Reviewer #4 (Public Review):

This is potentially a landmark study with far-reaching consequences for archaeology, palaeoanthropology, and more widely. The antiquity of intentional human mark marking is a hot topic but this study – understood as initial – has as yet incomplete sources of evidence and methods; and it will be interesting to follow how the study develops in subsequent studies.

Strengths and points to build on:

* Heuristic potential: As knowledge advances it poses a risk to accepted knowledge – and we should accept that one such risk is moving on from long-held disciplinary tenets. In this case, there has been a growing quantum of evidence – all hotly debated – for the deep antiquity of mark-making and even symbolism by species other than ourselves. Most researchers now accept Neanderthal symbolic capacity actualised in burials, intentional mark-making and the like. The evidence here presented is not unequivocal but is very suggestive and an ideal test case for applying multi-disciplinary techniques of analysis and interpretation beyond the expertise of the listed authors *see comments in 'weaknesses'). This work by itself may be equivocal but when taken together with other such work, points to a 'human' sensu lato past that is as complex as it is long. This work then helps all researchers to at least be alive to the possibility of things like anthropic marks and residues in a context not normally thought to have it.

* Decentering speciesism: As per the above comment, I appreciate empirical studies that erode speciesism – in particular studies that open up our minds to the possibility that multiple members of the Genus Homo were capable of intentional mark-making and even 'symbolic' behaviour, though this latter term is not well understood or uniformly used. This is probably because of continuous unconscious bias on our part as currently the only exemplar of our genus living - in contrast to most of the past in which different species and genera co-existed - if not on the same landscape and/or at exactly the same time, then with enough overlap that people would have realised 'others' were about either by sight and/or by encountering their physical remains and artefacts.

* Problematising 'firsts' and deep time: A strength – but which needs to be developed in this manuscript – is our understanding of time and change. We have a plethora of dating techniques but relatively few substantive monographs, articles, and think tanks on time – and especially on how change comes about and what causes it. This leads us to privilege 'firsts' and the 'oldest' finds in 'deep' time above those that are more recent and in 'shallow' time. I would suggest in addition to the claims for the oldest of the reported marks, the authors develop nascent remarks on the possibility the suite of marks may have been made over time. This will help counter criticism that these marks – if established to be anthropic – were not just a singularity, but part of patterned behaviour, which would move it towards the realm of 'symbolic' cognitive behaviour. And indeed, it would be good to hear more about why in this place, these marks were made to establish a replicable model for identifying early anthropic marks.

Ultimately, this manuscript presents evidence that those who are pro the deep antiquity of intentional mark-making by Homo (and possibly even other genera) will find enough evidence to support; while those sceptical of such claims will find enough methodological flaws and evidential limits to refute those claims. The next decade of work will likely be definitive and this article makes a key contribution to the debate.

Weaknesses and points to attend to:

* Definitions: The term 'rock engraving' is used rather uncritically and also the term 'etching' – and it would be useful to have a short definition of how the authors understand the term. Rock art scholars regularly debate these terms and whether they are or are not 'rock art' with its overwhelmingly visual bias; which this discovery may usefully help overthrow and advance.

* Dating: There is no evidence provided for dating the marks found in the cave system. They could, for example, have been made more recently than the dates claimed – and by another species (if we accept their anthropogenic authorship). This is a perennial problem of much rock art research – especially when it comes to understanding the wider archaeological/palaeoanthropological context. More crucially, accurate dating allows a more reliable understanding of authorship and who/what was responsible for a particular artefact or feature. This has not been demonstrated in this case, though we do have fossil evidence of Homo naledi in the cave system. The article title is this incorrect / and unsupported claim as the marks, if they are anthropic, have not been dated and are of unknown age. The authors allow that there may have been multiple episodes, but not that the marks can belong to a time other than they posit – either earlier, later, or distributed over a long period as the authors allow for in their concluding remarks.

* Authorship: The study does not utilise either a geoscientist as one of the authorial team, or a rock art specialist. These are key oversights as the former would help better contextualise the dating of the marks reported on, as well as explore alternative non-anthropogenic agents that may have created the marks reported on. For example, the marks and 'pitting' etc may be the result of water bringing abrasive agents during times of flooding, hitting prominent rock features in the cave system. Some explanation is given from lines 114-124, but are uncited. The overlying 'sediment' may be similar to the mondmilch found in cave systems and which is of natural origin. It may be that these non-anthropogenic causes are easy to discount; but the arguments do need to be made. Or, that the polishing was made by Homo naledi brushing against the surfaces as they moved in the cave system, independent of any mark-making. A Table showing the pros and cons of intentional anthropic versus natural authorship would be very effective - as well as showing some of the natural linear marks in the cave system to avoid any confirmation or similar bias. FTIR analysis of the panel A-C would be more than useful to determine whether an additional layer of material has been added. This is mentioned for future work, but this seems a rather post-hoc research programme.

* Use-wear analysis: If the marks are anthropic in origin; they are likely to have been made by a stone tool, which would leave characteristic marks, directionality and sequencing, distinct from natural causes. It is vital this work – such as was done on the Blombos engraved ochre – is done here – for example, linking to the chert and other tools described on lines 152-158. Note Figure 19, of such a tool, is very hard to make out. The Blombos – and Klasies River Mouth engraved ochres (curiously not referenced) – have very similar geometric markings and there is a real opportunity to compare these in securely dated contexts of 70-120 kya –which could support the argument made here for Homo naledi's cognitive capacity. On figure 16 it would be good to know on what basis some marks were selected as anthropic – and why others were not; this would help demonstrate the methodology and ability to distinguish between the two kinds of marks.

* Viewshed: The rock art specialist would have added essential expertise on how to study anthropic marks. For example, the images of the marks shown are all of individual or small collections of motifs rather than showing each panel as well as all panels together, to help understand the iconographic context as an ensemble – a 'feature' rather than isolated 'artefacts' or 'motifs'. Line 60 mentions being able to see these as a 'triptych' but the reader is not able to have this view in this manuscript. From the cave map, it is not clear whether all three 'panels' (an unfortunate art historical term that suggests a framed entity - better to use a term like 'cluster') can be viewed simultaneously or in sequence. The view shed in relation to the area where the bodies were recovered is vaguely stated as 'only a few metres away' and is worth developing. I understand 3D scans have been made so it would be useful to have a version showing the marks in relation to where the bodies were recovered and as a 3-cluster ensemble.

* Image enhancements: Also, in addition to polarised images, have colour enhancement tools like DStretch been tried to see if, for example, attempts at colouring with different coloured sands were made? Similarly, a 3D scan of the motif and panel – (Metashape is mentioned but not shown) – might assist in understanding how the marks and the rock they are on might relate to each other- as research in European upper Palaeolithic contexts has shown. Here, experimenting with different kinds of lighting - or in the absence of lighting, of tactility and how these marks and their rock support may have been experienced by those who may have made and interacted with them? As a note, it would be useful to have a scale in each image of the 'engravings' and it is a pity the one in situ photograph with the scale is not a standard rock art colour-corrected scale as is commonly used in rock art research.

Author Response:

We would like to thank the eLife reviewers for the considerable time and effort they have invested to review these manuscripts. We have also benefited from a previous round of review of the manuscript describing the proposed burial features, which underwent two rounds of revisions in a high-impact journal over a period of approximately 8 months during 2022 and early 2023. Both sets of reviews have reflected mixed responses to the evidence we have presented, with one reviewer recommending acceptance with minor editorial revisions, two recommending acceptance with minor revisions and the fourth recommending rejection based upon similar arguments to those reflected by some of the reviewers in this current round of reviews in eLife. Ultimately the managing editor of this first journal took the decision that the review process could not be completed in a timely manner and rejected the manuscript although the submission here reflected our consideration of these reviewers suggestions.

We have chosen in this initial response to the eLife reviews to include some references to the previous anonymous reviews in order to illustrate differences of opinion and differences in revision suggestions within the review process. Our goal is to offer maximal insight into our decision-making process and to acknowledge the considerable time and effort put into the assessment of these manuscripts by reviewers (for eLife and in the case of the earlier review process). We hope that this approach will assist the readers, and reviewers, of our manuscripts in understanding why we are proceeding with certain decisions during the revision process.

This is a new process for us and the reviewers, and one way in which it significantly differs from more traditional review is that both the reviews and our reply will be public well in advance of our revisions to the manuscript. Indeed, considering the scope of the reviews, some of those revisions may take considerable time, although many can be accomplished fairly easily. Thus, we are not in a position to say that we have solved every issue raised by the reviewers. Instead, we will examine what appear to be the key critical issues raised regarding the data and the analyses and how we propose to address these as we revise the papers. We will also address several philosophical and ethical issues raised by the reviews and our proposal for dealing with these. More specific editorial and citational recommendations will be dealt with on a case-by-case basis, and we do not address these point-by-point in this reply. Please note, this response to the reviewers is not the revision of the manuscript and is only the initial opinion of the corresponding authors with some guidance from the larger group of authors of all three papers. Our final submitted revision will reflect the input of all authors included on those submissions.

We took the decision to submit three separate papers consciously. The two different categories of evidence, burials and engravings, involve different kinds of analysis and different (although overlapping) teams of researchers, and we recognized that each deserved their own presentation and assessment. Meanwhile, together they inform the context of H. naledi in a way that requires some synthetic discussion, in which both kinds of evidence are relevant, leading to a third paper. But the mutual relevance of these different kinds of evidence and their review by a common set of reviewers naturally raises cross-cutting issues, and the reviewers have cross-referenced the three articles. This has sometimes led to suggestions about one manuscript based on the contents of another. Considering the situation, we accepted the recommendation that it would be clearer to consider all three articles in a single reply. Thus, while each of the three papers will proceed separately during the revision process, it will be necessary to highlight across all three papers occasionally in our responses.

Scientific Issues:

In reading the reviews, we feel there are 9 critical points/assertions raised by one or more of the reviewers that present a problem for, or challenge to, our hypothesis that the observed evidence (bone accumulations and engravings) described in the Dinaledi subsystem are of intentional naledigenic origin. These are:

1. The evidence presented does not demonstrate a clear interruption of the floor sediments, thus failing to demonstrate excavated holes.

2. The sediments infilling the holes where the skeletal remains are found have not been demonstrated to originate from the disruption of the floor sediments and thus could be part of a natural geological process (e.g. water movement, slumping) or carnivore accumulations.

3. Previous geological interpretations by our research group have given alternative geological explanations for formation of the bony accumulations that contradict the present evidence presented here and result in alternative origins hypotheses.

4. Burial cannot be effectively assessed without complete excavation of the features and site.

5. The skeletal remains as presented do not conform clearly to typical body arrangement/positions associated with human (Homo sapiens) burials.

6. There is no evidence of grave goods or lithic scatters that are typically associated with human burials.

7. Humans may have been involved with the creation of either the Homo naledi bone accumulations, the engravings, or both.

8. Without a date of the engravings, the null hypothesis should be the engravings were created by Homo sapiens.

9. The null hypothesis for explanation of the skeletal remains in this situation should be “natural accumulation”.

Our analysis of the Dinaledi Feature 1 leads us to accept that the laminated orange-red mudstone (LORM) sedimentary layer is interrupted, indicating a non-natural intervention, and that the hole created by the interruption was then filled by both a fleshed body (and perhaps parts of other bodies) which were then covered by sediment that originated from the hole that was dug. We recognize that the four eLife reviewers are not convinced that our presentation is sufficient to establish this. Interestingly, this was not the universal opinion of earlier reviewers of the initial manuscript several of whom felt we had adequately supported this hypothesis. The lack of clarity in this current version of the burial manuscript is our responsibility. In the upcoming revision of this paper to be submitted, we will take the reviewers’ critiques to heart and add additional figures that illustrate better the disruption of the LORM and clarify the sedimentological data showing the material covering the skeletal remains in the hole are the disrupted sediments excavated from the same hole. We are proposing to isolate this most critical evidence for burial into a separate section in the revised submission based on the reviewers’ comments. The fact that the LORM layer is disrupted, a fleshed body was placed in the hole created by this disruption, and the body (and perhaps parts of other bodies) was/were then covered by the same sediments from the hole is the central feature of our hypothesis that the bone accumulations observed reflect a burial and not a natural process.

The possibility of fluvial transport or involvement in the subsystem is a topic that we have addressed extensively in past work, and it is clear from these reviews that we must enhance our current manuscript to discuss this issue at greater length. Our previous work (Dirks et al. 2015; Dirks et al. 2017) emphasized that fluvial transport of whole bodies into the subsystem was precluded by several lines of sedimentological evidence. We excavated a rich accumulation of skeletal remains, including articulated limbs and other elements in subvertical orientations inconsistent with slow sedimentary infill, which were difficult to explain without positing either a large and dense pile of bodies and/or sediment movement. We encountered fractured chunks of laminated orange-red mudstone (LORM) in random orientations within our excavation area, within and among skeletal remains, which directly refuted that the remains were inundated with water at the time of burial, and this limited the possibility of fluvial transport. Water flow sufficient to displace bodies or complete skeletal evidence would also transport large and course sediment, which is absent from the subsystem, and would sort the commingled skeletal material that we found by size, which we do not observe. But our excavation only covered less than a square meter at very limited depth, and this was the limit to our knowledge of subsurface sediment. We thus were left with uncertainty that led us to suggest the possibility of sediment slumping or movement into subsurface drains, although these were not observed near our excavation. Our current work expands our knowledge of the subsurface and presents an alternative explanation for the disposition of skeletal remains from our earlier excavation. But we acknowledge that this new explanation is vulnerable to our own previous published proposals, and we must do a better job of explaining how the new information addresses our previous suggestions. By not clearly creating a section where we explained how these previous hypotheses were now nullified by new evidence, we clearly confused the reviewers with our own previous work. We will revise the manuscript by enhancing the review of the significant geological evidence demonstrating that there is no significant fluvial action in the system and making it clear how the burial hypothesis provides a clearer explanation for the situation of skeletal remains from our previous excavation work.

One of the central issues raised by reviewers has been a perceived need to excavate these features completely, totally exhuming all skeletal remains from them. Reviewers have written that it is necessary to identify every skeletal element that is present and account for any missing elements. On this point, we have both ethical and scientific differences from these reviewers. We express our ethical concerns first. Many of the best-preserved possible burials ever discovered by archaeologists were subjected to total excavation and exhumation. Cases like La Chapelle-aux-Saints, La Ferrassie, and Skhūl were fully excavated at a time when data recording and excavation methods did not include the range of spatial and geomorphological approaches that later became routine. The judgment of early investigators that these situations were intentional burials was challenged by later workers, and the kind of information that might enable better tests had been irrevocably lost (Gargett 1999; Dibble et al. 2015; Rendu et al. 2014).

Later, improved excavation standards have not sufficed to remove uncertainty or debate about possible burials. For example, it was long presumed that well-preserved remains of young children were by themselves diagnostic of intentional burial, such as those from Dederiyeh, Border Cave, or Roc de Marsal. Such cases were also fully excavated, with adequate documentation of the positioning of skeletal remains and their surrounding stratigraphic situation, but such cases were later challenged on several bases and the complete exhumation of material has confused or precluded testing of new hypotheses (e.g. Gargett 1999). The case of Roc de Marsal is one in which data from the initial excavation combined with data from the initial excavation combined with re-excavation and geoarchaeological analysis led to a naturalistic interpretation of the skeletal material (Sandgathe et al. 2011; Goldberg et al. 2017). But even in this case, the researchers erred in their interpretation of the skeleton’s situation due to a lack of identification of parts of the infant’s skeleton (Gómez-Olivencia and García-Martinez 2019). That is to say, it is not only the burial hypothesis but other hypotheses that suffer from complete excavation. Researchers concerned with preserving all possible information have sometimes taken extraordinary measures to remove and study possible burials at high-resolution in the laboratory. Such was the case of the Shanidar IV burial removed from the site and transported in plaster jacket by Solecki, which led to the disruption and loss of internal stratigraphic information (Pomeroy et al. 2020). Arguably, the current state of the art is full excavation with partial preparation, such as that undertaken at Panga ya Saidi (Martinón-Torres et al. 2021). But again, any future attempt to reinterpret or test the hypothesis of burial must rely on the adequacy of documentation as the original context has been removed.

In our decision to leave material in place as much as possible, we are expanding upon standard practice to leave witness sections and unexcavated areas for future research. The situation is novel, representing possible burials by a nonhuman species, and that makes it doubly important in our opinion to be conservative in not fully exhuming the skeletal material from its context. We anticipate that many other researchers, including future investigators, will suggest additional methods to further test the hypothesis of burial, something that would be impossible if we had excavated the features in their entirety prior to publishing a description of our work. We believe strongly that our ethical responsibility is to publish the work and the most likely interpretation while leaving as much evidence in place as possible to enable further testing and replication. We welcome the suggestions of additional methods/analyses to test the H. naledi burial hypothesis.

This being said, we also observe that total exhumation would not resolve the concerns raised by the reviewers. The recommendation of total exhumation is in pursuit of a full account of all skeletal material present and its preservation and spatial situation, in order to demonstrate that they conform to body positions comparable to human burials. As has been highlighted in forensic casework, the excavation of an inhumation feature does not necessarily provide an accurate spatial or anatomical manifest of the stratigraphical relationships between the body, encapsulating matrix, and any cut present due to preservational, taphonomic and operational factors (Dirkmaat and Cabo, 2016; Hunter, 2014). In particular, in cases where skeletal elements are highly fragmented, friable, or degraded (such as through bioerosion) then complete excavation—even under controlled laboratory conditions—may destroy bone and severely limit skeletal identification (Henderson, 1997; Hochrein, 2002; Owsley and Compton, 1997), particularly in elements where the ratio of trabecular to cortical bone is high (Darwent and Lyman, 2002; Lyman, 1994). As such, non-invasive methods of 3D and 4D modelling (preservation in situ) are often considered preferable to complete necropsy or excavation (preservation by record) where appropriate (Bolliger and Thali, 2009; Dell’Unto and Landeschi, 2022; Randolph-Quinney et al., 2018; Silver, 2016). 

The test of burial is not primarily positional, but taphonomic and geological. The position and number of bones can elaborate on process-driven questions of decay and destruction in the burial environment, or post-mortem modification, but are not singularly indicative of whether the remains were intentionally buried – the post-mortem narrative of all the processes affecting the cadaveric island is required (Knüsel and Robb, 2016). In previous cases, researchers have disputed or accepted the hypothesis of intentional hominin burial based upon assumptions about how modern humans or Neandertals would have positioned bodies, with the idea that some positions reflect ritual intent while others do not. But applying such assumptions is unjustifiable, particularly for a species like H. naledi, whose culture may have differed fundamentally from our own. Our work acknowledges that the present evidence does not enable a full reconstruction of the burial positions, but it does show that fleshed remains were encased in sediment prior to decomposition of soft tissue, and that subsequent spatial changes can be most parsimoniously explained by natural decomposition within sedimentary matrix contained within a burial feature (after Green, 2022; Mickleburgh and Wescott, 2018; Mickleburgh et al., 2022). If the argument is that extraordinary claims require extraordinary evidence, we feel that the evidence documents excavation and interment (and will do so more clearly in the revision) and the fact of the remains do not match a “typical” human burial in body positioning is not in itself evidence that these are not H. naledi burials.

We feel that the reviewers (in keeping with many palaeoanthropologists) have a clear idea of what they “think” a burial should look like in an idealised sense, but this platonic ideal of burial form is not matched by the extensive literature in archaeothanatology, funerary archaeology and forensic science which indicates enormous variability in the activity, morphology and post-mortem system experienced by the human body in cases of interment and body disposal (e.g. Aspöck, 2008; Boulestin and Duday, 2005 and 2006; Connelly et al., 2005; Channing and Randolph-Quinney, 2006; Cherryson, 2008; Donnelly et al., 1995; Finley, 2000; Hunter, 2014; Parker Pearson, 1999; Randolph-Quinney, 2013). Decades of experience in the identification, recovery and interpretation of clandestine, deviant, and non-formal burials indicates the platonic ideal is rare, and in many contexts, the exception (Cherryson, 2008; Parker Pearson, 1999). This variability is particularly relevant to morphological traits in burial context, such as the informal nature of the grave cut in plan and section, shallow burial depth, and initial disposition of body (placement) during the early post-mortem period. These might run counter to the expectations of reviewers or others referencing the fossil hominin record, but are well accepted within the communities of researchers investigating Holocene archaeological sites and forensic contexts.

It is encouraging to see reviewers beginning to incorporate the extensive (often experimentally derived) literature from archaeothanatology and forensic taphonomy in their deliberations, and we will be taking these comments on board going forward. In particular, we acknowledge reviewers’ comments and the need to construct a more detailed post-mortem narrative, accounting for joint disarticulation (labile versus persistent joints etc), displacement, and final disposition of elements within the burial space. As such we will incorporate the hierarchy of decomposition (rank order disarticulation), associations between regions of anatomical association, areas of disassociation, and the voids produced during decomposition (after Mickleburgh and Wescott, 2018; Mickleburgh et al., 2022) into our narrative. In doing so we acknowledge the tensions between the inductive archaeolothanatological narrative-driven approach (e.g. Duday, 2005 & 2009) versus robust decomposition data derived from human forensic taphonomic experimentation recently articulated by Schotsmans and colleagues (2022) - noting that we will highlight comparative data based on forensic experimental casework and actualistic modelling over inductive intuitive approaches which come with significant evidential shortcomings (Bristow et al. 2011).

Finally, from a taphonomic perspective it is worth pointing out to reviewers that we have already addressed the issue of lack of taphonomic evidence for carnivore involvement in the formation of the Dinaledi assemblage (Dirks, et al., 2016). Absence of any carnivore-induced bone surface modifications, patterns of skeletal part representation, and a total absence of any carnivore remains found within the Dinaledi chamber (following Kuhn and colleagues, 2010) lead us to reject carnivores as possible vectors of body accumulation within the Dinaledi Chamber and Hill Antechamber.

Reviewers suggest that without a date derived from geochronological methods, the engravings cannot be associated with H. naledi, and that it is possible (or probable) that the engravings were done in the recent past by H. sapiens. This suggestion neglects the context of the site. We have previously documented the structure and extremely limited accessibility of the Dinaledi subsystem. This subsystem was not recorded on maps of the documented Rising Star Cave system prior to our work and its discovery by our teams. Furthermore, there is no evidence of prehistoric human activity in the areas of the cave related to possible subterranean entrances There is no evidence that humans in the past typically ventured into such extreme spaces like those of Rising Star. It is clear from the presence of the remains of many individuals that H. naledi ventured into these spaces again and again. It is likely that H. naledi moved through these spaces more easily than humans do based on their physique. We show that the engravings overlay each other suggesting multiple engraving events.  These engravings took time and effort and the only evidence for use of the Dinaledi subsystem by any hominin is by H. naledi. The context leads to the null hypothesis that H. naledi made the marks. In our revision, we will elaborate on this argument to clarify the evidence for our stance on this hypothesis. Several reviewers took issue with the title of the engraving paper as we did not insert a qualifier in front of the suggested date range for the engravings. We deliberately left out qualifying language so that the title took the form of a testable hypothesis rather than a weak assertation. Should future work find the engravings were not produced within this time range, then we will restate this hypothesis.

Finally, with regards to the engravings we have chosen to report them because they exist. Not reporting the presence of engraved marks on the walls of a cave above hypothesized burials would be tantamount to leaving relevant evidence out of the description of an archeological context. We recognize and state in our manuscript that these markings require substantial further study, including attempts at geochronological dating. But the current evidence is clearly relevant to the archaeological context of the subsystem. We take a similar stance with reporting the presence of the tool shaped artefact near the hand of the H. naledi skeleton in the Hill Antechamber. It is evident that this object requires further study, as we stated in our manuscript, but again omitting it from our study would be leaving out relevant evidence.

Some have suggested that the null hypothesis should be that all of these observed circumstances are of natural origin. Our team took this approach in our early investigation of the Dinaledi subsystem (Dirks et al. 2015). We adopted the null hypothesis that the geological processes involved in the accumulation of H. naledi skeletal remains were “natural” (e.g., non-naledigenic involvement), and we were able to reject many alternative explanations for the assemblage, including carnivore accumulation, “death trap” accumulation, and fluvial transport of bodies or bones (Dirks et al. 2015). This led us to the hypothesis that H. naledi were involved in bringing the bodies into the spaces where they were found. But we did not hypothesize their involvement in the formation of the deposit itself beyond bringing the bodies to the location.

This approach seems conservative. It followed the traditional view that small-brained hominins do not engage in cultural practices. But we recognize in hindsight that this null hypothesis approach did harm to our analyses. It impeded us from recognizing within our initial excavations of the puzzle box area and other excavations between 2014 – 2017 that we might be encountering remains that were intrusive in the sedimentary floor of the chamber. If we had approached the accumulation of a large number of hominins from the perspective of the null hypothesis being that the situation was likely cultural, we perhaps would have collected evidence in a slightly different manner. We certainly note that if the Dinaledi system had been full of the remains of modern humans, there would have been little doubt that the null hypothesis would have been that this was a cultural space and not a “natural space”.  We therefore respectfully disagree with the reviewers who continue to support the idea that we should approach hominin excavations with the null hypothesis that they will be natural (specifically non-cultural) in origins. If excavations continue with this mindset we believe that potential cultural evidence is almost certain to be lost.

There has been a gradient across paleoanthropological excavations, archaeological work, and forensic investigation, with increasing precision of context. The reality is that the recording precision and frame of approach is typically different in most paleontological excavations than in those related to contemporary human remains. If anything comes from the present discussion of whether the Dinaledi system is a burial site for H. naledi or not, we hope that by taking seriously the possibility of deep cultural dynamics of hominins, we will encourage other teams to meet the highest standards of excavation in order to preserve potential cultural evidence. Given H. naledi’s cranial capacity we suggest that even very early hominin skeletal assemblages should be re-examined, if there is sufficient evidence or records available.  These would include examples such as the A.L. 333 Au. afarensis site (the so called First Family site in Hadar Ethiopia), the Dikika infant skeleton, WT 15000 (Turkana Boy) and even A.L. 288 (Lucy) as such unusual taphonomic situations where skeletons are preserved cannot be simply explained away as “natural” in origin, based solely on the cranial capacity and assumed lack of cognitive and cultural complexity of the hominins as emphasized by us in Fuentes et al. (2023). We are not the first to observe that some very early hominin situations may represent early mortuary activity (Pettitt 2013), but we would advocate a step further. We suggest it may be damaging to take “natural accumulation” as the standard null hypothesis for hominin paleoanthropology, and that it is more conservative in practice to engage remains with the null hypothesis of possible cultural formation.

We are deeply grateful for the time and effort all of the 8 reviewers (across three reviews) have taken with this work.  We also acknowledge the anonymous reviewers from previous submissions who’s opinions and comments will have made the final iterations of these manuscripts better for their efforts. As this process is rather public and includes commentary outside of the eLife forum, we ask that the efforts of all 37 authors and 8 reviewers involved be respected and that the discourse remain professional in all venues as we study this fascinating and quite complex occurrence. We appreciate also the efforts of members of the public who have engaged with this relatively new process where preprints are posted prior to the reviews allowing comments and interactions from colleagues and the public who are normally not part of the internal peer review process.  We believe these interactions will make for better final papers. We feel we have met the standards of demonstrating burials in H. naledi and that the engraving are most likely associated with H. naledi. However, given the reviews we see many areas where our clarity and context, and analyses, were less strong than they can be. With the clarifications and additions taken on board through these review processes the final papers will be stronger and clearer. We, recognize that this is an ongoing process of scientific investigation and further work will allow continued, and possibly better, evaluation of these hypothesis and others.

Lee R Berger, Agustín Fuentes, John Hawks, Tebogo Makhubela

Works cited:

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Author Response:

We would like to thank the eLife reviewers for the considerable time and effort they have invested to review these manuscripts. We have also benefited from a previous round of review of the manuscript describing the proposed burial features, which underwent two rounds of revisions in a high-impact journal over a period of approximately 8 months during 2022 and early 2023. Both sets of reviews have reflected mixed responses to the evidence we have presented, with one reviewer recommending acceptance with minor editorial revisions, two recommending acceptance with minor revisions and the fourth recommending rejection based upon similar arguments to those reflected by some of the reviewers in this current round of reviews in eLife. Ultimately the managing editor of this first journal took the decision that the review process could not be completed in a timely manner and rejected the manuscript although the submission here reflected our consideration of these reviewers suggestions.

We have chosen in this initial response to the eLife reviews to include some references to the previous anonymous reviews in order to illustrate differences of opinion and differences in revision suggestions within the review process. Our goal is to offer maximal insight into our decision-making process and to acknowledge the considerable time and effort put into the assessment of these manuscripts by reviewers (for eLife and in the case of the earlier review process). We hope that this approach will assist the readers, and reviewers, of our manuscripts in understanding why we are proceeding with certain decisions during the revision process.

This is a new process for us and the reviewers, and one way in which it significantly differs from more traditional review is that both the reviews and our reply will be public well in advance of our revisions to the manuscript. Indeed, considering the scope of the reviews, some of those revisions may take considerable time, although many can be accomplished fairly easily. Thus, we are not in a position to say that we have solved every issue raised by the reviewers. Instead, we will examine what appear to be the key critical issues raised regarding the data and the analyses and how we propose to address these as we revise the papers. We will also address several philosophical and ethical issues raised by the reviews and our proposal for dealing with these. More specific editorial and citational recommendations will be dealt with on a case-by-case basis, and we do not address these point-by-point in this reply. Please note, this response to the reviewers is not the revision of the manuscript and is only the initial opinion of the corresponding authors with some guidance from the larger group of authors of all three papers. Our final submitted revision will reflect the input of all authors included on those submissions.

We took the decision to submit three separate papers consciously. The two different categories of evidence, burials and engravings, involve different kinds of analysis and different (although overlapping) teams of researchers, and we recognized that each deserved their own presentation and assessment. Meanwhile, together they inform the context of H. naledi in a way that requires some synthetic discussion, in which both kinds of evidence are relevant, leading to a third paper. But the mutual relevance of these different kinds of evidence and their review by a common set of reviewers naturally raises cross-cutting issues, and the reviewers have cross-referenced the three articles. This has sometimes led to suggestions about one manuscript based on the contents of another. Considering the situation, we accepted the recommendation that it would be clearer to consider all three articles in a single reply. Thus, while each of the three papers will proceed separately during the revision process, it will be necessary to highlight across all three papers occasionally in our responses.

Scientific Issues:

In reading the reviews, we feel there are 9 critical points/assertions raised by one or more of the reviewers that present a problem for, or challenge to, our hypothesis that the observed evidence (bone accumulations and engravings) described in the Dinaledi subsystem are of intentional naledigenic origin. These are:

1. The evidence presented does not demonstrate a clear interruption of the floor sediments, thus failing to demonstrate excavated holes.

2. The sediments infilling the holes where the skeletal remains are found have not been demonstrated to originate from the disruption of the floor sediments and thus could be part of a natural geological process (e.g. water movement, slumping) or carnivore accumulations.

3. Previous geological interpretations by our research group have given alternative geological explanations for formation of the bony accumulations that contradict the present evidence presented here and result in alternative origins hypotheses.

4. Burial cannot be effectively assessed without complete excavation of the features and site.

5. The skeletal remains as presented do not conform clearly to typical body arrangement/positions associated with human (Homo sapiens) burials.

6. There is no evidence of grave goods or lithic scatters that are typically associated with human burials.

7. Humans may have been involved with the creation of either the Homo naledi bone accumulations, the engravings, or both.

8. Without a date of the engravings, the null hypothesis should be the engravings were created by Homo sapiens.

9. The null hypothesis for explanation of the skeletal remains in this situation should be “natural accumulation”.

Our analysis of the Dinaledi Feature 1 leads us to accept that the laminated orange-red mudstone (LORM) sedimentary layer is interrupted, indicating a non-natural intervention, and that the hole created by the interruption was then filled by both a fleshed body (and perhaps parts of other bodies) which were then covered by sediment that originated from the hole that was dug. We recognize that the four eLife reviewers are not convinced that our presentation is sufficient to establish this. Interestingly, this was not the universal opinion of earlier reviewers of the initial manuscript several of whom felt we had adequately supported this hypothesis. The lack of clarity in this current version of the burial manuscript is our responsibility. In the upcoming revision of this paper to be submitted, we will take the reviewers’ critiques to heart and add additional figures that illustrate better the disruption of the LORM and clarify the sedimentological data showing the material covering the skeletal remains in the hole are the disrupted sediments excavated from the same hole. We are proposing to isolate this most critical evidence for burial into a separate section in the revised submission based on the reviewers’ comments. The fact that the LORM layer is disrupted, a fleshed body was placed in the hole created by this disruption, and the body (and perhaps parts of other bodies) was/were then covered by the same sediments from the hole is the central feature of our hypothesis that the bone accumulations observed reflect a burial and not a natural process.

The possibility of fluvial transport or involvement in the subsystem is a topic that we have addressed extensively in past work, and it is clear from these reviews that we must enhance our current manuscript to discuss this issue at greater length. Our previous work (Dirks et al. 2015; Dirks et al. 2017) emphasized that fluvial transport of whole bodies into the subsystem was precluded by several lines of sedimentological evidence. We excavated a rich accumulation of skeletal remains, including articulated limbs and other elements in subvertical orientations inconsistent with slow sedimentary infill, which were difficult to explain without positing either a large and dense pile of bodies and/or sediment movement. We encountered fractured chunks of laminated orange-red mudstone (LORM) in random orientations within our excavation area, within and among skeletal remains, which directly refuted that the remains were inundated with water at the time of burial, and this limited the possibility of fluvial transport. Water flow sufficient to displace bodies or complete skeletal evidence would also transport large and course sediment, which is absent from the subsystem, and would sort the commingled skeletal material that we found by size, which we do not observe. But our excavation only covered less than a square meter at very limited depth, and this was the limit to our knowledge of subsurface sediment. We thus were left with uncertainty that led us to suggest the possibility of sediment slumping or movement into subsurface drains, although these were not observed near our excavation. Our current work expands our knowledge of the subsurface and presents an alternative explanation for the disposition of skeletal remains from our earlier excavation. But we acknowledge that this new explanation is vulnerable to our own previous published proposals, and we must do a better job of explaining how the new information addresses our previous suggestions. By not clearly creating a section where we explained how these previous hypotheses were now nullified by new evidence, we clearly confused the reviewers with our own previous work. We will revise the manuscript by enhancing the review of the significant geological evidence demonstrating that there is no significant fluvial action in the system and making it clear how the burial hypothesis provides a clearer explanation for the situation of skeletal remains from our previous excavation work.

One of the central issues raised by reviewers has been a perceived need to excavate these features completely, totally exhuming all skeletal remains from them. Reviewers have written that it is necessary to identify every skeletal element that is present and account for any missing elements. On this point, we have both ethical and scientific differences from these reviewers. We express our ethical concerns first. Many of the best-preserved possible burials ever discovered by archaeologists were subjected to total excavation and exhumation. Cases like La Chapelle-aux-Saints, La Ferrassie, and Skhūl were fully excavated at a time when data recording and excavation methods did not include the range of spatial and geomorphological approaches that later became routine. The judgment of early investigators that these situations were intentional burials was challenged by later workers, and the kind of information that might enable better tests had been irrevocably lost (Gargett 1999; Dibble et al. 2015; Rendu et al. 2014).

Later, improved excavation standards have not sufficed to remove uncertainty or debate about possible burials. For example, it was long presumed that well-preserved remains of young children were by themselves diagnostic of intentional burial, such as those from Dederiyeh, Border Cave, or Roc de Marsal. Such cases were also fully excavated, with adequate documentation of the positioning of skeletal remains and their surrounding stratigraphic situation, but such cases were later challenged on several bases and the complete exhumation of material has confused or precluded testing of new hypotheses (e.g. Gargett 1999). The case of Roc de Marsal is one in which data from the initial excavation combined with data from the initial excavation combined with re-excavation and geoarchaeological analysis led to a naturalistic interpretation of the skeletal material (Sandgathe et al. 2011; Goldberg et al. 2017). But even in this case, the researchers erred in their interpretation of the skeleton’s situation due to a lack of identification of parts of the infant’s skeleton (Gómez-Olivencia and García-Martinez 2019). That is to say, it is not only the burial hypothesis but other hypotheses that suffer from complete excavation. Researchers concerned with preserving all possible information have sometimes taken extraordinary measures to remove and study possible burials at high-resolution in the laboratory. Such was the case of the Shanidar IV burial removed from the site and transported in plaster jacket by Solecki, which led to the disruption and loss of internal stratigraphic information (Pomeroy et al. 2020). Arguably, the current state of the art is full excavation with partial preparation, such as that undertaken at Panga ya Saidi (Martinón-Torres et al. 2021). But again, any future attempt to reinterpret or test the hypothesis of burial must rely on the adequacy of documentation as the original context has been removed.

In our decision to leave material in place as much as possible, we are expanding upon standard practice to leave witness sections and unexcavated areas for future research. The situation is novel, representing possible burials by a nonhuman species, and that makes it doubly important in our opinion to be conservative in not fully exhuming the skeletal material from its context. We anticipate that many other researchers, including future investigators, will suggest additional methods to further test the hypothesis of burial, something that would be impossible if we had excavated the features in their entirety prior to publishing a description of our work. We believe strongly that our ethical responsibility is to publish the work and the most likely interpretation while leaving as much evidence in place as possible to enable further testing and replication. We welcome the suggestions of additional methods/analyses to test the H. naledi burial hypothesis.

This being said, we also observe that total exhumation would not resolve the concerns raised by the reviewers. The recommendation of total exhumation is in pursuit of a full account of all skeletal material present and its preservation and spatial situation, in order to demonstrate that they conform to body positions comparable to human burials. As has been highlighted in forensic casework, the excavation of an inhumation feature does not necessarily provide an accurate spatial or anatomical manifest of the stratigraphical relationships between the body, encapsulating matrix, and any cut present due to preservational, taphonomic and operational factors (Dirkmaat and Cabo, 2016; Hunter, 2014). In particular, in cases where skeletal elements are highly fragmented, friable, or degraded (such as through bioerosion) then complete excavation—even under controlled laboratory conditions—may destroy bone and severely limit skeletal identification (Henderson, 1997; Hochrein, 2002; Owsley and Compton, 1997), particularly in elements where the ratio of trabecular to cortical bone is high (Darwent and Lyman, 2002; Lyman, 1994). As such, non-invasive methods of 3D and 4D modelling (preservation in situ) are often considered preferable to complete necropsy or excavation (preservation by record) where appropriate (Bolliger and Thali, 2009; Dell’Unto and Landeschi, 2022; Randolph-Quinney et al., 2018; Silver, 2016). 

The test of burial is not primarily positional, but taphonomic and geological. The position and number of bones can elaborate on process-driven questions of decay and destruction in the burial environment, or post-mortem modification, but are not singularly indicative of whether the remains were intentionally buried – the post-mortem narrative of all the processes affecting the cadaveric island is required (Knüsel and Robb, 2016). In previous cases, researchers have disputed or accepted the hypothesis of intentional hominin burial based upon assumptions about how modern humans or Neandertals would have positioned bodies, with the idea that some positions reflect ritual intent while others do not. But applying such assumptions is unjustifiable, particularly for a species like H. naledi, whose culture may have differed fundamentally from our own. Our work acknowledges that the present evidence does not enable a full reconstruction of the burial positions, but it does show that fleshed remains were encased in sediment prior to decomposition of soft tissue, and that subsequent spatial changes can be most parsimoniously explained by natural decomposition within sedimentary matrix contained within a burial feature (after Green, 2022; Mickleburgh and Wescott, 2018; Mickleburgh et al., 2022). If the argument is that extraordinary claims require extraordinary evidence, we feel that the evidence documents excavation and interment (and will do so more clearly in the revision) and the fact of the remains do not match a “typical” human burial in body positioning is not in itself evidence that these are not H. naledi burials.

We feel that the reviewers (in keeping with many palaeoanthropologists) have a clear idea of what they “think” a burial should look like in an idealised sense, but this platonic ideal of burial form is not matched by the extensive literature in archaeothanatology, funerary archaeology and forensic science which indicates enormous variability in the activity, morphology and post-mortem system experienced by the human body in cases of interment and body disposal (e.g. Aspöck, 2008; Boulestin and Duday, 2005 and 2006; Connelly et al., 2005; Channing and Randolph-Quinney, 2006; Cherryson, 2008; Donnelly et al., 1995; Finley, 2000; Hunter, 2014; Parker Pearson, 1999; Randolph-Quinney, 2013). Decades of experience in the identification, recovery and interpretation of clandestine, deviant, and non-formal burials indicates the platonic ideal is rare, and in many contexts, the exception (Cherryson, 2008; Parker Pearson, 1999). This variability is particularly relevant to morphological traits in burial context, such as the informal nature of the grave cut in plan and section, shallow burial depth, and initial disposition of body (placement) during the early post-mortem period. These might run counter to the expectations of reviewers or others referencing the fossil hominin record, but are well accepted within the communities of researchers investigating Holocene archaeological sites and forensic contexts.

It is encouraging to see reviewers beginning to incorporate the extensive (often experimentally derived) literature from archaeothanatology and forensic taphonomy in their deliberations, and we will be taking these comments on board going forward. In particular, we acknowledge reviewers’ comments and the need to construct a more detailed post-mortem narrative, accounting for joint disarticulation (labile versus persistent joints etc), displacement, and final disposition of elements within the burial space. As such we will incorporate the hierarchy of decomposition (rank order disarticulation), associations between regions of anatomical association, areas of disassociation, and the voids produced during decomposition (after Mickleburgh and Wescott, 2018; Mickleburgh et al., 2022) into our narrative. In doing so we acknowledge the tensions between the inductive archaeolothanatological narrative-driven approach (e.g. Duday, 2005 & 2009) versus robust decomposition data derived from human forensic taphonomic experimentation recently articulated by Schotsmans and colleagues (2022) - noting that we will highlight comparative data based on forensic experimental casework and actualistic modelling over inductive intuitive approaches which come with significant evidential shortcomings (Bristow et al. 2011).

Finally, from a taphonomic perspective it is worth pointing out to reviewers that we have already addressed the issue of lack of taphonomic evidence for carnivore involvement in the formation of the Dinaledi assemblage (Dirks, et al., 2016). Absence of any carnivore-induced bone surface modifications, patterns of skeletal part representation, and a total absence of any carnivore remains found within the Dinaledi chamber (following Kuhn and colleagues, 2010) lead us to reject carnivores as possible vectors of body accumulation within the Dinaledi Chamber and Hill Antechamber.

Reviewers suggest that without a date derived from geochronological methods, the engravings cannot be associated with H. naledi, and that it is possible (or probable) that the engravings were done in the recent past by H. sapiens. This suggestion neglects the context of the site. We have previously documented the structure and extremely limited accessibility of the Dinaledi subsystem. This subsystem was not recorded on maps of the documented Rising Star Cave system prior to our work and its discovery by our teams. Furthermore, there is no evidence of prehistoric human activity in the areas of the cave related to possible subterranean entrances There is no evidence that humans in the past typically ventured into such extreme spaces like those of Rising Star. It is clear from the presence of the remains of many individuals that H. naledi ventured into these spaces again and again. It is likely that H. naledi moved through these spaces more easily than humans do based on their physique. We show that the engravings overlay each other suggesting multiple engraving events.  These engravings took time and effort and the only evidence for use of the Dinaledi subsystem by any hominin is by H. naledi. The context leads to the null hypothesis that H. naledi made the marks. In our revision, we will elaborate on this argument to clarify the evidence for our stance on this hypothesis. Several reviewers took issue with the title of the engraving paper as we did not insert a qualifier in front of the suggested date range for the engravings. We deliberately left out qualifying language so that the title took the form of a testable hypothesis rather than a weak assertation. Should future work find the engravings were not produced within this time range, then we will restate this hypothesis.

Finally, with regards to the engravings we have chosen to report them because they exist. Not reporting the presence of engraved marks on the walls of a cave above hypothesized burials would be tantamount to leaving relevant evidence out of the description of an archeological context. We recognize and state in our manuscript that these markings require substantial further study, including attempts at geochronological dating. But the current evidence is clearly relevant to the archaeological context of the subsystem. We take a similar stance with reporting the presence of the tool shaped artefact near the hand of the H. naledi skeleton in the Hill Antechamber. It is evident that this object requires further study, as we stated in our manuscript, but again omitting it from our study would be leaving out relevant evidence.

Some have suggested that the null hypothesis should be that all of these observed circumstances are of natural origin. Our team took this approach in our early investigation of the Dinaledi subsystem (Dirks et al. 2015). We adopted the null hypothesis that the geological processes involved in the accumulation of H. naledi skeletal remains were “natural” (e.g., non-naledigenic involvement), and we were able to reject many alternative explanations for the assemblage, including carnivore accumulation, “death trap” accumulation, and fluvial transport of bodies or bones (Dirks et al. 2015). This led us to the hypothesis that H. naledi were involved in bringing the bodies into the spaces where they were found. But we did not hypothesize their involvement in the formation of the deposit itself beyond bringing the bodies to the location.

This approach seems conservative. It followed the traditional view that small-brained hominins do not engage in cultural practices. But we recognize in hindsight that this null hypothesis approach did harm to our analyses. It impeded us from recognizing within our initial excavations of the puzzle box area and other excavations between 2014 – 2017 that we might be encountering remains that were intrusive in the sedimentary floor of the chamber. If we had approached the accumulation of a large number of hominins from the perspective of the null hypothesis being that the situation was likely cultural, we perhaps would have collected evidence in a slightly different manner. We certainly note that if the Dinaledi system had been full of the remains of modern humans, there would have been little doubt that the null hypothesis would have been that this was a cultural space and not a “natural space”.  We therefore respectfully disagree with the reviewers who continue to support the idea that we should approach hominin excavations with the null hypothesis that they will be natural (specifically non-cultural) in origins. If excavations continue with this mindset we believe that potential cultural evidence is almost certain to be lost.

There has been a gradient across paleoanthropological excavations, archaeological work, and forensic investigation, with increasing precision of context. The reality is that the recording precision and frame of approach is typically different in most paleontological excavations than in those related to contemporary human remains. If anything comes from the present discussion of whether the Dinaledi system is a burial site for H. naledi or not, we hope that by taking seriously the possibility of deep cultural dynamics of hominins, we will encourage other teams to meet the highest standards of excavation in order to preserve potential cultural evidence. Given H. naledi’s cranial capacity we suggest that even very early hominin skeletal assemblages should be re-examined, if there is sufficient evidence or records available.  These would include examples such as the A.L. 333 Au. afarensis site (the so called First Family site in Hadar Ethiopia), the Dikika infant skeleton, WT 15000 (Turkana Boy) and even A.L. 288 (Lucy) as such unusual taphonomic situations where skeletons are preserved cannot be simply explained away as “natural” in origin, based solely on the cranial capacity and assumed lack of cognitive and cultural complexity of the hominins as emphasized by us in Fuentes et al. (2023). We are not the first to observe that some very early hominin situations may represent early mortuary activity (Pettitt 2013), but we would advocate a step further. We suggest it may be damaging to take “natural accumulation” as the standard null hypothesis for hominin paleoanthropology, and that it is more conservative in practice to engage remains with the null hypothesis of possible cultural formation.

We are deeply grateful for the time and effort all of the 8 reviewers (across three reviews) have taken with this work.  We also acknowledge the anonymous reviewers from previous submissions who’s opinions and comments will have made the final iterations of these manuscripts better for their efforts. As this process is rather public and includes commentary outside of the eLife forum, we ask that the efforts of all 37 authors and 8 reviewers involved be respected and that the discourse remain professional in all venues as we study this fascinating and quite complex occurrence. We appreciate also the efforts of members of the public who have engaged with this relatively new process where preprints are posted prior to the reviews allowing comments and interactions from colleagues and the public who are normally not part of the internal peer review process.  We believe these interactions will make for better final papers. We feel we have met the standards of demonstrating burials in H. naledi and that the engraving are most likely associated with H. naledi. However, given the reviews we see many areas where our clarity and context, and analyses, were less strong than they can be. With the clarifications and additions taken on board through these review processes the final papers will be stronger and clearer. We, recognize that this is an ongoing process of scientific investigation and further work will allow continued, and possibly better, evaluation of these hypothesis and others.

Lee R Berger, Agustín Fuentes, John Hawks, Tebogo Makhubela

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

Summary: Silao et al make the intriguing observation that yeasts that are generally considered less pathogenic are unable to catabolize proline than Candida albicans. They then, in Candida albicans, construct mutants defective for the two key enzymes (Put1, Put2) required to convert proline to glutamate, which they show to be essential for proline utilization as an energy (carbon) and nitrogen source. The authors proceed to untangle the regulatory aspects of proline degradation, including the respective cellular localization of its key enzymes. They then make the important discovery that strains lacking either Put1 or Put2 suffer from a proline-dependent growth defect, which they attribute to resulting defects in mitochondrial metabolism.

The manuscript then goes on to analyze a broad range of infection models including: reconstituted human epithelial skin model, Drosophila, mouse systemic infections, organ colonization in these mice (kidney, spleen, brain, liver and histochemistry of the kidneys) as well as survival when incubated with cultured human neutrophils. Finally, they use yeast cells constitutively expressing yEmRFP (so that yeasts can be distinguished from other host cells) and coated with FITC before incubation with the host cells (which coats the wall of the original cells, but does not spread to progeny) and they go on to perform an impressive set of analyses of C. albicans growth within mouse kidneys both in vivo and ex vivo, exploiting an implanted window together with intravital imaging with a two photon microscope at different time points. The system is impressive and visualizes tissue invasion by hyphal cells beautifully. Finally, they compare the intra vital images from WT and put2-/- cells and show that, as in vitro, put2-/- cells do not form filaments and do not show extensive invasion of the kidney tissue. While the in vivo aspect of the study includes many different models, it finds defects in virulence for different subsets of put mutants and the relative importance of filamentation vs proline utilization for virulence is not conclusively resolved.

Overall, this is an important and timely manuscript, which significantly contributes to the understanding of how proline metabolism intersects with yeast fitness in the context of infections. However, there are several major concerns regarding some of the conclusions drawn from the study. In addition, some general recommendations that would improve the manuscript are provided.

Specifically, the manuscript provides a very detailed description of experiments and observations. However, in several parts it is difficult to follow and the reader needs more guidance about the logic involved in reaching conclusion. Specifically, several aspects of the paper are written for experts in Candida (yeast) metabolism. Here, explaining the rationale for some of the experiments, and providing more background information that is not obvious to a non-expert, is required.

In particular, writing a clear and measured summary sentence at the end of each paragraph and a conclusion paragraph that summarizes key findings in simple terms would help make the manuscript more digestible for readers.

In addition, the impressive microscopy and broad range of in vivo experiments is comprehensive but only adds incremental information relevant to proline metabolism-that filamentous growth in vivo and virulence is reduced in cells carrying some mutations in one or more put genes. However, this broad sweep of model systems and the development of the in vivo imagining system might have more impact in a separate paper focused on the real-time in vivo visualization of kidney invasion.

We thank Reviewer 1 for the extensive list of comments and have endeavored to adjust the manuscript to address all of the major and minor concerns. It is evident that Reviewer 1 clearly understood the significance of the work and we appreciate that the comments are presented in a positive manner intended to improve our manuscript.

Major comments:

1. The main finding that impressed this reviewer is that "removing the ability to catabolize proline, in an organism that evolved to catabolize it, leads to (growth) defects". This point could be better highlighted throughout the manuscript.

Thanks for the comment. We will adjust the text to reflect this suggestion.

1. The authors show that deletion strains for proline metabolism have defects that are important for in vivo pathogenicity. This is an important finding. However, as the manuscript reads now, it suggests that the main findings are that the ability to use proline in the respective host niche is key. Mechanistically, the manuscript revolves primarily around defects that arise when deleting PUT1 and/or PUT2 (i.e., an "unknown" toxicity of proline in the case of put1-/- (or put1-/- put2-/-) and the additional P5C-dependent toxicity for put2-/- mutants; see below).

Yes, the reviewer is correct in that we believe that proline catabolism is necessary to initiate and power hyphal growth, which is coupled to virulence. We have previously shown that upon phagocytosis by macrophages, the expression of Put1, Put2 and even Gdh2 are induced in phagocytized C. albicans cells, which is consistent with the analysis shown in Fig. 2D and Fig. S2B. Consequently, proline, or an amino acid that is metabolized via the proline catabolic pathway, must be present in the phagosomal compartment. However, as we now report, proline inhibits growth of cells lacking the capacity to catabolize it. Although we cannot differentiate the cause of reduced virulence in put mutants, i.e., the lack of energy due to the inability to catabolize proline vs proline toxicity, proline catabolism is clearly important and a robust indicator of virulence. As point 1, we have adjusted the text to make this clearer.

1. In order to claim that catabolizing prolines promotes pathogenicity (as opposed to the alternative hypothesis that the inability to catabolize proline leads to the observed defects), additional experiments would be required. For example, the put mutants would need to be compared with mutants that significantly reduce/impair proline uptake, such as the referenced gnp2 mutant (Garbe et al 2022). While the finding that less pathogenic yeast species are unable to catabolize proline is both intriguing and important, it also remains as is presented as a loose, non-quantitative correlation that only tangentially address the question of whether "proline catabolism is key for pathogenicity".

We have in fact already shown that proline uptake is required to induce filamentation (Martínez and Ljungdahl 2003, Fig. 6). The main point of our current work, which we believe is important and of general interest, is that C. albicans is adapted to use proline as sole energy source, which reflects the environment (humans) in which it evolved. See the response to point 2. Interestingly, the differences in the expression levels of Put1 (off in the absence of proline, induced robustly by proline) and Put2 (low level of constitutive expression, induced robustly by proline) suggest that cells are primed to decrease the likelihood of becoming inhibited by P5C, i.e., the constitutive expression of Put2 is able to ameliorate the potential toxicity of P5C. Regardless, the finding that put1 and put2 mutants exhibit significantly reduced virulence in two host models provides clear support for proline catabolism being key for C. albicans pathogenicity.

1. 238 onwards: The conclusion that "the primary growth inhibitory effect of proline is linked to catabolic intermediates formed by Put1 and that are metabolized further by Put2"does not appear to be fully supported by the evidence. Addition of proline to put1 mutants already reduced OD600 by ~50% (Figure 2); and is further reduced to ~10% when put2 is deleted. This implies that there are two inhibitory effects of proline, not one primary one. At the least, this option should be discussed, including why deletion of PUT1 leads to proline toxicity. The latter is not clear-is it that too much proline accumulates in the cell and this accumulation is toxic? If this is the case, the effect would be expected to be proline concentration dependent. Performing a relatively simple experiment as performed for the put2 mutant (Fig. 3 / S3F) may clarify this issue. Particularly, if the experiment would be coupled with intracellular quantification of proline.

Precisely! Proline toxicity is evident even in put1 mutants, clearly suggesting that proline, without being further catabolized, exerts a growth inhibitory effect (Fig. 3A). We traced this inhibitory effect to decreased mitochondrial respiration (Fig. 3E). There are two parameters to consider regarding the inhibitory effects of proline in put2 mutants. First, the presence of proline induces the expression of Put1 independent of Put2 (Fig. S2C), consequently, the levels of the toxic intermediate P5C increases (Fig. 3B). P5C has previously been postulated to inhibit mitochondrial respiration, which is well-aligned with our analysis (Fig. 3E; see response Point 5). We initially tested whether a proline-P5C cycle, suggested by work in mammalian cells, would play a role in proline-mediated toxicity; however, increasing cytoplasmic pools of proline by supplying high levels of glutamate (which according to work in mammalian cells should efficiently convert to cytoplasmic proline) did not occur; we did not see glutamate-enhanced Put1 expression (Fig. 2D, S2A, S2B). We agree with the reviewer with respect to the suggested experiment, and have monitored growth of put1 in media with different proline concentrations. The results are incorporated in the revised Fig. 3.

1. The caption "P5C mediates a respiratory block" is misleading, as the evidence is not that compelling: Although P5C increases in put2, but not in put1 mutants, and given that both single mutants experience a proline-dependent respiratory defect (Fig. 3E), the results suggest a more complex relationship.

Previous work using pure P5C (Ref. 36; Nishimura et al) showed that it targets respiration, hence the caption “respiratory block” in the header. In mammals, PRODH (Put1) physically interacts with mitochondrial respiratory complex II in the inner mitochondrial membrane (line 89-90), while P5CDH (Put2) is in the matrix. The put1 mutation might affect basal activity of the respiratory chain resulting in lowered respiration, which may compound when proline accumulates in the mitochondria. The inhibitory mechanism remains unknown, and in going forward we have begun characterizing various GFP-tagged respiratory complex components in put1 mutants and in strains co-expressing Put1-RFP (for interaction studies). The results are out of the scope of this current work.

1. The virulence assays and in vivo experiments do not present a unifying view: in Drosophila put2∆∆ is less virulent than put1∆∆, which appears similar to put3∆∆. Given that put2 mutants grow slowly, likely because of P5C inhibition, this seems logical. However, in mice, put3∆∆ remains highly virulent while put1∆∆ and put2∆∆ results for survival are mixed. Furthermore, in 4 mouse organs, put1∆∆ and put2∆∆ are not significantly different from one another but are different from wt, while put3∆∆ has no significant reduction in CFU. Kidney histology shows very little invasion by put1 and put2 and more by put3, but visually put3 appears to invade much less than the WT, and the human neutrophil experiment shows effects of put2 or put3 but not put1. This leaves the reader rather confused. It may be worth discussing the reasons for different results in different models. Is the availability of proline in each of the organisms and organs similar?

We thank the reviewer for these thoughtful observations, however, we note that all of the diverse assay systems employed provide a clear and consistent indication that the inability to completely catabolize proline significantly reduces virulence. This is well-aligned with our previous data regarding the need for proline catabolism to escape macrophages (Silao et al, 2019). The requirement for Put3 may not be very strict since the Put enzymes are still expressed in the absence of Put3 (Fig. 2D/S2A/S2B), indicating the activity of additional regulatory factors; hence, this may explain why the put3 strain behaves like wildtype in the murine model (Fig. 5B). The dispensability of Put3 in the murine model could be due to a lower neutrophil count and that murine neutrophils exhibit a lower affinity for fungal cells as compared to human blood (Machata et al., 2020, Front Immunol). The more pronounced requirement of Put3 to survive in whole human blood and when co-cultured with human neutrophils could indeed be linked to the need to rapidly derepress PUT1/PUT2 (and even other target genes) as suggested by the global RNASeq analysis that shows that proline catabolism is a core response of C. albicans during neutrophil interaction (Niemiec MJ et al., 2017, BMC Genomics). In Drosophila, a well-established model to study innate immunity, the presence of hemocytes that fulfill the equivalent functions of neutrophils and macrophages could explain the increased requirement for Put3. In summary, although it is impossible to know the precise mechanistic basis underlying the observed differences, we believe it unreasonable to expect that all mutations behave identically in each virulence model. In fact, differences considered trivial such as the use of mouse background can have profound effects on virulence. Presumably the differences we report are due to the specific nutrient composition (proline and metabolites feeding into the proline catabolic network) and physical parameters intrinsic to each model. For instance, Lionakis et al. (2013) suggested that filamentation occurs faster in the kidney compared to other organs, such as the liver/spleen, indicating the presence of kidney-specific cues that drive infections of this organ.

1. The ex vivo and in vivo analysis of the dynamics of C. albicans growth in the host is visually impressive, but it distracts from the focus of the paper and the metabolic findings. Showing that put mutant cells do not form filaments in vivo (as in vitro) does not add much conceptually to the paper. Furthermore, this lovely advance in in vivo visualization is lost at the end of this paper and the authors should consider whether it might fit better in manuscript that could really highlight the in vivo visualization approach.

We appreciate this comment. Indeed, our lab is at an advanced stage of completing a manuscript focused on the use of intravital and clearing microscopy to follow the onset of an upper urinary tract infection (UTI) in a murine candidemia model. However, our ability to visualize in 3D the onset of an infection in a living host is not a trivial achievement and we were impressed that it provided a clear answer as to whether a single C. albicans cell can initiate an infection and undergo morphogenesis leading to hyphal growth. Furthermore, we tested a put2 strain, the growth of which is highly sensitive to the presence of proline, and found that it did not exhibit filamentous growth. This clearly shows that cells colonizing the kidney are exposed to an environment that requires a functional proline catabolic network to exhibit filamentous growth, a characteristic of renal infections. Our results are consistent with the kidney being a metabolic hub for arginine/proline biosynthesis, which likely increases the levels of these amino acids in this organ.

1. The discussion of cells stained with FITC and expressing yEmRFP does not clearly point out that the FITC is only an indicator for those cells that were used to innoculate the tissue and that finding cells without FITC indicates that they are mitotic progeny, indicating that they have been dividing. The authors clearly understand this, but a naive reader may miss this important point if it is not stated explicitly.

We have adjusted the text to explicitly clarify this.

Minor comments:

1. Throughout: what is the distinction between utilization of proline for C or for energy? These terms seem to be used interchangeably.

C. albicans is heterotroph that can use proline to generate biomass (gluconeogenesis, etc) and its catabolism generates sufficient amounts of ATP to power growth. Thus, when proline is used as sole carbon source, it can also serves as the sole energy source. In the text, we have tried to be consistent using “carbon source” when discussing proline as a component of growth media, and “energy source” when discussing proline catabolism.

1. Introducing the schematic in Fig. 2A at the beginning of Figure 1, would help explain proline catabolism before delving into the growth experiments that rely upon this framework. This should include an explanation, for readers less familiar with the metabolic issues, of the main limitations to catabolizing proline, and the key issues for being able to use proline for nitrogen, carbon, and energy (potentially indicated in the overview figure, e.g. pointing towards gluconeogenesis etc.).

We have considered the reviewers suggestion, however, we believe that the placement of the schematic in Fig 2 is appropriate as is, and where it will hopefully enable readers to more readily grasp the strain construction and experiments documented in Fig.2.

1. Saccharomyces can only grow on proline as a nitrogen source, but not as energy/carbon source. Could the authors briefly mention or discuss why this is the case? This is not clearly apparent after reading the manuscript and it leaves the reader confused and trying to understand if the fact that proline is required for carbon utilization is a new finding of this paper or was already known. Do the authors think this is tied to the presence of complex 1 components in C. albicans that are not found in S. cerevisiae. Is this consistent for the pathogenic, but not the non-pathogenic yeasts analyzed in figure 1?

We have adjusted the text to clarify our thoughts regarding this. Indeed, we do believe that a major reason for the ability of C. albicans to efficiently grow using proline as a sole energy source is the presence of Complex I. However, C. glabrata appears to be able to grow well using proline as sole energy source despite apparently lacking Complex I. Consequently, alternative NADH dehydrogenases exist in C. glabrata, but how this is coupled to energy metabolism will require additional work that is out of the scope of the present work.

1. 100: While Gdh2 is apparently an important enzyme for generating ammonium, why is it not necessary for macrophage escape and virulence as shown in reference 18? A recent paper from Garbe et al (ref 12) suggests that Gnp2 is the major proline permease in C. albicans and what is known, and not known, about proline uptake would be good to mention, given that PUT gene functions require that proline enters the cells.

We have recently shown that ammonia generation by Gdh2 is dispensable for macrophage escape and documented that phagosome alkalinization is not a requisite for the induction of hyphal growth (Silao et al. 2020). We have referred to the work of Garbe et al., which is consistent with our previous work (Martinéz and Ljungdahl, 2004) where we reported that proline-dependent filamentation is dependent on Csh3. Csh3 is an ER membrane-localized chaperone responsible for catalyzing the proper folding of amino acid permeases, in csh3 null mutant strains, amino acid permeases accumulate in the ER as non-functional unfolded aggregates. Consistently, we have tested and found that proline-induced Put2-GFP expression is dependent on Csh3 (unpublished), clearly establishing that the regulatory effects of proline are dependent on its uptake. We have not generated a gnp2-/- strain, but suspect that we could find growth conditions where such a mutant would be refractory to proline induction. We have adjusted the text to include this information.

1. 116: Is the "low sugar environment of the host" referring to a specific niche, such as the GI tract, or human blood? Compared to most natural environments, glucose is abundant in the host, e.g., at ~5 mM, it is the most abundant metabolite in blood, and similarly, in the GI tract, levels can go beyond 50 mM glucose (see e.g. PMIDs 34371983, 21359215). Or is this comment indicating that the in vivo sugar concentration is lower than that in common lab growth media? Please spell out the niche/concentration for clarification - and compare that to other niches that are considered "high sugar environments".

We have adjusted the text to clarify our statement. The natural environment of C. albicans is the human host. Virulent infections are not within the GI with high sugar content, but rather result when C. albicans cells successfully cross into the blood with a relatively low glucose (5 mM), which importantly is a level that does not effectively repress mitochondrial function. A major point of our recent work is that laboratory experiments with C. albicans growing on YPD or SD with 2% glucose (111 mM) examine growth of cells with repressed mitochondrial functions.

1. 123: "proline as sole energy source" - suggest "is the source of carbon, nitrogen, and energy"

The text is adjusted (see response to Minor Point 1).

1. 142: it is worth noting to readers that C. neoformans is a basidiomycete and thus VERY distant from the other yeasts studied here-it is in a different major phylum of fungi.

Again, thanks for this suggestion, the text is adjusted. We included C. neoformans since the role of proline catabolism has been characterized and linked to its pathogenicity (reviewed in Christgen and Becker, 2018, Antioxi Redox Signal, Ref. 1).

1. 143: Here it is implied that put1 and put2 mutant strains do not grow on SPD, but this is not stated explicitly.

The put1 and put2 mutants are unable to grow in/on all media containing proline as sole nitrogen source. The phenotype is very tight that we were able to exploit this as a selection phenotype for reconstitution (Fig. 1A). We have adjusted the text to make this clear.

1. 151: The abbreviation SPG is not explained in main text. This was explained in the methods (1% glycerol as primary carbon source).

As suggested, we have defined SPG in the main text.

1. Paragraph 156 onwards: this section is particularly hard to read and very dense. Also, it is difficult to understand the significance of these experiments for the overall findings of the paper. Please at least provide a small conclusion / summary at the end of the paragraph that puts the findings into perspective.

We have adjusted text to make it more accessible.

1. Figure 2 C: simplifying the scheme (e.g. lots of redundant information, P2 and Mito - just give it one name) would help. This figure may be better in the supplementary material.

The schematic of our subcellular fractionation study uses standard designations routinely used by the cell biology community. We believe that its inclusion will help readers judge the how we mapped the intracellular localization of the reporter proteins, which is essential to understand the proline catabolic network.

1. Figure 2B: It is not directly apparent from the micrographs that Put1-RFP localisation is mitochondrial. Co-localisation of the RFP with a mitochondrial dye (e.g., mitotracker) or something similar is required to validate it.

We have previously reported that Put2 is a bona fide mitochondrial protein (by confocal microscopy, subcellular fraction, and co-localization with Mitotracker (Far Red) (Silao et al., Ref 17). The fact that the Put1-RFP associated fluorescence exhibits a distinct mitochondrial signature, is spatially exclusive and exhibits no overlap with the cytosolic pattern of Gdh2-GFP, co-fractionates with Put2-HA and the mitochondrial marker Atp1, should suffice to confirm that Put1-RFP is a mitochondrial localized protein.

1. Throughout the manuscript (figure legends): Suggest using "mean" instead of "Ave."

We have adjusted the legends.

1. 175: According to the 'Yeasttract' and 'Pathoyeasttract' databases, Put1 regulates at least 36 and 22 genes, in S. cerev. and C. alb., respectively (based on DNA binding and/or regulatory changes). The only gene in common between these two lists of genes is PUT1. Thus, it is quite likely that Put3 regulates many other processes that explain its function and that its major function may not be only to regulate Put1.

We assume that the reviewer is referring to Put3 (instead of Put1). Yes, Tebung et al. (2017) suggested that Put3 also regulates other genes. However, their data show that C. albicans put3 mutant was unable to grow in medium (YCB+Pro) compared to SPD (2% glucose as carbon source) where proline is used merely as a nitrogen source (Tebung et al., Fig. 3A). Our data in Fig. 1C shows that a put3 null strain exhibits residual growth on SPD, which aligns well with the expressed levels of PUT enzymes (Fig. 2D). Our conclusion is that despite being essential for rapid proline-dependent derepression of proline catabolic genes, Put3 is not the only transcription factor operating at the promoters of the PUT genes.

1. 175: Is it clear whether the Put3-independent mechanisms are positive or negative with respect to Put1?

We have accumulated evidence that an additional transcription factor positively regulates PUT1 expression and have a manuscript in preparation to describe this factors. The manuscript will focus on the Put3-independent regulation of PUT1, PUT2, and GDH2 expression.

1. 218: Suggestion: "growth was indistinguishable".Unless growth curves or growth rates are provided and if one time-point data are the basis for this point, than "rates" is not a relevant term.

The reviewer is correct; we will adjust the text accordingly. We have performed growth assays in a multi-well microplate format (Bioscreen) and found that the growth rates are not statistically different between WT, put1, put2, and put1 put2 strains in the presence and absence of proline in SD with 2% glucose. This is consistent with glucose repression of mitochondrial function, i.e., proline toxicity depends on derepression of mitochondrial function.

1. 256 onwards: did the authors test if the ROS scavenging effectively reduced ROS? i.e. does the luminol-HRP assay yield less ROS in +proline +scavenger treatment? This is necessary to effectively conclude that the growth inhibitory effect of proline is due to blocking respiration.

Indeed, we used NAC as a control in the luminol-HRP system and we saw reduction in ROS formation. In fact, this is the underlying reason why we used high levels of NAC for growth rescue (in Fig. 3D). We include the control data as Fig S3F.

1. The Figure captions are extremely lengthy and detailed, making it cumbersome to find the relevant information. Suggest moving some of the information, such as additional experimental details, into the methods section.

We have streamlined the figure legends.

1. 277-301: Phloxine is not exclusively a live/dead cell indicator-it is an indicator of metabolic activity. In Scerev. and Calb. it also indicates slower growth, opaque growth, and it has been used as an indicator of aneuploidy in C. glabrata (https://journals.asm.org/doi/10.1128/msphere.00260-22) and of diploids vs haploids in S. pombe. The colonies illustrated aer made up of many live cells, and thus the section "Defective proline utilization is linked to cell death" needs to be presented more carefully. In addition, it appears that this section shifts from using defined medium to using rich medium and 37C instead of 30C. Why was this shift necessary?

The reviewer is correct that phloxine (PXB) has been used to identify opaque growth (EFG1-dependent). However, the fact that the accumulation of PXB in the put mutants is evident in both SC5314 and cph1 efg1 backgrounds (Fig. 3G and Fig. S4C) suggests that we are not assaying opaque switching. We mention that we have observed an increase in the number of PI+ cells in put mutants under similar conditions, but as we pointed out, we were unable to reliably quantitate this by FACS due to the clumping of put mutants. Zheng et al 2022, the paper cited by the reviewer, used PXB to assess the ploidy of C. glabrata strains, but their assay was developed using 5 μg/ml PXB, half of the concentration we used. The homogenous accumulation of PXB as the macrocolonies grow (Fig. 3G), suggests that the accumulation is not a consequence of spontaneously occurring ploidy variations. Thus, we believe that the accumulation of PXB does indeed reflect enhanced cell death. The point here is to trace the consequences of proline toxicity and to test the dependency on mitochondrial function. We used complex media, which contains multiple nitrogen sources (amino acids, peptides), to specifically highlight the contribution of proline catabolism in the fitness of C. albicans. The put1, put2 and put1 put2 mutants grow normally on YPD+PXB (30 oC) without accumulating the dye; we only observed visible PXB uptake in put2 after 2-3 days in mature macrocolonies. We attribute the gradual increase in PXB accumulation to be a consequence of glucose becoming limiting, derepressing mitochondrial functions, a requisite for proline toxicity. Consistently, the accumulation is more evident in cells grown on non-fermentable C-sources (Fig. 3G and Fig S4C).

1. 295-301: Related to the point above, these results are hard to interpret due to the switch from defined medium in all prior experiments to rich growth medium here. Also, it is not clear why a 48h old YPD culture was chosen to show that the degree of PI staining correlates with mitochondrial activity - is this due to the culture age? It would be more clear to image cells grown on glucose vs. glycerol/lactate, or under repressive / de-repressive glucose concentrations (e.g., as shown in Fig. S4C where a PI+ difference is apparent for 0.2% glucose vs. 2% glucose at 30 oC).

See response to Point 19 for our rationale to switch to rich medium. We have adjusted the text to enhance its readability. In liquid YPD, all strains grow, however, we noticed that the put mutants tend to flocculate (sign of stress in yeast) when cells enter stationary phase, giving rise to erratic OD readings, particularly evident in the put1 mutant. At 48h, the cultures become dense and cells experience glucose limitation, derepress mitochondrial functions and exhibit maximal flocculation (Fig. S4D). In put mutants, the derepression of mitochondrial function results in proline sensitivity. We tested the notion that this would also increase cell death, which it does, see Fig. S4E.

1. 313-14: The statement 'the invasion process was dependent on the ability of cells to catabolize proline' doesn't take into account that put mutant cells are defective in filamentous growth irrespective of their utilization of proline...and like the efg1 cph1 double mutant.

Proline-induced filamentous growth is dependent on the catabolism of proline, which activates Efg1 and consequently the hyphal growth program. In Fig. 4A we show that put mutants grown on Spider media, initiate filamentation (as evidence by wrinkled colonies) but do not grow invasively (no halo). In Fig. 4B we developed and used a novel invasion assay to assess growth through a collagen plug. Similar to the control cph1 efg1 mutant, the put mutants exhibit drastically reduced capacity to penetrate through the plug, and reach the D10 media in the transwell (D10 = DMEM with 10% FBS). However, it is important to note that although these results are linked to two distinct processes - the filamentation defect of cph1 efg1 is due to the inability respond to multiple filamentation cues (e.g., CO2, 10% FBS, etc.), whereas the filamentation defect of the put mutants is linked to the inability to catabolize proline and to its toxicity. Clearly, the WT strain relies on proline catabolism, coming from one or three possible sources of proline (see response to Reviewer 3): 1) DMEM/F-12 medium used in the PureCol EZ Gel; 2) diffusion of nutrients up through the collagen from the recovery medium DMEM supplemented with 10% FBS; and 3) the proteolytic breakdown of collagen. Also, in contrast to the put mutants, WT cells are refractory to inhibition by proline.

1. 316-327: The results of the experiment described can only be interpreted as an effect of proline catabolism if the three strains (efg1 cph1; put1; put2) have similar growth rates as yeast cells in vitro. Why weren't the cells competed directly (efg1 cph1 vs put cells)?

We believe that the relevant comparisons are to WT. We recovered cells from the top of the collagen (see Fig. 4B inset) to monitor their ability to survive and grow on top of the collagen. We found that the ability to catabolize proline enables WT and cph1 efg1 cells to grow equally well (recovered similar ratio as starting input). This was not the case with the put mutants, they did not grow as well and almost 100% of the cells recovered were WT.23.

Fig 6: The logical order of the experiments, and in the text, is: 1) 4 h window, 2) 26 h window and then 3) ex vivo. The cartoon in 6B should be in this order as well.

Thanks for bringing this issue up. We have adjusted the figure and text placing the schematic time-lines in proper order.

1. 337: it is not clear what the 'direct exposure...' is trying to tell us. Can this be made more explicit?

The direct exposure means that the fungal cells are in contact with the culture media at the edges/border of the 3D skin model (see schematic diagram). Hence, fungal cells are in direct contact with 10% FBS, facilitating the observed filamentous growth. The inability of the put mutants to invade the skin model should be evaluated at the center of the artificial epithelium where there is likely a local increased concentration of proline stemming from the proteolytic activities associated with fibroblasts and keratinocytes.

1. 340-346: Here proteins with high proline content were used to ask if they could be induce transcription of PUT1 or PUT2 RNA and protein. This experiment is designed only to test the role of these proteins to induce utilization of nitrogen, as glucose is included in the medium. Given that these proline-rich proteins need to be lysed by proteases before they can be imported, and since no import pathways were tested, the results appear to tell us that mucin is more readily digested to peptides that contain proline-but why that is the case is not clear and how it relates to proline utilization is also not clear.

We thank the reviewer for raising this important point. First, we monitored protein not mRNA levels. We will adjust the text to provide better context for this experiment. Briefly, these experiments were initiated as we were perplexed as to why the wildtype cells took such a long time (14 days) to fully invade the collagen matrix (Fig. 4B); we naïvely assumed that fungal cells would secrete proteases to degrade the collagen and assimilate the liberated proline. In going forward, our experimental strategy was to incubate various proteins with a dense culture of cells in HBSS medium (pH 7.4) supplemented with low glucose (3.8 mM) and lactate (0.83 mM). This condition mimics interstitial fluid, where most broad range proteolytic enzymes are inactive or at least operating suboptimal. The results were clear; with the exception of mucin, the proteins did not stimulate Put1 or Put2 expression. We conclude that host-dependent processes play an important role on the release of the amino acids/peptides from these high-proline content proteins (see line 531-553 for discussion). The capacity of mucin to efficiently induce Put1 expression is interesting since mucin is abundant in the gut where systemic infections are thought to originate. It is important to be cautious here, we used a commercial mucin preparation (Sigma, 2 batches) that may contain degradation products, e.g., proline-rich peptides, that can easily be assimilated by C. albicans. Put1 expression is an excellent readout for proline uptake since its expression responds tightly to the presence of proline derived from exogenous supply or from intracellular conversion (Fig. 2D, S2A, S2B).

1. 363-369 An alternative is that Put3 induces different proteins important for growth.

We included this possibility in the revised text.

1. 379-380-the conclusion for this paragraph is somewhat of an overstatement as there is no analysis of the degree to which proline utilization is a predictor of virulence. It simply shows that put mutants affect the ability to survive in neutrophils.

We have adjusted the text.

1. Discussion: The statement that "S. cerevisiae" evolved in high sugar environments is debatable. The natural niche could well be forest soil and tree bark, or insect/wasp guts with arguably little glucose around.

The reviewer is correct, S. cerevisiae can be isolated from diverse environments with variable sugar contents, but it is the capacity to deal with high sugar environments that makes this yeast stand out in comparison to Candida spp. The unique attribute of S. cerevisiae have been exploited and truly benefited humankind in making alcohol and bread. We have amended the text to state this more accurately.

1. 469-470-how strong is the 'correlation' between the ability to utilize proline and virulence? Given that different mutants had different effects in different models, this seems like a very loose 'correlation'; it would be good to have some quantitative measures to make this claim.

We have used directed genetic approaches to determine whether a gene/protein is essential for virulence by testing them in currently available infection models. It is important to note that all virulence assays provided a consistent and clear read-out, namely that the inability to catabolize proline significantly reduced the expression of virulence characteristics. Presumably the differences we report are due to the specific nutrient composition (proline and metabolites feeding into the proline catabolic network) and physical parameters intrinsic to each model. In fact, the expression of virulence factors (i.e., hyphal growth) can significantly differ in different organs within a same mouse model (Lionakis et al., 2013) and that virulence outcomes can change depending on mouse background. We fail to see how this can be viewed as loose. This has not been shown before. Please refer to our response to major point 6.

1. 500: Was the experiment was done in larvae, and not in adult Drosophila? Fig 5 legend says flies and shows a picture of a fly and larvae are only mentioned much later in the text.

These experiments were performed using adult flies. We now include a reference regarding the levels of arginine in hemolymph in both larvae and adult Drosophila (Priyankage et al., 2012; Anal Chem).

1. 512:Why is it presumed that proline accumulates in the mitochondria in put1 mutants? How strong is the presumption?

Despite a great deal of efforts in many labs, the mechanism of proline transport across the mitochondrial membrane is not known. What has been shown in mammalian and plant systems is that proline can readily enter and accumulate in mitochondria where it is catabolized. (https://link.springer.com/article/10.1007/s00425-005-0166-z; https://www.sciencedirect.com/science/article/pii/0003986177902089). Our presumption that proline accumulates in the mitochondria is based on our finding that proline inhibits mitochondrial respiration when Put1, catalyzing the first oxidation reaction, is absent.

1. 539: why are MMPs important for digestion of collagen? This is not clear at this point of the Discussion.

In mammalians cells, some secreted MMPs have collagenase activity (e.g., MMP-1) that degrade proteins comprising the extracellular matrix, which releases proline. We emphasize this since the 3D skin model is comprised of dermal fibroblasts and keratinocytes that are known to secrete MMPs (Ref. 69).

1. 574: Concluding sentence of this paragraph seems unsubstantiated. There are at least two defects in put2 strains-hyphal growth and growth in general, presumably because of P5C accumulation.

See response to point 21. Proline-induced filamentous growth is dependent on its catabolism, which activates Efg1 and consequently the hyphal growth program. However, there are many potential cues in hosts that could induce hyphal growth in situ. Our finding that strains unable to catabolize proline do not filament, indicates that proline is a key modulator of virulence.

1. Fewer abbreviations would make the manuscript easier for non-experts to read. For example, P5C is not defined in the abstract. Furthermore, if an abbreviation is not used more than 3 times, it is not necessary to provide it (e.g., mammalian proteins in the last paragraph).

We have adjusted the text.

typos:

1. 82: should read 'is restricted to the mitoch...'

2. 102-103: should read 'to evade macrophages'

3. Fig. S4F is mislabelled as Fig. S4G.

Thanks!

**Referees cross-commenting**

Overall, we stand by our initial assessment of the study. However, we were not aware of previous studies that investigated proline utilization in yeasts, as noted by Rev # 2 (https://onlinelibrary.wiley.com/doi/epdf/10.1002/yea.1845). The current study suggests that using proline as an energy/carbon source is more wide-spread, beyond pathogenic yeasts. Further, the C. albicans strain they used for this study (ATCC 10231) was apparently unable to grow on proline in the quoted paper. In light of this, we think the authors should reference this study, tone down the claims about the clear correlation of pathogenicity and proline utilization, and address this apparent discrepancy with the indicated Candida albicans isolate. We note that our review considered this a paper mostly of interest to specialists.

Although other non-pathogenic fungi have been shown to use proline as pointed out by Reviewer 2, this metabolic attribute has not been previously tested in members of the pathogenic Candida spp. complex. We have included the reference and included a statement that many fungi, isolated from diverse environmental niches, can use proline as a carbon source.

Reviewer #1 (Significance (Required)):

1. The advance in this paper is conceptual for the proline utilization connection to virulence in a range of species and technical for the in vivo microscopy. Limitations are that the conceptual advance is based only on qualitative work in figure 1 and that the animal studies do not provide a conceptual advance, although the technical advance of in vivo visualization of kidney tissue is impressive and (to the knowledge of this reviewer) quite new as the only prior work was in mouse ears.

In response to the reviewer’s comment regarding Fig. 1, although it is qualitative, it is very reproducible. We even tried several clinical isolates of S. cerevisiae and observed consistent behavior to the standard laboratory strains (i.e., they do not grow on SP medium where proline is used as sole carbon/nitrogen/energy source). We tried to quantify growth of all strain in liquid SP medium at 30 oC using a TECAN microplate reader, but then the results show very erratic reading among species (and replicates) as each behaves differently; C. tropicalis, C. krusei, and C. parapsilosis form pseudohyphae and clump readily, while C. albicans forms hyphae and pseudohyphae.

2.The work fits well as an extension of the body of work from the corresponding author's lab with additions from the labs with expertise in models of infection.

1. People interested in yeast metabolism and pathogenic yeast virulence will be the audience for this paper and as written it is for a specialized audience interested in pathogenic yeast metabolism and, perhaps, (although not mentioned at all in the text) for those who want to try PUT gene products as new drug targets.

This was actually mentioned in the last paragraph of the discussion (line 581-582).

1. Reveiwer expertise is in pathogenic yeast biology and yeast metabolism. Little expertise in high tech microscopy.

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

The study is part of the continuous work by the authors to dissect the mechanism of utilization of proline as a carbon source in Candida spp. In particular, this work shows that the inability to process proline leads to accumulation of the toxic intermediate P5C and subsequent inhibition of mitochondrial respiration and toxic effect on the cells. Furthermore, the study demonstrates that proline utilization is important for C. albicans kidney colonization. The experiments are meticulously designed and the study adds to the overall understanding of the metabolic utilization of proline as a carbon source and its potential relevance for infection.

I find this work interesting, but the role of Put1 and Put2 in proline utilization is not particularly novel. The novelty here is the subcellular localization of the two proteins. Also, the importance of proline utilization for infection is unclear. The host-pathogen interaction assays are ambiguous as each assay gives different result. Lastly, the authors try to generalize the importance of use of proline as a energy source by other Candida spp.. This is not very surprising, given that it has been reported previously by others (example DOI: 10.1002/yea.1845) and that many pathogenic or closely related to C. albicans species use various amino acids, not only proline, as a carbon source.

Yes, as reviewer 2, we are not surprised that many of the pathogenic members of the Candida spp. complex are able to use proline, but this needed to be checked. The fact that proline can be used as a sole carbon/nitrogen/energy source clearly set them apart from the paradigm yeast S. cerevisiae. A major question is what amino acids are important in the context of the host? To assess this, we have used mutations that specifically block proline utilization. Our past studies demonstrating that proline catabolism is rapidly activated in C. albicans cells phagocytized by macrophages indicates that proline is present in the phagosomal compartment. Furthermore, put mutations clearly affect virulence in flies and murine systems. We are at a loss to understand why the reviewer believes that our data, which consistently shows that proline catabolism is important, is ambiguous.

The expectation that all three mutant strains, i.e., put1, put2 and put3, would behave identically in the different infection models reflects an unnuanced view of how infection works. In fact, differences considered trivial such as the use of mouse background can have a profound effects on virulence. Consequently, it is striking how the diverse infections models consistently and unequivocally demonstrate that proline catabolism affects virulence. Also, it should be appreciated that we are not testing mutations affecting proteins with many overlapping functions, where it may be appropriate to challenge claims as to their direct role in virulence. Here we tested mutants that lack the enzymes that catalyze proline utilization. A more reasonable expectation is that the virulence is commensurate to the specific nutrient composition of model systems (as asked by reviewer#1), which can fluctuate among models (see our response to the major comment 6 of reviewer 1). As it is not practical to precisely test the proline levels in the models, we have worked to identify and focus on critical phenotypes that can be analyzed in vitro. Our findings provide the basis for understanding the virulence and growth properties of the mutants in the context of the complex infection models.

Moreover, the authors take C. albicans as an example to demonstrate the role of PUT in invasion and infection. Proline is known stimulus for hyphal growth in this species, but many other Candida spp., including C. auris, do not filament. So how, aside from supporting growth, proline is linked to infection in these species? I think the authors oversell the importance of proline in Candida spp. pathogenesis and should tone this part down or remove completely. A new story that validates the importance of PUT in non-albicans species can bring clarity to why and where proline is critical for survival and infection.

The fact that proline supports growth in the host environment is one of the critical aspects of our work. The lack of appreciation for this finding represents a common misconception in infection biology. It is not just the ability to gain access to a host and initiate an infection that counts, it is equally important to sustain growth and to thrive within the host. Thus, the adaptation to the host environment is critical. Here we document that proline catabolism not only initiates but sustains an infection acting as a critical carbon/energy source. The inability of the put1 and put2 mutants, which are sensitive to proline, to grow and infect multiple models clearly suggests the substantial quantity of proline is accessible. Also, we have constructed C. glabrata (Fig. S1C) and C. auris (not shown) strains that lack the ability to catabolize proline, and are currently characterizing the virulence properties of these strains. This is out of the scope of the present study.

Major comments: I am not convinced by the data that proline is important to initiate infection. Candida infections of the kidney occur only at late stages of sepsis. The authors need more compelling data to prove that proline is important for infection in the host.

Again, not sure why there is such skepticism here, regardless of whether kidney infections occur late, the fact that in contrast to WT, we do not observe put mutants filamenting, clearly suggesting that the capacity to catabolize proline plays a role in the expression of virulence characteristics of C. albicans. Based on our findings using IVM, which provides 3D information, we can at least conclude that a single isolated C. albicans cell can initiate hyphal growth, initiating a point of infection. In addition, our newly added whole human blood data suggests that proline catabolism is required for survival in the blood; human blood contains high amount of proline, arginine, and ornithine that are all catabolized via the proline catabolic network.

Minor comments: I find the manuscript difficult to read and the discussion part is overly long. Some streamlining and adding a bit more explanation for the rationale of each experiment will make the work easier to follow. Some language/style needs refining as well.

We have attempted to take this critique into account during the revision of the manuscript and have streamlined the text and added explanations regarding the rationale underlying our experimental approaches.

**Referees cross-commenting**

In this manuscripts the authors clarify the cellular compartmentalization of steps in proline catabolism. However, it is not novel that proline is a valuable carbon source. The role of proline utilization for establishing or progression of infection remains ambiguous even after the authors provide different in vivo results. The overall significance of the study is limited.

Please refer to our comments below. We do not understand that the reviewers apparently question the obvious role of proline utilization facilitating virulence.

Reviewer #2 (Significance (Required)):

The strengths of this study are in the experimental design and variety. The data is well presented and visualized. The limitations are as pointed above - I find it especially difficult to figure out where, in a real infection scenario (e.g. breach of the gut barrier and entry into the bloodstream) proline will be the primary energy source. To me the significance of this work is minor.

C. albicans is the primary human fungal pathogen placed under the “Critical Priority Group” by WHO and yet our understanding of nutrient assimilation in this fungal pathogen is only a fraction of what is known in the model yeast S. cerevisiae, which has proven not to be the best paradigm for understanding the regulatory circuits operating in human fungal pathogens. This manuscript, as well as other recent publications, have revisited and corrected earlier assumptions regarding C. albicans growth, providing novel information that reflect important regulatory differences specifically relevant to the life of C. albicans in the host. For example, had it not been for the recent findings (Ref. 10, 18, 31) that show that proline utilization in C. albicans is not subject to nitrogen catabolite repression (NCR) and that glucose represses mitochondrial function, the perception in the field would remain that C. albicans cannot utilize proline as a carbon and/or nitrogen source in the presence of a “preferred” source of nitrogen, which is applicable in the blood that contains high concentrations of possible sources of carbon and nitrogen. Furthermore, the low but constitutive expression of Put2 and the tight highly responsive Put1 expression in response to proline (Fig. 2D, S2A, S2B), suggest that C. albicans is well equipped to productively anticipate proline availability depending on the host status, entirely consistent with its “opportunistic” character. The many incorrect and previously held assumptions regarding C. albicans, uncritically propagated in several influential reviews, likely have hampered efforts to develop novel antifungal therapies. We do not understand, nor accept the view that a more precise understanding of the proline catabolism is incremental.

The type of question raised by the reviewer is exactly what we hope to achieve in the future but to get there we have to have correct assumptions in place, and this is only possible if we have a more thorough understanding of the regulatory mechanisms driving proline utilization in C. albicans. The idea that certain proteins are refractory to degradation by C. albicans suggest that other external factors are triggering the release of amino acids from these proteins. This work however, suggest that proline is likely accessible in the gut due to the presence of proline-rich proteins like mucin (Fig. S5A/B).

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

The manuscript of Silao et al. describes an in-depth investigation of the role of Put1 and Put2 enzymes in proline catabolism and virulence in Candida albicans. This is an extension of previous work in this system. The basic biochemistry and genetics are solid and support the role of these enzymes in the proposed pathway and provide evidence that the build up a toxic intermediate in the absence of Put2 is likely involved in the poor growth of the strain when proline is the only carbon source.

Note that we observe the toxic effects of proline even when it is not the sole carbon source, however, and importantly, toxicity is dependent on mitochondrial function, which is repressed by high levels of glucose. Proline toxicity is observed when glycerol/lactate are present as carbon sources in addition to proline. Under these conditions, mitochondria are not repressed and exogenous proline impairs growth, particularly evident in put2 cells that accumulate the toxic intermediate P5C.

The conclusions regarding its role in virulence are less convincing, particularly the data derived from the collagen invasion assay, the ex vivo skin model and the ex vivo/in vivo imaging. The survival and fungal burden assays support a modest role in virulence and a modest reduction in infectivity (although the presented data for survival does not have statistical significance data reported for the kaplan analysis.

See below for response regarding collagen assay. We have included the significance values derived from Kaplan analysis in the revised Fig. 5B.

The manuscript is clearly written. The methods are well described.

**Referees cross-commenting**

I remain unconvinced of the broad significance of the advances and stand by my assessment that this is for the most part a reasonable study but does not move the field forward. The novel technical aspects are either extensions of previous in vivo imaging or are not well controlled (collagen invasion assay)s.

See below for response.

Reviewer #3 (Significance (Required)):

This is a detailed study of an area that is fairly mature and thus will be of interest to those in the field but does not represent a large advance and is thus truly incremental.

See below for response.

Major limitations of the work are as follows. First, the collagen invasion assay may be flawed. The recovery media is made with DMEM which is a medium that lacks proline and is fairly stringent. Control experiments need to be done to be sure that the mutants grow in the recovery medium. Second, the data from the RHE model are hard to interpret since so few cells are present in the tissue. It is hard to see if there are few filaments of if there are just too few cells to assess in the tissue. Third, in vitro experiments assessing the filamentation of the mutants in the medium in which these assays are preformed need to be done as controls. Candida albicans filaments in many conditions such as tissue culture medium. Spider medium is a strong inducer of filamentation but is very different than in vivo/ ex vivo conditions.

Related to the collagen invasion assay, there is a misunderstanding. The reviewer appears to confuse the put mutations with proline auxotrophy. The put mutants are proline prototrophs and can synthesize proline as they possess a full repertoire of biosynthetic enzymes. In contrast, the put mutants cannot utilize proline to obtain nitrogen or energy. In fact, the presence of excess proline imposes toxicity to the put mutants. There are three possible sources of proline. 1) PureCol EZ Gel is a ready-to-use collagen solution that forms a firm gel when warmed to 37 °C. It contains purified Type I bovine collagen (5 mg/ml) dissolved in DMEM/F-12 medium, which has multiple amino acids, including a substantial amount of arginine. 2) The recovery medium DMEM supplemented with 10% FBS. The presence of FBS provides amino acids and induces filamentous growth. As the reviewer points out, C. albicans grows in this media and exhibits filamentous growth. 3) The proteolytic breakdown of collagen is expected to liberate proline. Consequently, the poor growth of the mutants clearly demonstrate the importance of proline catabolism. Also, the fact that we recovered put mutants surviving on top of the collagen (Fig. 4B, inset) suggests that they remain viable but simply are unable to efficiently invade the collagen. Consistently, microscopic inspection of the wells of the put mutants showed extremely few or even complete absence of invading cells in the recovery medium. We will adjust the text and provide a more detailed description of the experimental set-up. In summary, the main concern of the reviewer with respect to lack of proline is not relevant.

Regarding the 3D-skin model, equal numbers of fungal cells were applied on top of the RHE. To avoid overgrowth, only low numbers (100 C. albicans cells) can be applied for the WT strain, and consequently for all other strains. In contrast to WT, which clearly proliferates, the apparent low level of put1 and put2 cells at the center of the 3D skin model is the consequence of poor growth. The upper layer of the RHE consists of stratified keratinocytes. To grow, WT fungal cells obtain proline either directly from the keratinocyte, from secreted proteases that liberate proline from keratin (proline not as abundant in keratin as in collagen, the main component of the dermis), or from the medium that basolaterally feeds the RHE. At the border of the model leakage from the medium can occur. Our results, showing poor growth of the mutants in the center of the 3D-skin model, entirely consistent with the collagen plug experiments, indicates that proline catabolism plays a determinant role to enable invasive growth.

Lastly, the imaging experiments are highly problematic. First, reference must be made to previous ex vivo imaging reported by the Lionakis lab in 2013. Second, the number of cells imaged is so low that there is no power to make any conclusions. At 24 hr, the mutants may be delayed in filamentation or they may be delayed in establishing infection. There is no way to know what is causing the apparent lack of filaments. This technique as presented is not any higher resolution than traditional histology and in fact histology would provide a more convincing case for reduced filamentation.

These considerations significantly reduce the overall significance of the work.

I work on Candida albicans.

We thank the reviewer for highlighting the beautiful study by Lionakis et al which document the host response, specifically the role of macrophages in mitigating C. albicans infection of the kidney. However, the reviewer apparently failed to recognize that their method is completely differed from ours. Lionakis et al. performed ex vivo imaging of kidney slices using regular confocal imaging, and the authors express an awareness regarding the limitations of this approach. In fact, these authors even state in their discussion that intravital microscopy should be pursued in the future to further investigate Candida-macrophage interactions in the kidney. Also, they point out that kidney-specific factors seem to facilitate rapid filamentous growth of C. albicans. In our work, we have experimentally addressed both of these astute statements. To our knowledge, our work is the first report of imaging a Candida cell infecting a kidney in a living mouse, which on its own is a major development and achievement considering the complexity of the kidney microenvironment. The finding that the put2 mutant does not exhibit filamentous growth in the kidney of a living mouse (24 h) is striking and strongly suggests that a substantial quantity of proline, or amino acids (e.g., arginine) that are metabolized via the proline catabolic network, is present in the kidney. This is clear based on finding that WT C. albicans cells respond accordingly to initiate hyphal growth. Consistent to this, it is well documented that the kidney is a major metabolic hub for arginine and proline metabolism. The work by Lionakis aligns remarkably well with our previous and current work in that put mutants exhibit greatly reduced survivability in co-culture with macrophages and do not evade these primary immune cells due to their inability to induce filamentous growth within the phagosome (Silao et al., 2019). We have adjusted the text to include a discussion that places our work in the context of the Lionakis work.

We have added a Fig. 6C showing an example of the scanned area of the kidney. Further we added the following in the revised legend to indicate that large areas of kidneys were imaged in our assessment of fungal growth and filamentation:

“Sites of colonization where localized using a spiral scan in the Las-X Navigator-module in the FITC channel. The entire area of the renal surface attached to the glass imaging window was scanned; circles highlight examples of regions of interest (ROI) exhibiting stronger and deviating fluorescence from the background. Each ROI was examined in detail using FITC, yEmRFP and autofluorescence. Scale bar, 500 µm.”

CONCLUDING STATEMENT – SUMMARY RESPONSE:

Our current work is based our previous discovery that proline metabolism provides energy to induce and support filamentous growth (PLoS Genetics, 2019). This turned out to be important since we also discovered that C. albicans cells depend on mitochondrial proline metabolism to evade engulfing macrophages, implicating this process as being an important virulence determinant. Consistently, using time-lapse microscopy, we subsequently found that proline catabolic enzymes are rapidly induced in C. albicans cells upon phagocytosis by macrophages. These results demonstrated that proline is present within phagosomes. As exciting as these findings are, they focused on a single phenotype, i.e., filamentation, and were obtained using in vitro experimental approaches. These results demanded that we pursue additional avenues to further characterize and test the in vivo relevance and merely provide a solid background for the current work.

In contrast to reviewer 2 and 3, we do not believe that our finding that proline catabolism plays such a critical role in virulence as being merely “incremental”. We also could not have foreseen that the ability to use proline as an energy source is a common feature of multiple fungal pathogens capable of causing human disease. This is conceptionally very important in that human fungal pathogens, unlike the well-studied yeast Saccharomyces cerevisiae, are not readily found out in nature, and thus have evolved to use a similar spectrum of nutrients as host cells, including cancer cells. It is important for the fungal pathogen community to realize that regulatory switches operating in C. albicans are wired substantially differently to those in S. cerevisiae, and are likely optimized to reflect the actual condition in the host environment. The growing appreciation that diverse cancers are able to shift metabolism to exploit proline as an energy source is strikingly and fascinatingly similar to our findings with pathogenic fungi. This represents a conceptual advance in that it points to the wealth of proline stored within extracellular matrix proteins as providing a potential and significant source of energy for virulent fungal and cancerous growth.

Finally, we strongly believe it is improper to extrapolate virulence properties based on in vitro findings, and that it is essential to actually test host-microbial pathogen interactions using refined in vivo models. Our successful use of advanced intravital microscopy goes beyond traditional and accepted murine infection models and has provided us with a unique state-of-the-art vantage point. Our findings that a single C. albicans cell is able to initiate and establish a site of infection in a kidney within a living mouse is itself important, and coupled to the novel finding that hyphal development at sites of infection depends on the ability of the fungal cells to catabolize proline must reflect the physiological conditions in the kidney. This is not an incremental finding, and we do not understand that reviewers 2 and 3 diminish the significance of these findings. Clearly, our manuscript provides a strong foundation for more detailed and advanced studies.

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

Summary:

Silao et al make the intriguing observation that yeasts that are generally considered less pathogenic are unable to catabolize proline than Candida albicans. They then, in Candida albicans, construct mutants defective for the two key enzymes (Put1, Put2) required to convert proline to glutamate, which they show to be essential for proline utilization as an energy (carbon) and nitrogen source. The authors proceed to untangle the regulatory aspects of proline degradation, including the respective cellular localization of its key enzymes. They then make the important discovery that strains lacking either Put1 or Put2 suffer from a proline-dependent growth defect, which they attribute to resulting defects in mitochondrial metabolism.

The manuscript then goes on to analyze a broad range of infection models including: reconstituted human epithelial skin model, Drosophila, mouse systemic infections, organ colonization in these mice (kidney, spleen, brain, liver and histochemistry of the kidneys) as well as survival when incubated with cultured human neutrophils. Finally, they use yeast cells constitutively expressing yEmRFP (so that yeasts can be distinguished from other host cells) and coated with FITC before incubation with the host cells (which coats the wall of the original cells, but does not spread to progeny) and they go on to perform an impressive set of analyses of C. albicans growth within mouse kidneys both in vivo and ex vivo, exploiting an implanted window together with intravital imaging with a two photon microscope at different time points. The system is impressive and visualizes tissue invasion by hyphal cells beautifully. Finally, they compare the intra vital images from WT and put2-/- cells and show that, as in vitro, put2-/- cells do not form filaments and do not show extensive invasion of the kidney tissue. While the in vivo aspect of the study includes many different models, it finds defects in virulence for different subsets of put mutants and the relative importance of filamentation vs proline utilization for virulence is not conclusively resolved.

Overall, this is an important and timely manuscript, which significantly contributes to the understanding of how proline metabolism intersects with yeast fitness in the context of infections. However, there are several major concerns regarding some of the conclusions drawn from the study. In addition, some general recommendations that would improve the manuscript are provided.

Specifically, the manuscript provides a very detailed description of experiments and observations. However, in several parts it is difficult to follow and the the reader needs more guidance about the logic involved in reaching conclusion. Specifically, several aspects of the paper are written for experts in Candida (yeast) metabolism. Here, explaining the rationale for some of the experiments, and providing more background information that is not obvious to a non-expert, is required.

In particular, writing a clear and measured summary sentence at the end of each paragraph and a conclusion paragraph that summarizes key findings in simple terms would help make the manuscript more digestible for readers.

In addition, the impressive microscopy and broad range of in vivo experiments is comprehensive but only adds incremental information relevant to proline metabolism-that filamentous growth in vivo and virulence is reduced in cells carrying some mutations in one or more put genes. However, this broad sweep of model systems and the development of the in vivo imagining system might have more impact in a separate paper focused on the real-time in vivo visualization of kidney invasion.

Major comments:

1. The main finding that impressed this reviewer is that "removing the ability to catabolize proline, in an organism that evolved to catabolize it, leads to (growth) defects". This point could be better highlighted throughout the manuscript.
2. The authors show that deletion strains for proline metabolism have defects that are important for in vivo pathogenicity. This is an important finding. However, as the manuscript reads now, it suggests that the main findings are that the ability to use proline in the respective host niche is key. Mechanistically, the manuscript revolves primarily around defects that arise when deleting PUT1 and/or PUT2 (i.e., an "unknown" toxicity of proline in the case of put1-/- (or put1-/- put2-/-) and the additional P5C-dependent toxicity for put2-/- mutants; see below).
3. In order to claim that catabolizing prolines promotes pathogenicity (as opposed to the alternative hypothesis that the inability to catabolize proline leads to the observed defects), additional experiments would be required. For example, the put mutants would need to be compared with mutants that significantly reduce/impair proline uptake, such as the referenced gnp2 mutant (Garbe et al 2022). While the finding that less pathogenic yeast species are unable to catabolize proline is both intriguing and important, it also remains as is presented as a loose, non-quantitative correlation that only tangentially address the question of whether "proline catabolism is key for pathogenicity".
4. 238 onwards: The conclusion that "the primary growth inhibitory effect of proline is linked to catabolic intermediates formed by Put1 and that are metabolized further by Put2"does not appear to be fully supported by the evidence. Addition of proline to put1 mutants already reduced OD600 by ~50% (Figure 2); and is further reduced to ~10% when put2 is deleted. This implies that there are two inhibitory effects of proline, not one primary one. At the least, this option should be discussed, including why deletion of PUT1 leads to proline toxicity. The latter is not clear-is it that too much proline accumulates in the cell and this accumulation is toxic? If this is the case, the effect would be expected to be proline concentration dependent. Performing a relatively simple experiment as performed for the put2 mutant (Fig. 3 / S3F) may clarify this issue. Particularly, if the experiment would be coupled with intracellular quantification of proline.
5. The caption "P5C mediates a respiratory block" is misleading, as the evidence is not that compelling: Although P5C increases in put2, but not in put1 mutants, and given that both single mutants experience a proline-dependent respiratory defect (Fig. 3E), the results suggest a more complex relationship.
6. The virulence assays and in vivo experiments do not present a unifying view: in Drosophila put2∆∆ is less virulent than put1∆∆, which appears similar to put3∆∆. Given that put2 mutants grow slowly, likely because of P5C inhibition, this seems logical. However, in mice, put3∆∆ remains highly virulent while put1∆∆ and put2∆∆ results for survival are mixed. Furthermore, in 4 mouse organs, put1∆∆ and put2∆∆ are not significantly different from one another but are different from wt, while put3∆∆ has no significant reduction in CFU. Kidney histology shows very little invasion by put1 and put2 and more by put3, but visually put3 appears to invade much less than the WT, and the human neutrophil experiment shows effects of put2 or put3 but not put1. This leaves the reader rather confused. It may be worth discussing the reasons for different results in different models. Is the availability of proline in each of the organisms and organs similar?
7. The ex vivo and in vivo analysis of the dynamics of C. albicans growth in the host is visually impressive, but it distracts from the focus of the paper and the metabolic findings. Showing that put mutant cells do not form filaments in vivo (as in vitro) does not add much conceptually to the paper. Furthermore, this lovely advance in in vivo visualization is lost at the end of this paper and the authors should consider whether it might fit better in manuscript that could really highlight the in vivo visualization approach.
8. The discussion of cells stained with FITC and expressing yEmRFP does not clearly point out that the FITC is only an indicator for those cells that were used to innoculate the tissue and that finding cells without FITC indicates that they are mitotic progeny, indicating that they have been dividing. The authors clearly understand this, but a naive reader may miss this important point if it is not stated explicitly.

Minor comments:

1. Throughout: what is the distinction between utilization of proline for C or for energy? These terms seem to be used interchangeably.
2. Introducing the schematic in Fig. 2A at the beginning of Figure 1, would help explain proline catabolism before delving into the growth experiments that rely upon this framework. This should include an explanation, for readers less familiar with the metabolic issues, of the main limitations to catabolizing proline, and the key issues for being able to use proline for nitrogen, carbon, and energy (potentially indicated in the overview figure, e.g. pointing towards gluconeogenesis etc.).
3. Saccharomyces can only grow on proline as a nitrogen source, but not as energy/carbon source. Could the authors briefly mention or discuss why this is the case? This is not clearly apparent after reading the manuscript and it leaves the reader confused and trying to understand if the fact that proline is required for carbon utilization is a new finding of this paper or was already known. Do the authors think this is tied to the presence of complex 1 components in C. albicans that are not found in S. cerevisiae. Is this consistent for the pathogenic, but not the non-pathogenic yeasts analyzed in figure 1?
4. 100: While Gdh2 is apparently an important enzyme for generating ammonium, why is it not necessary for macrophage escape and virulence as shown in reference 18? A recent paper from Garbe et al (ref 12) suggests that Gnp2 is the major proline permease in C. albicans and what is known, and not known, about proline uptake would be good to mention, given that PUT gene functions require that proline enters the cells.
5. 116: Is the "low sugar environment of the host" referring to a specific niche, such as the GI tract, or human blood? Compared to most natural environments, glucose is abundant in the host, e.g., at ~5 mM, it is the most abundant metabolite in blood, and similarly, in the GI tract, levels can go beyond 50 mM glucose (see e.g. PMIDs 34371983, 21359215). Or is this comment indicating that the in vivo sugar concentration is lower than that in common lab growth media? Please spell out the niche/concentration for clarification - and compare that to other niches that are considered "high sugar environments".
6. 123: "proline as sole energy source" - suggest "is the source of carbon, nitrogen, and energy"
7. 142: it is worth noting to readers that C. neoformans is a basidiomycete and thus VERY distant from the other yeasts studied here-it is in a different major phylum of fungi.
8. 143: Here it is implied that put1 and put2 mutant strains do not grow on SPD, but this is not stated explicitly.
9. 151: The abbreviation SPG is not explained in main text.
10. Paragraph 156 onwards: this section is particularly hard to read and very dense. Also, it is difficult to understand the significance of these experiments for the overall findings of the paper. Please at least provide a small conclusion / summary at the end of the paragraph that puts the findings into perspective.
11. Figure 2 C: simplifying the scheme (e.g. lots of redundant information, P2 and Mito - just give it one name) would help. This figure may be better in the supplementary material.
12. Figure 2B: It is not directly apparent from the micrographs that Put1-RFP localisation is mitochondrial. Co-localisation of the RFP with a mitochondrial dye (e.g., mitotracker) or something similar is required to validate it.
13. Throughout the manuscript (figure legends): Suggest using "mean" instead of "Ave."
14. 175: According to the 'Yeasttract' and 'Pathoyeasttract' databases, Put1 regulates at least 36 and 22 genes, in S. cerev. and C. alb., respectively (based on DNA binding and/or regulatory changes). The only gene in common between these two lists of genes is PUT1. Thus, it is quite likely that Put3 regulates many other processes that explain its function and that its major function may not be only to regulate Put1.
15. 175: Is it clear whether the Put3-independent mechanisms are positive or negative with respect to Put1?
16. 218: Suggestion: "growth was indistinguishable".Unless growth curves or growth rates are provided and if one time-point data are the basis for this point, than "rates" is not a relevant term.
17. 256 onwards: did the authors test if the ROS scavenging effectively reduced ROS? i.e. does the luminol-HRP assay yield less ROS in +proline +scavenger treatment? This is necessary to effectively conclude that the growth inhibitory effect of proline is due to blocking respiration.
18. The Figure captions are extremely lengthy and detailed, making it cumbersome to find the relevant information. Suggest moving some of the information, such as additional experimental details, into the methods section.
19. 277-301: Phloxine is not exclusively a live/dead cell indicator-it is an indicator of metabolic activity. In Scerev. and Calb. it also indicates slower growth, opaque growth, and it has been used as an indicator of aneuploidy in C. glabrata (https://journals.asm.org/doi/10.1128/msphere.00260-22) and of diploids vs haploids in S. pombe. The colonies illustrated aer made up of many live cells, and thus the section "Defective proline utilization is linked to cell death" needs to be presented more carefully. In addition, it appears that this section shifts from using defined medium to using rich medium and 37C instead of 30C. Why was this shift necessary?
20. 295-301: Related to the point above, these results are hard to interpret due to the switch from defined medium in all prior experiments to rich growth medium here. Also, it is not clear why a 48h old YPD culture was chosen to show that the degree of PI staining correlates with mitochondrial activity - is this due to the culture age? It would be more clear to image cells grown on glucose vs. glycerol/lactate, or under repressive / de-repressive glucose concentrations (e.g., as shown in Fig. S4C where a PI+ difference is apparent for 0.2% glucose vs. 2% glucose at 30{degree sign}C).
21. 313-14: The statement 'the invasion process was dependent on the ability of cells to catabolize proline' doesn't take into account that put mutant cells are defective in filamentous growth irrespective of their utilization of proline...and like the efg1 cph1 double mutant.
22. 316-327: The results of the experiment described can only be interpreted as an effect of proline catabolism if the three strains (efg1 cph1; put1; put2) have similar growth rates as yeast cells in vitro. Why weren't the cells competed directly (efg1 cph1 vs put cells)?
23. Fig 6: The logical order of the experiments, and in the text, is: 1) 4 h window, 2) 26 h window and then 3) ex vivo. The cartoon in 6B should be in this order as well.
24. 337: it is not clear what the 'direct exposure...' is trying to tell us. Can this be made more explicit?
25. 340-346: Here proteins with high proline content were used to ask if they could be induce transcription of PUT1 or PUT2 RNA and protein. This experiment is designed only to test the role of these proteins to induce utilization of nitrogen, as glucose is included in the medium. Given that these proline-rich proteins need to be lysed by proteases before they can be imported, and since no import pathways were tested, the results appear to tell us that mucin is more readily digested to peptides that contain proline-but why that is the case is not clear and how it relates to proline utilization is also not clear.
26. 363-369 An alternative is that Put3 induces different proteins important for growth.
27. 379-380-the conclusion for this paragraph is somewhat of an overstatement as there is no analysis of the degree to which proline utilization is a predictor of virulence. It simply shows that put mutants affect the ability to survive in neutrophils.
28. Discussion: The statement that "S. cerevisiae" evolved in high sugar environments is debatable. The natural niche could well be forest soil and tree bark, or insect/wasp guts with arguably little glucose around.
29. 469-470-how strong is the 'correlation' between the ability to utilize proline and virulence? Given that different mutants had different effects in different models, this seems like a very loose 'correlation'; it would be good to have some quantitative measures to make this claim.
30. 500: Was the experiment was done in larvae, and not in adult Drosophila? Fig 5 legend says flies and shows a picture of a fly and larvae are only mentioned much later in the text..
31. 512:Why is it presumed that proline accumulates in the mitochondria in put1 mutants? How strong is the presumption?
32. 539: why are MMPs important for digestion of collagen? This is not clear at this point of the Discussion.
33. 574: Concluding sentence of this paragraph seems unsubstantiated. There are at least two defects in put2 strains-hyphal growth and growth in general, presumably because of P5C accumulation.
34. Fewer abbreviations would make the manuscript easier for non-experts to read. For example, P5C is not defined in the abstract. Furthermore, if an abbreviation is not used more than 3 times, it is not necessary to provide it (e.g., mammalian proteins in the last paragraph).

Typos: 1. 82: should read 'is restricted to the mitoch...' 2. 102-103: should read 'to evade macrophages' 3. Fig. S4F is mislabelled as Fig. S4G.

Referees cross-commenting

Overall, we stand by our initial assessment of the study. However, we were not aware of previous studies that investigated proline utilization in yeasts, as noted by Rev # 2 (https://onlinelibrary.wiley.com/doi/epdf/10.1002/yea.1845). The current study suggests that using proline as an energy/carbon source is more wide-spread, beyond pathogenic yeasts. Further, the C. albicans strain they used for this study (ATCC 10231) was apparently unable to grow on proline in the quoted paper. In light of this, we think the authors should reference this study, tone down the claims about the clear correlation of pathogenicity and proline utilization, and address this apparent discrepancy with the indicated Candida albicans isolate. We note that our review considered this a paper mostly of interest to specialists.

Significance

1. The advance in this paper is conceptual for the proline utilization connection to virulence in a range of species and technical for the in vivo microscopy. Limitations are that the conceptual advance is based only on qualitative work in figure 1 and that the animal studies do not provide a conceptual advance, although the technical advance of in vivo visualization of kidney tissue is impressive and (to the knowledge of this reviewer) quite new as the only prior work was in mouse ears.
2. The work fits well as an extension of the body of work from the corresponding author's lab with additions from the labs with expertise in models of infection.
3. People interested in yeast metabolism and pathogenic yeast virulence will be the audience for this paper and as written it is for a specialized audience interested in pathogenic yeast metabolism and, perhaps, (although not mentioned at all in the text) for those who want to try PUT gene products as new drug targets.
4. Reviewer expertise is in pathogenic yeast biology and yeast metabolism. Little expertise in high tech microscopy.

Author Response

Reviewer #1 (Public Review):

Various parts of the premotor cortex have been implicated in choices underlying decisionmaking tasks. Further, norepinephrine has been implicated in modulating behavior during various decision-making tasks. Less work has been done on how noradrenergic modulation would affect M2 activity to alter decision-making, nor is it clear whether noradrenergic modulation effects on activity would differ between the male and female sexes.

This manuscript addresses some of these questions.

• In particular, clear sex differences in task engagement are seen.

• May also show some interesting differences and distributions of β2 adrenergic receptors in M2 between males and females.

We thank the reviewer for their summary of our findings and thoughtful critique of our manuscript. In our revised manuscript we have taken measures to address the reviewer’s comments in line (blue edits in text and revised figures) with direct responses outlined below. We believe these revisions improve the scientific rigor of our findings and provide relevant context for our studies. We hope that they have sufficiently addressed the reviewer’s concerns.

Less clear is the specificity of systemic antagonism of β adrenergic receptors on the changes in M2 activity reported. As propranolol was given systemically, changes in M2 firing rates could also be due to broader circuit (indirect) activity changes. As it was not given locally, nor were local receptor populations manipulated, one is unable to make the conclusion that changes in neural activity are due to the direct effects of adrenergic receptors within M2 populations.

We agree that propranolol driven changes in anterior M2 activity may arise via multiple mechanisms, including direct action on the adrenoreceptors within M2, and indirect action via other regions that project to M2. Although locally activating inhibitory interneurons within M2 is sufficient to disrupt cueguided action plans and behavior in a 2AFC task (Inagaki et al., 2018), our noradrenergic manipulation was not restricted to M2. We have clarified our conclusions and provided additional discussion to highlight that propranolol actions were multifaceted and that direct actions in M2 are likely working in concert with propranolol mediated actions in other regions.

Also not clear, is the contribution of M2 to this task, and whether the changes in M2 activity patterns observed are directly responsible for the behavioral disruptions measured.

We have revised our introduction and discussion to more clearly outline the critical role of cue-guided action plans in M2 for successful behavior in 2AFC tasks. Suppression of cue-guided activity in M2 results in behavioral performance at near chance levels, similar to what we saw in females after propranolol (Guo et al., 2017; Inagaki et al., 2018; Li et al., 2016). Furthermore, targeted photostimulation of action plan encoding neurons in M2 is sufficient to drive behavioral responses (Daie et al., 2021). In our investigations it is plausible to expect propranolol related disruptions in other cognitive, sensory or motor regions. Based on the strong foundational evidence for M2 activity in 2AFC, the propranolol driven changes in anterior M2 in females, whether direct or indirectly mediated, are likely sufficient to drive behavioral disruptions in accuracy and/or trial completion.

Reviewer #2 (Public Review):

This paper by Rodbarg et al describes an interesting study on the role of beta noradrenergic receptors in action-related activity in the premotor cortex of behaving rats. This work is precious because even if the action of neuromodulatory systems in the cortex is thought to be critical for cognition, there is very little data to actually substantiate the theories. The study is well conducted and the paper is well written. I think, however, that the paper could benefit from several modifications since I can see 3 major issues:

We thank the reviewer for their generous comments on the potential impact of our manuscript as well as their suggestions to improve this work. Below we outline responses to specific comments raised by the reviewer in addition to adresing them in the revised manuscript. We hope these responses sufficiently address the reviewer’s concerns.

Both from a theoretical and from a practical point of view, the emphasis on 'cue-related' activity and the potential influence of NA on sensory processing is problematic. First, recent studies in rodents and primates have clearly demonstrated that LC activation is more closely related to actions than to stimulus processing (see Poe et al, 2020 for review).

Indeed during optimal performance the peaks of LC activity are larger when PETH are aligned to action initiation rather than the cue itself (Clayton et al., 2004). This alignment resolves variability in decision processing times and omitted cues. Although LC responses align with action they are evoked by, and occur after, cue presentation with LC responses to visual cues occurring ~ 60ms after presentation (Aston-Jones & Bloom, 1981). The same behavioral action without preceding task relevant cues does not evoke an LC response (Rajkowski et al., 2004)

In our current study cues initiate activity in anterior M2, this is our primary interest and where our electrodes are placed. The window between cue delivery and action completion hones in on our goal of investigating the role for β noradrenergic signaling in target cortical processing, rather than LC explicitly. In both NHP and rodents NE signaling (and evoked LC) promotes sustained cortical representations between cue onset and actions across cortical regions (dlPFC, S1) (Ramos & Arnsten, 2007; Vazey et al., 2018; Wang et al., 2007). In the current study we aligned neural data to either cue presentation (Figure 3) or action (lever press; Figure 4). Both presentations support a critical role for β adrenoreceptor signaling in suppressing irrelevant information, resolving and maintaining action plans. A unique feature of aligning the data to cue onset is that it allows us to see how the neural activity changes not only on completed trials (that end with a lever press) but also on omitted trials (which strongly increase after propranolol). We propose the reason we are seeing large increases in omitted trials is because β adrenoreceptor blockade either directly or indirectly prevents anterior M2 from resolving an action plan.

Second, the analysis of neural activity around cue onset should be examined with spikes aligned on the action, since M2 is a motor region and raster plots suggest that activity is strongly related to action (I'll be more specific below).

We agree that M2 shows important action plan activity which we highlight throughout the manuscript. In cued tasks, M2 neurons have been shown to represent action plans starting at cue onset that continues up to behavioral execution. Neural data was examined and results presented aligned to cue onset (illustrated in Figure 3) and aligned to action - lever press (illustrated in Figure 4). The impact of propranolol in diminishing action plan selection was similar in both action, and cue-aligned analyses.

The distinction between neural activity and behavior or cognition is not always clear. I understand that spike count can be related to motor preparation or decision, but it should not be taken for granted that neuronal activity is action planning. The analysis should be clarified and the relation between neural activity, behavior, and potential hidden cognitive operations should be explicated more clearly.

We have worked to clarify in our revised introduction, results and discussion the specifics of the known roles of neural activity in M2 in both action planning and decision making. We further expand that the neuronal activity in our study may reflect potential changes in cognitive processing and thus alter resultant behavioral outcomes.

The sex difference is interesting, but at the moment it seems anecdotal. From a theoretical point of view, is there any ecological/ biological reason for a sex dependency of noradrenergic modulation of the cortex? Is there any background literature on sex differences in motor functions in rats, or in terms of NA action? If not, why does it matter (how does it change the way we should interpret the data?) From a practical point of view, is there a functional sex difference in absence of treatment, or is it that the drug has a distinct effect on males vs females? This has very distinct consequences, I think.

We did not find overt differences in behavior in the absence of treatment. Only when noradrenergic function was challenged using propranolol did we identify functional sex differences. We agree that this has very distinct consequences – specifically it supports sex differences that can be revealed by perturbations of normal function. These functional sex differences may be a result of differences in the anatomy of central noradrenergic systems, a hypothesis further supported by our mRNA expression findings and existing literature on LC anatomy across species (Bangasser et al., 2011, 2016; Luque et al., 1992; Mulvey et al., 2018; Ohm et al., 1997; Pinos et al., 2001). Collectively these results have potential ramifications for understanding sex differences in disease prevalence and targeted treatments.

Background literature supports some innate sex differences in motor function and executive function in rodents and humans. Of particular relevance to our investigation is an established difference in behavioral strategy with females being more risk averse than males (Grissom & Reyes, 2019). Ethologically risk adverse strategies may support parental care roles, and increased inhibitory mechanisms may be selected for in females. Although this strategy was not directly tested in our study, the large increase in omissions after propranolol seen in females is in line with avoiding risk (incorrect choices) during uncertainty (disrupted neural signaling). As with other executive functions, the utilization of norepinephrine within the cortex along with other neuromodulators, and local microcircuit interactions would all contribute to promoting risk averse behavior.

These issues could be clarified both in the introduction and in the discussion, but the authors might have a different view on what is theoretically relevant here. In the result section, however, I think that both the lack of specificity in the description of behavior and cognitive operation and the confusion between 'sensory' and 'motor' functions make it very difficult to figure out what is going on in these experiments, both at a behavioral and at a neurophysiological level. First, the description of the behavior in the task is clearly not sufficient, which makes the interpretation of the measures very difficult.

We have made an effort to better specify the task and relevant behavioral operations in both the methods and results and have included a clearer task schematic (Figure 1A). We agree that the confusion between ‘sensory’ and ‘motor’ functions may make it more difficult to understand the findings in this study. Anterior M2 plays a unique role in representing motor/action plans that can be informed by sensory information. This integrative function creates difficulty in parsing the neural activity of anterior M2 as strictly motor, sensory or cognitive. In attempts to improve clarity we have expanded and highlighted relevant information on the known roles of M2 in the introduction and discussion.

One possible interpretation of the effects of the drug is a decrease in motivation, for instance, due to a decrease in reward sensitivity or an increase in sensitivity to effort. But there are others. More importantly, none of these measures can be used to tease apart action preparation from action execution, even though the study is supposed to be about the former.

Neural activity during action planning, prior to action execution is known to be an essential function of M2 (Barthas & Kwan, 2017; Gremel & Costa, 2013; Guo et al., 2017; Inagaki et al., 2018, 2022; Li et al., 2016; Siniscalchi et al., 2016; Sul et al., 2011; Wei et al., 2019) for optimal performance in 2AFC tasks. In all, we found that the representation/separation of opposing action plans (a well validated function of M2) prior to responses (lever press) is degraded after propranolol, especially in females. We have provided additional emphasis on these foundational studies throughout our revised manuscript.

To minimize impact of motivational factors, effort and reward size remain consistent within our task, and all trials require a random initiation hold prior to cue delivery. As described in our general response to the editor above (Figure 1, above), we investigated whether motivational changes may be reflected in our M2 recordings. PETHs from the first and last 10 trials within saline sessions did not identify potential motivation related differences in anterior M2 activity. Similarly, across propranolol sessions the neural activity was consistent between early and late trials. We used early and late trials as there was a mild decrease in trial rate during saline sessions in both males and females, potentially indicative of motivation/reward sensitivity changes during these sessions. M2 neural responses consistently separate action plans (after saline) or failed to separate action plans (propranolol sessions).

Also, but this is less critical: In Figures 2C and D, it looks like there is a bimodal distribution for the effect of propranolol in females. Is there something similar in the neuronal effects of the drug? And in the distribution of receptors? Can it be accounted for by hormonal cycles/ anything else?

Although there is some clustering in behavioral outcomes all data passed normality assumption as appropriate. Propranolol treatments were not synchronized to hormonal cycles, and the data likely include animals at various hormonal stages. Similar clustering was not apparent in neuronal effects of propranolol, although propranolol increased variability in many measures.

In a pilot experiment we did not see any difference in baseline performance on our 2AFC task across the hormonal cycle (diestrous, proestrous, estrous or metestrous) of females in any measure including accuracy (F(3,33)=0.59, p=0.63, one-way ANOVA) and omissions (F(3,33)=0.51, p=0.68).

The description of neural activity is also very superficial. In general, it is not clear how spike count measures have been extracted. For example, legend and figure C are not clear, is the (long) period of cue presentation included in the 'decision time'?? "Cues were presented at a variable interval 200-700ms after initiation and until animals left the well, 'Well Exit'. The time from cue onset to well exit was identified as the decision time (yellow)." Yet on the figure only the period after cue presentation is in yellow. This is critical because, given the duration of the cue, the animals are probably capable of deciding (to exit the well) before the cue turns off. Indeed, as shown in fig 2D, the animals can decide within about 500 ms. So to what extent is the 'cue response' actually a 'decision response'?

We have clarified the task and spike count measurements in methods and added a revised task schematic. It is correct that the cues are available throughout the decision time (for up to 5 seconds or until well exit), and an action plan is generated before well exit/cues turn off as reflected by the separation of neural action plans (Fig 3, saline). Anterior M2 neurons maintain action plan representation from cue onset until the lever press under normal conditions (Fig 4, saline). These action plans encapsulate “cue responses” and “decision responses”. We have aligned neural data to discrete timestamps at either end of the window in which M2 processing is known to be critical, specifically between cues and actions (lever press) and focus on neural activity relative to those points. We refer to this activity throughout the manuscript as an ‘action plan’ as action planning functions of M2 activity have been well established in prior studies.

When looking at figure 3A, there is clearly a pattern on the raster, a line going from top left to bottom right. If the trials are sorted chronologically, something is happening over time. If, as I suspect, trials are sorted by ascending response time, this raster is showing that what authors are calling a 'response to cues' is actually a response around action. Basically, if propranolol slows down reaction time, the spikes will be delayed from cue onset only because they remain locked to the action. Then the whole analysis and interpretation need to be reconsidered. But it might be for the best: as I mentioned earlier, recent work on LC activity has clearly emphasized its influence on motor rather than sensory processing (Poe et al, 2020).

Figure 3A is a single neuron example, and data analyses focus on population-wide activity. Neural data is presented both aligned to cues, for all trials in which a cue was received, and aligned to lever press (action), for all trials on which a lever press occurred. In both cases, aligned to cue or aligned to action, the impact of propranolol is the same. β adrenoreceptor blockade reduces the separation of action plans in M2, severely so in females. However, a major finding is that females receive a cue but omit a large number of trials after propranolol, for this outcome the action does not occur. We propose this is due to the lack of action plan separation in anterior M2 (either directly or indirectly). When no behavioral response occurs, these trials cannot be aligned to action, yet we are still interested in the neural activity during the critical window between cue delivery and actions. We are not assigning this neural activity to sensory processing but using this discrete sensory event within our trials (cue) to align the data as there is substantial evidence that action plans in M2 arise after cue presentation in tasks such as ours where performance is guided by external cues.

Fig 2D-F: it is hard to believe that the increase in firing rate induced by propranolol in females is not significant. Presumably, because the range of the median firing rate is so high in the first place, distribution (2E) really indicates an increase in firing. Maybe some other test? e.g paired t.test, or standardized values (z.score) to get rid of variability in firing across neurons?

We agree that the session wide firing rate appears rightward shifted in females after propranolol. As our recordings were taken on different days, several days apart we cannot assume they are the same neurons for paired analyses. In our revised manuscript we evaluated these distributions using a MannWhitney test to increase power and decrease the impact of variability within the population. Previously we had used a Kolmogorov-Smirnov test. Using our new analysis, we can confirm that the propranolol significantly increases session wide firing rates in anterior M2 of females (p=0.027) but not males. This finding increases evidence for direct actions of propranolol within M2 and supports our hypothesis that propranolol leads to local disinhibition by reducing β noradrenergic signaling in interneurons and that without this noradrenergic tone anterior M2 is less efficient at suppressing irrelevant action plans.

Along those lines, would it be worth looking for effects on specific populations (interneurons) which are sometimes characterized by thinner spikes and higher mean firing rates? Given the distribution of beta receptors RNA on interneurons, one would actually expect an effect of propranolol on the firing rate irrespective of task events. Or what is it that prevents the influence of propranolol on interneurons from changing the firing rate? In any case, one of the strengths of this study is the localization of beta receptors on specific neuronal populations in the cortex, so I think that the authors should really try to build on it and find something related to the neurophysiological effects. Otherwise, one cannot exclude the possibility that the behavioral effects are not related to the influence of the drug on these receptors in that region.

Data were collected using stainless steel electrode arrays and our sample population of task related neurons is likely biased to pyramidal neurons, with a small number of fast spiking interneurons. We used validated spike waveform parameters of interneurons in premotor cortex (peak-to-trough ratio and duration; Giordano et al., 2023) in an attempt to isolate putative interneurons and found only a very small number of these cells in our recordings (n=5-7 per group). This population is too small to make any inferences about specific impacts. We have focused on the collective population activity of M2 as this is most strongly related to optimal action planning.

You are correct that from the given findings we cannot conclusively show that the results found here are a result of propranolol acting solely within anterior M2. We have made sure to clarify throughout our revised manuscript that the behavioral and physiological changes we identified are a result of collective direct and indirect actions of propranolol.

The conclusion that neuronal discrimination decreases because the proportion of neurons showing no effect increases is confusing (negative results, basically). It would be clearer if they were reporting the number of neurons that do show an effect, and presumably that this number shows a significant decrease.

The reviewer is correct that the number of neurons that do show an effect (task related activity) does significantly decrease with propranolol (from n=70 to 27 in females and n=71 to 48 in males). These n are now given adjacent to the proportions rather than at the end of the paragraph. Proportions were used for statistical analysis due to an overall decrease in the total number of units after propranolol. All PETH presented are from neurons that show some task related activity, these PETH confirm that neural activity no longer effectively discriminates/separates action plans in M2.

Figs 3F-I: a good proportion of neurons (at least 20%) show a significant encoding before cue onset. How is it possible? This raises the issue of noise level/ null hypothesis for this kind of repeated analysis. How did the author correct for multiple comparison issues?

In response to reviews, we have altered the manner in which we identify the significantly modulated neurons to increase rigor and no longer include these figures or analyses. The proportion of neurons showing action plan encoding prior to cue onset was likely an artifact of how the data was analyzed and an insufficient correction for multiple comparisons, allowing inclusion of internally generated action plans in some neurons.

The description of the action-related activity is globally confusing. Again, how can the authors discriminate between activity related to planning vs action itself? What is significant and what is not, in males vs females? What is being measured here? For example, a very unclear statement on line 238: "Propranolol primarily disrupted active inhibition of irrelevant action selection in M2 activity, reducing the ability to maintain action plan representation in M2, delaying lever press responses (Figure 4L, 4M)." What is 'active inhibition? What is an irrelevant action plan? What is selection? All of that should be defined using objective behavioral criteria and tested formally.

We have changed our wording to clarify what we are describing and why we have chosen the words we have, and to ensure consistency and objectivity throughout the manuscript. Much of the wording we have used – for example action planning or action plan selection, are the words used in the literature to describe M2 neural activity. We call the activity in M2 action planning (either externally/cue guided or internally guided) because that is what has been previously demonstrated. In our task design and analysis we are tracking cue guided actions, as opposed to internally guided.

We also separate the electrophysiology data as preferred and nonpreferred because the literature has shown individual M2 neurons show specific directional tuning as noted in our results, using the term ‘preferred’ encapsulates that tuning regardless of left/right direction. An example M2 neuron that increases activity for left cues and responses (preferred direction), will show active inhibition (low/negative z scores) on trials with right cues and responses (nonpreferred), other neurons would show the inverse relationship with direction.

A primary impact of propranolol was the loss of negative z-scores for nonpreferred trials ie neurons with a left preference that are usually inhibited on right trials were still firing and vice-versa. After propranolol neurons continue to fire for an irrelevant action plan (for the opposite direction), and the resulting population activity is not significantly different for opposing cues/responses. Behavioral responses normally occur after opposing action plans have significantly separated in M2, collapsing action plans by preventing relevant signaling (Guo et al., 2017; Inagaki et al., 2018; Li et al., 2016) or facilitating irrelevant signaling as we see here with propranolol leads impairments in 2AFC performance.

Also, the description of the classifier analysis should be more thorough. Referencing the toolbox is not sufficient to understand what has been done.

We have added additional explanation in both the methods and description of the results to clarify the functions of the neural decoding box and how we are using it to evaluate information encoding within M2. We have provided detail on how the algorithm was trained, how shuffled data was generated and how we determined significance of decoding accuracy.

Measuring Beta adrenoceptors is a great idea, and the results are interesting, especially the difference between neuron types. But again, how does that fit with neurophysiological results? Note, that since this is RNA measures, it should not be phrased as 'receptors' but 'receptors RNA' throughout. One possible interpretation of these anatomical results that cannot be reconciled with physiology is that protein expression at the membrane shows a distinct pattern.

We have changed the references to β receptor expression to β receptor mRNA expression throughout the manuscript. Although mRNA provides a valuable proxy for adrenoreceptor production, as noted by the reviewer protein expression at the membrane may differ. Reliable antibodies that allow quantitative analysis of membrane bound adrenoreceoptors in situ with co-labeling of specific cell types are limited. The goal of assessing mRNA expression within M2 was to determine if the functional sex differences we identified in M2 neurophysiology when manipulating β adrenoreceptor function could be mediated by basal differences in adrenoreceptors. The causal impact of differential mRNA expression in anterior M2 was not directly tested but our findings provide preliminary evidence that adrenoreceptor regulation may differ across sexes. Our results provide a plausible avenue for differential sensitivity to β adrenoreceptor manipulation across sexes, that may also be found in other brain regions.

In conclusion, I think that this is a very interesting study and that the results are potentially relevant for a wide audience. But the paper would clearly benefit from revisions. If the authors could clearly identify a significant relationship between the action of NA on beta receptors on specific cortical neurons, at a physiological and behavioral level, that would be a seminal study. At the moment, the evidence is not convincing enough but the data suggest that it is the case.

We thank the reviewer for the kind remarks. We have undertaken a number of new analyses, refined existing analysis and clarified our claims in the manuscript to improve rigor. Collectively our data reflect that the behavioral and neural deficits after systemic propranolol are likely due to both direct and indirect actions on M2. We believe this work is compelling and that it will inform future work investigating potential sex differences in central noradrenergic anatomy and functional sex differences after perturbations of noradrenergic signaling.

Author Response

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

(1) What's the rationale of trypsinizing the tissue prior to mitochondrial isolation? This is not standard for subsequent proteomics analysis. This step will inevitably cause protein loss, especially for the post mitochondrial fractions (PMF). Treating samples with 0.01ug/uL trypsin for 37oC 30 min is sufficient to partially digest a substantial portion of the proteome. If samples from different subjects were not of the same weight, then this partial digestion step may introduce artificial variability as variable proportions of proteins from different subjects would be lost during this step. In addition, the mitochondrial protein enrichment in the mito fraction, despite statistically significant, does not look striking (Figure 1E, ~30% mitochondrial proteins in the mito fraction). As a comparison, Williams et al., MCP 2018 seem to have obtained high mitochondrial protein content in the mito fraction without trpsinizing the frozen quadriceps using a similar SWATH-MS-based approach.

Trypsinisation of the tissue prior to mitochondrial isolation is based on previous work and a Nature Protocol (1, 2) which isolated mitochondria for skeletal muscle. The rationale is that it aids in mechanical homogenisation from highly fibrous tissues such as quadriceps muscle by digesting extracellular matrix proteins. The trypsin/protein ratio used to aid in this process is at least 400 times lower than the amount of trypsin used for formal proteomic tryptic digestion. Three pieces of evidence suggest this step has negligible effect on downstream proteomic analysis. First, because the trypsinisation buffer is detergent free, trypsin will only affect extracellular or exposed membrane proteins. Filtering our PMF dataset for proteins with ‘extracellular matrix’ gene ontology identifies at least 90 unique extracellular matrix proteins indicating good retention of proteins susceptible to partial digestion. Second, the trypsin dose used is 50 times lower than the concentration used for passaging cultured cells, which retain viability after trypsinisation. Third, and contrary to the point raised by the reviewer, we observe less missingness in PMF samples compared to mitochondrial samples. We thank the reviewer for bringing the Williams et al. 2018 MCP paper to our attention. We note that mitochondrial enrichment between the two papers is comparable (~2- fold). To improve clarity line 408 now reads: “Whole quadriceps muscle samples were prepared as previously described with modification (99, 100). First, tissue was snap frozen with liquid nitrogen…” and line 95 reads: “Mitochondrial proteins were defined based on their presence in MitoCarta 3.0 (24) and consistent with previous work (25) were approximately two-fold enriched in the mitochondrial fraction relative to the PMF (Fig 1E).”

(2) The authors mentioned that the proteomics data were Log2 transformed and median- normalized. Would it be possible to provide a bit more details on this? Were the subjects randomized?

Samples were randomised prior to sample processing and mass spectrometry analysis. Because of possible variation in total protein content, it is critical to normalise protein intensities between samples. Median normalisation adjusts the samples so that they have the same median, thereby accounting for technical variation. Log2 normalisation helps to achieve normal distributions, critical for many downstream statistical tests. Line 471 now reads: “…to achieve normal distributions and account for technical variation in total protein.”

(3) In Figure 1D, what were the numbers of mice the authors used for the CV comparisons in each group? Were they of similar age and sex? Were the differences in CV values statistically significant?

The mitochondrial and PMF proteomes originated from the same quadriceps sample from the same mouse, and thus the age and sex are the same across both proteomes. After quality control, we had mitochondrial proteomes for 194 mice and PMF proteomes for 215 mice. The overall CV in the mitochondrial fraction was significantly greater than in the PMF, however whether the source of this variation is biological, or the result of mitochondrial isolation is unclear and as such we have avoided making a statement within the body of the manuscript. We have now more clearly described the nature of the samples in the revised manuscript and added sample sizes to figure 1F.

(4) The authors stated in lines 155-157 that proteins negatively associated with the Matsuda index were further filtered by presence of their cis-pQTLs. Perhaps more explanations would be needed to justify this filtering criterion? Having a cis-pQTL would mean the protein abundance variation is explained by the variation in its coding gene, this however conceptually would not be relevant to its association with the Matsuda index. With the data that the authors have in hand, would it not be natural to align the Matsuda index QTL with the pQTLs (cis and trans if available), and/or to perform mediation analysis to examine causal relationships with statistical significance?

The rationale for filtering by cis-pQTL was not to study the genetics of either Matsuda or associated proteins but rather to identify proteins that were more likely to be causally associated with Matsuda Index as opposed to adaptively associated. To clarify this line 165 now reads: “Filtering based on cis-pQTL presence was based on the rationale that if genetic variation can explain protein abundance differences between mice, then we can be confident that phenotype (Matsuda Index) is not driving the observed differences and therefore the protein-phenotype associations are likely causal. Importantly, this assumption can only be made for cis-acting pQTLs.” Previous work by Matthew et al. (see https://qtlviewer.jax.org/) has demonstrated that cis-pQTL have markedly higher LOD scores than trans-pQTLs, and our own unpublished work suggests that trans-pQTLs do not reproduce well between datasets. The reviewer rightfully suggests aligning protein QTL with those for Matsuda. This is our long-term goal but to identify genome wide significant peaks associated with altered Matsuda will require many more mice than studied here.

(5) It seems a bit odd that the first half of the paper focused extensively on the authors' discoveries in the mitochondrial proteome, and how proteins involved in mitochondrial processes (such as complex I) were associated with Matsuda Index, but the final fingerprint list of insulin resistance, which contained 76 proteins, only had 7 mitochondrial proteins. Was this because many mitochondrial proteins were filtered out due to no cis-pQTL presenting?

There are three reasons our fingerprint is lacking mitochondrial proteins: 1) there are more non-mitochondrial than mitochondrial proteins in the muscle proteome; 2) we focussed on negatively associated proteins, and as demonstrated in figure 2c, the mitochondrial proteome is enriched for positively associated proteins; 3) as implied by the reviewer, we filtered for pQTL presence, further reducing the number of mitochondrial proteins in our fingerprint. To improve clarity, line 170 now reads: “Low mitochondrial representation in the fingerprint is the result of selecting negatively associating proteins, and as seen (Figure 2C) previously, the mitochondrial proteome is enriched for positive contributors to insulin resistance.”

(6) The authors found that thiostrepton-induced insulin resistance reversal effects were not through insulin signalling. It activated glycolysis but the mechanism of action was not clear. What are the proteins in the fingerprint list that led to identification of thiostrepton on CMAP?

Is thiostrepton able to bind or change the expression of these proteins? Since thiostrepton was identified by searching the insulin resistance fingerprint protein list against CMAP, it would be rational to think that it exerts the biological effects by directly or indirectly acting on these protein targets.

This is indeed the implication of our data. Because of the timescales involved it is unlikely that thiostrepton is changing fingerprint protein levels but could be binding to and inhibiting them. Searching the CMAP thiostrepton signature reveals ARHGDIB and NAGK as the fingerprint proteins with the most positive and negative fold-changes respectively perhaps suggesting they play a role in thiostrepton’s mechanism of action. Experiments are underway to test this hypothesis however these are beyond the scope of the current paper.

Reviewer #2 (Public Review):

Line 105: The observation that variance in respiratory proteins is stable while lipid pathways is variable is quite interesting. Is this due to lower overall levels of lipid metabolism enzymes (ex. do these differ substantially from similar pathways ranked from high-low abundance?).

The relationship between coefficient of variation (CV) and relative abundance of proteins is important to consider. To address this, we have now also performed GSEA on proteins ranked from high to low relative abundance. These comparisons have been added to supplementary figure 1 and line 110 now reads: “As a control experiment, we also performed enrichment analysis on proteins ranked by LFQ relative abundance. High CV pathways (enriched for high CV proteins) tended to be lower in relative abundance (enriched for low relative abundance proteins) (Supplementary Fig 1a, b). However, many high variability pathways, lipid metabolism for example, were not enriched in either direction based on relative abundance suggesting differences in relative abundance do not fully explain pathway variability differences.”

Line 154: the 664 associations are impressive and potentially informative. It would be valuable to know which of these co-map to the same locus - either to distinguish linkage in a 2mb window or identify any cis-proteins which directly exert effects in trans-

To assess this, we have analysed pQTL position relative to gene position to generate a ‘hotspot’ plot. We have also generated a histogram of this pQTL density (in a 2 Mbp window) and added these figures to figure 3. We did not detect any obvious pQTL hotspots, and the distribution of pQTLs across the genome appears fairly uniform. Line 159 now reads: “These were distributed across the genome and were predominately cis acting (Figure 3A)...”

Line 194: Cross-platform validation of the CMAP fingerprint results is an admirable set of validations. It might be good to know general parameters like how many compounds were shared/unique for each platform. Also the concordance between ranking scores for significant and shared compounds.

The Connectivity Map (CMap) query included 5163 compounds, the Prestwick library included 1120, and the overlap was 420. We have added these comparisons to supplementary figure 2. Supplementary figure 2 now also contains a comparison of CMap scores between overlapping compounds (found in CMap and the Prestwick library) against all significant compounds identified by CMap (supplementary figure 2b). Interestingly, compounds present in both platforms scored higher on average, suggesting the Prestwick library captures a significant proportion of highly scoring CMap candidates. Line 206 now reads: “In total, 420 compounds were found across both platforms, and these consensus compounds captured a significant proportion of highly scoring CMap compounds (Supplementary Figure 2A, B).”

Line 319: Another consideration in the molecular fingerprint is how unique these are for muscle. While studies evaluating gene expression have shown that many cis-eQTLs are shared across tissues, to my knowledge, this hasn't been performed systematically for pQTLs. Therefore, consider adding a point to the discussion pointing out that some of the proteins might be conserved pQTLs whereas others which would be more relevant here present unique druggable targets in muscle.

To examine tissue specificity, we determined whether our skeletal muscle fingerprint proteins were detected and contained a pQTL in two metabolically important tissues, liver and adipose. Despite detecting almost all the fingerprint proteins in both adipose and liver tissue, they were depleted for pQTL compared to skeletal muscle. These data have now been added to figure 3c. Line 172 now reads: “To assess the tissue specificity of our fingerprint we searched for the same proteins in metabolically important adipose and liver tissues. Despite detecting 94% and 82% of muscle fingerprint proteins across each tissue respectively, both adipose and liver were depleted for pQTL presence (Figure 3C) suggesting that regulation of our fingerprint protein abundance is specific to skeletal muscle.”

Line 332: These are fascinating observations. 1, that in general insulin signaling and ampk were not themselves shown as top-ranked enrichments with matsuda and that this was sufficient to alter glucose metabolism without changes in these pathways. While further characterization of this signaling mechanism is beyond the scope of this study, it would be good to speculate as to additional signaling pathways that are relevant beyond ROS (ex. CNYP2 and others)

We have now added further discussion to the manuscript to address this point., Line 347 now reads: “Aside from glycolysis, other pathways may be involved in enhancing insulin sensitivity. For example, the negatively associated protein ARHGDIA (Figure 2F) is a potent negative regulator of insulin sensitivity, and our fingerprint of insulin resistance contained its homologue ARHGDIB. Both ARHGDIA and ARHGDIB have been reported to inhibit the insulin action regulator RAC1 thus lowering GLUT4 translocation and glucose uptake. Further investigations may uncover a role for thiostrepton in modulating the RAC1 signalling pathway via ARHGDIB.”

Line: 314: Remove the statement: "While this approach is less powerful than QTL co- localisation for identifying causal drivers,", as I don't believe that this has been demonstrated. Clearly, the authors provide a sufficient framework to pinpoint causality and produce an actionable set of proteins.

We have edited line 314, which now reads: “Moreover, our approach has the major advantage that it requires far fewer mice to obtain meaningful outcomes (222 mice in this study) compared to that required for genetic mapping of complex traits like Matsuda Index.”

Line 346: I would highlight one more appeal of the approach adopted by the authors. Given that these compound libraries were prioritized from patterns of diverse genetics, these observations are inherently more-likely to operate robustly across target backgrounds.

This point is further supported by our thiostrepton results in both C57BL6/j and BXH9 mice. Line 317 now reads: “Furthermore, because we have used genetically diverse datasets (DOz mice and multiple cell lines in Connectivity Map) our findings are likely robust across diverse target backgrounds.”

Line 434: I might have missed but can't seem to find where the muscle data are available to researchers. Given the importance and novelty of these studies, it will be important to provide some way to access the proteomic data.

These data are now available via the ProteomeXchange Consortium. Line 465 now reads: “The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (104) partner repository with the dataset identifier PXD042277.”

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4. Gatti DM, Svenson KL, Shabalin A, Wu L-Y, Valdar W, Simecek P, et al. Quantitative Trait Locus Mapping Methods for Diversity Outbred Mice. G3 Genes|Genomes|Genetics. 2014;4(9):1623-33.

Reviewer #2 (Public Review):

Accumulating data suggests that the presence of immune cell infiltrates in the meninges of the multiple sclerosis brain contributes to the tissue damage in the underlying cortical grey matter by the release of inflammatory and cytotoxic factors that diffuse into the brain parenchyma. However, little is known about the identity and direct and indirect effects of these mediators at a molecular level. This study addresses the vital link between an adaptive immune response in the CSF space and the molecular mechanisms of tissue damage that drive clinical progression. In this short report the authors use a spatial transcriptomics approach using Visium Gene Expression technology from 10x Genomics, to identify gene expression signatures in the meninges and the underlying brain parenchyma, and their interrelationship, in the PLP-induced EAE model of MS in the SJL mouse. MRI imaging using a high field strength (11.7T) scanner was used to identify areas of meningeal infiltration for further study. They report, as might be expected, the upregulation of genes associated with the complement cascade, immune cell infiltration, antigen presentation, and astrocyte activation. Pathway analysis revealed the presence of TNF, JAK-STAT and NFkB signaling, amongst others, close to sites of meningeal inflammation in the EAE animals, although the spatial resolution is insufficient to indicate whether this is in the meninges, grey matter, or both.

UMAP clustering illuminated a major distinct cluster of upregulated genes in the meninges and smaller clusters associated with the grey matter parenchyma underlying the infiltrates. The meningeal cluster contained genes associated with immune cell functions and interactions, cytokine production, and action. The parenchymal clusters included genes and pathways related to glial activation, but also adaptive/B-cell mediated immunity and antigen presentation. This again suggests a technical inability to resolve fully between the compartments as immune cells do not penetrate the pial surface in this model or in MS. Finally, a trajectory analysis based on distance from the meningeal gene cluster successfully demonstrated descending and ascending gradients of gene expression, in particular a decline in pathway enrichment for immune processes with distance from the meninges.

Although these results confirm what we already know about processes involved in the meninges in MS and its models and gradients of pathology in sub-pial regions, this is the first to use spatial transcriptomics to demonstrate such gradients at a molecular level in an animal model that demonstrates lymphoid like tissue development in the meninges and associated grey matter pathology. The mouse EAE model being used here does reproduce many, although not all, of the pathological features of MS and the ability to look at longer time points has been exploited well. However, this particular spatial transcriptomics technique cannot resolve at a cellular level and therefore there is a lot of overlap between gene expression signatures in the meninges and the underlying grey matter parenchyma.

The short nature of this report means that the results are presented and discussed in a vague way, without enough molecular detail to reveal much information about molecular pathogenetic mechanisms.

The trajectory analysis is a good way to explore gradients within the tissues and the authors are to be applauded for using this approach. However, the trajectory analysis does not tell us much if you only choose 2 genes that you think might be involved in the pathogenetic processes going on in the grey matter. It might be more useful to choose some genes involved in pathogenetic processes that we already know are involved in the tissue damage in the underlying grey matter in MS, for which there is already a lot of literature, or genes that respond to molecules we know are increased in MS CSF, although the animal models may be very different. Why were C3 and B2m chosen here?

Strengths:<br /> - The mouse model does exhibit many of the features of the compartmentalized immune response seen in MS, including the presence of meningeal immune cell infiltrates in the central sulcus and over the surface of the cortex, with the presence of FDC's HEVs PNAd+ vessels and CXCL13 expression, indicating the formation of lymphoid like cell aggregates. In addition, disruption of the glia limitans is seen, as in MS. Increased microglial reactivity is also present at the pial surface.<br /> - Spatial transcriptomics is the best approach to studying gradients in gene expression in both white matter and grey matter and their relationship between compartments.<br /> - It would be useful to have more discussion of how the upregulated pathways in the two compartments fit with what we know about the cellular changes occurring in both, for which presumably there is prior information from the group's previous publications.

Limitations:<br /> - EAE in the mouse is not MS and may be far removed when one considers molecular mechanisms, especially as MS is not a simple anti-myelin protein autoimmune condition. Therefore, this study could be following gene trajectories that do not exist in MS. This needs a significant amount of discussion in the manuscript if the authors suggest that it is mimicking MS.<br /> - The model does not have the cortical subpial demyelination typical of MS and it is unknown whether neuronal loss occurs in this model, which is the main feature of cytokine-mediated neurodegeneration in MS. If it does not then a whole set of genes will be missing that are involved in the neuronal response to inflammatory stimuli that may be cytotoxic.<br /> - Visium technology does not get down to single cell level and does not appear to allow resolution of the border between the meninges and the underlying grey matter.<br /> - Neuronal loss in the MS cortex is independent of demyelination and therefore not related to remyelination failure. There does not appear to be any cortical grey matter demyelination in these animals, so it is difficult to relate any of the gene changes seen here to demyelination.<br /> - No mention of how the ascending and descending patterns of gene expression may be due to the gradient of microglial activation that underlies meningeal inflammation, which is a big omission.

Author Response:

We thank Reviewer #1 for their positive assessment of our work.

Reviewer #2 (Public Review):

[…] Although these results confirm what we already know about processes involved in the meninges in MS and its models and gradients of pathology in sub-pial regions, this is the first to use spatial transcriptomics to demonstrate such gradients at a molecular level in an animal model that demonstrates lymphoid like tissue development in the meninges and associated grey matter pathology. The mouse EAE model being used here does reproduce many, although not all, of the pathological features of MS and the ability to look at longer time points has been exploited well. However, this particular spatial transcriptomics technique cannot resolve at a cellular level and therefore there is a lot of overlap between gene expression signatures in the meninges and the underlying grey matter parenchyma.

We appreciate the reviewer’s concise summary and comments on our manuscript. We agree that the Visium spatial sequencing technology we applied is limited in its resolution and cannot precisely distinguish individual cells or anatomic regions. For that reason, there is undoubtedly some overlap between gene expression signatures in the meninges and underlying parenchyma, particularly in spots on the borders of the meningeal inflammation clusters. However, we believe that the majority of meningeal inflammation (“cluster 11”) spots are indeed in the meninges and represent the spatial transcriptome of that niche. To support this, in the revised manuscript we will provide H&E images with the UMAP clusters overlayed to demonstrate the anatomic borders that correlate with the clusters.

The short nature of this report means that the results are presented and discussed in a vague way, without enough molecular detail to reveal much information about molecular pathogenetic mechanisms.

We thank the reviewer for this comment. The goal of this work is to transcriptomically characterize the spatial relationship between areas of meningeal inflammation and the underlying parenchyma. While we agree that mechanistic studies are needed to further evaluate the role of presented signaling pathways, those experiments are beyond the scope of this brief report.

The trajectory analysis is a good way to explore gradients within the tissues and the authors are to be applauded for using this approach. However, the trajectory analysis does not tell us much if you only choose 2 genes that you think might be involved in the pathogenetic processes going on in the grey matter. It might be more useful to choose some genes involved in pathogenetic processes that we already know are involved in the tissue damage in the underlying grey matter in MS, for which there is already a lot of literature, or genes that respond to molecules we know are increased in MS CSF, although the animal models may be very different. Why were C3 and B2m chosen here?

We appreciate the reviewer’s points here. C3 and B2m were chosen as examples of genes that have differential fit to the gradient descending pattern to assist the reader in interpreting subsequent gene set trajectory analysis. However, we agree that there are many other genes of interest and will expand the number of genes displayed in our revised manuscript. 

Strengths: <br /> - The mouse model does exhibit many of the features of the compartmentalized immune response seen in MS, including the presence of meningeal immune cell infiltrates in the central sulcus and over the surface of the cortex, with the presence of FDC's HEVs PNAd+ vessels and CXCL13 expression, indicating the formation of lymphoid like cell aggregates. In addition, disruption of the glia limitans is seen, as in MS. Increased microglial reactivity is also present at the pial surface. <br /> - Spatial transcriptomics is the best approach to studying gradients in gene expression in both white matter and grey matter and their relationship between compartments. <br /> - It would be useful to have more discussion of how the upregulated pathways in the two .compartments fit with what we know about the cellular changes occurring in both, for which presumably there is prior information from the group's previous publications.

Limitations: <br /> - EAE in the mouse is not MS and may be far removed when one considers molecular mechanisms, especially as MS is not a simple anti-myelin protein autoimmune condition. Therefore, this study could be following gene trajectories that do not exist in MS. This needs a significant amount of discussion in the manuscript if the authors suggest that it is mimicking MS. <br /> - The model does not have the cortical subpial demyelination typical of MS and it is unknown whether neuronal loss occurs in this model, which is the main feature of cytokine-mediated neurodegeneration in MS. If it does not then a whole set of genes will be missing that are involved in the neuronal response to inflammatory stimuli that may be cytotoxic. <br /> - Visium technology does not get down to single cell level and does not appear to allow resolution of the border between the meninges and the underlying grey matter. <br /> - Neuronal loss in the MS cortex is independent of demyelination and therefore not related to remyelination failure. There does not appear to be any cortical grey matter demyelination in these animals, so it is difficult to relate any of the gene changes seen here to demyelination. <br /> - No mention of how the ascending and descending patterns of gene expression may be due to the gradient of microglial activation that underlies meningeal inflammation, which is a big omission.

We thank the reviewer for their insightful comments on the strengths and limitations of our study. Regarding the SJL EAE model we use in this paper, it certainly is not a perfect model of meningeal inflammation in MS, indeed we believe that no such animal model exists, but it does recapitulate several key features of human disease as described by the reviewer. Spatial transcriptomics of cortical grey matter lesions and overlying meninges of samples derived from patients with MS would be ideal, though access to this tissue is highly limited. In the revised manuscript we will include more detailed discussion of the limitations in applying these findings to MS. However, in addition to potential implications for MS research, our data contribute more generally to understanding of meningeal inflammation and penetrance of inflammation into brain tissue.

We acknowledge that sub-pial neuronal loss has not been assessed in SJL EAE, and if present it would increase the relevance of this model to neurodegeneration. We are currently working to assess this.

We agree with the reviewer that Visium technology is limited in its ability to discriminate individual cells, as discussed above (2.2).

We agree that gene expression by activated microglia is likely a major driver of the transcriptomic changes observed in the parenchyma, and thank the reviewer for highlighting this. We will add discussion of this to our revised manuscript, and intend to generate additional data regarding the contribution of subpial microglial activation to the measured transcriptomic changes.

Finally, we thank Reviewer #3 for their assessment of our work.

Author Response

eLife assessment:

Trypanosoma brucei evades mammalian humoral immunity through the expression of different variant surface glycoprotein genes. In this fundamental paper, the authors extend previous observations that TbRAP1 both interacts with PIP5pase and binds PI(3,4,5)P3, indicating a role for PI(3,4,5)P3 binding and suggesting that antigen switching is signal dependent. While much of the evidence is compelling, one reviewer suggested that the work would benefit from further controls.

We appreciate the evaluation of the work and agree that the findings substantially advance our understanding of antigenic variation. A detailed response to the public review is included below, which addresses and clarifies the issues raised by the reviewers, including those concerning controls. We also want to highlight the comment by Reviewer #3 “The methods used in the study are rigorous and well-controlled…. their results support the conclusions made in the manuscript.”. We hope this and our comments will help address the issue of controls in this eLife statement.

Reviewer #1 (Public Review):

Trypanosoma brucei undergoes antigenic variation to evade the mammalian host’s immune response. To achieve this, T. brucei regularly expresses different VSGs as its major surface antigen. VSG expression sites are exclusively subtelomeric, and VSG transcription by RNA polymerase I is strictly monoallelic. It has been shown that T. brucei RAP1, a telomeric protein, and the phosphoinositol pathway are essential for VSG monoallelic expression. In previous studies, Cestari et al. (ref. 24) have shown that PIP5pase interacts with RAP1 and that RAP1 binds PI(3,4,5)P3. RNAseq and ChIPseq analyses have been performed previously in PIP5pase conditional knockout cells, too (ref. 24). In the current study, Touray et al. did similar analyses except that catalytic dead PIP5pase mutant was used and the DNA and PI(3,4,5)P3 binding activities of RAP1 fragments were examined. Specifically, the authors examined the transcriptome profile and did RAP1 ChIPseq in PIP5pase catalytic dead mutant. The authors also expressed several C-terminal His6-tagged RAP1 recombinant proteins (full-length, aa1-300, aa301-560, and aa 561-855). These fragments’ DNA binding activities were examined by EMSA analysis and their phosphoinositides binding activities were examined by affinity pulldown of biotin-conjugated phosphoinositides. As a result, the authors confirmed that VSG silencing (both BES-linked and MES-linked VSGs) depends on PIP5pase catalytic activity, but the overall knowledge improvement is incremental. The most convincing data come from the phosphoinositide binding assay as it clearly shows that N-terminus of RAP1 binds PI(3,4,5)P3 but not PI(4,5)P2, although this is only assayed in vitro, while the in vivo binding of full-length RAP1 to PI(3,4,5)P3 has been previously published by Cestari et al (ref. 24) already. Considering that many phosphoinositides exert their regulatory role by modulating the subcellular localization of their bound proteins, it is reasonable to hypothesize that binding to PI(3,4,5)P3 can remove RAP1 from the chromatin. However, no convincing data have been shown to support the author’s hypothesis that this regulation is through an “allosteric switch”. Therefore, the title should be revised.

We appreciate the reviewer’s detailed evaluation of our work. There are a few general comments that we would like to clarify. We will break them into three points. All data included here are new and were not previously published.

i) “RNAseq and ChIPseq analyses have been performed previously …(ref. 24).” Reference 24 is Cestari et al. 2019, Mol Cell Biol. We, or others, have not published ChIP-seq of RAP1 in T. brucei. Previous work showed ChIP-qPCR, which analyses specific loci. The ChIP-seq shows genome-wide binding sites of RAP1, and new findings are shown here, including binding sites in the BES, MESs, and other genome loci such as centromeres. We also identified DNA sequence bias defining RAP1 binding sites (Fig 2A). We also show by ChIP-seq how RAP1-binding to these loci changes upon expression of catalytic inactive PIP5Pase. As for the RNA-seq, this is also the first time we show RNA-seq of T. brucei expressing catalytic inactive PIP5Pase, which establishes that the regulation of VSG silencing and switching is dependent on PIP5Pase enzyme catalysis, i.e., PI(3,4,5)P3 dephosphorylation. To improve clarity in the manuscript, we edited page 4, line 122, as follows: “We showed that RAP1 binds telomeric or 70 bp repeats (24), but it is unknown if it binds to other ES sequences or genomic loci.”

ii) “The in vivo binding of full-length RAP1 to PI(3,4,5)P3 has been previously published by Cestari et al. (ref. 24) already.”. We published in reference 24 that RAP1-HA can bind agarose beads-conjugated synthetic PI(3,4,5)P3. Here, we were able to measure T. brucei endogenous PI(3,4,5)P3 associated with RAP1-HA (Fig 4F). Moreover, we showed that the endogenous RAP1-HA and PI(3,4,5)P3 binding is about 100-fold higher when PIP5Pase is catalytic inactive than WT PIP5Pase. The data establish that in vivo endogenous PI(3,4,5)P3 binds to RAP1-HA and how the binding changes in cells expressing mutant PIP5Pase; this data is new and relevant to our conclusions.

iii) “no convincing data have been shown to support the author’s hypothesis that this regulation is through an “allosteric switch””. We show here in vitro and in vivo data supporting the conclusion. We show that PI(3,4,5)P3 binds to the N-terminus of rRAP1-His with a calculated Kd of about 20 µM (Fig 4B-E, Table 1). In contrast, we show by EMSA and binding kinetics by microscale thermophoresis that rRAP1-His binds to 70 bp and telomeric repeats via protein regions encompassing the Myb (central) or Myb-L domains (C-terminal) but not the N-terminus containing the VHP domain (Fig 3C-G, and Fig S5). Using microscale thermophoresis, we also show that rRAP1-His binds to 70 bp and telomeric repeats with Kd of 10 and 24 nM, respectively (Fig 3 and Table 1). Notably, we show that 30 µM of PI(3,4,5)P3, but not PI(4,5,)P2 – used as a control – disrupts rRAP1-His binding to 70 bp and telomeric repeats, changing Kds to about 188 and 155 nM, respectively (Fig 5A-C). We also show that PI(3,4,5)P3 does not disrupt the binding of rRAP1-His fragments (Myb or MybL) without the N-terminus domain (Fig S5), implying binding of PI(3,4,5)P3 to RAP1 N-terminus is required for displacement of RAP1 DNA binding domains (Myb and MybL) from telomeric and 70 bp repeats, and that PI(3,4,5)P3 is not competing for Myb or Myb-L binding to DNA. Moreover, we show that RAP1-HA binding to 70 bp and telomeric repeats in vivo is displaced in T. brucei cells expressing catalytic inactive PIP5Pase (Fig 5D-G), which we show results in RAP1-HA binding about 100-fold more endogenous PI(3,4,5)P3 than in T. brucei expressing WT PIP5Pase (Fig 4F). The in vivo data agrees with the in vitro data. The data show a typical allosteric regulator system, in which binding of a ligand to one site of the protein, here PI(3,4,5)P3 binding to RAP1 N-terminus, affects other domains (RAP1 Myb and Myb-L domains) binding to DNA. To improve the clarity of the title, we will change it in the revised version to imply a direct role of PI(3,4,5)P3 regulation of RAP1 in the process. This will provide more specific information to the readers and addresses the concern of the reviewer related to the “allosteric switch”. The new title will be: PI(3,4,5)P3 allosteric regulation of RAP1 controls antigenic switching in trypanosomes

There are serious concerns about many conclusions made by Touray et al., according to their experimental approaches:

1) The authors have been studying RAP1’s chromatin association pattern by ChIPseq in cells expressing a C-terminal HA tagged RAP1. According to data from tryptag.org, RAP1 with an N-terminal or a C-terminal tag does not seem to have identical subcellular localization patterns, suggesting that adding tags at different positions of RAP1 may affect its function. It is therefore essential to validate that the C-terminally HA-tagged RAP1 still has its essential functions. However, this data is not available in the current study. RAP1 is essential. If RAP1-HA still retains its essential functions, cells carrying one RAP1-HA allele and one deleted allele are expected to grow the same as WT cells. In addition, these cells should have the WT VSG expression pattern, and RAP1-HA should still interact with TRF. Without these validations, it is impossible to judge whether the ChIPseq data obtained on RAP1-HA reflect the true chromatin association profile of RAP1.

Tryptag data show both N- and C-terminus RAP1 with nuclear localization in procyclic forms, although there are differences in signal intensities in the images (http://tryptag.org/?id=Tb927.11.370). It is important to note that Tryptag data is from procyclic forms, and DNA constructs are not validated for their integration in the correct locus. As for the RAP1-HA localization in bloodstream forms, we demonstrated that C-terminally HA-tagged RAP1 co-localizes with telomeres by a combination of immunofluorescence and fluorescence in situ hybridization (Cestari and Stuart, 2015, PNAS), and RAP1-HA co-immunoprecipitate telomeric and 70 bp repeats (Cestari et al. 2019 Mol Cell Biol). We also showed by immunoprecipitation and mass spectrometry that HA-tagged RAP1 interacts with nuclear and telomeric proteins, including PIP5Pase (Cestari et al. 2019). Others have also tagged T. brucei RAP1 in bloodstream forms with HA without disrupting its nuclear localization (Yang et al. 2009, Cell; Afrin et al. 2020, Science Advances). As for the experiment suggested by the reviewer, there is no guarantee that cells lacking one allele of RAP1 will behave as wildtype, i.e., normal growth and repression of VSGs genes. Also, less than 90% of T. brucei TRF was reported to interact with RAP1 (Yang et al. 2009, Cell), which might be indirect via their binding to telomeric DNA repeats rather than direct protein-protein interactions.

2) Touray et al. expressed and purified His6-tagged recombinant RAP1 fragments from E. coli and used these recombinant proteins for EMSA analysis: The His6 tag has been used for purifying various recombinant proteins. It is most likely that the His6 tag itself does not convey any DNA binding activities. However, using His6-tagged RAP1 fragments for EMSA analysis has a serious concern. It has been shown that His6-tagged human RAP1 protein can bind dsDNA, but hRAP1 without the His6 tag does not. It is possible that RAP1 proteins in combination with the His6 tag can exhibit certain unnatural DNA binding activities. To be rigorous, the authors need to remove the His6 tag from their recombinant proteins before the in vitro DNA binding analyses are performed. This is a standard procedure for many in vitro assays using recombinant proteins.

We show in Fig 3C-G that His-tagged full-length rRAP1 does not bind to scrambled telomeric dsDNA sequences, which indicates that His-tagged rRAP1 does not bind unspecifically to DNA. Moreover, in Fig 3G, we show that His-tagged rRAP11-300 also does not bind to 70 bp or telomeric repeats. In contrast, full-length His-tagged rRAP1, rRAP1301-560, or rRAP1561-855 bind to 70 bp or telomeric repeats (Fig 3C-G). Since all proteins were His-tagged, the His tag cannot be responsible for the DNA binding.

As for the statement that human rRAP1-His has unspecific DNA binding properties, we could not find a reference to this statement; we cannot compare it without knowing the details of the experiment. Biochemical assays can result in unspecific binding depending on binding/buffer conditions. Also, humans and T. brucei RAP1 share only 15% of amino acid identity; unspecific binding to DNA could be specific to human RAP1.

3) It is unclear why Nanopore sequencing was used for RNAseq and ChIPseq experiments. The greatest benefit of Nanopore sequencing is that it can sequence long reads, which usually helps with mapping, particularly at genome loci with repetitive sequences. This seems beneficial for RAP1 ChIPseq analysis as RAP1 is expected to bind telomere repeats. However, for ChIPseq, the chromatin needs to be fragmented. Larger DNA fragments from ChIPseq experiments will decrease the accuracy of the final calculated binding sites. Therefore, ChIPseq experiments are not supposed to have long reads to start with, so Nanopore sequencing does not seem to bring any advantage. In addition, compared to Illumina sequencing, Nanopore sequencing usually yields smaller numbers of reads, and the sequencing accuracy rate is lower. The Nanopore sequencing accuracy may be a serious concern in the current study. All telomeres have the perfect TTAGGG repeats, all VSG genes have a very similar 3’ UTR, and all 70 bp repeats have very similar sequences. In fact, the active and silent ESs have 90% sequence identity. Are sequence reads accurately mapped to different ESs? How is the sequencing and mapping quality controlled? Furthermore, it is unclear whether the read depth for RNAseq is deep enough.

The mean sequence length for the ChIP-seq was about 500 bp (see Table S3), which helps to align reads to ESs and distinguish the different ESs, and it is a reasonable size range to define RAP1 binding sites. Although sequencing depths are usually higher in Illumina than in nanopore (all depending on the amount of sequencing), most Illumina short reads map to multiple genomic sequences, making it difficult to distinguish ESs. This is particularly important for RAP1 because it binds to repeats such as 70 bp and telomeric repeats. Mapping short reads to those regions would be virtually impossible; hence, our choice of nanopore sequencing. For RNA-seq, the ~500 bp read length help sequence alignment to the subtelomeric regions containing many VSG genes. The nanopore reads obtained here had an average sequencing score 12 (i.e., base call accuracy of 94%). Filtering reads with MAPQ ≥ 20 (99% probability of correct alignment) helped us to distinguish RAP1 binding to specific ESs, including silent vs active ES (ChIP-seq) or VSG sequences (RNA-seq). The details of the analysis and sequencing metrics (i.e., sequencing depth and read length) were described in the Methods section “Computational analysis of RNA-seq and ChIP-seq” and Table S3, respectively.

4) Many statements in the discussion section are speculations without any solid evidence. For example, lines 218 - 219 “likely due to RAP1 conformational changes”, no data have been shown to support this at all. In lines 224-226, the authors acknowledged that more experiments are necessary to validate their observations, so it is important for the authors to first validate their findings before they draw any solid conclusions. Importantly, RAP1 has been shown to help compact telomeric and subtelomeric chromatin a long time ago by Pandya et al. (2013. NAR 41:7673), who actually examined the chromatin structure by MNase digestion and FAIRE. The authors should acknowledge previous findings. In addition, the authors need to revise the discussion to clearly indicate what they “speculate” rather than make statements as if it is a solid conclusion.

The statement “likely due to RAP1 conformational changes” in lines 218-219 (page 6) is part of the Discussion. We did not make a strong statement but discussed a possibility. We believe that it is beneficial to the reader to have the data discussed, and we do not feel this point is overly speculative.

For lines 224-226 (page 6), the statement refers to the finding of RAP1 binding to centromeric regions by ChIP-seq, which is a new finding but not the focus of this work. Hence, future studies are necessary for this finding, and we believe it is appropriate in the Discussion to be upfront and highlight this point to the readers. However, for the RAP1 binding to telomeric ES sites, e.g., 70 bp repeats and telomeric repeats (the focus of this work), we validated the binding by EMSA and by performing binding kinetics using microscale thermophoresis.

We did not include Pandya et al. 2013 NAR because the authors demonstrated RAP1 compaction of chromatin to occur in procyclic forms only. Pandya et al. stated in their abstract: “no significant chromatin structure changes were detected on depletion of TbRAP1 in BF cells”. Hence, the suggested reference is not relevant to the context of our conclusions in bloodstream forms. Nevertheless, we have reviewed the Discussion to avoid broad speculations in the revised version of the manuscript.

There are also minor concerns:

1) In the PIP5Pase conditional knockout system, the WT or mutant PIP5Pase with a V5 tag is constitutively expressed from the tubulin array. What’s the relative expression level of this allele and the endogenous PIP5Pase? Without a clear knowledge of the mutant expression level, it is hard to conclude whether the mutant has any dominant negative effects or whether the mutant phenotype is simply due to a lower than WT PIP5pase expression level.

The relative mRNA levels of the exclusive expression of PIP5Pase Mut compared to the WT is available in the Data S1, RNA-seq. The Mut allele’s relative expression level is 0.85-fold to the WT allele (both from tubulin loci). We also showed by Western blot the WT and Mut PIP5Pase protein expression (Cestari et al. 2019, Mol Cell Biol). Concerning PIP5Pase endogenous alleles, we compared RNA-seq reads counts per million from the conditional null PIP5Pase cells exclusively expressing WT or the Mut PIP5Pase alleles (Data S1, this work) to our previous RNA-seq of single-marker 427 strain (Cestari et al. 2019, Mol Cell Biol). We used the single-maker 427 because the conditional null cells were generated in this strain background. The PIP5Pase WT and Mut mRNAs expressed from tubulin loci are 1.6 and 1.3-fold the endogenous PIP5Pase levels in single-marker 427, respectively. We include a statement in the Methods, page 7, lines 265-268: “The WT or Mut PIP5Pase mRNAs exclusively expressed from tubulin loci are 1.6 and 1.3-fold the WT PIP5Pase mRNA levels expressed from endogenous alleles in the single marker 427 strain. The fold-changes were calculated from RNA-seq reads counts per million from this work (WT and Mut PIP5Pase, Data S1) and our previous RNA-seq from single marker 427 strain (24).”

2) In EMSA analysis, what are the concentrations of the protein and the probe used in each reaction? The amount of protein used in the binding assay appears to be very high, and this can contribute to the observation that many complexes are stuck in the well. Better quality EMSA data need to be shown to support the authors’ claims.

All concentrations were provided in the Methods section. See page 9 Electrophoretic mobility shift assays: “100 nM of annealed DNA were mixed with 1 μg of recombinant protein…”. For microscale thermophoresis, also see page 9, Microscale thermophoresis binding kinetics: “1 μM rRAP1 was diluted in 16 two-fold serial dilutions in 250 mM HEPES pH 7.4, 25 mM MgCl2, 500 mM NaCl, and 0.25% (v/v) N P-40 and incubated with 20 nM telomeric or 70 bp repeats…”. Note that two different biochemical approaches, EMSA and microscale thermophoresis, were used to assess rRAP1-His binding to DNA. Both show similar results (Fig 3 and 5, and Fig S5; microscale thermophoresis shows the binding kinetics, data available in Table 1). The EMSA images clearly show the binding of RAP1 to 70 bp or telomeric repeats but not to scramble telomeric repeat DNA.

Reviewer #2 (Public Review):

This manuscript by Touray, et al. provides a significant new twist to our understanding of how antigenic variation may be regulated in T. brucei. Key aspects of antigenic variation are the mutually exclusive expression of a single antigen per cell and the periodic switching from expression of one antigen isoform to another. In this manuscript, the authors show, as they have previously shown, that depletion of the nuclear phosphatidylinositol 5-phosphatase (PIP5Pase) results in a loss of mutually exclusive VSG expression. Furthermore, using ChIP-seq, the authors show that the repressor/activator protein 1 (RAP1) binds to regions upstream and downstream of VSG genes located in transcriptionally repressed expression sites and that this binding is lost in the absence of a functional PIP5Pase. Importantly, the authors decided to further investigate this link between PIP5Pase and RAP1, a protein that has previously been implicated in antigenic variation in T. brucei, and found that inactivation of PIP5Pase results in the accumulation of PI(3,4,5)P3 bound to the RAP1 N-terminus and that this binding impairs the ability of RAP1 to bind DNA. Based on these observations, the authors suggest that the levels of PI(3,4,5)P3 may determine the cellular function of RAP1, either by binding upstream of VSG genes and repressing their function, or by not binding DNA and allowing the simultaneous expression of multiple VSG genes in a single parasite.

While I find most of the data presented in this manuscript compelling, there are aspects of Figure 1 that are not clear to me. Based on Figure 1F, the authors claim that transient inactivation of PIP5Pase results in a switch from the expression of one VSG isoform to another. However, I am not exactly sure what the authors are showing in this panel, nor do the data in Figure 1F seem to be consistent with those shown in Figure 1C. Based on Figure 1F, a transient inactivation of PIP5Pase appears to result in an almost exclusive switch to a VSG located in BES12. However, based on Figure 1E, the VSG transcripts most commonly found after a transient inactivation of PIP5Pase are those from the previously active VSG (BES1) and VSGs located on chr 1 and 6 (I believe). The small font and the low resolution make it impossible to infer the location of the expressed VSG genes, nor to confirm that ALL VSG genes located in expression sites are activated, as the authors claim. Also, I was not able to access the raw ChIP-seq and RNA-seq reads. Thus, could not evaluate the quality of the sequencing data.

We appreciate the reviewer’s comments and evaluation of our work. Fig 1E shows VSG-seq of a population after transient (24h) exclusive expression of the PIP5Pase mutant, followed by re-expression of the WT PIP5Pase allele for 60 hours (multiple VSGs are detected). As a control, it also shows VSG-seq in cells continuously expressing WT PIP5Pase (mostly VSG2, BES1 is detected). Fig 1F and Fig S1 show the sequencing of VSGs expressed by clones isolated (5-6 days of growth) after a temporary knockdown (24h) of PIP5Pase (tet -), followed by its re-expression. For comparison, no knockdown (tet +) was included. Fig 1F shows potential switchers in the population, the Fig 1E confirms VSG switching in clones.

To clarify the difference between Fig 1E and 1F, we edited the manuscript on page 3, lines 103-110: “To verify PIP5Pase role in VSG switching, we knocked down PIP5Pase for 24h (Tet -), then restored its expression (Tet +) and isolated clones by limiting dilution and growth for 5-6 days. Analysis of isolated clones after temporary PIP5Pase knockdown (Tet -/+) confirmed VSG switching in 93 out of 94 (99%) of the analyzed clones (Fig 1F, Fig S1). The cells switched to express VSGs from silent ESs or subtelomeric regions, indicating switching by transcription or recombination mechanisms. Moreover, no switching was detected in 118 isolated clones from cells continuously expressing WT PIP5Pase (Tet +, Fig 1F).”. We also edited Fig 1F to indicate temporary knockdown (Tet -/+) vs no knockdown (Tet -). The modifications will be available in the resubmitted version of the manuscript.

We agree that the heat map is difficult to read due to the amount of information. We will include in the revised version of the manuscript a table with the data in the supplementary information; the reader will be able to evaluate the data in detail.

A preference for switching to specific ESs has been observed in T. brucei (Morrison et al. 2005, Int J Parasitol; Cestari and Stuart, 2015, PNAS), which may explain several clones switching to BES12. Many potential switchers were detected in the VSG-seq (Fig 1F, the whole cell population is over 107 parasites), but not all potential switchers were detected in the clonal analysis because we analyzed 212 clones total, a fraction of the over 107 cells analyzed by VSG-seq (Fig 1E). Also, it is possible that not all potential switchers are viable. However, the point of the clonal analysis is to validate the VSG switching after genetic perturbation of PIP5Pase.

Fig 1C shows examples of ES derepression by RNA-seq after 24h exclusive expression of the mutant compared to WT PIP5Pase. The RNA-seq shows that all ESs are derepressed (Fig 1B). This can be visualized in the volcano plot (Fig 1B, BES and MES VSGs are labelled) and on the spreadsheet Data S1. Although all ESs are derepressed after PIP5Pase mutant expression, not all ESs are selected during switching, as observed in Fig 1E-F. This agrees with our previous observations in switching assays with proteins that control VSG switching (Cestari and Stuart, 2015, PNAS).

As for metrics of sequencing and raw sequencing data. See Methods section, page 13, lines 483-485: “Sequencing information is available in Table S3 and fastq data is available in the Sequence Read Archive (SRA) with the BioProject identification PRJNA934938.” Table S3 has a summary of sequencing data. Metrics information such as sequencing quality and analysis can be found in the Methods section “Computational analysis of RNA-seq and ChIP-seq”. The latter includes information about nanopore reads, i.e., mean Q-score of 12.

Reviewer #3 (Public Review):

In this manuscript, Touray et al investigate the mechanisms by which PIP5Pase and RAP1 control VSG expression in T. brucei and demonstrate an important role for this enzyme in a signalling pathway that likely plays a role in antigenic variation in T. brucei.

The methods used in the study are rigorous and well-controlled. The authors convincingly demonstrate that RAP1 binds to PI(3,4,5)P3 through its N-terminus and that this binding regulates RAP1 binding to VSG expression sites, which in turn regulates VSG silencing. Overall their results support the conclusions made in the manuscript.

There are a few small caveats that are worth noting. First, the analysis of VSG derepression and switching in Figure 1 relies on a genome that does not contain minichromosomal (MC) VSG sequences. This means that MC VSGs could theoretically be misassigned as coming from another genomic location in the absence of an MC reference. As the origin of the VSGs in these clones isn’t a major point in the paper, I do not think this is a major concern, but I would not over-interpret the particular details of switching outcomes in these experiments.

The authors state that “our data imply that antigenic variation is not exclusively stochastic.” I am not sure this is true. While I also favor the idea that switching is not exclusively stochastic, evidence for a signaling pathway does not necessarily imply that antigenic variation is not stochastic. This pathway could be important solely for lifecycle-related control of VSG expression, rather than antigenic variation during infection. Nevertheless, these data are critical for establishing a potential pathway that could control antigenic variation and thus represent a fundamental discovery.

Another aspect of this work that is perhaps important, but not discussed much by the authors, is the fact that signalling is extremely poorly understood in T. brucei. In Figure 1B, the RNA-seq data show many genes upregulated after expression of the Mut PIP5Pase (not just VSGs). The authors rightly avoid claiming that this pathway is exclusive to VSGs, but I wonder if these data could provide insight into the other biological processes that might be controlled by this signaling pathway in T. brucei.

Overall, this is an excellent study that represents an important step forward in understanding how antigenic variation is controlled in T. brucei. The possibility that this process could be controlled via a signalling pathway has been speculated for a long time, and this study provides the first mechanistic evidence for that possibility.

We thank the reviewer for the evaluation of our work. We agree that it is difficult to ensure the origin of all VSG genes not having minichromosome sequences; hence we did not emphasize this point in the manuscript. We used the 427-2018 reference genome assembled by PacBio and Hi-C (Muller et al. 2018, Nature), which we believe is the best assembly for the 427 strain, especially related to the VSG genes.

We also agree that having signaling controlling switching in vitro does not mean the switching necessarily occurs by signaling in vivo. Nevertheless, stochastic switching is an accepted model; but it has not been proved, whereas we provide molecular evidence that signaling can cause switching. To express this reviewer’s suggestion, we edited the Discussion, page 7, line 250: from “our data imply that antigenic variation is not exclusively stochastic” to “our data suggest that antigenic variation is not exclusively stochastic”.

Most of the RNA-seq data were VSGs genes/pseudogenes. Other genes upregulated included retrotransposons and DNA/RNA processing enzymes such as endonucleases and polymerases. We included in the Results, page 3, line 100: “Other genes upregulated include primarily retrotransposons, endonucleases, and polymerase proteins.”.

Reviewer #3 (Public Review):

It is well known that as seasonal day length increases, molecular cascades in the brain are triggered to ready an individual for reproduction. Some of these changes, however, can begin to occur before the day length threshold is reached, suggesting that short days similarly have the capacity to alter aspects of phenotype. This study seeks to understand the mechanisms by which short days can accomplish this task, which is an interesting and important question in the field of organismal biology and endocrinology.

The set of studies that this manuscript presents is comprehensive and well-controlled. Many of the effects are also strong and thus offer tantalizing hints about the endo-molecular basis by which short days might stimulate major changes in body condition. Another strength is that the authors put together a compelling model for how different facets of an animal's reproductive state come "on line" as day length increases and spring approaches. In this way, I think the authors broadly fulfill their aims.

I do, however, also think that there are a few weaknesses that the authors should consider, or that readers should consider when evaluating this manuscript. First, some of the molecular genetic analyses should be interpreted with greater caution. By bioinformatically showing that certain DNA motifs exist within a gene promoter (e.g., FSHbeta), one is not generating robust evidence that corresponding transcription factors actually regulate the expression of the gene in question. In fact, some may argue that this line of evidence only offers weak support for such a conclusion. I appreciate that actually running the laboratory experiments necessary to generate strong support for these types of conclusions is not trivial, and doing so may even be impossible. I would therefore suggest a clear admission of these limitations in the paper.

Second, I have another issue with the interpretation of data presented in Figure 3. The data show that FSHbeta increases in expression in the 8Lext group, suggesting that endogenous drivers likely act to increase the expression of this gene despite no change in day length. However, more robust effects are reported for FSHbeta expression in the 10v and 12v groups, even compared to the 8Lext group. Doesn't this suggest that both endogenous mechanisms and changes in day length work together to ramp up FSHbeta? The rest of the paper seemed to emphasize endogenous mechanisms and gloss over the fact that such mechanisms likely work additively with other factors. I felt like there was more nuance to these findings than the authors were getting into.

Third, studies 1 - 3 are well controlled; however, I'm left wondering how much of an effect the transitions in day length might have on the underlying molecular processes that mediate changes in body condition. While the changes in day length are themselves ecologically relevant, the transitions between day length states are not. How do we know, for example, that more gradual changes in day length that occur over long timespans do not produce different effects at the levels of the brain and body? This seemed especially relevant for study 3, where animals experience a rather sudden change in day length. I recognize that these experimental methods are well described in the literature, and they have been used by endocrinologists for a long time; nonetheless, I think questions remain.

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Reply to the reviewers

We thank the reviewers for their insights and comments on this manuscript. Specific responses to reviewer concerns are detailed below. We made a couple of significant changes based on the feedback. First, we performed more experiments to increase biologic replicates and then quantified image data for multiple figures. The new quantitative information added to Figure 3 fully supports our original conclusions about changes to the ONH in Hes-TKO mutants. The quantification of Atoh7, Otx2, Rbpms and Crx expressing cells among the different genotypes revealed interesting differences in Notch intracellular gene requirements for both RGC and cone development. The most startling outcome is that changes in both cell types correlate with significant changes in Otx2, but not Atoh7. This singular finding suggests interesting future work is needed, well beyond the scope of this paper about the molecular mechanisms underlying these cell fates. Second, our data presentation was reorganized with new information added to Fig 1 that clarifies the relationships between Hes1, Hes5, Foxg1 and Pax2; old Figs 6 & 7 about neurogenesis were merged; and some data moved to new Suppl Figs 2 and 5. The numbering for multiple figures changed and a new summary model (now Fig 8) is provided. In addition, the manuscript was completely rewritten to improve clarity. We hope this revised manuscript is acceptable for publication.

Reviewer #1 Summary:

In this study, the authors employed an impressive set of mouse mutant or Cre lines to investigate the complexity of Notch signaling across different stages of retinal development. These comprehensive analyses led to two main findings: 1. Sustained hes1 in the OHS/OS is Notch-independent; 2. Rbpj and Hes1 exhibited opposing roles in cone photoreceptor development. Although the study is potentially interesting, the current manuscript needs the essential research background and quantification, a lack of which significantly reduced the clarity of the manuscript and the credibility of the major conclusions. Also, how the authors organized the results is quite confusing, making the manuscript very difficult to follow.

Response: We agree with all reviewers concerning incomplete quantification of the data. We directly addressed this shortcoming in revised Figs 3 and 6 (the latter combines old Figs 6 +7). To do this, we repeated some IHC experiments to add more replicates and reorganized all of the neurogenesis phenotypic data figures. Our quantifications uncovered several surprising outcomes that clarify our model. For these reasons, the manuscript was exhaustively rewritten. We merged E13 neurogenesis data into revised Figure 6 and moved the most relevant E16 analyses to new supplemental data Fig 5. All changes made should make the paper easier to understand for retinal development, neurogenesis, and Notch pathway aficionados, in addition to readers lacking such expertise.

Major comments: 1. The authors needed to make the quantification for many analyses to strengthen the conclusions, such as Fig. 1F, 1G, and etc.

Response: We quantified optic nerve head (ONL) immunohistochemistry data in the revised Fig 3. We also quantified neurogenesis markers Atoh7, Otx2, Rbpms (RGCs), and Crx at E13 in revised Fig 6 (former Figs 6 and 7). Older stages were moved to a new Suppl Fig 5.

Respectfully, Hes5 mRNA expression in old Fig 1F and 1G shows that Hes5, like other retinal progenitor cell (RPC) markers, expanded in Rax-Cre deletion but not Chx10-Cre deletion conditions. This is analogous to Pax6 and Rax expansion in Rax-Cre;Hes1 CKO eyes and Pax2 mutants (doi: 10.1523/JNEUROSCI.2327-19.2020) (1). In revised Fig 1, we now show analogous expansion of Hes5 mRNA in Pax2 mutant retinas (compare Figs 1F-1I). Because Hes5 RNA in situ hybridization experiments are nonquantitative, we do not discuss the possibility of Hes5 mRNA level changes in labeled cells.

The authors reported many exciting results. However, further mechanistic insights are largely missing. They may focus on one of these exciting findings and give some mechanistic insights. For example, hes1 suppresses hes5 expression as the ONH boundary forms; hes1 expression in the ONH is Notch independent; differential influences of Rbpj and Hes1 on cone development. It is better for the authors to select one of these exciting findings and provide a deeper mechanistic study.

Response: This revision brings fresh focus to Notch regulation of RGC and photoreceptor development, particularly differential influences for Rbpj versus Hes1. We also better support our interpretation of image data in Fig 1. We include new data about the spatial relationships between Hes5-GFP/Pax2 and Hes5-GFP/Foxg1. In summary, we find that as Pax2 becomes restricted to the nasal optic cup prior to the onset of RGC genesis, it becomes mutually exclusive with Hes5-GFP, at the same time that Hes5-GFP+ cells coexpress Hes1. This is consistent with Hes1 indirectly regulating Hes5-GFP as a marker of neurogenic RPCs at the forming ONH. Furthermore, it emphasizes the importance of genetically teasing apart the separate and potentially compensatory roles for Hes1 versus Hes5 undertaken here. These relationships remain poorly resolved during vertebrate CNS development.

Some analyses lack an explanation of the rationale. For example, "To understand if the loss of multiple Hes genes is more catastrophic than Hes1 alone..."(PAGE 7). Please explain its significance.

Response: We assume the reviewer is referring to the first sentence of the last paragraph on this page. We analyzed Hes triple mutant mice (TKO) to understand if removing multiple Hes genes reveals redundant functions. This is an open question, given that Hes1 is expressed in the ONH/OS, which is normally devoid of Hes5 by the time retinal neurogenesis begins. These questions have only been explored in a handful of tissues throughout the body. Also see response to point 2 above. In general, we have expanded the rationale for all of the experiments throughout the revised manuscript.

Significance: In general, many results are quite interesting. However, the significance of these findings is largely hampered in the following aspects: 1. The authors were unable to provide the sufficient research contexts that are essential for understanding many results.2. Many conclusions were solely based on descriptive images but lacked statistical quantification, which significantly weakened many conclusions. 3. Many interesting findings are quite descriptive, and some mechanistic understandings of one of these exciting findings will be beneficial to improve the focus and significance of the study. Current format of the manuscript fits more specialized audience.

Response: During in vivo development, we wished to understand which particular Notch pathway genes can interact in a Notch-dependent versus a Notch-independent manner. Genetic (phenotypic) studies produce extremely rigorous datasets, in our opinion. This revision now extensively quantifies key findings. Here we dissected the "receipt" of a Notch signal by identically testing the functional requirements of particular pathway members. For Mastermind (Maml), there are 3 paralogues, double mutants for Maml1 and Maml3 are early lethal, and no floxed alleles exist, so it was logical to employ the ROSA-dnMaml mouse strain, particularly since it has been discussed throughout the Notch literature as "analogous" to removing either a Notch receptor or Rbpj. Our finding that the dnMAML allele does not function like a Rbpj null in the retina is important for researchers in the broad Notch field to consider when designing and interpreting experiments.

Reviewer #2: Hes genes are effectors of the Notch signaling pathway but can also act down-stream of other signaling cascades. In this manuscript the authors attempt to address the complexity of Hes effectors during optic cup development and retinal neurogenesis. To do so, they compared optic cup patterning and retinal neurogenesis in seven germline or conditional mutant mouse embryos generated with two spatio-temporally distinct Cre drivers. These lines allowed for the analysis of the consequences of perturbing the Notch ternary complex and multiple Hes genes alone or in combination. The authors show that the optic disc/nerve head is regulated by Notch independent Hes1 function. They also confirm that perturbation of Notch signaling interferes with cell proliferation enhancing the production of differentiated ganglion cells, whereas photoreceptor genesis requires both Rbpj and Hes1 with Notch dependent and independent mechanisms. This is a rather complex study that dissects further the role of the Notch pathway and Hes proteins during eye development, a topic that has been addressed in many previous studies but perhaps not with the details that the authors have used here. In this respect, this study adds to current literature but will likely be of interest to retina aficionados. The manuscript reads well and the figures are of very good quality. However, many of the statements are based on qualitative rather than on quantitative analysis. This should be, at least in some cases, remediated, despite the effort that this may require given the number of mouse lines used in the study.

Response: As described in the response to Reviewer 1, we agree and present considerably more quantification data. We extensively reorganized and rewrote this manuscript to emphasize that Hes1 in the ONH/OS is fully Notch-independent and highlight branchpoints in Notch-dependent signaling, for Rbpj versus Hes,1 during early retinal neurogenesis. It is too simplistic that the ternary complex (Rbpj-NICD-Maml) simply activates Hes1 (and/or multiple Hes genes) to regulate downstream signaling targets. This paradigm has been portrayed in the literature numerous times for many processes throughout vertebrate development, homeostasis or relative to particular diseases. By focusing on one tissue and a narrow window of development, our phenotypic studies delved more deeply to show the greater complexity and molecular cross-talk that we think underlie the modulation of signaling levels with in vivo context. Thus, our results are of broad interest and impact to the greater Notch field.

1. The title is somewhat misleading. The authors have explored mostly the role of Hes1, 3 and5. Although these are Notch effectors, there is already evidence that they participate in other pathways This is confirmed by the data present here. I would suggest to eliminate Notch from the title and use instead "Hes" to better reflect the findings. Furthermore, it is unclear why there is a reference to "mutations" or what are the Notch branchpoints to which the authors refer at the beginning of the discussion.

Response: We appreciate the reviewer’s viewpoint but disagree this paper is mostly about Hes genes, as there is a critical direct, comparable evaluation with Rbpj and dn-Maml. Direct comparison of 7 genotypes highlights where each pathway member exhibits idiosyncratic phenotypes. We are striving for a clear, simple title about a very complex topic, involving the in vivo genetic dissection of a signaling pathway. We modified the title to: "Notch pathway mutations do not equivalently perturb mouse embryonic retinal development "

1. "Although the Pax6-Pax2 boundary is intact in Rax-Cre;RbpjCKO/CKO eyes, ONH shape was attenuated compared to controls (Fig 3I)". This statement is arguable as the difference seems subtle. Perhaps some kind of quantification would help.

Response: We quantified Pax2+ cells (ONH domain) using the adjacent proximal terminus of the retinal pigmented epithelium (RPE) to indicate a transition from ONH to optic stalk (OS). We also quantified the number of Pax2+Pax6+ double positive cells where the 2 domains abut (boundary cells). Some higher magnification examples are now provided in Fig 3H';3K';3N'. Grossly, the imaging data support that the Pax2+ ONH is expanded in Chx10-Cre;TKO eyes, while boundary cells are most affected in Rax-Cre;HesTKO eyes, due to an expansion of retinal tissue. This is supported by our quantitative data (Fig 3O,3P). We observed even in controls that Pax2-expressing cells show some numerical variability. We attributed this to the position of the section through the ONH, which is a 3-dimsenional ring (torus). Therefore, we quantified additional wild-type controls and mutant samples in the new Fig 3O,3P graphs, improving statistical power, and allowing us to detect quantitative differences.

Page 12 first paragraph. "....but all other genotypes were unaffected". This statement is unclear. All lines in which the Rax-Cre has been used seem to have an increased number of apoptotic cells. This should be better explained

Response: Respectfully, only one genotype, Rax-Cre;Rbpj mutants contain a statistically significant increase in apoptotic cells (Fig 5P). This is demonstrated by one-way ANOVA analyses that included all pairwise comparisons. To ensure that the quantification was not misleading due to changes in tissue morphology, data in Figs 5, 6, and 7 were normalized to optic cup area. The area was traced in FIJI, creating a polygon whose area was determined in square microns. For every section image, the marker+ cells were divided by the square micron area of the retina (excluding the opening for the optic nerve). Such a method is critical for comparison across this allelic series, given the morphologic changes, differences in cell clustering where rosettes form, and reduced proliferation whenever Notch signaling is lost or reduced.

Page 12, end of second paragraph: "E13.5 Chx10-Cre;HesTKO eyes had a milder RGC phenotype (Figs 6G, 6N, 6U), but all other mutants were unaffected (Figs 6E, 6F, 6L, 6M, 6S, 6T). This statement is also rather subjective. The phenotype of Chx10-Cre;HesTKO is quite strong and the other mutants seem to have a phenotype. Some quantifications here will help.

Response: We agree and provide quantification for both Atoh7 and Rbpms positive cells in the revised Figure 6. This is now in the same figure with quantification of Otx2+, Otx2+Atoh7+ and Crx+ cells. The reviewer is correct that both ROSA-dnMaml and both HesTKO mutants have a statistically significant increase in RGCs. Surprisingly, neither of the Rbpj CKO mutants have this outcome (Fig 6Y).

1. Page 13, toward the bottom..."...but noted that Chx10-Cre RbpjCKO/CKO eyes were not different from controls (Figs 7E, 7AA)". Again, this statement is questionable as staining for both CRX and Rbpms seem reduced as compared to controls as quantifications in 7AA seems also to indicate (about half?). Did the authors calculate whether there is a statistical difference between controls and Chx10-Cre RbpjCKO/CKO ?

Response: Rbpms+ RGCs and Crx+ photoreceptor precursors were colabeled and quantified on sections for all genotypes. All counts were normalized to area as described above. Upon quantification and ANOVA with pairwise comparisons, there was no statistical difference in Crx+ or Rbpms+ cells between control and Chx10-Cre;Rbpj mutants (new Fig 6Y and Z).

In Fig 7CC the authors should make the effort of including at least one additional sample, 2 biological replicates seem insufficient to draw a conclusion.

Response: The Rax-Cre;Hes1CKO/+ X Hes1CKO/CKO matings stopped producing litters in late 2022. While this manuscript was out for review, we obtained younger mice, from which new control and Rax-Cre; Hes1 mutant littermates were collected, stained, imaged and quantified. Upon adding samples, we found that the outcome was unchanged, but the data better support the lack of a statistical difference in rods between genotypes at E17. These data were moved to revised Suppl Fig 5.

Significance: This is a rather complex study that dissects further the role of the Notch pathway and Hes proteins during eye development, a topic that has been addressed in many previous studies but perhaps not with the details that the authors have used here. In this respect, this study adds to current literature but will likely be of interest to retina aficionados. The manuscript reads well and the figures are of very good quality. However, many of the statements are based on qualitative rather than on quantitative analysis. This should be, at least in some cases, remediated, despite the effort that this may require given the number of mouse lines used in the study.

Response: To increase the impact of our manuscript, we quantified all markers except Tubb3, since its localization in cell bodies and axons make it impossible to assign to individual cells. We feel that this additional quantification strongly improves the quality of our findings and allowed us to make well-supported and novel conclusions. While we certainly believe that the retinal development community will find this paper of interest, it will also be of value to the broader Notch pathway scientific community. In this manuscript, we simultaneously compared phenotypes for Notch pathway genes in signal receiving cells. We could find essentially no studies like this for the mouse CNS and only a few from the Kopan lab about the kidney and immune system. Interestingly, one of us (NLB) is a coauthor on a recent paper about Notch signaling in the cortex, in which ROSA-dnMaml behaves analogously to Notch1CKO or RbpjCKO. This emphasizes that findings in one organ may not recapitulate the "rules" for this pathway for other cell types or tissues (doi: 10.1242/dev.201408)(2). Deeper understanding of how the Notch pathway in the retina functions, analogously or differently, is important. We feel our revised study advances when and where there are "branchpoints" in canonical signaling that may be overlooked in other developing tissues and organs.

Reviewer #3: I have reviewed a manuscript submitted by Bosze et al., which is entitled "Not all Notch pathway mutations are equal in the embryonic mouse retina". The authors focused on Notch signaling pathway. Notch signaling is deeply conserved across vertebrate and invertebrate animal species: in general, two transmembrane proteins, Delta and Notch, interact as a ligand and a receptor, respectively, which induces proteolytic cleavage of Notch receptors to generate Notch intracellular domain (NICD). NICD is translocated into nucleus, then forms the transcription factor complex including Rbpj (also referred to as CBF1) and Mastermind-like (Maml), and activates the transcription of Hes family transcription factors. Three Hes proteins, Hes1, 3, and 5, are important for nervous system development. In the vertebrate developing retina, these Hes proteins inhibit neurogenesis to maintain a pool of neural progenitor cells. In addition to their primary role in neurogenesis, the authors recently reported that Hes1 promotes cone photoreceptor differentiation. In the later stages of development, Hes proteins also promote Müller glial differentiation. In addition, Hes1 is highly expressed in the boundary between the neural retina and optic stalk and required for this boundary maintenance. To understand precise regulation of Notch component-mediated signaling network for retinal neurogenesis and cell differentiation, the authors compared retinal phenotypes in the knockdown of three Notch pathway components, that is (1) Hes1/3/5 cTKO, (2) Rbpj KO, and (3) dominant-negative Maml (dnMaml) overexpression, under the control of two Cre derivers; Rax-Cre and Chx10-Cre. First, the authors found that Hes1 expression in the boundary between optic stalk and neural retina is lost in Rax-Cre; Hes1/3/5 cTKO, but still retained in Rax-Cre; Rbpj KO and Rax-Cre; dnMaml overexpression, suggesting that Delta-Notch interaction is not required for Hes1 expression in the boundary between optic stalk and neural retina. Furthermore, Hes1 expressing boundary region expands distally at the expense of the neural retina in Chx10-Cre; Hes1/3/5 cTKO. Maintenance of ccd2 expression in this expanded boundary area suggests that Hes1 normally maintains a proliferative state in the optic stalk, which may allow these cells to differentiate into astrocyte in later stages. Second, in addition to precocious RGC differentiation in all the Notch component KO, the authors found that, as compared with wild-type, cone and rod photoreceptor genesis is highly enhanced in Rax-Cre; Rbpj KO and Rax-Cre; dnMaml overexpression and mildly enhanced in Chx10-Cre; dnMaml overexpression. On the other hand, in Rax-Cre; Hes1/3/5 cTKO, cone and rod photoreceptor genesis is not enhanced but similar to wild-type level. Since the authors previously reported that cone genesis is reduced in Rax-Cre; Hes1 cKO and Chx10-Cre; Hes1 cKO, so Rax-Cre; Hes1/3/5 cTKO may rescue decrease in cone genesis in single Hes1 cKO. The authors raise the possibility that elevated Hes5 expression in single Hes1 cKO may suppress cone photoreceptor genesis. The authors also found that amacrine cell genesis is significantly suppressed in Rax-Cre; Rbpj KO but not changed in Rax-Cre; dnMaml overexpression and Rax-Cre; Hes1/3/5 cTKO, suggesting that Rbpj is specifically required for amacrine cell genesis. From these observations, the authors propose that there are at least two branchpoints for photoreceptor and amacrine cell genesis in Notch component-mediated signaling network. Their findings are very interesting and provide some new insight on how Notch signaling components are integrated into other signaling pathways and promote to generate diverse but well-balanced retinal cell-types during retinal neurogenesis and cell differentiation, in addition to conventional classic view of Notch signaling pathway. However, one weak point is that, although the authors figured out what kinds of phenotypic difference appear in the KO retinas between these Notch components, the research result is descriptive and less analytical. Most of their conclusions may be supported by their previous works or others; it is still hypothetical. So, it is important to show more analytical data to support their interpretation and more clearly show what is new conceptual advance for Notch signaling pathways.

For example, sustained Hes1 expression in the boundary region between optic stalk and neural retina may be reminiscent to brain isthmus situation. I would like to request the authors to show more direct evidence that Hes1 regulation in optic stalk/retina boundary is independent of Delta-Notch interaction. One possible experiment is whether DAPT treatment phenocopies Rax-Cre; Rbpj KO and Rax-Cre; dnMaml overexpression (Hes1 in optic stalk boundary is normal?).

Response: Usage of the gamma secretase inhibitor DAPT is an interesting experiment as it can phenocopy the loss of Notch signaling in developing tissues. However, the reviewer's proposed DAPT experiment is problematic for two major reasons. First, DAPT blocks the gamma secretase complex, which has more than 90 protein targets in the cell membrane (3). Therefore, DAPT may not be informative for Hes1 regulation given the myriad of expected off-target effects. Second, it would be difficult to treat embryos at the relevant stages with DAPT. Injections into pregnant mice are lethal and we cannot localize drug to the relevant area during in vivo development. Our direct phenotypic comparisons with two Cre drivers strongly indicate that Hes1 is independent of canonical Notch signaling in the developing optic stalk.

We include an extra related data figure (Reviewer Fig 1) showing anti-Hes1 immunolabeling of E13.5 Rax-Cre;Notch1CKO/CKO (n=2) and E13.5 Rax-Cre;Notch2CKO/CKO eyes (n=3). The Notch1 mutant lost oscillating Hes1 expression in retinal progenitors, but the uniform Hes1 ONH domain remains. Interestingly, the Notch2 mutant had essentially no effect on Hes1 (oscillating or sustained), or Hes5 mRNA expression. A Notch2 RNA in situ hybridization demonstrates that Notch2 mRNA was lost in the E13 optic cup and RPE (Rax-Cre expressing tissues). These data emphasize: A) the Notch1-specific dependency of oscillating Hes1 expression in retinal progenitors is absent from the ONH; B) although coexpressed in the same tissue, Notch receptors have unequal activities.

Does Rax-Cre; Rbpj KO; Hes1-cKO phenocopy Rax-Cre; Hes1-cKO (or Rax-Cre; Hes1/3/5 cTKO)?

Response: This is a good question! The first author tried very hard to produce Rax-Cre; Rbpj CKO;Hes1 CKO double mutant embryos. However, these progeny could not be recovered from E10-E13 embryos, despite collecting more than 10 litters. Thus, it is likely that this genotype is lethal before eye formation.

Could the authors identify an enhancer element that drives Hes1 transcription in optic stalk/retina boundary, which should be not overlapped with that of NICD/ Rbpj binding motif? Such additional evidence will make their conclusion more convincing.

Response: Another interesting question. We have been working for >3 years on Hes1 cis regulatory enhancers, but the pandemic greatly delayed progress. The proximal Hes1 600bp upstream region is a generic enhancer that contains Hes1 binding sites for repressing its own expression (4) and has a pair of Rbpj consensus sites for Notch ternary complex activation of Hes1 expression (5,6). Nearby is a binding site occupied by Gli2 in the E16 mouse retina (7). Recently, it was shown that Ikzf4 binds slightly farther away (8). The upstream 1.8 kb region (including the 600bp just described) can drive destabilized GFP or dsRed reporters in early postnatal retinal explants (9). However, this sequence was used to make and analyze a classic Hes1-GFP transgenic reporter mouse, in which GFP was not expressed in the early embryonic mouse optic vesicle or cup (10). Therefore, any early eye-specific enhancer(s) are located farther upstream, in an intron, or downstream (or combination thereof). Public domain epigenetic and chromatin accessibility datasets support this idea. Identifying the gene regulatory logic for Hes1 expression in the eye will be an exciting future story, well beyond this manuscript. We are excited to use live imaging of enhancer reporters to discern oscillating versus sustained activity patterns during early ocular development.

Regarding the conclusion on new branchpoints on photoreceptor and amacrine cell genesis, a model shown in Figure 9 is still hypothetical. Figure 9B indicate a model in which the increase of Otx2+ cells and Crx+ cells in Rax-Cre; Rbpj KO is mediated by Hes1, which is presumed to be activated in Notch-independent signaling. However, Hes1 expression in the neural retina is markedly reduced in Rax-Cre; Rbpj KO (Fig. 2I), which does not fit in with the model.

Response: We removed Fig 9B and now present new models about the Notch-dependent versus -independent roles for both Rbpj and Hes1. The new summary is Fig 8.

So, I would like to request the authors to examine whether the increase of Otx2+ cells and Crx+ cells in Rax-Cre; Rbpj KO, (or Rax-Cre; dnMaml overexpression and Chx10-Cre; dnMaml overexpression) is inhibited by Hes1 KO.

Response: If we understand this correctly, it would mean generating double mutants, some of which we determined are not viable (see the response above, and Suppl Table 2). Given there is only a partial knockdown of Hes1 or Hes5 in either dnMaml mutant we do not believe repeating this in the Hes1 CKO genetic background to be informative and it would take 3 generations to perform.

Second, the authors concluded that both cone and rod genesis are enhanced in Rax-Cre; Rbpj KO by showing the data on Crx/Nr2e3 labeling in Rax-Cre; Hes1 cKO in Fig. 7BB. However, as the authors mentioned in the manuscript, Hes5 expression is elevated in Rax-Cre; Hes1 cKO (Fig. 1G). So, since Rax-Cre; Hes1 cKO has residual Hes activity in the retina, Fig. 7BB should be replaced with labeling of Crx/Nr2e3 in Rax-Cre; Hes1/3/5 cTKO.

Response: Unfortunately, Rax-Cre;HesTKO embryos do not live past E13 (Suppl Table 2). Thus, we cannot evaluate rods, whose genesis starts around E13.5. Revised Fig 1G shows the Hes5 domain is shifted with the expansion of retinal tissue in E13.5 Hes1 single mutants, but importantly, also analogously shifted in Pax2 mutants (Fig 1H). We do not conclude that mRNA levels are "elevated" since mRNA in situ hybridization is not a quantitative technique. Our initial examination of rods in E17 Rax-Cre;Hes1 CKO mutants tested the idea of a fate shift from cones to rods. However, deeper quantification (Suppl Fig 5) do not support such a fate change.

Furthermore, possibly, it is best to examine labeling of the retinas of Rax-Cre; Rbpj KO with rod and cone-specific markers and confirm that the number of both rods and cones is significantly increased. Third, as for defects in amacrine cells genesis in Rax-Cre; Rbpj KO, I would like to request the authors to show the data on Crx10-Cre; Rbpj KO. Although Rbpj KO is mosaic in Crx10-Cre; Rbpj KO, we can distinct Rbpj KO cells by GFP expression (Fig. S2C, C', C'). So, the authors can confirm that amacrine cell genesis is inhibited in a cell-autonomous manner in Crx10-Cre; Rbpj KO retinas but not in Crx10-Cre; dnMaml overexpression. Addition of such data will make the authors' conclusion is more convincing.

Response: Suppl Table 1 lists multiple references (two from the NLB lab) that demonstrated both a rod and cone increase in Rbpj loss-of-function conditions. Chx10;Rbpj CKO animals were evaluated by Zheng et al., who showed an amacrine loss phenotype in these mutants (11). This is equivalent to what we see in our Rax-Cre;Rbpj CKO data, but without the complications of Chx10 mosaic Cre expression upon Rbpj deletion.

Other comments: 1) Title of this manuscript is "Not all Notch pathway mutations are equal in the embryonic mouse retina". However, this title is quite obscure in what is research advancement of their findings. I suggest the authors to include more concrete and conclusive sentence in the title, for example "Hes and Rbpj differentially promotes retina/optic stalk boundary maintenance and photoreceptor genesis, in parallel with neurogenic inhibition by Notch signaling pathway".

Response: We appreciate the reviewer's perspective. We are striving for a relatively simple title about a very complex topic, involving the in vivo genetic dissection of a signaling pathway. We modified the title to "Notch pathway mutations do not equivalently perturb mouse embryonic retinal development ".

2) The "Results" section is a bit difficult to follow logics without detailed knowledge on roles of Notch signaling in mouse retinal development. I suggest the authors to improve a writing style of "Results" section for readers without such detailed knowledge on mouse Notch mutant phenotypes to follow logical flow more easily. There are many additional descriptions on research background before start to mention results. Such introductory sentences should be moved to the "Introduction" section, by which logical flow in the Results section should be simpler. In addition, the authors should show a concrete question at the beginning of each result subsection. Furthermore, the authors sometimes jump over from one result subsection and suddenly move to cite another figure panel in a far ahead subsection whose data has not been explained. Such a back-and-forth citation of figure data generally makes it difficult to follow logical flow.

Response: We now present a considerable amount of new quantified data, reorganized multiple figures, and extensively rewrote the paper. We significantly revised the summary figure to improve clarity. In addition, Suppl Table 1 provides a wealth of background information to orient the reader on this topic. We feel that this extensive revision has greatly improved the quality, logical flow, and readability of the manuscript.

3) In addition, figure configuration is not well organized. Each figure compared some particular marker expression in wild-type, Rax-Cre; HesTKO, Rax-Cre; Rbpj cKO, Rax-Cre; dn-Maml-GFP, Chx10-Cre; HesTKO, Chx10-Cre; Rbpj cKO, Chx10-Cre; dn-Maml-GFP. For example, Fig. 2 shows Hes1 for inhibition of neurogenesis, Fig. 3 shows Vsx2; Mitf and Pax2; Pax6 for retinal pigmented epithelium and optic stalk, Fig. 6 shows Atoh7, Rbpms, and Tubb3 for retinal ganglion cells. Fig. 7 shows Crx, Otx2, and Thrb2 for photoreceptor differentiation. Fig. 8 shows Prdm1, and Ptf1a for photoreceptors and amacrine cells. Although this figure configuration is convenient to show phenotypic difference between different genetic mutations, it is difficult to know how each differentiation steps are spatially and temporally coordinated during development. At least, I recommend the authors to show one summary figure, which shows spatio-temporal expression profile of retinal markers in wild-type mouse retinas.

Response: We recognize this point and completely reorganized and combined Figs 6 and 7 to improve clarity. New Figure 6 presents E13 quantification for Atoh7, Otx2, Atoh7/Otx2, Rbpms and Crx expressing retinal populations. E16-E17 data were condensed and moved to a new Suppl Fig 5.

4a) Page 7, line 7-10 "With earlier deletion using Rax-Cre, hes5 mRNA abnormally extended into the optic stalk": I wonder how the authors define the optic stalk. It is likely that optic stalk area (Pax2+, Vax1+ area) is shifted to more proximal (depart from the optic cup and move toward the brain), and neural retina is expanded accordingly (Fig. 4B, 4F), resulting in expansion of hes5 expression. Thus, it may be better to mention that optic stalk/neural retina boundary is abnormally shifted toward the brain.

Response: The retina, including the optic nerve head, ends where the adjacent RPE terminates. This is conspicuous morphologically in our sections. We also defined this by colabeling for Pax2 and Pax6, which is now quantified in revised Fig 3. To clarify this further, we added the words " in all panels the brain is to the right" in the Fig 4 legend.

4b) Page 8, line 14-15, "ONH/OS cells still express it (Hes1), demonstrating that sustained Hes1 is independent of Notch": I presume that Cre-Rax drives Cre in neural retina as well as optic stalk and pigmented epithelium. However, it is likely that Rbpj is not expressed in optic stalk/neural retina boundary area in wild type (Fig. S2A). No expression of Rbpj in optic stalk/neural retina boundary may support that Hes1 expression in this boundary area is Notch-independent. However, Rbpj expression is retained in some vitreal cells near optic nerve head in Rax-Cre; Rbpj-CKO retinas (Fig. S2B). What are these Rbpj+ cells? I would like to request the authors to confirm that Rbpj expression is completely absent in both neural retina and optic stalk in Rax-Cre; Rbpj-CKO mice. Otherwise, this conclusion is still not fully supported.

Response: We show the Rax-Cre lineage in Suppl Fig 2 via the Ai9 (tomato) reporter. The results are striking, with all of the optic cup derivatives (retina, RPE, ONH, optic stalk, and presumptive ciliary tissue and iris) being tomato positive, while the well-described population of vascular cells in the hyaloid space lack tomato expression. Furthermore, our figure shows that Rbpj expression is only absent from the optic cup derivates, rather than the vascular structures in the vitreous. Vascular cells also depend on the Notch pathway and express Rbpj. Based on considerable evidence from the literature and our lineage experiments, the population of cells the reviewer highlights represents the hyaloid vasculature and associated cell types. It does not represent any population that derives from neuroectoderm.

4c) Page 9, line 16-18, "Foxg1 had spread into the nasal optic stalk": Is Foxg1 expanded nasal area really "OS" rather than expanded retina? I suggest the authors to confirm molecular markers Pax2 expression is overlapped with Foxg1. Otherwise, it is difficult to conclude that foxg1 is expanded into the optic stalk territory, because foxg1 is normally a marker of retina. Indeed, Fig. 3K shows pax2 expression is shifted into more inside towards the brain, suggesting that neural retina is expanded. Please explain the situation.

Response: Foxg1 (BF-1) mRNA and protein are found in the nasal retina and are expressed in other brain tissues. Multiple studies show Foxg1 in the nasal side of the E10 optic cup/retina/optic stalk and developing hypothalamus (See extra data figure Reviewer Fig 2; top row figure is data from Smith et al., 2017 (12) with Foxg1 mRNA in purple. Also see our new manuscript panel Fig 1C. We include here for reviewers (extra data Reviewer Fig 2 showing E13 ocular cryosections colabeled for Foxg1 and Pax2, highlighting their relationship in the retina, optic stalk and adjacent forming hypothalamus. On page 9 the text now reads "At E13.5 Rax-Cre;HesTKO eyes, the Foxg1 nasal retinal domain was contiguous with the nasal optic stalk (Suppl Fig 4D). This is reminiscent of younger stages (Fig 1C), since normally at E13.5, Foxg1 in the nasal optic cup/retina is separated from expression in the ONH/OS (Suppl Fig 4A). Based on the expansion of Pax6, Vsx2 and Hes5 RPC domains into the optic stalk, we conclude that the change in Foxg1 similarly reflects an extension of retinal tissue."

4d) Page 10, line 4-5, In Rax-Cre; Hes1/3/5 cTKO eye, this tissue (RPE) extended into the optic stalk": This description seems to be incorrect. A part of Pax2 area, which is adjacent to the neural retina, contacts with RPE in wild type (Fig. 3AH), so most of RPE covers the neural retina even in Fig. 3DK.

Response: We disagree with the reviewer’s interpretation. Fig 3D shows Mitf labeling of RPE nuclei. Figure 3K shows the adjacent section labeled with Pax2 and Pax6 (labels both retina and RPE). As the retina extended "towards the brain", the RPE analogously extends and surrounds the retinal domain. We also added higher magnification data panels 3H, 3K and 3N, showing merged and single channels.

4e) Page 10, line 22-23, "For Chk10-Cre; Hes1/3/5 cTKO, there was a unique presence of ectopic Pax2 within the retinal territories": I wonder if this description is correct. I suspect that proliferative Pax2+ cells expand into regressing territory of Hes KO retinal cells, which undergo precocious neurogenesis and lose proliferative activity, in Chk10-Cre; HesTKO. In this case, it is possible that the Pax2/Pax6 interface may be maintained. Please show red and green channel panels for Fig. 3N to confirm that there is ectopic pax2 and pax6 double positive cells.

Response: New quantification in revised Fig 3 (see panels O,P) fully supports our original conclusion. Only Chx10-Cre;HesTKO mutants have a statistically significant increase in Pax2+ cells. There are not more Pax2+Pax6+ double labeled cells. Only this particular genotype has an increase in Pax2+ single labeled cells.

5a) Page 11, line 20-25. There seems to be inconsistency between result description and image data of Fig. 5A-G, and histogram Fig. 5O. Authors mentioned that a modest loss of pH3+ cell fraction in Chx10-Cre; Hes1/3/5 cTKO but not in Rax-Cre; Hes1/3/5 cTKO. However, Fig. 5D indicates severe reduction of pH3+ cell fraction in Rax-Cre; Hes1/3/5/ cTKO, which is similar to reduction of pH3+ cell fraction in Rex-Cre; Rbpj (Fig. 5B), but histogram data is different (Fig. 5O). Furthermore, pH3+ cell fraction is severely reduced in Chx10-Cre; ROSA(dn-Maml-GFP) (Fig. 5F) and modestly reduced in Chx10-Cre; Hes1/3/5 cTKO (Fig. 5G). However, pH3+ cell fraction seems to be normal in Chx10-Cre; Rbpj (Fig. 5E). These Chx10-Cre image data do not match the histogram of Fig. 5O. Please check their situation.

Response: Images in old Figs 5-8 were normalized using area measurements, see methods and above comments (note: old Figs 6&7 were combined into new Fig 6). One-way ANOVA with pairwise comparisons for each mutant genotype compared to control were calculated using Prism. All genotypes except two have a statistically significant loss of M phase cells and we discuss possibilities for this outcome (Fig 5O). A normalization method for the sampled area is an essential component of these studies since morphologic differences are apparent for particular genotypes. The quantitative data are consistent with our original conclusions.

5b) Fig. 5H-N, P: I wonder if the stage E13 is appropriate to evaluate cell death and survival because optic cup already becomes smaller in Rax-Cre; Rbpj, Hes1/3/5 cTKO, or ROSA(dn-MAML-GFP) than in wild-type control. I suggest the authors examine more earlier stage.

Response: While an earlier effect is possible, we only observed size differences in a subset of the genotypes. Thus, E13 serves as a critical timepoint to examine early developmental phenotypes across the totality of our mutant conditions. It is also first age when the ONH is fully formed.

5c) Page 12, line 19-20, "all other mutants (Chx10-Cre; Rbpj, and Chx10-Cre; ROSA(dn-MAML-GFP) were unaffected (Fig. 6EF, LM, ST)": It is likely that atoh7 expressing cells are mildly decreased and neuronal marker, Tubb3 and Rbpms-expressing cells are increased in Chx10-Cre; Rbpj, and Chx10-Cre; ROSA(dn-MAML-GFP). I requested the authors to evaluate the fraction of these markers in retinal area statistically in all the cases.

Response: As described above, we quantified Atoh7 and Rbpms nuclear expression by immunohistochemistry. We do not believe that Tubb3+ cells can be reliably quantified. Nonetheless, it is useful to qualitatively show the extent of excess neuron formation. Importantly, we observed that it is not the Atoh7 status that matters for RGC formation, rather it is the Otx2 expression status. This is in good agreement with single cell-RNA transcriptomics data from Wu et al 2021 showing that Atoh7 mRNA in all early transitional RPCs remains fairly constant and its loss does not block the formation of early RGC cell states (13). By contrast Otx2 fluctuates but remains expressed in transitional RPCs that progress to photoreceptor lineages.

6a) Page 7, line 19 "Ectopic blood vessels protruded from the ONH (Fig. 1K, 1L)": It is difficult to see blood vessel structures in these panels (Fig. 1I-L). Please show some molecular marker of blood vessels to confirm how blood vessel is organized in Hes1/3/5 cTKO.

Response: These vascular structures are highly conspicuous by morphology in the H&E insets. Nonetheless, we used adjacent P21 sections to immunolabel for Endomuscin (14) and Tubb3 antibodies. This colabeling confirms the morphology and position of ectopic blood vessels in the abnormal tissue masses in Chx10-Cre;HesTKO mutant eyes. Ectopic tissue contains only rare Tubb3+ cells or cell processes suggesting it is overwhelmingly nonneural. All P21 data were moved to a new Suppl Fig 2. A full detailing of vascular phenotypes is beyond the scope of this manuscript and, interestingly, would be potentially attributable to non-autonomous effects of perturbing the Hes genes in the adjacent retina.

6b) Fig. 5: Increase of pH3 fraction indicates several possibilities, for example (1) increased fraction of mitotic cells due to precocious neurogenesis, (2) increased fraction of mitotic cells due to activated cell proliferation of retinal progenitor cells, (3) increased cell-cycle arrest in M phase due to some stress response of progenitor cells. So, I suggest the authors to examine (1) BrdU percentage of retinal section area, (2) the percentage of pH3+ cells in PCNA+ retinal cells.

Response: The data listed in Suppl Table 1 presents a unified picture that disrupting Notch signaling reduced proliferation. This paradigm extends to other model organisms (e.g., Drosophila, chick, frog, zebrafish and even to nonneural tissues). We included the phospho-histone H3 staining so readers would see how the six mutants evaluated in this study align with this paradigm, providing confidence for the novel findings in other figures. A full evaluation of cell cycle kinetics is interesting, but beyond the scope and focus of this manuscript.

6c) Fig. 5: It is better that cell death fraction will be evaluated by TUNEL and labeling with anti-activated caspase 3 antibody.

Response: We disagree. The DNA repair enzyme PARP is inactivated upon cleavage by activated caspase 3. There are currently ~3,600 citations that use it as a marker of apoptosis. PARP also has a separate and very specific role in maintaining the integrity of sperm DNA. This antibody works on all metazoans and is amenable to many tissue preparations and fixatives, making it easy to use, robust and quantifiable.

7a) Please show red channel (Hes1) image in Fig1BC.

Response: This was added to Revised Fig 1 (Fig 1A).

7b) Fig. 1DH should be shown in neighbor. Fig. 1H should be assigned as Fig. 1E.

Response: The new Fig 1 layout addresses this point.

7c) Fig. S2D, F, H, J: Please show GFP green channel as well. Otherwise, it is difficult to see non-overlapping expression in optic stalk area.

Response: In the revision, this is Suppl Fig 3. Chx-10-Cre is not expressed by ONH-OS cells (1). The green and fuchsia overlap (coexpression) in RPCs is white, we feel this is fairly clear. If needed, all readers can turn on and off the green channel in the final PDF version of this figure to compare GFP with Hes1 expression for those panels.

7d) Fig. 9B: It is better to show Rax-Cre: Hes1/3/5 TKO rather than Rax-Cre: Hes1 cKO. 7e) Fig. 9B: Lettering "Rbpj mutant" should be revised as "Rax-Cre: Rbpj KO".

Response: Fig 9B was removed so these terms are now irrelevant. Our models are presented in new Fig 8.

Significance: The senior author of this manuscript, Dr. Nadean Brown, is an expert scientist who has investigate the role of Notch signaling pathway in vertebrate ocular tissue, including the neural retina and lens. In general, Notch signaling pathway consists of signaling stream from the interaction of Delta and Notch, Notch receptor activation by proteolytic cleavage, translocation of Notch intracellular domain (NICD) into nucleus, formation of transcription factor complex consisting of NICD/Rbpj/Maml, to the transcriptional activation of Notch target genes, Hes family transcription factors. Finally, Hes suppresses neurogenic program and maintain a pool of neural progenitor cells. Therefore, Notch is a key factor to regulate the balance between neurogenesis and progenitor proliferation. In this manuscript, the authors investigated retinal phenotypes in the knockout mice of different Notch signaling components, including Rbpj, Maml, and Hes. They found that functions of these three factors are not always equal in retinal cell differentiation; rather, they specifically regulate a particular step of retinal development. The authors propose the possibility that each of Notch signaling components may be modified by other signaling pathways and achieve some new roles beyond the conventional frame of classic Notch signaling pathway. In this point, this work has a potential to provide a new conceptual advance in the field of developmental and cell biology.

We fully agree this work is a significant advance for the fields of developmental and cell biology. Our findings provide new information and stimulate fresh ideas for anyone working on signal transduction and signal integration.

References cited:

1. Bosze et al., 2020 Journal of Neuroscience Vol 40:1501-13; Bosze et al. 2021 Dev Biol Vol 472:18-29.
2. Han et al., 2023 Development Vol 150 dev201408.
3. Kopan and Ilagan, 2004 Nat Rev Cell Biol. Vol 5:499-504
4. Hirata et al., 2002 Science Vol 298:840-3
5. Friedmann and Kovall, 2010 Protein Sci. Vol 19:34-46
6. Ong et al., 2006 JBC Voll24:5106-19
7. Wall et al., 2009 J Cell Biol. Vo 184: 101-12.
8. Javed et al., 2023 Development Vol 150:dev200436
9. Matuda and Cepko 2007 PNAS Vol 104: 1027-1032
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Author Response

Reviewer #2 (Public Review):

Associative learning assigns valence to sensory cues paired with reward or punishment. Brain regions such as the amygdala in mammals and the mushroom body in insects have been identified as primary sites where valence assignment takes place. However, little is known about the neural mechanisms that translate valence-specific activity in these brain regions into appropriate behavioral actions. This study identifies a small set of upwind neurons (UpWiNs) in the Drosophila brain that receive direct inputs from two mushroom body output neurons (MBONs) representing opposite valences. Through a series of behavioral, imaging, and electrophysiological experiments, the authors show that UpWiNs are differentially regulated by the two MBONs, i.e., inhibited by the glutamatergic MBON-α1(encoding negative valence) while activated by the cholinergic MBON-α3 (encoding positive valence). They also show that UpWiNs control the wind-directed behavior of flies. Activation of UpWiNs is sufficient to drive flies to orient and move upwind, and inhibition of UpWiNs reduces flies' upwind movement toward the source of reward-predicting odors (CS+). These results, together with existing knowledge about the function of the mushroom body in memory processing, suggest an appealing model in which reward learning decreases and increases the responses of MBON-α1 and MBON-α3 to the CS+ odor, respectively, and these changes cause UpWiNs to respond more strongly to the CS+ odor and drive upwind locomotion. Interestingly, in the final part of the results, the authors reveal a wind-independent function of UpWiNs: increasing the probability that flies will revisit the site where UpWiNs were activated. Thus, UpWiNs guide learned reward-seeking behavior with and without airflow. Although the mushroom body has been extensively studied for its role in learning and memory, the downstream neural circuits that read the information from the mushroom body to guide memory-driven behaviors remain poorly characterized. This study provides an important piece of the puzzle for this knowledge gap.

Strength

1) Memory studies have predominantly relied on binary choice (go or no-go) assays as measures of memory performance. While these assays are convenient and efficient, they fall short of providing a comprehensive understanding of underlying behavioral structures. In an effort to overcome this limitation, the current study used video recording and tracking software to delve deeper into memory-guided behavior. This innovative approach allowed the authors to uncover novel neurons and examine their contribution to behavior with a level of detail not possible with binary choice assays.

2) This study used electron microscopy-based Drosophila hemibrain connectome data to reveal the synaptic connection between UpWiNs and MBON-α1 and MBON-α3. Using this method, the study shows that a single UpWiN receives direct input from both MBON-α1 and MBON- α3, which is confirmed by a functional imaging experiment. The connectome dataset also reveals several neurons downstream of UpWiNs, opening avenues for further research into the neural mechanisms linking memory and behavior.

Weakness

1) The authors repeatedly state in the manuscript that MBON-α1 and MBON-α3 convey appetitive or aversive memories, respectively. This assertion may not be entirely accurate. Evidence from sugar reward conditioning experiments suggests that MBON-α3 is potentiated and required for sugar reward memory retrieval. Therefore, the compartmentalization for appetitive and aversive memories appears not as obvious at the level of MBONs.

What we intended was that activation of DANs in these compartments can induce aversive and appetitive memories, respectively, when paired with odors, and that these are the sole output pathway from these compartments to read out the memories in these compartments. As we previously proposed (Aso et al., 2014a eLife), these MBONs can integrate inputs from MBONs of other compartments and their activity can reflect appetitive memory stored as synaptic plasticity in other compartments. Since DANs in the α3 compartment respond to heat, bitter and electric shock but not sugar, the observation that MBON-α3 acquires an enhanced CS+ odor response after appetitive conditioning is presumably due to these intercompartmental connections rather than plasticity of KC-MBON synapses in the α3 compartment. In any case, the fact that excitatory activity of MBON-α1 and MBON-α3 conveys opposite valence of memory still holds true since appetitive conditioning induces depression and potentiation of odor responses, respectively.

To clarify this point, we now cited related literature in the following sentence in the final paragraph of Introduction: “UpWiNs receive inputs from several types of lateral horn neurons and integrate inhibitory and excitatory inputs from MBON-α1 and MBON-α3, which are the output neurons of MB compartments that store long-lasting appetitive or aversive memories, respectively (Aso and Rubin, 2016; Ichinose et al., 2015; Jacob and Waddell, 2022a; Pai et al., 2013; Yamagata et al., 2015).”

2) This study did not conclusively establish the importance of the MBON-α1/α3 to UpWiN pathways in memory-driven behavior. In the experiments shown in Figure 5, flies were trained to associate the activation of reward-related DANs with a specific odor (CS+). After conditioning, UpWiNs were observed to show enhanced responses to the CS+ odor. However, the results should be interpreted with caution because the driver line used to activate DANs (R58E02-LexAp65) labels not only DANs projecting to the MBON-α1 compartment, but all DANs in the protocerebral anterior medial (PAM) cluster. Thus, it remains unclear to what extent the observed enhanced responses are influenced by changes in inhibitory inputs from MBON-α1. While UpWiNs have been shown to play a critical role in the expression of sugar reward memory (Figure 7), it should be noted that UpWiNs receive inputs from multiple upstream neurons, making it difficult to accurately assess the contribution of MBON-α1/α3 to UpWiN pathways in UpWiN recruitment. Further research is needed to fully address this issue.

We totally agree with this point and added a sentence to explain an alternative mechanism. “This enhancement of CS+ response can be most easily explained as an outcome of disinhibition from MBON-α1 whose output had been decreased by memory formation; MBON-α1 is inhibitory to UpWiNs (Figure 4B) and MBON-α1 response to the CS+ is reduced following the same training protocol (Yamada et al. 2023). In addition to such a mechanism, plasticity in the β1 compartment may contribute to the enhanced CS+ response in UpWiNs because the driver R58E02 contains DANs in the β1 and glutamatergic MBON from the β1 directly synapse on the dendrites of MBON-α1 and MBON-α3. “

3) UpWind neurons (UpWiNs) were so named because their activation promotes upwind locomotion. However, when activated in the absence of airflow, flies show increased locomotor speed and an increased probability of revisiting the same location (Figure 7 and Figure 7-figure supplement 1). The revisiting behavior can be observed during the activation of UpWiNs, which is distinct from the local search behavior that typically begins after a reward stimulus is turned off (e.g., Gr64f-GAL4 results in Figure 7-figure supplement 1).

Return probability was calculated within a 15-s time window. High return probability during LED ON period (10-20s) in Figure 7-figure supplement 1 does not necessarily mean that flies returned during LED ON period. If a fly is at the position A when t=10s, to be counted as “returned”, it needs to move more than 10mm away from A and move back to the position less than 3mm distance from A by t=25s. In the case of sugar sensory neuron activation with Gr64f-GAL4, the peak of return probability is shifted toward a later time point because flies stop and extend proboscis during activation period.

Because revisiting a location can also be a consequence of repeated turns, it seems more accurate to describe UpWiNs as controlling the speed and likelihood of turns and promoting upwind movement by integrating with neurons that sense the direction of airflow.

The return probability plotted in Figure 7E is probability of return to the position at the end of LED period within 15s post LED period when angular speed of SS33917>CsChrimson and SS33918>CsChrimson flies are identical to empty-split-GAL4>CsChrimson control flies (Figure 7-figure supplement 1). Thus, revisiting behavior cannot be explained by a simple increase in turing probability.

Although functions of UpWiNs are not limited to promotion of wind-directed walking, we still think that the “UpWind Neurons” is a practical name for broad readers and oral communications at the current stage of investigations, because EM neuron IDs and names (SMP348, SMP353, SMP354, SLP399 and SLP400) are too lengthy and do not contain any functional information. We initially defined a set of 11 neurons labeled by SS33197 split-GAL4 as “UpWind Neurons (UpWiNs)” based on initial optogenetic screening (Figure 2A). We found other driver lines for mushroom body interneuron cell types that can promote release of dopamine and more robust returning phenotype (e.g. SS49755), but SS33917 remained to be the champion driver line for upwind locomotion phenotype.

Reviewer #3 (Public Review):

Aso et al. provide insight into how learned valences are transformed into concrete memory-driven actions, using a diverse set of proven techniques.

Here the authors use a four-armed arena to evaluate flies' preference for a reward-predicting odor and measure upwind locomotion. This behavioral paradigm was combined with the photoactivation of different memory-eliciting neurons, revealing that appetitive memories stored in different compartments of the mushroom bodies (center of olfactory memory) induce different levels of upwind locomotion. The authors then proceed to a non-exhaustive optogenetic screen of the neurons located downstream of the output neurons of the mushroom bodies (MBONs) and identify a group of 8-11 Cholinergic neurons promoting significant changes in upwind locomotion, the UpWins. By combining confocal immunolabelling of these neurons with electron microscope images, they manage to establish the UpWins' connectome within themselves and with the MBONs. Then, using two in vivo cell recording techniques, electrophysiology, and calcium imaging, they define that UpWins integrate both inhibitory and excitatory synaptic inputs from the MBONs encoding appetitive and aversive memory, respectively. In addition, they show that the UpWins' response to a reward-predicting odor is increased after appetitive training. On a behavioral level, the authors establish that the UpWins respond to wind direction only and are not involved in lower-level motor parameters, such as turning direction and acceleration. Finally, they demonstrate that the UpWins' activity is necessary for long-term appetitive memory retrieval, and even suggest a broader role for the UpWins in olfactory navigation, as their photoactivation increases the probability of revisiting behavior. In the end, the authors state that they provide new insights into how memory is translated into concrete behavior, which is fully supported by their data. Altogether, the authors present a pretty complete study that provides very interesting and reliable data, and that opens a new field of investigation into memory-driven behaviors.

Strengths of the study:

• To support their conclusions, the authors provide detailed data from different levels of analysis (behavioral, cellular, and molecular), using multiple sophisticated techniques.

• The measurement of multiple parameters in the behavioral analysis supports the strong changes in upwind locomotion. In addition, taken individually these parameters provide precise insights into how upwind locomotion changes, and allow the authors to more precisely define the role of the UpWins.

• The authors use split-Gal4 drivers instead of Gal4, allowing them to better refine neuron labelling.

The authors discussed and investigated all possible biases, making their data very reliable. For example, they demonstrated that the phenotypes observed in the behavioral assay were wind-directed behaviors and could not be explained by bias avoidance of the arena's center area.

Limitations of the study:

• In the absence of more precise drivers, the UpWins' labelling lacks precision. For example, there is no way to know exactly which UpWin is responding in the electrophysiological experiment presented in Figure 4.

We have ongoing efforts to generate split-GAL4 and split-LexA driver lines for specific subsets of UpWiN neurons, but the data using those lines are not ready for this manuscript. However, we would like to point out that historically, identification of a group of neurons with striking phenotype has been foundational to promote follow-up studies. A good example is P1 neurons for courtship behavior.

• The screening of neurons located downstream of the MBONs is not exhaustive, meaning that other groups of neurons might be involved in memory-driven upwind locomotion. Although, it does not diminish the authors' conclusions.

The UpWiNs is certainly not the only one cell type for mediating memory-driven upwind locomotion, since our and other groups’ studies (e.g. Matheson et al., 2022; PMCID: PMC9360402) identified a collection of cell types that can promote upwind locomotion upon optogenetic activation.

In 2021, we released images and driver lines of a larger collection of split-GAL4 driver lines at https://splitgal4.janelia.org. We are preparing a manuscript to provide anatomical descriptions of these lines. This collection of new drivers will help elucidate more comprehensive views of circuits for memory-driven actions.

• All data were obtained with walking flies. So far, there have been no experiments on flying flies.

This is an intriguing question and we mentioned in Discussion that “Our study was limited to walking behaviors, and the role of UpWiNs in flight behaviors remains to be investigated.”

Author Response

The following is the authors’ response to the original reviews.

Reviewer # 1

Specific comments

1) Figure 1: it is unclear how many mice were used for the described phenotypic analyses (panels D and E). Please clarify.

We acknowledge that we made a mistake in failing to clearly describe the phenotypic analyses. In Figure 1D and E, we performed statistical analysis on the number of TEBs in whole mammary mounts. One mouse stained a mammary whole mount with Carmine-alum staining. Thus, “n” represents the 10 mice we analyzed. We have modified the legend of Figure 1 to " D, E. Quantification of the average number of TEBs and bifurcated TEBs in littermate Crb3fl/fl (n=10) and Crb3fl/fl;MMTV-Cre (n=10) mice at 8 weeks old" in lines 909-911.

2) Figure 2: in panels B and C it is unclear how the data was quantified; the legend states "n=10", does this mean the experiment in B was done 10 times? And that 10 acini per condition were measured in panel C? In panel D a difference in 0.3% between NC and shCRB3 seems miniscule; do the authors mean 30% instead? And how many acini were counted per condition per (how many) experiments? Same applies to panels G and H, it is unclear how many cells were analyzed per (how many) experiments.

Thanks for your suggestions. We failed to describe the details of the statistical analysis well in the experimental method. To provide a brief overview of our statistical analysis method, we took 3-4 random bright-field micrographs of each well in the chamber slide system and repeated the experiment three times. We then counted the number of acini in all micrographs (Figure 2B) and examined the diameter of all acini in each photograph, averaging the values as data (Figure 2C). We also determined the percentage of aberrant acini in each photograph, which was used as an analysis value (Figure 2D). We carefully confirmed that the vertical axis of Figure 3D was indeed mislabeled and should mean 30%, and revised the original figure. For IF analysis of the mitotic spindle orientation during lumen formation, we examined the division angle of one cell in one acinus that was mitotically dividing, 3-4 acini were randomly examined in each well in the chamber slide system, and this experiment was repeated three times (Figure 2G and H). Therefore, we have provided a detailed description of these issues in the Figure 2 legend. The revised parts are found in lines 922-924, lines 926-927, lines 929-930, and line 932.

3) Figure 2: it would be desirable if authors were able to quantify the data in panels E and I.

Thank you for your comments. According to your suggestions, we performed the quantitative analysis of Figure 2E and I, which is now presented in the new Figure 2D and H.

4) For all cell-based assays using shRNA to knock down CRB3 (Fig. 2A-H; Fig. 3A-F; Fig. 4C-E; Fig. 5G-J; Fig. 6C; Fig. 7C, D; Fig. 8E-G), it would be desirable to perform rescue experiments to ensure that the observed phenotype of CRB3 depleted cells is specific and not due to off-target effects of the shRNA.

Yes, rescue experiments involving overexpression of CRB3 in CRB3 depleted cells can accurately account for the specific phenotype as well as eliminate the off-target effects of shRNA. However, our group has long focused on the role of the cell polarity protein CRB3 in contact inhibition and tumorigenesis. Our previous studies have ruled out the off-target effects of shRNA and reported that CRB3 regulates contact inhibition and tumorigenesis through Hippo or Wnt signaling pathways (Cell Death Dis 2017;8(1):e2546, Oncogenesis 2017;6(4):e322, J Cell Mol Med 2018;22(7):3423-33). Therefore, we will pay close attention to rescue experiments to ensure experimental integrity and phenotypic specificity in our subsequent studies.

5) Figure 3: how many cells were counted/measured per condition (in how many experiments) in panels B, D, H, F, G and H? In panels C and D, what is the CRB3 protein level in these cells? This is of relevance as protein overexpression per se could impinge on ciliation frequency. This question could be addressed by performing a western blot analysis with CRB3 antibody.

We did not clearly describe the measurement and statistical analysis methods in the previous manuscript. Similarly, we took 3-4 random IF and SEM micrographs of each sample in one experiment, and this experiment was repeated three times. Subsequently, the number of ciliated cells and total cells were counted, and the proportion of ciliated cells was calculated (Figure 3B, D and F). In these figures, the cilium length of representative ciliated cells was measured in each photograph. In the knockout mouse model, we needed to find the intact mammary ductal lumen and renal tubule in IF staining of mouse mammary and renal tissue sections, with 5-6 random fields micrographs taken per slice, and the proportion of ciliated cell was measured by counting and taking the average. A total of ten mice were repeated in these experiments (Figure 3G and H). Therefore, the legend of Figure 3G and H has been partially modified and a detailed description has been added to the Figure 3 legend. The revised parts are in lines 945-946, lines 950-951, line 953.

Thank you for your suggestions that we perform a western blot analysis with CRB3 antibody in Figure 3C and D. And we have added the western blotting with CRB3 analysis in the new Supplementary Figure 3A.

6) Figure 3G: it is very difficult to see that the red stained structures are primary cilia.

Yes, the staining structure of primary cilia in mammary ductal lumen are less clear than that of individual cells and in renal tubule in Figure 3G. We used recognized acetylated tubulin and γ-tubulin to stain the primary cilia, which were clearly labeled in individual cells. However, the labeled primary cilia in renal tubule were longer length and demonstrated a more pronounced structure than those in the mammary ductal lumen. In the mammary ductal lumen of the 10 mice we analyzed, the primary cilia showed shorter length and staining structure than the others shown in Figure 3G. This difference may be due to the distinct characteristics of primary cilia in different tissues.

7) Figure 4B: how many cells were analyzed in how many experiments?

Our statistical methods for analyzing cellular experiments using IF were essentially the same. We randomly selected 3-4 IF micrographs of each sample in one experiment, and this experiment was repeated three times. Subsequently, the number of colocalization cells and total cells were counted, and the proportion of cells with pericentrin and CRB3 colocalization was calculated (Figure 4B). The detailed description has been added to the Figure 4 legend. The revised part is in lines 962-963.

8) Lines 217-219: since the cells were not stained with a cilia marker, only a centrosome marker, the claim that CRB3 localizes to the base of cilia is unsubstantiated.

Thank you for your comments. The base of cilia is the basal body, which develops from the mother centriole of the centrosome (Cancer Res. 2006;66(13): 6463-7). Firstly, we found colocalization of CRB3 and pericentrin, a centrosome marker, in MCF10A cells (Figure 4A and B). Secondly, we verified the colocalization of CRB3 with γ-tubulin, a marker of basal body in primary cilia, in confluent quiescence cells (Figure 4C and D). In addition, we found that CRB3 was localized at the base of primary cilia labeled with acetylated tubulin (Figure 4E and F). Due to the species of commercialized CRB3 antibody, we were able to indirectly claim that CRB3 localizes to the base of cilia through these experiments.

9) Figure 3 and Figure 4: is it problematic to use gamma tubulin as centrosome marker if CRB3 depletion causes reduced centrosomal recruitment of gamma tubulin ring complex components? Also, in Figure S3A no gamma tubulin staining can be seen in the lower panel, why?

Thank you for your positive comments. As is well known, γ-tubulin is a marker of the centrosome, and we found that CRB3 depletion causes reduced centrosomal recruitment of gamma tubulin ring complex components. However, Our Figure 3 was illustrated the effect of CRB3 on ciliary assembly, and Figure 4 was analyzed the localization of CRB3 in primary cilia. In some reports on ciliary assembly, the fluorescent double staining of acetylated tubulin and γ-tubulin have been used to label primary cilia, and the effect of target genes on ciliary number and assembly were analyzed by these markers (Nature. 2013;502(7470): 254-7, Cell. 2007;130(4): 678-90 and so on). Although CRB3 affects the recruitment of gamma tubulin ring complex components, it does not affect the analysis of ciliary number and localization in Figures 3 and 4.

In Figure S3A, green staining labeled with γ-tubulin could be clearly found in the lower left panel. The representative area from the left amplification may have been poorly selected, resulting in no γ-tubulin staining on the right side. We have updated the lower right panel in the new Supplementary Figure 3B.

10) Figure S4A: the grouping of indicated proteins is factually wrong. For example, FBF1, SCLT1 and ODF2 are not IFT-B components, and several of the proteins indicated as localizing to the basal body also localize to (unciliated) centrioles. In contrast, CP110 is usually only found on unciliated centrioles and not mature basal bodies. Authors should consult the relevant literature and correct the figure accordingly. Alternatively, this misleading text/grouping could be removed from the figure. Furthermore, in the legend to Figure S4 there is no information provided about this quantitative analysis (how many independent experiments, which cells were analyzed etc.).

Thank you for your helpful suggestions. We have taken your advice and removed this misleading information from the manuscript, Supplementary Figure 4A and its corresponding legend. In the legend to Supplementary Figure 4A, we have added the detailed information for this quantitative analysis in the legend. The revised legend is shown in lines 1098-1100.

11) Figure S4B: how do authors know which of the bands correspond to CRB3 fusion protein?

Based on the construction strategy of the CRB3-GFP fusion protein (Figure 6D) and its base sequence, we were able to calculate its molecular weight. Then the molecular weight of CRB3-GFP fusion protein was verified by western blotting (Figure 6F and 7A). Meanwhile, exogenous overexpression allowed for the production of the CRB3-GFP fusion protein in large quantities. Due to these features, we could know that the band indicated by the black arrow is most likely CRB3-GFP fusion proteins. In order to check the molecular weight, we have labeled the key molecular weight markers in the new Supplementary Figure 4B.

12) Lines 251-253: this seems like data overinterpretation.

Thank you for your comments. We have revised this sentence in lines 252-254.

13) Lines 260-261: the data showing perturbed gamma tubulin localization is not convincing as data was not quantified.

According to your suggestions, we performed the quantitative analysis of Figure 4C, which is now presented in the new Figure 4E.

14) Figure 5H and Figure 6C: to show that the GCP6 IP actually worked, these blots should be probed also for GCP6.

Thank you for your good suggestions. We have added these blots probed for GCP6 in new Figure 5H and 6C.

15) Figure 5I: how many cells were analyzed in how many experiments?

Our statistical methods for analyzing cellular experiments using IF were essentially the same. We took 3-4 random IF micrographs of each sample in one experiment, and this experiment was repeated three times. The detailed description has been added to the Figure 5 legend. The revised part is in lines 992-994.

16) Figure S5: it looks like GPC6 and Rab11 are localizing all over the cell, are the antibodies used for the IFMs specific for these proteins?

After checking the specificity of these antibodies used for the IFMs, we have decided to delete the corresponding results in the Supplementary Figure 5 and their description in the original manuscript.

17) Lines 43, 89, and 314-315: the claim that CRB3 directly binds Rab11 is not supported by the data. The data provided only shows that these proteins interact indirectly. To show direct interaction, yeast-2-hybrid analysis or pull-down assays with purified proteins would be required.

Thank you for your positive comments. Since we were unable to complete the relevant experiments to demonstrate direct interaction of two proteins, we have revised our conclusions. Replace " CRB3 directly binds Rab11" with " CRB3 binds Rab11" in the manuscript.

18) Figure 6G and lines 314-315: this result is surprising as it indicates GTP- and GDP-locked versions of Rab11 have the same inhibitory effect on CRB3 binding? Please comment, and also indicate how data in Figure 6G was quantified (and how many independent experiments were used for the quantification).

We were also puzzled by the results shown in Figure 6G. Based on the western blotting bands, we suspected that there may have been some issues with the experiment. Specifically, we believed that the inefficient transfection of Flag-Rab11aWT, Flag-Rab11a[Q70L], Flag-Rab11a[S20V], and Flag-Rab11a[S25N] plasmids, as well as the insufficient amount of GFP antibody used in the co-IP experiment, led to the corresponding bands being too weak and masking the true differences.

To address this, we optimized the experimental conditions, strictly increased the experimental control, and repeated the experiment in triplicate. The new results are shown in the revised Figure 6G. The statistics from the three independent experiments revealed that CRB3b had a stronger interaction with Rab11a[Q70L] and Rab11a[S20V], while showing a weaker interaction with Rab11a[S25N], compared to Rab11aWT. As this result, we revised the original manuscript in lines 308-310 and added a detailed description to the Figure 6 legend in lines 1012-1013.

19) Figure 8G: data needs to be quantified.

Thank you for your comments. We replaced the unattractive bands in the western blotting of Figure 8G with better quality ones. The statistical analysis of the Figure 8G data is shown in Supplementary Figure 6.

Further minor comments

1) Abstract should indicate that this study describes conditional knockout of Crb3 in mouse mammary gland epithelial cells.

This is good writing advice. We have added the relevant description in lines 40-42.

2) Line 87: specify which gland (mammary?).

We have modified to " mammary gland" in line 87.

3) Line 140: sentence states that knockout of Crb3 is essential for branching morphogenesis in mammary gland development, I do not think this is correct.

We have removed the inappropriate finding.

4) Line 152: "formed more number" should be "formed more" or "formed higher number of".

We modified "formed more number" to "formed more" in line 154.

5) Lines 157-163: text and logic are difficult to follow for a non-expert.

We have modified the logic of this paragraph, as detailed in lines 158-165.

6) Figure 4A, C: figure resolution could be improved. It is difficult to see what the authors claim these figures are showing.

The clarity of the original images in Figure 4A and C is acceptable, while the images on the right are electronically enlarged. Although there is a decrease in pixels, it can still display our findings.

7) Figure 7D, E: images look pixelated.

The clarity of the original images in Figure 7D and E is acceptable using a laser confocal microscope, while the images on the right are electronically enlarged.

8) Line 222: unclear what authors mean by "detected a series".

We modified "detected a series" to "some important" in line 226.

9) Lines 221-225: which cells were used for the analysis in Fig. S4?

We used MCF10A cells for the analysis in Supplementary Figure 4, and modified its legend in line 1098.

10) Line 245: what is "cytomembrane"?

We modified "cytomembrane" to "cell membrane" in lines 246-247.

11) Lines 246-250: wording is unclear/difficult to understand.

We have modified this paragraph, as detailed in lines 248-251.

12) Line 273: should "regimented" be "sedimented"?

We modified "regimented" to "sedimented" in line 274.

13) Line 287-288: sentence does not make sense.

We have removed this sentence.

14) Figure 5A: it would be desirable to show the original dataset (Excel file) used for generating this figure.

To maintain data integrity, we should provide the original dataset (Excel file). However, there are some unpublished data in this file that we must withhold for the time being. If needed, the corresponding author can be requested to provide the file.

15) Lines 298-299: wording is unclear.

We have modified this sentence, as detailed in lines 296-298.

16) Lines 285-287: replace "instead of" with "but not".

We modified "instead of" to "but not" in line 286.

17) For all IFMs showing merged images of the green and red channel, please also show the red and green channel separately.

Most of our fluorescence images are presented separately for each channel in this manuscript, with only a few merged images due to space limitations. This type of presentation is commonly used in published papers.

18) Lines 326 and 327: replace "bonded" with "bound".

We have modified in lines 322-323.

19) Lines 327-328 and 361-364: wording is unclear/grammatically incorrect.

We have modified these paragraphs, as detailed in line 323 and lines 357-360.

20) Line 342: what is meant by "the combination of"?

We modified "the combination of" to "the binding of" in line 338.

21) Line 365: localization of what?

This means "subcellular localization" in lines 360-361.  

Reviewer # 2

Major points

1) CRB3 is present in mammals as 2 isoforms, A and B, originating from alternative splicing. In this study, the authors never mention this fact and when using approaches to KO or KD CRB3A/B they are likely to deplete both isoforms which have been shown to have different C-terminal domains and functions (Fan et al., 2007). This is also important for the CRB3 antibodies used in the study since according to the material and methods section they are either against the extracellular domain common to both isoforms or the intracellular domain which is only similar in the domain close to transmembrane between the 2 isoforms. Since the antibodies used in each figure are not detailed it is impossible to know if the authors are detecting CRB3A or B or both. Please provide the information and correct for the actual isoform detected in the data and conclusions.

Thanks for your positive comments. In mammals, CRB3 has two isoforms, CRB3a and CRB3b, distinguished by alternative splicing within the fourth exon of the CRB3 gene, which in turn produces a protein with 23 amino acid differences at the C terminus. Both CRB3a and CRB3b have mostly identical amino acid sequences, and have indistinguishable molecular weight sizes. As a result, the knockout mouse construction strategy and the design principles of RNAi sequences target both CRB3a and CRB3b. This is described in lines 100-104 and lines 149-150. Additionally, commercially available antibodies detect both CRB3a and CRB3b, as mentioned in line 123 and lines 636-637 in revised manuscript.

However, it should be noted that our CRB3 overexpression, as shown in the CRB3 structural domain in Figure 6D, refers specifically to the sequence of CRB3b. As a result, we have updated the original manuscript as well as the legends of Figures 3C, 3E, 4A, 5A, 5B, 6D-G, 7A, 7B and Supplementary Figure 2F-H, 3A, 4B, 6B to reflect this change. All instances of overexpressed CRB3 have been changed to CRB3b.

2) CRB3A and B have been localized in the cilium itself (Fan et al., 2004; 2007) but in the study CRB3A/B does not enter the cilium but is localized in the basal body (figure 4). How the authors reconcile these different localizations?

Indeed, we found that CRB3 is mainly localized at the basal body of the primary cilium, which differs from previous reports in the literature (Curr Biol. 2004;14(16):1451-61 and J Cell Biol. 2007;178(3):387-98). However, upon closer examination of one of these reports (Curr Biol. 2004;14(16):1451-61), it appears that CRB3 was actually scattered on the primary cilia, with a strong focus at the basal body. Additionally, in rat kidney collecting ducts, the localization of CRB3 on primary cilia was significantly reduced, with obvious localization at the basal body. Another study (J Cell Biol. 2007;178(3):387-98) also reported the co-localization of CRB3b and γ-tubulin in MDCK cells, which is consistent with our conclusion. We further verified the co-localization of CRB3 with the centrosome by overexpressing CRB3b in mammary epithelial cells, indicating that CRB3 mainly localizes to the basal body of the primary cilium. This information is discussed in the Discussion section of the manuscript (lines 400-410).

3) The authors use GFP-CRB3A/B, it is not stated which isoform, over-expression to localize CRB3A/B in MCF10A cells (figure 4A). The levels of expression appear to be very high in the GFP panel and it is likely that the secretory pathway of the cells is clogged with GFP-CRB3A/B in transit from the ER to the plasma membrane. Thus, the colocalization with pericentrin might be due to the accumulation of ER and Golgi around the centrosome. This colocalization should be done with the endogenous CRB3A/B and with a better resolution.

Thank you for your comments. We were also interested in the co-localization of endogenous CRB3 and centrosome proteins. However, the only commercial CRB3 antibody available is the rabbit species, and the pericentrin antibody (Abcam, ab4448) that is very useful is also the rabbit species. We had difficulty finding commercial centrosome-associated antibodies for other species. Therefore, we examined the co-localization of endogenous CRB3 with γ-tubulin in Figure 4C and combined the results with those of exogenous CRB3 to illustrate the co-localization of CRB3 with centrosomes.

4) The staining for CRB3A/B in figure 4C (red) is striking with a very strong accumulation in an undefined intracellular structure and the authors do not provide any explanation for such a difference with the GFP-CRB3A/B just above.

Thank you for your good suggestions. The immunofluorescence images of GFP-CRB3 in Figure 4a were obtained using a fluorescence microscope, while the images of endogenous CRB3 were obtained using a laser confocal microscope. The fluorescence microscope excites a fluorescent dye to emit a signal, which is amplified into a visible light signal and presents a full fluorescent signal. In Figure 4a, we can clearly see the full distribution of exogenous CRB3 in MCF10A cells, including its tight junctional localization consistent with previous reports in the literature and its co-localization with centrosomal proteins. On the other hand, laser confocal microscopy uses a laser as the light source to excite the fluorescence within the sample point by point. It employs a precision pinhole filtering technique with strong laminar imaging capabilities. In the specific analysis of endogenous CRB3 co-localization studies with centrosomes and primary cilium, signals at tight junctions must be excluded. Therefore, Figure 4c represents the fluorescence signal at the level of intracellular CRB3 co-localization with γ-tubulin. The two methods use different detection means and techniques, and are not directly comparable.

5) The staining in figure 4E is also different from those shown in figure 4F in which the CRB3A/B staining is right at the base of the axoneme while it is not the case in figure 4E where we can see a red dot close to but not right at the base of the axoneme.

Thank you for your comments. The new Figure 4F displays the localization relationship between CRB3 and primary cilium, analyzed using laser confocal microscopy. With the unique single-level detection function of this microscope, the problem of level selection may cause the red dots to appear close to, rather than right at the basal body of the primary cilium. However, the new Figure 4G, based on the use of 3D reconstruction scanning technique, clearly demonstrates the localization of CRB3 at the basal body of the primary cilium under the same cells and conditions.

6) The authors claim that CRB3A/B interacts directly with Rab11 but they only show co-immunoprecipitation experiments from cell lysates which do not support direct interactions. The only way to show a direct interaction is to produce both proteins in vitro. Thus, the term direct interaction should be removed.

Thank you for your positive comments. Since we were unable to complete the relevant experiments to demonstrate direct interaction of two proteins, we have revised our conclusions. Replace " CRB3 directly binds Rab11" with " CRB3 binds Rab11" in the manuscript.

7) In addition, the authors claim (Line 251/252) that Rab11 is necessary for the transport of CRB3A/B but they should KD Rab11 to show this.

Thank you for your good suggestions. It is essential to observe CRB3 trafficking after knockdown Rab11. However, in Figure 5C, we used the endocytosis inhibitor dynasore, which also inhibits Rab11-positive endosomes. This result shows that dynasore can significantly inhibit CRB3 trafficking in MCF10A cells. We believe that this experiment partially demonstrates that inhibiting Rab11 function can affect CRB3 trafficking.

8) The domain of CRB3A/B that is necessary for the interaction with Rab11 is the N-terminal part of the extracellular domain. This domain is thus inside the transport vesicles and not accessible from the cytoplasm. Given that Rab11 is a cytoplasmic protein, how the 2 proteins could interact across the membrane? The authors do not even discuss this essential point for their hypothesis.

Thank you for your positive comments. As shown in the schematic model in Figure 9, we believe that when cells form tight junctions, CRB3 is primarily located on the cell membrane. Subsequently, endosomes are involved in the intracellular degradation process of CRB3 on the cell membrane. Intracellular CRB3 can bind to Rab11 through the extracellular domain, which in turn participates in primary cilia assembly. We have made detailed modifications to lines 418-421.

9) Figures are not numbered.

Thank you for your comments. We have updated the numbers in the original manuscript as well as the legends of Figures 1D, 1E, 2B, 2D, 2F, 2G, 3B, 3D, 3F-H, 4B, 4E, 5I, 6, 8G and Supplementary Figure 1E, 2, 3C, 4A, 5B, 6.

Minor points

1) The authors cite several studies showing that a down regulation of CRB3A/B in human cells promotes cancer but other studies show the contrary: Lin et al., 2015 for example. Please discuss these discrepancies.

Thanks for your good suggestion. We have included additional studies with contrasting results in the discussion section, specifically in lines 378-380.

2) Line 98: "exhibit smaller" smaller than what?

We modified "exhibit smaller" to "exhibit smaller size" in line 97.

3) Line 152: "form more number, ..." ???

We modified "formed more number" to "formed more" in line 154.

4) Line 180: "Compared with the control, the number of cells with primary cilium was significantly increased ». To me it is the contrary! This part is not clear at all. Please rewrite.

We have revised the sentence in lines 183-185.

5) Authors should check and review extensively for improvements to the use of English.

Thanks for your good writing advice. We have carefully reviewed and revised the entire manuscript to improve its readability.

Reviewer #1 (Public Review):

In principle a very interesting story, in which the authors attempt to shed light on an intriguing and poorly understood phenomenon; the link between damage repair and cell cycle re-entry once a cell has suffered from DNA damage. The issue is highly relevant to our understanding of how genome stability is maintained or compromised when our genome is damaged. The authors present the intriguing conclusion that this is based on a timer, implying that the outcome of a damaging insult is somewhat of a lottery; if a cell can fix the damage within the allocated time provided by the "timer" it will maintain stability, if not then stability is compromised. If this conclusion can be supported by solid data, the paper would make a very important contribution to the field.

However, the story in its present form suffers from a number of major gaps that will need to be addressed before we can conclude that MASTL is the "timer" that is proposed here. The primary concern being that altered MASTL regulation seems to be doing much more than simply acting as a timer in control of recovery after DNA damage. There is data presented to suggest that MASTL directly controls checkpoint activation, which is very different from acting as a timer. The authors conclude on page 8 "E6AP promoted DNA damage checkpoint signaling by counteracting MASTL", but in the abstract the conclusion is "E6AP depletion promoted cell cycle recovery from the DNA damage checkpoint, in a MASTL-dependent manner". These 2 conclusions are definitely not in alignment. Do E6AP/MASTL control checkpoint signaling or do they control recovery, which is it?

Also, there is data presented that suggest that MASTL does more than just controlling mitotic entry after DNA damage, while the conclusions of the paper are entirely based on the assumption that MASTL merely acts as a driver of mitotic entry, with E6AP in control of its levels. This issue will need to be resolved.

and finally, the authors have shown some very compelling data on the phosphorylation of E6AP by ATM/ATR, and its role in the DNA damage response. But the time resolution of these effects in relation to arrest and recovery have not been addressed.

Revised manuscript:<br /> I think the authors did a good job in revising the paper, and provide compelling support for a timer function in the checkpoint. I do think they still have missed one important point how MASTL could act as a timer to control recovery. The data clearly show that MASTL somehow controls ATM/ATR activity, whilst their final model (fig.9) places MASTL upstream of CDK activity, without mentioning its feedback on ATM/ATR. I think there are 2 possible explanations for the timer function of MASTL they have discovered here, both may be relevant. The first is enhanced CDK activation by direct control of CDK phosphorylation through MASTL/B55/PP2A. The second is through MASTL-mediated shut-down of ATM/ATR activation (mechanism to be determined) which is also reported here. Their final model and discussion do not display sufficient appreciation for this latter option, and I would argue that the HU-recovery experiment shown in Fig.5B is actually in strong support of the second explanation, rather than the first.

Public Review:

In countries endemic for P vivax the need to administer a primaquine (PQ) course adequate to prevent relapse in G6PD deficient persons poses a real dilemma. On one hand PQ will cause haemolysis; on the other hand, without PQ the chance of relapse is very high. As a result, out of fear of severe haemolysis, PQ has been under-used.

In view of the above, the Authors have investigated in well-informed volunteers, who were kept under close medical supervision in hospital throughout the study, two different schedules of PQ administration: (1) escalating doses (to a total of 5-7 mg/kg); (2) single 45 mg dose (0.75 mg/kg).

It is shown convincingly that regimen (1) can be used successfully to deliver within 3 weeks, under hospital conditions, the dose of PQ required to prevent P vivax relapse.

As expected, with both regimens acute haemolytic anaemia (AHA) developed in all cases. With regimen (2), not surprisingly, the fall in Hb was less, although it was abrupt. With regimen (1) the average fall in Hb was about 4 G. Only in one subject the fall in Hb mandated termination of the study.

Since the data from the Chicago group some sixty years ago, there has been no paper reporting a systematic daily analysis of AHA in so many closely monitored subjects with G6PD deficiency. The individual patient data in the Supplementary material are most informative and more than precious.

Having said this, I do have some general comments.<br /> 1. Through their remarkable Part 1 study, the Authors clearly wish to set the stage for a revision of the currently recommended PQ regimen for G6PD deficient patients. They have shown that 5-7 mg/kg can be administered within 3 weeks, whereas the currently recommended regimen provides 6 mg/kg over no less than 8 weeks.<br /> 2. Part 2 aims to show that, as was known already, even a single PQ dose of 0.75 mg/kg causes a significant degree of haemolysis: G6PD deficiency-related haemolysis is characteristically markedly dose-dependent. Although they do not state it explicitly in these words (I think they should), the Authors want to make it clear that the currently recommended regimen does cause AHA.<br /> 3. Regulatory agencies like to classify a drug regimen as either SAFE or NOT-SAFE; they also like to decide who is 'at risk' and who is 'not at risk'. A wealth of data, including those in this manuscript, show that it is not correct to say that a G6PD deficient person when taking PQ is at risk of haemolysis: he or she will definitely have haemolysis. As for SAFETY, it will depend on the clinical situation when PQ is started and on the severity of the AHA that will develop.

The above three issues are all present in the discussion, but I think they ought to be stated more clearly.

Finally, by the Authors' own statement on page 15, the main limitation is the complexity of this approach. The authors suggest that blister packed PQ may help; but to me the real complexity is managing patients in the field versus the painstaking hospital care in the hands of experts, of which volunteers in this study have had the benefit. It is not surprising that a fall in Hb of 4 g/dl is well tolerated by most non-anaemic men; but patients with P vivax in the field may often have mild to moderate to severe anaemia; and certainly they will not have their Hb, retics and bilirubin checked every day. In crude approximation, we are talking of a fall in Hb of 4 G with regimen (1), as against a fall in Hb of 2 G with regimen (2), that is part of the currently recommended regimen: it stands to reason that, in terms of safety, the latter is generally preferable (even though some degree of fall in Hb will recur with each weekly dose). In my view, these difficult points should be discussed deliberately.

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Reply to the reviewers

1. General Statements [optional]

Reply to general assessment of referee #2:

1. General assessments: The current study adds some to these observations…some of these observations are incremental…biological significance is limited. While this reviewer does not suggest additional experimentation, this manuscript would be suitable as a resource paper.

Reply: It appears we were not clear enough in explaining the novel aspects of our study.

The starting points are two published studies from our lab demonstrating a global increase of ISGF3 association with ISG promoters in IFNγ-treated cells and a remarkable similarity of IFN-γ and type I IFN-induced early transcriptome changes. These findings challenge the notion in the field (as mentioned by the referee) that IFNγ specificity is produced by the predominant deployment of STAT1 homodimers. We thus tested the hypothesis that the specificity of the IFNγ-induced transcriptome is generated over time, rather than during the early response, and relies on secondary responses to transcription factors such as IRF1. In contrast, IRF1 plays no or only a small role in the type I IFN response that utilises ISGF3 and/or unknown secondary factors in the delayed response. We tested this hypothesis with PRO-seq technology to rule out confounding effects of mRNA processing over a 48h period. The data are clear in showing that many genes associated with the antibacterial or anti parasite profile of activated macrophages are indeed much more abundant in late-stage rather than briefly IFNγ-treated macrophages and these delayed changes are to a large extent dependent on IRF1. Our findings are based on the best available technologies, a combination of nascent transcript analysis with genetics and protein interaction studies. In addition, our findings rule out alternative models of sustained or secondary ISG transcription, such as the employment of alternative ISGF3 complexes (such as STAT2-IRF9) or of ISGF3 complexes formed with unphosphorylated STAT1 and STAT2. We provide evidence for higher order waves of transcription caused by unknow transcription factors that are produced by transcriptional activation of ISGF3 or IRF1 target genes and identify candidates among the AP1 and Ets transcription factor families. We agree that some of the data are confirmatory rather than novel (i.e. some of the genes we describe were known from previous literature to be IRF1 targets), but it is the systems approach of our study, and particularly the delineation of conditions under which the largely neglected delayed response diverts the IFNβ and IFNγ-induced transcriptomes, that generates a comprehensive and conclusive view of IFNγ acting predominantly as a macrophage activating factor, and IFNβ being an essential antiviral cytokine. We do think this main outcome is immunologically meaningful and not incremental. For this reason, we would prefer to publish the paper as a relevant contribution to innate immunology rather than a resource. Emphasizing our point, a paper appeared in ‘Cell’ while our study was under review, showing that human IRF1 mutations cause mendelian susceptibility to mycobacterial disease (MSMD), a term coined by JL Casanova and colleagues for immunological defects that reduce the ability of macrophages to cope with intracellular bacteria (new ref. 65). This important study emphasizes the main conclusions of our study about the relevance of IRF1 for macrophage activation. We discuss this paper on p. 14 lines 9-14.

Revision: We tried to better explain the scientific motivation for this study and the significance of the results (p. 4, lines, lines 12-25).

Revision plan: n. a.

2. Description of the planned revisions

Referee #3; major comment 1:

In Fig. 1d is difficult to interpret and misleading for many reasons. First, the cluster numbering is disconnected from the cluster order; why not numbering them based on the hierarchical clustering and writing the cluster number besides the cluster itself? Second, having a 2-color gradient is misleading; negative values shouldn't be in the same color tone than the positive values. Third, the authors did not provide adequate rationale behind using only the top 1,000 most expressed gene? Why not using all the differentially expressed genes in at least one of the condition to provide a comprehensive analysis? Could this potentially lead to bias in the data, and is there any information lost by not using the - lower - expressed genes fraction? Fourth, it is not clear what the color scale is representing and how the data was transformed. Was a mean centering of the expression values of the log2FC applied to the RNA-seq data to facilitate clustering? Mean centering and z-scoring is a common technique used to adjust expression data, but it can potentially exaggerate differences between samples. More information about the data and analysis should be provided, as it is difficult to determine whether this was a valid approach or not.

Reply:

• To create the heatmap, we used the pheatmap package from R and the cutree_rows option to separate 11 clusters with strikingly different patterns of gene expression based on visual exploration. The numbering was autogenerated by the program.
• The data is now shown in red-blue.
• We restricted our list to only 1000 genes from each comparison as we aimed to analyze the prominent patterns of gene expression across timepoints. Considering all differentially expressed genes based on a padj value would also include genes expressed at very low levels as evident from the low baseMean values obtained from DESeq2. Hence, we applied a selection of 1000 genes which effectively represented the major patterns of gene expression across timepoints.
• Variance stabilized transformation was applied on read counts obtained from PRO-seq using the DESeq2 package. The transformed reads were z-score normalized and used for performing hierarchical clustering by the “Ward.D2” method using the pheatmap package in R. A total of 3126 genes were used for this analysis. 11 distinct clusters were defined using cutree_rows option. The color scale represents z-score normalized counts. The genes represented in the heatmap were selected based on the following criteria: each timepoint of interferon treatment was compared to the homeostatic condition (untreated sample) in wildtype BMDMs. The differentially expressed genes from each comparison were selected based on the filtering criteria: absolute log2FoldChange >=1 and adjusted p value <0.01 by Wald test. Following the differential analysis, the first 1000 differentially expressed genes in each treatment condition (ordered based on adjusted p values) were selected for both IFN types and combined and selected for creating a list which consisted of 3126 unique genes. The scale in the heatmap represents z-scores of variance-stabilized reads, calculated across all genotype and treatment conditions, separately for each IFN type.

Revision plan: We will label the clusters with the cluster number next to it in addition to the color codes.

Referee #3; major comment 3:

The large standard deviation bars in the claim that ChIP data confirmed the binding of ISGF3 components to the promoter of Mx2 cast doubt on the validity of the results and conclusions. The authors should consider additional experiments or complementary analyses to validate their findings. Or alternative, to adjust their claims accordingly.

Reply: To demonstrate sufficient quality of the data the ratio of Stat1/ Stat2 was calculated for early (1.5hrs) and late (48h) separately. The unpaired two-tailed t test comparing this ratio between 1.5 hrs and 48hs, shows that they are not significantly different. This indicates that all ISGF3 components are associated with ISG during both early and delayed responses, i. e., that STAT2/IRF9 complexes are unlikely to contribute to delayed ISG control. However, we agree with the referee that the standard deviations of the kinetic ChIP experiment are high and that it would be good to generate additional data.

Revision plan: We will perform additional ChIP experiments to improve the statistical power of the results in fig. S2c.

Referee #3, major comment 6:

The authors interpret their ATAC-seq and ChIP-seq results based on a 2kb window to the TSS of genes, not considering relatively close enhancers or longer range cis-regulatory interactions in their interpretation. For example, they mention on p.7 "Contrasting the strong binding of IRF9 and IRF1 to the Mx2 (cluster 2) and Gbp2 (cluster 9) promoters, respectively, we saw no evidence for direct binding to Lrp11 (cluster 3) and Ptgs2 (cluster 10)", but on Fig 3d they show only the proximal regions. No scale bars are shown either. Moreover, exploring the same published IRF1 ChIP-seq dataset, there is a clear IRF1 binding site at the promoter of Ptgs2, while the authors report none.

Reply:

• According to the literature (e. g. refs. 11, 27), most IFN-induced accessibility changes occur in the vicinity of the TSS of ISG. This is further strengthened by the data shown in this manuscript. In addition, most functionally validated GAS and ISRE sequences are in the DNA interval chosen for our analysis. While distal ISG enhancers have been reported (e. g. DOI: 10.26508/lsa.202201823), an analysis beyond the placement of most control regions increases the risk of wrong assignments between ISG and their regulatory elements, hence the causality between transcription factor binding and accessibility changes.
• We extended the regions for the analysis of the Lrp11 and Ptgs2 regulatory regions and found no evidence for the binding of ISGF3 or IRF1. We find no evidence for a clear peak in the Ptgs2 promoter. There is a peak called by the Macs2 algorithm, but visual inspection of the track (bigwig file) shows it consists of a minor increase in reads above background that does not suggest a bona fide IRF1 binding site (see below). This view is supported by our inability to find an IRF binding site in the vicinity of the peak.

IRF1 binding indicated by bigWig browser tracks and corresponding peakfiles detected at the locus. We identified the peakfile from Langlais et al., 2016 and identified peaks using MACS2, however using mm10 genome as the analysis in the original paper was done with mm9 genome. The peak identified here appears to be an artefact of the MACS2 program as there is no evident enrichment at the gene promoter region upon inspection of the bigWig files.

Revision plan: Scales will be added to the browser tracks as requested.

Referee #3, major comment 7:

Lack of statistical analysis on chromatin accessibility claims: The authors claim that ATAC-seq data in BMDMs stimulated with IFNβ or IFNγ for a short (1.5 hours) or long (48 hours) period reveals a striking similarity between transcription and the general trends of chromatin accessibility at regions up to 1000 bp upstream of the TSS (Fig. 2a), suggesting continuous chromatin remodeling during the transcriptional response. However, I would like to know if this conclusion is well-supported by the correlation between the chromatin accessibility from ATAC-seq data from only one sample and the PRO-seq data.

Reply: See revision plan.

Revision plan: We will analyze single experiments whether they support the conclusions derived from the z-score of the triplicate samples.

Referee #3, major comment 8:

The need for additional experiments to verify claims such as the dependence of Ifi44 on IRF1 for gaining ATAC signal, as stated in the claim, "Expression required IRF1 for both, but accessibility of the Ifi44 regulatory region depended upon IRF1 whereas that of Gbp2 acquired an open structure independently of IRF1 (Fig. 5c).

Reply: We think the lack of clarity might be related to the size of figures 5a and 5b and the density of the dots in some areas of the plot. We agree it is very difficult to assign our gene labels unambiguously to a single dot.

Fig. 5a combines ATACseq data in wt and IRF1 knockout cells with the expression data from the Pro-seq experiment, Fig. 5b is the same set-up, but IRF9-deficient macrophages are analyzed.

Blue dots show ATACseq signals induced by IFN treatment. Violet dots represent genes that require IRF1 (Fig. 5a) or IRF9 (Fig. 5b) for transcriptional induction. Yellow dots mark genes such as IFI44 requiring IRF1 (Fig. 5a) or IRF9 (Fig. 5b) for both expression and the accessibility change in the promoter region. Fig. 5c visualizes representative examples of genes whose accessibility is coupled to the transcription factor dependence of the transcriptional induction (IFI44), or not (Gbp2). Thus Fig. 5c must be interpreted based on the dot color code in fig. 5a and we admit this has been difficult with the figure in its present form.

Revision plan: We will improve the clarity of figs 5a and 5b in several ways:

• We will label the panels to better indicate the intersected data sets.
• We will increase the size of the panels and figure legends and make sure that the correspondence between gene names and dots are unambiguous.
• We will include trend lines of the Ifi44 and Gbp2 genes to visualize their induction and IRF1 dependence.

Referee #3, major comment 13 (see also section 3):

The authors have not adequately addressed the methodological limitations in their discussion, which extends beyond the aforementioned comments. It is suggested they include a comprehensive discussion of the claims made pertaining to the necessity of IRF1 for accessibility and the potential biases in the interactomes, along with their associated consequences.

Reply: The contribution of IRF1 to the accessibility of ISG promoters emerges from the data in figures 5a, whose clarity will be improved (see reply to point 8). We do not interpret the impact of IRF1 beyond the data, in fact we state a relatively minor effect of IRF1 in the control of promoter accessibility (p. 10, lines 20-22) and we have added a reference in agreement with an impact of IRF1 on basal expression of antiviral genes (ref. 39, as suggested by the referee).

We have added discussion on potential limitations of the TurboID approach (p. 11, lines 22-24 and p. 15, lines 3-11).

Revision plan: Improvement of fig 5a (see ref. #3, point 8).

Referee #3, minor comment 2

Fig 1e. The color scales on the GO enrichment graphs are misleading since they use the same blue-to-red gradient for adj p-values ranging from 10-25 to 10-49 and 0.008 to 0.016, which could be considered non significant.

Reply: We agree that this is confusing. It results from automated assignments of the color gradients by the software.

Revision plan: We will investigate possibilities to change color codes for different ranges of p values.

Referee #3, minor comment 4

The incomplete schema in Figure 1a, which only focuses on PRO-seq and does not include the ATAC-seq element.

Reply: We will add a new figure to visualize the set-up of the ATAC seq experiments and their intersection with the Pro-seq data.

Revision plan: We will add a new figure in accordance with the referee’s request.

Referee #3, minor comment 6

The clearer labeling of Figure 5a and 5b.

Reply: Please refer to our reply to major point 8.

Referee #3, minor comment 10

Fig S1b, S3b. The PRO-seq was generated in triplicates, hence these graphs should include the Log2FC for the individual data points.

Reply: The Log2FC from DESeq2 were calculated from the triplicates, the software does not compute Log2FC from individual replicates.

Revision plan: We mention the p-values for the Log2FC to show the degree of consistency (figure legends). We will provide a table with log2FC and corresponding padj values of the genes represented at each timepoint (table_showing_padj_values_and_log2fc).

Referee #3, minor comment 12

In the genomic snapshot shown, only bars or fading triangles are shown in place of the gene body. The authors should provide an accurate gene structure; i.e., exons and introns.

Reply: We will try to include the exon-intron structure wherever the size of the figure allows this.

Revision: n. a.

Revision plan: If figure size permits, we will add the exon-intron structure of the genes in browser tracks as requested.

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

Referee #1, major comment 1

Figure 2. Difficult to interpret data as it is presented. Consider quantifying figure 2C in order to make "changes in Pol II pausing were more pronounced during IFNb signaling" statement more apparent.

Reply: We presented the pausing data in two different graphic representations (figures 2c and S2) to make the understanding of the information content easier. In hindsight we may have generated more confusion than clarity.

Revision: We removed the original figure 2c and replaced it with original figure S2. This representation is quite intuitive as the graphs represent a direct quantitative logarithmic display whether and how much the relative amount of paused polymerase changes when comparing IFN-treated and untreated cells. The calculation of these ratios is now explained better in the legend to figure 2.

Referee #1, major comment 2

How are you distinguishing autocrine signaling in the BMDMs driven by IFN treatment from late transcripts (for example, at 48 hours are differential genes due to autocrine cytokine signaling or are they truly late transcripts)?

Reply: We do not exclude autocrine effects. In case of ISG, the most likely autocrine factor would be secreted interferon. According to our Proseq data, the differentially expressed genes do not include any interferon genes. That being said, it is possible that the transcription factors from the AP1 family we hypothesize as drivers of secondary or tertiary waves of transcription are activated by non-IFN cytokines secreted from IFN-treated cells (see also reply to comment 3).

Revision: We now mention that enhanced IFN production is not sustaining ISG responses (p.5 lines 18/20). We mention the possibility that secreted factors may drive secondary or tertiary waves of ISG transcription (p. 8, lines 21/23).

Referee #1, major comment 3

Figure 3D. Authors choose Gbp2 (as positive control for IFNg driven gene), but don't show that Gbp2 is a IFNb independent gene. Consider using IRF1 KO BMDMs in this data as well.

Reply: This is a misunderstanding. Gbp2 is not shown as an IFNγ-specific gene (it’s induction by both IFN types has been shown previously and emerges from our Pro-seq analysis, see also response to minor issue no. 2). It represents the cluster of genes that are sustained specifically after IFNγ treatment in an IRF1-dependent manner. The purpose of fig. 3D is to show that not all ISGF3/IRF9-dependent genes have promoter binding sites for ISGF3 and not all IRF1-dependent genes have binding sites for IRF1. This suggests indirect effects of both transcription factors in sustaining IFN-induced transcription (in line with the referee’s comment 1).

Previous figure S3e (now S2f) confirms binding of IRF1 to the GBP2 promoter by ChIP with kinetics correlating to its transcriptional effect. This experiment is normalized with an IgG control. IRF1 knockout cells did not produce a ChIP signal with IRF1 antibody, as expected (data not shown).

Revision: We better explain the rationale behind the experiments shown in figure 3D (text on p8, lines 12-16). In addition, we show the trend line of Gbp2 expression in WT vs IRF1KO as well as that of additional genes showing delayed/sustained responses in the new Figure S3.

Referee #1, minor comment 2

Define known IFNg and IFNb driven genes when they are introduced in figure 2 rather than in discussion.

Reply: Following the referee’s suggestion we provide the examples of IFNβ and IFNγ-controlled genes and the characteristics of their regulation in the context of our description of the results displayed by fig. 2 (p.6 lines 15-21). This includes Gbp2 (see major issue no. 3).

Revision: The text on p. 6 lines 15-21 has been modified in accordance with the request.

Referee #1, minor comment 4

Unclear whether IRF1 expression in figure 3A is from whole cell lysate or nuclear fraction.

Reply: We indicate in the figure legend that whole cell lysates were used.

Revision: We added a sentence with the relevant information in the legend of figure 3.

Referee #1, minor comment 5

Authors suggest IFNb treatment induces less IRF1 at later time points, however loading control also seems slightly lower than other considerations. Is it possible that IFNb treated cells are dying at later time points, given that type I IFN signaling can be pro-apoptotic.

Reply: The graph below the blot represents quantified IRF1 signals, normalized to the loading control. It shows that the differences are not generated by unequal loading of the blotted gel. We and others have shown that IFNβ may indeed enhance macrophage death, however only when the cells are simultaneously infected with an intracellular pathogen (e.g. new ref. 25). These studies also show that treatment with IFNβ alone over periods used in the present study does not affect macrophage viability.<br /> Revision: We added a sentence about the viability of IFN-treated macrophages (p. 4, lines 31-32).

Revision plan: n. a.

Referee #2, major comment 3

The sequencing and BioID data are not submitted to public databases.

Reply: An accession number has been added.

Revision: The accession number was added on p.29, line 25.

Referee #3, major comment 1 (see also revision plan, section 2):

Revision: The rationale for using the top 1.000 genes is explained (p.5, lines 7-9). The description of the pro-seq read count processing has been extended in accordance with our reply to the referee in the legend of figure 1d and in the methods section (p. 33, lines following line 10.)

Referee #3, major comment 2

Fig 2c. The authors claim that RNA Pol II pausing is a major factor in controlling the dynamics of ISG transcription. However, they did not provide sufficient explanation of the results, and in all fairness there is not much variation between the clusters to sustain the claim that this is a major factor in ISG transcriptional control.

Reply: We agree with the referee that we cannot posit RNA pol II pausing as a major factor for the differences of transcriptional control of ISG in individual clusters. We have made sure to remove any statements suggesting this possibility. We also try to better integrate our findings with RNA pol II pausing into the existing literature.

Revision: We added relevant literature on p. 6 lines 28-30 and p. 7, lines 4-6.

Referee #3, major comment 4

On p.5, the authors mention "Representative browser tracks from the Gbp2 and Slfn1 genes further validate this observation" but they are simply referring to genome browser snapshot, i.e., specific genomic examples, extracting from the same single dataset. Without using an independent dataset, this can not "further validate" the initial findings.

Reply: We agree the wording is incorrect.

Revision: We changed the paragraph describing this experiment (p. 6, lines 15-21).

Referee #3, major comment 5

IRF1 was successfully pulled down with STAT1 bait but not in the reciprocal experiment. The author should discuss this point as it is important for the conclusions. Could it potentially indicate issues with the technique used, and if this could introduce any bias into the results. The statement, "In contrast, interactors of the IRF1 bait did not include STAT1. This discrepancy could result from steric constraints of the tagged proteins due to the limitation of the 10nm distance reached by the biotin ligase," does not seem to be sufficient to explain this discrepancy.

Reply: STAT1 was present in the IRF1 pull-down and the interaction increased significantly after IFN treatment but after normalization to the NLS control it did not conform to our criterium of a 95% confidence interval for the FDR. To be consistent we did not include it in the list of IRF1 interactors. We have observed on several occasions that the significance of proximity is not reciprocal, even for well- documented physical interactions. A prime example for this is the interaction between STAT1 and IRF9 in IFN-treated cells which is recorded in the STAT1 pull-down, but not that with IRF9 (ref. 10). Apart from steric reasons the lack of reciprocity may result from different signal/noise ratios in pull downs with different baits.

Revision: We mention that IRF1 was a STAT1 interactor below the statistical cut-off (p. 11, lines 26-28) as well as the possibility of different signal/noise ratios in the IRF1 and STAT1 pull-downs on p.11, lines 22-24.

Referee #3, major comment 9

In the figure legends, there is missing information about the number of times experiments were replicated, suggesting that some were done a single time. Moreover, some graphs are missing statistical analysis, e.g., in Fig S3cS3e, S3f, the ChIP-qPCR experiments were done on biological triplicates, there is no mention of statistical test performed, it is not mentioned what the error bars represents (SD, SEM, etc.) and the variance is large, but the authors still interpret these results as significant enrichment of the transcription factors to the Mx2 promoter.

Reply: Where missing the relevant information has been added to figure legends. In brief, all experiments represent at least three biological replicates. The only exception is the western blot shown in figure S3a, (no S2a) which represents two independent replicates. Here, the clarity of the difference of IRF1 expression and the fact that the only purpose is to show that Raw264.7 macrophages behave like bone marrow-derived macrophages in fig. 3a justifies the omission of another replicate (please see also answer to point 3).

Revision: The relevant information has been added to figure legends where necessary (figs. 1, a, 3a, 6a-f, S1, S4, S5).

Referee #3, major comment 10

Another example are the RNA Pol II pausing index ratios, which show minor variations and not are supported by statistics to support a possible significance. Proper description, replication and statistical analyses of the results are critical.

Reply: We agree.

Revision: Statistics underlying the RNA Pol II pausing data are included in supplementary data 2.

Referee #3, major comment 11

The authors used CRISPR-Cas9 genome editing to generate knockout cell lines. However, they did not verify the knockouts at the protein level. Further experiments could confirm that the targeted proteins are not expressed in the knockout cell lines.

Reply: We included a western blot showing the lack of IRF1 and STAT1 expression in the respective cell lines.

Revision: New figure S6.

Referee #3, major comment 12

On p.9, it is mentioned "IRF1 affects chromatin structure ...". Here chromatin structure is related to minor changes in chromatin accessibility, this can not be qualified as changes in chromatin structure.

Reply: ‘structure’ has been changed in accordance with the request.

Revision: ‚structure‘ has been replaced with ‘accessibility’. (p. 10, lines 19 and 21).

Referee #3, major comment 13 (see also section 2, revision plan, major comment 8)

The authors have not adequately addressed the methodological limitations in their discussion, which extends beyond the aforementioned comments. It is suggested they include a comprehensive discussion of the claims made pertaining to the necessity of IRF1 for accessibility and the potential biases in the interactomes, along with their associated consequences.

Reply: The contribution of IRF1 to the accessibility of ISG promoters emerges from the data in figures 5a, whose clarity will be improved (see reply to point 8). We do not interpret the impact of IRF1 beyond the data, in fact we state a relatively minor effect of IRF1 in the control of promoter accessibility (p. 10, lines 20-22) and we have added a reference in agreement with an impact of IRF1 on basal expression of antiviral genes (ref. 39, as suggested by the referee).

We have added discussion on potential limitations of the TurboID approach (p. 11, lines 22-24 and p. 15, lines 3-11).

Revision: Change of the discussion section (p. 11, lines 22-24 and p. 15, lines 3-11).

Revision plan: Improvement of fig 5a (see ref. #3, point 8).

Referee #3, major comment 15

The work should be discussed in the context of the demonstrated physiopathological evidence of the IRF1 and IRF9 functions. IRF9 (Hernandez et al., JEM 2018) and more recently IRF1 (Rosain et al Cell, 2023) were identified as causing non overlapping phenotypes in human patients carrying loss-of-function mutations for these genes. The authors must interpret their results in this context.

Reply: We thank the referee for reminding us about the importance of these papers for our work.

Revision: The papers have been mentioned and discussed (p. 13 lines 19-28 and p.14, lines 9-14).

Referee #3, minor comment 3

The inconsistency in the title referring to IFNb as Type 1 but using IFNg instead of Type 2 nomenclature, perhaps consistency is best.

Reply: We agree about the importance of consistency but find ourselves in yet another quandary. While the use of ‘type I IFN’ is clearly indicated and widely used as a collective name for this group of cytokines, the use of ‘type II IFN’ for IFNγ is rare because it is the only member of this type. Hence, we decided for sticking with convention at the expense of a bit of consistency. We agree about the title, though, and have changed type I IFN to IFNβ.

Revision: We adapted the title in agreement with the referee’s comment.

Referee #3, minor comment 5

Figure 6d includes a color scale of -1 to +3, but it is unclear what these values represent and how they were calculated per interactor. The figure legend should be revised to clarify this information.

Reply: We agree. The relevant information has been added to the figure legend.

Revision: We added information (log2FC with regard to the NLS control) to the legend of fig. 6d.

Referee #3, minor comment 9

Fig 1e, S1c. Graphs having circles of varying sizes in function of a value are named "bubble plots" and not "dot plots".

Reply: Thank you for pointing this out, we corrected our mistake.

Revision: We changed dot plot to bubble plot in legend to figure S1c.

Referee #3, minor comment 11

Fig S3c legend. It is mentioned "Graph represents RT-qPCR of genomic Mx2". RT-qPCR usually stands for reverse transcription quantitative PCR, hence we suggest to change to "ChIP-qPCR" or qPCR. Confusingly, in the literature the term "RT-PCR" is used for real-time PCR and "qPCR" for quantitative PCR. Also, the authors should be specific about the "genomic" region targeted; the graphs mention "promoter", hence it would be appropriate to use the same designation in the legend.

Reply: We agree and thank the referee for correction of the terminology.

Revision: We changed RT-PCR to qPCR throughout the manuscript. Moreover, we specifically refer to ‘promoter region’ as the amplified DNA.

Referee #3, minor comment 12

Fig S3e. The y-axis names are missing.

Reply: Thanks for spotting this.

Revision: The y axis in the figure received its proper label.

Referee #3, minor comment 14

Raw cells are sometimes spelled as "Raw" and other times as "RAW". Please choose one for consistency.

Revision: This inconsistency has been corrected

Referee #3, minor comment 15

In p.10 l.20, the figure number is missing.

Revision: We corrected this mistake.

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

Referee #1, minor comment 1

Simplify figure 4B- consider focusing on most differentially expressed genes between clusters

Reply: The purpose of fig. 4B is to provide a visual overview of the kinetics of eRNA transcription in response to both IFN types and of the effects of IRF9 and IRF1 knockouts. This information needs to be given to demonstrate the similarities and differences between the control of eRNA and the corresponding ISG transcripts in the different regulatory clusters (as shown in figs. 1d and 2a).

Simplifying the figure would mean to separate it according to time point, IFN type treatment or knock-out effect. We think this would require to mentally reassemble the figure to understand the interrelationships between these parameters. To our opinion the visual display of the data interrelationship in fig. 4B facilitates the impropriation of the information content.

Revision: n. a. - we hope our reasoning has become sufficiently clear.

Revision plan: n. a.

Referee #1, minor comment 3

Clarify which cell types (IRF1 KO vs IRF9 KO) are used in figure 5 A/B.

Reply: The cell type (bone marrow-derived macrophages) is mentioned in the first sentence of the figure legend. Since all experiments except the Bio-ID experiment were performed with this cell type we decided not to label each figure.

Revision: n. a.

Revision plan: n. a.

Referee #2, major comment 2 and referee #3, major comment 14

Ref #2: Biological significance is limited as this study is largely descriptive and they do not test the hits obtained from BioID.

Ref #3: Although the TurboID experiments identify known STAT1 and IRF1 interactors, the proposed new interactors are numerous, and none are validated through independent co-IP experiments. Moreover, the results are very noisy, with little differences between untreated BMDMs (where IRF1 is barely expressed) and IFN-treated conditions.

Reply: The big advantage of BioID or TurboID is the ability to score proximity and very transient interactions. Validating BioID hits with technologies such as coIP is not particularly useful as the two technologies will obviously produce different interactomes. In fact, we show in this manuscript that IRF1 and STAT1 show proximity, but they do not form a stable complex under co-IP conditions. This leaves genetic approaches (LOF or GOF) as alternatives. However, apart from the workload (> 100 genes would have to be knocked out or their products overexpressed), most of our hits are expected to produce very broad effects in such experiments, hard to interpret regarding ISGF3 and IRF1 activities.

In view of this situation, we publish exclusively the high confidence nuclear interactors identified in our screen: biological replicates were performed in triplicate, a stringent internal control (TurboID-NLS) was used, and a stringent statistical cut-off for high-confidence interactors (95% FDR between groups) was applied. We further account for the experimental situation by limiting interpretation of the data to confirmed molecular events. For example, STAT1 dimers and the ISGF3 complex are required for histone acetylation in response to IFN, and ISGF3 is known to contribute to the exchange of the H2AZ histone variant (refs 11, 14, 71, 72). Our data show that IRF1 contributes to promoter accessibility changes and this is in line with its proximity to a remodelling complex. Thus, the BioID data indeed validate previous findings. However, in agreement with the referee’s comment, some of the data remain descriptive (such as the intriguing proximity of both STAT1 and IRF1 to nuclear products of ISG). To determine the importance of this molecular proximity is a major undertaking and beyond the scope of this study.

Revision: We added discussion to state the difficulty of validating TurboID-based interactions and the limitations of the TurboID experiments (p.15 lines 3-11).

Referee #3, minor comment 1

In most graphs the expression values or log2FC are shown separately for IFNb and IFNg, however in the heatmaps (Fig 1d, S1d) the IFNb and IFNg results are intercalated keeping them side-by-side for each time point, which makes them more difficult to interpret.

Reply: We are in a quandary about the design of the figure. On the one hand our goal is to visualize gene clusters with distinct behaviors for each IFN type. For this purpose, it would be advantageous to separate the IFN types. On the other hand, we aim at showing similarities and differences between genes induced by each IFN type, for this purpose it is better to maintain the current sample order. While understanding the referee’s point, we prefer to keep the figure as it is, because the suggested change will not increase its overall clarity.

Revision: n. a.

Revision plan: n. a.

Referee #3, minor comment 7

The statement that "IFN-I are the more important mediators of antiviral immunity" is not entirely accurate and may be an oversimplification, as there are certainly articles which suggest a larger role for type ll IFN elements than type l (ref: Yamane D et al., 2019 Nature microbiology). While yes, IFN-I plays a critical role in the innate immune response to viral infections, IFNγ also has antiviral activity and is involved in the adaptive immune response to viral infections, and in some instances to a larger extent than IFN l.

Reply: The Yamane et al study (now mentioned on p 10, lines 22-25 and referenced) agrees with our findings because it shows that IRF1 contributes to the basal expression of an ISRE-driven ISG subset. Our statement about the predominant role of type I IFN versus IFNγ refers to genetic data in both humans (mainly Casanova’s work including effects of autoantibodies against type I IFN, see also the paper about human STAT2 deficiency in the June 15th issue of the JCI, https://doi.org/10.1172/JCI168321) and mice (hundreds of papers) showing that disruption of type I IFN synthesis or response causes profound effects of antiviral immunity (i.e. resulting susceptibilities are first and foremost to viral pathogens) whereas susceptibilities as a consequence of disrupting the IFNγ pathway are first and foremost to intracellular nonviral pathogens such a mycobacteria. In fact, the term mendelian susceptibility to mycobacterial disease (MSMD) was coined by Casanova and colleagues to describe a variety of human mutations that include those of the IFNγ, but not the type I IFN pathway.

Maybe more importantly, the Rosain et al. paper mentioned by the referee which appeared in ‘Cell’ while our study was under review, shows that human IRF1 mutations also fall into the MSMD category (new ref. 65). In contrast, the authors did not observe diminished antiviral immunity. This emphasizes the main conclusions of our study about the relevance of IRF1 for macrophage activation. We discuss this paper on p 14. lines 9-14.

Obviously, this does not exclude a role of type I IFN in nonviral infection or of IFNγ in viral infection, in fact much of our own work has been dedicated to a role of type I IFN in infections with L. monocytogenes. Nevertheless, we think that in a generic statement about the difference between type I IFN and IFNγ it is correct to label the former as predominantly antiviral and the latter predominantly as a macrophage activating factor against nonviral, intracellular pathogens.

Revision: We added discussion of Rosain et al. (ref. 65) on p 14. lines 9-14.

Referee #3, minor comment 8

The authors claim that a significant portion of ISG promoters is associated with ISGF3 upon IFNγ receptor engagement and that the transcriptomes of macrophages treated briefly with IFNβ or IFNγ exhibit remarkable similarity and sensitivity to Irf9 deletion. However, I am uncertain about the extent of consensus on this claim.

Reply: The data were surprising but supported by ChIP-seq and RNA-seq in wt and IRF9 ko macrophages (ref 10). Data in a follow-up study (ref. 11) and in this manuscript support our original conclusion by demonstrating the impact of the IRF9 ko on IFNγ responses. Importantly, we don’t claim this is true in all cell types, it may well depend on STAT/IRF9 expression levels and tonic IFN signaling.

Revision: n. a.

Revision plan: n. a.

This work has been peer reviewed in GigaScience (see Description), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

** Jack Pattee**

Overall, I think that this manuscript is strong and describes a well-formulated method to address a relevant problem. There are a few outstanding questions about the performance of the EraSOR method from my perspective, which I'll detail as follows.My understanding of reference [16] indicates that equation (3) of this manuscript only holds for null SNPs, i.e. if SNP g is not associated with the outcome Y. If this is the case, then this should be discussed in the manuscript. I wonder if this can partially explain the 'under-estimation' behavior we see in the application to real data in Supplementary Figure 3. In particular, I am referencing the behavior where the EraSOR correction will under-estimate the predictive accuracy of the PRS in the target data, i.e. where delta-R^2 is negative. This behavior is not seen in the simulation and warrants further investigation and discussion. While the bias appears small, for some cases delta-R^2 approaches -.025, which corresponds to an under-estimation of Pearson's r by roughly .15; this is substantial. Could it be the case that, for highly polygenic traits such as height and BMI, the null-SNP assumption is unreliable and the performance of EraSOR is degraded? Does a fundamental assumption of sparse genetic association underlie EraSOR?I recommend that the real data application play a larger role in the manuscript narrative and be moved out of the supplementary. The simulations are appreciated and helpful, but there is nuance in the analysis of real data that cannot be replicated in simulation.I believe the reference to "Supplementary Figure 2" on line 346 should actually be "Supplementary Figure 3". I believe that the axis labels in Supp Figure 3 are flipped.Lines 82 and 83 reference genetic stratification and subpopulations; I think the relevance of these concepts should be introduced more clearly and they should be defined in this context. EraSOR concerns the overestimation of predictive accuracy and association incurred by sample overlap between the base and target GWASs; to this reader, it's not clear what this central issue has to do with population stratification. I realize that the derivation of the LD score method is motivated heavily by correcting for stratification; however, these concepts should be introduced more clearly in this manuscript.Line 88: consider defining LD score l_j.Lines 94-96: consider outlining the mathematical consequence of the assumption that "the two outcomes and cohorts are identical." It's the case that N_1 = N_2 = N_c = N, correct?Line 109 / equation (11): My understanding is that the relevant quantity of this derivation is N_c / sqrt(N_1 N_2), which allows us to define the correct matrix C in expression (4). If this is the case, perhaps the quantity of interest should be moved to the LHS of the equation in the final line of the expression, for clarity.As discussed in the manuscript, the estimated heritability is in the denominator of the expression for N_c / sqrt(N_1 N_2). The authors correctly discuss that the method should not be applied when there is doubt as to whether the heritability is different from zero. I would take this a step further; in cases where the heritability is zero, we cannot meaningfully apply the EraSOR correction, and thus I am not sure of the utility of the 'type I error' simulations in the manuscript. Perhaps an explicit test for h^2 > 0 should be worked into the EraSOR workflow?Line 148 / expression (12): If beta has a normal distribution here, it is the case that all SNPs in the simulation are associated with the outcome Y. This is a somewhat unusual choice for the distribution of SNP effects in a simulation; other applications such as LDPred (Vilhjalmsson et al, AJHG 2015) and LassoSum (TSH Mak et al, Genetic Epi 2017) use a point-normal distribution for simulated SNP effects, which effectively simulates the sparsity frequently observed in nature. Is there a reference or justification for the non-sparse simulation structure here?Line 215: there may be a typo in the expression for the variance of the residual term. Is it the case that the variance of the residual depends on the variance of a covariance term? If so, I am confused as to the derivation.Line 241: 'triat' should be 'trait'.The simulation results in this paper are based on clumping and thresholding for PRS, which does not estimate joint SNP effects i.e. account for LD. Methods such as LDPred and LassoSum do so. Is there any reason to believe the results would be different for a method such as LassoSum?I am confused by the very low Fst between the simulated Finnish and Yoruban samples in simulation. As detailed on line 385: the reported Fst is > .1, but the simulated Fst is essentially zero. This seems likely to be an undesirable simulation artefact, and potentially invalidates the simulation study (or, at least, doesn't provide evidence that EraSOR functions correctly when Fst is large, which was the ostensible motivation for this simulation). Is there no way to effectively simulate populations with a larger Fst?

The logic Danforth uses to justify and explain to Hale why a lawyer is not necessary in this instance is a flawed logic.

He states that witchcraft is "an invisible crime" in which only the witch and victim are present, also that as the witch herself will hardly accuse herself, the court must rely upon the victim to testify buy identifying the witch in question.

BUT what he fails to take into account here is that he is assuming that the "victims" are actually victims in the first place and that their accusations are true. He has no real evidence of this other than the girls' confessions. Danforth thus makes a big mistake in assuming that their accusations are valid and to be believed.

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Reply to the reviewers

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

* Srinivasan et al. present a comprehensive study on systematizing the structure-dynamics-function relation of lipid transfer proteins (LTPs), combining extensive molecular simulations and complementary experiments. Indeed, the current state-of-the-art in the field is quite chaotic and fractional, and such systematic studies are necessary to advance our general and conceptual understanding of the mechanisms of action of LTPs. The selected techniques and research strategies are all suitable, their description is sufficient and enables reproducibility; the obtained results are carefully presented and discussed; the conclusions are adequately supported by the data.

Given my primarily computational background, I evaluated mainly the simulation part of the manuscript. Considering experiments, I do not see any significant flows or deficiencies that could diminish the value of the data and following conclusions given in the manuscript. I would even suggest improving the abstract by more explicitly saying that this work includes experimental measurements because it currently reads like purely computational work was performed. *

We thank Reviewer #1 for the positive evaluation of our work. The abstract has now been updated to include that our work allows us to interpret existing data but also to design and perform new experimental measurements.

* Major comments: *

1) Although I like the central message of the paper and have no objections, I am curious whether the conclusion "a more "dynamic" or/and "mobile" part of the protein interacts with the membrane or any other (macro)(bio)molecule" makes sense globally and is not limited to LTPs. For example, it is a reasonable assumption that a more flexible part of the protein, i.e., capable of adopting necessary binding configurations, would be a more likely interacting spot. Locking in a less flexible and more specific configuration upon binding with a target molecule is also anticipated and quite typical, e.g., when ligands interact with target proteins, thereby blocking their function. The authors themselves recognize this paradigm as referring to the enzymes' dynamics. It would be great if authors could comment more on dynamics-function relation, referring to the existing literature, where such observations were/were not observed for different protein families. Performing simulations on proteins that do not exhibit such a feature and do not belong to LTPs, but, e.g., structurally similar to some of the studied LTPs, would be an excellent addition too, highlighting this signature characteristic of LTPs.

We have now added a discussion comparing the mechanism we observe with those described for other proteins such as membrane transporters and receptors. Since those proteins are very different and have been already thoroughly characterized (including with molecular simulations) we don’t think that additional simulations are required. Also, concerning protein binding dynamics, we refer to the excellent review of Wade and coworkers: "Acc. Chem. Res. 2016, 49, 5, 809–815"

"____Notably, the conformational plasticity we observe for LTPs is reminiscent of other, previously described, functional protein mechanisms, including enzyme dynamics during catalysis (____DOI: 10.1126/science.1066176____), the alternating-access model of membrane transporters (____https://doi.org/10.1038/nsmb.3179____) or GPCR dynamics (____https://doi.org/10.1021/acs.chemrev.6b00177____). In all these cases, protein dynamics is strongly coupled to ligand binding (____https://doi.org/10.1021/acs.accounts.5b00516____) and protein function, be it for signaling, transport or enzymatic activity. Unlike for these fields, however, the contribution of structural and spectroscopic studies to uncover LTP dynamics remains quite limited, and our simulations provide an important contribution to fill this gap. We hope that our results will motivate researchers to increase efforts to experimentally quantify LTPs conformational plasticity, e.g. by structural determination of LTPs in different states (or bound to different lipids) or by single-molecule spectroscopy studies."

*Minor comments: *

*

1) Fig 1d. What is so special in Lysine compared to Arginine? Is there any disbalance in their presence in studied proteins? Any correlations between the binding affinity of certain amino acids and their overall presence on the protein surface? *

Indeed, there is disbalance in the presence of lysine and arginine residues in our proteins. The relation between the number of these residues in our dataset is Lys:Arg = 1.6:1. On top of that, and as described in (Tubiana T et al PLoS Comput Biol. 2022 ;18(12):e1010346) lysine is preferred over arginine in peripheral membrane proteins, likely because it induces fewer perturbations in the lipid bilayer. Our data also agree with Tubiana et al, concerning the correlation between abundance of specific residues on the protein surface and membrane binding.

* 2) Fig S1. GM2A and TTPA seem to be irreversibly adsorbed to the membrane on the microsecond timescale in most replicas. Is anything special in these proteins? Did this affect the sampling of a claimed membrane-binding interface?*

Our interpretation of the different adsorption profile of GM2A and TTPA is that these two proteins appear to have higher membrane affinity in our computational assay in comparison with the other proteins in our dataset. However, this has no effect on the membrane-binding interface as the proteins are still able to undergo significant tumbling before binding to the lipid bilayer, as demonstrated by the angle between the two main protein axes and the bilayer normal before membrane binding (Fig. S8 in Supplementary Information).

* 3) A related follow-up question. Multiple replicas were performed to identify the membrane-binding interface. However, if I understand well, the initial orientation of the protein with respect to the membrane was always the same. I found it a pity since performing multiple replicas starting from different initial geometries (e.g., rotating the protein in a somewhat systematic way) would likely result in a more efficient exploration of the conformation space. Can the authors comment on whether this predefined initial configuration could negatively affect the results? Performing a few additional simulations for the most problematic proteins I mentioned earlier (GM2A and TTPA) could be a nice opportunity to apply this strategy. *

In our protocol, all proteins start from the same initial orientation but undergo significant tumbling in solution before interacting with the lipid bilayer, including for the two most extreme cases, GM2A and TTPA (Fig. R1). Hence, we think that there is no bias for what pertains to the final membrane interacting region. We have added the Fig. R1 in Supplementary Information (Fig. S8) and added the following text in the Methods Section:

"____Despite starting from a single orientation, all proteins undergo extensive tumbling before binding to the bilayer, as illustrated by the angle between the two principal protein axes and the membrane normal for the two proteins that display the highest binding propensity, GM2A and TTPA (Fig. S8)."

* 4) How was the volume of the cavity affected by mutations in STARD11 and Mdm12? Do these data somehow correlate with the experimentally observed reduced efficiency of the lipid transfer? *

Our data on the volume of the cavity in STARD11 and Mdm12 are inconclusive. However, we caution from such a simplistic interpretation, since it completely neglects the lipid-bound conformation that normally has a much larger cavity than the apo form (Fig. 3).

*5) I would appreciate it if the authors considered playing with the templates of the main Figures at later stages because in the current version, and when printed on A4 paper, the readability of certain graphs and pictures is uncomfortable and sometimes even impossible. Obviously, the final schematics would depend on the journal and its formatting. *

We will modify the templates of the main Figures to improve readability according to journal formatting.

* **Referees cross-commenting** *

* I would like to acknowledge the thoughtful and detailed reviews provided by other reviewers. I do like their reports, and I believe that by addressing the reviewers' comments and incorporating their revisions, the article will significantly improve in terms of scientific rigor and contribution to the field. *

*Reviewer #1 (Significance (Required)):

This manuscript is a solid scientific work addressing gaps in our knowledge about Lipid Transfer Proteins by employing state-of-the-art methods. It advances the field on conceptual and fundamental levels. This study is of interest to both computational biophysicists and physical chemists (to whom I belong myself) as well as experimentalists, who seek a rational explanation of the experimental observations. *

We thank the reviewer again for the positive evaluation of the significance of our work.

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

* Summary:

In a combined computational and experimental study, the authors provide insights into general features of lipid transfer proteins (LTPs), which play key roles in lipid trafficking: Through molecular dynamics simulations of a diverse set of 12 shuttle-like LTPs, they demonstrate that LTPs consistently exist in an equilibrium between two or more conformations, whose populations are modulated by a bound lipid, and that residues significantly involved in these collective conformational changes typically interact with a membrane. Their simulations indicate that conformational plasticity is a general feature of LTPs, leading them to suggest that the ability to change conformations is essential for LTP function. They test the generality of this hypothesis through in cellulo assays of two LTPs (STARD11 and Mdm12) that were not originally simulated. While experiments of STARD11 support their hypothesis, those presented for Mdm12 provide ambiguous results. *

*

Major comments: *

* Throughout the manuscript, it's stated that common 'dynamical features' correlate with LTP function. The accuracy of this statement is unclear since 'dynamical features' are never precisely defined and, while equilibrium conformational ensembles are characterized, dynamics (ie kinetics or time-dependent observables) are not. Please clarify.*

We plan to improve the scholarly presentation of our article to clarify this issue. In short, two distinct properties modulate protein function: 1. Conformational plasticity, i.e. the (thermodynamic) ability of the protein to adopt different conformations (and with different populations depending on the bound substrate). 2. Conformational “dynamics”, i.e. the propensity to exchange between these different thermodynamic states. This ability depends on the free energy barriers between different states and it is intrinsically a kinetic (rather than thermodynamic) property.

*More importantly, further evidence is needed to determine a correlation with *function*. LTPs are suggested to have faster transfer rates (a measure of function) if the apo form adopts a substantial population of holo-like conformations, akin to enzyme preorganization. This is further tested by rationally mutating STARD11 and Mdm12. However, the support for this conclusion and if these mutations alter the LTPs conformational ensembles as desired is unclear: *

In our opinion, the interpretation suggested by Reviewer #2 that there is a “correlation” between transfer rates and the overlap of apo-like and holo-like conformations, though fascinating, cannot be derived from the available data at this stage, and we did not mean to imply as such. Rather, lipid transport is a complex phenomenon that involves several steps (membrane binding/unbinding, lipid uptake/release,…). Our simulations indicate that protein conformational plasticity, including potentially the overlap between apo-like and holo-like conformations, also influences lipid transfer rates. We will clarify this aspect in the text.

* Is there a quantitative correlation between the overlap of apo and holo conformational distributions (as could be quantified by KL divergence or Wasserstein distance, for example) and difference in transfer rates as suggested by Fig S6?*

We plan to compute quantitative correlation between apo and holo conformational distribution for Fig.S6 and for mutant simulations (see answer below) but, as discussed above, we are skeptical that we will observe a clear correlation.

* The conclusion and the generality of the findings would be greatly strengthened if a correlation can be shown for other LTPs through additional simulations of mutants whose transfer rates have been previously characterized experimentally in the literature. (For example: Ryan 2007 PMID 17344474, Grabon 2017 PMID 28718450, Iaea 2015 PMID 26168008, among many others)*

We are currently running simulations of several mutants to address this point and provide additional data/context.

* While differences in the apo conformational ensembles of the WT and mutants are observed in Fig S7b and d, if these mutations reduce overlap with holo-like conformations is not determined. Simulations of the WT holo forms are needed to properly test this hypothesis. *

We are currently performing these simulations.

• For Mdm12, mutations are specifically made to "lock the protein in the apo-like state;" however, the mutant adopts conformations distinct from the apo form as show in Fig S7d. How do the authors interpret the results of the cellular assays considering this and could it help explain why the mutant has similar kinetics to WT? What may explain the puzzling results of similar transfer kinetics but differing mitochondrial morphology? *

As discussed above, interpretation of lipid transport rates based exclusively on apo and holo conformational population is premature, as this is a complex mechanism that depends on many variables. For what concerns the experimental results, we think three explanations are possible: 1. Mitochondrial morphology could be more sensitive to small variations in lipid composition than our METALIC assay. 2. Our assay only quantifies transport of unsaturated PC and PE species, and we can’t quantify variations in transport of other lipid species that are likely to also be transported by ERMES, such as PS and PA. 3. According to a recent structural model (Wozny et al, Nature 618, 88–192, 2023), Mdm12 might be part of a tunnel-like LTP complex in which it doesn't establish direct interactions with nearby organellar membranes. As such, its mechanism might be different from the one described here for other shuttle-like lipid transport domains. We will discuss these possibilities in the main text.

• Confounding factors potentially complicate the interpretation of the in cellulo experiments. Simpler in vitro experiments may be better suited to determine if altering LTP's biophysical properties, namely rationally altering the population of apo- vs holo-like configurations, quantitatively affects transport rates as suggested.*

We agree with Reviewer #2 that this information could be useful. However, this is beyond our technical abilities, and it would require lengthy and expensive experiments that are unlikely to be completed within a reasonable time framework for a revision (3 months). We have rather opted to better discuss our model in the context of published in vitro lipid transport experiments.

• The abstract, intro, and title highlight that the manuscript's findings are indicative of and correlated with *function* but on p. 12 it's foreseen "that future studies will focus on the functional consequence of such observation." Please reconcile these conflicting statements and ensure connections to function are accurately described. The current title is rather bold. *

We will rewrite and clarify the extent of our hypotheses and validations.

* All mentions of "correlation" throughout the manuscript need to be quantitatively evaluated or properly qualified. In addition to that mentioned above regarding Fig S6, what is the correlation coefficient between residues' contribution to PC1 and membrane interaction frequency (Fig 2)? *

To address this point, we will quantify the correlation between residues' contribution to PC1 and membrane interaction frequency. However, we expect a low correlation between residues' contribution to PC1 and membrane interaction frequency for at least two main reasons. __ First, not all residues contributing to PC1 interact with membranes, but only a subset, as discussed above. Second, our methodology to compute membrane binding, based on the geometric distance between residues and bilayer, is intrinsically quite noisy (since residues in proximity of bona fide membrane binding regions will also appear as involved in membrane binding), thus making quantification of correlations somewhat inaccurate. Rather, we will try to explain in the text that our observations are not of "correlation" but rather of dependence/association, and we will use quantitative measures to quantify these properties (such as rank correlation coefficients or multivariate analyses).__

* Residue's contributions to collective conformational changes are found to be indicative of membrane binding. Yet, membrane interacting residues are identified from CG simulations that cannot capture such collective conformational changes due to the use of an elastic network. Given that the CG simulations agree with previous experimental findings, this suggests that collective conformational changes are not important for membrane binding. *

We disagree with this interpretation by Reviewer #2 of our data: we do not claim that residue's contributions to collective conformational changes is indicative of membrane binding. Rather, membrane binding happens at protein regions displaying high contribution to collective conformational changes. This distinction is subtle but important: protein motion does not determine membrane binding regions. Rather, it appears that, for LTPs, membrane binding regions are also characterized by collective motions (suggesting function). We will clarify this in the main text.

*Are similar conclusions drawn from residues' RMSFs? In other words, are local conformational fluctuations just as indicative of membrane binding? *

We will compute protein residues’ RMSFs and compare it with the membrane binding data. However, given that RMSF is representative of thermal fluctuations, we again expect a bad correlation between RMSF and membrane binding. On the other hand, we indeed observe that most membrane binding regions are protein loops, but this is not unexpected (e.g. Tubiana et al, PLoS Comput Biol. 2022 Dec; 18(12): e1010346.). However, such observation does not provide any information on lipid transport, but only on the mechanism of membrane binding. Rather, the observation of a relationship between membrane binding and global motion is more interesting, since the latter is often indicative of protein function.

*The stated correlation may in fact be spurious and instead arise because residues at the entrance to LTP's hydrophobic cavities need to be positioned at the membrane surface for productive lipid uptake and these same residues must undergo significant conformational changes to allow lipid entry. *

This is exactly what we think it is happening and what our data suggest. However, one must remember that our simulations allow us to predict the membrane binding interface, that is often difficult to determine experimentally (and often via indirect evidence). Hence our data provide novel evidence in this direction.

*Is proximity to cavity entrance more or less correlated with membrane binding than 'dynamics'? *

If we consider that, as discussed before, dynamics does not correlate with membrane binding (there are many dynamical regions that are not at the membrane interface), it is safe to assume that proximity to cavity entrance would correlate more with membrane binding. However, we have to consider that often we do not know where the cavity entrance in LTPs is located simply based on structure alone, and hence our approach provides important clues into this process.

p.12 speculatively suggests "the high degree of protein dynamics we observed in membrane proximal regions could potentially facilitate the energetically unfavorable reaction that involves the extraction of a lipid from a membrane." Yet, the logic behind this idea does not make sense since a free energy barrier, an equilibrium thermodynamic quantity, cannot be lowered by changes in dynamics. Please explain.*

Our current understanding of the mechanism of lipid extraction is quite poor. However, both using chemical intuition and following a recent MD study on one LTP (Rogers et al, 2023, Plos Comp Biol), it is safe to assume that the hydrophobic environment around the lipid is important for its stabilization in the lipid bilayer. Hence, reducing the number of hydrophobic contacts between the lipid and its environment could facilitate transport. A highly dynamic protein, by cycling between different conformations, could “stir” the bilayer, and hence decrease the number of contacts between the lipid and its environment favoring transport. We will clarify this point in the text.

*Examining how the LTPs impact membrane properties would offer insight into the functional relevance of such residues for lipid extraction. *

Indeed, our point above is connected to this one. We are performing simulations to compute hydrophobic contacts in bilayer as proposed in (Rogers et al, 2023, Plos Comp Biol).

The authors highlight that a bound lipid alters LTPs' conformational ensembles akin to "conformational selection" or "induced fit." How sensitive are these findings to the bound lipid species? Do LTPs with multiple known substrates exhibit an increasing diversity of holo conformations and are different conformations stabilized by different substrates? Would similar observations (Fig 3) be made with a lipid that is not known to be transferred by a given LTP? An interesting future direction would be to examine if lipid substrate specificity could be assessed by comparing conformational ensembles to that of a known substrate and/or by overlap with the apo ensemble.

We deem that the role of lipid specificity on LTP conformational plasticity is beyond the scope of the current work. While this topic is certainly worth future investigations, we must point out that (i) not all proteins bind/transport multiple lipids (at least according to current knowledge) and (ii) only few LTPs have been structurally characterized bound to different lipids (Osh4, Osh6, …). This limitation prevents a wide generalization, and we prefer not to speculate on this topic. So far, we have tested our approach for Osh4 bound to cholesterol or PI(4)P and found that indeed the protein exhibits different holo conformations (in agreement with the experimental data) when bound to different substrates. We have added a short comment on this topic in the Discussion section.

"____We foresee that future studies will focus on the functional consequence of such observation, and most notably to the characterization of the extent to which such conformational changes affect multiple steps of protein function, including membrane binding or lipid extraction and release, and whether these are further modulated when different lipids are being transported."

For LTPs to transfer lipids between membranes, transitions between apo and holo forms ought to occur when LTPs are membrane bound. How does membrane binding influence the conformational ensembles observed in solution? Does it promote conformational changes between apo- and holo-like structures, as suggested to regulate lipid uptake and release by previous studies of Osh/ORP, Ups/PRELI, and START family members? (For example: Miliara 2019 PMID 30850607, Watanabe 2015 PMID 26235513, Grabon 2017 PMID 28718450, Iaea 2015 PMID 26168008, Kudo 2008 PMID 18184806, Dong 2019 PMID 30783101) While answering these questions would require further computational effort, doing so will allow more accurate assessment of the role of conformational changes in LTP function.

We can’t unfortunately currently quantify how membrane binding influences the conformational ensembles observed in solution, as the slowdown in diffusion at the water-membrane interface makes this task computationally challenging (and certainly not feasible within the time framework of a review). We have so far tested two different proteins and have not succeeded in converging their conformational distribution when membrane-bound despite long MD simulations that lasted several months (even though the non-converged data indicate sampling of both “open” and “closed” conformations). Interestingly, our observations are in qualitative agreement with a recent study on CPTP (Rogers et al, PLOS Comp Biol, 2023), where membrane-bound CPTP is able to sample different conformations (“open” and “closed”) but not to transition between the two states in 300 ns-long MD simulations.

* The authors motivate the study with the *assumption* that a common molecular mechanism of LTP function exists. Yet LTPs have evolved diverse sequences, structures, and substrate preferences; thus there seems to be no a priori requirement (or even necessarily a benefit) for a single molecular mechanism. What evidence then supports this premise? While previous studies are limited to individual LTPs, when viewed altogether retrospectively, they suggest features that could be shared among LTPs. Synthesizing previous studies and more thoroughly referencing them (only 5 are cited in the intro on p. 3) would strengthen both the premise and findings of the manuscript. *

Indeed, despite having different structures, substrates and the ability to target distinct organelles, previous evidence on LTPs seem to suggest a potential role for protein conformational plasticity for function, e.g. for Osh/ORP (Jun Im et al, Nature 2005; Canagarajah et al, JMB 2008; Moser von Filseck et al, Nat Comm, 2015; Lipp et al, Nat Comm. 2019,...), StART (Arakane et al, PNAS, 1996; Feng et al, Biochemistry, 2000; Grabon et al, JBC, 2017; Khelashvili et al, eLife, 2019;...) and PITP domains (Tremblay et al, Archives of Biochemistry and Biophysics, 2005; Ryan et al, MBOC, 2007; …). Our simulations provide additional evidence in this direction and allow for generalizing these observations, allowing to draw parallelisms with “enzyme-like” or transporter-like” features that could be exploited for further design of testable hypotheses. We will rewrite our text to better contextualize/acknowledge previous findings and to clarify these points.

*The LTPs investigated are known to target distinct membranes. Should they then be expected to share structural or sequence-based features predictive of membrane binding interfaces, as motivates the analysis in Fig 1d, 1e, and S3? Or is it beneficial for LTPs to recognize membranes in different ways? *

Since membrane binding is membrane/organelle-specific, it is possible that residue’s diversity in membrane binding interfaces could indeed be beneficial for this diversity. We will add this comment as a potential explanation of our finding of a lack of conserved sequence-based features for membrane binding interfaces.

*

Minor comments:*

* 2 "making lipid transfer across the cytoplasm a potentially energetically favorable process": Is it meant that it is less energetically costly than transfer without a LTP? Why it would be energetically favorable is unclear (and would indicate that the LTP sequesters lipids away from membranes instead of transferring them between membranes). *

Yes, this is what we meant. We will rewrite this appropriately.

* 3 "The excellent agreement between the membrane interface determined from the simulations and the experimentally-proposed one available for... Osh6" is missing a citation. *

We have now added the relevant citation.

* The plots in Fig 1d and S3 are difficult to interpret. Bar plots, for example, would allow easier comparison and evaluation. Currently, it seems that most proteins individually exhibit some of the same trends observed among the whole set, counter to the conclusion on p 5. *

We will improve the presentation of our Figures.

* Negatively charged residues engage in a number of membrane interactions (Fig 1d and S3). What is a potential explanation for this unconventional observation? *

One possible interpretation is that negatively charged residues could interact with positively charged moieties (ethanolamine, choline) of PC and PE lipids.

* How much variance is captured by PC1, and how many PCs are needed to capture most of the variance in the conformations? *

PC1 explains 38 % of the total variance, by average, whereas PC2 accounts for 17 % of it. Therefore, PC1 and PC2 capture most of the variance in almost all cases.

We have also added this to the text:

"____We specifically focused on PC1 as it explains most of the variance in the dynamics (38% on average for all the proteins in our dataset, see Supplementary Table 2).____ "

We have computed this variance and we have added this analysis in Supplementary Information.

* Plots in Fig 3, especially panels c and d are difficult to see. Please make the panels larger (perhaps a 3 x 4 layout instead of 2 x 6 would work better). *

We will improve the presentation of our Figures.

* 8 "these conformational changes are localized in protein regions that interact with the lipid bilayer" is contradicted by the results in Fig 2b showing that all residues with large contributions to PC1 do not interact with the membrane and discussed on p 5. *

As discussed above, we don’t observe “correlation” between membrane binding and conformational plasticity, but we rather observe that membrane binding regions display high conformational plasticity (the opposite is not true). We will further clarify in the text.

*

8 "in the absence of bound lipids, it is able to sample multiple conformations" is not supported by the orange distributions in Fig 3d that appear unimodal. Is it instead meant that the apo form exhibits larger variance in cavity volume? *

Yes, this is what we meant. We’ll clarify.

*

Please clarify if the elastic network was constructed to maintain the holo or apo structures of each protein and if a bound lipid was used in the CG simulations. *

For membrane binding CG simulations, we used the apo structure and no bound lipid was used in the simulations. However, analogous simulations in the holo form (not shown) have essentially identical membrane binding interfaces.

*

Was *CHARMM* TIP3P used? *

Yes.

* Please clarify how membrane interacting residues were defined and how interaction frequency was calculated from the longest duration of interaction. *

We will add this explanation in the Methods. The method is identical to (Srinivasan et al, Faraday Discussion, 2021).

* Refs 16 and 45 refer to the same paper. *

Thanks, it is now corrected!

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

* General assessment: *

* The work aims to tackle a grand question regarding membrane homeostasis mechanisms-what are universal principles underlying LTP function-and offers initial insights; however, further evidence is needed to support the conclusions as written, and some key results require further investigation and explanation. *

*Advance and audience: *

*

By concurrently investigating the largest number of lipid transfer proteins to-date, the authors provide data invaluable for uncovering general mechanisms of non-vesicular lipid transport and advancing our understanding of membrane homeostasis mechanisms. By illuminating the wide-spread importance of conformational plasticity among lipid transfer proteins, the work presents a conceptual advance in our understanding of lipid transfer mechanisms and unifies previous studies. Because the manuscript emphasizes common biophysical principles and draws connections to enzyme biophysics, it ought to be of interest not only to membrane biologists but biochemists and molecular biologists more broadly.*

We thank Reviewer #2 for the very positive evaluation of the significance of our work and for the in-depth analysis provided that will certainly help improve the quality of our work.

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

*The article "Conformational dynamics of lipid transfer domains provide a general framework to decode their functional mechanism." by Sriraksha Srinivasan, Andrea DiLuca, Arun Peter, Charlotte Gehin, Museer Lone, Thorsten Hornemann, Giovanni D'Angelo and Stefano Vanni study the interaction of Lipid transport Domains with membranes. This is done mainly by molecular modelling but also with selected experimental validations. *

* Major comments: *

* - The key conclusions are generally well supported by the analysis. - The authors could however analyze in more details some aspects in which specific cases appear. For example, p3 "multiple binding and unbinding events, as shown by the minimum distance curves" does not give an entire description of the variability seen in Fig S1, e.g. LCN1 versus GM2A.*

We now discuss in more detail the variability seen in Fig. S1 and attribute it to different membrane binding affinities of the proteins in our dataset. We also discuss how this variability could reflect the diversity of organellar membranes to which these proteins bind in vivo.

"____Notably, the proteins in our dataset display distinct binding affinities, with some proteins showing very transient binding while others remain membrane-bound for most of the simulation trajectory (Fig. S1). This behavior could be, in part, attributed to the wide diversity of organellar membranes to which the LTDs in our dataset bind to in vivo, and to the comparative simplicity of our in silico model DOPC lipid bilayers."

• Later the "excellent agreement" for the data in Fig S2 is not quantified which does not allow the reader to know whether it better than would have been with other methods (SASA, OPM, DREAM). *

We have explicitly quantified this agreement by providing a direct comparison between the experimental results and our in silico assay, and we further compared it against two alternative methods: OPM and DREAMM. In detail, we have identified 12 experimentally-characterized spots suggested to be involved in membrane binding in our protein dataset (see shaded blue regions in Fig. S2). Of those 12, our method identifies all of them (100%), while DREAMM identifies 7 of them (58 %) and OPM 4 out of 8 (50 %), since of the 12 proteins we tested, only 7 are available in the OPM database. Overall, even if our approach is much noisier than the others, and thus suggesting multiple binding regions that are not currently supported by experimental observations, using physics-based methodologies appears to remain a preferable strategy to characterize the binding of peripheral proteins to lipid bilayers. Given the limited size of our dataset, we prefer not to make a direct comparison between our assay and OPM/DREAMM in the main text as this won't be representative of the various methodologies.

*p5 commenting on Fig2b the case of Osh6 that appears to disagree should probably be mentioned. *

We now discuss this case, and attribute to this disagreement to insufficient sampling for the peculiar case of Osh6:

"____One interesting exception in our database appears to be Osh6, where the experimentally determined membrane-binding region at the N-terminus (https://doi.org/10.1038/s41467-019-11780-y) is only marginally binding to the lipid bilayer in silico and it also appears to have limited contribution to PC1. However, our simulations are unable to sample the large conformational changes that the N-terminal lid of Osh6 has been proposed to undergo from its lipid-bound to its apo state, indicating that insufficient sampling could be the reason for this apparent discrepancy."

*

-The data and the methods are generally well presented allowing to be reproduced.

• The experiments adequately replicated with adequate statistical analysis. *

* Minor comments: *

* - When presenting the dataset the authors could probably detail a bit more the protocol undertaken to chose the cases. In particular it is unclear whether the chosen proteins have any membrane selectivity, which in principle could be affected by the choice of lipid used here.*

We have now added in Table 1 a column with a list of potential organelles the different LTPs have been shown to localize to (source: UniProt). As model membrane bilayer, we opted to use a pure DOPC bilayer, for both simplicity and to compare membrane binding in a uniform setting. We foresee that future studies investigating the membrane specificity of the various proteins will shed further light into the molecular mechanism of LTPs. Finally, we also indicate that our choice of proteins was mainly driven by the availability of lipid-bound structures in the protein data bank. We have added the following sentences in the main text:

"____Specifically, we selected all LTPs for which a crystallographic structure in complex with a lipid was available at the start of our project, plus two additional proteins (GM2A and LCN1) to increase the structural diversity of our dataset (Fig. 1a)"

and

"____Notably, the proteins in our dataset display distinct binding affinities, with some proteins showing very transient binding while other remain membrane-bound for most of the simulation trajectory (Fig. S1). This behavior could be, in part, attributed to the wide diversity of organellar membranes to which the LTDs in our dataset bind to in vivo, and to the comparative simplicity of our in silico model DOPC lipid bilayers."

*- The authors could probably give some indication of how much of the variance is explained by PC1 and comment briefly on the choice to ignore other PCs. *

PC1 explains 38 % of the total variance, on average. This means that PC1 has a large contribution to the variance, especially in comparison to the other PCs. For instance, PC2 only accounts for 17 % of the total variance. This is the reason we limited our discussion to PC1. We have added a table in supplementary Information quantifying the variance explained by PC1 and PC 2 and added the following sentence in the main text:

"____We specifically focused on PC1 as it explains most of the variance in the dynamics (38% on average for all the proteins in our dataset)____. "

* - When analyzing the residues involved in the interaction with the membrane the results could probably be compared with that of the systematic analysis performed recently: Tubiana, T., Sillitoe, I., Orengo, C., & Reuter, N. (2022). Dissecting peripheral protein-membrane interfaces. PLOS Computational Biology, 18(12), e1010346. *

We have added in the text a reference to the work by Tubiana et al and we have further stressed that our results agree with previous observations (including theirs). This includes the preference for Lys over Arg and the importance of protruding hydrophobes:

"____Concomitant analysis of all LTDs (Fig. 1d) indicates that the membrane binding interface of LTDs is enriched in the positively charged amino acid Lysine, as this amino acid is less membrane-disruptive than Arginine22, and aromatic/hydrophobic ones (Phe, Leu, Val, Ile). This confirms previous observations, as (i) binding of negatively charged lipids via positively charged residues and (ii) hydrophobic insertions are two of the main mechanisms involved in membrane binding by peripheral proteins22-27."

* - In the discussion on allostery/conformational selection might not be centered so much on enzymes. *

We thank the reviewer for this important observation. We have now included in the Discussion the following paragraph that provides additional references and discussion of membrane transporters and receptors.

"____Notably, the conformational plasticity we observe for LTPs is reminiscent of other, previously described, functional protein mechanisms, including enzyme dynamics during catalysis (____DOI: 10.1126/science.1066176____), the alternating-access model of membrane transporters (____https://doi.org/10.1038/nsmb.3179____) or GPCR dynamics (____https://doi.org/10.1021/acs.chemrev.6b00177____). In all these cases, protein dynamics is strongly coupled to ligand binding and protein function, be it for signaling, transport or enzymatic activity. Unlike for these fields, however, the contribution of structural and spectroscopic studies to uncover LTP dynamics remains quite limited, and our simulations provide an important contribution to fill this gap. We hope that our results will motivate researchers to increase efforts to experimentally quantify LTPs conformational plasticity, e.g. by structural determination of LTPs in different states (or bound to different lipids) or by single-molecule spectroscopy studies."

* Reviewer #3 (Significance (Required)): *

*

The article shows convincing results on the debated issue of the mechanism of lipid transport by lipid transfer proteins. *

First the study employs molecular modelling to allow a rather large test on 12 cases. The molecular dynamics experiments allow the authors to draw clear hypotheses on role of protein dynamics on the interaction with membranes and the effect on bound lipids on the modification of this dynamics.

*Then the authors use this knowledge to design experiments that largely confirm those hypotheses. The results should therefore be interesting for a large audience of biochemists and cell biologists interested in lipid transport in the cell. *

We thank Reviewer #3 for its very positive evaluation and contextualization of our work.

Some people believe that our feelings about love and our bodies are shaped when we are very young and that can affect us as we grow up. But this idea only focuses on the family and doesn't think about how other things and experiences in life can also shape our feelings. So, it's important to remember that there are many things that can make us feel different and that how we feel is not only because of our family.

Author Response:

Reviewer #1 (Public Review):

[…] The major strength of the study is the elegant and well-powered data set. Longitudinal data on this scale is very difficult to collect, especially with patient cohorts, so this approach represents an exciting breakthrough. Analysis is straightforward and clearly presented. However, no multiple comparison correction is applied despite many different tests. While in general I am not convinced of the argument in the citation provided to justify this, I think in this case the key results are not borderline (p<0.001) and many of the key effects are replications, so there are not so many novel/exploratory hypothesis and in my opinion the results are convincing and robust as they are. The supplemental material is a comprehensive description of the data set, which is a useful resource.

The authors achieved their aims, and the results clearly support the conclusion that the AD and mean confidence in a perceptual task covary longitudinally. I think this study provides an important impact to the project of computational psychiatry.Sspecifically, it shows that the relationship between transdiagnostic symptom dimensions and behaviour is meaningful within as well as across individuals.

Response: We thank the reviewer for their appraisal of our paper and positive feedback on the main manuscript and supplementary information. We agree with the reviewer that the lack of multiple comparison corrections can also justified by key findings being replications and not borderline significance. We have added this additional justification to the manuscript (Methods, Statistical Analyses, page 15, line 568: “Adjustments for multiple comparisons were not conducted for analyses of replicated effects”)

Reviewer #2 (Public Review):

[…] The major strength and contribution of this study is the use of a longitudinal intervention design, allowing the investigation of how the well-established link between underconfidence and anxious-depressive symptoms changes after treatment. Furthermore, the large sample size of the iCBT group is commendable. The authors employed well-established measures of metacognition and clinical symptoms, used appropriate analyses, and thoroughly examined the specificity of the observed effects.

However, due to the small effect sizes, the antidepressant and control groups were underpowered, reducing comparability between interventions and the generalizability of the results. The lack of interaction effect with treatment makes it harder to interpret the observed differences in confidence, and practice effects could conceivably account for part of the difference. Finally, it was not completely clear to me why, in the exploratory analyses, the authors looked at the interaction of time and symptom change (and group), since time is already included in the symptom change index.

Response: We thank the reviewer for their succinct summary of the main results and strengths of our study. We apologise for the confusion in how we described that analysis. We examine state-dependence., i.e. the relationship between symptom change and metacognition change, in two ways in the paper – perhaps somewhat redundantly. (1) By correlating change indices for both measures (e.g. as plotted in Figure 3D) and (2) by doing a very similar regression-based repeated-measures analysis, i.e. mean confidence ~ time*anxious-depression score change. Where mean confidence is entered with two datapoints – one for pre- and one for post-treatment (i.e. within-person) and anxious-depression change is a single value per person (between-person change score). This allowed us to test if those with the biggest change in depression had a larger effect of time on confidence. This has been added to the paper for clarification (Methods, Statistical Analysis, page 14, line 553-559: “To determine the association between change in confidence and change in anxious-depression, we used (1) Pearson correlation analysis to correlate change indices for both measures and, (2) regression-based repeated-measures analysis: mean confidence ~ time*anxious-depression score change, where mean confidence is entered with two datapoints (one for pre- and one for post-treatment i.e., within-person) and anxious-depression change is a single value per person (between-person change score)”).

The analyses have also been reported as regression in the Results for consistency (Treatment Findings: iCBT, page 5, line 197-204: ‘To test if changes in confidence from baseline to follow-up scaled with changes in anxious-depression, we ran a repeated measure regression analyses with per-person changes in anxious-depression as an additional independent variable. We found this was the case, evidenced by a significant interaction effect of time and change in anxious-depression on confidence (b=-0.12, SE=0.04, p=0.002)… This was similarly evident in a simple correlation between change in confidence and change in anxious-depression (r(647)=-0.12, p=0.002)”).

This longitudinal study informs the field of metacognition in mental health about the changeability of biases in confidence. It advances our understanding of the link between anxiety-depression and underconfidence consistently found in cross-sectional studies. The small effects, however, call the clinical relevance of the findings into question. I would have found it useful to read more in the discussion about the implications of the findings (e.g., why is it important to know that the confidence bias is state-dependent; given the effect size of the association between changes in confidence and symptoms, is the state-trait dichotomy the right framework for interpreting these results; suggestions for follow-up studies to better understand the association).

Response: Thank you for this comment. We have elaborated on the implications of our findings in the Discussion, including the relevance of the state-trait dichotomy to future research and how more intensive, repeated testing may inform our understanding of the state-like nature of metacognition (Discussion, Limitations and Future Directions, page 10, line 378-380: “More intensive, repeating testing in future studies may also reveal the temporal window at which metacognition has the propensity to change, which could be more momentary in nature.”).

Reviewer #3 (Public Review):

[…] I think these findings are exciting because they directly relate to one of the big assumptions when relating cognition to mental health - are we measuring something that changes with treatment (is malleable), so might be mechanistically relevant, or even useful as a biomarker?

This work is also useful in that it replicates a finding of heightened confidence in those with compulsivity, and lowered confidence in those with elevated anxious-depression.

One caveat to the interest of this work is that it doesn't allow any causal conclusions to be drawn, and only measures two timepoints, so it's hard to tell if changes in confidence might drive treatment effects (but this would be another study). The authors do mention this in the limitations section of the paper.

Another caveat is the small sample in the antidepressant group.

Some thoughts I had whilst reading this paper: to what extent should we be confident that the changes are not purely due to practice? I appreciate there is a relationship between improvement in symptoms and confidence in the iCBT group, but this doesn't completely rule out a practice effect (for instance, you can imagine a scenario in which those whose symptoms have improved are more likely to benefit from previously having practiced the task).

Response: We thank the reviewer for commenting on the implications of our findings and we agree with the caveats listed. We thank the reviewer for raising this point about practice effects. A key thing to note is that this task does not have a learning element with respect to the core perceptual judgement (i.e., accuracy), which is the target of the confidence judgment itself. While there is a possibility of increased familiarity with the task instructions and procedures with repeated testing, the task is designed to adjust the difficulty to account of any improvements, so accuracy is stable. We see that we may not have made this clear in some of our language around accuracy vs. perceptual difficulty and have edited the Results to make this distinction clearer (Treatment Findings: iCBT, pages 4-5, lines 184-189: “Although overall accuracy remained stable due to the staircasing procedure, participants’ ability to detect differences between the visual stimuli improved. This was reflected as the overall increase in task difficulty to maintain the accuracy rates from baseline (dot difference: M=41.82, SD=11.61) to follow-up (dot difference: M=39.80, SD=12.62), (b=-2.02, SE=0.44, p<0.001, r2\=0.01)”.)

However, it is true that there can be a ‘practice’ effect in the sense that one may feel more confident (despite the same accuracy level) due to familiarity with a task. One reason we do not subscribe to the proposed explanation for the link between anxious-depression change and confidence change is that the other major aspect of behaviour that improved with practice did so in a manner unrelated to clinical change. As noted above in the quoted text, participants’ discrimination improved from baseline to follow-up, reflected in the need for higher difficulty level to maintain accuracy around 70%. Crucially, this was not associated with symptom change. This speaks against a general mechanism where symptom improvement leads to increased practice effects in general. Only changes in confidence specifically are associated with improved symptoms. We have provided more detail on this in the Discussion (page 9, lines 324-326: “This association with clinical improvements was specific to metacognitive changes, and not changes in task performance, suggesting that changes in confidence do not merely reflect greater task familiarity at follow-up.”).

Relatedly, to what extent is there a role for general task engagement in these findings? The paper might be strengthened by some kind of control analysis, perhaps using (as a proxy for engagement) the data collected about those who missed catch questions in the questionnaires.

Response: Thank you for your comment. We included the details of data quality checks in the Supplement. Given the small number of participants that failed more than one attention checks (1% of the iCBT arm) and that all those participants passed the task exclusion criteria, we made the decision to retain these individuals for analyses. We have since examined if excluding these small number of individuals impacts our findings. Excluding those that failed more than one catch item did not affect the significance of results, which has now been added to the Supplementary Information (Data Quality Checks: Task and Clinical Scales, page 5, lines 181-185: “Additionally, excluding those that failed more than one catch item in the iCBT arm did not affect the significance of results, including the change in confidence (b=0.16, SE=0.02, p<0.001), change in anxious-depression (b=-0.32, SE=0.03, p<0.001), and the association between change in confidence and change in anxious-depression (r(638)=-0.10, p=0.011)”).

I was also unclear what the findings about task difficulty might mean. Are confidence changes purely secondary to improvements in task performance generally - so confidence might not actually be 'interesting' as a construct in itself? The authors could have commented more on this issue in the discussion.

Response: Thank you for this comment and sorry it was not clear in the original paper. As we discussed in a prior reply, accuracy – i.e. proportion of correct selections (the target of confidence judgements) are different from the difficulty of the dot discrimination task that each person receives on a given trial. We had provided more details on task difficulty in the Supplement. Accuracy was tightly controlled in this task using a ‘two-down one-up’ staircase procedure, in which equally sized changes in dot difference occurred after each incorrect response and after two consecutive correct responses. The task is more difficult when the dot difference between stimuli is lower, and less difficult when the dot difference between stimuli is greater. Therefore, task difficulty refers to the average dot difference between stimuli across trials. Crucially, task accuracy did not change from baseline to follow-up, only task difficulty. Moreover, changes in task difficulty were not associated with changes in anxious-depression, while changes in confidence were, indicating confidence is the clinically relevance construct for change in symptoms.

We appreciate that this may not have been clear from the description in the main manuscript, and have added more detail on task difficulty to the Methods (Metacognition Task, page 14, lines 540-542: “Task difficulty was measured as the mean dot difference across trials, where more difficult trials had a lower dot difference between stimuli.”) and Results (Treatment Findings: iCBT, pages 4-5, lines 184-186: “Although overall accuracy remained stable due to the staircasing procedure, participants’ ability to detect differences between the visual stimuli improved.”). We have also elaborated more on how improvements in symptoms are associated with change in confidence, not task performance in the Discussion (page 9, lines 324-326: “This association with clinical improvements was specific to metacognitive changes, and not changes in task performance, suggesting that changes in confidence do not merely reflect greater task familiarity at follow-up”).

To make code more reproducible, the authors could have produced an R notebook that could be opened in the browser without someone downloading the data, so they could get a sense of the analyses without fully reproducing them.

Response: Thank you for your comment. We appreciate that an R notebook would be even better than how we currently share the data and code. While we will consider using Notebooks in future, we checked and converting our existing R script library into R Notebooks would require a considerable amount of reconfiguration that we cannot devote the time to right now. We hope that nonetheless the commitment to open science is clear in the extensive code base, commenting and data access we are making available to readers.

Rather than reporting full study details in another publication I would have found it useful if all relevant information was included in a supplement (though it seems much of it is). This avoids situations where the other publication is inaccessible (due to different access regimes) and minimises barriers for people to fully understand the reported data.

Response: We agree this is good practice – the Precision in Psychiatry study is very large, with many irrelevant components with respect to the present study (Lee et al., BMC Psychiatry, 2023). For this reason, we tried to provide all that was necessary and only refer to the Precision in Psychiatry study methods for fine-grained detail. Upon review, the only thing we think we omitted that is relevant is information on ethical approval in the manuscript, which we have now added (Methods, Participants, page 11, lines 412-417: “Further details of the PIP study procedures that are not specific to this study can be found in a prior publication (21). Ethical approval for the PIP study was obtained from the Research Ethics Committee of School of Psychology, Trinity College Dublin and the Northwest-Greater Manchester West Research Ethics Committee of the National Health Service, Health Research Authority and Health and Care Research Wales”). If any further information is lacking, we are happy to include it here also.

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Reply to the reviewers

__Reviewer 1____: __

1-Localization of ESYT1 and SYNJ2BP

The claim of a localization at ER-mitochondria contacts relies on two type of assays. Light microscopy and subcellular fractionation. Concerning microscopy, while the staining pattern is obviously colocalizing with the ER (a control of specificity of staining using KO cells would nevertheless be desirable)

the idea that ESYT1 foci "partially colocalized with mitochondria" is either trivial or unfounded

Every cellular structure is "partially colocalized with mitochondria" simply by chance at the resolution of light microscopy

If the meaning of the experiment is to show that ESYT1 'specifically' colocalizes with mitochondria, then this isn't shown by the data

There is no quantification that the level of colocalization is more than expected by chance

nor that it is higher than that of any other ER protein

Moreover, the author's model implies that ESYT1 partial colocalization with mitochondria is, at least partially, due to its interaction with SYNJ2BP. This is not tested.

• To analyze and measure MERCs parameters and functions, we used a set of validated methods described in the following specialized review articles (Eisenberg-Bord, Shai et al. 2016, Scorrano, De Matteis et al. 2019).
• To support and confirm the localization of ESYT1-SYNJ2BP complex at MERCs, we performed supplementary BioID analysis using ER target BirA*, OMM targeted BirA* and ER-mitochondria tether BirA* (Table S1, Figure S1 and Figure 1 A and B). These results confirmed the specificity of the interaction of the 2 partners. ESYT1 is not identified as a prey in OMM BioID and SYNJ2BP is not identified in ER BioID, on the other hand both partners are identified in the ER-mitochondria tether BioID.
• To improve our description of the partial localization of ESYT1 at mitochondria, we performed a quantitative analysis using confocal microscopy on control human fibroblasts stably overexpressing SEC61B-mCherry as an ER marker which were labelled with ESYT1 and TOMM40 for mitochondria. We measured the % of ESYT1 signal colocalizing with mitochondria and the % of mitochondria positive for ESYT1 (Figure 1E).

• To demonstrate than ESYT1 partial colocalization with mitochondria is, at least partially, due to its interaction with SYNJ2BP, we performed a quantitative analysis using confocal microscopy. Human control fibroblasts, KO SYNJ2BP fibroblasts and SYNJ2BP overexpressing fibroblasts were labelled with ESYT1, TOMM40 for mitochondria and CANX for ER. We measured the % of ESYT1 signal colocalizing with mitochondria in each condition (Figure 3C). Membranes (MAM) can be purified and are enriched for proteins that localize at ER-mitochondria contacts. This idea originated in the early 90's and since then, myriad of papers has been using MAM purification, and whole MAM proteomes have been determined. Yet the evidence that MAM-enriched proteins represent bona fide ER-mitochondria-contact-enriched proteins (as can nowadays be determined by microscopy techniques) remain scarce. Here, anyway, ESYT1 fractionation pattern is identical to that of PDI, a marker of general ER, with no indication of specific MAM accumulation.

• To highlight the enrichment of ESYT1 in the MAM fraction, we quantified the ESYT1 signal in each fraction. Those results show a similar fractionation pattern than the MAM resident protein SIGMAR1 (Figure 1F). For SYNJ2BP, it is different as it is more enriched in the MAM than the general mitochondrial marker PRDX3. However, PRDX3 is a matrix protein, making it a poor comparison point, since SYNJ2BP is an OMM protein.

• To confirm the partial enrichment of SYNJ2BP in the MAM fraction compared to another outer mitochondrial membrane protein, we added the signal of the well characterized OMM protein CARD19 (Rios, Zhou et al. 2022). Again, the model implies that ESYT1 and SYNJ2BP accumulation in the MAM should be dependent on each other. This is not tested.

• As describe above, we demonstrated in Figure 3C than the accumulation of ESYT1 at mitochondria is, at least partially, dependent on the quantity of SYNJ2BP.

• We moreover showed a reciprocal effect in Figure 3E. A quantitative analysis using confocal microscopy demonstrated that the effect of SYNJ2BP overexpression on MERCs formation is partially dependent of the presence of ESYT1. 2-ESYT1-SYNJ2BP interaction.

The starting point of the paper is a BioID signal for SYNJ2BP when BioID is fused to ESYT1. One confirmation of the interaction comes in figure 4, using blue native gel electrophoresis and assessing comigration. Because BioID is promiscuous and comigration can be spurious, better evidence is needed to make this claim. This is exemplified by the fact that, although SYNJ2BP is found in a complex comigrating with RRBP1, according to the BN gel, this slow migrating complex isn't disturbed by RRBP1 knockdown, but is somewhat disturbed by ESYT1 knockdown. More than a change in abundance, a change in migration velocity when either protein is absent would be evidence that these comigrating bands represent the same complex.

• We showed in Figure 4C that the presence of SYNJ2BP in a complex of a similar molecular weight that ESYT1 (410KDa) is totally dependent of the presence of ESYT1, suggesting an interaction of the 2 proteins.

• To confirm this interaction, in figure 4A we analyzed on BN cells overexpressing SYNJ2BP together with a 3xFlag tagged version of ESYT1. As a result of the addition of the Flag tag, the complex positive for ESYT1 shifted to a higher molecular weight. The complex positive for SYNJ2BP shifted to a similar the molecular weight, demonstrating the interaction and dependence of the 2 partners. ESYT1-SYNJ2BP interaction needs to be tested by coimmunoprecipitation of endogenous proteins, yeast-2-hybrid, in vitro reconstitution or any other confirmatory methods.

• To confirm the interaction of the 2 partners, we performed co-immunoprecipitation of the ESYT1-3xFlag protein that we showed in Figure 1H to form complexes similar to the endogenous protein. SYNJ2BP is found as the strongest prey, followed by ESYT2 and SEC22B two described interactors of ESYT1, confirming the quality of the analysis (Table S2) (Giordano, Saheki et al. 2013, Gallo, Danglot et al. 2020). 3-Tethering by ESYT1- SYNJ2BP.

This is assessed by light and electron microscopy. Absence of ESYT1 decreases several metrics for ER-mitochondria contacts (whether absence of SYNJ2BP has the same effect isn't tested).

• Using PLA (proximity ligation assay) we demonstrated that the loss of SYNJ2BP leads to a decrease in MERCs (Figure 7 H and I), confirming previous studies (Ilacqua, Anastasia et al. 2022, Pourshafie, Masati et al. 2022). This interesting phenomenon could be due to many things, including but not limited to the possibility that "ESYT1 tethers ER to mitochondria".

This statement and the respective subheading title are therefore clearly overreaching and should be either supported by evidence or removed.

Indeed, absence of ESYT1 ER-PM tethering and lipid exchange could have knock-on effects on ER-mito contacts, therefore strong statements aren't supported.

Moreover, the effect on ER-mitochondria contact metrics could be due to changes in ER-mitochondria contact indeed but may also reflect changes in ER and/or mitochondria abundance and/or distribution, which favour or disfavour their encounter. Abundance and distribution of both organelles are not controlled for.

• The mitochondrial phenotypes caused by the loss of ESYT1 are all rescued by the introduction of an artificial mitochondrial-ER tether, demonstrating that they are due to loss of the tethering function of ESYT1. Finally, the authors repeat a finding that SYNJ2BP overexpression induces artificial ER-mitochondria tethering. Again, according to the model, this should be, at least in part, due to interaction with ESYT1. Whether ESYT1 is required for this tethering enhancement isn't tested.

• As described above, we demonstrated in Figure 3C that the accumulation of ESYT1 at mitochondria is, at least partially, dependent on the quantity of SYNJ2BP.

• We moreover showed a reciprocal effect in Figure 3F. A quantitative analysis using confocal microscopy demonstrated that the effect of SYNJ2BP overexpression on MERC formation is partially dependent of the presence of ESYT1. 4-Phenotypes of ESYT1/SYNJ2BP KD or KO.

The study goes in details to show that downregulation of either protein yields physiological phenotypes consistent with decreased ER-mitochondria tethering. These phenotypes include calcium import into mitochondria and mitochondrial lipid composition.

Figure 5 shows that histamine-evoked ER-calcium release cause an increase in mitochondrial calcium, and this increase is reduced in absence of ESYT1, without detectable change in the abundance of the main known players of this calcium import. This is rescued by an artificial ER-mitochondria tether. However, Figure 5D shows that the increase in calcium concentration in the cytosol upon histamine-evoked ER calcium release is equally impaired by ESYT1 deletion, contrary to expectation. Indeed, if the impairment of mitochondrial calcium import was due to improper ER-mitochondria tethering in ESYT1 mutant cells, one would expect more calcium to leak into the cytosol, not less.

The remaining explanation is that ESYT1 knockout desensitizes the cells to histamine, by affecting GPCR signalling at the PM, something unexplored here.

In any case, a decreased calcium discharge by the ER upon histamine treatment, explains the decreased uptake by mitochondria.

The authors argue that ER calcium release is unaffected by ESYT1 KO, but crucially use thapsigargin instead of histamine to show it. Thus, the most likely interpretation of the data is that ESYT1 KO affects histamine signalling and histamine-evoked calcium release upstream of ER-mitochondria contacts.

• Silencing ESYT1 impairs SOCE efficiency in Jurkat cells (Woo, Sun et al. 2020), but not in HeLa cells (Giordano, Saheki et al. 2013, Woo, Sun et al. 2020). Analysis of the role of ESYT1 in HeLa cells prevents confounding effects due to the loss of ESYT1 at ER-PM. In this model, knock-down of ESYT1 led to a decrease of mitochondrial Ca2+ uptake from the ER upon histamine stimulation, as monitored by genetically encoded Ca2+ indicator targeted to mitochondrial matrix (Figure 5A and B). ESYT1 silencing in HeLa cells did not impact ER Ca2+ store measured by the ER-targeted R-GECO Ca2+ probe (Figure 5C and D). The expression of the artificial mitochondria-ER tether was able to rescue mitochondrial Ca2+ defects observed in ESYT1 silenced cells (Figure 5B), confirming that the observed anomalies are specifically due to MERC defects.
• In contrast loss of ESYT1 impaired SOCE efficiency in fibroblasts (Figure 6 A and B). This phenotype was fully rescued by re-expression of ESYT1-Myc but not the artificial tether. We therefore investigated the influence of ESYT1 loss on cytosolic Ca2+ concentration following ATP (Figure 6F to H) or histamine stimulation (Figure S3 D to F), both of which showed a reduced cytosolic Ca2+ concentration and uptake in ESYT1 KO cells. This phenotype was fully rescued by the re-expression of ESYT1-Myc but not the artificial tether. Measurment of cytosolic Ca2+ after tharpsigargin treatment in Ca2+-fee media, an inhibitor of the sarco/endoplasmic reticulum Ca2+ ATPase SERCA that blocks Ca2+ pumping into the ER, showed that ESYT1 KO does not influence the total ER Ca2+ pool (Figure 6K and L). However, ER-Ca2+ release capacity upon histamine stimulation (Figure 6I and J) is decreased in ESYT1 KO cells. This phenotype was fully rescued by the re-expression of ESYT1-Myc but not the artificial tether. Loss of ESYT1 decreased the Ca2+ uptake capacities of mitochondria after activation with histamine (Figure S3 A to C) or ATP (Figure 6 C to E). This phenotype was rescued by re-expression of ESYT1-Myc and also the engineered ER-mitochondria tether. Thus, despite the ER-Ca2+ release defect observed after ESYT1 loss, the artificial tether fully rescued the mitochondrial phenotype.

• These results highlight the distinct and dual roles of ESYT1 in Ca2+ regulation at the ER-PM and at MERCs. The data with SYNJ2BP deletion are more compatible with decreased ER-mito contacts, as no decreased in cytosolic calcium is observed. This is compatible with the previously proposed role of SYNJ2BP in ER-mitochondria tethering, but the difference with ESYT1 rather argue that both proteins affect calcium signaling by different means, meaning they act in different pathways.

• We explain the different results concerning cytosolic calcium by the fact that ESYT1 is a bi-localized protein with dual functions on cellular calcium. Implicated both in SOCE at ER-PM and in mitochondrial calcium uptake at MERCs. On the other hand, SYNJ2BP is only present at MERCs and its loss do not influence PM-ER signaling or ER-Ca2+ release. Finally, the study delves into mitochondrial lipids to "investigated the role of the SMP-domain containing protein ESYT1 in lipid transfer from ER to mitochondria". In reality, it is not ER-mitochondria lipid transport that is under scrutiny, but general lipid homeostasis, and changes in ER-PM lipids could have knock-on effects on mitochondrial lipids without the need to invoke disruptions in ER-mitochondria transfer activity.

• The fact that the artificial tether, which specifically rescue MERCs, fully rescue the lipid phenotype argue for a direct loss of MERCs tethering function when ESYT1 is missing. The changes observed are interesting but could be due to anything. Surprisingly, PCA analysis shows that the rescue of the knockout by the ESYT1 gene clusters with the rescue by the artificial tether, and not with the wildtype. This indicates that overexpressing either ESYT1 or a tether cause similar lipidomic changes. These could be due, for instance, to ER stress caused by protein overexpression, and not to a rescue.

• In order to verify if the overexpression of ESYT1 or the artificial tether induces ER stress, we performed a WB analysis to compare markers of ER stress in control fibroblasts, KO ESYT1 fibroblasts, KO ESYT1 fibroblasts overexpressing ESYT1-Myc or the tether (Figure S4C). This showed no changes in the levels of several different markers of ER stress or cell death. __Reviewer 2____: __

1) the interaction between those proteins is direct,

2) if SYNJ2BP is necessary and sufficient to localize E-Syt1 at MERC, and

3) if MERCs extension induced by SYNJ2BP is dependent on E-Syt1.

Those points are important to investigate because SYNJ2BP has already been shown to induce MERCs by interacting with the ER protein RRBP1. In addition, some experiments need to be better quantified.

Major comments: E-syt1/SYNJ2BP in MERCs formation: the authors provide several convincing lines of evidence that both proteins are in the same complex (proximity labelling, localization in the same complex in BN-PAGE, localization in MAM) but it is not clear in which extent the direct interaction between both proteins regulates ER-mitochondria tethering. 1- Pull down experiments or BiFC strategy could be performed to show the direct interaction between both proteins.

• We showed in Figure 4C that the presence of SYNJ2BP in a complex of a similar molecular weight to that ESYT1 (410KDa) is totally dependent of the presence of ESYT1, suggesting an interaction of the 2 proteins.
• To confirm this interaction, in figure 4A we analyzed on BN cells overexpressing SYNJ2BP together with a 3xFlag tagged version of ESYT1. As a result of the addition of the Flag tag, the complex positive for ESYT1 shifted to a higher molecular weight. Significantly, the complex positive for SYNJ2BP shifted to a similar the molecular weight, demonstrating the interaction and dependence of the 2 protein partners.

• To confirm the interaction of the 2 partners, we performed co-immunoprecipitation of the ESYT1-3xFlag protein (Table S2). SYNJ2BP was found as the strongest prey, followed by ESYT2 and SEC22B two described interactors of ESYT1, confirming the quality of the analysis (Giordano, Saheki et al. 2013, Gallo, Danglot et al. 2020). 2- SYNJ2BP OE has already been demonstrated to increase MERCs and this being dependent on the ER binding partners RRBP1 (10.7554/eLife.24463). Therefore, it would be of interest to perform OE of SYNJ2BP in KO Esyt1 to address the question of whether ESyt1 is also required to increase MERCs.

• A quantitative analysis using confocal microscopy demonstrated that the effect of SYNJ2BP overexpression on MERCs formation is partially dependent of the presence of ESYT1 (Figure 3F). 3- The authors show that Esyt1 punctate size increases when SYNJ2BP is OE (Fig3C), but this can be indirectly linked to the increase of MERCs in the OE line. Thus, it could be interesting to test if the number/shape of E-syt1 punctate located close to mitochondria decreases in KO SYNJ2B. This could really show the dependence of SYNJ2BP for E-syt1 function at MERCs.

• To improve our description of the partial localization of ESYT1 at mitochondria, we performed a quantitative analysis using confocal microscopy on control human fibroblasts stably overexpressing SEC61B-mCherry as an ER marker which were labelled with ESYT1 and TOMM40 for mitochondria. We measured the % of ESYT1 signal colocalizing with mitochondria and the % of mitochondria colocalizing with ESYT1 (Figure 1E).

• To demonstrate than ESYT1 partial colocalization with mitochondria is, at least partially, due to its interaction with SYNJ2BP, we performed a quantitative analysis using confocal microscopy. Human control fibroblasts, KO SYNJ2BP fibroblasts and SYNJ2BP overexpressing fibroblasts were labelled with ESYT1, TOMM40 for mitochondria and CANX for ER. We measured the % of ESYT1 signal colocalizing with mitochondria in each condition (Figure 3C). Lipid analyses: the results of MS on isolated mitochondria clearly show that mitochondrial lipid homeostasis is affected on KO-Syt1 and rescued by expression of Syt1-Myc and artificial mitochondria-ER tether. However, p.15, the authors wrote "The loss of ESYT1 resulted in a decrease of the three main mitochondrial lipid categories CL, PE and PI, which was accompanied by an increase in PC ». As the results are expressed in mol%, this interpretation can be distorted by the fact that mathematically, if the content of one lipid decreases, the content of others will increase. I would suggest to express the results in lipid quantity (nmol)/mg of mitochondria proteins instead of mol%. This will clarify the role of E-Syt1 on mitochondrial lipid homeostasis and which lipid increase and decrease.

• We changed the sentence in the text as suggested. Also it could be of high interest to have the lipid composition of the whole cells to reinforce the direct involvement of E-Syt1 in mitochondrial lipid homeostasis and verify that the disruption of mitochondrial lipid homeostasis is not linked to a general perturbation of lipid metabolism as this protein acts at different MCSs.

• This is beyond the scope of the project and we would argue that the results of such an experiment would be difficult to interpret. To better understand the impact of Esyt1 of mitochondria morphology, the author could analyze the mitochondria morphology (size, shape, cristae) on their EM images of crt, KO and OE lines. Indeed, on OE (Fig3A), the mitochondria look bigger and with a different shape compared to crt.

• As we do not observe obvious differences in mitochondrial morphology between control, KO and OE fibroblasts we do not think that quantitative analysis would add to the understanding of the effect of ESYT1 on mitochondrial function. Also, they performed a lot of BN-PAGE. Is it possible to check whether the mitochondrial respiratory chain super-complexes are affected on Esyt1 KO line compared to crt?

• We decided to remove the data on the metabolic consequences of ESYT1 loss since it was too preliminary and required deeper investigations, focusing instead on the effect of ESYT1 loss on calcium homeostasis. Quantifications: some western blots needs to be quantified (Fig 5K, 6J, S3E);

• We did not observe obvious differences in the protein levels so we think that quantitation would not add significantly to the understanding of the differences in calcium dynamics that we report. Fig1A: Can the author provide a higher magnification of the triple labeling and perform quantification about the proportion of E-Syt1 punctate located close to mitochondria?

• We added higher magnification of the same area in all channels and arrows that point to the foci of ESYT1 colocalizing with both ER and mitochondria (Figure 1D).

• To improve our description of the partial localization of ESYT1 at mitochondria, we performed a quantitative analysis using confocal microscopy on control human fibroblasts stably overexpressing SEC61B-mCherry as an ER marker which were labelled with ESYT1 and TOMM40 for mitochondria. We measured the % of ESYT1 signal colocalizing with mitochondria and the % of mitochondria colocalizing with ESYT1 (Figure 1E). Minor comments:

• Fig1E + text: according to the legend, the BN-PAGE has been performed on Heavy membrane fraction. Why the authors speak about complexes at MAM in the text of the corresponding figure? Is-it the MAM or the heavy fraction (MAM + mito + ER...)? If BN have been performed from heavy membranes, it is not a real proof that E-syt1 is in MAMs.

• Heavy membranes have been used in this experiment. The text and conclusions have been changed accordingly.

• On fig3C (panel crt): it seems like SYNJ2BP dots are not co-localizaed with mito. Is this protein targeted to another organelle beside mitochondria?

• It is not described that SYNJ2BP would be targeted to another organelle beside mitochondria. It is possible that those dots outside of mitochondria could be non-specific signals from the antibody we used.

• Fig4A: can the author provide a control of protein loading (membrane staining as example) to confirm the decrease of E-Syt1 in siSYNJ2BP?

• As we performed this experiment only once we have removed the statement suggesting a decrease in ESYT1 protein in response to the siSYNJ2BP.

• Fig5E/F: it is not clear to me why the expression of E-Syt1 in the KO is not able to complement the KO phenotype for cytosolic Ca++. Can the authors comment this?

• We performed further analysis using ATP to trigger calcium release from the ER (figure 6 F to H). In those conditions, expression of ESYT1 in the KO is able to complement the KO phenotype for cytosolic Ca2+. __Reviewer 3____: __

Main points 1. Confirming the MERC localization of ESYT1 should include some more of tethering factors as demonstrated interactors (some are mentioned above) and should not be limited to lipid homeostasis.

• As shown in Figure 1B, VAPB, PDZD8 and BCAP31 are found as preys in the ESYT1 bioID analysis. Those proteins have been described as MERC tethers, their loss leading to mitochondrial calcium defects. To support and confirm the specificity of ESYT1-SYNJ2BP complex at MERCs, we performed a supplementary BioID analysis using ER targeted BirA* and OMM targeted BirA* (Table S1, Figure S1 and Figure 1 A and B). These results confirmed the specificity of the interaction of the 2 partners. ESYT1 is not identified as a prey in OMM BioID and SYNJ2BP is not identified in ER BioID. Additional ER-mitochondria tether BirA* analyses showed that tether-BirA* identified both ESYT1 and SYNJ2BP as a prey at MERCs, confirming the localisation of this interaction. Interestingly, a large majority of the known MERCs tethers VAPB-PTPIP51, MFN2, ITPRs, BCAP31 are also found as preys in the tether-BirA* (Figure 1B), confirming the quality of these data.
• To confirm the interaction of the 2 partners, we performed co-immunoprecipitation of the ESYT1-3xFlag protein. SYNJ2BP is found as the strongest prey, followed by ESYT2 and SEC22B two described interactors of ESYT1, confirming the quality of the analysis (Table S2) (Giordano, Saheki et al. 2013, Gallo, Danglot et al. 2020).

The fact that in ESYT1 KO cells both mitochondrial calcium transfer and cytosolic calcium accumulation are accompanied by decreased ER-cepia1ER signal decay upon histamine addition suggest that the main reason for ER-mitochondria calcium transfer defects are due to impaired SOCE. Calcium-free medium and histamine are used to show that ESYT1 does not affect ER calcium content. However, if it affects SOCE, then the absence of extracellular calcium would abolish such an effect; moreover, histamine does not test for leak effects. As additional information, the authors should investigate whether ER calcium content is affected by the presence of extracellular calcium in the ko scenario using thapsigargin. The authors should inhibit SOCE to test whether this mechanism is affected in ESYT1 KO and could account for observed signal differences. Excluding SOCE is critical, since any change in calcium entry from the outside would potentially negate a role of ESYT1 in mitochondrial calcium uptake.

• Silencing ESYT1 impairs SOCE efficiency in Jurkat cells (Woo, Sun et al. 2020), but not in HeLa cells (Giordano, Saheki et al. 2013, Woo, Sun et al. 2020). Analysis of the role of ESYT1 in HeLa cells prevents confounding effects due to the loss of ESYT1 at ER-PM. In this model, knock-down of ESYT1 led to a decrease of mitochondrial Ca2+ uptake from the ER upon histamine stimulation, as monitored by genetically encoded Ca2+ indicator targeted to mitochondrial matrix (Figure 5A and B). ESYT1 silencing in HeLa cells did not impact ER Ca2+ store measured by the ER-targeted R-GECO Ca2+ probe (Figure 5C and D). The expression of the artificial mitochondria-ER tether was able to rescue mitochondrial Ca2+ defects observed in ESYT1 silenced cells (Figure 5B), confirming that the observed anomalies are specifically due to MERC defects.
• In contrast loss of ESYT1 impaired SOCE efficiency in fibroblasts (Figure 6 A and B). This phenotype was fully rescued by re-expression of ESYT1-Myc but not the artificial tether. We therefore investigated the influence of ESYT1 loss on cytosolic Ca2+ concentration following ATP (Figure 6F to H) or histamine stimulation (Figure S3 D to F), both of which showed a reduced cytosolic Ca2+ concentration and uptake in ESYT1 KO cells. This phenotype was fully rescued by the re-expression of ESYT1-Myc but not the artificial tether. Measurment of cytosolic Ca2+ after tharpsigargin treatment in Ca2+-fee media, an inhibitor of the sarco/endoplasmic reticulum Ca2+ ATPase SERCA that blocks Ca2+ pumping into the ER, showed that ESYT1 KO does not influence the total ER Ca2+ pool (Figure 6K and L). However, ER-Ca2+ release capacity upon histamine stimulation (Figure 6I and J) is decreased in ESYT1 KO cells. This phenotype was fully rescued by the re-expression of ESYT1-Myc but not the artificial tether. Loss of ESYT1 decreased the Ca2+ uptake capacities of mitochondria after activation with histamine (Figure S3 A to C) or ATP (Figure 6 C to E). This phenotype was rescued by re-expression of ESYT1-Myc and also the engineered ER-mitochondria tether. Thus, despite the ER-Ca2+ release defect observed after ESYT1 loss, the artificial tether fully rescued the mitochondrial phenotype.
• These results highlight the distinct and dual roles of ESYT1 in Ca2+ regulation at the ER-PM and at MERCs.

The authors claim that ER-Geco measurements show that no change of ER calcium was observed. However, they use thapsigargin treatment and then get a peak, when the signal should show a decrease due to leak. This suggests they did not use ER-Geco in Figure S3C. What was measured and what does it mean?

• We used R-GECO (not ER-GECO) which measures the cytosolic calcium.
• We measured total ER Ca2+ store using the cytosolic-targeted R-GECO Ca2+ probe upon thapsigarin treatment, an inhibitor of the sarco/endoplasmic reticulum Ca2+ ATPase SERCA that blocks Ca2+ pumping into the ER (Figure 5C and D) and observed no difference in our different conditions.

The findings on growth in galactose medium are intriguing but are not accompanied by respirometry to confirm mitochondria are compromised upon ESYT1 KO.

• We decided to remove the data on the metabolic consequences of ESYT1 loss since it was to preliminary and required deeper investigations, focusing instead on the effect of ESYT1 loss on calcium homeostasis

Minor points: 1. The authors mention they measure mitochondrial uptake of "exogenous" calcium by applying histamine. They should specify that these measures transferred calcium from the ER rather than uptake of calcium from the exterior (directly at the plasma membrane).

• The text was clarified as suggested.

• Expression levels of IP3Rs are not very indicative of any change of their activity. The authors should discuss how ESYT1 could affect their PTMs.

• A large numer of post translational modifications are known to regulate IP3R activity (Hamada and Mikoshiba 2020), and it is possible that the loss of ESYT1 could interfere with these modifications, but an exploration of this issue is beyond the scope of this study. The text was clarified as suggested. Eisenberg-Bord, M., N. Shai, M. Schuldiner and M. Bohnert (2016). "A Tether Is a Tether Is a Tether: Tethering at Membrane Contact Sites." Dev Cell 39(4): 395-409.

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Reply to the reviewers

We are grateful to both reviewers for reviewing our manuscript, and for providing very helpful feedback as to how we can improve this work. We have now implemented nearly all of the changes as recommended, and provide responses to these points below.

In terms of novelty, while recent pre-prints and publications have suggested that the application of multi-omics analysis improves GRN inference, there has yet to be a systematic comparison of linear and non-linear machine learning methods for GRN prediction from single cell multi-omic data. here are many computational and statistical challenges to such a study, and we therefore believe that others in the field will be especially interested in our systematic comparison of network inference methods, especially given the increased interest and utility of multi-omic data.

In addition, we report the first comprehensive inference of GRNs in early human embryo development. This is a particularly challenging to study developmental context given genetic variation, limitations of sample size due to the precious nature of the material and regulatory constraints. We anticipate that the methodology we developed and datasets we generated will be informative for computational, developmental and stem cell biologists.

We have uploaded all the network predictions on FigShare and these can be accessed using the following link: https://doi.org/10.6084/m9.figshare.21968813. In addition, we anticipate that the computational and statistical codes and pipelines we developed (available on https://github.com/galanisl/early_hs_embryo_GRNs) will be applied to other cellular and developmental contexts, especially in challenging contexts such as human development, non-typical model organisms and in clinically relevant samples.

Reviewer 1

Major comments

- The proposed strategy (i.e. combining gene expression-based regulatory inference with cis-*regulatory evidence) have been well developed (and implemented) by multiple published works like SCENIC and CellOracle, which is also properly acknowledged by the authors in the discussion section too. This leads to a serious concern on the major methodological contribution of this work. *

We would like to note that our study is the first to comprehensively evaluate machine learning linear or non-linear gene regulatory network prediction strategies from single-cell transcriptional datasets combined with available multi-omic data. We also apply these methods to a challenging to study context of human early embryogenesis. There are specific methodological challenges arising in this context that other published work has not yet addressed. In particular, the precious nature of the source material means that sample sizes are limited, unlike the contexts where SCENIC and CellOracle were applied. Notably, the numbers of cells available for downstream analysis is typically several orders of magnitude fewer than when scRNA-seq data are collected from adult human tissue or from cell culture. This restriction on sample sizes places corresponding restrictions on statistical power, and is therefore likely to mean that different statistical network inference methodologies are optimal in specific contexts. Furthermore, the inclusion of multi-omic data from complementary platforms (such as ATAC-seq data) becomes even more important in this context to mitigate the effect of reduced sample sizes. These issues are very important for choice of gene regulatory network inference methodology in relation to studies of human embryo development, and ours is the first study to address these issues directly in any context. We have further clarified the novelty of our work in the manuscript in the abstract, introduction and discussion sections.

- Most of the compared network reconstruction methods involve hyper-parameters setup (e.g., *sparsity regularization weights of the regression methods). The authors did not discuss how these hyper-parameters were chosen. *

For sparse regression, the hyperparameter controlling sparsity was set by cross-validation (CV), using the internal CV function of the R package. All default settings for GENIE3 were used. This information has now been added to the manuscript (in the Methods section), along with a description of the implementation of the mutual information method we use.

- For the real-world blastocyst data, the network prediction methods were compared in terms of their reproducibility across validation folds (Fig. 3, Fig. S4-6). However, reproducibility does not necessarily imply accuracy. In fact, statistical learning methods are generally subject to the bias-variance tradeoff, where lower variance (i.e., higher reproducibility) could imply higher bias in model prediction. While there is a lack of gold-standard ground truth to evaluate network accuracy in real biological systems, silver-standards like the ranking of known regulatory interactions in the predictions could be employed as an indirect estimate.

We thank the reviewer for the opportunity to clarify this point. We would like to avoid any misunderstanding of the reproducibility statistic R, as follows. A higher value of R indicates that the fitted model would generalise well to new data; i.e., R=1 indicates that the model is robust (stable) to perturbations of the data-set. We note that this is not the same as analysing the residual variance of the data after model fitting and related over-fitting (i.e., bias-variance trade-off). The variance that is referred to when discussing bias-variance trade-off is the mean-squared error (of data compared to model), which is not the same as what is assessed by reproducibility statistic R . Specifically, R is a Bayesian estimate of the posterior probability of observing a gene regulation given the data. R is calculated by taking a random sample of the data, doing the network inference again, checking if each gene regulation still appears in the GRN, and then recording (as the R statistic) the average fraction of inclusions over many repetitions. So when we have R close to 1, this indicates that our model predictions generalise well to new data, which is the opposite of what is suggested in this comment. In summary, the accuracy quantified by the reproducibility statistic R relates to the stability of the model predictions to perturbation of the data. We thank the reviewer for the helpful comment to draw our attention to this point, and have now clarified this point in the manuscript on page 6 line 252.

- The gene set enrichment results were reported only on EPI and TE cell types (Fig. 4C and Fig. *S12), due to the reason that CA data is only available for TE and ICM. However, many of the other results presented in Fig. 3-6 did include the PE cell type albeit using the same CA data. It is not particularly convincing why the cell type inclusion standard for gene set enrichment is different from the other results. *

We thank the reviewer for noting this and would like to clarify that we restricted the analysis to the EPI and TE, because similar lists of gene-sets were not available for primitive endoderm, where it is currently unclear which pathways are most relevant to this cell type. This has now been clarified in the manuscript on page 8, line 337.

- The authors cited TF binding in cis-regulatory regions as supporting evidence of several MICA-inferred regulatory interactions (e.g., NANOG -> ZNF343). However, the same cis-regulatory *evidence has already been used in the CA filtering step. All interactions passing CA filtering should in principle have TF-binding support. It would be more convincing if the authors provided other types of evidence as independent support, such as genetic associations like eQTL, experimental perturbations like gene knockdown/knockout, etc. *

We appreciate the reviewer’s point. We address this by describing published ChIP-seq validation in human pluripotent stem cells which is widely used as a proxy for the study of the epiblast. We feel that the ChIP-seq validation in this context is an appropriate independent validation to support the MICA-inferred cis regulatory interactions predicted from the human embryo datasets we analysed. Our inferences from ATAC-seq data cannot identify TF-DNA binding directly. ChIP-seq data is a widely accepted independent methods to support the inferred interactions from ATAC-seq data.

We agree that knockdown/knockout would provide further evidence suggesting gene regulation, and indeed these are experiments we would like to conduct systematically in the future, but such perturbations are difficult to achieve at genome-wide scale, especially with very restricted quantities of human embryo material. Notably, these studies would not be evidence of direct regulation and the gold-standard in our opinion is to perturb the cis regulatory region to demonstrate its functional importance in gene regulation. These are important experiments to conduct systematically in the future. We also note that assessing quantitative trait loci in the context of human pre-implantation embryos is extremely challenging due to the restricted sample sizes and genetic variance in the samples collected.

*- Many of the MICA-inferred regulatory interactions do not exhibit Spearman correlation (Fig. 5, Fig. S17), which could probably be explained by the ability of mutual information to capture complex non-monotonic dependencies. It would be interesting to provide further investigation on these "uncorrelated" edges, which may help demonstrate the superiority of mutual information over Spearman correlation. *

This has been added as a new Fig.S18.

- The authors conducted immunostaining experiments to validate the MICA-inferred regulatory *interaction between TFAP2C and JUND. While the identified protein co-localization is a step further than RNA co-expression, it is still correlation rather than causality. Additional evidence like the effect of knockout/knockdown perturbations would be more convincing. *

We agree with Reviewer 1 that experimental perturbations of TFAP2C and JUND to determine what consequence this has for interactions between these proteins would be informative. However due to the complexity of such an investigation in human embryos, we feel that this is beyond the scope of the current study. One option is to conduct the perturbations in human pluripotent stem cells, however it is unclear if the GRN in this context reflects the same interactions as human embryos and is a distinct question to address in the future. Moreover, while knockdown/knockout studies would be suggestive of up-stream regulation, it will not address the question of whether this is a direct or indirect effect without systematic further analysis including transcription factor-DNA binding (such as CUT&RUN, CUT&Tag or ChIP-seq) analysis as well as perturbations of the putative cis regulatory regions. These are all exciting future experiments and our study provides us and others with hypotheses to functionally test in the future. These are future directions and we have clarified this in the discussion section on page 16, line 576.

__Minor comments __

• *The γ symbols in AP-2γ are not correctly rendered. *

We note that this applies only to the way AP-2γ appears on the Review Commons website, and we are trying to fix this issue. We hope this transformation after the manuscript upload will not apply to a subsequent transfer to a journal.

• The UMAP figures (Fig. 4A, Fig. S7) are of low resolution compared to other figures.

We thank the reviewer for noting this. These figures have now been added as vector graphics files to overcome this issue.

• As the authors are focused on studying the blastocyst regulatory network, the inferred regulatory interactions should be provided as supplementary data.

We have included all of the inferred gene regulatory interactions as a supplementary folder for the MICA predictions using FigShare: doi.org/10.6084/m9.figshare.21968813. We have included code to reproduce the inferred gene regulatory interactions for the other methods which we compared to MICA. Because this includes 100,000 regulatory interactions per method, we feel that it would be impractical to include the alternative inferred interaction as supplementary data.

Reviewer 2

Minor comments

*- In the abstract, it would be adequate to already mention which normalisation method works the best. *

This has now been added to the abstract and we appreciate this suggestion.

*- In Fig. 1: *

* Describe what are squares and circles

This information has been included in the figure 1 legend.

** In the GRNs refined by keeping CA-predicted regulations only, mention that this are Cis interactions *

We have modified the figure 1 legend and the text on page 5, line 224 to clarify that these are putative cis-regulatory interactions.

* The ATAC seq shows KRT8, GATA3, RELB motifs, while the rest of the figure is very general. Maybe make the ATAC-seq peaks panel also as a sketch and relate it to the square/circles graphs on the right hand side to showcase how the filtering of the network is performed.

We appreciate this suggestion and modified figure 1 accordingly.

** The caption says Five GRN inference approaches, while abstract and text say 4. If is clear after reading that the 5th is a random approach. However, it was a surprise at first. *

We have modified the figure 1 legend to clarify that we also compared random prediction in addition to the 4 GRN inference approaches.

*- How the Simulation study was performed is not understandable for non experts as it is described in the Methods section. This is an important approach in general, and I think the audience would benefit if the authors add a full section about it in their supplementary data. *

Further details have now been added to the subsection ‘simulation study’ in the Methods section.

*- Fig. 2: *

** As it is, it is hard to tell the difference between GRN inference methods for a given sample size and number of regulators. Could the authors add a comparative panel for this (maybe some scatter plots would be enough)? MI by itself looks worse here? *

We thank the reviewer for this helpful suggestion. This comparative plot has now been included in figure 2 and indicates that MI is on par with the other GRN inference methods using simulation RNA-seq data.

*- When mentioning "samples" (e.g. last paragraph of section 1 in results), do the authors refer to "cells"? *

We appreciate the reviewer pointing this out and have amended the text throughout to state that these are cells.

*- What about normalisation effects in the simulated data? *

With regards to the simulated data, normalisation effects are not relevant as we are generating data that are idealised and therefore not subject to unwanted sources of variation such as read depth. However, in future work, this could be investigated with an expanded simulation study and we appreciate the reviewer’s suggestion.

*- Figure S7 should be cited in the first paragraph of section 2 in results. *

This has now been cited.

*Could the authors add a panel to indicate whether the data is SMART-seq2 or 10X. *

We thank the reviewer for the suggestion to clarify this, which we think is an important point. We have included a statement that all data used was generated using the SMART-seq2 sequencing technique in the figure legend. The choice of sequencing method/depth of sequencing will likely impact on the choice of GRN inference method and we have also clarified this in the discussion section on page 13, line 516.

*- In the association of inferred GRNs to human blastocyst cell lineages, the authors find the GRN edges predicted that overlap between the 4 inference methods in each cell type. Do they, therefore, recommend to always use more than one GRN inference method? *

Identifying overlapping inferences by comparing more than one GRN inference method may be a strategy to identify network edges with more confidence due to the agreement between several inference methodologies. However, this strategy may also miss some edges which can only be detected by one method and not another. We have included a statement in the discussion section to clarify this point on page 15, line 571.

- If the CA data used was only generated for the TE and ICM only, how do the authors use it to perform MICA on PE?

We appreciate that this is confusing and have since revised the manuscript on page 5, line 223 to state that the inner cell mass (ICM), comprises EPI (epiblast) and PE (primitive endoderm) cells. It may be that we miss putative cis-regulatory interactions if the ICM CA data does not reflect developmentally progressed PE and EPI cells and we have noted this caveat in the discussion section on page 15, line 561.

Author Response

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

She et al studied the evolution of gene expression reaction norms when individuals colonise a new environment that exposes them to physiologically challenging conditions. Their objective was to test the "plasticity first" hypothesis, which suggest that traits that are already plastic (their value changes when facing a new environment compared to the original environment) facilitates the colonisation of novel environments, which, if true, would be predicted to result in the evolution of gene expression values that are similar in the population that colonised the new environment and evolved under these particular selection pressures. To test this prediction, they studied gene expression in cardiac and muscle tissues in individuals originating from three conditions: lowland individuals in their natural environment (ancestral state), lowland individuals exposed to hypoxia (the plastic response state), and a highland population facing hypoxia for several generations (the coloniser state). They classified gene expression patterns as maladaptive or adaptive in lowland individuals responding to short term hypoxia by classifying gene expression patterns using genes that differed between the ancestral state (lowland) and colonised state (highland). Genes expressed in the same direction in lowland individuals facing hypoxia (the plastic state) as what is found in the colonised state are defined as adaptative, while genes with the opposite expression pattern were labelled as maladaptive, using the assumption that the colonised state must represent the result of natural selection. Furthermore, genes could be classified as representing reversion plasticity when the expression pattern differed between the plasticity and colonised states and as reinforcement when they were in the same direction (for example more expressed in the plastic state and the colonised state than in the ancestral state). They found that more genes had a plastic expression pattern that was labelled as maladaptive than adaptive. Therefore, some of the genes have an expression pattern in accordance with what would be predicted based on the plasticity-first hypothesis, while others do not.

Thank you for a precise summary of our work. We appreciate the very encouraging comments recognizing the value of our work. We have addressed concerns from the reviewer in greater detail below.

Q1. As pointed out by the authors themselves, the fact that temperature was not included as a variable, which would make the experimental design much more complex, misses the opportunity to more accurately reflect the environmental conditions that the colonizer individuals face at high altitude. Also pointed out by the authors, the acclimation experiment in hypoxia lasted 4 weeks. It is possible that longer term effects would be identifiable in gene expression in the lowland individuals facing hypoxia on a longer time scale. Furthermore, a sample size of 3 or 4 individuals per group depending on the tissue for wild individuals may miss some of the natural variation present in these populations. Stating that they have a n=7 for the plastic stage and n= 14 for the ancestral and colonized stages refers to the total number of tissue samples and not the number of individuals, according to supplementary table 1.

We shared the same concerns as the reviewer. This is partly because it is quite challenging to bring wild birds into captivity to conduct the hypoxia acclimation experiments. We had to work hard to perform acclimation experiments by taking lowland sparrows in a hypoxic condition for a month. We indeed have recognized the similar set of limitations as the review pointed out and have discussed the limitations in the study, i.e., considering hypoxic condition alone, short time acclimation period, etc. Regarding sample sizes, we have collected cardiac muscle from nine individuals (three individuals for each stage) and flight muscle from 12 individuals (four individuals for each stage). We have clarified this in Supplementary Table 1.

Q2. Finally, I could not find a statement indicating that the lowland individuals placed in hypoxia (plastic stage) were from the same population as the lowland individuals for which transcriptomic data was already available, used as the "ancestral state" group (which themselves seem to come from 3 populations Qinghuangdao, Beijing, and Tianjin, according to supplementary table 2) nor if they were sampled in the same time of year (pre reproduction, during breeding, after, or if they were juveniles, proportion of males or females, etc). These two aspects could affect both gene expression (through neutral or adaptive genetic variation among lowland populations that can affect gene expression, or environmental effects other than hypoxia that differ in these populations' environments or because of their sexes or age). This could potentially also affect the FST analysis done by the authors, which they use to claim that strong selective pressure acted on the expression level of some of the genes in the colonised group.

The reviewer asked how individual tree sparrows used in the transcriptomic analyses were collected. The individuals used for the hypoxia acclimation experiment and represented the ancestral lowland population were collected from the same locality (Beijing) and at the same season (i.e., pre-breeding) of the year. They are all adults and weight approximately 18g. We have clarified this in the Supplementary Table S1 and Methods. We did not distinguish males from females (both sexes look similar) under the assumption that both sexes respond similarly to hypoxia acclimation in their cardiac and flight muscle gene expression.

The Supplementary Table 2 lists the individuals that were used for sequence analyses. These individuals were only used for sequence comparisons but not for the transcriptomic analyses. The population genetic structure analyzed in a previously published study showed that there is no clear genetic divergence within the lowland population (i.e., individuals collected from Beijing, Tianjing and Qinhuangdao) or the highland population (i.e., Gangcha and Qinghai Lake). In addition, there was no clear genetic divergence between the highland and lowland populations (Qu et al. 2020).

Q4. Impact of the work

There has been work showing that populations adapted to high altitude environments show changes in their hypoxia response that differs from the short-term acclimation response of lowland population of the same species. For example, in humans, see Erzurum et al. 2007 and Peng et al. 2017, where they show that the hypoxia response cascade, which starts with the gene HIF (Hypoxia-Inducible Factor) and includes the EPO gene, which codes for erythropoietin, which in turns activates the production of red blood cell, is LESS activated in high altitude individuals compared to the activation level in lowland individuals (which gives it its name). The present work adds to this body of knowledge showing that the short-term response to hypoxia and the long term one can affect different pathways and that acclimation/plasticity does not always predict what physiological traits will evolve in populations that colonize these environments over many generations and additional selection pressure (UV exposure, temperature, nutrient availability). Altogether, this work provides new information on the evolution of reaction norms of genes associated with the physiological response to one of the main environmental variables that affects almost all animals, oxygen availability. It also provides an interesting model system to study this type of question further in a natural population of homeotherms.

Erzurum, S. C., S. Ghosh, A. J. Janocha, W. Xu, S. Bauer, N. S. Bryan, J. Tejero et al. "Higher blood flow and circulating NO products offset high-altitude hypoxia among Tibetans." Proceedings of the National Academy of Sciences 104, no. 45 (2007): 17593-17598.

Peng, Y., C. Cui, Y. He, Ouzhuluobu, H. Zhang, D. Yang, Q. Zhang, Bianbazhuoma, L. Yang, Y. He, et al. 2017. Down-regulation of EPAS1 transcription and genetic adaptation of Tibetans to high-altitude hypoxia. Molecular biology and evolution 34:818-830.

Thank you for highlighting the potential novelty of our work in light of the big field. We found it very interesting to discuss our results (from a bird species) together with similar findings from humans. In the revised version of manuscript, we have discussed short-term acclimation response and long-term adaptive evolution to a high-elevation environment, as well as how our work provides understanding of the relative roles of short-term plasticity and long-term adaptation. We appreciate the two important work pointed out by the reviewer and we have also cited them in the revised version of manuscript.

Reviewer #2 (Public Review):

This is a well-written paper using gene expression in tree sparrow as model traits to distinguish between genetic effects that either reinforce or reverse initial plastic response to environmental changes. Tree sparrow tissues (cardiac and flight muscle) collected in lowland populations subject to hypoxia treatment were profiled for gene expression and compared with previously collected data in 1) highland birds; 2) lowland birds under normal condition to test for differences in directions of changes between initial plastic response and subsequent colonized response. The question is an important and interesting one but I have several major concerns on experimental design and interpretations.

Thank you for a precise summary of our work and constructive comments to improve this study. We have addressed your concerns in greater detail below.

Q1. The datasets consist of two sources of data. The hypoxia treated birds collected from the current study and highland and lowland birds in their respective native environment from a previous study. This creates a complete confounding between the hypoxia treatment and experimental batches that it is impossible to draw any conclusions. The sample size is relatively small. Basically correlation among tens of thousands of genes was computed based on merely 12 or 9 samples.

We appreciate the critical comments from the reviewer. The reviewer raised the concerns about the batch effect from birds collected from the previous study and this study. There is an important detail we didn’t describe in the previous version. All tissues from hypoxia acclimated birds and highland and lowland birds have been collected at the same time (i.e., Qu et al. 2020). RNA library construction and sequencing of these samples were also conducted at the same time, although only the transcriptomic data of lowland and highland tree sparrows were included in Qu et al. (2020). The data from acclimated birds have not been published before.

In the revised version of manuscript, we also compared log-transformed transcript per million (TPM) across all genes and determined the most conserved genes (i.e., coefficient of variance ≤  0.3 and average TPM ≥ 1 for each sample) for the flight and cardiac muscles, respectively (Hao et al. 2023). We compared the median expression levels of these conserved genes and found no difference among the lowland, hypoxia-exposed lowland, and highland tree sparrows (Wilcoxon signed-rank test, P<0.05). As these results suggested little batch effect on the transcriptomic data, we used TPM values to calculate gene expression level and intensity. This methodological detail has been further clarified in the Methods and we also provided a new supplementary Figure (Figure S5) to show the comparative results.

The reviewer also raised the issue of sample size. We certainly would have liked to have more individuals in the study, but this was not possible due to the logistical problem of keeping wild bird in a common garden experiment for a long time. We have acknowledged this in the manuscript. In order to mitigate this we have tested the hypothesis of plasticity following by genetic change using two different tissues (cardiac and flight muscles) and two different datasets (co-expressed gene-set and muscle-associated gene-set). As all these analyses show similar results, they indicate that the main conclusion drawn from this study is robust.

Q2. Genes are classified into two classes (reversion and reinforcement) based on arbitrarily chosen thresholds. More "reversion" genes are found and this was taken as evidence reversal is more prominent. However, a trivial explanation is that genes must be expressed within a certain range and those plastic changes simply have more space to reverse direction rather than having any biological reason to do so.

Thank you for the critical comments. There are two questions raised we should like to address them separately. The first concern centered on the issue of arbitrarily chosen thresholds. In our manuscript, we used a range of thresholds, i.e., 50%, 100%, 150% and 200% of change in the gene expression levels of the ancestral lowland tree sparrow to detect genes with reinforcement and reversion plasticity. By this design we wanted to explore the magnitudes of gene expression plasticity (i.e., Ho & Zhang 2018), and whether strength of selection (i.e., genetic variation) changes with the magnitude of gene expression plasticity (i.e., Campbell-Staton et al. 2021).

As the reviewer pointed out, we have now realized that this threshold selection is arbitrarily. We have thus implemented two other categorization schemes to test the robustness of the observation of unequal proportions of genes with reinforcement and reversion plasticity. Specifically, we used a parametric bootstrap procedure as described in Ho & Zhang (2019), which aimed to identify genes resulting from genuine differences rather than random sampling errors. Bootstrap results suggested that genes exhibiting reversing plasticity significantly outnumber those exhibiting reversing plasticity, suggesting that our inference of an excess of genes with reversion plasticity is robust to random sampling errors. We have added these analyses to the revised version of manuscript, and provided results in the Figure 2d and Figure 3d.

In addition, we adapted a bin scheme (i.e., 20%, 40% and 60% bin settings along the spectrum of the reinforcement/reversion plasticity). These analyses based on different categorization schemes revealed similar results, and suggested that our inference of an excess of genes with reversion plasticity is robust. We have provided these results in the Supplementary Figure S2 and S4.

The second issue that the reviewer raised is that the plastic changes simply have more space to reverse direction rather than having any biological reason to do so. While a causal reason why there are more genes with expression levels being reversed than those with expression levels being reinforced at the late stages is still contentious, increasingly many studies show that genes expression plasticity at the early stage may be functionally maladapted to novel environment that the species have recently colonized (i.e., lizard, Campbell-Staton et al. 2021; Escherichia coli, yeast, guppies, chickens and babblers, Ho and Zhang 2018; Ho et al. 2020; Kuo et al. 2023). Our comparisons based on the two genesets that are associated with muscle phenotypes corroborated with these previous studies and showed that initial gene expression plasticity may be nonadaptive to the novel environments (i.e., Ghalambor et al. 2015; Ho & Zhang 2018; Ho et al. 2020; Kuo et al. 2023; Campbell-Staton et al. 2021).

Q3. The correlation between plastic change and evolved divergence is an artifact due to the definitions of adaptive versus maladaptive changes. For example, the definition of adaptive changes requires that plastic change and evolved divergence are in the same direction (Figure 3a), so the positive correlation was a result of this selection (Figure 3d).

The reviewer raised an issue that the correlation between plastic change and evolved divergence is an artifact because of the definition of adaptive versus maladaptive changes, for example, Figure 3d. We agree with the reviewer that the correlation analysis is circular because the definition of adaptive and maladaptive plasticity depends on the direction of plastic change matched or opposed that of the colonized tree sparrows. We have thus removed previous Figure 3d-e and related texts from the revised version of manuscript. Meanwhile, we have changed Figure 3a to further clarify the schematic framework.

Reviewer #1 (Recommendations For The Authors):

Q1. Here are private recommendations that I think could help improve the manuscript. West-Eberhard was a pioneer back in 2003 in explicating the hypothesis of "plasticity first". I think it is important to cite their main work in the first paragraph of introduction and to use the term "plasticity-first", which is widely known among evolutionary biologists studying phenotypic plasticity, instead of "plasticity followed by genetic change", since the three papers cited in paragraph 1 call it « plasticity first ».

West-Eberhard, M.J. (2003) Developmental Plasticity and Evolution, Oxford University Press.

Thank you for suggesting West-Eberhard (2003) and we have cited this important work. We have also changed “plasticity followed by genetic change” to “plasticity first”.

Q2. Introduction. Line 5, Change for « On the one hand, if plasticity changes ... »

We have modified as suggested.

Q3. Line 52, Change for « ...same direction as adaptive evolution does ...»

We have modified as suggested.

Q4. Line 66,When presenting papers that address the plasticity and evolution of gene expression in response to environmental variables, paper by Morris et al is another example that could be useful to include (but this is only a suggestion in case the authors missed it).

Thank you for suggesting this nice work. We have cited Morris et al. (2014).

Q5. Line 94, Change for "We acclimated"

We have modified as suggested.

Q6. In Figure 3, the figure in panel A and B is labelled "normaxia", but I think that "normoxia" is usually the term used.

Thank you for spot the typo. We have modified Figure 3a and we no longer used the term “normaxia”.

Material and methods

It would be important to merge supplementary table 1 and 2 and only present the individuals that were used with their respective cardiac and muscle libraries (if they come from the same individual?). Also, the origin of the individuals used in the hypoxia experiment should be explained at the beginning of the methods section and explicated in the supplementary table. Information on sex or stage of development (juvenile? Adult? Male? female?) and time of year (in breeding stage? Pre-migration (if any), etc) would allow the reader to see that individuals from lowland differed only in their exposure to hypoxia or not, or if other variables may affect gene expression patterns. Similarly, if all individuals form the highland are males and the lowland hypoxia exposed individuals are females (or juveniles versus breeders, or different time of year, etc) this should be stated in the methods. Gene expression is labile so the reader should know if other variables influence the results presented or not.

Thank you for suggestion. We have added detailed information (i.e., age, collecting time and season) to the supplementary Table 1. We have also added this information to the Methods. Because the birds used in transcriptomic analysis (Supplementary Table 1) were different individuals from those used in the sequence analyses (Supplementary Table 2), these two tables cannot be merged.

References:

Campbell-Staton SC, Velotta JP, Winchell KM. 2021. Selection on adaptive and maladaptive genes expression plasticity during thermal adaptation to urban heat islands. Nat. Commun. 12: 6195.

Ghalambor CK, Hoke KL, Ruell EW, Fischer EK, Reznick DN, Hughes KA. 2015. Non-adaptive plasticity potentiates rapid adaptive evolution of gene expression in nature. Nature 525:372–375.

Hao et al. 2023. Divergent contributions of coding and noncoding sequences to initial high-altitude adaptation in passerine birds endemic to the Qinghai–Tibet Plateau. Mol. Ecol. Doi: 10.1111/mec.16942.

Ho WC, Zhang J. 2018. Evolutionary adaptations to new environments generally reverse plastic phenotypic changes. Nat. Commun. 9: 350.

Ho WC, Zhang J. 2019. Genetic gene expression changes during environmental adaptations tend to reverse plastic changes even after correction for statistical nonindependence. Mol. Biol. Evol. 36: 604–612.

Ho WC, Li D, Zhu Q, Zhang J. 2020. Phenotypic plasticity as a long-term memory easing readaptations to ancestral environments. Sci. Adv. 6: eaba3388.

Kuo KC, Yao CT, Liao BY, Weng MP, Dong F, Hsu YC, Hung CM. 2023. Weak gene-gene interaction facilitates the evolution of gene expression plasticity. BMC Biol. 21: 57.

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Reply to the reviewers

1. Point-by-point description of the revisions

Reviewer #1

Evidence, reproducibility and clarity (Required):

In this paper by Wideman et al, the authors seek to determine the role of cellular iron homeostasis in the pathogenesis of murine malaria.

The authors to attempt to disentangle the effects of anemia from that of cellular iron deficiency. The authors elegantly make use of a murine model of a rare human mutation in the transferrin receptor. This mutation leads to decreased receptor internalization and decreased cellular iron, but otherwise healthy mice. Using this model, the authors use a P. chabaudi infection model and show an increase in pathogen burden and a decrease in pathology. They show in some detail that the immune response to P. chabaudi infection is blunted, both T and B-cell responses are attenuated in the TfRY20H/Y20H model, and the block in proliferation can be rescued by exogenous iron supplementation. They also show that decreased cellular iron attenuates liver pathology through potentially multiple mechanisms.

Minor comments:

• The peak of parasitemia is relatively low (approx..3%) compared to other published studies (e.g. PMID: 22100995, 16714546, 31110285) where the peak in C57BL/6 mice reached 25 - 40%. Can the authors account for this low parasitemia?

Response: We thank the reviewer for their constructive comments and appreciate that they are highlighting this important point. It has previously been shown (PMID: 23217144, 23719378) that mosquito-transmission of P. chabaudi leads to significantly lower parasitaemia (“Recently mosquito-transmitted parasites were used to mimic a natural infection more closely, as vector transmission is known to regulate Plasmodium virulence and alter the host’s immune response (48-50). Consequently, parasitaemia is expected to be significantly lower upon infection with recently mosquito-transmitted parasites, compared to infection with serially blood-passaged parasites that are more virulent (48,49).”

• Figure 1K - At homeostasis, serum iron is low in TfR mice however increases to significantly higher than the WT mice at 8 days post infection. Do the authors have an explanation on why these dramatic changes in serum iron are seen?

Response: During malaria infection, RBC lysis releases haem and iron into circulation, which leads to an increase in serum iron levels. This effect is observed in both wild-type and TfrcY20H/Y20H mice infected with P. chabaudi (Supplementary Figure 1F & Figure 1K). However, the significantly higher serum iron levels observed in infected TfrcY20H/Y20H mice can likely be explained by their decreased capacity for transferrin receptor-1 mediated iron uptake, leading to relatively slower uptake and storage of circulating transferrin-bound iron into tissues. This has been clarified in the manuscript (line 142-143):

“The elevated serum iron observed in infected TfrcY20H/Y20H mice was consistent with their restricted capacity to take up transferrin-bound circulating iron into tissues.”

• Figure S3 - Is it surprising that no effects on splenic neutrophils are seen? Were neutrophils quantified at any other point? These would also be expected to have a role in both the control of malaria infection and on any pathology.

Response: We thank the reviewer for raising this interesting question. It is known that neutrophils can be sensitive to cellular iron deficiency (PMID: 36197985) and that neutrophils can play an important part in malaria infection (PMID: 31628160). However, the magnitude and significance of the neutrophil response to recently mosquito-transmitted P. chabaudi parasites has not been thoroughly investigated. A recent study demonstrated that monocytes and macrophages may be more important than granulocytes in the early response to recently mosquito-transmitted P. chabaudi infection (PMID: 34532703).

Moreover, we performed neutrophil quantifications in our initial experiments and found that the splenic neutrophil response was not altered in TfrcY20H/Y20H mice eight days after infection. Additionally, no neutrophil infiltration was observed in the liver of either genotype upon P. chabaudi infection. In light of these findings, we did not characterise the neutrophil response further, as it appeared unlikely that neutrophils were the principal causal agent of either the altered immunity or pathology, in this context. However, we agree with the reviewer that larger question of whether neutrophil iron plays a role in the pathology of malaria is an interesting open question which we hope future studies can elucidate.

A section was added to the discussion to address the role of innate immune cells in our model (line 354-363):

“The inhibited innate immune response to P. chabaudi in TfrcY20H/Y20H mice likely contributed to both the increased pathogen burden and the decreased liver pathology. Splenic MNPs are important for controlling parasitaemia (34,35,72), but MNPs are also vital for maintaining tissue homeostasis and preventing tissue damage in malaria (43,73). Although other innate cells, such as neutrophils, NK cells and γδT cells are an important part of the immune response to malaria, only the MNP response was distinctly impaired in TfrcY20H/Y20H mice. Notably, neutrophils are known to be sensitive to iron deficiency (16,74) and to affect both immunity and pathology in malaria (75,76). However, in the context of recently mosquito-transmitted P. chabaudi it appears that monocytes and macrophages, rather than granulocytes, may be particularly important for parasite control and tissue homeostasis (43,72).”

Changes to the text:

• Fig S1EandF - Please add to the figure legend that these were measured at homeostasis.

Response: This clarification has been added to the legend of Supplementary Figure 1 (line 954-957).

• Figure 3 - In the legend, H and I are the wrong way around.

Response: The legend of Figure 3 has been corrected accordingly (line 888-890).

• Figure 4 - please add the units of concentration of FeSO4 to all panels

Response: The units of concentration for FeSO4 and AFeC have been added to all panels of Figure 4 and 6, respectively.

• Line 246 - The authors state: "there was some evidence of decreased malaria-induced hepatomegaly" however there is no significant difference between WT and TfR mice and both show significant hepatomegaly. I feel that this line should be reworded.

Response: The sentence (line 252-254) has been reworded as follows:

“Furthermore, while both genotypes developed malaria-induced hepatomegaly, there was a trend toward less severe hepatomegaly in TfrcY20H/Y20H mice (Figure S5C).”

Significance (Required):

This work is one of the first to attempt to define the requirements for cellular iron in malaria infection. This is a difficult topic, as infection and associated inflammation and the red blood cell destruction caused by malaria all have complex effects on iron within the body. This study fits well with previous observations showing that anemia can be protective as it both prevents parasite growth and limit immunopathology. This work advances the field by demonstrating a cell intrinsic role for iron in malaria infection. There is a broad possible audience for this work, including malaria researchers, immunologists and people interested in the role or iron, both at a cellular level and systemically.

Reviewer #2

Evidence, reproducibility and clarity (Required):

In this manuscript, the authors have studied the role of iron deficiency in the host response to Plasmodium infection using a transgenic mouse model that carries a mutation in the transferrin receptor. They show that restricted cellular iron acquisition attenuated P. chabaudi infection- induced splenic and hepatic immune responses which in turn mitigated the immunopathology, even though the peak parasitemia was significantly high in the mutant mice. Interestingly, the course of parasite infection doesn't seem to be affected in the mutant mice compared to the wildtype mice despite the induction of poor immune responses. The authors show that the decreased cellular iron uptake broadly impact both innate and adaptive components of the immune system. Conversely, free iron supplementation restored the immune cell functions.

• The study is well performed, and the manuscript is well written. However, the authors should show how conserved the role of cellular iron is across other rodent malaria parasite species at least with * yoelii or P. berghei* blood stage infection models. This question becomes critical to address in order to understand broad relevance to human malaria infections where both the host and parasites are genetically diverse.

Response: We thank the reviewer for appreciating our study and for the thoughtful comments. We agree with the reviewer that the diverse genetic background of both parasites and hosts makes it difficult to draw broad conclusions about human malaria infection from animal studies performed in a laboratory setting. The recently mosquito-transmitted P. chabaudi chabaudi AS blood-stage infection model replicates many key features of mild to moderate malaria infection in humans, such as low parasitaemia, anaemia, cyto-adhesive sequestration in microvasculature, and self-resolving immunopathology. Importantly, the immune response elicited by recently mosquito-transmitted parasites also more closely mimics the immune response to a natural infection (PMID: 23719378). Therefore, we consider the recently mosquito-transmitted P. chabaudi chabaudi AS model as the most relevant to answer our particular research questions.

Furthermore, specific pathogen-free parasitised erythrocyte stabilates made from recently mosquito-transmitted P. berghei or P. yoelii parasites are unfortunately not readily accessible (e.g. through the European Malaria Reagent Repository), in contrast to P. chabaudi. Consequently, preparing and characterising recently mosquito-transmitted strains to perform the experiments suggested by the reviewer would require a substantial amount of additional time and labour, which we deem out of scope for this study.

In the design of our model we have also taken care to minimise the effects of anaemia, something which would be difficult or impossible to achieve using serially blood passaged P. yollii or P. berghei parasites. Both P. yoelii and P. berghei merozoites preferentially invade immature RBCs (PMID: 34322397) making readouts such as parasitaemia far more sensitive to small variations in erythropoietic output. In addition, the extensive RBC destruction caused by most serially blood-passaged murine Plasmodium strains would likely exaggerate any erythropoietic impairment caused by the TfrcY20H/Y20H mutation.

Although we strongly believe that the chosen mouse model of malaria is the most appropriate for our study, ultimately, no mouse model can replicate all features of human malaria infection. Inevitably, the direct relevance of animal studies for human infection will always be somewhat opaque. Hence, we respectfully disagree with the reviewer that repeating the experiments with additional murine malaria parasite species would allow us to extrapolate conclusions about human malaria infection. Such experiments would also conflict with the 3Rs principles that govern work with animals in the UK (https://nc3rs.org.uk/). Especially, because most strains of P. yoelii and P. berghei cause severe or non-resolving infections and have a significant negative impact on animal welfare.

In our opinion, the logical continuation of this study must be to utilise the insights from our research to inform future human studies on the relationships between iron deficiency and malaria-related immunopathology. However, we agree that this is an important topic and have added a section addressing the broad relevance of our findings to the discussion (line 393-396):

“It remains to be seen what the broader importance of cellular iron is in human malaria infection, in particular within the diverse genetic context of both humans and parasites found in malaria endemic regions. Murine models of malaria are useful in providing hypothesis-generating results, but such findings ultimately ought to be confirmed and developed further through studies in human populations.”

• Since, restricted cellular iron uptake mitigates the immunopathology, the authors should explore whether this could also relieve the cerebral malaria condition that is caused by the hyper inflammation in the brain. They should use the * berghei* ANKA parasite strain which causes t cerebral malaria in mice. I think would increase impact of the paper.

Response: Although we agree that this would be an interesting line of inquiry, we think that it is outside of the scope of this study, which predominantly aims to characterise and study the effects of cellular iron deficiency in host cells, particularly immune cells, during mild to moderate malaria infection. The severe pathology underlying cerebral malaria differs greatly from that of a self-resolving blood-stage infection. Furthermore, the relevance to human cerebral malaria of the P. berghei ANKA model is controversial within the field (PMID: 21288352) and as a severe infection its use would again conflict with the 3Rs principles.

Minor comments:

• Line 222: repeating word, "iron iron-supplemented...."

Response: The sentence has been corrected (line 228).

• Figure 3C, S4C & S5F: Why Mann-Whitney test is performed in these particular graphs, whereas rest of the two groups comparison were done using Welch's test? The authors should clearly mention this in the methods section.

Response: We apologise if this was unclear in the manuscript. We routinely tested all our datasets for normality to identify the appropriate tests for each dataset. In case of the graphs shown in figure 3C, S4C and S5F, the dataset did not pass the D’Agostino-Pearson normality test and we therefore applied a non-parametric test (i.e. Mann-Whitney), in contrast to the other datasets that passed the test for normal or lognormal distribution. This has been further clarified in the method section (line 581-586):

“The D’Agostino-Pearson omnibus normality test was used to determine normality/lognormality. Parametric statistical tests (e.g. Welch’s t-test) were used for normally distributed data. For lognormal distributions, the data was log-transformed prior to statistical analysis. Where data did not have a normal or lognormal distribution, or too few data points were available for normality testing, a nonparametric test (e.g. Mann-Whitney test) was applied.“

• Have authors explored whether gamma-delta T cell responses are affected in the mutant mouse strain compared to wildtype mice as they are one of the early responders and the key cytokine producing cells against the Plasmodium blood stage infection.

Response: __We thank the reviewer for this valuable comment. We briefly explored the role of γδT cells, but did not observe a significant difference in splenic γδT cell numbers between wild-type and TfrcY20H/Y20H mice, eight days post-infection (__Reviewer Figure 1). It is of course possible that γδT cell numbers were affected at an earlier stage, or that γδT cell function (e.g. cytokine production) was affected by cellular iron deficiency during P. chabaudi infection. However, γδT cells may also be less sensitive to cellular iron deficiency than conventional T cells, as has been previously demonstrated for developing T cells (PMID: 7957580).

A section was added to the discussion to address the role of innate immune cells in our model (line 354-363):

“The inhibited innate immune response to P. chabaudi in TfrcY20H/Y20H mice likely contributed to both the increased pathogen burden and the decreased liver pathology. Splenic MNPs are important for controlling parasitaemia (34,35,72), but MNPs are also vital for maintaining tissue homeostasis and preventing tissue damage in malaria (43,73). Although other innate cells, such as neutrophils, NK cells and γδT cells are an important part of the immune response to malaria, only the MNP response was distinctly impaired in TfrcY20H/Y20H mice. Notably, neutrophils are known to be sensitive to iron deficiency (16,74) and to affect both immunity and pathology in malaria (75,76). However, in the context of recently mosquito-transmitted P. chabaudi it appears that monocytes and macrophages, rather than granulocytes, may be particularly important for parasite control and tissue homeostasis (43,72).”

Significance (Required):

Overall, the study provides novel insights into the role of iron in the immune response to Plasmodium blood stage infection using a rodent malaria model and the interplay of infection, immunity and the development of pathology. As such it is an important study.

Reviewer #3

Evidence, reproducibility and clarity (Required):

Herein Wideman provide novel and important evidence on the role of iron availability for mounting an efficient immune response in a malaria infection model. They employed TfRC Y201H/Y201H mice which develop iron deficiency due to impaired cellular ingestion of transferrin bound iron. They found that those mice develop higher peak parasitemia after vector borne exposure to Pl. chabaudi chabaudi which was paralleled by an impaired immune response as reflected by altered CD4 cell activation, reduced IFN-g formation or reduced B-cell responsiveness. Those deficiencies could be re-covered upon ex vivo iron supplementation pointing to the importance of iron availability for mounting-CD4+ and B-cell specific anti-plasmodial immune responses at the initial phase of infection. However, TFRC mutated mice were able to clear infection over time in a comparable fashion to wt mice.

This excellent study is important in convincingly showing (by employing high quality immunological analyses) the importance of cellular iron deficiency on immune responses in an infection model of general interest. It also indicates that overwhelming immune response as seen in wt mice is associated with organ damage over time.

Minor comments:

• The authors should discuss why and how TFRC mutated mice were able to control infection over time in a comparable fashion as wt mice although peak parasitemia was significantly higher?

__Response: __We thank the reviewer for the helpful feedback on our study and for posing this interesting question. It does indeed appear as if the immune response, while significantly inhibited in the TfrcY20H/Y20H mice, is still sufficient to clear the infection. It is plausible that the early cell-mediated immune response is inhibited to the degree that parasite control is impaired, resulting in higher peak parasitaemia in TfrcY20H/Y20H mice. In contrast, parasite clearance is comparable and contemporary in both genotypes. Based on the fact that parasite clearance occurs at a time when a substantial adaptive immune response is expected to emerge, we hypothesize that this significantly contributes to pathogen clearance. Thus, it seems likely that the humoral response in TfrcY20H/Y20H mice, even if inhibited, may still be effective enough to clear the parasites and prevent recrudescence.

As malaria infection progresses, RBC loss and increasing anaemia also contributes to limiting exponential parasite growth. This occurs more or less equally in both genotypes, but it could be particularly important for parasite control in the TfrcY20H/Y20H mice that have an inhibited immune response.

We have added a section to the discussion to address this (line 380-386):

“Despite the higher peak parasitaemia in TfrcY20H/Y20H mice, both genotypes were able to clear P. chabaudi parasites at a comparable rate and prevent recrudescence. It follows that even a weakened humoral immune response appears to be sufficient to control P. chabaudi infection. However, our study did not investigate the effects of immune cell iron deficiency on the formation of long-term immunity, which may have been more severely affected. The impaired GC response, in particular, suggests that iron deficiency could counteract the formation of efficient immune memory to subsequent malaria infections.”

• The authors and others have previously shown (Frost J et al. Sci Adv 2022, Hoffmann et al. EBioMedicine 2021) that iron deficiency results in reduced neutrophil numbers in different infection models. This could also have contributed to the observed effect in initial infection control but may have also been linked altered histopathology seen in Figure 7. However, no mention of neutrophil numbers in this model is made. It would be important if the authors could provide information on neutrophil numbers (only if this analysis has been already performed) and discuss this issue in association with their observation.

Response: We appreciate that the reviewer has brought attention to this important topic. As they mention, iron deficiency can have a negative impact on the neutrophil response (PMID: 36197985, 34488018) but it can also cause a maladaptive excessive neutrophil response due to failed adaptive immunity (PMID: 33665641). In this study, we show that there is no difference in splenic neutrophil numbers between wild-type and TfrcY20H/Y20H mice, eight days after P. chabaudi infection (Figure S3B). Moreover, the histopathologists detected no liver neutrophil infiltration in either genotype, but rather observed infiltration of mononuclear leukocytes upon P. chabaudi infection. Hence, it appears unlikely that neutrophils were a major contributor to differences in either immunity or pathology in this specific context. However, we cannot definitively rule out that neutrophil numbers were affected earlier in the infection or that neutrophil function was impaired due to cellular iron deficiency.

A section was added to the discussion to address the role of innate immune cells in our model (line 354-363):

“The inhibited innate immune response to P. chabaudi in TfrcY20H/Y20H mice likely contributed to both the increased pathogen burden and the decreased liver pathology. Splenic MNPs are important for controlling parasitaemia (34,35,72), but MNPs are also vital for maintaining tissue homeostasis and preventing tissue damage in malaria (43,73). Although other innate cells, such as neutrophils, NK cells and γδT cells are an important part of the immune response to malaria, only the MNP response was distinctly impaired in TfrcY20H/Y20H mice. Notably, neutrophils are known to be sensitive to iron deficiency (16,74) and to affect both immunity and pathology in malaria (75,76). However, in the context of recently mosquito-transmitted P. chabaudi it appears that monocytes and macrophages, rather than granulocytes, may be particularly important for parasite control and tissue homeostasis (43,72).”

• In addition, alternative mechanism leading to immune tolerance and reduced tissue damage such as induction of heme oxygenase-1, which is also affected by systemic iron availability, should be discussed.

Response: __An addition was made to the results section and to Figure S5 to address this reviewer comment (line __269-274):

“In addition, we measured the expression of two genes that are known to have a hepatoprotective effect in the context of iron loading in malaria: Hmox1 (encodes haemoxygenase-1) and Fth1 (encodes ferritin heavy chain). Liver gene expression of Hmox1 was higher in TfrcY20H/Y20H mice, while the expression of Fth1 did not differ between genotypes, eight days after infection (Figure S5H-I). Thus, the higher expression of Hmox1 may have contributed to the hepatoprotective effect in TfrcY20H/Y20H mice.”

A relevant sentence was also added to the discussion (line 313-318):

“For example, HO-1 plays an important role in detoxifying free haem that occurs as a result of haemolysis during malaria infection, thus preventing liver damage due to tissue iron overload, ROS and inflammation (62). Interestingly, infected TfrcY20H/Y20H mice had higher expression of Hmox1, but levels of liver iron and ROS comparable to that of wild-type mice. Consequently, this may be indicative of increased haem processing that could have a tissue protective effect”

Significance (Required):

Important and intersting study highlighting the central role of iron homeostasis for immune repsonse to infection. General interest because iron deficiency has high prevalence in areas with high enedemic burden of infection

Reviewer's expertise: infectious disease, immunity, iron homeostasis-- both basic science and clincal expertise (more than 300 peer reviewed publications on these topcis)

Author Response

Reviewer #1 (Public Review):

The cerebral cortex, or surface of the brain, is where humans do most of their conscious thinking. In humans, the grooves (sulci) and bumps (convolutions) have a particular pattern in a region of the frontal lobe called Broca's area, which is important for language. Specialists study features imprinted on the internal surfaces of braincases in early hominins by casting their interiors, which produces so-called endocasts. A major question about hominin brain evolution concerns when, where, and in which fossils a humanlike Broca's area first emerged, the answer to which may have implications for the emergence of language. The researchers used advanced imaging technology to study the endocast of a hominin (KNM-ER 3732) that lived about 1.9 million years ago (Ma) in Kenya to test a recently published hypothesis that Broca's remained primitive (apelike) prior to around 1.5 Ma. The results are consistent with the hypothesis and raise new questions about whether endocasts can be used to identify the genus and/or species of fossils.

We would like to thank Rev. 1 for their comments on our paper.

Reviewer #2 (Public Review):

The authors tried to support the hypothesis that early Homo still had a primitive condition of Broca's cap (the region in fossil endocasts corresponding to Broca's area in the brain), being more similar to the condition in chimpanzees than in humans. The evidence from the described individual points to this direction but there are some flaws in the argumentation.

We are grateful to Rev. 2 for their comments, although we partially agree with some of them.

First, we would like to rectify the statement of Rev. 2 that we “tried to support the hypothesis that early Homo still had a primitive condition of Broca's cap”, indeed, our aim was to test this hypothesis and not to try to validate it.

First, only one human and one chimpanzee were used for comparison, although we know that patterns of brain convolutions (and in addition how they leave imprints in the endocranial bones) are very variable.

We understand the point raised by Rev. 2 about the variation of brain convolutions in humans and chimpanzees. We used atlases published by Connolly (1950), Falk et al. (2018) and de Jager et al. (2019, 2022) to analyse the endocast of KNM-ER 3732 and compare it to the extant human and chimpanzee cerebral conditions. However, in Figure 2, for the sake of clarity only two Homo and Pan specimens were used to illustrate the comparison (as it has been done in other published papers, e.g., Carlson et al., 2011; Science, Gunz et al., 2020 Sci Adv). In the revised version, we modified the manuscript to explain further our approach (line 156) “We used brain and endocast atlases published in Connolly (1950), Falk et al. (2018) and de Jager et al. (2019, 2022; see also www.endomap.org) for comparing the pattern identified in KNM-ER 3732 to those described in extant humans and chimpanzees. To the best of our knowledge, these atlases are the most extensive atlases of extant human and chimpanzee brains/endocasts available to date and are widely used in the literature to explore variability in sulcal patterns. In Figure 2, the extant human and chimpanzee conditions are illustrated by one extant human (adult female) and one extant chimpanzee (adult female) specimens from the Pretoria Bone Collection at the University of Pretoria (South Africa) and in the Royal Museum for Central Africa in Tervuren (Belgium), respectively (Beaudet et al., 2018).”.

Second, the evidence from this fossil specimen adds to the evidence of previously describe individuals but still not yet fully prove the hypothesis.

We tempered our discussion by concluding that (line 116) “Overall, the present study not only demonstrates that Ponce de León et al.’s (2021) hypothesis of a primitive brain of early Homo cannot be rejected, but also adds information […]”.

Third, there is a vicious circle in using primitive and derived features to define a fossil species and then using (the same or different) features to argue that one feature is primitive or derived in a given species. In this case, we expect members of early Homo to be derived compared to their predecessors of the genus Australopithecus and that's why it seems intriguing and/or surprising to argue that early Homo has primitive features. However, we should expect that there is some kind of continuum or mosaic in a time in which a genus "evolves into" another genus. This discussion requires far more discussions about the concepts we use, maybe less discussion about what is different between the two groups but more discussion about the evolutionary processes behind them.

We fully agree with Rev. 2 on this aspect. We believe that identifying these differences/similarities between fossil and extant hominids constitute the first step of a better understanding of the evolutionary mechanisms. Our work suggests indeed a certain continuity between genera and raises questions on the genus concept and how to interpret the specimens currently attributed to early Homo. In the revised version of the manuscript we included a reference to this possible scenario (line 134): “[…] or to the absence of a definite threshold between the two genera based on the morphoarchitecture of their endocasts (Wood and Collard, 1999).”.

Fourth, the data of convolutional imprints presented are rather subjective when identifying which impressions represent which brain convolutions. Not seeing an impression does not necessarily mean that the corresponding brain feature did not exist. Interestingly, the manuscript does not mention and discuss at all the frontoorbital sulcus. This is a sulcus that usually runs from the orbital surface of the frontal lobe up to divide the inferior frontal gyrus in chimpanzees, a condition totally different than in humans who do not have a frontoorbital sulcus. Could such a sulcus be identified, this would provide a far more convincing argument for a primitive condition in this specimen. In Australopithecus sediba, e.g., the condition in this region seems to be a mosaic in which some aspects of the morphology seem to be more modern while one of the sulcual impressions can well be interpreted as a short frontoorbital sulcus. For this specimen, by the way, I would come back to my third point above: some experts in the field might argue that this specimen could belong to Homo rather than Australopithecus...

We agree that the presence of a fronto-orbital sulcus would be more conclusive. However, this sulcus has not been identified in KNM-ER3732 and the region in which we would expect to find it is not preserved. As demonstrated by Ponce de León et al. (2021), because of the topographic relationships between sulci (and cranial structures), it is possible to interpret imprints on endocasts and the evolutionary polarity of some traits even in the absence of landmarks such as the fronto-orbital sulcus. In Australopithecus sediba the main derived feature of the endocast corresponds to the ventrolateral bulge in the left inferior frontal gyrus, and not to the sulcal pattern itself (Carlson et al., 2011 Science). However, the discussion around the taxonomic status of this taxon confirms the urgent need for reconsidering specimens from that time period and clarifying the mosaic-like or concerted evolution of the derived Homo-like traits within our lineage. Regarding the subjective nature of this approach, we invite readers to examine the specimen on MorphoSource (https://www.morphosource.org/concern/media/000497752?locale=en) and to request access to the National Museums of Kenya to the physical or virtual specimen to falsify our hypothesis.

According to my arguments above, I think that this manuscript might revive interesting discussions about this topic but it is not likely to settle them because the data presented are not strong enough to fully support the hypothesis.

We would be more than happy to consider new/other specimens with similar chronological and geographical contexts and investigate further this hypothesis in the future.

Reviewer #3 (Public Review):

The authors provide a detailed analysis of the sulcal and sutural imprints preserved on the natural endocast and associated cranial vault fragments of the KNM-ER3732 early Homo specimen. The analyses indicate a primitive ape-like organization of this specimen's frontal cortex. Given the geological age of around 1.9 million years, this is the earliest well-documented evidence of a primitive brain organization in African Homo.

In the discussion, the authors re-assess one of the central questions regarding the evolution of early Homo: was there species diversity, and if yes, how can we ascertain it? The specimen KNM-ER1470 has assumed a central role in this debate because it purportedly shows a more advanced organization of the frontal cortex compared to other largely coeval specimens (Falk, 1983). However, as outlined in Ponce de León et al. 2021 (Supplementary Materials), the imprints on the ER1470 endocranium are unlikely to represent sulcal structures and are more likely to reflect taphonomic fracturing and distortion. Dean Falk, the author of the 1983 study, basically shares this view (personal communication). Overall, I agree with the authors that the hypothesis to be tested is the following: did early Homo populations with primitive versus derived frontal lobe organizations coexist in Africa, and did they represent distinct species?

I greatly appreciate that the authors make available the 3D surface data of this interesting endocast.

We are grateful to Rev. 3 for their comments and for contextualizing our finding. We would also like to point out that, although the 3D surface can be viewed on MorphoSource, permission from the National Museums of Kenya has to be requested for studying the specimen and getting access to the physical specimen and/or the 3D model.

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Reply to the reviewers

We thank the reviewers for their comments and insights, we feel the manuscript is now greatly improved. Please find below our answers to the reviewer’s queries

Reviewer #1 (Evidence, reproducibility and clarity):

The manuscript by Niccoli et al. describes the identification of a novel modifier of C9orf72-derived toxicity based on the manipulation of the brain metabolic pathways. The premise for this work is supported by strong literature describing the aberrant glucose metabolism in FTD, AD and other degenerative disorders. The idea tested here is whether increasing the import of pyruvate produced in glia into neurons. They test three different types of importers and find that one of them, Bumpel, the orthologue of human SLC5A12, suppresses toxicity and reduces the accumulation of arginine-containing repeats, GP and PR. The authors investigate several potential mechanisms mediating this reduction of toxic DPRs, but do not find strong evidence linking pyruvate import and increase autophagy or mitochondria metabolism.

Overall, this is an interesting discovery based on a candidate approach that shows the power of Drosophila to efficiently identify novel mediators of neurodegeneration. The article is well written, although more detailed explanations of some experiments would be helpful. The weaknesses of the manuscript are the lack of a clear mechanism mediating the protective activity of pyruvate, the incomplete experiments lacking relevant controls, and the presentation of western blots.

Specific comments:

1. The reduced levels of DPRs require that the expression of C9 mRNA or the GR and PR constructs is examined by qPCR. In figure 3E, GP is not even detectable_

We agree with the reviewer, ideally we would have measured the RNA by qPCR. However, the C9 repeats and the DPR constructs are highly repetitive, it is therefore impossible to do a qPCR for them. The upstream and downstream sequence is identical for the C9 and the bumpel constructs, there isn’t, to our knowledge any unique sequence we can use to measure levels of expression in the presence of bumpel.

We did run a GFP control (Fig 2D) and did not see any difference and we have now carried out a qPCR for Gal4-GeneSwitch (Fig S3) to show that the levels of the driver do not change.

1. I wonder if there are constructs available to silence Bumpel or overexpress the human orthologues of bumpel. These would be nice controls for the effects observed with the Bumpel overexpression

This would be an extremely interesting experiment, however bumpel is normally only expressed in glia, therefore we can’t down-regulated it in glia whilst upregulating 36R in neurons, as we are limited to one driver (since everything is driven by the Gal4/UAS system). Expression of C9 in glia does not have a clear phenotype (our observation), so we can’t drive both in glia. We tried over-expressing the human homologue SLC5A12 , but it did not rescue the C9 phenotype (data not shown), possibly because it requires (like other human SLC5A type transporters) PDZK1 as extra co-factor (Srivastava S. et al, 2019), and this is not present in flies.

1. The argument about bumpel modulating autophagy downstream of Atg1 is not supported by the experimental data

We now have imaging data showing that bumpel modulates the formation of lysosomes, downstream of Atg1 (Fig 5). We also show that bumpel and Atg1 can act synergistically, leading to a much stronger rescue of C9 expression (See Fig 5I.), which also suggests that the two are acting at different points in the same pathway. We also show that bumpel rescues the downregulation of TFEB targets (Fig 5J)

1. Western blots throughout show no control lanes and in several occasions are created with cutout bands. The standard for this type of experiments should be more stringent, with entire gels showing all experimental conditions, which requires consistent methods and results vs selecting the best bands from different gels.

We apologise if this was mis-understood, the lanes shows are all from the same blot, where other samples were run too, and it would be confusing for the reader to include them. We have re-run samples where we had remaining sample from our quantifications, so that the lanes are now contiguous and we provide original blot images in the supplemental information for those we could not re-run. The control for all experiments are the C9 expressing line without bumpel, and this is always present, if the reviewer means we are missing -RU controls, these do not produce any DPRs so are not included in western blot or ELISA quantifications as the signal is not above back-ground.

1. For figures 2B and 5C, please, show representative WBs

These are ELISA quantifications, not western blots, we choose to run these when possible, as they are more quantitative.

1. Figure 5D describes the survival curve as significantly rescued. Statistical tests can indicate differences, but that is in no way convincing. The test may show the curves are different, but the abeta Atg1 flies also seem to start falling early, so an argument could be made in both directions, as a suppressor or an enhancer.

We agree the rescue is not strong enough, we have now removed this lifespan.

1. It is unclear why several results are placed in the supplemental materials. In general, all this material seems highly relevant and related to what is shown in the main figures

We are happy to include them in the main manuscript if this would help the reader, and we have now placed all mitochondrial data in Fig 4.

Minor comments:

Please, define several abbreviations throughout

We apologise for this over-sight, we have now does this.

A couple of sections could be improved by carefully sequencing human vs Drosophila background to advance the argument rather than going in circles. There is also a section on mitophagy in between two sections related to autophagy that could be sequenced better.

We have re-structured the sections, we think this has improved the flow.

There is a sentence at the end of page 6 that seems misplaced

We apologise for the over-sight, and we have removed this

Reviewer #1 (Significance):

Overall, this is an interesting discovery based on a candidate approach that shows the power of Drosophila to efficiently identify novel mediators of neurodegeneration. The article is well written, although more detailed explanations of some experiments would be helpful. The weaknesses of the manuscript are the lack of a clear mechanism mediating the protective activity of pyruvate, the incomplete experiments lacking relevant controls, and the presentation of western blots.

We thank the reviewer for the helpful comments, we have added some details in the methods section, we apologise for not having made it clear that the westerns were all derived from the same blot (we have now placed the originals in the supplemental materials). Regarding mechanism, we now show that bumpel over-expression increases clearance of late stage autolysosomes, possibly by increasing transcription of TFEB target lysosomal genes.

Reviewer #2 (Evidence, reproducibility and clarity):

Summary:<br /> Project investigates the role in dementias of glial glucose uptake, conversion to lactate and shuttling via transporters to neurons to produce pyruvate to fuel TCA cycle production of ATG. The experiments are conducted in Drosophila melanogaster, which have become a powerful model system for understanding neurodegeneration mechanisms associated with ALS/FTD associated C9orf72 pathology. Bumple misexpression is shown to rescue early death phenotype in flies expressing a C9orf72 expansion and flies expressing arginine containing di-peptide repeat proteins. The report describes novel insight into the function of bumpel, demonstrating that this conserved orthologue of human SLC14A functions as a sodium exchange transporter for monocarboxylates pyruvate and lactate. These findings conclude that increased neuronal pyruvate, but not its metabolites, rescues C9orf72 associated pathology.<br /> The authors next set out to describe the mechanism by which increase pyruvate rescues survival in C9orf72 expressing flies. Levels of autolysosomes were increased in C9orf72 expressing flies, and stimulation of autophagy by overexpression of atg1 shown to decrease levels of DPRs (though not to same extent as bumple expression). Expression of bumple in C9orf72 flies led to a modest increase in LC3-II, indicating increased autophagy. Co-overexpression of bumple and atg1 did not have an additive effect, suggesting bumple activates autophagy downstream or independent of atg1 activity. Finally the author extend their findings to amyloid models, suggest a common protective mechanism for elevating neuronal pyruvate levels in neurodegenerative disease.

Major comments

Prior data suggests that bumpel is expressed in glia (for example Yildirim et al 2022). In their study the authors do not present any data to demonstrate that the transporter is normally expressed in neurons in flies. This calls into questions the physiological relevance of their findings, that neuronal upregulation of bumpel is protective against C9orf72 associated pathology in neurons, from which it is reasonable for a reader to conclude that bumpel may be a neuronal target for therapeutic intervention. However, the report well demonstrates that regardless of whether the transporter in native to neurons, the increase in monocarboxylates it facilitates is projective against C9orf72 pathology and thus the overall conclusion of the project is supported by experimental evidence. The point of upregulation of a natively expressed gene versus misexpression of a glial enriched transporter should be considered in a bit more detail in the discussion text. The authors may consider speculating the identify of members of the sodium coupled monocarboxylate transporters that are enriched in neurons. Are any of the bumple human orthologues expressed in neurons?_

We thank the reviewer for this comment and suggestion. The reviewer correctly points out that we do not show whether there is a defect in pyruvate import in C9 expressing flies. We could not identify a validated sodium coupled pyruvate transporter in flies with a strong neuronal expression, we have added a comment in the discussion about this. There are a number of human homologues, some, such as SLC5A8, are expressed in neurons, thus providing a possible therapeutic target. We have added a sentence to this regard in the discussion.

[_OPTIONAL] cDNA overexpression of neuron specific sodium coupled monocarboxylate transporters in C9orf72 fly models would strengthen the conclusion their physiological relevance for ALS/FTD. Fly lines for these are not available in repositories, but could be generated and tested at reasonable cost (<£700, ~3 month duration).

This would be an ideal experiment, however, we could not find a neuronal sodium coupled transporter which is known to import monocarboxylates. There are a number of sodium coupled neuronal transporters, but they are mostly homologous to SLC5A6, which is a glucose coupled transporter. Going forward, we will screen a number of transporters to identify if there are any which import pyruvate.

The role of bumple expression in survival (Figure 1) could be a technical artifact due to dilution of Gal4 between C9orf72 and bumple-ORF transgenes. No expression control is shown (for example GFP, LacZ etc). This theory is unlikely as no improvement in survival was seen for the SLC14A class of transporters which have a matching site directed transgene insertion. For clarity this point relating to controls should be commented on in the text.

The reviewer is correct, there could be a dilution of the Gal4. We don’t like using GFP as a control as we have often seen a worsening when expressing other highly stable proteins at high levels. We have generated an “empty” flyORF line (generated by injecting the empty plasmid into the identical attP site), and used it as a control to check for dilution effects, bumpel still rescued relative to this control, we now include this is the supplementary (Fig S1B).

Reduced Mito-GFP levels are used to support a role for bumple in increasing mitophagy. As mito-GFP is a marker for mitochondria but not specifically mitophagy, an alternative explanation for decreased levels could be reduced mitochondria biogenesis. The text should be amended to clarify this point.<br /> The role of Pink1 RNAi in modifying mitophagy is a bit overstated. Whilst Pink1 is involved in stress associated mitophagy, its role in basal mitochondria turnover is less well defined. Text should be adapted.

We have added qualifying statements regarding the possibility of reduced mitochondrial biogenesis, and the fact that Pink1’s role in basal mitophagy is not very clear. The use of the mitophagy inducer drug, Kaempferol, however, suggests that mitophagy is unlikely to be a cause of the DRP reduction.

Minor comments

Introduction well describes current state of C9orf72 fly models. Introduction would benefit from a few comparable lines for AD models. The first paragraph of reports may also be better placed in the introduction._

We thank the reviewer for the suggestion, and have added a more in depth introduction to Aß and have moved the first paragraph of the results section to the introduction

Figure 1 presents survival for three SLC16A transporters and bumple. The C9 control curve appears to be consistent between charts, likely indicating the same control used across experiments, rather than independent controls for each chart. The authors should considered showing either all SLC16A and bumple data on a single chart, or clarify in the figure legend that a common control dataset is used. GFP control is used in later experiments (Figure 2).

We have now indicated that the SLC16A transporters were run together in the figure legend.

Choice of amyloid model needs a line of explanation, particularly with regard to extra/intracellular deposition of amyloid in this model.

We have now added a few sentences describing this when the model is introduced

Fruit Fly Injection method section needs a bit more detail to describe site of injection (head, body etc). This is not clear in the result section either.

We have now added this, the injection was done in the abdomen.

How were bumple orthologues identified? What degree of conservation (sequence homology etc?)

The bumpel orthologues are those identified as most similar by flybase. We have now added the degree of conservation in the text

The speculative mechanism for C9 pathology modification involves interaction of neurons and glia, monocarboxylate transporters and changes in autophagy activity. For clarity a diagram showing the model may be a helpful addition.

We have now added a diagram explaining how we think the rescue is achieved

Typos:<br /> Figure 1 Legend - "p values of ona way ANOVA "

We apologise for the error, and have now corrected it

Figure S2 Legend - Atg1 RNAi genotypes from S2 legend are mentioned erroneously

We apologise for the error, and have now corrected it

Repetition of text in results: "Bumpel, together with its paralogues kumpel and rumpel, is expressed in glia in flies, where it is thought to promote transport of substrates across the brain (31)."

We apologise and have rectified this

"Modulation of Atg1 when bumpel was co-overexpressed, however, did not affect GP<br /> levels (Fig 4E, F)" - Should be refering to Fig 4D, E)

We apologise and have rectified this

Reviewer #2 (Significance):

The study will be of broadly of interest to researcher working in the fields of neurodegeneration and metabolism, providing evidence for a protective role of elevated pyruvate in neuron that provide new understand relating to pathology in C9orf72 associated motor neuron disease and frontotemporal dementia.

Strengths:<br /> The study presents novel data to demonstrate that overexpression of fly monocarboxylate transporter bumple rescues an early death phenotype associate with ALS/FTD gene C9orf72. Any novel therapeutic strategies of ALS are of interest to the field, and the strategy demonstrated here may be readily translated to human cell culture systems for proof of principle translational studies to a more physiologically relevant system. This study further demonstrates the utility of invertebrate models to generate novel understanding of C9orf72 pathology.

Limitations:<br /> The study speculates that there is a link between pyruvate levels and increased autophagy, however the mechanisms by which this occurs is not defined in present study. This is a limitation of the experiment, though opens up an interesting question for future studies._

We thank the reviewer for their comments, and we have now added experiments characterising the role of bumpel in autophagy, particularly showing its rescue of a late autolysosomal block.

Reviewer expertise: The reviewer researches ALS and dementia associated neurodegeneration, utilising Drosophila, rodent and stem cell derived model systems.

Reviewer #3 (Evidence, reproducibility and clarity):

This is an interesting manuscript in which the authors provide evidence that elevated neuronal expression of the pyruvate transporter bumpel can partially rescue shortened lifespan in fly models of frontotemporal dementia and Alzheimer's disease. In addition, elevated neuronal bumpel expression can reduce accumulation of arginine containing FTD-linked dipeptide repeat proteins. Some evidence is presented that elevated neuronal bumpel expression may activate autophagy. These findings are novel and may have implications for therapeutic interventions based on pyruvate import/metabolism to treat neurodegenerative disorders. However, I have several concerns as follows:

Major Comments:

1. The authors provide no explanation as to why they targeted bumpel overexpression in neurons. Endogenous bumpel appears to be predominately expressed in glia cells so why not target these cells instead?

We wanted to increase pyruvate import in neurons, so we over-expressed a number of pyruvate transporter that were available in the fly ORF stock centre (so that they would all be inserted into the same site and therefore directly comparable), we were mainly interested in cell autonomous effects of importing glycolytic metabolites. Over-expressing bumpel in glia would be indeed an extremely interesting experiment, unfortunately we do not have the ability to express C9 in neurons while over-expressing bumpel in glia as we only have one over-expression system that works. We are working towards generating a new C9 model so we can then use the Gal 4 system to over-express bumpel in glia, but this is currently not available yet. Over-expression of C9 in glia is not toxic and not a good model of disease.

1. Data is shown that overexpressed bumpel can suppress GR and PR dipeptide repeat toxicity when these peptides are translated using an ATG start codon (Fig 2D,E). Does bumpel mediated neuroprotection also correlate with a reduction in DPR levels driven with an ATG start codon?

This would be a very interesting question, unfortunately, whist the Isaacs lab kindly made available the GR antibody for the initial ELISA experiment, we no longer have that antibody available and we do not have a working PR antibody. GR and PR westerns are not possible to carry out as the proteins are too positively charged to run. We do show that bumpel can down-regulate Aß from a UAS promoter, so its effect is not specific to RAN translation.

1. The authors provide some evidence suggesting that overexpression of bumpel increases autophagy in the fly brain. However, knockdown of Atg1 while co-expressing bumpel (Fig 4E) did not result in increased GP protein levels. In addition, Atg1 knockdown did not attenuate the protective effects of bumpel overexpression (Fig 4I), suggesting that bumpel is working through a pathway independent of autophagy to promote DPR clearance and protection against toxic peptide accumulation. The authors need to modify the interpretation of their data and temper their claim that autophagy contributes to bumpel-mediated protective effects in the CNS.

We apologise the data was not strong enough. We have now added evidence that bumpel acts downstream of Atg1, on late stage autolysosomal clearance. We also show that bumpel and Atg1 can act synergistically to improve the C9 phenotype when over-expressed, this is now described in Fig 5.

1. Although the authors present evidence that increased bumpel expression can activate autophagy, the data is not convincing that the neuroprotective effects associated with bumpel are mediated through autophagy. Pyruvate, in some circumstances, can non-enzymatically scavenge hydrogen peroxide or in other cases trigger oxidative stress resistance through hormetic ROS signaling. The authors should consider these alternative possibilities.

These are indeed possibilities, we have added a sentence to that effect in the discussion, we have now also showed that bumpel is affecting late clearance of autolysosomes, and is leading to an increase in TFEB targets.

1. The authors rely on overexpressing bumpel to attenuate C9 toxicity in flies. They should perform the opposite experiment and knockdown bumpel to demonstrate that reduced bumpel expression results in potentiation of C9 and amyloid beta neurotoxicity. In addition, then should show that knockdown of bumpel expression has some effect on autophagy.

This would be a very interesting experiment, unfortunately bumpel is expressed only in a few glia subtypes in a wild type fly, and we can’t downregulate it in glia while over-expressing toxic proteins in neurons, because of limitations of our expression system, both genes need to be over-expressed in the same cell type. We have tried downregulating bumpel in neurons, and don’t get an effect on phenotype, and no effect on DPR levels, but bumpel expression in neurons is extremely low. Moreover, bumpel has 2 paralogs, rumpel and kumpel,(also only present in glia) and all three need to be knocked out for phenotypes to become visible in glia (Yildirim et al, 2022). These experiments would be interesting but outside out scope.

We are in the process of generating new C9 models to be able to do these experiments, but these are currently outside the scope of this work.

Minor Comments:

1. Neuronal overexpression of bumpel appears to shorten lifespan of wild type flies (Fig 2A). It is possible that neuronal import of pyruvate may drive mitochondrial oxidative phosphorylation and ROS formation. The authors should comment on this possibility in the discussion._

This is a very good point, we have added a point to that effect.

1. In Fig 3 the authors used a mixture of sodium pyruvate and ethyl pyruvate to demonstrate the import properties of bumpel. The rationale for using ethyl pyruvate is unclear as this membrane-permeable metabolite can by-pass any transporters.

The ethyl pyruvate was only used in the injection of flies, not for the FRET experiments looking at the import properties of bumpel. Since we were not over-expressing bumpel, we needed the pyruvate to by-pass the requirement for a transporter. We were showing that delivery of pyruvate by another methods (other than by a transporter) was able to phenocopy the over-expression of bumpel, thus showing the effect is mediated by pyruvate entrance into the cell.

1. In the introduction several acronyms are used (i.e. GRN, MAPT, TREM2) that are not defined.

We apologise and have now rectified this.

Reviewer #3 (Significance):

To my knowledge, this is the first study to identify that bumpel can permit the import of pyruvate and lactate into neurons when ectopically expressed in the fly brain. The fact that increased neuronal pyruvate import can partially protect against toxic peptide accumulation is unexpected and quite novel. Although some evidence is presented that bumpel can trigger autophagy, it is not clear if autophagy is mediating bumpel neuroprotective effects. Alternative mechanisms related to pyruvate effects on ROS and oxidative stress resistance should be considered.

We thank the reviewer for their comments, and have added clarifying statements regarding the potential role of ROS.

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 comments and suggestions, which were very helpful to improve our manuscript. The revised manuscript notably includes the following improvements:

• To evaluate the relevance of identified candidate targets genes, we integrated an additional screening step in our method, corresponding to the analysis of RNAseq datasets specific of blood or brain cells. RNAseq data from irradiated hematopoietic stem cells or splenic cells were analyzed and included in the new Table S19, and RNAseq data from zika virus-infected neural progenitors were analyzed and included in the new Table S28. In addition, we also verified that the expression of a subset of blood related genes was decreased in the bone marrow cells of p53Δ31/Δ31 mice, known to exhibit increased p53 activity and to phenocopy dyskeratosis congenita (new Figure S8).
• Luciferase data were expanded to show that, for promoters exhibiting a significant p53-mediated repression in luciferase assays, the p53-dependent regulation was abrogated after mutation of the putative DREAM binding site (new Figures 2e and 2i).
• We found putative DREAM binding sites for 151 targets, and the predicted binding sites were precisely mapped relative to the position of ChIP peaks of DREAM subunits (E2F4 and LIN9) and to transcription start sites of target genes. These additional analyses, shown in the new Figures 3a and 3b, further suggest the reliability of our predicted binding sites. Notably, hypergeometric tests of the distribution of DREAM binding sites relative to E2F4/LIN9 ChIP peaks reveal a significant >1300-fold enrichment of these sites at ChIP peaks.
• We now present a detailed comparison of our results with those reported in other studies, notably the predicted E2F and CHR sites from the Target gene regulation database (new Figure S11), or the list of candidate DREAM targets suggested from Lin37 KO cells (new Figure S10 and new Table S35). This also leads us to discuss the different types of DREAM binding sites (bipartite sites (e.g. CDE/CHR or E2F/CLE) vs sites composed of a single E2F or a single CHR motif).
• We integrated updates of the Human phenotype ontology website to include the latest lists of genes related to blood or brain ontology terms in our analysis. In the previous version of the manuscript we had analyzed a total of 811 genes downregulated ≥ 1.5 fold upon bone marrow cell differentiation. Our revised manuscript now includes the analysis of 883 genes.
• Several improvements were made to present our results more clearly and with more details : 1) additional evidence that the differentiation of Hoxa9ER cells correlates with p53 activation is now provided in the new Figure S1; 2) the precise values for gene expression after bone marrow cell differentiation, as well as p53 regulation scores from the Target gene regulation databases are included in the new Tables S1, S5, S8, S11, S14, S20 and S23; 3) A Venn-like diagram was included to summarize the different steps of our approach in the new Figure 3c, with detailed lists of genes selected at each step in new Tables S17 and S26; 4) for genes associated with blood or brain genetic disorders, bibliographic references describing gene mutations and clinical traits were included in a new Table S36; 5) Figure 4a and Table S37 were improved to include evidence that increased BRD8 in glioblastoma cells leads to a decreased expression of several genes transactivated by p53.

Reviewer #1 (Evidence, reproducibility and clarity):

Summary<br /> In this paper the authors describe a data driven approach to identify and prioritise p53-DREAM targets whose repression might contribute to abnormal haematopoiesis and brain abnormalities observed in p53-CTD deleted mice. The premise is that in these mice, (where they have previously demonstrated p53 to be hyperactive in at least a subset of tissues), that the p53-p21-E2F/DREAM axis is at least in part responsible for observed phenotypes due to the repression of E2F and CDE/CHE element containing genes. Their approach to home in on relevant genes is based on transcriptomic gene ontology analysis of genes repressed in these disease settings where they primarily use publicly available data from HOXA9-ER regulated model of HSC expansion wherein they observe increases on p53-p21 expression upon differentiation where they demonstrate that p53-p21 DREAM target genes are suppressed as we would expect in this scenario where p53-p21 is activating withdrawal from cell cycle. They then spend a lot of effort analysing this datasets combining "gene-ontology", "disease phenotype" and "meta-ChIP-seq" analysis of public data to support the observation that mutations of genes suppressed in this manner are disproportionately linked to heritable haematopoetic and brain disorders. While these results are interesting in terms of framing a hypothesis about how mutations in p53-p21-DREAM regulated targets contribute to such conditions, they are to be expected given the now very well described impact of p53-p21 on both E2F4/DREAM targets.

We agree with the referee that the impact of p53-p21 on both E2F4/DREAM targets is well described. However, discussions with many scientists or clinicians specialized in bone marrow failure syndromes or microcephaly diseases led us to realize that most were not familiarized with the p53-DREAM pathway, so that a study that would bridge the gap between DREAM experts and bone marrow or microcephaly specialists would be particularly useful. In addition, we thought that strategies that would rely on disease-based ontology terms were likely to identify new targets, compared to previous studies that considered cell cycle regulation instead of disease phenotypes. Consistent with this, many genes we identified as candidate DREAM targets were not reported in previous studies. In addition, as detailed below, our positional frequency matrices led to identify DREAM binding sites that had not been predicted by previous approaches.

The natural progression of this work would be to go on to show this occurs in relevant cells or tissues derived from the p53-CTD mice as well as look at modulating target genes to understand underlying mechanisms and consequences.<br /> Rather than this, they focus on validating that a sub-set of these targets are indeed suppressed by specific p53 activation by MDM2 inhibitor Nutlin-3A in MEFs by qPCR and that mutation of predicted CDE CHR elements in luciferase constructs leads to increase luciferase activity. While these findings support their predictions, the results are entirely expected based on what is known about such targets and demonstrating that this occurs in MEFs does not closely relate to haematopoietic and brain cells they suggest this regulation is important. In fact, in the discussion, the authors comment on the importance of cell type context specificity in terms of discordance between predictions of TF binding sites and public datasets.

We agree that additional data from relevant cells or tissues were required to strengthen our conclusions. In the revised manuscript, we evaluated the relevance of candidate target genes related to blood ontology terms by integrating an additional screening step in our method, corresponding to the analysis of RNAseq datasets specific of blood cells. We analyzed dataset GSE171697, with RNAseq data from hematopoietic stem cells of unirradiated p53 KO, or unirradiated or irradiated WT mice, as well as dataset GSE204924, with RNAseq data from splenic cells of irradiated p53Δ24/- or p53+/- mice. The latter dataset appeared interesting because p53Δ24 is a mouse model prone to bone marrow failure and the spleen is a hematopoietic organ in mice. The analysis of these datasets is included in the new Table S19. In the datasets,increased p53 activity correlated with the downregulation of most of the 269 candidate DREAM targets. However, 56 genes which appeared upregulated in cells with increased p53 activity were considered poor candidate p53-DREAM targets and removed from further analyses, leading to a list of 213 genes that appeared as better candidate p53-DREAM targets related to blood abnormalities. Furthermore, we also verified that the expression of a subset of blood-related candidate genes was decreased in the bone marrow cells of p53Δ31/Δ31 mice (prone to bone marrow failure) compared to bone marrow cells from WT mice. This result is presented in the new Figure S8.

As for genes related to brain development, we discussed in the previous version of the manuscript that most genes mutated in syndromes of microcephaly or cerebellar hypoplasia are involved in ubiquitous cellular functions (chromosome condensation, mitotic spindle activity, tRNA splicing…), which suggested that our analysis of transcriptomic changes associated with bone marrow cell differentiation might also be used to identify brain specific targets. However, we agree with the referee that confirmation of these brain specific targets in a more relevant cellular context was preferable. In the revised manuscript, we included the analysis of datasets GSE78711 and GSE80434, containing RNAseq data from human cortical neural progenitors infected by the Zika virus (ZIKV) or mock-infected, because ZIKV was shown to cause p53 activation in cortical neural progenitors and microcephaly. This analysis is detailed in the new supplementary Table S28. In both datasets, increased p53 activity correlated with the downregulation of most of the 226 candidate DREAM targets. Sixty-four genes which appeared more expressed in ZIKV-infected cells were considered poor candidate p53-DREAM targets and removed from further analyses, leading to a list of 162 candidate p53-DREAM targets related to brain abnormalities. We think this significantly increases the relevance of our analysis of brain-specific targets.

Finally, they try and contextualise effects in glioblastoma data by correlating target gene expression with levels of BRD8 since it has recently been shown to attenuate p53 function in glioblastoma and show that some of the brain disease associated genes are expressed at higher levels in BRD8 high patient samples. It seems strange here that they do not also look at expression of p21 or other p53 targets that would help ascertain if p53 activity is indeed suppressed. Moreover, much more elegant methods for predicting transcription factor activity could be applied to this data.

We agree with the referee. Indeed, when we had performed the analysis of glioblastoma cells, we first verified that increased BRD8 levels correlated with decreased p21 levels in these cells. However, we had not included this verification in the previous version of the manuscript. In this revision, we improved the Figure 4 (and Table S37) reporting the analysis of glioblastoma cells to address this point. In Figure 4a, we now show the variations in mRNA levels between BRD8Low and BRD8High tumors, for BRD8 itself, as well as 5 genes well-known to be transactivated by p53 (p21, MDM2, BAX, GADD45A and PLK3) and the 77 p53-DREAM targets associated with microcephaly or cerebellar hypoplasia. The data clearly show that tumors with high BRD8 exhibit a decrease in the expression of p53 transactivated targets, and an increase in p53-DREAM repressed targets.

Major Comments<br /> The major result of this paper as it stands is the prioritisation of candidate genes in the p53-DREAM pathway involved in these conditions, and their refined approach used to identify and prioritise these genes and is such more of a starting point for further investigation. They fall short of demonstrating the relevance of their predictions physiologically in tissues from the mice and do not demonstrate functional importance of regulation of targets they put forward. Given that these genes will be co-ordinately regulated, without a mechanistic experiment in physiologically relevant model it is impossible to infer causality. For example, depleting individual targets in the HOXA9 model and evaluating impact on survival, proliferation and differentiation may be a (relatively) simple way to explore this, perhaps comparing to effects of p53 activating agents such as Nutlin-3A. Of note the authors (Jaber 2016 PMID: 27033104) and several other groups had (Fischer 2014 PMID: 25486564 McDade 2014 PMID: 24823795) previously demonstrated the link between p53-p21 and suppression of DNA-repair/Damage related genes (as is also observed here in particular FA-related genes that they discuss briefly here. I would have thought that this would be an obvious starting point for some mechanistic experiments and in fact I note this has been demonstrated before (Li et al 2018 PMID: 29307578)

The starting point of our study is not the prioritization of DREAM target genes, but rather the detailed phenotyping of p53Δ31/Δ31 mice that we performed in previous publications (Simeonova et al. Cell Rep 2013, Toufektchan et al. Nat. Commun. 2016), in which we mentioned phenotypical traits typical of dyskeratosis congenita and Fanconi anemia, including notably bone marrow failure and cerebellar hypoplasia.

We understand that depleting individual targets in the Hoxa9 system and evaluating impact on survival, proliferation and differentiation might seem appropriate to explore their potential causality. However, our previous work on Fanc genes leads us to think that this might not be informative. Regarding this, we now clearly discuss in the revised version of the manuscript : “Finding a functionally relevant [DREAM binding site] for Fanca, mutated in 60% of patients with Fanconi anemia [59,60], may help to understand how a germline increase in p53 activity can cause defects in DNA repair. Importantly however, we previously showed that p53Δ31/Δ31 cells exhibited defects in DNA interstrand cross-link repair, a typical property of Fanconi anemia cells, that correlated with a subtle but significant decrease in expression for several genes of the Fanconi anemia DNA repair pathway, rather than the complete repression of a single gene in this pathway [25]. Thus, the Fanconi-like phenotype of p53Δ31/Δ31 cells most likely results from a decreased expression of not only Fanca, but also of additional p53-DREAM targets mutated in Fanconi anemia such as Fancb, Fancd2, Fanci, Brip1, Rad51, Palb2, Ube2t or Xrcc2, for which functional or putative [DREAM binding sites] were also found with our systematic approach.” We further discuss in the manuscript how this may also apply to telomere-, ribosome-, of microcephaly-related genes.

The analysis of brain specific targets and the link to BRD8 sits largely as an aside and the analysis of patient data from glioblastomas is underdeveloped as noted above.

As we previously mentioned, the revised manuscript includes the analysis of RNAseq datasets from human cortical neural progenitors infected by the Zika virus (ZIKV) or mock-infected, which significantly increases the relevance of our analysis of brain-specific targets. Furthermore, we improved Figure 4 to present more clearly the impact of BRD8 levels on the expression of genes transactivated by p53 or repressed by p53-DREAM.

The computational methods applied are robust, albeit predominantly coorelative, in terms of identifying regulation of potential causative target genes, validated across human and mouse cell lines, and this indicates a role of these genes in the relevant conditions. However, further validation through application in a bulk or single cell RNAseq patient cohort, or at least an in vivo model would strengthen these conclusions and complement the work presented here which is based on in vitro mouse and human cells. This is pertinent as this study improves upon previously published approaches by focusing on "clinically relevant target genes". Additionally, this would exhibit the potential applications of the findings presented.

We thank the referee for this comment. As mentioned above, in the revised manuscript we analyzed RNAseq data from hematopoietic stem cells of unirradiated WT or p53 KO mice, or irradiated WT mice, and from splenic cells of irradiated p53D24/- or p53+/- mice, and quantified the expression of a subset of blood-related candidate genes in the bone marrow cells of p53Δ31/Δ31 mice (prone to bone marrow failure) and WT mice (new Figure S8 and Table S19). For genes related to brain development, we included the analysis of RNAseq data from human cortical neural progenitors infected by the Zika virus (ZIKV) or mock-infected (Table S28). These RNAseq analyses were added as an additional screening criterion in our approach, which significantly increased the relevance of the target genes identified.

In terms of statistical analysis, the hypergeometric test should be applied to assess significant enrichment of genes for example with CDE/CHR regions within the previously identified lists.

In the revised manuscript, we precisely mapped the DREAM binding sites in 50 bp windows within regions bound by E2F4 and/or LIN9, an analysis included in new Figure 3a. We then compared the distribution of DREAM binding sites at the level of ChIP peaks compared to their distribution over the entire genome and found a > 1300-fold enrichment of these sites at ChIP peaks. This significant enrichment (f=3 10-239 in a hypergeometric test) is most likely underestimated because mouse-human DNA sequence conservations were not determined for putative DBS over the full genome. These new analyses clearly reinforce our previous conclusions.

Minor Comments<br /> References are required for the genes listed which play a role in the diseases of interest.

In the revised manuscript, references are provided for genes which play a role in the diseases of interest. Due to the large number of added references, these were included in a new supplementary table, Table S36.

This paper would benefit from the inclusion of summary schematics and tables throughout (rather than relying only on somewhat unwieldy heatmaps which show little other than all these genes are co-ordinately regulated), this could include summaries of the methods applied, gene or CDE/CHR inclusion criteria, and Venn diagrams indicating the subsets of final genes identified through this approach.

We thank the referee for this suggestion. In the revised manuscript we provide a Venn-like diagram of the different steps of our approach (new Figure 3c), as well as tables listing the genes retained after each step of the selection (new Tables S17 and S26) and these additions improve the clarity of our manuscript.

Reviewer #1 (Significance):

In its current form this is a very limited study that would require significant additional work to move conclusions beyond correlation and hypothesis generation.<br /> Overall, while limited largely to target prioritisation, this research nicely exemplifies how genes affected by the p53-DREAM pathway can be robustly identified, providing a potential resource for individuals working on this pathway or on abnormal haematopoiesis and brain abnormalities. These results are complementary to work previously published by Fischer et al, which has been referenced throughout the analysis (highlighting Target Gene Regulation Database p53 and DREAM target genes) and discussion.

This paper will be of interest to researchers of blood/neurological diseases who can assess if these genes are dysregulated in their datasets, or those investigating the p53-DREAM pathway. This work represents a useful resource detailing genes affected by this pathway in these disease settings, however researchers of the p53-DREAM pathway may find this paper useful when planning an approach to identify and prioritise genes of interest.

We thank the reviewer for considering that our study represents a useful resource for researchers working on the p53-DREAM pathway, abnormal haematopoiesis and brain abnormalities, because it was exactly the purpose of our work. As mentioned above, we think that a study bridging the gap between DREAM experts and bone marrow or microcephaly specialists should be particularly useful.

We also agree with the referee that our approach could be used to identify DREAM targets relevant to other disease settings, and we now mentioned this clearly in the revised manuscript.

While our results are complementary to work previously published by Fischer et al and included in the Target gene regulation database, in the revised manuscript we discuss the novelty of our results in more details, notably by performing additional analyses. For example, our method identified bipartite DREAM binding sites for 151 candidate DREAM targets (of which 56 genes were not previously mentioned by Fischer et al.) and we now provide a detailed mapping (using 50 bp windows) of the bipartite DREAM binding sites we identified relative to ChIP peaks for DREAM subunits, then performed a similar mapping of the E2F and CHR sites included in the Target gene regulation database. Our predicted DREAM binding sites coincided with ChIP peaks more frequently (Figure 3a) than the predicted E2F or CHR from the Target gene regulation database (Figure S11), which further indicates the usefulness of our study as a resource.

Reviewer #2 (Evidence, reproducibility and clarity):

The authors used various systems including Hoxa9-indubible BMCs, human and mouse cells, WT and p53 knockout MEF, glioblastoma cells to screen p53-DREAM targets and observed distinct finding for each system. Since different cell types have various p53 activation and p53 target genes expression, the authors might want to select proper cell type(s) to screen p53-DREAM target genes and design experiments to confirm that these genes are really p53-DREAM target genes.

We agree that additional data from relevant cells or tissues were required to strengthen our conclusions. As mentioned in response to referee #1, in the revised manuscript we evaluated the relevance of candidate target genes related to blood ontology terms by integrating an additional screening step in our method, corresponding to the analysis of RNAseq dataset GSE171697, with data from hematopoietic stem cells of unirradiated or irradiated WT mice and unirradiated p53 KO mice , as well as RNAseq dataset GSE204924, with data from splenic cells of irradiated p53D24/- or p53+/- mice. As for genes related to brain development, we included the analysis of RNAseq datasets GSE78711 and GSE80434 for validation, two datasets from human cortical neural progenitors infected by the Zika virus or mock-infected. Together, the 4 datasets provide evidence for a p53-dependent downregulation in blood- and brain- relevant settings (new Tables S19 and S28).

Importantly, in the revision we also compared our list of 151 genes appearing as the best p53-DREAM candidates with the results of Magès et al., who analyzed, in murine cells with a CRISPR-mediated KO of Lin37 (a subunit of DREAM), the transcriptomic changes that follow a reintroduction of Lin37. This comparison is detailed in the discussion section, with the new Figure S10 and Table S35. We mention: “Our list of 151 genes overlaps only partially with the list of candidate DREAM targets obtained with this approach, with 51/151 genes reported to be downregulated in Lin37-rescued cells [17]. To better evaluate the reasons for this partial overlap, we extracted the RNAseq data from Lin37 KO and Lin37-rescued cells and focused on the 151 genes in our list. For the 51 genes that Mages et al. reported as downregulated in Lin37-rescued cells, an average downregulation of 14.8-fold was observed (Figure S10, Table S35). Furthermore, when each gene was tested individually, a downregulation was observed in all cases, statistically significant for 47 genes, and with a P value between 0.05 and 0.08 for the remnant 4 genes (Table S35). By contrast, for the 100 genes not previously reported to be downregulated in Lin37-rescued cells, an average downregulation of 4.7-fold was observed (Figure S10, Table S35), and each gene appeared downregulated, but this downregulation was statistically significant for only 35/100 genes, and P values between 0.05 and 0.08 were found for 23/100 other genes (Table S35). These comparisons suggest that, for the additional 100 genes, a more subtle decrease in expression, together with experimental variations, might have prevented the report of their DREAM-mediated regulation in Lin37-rescued cells.”

This comparison provides additional evidence that the 151 candidate target genes we identified are bona fide DREAM targets.

Specific comments:<br /> The authors need to describe and define HSC and Diff in Figure 1.

This has been corrected in the revised manuscript. “HSC” was replaced by “Hematopoietic Stem / Progenitor cells (+OHT)” and “Diff” was replaced by “Differentiated cells (5 days – OHT).

Are Figure 1B and 1D list genes p53 targets in bone marrow cells?

In the revised manuscript, we now analyzed RNAseq data to address this point. The question refers to lists of telomere-related genes (Figure 1b in both versions of the manuscript) and Fanconi-related genes (Figure 1d in the previous version, now Figure S2a), but could also apply to other lists of genes related to blood ontology terms (Figures S3-S5 in the revised manuscript). As mentioned in response to referee #1, in the revised manuscript we integrated an additional screening step in our method, corresponding to the analysis of RNAseq datasets specific of blood cells. We analyzed dataset GSE171697, with RNAseq data from hematopoietic stem cells of unirradiated WT or p53 KO mice, or irradiated WT mice, as well as dataset GSE204924, with RNAseq data from splenic cells of irradiated p53D24/- or p53+/- mice. The latter dataset appeared interesting because p53D24 is a mouse model prone to bone marrow failure and the spleen is a hematopoietic organ in mice. Furthermore, we also verified that the expression of a subset of blood-related candidate genes was decreased in the bone marrow cells of p53Δ31/Δ31 mice (prone to bone marrow failure) compared to bone marrow cells from WT mice, a result presented in the new Figure S8.

Where is the detailed information for mouse and human cells in Figure 1 and Figure 2?

In the first draft of the manuscript, supplementary tables provided precise values for ChIP binding. In the revised manuscript, we also provide the precise values for gene expression after bone marrow cell differentiation, as well as p53 regulation scores from the Target gene regulation databases. This additional information is included in the new Tables S1, S5, S8, S11, S14, S20 and S23.

Are Figure 3B list genes also p53 target genes in other cell types such as bone marrow cells and glioblastoma?

For genes in the Figure 3B of the previous version of the manuscript (now Figure 2B in the revised version), we now provide evidence that the blood-related genes are less expressed in the bone marrow cells of p53Δ31/Δ31 mice (mice with increased p53 activity and prone to bone marrow failure) compared to bone marrow cells from WT mice. This result is presented in the new Figure S8. For the brain-related genes of the same Figure, evidence of their p53-mediated regulation is provided by the RNAseq datasets GSE78711 and GSE80434, from human cortical neural progenitors infected by the Zika virus or mock-infected (analyzed in the new Table S28). Evidence of that a decreased p53 activity in glioblastomas correlates with increased expression of the brain-related genes of the same Figure is provided in supplementary Table S37.

Does BRD8high has high p53 and p21?

We now clearly show, in both Figure 4a and Table S37, that glioblastoma cells with high BRD8 exhibit a decreased expression of CDKN1A/p21 and other genes known to be transactivated by p53 (BAX, GADD45A, MDM2, PLK3), consistent with the fact that BRD8 attenuates p53 activity.

Are genes listed in Figure 4B all p53 target genes? can some validation be done?

For genes in Figure 4B, in the revision we focused on the genes that appeared more relevant, i.e. the 77 genes mutated in diseases with microcephaly or cerebellar hypoplasia. All the genes in Figure 4B are repressed in neural progenitors upon infection by the Zika virus, a virus known to cause p53 activation in those cells. This is reported in the new Table S28.

Reviewer #2 (Significance):

This is a potentially interesting study. The major limitation is the absence of validation from the screening. This study would definitely benefit the research community as long as some of the key findings are validated.

We thank the referee for this comment. We hope the new evidence in this revision provide the validation requested by the referee.

Reviewer #3 (Evidence, reproducibility and clarity):

In their work submitted to Review Commons, Rakotopare et al. aim to identify p53-DREAM target genes associated with blood or brain abnormalities. To this end, they utilize published data generated with a cellular model that results in cell-cycle exit and differentiation of murine bone marrow progenitor cells upon inducible expression of Hoxa9. By analyzing this gene expression data set published by Muntean et al., they find that multiple of the 3631 genes which are downregulated more than 1.5-fold in differentiated BMCs are also mutated in several disorders connected to proliferation and differentiation defects during hematopoiesis and brain development. By screening ChIP-seq data sets available at ChIP-Atlas, they find that the promoters of many of these genes are bound by DREAM complex components, and most of them were identified as genes indirectly repressed by p53 before (Fischer et al. 2016, targetgenereg.org). They then use a computational approach to identify putative CDE/CHR DREAM-binding sites in the promoters of 372 genes associated with blood/brain abnormalities which are downregulated in differentiated BMCs and bound by DREAM components. Out of the 173 candidate genes, they select twelve to analyze whether mutation of the putative DREAM binding sites results in increased activity of the promoters in luciferase reporter assays. The authors conclude that their findings suggest a general role for the p53-DREAM pathway in regulating hematopoiesis and brain development.<br /> While the study supports a large body of publications proving that repression of cell cycle genes by the DREAM complex is crucial for cell cycle arrest and exit, it is noted that none of the main conclusions here are unexpected or particularly exciting. All the analyses are based on data sets that compare gene expression in highly proliferative cells with cells that underwent terminal cell cycle exit. Thus, a large portion of the genes that are downregulated in differentiated BMCs are cell cycle genes and well-established targets of DREAM and E2F:RB complexes. Furthermore, it is not surprising that some of these pro-proliferative genes are mutated in diseases connected to proliferation defects like anemias or microcephaly.

We agree with the referee that the DREAM complex is well known to regulate cell cycle genes – in fact, this is what we mention in the first sentence of our introduction in both versions of our manuscript. However, as we already pointed out in response to Referee #1, many scientists or clinicians specialized in bone marrow failure syndromes or microcephaly diseases are not familiarized with the p53-DREAM pathway, and we think our study will be particularly useful to them. Furthermore, our strategy relying on disease-based ontology terms rather than cell cycle regulation led to identify many DREAM targets that were not reported in previous studies, and our positional frequency matrices led to identify DREAM binding sites not predicted by previous approaches. As discussed below, our revised manuscript provides a more detailed comparison of our findings with those from previous studies.

Additionally, I am not very enthusiastic about this manuscript because of several major concerns:

1. The authors draw conclusions about the p53-DREAM pathway based on data that was generated in a cellular differentiation model without convincingly showing that p53 plays a central role in gene repression in this experimental setup.<br /> (A) Rakotopare et al. define p53-DREAM target genes based on RNA expression data from proliferating precursor cells and non-proliferating, differentiated BMCs (Muntean et al., 2010). This paper has not studied whether p53 gets activated in the particular experimental setup during Hox9a-induced BMC differentiation. On page 4 of their manuscript, the authors state: "Consistent with the fact that BMC differentiation strongly correlates with p53 activation..." without citing any literature or explaining why this is supposed to be a fact. Furthermore, they imply that cell cycle gene repression in this model system depends on p53 because mRNA expression of the p53 targets p21 and Mdm2 was found to be increased in the differentiated cells (Fig. 1A, 5-fold and 2-fold, respectively). However, defining a large set of "p53-DREAM target genes" based on the moderate increase in mRNA levels of two genes that are known to be activated by p53 without showing any evidence that p53 is even involved in this effect during BMC differentiation is not appropriate.

We agree that Muntean et al. did not study whether p53 gets activated when BMCs differentiate in the Hox9a-ER system. We previously mentioned: “We observed that p53 activation correlated with cell differentiation in this system, because genes known to be transactivated by p53 (e.g. Cdkn1a, Mdm2) were induced, whereas genes repressed by p53 (e.g. Rtel1, Fancd2) were downregulated after tamoxifen withdrawal (Figure 1a)”. We had provided examples for 2 genes transactivated and 2 genes repressed, but clearly mentioned that they were given as examples. In the revised manuscript, we provide additional evidence with a new supplementary Figure that includes changes in expression for 15 additional genes known to be transactivated by p53, and 5 additional genes known to be repressed by p53 (Figure S1). In total, we now correlate HSC differentiation with p53 activation based on the expression of 24 well-known p53-regulated genes, which we hope is more convincing.

In addition, we changed our phrasing and mention “Consistent with the notion that BMC differentiation strongly correlates with p53 activation in this system, 72 of these 76 genes have negative score(s) in the Target gene regulation (TGR) database”.

(B) Interestingly, p53 is among the genes that get repressed on mRNA level in differentiated BMCs (Fig. 1B; Trp53), and the authors also identify the DREAM components E2F4 and LIN9 as bound to the p53 promoter by screening ChIP-Atlas data (Fig. 1C). Given that p53 has never been described as a DREAM target, I find this rather surprising and it makes me wonder whether appropriate parameters were selected for analyzing the ChIP data, particularly since the authors do not provide binding data for sets of non-cell cycle genes as a negative control.

We retrieved ChIP data from the ChIP Atlas database without any specific parameters, thus in a completely unbiased manner. Importantly however, for reasons detailed in the manuscript, we clearly mentioned that total ChIP scores <979/4000 were considered too low to reflect significant DREAM binding. The ChIP score for Trp53 was 630, which rapidly led us to eliminate this gene from our screen.

This ChIP score criterion was already mentioned in the previous version of our manuscript, but we think the addition of a Venn-like diagram (Figure 3c) and summary tables (S17 and S26) in the revised manuscript will probably make it easier to understand.

(C) Finally, the authors utilize the targetgenereg.org database to show that many of the genes they describe as p53-repressed were already identified as p53 targets. This database (Fischer et al. 2016) was created by performing a meta-analysis integrating a plethora of RNA-seq and ChIP-seq datasets with the aim to identify whether a particular gene gets up- or downregulated by p53, shows cell-cycle-dependent expression, is a DREAM/MuvB or E2F:RB target, etc. For example, 57 datasets analyzing p53-dependent RNA expression in human and 15 datasets generated with mouse cells were included, and a positive or negative score shows in how many of these experiments the gene was found to be up (positive score) or downregulated (negative score). Combining a large number of datasets in such a study is very helpful to get an idea if a gene is indeed generally regulated by a transcription factor, or if it just showed up in a few experiments - either as a false positive or because the regulation depends on a particular biological setting. The authors find most of the genes they identify as repressed in differentiated BMCs also as downregulated by p53 in targetgenereg.org, however, it remains unclear what parameters they used to define a gene as p53-repressed. For example, in the caption of Fig. 1C, they state: "According to the Target gene regulation database, 72/76 genes are downregulated upon mouse and/or human p53 activation." The four exemptions are SLX1B (human score: 0, mouse score : na), PML (+41, +9), RAD50 (0, na), and TNKS2 (+17, +4). However, there are several other genes that do not appear to be generally repressed by p53, e.g. HMBOX1 (+4, -2); UPF1 (+1, -2), SMG6 (+18, -2), CTC1 (-5, +11), etc. Thus, without providing details regarding the parameters they use to define p53-target genes, such statements are rather misleading. An easy way to solve this problem would be to show the p53 scores in the tables together with the E2F4/LIN9 ChIP data.

All the genes mentioned as downregulated by p53 had a negative TGR score in human and/or mouse cells. In the revised manuscript, we mention clearly what a negative TGR score means, by stating: “Consistent with the notion that BMC differentiation strongly correlates with p53 activation in this system, 72 of these 76 genes have negative p53 expression score(s) in the Target gene regulation (TGR) database [23], which indicates that they were downregulated upon p53 activation in most experiments carried out in mouse and/or human cells (Figure 1b, Table S1).” We agree with the referee that adding precise TGR scores is informative. In the revised manuscript, we provide the TGR scores for all the genes analyzed, as part of the new supplementary Tables S1, S5, S8, S11, S14, S20 and S23, together with their expression levels in undifferentiated or differentiated cells (as requested by Referee #2). The ChIP data are provided in separate tables (Tables S2, S3, S6, S7, S9, S10, S12, S13, S15, S16, S21, S22, S24 and S25).

1. The authors define a large set of genes containing "CDE-CHR" promoter elements and thereby ignore how these elements are defined and what properties they have.<br /> (A) At the beginning of the introduction, the authors state: "The DREAM complex typically represses the transcription of genes whose promoter contain a bipartite CDE/CHR binding site, with a cell cycle-dependent element (CDE) bound by E2F4 or E2F5, and a cell cycle gene homology region (CHR) bound by LIN54, the DNA binding subunit of MuvB (Zwicker et al., 1995; Müller and Engeland, 2010)."<br /> This statement is incorrect. The authors ignore that the CDE/CHR tandem site is just one of four promoter elements that have been shown to recruit DREAM for the transcriptional repression of several hundred genes. It has been studied in detail that DREAM can bind to the following promoter sites:<br /> (I) CHR elements - bound by DREAM via LIN54; also bound by the activator MuvB complexes B-MYB-MuvB and FOXM1-MuvB which results in maximum gene expression in G2/M<br /> (II) CDE-CHR tandem elements - like (I) but binding of DREAM can be stabilized via E2F4/DP interacting with a truncated E2F binding site. Since CDE elements do not represent functional E2F sites, E2F:RB complexes do not bind.<br /> (III) E2F binding sites - bound by DREAM via E2F4/DP; also bound by E2F:RB complexes and activator E2Fs which results in maximum gene expression in G1/S<br /> (IV) E2F-CLE tandem elements - like (III) but binding of DREAM can be stabilized via LIN54 interacting with a non-canonical CHR-like element. Since CLE elements do not represent functional CHR sites, B-MYB-MuvB and FOXM1-MuvB do not bind.<br /> Thus, these promoter sites have different functions and can be clearly distinguished from each other based on their properties - a fact that is completely ignored by the authors. Since the authors do not differentiate between G1/S and G2/M expressed genes and (CDE)-CHR and E2F-(CLE) sites, they identify CDE-CHR elements in G1/S genes that are functional E2F-(CLE) sites. A good example of this is the Rad51ap1 gene (and also the Rad51 gene that the Toledo lab described before as a CDE-CHR gene (Jaber et al. 2016)): these genes get expressed in G1/S and the promoters contain highly conserved E2F sites (parts of which the authors define as CDEs), and CLEs (which the authors define as CHRs). Furthermore, E2F:RB complexes bind to the promoters. Again: even though (CDE)-CHR and E2F-(CLE) sites both bind DREAM, they are otherwise functionally different in their ability to recruit non-DREAM complexes.

We agree that in the previous version of our manuscript we should have presented in more details the different types of DREAM binding sites and have corrected this in the revised manuscript. We now mention in the introduction that “The DREAM complex was initially reported to repress the transcription of genes whose promoter sequences contain a bipartite binding motif called CDE/CHR [19,20] (or E2F/CHR [21]), with a GC-rich cell cycle dependent element (CDE) that may be bound by E2F4 or E2F5, and an AT-rich cell cycle gene homology region (CHR) that may be bound by LIN54, the DNA-binding subunit of MuvB [19,20]. Later studies indicated that DREAM may also bind promoters with a single E2F binding site, a single CHR element, or a bipartite E2F/CHR-like element (CLE), and concluded that E2F and CHR elements are required for the regulation of G1/S and G2/M cell cycle genes, respectively [14,22].”

We hope that the referee will agree with this complete yet concise way of presenting DREAM binding sites. Importantly, we agree that CDE/CHR and E2F/CLE are sites bound by different non-DREAM complexes, but both sites are bound by DREAM, so it makes perfect sense to use them together to define positional frequency matrices for DREAM binding predictions. We would also like to point out that terms used to define DREAM binding sites may vary in the literature. For example, to our knowledge Müller et al. were the first to propose a clear distinction between “CDE/CHR” and “E2F/CLE” sites (Müller et al. (2017) Oncotarget 8, 97737-97748), yet Müller recently co-authored a review in which these two distinct terms were not used, but were replaced by a single, apparently more generic term of “E2F/CHR” (Fischer et al., (2022) Trends Biochem. Sci. 47, 1009-1022). In the revised manuscript we now clearly mention that we designed our positional frequency matrices to search for “bipartite DREAM binding sites”, i.e. sites that might be referred to as CDE/CHR, E2F/CLE or E2F/CHR sites in various publications.

(B) The authors identified putative CDE-CHR in the promoters of genes by building two position weight matrices (PWMs) based on 10 or 22 "validated CDE-CHR elements". However, since they include several genes that are clearly expressed in G1/S and contain E2F-(CLE) sites (e.g. Mybl2/B-myb, Rad51, Fanca, Fen1), it is not surprising that they identify a lot of putative CDE-CHR sites in genes that do not contain such elements.

As discussed above, both CDE/CHR and E2F/CLE are bipartite DREAM binding sites, and we now clearly state that we used bipartite DREAM binding sites to generate our positional frequency matrices and predict DREAM binding.

(C) Finally, in the discussion, the authors state: "A recent update (2.0) of the Target gene regulation database of p53 and cell cycle genes (www.targetgenereg.org) was recently reported to include putative DREAM binding sites for human genes (Fischer et al., 2022). However, this update only suggests potential E2F or CHR binding sites independently, a feature of little help to identify CDE/CHR elements. For example, targetgenereg 2.0 suggests several potential E2F sites, but no CHR site close to the transcription start site of FANCD2, despite the fact that we previously identified a functionally CDE/CHR element near the transcription start site of this gene (Jaber et al., 2016)." This statement highlights again that the authors don't seem to be aware of what specific properties distinct DREAM binding sites have, and that analyzing promoters for CHR and E2F sites separately generates much more meaningful results than the approach they chose. Also, the FANCD2 promoter binds DREAM as well as E2F:RB complexes and contains a highly conserved E2F binding site - which Jaber et al. mutated together with a potential downstream CLE element and named it "CDE/CHR".

In the revised manuscript, we provide a more detailed comparison between the bipartite DREAM binding sites predicted with our positional frequency matrices for 151 genes and the separate E2F and CHR predicted sites reported in the Target gene regulation database for the same set of genes. We now mention: “The Target gene regulation (TGR) database of p53 and cell-cycle genes was reported to include putative DREAM binding sites for human genes, based on separate genome-wide searches for 7 bp-long E2F or 5 bp-long CHR motifs [23]. We analyzed the predictions of the TGR database for the 151 genes for which we had found putative bipartite DBS. A total of 342 E2F binding sites were reported at the promoters of these genes, but only 64 CHR motifs. The similarities between the predicted E2F or CHR sites from the TGR database and our predicted bipartite DBS appeared rather limited: only 14/342 E2F sites overlapped at least partially with the GC-rich motif of our bipartite DBS, while 27/64 CHR motifs from the TGR database exhibited a partial overlap with the AT-rich motif. Importantly, most E2F and CHR sites from the TGR database mapped close to E2F4 and LIN9 ChIP peaks, but only 16% of E2Fs (54/342), and 33% of CHRs (21/64) mapped precisely at the level of these peaks (Figure S11), compared to 55% (83/151) of our bipartite DBS (Figure 3a). Thus, at least for genes with bipartite DREAM binding sites, our method relying on PFM22 appeared to provide more reliable predictions of DREAM binding than the E2F and CHR sites reported separately in the TGR database. Importantly however, predictions of the TGR database may include genes regulated by a single E2F or a single CHR that would most likely remain undetected with PFM22, suggesting that both approaches provide complementary results.”

1. The experimental approach chosen to validate CDE-CHR elements in a set of twelve promoters by luciferase reporter assays is not adequate.<br /> (A) Since the authors introduce point mutations in putative CDE and CHR elements in parallel, it is impossible to identify functional CDE elements. As explained above, a functional CDE is not required for binding of MuvB complexes and gene repression, and mutating the CHR alone would already lead to a loss of DREAM binding and to de-repression of a promoter. Thus, without mutating both sites of CDE-CHR elements separately, it is impossible to provide evidence that a putative CDE is functional.<br /> (B) As the putative CDE-CHR elements identified by the authors with a computational approach can overlap with functional E2F-(CLE) elements, the authors inactivate such sites by introducing mutations which leads to loss of DREAM binding and upregulation of the promoters, however, because of the problems described above, this experimental approach in the best case identifies DREAM binding sites, but does not differentiate between (CDE)-CHR and E2F-(CLE) elements.

Yes, we agree with this comment. As discussed above, our goal was to identify DREAM-binding sites, not to differentiate between CDE/CHR and E2F/CLE elements. In other words, we wanted to identify genes regulated by p53 and DREAM, but not distinguish between genes regulated by p53, DREAM and E2F/Rb versus those regulated by p53, DREAM and BMyb-MuvB or FoxM1-MuvB.

(C) The authors analyze the activities of wild-type and mutant promoters in proliferating NIH3T3 cells. Since the mutated promoters showed increased activity (about 2-3 fold), which would be expected when binding of DREAM gets abolished, they conclude: "...these experiments indicated that we could identify functional CDE/CHRs for 12/12 tested genes." In addition to the problems described above, a slight upregulation of promoter activities caused by the introduction of multiple point mutations close to the TSS is not sufficient to verify these elements. The increase in activity could occur independent of DREAM-binding by unrelated mechanisms. The authors should at least analyze the activities of the promoters with and without induction of p53. A loss of p53-dependent repression of the mutated promoters would prove that the elements are essential for p53-dependent repression. Furthermore, there are several experimental approaches to analyze whether DREAM binds to the putative promoter element and whether the introduced mutations disrupt binding (ChIP, DNA affinity purification, etc.).

In the revised manuscript, we show that the promoters of 7 of the tested genes, when cloned in luciferase reporter plasmids and transfected into NIH3T3 cells, exhibited a significant (> 1.4 fold) repression upon p53 activation by cell treatment with Nutlin, the Mdm2 antagonist. For these promoters, we showed that the p53-dependent repression was abrogated by mutating the identified DREAM binding site, which provided direct evidence that our positional frequency matrices can identify functionally relevant DREAM binding sites essential for p53-mediated repression. These experiments were added in Figures 2e and 2i.

Furthermore, as previously mentioned in response to referee #1, in the revised manuscript we precisely mapped the predicted DREAM binding sites for 151 genes in 50 bp windows within regions bound by E2F4 and/or LIN9, an analysis included in new Figure 3a. The distribution of these peaks clearly indicates that most predicted DREAM binding sites map precisely within a 50 bp-window encompassing the ChIP peaks, which represents an enrichment of at least a 1300-fold compared to the rest of the genome. This mapping strongly suggests that our predicted DREAM binding sites are functionally relevant.

Importantly, as shown in the new Figure S11, we carried out a similar mapping of the predicted E2F and CHR sites reported in the Target gene regulation (TGR) database and found that our predicted DREAM binding sites co-mapped with E2F4/LIN9 ChIP peaks more frequently than the E2F and CHR sites of the TGR database, which supports the conclusion that our positional frequency matrices bring new and improved predictions for DREAM binding.

1. Taken together, while over-simplifying mechanisms of cell cycle gene regulation, the authors largely ignore recent findings and publications regarding gene regulation by p53, E2F:RB, and DREAM/MuvB complexes:<br /> (A) Publications that show how DREAM binds to (CDE)-CHR sites and that experimentally defined a consensus motif for CHR elements (e.g. PMID: 27465258, PMID: 25106871).<br /> (B) Publications that identify p53-DREAM target genes by activating p53 in cells with or without functional DREAM complex (e.g. PMID: 31667499, PMID: 31400114).<br /> (C) Identification and comparison of (CDE)-CHR and E2F-(CLE) DREAM binding sites that have distinct functions in the activation of cell-cycle expression in G1/S and G2/M (e.g. PMID: 29228647, PMID: 25106871).<br /> These findings have been summarized in several review articles (e.g. PMID: 29125603, PMID: 28799433, PMID: 35835684). All of them describe the mechanisms I have mentioned above in detail, and since Rakotopare et al. cite one of the papers (Engeland 2018), I wonder even more why they did not design their experiments based on current knowledge.

The points (A) and (C) of this comment were largely discussed in our response to points 2 and 3 of the same referee. Briefly, in the revised manuscript we clearly mention CDE/CHR, E2F/CLE and E2F/CHR sites, as well as the functional differences between E2F and CHR sites with regards to cell cycle regulation, but all these sites were considered together in our positional frequency matrices because our goal was to identify genes regulated by p53 and DREAM, not to distinguish between genes regulated by p53, DREAM and E2F/Rb versus those regulated by p53, DREAM and BMyb-MuvB or FoxM1-MuvB.

Regarding point (B) of this comment, in the revised manuscript we performed a detailed comparison of our results with those of Mages et al. who analyzed, in murine cells with a CRISPR-mediated KO of Lin37 (a subunit of DREAM), the transcriptomic changes that follow a reintroduction of Lin37 (Mages et al. (2017) elife 6, e26876). This comparison is detailed in the discussion section, with New Figure S10 and Table S35. As mentioned in response to referee #2, this comparison is perfectly consistent with DREAM regulating the 151 genes for which we identified DREAM binding sites.

Minor concerns:

1. The authors state: "Importantly however, the relative importance of the p53-p21-DREAM pathway (called below p53-DREAM) remains controversial, because multiple mechanisms were proposed to account for p53-mediated gene repression (Peuget and Selivanova, 2021)." Even though Peuget & Selivanova do not agree that genes get repressed in response to p53 activation exclusively by the p21-DREAM pathway, they do not question that this mechanism is essential for the p53-dependent repression of a core set of cell cycle genes. Since I am also not aware of any publications that challenge the importance of the p53-p21-DREAM pathway, I do not agree with this statement.

As the referee pointed out, in the first version of the manuscript we wrote that “the relative importance of the p53-p21-DREAM pathway (called below p53-DREAM) remains controversial, because multiple mechanisms were proposed to account for p53-mediated gene repression (Peuget and Selivanova, 2021)”. The term “relative” was crucial in this sentence, because we wanted to say that the relative proportion of genes regulated by DREAM remained controversial. It seems to us that the title of the review by Peuget & Selivanova (“p53-dependent repression: DREAM or reality?”) emphasizes this controversy. Nevertheless, in the revised manuscript, we now mention : “The relative importance of this pathway remains to be fully appreciated, because multiple mechanisms were proposed to account for p53-mediated gene repression [18]”. We hope the referee will find this phrasing more acceptable.

1. Some parts of the manuscript are tiring to read - for example, pages 6, 7, and 8 which contain long listings and numbers of genes that are downregulated in differentiated BMC, found to be mutated in various disorders, bind DREAM components, were identified as downregulated by p53, etc. The authors may consider combining central parts of these data in a table that they show in the main manuscript which would make it easier to digest the information and at the same time significantly shorten the manuscript.

We apologize if some parts of the article were tiring to read. We hope that the addition of Tables S17 and S26, as well as the Venn-like diagram in Figure 3c, will improve the reading of the manuscript.

1. The supplementary tables (S1-S26) are combined in one Excel file with multiple tabs. The authors should label the tabs accordingly to make it easier for the reader to find a particular table.

We labelled the Excel tabs in the revised manuscript, as suggested.

1. At the end of page 6, the authors show that 17 genes found to be downregulated in differentiated BMCs are mutated in multiple bone marrow disorders, however, since they don't include references, it remains unclear where these mutations were originally described.

In the revised manuscript, we included a supplementary table (Table S36) with appropriate references for blood and/or brain related phenotypes for the 106 genes associated with blood or brain abnormalities.

1. On page 9, the authors state: "As a prerequisite to luciferase assays, we first verified that the expression of these genes, as well as their p53-mediated repression, can be observedin mouse embryonic fibroblasts (MEFs), because luciferase assays rely on transfections into MEFs (Figure 3b)." The authors don't explain why luciferase assays rely on transfections into MEFs and based on the caption of Fig. 3C, the luciferase assays were not performed in MEFs, but in NIH3T3 cells: "WT or mutant luciferase reporter plasmids were transfected into NIH3T3 cells..."

According to the American Type Culture Collection (ATCC), the NIH3T3 cell line is a mouse embryonic fibroblastic (MEF) cell line, which explains why we had tested the expressions of candidate target genes in MEFs. However, as we now clearly mention in the manuscript, this cell line exhibits an attenuated p53 pathway, which improves cell survival after transfection but leads to decreased p53-mediated repression. These points are now clearly mentioned in the text and in a new supplemental Figure (Figure S9).

Reviewer #3 (Significance):

While the study supports a large body of publications proving that repression of cell cycle genes by the DREAM complex is crucial for cell cycle arrest and exit, it is noted that none of the main conclusions here are unexpected or particularly exciting. All the analyses are based on data sets that compare gene expression in highly proliferative cells with cells that underwent terminal cell cycle exit. Thus, a large portion of the genes that are downregulated in differentiated BMCs are cell cycle genes and well-established targets of DREAM and E2F:RB complexes. Furthermore, it is not surprising that some of these pro-proliferative genes are mutated in diseases connected to proliferation defects like anemias or microcephaly.

Again, we agree with the referee that the DREAM complex is well known to regulate cell cycle genes, but many scientists or clinicians specialized in bone marrow failure syndromes or microcephaly diseases are not familiarized with the p53-DREAM pathway, and we think our study will be particularly useful to them. As for DREAM specialists, our strategy relying on disease-based ontology terms rather than cell cycle regulation led to identify many DREAM targets that were not reported in previous studies, and our positional frequency matrices led to identify DREAM binding sites not predicted by previous approaches. We hope that, by considering all these points together, the referee will acknowledge that our study provides a valuable resource for different types of readerships.

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Reviewer #1 (Evidence, reproducibility and clarity):

1) It is interesting MxDnaK1 seems to prefer cytosolic proteins while Mx-DnaK2 prefers inner membrane proteins. The domain-swapping experiments seem to suggest that the NBD is important for this difference. How NBD is important is not addressed. Is it due to ATP hydrolysis, NBD-SBD interaction, or co-chaperone interactions?

Answer: Thanks for your comments. We speculate that the co-chaperone interaction might be the key factor contributing to substrate differences. According to the working principle of Hsp70, its functional diversity is largely determined by substrate differences. Co-chaperones, such as JDPs, play a crucial role in this process as they possess the ability to bind substrates and facilitate their targeted delivery. Therefore, much of the functional diversity of the HSP70s is driven by a diverse class of JDPs 1,2. We found that NBD played important roles in cochaperone recognition of MxDnaKs. Additionally, it is generally accepted that the efficiency of ATP hydrolysis does not significantly impact the substrate recognition of Hsp70. Furthermore, if the NBD-SBD interaction is crucial, the substitution of either the NBD or SBDβ domain might result in similar cell phenotypes, as both alterations disrupt the original NBD-SBDβ interaction. We believe the DnaK proteins and their cochaperones both determine the substrate spectrums. We made corresponding modifications in the revised manuscript. (Page22; Line 488-494 in the marked-up manuscript)

2) About the interactome analysis, since apyrase was added to remove ATP, it's surprising multiple Hsp40s were found in their analysis. Hsp70-Hsp40 interaction is known to require ATP. This may suggest some of the proteins found in their interactome analysis are artifacts. The authors should perform negative controls for their interactome analysis, such as using a control antibody for their CO-IP and analyze any non-specific binding to their resin.

In addition, since JDPs were pull-down, is it possible some of the substrates identified are actually substrates for JDPs, not binding directly to DnaKs?

Answer: This is an interesting question. As you correctly noted, the interaction between Hsp70 and Hsp40 requires ATP. In our experiment, we used apyrase to remove ATP in order to promote tight binding of substrate by DnaK. This methodology was initially described by Calloni, G. et al in 20123, and the authors also identified the co-chaperone protein DnaJ, but with a concentration higher than 77% of the interactors. In our opinions, the incomplete removal of ATP could be the underlying cause of this phenomenon.

We apologize for the undetailed description in Methods. Actually, we implemented negative controls for each MxDnaK in order to eliminate the potential non-specific interactions with Protein A/G beads or antibodies. Specifically, we conducted a CO-IP experiment without the presence of antibodies to assess any non-specific binding to the Protein A/G beads. To further investigate non-specific binding to the antibodies of MxDnaK2 and MxDnaK1, we utilized the mxdnak2-deleted mutant (strain YL2216) and the MxDnaK1 swapping strain with the MxDnaK2 SBDα (strain YL2204), respectively. As the SBDα of MxDnaK1 was employed as antigen to generate antibodies, and YL2204 can’t be recognized by anti-MxDnaK1 (Figure S5). We believe these controls allowed us to evaluate and exclude the non-specific interactions in our CO-IP. We have improved our description in methods. (Page 27; Line 596-607)

While one of the main functions of JDPs is to interact with unfolded substrates and facilitate their delivery to Hsp70, there may still be substrates that do not directly bind to Hsp70. It’s thus possible that some of the substrates identified only bind to JDPs. We made corresponding modifications in the revised manuscript. (Page 14; Line 290-292)

3) For Figure S7, the pull-down assay used His6-tagged JDPs. Ni resin is known to bind Hsp70s non-specifically. It's not surprising DnaK showed up in all the pull-down lanes, especially considering how much DnaK was over-expressed. For some pull-down lanes, the amount of DnaK is much more than that of JDPs, further indicating artifact. The author should include negative controls such as JDPs without His6-tag or any irrelevant protein with His6 tag.

Answer: Thanks for your suggestion. As you and another reviewer pointed out, there were some flaws in the experimental design of the pulldown assay. These include the non-specific binding of Hsp70 proteins to nickel resin, the absence of a negative control without a tag, and the inappropriate selection of the MBP tag. Thus, we employed the nLuc assay as an alternative to the pulldown experiment to validate the interaction between DnaK and JDP (Figure S9). While our manuscript employed nLuc to confirm protein dimerization, it is worth noting that nLuc assay was originally devised for investigating protein interactions 4.

4) For the proposed dimer formation in Fig. 4C, there are multiple bands above the monomer bands. What are these forms? It seems the majority of the Cys residues that could form disulfide bonds are in the NBD of MxDnaK2 since constructs with MxDnaK2-NBD form some sort of high-MW bands above the monomer. Does MxDnaK1-NBD also contain Cys at the analogous positions? The fact that MxDnaK1 didn't show disulfide-bonded bands doesn't mean it doesn't form dimer. It depends on where the Cys residues are.

It's nice the authors did Fig. 4D. However, the authors should include a positive control to show how strong the signal is for a true interaction before interpreting their results.

Answer: Thank you very much for your comments. In at least three independent experiments, we consistently observed two unidentified bands within the molecular weight range of 70-100 kDa during the purification process of His6-MxDnaK2. These bands appeared to be intermediate in size between the monomeric and dimeric forms of His6-MxDnaK2, and disappeared upon DTT treatment. the unidentified band compositions have been confirmed by LC/MS. The upper band included MxDnaK2 (65.3 kDa) and anti-FlhDC factor of E. coli (WP_001300634.1, 27 kDa). In the lower band, we detected the presence of MxDnaK2 and the 50S ribosomal protein L28 of E. coli (WP_000091955.1, 9 kDa). Based on these findings, we conclude that these two additional bands are the result of the interaction between His6-MxDnaK2 and these two E. coli proteins. The related explanations have been added in the legend of Figure 5. (Page 42; Line 938-942)

We analyzed the presence of Cys in MxDnaK1 and MxDnaK2. The NBD region of MxDnaK2 contains two Cys, located at positions 15 and 319. MxDnaK1-NBD contain a Cys at position of 316, which is the analogous position of 319-Cys of MxDnaK2. The analogous position of 15-Cys of MxDnaK2 is a Val in MxDnaK1, which might be an important factor contributing to the inability of MxDnaK1 to form oligomers.

Thanks for your suggestion to add the positive control. We re-performed the nLuc assays including a positive control(αSyn). According to the working principle of the nLuc assay, the amount of fluorescent substrate is limited. Therefore, even for proteins that interact with each other, the fluorescence value gradually decreases and reaches a plateau, similar to the negative control. This gradual decline in fluorescence is a significant indicator of protein interaction. In Figure 4D (Figure 5D in the revision version), we only presented the results of the first 20 minutes of detection. The complete two-hour detection results have been added in the supplementary figure (Figure S14).

5) line 48: "human HSC70 and HSP70 are 85% identical, and the phenotypes of their knockout mutants are different, which is consistent with their largely nonoverlapping substrates" The authors completely ignored that the promoters of HSC70 and HSP70 are very different.

Answer: This is our carelessness. Yes, HSC70 and HSP70 exhibit distinct expression patterns, which play important roles in their functional diversity. We modified the sentence in the new version (Page 5; Line 58)

6) Line 69: "The two PRK00290 proteins, not the other Myxococcus Hsp70s, could alternatively compensate the functions of EcDnaK (DnaK of E. coli) for growth." Please add references for this statement.

Answer: Added, thanks.

7) line 191: What's the mechanism for DnaK's role in oxidative stress? Is the disulfide bond formation in Fig. 4 related? Does disulfide-bond change the activity of DnaK?

Answer: Thanks for your pertinent comments. Honestly, we have no idea about the mechanism for MxDnaK2's role in oxidative stress. In our previous studies, we determined that the deletion of mxdnaK2 resulted in a longer lag phase after H2O2 treatment. Here, our aim was to investigate the impact of region swapping on the cellular function of MxDnaK2. In other bacteria, the critical role that DnaK plays in resistance to oxidative stress stems from the pleotropic functions of this chaperone. By shortening the dwelling time that proteins spend in the unfolded state, the DnaK/DnaJ chaperone system minimizes the risk of metal-catalyzed carbonylation of the side chains of proline, lysine, arginine, and threonine residues, but none of them linked to the dimerization characteristic of DnaK 5-7.

8) Fig. S9 seems redundant.

Answer: Deleted, thanks.

9) line 263, "but the NBD exchange was almost equal to the deletion of the gene with respect to phenotypes." But, the mutant has >50% activity in Fig. 3F.

Answer: We apologize for the confusing description. The “phenotypes” here indicates “cell phenotypes”. What we really tried to say with this sentence is that the NBD swapping strain of either MxDnaK1 or MxDnaK2 presented identical cell phenotypes with the gene-deleted strain. As we have already provided a detailed description of this result earlier, now we consider this sentence to be redundant and have therefore deleted it. (Page 17; Line 355-356)

10) line 221-226: the logic is not quite clear.

Answer: We apologize for the confusing description. In M. xanthus DK1622, MxDnaK1 is essential for cell survival, and an insertion of a second copy of mxdnaK1 in the genome is required for deletion of the in-situ gene. Thus, To verify whether the NBD region is required for the essentiality of MxDnaK1, we performed the region swapping of the in situ MxDnaK1 gene in the att::mxdnaK1 mutant (a DK1622 mutant containing a second copy of mxdnaK1 at attB site), and successfully obtained the MxDnaK1 mutant swapped with the MxDnaK2 NBD region. The experiment indicated that the NBD of MxDnaK1 is essential for the cellular functions of the chaperone. We have added the information and modified the sentences in the manuscript. (Page 15; Line 308-319)

Minor concerns:

Please check spelling. There are some typos such as "HPPES" in the Methods.

Answer: Corrected. Many thanks.

My areas of expertise are protein biochemistry, genetics, and structural biology on heat shock proteins.

Reviewer #2 (Evidence, reproducibility and clarity):

Major comments:

The manuscript is very nice and interesting, although some of the authors' conclusions are perhaps not well supported by their data. For example:

1) In the pulldown experiments the lack of interaction between 2747-MxDnaK2, 3015-MxDnaK2 and 1145-MxDnaK1 should be shown in order to support the conclusion made in line 197-198,

Answer: This is our carelessness. As you and another reviewer pointed out, there are some flaws in the experimental design of the pulldown assay. These include the non-specific binding of Hsp70 proteins to nickel resin, the absence of a negative control without a tag, and the inappropriate selection of the MBP tag. Thus, we employed the nLuc assay as an alternative to the pulldown experiment to validate the interaction between DnaK and JDP (including 2747-MxDnaK2, 3015-MxDnaK2 and 1145-MxDnaK1 interaction) (Figure S9). While our manuscript employed nLuc to confirm protein dimerization, it is worth noting that nLuc assay was originally devised for investigating protein interactions 4.

2) The only evidence that the NBD of MxDnaK1 is essential for bacterial growth is that this mutation couldn´t be obtained in M. xanthus. However, it could be purified in E. coli. Could the authors do some experiments with the M. xanthus strain without the chromosomal MxDnaK1 and then introduce a plasmid with the mutated gene?

Answer: We apologize for the confusing description. Actually, we determined the NBD is essential not only from the mutation couldn’t be obtained. In M. xanthus DK1622, MxDnaK1 is essential for cell survival, and in-situ deletion of the gene could be obtained after an insertion of a second copy of mxdnaK1 in the genome at the attB site. To verify whether the NBD region is required for the essentiality of MxDnaK1, we performed the region swapping of the in situ MxDnaK1 gene in the att::_mxdnaK_1 mutant (a DK1622 mutant containing a second copy of _mxdnaK_1), and successfully obtained the MxDnaK1 mutant swapped with the MxDnaK2 NBD region. The experiment indicated that the NBD of MxDnaK1 is essential for the cellular functions of the chaperone. We have added the information and modified the sentences in the manuscript. (Page 15; Line 308-319)

3) All the experiments with purified proteins were done with MxDnaKs bearing His-tags. It doesn't say explicitly its position, but as they employed a pET28A it is likely that the tag is at the N-terminus, which is close to the linker region. As this tag might interfere, it should be removed for the experiments, or at least a control done with the tag removed.

Answer: We apologize for the lack of detailed description. As you pointed out, the His-tags are located at the N-terminus of DnaKs. The full lengths of MxDnaK1 and MxDnaK2 are 638 and 607 amino acids. The linker regions are located at amino acid positions 381-386 for MxDnaK1 and 387-392 for MxDnaK2. Therefore, we believe that the His-tag is not close to the linker regions. We have included the information in new manuscript. (Page 24; Line 544-546)

The purified His6-DnaK proteins were employed for holdase activity assays and in vitro dimerization assays. Several previous studies have utilized the same holdase activity assay method with His-tagged DnaK 8,9. We suggested that the His-tag did not interfere with the holdase activity of DnaK. To exclude the influence of His-tag on oligomerization, we conducted a control with the tag removed in the in vitro dimerization assay and the result show no difference (Figure S13).

4) The authors state that MxDnaK dimerized in vitro with the NBD, and to disrupt the dimer they used 100 mM DTT, which is a very high concentration. As the protein has the His-tag, it should be removed to corroborate that it is not interfering with the dimerization.

Answer: Thanks for your suggestion. As mentioned above, to exclude the influence of the His-tag on oligomerization, we conducted a control with the tag removed in the in vitro dimerization assay and the result show no difference (Figure S13).

5) Why were the pulldown experiments done with MBP-MxDnaKs? Can you show a negative control between the MBP and the JDPs to rule out this interaction? It will be more suitable to do the pulldown assays with the purified MxDnaK´s without the His-tags (and the His-tags JDP that were employed).

Answer: Thanks for your suggestion. As mentioned above, there are some flaws in the experimental design of the pulldown assay. Thus, we employed the nLuc assay as an alternative to the pulldown experiment to validate the interaction between MxDnaKs and JDPs (Figure S9).

Minor comments:

• E. coli´s DnaK is only essential in heat shock conditions and for lambda phage cycle. If MxDnaK1 is similar to this Hsp70, why the substitution of its NBD for the NBD MxDnaK2 would be lethal for bacterial growth?

Answer: Thanks for the comments. As you correctly point out, DnaK is nonessential in E. coli. But in some other bacteria, DnaK also plays an essential role in cell growth for different reasons 10-12. In our previous studies, we determined that MxDnaK1 is essential in M. xanthus DK1622, and the MxDnaK2 is nonessential. In this study, we performed region swapping and found that only the NBD of MxDnaK1 was unreplaceable. In our opinions, the result indicated that NBD play important roles in the functional diversity between MxDnaK1 and MxDnaK2.

• I think that the writing should be revised and in the supporting information the captions of the figures should include more information.

Answer: Thanks a lot for the suggestion. We revised the manuscript and added more information in the legends of supplementary figures.

Reviewer #2 (Significance):

-General assessment: This is a nice piece of work which would benefit from revision to address the comments above. The authors showed the roles and differences between two DnaK in the same organism. They track these differences to the subdomains of the MxDnaK´s and co-chaperones. It will be interesting for future works to explore more deeply the co-chaperones and their interactions.

-Advance: I think that this manuscript fills a gap regarding the role of DnaK duplicated in bacterial strains. -Audience: I would say that the audience is broad and includes scientists interested in protein folding and chaperones, as well as myxobacteria.

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

Summary: Kellner and Berlin present their research findings pertaining to the effect of GRIN2B variants that modify NMDA receptor function and pharmacology. While these mutations were published previously, the new manuscript provides a more thorough investigation into the effects that these variants pose when incorporated into heteromeric complexes with either wildtype GluN2B or GluN2A - NMDA receptors containing only a single mutated GluN2B subunits is more relevant to the disease cases because the associated patients are heterozygous for the variant. The authors achieved selective expression of receptor heteromeric complexes by utilising an established trafficking control system. The authors found that while a single variant subunit in the receptor complex is largely dominant in its effect on reducing glutamate potency of the NMDA receptor, it 's effect on receptor pharmacology varied. Unlike diheteromeric receptors containing mutated subunits, polyamine spermine potentiated GluN1/2B (but not GluN1/2A/2B) receptors that contained a single mutated GluN2B. In contrast, the neurosteroid, pregnenolone-sulfate (PS), was effective at potentiating the NMDA receptor currents (to varying degrees) regardless of the subunit composition. The potentiation of NMDA receptor currents by PS was also observed in neurons overexpressing the variants.

The techniques used in this study were appropriate to address the objectives and the overall effects are large, and generally convincing. I like the way the results are presented, although have a few (easily addressable) comments.

We thank the reviewer for the positive remarks on our manuscript.

Major comments:

#1 When incrementally adding drugs (e.g. traces in figures 5 and 6), it doesn't always appear like the response has plateaued before changing the solutions/drugs. Therefore, I am curious to what extent the effects observed are underestimated.

The reviewer is correct to note that some responses do not necessarily reach a plateau, despite our efforts reach steady-state (as shown in most figures, e.g., Figs. 1-4, 6b, etc.), in particular when applying pregnenolone-sulfate (PS) (Fig. 5a, all traces in middle and bottom rows). However, in several instances, this was unobtainable due the very slow effect of the neurosteroid (its mode of action is from within the membrane) and the very large size of the cell (~1 mm). For these reasons, these experiments mandated excessively long exposures (~minutes) of oocytes to glutamate and PS (see scale bar- 20 secs) to try to reach steady-state, however this also caused deterioration to some cells (which did not return to baseline- and were therefore discarded). Thus, we eventually converged on settings whereby we did not expose oocytes to more than 4 minutes of the drug. Nevertheless, to try to estimate the extent of the underestimation (if any), we fitted the currents (standard mono-exponential fit, as previously reported1–3 (Suppl. 5a). We found that our application times of PS were, on average, three time the response’s time constants (tau) (Suppl. 5b), and we found a very weak relationship (R2 = 0.09) between the response to PS and time of its application (Suppl. 5c). These are now explicitly mentioned in the text (line #203), and in the legend of Suppl. 5. These thereby suggest that the reaction reached approximately 95% (1 - 1/e^3) of the steady-state value, and we are therefore confident that we have very small, if any, underestimation the extent of PS potentiation.

2 Also, in relation to figure 6, to what extent does agonist application cause desensitization here? Looking at traces in Figure 6b it appears that there is some desensitization and it isn’t clear to what extent this persists during the solution changes.

Agonist desensitization of NMDARs-currents is a well-known phenomenon, but it is very well established that it is not always observed in cells, including neurons (e.g., 4–7). In general, we did not observe very frequent desensitization’s (we provide a larger variety of traces of desensitizing and non-desensitizing currents (Fig. 6b Suppl. 7e and Suppl. 8a). Nevertheless, we explicitly note that in neurons, currents that didn’t reach steady-state after application of 100 mM NMDA were excluded from analysis (Methods - Patch clamping of cultured neurons, line #474), and in most cases desensitization was minor (or absent) following application of 100 mM NMDA and 100 mM PS (Fig. 6b).

3 Could the authors conduct/show the controls where NMDA alone (for 50-60s), or NMDA followed by PE-S (without ifenprodil).

These recordings are now shown in Fig. 6b and Suppl. 8a, (as opposed to Suppl. 7e).

#4 Finally, figure 5 shows the effect of the neurosteroid (and ifenprodil) on NMDA-evoked currents in neurons overexpressing the GluN2B variants in neurons. However, there currents probably reflect a mixture of extrasynaptic and synaptic receptors. To what extent are synaptic NMDA receptors affected by the variants?

To show the extent of the effect of the variants over synaptic receptors, we recorded miniature NMDA-dependent EPSCs; mEPSCNMDA), as described in our previous report8. We find that the varinats completely eliminate the appearance of mEPSCs (Suppl. 7a, b). Change in minis’ frequency is not the result of a presynaptic change or a change in synapse number9, as we have shown that AMPAR-mEPSC frequency was unaffected by the variants (i.e., synapse number and probability of presynaptic release are unchanged by the variants).

  To further address this, we also explored the relative synaptic vs. extrasynaptic distribution of the variants by using the established MK-801-protocol (to block all synaptic receptors during spontaneous activity, leaving extrasynaptic receptors unblocked)10,11. In neurons overexpressing the GluN2B-*wt* subunit, we obtained an extrasynaptic fraction of 38%, highly consistent with previous reports12,13. Overexpression of the variants, however, yielded a significantly and higher fraction (~50%) of the remaining current, supposedly suggesting more variant receptors at extrasynaptic loci (__Suppl. 8b, c__). However, due to the experimental settings we have chosen, the results from this experiment represent quite the inverse when involving extreme LoF variants. Firstly, 100 mM NMDA does not saturate variant receptors (whether pure, mixed di- or tri-heteromers, see __Table 1__). Secondly, normal neurotransmission does not open synaptic receptors containing mutant GluN2B-subunits, attested by the complete absence of mEPSCs (see __Suppl. 7a, b and __8,9). Thus, during the 10 minutes exposure to MK-801, only (mostly) purely *wt* receptors are blocked by spontaneous synaptic activity, and thus the second bout of 100 mM NMDA solely exposes the remaining *wt*-receptors. An increase in the number reflects more *wt*-receptors at the extrasynapse than the synapse. Thus, the observed increase in the fraction of extrasynaptic receptors in neurons overexpressing the variants, implies that the number of *wt*-receptors is necessarily decreased from the synapse and increases at the extrasynapse. We deem this to ensue due to the incorporation of the variants at the synapse. This increase cannot be explained by an overall increase in membrane expression of *wt*-receptors in neurons overexpressing the variants, as these cells show a strong reduction in Imax  (see __Fig. 6c and Suppl. 7e__). This is now detailed in the text (lines #270-290).

Minor comments:

5 Looking at the fits in the graph of Figure 2b it appears that the slope on the concentration response curves is less steep for the mixed 2B-diheteromeric NMDA receptors. How much are the Hill coefficients changing and can this be interpreted to provide more mechanistic insight? Wouldn't it make sense to include the Hill coefficients in Table 1?

We agree with the reviewer’s observation. Actually, the mixed di-heteromers have a similar Hill coefficient (nH) as the purely di-heteromeric GluN2Bwt receptors (see Table 1), and these show the typical near nH ~1 (e.g., 14–16). The only diverging groups are the purely di-heteromeric variant-containing channels (G689C/S only containing receptors; nH~2). Although these may suggest positive cooperation between the subunits, we are less inclined to infer insights from the latter owing to the fact that we limited our examination to 10 mM glutamate (we limit exposure of oocytes to 10 mM glutamate due to artifacts arising past this concentration, as discussed in Kellner et al.8: Fig. 2—figure supplement 1). (this description is now mentioned in page lines #149, 318, 319).

6 The authors illustrate the changes in potency by the shift in the concentration response curves, but is there any change in efficacy? A simple way to illustrate this would be also present a simple graph showing the maximum current amplitudes (i.e. to 10 mM glutamate) for each of the receptor complexes.

We now provide these data in (Suppl. 2a, b). We would like to note however that the expression pattern of the tailed-receptors (i.e., subunits with carboxy-termini tagged with C1/C2 tails, see Fig. 1a) are less expressive in general when compared with the native subunits (Suppl. 2c). This description is detailed in lines# 162-166.

#7 The authors characterize the 'apparent' affinity (or potency) of the receptor using concentration-response curves, but numerous points in the manuscript refer to changes in affinity. None of the experiments shown directly measure affinity (which would require ligand-binding assays) and so the use of the word affinity is inaccurate/misleading. I suggest the authors replace the instances of the word 'affinity' with 'potency'.

We apologize for the confusion surrounding our use of the term affinity. In fact, we do initially define this term in introduction (page #4): “apparent glutamate affinity (EC50)” to differentiate from affinity (KD). Regardless, and to avoid confusion, we replaced all terms, as suggested by reviewer to potency.

#8 In the third line of the abstract, the authors wrote, 'for which there are no treatments' in relation to GRINopathies. My understanding is that there are symptomatic treatments but that there are no disease-modifying treatments.

Indeed, all current treatments are supportive, rather than provide a bona fide cure or disease-modifying. These are now better defined in the abstract.

#9 The authors have interchangeably used the terms NMDAR or GluNRs throughout the manuscript. I suggest sticking to one of these terms. I would suggest NMDARs since this is less likely to be misread as a a specific NMDA receptor subunit.

Agreed and corrected throughout manuscript.

#10 Typos: 1) Results paragraph 2 sentence one: 'We thereby produced GluN2B-wt, GluN2B-G689C and GluN2B-G689S subunits tagged with C1 or C2, co-expressed these along with the GluN1a-wt subunits in...') Results paragraph 2: '...but these were mainly noticeable when oocytes are were exposed to high (saturating) glutamate concentrations...'
3) Last sentence in the second to last paragraph of the results section entitled 'Mixed di-and tri-heteromeric channels...': 'This , PS may serve to rescue...'
4) Last sentence in last paragraph of the results section entitled 'Mixed di-and tri-heteromeric channels...': 'Despite the latter, we found no evidence for any direct effect of three different physiologically relevant concentrations of the drug on di- or tri-heteromeric receptors'

All typos corrected.

#11 Figures 1e, 2b, 3b: it would be helpful to add a legend to the graph so that the curves can be interpreted without having to read through the figure legend.

Corrected.

#12 The bar graphs in Figure 6 show individual data points but those in figures 4b and 5b don't. Can the authors please add the data points to these graph.

Individual data points have been added.

#13 It would be helpful to reviewers that future manuscripts by the authors include page numbers and line numbers.

Included.

**Referees cross-commenting**

#14 Reviewers 2 and 3 highlight an important issue concerning figure 6 and the extent to which the overexpressed variants subunits can compete and assemble with endogenous NMDA receptors (unlike the system where the surface expression of specific receptor complexes is controlled). Indeed in the recent paper by the same authors, the two variants differed in their surface expression (in HEK cells), with G689C expressing particularly poorly. With reference to the second minor comment of Reviewer 1, the maximum current amplitudes would of course need to be normalized to cell surface expression of the receptor to gain any insight into efficacy.

We provide maximal current amplitudes (Imax) as a proxy for expression level as typically done (e.g.,8,17). These are now shown in Suppl. 2a, b (and see our response to comment #6, above). We would like to emphasize that we find it challenging to gain insights about efficacy of the variants in neuronal synapses, as we purposefully express non-C1/C2 tagged subunits in neurons (as we covet assembly of the variants with endogenous subunits). Moreover, the C1/C2-tagged subunits (whether wt or variants) are less expressive compared to their non-tagged NMDAR-counterparts. For instance, tagged GluN2B-wt subunits express at ~50% compared to non-tagged GluN2B wt subunits (Suppl. 2c). Thus, we find that efficacy of the C1/C2 tagged-subunits is less relatable to the non-tagged subunits (which are used in neurons and likely more relevant to the disease).

Despite the latter, we deem that we have specifically addressed this issue by measuring miniature EPSCs (mEPSCs) (see our reply to comment #4, Suppl. 7a, b). Briefly, even though the non-tagged G689C expresses at ~40% compared to other subunits (in oocytes and mammalian cells8), in neurons it engenders a robust (and highly significant) negative effect over synaptic currents (mEPSCs), as strong as the G689S-variant which expresses much more robustly (non-tagged G689S expresses to same extent as wt subunits). This demonstrates that the reduced efficacy the tagged subunits is less relatable to the non-tagged subunits and, importantly, it does not hinder the variants’ ability to incorporate within the synapse and affect function (i.e., exert a dominant negative effect). Here, we extend these observations towards the major postnatal channel subtype, namely tri-heteromers (2A/2B*), and therefore demonstrate that the robust dominant negative effect of G689C and G689S variants is likely due to their ability to incorporate within the predominant receptor subtype at the synapse (Suppl. 8).

Reviewer #1 (Significance (Required)):

This study emphasizes the complex pattern of effects that variants can have on glutamate receptor function and pharmacology, especially considering the context of receptor subunit composition. The authors have followed up their previous findings on the same mutants (Kellner et al, 2021, Elife), but used a trafficking control system here to characterize properties of mutated receptor complexes that are most likely to exist in neurons. The authors show that the defective currents mediated by NMDA receptors containing a loss-of-function GluN2B variant can be enhanced by neurosteroids (and in the case of GluN1/2B receptors, polyamines also). Development and approval of neurosteroids for the clinic would be required for the findings to translate to a therapy for patients. Readers should also be aware that neurosteroids act on other receptors too (e.g. GABA receptors), which could complicate the outcome. The expertise of the reviewer is in glutamate receptors and synaptic transmission.

We agree with the reviewer’s comment pertaining to challenges in translating PS to the clinic. Indeed, we explicitly mentioned its inhibitory effect on GABAA receptors (see line #366-367 and reference 18), as well as note its potential negative effect over GluN2C/D-containing receptors (line #365 and reference 19). We further describe alternative neurosteroids and means to bypass the limitations of PS, for instance by use of 24(S)-hydroxycholesterol6,18 or synthetic analogues (SGE-201, SGE-301)6. Lastly, we also propose a novel therapeutic approach, for which we did not find any mentions in the literature with regard to GRINopathies, consisting of the use of the FDA-approved Efavirenz (anti-retroviral compound20) to promote activity of cytochrome P450 46A1 (CYP46A1) to increase amounts of 24-S in the brain (discussion, lines #370-383).

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

#1 The objective of this paper is to assess whether a single mutated subunit of GRIN can affect the function of various forms of NMDA receptors. In particular, this study investigates the functional consequences of a GRIN variant when assembled within tri-heteromers, containing 2 GluN1, 1 GluN2A and 1 GluN2B subunits, the major postnatal receptor type. For this purpose, the authors artificially forced the subunits to associate in predefined complexes, using chimeras of GRIN subunits fused to GABAb receptor retention control sites at the endoplasmic reticulum. This trick allows to control the stoichiometry of the channels at the membrane and thus to focus on the function of a single type of NMDA receptor.The take home message of the paper is that a single GluN2B‐variant, whether assembled with a GluN2B‐wt subunit to form mixed di‐heteromer or with a GluN2A‐wt‐subunit (tri‐heteromer), strongly impairs the receptor functioning, as reported by a decrease of the apparent glutamate affinity of the receptor.

Altogether, this is a straightforward study of great interest for the GRIN community.

We greatly appreciate the reviewer’s comment about the relevance of our work towards the GRIN-community.

2 However, the way the background and purpose of the study (title and abstract) are presented is a bit confusing for non-specialists and could be easily improved. Technical information, which is crucial to validate the conclusions drawn from data analysis, should be added to the article. Some additional experiments are suggested to consolidate the work. Finally, additional discussion points are strongly encouraged.

We apologize for not making the paper more accessible to a broader readership. We did so for the sake of brevity. Nevertheless, we have re-written major parts of the manuscript to address this issue and retitled the report: “Rescuing Tri-Heteromeric NMDA Receptor Function: The potential of Pregnenolone-Sulfate in Loss-of-Function GRIN2B Mutations”.

Specific comments

Abstract / Title:

3 This work shows that a single GRIN variant impairs the function of various forms of NMDA receptors. Several sentences in the title and the abstract are confusing for a non-specialized audience. "Two extreme Loss‐of‐Function GRIN2B‐mutations are detrimental to triheteromeric NMDAR‐function, but rescued by pregnanolone‐sulfate." "Here, we have systematically examined how two de novo GRIN2B variants (G689C and G689S) affect the function of di‐ and tri‐heteromers." The number of variants tested is not of capital importance in the title, especially because one could believe that both are tested at the same time; similarly, when variants are named in the abstract, the fact that only 1 variant is studied at a time should be clarified (G689C OR G689S). Indeed, the problem is obvious to those familiar with GRIN disorders, but if this paper is to be published in a journal reaching a large audience rather than a specialized audience, the title of the paper should be modified.

As noted in our reply to comment #1 of this reviewer, we apologize for not making the paper more accessible and have therefore changed the title and re-written major parts of the manuscript to address this issue. We would like to note that we appreciate the reviewer’s comment and intent to increase the readership of our manuscript.

#4 "We find that the inclusion of a single GluN2B‐variant within mixed di‐ or tri‐heteromeric channels is sufficient to prompt a strong reduction in the receptors' glutamate affinity, but these reductions are not as drastic as in purely di‐heterometric receptors containing two copies of the variants. This observation is supported by the ability of a GluN2B‐selective potentiator (spermine) to potentiate mixed diheteromeric channels." Please, clarify the link between the two sentences. How do spermin potentiation of mixed diheteromeric channels supports the observation that the inclusion of a single GluN2B‐variant has less effect than the inclusion of two variants?

Our intention was to highlight that mixed di-heteromeric channels (2B/2B*) are less “damaged” (this is the link) than purely di-heteromeric channels (2B*/2B*).Explicitly mixed di-heteromers show less reduction in glutamate potency AND are also spermine-responsive, whereas purely mutant di-heteromers (2B*/2B*) show reduced potency, BUT do not respond to spermine at all. We have rephrased the sentences in our current manuscript to be clearer:

For instance: The positive responses of mixed di-heteromers, compared to the null effect over pure di-heteromers, is likely the result of the restored pH-sensitivity of mixed di-heteromers (Suppl. 3). This was surprising as the minimal and essential rules of engagement for potentiation by spermine are not well established, particularly in the case of tri-heteromers21,22 (see discussion, lines #341-353).

Methods

#5 All this study is based on the use of a unique ER‐retention technique to limit expression of a desired receptor‐population at the membrane of cells. According to the ER system retention of GABAb receptor, used in this study, while C1/C1-fused subunits are retained in the ER, C2/C2 reach the cell surface and the association of C1/C2 in the ER enables cell-surface targeting of the heterodimer. However, GB2 does not contain any retention signal and can reach the cell surface in the absence of GB1, as a functionally inactive homo-dimer (doi: 10.1042/BJ20041435). If there is an experimental trick that prevents the addressing of C2/C2 to the cell membrane, it should be specified and explained. This is critically important for understanding which receptor populations the data are derived from: receptors containing C1/C2 fused subunits only as stated by the authors, or C1/C2 and C2/C2 fused subunits?

We base our experiments on two seminal reports—23,24—that have developed this unique method (which we also refer to in the text, lines# 112-116). Briefly, the method employs the binding motifs of GABAB1 (GB1) and GB2 subunits and ER-retention motifs (these are now better detailed in Methods section, line # 448). Previous reports explicitly demonstrate that C1/C1- OR C2/C2-containing receptors do not reach the plasma (or very minimally) and we have reproduced these data with our variants (C1/C1: Suppl. 1a-d; C2/C2: Fig. 1a-c).

Figures #6 NMDA-receptor current amplitude should be normalized by the membrane expression of the receptors. A preliminary experiment should measure the effective cell surface expression of each of the subunits in the different transfection conditions.

To address the effective cell surface expression, we employed Imax as a proxy for functional expression (e.g.,8,17). These are now shown in Suppl. 2a, b (and see our response to reviewer 1, comments #6 and 14). Expectedly, we find significantly reduced efficacy by the varinats compared to wt-receptors, and the purely mutant di-heteromeric receptors exhibit the weakest efficacy. We have also addressed this issue by measuring miniature EPSCs (mEPSCs) (see our reply to reviewer 1, comment #4,). We find the variants to abolish mEPSCNMDA frequency (Suppl. 7a, b). This shows that the variants’ reduced efficacy translates to elimination of synaptic activity (dominant negative effect) (also seen in Suppl. 8).

#7 Fig.1a

The scheme should include C2-C2 complexes and mention whether these complexes are expressed at the cell surface (see previous and following comments).

As noted in our reply to comment #5 of this reviewer (above), C2/C2-containing receptors do not reach the plasma membrane (Fig. 1a-c). To avoid confusion, we have now added this scheme to the cartoon presented in Fig. 1a and have provided a more detailed description of the method and clones produced in the Methods section (line # 448).

#8 Fig.1b and c

Current from cells transfected with GluN2B‐wt‐C1 and GluN2B‐wt‐C2 should be compared to current expressed in cells expressing untagged receptor subunits: GluN2B‐wt Current from cells transfected with GluN2B‐wt‐C1 alone should be shown as well (although expected to be retained in the ER) (as performed for GluN2A‐wt‐C1 GluN2B‐wt‐C1 in suppl Fig. 1a)

Current comparisons of oocytes expressing tagged GluN2B‐wt‐C1 and GluN2B‐wt‐C2 and non-tagged GluN2B‐wt are now demonstrated in Suppl. 2c. The results indicate that the “tags” (C1 and C2) affect the expression of the subunits. We have also added a sample trace of current from a cell expressing the GluN2B‐wt‐C1 alone (Fig. 1b).

9 How could you explain the null current from cells transfected with GluN2B‐wt‐C2 alone (Fig.1b middle, and 1c)? since GB2 does not contain any retention signal and can reach the cell surface in the absence of GB1, GluN2B‐wt‐C2 is supposed to reach the cell surface. This is a very important point to clarify (I am probably missing a technical detail) because if the sub-unit tagged with C2 does reach the cell surface, then all the results and conclusions drawn from the C1-C2 conditions are wrong and could be attributed to a mix of complexes containing either C1-C2 or C2-C2.

We now realize that the reviewer was missing a crucial technical detail, namely how the clones are designed. Briefly, all clones have ER retention motifs and cannot reach plasma membrane unless they necessarily assemble as C1/C223,24. Also, please see our replies to comments #5, 7 to this reviewer (and Methods section, line # 448).

My following comments are based on the assumption that only receptors containing C1-C2 tagged subunits reach the membrane (as assumed by the authors and suggested in Figure 1b middle), but explanations should absolutely be provided to convince the reader. Fig. 4a and 5a (see our above replies to comments #5, 7 and 9; and references 23,24).

#10 Please, keep the current scale constant between all current illustrations within the same figure (4a and 5a). Indeed, not only the Spermin- or SP- induced potentiation is an important data (which is presently quantified on the histograms of fig. 4b and 5b) but also knowing whether the amount of current recorded in cells expressing one mutant subunit in presence of SP (for example GluN2A‐wt‐C1 GluN2B‐G689S‐C2) is comparable to the current recorded in wt receptor-expressing cells (GluN2A‐wt‐C1 GluN2B‐wt‐C2) in absence of SP would be an excellent added value for the paper. A special figure could quantify this rescue effect of SP, measuring and comparing the mean currents recorded in these conditions (one current illustration is not sufficient given variations between similar samples). By the way 5mM glutamate might be an excessive concentration. At 1mM, the expected synaptic concentration of glutamate following action potential, according to figures 3 and Suppl1 the response of the mutated receptor is much lower than that of the WT which is already almost maximal. In these conditions, SP-induced potentiation by a factor of two of GluN2A‐wt‐C1 GluN2B‐G689S‐C2 current could be equivalent to control currents recorded in GluN2A‐wt‐C1 GluN2B‐wt‐C2 cells.

We have rescaled all current amplitudes in Figs. 4 and 5 to be identical in size for easier comparison.

We have added all current amplitudes to try to examine the rescue effect of the two drugs in cell overexpressing a specific channel subtype, as requested (Suppl. 4). We find that; indeed, the potentiated currents of the mutant receptors reach (or even surpass) the basal Imax (i.e., current before potentiation) of the non-mutated receptors (Suppl. 4, dashed statistics bar).

In neurons, we address this in two ways. First, we show that the total NMDA-current is reduced by expression of the variants, and this current is “normalized” by PS (Fig. 6a-c). Similar reductions in Imax (by the variants) are shown in Suppl. 7e (to provide more examples). Secondly, neurotransmission (i.e., 1 mM glutamate25,26) is not sufficient for activating mutant receptors, certainly not pre-di-heteromers (see Table1, EC50 and Suppl. 7a, b- no mEPSCs)27–29. Therefore, 5 mM was required. Together, these strongly suggest that PS may normalize the currents of different receptors that respond to PS (under physiological settings and not 1- or 5mM NMDA). As suggested by the reviewer, there are many subtypes, and some may be activated by ambient glutamate (as suggested by application of PS onto neurons without opening the receptors by NMDA; see Suppl. 7c, d).

#11 Fig. 6

Figure 6 is not convincing because cultured hippocampal neurons do express endogenous NMDA receptors. To what extent the recording currents are affected by endogenous, non-mutated GluN2B subunits? Western Blots showing an extinction of endogenous subunits expression when transfected tagged subunits are competitively expressed would be required.

We have previously shown that the two variants incorporate very efficiently within synapses, causing a very robust elimination of synaptic currents (by measuring miniature NMDA-dependent EPSCs; minis) [see Fig. 8 in Kellner et al. eLIFE, 202127, and see review by Sabo et al.9 ). Change in minis’ frequency can be interpreted as either a presynaptic change or a change in synapse number, however we observed that AMPAR-mEPSC frequency was unaffected by these variants. These imply that synapse number and probability of release are unchanged by the variants. As the experiments are performed in wild-type neurons, (which express wild-type GluN2A and -2B), the dramatic effects we observed on minis suggests a dominant-negative effect of these disease-associated GluN2B variants. These are consistent with our observations that mutant subunits can co-assemble with wild-type GluN2B and/or GluN2A subunits. We have now reproduced this experiment (in fact, we employ this strategy prior each experiment to ensure expression of the variants) (Suppl. 7a, b). This thereby shows that there are no available wt-receptors at the synapse.

As there are various pools of NMDARs at synaptic and extrasynaptic sites, we did not think that a western blot would sufficiently differentiate between the latter, and thereby would not provide insight about extinction of wt-receptors (which could be simply pushed to other sites compared to synapse). Moreover, the intracellular pool of receptors is much larger than the amount of NMDARs that can be detected at the membrane (e.g., 30,31), and therefore electrophysiology seemed to be a better means to monitor membrane receptors only:

Thus, to examine the distribution of the variants between synaptic- and extrasynaptic loci, we employed a standard procedure consisting of the use of the activity-dependent blocker MK-801 (Methods). Briefly, neurons were persistently bathed in TTX during which they were probed for Imax using 100 mM NMDA (to refrain from activating other GluRs), followed by application of MK-801 for 10 minutes to exclusively blocks synaptic receptors (that open following action-potential independent miniature neurotransmission). This thereby spares all extrasynaptic receptors from being blocked by MK-801, which are subsequently revealed by a second application of 100 mM NMDA (Suppl. 8a, inset)12. In neurons overexpressing the GluN2B-wt subunit, we obtained an extrasynaptic fraction of 38%, highly consistent with previous reports12,13. Overexpression of the variants, however, yielded a significantly and higher fraction (~50%) of remaining current (Suppl. 8b, c), but instead of reflecting a larger pool of extrasynaptic receptors, the experiment represents quite the inverse when involving LoF variants. Firstly, 100 mM NMDA does not saturate variant receptors (whether pure, mixed di- or tri-heteromers, see Table 1). Secondly, normal neurotransmission does not open synaptic receptors containing mutant GluN2B-subunits, attested by the complete absence of mEPSCs (see Suppl. 7). Thus, during the 10 minutes exposure to MK-801, only wt receptors are blocked by spontaneous synaptic activity, and thus the second bout of 100 mM NMDA solely exposes the remaining wt-receptors at the extrasynapse. Thus, the observed increase in the fraction of extrasynaptic receptors, in neurons overexpressing the variants, implies that the number of wt-receptors is necessarily decreased from the synapse and increases at the extrasynapse, most likely due to the incorporation of the variants at the synapse. This increase cannot be explained by an overall increase in membrane expression of wt-receptors in neurons overexpressing the variants, as these cells show, yet again, a strong reduction in Imax as seen above (see Fig. 6c and Suppl. 7e) (lLines #270-291). These thereby suggest that purely wt-receptors are not necessarily eliminated from the membrane (extinct), rather pushed outside of the synapse.

12 Fig.6b “PE-S” on the graph should be replaced by “PS”

Typo corrected.

Discussion #13 The authors are surprised by the fact (Fig.2) that 1 variant reduces the apparent glutamate affinity of the receptor, but not as much as 2 variants, despite the fact that "NMDARs opening requires all four subunits to be liganded (i.e., occupied by a ligand) which implies that the least affine subunit should have dominated the final affinity of the receptor". I agree that the difference is noticeable, however the glutamate affinity for receptors containing 1 variant is much closer to that of receptors containing 2 variants than that of wild-type receptors. Hence, the results obtained do not seem so surprising and could result, as rightly explained by the authors from a possible cooperativity between the subunits.

We agree with the reviewer that glutamate potency of receptors containing 1 variant subunit is much closer to that of receptors containing 2 variant subunits. However, we maintain our surprise because we expected it to equal (not just close) to the potency of the least affine subunit (the limiting factor). This is based on the notion that all four subunits need to be liganded for channel opening4,32–34. We gently raise the possibility of potential cooperativity (Table 1, see Hill-coefficient and 33,35,36), as well as mention that this may also stem from the variants’ lower proton sensitivity (Suppl. 3), which has also been shown to promote motions of the ATD (amino terminal domain) and increase open probability (positive cooperativity)36. Nevertheless, we are very careful with interpreting the Hill coefficient , as we limited exposure of oocytes to 10 mM glutamate due to artifacts arising past this concentration (see Kellner et al.8: Fig. 2—figure supplement 1). This description is now mentioned in page lines #149, 318, 319. Thus, even the slightest underestimation of the maximal reposnse would surely affect the slope.

#14 On the other hand, the data in Figure 6 are much more difficult to interpret and reconcile with the nature of the expressed receptor subunits (which this time is not controlled) nor their association within the same receptor. However, this aspect, which is essential to the understanding of the consequences of 1 variant on neuronal signalling, is not discussed: Whatever the stoichiometry of the complexes in the heterozygous disease, the mutated and wild type GluN2B subunits coexist in the same cell: Either within the same di-heteromeric complexes GluN2B-wt + GluN2B-mutant, or in separate complexes but nevertheless expressed in the same cell, in di heteromeric (GluN2B-wt + GluN2B-wt and GluN2B-mutant + GluN2B-mutant); or tri-heteromeric (GluN2A-wt + GluN2B-wt and GluN2A-wt + GluN2B-mutant) complexes. Assuming that half of the complexes remain wild-type, e.g. (GluN2A-wt + GluN2B-wt and GluN2A-wt + GluN2B-mutant) we would expect (Fig. 6) a small decrease in NMDA current (carried only by the half that expresses the mutated subunit, and whose function is not zero but only decreased by about 20% in response to 5 mM Glutamate, Fig. 3b). The same reasoning applies to the di-heteromeric conditions (GluN2B-wt + GluN2B-wt; GluN2B-mutant + GluN2B-mutant), here again the decrease observed Fig. 6b is difficult to reconcile with the responses measured Fig. 2b.

In other words, how to explain a 50% decrease of the currents, instead of the 10% expected by the previous reasoning. In this experiment we do not know which subunits are expressed, their proportions, nor how they are associated in functional complexes, which makes the interpretation of the data impossible. The only explanation, far-fetched, for 50 % decrease would be that the complexes were to contain all (or the vast majority) 1 wild-type subunits associated with 1 variant, then a homogeneous 50% reduction in current could be expected. But this extreme condition could only be possible in the case of di-heteromers, which is unlikely the case in Fig.6 as GluN2A currents are measured in presence of Ifenprodil. To conclude

1) the comparison of the currents in transfected and non-transfected neurons does not make sense in figure 6b which is not convincing because we do not know the nature of the currents actually measured. A comparison in controlled condition would make more sense (as I suggested in the criticism of figures 4, 5).

2) The reality of the combinations of expression and association between subunits within different complexes expressed in the same cell must be considered and taken into account in the interpretation of the data. Undoubtedly, the means of restoring the NMDA current will be different depending on the presence of mutated subunits in all functional channels or not.

Indeed, neurons express a variety of different combinations of channel stoichiometry, including following transfection with the variants. We do find find that the effect on whole-cell current is indeed ~50% (Fig. 6b, c), thereby safe to assume that 50% remain “wt”, but we do not know how they distribute between synaptic and extrasynaptic loci. Our results however argue against 50% remaining receptors at the synapse. First, mEPSCNMDA disappear (Suppl. 7a, b and see reply to comment #11 of this reviewer), but wt-receptors are still at the membrane, and they seem to be moving out of synapse (Suppl. 8). Thus, we can only state with higher certainty that the variant subunits are very efficient in incorporating within mixed or pure receptors, especially at the synapse.

  We also consider that the reduction in the whole-cell current observed in __Fig. 6b, c__ is not due to the remaining 50% GluN2B-*wt*-containing receptors, rather likely due to other variants, notably GluN2A, which are more prominent at postnatal stages37, such as in our case. In support, we see a large remaining current after saturating ifenprodil application (__Suppl. 7 e, f__)38. Thus, the variants incorporate within all 2A/2B membrane receptors, at the synapse and outside it (i.e., extrasynaptic) (see __Suppl. 8, c__).

**Referees cross-commenting**

The referees' comments are highly relevant. In particular, referee 3's comment 1 seems very interesting because it may help to better understand the discrepancy in the results observed in neurons, i.e. a 50% decrease in the currents induced by the expression of the mutant and wild type subunits in the same cells, whereas theoretically one would expect a 10% decrease of this current (cf. referee 2's 2nd comment in the discussion section). This comment 1 of referee 3 indeed stresses the fact that the control (non-transfected neurons) to which the heterozygous condition is compared is not the correct control, which should rather be neurons transfected with wild type receptor subunits. More generally, this comment underlines the importance of monitoring the effective membrane expression of the different subunits in each of the experimental conditions in order to be able to compare conditions and draw conclusions.

We initially did not perform this control as the literature paints a clear picture whereby expression of the GluN2B-subunit (without adding excess of the GluN1 subunit) does not instigate a robust increase in surface expression of NMDARs (and thus current remains the ~same) 4,39–43, and see our reply to comment #14 (above), and reviewer 3 comment 1 (below). Nevertheless, we have now performed this test by overexpressing GluN2B-wt. In support of previous reports, we do not find any statistical difference in current size between non-transfected neurons and neurons solely overexpressing the GluN2B-wt subunit (Fig. 6a, b). Furthermore, application of PS onto naïve or GluN2Bwt expressing neurons yields identical currents (Imax) and potentiation (Fig. 6c, d). These argue that we did not obtain “overexpression”.

We suggest that the 50% reduction in current size between neurons expressing the mutant and wt expressing neurons stems from the integration of mutant subunits and their dominant negative effect. Evidence for this incorporation is provided by the very strong reduction in synaptic currents (suppl 7a, b), and the supposedly higher abundance of wt-containing receptors in extrasynaptic regions (see reviewer 1 comment 4 and suppl 8). This is

Reviewer #2 (Significance required):

The novelty of the study, is to evaluate the consequences of a single mutated subunit within NMDA receptors affected by GRIN variant, to mimic the heterozygous condition of GRIN encephalopathies, this is of potential value for the field and the interest could also be extended to other genetic diseases (at least the experimental way to study the functioning of only one desired stoichiometric configuration). The strength of this paper is precisely to isolate technically and to study the functioning of a desired stoichiometric configuration only. The main limitation of the paper is the interpretation that the authors make of their data in a physio-pathological context. This work could be of interest for general audience, providing the title and summary are slightly modified. My area of expertise could not be closer to the topic of the article: Glutamate receptors; GRIN; molecular tinkering, cell culture, electrophysiology, receptor stoichiometry...

We thank the reviewer for noting the value in our work and its potential contribution and interest to the field and other diseases. Per reviewer’s suggestion, we have modified the title and text to suit a larger audience.

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

This paper is a follow up of an earlier paper published by the group (Kellner et al., eLife 2021), which aimed at characterizing the functional properties of two de novo GluN2B mutations in patients suffering from severe pediatric diseases, GluN2B-G689C and -G689S. NMDA receptors (NMDARs) are tetramers composed of two GluN1 and two GluN2 subunits. A single receptor can incorporate either two identical GluN2 subunits (di-heteromers) or two different GluN2 subunits (tri-heteromers), leading to a large diversity of NMDAR subtypes. The main NMDAR subtypes in the adult forebrain are GluN1/GluN2A and GluN1/GluN2B di-heteromers, as well as GluN1/GluN2A/GluN2B tri-heteromers. While the exact proportions of these three subtypes are still contentious, there are evidence that in the adult N1/2A/2B tri-heteromers form the major population of synaptic NMDARs in the adult forebrain. In addition, patients bearing pathogenic mutations are often heterozygous for the mutation, giving rise to mixed NMDARs incorporating one mutated and one intact GluN2 subunit. In their previous paper, Kellner et al. had shown that purely di-heteromeric GluN1/GluN2B-G689C and -G689S mutants display a drastic (> 1,000-fold) decrease of glutamate sensitivity and a decrease of surface expression. In the current paper, the authors characterize the effects of the -G689C and -G689S mutations on N1/2A/2B tri-heteromeric receptors, as well as on mixed di-heteromeric GluN1/GluN2B receptors containing one copy of the wild-type GluN2B subunit and one copy of the mutated GluN2B subunit. They show that one copy of the mutant subunit, either within mixed diheteromeric or tri-heteromeric receptors, is sufficient to decrease drastically glutamate sensitivity, although the shift in glutamate EC50 is not as strong as in pure di-heteromeric receptors (≈ 500-fold). They furthermore explore strategies to counteract the hypofunction induced by these mutations by testing the effect of positive allosteric modulators (PAMs). They show that spermine, a GluN2B-specific PAM, can potentiate the activity of mixed diheteromeric N1/2B but not N1/2A/2B tri-heteromers. However pregnenolone sulfate (a 2A/2B-specific PAM) can potentiate both the activity of mixed diheteromeric and tri-heteromeric NMDAR populations, either in oocytes or cultured neurons.I have very few major comments to make. The experiments are straightforward and the adequate controls have been made. Here are my two only major comments:

We thank the reviewer for the very detailed overview of our work and for appreciating our controls and methods.

#1 About the experiment on cultured neurons. The authors compare the currents of cultured neurons transfected with GluN2B-G689C and -G689S to non transfected neurons. The adequate control is rather neurons transfected with the wild-type GluN2B subunit to even out any phenomenon linked to transfection of the neuron. Given the overexpression that can occur after transfection, the effect of the mutations on the size of NMDAR currents might be even stronger than what the authors show. However in that case PS might not completely rescue mutant NMDAR currents to wild-type levels.

We initially did not perform this control as the literature paints a clear picture whereby expression of the GluN2B-subunit (without adding excess of the GluN1 subunit) does not instigate a robust increase in surface expression of NMDARs (and thus current remains the ~same) 4,39–43, and see our reply to comment #14 (above), and reviewer 3 comment 1 (below). Nevertheless, we have now performed this test by overexpressing GluN2B-wt. In support of previous reports, we do not find any statistical difference in current size between non-transfected neurons and neurons solely overexpressing the GluN2B-wt subunit (Fig. 6a, b). Furthermore, application of PS onto naïve or GluN2Bwt expressing neurons yields identical currents (Imax) and potentiation (Fig. 6c, d). These argue that we did not obtain “overexpression”. Thus, our results and interpretations hold true, and are therefore not underestimation of the effects of PS in neurons.

2 How come high concentrations of glutamate (>100µM) produce additional current on wt GluN1/GluN2B (with retention signals) compared to 100 µM glutamate, which is supposed to be saturating? It does not seem to stem from an osmotic effect since 10 mM glutamate does not produce any current on uninjected oocytes. Knowing that this "artefactual" effect might also occur in the mutant receptors, how do you take this effect into account when calculating the glutamate EC50s of the mutants? Given the drastic shift in EC50 produced by the mutant, taking into account this artefact is not going to change the conclusion, but the actual EC50s will be affected.

GluN1/GluN2B-wt receptors (with or without retention signals) are indeed saturated at 100 mM glutamate. However, excessively large concentrations of glutamate (>100 mM) may yield artefacts even in non-injected oocytes (in 10 mM, this occurs in ~20% of the cells, see Kellner et al 20218—Fig. 2 and Suppl. 1c, d) as well as in GluN2B-wt injected oocytes (supplementary Table 1 in 44). This is not due to osmolarity, as rightly mentioned by the reviewer (and below), rather possibly by endogenous glutamate receptors and transporters that do not readily contribute to current amplitude (these are extremely small currents), but can cause deterioration of the cell (and enhance ‘leak’) when activated for prolonged times by very large concentrations (e.g.,45). In fact, we explicitly report these to highlight potential artefacts, as these are often overlooked in the field. Regardless, most reports do no go past 100 mM glutamate, not even when describing GRIN2 mutations since most mutations do not cause such drastic shifts in potency as we observed (to the best of our knowledge only one report describes such an extreme LoF mutation for a GluN2A variant46). Of note, these effects are not seen when glycine is applied at high concentrations (supporting lack of effect by osmolarity)47. Thus, we refrained from testing concentrations past 10 mM, aware that it may yield a slight underestimation of glutamate potency (and perhaps the reason for the larger Hill coefficient, nH; see our reply to reviewer #1, comment #5). Importantly, despite the potential underestimation of the EC50, it does not change our conclusions as all groups are measured side-by-side (thus, the same underestimation equally applies to all other groups as well). We now mention this more in detail in the methods under the section – “Two Electrode Voltage Clamp recordings in Xenopus Laevis oocytes”.

Minor comments:

3 In the first paragraph of the "Results" section, when describing the design of the constructs used to force a heteromeric stoichiometry in recombinant systems, the authors do as if they had designed the constructs themselves "Briefly, we tagged...are retained in the ER (Fig. 1a)". Please rewrite this paragraph to show that you used constructs that had been previously designed by another group (Hansen et al., 2014).

We apologize. We did not mean to express that we have developed the method and indeed refer readers to the seminal works of those who did (Stroebel et al., 2014 and Hansen et al. 2014, lines #109-116). We did not go into details for the sake of brevity. We have rewritten this part to give proper acknowledgement to the method’s developers (also see Methods, line# 448).

4 I do not see any evidence of "positive cooperativity" between subunits in ref. 32. Ref. 32, to the best of my knowledge, states that in N1/2A/2B tri-heteromers, the 2A subunit sets the biophysical properties of the tri-heteromer. But there is no account of mixed di-heteromers. In addition, the cooperativity between the glutamate and glycine binding sites is negative.

The reviewer is correct, and we apologize for the mis-citation. Indeed, the cooperativity between glutamate- and glycine-binding is typically reported as negative48,49, and our intention was to highlight the strong cooperativity (whether positive or negative) observed between NMDAR-subunits and meant to cite the works of: 33,35,50 (lines . We have now rephrased the sentence: The divergence from this scenario suggests that the slight amelioration in potency could stem from positive cooperativity between the subunits50 (but see Hill coefficients in Table 1). Indeed, mixed receptors show restored proton sensitivity (Suppl. 3), which has been suggested to be coupled to other receptor features, notably increase in open probability.

5 Interpretation of spermine action within the Results section: it is striking indeed to observe that the mutations in the context of a mixed di-heteromer still allow spermine potentiation, while they abolish this potentiation in pure di-heteromers. As rightly said in the discussion, the regain of spermine potentiation in the mixed compared to the pure diheteromers is likely due to a more favorable transduction of spermine signaling to the pore, likely via a higher pH sensitivity of mixed di-heteromers compared to di-heteromers. I would thus avoid the terms of "one single intact interface" for the mixed di-heteromer, since both spermine binding sites are likely intact in this NMDAR configuration. How is pH sensitivity affected in the mixed di-heteromers?

We have performed a detailed pH dose-responses for the various channel types (Suppl 3). We find that GluN2B mixed di-heteromers exhibit similar IC­50 as pure GluN2B-wt di-heteromers, thus explaining their ability to undergo potentiation by spermine via alleviation of proton inhibition. We therefore further suggest that mixed di-heteromers’ have higher pH-sensitivity compared to pure mutant di-heteromers and this mat also contributes to their higher spermine sensitivity. Lastly, we observed that all GluN2A-wt-containing tri-heteromeric receptors were non-responsive to spermine (Fig. 4a). In fact, under our experimental conditions tri-heteromers underwent slight inhibition by spermine, regardless the identity of the GluN2B subunit (whether wt or variant) (Fig. 4b). Thus, as the tri-heteromers used here exhibit identical pH-sensitivity as 2B-di-heteromers, the only diverging aspect is the missing interface between the GluN1a and GluN2B subunits, demonstrating that potentiation by spermine requires at least one GluN2B-subunit with an intact proton sensitivity, and mandates two intact interfaces between GluN1-wt and GluN2B-wt subunits (Table 1)21.

6 In the methods section, the oocyte recording solution (likely Ringer and not Barth) does not contain any potassium. This is probably a typo. Could you correct the composition of your Ringer?

Corrected. We record NMDARs currents by use of a Barth solution containing (in mM): 100 NaCl, 0.3 BaCl2, 5 HEPES, pH 7.3 (adjusted with KOH, at ~2.5 mM) (as in 4,51).

7 There are several typos, especially in the Discussion.

We have corrected the typos throughout the publication.

**Referees cross-commenting**

I overall agree with the comments of reviewers 1 and 2. In particular, I agree that it is pointless to compare the absolute currents of non transfected neurons vs mutant-transfected neurons without an idea of receptor cell-surface expression.

We have performed this experiment (Fig. 6) and please see our reply to this reviewer’s comment #1.

I would like however to give some precisions about some comments of Reviewer 2. About the ER retention technique to express tri-heteromers: I didn't know that the C2 signal could be addressed to the membrane on its own. The lack of leak current stemming from C1-C1 or C2-C2 combinations has been demonstrated in the paper establishing the technique (Hansen et al, 2014), as well as in another paper that developed an analog technique based on GABAB retention signals (Stroebel et al., J Neurosci 2014). So it is fair to consider that the authors were not surprised by the lack of current when co-expressing two GluN2B subunits carrying the C2 signal.

We thank you for this addition and support for our observations.

About the comparison about absolute currents wt vs mutants, +/- spermine (Fig. 4a and 5a). I agree with reviewer 2 that being able to compare absolute currents of wt without spermine to mutant + spermine would be very interesting to see if spermine can actually rescue mutant hypofunction. However, to the defense of the authors, comparing absolute current values of recordings from Xenopus oocytes is meaningless. Indeed the variability of currents for the same construct and same day of experiment is too high (there can be up to a ten-fold difference between the lowest and the highest current of oocytes expressing the same construct the same experimental day). A way to investigate this aspect would be to estimate the open probability of the different constructs with or without spermine via the inhibition kinetics of an open channel blocker (e.g. MK801) and measure surface expression by Western blot, but I am not sure these experiments are worth it for the spermine experiment.

We agree with this reviewer about current size. It is quite variable among cells and would therefore introduce an additional variable and variability: the expression of these modified (C1/C2-tagged) subunits is dually affected by the mutation itself (Kellner et al. 2021) and by the introduction of the tagging (which really hampers there trafficking to membrane, Suppl. 2c); with unknown contribution of each variable. We thereby do not think these provide an added value to our conclusions, yet to grant reviewers’ no 2 request we have added __Suppl. 4 __which shows the rescue effect of the different drugs.

Reviewer #3 (Significance (Required)):

This paper is not of high significance since most of the characterization of the 2B-G689C and -G689S de novo mutants found in patients has already been published (Kellner et al., eLife 2021). However, this paper is worth publishing since it brings new data on the effect of the mutations on tri-heteromeric and mixed di-heteromeric NMDAR populations, which are likely the most abundant NMDAR populations in the patient's brain at adult stage. Tri-heteromeric and mixed NMDAR populations have often been overlooked when studying pathogenic NMDAR mutations due to the difficulty to express them specifically in recombinant systems. This paper (in addition to other papers in the field, see for instance Elmasri et al., Brain Sci. 2022; Li et al., Hum. Mutat. 2019) shows that the effect of the mutations on the receptor biophysical and pharmacological properties (but also on trafficking) differ whether the receptor contains one or two copies of the mutant subunit. This paper is of interest to scientists interested in NMDA receptor structure-function and pharmacology, as well as clinicians interested in GRINopathies (pathologies linked to NMDAR mutations).

I, the reviewer, am an expert in NMDAR structure-function and pharmacology. I believe I have sufficient expertise to evaluate the entirety of the paper.

We thank the reviewer for appreciating and acknowledging the merits of our work for publication.

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When it comes to data analysis, I usually think of data as a source of information rather than it being a research object by itself. The term “raw data” has been used in all my classes, starting from accounting and finishing with introduction to digital culture and information. Yes, we’ve talked about biases that come up in different data sets, but usually this conversation is related to so-called “post-production” of data – either us, students, using it, or someone else and we reverse engineered where it came from. So, reading about an approach to data, even ‘raw’ data, as a constructed artifact is very refreshing. It’s extremely important to look at how the raw data was collected and what was left out by collectors initially to have a full image of what’s going on.

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

Major:

- The statement (line 149'Together, our data suggest that systemic ecdysone levels are unlikely to be involved in modulating tumour-induced muscle detachment or to mediate the role of fatbody Insulin signalling in regulating muscle detachment.') is derived from an experiment with sterol free diet (in which 20HE is genetically addressed) and a pleiotropic experiment (PG>RasG12V). In neither paper nor the current manuscript, 20HE levels have been directly addressed.

Therefore, this statement needs further experimental support and discussion. Ecdysone is a critical hormone during development and especially growth-related effects central to this study. The authors should consider doing pharmacology or augment their claims here with genetic manipulation experiments of 20HE related genes in larvae (Leopold, Rewitz, Rideout, Drummond-Barbosa, Schuldiner labs) and adult animals using genetics, pharmacology or direct assessment of 20HE levels (RIPA, Edgar and Reiff labs).

The main point we were trying to convey is that we do not think global ecdysone levels plays a role in modulating fatbody insulin or tgfb signalling, which in turn affects muscle detachment. We are not claiming that edysone levels is not changing in control vs. tumour bearing animals. In fact, we predict that 20HE levels will be different in tumour bearing vs. control animals (as tumour bearing animals undergo developmental delay), but this is not the main point of our conclusions. We believe that our conclusions are supported by the experiment demonstrating global ecdysone alterations (via feeding sterol-free food) did not affect how fatbody Akt activation altered tgfb signalling and enhanced muscle integrity (Figure S1). Therefore, we don’t think measuring 20HE helps to support our conclusions. Pharmacological inhibition via feeding ecdysone inhibitors effectively demonstrate a similar point to feeding sterol-free food which we have already performed. We are happy to try direct manipulation of 20HE related genes (eip75B-RNAi) in the fatbody to see if this affects muscle detachment or pAkt and pMad levels in tumour bearing animals.

- In Fig.7 the authors used a sog-LacZ stock to show transcriptional activation in fatbody cells. This stock is based on P-element insertion in the according regulatory regions and supposed to express lacZ with an nls. I can clearly see lacZ in nuclei in Fig. 7H, whereas this is very hard to see in nuclei in Fig7i in the tumour model. In addition, lacZ is known for its high stability and not the best option. As this finding is vital for central claims of this study, it should be complemented by either qPCR for sog on fat body cells or using another readout by converting one of the two Mimic lines (BL42189/44958) into GFP sensors for sog.

We will add a counterstain to these images. We will also perform qPCR in the fatbody of control and cachectic animals to assess whether Sog transcription is altered. We agree converting one of the Mimic lines to a GFP sensor would be a good option, but this experiment would require getting new fly lines into Australia, which takes at least 2 months because of quarantine laws. We don’t believe this experiment would change the general conclusions of the paper, therefore would prefer not to do this experiment.

- I have similar problems with Fig.7B-F, as phosphorylated Mad should be translocated to the nucleus. In 7F the authors measure pMad over Dapi, which is the right way but it is hard to see pMad in the nucleaus apart from Fig7B, wheras in D and E, where the authors measure higher levels, I cannot identify clear pMad in nuclei. These images either need to show the Dapi channel or more representative images should be chosen like in Fig.4 with arrows pointing to measured nuclei. Fig.7C something went wrong with the compression of this image.

We will show more representative examples and fix Fig 7C.

- The proper function of RNAi stocks targeting genes like sog, mad, etc. is vital for this study as these lines are used throughout the study. Functional evidence of specific knockdown efficiency should be provided or references given in which these stocks were shown to provide functional knockdown on transcript or protein level.

We agree with the reviewer that this is an important point. We will demonstrate the knockdown of sog and mad (and other RNAis) used in the study by either referring to published data or demonstrate knockdown ourselves.

- Fig.S7 discusses appearance of gbb/Bmp7 and Sog/CHRD in human patients. The analysis the authors performed shows a correlation between both factors, but is hampered by the fact that datasets for peripheral tissues of cachexia patients are unavailable. The authors may consider sorting these after tumor entities in which cachexia occurs frequently vs. low occurrence and then check for both genes.

We will try this analysis.

Fig.5 M-P pMAd is not indicated in the Panels only the legend.

We will fix this error.

- Please follow FlyBase nomenclature, e.g. dlg1 for discs large 1 and unify in the whole manuscript and figure for all genes.

We will fix this error.

- For endogenous fusion proteins like Viking-GFP (e.g. vkg::GFP) choose a format to clearly decipher them from transcriptional readout stocks like sog-lacZ.

We will fix this error.

- The quantifications in most figures are quite small with tiny lettering and XY axis are difficult to read in letter/A4 size.

We will enlarge font size.

Minor:

1. Adjust in-figure caption alignments

2. Line 104: add comma RasV12, dlgRNAi

3. Line 114: replace little  not significant (n.s.)

4. Line 334: 'sogRNAi overexpression' to my knowledge, RNAi are expressed, not overexpressed.

5. Line 454: italicize r4>

6. Fig S4E: remove frame

7. Figures 6: It would be better to number and explain the pathway presented in the figure in text and fig legend.

8. Just a personal preference. Lettering of images in images is commonly done horizontally, here it appears like a mix between vertical and horizontal.

We will fix these minor errors.

Reviewer #2

Major comment

Their genetic experiments clearly showed that the reduction of insulin signaling activity in the fatbody induces upregulation of TGF-β signaling and Collagen accumulation. Then, how does TGF-β signaling induce Collagen accumulation?

From the experiments we have carried out, we do not have insights into how TGF-B signalling induce Collagen accumulation.

They showed that Rab10 knockdown and SPARC overexpression reduced the accumulation of fatbody ECM. Are Rab10 and SPARC expression regulated by TGF-β signaling?

We can address this point by assessing if Rab10 and SPARC expression is altered in cachectic fatbody.

Minor comments

Line 90: "Disc Large (Dlg) RNAi in the eye" must be "Discs Large (Dlg1) RNAi in the eye imaginal discs".

we will fix this error.

Figures 1D and 1L are from the same image. Also, Figures 1C and 1M are from the same image. Are both of them necessary to be shown in the different panels?

The duplication of 1C and 1M, was an error, we thank the reviewer for picking this up. We will fix this error. We will use different images for 1D and 1L.

Why are the staining patterns of anti-pAkt shown in Figures 1L and 1U so different? pAkt is not detected in the nuclei in Fig. 1L but its nuclear signal is clear in Fig. 1U.

We will show more representative images of these staining.

Figure 1: Images of counter staining for nuclei like DAPI should be also included for all these fatbody images.

We will show counter staining for DAPI.

Line 101: "Tumour specific ImpL2 inhibition was sufficient to reduce fatbody pAkt levels." Is this correct? ImpL2 inhibition in tumors should elevate the pAKT level in fatbody.

This was a mistake, we will fix this error.

Figure S1~S4: These figures and their legends do not correspond to each other. We thank the reviewer in picking up this error, there was an error in inserting the images into the text. S2 and S3 were swapped.

We will fix this error.

Line 189: The pAkt level in the muscle of tumour-bearing animals should be examined to confirm the activity of the insulin signaling is downregulated.

We will include this data.

Line 189: If the authors conclude that muscle insulin signaling predominantly regulates translation and atrophy, OPP assay for the muscle cells should be examined in the same experimental settings.

We will carry out OPP assay upon Akt overexpression in the muscle.

Line 247: The expression level of Rab10 and SPARC should be examined in the fatbody of tumour-bearing animals to see whether Rab10 is upregulated and SPARC is downregulated.

Line 247: If Rab10 upregulation and SPARC downregulation are the causes of the accumulation of ECM proteins in the fatbody of tumour-bearing animals, how the overexpressed Collagen proteins can be secreted from the fatbody cells?

We are not sure, but the overexpression of Collagen proteins is at an extremely high level, therefore, it is possible that some of it can be processed and secreted despite Rab10 upregulation and SPARC downregulation. We have carried out an experiment to overexpress Collagen proteins in the muscle, in this case, this manipulation did not rescue. This indicates that processing of Collagen in the fatbody is important, however, we do not know how the processing is regulated.

Line 347: Sog is a secreted BMP antagonist. Thus, it can be expected that the Sog overexpression downregulates TGF-β signaling in fatbody and muscle tissues. If the rescued phenotypes with Sog overexpression can be explained by this logic, pMad level should be examined in these experiments.

We have shown this data in Figure R-T. We will refer back to this data in Line 347.

Reviewer #3

Major comments:

- Are the key conclusions convincing?

Most of the conclusions are convincing. It is not clear however whether the ECM accumulation in the fat body of tumor animals is fibrotic and whether it is extracellular or in the cell cortex.

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

-The authors state in line 71 'This deposition of disorganized ECM leads to fibrotic ECM

accumulation.' The authors haven't really provided evidence for the ECM being fibrotic. The authors could either rephrase this or provide additional experimental evidence of fibrosis in the fat body.

We will tone down the claim that the ECM accumulation is fibrotic.

- 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 authors state in line 147" Finally, in tumor-bearing animals fed a sterol-free diet, that underwent a prolonged 3rd instar stage due to reduced ecdysone levels (Parkin and Burnet, 1986), we activated insulin signalling in the fatbody via Akt overexpression (QRasV12, scribRNAi). We found that this manipulation caused a significant decrease in pMad levels in the fatbody and a rescue of muscle detachment (Figure S1 D-I), similar to animals fed a standard diet (Figure 1 O-Q, Figure 2 F-H)." Since it's not already known what the extent of muscle integrity defect there is in tumors with additional sterol free diet, it would be important to show a non-tumor control for comparison in FigS1F. This would also then make it clear to what extent the defect is rescued by Akt overexpression.

We will include a non-tumour control for Fig S1F.

-The authors state in line 158 'Upon the knockdown of Impl2, we found that tumor gbb was not significantly altered (Figure S3A).' Even though this shows an indication that Gbb levels are not reduced, the n number is too low to state that it is non-significant. The authors should increase the n number here.

N=3 is generally enough to see a difference, we will include data done in parallel which shows Impl2 RNAi is sufficient to induce a reduction in Impl2 RNA levels. This will demonstrate that n=3 is sufficient to demonstrate a reduction in transcript levels if there is a reduction.

-The authors state in line 171 'Conversely, knockdown of gbb alone or knockdown of gbb together with ImpL2 significantly rescued the Nidogen overaccumulation defects observed at the plasma membrane of fatbody from tumor-bearing animals, while ImpL2RNAi alone did not (Figure S2 Q-U).' This is a somewhat misleading representation, since again no non-tumor control was used, so the extent of the rescue by gbb knowdown is not obvious. In FigS2P Nidogen levels in the tumor seem ~100% higher than in control. But in FigS2U, in which no control was included, the tumor+gbb knowdown seems ~ 20% lower than tumor. So it is probably a more moderate rescue, but that's only possible to assess by including a non-tumor control in FigS2U. Also the images in FigS2Q-T don't seem representative since they appear to show a much bigger difference in fluorescence intensity than ~20%. Please show more representative images.

We will include a non-tumour control for S2Q-T and show more representative pictures.

-The authors state in line 174 'Finally, co-knockdown of gbb and ImpL2 in the tumor significantly rescued the reduction in OPP and Nidogen levels observed in the muscles of tumor-bearing animals (Figure S3 B-I).'

Again, the single knockdowns and the non-tumor control are not shown in FigS3E and I and should be included for comparison and to see the contribution of each knockdown and to be able to judge the extent of the rescue.

We will include the single knockdowns and a wildtype control

-Regarding Fig3O: Is there a significant tumor muscle attachment defect here? In this graph the tumor only looks about 10% lower than the WT (rather than 40% in Fig2E). The other issue is the extremely low n number for WT. I would recommend increasing the n number for WT here and to indicate in the graph whether the tumor is significantly different to WT (or non-significant, in which case RabRNAi wouldn't actually 'rescue' the defect). In the present form, this graph is not very convincing.

We will increase the n number for WT for this experiment. The reduction in muscle detachment is 10% rather than 40% here is because this experiment was done at day 6, which we will indicate in the figure legend. The 40% reduction in Fig2E is because these samples were processed at day7. Rab10RNAi experiment was carried out at day 6, because by day7, the Rab10RNAi rescue is so good, most of the tumour bearing animals have pupated, thus the experiment could only be carried out at day6.

- Regarding Fig3W: A non-tumor control would be important to include to be able to judge the extent of muscle attachment defects and the extent of the rescue for UAS-Sparc. This will allow to assess the severity of muscle integrity defect in this particular experiment (since it appears to vary in different experiments e.g. muscle defect in tumor 40% in Fig2E and ~10% in Fig3O) and to assess the extent of rescue for the various genotypes.

We will include a non-tumour control for 3W.

-The authors show an accumulation of ECM in the fat body of tumors. It is not clear, whether this ECM accumulates intracellularly near the cell surface or extracellularly. The authors should assess this, maybe by doing electron microscopy.

We do not have an EM facility that can accommodate this experiment, thus doing EM is not an option for us. However, we can address whether the accumulation of ECM is intracellular or extracellular by performing an experiment, where we try perform antibody staining against Viking-GFP without permeabilizing the cells. If Viking is detected without permeabilization, it would indicate the accumulations are extracellular. This approach has been previously used to address this question in Zang et al., elife, 2015.

- 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 should be quite straightforward since they are mostly just repeating previous experiments with the appropriate controls and n numbers. I would think that they can be done within a few months. The electron microscopy should not take more than a few weeks and not be costly.

- Are the data and the methods presented in such a way that they can be reproduced?

-The details on how old animals used in each experiment were, are not easy to find and not written very clearly. They should be included in the each figure legend rather than summarising those details in the methods.

We will add the number of days in the figure legend.

-Also, in line 788 in the methods, several stocks are indicated as coming from particular labs (e.g. UAS-FOXO (Kieran Harvey), UAS-GFP (Kieran Harvey), UAS-lacZRNAi (Kieran Harvey), UAS-RasV12 (Helena Richardson), UAS-cg25C;UAS-Vkg (Brian Stramer)).

However, it is not clear whether these labs actually made these stocks and if so whether it has already been described in their papers how the lines were made. If the lines are unpublished, the detailed information should be given on how the lines were made. Or if the lines are published, the authors should provide the reference.

We will fix these references.

- Are the experiments adequately replicated and statistical analysis adequate?

In general, the n number is rather low in several experiments, especially n of 3 for many controls. And as I mentioned before, rescues of tumor phenotypes are often shown without including a non-tumor control, making it hard to judge the extent of the rescue. Sometimes this information can be found in other figures, but the reader should not have to search for it. And also the severity of the phenotype can vary from experiment to experiment.

We will include a non-tumour control when appropriate to address this.

Minor comments:

- Specific experimental issues that are easily addressable.

- Are prior studies referenced appropriately?

Yes, as far as I can tell.

- Are the text and figures clear and accurate?

-In the literature, people usually call it 'fat body' rather than 'fatbody'.

We will fix this error.

-The authors state in line 265 "Vkg accumulated in the membranes of fatbody where p60 was overexpressed using r4-GAL4 (Figure 5 A-C)."

This must be a typo. I think it is shown in Fig5E-G. Unless it's labelled wrongly in the figure and B, C and D show p60 rather than TorDN.

We will fix this error.

-The authors state in line 188 'This manipulation significantly rescued muscle integrity (Figure S4 A-C) and muscle atrophy (Figure S4 D-F), without affecting muscle ECM levels (Figure S4 G-H).' According to the graph in FigS4H this does actually 'affect muscle ECM levels' significantly, as in that it reduced Nidogen levels further. The authors could rephrase this.

We will reword this statement.

Author Response

The following is the authors’ response to the original reviews.

This important work reports the identification of a list of proteins that may participate in the clearance of paternal mitochondria during fertilization, which is known as essential for normal fertilization and embryonic and fetal development. While the main method used is state of the art and the supporting data are solid, the vigor of the biochemical assays and function validation is inadequate. This work will be of interest to developmental and reproductive biologists working on fertilization. Key revisions (for the authors) include 1) Use a mitochondria-enriched fraction instead of whole sperm for the assays, and add more control samples to monitor what got lost during sperm and oocyte treatments before the coincubation step. 2) Functional validation of the key proteins identified.

We thank Editors of eLife, as well as Special Issue Guest-Editors and Reviewers for a favorable assessment and helpful recommendations for key revisions. Provisional revisions included in our revised article are detailed below. We agree with Editors’ comment about the use of mitochondrion enriched fractions and additional functional validation of key proteins. In fact, we are developing experimental protocols for oocyte extract coincubation with isolated sperm heads and tails, and eventually with purified mitochondrial sheaths, to separate the ooplasmic sperm nucleus remodeling factors from the mitophagic ones. Such experiments, as well as functional validations using porcine zygotes are contingent upon anticipated post-pandemic rebound in the availability of porcine oocytes, obtained from ovaries harvested on slaughterhouse floors, requiring currently unavailable workforce which has hampered our access to this necessary resource.

Reviewer #1 (Peer Review):

Could the authors make clear how much the presented pictures reflect the described localisation? There is no information on the number of spermatozoa and embryos observed nor the fraction of these embryos showing the presented pattern of localisation. This must be included.

Two hundred spermatozoa were counted per replicate of the cell-free system co-incubation and 20 zygotes per replicate, with 3 replicates of immunolabelling for each phase/picture which were examined to establish the typical localization patterns that were observed. The displayed patterns were observed in 65 to 88% of examined spermatozoa/zygotes; varying dependent on protein, replicate, and phase of immunolabelling. In all cases, the signal displayed is the typical pattern that was displayed in most cells. This information has been added to the Materials and Methods section for clarification.

It is not clear if the authors also examined the localization of other proteins and obtained a different pattern than anticipated from the proteomic approach or if they only tested these 6 proteins and got a 100% of correlation.

These are the 6 proteins which were selected based on extensive literature review into known functions of all identified proteins, as well as extensive research into available and reliable antibodies to detect such proteins within our porcine systems. Even so, no particular localization patterns were anticipated; instead, we presented the patterns actually observed and even some patterns which defied our expectations (i.e., the localization of BAG5 in the sperm acrosome).

The authors use "MS" in the text to indicate "mitochondrial Sheath" and "Mass spectrometry". this is confusing.

The authors agree and the usage of MS as an acronym for either has been removed entirely to avoid confusion.

In the introduction the author refers to Ankel-Simons and Cummins, 1996 as a reference for the number of sperm mitochondria in mammalian species, this is incorrect since the quoted paper is about the number of mtDNA molecules and mentioned an earlier publication.

This has been revised and the appropriate citation has been used.

Reviewer #2 (Peer Review):

Major:

1) It has been proved from the earlier studies from this group that the porcine cell-free system is useful to observe spermatozoa interacting with ooplasmic proteins in a single trial and could recapitulate fertilization sperm mitophagy events that take place in a zygote without affecting later cell-division process. However, the post-fertilization sperm mitophagy process is a complex time-associated event that many processes that occur sequentially and interactively, which means ooplasmic proteins might be involved in this process but may not directly interact with sperm or may associate with sperm-ooplasmic protein complex at different time points. It is certainly a great advance already in knowledge to identify "the candidate players" from the list of 185 proteins; however, with the time-resolution (4 and 24hr) in the current study and without functional validation experiments at this stage, it is still difficult to postulate the importance of these identified proteins. The functional validation experimental designs, in my opinion, is critically important for better interpretation of the data.

The authors agree with this reviewer’s sentiments and do plan to conduct further functional analysis. This project was able to generate a list of candidate, sperm-mitophagy promoting proteins and we were further able to show that many of these proteins were detectable both via mass spectrometry and via immunocytochemistry in spermatozoa exposed to our cell-free system. Furthermore, similar localization patterns were found in spermatozoa that were detected within newly fertilized zygotes. These results boost our confidence in our cell-free system and show that our list of candidate proteins is truly a useful list for future localization and functional analyses. We are certainly aware that we have not captured every protein that may play a role in post-fertilization sperm mitophagy and that the proteins captured are just candidates until proven otherwise. Likewise, we have almost certainly captured multiple proteins that are currently candidates that will likely not be shown to play a role in postfertilization sperm mitophagy, while it is plausible that at least some of these candidate proteins do play a role in mitophagy and some of them likely participate (perhaps have yet to be described roles) in other fertilization events, in which we would be extremely interested in as well.

2) As shown in Figure 1, whole sperm was used in the co-incubation and the later MS analysis; thus, proteins identified in the current study might be relevant in fertilization processes other than postfertilization sperm mitophagy, as proteins identified in the current study may be associated with other parts of the sperm (e.g. sticky sperm head, e.g. PSMG2 associated with sperm midpieces, tail at 4hr coincubation, but then only associate with sperm head at 24hr co-incubation) rather than sperm midpiece, despite the fact that authors applied immunohistochemistry to show the localization of this protein, but the evidence is indirect, so how authors functionally differentiate these 6 identified proteins from sperm mitophagy process with other processes and to confirm (or to associate) the relevance of these proteins with sperm mitophagy process?

The authors agree that the 6 proteins which were further studied by using immunocytochemistry may be playing roles in other processes such as pronuclear formation. We discussed some potential roles including and beyond post-fertilization mitophagy, in the Supplemental Discussion. After reviewer comments, we moved the Supplemental Discussion back in the main Discussion section. Thus, this section now considers additional putative pathways in which the said 6 proteins cold participate, though we concede that thorough functional studies must still be performed.

3) Class 3 proteins were present in both the gametes or only the primed control spermatozoa, but are decreased in the spermatozoa after co-incubation, which authors interpreted as sperm-borne mitophagy determinants and/or sperm-borne proteolytic substrates of the oocyte autophagic system, this data categorization may need to be revised as sperm-borne proteolytic substrates of the oocyte autophagic system only, not for sperm borne mitophagy determinants. The argument for this disagreement is due to the fact that if the protein is a sperm-borne mitophagy determinant, after coincubation, to execute the mitophagy process, this protein should still be associated with the sperm at least at the early stage (of 4hr) (constant under MS detection when comparing control with 4hr treated) rather than being released from the sperm. Or alternatively, they could result in class 3 proteins (but not all those 6 were in class 3). Nevertheless, if these proteins serve as substrates, they can be used (consumed) and show decreased under MS detection.

This argument for redefining the Class 3 proteins more accurately is understood and we agree. The definition is revised in the paper.

4) Of particular interest among the 6 proteins that were further investigated. Unlike other proteins, MVP was highly significant (p<0.001) after 4hr incubation, but the significance became less after 24hr (p=0.19). Interpretation of this dynamic change in the relevance of the mitophagy process would facilitate the readers to understand the relevance and the role of MVP.

The differences in significance are likely influenced by the abundance of MVP detectable by mass spectrometry. As the time of cell-free system incubation increases, the variability between replicates also seemed to increase, likely due to the sustained proteolytic activity taking place in our system. This work was based on three replicates of mass spectrometry for each time point; additional replicates likely would have reduced the p-value for the 24hr cell-free data set, for MVP and potentially other proteins also. At both time points, MVP was only detectable in spermatozoa after they had been exposed to the cell-free system treatment which is the criteria that truly interested us more than the actual differences in content between the timepoints and is why it was added to our list of candidate proteins.

5) In figure 3, the association of ooplasmic MVP to sperm midpiece is not convincing enough as sperm midpiece and tail often show some levels of non-specific signals under fluorescent microscopy. And the dynamic association of ooplasmic MVP to sperm midpiece in Fig. 3F-G is difficult to reach a conclusion solely based on data presented in the manuscript. Additional negative control of sperm MVP staining from the primed and treated sperm would be helpful. Additionally, a quantitative comparison (15 vs 25hr) of sperm-associated MVP signals from the fertilized embryo or a stack image from different angles would clarify the doubts raised here.

For all images and all replicates, serum controls were also generated. These controls were then viewed under fluorescent microscope, and light intensities and exposures thresholds for each fluorescent light channel were set based on the background intensity that came from these nonimmune serum-treated control samples. We set our light intensity/acquisition time below a threshold where the non-specific signal began to appear. All the presented patterns are based on setting this peak intensity threshold and as such the signal we see should be the true signal. Furthermore, 200 spermatozoa were counted per treatment per replicate of the cell-free system co-incubation and 20 zygotes per replicate, with 3 replicates of immunolabelling for each protein and data point, which was used to represent the typical localization patterns that were observed. The displayed patterns were observed between in 65- 88% of examined spermatozoa/zygotes. Invariably, the signal displayed in the manuscript is the typical pattern that was seen in a majority of cells. This information has now been added to the Materials & Methods section for clarification.

6) Same concerns for the other 5 proteins (PSMG2, PSMA3, FUNDC2, SAMM50, BAG5) as indicated above.

See response to Question 5.

7) The patterns of these 6 proteins under the immunofluorescent study are confusing as the pattern varies after co-incubation (treated), and mostly, the signal of these proteins observed from the fertilized embryos is not really associated with sperm midpieces. Therefore, the evidence of these proteins involving in post-fertilization sperm mitophagy is, at this moment, weak based on the data presented. But the relevance of these proteins in events post-fertilization or early embryo development is certainly (evidence did not strong enough to support "sperm mitophagy," in my opinion).

The authors agree that some of these proteins seem to be playing roles beyond postfertilization sperm mitophagy and that there is a need for true functional studies before the authors can state with certainty that these proteins play a role in any of the discussed fertilization events. We state this in the discussion: “Considering the dynamic proteomic remodeling of both the oocyte and spermatozoa which takes place during early fertilization, these 185 proteins which have been identified likely play roles in processes beyond sperm mitophagy.” It should be noted that the authors went into greater detail about potential alternative protein functions based on the present data and literature review in the Supplemental Discussion. Based on this comment and other reviewer comments we have now included the Supplemental Discussion as part of the main Discussion section, and this will hopefully help clarify some of the authors’ thoughts about the 6 candidate proteins which were further analyzed during this study.

Minor:

1) To my understanding, statistical significance (relevance) is normally set at a p-value of either <0.1 or 0.05. The reason for loosening the p-value of 0.2 in the current study needs to be justified as this was not a common statistical criterium, and the interpretation of those candidates from this loosened criterium should also be careful.

The loosening of statistical relevance in this study to 0.2, only applied to our Class 1 proteins. This is because for a protein to fall into the Class 1 proteins it was a protein that was only present in samples after they were exposed to the cell-free system. In the case of these Class 1 proteins, this happened for all 3 replicates at each stated timepoint. We found this pattern of detection to be important whether the p-value fell under 0.1 or 0.2. As such, we loosened our statistical threshold for our Class 1 proteins. Any proteins added to our candidate list will be subject to further investigation before definitive conclusions can be drawn, and as such we think that capturing more proteins was more important for the goals of this study than limiting the number of proteins captured, especially for those Class 1 proteins. An explanation of this has been added to the Materials & Methods section Mass Spectrometry Data Statistical Analysis.

2) First cell cleavage of porcine embryo normally occurs within 48hr post-insemination or activation; therefore, the 4 and the 24hr time points used in the current study require justification included in the discussion or methods and material section.

First cleavage of porcine embryos normally occurs around 24 - 28 hours post-insemination. Thus, for both the cell-free system and the embryo studies we were capturing an advanced 1 cell stage zygote/zygote like system with our 24 hour and 25-hour time points.

3) In figure 2, colors used in different time points and in two different classes represent (sometimes) different protein categories, would be easier for the readers for quick comparisons if the same color could be used to represent the same protein category throughout the graph. (E.g, proteins for early zygote development are shown in red in "A", but blue in "B")

This has been corrected and the color scheme for Figure 2 has been revised for easier comparisons.

Reviewer #3 (Peer Review):

I am not used to seeing a supplementary discussion in a manuscript. I also believe it should be incorporated into normal discussion.

The Supplemental Discussion has been incorporated into the main Discussion now.

It would be very helpful to make an additional figure in which the proposed interactome of identified factors with the sperm mitochondria before and after incubation are drawn schematically and also which factors are not IDed in both cases (when comparing to somatic mito- or autophagy). This eases to get through the discussion and will beautifully summarize and illustrate the importance and progress that the authors have made with this assay.

We made a diagram that depicts the changes in protein localization patterns overtime within our cell-free system. This diagram has been added to the manuscript as Figure 9.

Reviewer #1 (Public Review):

In this manuscript, the authors used an unbiased method to identify proteins from porcine oocyte extracts associated with permeabilised boar spermatozoa in vitro. The identification of the proteins is done by mass spectrometry. A previous publication of this lab validated the cell-free extract purification methods as recapitulating early events after sperm entry in the oocyte. This novel method with mammalian gametes has the advantage that it can be done with many spermatozoa at the time and allows the identification of proteins associated with many permeabilised boar spermatozoa at the time. This allowed the authors to establish a list of proteins either enriched or depleted after incubation with the oocytes extract or even only associated with spermatozoa after incubation for 4h or 24h. The total number of proteins identified in their test is around 2 hundred and with very few present in the sample only when spermatozoa were incubated with the extracts. The list of proteins identified using this approach and these criteria provide a list of proteins likely associated with spermatozoa remnants after their entry and either removed or recruited for the transformation of spermatozoa-derived structures. Using WB and histochemistry labelling of spermatozoa and early embryos using specific antibodies the authors confirmed the association/dissociation of 6 proteins suspected to be involved in autophagy.

While this unique approach provides a list of potential proteins involved in sperm mitochondria clearance it's (only) a starting point for many future studies and does not provide the demonstration that any of these proteins has indeed a role in the processes leading to sperm mitochondria clearance since the protein identified may also be involved in other processes going-on in the oocyte at this time of early development.

We thank reviewer 1 for positive comments. We added a sentence in Discussion addressing the obvious shortcoming of present study, as further functional validations of candidate mitophagy factors are planned.

Concerning the localisation of the 6 proteins further analysed, the authors must add how much the presented picture represents the observed patterns. They must include the details on the fraction of spermatozoa and embryos displaying the presented pattern.

We now specify that the patterns depicted in manuscript are typical and representative of data from at least three replicates of immunolabeling in spermatozoa and zygotes. For each of these replicates, 200 spermatozoa were examined per replicate of the cell-free system co-incubation or 20 zygotes per replicate. The displayed patterns were observed between 65-88% in examined spermatozoa/zygotes. Invariably, the signal displayed in manuscript is the typical pattern that was seen in a majority of cells. This information has now been added to the Materials & Methods section for clarification.

Reviewer #2 (Public Review):

Mitochondria are essential cellular organelles that generate ATPs as the energy source for maintaining regular cellular functions. However, the degradation of sperm-borne mitochondria after fertilization is a conserved event known as mitophagy to ensure the exclusively maternal inheritance of the mitochondrial DNA genome. Defects on post-fertilization sperm mitophagy will lead to fatal consequences in patients. Therefore, understanding the cellular and molecular regulation of the postfertilization sperm mitophagy process is critically important. In this study, Zuidema et. al applied mass spectrometry in conjunction with a porcine cell-free system to identify potential autophagic cofactors involved in post-fertilization sperm mitophagy. They identified a list of 185 proteins that might be candidates for mitophagy determinants (or their co-factors). Despite the fact that 6 (out of 185) proteins were further studied, based on their known functions, using a porcine cell-free system in conjunction with immunocytochemistry and Western blotting, to characterize the localization and modification changes these proteins, no further functional validation experiments were performed. Nevertheless, the data presented in the current study is of great interest and could be important for future studies in this field.

We thank reviewer 2 for positive comments. As we explain in our response to Editors and Reviewer 1, further validation studies will be resumed once the availability of slaughterhouse ovaries for such studies improves. Examples of such functional validation of pro-mitophagic proteins SQSTM1 and VCP are included in our previous studies (DOI: 10.1073/pnas.1605844113 and DOI: 10.3390/cells10092450) that led to the development of cell-free system reported here, and are cited in present study.

Reviewer #3 (Public Review):

In this manuscript, a cytosolic extract of porcine oocytes is prepared. To this end, the authors have aspirated follicles from ovaries obtained from by first maturing oocytes to meiose 2 metaphase stage (one polar body) from the slaughterhouse. Cumulus cells (hyaluronidase treatment) and the zona pellucida (pronase treatment) were removed and the resulting naked mature oocytes (1000 per portion) were extracted in a buffer containing divalent cation chelator, beta-mercaptoethanol, protease inhibitors, and a creatine kinase phosphocreatine cocktail for energy regeneration which was subsequently triple frozen/thawed in liquid nitrogen and crushed by 16 kG centrifugation. The supernatant (1.5 mL) was harvested and 10 microliters of it (used for interaction with 10,000 permeabilized boar sperm per 10 microliter extract (which thus represents the cytosol fraction of 6.67 oocytes). The sperm were in this assay treated with DTT and lysoPC to prime the sperm's mitochondrial sheath. After incubation and washing these preps were used for Western blot (see point 2) for Fluorescence microscopy and for proteomic identification of proteins.

Points for consideration:

1) The treatment of sperm cells with DTT and lysoPC will permeabilize sperm cells but will also cause the liberation of soluble proteins as well as proteins that may interact with sperm structures via oxidized cysteine groups (disulfide bridges between proteins that will be reduced by DTT).

This is certainly a possibility, the lysoPC and DTT permeabilization steps were designed to mimic natural processing (plasma membrane removal and sperm protein disulfide bond reduction), which the spermatozoa would undergo during fertilization. However, we do realize that this is a chemically induced processing and thus is not a perfect recapitulation of fertilization processes. However, in this study and in previous studies with this system, we were able to show alignment between proteomic interactions taking place in the cell-free system and within the zygotes.

2) Figure 3: Did the authors really make Western blots with the amount of sperm cells and oocyte extracts as the description in the figures is not clear? This point relates to point 1. The proteins should also be detected in the following preparations (1) for the oocyte extract only (done) (2) for unextracted nude oocytes to see what is lost by the extraction procedure in proteins that may be relevant (not done) (3) for the permeabilized (LPC and DTT treated and washed) sperm only (not done) (4) For sperm that were intact (done) (5) After the assay was 10,000 permeabilized sperm and the equivalent of 6.67 oocyte extracts were incubated and were washed 3 times (or higher amounts after this incubation; not done). Note that the amount of sperm from one assay (10,000) likely will give insufficient protein for proper Western blotting and or Coomassie staining. In the materials and methods, I cannot find how after incubation material was subjected to western blotting the permeabilized sperm. I only see how 50 oocyte extracts and 100 million sperm were processed separately for Western blot.

The authors did make Western blots with the number of spermatozoa and oocytes stated in the materials and methods, a total protein equivalent of 10 to 20 million spermatozoa (equivalent to ~20-40 µg of total protein load) and 100 MII oocytes (equivalent to ~20 µg of total protein load). These numbers have been corrected in the Materials & Methods. Also, we did find in the Materials & Methods section that the Co-Incubation of Permeabilized Mammalian Spermatozoa with Porcine Oocyte Extracts section refers to using cell-free exposed spermatozoa for electrophoresis; however, for none of the presented Western blot work was this true. Rather, all of the presented Western blots as per their descriptions are utilizing ejaculated or capacitated sperm or oocytes. This line has been removed from the Materials & Methods to reduce confusion.

Regarding preparation (2), we have previously assessed the difference between oocyte extract and intact oocytes in this manner internally and we are certainly losing proteins due to the oocyte extraction process. We make caveats in this vein throughout the article such as: “Furthermore, this cell-free system while useful does not perfectly capture all the events which take place during in vivo fertilization. The cell-free system is intended to mimic early fertilization events but is presumably not the exact same as in vitro fertilization.”

3) Figures 4, 5, 6, 7, and 8 see point 2. I do miss beyond these conditions also condition 1 despite the fact that the imaged ooplasm does show positive staining.

For all the presented Western blots, the tissue type is stated in the image description and the protocol which was used to prepare these samples is stated in the Materials & Methods.

4) These points 1-3 are all required for understanding what is lost in the sperm and oocyte treatments prior to the incubation step as well as the putative origin of proteins that were shown to interact with the mitochondrial sheath of the oocyte extract incubated permeabilized sperm cells after triple washing. Is the origin from sperm only (Figs 5-8) or also from the oocyte? Is the sperm treatment prior to incubation losing factors of interest (denaturation by DTT or dissolving of interacting proteins preincubation Figs 3-8)?

The authors understand that there are proteins and interactions lost on both sides of the cellfree system equation and we have added a sentence to the Discussion to caveat this limitation in the system.

5) Mass spectrometry of the permeabilized sperm incubated with oocyte extracts and subsequent washing has been chosen to identify proteins involved in the autophagy (or cofactors thereof). The interaction of a number of such factors with the mitochondrial sheath of sperm has been shown in some cases from sperm and others for an oocyte origin. Therefore, it is surprising that the authors have not sub-fractionated the sperm after this incubation to work with a mitochondrial-enriched subfraction. I am very positive about the porcine cell-free assay approach and the results presented here. However, I feel that the shortcomings of the assay are not well discussed (see points 1-5) and some of these points could easily be experimentally implemented in a revised version of this manuscript while others should at least be discussed.

We agree that the use of a mitochondrial-enriched subfraction for further analysis would be interesting and useful. We are actively developing experimental protocols for oocyte extract coincubation with isolated sperm heads and tails, and eventually with purified mitochondrial sheaths. However, such experiments are contingent upon our access to porcine oocytes, which has continued to be a struggle since the COVID-19 pandemic compromised our ability to attain oocytes in large, cheap, and reliable quantities. This was a continuous problem with preparing materials for this very paper and has continued to be an issue for our laboratory as well as many others at our university and across the country. We continue to maximize oocytes every time we can get access to them, but the unfortunate reality is that this access has become sparce and unreliable over the past three years.

This reminds me of a metaphor my high school used to properly explain the difference between equality and equity. Let's say there's a fence, and on the other side is a baseball game that you and your friend are trying to peek over and watch. You each get a box to stand on, and now you can see over the fence! Your friend, however, is shorter than you and still can't reach. Although you may have the same box to stand on (equality), in order to get the same opportunity to watch the game you have to put effort into making sure that everyone actually receives that truly equal opportunity, e.g. another box for your friend.

Costanza-Chock's example of college admissions to explain equality vs. equity also make me think about what kinds of digital barriers exist in place to prevent restorative justice. Issues such as accessibility, class, and status keep coming up for me and now I'm wondering: How does class background influence the attempts made by digital humanities scholars who try to perform this restoration?

Author Response

The following is the authors’ response to the original reviews.

Reviewer 1 (Recommendations For The Authors):

1) The strikingly different conclusion from the previous Bourane study seems to stem from the experimental approaches. Rather than using genetic crosses that target all neurons from the hindbrain and spinal cord that express Npy at any point in development, Boyle et al target their manipulations specifically to the lumbar region of the superficial dorsal horn in adult mice using direct viral injections. Thus, Boyle is almost certainly manipulating much fewer neurons that the original study. How then is their behavioral effects so much greater? At the minimum, the authors need to discuss this discrepancy head on. Better would be a direct molecular/anatomical comparison of the neurons targeted by each approach. This could be done using Nyp-Cre mice crossed to a Rosa-LSL-reporter strain and quantifying the overlap with the same markers used here. Perhaps, the intersectional approach with Lbx1 resulted in labeling of a different population of neurons than the adult AAV injections? Although likely outside the scope, given this work directly questions the main conclusion of the Bourane paper, it will be important to see a replication of the original finding of selectivity to mechanical itch.

We agree that our approach should be manipulating a smaller population of neurons, and that it is therefore suprising that we see greater behavioural effects. Please see our response to "Weakness 1" of Reviewer 2 for consideration of this point. We have already provided a direct molecular comparison as requested by the reviewer, and this appears in Figure 1 supplement 1. Here we used tissue from NPY::Cre that had been crossed with Ai9 mice (i.e. a Rosa-LSL-reporter) and had received intraspinal injections of AAV.flex.GFP. We then characterised the neurochemistry of tdTomato+ cells that were GFP+ or GFP-negative.

2) The authors state that, "91.6% ± 0.3% of cells classed as Cre-positive cells were also Npy-positive, and these accounted for 62.1% ± 0.6% of Npy-positive cells" If I am reading this correctly, does that mean that 40% of the Npy+ cells are Cre negative? If so, how is this possible?

This interpretation is correct. For quantification of RNAscope data we used a cut-off level of 4 transcripts, and cells with fewer than 4 transcripts were classed as negative. It is likely that some of the NPY cells classified as negative for Cre would have had some Cre mRNA (sufficient to cause recombination), but at a level below this threshold. It is also possible that some NPY+ cells would fail to express Cre, since this is a BAC transgenic mouse, rather than a knock-in.

3) Similarly, the authors state that "great majority of FP-expressing neurons in laminae I-III were immunoreactive (IR) for NPY (78.5% ± 3.6%), and these accounted for 74.6% ± 109 1.9% of the NPY-IR neurons in this area". So does this mean 20% of the recombination is non-specific/in other cell types that could be involved in pain/itch sensation?

Our finding that 91.6% of cells with Cre mRNA were also positive for Npy mRNA (see above) indicates that Cre expression was largely restricted to NPY cells. The failure to detect NPY peptide in some of these cells probably results from the relatively low level of peptide seen in the cell bodies of peptidergic neurons, which results from the rapid transport of peptides into their axons.

4) Comparing Fig 3B and Fig4B it seems the control baseline von Frey responses are different. In fact, baseline response in Fig4b is quite like the CNO effect in Fig 3B. Unless I'm misunderstanding something, this seems quite odd?

We agree that there is a difference between the baseline responses. We are not aware of any particular reason for this, and we think that it reflects a degree of variability that is seen with the von Frey test. Interestingly, the baseline values for the SNI cohort (Fig 4E) lies between the values in Fig 3B and Fig 4B.

5) In Fig 4E, the behavior of the CNO treated mice is quite variable. Can the authors comment as to how this might be happening? Does the effect correlate with viral transduction?

We did not see any obvious correlation between the extent of viral transduction and the behaviour of individual mice.

6) Fig6, the PDyn-Cre experiment, is a bit of a non sequitur?

Please see our response to "Weakness 2" of Reviewer 2 for consideration of this point.

7) The conclusion is unusually long. I recommend trimming it to make it more concise.

We presume that this refers to the Discussion. However, this was ~1550 words, and we do not feel that that is unusually long.

Reviewer 2 (Public Review):

Weaknesses

1) There is inadequate discussion about previous studies of NPY interneurons. Specifically, the authors should address why a more restricted subset of these neurons (this study) have broader effects than seen previously.

We have expanded the discussion on the discrepancies between our findings and those reported previously. We state at the outset that we are targeting a more restricted population (lines 509-10), and we now go into more detail concerning both similarities and differences between our findings and the reasons that we think may underlie any discrepancies (various changes between lines 522-575).

2) I cannot see the reason for including results from manipulation of Dyn+ interneurons in this paper. First, the title does not reflect roles of spinal Dyn+ population. In addition, without further experiments characterizing relationships between NPY and Dyn interneurons in modulating itch and/or nociception, Dyn datasets seem to deviate from the main theme.

We had previously shown that activating Dyn-INs suppressed pruritogen-evoked itch (Huang et al 2018), but it was important to test whether silencing these cells would have the opposite effect. Our finding of overlap in function (i.e. both NPY-INs and Dyn-INs suppress itch, and that both innervate GRPR cells) provides strong evidence against the idea that neurochemically-defined interneuron populations have highly specific functions, and we now state this in the Discussion. The anatomical experiments (which follow on from the functional studies) provide important new information concerning synaptic circuitry of the dorsal horn, by showing that NPY-INs preferentially innervate GRPR cells, and provide around twice as many synapses on these cells, compared to the Dyn-INs. Interestingly, this correlates with the relatively large optogenetically-evoked IPSCs that we saw when NPY-INs were activated, compared to those reported by Liu et al (2019) when galanin-expressing (which largely correspond to Dyn-INs) were activated. By including these findings in the paper, we are able to make comparisons between these two populations.

3) While the authors provided convincing evidence that GRPR+ neurons serve as a downstream effector of NPY+ neuron evoked itch, the relationship between GRPR and NPY neurons in modulating pain is not examined. Therefore, Fig. 7B is pure speculation and should be removed.

We feel that our recent findings that GRPR neurons correspond to vertical cells, that they respond to noxious stimuli, and that activating them results in pain-related behaviours, makes it reasonable to speculate that the NPY/GRPR circuit may also be involved in the anti-nociceptive action of NPY cells. The legend for Fig 7B already refers to this as a "potential circuit", and we have toned down the corresponding part of the discussion to say that our findings "raise the possibility" that this is the case (lines 605-7). We feel that this part of the figure is important, as otherwise our summary diagram ignores some of the main findings of the paper, and we hope that this is now acceptable.

Recommendations For The Authors

1) Fig. 1G: the "misexpression" of tdTomato neurons was much more prominent in deep dorsal horn laminae but not in the superficial ones. Was this representative? Can the authors perform a laminae specific characterization?

We did test for this possibility in 2 NPY::Cre;Ai9 mice that had received intraspinal injections of AAV.flex.GFP, and found that there was a modest difference - 62% of tdTomato+ cells in laminae I-II, but only 39% of those in lamina III, were GFP+. This suggests that "misexpression" may have differed slightly between these regions. However, since the difference was quite modest, and we were only able to analyse tissue from two mice in this way, we did not include these findings in the paper.

2) I have a lot of problems interpreting the c-Fos data in Fig. 2 E and F. For the mCherry- population, how was the quantification performed? From the image, it does not look like 2030% of cells express c-Fos; at a minimum a clear stain of neurons would be needed. Similarly, the identification of NPY cells is not particularly convincing (e.g., middle arrowhead lower 2 panels in C).

We have provided further details on how the analysis was performed (changes made to lines 1016-29). NeuN staining was used to reveal all neurons, and a modified optical disector method was performed from somatotopically appropriate regions of the dorsal horn. As noted by the Reviewer, NeuN staining was required to allow identification of mCherrynegative cells. However, we have not included the NeuN immunoreactivity in the image, as this would add considerably to the complexity. These images are from single optical sections, and therefore the overall numbers of cells are low (in comparison to what would be seen in a projected image). The intensity of mCherry staining varied between cells. However, for all mCherry-positive cells (including the example referred to by the Reviewer), there was clear staining in the membrane, which could be followed in serial sections.

3) Please add individual data points for all quantifications.

These have been added.

Reviewer 3 Recommendations For The Authors:

1) It is somewhat surprising that there is no effect on CPP after activating spinal NPY neurons in neuropathic mice, given the almost complete rescue of hypersensitivity to baseline values in the nociceptive tests. Based on the methods, it appears that conditioning was carried out already 5 min after CNO injection. Yet, suppression of c-fos activity in excitatory spinal dh neurons was observed 30min after CNO injection. Also, it is not clear to me when CNO was injected prior to the nociceptive or CQ testing?

Have the authors considered that conditioning from 5-35 min after CNO injection might be too short after CNO injection to achieve a profound analgetic effect?

In a previous study (Polgár et al 2023), we had observed the timecourse of CNO-evoked itch and pain behaviours in mice in which GRPR cells expressed hM3Dq. We found that these started within 5 minutes of i.p. CNO injection (e.g. Fig S2 in that paper). In addition, the timecourse of action of gabapentin and CNO (both given i.p.) are likely to be similar, and there was a preference for the chamber paired with gabapentin. We are therefore confident that the conditioning period with CNO was adequate. We now explain this in the Methods section (lines 846-52). The timing of CNO injections for the nociceptive and CQ tests is now described (lines 749-55).

2) The authors claim that tonic pain was not affected based on the conditioned place preference test. Efficacy in withdrawal response tests and in the CPP differ by more than duration of the stimulus. I'd suggest using more cautious wording here.

We agree that caution is needed in interpreting the results of the CPP experiments. We have therefore replaced "does" with "may" in the Results section (line 336) and "did" with "may" in the Discussion (line 620).

3) On page 9 the authors state "...suggesting that they suppress the transmission of pain- and itch-related information in the dorsal horn." However, pain is not affected in the loss of function experiments suggesting some qualitative differences in the role of the NPY neurons in itch and pain. This should also be reflected more clearly in this statement and in the discussion e.g. "suppress itch" and "can suppress pain".

We accept the point made by the Reviewer. We have slightly altered the wording in lines 249-51 and 610 to reflect this.

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 four reviewers for their generally positive feedback on the manuscript. Below, we provide a point-by-point response to each reviewer.

We are performing new FCS and gradient measurements as suggested by the reviewers. We are confident we can have these completed within three months (accounting for the summer break).

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

*This manuscript reports a very thorough and careful study of the mobility of Bicoid in the early embryo, explored with single-point fluorescence correlation spectroscopy. Although previous groups have looked into this question in the past, the work presented here is novel and interesting because of the different Bicoid mutants and constructs the authors have examined, in particular with the goal of understanding the role of the protein DNA-binding homeodomain. The authors convincingly show that there is a significant increase in Bicoid dynamics from the anterior to the posterior region of the embryo, and that the homeodomain plays an important role in regulating the protein's dynamics. Their experiments are very well designed and carefully analyzed. The authors also modelled gradient formation to see whether this change in dynamics might play a role in setting the shape of the gradient. I am not sure I fully agree with their conclusion that it does, as mentioned in my comment below. However, it is an interesting discussion to have, and I think this paper makes a significant advance in our understanding of Bicoid's behavior in the early embryo. *

We thank the Reviewer for their positive comments and their suggestions for improving the manuscript. We will resolve the concerns raised by the reviewer with clarity in the revision. We will also add additional comment in the Discussion regarding the interpretation of our results.

*Major comments: *

• 1) Gradient profile quantification: Some of the conclusions made by the authors rely on the comparison between their model of gradient formation (as captured in the equations in lines 232 and 233) and the Bcd intensity profile measured in the embryos. Since the differences in gradient shape predicted by the different models are very small (see Fig. 3B, which is on a log scale and therefore emphasize small differences, and Fig. 3C), it is very important to understand how reliable the experimental concentration profiles are.*

This is a fair comment. It is worth noting that the key differences between the 1- and 2-component models are only apparent at large distances (and hence low concentrations) from the source.

We performed the quantification of the gradients in a manner similar to the Gregor lab, whereby the midsagittal plane is analysed. We used 488nm illumination (rather than 2-photon, as the Gregor lab does) so our measurements are likely noisier. However, we are not investigating the variability in the gradient here, but the mean extent. We currently correct background with a uniform subtraction, but we appreciate that is not the optimal method.

In the revised manuscript, we will repeat the above experiments using a 2-photon microscope. Further, we will image lines expressing His::mcherry without eGFP under the same imaging conditions to more accurately estimate the background signal. While we expect this to improve the data quality, we do not envisage significant change to the observed profiles based on prior experience.

At the moment, I do not find the evidence that [Bcd] concentration profile is more consistent with a 2-component diffusion model than a 1-component model very strong. A few comments related to this: * * 1a. Line 249, it is mentioned that: "observations ... incompatible with the SDD model". Which observations exactly are incompatible with the SDD model?

The key points are in the preceding paragraph. We will improve the model presentation in the Results and also include further contextualisation in the Discussion.

1b. In Fig. 3D, only the prediction of the 2-component model is shown. What would the simple 1-component diffusion model look like? Is it really incompatible with the data?

We agree with this comment and will provide the 1-component fit to the gradient profiles. We expect it to fit well for the anterior half of the embryo but fail at larger distances (as has been previously shown).

Regarding the FCS data, we also show one and two component fits. We will show the alternative fits – a 2 particle fit is clearly an improvement (see also related response to reviewer 2).

1c. Line 243: "The increased fraction in the fast form ... consistent with experimental observation of Bcd in the most posterior" (Mir et al.)". I am not sure how this is significant, since the simple model also predicts there will be Bcd in the posterior - the only difference is how much is there (as shown in Fig. 3C), and it's a very small difference.

The absolute differences are not large between the two models, but due to the observed clustering (Mir et al. 2018), even small differences can have very large effects. In the revision we will provide estimates of the actual concentration differences.

We are performing new experiments with the Fritzsche lab at Oxford to estimate if there is clustering of Bcd. We will also repeat our FCS experiments to validate our key conclusion of AP differences in diffusion of Bcd. These should be completed by the end of the summer.

1d. Since the difference between models is in the posterior region where Bcd concentration is very low, when comparing the models to the data the question of background subtraction is essential. How was the subtracted background (mentioned line 612) estimated?

See above response to the first comment.

1e. Along the same line, were the detectors on the Zeiss LSM analog or photon counting detectors, and how confident can we be that signal is exactly proportional to concentration?

We used PMTs and did not directly do photon counting. But the intensity is still proportional to the concentration. It is possible to estimate the absolute concentration value, e.g., Zhang et al., 2021 (https://doi.org/10.1016/j.bpj.2021.06.035). However, our main conclusions – especially regarding the spatially varying Bcd dynamics – are not dependent on this.

1f. Can the gradients created by the two Bcd mutants (FIg. 4B) be quantified as well, and are they any different from the original Bcd gradient?

We agree this would be useful. We will provide the gradient quantifications of the bcd mutants in the revision.

1e. What is the pink line in Figure 5C (I am assuming the green one is the same as in Fig. 3D)? It could be better to not use normalization here, or normalize everything respective to the eGFP::Bcd data to make comparison in relative concentrations in the posterior for different constructs more evident (also maybe different colors for the three different data sets would help clarity).

This is a fair comment, and we will create graphs with new data for better visualisation.

1f. Discussion, lines 402-403: Does the detailed shape of the Bcd in the posterior region matter at all, since the posterior is not a region where Bicoid is active, as far as we know? Could a varying Bcd dynamics have other consequences that would be more biologically relevant?

Bcd is now known to act at 70% EL (Singh et al., Cell Reports 2022). So, the gradient is relevant for a large extent of the embryo length, though it is not known if there is any effect in the most posterior region.

2) Model for gradient formation (lines 231-238): * * 2a. Whether the molecules of Bcd can change from their fast to slow form is never questioned. How do we know (or why might we suspect) they do exchange?

This is a good point. Within the nucleus, and based on our mutant data, we suspect the fast/slow forms correspond to unbound/bound DNA states.

In the cytoplasm, the dynamics are less clear. Bcd can bind to cytoskeletal elements (Cai et al., PLoS One 2017) as well as to Caudal mRNA. Therefore, it seems reasonable to have different effective dynamic modes – yet, how such switching occurs remains unclear.

Ultimately, our model approximates multiple dynamic modes that are integrated to drive Bcd motion. Including switching between states is a reasonable assumption based on what is known about cytoskeletal and protein dynamics, but we do not have a specific mechanism.

It is challenging to estimate a specific kon / koff rate, as the dynamic changes also depend on the diffusion – which itself is changing. For now, we believe our level of abstraction is appropriate given what is known about the system. It will be very interesting to explore the specific interactions underlying such behaviour in the future, but that is beyond this current manuscript.

2b. The values used in the model for alpha, beta_0 and rho_0 should be mentioned. Maybe having a table with all the parameters in the method section, or even in the supplementary section, would help. The exact values of alpha and beta matter, because if they are large (fast exchange) a single exponential gradient is to be expected, if they are 0 (no exchange) a double exponential gradient is to be expected, with intermediate behavior in between. Which case are we in here?

We agree and will add a more complete table in the revision.

3) Discussion about anomalous diffusion (lines 386-388): The 2-component model used by the authors to interpret their FCS data seems very well justified here (excellent fits with very small residuals). I agree with the authors' conclusion that "the dynamics of Bcd within the nucleus are more complicated than a simple model of bound versus unbound Bcd", but I don't see how that can lead to a diagnostic of anomalous diffusion instead. Maybe it is just a matter of exactly explaining what is meant by anomalous diffusion here (since this term is often used to mean different things). A more likely scenario I think, is that there are more than just two Bcd components in the system.

This is a good point, and we can’t easily differentiate two/multi- component fits from anomalous diffusion ones. This is a known problem. But we have recently shown in a collaboration with the Laurent Heliot lab (Furlan et al, Biophys J 2019), that anomalous diffusion is a good stable indicator of changes, even if it might not be the right model. We use anomalous diffusion as it stably predicts changes. We do not claim, however, that diffusion is anomalous. We will improve the discussion of these points in the revised manuscript.

4) Line 440 and after: What is the evidence that the transition between the two forms might vary non-linearly with Bcd concentration? How would that help adapt to different embryo sizes? It would be good to be more explicit here instead of just referring to another paper.

We will improve this discussion. The central point is that the action of Bicoid is unlikely to simply depend linearly on concentration as in that case the ratio of fast to slow forms would be constant across the embryo. Related to the above comment, it is important to emphasise that we are using a phenomenological model, not one based on a specific mechanism.

5) Since an important aspect of this work is the study of different Bcd constructs in vivo, it is important that these constructs are very clearly described, so the section on the generation of the fly lines (Methods) should be expanded. In particular: * * 5a. It seems that the eGFP:: NLS control used here was different from that first described in Ref. 64 (and used for FCS experiments in Ref. 30 and 36)? If so, what NLS sequence was used here, and precisely what type of eGFP was used (in particular, was the A206K mutation that prevents dimerization present in the eGFP used)? If it is the same construct as in Ref. 64, it should be mentioned explicitly. * * 5b. Were the mutant N51A and R54A lines gifts as well, or have they been described before? If so, previous publications should be referenced. If not, how the plasmid was introduced in the embryo should be briefly explained.

We agree and will expand on the fly lines in the revision.

6) Concentration calibration measurements (Methods Fig. 2, line 568 and on). It is well known that background noise is going to interfere with the measurement of N when the signal becomes equivalent to the background noise (Koppel 197, Phys Rev A 10:1938-1945, and for a recent discussion of this effect for morphogens in fly embryos: Zhang et al., 2021, Biophysical Journal 120,4230-4241). It is almost certain that in the low signal regions of the embryo (e.g. posterior cytoplasm) this is affecting the reported concentration, and should be at least acknowledged.

We agree with the reviewer. We will provide the SBR. We will also correct the N values based on the method followed in Zhang et al., 2021, Biophysical Journal 120,4230-4241.

*7) Reference 3 is mis-characterized in two different ways in the manuscript: * * 7a. Line 50: The conclusion in Ref. 3 was not that the gradient was due to a diffusive process, on the contrary Gregor et al. argued that Bcd was too slow to form such a long-range gradient by diffusion. Studies that do present data consistent with a morphogen gradient formation mechanism driven by diffusion are reference 5, reference 30, Zhou et al., Curr. Biol. 2012;22(8):668-75 and Müller et al., Science 336 (2012) 721-724. *

Gregor et al., do not argue against a diffusion process – indeed, they utilise a SDD model in their paper. However, they do extensively discuss how the predicted dynamics from the SDD model are not compatible with gradient formation as observed after n.c. 13. This problem was resolved to some degree by FCS measurements of Bcd (e.g., Dostatni lab, Development 2011) and the use of a Bcd tandem reporter which showed that production and degradation change during n.c. 14 (Durrieu et al., MSB 2018). We will improve the framing of these results in the revision.

7b. The diffusion coefficient estimated from FRAP measurements and reported in Ref. 3 (D = 0.4 micron^2/s) is mentioned a couple of times in the manuscript (line 66, line 395, line 411). However, this number is simply incorrect. When fast components (such as the ones clearly detected here by FCS) are present, they diffuse out of the photobleached area during the photobleaching step. If that is not corrected for during the analysis (and it wasn't in Ref. 3), then the recovery time measured is just equal to the photobleaching time, and has nothing to do with either the fast or slow fraction of the studied molecule - it has no other meaning than to give a lower bound on the value of the actual effective diffusion coefficient of the molecule. This effect (called the halo effect) is well known in the FRAP community (see e.g. Weiss 2004, Traffic 5:662-671), it has been experimental demonstrated to occur for Bcd-eGFP in the conditions used in Ref. 3 (Reference 30), and the actual diffusion coefficient that should have been extracted from the data presented in Ref. 3 has been recalculated by another group to be instead D = 0.9 micron^2/s (Castle et al., 2011, Cell. Mol. Bioeng. 4:116-121). It would therefore be better to report the corrected value from Castle et al. to help the field converge towards an accurate description of Bcd mobility.

We fully agree and will use the improved FRAP estimated value for Bcd.

*Minor comments and suggestions: *

• 8) Figure 1: From panel A, it seems that what is called "Anterior" and "Posterior" is about 150 micron away from the embryo mid-section, i.e. about 100 micron from either the anterior pole or the posterior pole (so not the tip of the embryo, but somewhere in the anterior half or posterior half). Maybe this should be made clear in the text. *

We have made changes in Figure 1A to indicate the region within which the FCS measurements are carried out. We have added the relevant details in the legend of figure 1 lines 137-138.

*9) Fig. 2A; It might be good to put this graph on a log scale, so that cytoplasmic values are seen more clearly. Also, what about reporting on nuclear to cytoplasmic ratios? *

We will rework on this graph and make necessary changes.

*10) Fig. 2: It could be interesting to plot D_effective as a function of the measured concentration of Bicoid in different locations, since the (interesting) suggestion is made several time that [Bcd] could the a determinant of the protein mobility. *

Our work provides an indication that Bcd concentration is connected to the diffusion. We did this by measuring at two locations. To extend this to a rigorous model would require substantial new measurement along the whole length of the embryo. While interesting, this represents a very large investment of time and lies beyond the current manuscript.

*11) Figure 3B&C: Is the curve for 2-component diffusion (without concentration dependence) for steady-state missing? *

We will clarify in the revision.

*12) Lines 78 and 471: What do the authors mean by "new reagents"? The word reagent evokes a chemical reaction, but there are none here. Do the authors mean new constructs? or new mutants? *

We have changed lines 78 and 479 from “new reagents” to new Bcd mutant eGFP lines”.

*13) Lines 57-59: Another good reference for FCS measurements performed to study the dynamics of a morphogen (in this case Dpp) is Zhou et al., Curr. Biol. 2012;22(8):668-75 *

We added this reference in no.70.

*14) Lines 109-111: A word must be missing. Precisely determined what? *

Precisely measure within cytoplasm, and nuclear compartments and also during interphase stages. We have changed to “precisely measure in the cytoplasmic and nuclear regions during the interphase stages of nuclear cycles (n.c.)12-14.” in line no.111-112.

*15) Line 278: The increase in the slow mode is expected. Maybe explicitly mention why. *

In line 286, we have added “due to the loss of Bcd binding to the DNA”.

*16) Line 282: "with the fast component increasing", maybe replace with "with the diffusion coefficient of the fast component increasing" or "with the fraction of the fast component increasing". *

We have changed line 289 “with the diffusion component of fast component increasing towards the posterior”.

*17) Line 517: Is there a reason why the dorsal surface is always placed in the coverslip? *

We have added these details in line 528-529 in Methods.

*18) Line 524 and on: FCS measurements: What was the duration of each individual FCS measurement? It is great that the exact number of measurements are reported in the supplementary! *

Thank you for the complement. Typically, cytoplasmic measurements are 60secs and nuclear measurements are 20-40s. We have added this in line no.528-529. We also added a column to indicate the duration of each of the measurements in the supplementary tables.

*19) An Airy unit of 120 um seems large in combination with an objective with a NA of 1.2, is there a reason for that? What was the radius of the resulting detection volume? *

Olympus microscopes have a 3x magnification stage in their confocals. This leads to the change in the Airy unit. Otherwise, it would be 40 mm.

*20) Thank you for detailing the reasons behind the choice of excitation power, an important and often omitted details. Where in the excitation path were the values of the laser power measured (before or after the objective?)? *

Thank you for the complement. The laser power is measured before the objective. We removed the objective and measured the laser power in the objective path.

*21) Line 585: "since the brightness of eGFP::Bcd..." do the authors mean the molecular brightness of a single eGFP::Bcd molecule, or the total fluorescence signal? *

It is the total fluorescence signal. We have edited line no.592.

*22) It would be good for reference to mention the approximate value of the molecular brightness recorded for these eGFP constructs at the laser power used. *

We will measure and tabulate in the revised manuscript.

*23) Reference 766: The year (and maybe other things) is missing. *

We have corrected this reference.

24) Figure 2 (Methods): The concentrations shown on the figure should be in nM not uM. * * Thanks for noticing – we have changed.

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

MAJOR POINTS

• 1) FCS measurements and fits *
• a) Please state the duration of each individual FCS measurement. *

In the cytoplasm, the measurements were carried out for 60 secs and in nuclei it is between 20-40s. We could not measure for 60s in the nuclei as the nuclear position fluctuates from its initial position. We will add another column to indicate the duration of FCS measurements in the supplementary tables.

b) The authors acknowledge potential issues with fluorophore photophysics and use different lag time ranges for the calibration dye Atto-488 (0.001 ms in Method Fig. 2) and eGFP (0.1 ms in the main figures). Given the strong influence of different parameters on data interpretation and conclusions, Method Fig. 2 should be repeated with purified eGFP. This is particularly relevant for the noisy FCS measurements in posterior regions.

Performing the experiment with purified eGFP will be a volume calibration. We routinely performed this before each imaging session, and that should be fluorophore independent. As noted by Reviewer 1, it is also important to be clear about background correction. We will provide brightness data for eGFP and background values in the revised manuscript. We can then use this to estimate the corrected concentrations.

We use 0.1 ms to start, as at that point any contribution from the photo-physics should have decayed (0.1 ms is about 3-5 times the day rate of the photophysical process, Sun et al., Analytical Chem 2015).

c) Please explain why no data is shown for "AN" around 0.1 ms lag time in Fig. 1B in contrast to all other figures.

We will add the data for AN from 0.01 in the revised figures.

d) Please state what the estimated diffusion coefficients with one-component model fits are. Please also explain why the fits in Fig. S1E do not reach a value of 1 and why they plateau higher than the experimental data at long lag times. Please constrain the fits to G=1 at 0.1 ms tau and G=0 at 1 s tau to make a fair comparison.

The experimental ACF curves reach 0 at long lag times as would be expected. The one-component fits, however, don’t describe the data well and as a result they do not reach 1 and 0 at short and long lag times, respectively. The fitting is done using a mean-squared estimation of the best approximation of the particular model function to the data. Fixing the parameters can be done, but it will further reduce fit accuracy and deviations will be larger. We will perform this analysis and tabulate the one component fits in supplementary 1 with necessary corrections.

e) Please assess the validity of all multi-component fits by comparing the relative quality of the models to the number of estimated parameters using the Akaike information criterion or similar approaches.

We will provide the values denoting the quality of the fits in the revision. We will provide the 3D 1 particle fit, the 3D 1 particle fit with triplet, the 3D 2 particle fit and the 3D 2 particle fit with triple and will provide appropriate measures of fit quality.

f) Please also present the Bcd-GFP fits with 0.001 ms that are mentioned in line 590, and present the results for the data that did not give comparable tau_D1 and tau_D2 values mentioned in line 593.

We will provide all the curves from 0.001ms in the supplementary. We did not provide these details as we have followed the methods from Abu Arish et al., 2010. As our cytoplasmic and nuclear TauD values match with Abu Arish et al., 2010 and Porcher et al., 2010, we thought the excess data would be redundant.

3) Bicoid gradient and modeling * a) Little et al. 2011 observed that the Bcd gradient decreases around n.c. 13. Can the authors of the present work observe a similar concentration decrease using FCS? This is important to i) validate the FCS concentration measurements, and ii) to resolve the controversy regarding "previous claims based on imaging the Bcd profile within nuclei, which predicted decrease in Bcd diffusion in later stages".*

This is a good point regarding conclusions from the previous literature. The Little et al. paper inferred that diffusion had to decrease from fitting to the gradient profiles. However, subsequent analysis from our lab (Durrieu et al., MSB 2018 [which uses a different method involving a tandem reporter for Bicoid] and this manuscript) strongly suggest that Bicoid remains dynamic, at least through n.c. 13 and early n.c. 14. One way to test this is to use SPIM-FCS, where longer time courses can be taken (though with slower time resolution in the FCS). We have performed preliminary experiments with SPIM-FCS and we will revisit this data to see if we can find evidence for changes in the diffusion.

We will also extend the Discussion to make the results clearer in terms of previous models and literature.

b) Please explain why the experimental Bcd-GFP gradient data does not reach a value of 1 (e.g. in Fig. 3D) despite normalization. Please also explain why the fits become flatter in Fig. 5B compared to the steep fit in Fig. 3D.

Both lines were measured under identical conditions. Therefore, we normalised to the maximum value of both experiments. We will redo, normalising to each individual experiment. Regarding Fig. 5C, the Bcd::eGFP curve is identical to Fig. 3D. The flatter curve is the line with eGFP tagged to a NLS alone.

c) For modeling, please take into account observations that the Bcd source is graded with a wide distribution (30-40% EL, see Spirov et al. 2009, Little et al. 2011, Cai et al. 2017 etc.). The extent of the source used in the present work (x_s=20 um, line 620) is at least five times too small.

Care must be taken in defining the source extent. The most careful measurements are reported in Little et al., PLoS Biology 2011 who performed single molecule FISH. They conclude “We demonstrate that all but a few mRNA particles are confined to the anterior 20% of the egg”. Further, the peak in the particle density is around 20-30um from the anterior (Figure 3, Little et al., PLoS Biology 2011), with the vast majority of counts being with 10% of the anterior pole. Further, Durrieu et al. MSB 2018, showed using a Bcd tandem reporter that there was unlikely to be an extended gradient of bcd mRNA (maximum extent of around 50um). Here, we used a simple source domain, which was arguable a little narrow, but not significantly so. We will increase the value in the revision, but the claim that there is an extended bcd mRNA gradient (Spirov et al., Development 2009) has not been substantiated by later experiments.

• d) Please discuss in the paper how well the simulations in Fig. 3B agree with the experimental data.*

We will provide these details in the revision.

• e) Please provide a precise estimate for the statement "Even with an effective diffusion coefficient of 7 μm2s-1, few molecules would be expected at the posterior given the estimated Bcd lifetime (30-50 minutes)" to turn this into a quantitative argument. How many molecules are expected to reach posterior in which model, and how does it compare to experimental observations?*

This can be estimated based on the root-mean-square distance for diffusive processes. We will provide this in the revision.

• f) The sentence "we find that a model of Bcd dynamics that explicitly incorporates fast and slow forms of Bcd (rather than a single "effective" dynamic mode) is consistent with a range of observations that are otherwise incompatible with the standard SDD model" needs to be toned down and corrected since a simple SDD appears to be sufficient to account for the observed gradients. If the authors disagree, please specifically point out in the paragraph around line 249 what observations exactly are incompatible with a standard SDD model.*

This is similar to the point raised by Reviewer 1. While the standard SDD model can explain the overall gradient shape, it is not compatible with the observed time scales and Bcd puncta tracked in the posterior pole. We will improve the Discussion around this point to make the distinctions between the models clearer.

• 5) Data presentation *
• a) In line 27 and 122 it would be better to rephrase the wording "find/found" and give credit to previous papers that first made these observations. *

We will edit in the revision.

• b) For the statement "This suggests that the dynamics of the fast fraction were not captured by previous FRAP measurements", please explain why this should not be the case even though the fast fraction is shown to be larger than the slow fraction in the current work.*

We will edit in the revision.

• c) Similarly, the sentence "The dynamics of the slower mode correspond closely to measured Bcd dynamics from FRAP" likely needs to be corrected since it neglects the contribution of the faster mode, which is fluorescent as well and should also contribute to the dynamics from FRAP.*

This is similar to the point raised by Reviewer 1 and we will edit in the revision.

d) In the absence of further evidence (see above), the sentences "We establish that such spatially varying differences in the Bcd dynamics are sufficient to explain how Bcd can have a steep exponential gradient in the anterior half of the embryo and yet still have an observable fraction of Bcd near the posterior pole" and "These results explain how a long- ranged gradient can form while retaining a steep profile through much of its range" in the abstract need to be toned down.

We are not sure here what needs to be toned down. Our results show that there are (at least) two dynamic forms of Bcd and, combined, they are capable of forming a long-ranged gradient while also ensuring the gradient remains steep in the anterior (because the diffusion coefficient itself varies across the embryo). We will go through these statements and make sure the meaning is clear.

e) The authors state that "However, we show that eGFP::Bcd in its fastest form can move quickly (~18 μm2s-1), and the fraction of eGFP::Bcd in this form increases at lower concentrations", but this has not been directly shown. Please tone down this statement or directly test the prediction that Bcd has a higher fraction of the fast form in earlier nuclear cycles when Bcd concentration is smaller.

This is a good suggestion, and we will test whether early nuclear cycles of the anterior domain show faster dynamics.

*MINOR POINTS * * 1) Introduction * * a) Please explain explicitly what exactly the contention in Bcd, Nodal and Wingless dynamics is in the cited references. *

We will add in the revision. b) In line 95, it would be better to state that this is a variation of the SDD model rather than "a new model". * We changed from “a new model” to “an improved version of SDD model” in the current version of the manuscript. 2) Methods * * a) The authors state that "The same software was also used to calculate the cross-correlation function", but I couldn't find any cross-correlation analyses. Please clarify. *

It is line 538. There is no cross correlation. We changed this to the autocorrelation function.

b) Please correct the "uM" typo to "nM" in the legend of Method Fig. 2A.

We have changed this in the current version.

• c) In the sentence "Further, since the brightness eGFP:Bcd in the anterior and posterior cytoplasm is lower compared to the nuclei", "brightness" probably needs to be changed to "concentration" since the molecular brightness is unlikely to change. *

We edited the line no.591.

• d) Please explain the background-correction method mentioned in line 612. Please also state at what temperature the experiments were performed.*

We will add a better background correction in the revision. Currently, it is the non-embryo background as background noise. The measurements are carried out at 25oC.

*3) Results * * a) Please provide labels for anterior, posterior, dorsal and ventral in Fig. 1A. * * b) Please explain the colors in Fig. 5C. * * c) Please explain the dashed lines in Fig. 3C. * We have edited Figure 1A and Figure 5C. We will edit Figure 3C in further revision.

*OPTIONAL * * 1) If possible, it would be helpful to mention whether the transgenic animals have any abnormal phenotypes or whether they can rescue the bcd mutant. * We will update in the revision.

*2) To validate the concentration measurements, it would be ideal if the authors could determine the Bcd concentration gradient using FCS along the anterior-posterior axis. This would also address whether there are further unexpected changes in diffusivity in medial regions and along the anterior-posterior axis that would have to be considered for modeling. * To measure the Bcd concentration using FCS along the whole axis would be a very challenging undertaking. To get the data for the two positions analysed already represents a significant amount of work. We have done SPIM-FCS measurements, and we will be repeating our FCS measurements in the Fritzsche lab at Oxford. Combined, we believe this provides sufficient corroboration of our results.

*3) Local photoconversion experiments, e.g. in Bcd-Dendra2 embryos if available, would provide compelling support for the relevance of the measurements in the current work. * This is a nice idea, but this would represent a substantial project in its own right and lies beyond the current work.

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

*In my estimation the experimental work is rigorous and the results fully support the conclusions of the authors. I was surprised, however, that the HD-only form localizes via very different and simpler dynamics than does full-length Bcd, but nevertheless forms at least a qualitatively similar gradient. That leads to the question as to whether the existence of the fast and slow forms and their different ratios in different parts of the embryo actually are physiologically relevant. I don't see a straightforward way to test this experimentally, because the mutations that effect Bcd gradient formation also affect essential functions of the protein that if abrogated produce severe downstream effects on embryonic development and lethality. However I would like to see this point at least addressed in the discussion. The data and the methods are presented in such a manner that they can be reproduced, and the number of replicates and statistical analysis is overall robust. * We thank the Reviewer for the positive and constructive review. They, like both previous reviewers, raise the issue of the model and how it fits with the data. As outlined above, we will improve this part of the data presentation and also the Discussion to make sure the main results are clear.

We agree that the underlying importance of the different dynamic forms of Bicoid – and why they change across the embryo – remains unknown. We believe that our careful characterisation of such behaviour is important nonetheless, as it reveals that: (1) morphogen dynamics are more complicated than typically modelled, and this may be just as relevant for ligands moving through extracellular space; and (2) dynamics can vary in space/time, providing an additional possible mechanism of control for regulating morphogen gradient profiles.

Of course, we would like to explore potential physiological relevance. Further exploration of the homeodomain and its role in regulating dynamics is a potential route, but that belongs in future work.

*Minor comments: *

• The presentation of the graphical data measuring Bcd levels along the a-p axis (Fig 1C, 1D, 4C-F and others) needs to be improved, because the grey lines that represent ACF curves are essentially invisible. This is partly because there is usually extensive overlap between the grey lines and other lines. This may be solved by using a more vivid colour than grey for the ACF curves, or perhaps the ACF lines could be made thicker but with some transparency so that overlapping data can be seen. In any event this aspect of the presentation needs to be improved. * We have made the ACF lines thicker to distinguish from the model fit.

*In Figs 2D and 2I measurements of statistical significance between the proportion of protein in fast and slow modes need to be added. * We will add in the revision.

*Relevant to line 174 and Fig 2, NLS should be defined when first used, the source of the NLS should be given (is it from Bcd?) and the rationale for looking at eGFP::NLS should be made explicit. *

We have added details on how the eGFP::NLS is generated in the methods.

*In Fig 3D the dashed lines need to be defined. I assume these are experimental error bars but this is not stated. *

We now state this in the legends.

*On lines 344-5, shouldn't this conclusion concern the HD rather than the NLS? * Yes, thanks for pointing it out it is related to only NLS not NLSHD. We removed this statement from line 351.

*On line 432, CAP is not an acronym, the correct term is 5' 'cap' or 'cap structure'. Also Cho et al. PMID 15882623 should be added to the references here. * We changed the corresponding section and added the references.

*On lines 446, 456, 469, and throughout: replace 'blastocyst' with 'blastoderm'. The former term is generally used for embryos that undergo full cellular divisions and cleavage in early embryogenesis, not for syncytial embryos such as Drosophila. * We have changed blastocyst to blastoderm throughout the manuscript.

Reviewer #4 (Evidence, reproducibility and clarity (Required)):

Major comments: The averaged autocorrelation curves were fitted to models of diffusion with one and two components. The one-component model was insufficient to reproduce the data and the two-component model seems to fit the data. Have the authors tested models with more than two components? Could it be possible to distinguish more Bcd populations?

While it is possible to fit with further components, it rarely provides useful further insight. In particular, the error in measuring three tau_D’s is typically very large. In addition, the improvement in the fit will be marginal, and thus the extra components cannot be justified statistically. Of course, we cannot exclude a third (or more) possible dynamic modes, but within the resolution of our FCS measurements two components with triplets are in general the maximum that can be accommodated without overfitting. We will provide evidence for this claim in the supplement of the revised manuscript.

In Figure 2E, the same concentration of eGFP::NLS is estimated to exist in the cytoplasm and nucleus. Since the NLS should target eGFP to the nucleus, what is the explanation for this observation? Is it possible that the method used to estimate the concentration of molecules is underestimating the concentration in the nucleus or the opposite in the cytoplasm?

This is a good observation. There are two possible explanations. First, the regular division cycles “reset” the nuclear levels. Therefore, differences may not be so large. Second, FCS measurements of concentration can be noisy, as they depend on the very short time scales in the measurement. We will double check our measurements and clarify this in our revision.

*In the simulation of the SDD model (Figure 3B), simulations at 10 min, 25 min and 120 min are shown. Assuming that 120 min corresponds to early nc14, are simulations at earlier timepoints corresponding to nc12 and nc13 indistinguishable from the profile at 120 min? This demonstration would further support the option to merge the data from all nuclear cycles. *

This is a good point. Here, we were primarily focused on showing the time evolution of the model, rather than directly mapping onto experiment. We will clarify in the revision.

*The results obtained with the BcdN51A mutant show an increase in diffusion speed, while retaining similar proportions of fast and slow populations. In the slow fraction, a new population is found. Assuming that the BcdN51A molecules cannot bind specifically to DNA due to the mutation, what would this newly found population correspond to? Could the authors explore the possibility of nonspecific binding to DNA? The article would also win by discussing more on this aspect or other options. *

This is an interesting question. Dslow for anterior nuclei of N51A mutants increases (Dslow from ~0.2um2/s to ~1.5 um2/s), and the proportion is similar to the slow fraction of WT Bcd in the anterior nuclei (F=50%). The Dslow values of bcdWT suggest that 0.2um2/s is a result of DNA binding. For bcdN51A, Dslow of 1.5 um2/s is suggestive of nonspecific interaction of bcdN51A to the DNA. Such a nonspecific interaction is also noticed in the case of NLS::eGFP, where we see a significant amount (Dslow~ 1-1.5 um2/s , F=20%) of slow form in the anterior nuclei, likely due to non-specific interaction with the DNA.

It is worth noting that the inactive homeodomain of transcription factor sex comb reduced (scr) also interacts non-specifically with DNA at high concentration (Vukojevic et al., PNAS 2010). Non-specific interaction of eGFP fluorophore is also noted to be higher in the nuclei of AT-1 cells that suggest “obstacle-free accessible space” is low in the nuclei (Wachsmuth et al., JMB 2000). Therefore, though we do not understand the specific mechanism, our results for N51 mutants are aligned with previous observations of intra-nuclei dynamics.

The experimental rational behind the BcdMM reporter needs to be better explained as it is not clear. It was previously shown that the N51A mutation disturbs zygotic hb activation and Caudal gradient formation (see Figure 3 in Niessing et al., 2000). Since N51A already causes a strong phenotype by disturbing hb expression and Cad gradient formation, what is the reasoning being adding extra mutations to this background? Since the mutations in the PEST domain and YIRPYL motif are involved in cad translational repression, it would be more interesting to add them to the R54A mutation and further study the repression of cad? It would also shed light on the unexpected no difference or even decrease in diffusion in the cytoplasm of the R54A mutant which should increase if indeed the cad mRNA binding is being repressed.

Our rationale was to remove more elements of Bcd to see if there was some degree of redundancy – at least in terms of the dynamics.

The Bicoid homeodomain N51A mutation is physiologically known to cause de-repression of caudal and inhibit hunchback expression. Mechanistically, nuclear Bcd activates hb transcription. However, in the cytoplasm Bcd interacts with other proteins and forms a complex to de-repress caudal. Bcd binds to caudal mRNA through its HD at one end of the complex. However, in the other end, other proteins in the complex are bound to the 5’cap region caudal mRNA. Our rationale for generating the MM mutation was that the N51A mutation may not be sufficient for Bcd to be released from the protein complex. Therefore, additional mutations to N51A may release Bcd from interactions with either DNA or with other proteins through PEST domain and YIRPYL motif.

*Have the authors confirmed that their BcdR54A indeed inhibits cad translation? *

We have not tested the eGFP:bcdR54A to inhibit cad translation. We will add the data in the revision.

*How many embryos of BcdMM were analysed? The authors should also provide a table with all the values in SI as they have done for all the other reporters. *

We will add this data with the revision.

*The claims with eGFP::NLSBcdHD need to be supported by data from multiple embryos. Even if multiple ACF curves are obtained from one embryo, analysing only one embryo is not sufficient. This would clarify the fact that this reporter seems to be able to reproduce the mobility of Bcd in the nucleus. *

We agree and we are arranging to collect more data. This should be completed by the end of the summer.

*According to the methods, all reporters were expressed in a bcd null background, made with the bcd1 allele. This allele is also known as bcd085 and according to Driever and Nusslein-Volhard, 1988 (PMID: 3383244), this allele only causes an intermediate phenotype. This indicates that a truncated version of the protein probably still exists on the embryo. Do the conclusions obtained here still hold if a truncated version of the Bcd protein exists in addition to their reporters? *

We used the bcdE1 mutant, a null mutant of bcd. This was used by Gregor et al., Cell 2007 in their generation of the original Bcd::eGFP. We have also recently generated a more complete bcdKO mutation (Huang et al., eLife 2017). Our embryos do not have a clear phenotype that we can relate to the specific bcd- background used. Nonetheless, we agree it is an important point to be clear about the genetic background and we will clarify in the revised manuscript.

Minor comments: * * In line 45: "Morphogens are signalling molecules", the authors should consider removing the word "signalling" since not all morphogens are, especially the one being studied, Bicoid. * * In lines 80-81 (and also throughout the text): "We measure the Bcd dynamics at multiple locations along the embryo AP-axis", should be more accurate and changed to anterior and posterior of the embryo. Using "multiple locations along the AP axis" is ambiguous and not exact for what was done.

Yes, this is a fair comment. We have edited these sections in the current manuscript.

*Throughout the article, the authors refer multiple times to "modes for/of Bcd transport". Since they or others have not proven that Bcd is being transported, which would involve at least another factor, the authors should replace transport by movement, diffusion or a similar word with which they are comfortable. *

We have changed transport to movement wherever relevant in the text.

*Suggestion: The authors claim that the Bcd gradient is exponential up to 60% of embryo length. Would this information allow a more precise calculation of the gradient decay length in the exponential region than the 80-100µm stated on line 202? *

This is an interesting point, but our results suggest that the idea of the decay length is not so applicable in the posterior region. There, the Bcd dynamics are generally quicker, thereby increasing l. Of course, we cannot discount possible spatial variation in degradation. However, in previous work, our Bcd tandem reporter (which is sensitive to changes in degradation) did not reveal spatial variation in degradation.

In lines 258-259, the sentence "Further, Bcd binds to caudal mRNA, repressing its expression in the cytoplasm" should be improved to clarify the role of Bcd in caudal mRNA translation repression and references should be added. This should also be corrected in the following paragraph.

We will add the necessary corrections in the revision.

*In line 262, "mutations" should be singular since it corresponds to only one amino acid mutation. *

We have corrected this.

*Figure 4J needs to be corrected as the fractions of the slow and fast populations do not correspond to what is shown in Table 3. For example, Fslow fraction of AC is ~45% in the figure while it is 36% in Table 3. The problem occurs in all fractions. *

We are sorry there is a mislabelling in the corresponding figure. AN is in the place of AC. We have edited figure 4J and removed the mislabelling.

*In the discussion, in lines 379-380, "Given the changing fractions of the fast and slow populations in space, the interactions between the populations are likely non-linear". What is the reasoning for non-linearity and not interchangeability? *

If the interactions between the two populations were linear, then the fraction in each form would be constant across the embryo. Some degree of nonlinearity is required in order to have spatially varying relative populations.

*In line 432 caudal should be italicized. *

We have edited this.

*In the discussion, the authors conclude that "In the nucleus, the two populations can be largely (though not completely) explained by Bcd binding to DNA". The discussion would win by explaining all the possible options. * We will add the necessary changes in the discussion. This is also related to above reviewer comments.

Author Response

Reviewer #1 (Public Review):

This paper studies color vision in anemonefish. The central conclusion of the paper is that anemonefish use signals from their UV cones to discriminate colors that would not otherwise be distinguishable; this differs from other fish in which UV cones extend the range of wavelengths of sensitivity but do not add a dimension to color vision. The work fits into a rich history of studies investigating how color vision fits into an animal's ecological niche. My primary concerns regard the microspectrophotometry data from single cones and some aspects of the presentation of the behavioral data.

Microspectrophotometry

The spectral properties of the cone types are a key issue for interpreting the results. These were measured using MSP, and fits are shown in Figure 2. The raw data shown in Fig. S1 appears more complicated than indicated in the main text. The templates miss the measurements across broad wavelength bands in each cone type. Particularly concerning is the high UV absorbance across cone types and the long-wavelength absorbance in the UV cone. It is not clear how this picture supports the relatively simple description of cone types and spectral sensitivities given in the main text and which forms the basis of the modeling.

Microspectrophotometry is an inherently noise-prone measurement technique, particularly for very small photoreceptor outer segments such as that of single cones, which are also difficult to detect as intact, isolated (nonoverlapping) cells. As such, the absorbance curve fitting and derived lambda max (λmax) values should be treated as estimates. The accuracy of these estimates is adequate for this type of study, and visual modelling results have been shown to be robust against small errors (±10 nm λmax) in photoreceptor sensitivity for multiple species [see Lind, O. & Kelber, A. (2009). Vis Res. 49(15), 1939-1947; and Bitton, PP. et al. (2017). PLOS ONE, 12: e0169810]. We consider it highly unlikely that small shifts in cone λmax from measurement error would make a meaningful difference to the colour discrimination thresholds.

It should be noted that the raw data shown in the original Supplementary Figure 1, included all scans overlain with an average absorbance curve for presentation purposes; however, the actual lambda max values for different cone types were measured and then averaged among individual scans fitted with photopigment absorbance curve templates. For clarity and transparency, we have now provided three multipaned plots (see Figure 1 – figure supplements 1-3) showing the individual pre- and post-bleach scans of absorbance spectra, fitted absorbance curve templates, and R2 values from the best visual pigment template fit.

It is worth noting that most of the cone absorbance spectra found in our study closely resemble those in λmax and quality to those measured in another anemonefish species (Amphiprion akindynos) [see Supplementary Figure 1 in Stieb S. et al. (2019). Sci Rep. 9, 16459]. These cone λmax values can also be reconciled with previous estimates on opsin λmax based on amino acid sequences and cone opsin expression in the A. ocellaris retina characterised in Mitchell LJ et al. (2021). GBE, 13: evab184.

Evidence that the unusual long-wavelength absorbance detected in a couple of the single cone (pre-bleach) measurements were not of visual pigment in origin comes from post-bleach scans, which showed their persistence (i.e., did not show a photobleaching response) and were likely instead contaminants (e.g., blood, RPE pigment). UV absorbance in some of the double cone measurements (above that expected of the prebleached beta peak from chromophore spectral absorption) can be attributed to either noise from scans as is quite typical of MSP and/or partial (accidental) bleaching from stray light sources. Although utmost care was taken to minimise contamination and unintended bleaching sometimes it is unavoidable.

We refer the Reviewer to multiple published studies for further examples of typical MSP measurements that share similar levels of noise to ours e.g., see Figure 1 in Knott B. et al. (2013). JEB, 216:4454-4461; Figure 3 in Schott, RK et al. (2015). PNAS, 113(2): 356-361; Figure 2 in Dalton BE et al. (2014). Proc R Soc B. 281; Figure 5 in Tosetto, JE et al. (2021). Brain Behav Evol. 96: 103-123.

Presentation

The results are not presented in a straightforward way - at least for this reviewer. What is missing for me is a clear link between the psychometric curves in Figure 3A and the discrimination thresholds indicated in Figure 3B and Figure 4. Figure 3A is only discussed in the text on line 289 - after Figure 4 has been introduced and discussed. It would have been very helpful for me if the psychometric curves were first introduced and described, then the relation to Figure 3B was clearly indicated (perhaps with a single psychometric curve as an example). Similarly for Figure 4 the relationship between specific psychometric curves and the threshold plotted would be quite helpful. Currently it takes a careful reading to understand why being below the dashed line in Figure 4 is important.

We have made the following changes, including the introduction of the psychometric curves earlier in the results (lines 236-249) and moved the psychometric function comparison before the mention of Figure 4. Additionally, to make the association between the plotted colour loci and psychometric curves clearer, we have added a smaller psychometric curve plot adjacent to the colour space (in Figure 3B) using red as an example which has an averaged psychometric curve overlying the individual fish curves. The figure caption (lines 250-274) explains that the plotted colour loci and given thresholds are mean values calculated from the individual fish behavioural data.

We have also added a brief reminder that the theoretical limit of colour discrimination is predicted by the RNL model as 1∆S, where in our task fish should be just able to distinguish targets from grey distractors (see lines 222-224). To clarify, the plotted values in Figure 4B are both the individual fish thresholds (points) and average threshold (black bar) per colour set. The individual threshold values are taken at a correct choice probability of 50% from fitted psychometric curves of fish behavioural performance (shown in Figure 3A).

RNL model

The data is fit and interpreted in the context of the receptor noise limited model. The paragraph in the discussion about complementary color pairs suggests that this model is incorrect (text around line 332). Consideration of how the results depend on the RNL model is important, especially given the interpretation here.

The inability of the RNL model to account for the observed asymmetry between color discrimination thresholds implies that they cannot be solely attributed to photoreceptor noise. We can therefore infer from the asymmetry that thresholds are set by a higher-level process, whether that involves post-receptor processes within the inner retina or in the brain remains to be investigated. As explained in lines 396-397 one possibility is that activation of the UV receptor suppresses noise in the visual pathway or enhances the saliency of colors for anemonefish. The high sensitivity to violet-green, which was found in all six of the fish tested, is consistent with the heightened saliency of this color (lines 397-399).

Figure 3B

This is the key figure in the paper. But several issues make seeing the data in this figure difficult. First, the important part of the figure is buried near the origin and hard to see. Can you show a surface that connects the thresholds in the different chromatic directions, or otherwise highlight the regions of discriminable and not discriminable colors?

See previous comment. In short, we have taken the advice of the Reviewer and added highlighted areas around the regions of discriminable colors in Figure 3B to help visually separate them from the non-discriminable regions of colors (from grey). Additionally, we have added an inset showing an enlarged image of the area surrounding the centre of colour space.

Reviewer #2 (Public Review):

Mitchell and colleagues examined the contribution of a UV-sensitive cone photoreceptor to chromatic detection in Amphiprion ocellaris, a type of anemonefish. First, they used biophysical measurements to characterize the response properties of the retinal receptors, which come in four spectrally-distinct subtypes: UV, M1, M2, and L. They then used these spectral sensitivities to construct a 4-dimensional (tetrahedral) color space in which stimuli with known spectral power distributions can be represented according to the responses they elicit in the four cone types. A novel five-LED display was used to test the fish's ability to detect "chromatic" modulations in this color space against a background of random-intensity, "achromatic" distractors that produce roughly equal relative responses in the four cone types. A subset of stimuli, defined by their high positive UV contrast, were more readily detected than other colors that contained less UV information. A well-established model was used to link calculated receptor responses to behavioral thresholds. This framework also enabled statistical comparisons between models with varying number of cone types contributing to discrimination performance, allowing inferences to be drawn about the dimensionality of color vision in anemonefish.

The authors make a compelling case for how UV light in the anemonefish habitat is likely an important ecological source of information for guiding their behavior. The authors are to be commended for developing an elegant behavioral paradigm to assess visual performance and for incorporating a novel display device especially suited to addressing hypotheses about the role of UV light in color perception. While the data are suggestive of behavioral tetrachromacy in anemonefish, there are some aspects of the study that warrant additional consideration:

1) One challenge faced by many biological imaging systems is longitudinal chromatic aberration (LCA) - that is, the focal power of the system depends on wavelength. In general, focal power increases with decreasing wavelength, such that shorter wavelengths tend to focus in front of longer wavelengths. In the human eye, at least, this focal power changes nonlinearly with wavelength, with the steepest changes occurring in the shorter part of the visible spectrum (Atchison & Smith, 2005). In the fish eye, where the visible spectrum extends to even shorter wavelengths, it seems plausible that a considerable amount of LCA may exist, which could in turn cause UV-enriched stimuli to be more salient (relative to the distractor pixels) due to differences in perceived focus rather than due solely to differences in their respective spectral compositions. Such a mechanism has been proposed by Stubbs & Stubbs (2016) as a means for supporting "color vision" in monochromatic cephalopods (but see Gagnon et al. 2016). It would be worth discussing what is known about the dispersive properties of the crystalline lens in A. ocellaris (or similar species), and whether optical factors could produce sufficient cues in the retinal image that might explain aspects of the behavioral data presented in the current study.

This is an interesting point, and we appreciate the reviewer’s thoughtful comment regarding this topic especially as LCA increases exponentially in the UV. Although we certainly cannot disprove such a mechanism in the present study, we are highly sceptical that LCA could be used by reef fish and is involved in the heightened saliency of UV stimuli. Previous work has found that LCA is mostly corrected for in the teleost retina of both marine and freshwater species by graded, multifocal lenses that focus different wavelengths at the same depth as their maximally sensitive cone photoreceptors [e.g., for evidence in African cichlids see Kröger, R. H. H. et al. (1999). J Comp Physiol. A, 184, 361-369; Malkki, P. E. & Kröger, R. H. H. (2005). J Opt. A, 7, 691-700; and for various reef fishes see Karpestam, B. et al. (2007). J Exp Biol., 210, 16: 2923-2931]. In essence, LCA is corrected in the eyes of many teleosts by accurately tuning longitudinal spherical aberration through having a graded density lens. We draw particular attention to the latter reference which comparatively examined the optical properties of reef fish lenses, including diurnal, planktivorous damselfishes (from the same family as anemonefishes, Pomacentridae). They found that not only were the lenses of these species highly UV-transmissive (as we show in anemonefish), but all were multifocal and capable of focusing both visible (non-UV) and UV wavelengths. Considering the coastal cephalopod species examined thus far, all of them contain only one type of visual pigment which is packed in their long photoreceptor (150-450µm long outer segment) across an entire retina (Chung and Marshall 2016, Proceeding B). Theoretically, given these long photoreceptors, the LCA and the resulting differentials of focal length onto different patches of photoreceptors or different depth of the outer segment might provide cues for colour discrimination even though no behavioural evidence exists to prove this hypothesis yet. Unlike the cephalopod case, the four specific spectral cones arranged in a mosaic pattern along with their very short outer segments (5-10µm) in the anemonefish retina likely makes the LCA less effective in this retinal design.

We have added a short paragraph (Lines 400-412) discussing the possibility of an optical mechanism contributing to heightened UV saliency with a particular focus on LCA and our thoughts on why we consider it an unlikely mechanism in anemonefish.

2) The authors provide a quantitative description of anemonefish visual performance within the context of a well-developed receptor-based framework. However, it was less clear to me what inferences (if any) can be drawn from these data about the post-receptoral mechanisms that support tetrachromatic color vision in these organisms. Would specific cone-opponent processes account for instances where behavioral data diverged from predictions generated with the "receptor noise limited" model described in the text? The general reader may benefit from more discussion centered on what is known (or unknown) about the organization of cone-opponent processing in anemonefish and related species.

In short, we do not know the specific opponent interactions of anemonefish cones. The RNL model assumes all possible opponent interactions in its calculations. From our results, very little can be said about the post-receptor mechanisms involved in their putative tetrachromatic vision. We would like to avoid overreaching beyond what our data can show. A future directions section has now been added to the discussion (lines 467-497), which briefly mentions the known UV opponency in larval zebrafish and that future investigation in anemonefish should attempt to disentangle the specific opponent (chromatic) and non-opponent (achromatic) circuits in the anemonefish retina.

Reviewer #3 (Public Review):

The comments below focus mainly on ways that the data and analysis as currently present do not to this reviewer compel the conclusions the authors wish to draw. It is possible that further analysis and/or clarification in the presentation would more persuasively bolster the authors' position. It also seems possible that a presentation with more limited conclusions but clarity on exactly what has been demonstrated and where additional future work is needed would make a strong contribution to the literature.

• Fig 3A. It might be worth emphasizing a bit more explicitly that the x-axis (delta S) is the result of a model fit to the data being shown, since this then means that if RNL model fit the data perfectly, all of the thresholds would fall at deltaS = 1. They don't, so I would like to see some evaluation from the authors' experience with this model as to whether they think the deviations (looks like the delta S range is ~0.4 to ~1.6 in Figure 4B) represent important deviations of the data from the model, the non-significant ANOVA notwithstanding. For example, Figure 4B suggests that the sign of the fit deviations is driven by the sign of the UV contrast and that this is systematic, something that would not be picked up by the ANOVA. Quite a bit is made of the deviations below, but that the model doesn't fully account for the data should be brought out here I think. As the authors note elsewhere, deviations of the data from the RNL model indicate that factors other than receptor noise are at play, and reminding the reader of this here at the first point it becomes clear would be helpful.

We have now stated more explicitly in the figure caption for Figure 3A, that the delta S values presented were calculated by fitting fish behavioral data to the RNL model. To test the overall effect that the sign of the UV contrast had on the discrimination threshold, we have now included ‘contrast’ (positive or negative) as another fixed effect in the linear mixed effects model. We have now included details of this test in the results which shows the systematic effect (lines 338-340). Additionally, as suggested we now briefly introduce in the results the idea that factors other than receptor noise are causing the observed deviations in data from the RNL model.

• Line 217 ff, Figure 4, Supplemental Figure 4). If I'm understanding what the ANOVA is telling us, it is that the deviations of the data across color directions and fish (I think these are the two factors based on line 649) is that the predictions deviate significantly from the data, relative to the inter-fish variability), for the trichromatic models but not the tetrachromatic model. If that's not correct, please interpret this comment to mean that more explanation of the logic of the test would be helpful.

The interpretation of the ANOVA by the Reviewer is mostly correct. We had the variables color set and Fish ID, with threshold delta S as the dependent variable. This showed that deviations from the predicted threshold were significant relative to the inter-fish variability for the trichromatic models. Missing details describing the ANOVA have now been added to the methods (lines 789-798).

Assuming that the above is right about the nature of the test, then I don't think the fact that the tetrachromatic model has an additional parameter (noise level for the added receptor type) is being taken into account in the model comparison. That is, the trichromatic models are all subsets of the tetrachromatic model, and must necessarily fit the data worse. What we want to know is whether the tetrachromatic model is fitting better because its extra parameter is allowing it to account for measurement noise (overfitting), or whether it is really doing a better job accounting for systematic features of the data. This comparison requires some method of taking the different number of parameters into account, and I don't think the ANOVA is doing that work. If the models being compared were nested linear models, than an F-ratio test could be deployed, but even this doesn't seem like what is being done. And the RNL model is not linear in its parameters, so I don't think that would be the right model comparison test in any case.

Typical model comparison approaches would include a likelihood ratio test, AIC/BIC sorts of comparisons, or a cross-validation approach.

If the authors feel their current method does persuasively handle the model comparison, how it does so needs to be brought out more carefully in the manuscript, since one of the central conclusions of the work hinges at least in part on the appropriateness of such a statistical comparison.

Our visual model comparisons were aimed at assessing whether a trichromatic or tetrachromatic model best fit the colour discrimination data. The trichromatic and tetrachromatic models assume two and three opponency pathways, respectively. If the fish were not tetrachromatic, and instead trichromatic, then we would expect that the RNL model should better fit the data with two opponency mechanisms (rather than three). Our reason for making this assessment, is because of the possibility that not all the cones could be contributing to colour vision and could be used exclusively for achromatic tasks (e.g., luminance vision or motion detection). However, according to our finding that the data best fit the tetrachromatic model (i.e., how the behavioural discrimination thresholds more closely fitted the theoretical prediction of 1∆S), it is likely that anemonefish used all four cones for colour vision.

We have also now repeated our analysis using unweighed delta S values which are calculated using general n-dimensional models of colour vision (using the PAVO2 package). These models essentially follow the same initial steps followed by the RNL model (and many others) but omit the receptor noise correction stage. After comparing (using ANOVA, see lines 303-311) the predicted thresholds with the data in this non-RNL space, it was found that again the tetrachromatic model predictions did not deviate significantly from the data relative to individual fish performance; however, we also found that the trichromatic model without M2 cone input no longer differed from the predicted values. In this case, it seems that the extra noise parameter did contribute to the difference in fit. Whether this is a biologically meaningful comparison (as all photoreceptors contain noise) is an open question. We have added a short statement explicitly framing our interpretation of anemonefish having a 3-D colour space to being in accordance with the closeness of RNL model predictions (lines 370-371, 506-508).

• Also on the general point on conclusions drawn from the model fits, it seems important to note that rejecting a trichromatic version of the RNL model is not the same as rejecting all trichromatic models. For example, a trichromatic model that postulates limiting noise added after a set of opponent transformations will make predictions that are not nested within those of RNL trichromatic models. This point seems particularly important given the systematic failures of even the tetrachromatic version of the RNL model.

This is a good point. We have limited our conclusions to specifically address trichromatic models generated within the framework of the RNL model by adding in the conclusion section that fish psychophysical thresholds were best explained by the RNL model when all four cone types contributed to colour vision (see lines 370-371, 506-508). In this same sentence, we have also added in parentheses that “suggesting (but not proving) tetrachromacy” (line 508). We have also edited the abstract to state that our results were “…best described by a tetrachromatic model using all four cone types…”, rather than stating we have shown tetrachromacy (lines 36-37).

• More generally, attempts to decide whether some human observers exhibit tetrachromacy have taught us how hard this is to do. Two issues, beyond the above, are the following. 1) If the properties of a trichromatic visual system vary across the retina, then by imaging stimuli on different parts of the visual field an observer can in principle make tetrachromatic discriminations even though visual system is locally trichromatic at each retinal location. 2) When trying to show that there is no direction in a tetrachromatic receptor space to which the observer is blind, a lot of color directions need to be sampled. Here, 9 directions are studied. Is that enough? How would we know? The following paper may be of interest in this regard: Horiguchi, Hiroshi, Jonathan Winawer, Robert F. Dougherty, and Brian A. Wandell. "Human trichromacy revisited." Proceedings of the National Academy of Sciences 110, no. 3 (2013): E260-E269. Although I'm not suggesting that the authors conduct additional experiments to try to address these points, I do think they need to be discussed. We agree with the reviewer, that colour discriminability achieved by tetrachromatic vision could in theory be achieved by the combined effect of localised, distinct forms of trichromacy. Evidence in other fishes suggests that such multiple forms of trichromacy across the retina likely exist in many species. However, the behavioural effects of this retinal setup remain to be studied likely due to its extremely difficult nature. We have added a new section titled “future directions” (Lines 474-489), in which we discuss the possibility that distinct forms of trichromacy in the anemonefish retina could in theory achieve colour discrimination on par with tetrachromatic vision. We also give suggestions on how this could be investigated.

Although we tried to include as many colour directions as practically possible in our experiment, we have certainly not provided an exhaustive range that completely encompasses anemonefish colour space. Whether 9 colour directions are adequate to assess the dimensionality of their color vision is difficult to say. As addressed in the previous comment, we now acknowledge this limitation by refining our conclusion, saying that our results do not prove tetrachromacy.

• Line 277 ff. After reading through the paper several times, I remain unsure about what the authors regard as their compelling evidence that the UV cone has a higher sensitivity or makes an omnibus higher contribution to sensitivity than other cones (as stated in various forms in the title, Lines 37-41, 56-57, 125, 313, 352 and perhaps elsewhere).

At first, I thought they key point was that the receptor noise inferred via the RNL model as slightly lower (0.11) for the UV cone than for the double cones (0.14). And this is the argument made explicitly at line 326 of the discussion. But if this is the argument, what needs to be shown is that the data reject a tetrachromatic version of the RNL model where the noise value of all the cones is locked to be the same (or something similar), with the analysis taking into account the fewer parametric degrees of freedom where the noise parameters are so constrained. That is, a careful model comparison analysis would be needed. Such an analysis is not presented that I see, and I need more convincing that the difference between 0.11 and 0.14 is a real effect driven by the data. Also, I am not sanguine that the parameters of a model that in some systematic ways fails to fit the data should be taken as characterizing properties of the receptors themselves (as sometimes seems to be stated as the conclusion we should draw).

We have performed various modelling scenarios where receptor noise was adjusted for each channel; however, the UV channel was consistently found to be more sensitive than the other channels. In (the original) Supplementary Figure 6 (now Figure 4 – figure supplements 1 and 2), we show predicted dS values calculated using receptor noise levels in the exact manner that the Reviewer suggests by ranging from 0.05 to 0.15, and most importantly, included scenarios where receptor noise was held equal across cone types and others where it was varied between single cones and double cones. None of the models adjusted the data so that sensitivity was equal across all four channels, which means that by an unknown mechanism, the UV channel is more sensitive, but this is unrelated to noise levels. Our best-fit receptor noise values of 0.11 (for single cones) and 0.14 (for double cones) are estimate values and should be treated as such till actual receptor noise measurements are made.

Then, I thought maybe the argument is not that the noise levels differ, but rather that the failures of the model are in the direction of thresholds being under predicted for discriminations that involve UV cone signals. That's what seems to be being argued here at lines 277 ff, and then again at lines 328 ff of the discussion. But then the argument as I read it more detail in both places switches from being about the UV cones per se to being about postive versus negative UV contrast. That's fine, but it's distinct from an argument that favors omnibus enhanced UV sensitivity, since both the UV increments and decrements are conveyed by the UV cone; it's an argument for differential sensitivity for increments versus decrements in UV mediated discriminations. The authors get to this on lines 334 of the discussion, but if the point is an increment/decrement asymmetry the title and many of the terser earlier assertions should be reworked to be consistent with what is shown.

To clarify our argument, we found that the colour discrimination thresholds were systematically lower than predicted by the RNL model for colours which elicited higher UV cone stimulation relative to other cone types. These colours we refer to as UV positive based on the sign direction of their contrast against grey distractors produced by higher UV/V LED channel (i.e., in a positive direction). Whereas colours with UV negative chromatic contrast had lower UV cone stimulation relative to the other cone types. Therefore, our interpretation of the importance of UV cone signals for colour discrimination are congruent with the results. In the discussion, we suggest a possibility that activation of the UV receptor suppresses noise downstream in the visual pathway or enhances the saliency of colours (see lines 397-398). This activation of the UV receptor would, of course, be at its highest for colours with positive UV chromatic contrast.

Note that we have added to the discussion the possibility that colour preferences or a difference in attentiveness might have contributed to differences in discrimination thresholds (see discussion lines 412-413, 427-428, 433-435, 456-466, and 469-473). However, we consider it a less likely explanation due to a couple of reasons, including 1) a lack of difference in responsiveness across colour sets in their timing to peck the target, and 2) any non-learnt bias would have likely been overridden or at least weakened by training prior to the experiment where colours were rewarded equally (see lines 462-466).

We have edited the results (lines 334-352) to make our point clearer and by changing the subtitle to be more explicit: “Lower discrimination thresholds induced by positive UV contrast”. The subsection begins by explaining the different types of UV chromatic contrast by elevation angle and, finally, how this division among colour sets was a major determinant of colour discrimination thresholds.

Perhaps the argument with respect to model deviations and UV contrast independent of sign could be elaborated to show more systematically that the way the covariation with the contrasts of the other cone stimulations in the stimulus set goes, the data do favor deviations from the RNL in the direction of enhanced sensitivity to UV cone signals, but if this is the intent I think the authors need to think more about how to present the data in a manner that makes it more compelling than currently, and walk the reader carefully through the argument.

We have added to the results the linear mixed-effects model output with ‘contrast’ (positive/negative) added as a fixed effect. This analysis shows that the sign direction of UV contrast was a strong predictor of threshold (see address to previous comments and lines 399-401, 790-799).

• On this point, if the authors decide to stick with the enhanced UV sensitivity argument in the revision, a bit more care about what is meant by "the UV cone has a comparatively high sensitivity (line 313 and throughout)" needs more unpacking. If it is that these cones have lower inferred noise (in the context of a model that doesn't account for at least some aspects of the data), is this because of properties of the UV cones, or the way that post-receptoral processing handles the signals from these cones mimicking a cone effect in the model. And if it is thought that it is because of properties of the cones, some discussion of what those properties might be would be helpful. As I understand the RNL model, relative numbers of cones of each type are taken into account, so it isn't that. But could it be something as simple as higher photopigment density or larger entrance aperture (thus more quantum catches and higher SNR)?

It is unknown what aspect of the cone morphology or physiology sets the activation or inactivation threshold. Electrophysiological data collected from the UV cones of other fish species e.g., in goldfish and zebrafish [see Hawryshyn & Beauchamp (1985). 25, Vis Res.; and Yoshimatsu et al. (2020). 107, Neuron.] show that they have exceptionally high sensitivity. What has not been shown is that having a UV cone can improve colour discrimination.

Previous quantitative cone opsin gene expression analysis showed that the single cone opsins (SWS1 and SWS2B) are expressed at lower levels than all double cone opsin genes. This difference in expression combined with the smaller size of single cone outer segments than the double cones make it unlikely that a larger photoreceptor size, higher volume or packing density of visual pigment is responsible. Contrary to our findings, these aspects of the different cone types (if they had an effect) would instead predict that double cones have a higher SNR, and non-UV colours would be more discriminable. We have now added these details to the discussion (see lines 391-397).

• Line 288 ff. The fact that the slopes of the psychometric functions differed across color directions is, I think, a failure of the RNL model to describe this aspect of the data, and tells us that a simple summary of what happens for thresholds at delta S = 1 does not generalize across color directions for other performance levels. Since one of the directions where the slope is shallower is the UV direction, this fact would seem to place serious limits on the claim that discrimination in the UV direction is enhanced relative to other directions, but it goes by here without comment along those lines. Some comment here, both about implications for fit of RNL model and about implications for generalizations about efficacy of UV receptor mediated discrimination and UV increment/decrement asymmetries, seems important.

The variation in the psychometric functions is difficult to interpret and cannot be explained by the RNL model. What the RNL model predicts is delta S based on low level factors (namely receptor noise). In the discussion, we completely agree with the notion that the asymmetry in thresholds from predicted values, and the variation in psychometric slopes cannot be explained by the RNL model, e.g., this is heavily implied by “colour discrimination thresholds cannot be directly attributed to noise in the early stages of the visual pathway…” (lines 388-390). To clarify the inability of the RNL model to account for this aspect of the data, we have included a statement (see line 390).

It is a good point that this could be an indication of heterogeneity in colour space. Heterogeneity in discrimination thresholds across animal colour space (both surrounding the threshold area and for more saturated regions) has been explored in detail using trichromatic triggerfish by Green N. F. et al. (2022). JEB, 7(225):jeb243533. We have added this idea to the discussion (see lines 490-498). For UV, it seems that two of the five fish (#34 and 20) had noticeably shallower curves than the others tested for UV (fish #19, 33, 36). Both also varied more in their ability to distinguish targets, as shown by their wider confidence intervals. One of these two fish (#34) was retested for UV at the end of the experiment, and in the secondary assessment had a steeper psychometric curve more in line with the other fish in the experiment (see Figure 3 – figure supplement 1 and added lines 247-250). Based on this discrepancy in performance between assessments, it is also possible that individual learning effects had a role in impacting the shape of the psychometric curve. Note, this had minimal effect on colour discrimination thresholds and any differences were in the direction of change observed across colour sets in the experiment (i.e., lower dS for UV positive directions).

• Line 357 ff. Up until this point, all of the discussion of differences in threshold across stimulus sets has been in terms of sensitivity. Here the authors (correctly) raise the possibility that a difference in "preference" across stimulus sets could drive the difference in thresholds as measured. Although the discussion is interesting and germaine, it does to some extent further undercut the security of conclusions about differential sensitivity across color directions relative to the RNL model predictions, and that should be brought out for the reader here. The authors might also discuss about how a future experiment might differentiate between a preference explanation and a sensitivity explanation of threshold differences.

We have now added a paragraph (see lines 469-473) discussing that future work should test for color preferences and suggest how this could be done using a similar foraging task. We also include our thoughts immediately prior on why it is unlikely that a colour preference was a major contribution towards the results. In short, we consider it unlikely as fish showed no evidence of reduced latency for pecking at targets across the colour sets and because the training regime prior to the experiment equally rewarded fish for all colours and would likely have overridden a strong preference (at least in this specific foraging context).

• RNL model. The paper cites a lot of earlier work that used the RNL model, but I think many readers will not be familiar with it. A bit more descriptive prose would be helpful, and particularly noting that in the full dimensional receptor space, if the limiting noise at the photoreceptors is Gaussian, then the isothreshold contour will be a hyper-ellipsoid with its axes aligned with the receptor directions.

There is now added explanation of the RNL model (see lines 141-151), particularly on its assumptions that it only receives chromatic input and that discrimination is limited by noise arising in the photoreceptors and not by any specific opponent mechanisms. We also added the mention of the expected hyper-ellipsoid shape of isothreshold contours if receptor noise is Gaussian. Note, while we appreciate the importance of the reader to understand the basic functionality of the model, we wanted to avoid overloading the introduction with details on the RNL model which is not the focus of the paper. The RNL model is well-established in the field of visual ecology and animal vision research for well over a decade and has been thoroughly dissected by previous methodological reviews. We refer to one of these more recent reviews by Olsson et al. (2018) Behav Ecol. 29(2):273-282, and direct the reader to the methods section for further details on the RNL model.

• Use of cone isolating stimuli? For showing that all four cone classes contribute to what the authors call color discrimination, a more direct approach would seem to be to use stimuli that target stimulation of only one class of cone at a time. This might require a modified design in which the distractors and target were shown against a uniform background and approximately matched in their estimated effect on a putative achromatic mechanism. Did the authors consider this approach, and more generally could they discuss what they see as its advantages and disadvantages for future work.

The Reviewer is correct in that a targeted approach of isolated cone stimulation would be the optimal approach to demonstrating tetrachromatic colour vision. However, the extreme spectral overlap in the absorption curves of anemonefish cones, particularly in the mid-wavelength region makes this problematic in using the current LED display. We added to the discussion ways that this could be studied in the future (see lines 474-489). This might be possible (but still challenging) using a monochromator, but such technology severely limits the diversity of stimuli which can be created and usually restricts experiments to a simple paired choice design (or grey card experiment). The traditional paired choice experiment requires animals to be trained to distinguish a specific colour, while the Ishihara-like task trains animals to distinguish targets using an odd-one-out approach. This latter approach is highly efficient, as it does not require retraining when testing a new colour (i.e., fish learnt the task not a specific colour). Here, we wanted to assess colour discrimination in multiple directions to compare performance, and the flexible LED display combined with a generalisable task was important.

The above assumes that anemonefish do not use multiple trichromatic systems. In which case, the use of standard experimental stimuli (e.g., a monochromator, an LED display) would be unsuitable as they illuminate the whole retina. To definitively test the range of opponent interactions, it would be necessary to make electrophysiological measurements targeting the transmitting neurons using a retinal multielectrode array (MEA) approach or by in-vivo calcium imaging (lines 484-486).

We understand that our results are not a direct test of the dimensionality of anemonefish colour vision and should not be interpreted as such, as we do not have direct evidence of tetrachromacy. To recognize this limitation of our data, we have drawn back some of our conclusive statements that claimed to have demonstrated tetrachromacy.

Reviewer #3 (Public Review):

The comments below focus mainly on ways that the data and analysis as currently present do not to this reviewer compel the conclusions the authors wish to draw. It is possible that further analysis and/or clarification in the presentation would more persuasively bolster the authors' position. It also seems possible that a presentation with more limited conclusions but clarity on exactly what has been demonstrated and where additional future work is needed would make a strong contribution to the literature.

* Fig 3A. It might be worth emphasizing a bit more explicitly that the x-axis (delta S) is the result of a model fit to the data being shown, since this then means that if RNL model fit the data perfectly, all of the thresholds would fall at deltaS = 1. They don't, so I would like to see some evaluation from the authors' experience with this model as to whether they think the deviations (looks like the delta S range is ~0.4 to ~1.6 in Figure 4B) represent important deviations of the data from the model, the non-significant ANOVA notwithstanding. For example, Figure 4B suggests that the sign of the fit deviations is driven by the sign of the UV contrast and that this is systematic, something that would not be picked up by the ANOVA. Quite a bit is made of the deviations below, but that the model doesn't fully account for the data should be brought out here I think. As the authors note elsewhere, deviations of the data from the RNL model indicate that factors other than receptor noise are at play, and reminding the reader of this here at the first point it becomes clear would be helpful.

* Line 217 ff, Figure 4, Supplemental Figure 4). If I'm understanding what the ANOVA is telling us, it is that the deviations of the data across color directions and fish (I think these are the two factors based on line 649) is that the predictions deviate significantly from the data, relative to the inter-fish variability), for the trichromatic models but not the tetrachromatic model. If that's not correct, please interpret this comment to mean that more explanation of the logic of the test would be helpful.

Assuming that the above is right about the nature of the test, then I don't think the fact that the tetrachromatic model has an additional parameter (noise level for the added receptor type) is being taken into account in the model comparison. That is, the trichromatic models are all subsets of the tetrachromatic model, and must necessarily fit the data worse. What we want to know is whether the tetrachromatic model is fitting better because its extra parameter is allowing it to account for measurement noise (overfitting), or whether it is really doing a better job accounting for systematic features of the data. This comparison requires some method of taking the different number of parameters into account, and I don't think the ANOVA is doing that work. If the models being compared were nested linear models, than an F-ratio test could be deployed, but even this doesn't seem like what is being done. And the RNL model is not linear in its parameters, so I don't think that would be the right model comparison test in any case.

Typical model comparison approaches would include a likelihood ratio test, AIC/BIC sorts of comparisons, or a cross-validation approach.

If the authors feel their current method does persuasively handle the model comparison, how it does so needs to be brought out more carefully in the manuscript, since one of the central conclusions of the work hinges at least in part on the appropriateness of such a statistical comparison.

* Also on the general point on conclusions drawn from the model fits, it seems important to note that rejecting a trichromatic version of the RNL model is not the same as rejecting all trichromatic models. For example, a trichromatic model that postulates limiting noise added after a set of opponent transformations will make predictions that are not nested within those of RNL trichromatic models. This point seems particularly important given the systematic failures of even the tetrachromatic version of the RNL model.

* More generally, attempts to decide whether some human observers exhibit tetrachromacy have taught us how hard this is to do. Two issues, beyond the above, are the following. 1) If the properties of a trichromatic visual system vary across the retina, then by imaging stimuli on different parts of the visual field an observer can in principle make tetrachromatic discriminations even though visual system is locally trichromatic at each retinal location. 2) When trying to show that there is no direction in a tetrachromatic receptor space to which the observer is blind, a lot of color directions need to be sampled. Here, 9 directions are studied. Is that enough? How would we know? The following paper may be of interest in this regard: Horiguchi, Hiroshi, Jonathan Winawer, Robert F. Dougherty, and Brian A. Wandell. "Human trichromacy revisited." Proceedings of the National Academy of Sciences 110, no. 3 (2013): E260-E269. Although I'm not suggesting that the authors conduct additional experiments to try to address these points, I do think they need to be discussed.

* Line 277 ff. After reading through the paper several times, I remain unsure about what the authors regard as their compelling evidence that the UV cone has a higher sensitivity or makes an omnibus higher contribution to sensitivity than other cones (as stated in various forms in the title, Lines 37-41, 56-57, 125, 313, 352 and perhaps elsewhere).

At first, I thought they key point was that the receptor noise inferred via the RNL model as slightly lower (0.11) for the UV cone than for the double cones (0.14). And this is the argument made explicitly at line 326 of the discussion. But if this is the argument, what needs to be shown is that the data reject a tetrachromatic version of the RNL model where the noise value of all the cones is locked to be the same (or something similar), with the analysis taking into account the fewer parametric degrees of freedom where the noise parameters are so constrained. That is, a careful model comparison analysis would be needed. Such an analysis is not presented that I see, and I need more convincing that the difference between 0.11 and 0.14 is a real effect driven by the data. Also, I am not sanguine that the parameters of a model that in some systematic ways fails to fit the data should be taken as characterizing properties of the receptors themselves (as sometimes seems to be stated as the conclusion we should draw).

Then, I thought maybe the argument is not that the noise levels differ, but rather that the failures of the model are in the direction of thresholds being under predicted for discriminations that involve UV cone signals. That's what seems to be being argued here at lines 277 ff, and then again at lines 328 ff of the discussion. But then the argument as I read it more detail in both places switches from being about the UV cones per se to being about postive versus negative UV contrast. That's fine, but it's distinct from an argument that favors omnibus enhanced UV sensitivity, since both the UV increments and decrements are conveyed by the UV cone; it's an argument for differential sensitivity for increments versus decrements in UV mediated discriminations. The authors get to this on lines 334 of the discussion, but if the point is an increment/decrement asymmetry the title and many of the terser earlier assertions should be reworked to be consistent with what is shown.

Perhaps the argument with respect to model deviations and UV contrast independent of sign could be elaborated to show more systematically that the way the covariation with the contrasts of the other cone stimulations in the stimulus set goes, the data do favor deviations from the RNL in the direction of enhanced sensitivity to UV cone signals, but if this is the intent I think the authors need to think more about how to present the data in a manner that makes it more compelling than currently, and walk the reader carefully through the argument.

* On this point, if the authors decide to stick with the enhanced UV sensitivity argument in the revision, a bit more care about what is meant by "the UV cone has a comparatively high sensitivity (line 313 and throughout)" needs more unpacking. If it is that these cones have lower inferred noise (in the context of a model that doesn't account for at least some aspects of the data), is this because of properties of the UV cones, or the way that post-receptoral processing handles the signals from these cones mimicking a cone effect in the model. And if it is thought that it is because of properties of the cones, some discussion of what those properties might be would be helpful. As I understand the RNL model, relative numbers of cones of each type are taken into account, so it isn't that. But could it be something as simple as higher photopigment density or larger entrance aperture (thus more quantum catches and higher SNR)?

* Line 288 ff. The fact that the slopes of the psychometric functions differed across color directions is, I think, a failure of the RNL model to describe this aspect of the data, and tells us that a simple summary of what happens for thresholds at delta S = 1 does not generalize across color directions for other performance levels. Since one of the directions where the slope is shallower is the UV direction, this fact would seem to place serious limits on the claim that discrimination in the UV direction is enhanced relative to other directions, but it goes by here without comment along those lines. Some comment here, both about implications for fit of RNL model and about implications for generalizations about efficacy of UV receptor mediated discrimination and UV increment/decrement asymmetries, seems important.

* Line 357 ff. Up until this point, all of the discussion of differences in threshold across stimulus sets has been in terms of sensitivity. Here the authors (correctly) raise the possibility that a difference in "preference" across stimulus sets could drive the difference in thresholds as measured. Although the discussion is interesting and germaine, it does to some extent further undercut the security of conclusions about differential sensitivity across color directions relative to the RNL model predictions, and that should be brought out for the reader here. The authors might also discuss about how a future experiment might differentiate between a preference explanation and a sensitivity explanation of threshold differences.

* RNL model. The paper cites a lot of earlier work that used the RNL model, but I think many readers will not be familiar with it. A bit more descriptive prose would be helpful, and particularly noting that in the full dimensional receptor space, if the limiting noise at the photoreceptors is Gaussian, then the isothreshold contour will be a hyper-ellipsoid with its axes aligned with the receptor directions.

* Use of cone isolating stimuli? For showing that all four cone classes contribute to what the authors call color discrimination, a more direct approach would seem to be to use stimuli that target stimulation of only one class of cone at a time. This might require a modified design in which the distractors and target were shown against a uniform background and approximately matched in their estimated effect on a putative achromatic mechanism. Did the authors consider this approach, and more generally could they discuss what they see as its advantages and disadvantages for future work.

Author Response

Reviewer #1 (Public Review):

Precise regulation of gamete fusion ensures that offspring will have the same ploidy as the parents. However, breaking this regulation can be useful for plant breeding. Haploid induction followed by chemical-induced genome doubling can be used to fix desirable genotypes, while triparental hybrids where two sperm cells with two different genotypes fertilize an egg cell can be advantageous for bypassing hybridization barriers to create interspecies hybrids with increased fitness. This manuscript follows up on a previous study from the same research group that used a clever high throughput polyspermy detection assay (HIPOD) to show that wild-type Arabidopsis naturally forms triparental hybrids at very low frequencies (less than 0.05% of progeny) and that these triparental hybrids can bypass dosage barriers in the endosperm (Nakel, et al., 2017). Mao and co-authors hypothesized that mutants that conferred polytubey, the attraction of multiple pollen tubes by mutant female gametophytes, would also increase the rate of triparental hybrids. They used a double mutant in the endopeptidase genes ECS1 and ECS2 which had previously been reported to induce supernumerary pollen tube attraction to test this hypothesis with their two-component HIPOD system in which one pollen donor constitutively expresses the mGAL4-VP16 transcription factor while the second pollen donor carries an herbicide resistance gene regulated by the GAL4-responsive UAS promoter. Triparental hybrids are detected as herbicide-resistant progeny from wild-type Arabidopsis flowers that have been pollinated by the two paternal genotypes. The authors convincingly show that the ecs1 ecs2-1 double mutant more than doubled the frequency of triparental, triploid hybrids in HIPOD crosses. They next tested the hypothesis that this increase in triparental hybrids was due to a gametophytic effect by using an ecs1-/- ecs2-1/ECS2 maternal parent in the HIPOD assay and testing whether the ecs2-1 mutant allele was preferentially inherited in triparental hybrids. The mutant allele was inherited at a much higher rate than expected, confirming their hypothesis.

The triparental hybrid results with the ecs1 ecs2 mutant were not that surprising since the presence of extra sperm cells gives more opportunities for triparental hybrids to form, especially if gamete fusion is misregulated. However, an unexpected result came when the authors used aniline blue staining to analyze the ecs1 ecs2 polytubey phenotype. They confirmed that the double mutant had increased levels of polytubey compared to wild-type ovules, but they also noticed that 13% of seeds were not developing normally. This phenotype was confirmed with a second ecs2 allele and was complemented with both ECS1 and ECS2 transgenes under their native promoters. Microscopic analysis revealed normal gametophyte morphology before fertilization, but 8% of pollinated ovules failed to develop an embryo and 7% failed to develop endosperm, suggesting single fertilization events. In a logical set of experiments, they followed up on this result by crossing ecs1 ecs2 with pollen carrying a fluorescent reporter that would be expressed in developing embryos and endosperm. In this experiment, they were again surprised. Some of the wild-type-looking seeds lacked a paternal contribution (i.e. no fluorescent signal from the paternal reporter construct) in the embryo. This prompted them to look more closely at the progeny, upon which they detected small plants that were haploid. They confirmed the haploid nature by chromosome spreads. Finally, they used interaccession crosses between ecs1 ecs2 (Col-0) and Landsberg to verify that haploid progeny only carried maternal alleles of markers on all five chromosomes, indicating that the ecs1 ecs2 genotype can induce maternal haploids.

This interesting study highlights the importance of following up on unexpected results. The conclusions are well-supported by the data and quite exciting. Paternal haploid inducers have been discovered in several species, but this is one of only two examples of maternal haploid induction. While the percentage of maternal haploids is very low, this phenomenon could be useful for plant breeding.

Weaknesses

The data in the manuscript is intriguing, but the question of how the same mutant combination promotes the formation of both triploid and haploid progeny remains unanswered and is not thoroughly discussed, nor is any model suggested for how the ECS1/2 peptidases could play a role in regulating gamete fusion and/or repressing parthenogenesis. A second unanswered question is whether the maternal haploids are a result of failed plasmogamy or karyogamy between the egg and sperm leading to parthenogenesis or a result of paternal genome elimination after plasmogamy. In figure 3B, the authors attempted to test whether plasmogamy occurs between the male and female gametes in ecs1 ecs2 ovules by crosses with pollen that expresses a mitochondrial marker under control of the pRPS5a promoter which is active in sperm cells as well as embryos and endosperm of fertilized ovules. This experiment allowed them to detect sperm cells that had not fused with the egg and central cell at 2 days after pollination. They also counted the percentage of seeds that expressed the mitochondrial marker in both embryo and endosperm at 2 DAP and found that ecs1 ecs2 mutants had a 20% reduction of visible mitochondria in embryo sacs compared to wildtype. They conclude that the result indicates a potential plasmogamy defect. However, the dependability of this marker is questionable since only ~55% of wild-type seeds had detectable signal in the embryo and endosperm. The authors imply that this experiment could be used to test plasmogamy, but it is not clear how any conclusions related to the abnormal seed phenotype could be drawn from examining the rate of signal in both the embryo and endosperm. Since the mitochondrial marker was not expressed from a sperm-specific promoter, the fluorescent signal at 2DAP is likely due to new gene expression from pRPS5a in the fertilized embryo and endosperm, not an indication of the presence of sperm-derived mitochondria. Perhaps an earlier timepoint could be used as well as a spermspecific promoter instead of pRPS5a to answer the question of whether plasmogamy is happening in the ecs1 ecs2 ovules.

Thanks for the suggestion. We here provide two additional new data sets to provide evidence that ecs1 ecs2 mutant plants indeed exhibit single fertilization that lead to fertilization recovery.

We determined the fertilization failure by checking the decondensation HTR10-RFP labelled sperm nuclei 8-10 HAP (Figure 3B) and the frequency of heterofertilization through dual pollination experiment (Figure 3C-E) (see above).

Reviewer #2 (Public Review):

The manuscript reports the triploid and haploid productions using an ecs1ecs2 mutant as the maternal donor, in addition to the evaluation of the sexual process observed in the mutant. The indicated data show exquisite quality. To improve the content, I recommend carefully reconsidering the descriptions because some of the insights would cause a stir in the controversy regarding ECS1&2 functions in plant reproduction.

Strengths

Triploid production by a combination of ecs1ecs2 mutant and HIPOD system has potential as a future plant breeding tool. Moreover, it's intriguing that both triploid and haploid productions were achieved using the same mutant as a maternal donor. I think authors can claim the value of their results more by adding descriptions about the usefulness of the aneuploid plants in plant breeding history.

The evidence of the persistent synergid nucleus (Figure 3A) is critical insight reported by this study. As Maruyama et al. (2013) reported by live cell imaging, synergid-endosperm fusion had occurred at the two endosperm nuclei stage. It would be valuable to claim the observed fact by citing Maruyama's previous observation.

Weakness

As the authors suggested, the higher triploid frequency observed in ecs1ecs2 than WT was likely caused by the increased polyspermy. However, it also could be that reduction of normal seed number in ecs1ecs2 (whichever is due to failure of fertilization or embryo development arrest) accounts for the increased frequency of the triploid compared to WT.

The results in Figure 3C-E suggested the single fertilization for both egg and central cells at similar frequencies. This is an exciting result, but it is still possible that the fertilized egg or central cell degenerated after fertilization resulting in the disappearance of paternally inherited fluorescence. Evaluation of fertilization patterns at 7-10HAP in ecs1ecs2 mutant may provide more confident insight, although unfused sperm cell was evaluated at 1DAP (Figure 3-figure supplement 1B). The fertilization states can be distinguished depending on the HTR10RFP sperm nuclei morphology and their positions, as reported by Takahashi et al (2018).

Thank you for your suggestion. We added the requested experiment see Figure 3B in the revised manuscript. In addition, we conducted a dual pollination experiment, that provides evidence for the activation of the fertilization recovery machinery (Figure 3C-E) (see above).

Several recent studies have reported exciting insights on ECS1&2 functions; however, various results from different laboratories have raised controversy. Though, the commonly found feature is the repression of polytubey. For readers, it would be helpful to organize the explanation about which insights are concordant or different.

Thank you for your suggestion. We now indicate using terms like in line with or in contrast to, where our data confirms /or contradicts with previous data.

In addition, a drawing that explains the time course in the process from pollination to seed development (up to 6DAP) based on WT would help to understand which point is evaluated in each data.

Thank you for your suggestion. We added a model figure (Figure 4E) at the end of the manuscript that brings the concepts together and facilitates the understandings.

Reviewer #3 (Public Review):

In this manuscript, Mao et al. reported that the two proteases ECS1 and ECS2 participate in both polyspermy block and gamete fusion in Arabidopsis thaliana. The authors could observe polytubey phenotype which has been reported previously and obtain both triparental plants and haploids in ecs1 ecs2 mutants. Therefore, they proposed that the triparental plants resulted from the polytubey block defect, whereas the haploids were caused by the gamete fusion defect. Together with two other previous reports, I think it is very interesting to see these two proteases participating in so many different but connected processes. Although they did not provide the molecular mechanism of how ECS participated in polyspermy block and gamete fusion, their findings provide more options for and thus promote plant breeding. The work may have a wide application in the future and will be of broad interest to cell biologists working on gamete fusion and plant breeders.

We thank the reviewer for their positive comments.

Although most of the conclusions in this paper are well supported by the data, it could be improved with a minor revision including providing clearer data analysis and descriptions, images with higher resolution, and more discussions.

Author Response

Reviewer #2 (Public Review):

Here, a simple model of cerebellar computation is used to study the dependence of task performance on input type: it is demonstrated that task performance and optimal representations are highly dependent on task and stimulus type. This challenges many standard models which use simple random stimuli and concludes that the granular layer is required to provide a sparse representation. This is a useful contribution to our understanding of cerebellar circuits, though, in common with many models of this type, the neural dynamics and circuit architecture are not very specific to the cerebellum, the model includes the feedforward structure and the high dimension of the granule layer, but little else. This paper has the virtue of including tasks that are more realistic, but by the paper’s own admission, the same model can be applied to the electrosensory lateral line lobe and it could, though it is not mentioned in the paper, be applied to the dentate gyrus and large pyramidal cells of CA3. The discussion does not include specific elements related to, for example, the dynamics of the Purkinje cells or the role of Golgi cells, and, in a way, the demonstration that the model can encompass different tasks and stimuli types is an indication of how abstract the model is. Nonetheless, it is useful and interesting to see a generalization of what has become a standard paradigm for discussing cerebellar function.

We appreciate the Reviewer’s positive comments. Regarding the simplifications of our model, we agree that we have taken a modeling approach that abstracts away certain details to permit comparisons across systems. We now include an in-depth discussion of our simplifying assumptions (Assumptions & Extensions section in the Discussion) and have further noted the possibility that other biophysical mechanisms we have not accounted for may also underlie differences across systems.

Our results predict that qualitative differences in the coding levels of cerebellum-like systems, across brain regions or across species, reflect an optimization to distinct tasks (Figure 7). However, it is also possible that differences in coding level arise from other physiological differences between systems.

Reviewer #3 (Public Review):

1) The paper by Xie et al is a modelling study of the mossy fiber-to-granule cell-to-Purkinje cell network, reporting that the optimal type of representations in the cerebellar granule cell layer depends on the type task. The paper stresses that the findings indicate a higher overall bias towards dense representations than stated in the literature, but it appears the authors have missed parts of the literature that already reported on this. While the modelling and analysis appear mathematically solid, the model is lacking many known constraints of the cerebellar circuitry, which makes the applicability of the findings to the biological counterpart somewhat limited.

We thank the Reviewer for suggesting additional references to include in our manuscript, and for encouraging us to extend our model toward greater biological plausibility and more critically discuss simplifying assumptions we have made. We respond to both the comment about previous literature and about applicability to cerebellar circuitry in detail below.

2) I have some concerns with the novelty of the main conclusion, here from the abstract: ’Here, we generalize theories of cerebellar learning to determine the optimal granule cell representation for tasks beyond random stimulus discrimination, including continuous input-output transformations as required for smooth motor control. We show that for such tasks, the optimal granule cell representation is substantially denser than predicted by classic theories.’ Stated like this, this has in principle already been shown, i.e. for example: Spanne and Jo¨rntell (2013) Processing of multi-dimensional sensorimotor information in the spinal and cerebellar neuronal circuitry: a new hypothesis. PLoS Comput Biol. 9(3):e1002979. Indeed, even the 2 DoF arm movement control that is used in the present paper as an application, was used in this previous paper, with similar conclusions with respect to the advantage of continuous input-output transformations and dense coding. Thus, already from the beginning of this paper, the novelty aspect of this paper is questionable. Even the conclusion in the last paragraph of the Introduction: ‘We show that, when learning input-output mappings for motor control tasks, the optimal granule cell representation is much denser than predicted by previous analyses.’ was in principle already shown by this previous paper.

We thank the Reviewer for drawing our attention to Spanne and Jo¨rntell (2013). Our study shares certain similarities with this work, including the consideration of tasks with smooth input-output mappings, such as learning the dynamics of a two-joint arm. However, our study differs substantially, most notably the fact that we focus our study on parametrically varying the degree of sparsity in the granule cell layer to determine the circumstances under which dense versus sparse coding is optimal. To the best of our ability, we can find no result in Spanne and J¨orntell (2013) that indicates the performance of a network as a function of average coding level. Instead, Spanne and Jo¨rntell (2013) propose that inhibition from Golgi cells produces heterogeneity in coding level which can improve performance, which is an interesting but complementary finding to ours. We therefore do not believe that the quantitative computations of optimal coding level that we present are redundant with the results of this previous study. We also note that a key contribution of our study is mathemetical analysis of the inductive bias of networks with different coding levels which supports our conclusions.

We have included a discussion of Spanne and Jo¨rntell (2013) and (2015) in the revised version of our manuscript:

"Other studies have considered tasks with smooth input-output mappings and low-dimensional inputs, finding that heterogeneous Golgi cell inhibition can improve performance by diversifying individual granule cell thresholds (Spanne and J¨orntell, 2013). Extending our model to include heterogeneous thresholds is an interesting direction for future work. Another proposal states that dense coding may improve generalization (Spanne and Jo¨rntell, 2015). Our theory reveals that whether or not dense coding is beneficial depends on the task."

3) However, the present paper does add several more specific investigations/characterizations that were not previously explored. Many of the main figures report interesting new model results. However, the model is implemented in a highly generic fashion. Consequently, the model relates better to general neural network theory than to specific interpretations of the function of the cerebellar neuronal circuitry. One good example is the findings reported in Figure 2. These represent an interesting extension to the main conclusion, but they are also partly based on arbitrariness as the type of mossy fiber input described in the random categorization task has not been observed in the mammalian cerebellum under behavior in vivo, whereas in contrast, the type of input for the motor control task does resemble mossy fiber input recorded under behavior (van Kan et al 1993).

We agree that the tasks we consider in Figure 2 are simplified compared to those that we consider elsewhere in the paper. The choice of random mossy fiber input was made to provide a comparison to previous modeling studies that also use random input as a benchmark (Marr 1969, Albus 1971, Brunel 2004, Babadi and Sompolinsky 2014, Billings 2014, LitwinKumar et al., 2017). This baseline permits us to specifically evaluate the effects of lowdimensional inputs (Figure 2) and richer input-output mappings (Figure 2, Figure 7). We agree with the Reviewer that the random and uncorrelated mossy fiber activity that has been extensively used in previous studies is almost certainly an unrealistic idealization of in vivo neural activity—this is a motivating factor for our study, which relaxes this assumption and examines the consequences. To provide additional context, we have updated the following paragraph in the main text Results section:

"A typical assumption in computational theories of the cerebellar cortex is that inputs are randomly distributed in a high-dimensional space (Marr, 1969; Albus, 1971; Brunel et al., 2004; Babadi and Sompolinsky, 2014; Billings et al., 2014; Litwin-Kumar et al., 2017). While this may be a reasonable simplification in some cases, many tasks, including cerebellumdependent tasks, are likely best-described as being encoded by a low-dimensional set of variables. For example, the cerebellum is often hypothesized to learn a forward model for motor control (Wolpert et al., 1998), which uses sensory input and motor efference to predict an effector’s future state. Mossy fiber activity recorded in monkeys correlates with position and velocity during natural movement (van Kan et al., 1993). Sources of motor efference copies include motor cortex, whose population activity lies on a lowdimensional manifold (Wagner et al., 2019; Huang et al., 2013; Churchland et al., 2010; Yu et al., 2009). We begin by modeling the low dimensionality of inputs and later consider more specific tasks."

4) The overall conclusion states: ‘Our results....suggest that optimal cerebellar representations are task-dependent.’ This is not a particularly strong or specific conclusion. One could interpret this statement as simply saying: ‘if I construct an arbitrary neural network, with arbitrary intrinsic properties in neurons and synapses, I can get outputs that depend on the intensity of the input that I provide to that network.’ Further, the last sentence of the Introduction states: ‘More broadly, we show that the sparsity of a neural code has a task-dependent influence on learning...’ This is very general and unspecific, and would likely not come as a surprise to anyone interested in the analysis of neural networks. It doesn’t pinpoint any specific biological problem but just says that if I change the density of the input to a [generic] network, then the learning will be impacted in one way or another.

We agree with the Reviewer that our conclusions are quite general, and we have removed the final sentence as we agree it was unspecific. However, we disagree with the Reviewer’s paraphrasing of our results.

First, we do not select arbitrary intrinsic properties of neurons and synapses. Rather, we construct a simplified model with a key quantity, the neuronal threshold, that we vary parametrically in order to assess the effect of the resulting changes in the representation on performance. Second, we do not vary the intensity/density of inputs provided to the network – this is fixed throughout our study for all key comparisons we perform. Instead, we vary the density (coding level) of the expansion layer representation and quantify its effect on inductive bias and generalization. Finally, our study’s key contribution is an explanation of the heterogeneity in average coding level observed across behaviors and cerebellum-like systems. We go beyond the empirical statement that there is a dependence of performance on the parameter that we vary by developing an analytical theory. Our theory describes the performance of the class of networks that we study and the properties of learning tasks that determine the optimal expansion layer representation.

To clarify our main contributions, we have updated the final paragraph of the Introduction. We have also removed the sentence that the Reviewer objects to, as it was less specific than the other points we make here.

"We propose that these differences can be explained by the capacity of representations with different levels of sparsity to support learning of different tasks. We show that the optimal level of sparsity depends on the structure of the input-output relationship of a task. When learning input-output mappings for motor control tasks, the optimal granule cell representation is much denser than predicted by previous analyses. To explain this result, we develop an analytic theory that predicts the performance of cerebellum-like circuits for arbitrary learning tasks. The theory describes how properties of cerebellar architecture and activity control these networks’ inductive bias: the tendency of a network toward learning particular types of input-output mappings (Sollich, 1998; Jacot et al., 2018; Bordelon et al., 2020; Canatar et al., 2021; Simon et al., 2021). The theory shows that inductive bias, rather than the dimension of the representation alone, is necessary to explain learning performance across tasks. It also suggests that cerebellar regions specialized for different functions may adjust the sparsity of their granule cell representations depending on the task."

5) The interpretation of the distribution of the mossy fiber inputs to the granule cells, which would have a crucial impact on the results of a study like this, is likely incorrect. First, unlike the papers that the authors cite, there are many studies indicating that there is a topographic organization in the mossy fiber termination, such that mossy fibers from the same inputs, representing similar types of information, are regionally co-localized in the granule cell layer. Hence, there is no support for the model assumption that there is a predominantly random termination of mossy fibers of different origins. This risks invalidating the comparisons that the authors are making, i.e. such as in Figure 3. This is a list of example papers, there are more: van Kan, Gibson and Houk (1993) Movement-related inputs to intermediate cerebellum of the monkey. Journal of Neurophysiology. Garwicz et al (1998) Cutaneous receptive fields and topography of mossy fibres and climbing fibres projecting to cat cerebellar C3 zone. The Journal of Physiology. Brown and Bower (2001) Congruence of mossy fiber and climbing fiber tactile projections in the lateral hemispheres of the rat cerebellum. The Journal of Comparative Neurology. Na, Sugihara, Shinoda (2019) The entire trajectories of single pontocerebellar axons and their lobular and longitudinal terminal distribution patterns in multiple aldolase C-positive compartments of the rat cerebellar cortex. The Journal of Comparative Neurology.

6) The nature of the mossy fiber-granule cell recording is also reviewed here: Gilbert and Miall (2022) How and Why the Cerebellum Recodes Input Signals: An Alternative to Machine Learning. The Neuroscientist. Further, considering the re-coding idea, the following paper shows that detailed information, as it is provided by mossy fibers, is transmitted through the granule cells without any evidence of re-coding: Jo¨rntell and Ekerot (2006) Journal of Neuroscience; and this paper shows that these granule inputs are powerfully transmitted to the molecular layer even in a decerebrated animal (i.e. where only the ascending sensory pathways remains) Jo¨rntell and Ekerot 2002, Neuron.

We agree that there is strong evidence for a topographic organization in mossy fiber to granule cell connectivity at the microzonal level. We thank the Reviewer for pointing us to specific examples. We acknowledge that our simplified model does not capture the structure of connectivity observed in these studies.

However, the focus of our model is on cerebellar neurons presynaptic to a single Purkinje cell. Random or disordered distribution of inputs at this local scale is compatible with topographic organization at the microzonal scale. Furthermore, while there is evidence of structured connections at the local scale, models with random connectivity are able to reproduce the dimensionality of granule cell activity within a small margin of error (Nguyen et al., 2022). Finally, our finding that dense codes are optimal for learning slowly varying tasks is consistent with evidence for the lack of re-coding – for such tasks, re-coding may absent because it is not required.

We have dedicated a section on this issue in the Assumptions and Extensions portion of our Discussion:

"Another key assumption concerning the granule cells is that they sample mossy fiber inputs randomly, as is typically assumed in Marr-Albus models (Marr, 1969; Albus, 1971; LitwinKumar et al., 2017; Cayco-Gajic et al., 2017). Other studies instead argue that granule cells sample from mossy fibers with highly similar receptive fields (Garwicz et al., 1998; Brown and Bower, 2001; J¨orntell and Ekerot, 2006) defined by the tuning of mossy fiber and climbing fiber inputs to cerebellar microzones (Apps et al., 2018). This has led to an alternative hypothesis that granule cells serve to relay similarly tuned mossy fiber inputs and enhance their signal-to-noise ratio (Jo¨rntell and Ekerot, 2006; Gilbert and Chris Miall, 2022) rather than to re-encode inputs. Another hypothesis is that granule cells enable Purkinje cells to learn piece-wise linear approximations of nonlinear functions (Spanne and J¨orntell, 2013). However, several recent studies support the existence of heterogeneous connectivity and selectivity of granule cells to multiple distinct inputs at the local scale (Huang et al., 2013; Ishikawa et al., 2015). Furthermore, the deviation of the predicted dimension in models constrained by electron-microscopy data as compared to randomly wired models is modest (Nguyen et al., 2022). Thus, topographically organized connectivity at the macroscopic scale may coexist with disordered connectivity at the local scale, allowing granule cells presynaptic to an individual Purkinje cell to sample heterogeneous combinations of the subset of sensorimotor signals relevant to the tasks that Purkinje cell participates in. Finally, we note that the optimality of dense codes for learning slowly varying tasks in our theory suggests that observations of a lack of mixing (J¨orntell and Ekerot, 2002) for such tasks are compatible with Marr-Albus models, as in this case nonlinear mixing is not required."

7) I could not find any description of the neuron model used in this paper, so I assume that the neurons are just modelled as linear summators with a threshold (in fact, Figure 5 mentions inhibition, but this appears to be just one big lump inhibition, which basically is an incorrect implementation). In reality, granule cells of course do have specific properties that can impact the input-output transformation, PARTICULARLY with respect to the comparison of sparse versus dense coding, because the low-pass filtering of input that occurs in granule cells (and other neurons) as well as their spike firing stochasticity (Saarinen et al (2008). Stochastic differential equation model for cerebellar granule cell excitability. PLoS Comput. Biol. 4:e1000004) will profoundly complicate these comparisons and make them less straight forward than what is portrayed in this paper. There are also several other factors that would be present in the biological setting but are lacking here, which makes it doubtful how much information in relation to the biological performance that this modelling study provides: What are the types of activity patterns of the inputs? What are the learning rules? What is the topography? What is the impact of Purkinje cell outputs downstream, as the Purkinje cell output does not have any direct action, it acts on the deep cerebellar nuclear neurons, which in turn act on a complex sensorimotor circuitry to exert their effect, hence predictive coding could only become interpretable after the PC output has been added to the activity in those circuits. Where is the differentiated Golgi cell inhibition?

Thank you for these critiques. We have made numerous edits to improve the presentation of the details of our model in the main text of the manuscript. Indeed, granule cells in the main text are modeled as linear sums of mossy fiber inputs with a threshold-linear activation function. A more detailed description of the model for granule cells can now be found in Equation 1 in the Results section:

"The activity of neurons in the expansion layer is given by: h = φ(Jeffx − θ), (1) where φ is a rectified linear activation function φ(u) = max(u,0) applied element-wise. Our results also hold for other threshold-polynomial activation functions. The scalar threshold θ is shared across neurons and controls the coding level, which we denote by f, defined as the average fraction of neurons in the expansion layer that are active."

Most of our analyses use the firing rate model we describe above, but several Supplemental Figures show extensions to this model. As we mention in the Discussion, our results do not depend on the specific choice of nonlinearity (Figure 2-figure supplement 2). We have also considered the possibility that the stochastic nature of granule cell spikes could impact our measures of coding level. In Figure 7-figure supplement 1 we test the robustness of our main conclusion using a spiking model where we model granule cell spikes with Poisson statistics. When measuring coding level in a population of spiking neurons, a key question is at what time window the Purkinje cell integrates spikes. For several choices of integration time windows, we show that dense coding remains optimal for learning smooth tasks. However, we agree with the Reviewer that there are other biological details our model does not address. For example, our spiking model does not capture some of the properties the Saarinen et al. (2008) model captures, including random sub-threshold oscillations and clusters of spikes. Modeling biophysical phenomena at this scale is beyond the scope of our study. We have added this reference to the relevant section of the Discussion:

"We also note that coding level is most easily defined when neurons are modeled as rate, rather than spiking units. To investigate the consistency of our results under a spiking code, we implemented a model in which granule cell spiking exhibits Poisson variability and quantify coding level as the fraction of neurons that have nonzero spike counts (Figure 7-figure supplement 1; Figure 7C). In general, increased spike count leads to improved performance as noise associated with spiking variability is reduced. Granule cells have been shown to exhibit reliable burst responses to mossy fiber stimulation (Chadderton et al., 2004), motivating models using deterministic responses or sub-Poisson spiking variability. However, further work is needed to quantitatively compare variability in model and experiment and to account for more complex biophysical properties of granule cells (Saarinen et al., 2008)."

A second concern the Reviewer raises is our implementation of Golgi cell inhibition as a homogeneous rather than heterogeneous input onto granule cells. In simplified models, adding heterogeneous inhibition does not dramatically change the qualitative properties of the expansion layer representation, in particular the dimensionality of the representation (Billings et al., 2014, Cayco-Gajic et al., 2017, Litwin-Kumar et al., 2017). We have added a section about inhibition to our Discussion:

"We also have not explicitly modeled inhibitory input provided by Golgi cells, instead assuming such input can be modeled as a change in effective threshold, as in previous studies (Billings et al., 2014; Cayco-Gajic et al., 2017; Litwin-Kumar et al., 2017). This is appropriate when considering the dimension of the granule cell representation (Litwin-Kumar et al., 2017), but more work is needed to extend our model to the case of heterogeneous inhibition."

Regarding the mossy fiber inputs, as we state in response to paragraph 3, we agree with the Reviewer that the random and uncorrelated mossy fiber activity that has been used in previous studies is an unrealistic idealization of in vivo neural activity. One of the motivations for our model was to relax this assumption and examine the consequences: we introduce correlations in the mossy fiber activity by projecting low-dimensional patterns into the mossy fiber layer (Figure 1B):

"A typical assumption in computational theories of the cerebellar cortex is that inputs are randomly distributed in a high-dimensional space (Marr, 1969; Albus, 1971; Brunel et al., 2004; Babadi and Sompolinsky, 2014; Billings et al., 2014; Litwin-Kumar et al., 2017). While this may be a reasonable simplification in some cases, many tasks, including cerebellumdependent tasks, are likely best-described as being encoded by a low-dimensional set of variables. For example, the cerebellum is often hypothesized to learn a forward model for motor control (Wolpert et al., 1998), which uses sensory input and motor efference to predict an effector’s future state. Mossy fiber activity recorded in monkeys correlates with position and velocity during natural movement (van Kan et al., 1993). Sources of motor efference copies include motor cortex, whose population activity lies on a low-dimensional manifold (Wagner et al., 2019; Huang et al., 2013; Churchland et al., 2010; Yu et al., 2009). We begin by modeling the low dimensionality of inputs and later consider more specific tasks.

We therefore assume that the inputs to our model lie on a D-dimensional subspace embedded in the N-dimensional input space, where D is typically much smaller than N (Figure 1B). We refer to this subspace as the “task subspace” (Figure 1C)."

The Reviewer also mentions the learning rule at granule cell to Purkinje cell synapses. We agree that considering online, climbing-fiber-dependent learning is an important generalization. We therefore added a new supplemental figure investigating whether we would still see a difference in optimal coding levels across tasks if online learning were used instead of the least squares solution (Figure 7-figure supplement 2). Indeed, we observed a similar task dependence as we saw in Figure 2F. We have added a new paragraph in the Discussion under Assumptions and Extensions describing our rationale and approach in detail:

"For the Purkinje cells, our model assumes that their responses to granule cell input can be modeled as an optimal linear readout. Our model therefore provides an upper bound to linear readout performance, a standard benchmark for the quality of a neural representation that does not require assumptions on the nature of climbing fiber-mediated plasticity, which is still debated. Electrophysiological studies have argued in favor of a linear approximation (Brunel et al., 2004). To improve the biological applicability of our model, we implemented an online climbing fiber-mediated learning rule and found that optimal coding levels are still task-dependent (Figure 7-figure supplement 2). We also note that although we model several timing-dependent tasks (Figure 7), our learning rule does not exploit temporal information, and we assume that temporal dynamics of granule cell responses are largely inherited from mossy fibers. Integrating temporal information into our model is an interesting direction for future investigation."

Finally, regarding the function of the Purkinje cell, our model defines a learning task as a mapping from inputs to target activity in the Purkinje cell and is thus agnostic to the cell’s downstream effects. We clarify this point when introducing the definition of a learning task:

"In our model, a learning task is defined by a mapping from task variables x to an output f(x), representing a target change in activity of a readout neuron, for example a Purkinje cell. The limited scope of this definition implies our results should not strongly depend on the influence of the readout neuron on downstream circuits."

8) The problem of these, in my impression, generic, arbitrary settings of the neurons and the network in the model becomes obvious here: ‘In contrast to the dense activity in cerebellar granule cells, odor responses in Kenyon cells, the analogs of granule cells in the Drosophila mushroom body, are sparse...’ How can this system be interpreted as an analogy to granule cells in the mammalian cerebellum when the model does not address the specifics lined up above? I.e. the ‘inductive bias’ that the authors speak of, defined as ‘the tendency of a network toward learning particular types of input-output mappings’, would be highly dependent on the specifics of the network model.

We agree with the Reviewer that our model makes several simplifying assumptions for mathematical tractability. However, we note that our study is not the first to draw analogies between cerebellum-like systems, including the mushroom body (Bell et al., 2008; Farris, 2011). All the systems we study feature a sparsely connected, expanded granule-like layer that sends parallel fiber axons onto densely connected downstream neurons known to exhibit powerful synaptic plasticity, thus motivating the key architectural assumptions of our model. We have constrained anatomical parameters of the model using data as available (Table 1). However, we agree with the Reviewer that when making comparisons across species there is always a possibility that differences are due to physiological mechanisms we have not fully understood or captured with a model. As such, we can only present a hypothesis for these differences. We have modified our Discussion section on this topic to clearly state this.

"Our results predict that qualitative differences in the coding levels of cerebellum-like systems, across brain regions or across species, reflect an optimization to distinct tasks (Figure 7). However, it is also possible that differences in coding level arise from other physiological differences between systems."

9) More detailed comments: Abstract: ‘In these models [Marr-Albus], granule cells form a sparse, combinatorial encoding of diverse sensorimotor inputs. Such sparse representations are optimal for learning to discriminate random stimuli.’ Yes, I would agree with the first part, but I contest the second part of this statement. I think what is true for sparse coding is that the learning of random stimuli will be faster, as in a perceptron, but not necessarily better. As the sparsification essentially removes information, it could be argued that the quality of the learning is poorer. So from that perspective, it is not optimal. The authors need to specify from what perspective they consider sparse representations optimal for learning.

This is an important point that we would like to clarify. It is not the case that sparse coding simply speeds up learning. In our study and many related works (Barak et al. 2013; Babadi and Sompolinsky 2014; Litwin-Kumar et al. 2017), learning performance is measured based on the generalization ability of the network – the ability to predict correct labels for previously unseen inputs. As our study and previous studies show, sparse codes are optimal in the sense that they minimize generalization error, independent of any effect on learning speed. To communicate this more effectively, we have added the following sentence to the first paragraph of the Introduction:

"Sparsity affects both learning speed (Cayco-Gajic et al., 2017), and generalization, the ability to predict correct labels for previously unseen inputs (Barak et al., 2013; Babadi and Sompolinsky, 2014; Litwin-Kumar et al., 2017)."

10) Introduction: ‘Indeed, several recent studies have reported dense activity in cerebellar granule cells in response to sensory stimulation or during motor control tasks (Knogler et al., 2017; Wagner et al., 2017; Giovannucci et al., 2017; Badura and De Zeeuw, 2017; Wagner et al., 2019), at odds with classic theories (Marr, 1969; Albus, 1971).’ In fact, this was precisely the issue that was addressed already by Jo¨rntell and Ekerot (2006) Journal of Neuroscience. The conclusion was that these actual recordings of granule cells in vivo provided essentially no support for the assumptions in the Marr-Albus theories.

In our reading, the main finding of J¨orntell and Ekerot (2006) is that individual granule cells are activated by mossy fibers with overlapping receptive fields driven by a single type of somatosensory input. However, there is also evidence of nonlinear mixed selectivity in granule cells in support of the re-coding hypothesis (Huang et al., 2013; Ishikawa et al., 2015). Jo¨rntell and Ekerot (2006) also suggest that the granule cell layer shares similar topographic organization as mossy fibers, organized into microzones. The existence of topographic organization does not invalidate Marr-Albus theories. As we have suggested earlier, a local combinatorial expansion can coexist with a global topographic organization.

We have described these considerations in the Assumptions and Extensions portion of the Discussion:

"Another key assumption concerning the granule cells is that they sample mossy fiber inputs randomly, as is typically assumed in Marr-Albus models (Marr, 1969; Albus, 1971; LitwinKumar et al., 2017; Cayco-Gajic et al., 2017). Other studies instead argue that granule cells sample from mossy fibers with highly similar receptive fields (Garwicz et al., 1998; Brown and Bower, 2001; J¨orntell and Ekerot, 2006) defined by the tuning of mossy fiber and climbing fiber inputs to cerebellar microzones (Apps et al., 2018). This has led to an alternative hypothesis that granule cells serve to relay similarly tuned mossy fiber inputs and enhance their signal-to-noise ratio (Jo¨rntell and Ekerot, 2006; Gilbert and Chris Miall, 2022) rather than to re-encode inputs. Another hypothesis is that granule cells enable Purkinje cells to learn piece-wise linear approximations of nonlinear functions (Spanne and J¨orntell, 2013). However, several recent studies support the existence of heterogeneous connectivity and selectivity of granule cells to multiple distinct inputs at the local scale (Huang et al., 2013; Ishikawa et al., 2015). Furthermore, the deviation of the predicted dimension in models constrained by electron-microscopy data as compared to randomly wired models is modest (Nguyen et al., 2022). Thus, topographically organized connectivity at the macroscopic scale may coexist with disordered connectivity at the local scale, allowing granule cells presynaptic to an individual Purkinje cell to sample heterogeneous combinations of the subset of sensorimotor signals relevant to the tasks that Purkinje cell participates in. Finally, we note that the optimality of dense codes for learning slowly varying tasks in our theory suggests that observations of a lack of mixing (J¨orntell and Ekerot, 2002) for such tasks are compatible with Marr-Albus models, as in this case nonlinear mixing is not required."

We have also included the Jo¨rntell and Ekerot (2006) study as a citation in the Introduction:

"Indeed, several recent studies have reported dense activity in cerebellar granule cells in response to sensory stimulation or during motor control tasks (Jo¨rntell and Ekerot, 2006; Knogler et al., 2017; Wagner et al., 2017; Giovannucci et al., 2017; Badura and De Zeeuw, 2017; Wagner et al., 2019), at odds with classic theories (Marr, 1969; Albus, 1971)."

11) Results: 1st para: There is no information about how the granule cells are modelled.

We agree that this should information should have been more readily available. We now more completely describe the model in the main text. Our model for granule cells can be found in Equation 1 in the Results section and also the Methods (Network Model):

"The activity of neurons in the expansion layer is given by: h = φ(Jeffx − θ), (2)

where φ is a rectified linear activation function φ(u) = max(u,0) applied element-wise. Our results also hold for other threshold-polynomial activation functions. The scalar threshold θ is shared across neurons and controls the coding level, which we denote by f, defined as the average fraction of neurons in the expansion layer that are active."

12) 2nd para: ‘A typical assumption in computational theories of the cerebellar cortex is that inputs are randomly distributed in a high-dimensional space.’ Yes, I agree, and this is in fact in conflict with the known topographical organization in the cerebellar cortex (see broader comment above). Mossy fiber inputs coding for closely related inputs are co-localized in the cerebellar cortex. I think for this model to be of interest from the point of view of the mammalian cerebellar cortex, it would need to pay more attention to this organizational feature.

As we discuss in our response to paragraphs 5 and 6, we see the random distribution assumption at the local scale (inputs presynaptic to a single Purkinje cell) as being compatible with topographic organization occurring at the microzone scale. Furthermore, as discussed earlier, we specifically model low-dimensional input as opposed to the random and high-dimensional inputs typically studied in prior models.

"A typical assumption in computational theories of the cerebellar cortex is that inputs are randomly distributed in a high-dimensional space (Marr, 1969; Albus, 1971; Brunel et al., 2004; Babadi and Sompolinsky, 2014; Billings et al., 2014; Litwin-Kumar et al., 2017). While this may be a reasonable simplification in some cases, many tasks, including cerebellumdependent tasks, are likely best-described as being encoded by a low-dimensional set of variables. For example, the cerebellum is often hypothesized to learn a forward model for motor control (Wolpert et al., 1998), which uses sensory input and motor efference to predict an effector’s future state. Mossy fiber activity recorded in monkeys correlates with position and velocity during natural movement (van Kan et al., 1993). Sources of motor efference copies include motor cortex, whose population activity lies on a low-dimensional manifold (Wagner et al., 2019; Huang et al., 2013; Churchland et al., 2010; Yu et al., 2009). We begin by modeling the low dimensionality of inputs and later consider more specific tasks. We therefore assume that the inputs to our model lie on a D-dimensional subspace embedded in the N-dimensional input space, where D is typically much smaller than N (Figure 1B). We refer to this subspace as the “task subspace” (Figure 1C)."

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Author Response

Reviewer #1 (Public Review):

In this manuscript, Huang et al., assess cognitive flexibility in rats trained on an animal model of anorexia nervosa known as activity-based anorexia (ABA). For the first time, they do this in a way that is fully automated and free from experimenter interference, as apparently experimenter interference can affect both the development of ABA as well as the effect on behaviour. They show that animals that are more cognitively flexible (i.e. animals that had received reversal training) were better able to resist weight loss upon exposure to ABA, whereas animals exposed to ABA first show poorer cognitive flexibility (reversal performance).

Strengths:

• The development of a fully-automated, experimenter-free behavioural assessment paradigm that is capable of identifying individual rats and therefore tracking their performance.

• The bidirectional nature of the study - i.e. the fact that animals were tested for cognitive flexibility both before and after exposure to ABA, so that direction of causality could be established.

• The analyses are rigorous and the sample sizes sufficient.

• The use of touchscreens increases the translational potential of the findings.

Weaknesses

• Some descriptions of methods and results are confusing or insufficiently detailed.

We have been through all methods and results to include additional details as requested by this reviewer below.

It seems to me that performance on the pairwise discrimination task cannot be directly (statistically) compared to performance on reversal (as in Figure 4E), as these are tapping into fundamentally different cognitive processes (discrimination versus reversal learning). I think comparing groups on each assessment is valid, however.

We agree that discrimination and reversal are different cognitive processes, and statistical comparisons between these two components of the task were only made when examining the speed of learning in the validation of the novel testing system. Moreover, our inclusion of the pink and purple bars on graphs such as Figure 4C & 4E represent “main effects of ABA exposure”, regardless of learning phase (PD or reversal) rather than, as you describe, comparing PD to R1. Perhaps this comparison wasn’t clear, so we have amended the text to say ‘main effect of ABA exposure p=.0017’ rather than just “exposure”.

Not necessarily a 'weakness' but I would have loved to see some assessment of the alterations in neural mechanisms underlying these effects, and/or some different behavioural assessments in addition to those used here. In particular, the authors mention in the discussion that this manipulation can affect cholinergic functioning in the dorsal striatum We (Bradfield et al., Neuron, 2013) and a number of others have now demonstrated that cholinergic dysfunction in the dorsomedial striatum impairs a different kind of reversal learning that based on alterations in outcome identity and thus relies on a different cognitive process (i.e. 'state' rather than 'reward' prediction error). It would be interesting perhaps in the future to see if the ABA manipulation also alters performance on this alternative 'cognitive flexibility' task.

This is an excellent suggestion and we have already begun exploring this in other ongoing work in the laboratory. Due to ‘compulsive’ wheel running being a hallmark of ABA, we are interested in determining if this also translates to a goal-directed action impairment using the well-established outcome-specific devaluation task. Perhaps with ABA it may be more relevant to investigate outcome-reversals rather than stimulus-reversals, and if this is the case, it would further support the use of the ABA model for investigating cognitive dysfunction relevant to AN. We have included an additional section in the discussion text relating to our hypotheses regarding outcome-specific reversal learning in the ABA model.

Nevertheless, I certainly think the manuscript provides a solid appraisal of cognitive flexibility using more traditional tasks, and that the authors have achieved their aims. I think the work here will be of importance, certainly to other researchers using the ABA model, but perhaps also of translational importance in the future, as the causal relationship between ABA and cognitive inflexibility is near impossible to establish using human studies, but here evidence points strongly towards this being the case.

Reviewer #2 (Public Review):

Huang and colleagues present data from experiments assessing the role of cognitive inflexibility in the vulnerability to weight loss in the activity-based anorexia paradigm in rats. The experiments employ a novel in-home cage touchscreen system. The home cage touch screen system allows reduced testing time and increased throughput compared with the more widely used systems resulting in the ability to assess ABA following testing cognitive flexibility in relatively young female rats. The data demonstrate that, contrary to expectations, cognitive inflexibility does not predispose to greater ABA weight loss, but instead, rats that performed better in the reversal learning task lost more weight in the ABA paradigm. Prior ABA exposure resulted in poorer learning of the task and reversal. An additional experiment demonstrated that rats that had been trained in reversal learning resisted weight loss in the ABA paradigm. The findings are important and are clearly presented. They have implications for anorexia nervosa both in terms of potentially identifying those at risk also in understanding the high rates of relapse.

Thanks for a great summary of the manuscript.

Reviewer #3 (Public Review):

Activity-based anorexia (ABA), which combines access to a running wheel and restricted access to food, is a most common paradigm used to study anorexic behavior in rodents. And yet, the field has been plagued by persistent questions about its validity as a model of anorexia nervosa (AN) in humans. This group's previous studies supported the idea that the ABA paradigm captures cognitive inflexibility seen in AN. Here they describe a fully automated touchscreen cognitive testing system for rats that makes it possible to ask whether cognitive inflexibility predisposes individuals to severe weight loss in the ABA paradigm. They observed that cognitive inflexibility was predictive of resistance to weight loss in the ABA, the opposite of what was predicted. They also reported reciprocal effects of ABA and cognitive testing on subsequent performance in the other paradigm. Prior exposure to the ABA decreased subsequent cognitive performance, while prior exposure to the cognitive task promoted resistance to the ABA. Based on these findings, the authors argue that the ABA model can be used to identify novel therapeutic targets for AN.

The strength of this manuscript is primarily as a methods paper describing a novel automated cognitive behavioral testing system that obviates the need for experimentalist handling and single housing, which can interfere with behavioral testing, and accelerate learning on the task. Together, these features make it feasible to perform longitudinal studies to ask whether cognitive performance is predictive of behavior in a second paradigm during adolescence, a peak period of vulnerability for many psychiatric disorders. The authors also used machine learning tools to identify specific behaviors during the cognitive task that predicted later susceptibility to the ABA paradigm. While the benefits of this system are clear, the rigor and reproducibility of experiments using this paradigm would be enhanced if the authors provided clear guidelines about which parameters and analyses are most useful. In their absence, the large amount of data generated can promote p-hacking.

The authors use their automated behavioral testing paradigm to ask whether cognitive inflexibility is a cause or consequence of susceptibility to ABA, an issue that cannot be addressed in AN. They provide compelling evidence that there are reciprocal effects of the two behavioral paradigms, but do not perform the controls needed to evaluate the significance of these observations. For example, the learning task involves sucrose consumption and food restriction, conditions that can independently affect susceptibility to the ABA. Similarly, the ABA paradigm involves exercise and restricted access to food, which can both affect learning.

In the Discussion, the authors hypothesize that the ABA paradigm produces cognitive inflexibility and argue that uncovering the underlying mechanism can be used to identify new therapeutic targets for AN. The rationale for their claim of translational relevance is undermined by the fact that the biggest effect of the ABA paradigm is seen in the pair discrimination task, and not reversal learning. This pattern does not fit clinical observations in AN.

In summary, the significance of this manuscript lies in the development of a new system to test cognitive function in rats that can be combined with other paradigms to explore questions of causality. While the authors clearly demonstrate that cognitive flexibility does not promote susceptibility to ABA, the experiments presented do not provide a compelling case that their model captures important features of the pathophysiology of AN.

We thank the reviewer for this detailed review and note that we have now both explicitly defined the most useful parameters for analyses from the novel touchscreen system as well as removed some comparisons that could be considered superfluous. We argue that the additional information provided by the machine learning analyses are, at this stage, exploratory, and rather than reveal independent descriptions of behavioural change in ABA exposed versus naïve rats this information will aid in the generation of hypotheses to be tested in future studies. Therefore, the figures pertaining to these analyses have now been provided as supplements to Figures 3 & 4 (Figure 3-figure supplement 3; Figure 4-figure supplements 3&4). We have also clarified our intention to explore possible behavioural differences using this technique in the methods and discussion.

We have also completed the essential control experiment, defined in the “essential revisions” section of this review, whereby we show only moderate impairments in reversal learning following a matched period of food restriction without rapid weight loss, suggesting that the substantial impairment seen following ABA exposure was not due to food restriction alone (see updated Figure 4 and supplements).

However, we do not agree with this reviewer “that the biggest effect of the ABA paradigm is seen in the pair discrimination task” and point to the outcomes of both reciprocal experiments.

In the first experiment, rats that went onto be susceptible or resistant to ABA did not differ on pairwise discrimination learning but specifically on performance at the reversal of reward contingencies (Figure 3B & E). Although this result was not in the hypothesised direction, this suggests that reversal learning specifically and not pairwise discrimination can differentiate those rats that go on to be susceptible to weight loss. We have included additional discussion in the text related to this finding (see line 490-497).

In the second experiment, it is clear by the number of ABA exposed rats that were unable to learn the reversal component even after being able to learn pairwise discrimination, that flexible learning is more impaired by ABA. While it is true that ABA exposed rats that were successful in learning the reversal task were slower to learn the pairwise discrimination component than naïve rats (Figure 4E), this was not related to their ability to learn the reversal task overall – with equivalent learning rates in pairwise discrimination to ABA exposed rats that failed to learn the reversal component (Figure 4G-I). The absence of significant differences between ABA exposed and naïve animals in Figure 4F relates to the fact that the large proportion of ABA exposed animals never reached performance criterion in the reversal phase of the task and therefore data from these animals could not be included in the figure. This is where the trials completed within each session becomes important for interpretation (i.e. Figure 4-figure supplement 1M-O), whereby ABA exposure caused impaired responding specifically within the reversal phase of the task. The results text has been updated to better reflect this critical point.

Overall, this suggests that the impairment in cognitive flexibility caused by ABA exposure was related both to an associative learning impairment (slower to learn PD than naïve animals) and an impairment in the integration of new and existing learning (failure to learn R1 in a large proportion of animals).

Pauline van Mourik Broekman: And community is also always in/with space. In that respect, it seems so important to recognise how hard editors of ‘living books’ actually find it to encourage the reuse/appropriation/disappropriation offered up, and quite how much (material, socialised) time and care it takes to coax – and perform – this activity sensitively, on- and offline, with all the nuances you’ve described (and which run counter to the ‘social’ as the metricised communicating human being is now supposed to perform – and seek – it, and whose conditions of ‘communication’ Jodi Dean has done a lot to theorise).

My PhD research on early Soviet life made me realise it is just really hard to conceive of the experience of true convulsive collectivity (a loss of individuality that I realise may be different, but that I hope might also be compared to the forms of subjectivation inherent in disappropriation?). And how creativity, let alone ‘authorship’, might be experienced within that. Do we (and I am thinking here especially of scholarly workers) come anything close to Walter Benjamin’s experience, from 1927, of how “Each thought, each day, each life lies here [in Moscow] as on a laboratory table. … No organism, no organisation, can escape this process.” Sensations which are also documented in Richard Stites’ Revolutionary Dreams: Utopian Vision and Experimental Life in the Russian Revolution, Oxford: Oxford University Press, 1989; and similarly, in Kristin Ross’s works on the Paris commune (Ross, 2008, 2015). The Soviet concept of the ‘social condenser’ is fascinating in this respect in that it places architecture, and space/s, right in the centre of psychosocial subjectivation, as a potentially intensifying, opening or collectivising force in social movement and change (as some have commented, these might importantly be separated into ‘planned’ and ‘accidental’ social condensers, meaning those which are forward-looking and intentional, or retroactively recognised for their capacities).

If, as Teju Cole so memorably described, we have achieved the sort of collective spectacular alienation wherein we can witness ‘death in the browser tab’ while sitting still in front of a computer and toggling between that and other media ‘content’ (The New York Times Magazine, 2015, and online: https://www.nytimes.com/2015/05/24/magazine/death-in-the-browser-tab.html, how are we to expand living books’ writerly ‘space’ such that the tabs which living books’ readers/writers painstakingly write into might truly act as social condensers, in line with the more fervent hopes and dreams of ‘radical’ open access? As we sit at those computers, writing, our bodies slumped in chairs and our eyes tired and glazed, should we, can we, seek an experience of elated social dissolution the likes of which I’ve in recent times only seen described by authors contemplating the psychological experience of riots (e.g. Hannah Black, 2022; Tobi Haslett, 2021; Adrian Wohlleben, 2021). It is a vain imagining, probably, but I can’t help but wonder how might try and think of these phenomena together, or at least as potentially related? To me it seems inevitably to point to the fact that we cannot conceive of digital materials outside of the spaces in which they are engaged with. I’ve found Mark Nowak’s Social Poetics (2020) and June Jordan’s Poetry for the People (1995) some of the more helpful sources to think this relationship through (though I realise there are countless others). It also seems telling that they are to a lesser or greater extent centred in interpretations of communal pedagogy.

Author Response

Reviewer #1 (Public Review):

Strengths

This paper is well situated theoretically within the habit learning/OCD literature. Daily training in a motor-learning task, delivered via smartphone, was innovative, ecologically valid and more likely to assay habitual behaviors specifically. Daily training is also more similar to studies with non-humans, making a better link with that literature. The use of a sequential-learning task (cf. tasks that require a single response) is also more ecologically valid. The in-laboratory tests (after the 1 month of training) allowed the researchers to test if the OCD group preferred familiar, but more difficult, sequences over newer, simpler sequences.

The authors achieved their aims in that two groups of participants (patients with OCD and controls) engaged with the task over the course of 30 days. The repeated nature of the task meant that 'overtraining' was almost certainly established, and automaticity was demonstrated. This allowed the authors to test their hypotheses about habit learning. The results are supportive of the authors' conclusions.

We truly appreciate the positive assessment of referee 1, particularly the consideration that our study is theoretically strong and that ‘the results are supportive of the authors' conclusions’. This is an important external endorsement of our conclusions, contrasting somewhat with the views of referee 2.

Weaknesses

The sample size was relatively small. Some potentially interesting individual differences within the OCD group could have been examined more thoroughly with a bigger sample (e.g., preference for familiar sequences). A larger sample may have allowed the statistical testing of any effects due to medication status.

The authors were not able to test one criterion of habits, namely resistance to devaluation, due to the nature of the task

We agree with the reviewer that the proof of principle established in our study opens new avenues for research into the psychological and behavioral determinants of the heterogeneity of this clinical population. However, considering the study timeline and the pandemic constraints, a bigger sample was not possible. Our sample can indeed be considered small if one compares it with current online studies, which do not require in-person/laboratory testing, thus being much easier to recruit and conduct. However, given the nature of our protocol (with 2 demanding test phases, 1-month engagement per participant and the inclusion of OCD patients without comorbidities only) and the fact that this study also involved laboratory testing, we consider our sample size reasonable and comparable to other laboratory studies (typically comprising on average between 30-50 participants in each group).

This article is likely to be impactful -- the delivery of a task across 30 days to a patient group is innovative and represents a new approach for the study of habit learning that is superior to an inlaboratory approach.

An interesting aspect of this manuscript is that it prompts a comparison with previous studies of goal-directed/habitual responding in OCD that used devaluation protocols, and which may have had their effects due to deficits in goal-directed behavior and not enhanced habit learning per se.

Thank you for acknowledging the impact of our study, in particular the unique ability of our task to interrogate the habit system.

Reviewer #2 (Public Review):

In this study, the researchers employed a recently developed smartphone application to provide 30 days of training on action sequences to both OCD patients and healthy volunteers. The study tested learning and automaticity-related measures and investigated the effects of several factors on these measures. Upon training completion, the researchers conducted two preference tests comparing a learned and unlearned action sequences under different conditions. While the study provides some interesting findings, I have a few substantial concerns:

1) Throughout the entire paper, the authors' interpretations and claims revolve around the domain of habits and goal-directed behavior, despite the methods and evidence clearly focusing on motor sequence learning/procedural learning/skill learning. There is no evidence to support this framing and interpretation and thus I find them overreaching and hyperbolic, and I think they should be avoided. Although skills and habits share many characteristics, they are meaningfully distinguishable and should not be conflated or mixed up. Furthermore, if anything, the evidence in this study suggests that participants attained procedural learning, but these actions did not become habitual, as they remained deliberate actions that were not chosen to be performed when they were not in line with participants' current goals.

We acknowledge that the research on habit learning is a topic of current controversy, especially when it comes to how to induce and measure habits in humans. Therefore, within this context referee’s 2 criticism could be expected. Across disQnct fields of research, different methodologies have been used to measure habits, which represent relaQvely stereotyped and autonomous behavioral sequences enacted in response to a specific sQmulus without consideraQon, at the Qme of iniQaQon of the sequence, of the value of the outcome or any representaQon of the relaQonship that exists between the response and the outcome. Hence these are sQmulus-bound responses which may or may not require the implementaQon of a skill during subsequent performance. Behavioral neuroscienQsts define habits similarly, as sQmulus-response associaQons which are independent of reward or outcome, and use devaluaQon or conQngency degradaQon strategies to probe habits (Dickinson and Weiskrantz, 1985; Tricomi et al., 2009). Others conceptualize habits as a form of procedural memory, along with skills, and use motor sequence learning paradigms to invesQgate and dissect different components of habit learning such as acQon selecQon, execuQon and consolidaQon (Abrahamse et al., 2013; Doyon et al., 2003; Squire et al., 1993). It is also generally agreed that the autonomous nature of habits and the fluid proficiency of skills are both usually achieved with many hours of training or pracQce, respecQvely (Haith and Krakauer, 2018).

We consider that Balleine and Dezfouli (2019) made an excellent attempt to bring all these different criteria within a single framework, which we have followed. We also consider that our discussion in fact followed a rather cautious approach to interpretation solely in terms of goaldirected versus habitual control.

Referee 2 does not actually specify criteria by which they define habits and skills, except for asserting that skilled behavior is goal-directed, without mentioning what the actual goal of the implantation of such skill is in the present study: the fulfillment of a habit? We assume that their definition of habit hinges on the effects of devaluation, as a single criterion of habit, but which according to Balleine and Dezfouli (2019) is only 1 of their 4 listed criteria. We carefully addressed this specific criterion in our manuscript: “We were not, however, able to test the fourth criterion, of resistance to devaluation. Therefore, we are unable to firmly conclude that the action sequences are habits rather than, for example, goal-directed skills. Regardless of whether the trained action sequences can be defined as habits or goal-directed motor skills, it has to be considered…”. Therefore, we took due care in our conclusions concerning habits and thus found the referee’s comment misleading and unfair.

We note that our trained motor sequences did in fact fulfil the other 3 criteria listed by Balleine and Dezfouli (2019), unlike many studies employing only devaluation (e.g. Tricomi et al 2009; Gillan et al 2011). Moreover, we cited a recent study using very similar methodology where the devaluation test was applied and shown to support the habit hypothesis (Gera et al., 2022).

Whether the initiation of the trained motor sequences in experiment 3 (arbitration) are underpinned by an action-outcome association (or not) has no bearing on whether those sequences were under stimulus-response control after training (experiment 1). Transitions between habitual and goal-directed control over behavior are quite well established in the experimental literature, especially when choice opportunities become available (Bouton et al (2021), Frölich et al (2023), or a new goal-directed schemata is recruited to fulfill a habit (Fouyssac et al, 2022). This switching between habits and goal-directed responding may reflect the coordination of these systems in producing effective behavior in the real world.

• Fouyssac M, Peña-Oliver Y, Puaud M, Lim NTY, Giuliano C, Everitt BJ, Belin D. (2021).Negative Urgency Exacerbates Relapse to Cocaine Seeking After Abstinence. Biological Psychiatry. doi: 10.1016/j.biopsych.2021.10.009

• Frölich S, Esmeyer M, Endrass T, Smolka MN and Kiebel SJ (2023) Interaction between habits as action sequences and goal-directed behavior under time pressure. Front. Neurosci. 16:996957. doi: 10.3389/fnins.2022.996957

• Bouton ME. 2021. Context, attention, and the switch between habit and goal-direction in behavior. Learn Behav 49:349– 362. doi:10.3758/s13420-021-00488-z

2) Some methodological aspects need more detail and clarification.

3) There are concerns regarding some of the analyses, which require addressing.

We thank referee 2 for their detailed review of the methods and analyses of our study and for the helpful feedback, which clearly helps improve our manuscript. We will clarify the methodological aspects in detail and conduct the suggested analysis. Please see below our answers to the specific points raised.

Introduction:

4) It is stated that "extensive training of sequential actions would more rapidly engage the 'habit system' as compared to single-action instrumental learning". In an attempt to describe the rationale for this statement the authors describe the concept of action chunking, its benefits and relevance to habits but there is no explanation for why sequential actions would engage the habit system more rapidly than a single-action. Clarifying this would be helpful.

We agree that there is no evidence that action sequences become habitual more readily than single actions, although action sequences clearly allow ‘chunking’ and thus likely engage neural networks including the putamen which are implicated in habit learning as well as skill. In our revised manuscript we will instead state: “we have recently postulated that extensive training of sequential actions could be a means for rapidly engaging the ‘habit system’ (Robbins et al., 2019)]”

5) In the Hypothesis section the authors state: “we expected that OCD patients... show enhanced habit attainment through a greater preference for performing familiar app sequences when given the choice to select any other, easier sequence”. I find it particularly difficult to interpret preference for familiar sequences as enhanced habit attainment.

We agree that choice of the familiar response sequence should not be a necessary criterion for habitual control although choice for a familiar sequence is, in fact, not inconsistent with this hypothesis. In a recent study, Zmigrod et al (2022) found that 'aversion to novelty' was a relevant factor in the subjective measurement of habitual tendencies. It should also be noted that this preference was present in patients with OCD. If one assumes instead, like the referee, that the familiar sequence is goal-directed, then it contravenes the well-known 'egodystonia' of OCD which suggests that such tendencies are not goal-directed.

To clarify our hypothesis, we will amend the sentence to the following: “Finally, we expected that OCD patients would generally report greater habits, as well as attribute higher intrinsic value to the familiar app sequences manifested by a greater preference for performing them when given the choice to select any other, easier sequence”.

A few notes on the task description and other task components:

6) It would be useful to give more details on the task. This includes more details on the time/condition of the gradual removal of visual and auditory stimuli and also on the within practice dynamic structure (i.e., different levels appear in the video).

These details will be included in the revised manuscript. Thank you for pointing out the need for further clarification of the task design.

7) Some more information on engagement-related exclusion criteria would be useful (what happened if participants did not use the app for more than one day, how many times were allowed to skip a day etc.).

This additional information will be added to the revised manuscript. If participants omitted to train for more than 2 days, the researcher would send a reminder to the participant to request to catch up. If the participant would not react accordingly and a third day would be skipped, then the researcher would call to understand the reasons for the lack of engagement and gauge motivation. The participant would be excluded if more than 5 sequential days of training were missed. Only 2 participants were excluded given their lack of engagement.

8) According to the (very useful) video demonstrating the task and the paper describing the task in detail (Banca et al., 2020), the task seems to include other relevant components that were not mentioned in this paper. I refer to the daily speed test, the daily random switch test, and daily ratings of each sequence's enjoyment and confidence of knowledge.

If these components were not included in this procedure, then the deviations from the procedure described in the video and Banca al. (2020) should be explicitly mentioned. If these components were included, at least some of them may be relevant, at least in part, to automaticity, habitual action control, formulation of participants' enjoyment from the app etc. I think these components should be mentioned and analyzed (or at least provide an explanation for why it has been decided not to analyze them).

This is also true for the reward removal (extinction) from the 21st day onwards which is potentially of particular relevance for the research questions.

The task procedure was indeed the same as detailed in Banca et al., 2020. We did not include these extra components in this current manuscript for reasons of succinctness and because the manuscript was already rather longer than a common research article, given that we present three different, though highly inter-dependent, experiments in order to answer key interrelated questions in an optimal manner. However, since referee 2 considers this additional analysis to be important, we will be happy to include it in the supplementary material of the revised manuscript.

Training engagement analysis:

9)I find referring to the number of trials including successful and unsuccessful trials as representing participants "commitment to training" (e.g. in Figure legend 2b) potentially inadequate. Given that participants need at least 20 successful trials to complete each practice, more errors would lead to more trials. Therefore, I think this measure may mostly represent weaker performance (of the OCD patients as shown in Figure 2b). Therefore, I find the number of performed practice runs, as used in Figure 2a (which should be perfectly aligned with the number of successful trials), a "clean" and proper measure of engagement/commitment to training.

We acknowledge referee’s concern on this matter and agree to replace the y-axis variable of Figure 2b to the number of performed practices (thus aligning with Figure 2a). This amendment will remove any potential effect of weaker performance on the engagement measurement and will provide clearer results.

10) Also, to provide stronger support for the claim about different diurnal training patterns (as presented in Figure 2c and the text) between patients and healthy individuals, it would be beneficial to conduct a statistical test comparing the two distributions. If the results of this test are not significant, I suggest emphasizing that this is a descriptive finding.

We will conduct the statistical test and report accordingly.

Learning results:

11) When describing the Learning results (p10) I think it would be useful to provide the descriptive stats for the MT0 parameter (as done above for the other two parameters).

Thank you for pointing this out. The descriptive stats for MT0 will be added to the revised version of the manuscript.

12) Sensitivity of sequence duration and IKI consistency (C) to reward:

I think it is important to add details on how incorrect trials were handled when calculating ∆MT (or C) and ∆R, specifically in cases where the trial preceding a successful trial was unsuccessful. If incorrect trials were simply ignored, this may not adequately represent trial-by-trial changes, particularly when testing the effect of a trial's outcome on performance change in the next trial.

This is an important question. Our analysis protocol was designed to ensure that incorrect trials do not contaminate or confound the results. To estimate the trial-to-trial difference in ∆MT (or C) and ∆R, we exclusively included pairs of contiguous trials where participants achieved correct performance and received feedback scores for both trials. For example, if a participant made a performance error on trial 23, we did not include ∆R or ∆MT estimates for the pairs of trials 23-22 and 24-23. Instead of excluding incorrect trials from our analyses, we retained them in our time series but assigned them a NaN (not a number) value in Matlab. As a result, ∆R and ∆MT was not defined for those two pairs of trials. Similarly for C. This approach ensured that our analyses are not confounded by incremental or decremental feedback scores between noncontiguous trials. In the past, when assessing the timing of correct actions during skilled sequence performance, we also considered events that were preceded and followed by correct actions. This excluded effects such as post-error slowing from contaminating our results (Herrojo Ruiz et al., 2009, 2019). Therefore, we do not believe that any further reanalysis is required.

• Ruiz MH, Jabusch HC, Altenmüller E. Detecting wrong notes in advance: neuronal correlates of error monitoring in pianists. Cerebral cortex. 2009 Nov 1;19(11):2625-39.

• Bury G, García-Huéscar M, Bhattacharya J, Ruiz MH. Cardiac afferent activity modulates early neural signature of error detection during skilled performance. NeuroImage. 2019 Oct 1;199:704-17.

13) I have a serious concern with respect to how the sensitivity of sequence duration to reward is framed and analyzed. Since reward is proportional to performance, a reduction in reward essentially indicates a trial with poor performance, and thus even regression to the mean (along with a floor effect in performance [asymptote]) could explain the observed effects. It is possible that even occasional poor performance could lead to a participant demonstrating this effect, potentially regardless of the reward. Accordingly, the reduced improvement in performance following a reward decrease as a function of training length described in Figure 5b legend may reflect training-induced increased performance that leaves less room for improvement after poor trials, which are no longer as poor as before. To address this concern, controlling for performance (e.g., by taking into consideration the baseline MT for the previous trial) may be helpful. If the authors can conduct such an analysis and still show the observed effect, it would establish the validity of their findings."

Thank you for raising this point. Figure 5b illustrates two distinct effects of reward changes on behavioral adaptation, which are expected based on previous research.

I. Practice effects: Firstly, we observe that as participants progress across bins of practice, the degree of improvement in behavior (reflected by faster movement time, MT) following a decrease in reward (∆R−) diminishes, consistent with our expectations based on previous work. Conversely, we found that ∆MT does not change across bins of practices following an increase in reward (∆R+). We appreciate the reviewer's suggestion regarding controlling for the reference movement time (MT) in the previous trial when examining the practice effect in the p(∆T|∆R−) and p(∆T|∆R+) distributions. In the revised manuscript, we will conduct the proposed control analysis to better understand whether the sensitivity of MT to score decrements changes across practice when normalising MT to the reference level on each trial. But see below for a preliminary control analysis.

II. Asymmetry of the effect of ∆R− and ∆R+ on performance: Figure 5b also depicts the distinct impact of score increments and decrements on behavioural changes. When aggregating data across practice bins, we consistently observed that the centre of the p(∆T|∆R−) distribution was smaller (more negative) than that of p(∆T|∆R+). This suggests that participants exhibited a greater acceleration following a drop in scores compared to a relative score increase, and this effect persisted throughout the practice sessions. Importantly, this enhanced sensitivity to losses or negative feedback (or relative drops in scores) aligns with previous research findings (Galea et al., 2015; Pekny et al., 2014; van Mastrigt et al., 2020).

We have conducted a preliminary control analysis to exclude the potential impact that reference movement time (MT) values could have on our analysis. We have assessed the asymmetry between behavioural responses to ∆R− and ∆R+ using the following analysis: We estimated the proportion of trials in which participants exhibited speed-up (∆T < 0) or slow-down (∆T > 0) behaviour following ∆R− and ∆R+ across different practice bins (bins 1 to 4). By discretising the series of behavioural changes (∆T) into binary values (+1 for slowing down, -1 for speeding up), we can assess the type of changes (speed-up, slow-down) without the absolute ∆T or T values contributing to our results. We obtained several key findings:

• Consistent with expectations (sanity check), participants exhibited more instances of speeding up than slowing down across all reward conditions.

• Participants demonstrated a higher frequency of speeding up following ∆R− compared to ∆R+, and this asymmetry persisted throughout the practice sessions (greater proportion of -1 events than +1 events). 53% events were speed-up events in the in the p(∆T|∆R+) distribution for the first bin of practices, and 55% for the last bin. Regarding p(∆T|∆R-), there were 63% speed-up events throughout each bin of practices, with this proportion exhibiting no change over time.

• Accordingly, the asymmetry of reward changes on behavioural adaptations, as revealed by this analysis, remained consistent across the practice bins.

Thus, these preliminary findings provide an initial response to referee 2 and offer valuable insights into the asymmetrical effects of positive/negative reward changes on behavioural adaptations. We plan to include these results in the revised manuscript, as well as the full control analysis suggested by the referee. We will further expand upon their interpretation and implications.

14) Another way to support the claim of reward change directionality effects on performance (rather than performance on performance), at least to some extent, would be to analyze the data from the last 10 days of the training, during which no rewards were given (pretending for analysis purposes that the reward was calculated and presented to participants). If the effect persists, it is less unlikely that the effect in question can be attributed to the reward dynamics.

The reviewer’s concern is addressed in the previous quesQon. Also, this analysis would not be possible because our Gaussian fit analyses use the Qme series of conQnuous reward scores, in which ∆R− or ∆R+ are embedded. These events cannot be analyzed once reward feedback is removed because we do not have behavioral events following ∆R− or ∆R+ anymore.

15) This concern is also relevant and should be considered with respect to the sensitivity of IKI consistency (C) to reward. While the relationship between previous reward/performance and future performance in terms of C is of a different structure, the similar potential confounding effects could still be present.

We will conduct this analysis for the revised manuscript, similarly to the control analysis suggested by referee 2 on MT. Our preliminary control analysis, as explained above, suggests that the fundamental asymmetry in the effect of ∆R+ and ∆R+ on behavioral changes persists when excluding the impact of reference performance values in our Gaussian fit analysis.

16) Another related question (which is also of general interest) is whether the preferred app sequence (as indicated by the participants for Phase B) was consistently the one that yielded more reward? Was the continuous sequence the preferred one? This might tell something about the effectiveness of the reward in the task.

We have now conducted this analysis. There is in fact no evidence to conclude that the continuously rewarded sequence was the preferred one. The result shows that 54.5% of HV and 29% of the OCD sample considered the continuous sequence to be their preferred one. Of note, this preference may not necessarily be linked to the trial-by-trial reward sensitive analysis. The latter assesses how learning may be affected by reward. The overall preference may be influenced by many other factors, such as, for example, the aesthetic appeal of particular combinations of finger movements.

Regarding both experiments 2 and 3:

17) The change in context in experiment 2 and 3 is substantial and include many different components. These changes should be mentioned in more detail in the Results section before describing the results of experiments 2 and 3.

Following referee’s advice, we will move these details (currently written in the Methods section) to the Results section, when we introduce Phase B and before describing the results of experiments 2 and 3.

Experiment 2:

18) In Experiment 2, the authors sometimes refer to the "explicit preference task" as testing for habitual and goal-seeking sequences. However, I do not think there is any justification for interpreting it as such. The other framings used by the authors - testing whether trained action sequences gain intrinsic/rewarding properties or value, and preference for familiar versus novel action sequences - are more suitable and justified. In support of the point I raised here, assigning intrinsic rewarding properties to the learned sequences and thereby preferring these sequences can be conceptually aligned with goal-directed behavior just as much as it could be with habit.

We clearly defined the theoretical framing of experiment 2 as a test of whether trained action sequences gain intrinsic value and we are pleased to hear that the referee agrees with this framing. If the referee is referring to the paragraph below (in the Discussion), we actually do acknowledge within this paragraph that a preference for the trained sequences can either be conceptually aligned with a habit OR a goal-directed behavior.

“On the other hand, we are describing here two potential sources of evidence in favor of enhanced habit formation in OCD. First, OCD patients show a bias towards the previously trained, apparently disadvantageous, action sequences. In terms of the discussion above, this could possibly be reinterpreted as a narrowing of goals in OCD (Robbins et al., 2019) underlying compulsive behavior, in favor of its intrinsic outcomes”

This narrowing of goals model of OCD refers to a hypothetically transiQonal stage of compulsion development driven by behavior having an abnormally strong, goal-directed nature, typically linked to specific values and concerns.

If the referee is referring to the penulQmate sentence of hypothesis secQon, this has been amended in response to Q5. We cannot find any other possible instances in this manuscript stating that experiment 2 is a test of habitual or goal-directed behavior.

Experiment 3:

19) Similar to Experiment 2, I find the framing of arbitration between goal-directed/habitual behavior in Experiment 3 inadequate and unjustified. The results of the experiment suggest that participants were primarily goal-directed and there is no evidence to support the idea that this reevaluation led participants to switch from habitual to goal-directed behavior.

Also, given the explicit choice of the sequence to perform participants had to make prior to performing it, it is reasonable to assume that this experiment mainly tested bias towards familiar sequence/stimulus and/or towards intrinsic reward associated with the sequence in value-based decision making.

This comment is aligned with (and follows) the referee’s criticism of experiment 1 not achieving automatic and habitual actions. We have addressed this matter above, in response 1 to Referee 2.

Mobile-app performance effect on symptomatology: exploratory analyses:

20) Maybe it would be worth testing if the patients with improved symptomatology (that contribute some of their symptom improvement to the app) also chose to play more during the training stage.

We have conducted analysis to address this relevant question. There is no correlation between the YBOCS score change and the number of total practices, meaning that the patients who improved symptomatology post training did not necessarily chose to play the app more during the training stage (rs = 0.25, p = 0.15). Additionally, we have statistically compared the improvers (patients with reduced YBOCS scores post-training) and the non-improvers (patients with unchanged or increased YBOCS scores post-training) in their number of app completed practices during the training phase and no differences were observed (U = 169, p = 0.19).

Discussion:

21) Based on my earlier comments highlighting the inadequacy and mis-framing of the work in terms of habit and goal-directed behavior, I suggest that the discussion section be substantially revised to reflect these concerns.

We do not agree that the work is either "inadequate or mis-framed" and will not therefore be substantially revising the Discussion. We will however clarify further the interpretation we have made and make explicit the alternative viewpoint of the referee. For example, we will retitle experiment 3 as “Re-evaluation of the learned action sequence: possible test of goal/habit arbitration” to acknowledge the referee’s viewpoint as well as our own interpretation.

22) In the sentence "Nevertheless, OCD patients disadvantageously preferred the previously trained/familiar action sequence under certain conditions" the term "disadvantageously" is not necessarily accurate. While there was potentially more effort required, considering the possible presence of intrinsic reward and chunking, this preference may not necessarily be disadvantageous. Therefore, a more cautious and accurate phrasing that better reflects the associated results would be useful.

We recognize that the term "disadvantageously" may be semantically ambiguous for some readers and therefore we will remove it.

Materials and Methods:

23) The authors mention: "The novel sequence (in condition 3) was a 6-move sequence of similar complexity and difficulty as the app sequences, but only learned on the day, before starting this task (therefore, not overtrained)." - for the sake of completeness, more details on the pre-training done on that day would be useful.

Details of the learning procedure of the novel sequence (in condition 3, experiment 3) will be provided in the methods of the revised version of the manuscript.

Minor comments:

24) In the section discussing the sensitivity of sequence duration to reward, the authors state that they only analyzed continuous reward trials because "a larger number of trials in each subsample were available to fit the Gaussian distributions, due to feedback being provided on all trials." However, feedback was also provided on all trials in the variable reward condition, even though the reward was not necessarily aligned with participants' performance. Therefore, it may be beneficial to rephrase this statement for clarity.

We will follow this referee’s advice and will rephrase the sentence for clarity.

25) With regard to experiment 2 (Preference for familiar versus novel action sequences) in the following statement "A positive correlation between COHS and the app sequence choice (Pearson r = 0.36, p = 0.005) further showed that those participants with greater habitual tendencies had a greater propensity to prefer the trained app sequence under this condition." I find the use of the word "further" here potentially misleading.

The word "further" will be removed.

Reviewer #2 (Public Review):

In this study, the researchers employed a recently developed smartphone application to provide 30 days of training on action sequences to both OCD patients and healthy volunteers. The study tested learning and automaticity-related measures and investigated the effects of several factors on these measures. Upon training completion, the researchers conducted two preference tests comparing a learned and unlearned action sequences under different conditions. While the study provides some interesting findings, I have a few substantial concerns:

1. Throughout the entire paper, the authors' interpretations and claims revolve around the domain of habits and goal-directed behavior, despite the methods and evidence clearly focusing on motor sequence learning/procedural learning/skill learning. There is no evidence to support this framing and interpretation and thus I find them overreaching and hyperbolic, and I think they should be avoided. Although skills and habits share many characteristics, they are meaningfully distinguishable and should not be conflated or mixed up. Furthermore, if anything, the evidence in this study suggests that participants attained procedural learning, but these actions did not become habitual, as they remained deliberate actions that were not chosen to be performed when they were not in line with participants' current goals.<br /> 2. Some methodological aspects need more detail and clarification.<br /> 3. There are concerns regarding some of the analyses, which require addressing.

Please see details below, ordered by the paper sections.

Introduction:<br /> It is stated that "extensive training of sequential actions would more rapidly engage the 'habit system' as compared to single-action instrumental learning". In an attempt to describe the rationale for this statement the authors describe the concept of action chunking, its benefits and relevance to habits but there is no explanation for why sequential actions would engage the habit system more rapidly than a single-action. Clarifying this would be helpful.

In the Hypothesis section the authors state: "we expected that OCD patients... show enhanced habit attainment through a greater preference for performing familiar app sequences when given the choice to select any other, easier sequence." I find it particularly difficult to interpret preference for familiar sequences as enhanced habit attainment.

A few notes on the task description and other task components:<br /> It would be useful to give more details on the task. This includes more details on the time/condition of the gradual removal of visual and auditory stimuli and also on the within practice dynamic structure (i.e., different levels appear in the video).

Some more information on engagement-related exclusion criteria would be useful (what happened if participants did not use the app for more than one day, how many times were allowed to skip a day etc.).

According to the (very useful) video demonstrating the task and the paper describing the task in detail (Banca et al., 2020), the task seems to include other relevant components that were not mentioned in this paper. I refer to the daily speed test, the daily random switch test, and daily ratings of each sequence's enjoyment and confidence of knowledge.<br /> If these components were not included in this procedure, then the deviations from the procedure described in the video and Banca al. (2020) should be explicitly mentioned. If these components were included, at least some of them may be relevant, at least in part, to automaticity, habitual action control, formulation of participants' enjoyment from the app etc. I think these components should be mentioned and analyzed (or at least provide an explanation for why it has been decided not to analyze them).<br /> This is also true for the reward removal (extinction) from the 21st day onwards which is potentially of particular relevance for the research questions.

Training engagement analysis:<br /> I find referring to the number of trials including successful and unsuccessful trials as representing participants "commitment to training" (e.g. in Figure legend 2b) potentially inadequate. Given that participants need at least 20 successful trials to complete each practice, more errors would lead to more trials. Therefore, I think this measure may mostly represent weaker performance (of the OCD patients as shown in Figure 2b). Therefore, I find the number of performed practice runs, as used in Figure 2a (which should be perfectly aligned with the number of successful trials), a "clean" and proper measure of engagement/commitment to training.

Also, to provide stronger support for the claim about different diurnal training patterns (as presented in Figure 2c and the text) between patients and healthy individuals, it would be beneficial to conduct a statistical test comparing the two distributions. If the results of this test are not significant, I suggest emphasizing that this is a descriptive finding.

Learning results:<br /> When describing the Learning results (p10) I think it would be useful to provide the descriptive stats for the MT0 parameter (as done above for the other two parameters).

Sensitivity of sequence duration and IKI consistency (C) to reward:<br /> I think it is important to add details on how incorrect trials were handled when calculating ∆MT (or C) and ∆R, specifically in cases where the trial preceding a successful trial was unsuccessful. If incorrect trials were simply ignored, this may not adequately represent trial-by-trial changes, particularly when testing the effect of a trial's outcome on performance change in the next trial.

I have a serious concern with respect to how the sensitivity of sequence duration to reward is framed and analyzed. Since reward is proportional to performance, a reduction in reward essentially indicates a trial with poor performance, and thus even regression to the mean (along with a floor effect in performance [asymptote]) could explain the observed effects. It is possible that even occasional poor performance could lead to a participant demonstrating this effect, potentially regardless of the reward. Accordingly, the reduced improvement in performance following a reward decrease as a function of training length described in Figure 5b legend may reflect training-induced increased performance that leaves less room for improvement after poor trials, which are no longer as poor as before. To address this concern, controlling for performance (e.g., by taking into consideration the baseline MT for the previous trial) may be helpful. If the authors can conduct such an analysis and still show the observed effect, it would establish the validity of their findings."<br /> Another way to support the claim of reward change directionality effects on performance (rather than performance on performance), at least to some extent, would be to analyze the data from the last 10 days of the training, during which no rewards were given (pretending for analysis purposes that the reward was calculated and presented to participants). If the effect persists, it is less unlikely that the effect in question can be attributed to the reward dynamics.<br /> This concern is also relevant and should be considered with respect to the Sensitivity of IKI consistency (C) to reward (even though the relationship between previous reward/performance and future performance in terms of C is of a different structure).<br /> This concern is also relevant and should be considered with respect to the sensitivity of IKI consistency (C) to reward. While the relationship between previous reward/performance and future performance in terms of C is of a different structure, the similar potential confounding effects could still be present.

Another related question (which is also of general interest) is whether the preferred app sequence (as indicated by the participants for Phase B) was consistently the one that yielded more reward? Was the continuous sequence the preferred one? This might tell something about the effectiveness of the reward in the task.

Regarding both experiments 2 and 3:<br /> The change in context in experiment 2 and 3 is substantial and include many different components. These changes should be mentioned in more detail in the Results section before describing the results of experiments 2 and 3.

Experiment 2:<br /> In Experiment 2, the authors sometimes refer to the "explicit preference task" as testing for habitual and goal-seeking sequences. However, I do not think there is any justification for interpreting it as such. The other framings used by the authors - testing whether trained action sequences gain intrinsic/rewarding properties or value, and preference for familiar versus novel action sequences - are more suitable and justified. In support of the point I raised here, assigning intrinsic rewarding properties to the learned sequences and thereby preferring these sequences can be conceptually aligned with goal-directed behavior just as much as it could be with habit.

Experiment 3:<br /> Similar to Experiment 2, I find the framing of arbitration between goal-directed/habitual behavior in Experiment 3 inadequate and unjustified. The results of the experiment suggest that participants were primarily goal-directed and there is no evidence to support the idea that this re-evaluation led participants to switch from habitual to goal-directed behavior.<br /> Also, given the explicit choice of the sequence to perform participants had to make prior to performing it, it is reasonable to assume that this experiment mainly tested bias towards familiar sequence/stimulus and/or towards intrinsic reward associated with the sequence in value-based decision making.

Mobile-app performance effect on symptomatology: exploratory analyses:<br /> Maybe it would be worth testing if the patients with improved symptomatology (that contribute some of their symptom improvement to the app) also chose to play more during the training stage.

Discussion:<br /> Based on my earlier comments highlighting the inadequacy and mis-framing of the work in terms of habit and goal-directed behavior, I suggest that the discussion section be substantially revised to reflect these concerns.

In the sentence "Nevertheless, OCD patients disadvantageously preferred the previously trained/familiar action sequence under certain conditions" the term "disadvantageously" is not necessarily accurate. While there was potentially more effort required, considering the possible presence of intrinsic reward and chunking, this preference may not necessarily be disadvantageous. Therefore, a more cautious and accurate phrasing that better reflects the associated results would be useful.

Materials and Methods:<br /> The authors mention: "The novel sequence (in condition 3) was a 6-move sequence of similar complexity and difficulty as the app sequences, but only learned on the day, before starting this task (therefore, not overtrained)." - for the sake of completeness, more details on the pre-training done on that day would be useful.

Minor comments:<br /> In the section discussing the sensitivity of sequence duration to reward, the authors state that they only analyzed continuous reward trials because "a larger number of trials in each subsample were available to fit the Gaussian distributions, due to feedback being provided on all trials." However, feedback was also provided on all trials in the variable reward condition, even though the reward was not necessarily aligned with participants' performance. Therefore, it may be beneficial to rephrase this statement for clarity.

With regard to experiment 2 (Preference for familiar versus novel action sequences) in the following statement "A positive correlation between COHS and the app sequence choice (Pearson r = 0.36, p = 0.005) further showed that those participants with greater habitual tendencies had a greater propensity to prefer the trained app sequence under this condition." I find the use of the word "further" here potentially misleading.

Reviewer #2 (Public Review):

Olszyński et al. claim that they identified a "new-type" ultrasonic vocalization around 44 kHz that occurs in response to prolonged fear conditioning (using foot-shocks of relatively high intensity, i.e. 1 mA) in rats. Typically, negative 22-kHz calls and positive 50-kHz calls are distinguished in rats, commonly by using a frequency threshold of 30 or 32 kHz. Olszyński et al. now observed so-called "44-kHz" calls in a substantial number of subjects exposed to 10 tone-shock pairings, yet call emission rate was low (according to Fig. 1G around 15%, according to the result text around 7.5%). They also performed playback experiments and concluded that "the responses to 44-kHz aversive calls presented from the speaker were either similar to 22-kHz vocalizations or in-between responses to 22-kHz and 50-kHz playbacks".

Strengths: Detailed spectrographic analysis of a substantial data set of ultrasonic vocalizations recorded during prolonged fear conditioning, combined with playback experiments.

Weaknesses: I see a number of major weaknesses.

While the descriptive approach applied is useful, the findings have only focused importance and scope, given the low prevalence of "44 kHz" calls and limited attempts made to systematically manipulate factors that lead to their emission. In fact, the data presented appear to be derived from reanalyses of previously conducted studies in most cases and the main claims are only partially supported. While reading the manuscript, I got the impression that the data presented here are linked to two or three previously published studies (Olszyński et al., 2020, 2021, 2023). This is important to emphasize for two reasons: 1) It is often difficult (if not impossible) to link the reported data to the different experiments conducted before (and the individual experimental conditions therein). While reanalyzing previously collected data can lead to important insight, it is important to describe in a clear and transparent manner what data were obtained in what experiment (and more specifically, in what exact experimental condition) to allow appropriate interpretation of the data. For example, it is said that in the "trace fear conditioning experiment" both single- and group-housed rats were included, yet I was not able to tell what data were obtained in single- versus group-housed rats. This may sound like a side aspect, however, in my view this is not a side aspect given the fact that ultrasonic vocalizations are used for communication and communication is affected by the social housing conditions. 2) In at least two of the previously published manuscripts (Olszyński et al., 2021, 2023), emission of ultrasonic vocalizations was analyzed (Figure S1 in Olszyński et al., 2021, and Fig. 1 in Olszyński et al., 2023). This includes detailed spectrographic analyses covering the frequency range between 20 and 100 kHz, i.e. including the frequency range, where the "new-type" ultrasonic vocalization, now named "44 kHz" call, occurs, as reflected in the examples provided in Fig. 1 of Olszyński et al. (2023). In the materials and methods there, it was said: "USV were assigned to one of three categories: 50-kHz (mean peak frequency, MPF >32 kHz), short 22-kHz (MPF of 18-32 kHz, <0.3 s duration), long 22-kHz (MPF of 18-32 kHz, >0.3 s duration)". Does that mean that the "44 kHz" calls were previously included in the count for 50-kHz calls? Or were 44 kHz calls (intentionally?) left out? What does that mean for the interpretation of the previously published data? What does that mean for the current data set? In my view, there is a lack of transparency here.

Moreover, whether the newly identified call type is indeed novel is questionable, as also mentioned by the authors in their discussion section. While they wrote in the introduction that "high-pitch (>32 kHz), long and monotonous ultrasonic vocalizations have not yet been described", they wrote in the discussion that "long (or not that long (Biały et al., 2019)), frequency-stable high-pitch vocalizations have been reported before (e.g. Sales, 1979; Shimoju et al., 2020), notably as caused by intense cholinergic stimulation (Brudzynski and Bihari, 1990) or higher shock-dose fear conditioning (Wöhr et al., 2005)" (and I wish to add that to my knowledge this list provided by the authors is incomplete). Therefore, I believe, the strong claims made in abstract ("we are the first to describe a new-type..."), introduction ("have not yet been described"), and results ("new calls") are not justified.

In general, the manuscript is not well written/ not well organized, the description of the methods is insufficient, and it is often difficult (if not impossible) to link the reported data to the experiments/ experimental conditions described in the materials and methods section. For example, I miss a clear presentation of basic information: 1) How many rats emitted "44 kHz" calls (in total, per experiment, and importantly, also per experimental condition, i.e. single- versus group-housed)? 2) Out of the ones emitting "44 kHz" calls, what was the prevalence of "44 kHz" calls (relative to 22- and 50-kHz calls, e.g. shown as percentage)? 3) How did this ratio differ between experiments and experimental conditions? 4) Was there a link to freezing? Freezing was apparently analyzed before (Olszyński et al., 2021, 2023) and it would be important to see whether there is a correlation between "44-kHz" calls and freezing. Moreover, it would be important to know what behavior the rats are displaying while such "44-kHz" calls are emitted? (Note: Even not all 22-kHz calls are synced to freezing.) All this could help to substantiate the currently highly speculative claims made in the discussion section ("frequency increases with an increase in arousal" and "it could be argued that our prolonged fear conditioning increased the arousal of the rats with no change in the valence of the aversive stimuli"). Such more detailed analyses are also important to rule out the possibility that the "new-type" ultrasonic vocalization, the so-called "44 kHz" call, is simply associated with movement/ thorax compression.

The figures currently included are purely descriptive in most cases - and many of them are just examples of individual rats (e.g. majority of Fig. 1, all of Fig. 2 to my understanding, with the exception of the time course, which in case of D is only a subset of rats ("only rats that emitted 44-kHz calls in at least seven ITI are plotted" - is there any rationale for this criterion?)), or, in fact, just representative spectrograms of calls (all of Fig. 3, with the exception of G, all of Fig. 4). Moreover, the differences between Fig. 5 and Fig. 6 are not clear to me. It seems Fig. 5B is included three times - what is the benefit of including the same figure three times? A systematic comparison of experimental conditions is limited to Fig. 7 and Fig. 8, the figures depicting the playback results (which led to the conclusion that "the responses to 44-kHz aversive calls presented from the speaker were either similar to 22-kHz vocalizations or in-between responses to 22-kHz and 50-kHz playbacks", although it remains unclear to me why differences were seen b e f o r e the experimental manipulation, i.e. the different playback types in Fig. 8B).

Related to that, I miss a clear presentation of relevant methodological aspects: 1) Why were some rats single-housed but not the others? 2) Is the experimental design of the playback study not confounded? It is said that "one group (n = 13) heard 50-kHz appetitive vocalization playback while the other (n = 16) 22-kHz and 44-kHz aversive calls". How can one compare "44 kHz" calls to 22- and 50-kHz calls when "44 kHz" calls are presented together with 22-kHz calls but not 50-kHz calls? What about carry-over effects? Hearing one type of call most likely affects the response to the other type of call. It appears likely that rats are a bit more anxious after hearing aversive 22-kHz calls, for example. Therefore, it would not be very surprising to see that the response to "44 kHz" calls is more similar to 22-kHz calls than 50-kHz calls. Of note, in case of the other playback experiment it is just said that rats "received appetitive and aversive ultrasonic vocalization playback" but it remains unclear whether "44 kHz" calls are seen as appetitive or aversive. Later it says that "rats were presented with two 10-s-long playback sets of either 22-kHz or 44-kHz calls, followed by one 50-kHz modulated call 10-s set and another two playback sets of either 44-kHz or 22-kHz calls not previously heard" (and wonder what data set was included in the figures and how - pooled?). Again, I am worried about carry-over effects here. This does not seem to be an experimental design that allows to compare the response to the three main call types in an unbiased manner. Of note, what exactly is meant by "control rats" in the context of fear conditioning is also not clear to me. One can think of many different controls in a fear conditioning experiment. More concrete information is needed.

Reviewer #2 (Public Review):

Theta-nested gamma oscillations (TNGO) play an important role in hippocampal memory and cognitive processes and are disrupted in pathology. Deep brain stimulation has been shown to affect memory encoding. To investigate the effect of pulsed CA1 neurostimulation on hippocampal TNGO the authors coupled a physiologically realistic model of the hippocampus comprising EC, DG, CA1, and CA3 subfields with an abstract theta oscillator model of the medial septum (MS). Pathology was modeled as weakened theta input from the MS to EC simulating MS neurodegeneration known to occur in Alzheimer's disease. The authors show that if the input from the MS to EC is strong (the healthy state) the model autonomously generates TNGO in all hippocampal subfields while a single neurostimulation pulse has the effect of resetting the TNGO phase. When the MS input strength is weaker the network is quiescent but the authors find that a single CA1 neurostimulation pulse can switch it into the persistent TNGO state, provided the neurostimulation pulse is applied at the peak of the EC theta. If the MS theta oscillator model is supplemented by an additional phase-reset mechanism a single CA1 neurostimulation pulse applied at the trough of EC theta also produces the same effect. If the MS input to EC is weaker still, only a short burst of TNGO is generated by a single neurostimulation pulse. The authors investigate the physiological origin of this burst and find it results from an interplay of CAN and M currents in the CA1 excitatory cells. In this case, the authors find that TNGO can only be rescued by a theta frequency train of CA1 pulses applied at the peak of the EC theta or again at either the peak or trough if the MS oscillator model is supplemented by the phase-reset mechanism.

The main strength of this model is its use of a fairly physiologically detailed model of the hippocampus. The cells are single-compartment models but do include multiple ion channels and are spatially arranged in accordance with the hippocampal structure. This allows the understanding of how ion channels (possibly modifiable by pharmacological agents) interact with system-level oscillations and neurostimulation. The model also includes all the main hippocampal subfields. The other strength is its attention to an important topic, which may be relevant for dementia treatment or prevention, which few modeling studies have addressed.

The work has several weaknesses. First, while investigations of hippocampal neurostimulation are important there are few experimental studies from which one could judge the validity of the model findings. All its findings are therefore predictions. It would be much more convincing to first show the model is able to reproduce some measured empirical neurostimulation effect before proceeding to make predictions. Second, the model is very specific. Or if its behavior is to be considered general it has not been explained why. For example, the model shows bistability between quiescence and TNGO, however what aspect of the model underlies this, be it some particular network structure or particular ion channel, for example, is not addressed. Similarly for the various phase reset behaviors that are found. We may wonder whether a different hippocampal model of TNGO, of which there are many published (for example [1-6]) would show the same effect under neurostimulation. This seems very unlikely and indeed the quiescent state itself shown by this model seems quite artificial. Some indication that particular ion channels, CAN and M are relevant is briefly provided and the work would be much improved by examining this aspect in more detail. In summary, the work would benefit from an intuitive analysis of the basic model ingredients underlying its neurostimulation response properties. Third, while the model is fairly realistic, considerable important factors are not included and in fact, there are much more detailed hippocampal models out there (for example [5,6]). In particular, it includes only excitatory cells and a single type of inhibitory cell. This is particularly important since there are many models and experimental studies where specific cell types, for example, OLM and VIP cells, are strongly implicated in TNGO. Other missing ingredients one may think might have a strong impact on model response to neurostimulation (in particular stimulation trains) include the well-known short-term plasticity between different hippocampal cell types and active dendritic properties. Fourth the MS model seems somewhat unsupported. It is modeled as a set of coupled oscillators that synchronize. However, there is also a phase reset mechanism included. This mechanism is important because it underlies several of the phase reset behaviors shown by the full model. However, it is not derived from experimental phase response curves of septal neurons of which there is no direct measurement. The work would benefit from the use of a more biologically validated MS model.

[1] Hyafil A, Giraud AL, Fontolan L, Gutkin B. Neural cross-frequency coupling: connecting architectures, mechanisms, and functions. Trends in neurosciences. 2015 Nov 1;38(11):725-40.

[2] Tort AB, Rotstein HG, Dugladze T, Gloveli T, Kopell NJ. On the formation of gamma-coherent cell assemblies by oriens lacunosum-moleculare interneurons in the hippocampus. Proceedings of the National Academy of Sciences. 2007 Aug 14;104(33):13490-5.

[3] Neymotin SA, Lazarewicz MT, Sherif M, Contreras D, Finkel LH, Lytton WW. Ketamine disrupts theta modulation of gamma in a computer model of hippocampus. Journal of Neuroscience. 2011 Aug 10;31(32):11733-43.

[4] Ponzi A, Dura-Bernal S, Migliore M. Theta-gamma phase-amplitude coupling in a hippocampal CA1 microcircuit. PLOS Computational Biology. 2023 Mar 23;19(3):e1010942.

[5] Bezaire MJ, Raikov I, Burk K, Vyas D, Soltesz I. Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit. Elife. 2016 Dec 23;5:e18566.

[6] Chatzikalymniou AP, Gumus M, Skinner FK. Linking minimal and detailed models of CA1 microcircuits reveals how theta rhythms emerge and their frequencies controlled. Hippocampus. 2021 Sep;31(9):982-1002.

Author Response

eLife assessment

This study assesses homeostatic plasticity mechanisms driven by inhibitory GABAergic synapses in cultured cortical neurons. The authors report that up- or down-regulation of GABAergic synaptic strength, rather than excitatory glutamatergic synaptic strength, is critical for homeostatic regulation of neuronal firing rates. The reviewers noted that the findings are potentially important, but they also raised questions. In particular, the evidence supporting the findings is currently incomplete and demonstration of independent regulation of mEPSCs and mIPSCs is a necessary experiment to support the major claims of the study.

We appreciate the detailed, thoughtful assessment of our paper by the reviewers and editors and will submit a revised version in the future that addresses the reviewers’ comments as detailed below in response to each concern. We will include a more open discussion of alternative possibilities. Further, we will repeat the optogenetic experiments assessing AMPAergic scaling in our mouse cortical cultures in order to demonstrate independent regulation of mEPSCs and mIPSCs as suggested.

Reviewer #1 (Public Review):

In the manuscript titled "GABAergic synaptic scaling is triggered by changes in spiking activity rather than transmitter receptor activation," the authors present an investigation of the role of GABAergic synaptic scaling in the maintenance of spike rates in networks of cultured neurons. Their main findings suggest that GABAergic scaling exhibits features consistent with a key homeostatic mechanism that contributes to the stability of neuronal firing rates. Their data demonstrate that GABAergic scaling is multiplicative and emerges when postsynaptic spike rates are altered. Finally, their data suggest that, in contrast to their prior data on glutamatergic scaling, GABAergic scaling is driven by spike rates. The authors set the paper up as an argument that GABAergic scaling, rather than glutamatergic scaling, serves as the critical homeostatic mechanism for spike rate regulation.

While the paper is ambitious in its rhetorical scope and certainly presents intriguing findings, there are several serious concerns that need to be addressed to substantiate the interpretations of the data. For example, the CTZ data do not support the interpretations and conclusions drawn by the authors. Summarily, the authors argue that GABAergic scaling is measuring spiking (at the time scale of the homeostatic response, which they suggest is a key feature of a homeostat) yet their data in figure 5B show more convincingly that CTZ does not influence spiking levels - only one out of four time points is marginally significant (also, I suspect that the bootstrapping method mentioned in line 454-459 was conducted as a pairwise comparison of distributions. There is no mention of multiple comparisons corrections, and I have to assume that the significance at 3h would disappear with correction).

We certainly understand the criticism here (similar to reviewer 2’s third point). In our resubmission we will do a better job discussing these complications, which we now summarize. First, we are presenting our entire dataset to be as transparent as possible. Unlike most synaptic scaling studies (including our own) that apply drugs to alter activity and assess mPSC amplitude at the final time point, here we are actually showing CTZ’s effect on spiking activity within the culture over time. This is critical because it has informed us of the drug’s true effect on spiking, the variability that is associated with these perturbations, and the ability and timing of the cultured network to homeostatically recover initial levels. This was important because it revealed that the drugs do not always influence activity in the way we assume, and this provides greater context to our results. Second, we are showing all of our data, and presenting it using estimation statistics which go beyond the dichotomy of a simple p value yes or no (Ho J, Tumkaya T, Aryal S, Choi H, Claridge-Chang A. 2019. Moving beyond P values: data analysis with estimation graphics. Nat Methods 16: 565-66). Estimation statistics have become a more standard statistical approach in the last 15 years and is the preferred method for the Society for Neuroscience’s eNeuro Journal. This method shows the effect size and the confidence interval of the distribution. For the 3 hr time point in Fig. 5B the CTZ/ethanol vs. ethanol data points exhibit very little overlap and the effect size demonstrates a near doubling of spike frequency, and the confidence interval shows a clear separation from 0. This was a pairwise comparison as we compared values at each time point after the addition of ethanol or ethanol/CTZ. Third, the plots illustrate an upward trend in spike frequency at 1 and 6 hrs, but that there is also clear variability. It is important to note that while these recordings help us to understand effects on spiking across the cultured network, they cannot directly speak to spiking activity in the principal neurons that we target. This complication along with the variability inherent in these cultures could make simple comparisons difficult to interpret. Regardless, we do see some increase in spiking with CTZ and we clearly see increases in mIPSC amplitude, thus providing some support for the idea that spiking could be a critical player in terms of GABAergic scaling, particularly when put in the context of our other findings. However, it is important to recognize that something other than total spike rate may contribute to GABAergic scaling, such as the pattern of spiking that produces a particular calcium transient, and this will be discussed in the resubmission.

Then, the fact that TTX applied on top of CTZ drives a increase in mIPSC amplitude is interpreted as a conclusive demonstration that GABAergic scaling is sensing spiking. It is inevitable, however, that TTX will also severely reduce AMAP-R activation - a very plausible alternative explanation is that the augmentation of AMPAR activation caused by CTZ is not sufficient to overcome the dramatic impact of TTX. All together, these data do not provide substantial evidence for the conclusion drawn by the authors.

We understand this point when considering the CTZ/TTX experiments by themselves. However, spiking appears to be a more straightforward trigger when the CTZ/TTX results are coupled with the prevention of GABAergic downscaling by optogenetic restoration of spiking in the presence of AMPAR antagonists. Further, an important point here is that our results with TTX vs. TTX + CTZ are different for GABAergic scaling (no difference) and AMPAergic scaling (CTZ diminished upward scaling) suggesting different triggers for the two forms of scaling. We will make this more clear in our resubmission.

Specific points:

• The logic of the basis for the argument is somewhat flawed: A homeostat does not require a multiplicative mechanism, nor does it even need to be synaptic. Membrane excitability is a locus of homeostatic regulation of firing, for example. In addition, synapse-specific modulation can also be homeostatic. The only requirement of the homeostat is that its deployment subserves the stabilization of a biological parameter (e.g., firing rate).

We agree with the reviewer and should not have suggested that this was a necessary requirement for a spike rate hemostat. What we should have said was that historically this definition has been attributed to AMPAergic scaling, which is thought to be a spike rate homeostat. We will correct this in the resubmission.

• Line 63 parenthetically references an important, but contradictory study as a brief "however". Given the tone of the writing, it would be more balanced to give this study at least a full sentence of exposition.

Agreed, we will do this.

• The authors state (line 11) that expression of a hyperpolarizing conductance did not trigger scaling. More recent work ('Homeostatic synaptic scaling establishes the specificity of an associative memory') does this via expression of DREADDs and finds robust scaling.

The purpose of citing this study was to argue that the spike rate homeostat hypothesis doesn’t make sense for AMPAergic scaling based on a study that hyperpolarized an individual cell while leaving the rest of the network unaltered and therefore leaving network activity and neurotransmission largely normal. In this case scaling was not triggered, suggesting reduced spike rate within an individual cell was insufficient to trigger scaling. The study that the reviewer refers to hyperpolarizes a majority of cells in the network and therefore will also alter neurotransmission throughout the network, which does not separate the importance of spiking and receptor activation as in the above-mentioned study. We will make this point more clearly in the resubmission.

• Supplemental figure 1 looks largely linear to me? Out of curiosity, wouldn't you expect the left end to be aberrant because scaling up should theoretically increase the strength of some synapses that would have been previously below threshold for detection?

We agree that the scaling ratio plot is largely linear. To be clear, the linearity of the ratio plot was interesting but our main point here was that this line had a positive slope meaning ratios (CNQX mPSC amplitudes/control mPSC amplitudes) got bigger for the larger CNQX-treated mPSCs. Alternatively, a multiplicative relationship where mPSCs are all increased by a single factor (e.g. 2X) would be a flat line with 0 slope at the multiplicative value (e.g. 2). In terms of the left side of the plot, we do see values that rise abruptly from 1 - this is partially obstructed by the Y axis in this figure and we will adjust this. This left part of the plot is likely due the CNQX-induced increases in mPSC amplitudes of mini’s that were below our detection threshold of 5pA. Therefore, mini’s that were 4pAs could now be 5pAs after CNQX treatment and these are then divided by the smallest control mPSCs which are 5 pAs (ratio of 1). We will try to do a better job describing this in the resubmission.

Given that figure 2B also shows warping at the tail ends of similar distributions, how is this to be interpreted?

The left side of the ratio plot shows evidence consistent with the idea that mIPSCs are dropping into the noise after CNQX treatment (similar to above argument), while most of the distribution suggests mIPSCs are reduced to 50% by CNQX treatment. On the right side of the ratio plot the values appear to mostly increase. We are not sure why this is happening, but it looks like some mIPSCs are not purely multiplicative at 0.5, particularly in TTX. It is also important to point out that this is a relatively small percent of the total population and the biggest mPSCs can vary to a great degree from one cell to the next. We will discuss this in the resubmission.

• The readability of the figures is poor. Some of them have inconsistent boundary boxes, bizarre axes, text that appears skewed as if the figures were quickly thrown together and stretched to fit.

We will address these issues in the resubmission.

• I'm concerned about the optogenetic restoration of activity experiment. Cortical pyramidal neuron mean firing rates are log normally distributed and span multiple orders of magnitude. The stimulation experiments can only address the total firing at a network-level - given than a network level "mean" is meaningless in a lognormal distribution, how are we to think about the effect of this manipulation when it comes to individual neurons homeostatically stabilizing their own activities? In essence, the argument is made at the single-neuron level, but the experiment is conducted with a network-level resolution.

As described above, we do not have the capacity to know what the actual firing rate of a particular neuron was before and after introducing a drug and so we cannot absolutely say that we have restored the original firing rates of neurons. However, there is reason to believe that this is achieved to some extent. Our optogenetic stimulation is only 50-100 ms long activating a subset of neurons. This is sufficient to provide a synaptic barrage that then triggers a full blown network burst where the majority of spikes occur, but this is after the light is off. In other words, the optogenetic light pulse only initiates what becomes a normal network burst that fortunately allows the individual cells to express their relatively normal (pre-drug) activity pattern. In our previous study we show that this is the case for individual units - the spiking of an individual unit during a burst is similar before and after CNQX/optostim (see Figure 4b and Suppl. Fig 4 in Fong et al. 2015 Nat. Comm.). We are not claiming that we have restored spiking to exactly the pre-drug state, but bring it back toward those levels and we see this is associated with a return of the mIPSC amplitude to near control levels. We will include a description of this in the resubmission.

• Line 198-99: multiplicativity is not a requirement of a homeostatic mechanism.

• Line 264-265 - again, neither multiplicativity and synaptic mechanisms are fundamentally any more necessary for a homeostatic locus than anything else that can modulate firing rate in via negative feedback.

Agreed, see above discussion of homeostat requirement. Will adjust these statements in our resubmission.

• 277: do you mean AMPAR?

We were not clear enough here. We actually do mean GABAR. The idea is that CTZ increases network activity and thus increases both AMPAergic and GABAergic transmission. We will clarify this in the resubmission.

• Example: Figure 1A is frustratingly unreadable. The axes on the raster insets are microscopic, the arrows are strangely large, and it seems unnecessary to fill so much realestate with 4 rasters. Only one is necessary to show the concept of a network burst. The effect of time+CNQX on the frequency of burst is shown in B and C.

• Example: Figure 2 appears warped and hastily assembled. Statistical indications are shown within and outside of bounding boxes. Axes are not aligned. Labels are not aligned. Font sizes are not equal on equivalent axes.

We will adjust these issues in the resubmission.

• The discussion should include mention of the limitations and/or constraints of drawing general conclusions from cell culture.

We agree and will adjust the discussion. Also, this is why we cited studies that argue GABAergic neurons have a particularly important role in homeostatic regulation of firing following sensory deprivations in vivo.

• The discussion should include mention of the role of developmental age in the expression of specific mechanisms. It is highly likely that what is studied at ~P14 is specific to early postnatal development.

We will discuss caveats of cortical cultures at DIV 14-20.

It is essential to ensure that the data presented in the paper adequately supports the conclusions drawn. A more cautious approach in interpreting the results may lead to a stronger argument and a more robust understanding of the underlying mechanisms at play.

Agreed.

Reviewer #2 (Public Review):

Synaptic scaling has long been proposed as a homeostatic mechanism for the regulation for the activity of individual neurons and networks. The question of whether homeostasis is controlled by neuronal spiking or by the activation of specific receptor populations in individual synapses has remained open. In a previous work, the Wenner group had shown that upscaling of glutamatergic transmission is triggered by direct blockade of glutamate receptors rather than by the concomitant reduction in firing rate (Nat Comm 2015). In this manuscript they investigate the mechanisms regulating scaling of GABA-mediated responses in cortical cell cultures using whole-cell recordings to detect GABAergic currents and multielectrode arrays to monitor global firing activity, and find that spiking plays a fundamental role in scaling.

Initially, the authors show that chronic blockade (24 h) of glutamatergic transmission by CNQX first reduces spontaneous spiking (at 2 h), but later (24 h) firing grows back towards higher frequencies, suggesting a compensatory mechanism. Then it is shown that either chronic CNQX treatment or TTX cause a reduction in the amplitude of GABAergic mIPSCs. Effects of CNQX on IPSCs are then reverted by replacing spontaneous network firing by chronic optogenetic stimulation of the entire culture, also indicating that GABAergic transmission is homeostatically regulated by global firing. Enhancing glutamatergic transmission with CTZ increases mIPSC amplitude, while addition of TTX in the presence of CTZ causes the opposite effect. Finally, increasing spiking activity using bicuculline also increases mIPSC amplitude, and the authors conclude that spiking activity rather than neurotransmission control homeostatic GABA scaling. The manuscript shows interesting properties in the regulation of global GABAergic transmission and highlight the important role of spiking activity in triggering GABA scaling. However, it is strongly recommended to address some caveats in order to better support the conclusions presented in the manuscript.

Major points:

1) The reason why CNQX does not completely eliminate spiking is unclear (Fig. 1). What is the circuit mechanism by which spiking continues, although at lower frequency, in the absence of AMPA-mediated transmission and what the mechanism by which spiking frequency grows back after 24h (still in the absence of AMPA transmission)?

Is it possible that NMDA-mediated transmission takes over and triggers a different type of network plasticity?