associated with antibody binding with respect to spike protein in the omicron variant. It rigorously tests a large suite of mutations in the omicron receptor binding domain, highlighting differences in how mutation effects affect binding to certain therapeutic antibodies.

      Interestingly, mutations of large effect drive escape from binding to certain antibodies, but not others (S309). The difference in the mutational signature is the most interesting finding, and in particular, the signature of how higher-order epistasis manifests in the partial escape in S309, but less so in the full escape of other antibodies.

      The results are timely, the scope enormous, and the analyses responsible.

      My only main criticisms walk the stylistic/scientific line: many of the others have pioneered discussions and methods relating to the measurement of epistasis in proteins and other biomolecules. While I recognize that the purpose of this study is focused on the public health implications, I would have appreciated more of a dive into the peculiarity of the finding with respect to epistasis. I think the authors could achieve this by doing the following:

      a) Reconciling discussions around the mutation effects in light of contemporary discussions of global epistasis "vs" idiosyncratic epistasis, etc. Several of the authors of the manuscript have written other leading manuscripts of the topic. I would appreciate it if the authors couched the findings within other studies in this arena.

      We added a discussion related to global epistasis at the end of the “Epistasis Analysis” methods section. We tried to highlight that the cause and relevance of global epistasis phenomena are quite different at molecular and at organismic level.

      B) While the methods used to detect epistasis in the manuscript make sense, the authors surely realize that methods used to measure is a contentious dimension of the field. I'd appreciate an appeal/explanation as to why their methods were used relative to others. For example, the Lasso correction makes sense, but there are other such methods. Citations and some explanation would be great.

      We added more context and justification in the methods section (Epistasis Analysis). We used Lasso correction not particularly to obtain a sparser representation of the epistasis coefficients (an assumption that is not always valid, particularly within proteins) but rather to reduce instabilities created by the Tobit model inference. In this inference, the model coefficients are unbounded. Thus, if one mutation causes a complete binding loss, all epistatic terms associated with this mutation are not constrained and can become very large in magnitude. A Lasso term with a small coefficient constrains these coefficients but will have a limited influence on the other coefficients.

      Lastly (somewhat relatedly), I found myself wanting the discussion to be bolder and more ambitious. The summary, as I read it, is on the nose and very direct (which is appropriate), but I want more: What do the findings say for greater discussions surrounding evolution in sequence space? For discussions of epistasis in proteins of a certain kind? In, my view, this data set offers fodder for fundamental discussion in evolutionary biology and evolutionary medicine. I recognize, however, the constraints: such topics may not be within the scope of a single paper, and such discussions may distract from the biomedical applications, which are more relevant for human health.

      But I might say something similar about the biomedical implications: the authors do a good job outlining exactly what happened, but what does this say about patterns (the role of mutations of large effect vs. higher-order epistasis) in some traits vs others? Why might we expect certain patterns of epistasis with respect to antibody binding relative to other pathogenic virus phenotypes?

      We agree that these are interesting questions, and have added a paragraph in the discussion to explore these points.

      In summary: rigorous and important work, and I congratulate the authors.

    1. Author Response

      Reviewer #1 (Public Review):

      In this work, the authors investigate a means of cell communication through physical connections they call membrane tubules (similar or identical to the previously reported nanotubes, which they reference extensively). They show that Cas9 transfer between cells is facilitated by these structures rather than exosomes. A novel contribution is that this transfer is dependent on the pair of particular cell types and that the protein syncytin is required to establish a complete syncytial connection, which they show are open ended using electron microscopy.

      The data is convincing because of the multiple readouts for transfer and the ultrastructural verification of the connection. The results support their conclusions. The implications are obvious, since it represents an avenue of cellular communication and modifications. It would be exciting if they could show this occurring in vivo, such as in tissue. The implication of this would be that neighboring cells in a tissue could be entrained over time through transfer of material.

      Thank the reviewer for his/her comments and suggestion. It’s possible that the thick tubular connections found in this study also exist in vivo. A previous study reported that TNT-like structures were found in mouse or human primary tumor cells (PMID: 34494703; PMID: 34795441). Our transfer assays could be adopted to evaluate such transfer in primary cultures and in vivo. We anticipate this for future work.

      Reviewer #2 (Public Review):

      There is a lot of interest in how cells transfer materials (proteins, RNA, organelles) by extracellular vesicles (EV) and tunneling nanotubes (TNTs). Here, Zhang and Schekman developed quantitative assays, based on two different reporters, to measure EV and direct contact-dependent mediated transfer. The first assay is based on transfer of Cas9, which then edits a luciferase gene, whose enzymatic activity is then measured. The second assay is based on a split-GFP system. The experiments on EV trafficking convincingly show that purified exosomes, or any other diffusible agent, are unable to transfer functional Cas9 (either EV-tethered or untethered) and induce significant luciferase activity in acceptor cells. The authors suggest a plausible model by which Cas9 (with the gRNA?) gets "stuck" in such vesicles and is thus unable to enter the nucleus to edit the gene.

      To test alternative pathways of transfer, e.g. by direct cell-cell contact, the authors co-cultured donor and acceptor cells and detect significant luciferase activity. The split GFP assay also showed successful transfer. The authors further characterize this process by biochemical, genetic and imaging approaches. They conclude that a small percentage of cells in the population produce open-ended membrane tubules (which are wider and distinct from TNTs) that can transfer material between cells. This process depends on actin polymerization but not endocytosis or trogocytosis. The process also seems to depend on endogenously expressed Syncytin proteins - fusogens which could be responsible for the membrane fusion leading to the open ends of the tubules.

      The paper provides additional solid evidence to what is already known about the inefficiency of EV-mediated protein transport. Importantly, it provides an interesting new mechanism for contact-dependent transport of cellular material and assigns valuable new information about the possible function of Syncytins. However, the evidence that the proteins and vesicles transfer through the tubules is incomplete and a few more experiments are required. In addition, certain inconsistencies within the paper and with previous literature need to be resolved. Finally, some parts of the text, methods and the figures require re-writing or additional information for clarity.

      Major comments

      1) In Figure 1F, the authors compare the function of exosome-transported SBP-Cas9-GFP vs. transient transfection of SBP-Cas9-GFP. It is not clear if the cells in the transiently transfected culture also express the myc-str-CD63 and were treated with biotin. It is important to determine if CD63-tethering itself affects Cas9 function.

      Thank the reviewer for his comments and suggestions. We now show in Figure 1- figure supplement 1D that CD63-tethering itself does not affect Cas9 function.

      2) The authors do not rule out that TNTs are a mode of transfer in any of their experiments. Their actin polymerization inhibition experiments are also in-line with a TNT role in transfer. This possibility is not discussed in the discussion section.

      Yes, the results in this study do not rule out a role for TNTs in the transfer. At present, we are not aware of conditions that would functionally distinguish transfer mediated by TNTs and thick tubules. We have now included this in the Discussion section.

      3) Issues with the Split GFP assay:

      a) On page 4, line 176, the authors claim that "A mixture of cells before co-culture should not exhibit a GFP signal". However, this result is not presented.

      The results of mixture experiment are included in Figure 2-figure supplement 1D, E.

      b) The authors show in Figure 2C and F that in MBA/HEK co-culture or only HEK293T co-culture, there are dual-labeled, CFP-mCherry, cells. First - what is the % of this sub-population? Second, the authors dismiss this population as cell adhesion (Page 5, line 192) - but in the methods section they claim they gated for single particles (page 17, line 642), supposedly excluding such events. There is a simple way to resolve this - sort these dual labeled cells and visualize under the microscope. Finally - why do the authors think that the GFP halves can transfer but not the mature CFP or mCherry?

      The plot in the Figure 2C and F are displayed in an all-cell mode, not in singlet mode. The percentage of dual-labeled CFP-mCherry in singlet was 0-0.2%. Thus, most of the signal was from doublet, or cell adhesion. We did not claim that the mature CFP or mCherry cannot be transferred. We suggested that the GFP signal of split-GFP recombination may be a more accurate reflection of cytoplasmic transfer between cells. In contrast, mature CFP or mCherry may simply attach to the cell surface but not enter into the other cells.

      c) In the Cas9 experiments - the authors detect an increase in Nluc activity similar in order of magnitude that that of transient transfection with the Cas9 plasmid - suggesting most acceptor cells now express Nluc. However, only 6% of the cells are GFP positive in the split-GFP assay. Can the authors explain why the rate is so low in the split-GFP assay? One possibility (related to item #2 above) is that the split-GFP is transferred by TNTs.

      The Cas9-based Nluc activity assay is more sensitive as it measures an enzyme with a very high turnover number. The split-GFP assay requires a transfer of GFP fragments to produce intact GFP molecules where the signal is not amplified. We think this explains the dramatic increase in a signal once Cas9 is transferred. Our cell sorting results suggest that at least 6% of the receptor cells are transferred in the co-cultures. Of course, nothing in either analysis rules out a role for TNTs in this transfer.

      4) The membrane tubules, the membrane fusion and the transfer process are not well characterized:

      a) The suggested tubules are distinct from TNTs by diameter and (I presume, based on the images) that they are still attached to the surface - whereas TNTs are detached. However, how are these structures different from filopodia except that they (rarely) fuse?

      We used TIRF microscopy and found that the thick tubules are not attached to the surface (not shown). Filopodia are much closer in diameter to TNTs (0.1-0.4 micron). The thick tubules we observe are much thicker (2-4 micron in diameter).

      b) Figure 5E shows that the acceptor cells send out a tubule of its own to meet and fuse. Is this the case in all 8 open-ended tubules that were imaged? Is this structure absent in the closed-ended tubules (e.g. as seen in Figures 6 & 8)?

      Around half of open-ended tubules appeared to emanate from acceptor cells. Likewise, for closed-ended tubules, for example, in Figure 6E where a recipient HEK293T cell projected a short tubule.

      c) The authors suggest a model for transport of the proteins tethered to vesicles (via CD63 tethering). However, the data is incomplete.

      i) They show only a single example of this type of transport, without quantification. How frequent is this event?

      The transport of the proteins tethered to vesicles (via CD63 tethering) were found in all 8 open-ended tubules that we detected in this study.

      ii) Furthermore, the labeling does not conclusively show that these are vesicles and not protein aggregates. Labeling of the vesicle - by dye or protein marker will be useful to determine if these are indeed vesicles, and which type.

      In Figure 4B, the moving punctum in a tubular connection appears to contain SBP-Cas9-GFP, Streptavidin-CD63-mCherry, and the cell surface WGA conjugate that may have been internalized into a donor cell endosome, which indicates that the moving punctum is vesicle type. Nonetheless, in general we cannot distinguish the forms of Cas9 that are transferred and become localized to the nucleus of target cells and we make no claim other than to suggest this possibility that Cas9 may be transferred as an aggregate.

      iii) The data from Figure 2 suggest (if I understand correctly) transfer of the CD63-tethered half-GFP, further strengthening the idea of vesicular transfer. However, the authors also show efficient transfer of untethered Cas9 protein (Figure 2A and other figures). Does this mean that free protein can diffuse through these tubules? The Cas9 has an NLS so the un-tethered versions should be concentrated in the nucleus of donor cells. How, then, do they transfer? The authors do not provide visual evidence for this and I think it is important they would.

      Based on the results using the Cas9-based luciferase assay (His- or SBP-tagged Cas9) (Figure 2A) and split-GFP assay (free GFP1-10) (Figure 2G), we suggest that free protein could be transferred between cells. Our current imaging approach is not designed to quantify protein diffusion. However, we are able to detect from images that Cas9-GFP does not colocalize exclusively with CD63 or concentrate in the nucleus, but also appears in the cytoplasm. These data indicate that both vesicle association and free diffusion may mediate the transfer through tubules. We thank the referee for emphasizing this issue which we will consider for future work to distinguish the transfer types through tubules.

      iv) In Figures 6 & 8, where transfer is diminished, there are still red granules in acceptors cells (representing CD63-mcherry). Does this mean that vesicles do transfer, just not those with Cas9-GFP? Is this background of the imaging? The latter case would suggest that the red granule moving from donor to acceptor cells in figure 4 could also be "background". This matter needs to be resolved.

      There are a few red puncta in the acceptor cell in Figure 6B. Since the acceptor cell is close to and overlapped with other donor cells containing CD63-mCherry, the red signal may, as the reviewer suggests, be from donor cells and not as a result of transfer through tubular connections. However, donor-acceptor cultures of HEK293T where transfer is not observed, little CD63-mCherry signal, for example, in Figure 6a, was seen in acceptor cells, even during several hours of observation (Figure 6- figure supplement video). A minor red signal could arise from exosomes secreted by donor cells that are internalized by acceptor cells. Images of single-culture receptor cells were added in Figure 4- figure supplement 1.

      For Figure 8, we used MDA-MB-231 syncytin-2 knock-down cells containing Fluc:Nluc:mCherry as the receptor cell, thus in these experiments the red signal most likely represents mCherry expressed in the acceptor cells.

      In Figure 4, we observed moving punctum in a tubular connection which contained co-localized green, red, and purple signals, corresponding to SBP-Cas9-GFP, streptavidin-CD63-mCherry, and the WGA conjugate, respectively. The video of punctum transport (Figure 4-figure supplement video) suggests that the red signal is not “background”.

      5) Why do HEK293T do not transfer to HEK293T?

      a) A major inexplicable result is that HEK293T express high levels of both Syncytin proteins (Figure 7 - supp figure 1A) yet ectopic expression of mouse Syncytin increases transfer (Figure 7E). Why would that be? In addition, Fig 3A shows high transfer rates to A549 cells - which express the least amount of Syncytin. The authors suggest in the discussion that Syncytin in HEK293T might not be functional without real evidence.

      We cannot yet explain why the basal level of syncytin expressed in HEK293 cells is insufficient to promote open-ended tubular connections between these cells. It could be that the proteins are not well represented in a processed form at the cell surface. Nonetheless, ectopic expression of mouse syncytin-A in HEK293T produced some increased transfer but less than when syncytin-A is ectopically expressed in MDA-MB-231 cells (up to 4-fold vs. 30-fold change of Nluc/Fluc signal) (Figure 7E). Furthermore, we have added new results which show that apparent furin-processed forms of syncytin-A, -1 and -2 can be detected by cell surface biotinylation in transfected MDA-MB-231 cells (Figure 8-figure supplement 1D). All we demonstrate is that syncytin in the acceptor cell is required for fusion and we make no claim that it is the only protein or lipid at the cell surface in the acceptor cell required for fusion. Clearly, more work is essential to establish the complexity of this fusion reaction.

      For A549 cells, syncytin-1 is highly expressed in A549 cells, thus it is possible that syncytin-1 in A549 plays crucial roles in the process.

      b) In addition - previous publications (e.g. PMID: 35596004; 31735710) show that over expression of syncytin-1 or -2 in HEK293T cells causes massive cell-cell fusion. The authors do not provide images of the cells, to rule out cell-cell fusion in this particular case.

      Overexpression of syncytin-1 or -2 in cells indeed causes massive cell-cell fusion, while overexpression of syncytin-A induced much less cell fusion than syncytin-1, or -2. We have now added new images shown in Figure 8-figure supplement 1A-C to document these observations. It may be that overexpressed human syncytins are better represented in a furin-processed form in both cell types. In contrast, we did not observe donor-acceptor cell fusion at basal levels of expression of syncytin in HEK293T and MDA-MB-231. For example, the Figure 4-figure supplement video shows that tubular structures were seen to form and break during the course of visualization with a tubule fusion event but no cell fusion to form heterokaryons.

      Reviewer #3 (Public Review):

      In this manuscript, Zhang and Schekman investigated the mechanisms underlying intercellular cargo transfer. It has been proposed that cargo transfer between cells could be mediated by exosomes, tunneling nanotubes or thicker tubules. To determine which process is efficient in delivering cargos, the authors developed two quantitative approaches to study cargo transfer between cells. Their reporter assays showed clearly that the transfer of Cas9/gRNA is mediated by cell-cell contact, but not by exosome internalization and fusion. They showed that actin polymerization is required for the intercellular transfer of Cas9/gRNA, the latter of which is observed in the projected membrane tubule connections. The authors visualized the fine structure of the tubular connections by electron microscopy and observed organelles and vesicles in the open-ended tubular structure. The formation of the open-ended tubule connections depends on a plasma membrane fusion process. Moreover, they found that the endogenous trophoblast fusogens, syncytins, are required for the formation of open-ended tubular connections, and that syncytin depletion significantly reduced cargo Cas9 protein transfer.

      Overall, this is a very nice study providing much clarity on the modes of intercellular cargo transfer. Using two quantitative approaches, the authors demonstrated convincingly that exosomes do not mediate efficient transfer via endocytosis, but that the open-ended membrane tubular connections are required for efficient cargo transfer. Furthermore, the authors pinpointed syncytins as the plasma membrane fusogenic proteins involved in this process. Experiments were well designed and conducted, and the conclusions are mostly supported by the data. My specific comments are as follows.

      1) The authors showed that knocking down actin (which isoform?) in both donor and acceptor cells blocked transfer, and more so in the acceptor cells perhaps due to the greater knockdown efficiency in these cells. However, Arp2/3 complex knockdown in donor cells, but not recipient cell, reduced Cas9 transfer. It would be good to clarify whether the latter result suggests that the recipient cells use other actin nucleators rather than Arp2/3 to promote actin polymerization in the cargo transfer process. Are formins involved in the formation of these tubular connections?

      We thank the reviewer for his/her comments and suggestions. Beta-actin was knocked down in this study. We tried a formin inhibitor, SMIFH2 which resulted in a decrease the Cas9 transfer between cells (Figure 3F).

      2) The authors provided convincing evidence to show that the tubular connections are involved in cargo transfer. Intriguingly, in Figure 4-figure supplement video (upper right), protein transfer appeared to occur along a broad cell-cell contact region instead of a single tubular connection. How often does the former scenario occur? Is it possible that transfer can happen as long as cells are contacting each other and making protrusions that can fuse with the target cell?

      In the Figure 4-figure supplement video (upper right), it may be that several membrane tubes from several different donor cells contact at sites close to one another on the recipient cell resulting in the appearance a broad cell-cell contact. This was a rare observation. In our quantification, only 8 connections were open-ended in 120 cell-cell contact junctions. Once open-ended, or plasma membrane fused, cargo transfer is observed.

      3) The requirement of MFSD2A in both donor (HEK293T) and recipient (MDA-MB-231) cells is consistent with a role for syncytin-1 or 2 in both types of cells. Since HEK293T cells contain both syncytins and MFSD2A but cargo transfer does not occur among these cells, does this suggest that syncytins and/or MFSD2A are only trafficked to the HEK293T cell membrane in the presence of MDA-MB-231 cells?

      A proper answer to this question requires the visualization of syncytins and MFSD2A. The commercial syncytin antibodies were inadequate for immunofluorescence. In advance of the more detailed effort required to tag the genes for endogenous syncytin 1 and 2, we performed live cell imaging and surface biotin labeling of cells transiently transfected to express fluorescently-tagged forms of syncytin-1, -2 and -A. We now show that syncytin-A, -1, and -2 partially localize to the plasma membrane or the cell surface of MDA-MB-231 and at points of cell-cell contact. In fact, overexpression of codon-optimized human syncytin-1, and -2 induced dramatic HEK293T cell-cell fusion. However, at basal levels of syncytin expression, HEK293T could not form open-ended tubular connections, which may be because the basal level of syncytins are not well represented in a processed form at the cell surface or their activity is limited by unknown factors.

      As an independent test of cell surface localization, we used surface biotinylation to show that a fraction of the syncytins can be labeled externally (Figure 8-figure supplement 1D). This fraction shows evidence of proteolytic processing consistent with furin cleavage whereas the overwhelming majority of transfected syncytins detected in a blot of lysates suggests that most remain in the unprocessed precursor form, consistent with the punctate and reticular fluorescence images (Figure 8-figure supplement 1A-C).

      We used IF and GFP-tagged MFSD2A and found this protein partially localized to the plasma membrane of HEK293T cells (Figure 9E, F). Given the results reveal that cargos could be transferred among MDA-MB-231 cells (Figure 2G), syncytin and its receptor appear to function in transfer among these cells.

    1. I mentioned that I regarded this as a discovery. I would like to amplify that statement. We have known for centuries that catharsis and emotional release were helpful. Many new methods have been and are being developed to bring about release, but the principle is not new. Likewise, we have known since Freud's time that insight, if it is accepted and assimilated by the client, is therapeutic. The principle is not new. Likewise we have realized that revised action patterns, new ways of behaving, may come about as a result of insight. The principle is not new.

      Rogers acknowledges the success of the client focused therapy approach. In this passage he explains that the client focused therapy approach has developed expectations a guide so to speak. The therapist has a great idea or as he put it predicted the outcome of the therapy. I think this is important because the therapist can say if therapy is indeed working. Blueprint.

    1. Racism, sexism, ageism, homophobia, some social movements asserted,are distinct forms of oppression with their own dynamics apart from the dynamics of class, even though they may interact with class oppression.

      I like how the author highlights different forms of oppression that people may not think of. We usually automatically think of racism when it comes to oppression, but oppression comes in all types of forms. Referring back to the beginning of 'Oppression as a structural concept,' I think people would not term oppression to many situations because it is thought that to be oppressed there must be some type of dictator/tyrant in rule, but that is not the case. We fail to forget the meaning of oppression which from Merriam-Webster Dictionary means "unjust or cruel exercise of authority or power." We see this unjust authority of power everyday in society in which people exercise their privilege in unjust ways towards the less privileged. One example of this in our society is the murder of George Floyd in which one person unjustly and inhumanely took advantage of their power/authority over another. This is oppression, and we tend to not think of situations like this one, that happen on a daily basis, as such.

    2. Here she does answer one of my questions. I was wondering how to criteria was going to be applied. According to her, presence of any of these categories is grounds for labeling the group as oppressed, which makes sense. However, I do think this may be slightly problematic because it almost equalizes oppression between groups. This is not to say that we should be comparing who is more oppressed than who BUT I do think there are some groups that need a little more attention than others in certain aspects of life.

    3. Social justice, I shall arguein later chapters, requires not the melting away of differences, but institutionsthat promote reproduction of and respect for group •differences withoutoppression

      I agree with this. I don't believe it possible to eliminate groups and I don't think it is necessary. We are all unique and our uniqueness helps us identify who we are. We can't strip that away. Just the way we view other peoples uniqueness need to change. Our differences shouldn't be criticized. Some people may not agree with some differences but that doesn't mean they should disrespect it either. It is unrealistic to think people will accept everything but as long as it is respected than we can have group differences without oppression.

    1. Author Response

      Reviewer #1 (Public Review):

      1) The authors show that there are several classes of Snf1 targets (Fig. 3e), most notably some that are phosphorylated immediately after Snf1 activation by glucose (<5 min) and others that are only phosphorylated after 15 min. In a simple view, all direct Snf1 targets should be phosphorylated immediately after Snf1 activation. Is that the case? What is the overlap between the direct targets found using the OBIKA assay and the slow and fast responding in vivo targets? What about the phosphorylation motif, does it differ between the groups? These points are not discussed in the text except to point out that the direct Snf1 target Msn4 is among the slowly phosphorylated group.

      This is a very good point and we have performed the suggested analysis, which resulted in an interesting finding that we describe now in the text as follows:

      “Notably, of the 145 confirmed target sites, 81 (i.e. 72%) were significantly regulated after both 5 min and 15 min. Of the remaining 64 sites, 32 responded only after 5 min, while the other 32 responded only after 15 min. Some of the former residues are located within Snf1 itself, the -subunit of the Snf1 complex (i.e. Sip1), the Snf1-targeting kinase Sak1, or Mig1, while some of the latter are located within the known Snf1-interacting proteins such as Gln3, Msn4, and Reg1. These observations indicate that Snf1-dependent phosphorylation initiates, as expected, within the Snf1 complex and then progresses to other effectors. Interestingly, based on the residues that responded exclusively after 5 min, we retrieved a perfect Snf1 consensus motif (i.e. an arginine residue in the -3 position and a leucine residue in the +4 position; Supplementary figure 2A). The one retrieved for the residues that respond exclusively at 15 min, in contrast, significantly deviated from this consensus motif (Supplementary figure 2B). The slight temporal deferral of Snf1 target phosphorylation may therefore perhaps in part be explained by reduced substrate affinity due to consensus motif divergence.”

      2) The data showing that Snf1-dependent phosphorylation of Pib2 plays a key role in triggering inhibition of TORC1 is convincing but is entirely dependent on a rescue of the TORC1 inhibition defect seen in cells where Snf1 is inhibited. That is, TORC1 is normally inactivated during glucose starvation; this does not occur when Snf1 is inhibited by 2nm-pp1 but does occur when Snf1 is inhibited in a strain carrying a phosphomimetic version of Pib2 (Pib2SESE). This indicates that Pib2 phosphorylation is sufficient to replace Snf1 signaling and inhibit TORC1 during glucose starvation. However, in a simple model, a phosphodead version of Pib2 (SASA) should have the opposite effect. That is TORC1 should remain active during glucose starvation in the Pib2SASA strain-but that is not the case (Fig. 4g). This point is not discussed in the paper; why do the authors think that TORC1 is inhibited normally in the SASA mutant inhibits TORC1 normally?

      We fully agree with this statement and have highlighted and discussed this issue now in the last paragraph of the results section (where we think this fits best) as follows:

      “In contrast, the separated and combined expression of Sch9S288A and Pib2S268A,S309A showed, as predicted, no significant effect in the same experiment. Unexpectedly, however, the latter combination did not result in transient reactivation of TORC1, like we observed in glucose-starved, Snf1-compromised cells. This may be explained if TORC1 reactivation would rely on specific biophysical properties of the non-phosphorylated serines within Sch9 and Pib2 that may not be mimicked by respective serine-to-alanine substitutions. Alternatively, Snf1 may employ additional parallel mechanisms (perhaps through phosphorylation of Tco89, Kog1, and/or other factors; see above) to prevent TORC1 reactivation even when Pib2 and Sch9 cannot be appropriately phosphorylated. While such models warrant future studies, our current data still suggest that Snf1-mediated phosphorylation of Pib2 and Sch9 may be both additive and together sufficient to appropriately maintain TORC1 inactive in glucose-starved cells”

      Reviewer #2 (Public Review):

      1) Because PIB2 is a major focus of the manuscript, I was surprised that it was not discussed in the introduction. I think it would be appropriate to discuss prior evidence linking this protein to TORC1.

      We thank the reviewer for this suggestion. Pib2 and its role in TORC1 control is now described in the introduction.

      2) The authors introduce mutations into PIB2 at two sites determined to be phosphorylated by SNF1, at S268 and S309. Somewhat confusing results are obtained, in that the PIB2 null and phosphomimic mutants (S268E and S309E) confer a similar TORC1 phenotype, compared to the S268A S308A mutant. These results require further explanation than simply that "TORC1 inactivation defect in SNF1-compromised cells is due to a defect in PIB1 phosphorylation". This is particularly intriguing given that the opposite results are observed with the SCH9 mutants, where the null and alanine mutants confer a similar phenotype compared to the S to E mutants.

      The finding that both loss of Pib2 and expression of the phosphomimetic allele yield the same phenotype is indeed counterintuitive. Hence, we fully agree with the criticism put forward here. We believe that the underlying reason for our observation is based on the unique property of Pib2 in having both a C-terminal TORC1-activating domain (CAD) and an-N-terminal TORC1-inhibitory domain (NID). We have addressed this point briefly in the discussion ("Our current data favor a model according to which Snf1-mediated phosphorylation of the Kog1-binding domain in Pib2 weakens its affinity to Kog1 and thereby reduces the TORC1-activating influence of Pib2 that is mediated by the C-terminal TORC1-activating (CAD) domain via a mechanism that is still largely elusive"), but now also address this issue in the results section as suggested.

      3) The authors conclude, based on the co-IP data in Figure 4H, that interactions between KOG1 and PIB2 are direct. However, it remains possible that interactions between these proteins are mediated by other components of TORC1 or within cells. This should be addressed.

      Please note that the Kog1-Pib2 interaction has previously been demonstrated by different methods. Accordingly, Pib2 has not only been shown to interact with Kog1 (or TORC1) in co-IP studies in vivo (PMID: 30485160, PMID: 29698392), but also by co-IP studies in vitro (PMID: 29698392, PMID: 28483912, PMID: 34535752). In addition, the interaction between Kog1-Pib2 has also been dissected (down to defined domains) by classical two hybrid analyses (PMID: 28481201). All of these studies are cited now in the introduction where Pib2 is discussed.

      4) The authors demonstrate convincingly that the PIB2 and SCH9 SNF1-specific phospho-site mutants have a detectable effect on TORC1, primarily by examining TORC1-dependent phosphorylation of SCH9. What is unclear is whether phosphorylation at these sites has a significant physiological impact on cells. It appears that the rapamycin hyper-sensitivity displayed in Figure 6E is the only data presented to address this question. It would be appropriate for the authors to comment further on the significance of SNF1-dependent phosphorylation of these two substrates.

      To further address the physiological role of the Snf1-dependent phosphorylation of Sch9 and Pib2 combined, we newly assessed the growth rate of the strain that expresses the Sch9SE and Pib2SESE alleles combined. Accordingly, we found the snf1as pib2SESE sch9SE strain to exhibit a significantly higher doubling time than the snf1as strain on both low-nitrogen-containing media and standard synthetic complete media. This is now included in the text (results section).

      Reviewer #3 (Public Review):

      1) Conceptually, the manuscript shows that Snf1 activity is important for the acute inhibition of TORC1 during glucose starvation. However, this is mainly restricted to 10 and 15 minutes of glucose starvation. After 20 minutes, TORC1 is inhibited by some unknown mechanisms independent of Snf1 (Hughes Hallet et al). This raises concern regarding the physiological relevance of Snf1-mediated TORC1 inhibition during acute glucose stress. The authors show that this regulation is important for the survival of cells under TORC1 inhibition. How do the authors envision that the acute role of Snf1 plays an important long-term physiological relevance during rapamycin treatment? Providing more support for the physiological relevance of this regulation will make this study of interest to a broad readership.

      Please see our response to point 4 of reviewer #2.

      2) Another major concern of the manuscript is the inconsistencies between the various representative immunoblots and their quantifications. The effect of AMPK activity on TORC1 signaling under glucose starvation seems very subtle. A few specific concerns are mentioned below:

      a) In figure 1A, the increase in TORC1 activity upon inhibition of analogue sensitive Snf1as by 2NM-PP1 is very marginal. Although quantification shows a significant increase, a representative western blot figure should be shown.

      We have replaced the original immunoblots with more representative ones in Figure 1A.

      b) Does deleting Snf1 itself have any effect on TORC1 activity? Lane 4 of figure 1A shows reduced activity compared to lane 1.

      TORC1 activity is generally assessed as the ratio between phosphorylated Sch9 and total Sch9 (see also below under (e)). Accordingly, based on the quantification of 6 blots (we added two more experiments to address this point; Figure 1B), loss of Snf1 has no significant impact on TORC1 activity in exponentially growing cells, as we expected.

      c) To show the effect of Snf1 on the repression of TORC1, the time-course experiments are run on two separate gels in figure 1C. Hence, it is difficult to compare the effect of Snf1 on unscheduled reactivation of TORC1 under glucose starvation.

      Please note that the data of the two blots were cross-normalized to the sample from exponentially growing cells (labeled “Exp”; i.e. the same sample was loaded on the two blots) in order to compare and quantify the effects of Snf1.

      d) In figure 1E, the effect of Reg1 deletion on TORC1 activity seems minor as both phospho- and total levels of Sch9 are reduced.

      As correctly pointed out by this reviewer, we consistently found the total Sch9 levels to be lower in reg1Δ cells when compared to wild-type cells. To assess TORC1 activity, we therefore always determine the ratio between phosphorylated Sch9 and total Sch9, and the respective ratio is significantly different in reg1∆ cells when compared to wild-type cells. We speculate that the reduced Sch9 levels in this mutant are caused by the reduced growth rate (PMID: 22140226) and hence lower protein synthesis rate (to which translation of SCH9 mRNA may be specifically sensitive).

      Since further mechanistic insights are based on these initial findings of figure 1, solidifying these observations is very important.

      3) In figure S1, the analogue sensitive Snf1as shows significant reduction in its activity (reduced S79 phosphorylation of ACC1-GFP). This raises the concern of whether this genetic background is an ideal system to resolve the mechanism of TORC1 suppression.

      The Snf1as allele is indeed hypomorphic, which we acknowledge appropriately in the text. We would like to point out however, that we took great care in each experiment to include the DMSO control that allowed us to unequivocally assign any observed effects to the specific drug-mediated inhibition of Snf1as. Importantly, we think that the hypomorphic nature of the Snf1as allele (which allows normal growth on non-fermentable carbon sources) represents a minor trade-off when compared to the advantages that this allele provides over the use of a snf1∆ strain, which exhibits a fundamentally reprogrammed transcriptome/proteome (PMID: 17981722). Accordingly, this allele allows the assessment of Snf1 inhibition on very short time scales while minimizing confounding large-scale proteome rearrangements that may indirectly affect the studies. Moreover, use of the Snf1as allele also allowed us to compare our results more directly with other phosphoproteome studies that used the same allele (PMID: 25005228, PMID: 28265048). Finally, please also note that our main conclusions (on Snf1-mediated control of TORC1) are corroborated by additional genetic data such as the ones in Figure 1A/E where we use snf1∆ and reg1∆ cells.

      4) In figure 2, during glucose restimulation, there is increased retention of Snf1as-pThr210 in the presence of 2NM-PP1. This suggests that the upstream glucose sensing pathway as well as Snf1 might be more active than in DMSO-treated cells. This also raises concerns regarding the suitability of the genetic background for the study. Can authors comment on why this phosphorylation persists? Does the phosphoproteomic analysis give any hint for this phenotype?

      This is a very good point. In fact, we forgot to mention in the text that the observed effect of the 2NM-PP1 treatment on Snf1-Thr210 phosphorylation has already been studied and mechanistically explained earlier (PMID: 23184934). Accordingly, the entry of the drug into the broader catalytic cleft of the Snf1as mutant causes the catalytic domain to be stabilized in a conformation, which prevents dephosphorylation of pThr210 by the dedicated Glc7-Reg1 phosphatase heterodimer. This can be observed each time when we compared 2NM-PP1- and DMSO-treated cells and probed for Snf1-Thr210 phosphorylation. This is, in fact, an independent control for proper 2NM-PP1 functioning. We have now added a sentence (including reference) that pinpoints this issue in the text.

      5) In figure 4H, where authors claim reduced binding of Kog1 to Pib2SESE, levels of Kog1 in input are also reduced. Can authors provide further support using colocalization studies? Also, does Pib2SESE has any defect in forming Kog1 bodies?

      We took great care to load equal amounts of IPed Pib2-myc variants and then normalized the co-IPed Kog1-HA on the IPed Pib2-myc variant levels. The Kog1-HA input levels vary a bit between the 4 experiments, but they are on average not significantly lower in Pib2SESE-myc-expressing cells when compared to WT cells. In addition, in our Co-IP experiments, the beads are saturated with Pib2-myc variants and Kog1-HA levels are generally not limiting. We therefore deem it fair to say that the Pib2SESE has a reduced affinity for Kog1. Based on our experience with other co-localization studies of membrane-bound proteins and protein complexes (e.g. TORC1 versus EGOC), we find it extremely difficult to quantify local interactions by fluorescence microscopy (unless they are close to all or nothing). In this case, where we have a partial defect in the interaction between Kog1 and Pib2SESE, we anticipate that such analyses will not allow us to draw additional conclusions.

      Regarding the issue of Kog1/TORC1-body formation: all of our mutations in PIB2 and SCH9 were introduced (by CRISPR-Cas9) in the genome of our snf1as strain, which was used throughout this study. To analyze Kog1/TORC1-bodies, we have therefore first tried to C-terminally tag KOG1 with GFP in the genome of our strain background (similarly as was done in the original description of Kog1 bodies; PMID: 26439012). However, because all our attempts failed to create KOG1-GFP in our strain, we assumed that this construct may be lethal in our strain background. This is not completely unexpected, as it is known that the Kog1-GFP allele is hypomorphic and temperature sensitive (PMID: 19144819). In an alternative approach, we have therefore set out to study TORC1 body formation in our strains by using a GFP-TOR1 allele that can be integrated into the genome and that expresses functional TORC1 (PMID: 25046117). As we have described earlier, the respective GFP-Tor1 construct localized on vacuolar membranes and on foci that we previously have shown to correspond to signaling endosomes (PMID: PMID: 30732525, 30527664). Unexpectedly, however, when we starved the respective cells for glucose, the number of GFP-Tor1 foci did only marginally increase (20%) in our strain background over a period of up to 1 hour. Given these various unexpected issues, we prefer to not include any of these preliminary data in the current version of our manuscript, but to rather follow up on these observations in a separate study. We deem this particularly justified as the current literature on TORC1-body and TOROID formation also appears controversial and may need further clarification. For instance, while TORC1-body formation has been suggested to represent a Snf1-dependent process that is dispensable for TORC1 inhibition (PMID: 30485160), TOROID formation has been suggested to represent a Snf1-independent process that is mechanistically linked to TORC1 inhibition (PMID: 28976958).

      6) In figure 5F, where the authors claim the Sch9SE mutant has lower TORC1 activity, the difference is very minor. Furthermore, corresponding lanes also show reduced levels of Snf1as expression. Hence, improved blots are required here. Also, an in vitro kinase assay with full-length Sch9 KD with and without the Ser288 mutation could solidify the observation that phosphorylation of Ser288 indeed affects TORC1-mediated phosphorylation.

      We have replaced the blots in Figure 5F with an alternative set that more clearly highlights the (statistically significant) differences, while also exhibiting more equal levels of Snf1as levels. Regarding the in vitro kinase assays: we have repeatedly tried to perform TORC1 kinase assays on full length Sch9KD without success. We currently believe that proper TORC1-mediated phosphorylation of Sch9 may have to occur on membranes to which both TORC1 and Sch9 are tethered through phospholipid interactions (PMID: 29237820). We are trying to set up such a system on liposomes, but we assume that this will be a major effort that cannot be resolved in due time.

      7) In figure 6E, the Sch9SE mutant shows no effect in the presence of rapamycin. Thus, in vivo, phosphorylation at Ser288 may not be perturbing the phosphorylation of Sch9 by TORC1.

      When cells are grown on glucose where TORC1 is highly active (as in Fig. 6E or 6A/B in Exp), expression of Sch9SE has no significant effect indeed. However, in glucose-starved cells, where TORC1 activity is low, expression of the Sch9S288E allele clearly and significantly contributes to inhibition of Sch9-Thr737 phosphorylation by TORC1 (Figure 6A/B and Figure 5F/G).

      8) According to the author's proposed mechanism, TORC1 activity in Pib2SASA or Pib2SASA/Sch9SA backgrounds should be higher during glucose starvation compared to the control strains. However, glucose starvation shows a similar level of reduction in TORC1 activity in these backgrounds. This raises concern regarding the proposed mechanism. The authors mainly base their conclusions on Ser to Glutamate mutants. The authors should be cautious that Ser to Glutamate changes may also affect the protein structure which can confer similar phenotypes. How do the authors justify this discrepancy?

      Please see our response to point 2 of reviewer #1.

    1. Author Response

      Reviewer #1 (Public Review):

      The authors sequence some of the oldest maize macroremains found to date, from lowland Peru. They find evidence that these specimens were already domesticated forms. They also find a lack of introgression from wild maize populations. Finally, they find evidence the Par_N16 sample already carried alleles for lowland adaptation.

      Overall I think this is an interesting topic, the study is well-written and executed for the most part. I have a variety of comments, most important of which revolve around methodological clarity. I will give those comments first.

      1) The authors should say in the Results section how "alleles previously reported to be adaptive to highlands and lowlands, specifically in Mesoamerica or South America" were identified in Takuno et al. 2015. What method was used? I see this partly comes in the Discussion eventually, but it would help to have it in the Results with more detail. The answer to this question would help a skeptical reader decide the appropriateness of the resource, given that many selection scans have been performed on maize genomes, the choice would ideally not be arbitrary.

      This was explained in more detail in the Material and Methods section, to keep the Results and Discussion sections more concise. However, we agree that adding a brief explanation in the Results section would be useful and we have modified the revised version accordingly. Now the relevant part of the section Specific adaptation to lowlands in Mesoamerica and South America reads as follows: “To assess this, we identified in Par_N16 all covered SNPs with alleles previously reported to be adaptive to highlands and lowlands, specifically in Mesoamerica or South America by Takuno and coworkers (Takuno et al., 2015). These authors used genome-wide SNP data from 94 Mesoamerican and South American landraces and identified SNPs with significant FST values to infer which allele was likely adaptive. For example, those SNPs showing significant FST only in Mesoamerica, were characterized as adaptive for lowlands if they were at high frequency in the lowland population and at low frequency in the highland population, and vice versa. The same was applied for South America (Takuno et al., 2015). They identified 668 Mesoamerican and 390 South American previously reported adaptive SNPs, from which 32 and 20 were covered in Par_N16, respectively.”

      2) How were the covered putative adaptive SNPs distributed in the genome? Were any clustered and linked? The random sampled SNPs should be similarly distributed to give an appropriate null.

      The SNPs in Takuno et al. (2015) are in general at a median distance of 353 bp from each other. The 20 adaptive sites covered in Par_N16 for South America (SA) are at a median distance of 8,301,843 bp (approximately 8.3 Mbp), while the 32 for Mesoamérica (MA) are at a median distance of 24,295,968 bp (approximately 24.3 Mbp). SNPs in five pairs from Mesoamerica are closer than 100 bp between them, but each pair is at a considerable distance (beyond 1 cM) from each other and from other SNPs covered in Par_N16. This same happens for only one SNP pair from South America. Then, in general, the covered adaptive SNPs are not clustered. For our random samples, the range of genomic distances between SNPs is similar to those of adaptive SNPs. This shows that our null distributions are adequate for our statistical purposes. The genomic positions of covered adaptive sites in Par_N16 are now included in a new Table in the revised version (Supplementary File 2). We have included these observations in the main text (section Specific adaptation to lowlands in Mesoamerica and South America), as follows: “In general, adaptive SNPs represented in Par_N16 were not clustered. The 20 South American adaptive SNPs are at a median distance of 8,301,843 bp, while the 32 Mesoamerican SNPs are at a median distance of 24,295,968 bp (Supplementary File 2). SNPs in five pairs from MA are closer than 100 bp between them, but each pair is at a considerable distance (beyond 1 cM) from each other and from other SNPs. This same happens for only one SNP pair from SA. Then, although at low proportions, the adaptive SNPs in Par_N16 are a bona fide representation of different genomic responses to selection pressures...” and “We analyzed some of these random samples and observed a similar behavior as the adaptive SNPs regarding the range of distances between SNPs (Fig, S18).”

      3) How is genetic similarity calculated? It should be briefly described in the Results.

      This is formally explained in the Material and Methods section, but now we have included a brief description in the Results section (Specific adaptation to lowlands in Mesoamerica and South America) as follows: “The allelic similarity is the average of the frequencies of the Par_N16 alleles in the intersected sites with each test population (see Material and Methods).”

      4) It would help for the authors to state why they focus on Par_N16, I did not see this in my reading. Presumably, the analyses done are because of the higher quality data, but it would also help to mention why Par_N16 was sequenced in an additional run.

      Indeed, Par_N16 has an endogenous DNA content of 1.1 %, while the other two samples presented a very low DNA content (0.2%). Therefore, we decided to invest more in the best sample, as a cost/benefit decision for additional sequencing. We have included brief explanations of this in the revised text. In the Results section Paleogenomic characterization of ancient maize samples, it reads as follows: “Due to its higher endogenous DNA content (one order of magnitude larger, we further sequenced the Par_N16 library, obtaining 459M additional reads, to generate a total of 851M for this sample (Table 2).” and “To determine if the specific elimination of C to T and G to A modifications could bias the results in favor of maize rather than teosinte alleles, an additional database was generated in which all transitions were eliminated (i.e., only transversions were included) in Par_N16 only, because it was the only sample with enough sequencing data to conduct this experiment.” While in the section Tests of gene flow from mexicana, is as follows: “Par_N16 was the only sample with enough DNA sequence data to perform this analysis. All the samples showed the same phylogenetic position; therefore, Par N 16 was considered to be representative of ancient Paredones maize.”

      5) In the sections on phylogenetic analysis, introgression, and D statistics, the authors could do a better job specifically indicating how the results support their conclusions.

      Precise indications of how our results support our conclusions are given in the Discussion section. Nevertheless, we added relevant sentences in the specified sections. In the section Relationship between ancient maize, extant landraces, and Balsas teosinte, we added the following: “Thus, based on genome-wide relatedness, Paredones maize clusters with extant domesticated Andean landraces, supporting both, a single origin for maize and that these Peruvian samples were already domesticated.” In the section on introgression and D-statistics (Tests of gene flow from mexicana), we improved the last sentence as follows: “These results consistently show the absence of significant gene flow between Par_N16 and mexicana, implying that the lineage that gave rise to Paredones maize left Mesoamerica without relevant introgressions from this teosinte.”

      Reviewer #2 (Public Review):

      In this foundational article, the authors conduct an ancient DNA characterization of maize unearthed in archaeological contexts from Paredones and Huaca Prieta in the Chicama river valley of Peru. These maize specimens were recovered by painstakingly controlled excavation. Their context would appear to be beyond reproach though the individual radiocarbon determinations should be subject to further scrutiny.

      1) Radiocarbon determination for at least one of the maize cobs analyzed for aDNA is not a direct date, but dates associated material. The authors should provide a table of the direct dates on the specimens that were analyzed for ancient DNA. They should also specify the type and quantity of material sent and whether the cob, glumes, pith, or husks were submitted for dates. Include δ13C determinations for each cob with laboratory analysis numbers because there is justifiable concern that at least one of these cob dates has a δ13C value suggesting the material dated is not maize. Generally, the δ13C for maize ranges from -14 to -7. One or more of the specimens subjected to ancient DNA analysis in this paper have δ13C values far outside of this confidence interval.

      The indirect radiocarbon date on a maize cob was derived from a single piece of wood charcoal in a hearth directly associated with the analyzed cob, both embedded in a thin intact floor in Unit 20 at the Paredones site. The assay on the charcoal and the floor are in an undisturbed stratigraphic context and are in agreement with assays on other maize and charcoal remains in floors both above and below the hearth. We have included this information in Table 1 in the revised version. The information sought by Reviewer 2 on the studied cobs was published previously in Grobman et al. 2012 and in Dillehay 2017. Since details of the cobs were published, we decided to submit only what we thought were pertinent data for this manuscript.

      As for the δ13C reading of one cob outside of the confidence interval for maize, the dated specimen with this value is a maize husk fragment. Both the macro- and micro-morphology and the ancient DNA analysis of the husk demonstrated it was maize. We do not understand what affected the δ13C value for this specimen. Similarly, three human skeletons from deeper site levels have δ13C values greater than the expected range for human remains.

      2) From the perspective of future scientists being able to repeat the analyses performed here, I would hope that all details of specimen treatment, extraction methods, read length and quality would need to be assiduously described. Routine analytical results should be reported so that comparisons with earlier and future results are facilitated, and not made difficult to decipher or search for.

      The general procedures for accurate ancient DNA extraction were described in Vallebueno-Estrada et al. 2016 and we do not see the need to repeat this information in this article. Specific aspects of sample treatment and DNA extraction of the samples analyzed here are described in the Material and Methods, section on Extraction and sequencing of ancient samples. Results on quality (percentage of endogenous DNA, quality-filtered reads, mapped reads to either repetitive or unique regions, amount of sequence mapped, mapping Phred scores, estimated error rates, percentage of deamination, fragment median lengths, percentage of sites with signatures of molecular damage, number of unique genomic sites covered and their corresponding average sequencing depth) are described in the Results, section Paleogenomic characterization of ancient maize samples. This section also includes the number of SNPs in relation to the reference and the number of intersected SNPs between our samples and the HapMap3 database. In addition, complementary information to this section is included in Tables 2-4 and supplementary Figures S2-S6, as properly referenced in the last mentioned section.

      3) The aDNA analysis may or may not be affected by the anomalous δ13C values but one would anticipate that standard aDNA extraction and analysis protocols would provide a means by which the specimen's preservation of the specimens could be ascertained, for example, perhaps deamination and fragmentation rates could be compared or average read length evaluated with modern-contemporary materials so that preservation of the Paredones samples relative to that of maize in the CIMMYT germplasm bank and the San Marcos specimens investigated by the same researchers can be evaluated.

      Average read length from contemporary material depends more on the sequencing platform than sample preservation. For example, Illumina can only read fragments of hundreds of base pairs, while MinIon or PacBio can read fragments in the order of kb. Also, deamination is not an issue in DNA extracted from modern material (unless bisulfite is used for methylation detection). Comparison with San Marcos samples indicates that Paredones samples are heavily degraded, although this is not a function of time only (humidity, temperature, and pH are among other relevant factors). Therefore, to avoid misleading interpretations, we are not including a comparison with San Marcos samples in the revised version.

      4) The size and shape of the cobs depicted are similar to specimens occurring much later in Mesoamerican assemblages. For example, the approximate rachis diameter of the San Marcos specimens depicted by Valle-Bueno et al. (2016: Fig.1) averages less than 0.5cm while the specimens depicted in Valle-Bueno et al. (this manuscript) average 1.0 cm. The former - San Marcos - specimens are dated at 5300-4970 BP cal while the larger - Paredones - specimens date roughly 6777 - 5324 BP cal. The considerable disparity among the smaller more recent specimens compared to the very much larger putatively older specimens suggests the Paredones specimen's radiocarbon determinations are equivocal. The authors point this out but repeatedly state these cobs are the most ancient; a conundrum that should be resolved.

      Radiocarbon determinations in Paredones are not equivocal, on the contrary, they are perfectly in agreement with and supported by the unimpeachable stratigraphy of the site and by more than 150 other radiocarbon and OSL dates from Paredones and nearby excavated contexts. The difference in morphology between the more recent samples from Tehuacan and the more ancient samples from Paredones is exactly the paradox we try to address. Our results indicate that the rapid migration and adaptation of maize to the coast of Peru in comparison with a slower migration and adaptation to Tehuacan lands explains this apparent conundrum. This rapid movement and migration allowed the presence of more “modern” maize in Peru than in Tehuacan on the respective dates. This more rapid maize development also coincides with more rapid and advanced socio-cultural transformations in Peru, including proto-urbanism (i.e, first cities), early religious symbolism, long-distance irrigation canals, and other major innovations that far exceed what was happening in Mesoamerica at the time.

      5) I would suggest the authors consider redating these three specimens and if they do, hope that they will prepare the laboratory personnel with depositional environment information. MacNeish was skeptical about late dates on maize at Tehuacan, at first. Adovasio was initially certain about maize's associated dates from Meadowcroft. One would prefer to be reasonably certain the foundation this article creates is solid; the author's repeated reference to these cobs as the most ancient in the Americas should be reaffirmed so retraction will not be necessary.

      As discussed in Grobman et al. 2012 and in Dillehay 2017, we do not confide in C14 dating of unburned corn remains due to the possible intrusion of fungi in the soft cellular structure of cobs. The chrono-stratigraphically acceptable dates on cobs and other maize remains were taken on burned and hard tissue remains, such as husks. See detailed discussion in Supplementary Materials.

      MacNeish and Adovasio were excavating cave and rock shelter sites, which are known to often have areas of stratigraphically disturbed deposits. Paredones, Huaca Prieta, SR-18 and other Preceramic sites excavated in the study area here contain late to early varieties of maize and radiocarbon assays that are in chrono-stratigraphic agreement. As noted in the main text and in prior publications, these sites are open air localities with clear stratigraphy defined by intact floor and fill sequences, with no tree root, animal burrowing, or other major taphonomic disturbances.There were occasional hearths and pits (i.e., human burials) that intruded into deeper floor-fill sequences but none of the assayed and studied maize samples were derived from these contexts. Once again, we encourage readers to examine the stratigraphy shown in the main text and in Grobman et al. (2012) and Dillehay (2017). Moreover, as noted in the text, there is a growing number of Preceramic sites in South America that date between 6800 and 6000 years ago and later that contain micro-maize remains (see Kistler et al., 2018). Not all of these sites are well-dated and present reliable contexts, but several have good chrono-stratigraphic settings and micro-evidence (e.g., phytoliths, starch grains) indicative of a maize presence at or prior to 6000 years ago.

    1. First, I am a big fan of Chris’ posts. He is our best historian. Second, I did not challenge his ideas but asked for clarification about some terms which I believe are of general interest. Chris is well-positioned to answer my questions. Third, statistical mechanics is more about microscopic systems that do not evolve. As we know, ideas (from concepts to theories) evolve and generally emerge from previous ideas. Emergence is the key concept here. I suggested Phenomics as a potential metaphor because it represents well the emergence of some systems (phenotypes) from pre-existing ones (genotypes).

      reply to u/New-Investigator-623 at https://www.reddit.com/r/antinet/comments/10r6uwp/comment/j6wy4mf/?utm_source=reddit&utm_medium=web2x&context=3

      Ideas, concepts, propositions, et al. in this context are just the nebulous dictionary definitions. Their roots and modern usage have so much baggage now that attempting to separate them into more technical meanings is difficult unless you've got a solid reason to do so. I certainly don't here. If you want to go down some of the rabbit hole on the differences, you might appreciate Winston Perez' work on concept modeling which he outlines with respect to innovation and creativity here: https://www.youtube.com/watch?v=gGQ-dW7yfPc.

      I debated on a more basic framing of chemistry or microbiology versus statistical mechanics or even the closely related statistical thermodynamics, but for the analogy here, I think it works even if it may scare some off as "too hard". With about 20 linear feet of books in my library dedicated to biology, physics, math, engineering with a lot of direct focus on evolutionary theory, complexity theory, and information theory I would suggest that the underlying physics of statistical mechanics and related thermodynamics is precisely what allows the conditions for systems to evolve and emerge, for this is exactly what biological (and other) systems have done. For those intrigued, perhaps Stuart Kauffman's Origins of Order (if you're technically minded) or At Home in the Universe (if you're less technically oriented) are interesting with respect to complexity and emergence. There's also an interesting similar analogy to be made between a zettelkasten system and the systems described in Peter Hoffman's book Life's Rachet. I think that if carefully circumscribed, one could define a zettelkasten to be "alive". That's a bigger thesis for another time. I was also trying to stay away from the broad idea of "atomic" and drawing attention to "atomic notes" as a concept. I'm still waiting for some bright physicist to talk about sub-atomic notes and what that might mean... I see where you're going with phenomics, but chemistry and statistical mechanics were already further afield than the intended audience who already have issues with "The Two Cultures". Getting into phenomics was just a bridge too far... not to mention, vastly more difficult to attempt to draw(!!!). 😉 Besides, I didn't want Carol Greider dropping into my DMs asking me why didn't I include telomeres or chancing an uncomfortable LAX-BWI flight and a train/cab ride into Baltimore with Peter Agre who's popped up next to me on more than one occasion.

      Honestly, I was much less satisfied with the nebulousness of "solution of life"... fortunately no one seems to be complaining about that or their inability to grapple with catalysis. 🤷🏼

  6. blogs.baruch.cuny.edu blogs.baruch.cuny.edu
    1. objective,impersonal,formal,

      This may be surprising to hear from a journalism major, but I don't believe that objectivity truly doesn't exist in writing. We can try to remain as impartial as we can, but we are human and prone to error. Bias will slip in. However, a work that is truly objective would just be boring! Then there's the question of "seems objective to who?" To journalists like Wesley Lowrey, objectivity skews to what a perceived white and male audience will think. When one considers the realm of academia, do these "many professors" think the same? What does that say about academia itself?

    1. Set Clear Classroom Expectations For AI-Generated Writing

      I think this is crucial, along with educating the public, parents, students, other teachers about what it can and can't do. The more familiar people are then scarmongering and negative attitudes towards its use might be addressed quickly. Everyone is an expert and have their own views based on what they have read or been told so we may as well do what we can to promote ethical and sensible/useful examples of its application support teaching and learning.

    1. Are there symbols for 'supported by' or 'contradicted by' etc. to show not quite formal logical relations in a short hand?

      reply to u/stjeromeslibido at https://www.reddit.com/r/Zettelkasten/comments/10qw4l5/are_there_symbols_for_supported_by_or/

      In addition to the other excellent suggestions, I don't think you'll find anything specific that that was used historically for these, but there are certainly lots of old annotation symbols you might be able to co-opt for your personal use.

      Evina Steinova has a great free cheat sheet list of annotation symbols: The Most Common Annotation Symbols in Early Medieval Western Manuscripts (a cheat sheet).

      More of this rabbit hole:

      (Nota bene: most of my brief research here only extends to Western traditions, primarily in Latin and Greek. Obviously other languages and eras will have potential ideas as well.)

      Tironian shorthand may have something you could repurpose as well: https://en.wikipedia.org/wiki/Tironian_notes

      Some may find the auxiliary signs of the Universal Decimal Classification useful for some of these sorts of notations for conjoining ideas.


      Given the past history of these sorts of symbols and their uses, perhaps it might be useful for us all to aggregate a list of common ones we all use as a means of re-standardizing some of them in modern contexts? Which ones does everyone use?

      Here are some I commonly use:

      Often for quotations, citations, and provenance of ideas, I'll use Maria Popova and Tina Roth Eisenberg's Curator's Code:

      • ᔥ for "via" to denote a direct quotation/source— something found elsewhere and written with little or no modification or elaboration (reformulation notes)
      • ↬ for "hat tip" to stand for indirect discovery — something for which you got the idea at a source, but modified or elaborated on significantly (inspiration by a source, but which needn't be cited)

      Occasionally I'll use a few nanoformats, from the microblogging space, particularly

      • L: to indicate location

      For mathematical proofs, in addition to their usual meanings, I'll use two symbols to separate biconditionals (necessary/sufficient conditions)

      • (⇒) as a heading for the "if" portion of the proof
      • (⇐) for the "only if" portion

      Some historians may write 19c to indicate 19th Century, often I'll abbreviate using Roman numerals instead, so "XIX".

      Occasionally, I'll also throw drolleries or other symbols into my margins to indicate idiosyncratic things that may only mean something specifically to me. This follows in the medieval traditions of the ars memoria, some of which are suggested in Cornwell, Hilarie, and James Cornwell. Saints, Signs, and Symbols: The Symbolic Language of Christian Art 3rd Edition. Church Publishing, Inc., 2009. The modern day equivalent of this might be the use of emoji with slang meanings or 1337 (leet) speak.

  7. Jan 2023
    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Manuscript number: RC-2021-01111

      Corresponding author(s): Esther Stoeckli

      [Please use this template only if the submitted manuscript should be considered by the affiliate journal as a full revision in response to the points raised by the reviewers.

      If you wish to submit a preliminary revision with a revision plan, please use our "Revision Plan" template. It is important to use the appropriate template to clearly inform the editors of your intentions.]

      1. General Statements [optional]

      Dear editors at Review Commons

      Thanks for your patience. We have finally carried out a full revision of our originally submitted manuscript summarizing our findings on the role of Cables1 in axon guidance.

      In our study, we provide in vitro and in vivo evidence for a role of Cables1 as a linker between axon guidance signaling pathways. Commissural axons in the developing spinal cord leave their intermediate target, the floor plate, due to a switch from attraction to repulsion mediated by the specific trafficking of Robo1 receptors to the growth cone surface. The presence of Robo1 on growth cones after contact with the floor plate allows them to respond to Slit, the negative guidance cue associated with the floor plate. After leaving the floor plate on the contralateral side, growth cones respond to a Wnt gradient along the antero-posterior axis. The responsiveness to Wnt of post- but not pre-crossing axons is regulated by the trafficking of Fzd3 receptors to the growth cone membrane of post-crossing axons (Alther et al., 2016), but also by the specific phosphorylation of β-Catenin at tyrosine Y489 by Abl kinase. Cables1 mediates this phosphorylation by transferring Abl kinase from the C-terminus of Robo1 to β-Catenin (this study).

      The revised version of the manuscript contains additional experiments in vitro, in vivo and ex vivo combined with live imaging to further support our conclusion about the role of Cables1 as a linker between Robo/Slit and Wnt signaling.

      It took as longer than expected to carry out these new experiments, as Nikole Zuñiga, the first author of the paper, left the lab after her PhD defense to take up a job in industry. Unfortunately for the study, but fortunately for Giuseppe Vaccaro, he also got a job soon after taking over the project. Therefore, the revision was delayed again. We hope that the additional experiments will solve the issues that were raised by the reviewers. We thank them for their contributions and suggestions.

      Best regards

      Esther Stoeckli

      2. Point-by-point description of the revisions

      This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

      Point to point response to reviewers’ comments

      Reviewer #1 (Evidence, reproducibility and clarity (Required)): In this work by Zuñiga et al. the authors study the role of the adaptor protein Cables1 on the guidance of post-comissural spinal cord neurons. They hypothesize that commissural axons need Cables1 to leave the floor plate and turn to ascend to the brain. They propose that during this process, Cables1 acts as a linker of two key axon guidance pathways, Slit and Wnt. Cables1 would localize β-catenin phosphorylated at tyrosine 489 to the distal axon and this would be necessary for the correct turning and navigation of post-crossing commissural axons. Although the work may be potentially interesting, there are major issues that authors need to address in order to state their claims:

      -Fig. 2. To visualize the axonal phenotype after downregulation of Cables1 the authors use DiI labelling. This difficults the interpretation of the results as both electroporated and non- electroporated axons are labelled. Since the authors have a Math1::tdTomatoF reporter construct (as in Fig. 3), it would be desirable to use this construct Math1::tdTomatoF in combination with the dsCables1 plasmid to better visualize the phenotype. Alternatively and less preferred, GFP signal should be also shown in Fig.2B experiments.

      We respectfully disagree. Most likely, the reviewer thinks about a defined nerve that has a particular trajectory and then when labelled with a fluorescent marker, deviations from this pathway, or defasciculated growth can be easily visualized. However, in the spinal cord, the dI1 axons run ventrally more like a ‘curtain’. Therefore, the aberrant behavior of axons is difficult to see. We therefore, opted for the alternative suggestion and added the GFP images to visualize clearly that the axons labelled with DiI are from the injected area. We also would like to add that we are extremely careful in injecting DiI only to the dI1 population of commissural axons to avoid mixing populations with different trajectories. As the analysis is done by a person blind to the experimental condition, we are convinced that our way of analyzing the phenotype is valid. An approach that has been successfully used by many groups for decades now. Please also keep in mind that we are always comparing groups of embryos with each other. Furthermore, having axons traced by DiI which were not targeted by dsRNA electroporation would not increase but rather decrease the likelihood of aberrant behavior. Therefore, we are convinced that our method of quantification is valid.

      However, we have added new experiments using live-imaging which also demonstrate that many axons in embryos electroporated with dsCables1 fail to turn properly at the floor-plate exit site (see Movie 2). These experiments provide additional evidence for the validity of our results.

      -Fig. 2B and Supp.Fig.3. Comparable DiI labellings should be shown in the different conditions. The three examples shown in this panel despite different amount of DiI-labeled axons making it difficult to compare them.

      We have exchanged the image of the control-treated embryo in Figure 2 to have more comparable DiI injection sites. However, as we detail in our Material & Method section, the quantification was done in such a way that the number of axons does not matter. We rephrased this paragraph to make this point more clear (lines 630ff). Please also refer to the GFP-expressing control sample shown in Figure 6A.

      We counted a DiI injection site as showing floor-plate stalling when at least 50% of the fibers entering the floor plate failed to reach the exit site. Similarly, ‘No turn’ means that at least 50% of the axons at the exit site failed to turn rostrally. Because, these two phenotypes are not independent of each other (100% stalling prevents the analysis of the turning phenotype), we only did a statistical analysis for the DiI injection sites with correctly turning axons. We also would like to point out that we hardly had injection sites where it was difficult to decide whether the 50% threshold was reached or not.

      -Fig. 2D. An scheme depicting the different phenotypes: "normal", "FP stalling" and "no turn" would help to understand the results. They can use schemes similar to those shown in Fig. 2K Parra et al. 2010.

      We have added a scheme outlining the different phenotypes, as suggested to Figure 2A.

      -Fig. 3A. The open-book drawing is confusing. It seems that they are analyzing open-book preparations in this experiment when this is not the case.

      Now Figure 4: We have changed the schematic explaining our experimental design. We wanted to illustrate that we only took the dorsal-most part of the spinal cord, dissected from open-book preparations of the spinal cord, as explants to avoid the inclusion of other cell types.

      -Fig. 3B. Authors claim that Cables1 is not required in pre-crossing axons as dsCables electroporation does not affect axonal growth of DiI neurons taken at HH22. However, to be sure that Cables1 mRNA levels are downregulated in pre-crossing axons, relative levels of Cables1 mRNA and/or protein should be also determined at HH22 not only at HH25.

      We have clarified the quantification of downregulation efficiency. The qPCR data are taken from HH23, that is one day after electroporation. The Western blot data show differences in protein levels at HH25, that is 2 days after electroporation. In both cases, the downregulation efficiency is about 50%. This means that we got rid of all Cables1 mRNA, as we successfully transfected 50% of the cells in the targeted area (52.5% in n=4 embryos). The cell numbers were determined by counting the ratio of GFP-positive cells from transfected spinal cords in a single cell suspension.

      -Fig. 4. The incapacity of Slit to induce axonal retraction in dsCables1 neurons is used to conclude that Cables1 is required to respond to Slit. However, downregulation of Cables1 by itself is even more effective inhibiting axonal growth than Slit treatment. Upon this strong effect as a background, it is difficult to assay slit response. Authors should point this observation in the manuscript.

      We disagree. There is no significant difference between the neurite lengths between the control neurons in the presence of Slit and the neurons lacking Cables1 (dsCables1), p=023, or the neurons lacking Cables1 in the presence of Slit (dsCables1 and Slit), p>0.9999. As seen in the images and also from the measured neurite lengths, axons still show growth and further reduction would have been possible. We would also like to point out that the conclusion from this experiment is that Cables1 is required for the response of axons to either Slit or Wnt.

      To support our claims, we have added another experiment addressing the need for Cables1 for post-crossing axons’ responsiveness to Slit by downregulation of Robo receptors (Figure 10). These experiments confirmed that Slit/Robo signaling is required for the effect of Cables1 on post-crossing axons, in line with our final conclusion that Slit binding to Robo triggers internalization and Cables then transfers Abl from the C-term of Robo to β-Catenin. This results in phosphorylation of β-Catenin at tyrosine489 (β-Catenin pY489) and responsiveness to Wnt5a.

      -Fig. 5B. In this Figure they do not differentiate between FP stalling or no turn phenotypes. A quantification taking into account the different phenotypes as shown in Fig.2D should be included.

      Done, as suggested. This is Figure 6C in the revised manuscript.

      -Fig. 6D,E. As postulated in the manuscript and based on the Rhee, et al. paper, the β-catenin phosphorylation is triggered by Abl quinase upon Slit-Robo signaling. How the authors explain then that isolated cells with axons growing on a plate recapitulate specific distal phosphorilation of β-catenin at Y489 in the absence of Slit signaling? This experiment shows that postcrossing axons contain more phosphorylated β-catenin as an intrinsic characteristic rather than as a consecuence of contact with floor plate signals. Authors should try a similar experiment but exposing the neurons (or explants) to Slit. Also, why β-catenin phosphorylation was not measured at the growth cone?

      In Figure 6D and E (now Figure 7D,E), we compare pre- and post-crossing axons. Post-crossing axons do have ‘a memory’ of their contact with the floor plate, as this contact has changed the localization of Robo receptors to the surface (Philipp et al., 2012; Alther et al., 2016). Floor-plate contact also initiates differences in gene expression (e.g. Hhip expression in a Shh-and Glypican-dependent manner; Wilson and Stoeckli, 2013). The difference in Robo localization has also been described by others (Pignata et al., Cell Rep 29(2019)347).

      In fact, the distal localization of pY-489 β-Catenin is in perfect agreement with our results: The localization of Robo1 on the distal portion of the axon is in line with published data from our own lab but also from the Castellani and the Tessier-Lavigne lab. Our results suggest that Cables is recruited to Abl bound to the C-term of Robo. Cables transfers Abl then to β-Catenin which is phosphorylated by Abl. Thus pY-489 β-Catenin would be localized predominantly where Robo is localized, i.e. the distal axon. In support of these results, experiments added to the revised version of the manuscript indicate that the response to Slit is required for the increase in β-Catenin pY489 (Figure 10B).

      -Fig. 7. CAG::hrGFP electroporation is not specific for dl1 neurons. This experiment should be performed with Math1::tdTomatoF in order to analyze β-cat pY489 with or without dsCables1 specifically in dl1 neurons. Also, why GFP staining at the growth cones in Fig.7B is not visible in the axon?

      As indicated in our schematic drawing (Figure 7A) we only cultured explants from the dorsal-most part of open-book preparations of spinal cords, making sure that our cultures are not mixtures with more ventral populations of neurons. We opted for CAG::hrGFP because Math1 is a weak promoter and the expression of GFP was very difficult to see after dissociating cells and culturing them in vitro. We used a GFP version that is not farnesylated to avoid interference with axonal staining of pY-489 β-Catenin. Therefore, GFP is not visible in axons with the imaging conditions used.

      -Fig. 8. This experiment does not distinguish whether phosphorylated β-Cat is necessary for the correct navigation of post-crossing commissural axons (as it is claimed in the abstract) or it is also required for midline crossing. As it has been previously shown, correct navigation of post-crossing commisusal axons is a Wnt5 dependent process. As dsCables1 abrogates Wnt5a responsiveness (Fig.4B,C), does the phosphomimetic β-catenin Y489E construc rescue the Wnt5a response in dsCables1 electroporated neurons? Moreover, can the phosphomimetic β-catenin Y489E construc rescue the Slit response in dsCables1 electroporated neurons? Testing these effects on explants as in Fig. 4B,C but including phosphomimetic β-catenin, will help to understand to what extend phosphorylation of β-catenin is important for crossing, turning or both processes.

      Yes, the phosphomimetic Y489E version of β-Catenin reduces the percentage of DiI injections sites with aberrant axonal navigation to control levels (Figure 9 in the revised manuscript). In contrast, a mutant version of β-Catenin that cannot be phosphorylated, β-CateninY489F, cannot rescue the axon guidance phenotype seen in the absence of Cables1.

      -How do the authors envision the mechanism of Cables1/β-catenin mediated crossing and turning? A working model summarizing their hypothesis would help the reader to understand the results.

      **Minor points:** -Homogeneize the term "scale bars" or "bars" in the Figure Legends.

      done

      -Scale bar of insets in Fig.1C is missing.

      The scale bar is now added, we apologize for the mistake.

      -The antisense control for Cables probe should be shown at HH-22/24. Otherwise is not possible to distinguish whether they do not detect signal because is a negative control or because Cables1 is not expressed at HH25.

      We have added the image of an adjacent section hybridized with the sense probe for HH25, in addition to HH22 to clarify that Cables expression is higher during floorplate crossing, exiting and turning rostrally but then levels decrease when post-crossing axons have initiated their growth along the rostro-caudal axis.

      -Figure legend for Fig. 2D is missing

      corrected

      -Fig. 8B right panel is contaminated with growthing axons coming from the below DiI injection. Please replace the picture.

      We have changed the outline of this figure.

      -The quantification of the different phenotypes "FP stalling", "no turn" should be better explained in the Mat and Met section. The sentence " more than 50% of the axons...." is not clear. Was this measured by eye? Otherwise, please indicate the soIware used to measure.

      Yes, as mentioned above, it was hardly ever a close call. It is very easy for a person blind to the experimental condition to go through the DiI injection sites of an open-book preparation and to assess whether 50% or more of the axons that enter the floorplate reach the exit site, or not. Similarly, it is very easy to do the same for the turning behavior. We have changed the text describing this method of quantification to be more explicit (lines 630ff).

      -Provide the quantification of the WB in Supplementary Fig. 2B normalising to Gapdh.

      Added as Supplementary Figure 2C.

      Reviewer #1 (Significance (Required)): Previous results have demonstrated that Slit-induced modulation of adhesion is mediated by cables that links Robo-bound Abl kinase to N-cadherin-bound betacat (Rhee et al., 2007). Here the authors propose that a similar mechanism is operating in commissural neurons leave the midline after crossing and turn immediately after. The role of Cables in the process has not been previously addressed. Thus, after proper addressing of my main concerns, I consider this paper may advance in our knowlege of how growing axons navigate intermediate targets.

      We appreciate this positive evaluation of our study and hope that the additional experiments and more detailed explanations have helped clarify open questions of the reviewer.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)): In this paper entitled "Cables1 links Slit/Robo and Wnt/Frizzled signaling in commissural axon guidance", authors aim to the find the mechanisms the coordinate the floor plate exit and the rostral turning of commissural axons. During development thousands of axons have to navigate long distances to reach their targets and build functional circuits. To facilitate their journey, their paths is divided into small portions by intermediated targets. The most studied intermediated target is the floorplate (FP) at the midline of the ventral spinal cord. Glia cells forming the FP express plethora of guidance cues. Commissural neurons, which have their cells bodies located in the dorsal part of the spinal cord, send their axons towards the FP. These axons are first attracted by the FP which facilitate their entry within the FP. However, they switch this attractive response into a repulsive one in order to exit the FP and turn rostrally to connect their brain targets. In order to ensure that this process will go smoothly, commissural axons have to adapt the composition of their receptors and the signaling pathways to switch from attractiveness to repulsion. So far, many processes have been involved such as the alternative splicing of receptors (Robo3; Chen et al Neuron 2008), protease regulation of receptor expression (Nawabi et al Genes & Dev 2010), trafficking of receptors, or their interaction profiles (Delloye et al Nat Neuro 2015). However, it is still not clear how 2 events (here exit from the FP and rostral turning) are linked. Authors propose an original mechanism that involved the adaptor protein Cables 1. This protein has been shown to link the Robo/Slit1 signaling to Cadherins. Cables regulates the repulsive response to Slit and adhesion by the phosphoryla4on of b-Catenin by the kinase Abelson (Rhee et al Nat Cell Biol 2007). The story developed here is very original and interesting: Cables would link the exit of FP (mediated by Robo/Slit signaling) and the rostral turning of the commissural axons (controlled by the Wnt/Fzd pathway. Below I'm proposing some experiments as many questions raised upon reading this beautiful work. The experiments are sound and could be reproducible. The statistic analysis looks fine.

      We thank the reviewer for this positive assessment of our study.

      I would suggest some experiments to strengthen the whole work: •Authors might want to consider to perform some biochemistry experiments to show that Cables is able to interact with Robo1 and Fzd3: are these proteins in the same molecular complex? They could do 2 experiments: one in vitro by transfecting a cell line (such as HEK293 or cos cells) with plasmids coding for Robo1, Cables and Fzd3 or at least Cables and Fzd3 (as for Robo1/Cables they could refers to Rhee et al 2007). Another one would be in vivo: extracting proteins from the pre-crossing stage, the FP and post crossing stage; immunoprecipitation of Cables1 and see whether Robo1 and/or Fzd are pull down with Cables 1.

      We decided not to do these experiments, as we felt that this would go beyond the current study. In fact, for our effects it is not necessary that Cables interacts physically with Robo or Fzd3. The important aspect is that Abl bound to Robo is transferred by Cables to β-Catenin. A direct interaction with Fzd3 is not necessary.

      • From the pictures it seems that most of the axons are stalling in the FP when embryos are electroporated with dsCables1. It would be nice to show more examples of axons that are able to exit the FP but have turning problems. Given the data, as it is presented, it seems that Cables regulates more the FP exit (and therefore, as it was shown in Rhee et al, the responsiveness to Robo/Slit signaling).

      The major phenotype is ‘no turn’. However, as we describe in response to reviewer 1 and in the manuscript, the ‘floorplate stalling’ and the ‘no turn’ phenotypes are not independent of each other. At DiI injection sites, where almost all axons stall in the floorplate, the turning cannot be assessed. Thus, the ‘no turn’ phenotype tends to be underestimated in conditions where floorplate crossing is also affected, as is the case after silencing Cables1.

      In the same line, in Fig 4, Authors need to add a condition using dsCables and ds Fzd in order to see the effect of Cables on axon turning (response to Wnt). As it is this figure supports the role of Cables on FP exit but it's hard to make the link with commissural axon responsiveness to Wnt.

      We belief that experiment 4 clearly demonstrates the absence of the Wnt responsiveness, as axons fail to grow in response to Wnt when they extend from neurons transfected with dsCables1 (Figure 4C). Because dsCables1 alone already abolishes all responsiveness to Wnt, the removal of Fzd at the same time would not change anything.

      • Authors aim to show that Cables is a linker between 2 events: maybe it should be nice to try to disconnect these events. One way would be (if technically possible) to modulated expression of Cables at different stages. What would happen if Cables was down regulated upon FP crossing? Would axons still be able to respond to Wnt? The question I'm wondering about is whether the responsiveness to Slit and Wnt is acquired at the same time or whether axons should become sensitive to Slit and this event will prime them to respond to Slit. In order to address the following experiment could be performed: explants from HH22-HH23 embryos, could be treated with medium containing Slit first and then Wnt or vice et versa and perform some collapse assay.

      Unfortunately, the experiment as proposed by the reviewer is not possible. The axons take on average 5.5 hours to cross the floorplate (entry – exit; Dumoulin et al., 2021). Most importantly, the protein that is already made before axons are at the exit site, could not be removed. Therefore, it is not possible to prevent the production of Cables only after axons have crossed the midline. As shown in Figure 1, Cables1 mRNA is present at HH22, that is when axons have reached and are about to enter the floorplate. We also do not belief that the in vitro experiment suggested by the reviewer would work. We would have to wash cell intensively to remove Slit added to the medium. This would interefere with their potential to grow in response to Wnt immediately after addition. However, we added experiments where we looked at the effect of Wnt after removal of Robo (Figure 10). These experiments demonstrate that responsiveness to Wnt can only be established when axons can respond to Slit, i.e. when Robo is activated.

      • In Fig3 I was wondering whether post crossing axons were growing less because of the change in the regulation of adhesion: Rhee et al shows that Cables is able to modulate adhesion through N-cadherin. It would be interesting to perform immunostaining on these explant cultures to assess any change in adhesion molecules.

      We have not found any changes in the expression levels of Contactin-2 (Axonin-1), NrCAM, or most importantly β1-Integrin, as our cultures grow on laminin.

      • It is not clear whether Robo1 and/or Fzd induces the phosphorylation of b-catenin: is the Robo1/Slit binding induce the phosphorylation of b-cat and this event will prime the axons to respond to Wnt/Fzd? Or Wnt/Fzd is also able to control b-cat phosphorylation?

      We have added an experiment, where we remove Robo1 from commissural neurons and compare pY489 β-Catenin levels (Figure 10). Furthermore, we demonstrate that in the absence of Robo1, Wnt has no stimulatory effect on axons (Figure 10C,D). These experiments supports our conclusion that Cables1 transfers Abl kinase from the C-terminal part of Robo to β-Catenin, which gets phosphorylated and thus is ready to act in the Wnt signaling pathway.

      • The staining with the antibody needs to be detailed: as it is reported this antibody recognizes "a domain of Cables1 that is 90% identical to the corresponding region of Cables2": it seems that the Cables protein enrichment in the floor plate (around the central canal) is Cables 2 as its mRNA expression matches this profile of expression. The one expressed in the crossing axons might be Cables 1: one way to verify this, is to perform the staining on sections from embryos electroporated with dsCables 1. This is a very important control of the antibody to reinforce this point of the paper.

      We belief that the staining of the cells around the central canal could be due to endfeet of precursors spanning the neural tube from the apical to the basal side. All cells seem to express some Cables1 (Figure 1B,C). As we did not find any effect of Cables2 on commissural axon navigation and we do not use antibodies to functionally interfere with Cables1 function, we did not do this experiment, as the antibody is not able to distinguish the two proteins. Most likely, there is little, if any, Cables2 expressed in the spinal cord during this time window. We still did some functional analyses but found no effect on axon guidance (Supplementary Figure 3).

      • In Figures 3-4: why not performing some co culture of spinal cord explants with COS or HEK 293 cells expressing Slit1 or Wnt? This experiment will provide a clear-cut response to see the role of Cables in axon guidance. As there it is, Fig3 shows a role of Cables in axon growth but not guidance.

      We respectfully disagree that in vitro experiment would help to show guidance versus growth. Guidance can only be shown in vivo. This is what we do. Our in vitro results are only included to address specific responsiveness of axons or expression changes in total β-Catenin or pY489 β-Catenin. But all our conclusions about the role of Cables in axon guidance are demonstrated in vivo. Experiments using co-cultures of axons with COS or HEK cells would be impossible to control for timing and amount of Slit or Wnt release.

      • In Figure 6: my understanding of axon guidance is that every guidance decision happens at the level of the growth cone. However, it seems that in post crossing stage, there is a strong decrease of b-cat and phosphor- b cat within the growth cone compared to the precrossing stage. If beta cat is the effector of Cables to link Robo/Slit and Wnt/Fzd signaling I would expect it to be localized at the growth cone. I think authors should discuss this point. Regarding the normalization, it would be better to counterstaing the neurons with actin and use the measure of its fluorescence to normalize phopho-beta cat.

      There must be a misunderstanding. We do not demonstrate or claim that there is a decrease in β-Catenin or pY489 β-Catenin between pre- and post-crossing axons. We only demonstrate that the distribution of pY489 β-Catenin is clustered in distal post- but not pre-crossing axons. This change in localization of pY489 β-Catenin is supporting our model that Cables1 transfers Abl kinase to β-Catenin and phosphorylates it and prepare it for signaling in the Wnt pathway. And, as demonstrated pY489 β-Catenin and β-Catenin are in the growth cone. However, for quantification we concentrated on the axon, as the difference in growth cone morphology would have complicated the quantification.

      **Minor points:** •In figure 2: it seems that there are few axons labelled with DiI in the dsCables1 condition (Fig2B): it would be the choice of the picture or maybe the downregulation of Cables 1 interfere with the survival of dl1 neurons (even though in supp 1C it is shown that most of the populations are still there with no difference with the control side) or maybe some axons are delayed to reach to FP on time: the picture is focused on the FP: are there any axons still growing in the side of the open book preparation? Again, the picture that could be misleading.

      We have exchanged the images for alternatives with a better matched number of DiI-labelled axons. There is indeed no evidence for cell death, as axons are still there at normal numbers when we analyze open-book preparations a day later than usually. The difference in the number of axons labelled by DiI is only due to the variability in the amount of DiI injected per injection site.

      • In Fig1 legends, maybe Authors wanted to write "At HH18 dl1 commissural neurons start to extend their axons in the ventral spinal cord"?

      No, what we mean is, as shown in Figure 1A, that axons emerge from the cell body at this time. They reach the ventral spinal cord by HH21 and the floor plate by HH22.

      • I would also remove the yellow shadow on the Fig1A: it could be misleading as at first glance the reader might wonder whether there are 2 populations of dl1 neurons.

      We have done as suggested to make the image clearer.

      Reviewer #2 (Significance (Required)): It is still not clear how axons cross the midline. So far, many processes have been involved such as the alternative splicing of receptors (Robo3; Chen et al Neuron 2008), protease regulation of receptor expression (Nawabi et al Genes & Dev 2010), trafficking of receptors, or their interaction profiles (Delloye et al Nat Neuro 2015). However, it is still not clear how 2 events (here exit from the FP and rostral turning) are linked. Authors propose an original mechanism that involved the adaptor protein Cables 1. This protein has been shown to link the Robo/Slit1 signaling to Cadherins. Cables regulates the repulsive response to Slit and adhesion by the phosphorylation of b-Catenin by the kinase Abelson (Rhee et al Nat Cell Biol 2007). The audience that will be interested in this work is the neurodevelopment filed, axon regeneration field and overall people interested in neuronal circuit formation and function. My field of expertise is molecular and cellular neuroscience applied to axon guidance (crossing the FP) in mice models, axon regeneration and circuit formation.

      We are happy to learn about the positive assessment of our work by a specialist.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)): In their manuscript, Zuniga and Stoeckli characterize the role of Cables in commissural axon guidance in the developing chick spinal cord. Based on a combination of in vitro outgrowth assays and in vivo dye-tracing experiments, the authors propose that Cables participates in both normal repulsive responses to Slit and attractive responses to Wnt5. Using combinations of low-does knock down of cables/robo and or B-catenin, the author suggest an in vivo link between these pathways. Using IF with phospho-specific antibodies to B-catenin, the authors suggest that there is elevated P-Bcatenin in the post-crossing segments of distal axons. While potentially interesting, the present study falls short of adequately supporting the major claims. In addition, there are several instances where experiments lack appropriate controls.

      **Specific Comments** The conclusions reached by the authors are over-stated given the experiments performed. For example, the authors describe 'silencing' cables throughout the paper; however, the knock down that they achieve is approximately 50%. Indeed, it is quite surprising that such strong effects on growth/guidance can be achieved with a two-fold depletion of the gene product. Nevertheless, the rescue experiments provide nice evidence that dsRNA for Cables is causing a phenotype. This partial knockdown precludes strong conclusions, like for Figure 3, where they state that 'Cables is not required for pre-crossing.' The language needs to be tempered.

      We rephrased the paragraph where we describe the effect of Cables 1 and the efficiency of downregulation to stress that the parameters that we use for electroporation result in around 50% of the cells successfully transfected (lines 154 – 162, and legend of Supplementary Figure 2). Therefore, to find mRNA levels and protein levels reduced to about half indicates that our method is extremely efficient and removes the targeted mRNA and the protein almost completely. We need to point out here that we always analyze the temporal expression pattern to electroporated embryos before the protein of interest has accumulated, as in ovo RNAi obviously does not remove protein but only prevents translation and therefore the synthesis of new protein. As proteins can be extremely stable compared to the time line of embryonic development, we inject and electroporate dsRNA before we find expression of mRNA.

      Figure 4: the authors use bath application of Slit and Wnt to test effects of cables on Slit and Wnt responses. The observed effect sizes are very small and a single assay of this type does not allow such strong conclusions like 'loss of Cables prevents responsiveness.' Again, it is difficult to imagine that 50% reduction would completely prevent responses, raising questions about the suitability of this assay for measuring responsiveness- perhaps growth cone collapse would give more convincing results.

      As mentioned above, we are almost completely eliminating the targeted protein in the transfected neurons. For the explants, we only looked at the neurons expressing td-Tomato driven by the Math1 promoter. Thus, these neurons were transfected. Obviously, we cannot be sure that 100% of our cells took up the plasmid and the dsRNA, but the chances are very high that this is the case based on the ration between plasmid and dsRNA.

      Figure 5: The authors should more clearly document the effects they are seeing in these manipulations. As written, all we know is that there are 'significant effects on axon guidance.' What are these effects? Do they see the predicted differences between robo/cables and Bcatenin/cables phenotypes? e.g re-crossing defects in the case of robo and anterior turning defects in the case of B-catenin?

      We have added the analysis of the detailed axon guidance problems seen in the absence of Robo1, Cables1, βCatenin, or combinations, now Figure 6C. Indeed, we find that the phenotype ‘no turn’ is more prevalent in the condition with loss of both Cables and βCatenin. However, as mentioned above in response to a question raised by Reviewer 2, the two phenotypes are not independent of each other. Stalling in the floor plate of the majority of axons prevents the analysis of the turning phenotype. That is why we only use the ‘normal’ DiI injection sites for the statistical analysis.

      Also related to Figure 5: The authors do not validate the dsRNA knockdown of either Robo or B catenin. It is unclear what the interpretation or expectation of the triple knock down condition is.

      We have used the same ESTs to produce dsRNA derived from Robo and βCatenin in our previous publications (Alther et al., Development 143(2016)994; Avilés and Stoeckli Dev Neurobiol 76(2016)190). Therefore, we only repeated the functional experiments to verify reproducibility of the effect but we did not quantify the efficiency of downregulation in detail again.

      Figure 6: For this reviewer images showing enhanced P-Catenin in post-crossing distal axons is not convincing. The differences are not obvious by eye and the quantification suggests an ~30% increase. In contrast a nearly 4-fold increase is reported in Figure 7 for this same measurement. This raises concerns about the reproducibility of this 'phenotype.'

      Staining intensities are subject to batch-to-batch variability. Therefore, the experiments shown in Figure 7 (Figure 6 in the original manuscript) cannot be directly compared to the levels in Figure 8 (previously Figure 7). However, within the experiments, we carefully normalized data. We do not make any claims about absolute staining intensities.

      Also related to Figure 6: No validation of antibody specificity is provided or described.

      Again, please keep in mind that we do not make any claims about absolute values. All are results are based on stainings with the same antibody and comparison between different areas of the same axons. Therefore, the specificity of the antibody is important but not a fundamental aspect of our results.

      Figure 8: As for figure 5, phenotypic documentation is incomplete. In addition, no controls are shown to assure that the different mutant forms of B-catenin are comparably expressed, nor is there an unmutated wild-type control. The authors state that expression of these constructs alone has no effect on normal guidance; however, the supplemental data 6B would seem to indicate that both forms lead to increases abnormal phenotypes.

      There is an increase in the number of injection sites with aberrant axon guidance, however, this was not significant. We cannot exclude the possibility that premature expression, or overexpression of βCatenin pY489E or βCatenin pY489F does interfere with the endogenous βCatenin pY489. We still decided to keep these experiments in the revised version of the manuscript as they support our conclusion that Cables1 is required for axonal responsiveness to Slit and Wnts, and that this effect is mediated by phosphorylation of βCatenin at Y489. We are aware that this experiment in isolation is not sufficient.

      Reviewer #3 (Significance (Required)): The work builds on in vitro observa4ons from Rhee, 2007 about links between Robo signaling and Cables func4on. If adequately demonstrated, integra4on and coordina4on of Robo and Wnt axon guidance pathways is quite significant.

      We thank the reviewer for this positive assessment.

    1. When we talk here about the poverty level, then, keep in mind that we are talking only about official poverty and that there are many families and individuals living in near poverty who have trouble meeting their basic needs, especially when they face unusually high medical expenses, motor vehicle expenses, or the like.

      This is very eye-opening to think about. Although one may not "officially" be living in poverty, meeting basic needs can still be difficult. Even for those whose basic needs are met, I often wonder when is the last time one under this category was able to buy an extra or "luxury" for themself such as a dessert or a new piece of clothing. Just because someone is not living in poverty does not mean they are not struggling. Even if all basic needs are met, it can be discouraging when you can never splurge on something special for yourself or your family.

    1. And yet researchers have found significant positive results when black and Hispanic students have teachers who match their race or ethnicity: better attendance, fewer suspensions, more positive attitudes, and higher test scores, graduation rates and college attendance. Teachers of color also have higher expectations for students of color, which may fuel the other gains.

      Coming from a white student from a predominantly white school, I had never even considered the relatability aspect of teaching concerning race. I think it is encouraging to know that while ethnic students may feel underrepresented in some schools across America, we as future educators and principals can seek out ethic teachers to support and relate to students of their own race, as well as try to include students that are different from us when we can.

    1. Author Response

      Reviewer #1 (Public Review):

      Animal colour evolution is hard to study because colour variation is extremely complex. Colours can vary from dark to light, in their level of saturation, in their hue, and on top of that different parts of the body can have different colours as well, as can males and females. The consequence of this is that the colour phenotype of a species is highly dimensional, making statistical analyses challenging.

      Herein the authors explore how colour complexity and island versus mainland dwelling affect the rates of colour evolution in a colourful clade of birds: the kingfishers. Island-dwelling has been shown before to lead to less complex colour patterns and darker coloration in birds across the world, and the authors hypothesise that lower plumage complexity should lead to lower evolutionary rates. In this paper, the authors explore a variety of different and novel statistical approaches in detail to establish the mechanism behind these associations.

      There are three main findings: (1) rates of colour evolution are higher for species that have more complex colour phenotypes (e.g. multiple different colour patches), (2) rates of colour evolution are higher on island kingfishers, but (3) this is not because island kingfishers have a higher level of plumage complexity than their mainland counterparts.

      I think that the application of these multivariate methods to the study of colour evolution and the results could pave the way for new studies on colour evolution.

      We appreciate this positive comment about our manuscript.

      I do, however, have a set of suggestions that should hopefully improve the robustness of results and clarity of the paper as detailed below:

      1) The two main hypotheses tested linking plumage complexity and island-dwelling to rates of colour evolution seem rather disjointed in the introduction. This section should integrate these two aspects better justifying why you are testing them in the same paper. In my opinion, the main topic of the paper is colour evolution, not island-mainland comparisons. I would suggest starting with colours and the challenges associated with the study of colour evolution and then introducing other relevant aspects.

      We implemented this suggestion by reorganizing the introduction to introduce color/and challenges with studying it (para 1), then we discuss plumage complexity (para 2). We follow this with a paragraph about the importance of islands in testing evolutionary hypotheses (para 3), and onto kingfishers as a model system (para 4) and our hypothesis/predictions (para 5).

      2) Title: the title refers to both complex plumage and island-dwelling, but the potential effects of complexity should apply regardless of being an island or mainland-dwelling species, am I right? Consider dropping the reference to islands in the title.

      We removed “island” from the title.

      3) The results encompass a large variety of statistical results some closely related to the main hypothesis (eg island/mainland differences) tested and others that seem more tangential (differences between body parts, sexes). Moreover, quite a few different approaches are used. I think that it would be good to be a bit more selective and concentrate the paper on the main hypotheses, in particular, because many results are not mentioned or discussed again outside the Results section.

      We removed analyses that we felt were distracting from our main point (e.g., MCMCglmm) and streamlined our approach to use PGLS methods for both rates (phylolm) and multivariate color patterns (d-PGLS). The relevance of sex differences in coloration is also made more clear, as we added details about how we tested for a relationship between male and female coloration and that we use this strong correlation as a justification for averaging color by species (e.g., see lines 369-375).

      4) Related to the previous section, the variety of analytical approaches used is a bit bewildering and for the reader, it is unclear why different options were used in different sections. Again, streamlining would be highly desirable, and given the novel nature of the analytical approach (as far as I know, many analytical approaches are applied for the first time to study colour evolution) it would be good to properly explain them to the reader, highlighting their strengths and weaknesses.

      We appreciate the suggestion and have now included a workflow diagram, as suggested (see Figure 1). We further added considerable detail to the Methods (old length = 502 words, new length = 1355 words) and mention caveats of the approaches we have taken (e.g., line 308: “We used photosensitivity data for the blue tit (Hart et al., 2000) due to the limited availability of sensitivity data for other avian species”).

      5) The Results section contains quite a bit of discussion (and methods) despite there being a separate Discussion section. I suggest either separating them better or joining them completely.

      We appreciate this. We were following other eLife articles that include more discussion within the Results, therefore we would prefer to leave these aspects in place. However, we did move a considerable amount of information from the Results section to the Methods section. In addition, we also reorganized the Results to better match the logical flow of the Introduction. The end result, we hope, is a Results section that is considerably more streamlined.

      6) The main analyses of colour evolutionary rates only include chromatic aspects of colour variation. Why was achromatic variation (i.e. light to dark variation) not included in the analyses? I think that such variation is an important part of the perceived colour (e.g. depending on their lightness the same spectral shape could be perceived as yellow or green, black or grey or white). I realize that this omission is not uncommon and I have done so myself in the past, but I think that in this case, it is highly relevant to include it in the analyses (also because previous work suggests that island birds are darker than their mainland counterparts). This should be possible, as achromatic variation may be estimated using double cone quantum catches (Siddiqi et al., 2004) and the appropriate noise-to-signal ratios (Olsson et al., 2018). Adding one extra dimension per plumage patch should not pose substantial computational difficulties, I think.

      We incorporated this suggestion and we have now fully integrated achromatic color variation into all of our analyses. These new analyses let us compare results to previous work showing that island birds are darker than mainland counterparts. We further discuss the caveats of chromatic and achromatic channels (e.g., lines 313-317: “Although it is possible, in theory, to combine chromatic and achromatic channels of color variation in a single analysis (Pike, 2012), we opted to analyze them separately, as these different channels are likely under different selection pressures (Osorio and Vorobyev, 2005).”).

      7) The methods need to be much better explained. Currently, some methods are explained in the main text and some in the methods section. All methods should be explained in detail in the methods section and I suggest that it would be better to use a more traditional manuscript structure with Methods before Results (IMRaD), to avoid repetition (provided this is allowed by the journal). Whenever relevant the authors need to explain the choice of alternative approaches. Many functions used have different arguments that affect the outcome of the analyses, these need to be properly explained and justified. In general, most readers will not check the R script, and the methods should be understandable to readers that are not familiar with R. This is particularly important because I think that the methodological approach used will be one of the main attractions of the manuscript, and other researchers should be able to implement it on their own data with ease. Judging from the R script, there are quite a few analyses that were not reported in the manuscript (e.g. multivariate evolutionary rates being higher in forest species). This should be fixed/clarified.

      We clarified several methodological details in the manuscript (e.g., added package versions throughout, mention the permutation option used for compare.evol.rates, cited RPANDA) and modified the Methods section considerably to make logical connections among the sections. We also checked and cleaned up the R markdown file to ensure the analyses were in sync with the manuscript analyses.

      Reviewer #2 (Public Review):

      In "Complex plumages spur rapid color diversification in island kingfishers (Aves: Alcedinidae)", Eliason et al. link intraspecific plumage complexity with interspecific rates of plumage evolution. They demonstrate a correlation here and link this with the distinction between island and mainland taxa to create a compelling manuscript of general interest on drivers of phenotypic divergence and convergence in different settings.

      This will be a fantastic contribution to the literature on the evolution of plumage color and pattern and to our understanding of phenotypic divergence between mainland and island taxa. A few key revisions can help it get there. This paper needs to get, fairly quickly, up to a point where the difference between plumage complexity and color divergence is defined carefully. That should include hammering home that one is an intraspecific measure, while one is an interspecific measure. It took me three reads of the paper to be able to say this with confidence. Leading with that point will greatly improve the paper if that point gets forgotten then the premise of the paper feels very circular.

      We hope our considerable modifications throughout–including explicitly mentioning that complexity is an intraspecific measure whereas rates are interspecific (e.g., see lines 65, 140, 170, 667)–have made the premise of the paper more clear. We also added a new workflow figure (Figure 1) that includes example species pairs showing cases in which intraspecific plumage complexity and interspecific color divergence could show a negative relationship, rather than a positive one as we predict in the manuscript. We discuss this detail in lines 159-161 (“However, this is not necessarily the case, as there are examples within kingfishers that show simple plumages yet high color divergence, as well as complex plumages with little evolutionary divergence (Figure 1B).”).

      Also importantly, somewhere early on a hypothesized causal pathway by which insularity, plumage complexity, and color divergence interact needs to be laid out. The analyses that currently follow are good ones, and not wrong, but it's challenging to assess whether they are the right ones to run because I'm not following the authors' reasoning very well here. I think it's possible a more holistic analysis could be done here, but I'll refrain from any such suggestions until I better get what the authors are trying to link.

      We overhauled the Introduction. This included adding lines that connect the ideas of complexity and insularity (lines 65-58: “intraspecific plumage complexity (i.e., the degree of variably colored patches across a bird's body) could be a key innovation that drives rates of color evolution in birds and should be considered alongside ecological and geographic hypotheses.”) and insularity and color divergence (lines 69-85). We also rethought the analyses and now include PGLS analyses using tip-based rates that allow us to account for both insularity and complexity in the same analysis.

      We also need something near the top that tells us a bit more about the biogeography of kingfishers. Are kingfisher species always allopatric? I know the answer is no, but not all readers will. What I know less well though is whether your insular species are usually allopatric. I suspect the answer is yes, but I don't actually know.

      Great point. We have added details to the manuscript to clarify this (e.g., line 214: “The number of sympatric lineages ranged from 1–9 on islands, and 6–38 for mainland taxa.”).

      In short, how do the authors think allopatry/sympatry/opportunity for competition link to mainland vs. island link to plumage complexity? And rates of color evolution? Make this clear upfront.

      We believe our revised introduction makes these connections much clearer.

    1. Prosecuting the officer who shot Michael Brown, or investigating and integrating Ferguson’s police department,

      Single entities or individuals are much easier for people to take on and feel like they're achieveing than something systemic. It's important, but too often people miss the bigger picture. In a uhl class I took over the summer a couple of years ago, we discussed how it's often very very difficult for people to really conceptualize what a large amount of data means, and to apply that to how it affects individuals. The people being affected become nothing more than numbers. It is therefore easier to focus on Michael Brown or a single police department than it is to reckon with the prospect of millions upon millions of people facing discriminatory practices and policies. The individual like Michael Brown of course deserves attention and justice, but too often we let the isolated incident or the fact that not as many similar incidents go viral get in the way of seeing a wider pattern. I think it also has something to do with guilt. Tackling systems that harm people of color to an extent also means admitting that you yourself may be benefiting from those systems or admitting that you have privelege, which is not always an easy thing for everyone to do.

    1. The digital,like any tool, institution, or system across society, from law and medicineto the academy, will be radical or transformative only to the extent that

      This is a crucial point and I think it is something that anybody studying any type of history should be very aware of. As valuable as the data that we find may be as far as developing a factual and statistical basis, it is imperative that we manage to distinguish our analysis of said data by taking into consideration the way others might have viewed it at the time it was gathered.

  8. evergreen0-my.sharepoint.com evergreen0-my.sharepoint.com
    1. So, why should we ask students to read in digital envi-ronments given all that we know about how they feel aboutreading books? If student preferences and dispositions areclear, why should we try to advance practices that may runcontrary to those preferences? The conclusion to Baron etal.’s (2017) intensive study may be a good starting point tothis conversation, as they suggest that “we should devoteserious research attention to the question of what kinds oftexts or subject matters make most educational sense inwhich formats. As in so much of education, one size likelydoesn’t fit all” (p. 603)

      I think this is a key idea, we need to look not for a one size all solution to our influx of information, but instead we should make it easily adaptable to anyone looking to learn. Some texts make sense to hold in your hands, other don't. But I think we should digitze everything for record and accessibility, as well as a comparison of how it is written and how it ages.

    1. A little child of six years was extremely [26] sick in the Mission of saint Michel. His mother was unable to contain her tears, seeing the excess of his pain, and the approach of death to this her only son. " My mother," said to her this child, " why do you weep your tears will not give me back my health; but rather let us pray to God together, so that I may [page 111] be very happy in Heaven." After some prayers, his mother said to him, " My son, I must carry thee to Sainte Marie, so that the French may restore thee thy health." " Alas! my mother," said to her thief little innocent, " I have a fire burning in my head could they indeed quench it? I no longer think o life,—have no desire of it for me; but I will wart you of my death, and, when it is near, I will pray you to carry me to Sainte Marie, for I wish to die there, and to be buried there with the excellent Christians." In fact, some days later, this child warned his mother that his death was near, and that it was time to carry him to us. It is the custom in these countries, when any one is near death, to make a solemn feast to which are invited all the friends and the most considerable persons,—about a hundred. The mother would not [27] fail in this obligation,—desiring also to apprise all the people of the sentiments which her son had toward the Faith. This child, having seen the preparations for the feasts said to her: " What! my mother, would you have me sin so nigh to my death? I renounce all these superstitions of the country; I wish to die a good Christian." This child believed that that custom was among the number of those forbidden; and although his mother, an excellent Christian, assured him that there was no evil in that, he would never believe her, and could not resolve to comply with her wish, until the Father who has charge of that Mission had assured him that in that feast there was no sin. This little Angel was brought to us; and he died in our arms, praying even till death, and telling us that he was going straight to Heaven, and that he would pray to God for us; and he even asked his mother [page 113] for which of his relatives she wished him to pray chiefly, when he should be near God,—saying that no doubt he would be heard. He has been; for, shortly after his death, an uncle of his, one of those most rebellious against the Faith in these countries, and an aunt of his, asked us for instruction, and have become Christians. [28] A little girl of five years, at the Mission of saint Ignace, of Infidel parents, came every day to prayers, morning and evening. She had so constantly adhered to this duty, even against the wishes and the prohibitions of her parents, that we could not refuse her Holy Baptism,—seeing that the spirit of the Faith was abundantly compensating in her for the years that she might lack in order freely to dispose of herself in a matter wherein grace has more right than nature. Some time after, this child fell sick; the Infidel parents, having recourse to the superstitions of the country, sent to fetch the Magician,—or, to speak more correctly, an impostor who made profession of that trade of hell. This juggler does not fail to say, as is his wont, that a certain Demon had reduced their daughter to that state; and that, in order to expel him, it was necessary to present the patient with some embellishments and ornaments of clothing, of which the girls of that age are sufficiently desirous. The little sick girl, although she was very low, nevertheless had strength enough, and her faith gave her courage enough, to belie this impostor. " I am a Christian," she said to her parents; " the Devils have no longer [29] any power over me. I do not consent to the sin that you have just committed, in consulting the Demons; I do not wish their remedies. God alone will cure me; let [page 115] this Magician go away." The father and mothers and all those present, were much astonished at this rebuke,—so innocent, but yet so efficacious that they made that juggler withdraw, not wishing to grieve this sick child. But their astonishment increased when, on that very day, this child asked to be carried to the Church, asserting that she would get well,—as, in fact, it happened. This event has beers the means of converting the father and the mother, who have adopted their daughter's faith, and have received Baptism after her,—blessing God for having called them with so much gentleness. A young girl of fifteen years, among the most accomplished in the country, still a Catechumen, had been taken captive toward the end of last year's disinter; the enemies, however, had spared her life, and she remained with them in her captivity. She was the daughter and sister of two excellent Christians, who had no greater regret in the loss which they had incurred, than that this poor captive had not [30] yet been baptized. She, too, in her captivity did not forget her faith and often exclaimed to God: " My God,—and the God of my mother and my sister, who know you better than I, and who serve you so faithfully,—have pity on me! I have not been baptized; grant me this favor before I diets One day, when this poor afflicted one was in a field of Indian corn, which she was planting for those whose slave she was, she heard voices from Heaven which were singing a ravishing music in the air, from the chant of our Vespers, which she had formerly heard. She looks about her, supposing that some Frenchmen would accost her; but she sees nothing else. she kneels down, and prays to God [page 117] with all her heart; and she conceives a hope of seeing herself delivered from her captivity, though she sees neither means nor any probability of this Some days afterward, the same thing happens to her; she kneels again, with the same sentiments. Finally, having for the third time heard these same voices from Heaven,—and feeling her confidence increased, and her courage more animated,—she prays to God and hastens into a road which she [31] did not know, in order to return to these countries, without victuals, without provisions, without escort, but not without the guidance of him alone who had inspired her, and who gave her sufficient strength to arrive here, having traveled more than eighty leagues without any evil encounter. She asked us for Baptism from the day of her arrival; and, seeing the hand of God over her with so much love, we could not put her off. she had come straight to this house of Sainte Marie, although her shorter way would have carried her to the village to which her parents belonged. Since then, she has continually increased in fervor, and cannot grow weary with relating to every one the mercies of God. Often, in her captivity, she found herself solicited to what she could not grant without losing innocence; but never could they draw from her lips even a single word of agreement. She even carried this so far that, seeing her in this disposition, which was not pleasing to those shameless Barbarians, some had often spoken of beating her to death; and she was awaiting that death with patience, preferring to die rather than to commit any sin. This chapter would have no end, if I [32] should relate the effects of grace upon these poor Savages,—[page 119] which we admire every day, and for which we will bless God forever in Heaven, without weariness and without distaste. I cannot, however, omit a sufficiently prevailing sentiment of many good Christians, who—having lost all their property, their children, and what they had most precious in this world, and being even upon the point of undergoing a voluntary exile from their country which they were forsaking in order to avoid the cruelty of the Iroquois, their enemies—thanked God for it, and said to him: " My God, may you be blessed; I cannot regret these losses, since the Faith has taught me that the love which you have for the Christians is not in regard to the goods of this world, but for eternity. I bless you in my losses, with as good a heart as I have ever done; for you are my Father, and it is enough that I know that you love me, that I should be content with all the evils which can happen to me.

      stories about devout convert Christians- mostly children

    1. Others attribute this fall to another cause, which seems to have some relation to the case of Adam, but falsehood makes up the greater part of it. They say that the husband of Aataentsic, being very sick, dreamed that it was necessary to cut down a certain tree from which those who abode in Heaven obtained their food; and that, as soon as he ate of the fruit, [page 127] he would be immediately healed. Aataentsic, knowing the desire of her husband, takes his axe and goes away with the resolution not to make two trips of it; but she had no sooner dealt the first [88] blow than the tree at once split, almost under her feet, and fell to this earth; whereupon she was so astonished that, after having carried the news to her husband, she returned and threw herself after it. Now, as she fell, the Turtle, happening to raise her head above water, perceived her; and, not knowing what to decide upon, astonished as she was at this wonder, she called together the other aquatic animals to get their opinion. They immediately assembled; she points out to them what she saw, and asks them what they think it fitting to do. The greater part refer the matter to the Beaver, who, through courtesy, hands over the whole to the judgment of the Turtle, whose final opinion was that they should all promptly set to work, dive to the bottom of the water, bring up soil to her, and put. it on her back. No sooner said than done, and the woman fell very gently on this Island. Some time after, as she was with child when she fell, she was delivered of a daughter, who almost immediately became pregnant. If you ask them how, you puzzle them very much. At all events, they tell you, she was pregnant. Some throw the blame upon some strangers, [89] who landed on this Island. I pray you make this agree with what they say, that, before Aataentsic fell from the Sky, there were no men on earth. However that may be, she brought forth two boys, Tawiscaron and Iouskeha, who, when they grew up, had some quarrel with each other; judge if this does not relate in some way to the murder of Abel. They came to blows, but with very different [page 129] weapons. Iouskeha had the horns of a Stag; Tawiscaron, who contented himself with some fruits of the wild rosebush, was persuaded that, as soon as he had struck his brother, he would fall dead at his feet. But it happened quite differently from what he had expected; and Iouskeha, on the contrary, struck him so rude a blow in the side, that the blood came forth abundantly. This poor wretch immediately fled; and from his blood, with which the land was sprinkled, certain stones sprang up, like those we employ in France to fire a gun,—which the Savages call even to-day Tawiscara, from the name of this unfortunate. His brother pursued him, and finished him. This is what the greater part believe concerning the origin of these Nations.

      comparable attributes to other religious stories

    2. They recognize as head of their Nation a certain woman whom they call Ataentsic, who fell among them, they say, from Heaven. For they think the Heavens existed a long time before this wonder; but they cannot tell you when or how its great bodies were drawn from the abysses of nothing. They suppose, even, that above the arches of the Sky there was and still is a land like ours, with woods, lakes, rivers and fields, and Peoples who inhabit them. They do not agree as to the manner in which this so fortunate descent occurred. [87] Some say that one day, as she was working in her field, she perceived a Bear; her dog began to pursue it and she herself afterwards. The Bear, seeing himself closely pressed, and seeking only to escape the teeth of the dog, fell by accident into a hole; the dog followed him. Aataentsic, having approached this precipice, finding that neither the Bear nor the dog were any longer to be seen, moved by despair, threw herself into it also. Nevertheless, her fall happened to be more favorable than she had supposed; for she fell down into the waters without being hurt, although she was with child,—after which, the Waters having dried up little by little, the earth appeared and became habitable. Others attribute this fall to another cause, which seems to have some relation to the case of Adam, but falsehood makes up the greater part of it. They say that the husband of Aataentsic, being very sick, dreamed that it was necessary to cut down a certain tree from which those who abode in Heaven obtained their food; and that, as soon as he ate of the fruit, [page 127] he would be immediately healed. Aataentsic, knowing the desire of her husband, takes his axe and goes away with the resolution not to make two trips of it; but she had no sooner dealt the first [88] blow than the tree at once split, almost under her feet, and fell to this earth; whereupon she was so astonished that, after having carried the news to her husband, she returned and threw herself after it. Now, as she fell, the Turtle, happening to raise her head above water, perceived her; and, not knowing what to decide upon, astonished as she was at this wonder, she called together the other aquatic animals to get their opinion. They immediately assembled; she points out to them what she saw, and asks them what they think it fitting to do. The greater part refer the matter to the Beaver, who, through courtesy, hands over the whole to the judgment of the Turtle, whose final opinion was that they should all promptly set to work, dive to the bottom of the water, bring up soil to her, and put. it on her back. No sooner said than done, and the woman fell very gently on this Island. Some time after, as she was with child when she fell, she was delivered of a daughter, who almost immediately became pregnant. If you ask them how, you puzzle them very much. At all events, they tell you, she was pregnant. Some throw the blame upon some strangers, [89] who landed on this Island. I pray you make this agree with what they say, that, before Aataentsic fell from the Sky, there were no men on earth. However that may be, she brought forth two boys, Tawiscaron and Iouskeha, who, when they grew up, had some quarrel with each other; judge if this does not relate in some way to the murder of Abel. They came to blows, but with very different [page 129] weapons. Iouskeha had the horns of a Stag; Tawiscaron, who contented himself with some fruits of the wild rosebush, was persuaded that, as soon as he had struck his brother, he would fall dead at his feet. But it happened quite differently from what he had expected; and Iouskeha, on the contrary, struck him so rude a blow in the side, that the blood came forth abundantly. This poor wretch immediately fled; and from his blood, with which the land was sprinkled, certain stones sprang up, like those we employ in France to fire a gun,—which the Savages call even to-day Tawiscara, from the name of this unfortunate. His brother pursued him, and finished him. This is what the greater part believe concerning the origin of these Nations.

      It is interesting how similar the story is to the Christian creation belief, and many others.

    3. The Captains of the village, having heard these stories, sent for me and said, "My nephew, here is what so-and-so says; what dost thou answer to it? We are ruined, for the corn will not ripen. If at least we should die by the hands and arms of our enemies who are ready to burst upon us, well and good,—we should not at any rate pine away; but if, having escaped from their fury, we are exposed to famine, that would be to go from bad to worse. What dost thou think of it? Thou dost not wish to be the cause of our death? besides, it is of as much importance to thee as to us. We are of the opinion that thou shouldst take down that Cross, and hide it awhile in thy Cabin, or even in the lake, so that the thunder and the clouds may not see it, and no longer fear it; and then after the harvest thou mayest set it up again." To this I answered, " As for me, I shall never take down nor hide the Cross [30] where died he who is the cause of all our blessings. For yourselves, if you wish to take it down, consider the matter well; I shall not be able to hinder you, but take care that, in taking it down, you do not make God angry and increase your own misery

      this is very interesting.

    4. They seek Baptism almost entirely as an aid to health. We try to purify this intention, and to lead them to receive from the hand of Cod alike sickness and health, death and life; and teach them that the life-giving waters of Holy [6] Baptism principally impart life to the soul, and not to the body. However, they have the opinion so deeply rooted that the baptized, especially the children, are no longer sickly, that soon they will have spread it abroad and published it everywhere. The result is that they are now bringing us children to baptize from two, three, yes, even seven leagues away

      It seems the language barrier played a huge part in the understanding of baptism. Makes me think the missionaries may have moved too fast in their efforts to convert the natives.

    1. It’s also easy to blame colleges for not meeting the literacyneeds of the populace. Ironically, many state-supported univer-sities are no longer able to offer remedial courses for studentswho may need some additional help to succeed in college, in partbecause state legislatures, ready to trim university budgets, do notwant to pay for courses that may limit a student’s ability to finisha bachelor’s degree in four years. So the courses that have oftenhelped students prepare for the rigor of academic writing and thesophistication of writing informed by knowledge of rhetorical prin-ciples are actually being cut even as the public continues to declarethat literacy is in decline.Rather than thinking of writing instruction as a form of triage,inoculation, or clinical diagnostic generated to protect the middleclass from the ravages of illiteracy, we benefit from thinking ofwriting instruction as a means of helping students improve theirabilities to engage in public discourse in all its varied forms. Whatwriting teachers have known for generations is that writing is notan end in itself—it is a method of invention that gives shape to ourview of the world and empowers us to engage in discourse with ourfellow humans. There are few things more important than that.There is no literacy crisis. Instead, the concept of literacy contin-ues to become more complex as we expect people to know howto produce and understand texts in multiple forms, whether writ-ten, visual, or otherwise. Like all human institutions, education isinherently flawed, and teachers, students, parents and others mustalways consider ways and initiatives to improve literacy education.Further ReadingFor more about the study of literacy in the United States, seeDeborah Brandt’s Literacy in American Lives (Cambridge UniversityPress), which offers several case studies of how Americans gainliteracy by what Brandt calls sponsors of literacy, people or thingsthat control individuals’ access to literacy instruction. Additionally,see the New London Group’s Multiliteracies (Routledge). The NewLondon Group, a group of ten scholars, acknowledges that tech-nology plays a significant role in how literacy expectations haveshifted.For more on how writing scholars are thinking about the transfer

      Why is the government cutting budgets in the universities for the courses that help students to prepare for a rigor of academic writing? Did they not interesting in literary courses or they think it won't be necessary to put a lot money while other subject like science and technology are there?

    1. Author Response

      Reviewer #1 (Public Review):

      In this study, the protein composition of exocytotic sites in dopaminergic neurons is investigated. While extensive data are available for both glutamatergic and GABA-ergic synapses, it is far less clear which of the known proteins (particularly proteins localized to the active zone) are also required for dopamine release, and whether proteins are involved that are not found in "classical" synapses. The approach used here uses proximity ligation to tag proteins close to synaptic release sites by using three presynaptic proteins (ELKS, RIM, and the beta4-subunit of the voltage-gated calcium channel) as "baits". Fusion proteins containing BirA were selectively expressed in striatal dopaminergic neurons, followed by in-vivo biotin labelling, isolation of biotinylated proteins and proteomics, using proteins labelled after expression of a soluble BirAconstruct in dopaminergic neurons as reference. As controls, the same experiments were performed in KO-mouse lines in which the presynaptic scaffolding protein RIM or the calcium sensor synaptotagmin 1 were selectively deleted in dopaminergic neurons. To control for specificity, the proteomes were compared with those obtained by expressing a soluble BirA construct. The authors found selective enrichments of synaptic and other proteins that were disrupted in RIM but not Syt1 KO animals, with some overlap between the different baits, thus providing a novel and useful dataset to better understand the composition of dopaminergic release sites.

      Technically, the work is clearly state-of-the-art, cutting-edge, and of high quality, and I have no suggestions for experimental improvements.

      We thank the reviewer for this summary and for pointing out the high quality of the work.

      On the other hand, the data also show the limitations of the approach, and I suggest that the authors discuss these limitations in more detail. The problem is that there is very likely to be a lot of non-specific noise (for multiple reasons) and thus the enriched proteins certainly represent candidates for the interactome in the presynaptic network, but without further corroboration it cannot be claimed that as a whole they all belong to the proteome of the release site.

      We fully agree with the reviewer. Most importantly, we have changed the final section from “Conclusions” to “Summary of conclusions and limitations” (lines 501-518) to summarize the limitations with equal weight to the conclusions. In the revised manuscript, we also included many specific additional points in this respect throughout the discussion and the results: many hits could be noise (lines 458, 478-479), thresholding affects the inclusion of proteins in the release site dataset (lines 208-215), the seven-day time window could deliver interactors from the soma to the synapse (lines 493-495), specific oddities (for example histones, lines 482-485), iBioID does not deliver an interactome per se but is simply based on proximity (lines 505-508), and several more. We also clearly state that each specific hit needs follow-up studies (lines 501-503: ” Each protein will require validation through morphological and functional characterization before an unequivocal assignment to dopamine release sites is possible.”), and a similar statement was added on lines 374-375.

      Reviewer #2 (Public Review):

      The Kaiser lab has been on the forefront in understanding the mechanism of dopamine release in central mammalian neurons. assessing dopamine neuron function has been quite difficult due to the limited experimental access to these neurons. Dopamine neurons possess a number of unique functional roles and participate in several pathophysiological conditions, making them an important target of basic research. This study here has been designed to describe the proteome of the dopamine release apparatus using proximity biotin labeling via active zone protein domains fused to BirA, to test in which ways its proteome composition is similar or different to other central nerve terminals. The control experiments demonstrating proper localization as well as specificity of biotinylation are very solid, yielding in a highly enriched and well characterized proteome data base. Several new proteins were identified and the data base will very likely be a very useful resource for future analysis of the protein composition of synapse and their function at dopamine and other synapses.

      We thank the reviewer for this positive assessment of our work.

      Major comment:

      The authors find that loss of RIM leads to major reduction in the number of synaptically enriched proteins, while they did not see this loss of number of enriched proteins in the Syt1-KO's, arguing for undisrupted synaptome. Maybe I missed this, but which fraction of proteins and synaptic proteins are than co-detected both in the Syt1 and control conditions when comparing the Venn diagrams of Fig2 and Fig 3 Suppl. 2? This analysis may provide an estimate of the reliability of the method across experimental conditions.

      We thank the reviewer for proposing to be clear in the comparison of the control and Syt-1 cKODA data. A direct comparison of hit numbers is included on lines 323-324, with 37% overlap between control and Syt-1 cKODA (vs. 15% between control and RIM cKODA). A direct mapping of this overlap is included in Fig. 4E. We think that this direct comparison is complicated by a number of factors, as outlined below, and did our best to include these complications in the discussion, including the last section (lines 501-518).

      First, to assess overall similarity, the initial comparison should be to assess axonal proteins identified in the BirA-tdTomato samples. These datasets are quite similar, with 671 (control) and 793 (Syt-1 cKODA) proteins detected, and a high overlap of 601 proteins. We think that this indicates that the experiment per se is quite reproducible. The comparison of the release site proteome between control and Syt-1 cKODA is more complicated. We think that the main point of this comparison is that the overall number of hits is quite similar, with 450 hits in the Syt-1 cKODA proteome and 527 hits in the control proteome, and we now show that this similarity holds across multiple thresholds (lines 298-301; ≥ 1.5: Syt-1 cKODA 602 hits, control 991, ≥ 2.0: 450/527, ≥ 2.5: 252/348). Detailed analyses of overlap reveals that known active zone proteins such as Bassoon, CaV2 channels, RIMs, and ELKS proteins are present in both proteomes, but the overlap is partial and incomplete with 191 proteins found in both proteomes. As discussed throughout and summarized on lines 501-518, the reasons for this partial overlap may be manifold. Trivially, it could be explained by noise or non-saturation (“incompleteness”) of the proteome. We also think that the Syt-1 proteome is not expected to be identical because there is a strong release deficit in these mice. If Syt-1 has a dopamine vesicle docking function (which it does at conventional synapses [4]), this could influence the proteome. We note that protein functions in the dopamine axon are not well established, but inferred from studies of classical synapses.

      We have scrutinized the manuscript to not express that the control and Syt-1 cKODA proteomes are identical; we know they are not and discuss the example of α-synuclein specifically (Fig. 6, lines 347-362). Rather, the striking part is that the extent of the proteomes with high hit number, much higher than RIM cKODA, are similar. Specific hits have to be assessed in a detailed way, one hit at a time, in future studies, as expressed unequivocally on lines 501-503).

      Reviewer #3 (Public Review):

      In this study Kershberg et al use three novel in vivo biotin-identification (iBioID) approaches in mice to isolate and identify proteins of axonal dopamine release sites. By dissecting the striatum, where dopamine axons are, from the substantia nigra and VTA, where dopamine somata are, the authors selectively analyzed axonal compartments. Perturbation studies were designed by crossing the iBioID lines with null mutant mice. Combining the data from these three independent iBioID approaches and the fact that axonal compartments are separated from somata provides a precise and valuable description of the protein composition of these release sites, with many new proteins not previously associated with synaptic release sites. These data are a valuable resource for future experiments on dopamine release mechanisms in the CNS and the organization of the release sites. The BirA (BioID) tags are carefully positioned in three target proteins not to affect their localization/function. Data analysis and visualization are excellent. Combining the new iBioID approaches with existing null mutant mice produces powerful perturbation experiments that lead and strong conclusions on the central role of RIM1 as central organizers of dopamine release sites and unexpected (and unexplained) new findings on how RIM1 and synaptotagmin1 are both required for the accumulation of alpha-synuclein at dopamine release sites.

      We thank the reviewer for assessing our paper, for summarizing our main findings, and for expressing genuine enthusiasm for the approach and the outcomes.

      It is not entirely clear how certain decisions made by the authors on data thresholds may affect the overall picture emerging from their analyses. This is a purely hypothesis-generating study. The authors made little efforts to define expectations and compare their results to these. Consequently, there is little guidance on how to interpret the data and how decisions made by the authors affect the overall conclusions. For instance, the collection of proteins tagged by all three tagging strategies (Fig 2) is expected to contain all known components of dopamine release sites (not at all the case), and maybe also synaptic vesicles (2 TM components detected, but not the most well-known components like vSNAREs and H+/DA-transporters), and endocytic machinery (only 2 endophilin orthologs detected). Whether or not a more complete collection the components of release sites, synaptic vesicles or endocytic machinery are observed might depend on two hard thresholds applied in this study: (a) "Hits" (depicted in Fig 2) were defined as proteins enriched {greater than or equal to} 2-fold (line 178) and peptides not detected in the negative control (soluble BirA) were defined as 0.5 (line 175). How crucial are these two decisions? It would be great to know if the overall conclusions change if these decisions were made differently.

      We agree with the reviewer that the thresholding decisions are important and have now better incorporated the rationale for these decisions in the manuscript.

      Two-fold enrichment threshold. As outlined in the response to point 1 in the editorial decision letter, we now include figure supplements to illustrate the composition of the control proteome if we apply 1.5- or 2.5-fold enrichment thresholds (Fig. 2 – figure supplements 1 and 2) instead of the 2.0-fold threshold used in Fig. 2. This leads to more or less hits (991 and 348, respectively) compared to the 2.0-fold threshold (527 hits). It is noteworthy that the SynGO-overlap is the highest with the 2.0 threshold (37% vs. 31% at 1.5 and 33% at 2.5, Fig. 2 – figure supplement 3), justifying this threshold experimentally in addition to what was done in previous work [1,2]. These data are now described on lines 208-215 of the manuscript. When we apply these different thresholds to RIM and Syt-1 cKODA datasets, the finding that RIM ablation disrupts release site assembly persists. The following hit numbers were observed in the mutants at the 1.5, 2.0 and 2.5 enrichment thresholds, respectively: RIM cKODA 268, 198 and 82 hits; Syt cKODA 602, 450 and 252 hits. Hence, the extent of the release site proteome remains much smaller after RIM ablation independent of the enrichment threshold, bolstering the conclusion that RIM is an important scaffold for these release sites. This is included in the revised manuscript on lines 298301.

      Undetected peptides in BirA-tdTomato. We did not express this well enough in the manuscript. The undetected proteins were set to 0.5 such that a protein that was detected with a specific bait but not with BirA-tdTomato could be illustrated with a specific circle size, not to determine inclusion in the analyses. If the average peptide count across repeats with a specific bait was 1, this resulted in inclusion in Fig. 2 and consecutive analyses with the smallest circle size. Hence, this decision was made to define circle size. It did not affect inclusion in Fig. 2 beyond the following two points. If one were to further decrease it, this might result in including peptides that only appeared once as a single peptide for some of the experiments, which we wanted to avoid. If one would set it higher (to 1), this artificial threshold would be equal to proteins that were actually detected experimentally multiple times, which we wanted to avoid as well. We have now clarified this on lines 165-167 and lines 1119-1121.

      Expected proteins. In general, interpreting our dataset with a strong prior of expected proteins is difficult. The literature on release site proteins specifically characterized for dopamine is limited. We have found Bassoon, RIM, ELKS and Munc13 to be present using 3D-SIM superresolution microscopy [5,6], and we indeed found these proteins in the data as discussed on lines 227-232 and lines 423-445 in the revised manuscript. The prediction for vesicular and endocytic proteins is complicated. Release sites are sparse [5,7], and vesicle clusters are widespread in the dopamine axon, in some cases filling most of the axon (for example, see extended vesicle clusters filling much of the dopamine axon in Fig. 7E of [5]). Furthermore, docking in dopamine axons has not been characterized, and it is unclear how frequently vesicles are docked. Hence, it is not clear whether vesicular proteins should be concentrated at release sites compared to the rest of the axon (the BirA-tdTomato proteome we use for normalization). Similar points can be made for proteins for endocytosis and recycling of dopamine vesicles. Within the dopamine system, it is unclear whether the recycling pathway is close to the exocytic sites. One consistent finding across functional studies is that depletion after activity is unusually long-lasting in the dopamine system, for tens of seconds, even after only mild stimulation [5,8–13]. Hence, endocytosis and RRP replenishment might be very slow in these axons. It is not certain that endocytic factors are predeployed to the plasma membrane, and if they are, it is unclear how close to release sites they would be. As such, we agree with the reviewer that the proteome we describe is a hypothesisgenerator. With the limited knowledge on dopamine release, predictions beyond the previously characterized proteins in dopamine axons are difficult to make.

      We thank the reviewer for suggesting to include a better analysis of different thresholds and for giving us the opportunity to clarify the other points that were raised.

      Given the good separation of the axonal compartment from the somata (one of the real experimental strengths of this study), it is completely unexpected to find two histones being enriched with all three tagging strategies (Hist1h1d and 1h4a). This should be mentioned and discussed.

      We agree with the reviewer and have addressed this point in the manuscript. This could either reflect noise, or there could be more specific reasons behind it. The manuscript now states on lines 482-485: “It is surprising that Hist1h1d and Hist1h4a, genes encoding for the histone proteins H1.3 and H4, were robustly enriched (Fig. 2A). These hits might be entirely unspecific, or their co-purification could be due to biotinylation of H1 and H4 proteins (Stanley et al., 2001). It is also possible that there are unidentified synaptic functions of some of the unexpected proteins.” Ultimately, we do not know why these proteins are enriched, and we state clearly in the section “Summary of conclusions and limitations” that each new hit has to be validated in future studies (lines 501-503).

      It would also help to compare the data more systematically to a previous study that attempted to define release sites (albeit not dopamine release sites) using a different methodology (biochemical purification): Boyken et al (only mentioned in relation to Nptn, but other proteins are observed in both studies too, e.g. Cend1).

      We agree with the reviewer that Boyken et al, 2013 [14] is an important resource for our paper and for the assessment of the proteomic composition of release sites. We have now introduced links and citations to this paper multiple times (for example, on lines 231, 241, 430, 443, 481) and have expanded the discussion of overlap between these proteomes, including on Cend1 (lines 479482).

      We think that a systematic comparison with Boyken et al, 2013 [14] is complicated because (1) so little is known about dopamine release mechanics and (2) because the approach is very different between the two papers. In respect to (1), most prominently, it is not certain how frequently vesicles are docked in the dopamine axon. Only ~25% of the varicosities contain these release sites, and vesicle docking has not been characterized in striatal dopamine axons to the best of our knowledge. Hence, how a docking site at a classical synapse compares to a dopamine release site remains unclear at the outset. For point (2), the key difference is that “within dataset normalizations” are very different in these two studies. In our iBioID dataset, we normalize to soluble proteins defined as proximity to BirA-tdTomato. In ref. [14], the authors express enrichment over “light”, regular synaptic vesicles purified with the same approach. This has a major impact on the proteome that strongly influences a direct comparison of hits, because there are large differences in the normalization. While each normalization makes sense for the respective paper, it complicates direct comparison.

      With these points in mind, we have compared hits across both datasets class-by-class. For some classes, the datasets have reasonable overlap for ≥ 2-fold enriched proteins: for example for active zone proteins (3 of 7 hits in [14] appear in our control proteome) and adhesion and cell surface proteins (8 of 18). For other classes, the overlap is limited: for example for nucleotide metabolism/protein synthesis (0 of 16 hits in [14] appear in our dataset) and cytoskeletal proteins (5 of 29). We hope the reviewer agrees, that given these factors, the analyses and discussion needed for a systematic comparison goes beyond the scope of our paper. We have instead added a number of references to Boyken et al., 2013 [14], as outlined above, when direct comparison is meaningful.

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      Reply to the reviewers

      Manuscript number: RC-2022-01758

      Corresponding author(s): Harbison, Susan and Souto-Maior, Caetano

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      1. General Statements [optional]

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      We thank the reviewers for their time and care in evaluating our manuscript. They raise several important points, which we have addressed, resulting in a greatly improved manuscript. Please note that we numbered the comments from both reviewers for ease of reference, as we cross-referenced comments in some cases. Reviewer comments are in italics; our responses are provided in plain text.

      2. Point-by-point description of the revisions

      This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      *Summary*:

      *The authors of this work generated a Sleep Advanced Intercross Population from 10 extreme sleeper Drosophila Genetics Reference Panel. This new out-bred population was subjected to a artificial selection with the aim of understanding the genes underlying the sleep duration differences between three populations: short-sleep, unselected, and long-sleep. Using analysis of variance the authors identified up to nearly 400 of genes that were significant selected over the various generations and showed opposite trends for long and short sleep, thus potentially relevant for the regulation of sleep duration. 85 of these genes were consistent between male and females sub-populations, suggesting a small number of genetic divergences may underlie sex-independent mechanisms of sleep.

      Given the time-course nature of the generational data obtained, the authors studied potential correlations and interactions between these 85 identified candidate genes. Initially, the authors used pairwise Spearman correlation, noticing how this method could not filter most of pairwise interaction (around 40% of all possibilities were significant). To overcome the linear limitations of the previous approach, the authors implemented a more complex, non-linear Gaussian process model able to account for pairwise interactions. This new approach was able to identify a smaller number of different, and potentially more informative, correlations between the candidate genes previously identified.

      Lastly, with genetic manipulations, the authors show in vivo that a subset of the candidate genes is causally related with the sleep duration as well as partially validating some of the correlation identified by their new model.

      The authors conclude that, given the non-linear and complex nature of biological systems, simplistic linear approaches may not suffice to fully capture underlying mechanisms of complex traits such as sleep.

      *Major comments*

      1. Most of the the work presented focus on the computational and statistical analysis of different populations submitted (or not) to a process of artificial selection for short or long sleep duration. As such, the amount of potentially relevant biological conclusions to be tested is mostly unfeasible. The authors already present additional experiments to partially support some, though not all, of their findings. Given the manuscript is written as a method innovation, these additional experiments illustrate the potential uses of the method described. *

      Our response: The reviewer raises a very important point, one that is at the very impetus of our work. We agree that it is not possible to test all combinations of genes in all contexts to determine whether they influence sleep or not. In contrast to the situation for circadian rhythms, where the core clock is controlled by just four genes, recent work has concluded that sleep is a set of complex traits influenced by large numbers of genes. Robust computational methods are needed to identify the complex interactions among genes. The current manuscript is a first step towards achieving this goal.

      *(OPTIONAL) However, since the one of the focuses of this work in identifying potential gene interactions, it would be interesting if the authors could test a "double knockout" and perhaps demonstrate evidence for epistasis between two of the identified genes. Having access to single mutants, this experiment should be realistic. However, I have no hands-on experience working with Drosophila and I am unable to accurately estimate the amount of resources and time such and experiment could take. My initial guess would be 3-6 months work should suffice. *

      Our response: The reviewer makes an interesting proposal. While such an experiment would provide some additional information, our method does not make any prediction about what a double knockout would do, either to the sleep phenotypes or to gene expression.

      2. In regards to the gene CG1304, it seems to be an important example used throughout the manuscript. It should be carefully re-analyzed as was considered for interaction analyses without showing opposite trends for short- and long-sleep populations (see minor comments on figure 2).

      Our response: We are not entirely certain that we understand the reviewer’s point. We note that significant genotype-by-selection-scheme interactions may not manifest as opposite trends and this is not what is being tested for significance. The likelihood ratio is a test for a significant effect of including sel x gen coefficients for both short and long schemes; therefore, GLM significance may mean that either one or the two selection schemes are significantly different from controls, not from each other. We could, for instance, apply three different tests: one (i) comparing between long and short flies; the second (ii) __comparing short flies to controls; and the third (iii) __comparing long to controls and find that the first test is significant — i.e. short is different from long — and that the two others are not — i.e. neither scheme is found to be different from controls. The opposite could also happen: short and long flies may not be different from each other, but with both being different from controls.

      Since we are interested in identifying differences of either to controls, our choice of statistical test is equivalent to performing tests (ii) __and (iii)__ without the need to perform and correct for multiple tests. While there are caveats to this choice (like all choices), linear model-based differential expression analysis has its own caveats, and has limited ability to pick up arbitrary trends, so it serves as a coarse-grained filter for large shifts since it’s too costly (computationally) to run the Gaussian process on 50 million pairwise combinations.

      *3. One major comment would be that the claim that the Gaussian process method is more sensitive and specific than simpler approaches, though intuitively understandable, does not seem to be fully correct from a strict statistical point of view, given the lack of a gold standard reference to compare if the new method is indeed picking more true positives/negatives. I would reconsider re-rephrasing such statement in the absence of a biologically relevant validation set. *

      Our response: We agree with the reviewer that there is no ‘gold standard’ reference data set with which to compare our findings. We have softened this language a bit in response, where it occurs in both the Abstract and the Results.

      Under Abstract, we changed “Our method not only is considerably more specific than standard correlation metrics but also more sensitive, finding correlations not significant by other methods” to “Our method appears to be not only more specific than standard correlation metrics but also more sensitive, finding correlations not significant by other methods.”

      Under Results, we changed “Therefore, computing correlations between genes using covariance estimates from the Gaussian Processes greatly increases specificity over direct correlations. Furthermore, the Gaussian processes are not only more specific but more sensitive…” to “Therefore, computing correlations between genes using covariance estimates from the Gaussian Processes appears to increase specificity over direct correlations. Furthermore, the Gaussian Processes appear to be more sensitive…”

      *4. Finally, the study appears to be well powered and it is clear that the authors were careful in their explanation of the statistical methods. However, I could not find the copy of the code/script used for the model. Without it, it would be very difficult to fully reproduce the results as both the language used (Stan) and the method itself are not common in the sleep research field. *

      Our response: We thank the reviewer for noticing this, and apologize for this oversight. The code used for analysis has been deposited in GitHub under: https://github.com/caesoma/Multiple-shifts-in-gene-network-interactions-shape-phenotypes-of-Drosophila-melanogaster.

      We have noted the script location in the Data Availability statement. We added a statement to read “All scripts used for the model have been deposited in Git Hub https://github.com/caesoma/Multiple-shifts-in-gene-network-interactions-shape-phenotypes-of-Drosophila-melanogaster.”

      * * *Minor comments* * 5. The statistical cut-off used for gene expression hierarchical GLMM after BH correction was of 0.001, which is 50 times more strict than the common 0.05. Could the authors comment on how this choice may impact the results compared to those available in the literature and on the rational for choosing such a value.*

      Our response: A FDR of 0.05 would increase the number of genes identified (3,544 for females; 1,136 for males, with 462 overlapping). The FDR of 0.001 is consistent with the lowest threshold typically used for gene expression data collected during other artificial selection experiments (Mackay et al., 2005; Morozova et al., 2007; Edwards et al., 2006), though thresholds as high as 0.20 have been used (Sorensen et al., 2007). We have added to the last statement to the Methods and Materials section under “Generalized Linear Model analysis of expression data” to read “Model p-values were corrected for multiple testing using the Benjamini-Hochberg method (Benjamini and Hochberg, 1995), with significance defined at the 0.001 level, consistent with the lower threshold applied in other artificial selection studies (Mackay et al., 2005; Morozova et al., 2007; Edwards et al., 2006).”

      *6. Heritability calculations are not mentioned in the methods. Could it be useful to include a small paragraph? Could a small comment be done on the differences in h2 for the short sleep replicates which show ~10x difference? *

      Our response: We thank the reviewer for noticing this omission and apologize for the oversight. We have added the following statements to the Methods and Materials under “Quantitative genetic analyses of selected and correlated phenotypic responses.”

      “We estimated realized heritability h2 using the breeder’s equation:

      h2 = ΣR/ΣS

      where ΣR and ΣS are the cumulative selection response and differential, respectively (Falconer and Mackay, 1996). The selection response is computed as the difference between the offspring mean night sleep and the mean night sleep of the parental generation. The selection differential is the difference between the mean night sleep of the selected parents and the mean night sleep of the parental generation.”

      Additionally, we thank the reviewer for noticing the large difference in the realized heritability between the short sleeping population replicates; the heritability for replicate 1 is a typo and should be 0.169, not 0.0169. Hence, the heritabilities of both replicate populations are quite similar, i.e., 0.169 for replicate 1 and 0.183 for replicate 2. We have corrected this error in the Results.

      7. In regards to the model implementation, what would be the implications of not enforcing positive semi-definiteness on the co-variance matrix, given than that these are strictly positive semi-defined?

      Our response: All covariance matrices are by definition positive semi-definite (PSD), since they cannot yield negative values for the probabilities associated to them, so it would not be possible to relax that assumption generally. The only choice we could make would be on the number of genes included (M) in each multi-channel gaussian process model, and this in turn would by design enforce positive semi-definiteness on an matrix of size MN, (N being the number of generations). As noted in the appendix, “enforcing” positive semi-definiteness on smaller blocks of a larger 2D-array of covariances (which is not itself a covariance matrix) does not imply the latter is PSD and therefore seems like a softer constraint. In practice scaling up to a model where M >> 40 is not trivial from a computational and inference point of view, so the choice of smaller M is in a way imposed on us, and fortunately it is the less limiting one. We provide the appendix as a general clarification on the subtleties of Gaussian Processes, but a comprehensive assessment is beyond the multidisciplinary scope of this article and would require a narrower mathematical/statistical description in a standalone methodological article or technical note.

      1. *The methods mention that PCA projection were performed on the first 3 components, however only the first two are showed. *

      Our response: PCA was performed on 10 components, although the algorithms will commonly compute all components and return only the selected number. The variance of the third component is smaller than ~5% (that of the second PC). In practice PC1 is by itself enough to show the clear separation of expression per sex with ~65% of the variance; PC2 is in fact only shown to improve visualization. Plots of the remaining components will not show clear separation among samples as the variance explained is so small. We have corrected the Methods to indicate that PCA was performed on 10 components rather than 3.

      *9. Figure 1 refers to the mean night sleep time of the population. Could some measurement of variability (se or sd) be represented to provide a general idea of the distribution of the values? Additionally, the standard deviation of associated with the CVe estimates are mentioned but not showed explicitly. Could they maybe be added to the text as to illustrate how much such deviations were reduced? *

      Our response: We thank the reviewer for this comment. Including either the standard errors or standard deviations on the plot of the response to selection (Figure 1A) makes visualization unwieldy; thus we have added an additional supplemental table, Supplementary Table S15, that contains the mean night sleep, standard deviation, and number of flies measured for each generation in each replicate population. We also added a plot of the standard deviation in night sleep per generation to Supplemental Figure S2 (letter “Q” in the figure) so that the reduction over time in each population can be seen.

      Under “Data Availability,” We added the following: “Night sleep phenotypes per selection scheme/sex/generation/population replicate are listed in Table S15.”

      *10. Figure 2 shows the linear model fits for gene CG1304. I find this gene on the list of significant genes for both sexes (tables S5/6), but it does not seem to be one that shows opposite trend for short- and long-sleep (tables S7/8). Surprisingly, it shows up again on table S10! However, the text introducing the figure reads like this should be one of the 85 sex-independent genes. Would it be best to provide an example of what a significant gene looks like? *

      Our response: As mentioned in our response to comment #2 above, significance in the likelihood-ratio test does not imply opposite trends between long and short selection schemes, but between a model that includes specific slope coefficients for selection scheme by generation (both long and short) compared to a reduced model where the only slope is one associated to generation and therefore independent of selection scheme.

      11. *Figure 3 would be interesting to have both the GP correlations and the Spearman correlations to illustrate the methodological differences. I would be curious to see at least one pairwise expression scatter-plot as well just to see how they correlate in one plot. *

      __Our response: __Table S11 contains all (significant and nonsignificant) GP and Spearman values side-by-side for comparison. High correlations are likely to conform to the Spearman assumptions of a monotonic relationship; nevertheless, this will not be so for the majority of genes since the difference in the number of Spearman and GP-significant genes is tenfold or more, so it would be misleading to focus on individual-gene relationships without taking into consideration the transcriptome wide results for any method employed.

      We would like to stress that there is nothing particularly special about CG1304 in and of itself; furthermore, there are no “representative” genes or figures in this manuscript. Instead, CG1304 is chosen because its GLM and GP fits are illustrative of the limitations and capabilities of each model to pick up certain kinds of trends, and especially because it is especially instructive of how correlations arise from the GP model, which may not be intuitively clear to all readers.

      12. Figures 3S3/4 are described as showing single- and multi-channel models don't change substantially. Would this be expected and why?

      Our response: This is not necessarily expected, as scaling up from a single to a multi-channel model will add additional parameters as well as constraints, like positive the semi-definiteness mentioned in comment #7 above. If that seemed to have considerable impact on the fits it could challenge our assumption that the signal variance parameters estimated from the single-channel are good priors for the same parameters in the two-channel model (although this is not a hard constraint, so in the worst case the result could still only be a slight bias).

      *13. Having build different networks of pairwise associations of genes (projecting on a unified network as illustrated on figure 5), it could in interesting to compare the network topologies at a basic level such as node degrees, overlapping sub-networks, are they potentially scale free as previously described for biological systems, etc. *

      __Our response: __The reviewer makes an interesting point. Indeed summaries of the network could be useful information about the system level parameters, which are the main results of this paper. We now include the number of connections (i.e., the degree) to each gene in each of the four networks presented in Figure 5 in a new supplemental Table (Table S13). We also plot the distribution of node connectivity below. The distributions do not appear random (i.e., a normal distribution), and appear closer to a power-law or scale-free distribution. However, the small size and low average degree of these networks make a formal test unfeasible, and a recent study suggests that a log-normal distribution is in general more likely than a power-law distribution (Broido et al., Nat Comm, 2019), so we lack the evidence to claim that these networks are scale-free.

      We have added to the Results under “Gaussian Process model analysis uncovers nonlinear trends and specifically identifies covariance in expression between genes”: “Table S13 lists the number of connections (degrees) that each gene has with others in the network. The average number of connections for long-sleeper males was 2.6; the other three networks had average degrees of 2.0 or less (2.0 for long-sleeper females and short-sleeper males; 1.75 for short-sleeper females).”

      *14. On table S6 I noticed some gene symbols were loaded as dates (1-Dec) *

      Our response: We thank the reviewer for noticing this, the gene symbol is supposed to be dec. We have corrected this in Table S6 (now Table S7).

      1. *In results, the phenotypical response to artificial selection is sometimes described in minutes, other times in hours. Though this is an hurdle, it could make the values easier to compere if they were consistently formatted as minutes (hours). *

      Our response: We are unsure what the reviewer is referring to. We only see one sentence in which we used hours, and that was the concluding sentence under Results, “Phenotypic response to artificial selection.” The remainder of the manuscript refers to sleep times in minutes, phenotypes in all of the figures are plotted as minutes, and all of the supplemental material refers to times in minutes.

      16. *Over 99% of chains converged after three runs. Even though the reasons for the lack of convergence of these chains was not investigated, could this be a relevant effect? 1% of 3570 interactions is still 35 potential interactions. Do the non convergent chains relate with specific genes? *

      Our response: Bayesian MCMC inference is a stochastic algorithm, so there is a finite chance that any given run doesn’t converge, and that means that all eight parallel chains must converge and mix as measured by the stringent choice of R-hat metric being within 0.05 of unity. Relaxing the interval to 0.1 or 0.2 could still be acceptable, but we made the choice of a stringent threshold to avoid making interpretations on less-than-ideal runs. There is no evidence that there is any gene-specific problem, usually it would be one out of eight chains that would not mix well and throw off the diagnostic metrics (like relaxing the metrics, an acceptable approach could be accepting a run with 6-7 chains converging properly, but we decided to rerun all chains and only accept 100% convergence but accept a possible loss). Non-converging/nonmixing runs are likely to eventually do so, but since were are running tens of thousands of runs (3570 pairwise combinations × 3 schemes × 8 chains) a massively parallel implementation in a HPC cluster is required. Finally, seeing that 145 is ~4% of the total number of interactions, a naïve expectation would be that no more than one interaction would come out significant — while there is a chance that an interesting interaction was identified, the same can be said for potential false negatives computed using the GLM, which is a consequence of working at a high-throughput scale.

      17. The GO terms identified as significantly enriched after pvalue correction point to a clear association of the 85 genes identified with Serine proteases. Could this be discussed further to highlight biological findings of the work in the context of neuronal function or sleep regulation?

      Our response: The reviewer is correct, nine putative Serine proteases are significantly enriched among the 85 genes. All nine exhibit some expression in neurons and in epithelial cells, and all are expressed at the adult stage. The appearance of these enzymes is interesting given their role in proteolysis.

      We have updated the Discussion to read, “Interestingly, our Gene Ontology analysis identified nine genes from the 85-gene network with predicted Serine endopeptidase/peptidase/hydrolase activity: CG1304, CG10472, CG14990, CG32523, CG9676, grass, Jon65Ai, Jon65Aii, and Jon99Fii. All of these genes are expressed in neurons and epithelial cells, and all genes are expressed at the adult stage (Li et al., 2022). Serine proteases are a large group of proteins (257 in Drosophila) that perform a variety of functions (Cao and Jiang, 2018). Their predicted enzymatic activity suggests a putative role in proteolysis. This is an intriguing observation given pioneering work in mammals which suggested a role for sleep in exchanging interstitial fluid and metabolites between the brain and cerebral spinal fluid (Xie et al., 2013). Recent work demonstrated that a similar function is conserved in flies via vesicular trafficking through the fly blood-brain barrier (Artiushin et al., 2018). It would be interesting to determine whether these genes function in this process.”

      *18. Could the authors discuss the little overlap between males/females and shot/long sleep for 145 gene pairs identified after the MCMC runs. Similarly, how can the network differences be explained from a biological/evolutionary perspective? *

      Our response: The reviewer asks an interesting question. We did not detect sex-specific responses to artificial selection for long or short sleep in the present experiment. Yet differences in gene expression network pairs between males and females exist, and as the reviewer mentions, we also observed differences in network pairs between long sleepers and short sleepers. These differences reflect an inescapable conclusion: a given sleep duration phenotype can originate from more than one gene expression network configuration.

      19. *In the mutational analyses it is pointed out that CG12560 and Jon65Aii only affect females significantly. However, in the following sentence, the authors claim these two genes had the greatest effect on both sexes, which seems contradictory, at least in the way it is described. *

      Our response: Our wording may have been confusing, given that it came after a comment about Jon65Aii. Our exact statement was “Effects of the Minos insertions on night sleep duration were stronger in females than in males; when sexes were examined separately, only mutations in CG12560 and Jon65Aii affected male night sleep duration.” This was meant to convey that the effects of all Minos insertions were the same directionally for both males and females, but that only CG12560 and Jon65Aii insertions had statistically significant effects on each sex separately. We have re-worded this sentence to read “All Minos insertions had the same directional effect on night sleep for both males and females, but only the CG12560 and Jon65Aii insertions had statistically significant effects on night sleep on each sex separately.”

      20. *Maybe a small comment on how unchanged expression could lead to the observed phenotypical variation could help understanding how Minos mutations effects are biological mediated for those not familiar with the method. This seems to be the authors expectation so, could it be non-functional proteins or something else? *

      Our response: The reviewer raises an interesting point. We did not observe changes in gene expression for CG13793, Cyp6a16, or hiw compared to w1118 controls. Thus far, we have examined gene expression relative to the control for a single timepoint, and only in pooled whole flies. Differential gene expression between the Minos mutants and controls might occur at a different timepoint, or in a small set of key neurons that would be undetectable when comparing whole flies.

      We expand on this in Results, under “Mutational analyses confirms the role of candidate genes and interacting expression networks in sleep”: “Potential reasons for the lack of a significant change in gene expression in the remaining lines include: the position of the insertion within the targeted gene, which has variable effects on its expression; the relatively low statistical power of the experiment; confining our observation to a single timepoint during the day; or pooling whole flies, which might obscure gene expression changes occurring at a single-tissue level.”

      *21. The assumption that interacting genes would have their expression ratio changed by the Minos insertion would hold on situation where the affected gene causally interferes with the candidates expression. As far as I understand, causality cannot be inferred by the proposed method. Thus in a situation where both genes are co-regulated by a third factor, no change in expression ratio is to expected. How would the authors re-interpret their final result when considering this direct vs indirect interaction distinction? *

      Our response: Our method only gives us the hypothesis that two genes interact based on their correlation, and that is what we test using the Minos insertions. We do not as yet have a way to identify a third gene or factor that might be regulating the two. Given the number of genes affecting sleep, it is quite likely that there are such factors, but we can only report and test what we’ve observed. Any interpretation based on an arbitrary third factor would be purely speculative.

      **Referees cross-commenting**

      22. *I agree with Reviewer #2 comments which, to me, reads as generally pointing out the lack of biological interpretation of the results (and thus connecting this study with previous literature). Adding this component would make the manuscript well-rounded and attractive to a wider audience. *

      Our response: We agree with both reviewers that additional biological interpretation of the results would make the manuscript more attractive to a wider audience. Accordingly, we have added the following paragraph to the Discussion: “The genes we identify herein overlap and extend previous work. Of the 1,140 genes implicated in the generalized linear model, 151 (13.2 percent) overlapped with previous candidate gene, random mutagenesis, gene expression, and genome-wide association studies of sleep and circadian behavior in flies (Pegoraro e t al., 2022; Dissel et al., 2015; Seugnet et al., 2017; Shalaby et al., 2018; Thimgan et al., 2010, Thimgan et al., 2018, He et al., 2013; Mallon et al., 2014; Roessingh et al., 2019, Feng et al., 2018; Lee et al., 2021; Khoury et al., 2020; Wu et al., 2018; Harbison et al., 2013; Harbison et al., 2009; Harbison et al., 2017; Harbison et al., 2019). Notably, previous studies identified the genes CG17574, cry, dro, mip120, Mtk, NPFR1, pdgy, PGRP-LC, Shal, and vari as affecting sleep duration (Feng e t al., 2018, Dissel et al., 2015; Pegoraro et al., 2022; Thimgan et al., 2018; Mallon et al., 2014; He et al., 2013; Khoury et al., 2020; Harbison et al., 2013). Two genes, ringer and mip120, overlapped with our previous study of DNA sequence variation in flies selected for long and short sleep (Harbison et al., 2017). In that study we identified a polymorphism in an intron of ringer that changed in allele frequency with selection, with increases in the population frequency of the ‘G’ allele with increasing sleep, while the frequency of the ‘A’ allele increased with decreasing sleep. When the selective breeding procedure was relaxed, the frequency of the ‘G’ allele increased in short-sleeping populations, paralleling an increase in sleep (Souto-Maior et al., 2020). One possibility is that this polymorphism contributes to the changes in gene expression in ringer that we observed in the present study. Of the 85 genes common to both sexes that we used in the gene interaction networks, 11 (13 percent) appear in other studies of sleep: CG10444, CG2003, CG5142, CG6785, CG9114, CG9676, CR42646, hiw, NPFR1, Tie, and wb (He et al., 2013; Seugnet et al., 2017; Wu et al., 2018; Harbison et al., 2013). Thus, our study corroborates genes known to affect sleep, and identifies new candidate genes for sleep as well.”

      Reviewer #1 (Significance (Required)):

      *This study proposes the application of advanced non-linear methods to study complex traits such as sleep. As implemented, Gaussian Processes are able to identify non-linear correlations between two biological features (e.g. transcripts) over time (e.g. generations), representing an attempt to push the analytical methods available beyond the single gene paradigm. As such, more than the relevance of the biological results themselves, the authors focus on the explaining and illustrating the application of methodological advances obtained, and its relevance to obtain a better understanding of biological systems.

      However the mathematical principles required to understand the implemented method are not trivial and require advanced knowledge of machine learning and statistics. This is a potential barrier, though not an impediment, to its quick and wide adoption by the community. In addition, even if demonstrated to be a valid method when working with Drosophila, the resolution required to perform such a study may be difficult to obtain with other model systems, which would likely require further refinement of the statistical approach.

      The main audience interested in this work would be basic sleep researchers. However, this work is also related to the understanding gene selection over an artificial evolutionary process, thus evolutionary and developmental biologist may be also be interested. The methodology itself, already used in other fields of study, is a general statistical tool that could be adopted by a broad range of researchers for a diversity of topics. As such, I believe with this work, the authors will be able to stimulate the development and/or rethinking of the available analytical methods to study complex biological systems, though this would likely be done either in collaboration with the authors themselves or by a specific subset of researchers who regularly work with advanced mathematical, statistical and computational principles.

      (disclaimer) My mathematical formation does not reach the PhD level expertise that may be required to fully understand the methodology described. I have never personally worked with D. melonogaster or used Gaussian Processes in a professional setting. As such, I may not be able to fully evaluate/appreciate the more detailed technical aspects of this work.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Souto-Mairo et al. reports phenotypic and genotypic effects of artificially selecting for short and long sleep in flies. They generated an impressive time-series dataset where one could examine genetic and phenotypic changes across time (generations, total 13 generations) in response to the selection pressure. The authors explored the relationships between pairs of genes in addition to just identifying potential candidate genes involved in the regulation of the amount of sleep.

      Major points:

      1. Harbison et al 2017: This study seems to be a continuation of Harbison et al 2017. There needs to be a clearer approach in the text (introduction?) in elucidating how this study is really advancing the findings of Harbison et al., 2017. Do the two studies use the same selection lines? If not, how are they different? If they are not different, what might cause the phenotypes evolving differently? For example, day sleep, day bout number do not respond to the selection pressure similarly in both studies etc. *

      Our response: We would like to emphasize that this study is not a continuation of the Harbison et al., PLoS Genetics, 2017 paper, where we examined the changes in DNA sequence during artificial selection, and it does not use the same selection lines. The fact that the two studies are different can be seen from an examination of Figure 1A of the current study and Figure 1A of the Harbison et al 2017 study. The trajectories of each population across generation are very different. Out of convenience, we used the same nomenclature to refer to the populations in both studies (L1, L2, S1, S2, etc.), and apologize if this is the source of the confusion. Both studies do originate from the same outbred population, however, and to get to the broader question that the reviewer is asking, should one expect to see the same correlated responses to selection for night sleep among selection lines originating from the same outbred population? The answer is no, not unless the selected trait and the responding trait have a genetic correlation of 1.0. We previously estimated the correlation between day sleep and night sleep to be between 0.29 - 0.38 and between day bout number and night sleep to be -0.05 (Harbison et al., 2013; Harbison et al. 2009). In the Harbison et al. 2017 study we noted that day sleep and day bout number had correlated responses to selection for night sleep, but neither have correlated responses in the current study. The relatively low genetic correlations between these two measures and night sleep explain why we do not see a consistent correlated response among studies.

      We didn’t really elaborate on these observations in the manuscript, and so have added to the Results under “Correlated response of other sleep traits to selection for night sleep” the following: “These correlated responses concur with previous observations we made in selected populations originating from the same outbred population for night sleep and night average bout length, and night sleep and sleep latency (Harbison et al., 2017). However, unlike the previous study, we did not see a correlated response between night sleep and day sleep, and night sleep and day bout number (Harbison et al., 2017). The lack of correlated response reflects the relatively low genetic correlations these two traits have with night sleep (Harbison et al., 2013; Harbison et al., 2009).”

      2. Zeitgeber Time (ZT) for RNA collection: It is puzzling that the study reports that the RNA was collected at 12 PM. I do not understand what this information means; especially in a project where one is working with sleep. The authors might want to report ZT. Also, why a particular ZT was chosen should be discussed. These genes are potential sleep-relevant genes - hence it is not too esoteric to think that the ZT of data collection matters a lot as some of them might be cycling. To get a more appropriate picture, multiple time points of data collection might be even better. The authors seem to have ignored this crucial aspect of a clock/sleep study - time of data collection and how time of data collection might shape your findings.

      Our response: We agree with the reviewer that it would be better to have multiple timepoints for collection, but this is difficult to implement in practice as it would require an additional 5,280 flies per generation (4 pools of 10 flies per sex per population) for 12 timepoints as recommended by Hughes et al., JBR, 2017. We mention collection time in the Methods and Materials because we are aware of the changes in gene expression over the circadian day. 12PM is the midpoint between the start of the lights-on and lights-off period (i.e., ZT6), and was chosen arbitrarily. We have added the ZT notation to the Methods and Materials for clarity.

      3. Short sleeping flies: Are there reports of flies sleeping this less? "We found 2,830 interactions; 8 of these were one of the 3,570 between the 85 genes, but none of them overlapped with the 145 gene pairs found to be different from controls. The gene interactions we observed may therefore be unique to extreme sleep." What is extreme sleep? How does this study then claim to have identified evolution of potential sleep-relevant gene expression for normal, physiologically relevant sleep?

      Our response: Our statement was not very well worded, and we thank the reviewer for noticing this. What we intended to say was that the lack of overlap between our data and a known protein-protein interaction database may due to the interactions being unique to sleep as opposed to some other complex trait. We have re-worded this statement to say “The gene interactions we observed may therefore be unique to sleep.”

      *Minor points:

      4. The article uses an unnecessarily defensive tone to establish their approach to understand underlying mechanisms of sleep 'better' than that of the others (in both introduction and discussion): "In spite the large amount of studies and data generated for many systems, identifying underlying processes is still very rare; this is clear indication that better methods are needed to obtain understanding of biological processes from data." The 'still very rare' part is just factually incorrect and misleading as far as sleep is concerned. Even if we just see Drosophila studies on sleep, there is a huge progress that's being made in terms of genes, neurons and circuits relevant for sleep: both in terms of baseline sleep as an output of the circadian clock and the rebound/homeostatic sleep. Most, if not all, of these elegant and pioneering studies from multiple, independent groups took approaches that did not require artificial selection regimes. As a substitution for their defense, the authors might attempt to present their findings in the context of the existing knowledge of sleep in flies. For example, what about genes already implicated in sleep? Do they show up in their analysis? For example, Sleepless, DATfmn, Sandman, AstA, AstA-receptor, Wide-awake etc. This could really help the manuscript.*

      Our response: We certainly did not intend for this statement to suggest that no progress had been made in the identification of genes and circuits for sleep, and we agree that elegant and pioneering approaches have made significant progress in our understanding of the phenomenon. Rather, we were thinking more in terms of fully described biochemical networks. To avoid this interpretation by other readers, we have altered the “still very rare” sentence in the Introduction to read: “Despite the large amount of studies and data generated for many systems, a full understanding of underlying processes has not yet been achieved…’

      We also agree with the reviewer that it would be helpful to put our work in the context of what is already known in flies. We have added the following paragraph to the Discussion to relate the work with previous work on sleep in flies: “The genes we identify herein overlap and extend previous work. Of the 1,140 genes implicated in the generalized linear model, 151 (13.2 percent) overlapped with previous candidate gene, random mutagenesis, gene expression, and genome-wide association studies of sleep and circadian behavior in flies (Pegoraro e t al., 2022; Dissel et al., 2015; Seugnet et al., 2017; Shalaby et al., 2018; Thimgan et al., 2010, Thimgan et al., 2018, He et al., 2013; Mallon et al., 2014; Roessingh et al., 2019, Feng et al., 2018; Lee et al., 2021; Khoury et al., 2020; Wu et al., 2018; Harbison et al., 2013; Harbison et al., 2009; Harbison et al., 2017; Harbison et al., 2019). Notably, previous studies identified the genes CG17574, cry, dro, mip120, Mtk, NPFR1, pdgy, PGRP-LC, Shal, and vari as affecting sleep duration (Feng e t al., 2018, Dissel et al., 2015; Pegoraro et al., 2022; Thimgan et al., 2018; Mallon et al., 2014; He et al., 2013; Khoury et al., 2020; Harbison et al., 2013). Two genes, ringer and mip120, overlapped with our previous study of DNA sequence variation in flies selected for long and short sleep (Harbison et al., 2017). In that study we identified a polymorphism in an intron of ringer that changed in allele frequency with selection, with increases in the population frequency of the ‘G’ allele with increasing sleep, while the frequency of the ‘A’ allele increased with decreasing sleep. When the selective breeding procedure was relaxed, the frequency of the ‘G’ allele increased in short-sleeping populations, paralleling an increase in sleep (Souto-Maior et al., 2020). One possibility is that this polymorphism contributes to the changes in gene expression in ringer that we observed in the present study. Of the 85 genes common to both sexes that we used in the gene interaction networks, 11 (13 percent) appear in other studies of sleep: CG10444, CG2003, CG5142, CG6785, CG9114, CG9676, CR42646, hiw, NPFR1, Tie, and wb (He et al., 2013; Seugnet et al., 2017; Wu et al., 2018; Harbison et al., 2013). Thus, our study corroborates genes known to affect sleep, and identifies new candidate genes for sleep as well.”

      Reviewer #2 (Significance (Required)):

      5. I believe that the authors should attempt to put this study in the context of what is already known in sleep in flies and how this study advances the knowledge. And how the knowledge generated by this study would help other sleep researchers, who, for obvious reasons, would like to employ techniques other than artificial selection and big data. The data is elegant. The work seems to be extremely laborious. Nonetheless, as it stands now, this manuscript is only very specific for an audience who work with artificial selection to understand underlying genetics of behavior. In fact, even within the fly sleep field, most people might not find this manuscript very useful.

      Our response: The reviewer may not have considered the wider application of this work. This framework is applicable to any data set of gene expression sampled across time, whether sampled across generation, as we did, or across the 24-hour circadian day, or sampled at other time intervals. We have added a statement to the Discussion to stress this fact: “The Gaussian Processes we apply herein have broad applications to other experimental designs, such as gene expression measured at varying time intervals over the circadian day, or time-based sampling of gene expression responses to drug administration.”

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      Referee #2

      Evidence, reproducibility and clarity

      Souto-Mairo et al. reports phenotypic and genotypic effects of artificially selecting for short and long sleep in flies. They generated an impressive time-series dataset where one could examine genetic and phenotypic changes across time (generations, total 13 generations) in response to the selection pressure. The authors explored the relationships between pairs of genes in addition to just identifying potential candidate genes involved in the regulation of the amount of sleep.

      Major points:

      1. Harbison et al 2017: This study seems to be a continuation of Harbison et al 2017. There needs to be a clearer approach in the text (introduction?) in elucidating how this study is really advancing the findings of Harbison et al., 2017. Do the two studies use the same selection lines? If not, how are they different? If they are not different, what might cause the phenotypes evolving differently? For example, day sleep, day bout number do not respond to the selection pressure similarly in both studies etc.
      2. Zeitgeber Time (ZT) for RNA collection: It is puzzling that the study reports that the RNA was collected at 12 PM. I do not understand what this information means; especially in a project where one is working with sleep. The authors might want to report ZT. Also, why a particular ZT was chosen should be discussed. These genes are potential sleep-relevant genes - hence it is not too esoteric to think that the ZT of data collection matters a lot as some of them might be cycling. To get a more appropriate picture, multiple time points of data collection might be even better. The authors seem to have ignored this crucial aspect of a clock/sleep study - time of data collection and how time of data collection might shape your findings.
      3. Short sleeping flies: Are there reports of flies sleeping this less? "We found 2,830 interactions; 8 of these were one of the 3,570 between the 85 genes, but none of them overlapped with the 145 gene pairs found to be different from controls. The gene interactions we observed may therefore be unique to extreme sleep." What is extreme sleep? How does this study then claim to have identified evolution of potential sleep-relevant gene expression for normal, physiologically relevant sleep?

      Minor points:

      The article uses an unnecessarily defensive tone to establish their approach to understand underlying mechanisms of sleep 'better' than that of the others (in both introduction and discussion): "In spite the large amount of studies and data generated for many systems, identifying underlying processes is still very rare; this is clear indication that better methods are needed to obtain understanding of biological processes from data." The 'still very rare' part is just factually incorrect and misleading as far as sleep is concerned. Even if we just see Drosophila studies on sleep, there is a huge progress that's being made in terms of genes, neurons and circuits relevant for sleep: both in terms of baseline sleep as an output of the circadian clock and the rebound/homeostatic sleep. Most, if not all, of these elegant and pioneering studies from multiple, independent groups took approaches that did not require artificial selection regimes. As a substitution for their defense, the authors might attempt to present their findings in the context of the existing knowledge of sleep in flies. For example, what about genes already implicated in sleep? Do they show up in their analysis? For example, Sleepless, DATfmn, Sandman, AstA, AstA-receptor, Wide-awake etc. This could really help the manuscript.

      Significance

      I believe that the authors should attempt to put this study in the context of what is already known in sleep in flies and how this study advances the knowledge. And how the knowledge generated by this study would help other sleep researchers, who, for obvious reasons, would like to employ techniques other than artificial selection and big data.

      The data is elegant. The work seems to be extremely laborious. Nonetheless, as it stands now, this manuscript is only very specific for an audience who work with artificial selection to understand underlying genetics of behavior. In fact, even within the fly sleep field, most people might not find this manuscript very useful.

    1. Author Response

      Reviewer #1 (Public Review):

      Doostani et al. present work in which they use fMRI to explore the role of normalization in V1, LO, PFs, EBA, and PPA. The goal of the manuscript is to provide experimental evidence of divisive normalization of neural responses in the human brain. The manuscript is well written and clear in its intentions; however, it is not comprehensive and limited in its interpretation. The manuscript is limited to two simple figures that support its concussions. There is no report of behavior, so there is no way to know whether participants followed instructions. This is important as the study focuses on object-based attention and the analysis depends on the task manipulation. The manuscript does not show any clear progression towards the conclusions and this makes it difficult to assess its scientific quality and the claims that it makes.

      Strengths:

      The intentions of the paper are clear and the design of the experiment itself is simple to follow. The paper presents some evidence for normalization in V1, LO, PFs, EBA, and PPA. The presented study has laid the foundation for a piece of work that could have importance for the field once it is fleshed out.

      Weakness:

      The paper claims that it provides compelling evidence for normalization in the human brain. Very broadly, the presented data support this conclusion; for the most part, the normalization model is better than the weighted sum model and a weighted average model. However, the paper is limited in how it works its way up to this conclusion. There is no interpretation of how the data should look based on expectations, just how it does look, and how/why the normalization model is most similar to the data. The paper shows a bias in focusing on visualization of the 'best' data/areas that support the conclusions whereas the data that are not as clear are minimized, yet the conclusions seem to lump all the areas in together and any nuanced differences are not recognized. It is surprising that the manuscript does not present illustrative examples of BOLD series from voxel responses across conditions given that it is stated that it is modeling responses to single voxels; these responses need to be provided for the readers to get some sense of data quality. There are also issues regarding the statistics; the statistics in the paper are not explicitly stated, and from what information is provided (multiple t-tests?), they seem to be incorrect. Last, but not least, there is no report of behavior, so it is not possible to assess the success of the attentional manipulation.

      We appreciate the reviewer’s feedback on providing more information so that the scientific quality of our work can be assessed. We have now added a new figure including BOLD responses in different conditions, as well as how we expected the data to look and the interpretations. To provide extra evidence for data quality and reliability, we have included BOLD responses of different conditions for odd and even runs separately in the supplementary information.

      In order to avoid any bias in presentation, we have now visualized the results from all areas with the same size and in a more logical order. However, we have also modified all results to include only those voxels in each ROI that were active for the stimuli presented in the main task based on the comment of one of the reviewers. According to the current results, there is no difference in the efficiency of the normalization model in different regions, which we have reported in the results section.

      Regarding the statistics, we have corrected the problem. We have performed ANOVA tests, have corrected all results for multiple comparisons, and have added a statistics subsection in the methods section to explicitly explain the statistics.

      Finally, we have added the report of the reaction time and accuracy in the results section and the supplementary information. As stated, average performance was above 86% in all conditions, confirming that the participants correctly followed the instructions and that the attentional manipulation was successful.

      We hope that the reviewer would find the manuscript improved and that the new analyses, figures, and discussions would address the reviewer’s concerns.

      Reviewer #2 (Public Review):

      My main concern is in regards to the interpretation of these results has to do with the sparseness of data available to fit with the models. The authors pit two linear models against a nonlinear (normalization) model. The predictions for weighted average and summed models are both linear models doomed to poorly match the fMRI data, particularly in contrast to the nonlinear model. So, while I appreciate the verification that responses to multiple stimuli don't add up or average each other, the model comparisons seem less interesting in this light. This is particularly salient of an issue because the model testing endeavor seems rather unconstrained. A 'true' test of the model would likely need a whole range of contrasts tested for one (or both) of the stimuli, Otherwise, as it stands we simply have a parameter (sigma) that instantly gives more wiggle room than the other models. It would be fairer to pit this normalization model against other nonlinear models. Indeed, this has been already been done in previous work by Kendrick Kay, Jon Winawer and Serge Dumoulin's groups. So far, may concern above has only been in regards to the "unattended" data. But the same issue of course extends to the attended conditions. I think the authors need to either acknowledge the limits of this approach to testing the model or introduce some other frameworks.

      We thank the reviewer for their feedback. We have taken two approaches to answer this concern. First, we have included simulations of neural population responses to attended and unattended stimuli. The results demonstrate that with our cross-validation approach, the normalization model is only a better fit if the computation performed at the neural level for multiple-stimulus responses is divisive normalization. Otherwise, the weighted sum or the weighted average models are better fits to the population response when the neurons respectively sum or average responses. These results suggest that the normalization model provides a better fit to the data because the underlying computation performed by the neurons is divisive normalization, not because of the model’s non-linearity.

      In a second approach, we tested a nonlinear model, which was a generalization of the weighted sum and the weighted average models with an extra saturation parameter (with even more parameters than the normalization model). The results demonstrated that this model was also a worse fit than the normalization model.

      Regarding the reviewer’s comment on testing for a range of contrasts, as we have emphasized now in the discussion, here, we have used single-, multiple-, attended- and unattended-stimulus conditions to explore the change in response and how the normalization model accounts for the observed changes in different conditions. While testing for a range of contrasts would also be interesting, it would need a multi-session fMRI experiment to test for a range of contrasts with isolated and paired stimulus conditions in the presence and absence of attention. Moreover, the role of contrast in normalization has been investigated in previous studies, and here we added to the existing literature by exploring responses to multiple objects, and investigating the role of attention. Finally, since the design of our experiment includes presenting superimposed stimuli, the range of contrasts we can use is limited. Low-contrast superimposed stimuli cannot be easily distinguished, and high-contrast stimuli block each other.

      We hope that the reviewer would find the manuscript improved and that the new models, simulations, analyses, and discussions would address the reviewer’s concerns.

      Reviewer #3 (Public Review):

      In this paper, the authors model brain responses for visual objects and the effect of attention on these brain responses. The authors compare three models that have been studied in the literature to account for the effect of attention on brain responses to multiple stimuli: a normalization model, a weighted average model, and a weighted sum model.

      The authors presented human volunteers with images of houses and bodies, presented in isolation or together, and measured fMRI brain activity. The authors fit the fMRI data to the predictions of these three models, and argue that the normalization model best accounts for the data.

      The strengths of this study include a relatively large number of participants (N=19), and data collected in a variety of different visual brain regions. The blocked design paradigm and the large number of fMRI runs enhance the quality of the dataset.

      Regarding the interpretation of the findings, there are a few points that should be considered: 1) The different models that are being studied have different numbers of free parameters. The normalization model has the highest number of free parameters, and it turns out to fit the data the best. Thus, the main finding could be due to the larger number of parameters in the model. The more parameters a model has, the higher "capacity" it has to potentially fit a dataset. 2) In the abstract, the authors claim that the normalization model best fits the data. However, on closer inspection, this does not appear to be the case systematically in all conditions, but rather more so in the attended conditions. In some of the other conditions, the weighted average model also appears to provide a reasonable fit, suggesting that the normalization model may be particularly relevant to modeling the effects of attention. 3) In the primary results, the data are collapsed across five different conditions (isolated/attended for preferred and null stimuli), making it difficult to determine how each model fares in each condition. It would be helpful to provide data separately for the different conditions.

      We thank the reviewer for their feedback.

      Regarding the reviewer’s concern about the number of free parameters, we have introduced a simulation approach, demonstrating that with our cross-validation approach, a model with a higher number of parameters is not a good fit when the underlying neural computation does not match the computation performed by the model. Moreover, we have now included another nonlinear model with 5 parameters that performs worse than the normalization model. Besides, we have used the AIC measure in addition to cross-validation for model comparison, and the AIC measure confirms the previous results.

      Regarding the difference in the efficiency of the normalization model across conditions, after selecting the voxels that were active during the main task in each ROI (done according to the suggestion of one of the reviewers to compensate for the difference in size of localizer and task stimuli), we observed that the normalization model was a better fit for both attended and unattended conditions. However, since the weighted average model results were also close to the data in unattended conditions, we have discussed the unattended condition separately and have discussed the relevance of our results to previous reports of multiple-stimulus responses in the absence of attention.

      Finally, concerning model comparison for different conditions, we have calculated the models’ goodness of fit across conditions for each voxel. The reason for calculating the goodness of fit in this manner was to evaluate model fits based on their ability in predicting response changes with the addition of a second stimulus and with the shifts of attention. Since correlation is blind to a systematic error in prediction for all voxels in a condition, calculating the goodness of fit across voxels would lead to misinterpretation. We have now included a figure in the supplementary information illustrating the method we used for calculating the goodness of fit.

      We hope that the reviewer would find the manuscript improved and that the new analyses, simulations, figures, and discussions would address the reviewer’s concerns.

    2. Reviewer #2 (Public Review):

      My main concern is in regards to the interpretation of these results has to do with the sparseness of data available to fit with the models. The authors pit two linear models against a nonlinear (normalization) model. The predictions for weighted average and summed models are both linear models doomed to poorly match the fMRI data, particularly in contrast to the nonlinear model. So, while I appreciate the verification that responses to multiple stimuli don't add up or average each other, the model comparisons seem less interesting in this light. This is particularly salient of an issue because the model testing endeavor seems rather unconstrained. A 'true' test of the model would likely need a whole range of contrasts tested for one (or both) of the stimuli, Otherwise, as it stands we simply have a parameter (sigma) that instantly gives more wiggle room than the other models. It would be fairer to pit this normalization model against other nonlinear models. Indeed, this has been already been done in previous work by Kendrick Kay, Jon Winawer and Serge Dumoulin's groups. So far, may concern above has only been in regards to the "unattended" data. But the same issue of course extends to the attended conditions. I think the authors need to either acknowledge the limits of this approach to testing the model or introduce some other frameworks.

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      Reply to the reviewers

      We are very grateful to the reviewers for their constructive comments. In response to their critiques, we have made extensive modifications to the manuscript, including documenting new experiments and analyses, and improving data presentation. Here we provide a point-by-point response to the reviewers’ comments.

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Summary:

      It is well established that localization of oskar (osk) RNA in the Drosophila ovary proceeds in multiple steps. The first step depends upon dynein and results in delivery of osk into the oocyte. The second step involves kinesin-driven transport of osk to the oocyte posterior pole. The manuscript by Gáspár et al brings together several lines of evidence that support an tantagonistic relationship with respect to motor binding between two osk-interacting proteins, Egalitarian (Egl) and Staufen (Stau). As staufen RNA and protein accumulate in the oocyte, Egl dissociates from osk, down-regulating dynein and enabling the second stage of osk transport to begin.

      Major comments:

      In general the experimental results support the conclusions drawn, and the paper includes a strong mix of in vitro and in vivo approaches. Nevertheless I have a few concerns.

      (1)In Fig 1D it is apparent that stau KD increases the speed of both plus-end and minus-end runs to a highly significant degree, not just minus-end runs. The stimulating effect of loss of Stau on speed of plus-end runs is not mentioned in the text, and it perhaps muddies the argument that Stau is simply a negative regulator of dynein-dependent minus-end directed transport. This result needs to be explicitly discussed in the text.

      We thank the reviewer for this important comment. Indeed, our previous analysis of the overall population of oskar RNPs showed that plus-end-directed runs had increased velocity in the absence of Staufen (although the magnitude of the effect was considerably smaller than observed for minus-end-directed runs). The reviewer’s comment prompted us to analyze the effects on motility in more detail. In particular, we have now stratified the data based on the RNA content of the RNPs to control for effects of Staufen depletion on RNA copy number of the motile oskar RNPs. These analyses, which are documented in Fig 1B-F of the revised manuscript and discussed between lines 96-143, indicate that the previous velocity and run length data was somewhat confounded by the Staufen-depleted condition having a lower fraction of moving complexes with a large RNA content, which generally move more slowly. Accounting for this effect shows that impairing Staufen has no significant effect on plus-end-directed run lengths, whereas minus-end-directed run lengths are substantially increased. The velocity of runs is also specifically increased in the minus-end direction in the Staufen-depleted background for RNPs that have a relative RNA content of 1 or 2 units, which represent the majority of the RNP population in that genotype. Whilst RNPs with larger RNA content (2 relative units) do have significantly higher plus-end-directed velocity compared to the same category in the control, the effect is of much smaller magnitude than observed for minus-end-directed movements by this population. To help clarify these results, magnitudes of the effects are now shown in the new Fig. 1 E and F.

      These data strengthen the case that Staufen predominantly affects minus-end-directed motion. Given many documented examples of the interdependence of dynein and kinesin on bidirectional cargoes (Hancock et al. 2014), it is conceivable that the modest effects on plus-end-directed velocity for a subset of RNPs arise indirectly from the influence of Staufen on dynein activity. However, we agree with the reviewer that we should not rule out the alternative possibility that Staufen has additional roles in regulating oskar transport, including potentially modulating kinesin-1 directly. We have therefore added a section to the Discussion that covers this issue (lines 496-514).

      (2) I recognize the importance of quantitative imaging to rigorously measure small differences in localization patterns. Nevertheless I find the data in Fig 3 extremely difficult to interpret. Presumably there is standard deviation everywhere there is green signal, but the magenta signal that corresponds to SD is not visible in most places that are green. I suggest adding to Fig 3 a single representative image for each genotype to illustrate each localization pattern, as well as a much clearer explanation of the quantitative imaging data. Perhaps the quantitative images could be moved to a supplemental figure.

      Reviewer 2 also suggested that we include representative images in addition to the quantitative readout. We have now replaced the old Figure 3 with a new one showing representative examples of oskar distribution in the different genotypes and moved the quantitative images to the supplement (Figure S4). We have also improved the legends and labeling of this supplementary figure to add clarity.

      **Minor comments:**

      (1)Color/density scales should be added to Figs 1A and S1A, otherwise the yellow/white signal at the posterior could be interpreted as something other than high abundance.

      We thank the reviewer for spotting this. We have now added a color scale to the relevant figures.

      (2)In Fig 4A and 4C, I find it odd to have different halves of images photographed under different intensity settings and would prefer duplicate whole images.

      We used this layout to illustrate in the most compact way possible the (co)localization of the two RBPs and oskar RNA in the nurse cell and oocyte compartments, where signal intensities can differ dramatically. Following the reviewer’s comment, we now show whole images with different intensity settings (Figure 4 A, A’, C, C’).

      (3)The references to Fig 3G on page 13 should be corrected to Fig 4G.

      We thank the reviewer for spotting this error, which has now been corrected.

      Reviewer #1 (Significance (Required)):

      The paper represents a substantial advance over existing knowledge and it extends our understanding about how RNAs can shuttle between different motor proteins to achieve a localized pattern. However, the Mohr et al 2021 PLoS Genetics paper covers some of the same ground. As that paper has now been published for several months, I believe a revised version of this paper should discuss that other work more prominently, making it apparent where the two studies concur and where this study extends the conclusions of the other one. If there are any contradictions between the two, those should be made explicit as well.

      We had discussed the Mohr et al. study in our manuscript, which came out when our work was in preparation. Following the reviewer’s comment, we now address explicitly how our study differs from theirs and how our work extends their findings. The relevant paragraphs in the Discussion begin on lines 437 and 496. Briefly, a key point of difference is that Mohr et al. focused on the Transport and Anchoring Sequence (TAS) (including its ability to associate with Egl) and other Staufen recognition sites (SRSs) in oskar mRNA. Their study also includes an experiment examining the effect of Egl overexpression on oskar localization (as described in our original submission). In contrast, our study directly examines the interplay between the RBPs Staufen and Egl on oskar RNPs. We are the first to show that Staufen directly antagonizes dynein-based transport and that this is associated, at least in part, with an ability to impair Egl association with RNPs. Moreover, we provide insights into the in vivo role of Egl/BicD in recruitment vs activation of dynein on RNPs and how the activity of Staufen is coordinated in space and time via Egl-mediated delivery of stau mRNA, which constitutes a novel type of feed-forward mechanism. We do not believe there are any contradictions between the two studies.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      In this manuscript, Gáspár et al. investigated the molecular mechanisms underlying the switching of motors for osk mRNA transport in the Drosophila ovary: from dynein in the nurse cells to kinesin-1 in the oocyte. They demonstrated that it requires two RNA-binding proteins, Egalitarian (Egl) and Staufen (Stau) to achieve the posterior localization of osk mRNA in the oocyte. Their data show that Egl is responsible for the stau mRNA transport into the oocyte, while Stau protein inhibits Egl-dependent dynein transport in the oocyte. Thus, they proposed a feed-forward mechanism in which Egl transports mRNA encoding its own antagonist Stau into the oocyte and thus achieves the switch of the osk mRNA transport from dynein to kinesin-1.

      The antagonistic interaction between Egl and Staufen is well documented both in vitro and in vivo. All the results are carefully analyzed, but the data presentation is not reader-friendly. Overall, our main concern is about the role of Staufen in osk mRNA transport.

      **Here are specific points:**

      (1)According to the model, lack of Stau should result in failure of displacing Egl from the RNP complex and thus more dynein-driven transport in the oocyte. However, the increase of minus-end run length in stau-RNAi is very small (Figure 1E). It makes us wonder whether Stau is not a dominant inhibitor of Egl/dynein transport of osk RNPs. On the other hand, the speed increase of minus-end run in stau-RNAi is more dramatic than the run length (Figure 1D-1E). Does it mean that in stau-RNAi dynein-driven osk transport has a shorter duration of run? Additionally, in Figure 1D, there is a statistically-significant increase of plus-end-directed transport velocity in stau-RNAi. While the author did mention that in the results "analysis of the speed and length of oskar RNP runs in ooplasmic extracts indicated that Khc activity was not compromised upon staufen knock-down", it does not explain the increased velocity towards the plus-end.

      We thank the reviewer for these insightful comments.

      We and others (Zimyanin et al. 2008; Gaspar et al., 2014) have shown that there is only a small posterior-directed bias in oskar RNP transport in the wild-type ooplasm at mid-oogenesis. Thus, small increases in minus-end-directed transport parameters are expected to be sufficient for anterior mislocalization of a subset of RNPs, as is seen in stau mutants (note that we would not expect a dramatic increase in minus-end-directed motile properties in the stau RNAi condition, as a significant fraction of oskar RNA is targeted posteriorly). To allow the readers to better judge the magnitude of the effects, we now include the percentage change in mean velocity and run length values on the graphs (new Figure 1E and F).

      Regarding the reviewer’s question about the run duration, indeed it is shorter for the minus-end directed runs in the absence of Staufen. In the motor field, it is typical to present velocity and run length only because duration is dependent on these two parameters.

      Reviewer 1 also made a similar comment about plus-end directed velocity of RNPs. As we wrote in response to their comment, we have now stratified the data based on the RNA content of the RNPs to control for effects of Staufen depletion on RNA copy number of the motile oskar RNPs. These analyses, which are documented in Fig 1 B-F of the revised manuscript and discussed between lines 96-143, indicate that the previous velocity and run length data were somewhat confounded by the Staufen-depleted condition having a lower fraction of moving complexes with a large RNA content, which generally move more slowly. Accounting for this effect shows that impairing Staufen has no significant effect on plus-end-directed run lengths, whereas minus-end-directed run lengths are substantially increased. The velocity of runs is also increased only in the minus-end direction in the Staufen-depleted background for RNPs that have a RNA content of 1 or 2 relative units, which represent the majority of the RNP population in that genotype. Whilst RNPs with larger RNA content (2 relative units) do have significantly higher plus-end-directed velocity compared to the same category in the control, the effect is of much smaller magnitude than observed for minus-end-directed movement for this population.

      These data strengthen the case that Staufen predominantly affects minus-end-directed motion. Given many documented examples of the interdependence of dynein and kinesin on cargoes (Hancock et al., 2014), it is conceivable that the modest effects on plus-end-directed velocity arise indirectly due to the influence of Staufen on dynein activity. However, we agree with the reviewer that we should not rule out the alternative possibility that Staufen has additional roles in regulating oskar transport, including potentially modulating kinesin-1 activity directly. We have therefore added a section to the Discussion that covers this issue (lines 496-514).

      (2) What happened to osk mRNP transport in nurse cells with Staufen overexpression? The authors briefly mentioned that "GFP-Staufen overexpression has no major effect on the localization of oskar (Fig S1F-I)" on page 10. This is quite puzzling, as the authors propose that Staufen antagonized the Egl/dynein-driven transport. If the model holds true, we would expect to see that overexpression of Staufen causes less osk transport in nurse cells and thus less osk accumulated in the oocyte. Can the authors examine the osk mRNP transport in nurse cells in control and in GFP-Staufen overexpressing mutant and quantify the total amount of osk mRNA in the oocyte in control and after GFP-Staufen overexpression?

      We showed in the initial submission that strong overexpression of GFP-Staufen in early oogenesis (e.g. with osk-Gal4) disrupts oskar localization, including causing ectopic accumulation in the nurse cells (Fig S7F and G, now marked with arrowheads). Fig S1F-I, to which the reviewer refers, documents an experiment in which the expression of GFP-Staufen was directly driven by the maternal tubulin promoter (i.e. not through the UAS-Gal4 system; now indicated in Fig. S1F). We had assumed that the difference in behavior of the different GFP-Staufen transgenes was caused by the timing and the amount of overexpression – maternal Gal4 drivers are capable of very strong and, in the case of osk-Gal4, early expression of UAS transgenes. Prompted by the reviewer, we have now examined GFP-Staufen expression in these lines in more detail. This confirmed our previous assumptions about timing and levels of ectopic expression. We now included a new panel Fig S7I to document the expression of maternal tubulin promoter-driven GFP-Staufen and have updated the manuscript to include details about the mode of Staufen overexpression used in different experiments (lines 205, 408-417).

      (3)Is osk mRNP transport in the nurse cells affected by stau-RNAi? The authors showed the Khc association with oskar mRNPs in the nurse cells in Figure 1C. We hope they could quantify the velocity and run length of the osk mRNP particles in nurse cells and compare control with stau-RNAi.

      We have never succeeded in making squashes of nurse cells that maintain oskMS2 RNA transport. Therefore, we are unable to evaluate directional transport of oskar in these cells. However, Staufen does not accumulate to appreciable levels in the nurse cells, as shown by Little et al., 2015 and also Figure 4A and A’ (left panels). Moreover, we did not detect significant colocalization between Staufen and oskar in the nurse cells (Fig. 4B). Therefore, depletion of Staufen with RNAi is not expected to influence motility of oskar in this part of the egg chamber.

      (4)The kymograms of in vitro motility assays (Figure 2A and Figure S2) clearly showed two different moving populations, fast and slow. Did the authors include both types of events in their quantifications? What are the N numbers for each quantification? What do the dots mean in Figure 2B-2G? Does each dot represent a single track in the kymograph? If so, we believe that the sample sizes are too small for in vitro motility assay.

      For completeness, we did not exclude particles from our analysis based on their speed of movement. We have now made this point clear in an updated section of the Methods (lines 799-802), which provides additional information on particle inclusion criteria.

      We did document in the legends what the dots represent (values for single microtubules). We have now also included information on the number of complexes analyzed, which is 586-1341 single RNA particles or 1247-2207 single dynein particles per condition. These sample sizes are considerably larger than those used in most in vitro motility studies.

      (5)The in vitro motility assay showed that Staufen impairs dynein-driven transport of osk 5'-UTR (Figure 2). Based on these data, it is unclear whether the effect of Staufen is osk mRNA-dependent or Egl-dependent. We suggest performing the motility assay in the absence of osk 5'-UTR and Egl. Dynein, dynactin, and BicD should be sufficient to constitute the processive dynein complex in vitro. The addition of Staufen to the dynein complex will help to understand whether Staufen could directly affect dynein activity. We bring up this point because we noticed that the Staufen displacement of Egl in osk RNPs does not alter the amount of dynein complex associated (Figure 6), implying that Staufen inactivates dynein activity on the RNP complex, independently of Egl-driven dynein recruitment.

      We cannot look at transport of dynein in the presence of only dynactin and full-length BicD as BicD is not activated (and thus unable to effectively bind dynein and dynactin) without Egl and RNA (McClintock et al. 2018, Sladewski et al. 2018). However, the reviewer’s comment prompted us to investigate the effect of Staufen on dynein-dynactin motility that is stimulated by the constitutively active truncated mammalian BicD2, so called BicD2N (Schlager et al. 2014, McKenney et al. 2014). We find that Staufen partially inhibits DDB motility but not to the extent seen with the full-length BicD in the presence of Egl and RNA (new main figure panels 2H and I, and Figure S3). As stated between lines 187-188, these data suggest that Staufen inhibits both the activation of dynein-dynactin motility by BicD proteins, as well as stimulation of this event by Egl and RNA. This finding is also incorporated in a new section of the Discussion that covers possible roles of Staufen in addition to competing for Egl’s binding to RNA (between lines 496-514). We are very grateful to the reviewer for suggesting this approach, as it has provided significant new insight into Staufen’s function.

      (6)In Figure 4, it is hard to see any colocalization between GFP and osk mRNA. And the authors compared overexpressed Egl-GFP (driven by mat atub-Gal4 in mid-oogenesis) with Staufen-GFP under its endogenous promoter. An endogenous promoter-driven Egl-GFP would be much more appropriate for the comparison.

      Colocalization between GFP and oskar signals is seen as white in Fig. 4A and C. We have now added arrows to highlight a few examples of colocalization. The degree of colocalization was quantified in an unbiased fashion (shown in panels Fig 4B and D).

      Regarding the expression of Egl-GFP: it was driven directly by the aTub84B promoter and not by matTub-Gal4. Western blot analysis performed in response to the reviewer’s comment shows that Egl-GFP is expressed at similar levels to endogenous Egl in this line (new Fig. S5I).

      (7)In a recent publication (Mohr et al., 2021), a different model was proposed, in which Egl mediates transport, and Staufen facilitates the dissociation from the transport machinery for posterior anchoring. Although the authors referred to their paper in the discussion, they should acknowledge the differences and try to reconcile it (at least in the discussion).

      We now further discuss our work in the light of the findings by Mohr et al. (a request also made by Reviewer 1) (in paragraphs starting on lines 436 and 496). In our opinion, the data of Mohr et al. in fixed material cannot discriminate between effects of Staufen (or the TAS) on transport vs anchorage. In contrast, our dynamic imaging in vitro and ex vivo shows unambiguously that Staufen can modulate transport processes. As accumulation of RNA at the cortex is dependent on directional transport, we do not think it necessary to invoke a separate anchorage role of Staufen. We have now raised the possibility that transport and cortical localization are two facets of the same underlying process in the hope that this will stimulate further investigation (lines 455-459).

      (8)In the feed-forward model, Egl is required for the staufen mRNA transport from the nurse cells to the oocyte. Are Egl-GFP dots colocalized with staufen mRNAs in the nurse cells?

      We showed in Fig 7I of the original submission that Egl-GFP puncta are colocalized with stau mRNAs in nurse cells. Indeed, this is a key piece of evidence for our model. These data are now in Figure 7F.

      Furthermore, to our understanding, in this model, the translation of the staufen mRNA would be critical for the switching motors between dynein and kinesin-1. In this sense, staufen mRNA translation is either suppressed in the nurse cells or only activated in the oocytes. I think the authors should at least address this point in the discussion.

      This is another excellent suggestion. We have now included in the Discussion (from line 525) the point that Staufen translation may be suppressed during transit to the oocyte or that the protein may be translated en route but only build up to meaningful levels where the RNA is concentrated in the oocyte.

      **Minor points:**

      1)I hope the authors would show the osk mRNA localization in egl mutant in in individual stage 9 egg chambers. I can only find the osk mRNA in egl-RNAi early stage egg chambers (Figure 7E), in which osk mRNA still shows an accumulation in the oocyte, although to a much lesser extent compared to control. In another publication (Sanghavi et al., 2016), it seems that the knockdown of Egl by RNAi causes some retention of osk mRNA in the nurse cells; but there are still noticeable amount of osk mRNA in the oocyte (Figure 3A-B). We wonder whether the authors could quantify the amount of osk mRNA both in the nurse cells and in the oocyte of control and egl-RNAi. Also I wonder whether the authors could comment on fact that some osk mRNA transported into the oocyte. Could it be due to an egl-independent transport mechanism?

      egl null mutants do not reach stage 9 due to a defect in retention of oocyte fate, hence the use of egl RNAi in our study and the one by Sanghavi et al. Whilst we can’t rule out a (minor) Egl-independent mechanism for localizing oskar RNA in the oocyte, to date no other pathway has been implicated in the delivery of this or any other mRNA from the nurse cells. We favor a scenario in which residual oskar accumulation in the oocyte in egl RNAi egg chambers is due to incomplete depletion of Egl protein in the knockdown condition. We have noted this in the relevant figure legend and also clarify that the RNAi is a tool for knockdown in line 383 of the Results section.

      The below plot shows a quantification of oskar mRNA localization in egl and control RNAi egg chambers, which the reviewer was wondering about.

      In the egl RNAi egg-chambers, there is a significant increase in the mean signal intensity of oskar mRNA in the nurse cells, while oskar mRNA levels are substantially reduced in the oocyte, in line with the findings of Sanghavi et al., 2016.

      2)It is always nice to how the average distribution of osk mRNA (e.g., Figure 3, Figure S1, and Figure S3). But we recommend having a representative image of each genotype (a single egg) next to the average distribution. It will help the readers to better appreciate the differences among these genotypes.

      This suggestion was also made by Reviewer 1. We have added representative images to Figure 3 and moved the images depicting average distributions to the supplement (Fig S4). We have also improved the legend and labeling for Fig S4.

      3)The figure legends are overall hard to read and sometimes impossible to get information about the experiments (for example, Figure 4 legend). Can the authors improve their figure legends making them reader-friendly?

      We have edited the legends to make them clearer, including an extensive reworking of those for Figure 4. We thank the reviewer for encouraging us to do this.

      4)For moderate overexpression, the authors used P{matα4-GAL-VP16} (FBtp0009293). However, there are two different transgenic lines associated with FBtp0009293 (V2H and V37), which have slightly different expression levels. The authors should specify which line they used in the experiments.

      The matTub-Gal4 transgene we used in our study is inserted in the 2nd chromosome. We now mention this in the Methods section (line 567). We received this line from another lab many years ago, with no additional information provided.

      5) On page 13 "PCR on egg-chambers co-expressing Egl-GFP and either staufen RNAi or a control RNAi (white) in the germline (Fig 3G)", it should be Figure 4G.

      We apologize for this mistake, which has now been fixed.

      Reviewer #2 (Significance (Required)):

      see above

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Some additional experimental evidence is needed to solidify the conclusions and provide definitive support for this model, as discussed below.

      Biochemical experiments using UV crosslinking and GFP immunoprecipitation followed by quantitative PCR were performed to show that Staufen antagonizes the association of Egl with oskar mRNA in vivo. -The authors need to show the quantitative analysis, which was not present in the figure, specifically the effects of Staufen RNAi compared to control.

      These quantitative data, which are key for our model, were shown in the original submission (Fig 4G in the original and revised manuscript). We mistakenly called out the panel as 3G in the original submission. We apologize for this error, which has now been dealt with.

      Is the ability of Staufen to antagonize and displace Egl dependent on Staufen binding to Oscar RNA? Will a Staufen mutant that can't bind to RNA also displace Egl? Alternatively, the mechanism may be independent of RNA binding and perhaps due to protein-protein interactions.

      While the details of how Staufen displaces Egl are certainly an interesting topic for future research, we consider that addressing this goes well beyond the scope of this study, which already covers a lot of ground. Staufen contains four double stranded RNA-binding domains, and deleting or mutating all of these domains is likely to interfere with overall folding of Staufen, thus confounding the interpretation of the results.

      As an alternative approach to elucidating RNA-dependent vs RNA-independent roles of Staufen, we have now assessed the effect of the protein on in vitro motility of dynein-dynactin complexes formed in the presence of a constitutively active truncation of mammalian BicD2 (BicD2N). We find that Staufen partially inhibits motility of these ‘DDB’ complexes but not to the extent seen with the full length BicD in the presence of Egl and RNA (new Fig 2H, I and S3). As stated in the manuscript (lines 187-188) these data suggest that Staufen inhibits both the activation of dynein-dynactin motility by BicD proteins, as well as stimulation of this event by Egl and RNA. We believe these experiments provide significant new insight into Staufen’s function. This finding is also incorporated into a new section of the Discussion dealing with potential roles of Staufen in addition to displacing Egl from RNPs.

      A key question addressed is how does Staufen play a role in directing Oscar RNA localization to the posterior pole. The spatiotemporal control of Staufen at stage 9 seems to be a critical step. A number of experiments are performed to show that Staufen RNA enters the oocyte and accumulates to anterior pole through a process dependent on Egl (Fig. 7).

      -Definitive evidence is needed to show the role of 3'UTR of Stau and Egl binding. As it stands now, no evidence is presented to prove that delivery of staufen RNA via Egl, rather than dumping of Staufen protein into oocytes is the necessary trigger for the switch. It is well known that Staufen protein is also transported through ring canals to deliver Staufen into oocytes. There is no need to invoke an additional mechanism of Egl mediated staufen mRNA delivery. A key experiment is to perturb the Egl interaction with staufen 3'UTR and show this is a necessary component to impact oscar. Related to this comment, they should first perform biochemistry IP and PCR to demonstrate association of Egl with staufen RNA, and then somehow perturb this interaction to assess effects on oscar RNA localization. For example, is the 3'UTR of staufen RNA necessary for this mechanism? What if staufen RNA was ectopically localized in some inappropriate manner, for example localized to posterior pole? Would this prevent the switch of oscar RNA to move to posterior pole? The key question is: is it necessary that translation of Stau be coupled to Egl in order to drive the switch.

      Mapping of the Egl-binding site in stau mRNA is a major undertaking requiring the production and evaluation of multiple new transgenic fly lines. We feel that this would constitute an entirely new study. Moreover, multiple lines of evidence already support a functional interaction between Egl and stau mRNA, notably the presence of Egl on stau RNPs (previously Fig. 7I, now Fig. 7F), the strongly impaired accumulation of stau mRNA in the oocyte of egl RNAi egg chambers, and the ability of Egl overexpression to reposition a subset of the stau mRNA population at the anterior cortex.

      We have now performed new experiments and analyses to test the alternative hypothesis that Staufen protein is transported into the oocyte in the absence of stau mRNA transport. We find that disrupting Egl function with RNAi impairs localisation of both stau mRNA and protein in the proto-oocyte (new Figure 7A-D). As Egl has no known function in protein transport, these data argue against an RNA-independent mechanism for Staufen protein delivery. Moreover, we showed that both stau mRNA and Staufen are enriched in early oocytes lacking oskar mRNA, the main target of Staufen protein in the female germline. This result shows that Staufen protein is not appreciably transported from the nurse cells to the oocyte by hitchhiking on its RNA targets.

      Whilst Mhlanga et al. 2009 did report transport of large GFP-Staufen particles through ring canals, the line used (matTub4>GFP-Staufen from the St Johnston lab, which was also used for our rescue experiments) is known to make protein aggregates which is not the case for the endogenous protein (Zimyanin et al., 2008 and our new Figures 7B and S7E-I) and are therefore likely to be artefactual. Neither we, nor previous studies (Little et al., NCB, 2015), detected endogenous Staufen protein in nurse cells.

      Finally, the reviewer asks if coupling Staufen translation to Egl-mediated enrichment of stau mRNA in the oocyte is important: we showed in the original submission that strong overexpression of GFP-Staufen by Gal4 drivers leads to mislocalization of Staufen in the nurse cells of early egg-chambers, presumably due to saturation of the Egl-based transport machinery. In these egg-chambers, we observed defects in RNA enrichment in the primordial oocyte and defects in oogenesis, consistent with the need to exclude Staufen protein from the nurse cells.

      These findings are now presented in new panels of the updated Figures 7 and S7, with the corresponding section of the manuscript revised accordingly (lines 408-417). We think that altogether these lines of evidence strongly support our model that Egl transports stau mRNA into the developing oocyte and that this process is pivotal for oskar RNA localization.

      **Minor comments**

      "Substantially more oskar mRNA was co-immunoprecipitated with Egl-GFP from extracts of egg-chambers expressing staufen RNAi compared to the control (Fig 3G). -This data is not shown in 3G, but rather only in Fig. S4H which needs quantitative analysis shown.

      This point stems from us calling out the wrong panel in the first submission; this has now been addressed, as described above. We apologize for the error.

      "Addition of recombinant Staufen to the Egl, BicD, dynein and dynactin assembly mix significantly reduced the number of oskar mRNA transport events (Fig. 2A and B)."

      -In Fig. 2A, the Y axis shows velocity not number of transport events

      Fig 2A is a kymograph that is representative of the overall effect, where the Y-axis represents time. The reviewer may be referring to Fig 2B but this shows the frequency of processive oskar RNA movements (expressed as ‘number / micron / minute’), not velocity (micron/minute).

      Fig. 3. - This is very unclear figure as to what is being shown. More details are needed to explain the figure, and add arrows to help reader note what is being described.

      We have changed this figure to show representative images of individual egg chambers, as requested by the other two reviewers. The original Fig 3 is now moved to the Supplement as Fig S4. We have added arrows to the figure to indicate the anterior mislocalization of oskar mRNA and edited the legend for clarity.

      Staufen may also be required for the efficient release of the mRNA from the anterior cortex. This may reflect a role of Staufen in the coupling of the mRNA to the kinesin-dependent posterior transport pathway. This could be discussed as another aspect of the inhibition of dynein and handoff to kinesin.

      This is an interesting idea but it does not fit with our observation that Staufen depletion does not alter the association of oskar RNPs with kinesin-1 (originally Fig. 1C, now Fig. 1D). We do, however, now include in the Discussion a section on other ways, in addition to promoting Egl disassociation, that Staufen might orchestrate oskar mRNA transport.

      Reviewer #3 (Significance (Required)):

      This elegant manuscript by Gaspar et al provides new insight into the spatiotemporal regulation of Staufen mediated localization of oscar mRNA to the posterior pole in Drosophila oocytes. Here the authors demonstrate the competitive displacement of the RNA binding protein Egalitarian, which antagonizes dynein dependent localization at the anterior pole. This work done in this well characterized model of mRNA localization in Drosophila oocytes has broader implications for how the bidirectional transport of mRNAs is regulated in other polarized and highly differentiated cells, where very little is know about how mRNA transport direction might be regulated by opposing activities of kinesin and dynein motors. The strengths of this study are the integration of microscopy, biochemisty and genetic mutants to provide very nice experimental support for the two major aspects to the proposed model: 1) the competition between Staufen and Egl on oscar RNA which affects localization, 2) evidence for Egl mediated localization of staufen RNA into the oocyte as a key trigger for competitive displacement to bias localization of oscar RNA via kinesin. However, some additional experimental evidence is needed to solidify the conclusions and provide definitive support for this model, as discussed in other section.

    1. They will convey the degree to which they disagree and the respect or contempt they feel for this opposing view. Common Phrases That Introduce Counterarguments Attitude to the Counterargument Phrases Negative  The writer thinks the counterargument is completely wrong. It is a popular misconception that_____________. Some have fallen for the idea that_____________. Many people mistakenly believe that_____________. Neutral  The writer is about to describe a counterargument without giving their opinion yet. Many people think _____________. Some, on the other hand, will argue that _____________. Some might disagree, claiming that _____________. Of course, many have claimed that _____________. Some will take issue with _____________, arguing that _____________. Some will object that _____________. Some will dispute the idea that _____________, claiming that _____________. One criticism of this way of thinking is that _____________. Note that these neutral examples don’t tell us whether the writer thinks the counterargument has any validity. Usually, the writer will want to follow them with a sentence that does reveal their opinion. Positive  The writer sees some merit in the counterargument. They agree with it even though it hurts their argument. This is called concession. It is true that ___________. I do concede_____________. We should grant that_____________. We must admit that_____________. I acknowledge that _____________. X has a point that _____________. Admittedly, _____________. Of course, _____________. To be sure, _____________. There may be something to the idea that _____________.

      I haven't thought about the attitude an author can have toward a counter argument. Rarely am I focusing on how they present opposing views to their argument as. I'm usually trying to dissect their argument or disprove it myself.

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      Reply to the reviewers

      Response to Reviewer Comments

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Summary:

      In developing systems, morphogens gradients pattern tissues such that cells along the patterning length sense varying levels of the morphogen. This process has a low positional error even in the presence of biological noise in numerous tissues including the early embryo of the Drosophila melanogaster. The authors of this manuscript developed a mathematical model to test the effect of noise and mean cell diameter on gradient variability and the positional error they convey.

      They solved the 1D reaction-diffusion equation for N cells with diameters and kinetic parameters sampled from a physiologically relevant mean and coefficient of variation (CV). They fit the resulting morphogen gradients to a hyperbolic cosine profile and determined the decay length (DL) and amplitude (A) for a thousand independent runs and reported the CV in DL and A.

      The authors found that CV in DL and A increases with increase in mean cell diameter. They propose a mathematical relationship between CV in DL scales as an inverse-square-root of N. Whereas the CV in DL and A is a weak function of CV of cell surface area (CVa) if CVa __They further looked at the shift in readout boundaries and compared four different readout metrics: spatial averaging, centroid readout, random readout and readout along the length of the cilium. Their results show that spatial averaging and centroid have a high readout precision.

      They finally showed that the positional error (PE) increases along the patterning length of the tissue and increases with increasing mean cell diameter.

      The authors also supported their theoretical and simulated results by looking at mean cell areas reported for in patterning tissues in literature which also have a higher readout precision with smaller cell diameters.

      Major comments:

      Most of the key conclusions are convincing. However, there are four major points that should be addressed. First, the authors conclude the section titled, "The positional error scales with the square root of the average cell diameter," by saying that morphogen systems with small cells can have high precision in absolute length scales, but not on the scale of one cell diameter. They state this would result in salt and pepper patterns in the transition zones. The authors should either support this with biological examples or explain why this is not observed experimentally.

      We thank the referee for pointing out this imprecise comment, which we have removed. The exact nature of transition zones between patterning domains is a subject of ongoing research in our group, and goes beyond the scope of the present work. We will be sharing our results on this aspect in a separate forthcoming publication.

      Second, perhaps the main conclusion of the paper is that morphogen gradients pattern best when the average cell diameter is small. The authors support this by reviewing the apical cell area of epithelial systems that are known to be patterned by morphogens and those that are not (presumably taking apical cell area as a proxy for cell diameter). However, the key parameter is not absolute cell diameter, but the cell diameter relative to the morphogen length scale. The authors should report the ratio of these two quantities in their literature analysis.

      Since cell areas and cell diameters are monotonically increasing functions of one another for reasonably regular cell shapes, we indeed consider apical cell areas as proxies for the cell diameter, as the referee correctly noted. Cell areas are more frequently reported in the literature than cell diameters, which is why we compiled these in our analysis.We have now revised our analysis of the effect of the cell diameter on patterning precision to further length scales relevant in the patterning process. We show by example of the Drosophila wing disc how the parallel changes in cell diameter and morphogen source size compensate for the increase in gradient length and domain size, which would otherwise reduce patterning precision over time as the readout positions shift away from the source to maintain the same relative position in the growing wing disc.

      Lamentably, accurate measurements of morphogen gradients in epithelial tissues are still rare. In fact, among the listed tissues that are patterned by gradients, we are only aware of measurements of the SHH and BMP gradients in the mouse NT (lambda = 20 µm) and of the Dpp gradients in the Drosophila wing and eye discs [Wartlick, et al., Science, 2011 & Wartlick et al., Development, 2014]. We agree that it would be great if experimental groups would measure this in more tissues. In this revised and extended analysis, we show that the positional error increases with the cell diameter in absolute terms, not only relative to any reference length, be it the gradient length or cell diameter.

      Third, as part of their literature analysis, the authors state that in the Drosophila syncytium, there are morphogen gradients, but they imply that because these gradients operate prior to cellularization, one cannot use the large distances between nuclei as counter evidence to their main conclusion. Rather than simply dismissing the case of the Drosophila syncytium, the authors should explain why this case does not apply, using reasoning based on their model assumptions.

      Our paper is concerned with patterning of epithelia (which we now make clearer in the manuscript), and we would not want to stretch our paper to other tissue types, as the reaction-diffusion process in them differs. But we do not share the referee’s sentiment that the syncytium would present a counter-example. Since our model explicitly represents kinetic variability between spatial regions bounded by cell membranes, which are absent in the syncytium, our model is not directly applicable to it. We now provide this argument in the discussion, as requested by the referee.

      At 100 µm [Gregor et al., Cell, 2007], the Bicoid gradient is 5 times longer than the SHH/BMP gradients in the mouse neural tube and more than 10 times the reported length of the WNT gradient in the Drosophila wing disc [Kicheva et al., Science, 2007]. The nuclei become smaller as they divide because the anterior-posterior length of the Drosophila embryo remains about 500 µm [Gregor et al., Cell, 2007], but even at the earliest patterning stage their diameter will not be larger than 10 µm at midinterphase 12 [Gregor et al., Cell, 2007, Fig. 3A].

      Fourth, related to the above: the authors then state that there are no morphogen gradients known during cellularization. Unless I am misunderstanding their point, this is untrue. The Dpp gradient acts during the process of cellularization and specifies at least three distinct spatial domains of gene expression. Furthermore, not long after gastrulation, EGFR signaling patterns the ventral ectoderm into at least two distinct domains of gene expression. What are the cell areas in that case?

      Unfortunately, the referee does not provide literature references, and we were not able to find anything in the literature ourselves. We have now rephrased the statement to “we are not aware of morphogen gradient readout during cellularisation”.

      Minor comments:

      Figs 1cd:

      The way the system is set-up: (DL = 20 micron, Patterning Length (LP) = 250 micron, Nominal cell diameter (D) = 5 micron) the DL/L ~ 0.08 which makes the exponential profile far to a small value around 100 micron. This means in all these simulations, the LP was only around 100 micron, cells beyond that saw nearly zero concentration.

      Because of this, when diameters were varied from 0.2 - 40 micron, there could be as few as 2.5 cells in the "patterning region" which could be responsible for higher variability in DL and A.

      Patterning in the neural tube works across several 100 µm. At x=100µm, there is still exp(-5)=0.0067 of the signal left, which likely well translates into appreciable numbers of the morphogen molecule (see [Vetter & Iber, 2022] for a discussion of concentration ranges cells might sense). Unfortunately, very little is known about absolute morphogen numbers in the different patterning systems — experimental data is available only on relative scales, not in absolute nu mbers. While more quantitative experiments are still outstanding, modeling work needs to be based on reasonable assumptions. The seemingly quick decay of exponential profiles (when plotted on a linear scale) can be deceiving. In fact, exponential profiles describe the same fold-change over repeated equal distances, which makes them biologically very useful for different readout mechanisms operating on different levels of morphogen abundance. Our simulations are not limited to a patterning length of 100µm. Our work merely shows that variable exponential gradients stay precise over a long distance. We draw no conclusion on whether cells are able to interpret the low morphogen concentrations that arise far in the patterning domain - this aspect certainly deserves further research.

      The referee’s observation is correct in that for a cell diameter of up to 40 µm, there are only few cells in the patterning domain (namely down to about six, for a length of 250µm, as used in the simulations). It is also correct that this is the reason why gradients in such a tissue have greater variability in lambda and C0. This is precisely the main point we are making in this study: The narrower the cells in a tissue of given size, the less variable the morphogen gradients, and the more accurate the positional information they carry. Conversely, the wider the cells in x direction, the more variable the gradients.

      Would any of the results change if DL/L was higher, around 0.2?

      As we consider steady state gradients, nothing changes if we fix the (mean) gradient decay length and only shorten the patterning domain, except for a small boundary effect at the far end of the tissue due to zero-flux conditions applied there. At a fixed gradient length, the steady-state gradients just extend further if DL/L is increased (for example to 0.2), reaching lower concentrations, but the shape remains unchanged, and so does the morphogen concentration at a given absolute readout position.

      To demonstrate what happens at DL/L = 0.2, as requested by the referee, we repeated simulations with an increased gradient decay length of DL=50 micrometers; the length of the patterning domain remained unchanged at L=250 micrometers. As it is not possible to include image files in this response, we have made the plots available at https://git.bsse.ethz.ch/iber/Publications/2022_adelmann_vetter_cell_size/-/blob/main/revision_increased_dl.pdf for the time of the reviewing process. The plots show the resulting gradient variability, which is analogous to Fig 1c,d in the original manuscript. For both gradient parameters, we still recover the identical scaling laws.

      The source region is 25 microns in length and all cell diameters above 25 micron get defaulted back to 25 micron which explains the flatness lines in the region beyond mu_delta/mu_DL> 1

      Thanks for pointing this out. We now mention this in the manuscript. Note that it’s the ratio mu_delta/L_s that matters, not mu_delta/mu_lambda. It just so happens in this case, that both are nearly equal, because L_s=5*mu_lambda/4 in our simulations.

      Results:

      Pg 2 (bottom left): In the git repository code, the morphogen gradients are fit to a hyperbolic cosines function (described in reference 19) which is not described in the main text. Having this in the main text would help readers understand why fig 1c has variation in d only, D only and all k parameters whereas fig 1d has variation with all individual parameters p, d and D and all k.

      The reason why the impact of CV_p alone on CV_lambda is not plotted in Fig 1c is that it is minuscule. We now mention this in the figure legend. This follows from the fact that the gradient length lambda is determined in the patterning domain, whereas the production rate p sets the morphogen concentration in the source domain, and thus, the gradient amplitude, but not its characteristic length. This is unrelated to the functional form used to fit the shape of the gradients, be it exponential or a hyperbolic cosine. We mention that we fit hyperbolic cosines to the numerical gradients in section Gradient parameter extraction in the Methods section, and we refer the interested reader to the original reference [Vetter & Iber, 2022], which contains all mathematical details, should they be needed.

      Figure 3b:

      In figures where markers are overlapping perhaps the authors can use a "dot" to identify one set of simulations and a "o" to identify the ones under it. The way the plots are set up currently makes it hard for the reader to understand where certain points on the plot are.

      We use a color code to represent the readout strategy and different symbols to represent the cell diameter in Fig 3b. We agree that for the smallest of the cell diameters, the diamond-shaped data points lie so close that they are not easy to tell apart at first sight. For this reason, we chose different symbol sizes. We would like to keep the symbols as they are to maintain visual consistency with the other figures, which we think is an important feature of our presentation that facilitates the interpretation. Note that all our figures are vector graphics, which allow the reader to zoom in arbitrarily deep, and to easily distinguish the data points. Note also that in this particular case, telling the data points apart is not necessary; recognizing that they are nearly identical is sufficient for the interpretation of our results.

      Methods:

      The Methods can be more descriptive to include certain aspects of the simulations such as adjusted lambda which is only described in the code and not the main text or supplementary.

      We apologize for this omitted detail. As shown in Fig. 8g in [Vetter & Iber, 2022], the mean fitted value of lambda drifts away from the prescribed value, depending on which of the kinetic parameters are varied, and by how much. To report the true observed mean gradient length in our results, we corrected for this drift in our implementation, as the referee correctly noticed. We now describe this in the methods section, and we have extended the methods also on other aspects.

      Git code:

      The git code function handles do not represent figure numbers and should be updated to make it easier for readers to find the right code

      Thank you for pointing this out — it was an oversight from an earlier preprint version. The function names now correspond to the figure numbers.

      Reviewer #1 (Significance (Required)):

      This manuscript contributes certain key aspects to the patterning domain. The three most important contributions of this work to the current literature are: (1) the scaling relationships developed here are important, (2) the idea that PE increases at the tail-end of the morphogen profile is nicely shown and (3) Comparison of various readout strategies.

      Thank you for the positive assessment.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary:

      How morphogen gradients yield to precise patterning outputs is an important problem in developmental biology. In this manuscript, Adelmann et al. study the impact of cell size in the precision of morphogen gradients and use a theoretical framework to show that positional error is proportional to the square root of cell diameter, suggesting that the smaller the cells in a patterning field, the more precise patterns can be established against morphogen gradient variability. This result remains true even when cells average the morphogen signal across their surface or spatial correlations between cells are introduced. Thus, the authors suggest that epithelial tissues patterned by morphogen gradients buffer morphogen variability by reducing apical cell areas and support their hypothesis by examining several experimental examples of gradient-based vs. non-gradient-based patterning systems.

      Major comments:

      While the idea that smaller cells yield to more precise morphogen gradient outputs is attractive, it is unclear whether patterning systems use this strategy to make patterns more precise, as there are several mechanisms that could achieve precision. Do actual developmental systems use it as a mechanism to increase precision? Or precision is achieved through other mechanisms (for example, cell sorting as in the zebrafish neural tube; Xiong et al. Cell, 2013). Indeed, classical patterning work on Drosophila embryo suggest that segmentation patterns are of an absolute size rather by an absolute number of cells (Sullivan, Nature, 1987). According to the authors, the patterning stripes should be more precise when embryos have higher cell densities than in the wild-type, but stripes are remarkably precise in wild-type embryos. This is likely due to other precision-ensuring mechanisms (such as downstream transcriptional repressors, in this case).

      We want to emphasize that our predictions concern the precision of the gradients, not the precision of their readout, which can be strongly affected by readout noise, as we will show in a forthcoming paper. Cell sorting can sharpen boundaries in the transition zone, but this would not address errors in target domain sizes and is thus different from gradient precision as we discuss it here. Also, cell sorting as observed in the zebrafish neural tube requires higher cell motility than what is observed in most epithelial tissues. The work by Sullivan, Nature, 1987, is concerned with patterning of the early Drosophila embryo, and the stripes are defined already before cellularisation. We are unfortunately not aware of any work that quantified gradient precision at different cell densities in epithelia. This would, of course, be highly interesting data and would indeed put our predictions to a test. We are, to the best of our knowledge, the first to propose this principle with the present work. We have now made these points and distinctions clearer in the revised manuscript. Thank you for bringing this up.

      Their modeling approach is based on exponential gradients formed by diffusion and linear degradation, but in reality, actual morphogen gradients are affected by receptor and proteoglycan binding and are likely not simply exponential and/or interpreted at the steady state. Do the main results of the manuscript hold even for non-exponential gradients or before they reach a steady state?

      We can confirm that our results also hold for non-exponential gradients, as they emerge for example when morphogen degradation is self-enhanced (i.e., non-linear). This result will be published in a follow-up study [BioRxiv: 10.1101/2022.11.04.514993], which we now cite in the concluding remarks in the revised manuscript.

      The analysis of pre-steady-state gradients lies outside of the scope of the present work, and so the question as to whether our results are applicable to them as well, remains to be answered in future research. We have added a comment on this to the discussion.

      In their Discussion section, the authors note that several patterning systems, such as the Drosophila wing and eye discs, show smaller cells near the morphogen source relative to other regions in the tissue. This observation suggests a prediction of the authors' hypothesis that can be tested experimentally. In the Drosophila wing and eye discs genetic mosaics of ectopic morphogen sources (such as Dpp) can (and have) been made. Therefore, one could predict that the patterning outputs in a region of larger cross-sectional areas will be more imprecise than in the endogenous source. Since this is a theoretical paper, it is understandable that authors are not going to make this experiment themselves, but I wonder if they can use published data to test this prediction or at least mention it in the manuscript to offer the experimental biology reader an idea of how their hypothesis can be tested experimentally.

      We appreciate that the referee would like to help us inspire the experimental community. Unfortunately, the problem with the proposal is that Dpp has been shown to result in a lengthening of the cells (and thus a smaller cell width) [Widmann & Dahman, J Cell Sci, 2009]. The Dpp gradient thus ensures a small cell width close to its source, which makes it virtually impossible to test this proposal experimentally in the suggested way. Nevertheless, we have added brief comments on potential experimental testing of our predictions to the discussion.

      Other comments:

      The Methods section should be expanded and should include more details about how authors consider cell size in their simulations. As presented, I believe that experimental biologists will not be able to grasp how the analysis was done.

      We have expanded on the technical details of our model in the methods section, in particular in relation to the cell size, as requested. To avoid being overly redundant with existing published descriptions of the modeling details [Vetter & Iber, 2022], we focus here on a description of what has not been covered already, and refer the interested reader to our previous publication. It is inevitable for any kind of work, be it theoretical or experimental, to be less accessible to experts in other disciplines, but we believe that the presentation of our results is independent enough of modeling aspects to be accessible to experimental biologists, too.

      Authors use adjectives such as 'little' as 'small' without a comparative reference. For example in the abstract, the authors say that apical areas "are indeed small in developmental tissues." What does "small" mean? This should be avoided throughout the text.

      We thank the referee for raising this point. Where appropriate, we changed the phrasing accordingly to clarify what the comparative reference is. We leave all sentences unchanged where the statement holds in absolute terms. Note that in the substantially revised analysis on the impact of the different length scales involved in the patterning process, we now explicitly show with simulation data and theory that the absolute positional error increases with increasing absolute cell diameter.

      Reviewer #2 (Significance (Required)):

      Overall, I believe that the manuscript is well written and deserves consideration for publication. However, authors should consider the points outlined above in order to make their manuscript more accessible and relevant to the developmental biology community.

      Thank you for the positive assessment.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      In their mansucript "Impact of cell size on morphogen gradient precision" the authors Adelmann, Vetter and Iber numerically analyse a one-dimensional PDE-based model of morphogen gradient formation in tissues in which the cell sizes and cell-specific parameters locally affecting the gradient properties are varied according to predefined distributions. They find that the average cell size has the largest impact on the variance of the gradient shape and the read-out precision downstream, while other factors such as details of the readout mechanism have markedly less influence on these properties. In addition they demonstrate that averaging gradient concentrations over typical cell areas induces a shift of the readout position, which however appears to be insignificant (~1% of the cell diameter) for typical parameters.

      Overall this manuscript is in very good shape already and tackles an interesting topic. I still would like the authors to address the comments below before I would recommend any publication. My main criticism pertains to some of the authors' derivations which, as I find, partly do deserve more detail, and to their conclusions about gradient readout precision.

      Thank you for the positive assessment.

      MAJOR COMMENTS

      p. 1, left column: The positional error of the readout position does not only depend on the variation of the gradient parameters, as suggested by the first part of the introduction. A very important factor is also the fluctuations due to random arrival of molecules to the promoters that perform the readout due to the limited (and typically low) molecule number. In fact, for positions very distant to the source of the gradient, this noise source is expected to be dominant over gradient shape fluctuations. Importantly, these fluctuations also arise for non-fluctuating, "perfect" gradient inputs if copy numbers of the morphogen molecules are limited (which they always are). This important contribution to the noise is neglected in the work of the authors. This is OK if the purpose is focusing on the origin and influence of the gradient shape fluctuations, but that focus should be clearly highlighted in the introduction, saying explicitly that noise due to diffusive arrival of transcription factors is not taken into account in the given work (see, e.g., Tkacik, Gregor, Bialek, PLoS ONE 3, 2008)

      In the present work, only precision of the gradients, but not the readout itself is studied. We have now mentioned this more explicitly in the introduction. We also acknowledge the fact that the readout itself introduces additional noise into the system. We are currently finishing up work that addresses exactly this subject, which is outside of the scope of the present paper.

      What may have led to misinterpretation of the scope of our work is that we called x_theta the readout position. x_theta defines the location where cells sense (i.e., read out) a certain concentration threshold, and is not meant to be interpreted as the location of a certain readout (a downstream transcription factor) of the morphogen. We have made this distinction clearer in the revised manuscript.

      p.1, right column: Why exactly are the parameters p, d, D assumed to follow a log-normal distribution? Such a distribution has been verified for cell size, but the rationale behind choosing it also for the named parameters should be explained, in particular for D. Why would D depend on local properties of the cell? Which diffusion / transport mechanism precisely is assumed here?

      The motivations for the used log-normal distributions for the kinetic parameters are the following:

      The morphogen production rates, degradation rates and diffusivities must be strictly positive. This rules out a normal distribution. The probability density of near-zero kinetic parameters must vanish quickly, as otherwise no successful patterning can occur. For example, a tiny diffusion coefficient would not enable morphogen transport over biologically useful distances within useful timeframes. This rules out a normal distribution truncated at zero, because very low diffusivities would occur rather frequently for such a distribution. Given the absence of reports on distributions for p, d, D from the literature, we chose a plausible probability distribution that fulfills the above two criteria and possesses just two parameters, such that they are fully defined by a mean value and coefficient of variation. This is given by a lognormal distribution. Our results are largely independent of the exact choice of probability distribution assumed for the kinetic parameters, under the constraints mentioned above. To demonstrate this, we have repeated a set of simulations with a gamma distribution with equal mean and variance as used for the lognormal distribution. Below are some simulation results for a gamma distribution with shape parameters a = 1/CV^2 and inverse scale parameter b = mu*CV^2 with CV = 0.3 as used in the results shown in the paper. As can be appreciated from these plots, the results do not change substantially, and our conclusions still hold. As we believe this information is potentially relevant for the readership of our paper, we have added this result and discussion to the supplement and to the conclusion in the main text.

      We assume extracellular, Fickean morphogen diffusion with effective diffusivity D along the epithelial cells, as specified by Eq. 2. We now state this more explicitly just below Eq. 2 in the revised manuscript. Cell-to-cell variability in the effective diffusivity may arise from effects that alter the effective diffusion path and dynamics along the surface of cells, which we do not model explicitly, but lump into the effective values of D. Such effects may include different diffusion paths (different tortuosities) or transient binding, among others.

      Moreover, is there any relationship between A_i and p_i, d_i and D_i, or are these parameters varied completely independently? If yes, is there a justification for that?

      The parameters are all varied independently, as written in the paragraph below Eq. 2 on the first page (“drawn for each cell independently”). To our knowledge there is no reported evidence for correlations between cell areas, morphogen production rates, degradation rates, or transport rates across epithelia, that we could base our model on. The choice of independent cell parameters therefore represents a plausible model of least assumptions made. Note that we explore the effect of potential spatial correlations in the kinetic parameters between neighboring cells in the section “The effect of spatial correlation”, finding that such correlations, if at all present, are unlikely to significantly alter our results.

      p. 2, right column, section on "Spatial averaging": First of all, how is "averaging" exactly defined here? Do the authors assume that the cells can perfectly integrate over their surface in the dimensions perpendicular to their height? If yes, then this should be briefly mentioned here. Secondly, the shift \Delta x calculated by the authors ultimately seems to trace back to the fact that the cells average over an exponential gradient, whose derivative also is exponential, such that levels further to the anterior from the cell center are higher (on average) than levels to the posterior of it. I suppose, therefore, that a similar calculation for linear gradients would not lead to any shift. If these things are true they deserve being mentioned in this part of the manuscript because they provide an intuitive explanation for the shift. Thirdly, in Fig. 2A the cell sizes seem exaggerated with respect to the gradient length. This seems fine for illustrative purposes, but if it is the case it should be mentioned. Also, I believe that this figure panel would benefit from showing another readout case where the average concentration e.g. in cell 1 maps to its corresponding readout position, in order to show that this process repeats in every cell. Moreover, it could be indicated that in the shown case C_\theta matches the average concentration in cell 2 at the indicated position.

      Spatial averaging is defined as perfect integration along the spatial coordinate over a length of 2r (which can generally be equal to, or smaller than, or larger than one cell diameter) as detailed in the supplementary material. In simulations, we use the trapezoid method for numerical integration to get the average concentration a cell experiences along its surface area perpendicular to their height.

      The reviewer is correct, that the shift is a consequence of averaging over an exponential gradient. The average of an exponential gradient is higher compared to the concentration at the centroid of the cell, thus the small shift. This is mentioned e.g. in the caption of Fig. S1, but also in the main text (“spatial averaging of an exponential gradient results in a higher average concentration than centroid readout”). We have now added this information also to the caption of Fig. 2. As pointed out correctly by the referee, linear gradients would not result in such a shift. A brief comment on this has been added to the revised manuscript.

      We now mention that the cell size is exaggerated in comparison to the gradient decay length for illustration purposes in the schematic of Fig. 2a, as requested.

      Unfortunately, we had a hard time following the reviewer’s final point. We show a specific readout threshold concentration, C_theta, in Fig. 2a. A cell determines its fate based on whether its sensed (possibly averaged) concentration is greater or smaller than C_theta. In the illustration, cells 1 and 2 sense a concentration greater than C_theta, and all further cells sense a concentration smaller than C_theta. Cell fate boundaries necessarily develop at cell boundaries (here; between cells 2 and 3, red). Additionally, the readout position for a continuous domain, where morphogen sensing can occur at an arbitrary point along the patterning axis, is shown (blue). This position can be different from the one restricted to cell borders. Thus, different readout positions in the patterning domain result from the two scenarios, which is what the schematic illustrates. Given that our illustration seems to go well with the other referees, we are unsure in what way it could be improved.

      As for the significance of the magnitude of the shift for typical parameters as calculated by the authors: I believe that it could be said more explicitly and clearly that under biological conditions the calculated shift overall seems insignificant, as it amounts to a small fraction of the cell diameter.

      We have made this more explicit in the text.

      Finally, and most importantly: The term "spatial averaging" can have a different meaning in developmental biology than the one employed by the authors. While the authors mean by it that individual cells average the gradient concentration over their area, in other works "spatial averaging" typically means that individual cells sense "their" gradient value (by whatever mechanism) and then exchange molecules activated by it, which encode the read-out gradient value downstream, between neighboring cells, in order to average out the gradient values "measured" under noisy conditions. The noise reduction effect of such spatial averaging can be very significant, as evidenced by this (incomplete) list of works which the authors can refer to:

      - Erdmann, Howard, ten Wolde, PRL 103, 2009

      - Sokolowski & Tkacik, PRE 91, 2015

      - Ellison et al., PNAS 113, 2016

      - Mugler, Levchenko, Nemenman, PNAS 113, 2016

      The main point, however, is that this is a different mechanism as the one described by the authors, and this should be clearly mentioned in order to distinguished them. I would therefore also advise the authors to make the section title more precise here, by changing "Spatial averaging barely affects ..." to "Spatial averaging across the cell area barely affects ..." for clarity.

      Most theory development has previously indeed been done with the syncitium of the early Drosophila embryo in mind. However, most patterning in development happens in epithelial (or mesenchymal) tissues, where spatial averaging via translated proteins is not as straightforward and natural as in a syncitium. In fact, a bucket transport of a produced protein from cell to cell would be difficult to arrange (as upon internalization, degradation would have to be prevented), be subject to much molecular noise, and be rather slow. Our paper is concerned with patterning in epithelia, which we have now stated more clearly in the manuscript.

      Regarding the section title: Our analysis does not only cover spatial morphogen averaging over the cell area, but it also includes averaging radii below (in the theory) and far above (in the theory and in the new Fig. 4c, previously 3c) half a cell diameter. With cilia of sufficient length r, epithelial cells could potentially average over spatial regions extending further than their own cell area, without need for inter-cellular molecular exchange between neighboring cells. This is the kind of spatial averaging we explored here. Restricting the section title to the cell area only would therefore be misleading. However, we agree with the referee that the distinction between different meanings of “spatial averaging” is important, and we now emphasize our interpretation and the scope of our work more in the revised text.

      p. 3, Figure 3: It would be good to highlight the fact that the colours in panel A correspond to the bullet colors in the other panels also in the main text.

      We now added this also in the main text.

      As to the comparison of different readout strategies: How exactly were the different readout mechanisms compared on the mathematical side? More precisely: How was the readout by the whole area matched (in terms of fluxes) to the readout at a single point, be it in the center of the cell or a randomly chosen point? How was it ensured that the comparison is done at equal footing?

      Our model considers that a cell can sense a single concentration even if it is exposed to a gradient of concentrations. Assuming the French flag model is correct, a cell must make a binary decision based on a sensed concentration in order to determine its fate. The different readout strategies are hypothetical and simplified mechanisms for how a cell could, in principle, detect a local morphogen signal. It is unclear to us what the referee is referring to when mentioning “matching in terms of fluxes”, as there are no fluxes involved in the modeled readout strategies. We make no assumption on the underlying biochemical mechanism that would allow cells to implement one of the strategies. The main goal of this analysis was to determine whether various different sensing strategies had a significant effect on the precision of morphogen gradients experienced by cells. To assure that we can compare the different mechanisms at equal footing, we simulated gradients and then calculated from each gradient the readout concentration in each cell and for each of the methods.

      p. 3, right column: "... similar gradient variabilities, and thus readout precision": Linking to comment 1 above, this is strictly speaking only the case when the only source of fluctuations in the readout is the gradient fluctuations. I would therefore leave this statement out.

      To avoid confusion, we have removed parts of the sentence. Thank you for pointing this out.

      p. 3, section on positional error (right column): In this part I had most troubles following the thoughts of the authors.

      First of all, the measure that the authors use for the positional error is sigma_x / mu_lambda, i.e. the standard deviation of the readout position relative to the gradient length. The question is whether this is the correct measure. It should be specified what the motivation for normalizing by mu_lambda is. In the end, one could argue, what the cells really do care about would be that the developmental process can assign cell fates with single cell precision, for which the other measure shown in Eq. (6) is the representative one. Now in contrast to the former measure, the latter actually increases with decreasing cell diameter.

      We thank the referee for raising this point, and acknowledge that we have not presented this aspect well enough. We have rewritten the entire section and the discussion about biological implications. Instead of normalizing with a constant mean gradient length in the formulas and figures, which has left room for misinterpretation, we now instead varied all relevant length scales in the patterning system, to determine the impact of each of them independently on the positional error. We now show that the positional error increases (to leading order) proportionally to the mean gradient length, the square root of the cell diameter, the square root of the location in the patterned tissue, and inversely proportional to the length of the source domain. We support these new aspects with new simulation data (Fig. 2E-2H, Fig. 3D-G, Fig. S5, Fig. S6). As the positional error is now reported in absolute terms, rather than relative to a particular length scale, the question of the relevant scale is addressed. We now show that the absolute positional error increases with increasing absolute cell diameter.

      We believe that this extension provides additional important insight into what affects the patterning precision. We thank the referee very much for motivating us to expand our analysis.

      Secondly, even when the former measure (sigma_x / mu_lambda) is employed, Fig. 3(D) shows that while it decreases with decreasing cell diameters, in the regime of small diameters the std. dev. of the readout position becomes larger than the average cell diameter, which actually would mean that cell fates cannot be assigned with single-cell precision. While the authors later report both quantities for specific gradients, it should be clarified beforehand which of the measures is the relevant one.

      This has now been addressed by considering absolute length scales as discussed at length in our answer to the previous point.

      Moreover, in the following derivations, mu_x is not properly introduced. What exactly is the definition of that quantity? Is it the mean readout position? If yes, it is not clear why exactly it would be interesting and relevant to the cell. This should be properly explained in a way that does not require the reader to look up further details in another publication.

      The referee is correct in that mu_x is the mean readout position. We apologize for not being clear enough on this, and have now defined this in the introduction together with the definition of sigma_x.

      At the end of this section the authors come back to the sigma_x / mu_delta measure again and indeed point out that it increases with decreasing mu_delta, which causes a bit of confusion because the initial part of the section only talks about the increase of the pos. error with mu_delta. Overall I find that this section should be rewritten more clearly. Right now it leaves the reader with the "take home message" that small cells are good because they lead to smaller pos. error, but when the--in my opinion--relevant measure (sigma_x/mu_delta) is employed the opposite is the case. This is confusing and unclear about the authors' intentions in that part.

      See the answer above. The “take-home message” is now reformulated in absolute terms regarding the effect of cell diameter, rather than relative to a certain choice of reference scale. Our new analysis revealed a new relative ratio that determines the positional error, mu_lambda/L_s. We now discuss this relative measure also regarding its biological significance. Once again, we thank the referee for pointing us at this source of confusion, the elimination of which allowed us to improve our analysis.

      __Finally, the authors could also supplement the numbers that they name for the FGF8 and SHH gradients by the known numbers for the Bcd gradient in Drosophila, which has been studied excessively and constitutes a paradigm of developmental biology. Here mu_delta ~= 6.5 um, while mu_lambda ~= 100 um, such that mu_delta/mu_lambda While we appreciate that most theoretical work has been done for syncytia, this paper is concerned with patterning of epithelia, which have different patterning constraints, as also explained in a reply further above. We now make the scope of our work clearer in the revised manuscript. But as the referee points out, the diameter of the nucleus relative to the gradient length is such that gradients can be expected to be sufficiently precise.

      p. 4, section on the effect of spatial correlation: Here the authors chose to order the kinetic parameters in ascending or descending order. Is there any biological motivation for that particular choice? Other types of correlations seem possible, e.g. imposing the rule that successive parameter values are sampled starting from the previous value, p_i+1 = o_i +- delta_i+1 where delta_i+1 are random numbers with a defined variance.

      In the simulations we go from zero correlation (every cell has independent kinetic parameters) to maximal correlation (every cell has the same parameters, resulting effectively in a patterning domain that consists of a single effective “cell”), see Fig. S3. Biologically plausible correlations in between these extremes should retain the same kinetic variability levels (same CVs) which we took from the measured range reported in the literature. We accomplish this by ordering the parameters after independently sampling the parameters for each cell from probability distributions with the desired CV. The motivation for this approach is that this produces a type of maximal correlation that still reflects the measured biological cell-to-cell variability, to demonstrate in Fig. S3, that even such a maximal degree of spatial correlation does not qualitatively alter our results. The kind of correlation that the referee suggests introduces a spatial correlation length that lies in between the extremes that we simulated. Since even for maximal correlation using the ordering approach, we find our conclusions to still apply, we have no reason to expect that intermediate levels of correlation would behave any differently.

      The idea brought forward by the referee effectively introduces a correlation length scale. We discuss this case in the paper, noting that the positional error will scale as x~N , where N is the number of cells sharing the same kinetic parameters. A correlation length scale will be proportional to N and will therefore simply uniformly scale the positional error accordingly, but will likely not reveal any new insight beyond that.

      Moreover, using the idea of the referee as an additional way to introduce correlation is difficult to realise in practice, as we need to recover the mean and variance of the kinetic parameters, while ensuring strict positivity for each of them. A simple random walk, as proposed, would not lend itself easily to achieve this without introducing a bias in the distribution, because negative values need to be prevented. As explained in a reply further above, an important feature of the kinetic parameters is that they are not too small to prevent the formation of a meaningful gradient, which is not straightforward to ensure with the proposed method.

      We acknowledge that there are different types of correlations conceivable, but we expect these correlations to lie between the two extremes that we present in the paper, which show no qualitative difference in the results.

      p.5, Discussion: "..., but with nuclei much wider than the average cell diameter". To be honest, I could not completely imagine what is meant with this sentence. Intuitively, it seems that the nuclei cannot be larger than the cells, but I suppose that some kind of special anisotropy is considered here? In any case, this should be made precise.

      The main tissues that are patterned by gradients are epithelia. Our paper focuses on such tissues. It is a well-known feature of pseudostratified epithelia that nuclei are on average wider than the cell width averaged over the apical-basis axis. Nature solves this problem by stacking nuclei above each other along the apical-basal axis, resulting in a single-layered tissue that appears to be a multi-layered stratified tissue when only looking at nuclei. For a schematic illustration of this, see Fig. 1 in [DOI: 10.1016/j.gde.2022.101916]. An image search for “pseudostratified epithelia” on Google yields a plethora of microscopy images. Right at the end of the quote recited by the referee, we also cite our own study [Gomez et al, 2021], which quantifies this in Fig. 5.

      Moreover, I find that the conclusion that morphogen gradients "provide precise positional information even far away from the morphogen source" goes to far based on the authors' work, precisely for the fact input fluctuations due to limited morphogen copy number, which can become detrimentally low far away from the source, are not considered, neither the timescales needed to both establish and sample such low concentrations far away from the source. While thus, according to the work of the authors, the fluctuations in the morphogen signal may be favorably small, these other factors are supposed to exert a strong limit on positional information. This conclusion therefore seems unjustified and should be toned down, or even better taken out and replaced by a more accurate one, which only focuses on the gradient shape fluctuations, not on the conveyed positional information.

      There is no evidence so far that morphogen gradient concentrations become too low to be sensed by epithelial cells, to the best of our knowledge. What we show is that the gradient variability between embryos remains low enough that precise patterning remains possible. Whether the morphogen concentration remains high enough to be read out reliably by cells is a subject that requires future research. Genetic evidence from the mouse neural tube demonstrates that the SHH gradient is still sensed at a distance beyond 15 lambda (SHH signalling represses PAX7 expression at the dorsal end of the neural tube) [Dessaud et al., Nature, 2007], where an exponential concentration has dropped more than 3-million-fold.

      As the referee correctly recites, we state that “morphogen gradients remain highly accurate over very long distances, providing precise positional information even far away from the morphogen source”. This statement is restricted to the positional information that the gradients convey, and does not touch potentially precision-enhancing or -deteriorating readout effects, nor does it concern the absolute number of morphogen molecules.

      Positional information goes through several steps. The gradients themselves convey a first level of positional information, by being variable in patterning direction, as quantified by the positional error. This is what we draw our conclusion about. This positional information from the gradients can then be translated into positional information further downstream, by specific readout mechanisms, inter-cellular processes, temporal averaging, etc. About these further levels of positional information, we make no statement.

      We therefore disagree that our conclusion is unjustified. In fact, we have phrased it exactly having the limited scope of our study in mind, making sure that we restrict the conclusion to the gradients themselves.

      MINOR COMMENTS

      - p. 1: "and find that positional accuracy is the higher, the narrower the cells".

      (This sentence, however, should be anyhow revised in view of major comment 5 above.)

      We have added “the”.

      - p. 4: "... with an even slightly smaller prefactor."

      We have removed “even”.

      Reviewer #3 (Significance (Required)):

      I believe that this work is significant to the community working on the theoretical foundations of morphogen gradient precision in developmental systems. The main interesting findings are that small cell diameters lead to smaller positional error (although the relevant measure should be clarified according to my comment no. 5), and that the gradient shape fluctuations are surprisingly robust with respect to the readout mechanism.

      Its limitations consist of the fact that the impact of small copy numbers on the readout and associated timescales are neglected, such that the findings of the authors on gradient robustness cannot be simply transferred by simple conversion formulas to readout robustness / positional information. Comment 5 goes hand in hand with this, as a different conclusion may emerge depending on how the relevant positional error measure is defined. This should be fixed by the authors as indicated in the main part of the report.

      Thank you for your assessment.

    1. Author Response

      Reviewer #1 (Public Review):

      Major points:

      1) How STC1 controls changes in MSCs' ability for hampering CAR-T cell-mediated anti-tumor responses is unclear.

      In this study, we demonstrated that the presence of STC1 is critical for MSCs to exert their immunosuppressive role by inhibiting cytotoxic T cell subsets, activating key immune suppressive/escape related molecules such as IDO and PD-L1, and crosstalking with macrophages in the TME. These immunosuppressive functions of MSC could be significantly hampered when the STC1 gene was knockdown. Considering that staniocalcin-1 is glycoprotein hormone that is secreted into the extracellular matrix in a paracrine manner, we would conclude that the role of STC-1 is not to alter the function of MSCs intracellularly. Rather, it facilitates the immunosuppressive capabilities of MSCs through extracellular secretion into the TME as a pleiotropic factor, thus impacting the functioning of T cells, cancer cells and other immune cells.

      The reviewer's question is well taken, and we have added the points mentioned above to the Discussion section to ensure a more comprehensive conclusion. Moreover, a recent study published in Cancer Cell, which was suggested by the other reviewer, is consistent with our results. It has provided further mechanistic information on how stanniocalcin-1 impacts immunotherapy efficacy and T cell activation. The reference has been cited and discussed as shown below.

      "In this model, activated macrophages or stress signals during CAR-T therapy may prompt MSCs to secret staniocalcin-1 into the extracellular matrix of TME, serving as a pleiotropic factor to negatively impact the function of T cells and stimulate the expression of molecules that inactivate immune responses, ultimately providing an immunosuppressive effect of MSC." (page 22, highlighted). "In line with our study, it was recently reported that stanniocalcin-1 negatively correlates with immunotherapy efficacy and T cell activation by trapping calreticulin, which abrogates membrane calreticulin-directed antigen presentation function and phagocytosis [50]." (Page 20, highlighted)

      2) Is ROS important? It is not tested directly.

      ROS plays an important role during immune response, which are released by neutrophils and macrophages. Not only do they act as key mediators of the adaptive immune response, but they also have the ability to modulate the activation of B-cells and T-cells. In our study, we suggest that ROS may be involved in NLRP3 inflammasome activation and the expression and secretion of STC1. Although we did not pursue this line of inquiry further as it was beyond the scope of our paper, we have included additional relevant research in Discussion and a reference is provided.

      "It has been proved that the expression and secretion of STC1 in multiple cell lines can be stimulated by external stimuli, including cytokines and oxidative stress [26]." (Page 21, highlighted)

      3) The changes in CD8 and Treg are not convincing. Moreover, it is not tested how these changes can be elicited by the presence of MSCs.

      We have included additional in vivo data to assess the levels of Treg cells and CD8+ in this revised manuscript. This not only confirms the alterations of CD8 and Treg, but also offers additional line of evidence to further analyze the influence of MSCs on CAR-T in vivo. The findings are presented in Figure 4B, and the corresponding discussion can be found on Page 17 (highlighted).

      Reviewer #2 (Public Review):

      Major points:

      1) STC-1 is expressed and secreted by many human cancer cells. This should be discussed in the introduction or discussion with more inter-related background info on both its regulation in cancer cells and secretion pattern into TME. It is important because you state that the STC-1 secreted by MSC has such strong functions, then how about those produced and secreted by cancer cells? Are those also stimulated by macrophages or other components in TME? Do they have possible functions in helping cancer cell to escape the immune surveillance mechanisms?

      Thanks for the suggestion. We have added more details about the regulation and secretion of STC-1 in cancer cells (see below). The information is added to both the introduction and discussion (highlighted on pages 4 and 21), and all the above questions are addressed.

      "It was proved that STC1 is involved in several oxidative and cancer-related signaling pathways such as NF-κB, ERK, and JNK pathways [26,27]. The expression and secretion of STC1 in cancer tissue can be stimulated by external stimulus including external cytokines and oxidative stress [26]. Under hypoxia conditions, STC1 could be modulated by HIF-1 to facilitate the reprogramming of tumor metabolism from oxidative to glycolytic metabolism [28]. STC1 was also reported to participate in the process of epithelial-to-mesenchymal transition (EMT), which is associated with tumor invasion and the reshape the tumor microenvironment, as well as increasing therapy resistance [29]." (Page 4)

      "It has been proved that the expression and secretion of STC1 in multiple cell lines can be stimulated by external stimuli including cytokines and oxidative stress [26]." (Page 21)

      2) In Figure 4B, using a single marker of IL-1β to show the immune suppressive capability of MSC in vivo is not sufficient, staining for CD4+ and CD8+ should also be included to demonstrate whether MSC could modulate T cell compositions, which can give more direct evidence about MSC's impacts on CAR-T cell.

      The above experiments were done as suggested, and the data were presented in figure 4B. Explanations of the results are shown on page 17 Results section and page 21 Discussion section (highlighted).

      3) One of the major risks associated with CAR-T therapy is an excessive immune response that causes cytokine release syndrome. MSCs have been used in clinics as a way to suppress immune response including post-CAR-T. What does the author think about using MSC with STC-1 knockout? Can it still help reduce toxicity while maintaining CAR-T efficacy? This might be a potential application.

      This is definitely an interesting idea. Based on the data presented in the current study, it is clear that knockdown of STC-1 would abrogate the immune-suppressive impact of MSC, and therefore affect CAR-T efficacy. However, whether the presence of MSC can help reduce cytokine release syndrome when losing the function of STC-1 requires further study. We agree with the reviewer, and we had briefly discussed this possibility at the very end of the discussion as shown below (Page 22, highlighted).

      "… the findings we presented here are no doubt that would have potential clinical applications toward improving the efficiency of CAR-T therapy as well as reducing the excessive toxicity by modulating the level of STC1 in TME".

      4) There was a recent study published in Cancer Cell (Lin et al. Stanniocalcin 1 is a phagocytosis checkpoint driving tumor immune resistance. 2021), and they also reported that STC1 negatively correlates with immunotherapy efficacy and patient survival. It should be cited, and in fact, it provided support to the authors' present study with completely different experimental settings.

      Thanks for providing this important information. It is an excellent study and consistent with our findings. The reference was added and discussed on page 20 (highlighted) as shown below.

      "In line with our study, it was recently reported that stanniocalcin-1 negatively correlates with immunotherapy efficacy and T cell activation by trapping calreticulin, which abrogates membrane calreticulin-directed antigen presentation function and phagocytosis [50]"

    1. Author Response

      Reviewer #1 (Public Review):

      This theoretical (computational modelling) study explores a mechanism that may underlie beta (13-30Hz) oscillations in the primate motor cortex. The authors conjecture that traveling beta oscillation bursts emerge following dephasing of intracortical dynamics by extracortical inputs. This is a well written and illustrated manuscript that addressed issues that are both of fundamental and translational importance.

      We are pleased by the reviewer’s judgement about the importance of the question that we consider and about the presentation of our manuscript.

      Unfortunately, existing work in the field is not well considered and related to the present work. The rationale of the model network follows closely the description in Sherman et al (2016). The relation (difference/advance) to this published and available model needs to be explicitly made clear. Does the Sherman model lack emerging physiological features that the new proposed model exhibits?

      We view the work of Sherman et al (2016) and ours as complementary. Sherman et al propose a model of a single E-I module, using the terminology of our manuscript, that is much more detailed than ours since it approximately accounts for the layered structure of the cortex using two layers of multi-compartment spiking neurons, each comprising 100 excitatory neurons and 35 inhibitory neurons. This allows a detailed comparison of the model with local MEG signals. We used a much simpler description and only describe the population behavior of local E and I neurons populations in each module. However, contrary to Sherman’s model, this allows us to address the spatial aspect of beta oscillations which is the main target of our work. Our simple description of a local E-I module allows us to consider several hundred E-I modules with a spatially-structured connectivity and to analyze the spatio-temporal characteristics of beta activity. We have now described the relation of our work with Sherman et al (2019) in the discussion section (lines 540-547).

      The authors may also note the stability analysis in: Yaqian Chen et al., “Emergence of Beta Oscillations of a Resonance Model for Parkinson’s Disease”, Neural Plasticity, vol. 2020, https://doi.org/10.1155/2020/8824760

      We thank the reviewer for pointing out this paper that had escaped our notice. It presents the stability analysis of a single E-I module with propagation delay (and instantaneous synapses). At the mathematical level, the analysis brings little as compared to the much older article of Geisler et al., J Neurophys (2005) that we cite. However, the model specifically proposes to describe beta oscillations in the motor cortex as arising from the interaction between excitatory and inhibitory neurons, as we do. Therefore, we included this reference as well as a reference to the previous work of Pavlides et al., PLoS Comp Biol (2015) where the model was developed.

      The model-based analysis of the traveling nature of the beta frequency bursts appears to be the most original component of the manuscript. Unfortunately, this is also the least worked out component. The phase velocity analysis is limited by the small number (10 x 10) of modeled (and experimentally recorded) sites and this needs to be acknowledged.How were border effects treated in the model and which are they?

      We thank the reviewer for these points which gave us the opportunity to clarify them and improve our manuscript. As described in Methods: Simulations (line 847 and seq.) and shown in Fig. S2 (Fig. S10 in the original submission), we actually simulated our model on a 24 × 24 grid and did all our measurements in a central 10×10 grid to take into account that the electrode covers only part of the motor cortex. In addition to minimize border effects, we added on each side of the 24×24 grid two rows of E-I modules kept at their (non-oscillating) fixed points of stationary activity, as depicted in Fig. S2. In order to address the concern of the reviewer, and to check that indeed border effects had a minimal impact on our results, we have performed a new set of simulations on a 24×24 grid with periodic boundary conditions. The results are shown in the new supplementary Fig. S9 and are indistinguishable from those reported in the main text and figures. In particular, the proportion of the different wave types and the wave speeds are unaffected by this change of boundary conditions. A paragraph has been added in the revised version (lines 371-378) to discuss this point.

      How much of the phase velocities are due to unsynchronized random fluctuations? At least an analysis of shuffled LFPs needs to be performed.

      The phase velocities are indeed due to unsynchronized random fluctuations (coming from the finite number of neurons in each of our modules as well as, and more importantly, from the uncorrelated local external inputs). In order to check that the spatial-structure of connectivity was important, we followed the suggestion of the reviewer and also performed a new set of simulations to provide a further test. As proposed by the reviewer, after performing the simulations we shuffled in space the signal of the different electrodes and also did a parallel analysis where we shuffled the signal from different electrodes in the recording. We then reclassified the shuffled simulations/recordings in exactly the same way as the original ones. As shown in the new additional Fig. S16, this resulted in the full elimination of time frames classified as “planar waves” both in the model and in the experimental recordings. Additionally, it little modified the proportion of “synchronized” or “random” episodes which is intuitively understandable since shuffling does not change the nature of these states. In order to further assess the impact of connections between modules, we also decided to suppress them, namely to put their range l to zero. In order to avoid modifying the working point of a local module by this manipulation, we focused on the case without propagation delay. Without long-range connection, the local dynamics of each module is little modified. However, as shown in the new Fig. S18a, synchronization between neighboring modules is strongly decreased and the proportion of the different wave types is entirely changed: synchronized states and planar waves disappear and are replaced by random states. These results are described in two new paragraphs (lines 401-414 and lines 431-435).

      Is there a relationship between the localizations of the non-global external input and the starting sites of the traveling waves?

      This is also an interesting question that parallels some asked by the other reviewers and which we did our best to address. As described in the “Essential revisions” point 5) above, we aligned all “planar wave events” in space and time with the help of the spatio-temporal phase maps of the oscillations. We did find that planar waves were preceded by an increase in the global synchronization index σp, both in simulations and in experiments. In simulations this increase also corresponded to a shift of the global inputs away from their mean, as depicted in the new Fig. 4 in the main manuscript. However, no significant average spatio-temporal profile of the local inputs emerged when we used these temporal alignments. This is presumably due to the large variability of local inputs that can give rise to planar waves. We have described these results in the new section “Properties of planar waves and characteristics of their inputs”.

      In summary, this work could benefit from a widening of its scope to eventually inspire new experimental research questions. While the model is constructed well, there is insufficient evidence to conclude that the presented model advances over another published model (e.g. Sherman et al., 2016).

      As described in the “Essential revisions” and the discussion section of the manuscript, our work highlights a number of questions that can (and hopefully will) inspire new experimental research. We also hope that we have clarified above that our model complements Sherman et al.’s model and advances it as far as the spatial aspects of beta oscillations in motor cortex are concerned.

      Reviewer #2 (Public Review):

      Kang et. al., model the cortical dynamics, specifically distributions of beta burst durations and proportion of different kind of spatial waves using a firing rate model with local E-I connections and long range and distance dependent excitatory connections. The model also predicts that the observed cortical activity may be a result of non stationary external input (correlated at short time scales) and a combination of two sources of input, global and local. Overall, the manuscript is very clear, concise and well written. The modeling work is comprehensive and makes interesting and testable predictions about the mechanism of beta bursts and waves in the cortical activity. There are just a few minor typos and curiosities if they can be addressed by the model. Notwithstanding, the study is a valuable contribution towards developing data driven firing rate.

      We really appreciate the positive comments of the reviewer and thank her/him for them. We have done our best to correct the typos and to address the questions raised by the reviewer.

      1) The model beautifully reproduces the proportion of different kind of waves that can be seen in the data (Fig 3), however the manuscript does not comment on when would a planar/random wave appear for a given set of parameters (eg. fixed v ext, tau ext, c) from the mechanistic point of view. If these spatio-temporal activities are functional in nature, their occurrence is unlikely to be just stochastic and a strong computational model like this one would be a perfect substrate to ask this question. Is it possible to characterize what aspects of the global/local input fluctuations or interaction of input fluctuations with the network lead to a specific kind of spatio-temporal activity, even if just empirically ?

      This is an important question that parallels some asked by the other reviewers and which we did our best to address. As described in the “Essential revisions” paragraph above, we aligned all “planar wave events” either in phase or at their starting time points. We did find that planar waves were preceded by an increase in the global synchronization index σp, both in simulations and in experiments. In simulations this increase also corresponded to a shift of the global inputs away from their mean, as depicted in the new Fig. 4 in the main manuscript. When we used the same alignment to average spatio-temporal local inputs, we did not see the emergence of any significant patterns. This presumably reflects the high variability of local inputs able to produce a planar wave.

      Do different waves appear in the same trial simulation or does the same wave type persist over the whole trial? If former, are the transition probabilities between the different wave types uniform, i.e probability of a planar wave to transit into a synchronized wave equal to the probability of a random wave into synchronized wave?

      In the same trial simulation, different types of waves indeed successively appear. The curiosity of the reviewer led us to investigate this interesting point. Since time frames classified as random or synchronized are much more numerous than the planar (and radial) wave ones, it is much more probable that a planar wave transits into a synchronized or a random pattern than the reverse process (i.e., synchronized and random patterns preferentially transit into each other). Nonetheless, we considered questions related to the one of the reviewer. What are the states preceding a planar wave event? Given that a planar wave episode is preceded by a random (or synchronous) episode, is it more likely to be followed by a random or by a synchronous event? We actually find that the entry state is prominently a synchronized state. Furthermore, when the entry state is synchronized, the exit state is also synchronized much more often than would be expected by chance. This shows that most often, planar waves are created from an underlying synchronized persistent state. This has been described in the revised manuscript (lines 443-451).

      2) Denker et al 2018, also reports a strong relationship between the spatial wave category, beta burst amplitude, the beta burst duration and the velocity (Fig 6E - Denker et. al), eg synchronized waves are fastest with the highest beta amplitude and duration. Was this also observed in the model ?

      We had long exchanges with Michael Denker about his analysis since there are some differences between his code and what is described in Denker et al. (2017), possibly because of several typos in the Method section of Denker et al (2017). We have checked that the results of our code agree with his but there are some differences with the results obtained on the available datasets and those reported in Denker et al from other data sets. We have now provided the detailed statistics of the different wave types as obtained by our analysis in the simulation of model SN (Fig. S9) and SN’ (Fig. S11) and in the recordings for monkey L (Fig. S10) and monkey N (Fig. S12). In the recording data, the amplitude and speed of the synchronized and planar waves are comparable and higher than in the radial and random wave types. The duration of synchronized events is longer than the one of planar waves and of the other waves types. Comparable results are obtained in the simulations with nonetheless a few differences: the mean amplitude of planar waves is somewhat larger than those of synchronized states, the hierarchy of duration in the different states is respected but the duration themselves are longer in the simulations than in the recordings (about 40 % for the planar waves and almost two times longer for the synchronized states). We attribute these differences to the fact synchronization is slightly less effective in the recordings than in the model. Long synchronization episodes in the recordings are often cut-off by a few time frames where the synchronization index goes below the threshold value for a synchronized pattern. This happens rarely enough not to affect much the global statistics of the different states but it as a much more visible effect on the measured duration of the synchronized states.

      Reviewer #3 (Public Review):

      In this manuscript, the authors consider a rate model with recurrently connections excitatory-inhibitory (E-I) modules coupled by distance-dependent excitatory connections. The rate-based formulation with adaptive threshold has been previously shown to agree well with simulations of spiking neurons, and simplifies both analytical analysis and simulations of the model. The cycles of beta oscillations are driven by fluctuating external inputs, and traveling waves emerge from the dephasing by external inputs. The authors constrain the parameters of external inputs so that the model reproduces the power spectral density of LFPs, the correlation of LFPs from different channels and the velocity of propagation of traveling waves. They propose that external inputs are a combination of spatially homogeneous inputs and more localized ones. A very interesting finding is that wave propagation speed is on the order of 30 cm/s in their model which is consistent with the data but does not depend on propagation delays across E-I modules which may suggest that propagation speed is not a consequence of unmylenated axons as has been suggested by others. Overall, the analysis looks solid, and we found no inconsistency in their mathematical analysis.

      We thank the reviewer for his comments and for his expert review.

      However, we think that the authors should discuss more thoroughly how their modeling assumptions affect their result, especially because they use a simple rate-based model for both theory and simulations, and a very simplified proxy for the LFPs.

      In the revised manuscript, we have performed additional simulations to test different modeling assumptions as suggested by the reviewer and discussed further below.

      The authors introduce anisotropy in the connectivity to explain the findings of Rubino et al. (2006), showing that motor cortical traveling waves propagate preferentially along a specific axis. They introduce anisotropy in the connectivity by imposing that the long range excitatory connections be twice as long along a given axis, and they observe waves propagating along the orthogonal axis, where the connectivity is shorter range. Referring specifically to the direction of propagation found by Rubino et al, could the authors argue why we should expect longer range connections along the orthogonal axis? In fact, Gatter and Powell (1978, Brain) documented a preponderance of horizontal axons in layers 2/3 and 5 of motor cortex in non-human primates that were more spatially extensive along the rostro-caudal dimension as compared with the medio-lateral dimension, and Rubino et al. (2006) showed the dominant propagation direction was along the rostro-caudal axis. This is inconsistent with the modeling work presented in the current manuscript.

      This is an important comment and we thank the reviewer for pointing out these data in Gatter and Powell (1978). Since the experimental data show that planar wave propagation directions are anisotropically distributed, we have tried and investigated what the underlying mechanism of this anisotropy could be in the framework of our model. Anisotropy in connectivity is an obvious possibility. Given our result, and the data of Gatter and Powell, it appears however that it is not the underlying cause of the observed anisotropy direction in the motor cortex (in the framework of our model). We have thus investigated another possibility, namely that the local external inputs are anisotropically targeting the motor cortex, being more spread out along a given axis (lines 510-529 and new Fig. 5g-l). We find that planar waves propagate preferentially along the orthogonal axis. This leads us to conclude that the observed propagation anisotropy could be of consequence of the external input being more spread out along the medio-lateral axis. Data addressing this issue could be obtained using retroviral tracing techniques.

      The clarity and significance of the work would greatly improve if the authors discussed more thoroughly how their modeling assumptions affect their result. In particular, the prediction that external inputs are a combination of local and global ones relies on fitting the model to the correlation between LFPs at distant channels. The authors note that when the model parameter c=1, LFPs from distant channels are much more correlated than in the data, and thus have to include the presence of local inputs. We wonder whether the strong correlation between distant LFPs would be lower in a more biologically realistic model, for example a spiking model with sparse connectivity and a spiking external population, where all connections are distant dependent. While the analysis of such a model is beyond the scope of the present work, it would be helpful if the authors discussed if their prediction on the structure of external inputs would still hold in a more realistic model.

      This is a legitimate question that we indeed asked ourselves. In a previous work with a simpler chain model, we only considered finite size fluctuations. We found good agreement between our simplified description of finite size fluctuations and simulations of a spiking network with fully connected modules and sparse distance-dependent connectivity. This leads us to believe that our description of finite-size fluctuations is reliable in this setting. Assuming that it is the case, we find that with 104 neurons or more per module finite size noise is not strong enough to replace our local external inputs. Even with 2000 neurons per modules the intrinsic fluctuations the network is very synchronized (new Fig. S15e-g). With 200 neurons per module, the intrinsic fluctuations are strong enough to replace the fluctuating local inputs (Fig. S15a-d) but this is quite a low number. Our description of local noise would have to underestimate the fluctuation in a more sparsely connected network by a significant amount for agreement with the data to be obtained without local inputs. Moreover, it seems to us quite plausible that different regions of motor cortex receive different inputs but, of course, this can only settled by further experiments. Together with the new Fig. S15, we have added a paragraph to address this question in the manuscript (lines 379-400).

    2. Reviewer #3 (Public Review):

      In this manuscript, the authors consider a rate model with recurrently connections excitatory-inhibitory (E-I) modules coupled by distance-dependent excitatory connections. The rate-based formulation with adaptive threshold has been previously shown to agree well with simulations of spiking neurons, and simplifies both analytical analysis and simulations of the model. The cycles of beta oscillations are driven by fluctuating external inputs, and traveling waves emerge from the dephasing by external inputs. The authors constrain the parameters of external inputs so that the model reproduces the power spectral density of LFPs, the correlation of LFPs from different channels and the velocity of propagation of traveling waves. They propose that external inputs are a combination of spatially homogeneous inputs and more localized ones. A very interesting finding is that wave propagation speed is on the order of 30 cm/s in their model which is consistent with the data but does not depend on propagation delays across E-I modules which may suggest that propagation speed is not a consequence of unmylenated axons as has been suggested by others. Overall, the analysis looks solid, and we found no inconsistency in their mathematical analysis. However, we think that the authors should discuss more thoroughly how their modeling assumptions affect their result, especially because they use a simple rate-based model for both theory and simulations, and a very simplified proxy for the LFPs.

      The authors introduce anisotropy in the connectivity to explain the findings of Rubino et al. (2006), showing that motor cortical traveling waves propagate preferentially along a specific axis. They introduce anisotropy in the connectivity by imposing that the long range excitatory connections be twice as long along a given axis, and they observe waves propagating along the orthogonal axis, where the connectivity is shorter range. Referring specifically to the direction of propagation found by Rubino et al, could the authors argue why we should expect longer range connections along the orthogonal axis? In fact, Gatter and Powell (1978, Brain) documented a preponderance of horizontal axons in layers 2/3 and 5 of motor cortex in non-human primates that were more spatially extensive along the rostro-caudal dimension as compared with the medio-lateral dimension, and Rubino et al. (2006) showed the dominant propagation direction was along the rostro-caudal axis. This is inconsistent with the modeling work presented in the current manuscript.

      The clarity and significance of the work would greatly improve if the authors discussed more thoroughly how their modeling assumptions affect their result. In particular, the prediction that external inputs are a combination of local and global ones relies on fitting the model to the correlation between LFPs at distant channels. The authors note that when the model parameter c=1, LFPs from distant channels are much more correlated than in the data, and thus have to include the presence of local inputs. We wonder whether the strong correlation between distant LFPs would be lower in a more biologically realistic model, for example a spiking model with sparse connectivity and a spiking external population, where all connections are distant dependent. While the analysis of such a model is beyond the scope of the present work, it would be helpful if the authors discussed if their prediction on the structure of external inputs would still hold in a more realistic model.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity):

      Summary:<br /> The Authors report on the synthesis and characterization of a class of small molecules, the tanshinone mimics (TMs), which interfere with binding of the RNA binding protein (RBP) HuR to its mRNA targets. HuR is an important regulator of mRNA stability and translation of genes involved in key homeostatic (cell cycle, stress response) and pathologic process (inflammation, carcinogenesis). In particular, the first part of the study describes the compounds' chemical synthesis and some pharmacokinetic parameters (i.e., definition of molecular binding, solubility, bioavailability, prodrug generation etc). The second part undertakes, in in vitro and ex-vivo model of LPS-induced mouse macrophage activation, the identification of HuR-bound mRNA targets, which is then evaluated within the global LPS-induced transcriptome; finally, the study evaluates the ability of TMs to inhibit HuR-mediated proinflammatory gene regulation, indicating their use and potential value as therapeutic anti-inflammatory strategy.<br /> Major comments:<br /> The manuscript contains a wealth of data generated from different experimental systems, spanning from synthetic chemistry to preclinical models of gene regulation, requiring cultural backgrounds in chemistry and biology as well. The key conclusions are well supported by the data, but it takes a great effort to get to the core results and thus critically read and evaluate their interpretation. Although the complexity and sheer size of data sets generated lends itself to a hard read, this is further complicated by data presentation, which especially in the second part needs to be significantly improved to gain clarity and focus. For ease of referral, specific comments will be addressed related to Figures whenever possible.<br /> 1.1 • Page 15: To measure TM7nox disrupting ability of HuR:mRNA complex for the HTRF assay (Figure 2G) and for biotin pull down assay (Figure 5C), it was chosen a biotinylated probe containing the AU rich elements of the TNFα, as known HuR target. Please comment on the rationale, and whether could it be relevant reevaluate these parameters post-hoc, based on the sequences identified in HuR targets more susceptible of modulation by TM compound (listed in table 1, Figure 5 A/B) and based on the absence of regulation of TNFa (Figures 3D, 4D, 7A) found in the tested systems.

      R1.1 - We thank the reviewer for this observation. We have been using the biotinylated probe containing the AU-rich elements of TNFα as a representative probe for HuR for biochemical assays in several articles (PMID: 29313684, PMID: 26553968, PMID: 23951323). As the reviewer suggests, a posteriori, it is worth reevaluating the representative probe to be used for evaluating the disrupting ability of TMs based on the data we present here. Indeed, we will tackle this problem in our following efforts, as it is a meaningful although time-consuming task which is outside of the scope of this manuscript.

      1.2 • Page 16-18: Description of the RNAseq data shown in Figure 3 should be more centered around the main findings regarding the effect of TMnox that are further pursued in the study: that is, (Figure 3B), the 249 downregulated DEGs found modulated by TM7nox in presence of LPS, where was observed a strong enrichment of categories related to the inflammatory response: cytokines (Il1b, Cxcl10, Il10, Il19, Il33), immune cell chemotaxis (Ccl12, Ccl22, Ccl17, Ccl6) and innate immune response.

      The description of the GO for the remaining data should be shortened to main points, perhaps reporting what described in the results with each section of the Venn in a table, while referring to the whole list in the supplements as already done. This could replace Figures 3D, E which do not add substantially to what provided in the supplementary table 2 and to which they can be added as further visualization.

      R1.2 - We thank the reviewer for this suggestion, accordingly, we simplified the text keeping only the description of the genes modulated by TM7nox during LPS treatment. The other information originally there was moved to Supplementary table 2. Revised figures 3E and 3F now focus only on the 249 downregulated genes of this group.

      1.3 • Page 18-19: Description of the results of the RIP-seq shown in Figure 4 set is very confusing: onward from the line "477 HuR-bound transcripts (log2 FC > 3) were also upregulated by LPS at the transcriptional level..." the numbers do not match or reconcile with those shown in the Venn diagram (Fig. 4B) nor with those listed in the figure legend of Figure S8.

      R1.3 - We agree with the reviewer, we apologize for having reported the wrong numbers, and we clarified this point in general by deeply revising the text. A more precise explanation of the selection procedure for the genes of interest is now reported and better explained also by adding a scheme (Fig 4D in the revised manuscript).

      1.4 Moreover, as previously remarked for Figure 3 (and even more for this dataset in which initial description of Venn in 4B is unclear), panel 4E does not add as much to the info provided in Table 1/supplementary Table 1, where they can eventually be added as further data visualization; Instead, Figure S8 displays very informative data merging together the results obtained in RNAseq (Fig. 3) and RIP-Seq (Fig.4) and should be displayed in Figure 4, as in the result section they are indeed presented together.

      R1.4 - We agree with this remark, thus we have removed the old panels 3E in S8C and 4E in S9B, and we now provide the information previously contained in old S8 in the main figure 4E of the revised manuscript.

      1.5 • Page 19-20: Description of the modulation by TM7nox of HuR binding to specific consensus sequences is summarized at the end of the relative paragraph as follows: "TM7nox reshapes HuR binding to target genes in presence of LPS by disrupting the binding of HuR towards target genes containing a lower number of HuR consensus sequences than the average observed in the HuR-bound transcripts". Understanding of these data through the provided text and the Supplementary Figure 9 is very laborious and referring of an entire dataset to a supplementary figure makes it even harder. It would be best to show this as main figure, not supplemental, either adding a Venn diagram as in 3B/4B showing to which dataset each part of the analysis refers, or even more efficaciously, extrapolate a representative gene set for the main analyses showing TM7nox activity in target genes with higher vs lower consensus sequences; same approach for the analysis in Figure 9B, where the effect on a gene with sequence #1 or #10 could be compared with one bearing sequence #3 for example.

      R1.5 - We agree with the reviewer, thus we moved the information of old S9 in figure 4C of the revised manuscript. We deeply revised the information provided also by taking into account the request to compare this experiment to the one in Lal et al. NAR 2017 (please see also R2.4). We made an effort to identify a subset of genes that follow a coherent modulation, identifying 82 genes highlighted in Supplementary Table 1. All such genes show increased expression during LPS or LPS/TMnox vs DMSO conditions, and decreased association to HuR during LPS/TMnox vs LPS. As 47 of these, i.e. more that 50%, contain less AU rich sequences than the average (highlighted in Supplementary Table 1), we can consider them as a representative gene ensemble modulated in accordance with the presence of AU rich sequences.

      1.6 • Page 21: Description of the effect of three TMs (TM6, TM7nox and TM7nred) on LPS response in macrophages at the single gene level (Figure 5 and Figure 6): TM6 and TM7nox were used in exps in Fig. 5 A and E, while only TM7nred was used for CXCL10 secretion analysis (fig.5 D and F): please describe the compound choices' rationale (as done for experiments in Figure 6).

      R1.6 – Following the reviewer suggestion, we now explain our rationale in choosing the small molecules, that is the use of the ones bearing the active quinone species. We have performed additional experiments, and now we report TM6n, TM7nox, and the control DHTS activity in decreasing the secretion of Cxcl10 (figure 5E in the revised manuscript). All compounds behave similarly in this experiment. TM7nred is now used to show its equivalence to TM7nox in figure 5E and in figure 6 of the revised manuscript.

      1.7 • Page 21-22: The effect on HuR expression of siRNA silencing and, more importantly, of TMs shown in Figure 6A, first panel, should be visualized at protein level by western blot. This is an important point as for CXCL10 and iL1b there seems to be an additive effect between decreased HuR levels and pharmacological blocking.

      R1.7 - Following the reviewer suggestion, we now show the protein level as measured by intracellular Elisa; as we were not able to detect the proteins by western blot. The protein level is in general agreement with the gene expression level. We do not observe an additive effect by pharmacological inhibition during HuR silencing, but we rather observe a slight increase in the protein level during HuR silencing. We do not have an explanation for this effect, which may depend on several reasons - for example, an aspecific effect of the TMs when their molecular target HuR is absent.

      1.8 • Page 24: please rephrase the statement 'These observations suggest the utilization of TMs in autoinflammatory and autoimmune diseases' as 'These observations suggest the evaluation of TMs in specific preclinical models for autoinflammatory and autoimmune diseases'.

      R1.8 - We fully agree with the reviewer, and we changed the text in the revised manuscript accordingly.

      1.9 • In the discussion, please include a paragraph with study limitation and possible biases (for example, the choice of RNP-IP without crosslinking has pros and cons).

      R1.9 – Thank you for the good suggestion, we added a paragraph in the discussion which describes study limitations due to the utilization of RNP-IP vs crosslinking.

      The data and the methods are correctly presented for reproducibility, replicates and statistical analysis are adequate. Minor comments: 1.10 • At least in the single gene validation experiments (Fig.5), a negative control (such as recombinant HuR with mutated RRMs in trans-, or ARE-less/non-HuR targetable sequence in cis, or inactive TM) would be advisable.

      R1.10- We thank the reviewer for the suggestion. Accordingly, we used an ARE-less/non-HuR targetable gene as RPLP0 for validation.

      1.11 • Figure 6B/C: for immunofluorescence panels, zooming on a smaller number of cells will render more visible HuR and NFkB nucleocytoplasmic shuttling, given that quantification and statistics are provided by imaging software. Negative control stainings (secondary Abs only) should be included.

      R1.11 – In accordance with this suggestion, we now report a higher magnification of the immunofluorescence images. We also report the standard DHTS effect, showing a difference vs TMnox activity which may suggest its impact on NFkB shuttling.

      1.12 • Figure 7A: in the X axis LPS+8n is indicated: is it a typo for LPD+6n or was compound TM8n indeed used?

      R1.12 – Thanks for your spotting our mistake, the prodrug 8 described in figure 1 was used.

      1.13 • In the Methods section please include protocols and materials for immunofluorescence (results shown in Fig. 6B/C).

      R1.13 – As for your suggestion, protocols and materials for immunofluorescence were added to the methods.

      1.14 • There are some typos and repetition in figure legends (legend Figure S9).

      R1.14- Thank you for this, we revised all the figure legends.

      Prior studies are referenced appropriately. Review Cross-commenting I fully agree with the Reviewer's remarks. I would add that a general concern expressed is that this manuscript in its present form has a readership issue: the first part is for chemistry/pharmacology audience, the second is biology-based. Splitting the work has been suggested; or, the Authors may decide which part is more impactful and present the other in a streamlined version.

      Reviewer #1 (Significance):

      This is a large study reporting progress in the development of synthetic antagonists of HuR function, which is the Authors' well-established line of research. The TM compounds are small molecules with anti-inflammatory effects with strong potential for therapeutic use due to selected inhibition of HuR-mediated upregulation of proinflammatory molecules. The physicochemical and early biological characterization done in this study will allow further testing of their efficacy and of the overall role of HuR-mediated regulation as targetable mechanism in several preclinical human disease models. Targeting of the RNA-binding protein HuR has been tackled as therapeutic approach in cancer, less in chronic immune and inflammatory diseases despite many common mechanisms and mediators. This study could be well received by researchers involved in basic science and drug development (chemistry, biochemistry/biophysics, pharmacology, computational modeling) and biologists/physician scientists interested in testing these compounds in translational research settings where HuR-driven functions can be relevant (cancer, chronic inflammation), though the chemical part would be less accessible to the latter audience. Reviewer's background is in preclinical human models of chronic inflammation with interest in posttranscriptional gene regulation with familiarity with RNAseq and RIPseq dataset and analysis. For the part of the manuscript regarding the synthesis and physicochemical characterization of the TN compound I requested assistance to a faculty from the chemistry department with expertise in that field, who did not request any specific clarification or addendum.

      Reviewer #2 (Evidence, reproducibility and clarity):

      In the manuscript entitled "HuR modulation with tanshinone mimics impairs LPS response in murine macrophages" the authors have described the synthesis and application of small molecule mimics of the naturally occurring compound tanshinone, which is known to inhibit the binding of the RBP HuR to a class of its mRNA targets. The authors have shown that the tanshinone mimics (TMs) used by them block the binding of RRM1-2 of HuR to ARE-containing RNA in vitro, and reduce the interaction of HuR with a set of ARE-containing mRNAs in LPS-treated mouse macrophage cells. This reduction of interaction of HuR with some of these mRNAs correlates with the reduction in their level in the cells treated with the TMs, and in the secreted level of their proteins in the serum of animals with LPS-induced peritonitis. Together, the study demonstrates the role of these TMs as modulators of the LPS-induced inflammatory response by blocking the binding of HuR to a subset of LPS-induced inflammatory mRNAs and thereby downregulating their mRNA and protein levels in inflammatory cells. The manuscript describes a comprehensive study, ranging from chemical synthesis of TMs, MD simulations to demonstrate the binding site of the TMs to the cleft formed by the RRM1-linker-RRM2 domains of HuR, which has been shown in crystal structure to be the main binding site of A/U-rich RNA molecules, in vitro studies showing the ability of the TMs to hinder ARE-containing RNA binding to HuR RRM1-2, whole transcriptome analysis to show the effect of the TMs on LPS-induced differential gene expression in murine macrophages, and on HuR binding to target mRNAs, and animal studies to show the effect of the TMs on the level of some inflammatory mediators in the serum of mice with LPS-induced peritonitis. The results are quite convincing and is in line with what is generally known about the effect of HuR on the expression of a large number of genes encoding pro-inflammatory proteins, and the ability of tanshinone derivatives/mimics in inhibiting HuR binding to target mRNAs. The authors put these two information together in this study and the results are on expected lines. While the results are convincing and quite comprehensive, I would suggest the following in order to substantiate and strengthen the results: 2.1. The experiments do not have any "positive control", such that the performance of the TMs can be compared with that of a molecule with known HuR binding inhibition activity, such as DHTS. It would be good to have such a comparison, to understand whether the TMs work similar to DHTS or differently, both qualitatively in terms of the mRNA targets which they affect and the extent of their anti-inflammatory activity.

      R2.1- We added DHTS as a comparison to TMs, following the reviewer’s comment. In this model, the net effect of DHTS is partially overlapping with TMs, at least for the parameters that we checked (see Figure 5, 6 and 7), showing some differences in the modulation of NF-kB shuttling upon LPS stimulation. Therefore, we suggest that DHTS and TMs show partially different effects on mRNA targets and in terms of anti-inflammatory activities.

      2.2. It is not clear to me whether the mRNAs which show differential expression in the RNAseq analysis of cells treated with LPS and TMs are exactly the ones which show difference in binding with HuR in the RIPseq analysis in presence of the TMs. This analysis is important for a number of reasons: all the mRNA binding targets of HuR are not affected by HuR at the level of mRNA stability, many are affected at the level of translation, without change in mRNA level. These mRNAs should therefore show change in binding of HuR in the RIPseq assay in presence of TM, but not show change in expression. Secondly, there may be mRNAs which show a change in expression in presence of TMs, but do not show binding of HuR, suggesting pleiotropic roles of the TMs. Therefore, instead of an overall correlation between differential expression and change in HuR binding of mRNAs, a table comparing the RIPseq status of individual mRNAs with that of their differential expression status, in presence and absence of LPS/TMs would be useful, further designating the different groups of mRNAs based on these differential status (change in HuR binding/change in expression, change in HuR binding/no change in expression etc.).

      R2.2 – We tried to rationalize the data following the reviewer’ suggestion, however, we could not fully adopt this strategy due to the complexity of the experiment design. Indeed, we have focused our attention on the effect of TMs during LPS stimulus, which induces a strong transcriptional response, rather than in steady state conditions. This is why we reported the overall correlation of LPS vs DMSO and TM7nox/LPS vs DMSO. Then, we evaluated whether the observed difference in the correlation may be reflected on a change of HuR binding, and we checked the RIPseq status during co-treatment vs LPS. This was the case for a subset of genes that are reported in Supplementary Table 1. Nevertheless, to be fully compliant with the reviewer’s request we now report a Supplementary Table 1 containing the entire gene list, so that the reader can immediately filter out the subsets according only to the comparison TM7nox/LPS vs LPS.

      2.3. Nuclear/cytoplasmic localization of HuR and NFkb is impossible to discern at the magnification of the immunofluorescence images in Fig 6 B and C. Higher magnification images are required to understand changes in localization.

      R2.3 – In accordance with this suggestion, we now report higher magnification, please see also R1.11. We do not observe any change in nuclear/cytoplasmic localization of HuR and NFkb due to TMs treatment. We rather observe LPS-induced NFkB nuclear accumulation, ActD-induced HuR cytoplasmic shuttling and inhibition of NFkB translocation, during LPS and DHTS treatment.

      2.4. It has been shown that DHTS-I increases the binding of HuR to the mRNAs with longer 3'UTR and with higher density of U/AU-rich elements, whereas it reduces the interaction of HuR with the mRNAs having shorter 3'UTR and with low density of U/AU-rich elements (Lal et al., NAR, 2017). It is not clear if the same is observed in case of the TMs or not, and such a comparative analysis would be useful to address this point.

      R2.4 – We re-analysed the data, checking the density of U/AU rich elements and the length of the 3’UTR of the displaced mRNA as in Lal et al. NAR 2017. Although we could not compare DHTS and TMs within the same biological system, it appears that the rules dictating their mechanism of action are similar.

      I think that the above suggested points are feasible as most of them really involve re-analysis of existing data. Only the suggestion to add DHTS or tanshinone as a positive/comparison control will require experimentation and addition of new data.

      Review Cross-commenting

      I think most of the reviewers' comments coincide in the evaluation of the manuscript. I would especially like to draw attention to the fact that all three reviewers found that the content and form of data presented in the paper is very dense and bogs down the reader and distracts from the overall focus of the manuscript.

      Reviewer #2 (Significance):

      The work described in the manuscript is comprehensive as it ranges from chemical synthesis and in vitro evaluation of the TMs to the characterization of their effects in vivo. Although the effect of tanshinone derivatives on HuR mRNA target binding is known, and the effect of HuR on inflammatory gene expression is also known, the manuscript is significant as it brings these two information together and tests the effect of these TMs on HuR-mediated regulation of inflammatory gene expression.<br /> However the extensiveness of the work also makes it quite dense, and I feel that the focus of the paper is often lost in the details. Also, the text of the manuscript is dense and verbose and uses many irregular grammatical and phraseological usages, for eg "their<br /> modulation or mis-localization lead to the insurgence of complex phenotypes and diseases". It appears to me that it would be ideal if the chemical synthesis, MD simulation studies and in vitro studies are presented in an independent manuscript. Also, that would allow a more exhaustive referencing of the known studies in literature where tanshinone derivatives, and other small molecules, have been used to modulate HuR binding to mRNA targets.<br /> This work would be of interest to molecular cell biologists in general and RNA biologists in particular, especially those who are studying RNA-protein interactions, and scientists who are interested in drug development using RNA-protein interactions as drug targets.<br /> My interest in the work lies in my expertise in studying RNA-protein interactions, especially of RNA-binding proteins such as HuR involved in regulating the translation of mRNAs encoded by inflammatory genes. I do not have expertise in chemical synthesis and am therefore not qualified to evaluate the first set of results describing the chemical synthesis of TMs.

      Reviewer #3 (Evidence, reproducibility and clarity):

      In this study, the authors investigated the modulation of HuR by tanshinone mimics and how it mitigates LPS response in murine macrophages. This represents a nice integration of synthetic chemistry, molecular simulations, and in vitro as well as in vivo experimental validations. Overall, this is an interesting study, and will add to the growing interest in HuR in inflammatory-mediated disease. The paper contains a lot of data (actually several papers in one) which may bog down the reader and distract from the overall message. it is suggested that they condense the data and simplify the figures and use more supplemental figures.<br /> Major Comments:<br /> 3.1. The authors have shown the dose response and cytotoxicity effect of tanshinone mimics; The authors show that TMs affect the overall HuR mRNA but they don't show protein levels.

      R3.1 – In accordance with the reviewer’s comment, we now show also protein levels, as we performed intracellular ELISA (Figure 6 in the revised manuscript); please see also R1.7.

      3.2. It is unclear the timing of certain experiments for LPS vs TMs (whether macrophages were pre-treated with TMs before LPS)-e.g fig 5. The authors should clarify for all experiments as the long-term clinical paradigm would be treatment after inflammation has been established.

      R3.2 – In most experiments TMs are co-administered with LPS. Only in one of the two protocols used for Actinomycin D chase experiment TMs are added after LPS with Act D, as we wanted to discriminate between transcriptional and post-transcriptional effects of TMs (see also R3.3).

      3.3. They have also identified differentially expressed genes which are RNA binding ligands of HuR by RIP-Seq. However, it would be necessary to check whether TM7nox affects the stability of those targets before conclusions can be made that TMs don't inhibit the primary transcriptional response (as mentioned in the Discussion section). Transcriptional effects of HUR chemical inhibition or genetic silencing has been reported previously in other cell systems.

      R3.3 – The reviewer is entirely correct, and we accordingly amended our conclusions. Indeed, TMs have an impact on gene transcription during co-administration with LPS as now suggested by Actinomycin D chase experiments reported in Figure 6C in the revised data and discussion in the manuscript.

      3.4. HuR competes with many RBPs (e.g. TTP and KSRP) as well as microRNAs (including miR-21 and miR-122) to regulate the stability/translational efficiency of several AU-rich transcripts. Does TM binding to HuR lead to increase access of these RBPs/microRNA to the transcripts? This could be addressed by RNA IP with antibodies to TTP or KSRP.

      R3.4 – The reviewer is suggesting an important experiment that requires multiple controls and significant efforts. Indeed, we are planning to study the specificity of TMs, and we prefer to tackle and report this point in a later publication.

      3.5. Another aspect of HuR functioning is the dimerization of HuR. HuR dimerization has been linked with many pathophysiologic conditions. The authors should show the effect of TM7nox on HuR dimerization. In figure 2, for example, there is a suggestion of this in the representative EMSAs where an intermediate shifted band is seen with the addition of TMs. Also, the legend should make clear which ligand is being tested in the modeling (purple structure) versus the RNA probe in the EMSAs. It would help the reader to identify the RNA probe used-e.g. "5′-DY681-labeled ARE RNA probe.

      R3.5 – We agree with the reviewer’s suggestion, and we investigated whether TM7nox influences HuR dimerization in the absence of RNA as performed in PMID 17632515 (Meisner et al 2007). We used MS-444 as a positive control, and we did not observe inhibition of dimerization by TMs at least at the used dosages. Data are reported in Supplementary Figure S6B of the revised manuscript.

      3.6. HuR does alter M2-associated targets like IL-10 and this should be addressed more directly. Fig. 3 suggests that IL-10 is reduced by TM7nox but the variance is so high that the statistics show NS. HuR regulates IL-10 in other cellular contexts and this would be important to determine for TM7 in the long run.

      R3.6 – Although we acknowledge its relevance, however, we did not investigate this gene directly. The variance becomes significant in the RIP-seq experiment (Supplementary Figure 9D). Therefore, we confirm that Il10 is among the 47/82 genes that show the same behavior as Cxcl10, Il1b and many other cytokines as Ccl12, Ccl7, Fas, Il1a, Il33; in conclusion, it is among the restricted list of genes modulated by TM7nox according to the presence of less AU rich sequences than average.

      3.7. Fig. 5-10 um of the TM used here produces significant toxicity to BMDM according to fig. S7. This may distort the ELISA/qPCR results as the RNA levels may be lower due to toxicity. The authors should address this or use a lower dose that is not toxic.

      R3.7 – The viability curves mentioned by the reviewer are run at 24-48 hours, and no toxic effects have been observed using TMs after 6 hours of treatment.

      3.8. In Fig 6 the immunocytochemistry is difficult to interpret as the magnification is too small to appreciate the N/C ratio. The investigators should provide higher magnification. A nuclear/cytoplasmic western blot is recommended as well to confirm that TM does not impair HuR shuttling (or NFkb shifts). This is an important area as there is a suggestion that TM blocks dimerization (Fig. 2) which does impair shuttling. Also, the modeling data suggest that TMs appear to sit in a similar groove between RRM1 and 2 as other HuR inhbitors that block shuttling.

      R3.8 – This point has also been raised by other reviewers, and we replied in R2.3 and R1.11. We understand the reviewer’s points, and we agree with the observation. However, we do not observe a change in HuR nuclear/cytoplasmic shuttling by immunofluorescence, neither we see an effect on HuR dimerization.

      3.9. IL-6 does not appear to be affected by TM treatment after LPS stimulation in vivo or in vitro -either mRNA or protein. However, DHTS did suppress this cytokine. The authors should address this discrepancy. Likewise, TNFa data here show no change and possibly a trend upward (Fig 3,4 and 7). This is in contrast to the effect of DHTS on TNF-a reported by the authors in a prior publication (D'Agnistino et al). The authors should address this discrepancy. There are reports suggesting that HuR is a translational inhibitor of TNFa in macrophages--Katsanou V, Papadaki O, Milatos S, Blackshear PJ, Anderson P, Kollias G, Kontoyiannis DL. HuR as a negative posttranscriptional modulator in inflammation (PMID 16168373)

      R3.9 – The reviewer’s comments are correct, but we do not have an explanation for this. In theory, there could be several possibilities such as 1) a DHTS effect on NFkB, 2) the fact that previously mentioned experiments with DHTS are not run with the same cells-at the same doses and timing as our current TM experiments, and 3) that HuR silencing is only partially overlapping with TMs treatment also in our recent experiments. Irrespective of specific transcripts, we think we have shown that TMs’ mechanism of action involves the modulation of HuR binding at the transcriptional level in our experimental condition.

      Review Cross-commenting

      I think the other reviewers' comments are pertinent and well thought out. I have no further suggestions.

      Reviewer #3 (Significance):

      The characterization of novel HuR inhibitors derived from tanshinones is an important advancement to the field which is rapidly growing. This complements other work with small molecule inhibitors and will allow the field to better understand the role of HuR in different disease contexts (cancer, neuroinflammatory etc) and cell types (e.g. macrophages, microglia, astrocytes). The ultimate significance is the clinical application of the inhibitors and the more options the better, particularly if there are toxic effects of some. My expertise is in post-trasnscriptional regulation of cytokines and we have already characterized some potent effects in cancer.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #3

      Evidence, reproducibility and clarity

      In this study, the authors investigated the modulation of HuR by tanshinone mimics and how it mitigates LPS response in murine macrophages. This represents a nice integration of synthetic chemistry, molecular simulations, and in vitro as well as in vivo experimental validations. Overall, this is an interesting study, and will add to the growing interest in HuR in inflammatory-mediated disease. The paper contains a lot of data (actually several papers in one) which may bog down the reader and distract from the overall message. it is suggested that they condense the data and simplify the figures and use more supplemental figures.

      Major Comments:

      1. The authors have shown the dose response and cytotoxicity effect of tanshinone mimics; The authors show that TMs affect the overall HuR mRNA but they don't show protein levels.
      2. It is unclear the timing of certain experiments for LPS vs TMs (whether macrophages were pre-treated with TMs before LPS)-e.g fig 5. The authors should clarify for all experiments as the long-term clinical paradigm would be treatment after inflammation has been established.
      3. They have also identified differentially expressed genes which are RNA binding ligands of HuR by RIP-Seq. However, it would be necessary to check whether TM7nox affects the stability of those targets before conclusions can be made that TMs don't inhibit the primary transcriptional response (as mentioned in the Discussion section). Transcriptional effects of HUR chemical inhbiition or genetic silencing has been reported previously inother cell systems.
      4. HuR competes with many RBPs (e.g. TTP and KSRP) as well as microRNAs (including miR-21 and miR-122) to regulate the stability/translational efficiency of several AU-rich transcripts. Does TM binding to HuR lead to increase access of these RBPs/microRNA to the transcripts? This could be addressed by RNA IP with antibodies to TTP or KSRP.
      5. Another aspect of HuR functioning is the dimerization of HuR. HuR dimerization has been linked with many pathophysiologic conditions. The authors should show the effect of TM7nox on HuR dimerization. In figure 2, for example, there is a suggestion of this in the representative EMSAs where an intermediate shifted band is seen with the addition of TMs. Also, the legend should make clear which ligand is being tested in the modeling (purple structure) versus the RNA probe in the EMSAs. It would help the reader to identify the RNA probe used-e.g. "5′-DY681-labeled ARE RNA probe.
      6. HuR does alter M2-associated targets like IL-10 and this should be addressed more directly. Fig. 3 suggests that IL-10 is reduced by TM7nox but the variance is so high that the statistics show NS. HuR regulates IL-10 in other cellular contexts and this would be important to determine for TM7 in the long run.
      7. Fig. 5-10 um of the TM used here produces significant toxicity to BMDM according to fig. S7. This may distort the ELISA/qPCR results as the RNA levels may be lower due to toxicity.The authors should address this or use a lower dose that is not toxic.
      8. In Fig 6 the immunocytochemistry is difficult to interpret as the magnification is too small to appreciate the N/C ratio. The investigators should provide higher magnification and provide examples of ActD, LPS and LPS + drug. A nuclear/cytoplasmic western blot is recommended as well to confirm that TM does not impair HuR shuttling (or NFkb shifts). This is an important area as there is a suggestion that TM blocks dimerization (Fig. 2) which does impair shuttling. Also, the modeling data suggest that TMs appear to sit in a similar groove between RRM1 and 2 as other HuR inhbitors that block shuttling.
      9. IL-6 does not appear to be affected by TM treatment after LPS stimulation in vivo or in vitro -either mRNA or protein. However, DHTS did suppress this cytokine. The authors should address this discrepancy. Llikewise, TNFa data here show no change and possibly a trend upward (Fig 3,4 and 7). This is in contrast to the effect of DHTS on TNF-a reported by the authors in a prior publication (D'Agnistino et al). The authors should address this discrepancy. There are reports suggesting that HuR is a translational inhbitor of TNFa in macrophages--Katsanou V, Papadaki O, Milatos S, Blackshear PJ, Anderson P, Kollias G, Kontoyiannis DL. HuR as a negative posttranscriptional modulator in inflammation (PMID 16168373)

      Review Cross-commenting

      I think the other reviewers' comments are pertinent and well thought out. I have no further suggestions.

      Significance

      The characterization of novel HuR inhibitors derived from tanshinones is an important advancement to the field which is rapidly growing. This complements other work with small molecule inhibitors and will allow the field to better understand the role of HuR in different disease contexts (cancer, neuroinflammatory etc) and cell types (e.g. macrophages, microglia, astrocytes). The ultimate significance is the clinical application of the inhibitors and the more options the better, particularly if there are toxic effects of some. My expertise is in post-trasnscriptional regulation of cytokines and we have already characterized some potent effects in cancer.

    1. As citizens, we need not think that the conflict between ideas and ideologies is bad.

      This sentence supports the main argument. We are often pinned against one another when we have different political views. If leaders respect and accept their opponent's ideas, we may also reciprocate that same respect to fellow community members and leaders.

    1. Notice that attempting to summarize each claim can actually take more space than the original text itself if we are summarizing in detail and trying to be very precise about what the text claims and implies. Of course, we won’t want to or need to do this in such detail for every paragraph of every reading we are assigned to write about. We can resort to it when the argument gets harder to follow or when it’s especially important to be precise.

      Would it be every assumption that we may think is a fact, but ending it with a question in the sentence that we gave? Such as the example that is provided. It gives a fact, but then asks questions about it. Either it could be a claim of policy, fact, or value.

    1. Holidays moved from the realm of private affairs to commercial affairs, and such a move may have contributed to the depersonalization of these occasions.

      This makes me think about stores. The seasonal time change of consumption marked by holiday products. The target collectible birds for every season. The fastidious change of these products as soon as one holiday is over. We have to gear up and buy buy buy for the next small break. Churning out new products and accumulating them as the days and years go by. A cycle of consumption.

    1. It was as if there was a switch in my brain that regulated how I saw the world: It was off when I walked around alone, and it was on when I was out in the world with my parents. It was only when I visited that medical center with my mom that I realized the switch was the same one I flipped on whenever I stepped on my skateboard.

      Why now? I don't know much about disability and design, but this may be a metaphor for how we think about people with opposing viewpoints. Often people with different opinions are looking at the same thing in the same way, but with a different base experience.

    1. Author Response

      Reviewer #1 (Public Review):

      This study investigates the psychological and neurochemical mechanisms of pain relief. To this end, 30 healthy human volunteers participated in an experiment in which tonic heat pain was applied. Three different trial types were applied. In test trials, the volunteers played a wheel of fortune game in which wins and losses resulted in decreases and increases of the stimulation temperature, respectively. In control trials, the same stimuli were applied but the volunteers did not play the game so that stimulation decreases and increases were passively perceived. In neutral trials, no changes of stimulation temperature occurred. The experiment was performed in three conditions in which either a placebo, or a dopamineagonist or an opioid-antagonist was applied before stimulations. The results show that controllability, surprise, and novelty-seeking modulate the perception of pain relief. Moreover, these modulations are influenced by the dopaminergic but not the opioidergic manipulation.

      Strengths

      • The mechanisms of pain relief is a timely and relevant basic science topic with potential clinical implications.

      • The experimental paradigm is innovative and well-designed.

      • The analysis includes advanced assessments of reinforcement learning.

      Weaknesses

      • There is no direct evidence that the opioidergic manipulation has been effective. This weakens the negative findings in the opioid condition and should be directly demonstrated or at least critically discussed.

      We agree that we cannot provide direct evidence on the effectiveness of the opioidergic manipulation in our study. However, previous literature strongly suggests that a dose of 50 mg naltrexone (p.o.) is effective in blocking 𝜇-opioid receptors in humans. Using positron emission tomography, Weerts et al. (2013) found a blockage of 𝜇-opioid receptors of more than 90% with 50 mg naltrexone (p.o.) although given repeatedly 4 days in a row. In addition, convincing effects on behavioral functions have been reported with comparable doses that support the efficacy of the opioidergic manipulation. For example, Chelnokova et al. (2014) found attenuating effects of 50 mg naltrexone (p.o.) on wanting as well as liking of social rewards, implicating the involvement of endogenous opioids in the processing of rewarding stimuli. The same dose was also found to attenuate reward directed effort exerted in a value-based decision-making task (Eikemo et al., 2017). Moreover, 50mg of naltrexone (p.o.) have been shown to reduce endogenous pain inhibition induced by conditioned pain modulation (King et al., 2013) and to reduce the perceived pleasantness of pain relief (Sirucek et al., 2021). Thus, based on the available literature we assume the effectiveness of our opioidergic manipulation. A corresponding reasoning including a note of caution on the of the lack of a direct manipulation check of the opioidergic manipulation can be found in the manuscript in the Discussion:

      “The doses and methods used here are comparable to those used in other contexts which have identified opioidergic effects. Using positron emission tomography, Weerts et al. (2013) found a blockage of opioid receptors of more than 90% by 50 mg of naltrexone (p.o.) in humans given repeatedly over 4 days. In addition, effects on behavioral functions have been reported with comparable doses that support the efficacy of the opioidergic manipulation. Chelnokova et al. (2014) found attenuating effects of 50 mg naltrexone (p.o.) on wanting as well as liking of social rewards, implicating the involvement of endogenous opioids in the processing of rewarding stimuli. The same dose was also found to attenuate reward directed effort exerted in a value-based decision-making task (Eikemo et al., 2017). Moreover, 50 mg of naltrexone (p.o.) have been shown to reduce endogenous pain inhibition induced by conditioned pain modulation (King et al., 2013). Thus, based on the literature we assume that the opioidergic manipulation was effective in this study, although we do not have a direct manipulation check of this pharmacological manipulation. Despite its effectiveness in blocking endogenous opioid receptors, the effect of naltrexone on reward responses was found to be small (Rabiner et al., 2011). Hence, a lack of power may have limited our chances to find such effects in the present study.”

      • The negative findings are exclusively based on the absence of positive findings using frequentist statistics. Bayesian statistics could strengthen the negative findings which are essential for the key message of the paper.

      We agree with the reviewers that the power may not have been sufficient to detect potentially small effects of the pharmacological manipulations. The power calculation was based on the design and the medium effect size found in a previous study using a comparable experimental procedure for assessing pain-reward interactions (Becker et al., 2015). To acknowledge this weakness, we clarified in the manuscript the description of the a priori sample size calculation as follows:

      “The power estimation was based on the design and the finding of a medium effect size in a previous study using a comparable version of the wheel of fortune game without pharmacological interventions (Becker et al., 2015). The a priori sample size calculation for an 80% chance to detect such an effect at a significance level of 𝛼=0.05 yielded a sample size of 28 participants (estimation performed using GPower (Faul et al., 2007 version 3.1) for a repeated-measures ANOVA with a three-level within-subject factor)."

      Further, we did not aim to claim that endogenous opioids do not affect the perception of pain relief. Our phrasing in describing the results was in several instances too bold. The aim of the pharmacological manipulations was to investigate effects of dopamine and endogenous opioids on endogenous modulation of perceived intensity of pain relief. Here, we expected dopamine to enhance such endogenous modulation and naltrexone to reduce this modulation. The higher average pain modulation under naltrexone compared to placebo found in VAS ratings (naltrexone: -10.09, placebo: -7.31, see Table 1) suggests an increase in pain modulation by naltrexone compared to placebo, although this did not reach statistical significance, which is the opposite of what we had expected (see comment #11). Therefore, we concluded that we have no evidence to support our hypothesis of reduced endogenous modulation of pain relief by naltrexone. We do not want to claim that there are no effects of endogenous opioids on pain modulation. Although Bayesian statistics might be used to support such an interpretation, we think this might be misleading in our context here due to the considerations on the lack of power (which also affects null-hypothesis testing in Bayesian statistics) and the lack of a direct manipulation check mentioned above. Since we expected opposite effects of levodopa and naltrexone on pain modulation, we did not intend to compare these effects directly to avoid a distortion of the results. According to our hypotheses, we expected to see increased modulation of pain relief with enhanced dopamine availability and decreased modulation of pain relief with blocking of opioid receptors (see also comment #11). However, we had no a priori assumptions on potential differences in the absolute changes induced by the drug manipulations. Based on these considerations, we did now not include further direct comparisons of the effects of both drugs. Rather, we carefully went through the manuscript to tone down the descriptions and interpretations of our null findings and adjusted the respective section of the discussion to better reflect this interpretation.

      • The effects were found in one (pain intensity ratings) but not the other (behaviorally assessed pain perception) outcome measure. This weakens the findings and should at least be critically discussed.

      We thank the reviewers for highlighting this important aspect. We have considered the two outcome measures as indicative of two different aspects or dimensions of the pain experience, based also on previous results in the literature. Within our procedure, the ratings indicate the momentary perception of the stimulus intensity after phasic changes in nociceptive input (outcomes), while the behavioral measure indicates perceptual within-trial sensitization or habituation in response to the tonic stimulation within each trial. Supporting the assumption of such two different aspects, it has been shown before that pain intensity ratings and behavioral discrimination measures can dissociate (Hölzl et al., 2005). In line with the assumption that both outcome measures assess different aspects of the pain experience, a differential effect of controllability on these two outcome measures is conceivable. Similarly, Becker et al. (2015), using a very similar experimental paradigm, did only find endogenous pain facilitation in the losing condition of the wheel of fortune game in pain ratings but not in the behavioral outcome measure, while they found endogenous inhibition in both measures. Compared to Becker et al. (2015), we implemented here smaller changes in stimulation intensity as outcomes in the wheel of fortune game (-3°C vs -7°C for win trials, +1°C vs +5°C for lose trials), potentially resulting in the differential effects here. Nevertheless, we agree that this reasoning needs a more explicit discussion in the manuscript and we included the following sentences to the Discussion section:

      “Although we did not assess the affective component of the relief experience, we implemented two outcome measures that are assumed to capture independent aspects of the pain experience: VAS ratings indicate perception of phasic changes (outcomes), while the behavioral measure indicates perceptual within-trial sensitization or habituation in response to the tonic stimulation within each trial. We found enhanced endogenous modulation by controllability and unpredictability in the VAS ratings, in line with the view that endogenous modulation enhances behaviorally relevant information. In contrast, the within-trial sensitization did not differ between the active and passive conditions under placebo. In contrast, in a previous study using a similar experimental paradigm Becker et al. (2015) found a reduction of within-trial sensitization after pain relief outcomes by controllability. Compared to this study, we implemented here smaller changes in stimulation intensity as outcomes in the wheel of fortune (-3 °C vs -7 °C for pain relief), potentially explaining the differential results.“

      • The instructions given to the participants should be specified. Moreover, it is essential to demonstrate that the instructions do not yield differences in other factors than controllability (e.g., arousal, distraction) between test and control trials. Otherwise, the main interpretation of a controllability effect is substantially weakened.

      Thanks for pointing out that specific information on instructions given to the participants was missing. We agree that factors other than controllability would confound the interpretation of differences between test and control trials. We aimed minimizing nonspecific effects of arousal and/or distraction while still giving all needed information with our instructions (see below). In addition, control and test trials were kept as similar as possible. In order to check for unspecific effects of arousal and/or distraction, we also included lose trials in the game as an additional control condition. For clarifying participants’ instructions, we added the following paragraph to the Materials and methods section: “The participants were instructed that there were two types of trials: trials in which they could choose a color to bet on the outcome of the wheel of fortune and trials in which they had no choice. Specifically, they were told that in the first type of trials they could use the left and right mouse button, respectively, to choose between the pink and blue section of the wheel of fortune. Participants were further instructed that if the wheel lands on the color they had chosen they will win, i.e. that the stimulation temperature will decrease, while if the wheel lands on the other color, they will lose, i.e. that the stimulation temperature will increase. For the second type of trials, participants were instructed that they could not choose a color, but were to press a black button, and that after the wheel stopped spinning the temperature would by chance either increase, decrease, or remain constant.”

      In general, both arousal and distraction can be assumed to affect pain perception. If the active condition in the wheel of fortune resulted in higher arousal and/or distraction this should result in comparable effects on intensity ratings in both the win and lose outcomes compared to the passive condition. In contrast, controllability is expected to have opposite effects on pain perception in win and lose trials (decreased pain perception after winning and increased pain perception after losing in the active compared to the passive condition). These opposite effects of controllability are tested by the interaction ‘outcome × trial type’ when fitting separate models for each drug condition, which should be zero if unspecific effects of arousal and/or distraction predominated. Instead, we found a significant interaction in these models, confirming opposing effects of controllability in win and lose outcomes and contradicting such unspecific effects. We added this reasoning, marked in red here, to the Results section to better highlight this line of reasoning, as follows:

      “To test whether playing the wheel of fortune induced endogenous pain inhibition by gaining pain relief during active (controllable) decision-making, a test condition in which participants actively engaged in the game and ‘won’ relief of a tonic thermal pain stimulus in the game was compared to a control condition with passive receipt of the same outcomes (Figure 1). As a further comparator the game included an opposite (‘lose’) condition in which participants received increases of the thermal stimulation as punishment. This active loss condition was also matched by a passive condition involving receipt of the same course of nociceptive input. Comparing the effects of active versus passive trials between the pain relief and the pain increase condition (interaction ‘outcome × trial type’) allowed us to test for unspecific effects such as arousal and/or distraction. If effects seen in the active compared to the passive condition were due to such unspecific effects, then actively engaging in the game should affect comparably pain in both win and lose trials. In contrast, if the effects were due to increased controllability, pain inhibition should occur in win trials and pain facilitation in lose trials.”

      • The blinding assessment does not rule out that the volunteers perceived the difference between placebo on the one hand and levodopa/naltrexone on the other hand. It is essential to directly show that the participants were not aware of this difference.

      We based our assessment of blinding on the fact that for none of the drug conditions the frequency of guessing correctly which drug was ingested was above chance (see Results section, page 8, lines 201ff). In addition, the frequency of side effects reported by the participants did not differ between the three drug conditions, supporting this notion indirectly. However, we agree with the reviewer that this does not rule out completely that participants may have perceived a difference between the placebo and the levodopa/naltrexone conditions. We ran additional analyses to test whether participants were more likely to answer correctly that they had ingested an active drug and whether they were more likely to report side effects in the active drug conditions compared to the placebo condition. In 7 out of 28 placebo sessions (25%) the participants assumed incorrectly to have ingested one of the active drugs. In 12 out of 43 drug sessions (21.8%) the participants assumed correctly that they had ingested one of the active drugs. These frequencies did not differ between placebo sessions on the one hand and the levodopa and naltrexone active drug sessions on the other hand (𝜒)(1) = 0.11, p = 0.737). In 9 out of 28 placebo sessions (32.1%) and in 23 out of 55 drug sessions (41.8%) participants reported to be tired at the end of the session. The frequency of reporting tiredness did not significantly differ between placebo sessions on the one hand and drug sessions on the other hand (𝜒)(1) = 1.06, p = 0.304). No other side effects were reported. We added the following information, marked in red here, to the Results section:

      “In 32 out of 83 experimental sessions subjects reported tiredness at the end of the session. However, the frequency did not significantly differ between the three drug conditions (𝜒)(2) = 2.17, p = 0.337) or between the placebo condition compared to the levodopa and naltrexone condition (𝜒)(1) = 1.06, p = 0.304). No other side effects were reported. To ensure that participants were kept blinded throughout the testing, they were asked to report at the end of each testing session whether they thought they received levodopa, naltrexone, placebo, or did not know. In 43 out of 83 sessions that were included in the analysis (52%), participants reported that they did not know which drug they received. In 12 out of 28 sessions (43%), participants were correct in assuming that they had ingested the placebo, in 6 out of 27 sessions (22%) levodopa, and in 2 out of 28 sessions (7%) naltrexone. The amount of correct assumptions differed between the drug conditions (𝜒)(2) = 7.70, p = 0.021). However, posthoc tests revealed that neither in the levodopa nor in the naltrexone condition participants guessed the correct pharmacological manipulation significantly above chance level (p’s > 0.997) and the amount of correct assumptions did not differ significantly between placebo compared to levodopa and naltrexone sessions (𝜒)(1) = 0.11, p = 0.737), suggesting that the blinding was successful.”

      • The effects of novelty seeking have been assessed in the placebo and the levodopa but not in the naltrexone conditions. This should be explained. Assessing novelty seeking effects also in the naltrexone condition might represent a helpful control condition supporting the specificity of the effects in the naltrexone condition.

      We thank the reviewer for this interesting suggestion. Indeed, we did not report the association of pain modulation with novelty seeking in the naltrexone condition, because we did not have an a-priori hypothesis for this relationship. We now included correlations for all three drug conditions, testing if higher novelty seeking was associated with greater perceptual modulation in the active vs. passive condition. In line with comment 3, we applied a correction for multiple comparisons here (Bonferroni-Holm correction). This correction caused the correlation in the placebo condition to be no longer significant with an adjusted p-value of 0.073 (r = -0.412), while the correlation stays significant in the levodopa condition (r = -0.551, p = 0.013). Because of a reasonable effect size of the correlation under placebo (i.e. r = -0.412), we still report this correlation to highlight the increase under levodopa, while emphasizing that this correlation not significant We carefully toned down the interpretation of this correlation to reflected the change in significance with the correction for multiple testing.

      We added the following information, marked in red here, in the Results section:

      “Previous data suggest that endogenous pain inhibition induced by actively winning pain relief is associated with a novelty seeking personality trait: greater individual novelty seeking is associated with greater relief perception (pain inhibition) induced by winning pain relief (Becker et al., 2015). Similar to these results, we found here that endogenous pain modulation, assessed using self-reported pain intensity, induced by winning was associated with participants’ scores on novelty seeking in the NISS questionnaire (Need Inventory of Sensation Seeking; Roth & Hammelstein, 2012; subscale ‘need for stimulation’ (NS)), although this correlation failed to reach statistical significance after correction for multiple comparisons using Bonferroni-Holm method (r = -0.412, p = 0.073). A significant association between novelty seeking and endogenous pain modulation was found in the levodopa condition (r = 0.551, p = 0.013). More importantly, the higher a participants’ novelty seeking score in the NISS questionnaire, the greater the levodopa-related endogenous pain modulation when winning compared to placebo (NISS NS: r = -0.483, p = 0.034 Figure 7). In contrast, higher novelty seeking scores were not correlated with stronger pain modulation induced by winning in the naltrexone condition (r = 0.153, p = 0.381) and the naltrexone induced change in pain modulation showed no significant association with novelty seeking (r = 0.239, p = 0.499). Pain modulation after losing was not associated with novelty seeking in placebo (r = 0.083, p = 0.866), levodopa (r = -0.164, p = 0.783), or naltrexone (r = 0.405, p = 0.133).

      No significant correlations with NISS novelty seeking score were found for behaviorally assessed pain modulation in the placebo, levodopa and naltrexone conditions during pain relief or pain increase (|r|’s < 0.35, p’s > 0.238). Similarly, the difference in pain modulation during pain relief or pain increase between the levodopa and the placebo condition and between the naltrexone and the placebo condition did also not correlate with novelty seeking (|r|’s < 0.22, p’s > 0.576).” <br /> We also edited the interpretation of the correlation in the Discussion:

      “Overall, all three predictions were largely borne out by the data: relief perception as measured by VAS ratings was enhanced by controllability, unpredictability and showed a medium sized - although not significant - association with the individual novelty-seeking tendency,”

      • The writing of the manuscript is sometimes difficult to follow and should be simplified for a general readership. Sections on the information-processing account of endogenous modulation in the introduction (lines 78-93), unpredictability and endogenous pain modulation in the results (lines 278-331) are quite extensive and add comparatively little to the main findings. These sections might be shortened and simplified substantially. Moreover, providing a clearer structure for the discussion by adding subheadings might be helpful.

      We have reworked the manuscript to make it easier to follow. Specifically, we reworked the Introduction section to simplify it and to make it more concise. Further, we also shortened the extensive descriptions of modeling procedures that are not central for understanding the main findings. We think that these additions make it easier to follow the manuscript and our line of arguments, and to understand the applied analysis strategies.

      • Effect sizes are generally small. This should be acknowledged and critically discussed. Moreover, effect sizes are given in the figures but not in the text. They should be included to the text or at least explicitly referred to in the text.

      We agree that the effect sizes we report appear generally small. Importantly, the effect sizes were calculated by dividing differences in marginal means by the pooled standard deviation of the residuals and the random effects to obtain an estimate of the effect size of the underlying population rather than only for our sample. This procedure was used for the purpose of achieving more generalizable estimates. Due to considerable variance between subjects in our sample, this procedure resulted in comparatively small effect sizes. Nevertheless, we think this calculation of effects sizes results in more informative values because they can be viewed as estimates of population effects. We added specific information on the calculation of the effect sizes and a brief explanation that this procedure results in comparatively small effect sizes estimates to the Materials and methods and to the Results section (see below). In addition, we included standardized effect sizes whenever we report the respective post-hoc comparisons in the Results section.

      “Effects sizes were calculated by dividing the difference in marginal means by the pooled standard deviation of the random effects and the residuals providing an estimate for the underlying population (Hedges, 2007).” (Materials and methods section)

      “We used post-hoc comparisons to test direction and significance of differences in either outcome condition and report standardized effect sizes for these differences. Note that all reported effect sizes account for random variation within the sample, providing an estimate for the underlying population; due to considerable variance between participants in the present study, this results in comparatively small effect sizes.” (Results section)

      • The directions of dopamine and opioid effects on pain relief should be discussed.

      We amended our explanation of the hypothesis on the expected drug effects. As outlined there, we indeed expected opposite effects of levodopa and naltrexone on endogenous pain modulation in the active vs. the passive condition of the wheel of fortune.

      Reviewer #2 (Public Review):

      This study used the tonic heat stimulation combined with the probabilistic relief-seeking paradigm (which is a wheel of fortune gambling task) to manipulate the level of controllability and predictability of pain on 30 healthy participants. The authors focused on the influence of controllability and unpredictability on pain relief using pain reports and computational models and examined the involvement of dopamine and opioids in those effects. For that, the authors conducted the three-day experiments, which involved placebo, levodopa (dopamine precursor), and naltrexone (opioid receptor antagonist) administration on separate days. Lastly, the authors examined the relationship between dopamine-induced pain relief and novelty-seeking traits.

      This is a strong and well-performed study on an important topic. The paper is well-written. I really enjoyed reading the introduction and discussion and learned a lot. Below, I have a few minor comments.

      First, given that the Results section comes before the Methods section, it would be helpful to include some method and experimental design-related information crucial for the understanding of the results in the Results section. For example, how long was the thermal stimulus? What was the baseline temperature? etc. Maybe this information can be included in the caption of Figure 1.

      We thank the reviewer for this helpful suggestion. We agree that due to the order of the manuscript sections, more information on experimental design and the statistical analysis strategies should be included in the results section. Accordingly, we included more detailed information on the analysis strategies in the Results section (please see responses to comments #5 & #9). In addition, we added more detailed information on the experimental design and information such as the duration of the stimuli and the baseline temperature, marked in red below, to the caption of Figure 1 (Results section).

      “Figure 1: Time line of one trial with active decision-making (test trials) of the wheel of fortune game. Experimental pain was implemented using contact heat stimulation on capsaicin sensitized skin on the forearm. In each trial, the temperature increased from a baseline of 30 °C to a predetermined stimulation intensity perceived as moderately painful. In each testing session, one of the two colors (pink and blue) of the wheel was associated with a higher chance to win pain relief (counterbalanced across subjects and drug conditions). Pain relief (win) as outcome of the wheel of fortune game (depicted in green) and pain increase (loss; depicted in red) were implemented as phasic changes in stimulation intensity offsetting from the tonic painful stimulation. Based on a probabilistic reward schedule for theses outcomes, participants could learn which color was associated with a better chance to win pain relief. In passive control trials and neutral trials participants did not play the game, but had to press a black button after which the wheel started spinning and landed on a random position with no pointer on the wheel. Trials with active decision-making were matched by passive control trials without decision making but the same nociceptive input (control trials), resulting in the same number of pain increase and pain decrease trials as in the active condition. In neutral trials the temperature did not change during the outcome interval of the wheel. Two outcome measures were implemented in all trial types: i) after the phasic changes during the outcome phase participants rated the perceived momentary intensity of the stimulation on a visual analogue scale (‘VAS intensity’); ii) after this rating, participants had to adjust the temperature to match the sensation they had memorized at the beginning of the trial, i.e. the initial perception of the tonic stimulation intensity (‘self-adjustment of temperature’). This perceptual discrimination task served as a behavioral assessment of pain sensitization and habituation across the course of one trial. One trial lasted approximately 30 s, phasic offsets occurred after approximately 10 s of tonic pain stimulation. Adapted from Becker et al. (2015).”

      Second, it would be helpful if the authors could provide their prior hypotheses on the drug effects. It could be a little bit confusing that the goal of using these drugs given that levodopa is a precursor of dopamine, whereas naltrexone is the opioid antagonist, i.e., the effects on the target neurotransmitters seem the opposite. Then, I wondered if the authors expected to see the opposite effects, e.g., levodopa enhances pain relief, while naltrexone inhibits pain relief, or to see similar effects, e.g., both enhance pain relief. Clarifying which direction of expected effects would be helpful for novice readers.

      We thank the reviewer for pointing out that information on the expected drug effects should be explained in more detail. Indeed, we expected opposite effects of levodopa and naltrexone with respect to the effect of controllability on pain relief. Levodopa, as a precursor of dopamine, enhances dopamine availability and thus, phasic release of dopamine in response to events, for example, the reception of reward. Accordingly, we hypothesized that endogenous modulation by relief outcomes are increased in the active (reward) compared to the passive condition. In contrast, naltrexone blocks opioid receptors and as such it has been reported that naltrexone blocks placebo analgesia as a type of endogenous pain inhibition. Correspondingly, we hypothesized that naltrexone decreases endogenous pain modulation induced by actively winning pain relief compared to the passive condition. We expanded the explanation of these hypotheses in the Introduction section as follows:

      “We expected increased dopamine availability to enhance phasic release of dopamine in response to rewards, and hence, to increase the effect of active compared to passive reception of pain relief. In contrast, we expected the inhibition of endogenous opioid signaling to decrease the effect of active controllability on pain relief. The latter is based on the observation that blocking of opioid receptors attenuates other types of endogenous pain inhibition such as placebo analgesia (Benedetti, 1996; Eippert et al., 2009) or conditioned pain modulation (King et al., 2013). “

      Third, on the "Behaviorally assessed pain perception" results in Figs. 2D-F, I wonder why the results for the "pain increase" were still positive. Were the y values on the plots the temperature that participants adjusted (i.e., against the temperature right before the temperature adjustment)? or are the values showing the differences from the baseline (i.e., against the baseline temperature)?

      The behavioral measure was calculated as the difference in temperatures between the memorization interval at the beginning of the trial (i.e. the predetermined temperature perceived as moderately painful) minus the self-adjusted temperature at the end of the trial so that positive values indicate sensitization (i.e. an increase in sensitivity) and negative values indicate habituation (i.e. a decrease in sensitivity) across the stimulation within on trial (i.e. approx. 30 seconds of stimulation). In general, for a stimulation of approximately 30 seconds with intensities perceived as painful, perceptual sensitization is expected to occur (Kleinböhl et al., 1999).

      The outcome of the wheel of fortune game, i.e. the phasic decrease (winning) or increase (losing) in stimulation intensity, should indeed have opposite effects on this sensitization. A decrease in nociceptive input negatively reinforces pain perception, as seen in stronger sensitization in win trials, while an increase in nociceptive input punishes pain perception, as seen in reduced perceptual sensitization in lose trials. Using the a very similar task, Becker et al. (2015) found values indicating habituation within trials with temperature increases in lose outcomes. However, in this previous study, increases of +5°C were used for lose outcomes (as compared to +1 °C in the present study). Thus, in the present study the comparatively small increase in absolute stimulation temperature may not have been sufficient to induce within trial habituation to the tonic heat pain stimulation.

      Nevertheless, independent of the effect of the outcome (increase or decrease of the stimulation intensity) our focus was on the additional effect that controllability (active vs. passive condition) had on the perception of the underlying tonic stimulation within each outcome condition (i.e. on the same nociceptive input). Here we expected to see endogenous inhibition after winning and endogenous facilitation after losing in the active compared to the passive condition.

      We added more detailed information on the calculation of the behavioral measure and the expected perceptual modulation within each trial due to the stimulus duration in the Methods section as well as in the Results section.

      Methods section:

      “After this rating, participants had to adjust the stimulation temperature themselves to match the temperature they had memorized at the beginning of the trial. This self-adjustment operationalizes a behavioral assessment of perceptual sensitization and habituation within one trial (Becker et al., 2011, 2015; Kleinböhl et al., 1999). Participants adjusted the temperature using the left and right button of the mouse to increase and decrease the stimulation temperature. The behavioral measure was calculated as the difference in temperatures in the memorization interval at the beginning of each trial minus this selfadjusted temperature at the end of each trial. Positive values, i.e. self-adjusted temperatures lower than the stimulation intensity at the beginning of the trial, indicate perceptual sensitization, while negative values indicate habituation.” Results section:

      “Positive values (i.e. lower self-adjusted temperatures compared to the stimulation intensity at the beginning of the trial) indicate perceptual sensitization across the course of one trial of the game, negative values indicate habituation. For tonic stimulation at intensities that are perceived as painful, perceptual sensitization is expected to occur (Kleinböhl et al., 1999). Differences between the outcome conditions (win, lose) reflect the effect of the phasic changes on the perception of the underlying tonic stimulus. Differences between active and passive trials reflect the effect of controllability on this perceptual sensitization within each outcome condition.”

      Lastly, I wonder if it is feasible or not, but examining the effects of dopamine antagonists will be helpful for obtaining a more definitive answer to the role of dopamine in information-related pain relief. This could be a good suggestion for future studies.

      We thank the reviewer for this suggestion. We agree that antagonistic manipulation of the dopaminergic system could provide further insights and confirm the role of dopamine in shaping pain related perception and behavior. Moreover, we think that bidirectional manipulations of opioidergic signaling could also provide valuable insights and should be used for future research. We added the following sentences to the Discussion section:

      “Because the mechanisms underlying learning from pain and pain relief and their recursive influence on pain perception may contribute to the development and maintenance of chronic pain, it is crucial to better understand the roles of dopamine and endogenous opioids in these mechanisms. Accordingly, bidirectional manipulations of both transmitter systems should be used in future studies to better characterize their respective roles in shaping behavior and perception.“

    1. hey can act in waysthat benefit a small percentage of the group and unknowingly destroy all ofhuman life.

      This makes me think of where we are as a human race and what is happening to the environment. Industrialization, wealth, globalization, control of resources is driven by greed and profit for the few, and it is resulting in a crisis that the world may never recover from.

    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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      Reply to the reviewers

      We are obviously very pleased with the general support expressed by the referees, and appreciate their critical comments. We detail below how we propose to respond to their suggestions and queries.

      In view of the fact that my lab is no longer in existence, I will have to rely on the kind generosity of my colleagues at EMBL to host former team members (the two first authors) for a limited period to come back to Heidelberg to carry out any further experimental work that may be needed. This means we will have to limit the work we can do to those experiments with the highest priority. However, we are optimistic that we will be able to obtain indicative results.

      We will also follow most of the referees’ other suggestions and requests for additional data and quantifications, as outlined (or already included) below.

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Summary: ASC is the Pyrin/CARD-containing adapter protein that functions as a core component of inflammasome signaling complexes. ASC functions downstream of various NLR- and ALR-inflammasome initiator proteins and upstream of the inflammatory caspases that function as inflammasome effector enzymes. This study uses a novel chimeric construct (Opto-ASC) comprising the Arabidopsis photo-oligomerizable cryptochrome 2 (Cry2-olig) protein with zebrafish ASC to generate transgenic zebrafish larvae wherein ASC oligomerization can be rapidly, dynamically and spatially induced by blue light illumination of either the entire larva or single cells within discrete tissues of an intact larva. Induction of these "opto-inflammasome" complexes is coupled with state-of-the-art, live-cell optical imaging of multiple single cell and integrative tissue parameters to assay various modes of regulated cell death within the peridermal and basal cellular layers of the larval skin. This experimental model was further combined with genetic manipulation of the expression of various zebrafish inflammatory or apoptotic caspases, as well as the two zebrafish members of the gasdermin family of pore-forming proteins which can mediate disruption of plasma membrane permeability without (pre-lytic) or with (pyroptosis) progression to lytic cell death.

      The main results of the study are: 1) introduction of a novel experimental system for dynamic and spatially resolved ASC oligomerization and speck formation within the cells of intact epithelial tissues of a living organism; 2) the ability of these optically induced ASC oligomers/specks to drive multiple modes of regulated cell death which exhibit some (but not all) features of lytic pyroptosis or non-lytic apoptosis depending on cell type and tissue location; 3) the ability of the dying epithelial cells containing optically-induced ASC specks to coordinate rapid adaptive responses in adjacent non-dying cells to maintain integrity/ continuity of skin epithelial barrier; and 4) unexpectedly, no obvious role for either of the two zebrafish gasdermins in the regulated cell death responses.

      Major Comments:

      1. Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them? The major goal of this MS is to present a new experimental model (optogenetic activation of ASC oligomerization in transgenic zebrafish) that has the potential to provide new insights regarding the multiple mechanisms by which ASC can regulate inflammasome/ cell death signaling responses in the context of an intact organism. As noted above, some of the observed results are unexpected (e.g., lytic cell death independent of the zebrafish gasdermins in particular epithelial cells) and may reflect mechanisms unique to zebrafish as a non-mammalian vertebrate model versus the mammalian experimental systems (murine and human) that have informed most of our current understanding of how ASC coordinates inflammasome and cell death responses. However, the authors have used rigorous genetic approaches to rule out trivial explanations for the unexpected observations. Thus, no major additional experiments are required to support the claims and conclusions presented in the MS.

      2. Are the suggested experiments realistic in terms of time and resources? Yes. It would help if you could add an estimated time investment for substantial experiments: A few weeks.

      3. Are the data and the methods presented in such a way that they can be reproduced? Are the experiments adequately replicated and statistical analysis adequate? Yes.

      4. Are the experiments adequately replicated and statistical analysis adequate? Yes.

      Minor comments

      1. Specific experimental issues that are easily addressable:

      There's a significant concern with the use of LDC7559 (line 387) as a putative small molecule inhibitor of gasdermin D function to test roles (or lack thereof) of the zebrafish gasdermins in the ASC-triggered lytic cell death responses. A recent study (Amara et al. 2021. Cell. PMID34320407) has reported that LDC7559 does not inhibit gasdermin D (and possibly other gasdermins) but rather acts as an allosteric activator of PFKL (phosphofructosekinase-1 liver type) in neutrophils and thereby suppress generation of the NADPH required for the phagocytic oxidative burst and consequent NETosis. Thus, use of LDC7559 as a presumed gasdermin inhibitor in the current MS is problematic and should be deleted. As an alternative pharmacological approach to suppress gasdermin function, the authors might consider the use of either disulfiram (Hu et al. 2020. Nature Immunology. PMID32367036) and/or dimethylfumarate (Humphries et al. Science. 2020. PMID32820063). These would be straightforward additional experiments.

      We have ordered the reagents to do these experiments. We are optimistic that we will obtain data that will strengthen this part of the ms and be a pointer for future studies by others.

      We propose to keep the information on LDC7559 included, but to discuss the reservations the referee lists above - otherwise, others might ask why we did not even try this inhibitor. .

      Are prior studies referenced appropriately? there are some problems; see below. 2a. One paper is cited twice in lines 724-726 and 727-729. 2b. Another paper is cited twice in lines 790-792 and 793-795. 2c. No journal is included for the referenced study by Shkarina et al in lines 827-828. 2d. No journal is included for the referenced study by Stein et al in lines 831-832. 2e. No journal is included for the referenced study by Masumoto et al in lines 793-795. 2f. No journal is included for the referenced study by Kuri et al in lines 774-775.

      We are embarrassed about these omissions and mistakes and have corrected them..

      Are the text and figures clear and accurate? Generally, yes but with a few exceptions noted below: 3a. line 28: "morphological distinct" should read "morphologically distinct" 3b. line 161: this sentence contains in parentheses "for how long?" I think this was a comment by one author that wasn't removed from the final submitted MS 3c. line 945: spelling "balck" > "black" 3d. line 268: "whereas showed a delayed speck formation": the authors need to specify what model/ condition showed a delayed speck formation 3e. line 262: spelling "egnerated" > "generated"

      Thank you, all corrected.

      CROSS-CONSULTATION COMMENTS I also agree with the comments of the other 2 reviewers. Between the 3 sets of comments and suggestions, the aggregate review will provide the authors with a suitable range of feasible recommendations that will improve an already strong MS.

      Reviewer #1 (Significance (Required)):

      1. General assessment: As noted above, this the major goal of this MS is to present a new experimental model (optogenetic activation of ASC oligomerization in transgenic zebrafish) that has the potential to provide new insights regarding the multiple mechanisms by which ASC can regulate inflammasome/ cell death signaling responses in the context of an intact organism. The authors have used rigorous genetic approaches to rule out trivial explanations for the unexpected observations. In general, the MS describes an elegant model system that will provide a platform for identifying new mechanisms of ASC-dependent inflammasome signaling and regulated cell death.

      2. Advance: This MS describes a highly novel experimental model. Zebrafish are increasingly being used as a genetically tractable model for inflammasome signaling within integrated tissues of intact organism. As noted above, the advances are technical but also conceptual. Future application of this novel model is likely to yield identification of new mechanisms for ASC function in innate immunity and regulated cell death within the context of tissue homeostasis and host defense.

      3. Audience: Basic research and discovery.

      4. Please define your field of expertise with a few keywords to help the authors contextualize your point of view: My group investigates multiple aspects of inflammasome signaling biology at the cellular level with an emphasis on cell-type specific roles for gasdermins in coordinating downstream innate immune responses to inflammasome activation in various myeloid leukocytes (macrophages, dendritic cells, neutrophils, eosinophils, mast cells).

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Programmed cell death is critical for host defense and tissue homeostasis. How dead cells initiate cellular responses in the microenvironment with neighbouring cells in vivo is still largely unknown. The authors have chosen a Zebrafish model to tackle this question, given that this model shows advantages for imaging and addresses these pathways in a complex in vivo setting. Their recent development of light-induced activation of caspases (published in JEM) enabled them to investigate cellular responses to a specific type of cell death in vivo at a single cell resolution. In this study, the author further developed a light-induced activation of ASC to specifically look at inflammasome activation-mediated cell death in vivo. The authors have successfully established this system in zebrafish and also observed that Opto-Asc-induced cell death showed different phenotypes as compared to Opto-caspase-a/b-induced cell death. However, it is not really clear why. I have a few specific comments to be addressed or discussed.

      1. In Fig.3 and Fig.4, the majority of Opto-Asc localizes to the plasma membrane but not endogenous Asc. It seems that tagging affects its localization, which could potentially explain its slow kinetics in oligomerization.

      That is an interesting suggestion. The membrane enrichment is indeed reproducible and we have no full explanation for it. However, ASC itself seems to have some affinity for the cell cortex as seen by its association with the apical actin ridges in keratinocytes in the resting state (see e.g. figure 3A). Affinity of ASC for actin is also documented in the literature:(F-actin dampens NLRP3 inflammasome activity via flightless-1 and LRRFIP2 OPEN; https://doi.org/10.1038/srep29834).

      Perhaps the fusion to the optogenetic module somehow enhances the affinity through the initial dimerization. But we can only speculate and have no further evidence that would allow reliable conclusions.

      In Fig.7, the authors showed that deletion of Caspb, but not Caspa, affected the apical extrusion, without affecting cell death. This may indicate that other caspases, like Caspase-8 or/and caspase-3 were involved. This could be addressed through deletion of Caspase-8 or/and caspase-3.

      These are experiments we had in fact done. Unfortunately, they did not allow us to address the question, because the deletions resulted in embryonic lethality. We have added this information to the text.

      It is very surprising that Opto-Asc-mediated cell death is not dependent on Gasdermins, at least in Caspb-dependent apically extruded dead cells.

      Indeed – but see comment by and our response to reviewer 1. We hope to be able to provide additional data.

      CROSS-CONSULTATION COMMENTS I agree with the other two reviewers and don't have further comments.

      Reviewer #2 (Significance (Required)):

      The Opto-Asc zebrafish model developed in this study will enable us to specifically look at inflammasome-mediated cell death in vivo. This model is more physiologically relevant compared to Opto-caspase1 model.

      Audience interested in physiological function of inflammasome activation, but it is questionable whether such a tool will address mechanisms in mammalian cells. Eventually, more evidence for the latter could be provided.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      In this article, de Carvahlo and colleagues describe a novel optogenetic tool allowing single cell and temporally controlled induction of ASC clusters in vivo (in zebrafish), a central adaptator protein of the inflammasome complex which is involved in the induction of pyroptosis. This alternative mode of programmed cell death is involved in pathogen response and promote cell swelling and the release of pro-inflammatory factors. Previous works have shown that the inflammasome activation is associated with the formation of a large cluster of ASC protein (called speck) which promotes then the recruitment and the activation of caspase 1. Specks were previously characterised by the same group in vivo (in zebrafish larvae) and could be induced by the overexpression of ASC protein. This however was not compatible with fine spatio-temporal control of speck formation, thus preventing very refined characterisation of the dynamics and the distinction of the cell autonomous and non-cell autonomous effects.

      By fusing ASC to the blue-light sensitive oligomerising protein Cry2-olig under the control of a heat shock promoter, they could induce time controlled induction of speck at the single cell level, which is then followed by cell extrusion and cell death both in the periderm and the basal cell of the skin of zebrafish larvae. Doing so, they could characterise the dynamics of speck formation as well as key paramters affecting its dynamics and the subsequent extrusion. While ASC activation led to apical or basal extrusion in the periderm layer followed by non-apoptotic cell death, it triggers basal extrusion and apoptosis in the basal layer. Importantly, periderm cell elimination does not seem to strictly follow all the features of pyroptosis as it does not require GSDM, and relies on Caspb (not Caspa). It is also associated with strong Calcium release both in the dying and neighbouring cells.

      The authors performed a very careful characterisation of the tools and the optimisation of the condition to form speck and eliminate cells. The experiments are very well performed with all the necessary controls. The results, while to some extend still hard to fully interpret for some aspects, illustrate the plasticity of cell death and cell extrusion, which include several very interesting observations on the direction of extrusion, putative compensatory modes of cell death upon Caspase1 perturbation and the difference of response to ASC clustering depending on the tissue layer. While it is not the main point of this study, the observation that the direction of extrusion can vary very significantly in different genetic backgrounds is also extremely interesting.

      The atypical cell elimination revealed in the system may require further characterisation in the future and suggest that the tools may not be the best to study bona fide pyroptosis. However, I don't believe there is always such strict separation between the modes of cell death and I am sure that it could lead to very interesting insights on inflammasome formation, extrusion and charcaterisation of downstream signalling in vivo, so overall this will be a very interesting resource for the community working on inflammasome, cell death and extrusion.

      I have some suggestions that could help to better characterise the mode of elimination as well as the mechanism of speck formation. I have also some suggestions for comparison with other published results as well as some text editing.

      Main points :

      1. So far, it remains a bit unclear how the authors define precisely speck versus any aggregate and the light induced clusters of Cry2 olig. Is it related to the timescale of formation and/or the lifetime of the aggregates? Is it related to their size?

      There Is no ‘formal’ definition of an inflammatory speck apart from it being the unusually large aggregates that ASC forms once it is activated. Light-induced clusters of Cry2Olig alone, or of Cry2olig fusions with proteins that do not normally oligomerize are much smaller (extensive documentation in the literature).

      A speck is thus a stable aggregate of ASC which is usually around 1 µm in size and is able to activate downstream caspases. But neither of these aspects alone are unique to ASC: prion-like structures can also be large aggregates (indeed ASC-specks have been compared to prions), and much smaller molecular assemblies can activate caspases. Thus ‘speck’ is more an operational definition, and ‘natural’ specks do have both of these properties, but as our experiments show, the properties can actually be separated. I would rather not try to narrow or change the definition, but leave any further discussion to the experts in the field.

      Figure 4E shows a number of variants of ‘speck’-like and other multimers: ASC-mKate and Opto-ASC form large single specks in the presence of endogenous ASC. Opto-ASC specks are only slightly smaller than those formed by endogenously tagged ASC-GFP (see also Supplementary Figure 2E.. Opto-PYD recruits endogenous ASC and becomes incorporated into a speck of approximately the same size, while Opto-CARD does so less efficiently. All of these kill cells. In the absence of endogenous ASC, Opto-ASC forms much smaller specks, and very many in each cell, but these are still functional as seen by the fact that they still kill cells (not the large spot at t = 60 min in the right half of Fig. 4E is not a speck, but the contracted dying cell). Both Opto-PYD and Opto-CARD also form only the small aggregates (quantification will be included), with Opto-PYD still killing the cell by virtue of its ability to recruit caspases via their PYD, whereas Opto-CARD does not.

      Since the authors use most of the time constant blue light illumination, could they also assess how long the speck remains after stopping blue light exposure and quantify their lifetime (relative to the CRY2olig cluster lifetime)?

      Briefly, any speck that contains a functional ASC moiety remains stable and does not disassemble once the blue light is turned off. In skin cells it is not possible to make quantitative measurements because they are killed by the speck. Opto-ASC specks remain stable until they are taken up by macrophages, as originally reported for ASC-GFP specks in Kuri et al. 2017.

      Stability can best be assessed in muscle cells, which do not die upon speck formation. The figure below shows that specks begin to form within minutes of a short pulse of illumination and remain stable (and indeed grow further) for at least 60 min.

      Here is an example:

      Revisions Figure A:

      __Stability of __Opto-ASC specks in muscle cells after exposure to a single pulse of blue light

      Specks in muscle cells expressing Opto-AscTg(mCherry-Cry2olig-asc) are induced by a single illumination with blue light (488nm) at t = 0 for 32 seconds. Multiple oligomers begin to form within 6 minutes, continue to gradually increase in number and, and remain until the end of the movie (60 mins).

      Cell outlines in the overlying epithelium labeled by AKT-PH-GFP are faintly visible in the first frame. Scale bar is 20 mm.

      Similarly could they provide some comparison of the size and localisation of CRY2 olig clusters compared to the speck.

      For size, see above. In addition, the size of the Cry2 oligomers as well as of Opto-ASC specks can vary with expression levels.

      For location, Cry2olig clusters are usually distributed throughout the cell, as seen in most of the right panels in Fig 4E, and in earlier work in cultured cells (e.g. Taslimi et al 2014). ASC specks can form anywhere in the cell, while Cry2olig-ASC has a preference for the cell cortex, but this is not absolute. In keratinocytes, but not in basal cells, the speck usually forms close to the lateral membrane. In the absence of endogenous ASC no real speck is formed but Opto-ASC in this case shows no clear localisation of Opto-ASC to the membrane.

      In view of the variation we see, a strict quantification is difficult: what would be the ‘correct’ definition of classes to look at? To make statistically significant statements, we would need an enormous number of examples in which we could control for all of the variation of expression levels, cell size, day to day variation etc, and we currently don’t have these. We hope the qualitative evidence in the micrographs we show represents the differences well, and we will be happy to provide a larger number of images, if the referees feel this would be helpful.

      With the non functional CRY2olig Asc fusion (Cter fusion), do they still see transient olig2 clustering which then reverse when blue light illumination is gone? I think it might be useful to clarify these points in the main text since most of the quantifications are based on speck localisation/numbering, so their characteristics have to be very well defined.

      That would be interesting to work out, but after our initial experiments with this construct, we did not pursue this further, since it was not a pressing issue at the time. If we can fit this into our planned experimental time table, we will re-assess it. However, while of interest, we feel these data would not add substantially to what we know at this point.

      1. In all the snapshots of speck formation, there seems to be a relative enrichment of the ASC signal at the cytoplasmic membrane (relative to the cytoplasm) prior to strong speck formation. This seems specific of optoASC as it does not seem to happen for the endogeneous ASC or upon overexpression of ASC-mKate (both in this study and in the previous study published by the same group). Is this apparent membrane enrichment something reproducible? (I see that on pretty much every example of this manuscript). If so what could be the explanation? Is there an actual recruitment at the membrane or is it because the membrane/cortical pool takes longer to be recruited in the speck (hence looking relatively more enriched at intermediate time points).

      See our speculations in response to point 1 of the first referee.

      We too would really like to understand this, but see no easy and efficient way of testing it at this point.

      1. There is also a very distinctive ring accumulation that seems to match with apical constriction and/or a putative actomyosin ring (since this is perfectly round, it could match with a structure with high line tension) (see Figure 1E, Figure 3B, Figure 4D...). Is it something already known? Could the authors comment a bit more on this? This could suggest that ASC accumulates in actomyosin cortex, which would be a very interesting property.

      We see that we had failed to be clear about this.

      There are two types of actin-labelled rings that appear around dying cells. One is formed by the epithelial cells that surround the dying cell. This structure becomes visible as soon as the cell begins to shrink. That it is formed by the surrounding cells is clear from mosaics where the dying cell does not express the actin marker (e.g. suppl. Figure 4A) and the parts of the ring are seen only in the subset of surrounding cells that do express the marker. This ring is also not circular, but follows the polygonal shape of the shrinking cell. We believe that this is the contractile structure that closes the wound, as observed in many other cases of wound healing.

      The other is the one the referee describes here. It is formed within the dying cell, as shown by the fact that it is visible in labelled cells when all the surrounding cells are negative for the marker. The other difference is that it appears only once the dying cell has already contracted considerably and begins to round up and be extruded (most clearly seen in Fig. 1E). The third referee had raised a similar point in relation to the same structure seen in Fig. 6C, and we provide below the requested analysis. It relies on resolution in the y-axis, which is unsatisfactory, but nevertheless, it is clear that this ring is in a plane above the apical surface of the epithelium (marked by the red membrane marker, i.e is present in the detaching cell. It may well simply be actin appearing in the entire cortex of the cell as it rounds up and looking like a ring when seen from above. A completely different method for imaging would have to be set up to document this reliably, but we hope that these explanations help to clarify the confusion we may have created.

      We do not see this accumulation in cells that leave the epithelium towards the interior (see figure in the response to ‘minor points’ below).

      In the end, since cell death can also occur without visible speck formation, I am wondering if they are eventually the most relevant structure to be quantified. Is it because speck can be dissolved upon caspase activation and could it relates to the speed at which caspase are activated (which may not leave enough time for strong aggregation and visible speck formation)? I believe it would help to get more explanation/discussion on this point.

      As already mentioned above, it is indeed not obvious what the significance of the large speck is (and it is extremely puzzling why it is that normally one a single one forms in each cell). We agree that it is not necessarily functionally relevant for the signalling outcome to quantify this property – but nor was this the purpose of this work. Regardless of what kind of aggregate is formed, the optogenetic tool allows the induction of ASC-dependent cell death, and therefore the study of the ensuing cellular events.

      The compensatory mechanisms that lead to cell death/extrusion despite depletion of caspb is very interesting. Could the authors use some pan caspase inhibitor (like zvad FMK) to confirm that this block opto-ASC cell death also in this context? Alternatively could they check the status of effector caspase activation using live probe (nucview) or immunostaining in the context of caspb depletion?

      Those would be interesting avenues to pursue. However, for the reason stated above (Leptin lab closing down, members of fish group no longer at EMBL), we are forced to restrict ourselves to the most important experiments, and think we should prioritize the ones mentioned above.

      1. If I understand well, Figure 7C on the right side suggest that the double KO cells don't extrude (if indeed "no change" mean no extrusion, by the way this nomenclature may deserve some clarification in the legend). I don't think these results are mentioned at any point in the main text, but it would be important to include them (since this is an important control).

      This interpretation is in fact correct, and we have changed the labelling in the figure to ‘no immediate death’

      1. Waves of calcium following cell death and cell extrusion have been previously characterised (Takeushi et al. Curr Biol 2020, Y Fujita group). Interestingly, in this previous article they observed waves of calcium near Caspase8 induced death (in MDCK) as well as near laser induced death in zebrafish, while apparently the authors don't see such Calcium waves upon Caspase8 activation in the zebrafish here. I think it would be important to include a comparison of the authors results with this previous paper in the discussion

      We have included this in our discussion.

      There is also a previous study which characterised the impact of caspase1 on cell extrusion (Bonfim Melo et al. Cell Report 2022, A. Yap lab) which promotes apical extrusion in Caco2 cells. I think it would also be important to include this work in the discussion and to compare with the results obtain here in vivo.

      We have included this in our discussion.

      Other minor points:

      1. Line 439: are the numbers given in percentage? if these are absolute numbers, it is out of how many cells ? Same remark line 445: what are the number of cases representing? (percentage?)

      We have rephrased this to make it unambiguous.

      Figure 5: could the authors show periderm and basal cell extrusion with the same type of markers? (membrane or actin or ZO1)? This would help to really compare accurately the morphology and the remodellings associated.

      We used Utr-mNeonGreen to lable actin both in periderm and basal cells. Actin labeling of extruded periderm cells is shown in figure 6C, actin labeling of a dying basal cells and the overlying periderm cells is shown in supplementary figure 5A.

      Is there any obvious differences in cell size or characteristic cell shape between the classic lab strains (golden, AB, AB2B2) and the WIK and experiment strain used here? I do acknowledge that this is clearly not the focus of this study, but given this striking difference (which is related to an important question in the field of extrusion), it would interesting to mention this if there is anything obvious.

      We will make these measurements and include the data.

      1. Figure 6C: what is exactly the localisation in Z of this strong actin accumulation observed during apical extrusion? Is it apical or is it rather on the basal side of the cell? A lateral view of actin could be useful in this figure for all the different conditions described.

      See response to ‘main point 3’ above.

      The images that show this are below. However, even from these images it is hard to appreciate the locations. They are in fact much easier to see by following the movies over time, and through the z-sections at any given time point. We will of course submit the movies with the manuscript.

      Revisions figure B:

      Localization of actin in the yz and xz planes in Opto-Asc-induced cell death and Opto-caspase-8-induced apoptosis

      Orthogonal projections of images of apically (A) and basally (B, C) extruded cells at four time points from time lapse recordings. Each time point shows the x-z plane and the orthogonal yz and and xz planes, in which the apical sides of the epithelium faces the x-z image.

      Actin is labeled with mNeonGreen-UtrCH (cyan), plasma membranes and internal membranes by lyn-tagRFP (magenta). Actin is initially concentrated in the apical cortical ridges of periderm cells.

      1. Apically extruded cell after death is induced by Opto-Asc. As the cell dies actin is lost from the apical ridges and accumulates in the cell cortex in a plane above the original apical surface of the epithelium
      2. Basally extruded cell after death is induced by Opto-Asc. Actin is retained in the apical ridges as the cell shrinks and moves below the epithelium within the dying cell.
      3. Basally extruded cell after death is induced by Opto-Caspase 8. The apical surfaces forms a transient dome in which the actin ridges remain intact before the dying cell is internalized. .

      Figure S3B: could the authors show the utrophin-neonGreen channel separatly? Is there a ring of actin in the dying cell? Also are the membrane protrusion formed more basally? (I suspect this is a z projection, but this would need to be specified in the legend).

      1. Figure 4A legend: I guess the authors meant red arrowheads rather than frame ? This has been corrected

      2. I list below a number of typos I could find in the main text

      Thanks for noticing these, we have corrected all of these, as well as further typos we found.

      Line 29: in Line 30: but Line 151 : from the ...[...] (tissue ?) Line 161: there is most likely a text commenting that was not removed (for how long?) Line 262: generated (egnrtd) Line 268: whereas showed a delay (the subject is missing) Line 269: a point is missing Line 362: which the lack Line 368: a point is missing Line 400: a space is lacking "cellsdepending" Line 438: shrinkwe (space) Line 459 : or I infections Line 525: there is a point missing.

      CROSS-CONSULTATION COMMENTS I generally agree with all the comments raised by the other reviewers which partially overlap with comments I had (see for instance referee two for the role of other caspases and the membrane localisation of the probe).

      Reviewer #3 (Significance (Required)):

      In this article, de Carvahlo and colleagues describe a novel optogenetic tool allowing single cell and temporally controlled induction of ASC clusters in vivo (in zebrafish), a central adaptator protein of the inflammasome complex which is involved in the induction of pyroptosis. This alternative mode of programmed cell death is involved in pathogen response and promote cell swelling and the release of pro-inflammatory factors. Previous works have shown that the inflammasome activation is associated with the formation of a large cluster of ASC protein (called speck) which promotes then the recruitment and the activation of caspase 1. Specks were previously characterised by the same group in vivo (in zebrafish larvae) and could be induced by the overexpression of ASC protein. This however was not compatible with fine spatio-temporal control of speck formation, thus preventing very refined characterisation of the dynamics and the distinction of the cell autonomous and non-cell autonomous effects.

      By fusing ASC to the blue-light sensitive oligomerising protein Cry2-olig under the control of a heat shock promoter, they could induce time controlled induction of speck at the single cell level, which is then followed by cell extrusion and cell death both in the periderm and the basal cell of the skin of zebrafish larvae. Doing so, they could characterise the dynamics of speck formation as well as key paramters affecting its dynamics and the subsequent extrusion. While ASC activation led to apical or basal extrusion in the periderm layer followed by non-apoptotic cell death, it triggers basal extrusion and apoptosis in the basal layer. Importantly, periderm cell elimination does not seem to strictly follow all the features of pyroptosis as it does not require GSDM, and relies on Caspb (not Caspa). It is also associated with strong Calcium release both in the dying and neighbouring cells.

      The authors performed a very careful characterisation of the tools and the optimisation of the condition to form speck and eliminate cells. The experiments are very well performed with all the necessary controls. The results, while to some extend still hard to fully interpret for some aspect, illustrate the plasticity of cell death and cell extrusion, which include several very interesting observations on the direction of extrusion, putative compensatory modes of cell death upon Caspase1 perturbation and the difference of response to ASC clustering depending on the tissue layer. While it is not the main point of this study, the observation that the direction of extrusion can vary very significantly in different genetic backgrounds is also extremely interesting.

      The atypical cell elimination revealed in the system may require further characterisation in the future and suggest that the tools may not be the best to study bona fide pyroptosis. However, I don't believe there is always such strict separation between the modes of cell death and I am sure that it could lead to very interesting insights on inflammasome formation, extrusion and charcaterisation of downstream signalling in vivo, so overall this will be a very interesting resource for the community working on inflammasome, cell death and extrusion.

      My expertise are in cell extrusion, optogenetics, apoptosis and epithelial mechanics. I am not a specialist of the inflammasome and pyroptosis.

    1. Adam Marshall Dobrin • You Technocrat Founder at XCALIBER DAO/ARKLOUD.XYZ. Writer. Coder. Futurologist. Aspiring dad. 1m • 1 minute ago I came to the particular city that I am in to prove that Operation Gunsider and Project Y were "ruce's" ... #informationoperations that were part of a grand design that literally includes the whole of "Majestic" which is another key word in the research path to where we are going.It includes more than that, much more--on this song and who you all are. Closer to God, than ... "most." Closer to me, too. It includes the entirety of the KJV and "all of religion as seen through the eyes of the Christ." It includes missions to teach Latin and English and "reading and writing" to the entirety of humanity; and at this point we have to pause and really understand what is going on.We have an "Adam code" that is something like ##305407; its a word that includes research and development on what to do when the "everybody up?" generation fails or succeeds; it is a way to get "way more voters involved" in a place where we once had a world that could have have saved its past, clearly do to the inability to see it at all, much less travel to it. Today I need basic computer knowledge and general concepts of things like terraforming and physics added to the list of things that are "required to vote" in the Constitutional realms considerably here perhaps the somewhere between the third and the fifth Houses of the Capitol of the United States.I would like to make the entirety of the past, the entire A.D. timeline and perhaps something bigger than that "intelligent, omni-important, and oligarchical rulers of themselves." I would like to see Technocracy flourish as a word that literally involves the Halo of Cortana and its connection to "how we vote." I wrote for a brief time on how to engage an audience in something called "subconscious voting" and how to connect "checking your vote" to the only Labor the Party has to accomplish on it's WED/hour of "required work left once we are done with ... automation, roboticization and the revolution colloquially associated with Bolshevik and Ford.I need us to think today what kind of classes we would need to put together for ... "members of the midieval civilization of lore" ... people who coincided in cities with Cathedral's or Mission's that match the architecture associated with the One True Church--whether it be the source of the Spanish Armada or the Eastern Orthodox Byzantine Fault. What kind of classes are required to understand things like "game theory" and "solar fusion" and also the inner workings of Heaven enough to intelligently vote on whether or not another group of people, for instance, is "educated enough to be considered a peer, or a citizen."UK Home Office U.S. Immigration and Customs Enforcement (ICE) Immigration And Nationality Services (IANS) It's interesting to "see this answer" INS has aided me here in assuming you understand that acronym has changed from the historical truth, as we consider "naturalization" and what kind of history/nationhttps://lnkd.in/dvUKdGZf

      OPERATION JAZZORCIVILIZE

      Jazzercize is something my mommy did around the time I was born. It's literally just "jamboree" or some kind of popular women's ((predominantly)) exercize group. They met all over America in the 80's and they wore some funny socks ;) It is the word associated with "changing everybody up" to include the entirety of the capable group of humanity ever living on a rock with religion. It could be bigger than that, but here I've sort of defined it to literally link with significance only the Church of Rome and things that came after it. It is literally what it is, the A.D. timeline. It most likely includes a group of "less than all" who carried things like knowledge and Asimov's Foundation from the Pentagon Technocrat's "Torah guild" ... many thousands of years before the day Christ appears to have been born or died in history.

      this is a big deal. I have come to a place in Deseret I associate with a military group that is literally and ((I pray)) responsible for the colonization or the co-colonization of the known galaxy. I believe we have a number of coveted extra-galactic operations aswell, and that they include Soviet and American as well as European operations outside of Deseret. I have come here to prove that Operation's Gunsider, Holocaust and Y are "Information Operations" which is modern NSA-talk for "propaganda designed for a purpose." I do not believe the technologies are real, and it's important to understand I lived through the time others call "the Cold War" and saw with my own eyes videos of rocket's traveling along United States Federal USHWY1 up and down the Eastern Seaboard ... rather than I-95 though it existed because of known and intentional fortifications on that road for equipment so heavy it would crumble bridges. We are in a place where ... London may be the only bridge left in existence after the move from NM to NV .. if you know what it means to lose pillars of Samson in a place like the Holy Temple's heart.

      I need this to be taken seriously. If we want to stop moving towards a point where we are going to be angrier with each other than we should be; I need someone in the world with a public company to hire me to build something ... "more public than companies." It starts with software and it ends with codification in the Constitution and beyond. It's a "big deal" this is a revolution bigger than the invention of voting and money; this is big. I need a pay check from a company with that kind of oversight at the very least.

      I am open to FTSE, CAC, ASX, DAX, or similar companies on those exchanges to ones listed on the S&P 500 or the DOW. The exchanges listed are not all inclusive, but it means something that I "know what they are" I studied them and we need something at least as big as an entity governed by laws to be listed on "those" ... a private company in Dubai, for instance; is not large enough to do this properly.

      I came to the particular city that I am in to prove that Operation Gunsider and Project Y were "ruce's" ... #informationoperations that were part of a grand design that literally includes the whole of "Majestic" which is another key word in the research path to where we are going.

      It includes more than that, much more--on this song and who you all are. Closer to God, than ... "most." Closer to me, too. It includes the entirety of the KJV and "all of religion as seen through the eyes of the Christ." It includes missions to teach Latin and English and "reading and writing" to the entirety of humanity; and at this point we have to pause and really understand what is going on.

      We have an "Adam code" that is something like ##305407; its a word that includes research and development on what to do when the "everybody up?" generation fails or succeeds; it is a way to get "way more voters involved" in a place where we once had a world that could have have saved its past, clearly do to the inability to see it at all, much less travel to it. Today I need basic computer knowledge and general concepts of things like terraforming and physics added to the list of things that are "required to vote" in the Constitutional realms considerably here perhaps the somewhere between the third and the fifth Houses of the Capitol of the United States.

      I would like to make the entirety of the past, the entire A.D. timeline and perhaps something bigger than that "intelligent, omni-important, and oligarchical rulers of themselves." I would like to see Technocracy flourish as a word that literally involves the Halo of Cortana and its connection to "how we vote." I wrote for a brief time on how to engage an audience in something called "subconscious voting" and how to connect "checking your vote" to the only Labor the Party has to accomplish on it's WED/hour of "required work left once we are done with ... automation, roboticization and the revolution colloquially associated with Bolshevik and Ford.

      I need us to think today what kind of classes we would need to put together for ... "members of the midieval civilization of lore" ... people who coincided in cities with Cathedral's or Mission's that match the architecture associated with the One True Church--whether it be the source of the Spanish Armada or the Eastern Orthodox Byzantine Fault.

      What kind of classes are required to understand things like "game theory" and "solar fusion" and also the inner workings of Heaven enough to intelligently vote on whether or not another group of people, for instance, is "educated enough to be considered a peer, or a citizen."

      UK Home Office U.S. Immigration and Customs Enforcement (ICE) Immigration And Nationality Services (IANS)

      It's interesting to "see this answer" INS has aided me here in assuming you understand that acronym has changed from the historical truth, as we consider "naturalization" and what kind of history/nation

      https://lnkd.in/dvUKdGZf

    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Response to reviewers

      Reviewer #1

      Reviewer #1 (evidence, reproducibility and clarity (required)):

      Winter et al. present a study of Ebola virus fusion in the acidic environment of the late endosome. Based on cryo-ET of Ebola virions undergoing entry into cells, they note that the VP40 matrix is disassembled and dissociated from the viral membrane in virions seen in the endosome. Subsequent in vitro and computational analyses suggest that protons diffuse across the viral membrane and neutralize anionic lipids on the inner leaflet. They argue that this loss of negative charge reduces the affinity of VP40 for the viral membrane. They further suggest that VP40 dissociation from the viral membrane precedes GP-mediated membrane fusion and contributes to reduction in the energy barrier for membrane stalk formation. Whereas most studies have focused on the importance of acidic pH in triggering GP conformational changes during fusion, the present work contributes new appreciation for VP40-membrane interactions.

      We would like to thank the reviewer for all the insightful comments and appreciation of the novelty.

      In the cryo-ET experiments aimed at visualizing Ebola entry, do the authors see evidence of viral membrane fusion? There is no mention of this in the text. Knowing that the virions that show disassembly of the VP40 matrix are in fact the virions that productively enter cells would support the conclusions of the study. As is stands, one is forced to wonder whether the virions that show VP40 disassembly prior to fusion ultimately fuse.

      *We first note that the EBOV virions shown in Figure 1 entering host cells were captured by cryo-ET at 48 hours post infection and resulted from 2-3 rounds of infection, thus the virions can productively enter the cells by micropinocytosis. Virions that are not able to undergo membrane fusion would be processed in the lysosomes and would not be detectable by cryo-ET at 48 hours post infection. In addition, the virions captured in late endosomes contain nucleocapsids, hence these virions are likely infectious. Together, this is good evidence that we really see events after successful membrane fusion. *

      *We fully agree with the reviewer that capturing a fusion event would provide further proof that fusion depends on prior disassembly of the VP40 matrix layer. To address this, we acquired additional data on cells infected at different time-points post-infection (15 cells imaged); regrettably, we have not been successful in capturing a membrane fusion event, presumably due its fast kinetics. In this study we are technically limited with the amount of the virus we can use for infection in BSL4. The current dataset was generated at an MOI of 0.1 and this makes capturing entry events difficult as we would need an MOI of at least 100-1000 to increase the chances of capturing such a rare event. *

      *Considering the technical difficulties to perform the experiment under BSL4 conditions, we have in addition performed a similar experiment using EBOV VLPs at high concentration (estimated MOI > 100) composed of VP40 and GP (Fig. S5). Despite the high VLP concentration, we could only find 2 tomograms out of 18 tomograms showing VLP entry events. These clearly show that the VP40 matrix is disassembled in VLPs residing in endosomes. The same lamellae displayed sites of viral fusion as evident from enlarged endosomal membrane surfaces studded with GPs facing endosomal lumina. Hence, this new data supports our results that VLPs that undergo VP40 disassembly are able to fuse. We have included the new supplementary figure S5 and added the following sentence to the main text: *

      Lines 96-102: “We were not able to capture virions residing in endosomes in the process of fusing with the endosomal membrane, presumably because virus membrane fusion is a rapid event. However, in a similar experiment using EBOV VLPs composed of VP40 and GP, we could confirm the absence of ordered VP40 matrix layers in VLPs inside endosomal compartments. Moreover, we were able to capture one fusion event and several intracellular membranes studded with luminal GPs, indicating that fusion had taken place (Fig. S5).”

      In the cryo-ET experiments that evaluate VP40 disassembly in vitro, why do the authors leave out NP from their VLP preparations? There is some evidence in the literature (Li et al., JVI 2016) that NP is necessary to form particles with native morphology. If the authors feel that NP is not necessary for their experiment, perhaps this could be noted.

      *Thank you very much for this important comment. Throughout this study, we mainly focused on the fate of the VP40 matrix during entry and thus reduced the complexity of the VLPs used to the minimum – VP40 and GP, so indeed NP was left out before. To address the role of the nucleocapsid in Ebola VLPs uncoating, we have now also included data on VLPs prepared by expression of nucleocapsid components (NP, VP24 and VP35) in addition to GP and VP40. Cryo-ET analysis of these VLPs showed that VLPs mainly contain loosely coiled nucleocapsid. This is consistent with a study by Bharat et al 2012, which shows that compared to virions, VLPs displayed heterogeneous nucleocapsid assembly states and reduced incorporation of nucleocapsids. It is important to note that VLPs containing nucleocapsid also displayed disassembled VP40 matrices at low pH (Fig. S7). Hence, nucleocapsid proteins do not influence the VP40 disassembly driven by low pH and GP-VP40 VLPs can be used as model to study VP40 uncoating. *

      *We included a statement shown on lines 150-153: “We further repeated the experiment using VLPs composed of VP40, GP and the nucleocapsid proteins NP, VP24 and VP35, and observed the same low pH-phenotype described above. These results show that nucleocapsid proteins do not influence the VP40 disassembly driven by low pH.” *

      The authors argue that acidic pH neutralizes the charge of PS phospholipids, thereby removing the electrostatic interactions of basic residues in VP40 and PS. They also note in the Methods section that 7 amino acids in VP40 are predicted by PROPKA to be protonated at pH 4.5. If the authors feel that protonation of these 7 amino acids is not involved in the loss of affinity for PS, this could be stated explicitly and justified. Could the protonation of these 7 amino acids contribute to disassembly of the VP40 lattice, rather than dissociation from the membrane?

      Thank you for this interesting comment. We note that the amino acids predicted to be protonated (*E76, E325, H61, H124, H210, H269, H315, see below) are far away from the interaction interface with the membrane and also away from the intra-dimerization domain. Hence, they do not likely contribute to the loss of affinity for PS but may contribute to conformational changes that facilitate the disassembly of the VP40 matrix. For clarification, we have added the following statement to the methods section: *

      Lines 541-544: “Importantly, these residues are located away from the interaction interface of VP40 with the membrane and their protonation accordingly does not influence membrane-binding. However, protonation of these residues may contribute to conformational changes that facilitate the VP40 matrix disassembly.

      Minor: Figure S5C is difficult to interpret. The red frame on the bars that indicates data acquired at low pH is nearly invisible. Better might be to indicate explicitly (ie, with words) the pH at which data were acquired.

      Thank you very much for this comment. We have changed the design of the graph accordingly. Please note that the figure numbering has changed and that Figure S5C is now Figure S6C.* * Reviewer #1 (significance (required)): The significance of the study stems from the idea that the VP40 lattice and its interaction with the viral membrane plays a direct role in facilitating viral fusion. To my knowledge, this has not been previously addressed. The significance would be considerably increased if the authors were able to demonstrate by cryo-ET that the virions with disassembled VP40 were in fact the virions that productively fused. Nonetheless, this work should be of broad interest to researchers studying viral fusion as it may represent a phenomenon relevant to numerous viruses that enter cells via the endocytic route.

      Reviewer #2 Reviewer #2 (evidence, reproducibility and clarity (required)):

      The manuscript by Winter et al., entitled "The Ebola virus VP40 matrix undergoes endosomal disassembly essential for membrane fusion" describes the structural aspects of the events that precede Ebola virus (EBOV) membrane fusion in late endosome and virion uncoating in the cytosol. By combining state-of-the-art cryo-electron tomography (cryo-ET) with biophysical and computational techniques, they have elucidated the pivotal role of the ebolaviral matrix virion protein 40 (VP40) in modulating the fusion process, in particular discovering that disassembly of the VP40 ordered lattice is low pH-driven, occurs despite the absence of a viral ion channel within the filovirus envelope and takes place through the weakening of VP40 interactions with lipids at the interface between the ebolaviral envelope and matrix. Overall, the manuscript is well written and the research work is very well conceived, with solid orthogonal experimental approaches that mutually validate their respective results. It is opinion of this reviewer that the paper contributes to the elucidation of a key step in the EBOV infection cycle and that it will be of great interest for the readership of Review Commons and for the community of structural biologists. Therefore, I recommend the publication of this paper, however after some minor revision to the text, the figures and the figure legends, which show inconsistencies in the terminology used, the acronyms and could be easily improved by some little graphical editing.

      Thank you very much for your positive feedback and your comments.

      Comments:

      • By starting their abstract and introduction sessions with the term "Ebola viruses" the authors are (on purpose?) preparing the reader to the implicit statement that their findings could be a paradigm model for the other members of the Ebolavirus genus. This is an exciting picture, especially in perspective of VP40-targeting drugs development. Therefore, although conclusions in this sense would probably require further studies, I encourage the authors to implement their figure 3 (or related supplementary figure) with a multiple-sequence alignment, and the relative text in the manuscript, by showing if and how much the basic patch at the C-terminus of VP40 is conserved within the Ebolavirus genus, especially the residues Lys224, Lys225, Lys274 and Lys275.

      Thank you very much for this comment. We have added a corresponding sequence alignment highlighting the high conservation of the basic patch of amino acids across all Ebola virus species (Suppl. Fig. S6). In the text, we refer to the sequence conservation as follows:

      Lines 213-215: “These interactions are driven by basic patches of amino acids which are highly conserved across all EBOV species (Fig. S8 H), further emphasizing their importance in adaptable membrane binding.”

      • It is a bit inconvenient for the reader to follow how a story unfolds while jumping back and forth between figures, and this is why I would recommend to move the period of the sentence at lines 88-91 to the session where figure 5 is discussed.

      *We refer in fact to Figure 1 and fixed the reference accordingly (line 95). *

      • Please, avoid the use of the slang "Ebola" without the apposition "virus", and make the text consistent throughout the manuscript by only using the acronym of each term after it was introduced for the first time.

      Thank you for this comment. We have thoroughly revised the use of technical terms.

      Minor revisions: Line 1: "matrix protein undergoes" We refer here to the entire VP40 matrix layer composed of many VP40 proteins and not to single VP40 proteins (as the individual proteins do not disassemble, but their macromolecular assembly does). For clarification, we changed the title to “matrix layer undergoes”.

      Line 19: "the matrix viral protein 40 (VP40)" We have corrected the statement.

      Line 18: considering that a virus "exists" in the form of a virion while temporarily located outside the cell, and as a "molecular entity" consisting of viral proteins and nucleic acids organised in macromolecular complexes during its life cycle inside the infected cell, this reviewer encourages the authors to rephrase as follows: " Ebola viruses (EBOVs) virions are filamentous particles, ..." Thank you for your suggestion. We have rephrased it to: „Ebola viruses (EBOVs) assemble into filamentous virions“ (line 18).

      Lines 35-36 and line 40: "that is determined by the matrix made up by the viral protein 40 (VP40), which drives ..." And then, directly use the acronym VP24 at line 40

      We have corrected the statement.

      Line 40: as VP24 and VP35 interact with NP but do not interact with the ssRNA genome, please rephrase as follows "the nucleoprotein (NP) which encapsidates the ssRNA genome, and the viral proteins VP24 and VP35 which, together with NP, form the nucleocapsid"

      We have corrected the statement.

      Lines 47-48: "...fusion glycoprotein (GP)...[...] the ebolaviral envelope"

      We have corrected the statement.

      Line 51: "...remarkably long virion of EBOVs undergoes..."

      We have rephrased the statement: line 55: “…remarkably long EBOV virions undergo…”

      Line 63: "... in vitro, and in endo-lysosomal compartments in situ, by cryo-electron..."

      We have corrected the statement.

      Lines 70-71: " to shed light on EBOVs ... [...] with EBOV (Zaire ebolavirus species, Mayinga strain) in biosafety level 4 (BSL4) containment"

      We have corrected the statement.

      Line 72: chemically fixed by? (PFA and GA acronyms have been annotated in figure 1, but should be first mentioned in their explicit form in the text)

      We have now mentioned annotations for GA and PFA both in the main text and in the figure legend in their explicit forms.

      Line 73 (cryo-FIB)

      We have corrected the acronym.

      Line 80: EBOV virions

      We have corrected the statement.

      Figure 1A and line 97: for consistency with the terminology used in the main text, should be perhaps in the second step preferred the term "vitrification" instead of cryofixation? Readers not familiar with the field could be confused by the use of the two synonyms

      We have replaced the term as suggested.

      Lines 92-93: "...these data indicate [...] and suggest..."

      We have corrected the statement.

      Figure 1C and line 100: in the color legend EBOV is annotated as dark teal, however in the segmentation of the reconstructed tomogram there are three objects, one of which in dark teal is evidently a portion of EBOV virion inside the endosome, and other two are in different shades of green. What are those? Please, could author specify their identity in the figure legend with their corresponding color code? The same applies to supplementary figure S2 (see comment below).

      Thank you very much for this comment. All three green objects are EBOV virions. For clarification, we have added numbers 1-3 to the figure and legend and adjusted the text in the legend accordingly (lines 109-110).

      Line 95: "...tomography of EBOV virions..."

      We have corrected the statement.

      Line 98: "...showing EBOV virions..." (This reviewer refers to the use of the term 'EBOVs' as for different species within the genus rather than for different EBOV particles within a dataset)

      We have corrected the statement.

      Line 105: "... a purified EBOV before..." *We realized a mistake in our phrasing: the virion shown in Fig. 1 H is not purified, but a virion found adjacent to the plasma membrane of an infected cell. We have changed the phrasing accordingly (lines 117-118). *

      Line 110 and 113: "...EBOV matrix..." And "EBOV virus-like particles (VLP)"

      We have corrected the statement.

      Line 140, 141, 145 and 147: "EBOV VLPs" and "EBOV VLP"; idem at lines 188-189, 209 and anywhere else in the manuscript (including figure 4A) We have corrected the use of “EBOV VLP(s)” as suggested.

      Line 235: "influenza virus ion channel..."

      We have corrected the statement.

      Line 249: please, use directly the above-introduced acronym for the detergent

      We have revised the use of acronyms.

      Figure 5F: in plot's X axis label: thermolysin (T)?

      Yes, this is correct and stated in the figure legend.* * Line 342: "EBOV have remarkably long..."

      We have corrected the statement.

      Line 420 "...matrix-specific"

      We have corrected the spelling error.

      Line 464: "grids"

      We have corrected the spelling error.

      Line 465: "for cryo-FIB milling"

      We have corrected the statement.

      Line 611: "influenza virus M2 ..." (Please, from which influenza virus strain does the gene come from? Alternatively, which is the NCBI Protein and/or UniProt database code?)

      We have added the information to the Methods (line 648): “….A/Udorn/307/1972 (subtype H3N2))…”

      Line 623: please, use the above-designated acronym for the detergent

      *We have used the acronym as suggested. *

      Line 716: "...based on cryo-ET..." We have corrected the statement.

      Line 718: "influenza virus" We have corrected the term.

      Line 734: "cryo-ET data" We have corrected the term.

      Fig. S8: for consistency with the main text, "thermolysin" We have corrected the spelling of thermolysin throughout the manuscript.

      Fig. S2, C and F: are these EBOV virions (as mentioned in the figure title) or EBOV VLPs (as the legends in the two panels of this figure seem to suggest)? Please, the authors should clarify

      Thank you very much for spotting this mistake! These are indeed EBOV virions and we have changed the legends within the figure accordingly.

      Line 1046: "malleable lipid envelope of the EBOV"; this adjective sounds confusing; the reviewer encourages the authors to rephrase for more clarity.

      We have removed the adjective „malleable”.

      Reviewer #2 (significance (required)): see above.

      __Reviewer #3__Reviewer #3 (evidence, reproducibility and clarity (required)):

      Winter and colleagues describe the molecular architecture of Ebola virus during entry into host cells. The main claims of the paper are that VP40 is disassembled prior to fusion. Disassembly is driven by the low pH environment in the endosomes. PH-induced uncoating works via "passive equilibration" because the Ebola virus envelope does not contain an ion channel. The authors conclude that structural remodeling of VP40 acts as a molecular switch coupling uncoating to fusion. The main novel results of the manuscript are: In situ cryo-ET of endosomal compartments shows EBOV particles with intact condensed nucleocapsids and disordered protein densities that may relate to detached VP40. Five EBOV particles were imaged in the endosome and all had detached VP40 layers. Controls, budding virions and extracellular virions showed intact VP40 layers. Incubation of VP40-Gp VLPs with a pH 4.5 buffer leads to the disorder of the VP40 matrix in vitro, which is independent of Gp presence in the VLPs. MD simulation showed VP40 dimer binding to model membranes containing 30 % PS at pH7 and reduced binding at pH 4.5. Lipidomics revealed the lipid composition of VP40-Gp VLPs demonstrating 9% PS.

      VP40-PHluorin fusions were used to determine acidification of VLPs in vitro and to calculate a permeability coefficient of 1.2 Å sec-1, which is quite low compared to the permeability of the plasma membrane (345 Å sec-1). Next they modeled membrane fusion showing that fusion is more favorable after VP40 disassembly, especially favoring stalk formation. The authors propose further that fusion pore opening is more favorable in the presence of VP40. The authors claim that strong interactions of lipids with VP40 stabilizes the hemifusion intermediate. VP40 Gp VLPs can enter host cells independent of pH once Gp has been activated by thermolysin.

      We thank the reviewer for these interesting comments and valuable suggestions.

      Some of the results are over interpreted and require appropriate modifications.

      Main points that need to be addressed: Imperfections of the membrane could be induced by proteins. Does acidification of the virion depend on GP and its transmembrane region? This can be tested with chimeric GP replacing its TM by unrelated trimeric TMs.

      We agree that this is important to consider. We have addressed this question in Fig. 2 K using VLPs composed of VP40 alone. These VLPs lack GP and still display luminal acidification as evident from the disassembled VP40 matrix when incubated at low pH. Therefore, acidification does not depend on GP. For clarification, we have adjusted the following sentence in the discussion:

      Lines 410-413: “Using VLPs of minimal protein composition (VP40 and GP, and VP40 alone), we show that VP40‑disassembly, i.e. the detachment of the matrix from the viral envelope is triggered by low endosomal pH (Fig. 2). This indicates that VP40 disassembly does not depend on structural changes of other viral proteins, including GP, and is driven solely by the acidic environment.*” *

      Virus entry assays, line 292. The low pH is not only used for Gp cleavage, but induces the conformational changes leading to the post fusion conformation of Gp2. The authors need to check what happens to Gp once it is cleaved by thermolysin. Is this sufficient to induce the conformational changes in Gp? And if so how does entry of such VLPs work, because once the conformational change is triggered, GP2 will adopt the post fusion conformation which is inactive in fusion. This requires further clarification.

      To our knowledge, there is only one study showing that EBOV GP2 changes conformation at low pH in the form of a re-arrangement of the fusion peptide from an extended loop to a kinked conformation (Gregory et al 2011). Importantly, low pH alone is not sufficient to trigger GP mediated membrane fusion and NPC1 is needed as a trigger for membrane fusion process (Das et al, 2020). Hence proteolytically processed GP requires NPC1 binding to change its conformation to post-fusion state. We addressed this question by using pre-cleaved (= GP2) and low pH- treated VLPs in our entry assay (Fig. 5 F). Since low pH-treated VLPs enter host cells as efficiently as VLPs incubated at neutral pH, and low pH-treated and additionally pre-cleaved VLPs enter even more efficiently, it is highly unlikely that low pH triggers the post-fusion conformation as this should inhibit virus entry (as the reviewer pointed out). In conclusion, low pH does not induce the post-conformation in GP2 and we have included a respective sentence for clarification:

      Lines 339-343: * Since thermolysin-treated EBOV VLPs efficiently enter untreated host cells at neutral and low pH, we further conclude that low pH alone does not induce the GP2 post-fusion conformation, which would inhibit virus entry. Together, this suggests a role of low endosomal pH beyond proteolytic processing of EBOV GP, likely for the disassembly of the VP40 matrix.”*

      In the fusion model, the authors claim that VP40 disassembly is more favorable for stalk formation, which is likely true. However, they also claim that strong VP40 interaction, which I would interpret as VP40 filaments interacting with the membrane, favor fusion pore opening. The tomograms and the in vitro experiments with VLPs indicate that the complete VP40 matrix is detached from the membrane under low pH conditions.

      We would like to stress that the modelling results for hemifusion formation and pore opening are independently calculated but have to be interpreted together because they occur sequentially. Hemifusion precedes formation of the pore and hence even though the model shows that the fusion pore opening is favored in the presence of VP40 interaction, membrane fusion cannot proceed to this stage because hemifusion is blocked until the VP40 matrix layer disassembles from the membrane. We apologize for lack of clarity, and we have added the sentences:

      Lines 315-318: “However, it is important to note that hemifusion precedes pore formation in the membrane fusion pathway. Since the disassembly of the VP40 matrix is required for hemifusion and hence for the initiation of membrane fusion, it determines the outcome of the membrane fusion pathway.*” *

      VLPs are purified. Can the authors exclude the possibility that the purification protocol does not damage the VLP membrane leading to in vitro acidification in a low pH environment? Can some of the assays be repeated with non-purified VLPs?

      *Thank you very much for this important comment. To address this question, we had performed the cryo-ET experiments using purified and unpurified VLPs and found that they are virtually indistinguishable. Importantly, unpurified VLPs also undergo VP40 disassembly. We now show images from unpurified VLPs in a supplementary figure (Fig. S7). Thereby, the manuscript contains data of purified VLPs while we also provide proof that the purification protocol does not influence the disassembly of the VP40 matrix. We added the following explanatory sentence to the main text: *

      Lines 151-156: “*We further repeated the experiment using VLPs composed of VP40, GP and the nucleocapsid proteins NP, VP24 and VP35, and observed the same low pH-phenotype described above (Fig. S5 C). Performing the experiments on unpurified VLPs harvested from the supernatant of transfected cells confirmed that the purification protocol applied did not influence the disassembly of the VP40 matrix (Fig. S7). “ *

      Does acidification only work at pH 4.5?

      *We also attempted to verify the acidification of VLPs at higher pH (~5.5. and ~6.0) by cryo-ET, however, subtle structural differences were difficult to quantify. Considering the lower permeability of the VLP membrane compared to the plasma membrane, we think that acidification occurs indeed also at higher pH (as shown for cells), albeit at slower kinetics. *

      Minor points Line 37: Ruigrok et al. 2000 J Mol Biol showed first that Ebola VP40 requires negatively charged lipids for interaction.

      *Thank you for pointing out this reference. We have included it in the text. *

      Fig. 1f: Is VP40 detaching as a filament?

      We have not observed that VP40 detaches as a filament or a linear segment of multiple VP40 dimers. *Since the VP40 dimer is inherently flexible (Fig. 3, Fig. S8) and can rotate along the N- and C-terminal intra- and inter-dimer interfaces, we believe disassembly occurs in a non-ordered fashion (not as filaments, see also Figure 2 G-K). *

      References 8 and 28 are the same. We have corrected the reference duplication.

      Lipidomics: The authors find only 9% PS in the VLPs. How do these results compare to the composition of other enevloped viruses that have been reported to assemble on negatively charged lipids.

      *We compared the lipid composition of the EBOV VLPs to the lipid composition of influenza viruses and HIV, which both bud from the plasma membrane and require negatively charged lipids. When grown in eggs, the envelope of influenza viruses contains 22-25 % PS (Ivanova et al 2015, Li et al 2011), and approximately 12% when produced from MDCK cells (Gerl et al 2012). The envelope of HIV virions produced from HeLa or MT4 cells contains 10-15% PS. These numbers suggest that the producing cell line strongly influences the lipid composition of the virus particles. Besides differences in the producing cell line, the lower amount of PS found in EBOV VLPs could have multiple implications: first, apart from PS, PIP2 has also been shown to interact specifically with VP40 at budding sites in the plasma membrane (Jeevan et al 2017, Johnson et al 2018) and thus also contributes to virion assembly (potentially allowing for a lower PS concentration); second, as recently shown for paramyxoviruses (Norris et al 2022), binding of PS to viral proteins is not based on charge alone but may include specific binding – in which case a high affinity of viral proteins to PS may allow for a lower PS concentration in the target membrane. Overall, the rather low PS content in Ebola VLPs might be important for VP40 interaction and low pH-driven disassembly. *

      EBO virus was suggested to assemble at lipid rafts. Is this reflected by the lipid composition?

      *Yes, that is correct. A hallmark of lipid rafts is the enrichment of cholesterol and sphingomyelin (~32 mol% cholesterol, ~ 14 mol% sphingomyelin) in the microdomains (Pike et al 2002). The lipid composition of the EBOV VLPs determined in our study (~ 39% cholesterol and ~10 mol% sphingomyelin) is consistent with the assembly at lipid rafts. Minor differences stem from the different cell lines and lipidomic approaches used to determine the lipid species. *

      Reviewer #3 (significance (required)): In summary, the manuscript is of high technical quality and the observation that VP40 detaches from the viral membrane prior to membrane fusion is novel and interesting to the field of virus fusion. How acidification occurs in the absence of an ion channel remains to be determined. The authors provide little insight how this might work. The strong part of the manuscript is the EM part, which shows convincing detachement of the VP40 matrix. I cannot comment too much on the modelling part, which, however, sounds solid.

    1. THE **MONO**LOGUE **C**ONTINUES, **UNDERSTAND ME**. It doesn’t take much “thought” to see these star charts–our Astrological road maps to ‘wisdom of the Ancients’ might actually be something closer to road maps than I could have previously fathomed–let alone imagined. I’m staring at “Monoceros” and seeing it’s definately connected to “the kissing disease” and to Eros and to Cupid–and seeing … this one not for the first time that character linked to Orion and to the “Speare” of Sagittarius. I’ve commented … ‘on the show in my head’ that it seems the entirety of the Milky Way might be something like our world … it could be a microcosmic map to something much larger–it could be the seed of “galaxies” in this place that might very well be the “thing” that connects the end and the beginning; rather than the beginning and the end as I once … commented was the original “glyph” i read in the letter “H.”

      THE MONOLOGUE CONTINUES, UNDERSTAND ME.

      It doesn’t take much “thought” to see these star charts–our Astrological road maps to ‘wisdom of the Ancients’ might actually be something closer to road maps than I could have previously fathomed–let alone imagined. I’m staring at “Monoceros” and seeing it’s definately connected to “the kissing disease” and to Eros and to Cupid–and seeing … this one not for the first time that character linked to Orion and to the “Speare” of Sagittarius.

      I’ve commented … ‘on the show in my head’ that it seems the entirety of the Milky Way might be something like our world … it could be a microcosmic map to something much larger–it could be the seed of “galaxies” in this place that might very well be the “thing” that connects the end and the beginning; rather than the beginning and the end as I once … commented was the original “glyph” i read in the letter “H.”

      --

      someone commented on the site, they posted a picture from my earlier work ... the "WHY?" one that depicted starvation and crucifixion and no press.

      in related news the LA Times spoke, it echoed "and he's thinking about his own mortality" seconds after the event ... the self questioning of whether or not I have any "kind of divinity" in me at all. Dana too, has echoed back that there's a message I am missing.

      I forgot to mention the press junket's every day, that was a kind of speech that you can't really "feel" in the rest of the articles that talk about things like walls and "something missing." Acosta may have written more on the reason, but I wasn't able to find out exactly what it was they were saying.

      Lately science has started talking about things ... "going haywire" I'm here with "IGNITION" and LLNL on my mind, and also the power of star creation and destruction connecting to the Pentagon and Deuteronomy and ... deuterium and fusion and fission and the Vooshan Young.

      I imagine some people read through this feed, the one I'm posting to. I've just posted this:

      This is what I have to do to ensure things "aren't vanished upon death" or worse, while I'm still typing about them in the very same day. The post was "vanished" from github, and I mean; it's here for the protection of not just veracity and Americana, but Hypothesis itself.

      Edit: the Github posting didn't disappear it was just marked as closed; along with an explanation "about hearing and answering before."

      There's a fortune in building the thing; and putting it together; it's basically "the next big thing" a news and "what's popular on the web" aggregator that has advanced search and friendship capabilities. Integrating with LinkedIn and Facebook and Twitter and ... "most of all abstracting those things with an identity system that ties directly to IPFS and strong identity validation and authentication--

      Out of the Ether ..

      PS: noting that this link ties to the root directory of fromthemachine dot org and it's already an aged post about this very thing, building something with Ethereum that "is glaringly missing" .. including "find your friends" integration that doesn't require them to send you a long random string of letters and numbers--just being "already connected". Creating a Wallet/Address system that ties together the social networks and "crypto trading" is glaringly missing, and we can already see applications like Snapchat and Tik-Tok that have done it in a way that creates a significant growth factor ... I mean it almost instantly made Tik Tok as big as Instagram.

      This tool is a key; being able to see "what everyone is saying about the front page news on the outlets you read every day" in a news feed and interface similar to Facebook's ... "I think that's a game changer for me; I would use it."

    1. Note 9/8j says - "There is a note in the Zettelkasten that contains the argument that refutes the claims on every other note. But this note disappears as soon as one opens the Zettelkasten. I.e. it appropriates a different number, changes position (or: disguises itself) and is then not to be found. A joker." Is he talking about some hypothetical note? What did he mean by disappearing? Can someone please shed some light on what he really meant?

      On the Jokerzettel

      9/8j Im Zettelkasten ist ein Zettel, der das Argument enthält, das die Behauptungen auf allen anderen Zetteln widerlegt.

      Aber dieser Zettel verschwindet, sobald man den Zettelkasten aufzieht.

      D.h. er nimmt eine andere Nummer an, verstellt sich und ist dann nicht zu finden.

      Ein Joker.

      —Niklas Luhmann, ZK II: Zettel 9/8j

      Translation:

      9/8j In the slip box is a slip containing the argument that refutes the claims on all the other slips. But this slip disappears as soon as you open the slip box. That is, he assumes a different number, disguises himself and then cannot be found. A joker.

      Many have asked about the meaning of this jokerzettel over the past several years. Here's my slightly extended interpretation, based on my own practice with thousands of cards, about what Luhmann meant:

      Imagine you've spent your life making and collecting notes and ideas and placing them lovingly on index cards. You've made tens of thousands and they're a major part of your daily workflow and support your life's work. They define you and how you think. You agree with Friedrich Nietzsche's concession to Heinrich Köselitz that “You are right — our writing tools take part in the forming of our thoughts.” Your time is alive with McLuhan's idea that "The medium is the message." or in which his friend John Culkin said, "We shape our tools and thereafter they shape us."

      Eventually you're going to worry about accidentally throwing your cards away, people stealing or copying them, fires (oh! the fires), floods, or other natural disasters. You don't have the ability to do digital back ups yet. You ask yourself, can I truly trust my spouse not to destroy them?,What about accidents like dropping them all over the floor and needing to reorganize them or worse, the ghost in the machine should rear its head?

      You'll fear the worst, but the worst only grows logarithmically in proportion to your collection.

      Eventually you pass on opportunities elsewhere because you're worried about moving your ever-growing collection. What if the war should obliterate your work? Maybe you should take them into the war with you, because you can't bear to be apart?

      If you grow up at a time when Schrodinger's cat is in the zeitgeist, you're definitely going to have nightmares that what's written on your cards could horrifyingly change every time you look at them. Worse, knowing about the Heisenberg Uncertainly Principle, you're deathly afraid that there might be cards, like electrons, which are always changing position in ways you'll never be able to know or predict.

      As a systems theorist, you view your own note taking system as a input/output machine. Then you see Claude Shannon's "useless machine" (based on an idea of Marvin Minsky) whose only function is to switch itself off. You become horrified with the idea that the knowledge machine you've painstakingly built and have documented the ways it acts as an independent thought partner may somehow become self-aware and shut itself off!?!

      https://www.youtube.com/watch?v=gNa9v8Z7Rac

      And worst of all, on top of all this, all your hard work, effort, and untold hours of sweat creating thousands of cards will be wiped away by a potential unknowable single bit of information on a lone, malicious card and your only recourse is suicide, the unfortunate victim of dataism.

      Of course, if you somehow manage to overcome the hurdle of suicidal thoughts, and your collection keeps growing without bound, then you're sure to die in a torrential whirlwind avalanche of information and cards, literally done in by information overload.

      But, not wishing to admit any of this, much less all of this, you imagine a simple trickster, a joker, something silly. You write it down on yet another card and you file it away into the box, linked only to the card in front of it, the end of a short line of cards with nothing following it, because what could follow it? Put it out of your mind and hope your fears disappear away with it, lost in your box like the jokerzettel you imagined. You do this with a self-assured confidence that this way of making sense of the world works well for you, and you settle back into the methodical work of reading and writing, intent on making your next thousands of cards.

    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      Manuscript number: RC-2022-01756

      Corresponding author(s): Wenya, Hou

      1. General Responses

      Dear Editors and Reviewers,

      We deeply appreciate all critical comments and constructive suggestion from all Reviewers, which have inspired us to conceive at least 8 new important experiments and mathematic analysis/modeling (shown in dark red). In addition, we will include more repeats with quantification for spot assays (with more HU doses) and biochemical experiments as well as language revision (shown in orange).

      Below we only list the general response to the Major Concerns raised by at least two Reviewers:

      • To perform mathematic analysis of the single-cell quantitative data (Fig 4, Fig 5 and Fig S4) (Analysis #1).

      50% Sic1 degradation time from Sic1peak

      WT SC

      7.62 min

      whi7 whi5 SC

      7.91 min

      WT HU

      36 min

      whi7 whi5 del HU

      7.49 min

      50% nuclear exit time of Whi5

      WT SC

      4.69 min

      rad53Δsml1Δ SC

      7.60 min

      WT HU

      22.33 min

      rad53Δsml1ΔHU

      13.41 min

      Table R1. 50% Sic1 degradation time calculated from Sic1peak and 50% nuclear exit time of Whi5 based on the experimental data shown in Fig 5 and Fig 4, respectively.

      (2) To reinterpret the HU-induced extension of G1/S transition with an updated model (Analysis #2).

      (3) predict that like WHI7/5 overexpression, CKS1 deletion (PMID: 7958905) or sic1 mutants with longer destruction timing (T2,5S-VLLPP or T2,5S-RXL reported in Fig. 6C, PMID: 32296067), can suppress the HU sensitivity of rad53 mutants according to our model. Moreover, their suppression effects should be epistatic to WHI7/5 overexpression. Alternatively, the dosage suppression of WHI7/5 might be reversed by CKS1 overexpression or sic1 mutants with shorter destruction timing (unfortunately no such mutant has been reported yet). We will perform this set of genetic experiment to test these predictions and thereby functionally reinforce the Whi7/5-Cks1-Sic1 axis (Experiment #1).

      (4) do DNA replication profiling to examine the number of origin firing or replication capacity (Experiment #2).

      (5) To address the suppression effect of phosphorylation in Fig 2E. We agree that the phenotypes of the A-mutants of Whi7 have a weak difference compared with WT, but become much stronger (5-fold difference between two dilutions) compared with the D-mutants. As shown lately in Fig 3, phosphorylation solely facilitates protein stabilization/total levels, which can be masked by ectopic overexpression from an extra plasmid. Moreover, phosphorylation does NOT enhance Whi7’s interaction with Cks1. We should tune down the contribution of phosphorylation and focus more on the stability/protein level. Furthermore, we will do competition assays using A-/D- mutants with GFP and RFP labels (Experiment #3), and add back whi7 13A or 13D in its endogenous locus in the whi7Δwhi5Δ double mutant to test the effect on Sic1 turnover (Experiment #4).

      (6) To add more repeats with quantification for spot assays (with more HU doses) and biochemical experiments (shown in orange).

      Besides reinforcing the current model, these experiments, analysis and re-interpretation may help to clarify two concepts which remain elusive in current version:

      • S-CDK activation can switch from an abrupt/all-or-none pattern under normal condition to a gradually flattened one under replication stress.
      • Consequently, the Whi7/5-Cks1-S-CDKs axis may determine replication capacity and/or number of origin firing. Thus, we did not include a preliminary revision this time due to significant changes. We plan to request at least 6 months for an extensive full revision (e.g., from a short letter to a regular article) to improve this study to a higher level with more general significance. Therefore, we request a revision opportunity from The EMBO Journal.

      2. Point-to-point responses

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      SUMMARY

      This work begins with a heterologous screen, introducing human genes in double mec1,sml1 yeast deletants, which are alive, but sensitive to hydroxyurea. The readout was mec1,sml1 proliferation in the presence of hydroxyurea. They found that mec1,sml1 yeast mutants carrying the human RB1 gene (a G1/S transcriptional repressor) proliferated on hydroxyurea. Then, they test if known yeast G1/S transcriptional repressors (Whi5 and Whi7) could have similar effects if provided at higher than normal levels (they did). With this initial result, followed up by a variety of experiments, the authors then go on to propose that replication stress, which activates Mec1 and Rad53, triggers the phosphorylation of Whi7 (by Mec1) and Whi5 (by both Rad53 and Mec1) blocking their eviction from the nucleus, allowing them instead to bind and inhibit Cks1, a Cdk processivity factor, needed for the complete phosphorylation and degradation of a Cdk inhibitor, Sic1. This is different from published work a decade earlier in mammalian cells (ref. 37; Bertoli et al.), which showed that upon replication stress, Chk1 phosphorylates G1/S transcriptional repressors to maintain G1/S transcription, which could help genome stability. Here, the authors propose that replication stress could block the G1/S transition. While the model and some of the experiments are interesting, the rationale for some experiments was shaky, and the data do not fully support the conclusions.

      MAJOR POINTS

        • Any cell that undergoes DNA replication must have already destroyed Sic1. It has been known for 25+ years that targeting Sic1 is the only necessary function of G1/Cdk to enable DNA replication (PMID: 8755551). Sic1 does not reappear until the M/G1 transition. Hence, in the authors' model, where cells are already in the S phase, how can multisite phosphorylation and degradation of Sic1 be the critical and final output of the pathway they propose when there shouldn't be any Sic1 around, to begin with? Why would a cell that has already completed Start and the G1/S transition, is in the S phase and experiencing replication stress, care about going through the G1/S? A: Yes, S-CDK activity is regarded as an abrupt or so-called “all-or-none transition” due to a relative short half-life of Sic1 controlled by a robust double-negative feedback loop (PMID: 24130459; 23230424). Sic1 degradation requires multi-phosphorylation events including prime phosphorylation by G1-CDKs, two opposing multi-phosphorylation by S-CDK complex (Clb5–Cdk1–Cks1), one to trigger phosphodegrons and the other to terminate the degron route (PMID: 32296067). The timing and speed (or “sharpness”) of Sic1 degradation is determined by G1-CDKs and S-CDKs, respectively (PMID: 24130459 and PMID: 32296067). Sic1 degradation is not an instantaneous “all-or-none” event even under the optimal growth conditions. The Sic1 destruction timing calculated from Start (defined as 50% nuclear exit of Whi5) is about 14.2 min, whereas the time between Start and Sic1peak is about 5 min from independent studies (Fig 4G, PMID: 24130459; Fig. 6C, PMID: 32296067; Fig. 7B, 32976810). Similarly, the 50% Sic1 degradation time calculated from Sic1peak (50% of Sic1peak) is about 8 min for WT and whi7, in agreement with the results in Figure 2E, PMID: 24130459. However, in the presence of HU, the 50% of Sic1peak time remains constant (7.49 min) in whi7Δwhi5Δ cells but becomes greater than 36 min in WT. Meanwhile, the 50% nuclear exit time of Whi5 (Start) is about 22 min in WT compared to 13 min in rad53Δsml1*Δ upon HU treatment.

      50% Sic1 degradation time from Sic1peak

      WT SC

      7.62 min

      whi7 whi5 SC

      7.91 min

      WT HU

      36 min

      whi7 whi5 del HU

      7.49 min

      50% nuclear exit time of Whi5

      WT SC

      4.69 min

      rad53Δsml1Δ SC

      7.60 min

      WT HU

      22.33 min

      rad53Δsml1ΔHU

      13.41 min

      Table R1. 50% Sic1 degradation time calculated from Sic1peak and 50% nuclear exit time of Whi5 based on the experimental data shown in Fig 5 and Fig 4, respectively.

      Therefore, G1/S transition is a “transition zone” (from Start to 50% of Sic1peak) rather than a single borderline. The key finding of this study is that in the presence of HU, Sic1 degradation speed/sharpness is significantly reduced (Figure 5), mechanistically due to the inhibition of S-CDK-Cks1 by Whi7/5. This eventually reflects a flattened S-CDK activity curve, no longer an “all-or-none activation” any more upon replication stress. S-CDKs phosphorylate the two essential targets (Sld2 and Sld3) to enable DNA replication. Therefore, the Sic1 levels determine the S-CDK activities, which in turn determine the DNA replication capacity (the maximal amount of DNA a cell can synthesize per unit time). In sum, under optimal conditions, the S-CDK activity appears an abrupt/sharp transition and cells replicate DNA in its maximum capacity (i.e., minimal S phase length). When cells encounter replication stress (HU), S-CDK is activated very slowly (very low Sic1 destruction speed) and replicate DNA with a low capacity (slow fork speed and/or few origin firing) to meet the limited resource. Recently, the de Bruin group demonstrates that replication capacity can be tuned by E2F-dependent transcription (includes S-Cyclin genes) in mammalian cells (PMID: 32665547).

      Inspired by these questions, we plan to

      (1) perform mathematic analysis of the single-cell quantitative data (Fig. 5 and S4) (Analysis #1).

      (2) reinterpret the HU-induced extension of G1/S transition with an updated model (Analysis #2).

      (3) predict that like WHI7/5 overexpression, CKS1 deletion (PMID: 7958905) or sic1 mutants with longer destruction timing (T2,5S-VLLPP or T2,5S-RXL reported in Fig. 6C, PMID: 32296067), can suppress the HU sensitivity of rad53 mutants according to our model. Moreover, their suppression effects should be epistatic to WHI7/5 overexpression. Alternatively, the dosage suppression of WHI7/5 might be reversed by CKS1 overexpression or sic1 mutants with shorter destruction timing (unfortunately no such mutant has been reported yet). We will perform this set of genetic experiment to test these predictions and thereby functionally reinforce the Whi7/5-Cks1-Sic1 axis (Experiment #1).

      (4) do DNA replication profiling to examine the number of origin firing or replication capacity (Experiment #2).

      • The results in Figure 2C are confusing and difficult to interpret. For example, comparing lane 8 (WT without hydroxyurea) to lane 7 (WT with hydroxyurea), it appears that there is more phosphorylated Whi7 in lane 7 (hydroxyurea treatment) than in lane 8 (no treatment). But, the ratio of phosphorylated/unphosphorylated Whi7 is not that different (there is very little unphosphorylated Whi7 in lane 8). Same problem when comparing lanes 3 and 4. I understand that they later show that Whi7 is stabilized by hydroxyurea, but from the data in this figure, what exactly can they conclude here?*

      A: Yes, phosphorylation is a bit confusing according to the current statement. Without HU, Whi7 is phosphorylated by G1-CDKs with a much less total protein level as well. With HU, whi7 is phosphorylated by Mec1 and Rad53, because Whi7-P largely disappeared in rad53 mutant (lane 1) and 13A (with all putative Mec1-Rad53 sites mutated, lane 5). Lanes 3 and 4 are biological repeats of Lanes 7-8 with less loading. We will clarify our statement.

      • Their data in Figure 2E show that phosphorylation of Whi7 is not required for suppressing the lethality of rad53,sml1 cells treated with hydroxyurea. Cells carrying Whi7-41A (lacking all possible phosphorylations) suppressed nearly as well as wild-type Whi7 did. The purported differences in the suppression are minuscule at best and not evident at the dilutions tested. It is not clear at all how they can conclude that phosphorylation of Whi7 has anything to do with the ability of Whi7 overexpression to suppress the lethality of rad53,sml1 cells.*

      A: Yes, we agree that the phenotypes of the A-mutants of Whi7 have a weak difference compared with WT, but become much stronger (5-fold difference between two dilutions) compared with the D-mutants. As shown lately in Fig 3, phosphorylation solely facilitates protein stabilization/total levels, which can be masked by ectopic overexpression from an extra plasmid. Moreover, phosphorylation does NOT enhance Whi7’s interaction with Cks1.

      Anyway, we should tune down the contribution of phosphorylation and focus more on the stability/protein level. Furthermore, we will do competition assays using A-/D- mutants with GFP and RFP labels __(Experiment #3) __and add back whi7 13A or 13D in its endogenous locus in the whi7

      • For all the arguments they make about this new role of Whi5 and Whi7 at Start, they do not examine size homeostasis or the kinetics of cell cycle progression in any of their experiments and their mutants, with or without hydroxyurea treatment.*

      A: Good suggestion. We will examine size homeostasis, budding index or the cell cycle progression in the related experiments (Experiment #5). In Fig. S5, we only showed the cell cycle progression profiles in wild-type cells carrying an extra copy of Whi7 WIQ or Whi7 WIQ ΔC. WIQ mutant (without Swi6 binding activity) significantly slowed the cell cycle progression under normal conditions.

      • The Sic1 stability experiments they show in Figure 5 are nice. They would need to be extended to their various mutants, including their Whi7 phosphomutants, to make a case for phosphorylation by Rad53 and Mec1 in this process.*

      A: Thanks, very good suggestion, we will add back whi7 13A or 13D in its endogenous locus in the whi7Δwhi5Δ double mutant (Experiment #4), to avoid the effects of overexpression.

      MINOR POINTS

        • The language is awkward. Editing for style will be necessary.* A: We will request language editing.
      1. They use different hydroxyurea doses in the experiments they show, making it difficult to conclude much when comparing different figures. Why aren't they consistent from experiment to experiment?*

      A: Sorry for the confusing. We used at least three HU concentration gradients in each experiment, but only showed one of them to save the space for a short article. Notably, S. cerevisiae has a much broader range of HU doses (up to 300 mM) than other species (less than 10 mM). We’ll add other Figures during revision.

      **Referees cross-commenting**

      Overall, all reviews are well-aligned. The points raised by the other reviewers are valid, and the reviews are thorough and detailed. I don't know whether the authors will be able to respond since the list is quite long. Even if they do, the manuscript will look very different. I do not have anything else to add.

      Reviewer #1 (Significance (Required)):

      The manuscript presents some interesting data, most notably the role of Whi7 and Whi5 in the stability of Sic1 in vivo and the various in vitro experiments the authors present. The advance is conceptual and mechanistic, offering a different and unanticipated model for the role of these proteins at Start, under replication stress. Unfortunately, the significance of the manuscript is limited. A convincing case for their model and its importance has not been made. For example, their data in Figure 2E, measuring the ability of phosphomutants to suppress the lethality of rad53,sml1 cells upon replication stress, is underwhelming and undermines the importance of the study, particularly to a wider audience.

      A: Thanks for the suggestion, we will improve the model as discussed above.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary:

      Jin et al demonstrate a novel type of regulation of the G1/S transition in response to hydroxyurea stress. They approach this by first screening a library of human proteins (cDNA on yeast plasmids) for repressors of the mec1 or rad53 HU sensitivity. HU inhibits ribonucleotide reductase and thus lowers dNTP pools needed for S-phase. This slows replication and leads to stalled replication forks, triggering a "replication stress" response, which is executed by the kinases Mec1 and Rad53. Deletions of mec1 or rad53 are viable in unstressed conditions (with additional sml1 deletion), but are lethal on even low doses of HU. One main hit that rescued this lethality was the human G1/S inhibitor RB. They then went on to confirm that also the yeast analogs Whi5 and Whi7 can rescue mec1 or rad53 lethality when overexpressed. To track down the mechanism, the authors do a variety of genetic and biochemical assays. The resulting model is that Mec1 and Rad53 phosphorylate and stabilize Whi7, which binds to and inhibits the S-phase-CDK complex via the processivity factor Cks1. So on top of acting as a transcriptional repressor, Whi7 (and probably also Whi5) is also a direct interactor and inhibitor of CDK. The binding of Whi7 to Cks1-Clb5/6-CDK prevents the hyperphosphorylation and degradation of the inhibitor Sic1, and thus slows the G1/S transition in response to HU.

      Major comments:

      - Are the key conclusions convincing?

      ->Overall I think the sum of the evidence supports the suggested model, individual claims though are on somewhat shaky grounds based often on single replicates, see below.

      My main conceptual issue may be somewhat just a "semantic" problem. In my understanding "replication stress" refers to stalled replications forks and/or large stretches of single-strand DNA which then triggers a checkpoint response. So how would slowing the G1/S transition help to deal with "replication stress", if replication is not yet happening in these cells? I am assuming Mec1 senses dNTP depletion also in the absence of replication and that is how Mec1 and Rad53 become active in G1. But then maybe the model and the arguments can be phrased differently? What exactly is slowing down Sic1 degradation doing for the cell? Replenishing dNTP pools before the first origins fire? Or is maybe Sic1 not the most important target of this regulation? Maybe also during S-phase, partially inhibiting CDK is beneficial, maybe to stretch out origin firing... or?

      A: Thank you, very good suggestion. This also helps to address the Major Point 1 raised by Reviewer #1. This also reminds us about the work from Pasero’s group demonstrating that Mec1 is activated at the onset of normal S phase by low dNTPs (PMID: 32169162). We will revise the text, and do DNA replication profiling __(Experiment #2) __to examine the number of origin firing or replication speed. Also see response to Point 1 of Reviewer #1.

      - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

      ->Most of the work is done on Whi7 and then some Whi5 in the end, I would tone down on the Whi5 claims a bit.

      A: Very good suggestion. We have to include Whi5 in the story because it plays a redundant role with Whi7.

      - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

      -> Since the authors are clearly able to do quantitative live cell imaging, I do not understand why they do not quantify Whi7 concentrations and localization in response to HU instead of using Western blots of synchronized cells. This would make the whole thing much more credible, especially given the current lack of replicates (see below). This would also allow correlating the timing and amount of the Whi7 response with the stabilizing of Sic1 in single cells.

      A: Yes, we tried but did not see Whi7-GFP clearly because of its very low protein abundance, which is also not shown in literature as far as we know. Only overexpressed Whi7 fluorescence detection(PMID: 33443080).

      ->The causality of phosphorylation being required for stabilization seems plausible from the genetics, but is far from clear in the western blots. Here, concentration increase seems to precede phosphorylation. Could this due to induced Whi7 transcription?

      A: Good suggestion. We will detect Whi7 mRNA levels through qPCR (Experiment #6).

      ->Many if not most claims are based on single replicates. See below.

      - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

      -I am not suggesting any different types of experiments or new methods, so it should be doable within a few weeks.

      - Are the data and the methods presented in such a way that they can be reproduced?

      -I would suggest the authors spell out all of their experimental procedures instead of referring to "as described previously". I think everyone knows the pains of going on a wild goose chase of following references to the original method description.

      A: Good suggestion. I will described all experimental procedures to replace "as described previously".

      - Are the experiments adequately replicated and statistical analysis adequate?

      -The key weakness of this entire paper is imho that many claims are based on single experiments, that are neither replicated nor quantified. For example, all the co-IPs (such as 1E or 3F) should be replicated and the ratio of bait to target quantified and averaged.

      A: Good suggestion. We will show the biological repeats and quantification.

      -If a claim is made regarding increased phosphorylation in vivo, then again this should be replicated and the ratio of phosphorylated to unphosphorylated bands quantified. In many Whi7 gels it looks like it is mainly the total amount of the protein that is changing rather than the phosphorylation state. But again, by eye and from a single replicate, this is hard to tell.

      A: Good suggestion. We will add more repeats.

      -A similar thing holds true for the spot assays. Spot assays are great to show lethality and rescue as in the first figure. But making semi-quantitative claims of different degrees of "partial rescue" from a single spot assay is a bit speculative. This seems especially true since the authors are using different and seemingly random HU concentrations for every spot assay, which suggests that the effect is not very robust and can only be seen in very specific concentration ranges. If e.g. the degree of rescue between WT, A and D mutants or truncations matters for the model/the storyline, then more quantitative growth or competition assays should be added.

      A: Good suggestion. sorry for the confusing. We used at least three HU concentration gradients in each experiment, but only showed one of them to save the space for a short article. Notably, S. cerevisiae has a much broader range of HU doses (up to 300 mM) than other species (less than 10 mM). We’ll add other Figures during revision, and do competition assays using A-/D- mutants with GFP and RFP labels

      Minor comments:

      - Specific experimental issues that are easily addressable.

      ->At least some of the alpha-factor release experiments should contain infos on budding index and/or DNA content to understand see the delay in timing by HU addition.

      A: Good suggestion. We will examine size homeostasis, budding index or the cell cycle progression in the related experiments (Experiment #5).

      - Are prior studies referenced appropriately?

      ->Seems fine from the G1/S side, but I don't know the Mec1/Rad53 literature well enough to judge.

      - Are the text and figures clear and accurate?

      ->The authors could do another round of proofing figures and legends. For example, Fig 5C contains scale bars that are not defined, blot 3E has an asterix labeling that is not defined, the model in 5E has misspelled "degradation"...

      A: We will proofread and revise the full text again.

      - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      -> The authors use a lot of different mutants (especially for Whi7). Even for someone who knows the proteins fairly well, it is hard to remember throughout the text which abbreviation is relating to which mutations and which function that is addressing. Maybe occasionally remind the reader of what the mutant is or use terms like Whi7non-binding rather than WIQ.

      A: Thank you for your suggestion. We will add (TF non-binding) after WIQ.

      ->The text could also use another round of proof-reading. The overall flow of the storyline is easily comprehensible, but sometimes there is a sudden switch of topics or new proteins come out of nowhere. Some expressions are used in a way that is not common English.

      A: We will request language editing.

      Reviewer #2 (Significance (Required)):

      - Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

      ->This study is a major conceptual contribution to understanding G1/S regulation in perturbed conditions (assuming the results can be replicated and quantified as detailed above). That Whi7 (and maybe Whi5) directly inhibit Clb5/Clb6-CDK through Cks1 binding is an important addition/modification to the current model and may well be important beyond genotoxic stress.

      A: Thanks and we’ll reinforce it with more repeats and quantification.

      - Place the work in the context of the existing literature (provide references, where appropriate).

      ->The authors do this reasonably well.

      - State what audience might be interested in and influenced by the reported findings.

      -> Anyone in the yeast cell cycle/replication field should find this interesting. It should also have important implications for the mammalian cell cycle/replication/DNA damage field.

      - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

      ->I am well familiar with G1/S control and all the methods used in the study. I am not an expert on replication stress/DNA damage/ checkpoint signaling.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Summary

      In their manuscript "Transcription-independent hold of the G1/S transition is exploited to cope with DNA replication stress", Jin et al. intend to show that Retinoblastoma-like G1/S transcriptional repressors can also work as S-CDK-Cks1 inhibitors in response to DNA replication stress, hence prolongating the G1/S transition to enable cells to deal with replication stress. In particular, they aim to identify the mechanism by which Whi7/Srl3 (Suppressor of Rad53 Lethality) rescues the lethality of rad53 yeast mutants. Even though their very first experiment is performed using human RB1, the remainder or the work is performed in the yeast model organism. Experimental methods used include mostly immunoprecipitation experiments (Western blots), spot assays, and some single cell microscopy (not specified if widefield or confocal).

      Major comments

      1) the authors refer to a cross-species screen where they aim to detect human proteins that rescue, upon overexpression, the yeast mec1Dsml1D and rad53Dsml1D lethality (of note, not mec1D/rad53D: why?). They identify hRB1 this way. But the entire screen data is missing, either is the analysis pipeline and "hit selection thresholds" (if applicable). Then no more experiments are performed on human cells or using human proteins. In my opinion this cross-specie approach is not necessary, or not developed enough.

      A: Yes, we have only performed a pilot screen based on the growing on 4 mM HU. We consider removing it. The reason to use mec1Dsml1D for genetic screen is that mec1D/rad53D cells are dead even without HU, whereas dissection assays do not fit for large-scale screening.

      2) Moreover, the interpretation of the data provided as a whole is strongly complicated by the variability in the HU doses used to trigger the Mec1/Rad53 response. While most immunoprecipitation experiments are performed with 200mM, spot assays are performed at various HU concentrations ranging from 3 to 21mM (and exploring the entire range). Sometimes HU concentrations differ on the same Figure panels. Downstream effects of such diverse HU concentrations might also be very diverse and due to this it is difficult to get an understanding of how the different experiments fit together.

      A: Sorry for the confusing. We used at least three HU concentration gradients in each experiment, but only showed one of them to save the space for a short article. Notably, S. cerevisiae has a much broader range of HU doses (up to 300 mM) than other species (less than 10 mM). Spot assays (HU are persistent) are mostly done in the mec1Dsml1D and rad53Dsml1D background (sensitive to 4 mM HU), whereas the IP experiments (only 2-3 h treatment and then removal) are mainly performed in WT or at least in comparison with WT background (resistant up to 250 mM HU). We’ll add other Figures during revision.

      3) Likewise, some experiments are performed only on rad53D backgrounds, or only on mec1D backgrounds (e.g. Fig1B and Fig1F, respectively), while results are claimed valid for the two gene deletion backgrounds.

      A: Thank you. We will add some “not shown data” and remove the invalid claims without data.

      4) Finally, the experiments performed in this study and/or their quantitative analysis are insufficient to support several of the claims, and results are often "over-interpreted". Below I have listed some of such insufficient experiments/analyses, in regard of the interpretation that the authors make of each piece of data.

      - Fig1B could indeed show that Whi7 could rescue rad53D lethality but it is hard to judge from just one tetrad. Many tetrads should be shown to exclude "random sampling" effects.

      A: Thank you. We will add more repeats and remove over-statements. Fig 1B was carried out for at least 12 tetrads but the original picture has been unintentionally lost. We can repeat it if necessary, but the result was validated by the plasmid shuffling experiment (Fig 1C).

      - Fig1F indeed shows that the rescue effect of Whi7 overexpression on mec1Dsml1D lethality in HU does not require its G1/S transcription factor-binding motif (GTB); however, it does not prove that it is independent on any putative effects that Whi7 could have on transcription (it could affect other transcription factors, or even the same ones via other domains).

      A: Good suggestion. As far as we know, there are no reports proving that Whi7 binds to other transcription factors. To rule out this possibility, we will detect whether overexpression of WHI7 affects the transcription of representative G1/S genes (Experiment #7).

      - FigS2A does not really support the authors' claim that Whi7 is hyperphosphorylated upon HU-treatment: the first lane before HU treatment already show the same hyperphosphorylated bands than the second lane (see "darker exposure"); however, the signal intensity is clearly lower so the overall levels of Whi7 are clearly increased by HU, rather than the relative fractions of phosphorylated species.

      A: Yes, we will modify the statement as suggested.

      - Fig2B shows that HU-dependent increase in Whi7 levels is partially abrogated in rad53Dsml1D and mec1Dsml1D mutant backgrounds, which demonstrates that Whi7 upregulation requires either Rad53 or Sml1, and Mec1 or Sml1, but not Rad53/Mec1 as claimed by the authors.

      A: Thank you, we will revise the statement. The only known function of Sml1 is a small unstructured protein inhibitor of Rnr1.

      - Likewise, Fig2B does not show any significant Whi7 phosphorylation following HU-treatment in the whi7-13AP mutant with all CDK consensus sites mutated to alanine. There is indeed a slightly slower migrating band appearing as acknowledge by the authors, which also appears in the mec1Dsml1D and rad53Dsml1D backgrounds. Again here, higher Whi7 levels in the WT background make the comparison with mec1Dsml1D and rad53Dsml1D backgrounds almost impossible. Quantification of the blots, including normalization of the signals of each phosphorylated band to the total signal, could help. But overall this figure does not demonstrate any Mec1/Rad53-dependent Whi7 phosphorylation following HU treatment. The phostag gel Fig2C might show the same result, as the differences in phosTag signals between different conditions might just simply reflect the differences in total amount of Whi7 between those same conditions. However, I acknowledge that Figs 2D and S2C shows Rad53- and Mec1-triggered Whi7 phosphorylation in vitro, but the conditions of this experiments likely differ a lot from in vivo context (kinase levels, competing substrates, presence of co-factors...).

      A: Thank you, we will quantify the blotting as suggested.

      - Along the same lines, Fig3E seems to show that truncation of Whi7 C terminus slightly reduces its efficiency in pulling down Cks1 (indicating reduced interaction). However, the total amount of WT Whi7 in the pull down seems to exceed the total amount of Whi7-DeltaC protein, which could in part explain the difference in Cks1 signal. Here again, quantification of the WB signals and adequate normalization would maybe make this figure more convincing.

      A: Good suggestion. We will show the biological repeats and quantification.

      - Fig4A-B (Whi5 GFP data): the cell representing the absence of HU shows Whi5 nuclear export and therefore likely passes through G1/S; the HU-treated cell shown as example does not export Whi5 from the nucleus, certainly because it does not pass G1/S. IMHO this demonstrates that the G1/S transition is delayed in HU-treated cells (as shown previously), irrespective of any role of Whi5 or Whi7 in this delay.

      - Likewise, Fig4C shows the absence of HU-induced delay in Whi5 nuclear export in rad53Dsml1D cells; however, while the authors claim this indicates "Rad53-dependent nuclear detention of Whi5", it is equally plausible that it indicates that rad53Dsml1D cells do not delay the G1/S transition under HU treatment.

      A: good comments. We should claim both possibilities at this stage. Previous studies mainly show delays in the Start stage (e.g., down-regulate SBF transcription). CLN1/2 deletion is known to delay DNA replication in a Sic1-dependent manner albeit with unknown mechanism in the S-CDK activation stage.

      - The same ambiguity holds for Fig5A,B (Sic1-GFP quantification in whi5Dwhi7D double deletion strain following release from alpha factor block): indeed Sic1 is degraded fast after release from alpha factor block both in presence of HU, while in WT cells Sic1 is not immediately degraded in presence of HU. While authors claim that "Whi7 and Whi5 significantly slow down the Sic1 degradation", this result could also likely reflect that whi5Dwhi7D cells pass G1/S even in this context, and therefore that whi5 or whi7 or both have a role in maintaining cells in G1, not showing any direct implication of Whi5/Whi7 in Sic1 degradation.

      A: good comments. It only provides some indirect hints. For instance, whi5Dwhi7D cells pass G1/S in a same timing as WT in the absence of HU (Fig. S4), indicating that the role of Whi5/7 in the G1/S delay is related to additional checkpoint function, not normal G1 maintaining function. Moreover, it should be combined with other results, for example, dosage suppression effects in the presence of HU and inhibitory effects in the absence of HU. Direct evidence of Whi5/Whi7 in Sic1 degradation and Cks1 inhibition comes only from the biochemical experiments shown in Fig 3E-3H.

      - FigS5: the authors show here that overexpression of Whi7-WIQ (that does not bind SBF) slows down the G1/S transition following release from alpha factor blockade, but this data does not demonstrate anything related to the role of Whi7 in the DNA replication stress response. Indeed, since Whi7 sequesters Cln3 in the ER (independent of any putative role on transcription regulation), its overexpression could simply reflect an increased sequestering of Cln3 pool. What does this result become in a cln3D background?

      A: Very good suggestion. We will check whether cln3Δ affects the suppression effect of Whi7 (Experiment #8).

      Due to the fundamental concerns raised above in the interpretation of the data, it is difficult to predict the outcome of more controlled experiments that would aim to prove the same statements. This makes the estimation of the time and resources required to complete the study almost impossible.

      Minor comments

      Owing to the major comments above, an important re-structuration of the study is required, and minor comments I may have on this version are likely to be irrelevant to the revised manuscript.

      Reviewer #3 (Significance (Required)):

      The study aims to establish a molecular link between the progression through the G1/S transition and the DNA damage and DNA replication stress responses. Establishing molecular links between different phases of the cell cycle is an important question in basic research, and might be of interest for a broad range of cell biologists, even though the study is conducted in a model organism (budding yeast). The link proposed involves G1/S inhibitors Whi5 and Whi7, that would bind and inhibit the Cks1 subunit of S-CDK complexes, downstream of Rad53 and Mec1 signaling. The authors confirm some known results (e.g., Whi7 overexpression bypasses rad53 lethality in presence of HU) and gather new pieces of data using well-established methods (immunoprecipitation, spot assays, fluorescence microscopy). However, many experiments reported in this study are not sufficient to support the authors' claims, and therefore the novel mechanistic insight that this study ambitions to provide is not established.

      My scientific background being more in bio-imaging than in biochemistry, it is possible that I missed some hands-on experience to correctly interpret artefacts on western blots, however I do not feel like I missed sufficient expertise to evaluate any section of the manuscript.