pon coming into class, he will sit down and then almost immediately get back up and walk over to my Take a Break station to play with the glitter bottles, walk around the room to an area of his choice, or go toward the piano and other instruments to explore.
It is interesting to see how Carson uses and utilizes his surroundings. He tends to explore and Id assume he does so to clear his mind or see new things.
In the beginning, it is often helpful to place students with differences and disabilities near an excellent student who can model appropriate behaviors.
This can be good, as long as the "model student" does not feel uncomfortable about being an example for their classmate, but this most likely would not be an issue. I do agree that learning by observing ones peers is a great method.
it is imperative for music educators to strive for a caring, inclusive environment that is conducive for all students to learn.
This is, again, something that is so important. If you want your students to enjoy learning and enjoy being at school in your class, you have to create an environment that will foster that feeling.
Carson loves knowing when his teachers care about his well-being
I think this is something that educators forget about sometimes. Students just want to be known and seen by their teachers. If they feel that way, they will be more open to participating and overall more engaged in the classroom.
Allow opportunities to respond during instruction that include time allotted for visual, kinesthetic, and oral responses. In addition, use an instructional model that allows students to respond individually, in small groups or in large groups
I can connect this to my own placement. When I have taught in self-contained autism classes, students can respond to the music by singing and dancing to the movements (oral and kinesthetic), as well as choose what songs they want by pointing to a choice board I hand them. The choice board encourages them to use their words (Ex. "I want..") or use their communication device.
In these cases, create a special signal or gesture to let this student know that their behavior is not appropriate.
In the special education classes I've observed and taught, silent gestures and signals have been ineffective. Perhaps this could work in an inclusive classroom rather than a self-contained one? In my placement, when a student is displaying inappropriate behavior, it's accepted schoolwide for teachers to say "No thank you" and give the student a simple and quick redirection. This saying and redirection is used by all teachers consistently, so it has been effective.
I told him that he could come up with an eight-beat rhythmic pattern and then hand the materials over to me (“1, 2, ready, go,” he played, and then passed the materials back with no problem).
This is so smart. Kids love to be creative and show off what they can do, so this was a great way of getting Carson's mood back up.
Some teachers create class rules that are too vague, ask too much or too little of students, or compile a lengthy list of rules that are difficult to remember, comply with, and enforce. Begin with a few rules that are general enough to be adapted to many situations and are easy to remember.
I appreciate this reminder. Vague rules help nobody, and lengthy rules are difficult to remember. For teachers with a list of rules, I've observed that they often have a designated rule poster in their classroom, so students always know where to refer to make good choices.
Creating a classroom culture that includes a regular and efficient manner of communicating and enforcing rules is important.
I've found that my time as a student teacher, children need structure more than we think they do. Even as adults, we crave structure and stability. I've noticed in texts that structure is especially encouraged for special education classes, but I feel this should be applied to all classes of every age and level.
The proximity of the student (especially one who has the potential to disrupt class) to the music teacher is an important first step in managing behavior.
I witnessed how effective student-to-teacher proximity is today during my placement. In a self-contained autism classroom, all of the paraprofessionals, as well as their teacher, were absent. One substitute para and one substitute teacher were placed there instead for the day. This caused the students to display more class-disruptive behaviors than normal. With a lack of their familiar paras and a lack of adults, the classroom management was lower than usual.
Carson loves routine and knowing details
These are common traits in people with special needs, especially those on the autism spectrum. It is incredibly important to know this about your students with special needs, and it seems like the teacher in this vignette was doing an excellent job!
If a student is having great difficulty following the class rules, write or draw a picture of the rule on a note card and have the student put the card in their pocket to assist in remembering that rule.
I really like this strategy because while it encourages visual and tactical learning, it also serves as a gentle reminder to follow the rules established in the classroom, rather than a punishment
Many students with disabilities have communication delays. This leads them to act out to express dissatisfaction with their surroundings. That does not mean they should not face consequences; however, as mentioned earlier, teachers have been known to label a child as a “bad kid” when in fact there is a simple communication barrier or misunderstanding within the classroom.
I really appreciate this. This is deeply shows how easily behavior can be misunderstood when communication is difficult. Growing up I've lived through situations where I acted out in class, not because I felt the urge to misbehave, but because I didn't have the words to express my feelings. It's important for teachers to be patient and have empathy toward students instead of labeling students "bad kids".
Students who are developmentally able and less affected by their disabilities often appreciate the opportunity to participate in the creation of their own behavior plans, expectations, and consequences.
It's important to understand that students deserve to have a voice in their own behavioral plans. It'll help them feel more respected and responsible for their own actions, while ensuring they feel heard.
In addition, ensuring that students with differences and disabilities are actively engaged with other students may lessen the severity or frequency of outbursts and other inappropriate behaviors.
This is very important in not only the classroom setting, but also in the workplace. This ensures that people don't feel isolated, and frustrated. It's important to know that active engagement promotes social integration, acceptance and shared learning experiences.
Music educators tend to be isolated within public schools. They are often the only teacher or one of the few music teachers within a school building. Many travel between buildings. This can be a disadvantage in understanding the social structure within a school.
This is an ongoing issue in today's society, as many schools are short in the music department. In my experience, there was only one music staff in my High School building. This in turn, can make professional support and creative idea sharing, a lot difficult for music educators.
Music can be the catalyst for students to develop healthy self-concepts and establish positive relationships throughout their time in public school. These concepts and relationships continue with students (with and without differences and disabilities) as they leave public school settings and continue their lives as adults.
This chapter emphasizes proactive student-centered strategies for inclusive music classrooms, from physical setup and behavior plans to socialization and ethical care. How can music educators balance the need for structure with the flexibility required to support disabled learners in high-performance settings?
This type of data collection is sometimes referred to as a functional behavioral analysis and the three steps may be called “ABC,” or antecedent, behavior, and consequence (Barnhill, 2005).
This passage reinforces how music educators can contribute to meaningful data intervention plans, even in non-core subjects.
. Music educators should treat paraprofessionals as team members in classrooms and provide them with information prior to class time to allow them to learn the lesson and prepare to participate in instruction. This allows paraprofessionals the opportunity to share any additional information that may assist in the teaching and learning process and shows them that their participation in the process is valued.
How can music educators ensure paraprofessionals feel empowered to participate in teaching, especially if they don't know a lot about music?
s, he gets a chance to play a short improvisation on the piano for his classmates, and during class, he gets to be a volunteer for each of our activities if he follows the directions of sitting in a circle with us and keeping his motions and sounds to a minimum. His class last year was very welcoming of him and understood that he learns differently and helped him in ways that they could, whether it be directing him to the circle carpet or in line at the end of class.
a strategy reframed by thinking about classroom incentives. Instead of external reward, the teachers use musical expression and leadership roles to motivate Carson.
It is also important to point out that using the words “good behavior” and “bad behavior” can be problematic. If a student hears that they are exhibiting “bad” behavior often, the student can develop self-esteem issues. Even worse, the student can start to build an identity that is centered around “bad” behavior as a way of gaining the attention of the teacher. Just refer to behaviors as what they are: behaviors.
Labeling as "good" or "bad" can harm a student's esteem and identity. Describing actions neutrally to avoid reinforcement of negative self-concepts.
As you read each source, take a minute to evaluate the reliability of each source you find
Looking at multiple sources ensures your reliability and cancels out any false information
Your sources will include both primary sources and secondary sources. As you conduct research, you will want to take detailed, careful notes about your discoveries
Including both primary sources and secondary sources is important because both offer different/similar perspectives.
The following are examples of secondary sources: Magazine articles Biographical books Literary and scientific reviews Television documentaries
Secondary source examples
Other primary sources include the following: Research Articles Literary Texts Historical documents such as diaries or letters Autobiographies or other personal accounts Podcasts
Primary source examples
The textbook Successful Writing explains that writers need a thesis statement to provide a specific focus for their essay and to organize what they will discuss in the body of their writing. A thesis statement is an argumentative central claim in a paper; the entire paper is focused on demonstrating that claim as a valid perspective. Your thesis statement should be in your introduction because you must make sure that the audience is aware of your paper’s intent so that there is clarity from the outset. Consider placing the thesis toward the bottom of your introduction. This allows you a few sentences to introduce the concept and prepare the reader for your purpose.
A thesis statement is very useful and needed for your essay
selecting some text
test
Ever since Coyote closed the door the spirits of the dead have wandered over the earth, trying to find some place to go, until at last they find the road to spirit land.
This line shows how myths explain natural and emotional realities — in this case, grief and the unknown after death. The story uses the wandering spirits to make sense of human sorrow and the mystery of where souls go. It’s both poetic and tragic.
Coyote jumped up and said that people ought to die forever because there was not enough food or room for everyone to live forever.
This moment echoes Lewis Hyde’s idea of the trickster as a boundary-breaker. Like Coyote, he challenges the community’s decision and insists that death should be permanent. In doing so, he breaks the social and moral order that values life and rebirth. Although his actions appear selfish and cruel, they ultimately reshape the natural balance, showing how tricksters bring transformation through chaos and contradiction.
Rather than being “fooled”by the occasional errors of Annotator1, Cellpose-SAM reverts to its inductive biases to selectively learnthe generalizable structure in the data.
It'd be cool to show some examples of this!
hus, Cellpose-SAM can run out-of-the-box on images that have been acquired with varyinglevels of image degradation, at different pixel sizes orin arbitrary channel order, substantially simplifying thelogistics typically associated with setting up an image
What is your intuition for the origin of this capability? e.g. to what extent is it likely due to the size of the SAM encoder, its architecture, the size of the pretraining dataset, etc.
We reducedthis to 256x256 and 8x8, which required us to adaptthe position embeddings and patch embedding filtersvia appropriate subsampling.
Does this later the number of parameters in the pre-trained SAM model? And would it be expected to degrade performance? (of course the tradeoff could still be worthwhile, but it would be nice to know if this downsampling comes with a cost)
The most important aspect of biological software is thatit works well in the hands of biologists.
This is a bit of a nit, but I think it would be worth clarifying what is meant by "works well" as it could be interpreted to mean "easy to use", which feels much less important than that biological software produce correct and reproducible results.
Since we are especially interested in generalizationperformance, we wanted to choose a test datasetin which images are relatively different from thosein the training set.
This could be prohibitively expensive, but a more robust test of generalization would be some kind of leave-one-dataset-out cross-validation (e.g. calculate a dataset-dataset similarity and hold out the dataset least similar to all other datasets)
We also reverted the local attention layers of thecustom ViT transformer from SAM back to the defaultglobal attention of ViT-L, with almost no runtime penalty
what was the motivation for making this change?
what is developmental science?
Relationships charac-terized as Andhra riste were not as binding as those of the Lashkarwala orSheharwala riste. They did not entail rigid responsibilities and obligationsas the guru-cela bond did, nor were they restricted to members of one’s ownlineage or hijra house. The most common of such relationships were thosebetween “sisters” (behen), and that between a “mother” and her “daughter”(ma-beti relationships).
Reddy distinguishes two different relationships/bindings in the community: the guru-cela relationship (focus on lineage and hierarchy and entails obligations), and the sister and mother-daughter relationship (motivated by mutual affection, focus on love and caring). I note that she didn't discuss how the sister relationship is organized for hijras, instead comparing the guru-cela and mother-daughter as parallel. As she argues that this latter form were not restricted by houses or lineages, will it unsettle the Hijra's rigid kinship structure by houses? How does the kinship incorporate this form of relationality, or even use it to recuperate the need for care, which is sometimes absent in the obligations-bound guru-cela relationship?
it is moreproductive to see these kinship patterns as a complex web of significations,a web of emotional tensions between real people, fraught with ambiguousmeanings—an “architecture of conflicting desires” as Trawick notes (152)—that fundamentally constitutes hijra/koti identity. If desire or love plays acentral role in the lives of hijras and kotis, it is through the various, am-biguous, and conflicting patterns of kinship—the affective bonds of guruand cela, “milk” mother and daughter, sister and gurubhai, mother and son,husband and wife—that this love is made manifest
Hooray, at the final bit of the argument in this chapter. Reddy employed the notion of the architecture of conflicting desires from Trawick. It is an architecture, a system for organizing and managing the ambiguous forces in "various polysemic" desires and relations. The kinship system here is the way to make such ambiguity the meaning-maker. This goes back to her critiques of psychoanalytical, relational, and family explanations. The architecture helps us to think about how ambiguity manifested in these practices of desires, and how in turn it is managed in this way.
nstead, I would argue,understanding these options not as dichotomous ideological oppositions butas subtle tensions reflected through the various polysemic, affective bondsof hijras and other kotis is imperative.
Reddy shows her concerns with the "resistant" discourse that reads hijras' kinship model as an opposition against the traditional heteronormative one. Reddy pays attention to the fact that hijras also internalize and adopt many ideas and terminologies from others (guru-cela is a clear example of how toxic it can be sometimes). And this understanding somehow feeds into the heteronormative hegemony (as if all other alternatives have to have something to do with it). I love how she used the word "polysemic," which really allows us to see the possibilities in the hijras' community, which does not exclude the role of power relations. It is not the dichotomy of conforming/resisting. Instead, the hijras create a space in the ambiguity in between.
his statement ignores the existence of thespecific elaborations of hijra and koti kinship, the patterns of caring andrelatedness within the community, and their fundamental resonance withbroader mainstream societal patterns, structures, and sentiments
Many vignettes above called back here! Reddy criticized the recent scholarship that resolves the theoretical problem raised by hijra with their investment in the family. Reddy has shown that this family is not what we usually conceive: multidimensional, inclusive, and elastic. The family structure of hijra does not oppose the mainstream family value of India, but precisely in the space of ambiguity, it finds its legitimacy and possibility to sustain.
Although the “desire for fusion” or “the cultural pref-erence for integration” rather than individuation does address, to some ex-tent, kotis’ desire for kinship and perhaps the existence of certain significantbonds, it does not really explain why they adopt the specific kin and therituals or practices they do, nor does it satisfactorily explain the power dif-ferentials evident in other relationships within the community. Likewise,the relational argument potentially accounts for the ubiquitous need for“our people,” but it reveals nothing significant about the specific structuresof caring and the particular constructions of kinship that I have describedamong hijras and kotis.
The paragraphs above criticized the psychoanalysis method; here, Reddy also argues that the relational framework is unsatisfying. Indeed, identities are all relational, but it seems to be an easier option not to obscure the specificity of practices and rituals - why in this way? As other relations also provide identifications, why do this and why insist on becoming hijra despite all the hardship and the sacrifices? Reddy further pushes Don Kulick's idea of relational femininity, which glosses many difficulties between the ideal and practices for travestis (maybe they do love their boyfriends!).
erhaps, as these scholars maintain, oneof the reasons being alone—without a kin network—is so inconceivablein India is because identity is largely relationally constructed and context-dependent to a greater (and different) degree than it is in the West (Marriott1976; Shweder and Bourne 1984; Ramanujan 1990; Shweder, Mahapatra,and Miller 1990).
This passage delivered the punch of the argument: hijra is a relational identity. This echoes the idea of relational femininity of Travestis in Kulick's ethnography. An identity of self can only emerge in a relational network with others (whether the in-groups or out-groups). Then Reddy criticized Western scholarship that views identity as a matter of individuals, neglecting the significance of specific contexts that condition the manifestation of such identity.
In India, an individual’s sense of self, theyargue, is fundamentally connected to a desire for incorporation, for fusionwith the (maternal) world, rather than a greater differentiation of self fromothers. According to these (male) authors, integration, in this context, moreoften than not implies the desire for an idealized relationship with one’smother (Kakar 1989).
Reddy tries to build dialogue with the Indian psychoanalysis literature that seems to insist on an essentialist approach. This view naturalizes the hijra identity as something intact and given, as simply a desire for fusion with the world. ijras' desires were essentialized as the desires for fusion. Therefore, the intricated innovation of hijra kinship structure can be reduced to their "desire for an idealized relationship" with their maternal mother. Nevertheless, as Reddy also already shown, many of the hijras did maintain good relationship with their biological mothers, most often as their only connection with their natal family. Reddy wages a critique of the psychoanalytic approach and argues for a more in-depth examination of the hijra's kinship to understand their desires not as a taken-for-granted fact but as a mediation (ambiguity_ of the negotiation of their status quo in the society and their own agency.
or them, all kotis are hamare log or manollu (ourpeople), in opposition to pantis who are “othered,” both as objects of desireagainst whom kotis define themselves as well as subjects who instantiatethe gender norm
Hijras identify all Pantis as the othered, denying their membership to join the kinship network of Kotis. Therefore, sexual penetration (penetrator/penetratee) serves as a principal distinguisher for the hijras' relationality. However, at the same time, the desires for panti/coexist with this exclusive concept of family. For example, in previous chapters, Reddy introduced a vignette of a panti who is viewed as a member of the family, who basically lives at the waterbank with the hijras. Many other instances illustrate this physical proximity with their husbands. It once again demonstrated the elastic application of the family system. It is a kind of family that does not exclude other forms of kinship but allows them to coexist. Moreover, it also relates ot Kulick's account of travestis wherein they could reverse the othering, reiterating their subjectivity.
As the above vignette indicates, aside from the privileging of the rit,hijras adopt a shifting signifier in their demarcation of an insider/outsiderboundary. For the most part, family for hijras refers to other hijras, andyet not all non-hijras are excluded from consideration: non-hijra kotis arealso considered manollu (our people). The use of this term implies a wider,shared community of actors. It is a contextual signifier, dependent to somedegree on the particular actors present. For hijras, manollu refers to themembers of their own in-group—hijras—in the context of other kotis, butit refers to the entire koti community when the social context includes pantis(or narans
Reddy adopted a semiotic analytical framework here to understand the ambiguity/ and the flexibility of the kinship conception. The subtle difference between family and manollu is interesting, as it shows how they relate to the wider Koti community and other communities they deem with less izzat. It also makes me wonder if it is possible to extend such kin identification to their biological mothers, as Reddy states, women can also be included in. It also differs from the biological family, which is rigid and passes down through vertical lineage only. Perhaps the kinship system of hijra elucidates on how the a system of relationality can be organized multidimensionally.
Despite the retention of this strong link between natal mother and sonin practice, such a relationship went against the ideal norms of the hijracommunity. The renunciation of natal kinship ties is a clear marker of hijraidentity, serving to differentiate them from other kotis such as the zenanas,as the latter explicitly stated.
Another paradox arises: the complicated presence of multiple kinship relations, the hijra kinship and the natal kinship. While it is ideal for hijra to completely renunciate their natal family as the hijra family is supposed to replace the latter, there is a lot more elasticity in practices. It is also interesting to see that the natal family sometimes recovers the individuals when the hijra kinship fails to extend the support and care. The lasting relationship with their natal mothers also points to the differences from the hijra mother-daughter relationship. Do hijras understand them as two fundamentally different relationships?
. Despite their marginality,concern for their izzat appears to motivate many of their actions. Surekhaexplicitly expressed this sentiment when she said, “Having a husband givesyou some izzat [in the eyes of society].”While “marriage” or maintaining a jodi appears to be a cherished ideal forhijras, it is clearly not without ambivalence. Hijras are officially discouragedby senior hijras from maintaining relationships with pantis.
The question of Izzat that echoes the title of the book, reiterates the inquiry of what it means to be a hijra in a specific sociocultural context of India. Reddy here draws our attention to another paradox: the ascetic, asexual ideal and the desire for a husband for gender affirmation. This passage also responds to the two issues outlined on page 169 (idealization of marriage and the ambivalence in their feelings towards men). It is because such ambiguity makes sense of their hijra identity. Izzat is at the heart of the ambiguity knot of the Hijra kinship system. It somehow resonates with the Travesti. Despite the very different attitude towards husbands/boyfriends, both groups understand their romantic partners as an example of their status in the in-group.
then ambiguity must bea key component of that whole, a key feature of the communicative systemby which that whole is maintained,” writes Margaret Trawick (1990, 41).Perhaps, with regard to hijras’ worldview too, it is intentional ambiguitythat best describes their “paradoxical behavior.” As with Trawick’s Tamilfamily, if such ambiguity or “paradoxical behavior” could be explained atall, it was often in terms of love or desire (
Reddy demonstrated that the sense of ambiguity has orchestrated the everyday life of the hijra community and their identity of the self, from guru-cela and mother-daughter, and the natal family relationships, to their attitudes towards husbands and marriage, to their ideal and reality. For her, this intentional ambiguity may serve both as a means and an end of this kinship model to make sense of their unique position in the gender milieu of India. She concludes her argument, drawing on the connection between such a paradoxical nature and love or desires. Intentional ambiguity explains the impossibility to realize those ideals, or in other words, it strategizes such impasse as productive where individuals can find belonging. Ambiguity is precisely the meaning-making here.
an instance of how,at particular moments, our socially produced worlds sometimes becomenaturalized into “new” forms of caring
I understand that Reddy is very cautious about making the parallel connection between the heteronormative consanguineal relationship and the mother-daughter ties in the hijra community. This sentence implies a temporal order, that the biological form precedes the other (maybe I am wrong). Nevertheless, the appearance of "newness" reflects the social construction as the basis of our kinship.
Mothers often appeared to have greater affection for daughters thanfor their celas, even though there was no denying the greater significanceand legitimacy of the guru-cela bond over the ma-beti one
Just a random pondering: how would the celas and daughters of one hijra interact with each other? And does this scale of affection also engender another form of hierarchy and evaluation?
When I asked Rajeshwari why they adopted daughters, she told me itwas to extend their kin relations, their sambandam. Daughters would morethan likely be celas of other hijras with whom they could then form analliance, she explained. Such relationships also serve publicly to strengthenties between hijras, through a symbolic ritual enactment. By developingthese bonds, each hijra is able to establish relationships with other hijras,thereby not only widening the kinship network but also cementing ties,as in Pierre Bourdieu’s (1977) notion of “practical kinship.” For instance,among kandra hijras, dudh behans would “exchange” celas, making thesecelas their respective dudh betis
It is interesting to see that though the mother-daughter relationship is founded upon a mutual affection, it still entails a power relation within the larger hijra community as a means of negotiation and alliances. The term "extend" implies the horizontal orientation of the mother-daughter relationship, which supplements the vertical hierarchy of the guru-cela relationship. Also, they can be interchangeable, as the interlocutors showed that the guru would exchange celas. I dont know whether this alliance also creates a friction between the hijras? For instance, if two gurus exchanged their celas as the other's daughter, will one step out to protect her daughter while she was facing mistreatment by the other guru? And what will happen if the cela decides to switch to another guru, will this mother-daughter tie not be obstructed? Overall, this passage clearly demonstrates that the this horizontal mother-daughter love is not the exception of the hijra kinship, but a constituent of the holistic part of what animates it, opening up venues for ambiguity (Reddy will talk about it later!).
he then pours somemilk, using a cup held over her breast, into the mouths of the prospectivebetis, thereby sealing this relationship with “her” milk. To further seal thebond thus forged, each of the prospective dudh behans pricks her fingerand lets a few drops of blood flow into the cup of milk, which is then sharedby all of them, mother and sisters
The emphasis on milk, a substance, is interesting. This ritual contrasts with the guru-cela one, in which the centre is the monetary "offering" and kissing the feet of the guru and nayak. In this ritual, the transmittable liquid—blood and milk—fuses with one another, sealing the bond in a nursing gesture. It is the mother who enacts the act of care to the daughters. These two rituals sort of indicate an opposite relationship dynamic and orientations of care.
1
C handra Mohanty, mycolleague in W om en’s Studies, shared these concerns
批注
Ya que escribo como trabajo, muchas veces me han preguntado si no creo que seré reemplazado por una inteligencia artificial. Yo creo que no. Aunque seguramente muchas personas usarán estas herramientas para escribir cosas, consideren lo que pasaría si todo el texto del mundo fuera creado por IA: los modelos de lenguaje en los que están basados estas herramientas simplemente regurgitarían infinitamente otros textos, si bien coherentes, de baja calidad y de dudosa verosimilitud ya regurgitados por otra inteligencia artificial. Eventualmente habría un mercado para algún humano que entrara, cuando menos, a revisar, a editar, a hacer algo con el texto. A escribir.
La IA puede escribir, sí, pero no puede tener algo que decir y mientras exista alguien que piense, cuestione, viva y sienta va a ser necesario que un humano esté ahí, al menos para revisar, reinterpretar y darle alma a lo que se escribe. Sara Sarria
Por su parte, las redes sociales (en un sentido amplio que incluye foros y blogs) atrofiaron nuestro sentido de habitar una realidad común. Pero a cambio nos dieron la posibilidad de cambiar las dinámicas del poder de la información. Ahora “cualquiera” (en el sentido de Ratatouille) puede hacer escuchar su voz, no sólo los guardianes de la información a los que hemos estado acostumbrados. Esto tiene sus cosas buenas y malas, pero sin duda ha cambiado cómo vivimos e interactuamos.
Lo que plantea sobre las redes es muy cierto, antes unos pocos hablaban y el resto escuchaba, ahora cualquiera puede opinar, pero eso también hizo que cada uno viva en su propia “burbuja”. Ganamos voz pero perdimos un poco el sentido de realidad compartida "No todos estamos viendo el mismo mundo". Sara Sarria
Values often suggest how people should behave, but they don’t accurately reflect how people do behave. Values portray an ideal culture, the standards society would like to embrace and live up to. But ideal culture differs from real culture. In an ideal culture, there would be no traffic accidents, murders, poverty, or racial tension. But in real culture, police officers, lawmakers, educators, and social workers constantly strive to prevent or address these issues. American teenagers are encouraged to value celibacy. However, the number of unplanned pregnancies among teens reveals that the ideal alone is not enough to spare teenagers the potential consequences of having sex.
This section of this chapter provides really good topics to annotate on. I believe that especially in generation Z, value lacks effectiveness and importance because of how everyone in my generation care more about perception and reward rather than living up to values I believe that not many people my age even care to live up to their values and beliefs because of the fact that everyone is so engulfed in social media and disregard how dangerous social media's influence can truly be
Consider the value that the U.S. places upon youth. Children represent innocence and purity, while a youthful adult appearance signifies sexuality. Shaped by this value, individuals spend millions of dollars each year on cosmetic products and surgeries to look young and beautiful.
This is a very interesting take on values and beliefs in the US. This section of the article in particular makes so much sense, as I can relate to this to my everyday life. It's no question how a youthful adult appearance signifies sexuality, and It can be a very frightening and unsafe thing because of the dangers that goes on with sexual harrasment in the world going on today. This section states that children represent innocence and purity, but in all honstly and unfortunately, it feels as though there's not much innocence and purity left in children anymore because of the rise in child trafficking and sexual abuse either.
Verantwortlich gemäß § 5 DDG (Digitale-Dienste-Gesetz):
High, Low, and Popular Culture
High, Low and Popular Culture summary: This section explains the different types of culture mentioned in the subheading being, high, low and popular culture. High culture is often associated with intellectualism, political power and prestige. Low culture is often associated with experiences and attitudes that exist in the lowest segments of society. Popular culture refers to cultural experiences and attitudes that exist in mainstream society. A few examples of this are, baseball games, music, anime and cosplay.
Nonmaterial culture, in contrast, consists of the ideas, attitudes, and beliefs of a society. These are things you cannot touch. They are intangible. You may believe that a line
physical properties that are of some culture that can be touched are tangible whereas, nonmaterial culture are just mainly ideas of a society. Ideas of a society are not physical properties that you can touch, therefore, they are intangible.
marriage is generally seen as an individual choice made by two adults, based on mutual feelings of love.
Its interesting to me how in other nations and in different times, marriages are arranged due to culture and without individual choice of the person. Even if you hadn't gotten to know your wife or husband, that youre inevtiably bound to them for life without really knowing what theyre capable of doing to you. That is a very scary thought.
Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.
Learn more at Review Commons
Manuscript number: RC-2024-02830
Corresponding author(s): Julien, Sage
We thank the Reviewers for a fair review of our work and helpful suggestions. We have significantly revised the manuscript in response to these suggestions. We provide a point-by-point response to the Reviewers below but wanted to highlight in our response a recurring concern related to the strong cell cycle arrest observed upon the acute FAM53C knock-down being different than the limited phenotypes in other contexts, including the knockout mice and DepMap data.
First, we now show that we can recapitulate the strong G1 arrest resulting from the FAM53C knock-down using two independent siRNAs in RPE-1 cells, supporting the specificity of the effects.
Second, the G1 arrest that results from the FAM53C knock-down is also observed in cells with inactive p53, suggesting it is not due to a non-specific stress response due to “toxic” siRNAs. In addition, the arrest is dependent on RB, which fits with the genetic and biochemical data placing FAM53C upstream of RB, further supporting a specific phenotype.
Third, we have performed experiments in other human cells, including cancer cell lines. As would be expected for cancer cells, the G1 arrest is less pronounced but is still significant, indicating that the G1 arrest is not unique to RPE-1 cells.
Fourth, it is not unexpected that compensatory mechanisms would be activated upon loss of FAM53C during development or in cancer – which may explain the lack of phenotypes in vivo or upon long-term knockout. This has been true for many cell cycle regulators, either because of compensation by other family members that have overlapping functions, or by a larger scale rewiring of signaling pathways.
__Reviewer #1 (Evidence, reproducibility and clarity (Required)): __
Summary:
Taylar Hammond and colleagues identified new regulators of the G1/S transition of the cell cycle. They did so by screening public available data from the Cancer Dependency Map, and identified FAM53C as a positive regulator of the G1/S transition. Using biochemical assays they then show that FAM53 interacts with the DYRK1A kinase to inhibit its function. DYRK1A in its is known to induce degradation of cyclin D, leading the authors to propose a model in which DYRK1A-dependent cyclin D degradation is inhibited by FAM53C to permit S-phase entry. Finally the authors assess the effect of FAM53C deletion in a cortical organoid model, and in Fam53c knockout mice. Whereas proliferation of the organoids is indeed inhibited, mice show virtually no phenotype.
Major comments:
The authors show convincing evidence that FAM53C loss can reduce S-phase entry in cell cultures, and that it can bind to DYRK1A. However, FAM53 has multiple other binding partners and I am not entirely convinced that negative regulation of DYRK1A is the predominant mechanism to explain its effects on S-phase entry. Some of the claims that are made based on the biochemical assays, and on the physiological effects of FAM53C are overstated. In addition, some choices made methodology and data representation need further attention.
We now extended these experiments to conditions where we inhibited DYRK1A, and we also compared these data to experiments in p53-null RPE-1 cells. Altogether, these experiments point to activation of p53 downstream of DYRK1A activation upon FAM53C knock-down, and indicate that p21 is not the only critical p53 target in the cell cycle arrest observed in FAM53C knock-down cells (Figure 4 and Figure S4).
The authors do not convincingly show that FAM53C acts as a DYRK1A inhibitor in cells. Figures 4B+C and S4B+C show extremely faint P-CycD1 bands, and tiny differences in ratios. The P values are hovering around the 0.05, so n=3 is clearly underpowered here. Total CycD1 levels also correlate with FAM53C levels, which seems to affect the ratios more than the tiny pCycD1 bands. Why is there still a pCycD1 band visible in 4B in the GFP + BTZ + DYRK1Ai condition? And if I look at the data points I honestly don't understand how the authors can conclude from S4C that knockdown of siFAM53C increases (DYRK1A dependent) increases in pCycD1 (relative to total CycD1). In figure 5C, no blot scans are even shown, and again the differences look tiny. So the authors should either find a way to make these assays more robust, or alter their claims appropriately.
We appreciate these comments from the Reviewer and have significantly revised the manuscript to address them.
The analysis of Cyclin D phosphorylation and stability are complicated by the upregulation of p21 upon FAM53C knock-down, in particular because p21 can be part of Cyclin D complexes, which may affect its protein levels in cells (as was nicely showed in a previous study from the lab of Tobias Meyer – Chen et al., Mol Cell, 2013). Instead of focusing on Cyclin D levels and stability, we refocused the manuscript on RB and p53 downstream of FAM53C loss.
We removed previous panel 4B from the revised manuscript. For panels 4E and S4B (now panels S3J and S3K)), we used a true “immunoassay” (as indicated in the legend – not an immunoblot), which is much more quantitative and avoids error-prone steps in standard immunoblots (“Western blots”). Briefly, this system was developed by ProteinSimple. It uses capillary transfer of proteins and ELISA-like quantification with up to 6 logs of dynamic range (see their web site https://www.proteinsimple.com/wes.html). The “bands” we show are just a representation of the luminescence signals in capillaries. We made sure to further clarify the figure legends in the revised manuscript.
The representative Western blot images for 5C-D (now 5F-G) in the original submission are shown in Figure 5E, we apologize if this was not clear. The differences are small, which we acknowledge in the revised manuscript. Note that several factors can affect Cyclin D levels in cells, including the growth rate and the stage of the cell cycle. Our FACS analysis shows that normal organoids have ~63% of cells in G1 and ~13% in S phase; the overall lower proportion of S-phase cells in organoids may make the immunoblot difference appear smaller, with fewer cycling cells resulting in decreased Cyclin D phosphorylation.
Nevertheless, the Reviewer brings up a good point and comments from this Reviewer and the others made us re-think how to best interpret our results. As discussed above, we re-read carefully the Meyer paper and think that FAM53C’s role and DYRK1A activity in cells may be understood when considering levels of both CycD and p21 at the same time in a continuum. While our genetic and biochemical data support a role for FAM53C in DYRK1A inhibition, it is likely that the regulation of cell cycle progression by FAM53C is not exclusively due to this inhibition. As discussed above and below, we noted an upregulation of p21 upon FAM53C knock-down, and activation of p53 and its targets likely contributes significantly to the phenotypes observed. We added new experiments to support this more complex model (Figure 4 and Figure S4, with new model in S4L).
The experiments to test if DYRK1A inhibition could rescue the G1 arrest observed upon FAM53C knockdown are not entirely convincing either. It would be much more convincing if they also perform cell counting experiments as they have done in Figures 1F and 1G, to complement the flow cytometry assays. I suggest that the authors do these cell counting experiments in RPE1 +/- P53 cells as well as HCT116 cells. In addition, did the authors test if P21 is induced by DYRK1Ai in HCT116 cells?
We repeated the experiments with the DYRK1A inhibitor and counted the cells. In p53-null RPE-1 cells, we found that cell numbers do not increase in these conditions where we had observed a cell cycle re-entry (Fig. 4E), which was accompanied by apoptotic cell death (Fig. S4I). Thus, cells re-enter the cell cycle but die as they progress through S-phase and G2/M. We note that inhibition of DYRK1A has been shown to decrease expression of G2/M regulators (PMID: 38839871), which may contribute to the inability of cells treated to DYRK1Ai to divide. Because our data in RPE-1 cells showed that p21 knock-down was not sufficient to allow the FAM53C knock-down cells to re-enter the cell cycle, we did not further analyze p21 in HCT-116 cells.
The data in Figure 5C and 5D are identical, although they are supposed to represent either pCycD1 ratios or p21 levels. This is a problem because at least one of the two cannot be true. Please provide the proper data and show (representative) images of both data types.
We apologize for these duplicated panels in the original submission. We now replaced the wrong panel with the correct data (Fig. 5F,G).
Line 246: "Fam53c knockout mice display developmental and behavioral defects." I don't agree with this claim. The mutant mice are born at almost the expected Mendelian ratios, the body weight development is not consistently altered. But more importantly, no differences in adult survival or microscopic pathology were seen. The authors put strong emphasis on the IMPC behavioral analysis, but they should be more cautious. The IMPC mouse cohorts are tested for many other phenotypes related to behavior and neurological symptoms and apparently none of these other traits were changed in the IMPC Famc53c-/- cohort. Thus, the decreased exploration in a new environment could very well be a chance finding. The authors need to take away claims about developmental and behavioral defects from the abstract, results and discussion sections; the data are just too weak to justify this.
We agree with the Reviewer that, although we observed significant p-values, this original statement may not be appropriate in the biological sense. We made sure in the revised manuscript to carefully present these data.
Minor comments:
Can the authors provide a rationale for each of the proteins they chose to generate the list of the 38 proteins in the DepMap analysis? I looked at the list and it seems to me that they do not all have described functions in the G1/S transition. The analysis may thus be biased.
To address this point, we updated Table S1 (2nd tab) to provide a better rationale for the 38 factors chosen. Our focus was on the canonical RB pathway and we included RB binding proteins whose function had suggested they may also be playing a role in the G1/S transition. We do agree that there is some bias in this selection (e.g., there are more RB binding factors described) but we hope the Reviewer will agree with us that this list and the subsequent analysis identified expected factors, including FAM53C. Future studies using this approach and others will certainly identify new regulators of cell cycle progression.
Figure 1B is confusing to me. Are these just some (arbitrarily) chosen examples? Consider leaving this heatmap out altogether, of explain in more detail.
We agree with the Reviewer that this panel was not necessarily useful and possibly in the wrong place, and we removed it from the manuscript. We replaced it with a cartoon of top hits in the screen.
The y-axes in Figures 2C, 2D, 2E, and 4D are misleading because they do not start at 0. Please let the axis start at 0, or make axis breaks.
We re-graphed these panels.
Line 229: " Consequences ... brain development." This subheader is misleading, because the in vitro cortical organoid system is a rather simplistic model for brain development, and far away from physiological brain development. Please alter the header.
We changed the header to “Consequences of FAM53C inactivation in human cortical organoids in culture”.
Figure S5F: the gating strategy is not clear to me. In particular, how do the authors know the difference between subG1 and G1 DAPI signals? Do they interpret the subG1 as apoptotic cells? If yes, why are there so many? Are the culturing or harvesting conditions of these organoids suboptimal? Perhaps the authors could consider doing IF stainings on EdU or BrdU on paraffin sections of organoids to obtain cleaner data?
Thank you for your feedback. The subG1 population in the original Figure S5F represents cells that died during the dissociation step of the organoids for FACS analysis. To address this point, we performed live & dead staining to exclude dead cells and provide clearer data. We refined gating strategy for better clarity in the new S5F panel.
Figure S6A; the labeling seems incorrect. I would think that red is heterozygous here, and grey mutant.
We fixed this mistake, thank you.
__Reviewer #1 (Significance (Required)): __
The finding that the poorly studied gene FAM53C controls the G1/S transition in cell lines is novel and interesting for the cell cycle field. However, the lack of phenotypes in Famc53-/- mice makes this finding less interesting for a broader audience. Furthermore, the mechanisms are incompletely dissected. The importance of a p53-indepent induction of p21 is not ruled out. And while the direct inhibitory interaction between FAM53C and DYRK1A is convincing (and also reported by others; PMID: 37802655), the authors do not (yet) convincingly show that DYRK1A inhibition can rescue a cell proliferation defect in FAM53C-deficient cells.
Altogether, this study can be of interest to basic researchers in the cell cycle field.
I am a cell biologist studying cell cycle fate decisions, and adaptation of cancer cells & stem cells to (drug-induced) stress. My technical expertise aligns well with the work presented throughout this paper, although I am not familiar with biolayer interferometry.
__Reviewer #2 (Evidence, reproducibility and clarity (Required)): __
Summary
In this study Hammond et al. investigated the role of Dual-specificity Tyrosine Phosphorylation regulated Kinase 1A (DYRK1) in G1/S transition. By exploiting Dependency Map portal, they identified a previously unexplored protein FAM53C as potential regulator of G1/S transition. Using RNAi, they confirmed that depletion of FAM53C suppressed proliferation of human RPE1 cells and that this phenotype was dependent on the presence protein RB. In addition, they noted increased level of CDKN1A transcript and p21 protein that could explain G1 arrest of FAM53C-depleted cells but surprisingly, they did not observe activation of other p53 target genes. Proteomic analysis identified DYRK1 as one of the main interactors of FAM53C and the interaction was confirmed in vitro. Further, they showed that purified FAM53C blocked the ability of DYRK1 to phosphorylate cyclin D in vitro although the activity of DYRK1 was likely not inhibited (judging from the modification of FAM53C itself). Instead, it seems more likely that FAM53C competes with cyclin D in this assay. Authors claim that the G1 arrest caused by depletion of FAM53C was rescued by inhibition of DYRK1 but this was true only in cells lacking functional p53. This is quite confusing as DYRK1 inhibition reduced the fraction of G1 cells in p53 wild type cells as well as in p53 knock-outs, suggesting that FAM53C may not be required for regulation of DYRK1 function. Instead of focusing on the impact of FAM53C on cell cycle progression, authors moved towards investigating its potential (and perhaps more complex) roles in differentiation of IPSCs into cortical organoids and in mice. They observed a lower level of proliferating cells in the organoids but if that reflects an increased activity of DYRK1 or if it is just an off target effect of the genetic manipulation remains unclear. Even less clear is the phenotype in FAM53C knock-out mice. Authors did not observe any significant changes in survival nor in organ development but they noted some behavioral differences. Weather and how these are connected to the rate of cellular proliferation was not explored. In the summary, the study identified previously unknown role of FAM53C in proliferation but failed to explain the mechanism and its physiological relevance at the level of tissues and organism. Although some of the data might be of interest, in current form the data is too preliminary to justify publication.
Major points
Experiments focusing on the cell cycle progression were done in a single cell line RPE1 that showed a strong sensitivity to FAM53C depletion. In contrast, phenotypes in IPSCs and in mice were only mild suggesting that there might be large differences across various cell types in the expression and function of FAM53C. Therefore, it is important to reproduce the observations in other cell types.
As mentioned above, we have new data indicating that other cell lines arrest in G1 upon FAM53C knock-down (three cancer cell lines) (Fig. S1E,F and Fig. 4F).
Authors state that FAM53C is a direct inhibitor of DYRK1A kinase activity (Line 203), however this model is not supported by the data in Fig 4A. FAM53C seems to be a good substrate of DYRK1 even at high concentrations when phosphorylations of cyclin D is reduced. It rather suggests that DYRK1 is not inhibited by FAM53C but perhaps FAM53C competes with cyclin D. Further, authors should address if the phosphorylation of cyclin D is responsible for the observed cell cycle phenotype. Is this Cyclin D-Thr286 phosphorylation, or are there other sites involved?
We revised the text of the manuscript to include the possibility that FAM53C could act as a competitive substrate and/or an inhibitor.
We removed most of the Cyclin D phosphorylation/stability data from the revised manuscript. As the Reviewers pointed out, some of these data were statistically significant but the biological effects were small. As discussed above in our response to Reviewer #1, the analysis of Cyclin D phosphorylation and stability are complicated by the upregulation of p21 upon FAM53C knock-down, in particular because p21 can be part of Cyclin D complexes, which may affect its protein levels in cells (as was nicely showed in a previous study from the lab of Tobias Meyer – Chen et al., Mol Cell, 2013). Instead of focusing on Cyclin D levels and stability, we refocused the manuscript on RB and p53 downstream of FAM53C loss.
We note, however, that we used specific Thr286 phospho-antibodies, which have been used extensively in the field. Our data in Figure 1 with palbociclib place FAM53C upstream of Cyclin D/CDK4,6. We performed Cyclin D overexpression experiments but RPE-1 cells did not tolerate high expression of Cyclin D1 (T286A mutant) and we have not been able to conduct more ‘genetic’ studies.
At many places, information on statistical tests is missing and SDs are not shown in the plots. For instance, what statistics was used in Fig 4C? Impact of FAM53C on cyclin D phosphorylation does not seem to be significant. In the same experiment, does DYRK1 inhibitor prevent modification of cyclin D?
As discussed above, we removed some of these data and re-focused the manuscript on p53-p21 as a second pathway activated by loss of FAM53C.
Validation of SM13797 compound in terms of specificity to DYRK1 was not performed.
This is an important point. We had cited an abstract from the company (Biosplice) but we agree that providing data is critical. We have now revised the manuscript with a new analysis of the compound’s specificity using kinase assays. These data are shown in Fig. S3F-H.
A fraction of cells in G1 is a very easy readout but it does not measure progression through the G1 phase. Extension of the S phase or G2 delay would indirectly also result in reduction of the G1 fraction. Instead, authors could measure the dynamics of entry to S phase in cells released from a G1 block or from mitotic shake off.
The Reviewer made a good point. As discussed in our response to Reviewer #1, with p53-null RPE-1 cells, we found that cell numbers do not increase in these conditions where we had observed a cell cycle re-entry (Fig. 4E), which was accompanied by apoptotic cell death (Fig. S4I). Thus, cells re-enter the cell cycle but die as they progress through S-phase and G2/M. We note that inhibition of DYRK1A has been shown to decrease expression of G2/M regulators (PMID: 38839871), which may contribute to the inability of cells treated to DYRK1Ai to divide. Because our data in RPE-1 cells showed that p21 knock-down was not sufficient to allow the FAM53C knock-down cells to re-enter the cell cycle, we did not further analyze p21 in HCT-116 cells. These data indicate that G1 entry by flow cytometry will not always translate into proliferation.
Other points:
Fig. 2C, 2D, 2E graphs should begin with 0
We remade these graphs.
Fig. 5D shows that the difference in p21 levels is not significant in FAM53C-KO cells but difference is mentioned in the text.
We replaced the panel by the correct panel; we apologize for this error.
Fig. 6D comparison of datasets of extremely different sizes does not seem to be appropriate
We agree and revised the text. We hope that the Reviewer will agree with us that it is worth showing these data, which are clearly preliminary but provide evidence of a possible role for FAM53C in the brain.
Could there be alternative splicing in mice generating a partially functional protein without exon 4? Did authors confirm that the animal model does not express FAM53C?
We performed RNA sequencing of mouse embryonic fibroblasts derived from control and mutant mice. We clearly identified fewer reads in exon 4 in the knockout cells, and no other obvious change in the transcript (data not shown). However, immunoblot with mouse cells for FAM53C never worked well in our hands. We made sure to add this caveat to the revised manuscript.
__Reviewer #2 (Significance (Required)): __
Main problem of this study is that the advanced experimental models in IPSCs and mice did not confirm the observations in the cell lines and thus the whole manuscript does not hold together. Although I acknowledge the effort the authors invested in these experiments, the data do not contribute to the main conclusion of the paper that FAM53C/DYRK1 regulates G1/S transition.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
This paper identifies FAM53C as a novel regulator of cell cycle progression, particularly at the G1/S transition, by inhibiting DYRK1A. Using data from the Cancer Dependency Map, the authors suggest that FAM53C acts upstream of the Cyclin D-CDK4/6-RB axis by inhibiting DYRK1A.
Specifically, their experiments suggest that FAM53C Knockdown induces G1 arrest in cells, reducing proliferation without triggering apoptosis. DYRK1A Inhibition rescues G1 arrest in P53KO cells, suggesting FAM53C normally suppresses DYRK1A activity. Mass Spectrometry and biochemical assays confirm that FAM53C directly interacts with and inhibits DYRK1A. FAM53C Knockout in Human Cortical Organoids and Mice leads to cell cycle defects, growth impairments, and behavioral changes, reinforcing its biological importance.
Strength of the paper:
The study introduces a novel cell cycle control signalling module upstream of CDK4/6 in G1/S regulation which could have significant impact. The identification of FAM53C using a depmap correlation analysis is a nice example of the power of this dataset. The experiments are carried out mostly in a convincing manner and support the conclusions of the manuscript.
Critique:
1) The experiments rely heavily on siRNA transfections without the appropriate controls. There are so many cases of off-target effects of siRNA in the literature, and specifically for a strong phenotype on S-phase as described here, I would expect to see solid results by additional experiments. This is especially important since the ko mice do not show any significant developmental cell cycle phenotypes. Moreover, FAM53C does not show a strong fitness effect in the depmap dataset, suggesting that it is largely non-essential in most cancer cell lines. For this paper to reach publication in a high-standard journal, I would expect that the authors show a rescue of the S-phase phenotype using an siRNA-resistant cDNA, and show similar S-phase defects using an acute knock out approach with lentiviral gRNA/Cas9 delivery.
We thank the Reviewer for this comment. Please refer to the initial response to the three Reviewers, where we discuss our use of single siRNAs and our results in multiple cell lines. Briefly, we can recapitulate the G1 arrest upon FAM53C knock-down using two independent siRNAs in RPE-1 cells. We also observe the same G1 arrest in p53 knockout cells, suggesting it is not due to a non-specific stress response. In addition, the arrest is dependent on RB, which fits with the genetic and biochemical data placing FAM53C upstream of RB, further supporting a specific phenotype. Human cancer cell lines also arrest in G1 upon FAM53C knock-down, not just RPE-1 cells. Finally, we hope the Reviewer will agree with us that compensatory mechanisms are very common in the cell cycle – which may explain the lack of phenotypes in vivo or upon long-term knockout of FAM53C.
2) The S-phase phenotype following FAM53C should be demonstrated in a larger variety of TP53WT and mutant cell lines. Given that this paper introduces a new G1/S control element, I think this is important for credibility. Ideally, this should be done with acute gRNA/Cas9 gene deletion using a lentiviral delivery system; but if the siRNA rescue experiments work and validate an on-target effect, siRNA would be an appropriate alternative.
We now show data with three cancer cell lines (U2OS, A549, and HCT-116 – Fig. S1E,F and Fig. 4F), in addition to our results in RPE-1 cells and in human cortical organoids. We note that the knock-down experiments are complemented by overexpression data (Fig. 1G-I), by genetic data (our original DepMap screen), and our biochemical data (showing direct binding of FAM53C to DYRK1A).
3) The western blot images shown in the MS appear heavily over-processed and saturated (See for example S4B, 4A, B, and E). Perhaps the authors should provide the original un-processed data of the entire gels?
For several of our panels (e.g., 4E and S4B, now panels S3J and S3K)), we used a true “immunoassay” (as indicated in the legend – not an immunoblot), which is much more quantitative and avoids error-prone steps in standard immunoblots (“Western blots”). Briefly, this system was developed by ProteinSimple. It uses capillary transfer of proteins and ELISA-like quantification with up to 6 logs of dynamic range (see their web site https://www.proteinsimple.com/wes.html). The “bands” we show are just a representation of the luminescence signals in capillaries. We made sure to further clarify the figure legends in the revised manuscript.
Data in 4A are also not a western blot but a radiograph.
For immunoblots, we will provide all the source data with uncropped blots with the final submission.
4) A critical experiment for the proposed mechanism is the rescue of the FAM53C S-phase reduction using DYRK1A inhibition shown in Figure 4. The legend here states that the data were extracted from BrdU incorporation assays, but in Figure S4D only the PI histograms are shown, and the S-phase population is not quantified. The authors should show the BrdU scatterplot and quantify the phenotype using the S-phase population in these plots. G1 measurements from PI histograms are not precise enough to allow for conclusions. Also, why are the intensities of the PI peaks so variable in these plots? Compare, for example, the HCT116 upper and lower panels where the siRNA appears to have caused an increase in ploidy.
We apologize for the confusion and we fixed these errors, for most of the analyses, we used PI to measure G1 and S-phase entry. We added relevant flow cytometry plots to supplemental figures (Fig. S1G, H, I, as well as Fig. S4E and S4K, and Fig. S5F).
5) There's an apparent contradiction in how RB deletion rescues the G1 arrest (Figure 2) while p21 seems to maintain the arrest even when DYRK1A is inhibited. Is p21 not induced when FAM53C is depleted in RB ko cells? This should be measured and discussed.
This comment and comments from the two other Reviewers made us reconsider our model. We re-read carefully the Meyer paper and think that DYRK1A activity may be understood when considering levels of both CycD and p21 at the same time in a continuum (as was nicely showed in a previous study from the lab of Tobias Meyer – Chen et al., Mol Cell, 2013). While our genetic and biochemical data support a role for FAM53C in DYRK1A inhibition, it is obvious that the regulation of cell cycle progression by FAM53C is not exclusively due to this inhibition. As discussed above and below, we noted an upregulation of p21 upon FAM53C knock-down, and activation of p53 and its targets likely contributes significantly to the phenotypes observed. We added new experiments to support this more complex model (Figure 4 and Figure S4, with new model in S4L).
__Reviewer #3 (Significance (Required)): __
In conclusion, I believe that this MS could potentially be important for the cell cycle field and also provide a new target pathway that could be relevant for cancer therapy. However, the paper has quite a few gaps and inconsistencies that need to be addressed with further experiments. My main worry is that the acute depletion phenotypes appear so strong, while the gene is non-essential in mice and shows only a minor fitness effect in the depmap screens. More convincing controls are necessary to rule out experimental artefacts that misguide the interpretation of the results.
We appreciate this comment and hope that the Reviewer will agree it is still important to share our data with the field, even if the phenotypes in mice are modest.
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
This paper identifies FAM53C as a novel regulator of cell cycle progression, particularly at the G1/S transition, by inhibiting DYRK1A. Using data from the Cancer Dependency Map, the authors suggest that FAM53C acts upstream of the Cyclin D-CDK4/6-RB axis by inhibiting DYRK1A.
Specifically, their experiments suggest that FAM53C Knockdown induces G1 arrest in cells, reducing proliferation without triggering apoptosis. DYRK1A Inhibition rescues G1 arrest in P53KO cells, suggesting FAM53C normally suppresses DYRK1A activity. Mass Spectrometry and biochemical assays confirm that FAM53C directly interacts with and inhibits DYRK1A. FAM53C Knockout in Human Cortical Organoids and Mice leads to cell cycle defects, growth impairments, and behavioral changes, reinforcing its biological importance.
Strength of the paper:
The study introduces a novel cell cycle control signalling module upstream of CDK4/6 in G1/S regulation which could have significant impact. The identification of FAM53C using a depmap correlation analysis is a nice example of the power of this dataset. The experiments are carried out mostly in a convincing manner and support the conclusions of the manuscript.
Critique:
In conclusion, I believe that this MS could potentially be important for the cell cycle field and also provide a new target pathway that could be relevant for cancer therapy. However, the paper has quite a few gaps and inconsistencies that need to be addressed with further experiments. My main worry is that the acute depletion phenotypes appear so strong, while the gene is non-essential in mice and shows only a minor fitness effect in the depmap screens. More convincing controls are necessary to rukle out experimental artefacts that misguide the interpretation of the results.
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Summary
In this study Hammond et al. investigated the role of Dual-specificity Tyrosine Phosphorylation regulated Kinase 1A (DYRK1) in G1/S transition. By exploiting Dependency Map portal, they identified a previously unexplored protein FAM53C as potential regulator of G1/S transition. Using RNAi, they confirmed that depletion of FAM53C suppressed proliferation of human RPE1 cells and that this phenotype was dependent on the presence protein RB. In addition, they noted increased level of CDKN1A transcript and p21 protein that could explain G1 arrest of FAM53C-depleted cells but surprisingly, they did not observe activation of other p53 target genes. Proteomic analysis identified DYRK1 as one of the main interactors of FAM53C and the interaction was confirmed in vitro. Further, they showed that purified FAM53C blocked the ability of DYRK1 to phosphorylate cyclin D in vitro although the activity of DYRK1 was likely not inhibited (judging from the modification of FAM53C itself). Instead, it seems more likely that FAM53C competes with cyclin D in this assay. Authors claim that the G1 arrest caused by depletion of FAM53C was rescued by inhibition of DYRK1 but this was true only in cells lacking functional p53. This is quite confusing as DYRK1 inhibition reduced the fraction of G1 cells in p53 wild type cells as well as in p53 knock-outs, suggesting that FAM53C may not be required for regulation of DYRK1 function. Instead of focusing on the impact of FAM53C on cell cycle progression, authors moved towards investigating its potential (and perhaps more complex) roles in differentiation of IPSCs into cortical organoids and in mice. They observed a lower level of proliferating cells in the organoids but if that reflects an increased activity of DYRK1 or if it is just an off target effect of the genetic manipulation remains unclear. Even less clear is the phenotype in FAM53C knock-out mice. Authors did not observe any significant changes in survival nor in organ development but they noted some behavioral differences. Weather and how these are connected to the rate of cellular proliferation was not explored. In the summary, the study identified previously unknown role of FAM53C in proliferation but failed to explain the mechanism and its physiological relevance at the level of tissues and organism. Although some of the data might be of interest, in current form the data is too preliminary to justify publication.
Major points
Other points
Main problem of this study is that the advanced experimental models in IPSCs and mice did not confirm the observations in the cell lines and thus the whole manuscript does not hold together. Although I acknowledge the effort the authors invested in these experiments, the data do not contribute to the main conclusion of the paper that FAM53C/DYRK1 regulates G1/S transition.
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Summary:
Taylar Hammond and colleagues identified new regulators of the G1/S transition of the cell cycle. They did so by screening public available data from the Cancer Dependency Map, and identified FAM53C as a positive regulator of the G1/S transition. Using biochemical assays they then show that FAM53 interacts with the DYRK1A kinase to inhibit its function. DYRK1A in its is known to induce degradation of cyclin D, leading the authors to propose a model in which DYRK1A-dependent cyclin D degradation is inhibited by FAM53C to permit S-phase entry. Finally the authors assess the effect of FAM53C deletion in a cortical organoid model, and in Fam53c knockout mice. Whereas proliferation of the organoids is indeed inhibited, mice show virtually no phenotype.
Major comments:
The authors show convincing evidence that FAM53C loss can reduce S-phase entry in cell cultures, and that it can bind to DYRK1A. However, FAM53 has multiple other binding partners and I am not entirely convinced that negative regulation of DYRK1A is the predominant mechanism to explain its effects on S-phase entry. Some of the claims that are made based on the biochemical assays, and on the physiological effects of FAM53C are overstated. IN addition, some choices made methodology and data representation need further attention.
Minor comments:
The finding that the poorly studied gene FAM53C controls the G1/S transition in cell lines is novel and interesting for the cell cycle field. However, the lack of phenotypes in Famc53-/- mice makes this finding less interesting for a broader audience. Furthermore, the mechanisms are incompletely dissected. The importance of a p53-indepent induction of p21 is not ruled out. And while the direct inhibitory interaction between FAM53C and DYRK1A is convincing (and also reported by others; PMID: 37802655), the authors do not (yet) convincingly show that DYRK1A inhibition can rescue a cell proliferation defect in FAM53C-deficient cells.
Altogether, this study can be of interest to basic researchers in the cell cycle field.
I am a cell biologist studying cell cycle fate decisions, and adaptation of cancer cells & stem cells to (drug-induced) stress. My technical expertise aligns well with the work presented throughout this paper, although I am not familiar with biolayer interferometry.
JSport
JotSpot
micro SCS
Micro SaaS
Software as a Service
involving people serving people and through through involving people so that each of them can do it for themselves or do it together.
Do it yOurselves
autooasis
Autopoiesis
above the threshold What is required?
Sweet spot
Goldilocks
system that we really really need and we that we want which is above the threshold
Sweet spot not sweat spot
auto
Autopoietic
peer suite and indie web,
Peersuite indyweb
expense
Detriment
maximize your returns
Never mind the bad consewuences
And missing out on empowering people that could create exponentially more value at scale
secret source
Sauce
jewel of that is to ensure that every anything you you create is open source
Sauce
pursuits space
Peersuite.space
open source is not enough
X
Sharing the seed. We're sharing a seed that's going to grow more apple trees
Sharinh
suboptimal
Not what we would need
. It ain't what you do but the way that you
X
open source closed construct an open construct. >>
Open/Closed
Source Construct Sauce
vantage point
X
scrubbling through mountains of information
Vantage point
viewpoint
X
doing the job for what we are here for is to understand the world and discover and and find and act accordingly.
Spell out better
that's the starting point the human potential
X
servants
Men behind the machines
computer potential for enhancing human potential
Not the other way round primmarily
alphabet
Graffity
personal capacities
Expsnd Human Potential
personal digital system
Personal Digital Assistant
book
DynaBook
Interpersonal and mutual learning
InterPersonal Mutual Learning
Symmathesy
Software is a Symmathetic Generative Conversation conversence about and for augmenting human capacity for mutual learning and co-lab-oration augmenting human capacity for consolience
Mutual learning.
X
augment the human intellect
InterExtellect
Indie Learning Commons
Indy Learning Commons
Add link
objective
intent
Commitment to work on its articulation explicit formulation leading to its emergent coevolutiinary through co-lab-oration to its Realization in the medium of the working software
structures
Structure and INTERPERSONAL Auto-poietic/nomous co-lab-oration
Riffing on the annotation margins
Say the what you feel and mean what you say
Don't prepare the words prepare the feelings
Transmute the caterpillars of verbal narrative trails flights insights as butterflies in flight
Inicial
Mudou a natureza do recurso em sede de recurso no processo de PC (de autofinanciamento para doação à campanha doadora é a companheira do candidato.
Não obstante, chama atenção, ainda, o fato de que no mesmo dia (07.10.2024- um dia após a realização das eleições) em que foi feita a suposta doação por sua companheira do valor de R$ 18.345,00 (dezoito mil e trezentos e quarenta e cinco reais), momentos antes, o candidato efetuou um pagamento do mesmo valor (R$ 18.345,00) à empresa NOVA COLOR GRÁFICA E EDITORA LTDA, conforme consta no extrato da prestação de contas do candidato (fl. 190 do PPC em anexo).
Juntou-se aos autos da prestação e contas, ainda, as notas fiscais da empresa NOVA COLOR GRÁFICA E EDITORA LTDA emitidas em 20 de agosto de 2024 referentes a serviços gráficos prestados ao candidato (confecção de adesivos, bandeiras, santinhos, praguinha etc) no exato valor de R$ 18.345,00 (dezoito mil e trezentos e quarenta e cinco reais), as quais, conforme extratos bancários, somente foram pagas no dia 07.10.24 (dia seguinte à eleição) por meio de recursos transferidos da conta de Amanda Cristina de Azevedo Mozer pelo candidato
Defesa
Já de partida, afirmamos que o descumprimento do limite de autofinanciamento por ele estabelecido não configura abuso de poder econômico, devendo ser apreciado unicamente pela ótica do art. 30-A da Lei das Eleições.
Basta uma mera consulta nos registros oficiais dessa Especializada para verificar que, enquanto, o Investigado teve despesas que montaram à R$30.408,00 (trinta mil, quatrocentos e oito reais)1 , o candidato mais votado no sufrágio proporcional de 2024 - Sr. Raphael Braga - despendeu recursos na monta de R$ 35.000,00 (trinta e cinco mil reais)2 . Como pode se afirmar que o excesso de gastos impactou nas eleições municipais se o candidato, ora investigado, sequer ficou a frente dos demais candidatos, tendo que, efetivamente, se digladiar com os demais para que seu intento fosse bem sucedido?
E assim se passa, pois, com a criação e fixação do teto global de gastos, há reconhecimento prévio, pelo sistema normativo, que o emprego de recursos além o limite máximo permitido não enseja quebra de normalidade e de legitimidade das eleições, não possuindo, pois, gravidade apta a macular o pleito, caso se leve em consideração a distribuição de votos entre os candidatos eleitos nas proporcionais. Com efeito, para justificar a suposta ocorrência de abuso de poder econômico, pelo descumprimento do limite de autofinanciamento durante o período eleitoral, o parquet assevera unicamente o quantitativo gasto, sem se ater à realidade fática das eleições proporcionais do município de Armação dos Búzios Justamente por isso, a jurisprudência do TSE firmou o entendimento de que não basta o descumprimento de normas de arrecadação para as graves sanções do art. 30-A da Lei nº 9.504/97, sendo necessária afetação da normalidade das eleições
Alegações finais MP
No entanto, merece destaque que o Município de Armação dos Búzios teve o total de 27.506 (vinte e sete mil quinhentos e seis) eleitores que compareceram às urnas nas eleições municipais de 2024 e que o candidato se elegeu com 1.206 (mil duzentos e seis votos) pelo partido MDB pelo quociente partidário, sendo que o segundo candidato de seu partido MDB (Victor Santos – eleito por média) obteve 1.078 votos e o terceiro candidato mais votado do seu partido ( Gugu de Nair) obteve 957 votos, ou seja, uma diferença de apenas 128 votos para o segundo candidato e 249 votos para o terceiro candidato. Por análise primária e lógica, obviamente se o Representado tivesse extrapolado minimamente o valor do autofinanciamento, apesar de ter cometido irregularidade eleitoral, poderia não ter impactado no resultado das eleições. No entanto, ele extrapolou em mais de 300% (trezentos por cento) do limite do autofinanciamento, valor que claramente possui o condão para comprometer a integridade do pleito, especialmente se tratando de Comarca com número reduzido de eleitores e com diferença pequena de votos entre os candidatos, conforme demonstrado acima.
A jurisprudência massiva do TSE, inclusive as colacionadas pelo Representado na sua defesa vem entendendo que a extrapolação do limite de autofinanciamento, por si só, não é capaz de ensejar o reconhecimento e aplicação do abuso de poder econômico, sendo necessária a aplicação dos princípios da proporcionalidade e razoabilidade exigindo-se a presença cumulativa de três requisitos para afastar o abuso do poder econômico: (i) percentual de irregularidade inferior a 10% do total movimentado; (ii) ausência de má-fé; e (iii) irrelevância do valor absoluto.
Dessa forma, evidente que a prática do Representado configura abuso do poder econômico, visto que, sob o parâmetro da própria jurisprudência do TSE, quando analisado o caso concreto sob a ótica dos princípios da proporcionalidade e razoabilidade, ao extrapolar o limite de autofinanciamento (essencial para evitar vantagens indevidas e proteger o princípio da isonomia no pleito eleitoral - art. 27, § 4º, da Resolução TSE nº 23.607/2019) o Representado praticou ato de abuso de poder econômico que causou desequilíbrio no pleito.
PAREI ALEGAÇÕES FINAIS réu ID 32649352
General comment. We need to guide/limit the number of H2s in a lesson. These are all H2s, and there are 6, which is a lot for one lesson.
: The Building Blocks of System Dynamics
Simplify titles when possible. Ive been seeing some very long and verbose titles.
Can you think of a team, business unit, or process in your organization that feels stable, even though change is constantly occurring? What keeps it in balance?
This is a good reflection.
Closing Thoughts Systems thinking isn’t just about connecting the dots. It’s about understanding why those dots behave the way they do. By learning to see steady states, manage stocks and flows, set boundaries intentionally, and surface governance dynamics, you gain the tools to shape systems and not just react to them.
Let's talk about closing thoughts, summaries, etc. and ways to close lessons. Do we need a closing thought? AI does this, but it rarely feels valuable.
This feels fluffy, but maybe it's because of the "It's not just X, it's X." structure.
d Dynamic Balance
What is the definition of dynamic balance?
Stocks are the accumulations in a system, like the water in a pond, the money in a savings account, or the number of trained staff in an organization.
This could be more effective if we're focused on weaving the example we introduced above throughout (intentionally). ie, take out pond analogy, for example, Stocks are,..
Other examples of stocks include...
and Dynamic Balance
Do we need this second part of the title? the term Dynamic balance isn't used.
Reflection prompt:
Terms like "reflection prompt" and "discussion prompt" are id language, not ideal for students. How can we standardize this in our courses?
Take a moment to reflect.??? Maybe an icon?
Let’s begin with a natural example: a pond.
This is conversational and uses an analogy. As someone 'learning' this, this is easy to understand and the visual adds to the learning experience here.
good tone, good analogy, good use of an image
Die Pfadgelegenheit einen Diskurs aufzugleisen. Denn ist ein Diskurs mit einem bestimmten Frame erstmal etabliert, akkumuliert er alle Aufmerksamkeit und alle debattieren über das für und wieder der Vorschläge innerhalb dieses Frames. Präziser: Der Frame generiert semantische und diskursive Pfadgelegenheiten, die alle pfadabhängig von seinen unausgesprochenen Setzungen und Weglassungen bleiben. Die Pfadgelegenheit der diskursiven Aufgleisung hat selbst diverse Pfadabhängigkeiten: materielle Infrastrukturen, also Medien, um am Diskurs teilzunehmen (internet, Social Media, Medienökosystem) mit möglichst breiter etablierter Zuhörerschaft (zb. Economist). Dazu bedarf es eine möglichst herausgehobene gesellschaftliche Stellung des Sprechenden (bekannt, renommiert, oder institutionell herausgehoben, zb. CEO von Signal) und dann – das ist der kontingente Part – Timing
Wandel findet zwar statt, aber langsam und der äußert sich z.B. in Reibungsereignissen zwischen den materiellen Infrastrukturen, die unsere Leben umgestalten und den inadäquaten Versuchen, sie zu beschreiben, geschweige denn, zu regulieren.
Das führt mich zu einem grundsätzlichen Kritikpunkt bei fast allen linken Utopie-Projekten: Machtfragen werden aus diesen Zukunftsvisionen meistens einfach ausgeklammert, ganz so, als ob mit der Überwindung des Kapitalismus auch alle Machtungleichgewichte aus dem Fenster fliegen? Schimpft mich liberal, aber ich bin nach wie vor der Überzeugung, dass das Rumlaborieren an neuen Formen der Ressourcenallokation nur halb so wichtig ist, wie die Entwicklung von dynamischen Systemen der Macht-Einhegung. Und nein, der Hinweis auf „Demokratie“ reicht mir nicht.
It's all driven by who gets what
eLife Assessment
This work reveals metabolic pathways and molecular events mechanistically linked to B cell activation. Using an unbiased, comprehensive proteome profiling method and various functional validation approaches, this study generated convincing evidence suggesting a role for amino acid uptake, cholesterol accumulation, and protein prenylation in the proliferation, survival, and biogenesis of B cells stimulated with LPS and other activating stimuli. The significance of the findings is considered to be fundamental, in that they will advance our understanding of cell metabolism during B cell activation.
Reviewer #1 (Public review):
The work presented by Cheung et al. used a quantitative proteomics method to capture molecular changes in B cells exposed to LPS and IL-4, a combination of stimuli activating naive B cells. Amino acid transporters, cholesterol biosynthetic enzymes, ribosomal components, and other proteins involved in cell proliferation were found to increase in stimulated B cells. Experiments involving genetic loss-of-function (SLC7A5), pharmacological inhibition (HMGCR, SQLE, prenylation), and functional rescue by metabolites (mevalonate, GGPP) validated the proteomics data and revealed that amino acid uptake, cholesterol/mevalonate biosynthesis, and cholesterol uptake played a crucial role in B cell proliferation, survival, biogenesis, and immunoglobulin class switching. Experiments involving cholesterol-free medium showed that both biosynthesis and LDLR-mediated uptake catered to the cholesterol demand of LPS/IL-4-stimulated B cells. A role for protein prenylation in LDLR-mediated cholesterol uptake was postulated and backed by divergent effects of GGPP rescue in the presence and absence of cholesterol in culture medium.
Strengths:
The discovery was made by proteome-wide profiling and unbiased computational analysis. The discovered proteins were functionally validated using appropriate tools and approaches. The metabolic processes identified and prioritized from this comprehensive survey and systematic validation are highly likely to represent mechanisms of high importance and influence. Analysis of immune cell metabolism at the protein level is relatively compared to transcriptomic and metabolomic analysis.
The conclusions from functional validation experiments were supported by clear data and based on rational interpretations. This was enabled by well-established readouts/analytical methods used to analyze cell proliferation, viability, size, cholesterol content, and transporter/enzyme function. The data generated from these experiments strongly support the conclusions.
This work reveals a complex, yet intriguing, relationship between cholesterol metabolism and protein prenylation as they serve to promote B cell activation. The effects of pharmacological inhibition and metabolite replenishment on the cholesterol content and activation of B cells were precisely determined and logically interpreted.
Weaknesses:
The findings of this study were obtained almost exclusively from ex vivo B cell stimulation experiments. Their contribution to B cell state and B-cell-mediated immune responses in vivo was not explored. Without in vivo data, the study still provides valuable mechanistic information and insights, but it remains unknown, and there is no discussion about how the identified mechanisms may play out in B cell immunity.
The role of HMGCR, SQLE, and prenylation in B cell activation was assessed using pharmacological inhibitors. Evidence from other loss-of-function approaches, which could strengthen the conclusions, does not exist. This is a moderate weakness.
Reviewer #2 (Public review):
This study uses mass spectrometry to quantify how LPS and IL-4 modify the mouse B cell proteome as naïve cells undergo blastogenesis and enter the cell cycle. This analysis revealed changes in key proteins involved in amino acid transport and cholesterol biosynthesis. Genetic and pharmacological experiments indicated important roles for these metabolic processes in B cell proliferation.
This work provides new information about the regulation of TI B cell responses by changes in cell metabolism and also a comprehensive mass spectrometry dataset, which will be an important general resource for future studies. The experiments are thorough and carefully carried out. The majority of conclusions are backed up by data that is shown to be highly significant statistically.
The study would be strengthened by additional experiments to determine whether the detected changes are unique to stimulation with LPS + IL-4 or more generic responses of resting B cells to mitogenic agonists.
Author response:
Reviewer #1:
We agree with the reviewer that a limitation of our study is its focus on cell-based assays rather than in vivo experiments. We did consider evaluating the effects of statins on B cell responses in vivo; however, this approach is complicated by findings that statins can influence antigen presentation by dendritic cells, thereby impacting antibody responses (Xia et al, 2018). One possible solution would be to use B cell-specific conditional knockout models to study the roles of the identified proteins in an in vivo context. However, we currently do not have access to these models and were therefore unable to include such experiments within a feasible timeframe. We will revise the discussion section to acknowledge these points.
The reviewer also noted that our study assessed the roles of HMGCR, SQLE, and prenylation in B cell activation using pharmacological inhibitors and genetic knockdown/out approaches. Loss-of-function techniques such as RNAi, siRNA, and CRISPR can be challenging to apply to primary B cells, but we are exploring their feasibility for future revisions. While we acknowledge the limitations of using pharmacological inhibitors, we have taken several steps to mitigate these, including targeting multiple steps in the cholesterol biosynthetic pathway using structurally distinct inhibitors and conducting rescue experiments by supplementing downstream metabolites. To further investigate potential off-target effects of statins, we have recently performed proteomic analysis of B cells treated with and without fluvastatin. The data suggest that fluvastatin primarily affects cholesterol metabolism and does not cause widespread off-target effects. We will include this new data in the revised manuscript.
Reviewer #2:
The reviewer suggested that the study would be strengthened by determining whether the observed changes are specific to LPS + IL-4 stimulation or represent a more general B cell response to mitogenic signals.
A complementary study by James et al. (James et al, 2024) investigated murine B cells stimulated via the B cell receptor (BCR) and CD40, using anti-IgM and anti-CD40 antibodies alongside IL-4. Their proteomic analysis showed that such co-stimulation induces a fivefold increase in total cellular protein mass within 24 hours, mirroring our findings with LPS + IL-4. They also reported upregulation of proteins associated with cell cycle progression, ribosome biogenesis, and amino acid transport. Furthermore, by using SLC7A5 knockout mice, they demonstrated that this transporter is required for B cell activation. We will expand our discussion to include and these findings. We will also expand on the final figure in our paper showing that the effects of statins are not limited to LPS.
References:
James O, Sinclair LV, Lefter N, Salerno F, Brenes A & Howden AJM (2024) A proteomic map of B cell activation and its shaping by mTORC1, MYC and iron. bioRxiv 2024.12.19.629506 doi:10.1101/2024.12.19.629506 [PREPRINT]
Xia Y, Xie Y, Yu Z, Xiao H, Jiang G, Zhou X, Yang Y, Li X, Zhao M, Li L, et al (2018) The Mevalonate Pathway Is a Druggable Target for Vaccine Adjuvant Discovery. Cell 175: 1059-1073.e21
HISTORICAL DEVELOPMENT OF INDUSTRIAL SAFETY IN NIGERIA
HISTORICAL DEVELOPMENT OF INDUSTRIAL SAFETY
THE RELATIONSHIP BETWEEN SAFETY AND INDUSTRY
THE RELATIONSHIP BETWEEN SAFETY AND INDUSTRY
WHAT IS INDUSTRIAL SAFETY
Manage activities in an industry to maintain safety and health by minimizing hazards, risks, accident and near misses.
WHAT IS INDUSTRY
Manufacturers or businesses that produces a particular kind of goods or render a particular kind of service, considered invidually or collectively.
WHAT IS ENVIRONMENT
It is machinery, equipment, materials, facilities and activities in the workplace
WHAT IS SAFETY?
It is protection of people in a work setting from physical injury. It is also a state that prevents damage to equipment.
Occupational health and safety
Occupation health and safety is a fundamental topic in every productive organization as health and safety are not negotiable in any activity. It aims to adapt work to people such that the working conditions does not negatively impact the physical, mental or social well being of workers.
WHAT IS HEALTH
In the context of occupational health and safety is the protection of the mind and body of workers from illness resulting fom materials, processes and procedures in the workplace.
person and being are already married in reality
Being
Process
subjective self-conscious oriented towards his own identity
Nothing you can do but learn to be you through time
what is learning
How does it grow
The Marriage of Medieval Metaphysics and Modern Personalism
Word on Fire Institute 174K subscribers
he had fallen in love and moved to Paris topursue her dream of becoming a war correspond
test annonation
reply to u/warriorkitten18 at https://reddit.com/r/adhdwomen/comments/1mqzm12/index_card_system_please_discuss/
Apologies for the delay in reply. I've read extensively about card indexes for productivity and the variety of systems and uses over the past couple of centuries.
Given your context, I'd recommend reading one book which describes an index card system in full, walks you through it card by card, helps you make it, and describes how it's used. It's thorough, but fully adaptable to your particular needs. Best, it's written by two women in the early 1980s and though there wasn't much in the culture about ADHD at the time, I suspect that one or both of these women were coping with nearby neurodivergent issues (not to mention the eternal brain fog forced by pregnancy, lack of sleep, and early childhood woes.)
Young, Pam, and Peggy Jones. 1981. Sidetracked Home Executives: From Pigpen to Paradise. ed. Sydney Craft Rozen. New York: Warner Books. http://archive.org/details/sidetrackedhomee00youn (November 3, 2023).
If you need other perspectives, there are also areas with potential solutions like the Bullet Journal (notebooks), Getting Things Done (GTD), Hipster PDA, and a variety of others. Almost all of these were built on the ideas behind the early 1900s version of the Memindex, which I've written about here: https://boffosocko.com/2023/03/09/the-memindex-method-an-early-precursor-of-the-memex-hipster-pda-43-folders-gtd-basb-and-bullet-journal-systems/. They're all roughly the same in shape, practice, and philosophy, but the Young/Jones SHE version uses index cards, speaks directly to your use case, and suggests an approach for the ADHD set. Assuredly a nearby library can get you a copy, you can find them used, or read the linked online version which you can check out.
If you'd like to see portions of my personal system, I've written a bit about it along with lots of other resources at https://boffosocko.com/research/zettelkasten-commonplace-books-and-note-taking-collection/#Productivity
It's worked well for me for many years. The secret is to read the basics and then adapt the pieces of the system to suit your own needs and methods of working. For example, I love crossing things off of lists, which my index cards didn't really encourage because you do them and move them to the next day/week/month/year forward, but I bought a rotating date stamp that allows me to stamp each card with the date as "done" before re-filing it. For me, the haptic feedback of the "thonk" of the stamp is even better than crossing things off and gives me a sense of accomplishment when I see them and finish filling an entire card up with dates.
A decade on, the best part of my collection are the separate index cards I had laying around while using the rest of the system and on which I wrote down quotes from my daughter, new words as she learned them, words she made up, goofy jokes, etc.
Enough for now, the card for the dog groomer came up yesterday, so I'm off to take our dog to the appointment I made when I saw it. Thonk! Refiled for next month's reminder.
Good luck.
Across, on the other side, were fields of grain and trees along the banks of the Ebro. Far away, beyond the river, were mountains. The shadow of a cloud moved across the field of grain and she saw the river through the trees.
The contrasting landscapes that are dry and barren on one side, and fertile and alive on the other, mirror the couple’s decision. One path leads to life and connection (keeping the baby), and the other to emptiness (abortion and separation). The river represents the natural flow of life that she’s tempted to hold onto.
“Then I’ll do it. Because I don’t care about me.”
This line reveals the girl’s emotional surrender. She’s willing to undergo the abortion not out of personal choice but to please the man. Hemingway uses this brief, restrained dialogue to show her emotional pain in their relationship. He’s detached, while she’s sacrificing herself for his comfort.
But if I do it, then it will be nice again if I say things are like white elephants, and you’ll like it?”
The “white elephants” symbolize something unwanted or burdensome, which, in this case, is the pregnancy. The man tries to make light of it, but the girl’s repetition of the phrase shows her deeper emotional conflict and the couple’s inability to communicate honestly.
“Doesn’t it mean anything to you? We could get along.”
Jig’s plea reveals her emotional vulnerability. She’s seeking reassurance and love rather than logic. The phrase “we could get along” shows she associates keeping the baby with saving their relationship. It's a contrast to the man's practical reasoning.
“I know. Could we have another beer?”
The request to drink again is definitely an attempt to avoid the emotional tension. Alcohol becomes a symbol of distraction and denial, emphasizing the couple’s inability to communicate meaningfully about their situation.
história nas temporalidades digitais
Tese da Taciana
The not-for-profit organization Project Implicit (https://www.projectimplicit.net/) has been researching subconscious biases for years and has developed several, free online tests. (https://implicit.harvard.edu/implicit/takeatest.html) The tests can help you understand your proclivities and subconscious biases. Knowing your biases may help you begin to overcome them.
IAT's have been under scrutiny for some time.They are commonly skewed towards perspectives gained from specific demographics and can be manipulated quite easily to reflect personal pejoratives that align with an agency's codes of ethics. this suggestion should be given with caveats.
“We think there’s no normal way to act on a 911 call,” said the lead investigator, Jessica Salerno, a social psychologist at ASU. Given the gamut of human emotion, she explained, anyone claiming to know the right and wrong way to speak during an emergency has seen too much television.
It's nuts that this has to be said.
since 〈~x|~p〉 = ei~p·~x(2π)3/2 ,
test
Example 8: Combustion of Organic Molecules
Read this again and study it multiple times
private
Not personalized (without cookies)
and high risk of cardiovascular disorders
How can auditory stimuli affect cardiovascular health?
Though the treatment of autistic children was markedlymore brutal, both programs shared one vision—to make the children “indistin-guishable” from others
It is about upholding a norm. It is less so about protecting children then it is upholding sociocultural values.
Our access to technology gives us advantages in accessing information that many around the world do not have.
This is true because some places around the world have no internet access at all so they don't know about many things that aren't in books.
The idea that status impacts your access to information is nothing new. What is relatively new is that librarians and others are critically talking about it as it relates to higher education.
I agree with this because information truly is a privilege.
Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.
Learn more at Review Commons
Since we are at the stage of simply proposing a Revision Plan to an affiliate journal, there is not a revised version of the manuscript yet. But we honestly thank the three reviewers for their important input, which we are taken into consideration very seriously.
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Major Comments:
It is interesting case study but the main problem with the study is the use of an unsuitable tardigrade model species. It was shown in the past that Hypsibius exemplaris is not a good model species to test tardigrade survival under extreme stress. Of course, results of Hypsibius exemplaris can be published but from the entire manuscript all general comments that tardigrades react in this or in different way need to be removed. This is characteristic only to Hypsibius exemplaris species which is a poor model for studies focused on environmental stressTo present general conclusions use few different tardigrade species or at least a correct tardigrade species with confirmed high resilience for different kind of stress like Milnesium, Ramazzottius, Paramacrobiotus or similar must be tested. Based on present study I can only propose to publish this manuscript as a case study for one poorly stress resistant eutardigrade species, without any general conclusions about other tardigrades. See: Poprawa, I., Bartylak, T., Kulpla, A., Erdmann, W., Roszkowska, M., Chajec, Ł., Kaczmarek, Ł., Karachitos, A. & Kmita, H. (2022) Verification of Hypsibius exemplaris Gąsiorek et al., 2018 (Eutardigrada; Hypsibiidae) application in anhydrobiosis research. PLoS ONE 17(3): e0261485.
Minor comments:
Studies described in the manuscript are very interesting for many potential readers, however manuscript need to be modified as case study for one tardigrades species without generalization of the results for all tardigrades. It is very important to not suggest that all tardigrades react in the same way especially that species used is not a good candidate for this type of studies (see my major comments).
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Summary:
This manuscript studies the effects of genotoxic stress using zeocin, a bleomycin-family drug, in the tardigrade species H. exemplaris. In a first experimental set, the authors evaluate the survival of the organisms as well as the levels of DNA damage.
A RT-qPCR analysis of a set of DNA repair genes identified in a previous study by another group (Clark-Hachtel, Courtney M. et al.; Curr Biol, Vol. 34, Issue 9, 1819-1830.e6) and a comet assay reveal the damage observed during treatment.
Experiments on fasting animals show variations in animal size that overlap with those seen in groups of animals treated with the genotoxic drug. Physiological variations are also observed, such as lipid loss and cuticle alteration.
In a subsequent experimental set, the authors indicate that the genotoxic drug blocks DNA replication and activates DNA repair systems in various tissues, particularly the digestive tissue, which appears to be specifically targeted in terms of its replicative capacity following DNA damage caused by the drug. A sensitivity study of tardigrade embryo development then shows that their proliferative capacity, which is highly dependent on replication, mobilizes different sets of DNA repair genes that may be more closely associated with replication than in adults.
Finally, a comparative study of the development of two organisms (C. elegans and planarian) also shows sensitivity to drugs that disrupt the replication process during development.
The authors conclude from all of this work that the cells of the animals' intestines are the main target of the genotoxic stress induced by the drug. The effects of disruption of the normal replication process in intestinal cells are thought to be the cause of the observed loss of tissue homeostasis (loss of lipids and tissue renewal capacity).
Major comments:
Zeocin is a drug derived from bleomycin but has not yet been extensively studied. Could you give examples of the use/validation of zeocin as a radiomimetic in other biological systems?
Similarities in transcriptional responses between UV and dehydration genotoxic stresses have already been observed (Yoshida et al., 2022; BMC Genomics 23, 405) in a tardigrade species closely related to H. exemplaris (R. varieornatus). However, no correlation in transcriptional responses could be observed after treating H. exemplaris with genotoxic stresses such as desiccation and 500 Gy gamma ray irradiation (Clark-Hachtel, Courtney M. et al.; Curr Biol, Vol 34, Issue 9, 1819 - 1830.e6). These results indicate that, depending on the type of genotoxic stress, transcriptomic responses can appear to be very different and sometimes uncorrelated, particularly in the species H. exemplaris. Bleomycin has been studied in previous reports (refs Yoshida Y, et al. Proc Jpn Acad Ser B Phys Biol Sci. 2024 100(7):414-428; Clark-Hachtel, Courtney M. et al.; Curr Biol, Vol 34, Issue 9, 1819 - 1830.e6; Marwan Anoud et al., 2024, eLife 13:RP92621), which used a transcriptomic study to confirm that it behaves as a radiomimetic for the species H. exemplaris.
On the other hand, since zeocin is a bleomycin-family drug, it is possible that its effects may differ slightly from those of bleomycin, exhibiting specific effects as observed by comparison of chemical radiomimetic and radiation treatments.
A control experiment comparing the effects of bleomycin and zeocin using RNAseq would validate that their use is equivalent.
It would be interesting to block replication in embryos and assess whether the same sets of DNA repair genes are induced when compared with treatment with zeocin only. Additionally, it will be interesting to redo the same DNA replication block experiments with additional treatment to compare the induced sets of DNA reparation genes. This will help to understand the true effect that will be directly imputable to zeocin.
Minor comments:
The data are well presented, and the experiments are well described for general understanding. Previous studies in this field have been well referenced. However, the link between DNA damage caused by the drug and its impact on replication needs to be better explained.
Finally, the use of the drug zeocin should be validated in this system by comparison with bleomycin.
This study evaluates the resistance of a species of tardigrades to genotoxic stress. Several previous studies have conducted this type of experiment using the same species with consistent results and using the same type of genotoxic chemical drug : bleomycin. In this study, a new genotoxic drug is evaluated for its effects on DNA damage as well as on the survival of organisms and their embryonic development. Definitive validation experiments of this new genotoxic chemical tool are necessary to determine its similarities with drugs already known for their effects in the literature.
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
This manuscript concerns tardigrade sensitivity to genotoxic stress. Using the radiomimetic drug Zeocin to induce DNA breaks, authors show that continuous exposure progressively kills tardigrades, accompanied by striking body shrinkage and lipid depletion. Authors show that germ cells and embryos, with their high proliferation rates, show heightened sensitivity. To resume, their findings pinpoint DNA replication as an Achilles' heel of organismal survival under genotoxic stress.
Major comments:
The claims and conclusions in this article are not sufficiently supported by the data. They require additional experiments or analyses.
The fundamental problem with this paper is the use of a single molecule, Zeocin, as a radiomimetic. It is absolutely essential to compare the results obtained with radiation. In the bibliography, researchers compare a drug with radiation. Bob Goldstein, for example, in his 2024 Current Biology paper uses radiation and bleomycin. The same is true for Concordet in his 2024 elife paper. Zeocin has been used very little on tardigrades. It cannot be used alone to draw conclusions from this study.
Additionally, at the beginning of the paper, the authors tested different concentrations of Zeocin. They showed results at two concentrations : 100ug/ml and 1mg/ml. In the remainder of the paper, only the latter concentration is used. This is not sufficient. The analyses should have been conducted in parallel on several concentrations in order to compare and analyze a potential dose-dependent effect.
Finally, the authors focused on two types of cells that have the particularity of replicating themselves: gut cells and storage cells. It would have been necessary to work on other cell types to compare the results.
The realization of these additional experiences are completely realistic.
The data and methods are presented in a reproducible manner. But experiments sometimes lack independent replicates and need to be reproduced.
The legend to Figure 1, for example, indicates that the experiment was conducted with 3 to 7 biological replicates and 60 to 120 animals. These are still very different numbers. And this can lead to significant bias.
For the other figures, no biological replicates were indicated and the numbers « n » are sometimes very different, as in Figure 4 with n=107 and n=166. A little more homogenization allows for better robustness of the results. And biological replicates are essential.
Sometimes there are some unclear elements in the figures. In Figure 3, if I understand correctly, A and B show the gut cells (adult) and C and D the storage cells (juvenile). The size difference is not very clear in this image. How old is the juvenile compared to this adult?
This study, if confirmed by additional experiments that are absolutely essential to validate these conclusions, will be interesting for the community of researchers working on tardigrades, even if the effects of genotoxic stress on tardigrades are already widely studied.
This study is relatively complete on only one molecule, Zeocin, at a concentration of 1 mg/ml. To be relevant, another genotoxic stress should be included in the study. And the study should also be conducted at the concentration of 100 ug/ml, which did show effects but was abandoned for the rest of the study. Similarly, only storage cells and gut cells were studied given their replication capacity. Other cell types should have been included in the study for comparison.
Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.
Learn more at Review Commons
We would like to thank all the reviewers for their valuable comments and criticisms. We have thoroughly revised the manuscript and the resource to address all the points raised by the reviewers. Below, we provide a point-by-point response for the sake of clarity.
Reviewer #1
__Evidence, reproducibility and clarity __
Summary: This manuscript, "MAVISp: A Modular Structure-Based Framework for Protein Variant Effects," presents a significant new resource for the scientific community, particularly in the interpretation and characterization of genomic variants. The authors have developed a comprehensive and modular computational framework that integrates various structural and biophysical analyses, alongside existing pathogenicity predictors, to provide crucial mechanistic insights into how variants affect protein structure and function. Importantly, MAVISp is open-source and designed to be extensible, facilitating reuse and adaptation by the broader community.
Major comments: - While the manuscript is formally well-structured (with clear Introduction, Results, Conclusions, and Methods sections), I found it challenging to follow in some parts. In particular, the Introduction is relatively short and lacks a deeper discussion of the state-of-the-art in protein variant effect prediction. Several methods are cited but not sufficiently described, as if prior knowledge were assumed. OPTIONAL: Extend the Introduction to better contextualize existing approaches (e.g., AlphaMissense, EVE, ESM-based predictors) and clarify what MAVISp adds compared to each.
We have expanded the introduction on the state-of-the-art of protein variant effects predictors, explaining how MAVISp departs from them.
- The workflow is summarized in Figure 1(b), which is visually informative. However, the narrative description of the pipeline is somewhat fragmented. It would be helpful to describe in more detail the available modules in MAVISp, and which of them are used in the examples provided. Since different use cases highlight different aspects of the pipeline, it would be useful to emphasize what is done step-by-step in each.
We have added a concise, narrative description of the data flow for MAVISp, as well as improved the description of modules in the main text. We will integrate the results section with a more comprehensive description of the available modules, and then clarify in the case studies which modules were applied to achieve specific results.
OPTIONAL: Consider adding a table or a supplementary figure mapping each use case to the corresponding pipeline steps and modules used.
We have added a supplementary table (Table S2) to guide the reader on the modules and workflows applied for each case study
We also added Table S1 to map the toolkit used by MAVISp to collect the data that are imported and aggregated in the webserver for further guidance.
- The text contains numerous acronyms, some of which are not defined upon first use or are only mentioned in passing. This affects readability. OPTIONAL: Define acronyms upon first appearance, and consider moving less critical technical details (e.g., database names or data formats) to the Methods or Supplementary Information. This would greatly enhance readability.
We revised the usage of acronyms following the reviewer’s directions of defying them at first appearance.
We thank the reviewer for noticing and praising the availability of the tools of MAVISp. Our MAVISp framework utilizes methods and scores that incorporate machine learning features (such as EVE or RaSP), but does not employ machine learning itself. Specifically, we do not use PyTorch and do not utilize features in a machine learning sense. We do extract some information from the AlphaFold2 models that we use (such as the pLDDT score and their secondary structure content, as calculated by DSSP), and those are available in the MAVISp aggregated csv files for each protein entry and detailed in the Documentation section of the MAVISp website.
We have removed this section and included a mention in the conclusions as part of the future directions.
Minor comments: - Most relevant recent works are cited, including EVE, ESM-1v, and AlphaFold-based predictors. However, recent methods like AlphaMissense (Cheng et al., 2023) could be discussed more thoroughly in the comparison.
We have revised the introduction to accommodate the proper space for this comparison.
We have revised Figure 2 and presented only one case study to simplify its readability. We have also changed Figure 3, whereas retained the other previous figures since they seemed less problematic.
We have done a proofreading of the entire article, including the points above
Significance
General assessment: the strongest aspects of the study are the modularity, open-source implementation, and the integration of structural information through graph neural networks. MAVISp appears to be one of the few publicly available frameworks that can easily incorporate AlphaFold2-based features in a flexible way, lowering the barrier for developing custom predictors. Its reproducibility and transparency make it a valuable resource. However, while the technical foundation is solid and the effort substantial, the scientific narrative and presentation could be significantly improved. The manuscript is dense and hard to follow in places, with a heavy use of acronyms and insufficient explanation of key design choices. Improving the descriptive clarity, especially in the early sections, would greatly enhance the impact of this work.
Advance
to the best of my knowledge, this is one of the first modular platforms for protein variant effect prediction that integrates structural data from AlphaFold2 with bioinformatic annotations and even clinical data in an extensible fashion. While similar efforts exist (e.g., ESMfold, AlphaMissense), MAVISp distinguishes itself through openness and design for reusability. The novelty is primarily technical and practical rather than conceptual.
Audience
this study will be of strong interest to researchers in computational biology, structural bioinformatics, and genomics, particularly those developing variant effect predictors or analyzing the impact of mutations in clinical or functional genomics contexts. The audience is primarily specialized, but the open-source nature of the tool may diffuse its use among more applied or translational users, including those working in precision medicine or protein engineering.
Reviewer expertise: my expertise is in computational structural biology, molecular modeling, and (rather weak) machine learning applications in bioinformatics. I am familiar with graph-based representations of proteins, AlphaFold2, and variant effects based on Molecular Dynamics simulations. I do not have any direct expertise in clinical variant annotation pipelines.
Reviewer #2
__Evidence, reproducibility and clarity __
Summary: The authors present a pipeline and platform, MAVISp, for aggregating, displaying and analysis of variant effects with a focus on reclassification of variants of uncertain clinical significance and uncovering the molecular mechanisms underlying the mutations.
Major comments: - On testing the platform, I was unable to look-up a specific variant in ADCK1 (rs200211943, R115Q). I found that despite stating that the mapped refseq ID was NP_001136017 in the HGVSp column, it was actually mapped to the canonical UniProt sequence (Q86TW2-1). NP_001136017 actually maps to Q86TW2-3, which is missing residues 74-148 compared to the -1 isoform. The Uniprot canonical sequence has no exact RefSeq mapping, so the HGVSp column is incorrect in this instance. This mapping issue may also affect other proteins and result in incorrect HGVSp identifiers for variants.
We would like to thank the reviewer for pointing out these inconsistencies. We have revised all the entries and corrected them. If needed, the history of the cases that have been corrected can be found in the closed issues of the GitHub repository that we use for communication between biocurators and data managers (https://github.com/ELELAB/mavisp_data_collection). We have also revised the protocol we follow in this regard and the MAVISp toolkit to include better support for isoform matching in our pipelines for future entries, as well as for the revision/monitoring of existing ones, as detailed in the Method Section. In particular, we introduced a tool, uniprot2refseq, which aids the biocurator in identifying the correct match in terms of sequence length and sequence identity between RefSeq and UniProt. More details are included in the Method Section of the paper. The two relevant scripts for this step are available at: https://github.com/ELELAB/mavisp_accessory_tools/
- The paper lacks a section on how to properly interpret the results of the MAVISp platform (the case-studies are helpful, but don't lay down any global rules for interpreting the results). For example: How should a variant with conflicts between the variant impact predictors be interpreted? Are specific indicators considered more 'reliable' than others?
We have added a section in Results to clarify how to interpret results from MAVISp in the most common use cases.
We thank the reviewer for spotting this inconsistency. This part in the main text was left over from a previous and preliminary version of the pre-print, we have revised the main text. Supplementary Text S4 includes the correct reference for the value in light of the benchmarking therewithin.
We have changed the structure of the webserver in such a way that now the whole website opens as its own separate window, instead of being confined within the size permitted by the website at DTU. This solves the fixed window size issue. Hopefully, this will improve the user experience.
We have refactored the web app by adding filtering functionality, both for the main protein table (that can now be filtered by UniProt AC, gene name or RefSeq ID) and the mutations table. Doing this required a general overhaul of the table infrastructure (we changed the underlying engine that renders the tables).
The table overhauls fixed both of these issues
We clarified the meaning of the reference column in the Documentation on the MAVISp website, as we realized it had confused the reviewer. The reference column is meant to cite the papers where the computationally-generated MAVISp data are used, not external sources. Since we also have the experimental data module in the most recent release, we have also refactored the MAVISp website by adding a “Datasets and metadata” page, which details metadata for key modules. These include references to data from external sources that we include in MAVISp on a case-by-case basis (for example the results of a MAVE experiment). Additionally, we have verified that the papers using MAVISp data are updated in https://elelab.gitbook.io/mavisp/overview/publications-that-used-mavisp-data and in the csv file of the interested proteins.
Here below the current references that have been included in terms of publications using MAVISp data:
SMPD1
ASM variants in the spotlight: A structure-based atlas for unraveling pathogenic mechanisms in lysosomal acid sphingomyelinase
Biochim Biophys Acta Mol Basis Dis
38782304
https://doi.org/10.1016/j.bbadis.2024.167260
TRAP1
Point mutations of the mitochondrial chaperone TRAP1 affect its functions and pro-neoplastic activity
Cell Death & Disease
40074754
https://doi.org/10.1038/s41419-025-07467-6
BRCA2
Saturation genome editing-based clinical classification of BRCA2 variants
Nature
39779848
0.1038/s41586-024-08349-1
TP53, GRIN2A, CBFB, CALR, EGFR
TRAP1 S-nitrosylation as a model of population-shift mechanism to study the effects of nitric oxide on redox-sensitive oncoproteins
Cell Death & Disease
37085483
10.1038/s41419-023-05780-6
KIF5A, CFAP410, PILRA, CYP2R1
Computational analysis of five neurodegenerative diseases reveals shared and specific genetic loci
Computational and Structural Biotechnology Journal
38022694
https://doi.org/10.1016/j.csbj.2023.10.031
KRAS
Combining evolution and protein language models for an interpretable cancer driver mutation prediction with D2Deep
Brief Bioinform
39708841
https://doi.org/10.1093/bib/bbae664
OPTN
Decoding phospho-regulation and flanking regions in autophagy-associated short linear motifs
Communications Biology
40835742
10.1038/s42003-025-08399-9
DLG4,GRB2,SMPD1
Deciphering long-range effects of mutations: an integrated approach using elastic network models and protein structure networks
JMB
40738203
doi: 10.1016/j.jmb.2025.169359
Entering multiple mutants in the "mutations to be displayed" window is time-consuming for more than a handful of mutants. Suggestion: Add a box where multiple mutants can be pasted in at once from an external document.
During the table overhaul, we have revised the user interface to add a text box that allows free copy-pasting of mutation lists. While we understand having a single input box would have been ideal, the former selection interface (which is also still available) doesn’t allow copy-paste. This is a known limitation in Streamlit.
Minor comments
We have done proofreading on the final version of the manuscript
Yes, we are aware of this. It is far from trivial to properly import the datasets from multiplex assays. They often need to be treated on a case-by-case basis. We are in the process of carefully compiling locally all the MAVE data before releasing it within the public version of the database, so this is why they are missing. We are giving priorities to the ones that can be correlated with our predictions on changes in structural stability and then we will also cover the rest of the datasets handling them in batches. Having said this, we have checked the dataset for BRCA1, HRAS, and PPARG. We have imported the ones for PPARG and BRCA1 from ProtGym, referring to the studies published in 10.1038/ng.3700 and 10.1038/s41586-018-0461-z, respectively. Whereas for HRAS, checking in details both the available data and literature, while we did identify a suitable dataset (10.7554/eLife.27810), we struggled to understand what a sensible cut-off for discriminating between pathogenic and non-pathogenic variants would be, and so ended up not including it in the MAVISp dataset for now. We will contact the authors to clarify which thresholds to apply before importing the data.
In the KRAS case study presented in MAVISP, we utilized the protein abundance dataset reported in (http://dx.doi.org/10.1038/s41586-023-06954-0) and made available in the ProteinGym repository (specifically referenced at https://github.com/OATML-Markslab/ProteinGym/blob/main/reference_files/DMS_substitutions.csv#L153). We adopted the precalculated thresholds as provided by the ProteinGym authors. In this regard, we are not really sure the reviewer is referring to this dataset or another one on KRAS.
We improved the description of our classification strategies for both modules in the Documentation page of our website. Also, we explained more clearly the possible sources of ‘uncertain’ annotations for the two modules in both the web app (Documentation page) and main text. Briefly, in the STABILITY module, we consider FoldX and either Rosetta or RaSP to achieve a final classification. We first classify one and the other independently, according to the following strategy:
If DDG ≥ 3, the mutation is Destabilizing If DDG ≤ −3, the mutation is Stabilizing If −2 We then compare the classifications obtained by the two methods: if they agree, then that is the final classification, if they disagree, then the final classification is Uncertain. The thresholds were selected based on a previous study, in which variants with changes in stability below 3 kcal/mol were not featuring a markedly different abundance at cellular level [10.1371/journal.pgen.1006739, 10.7554/eLife.49138]
Regarding the LOCAL_INTERACTION module, it works similarly as for the Stability module, in that Rosetta and FoldX are considered independently, and an implicit classification is performed for each, according to the rules (values in kcal/mol)
If DDG > 1, the mutation is Destabilizing. If DDG Each mutation is therefore classified for both methods. If the methods agree (i.e., if they classify the mutation in the same way), their consensus is the final classification for the mutation; if they do not agree, the final classification will be Uncertain.
If a mutation does not have an associated free energy value, the relative solvent accessible area is used to classify it: if SAS > 20%, the mutation is classified as Uncertain, otherwise it is not classified.
Thresholds here were selected according to best practices followed by the tool authors and more in general in the literature, as the reviewer also noticed.
The last of these points is not an application of MAVISp, but rather a way in which external data can help validate MAVISp results. Furthermore, none of the examples given demonstrate an application in benchmarking (what is being benchmarked?).
We have revised the statements to avoid this confusion in the reader.
We have removed this section and included a mention in the conclusions as part of the future directions.
The reviewer’s interpretation on the second legend is correct - it does refer to the ClinVar classification. Nonetheless, we understand the positioning of the legend makes understanding what the legend refers to not obvious. We also revised the captions of the figures in the main text. On the web app, we have changed the location of the figure legend for the ClinVar effect category and added a label to make it clear what the classification refers to.
We have corrected this in the text and the statements related to it.
Significance
Platforms that aggregate predictors of variant effect are not a new concept, for example dbNSFP is a database of SNV predictions from variant effect predictors and conservation predictors over the whole human proteome. Predictors such as CADD and PolyPhen-2 will often provide a summary of other predictions (their features) when using their platforms. MAVISp's unique angle on the problem is in the inclusion of diverse predictors from each of its different moules, giving a much wider perspective on variants and potentially allowing the user to identify the mechanistic cause of pathogenicity. The visualisation aspect of the web app is also a useful addition, although the user interface is somewhat awkward. Potentially the most valuable aspect of this study is the associated gitbook resource containing reports from biocurators for proteins that link relevant literature and analyse ClinVar variants. Unfortunately, these are only currently available for a small minority of the total proteins in the database with such reports. For improvement, I think that the paper should focus more on the precise utility of the web app / gitbook reports and how to interpret the results rather than going into detail about the underlying pipeline.
We appreciate the interest in the gitbook resource that we also see as very valuable and one of the strengths of our work. We have now implemented a new strategy based on a Python script introduced in the mavisp toolkit to generate a template Markdown file of the report that can be further customized and imported into GitBook directly (https://github.com/ELELAB/mavisp_accessory_tools/). This should allow us to streamline the production of more reports. We are currently assigning proteins in batches for reporting to biocurator through the mavisp_data_collection GitHub to expand their coverage. Also, we revised the text and added a section on the interpretation of results from MAVISp. with a focus on the utility of the web-app and reports.
In terms of audience, the fast look-up and visualisation aspects of the web-platform are likely to be of interest to clinicians in the interpretation of variants of unknown clinical significance. The ability to download the fully processed dataset on a per-protein database would be of more interest to researchers focusing on specific proteins or those taking a broader view over multiple proteins (although a facility to download the whole database would be more useful for this final group).
While our website only displays the dataset per protein, the whole dataset, including all the MAVISp entries, is available at our OSF repository (https://osf.io/ufpzm/), which is cited in the paper and linked on the MAVISp website. We have further modified the MAVISp database to add a link to the repository in the modes page, so that it is more visible.
My expertise. - I am a protein bioinformatician with a background in variant effect prediction and large-scale data analysis.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
Evidence, reproducibility and clarity:
Summary:
The authors present MAVISp, a tool for viewing protein variants heavily based on protein structure information. The authors have done a very impressive amount of curation on various protein targets, and should be commended for their efforts. The tool includes a diverse array of experimental, clinical, and computational data sources that provides value to potential users interested in a given target.
Major comments:
Unfortunately I was not able to get the website to work correctly. When selecting a protein target in simple mode, I was greeted with a completely blank page in the app window. In ensemble mode, there was no transition away from the list of targets at all. I'm using Firefox 140.0.2 (64-bit) on Ubuntu 22.04. I would like to explore the data myself and provide feedback on the user experience and utility.
We have tried reproducing the issue mentioned by the reviewer, using the exact same Ubuntu and Firefox versions, but unfortunately failed to produce it. The website worked fine for us under such an environment. The issue experienced by the reviewer may have been due to either a temporary issue with the web server or a problem with the specific browser environment they were working in, which we are unable to reproduce. It would be useful to know the date that this happened to verify if it was a downtime on the DTU IT services side that made the webserver inaccessible.
I have some serious concerns about the sustainability of the project and think that additional clarifications in the text could help. Currently is there a way to easily update a dataset to add, remove, or update a component (for example, if a new predictor is published, an error is found in a predictor dataset, or a predictor is updated)? If it requires a new round of manual curation for each protein to do this, I am worried that this will not scale and will leave the project with many out of date entries. The diversity of software tools (e.g., three different pipeline frameworks) also seems quite challenging to maintain.
We appreciate the reviewer’s concerns about long-term sustainability. It is a fair point that we consider within our steering group, who oversee and plans the activities and meet monthly. Adding entries to MAVISp is moving more and more towards automation as we grow. We aim to minimize the manual work where applicable. Still, an expert-based intervention is really needed in some of the steps, and we do not want to renounce it. We intend to keep working on MAVISp to make the process of adding and updating entries as automated as possible, and to streamline the process when manual intervention is necessary. From the point of view of the biocurators, they have three core workflows to use for the default modules, which also automatically cover the source of annotations. We are currently working to streamline the procedures behind LOCAL_INTERACTION, which is the most challenging one. On the data manager and maintainers' side, we have workflows and protocols that help us in terms of automation, quality control, etc, and we keep working to improve them. Among these, we have workflows to use for the old entries updates. As an example, the update of erroneously attributed RefSeq data (pointed out by reviewer 2) took us only one week overall (from assigning revisions and importing to the database) because we have a reduced version of Snakemake for automation that can act on only the affected modules. Also, another point is that we have streamlined the generation of the templates for the gitbook reports (see also answer to reviewer 2).
The update of old entries is planned and made regularly. We also deposit the old datasets on OSF for transparency, in case someone needs to navigate and explore the changes. We have activities planned between May and August every year to update the old entries in relation to changes of protocols in the modules, updates in the core databases that we interact with (COSMIC, Clinvar etc). In case of major changes, the activities for updates continue in the Fall. Other revisions can happen outside these time windows if an entry is needed or a specific research project and needs updates too.
Furthermore, the community of people contributing to MAVISp as biocurators or developers is growing and we have scientists contributing from other groups in relation to their research interest. We envision that for this resource to scale up, our team cannot be the only one producing data and depositing it to the database. To facilitate this we launched a pilot for a training event online (see Event page on the website) and we will repeat it once per year. We also organize regular meetings with all the active curators and developers to plan the activities in a sustainable manner and address the challenges we encounter.
As stated in the manuscript, currently with the team of people involved, automatization and resources that we have gathered around this initiative we can provide updates to the public database every third month and we have been regularly satisfied with them. Additionally, we are capable of processing from 20 to 40 proteins every month depending also on the needs of revision or expansion of analyses on existing proteins. We also depend on these data for our own research projects and we are fully committed to it.
Additionally, we are planning future activities in these directions to improve scale up and sustainability:
We thank the reviewer for this comment - we are aware of the upcoming EOL of Python 3.9. We tested MAVISp, both software package and web server, using Python 3.10 (which is the minimum supported version going forward) and Python 3.13 (which is the latest stable release at the time of writing) and updated the instructions in the README file on the MAVISp GitHub repository accordingly.
We plan on keeping track of Python and library versions during our testing and updating them when necessary. In the future, we also plan to deploy Continuous Integration with automated testing for our repository, making this process easier and more standardized.
I appreciate that the authors have made their code and data available. These artifacts should also be versioned and archived in a service like Zenodo, so that researchers who rely on or want to refer to specific versions can do so in their own future publications.
Since 2024, we have been reporting all previous versions of the dataset on OSF, the repository linked to the MAVISp website, at https://osf.io/ufpzm/files/osfstorage (folder: previous_releases). We prefer to keep everything under OSF, as we also use it to deposit, for example, the MD trajectory data.
Additionally, in this GitHub page that we use as a space to interact between biocurators, developers, and data managers within the MAVISp community, we also report all the changes in the NEWS space: https://github.com/ELELAB/mavisp_data_collection
Finally, the individual tools are all available in our GitHub repository, where version control is in place (see Table S1, where we now mapped all the resources used in the framework)
In the introduction of the paper, the authors conflate the clinical challenges of variant classification with evidence generation and it's quite muddled together. They should strongly consider splitting the first paragraph into two paragraphs - one about challenges in variant classification/clinical genetics/precision oncology and another about variant effect prediction and experimental methods. The authors should also note that they are many predictors other than AlphaMissense, and may want to cite the ClinGen recommendations (PMID: 36413997) in the intro instead.
We revised the introduction in light of these suggestions. We have split the paragraph as recommended and added a longer second paragraph about VEPs and using structural data in the context of VEPs. We have also added the citation that the reviewer kindly recommended.
Also in the introduction on lines 21-22 the authors assert that "a mechanistic understanding of variant effects is essential knowledge" for a variety of clinical outcomes. While this is nice, it is clearly not the case as we can classify variants according to the ACMG/AMP guidelines without any notion of specific mechanism (for example, by combining population frequency data, in silico predictor data, and functional assay data). The authors should revise the statement so that it's clear that mechanistic understanding is a worthy aspiration rather than a prerequisite.
We revised the statement in light of this comment from the reviewer
In the structural analysis section (page 5, lines 154-155 and elsewhere), the authors define cutoffs with convenient round numbers. Is there a citation for these values or were these arbitrarily chosen by the authors? I would have liked to see some justification that these assignments are reasonable. Also there seems to be an error in the text where values between -2 and -3 kcal/mol are not assigned to a bin (I assume they should also be uncertain). There are other similar seemingly-arbitrary cutoffs later in the section that should also be explained.
We have revised the text making the two intervals explicit, for better clarity.
On page 9, lines 294-298 the authors talk about using the PTEN data from ProteinGym, rather than the actual cutoffs from the paper. They get to the latter later on, but I'm not sure why this isn't first? The ProteinGym cutoffs are somewhat arbitrarily based on the median rather than expert evaluation of the dataset, and I'm not sure why it's even worth mentioning them when proper classifications are available. Regarding PTEN, it would be quite interesting to see a comparison of the VAMP-seq PTEN data and the Mighell phosphatase assay, which is cited on page 9 line 288 but is not actually a VAMP-seq dataset. I think this section could be interesting but it requires some additional attention.
We have included the data from Mighell’s phosphatase assay as provided by MAVEdb in the MAVISp database, within the experimental_data module for PTEN, and we have revised the case study, including them and explaining better the decision of supporting both the ProteinGym and MAVEdb classification in MAVISp (when available). See revised Figure3, Table 1 and corresponding text.
The authors mention "pathogenicity predictors" and otherwise use pathogenicity incorrectly throughout the manuscript. Pathogenicity is a classification for a variant after it has been curated according to a framework like the ACMG/AMP guidelines (Richards 2015 and amendments). A single tool cannot predict or assign pathogenicity - the AlphaMissense paper was wrong to use this nomenclature and these authors should not compound this mistake. These predictors should be referred to as "variant effect predictors" or similar, and they are able to produce evidence towards pathogenicity or benignity but not make pathogenicity calls themselves. For example, in Figure 4e, the terms "pathogenic" and "benign" should only be used here if these are the classifications the authors have derived from ClinVar or a similar source of clinically classified variants.
The reviewer is correct, we have revised the terminology we used in the manuscript and refers to VEPs (Variant Effect Predictors)
Minor comments:
The target selection table on the website needs some kind of text filtering option. It's very tedious to have to find a protein by scrolling through the table rather than typing in the symbol. This will only get worse as more datasets are added.
We have revised the website, adding a filtering option. In detail, we have refactored the web app by adding filtering functionality, both for the main protein table (that can now be filtered by UniProt AC, gene name, or RefSeq ID) and the mutations table. Doing this required a general overhaul of the table infrastructure (we changed the underlying engine that renders the tables).
The data sources listed on the data usage section of the website are not concordant with what is in the paper. For example, MaveDB is not listed.
We have revised and updated the data sources on the website, adding a metadata section with relevant information, including MaveDB references where applicable.
Figure 2 is somewhat confusing, as it partially interleaves results from two different proteins. This would be nicer as two separate figures, one on each protein, or just of a single protein.
As suggested by the reviewer, we have now revised the figure and corresponding legends and text, focusing only on one of the two proteins.
Figure 3 panel b is distractingly large and I wonder if the authors could do a little bit more with this visualization.
We have revised Figure 3 to solve these issues and integrating new data from the comparison with the phosphatase assay
Capitalization is inconsistent throughout the manuscript. For example, page 9 line 288 refers to VampSEQ instead of VAMP-seq (although this is correct elsewhere). MaveDB is referred to as MAVEdb or MAVEDB in various places. AlphaMissense is referred to as Alphamissense in the Figure 5 legend. The authors should make a careful pass through the manuscript to address this kind of issues.
We have carefully proofread the paper for these inconsistencies
MaveDB has a more recent paper (PMID: 39838450) that should be cited instead of/in addition to Esposito et al.
We have added the reference that the reviewer recommended
On page 11, lines 338-339 the authors mention some interesting proteins including BLC2, which has base editor data available (PMID: 35288574). Are there plans to incorporate this type of functional assay data into MAVISp?
The assay mentioned in the paper refers to an experimental setup designed to investigate mutations that may confer resistance to the drug venetoclax. We started the first steps to implement a MAVISp module aimed at evaluating the impact of mutations on drug binding using alchemical free energy perturbations (ensemble mode) but we are far from having it complete. We expect to import these data when the module will be finalized since they can be used to benchmark it and BCL2 is one of the proteins that we are using to develop and test the new module.
Reviewer #3 (Significance (Required)):
Significance:
General assessment:
This is a nice resource and the authors have clearly put a lot of effort in. They should be celebrated for their achievments in curating the diverse datasets, and the GitBooks are a nice approach. However, I wasn't able to get the website to work and I have raised several issues with the paper itself that I think should be addressed.
Advance:
New ways to explore and integrate complex data like protein structures and variant effects are always interesting and welcome. I appreciate the effort towards manual curation of datasets. This work is very similar in theme to existing tools like Genomics 2 Proteins portal (PMID: 38260256) and ProtVar (PMID: 38769064). Unfortunately as I wasn't able to use the site I can't comment further on MAVISp's position in the landscape.
We have expanded the conclusions section to add a comparison and cite previously published work, and linked to a review we published last year that frames MAVISp in the context of computational frameworks for the prediction of variant effects. In brief, the Genomics 2 Proteins portal (G2P) includes data from several sources, including some overlapping with MAVISp such as Phosphosite or MAVEdb, as well as features calculated on the protein structure. ProtVar also aggregates mutations from different sources and includes both variant effect predictors and predictions of changes in stability upon mutation, as well as predictions of complex structures. These approaches are only partially overlapping with MAVISp. G2P is primarily focused on structural and other annotations of the effect of a mutation; it doesn’t include features about changes of stability, binding, or long-range effects, and doesn’t attempt to classify the impact of a mutation according to its measurements. It also doesn’t include information on protein dynamics. Similarly, ProtVar does include information on binding free energies, long effects, or dynamical information.
Audience:
MAVISp could appeal to a diverse group of researchers who are interested in the biology or biochemistry of proteins that are included, or are interested in protein variants in general either from a computational/machine learning perspective or from a genetics/genomics perspective.
My expertise:
I am an expert in high-throughput functional genomics experiments and am an experienced computational biologist with software engineering experience.
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Summary:
The authors present MAVISp, a tool for viewing protein variants heavily based on protein structure information. The authors have done a very impressive amount of curation on various protein targets, and should be commended for their efforts. The tool includes a diverse array of experimental, clinical, and computational data sources that provides value to potential users interested in a given target.
Major comments:
Unfortunately I was not able to get the website to work properly. When selecting a protein target in simple mode, I was greeted with a completely blank page in the app window, and in ensemble mode, there was no transition away from the list of targets at all. I'm using Firefox 140.0.2 (64-bit) on Ubuntu 22.04. I would have liked to be able to explore the data myself and provide feedback on the user experience and utility.
I have some serious concerns about the sustainability of the project and think that additional clarifications in the text could help. Currently is there a way to easily update a dataset to add, remove, or update a component (for example, if a new predictor is published, an error is found in a predictor dataset, or a predictor is updated)? If it requires a new round of manual curation for each protein to do this, I am worried that this will not scale and will leave the project with many out of date entries. The diversity of software tools (e.g., three different pipeline frameworks) also seems quite challenging to maintain.
On the same theme, according to the GitHub repository, the program relies on Python 3.9, which reaches end of life in October 2025. It has been tested against Ubuntu 18.04, which left standard support in May 2023. The authors should update the software to more modern versions of Python to promote the long-term health and maintainability of the project.
I appreciate that the authors have made their code and data available. These artifacts should also be versioned and archived in a service like Zenodo, so that researchers who rely on or want to refer to specific versions can do so in their own future publications.
In the introduction of the paper, the authors conflate the clinical challenges of variant classification with evidence generation and it's quite muddled together. The y should strongly consider splitting the first paragraph into two paragraphs - one about challenges in variant classification/clinical genetics/precision oncology and another about variant effect prediction and experimental methods. The authors should also note that they are many predictors other than AlphaMissense, and may want to cite the ClinGen recommendations (PMID: 36413997) in the intro instead.
Also in the introduction on lines 21-22 the authors assert that "a mechanistic understanding of variant effects is essential knowledge" for a variety of clinical outcomes. While this is nice, it is clearly not the case as we are able to classify variants according to the ACMG/AMP guidelines without any notion of specific mechanism (for example, by combining population frequency data, in silico predictor data, and functional assay data). The authors should revise the statement so that it's clear that mechanistic understanding is a worthy aspiration rather than a prerequisite.
In the structural analysis section (page 5, lines 154-155 and elsewhere), the authors define cutoffs with convenient round numbers. Is there a citation for these values or were these arbitrarily chosen by the authors? I would have liked to see some justification that these assignments are reasonable. Also there seems to be an error in the text where values between -2 and -3 kcal/mol are not assigned to a bin (I assume they should also be uncertain). There are other similar seemingly-arbitrary cutoffs later in the section that should also be explained.
On page 9, lines 294-298 the authors talk about using the PTEN data from ProteinGym, rather than the actual cutoffs from the paper. They get to the latter later on, but I'm not sure why this isn't first? The ProteinGym cutoffs are somewhat arbitrarily based on the median rather than expert evaluation of the dataset and I'm not sure why it's even worth mentioning them when proper classifications are available. Regarding PTEN, it would be quite interesting to see a comparison of the VAMP-seq PTEN data and the Mighell phosphatase assay, which is cited on page 9 line 288 but is not actually a VAMP-seq dataset. I think this section could be interesting but it requires some additional attention.
The authors mention "pathogenicity predictors" and otherwise use pathogenicity incorrectly throughout the manuscript. Pathogenicity is a classification for a variant after it has been curated according to a framework like the ACMG/AMP guidelines (Richards 2015 and amendments). A single tool cannot predict or assign pathogenicity - the AlphaMissense paper was wrong to use this nomenclature and these authors should not compound this mistake. These predictors should be referred to as "variant effect predictors" or similar, and they are able to produce evidence towards pathogenicity or benignity but not make pathogenicity calls themselves. For example, in Figure 4e, the terms "pathogenic" and "benign" should only be used here if these are the classifications the authors have derived from ClinVar or a similar source of clinically classified variants.
Minor comments:
The target selection table on the website needs some kind of text filtering option. It's very tedious to have to find a protein by scrolling through the table rather than typing in the symbol. This will only get worse as more datasets are added.
The data sources listed on the data usage section of the website are not concordant with what is in the paper. For example, MaveDB is not listed.
I found Figure 2 to be a bit confusing in that it partially interleaves results from two different proteins. I think this would be nicer as two separate figures, one on each protein, or just of a single protein.
Figure 3 panel b is distractingly large and I wonder if the authors could do a little bit more with this visualization.
Capitalization is inconsistent throughout the manuscript. For example, page 9 line 288 refers to VampSEQ instead of VAMP-seq (although this is correct elsewhere). MaveDB is referred to as MAVEdb or MAVEDB in various places. AlphaMissense is referred to as Alphamissense in the Figure 5 legend. The authors should make a careful pass through the manuscript to address this kind of issues.
MaveDB has a more recent paper (PMID: 39838450) that should be cited instead of/in addition to Esposito et al.
On page 11, lines 338-339 the authors mention some interesting proteins including BLC2, which has base editor data available (PMID: 35288574). Are there plans to incorporate this type of functional assay data into MAVISp?
General assessment:
This is a nice resource and the authors have clearly put a lot of effort in. They should be celebrated for their achievments in curating the diverse datasets, and the GitBooks are a nice approach. However, I wasn't able to get the website to work and I have raised several issues with the paper itself that I think should be addressed.
Advance:
New ways to explore and integrate complex data like protein structures and variant effects are always interesting and welcome. I appreciate the effort towards manual curation of datasets. This work is very similar in theme to existing tools like Genomics 2 Proteins portal (PMID: 38260256) and ProtVar (PMID: 38769064). Unfortunately as I wasn't able to use the site I can't comment further on MAVISp's position in the landscape.
Audience:
MAVISp could appeal to a diverse group of researchers who are interested in the biology or biochemistry of proteins that are included, or are interested in protein variants in general either from a computational/machine learning perspective or from a genetics/genomics perspective.
My expertise:
I am an expert in high-throughput functional genomics experiments and am an experienced computational biologist with software engineering experience.
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Summary:
The authors present a pipeline and platform, MAVISp, for aggregating, displaying and analysis of variant effects with a focus on reclassification of variants of uncertain clinical significance and uncovering the molecular mechanisms underlying the mutations.
Major comments:
Minor comments
The last of these points is not an application of MAVISp, but rather a way in which external data can help validate MAVISp results. Furthermore, none of the examples given demonstrate an application in benchmarking (what is being benchmarked?). - Transcription factors section. This section describes an intended future expansion to MAVISp, not a current feature, and presents no results. As such, it should probably be moved to the conclusions/future directions section. - Figures. The dot-plots generated by the web app, and in Figures 4, 5 and 6 have 2 legends. After looking at a few, it is clear that the lower legend refers to the colour of the variant on the X-axis - most likely referencing the ClinVar effect category. This is not, however, made clear either on the figures or in the app. - "We identified ten variants reported in ClinVar as VUS (E102K, H86D, T29I, V91I, P2R, L44P, L44F, D56G, R11L, and E25Q, Fig.5a)"
E25Q is benign in ClinVar and has had that status since first submitted.
Platforms that aggregate predictors of variant effect are not a new concept, for example dbNSFP is a database of SNV predictions from variant effect predictors and conservation predictors over the whole human proteome. Predictors such as CADD and PolyPhen-2 will often provide a summary of other predictions (their features) when using their platforms. MAVISp's unique angle on the problem is in the inclusion of diverse predictors from each of its different moules, giving a much wider perspective on variants and potentially allowing the user to identify the mechanistic cause of pathogenicity. The visualisation aspect of the web app is also a useful addition, although the user interface is somewhat awkward. Potentially the most valuable aspect of this study is the associated gitbook resource containing reports from biocurators for proteins that link relevant literature and analyse ClinVar variants. Unfortunately, these are only currently available for a small minority of the total proteins in the database with such reports.
For improvement, I think that the paper should focus more on the precise utility of the web app / gitbook reports and how to interpret the results rather than going into detail about the underlying pipeline.
In terms of audience, the fast look-up and visualisation aspects of the web-platform are likely to be of interest to clinicians in the interpretation of variants of unknown clinical significance. The ability to download the fully processed dataset on a per-protein database would be of more interest to researchers focusing on specific proteins or those taking a broader view over multiple proteins (although a facility to download the whole database would be more useful for this final group).
My expertise.
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Summary: This manuscript, "MAVISp: A Modular Structure-Based Framework for Protein Variant Effects," presents a significant new resource for the scientific community, particularly in the interpretation and characterization of genomic variants. The authors have developed a comprehensive and modular computational framework that integrates various structural and biophysical analyses, alongside existing pathogenicity predictors, to provide crucial mechanistic insights into how variants affect protein structure and function. Importantly, MAVISp is open-source and designed to be extensible, facilitating reuse and adaptation by the broader community.
Major comments:
Minor comments:
Page 3, line 46: "MAVISp perform" -> "MAVISp performs"
Page 3, line 56: "automatically as embedded" -> "automatically embedded"
Page 3, line 57: "along with to enhance" -> unclear; please revise
Page 4, line 96: "web app interfaces with the database and present" -> "presents"
Page 6, line 210: "to investigate wheatear" -> "whether"
Page 6, lines 215-216: "We have in queue for processing with MAVISp proteins from datasets relevant to the benchmark of the PTM module." -> unclear sentence; please clarify
Page 15, line 446: "Both the approaches" -> "Both approaches"
Page 20, line 704: "advantage of multi-core system" -> "multi-core systems"
General assessment: the strongest aspects of the study are the modularity, open-source implementation, and the integration of structural information through graph neural networks. MAVISp appears to be one of the few publicly available frameworks that can easily incorporate AlphaFold2-based features in a flexible way, lowering the barrier for developing custom predictors. Its reproducibility and transparency make it a valuable resource. However, while the technical foundation is solid and the effort substantial, the scientific narrative and presentation could be significantly improved. The manuscript is dense and hard to follow in places, with a heavy use of acronyms and insufficient explanation of key design choices. Improving the descriptive clarity, especially in the early sections, would greatly enhance the impact of this work.
Advance: to the best of my knowledge, this is one of the first modular platforms for protein variant effect prediction that integrates structural data from AlphaFold2 with bioinformatic annotations and even clinical data in an extensible fashion. While similar efforts exist (e.g., ESMfold, AlphaMissense), MAVISp distinguishes itself through openness and design for reusability. The novelty is primarily technical and practical rather than conceptual.
Audience: this study will be of strong interest to researchers in computational biology, structural bioinformatics, and genomics, particularly those developing variant effect predictors or analyzing the impact of mutations in clinical or functional genomics contexts. The audience is primarily specialized, but the open-source nature of the tool may diffuse its use among more applied or translational users, including those working in precision medicine or protein engineering.
Reviewer expertise: my expertise is in computational structural biology, molecular modeling, and (rather weak) machine learning applications in bioinformatics. I am familiar with graph-based representations of proteins, AlphaFold2, and variant effects based on Molecular Dynamics simulations. I do not have any direct expertise in clinical variant annotation pipelines.
eLife Assessment
This important study advances our understanding of how cellular quality control machinery influences cystic fibrosis (CF) drug responsiveness by systematically analyzing the effects of the chaperone calnexin on more than two hundreds of CFTR (cystic fibrosis transmembrane regulator) variants. The evidence supporting the conclusions is convincing, with a comprehensive deep mutational scanning methodology and rigorous quantitative analysis. The findings reveal that calnexin is critical for both CFTR protein expression and corrector drug efficacy in a variant-specific manner, providing invaluable insights that could guide the development of personalized CF therapies. This work will be of significant interest to researchers in protein folding, CF drug development, and genetic disease therapeutics.
Reviewer #1 (Public review):
Summary:
This research investigates how the cellular protein quality control machinery influences the effectiveness of cystic fibrosis (CF) treatments across different genetic variants. CF is caused by mutations in the CFTR gene, with over 1,700 known disease-causing variants that primarily work through protein misfolding mechanisms. While corrector drugs like those in Trikafta therapy can stabilize some misfolded CFTR proteins, the reasons why certain variants respond to treatment while others don't remain unclear. The authors hypothesized that the cellular proteostasis network-the machinery that manages protein folding and quality control-plays a crucial role in determining drug responsiveness across different CFTR variants. The researchers focused on calnexin (CANX), a key chaperone protein that recognizes misfolded glycosylated proteins. Using CRISPR-Cas9 gene editing combined with deep mutational scanning, they systematically analyzed how CANX affects the expression and corrector drug response of 234 clinically relevant CF variants in HEK293 cells.
In terms of findings, this study revealed that CANX is generally required for robust plasma membrane expression of CFTR proteins, and CANX disproportionately affects variants with mutations in the C-terminal domains of CFTR and modulates later stages of protein assembly. Without CANX, many variants that would normally respond to corrector drugs lose their therapeutic responsiveness. Furthermore, loss of CANX caused broad changes in how CF variants interact with other cellular proteins, though these effects were largely separate from changes in CFTR channel activity.
This study has some limitations: the research was conducted in HEK293 cells rather than lung epithelial cells, which may not fully reflect the physiological context of CF. Additionally, the study only examined known disease-causing variants and used methodological approaches that could potentially introduce bias in the data analysis.
How cellular quality control mechanisms influence the therapeutic landscape of genetic diseases is an emerging field. Overall, this work provides important cellular context for understanding CF mutation severity and suggests that the proteostasis network significantly shapes how different CFTR variants respond to corrector therapies. The findings could pave the way for more personalized CF treatments tailored to patients' specific genetic variants and cellular contexts.
Strengths:
(1) This work makes an important contribution to the field of variant effect prediction by advancing our understanding of how genetic variants impact protein function.
(2) The study provides valuable cellular context for CFTR mutation severity, which may pave the way for improved CFTR therapies that are customized to patient-specific cellular contexts.
(3) The research provides further insight into the biological mechanisms underlying approved CFTR therapies, enhancing our understanding of how these treatments work.
(4) The authors conducted a comprehensive and quantitative analysis, and they made their raw and processed data as well as analysis scripts publicly available, enabling closer examination and validation by the broader scientific community.
Comments on revisions:
The authors have addressed my concerns. If Document S1 is part of the final published version, this will address one of my previous concerns about potential skew and bias in the read data (Weakness 3, Methodological Choices).
Reviewer #2 (Public review):
In this work, the authors use deep mutational scanning (DMS) to examine the effect of the endogenous chaperone calnexin (CANX) on the plasma membrane expression (PME) and potential pharmacological stabilization cystic fibrosis disease variants. This is important because there are over 1,700 loss-of-function mutations that can lead to the disease Cystic Fibrosis (CF), and some of these variants can be pharmacologically rescued by small-molecule "correctors," which stabilize the CFTR protein and prevent its degradation. This study expands on previous work to specifically identify which mutations affect sensitivity to CFTR modulators, and further develops the work by examining the effect of a known CFTR interactor-CANX-on PME and corrector response.
Overall, this approach provides a useful atlas of CF variants and their downstream effects, both at a basal level as well as in the context of a perturbed proteostasis. Knockout of CANX leads to an overall reduced plasma membrane expression of CFTR with CF variants located at the C-terminal domains of CFTR, which seem to be more affected than the others. This study then repeats their DMS approach, using PME as a readout, to probe the effect of either VX-445 or VX-455 + VX-661-which are two clinically relevant CFTR pharmacological modulators. I found this section particularly interesting for the community because the exact molecular features that confer drug resistance/sensitivity are not clear. When CANX is knocked out, cells that normally respond to VX-445 are no longer able to be rescued, and the DMS data show that these non-responders are CF variants that lie in the VX-445 binding site. Based on computational data, the authors speculate that NBD2 assembly is compromised, but that remains to be experimentally examined. Cells lacking CANX were also resistant to combinatorial treatment of VX-445 + VX-661, showing that these two correctors were unable to compensate for the lack of this critical chaperone.
One major strength of this manuscript is the mass spectrometry data, in which 4 CF variants were profiled in parental and CANX KO cells. This analysis provides some explanatory power to the observation that the delF508 variant is resistant to correctors in CANX KO cells, which is because correctors were found not to affect protein degradation interactions in this context. Findings such as this provide potential insights into intriguing new hypothesis, such as whether addition of an additional proteostasis regulators, such as a proteosome inhibitor, would facilitate a successful rescue. Taken together, the data provided can be generative to researchers in the field and may be useful in rationalizing some of the observed phenotypes conferred by the various CF variants, as well as the impact of CANX on those effects.
To complete their analysis of CF variants in CANX KO cells, the research also attempted to relate their data, primarily based on PME, to functional relevance. They observed that, although CANX KO results in a large reduction in PME (~30% reduction), changes in the actual activation of CFTR (and resultant quenching of their hYFP sensor) were "quite modest." This is an important experiment and caveat to the PME data presented above since changes in CFTR activity does not strictly require changes in PME. In addition, small molecule correctors also do not drastically alter CFTR function in the context of CANX KO. The authors reason that this difference is due to a sort of compensatory mechanism in which the functionally active CFTR molecules that are successfully assembled in an unbalanced proteostasis system (CANX KO) are more active than those that are assembled with the assistance of CANX. While I generally agree with this statement, it is not directly tested and would be challenging to actually test.
The selected model for all the above experiments was HEK293T cells. The authors then demonstrate some of their major findings in Fischer rat thyroid cell monolayers. Specifically, cells lacking CANX are less sensitive to rescue by CFTR modulators than the WT. This highlights the importance of CANX in supporting the maturation of CFTR and the dependence of chemical correctors on the chaperone. Although this is demonstrated specifically for CANX in this manuscript, I imagine a more general claim can be made that chemical correctors depend on a functional/balanced proteostasis system, which is supported by the manuscript data. I am surprised by the discordance between HEK293T PME levels compared to the CTFR activity. The authors offer a reasonable explanation about the increase in specific activity of the mature CFTR protein following CANX loss.
For the conclusions and claims relevant to CANX and CF variant surveying of PME/function, I find the manuscript to provide solid evidence to achieve this aim. The manuscript generates a rich portrait of the influence of CF mutations both in WT and CANX KO cells. While the focus of this study is a specific chaperone, CANX, this manuscript has the potential to impact many researchers in the broad field of proteostasis.
Comments on revisions:
The authors address my concerns. I appreciate seeing that the UPR probably isn't activated, ruling out that less PME is simply due to less CF protein.
Author response:
The following is the authors’ response to the original reviews
Reviewer 1 (Public review):
This research investigates how the cellular protein quality control machinery influences the effectiveness of cystic fibrosis (CF) treatments across different genetic variants. CF is caused by mutations in the CFTR gene, with over 1,700 known disease-causing variants that primarily work through protein misfolding mechanisms. While corrector drugs like those in Trikafta therapy can stabilize some misfolded CFTR proteins, the reasons why certain variants respond to treatment while others don't remain unclear. The authors hypothesized that the cellular proteostasis network-the machinery that manages protein folding and quality control-plays a crucial role in determining drug responsiveness across different CFTR variants. The researchers focused on calnexin (CANX), a key chaperone protein that recognizes misfolded glycosylated proteins. Using CRISPR-Cas9 gene editing combined with deep mutational scanning, they systematically analyzed how CANX affects the expression and corrector drug response of 234 clinically relevant CF variants in HEK293 cells.
In terms of findings, this study revealed that CANX is generally required for robust plasma membrane expression of CFTR proteins, and CANX disproportionately affects variants with mutations in the C-terminal domains of CFTR and modulates later stages of protein assembly. Without CANX, many variants that would normally respond to corrector drugs lose their therapeutic responsiveness. Furthermore, loss of CANX caused broad changes in how CF variants interact with other cellular proteins, though these effects were largely separate from changes in CFTR channel activity.
This study has some limitations: the research was conducted in HEK293 cells rather than lung epithelial cells, which may not fully reflect the physiological context of CF. Additionally, the study only examined known diseasecausing variants and used methodological approaches that could potentially introduce bias in the data analysis.
We agree that the approaches employed here are not fully physiological, though we would remind the reviewer that we previously benchmarked the results generated by this experimental platform against a variety of other published datasets (PMID: 37253358). Regarding the issue of bias, we outline several pieces of evidence suggesting we retain robust and near-uniform sampling of these variants across these experimental conditions. We hope our comments below address all of these concerns. Overall, we believe deep mutational scanning is actually remarkably unbiased relative to other approaches due to the fact that all measurements are taken from a single dish of cells that is processed in parallel. Moreover, we show the trends are highly reproducible across replicates and users (see Figure S1).
How cellular quality control mechanisms influence the therapeutic landscape of genetic diseases is an emerging field. Overall, this work provides important cellular context for understanding CF mutation severity and suggests that the proteostasis network significantly shapes how different CFTR variants respond to corrector therapies. The findings could pave the way for more personalized CF treatments tailored to patients' specific genetic variants and cellular contexts.
Strengths:
(1) This work makes an important contribution to the field of variant effect prediction by advancing our understanding of how genetic variants impact protein function.
(2) The study provides valuable cellular context for CFTR mutation severity, which may pave the way for improved CFTR therapies that are customized to patient-specific cellular contexts.
(3) The research provides further insight into the biological mechanisms underlying approved CFTR therapies, enhancing our understanding of how these treatments work.
(4) The authors conducted a comprehensive and quantitative analysis, and they made their raw and processed data as well as analysis scripts publicly available, enabling closer examination and validation by the broader scientific community.
We are grateful for this broad perspective on the general relevance of this work.
Weaknesses:
(1) The study only considers known disease-causing variants, which limits the scope of findings and may miss important insights from variants of uncertain significance.
We agree with this caveat. A more comprehensive library of CFTR variants will undoubtedly be useful for assigning variants of uncertain significance, though we note that such a large library would involve trade-offs in depth/ coverage that will compromise the sensitivity/ precision of the measurements. This will, in turn, make it challenging to compare the effects of CFTR modulators across the spectrum of clinical variants. For this reason, we believe the current library will remain a useful tool for CF variant theratyping.
(2) The cellular context of HEK293 cells is quite removed from lung epithelia, the primary tissue affected in cystic fibrosis, potentially limiting the clinical relevance of the findings.
We concede this limitation, but note that we did carry out functional measurements in FRT monolayers, which are a prevailing model that closely mimics pharmacological outcomes in the clinic (see Fig. 6).
(3) Methodological choices, such as the expansion of sorted cell populations before genetic analysis, may introduce possible skew or bias in the data that could affect interpretation.
We respectfully disagree with this point. The recombination system we employ in these studies generates millions of recombinant cells per transfection, which corresponds to tens of thousands of clones per variant. Moreover, our sequencing data contain exhaustive coverage of every variant characterized herein within each of the final data sets. Generally, we do not see any evidence to suggest certain variants are lost from the population. We note that, while HEK293T cells are not the most physiological relevant system, they are robust to uniformly express these variants in a manner that provides a precise comparison of their effects and/ or response to CFTR modulators. To address this concern, we added Document S1 to the revised draft, which shows the total number of reads for each variant within each fraction and each experiment.
(4) While the impact on surface trafficking is convincingly demonstrated, how cellular proteostasis affects CFTR function requires further study, likely within a lung-specific cellular context to be more clinically relevant.
We agree with this caveat.
Reviewer 1 (Recommendations for the authors):
Major Issues
Cell Growth Bias? After sorting cell populations into quartiles, cells were expanded before genetic analysis - if CFTR variants affect cell doubling time (e.g., severely misfolded variants causing cellular stress), this could skew variant abundance within sorted quartiles and bias results.
Based on several observations, we do not believe this to be a significant issue. First, we note that we previously benchmarked the quantitative outputs of these experiments against a variety of other investigations and found very good agreement with previous variant classifications and expression levels (PMID: 37253358). If there were significant bias, we believe this would have come up in our efforts to benchmark the assay. Second, we note that we typically create recombinant cell lines that express WT or ΔF508 CFTR only alongside each recombinant cellular library. Importantly, we have never observed any difference in the growth rate of cultures expressing different CFTR variants. Third, even if cells expressing certain variants grow slower, it seems likely this slow growth would consistently occur in the context of each sorted subpopulation. Given that scores are derived from the relative amount of identifications across each subpopulation, we do not suspect this should impact the scoring. Overall, we believe the robustness of this cell line is a key feature that allows us to avoid any such issues related to proteostatic toxicity.
(1) Please add methodological detail. The data analysis pipeline lacks adequate description beyond referencing prior studies - essential details about what the Plasma Membrane Expression (PME) values represent (fold enrichment vs input library) and calculation methods must be provided.
We thank the reviewer for this helpful comment. We have added the text below to the revised manuscript in order to provide more detail to the reader:
“Briefly, low quality reads that likely contain more than one error were first removed from the demultiplexed sequencing data. Unique molecular identifier sequences within the remaining reads were then counted within each sample to track the relative abundance of each variant. To compare read counts across fractions, the collection of reads within each population were then randomly down-sampled to ensure a consistent total read count across each sub-population. The surface immunostaining of each variant was then estimated by calculating the the weighted-average immunostaining intensity for each variant using the following equation:
where ⟨I⟩<sub>variant</sub> is the weighted-average fluorescence intensity of a given variant, ⟨F⟩<sub>i</sub> is the mean fluorescence intensity associated with cells from the ith FACS quartile, and Ni is the number of variant reads in the i<sup>th</sup> FACS quartile. Variant intensities from each replicate were normalized relative to one another using the mean surface immunostaining intensity of the entire recombinant cell population for each experiment to account for small variations in laser power and/ or detector voltage. Finally, to filter out any noisy scores arising from insufficient sampling, we repeated the down-sampling and scoring process then rejected any variant measurements that exhibit more than X% variation in their intensity scores across the two replicate analyses. The reported intensity values represent the average normalized intensity values from two independent down-sampling iterations across three biologicals replicates.”
(3) Add detail on library composition. The distribution of CFTR variants within the parental HEK293T library after landing pad insertion needs documentation, including any variant dropout or overrepresentation issues.
As noted in our previous work (PMID: 37253358), our CF variant library is quite uniform, with each mutant contributing on average, 0.43% of the library with a standard deviation of +/- 0.16%. This corresponds to an average read depth of over 40K reads per variant, per experimental condition in the final analyses. Indeed, the most abundant variant in the pool was ΔF508 (1.67% of total reads). In contrast, the least sampled variant was S549R (1647T>G) was still sampled an average of 3,688 times per replicate, which corresponds to 0.09% of the total reads. See Doc S1.
(4) Documentation of CFTR variant overlap between parental and CANX KO HEK293T libraries is needed, including whether every variant was present at equivalent input abundance in both libraries.
We thank the reviewer for this suggestion. Though there are small deviations in the composition of recombinant parental and knockout cell lines, the relative abundances of individual variants within the recombinant populations only differs by an average of 18.5% between the parental and knockout lines. There are no cases in which we observe a single variant increasing by more than 50% in the knockout line relative to the parent. However, there is a single variant, Y563N, that exhibits a 96% decrease in its abundance in the context of the knockout cell line. Nevertheless, even this variant was sampled over 1,000 times, and it’s final score passed all quality control metrics. In the revised draft, we have provided a complete table containing the total number of reads and percent of total reads for each variant for each cell line and condition (see Doc. S1).
(5) The section reporting CANX impact on functional rescue of CF variants requires clearer logic flow - the conclusion about higher specific activity of CFTR assembled without CANX appears misleading, given later discussion about CANX allowing suboptimally folded CFTR to traffic to the surface.
We apologize for any confusion. We invoked the term “specific activity” in the enzymological sense, which is to say the proportion of active enzyme (i.e. channel) at the plasma membrane differs in the knockout line. The logic is quite simple- if protein levels are lower while ion conductance remains the same in the knockout cells, then a higher proportion of the mature channels must be inactive in the parental cell line. Thus, we suspect fewer of the channels at the plasma membrane are active in the context of the parental cell line containing CANX. We considered modifications to the text in the discussion, but ultimately feel the current text strikes a reasonable balance between nuance and simplicity.
(6) In your discussion, consider that HEK293T cellular context differs significantly from lung epithelia, and the hYFP quenching assay may have insufficient dynamic range or high noise for detecting relevant functional differences.
We modified the following sentence in the discussion to introduce this possibility:
“While these discrepancies could stem from differences in the dynamic range of the functional assays, they may also suggest the stringency of QC is more finely tuned to ion channel biosynthesis in epithelial monolayers.”
Minor Issues
(1) Include immunostaining quartiles as a supplementary figure overlaid on Figure 1A, and clarify whether quartiles were consistent across experiments or adjusted for each sort.
We added a new figure to demonstrate the gating approach in the revised manuscript (see Fig. S10). We have also added the following text to the Methods section:
“Sorting gates for surface immunostaining were independently set for each biological replicate and in each condition to ensure that the population was evenly divided into four equal subpopulations.”
(2) Figure 2C improvements. Flip the figure 180 degrees to position MSD1 and NBD1 on the left, replace the blue-to-red color scale with yellow-to-blue or monochromatic scaling for better intermediate value differentiation.
Respectfully, we prefer not to do this so that our figures can be easily compared across our previous and forthcoming publications. We chose this rendering because this view depicts certain trends in variant response more clearly.
(3) Indicate the location of ECL4 on the protein structure shown in Figure 2C for better reference.
We appreciate the suggestion. However, most of ECL4 is missing from the experimental cryo-EM models of CFTR due to a lack of density. For this reason, we did not modify the figure.
Reviewer 2 (Public review):
In this work, the authors use deep mutational scanning (DMS) to examine the effect of the endogenous chaperone calnexin (CANX) on the plasma membrane expression (PME) and potential pharmacological stabilization cystic fibrosis disease variants. This is important because there are over 1,700 loss-of-function mutations that can lead to the disease Cystic Fibrosis (CF), and some of these variants can be pharmacologically rescued by small-molecule "correctors," which stabilize the CFTR protein and prevent its degradation. This study expands on previous work to specifically identify which mutations affect sensitivity to CFTR modulators, and further develops the work by examining the effect of a known CFTR interactor-CANX-on PME and corrector response.
Overall, this approach provides a useful atlas of CF variants and their downstream effects, both at a basal level as well as in the context of a perturbed proteostasis. Knockout of CANX leads to an overall reduced plasma membrane expression of CFTR with CF variants located at the C-terminal domains of CFTR, which seem to be more affected than the others. This study then repeats their DMS approach, using PME as a readout, to probe the effect of either VX-445 or VX-455 + VX-661-which are two clinically relevant CFTR pharmacological modulators. I found this section particularly interesting for the community because the exact molecular features that confer drug resistance/sensitivity are not clear. When CANX is knocked out, cells that normally respond to VX-445 are no longer able to be rescued, and the DMS data show that these non-responders are CF variants that lie in the VX-445 binding site. Based on computational data, the authors speculate that NBD2 assembly is compromised, but that remains to be experimentally examined. Cells lacking CANX were also resistant to combinatorial treatment of VX-445 + VX-661, showing that these two correctors were unable to compensate for the lack of this critical chaperone.
One major strength of this manuscript is the mass spectrometry data, in which 4 CF variants were profiled in parental and CANX KO cells. This analysis provides some explanatory power to the observation that the delF508 variant is resistant to correctors in CANX KO cells, which is because correctors were found not to affect protein degradation interactions in this context. Findings such as this provide potential insights into intriguing new hypothesis, such as whether addition of an additional proteostasis regulators, such as a proteosome inhibitor, would facilitate a successful rescue. Taken together, the data provided can be generative to researchers in the field and may be useful in rationalizing some of the observed phenotypes conferred by the various CF variants, as well as the impact of CANX on those effects.
To complete their analysis of CF variants in CANX KO cells, the research also attempted to relate their data, primarily based on PME, to functional relevance. They observed that, although CANX KO results in a large reduction in PME (~30% reduction), changes in the actual activation of CFTR (and resultant quenching of their hYFP sensor) were "quite modest." This is an important experiment and caveat to the PME data presented above since changes in CFTR activity does not strictly require changes in PME. In addition, small molecule correctors also do not drastically alter CFTR function in the context of CANX KO. The authors reason that this difference is due to a sort of compensatory mechanism in which the functionally active CFTR molecules that are successfully assembled in an unbalanced proteostasis system (CANX KO) are more active than those that are assembled with the assistance of CANX. While I generally agree with this statement, it is not directly tested and would be challenging to actually test.
The selected model for all the above experiments was HEK293T cells. The authors then demonstrate some of their major findings in Fischer rat thyroid cell monolayers. Specifically, cells lacking CANX are less sensitive to rescue by CFTR modulators than the WT. This highlights the importance of CANX in supporting the maturation of CFTR and the dependence of chemical correctors on the chaperone. Although this is demonstrated specifically for CANX in this manuscript, I imagine a more general claim can be made that chemical correctors depend on a functional/balanced proteostasis system, which is supported by the manuscript data. I am surprised by the discordance between HEK293T PME levels compared to the CTFR activity. The authors offer a reasonable explanation about the increase in specific activity of the mature CFTR protein following CANX loss.
For the conclusions and claims relevant to CANX and CF variant surveying of PME/function, I find the manuscript to provide solid evidence to achieve this aim. The manuscript generates a rich portrait of the influence of CF mutations both in WT and CANX KO cells. While the focus of this study is a specific chaperone, CANX, this manuscript has the potential to impact many researchers in the broad field of proteostasis.
We thank the reviewer for their thoughtful and comprehensive perspectives on the scope and relevance of this work.
Reviewer 2 (Recommendations for the authors):
While I did not identify any major weaknesses in this manuscript, I offer some suggestions below, as well as some conclusions to consider:
(1) Missing period at the end of line 51.
We thank the reviewer for catching this grammatical error and have added proper punctuation.
(2)Figure S1 "repre-sent"??
We have corrected this punctuation error.
(3) Figure S2 missing parentheses A)
We have corrected the punctuation error.
(4) Figure S5, "B) The total ΔRMSD of the active conformation of NBD2 is shown for variants bound to VX-445. Red bars show increasing deviations from the native NBD2 conformation in the mutant models, and blue bars show how much VX-445 suppresses these conformational defects in NBD2."
VX-445 should not bind/stabilize the G85E from the calculations in Figure S5A. As a confirmation, it would be nice to see the calculated hypothetical effect of VX-445 in the G85E variant as performed for L1077P and N1303K. I also want to point out that G58E is referred to as being non-responsive in S5A, but then in S5D, N103K is referred to as non-responsive, but this variant falls pretty far below the stabilized region calculated in S5A, right?
We agree that it would be insightful to examine the RMSD changes in a non-responsive variant such as G85E. We added the G85E NBD2 ∆RMSD to Supplemental Figure S5B and a G85E ∆RMSD structure map as an additional subpanel at Supplemental Figure S5C. As the reviewer expected, VX-445 fails to confer any stability to G85E as shown by a lack of significant change in NBD2 ∆RMSD or any visible ∆RMSD throughout the structure. Finally, we acknowledge that N1303K falls below the stabilized region as calculated in S5A. However, we note that the binding energy only suggests it is likely to interact with the protein- this does not to necessarily mean that binding will allosterically suppress conformational defects in NBD2. Moreover, this is simply an in silico calculation, that does not necessarily capture all of the nuanced interactions in the cell (or lack thereof). We have corrected this in the Figure S5 caption, which reads as follows:
“Maps of the change in RMSD between N1303K modeled with and without VX-445 shows that few structural regions are stabilized by VX-445 for N1303K, which responds poorly to VX-445 in vitro.”
(5) "stan-dard" standard?
We have corrected this punctuation error.
(6) Line 270, "these variants" is written twice
We have corrected this typographical error.
(7) Figure 6 B. What is being compared? The text writes "there are prominent differences in the activity of these variants [those with CANX] (two-way ANOVA, p = 3.8 x 10-27." Does this mean WT vs. delF508, P5L, V232D, T1036N, and I1366N combined? I have not seen a set of 5 variables compared to a single variable. Usually, it would be WT vs. DelF508, WT vs. P5L, WT vs. V232D...right? Maybe this is normal in this specific field. The same goes for the CANX knockout comparison "(two-way ANOVA, p = 0.06).".
In this instance, the two-way ANOVA test is evaluating whether there are differences in the half-lives of individual variants and/ or systematic differences across the variant measurements in the knockout line relative to the parental cells. The test gives independent p-values for these two variables (variant and cell line). We chose this test because it makes it clear that, when you consider the trends together, one variable has a significant effect while the other does not.
(8) Why don't the CFTR modulators rescue CFTR activity in the WT FRT monolayers?
We thank the reviewer for this inquiry. Please note that compared to DMSO, VX-661 does significantly enhance the forskolin-mediated response of WT-CFTR (red asterisk). Treatments with VX-445 alone, VX-661+VX-445, or VX-661+VX-445+VX-770 showed no significant forskolin stimulation of WT-CFTR. These observations could be attributable to the brief period in which WT-CFTR cDNA is transiently transfected. However, it is not necessarily anticipated that modulators would enhance WT-CFTR function. Correctors and potentiators are designed to rescue processing and gating abnormalities, respectively. WT-CFTR channels do not exhibit such defects.
In both constitutive overexpression systems and primary human airway epithelia, published literature demonstrates that prolonged exposure to CFTR modulators has resulted in variable consequences on WT-CFTR activity. For example, forskolin-mediated responsiveness of WT-CFTR is not altered by chronic application of VX-445 (PMID: 34615919) nor VX-770 (PMID: 28575328, 27402691, 37014818). In contrast, short-circuit current measurements show that forskolin stimulation of WT-CFTR is augmented by chronic treatment with VX-809 (PMID: 28575328), an analog of VX-661. Thus, our findings are congruent with observations reported by other groups.
(9) General comment: As someone not familiar with the field, it would be nice to see the structures of VX-445 and VX-661 somewhere in the figures or at least in the SI.
We appreciate this suggestion, but do not feel that we include enough structural analyses to justify a stand-alone figure for these purposes. The structures of these compounds are easily referenced on a variety of internetbased resources.
(10) Weakness: As an ensemble, the data points CANX as required for plasma membrane expression, particularly those that lie in the C-terminal domain, but when considering individual CF variants, there is no clear trend. Similarly, when looking at the effect of the pharmacological correctors on PME, no variant strays from the linear trend.
We generally agree that the predominant trend is a uniform decrease in CFTR PME across all variants and that individual variant effects are hard to generalize. Indeed, this latter point has been widely appreciated in the CF community for several decades. Our approach exposes this variability in detail, but we concede that we cannot yet fully interpret the full complexity of the trends.
(11) Something to consider: Knockout of calnexin, a central ER chaperone, is going to set off the UPR, which in turn will activate the ISR and attenuate translation. From what I can tell, in general, all CF variant PME is decreased. Is this simply because less CF protein is being synthesized?
The reviewer raises an excellent point. However, to investigate this possibility further, we compared whole-cell proteomic data for the parental and knockout cell lines. Our analysis suggests there is no significant upregulation of proteins associated with UPR activation, as is shown in the graphic to the right. In fact, only proteins associated with the PERK branch of the UPR exhibit any statistically significant changes between these two cell lines across three biological replicates. Based on this consideration, we suspect any wider changes in ER proteostasis must be relatively subtle.
Author response image 1.
Why does adding structure to AI workflows work so well? Fundamentally, there are four key reasons. Methodologies like FRAME™:
Why create a structured workflow?
What you’re doing: Turning one-off prompts into reusable systems.Once you’ve perfected a workflow, you have a proven recipe. Now you can decide how to operationalise it. There are three options:Create a Prompt Template when you want to use it regularly for personal reuse onlyBuild a Custom GPT or Bot when you want to share a task-specific workflow with a team for cross-team quality and efficiency gains Create an Automated Agent when you want to trigger the workflow automatically in certain conditions
How to create reusable systems
AI’s first draft is rarely its best. This is where quality assurance happens.The process:
AI refinement process for QA
File format matters. Here’s the reliability ranking for how well AI reads different formats:.txt / .md — Minimal noise, clear structure (best)JSON / CSV — Great for structured dataDOCX — Fine if formatting is simpleDigital PDFs — Extraction can mix headers, footers, columnsPPTX — Text order can be unpredictableScanned PDFs / images — Worst; requires OCR, highly error-prone
How AI reads file formats and what they are good for
Consensus, Perplexity Pro, Elicit, or STORM by Stanford—to gather peer-reviewed findings about your specific task.
AI powered research tools - built for academics
FRAME™ is a 5-step AI workflow designed for L&D teams. It’s purpose is to tap into AI’s strengths (speed, creativity, pattern recognition) while intentionally offsetting its known weaknesses (guessing, drift, generic-ness).
Learning Design workflow to best use AI
TLDR: When working with LLMs, the risks for the L&D workflow and its impact on substantive learning are real:Hallucination — LLMs invent plausible-sounding facts that aren’t trueDrift — LLM outputs wander from your brief without clear constraintsGeneric-ness — LLMs surface that which is most common, leading to homogenisation and standardisation of “mediocre”Mixed pedagogical quality — LLMs do not produce outputs which are guaranteed to follow evidence-based practiceMis-calibrated trust — LLMs invite us to read guesswork as dependable, factual knowledge These aren’t edge cases or occasional glitches—they’re inherent to how AI / all LLMs function. Prediction machines can’t verify truth. Pattern-matching can’t guarantee validity. Statistical likelihood doesn’t equal quality.
Real inherent issue using AI for learning.
Google hasn’t publicly revealed LearnLM’s exact dataset, but we know from published research papers that its training included:Real tutor–learner dialoguesReal essays, homework problems, diagrams + expert feedbackExpert pedagogy rubrics collected from education experts to train reward models and guide tuning.Education-focused guidelines, developed with education partners (e.g., ASU, Khan Academy, Teachers College, etc.).
Google Learns training data 10/25
Google’s LearnLM: an LLM built specifically to “fix” the pedagogical shortcomings of general LLMs and optimise AI’s performance in learning and education-related tasks (try it for yourself in Google’s AI Studio, here).
Google Learn LM
specialised” LLMs — i.e. models which are trained on specific sorts of data and optimised for specific sorts of tasks.
Specialized LLMs - specialized data sets
AI’s instructional design “expertise” is essentially a statistical blend of everything ever written about learning—expert and amateur, evidence-based and anecdotal, current and outdated. Without a structured approach, you’re gambling on which patterns the model draws from, with no guarantee of pedagogical validity or factual accuracy.
Issue with applying general LLMs to instructional design
quality of AI’s predictions depends ultimately on the data it’s been trained on -- i.e. the resources it’s been fed by those who built it.
Training data sets are critical to good results
general-assistance Large Language Models (LLMs) -- tools like ChatGPT, Copilot, Gemini and Claude (Taylor & Vinauskaitė, 2025).
General assistance Large Language Models - work on "patterns and predictions - what is most statistically likely to come next, not what is optimal"-------Lack of true understanding is a real issue!
Violet's Country Cottage
Lady Penelope and I spent time baking gingerbread biscuits in the kitchen today. Brains wanted to help, but questioned in advance the difference between gingerbread 'men' and gingerbread 'boys' and how boys actually become men through puberty in the process.
Introduction
Quick test
ля этого онипользуются языком, состоящим из слов: «блок», «колонна», «плита», «балка». А выкрикивает эти слова, В доставляет тот камень,который его научили подавать при соответствующей команде.
В схеме Августина можно указывать на объекты ирл, а можно абстрактно «указывать» словами
бъяснениям где-то наступает конец. — Но каково же значение слова «пять»? — Речь здесь совсем не об этом, а только о том, как употребляется слово «пять».
числа не существуют в реальности, поэтому схема с указыванием на объект тут не работает
Это значениесоотнесено с данным словом. Оно — соответствующий данномуслову объект.
всегда ли указывает на конкретный объект? кайнда нет
«Cum ipsi (majores homines) appellabant rem aliquam, et cum secundum earn vocem corpusad aliquid movebant, videbam, et tenebam hoc ab eis vocari rem il-lam, quod sonabant, cum earn vellent ostendere. Hoc autem eos velleex motu corporis aperiebatur: tamquam verbis naturalibus omniumgentium, quae fiunt vultu et nutu oculorum, ceterorumque membro-rum actu, et sonitu vocis indicante affectionem animi in petendis, ha-bendis, rejiciendis, fugiendisqve rebus. Ita verba in variis sententiislocis suis posita, et crebro audita, quarum rerum signa essent,paulatim colhgebam, measque jam voluntates, edomito in eis signisore, per haec enutitiabam»
“Наблюдая, как взрослые, называя какой-нибудь предмет, поворачивались в его сторону, я постигал, что предмет обозначается произносимыми им звуками, поскольку они указывали на него. А вывод этот я делал из их жестов, этого естественного языка всех народов, языка, который мимикой, движениями глаз, членов тела, звучанием голоса выражает состояние души — когда чего-то просят, получают, отвергают, чуждаются. Так постепенно я стал понимать, какие веши обозначаются теми словами, которые я слышал вновь и вновь произносимыми в определенных местах различных предложений. И когда мои уста привыкли к этим знакам, я научился выражать ими свои желания” (лат.)
The color line is a metaphor used to explain the assumptions people make about Black people. They think Black people do negative things because its a part of their nature.
Identity exists in the context of race, social class, gender, sexual orientation, language, culture, geography, physical ability, religion, and so much more
Disinterestedness – Scientists should focus on truth, not personal gain. That means putting curiosity and honesty above fame or money.
This is something that society struggles with as a whole so its no surprise science is affected by it. People are often motivated by the money or fame they can get rather than making decisions that not just benefit themselves or their company but also society. The idea of bettering for society instead of just for money is becoming a more popular idea again so hopefully we will soon see that reflected in the science industry.
What gets studied, how it’s studied, and whose voices are heard all depend on the world around us.
A great example of this is the space race. The world was competing to be the first on the moon. Many scientists joined the race while the people where helping to fund the tests and experiments. Because the world was focus on space so was science and because science was studying space the people wanted to help fund the science.
you need help please don't hesit
Test
Global Importance
Additional headers can be found here: https://suntheticsml.quarto.pub/platform_redesign_wireframe/modeling/explain-variable-importance.html
Global Importance, Local Importance, Variable Interactions
Global Prediction Summaries
For the platform, there are also other headers in this wireframe:https://suntheticsml.quarto.pub/platform_redesign_wireframe/modeling/explore-predictive-space.html
I don't have all the graphs from the platform, and some won't be shown for the automated report, so it is good to review these as well.
There are: Global Prediction Summaries, Single-Variable Exploration, Multi-Variable Exploration, and Interactive Predictions.
One of my favorite examples in the book is how temporal landmarks distort judgment. This is how we relate to time and how we use time to structure days, weeks, months, and years.
!
they help us make shortcuts in our decision-making processes that serve us well most of the time.
*
but details and the big picture aren’t always congruent.
We tend to downplay anything that seems distant, be it a future event or something happening to a stranger, and ignore its significance in our decision-making process.
Your decisionscape is the mental equivalent; it’s what you put into consideration, how you foreground and background things, and where you put the focus of your attention.
!
Artists use distance and diminution to draw big things in the foreground and small things in the background
? I don't know what diminution is and how it applies to the situation.
A decisionscape is the cognizant equivalent of an artist’s canvas.
*!
We operate and make decisions within bounds that are entirely invisible to us.
!