Reviewer #1 (Public Review):

Summary:

Tsai and Seymen et al. investigate associations between RTE expression and methylation and age and inflammation, using multiple public datasets. The concept of the study is in principle interesting, as a systematic analysis of RTE expression during human aging is lacking. Unfortunately, the reliance on expression microarray data, used to perform the core analysis of the paper places much of the study on shaky ground. The findings of the study would not be sufficiently supported until the authors validate them with more suitable methods.

Strengths:

This is a very important biological problem.

Weaknesses:

RNA microarray probes are obviously biased to genes, and thus quantifying transposon analysis based on them seems dubious. Based on how arrays are designed there should at least be partial (perhaps outdated evidence) that the probe sites overlap a protein-coding or non-coding RNA. The authors state they only used intergenic probes, but based on supplementary files, almost half of RTE probes are not intergenic but intronic (n=106 out of 264). This is further complicated by the fact that not all this small subset of probes is available in all analyzed datasets. For example, 232 probes were used for the MESA dataset but only 80 for the GTP dataset. Thus, RTE expression is quantified with a set of probes which is extremely likely to be highly affected by non-RTE transcripts and that is also different across the studied datasets. Differences in the subsets of probes could very well explain the large differences between datasets in multiple of the analyses performed by the authors, such as in Figure 2a, or 3a. It is nonetheless possible that the quantification of RTE expression performed by the authors is truly interpretable as RTE expression, but this must be validated with more data from RNA-seq. Above all, microarray data should not be the main type of data used in the type of analysis performed by the authors.

Reviewer #2 (Public Review):

Summary:

Yi-Ting Tsai and colleagues conducted a systematic analysis of the correlation between the expression of retrotransposable elements (RTEs) and aging, using publicly available transcriptional and methylome microarray datasets of blood cells from large human cohorts, as well as single-cell transcriptomics. Although DNA hypomethylation was associated with chronological age across all RTE biotypes, the authors did not find a correlation between the levels of RTE expression and chronological age. However, expression levels of LINEs and LTRs positively correlated with DNA demethylation, and inflammatory and senescence gene signatures, indicative of "biological age". Gene set variation analysis showed that the inflammatory response is enriched in the samples expressing high levels of LINEs and LTRs. In summary, the study demonstrates that RTE expression correlates with "biological" rather than "chronological" aging.

Strengths:

The question the authors address is both relevant and important to the fields of aging and transposon biology.

Weaknesses:

The choice of methodology does not fully support the primary claims. Although microarrays can detect certain intergenic transposon sequences, the authors themselves acknowledge in the Discussion section that this method's resolution is limited. More critical considerations, however, should be addressed when interpreting the results. The coverage of transposon sequences by microarrays is not only very limited (232 unique probes) but also predetermined. This implies that any potential age-related overexpression of RTEs located outside of the microarray-associated regions, or of polymorphic intact transposons, may go undetected. Therefore, the authors should be more careful while generalising their conclusions.

Additionally, for some analyses, the authors pool signals from RTEs by class or family, despite the fact that these groups include subfamilies and members with very different properties and harmful potentials. For example, while sequences of older subfamilies might be passively expressed through readthrough transcription, intact members of younger groups could be autonomously reactivated and cause inflammation. The aggregation of signals by the largest group may obscure the potential reactivation of smaller subgroups. I recommend grouping by subfamily or, if not possible due to the low expression scores, by subgroup. For example, all HERV subfamilies are from the ERVL family.

Next, Illumina arrays might not accurately represent the true abundance of TEs due to non-specific hybridization of genomic transposons. Standard RNA preparations always contain traces of abundant genomic SINEs unless DNA elimination is specifically thorough. The problem of such noise should be addressed.

Lastly, scRNAseq was conducted using 10x Genomics technology. However, quantifying transposons in 10x sequencing datasets presents major challenges due to sparse signals. Smart-seq single-cell technology is better suited to this particular purpose. Anyway, it would be more convincing if the authors demonstrated TE expression across different clusters of immune cells using standard scRNAseq UMAP plots instead of boxplots.

I recommend validating the data by RNAseq, even on small cohorts. Given that the connection between RTE overexpression and inflammation has been previously established, the authors should consider better integrating their observations into the existing knowledge.

This is now called MITRE ATT&CK drop down menu

if you are doing this from scratch you will have to create a vm1 in RG1

Author response:

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

eLife assessment

This study provides an important finding that the local abundance of metabolites impacts the biology of the tumor microenvironment by utilizing kidney tumors from patients and adjacent normal tissues. The evidence supporting the claims of the authors is convincing although certain caveats need to be taken into consideration as the authors acknowledged in the paper. The work will be of interest to the research community working on metabolism and on kidney cancer especially.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The present study addresses how the local abundance of metabolites impacts the biology of the tumor microenvironment. The authors enroll patients harboring kidney tumors and use freshly resected tumor material for metabolic studies. Specifically, the authors separate the adjacent normal kidney tissue from the tumor material and then harvest the interstitial fluid from the normal kidney (KIF) or the tumor (TIF) for quantitative metabolomics. The plasma samples from the patient are used for comparison. Additionally, the authors also compare metabolite levels in the plasma of patients with kidney versus lung cancer (or healthy donors) to address how specific tumor types might contribute to circulating levels of metabolites. Altogether, the authors find that the metabolite levels in the KIF and TIF, although vastly different than plasma, are largely overlapping. These findings indicate that tissue of origin appears to have a stronger role in determining the local metabolic environment of tumors than the genetics or biochemistry of the tumor itself.

Strengths:

The biggest strength of the current study is the use of human patient-derived samples. The cohort size (~50 patients) is relatively large, which adds to the rigor of the work. The work also relies on a small pool of metabolites that can be quantitatively measured using methods developed by the authors. Focusing on a smaller metabolic pool also likely increases the signal-to-noise ratio and enables the more rigorous determination of any underlying differences. The manuscript is well-written and highlights both the significance of the findings and also acknowledges many of the caveats. The recognition of the metabolic contributions of surrounding normal tissue as the primary driver of local nutrient abundance is a novel finding in the work, which can be leveraged in future studies.

We thank the Reviewer for their careful evaluation of the study and for their supportive comments.

Weaknesses:

The work has certain caveats, some of which have been already recognized by the authors. These include the use of steady-state metabolites and the possibility of cross-contamination of some TIF into the adjacent KIF. This study is also unable to distinguish the mechanisms driving the metabolic changes in KIF/TIF relative to circulating levels in plasma.

We agree with the Reviewer that these are important caveats to consider when interpreting the results of this study.

The relative similarity of KIF and TIF is quite surprising. However, this interpretation is presently based on a sampling of only ~100 polar metabolites and ~200 lipid molecules. It is, perhaps, possible that future technological developments that enable more comprehensive quantitative metabolic profiling might distinguish between KIF and TIF composition.

The Reviewer raises another important point that our interpretation of KIF vs TIF is limited to the ~300 metabolites we measured. We agree it would be worthwhile quantifying more metabolites where technically feasible to further characterize similarities and differences in nutrient availability between tumor and normal tissues.

In vitro, tissue culture is recognized to suffer from ‘non-physiological’ nutrient dependencies, which are impacted by the composition of culture media. Thus, in vivo studies remain our current gold-standard in mechanistic studies of tumor metabolism. It is presently unclear whether the findings of this work will be recapitulated in any of the kidney cancer in vivo models and thus be functionally testable.

We thank the Reviewer for calling attention to the limitations of cell culture media in studying tumor metabolism. While both in vitro and in vivo approaches have inherent limitations, formulating culture media based on metabolite concentrations measured here and in other studies provides a tool to study the influence of nutrient availability on kidney cell or kidney cancer cell phenotypes in vitro. We also agree with the Reviewer that determining whether the findings in our study are recapitulated in mouse models of kidney cancer, as this might enable investigation into the factors that modulate nutrient availability in this tissue context.

Reviewer #2 (Public Review):

The study employs quantitative metabolomic and lipidomic analyses to scrutinize tumor interstitial fluid (TIF), adjacent normal kidney interstitial fluid (KIF), and plasma samples from renal cell carcinoma (RCC) patients. The authors delve into the intricate world of renal cell carcinoma and its tumor microenvironment, shedding light on the factors that shape nutrient availability in both cancerous and adjacent normal tissues. The authors prove that non-cancer-driven tissue factors play a dominant role in shaping nutrient availability in RCC. This finding opens up new avenues for research, suggesting that the tumor microenvironment is profoundly influenced by factors beyond the presence of cancer cells. This study not only contributes valuable insights into RCC metabolism but also prompts a reevaluation of the factors governing nutrient availability in tumor microenvironments more broadly. Overall, it represents a significant step forward in our understanding of the intricate interplay between cancer and its surrounding milieu.

We thank the Reviewer for their evaluation of our work and for their supportive comments.

The study is overall well-constructed, including appropriate analysis. Likewise, the manuscript is written clearly and supported by high-quality figures. Since the authors exclusively employed samples from RCC patients and did not include kidney interstitial fluid and plasma samples from healthy individuals, we cannot accurately assess the true significance and applicability of the results until the role of cancer cells in reshaping KIF is understood. In essence, some metabolite levels in the tumor interstitial fluid did not show an increase or decrease compared to the adjacent normal kidney interstitial fluid. However, the levels of these metabolites in both TIF and KIF might be higher or lower than those in kidney interstitial fluid from healthy individuals, and the roles of these metabolites should not be overlooked. Similar concerns extend to plasma levels, emphasizing the importance of metabolites that synchronously change in RCC TIF, KIF, and plasma-whether elevated or reduced.

We agree with the Reviewer that an important caveat in considering the study findings is that we do not have KIF values from healthy individuals. Since resection of normal kidney is not a common procedure, obtaining KIF samples from healthy patients was not possible to complement our analysis. We further agree that the metabolite levels we measured in KIF or plasma are plausibly impacted by the presence of RCC. We did compare the composition of polar metabolites in the plasma from RCC, lung cancer, and healthy patients, highlighting how cystine is affected by tumor presence and/or sample collection methodology. We also point out that factors such as diet will impact metabolites in both blood and tissues.

Reviewer #3 (Public Review):

In this study, the authors utilized mass spectrometry-based quantification of polar metabolites and lipids in normal and cancerous tissue interstitial fluid and plasma. This showed that nutrient availability in tumor interstitial fluid was similar to that of interstitial fluid in adjacent normal kidney tissue, but that nutrients found in both interstitial fluid compartments were different from those found in plasma. This suggests that the nutrients in kidney tissue differ from those found in blood and that nutrients found in kidney tumors are largely dictated by factors shared with normal kidney tissue. Those data could be useful as a resource to support further study and modeling of the local environment of RCC and normal kidney physiology.

We thank the Reviewer for their time considering our paper and for their supportive comments.

In Figures 1D and 1E, there were about 30% of polar metabolites and 25% of lipids significantly different between TIF and KIF, which could be key factors for RCC tumors. This reviewer considers that the authors should make comments on this.

We agree with the Reviewer that the metabolites that significantly differ between TIF and KIF are of interest, particularly for those studying RCC tumor metabolism. We comment on some of the metabolites driving differences between TIF and KIF in our discussion of Figure 2, and in the revised manuscript we now include a new figure showing a heatmap that enables visualization of these metabolites (Figure 2-Supplement 1A-B).

Recommendations for the authors:

From the Reviewing Editor:

Figure 2 needs to plot heatmaps for both upregulated and downregulated metabolites in TIF.

We agree and now include heatmaps for significantly differing polar metabolites and lipids in TIF vs KIF as requested by Reviewer 3 (Figure 2-Supplement 1A-B). For completeness, we also include heatmaps for metabolites differing between healthy and RCC plasma (Figure 2-Supplement 2C) and for NSCLC and RCC plasma (Figure 2-Supplement 2D).

There is a need to show whether the differences in these metabolites between plasma and tissue interstitial fluid are specific to RCC patients or if they are also present in normal individuals.

Unfortunately, it has not been possible for us to collect KIF from healthy individuals. Since resection of normal kidney is not a common procedure, we have no way to obtain sufficient KIF samples from healthy patients for this measurement. We discuss this as a limitation of the study.

Reviewer #1 (Recommendations For The Authors):

a. The authors should provide additional details about the methodology to separate the KIF and TIF. Contaminating metabolites from surrounding tissue or the peritoneal fluids could impact interpretation and it would be helpful to understand how these challenges were addressed during tissue collection for this study. Additionally, was the collected tissue minced or otherwise dissociated? If so, could these procedures cause tissue lysis and contaminate the KIF/TIF with intracellular components?

We thank the Reviewer for the suggestions to include more information about the sampling methodology. Care was taken to minimize cell lysis incurred by the processing methodology as tissues were not minced, smashed, nor dissociated, however there is still a possibility of some level of tissue lysis that is pre-existing or occurs during the isolation procedure. We note this caveat in the text (lines 218-220) and have updated the Methods with more details of the sampling and processing of the samples.

b. Although the authors focus on metabolites that are elevated in TIF (relative to KIF and plasma), it would be equally relevant to consider the converse. Metabolites that are reduced in TIF, either due to underproduction or overconsumption, could render the tumors auxotrophic for some critical dependencies and identify some novel metabolic vulnerabilities. In this regard, Figure 2 could have a heatmap of the top metabolites that are elevated and depleted specifically in the TIF.

We agree with the Reviewer it is useful to include heatmaps to better display the metabolites that significantly differ between TIF and KIF and now include these in Figure 2-Supplement 1A-B.

c. The future utilization of this knowledge would depend on our ability to model these differences. Would interstitial tissue from a normal mouse kidney or tumor-bearing mouse kidney recapitulate the same differences relative to mouse plasma?

We agree with the Reviewer that it would be worth determining whether the findings in our study are recapitulated in mouse models of kidney cancer, which would support future investigation into the factors that modulate nutrient availability. This is an interesting question, but we did not have access to endogenously arising models of RCC, which have been a limitation for the field, and comparison of normal mouse kidney metabolite data to human metabolite data is problematic for obvious reasons. Thus, we had no choice but to discuss this as a limitation of the study.

Reviewer #2 (Recommendations For The Authors):

In this study, Abbott et al. investigated the metabolic profile of renal cell carcinoma (RCC) by analyzing the tumor interstitial fluid (TIF), adjacent normal kidney interstitial fluid (KIF), and plasma samples from patients. The results indicate that nutrient composition in TIF closely resembles that of KIF, suggesting that tissue-specific factors, rather than tumor-driven alterations, have a more significant impact on nutrient levels. These findings are interesting. The study is overall well-constructed, including appropriate analysis, and the manuscript is written clearly and supported by high-quality figures. However, some issues are raised which if addressed, would strengthen the paper.

We thank the Reviewer for their suggestions to improve the paper.

The authors found a difference in the number of metabolites when comparing TIF or KIF lipid composition with plasma. The discoveries are intriguing; however, I am keen to understand whether the differences in these metabolites between plasma and tissue interstitial fluid are specific to RCC patients or if they are also present in normal individuals. I am particularly interested in identifying which metabolites could serve as potential diagnostic markers, intervention targets, or potentially reshape the tumor microenvironment. Because, even though some metabolite levels show no difference between TIF and KIF in RCC patients, I wonder if these metabolite levels in KIF increase or decrease compared to the interstitial fluid in healthy individuals. I am intrigued by the metabolites that simultaneously increase or decrease in both TIF and KIF compared to the kidney interstitial fluid in healthy individuals.

We agree with the Reviewer that it would be interesting to measure kidney interstitial fluid from healthy patients to be able to compare metabolites changing due to the presence of RCC tumor. As we discuss in response to the public review, this was not possible as we could not obtain material from healthy individuals for analysis. Nevertheless we agree it warrants future study if material were available.

The analysis conducted using plasma from healthy donors, as applauded by the author, is noteworthy. The author seems to have found that cystine levels do not differ between RCC patient plasma and tissue interstitial fluid. However, considering that in patient plasma, the cystine concentration is approximately two-fold higher than in plasma from healthy individuals, likely, cystine levels in patient tissue fluid have also increased nearly two-fold compared to levels in the interstitial fluid of normal kidney tissues. This finding aligns with the discovery of elevated GSH levels in cancer cells.

We agree with the Reviewer that a higher cystine concentration in RCC patient plasma and interstitial fluid is interesting, and also considered this in relationship to past findings including reports of elevated GSH levels in RCC. However, we think this observation is driven at least in part by the fasting status of the patients pre-surgery. This does not rule out some part being related to the presence of the tumor, as this would be consistent with elevated GSH levels as noted by the Reviewer. Future studies will be needed to further delineate the factors that impact elevated cystine levels in both interstitial fluid and plasma.

Some minor typos, such as "HIF1􀀀-driven" should be corrected.

We thank the Reviewer for pointing out this typo and we have corrected it in the revised manuscript.

Reviewer #3 (Public Review):

In this study, the authors utilized mass spectrometry-based quantification of polar metabolites and lipids in normal and cancerous tissue interstitial fluid and plasma. This showed that nutrient availability in tumor interstitial fluid was similar to that of interstitial fluid in adjacent normal kidney tissue, but that nutrients found in both interstitial fluid compartments were different from those found in plasma. This suggests that the nutrients in kidney tissue differ from those found in blood and that nutrients found in kidney tumors are largely dictated by factors shared with normal kidney tissue. Those data could be useful as a resource to support further study and modeling of the local environment of RCC and normal kidney physiology.

eLife assessment

This study provides an important finding that the local abundance of metabolites impacts the biology of the tumor microenvironment by utilizing kidney tumors from patients and adjacent normal tissues. The evidence supporting the claims of the authors is convincing. The work will of interest to the research community working on metabolism and kidney cancer especially.

Reviewer #1 (Public Review):

(a) Summary: The present study addresses how the local abundance of metabolites impacts the biology of the tumor microenvironment. The authors enroll patients harboring kidney tumors and use freshly resected tumor material for metabolic studies. Specifically, the authors separate the adjacent normal kidney tissue from the tumor material and then harvest the interstitial fluid from the normal kidney (KIF) or the tumor (TIF) for quantitative metabolomics. The plasma samples from the patient are used for comparison. Additionally, the authors also compare metabolite levels in the plasma of patients with kidney versus lung cancer (or healthy donors) to address how specific tumor types might contribute to circulating levels of metabolites. Altogether, the authors find that the metabolite levels in the KIF and TIF, although vastly different than plasma, are largely overlapping. These findings indicate that tissue of origin appears to have a stronger role in determining the local metabolic environment of tumors than the genetics or biochemistry of the tumor itself.

(b) Strengths: The biggest strength of the current study is the use of human patient-derived samples. The cohort size (~50 patients) is relatively large, which adds to the rigor of the work. The work also relies on a small pool of metabolites that can be quantitatively measured using methods developed by the authors. Focusing on a smaller metabolic pool also likely increases the signal-to-noise ratio and enables the more rigorous determination of any underlying differences. The manuscript is well-written and highlights both the significance of the findings and also acknowledges many of the caveats. The recognition of the metabolic contributions of surrounding normal tissue as the primary driver of local nutrient abundance is a novel finding in the work, which can be leveraged in future studies.

(c) Weaknesses: The work has certain caveats, some of which have been already recognized by the authors. These include the use of steady-state metabolites and the possibility of cross-contamination of some TIF into the adjacent KIF. This study is also unable to distinguish the mechanisms driving the metabolic changes in KIF/TIF relative to circulating levels in plasma.

The relative similarity of KIF and TIF is quite surprising. However, this interpretation is presently based on sampling of only ~100 polar metabolites and ~200 lipid molecules. It is, perhaps, possible that future technological developments that enable more comprehensive quantitative metabolic profiling might distinguish between KIF and TIF composition.

In vitro tissue culture is recognized to suffer from 'non-physiological' nutrient dependencies, which are impacted by the composition of culture media. Thus, in vivo studies remain our current gold-standard in mechanistic studies of tumor metabolism. It is presently unclear whether the findings of this work will be recapitulated in any of the kidney cancer in vivo models and thus be functionally testable.

The authors have acknowledged these caveats and where possible provided textual clarifications and updated figures in their revised manuscript. Future work will be required to model these changes in animal models.

Reviewer #2 (Public Review):

The study employs quantitative metabolomic and lipidomic analyses to scrutinize tumor interstitial fluid (TIF), adjacent normal kidney interstitial fluid (KIF), and plasma samples from renal cell carcinoma (RCC) patients. The authors delve into the intricate world of renal cell carcinoma and its tumor microenvironment, shedding light on the factors that shape nutrient availability in both cancerous and adjacent normal tissues. The authors prove that non-cancer-driven tissue factors play a dominant role in shaping nutrient availability in RCC. This finding opens up new avenues for research, suggesting that the tumor microenvironment is profoundly influenced by factors beyond the presence of cancer cells. This study not only contributes valuable insights into RCC metabolism but also prompts a reevaluation of the factors governing nutrient availability in tumor microenvironments more broadly. Overall, it represents a significant step forward in our understanding of the intricate interplay between cancer and its surrounding milieu.

The study is overall well-constructed, including appropriate analysis. Likewise, the manuscript is written clearly and supported by high-quality figures. Since the authors exclusively employed samples from RCC patients and did not include kidney interstitial fluid and plasma samples from healthy individuals, we cannot accurately assess the true significance and applicability of the results until the role of cancer cells in reshaping KIF is understood. In essence, some metabolite levels in the tumor interstitial fluid did not show an increase or decrease compared to the adjacent normal kidney interstitial fluid. However, the levels of these metabolites in both TIF and KIF might be higher or lower than those in kidney interstitial fluid from healthy individuals, and the roles of these metabolites should not be overlooked. Similar concerns extend to plasma levels, emphasizing the importance of metabolites that synchronously change in RCC TIF, KIF, and plasma-whether elevated or reduced.

mit einem Original in Größe und Ausführung genau übereinstimmende Nachbildung, Wiedergabe, besonders als fotografische Reproduktion (Duden)

Ist das vergleichbar damit, dass es vor Jahren gestattet war, dass man eine Kopie von DVD oder CD für den Privatgebrauch machen durfte?

Wie wird mit dem Urheberrecht verfahren? Es gab vor vielen Jahren Proteste als Google damit begann, Texte zu digitalisieren. Bei historischen Texten ist das Urheberrecht abgelaufen und zeitgenössische Verfasser:innen müssen sich oftmals einverstanden erklären, wenn ihre Werke im open access erhältlich sind. Was ist mit den Fällen, die dazwischen liegen? Oder greift 'schlicht' die Begründung, dass ein Text aus nicht-kommerziellen oder aus wissenschaftlichen Gründen genutzt werden soll?

I think that this passage points out the importance of having supportive learning environments that foster trustworthy relationships between faculty and students. As a student, I know I get a lot of anxiety when I have to go ask my advisor a small question. For those that are undocumented, I completely understand why some are weary of reaching out for help from school faculty.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

Learn more at Review Commons

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Reply to the reviewers

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

The manuscript by Xie et al investigates the role of efemp1 in mediating ocular growth. Efemp1, a secreted extracellular matrix glycoprotein, was previously identified as a myopia-risk gene in human GWAS studies. Given that myopia is linked to aberrant eye shape, the authors investigated whether and how this gene mediates eye growth. Using a CRISPR based approach in zebrafish the authors knocked out efemp1 specifically in the retina and established that a myopic eye results. They went further and investigated visual function in these mutant fish using the optomotor response and electroretinograms. As dark-rearing in many animal models has been linked to the induction of myopia, the authors examined the effects of a dark-rearing regimen in efemp1 mutants and found surprisingly that they did not show signs of myopia. Lastly, the expression and distribution of several myopia-associated genes was investigated in the retina of efemp1 mutants and following dark-rearing.

1. The starting point for this study was the generation of a "retina-specific knockout mutant of the efemp1 gene". However, evidence for a 'successful' knockout at the protein level is missing.

We have clarified the exact nature of our efemp12C-Cas9 model further. The mutants have mosaic genetic modification that do not simply lead to gene deletion (knockout). We have reworded throughout the manuscript to avoid statement indicating the efemp1 2C-Cas9 fish as a knockout model and instead used “genetic modification” or “genetic disruption”, etc:

“This gene editing system led to mosaic retinal mutations; each Cas9-expressing retinal cell that were driven by the rx2 promoter would perform its own CRISPR gene editing process, and as a result, even within an individual retina, there were different types of indels (e.g., loss- or gain-of-function mutations, or milder mutations that may cause mislocalization) in different cells.” (Line 104–108).

For the same reason, it is very challenging to show such a mosaic genetic editing in the protein level. First of all, we were not able to find commercial anti-EFEMP1 antibody for zebrafish that targets specifically the editing sites in our fish model. This means that mutated efemp1 DNAs that were transcribed and translated would produce mutant EFEMP1 protein that might still be recognized by an anti-EFEMP1 antibody, although their dysfunction might manifest as altered distribution and thus abnormal ocular development.

On the other hand, in this study we used a headloop PCR technique, a sensitive genotyping approach that specifically suppresses amplification of wild-type but not mutated efemp1 DNA to show that there were genetic modifications in our mutants. However, likely due to the patchy distribution of Cas9-expressing retinal cells (Fig 1A′) and the non-uniform nature of gene editing, our genotyping results showed weak mutant bands (Fig 1C–D), implicating low editing rates. The fact that only a proportion of mutations would result in loss of the protein would make it difficult to distinguish the gene editing in the retina via immunostaining or western blot.

We have added following in the Results section to indicate the difficulties in showing genetic modification at the protein level for the efemp12C-Cas9 model:

“On the other hand, due to the mosaic nature of the gene editing, the patchiness of Cas9-expressing retinal cells (Fig 1A′) and the potentially low editing rate, as well as the unavailability of commercial anti-EFEMP1 antibodies that targets specifically the CRISPR editing sites, efemp1 modification in our mutant model at the protein level is challenging to show.” (Line 125–128)

Immunostaining for Efemp1 in sections of the entire retina from control and mutant fish would have helped here. It is only in Figure 7 B, C that portions of the inner retina from control and efemp1 2c-Cas9 fish are shown with Efemp1 immunostaining. Control and mutant retinae show slight relative differences in Efemp1 fluorescence levels which are difficult to reconcile with a knock-out scenario.

As mentioned above, our model is not simply a knockout but a combination of a range of indels that may produce mutant proteins. At least some of them are therefore still likely to bind with the anti-EFEMP1 antibody used in the present study; the antibody does not bind to EFEMP1 regions corresponding to sgRNAs target sites on zebrafish efemp1 DNA. We have added this detail in the Methods to clarify.

“Noting that the anti-EFEMP1 antibody does not bind to EFEMP1 regions corresponding to sgRNAs target sites on zebrafish efemp1 DNA, thus mutant proteins (if any) may still be labeled by the antibody.” (Line 790–792)

Therefore, it makes sense that our result showed differences in relative EFEMP1 fluorescence between groups across the inner retina rather than complete loss of EFEMP1 immunostaining in mutant retinas.

resumably this phenotype is a result of the mosaic expression of Cas9 (GFP) shown in Fig 1? Can the authors explain the reason for this mosaicism?

We believe that the “mosaic expression of Cas9” the reviewer mentioned is the “patchy distribution of Cas9-expressing retinal cells” as we mentioned in the above response. Yes this is also partially the reason why mutant retinas still present EFEMP1 immunostaining. The patchy (or mosaic) Cas9 expression in the retina of our mutant model can be because we use the Gal4/UAS system to drive the 2C-Cas9 gene editing system. Mosaic expression has long been noticed as a drawback of the Gal4/UAS system. We have modified the manuscript to explain the mosaic Cas9 expression in the mutant retina:

“The patchiness of Cas9 expression in the mutant retina may attribute to the Gal4/UAS system (Halpern et al., 2008).” (Line 103–104)

Given this mosaic expression would one expect Efemp1 immunoreactive areas intermingled with areas devoid of Efemp1 in the mutant retina?

This happens only in cells that CRIPSR eliminates production of EFEMP1, but due to patchy Cas9 expression and perhaps only a little proportion of Cas9-positive cells will lose EFEMP1 protein, our immunostaining did not show apparent intermingling. Importantly, it is worth noting that as our explanation above, anti-EFEMP1 antibody may be able to bind with mutant EFEMP1 proteins and thus EFEMP1 immunostaining will still present in retinal cells with successful gene editing.

Further, do deficits in the various functional assays the authors perform correlate with the degree of mosaicism?

We appreciate the reviewer’s interesting idea. As primary goal of the present study is to determine whether retinal-specific efemp1 modification has any effect on ocular refraction, we aimed to use fish with as more Cas9-expressing cells as possible for functional analysis, and thus fish used were not expected to have discernible difference in degree of Cas9 expression mosaicism. Therefore, it is not known that whether there is a correlation between ocular deficits and Cas9 expression mosaicism. We thank the reviewer’s suggestion and will bear this idea in mind for future experimental design.

In the same vein, in Figure 2 the authors refer to variation in GFP levels in the efemp12c-Cas9. It is not clear whether the authors mean levels of GFP in individual cells or numbers of GFP+ cells. Presumably the latter. Could the authors clarify?

We have added details in the Methods of the manuscript to clarify:

“Post-hoc retinal histology indicated that intensity of eGFP fluorescence is corresponding to eGFP positive cell number; fish with higher eGFP fluorescence level had more eGFP positive cells.” (Line 723–725)

In my opinion understanding and characterizing the efemp12c-Cas9 fish thoroughly is key to interpreting the phenotypes the authors show subsequently.

We agree with the reviewer. Due to the characteristics of our 2C-Cas9 model mentioned above, headloop PCR, which is highly sensitive for determining occurrence of gene mutations regardless indel types, is so far the most practical approach for us to provide evidence of successful gene editing. Because there was limited means to show gene modification in the protein level for our mutant model (as mentioned above), we instead provided functional verification of gene modification using OMR. We showed that functionally our 2C-Cas9 model have comparable phenotype with efemp1-knockdown zebrafish that have robust gene disruption induced by morpholino. Overall, with this evidence we believe that there were efemp1 modification in our fish model. Given no other manipulations, the phenotypes are presumably due to the mosaic mutations generated here. We would speculate (though have no data to show this) that a more even and complete knockout of Efemp1 throughout all of the retinal neurons would increase the size of the phenotypic changes seen even more. It was important for us to target the eye to assess the role in the local emmetropisation processes rather than mixing it with possible other CNS defects confounding the phenotype. We were excited to be able to observe quantifiable phenotypes even with such a mosaic randomized mutation model shown here and believe it gives more strength to the role of Efemp1.

Reviewer #1 (Significance (Required)):

The wide range of assays the authors perform to assess visual deficits is commendable. Such a comprehensive approach ranging from anatomical, behavioral and electrophysiological assays is poised to identify changes that could otherwise be overlooked. Given the increasing use of zebrafish as models of ocular diseases, this study provides a solid roadmap of the types of analysis possible. This work should be interesting to researchers in the field of myopia research and to basic vision researchers interested in using the zebrafish as a model organism.

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

Summary: In this study, the authors used the zebrafish model to study efemp1, a gene that was previously found to be associated with myopia. They used CRISPR-Cas9 to create specific efemp1 knockout in the retina in a mosaic manner. They used a few histological and physiological techniques to evaluate the resulting mutant and found that the efemp1 mutants developed symptoms that are consistent with myopia. The authors further quantified the expression of a few potential target genes in the eye that are potentially implicated in myopia phenotype. The authors also evaluated the differential phenotype of the efemp1 mutant grown in different light conditions that might contribute to myopia.

Major comments:

Overall, the authors have provided convincing evidence of the phenotype created by their efemp1 perturbation. Their experiments were thoroughly done and extensively analyzed. They even discussed some potential shortcomings of their study. Their study is a nice first step towards a better understanding of the efemp1 gene function in ocular growth and in myopia. All my comments below should be addressed by clarifications and discussions and not by any new experiments or projects.

Minor comments:

1. Elaborate the rationale for choosing efemp1 from the original GWAS study for zebrafish investigation. The authors only mentioned that this gene is among the highest in the rank and its role in myopia is not clear. However, there are quite a few other genes in the GWAS study that were ranked as high, if not higher than efemp1.

We thank the reviewer for the suggestion. Firstly, in a previous study, we used the high-throughput zebrafish optomotor response assay coupled with morpholino gene knockdown to screen top-ranked myopia-risk genes from the GWAS study. To use zebrafish as a model, additionally we took into account several other factors including existence of zebrafish orthologues, gene expression in the eye, association of ocular phenotypes with risk genes shown in previous zebrafish studies, fatality of gene depletion and availability of characterized morpholinos to prioritize GWAS-associated risk genes for screening. With significant reduction of OMR responses in efemp1 morphants, efemp1 was selected as a gene of interest for investigation. As our pre-screen is currently an unpublished study, we were not showing the data in the manuscript, but we are happy to show relevant results to the reviewer if requested. To clarify the selection of this gene, we have added a brief statement in the Introduction:

“Our previous study (unpublished) screening GWAS-associated myopia-risk genes with high-throughput optomotor response measurement and morpholino gene knockdown indicated that knockdown of efemp1 in larval zebrafish reduced spatial-frequency tuning function, making efemp1 a candidate gene worth for further investigation for myopia development.” (Line 53–57)

On the other hand, in the Introduction of our manuscript, we indeed had covered that in humans, efemp1 disruptions, with either gain- or loss-of functions, would lead to visual disease, such as Malattia Leventinese, doyne honeycomb retinal dystrophy, juvenile-onset open-angle glaucoma, or high myopia. These also implicated the importance in understanding the role of efemp1 in ocular development.

Elaborate the rationale for choosing retina as the target tissue of efemp1 knockout, especially when the original GWAS study indicated the expression of EFEMP1 is in cornea, RPE, and sclera, but not in retinal cells.

Firstly, efemp1 is expressed in the retina as shown by our immunostaining in zebrafish and in situ hybridization in mouse in a previous study (PMID: 26162006). We have modified the manuscript to clarify this point:

“EFEMP1 is a secreted extracellular matrix glycoprotein widely expressed throughout the human body, especially in elastic fiber-rich tissues, for examples, the brain, lung, kidney and eye including the retina (Livingstone et al., 2020; Mackay et al., 2015).” (Line 51–53)

In future studies it will be interesting to perform similar somatic efemp1 manipulation in other ocular tissues to examine whether this gene has tissue-dependent functions for ocular growth. Nonetheless, our results demonstrated that at the very least retinal efemp1 is involved in ocular development.

Secondly, the rx2 gene is indeed also expressed in the RPE in zebrafish (PMID: 11180949), meaning that there were also RPE cells expressing Cas9 driven by rx2. We have added this detailed to the manuscript:

“In this transgenic zebrafish line, Tg(rx2:Gal4) is expressed specifically in the retina and the RPE (Chuang and Raymond, 2001), due to the retina-specific retinal homeobox gene 2 (rx2) promoter.” (Line 95–97)

Importantly, as myopia generally develops due to dysregulated gene-environment interactions, modification of efemp1 specifically in the light-sensing retina allowed us to investigate the interaction of efemp1 with visual environment. We have added this point to the manuscript:

“In order to investigate the role of the efemp1 gene and its interaction with visual environment, we first generated a zebrafish line with efemp1 modification specifically in the retina (efemp12C-Cas9; Fig 1A), the light-sensing tissue in the eye, using a 2C-Cas9 somatic CRISPR gene editing system (Di Donato et al., 2016).” (Line 92–95)

Discuss possible ways of modifying efemp1 gene in the retina that would be more uniform and would not create mosaicism and/or heterogenous mutations that can complicate downstream characterizations and interpretations as the authors currently experienced.

We appreciate the reviewer’s suggestion. One possible way of generating uniform tissue-specific gene modification is to use the Cre-loxP recombination system. We have modified the Discussion of manuscript as per reviewer’s suggestion:

“To avoid such heterogeneous tissue-specific gene editing, the Cre-LoxP system is an option­: using tissue-specific driven Cre recombination to delete LoxP flanked exons of the target gene.” (Line 482–486)

• Added to discussion –

The authors should elaborate further on the effect of the mosaicism and heterogenous mutations on efemp1, a presumably excreted protein, on regulating the ocular growth.

We appreciate the reviewer’s interesting point of view. However, it is very difficult to identify a regionalized effect of mosaicism and heterogenous mutations of efemp1 on ocular growth even with dissected eyes. It is likely that distribution of Cas9-expressing cells was mosaic but still overall even across the retina. Perhaps in other models that allow controlled regional efemp1 manipulation in the eye, for sample, using gene promoters that present dorsal to ventral gradient, comparisons between modified and unmodified regions in the same eye will help to unravel whether efemp1 regulates eye growth only around the location where it was produced.

How did the downstream genes they studied affect by the messing up of the extracellular Efemp1? Is it through altering the Egf signal transduction?

Throughout the Discussion we have tried to cover how efemp1 disruption affect myopia-associated genes where it is possible by linking our results with literature. However, there were not enough details from the literature showing direct pathways between efemp1 and the tested myopia-risk genes. These will be interesting topics for further investigation. To our knowledge, there is no evidence that myopia-associated genes we analyzed in the study are transduced by Egf signaling.

If possible, discuss the original SNP that was associated with efemp1 and the potential mechanisms through which the SNP affects human EFEMP1; Then, discuss how the study of zebrafish efemp1 mutant can aid our understanding of the human's SNP.

Unfortunately, this information is not available. In the meta-analysis our work is based on Efemp1 ranked highly based on biological and statistical evidence. In figure 5 of Tedje et al., 2018, we can see Efemp1 in the first place. Where available, the annotation (light blue column) would indicate whether the variant was found in exonic, UTR or transcribing RNA. Nothing was identified for Efemp1 – which could mean that it is expressed in regulatory sequencing further away.

Typo: Page 15, Line 299: Loss of this gene "promotes".

Thanks to the reviewer, we have corrected the typo.

Reviewer #2 (Significance (Required)):

This study is an interesting and potentially significant addition to the ophthalmology field, as it conducted an initial characterization of a candidate gene for myopia in zebrafish and observed a relevant phenotype after the gene knockout. Colleagues in the myopia field will find the results interesting. In addition, colleagues in the zebrafish field will find the in-depth characterizations and tools used in the paper very informative.

I have conducted research in the human genetics of ophthalmology, gene expression analysis, zebrafish eye development and diseases. I believe my background allows me to effectively appreciate and evaluate the findings of this manuscript.

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

Summary The authors use a retinal-specific promoter to target zebrafish efemp1 for inactivation to study its effects on the eye. Their use of the DiDonato/del Bene 2C-Cas9 system is a good method to target only cells that express a specific promoter i.e. rx2. Following this (mosaic and transient) targeting of efemp1, the authors describe enlarged eyes and myopia development, as well as reduced spatial visual sensitivity and altered retinal function by ERG analysis. Furthermore, expression levels of egr1, tgfb1a, vegfb, and rbp3 are altered, as well as Timp2 and Mmp2 proteins. Finally, dark-rearing of efemp1 mutant fish is reported to lead to emmetropization, rather than myopia.

Major comments

1. The data presented by the authors are interesting, and likely due to efemp1 disruption in the eye. However, the authors should clarify or explain several points, and improve on experimental rigor. Figure 1 C, D- PCRs are not convincing for loss of efemp1. The authors should consider PCR reactions that would show deletion driven by both CRISPRs, or an RFLP reaction based on conventional PCR that would show differences if individual CRISPRs were effective.

Our zebrafish model is not simply a complete knockout model (please see response to reviewer 1’s comment 1 for details). In our model, even in a retina, there will be different indels in cells that expresses Cas9, including gain- or loss-of-function mutations, or mutations that do not even influence its function. In some cases, even with CRISPR cutting, DNA will recover to be wildtype. Thus, even with FACS to sort for Cas9+ (GFP+) cells, it is not possible to provide evidence for such gene modification using conventional PCR, because as long as there is a unmutated target sequence there will be PCR production. Because of this, headloop PCR as a well-established, highly sensitive approach is specifically suitable for our case.

There needs to be better evidence that efemp1 is being edited (e.g. Western blot, or qPCR).

As described in our response to reviewer 1’s comment 1, due the way efemp1 gene was modified in the retina in our model and the unavailability of suitable commercial antibodies, western blot is currently not an option for us. For qPCR, theoretically it is a way to show genetic modification at the transcriptional level, if combined with FACS from dissected eyes and sgRNA target sites specific primers. However, in reality it is not very practical to perform. First of all, even in our model with more Cas9+ cells, due to the patchy expression, the number of these cells are in fact low in a retina. This means that the number of fish to get enough cells for RNA isolation would be much higher, likely to be hundreds of fish. Moreover, in each clutch the number of fish with higher Cas9+ cell number is generally low, estimated to be only ~5%. Overall, this indicates that a large number of fish are required to even just get one sample for such an experiment. With evidence from headloop PCR and visual phenotype verification (OMR; Fig 1E–H and Fig S1), we believe it is certain that efemp1 gene has been modified. As mentioned also, the ability to identify quantifiable phenotypic differences in this model despite the mosaic Cas9 activity and random indels in different cells is highly suggestive of a full knockout of Efemp1 in the eye causing an even larger phenotype.

The data in Figure 7 are not convincing that EFEMP1 protein levels are substantially reduced in mutants.

This is expected. Please see response to reviewer 1’s comment 2.

Why are efemp12C-Cas9 eyes smaller with normal lighting? (Figure S2)

Fig S2 showed that efemp1*2C-Cas9 fish have smaller eye size than control fish only at 2 weeks of age. As shown by our survival data (Fig 2C), fish with more severe gene modification (implicated by more GFP+ cells, GFP+++ fish) are possibly died by 4 weeks of age, likely due to severe deficits in visually driven predation and subsequently nutrition deficiency. These fish thus gradually develop smaller size of the body including the eye with age, compared to control fish. Therefore, it makes senses that overall mutant fish have smaller eyes at 2 weeks of age but as GFP+++ fish die by 4 weeks, the group averaged eye size returned to a level similar to control fish. The fish survived are likely the ones that have mild mutations, which allow them to remain some levels of vision for feeding and develop without discernibly smaller eye size. Because there was variability of eye size in zebrafish caused by either development or gene manipulation, we used a relative calculation (ratio of retinal radius to lens radius) as a myopia index for comparison.

The clustering of datapoints in Figure 2B, 4B, overlaps extensively between control and mutant, and it is not easy to be sure that the high significance scores (***) are accurate.

We thank the reviewer for pointing out this concern. Though data points overlapped to some levels, in general difference between group means were apparent and the range that they deviate (i.e., mean ± SEM) were barely overlap. We realise it was difficult to see the SEM error bars, as they were so close to the mean, that they were hard to distinguish. We have adjusted our figures for clearer visualization of the error bars. Hopefully this will better show how far apart the data are as reflected by the significance scores.

The authors should consider discussing whether loss of efemp1 is developmental only, or sustained. rx2 is likely to be switched off after development, and retinal cells that arise after the rx2:Gal4 ceases to be active will have a normal quotient of efemp1.

Genetic modification in our mutant model is sustained. It is true that rx2 is only transiently expressed during early development, but once gDNA in a cell was modified by a CRISPR editing event driven by the 2C-Cas9 system, it remains throughout cell divisions (the same mutation would be copied during DNA synthesis) and cell lifetime. In addition, it has been showed that in adult teleost activation of rx2 in retinal stem cells in the retinal ciliary marginal zone determines its fate to form retinal neurons (PMID: 25908840). Therefore, in new neurons derived from retinal stem cells in the adult zebrafish retina, there is expression of rx2 to drive the 2C-Cas9 system for genetic modification. We have added relevant details to the Result section:

“Despite the mosaicism, the mutations resulted from the 2C-Cas9 system in retinal cells is expected to be sustained. Also, in adult teleost, activation of rx2 in retinal stem cells in the retinal ciliary marginal zone determines its fate to form retinal neurons (Reinhardt et al., 2015). This suggested that in new neurons derived from retinal stem cells in the adult zebrafish retina, there may be expression of rx2 to drive the 2C-Cas9 system for genetic modification.” (Line 108–116)

The authors should also consider a more detailed discussion of the mechanism mediated by/through efemp1 that alters retinal function and expression of other genes.

We appreciate the reviewer’s suggestion. It is possible to add more detailed discussion to the manuscript for potential mechanistic links, but ultimately such content would be highly speculative and may lead to over-interpretation of the data. Moreover, a comprehensive overview of detailed mechanisms of how efemp1 may alter retinal function and expression of relevant genes will require space and significantly lengthen the manuscript, with however only minimal improvement. Therefore, we believe it is reasonable to only touch the most relevant as we did for the manuscript.

Finally, since a full mouse knockout of efemp1 exists (Daniel et al, 2020), it is not clear why a retinal-specific zebrafish model would give better insight into the phenotype.

There are several advantages of our 2C-Cas9 zebrafish model. Firstly, with a retinal specific modification of the efemp1 gene, we are able to rule out systematic effect. Essentially our focus is the role of efemp1 in specifically ocular development. Secondly, with their smaller size, rapid development, high reproductivity, and ease of genetic and environmental manipulations, zebrafish allow us to perform large-scale high-throughput investigation with different genetic and environmental combinations. Furthermore, by changing the promoter that drives Gal4 expression in our model, we can target precisely different retinal neuron subtypes to characterize which and how different visual circuits are involved.

Minor comments

"Myopia is the most common ocular disorder" is overly broad and needs qualifiers.

We appreciate the reviewer’s rigorousness. However, myopia is in fact the most common ocular disorder around the world. We have mentioned in the Introduction that “Myopia (short-sightedness) is now the most common visual disorder, and is predicted to impact approximately half of the world population by 2050 (Holden et al., 2016).” Therefore, we believe a qualifier is appropriate.

Line 36 - what ocular changes cannot be easily managed?

We thank the reviewer’s suggestion. We have modified the Introduction manuscript to add some examples:

“Although considered manageable with optical correction, the development of high levels of myopia (or pathological myopia) brings with it ocular changes that promote eye diseases that cannot be easily managed (glaucoma, cataract, myopic maculopathy, etc.) (Hayashi et al., 2010; Ikuno, 2017; Marcus et al., 2011).” (Line 34–37)

Why does loss of retinal efemp1 cause reduced OMR response? Unlikely to be refractive error at this stage.

We have modified the Discussion as per the reviewer’s concern:

“We noticed that although 5 dpf efemp12C-Cas9 fish overall were not myopic relative to efemp1+/+ fish (Fig 2B), they showed reduced spatial-frequency tuning function (Fig 1E–H). This phenotype, if not due to refractive error, can be a result of altered visual processing, as aberrant extracellular matrix caused by efemp1 disruption may lead to dysfunctional synapses (Dityatev and Schachner, 2006).” (Line 491–494)

Which Timp2 (Timp2a or Timp2b) is visualized in Figure 7?

Thanks to the reviewer for raising this point. We have added relevant details to the Methods:

“The anti-TIMP2 antibody was developed based on human TIMP2. In a previous study this antibody was showed to label for zebrafish TIMP2a (Zhang et al., 2003). As similarity of zebrafish TIMP2b to human TIMP2 is much lower than that of zebrafish TIMP2a (60.55% vs. 71.23%), labelling of zebrafish TIM2b is less likely. Yet, we are not able to completely rule out this possibility due to lack of information of the exact immunogen.” (Line 790–794)

Why is the inner retina studied for altered protein expression, but not the rest of the eye? Myopia is primarily driven by growth of the outer retinal/sclera.

The reason why we focused on the inner retina is that in our study, prominent expression differences of our proteins of interest between groups were mainly noticed on the inner but not outer retina. We agree that the outer retina is a key driver for visually regulated ocular growth, yet the inner retina also plays a crucial role. There is abundant evidence that the inner retina is involved in development of ocular refraction. For examples, Cx36, Egr1 and dopamine pathways in the inner retina have been reported to be associated with regulation of ocular refraction (PMID: 10412059; PMID: 28602573; PMID: 25052990; PMID: 32547367). We believe it is reasonable to focus on the inner retina, were we observed robust quantifiable expression for the tested proteins in our case.

Reviewer #3 (Significance (Required)):

• General assessment: This study uses retinal-specific inactivation of efemp1 with a clever methodology to study its effects on the eye. However, the necessity of these experiments is not well explained, as a full mouse knockout line exists. • Advance: There are some interesting observations about gene expression following efemp1 inactivation, and useful experiments that look at the combination of genetics with environmental conditions on refractive error. This builds on studies by the Hulleman group on efemp1's role in the eye by adding functional information. • Audience: This will be of interest to both basic researchers and clinicians who study genetic influencers of the eye.

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

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Referee #3

Evidence, reproducibility and clarity

Summary

The authors use a retinal-specific promoter to target zebrafish efemp1 for inactivation to study its effects on the eye. Their use of the DiDonato/del Bene 2C-Cas9 system is a good method to target only cells that express a specific promoter i.e. rx2. Following this (mosaic and transient) targeting of efemp1, the authors describe enlarged eyes and myopia development, as well as reduced spatial visual sensitivity and altered retinal function by ERG analysis. Furthermore, expression levels of egr1, tgfb1a, vegfb, and rbp3 are altered, as well as Timp2 and Mmp2 proteins. Finally, dark-rearing of efemp1 mutant fish is reported to lead to emmetropization, rather than myopia.

Major comments

The data presented by the authors are interesting, and likely due to efemp1 disruption in the eye. However, the authors should clarify or explain several points, and improve on experimental rigor. Figure 1 C, D- PCRs are not convincing for loss of efemp1. The authors should consider PCR reactions that would show deletion driven by both CRISPRs, or an RFLP reaction based on conventional PCR that would show differences if individual CRISPRs were effective. There needs to be better evidence that efemp1 is being edited (e.g. Western blot, or qPCR). The data in Figure 7 are not convincing that EFEMP1 protein levels are substantially reduced in mutants. Why are efemp12C-Cas9 eyes smaller with normal lighting? (Figure S2) The clustering of datapoints in Figure 2B, 4B, overlaps extensively between control and mutant, and it is not easy to be sure that the high significance scores (***) are accurate. The authors should consider discussing whether loss of efemp1 is developmental only, or sustained. rx2 is likely to be switched off after development, and retinal cells that arise after the rx2:Gal4 ceases to be active will have a normal quotient of efemp1. The authors should also consider a more detailed discussion of the mechanism mediated by/through efemp1 that alters retinal function and expression of other genes. Finally, since a full mouse knockout of efemp1 exists (Daniel et al, 2020), it is not clear why a retinal-specific zebrafish model would give better insight into the phenotype.

Minor comments

"Myopia is the most common ocular disorder" is overly broad and needs qualifiers. Line 36 - what ocular changes cannot be easily managed? Why does loss of retinal efemp1 cause reduced OMR response? Unlikely to be refractive error at this stage. Which Timp2 (Timp2a or Timp2b) is visualized in Figure 7? Why is the inner retina studied for altered protein expression, but not the rest of the eye? Myopia is primarily driven by growth of the outer retinal/sclera.

Significance

• General assessment: This study uses retinal-specific inactivation of efemp1 with a clever methodology to study its effects on the eye. However, the necessity of these experiments is not well explained, as a full mouse knockout line exists.
• Advance: There are some interesting observations about gene expression following efemp1 inactivation, and useful experiments that look at the combination of genetics with environmental conditions on refractive error. This builds on studies by the Hulleman group on efemp1's role in the eye by adding functional information.
• Audience: This will be of interest to both basic researchers and clinicians who study genetic influencers of the eye.

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

Learn more at Review Commons

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Referee #2

Evidence, reproducibility and clarity

Summary:

In this study, the authors used the zebrafish model to study efemp1, a gene that was previously found to be associated with myopia. They used CRISPR-Cas9 to create specific efemp1 knockout in the retina in a mosaic manner. They used a few histological and physiological techniques to evaluate the resulting mutant and found that the efemp1 mutants developed symptoms that are consistent with myopia. The authors further quantified the expression of a few potential target genes in the eye that are potentially implicated in myopia phenotype. The authors also evaluated the differential phenotype of the efemp1 mutant grown in different light conditions that might contribute to myopia.

Major comments:

Overall, the authors have provided convincing evidence of the phenotype created by their efemp1 perturbation. Their experiments were thoroughly done and extensively analyzed. They even discussed some potential shortcomings of their study. Their study is a nice first step towards a better understanding of the efemp1 gene function in ocular growth and in myopia. All my comments below should be addressed by clarifications and discussions and not by any new experiments or projects.

Minor comments:

• Elaborate the rationale for choosing efemp1 from the original GWAS study for zebrafish investigation. The authors only mentioned that this gene is among the highest in the rank and its role in myopia is not clear. However, there are quite a few other genes in the GWAS study that were ranked as high, if not higher than efemp1.
• Elaborate the rationale for choosing retina as the target tissue of efemp1 knockout, especially when the original GWAS study indicated the expression of EFEMP1 is in cornea, RPE, and sclera, but not in retinal cells.
• Discuss possible ways of modifying efemp1 gene in the retina that would be more uniform and would not create mosaicism and/or heterogenous mutations that can complicate downstream characterizations and interpretations as the authors currently experienced.
• The authors should elaborate further on the effect of the mosaicism and heterogenous mutations on efemp1, a presumably excreted protein, on regulating the ocular growth. How did the downstream genes they studied affect by the messing up of the extracellular Efemp1? Is it through altering the Egf signal transduction?
• If possible, discuss the original SNP that was associated with efemp1 and the potential mechanisms through which the SNP affects human EFEMP1; Then, discuss how the study of zebrafish efemp1 mutant can aid our understanding of the human's SNP.
• Typo: Page 15, Line 299: Loss of this gene "promotes".

Significance

This study is an interesting and potentially significant addition to the ophthalmology field, as it conducted an initial characterization of a candidate gene for myopia in zebrafish and observed a relevant phenotype after the gene knockout. Colleagues in the myopia field will find the results interesting. In addition, colleagues in the zebrafish field will find the in-depth characterizations and tools used in the paper very informative.

I have conducted research in the human genetics of ophthalmology, gene expression analysis, zebrafish eye development and diseases. I believe my background allows me to effectively appreciate and evaluate the findings of this manuscript.

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

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

The manuscript by Xie et al investigates the role of efemp1 in mediating ocular growth. Efemp1, a secreted extracellular matrix glycoprotein, was previously identified as a myopia-risk gene in human GWAS studies. Given that myopia is linked to aberrant eye shape, the authors investigated whether and how this gene mediates eye growth. Using a CRISPR based approach in zebrafish the authors knocked out efemp1 specifically in the retina and established that a myopic eye results. They went further and investigated visual function in these mutant fish using the optomotor response and electroretinograms. As dark-rearing in many animal models has been linked to the induction of myopia, the authors examined the effects of a dark-rearing regimen in efemp1 mutants and found surprisingly that they did not show signs of myopia. Lastly, the expression and distribution of several myopia-associated genes was investigated in the retina of efemp1 mutants and following dark-rearing.

The starting point for this study was the generation of a "retina-specific knockout mutant of the efemp1 gene". However, evidence for a 'successful' knockout at the protein level is missing. Immunostaining for Efemp1 in sections of the entire retina from control and mutant fish would have helped here. It is only in Figure 7 B, C that portions of the inner retina from control and efemp12c-Cas9 fish are shown with Efemp1 immunostaining. Control and mutant retinae show slight relative differences in Efemp1 fluorescence levels which are difficult to reconcile with a knock-out scenario. Presumably this phenotype is a result of the mosaic expression of Cas9 (GFP) shown in Fig 1? Can the authors explain the reason for this mosaicism? Given this mosaic expression would one expect Efemp1 immunoreactive areas intermingled with areas devoid of Efemp1 in the mutant retina? Further, do deficits in the various functional assays the authors perform correlate with the degree of mosaicism? In the same vein, in Figure 2 the authors refer to variation in GFP levels in the efemp12c-Cas9. It is not clear whether the authors mean levels of GFP in individual cells or numbers of GFP+ cells. Presumably the latter. Could the authors clarify? In my opinion understanding and characterizing the efemp12c-Cas9 fish thoroughly is key to interpreting the phenotypes the authors show subsequently.

Significance

The wide range of assays the authors perform to assess visual deficits is commendable. Such a comprehensive approach ranging from anatomical, behavioral and electrophysiological assays is poised to identify changes that could otherwise be overlooked. Given the increasing use of zebrafish as models of ocular diseases, this study provides a solid roadmap of the types of analysis possible. This work should be interesting to researchers in the field of myopia research and to basic vision researchers interested in using the zebrafish as a model organism.

eLife assessment

This valuable paper characterizes a murine model for congenital cystic airway abnormalities (CPAM). In contrast to previous assumptions that only epithelial cells are involved in the formation of pulmonary cysts, the authors provide compelling new evidence that defective BMP signaling in lung mesenchymal cells can disrupt airway development. Knowing that proper BMP signaling in mesenchymal cells is required for normal cyst-free lungs could potentially pave the way to understanding and preventing CPAM in infants at risk for this common disorder, which can be fatal if untreated. The relevance of the murine model could be enhanced by further molecular and histological comparison with human cysts.

Reviewer #2 (Public Review):

Congenital cystic airway abnormalities (CPAM) are a common poorly understood disorder in airway lung development that can be fatal if not effectively treated at birth. This study by Luo and colleagues provides compelling new evidence that bone morphogenetic protein signaling in distal mesenchymal cells is required for normal mouse lung development. Genetic loss of BMP receptor in mice and in fetal mesenchymal cells causes type 2 or alveolar-like CPAM pathology. Furthermore, this is associated with changes in expression of Sox2-Sox9 suggesting defects in the proximal to distal cellularity of the lung. Interestingly, cysts are formed even when SMAD1 and 5, two major downstream effects of BMP signaling are deleted suggesting a role for non-canonical BMP signalling. Furthermore, they were independent of ablating BMP signaling in non-vascular mesenchymal cells. The findings are compelling and provide strong evidence that cystic lung development is caused by loss of non-canonical BMP signaling in mesenchymal cells. The main weakness of the paper is that it does not identify the downstream non-canonical effector of mesenchymal BMP signaling. The authors provide a plausible suggestion that it may be p38 MAPK that deserves further investigation. Despite this minor weakness, the overall findings are novel and considered important because they provide a foundation for new studies, including experiments that may produce drugs designed to prevent or treat newborn infants with CPAM.

Padarius leako matavimą, tada Glassfish'a ir Newt'a parametru patikra, šitos vietos nereikia - tai bus daroma vėliau. Nustatom max energiją (pvz: 100kHz). Taip pat prie esamos srovės (kuri padaro išvadinę galią maksimalia (pvz 20W)) pridedame 2,5A. Taip pat juodą plokštelę padedame šalia grasshoperio, kad matavimai būtų tikslesni. P.S. Gustas is PP rms nedaro.

eLife assessment

This important study presents a novel pipeline for the large-scale genomic prediction of members of the non-ribosomal peptide group of pyoverdines based on a dataset from nearly 2000 Pseudomonas genomes. The advance presented in this study is largely based on solid evidence, although some main claims are only incompletely supported. This study on bacterial siderophores has broad theoretical and practical implications beyond a singular subfield.

Reviewer #1 (Public Review):

The manuscript introduces a bioinformatic pipeline designed to enhance the structure prediction of pyoverdines, revealing an extensive and previously overlooked diversity in siderophores and receptors. Utilizing a combination of feature sequence and phylogenetic approaches, the method aims to address the challenging task of predicting structures based on dispersed gene clusters, particularly relevant for pyoverdines.

Predicting structures based on gene clusters is still challenging, especially pyoverdines as the gene clusters are often spread to different locations in the genome. An improved method would indeed be highly useful, and the diversity of pyoverdine gene clusters and receptors identified is impressive.

However, so far the method basically aligns the structural genes and domains involved in pyoverdine biosynthesis and then predicts A domain specificity to predict the encoded compounds. Both methods are not particularly new as they are included in other tools such as PRISM (10.1093/nar/gkx320 ) or Sandpuma (https://doi.org/10.1093/bioinformatics/btx400) among others. The study claims superiority in A domain prediction compared to existing tools, yet the support is currently limited, relying on a comparison solely with AntiSMASH. A more extensive and systematic comparison with other tools is needed.

Additionally, in contradiction to the authors' claims, the method's applicability seems constrained to well-known and widely distributed gene clusters. The absence of predictions for new amino acids raises concerns about its generalizability to NRPS beyond the studied cases.

The manuscript lacks clarity on how the alignment of structural genes operates when dealing with multiple NRPS gene clusters on different genome contigs. How would the alignment of each BGC work?

Another critical concern is that a main challenge in NRPS structure prediction is not the backbone prediction but rather the prediction of tailoring reactions, which is not addressed in the manuscript at all, and this limitation extensively restricts the applicability of the method.

The manuscript presents a potentially highly useful bioinformatic pipeline for pyoverdine structure prediction, showcasing a commendable exploration of siderophore diversity. However, some of the claims made remain unsubstantiated. Overall, while the study holds promise, further validation and refinement are required to fulfill its potential impact on the field of bioinformatic structure prediction.

Reviewer #2 (Public Review):

Pyoverdines, siderophores produced by many Pseudomonads, are one of the most diverse groups of specialized metabolites and are frequently used as model systems. Thousands of Pseudomonas genomes are available, but large-scale analyses of pyoverdines are hampered by the biosynthetic gene clusters (BGCs) being spread across multiple genomic loci and existing tools' inability to accurately predict amino acid substrates of the biosynthetic adenylation (A) domains. The authors present a bioinformatics pipeline that identifies pyoverdine BGCs and predicts the A domain substrates with high accuracy. They tackled a second challenging problem by developing an algorithm to differentiate between outer membrane receptor selectivity for pyoverdines versus other siderophores and substrates. The authors applied their dataset to thousands of Pseudomonas strains, producing the first comprehensive overview of pyoverdines and their receptors and predicting many new structural variants.

The A domain substrate prediction is impressive, including the correction of entries in the MIBiG database. Their high accuracy came from a relatively small training dataset of A domains from 13 pyoverdine BGCs. The authors acknowledge that this small dataset does not include all substrates, and correctly point out that new sequence/structure pairs can be added to the training set to refine the prediction algorithm. The authors could have been more comprehensive in finding their training set data. For instance, the authors claim that histidine "had not been previously documented in pyoverdines", but the sequenced strain P. entomophila L48, incorporates His (10.1007/s10534-009-9247-y). The workflow cannot differentiate between different variants of Asp and OHOrn, and it's not clear if this is a limitation of the workflow, the training data, or both. The prediction workflow holds up well in Burkholderiales A domains, however, they fail to mention in the main text that they achieved these numbers by adding more A domains to their training set.

To validate their predictions, they elucidated structures of several new pyoverdines, and their predictions performed well. However, the authors did not include their MS/MS data, making it impossible to validate their structures. In general, the biggest limitation of the submitted manuscript is the near-empty methods section, which does not include any experimental details for the 20 strains or details of the annotation pipeline (such as "Phydist" and "Syndist"). The source code also does not contain the requisite information to replicate the results or re-use the pipeline, such as the antiSMASH version and required flags. That said, skimming through the source code and data (kindly provided upon request) suggests that the workflow itself is sound and a clear improvement over existing tools for pyoverdine BGC annotation.

Predicting outer membrane receptor specificity is likewise a challenging problem and the authors have made a promising achievement by finding specific gene regions that differentiate the pyoverdine receptor FpvA from FpvB and other receptor families. Their predictions were not tested experimentally, but the finding that only predicted FpvA receptors were proximate to the biosynthesis genes lends credence to the predictive power of the workflow. The authors find predicted pyoverdine receptors across an impressive 468 genera, an exciting finding for expanding the role of pyoverdines as public goods beyond Pseudomonas. However, whether or not these receptors can recognize pyoverdines (and if so, which structures!) remains to be investigated.

In all, the authors have assembled a rich dataset that will enable large-scale comparative genomic analyses. This dataset could be used by a variety of researchers, including those studying natural product evolution, public good eco/evo dynamics, and NRPS engineering.

Reviewer #3 (Public Review):

Summary:

Secondary metabolites are produced by numerous microorganisms and have important ecological functions. A major problem is that neither the function of a secondary metabolite enzyme nor the resulting metabolite can be precisely predicted from gene sequence data.

In the current paper, the authors addressed this highly relevant question.

The authors developed a bioinformatic pipeline to reconstruct the complete secondary metabolism pathway of pyoverdines, a class of iron-scavenging siderophores produced by Pseudomonas spp. These secondary metabolites are biosynthesized by a series of non-ribosomal peptide synthetases and require a specific receptor (FpvA) for uptake. The authors combined knowledge-guided learning with phylogeny-based methods to predict with high accuracy encoding NRPSs, substrate specificity of A domains, pyoverdine derivatives, and receptors. After validation, the authors tested their pipeline with sequence data from 1664 phylogenetically distinct Pseudomonas strains and were able to determine 18,292 enzymatic A domains involved in pyoverdine synthesis, reliably predicted 97.8% of their substrates, identified 188 different pyoverdine molecule structures and 4547 FpvA receptor variants belonging to 94 distinct groups. All the results and predictions were clearly superior to predictions that are based on antiSMASH. Novel pyoverdine structures were elucidated experimentally by UHPLC-HR-MS/MS.

To assess the extendibility of the pipeline, the authors chose Burkholderiales as a test case which led to the results that the pipeline consistently maintains high prediction accuracy within Burkholderiales of 83% which was higher than for antiSMASH (67%).

Together, the authors concluded that supervised learning based on a few known compounds produced by species from the same genus probably outperforms generalized prediction algorithms trained on many products from a diverse set of microbes for NRPS substrate predictions. As a result, they also show that both pyoverdine and receptor diversity have been vastly underestimated.

Strengths:

The authors developed a very useful bioinformatic pipeline with high accuracy for secondary metabolites, at least for pyoverdines. The pipelines have several advantages compared to existing pipelines like the extensively used antiSMASH program, e.g. it can be applied to draft genomes, shows reduced erroneous gene predictions, etc. The accuracy was impressively demonstrated by the discovery of novel pyoverdines whose structures were experimentally substantiated by UHPLC-HR-MS/MS.

The manuscript is very well written, and the data and the description of the generation of pipelines are easy to follow.

Weaknesses:

The only major comment I have is the uncertainty of whether the pipeline can be applied to more complex non-ribosomal peptides. In the current study, the authors only applied their pipeline to a very narrow field, i.e., pyoverdines of Pseudomonas and Burkholderia strains.

Author response:

eLife assessment

This study provides valuable evidence indicating that Syngap1 regulates the synaptic drive and membrane excitability of parvalbumin- and somatostatin-positive interneurons in the auditory cortex. Since haplo-insufficiency of Syngap1 has been linked to intellectual disabilities without a well-defined underlying cause, the central question of this study is timely. However, the support for the authors' conclusions is incomplete in general and some parts of the experimental evidence are inadequate. Specifically, the manuscript requires further work to properly evaluate the impact on synaptic currents, intrinsic excitability parameters, and morphological features.

We are happy that the editors found that our study provides valuable evidence and that the central question is timely. We thank the reviewers for their detailed comments and suggestions. Below, we provide a point-by-point answer (in blue) to the specific comments and indicate the changes to the manuscript and the additional experiments we plan to perform to answer these comments.

Public Reviews:

Reviewer #1 (Public Review):

The study is designed to assess the role of Syngap1 in regulating the physiology of the MGE-derived PV+ and SST+ interneurons. Syngap1 is associated with some mental health disorders, and PV+ and SST+ cells are the focus of many previous and likely future reports from studies of interneuron biology, highlighting the translational and basic neuroscience relevance of the authors' work.

Strengths of the study are using well-established electrophysiology methods and the highly controlled conditions of ex vivo brain slice experiments combined with a novel intersectional mouse line, to assess the role of Syngap1 in regulating PV+ and SST+ cell properties. The findings revealed that in the mature auditory cortex, Syngap1 haploinsufficiency decreases both the intrinsic excitability and the excitatory synaptic drive onto PV+ neurons from Layer 4. In contrast, SST+ interneurons were mostly unaffected by Syngap1 haploinsufficiency. Pharmacologically manipulating the activity of voltage-gated potassium channels of the Kv1 family suggested that these channels contributed to the decreased PV+ neuron excitability by Syngap insufficiency. These results therefore suggest that normal Syngap1 expression levels are necessary to produce normal PV+ cell intrinsic properties and excitatory synaptic drive, albeit, perhaps surprisingly, inhibitory synaptic transmission was not affected by Syngap1 haploinsufficiency.

Since the electrophysiology experiments were performed in the adult auditory cortex, while Syngap1 expression was potentially affected since embryonic stages in the MGE, future studies should address two important points that were not tackled in the present study. First, what is the developmental time window in which Syngap1 insufficiency disrupted PV+ neuron properties? Albeit the embryonic Syngap1 deletion most likely affected PV+ neuron maturation, the properties of Syngap-insufficient PV+ neurons do not resemble those of immature PV+ neurons. Second, whereas the observation that Syngap1 haploinsufficiency affected PV+ neurons in auditory cortex layer 4 suggests auditory processing alterations, MGE-derived PV+ neurons populate every cortical area. Therefore, without information on whether Syngap1 expression levels are cortical area-specific, the data in this study would predict that by regulating PV+ neuron electrophysiology, Syngap1 normally controls circuit function in a wide range of cortical areas, and therefore a range of sensory, motor and cognitive functions. These are relatively minor weaknesses regarding interpretation of the data in the present study that the authors could discuss.

We agree with the reviewer on the proposed open questions, which we will certainly discuss in the revised manuscript we are preparing. We do have experimental evidence suggesting that Syngap1 mRNA is expressed by PV+ and SST+ neurons in different cortical areas, during early postnatal development and in adulthood; therefore, we agree that it will be important, in future experiments, to tackle the question of when the observed phenotypes arise.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors investigated how partial loss of SynGap1 affects inhibitory neurons derived from the MGE in the auditory cortex, focusing on their synaptic inputs and excitability. While haplo-insufficiently of SynGap1 is known to lead to intellectual disabilities, the underlying mechanisms remain unclear.

Strengths:

The questions are novel

Weaknesses:

Despite the interesting and novel questions, there are significant concerns regarding the experimental design and data quality, as well as potential misinterpretations of key findings. Consequently, the current manuscript fails to contribute substantially to our understanding of SynGap1 loss mechanisms and may even provoke unnecessary controversies.

Major issues:

(1) One major concern is the inconsistency and confusion in the intermediate conclusions drawn from the results. For instance, while the sEPSC data indicates decreased amplitude in PV+ and SOM+ cells in cHet animals, the frequency of events remains unchanged. In contrast, the mEPSC data shows no change in amplitudes in PV+ cells, but a significant decrease in event frequency. The authors conclude that the former observation implies decreased excitability. However, traditionally, such observations on mEPSC parameters are considered indicative of presynaptic mechanisms rather than changes of network activity.‎ The subsequent synapse counting experiments align more closely with the traditional conclusions. This issue can be resolved by rephrasing the text. However, it would remain unexplained why the sEPSC frequency shows no significant difference. If the majority of sEPSC events were indeed mediated by spiking (which is blocked by TTX), the average amplitudes and frequency of mEPSCs should be substantially lower than those of sEPSCs. Yet, they fall within a very similar range, suggesting that most sEPSCs may actually be independent of action potentials. But if that was indeed the case, the changes of purported sEPSC and mEPSC results should have been similar.

We understand the reviewer’s perspective; indeed, we asked ourselves the very same question regarding why the sEPSC and mEPSC frequency fall within a similar range when we analysed neuron means (bar graphs). We have already recorded sEPSCs followed by mEPSCs from several PV neurons (control and cHet) and are in the process of analyzing the data. We will add this data to the revised version of the manuscript. We will also rephrase the manuscript to present multiple potential interpretations of the data.

We hope that we have correctly interpreted the reviewer's concern. However, if the question is why sEPSC amplitude but not frequency is affected in cHet vs ctrl then the reviewer’s comment is perhaps based on the assumption that the amplitude and frequency of miniature events should be lower for all events compared to those observed for spontaneous events. However, it's essential to note that changes in the mean amplitude of sEPSCs are primarily driven by alterations in large sEPSCs (>9-10pA, as shown in cumulative probability in Fig. 1b right), with smaller ones being relatively unaffected. Consequently, a reduction in sEPSC amplitude may not necessarily result in a significant decrease in frequency since their values likely remain above the detection threshold of 3 pA. This could explain the lack of a significant decrease in average inter-interval event of sEPSCs (as depicted in Fig. 1b left).

If the question is whether we should see the same parameters affected by the genetic manipulation in both sEPSC and mEPSC, then another critical consideration is the involvement of the releasable pool in mEPSCs versus sEPSCs. Current knowledge suggests that activity-dependent and -independent release may not necessarily engage the same pool of vesicles or target the same postsynaptic sites. This concept has been extensively explored (reviewed in Kavalali, 2015). Consequently, while we may have traditionally interpreted activity-dependent and -independent data assuming they utilize the same pool, this is no longer accurate. The current discussion in the field revolves around understanding the mechanisms underlying such phenomena. Therefore, comparisons between sEPSCs and mEPSCs may not yield conclusive data but rather speculative interpretations. For a rigorous analysis, particularly in this context involving thousands of events, it is essential to assess these data sets (mEPSCs vs sEPSCs) separately and provide cumulative probability curves. This approach allows for a more comprehensive understanding of the underlying distributions and helps to elucidate any potential differences between the two types of events. We will rephrase the text, and as mentioned above, add additional data, to better reflect these considerations.

(2) Another significant concern is the quality of synapse counting experiments. The authors attempted to colocalize pre- and postsynaptic markers Vglut1 and PSD95 with PV labelling. However, several issues arise. Firstly, the PV labelling seems confined to soma regions, with no visible dendrites. Given that the perisomatic region only receives a minor fraction of excitatory synapses, this labeling might not accurately represent the input coverage of PV cells. Secondly, the resolution of the images is insufficient to support clear colocalization of the synaptic markers. Thirdly, the staining patterns are peculiar, with PSD95 puncta appearing within regions clearly identified as somas by Vglut1, hinting at possible intracellular signals. Furthermore, PSD95 seems to delineate potential apical dendrites of pyramidal cells passing through the region, yet Vglut1+ partners are absent in these segments, which are expected to be the marker of these synapses here. Additionally, the cumulative density of Vglut2 and Vglut1 puncta exceeds expectations, and it's surprising that subcortical fibers labeled by Vglut2 are comparable in number to intracortical Vglut1+ axon terminals. Ideally, N(Vglut1)+N(Vglut2) should be equal or less than N(PSD95), but this is not the case here. Consequently, these results cannot be considered reliable due to these issues.

We apologize, as it appears that the images we provided have caused confusion. The selected images represent a single focal plane of a confocal stack, which was visually centered on the PV cell somata. We chose just one confocal plane because we thought it showed more clearly the apposition of presynaptic and postsynaptic immunolabeling around the somata. In the revised version of the manuscript, we will provide higher magnification images, which will clearly show how we identified and selected the region of interest for the quantification of colocalized synaptic markers. In our confocal stacks, we can also identify PV immunolabeled dendrites and colocalized vGlut1/PSD95 or vGlut2/PSD95 puncta on them; but these do not appear in the selected images because, as explained, only one focal plane, centered on the PV cell somata, was shown.

We acknowledge the reviewer's point that in PV+ cells the majority of excitatory inputs are formed onto dendrites; however, we focused on the somatic excitatory inputs to PV cells, because despite their lower number, they produce much stronger depolarization in PV neurons than dendritic excitatory inputs (Hu et al., 2010; Norenberg et al., 2010). Further, quantification of perisomatic putative excitatory synapses is more reliable since by using PV immunostaining, we can visualize the soma and larger primary dendrites, but smaller, higher order dendrites are not be always detectable. Of note, PV positive somata receive more excitatory synapses than SST positive and pyramidal neuron somata as found by electron microscopy studies in the visual cortex (Hwang et al., 2021; Elabbady et al., 2024).

Regarding the comment on the density of vGlut1 and vGlut2 puncta, the reason that the numbers appear high and similar between the two markers is because we present normalized data (cHet normalized to their control values for each set of immunolabelling) to clearly represent the differences between genotypes. This information is present in the legends but we apologize for not clearly explaining it the methods section. We will provide a more detailed explanation of our methods in the revised manuscript.

Briefly, immunostained sections were imaged using a Leica SP8-STED confocal microscope, with a 63x (NA 1.4) at 1024 X 1024, z-step =0.3 μm, stack size of ~15 μm. Images were acquired from the auditory cortex from at least 3 coronal sections per animal. All the confocal parameters were maintained constant throughout the acquisition of an experiment. All images shown in the figures are from a single confocal plane. To quantify the number of vGlut1/PSD95 or vGlut2/PSD95 putative synapses, images were exported as TIFF files and analyzed using Fiji (Image J) software. We first manually outlined the profile of each PV cell soma (identified by PV immunolabeling). At least 4 innervated somata were selected in each confocal stack. We then used a series of custom-made macros in Fiji as previously described (Chehrazi et al, 2023). After subtracting background (rolling value = 10) and Gaussian blur (σ value = 2) filters, the stacks were binarized and vGlut1/PSD95 or vGlut2/PSD95 puncta were independently identified around the perimeter of a targeted soma in the focal plane with the highest soma circumference. Puncta were quantified after filtering particles for size (included between 0-2μm2) and circularity (included between 0-1). Data quantification was done by investigators blind to the genotype, and presented as normalized data over control values for each experiment.

(3) One observation from the minimal stimulation experiment was concluded by an unsupported statement. Namely, the change in the onset delay cannot be attributed to a deficit in the recruitment of PV+ cells, but it may suggest a change in the excitability of TC axons.

We agree with the reviewer, please see answer to point below.

(‎4) The conclusions drawn from the stimulation experiments are also disconnected from the actual data. To make conclusions about TC release, the authors should have tested release probability using established methods, such as paired-pulse changes. Instead, the only observation here is a change in the AMPA components, which remained unexplained.

We agree with the reviewer and we will perform additional paired-pulse ratio experiments at different intervals. We will rephrase the discussion and our interpretation and potential hypothesis according to the data obtained from this new experiment.

(5) The sampling rate of CC recordings is insufficient ‎to resolve the temporal properties of the APs. Therefore, the phase-plots cannot be interpreted (e.g. axonal and somatic AP components are not clearly separated), raising questions about how AP threshold and peak were measured. The low sampling rate also masks the real derivative of the AP signals, making them apparently faster.

We acknowledge that a higher sampling rate could offer a more detailed analysis of the action potential waveform. However, in the context of action potential analysis, it is acceptable to use sampling rates ranging from 10 kHz to 20 kHz (Golomb et al., 2007; Stevens et al., 2021; Zhang et al., 2023), which are considered adequate in the context of the present study. Indeed, our study aims to evaluate "relative" differences in the electrophysiological phenotype when comparing groups following a specific genetic manipulation. A sampling rate of 10 kHz is commonly employed in similar studies, including those conducted by our collaborator and co-author S. Kourrich (e.g., Kourrich and Thomas 2009, Kourrich et al., 2013), as well as others (Russo et al., 2013; Ünal et al., 2020; Chamberland et al., 2023).

Despite being acquired at a lower sampling rate than potentially preferred by the reviewer, our data clearly demonstrate significant differences between the experimental groups, especially for parameters that are negligibly or not affected by the sampling rate used here (e.g., #spikes/input, RMP, Rin, Cm, Tm, AP amplitude, AP latency, AP rheobase).

Regarding the phase-plots, we agree that a higher sampling rate would have resulted in smoother curves and more accurate absolute values. However, the differences were sufficiently pronounced to discern the relative variations in action potential waveforms between the experimental groups.

A related issue is that the Methods section lacks essential details about the recording conditions, such as bridge balance and capacitance neutralization.

We indeed performed bridge balance and neutralized the capacitance before starting every recording. We will add the information in the methods.

(6) Interpretation issue: One of the most fundamental measures of cellular excitability, the rheobase, was differentially affected by cHet in BCshort and BCbroad. Yet, the authors concluded that the cHet-induced changes in the two subpopulations are common.

We are uncertain if we have correctly interpreted the reviewer's comment. While we observed distinct impacts on the rheobase (Fig. 7d and 7i), there seems to be a common effect on the AP threshold (Fig. 7c and 7h), as interpreted and indicated in the final sentence of the results section for Figure 7 (page 12). If our response does not address the reviewer's comment adequately, we would greatly appreciate it if the reviewer could rephrase their feedback.

(7) Design issue:

The Kv1 blockade experiments are disconnected from the main manuscript. There is no experiment that shows the causal relationship between changes in DTX and cHet cells. It is only an interesting observation on AP halfwidth and threshold. However, how they affect rheobase, EPSCs, and other topics of the manuscript are not addressed in DTX experiments.

Furthermore, Kv1 currents were never measured in this work, nor was the channel density tested. Thus, the DTX effects are not necessarily related to changes in PV cells, which can potentially generate controversies.

While we acknowledge the reviewer's point that Kv1 currents and density weren't specifically tested, an important insight provided by Fig. 5 is the prolonged action potential latency. This delay is significantly influenced by slowly inactivating subthreshold potassium currents, namely the D-type K+ current. It's worth noting that D-type current is primarily mediated by members of the Kv1 family. The literature supports a role for Kv1.1-containing channels in modulating responses to near-threshold stimuli in PV cells (Wang et al., 1994; Goldberg et al., 2008; Zurita et al., 2018). However, we recognize that besides the Kv1 family, other families may also contribute to the observed changes.

To address this concern, we will revise our interpretation. We will opt for the more accurate term "D-type K+ current" and only speculate about the involved channel family in the discussion. It is not our intention to open unnecessary controversy, but present the data we obtained. We believe this approach and rephrasing the discussion as proposed will prevent unnecessary controversy and instead foster fruitful discussions.

(8) Writing issues:

Abstract:

The auditory system is not mentioned in the abstract.

One statement in the abstract is unclear‎. What is meant by "targeting Kv1 family of voltage-gated potassium channels was sufficient..."? "Targeting" could refer to altered subcellular targeting of the channels, simple overexpression/deletion in the target cell population, or targeted mutation of the channel, etc. Only the final part of the Results revealed that none of the above, but these channels were blocked selectively.

We agree with the reviewer and we will rephrase the abstract accordingly.

Introduction:

There is a contradiction in the introduction. The second paragraph describes in detail the distinct contribution of PV and SST n‎eurons to auditory processing. But at the end, the authors state that "relatively few reports on PV+ and SST+ cell-intrinsic and synaptic properties in adult auditory cortex". Please be more specific about the unknown properties.

We agree with the reviewer and we will rephrase more specifically.

(9) The introduction emphasizes the heterogeneity of PV neurons, which certainly influences the interpretation of the results of the current manuscript. However, the initial experiments did not consider this and handled all PV cell data as a pooled population.

In the initial experiments, we handled all PV cell data together because we wanted to be rigorous and not make assumptions/biases on the different PV cells, which in later experiments we were to distinguish based on the intrinsic properties alone. We will make this point clear in the revised manuscript.

(10) The interpretation of the results strongly depends on unpublished work, which potentially provide the physiological and behavioral contexts about the role of GABAergic neurons in SynGap-haploinsufficiency. The authors cite their own unpublished work, without explaining the specific findings and relation to this manuscript.

We agree with the reviewer and apologize for the lack of clarity. Our unpublished work is in revision right now. We will provide more information and update references in the revised version of this manuscript.

(11) The introduction of Scholl analysis ‎experiments mentions SOM staining, however, there is no such data about this cell type in the manuscript.

We apologize for the error, we will change SOM with SST (SOM and SST are two commonly used acronyms for Somatostatin expressing interneurons).

Reviewer #3 (Public Review):

This paper compares the synaptic and membrane properties of two main subtypes of interneurons (PV+, SST+) in the auditory cortex of control mice vs mutants with Syngap1 haploinsufficiency. The authors find differences at both levels, although predominantly in PV+ cells. These results suggest that altered PV-interneuron functions in the auditory cortex may contribute to the network dysfunction observed in Syngap1 haploinsufficiency-related intellectual disability. The subject of the work is interesting, and most of the approach is direct and quantitative, which are major strengths. There are also some weaknesses that reduce its impact for a broader field.

(1) The choice of mice with conditional (rather than global) haploinsufficiency makes the link between the findings and Syngap1 relatively easy to interpret, which is a strength. However, it also remains unclear whether an entire network with the same mutation at a global level (affecting also excitatory neurons) would react similarly.

The reviewer raises an interesting and pertinent open question which we will address in the discussion of the revised paper.

(2) There are some (apparent?) inconsistencies between the text and the figures. Although the authors appear to have used a sophisticated statistical analysis, some datasets in the illustrations do not seem to match the statistical results. For example, neither Fig 1g nor Fig 3f (eNMDA) reach significance despite large differences.

We respectfully disagree, we do not think the text and figures are inconsistent. In the cited example, large apparent difference in mean values does not show significance due to the large variability in the data; further, we did not exclude any data points, because we wanted to be rigorous. In particular, for Fig.1g, statistical analysis shows a significant increase in the inter-mEPSC interval (*p=0.027, LMM) when all events are considered (cumulative probability plots), while there is no significant difference in the inter-mEPSCs interval for inter-cell mean comparison (inset, p=0.354, LMM). Inter-cell mean comparison does not show difference with Mann-Whitney test either (p=0.101, the data are not normally distributed, hence the choice of the Mann-Whitney test). For Fig. 3f (eNMDA), the higher mean value for the cHet versus the control is driven by two data points which are particularly high, while the other data points overlap with the control values. The Mann-Whitney test show also no statistical difference (p=0.174).

In the manuscript, discussion of the data is based on the results of the LMM analysis, which takes in account both the number of cells and the numbers of mice from which these cells are recorded. We chose this statistical approach because it does not rely on the assumption that cells recorded from same mouse are independent variables. In the supplemental tables, we provided the results of the statistical analysis done with both LMM and the most commonly used Mann Whitney (for not normally distributed) or t-test (for normally distributed), for each data set.

Also, the legend to Fig 9 indicates the presence of "a significant decrease in AP half-width from cHet in absence or presence of a-DTX", but the bar graph does not seem to show that.

We apologize for our lack of clarity. In legend 9, we reported the statistical comparisons between 1) cHET mice in absence of a-DTX and control mice and 2) cHET mice in presence of a-DTX and control mice. We will rephrase result description and the legend of the figure to avoid confusion.

(3) The authors mention that the lack of differences in synaptic current kinetics is evidence against a change in subunit composition. However, in some Figures, for example, 3a, the kinetics of the recorded currents appear dramatically different. It would be important to know and compare the values of the series resistance between control and mutant animals.

We agree with the reviewer that there appears to be a qualitative difference in eNMDA decay between conditions, although quantified eNMDA decay itself is similar between groups. We have used a cutoff of 15 % for the series resistance (Rs), which is significantly more stringent as compared to the cutoff typically used in electrophysiology, which are for the vast majority between 20 and 30%. To answer this concern, we re-examined the Rs, we compared Rs between groups and found no difference for Rs in eAMPA (13.2±0.5 in WT n=16 cells, 7 mice vs 13.7±0.3 in cHet n=14 cells, 7 mice, p=0.432 LMM) and eNMDA (12.7±0.7 in WT n=6 cells, 3 mice vs 13.8±0.7 in cHet n=6 cells, 5 mice, p=0.231, LMM). Thus, the apparent qualitative difference in eNMDA decay stems from inter-cell variability rather than inter-group differences. Notably, this discrepancy between the trace (Fig. 3a) and the data (Fig. 3f, right) is largely due to inter-cell variability, particularly in eNMDA, where a higher but non-significant decay rate is driven by a couple of very high values (Fig. 3f, right). In the revised manuscript, we will show traces that better represent our findings.

(4) A significant unexplained variability is present in several datasets. For example, the AP threshold for PV+ includes points between -50-40 mV, but also values at around -20/-15 mV, which seems too depolarized to generate healthy APs (Fig 5c, Fig7c).

We acknowledge the variability in AP threshold data, with some APs appearing too depolarized to generate healthy spikes. However, we meticulously examined each AP that spiked at these depolarized thresholds and found that other intrinsic properties (such as Rin, Vrest, AP overshoot, etc.) all indicate that these cells are healthy. Therefore, to maintain objectivity and provide unbiased data to the community, we opted to include them in our analysis. It's worth noting that similar variability has been observed in other studies (Bengtsson Gonzales et al., 2020; Bertero et al., 2020).

Further, we conducted a significance test on AP threshold excluding these potentially unhealthy cells and found that the significant differences persist. After removing two outliers from the cHet group with values of -16.5 and 20.6 mV, we obtain: -42.6±1.01 mV in control, n=33, 15 mice vs -36.2±1.1 mV in cHet, n=38 cells, 17 mice, ***p<0.001, LMM. Thus, whether these cells are included or excluded, our interpretations and conclusions remain unchanged.

We would like to clarify that these data have not been corrected with the junction potential. We will add this info in the revised version.

(5) I am unclear as to how the authors quantified colocalization between VGluts and PSD95 at the low magnification shown in Supplementary Figure 2.

We apologize for our lack of clarity. Although the analysis was done at high resolution, the figures were focused on showing multiple PV somata receiving excitatory inputs. We will add higher magnification figures and more detailed information in the methods of the revised version. Please also see our response to reviewer #2.

(6) The authors claim that "cHet SST+ cells showed no significant changes in active and passive membrane properties", but this claim would seem to be directly refused by the data of Fig 8f. In the absence of changes in either active or passive membrane properties shouldn't the current/#AP plot remain unchanged?

While we acknowledge the theoretical expectation that changes in intrinsic parameters should correlate with alterations in neuronal firing, the absence of differences in the parameters analyzed in this study should not overshadow the clear and significant decrease in firing rate observed in cHet SST+ cells. This decrease serves as a compelling indication of reduced intrinsic neuronal excitability. It's certainly possible that other intrinsic factors, not assessed in this study, may have contributed to this effect. However, exploring these mechanisms is beyond the scope of our current investigation. We will rephrase the discussion and add this limitation of our study in the revised version.

(7) The plots used for the determination of AP threshold (Figs 5c, 7c, and 7h) suggest that the frequency of acquisition of current-clamp signals may not have been sufficient, this value is not included in the Methods section.

This study utilized a sampling rate of 10 kHz, which is a standard rate for action potential analysis in the present context. We will describe more extensively the technical details in the method section of the revised manuscript we are preparing. While we acknowledge that a higher sampling rate could have enhanced the clarity of the phase plot, our recording conditions, as detailed in our response to Rev#2/comment#5, were suitable for the objectives of this study.

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Elabbady L, Seshamani S, Mu S, Mahalingam G, Schneider-Mizell C, Bodor AL, Bae JA, Brittain D, Buchanan J, Bumbarger DJ, Castro MA, Dorkenwald S, Halageri A, Jia Z, Jordan C, Kapner D, Kemnitz N, Kinn S, Lee K, Li K…Collman F (2024) Perisomatic features enable efficient and dataset wide cell-type classifications across large-scale electron microscopy volumes. bioRxiv, https://doi.org/10.1101/2022.07.20.499976

Goldberg EM, Clark BD, Zagha E, Nahmani M, Erisir A, Rudy B (2008) K+ Channels at the axon initial segment dampen near-threshold excitability of neocortical fast-spiking GABAergic interneurons. Neuron, 58, 387–400. https://doi.org/10.1016/j.neuron.2008.03.003

Golomb D, Donner K, Shacham L, Shlosberg D, Amitai Y, Hansel D. (2007). Mechanisms of firing patterns in fast-spiking cortical interneurons. PLoS Computational Biology, 38, e156. http://doi.org/10.1371/journal.pcbi.0030156

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Hwang YS, Maclachlan C, Blanc J, Dubois A, Petersen CH, Knott G, Lee SH (2021). 3D ultrastructure of synaptic inputs to distinct gabaergic neurons in the mouse primary visual cortex. Cerebral Cortex, 31, 2610–2624. http://doi.org/10.1093/cercor/bhaa378

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Stevens SR, Longley CM, Ogawa Y, Teliska LH, Arumanayagam AS, Nair S, Oses-Prieto JA, Burlingame AL, Cykowski MD, Xue M, Rasband MN (2021) Ankyrin-R regulates fast-spiking interneuron excitability through perineuronal nets and Kv3.1b K+ channels. Elife, 10, e66491. http://doi.org/10.7554/eLife.66491

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eLife assessment

This study provides valuable evidence indicating that SynGap1 regulates the synaptic drive and membrane excitability of parvalbumin- and somatostatin-positive interneurons in the auditory cortex. Since haplo-insufficiency of SynGap1 has been linked to intellectual disabilities without a well-defined underlying cause, the central question of this study is timely. However, the support for the authors' conclusions is incomplete in general and some parts of the experimental evidence are inadequate. Specifically, the manuscript requires further work to properly evaluate the impact on synaptic currents, intrinsic excitability parameters, and morphological features.

Reviewer #1 (Public Review):

The study is designed to assess the role of Syngap1 in regulating the physiology of the MGE-derived PV+ and SST+ interneurons. Syngap1 is associated with some mental health disorders, and PV+ and SST+ cells are the focus of many previous and likely future reports from studies of interneuron biology, highlighting the translational and basic neuroscience relevance of the authors' work.

Strengths of the study are using well-established electrophysiology methods and the highly controlled conditions of ex vivo brain slice experiments combined with a novel intersectional mouse line, to assess the role of Syngap1 in regulating PV+ and SST+ cell properties. The findings revealed that in the mature auditory cortex, Syngap1 haploinsufficiency decreases both the intrinsic excitability and the excitatory synaptic drive onto PV+ neurons from Layer 4. In contrast, SST+ interneurons were mostly unaffected by Syngap1 haploinsufficiency. Pharmacologically manipulating the activity of voltage-gated potassium channels of the Kv1 family suggested that these channels contributed to the decreased PV+ neuron excitability by Syngap insufficiency. These results therefore suggest that normal Syngap1 expression levels are necessary to produce normal PV+ cell intrinsic properties and excitatory synaptic drive, albeit, perhaps surprisingly, inhibitory synaptic transmission was not affected by Syngap1 haploinsufficiency.

Since the electrophysiology experiments were performed in the adult auditory cortex, while Syngap1 expression was potentially affected since embryonic stages in the MGE, future studies should address two important points that were not tackled in the present study. First, what is the developmental time window in which Syngap1 insufficiency disrupted PV+ neuron properties? Albeit the embryonic Syngap1 deletion most likely affected PV+ neuron maturation, the properties of Syngap-insufficient PV+ neurons do not resemble those of immature PV+ neurons. Second, whereas the observation that Syngap1 haploinsufficiency affected PV+ neurons in auditory cortex layer 4 suggests auditory processing alterations, MGE-derived PV+ neurons populate every cortical area. Therefore, without information on whether Syngap1 expression levels are cortical area-specific, the data in this study would predict that by regulating PV+ neuron electrophysiology, Syngap1 normally controls circuit function in a wide range of cortical areas, and therefore a range of sensory, motor and cognitive functions. These are relatively minor weaknesses regarding interpretation of the data in the present study that the authors could discuss.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors investigated how partial loss of SynGap1 affects inhibitory neurons derived from the MGE in the auditory cortex, focusing on their synaptic inputs and excitability. While haplo-insufficiently of SynGap1 is known to lead to intellectual disabilities, the underlying mechanisms remain unclear.

Strengths:

The questions are novel

Weaknesses:

Despite the interesting and novel questions, there are significant concerns regarding the experimental design and data quality, as well as potential misinterpretations of key findings. Consequently, the current manuscript fails to contribute substantially to our understanding of SynGap1 loss mechanisms and may even provoke unnecessary controversies.

Major issues:

(1) One major concern is the inconsistency and confusion in the intermediate conclusions drawn from the results. For instance, while the sEPSC data indicates decreased amplitude in PV+ and SOM+ cells in cHet animals, the frequency of events remains unchanged. In contrast, the mEPSC data shows no change in amplitudes in PV+ cells, but a significant decrease in event frequency. The authors conclude that the former observation implies decreased excitability. However, traditionally, such observations on mEPSC parameters are considered indicative of presynaptic mechanisms rather than changes of network activity.‎ The subsequent synapse counting experiments align more closely with the traditional conclusions. This issue can be resolved by rephrasing the text. However, it would remain unexplained why the sEPSC frequency shows no significant difference. If the majority of sEPSC events were indeed mediated by spiking (which is blocked by TTX), the average amplitudes and frequency of mEPSCs should be substantially lower than those of sEPSCs. Yet, they fall within a very similar range, suggesting that most sEPSCs may actually be independent of action potentials. But if that was indeed the case, the changes of purported sEPSC and mEPSC results should have been similar.

(2) Another significant concern is the quality of synapse counting experiments. The authors attempted to colocalize pre- and postsynaptic markers Vglut1 and PSD95 with PV labelling. However, several issues arise. Firstly, the PV labelling seems confined to soma regions, with no visible dendrites. Given that the perisomatic region only receives a minor fraction of excitatory synapses, this labeling might not accurately represent the input coverage of PV cells.<br /> Secondly, the resolution of the images is insufficient to support clear colocalization of the synaptic markers. Thirdly, the staining patterns are peculiar, with PSD95 puncta appearing within regions clearly identified as somas by Vglut1, hinting at possible intracellular signals. Furthermore, PSD95 seems to delineate potential apical dendrites of pyramidal cells passing through the region, yet Vglut1+ partners are absent in these segments, which are expected to be the marker of these synapses here.<br /> Additionally, the cumulative density of Vglut2 and Vglut1 puncta exceeds expectations, and it's surprising that subcortical fibers labeled by Vglut2 are comparable in number to intracortical Vglut1+ axon terminals. Ideally, N(Vglut1)+N(Vglut2) should be equal or less than N(PSD95), but this is not the case here. Consequently, these results cannot be considered reliable due to these issues.

(3) One observation from the minimal stimulation experiment was concluded by an unsupported statement. Namely, the change in the onset delay cannot be attributed to a deficit in the recruitment of PV+ cells, but it may suggest a change in the excitability of TC axons.

(‎4) The conclusions drawn from the stimulation experiments are also disconnected from the actual data. To make conclusions about TC release, the authors should have tested release probability using established methods, such as paired-pulse changes. Instead, the only observation here is a change in the AMPA components, which remained unexplained.

(5) The sampling rate of CC recordings is insufficient ‎to resolve the temporal properties of the APs. Therefore, the phase-plots cannot be interpreted (e.g. axonal and somatic AP components are not clearly separated), raising questions about how AP threshold and peak were measured. The low sampling rate also masks the real derivative of the AP signals, making them apparently faster.<br /> A related issue is that the Methods section lacks essential details about the recording conditions, such as bridge balance and capacitance neutralization.

(6) Interpretation issue: One of the most fundamental measures of cellular excitability, the rheobase, was differentially affected by cHet in BCshort and BCbroad. Yet, the authors concluded that the cHet-induced changes in the two subpopulations are common.

(7) Design issue:<br /> The Kv1 blockade experiments are disconnected from the main manuscript. There is no experiment that shows the causal relationship between changes in DTX and cHet cells. It is only an interesting observation on AP halfwidth and threshold. However, how they affect rheobase, EPSCs, and other topics of the manuscript are not addressed in DTX experiments.<br /> Furthermore, Kv1 currents were never measured in this work, nor was the channel density tested. Thus, the DTX effects are not necessarily related to changes in PV cells, which can potentially generate controversies.

(8) Writing issues:<br /> Abstract:<br /> The auditory system is not mentioned in the abstract.<br /> One statement in the abstract is unclear‎. What is meant by "targeting Kv1 family of voltage-gated potassium channels was sufficient..."? "Targeting" could refer to altered subcellular targeting of the channels, simple overexpression/deletion in the target cell population, or targeted mutation of the channel, etc. Only the final part of the Results revealed that none of the above, but these channels were blocked selectively.<br /> Introduction:<br /> There is a contradiction in the introduction. The second paragraph describes in detail the distinct contribution of PV and SST n‎eurons to auditory processing. But at the end, the authors state that "relatively few reports on PV+ and SST+ cell-intrinsic and synaptic properties in adult auditory cortex". Please be more specific about the unknown properties.

(9) The introduction emphasizes the heterogeneity of PV neurons, which certainly influences the interpretation of the results of the current manuscript. However, the initial experiments did not consider this and handled all PV cell data as a pooled population.

(10) The interpretation of the results strongly depends on unpublished work, which potentially provide the physiological and behavioral contexts about the role of GABAergic neurons in SynGap-haploinsufficiency. The authors cite their own unpublished work, without explaining the specific findings and relation to this manuscript.

(11) The introduction of Scholl analysis ‎experiments mentions SOM staining, however, there is no such data about this cell type in the manuscript.

Reviewer #3 (Public Review):

This paper compares the synaptic and membrane properties of two main subtypes of interneurons (PV+, SST+) in the auditory cortex of control mice vs mutants with Syngap1 haploinsufficiency. The authors find differences at both levels, although predominantly in PV+ cells. These results suggest that altered PV-interneuron functions in the auditory cortex may contribute to the network dysfunction observed in Syngap1 haploinsufficiency-related intellectual disability. The subject of the work is interesting, and most of the approach is direct and quantitative, which are major strengths. There are also some weaknesses that reduce its impact for a broader field.

(1) The choice of mice with conditional (rather than global) haploinsufficiency makes the link between the findings and Syngap1 relatively easy to interpret, which is a strength. However, it also remains unclear whether an entire network with the same mutation at a global level (affecting also excitatory neurons) would react similarly.

(2) There are some (apparent?) inconsistencies between the text and the figures. Although the authors appear to have used a sophisticated statistical analysis, some datasets in the illustrations do not seem to match the statistical results. For example, neither Fig 1g nor Fig 3f (eNMDA) reach significance despite large differences. Also, the legend to Fig 9 indicates the presence of "a significant decrease in AP half-width from cHet in absence or presence of a-DTX", but the bar graph does not seem to show that.

(3) The authors mention that the lack of differences in synaptic current kinetics is evidence against a change in subunit composition. However, in some Figures, for example, 3a, the kinetics of the recorded currents appear dramatically different. It would be important to know and compare the values of the series resistance between control and mutant animals.

(4) A significant unexplained variability is present in several datasets. For example, the AP threshold for PV+ includes points between -50-40 mV, but also values at around -20/-15 mV, which seems too depolarized to generate healthy APs (Fig 5c, Fig7c).

(5) I am unclear as to how the authors quantified colocalization between VGluts and PSD95 at the low magnification shown in Supplementary Figure 2.

(6) The authors claim that "cHet SST+ cells showed no significant changes in active and passive membrane properties", but this claim would seem to be directly refused by the data of Fig 8f. In the absence of changes in either active or passive membrane properties shouldn't the current/#AP plot remain unchanged?

(7) The plots used for the determination of AP threshold (Figs 5c, 7c, and 7h) suggest that the frequency of acquisition of current-clamp signals may not have been sufficient, this value is not included in the Methods section.

for - classification table - types of Buddhist practice - nondual vs classical

for - two types of mindfulness

Buddhist classifcation - two types of mindfulness - classical - requires - memory of specific Buddhist teachings - dhammas mental framework<br /> - ethical consideraions - think of these things - don't think of those things - nondual - not distracted by anything - nothing in particular to focus on - no objject of attention

for - John D. Dunne - Buddhist scholar - paper - Buddhist Styles of Mindfulness - A Heuristic Approach - to - citation - John Dunne

to - citation - John Dunne website and paper - citation - https://hyp.is/N348dga5Ee-vq5-ZnnVD9Q/docdrop.org/video/BNAVYglundg/

for - definition - mindfulness

definition - mindfulness - This is a 20th century Western, Buddhist psychology term which has two complimentary aspects - remembering / recollecting (smrti) - hold some mental object in mind and prevent it from drifting away - clear comprehension (samprajanya) - clear knowing through alert awareness - mental surveying / monitoring

for - definition - Bhavana - meditation - Sanskrit - samatha - vipassana

definition - Bhavana - meditation - Sanskrit - https://encyclopediaofbuddhism.org/wiki/Bh%C4%81van%C4%81 - cultivation - samatha-bhāvanā, the cultivation of calm-abiding - stabilizing attention leading to refined states of concentration - vipassanā-bhāvanā, the cultivation of insight<br /> - clearly noting what is arising from moment to moment

for . Evan Thompson - interview - Osher Center for Integrative Health - Harvard

to - Osher Center

Reviewer #1 (Public Review):

Summary:

This manuscript explores the impact of serotonin on olfactory coding in the antennal lobe of locusts and odor-evoked behavior. The authors use serotonin injections paired with an odor-evoked palp-opening response assay and bath application of serotonin with intracellular recordings of odor-evoked responses from projection neurons (PNs).

Strengths:

The authors make several interesting observations, including that serotonin enhances behavioral responses to appetitive odors in starved and fed animals, induces spontaneous bursting in PNs, directly impacts PN excitability, and uniformly enhances PN responses to odors.

Weakness:

The one remaining issue to be resolved is the theoretical discrepancy between the physiology and the behavior. The authors provide a computational model that could explain this discrepancy and provide the caveat that while the physiological data was collected from the antennal lobe, but there could be other olfactory processing stages involved. Indeed other processing stages could be the sites for the computational functions proposed by the model. There is an additional caveat which is that the physiological data were collected 5-10 minutes after serotonin application whereas the behavioral data were collected 3 hours after serotonin application. It is difficult to link physiological processes induced 5 minutes into serotonin application to behavioral consequences 3 hours subsequent to serotonin application. The discrepancy between physiology and behavior could easily reflect the timing of action of serotonin (i.e. differences between immediate and longer-term impact).

Overall, the study demonstrates the impact of serotonin on odor-evoked responses of PNs and odor guided behavior in locust. Serotonin appears to have non-linear effects including changing the firing patterns of PNs from monotonic to bursting and altering behavioral responses in an odor-specific manner, rather than uniformly across all stimuli presented.

Reviewer #2 (Public Review):

Summary:

The authors investigate the influence of serotonin on feeding behavior and electrophysiological responses in the antennal lobe of locusts. They find that serotonin injection changes behavior in an odor-specific way. In physiology experiments, they can show that projection neurons in the antennal lobe generally increase their baseline firing and odor responses upon serotonin injection. Using a modeling approach the authors propose a framework on how a general increase in antennal lobe output can lead to odor-specific changes in behavior.

Strengths:

This study shows that serotonin affects feeding behavior and odor processing in the antennal lobe of locusts, as serotonin injection increases activity levels of projection neurons. This study provides another piece of evidence that serotonin is a general neuromodulator within the early olfactory processing system across insects and even phyla.

Weaknesses:

I still have several concerns regarding the generalizability of the model and interpretation of results. The authors cannot provide evidence that serotonin modulation of projection neurons impacts behavior.

The authors show that odor identity is maintained after 5-HT injection, however, the authors do not show if PN responses to different odors were differently affected after serotonin exposure.

Regarding the model, the authors show that the model works for odors with non-overlapping PN activation. However, only one appetitive, one neutral, and one aversive odor has been tested and modeled here. Can the fixed-weight model also hold for other appetitive and aversive odors that might share more overlap between active PNs? How could the model generate BZA attraction in 5-HT exposed animals (as seen in behavior data in Figure 1) if the same PNs just get activated more?

The authors should still not exclude the possibility that serotonin injections could affect behavior via modulation of other cell types than projection neurons. This should still be discussed, serotonin might rather shut down baseline activation of local inhibitory neurons - and thus lead to the interesting bursting phenotypes, which can also be seen in the baseline response, due to local PN-to-LN feedback.

The authors did not fully tone down their claims regarding causality between serotonin and starved state behavioral responses.<br /> There is no proof that serotonin injection mimics starved behavioral responses.

eLife assessment

In this manuscript the authors present high-speed atomic force microscopy (HSAFM) to analyze real-time structural changes in actin filaments induced by cofilin binding. This important study enhances our understanding of actin dynamics which plays a crucial role in a broad spectrum of cellular activities. However, some technical questions remain, making the data interpretation incomplete.

Reviewer #1 (Public Review):

The authors provided a detailed analysis of the real-time structural changes in actin filaments resulting from cofilin binding, using High-Speed Atomic Force Microscopy (HSAFM). The cofilin family controls the lifespan of actin filaments in the cells by severing the filament and promoting depolymerization. Understanding the effects of cofilin on actin filament structure is critical. It is widely acknowledged that cofilin binding significantly shortens the pitch of the actin helix. The authors previously reported (1) that this shortening extends to the unbound region of the actin filament on the pointed end side of the cluster. In this study, the authors presented substantially improved AFM images and provide detailed accounts of the dynamics observed. It was found that a minimal cofilin-binding cluster, consisting of 2-4 molecules, could induce changes in the helical parameters over one or more actin crossover repeats. Adjacent to the cofilin-binding clusters, the actin crossovers were observed to shortened within seconds, and this shortening was limited to one side of the cluster. Additionally, the phosphate binding to the actin filament was observed to stabilize the helical twist, suggesting a mechanism in which cofilin preferentially binds to ADP-bound actin filaments. These findings significantly advance our understanding of actin filament dynamics which is essential for a wide of cellular processes.<br /> However, I propose that the sections about MAD and certain parts of the discussions need substantial revisions.

MAD analysis<br /> The authors have presented findings that the mean axial distance (MAD) within actin filaments exhibits a significant dependency on the helical twist, a conclusion not previously derived despite extensive analyses through electron microscopy (EM) and molecular dynamics (MD) simulations. Notably, the MAD values span from 4.5 nm (8.5 pairs per half helical pitch, HHP) to 6.5 nm (4.5 pairs/HHP) as depicted in Figure 3C. The inner domain (ID) of actin remains very similar across C, G, and F forms(2, 3), maintaining similar ID-ID interactions in both cofilactin and bare actin filaments, keeping the identical axial distance between subunits in the both states. This suggests that the ID is unlikely to undergo significant structural changes, even with fluctuations in the filament's twist, keeping the ID-ID interactions and the axial distances. The broad range of MAD values reported poses a challenge for explanation. A careful reassessment of the MAD analysis is recommended to ensure accuracy.<br /> In determining axial distances, the authors extracted measurements from filament line profiles. It is advised to account for potential anomalies such as missing peaks or pseudo peaks, which could arise from noise interference. An example includes the observation of three peaks in HHP6 of Figure Supplement 5C, corresponding to 4.5 pairs. Peak intervals measured from the graph were 5, 11.8, 8.7, and 5.7 nm. The second region (11.8 nm) appears excessively long. If one peak is hidden in the second region, the MAD becomes 5.5 nm.

Compiling histograms of axial distances (ADs) rather than focusing solely on MAD may provide deeper insights. If the AD is too long or too short, the authors should suspect the presence of missing peaks or pseudo-peaks due to noise. If 4.4 or 5.5 pairs/HHP regions tend to contain missing peaks and 7.5-8.5 pairs/HHP regions tend to contain pseudo peaks, this may explain the MAD dependency on the helical twist.

Additionally, Figure 3E indicates a first decay constant of 0.14 seconds, substantially shorter than the frame rate (0.5 sec/frame). This suggests significant variations in line profiles between frames, attributable either to overly rapid dynamics or a low signal-to-noise ratio. Implementing running frame averages (of 2-3 frames) is recommended to distinguish between these scenarios. If the dynamics are indeed fast, the averaged frame's line profile may degrade, complicating peak identification. Conversely, if poor signal-to-noise ratio is the cause, averaging frames could facilitate peak detection. In the latter case, the authors can find the optimal number of frame averages and obtain better line profiles with fewer missing and pseudo-peaks.

Discussions<br /> The authors suggest a strong link between the C-form of actin and the formation of a short pitch helix. However, Oda et al. (3) have demonstrated that the C-form is highly unstable in the absence of cofilin binding, casting doubt on the possibility of the C-form propagating without cofilin binding. Moreover, in one strand of the cofilactin, interactions between actin subunits are limited to those between the inner domains (ID-ID interactions), which are quite similar to the interactions observed in bare actin filaments. This similarity implies that ID-ID interactions alone are insufficient to determine the helical parameters, suggesting that the presence of cofilin is essential for the formation of the short pitch helix in the cofilactin filament. Thus, crossover repeats are not necessarily shortened even if the actin form is C-form.

Narita (4) proposes that the facilitation of cofilin binding may occur through a shortening in the helix pitch, independent of a change to the C-form of actin. Furthermore, the dissociation of the D-loop from an adjacent actin subunit leads directly to the transition of actin to the G-form, which is considered the most stable configuration for the actin molecule (3).

The mechanism by which the shortened pitch propagates remains a critical and unresolved issue. It appears that this propagation is not a result of the C-form's propagation but likely involves an unidentified mechanism. Identifying and understanding this mechanism represents an essential direction for future research.

(1) K. X. Ngo et al., a, Cofilin-induced unidirectional cooperative conformational changes in actin filaments revealed by high-speed atomic force microscopy. eLife 4, (2015).<br /> (2) K. Tanaka et al., Structural basis for cofilin binding and actin filament disassembly. Nature communications 9, 1860 (2018).<br /> (3) T. Oda et al., Structural Polymorphism of Actin. Journal of molecular biology 431, 3217-3228 (2019).<br /> (4) A. Narita, ADF/cofilin regulation from a structural viewpoint. Journal of muscle research and cell motility 41, 141-151 (2020).

Reviewer #2 (Public Review):

Summary:

This study by Ngo et al. uses mostly high-speed AFM to estimate conformational changes within actin filaments, as they get decorated by cofilin. The authors build on their earlier study (Ngo et al. eLife 2015) where they used the same technique to monitor the expansion of cofilin clusters on actin filaments, and the propagation of the associated conformational changes in the filament (reduction of the helical pitch). Here, they propose a higher-resolution description of the binding of cofilin to actin filaments.

Strengths:

The high speed AFM technique used here is quite original to address this question, compared to classical light and electron microscopy techniques. It can certainly bring valuable information as it provides a high spatial resolution while monitoring live events. Also, in this paper, a nice effort was made to make the 3D structures and conformational changes clear and understandable.

Weaknesses:

The paper also has a number of limitations, which I detail below.

In addition to AFM, the authors also propose a Principal Component Analysis (PCA) of exisiting structural data on actin protomers. However, this part seems very similar to another published work by others (Oda et al. JMB 2019), which is not even cited.

The asymmetrical growth of cofilin clusters has so far only been seen using AFM, by the same authors (Ngo et al. eLife 2015). Using fluorescent microscopy, others have reported a very symmetrical expansion of cofilin clusters (Wioland et al. Curr Biol 2017). This is not mentioned at all, here. It should be discussed, and explanations for this discrepancy could be proposed.

Regarding the AFM technique, I have the following concerns.

The filaments appear densely packed on the surface, and even clearly in register in some images (if not most images, e.g., Figs 3A, 4BC, 5A). Why is that? Isn't there a risk that this could affect the result? This suggests there is some interaction between the filaments.

The properties of the lipid layer and its interaction with the actin filaments are not clear at all. A poor control of these interactions is a problem if one aims to measure conformational changes at high resolution. The strength of the interaction appears tuned by the ratio of lipids put on the surface to change its electrostatic charge. A strong attachement likely does more than suppress torsional motion (as claimed in Fig 8A). It may also hinder cofilin binding in several ways (lower availability of binding sites on the filament facing the surface, electrostatic interactions between cofilin and the surface, etc.)

How do we know that the variations over time are not mostly experimental noise, i.e. variations between repeats of the same measurement? As shown in Fig 3, correlation is mostly lost from one image to the next, and rather stable after that.

The identification of cofilactin regions relies on the additional height of the "peaks", due to the presence of cofilin. It thus seems that cofilin is detected every half helical pitch (HHP), but not in between, thereby setting the resolution for the localization of cluster borders to one HHP. It thus seems difficult to claim that there is a change in helicity without cofilin decoration over this distance. In Fig 7, the change in helicity could be due to cofilin decoration that is undetected because cofilins have not yet reached the next peak.

Why I’m excited about Richard Polt’s book. by [[Scott K]]

eLife assessment

This study provides valuable insights into how chromatin-bound PfMORC controls gene expression in the asexual blood stage of Plasmodium falciparum. By interacting with key nuclear proteins, PfMORC is predicted to affect expression of genes important for host invasion and variable subtelomeric gene families. Correlating transcriptomic data with in vivo chromatin analysis, the study provides convincing evidence for the role of PfMORC in epigenetic transcriptional regulation.

Reviewer #1 (Public Review):

Summary:

The study provides valuable insights into the role of PfMORC in Plasmodium's epigenetic regulation, backed by a comprehensive methodological approach. The overarching goal was to understand the role of PfMORC in epigenetic regulation during asexual blood stage development, particularly its interactions with ApiAP2 TFs and its potential involvement in the regulation of genes vital for Plasmodium virulence. To achieve this, they conducted various analyses. These include a proteomic analysis to identify nuclear proteins interacting with PfMORC, a study to determine the genome-wide localization of PfMORC at multiple developmental stages, and a transcriptomic analysis in PfMORCHA-glmS knockdown parasites. Taken together, this study suggests that PfMORC is involved in chromatin assemblies that contribute to the epigenetic modulation of transcription during the asexual blood stage development.

Strengths:

The study employed a multi-faceted approach, combining proteomic, genomic, and transcriptomic analyses, providing a holistic view of PfMORC's role. The proteomic analysis successfully identified several nuclear proteins that may interact with PfMORC. The genome-wide localization offered valuable insights into PfMORC's function, especially its predominant recruitment to subtelomeric regions. The results align with previous findings on PfMORC's interaction with ApiAP2 TFs. Notably, the authors meticulously contextualized their findings with prior research adding credibility to their work.

Weaknesses:

While the study identifies potential interacting partners and loci of binding, direct functional outcomes of these interactions remain an inference. The use of the glmS ribozyme system to achieve a 50% reduction in PfMORC transcript levels makes it difficult to understand the role of PfMORC solely in terms of chromatin architecture without considering its impact on gene expression. Although assessing the overall impact of acute MORC depletion was beyond the scope of the study, it would have been informative.

eLife assessment

This manuscript reports important in vitro biochemical and in planta experiments to study the receptor activation mechanism of plant membrane receptor kinase complexes with non-catalytic intracellular kinase domains. Several lines of evidence convincingly show that one such putative pseudokinase, the immune receptor EFR achieves an active conformation following phosphorylation by a co-receptor kinase, and then in turn activates the co-receptor kinase allosterically to enable it to phosphorylate down-stream signaling components. This manuscript will be of interest to scientists focusing on cell signalling and allosteric regulation.

Reviewer #1 (Public Review):

Summary

The authors use an elegant but somewhat artificial heterodimerisation approach to activate the isolated cytoplasmic domains of different receptor kinases (RKs) including the receptor kinase BRI1 and EFR. The developmental RK BRI1 is known to be activated by the co-receptor BAK1. Active BRI1 is then able to phosphorylate downstream substrates. The immune receptor EFR is also an active protein kinase also activated by the co-receptor BAK1. EFR however appears to have little or no kinase activity but seems to use an allosteric mechanism to in turn enable BAK1 to phosphorylate the substrate kinase BIK1. EFR tyrosine phosphorylation by BAK1 appears to trigger a conformational change in EFR, activating the receptor. Likewise, kinase activating mutations can cause similar conformational transitions in EFR and also in BAK1 in vitro and in planta.

Strengths:

I particularly liked The HDX experiments coupled with mutational analysis (Fig. 2) and the design and testing of the kinase activating mutations (Fig. 3), as they provide novel mechanistic insights into the activation mechanisms of EFR and of BAK1. These findings are nicely extended by the large-scale identification of EFR-related RKs from different species with potentially similar activation mechanisms (Fig. 5).

Weaknesses:

In my opinion, there are currently two major issues with the present manuscript. (1) The authors have previously reported that the EFR kinase activity is dispensible for immune signaling (https://pubmed.ncbi.nlm.nih.gov/34531323/) but the wild-type EFR receptor still leads to a much better phosphorylation of the BIK1 substrate when compared to the kinase inactive D849N mutant protein (Fig. 1). (2) How the active-like conformation of EFR is in turn activating BAK1 is poorly characterized, but appears to be the main step in the activation of the receptor complex. Extending the HDX analyses to resting and Rap-activated receptor complexes could be a first step to address this question, but these HDX studies were not carried out due to technical limitations.

Overall this is an interesting study that aims to advance our understanding of the activation mechanisms of different plant receptor kinases with important functions in plant immunity.

Reviewer #2 (Public Review):

Summary:

Transmembrane signaling in plants is crucial for homeostasis. In this study, the authors set out to understand to what extent catalytic activity in the EFR tyrosine kinase is required in order to transmit a signal. This work was driven by mounting data that suggest many eukaryotic kinases do not rely on catalysis for signal transduction, relying instead on conformational switching to relay information. The crucial findings reported here involve the realisation that a kinase-inactive EFR can still activate (ie lead to downstream phosphorylation) of its partner protein BAK1. Using a convincing set of biochemical, mass spectrometric (HD-exchange) and in vivo assays, the team suggest a model in which EFR is likely phosphorylated in the canonical activation segment (where two Ser residues are present), which is sufficient to generate a conformation that can activate BAK1 through dimersation. A model is put forward involving C-helix positioning in BAK1, and the model extended to other 'non-RD' kinases in Arabidopsis kinases that likely do not require kinase activity for signaling.

Strengths:

The work uses logical and well-controlled approaches throughout, and is clear and convincing in most areas, linking data from IPs, kinase assays (including clear 32P-based biochemistry), HD-MX data (from non-phosphorylated EFR) structural biology, oxidative burst data and infectivity assays. Repetitions and statistical analysis all appear appropriate.<br /> Overall, the work builds a convincing story and the discussion does a clear job of explaining the potential impact of these findings (and perhaps an explanation of why so many Arabidopsis kinases are 'pseudokinases', including XPS1 and XIIa6, where this is shown explicitly).

Weaknesses:

No major weaknesses are noted from reviewing the data and the paper follows a logical course built on solid foundations; the use of Tables to explain various experimental data pertinent to the reported studies is appreciated.

(1) The use of a, b,c, d in Figures 2C and 3C etc is confusing to this referee, and is now addressed in the latest version<br /> (2) The debate about kinase v pseudokinases is well over a decade old. For non-experts, the kinase alignments/issues raised are in PMID: 23863165 and might prove useful if cited.<br /> (3) Early on in the paper, the concept of kinases and pseudokinases related to R-spine (and extended R-spine) stability and regulation really needs to be more adequately introduced to explain what comes next; e.g. some of the key work in this area for RAF and Tyr kinases where mutual F-helix Phe amino acid changes are evaluated (conceptually similar to this study of the E-helix Tyr to Phe changes in EFR) should be cited (PMID: 17095602, 24567368 and 26925779).<br /> (4) In my version, some of the experimental text is also currently in the wrong order (and no page numbers, so hard for me to state exactly where in the manuscript); However, I am certain that Figure 2C is mentioned in the text when the data are actually shown in Figure 3C for the EFR-SSAA protein.<br /> (5) Tyr 156 in PKA is not shown in Supplement 1, 2A as suggested in the text; for readers, it will be important to show the alignment of the Tyr residue in other kinases; this has been updated in the second version. Although it is clearly challenging to generate phosphorylated EFR (seemingly through Codon-expansion here?), it appears unlikely that a phosphorylated EFR protein, even semi-pure, couldn't have been assayed to test the idea that the phosphorylation drives/supports downstream signaling. What about a DD or EE mutation, as commonly used (perhaps over-used) in MEK-type studies?

Impact:

The work is an important new step in the huge amount of follow-up work needed to examine how kinases and pseudokinases 'talk' to each other in (especially) the plant kingdom, where significant genetic expansions have occurred. The broader impact is that we might understand better how to manipulate signaling for the benefit of plants and mankind; as the authors suggest, their study is a natural progression both of their own work, and the kingdom-wide study of the Kannan group.

Reviewer #3 (Public Review):

The study presents strong evidence for allosteric activation of plant receptor kinases, which enhances our understanding of the non-catalytic mechanisms employed by this large family of receptors.

Plant receptor kinases (RKs) play a critical role in transducing extracellular signals. The activation of RKs involves homo- or heterodimerization of the RKs, and it is believed that mutual phosphorylation of their intracellular kinase domains initiates downstream signaling. However, this model faces a challenge in cases where the kinase domain exhibits pseudokinase characteristics. In their recent study, Mühlenbeck et al. reveal the non-catalytic activation mechanisms of the EFR-BAK1 complex in plant receptor kinase signaling. Specifically, they aimed to determine that the EFR kinase domain activates BAK1 not through its kinase activity, but rather by utilizing a "conformational toggle" mechanism to enter an active-like state, enabling allosteric trans-activation of BAK1. The study sought to elucidate the structural elements and mutations of EFR that affect this conformational switch, as well as explore the implications for immune signaling in plants. To investigate the activation mechanisms of the EFR-BAK1 complex, the research team employed a combination of mutational analysis, structural studies, and hydrogen-deuterium exchange mass spectrometry (HDX-MS) analysis. For instance, through HDX-MS analysis, Mühlenbeck et al. discovered that the EFR (Y836F) mutation impairs the accessibility of the active-like conformation. On the other hand, they identified the EFR (F761H) mutation as a potent intragenic suppressor capable of stabilizing the active-like conformation, highlighting the pivotal role of allosteric regulation in BAK1 kinase activation. The data obtained from this methodology strengthens their major conclusion. Moreover, the researchers propose that the allosteric activation mechanism may extend beyond the EFR-BAK1 complex, as it may also be partially conserved in the Arabidopsis LRR-RK XIIa kinases. This suggests a broader role for non-catalytic mechanisms in plant RK signaling.

The allosteric activation mechanism was demonstrated for receptor tyrosine kinases (RTKs) many years ago. A similar mechanism has been suggested for the activation of plant RKs, but experimental evidence for this conclusion is lacking. Data in this study represent a significant advancement in our understanding of non-catalytic mechanisms in plant RK signaling. By shedding light on the allosteric regulation of BAK1, the study provides a new paradigm for future research in this area.

Erinnert mich an Erik Olin Wright.

Bakunin unterscheidet zwischen Politik auf lokaler und auf staatlicher Ebene. Das ist ebenso interessant und wahrscheinlich relevant, wie die Forderung nach einer Aufhebung des Gegensatzes von Stadt und Land bei Engels.

Heute ist die Frage, ob dieser politische Ansatz sich in eine Zeit der ökologischenKatastrophen oder Degradation übertragen lässt. Kann man den libertären Munizipalismus auch als Strategie gegen die Dominanz fossiler Machtrguppen formulieren? Oder sind unsere Bedingungen heute so anders, dass man mit diesem Versuch in lange vergangenen Debatten verharren würde.

Wenn man Argumente dafür sucht, Politik auf der Ebene von Städten oder Kommunen zu machen, findet man sie wohl bei Murray. Sein geografischer Ansatz (den er wohl nicht so benennt) erinnert mich dabei an David Harvey.

Was Murray ablehnt, ist nicht der Anarchismus als historische Bewegung sondern der individualistische Anarchismus, der sich in den USA entwickelt hatte. Er nimmt offenbar viel von der späteren Entwicklung vorweg – Kommodifizierung der persönlichen Gefühle und Vorlieben vs. Machkonzentration bei wenigen Oligarchen.

Das liest sich, als hätte Murray die Epoche der Elon Musks vorhergesehen.

Das ist dagegen bei der Degrowth-Bewegung gelungen. Sie ist tatsächlich in den letzten Jahren gewachsen.

Vielleicht kann man das, was Murray „communalism“ nennt, als eine verräumlichte, geografisch konzipierte Form der sozialen Ökologie verstehen. Sie entspricht, oder ist eher ein Gegenstück, zu dem, was man eher von konservativen Positionen aus als Geopolitik bezieichnet.

yes but... there is a general lack of acknowledgement of privilege asians experience in this piece

lack of critical thinking within our own communities

lump all communities of color together, claim allocation of resources to one will take away from resource allocation to another

thesis

interview

as opposed to a more diverse area

survey aspect

research q

importance of racial education + solidarity in activism... much of which is political radical

read: passive, liberal, appealing to white ruling class

sentiments present also in the asian am community... arguably more conservative talking points

key concept to comparing asian am activism to other groups

https://web.archive.org/web/20240430062049/https://ideophone.org/raspberry-pi400-kids-first-personal-computer/

Post by [[Mark Dingemanse]] about a RPi pc for his kids to explore. Sounds like something that is perfect for Y too.

from - Evan Thompson interview - Osher Center youtube channel podcast - citation of Dunne's paper - https://hyp.is/N348dga5Ee-vq5-ZnnVD9Q/docdrop.org/video/BNAVYglundg/

Summary of the Talk on the Future of Web Frameworks by Ryan Carniado

• Introduction and Background:

  • Ryan Carniado, creator of SolidJS, has extensive experience in web development spanning 25 years, having worked with various technologies including ASP.NET, Rails, and jQuery.
  • SolidJS was started in 2016 and reflects a shift towards new paradigms in web frameworks, particularly in the front-end JavaScript ecosystem.
  • Quote: "I've been doing web development now for like 25 years... it wasn't really until the 2010s that my passion reignited for front-end JavaScript."

• Core Themes and Concepts:

  • Modern front-end development heavily relies on components (e.g., class components, function components, web components) which serve as fundamental building blocks for creating modular and composable applications.
  • Components have runtime implications due to their update models and life cycles, influencing the performance and design of web applications.
  • Traditional component models use either a top-down diffing approach (like virtual DOM) or rely on compilation optimizations to enhance performance.
  • Quote: "Modern front-end development for years has been about components... however, in almost every JavaScript framework components have runtime implications."

• Reactive Programming and Fine-Grained Reactivity:

  • Ryan advocates for a shift towards reactive programming to manage state changes more efficiently. This approach is likened to how spreadsheets work, where changes in input immediately affect outputs without re-execution of all logic.
  • Fine-grained reactivity involves three primitives: signals (atomic atoms), derived state (computeds or memos), and side effects (effects). These primitives help manage state and side effects without heavy reliance on the component architecture or compilation.
  • Quote: "What if the relationship held instead? What if whenever we changed B and C, A also immediately updated? That's basically what reactive programming is."

• Practical Demonstration and Code Examples:

  • Ryan demonstrated the implementation of fine-grained reactivity using SolidJS, showing how state management and updates can be handled more efficiently compared to traditional methods that rely heavily on component re-renders and hooks.
  • The examples provided emphasized how reactive programming can simplify state management and improve performance by only updating components that need to change, reducing unnecessary re-renders.
  • Quote: "The problem is that if any state in this component changes, the whole thing reruns again... what if we didn't? What if components didn't dictate the boundary of our performance?"

• Performance Implications and Advantages:

  • The "reactive advantage" in SolidJS and similar frameworks lies in their ability to run components minimally, avoiding stale closures and excessive dependencies that can degrade performance.
  • Ryan highlighted that in reactive frameworks, component boundaries do not dictate performance; instead, performance optimization is achieved through smarter state management and reactive updates.
  • Quote: "Components run once... state is independent of components. Component boundaries are for your sake, how you want to organize your code, not for performance."

• Future Directions and Framework Evolution:

  • The talk touched on the broader impact of reactive programming and fine-grained reactivity on the evolution of web frameworks. This includes the potential integration with AI and compilers to further optimize performance and developer experience.
  • Ryan suggested that the future of web development might see more frameworks adopting similar reactive principles, possibly leading to a "reactive renaissance" in the industry.
  • Quote: "A revolution is not in the cards, maybe just a reactive Renaissance."

• Q&A and Additional Insights:

  • During the Q&A, Ryan discussed the potential application of SolidJS principles in environments like React Native and native code development, indicating the flexibility and broad applicability of reactive programming principles across different platforms and technologies.
  • Quote: "The custom renderer and stuff is not something you need a virtual DOM to... the reactive tree as it turns out is completely independent."

This background tells us the structure of the American races. But even though they are all immigrants at the beginning, they seen themselves as the owner of the land now and some of them are somewhat exclusive to the new immigrants.

面对这样的挑战，第一步，我们快速借调了一些比较懂 KV 引擎的同学，然后大家一起合作， 将大问题拆解为一系列的小问题，然后来回讨论，逐步解决这些问题，在解决了全部问题之后，做好各个子模块的接口定义和层次划分，然后高效完成研发，达成目标。

from - Youtube podcast - Osher Center channel guest - Evan Thompson interview - https://hyp.is/2IByFgazEe-JTGfoEos7Fw/docdrop.org/video/BNAVYglundg/

this sentence is basically thanking the community for playing the game, supporting the developers, and telling us to tune in to the 2024 championship to see who goes down int he vct history books as the worlds best players. i chose to annotate this sentence because i thought it was a sweet way to end an informational article by thanking the players who could make such developments possible through their support.

this passage basically tells us when the games are being held and their time and location being all around the globe. I highlighted this because I find it pretty remarkable that this is a world wide event celebrated by millions of people who are all connected via this game.

this sentence is describing how the dev team responsible for creating these vct team capsules took players preferences into account, and created the audio sound effects to the majority's liking.

this sentence is describing why they chose this certain weapon to represent the teams. i am commenting on how cleaver i thought this to be, becuase the "classic" gun is quote: "almost always in the players inventory and its the first item you are presented with when you start the game."

this explains the creative minds behind the design of the teams capsules and what it represents, therefore explaining the materials used to create them.

these phrases explain why the dev team chose to collaborate with the vct teams in the first place and goes on to explain that the profits go on to supporting the team whose capsule you purchased. this is a method because they are talking about money and supporting people with the money the consumers spend.

this whole article is in Korean, but it basically says, "these are the new weapon designs you can purchase based off of the pro teams playing in this year's tournament." since they are making merchandise available for those who play the game, it connects the teams with the regular/average players in the community by having you (the player) root for which team is your favorite by purchasing their merch. this is also the aim of the article.

This is something I experience after finishing movies and TV series. Feeling empty.

I also relate to this, I first joined some clubs that talked about the anatomy of the human body and medicine, I later transitioned to coding skills and now I'm studying to become a geologist.

As this is my first semester here at the University of Arizona, I have noticed that I have changed to the better. Becoming more independent and able to manage things by myself.

Having small class sizes allows teachers to help students effectively.

Focusing on the positive aspects is something to utilize so you don't loose focus of your goals.

Prioritizing things is pretty important, trying to give your all on a certain task is pretty important. However, utilizing one's free time to complete and give your all to complete everything no matter how trivial it seems is something that could lead to success.

FISHING AS A MAJOR SOURCE OF PROTEIN FOR INDIG COMMUNITIES IN BRAZIL!! USABLE IN THE PAPER

consistent with prior readings

Falta la tabla de todos los departamentos y su tasa

Porque sale doble valor

Porque sale doble valor?

San Clemente Syndrome

First contradiction? And allusion? What does this mean?

Message Appropriation

Four-stage theory of communication: production, circulation, use (distribution or consumption), reproduction

The source discussed a disturbing image of a Chinese worker surrounded by nearly 100 iPhones allegedly used to manipulate App Store rankings. The image, originally posted on Weibo, shows the worker in the cold, reinstalling apps on multiple devices to artificially boost their downloads and rankings on Apple's App Store. The manipulation involves services that can charge up to $65,000 per week for top rankings.

It is intriguing to see how the line between human and automated behavior on social media is becoming increasingly blurred. From a practical perspective, this blurring creates challenges in recognizing real interactions. From an ethical perspective, it also raises questions about accountability and transparency.

In this debate, “Taoists” may be inclined to wait to introduce “alt-text”, emphasizing natural integration and user readiness to maintain harmony. Consequentialists, by contrast, may argue for immediate implementation, focusing on the benefits of accessibility and considering the ethical costs of delaying the provision of these features to users who need them.

The view that virtue is a group effort, not just an individual effort, emphasizes the importance of social and community support in sustaining moral behavior. In contrast to an individualistic approach that focuses on individual autonomy and self-reliance to cultivate virtue, this view holds that our moral lives are deeply intertwined with the norms and values of the communities to which we belong. It also reinforces the notion that “the earth is slippery.”

aligns with their beliefs : rivers are where the outsiders are

even though they live in the same open layout building

Finding a good writing style for what you are writing about and who your audience may be. It can also help show off a chariters personality if needed when writing.

Being able to figure out how fast or slow an event takes place can change the story and what order it would be reliving for the story as a whole.

So make it as if the reader is actually there and is able to see everything for themselves. Don't be vague in describing be specific to what they may be seeing through the writing

I agree. I grew up having a computer, I learned how to type and use different softwares and I became good at playing around with the computer. My boyfriend didn't grow up with a computer and didn't have one until a few years ago. I see the differences between us because I can easily navigate a computer while it takes him a while to figure somthing out.

I think this is a great idea. I think allowing students to create a video, presentation, poster, etc. for an assignment allows students to pick their ideal way of learning.

I understand that closed captioning is new to universities and can be time consuming to go back and provide closed captioning to older videos. However, I believe because these institutions are meant to be for the people that pay money to attend them, their material should be accessible everyone despite a disability.

这里补充一个知识点。审计报告的结论有五种：1.标准无保留意见；2.带强调事项段的无保留意见；3.保留意见；4.否定意见；5.无法表示意见。其中，风险提示等级是逐级上升的，“标准无保留意见“表示没有发现风险，而“否定意见”和“无法表示意见”则提示有高度风险。普华永道对恒大的审计意见是第一种，“标准无保留意见”。 得到头条 100| 会计师是怎么工作的？

La pedagogía y la didáctica son dos campos estrechamente relacionados con la educación, pero con enfoques y objetivos distintos. La pedagogía se centra en los aspectos teóricos y filosóficos de la educación, mientras que la didáctica se ocupa de la aplicación práctica de los principios pedagógicos en el aula.

native healers as:

DOI: 10.1016/j.celrep.2021.108889

Resource: (BioLegend Cat# 802001, RRID:AB_2564645)

Curator: @Naa003

SciCrunch record: RRID:AB_2564645

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What is this?

DOI: 10.1016/j.celrep.2021.108883

Resource: (Sigma-Aldrich Cat# HPA004056, RRID:AB_1079226)

Curator: @Naa003

SciCrunch record: RRID:AB_1079226

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What is this?

DOI: 10.1101/2024.04.20.590419

Resource: CVCL-0574

Curator: @evieth

SciCrunch record: RRID:CVCL-0574

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What is this?

DOI: 10.1016/j.heliyon.2024.e29827

Resource: (NIH-ARP Cat# 2188-324, RRID:CVCL_0022)

Curator: @abever99

SciCrunch record: RRID:CVCL_0022

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What is this?

DOI: 10.1016/j.celrep.2024.114112

Resource: (IMSR Cat# CRL_027,RRID:IMSR_CRL:027)

Curator: @abever99

SciCrunch record: RRID:IMSR_CRL:027

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What is this?

DOI: 10.1016/j.celrep.2024.114112

Resource: (IMSR Cat# JAX_003782,RRID:IMSR_JAX:003782)

Curator: @abever99

SciCrunch record: RRID:IMSR_JAX:003782

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What is this?

DOI: 10.1016/j.celrep.2024.114112

Resource: (IMSR Cat# JAX_000664,RRID:IMSR_JAX:000664)

Curator: @abever99

SciCrunch record: RRID:IMSR_JAX:000664

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What is this?

DOI: 10.1016/j.celrep.2024.114112

Resource: (IZSLER Cat# BS CL 129, RRID:CVCL_0045)

Curator: @abever99

SciCrunch record: RRID:CVCL_0045

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What is this?

DOI: 10.1016/j.scr.2024.103427

Resource: (Proteintech Cat# 12096-1-AP, RRID:AB_2094914)

Curator: @abever99

SciCrunch record: RRID:AB_2094914

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What is this?

DOI: 10.1016/j.scr.2024.103427

Resource: (Lab Vision Cat# MS-231-PABX, RRID:AB_62248)

Curator: @abever99

SciCrunch record: RRID:AB_62248

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What is this?

DOI: 10.1016/j.celrep.2024.114152

Resource: RRID:Addgene_52961

Curator: @abever99

SciCrunch record: RRID:Addgene_52961

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What is this?

DOI: 10.1016/j.celrep.2024.114152

Resource: RRID:Addgene_73955

Curator: @abever99

SciCrunch record: RRID:Addgene_73955

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What is this?

DOI: 10.1016/j.celrep.2024.114152

Resource: RRID:Addgene_19444

Curator: @abever99

SciCrunch record: RRID:Addgene_19444

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What is this?

DOI: 10.1016/j.celrep.2024.114152

Resource: (CCLV Cat# CCLV-RIE 1018, RRID:CVCL_0063)

Curator: @abever99

SciCrunch record: RRID:CVCL_0063

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What is this?

DOI: 10.1016/j.celrep.2024.114152

Resource: (RCB Cat# RCB1189, RRID:CVCL_0006)

Curator: @abever99

SciCrunch record: RRID:CVCL_0006

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What is this?

DOI: 10.1016/j.celrep.2024.114146

Resource: (CLS Cat# 300324/p10280_RT-112, RRID:CVCL_1670)

Curator: @abever99

SciCrunch record: RRID:CVCL_1670

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What is this?

DOI: 10.1016/j.xcrm.2024.101528

Resource: (IMSR Cat# JAX_000664,RRID:IMSR_JAX:000664)

Curator: @abever99

SciCrunch record: RRID:IMSR_JAX:000664

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What is this?

DOI: 10.1016/j.xcrm.2024.101528

Resource: (ATCC Cat# HTB-73, RRID:CVCL_0600)

Curator: @abever99

SciCrunch record: RRID:CVCL_0600

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What is this?

DOI: 10.1016/j.xcrm.2024.101528

Resource: (NCI-DTP Cat# SK-MEL-5, RRID:CVCL_0527)

Curator: @abever99

SciCrunch record: RRID:CVCL_0527

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What is this?

DOI: 10.1016/j.xcrm.2024.101528

Resource: (ATCC Cat# HTB-38, RRID:CVCL_0320)

Curator: @abever99

SciCrunch record: RRID:CVCL_0320

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What is this?

DOI: 10.1016/j.xcrm.2024.101528

Resource: (BCRJ Cat# 0326, RRID:CVCL_1107)

Curator: @abever99

SciCrunch record: RRID:CVCL_1107

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What is this?

DOI: 10.1016/j.xcrm.2024.101528

Resource: (CCLV Cat# CCLV-RIE 1018, RRID:CVCL_0063)

Curator: @abever99

SciCrunch record: RRID:CVCL_0063

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What is this?

DOI: 10.1016/j.xcrm.2024.101528

Resource: (Bio X Cell Cat# BE0061, RRID:AB_1125541)

Curator: @abever99

SciCrunch record: RRID:AB_1125541

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What is this?

DOI: 10.1016/j.xcrm.2024.101528

Resource: (Bio X Cell Cat# BE0003-1, RRID:AB_1107636)

Curator: @abever99

SciCrunch record: RRID:AB_1107636

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What is this?

DOI: 10.1016/j.xcrm.2024.101528

Resource: (Bio X Cell Cat# BE0273, RRID:AB_2687796)

Curator: @abever99

SciCrunch record: RRID:AB_2687796

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What is this?

DOI: 10.1016/j.xcrm.2024.101528

Resource: (Bio X Cell Cat# BE0089, RRID:AB_1107769)

Curator: @abever99

SciCrunch record: RRID:AB_1107769

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What is this?

DOI: 10.1016/j.crmeth.2024.100741

Resource: (BCRJ Cat# 0319, RRID:CVCL_0618)

Curator: @evieth

SciCrunch record: RRID:CVCL_0618

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What is this?

DOI: 10.1016/j.crmeth.2024.100741

Resource: (ATCC Cat# CRL-10317, RRID:CVCL_0598)

Curator: @evieth

SciCrunch record: RRID:CVCL_0598

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What is this?

DOI: 10.1016/j.crmeth.2024.100741

Resource: (ATCC Cat# CRL-2322, RRID:CVCL_1247)

Curator: @evieth

SciCrunch record: RRID:CVCL_1247

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What is this?

DOI: 10.1016/j.crmeth.2024.100741

Resource: (ECACC Cat# 86082104, RRID:CVCL_0332)

Curator: @evieth

SciCrunch record: RRID:CVCL_0332

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What is this?

DOI: 10.1016/j.celrep.2024.114159

Resource: FlowJo (RRID:SCR_008520)

Curator: @abever99

SciCrunch record: RRID:SCR_008520

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What is this?

DOI: 10.1016/j.celrep.2024.114157

Resource: RRID:Addgene_60662

Curator: @abever99

SciCrunch record: RRID:Addgene_60662

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What is this?

DOI: 10.1016/j.celrep.2024.114157

Resource: (Abcam Cat# ab184337, RRID:AB_2916270)

Curator: @abever99

SciCrunch record: RRID:AB_2916270

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What is this?

DOI: 10.1016/j.celrep.2024.114149

Resource: Biorender (RRID:SCR_018361)

Curator: @abever99

SciCrunch record: RRID:SCR_018361

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What is this?

DOI: 10.1016/j.celrep.2024.114149

Resource: DESeq (RRID:SCR_000154)

Curator: @abever99

SciCrunch record: RRID:SCR_000154

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What is this?

DOI: 10.3390/cancers16081540

Resource: (ECACC Cat# 85062806, RRID:CVCL_0428)

Curator: @evieth

SciCrunch record: RRID:CVCL_0428

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What is this?

DOI: 10.3390/cancers16081540

Resource: (IZSLER Cat# BS TCL 4, RRID:CVCL_0186)

Curator: @evieth

SciCrunch record: RRID:CVCL_0186

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What is this?

DOI: 10.3390/cancers16081540

Resource: (DSMZ Cat# ACC-162, RRID:CVCL_1847)

Curator: @evieth

SciCrunch record: RRID:CVCL_1847

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What is this?

DOI: 10.1016/j.celrep.2024.114138

Resource: RRID:WB-STRAIN:WBStrain00026455

Curator: @abever99

SciCrunch record: RRID:WB-STRAIN:WBStrain00026455

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What is this?

DOI: 10.1016/j.celrep.2024.114138

Resource: RRID:WB-STRAIN:WBStrain00005592

Curator: @abever99

SciCrunch record: RRID:WB-STRAIN:WBStrain00005592

---

What is this?

DOI: 10.1016/j.celrep.2024.114138

Resource: RRID:WB-STRAIN:WBStrain00032678

Curator: @abever99

SciCrunch record: RRID:WB-STRAIN:WBStrain00032678

---

What is this?

DOI: 10.1016/j.celrep.2024.114138

Resource: RRID:WB-STRAIN:WBStrain00037720

Curator: @abever99

SciCrunch record: RRID:WB-STRAIN:WBStrain00037720

---

What is this?

DOI: 10.1016/j.celrep.2024.114138

Resource: RRID:WB-STRAIN:WBStrain00000259

Curator: @abever99

SciCrunch record: RRID:WB-STRAIN:WBStrain00000259

---

What is this?

DOI: 10.1016/j.celrep.2024.114138

Resource: RRID:WB-STRAIN:WBStrain00024035

Curator: @abever99

SciCrunch record: RRID:WB-STRAIN:WBStrain00024035

---

What is this?

DOI: 10.1016/j.celrep.2024.114138

Resource: (WB Cat# WBStrain00024040,RRID:WB-STRAIN:WBStrain00024040)

Curator: @abever99

SciCrunch record: RRID:WB-STRAIN:WBStrain00024040

---

What is this?

DOI: 10.1016/j.celrep.2024.114138

Resource: RRID:WB-STRAIN:WBStrain00031649

Curator: @abever99

SciCrunch record: RRID:WB-STRAIN:WBStrain00031649

---

What is this?

DOI: 10.1016/j.celrep.2024.114138

Resource: RRID:WB-STRAIN:WBStrain00036241

Curator: @abever99

SciCrunch record: RRID:WB-STRAIN:WBStrain00036241

---

What is this?

DOI: 10.1016/j.celrep.2024.114138

Resource: RRID:WB-STRAIN:WBStrain00031520

Curator: @abever99

SciCrunch record: RRID:WB-STRAIN:WBStrain00031520

---

What is this?

DOI: 10.1016/j.celrep.2024.114138

Resource: (WB Cat# WBStrain00034074,RRID:WB-STRAIN:WBStrain00034074)

Curator: @abever99

SciCrunch record: RRID:WB-STRAIN:WBStrain00034074

---

What is this?

DOI: 10.1016/j.celrep.2024.114138

Resource: RRID:WB-STRAIN:WBStrain00000264

Curator: @abever99

SciCrunch record: RRID:WB-STRAIN:WBStrain00000264

---

What is this?

DOI: 10.1016/j.celrep.2024.114138

Resource: RRID:WB-STRAIN:WBStrain00000262

Curator: @abever99

SciCrunch record: RRID:WB-STRAIN:WBStrain00000262

---

What is this?

DOI: 10.1016/j.celrep.2024.114138

Resource: RRID:WB-STRAIN:WBStrain00004921

Curator: @abever99

SciCrunch record: RRID:WB-STRAIN:WBStrain00004921

---

What is this?

DOI: 10.1016/j.celrep.2024.114138

Resource: (WB Cat# WBStrain00034068,RRID:WB-STRAIN:WBStrain00034068)

Curator: @abever99

SciCrunch record: RRID:WB-STRAIN:WBStrain00034068

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What is this?

DOI: 10.1016/j.immuni.2024.04.003

Resource: (IMSR Cat# JAX_003584,RRID:IMSR_JAX:003584)

Curator: @abever99

SciCrunch record: RRID:IMSR_JAX:003584

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What is this?

DOI: 10.1016/j.immuni.2024.04.003

Resource: (IMSR Cat# JAX_013181,RRID:IMSR_JAX:013181)

Curator: @abever99

SciCrunch record: RRID:IMSR_JAX:013181

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What is this?

DOI: 10.1016/j.immuni.2024.04.003

Resource: (IMSR Cat# JAX_002014,RRID:IMSR_JAX:002014)

Curator: @abever99

SciCrunch record: RRID:IMSR_JAX:002014

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What is this?

DOI: 10.1016/j.immuni.2024.04.003

Resource: (IMSR Cat# JAX_004194,RRID:IMSR_JAX:004194)

Curator: @abever99

SciCrunch record: RRID:IMSR_JAX:004194

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What is this?

DOI: 10.1016/j.immuni.2024.04.003

Resource: (IMSR Cat# JAX_008449,RRID:IMSR_JAX:008449)

Curator: @abever99

SciCrunch record: RRID:IMSR_JAX:008449

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What is this?

DOI: 10.1016/j.immuni.2024.04.003

Resource: (IMSR Cat# JAX_016961,RRID:IMSR_JAX:016961)

Curator: @abever99

SciCrunch record: RRID:IMSR_JAX:016961

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What is this?

DOI: 10.1016/j.immuni.2024.04.003

Resource: (IMSR Cat# JAX_016958,RRID:IMSR_JAX:016958)

Curator: @abever99

SciCrunch record: RRID:IMSR_JAX:016958

---

What is this?

DOI: 10.1016/j.immuni.2024.04.003

Resource: RRID:IMSR_JAX:003611

Curator: @abever99

SciCrunch record: RRID:IMSR_JAX:003611

---

What is this?

DOI: 10.1016/j.immuni.2024.04.003

Resource: (IMSR Cat# JAX_007905,RRID:IMSR_JAX:007905)

Curator: @abever99

SciCrunch record: RRID:IMSR_JAX:007905

---

What is this?

DOI: 10.1016/j.immuni.2024.04.003

Resource: (IMSR Cat# JAX_000664,RRID:IMSR_JAX:000664)

Curator: @abever99

SciCrunch record: RRID:IMSR_JAX:000664

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What is this?