adapting others opinions to conform is the death of oneself

Taking ideas from other people to use as your own instead of creating one. The idea of being original to nothing being original

A reminder, almost like a conscious reminding the soul?

Strength in wildlife connecting back to reliance.

This is such a powerful statement, its very unique

Do not seek outside yourself, prevalent to the title at hand

I don't know why I laughed at this

I get that; appreciating the silence of life

Supreme Cause?

what is it?

what is "this" exactly?

if you stop putting in effort / get lazy your ability to influence change goes away

focus on the now; don't dwell on the past or future, stay present

this also has me thinking a lot. I can't tell if its actually an impressive statement or if its just worded fancily

wow this really got me thinking

all is one

I dont know if "Self" has been capitalized this whole time but this is the first time it's stuck out to me

im wondering the same

you exist in the world, the world doesn't necessarily exist for you. I don't know this is a weird statement

why not now? why just the future?

I don't know why but I thought of the quote "to be loved is to be understood"

consistency can be terrifying but also comforting. depends on the person

why is this quoted? where is it from?

sometimes people give in

is this basically saying that when you send out your love into the distance it harms those closest to you?

sometimes the truth is hard to hear

hm I can't tell if I like this statement or not

words having an impact; hold so much truth / importance that they cannot be ignored

chased not the chaser

don't underestimate the young

who? what?

follow what has been put in place; destiny

hm what power?

be spontaneous, be yourself, be true and ignore the voices saying otherwise

dismissing own thoughts because we tend to carry the mindset that what we think is "wrong" in a way or not as important as other peoples' thoughts

connecting to wild-life and animals way of survival

I think this could mean that whatever we do, good or bad, it will follow us (like shadows) for the rest of our lives

Beat the standard truth, and prevail over it.

Omg we made it, we are peak american lit

Just do it - John Cena

Be so fr. No he did not and you saying everyone has the same blood, means you should have known better.

Its giving, "We cry, your cry, we all crew"

Just a big circle

You gott create work, not study his

Who are the aliens?

The name, wonder why he puts it almost at the end?

Kinda like Farmer James

Of the mind?

As long as your intentions are pure, do whatever you want.

Okay he kinda ate here.

Everyone is the same.

Here we go again

Move on or stay depressed.

Youth better than older generation?

God doesn't let you do/think certaint hings though so how is that self freedom?

own guide; self-reliant; in control of your own life and "destiny"

Be true to yourself, but still believe in god.

1 ruler has an immense amount of power over the lives of millions.

Seems like he really wants/likes attention. Also still no mention of women.

What if its bad? Would that change anything, or should you still be true to yourself at the expense of others?

Burn it

The law does this a lot, you can't trust only your memory.

Depends on the person

Being nice gets you way more things than being mean, just saying.

As long as you stay true to yourself you can/will be great

Again he doesn't care about others, only what is true to himself.

Would he say the same towards modern day things like charities and small buisnesses?

meaning "going" - roots in Caribbean and U.S. Southern regional manners of speech, showing how the pains of the singer are likely responsive to the pains of the American Empire - both colonialism and segregationist pains and sufferings caused by America are resulting in African-American and global African sufferings and this general sense of exhaustion

personification of the piano itself as singing - not just an accompaniment which is subservient to the singer but the instruments itself are powerful, vocal, and centered

however, also implies that something is being done to the piano that forces it to sing (rather than singing, its moaning - involuntary and pained rather than joyous or enthusiastic)

connected with the sense of pathos throughout the poem - the forcing of the piano to play is reflective of the singer themselves who seems to be forced by something itself to play; not an excited performance but a 'weary' one

patients' lipid panel should be measured every 3 months to 1 yr if patients have statin

EKG shows elevated ST wave and depressed T wave:

General summary: In AMI, there is transmural injury - infarction damages the entire thickness of the heart across all layers, this is worse than subendocardial injury where only the subendocardium (inner layers of the heart) is damaged

EKG will show inverted T wave ST elevation for STEMI and ST depression for NSTEMI, prolonged Q wave

Normally, ventricles repolarize from epicardium to endocardium. During ischemia or MI, AP duration is reduced, and ventricles repolarizes from endocardium to epicardium instead. This reversed repolarization leads to an inverted T-wave on ECG (negative deflection).

Angina does not change the cardiac biomarkers

Acute MI do --> elevated troponin that peaks and dies faster than CK-MB that takes longer

Troponin I = sensitive and specific, rise in ~4hr and peak in 24hr, lasts 7-10 days

CK-MB: less sensitive and specific, rise in 4-8hr and lasts 48-72 hr (2-3 days), good for looking for reinfarction

Unstable angina, atherosclerotic plaque has ruptured forming a thrombi that partially blocked blood flow

His angina has worsened to an extent where taking nitroglycerin isn't relieving the symptoms

He may be experiencing a side effect of nitroglycerin overdose, ends up having reflex tachycardia and feeling worse

Chapman's reflexes at the bilateral second intercostal spaces - relates to viscerosomatic dysfunction on BETH - Bronchus, Thoracic, Heart, Esophagus

Stomach ulcer Prevacid is indicated for the treatment of patients with H. pylori infection and duodenal ulcer disease (active or one year history of a duodenal ulcer) to eradicate H. pylori

HE SMOKES! what does "he thinks he eats good" means...

Unstable angina (probably no longer stable) Myocardial Infarction Pulmonary Embolism, Coronary Embolism, etc (more unlikely because they tend to cause shortness of breath which the patient doesn't have) GERD

I actually like that he is advocating his system as being more effective than most systems of legal research used in his time. I think that one of the problems that I've noticed amongst lawyers, is that many are too adversarial. They often don't want to help each other, and sometimes actively work to undermine each other. The "cagey, old lawyer" hiding the book is an example of a common behavior amongst the lawyers who want to be sharks. They are jealous of their knowledge and experience, and rather than sharing that knowledge and experience with the inexperienced or ignorant, they use that knowledge and experience to eat their young. Even Gardner displays some of that attitude, making fun of the uneducated mountaineer, and not showing much empathy for the frustrated widow who was desperate to find some case that would help her cause.

Not only is this story funny but it did get me thinking: What do you do when somebody tries to sabotage your legal research process? Sure, such a process may be unethical and likely to result in those who sabotage their opponents being punished but that does not mean it is not done. If you not only have to deal with the difficulties of legal research but also of somebody trying to prevent you from doing legal research that may be a situation rare enough for few to write about yet risky enough to be concerning for those doing legal research.

Reading this initially this comment really stood out to me because of how odd it was. "What is he talking about? You can easily find a hundred different research outlines with a simple search." I looked up the paper and found out it was published in 1953. These sentences show how different the legal world has become in just 70 years. With the advent of the internet and websites such as Westlaw and Lexis the difficulty of doing legal research has substantially decreased. There was once a time where even the knowledge of how to research was scarce and I am glad that time no longer exists.

A mysterious ghastly figure that wins the Mariner's soul in a gamble. Then he is faced with a consequence worth than death: life-in-death, he will have to wander the world meaninglessly and unable to fade away.

Definition (n): a specific time of day dedicated to prayer, the ninth canonical hour being dedicated to God the Son. Originated with the Catholic tradition. 'Vesper' means 'evening' in Latin and was used to describe the time of evening prayers. The specific phrase 'vesper's nine' is unique to Coleridge's poem and means that the Albatross perched for nine days (or through nine vespers). KF

The polar spirit is not an actual character, more of a presence. A looming presence throughout the reading that punishes the Mariner and makes sure to carry out his curse of suffering. LS

The Albatross is a creature of love and grace that was introduced to the sea men. It symbolizes innocence and love, and it is believed to be symbolism for Jesus Christ. It brings many happy gifts to the sailors, but it is killed by the mariner. LS

The Mariner's crew are an impressionable bunch, they are originally captivated by the Albatross, but only for what it could provide and not the creature itself. They are indifferent to its killing and only regret it because they can no longer reap the rewards of it's presence. LS

The Wedding Guest is a listener to the Mariner's story, they are perhaps seen as naive and in need of this story to gain the experience to witness a wedding. They leave the story feeling wiser. LS

The story's protagonist. An old sailor who is perhaps jaded and didn't have much hope left even prior to meeting the Albatross. After inevitably killing the Albatross he is cursed to forever be tortured and forced to tell his tale. LS

Author response:

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

Reviewer #1 (Public Review): 

Summary: 

Dr. Santamaria's group previously utilized antigen-specific nanomedicines to induce immune tolerance in treating autoimmune diseases. The success of this therapeutic strategy has been linked to expanded regulatory mechanisms, particularly the role of T-regulatory type-1 (TR1) cells. However, the differentiation program of TR1 cells remained largely unclear. Previous work from the authors suggested that TR1 cells originate from T follicular helper (TFH) cells. In the current study, the authors aimed to investigate the epigenetic mechanisms underlying the transdifferentiation of TFH cells into IL-10-producing TR1 cells. Specifically, they sought to determine whether this process involves extensive chromatin remodeling or is driven by preexisting epigenetic modifications. Their goal was to understand the transcriptional and epigenetic changes facilitating this transition and to explore the potential therapeutic implications of manipulating this pathway. 

The authors successfully demonstrated that the TFH-to-TR1 transdifferentiation process is driven by pre-existing epigenetic modifications rather than extensive new chromatin remodeling. The comprehensive transcriptional and epigenetic analyses provide robust evidence supporting their conclusions. 

Strengths: 

(1) The study employs a broad range of bulk and single-cell transcriptional and epigenetic tools, including RNA-seq, ATAC-seq, ChIP-seq, and DNA methylation analysis. This comprehensive approach provides a detailed examination of the epigenetic landscape during the TFH-to-TR1 transition. 

(2) The use of high-throughput sequencing technologies and sophisticated bioinformatics analyses strengthens the foundation for the conclusions drawn. 

(3) The data generated can serve as a valuable resource for the scientific community, offering insights into the epigenetic regulation of T-cell plasticity. 

(4) The findings have significant implications for developing new therapeutic strategies for autoimmune diseases, making the research highly relevant and impactful. 

We thank the reviewer for providing constructive feedback on the manuscript.

Weaknesses: 

(1) While the scope of this study lies in transcriptional and epigenetic analyses, the conclusions need to be validated by future functional analyses. 

We fully agree with the reviewer’s suggestion. We have added the following text to the Discussion to address this concern: “The current study provides a foundational understanding of how the epigenetic landscape of TFH cells evolves as they transdifferentiate into TR1 progeny in response to chronic ligation of cognate TCRs using pMHCII-NPs. Our current studies focus on functional validation of these observations, by carrying out extensive perturbation studies of the TFH-TR1 transdifferentiation pathway in conditional transcription factor gene knock-out mice. In these ongoing studies, genes coding for a series of transcription factors expressed along the TFH-TR1 pathway are selectively knocked out in T cells, to ascertain (i) the specific roles of key transcription factors in the various cell conversion events and transcriptional changes that take place along the TFH-TR1 cell axis; (ii) the roles that such transcription factors play in the chromatin re-modeling events that underpin the TFH-TR1 transdifferentiation process; and (iii) the effects of transcription factor gene deletion on phenotypic and functional readouts of TFH and regulatory T cell function.”

(2) This study successfully identified key transcription factors and epigenetic marks. How these factors mechanistically drive chromatin closure and gene expression changes during the TFH-to-TR1 transition requires further investigation. 

Agreed. Please see our response to point #1 above.  

(3) The study provides a snapshot of the epigenetic landscape. Future dynamic analysis may offer more insights into the progression and stability of the observed changes. 

We have previously shown that the first event in the pMHCII-NP-induced TFH-TR1 transdifferentiation process involves proliferation of cognate TFH cells in the splenic germinal centers. This event is followed by immediate transdifferentiation of the proliferated TFH cells into transitional and terminally differentiated TR1 subsets. Although the snapshot provided by our single cell studies reported herein documents the simultaneous presence of the different subsets composing the transdifferentiation pathway at any given time point, the transdifferentiation process itself is extremely fast, such that proliferated TFH cells already transdifferentiate into TR1 cells after a single pMHCII-NP dose (Sole et al., 2023a). This makes it extremely challenging to pursue dynamic experiments. Notwithstanding this caveat, ongoing studies of cognate T cells post treatment withdrawal, coupled to single cell studies of the TFHTR1 pathway in transcription factor gene knockout mice exhibiting perturbed transdifferentiation processes are likely to shed light into the progression and stability of the epigenetic changes reported herein. 

To address this limitation in the manuscript, we have added the following paragraph to the Discussion: “Although the snapshot provided by our single cell studies reported herein documents the simultaneous presence of the different subsets composing the TFH-TR1 cell pathway upon the termination of treatment, the transdifferentiation process itself is extremely fast, such that proliferated TFH cells already transdifferentiate into TR1 cells after a single pMHCII-NP dose (6). This makes it extremely challenging to pursue dynamic experiments. Notwithstanding this caveat, ongoing studies of cognate T cells post treatment withdrawal, coupled to single cell studies of the TFH-TR1 pathway in transcription factor gene knockout mice exhibiting perturbed transdifferentiation processes are likely to shed light into the progression and stability of the epigenetic changes reported herein”. 

Reviewer #1 (Recommendations for the authors): 

The authors may consider the following suggestions to improve this study: 

(1) The authors may include a brief background on type 1 diabetes and the model involving BDC2.5 T cells to provide context for readers who may not be familiar with these aspects. 

We have added this information to the first paragraph in the Results section: “BDC2.5mi/I-Ag7-specific CD4+ T cells comprise a population of autoreactive T cells that contribute to the progression of spontaneous autoimmune diabetes in NOD mice. The size of this type 1 diabetes-relevant T cell specificity is small and barely detectable in untreated NOD mice, but treatment with cognate pMHCII-NPs leads to the expansion and formation of antidiabetogenic TR1 cells that retain the antigenic specificity of their precursors (3). As a result, treatment of hyperglycemic NOD mice with these compounds results in the reversal of type 1 diabetes (3).”

(2) It is understandable that further biological and functional experiments are beyond the scope of this paper, but it would be of interest to know how the authors envision future studies based on the transcriptional and epigenetic information obtained thus far. 

We have added the following text to the Discussion section: “The current study provides a foundational understanding of how the epigenetic landscape of TFH cells evolves as they transdifferentiate into TR1 progeny in response to chronic ligation of cognate TCRs using pMHCII-NPs. Our current studies focus on functional validation of these observations, by carrying out extensive perturbation studies of the TFH-TR1 transdifferentiation pathway in conditional transcription factor gene knock-out mice. In these ongoing studies, genes coding for a series of transcription factors expressed along the TFH-TR1 pathway are selectively knocked out in T cells, to ascertain (i) the specific roles of key transcription factors in the various cell conversion events and transcriptional changes that take place along the TFH-TR1 cell axis; (ii) the roles that such transcription factors play in the chromatin re-modeling events that underpin the TFH-TR1 transdifferentiation process; and (iii) the effects of transcription factor gene deletion on phenotypic and functional readouts of TFH and regulatory T cell function.”

(3) The authors may consider adjusting figures where genes are crowded or difficult to read due to small font size. 

Figures with crowded text have been modified to facilitate reading.

Reviewer #2 (Public Review): 

Summary: 

This study, based on their previous findings that TFH cells can be converted into TR1 cells, conducted a highly detailed and comprehensive epigenetic investigation to answer whether TR1 differentiation from TFH is driven by epigenetic changes. Their evidence indicated that the downregulation of TFH-related genes during the TFH to TR1 transition depends on chromatin closure, while the upregulation of TR1-related genes does not depend on epigenetic changes. 

Strengths: 

(1) A significant advantage of their approach lies in its detailed and comprehensive assessment of epigenetics. Their analysis of epigenetics covers chromatin open regions, histone modifications, DNA methylation, and using both single-cell and bulk techniques to validate their findings. As for their results, observations from different epigenetic perspectives mutually supported each other, lending greater credibility to their conclusions. This study effectively demonstrates that (1) the TFH-to-TR1 differentiation process is associated with massive closure of OCRs, and (2) the TR1-poised epigenome of TFH cells is a key enabler of this transdifferentiation process. Considering the extensive changes in epigenetic patterns involved in other CD4+ T lineage commitment processes, the similarity between TFH and TR1 in their epigenetics is intriguing. 

(2) They performed correlation analysis to answer the association between "pMHC-NPinduced epigenetic change" and "gene expression change in TR1". Also, they have made their raw data publicly available, providing a comprehensive epigenomic database of pMHC-NPinduced TR1 cells. This will serve as a valuable reference for future research. 

We thank the reviewer for his/her constructive feedback and suggestions for improvement of the manuscript.

Weaknesses: 

(1) A major limitation is that this study heavily relies on a premise from the previous studies performed by the same group on pMHC-NP-induced T-cell responses. This significantly limits the relevance of their conclusion to a broader perspective. Specifically, differential OCRs between Tet+ and naïve T cells were limited to only 821, as compared to 10,919 differential OCRs between KLH-TFH and naïve T cells (Figure 2A), indicating that the precursors and T cell clonotypes that responded to pMHC-NP were extremely limited. This limitation should be clearly discussed in the Discussion section. 

We agree that this study focuses on a very specific, previously unrecognized pathway discovered in mice treated with pMHCII-NPs. Despite this apparent narrow perspective, we now have evidence that this is a naturally occurring pathway that also develops in other contexts (i.e., in mice that have not been treated with pMHCII-NPs). Furthermore, this pathway affords a unique opportunity to further understand the transcriptional and epigenetic mechanisms underpinning T cell plasticity; the findings reported can help guide/inform not only upcoming translational studies of pMHCII-NP therapy in humans, but also other research in this area. 

We have added the following text to the Discussion to address this limitation: “Although the TFH-TR1 transdifferentiation was discovered in mice treated with pMHCII-NPs, we now have evidence that this is a naturally occurring pathway that also develops in other contexts (i.e., in mice that have not been treated with pMHCII-NPs). Importantly, the discovery of this transdifferentiation process affords a unique opportunity to further understand the transcriptional and epigenetic mechanisms underpinning T cell plasticity; the findings reported here can help guide/inform not only upcoming translational studies of pMHCII-NP therapy in humans, but also other research in this area”.

We acknowledge that, in the bulk ATAC-seq studies, the differences in the number of OCRs found in tetramer+ cells or KLH-induced TFH cells vs. naïve T cells may be influenced by the intrinsic oligoclonality of the tetramer+ T cell pool arising in response to repeated pMHCII-NP challenge (Sole et al., 2023a). However, we note that our scATAC-seq studies of the tetramer+ T cell pool found similar differences between the oligoclonal tetramer+ TFH subpool and its (also oligoclonal) tetramer+ TR1 counterparts (i.e., substantially higher number of OCRs in the former vs. the latter relative to naïve T cells). 

This has been clarified in the revised version of the manuscript, by adding the following text to the last paragraph of the Results subsection entitled “Contraction of the chromatin in pMHCII-NP-induced Tet+ vs. TFH cells at the bulk level”: “We acknowledge that, in the bulk ATAC-seq studies, the differences in the number of OCRs found in tetramer+ cells or KLHinduced TFH cells vs. naïve T cells may be influenced by the intrinsic oligoclonality of the tetramer+ T cell pool arising in response to repeated pMHCII-NP challenge (6). However, we note that scATAC-seq studies of the tetramer+ T cell pool found similar differences between the oligoclonal tetramer+ TFH subpool and its (also oligoclonal) tetramer+ TR1 counterparts (i.e., substantially higher number of OCRs in the former vs. the latter relative to naïve T cells)”.

(2) This article uses peak calling to determine whether a region has histone modifications, claiming that the regions with histone modifications in TFH and TR1 are highly similar. However, they did not discuss the differences in histone modification intensities measured by ChIP-seq. For example, as shown in Figure 6C, IL10 H3K27ac modification in Tet+ cells showed significantly higher intensity than KLH-TFH, while in this article, it may be categorized as "possessing same histone modification region". This will strengthen their conclusions.

We appreciate your suggestion to discuss differences in histone modification intensities as measured by ChIP-seq. However, we respectfully disagree with the reviewer’s interpretation of these data.

Our study primarily focuses on the identification of epigenetic similarities and differences between pMHCII-NP-induced tetramer+ cells and KLH-induced TFH cells relative to naive T cells. The outcome of direct comparisons of histone deposition (ChIP-seq) between these cell types is summarized in the lower part of Figure 4B and detailed in Datasheet 5. Throughout this section, we mention the number of differentially enriched regions, their overlap with OCRs shared between tetramer+ TFH and tetramer+ TR1 cells based on scATAC-seq data, and the associated genes. Clearly, the epigenetic modifications that TR1 cells inherit from TFH cells were acquired by TFH cells upon differentiation from naïve T cell precursors. 

Regarding the specific point raised by the reviewer on differences in the intensity of the H3K27Ac peaks linked to Il10 in Figure 6C, we note that the genomic tracks shown are illustrative. Thorough statistical analyses involving signal background for each condition and p-value adjustment did not support differential enrichment for H3K27Ac deposition around the Il10 gene between pMHCII-NP-induced tetramer+ T cells and KLH-induced TFH cells. 

This has now been clarified by adding the following text to the end of the Results subsection entitled ”H3K4me3, H3K27me3 and H3K27ac marks in genes upregulated during the TFH-to-TR1 cell conversion are already in place at the TFH cell stage”: “We note that, although in the representative chromosome track views shown in Fig. 6C there appear to be differences in the intensity of the peaks, thorough statistical analyses involving signal background for each condition and p-value adjustment did not support differential enrichment for histone deposition around the Il10 gene between pMHCII-NP-induced tetramer+ T cells and KLH-induced TFH cells.” 

We have also clarified this in the corresponding section of the Methods section (“ATACseq and ChIP-seq” under “Bioinformatic and Statistical Analyses”): “Given that peak calling alone does not account for variations in the intensity of histone mark deposition, analysis of differential histone deposition includes both qualitative and quantitative assessments. Whereas qualitative assessment involves evaluating the overall pattern and distribution of the various histone marks, quantitative assessment measures the intensity and magnitude of histone mark deposition.”

(3) Last, the key findings of this study are clear and convincing, but some results and figures are unnecessary and redundant. Some results are largely a mere confirmation of the relationship between histone marks and chromatin status. I propose to reduce the number of figures and text that are largely confirmatory. Overall, I feel this paper is too long for its current contents. 

We understand your concern about the potential redundancy of some results and figures. Our aim in including these analyses was to provide a comprehensive understanding of the intricate relationships between epigenetic features and transcriptomic differences. We believe that a detailed examination of these relationships is crucial for several reasons: (i) the breadth of the data allows for a thorough exploration of the relationships between histone marks, open chromatin status and transcriptional differences. This comprehensive approach helps to ensure that our conclusions are robust and well-supported; (ii) some of the results that may appear confirmatory are, in fact, important for validating and reinforcing the consistency of our findings across different contexts. These details are intended to provide a nuanced understanding of the interactions between epigenetic features and gene expression; and (iii) By presenting a detailed analysis, we aim to offer a solid foundation for future research in this area. The extensive data presented will serve as a valuable resource for others in the field who may seek to build on our findings.

That said, we have carefully reviewed the manuscript to identify and streamline elements that might be perceived as overly redundant, while retaining the depth of analysis that we believe is essential.

Reviewer #2 (Recommendations for the authors): 

(1) In Figure 1E, the text states "94% (n=217/231) of the genes associated with chromatin regions that had closed during the TFH-TR1 conversion,", but n=231 do not match with n=1820 provided in Figure 1D as downregulated genes. This is one of the examples that do not match numbers among figures or lack sufficient explanations. Please check those numbers carefully and add some sentences if necessary. 

We note that the text referring to Figure 1D describes the total number of differentially expressed genes between Tet+ TR1 and Tet+ TFH cells using the scMultiome dataset (n = 2,086 genes downregulated in the former vs. the latter; and n = 266 genes upregulated in the former vs. the latter). The text in the paragraph that follows (referring to Figure 1E) focuses exclusively on the genes that had closed chromatin regions during the TFH-to-TR1 conversion, to ascertain whether or not chromatin closure was indeed associated with such gene downregulation. 

We have modified the first sentence in the paragraph referring to Figure 1E to clarify this point for the reader: “Further analyses focusing on the genes that had closed chromatin regions during the TFH-to-TR1 conversion, confirmed…”

eLife Assessment

This study provides important information on pre-existing epigenetic modification in T cell plasticity. The evidence supporting the conclusions is compelling, supported by comprehensive transcriptional and epigenetic analyses. The work will be of interest to immunologists and colleagues studying transcriptional regulation.

Reviewer #1 (Public review):

Summary:

Dr. Santamaria's group previously utilized antigen-specific nanomedicines to induce immune tolerance in treating autoimmune diseases. The success of this therapeutic strategy has been linked to expanded regulatory mechanisms, particularly the role of T-regulatory type-1 (TR1) cells. However, the differentiation program of TR1 cells remained largely unclear. Previous work from the authors suggested that TR1 cells originate from T follicular helper (TFH) cells. In the current study, the authors aimed to investigate the epigenetic mechanisms underlying the transdifferentiation of TFH cells into IL-10-producing TR1 cells. Specifically, they sought to determine whether this process involves extensive chromatin remodeling or is driven by pre-existing epigenetic modifications. Their goal was to understand the transcriptional and epigenetic changes facilitating this transition and to explore the potential therapeutic implications of manipulating this pathway.

The authors successfully demonstrated that the TFH-to-TR1 transdifferentiation process is driven by pre-existing epigenetic modifications rather than extensive new chromatin remodeling. The comprehensive transcriptional and epigenetic analyses provide robust evidence supporting their conclusions.

Strengths:

(1) The study employs a broad range of bulk and single-cell transcriptional and epigenetic tools, including RNA-seq, ATAC-seq, ChIP-seq, and DNA methylation analysis. This comprehensive approach provides a detailed examination of the epigenetic landscape during the TFH-to-TR1 transition.

(2) The use of high-throughput sequencing technologies and sophisticated bioinformatics analyses strengthens the foundation for the conclusions drawn.

(3) The data generated can serve as a valuable resource for the scientific community, offering insights into the epigenetic regulation of T cell plasticity.

(4) The findings have significant implications for developing new therapeutic strategies for autoimmune diseases, making the research highly relevant and impactful.

Weaknesses:

(1) While the study focuses on transcriptional and epigenetic analyses, the authors are currently undertaking efforts to validate these findings functionally. Ongoing research aims to further explore the roles of key transcription factors in the TFH-to-TR1 transition, reflecting the authors' commitment to building on the insights gained from this study.

(2) The identification of key transcription factors and epigenetic marks is a strong foundation for future work. The authors are actively investigating how these factors drive chromatin remodeling, which will enhance the mechanistic understanding of the TFH-to-TR1 process in future studies.

(3) Although the study provides a valuable snapshot of the epigenetic landscape, the authors are pursuing additional research to assess the dynamics of these changes over time. These ongoing efforts will contribute to a deeper understanding of the stability and progression of the observed epigenetic modifications.

Comments on revised version:

The authors have effectively discussed and addressed all previously raised questions. There are no further concerns.

Reviewer #2 (Public review):

Summary:

This study, based on their previous findings that TFH cells can be converted into TR1 cells, conducted a highly detailed and comprehensive epigenetic investigation to answer whether TR1 differentiation from TFH is driven by epigenetic changes. Their evidence indicated that the downregulation of TFH-related genes during the TFH to TR1 transition depends on chromatin closure, while the upregulation of TR1-related genes does not depend on epigenetic changes.

Strengths:

A significant advantage of their approach lies in its detailed and comprehensive assessment of epigenetics. Their analysis of epigenetics covers chromatin open regions, histone modifications, DNA methylation, and using both single-cell and bulk techniques to validate their findings. As for their results, observations from different epigenetic perspectives mutually supported each other, lending greater credibility to their conclusions. This study effectively demonstrates that 1. the TFH-to-TR1 differentiation process is associated with massive closure of OCRs, and 2. the TR1-poised epigenome of TFH cells is a key enabler of this transdifferentiation process. Considering the extensive changes in epigenetic patterns involved in other CD4+ T lineage commitment processes, the similarity between TFH and TR1 in their epigenetics is intriguing.

They performed correlation analysis to answer the association between "pMHC-NP-induced epigenetic change" and "gene expression change in TR1". Also, they have made their raw data publicly available, providing a comprehensive epigenomic database of pMHC-NP induced TR1 cells. This will serve as a valuable reference for future research.

Weaknesses:

A major limitation is that this study heavily relies on a premise from the previous studies performed by the same group on pMHC-NP-induced T cell responses. This significantly limits the relevance of their conclusion to a broader perspective. Specifically, differential OCRs between Tet+ and naïve T cells were limited to only 821, as compared to 10,919 differential OCRs between KLH-TFH and naïve T cells (Fig. 2A), indicating that the precursors and T cell clonotypes that responded to pMHC-NP were extremely limited. I acknowledge that this limitation has been added and discussed in the Discussion section of the revised manuscript.

eLife Assessment

The authors have developed a valuable approach that employs cell-free expression to reconstitute ion channels into giant unilamellar vesicles for biophysical characterisation. The work is convincing and will be of particular interest to those studying ion channels that primarily occur in organelles and are therefore not amenable to be studied by more traditional methods.

Reviewer #1 (Public review):

Summary:

The authors have developed a valuable method based on a fully cell-free system to express a channel protein and integrated it into a membrane vesicle in order to characterize it biophysically. The study presents a useful alternative to study channels that are not amenable to be studied by more traditional methods.

Strengths:

The evidence supporting the claims of the authors is solid and convincing. The method will be of interest to researchers working on ionic channels, allowing to study a wide range of ion channel functions such as those involved in transport, interaction with lipids or pharmacology.

Weaknesses:

The inclusion of a mechanistic interpretation how the channel protein folds into a protomer or a tetramer to become functional into the membrane, would strengthen the study.

Comments on revised version:

In the revised version, the authors did not experimentally addressed how tetrameric or protomeric proteins are actually produced. However, they performed new experiments to assess the amount of tetramers that are being actually formed. They used a size-exclusion chromatography to conclude that the protomers and tetramers species of complexes are formed and assembled.

The authors have addressed most of my minor concerns and have modified or updated the manuscript following my recommendations, so I have no further comments.

Reviewer #2 (Public review):

It is challenging to study the biophysical properties of organelle channels using conventional electrophysiology. The conventional reconstitution methods requires multiple steps and can be contaminated by endogenous ionophores from the host cell lines after purification. To overcome this challenge, in this manuscript, Larmore et al. described a fully synthetic method to assay the functional properties of the TRPP channel family. The TRPP channels are an important organelle ion channel family that natively traffic to primary cilia and ER organelles. The authors utilized cell-free protein expression and reconstitution of the synthetic channel protein into giant unilamellar vesicles (GUV), the single channel properties can be measured using voltage-clamp electrophysiology. Using this innovative method, the authors characterized their membrane integration, orientation, and conductance, comparing the results to those of endogenous channels. The manuscript is well-written and may present broad interest to the ion channel community studying organelle ion channels. Particularly because of the challenges of patching native cilia cells, the functional characterization is highly concentrated in very few labs. This method may provide an alternative approach to investigate other channels resistant to biophysical analysis and pharmacological characterization.

Comments on revised version:

The authors have addressed my concerns. This excellent method manuscript would benefit the study of organelle channels.

Author response:

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

Public reviews:

Reviewer #1 (Public Review):

Summary:

The authors have developed a valuable method based on a fully cell-free system to express a channel protein and integrate it into a membrane vesicle in order to characterize it biophysically. The study presents a useful alternative to study channels that are not amenable to being studied by more traditional methods.

Strengths:

The evidence supporting the claims of the authors is solid and convincing. The method will be of interest to researchers working on ionic channels, allowing them to study a wide range of ion channel functions such as those involved in transport, interaction with lipids, or pharmacology.

Weaknesses:

The inclusion of a mechanistic interpretation of how the channel protein folds into a protomer or a tetramer to become functional in the membrane would strengthen the study.

Work from other labs has described key factors which can improve expression and artificial lipid integration of cellfree derived transmembrane proteins (PMIDs: 35520093, 29625253, 26270393) . However, a significant number of additional experiments would be needed to elucidate the exact biophysical properties governing channel assembly of synthetically derived polycystins. We carried out additional biochemical experiments to address these concerns (see new Figure 1— figure supplement 1 D, E). We used fluorescence-detection size-exclusion chromatography (FSEC) with the goal of understanding how much of the CFE-derived protomers are biochemically folding and assembly into functional tetramers upon incorporation into SUVs. When compared to protein recombinant sources from HEK cells, the production of assembled channels is less than 4% when using the CFE+SUV approach, an estimate based on the oligomer peak fluorescence. In the absence of chaperones found in cells, the assembly of synthetically derived protomers into tetramers is likely intrinsic to the chemical properties of the proteins, and the biophysical principles governing helical membrane protein when inserted into the lipid membrane  (PMID:35133709). We have added our interpretation in lines 111-121.

Reviewer #2 (Public Review):

It is challenging to study the biophysical properties of organelle channels using conventional electrophysiology. The conventional reconstitution methods require multiple steps and can be contaminated by endogenous ionophores from the host cell lines after purification. To overcome this challenge, in this manuscript, Larmore et al. described a fully synthetic method to assay the functional properties of the TRPP channel family. The TRPP channels are an important organelle ion channel family that natively traffic to primary cilia and ER organelles. The authors utilized cell-free protein expression and reconstitution of the synthetic channel protein into giant unilamellar vesicles (GUV), the single channel properties can be measured using voltage-clamp electrophysiology. Using this innovative method, the authors characterized their membrane integration, orientation, and conductance, comparing the results to those of endogenous channels. The manuscript is well-written and may present broad interest to the ion channel community studying organelle ion channels. Particularly because of the challenges of patching native cilia cells, the functional characterization is highly concentrated in very few labs. This method may provide an alternative approach to investigate other channels resistant to biophysical analysis and pharmacological characterization.

Thank you for evaluating our manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) It would be useful to explain how the Polycystin protein is folded under the experimental conditions used. The expression data shown in Figure 1 Supplement 1B show different protein concentrations of protomer or tetramer. However, it is not described how each form is identified and distinguished. It is also important to mention in the manuscript that this method is only applicable to membrane channels that do not require chaperons for its folding and expression into the membrane. How is the tetramer mechanistically conformed? In line 184, it is stated that this method can be leveraged for studying the effects of channel subunit composition. Would this method allow the expression of two different subunit proteins in order to produce a heteromeric channel?

In Figure 1—figure supplement 1B, total fluorescence from the synthesized channel-GFP was measured. Protein concentration was calculated based on the linear regression of the GFP standards. Monomeric protein concentration was reported directly from total fluorescence. Tetrameric protein concentration was calculated by dividing the fluorescence by four, and subsequently calculating the concentration based off the GFP standards. 

This is a good point. Based on your suggestion, we carried out additional biochemical experiments (see new Figure 1— figure supplement 1 D, E). We used fluorescence-detection size-exclusion chromatography (FSEC) with the goal of understanding how much of the CFE-derived protomers are biochemically folding and assembly into functional tetramers upon incorporation into SUVs. As controls we produced recombinant PKD2-GFP and PKD2L1GFP channels as elution time standards and to compare the relative production of tetrameric channels generated when using the two expression systems. The synthetically derived polycystin channels indeed produced tetramers and protomers, which supports feasibility of using this method to assay their functional properties.  When compared to protein recombinant sources from HEK cells, the production of assembled channels is less than 4% when using the CFE+SUV approach, an estimate based on the oligomer peak fluorescence. We speculate that assembly of synthetically derived protomers into tetramers is likely intrinsic to the chemical properties of the proteins, and the biophysical principles governing helical membrane protein when inserted into the lipid membrane (PMID: 35133709). Although an interesting question, a systematic analysis of these channel-lipid interactions is beyond the scope of this eLife Report but can be addressed in future studies. The limitation of using this method to characterize channels which fold and membrane integrate without the aid of molecular chaperones is now stated in lines 201205. In principle, the CFE-GUV method can be deployed to co-express different subunits to produce heteromeric channels. We have modified the text lines 192-197 to be clearer on this point.

(2) The type of plasmid (and promoter) required for this methodology should be mentioned.

Added to the methods (lines 210-211). “PKD2 and PKD2L1 are in pET19b plasmid under T7 promoter.”

(3) Since this paper is methodological, it would be useful to have some information about the stability of the GUVs containing the synthetic channel. In Methods, it is stated that GUV vesicles are used on the same day (line 207). And in line 193 it says that the reactions (?) are placed at 4{degree sign}C for storage.

Restated in lines 226-228: GUVs are electroformed and used for electrophysiology the same day. SUVs with channel incorporated are stored at 4°C for 3 days.

(4) A comment reasoning why the PKD2 protein is more frequently incorporated into the membrane in comparison to PKD2L1 should be included. A brief description of the differences between these two proteins would also be helpful for the reader.

In terms of overall protein production and oligomeric assembly— more PKD2L1 channels are produced compared to PKD2 (see new Figure 1C, and Figure 1— figure supplement 1 D, E). In lines 149-155 we note single channel openings were frequently observed for the high expressing PKD2L1 channels, but this often resulted in patch instability. As a result, GUV patches with lower expressing PKD2-GFP channel were more stable and thus more successfully recorded from. We have revised the text to be clearer on this point.

(5) There are no methods for preparing hippocampal neurons or IMCD cells shown in Figure 4 Supplement 1. Instead, the method of mammalian cultures provided corresponds to HEK 293T cells.

This information has been added to lines 273-284.

(6) Minor:

In Figure 2C, please include the actual % of the Cell488+Surface647+Clear lumen vesicles.

Added

Line 99, 108: Figures 1B and 1C are swapped. Please correct.

Corrected in figure and figure legends.

Line 108: misspelling: effect.

Done

Line 109: check sentence: verb is missing.

Sentence now reads “Minimal changes in fluorescence were detected when a control plasmid (Ctrl) encoding a non- fluorescent protein (dihyrofolate reductase) was used in the reaction.”

Line 145: recoding. Correct.

Recoding changed to recordings

Line 169: "from" is missing (recorded from MCD cilia).

Added

Line 169: In Table 1, the PKD2 K+ conductance magnitudes recorded from IMCD cilia were significantly smaller, not larger as stated, than those assayed using CFE-GUV system. Please correct.

Corrected

Line 180: "of" is missing (adaptation of CFE derived...).

Corrected

Line 182: "to" is missing (generalized to other channels).

Corrected

Line 193: "in" 4ºC, correct at.

Corrected

Line 197: replace "mole" for "mol".

Corrected

Line 207: are used "within the" same day.

Corrected

Line 210: c-terminally. C-should be capital letter.

Corrected

Line 231: n-terminally. N- should be capital letter.

Corrected

Reviewer #2 (Recommendations For The Authors):

The authors validated their method using PKD2 and PKD2L1 channels, demonstrating the potential of this approach. However, a few points merit further clarification or validation:

(1) Stability of the protein vesicles for recording. The authors observed membrane instability during voltage transitions. It would be beneficial to discuss potential solutions to enhance stability.

In lines 197-202, we have added a discussion of potential solutions to enhance stability. CsF in the intracellular saline could be added to stabilize the GUV membranes. CsF is frequently added to stabilize whole cell membranes in HTS planer patch clamp recording. We did not explore this formulation because Cs+ would limit outward polycystin conductance. We also suggest but did not test altering the membrane formulation of GUVs with additional cholesterol to stabilize these recordings.

(2) Validation. Further discussion on how broadly this method can be applied to other channels would strengthen the manuscript.

We have included further discussion on this point in lines 190-206. 

(3) Protein production estimated by a standard GFP absorbance assay. The estimation of protein production using GFP absorption may be affected by improperly folded protein. Additional validation methods could be considered.

C-terminal GFP fluorescence has been widely used in expression systems to designate proper folding of the target protein upstream of the GFP-tag (PMID: 22848743, PMID: 21805523, PMID: 35520093). Nonetheless we have conducted additional experiments designed to estimate the amount of assembled PKD2 and PKD2L1 channels generated using the CFE method. In the new Figure 1— figure supplement 1 D, E, we carried out fluorescencedetection size-exclusion chromatography and compared channel assembly of recombinant and CFE+SUV derived PKD2-GFP and PKD2L1-GFP. Here, we clearly observed tetrameric and protomeric forms of the channels using the synthetic approach, which supports feasibility of using this method to assay their functional properties (see new Figure 1— figure supplement 1 D, E).  When compared to protein recombinant sources from HEK cells, the production of assembled channels is less than 4% when using the CFE+SUV approach, an estimate based on the oligomer peak fluorescence. 

(4) Single channels were observed more frequently from PKD2 incorporated GUVs compared to PKD2L1. Does this just randomly happen or is there a reason behind this difference?

In terms of overall protein production and oligomeric assembly— more PKD2L1 channels are produced compared to PKD2 (Figure 1C, and Figure 1— figure supplement 1 D, E). This is apparent whether the channels are produced recombinantly in cells or when using the cell-free method (Figure 1— figure supplement 1 D, E). In lines 149-155, we note single channel openings were frequently observed but that the high expression of the PKD2L1 often resulted in patch instability. As a result, GUV patches the lower expressing PKD2-GFP channel were more stable and thus more successfully recorded from. As requested, we have included a brief description of the two proteins in lines 76-78. 

(5) Additional validation or clarification for examining the channel orientation may strengthen the manuscript.

We have modified the text to make this point clearer. 

(6) Advantage and limitations. The authors compared the recordings from hippocampal primary cilia membranes, noting differences in conductance magnitudes compared to the GUV method. Further discussing the limitations and advantages of this approach for the biophysical properties of organelle channels would be beneficial.

We have revised the final paragraph to discuss the limitations of this method.

(7) Including experiments that demonstrate ligand-induced activation or inhibition to further validate the current using this method would strengthen the manuscript (optional, not required).

Despite our best attempts, exchange of the external bath to apply inhibitors (Gd3+, La3+) resulted in GUV patch instability. Our plans are to investigate ways to stabilize the high resistance seals to develop pharmacological screening using the CFE+GUV method.

Author response:

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

We would like to thank the reviewers for their interest in our studies. In response to their comments, we have conducted additional experiments and made the necessary revisions to the manuscript. The new studies included to address the reviewers’ comments are shown in Figure 1B, 1F, Figure 2—figure supplement 1, Figure 3, Figure 3—figure supplement 1, Figure 3—figure supplement 2, Figure 3—figure supplement 3, Figure 4E, Figure 4—figure supplement 1, Figure 5, Figure 5—figure supplement 1, Figure 5—figure supplement 2D, and Figure 6. We are grateful for the critiques, which have helped us substantially improve the quality of the manuscript.

Below, we have provided a point-by-point response to the reviewers’ comments.  

Public Reviews:

Reviewer #1 (Public Review):

In this paper, the authors show that disruption of calcineurin, which is encoded by tax-6 in C. elegans, results in increased susceptibility to P. aeruginosa, but extends lifespan. In exploring the mechanisms involved, the authors show that disruption of tax-6 decreases the rate of defecation leading to intestinal accumulation of bacteria and distension of the intestinal lumen. The authors further show that the lifespan extension is dependent on hlh-30, which may be involved in breaking down lipids following deficits in defecation, and nhr-8, whose levels are increased by deficits in defecation. The authors propose a model in which disruption of the defecation motor program is responsible for the effect of calcineurin on pathogen susceptibility and lifespan, but do not exclude the possibility that calcineurin affects these phenotypes independently of defecation.

We thank the reviewer for providing an excellent summary of our work. We have performed additional experiments as suggested by both the reviewers and believe we have thoroughly addressed all the reviewers' concerns.

Reviewer #2 (Public Review):

The manuscript titled "Calcineurin Inhibition Enhances Caenorhabditis elegans Lifespan by Defecation Defects-Mediated Calorie Restriction and Nuclear Hormone Signaling" by Priyanka Das, Alejandro Aballay, and Jogender Singh reveals that inhibiting calcineurin, a conserved protein phosphatase, in C. elegans affects the defecation motor program (DMP), leading to intestinal bloating and increased susceptibility to bacterial infection. This intestinal bloating mimics calorie restriction, ultimately resulting in an enhanced lifespan. The research identifies the involvement of HLH-30 and NHR-8 proteins in this lifespan enhancement, providing new insights into the role of calcineurin in C. elegans DMP and mechanisms for longevity.

The authors present novel findings on the role of calcineurin in regulating the defecation motor program in C. elegans and how its inhibition can lead to lifespan enhancement. The evidence provided is solid with multiple experiments supporting the main claims.

Strengths:

The manuscript's strength lies in the authors' use of genetic and biochemical techniques to investigate the role of calcineurin in regulating the DMP, innate immunity, and lifespan in C. elegans. Moreover, the authors' findings provide a new mechanism for calcineurin inhibitionmediated longevity extension, which could have significant implications for understanding the molecular basis of aging and developing interventions to promote healthy aging.

(1) The study uncovers a new role for calcineurin in the regulation of C. elegans DMP and a potential novel pathway for enhancing lifespan via calorie restriction involving calcineurin, HLH-30, and NHR-8 in C. elegans.

(2) Multiple signaling pathways involved in lifespan enhancement were investigated with fairly strong experimental evidence supporting their claims.

We thank the reviewer for an excellent summary of our work and for highlighting the strengths of the findings.

Weaknesses:

The manuscript's weaknesses include the lack of mechanistic details regarding how calcineurin inhibition leads to defects in the DMP and induces calorie restriction-like effects on lifespan.

The exact site of calcineurin action, i.e., whether in the intestine or enteric muscles (Lee et al., 2005), and the possible molecular mechanisms linking calcineurin inhibition, DMP defects, and lifespan were not adequately explored. Although characterization of the full mechanism is probably beyond the scope of this paper, given the relative simplicity and advantages of using C. elegans as a model organism for this study, some degree of rigor is expected with additional straightforward control experiments as listed below:

The authors state that tax-6 knockdown animals had drastically reduced expulsion events (Figure 2G), leading to irregular DMP (Lines 144-145), but did not describe the nature of DMP irregularity. For example, did the reduced expulsion events still occur with regular intervals but longer cycle lengths? Or was the rhythmicity completely abolished? The former would suggest the intestine clock is still intact, and the latter would indicate that calcineurin is required for the clock to function. Therefore, ethograms of DMP in both wild-type and tax6 mutant animals are warranted to be included in the manuscript. Along the same line, besides the cycle length, the three separable motor steps (aBoc, pBoc, EMC) are easily measurable, with each step indicating where the program goes wrong, hence the site of action, which is precisely the beauty of studying C. elegans DMP. Unfortunately, the authors did not use this opportunity to characterize the exact behavior phenotypes of the tax-6 mutant to guide future investigations. Furthermore, it is interesting that about 64% of tax-6 (p675) animals had normal DMP. The authors attributed this to p675 being a weak allele. It would be informative to further examine tax-6 RNAi as in other experiments or to make a tax-6 null mutant with CRISPR. In addition, in one of the cited papers (Lee et al., 2005), the exact calcineurin loss-of-function strain tax-6(p675) was shown to have normal defecation, including normal EMC, while the gain-of-function mutant of calcineurin tax-6(jh107) had abnormal EMC steps. It wasn't clear from Lee et al., 2005, if the reported "normal defecation" was only referring to the expulsion step or also included the cycle length. Nevertheless, this potential contradiction and calcineurin gain-of-function mutant is highly relevant to the current study and should be further explored as a follow-up to previously reported results. For some of the key experiments, such as tax-6's effects on susceptibility to PA14, DMP, intestinal bloating, and lifespan, additional controls, as the norm of C. elegans studies, including second allele and rescue experiments, would strengthen the authors' claims and conclusions.

We have now included lifespan, survival on P. aeruginosa, and DMP data using an additional knockout allele, tax-6(ok2065). Additionally, we have added ethograms of DMP for both tax-6 RNAi and the tax-6(ok2065) mutant. Our observations indicate that tax-6 inhibition leads to a complete loss of DMP rhythmicity, suggesting that calcineurin is essential for maintaining the DMP clock. While characterizing the DMP, we noticed that expulsion events appeared superficial in the tax-6(ok2065) mutant, with little to no gut content released. Consequently, we examined the movement of gut content and found that both tax-6(ok2065) mutants and tax-6 knockdown animals showed significantly reduced gut content movement. The new findings on the characterization of DMP are presented in Figure 2—figure supplement 1, Figure 3, Figure 3—figure supplement 1, and Figure 3—figure supplement 2. The text in the results section reads (lines 160-176): “Next, we investigated whether the reduced number of expulsion events was due to regular intervals with longer cycle lengths or if rhythmicity was entirely disrupted upon tax-6 knockdown. To assess this, we obtained ethograms of the DMP for N2 animals grown on control and tax-6 RNAi. While animals on control RNAi displayed regular cycles of pBoc, aBoc, and EMC, the tax-6 RNAi animals exhibited disrupted rhythmicity (Figure 3A and Figure 3—figure supplement 1). Most tax-6 knockdown animals lacked the pBoc and aBoc steps and had sporadic expulsion events. Isolated pBoc events were occasionally observed, indicating a complete loss of rhythmicity in tax-6 knockdown animals. Ethograms for tax-6(ok2065) animals also showed disrupted rhythmicity (Figure 3B and Figure 3—figure supplement 2). Although the number of expulsion events appeared higher in tax-6(ok2065) animals compared to tax-6 RNAi animals (Figure 3—figure supplement 1 and 2), these expulsion events seemed superficial, releasing little to no gut content. This suggested slow movement of gut content in tax6(ok2065) animals, leading to constipation and intestinal bloating. We examined gut content movement by measuring the clearance of blue dye (erioglaucine disodium salt) from the gut. The clearance was significantly slower in tax-6(ok2065) animals compared to N2 animals (Figure 3C), indicating impaired gut content movement due to the loss of tax-6. Similarly, tax-6 knockdown animals also showed significantly slowed gut content movement (Figure 3D).”

Moreover, we have added a potential reason for the tax-6(p675) contradictory results from Lee et al., 2005 (lines 154-159): “At the 1-day-old adult stage, about 36% of tax-6(p675) animals showed irregular and slowed DMP, while the remainder had regular DMP (Figure 2H), suggesting that tax-6(p675) is a weak allele. The fraction of the animals with irregular DMP appeared to increase with age, indicating that this phenotype might be agedependent. This may also explain why tax-6(p675) animals were reported to have a normal defecation cycle in an earlier study (Lee et al., 2005).”

The second weakness of this manuscript is the data presentation for all survival rate curves. The authors stated that three independent experiments or biological replicates were performed for each group but only showed one "representative" curve for each plot. Without seeing all individual datasets or the averaged data with error bars, there is no way to evaluate the variability and consistency of the survival rate reported in this study.

We now provide all replicates data in the source data files.

Overall, the authors' claims and conclusions are justified by their data, but further experiments are needed to confirm their findings and establish the detailed mechanisms underlying the observed effects of calcineurin inhibition on the DMP, calorie restriction, and lifespan in C. elegans.

We have conducted additional experiments to elucidate the role of calcineurin in the DMP and to investigate the impact of the DMP on calorie restriction and lifespan in C. elegans, as described in the various responses to the reviewers’ comments. 

Recommendations for the authors:

Our specific comments to guide the authors, should they choose to revise the manuscript:

The RNAi experiments in the eat-2 mutant background are difficult to interpret. If these animals are eating fewer bacteria, it is possible that there is also less tax-6 dsRNA being ingested and therefore less tax-6 inactivation. These experiments should be conducted with a tax-6 null allele.

We have included lifespan experiments with the eat-2(ad465);tax-6(ok2065) double mutant, along with the individual single mutant controls, as shown in Figure 4E. These results demonstrate that the eat-2 mutation does not further extend the lifespan of the tax-6(ok2065) mutant. Additionally, we confirmed that the eat-2(ad465) mutants do not exhibit defects in feeding-based RNAi (Figure 4—figure supplement 1).

While aak-2, hlh-30, and nhr-8 mutants may not have an eat phenotype, the negative tax-6 RNAi results should be confirmed with a tax-6 null mutant to obviate the consideration that these background mutations reduce RNAi efficacy.

The genes hlh-30 and nhr-8 are located very close to tax-6 on chromosome IV (https://wormbase.org//#012-34-5), which made it challenging to generate double mutants. However, we tested the RNAi sensitivity of the hlh-30(tm1978) and nhr-8(ok186) mutants and confirmed that they are not defective in RNAi (Figure 5—figure supplement 1). We also found that tax-6 RNAi disrupted the DMP in both hlh-30(tm1978) and nhr-8(ok186) mutants (Figure 5—figure supplement 2). Furthermore, our results show that hlh-30(tm1978) and nhr-8(ok186) animals have increased susceptibility to P. aeruginosa upon tax-6 knockdown (Figure 6A, B), indicating that tax-6 RNAi was effective in these mutants. Since the phenotype in the aak-2 mutant was only partially observed, we did not conduct further experiments with aak-2 mutants.

Reviewer #1 (Recommendations For The Authors):

The low penetrance of defecation cycle defects in tax-6(p675) worms brings into question the role of the defecation deficits in the phenotypes caused by the disruption of tax6. At the same time, the low penetrance provides a golden opportunity to test this. Do tax6(p675) worms with a normal defecation cycle length have extended longevity? Increased susceptibility to bacterial pathogens? Smaller body size? Distended lumen? Decreased fat accumulation? Increased pha-4 and nhr-8 expression? It would be relatively straightforward to measure defecation cycle length in individual tax-6(p675) worms, bin them into normal defecation and slow defecation groups, and then compare the above-mentioned phenotypes.

We appreciate the reviewer's interesting suggestion. However, the DMP defect phenotype in tax-6(p675) worms appears to be age-dependent, with the number of DMPdefective worms increasing as they age. Additionally, we observed that exposure to P. aeruginosa accelerates the onset of DMP defects in tax-6(p675) worms. As a result, tax6(p675) worms are not suitable for the type of experiments the reviewer suggested. Nevertheless, we believe that the additional data using the tax-6(ok2065) mutant, along with the characterization of ethograms of DMP, firmly establishes the role of calcineurin in maintaining a regular DMP in C. elegans.

Another way to dissect specific effects of calcineurin disruption from phenotypes resulting from defecation motor program deficits would be to further characterize other worms with deficits in defecation (flr-1, nhx-2, pbo-1 RNAi). It is mentioned that they have decreased lifespan. Do they also show increased susceptibility to bacterial pathogens? Do they show decreased fat? Is their lifespan dependent on HLH-30 and NHR-8?

We thank the reviewer for this important suggestion. We have now included data with flr-1, nhx-2, and pbo-1 RNAi, which shows that the knockdown of these genes also enhances susceptibility to P. aeruginosa (Figure 3—figure supplement 3G). Knockdown of these genes is already known to reduce fat levels in N2 worms, and we demonstrate that they similarly reduce fat levels in hlh-30(tm1978) and nhr-8(ok186) animals (Figure 5B, C, F, G). Additionally, we found that the increased lifespan observed upon knockdown of these genes (as well as with tax-6 knockdown) is dependent on HLH-30 and NHR-8 (Figure 5A, D).

To place "enhanced susceptibility to pathogen" within the proposed model, it would be important to examine the effect of HLH-30 and NHR-8 disruption on this phenotype. The proposed model suggests that this phenotype is independent of HLH-30 and NHR-8, but this should be tested experimentally. Similarly, it would be important to test the effect of HLH-30 and NHR-8 disruption on defecation cycle length to determine if defecation deficits are upstream or downstream of deficits in the defecation motor program

We show that the knockdown of tax-6 leads to defects in the DMP in hlh30(tm1978) and nhr-8(ok186) animals (Figure 5—figure supplement 2). Moreover, we show that hlh-30(tm1978) and nhr-8(ok186) animals have increased susceptibility to P. aeruginosa upon tax-6 knockdown (Figure 6A, B). These results are described as (lines 279-285): “Given that HLH-30 and NHR-8 are essential for lifespan extension upon calcineurin inhibition, we investigated whether these pathways also influence survival in response to P. aeruginosa infection following calcineurin knockdown. Both hlh-30(tm1978) and nhr-8(ok186) animals showed significantly reduced survival upon tax-6 RNAi (Figure 6A, B). These findings suggested that the reduced survival on P. aeruginosa following calcineurin inhibition is independent of HLH-30 and NHR-8 and is more likely due to increased gut colonization by P. aeruginosa resulting from DMP defects (Figure 6C).”

Is the lifespan of tax-6(p675) increased? This would be important to measure and include in Figure 1.

Indeed, the lifespan of tax-6(p675) mutants is increased. We have included the lifespan of tax-6(p675) and tax-6(ok2065) in Figure 1F.

In Figure 2, disruption of tax-6 appears to result in a clear decrease in body size. To what extent is the decrease in fat/worm in Figure 3 simply a result of the worms being smaller? Perhaps, a measurement of Oil-Red-O intensity PER AREA would be a more appropriate measure.

The ORO intensity values we had shown per animal were already area normalized. We have now indicated this in the Figure Legends.

There are multiple long-lived mutant strains such as clk-1 and isp-1 that have an increased defecation cycle length. To what extent do these worms exhibit phenotypes similar to tax-6 disruption? isp-1 have increased resistance to bacterial pathogens suggesting that defecation motor program deficits are not sufficient to increase susceptibility to bacterial pathogens.

We have now examined the clk-1 and isp-1 mutants and found that these mutants exhibit reduced gut colonization by P. aeruginosa compared to N2 animals. This reduction in colonization may be attributed to the slowed pharyngeal pumping rates observed in these mutants. These findings suggest that the phenotypes associated with a slow DMP versus a disrupted DMP could be significantly different. The manuscript with the new data on these mutants reads (lines 177-192): “We then explored whether the disruption of DMP rhythmicity due to tax-6 knockdown affected P. aeruginosa responses similarly to longer but regular DMP cycles. To do this, we studied P. aeruginosa colonization in clk-1(qm30) and isp1(qm150) mutants, which have regular but extended DMP cycles (Feng et al., 2001; Wong et al., 1995). Interestingly, both clk-1(qm30) and isp-1(qm150) mutants showed significantly reduced intestinal colonization by P. aeruginosa compared to N2 animals (Figure 3—figure supplement 3A-D). This reduced colonization could be attributed to their significantly decreased pharyngeal pumping rates (Wong et al., 1995; Yee et al., 2014), suggesting a lower intake of bacterial food in these mutants. While the survival of clk-1(qm30) animals on P. aeruginosa was comparable to N2 animals (Figure 3—figure supplement 3E), isp1(qm150) animals exhibited significantly improved survival (Figure 3—figure supplement 3F). Conversely, knockdown of flr-1, nhx-2, and pbo-1 in N2 animals resulted in significantly reduced survival on P. aeruginosa compared to control RNAi (Figure 3—figure supplement 3G). Knockdown of these genes causes complete disruption of DMP rhythmicity, increasing gut colonization by P. aeruginosa (Singh and Aballay, 2019a). Overall, these findings demonstrated that calcineurin is crucial for maintaining the DMP ultradian clock, and its inhibition increases susceptibility to P. aeruginosa by disrupting the DMP.”

Line 192. This statement is speculative. There is no evidence that HLH-30 is mediating lipid depletion in these worms.

We have removed this statement. We observed that the knockdown of flr-1, nhx2, and pbo-1 resulted in significant fat depletion in hlh-30(tm1978) animals (Figure 5B, C). Additionally, tax-6 knockdown also caused a small but significant reduction in fat levels in hlh-30(tm1978) animals. This contrasts with our initial submission, possibly due to the increased number of animals included in the analysis. These findings suggest that the increase in lifespan due to DMP defects requires HLH-30, likely through a mechanism independent of HLH-30’s role in fat depletion. We have updated the manuscript text and model (Figure 6C) accordingly.

In Figure S2, tax-6 RNAi appears to have a more detrimental effect in pmk-1 mutants than the other mutants. The authors should comment on this.

We have added the following sentence in the manuscript (lines 123-125): “The knockdown of tax-6 appeared to have a more pronounced effect in pmk-1(km25) mutants than in other mutants, suggesting that inhibition of tax-6 might exacerbate the adverse effects observed in pmk-1(km25) mutants.”

Reviewer #2 (Recommendations For The Authors):

Line 192-193: The statement is confusing and not accurate because HLH-30 did not enhance lifespan with or without calcineurin (Figure 4A and S4A, also in Lapierre 2023). The takeaway should be along the lines of calcineurin inhibition enhancing lifespan through HLH-30 or HLH-30 being required for lifespan enhancement via calcineurin inhibition.

We have removed this statement. We now state (lines 237-239): “Knockdown of tax-6 did not extend the lifespan of hlh-30(tm1978) animals (Figure 5A), indicating that HLH-30 is required for the increased lifespan observed with calcineurin inhibition.”

Line 261: Similar to the point above. Where is the data showing NHR-8 increases lifespan with or without calcineurin?

We have removed this sentence.

Figure 1 legend line 699: animals per condition per replicate >90, but in the Method section Line 317, it says more than 80 animals per condition per replicate. Could be more accurate.

We have now specified in the Methods section that the exact number of animals per condition is provided in the source data files. Since different lifespan curves within a given figure panel had varying numbers of animals, we have indicated the lower boundary for all curves (including the replicates). The precise number of animals for each lifespan experiment is available in the source data files.

Figures 2F and G, "tax-6" should be labeled as "tax-6 RNAi" to be consistent with other figures.

We thank the reviewer for this suggestion and have updated the label to “tax-6 RNAi”.

In summary, we would like to thank the reviewers again for providing constructive critiques. We believe we have fully addressed all the concerns of the reviewers by carrying out several new experiments and modifying the text. The manuscript has undergone substantial revision and has thereby improved significantly. We do hope that the evidence in support of the conclusions is found to be complete in the revised manuscript.

eLife Assessment

This important study reveals insights into how calcineurin influences C. elegans pathogen susceptibility and lifespan through its role in controlling the defecation motor program. The authors provide convincing evidence to support a new mechanism through which calcineurin impacts longevity. This work will be of interest to investigators studying host-pathogen interactions and aging in a number of experimental systems.

Reviewer #1 (Public review):

In this paper, the authors show that disruption of calcineurin, which is encoded by tax-6 in C. elegans, results in increased susceptibility to P. aeruginosa but extends lifespan. In exploring the mechanisms involved, the authors show that disruption of tax-6 decreases the rate of defecation leading to intestinal accumulation of bacteria and distension of the intestinal lumen. The authors further show that the lifespan extension is dependent on hlh-30, which may be involved in breaking down lipids following deficits in defecation, and nhr-8, whose levels are increased by deficits in defecation. The authors propose a model in which disruption of the defecation motor program is responsible for the effect of calcineurin on pathogen susceptibility and lifespan, but do not exclude the possibility that calcineurin affects these phenotypes independently of defecation.

Reviewer #2 (Public review):

The relationships between genes and phenotypes are complex and the impact of deleting or a gene can often have multifaceted and unforeseen consequences. This paper dissected the role of calcineurin, encoded by tax-6, in various phenotypes in C. elegans, including lifespan, pathogen susceptibility, the defecation motor program, and nutrient absorption or calorie restriction, through a series of genetic and behavioral analyses. Many genes in these pathways were tested yielding robust results. Classic epistasis analyses were used to distinguish between genes operating in the same or separate pathways. Researchers in the related fields will be very interested in looking through the data presented in this paper in great detail and benefit from it.

Overall, this paper supports a model in which the increased lifespan and heightened pathogen susceptibility observed following calcineurin inhibition result from the disruptions in the defecation motor program but through distinct pathways. A defective defecation motor program leads to intestine bloating and compromised nutrient absorption. Calorie restriction resulting from poor nutrient absorption affects lifespan, whereas increased colonization in the bloated intestine heightens pathogen susceptibility. The observation that knockdown of several other DMP-related genes also results in increased lifespan and pathogen susceptibility further reinforces the proposed model.

eLife Assessment

This important study presents a high-resolution cryoEM structure of the supercomplex between photosystem I (PSI) and fucoxanthin chlorophyll a/c-binding proteins (FCPs) from the model diatom Thalassiosira pseudonana CCMP1335, revealing subunits, protein:protein interactions and pigments not previously seen in other diatoms or red/green photosynthetic lineages. Combining structural, sequence and phylogenetic analyses, the authors provide convincing evidence of conserved motifs crucial for the binding of FCPs, accompanied by interesting speculation about the mechanisms governing the assembly of PSI-FCP supercomplexes in diatoms and their implications for related PSI-LHC supercomplexes in plants. The findings set the stage for functional experiments that will further advance the fields of photosynthesis, bioenergy, ocean biogeochemistry and evolutionary relationships between photosynthetic organisms.

Reviewer #1 (Public review):

The authors present the cryo-EM structure of PSI-fucoxanthin chlorophyll a/c-binding proteins (FCPs) supercomplex from the diatom Thalassiosira pseudonana CCMP1335 at a global resolution of 2.3 Å. This exceptional resolution allows the authors to construct a near-atomic model of the entire supercomplex and elucidate the molecular details of FCPs arrangement. The high-resolution structure reveals subunits not previously identified in earlier reconstructions and models, as well as sequence analysis of PSI-FCPIs from other diatoms and red algae. Additionally, the authors use their model in conjunction with a phylogenetic analysis to compare and contrast the structural features of the T. pseudonana supercomplex with those of Chaetoceros gracilis, uncovering key structural features that contribute to the efficiency of light energy conversion in diatoms.

The study employs the advanced technique of single particle cryo-electron microscopy to visualize the complex architecture of the PSI supercomplex at near-atomic resolution and analyze the specific roles of FCPs in enhancing photosynthetic performance in diatoms.

Overall, the approach and data are both compelling and of high quality. The paper is well written and will be of wide interest for comprehending the molecular mechanisms of photosynthesis in diatoms. This work provides valuable insights for applications in bioenergy, environmental conservation, plant physiology, and membrane protein structural biology.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

The authors present the cryo-EM structure of of PSI-fucoxanthin chlorophyll a/c-binding proteins (FCPs) supercomplex from the diatom Thalassiosira pseudonana CCMP1335 at a global resolution of 2.3 Å. This exceptional resolution allows the authors to construct a near-atomic model of the entire supercomplex and elucidate the molecular details of FCPs arrangement. The high-resolution structure reveals subunits not previously identified in earlier reconstructions and models, as well as sequence analysis of PSI-FCPIs from other diatoms and red algae. Additionally, the authors use their model in conjunction with a phylogenetic analysis to compare and contrast the structural features of the T. pseudonana supercomplex with those of Chaetoceros gracilis, uncovering key structural features that contribute to the efficiency of light energy conversion in diatoms.

The study employs the advanced technique of single particle cryo-electron microscopy to visualize the complex architecture of the PSI supercomplex at near-atomic resolution and analyze the specific roles of FCPs in enhancing photosynthetic performance in diatoms.

Overall, the approach and data are both compelling and of high quality. The paper is well written and will be of wide interest for comprehending the molecular mechanisms of photosynthesis in diatoms. This work provides valuable insights for applications in bioenergy, environmental conservation, plant physiology, and membrane protein structural biology.

We thank you very much for your highly positive evaluation and comments on our manuscript.

Reviewer #2 (Public Review):

Summary:

This manuscript elucidated the cryo-electron microscopic structure of a PSI supercomplex incorporating fucoxanthin chlorophyll a/c-binding proteins (FCPs), designated as PSI-FCPI, isolated from the diatom Thalassiosira pseudonana CCMP1335. Combining structural, sequence, and phylogenetic analyses, the authors provided solid evidence to reveal the evolutionary conservation of protein motifs crucial for the selective binding of individual FCPI subunits and provided valuable information about the molecular mechanisms governing the assembly and selective binding of FCPIs in diatoms.

Strengths:

The manuscript is well-written and presented clearly as well as consistently. The supplemental figures are also of high quality.

Weaknesses:

Only minor comments (provided in recommendations for authors) to help improve the manuscript.

We thank you very much for your highly positive evaluation and comments on our manuscript.

Reviewer #3 (Public Review):

Summary:

Understanding the structure and function of the photosynthetic machinery is crucial for grasping its mode of action. Photosystem I (PSI) plays a vital role in light-driven electron transfer, which is essential for generating cellular reducing power. A primary strategy to mitigate light and environmental stresses involves incorporating peripheral light-harvesting proteins. Among various lineages, the number of LHCIs and their protein and pigment compositions differ significantly in PSI-LHCI structures. However, it is still unclear how LHCIs recognize their specific binding sites in the PSI core. This study aims to address this question by obtaining a high-resolution structure of the PSI supercomplex, including fucoxanthin chlorophyll a/c-binding proteins (FCPs), referred to as PSI-FCPI, isolated from the diatom Thalassiosira pseudonana. Through structural and sequence analyses, distinct protein-protein interactions are identified at the interfaces between FCPI and PSI subunits, as well as among FCPI subunits themselves.

Strengths:

The primary strength of this work lies in its superb isolation and structural determination, followed by clear discussion and conclusions. However, the interactions among the protein complexes and their relevance in formulating general rules are not definitively established. While efficiency is a crucial aspect, preventing damage is equally important, and currently, we cannot infer this from the provided structures.

Weaknesses:

The interactions among the protein complexes and their relevance in formulating general rules are not definitively established. While efficiency is a crucial aspect, preventing damage is equally important, and currently, we cannot infer this from the provided structures.

We thank you very much for your highly positive evaluation and comments on our manuscript. This study is aimed to decipher the interactions among different protein subunits within the PSI-FCPI supercomplex, from which we wish to draw their relevance in formulating general rules. While we agree that damage is equally important, it is unclear to us what kind of damage you are mentioning, and we consider that this may need to be treated in another publication, as we cannot elucidate everything in one paper.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Line 69: "Diatoms are one of the most important phytoplankton in aquatic environments and contribute to the primary production in the ocean remarkably." Check the sentence, something is missing.

We modified the sentence as follow:

"Diatoms are among the most essential phytoplankton in aquatic environments, playing a crucial role in the global carbon cycle, supporting marine food webs, and contributing significantly to nutrient cycling, thus ensuring the health and sustainability of marine ecosystems"

(2) Supplementary Figure 1B: The SDS-PAGE gel shows multiple bands. Do the authors know the identity of these proteins, or have they considered analyzing the bands using mass spectrometry? The band at ~17 kDa is particularly intense. Could you comment on this? Have you tried running a Native-PAGE gel?

We did not identify protein bands by MS analysis. The protein bands in the PSI-FCPI supercomplex of this diatom have been identified by Ikeda et al. 2013. The protein bands of our sample were similar to those of Ikeda et al. 2013. To explain this, we modified the sentences and cited Ikeda et al. 2013 in the revised manuscript (lines 89-91).

"The PSI-FCPI supercomplexes were purified from the diatom T. pseudonana CCMP1335 and analyzed by biochemical and spectroscopic techniques (Fig. S1). Notably, the protein bands of PSI-FCPI closely resembled those reported in a previous study (31)."

The ~17 kDa protein band appears to be FCPIs, which was identified in Ikeda et al. 2013. We did not perform BN-PAGE of this sample; however, we performed trehalose density gradient centrifugation (Fig. S1A).

(3) Can the authors comment on the position of the FCPI subunits in the PSI supercomplex in diatoms compared to the arrangement of LHCIs in complex with PSI in cyanobacteria, green algae, and angiosperms? This information would be useful to incorporate into the text.

We previously compared the PSI-FCPI structures of the diatom C. gracilis to the PSI-LHCI structures of land plant, green alga, and red alga (Nagao et al., 2020). Also, Xu et al. 2020 compared the C. gracilis PSI-FCPI structure to the PSI-LHCI structures of land plant, green alga, and red alga. The binding sites between FCPIs and LHCIs are conserved to some extent. However, our recent study revealed that no orthologous relationship exists among LHCs bound to PSI between primitive red algae and diatoms (Kato et al., 2024). Consequently, we found that the information obtained from structural comparisons alone is extremely limited. To avoid misinterpretation, this study focused on comparing the structures and amino acid sequences of FCPIs between T. pseudonana and C. gracilis.

(4) Line 104: Despite achieving high resolution, the authors modeled only six lipid densities (the PDB model contains actually 9 lipids, you should correct it in the text). Do you believe this is due to the detergent used for purification? Can you comment on the position, identity, and potential role of the lipids within your model?

There are 6 lipids associated with the PSI core and 3 with FCP, giving rise to a total of 9 lipids. We have described it in our original text (lines 102-104 in the modified manuscript). Additionally, our structure reveals unidentified densities which likely represent lipids; they are modeled as 88 unknown lipids (UNLs). Thus, there are more lipids in the supercomplex. However, we also observed 4 β-DDM molecules (LMT) in the structure, which are used as detergents. Thus, it is possible that some lipids have dissociated and replaced by detergents. Many of the observed lipids are located between subunits, likely contributing to the stabilization of the complex.

(5) Line 111: The global resolution is very high. Why does the unknown protein have such low resolution that it was impossible to model it properly and perform de novo identification from the density map? Is it due to a lower abundance of particles with this subunit bound? Have you tried improving this with 3D classification/ focus refinement /density modification?

The Unknown subunit (UNK) is located peripherally, and its density is significantly lower compared to the neighboring subunits, which may suggest a low abundance. We applied density modification using Topaz for 3D map denoising, but the effect was minimal. As the low abundance of UNK may be the cause, 3D classification and focus refinement also had limited impact.

(6) Figure 2A: It would be useful to show the density map for the subunit together with the model, especially to demonstrate visualization of the long loop.

We added the model and map of Psa29 to Figure S4C in the revised manuscript.

(7) Given the proximity of Psa29 to PsaC, is the protein involved in electron shuttling? If so, could you comment on this? In line 131, you state that Psa29 was not found in other organisms. Can the authors speculate on the potential role of this protein in diatoms?

We have no idea about the function of Psa29 at present. However, Psa29 does not contain any cofactors, indicating no contribution of it to electron transfer reactions. To understand the function of Psa29, a deletion mutant of this gene is required for examining its functional and physiological roles in diatom photosynthesis. To explain this, we added the following sentences to the revised manuscript (lines 129-133):

"However, the functional and physiological roles of Psa29 remain unclear at present. It is evident that Psa29 does not have any pigments, quinones, or metal complexes, suggesting no contribution of Psa29 to electron transfer reactions within PSI. Further mutagenesis studies will be necessary to investigate the role of Psa29 in diatom photosynthesis."

(8) Line 163: "Among the FCPI subunits, only FCPI-1 has BCRs in addition to Fxs and Ddxs (Figure S6A). FCPI-1 is a RedCAP, which belongs to the LHC protein superfamily but is distinct from the LHC protein family (6, 7)." It would be useful if the authors could add the carotenoid model embedded in the cryoEM density map to the figure to show the features that led to modeling BCR instead of other carotenoids. Additionally, it would be helpful to include in the text why RedCAPs differ from LHCIs and their proposed role.

We added the model and map of two BCRs in FCPI-1 (RedCAP) to Figure S4F in the revised manuscript.

Phylogenetic analysis showed that RedCAPs are distinct from the LHC protein family. This has been explained in lines 163-164. Also, the functional and physiological roles of RedCAP remain unclear. To explain this, we added the sentence "; however, the functional and physiological roles of RedCAP remain unclear" to the revised manuscript (lines 164-165).

(9) Line 185: "However, it is unknown (i) whether CgRedCAP is indeed bound to the C. gracilis PSI-FCPI supercomplex and (ii) if a loop structure corresponding to the Q96-T116 loop of TpRedCAP exists in CgRedCAP." Have the authors attempted to model the protein using AlphaFold? If so, are there significant differences? Could you speculate on the absence of RedCAP in C. gracilis? Do you believe it is due to using a different detergent or related to environmental factors?

We did not model CgRedCAP using AlphaFold. Our recent study “Kato et al. 2024” proposed that CgRedCAP binds to the LHCI-1 site in the PSI-FCPI structure based on sequence comparison. There are two types of PSI-FCPI supercomplexes, one having 16 FCPIs and the other having 24 FCPs, from C. gracilis. The different antenna sizes may depend on the growth conditions of C. gracilis (Nagao et al. 2020). These explanations were already described in the manuscript (lines 243-246).

(10) Line 193: Figure 8 is mentioned before Figures 4-7.

We are sorry for the mistake of Figure number. Figure 8 is Supplementary Figure 8, so that we modified Fig. S8B in the revised manuscript.

(11) Line 223: FCPI-4 interacts only with FCPI-5, primarily through the interaction of Y196/4 with the FCPI-5 backbone. Is this interaction facilitated by other factors such as lipids, carotenoids, or other ligands? Also, FCPI-4 occupies a peculiar position compared to other LHCIs proteins (it is peripheral to FCPI-4 and FCPI-5). Do you believe this could be due to a transient interaction with the complex? Could the presence of this protein be related to the growth conditions experienced by the plant? Are there any literature reports on environmental conditions influencing FCPI arrangements? Including this information in the text would be interesting.

Y196/4 interacts with only backbones by hydrogen-bond interactions; therefore, other cofactors do not contribute to the interactions.

We do not believe that the interaction of FCPI-4 is transient; rather, this binding appears to be stable within the complex. Given that the PSI-FCPI supercomplexes were isolated by anion exchange chromatography, FCPI-4 and FCPI-5 are tightly associated within this complex. However, it is important to note that the expression of diatom FCPI proteins can indeed vary depending on growth conditions, as highlighted in our previous study (Nagao et al., 2020). While the peculiar position of FCPI-4 may not be directly related to transient interactions, environmental conditions could still influence the overall arrangement and expression levels of FCPIs. This information has already been described in the manuscript (lines 243-246).

(12) Given the high resolution of your map, the overall model quality does not seem to match the map quality. Specifically, the clash score (10) and sidechain outliers (3%) are elevated. Could you comment on this? Do you believe it is related to the high number of ligands?

Our structure contains a total of 295 ligands, including cofactors, detergents, and unknown lipids. We believe the high clash score and number of sidechain outliers are due to the large number of ligands present.

(13) Supplementary Figure 2: You should show the 3D classes that were discarded.

According to your comment, we added the 3D classes that were discarded and the sentence "Red boxes highlight selected particles from each 3D classification." to Figure S2 and its legend in the revised manuscript.

(14) Which masks were used for refinement? How were they generated, and which parameters were chosen? This information should be added to the Materials and Methods section. You should show the masks used during classification, for example.

We used a 240 Å spherical mask for refinement and classification, without applying any reference mask as input. To explain this, we added the corresponding sentence to Methods in the revised manuscript (lines 347-348) as follow:

"A 240-Å spherical mask was used during the 3D classification and refinement processes."

(15) Were any extra proteins detected in the early stages of the cryoEM analysis (i.e., 2D classification) that were discarded? Could you visualize the superior oligomeric states of the supercomplex?

In the single-particle analysis, no larger particles than the analyzed complex were detected. The results of 2D classification using a sufficiently large spherical mask with a diameter of 320 Å are shown below.

Author response image 1.

(16) Have you tried using cryoSPARC for data analysis? If so, could you comment on that?

We did not use cryoSPARC for data analysis.

Reviewer #2 (Recommendations For The Authors):

I have some minor comments below to help improve the manuscript. The line numbers below refer to those in the Word version of the manuscript.

(1) Figure 1 legend, line 559, "membrane normal"? Panel A and B, structures with the same colors, do they refer to the closely related or interacted parts? For example, the red color for FCP1-1 in A and PsaA in B. If not, the authors may want to clarify it.

The term 'membrane normal' refers to the direction perpendicular to the surface of a membrane. It is a concept frequently used in physics and biology to describe the orientation relative to the membrane's plane.

We do not refer to either the closely related or interacted parts used in Figure 1. According to your comments, the colors of subunits were revised in the revised manuscript.

(2) Line 109-117. "Psa28 is a novel subunit found in the C. gracilis PSI-FCPI structure, and its name follows the nomenclature as suggested previously (31).... After psaZ, the newly identified genes should be named psa27, psa28, etc., and the corresponding proteins are called Psa27, Psa28, etc... Psa28 was also named PsaR in the PSI-FCPI structure of C. gracilis (16)". It is confusing. Was Psa28 named twice, PsaR and Psa28? It would be helpful to add a simple explanation here.

According to your comment, we modified the sentence as follow (lines 117-118):

" However, Xu et al. named the subunit as PsaR in the PSI-FCPI structure of C. gracilis "

(3) Line 134, "One of the Car molecules in PsaJ was identified as ZXT103 in the T. pseudonana PSI-FCPI structure but it is BCR112 in the C. gracilis PSI-FCPI structure (15)". Figure S4D mentioned BCR863 but did not mention BCR112. Figure S4C, D, it may need better explanations of the colors and labels, and indicate which parts are from T. pseudonana or C. gracilis.

BCR112 was misnumbered; the correct number is BCR103. In response to your comments, we revised Figure S4C and D by labeling the characteristic pigments in the revised manuscript.

(4) Figure S7, although mentioned in the legend, it would be helpful to label interaction pairs on the figure directly with corresponding colours.

According to your comments, we modified the Figure and legends in the revised manuscript.

(5) Figure 3E, it is better to avoid red/green colours in one figure as some readers may be colour-blind. It would also be helpful to label each FCPI with the same colour as its structure on the figure directly.

According to your comments, we modified Figure 3E in the revised manuscript.

(6) Line 185, "structures similar to the Q96-T116 loop in TpRedCAP found in the present study (Figure 8B).". The authors refer to Figure S8B? I have the same comment for line 186, Figure 8C.

We are sorry for the mistake of Figure number. Figure 8 is Supplementary Figure 8, so we modified it as Fig. S8B in the revised manuscript.

(7) Line 270, "TpLhcq10 cannot bind at the FCPI-2 site". Why not use FCPI-3 for TpLhcq10?

This means that the gene product of TpLhcq10 binds at the FCPI-3 site but not at the other sites such as FCPI-2. To avoid misreading, we modified the sentence as follows:

"TpLhcq10 binds specifically at the FCPI-3 site but not at the other sites such as FCPI-2" (lines 278-279)

Reviewer #3 (Recommendations For The Authors):

I have no technical or conceptual suggestions at the current stage.

Thank you.

It feels like a William Playfair moment - the idea that numbers can be represented in graphs, charts - can now be applied to anything else. We're still imagining the forms; network/knowledge graphs are trendy (to what end though) - what else?

Information in various formats - an essay, a map, a chart, number equations, a music score, a receipt - are externalisation of some thought process, to some end.

Verene's helpful definition of the Humanities.

Author response:

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

Recommendations for the authors:

- The authors should think about revising the terminology used to describe electrophysiological data in zebrafish (Fig.5): "posterior" hair cells in a neuromast are sensitive to posterior-to-anterior flow, which is currently termed "anterior". This is confusing because when "posterior" or "anterior" is used, for instance in the labels of the figure, one may get confused about whether this applies to hair-cell position or directionality of the stimulus. It would help to always use clearer terminology for the stimulus (e.g. posterior-to-anterior (P-to-A) as in Kindig 2023, or "from the tail"). Also, the authors may want to clarify what we should see in Fig.5 demonstrating that posterior hair cells, with reversed hair-bundle polarity, actually evince transduction of similar magnitude as anterior hair cells, with normal polarity of their hair bundles. 

This nomenclature can indeed be confusing. Per the reviewers request we have changed the terminology to always refer to the direction of flow sensed by the hair cells. For example, HCs that respond to posterior-directed flow or anterior-directed flow. We now denote these HCs as (A to P) and (P to A), respectively in the Figure for clarity. We have modified Figure 5, the Figure 5 legend and Results (starting line 339) to reflect these changes.

In addition, in our results we now provide more context when comparing the response magnitude of the anterior-sensing hair cells in gpr156 mutants to the response magnitude of the two diVerent orientations of hair cells in controls.

- Also, does it make sense that there is no defect in MET for mouse otolith organs with deleted GPR156, whereas there is a diVerence in the zebrafish lateral line? It would help motivate the study on mechanoelectrical transduction (see comment of Reviewer 1 below). 

We previously discussed this point and recognized that subtle eVects remain possible in mouse (previously Discussion line 614). We have now  modified the text in the Discussion to better emphasize this point (new line 627). The Eatock lab is currently working on developing calcium imaging in the mouse utricle to revisit this question in a future study. "Subtle e)ects remain possible, however, given the variance in single-cell electrophysiological data from both control and mutant mice.  Nevertheless, current results are consistent with normal HC function in the Gpr156 mouse mutant, a prerequisite to interrogate how non-reversed HCs a)ects vestibular behavior."

To help motivate transduction studies starting in the second Result paragraph, we added a transition at Line 205 that was indeed lacking:

"Gpr156 inactivation could be a powerful model to specifically ask how HC reversal contributes to vestibular function. However, GPR156 may have other confounding roles in HCs besides regulating their orientation, similar to EMX2, which impacts mechanotransduction in zebrafish HCs (Kindig et al., 2023) and a)erent innervation  in mouse and zebrafish HCs (Ji et al., 2022; Ji et al., 2018)."

(1) One overarching objective of this study was to use the Gpr156 KO model to discover how polarity reversal informs vestibular function (Introduction, overall summary in the last paragraph) . Pairing behavioral defects with hair cell orientation is only possible if hair cell transduction is normal, which had to be tested.

(2) The notion that experiments that produced negative results are unecessary and are not properly motivated can only apply in retrospect. At early stages we performed electrophysiology because we did not know whether transduction would be normal in absence of GPR156. We also did not know whether innervation would be normal. The fact that both appear normal makes Gpr156 KO a better model to address the importance of orientation reversal (conclusion of the Discussion line 705).

See also reply to Reviewer #1 below.

Reviewer #1 (Recommendations For The Authors): 

Fig1, panel B appears to show diVerent focal planes for Gpr156del/+ and Gpr156del/del. 

Figure 1B had control and mutant panels at slightly diVerent focal planes indeed. We swapped the right (mutant) panel image and adjusted intensities in the control image to match adjustments of the new mutant image.  

Given that this work is largely about polarity and connectivity to neurons, I do not understand the need to assess mechanosensitivity in Gpr156 mutants. Please explain in the text, as follows: "After establishing normal numbers and types of mouse vestibular HCs, we assessed whether HCs respond normally to hair bundle deflections in the absence of GPR156." We did this because... 

Please see reply above in 'Recommendations for the authors' for comment about the need to assess mechanosensitivity. We agree that this transition was lacking, and we added an explanation as recommended:

"Gpr156 inactivation could be a powerful model to specifically ask how HC reversal contributes to vestibular function. However, GPR156 may have other confounding roles in HCs besides regulating their orientation, similar to EMX2, which impacts mechanotransduction in zebrafish HCs (Kindig et al., 2023) and a)erent innervation  in mouse and zebrafish HCs (Ji et al., 2022; Ji et al., 2018)."

Anyway, the data in Figures 2, 3 and 4 seems somewhat superfluous to the main message of the paper. 

Please see reply above in 'Recommendations for the authors'. This data may appear superfluous in retrospect but we could not claim that behavioral changes in Gpr156 mutants reflect the role of the line of polarity reversal if, for example, hair cell transduction was abnormal. We had to perform experiments to figure this out. We were further motivated as data began to emerge from the zebrafish lateral line that showed eVects on HC transduction. Although we did not get positive results on this question in the mouse, we think the diVerence between models should be included as a significant part of the narrative.

eLife Assessment

This valuable study provides convincing evidence that mutant hair cells with abnormal, reversed polarity of their hair bundles in mouse otolith organs retain wild-type localization, mechanoelectrical transduction and firing properties of their afferent innervation, leading to mild behavioral dysfunction. It thus demonstrates that the bimodal pattern of afferent nerve projections in this organ is not causally related to the bimodal distribution of hair-bundle orientations, as also confirmed in the zebrafish lateral line. The work will be of interest to scientists interested in the development and function of the vestibular system as well as in planar-cell polarity.

Reviewer #1 (Public review):

Summary:

The authors aim at dissecting the relationship between hair-cell directional mechanosensation and orientation-linked synaptic selectivity, using mice and the zebrafish. They find that Gpr156 mutant animals homogenize the orientation of hair cells without affecting the selectivity of afferent neurons, suggesting that hair-cell orientation is not the feature that determines synaptic selectivity. Therefore, the process of Emx2-dependent synaptic selectivity bifurcates downstream of Gpr156.

Strengths:

This is an interesting and solid paper. It solves an interesting problem and establishes a framework for the following studies. That is, to ask what are the putative targets of Emx2 that affect synaptic selectivity.<br /> The quality of the data is generally excellent.

Weaknesses:

The feeling is that the advance derived from the results is very limited.

Comments on revised version:

I am happy with the authors' reply and do not wish to modify my initial assessment.

Reviewer #2 (Public Review):

Summary:

The authors inquire in particular whether the receptor Gpr156, which is necessary for hair cells to reverse their polarities in the zebrafish lateral line and mammalian otolith organs downstream of the differential expression of the transcription factor Emx2, also controls the mechanosensitive properties of hair cells and ultimately an animal's behavior. This study thoroughly addresses the issue by analyzing the morphology, electrophysiological responses, and afferent connections of hair cells found in different regions of the mammalian utricle and the Ca2+ responses of lateral line neuromasts in both wild-type animals and gpr156 mutants. Although many features of hair cell function are preserved in the mutants-such as development of the mechanosensory organs and the Emx2-dependent, polarity-specific afferent wiring and synaptic pairing-there are a few key changes. In the zebrafish neuromast, the magnitude of responses of all hair cells to water flow resembles that of the wild-type hair cells that respond to flow arriving from the tail. These responses are larger than those observed in hair cells that are sensitive to flow arriving from the head and resemble effects previously observed in Emx2 mutants. The authors note that this behavior suggests that the Emx2-GPR156 signaling axis also impinges on hair cell mechanotransduction. Although mutant mice exhibit normal posture and balance, they display defects in swimming behavior. Moreover, their vestibulo-ocular reflexes are perturbed. The authors note that the gpr156 mutant is a good model to study the role of opposing hair cell polarity in the vestibular system, for the wiring patterns follow the expression patterns of Emx2, even though hair cells are all of the same polarity. This paper excels at describing the effects of gpr156 perturbation in mouse and zebrafish models and will be of interest to those studying the vestibular system, hair cell polarity, and the role of inner-ear organs in animal behavior.

The study is exceptional in including, not only morphological and immunohistochemical indices of cellular identity but also electrophysiological properties. The mutant hair cells of murine maculæ display essentially normal mechanoelectrical transduction and adaptation-with two or even three kinetic components-as well as normal voltage-activated ionic currents.

Last year, scholar Sophie Perkel noted how the ambiguous “he”, who plagues this poem makes its final appearance in this line, “He who was living is now dead.” There is a finality of death, different from the amorphous cycle of life and rebirth that has plagued the poem thus far, as this “he” dies not only because Eliot wrote it so, but because “he” never appears again; the pronoun dies from the remaining stanzas. This finality is also emphasized by the following line, which follows a similar grammatical pattern, but differs in number of the subject (singular versus plural) and form of the final word (dead vs. dying). The two lines follow this grammatical structure: First person singular/plural - imperfect verb in relative clause - present verb - adjective/present participle. The imperfect verb is the most recent form of past tense, indicating a freshness to the living, and the present provides vividness for the current state of death. But death and dying have far more contrast: death is an adjective, used to directly describe and define “he”. There is no verby-ness in its form, instead more analogous to the noun death. Conversely, “we” are “dying”, a present participle, a verbal adjective meaning continuous action. In this moment of time we are still going through the process of dying, it is not yet complete. With this continuous form the first person pronoun continues to appear in alive use throughout the remainder. “We” might be dying, but we are not yet dead.

I am interested in the function of the title in concluding the poem. The “Apocalypse of John” is the final book in the New Testament and the “only apocalyptic document in the New Testament canon”. Considering the entire poem as one entity with multiple books, “What the Thunder Said” is the “Apocalypse” of the TWL canon. Curiously, the Greek definition of apokálypsis is “unveiling” or finding out a secret. There isn’t any dystopian world-ending connotation. Rather, apocalypse is a revelation, an intellectual rite of passage from past ignorance to present learning. This section can be read as the aftermath of the preceding four – thunder is the dreadful noise after a lighting strike, the product of water and fire and light (and chess…), the consequence of human sin and God’s anger.

Adapting the Greek definition of “apocalypse”, we discover new meaning in the beginning of ends. We wrongly assume the world is ending and the waste land is doomed to remain barren, when in fact, it is growing back “with a little patience”. Keats, as in “What The Trush Said”, would argue that “spring will be a harvest-time” for those “whose face hath felt the Winter's wind”, and those who face adversity such as “the torchlight red on sweaty faces”, the “frosty silence in the gardens”, or the “agony in stony places” are spiritually resurrected (Eliot). After winter, the bodies buried in the garden have sprouted, and the Thunder says we must hold on.

Note: a breakfast buddy of mine (who is much more well-versed in biblical lore) astutely observed that in the Book of Revelations, a bunch of people die. Will they be buried? Will the cycle of TWL repeat, infinitely throbbing between life, death, and rebirth?

The heading and first three lines of this section are an intricate reworking of the scene of the death of Jesus Christ in the Bible. In order to understand this collage of biblical material, the reader must first allow the title of the section to become the first line of the poem, which is grammatically sound as an antecedent before the list of prepositions in the following three lines beginning with “after.” With this in mind, we can turn to “thunder” as it appears in the heading for this section, where Eliot seems to render it its own character in the poem. We can identify the “thunder” as some sort of divine being. After Jesus’s death in the Book of John, it is written that “The crowd that stood there and heard it said that it had thundered” (John 12:29). Presumably, the thunder occurred by the hand of God, or represents the voice of God. In fact, Eliot’s God might represent more than just the Christian God. In Themis’s essay, she claims that “the thunderbolt was to the primitive Greek not the symbol or attribute of the god, but itself the divine thing, the embodiment and vehicle of the god” (Themis, 62). Given both religions justify the thunderbolt as a celestial voice of sorts, we continue to the following lines, which appear to take the form of the chronology of the death of Jesus as it appears in the book of John. First, the “torchlight” in “The Waste Land” surely references the Pharisees who Jesus himself describes as holding “torches” (John 18:3). In the second line, the “frosty silence in the gardens” is reflected in the biblical text when Jesus takes his “disciples over the brook Cedron, where was a garden” (John 18:1), the garden which is described as “cold” (John 18:18). “Silence” may refer to Peter when he “smote the high priest's servant, and cut off his right ear” (18:10). Then, “stone agony” likely nods to the flogging of Jesus in John 19:1. Given the grammatical mechanics of these lines, it appears that the setting of “What the Thunder Said” is Jerusalem, when the thunder sounded right after Jesus died. In other words, perhaps the text that follows is through the voice of God, an interesting lens through which to approach the section.

In his annotation from last year, Parth draws a very interesting parallel between the onomatopoeic dripping of water and atonal music: “this onomatopoeia…lacks a concrete framework with which the notes—"drip" and "drop"—arrange themselves, nor does it have a "triad" that the notes "drip" and "drop" must return to.” Parth goes on to interpret the atonality of water as related to purity and constancy. Building on-bending, if you will-Parth’s analysis, an interesting added dimension here is that, if the grounded reality of this section is atonal, the portions where water is hypothetically present induces a more perplexing state because we are goaded with the almost tangible yet unrealizable presence of abundant water. Music traditionally has key because it is pleasant and reassuring for eon-conditioned human ears and consciousnesses. So, by creating tension between atonally dripping water and the promise of abundant water, Eliot casts the presence of water as a more abstract and less certain entity.

This interpretation has some logical basis because, unlike atonal music which is characterizable despite being disorienting at first, the water here only exists in the mind of the speaker who is yearning for hydration, ostensibly beyond the absolute comprehension of even the most astute scholar in a similar way to how the water is beyond the speaker’s scope of consumption. This taunting absence is extended even to sweat, which is somehow “dry” and “sterile” thunder which offers the promise of torrential rain and salvation but never actually offers such a luxury. Also, in analyzing the promise of water, the contrast between it–the most essential substance for human life and rocks–rather useless objects–is also quite interesting. Rocks are quite abundant but “Amongst the rock one cannot stop or think.” Thus, at an obvious level, a broader interpretation here is that Eliot is highlighting the criticality of certain substances to human existence and vividly illustrating the unique torture of not having access to them through the creation of a hypothetical. However, an added layer of nuance can be unlocked by contrasting Eliot with Keats.

Of course, Keats’ work is a more optimistic one in that it casts nature as offering realizable solace while Eliot creates a dynamic verging on torture. More interesting, however, is the contrasting messages on renewal that emerge. Despite not necessarily providing detailed reasoning, indeed rejecting convoluted thought processes generally–as he writes to “fret not after knowledge–I have none”–Keats suggests the presence of some larger force that will assure renewal and ensure that “spring will be a harvest-time.” Eliot, on the other hand, offers an abundance of twisting, looping, and otherwise confusing thoughts on water’s presence, or the lack thereof, and by extension suggests that renewal, which requires water, is not guaranteed. In TWL, there is only the rawness of nature and the speaker, no larger force that allows one to exist in the bliss of having no knowledge and believing in external forces.

Tying this message on optimism back into my preceding analysis on Eliot’s gauding with water in a wasteland where there is no water–interspersing comfort-inducing tone-into atonal music–the more profound commentary of this section seems to be that humans exist in an inherently frail state where the lack of even the simplest substance of water–that is taken for granted with increasing frequency as development grows–can render us impotent and open to the emotional manipulation that the speaker in this section suffers as he yearns for water, a reflection of his physical suffering.

Fact Verification System

Important video for the lecture

eLife Assessment

The authors describe a model for tracking time-varying functional connectivity between neurons from multi-electrode spike recordings. This is an interesting and potentially useful approach to an open problem in neural data analysis, and could be an essential tool for investigating the neural code from large-scale in-vivo recordings of spiking activity. However, the evidence is incomplete: systematic comparisons with existing methods and/or demonstration of its utility relative to conventional methods are essential to demonstrate the usefulness of the method.

Reviewer #1 (Public review):

Summary:

This work proposes a new method, DyNetCP, for inferring dynamic functional connectivity between neurons from spike data. DyNetCP is based on a neural network model with a two-stage model architecture of static and dynamic functional connectivity.<br /> This work evaluates the accuracy of the synaptic connectivity inference and shows that DyNetCP can infer the excitatory synaptic connectivity more accurately than a state-of-the-art model (GLMCC) by analyzing the simulated spike trains. Furthermore, it is shown that the inference results obtained by DyNetCP from large-scale in-vivo recordings are similar to the results obtained by the existing methods (jitter-corrected CCG and JPSTH). Finally, this work investigates the dynamic connectivity in the primary visual area VISp and in the visual areas using DyNetCP.

Strengths:

The strength of the paper is that it proposes a method to extract the dynamics of functional connectivity from spike trains of multiple neurons. The method is potentially useful for analyzing parallel spike trains in general, as there are only a few methods (e.g. Aertsen et al., J. Neurophysiol., 1989, Shimazaki et al., PLoS Comput Biol 2012) that infer the dynamic connectivity from spikes. Furthermore, the approach of DyNetCP is different from the existing methods: while the proposed method is based on the neural network, the previous methods are based on either the descriptive statistics (JSPH) or the Ising model.

Weaknesses:

Although the paper proposes a new method, DyNetCP, for inferring the dynamic functional connectivity, its strengths are neither clear nor directly demonstrated in this paper. That is, insufficient analyses are performed to support the usefulness of DyNetCP.<br /> First, this paper attempts to show the superiority of DyNetCP by comparing the performance of synaptic connectivity inference with GLMCC (Fig. 2). However, the improvement in the synaptic connectivity inference does not seem to be convincing. While this paper compares the performance of DyNetCP with a state-of-the-art method (GLMCC), there are several problems with the comparison. For example,

(1) It is unclear how accurately the proposed method can infer the dynamic connectivity.<br /> (2) This paper does not compare with existing approach (e.g., classical JPSTH: Aertsen et al., J. Neurophysiol., 1989, and other methods : Stevenson and Koerding, NIPS, 2011; Linderman et al., NIPS, 2014; Song et al., J. Neurosci. Methods, 2015), and<br /> (3) only a population of neurons generated from the Hodgkin-Huxley model was evaluated.

Thus, the results in this paper are not sufficient to conclude the superiority of DyNetCP in the estimation of synaptic connections. In addition, this paper compares the proposed method with the standard statistical methods Jitter-corrected CCG (Fig. 3) and JPSTH (Fig. 4). Unfortunately, these results do not show the superiority of the proposed method. It only shows that the results obtained by the proposed method are consistent with those obtained by the existing methods (CCG or JPSTH). This paper also compares the proposed method with the standard statistical methods, such as jitter-corrected CCG (Fig. 3) and JPSTH (Fig. 4). It only shows that the results obtained by the proposed method are consistent with those obtained by the existing methods (CCG or JPSTH), which does not show the superiority of the proposed method.

In summary, although DyNetCP has the potential to infer the dynamic (time-dependent) correlation more accurately than existing methods, the paper does not provide sufficient analysis to make this claim. It is also unclear whether the proposed method is superior to the existing methods for estimating functional connectivity, such as JPSTH and statistical approach (Stevenson and Koerding, NIPS, 2011; Linderman et al., NIPS, 2014). Thus, the strength of DyNetCP is unclear.

Reviewer #2 (Public review):

Summary:

Here the authors describe a model for tracking time-varying coupling between neurons from multi-electrode spike recordings. Their approach extends a GLM with static coupling between neurons to include dynamic weights, learned by a long-short-term-memory (LSTM) model. Each connection has a corresponding LSTM embedding and is read-out by a multi-layer perceptron to predict the time-varying weight.

Strengths:

This is an interesting approach to an open problem in neural data analysis. I think, in general, the method would be interesting to computational neuroscientists.

Weaknesses:

It is somewhat difficult to interpret what the model is doing. I think it would be worthwhile to add some additional results that make it more clear what types of patterns are being described and how.

Major Issues:

Simulation for dynamic connectivity. It certainly seems doable to simulate a recurrent spiking network whose weights change over time, and I think this would be a worthwhile validation for this DyNetCP model. In particular, I think it would be valuable to understand how much the model overfits, and how accurately it can track known changes in coupling strength. If the only goal is "smoothing" time-varying CCGs, there are much easier statistical methods to do this (c.f. McKenzie et al. Neuron, 2021. Ren, Wei, Ghanbari, Stevenson. J Neurosci, 2022), and simulations could be useful to illustrate what the model adds beyond smoothing.

Stimulus vs noise correlations. For studying correlations between neurons in sensory systems that are strongly driven by stimuli, it's common to use shuffling over trials to distinguish between stimulus correlations and "noise" correlations or putative synaptic connections. This would be a valuable comparison for Fig 5 to show if these are dynamic stimulus correlations or noise correlations. I would also suggest just plotting the CCGs calculated with a moving window to better illustrate how (and if) the dynamic weights differ from the data.

Minor Issues:

Introduction - it may be useful to mention that there have been some previous attempts to describe time-varying connectivity from spikes both with probabilistic models: Stevenson and Kording, Neurips (2011), Linderman, Stock, and Adams, Neurips (2014), Robinson, Berger, and Song, Neural Computation (2016), Wei and Stevenson, Neural Comp (2021) ... and with descriptive statistics: Fujisawa et al. Nat Neuroscience (2008), English et al. Neuron (2017), McKenzie et al. Neuron (2021).

In the sections "Static DyNetCP ...reproduce". It may be useful to have some additional context to interpret the CCG-DyNetCP correlations and CCG-GLMCC correlations (for simulation). If I understand right, these are on training data (not cross-validated) and the DyNetCP model is using NM+1 parameters to predict ~100 data points (It would also be good to say what N and M are for the results here). The GLMCC model has 2 or 3 parameters (if I remember right?).

In the section "Static connectivity inferred by the DyNetCP from in-vivo recordings is biologically interpretable"... I may have missed it, but how is the "functional delay" calculated? And am I understanding right that for the DyNetCP you are just using [w_i\toj, w_j\toi] in place of the CCG?

Author response:

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

We thank the reviewers for the constructive criticism and detailed assessment of our work which helped us to significantly improve our manuscript. We made significant changes to the text to better clarify our goals and approaches. To make our main goal of extracting the network dynamics clearer and to highlight the main advantage of our method in comparison with prior work we incorporated Videos 1-4 into the main text. We hope that these changes, together with the rest of our responses, convincingly demonstrate the utility of our method in producing results that are typically omitted from analysis by other methods and can provide important novel insights on the dynamics of the brain circuits. 

Reviewer #1 (Public Review):

(1) “First, this paper attempts to show the superiority of DyNetCP by comparing the performance of synaptic connectivity inference with GLMCC (Figure 2).”

We believe that the goals of our work were not adequately formulated in the original manuscript that generated this apparent misunderstanding. As opposed to most of the prior work focused on reconstruction of static connectivity from spiking data (including GLMCC), our ultimate goal is to learn the dynamic connectivity structure, i.e. to extract time-dependent strength of the directed connectivity in the network. Since this formulation is fundamentally different from most of the prior work, therefore the goal here is not to show the “improvement” or “superiority” over prior methods that mostly focused on inference of static connectivity, but rather to thoroughly validate our approach and to show its usefulness for the dynamic analysis of experimental data. 

(2) “This paper also compares the proposed method with standard statistical methods, such as jitter-corrected CCG (Figure 3) and JPSTH (Figure 4). It only shows that the results obtained by the proposed method are consistent with those obtained by the existing methods (CCG or JPSTH), which does not show the superiority of the proposed method.”

The major problem for designing such a dynamic model is the virtual absence of ground-truth data either as verified experimental datasets or synthetic data with known time-varying connectivity. In this situation optimization of the model hyper-parameters and model verification is largely becoming a “shot in the dark”. Therefore, to resolve this problem and make the model generalizable, here we adopted a two-stage approach, where in the first step we learn static connections followed in the next step by inference of temporally varying dynamic connectivity. Dividing the problem into two stages enables us to separately compare the results of both stages to traditional descriptive statistical approaches. Static connectivity results of the model obtained in stage 1 are compared to classical pairwise CCG (Fig.2A,B) and GLMCC (Fig.2 C,D,E), while dynamic connectivity obtained in step 2 are compared to pairwise JPSTH (Fig.4D,E).

Importantly, the goal here therefore is not to “outperform” the classical descriptive statistical or any other approaches, but rather to have a solid guidance for designing the model architecture and optimization of hyper-parameters. For example, to produce static weight results in Fig.2A,B that are statistically indistinguishable from the results of classical CCG, the procedure for the selection of weights which contribute to averaging is designed  as shown in Fig.9 and discussed in details in the Methods. Optimization of the L2 regularization parameter is illustrated in Fig.4 – figure supplement 1 that enables to produce dynamic weights very close to cJPSTH as evidenced by Pearson coefficient and TOST statistical tests. These comparisons demonstrate that indeed the results of CCG and JPSTH are faithfully reproduced by our model that, we conclude, is sufficient justification to apply the model to analyze experimental results. 

(3) “However, the improvement in the synaptic connectivity inference does not seem to be convincing.”

We are grateful for the reviewer to point out to this issue that we believe, as mentioned above, results from the deficiency of the original manuscript to clarify the major motivation for this comparison. Comparison of static connectivity inferred by stage 1 of our model to the results of GLMCC in Fig.2C,D,E is aimed at optimization of yet another two important parameters - the pair spike threshold and the peak height threshold. Here, in Fig. 2D we show that when the peak height threshold is reduced from rigorous 7 standard deviations (SD) to just 5 SD, our model recovers 74% of the ground truth connections that in fact is better than 69% produced by GLMCC for a comparable pair spike threshold of 80. As explained above, we do not intend to emphasize here that our model is “superior” since it was not our goal, but rather use this comparison to illustrate the approach for optimization of thresholds for units and pairs filtering as described in detail in Fig. 11 and corresponding section in Methods.

To address these misunderstandings and better clarify the goal of our work we changed the text in the Introductory section accordingly. We also incorporated Videos 1-4 from the Supplementary Materials into the main text as Video 1, Video 2, Video 3, and Video 4. In fact, these videos represent the main advantage (or “superiority”) of our model with respect to prior art that enables to infer the time-dependent dynamics of network connectivity as opposed to static connections.

(4) “While this paper compares the performance of DyNetCP with a state-of-the-art method (GLMCC), there are several problems with the comparison. For example: 

(a) This paper focused only on excitatory connections (i.e., ignoring inhibitory neurons). 

(b) This paper does not compare with existing neural network-based methods (e.g., CoNNECT: Endo et al. Sci. Rep. 2021; Deep learning: Donner et al. bioRxiv, 2024).

(c) Only a population of neurons generated from the Hodgkin-Huxley model was evaluated.”

(a) In general, the model of Eq.1 is agnostic to excitatory or inhibitory connections it can recover. In fact, Fig. 5 and Fig.6 illustrate inferred dynamic weights for both excitatory (red arrows) and inhibitory (blue arrows) connections between excitatory (red triangles) and inhibitory (blue circles) neurons. Similarly, inhibitory and excitatory dynamic interactions between connections are represented in Fig. 7 for the larger network across all visual cortices.

(b) As stated above, the goal for the comparison of the static connectivity results of stage 1 of our model to other approaches is to guide the choice of thresholds and optimization of hyperparameters rather than claiming “superiority” of our model. Therefore, comparison with “static” CNN-based model of Endo et al. or ANN-based static model of Donner et al. (submitted to bioRxiv several months after our submission to eLife) is beyond the scope of this work. 

(c) We have chosen exactly the same sub-population of neurons from the synthetic HH dataset of Ref. 26 that is used in Fig.6 of Ref. 26 that provides direct comparison of connections reconstructed by GLMCC in the original Ref.26 and the results of our model. 

(5) “In summary, although DyNetCP has the potential to infer synaptic connections more accurately than existing methods, the paper does not provide sufficient analysis to make this claim. It is also unclear whether the proposed method is superior to the existing methods for estimating functional connectivity, such as jitter-corrected CCG and JPSTH. Thus, the strength of DyNetCP is unclear.”

As we explained above, we have no intention to claim that our model is more accurate than existing static approaches. In fact, it is not feasible to have better estimation of connectivity than direct descriptive statistical methods as CCG or JPSTH. Instead, comparison with static (CCG and GLMCC) and temporal (JPSTH) approaches are used here to guide the choice of the model thresholds and to inform the optimization of hyper-parameters to make the prediction of the dynamic network connectivity reliable. The main strength of DyNetCP is inference of dynamic connectivity as illustrated in Videos 1-4. We demonstrated the utility of the method on the largest in-vivo experimental dataset available today and extracted the dynamics of cortical connectivity in local and global visual networks. This information is unattainable with any other contemporary methods we are aware of. 

Reviewer #1 (Recommendations for the Authors):

(6) “First, the authors should clarify the goal of the analysis, i.e., to extract either the functional connectivity or the synaptic connectivity. While this paper assumes that they are the same, it should be noted that functional connectivity can be different from synaptic connectivity (see Steavenson IH, Neurons Behav. Data Anal. Theory 2023).”

The goal of our analysis is to extract dynamics of the spiking correlations. In this paper we intentionally avoided assigning a biological interpretation to the inferred dynamic weights. Our goal was to demonstrate that a trough of additional information on neural coding is hidden in the dynamics of neural correlations. The information that is typically omitted from the analysis of neuroscience data. 

Biological interpretation of the extracted dynamic weights can follow the terminology of the shortterm plasticity between synaptically connected neurons (Refs 25, 33-37) or spike transmission strength (Refs 30-32,46). Alternatively, temporal changes in connection weights can be interpreted in terms of dynamically reconfigurable functional interactions of cortical networks (Refs 8-11,13,47) through which the information is flowing. We could not also exclude interpretation that combines both ideas. In any event our goal here is to extract these signals for a pair (video1, Fig.4), a cortical local circuit (Video 2, Fig.5), and for the whole visual cortical network (Videos 3, 4 and Fig.7). 

To clarify this statement, we included a paragraph in the discussion section of the revised paper. 

(7) “Finally, it would be valuable if the authors could also demonstrate the superiority of DyNetCP qualitatively. Can DyNetCP discover something interesting for neuroscientists from the large-scale in vivo dataset that the existing method cannot?”

The model discovers dynamic time-varying changes in neuron synchronous spiking (Videos 1-4) that more traditional methods like CCG or GLMCC are not able to detect. The revealed dynamics is happening at the very short time scales of the order of just a few ms during the stimulus presentation. Calculations of the intrinsic dimensionality of the spiking manifold (Fig. 8) reveal that up to 25 additional dimensions of the neural code can be recovered using our approach. These dimensions are typically omitted from the analysis of the neural circuits using traditional methods.  

Reviewer #2 (Public Review):

(1) “Simulation for dynamic connectivity. It certainly seems doable to simulate a recurrent spiking network whose weights change over time, and I think this would be a worthwhile validation for this DyNetCP model. In particular, I think it would be valuable to understand how much the model overfits, and how accurately it can track known changes in coupling strength.”

We are very grateful to the reviewer for this insight. Verification of the model on synthetic data with known time-varying connectivity would indeed be very useful. We did generate a synthetic dataset to test some of the model performance metrics - i.e. testing its ability to distinguish True Positive (TP) from False Positive (FP) “serial” or “common input” connections (Fig.10A,B). Comparison of dynamic and static weights might indeed help to distinguish TP connections from an artifactual FP connections. 

Generating a large synthetic dataset with known dynamic connections that mimics interactions in cortical networks is, however, a separate and not very trivial task that is beyond the scope of this work. Instead, we designed a model with an architecture where overfitting can be tested in two consecutive stages by comparison with descriptive statistical approaches – CCG and JPSTH. Static stage 1 of the model predicts correlations that are statistically indistinguishable from the CCG results (Fig.2A,B). The dynamic stage 2 of the model produce dynamic weight matrices that faithfully reproduce the cJPSTH (Fig.4D,E). Calculated Pearson correlation coefficients and TOST testing enable optimizing the L2 regularization parameter as shown in Fig.4 – supplement 1 and described in detail in the Methods section. The ability to test results of both stages separately to descriptive statistical results is the main advantage of the chosen model architecture that allow to verify that the model does not overfit and can predict changes in coupling strength at least as good as descriptive statistical approaches (see also our answer above to the Reviewer #1 questions).

(2) “If the only goal is "smoothing" time-varying CCGs, there are much easier statistical methods to do this (c.f. McKenzie et al. Neuron, 2021. Ren, Wei, Ghanbari, Stevenson. J Neurosci, 2022), and simulations could be useful to illustrate what the model adds beyond smoothing.”

We are grateful to the reviewer for bringing up these very interesting and relevant references that we added to the discussion section in the paper. Especially of interest is the second one, that is calculating the time-varying CCG weight (“efficacy” in the paper terms) on the same Allen Institute Visual dataset as our work is using. It is indeed an elegant way to extract time-variable coupling strength that is similar to what our model is generating. The major difference of our model from that of Ren et al., as well as from GLMCC and any statistical approaches is that the DyNetCP learns connections of an entire network jointly in one pass, rather than calculating coupling separately for each pair in the dataset without considering the relative influence of other pairs in the network. Hence, our model can infer connections beyond pairwise (see Fig. 11 and corresponding discussion in Methods) while performing the inferences with computational efficiency. 

(3) “Stimulus vs noise correlations. For studying correlations between neurons in sensory systems that are strongly driven by stimuli, it's common to use shuffling over trials to distinguish between stimulus correlations and "noise" correlations or putative synaptic connections. This would be a valuable comparison for Figure 5 to show if these are dynamic stimulus correlations or noise correlations. I would also suggest just plotting the CCGs calculated with a moving window to better illustrate how (and if) the dynamic weights differ from the data.”

Thank you for this suggestion. Note that for all weight calculations in our model a standard jitter correction procedure of Ref. 33 Harrison et al., Neural Com 2009 is first implemented to mitigate the influences of correlated slow fluctuations (slow “noise”). Please also note that to obtain the results in Fig. 5 we split the 440 total experimental trials for this session (when animal is running, see Table 1) randomly into 352 training and 88 validation trials by selecting 44 training trials from each configuration of contrast or grating angle and 11 for validation. We checked that this random selection, if changed, produced the very same results as shown in Fig.5. 

Comparison of descriptive statistical results of pairwise cJPSTH and the model are shown in Fig. 4D,E. The difference between the two is characterized in Fig.4 – supplement 1 in detail as evidenced by Pearson coefficient and TOST statistical tests.

Reviewer #2 (Recommendations for the Authors):

(4) “The method is described as "unsupervised" in the abstract, but most researchers would probably call this "supervised" (the static model, for instance, is logistic regression).”

The model architecture is composed of two stages to make parameter optimization grounded. While the first stage is regression, the second and the most important stage is not. Therefore, we believe the term “unsupervised” is justified. 

(5) “Introduction - it may be useful to mention that there have been some previous attempts to describe time-varying connectivity from spikes both with probabilistic models: Stevenson and Kording, Neurips (2011), Linderman, Stock, and Adams, Neurips (2014), Robinson, Berger, and Song, Neural Computation (2016), Wei and Stevenson, Neural Comp (2021) ... and with descriptive statistics: Fujisawa et al. Nat Neuroscience (2008), English et al. Neuron (2017), McKenzie et al. Neuron (2021).”

We are very grateful to both reviewers for bringing up these very interesting and relevant references that we gladly included in the discussions within the Introduction and Discussion sections. 

(6) “In the section "Static connectivity inferred by the DyNetCP from in-vivo recordings is biologically interpretable"... I may have missed it, but how is the "functional delay" calculated? And am I understanding right that for the DyNetCP you are just using [w_i\toj, w_j\toi] in place of the CCG?”

The functional delay is calculated as a time lag of the maximum (or minimum) in the CCG (or static weight matrix). The static weight that the model is extracting is indeed the wiwj product. We changed the text in this section to better clarify these definitions. 

(7) “P14 typo "sparce spiking" sparse”

Fixed. Thank you. 

(8) “Suggest rewarding "Extra-laminar interactions reveal formation of neuronal ensembles with both feedforward (e.g., layer 4 to layer 5), and feedback (e.g., layer 5 to layer 4) drives." I'm not sure this method can truly distinguish common input from directed, recurrent cortical effects. Just as an example in Figure 5, it looks like 2->4, 0->4, and 3>2 are 0 lag effects. If you wanted to add the "functional delay" analysis to this laminar result that could support some stronger claims about directionality, though.”

The time lags for the results of Fig. 5 are indeed small, but, however, quantifiable. Left panel Fig. 5A shows static results with the correlation peaks shifted by 1ms from zero lag.

(9) “Methods - I think it would be useful to mention how many parameters the full DyNetCP model has.”

Overall, after the architecture of Fig.1C is established, dynamic weight averaging procedure is selected (Fig.9), and Fourier features are introduced (Fig.10), there is just a few parameters to optimize including L2 regularization (Fig.4 – supplement 1) and loss coefficient  (Fig.1 – figure supplement 1A). Other variables, common for all statistical approaches, include bin sizes in the lag time and in the trial time. Decreasing the bin size will improve time resolution while decreasing the number of spikes in each bin for reliable inference. Therefore, number of spikes threshold and other related thresholds α𝑠 , α𝑤 , α𝑝 as well as λ𝑖λ𝑗, need to be adjusted accordingly (Fig.11) as discussed in detail in the Methods, Section 4. We included this sentence in the text. 

(10) “It may be useful to also mention recent results in mice (Senzai et al. Neuron, 2019) and monkeys (Trepka...Moore. eLife, 2022) that are assessing similar laminar structures with CCGs.”

Thank you for pointing out these very interesting references. We added a paragraph in “Dynamic connectivity in VISp primary visual area” section comparing our results with these findings. In short, we observed that connections are distributed across the cortical depth with nearly the same maximum weights (Fig.7A) that is inconsistent with observed in Trepka et al, 2022 greatly diminished static connection efficacy within <200µm from the source. It is consistent, however, with the work of Senzai et al, 2019 that reveals much stronger long-distance correlations between layer 2/3 and layer 5 during waking in comparison to sleep states. In both cases these observations represent static connections averaged over a trial time, while the results presented in Video 3 and Fig.7A show strong temporal modulation of the connection strength between all the layers during the stimulus presentation. Therefore, our results demonstrate that tracking dynamic connectivity patterns in local cortical networks can be invaluable in assessing circuitlevel dynamic network organization.

eLife Assessment

This work provides an important and novel framework for interpreting the interactions between recurrent dynamics across stages of neural processing. The authors report that two different kinds of dynamics exist in recurrent networks differing in the extent to which they align with the output weights. The authors also present convincing evidence that both types of dynamics exist in the brain.

Reviewer #1 (Public review):

Summary:

In this work, authors utilize recurrent neural networks (RNNs) to explore the question of when and how neural dynamics and the network's output are related from a geometrical point of view. The authors found that RNNs operate between two extremes: an 'aligned' regime in which the weights and the largest PCs are strongly correlated and an 'oblique' regime where the output weights and the largest PCs are poorly correlated. Large output weights led to oblique dynamics, and small output weights to aligned dynamics. This feature impacts whether networks are robust to perturbation along output directions. Results were linked to experimental data by showing that these different regimes can be identified in neural recordings from several experiments.

Strengths:

Diverse set of relevant tasks<br /> Similarity measure well chosen<br /> Explored various hyperparameter settings

Weaknesses:

One of the major connections to found BCI data with neural variance aligned to the outputs. Maybe I was confused about something, but doesn't this have to be the case based on the design of the experiment? The outputs of the BCI are chosen to align with the largest principal components of the data.

Proposed experiments maybe have already been done (New neural activity patterns emerge with long-term learning, Oby et al. 2019). My understanding of these results is that activity moved to be aligned as the manifold changed, but more analyses could be done to more fully understand the relationship between those experiments and this work.

Analysis of networks was thorough, but connections to neural data were weak. I am thoroughly convinced of the reported effect of large or small output weights in networks. I also think this framing could aid in future studies of interactions between brain regions.

This is an interesting framing to consider the relationship between upstream activity and downstream outputs. As more labs record from several brain regions simultaneously, this work will provide an important theoretical framework for thinking about the relative geometries of neural representations between brain regions.

It will be interesting to compare the relationship between geometries of representations and neural dynamics across connected different brain areas that are closer to the periphery vs. more central.

Exciting to think about the versatility of the oblique regime for shared representations and network dynamics across different computations.

Versatility of oblique regime could lead to differences between subjects in neural data.

Reviewer #2 (Public review):

Summary:

This paper tackles the problem of understanding when the dynamics of neural population activity do and do not align with some target output, such as an arm movement. The authors develop a theoretical framework based on RNNs showing that an alignment of neural dynamics to an output can be simply controlled by the magnitude of the read-out weight vector while the RNN is being trained: small magnitude vectors result in aligned dynamics, where low-dimensional neural activity recapitulates the target; large magnitude vectors result in "oblique" dynamics, where encoding is spread across many dimensions. The paper further explores how the aligned and oblique regimes differ, in particular that the oblique regime allows degenerate solutions for the same target output.

Strengths:

- A really interesting new idea that different dynamics of neural circuits can arise simply from the initial magnitude of the output weight vector: once written out (Eq 3) it becomes obvious, which I take as the mark of a genuinely insightful idea

- The offered framework potentially unifies a collection of separate experimental results and ideas, largely from studies of motor cortex in primate: the idea that much of the ongoing dynamics do not encode movement parameters; the existence of the "null space" of preparatory activity; and that ongoing dynamics of motor cortex can rotate in the same direction even when the arm movement is rotating in opposite directions.

- The main text is well written, with a wide-ranging set of key results synthesised and illustrated well and concisely

- Shows the occurrence of the aligned and oblique regimes generalises across a range of simulated behavioural tasks

- A deep analytical investigation of when the regimes occur and how they evolve over training

- Shows where the oblique regime may be advantageous: allows multiple solutions to the same problem; and differs in sensitivity to perturbation and noise

- An insightful corollary result that noise in training is needed to obtain the oblique regime

- Tests whether the aligned and oblique regimes can be seen in neural recordings from primate cortex in a range of motor control tasks

- The revised text offers greater clarity and precision about when the aligned and oblique regimes occur and in the interpretation of the analyses of neural data

Weaknesses:

- The depth of analytical treatment in the Methods is impressive; however, the paper and the Methods analyses are largely independent, with the numerous results in the latter not being mentioned in the Results or Discussion. It in effect operates as two papers.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this work, the authors utilize recurrent neural networks (RNNs) to explore the question of when and how neural dynamics and the network's output are related from a geometrical point of view. The authors found that RNNs operate between two extremes: an 'aligned' regime in which the weights and the largest PCs are strongly correlated and an 'oblique' regime where the output weights and the largest PCs are poorly correlated. Large output weights led to oblique dynamics, and small output weights to aligned dynamics. This feature impacts whether networks are robust to perturbation along output directions. Results were linked to experimental data by showing that these different regimes can be identified in neural recordings from several experiments.

Strengths:

A diverse set of relevant tasks.

A well-chosen similarity measure.

Exploration of various hyperparameter settings.

Weaknesses:

One of the major connections found BCI data with neural variance aligned to the outputs.

Maybe I was confused about something, but doesn't this have to be the case based on the design of the experiment? The outputs of the BCI are chosen to align with the largest principal components of the data.

The reviewer is correct. We indeed expected the BCI experiments to yield aligned dynamics. Our goal was to use this as a comparison for other, non-BCI recordings in which the correlation is smaller, i.e. dynamics closer to the oblique regime. We adjusted our wording accordingly and added a small discussion at the end of the experimental results, Section 2.6.

Proposed experiments may have already been done (new neural activity patterns emerge with long-term learning, Oby et al. 2019). My understanding of these results is that activity moved to be aligned as the manifold changed, but more analyses could be done to more fully understand the relationship between those experiments and this work.

The on- vs. off-manifold experiments are indeed very close to our work. On-manifold initializations, as stated above, are expected to yield aligned solutions. Off-manifold initializations allow, in principle, for both aligned and oblique solutions and are thus closer to our RNN simulations. If, during learning, the top PCs (dominant activity) rotate such that they align with the pre-defined output weights, then the system has reached an aligned solution. If the top PCs hardly change, and yet the behavior is still good, this is an oblique solution. There is some indication of an intermediate result (Figure 4C in Oby et al.), but the existing analysis there did not fully characterize these properties. Furthermore, our work suggests that systematically manipulating the norm of readout weights in off-manifold experiments can yield new insights. We thus view these as relevant results but suggest both further analysis and experiments. We rewrote the corresponding section in the discussion to include these points.

Analysis of networks was thorough, but connections to neural data were weak. I am thoroughly convinced of the reported effect of large or small output weights in networks. I also think this framing could aid in future studies of interactions between brain regions.

This is an interesting framing to consider the relationship between upstream activity and downstream outputs. As more labs record from several brain regions simultaneously, this work will provide an important theoretical framework for thinking about the relative geometries of neural representations between brain regions.

It will be interesting to compare the relationship between geometries of representations and neural dynamics across connected different brain areas that are closer to the periphery vs. more central.

It is exciting to think about the versatility of the oblique regime for shared representations and network dynamics across different computations.

The versatility of the oblique regime could lead to differences between subjects in neural data.

Thank you for the suggestions. Indeed, this is precisely why relative measures of the regime are valuable, even in the absence of absolute thresholds for regimes. We included your suggestions in the discussion.

Reviewer #2 (Public Review):

Summary:

This paper tackles the problem of understanding when the dynamics of neural population activity do and do not align with some target output, such as an arm movement. The authors develop a theoretical framework based on RNNs showing that an alignment of neural dynamics to output can be simply controlled by the magnitude of the read-out weight vector while the RNN is being trained. Small magnitude vectors result in aligned dynamics, where low-dimensional neural activity recapitulates the target; large magnitude vectors result in "oblique" dynamics, where encoding is spread across many dimensions. The paper further explores how the aligned and oblique regimes differ, in particular, that the oblique regime allows degenerate solutions for the same target output.

Strengths:

- A really interesting new idea that different dynamics of neural circuits can arise simply from the initial magnitude of the output weight vector: once written out (Eq 3) it becomes obvious, which I take as the mark of a genuinely insightful idea.

- The offered framework potentially unifies a collection of separate experimental results and ideas, largely from studies of the motor cortex in primates: the idea that much of the ongoing dynamics do not encode movement parameters; the existence of the "null space" of preparatory activity; and that ongoing dynamics of the motor cortex can rotate in the same direction even when the arm movement is rotating in opposite directions.

- The main text is well written, with a wide-ranging set of key results synthesised and illustrated well and concisely.

- The study shows that the occurrence of the aligned and oblique regimes generalises across a range of simulated behavioural tasks.

- A deep analytical investigation of when the regimes occur and how they evolve over training.

- The study shows where the oblique regime may be advantageous: allows multiple solutions to the same problem; and differs in sensitivity to perturbation and noise.

- An insightful corollary result that noise in training is needed to obtain the oblique regime.

- Tests whether the aligned and oblique regimes can be seen in neural recordings from primate cortex in a range of motor control tasks.

Weaknesses:

- The magnitude of the output weights is initially discussed as being fixed, and as far as I can tell all analytical results (sections 4.6-4.9) also assume this. But in all trained models that make up the bulk of the results (Figures 3-6) all three weight vectors/matrices (input, recurrent, and output) are trained by gradient descent. It would be good to see an explanation or results offered in the main text as to why the training always ends up in the same mapping (small->aligned; large->oblique) when it could, for example, optimise the output weights instead, which is the usual target (e.g. Sussillo & Abbott 2009 Neuron).

We understand the reviewer’s surprise. We chose a typical setting (training all weights of an RNN with Adam) to show that we don’t have to fine-tune the setting (e.g. by fixing the output weights) to see the two regimes. However, other scenarios in which the output weights do change are possible, depending on the algorithm and details in the way the network is parameterized. Understanding why some settings lead to our scenario (no change in scale) and others don’t is not a simple question. A short explanation here, nonetheless:

- Small changes to the internal weights are sufficient to solve the tasks.

- Different versions of gradient descent and different ways of parametrizing the network lead to different results in which parts of the weights get trained. This goes in particular for how weight scales are introduced, e.g. [Jacot et al. 2018 Neurips], [Geiger et al. 2020 Journal of Statistical Mechanics], or [Yang, Hu 2020, arXiv, Feature learning in infinite-width networks]. One insight from these works is that plain gradient descent (GD) with small output weights leads to learning only at the output (and often divergence or unsuccessful learning). For this reason, plain GD (or stochastic GD) is not suitable for small output weights (the aligned regime). Other variants of GD, such as Adam or RMSprop, don’t have this problem because they shift the emphasis of learning to the hidden layers (here the recurrent weights). This is due to the normalization of the gradients.

- FORCE learning [Sussillo & Abbott 2009] is somewhat special in that the output weights are simultaneously also used as feedback weights. That is, not only the output weights but also an additional low-rank feedback loop through these output weights is trained. As a side note: By construction, such a learning algorithm thus links the output directly to the internal dynamics, so that one would only expect aligned solutions – and the output weights remain correspondingly small in these algorithms [Mastrogiuseppe, Ostojic, 2019, Neural Comp].

- In our setting, the output is not fed back to the network, so training the output alone would usually not suffice. Indeed, optimizing just the output weights is similar to what happens in the lazy training regime. These solutions, however, are not robust to noise, and we show that adding noise during the training does away with these solutions.

To address this issue in the manuscript, we added the following sentence to section 2.2: “While explaining this observation is beyond the scope of this work, we note that (1) changing the internal weights suffices to solve the task, and that (2) the extent to which the output weights change during learning depends on the algorithm and specific parametrization [21, 27, 85].”

- It is unclear what it means for neural activity to be "aligned" for target outputs that are not continuous time-series, such as the 1D or 2D oscillations used to illustrate most points here.

Two of the modeled tasks have binary outputs; one has a 3-element binary vector.

For any dynamics and output, we compare the alignment between the vector of output weights and the main PCs (the leading component of the dynamics). In the extreme of binary internal dynamics, i.e., two points {x_1, x_2}, there would only be one leading PC (the line connecting the two points, i.e. the choice decoder).

- It is unclear what criteria are used to assign the analysed neural data to the oblique or aligned regimes of dynamics.

Such an assignment is indeed difficult to achieve. The RNN models we showed were at the extremes of the two regimes, and these regimes are well characterized in the case of large networks (as described in the methods section). For the neural data, we find different levels of alignment for different experiments. These differences may not be strong enough to assign different regimes. Instead, our measures (correlation and relative fitting dimension) allow us to order the datasets. Here, the BCI data is more aligned than non-BCI data – perhaps unsurprisingly, given the experimental design of the prior and the previous findings for the rotation task [Russo et al, 2018]. We changed the manuscript accordingly, now focusing on the relative measure of alignment, even in the absence of absolute thresholds. We are curious whether future studies with more data, different tasks, or other brain regions might reveal stronger differentiation towards either extreme.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

There's so much interesting content in the supplement - it seemed like a whole other paper! It is interesting to read about the dynamics over the course of learning. Maybe you want to put this somewhere else so that more people read it?

We are glad the reviewer appreciated this content. We think developing these analysis methods is essential for a more complete understanding of the oblique regime and how it arises, and that it should therefore be part of the current paper.

Nice schematic in Figure 1.

There were some statements in the text highlighting co-rotation in the top 2 PCs for oblique networks. Figure 4a looks like aligned networks might also co-rotate in a particular subspace that is not highlighted. I could be wrong, but the authors should look into this and correct it if so. If both aligned and oblique networks have co-rotation within the top 5 or so PCs, some text should be updated to reflect this.

This is indeed the case, thanks for pointing this out! For one example, there is co-rotation for the aligned network already in the subspace spanned by PCs 1 and 3, see the figure below. We added a sentence indicating that co-rotation can take place at low-variance PCs for the aligned regime and pointed to this figure, which we added to the appendix (Fig. 17).

While these observations are an important addition, we don’t think they qualitatively alter our results, particularly the stronger dissociation between output and internal dynamics for oblique than aligned dynamics.

Figure 4 color labels were 'dark' and 'light'. I wasn't sure if this was a typo or if it was designed for colorblind readers? Either way, it wasn't too confusing, but adding more description might be useful.

Fixed to red and yellow.

Typo "Aligned networks have a ratio much large than one"

Typo "just started to be explored" Typo "hence allowing to test"

Fixed all typos.

Reviewer #2 (Recommendations For The Authors):

- Explain/discuss in the main text why the initial output weights reliably result in the required internal RNN dynamics (small->aligned; large->oblique) after training. The magnitude of the output weights is initially discussed as being fixed, and as far as I can tell all analytical results (sections 4.6-4.9) also assume this. But in all trained models that make up the bulk of the results (Figures 3-6) all three weight vectors/matrices (input, recurrent, and output) are trained by gradient descent. It would be good to see an explanation or results offered in the main text as to why the training always ends up in the same mapping (small->aligned; large->oblique) when it could, for example, just optimise the output weights instead.

See the answer to a similar comment by Reviewer #1 above.

- Page 6: explain the 5 tasks.

We added a link to methods where the tasks are described.

- Page 6/Fig 3 & Methods: explain assumptions used to compute a reconstruction R^2 between RNN PCs and a binary or vector target output.

We added a new methods section, 4.4, where we explain the fitting process in Fig. 3. For all tasks, the target output was a time series with P specified target values in N_out dimensions. We thus always applied regression and did not differentiate between binary and non-binary tasks.

- Page 8: methods and predictions are muddled up: paragraph ending "along different directions" should be followed by paragraph starting "Our intuition...". The intervening paragraph ("We apply perturbations...") should start after the first sentence of the paragraph "To test this,...".

Right, these sentences were muddled up indeed. We put them in the correct order.

- Page 10: what are the implications of the differences in noise alignment between the aligned and oblique regimes?

The noise suppression in the oblique regime is a slow learning process that gradually renders the solution more stable. With a large readout, learning separates into two phases. An early phase, in which a “lazy” solution is learned quickly. This solution is not robust to noise. In a second, slower phase, learning gradually leads to a more robust solution: the oblique solution. The main text emphasizes the result of this process (noise suppression). In the methods, we closely follow this process. This process is possibly related to other slow learning process fine-tuning solutions, e.g., [Blanc et al. 2020, Li et al. 2021, Yang et al. 2023]. Furthermore, it would be interesting to see whether such fine-tuning happens in animals [Ratzon et al. 2024]. We added corresponding sentences to the discussion.

- Neural data analysis:

(i) Page 11 & Fig 7: the assignment of "aligned" or "oblique" to each neural dataset is based on the ratio of D_fit/D_x. But in all cases this ratio is less than 1, indicating fewer dimensions are needed for reconstruction than for explaining variance. Given the example in Figure 2 suggests this is an aligned regime, why assign any of them as "oblique"?

We weakened the wording in the corresponding section, and now only state that BCI data leans more towards aligned, non-BCI data more towards oblique. This is consistent with the intuition that BCI is by construction aligned (decoder along largest PCs) and non-BCI data already showed signs of oblique dynamics (co-rotating leading PCs in the cycling task, Russo et al. 2018).

We agree that Fig 2 (and Fig 3) could suggest distinguishing the regimes at a threshold D_fit/D_x = 1, although we hadn’t considered such a formal criterion.

(ii) Figure 23 and main text page 11: discuss which outputs for NLB and BCI datasets were used in Figure 7 & and main text; the NLB results vary widely by output type - discuss in the main text; D_fit for NLB-maze-accuracy is missing from panel D; as the criterion is D_fit/D_x, plot this too.

We now discuss which outputs were used in Fig. 7 in its caption: the velocity of the task-relevant entity (hand/finger/cursor). This was done to have one quantity across studies. We added a sentence to the main text, p. 11, which points to Fig 22 (which used to be Fig 23) and states that results are qualitatively similar for other decoded outputs, despite some fluctuations in numerical values and decodability.

Regarding Fig 22: D_fit for NLB-maze-accuracy was beyond the manually set y-limit (for visibility of the other data points). We also extended the figure to include D_fit/D_x. We also discovered a small bug in the analysis code which required us to rerun the analysis and reproduce the plots. This also changed some of the numbers in the main text.

- Discussion:

"They do not explain why it [the "irrelevant activity"] is necessary", implies that the following sentence(s) will explain this, but do not. Instead, they go on to say:

"Here, we showed that merely ensuring stability of neural dynamics can lead to the oblique regime": this does not explain why it is necessary, merely that it exists; and it is unclear what results "stability of neural dynamics" is referring to.

We agree this was not a very clear formulation. We replaced these last three sentences with the following:

“Our study systematically explains this phenomenon: generating task-related output in the presence of large, task-unrelated dynamics requires large readout weights. Conversely, in the presence of large output weights, resistance to noise or perturbations requires large, potentially task-unrelated neural dynamics (the oblique regime).”

- The need for all 27 figures was unclear, especially as some seemed not to be referenced or were referenced out of order. Please check and clarify.

Fig 16 (Details for network dynamics in cycling tasks) and Fig 21 (loss over learning time for the different tasks) were not referenced, and are now removed.

We also reordered the figures in the appendix so that they would appear in the order they are referenced. Note that we added another figure (now Fig. 17) following a question from Reviewer #1.

A source is the decontextualised truth about the existence of an event in history and a historian must present the context for said event.

These are different sources that help historians understand past cultures and events.

eLife Assessment

This study presents useful findings on the role of the small GTPase Rab3A in homeostatic synaptic plasticity following activity suppression. While the study demonstrates that Rab3A is required for homeostatic scaling, the evidence supporting the model put forward by the authors is incomplete. The work will be of interest to the field of synaptic transmission and synaptic plasticity.

Reviewer #1 (Public review):

Koesters and colleagues investigated the role of the small GTPase Rab3A in homeostatic scaling of miniature synaptic transmission in primary mouse cortical cultures using electrophysiology and immunohistochemistry. The major finding is that TTX incubation for 48 hours does not induce an increase in the amplitude of excitatory synaptic miniature events in neuronal cultures derived from Rab3A KO and Rab3A Earlybird mutant mice. NASPM application had comparable effects on mEPSC amplitude in control and after TTX, implying that Ca2+-permeable glutamate receptors are unlikely modulated during synaptic scaling. Immunohistochemical analysis revealed no significant changes in GluA2 puncta size, intensity, and integral in control and Rab3A KO cultures. Finally, they provide evidence that loss of Rab3A in neurons, but not astrocytes, blocks homeostatic scaling. Based on these data, the authors propose a model in which neuronal Rab3A is required for homeostatic scaling of synaptic transmission through GluA2-dependent and independent mechanisms.

While the title of the manuscript is mostly supported by data of solid quality, many conclusions, as well as the final model, cannot be derived from the results presented. Importantly, the data do not support that GluA2 levels change upon TTX treatment in control cultures, rendering conclusions regarding Rab3A's role in TTX-dependent GluA2 modulation spurious. Other aspects of the model, such as a Rab3A-dependent release of a tropic factor, cannot be derived from the data.

The following points should be addressed:

(1) There is no (significant) increase in GluA2 levels (intensity, area, or integral) upon TTX treatment in controls (Fig. 5). Conclusions regarding Rab3As role in TTX-dependent GluA2 modulation should be revised accordingly. Hence, the data shown in Fig. 4 - 7 do not allow drawing conclusions in the context of Rab3A-dependent GluA2 modulation and scaling.

(2) The effects of Rab3A on TTX-induced mini frequency modulation remains unclear, because TTX does not induce a change in mini frequency in the Rab3A+/Ebd control (Fig. 2). The respective conclusions should be revised accordingly (l. 427).

(3) The model is still not supported by the data. In particular, data supporting a negative regulation of Rab3A by APs, Rab3A-dependent release of a tropic factor, or a Rab3A-dependent increase in GluA2 abundance are not presented.

(4) Data points are not overlapping and appear "quantal" in most box plots. How were the data rounded?

Reviewer #2 (Public review):

In the revised manuscript, the authors investigated the role of a presynaptic protein, Rab3A, in the homeostatic synaptic plasticity in cultured cortical neurons. The study was motivated by their previous findings that Rab3A is required for expression of similar homeostatic mechanisms at the neuromuscular junction. The authors first show that untreated WT neurons express homeostatic synaptic plasticity in response to 48h of TTX treatment (upregulation of both mEPSC amplitude and frequency), whereas neurons lacking Rab3A or carrying a dominant negative mutated Rab3A (earlybird) do not. They also demonstrate that only neuronal, but not glial Rab3A is responsible for this defect. Furthermore, they confirm the increased mEPSC amplitudes in WT neurons reflect the addition of GluA2-containing AMPA receptors rather than calcium-permeable ones, as previously reported by multiple labs. However, the increase in mEPSC amplitude is not accompanied by a corresponding upregulation of GluA2 synaptic clusters according to their IHC data (cluster size and intensity trend slightly upwards but not reaching significance). They further show that this modest upward trend is absent in Rab3A KO neurons, and conclude that Rab3A is involved in postsynaptic GluA2 upregulation during homeostatic synaptic plasticity.

When compared to the original version, the authors have done an admirable job in switching to more established ways to assess homeostatic synaptic plasticity by comparing the mean mEPSC amplitude and frequency, which has greatly improved the legibility of the manuscript to the public. Their data in Figures 1,2, and 8 clearly demonstrate that functional Rab3A in cortical neurons is required for the homeostatic regulation of mEPSCs.

However, the authors still have not provided further investigation of the mechanisms behind the role of Rab3A in this form of plasticity, and the revision therefore has added little to the significance of the study. Moreover, the experimental design for the investigation of the mismatch between mEPSC amplitude and GluA2 cluster fluorescence remains questionable, making it difficult to draw any credible conclusions from groups of data that not only look similar to the eye but also show no significance statistically.

A major claim the authors want to make is that Rab3A, although a presynaptic protein, regulates postsynaptic GluA2, and they do this by showing in Figure 5 that the upward trend of GluA2 cluster size and intensity is absent in Rab3A KO neurons. First, it is difficult to convince readers that this upward trend is real in Figures 5B-D without getting more samples. Second, the authors pick GluA2 clusters on the primary dendrites, whereas mEPSC events come from a much larger synapse population (e.g., more distal), therefore it makes sense that these two forms of measurement do not show matching changes, and this caveat could be addressed by sampling more diverse dendritic locations. Without a convincing phenotype in WT neurons, the support for this claim is weak.

Another claim of the authors is that this mismatch between mEPSC amplitude and GluA2 cluster sizes with the same culture suggests there are other factors contributing to the mEPSC amplitude. They do this by comparing results from individual culture dissociations, which greatly suffer from undersampling (Figure 6). In particular, they point out that 2 out of 3 dissociations show "matching" upward trends in mEPSC and GluA2 cluster (figure 6A and 6B) while the third one shows opposite trends, and use this to support their claim. Anyone who has done culture preparation would know the high variability between dissociations, which is why culture data are always pooled for assessment of any population trend. Anything could have happened to this particular dissociation (culture #3, figure 6C), and drawing conclusion from this single incident does little to support this claim. At least, they should double the dissociation numbers, and their claim would be much more convincing if a similar phenomenon occurs again. Besides, as mentioned above, all these mismatching trends could just be due to sampling differences.

In summary, this study establishes that neuronal Rab3A plays a role in homeostatic synaptic plasticity, but so do a number of other molecules that have been implicated in homeostatic synaptic plasticity in the past two decades (only will grow with the new techniques such as RNAseq). Without going beyond this finding and demonstrating how exactly Rab3A participates in the induction and/or expression of this form of plasticity, or maybe the potential Rab3A-mediated functional and behavioral defects in vivo, the contribution of the current study to the field is limited. However, given the presynaptic location of Rab3A, this finding could serve as a starting point for researchers interested in pre-postsynaptic cross-talk during homeostatic plasticity in general.

Reviewer #3 (Public review):

This manuscript presents a number of interesting findings that have the potential to increase our understanding of the mechanism underlying homeostatic synaptic plasticity (HSP). The data broadly support that Rab3A plays a role in HSP, although the site and mechanism of action remain uncertain.

The authors clearly demonstrate the Rab3A plays a role in HSP at excitatory synapses, with substantially less plasticity occurring in the Rab3A KO neurons. There is also no apparent HSP in the Earlybird Rab3A mutation, although baseline synaptic strength seems already elevated. In this context, it is unclear if the plasticity is absent or just occluded by a ceiling effect due the synapses already being strengthened. Occlusion may also occur in the mixed cultures, with Rab3A missing from neurons but not astrocytes. The authors do appropriately discuss both options. There are also differences in genetic background between the Rab3A KO and Earlybird mutants that could also impact the results, which are also noted. The authors have solid data showing that Rab3A is unlikely to be active in astrocytes, Finally, they attempt to study the linkage between synaptic strength during HSP and AMPA receptor trafficking and conclude that trafficking may not be solely responsible for the changes in synaptic strength.

Strengths:

This work adds another player into the mechanisms underlying an important form of synaptic plasticity. The plasticity is likely only reduced, suggesting Rab3A is only partially required and perhaps multiple mechanisms contribute. The authors speculate about some possible novel mechanisms.

However, the conclusions on the partial dissociation of AMPAR trafficking and synaptic response are made from somewhat weaker data. On average, across 3 culture sets, they saw similar magnitude of change in mEPSC amplitude and GluA2 cluster area and integral, but the GluA2 data was not significant. This is likely due to the nature of the datasets. Their imaging method involves only assessing puncta pairs (GluA2/VGlut1) clearly associated with a MAP2 labeled dendrite. This is a small subset of synapses, with usually less than 20 synapses per neuron analyzed (as stated by the authors). The mEPSC recordings will be averaging across several hundred events, which likely represent a hundred or more synapses given reasonable expectations on release probability. It has been reported, in work from this lab as well as by direct monitoring of tagged AMPARs during HSP (Wang, et al., 2019), that individual synapses are quite variable in their response. So there will almost necessarily be higher variability in the imaging data due to the smaller number of synapses sampled. The overall trends, though, are in alignment with previous data implicating receptor trafficking as the mechanism for HSP. However, the authors go on to evaluate each of the individual cultures, where 2 show similar changes between the mEPSC data and GluA2 clusters, and 1 culture showing little/no change in GluA2 clusters. The n's are very low here, and none of the datasets are significant. They want to conclude for this culture, there was a change in mEPSC amplitude that was not accompanied by a change in GluA2 at synaptic sites. But these data are collected from different coverslips, and due to the low n's, the potential under-sampling of the GluA2 clusters, and neuron-to-neuron variability, it is very hard to distinguish if this apparent difference is a methodological issue rather than a biological one. Much stronger data would be necessary to conclude that additional factors beyond receptor trafficking are required for HSP.

Other questions arise from the NASPM experiments, used to justify looking at GluA2 (and not GluA1) in the immunostaining. First, there is a frequency effect that is unclear in origin. One would expect NASPM to merely block some fraction of the post-synaptic current, and not affect pre-synaptic release or block whole synapses. However the change in frequency seems to argue (as the authors do) that some synapses only have CP-AMPARs, while the rest of the synapses have few or none. Another possibility is that there are pre-synaptic NASPM-sensitive receptors that influence release probability. Further, the amplitude data show a strong trend towards smaller amplitude following NASPM treatment (Fig 3B). The p value for both control and TTX neurons was 0.08 - it is very difficult to argue that there is no effect. The decrease on average is larger in the TTX neurons, and some cells show a strong effect. It is possible there is some heterogeneity between neurons on whether GluA1/A2 heteromers or GluA1 homomers are added during HSP. This would impact the weakly supported conclusions about the GluA2 imaging vs mEPSC amplitude data.

Unaddressed issues that would greatly increase the impact of the paper:

(1) Is Rab3A acting pre-synaptically, post-synaptically or both? The authors provide good evidence that Rab3A is acting within neurons and not astrocytes. But where it is acting (pre or post) would aid substantially in understanding its role. They could use sparse knock-down of Rab3A, or simply mix cultures from KO and WT mice (with appropriate tags/labels). The general view in the field has been that HSP is regulated post-synaptically via regulation of AMPAR trafficking, and considerable evidence supports this view. The more support for their suggestion of a pre-synaptic site of control, the better.

(2) Rab3A is also found at inhibitory synapses. It would be very informative to know if HSP at inhibitory synapses is similarly affected. This is particularly relevant as at inhibitory synapses, one expects a removal of GABARs (ie the opposite of whatever is happening at excitatory synapses). If both processes are regulated by Rab3A, this might suggest a role for this protein more upstream in the signaling; an effect only at excitatory synapses would argue for a more specific role just at these synapses.

Author response:

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

The detailed, thorough critique provided by the three reviewers is very much appreciated. We believe the manuscript is greatly improved by the changes we have made based on those reviews. The major changes are described below, followed by a point by point response.

Major Changes:

(1) We revised our model (old Fig. 10; new Fig. 9) to keep the explanation focused on the data shown in the current study. Specifically, references to GTP/GDP states of Rab3A and changes in the presynaptic quantum have been removed and the mechanisms depicted are confined to pre- or post-synaptic Rab3A participating in either controlling release of a trophic factor that regulates surface GluA2 receptors (pre- or postsynaptic) or directly affecting fusion of GluA2-receptor containing vesicles (postsynaptic).

(2) We replaced all cumulative density function plots and ratio plots, based on multiple quantile samples per cell, with box plots of cell means. This affects new Figures 1, 2, 3, 5, 6, 7 and 8. All references to “scaling,” “divergent scaling,” or “uniform scaling,” have been removed. New p values for comparison of means are provided above every box plot in Figures 1, 2, 3, 5, 6, 7 and 8. The number of cultures is provided in the figure legends.

(3) We have added frequency to Figures 1, 2 and 8. Frequency values overall are more variable, and the effect of activity blockade less robust, than for mEPSC amplitudes. We have added text indicating that the increase in frequency after activity blockade was significant in neurons from cultures prepared from WT in the Rab3A+/- colony but not cultures prepared from KO mice (Results, lines 143 to 147, new Fig. 1G. H). The TTX-induced increase in frequency was significant in the NASPM experiments before NASPM, but not after NASPM (Results, lines 231 to 233, new Fig. 3, also cultures from WT in Rab3A+/- colony). The homeostatic plasticity effect on frequency did not reach significance in WT on WT glia cultures or

WT on KO glia cultures, possibly due to the variability of frequency, combined with smaller sample sizes (Results, lines 400 to 403, new Fig. 8). In the cultures prepared from WT mice in the Rab3A+/Ebd colony, there was a trend towards higher frequency after TTX that did not reach statistical significance, and in cultures prepared from mutant mice, the p value was large, suggesting disruption of the effect, which appears to be due to an increase in frequency in untreated cultures, similar to the behavior of mEPSC amplitudes in neurons from mutant mice (Results, lines 161-167). In sum, the effect of activity on frequency requires Rab3A and Ca2+-permeable receptors, and is mimicked by the presence of the Rab3A Earlybird mutant. We have also added a discussion of these results (Discussion, lines 427-435). 

(4) In the revised manuscript we have added analysis of VGLUT1 levels for the same synaptic sites that we previously analyzed GluA2 levels, and these data are described in Results, lines 344 to 371, and appear in new Table 2. In contrast to previous studies, we did not find any evidence for an increase in VGLUT1 levels after activity blockade. We reviewed those studies to determine whether there might be differences in the experimental details that could explain the lack of effect we observed. In (De Gois et al., 2005), the authors measured mRNA and performed western blots to show increases in VGLUT1 after TTX treatment in older rat cortical cultures (DIV 19). The study performs immunofluorescence imaging of VGLUT1 but only after bicuculline treatment (it decreases), not after TTX treatment. In (Wilson et al.,

2005), the hippocampal cultures are treated with AP5, not TTX, and the VGLUT1 levels in immunofluorescence images are reported relative to synapsin I. That the type of activity blockade matters is illustrated by the failure of Wilson and colleagues to observe a consistent increase in VGLUT1/Synapsin ratio in cultures treated with AMPA receptor blockade (NBQX; supplementary information). These points have been added to the Discussion, lines 436 to 447.)

Reviewer #1:

(1) (model…is not supported by the data), (2) (The analysis of mEPSC data using quantile sampling…), (3) (…statistical analysis of CDFs suffers from n-inflation…), (4) (How does recording noise and the mEPSC amplitude threshold affect “divergent scaling?”) (5) (…justification for the line fits of the ratio data…), (7) (A comparison of p-values between conditions….) and (10) (Was VGLUT intensity altered in the stainings presented in the manuscript?)

The major changes we made, described above, address Reviewer #1’s points. The remaining points are addressed below.

(6) TTX application induces a significant increase in mEPSC amplitude in Rab3A-/- mice in two out of three data sets (Figs. 1 and 9). Hence, the major conclusion that Rab3A is required for homeostatic scaling is only partially supported by the data. 

The p values based on CDF comparisons were problematic, but the point we were making is that they were much larger for amplitudes measured in cultures prepared from Rab3A-/- mice (Fig. 1, p = 0.04) compared to those from cultures prepared from Rab3A+/+ mice (Fig. 1, p = 4.6 * 10-4). Now that we are comparing means, there are no significant TTX-induced effects on mEPSC amplitudes for Rab3A-/- data. However, acknowledging that some increase after activity blockade remains, we describe homeostatic plasticity as being impaired or not significant, rather than abolished, by loss of Rab3A, (Abstract, lines 37 to 39; Results, lines 141 to 143; Discussion, lines 415 to 418).

(8) There is a significant increase in baseline mEPSC amplitude in Rab3AEbd/Ebd (15 pA) vs. Rab3AEbd/+ (11 pA) cultures, but not in Rab3A-/- (13.6 pA) vs. Rab3A+/- (13.9 pA). Although the nature of scaling was different between Rab3AEbd/Ebd vs. Rab3AEbd/+ and Rab3AEbd/Ebd with vs. without TTX, the question arises whether the increase in mEPSC amplitude in Rab3AEbd/Ebd is Rab3A dependent. Could a Rab3A independent mechanism occlude scaling?

The Reviewer is concerned that the increase in mEPSC amplitude in the presence of the Rab3A point mutant may be through a ‘non-Rab3A’ mechanism (a concern raised by the lack of such effect in cultures from the Rab3A-/- mice), and secondly, that the already large mEPSC cannot be further increased by the homeostatic plasticity mechanism. It must always be considered that a mutant with an altered genetic sequence may bind to novel partners, causing activities that would not be either facilitated or inhibited by the original molecule. We have added this caveat to Results, lines 180 to 186 We added that a number of other manipulations, implicating individual molecules in the homeostatic mechanism, have caused an increase in mEPSC amplitude at baseline, potentially nonspecifically occluding the ability of activity blockade to induce a further increase (Results lines 186 to 189). Still, it is a strong coincidence that the novel activity of the mutant Rab3A would affect mEPSC amplitude, the same characteristic that is affected by activity blockade in a Rab3A dependent manner, a point which we added to Results, lines 189 to 191.

(9) Figure 4: NASPM appears to have a stronger effect on mEPSC frequency in the TTX condition vs. control (-40% vs -15%). A larger sample size might be necessary to draw definitive conclusions on the contribution of Ca2+-permeable AMPARs.

Our results, even with the modest sample size of 11 cells, are clear: NASPM does not disrupt the effect of TTX treatment on mEPSC amplitude (new Fig. 3A). It also looks like there is a greater magnitude effect of NAPSM on frequency in TTX-treated cells; we note this, but point out that nevertheless, these mEPSCs are not contributing to the increase in mEPSC amplitude (Results, lines 238-241). 

(11) The change in GluA2 area or fluorescence intensity upon TTX treatment in controls is modest. How does the GluA2 integral change?

We had reported that GluA2 area showed the most prominent increase following activity blockade, with intensity changing very little. When we examined the integral, it closely matched the change in area. We have added the values for integral to new Fig. 5 D, H; new Fig. 6 A-C; new Fig. 7 A-C and new Table 1 (for GluA2) and new Table 2 (for VGLUT1). These results are described in the text in the following places: Results, lines 289-292; 298-299; 311-319; 328-324). For VGLUT1, both area and intensity changed modestly, and the integral appeared to be a combination of the two, being higher in magnitude and resulting in smaller p values than either area or intensity (Results, lines 344-348; 353-359; new Table 2).

(12) The quantitative comparison between physiology and microscopy data is problematic. The authors report a mismatch in ratio values between the smallest mEPSC amplitudes and the smallest GluA2 receptor cluster sizes (l. 464; Figure 8). Is this comparison affected by the fluorescence intensity threshold? What was the rationale for a threshold of 400 a.u. or 450 a.u.? How does this threshold compare to the mEPSC threshold of 3 pA.

This concern is partially addressed by no longer comparing the rank ordered mEPSC amplitudes with the rank ordered GluA2 receptor characteristics. We had used multiple thresholds in the event that an experiment was not analyzable with the chosen threshold (this in fact happened for VGLUT1, see end of this paragraph). We created box plots of the mean GluA2 receptor cluster size, intensity and integral, for experiments in which we used all three thresholds, to determine if the effect of activity blockade was different depending on which threshold was applied, and found that there was no obvious difference in the results (Author response image 1). Nevertheless, since there is no need to use a different threshold for any of the 6 experiments (3 WT and 3KO), for new Figures 5, 6 and 7 we used the same threshold for all data, 450; described in Methods, lines 746 to 749. For VGLUT1 levels, it was necessary to use a different threshold for Rab3A+/+ Culture #1 (400), but a threshold of 200 for the other five experiments (Methods, lines 751-757). The VGLUT1 immunofluorescent sites in Culture #1 had higher levels overall, and the low threshold caused the entire AOI to be counted as the synapse, which clearly included background levels outside of the synaptic site. Conversely, to use a threshold of 400 on the other experiments meant that the synaptic site found by the automated measurement tool was much smaller that what was visible by eye. In our judgement it would have been meaningless to adhere to a single threshold for VGLUT1 data.

Author response image 1.

Using different thresholds does not substantially alter GluA2 receptor cluster size data. A) Rab3A+/+ Culture #1, size data for three different thresholds, depicted above each graph. B) Rab3A+/+ Culture #2, size data for three different thresholds, depicted above each graph. Note scale bar in A is different from B, to highlight differences for different thresholds. (Culture #3 was only analyzed with 450 threshold).

The conclusion that an increase in AMPAR levels is not fully responsible for the observed mEPSC increase is mainly based on the rank-order analysis of GluA2 intensity, yielding a slope of ~0.9. There are several points to consider here: (i) GluA2 fluorescence intensity did increase on average, as did GluA2 cluster size.

(ii) The increase in GluA2 cluster size is very similar to the increase in mEPSC amplitude (each approx. 1820%). (iii) Are there any reports that fluorescence intensity values are linearly reporting mEPSC amplitudes (in this system)? Antibody labelling efficiency, and false negatives of mEPSC recordings may influence the results. The latter was already noted by the authors.

Our comparison between mEPSC amplitude and GluA2 receptor cluster characteristics has been reexamined in the revised version using means rather than rank-ordered data in rank-order plots or ratio plots. Importantly, all of these methods revealed that in one out of three WT cultures (Culture #3) GluA2 receptor cluster size (old Fig. 8, old Table 1; new Fig. 6, new Table 1), intensity and integral (new Fig. 6, new Table 1) values decreased following activity blockade while in the same culture, mEPSC amplitudes increased. It is based on this lack of correspondence that we conclude that increases in mEPSC amplitude are not fully explained by increases in GluA2 receptors, and suggest there may be other contributors. These points are made in the Abstract (lines 108-110); Results (lines 319 to 326; 330337; 341-343) and the Discussion (lines 472 to 474). To our knowledge, there are not any reports that quantitatively compare receptor levels (area, intensity or integrals) to mEPSC amplitudes in the same cultures. We examined the comparisons very closely for 5 studies that used TTX to block activity and examined receptor levels using confocal imaging at identified synapses (Hou et al., 2008; Ibata et al., 2008; Jakawich et al., 2010a; Xu and Pozzo-Miller, 2017; Dubes et al., 2022). We were specifically looking for whether the receptor data were more variable than the mEPSC amplitude data, as we found. However, for 4 of the studies, sample sizes were very different so that we cannot simply compare the p values. Below is a table of the comparisons.

Author response table 1.

In Xu 2017 the sample sizes are close enough that we feel comfortable concluding that the receptor data were slightly more variable (p < 0.05) than mEPSC data (p<0.01) but recognize that it is speculative to say our finding has been confirmed. A discussion of these articles is in Discussion, lines 456-474.

(iv) It is not entirely clear if their imaging experiments will sample from all synapses. Other AMPAR subtypes than GluA2 could contribute, as could kainite or NMDA receptors.

While our imaging data only examined GluA2, we used the application of NASPM to demonstrate Ca2+permeable receptors did not contribute quantitatively to the increase in mEPSC amplitude following TTX treatment. Since GluA3 and GluA4 are also Ca2+-permeable, the findings in new Figure 3 (old Fig. 4) likely rule out these receptors as well.  There are also reports that Kainate receptors are Ca2+-permeable and blocked by NASPM (Koike et al., 1997; Sun et al., 2009), suggesting the NASPM experiment also rules out the contribution of Kainate receptors. Finally, given our recording conditions, which included normal magnesium levels in the extracellular solution as well as TTX to block action-potential evoked synaptic transmission, NMDA receptors would not be available to contribute currents to our recordings due to block by magnesium ions at resting Vm. These points have been added to the Methods section, lines 617 to 677 (NMDA); 687-694 (Ca2+-permeable AMPA receptors and Kainate receptors).

Furthermore, the statement “complete lack of correspondence of TTX/CON ratios” is not supported by the data presented (l. 515ff). First, under the assumption that no scaling occurs in Rab3A-/-, the TTX/CON ratios show a 20-30% change, which indicates the variation of this readout. Second, the two examples shown in Figure 8 for Rab3A+/+ are actually quite similar (culture #1 and #2, particularly when ignoring the leftmost section of the data, which is heavily affected by the raw values approaching zero.

We are no longer presenting ratio plots in the revised manuscript, so we do not base our conclusion that mEPSC amplitude data is not always corresponding to GluA2 receptor data on the difference in behavior of TTX/CON ratio values, but only on the difference in direction of the TTX effect in one out of three cultures. We agree with the reviewer that the ratio plots are much more sensitive to differences between control and treated values than the rank order plot, and we feel these differences are important, for example, there is still a homeostatic increase in the Rab3A-/- cultures, and the effect is still divergent rather than uniform. But the comparison of ratio data will be presented elsewhere.

(13) Figure 7A: TTX CDF was shifted to smaller mEPSC amplitude values in Rab3A-/- cultures. How can this be explained?

While this result is most obvious in CDF plots, we still observe a trend towards smaller mEPSC amplitudes after TTX treatment in two of three individual cultures prepared from Rab3A-/- mice when comparing means (new Fig. 7, Table 1) which did not reach statistical significance for the pooled data (new Fig. 5, new Table 1). There was not any evidence of this decrease in the larger data set (new Fig. 1) nor for Rab3A-/- neurons on Rab3A+/+ glia (new Fig. 8). Given that this effect is not consistent, we did not comment on it in the revised manuscript. It may be that there is a non-Rab3A-dependent mechanism that results in a decrease in mEPSC amplitude after activity blockade, which normally pulls down the magnitude of the activity-dependent increase typically observed. But studying this second component would be difficult given its magnitude and inconsistent presentation.

Reviewer #1 (Recommendations For the Authors):

(1) Abstract, last sentence: The conclusion of the present manuscript should be primarily based on the results presented. At present, it is mainly based on a previous publication by the authors.

We have revised the last sentence to reflect actual findings of the current study (Abstract, lines 47 to 49).

(2) Line 55: “neurodevelopmental”

This phrase has been removed.

(3) Line 56: “AMPAergic” should be replaced by AMPAR-mediated

This sentence was removed when all references to “scaling” were removed; no other instances of “AMPAergic” are present.

(4) Figure 9: The use of BioRender should be disclosed in the Figure Legend.

We used BioRender in new Figures 3, 7 and 8, and now acknowledge BioRender in those figure legends.

(5) Figure legends and results: The number of cultures should be indicated for each comparison.

Number of cultures has been added to the figure legends.

(6) Line 289: A comparison of p-values between conditions does not allow any meaningful conclusions.

Agreed, therefore we have removed CDFs and the KS test comparison p values. All comparisons in the revised manuscript are for cell means.

(7) Line 623ff: The argument referring to NMJ data is weak, given that different types of receptors are involved.

We still think it is valid to point out that Rab3A is required for the increase in mEPC at the NMJ but that ACh receptors do not increase (Discussion, lines 522 to 525). We are not saying that postsynaptic receptors do not contribute in cortical cultures, only that there could be another Rab3A-dependent mechanism that also affects mEPSC amplitude.

(8) Plotting data points outside of the ranges should be avoided (e.g., Fig. 2Giii, 7F).

These two figures are no longer present in the revised manuscript. In revising figures, we made sure no other plots have data points outside of the ranges.

(9) The rationale for investigating Rab3AEbd/Ebd remains elusive and should be described.

A rationale for investigating Rab3AEbd/Ebd is that if the results are similar to the KO, it strengthens the evidence for Rab3A being involved in homeostatic synaptic plasticity. In addition, since its phenotype of early awakening was stronger than that demonstrated in Rab3A KO mice (Kapfhamer et al., 2002), it was possible we would see a more robust effect. These points have been added to the Results, lines 118 to 126.

(10) Figures 3 and 4, as well as Figure 5 and 6 could be merged.

In the revised version, Figure 3 has been eliminated since its main point was a difference in scaling behavior. Figure 4 has been expanded to include a model of how NASPM could reduce frequency (new Fig. 3.) Images of the pyramidal cell body have been added to Figure 5 (new Fig. 4), and Figure 6 has been completely revised and now includes pooled data for both Rab3A+/+ and Rab3A-/- cultures, for mEPSC amplitude, GluA2 receptor cluster size, intensity and integral.

(11) Figure 5: The legend refers to MAP2, but this is not indicated in the figure.

MAP2 has now been added to the labels for each image and described in the figure legend (new Fig. 4).

Reviewer #2:

Technical concerns:

(1) The culture condition is questionable. The authors saw no NMDAR current present during spontaneous recordings, which is worrisome since NMDARs should be active in cultures with normal network activity (Watt et al., 2000; Sutton et al., 2006). It is important to ensure there is enough spiking activity before doing any activity manipulation. Similarly it is also unknown whether spiking activity is normal in Rab3AKO/Ebd neurons.

In the studies cited by the reviewer, NMDA currents were detected under experimental conditions in which magnesium was removed. In our recordings, we have normal magnesium (1.3 mM) and also TTX, which prevents the necessary depolarization to allow inward current through NMDA receptors. This point has been added to our Methods, lines 674 to 677. We acknowledge we do not know the level of spiking in cultures prepared from Rab3A+/+, Rab3A-/- or Rab3A_Ebd/Ebd_ mice. Given the similar mEPSC amplitude for untreated cultures from WT and KO studies, we think it unlikely that activity was low in the latter, but it remains a possibility for untreated cultures from Rab3A_Ebd/Ebd_ mice, where mEPSC amplitude was increased. These points are added to the Methods, lines 615 to 622.

(2) Selection of mEPSC events is not conducted in an unbiased manner. Manually selecting events is insufficient for cumulative distribution analysis, where small biases could skew the entire distribution. Since the authors claim their ratio plot is a better method to detect the uniformity of scaling than the well-established rank-order plot, it is important to use an unbiased population to substantiate this claim.

We no longer include any cumulative distributions or ratio plot analysis in the revised version. We have added the following text to Methods, lines 703 to 720:

“MiniAnalysis selects many false positives with the automated feature when a small threshold amplitude value is employed, due to random fluctuations in noise, so manual re-evaluation of the automated process is necessary to eliminate false positives. If the threshold value is set high, there are few false positives but small amplitude events that visually are clearly mEPSCs are missed, and manual re-evaluation is necessary to add back false negatives or the population ends up biased towards large mEPSC amplitudes. As soon as there is a manual step, bias is introduced. Interestingly, a manual reevaluation step was applied in a recent study that describes their process as ‘unbiased (Wu et al., 2020). In sum, we do not believe it is currently possible to perform a completely unbiased detection process. A fully manual detection process means that the same criterion (“does this look like an mEPSC?”) is applied to all events, not just the false positives, or the false negatives, which prevents the bias from being primarily at one end or the other of the range of mEPSC amplitudes. It is important to note that when performing the MiniAnalysis process, the researcher did not know whether a record was from an untreated cell or a TTX-treated cell.”

(3) Immunohistochemistry data analysis is problematic. The authors only labeled dendrites without doing cell-fills to look at morphology, so it is questionable how they differentiate branches from pyramidal neurons and interneurons. Since glutamatergic synapse on these two types of neuron scale in the opposite directions, it is crucial to show that only pyramidal neurons are included for analysis.

We identified neurons with a pyramidal shape and a prominent primary dendrite at 60x magnification without the zoom feature. This should have been made clear in the description of imaging. We have added an image of the two selected cells to our figure of dendrites (old Fig. 5, new Fig. 4), and described this process in the Methods, lines 736 to 739, and Results, lines 246 to 253. Given the morphology of the neurons selected it is highly unlikely that the dendrites we analyzed came from interneurons.

Conceptual Concerns

The only novel finding here is the implicated role for Rab3A in synaptic scaling, but insights into mechanisms behind this observation are lacking. The authors claim that Rab3A likely regulates scaling from the presynaptic side, yet there is no direct evidence from data presented. In its current form, this study’s contribution to the field is very limited.

We have demonstrated that loss of Rab3A and expression of a Rab3A point mutant disrupt homeostatic plasticity of mEPSC amplitudes, and that in the absence of Rab3A, the increase in GluA2 receptors at synaptic sites is abolished. Further, we show that this effect cannot be through release of a factor, like TNFα, from astrocytes. In the new version, we add the finding that VGLUT1 is not increased after activity blockade, ruling out this presynaptic factor as a contributor to homeostatic increases in mEPSC amplitude. We show for the first time by examining mEPSC amplitudes and GluA2 receptors in the same cultures that the increases in GluA2 receptors are not as consistent as the increases in mEPSC amplitude, suggesting the possibility of another contributor to homeostatic increases in mEPSC amplitude. We first proposed this idea in our previous study of Rab3A-dependent homeostatic increases in mEPC amplitudes at the mouse neuromuscular junction. In sum, we dispute that there is only one novel finding and that we have no insights into mechanism. We acknowledge that we have no direct evidence for regulation from the presynaptic side, and have removed this claim from the revised manuscript. We have retained the Discussion of potential mechanisms affecting the presynaptic quantum and evidence that Rab3A is implicated in these mechanisms (vesicle size, fusion pore kinetics; Discussion, lines 537 to 563). One way to directly show that the amount of transmitter released for an mEPSC has been modified after activity blockade is to demonstrate that a fast off-rate antagonist has become less effective at inhibiting mEPSCs (because the increased glutamate released out competes it; see (Liu et al., 1999) and (Wilson et al., 2005) for example experiments). This set of experiments is underway but will take more time than originally expected, because we are finding surprisingly large decreases in frequency, possibly the result of mEPSCs with very low glutamate concentration that are completely inhibited by the dose used. Once mEPSCs are lost, it is difficult to compare the mEPSC amplitude before and after application of the antagonist. Therefore we intend to include this experiment in a future report, once we determine the reason for the frequency reduction, or, can find a dose where this does not occur.

(1) Their major argument for this is that homeostatic effects on mEPSC amplitudes and GluA2 cluster sizes do not match. This is inconsistent with reports from multiple labs showing that upscaling of mEPSC amplitude and GluA2 accumulation occur side by side during scaling (Ibata et al., 2008; Pozo et al., 2012; Tan et al., 2015; Silva et al., 2019). Further, because the acquisition and quantification methods for mEPSC recordings and immunohistochemistry imaging are entirely different (each with its own limitations in signal detection), it is not convincing that the lack of proportional changes must signify a presynaptic component.

Within the analyses in the revised manuscript, which are now based only on comparison of cell/dendrite means, we find a very good match in the magnitude of increase for the pooled data of mEPSC amplitudes and GluA2 receptor cluster sizes (+19.7% and +20.0% respectively; new Table 1). However, when looking at individual cultures, we had one of three WT cultures in which mEPSC amplitude increased 17.2% but GluA2 cluster size decreased 9.5%. This result suggests that while activity blockade does lead to an increase in GluA2 receptors after activity blockade, the effect is more variable than that for mEPSC amplitude. We went back to published studies to see if this has been previously observed, but found that it was difficult to compare because the sample sizes were different for the two characteristics (see Author response table 1). We included these particular 5 studies because they use the same treatment (TTX), examine receptors using imaging of identified synaptic sites, and record mEPSCs in their cultures (although the authors do not indicate that imaging and recordings are done simultaneously on the same cultures.) Only one of the studies listed by the Reviewer is in our group (Ibata et al., 2008). The study by (Tan et al., 2015) uses western blots to measure receptors; the study by (Silva et al., 2019) blocks activity using a combination of AMPA and NMDA receptor blockers; the study by (Pozo et al., 2012) correlates mEPSC amplitude changes with imaging but not in response to activity blockade, instead for changing the expression of GluA2. While it may seem like splitting hairs to reject studies that use other treatment protocols, there is ample evidence that the mechanisms of homeostatic plasticity depend on how activity was altered, see the following studies for several examples of this (Sutton et al., 2006; Soden and Chen, 2010; Fong et al., 2015). A discussion of the 5 articles we selected is in the revised manuscript, Discussion, lines 456 to 474. In sum, we provide evidence that activity blockade is associated with an overall increase in GluA2 receptors; what we propose is that this increase, being more variable, does not fully explain the increase in mEPSC amplitude. However, we acknowledge that the disparity could be explained by the differences in limitations of the two methods (Discussion, lines 469-472).

(2) The authors also speculate in the discussion that presynaptic Rab3A could be interacting with retrograde BDNF signaling to regulate postsynaptic AMPARs. Without data showing Rab3A-dependent presynaptic changes after TTX treatment, this argument is not compelling. In this retrograde pathway, BDNF is synthesized in and released from dendrites (Jakawich et al., 2010b; Thapliyal et al., 2022), and it is entirely possible for postsynaptic Rab3A to interfere with this process cell-autonomously.

We have added the information that Rab3A could control BDNF from the postsynaptic cell and included the two references provided by the reviewer, Discussion, lines 517 to 518. We have added new evidence, recently published, that the Rab3 family has been shown to regulate targeting of EGF receptors to rafts (among other plasma membrane molecules), with Rab3A itself clearly present in nonneuronal cells (Diaz-Rohrer et al., 2023) (added to Discussion, lines 509 to 515).

(3) The authors propose that a change in AMPAR subunit composition from GluA2-containing ones to GluA1 homomers may account for the distinct changes in mEPSC amplitudes and GluA2 clusters. However, their data from the NASPM wash-in experiments clearly show that the GluA1 homomer contributions have not changed before and after TTX treatment.

We have revised this section in the Discussion, lines 534 to 536, to clarify that any change due to GluA1 homomers should have been detectable by a greater ability of NASPM to reverse the TTX-induced increase.

Reviewer #2 (Recommendations for the Authors):

For authors to have more convincing arguments in general, they will need to clarify/improve certain details in their data collection by addressing the above technical concerns. Additionally, the authors should design experiments to test whether Rab3A regulates scaling from pre- or post-synaptic site. For example, they could sparsely knock out Rab3A in WT neurons to test the postsynaptic possibility. On the other hand, their argument for a presynaptic role would be much more compelling if they could show whether there are clear functional changes such as in vesicle sizes and release probability in the presynaptic terminal of Rab3AKO neurons.

An important next step is to identify whether Rab3A is acting pre- or post-synaptically (Discussion, lines 572 to 573), but these experiments will be undertaken in the future. It would not add much to simply show vesicle size is altered in the KO (and we do not necessarily expect this since mEPSC amplitude is normal in the KO). It will be very difficult to establish that vesicle size is changing with activity blockade and that this change is prevented in the Rab3A KO, because we are looking for a ~25% increase in vesicle volume, which would correspond to a ~7.5% increase in diameter. Finally, we do not believe demonstrating changes in release probability tell us anything about a presynaptic role for Rab3A in regulating the size of the presynaptic quantum.

Reviewer #3 (Public Review)

Weaknesses: However, the rather strong conclusions on the dissociation of AMPAR trafficking and synaptic response are made from somewhat weaker data. The key issue is the GluA2 immunostaining in comparison with the mEPSC recordings. Their imaging method involves only assessing puncta clearly associated with a MAP2 labeled dendrite. This is a small subset of synapses, judging from the sample micrographs (Fig. 5). To my knowledge, this is a new and unvalidated approach that could represent a particular subset of synapses not representative of the synapses contributing to the mEPSC change (they are also sampling different neurons for the two measurements; an additional unknown detail is how far from the cell body were the analyzed dendrites for immunostaining.) While the authors acknowledge that a sampling issue could explain the data, they still use this data to draw strong conclusions about the lack of AMPAR trafficking contribution to the mEPSC amplitude change. This apparent difference may be a methodological issue rather than a biological one, and at this point it is impossible to differentiate these. It will unfortunately be difficult to validate their approach. Perhaps if they were to drive NMDAdependent LTD or chemLTP, and show alignment of the imaging and ephys, that would help. More helpful would be recordings and imaging from the same neurons but this is challenging. Sampling from identified synapses would of course be ideal, perhaps from 2P uncaging combined with SEP-labeled AMPARs, but this is more challenging still. But without data to validate the method, it seems unwarranted to make such strong conclusions such as that AMPAR trafficking does not underlie the increase in mEPSC amplitude, given the previous data supporting such a model.

In the new version, we soften our conclusion regarding the mismatch between GluA2 receptor levels and mEPSC amplitudes, now only stating that receptors may not be the sole contributor to the TTX effect on mEPSC amplitude (Discussion, lines 472 to 474). With our analysis in the new version focusing on comparisons of cell means, the GluA2 receptor cluster size and the mEPSC amplitude data match well in magnitude for the data pooled across the 3 matched cultures (20.0% and 19.7%, respectively, see new Table 1). However, in one of the three cultures the direction of change for GluA2 receptors is opposite that of mEPSC amplitudes (Table 1, Culture #3, -9.5% vs +17.2%, respectively).

It is unlikely that the lack of matching of homeostatic plasticity in one culture, but very good matching in two other cultures, can be explained by an unvalidated focus on puncta associated with MAP2 positive dendrites. We chose to restrict analysis of synaptic GluA2 receptors to the primary dendrite in order to reduce variability, reasoning that we are always measuring synapses for an excitatory pyramidal neuron, synapses that are relatively close to the cell body, on the consistently identifiable primary dendrite. We measured how far this was for the two cells depicted in old Figure 5 (new Fig. 4). Because we always used the 5X zoom window which is a set length, and positioned it within ~10 microns of the cell body, these cells give a ball park estimate for the usual distances. For the untreated cell, the average distance from the cell body was 38.5 ± 2.8 µm; for the TTX-treated cell, it was 42.4 ± 3.2 µm (p = 0.35, KruskalWallis test). We have added these values to the Results, lines 270 to 274.

We did not mean to propose that AMPA receptor levels do not contribute at all to mEPSC amplitude, and we acknowledge there are clear cases where the two characteristics change in parallel (for example, in the study cited by Reviewer #2, (Pozo et al., 2012), increases in GluA2 receptors due to exogenous expression are closely matched by increases in mEPSC amplitudes.) What our matched culture experiments demonstrate is that in the case of TTX treatment, both GluA2 receptors and mEPSC amplitudes increase on average, but sometimes mEPSC amplitudes can increase in the absence of an increase in GluA2 receptors (Culture #3, Rab3A+/+ cultures), and sometimes mEPSC amplitudes do not increase even though GluA2 receptor levels do increase (Culture #3, Rab3A-/- cultures). Therefore, it would not add anything to our argument to examine receptors and mEPSCs in NMDA-dependent LTP, a different plasticity paradigm in which changes in receptors and mEPSCs may more closely align. It has been demonstrated that mEPSCs of widely varying amplitude can be recorded from a single synaptic site (Liu and Tsien, 1995), so we would need to measure a large sample of individual synapse recordings to detect a modest shift in average values due to activity blockade. In addition, it would be essential to express fluorescent AMPA receptors in order to correlate receptor levels in the same cells we record from (or at the same synapses). And yet, even after these heroics, one is still left with the issue that the two methods, electrophysiology and fluorescent imaging, have distinct limitations and sources of variability that may obscure any true quantitative correlation.

Other questions arise from the NASPM experiments, used to justify looking at GluA2 (and not GluA1) in the immunostaining. First, there is a frequency effect that is quite unclear in origin. One would expect NASPM to merely block some fraction of the post-synaptic current, and not affect pre-synaptic release or block whole synapses. It is also unclear why the authors argue this proves that NASPM was at an effective concentration (lines 399-400). Further, the amplitude data show a strong trend towards smaller amplitude. The p value for both control and TTX neurons was 0.08 – it is very difficult to argue that there is no effect. And the decrease is larger in the TTX neurons. Considering the strong claims for a presynaptic locus and the use of this data to justify only looking at GluA2 by immunostaining, these data do not offer much support of the conclusions. Between the sampling issues and perhaps looking at the wrong GluA subunit, it seems premature to argue that trafficking is not a contributor to the mEPSC amplitude change, especially given the substantial support for that hypothesis. Further, even if trafficking is not the major contributor, there could be shifts in conductance (perhaps due to regulation of auxiliary subunits) that does not necessitate a pre-synaptic locus. While the authors are free to hypothesize such a mechanism, it would be prudent to acknowledge other options and explanations.

We have created a model cartoon to explain how NASPM could reduce mEPSC frequency (new Fig. 3D). mEPSCs that arise from a synaptic site that has only Ca2+-permeable AMPA receptors will be completely blocked by NASPM, if the NASPM concentration is maximal. The reason we conclude that we have sufficient NASPM reaching the cells is that the frequency is decreased, as expected if there are synaptic sites with only Ca2+-permeable AMPA receptors. We previously were not clear that there is an effect of NASPM on mEPSC amplitude, although it did not reach statistical significance (new Fig. 3B). Where there is no effect is on the TTX-induced increase in mEPSC amplitude, which remains after the acute NASPM application (new Fig. 3A). We have revised the description of these findings in Results, lines 220 to 241. In reviewing the literature further, we could find no previous studies demonstrating an increase in conductance in GluA2 or Ca2+-impermeable receptors, only in GluA1 homomers. In other words, any conductance change would have been due to a change in GluA1 homomers, and should have been visible as a disruption of the homeostatic plasticity by NASPM application. We have added text to Results, lines 211 to 217; 236-241; Discussion, lines 420 to 422; 526-536 and Methods, lines 685 to 695 regarding this point.

The frequency data are missing from the paper, with the exception of the NASPM dataset. The mEPSC frequencies should be reported for all experiments, particularly given that Rab3A is generally viewed as a pre-synaptic protein regulating release. Also, in the NASPM experiments, the average frequency is much higher in the TTX treated cultures. Is this statistically above control values?

This comment is addressed by the major change #3, above.

Unaddressed issues that would greatly increase the impact of the paper:

(1) Is Rab3A activity pre-synaptically, post-synaptically or both. The authors provide good evidence that Rab3A is acting within neurons and not astrocytes. But where is it acting (pre or post) would aid substantially in understanding its role (and particularly the hypothesized and somewhat novel idea that the amount of glutamate released per vesicle is altered in HSP). They could use sparse knockdown of Rab3A, or simply mix cultures from KO and WT mice (with appropriate tags/labels). The general view in the field has been that HSP is regulated post-synaptically via regulation of AMPAR trafficking, and considerable evidence supports this view. The more support for their suggestion of a pre-synaptic site of control, the better.

This is similar to the request of Reviewer #2, Recommendations to the Authors. An important next step is to identify whether Rab3A is working pre- or postsynaptically. However, it is possible that it is acting pre-synaptically to anterogradely regulate trafficking of AMPAR, as we have depicted in our model, new Fig. 9. To demonstrate that the presynaptic quantum is being altered, we would need to show that vesicle size is increased, or the amount of transmitter being released during an mEPSC is increased after activity blockade. To that end, we are currently performing experiments using a fast off-rate antagonist. As described above in response to Reviewer #2’s Conceptual Concerns, we find dramatic decreases in frequency not explained by the 30-60% inhibition observed for the largest amplitude mEPSCs, which suggests the possibility that small mEPSCs are more sensitive than large mEPSCs and therefore may have less transmitter. Due to these complexities and the delay while we test other antagonists to see if the effect is specific to fast-off rate antagonists, we are not including these results here.

(2) Rab3A is also found at inhibitory synapses. It would be very informative to know if HSP at inhibitory synapses is similarly affected. This is particularly relevant as at inhibitory synapses, one expects a removal of GABARs and/or a decrease of GABA-packaging in vesicles (ie the opposite of whatever is happening at excitatory synapses.). If both processes are regulated by Rab3A, this might suggest a role for this protein more upstream in the signaling, an effect only at excitatory synapses would argue for a more specific role just at these synapses.

It will be important to determine if homeostatic synaptic plasticity at inhibitory synapses on excitatory neurons is sensitive to Rab3A deletion, especially in light of the fact that unlike many of the other molecules implicated in homeostatic increases in mEPSCS, Rab3A is not a molecule known to be selective for glutamate receptor trafficking (in contrast to Arc/Arg3.1 or GRIP1, for example). Such a study would warrant its own publication.

Reviewer #3 (Recommendations for the Authors):

There are a number of minor points or suggestions for the authors:

Is RIM1 part of this pathway (or expected to be)? Some discussion of this would be nice.

RIM, Rab3-interacting molecule, has been implicated at the drosophila neuromuscular junction in a presynaptic form of homeostatic synaptic plasticity in which evoked release is increased after block of postsynaptic receptors (Muller et al., 2012), a plasticity that also requires Rab3-GAP (Muller et al., 2011). To our knowledge there is no evidence that RIM is involved in the homeostatic plasticity of mEPSC amplitude after activity blockade by TTX. The Rim1a KO does not have a change in mEPSC amplitude relative to WT (Calakos et al., 2004), but that is not unexpected given the normal mEPSC amplitude in neurons from cultures prepared from Rab3A-/- mice in the current study. It would be interesting to look at homeostatic plasticity in cortical cultures prepared from Rim1a or other RIM deletion mice, but we have not added these points to the revised manuscript since there are a number of directions one could go in attempting to define the molecular pathway and we feel it is more important to discuss the potential location of action and physiological mechanisms.

Is the Earlybird mutation a GOF? More information about this mutation would help.

We have added a description of how the Earlybird mutation was identified, in a screen for rest:activity mutants (Results, lines 118 to 123). Rab3A Earlybird mice have a shortened circadian period, shifting their wake cycle earlier and earlier. When Rab3A deletion mice were tested in the same activity raster plot measurements, the shift was smaller than that for the Earlybird mutant, suggesting the possibility that it is a dominant negative mutation.

The high K used in the NASPM experiments seems a bit unusual. Have the authors done high K/no drug controls to see if this affects the synapses in any way?

We used the high K based on previous studies that indicated the blocking effect of the Ca2+-permeable receptor blockers was use dependent (Herlitze et al., 1993; Iino et al., 1996; Koike et al., 1997). We reasoned that a modest depolarization would increase the frequency of AMPA receptor mEPSCs and allow access of the NASPM.  We have added this point to the Methods, lines 695 to 708. 

The NASPM experiments do not show that GluA1 does not contribute (line 401), only that GluA1 homomers are not contributing (much – see above). GluA1/A2 heteromers are quite likely involved. Also, the SEM is missing from the WT pre/post NASPM data.

Imaging of GluA2-positive sites will not distinguish between GluA2 homomers and GluA2-GluA1 heteromers, so we have added this clarification to Results, lines 242 to 246. We have remade the NASPM pre-post line plots so that the mean values and error bars are more visible (new Fig. 3B, C).

It seems odd to speculate based on non-significant findings (line 650-1), with lower significance (p = 0.11) than findings being dismissed in the paper (NASPM on mEPSC amplitude; p = 0.08).

We did not mean to dismiss the effect of NASPM on mEPSC amplitude (new Fig. 3B), rather, we dismiss the effect of NASPM on the homeostatic increase in mEPSC amplitude caused by TTX treatment (new Fig. 3A). We have emphasized this distinction in Results, lines 223 to 225, and Discussion, lines 420 to 422, as well as adding that the stronger effect of NASPM on frequency after TTX treatment suggests an activity-dependent increase in the number of synapses expressing only Ca2+ permeable homomers (Results, lines 236 to 241; Discussion, lines 431 to 435).

Fig. 4 could be labeled better (to make it clear that B is amplitude and C is freq from the same cells).

Fig. 4 has been revised—now the amplitude and frequency plots from the same condition (new Fig. 3, B, C; CON or TTX) are in a vertical line and the figure legend states that the frequency data are from the same cells as in Fig. 3A.

The raw amplitude data seems a bit hidden in the inset panels – I would suggest these data are at least as important as the cumulative distributions in the main panel. Maybe re-organizing the figures would help.

We have removed all cumulative distributions, rank order plots, and ratio plots. The box plots are now full size in new Figures 1, 2, 5, 6, 7 and 8.

I’m not sure I would argue in the paper that 12 cells a day is a limiting issue for experiments. It doesn’t add anything and doesn’t seem like that high a barrier. It is fine to just say it is difficult and therefore there is a limited amount of data meeting the criteria.

We have removed the comment regarding difficulty.

Calakos N, Schoch S, Sudhof TC, Malenka RC (2004) Multiple roles for the active zone protein RIM1alpha in late stages of neurotransmitter release. Neuron 42:889-896.

De Gois S, Schafer MK, Defamie N, Chen C, Ricci A, Weihe E, Varoqui H, Erickson JD (2005) Homeostatic scaling of vesicular glutamate and GABA transporter expression in rat neocortical circuits. J Neurosci 25:7121-7133.

Diaz-Rohrer B, Castello-Serrano I, Chan SH, Wang HY, Shurer CR, Levental KR, Levental I (2023) Rab3 mediates a pathway for endocytic sorting and plasma membrane recycling of ordered microdomains. Proc Natl Acad Sci U S A 120:e2207461120.

Dubes S, Soula A, Benquet S, Tessier B, Poujol C, Favereaux A, Thoumine O, Letellier M (2022) miR-124dependent tagging of synapses by synaptopodin enables input-specific homeostatic plasticity. EMBO J 41:e109012.

Fong MF, Newman JP, Potter SM, Wenner P (2015) Upward synaptic scaling is dependent on neurotransmission rather than spiking. Nat Commun 6:6339.

Herlitze S, Raditsch M, Ruppersberg JP, Jahn W, Monyer H, Schoepfer R, Witzemann V (1993) Argiotoxin detects molecular differences in AMPA receptor channels. Neuron 10:1131-1140.

Hou Q, Zhang D, Jarzylo L, Huganir RL, Man HY (2008) Homeostatic regulation of AMPA receptor expression at single hippocampal synapses. Proc Natl Acad Sci U S A 105:775-780.

Ibata K, Sun Q, Turrigiano GG (2008) Rapid synaptic scaling induced by changes in postsynaptic firing. Neuron 57:819-826.

Iino M, Koike M, Isa T, Ozawa S (1996) Voltage-dependent blockage of Ca(2+)-permeable AMPA receptors by joro spider toxin in cultured rat hippocampal neurones. J Physiol 496 ( Pt 2):431437.

Jakawich SK, Neely RM, Djakovic SN, Patrick GN, Sutton MA (2010a) An essential postsynaptic role for the ubiquitin proteasome system in slow homeostatic synaptic plasticity in cultured hippocampal neurons. Neuroscience 171:1016-1031.

Jakawich SK, Nasser HB, Strong MJ, McCartney AJ, Perez AS, Rakesh N, Carruthers CJ, Sutton MA (2010b) Local presynaptic activity gates homeostatic changes in presynaptic function driven by dendritic BDNF synthesis. Neuron 68:1143-1158.

Kapfhamer D, Valladares O, Sun Y, Nolan PM, Rux JJ, Arnold SE, Veasey SC, Bucan M (2002) Mutations in Rab3a alter circadian period and homeostatic response to sleep loss in the mouse. Nat Genet 32:290-295.

Koike M, Iino M, Ozawa S (1997) Blocking effect of 1-naphthyl acetyl spermine on Ca(2+)-permeable AMPA receptors in cultured rat hippocampal neurons. Neurosci Res 29:27-36.

Liu G, Tsien RW (1995) Properties of synaptic transmission at single hippocampal synaptic boutons. Nature 375:404-408.

Liu G, Choi S, Tsien RW (1999) Variability of neurotransmitter concentration and nonsaturation of postsynaptic AMPA receptors at synapses in hippocampal cultures and slices. Neuron 22:395409.

Muller M, Pym EC, Tong A, Davis GW (2011) Rab3-GAP controls the progression of synaptic homeostasis at a late stage of vesicle release. Neuron 69:749-762.

Muller M, Liu KS, Sigrist SJ, Davis GW (2012) RIM controls homeostatic plasticity through modulation of the readily-releasable vesicle pool. J Neurosci 32:16574-16585.

Pozo K, Cingolani LA, Bassani S, Laurent F, Passafaro M, Goda Y (2012) beta3 integrin interacts directly with GluA2 AMPA receptor subunit and regulates AMPA receptor expression in hippocampal neurons. Proc Natl Acad Sci U S A 109:1323-1328.

Silva MM, Rodrigues B, Fernandes J, Santos SD, Carreto L, Santos MAS, Pinheiro P, Carvalho AL (2019) MicroRNA-186-5p controls GluA2 surface expression and synaptic scaling in hippocampal neurons. Proc Natl Acad Sci U S A 116:5727-5736.

Soden ME, Chen L (2010) Fragile X protein FMRP is required for homeostatic plasticity and regulation of synaptic strength by retinoic acid. J Neurosci 30:16910-16921.

Sun HY, Bartley AF, Dobrunz LE (2009) Calcium-permeable presynaptic kainate receptors involved in excitatory short-term facilitation onto somatostatin interneurons during natural stimulus patterns. J Neurophysiol 101:1043-1055.

Sutton MA, Ito HT, Cressy P, Kempf C, Woo JC, Schuman EM (2006) Miniature neurotransmission stabilizes synaptic function via tonic suppression of local dendritic protein synthesis. Cell 125:785-799.

Tan HL, Queenan BN, Huganir RL (2015) GRIP1 is required for homeostatic regulation of AMPAR trafficking. Proc Natl Acad Sci U S A 112:10026-10031.

Thapliyal S, Arendt KL, Lau AG, Chen L (2022) Retinoic acid-gated BDNF synthesis in neuronal dendrites drives presynaptic homeostatic plasticity. Elife 11.

Wilson NR, Kang J, Hueske EV, Leung T, Varoqui H, Murnick JG, Erickson JD, Liu G (2005) Presynaptic regulation of quantal size by the vesicular glutamate transporter VGLUT1. J Neurosci 25:62216234.

Wu YK, Hengen KB, Turrigiano GG, Gjorgjieva J (2020) Homeostatic mechanisms regulate distinct aspects of cortical circuit dynamics. Proc Natl Acad Sci U S A 117:24514-24525.

Xu X, Pozzo-Miller L (2017) EEA1 restores homeostatic synaptic plasticity in hippocampal neurons from Rett syndrome mice. J Physiol 595:5699-5712.

Bold

for - question - @Gyuri - How would Indyweb do this?

There is a lot of antisocial media information online. Part of those people who get involved in these steams this way could attract people's attention and make more people notice their idea. So the "rule" build by the social media platform is necessary, such as the limitation of specific bad words and high attention and sensibility to potential anti-social behaviors.

Good advice for the presenters using macOS

When we first get in touch with the name and content of this course and book. The limited thinking was social media only included platforms online. But we forgot about what those online stuff comes from and retrieve from. People's passion and curiosity in the public contribute to the development of society and future things.

Deliver Progressive Web Apps with the capability of allowing fast, reliable, and engaging user experiences across platforms. PWAs are the best of both worlds, taking all that a web app has to offer you and pairing it with everything that its rival-the mobile application-provides, thus including offline functionality, push notifications, and an interface similar to that of an app-but without the app store. Whether better engagement or performance, PWAs are a cost-effective option for business companies.

Got an error message when running this code. The code below worked for me:

stargazer(TSMA_mod1, TSMA_mod2, TSMA_mod3, TSMA_mod4, TSMA_mod5, TSMA_mod6, title = "Regressions Using Massachusetts Test Score Data", type = "text", digits = 3, se = rob_se, dep.var.caption = "Dependent Variable: Test Score", column.labels = c("(I)", "(II)", "(III)", "(IV)", "(V)", "(VI)"))

This last line shows that despite her efforts to obliterate herself and thus (end the novel), it is in vain, there is a hopeful light in her abduction!

I think this is a hopeful ending, as out of the many options she lays out for us on what she can do -- there is a new option, to be taken away. Not to be killed in a salvaging or hung on the wall, but just taken away-- She has opened an escape outside of herself.

Naming is such a powerful mark of identity. In it, it defers what the object/person actually is. The executions are thus called salvagings, the abusers are called Aunts as a form of a mother's harsh love, and the place of happiness is called Jezebel's. In a sense, morality is shifted (ironically) through the use of contradicting nomenclature.

I feel like this is a very smart approach by the designers as sometimes the "x" is too small and can't be pressed so it just opens the website instead or it takes a while to show up, in most of the users are too lazy to go back to what they were doing and end up watching the whole ad. This for sure leads to the product getting views and even sales. It is so crazy how the whole world is hooked to social media and their phones all day and the designers just make money out of it

Knowing vs Witnessing, witnessing behavior like this and also going through it causes so much trauma. It shows how inhumane people treated them.

Passing down history through family has such a warm meaningful feeling. Knowing that this information comes from your ancestry must mean a lot.

Understanding what it means to be a Dakota woman, is such a powerful statement due to what she unravels later in the text.

I find it interesting how complications are simplified overtime if in the 1980s and 1990s, BBS was commonly used, to me it doesn't seem very intriguing as if it looks very dry without any pictures or a comment section where everyone can fight lol, like if we can not post memes on social media what's the point. However now, social media has become so much more simple and fun. We go to social media apps such as TikTok or Instagram to take our mind off from work and if I see the picture of BBS it looks like coding, so I wouldn't really use a site that reminds me of work even in my free time.

Shifting our focus will help us better understand techniques that would genuinely be useful for students. This will help them learn better and grasp the material easier.

The fact that was written in 2011, and people still feel like this in 2024 shows us how we haven't improved.

This is a point that many teachers should pay attention to, not everyone is good at test taking. There are other ways for students to be able to prove they know the material.

Crucial point, understanding the thought process of how students value their education.

Many students feel this way, and I feel this is what takes the passion out of learning. All you focus on is getting a good grade.

eLife Assessment

This study provides important findings that during credit assignment, the lateral orbitofrontal cortex (lOFC) and hippocampus (HC) encode causal choice representations, while the frontopolar cortex (FPl) mediates HC -lOFC interactions when the causality needs to be maintained over longer distractions. While this research offers compelling evidence and employs sophisticated multivariate pattern analysis, there are some concerns regarding a) task design which may have oversimplified real-world credit assignment complexities, and b) the interpretation of results. This work will be of interest to cognitive and computational neuroscientists who work on value-based decision-making and fronto-hippocampal circuits.

Reviewer #1 (Public review):

Summary:

The authors conducted a study on one of the fundamental research topics in neuroscience: neural mechanisms of credit assignment. Building on the original studies of Walton and his colleagues and subsequent studies on the same topic, the authors extended the research into the delayed credit assignment problem with clever task design, which compared the non-delayed (direct) and delayed (indirect) credit assignment processes. Their primary goal was to elucidate the neural basis of these processes in humans, advancing our understanding beyond previous studies.

Strengths:

(1) Innovative task design distinguishing between direct and indirect credit assignment.

(2) Use of sophisticated multivariate pattern analysis to identify neural correlates of pending representations.

(3) Well-executed study with clear presentation of results.

(4) Extension of previous research to human subjects, providing valuable comparative insights.

Considerations for Future Research:

(1) The task design, while clear and effective, might be further developed to capture more real-world complexity in credit assignment.

(2) There's potential for deeper exploration of the role of task structure understanding in credit assignment processes.

(3) The interpretation of lateral orbitofrontal cortex (lOFC) involvement could be expanded to consider its role in both credit assignment and task structure representation.

Achievement of Aims and Support of Conclusions:

The authors successfully achieved their aim of investigating direct and indirect credit assignment processes in humans. Their results provide valuable insights into the neural representations involved in these processes. The study's conclusions are generally well-supported by the data, particularly in identifying neural correlates of pending representations crucial for delayed credit assignment.

Impact on the Field and Utility of Methods:

This study makes a significant contribution to the field of credit assignment research by bridging animal and human studies. The methods, particularly the multivariate pattern analysis approach, provide a robust template for future investigations in this area. The data generated offers valuable insights for researchers comparing human and animal models of credit assignment, as well as those studying the neural basis of decision-making and learning.

The study's focus on the lOFC and its role in credit assignment adds to our understanding of this brain region's function.

Additional Context and Future Directions:

(1) Temporal ambiguity in credit assignment: While the current design provides clear task conditions, future studies could explore more ambiguous scenarios to further reflect real-world complexity.

(2) Role of task structure understanding: The difference in task comprehension between human subjects in this study and animal subjects in previous studies offers an interesting point of comparison.

(3) The authors used a sophisticated method of multivariate pattern analysis to find the neural correlate of the pending representation of the previous choice, which will be used for the credit assignment process in the later trials. The authors tend to use expressions that these representations are maintained throughout this intervening period. However, the analysis period is specifically at the feedback period, which is irrelevant to the credit assignment of the immediately preceding choice. This task period can interfere with the ongoing credit assignment process. Thus, rather than the passive process of maintaining the information of the previous choice, the activity of this specific period can mean the active process of protecting the information from interfering and irrelevant information. It would be great if the authors could comment on this important interpretational issue.

(4) Broader neural involvement: While the focus on specific regions of interest (ROIs) provided clear results, future studies could benefit from a whole-brain analysis approach to provide a more comprehensive understanding of the neural networks involved in credit assignment.

Reviewer #2 (Public review):

Summary:

The present manuscript addresses a longstanding challenge in neuroscience: how the brain assigns credit for delayed outcomes, especially in real-world learning scenarios where decisions and outcomes are separated by time. The authors focus on the lateral orbitofrontal cortex and hippocampus, key regions involved in contingent learning. By integrating fMRI data and behavioral tasks, the authors examined how neural circuits maintain a causal link between past decisions and delayed outcomes. Their findings offer insights into mechanisms that could have critical implications for understanding human decision-making.

Strengths:

(1) The experimental designs were extremely well thought-out. The authors successfully coupled behavioral data and neural measures (through fMRI) to explore the neural mechanisms of contingent learning. This integration adds robustness to the findings and strengthens their relevance.

(2) The emphasis on the interaction between the lateral orbitofrontal cortex (lOFC) and hippocampus (HC) in this study is very well-targeted. The reported findings regarding their dynamic interactions provide valuable insights into contingent learning in humans.

(3) The use of an advanced modeling framework and analytical techniques allowed the authors to uncover new mechanistic insights regarding a complex case of the decision-making process. The methods developed will also benefit analyses of future neuroimaging data on a range of decision-making tasks as well.

Weaknesses:

Given the limited temporal resolution of fMRI and that the measured signal is an indirect measure of neural activity, it is unclear the extent to which the reported causality reflects the true relationship/interactions between neurons in different regions.

Reviewer #3 (Public review):

The authors apply multivoxel decoding analyses from fMRI during reward feedback about the cues previously chosen that led to that feedback. They compare two versions of the task - one in which the feedback is provided about the current trial, and one in which the feedback is provided about the previous trial. Reward probability changes slowly over time, so subjects need to identify which cues are leading to reward at a given time. They find that evidence for recall of the cue in the lateral orbitofrontal cortex (lOFC) and hippocampus (HC). They also find that in the second condition, where feedback is for the one-back trial, this representation is mediated by the lateral frontal pole (FPl).

Overall, the analyses are clean and elegant and seem to be complete. I have only a few comments.

(1) They do find (not surprisingly) that the one-back task is harder. It would be good to ensure that the reason that they had more trouble detecting direct HC & lOFC effects on the harder task was not because the task is harder and thus that there are more learning failures on the harder one-back task. (I suspect their explanation that it is mediated by FPl is likely to be correct. But it would be nice to do some subsampling of the zero-back task [matched to the success rate of the one-back task] to ensure that they still see the direct HC and lOFC there).

(2) The evidence that they present in the main text (Figure 3) that the HC and lOFC are mediated by FPl is a correlation. I found the evidence presented in Supplemental Figure 7 to be much more convincing. As I understand it, what they are showing in SF7 is that when FPl decodes the cue, then (and only then) HC and lOFC decode the cue. If my understanding is correct, then this is a much cleaner explanation for what is going on than the secondary correlation analysis. If my understanding here is incorrect, then they should provide a better explanation of what is going on so as to not confuse the reader.

(3) I like the idea of "credit spreading" across trials (Figure 1E). I think that credit spreading in each direction (into the past [lower left] and into the future [upper right]) is not equivalent. This can be seen in Figure 1D, where the two tasks show credit spreading differently. I think a lot more could be studied here. Does credit spreading in each of these directions decode in interesting ways in different places in the brain?

The impact of action in the financial markets do not result in immediate outcomes. Instead actions will have underlying 'invisible' impacts that will go unnoticed for an extended time or until there is a major failure.

Before the introduction of Lycurgus, there was extreme wealth inequality between the plebeians and the ruling elites, resulting in social tension and discord between the people of Sparta. In order for the nation to become self-sufficient and harmonious, Lycurgus implemented a drastic solution to redistribute wealth throughout through the land by dividing the land into equal portuons between the elites and the perioeci. It is an interesting afterthought after all, as it closely resembles one of the policies of an utopian society by eliminating the economic foundations of class distinction based on wealth. There was also mentions of switching gold and silver currency with that of iron, to eliminate materialism and encourage the prople to focus more on equality and austerity. Although it is an ideal theory, the real life applications of such theories has too many limitations that stops it from becoming an ideal method to achieve equality. Past explorations into enforcing a state of economic equality throughout modern history has failed, such as the implementation of collectivism throughout the USSR and Post WW2 China. The juxtaposition between soviet collectivisation and Spartan Syssitia is clear, one focuses on trying to provide and distribute food to the masses, while the other one's main focus is to enforce equality. The irony, unfortunately, is clear. Both of them, in the effort to eliminate hierarchies, inadvertently created new ones.

The text emphasizes that learning should extend beyond the classroom to include adults and activists from the local community. This points to the importance of connecting students with real-world examples and mentors who can enrich their educational experience. The statement about students feeling valued for their ideas, attitudes, and skills highlights the psychological and emotional aspects of education.

The text opens with a shift in the educational environment, signaling a change in the author's experience with Ms. Hill's class, suggesting that the dynamics of classroom discussions can significantly impact students' emotional and academic well-being

The text starts with the assertion that the school system privileges individuals who align with the dominant culture, which is typically associated with middle-class and upper-middle-class values and behaviors. This points to systemic biases within educational institutions that favor certain cultural norms.

The text identifies that there are fewer stereotypes regarding academic success for Latino and African American students. This observation highlights a disparity in societal perceptions of different racial groups. The text states that the absence of stereotypes for Latino and African American students can lead to a lack of recognition of their hard work and achievements.

The text points out that there are fewer stereotypes surrounding academic success for Latino and African American students compared to their White and Asian American counterparts. This absence can lead to a lack of recognition of the hard work and achievements of these communities.

Title should be verify state of an object

This syntax is wrong there is no control here it should be: Verify the value of "John" Text is within ...

This example is wrong as it shows an icon with an associated text. it should be: Verify the alarm exists This also brings the question of the syntax since the <text for identification> should be optional

from : Custom Apps Drive HTTP actions peergos book

drive HTTP actions

This text highlights the critical issue of educational inequality and the ways in which institutional structures can perpetuate disparities among students. The advantage enjoyed by students from private feeder schools raises questions about fairness and access to high quality education.

The text further illustrates the flexible tracking system by detailing the options available to ninth graders for foreign language electives. This text highlights the evolving nature of tracking in American high schools, emphasizing the balance between maintaining academic standards and providing students with greater flexibility and choice.

The text acknowledges that schools have limited ability to change the inequalities in students' backgrounds, such as socioeconomic status or family circumstances.

The text highlights a contradiction between the school’s focus on diversity and the ongoing achievement gap. Berkeley High is described as having institutional features, such as courses and departments that celebrate diversity, yet these efforts are not translating into equal academic success for all students.

Due to the apparent issues with this type of thinking, some changes in common pedagogy have occurred since then. As someone with no prior computer-science knowledge, I have seen that this course is far more focused on thinking about the art of problem solving, then it is about the logic of machines. We are able to atomically break down the way our programs work to the computer with the stepper, but just as often it's useful instead to undergo a design process. We think about what we want to achieve, and break it down into goals a human finds intuitive, rather than focusing on the thought process of the computer.

Parnas says that it is unlikely that research towards automatic programming is going to bring real results. However, with the creation of public AI such as ChatGPT for public use, you could say that the military has better AI than the public. Thus, the military's automatic programming has significantly improved from his time.

Getting inspired by things he encountered at the cemetery is an excellent idea. I liked that and I am planing to use it in the future.

Great start of describing writing place environment and how change based on the writer state of mind

Gawain's reputation holds a lot of weight both with Lady Bertilak and Lord Bertilak, but in different contexts. The Lady's image of Gawain lies in courtly manner. The Lord's image of Gawain lies in his brave, honest, and other knightly attributes. Both of which he must uphold. The modern equivalent is having our reputation precede us and feeling pressure to uphold our image or face the consequences of failing.

Aside from christian discourse, the hyper fixation on male homosexual relation in comparison to female homosexual relations is extremely present in the modern world. Perhaps it has something to do with societal norms and how cultures view masculinity. To be masculine is to refrain from emotional and physical expression of love, care, and tenderness. Tenderness and softness are expected traits of femininity, maybe this is why it's so culturally consuming to see two men engage physical and emotional romance.

The interpretation of a kiss between men can greatly vary depending on the cultural context. I argue that if a 21st century, american, heterosexual, male were to be transported into the medieval period and integrated into society, he would be able to become accustom to kisses between men as greetings, homage, or parting of ways. It is easier to adapt the the dominant cultural norms rather than vice versa.

One example I can think of is TikTok and Xiaohongshu. Both have graphic sharing functions. But the next steps are slightly different. While TikTok focuses on fostering group chats and building connections between different users, Xiaohongshu follows up by guiding users to post comments and chat individually.

The narrator's feelings change from giving money to feeling hatred. This shows how past relationships can be complicated and filled with regret.

The music is a rare moment of beauty in a harsh world. It shows that even in tough times, there’s a desire to express feelings and find joy.

The laughter is mean and not joyful, showing how tough and unkind their environment is. It highlights the sadness and challenges children face.

"Pornography" as in books about LGBTQ+ people?

The text acknowledges the difficulty in precisely determining the effects of various family-related factors on children's school readiness. The text asserts that the rise in income inequality since the 1970s and the concurrent widening gap in school success between children from different income levels is not a coincidence.

The text also highlights the importance of family structure in shaping children's academic trajectories. Both Anthony and Harold were raised primarily by single mothers, a situation that is common in low-income families but rare in high-income families.

The text begins by discussing the impact of income inequality on children’s educational success. The circumstances that Anthony and Harold grew up in, left them at a disadvantage compared to children from wealthier families. Although some inequality has always existed, the income gap has widened significantly over the past four decades, which has exacerbated disparities in educational opportunities and outcomes.

The text emphasizes that even modest income increases can lead to measurable improvements in children’s achievement test scores, raising them by the equivalent of about 20 SAT points. The text suggests that increasing income may be one way to help narrow the achievement gap.

Income is a major factor in determining a child's academic success. National studies consistently show that children from higher-income families perform better in school, and the achievement gaps between wealthy and low-income children have grown significantly over time. These gaps highlight the powerful influence of socioeconomic status on educational outcomes

The bar chart in figure 3.1 visually represents the magnitude of income-based achievement gaps. This quantitative approach illustrates how substantial these gaps are, emphasizing that income plays a powerful role in determining educational success.

The text highlights that beyond academic skills, children from higher-income families tend to have advantages in behavioral and emotional well-being. They are more engaged in school, less likely to engage in antisocial behavior, and less likely to struggle with mental health issues compared to their lower-income peers. These behavioral and emotional differences still contribute to overall school success and remain stable throughout elementary and high school.

The use of a "100-point difference" to represent one standard deviation allows for the measurement of how much high- and low-income children's achievements differ over time. The reference to figure 3.1 suggests that the size of the gap between high- and low-income children is significant, underlining income as one of the most powerful factors influencing academic success.

The text points to how socioeconomic factors such as family income, parental education, and family structure can significantly shape a child’s future, perpetuating cycles of inequality

I did not know that. No wonder clocks have been so relevant through the centuries.

I find it interesting how there were multiple languages used amongst Rome. Making the use of languages very diverse. From lower classes to upper classes.

When colonizers forced Indigenous peoples and African peoples not to speak their languages, that imapcted the world more than we understand

It is amazing how much culture impacts our outlook on life and how we respond to events, big or small

Wouldn't that be considered a scroll, rather than a book?

Mechanics to this day are crucial to our society.

I am curious as to how water was used in order to abstract golds.

I can understand as to why clocks are heavily referenced, mainly because of their longevity of relevance.

I find it super interesting that an invention from way back then, is still essential in todays society.