The "Shop" button is connected to the Operable POUR rule. The button makes it easy for users to navigate the page and access products. It is easy to see because it stands out on the screen. The majority of users immediately understand what it does because the text on it is straightforward. Additionally, Nike needs to verify that the "Shop" button functions with a keyboard. Due to motor limitations, some users are unable to rely on a mouse.

Under the Understandable section, this displays the POUR guidelines. Nike uses short, straightforward words in the menu to make it easier for users to understand where each link leads. Options such as "Men," "Women," and "Kids" are being displayed. Then there's no need to speculate. You can get what you want by selecting an attachment.

The POUR accessibility rules and its Perceivable rule are related to this section. The large white title stands out nicely because the background is darker. The graphic may still be difficult to read in some places. The text may become blurry due to the smaller white words sitting on top of various colours. Nike can improve the contrast or add a plain block behind the text to help those with low vision.

Cassio acts smart: he knows that if he drinks, he'll lose control. But Iago is smarter and uses the atmosphere and social pressure to push him. Iago never invents a weakness — he only exploits one that already exists, exactly as he will do with Othello's jealousy.

Both characters show their trust in Iago, and Othello himself calls him "honest" — the most ironic word of the play. They don't just trust him, they give him authority over the watch: they let the wolf be the shepherd.

Cassio makes a reference to the devil, stating that his loss of reputation was only for the devil’s amusement and nothing else afterwards. Iago’s line earlier in the play, “I am not what I am,” plays into this reference to the devil. The story seemingly sets up Iago as a direct comparison to the devil, and Cassio says that his reputation was the expense of the devil’s amusement.

Here is another instance of Iago saying things that would normally be directly insulting or extremely foul and seemingly getting away with is because of his smooth manner of talking and persuasion. Hi did this earlier with Roderigo, hurling offensive lines towards him at times with Roderigo not catching a single one of them.

Asking the people in the fight who started the fight seems like a bad idea, but Othello does this regardless. Iago also seemingly planned this to happen, meaning that he knew that Othello would ask such a controversial question to settle the dispute. This demonstrates the sheer extent to which Iago knows Othello and his sense of trust in people.

I see several analogies in this line. First, the Englishman overpowering everyone else in drinking could be an analogy to the British empire’s near unstoppable nature at one point in history. Secondly, this could be analogy for Iago’s own prowess in terms of manipulation, with him long outlasting everyone in his competition to different degrees. He also says that the Englishman would outdrink Cassio first, which lines up with analogy because Cassio is Iago’s first victim, in a sense.

For anyone tracking this preprint, this study has now been officially published in Nature (as of July 22, 2026) with a modified title. Published Title: A global molecular code for birth order and neuronal identity in Drosophila Citation: Nature volume 657, pages 202–212 (2026) DOI: https://doi.org/10.1038/s41586-026-10797-w

Another key conclusion, supporting their idea that hippos have broader ecological effects than just adding nutrients.

This is a conclusion they've reached from their results. This supports their hypothesis that hippo subsidies have a stronger ecological influence when river flow is reduced, showing that hydrology can affect the relationship between hippos and aquatic ecosystems.

This is a big research gap, and could potentially give scientists new methods of studying the Hippos

The fixed end time creates a countdown: everything must go wrong before the bell strikes eleven, which builds tension. Othello sets the limits of the celebration, but Iago has already planned what will happen inside them.

By celebrating the victory and the marriage together, Othello ties his public role to his private life. This gives Iago his opening: if one falls, the other follows. Attacking the marriage is now enough to destroy the general too.

This moment benefits Iago: with military discipline suspended, he finally gets the chance to put his plan in motion. The irony is that Othello himself gives the order — he opens the door to his own downfall, and Cassio's drunken fight follows straight away.

It is not clear to me how the problems turns from a minimization problem to a maximization problem.

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eLife Assessment

This study presents a useful finding on the interplay of CCL5 and miR-324-5p during ischemic stroke injury. The authors have addressed some of the concerns in the revision. The methodological information has been updated, and additional experiments, such as FISH, have been included. After the revision, the evidence supporting the claims of the authors is solid. The work will be of interest to neuroscientists working on brain injury such as stroke.

Reviewer #1 (Public review):

Summary:

Here, the authors attempt to show that CCL5 is increased after stroke, possibly due to decreased miR-324, and that this is a modifiable system to decrease stroke damage. By bidirectionally manipulating CCL5 levels through direct injection of CCL5; a CCL5 blocking antibody; miR324; miR324 antagomir; or CCR5-blocking Maraviroc, they broadly show improvement with lower CCL5 levels. This includes infarct size, behavioral analysis, and immunohistochemical analysis of astrocytes, microglia, and neurons. They further try to mechanistically tie miR324 and CCL5 in astrocytes specifically to stroke-induced changes using a neuronal/astrocytic coculture system. They argue that decreasing CCL5 leads to increased ERK and CREB phosphorylation as a potential neuroprotective mechanism. CCL5 is one potential ligand for CCR5, and recent work identified CCR5 as a targetable mechanism by clinically-approved drug Maraviroc to enhance stroke recovery. Particularly given the high level of interest in CCR5 in stroke recovery, the focus on CCL5 - one of CCR5's potential ligands - and its miR regulation is an exciting expansion of this area of stroke biology.

Strengths:

The authors' findings that decreasing CCL5 acutely after stroke shows behavioral improvement appear robust. This broadly replicates work from other groups, although the finding that miR324 manipulation can phenocopy direct CCL5 manipulation is novel and intriguing. However, many of their other claims are difficult to evaluate based on a combination of missing methodological information, inappropriate statistical testing, and a flawed culture system.

Weaknesses:

Broadly speaking, the manuscript takes a zoomed-out view of what is fundamentally highly localized biology.

(1) miRNA-based regulation, by definition, has to include miR and mRNA in the same cell type; as the authors note, CCL5 is expressed in many cells. It is therefore impossible to propose any interaction on the basis of the tissue-level changes described; any evidence of in vivo cell-type specificity would dramatically improve the claims.

(2) The authors treat an extensive area of ipsilesional cortex uniformly as "IP". Astrocytic and microglial responses to localized injuries such as stroke are highly location-dependent and undoubtedly change dramatically within this area. The presented data cannot be interpreted without confirmation that these were taken at identical distances from the injury, and what that distance was. These do not appear to be adjacent to the injury, where the responses would presumably be the most informative. Similarly, it is difficult to interpret the neuronal Sholl and spine data without more information on where within the large IP region these neurons were found.

The authors attempt to narrow in on cell-type specificity via culture. However, astrocytes are notoriously prone to a dramatic change in culture and require careful methods (immunopanning; see eg doi: 10.1016/j.neuron.2011.07.022) to maintain much resemblance to their in vivo counterpart. It is difficult to conclude much about the role of astrocytes in the CCL5 pathway based on the use of this shaking-based culture system, particularly in the absence of cell-type specific validation in vivo.

There is missing methodological information, including infarct size measurements, TUNEL staining, and statistical testing. The TTC figures look very odd, like a collection of overlapping stars have been placed on the images rather than the natural relatively smooth infarct edges one would expect. It is unclear if the infarct volume measurements accounted for edema, as is standard; there is no description of the protocol used for quantification. It is also unclear if the infarct volume measurement comparisons were also done with t-tests vs ANOVA, as the statistical test used is not listed in the figure legends. In numerous cases where statistical testing is listed, repeated t-tests between subgroups are used vs the more appropriate ANOVA (assuming normality; nonparametric testing as appropriate), making it difficult to have confidence in the results.

Reviewer #2 (Public review):

The authors presented evidence from various in vivo and in vitro experiments demonstrating the mutual interaction between CCL5 and astrocytic miR-342-5p in the ipsilateral core of cerebral ischemia. However, miR-342-5p was downregulated only late after MCAO (D3-7). Additionally, this downregulation was observed not only in the ipsilateral core but also in the ipsilateral penumbra and contralateral sides. Therefore, it is not convincing that the upregulation of CCL5 in the ipsilateral core at later time points (D3 and D7) is attributable to the decreased expression of miR-342-5p. In particular, infarct injury was already evident within a short time period (say 24 h) following MCAO.

(1) The temporal and spatial expression patterns of miR-324-5p do not match those of CCL-5, especially at D1 and D3 (see Figure 1C, 1D). Despite the inverse relationship between miR-324-5p and CCL-5 expression at D7 after MCAO, what was the purpose of administering miR-324-5p agomir (or antagomir) at D1 post-MCAO? If the connection cannot be clearly established, the conclusion reached at the end will be difficult to accept.

(2) Would administering miR-342-5p or anti-CCL5 at later time points (e.g., after D3) reduce infarct size or improve functional recovery? If this is not the case, the effect of CCL5 on neuronal cell damage (infarct size formation) must occur within a very short time after MCAO. Additionally, if the increased CCL5 expression is due to the downregulation of miR-342-5p, its impact would likely be less significant.

(3) While the study offers valuable insights into the roles of CCL5 and its connection with the regulation of miR-342-5p (though this connection is somewhat weak), it is recommended that the authors explore potential translational applications of these findings.

Overall, given the experimental designs and results, it is difficult to support the conclusions drawn in the manuscript.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

(1) miRNA-based regulation, by definition, requires the miRNA and its target mRNA to be present in the same cell type. CCL5 is expressed in many cell types, making it impossible to propose any miRNA–mRNA interaction based solely on tissue-level expression changes. In vivo cell-type specificity data would substantially strengthen the claims.

We fully agree with this critique, and we consider this the most critical point to address. To directly examine cell-type-specific expression in vivo, we performed FISH combined with immunofluorescence for GFAP (astrocytes) or IBA1 (microglia) in the peri-infarct region of MCAO mice at D3 (Figure 1E–G). Our results show that Ccl5 mRNA is expressed in both GFAP-positive astrocytes and IBA1-positive microglia, with comparable co-localization rates across both cell populations. In contrast, miR-324-5p showed a significantly higher positive rate in astrocytes than in microglia (Figure 1G). Given that miR-324-5p is more abundantly expressed in astrocytes, the regulatory capacity of the miR-324-5p/CCL5 axis is predicted to be more pronounced in this cell type. These findings provide direct in vivo evidence supporting an astrocyte-predominant miR-324-5p/CCL5 regulatory interaction in the peri-infarct region. We acknowledge that the current data do not constitute cell-type-specific in vivo manipulation, and we discuss this limitation and propose future directions (including viral or transgenic approaches) in the revised Discussion.

(2) The authors treat an extensive area of ipsilesional cortex uniformly as "IP." Astrocytic and microglial responses to localized injuries such as stroke are highly location-dependent and undoubtedly change dramatically within this area. Data cannot be interpreted without confirmation that samples were collected at identical, defined distances from the injury. Similarly, it is difficult to interpret the Sholl and spine data without knowing where within the large IP region these neurons were found.

We thank the reviewer for identifying this important concern. Upon review, we recognized that part of the tissue samples used for qPCR, ELISA and Western blot in the original submission were collected from an excessively broad region of the ipsilateral cortex, which likely introduced heterogeneity into the data. We have re-collected these samples specifically from the peri-infarct zone, defined as the 1–2 mm cortical rim immediately surrounding the visibly pale infarct core, beginning from the second and third coronal slices from the most rostral aspect of the cerebral cortex. The qPCR, ELISA and Western blot data have been updated accordingly (Figures 1C–D, 2A, 3A, 6A), and the sampling definition has been specified in the Methods. We also confirmed that all immunofluorescence and Golgi staining analyses were performed within this same peri-infarct zone, where cells retain intact morphology and show the most informative between-group differences. This sampling region has now been defined consistently across all in vivo analyses in the revised manuscript.

(3) Astrocytes are notoriously prone to dramatic change in serum-containing culture. The shaking-based culture system makes it difficult to conclude much about the role of astrocytes in the CCL5 pathway, particularly without cell-type-specific validation in vivo.

We acknowledge this limitation. We attempted to implement the immunopanning protocol described by Barres et al. (doi: 10.1016/j.neuron.2011.07.022) to obtain a more purified astrocyte culture. However, several essential reagents — including sodium selenite, putrescine, and N-acetyl-L-cysteine — could not be procured due to import and purchasing restrictions in our region. The use of a commercially available O4 antibody substitute (clone O4, R&D, MAB1326) in place of O4 hybridoma supernatant, combined with the absence of these chemicals, likely contributed to the very low astrocyte yields and poor cell viability observed across multiple independent attempts. We therefore retained the shaking-based isolation and purification method for the present study.

To characterize the composition of our primary cortical astrocyte cultures, we have included immunofluorescence data from co-labeling of GFAP with Tuj1, Olig2, and IBA1 at P0 and P1 in Supplementary Figure S4, confirming that GFAP-positive cells comprised approximately 88% of total cells at P1. We have also added a paragraph to the Discussion acknowledging that more refined culture systems, as well as cell-type-specific in vivo manipulation of CCL5 and miR-324-5p via viral or transgenic approaches, would further consolidate the conclusions of the present study.

(4) Missing methodological information, including infarct size measurements, TUNEL staining, and statistical testing.

We apologize for these omissions. Detailed descriptions of infarct volume quantification (including the edema-correction formula), TUNEL staining procedures, NeuN/TUNEL co-labeling, and all statistical tests have been added to the Methods section.

(5) The TTC figures appear unusual, with infarct edges resembling overlapping stars rather than natural smooth boundaries. It is unclear whether infarct volume measurements accounted for edema, and no quantification protocol is described.

We apologize for the confusion. The unusual appearance of the infarct edges in the original TTC figures resulted from dotted-line annotations we had added to highlight the infarct boundaries; these have now been removed to present the unmodified TTC images (Figure 2B, 3B). Infarct volume was corrected for edema-induced hemispheric swelling using the formula: [(contralateral hemisphere volume − ipsilateral non-infarcted volume) / contralateral hemisphere volume] × 100%. This formula and the complete quantification protocol have been added to the Methods section.

(6) Repeated t-tests between subgroups are used instead of the more appropriate ANOVA, making it difficult to have confidence in the results.

We agree. We identified that t-tests had been applied inappropriately in the original qPCR and ELISA analyses. All qPCR and ELISA data have been re-analyzed using two-way ANOVA with Tukey's post-hoc test, as appropriate for datasets with multiple groups and time points. We have also reviewed all other figures and corrected any inappropriate use of t-tests.

Reviewer #2 (Public review):

(1) The temporal and spatial expression patterns of miR-324-5p do not match those of CCL5, especially at D1 and D3. Despite the inverse relationship between miR-324-5p and CCL5 being apparent only at D7 after MCAO, what was the purpose of administering miR-324-5p agomir (or antagomir) at D1 post-MCAO? If the connection cannot be clearly established, the conclusion reached at the end will be difficult to accept.

We thank the reviewer for identifying this critical issue. Upon re-examination, we recognized that the original qPCR and ELISA samples had been collected from an excessively broad region of the ipsilateral cortex, which likely introduced heterogeneity into the expression data and obscured the true temporal dynamics in the viable peri-infarct tissue. We have re-collected samples specifically from the peri-infarct zone — defined as the 1–2 mm cortical rim immediately surrounding the infarct core — and updated the data accordingly (Figures 1C–D, 2A, 3A).

The updated data reveal a clearer and more consistent temporal pattern. Ccl5 mRNA levels in the IP region are significantly elevated as early as D1 compared with sham controls, and continue to increase progressively through D7. Regarding miR-324-5p, although IP region levels at D1 do not yet differ significantly from sham controls, they are already significantly lower than in the contralateral CP region at this early time point. This ipsilateral-versus-contralateral difference at D1 indicates that miR-324-5p downregulation begins in the acute phase following stroke, even before it reaches statistical significance relative to the sham baseline. From D3 onwards, miR-324-5p levels in the IP region are significantly reduced relative to both sham and CP groups, coinciding with the period of sustained and progressive CCL5 upregulation. Taken together, these updated findings support an early and progressive inverse relationship between miR-324-5p and CCL5 in the peri-infarct cortex following MCAO, consistent with our previously published finding that miR-324-5p suppresses astrocytic CCL5 expression (Sun et al., Cell Death Dis., 2019), and functionally validated by the ELISA data showing that miR-324-5p agomir injection significantly reduces CCL5 protein concentrations in the IP region at D3 and D7 (Figure 3A).

Regarding the rationale for administering miR-324-5p agomir at D1: this timing was chosen to model a clinically realistic therapeutic scenario targeting the early post-stroke period. MicroRNA agomir/antagomir interventions typically require 2–10 days to achieve peak target gene modulation in the mouse brain; administration at D1 therefore ensures that meaningful miR-324-5p-mediated suppression of CCL5 is achieved during the critical acute-to-subacute transition period, as confirmed by the ELISA results at D3 (Figure 3A).

(2) Would administering miR-342-5p or anti-CCL5 at later time points (e.g., after D3) reduce infarct size or improve functional recovery? If this is not the case, the effect of CCL5 on neuronal cell damage must occur within a very short time after MCAO. Additionally, if the increased CCL5 expression is due to the downregulation of miR-342-5p, its impact would likely be less significant.

We acknowledge that the current study did not include experimental groups with delayed administration, and we recognize this as a limitation.

However, several points inform our interpretation. First, as described in our response to Comment 1 above, the updated data demonstrate that miR-324-5p downregulation in the IP region is already detectable relative to the contralateral CP region at D1 and progresses further through D3 and D7, indicating that the miR-324-5p/CCL5 regulatory axis is engaged from the acute phase of stroke, providing a biological basis for early intervention. Second, in experimental stroke models, the infarct core is largely established within the first 24–72 h following vessel occlusion, with the majority of ischemic neuronal death occurring during this window. CCL5, as a pro-inflammatory mediator, is expected to amplify immune cell recruitment and inflammatory cascades most consequentially during this early period, making D1 administration mechanistically rational for limiting neuronal loss. Third, the superior early behavioral outcomes in the CCL5 antibody group relative to the miR-324-5p agomir group (Figures 2D–E, 3D–E) support the value of early CCL5 suppression. As the antibody acts immediately while the agomir requires time for post-transcriptional regulation, this difference highlights that timely agomir delivery is essential to achieve effective CCL5 suppression during the critical early window.

(3) The study would benefit from the exploration of potential translational applications.

We thank the reviewer for this constructive suggestion. We are planning to investigate whether astrocyte-derived extracellular vesicles engineered to overexpress miR-324-5p can enhance neurological recovery after stroke. Extracellular vesicles offer several translational advantages: they can traverse the blood-brain barrier, provide a stable and biocompatible vehicle for miRNA delivery, and may be less immunogenic than viral approaches. This would leverage the neuroprotective regulatory mechanism identified in the present study while offering a clinically viable delivery strategy.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) In vivo cell-type specificity data for CCL5/miR-324-5p would substantially strengthen the manuscript.

We have performed FISH combined with GFAP and IBA1 immunofluorescence in the peri-infarct region of MCAO mice at D3 to characterize the cell-type-specific in situ expression of Ccl5 mRNA and miR-324-5p (Figure 1E–G).

(2) Cell-type-specific in vivo manipulation of CCL5/miR-324-5p (e.g., virally or transgenically) would further substantiate the conclusions. A more compelling astrocytic culture model is needed.

We fully agree that cell-type-specific in vivo manipulation would represent a major advance. As described in our response to Comment 3 above, we were unable to successfully implement immunopanning in the current study. The FISH data provide in vivo evidence supporting the astrocyte-enriched expression of miR-324-5p in the peri-infarct region. We have added a Discussion paragraph explicitly identifying viral or transgenic astrocyte-specific manipulation of CCL5 and miR-324-5p in vivo as a critical next step to validate and extend the conclusions of this study.

(3) The measurement shown in Figure 4D is unclear. A more informative measure might be TUNEL/DAPI, with additional cell-type-specific markers to identify what cells are dying in what proportions.

We agree with this suggestion and have revised the quantification accordingly. In the updated manuscript, apoptotic cell death is reported as the proportion of TUNEL-positive cells among total DAPI-positive nuclei (TUNEL/DAPI). As a complementary cell-type-specific measure, we quantified the proportion of NeuN-positive neurons among total DAPI-positive nuclei (NeuN/DAPI) to specifically assess neuronal survival within the co-culture system. These two measures together provide a clear and interpretable readout of both overall cell death and neuronal viability under each experimental condition. The revised quantification is presented in Figure 4C–E and Figure 5B–D.

(4) qPCR and ELISA data should be normalized to internal controls, sham values should be presented, and ANOVA (or appropriate non-parametric tests) should be used.

All qPCR data are now normalized to Gapdh (for mRNA) or U6 snRNA (for miRNA). Sham group values are presented in all relevant figures. Two-way ANOVA with Tukey's post-hoc test is now used for all qPCR and ELISA comparisons. The updated statistical approach is summarized in the Statistical Analysis section of the Methods.

(5) The description "within 24 hrs" for the timing of CCL5 in vivo manipulation is ambiguous.

We have revised the description to "at 24 h post-MCAO" throughout the Methods and Results sections to specify the precise time point of intervention.

(6) Only some statistical comparisons are shown in Figure 2E, which inaccurately implies that the other groups are not different.

We have updated Figure 2E and Figure 3E to include all statistically significant pairwise comparisons, ensuring that the significance markers accurately represent the complete set of statistical relationships among all groups.

(7) "Activation" is not the appropriate term for astrocytes in pathological contexts; A1/A2 terminology should be removed.

We thank the reviewer for this important correction. In line with the consensus recommendations by Escartin et al. (Nat Neurosci, 2021), we have replaced all instances of "astrocyte activation" in pathological contexts with "astrocyte reactivity" or "reactive astrogliosis" throughout the manuscript, including the Abstract, Results, and Discussion. All references to A1 and A2 subtypes have been removed from the Discussion.

(8) There are typographical errors, and the repeated use of "Besides" is awkward.

We have carefully proofread the entire manuscript to correct typographical errors. All instances of "Besides" used as a sentence-opening connector have been replaced with contextually appropriate alternatives, such as "Furthermore," "Moreover," "In addition," or "Additionally."

eLife Assessment

This study presents a valuable finding on the condition dependence of autophagy-mediated lifespan regulation in C. elegans. The evidence is solid, as the data broadly support the main claims, although variability between biological replicates in several of the performed experiments and mechanistic exploration with only a limited set of experimental techniques leave some conclusions less firmly established. The work will be of interest to researchers studying autophagy, ageing, and intracellular trafficking.

Reviewer #1 (Public review):

Summary:

Hsiung et al. investigated whether the effects of autophagy gene knockdown on the lifespan of long-lived C. elegans mutants depend on experimental conditions. The authors first compiled published data on autophagy-dependent lifespan regulation in daf-2 and wild-type backgrounds, highlighting that prior results are notably inconsistent and likely context-dependent. They then systematically tested the lifespan effects of RNAi knockdown of six autophagy genes (atg-2, atg-4.1, atg-9, atg-13, atg-18, and bec-1) in wild-type (N2), daf-2 (reduced insulin/IGF-1 signalling), and glp-1 (germlineless) animals, while varying temperature, daf-2 allele, FUDR concentration, and bacterial infection status.

The key findings are as follows. In wild-type animals, lifespan suppression by most autophagy gene knockdowns was more pronounced at 20{degree sign}C than at 25{degree sign}C, where little or no effect was observed. In daf-2 mutants, stronger lifespan suppression was seen in the weaker daf-2(e1368) allele at 20{degree sign}C, but not in the stronger daf-2(e1370) allele, and effects were largely absent at 25{degree sign}C. In glp-1 mutants, four of six gene knockdowns suppressed lifespan to a greater extent than in N2, though again in a temperature-dependent manner. FUDR at a high concentration (800 µM) abolished the life-shortening effects of most knockdowns and, in the case of atg-9 and atg-13, led to lifespan extension. Kanamycin treatment to eliminate bacterial proliferation did not fully account for the lifespan effects, suggesting that increased susceptibility to infection is not the primary mechanism. The authors also tested the programmed aging hypothesis that autophagy promotes lifespan reduction through biomass repurposing, but found no changes in vitellogenin levels upon knockdown of any of the six genes.

Altogether, among all genes tested, atg-18 knockdown produced the strongest and most consistent lifespan suppression across nearly all conditions, including both daf-2 and glp-1 backgrounds. The authors probed whether atg-18 acts through the FOXO transcription factor DAF-16 by examining dauer formation and ftn-1 expression, but found no evidence for this, suggesting a DAF-16-independent mechanism.

Strengths:

The primary strength of this work lies in its systematic and comprehensive approach to dissecting how experimental variables influence the outcome of autophagy-lifespan epistasis tests. The compilation of prior data alongside the authors' own multi-condition dataset is a genuinely useful resource for the field. The study raises a timely and important point about condition selection bias in interpreting autophagy lifespan relationships, with broader relevance to C. elegans studies. The finding that atg-18 behaves distinctly from other autophagy genes across a range of experimental conditions is particularly noteworthy and provides an interesting direction for future mechanistic investigation.

Comments on revised version.

The authors have carefully addressed the concerns raised in the previous review and have incorporated most of the suggested revisions. In particular, the revised manuscript provides greater clarity regarding the effects of glp-1 and FUDR and improves the presentation and interpretation of the experimental findings. The authors have also provided useful clarification regarding the variability between lifespan experiments, the number of biological replicates, and the interpretation of RNAi efficacy.

I have only two minor suggestions concerning the wording and organization of the manuscript, which I have communicated separately to the authors. These points are intended to further improve clarity and accuracy and do not affect my overall assessment of the study.

Overall, I am satisfied with the revisions and consider the manuscript to be a valuable contribution to our understanding of the context dependence of autophagy-mediated lifespan regulation.

Reviewer #2 (Public review):

Summary:

This study examines how genes involved in cellular recycling (autophagy) influence lifespan under different experimental conditions. The findings help clarify why previous studies have reported conflicting results about whether blocking autophagy shortens or extends lifespan. The work will be of interest to researchers studying aging and cellular stress responses, particularly those using model organisms.

Strengths:

The findings are valuable, as they help resolve inconsistencies within a specific subfield of aging research. The evidence presented is solid, as the data broadly support the primary claims of the study. In addition, the discussion is thorough and thoughtfully integrates the findings within the broader context of the field.

Weaknesses:

Additional functional validation would further strengthen the conclusions.

Comments on revised version.

I have reviewed the revised manuscript. Overall, the authors have addressed most of my concerns, and the revised manuscript has been improved. The study provides a comprehensive examination of the context-dependent effects of autophagy gene knockdown on lifespan in C. elegans. The data are solid and provide valuable information for understanding the conflicting results currently reported in the literature regarding the role of autophagy in longevity.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

Hsiung et al. investigated whether the effects of autophagy gene knockdown on the lifespan of long-lived C. elegans mutants depend on experimental conditions. The authors first compiled published data on autophagy-dependent lifespan regulation in daf-2 and wild-type backgrounds, highlighting that prior results are notably inconsistent and likely context-dependent. They then systematically tested the lifespan effects of RNAi knockdown of six autophagy genes (atg-2, atg4.1, atg-9, atg-13, atg-18, and bec-1) in wild-type (N2), daf-2 (reduced insulin/IGF-1 signalling), and glp-1 (germlineless) animals, while varying temperature, daf-2 allele, FUDR concentration, and bacterial infection status.

The key findings are as follows. In wild-type animals, lifespan suppression by most autophagy gene knockdowns was more pronounced at 20˚C than at 25˚C, where little or no effect was observed. In daf-2 mutants, stronger lifespan suppression was seen in the weaker daf-2(e1368) allele at 20˚C, but not in the stronger daf-2(e1370) allele, and effects were largely absent at 25˚C. In glp-1 mutants, four of six gene knockdowns suppressed lifespan to a greater extent than in N2, though again in a temperature-dependent manner. FUDR at a high concentration (800 µM) abolished the life-shortening effects of most knockdowns and, in the case of atg-9 and atg-13, led to lifespan extension. Kanamycin treatment to eliminate bacterial proliferation did not fully account for the lifespan effects, suggesting that increased susceptibility to infection is not the primary mechanism. The authors also tested the programmed aging hypothesis that autophagy promotes lifespan reduction through biomass repurposing, but found no changes in vitellogenin levels upon knockdown of any of the six genes.

Altogether, among all genes tested, atg-18 knockdown produced the strongest and most consistent lifespan suppression across nearly all conditions, including both daf-2 and glp-1 backgrounds. The authors probed whether atg-18 acts through the FOXO transcription factor DAF-16 by examining dauer formation and ftn-1 expression, but found no evidence for this, suggesting a DAF-16-independent mechanism.

We thank the reviewer for their meticulous review of the manuscript. Responding to their comments (particularly those not in the public review) has improved it substantially.

Strengths:

The primary strength of this work lies in its systematic and comprehensive approach to dissecting how experimental variables influence the outcome of autophagy-lifespan epistasis tests. The compilation of prior data alongside the authors' own multi-condition dataset is a genuinely useful resource for the field. The study raises a timely and important point about condition selection bias, which is relevant not only to autophagy research but to C. elegans aging studies more broadly. The finding that atg-18 behaves distinctly from other autophagy genes across all conditions is noteworthy and opens avenues for future mechanistic work.

Weaknesses:

Despite its breadth, the study has several weaknesses that limit the strength of some conclusions.

(1) Variability in control lifespan data. The N2 lifespan values under ostensibly identical conditions (e.g., GFP RNAi at 20˚C) differ substantially across experiments (compare Tables S2, S5, S6, S7, and S9). Since N2 serves as the baseline for calculating whether the effect is greater in long-lived mutants via Cox proportional hazard (CPH) analysis, this variability in controls directly affects the reliability of those comparisons.

Such inter-trial variability in N2 lifespan is not unusual in studies of C. elegans aging, ostensibly identical conditions notwithstanding, and its causes are unknown. A careful C. elegans lifespan study from 2017 compared results of tests performed independently at 3 sites under similar conditions. This showed that inter-trial variation occurred mainly at each site over time, rather than between sites (Lucanic et al., 2017). We note that the data for our study was gathered over a 5-year period. One possibility is that this long time frame may have contributed to the variability seen. 

Regarding statistical tests (whether the log-rank test to assess differences between pairs of populations, or the CPH test to assess differences between effects of treatments under two conditions): comparisons were made either of data within individual trials, or for pooled data. Thus, valid comparisons were made. We did not compare controls from one trial with treatments from another, which would have yielded misleading results. 

(2) Limited biological replication. Most experiments were performed with only two biological replicates. In several cases, the two replicates yield contradictory outcomes: one showing significant lifespan suppression and the other showing no effect or even extension. The authors combine these into cumulative datasets for analysis, which, while not incorrect in principle, may obscure genuine irreproducibility. Given that the central message of the paper concerns variability and condition dependence, additional replication would have substantially strengthened confidence in the reported results.

This is a natural issue to raise. In tests of an effect of a given treatment on C. elegans lifespan, a minimum of 3 trials is standard, and in this lab as a rule we follow this convention. However, after careful consideration during the design stages we opted not to do so for this particular study. Our reasons are set out in the manuscript as follows.

“A methodological note: for tests of effects of a given intervention on C. elegans lifespan an often-applied standard is to include 3 biological replicates. This is true of several recent studies where the effect of knockdown of a single atg gene on daf-2 longevity was studied (Minnerly et al., 2017; Wilhelm et al., 2017; Yang et al., 2024). However, given that the present condition dependence study effectively performs this test in 18 different ways, involving RNAi of 6 atg genes, 2 daf-2 mutants and 2 temperatures, N = 2 biological replicates were judged to be sufficient to draw robust conclusions; similarly, an earlier study of RNAi 14 atg genes under two conditions used 2-3 biological replicates (Hashimoto et al., 2009); for an overview of N sizes in previous studies, see Table S1.”

While this approach has yielded robust broad conclusions (e.g. that atg gene RNAi generally does not suppress daf-2(e1370) Age), it is true that for any one given treatment (say, effects of atg-9 RNAi on daf-2(e1370) Age) one may not draw conclusions with a high degree of confidence, and we do not do so. We therefore argue that in a study of this nature, as for instance in a whole genome RNAi screen for lifespan effects, it is reasonable and expedient to drop below the 3 replicates minimum standard; here we agree with the Nishida lab’s similar judgement, and from that study too robust conclusions may be drawn.

(3) Low sample sizes in individual trials. A number of lifespan assays were conducted with only 40-50 worms per replicate, and in some cases, as few as 30. Such sample sizes are below the standard commonly used in the C. elegans aging field and are likely to contribute to the variability observed.

Please see our response to point 2, which in essence responds to this concern.

(4) RNAi efficacy measured only in N2 at 20˚C. The authors demonstrated that atg-2 and atg-4.1 RNAi did not significantly reduce target mRNA levels, which may explain their weaker lifespan effects. However, these same RNAi treatments significantly affected lifespan in several other conditions (e.g., daf-2(e1368) at 20˚C, glp-1 at 20˚C and 25˚C, and N2 with 15 µM FUDR). Measuring RNAi efficacy across different genetic backgrounds and conditions would be needed to properly interpret these variable results.

The study would indeed be strengthened by inclusion of target atg mRNA measurements under all of the various conditions tested. However, this would have required a very large number of qPCR tests to be run; we note that in previous assessments of atg RNAi effects on lifespan (listed in Table S1 and Table S6), such tests were rarely performed. Regarding RNAi effects on daf-2 mutants, we note in the text the following: “While mRNA levels after RNAi under the various other conditions tested were not assayed, reduced IIS (including daf2(e1370)) has been shown to intensify the RNAi response (Wang and Ruvkun, 2004), thus lack of effect on lifespan in daf-2(e1370) is unlikely to reflect suppression of mRNA knockdown.” Here we have at least assessed, using N2, the most important issue relating to RNAi efficacy: the differential effects of different RNAi feeding clones on atg mRNA levels.

(5) Incomplete mechanistic exploration. The investigation of why atg-18 knockdown has uniquely strong effects was limited to DAF-16. Given published evidence that atg-18 may regulate HLH-30/TFEB, a master transcriptional regulator of autophagy and lysosomal biogenesis, testing whether atg-18 specifically affects HLH-30 nuclear localisation or activity could have provided valuable mechanistic insight and would distinguish atg-18 from the other genes tested.

We would have readily investigated this. However we learned of the interactions between atg-18 and hlh-30 only in Nov 2025, when one of us (David Gems) bumped into a member of Evandro Fan’s research group at an aging meeting at the Crick Institute in London. Their findings were very interesting for us, as they offered a possible explanation for the seeming idiosyncrasy of atg-18 RNAi effects on lifespan. This subject is currently under investigation by the Fan lab at the University of Oslo, who recently posted a preprint describing the work (Schmauck-Medina et al., 2026).

Reviewer #2 (Public review):

Summary:

This study examines how genes involved in cellular recycling (autophagy) influence lifespan under different experimental conditions. The findings help clarify why previous studies have reported conflicting results about whether blocking autophagy shortens or extends lifespan. The work will be of interest to researchers studying aging and cellular stress responses, particularly those using model organisms.

We thank the reviewer for their helpful remarks. Responding to their comments (particularly those not in the public review) has enabled us to improve it.

Strengths:

The findings are valuable, as they help resolve inconsistencies within a specific subfield of aging research. The evidence presented is solid, as the data broadly support the primary claims of the study. In addition, the discussion is thorough and thoughtfully integrates the findings within the broader context of the field.

Weaknesses:

Additional functional validation would further strengthen the conclusions.

We very much agree. Our original plan for this study was to include autophagic flux assays under different conditions. However, this line of investigation led us to a careful reassessment of reporter-based approaches to measuring autophagic flux in C. elegans, and attempts to improve them. This includes development of an automated, AI-based quantitative image analysis pipeline to improve reproducibility and data interpretation across studies. This investigation is still ongoing, and we are currently preparing a separate manuscript focused specifically on methodological clarity and quantitative assessment of autophagic flux.

Recommendations for the authors:

Reviewing Editor Comments:

To increase the evidence provided by the authors, they should at least address the comments from Reviewer 1 regarding the experimental inconsistencies. We acknowledge that the additional experiments suggested by Reviewer 2 regarding the use of C. elegans mutants and additional methods to assess autophagic flux would likely be a lot of additional work. However, adding results from such experiments would, of course, make the evidence more compelling.

Reviewer #1 (Recommendations for the authors):

Writing and presentation

(1) The abstract discusses results for daf-2 in detail but does not mention the glp-1 findings. Given that a substantial portion of the study addresses glp-1 longevity, including a summary of those results in the abstract would better represent the scope of the work.

Agreed. glp-1 is now referred to in the abstract.

(2) In the abstract, the sentence regarding FUDR effects is placed between statements about daf2, while it is referring to N2 lifespans, which may give the impression that the FUDR results were obtained in a daf-2 background. Consider restructuring this section for clarity.

Agreed. To improve clarity this now reads as follows. “In wild-type C. elegans, FUDR at a high concentration caused knockdown of several atg genes to increase lifespan”

(3) The definition of "robust" used in Figure 4E could be misleading to readers. For instance, bec-1 and atg-4.1 knockdowns in glp-1 at 20˚C are classified as robust, yet the actual percent suppression is modest (~8% and ~3%, respectively) and non-consistent in individual replicates. The "robust" designation arises because these knockdowns slightly increased N2 lifespan. Clarifying the definition in the figure legend or text would help readers interpret this correctly.

For glp-1 at 20˚C robust suppression is seen with atg-2, atg-18 and bec-1 RNAi, not atg-4.1 RNAi. But regarding bec-1: yes, the suppression is modest. bec-1 RNAi is an unusual case insofar as it meets the <30% definition partly because it caused an increase in N2 lifespan. Under the circumstances, arguably, it makes little sense to view it as an example of robust suppression, and Figure 4E has been altered accordingly, with a note added to the legend as follows. “Note that the bec-1 RNAi effect on glp-1 at 20˚C is not classified as robust here even though it reduces lifespan to within <30% of the mean lifespan of N2 under bec-1 RNAi, since the fact that it does so partly reflects an increase in N2 lifespan, rather than a robust life-shortening effect on glp-1.”

To try to improve clarity we have altered the definition of “robust” to read as follows. “R, robust suppression, i.e. knockdown reduces the extended lifespan of daf-2 or glp-1 to within <30% of the mean lifespan of N2 under the same RNAi. This designation (“robust”) indicates a high degree of suppression of the mutant longevity phenotype (see Figure 2, Figure 5 and Fig. S2).”

(4) On page 12, the text should read "9/30 suppresses robustly" (currently appears to contain a numerical error).

Fixed. This now reads “In 8/30 the RNAi effect was robust, i.e. the mutant longevity was largely suppressed.” (Now 8/30 since bec-1/glp-1/20˚C is no longer viewed as robust suppression).

(5) In Supplementary Sheet 8, the lifespan data from Hashimoto et al. are presented in a different format than the data from other studies. Standardizing the presentation would improve readability.

This is Supplementary Table 1. The inconsistencies have been ironed out.

Data and calculations:

(6) In Supplementary Table S3, the ΔΔCt values for atg-9 appear to be incorrect. Please verify and correct.

We thank the reviewer for highlighting this error. The ΔΔCt values for atg-9 in Supplementary Table S3 were incorrectly entered; the Fold Change values had been mistakenly placed in that column. This error has now been corrected. Please note that analyses and conclusions reported in the manuscript were based on the correct values.

(7) In Supplementary Table S4, the standard deviation values do not match my independent calculations. Please double-check these values.

This is correct: there was an error in the standard deviation (SD) values. We thank the reviewer for their diligence. We have updated both the SD and SEM (standard error of mean) columns in Table S4. The mean ΔΔCt values remain unchanged, as do the conclusions from analyses and statistical tests.

(8) In Supplementary Table S7 (kanamycin experiment), there appear to be several errors in the percent change calculations. Additionally, the statement "In the absence of Kan, atg-13 RNAi caused a slight reduction in lifespan" is not supported by the combined data, which actually shows a slight increase. Given that the reported changes are subtle but statistically significant, it would be prudent to re-verify the p-value calculations as well before drawing conclusions from this experiment.

The calculation for trial 1 atg-13 (-Kan) as a percentage of control (L4440 Kan) has been corrected so that the mean lifespan of the knockdown is divided by the mean lifespan of the control. In the previous version, the ratio was inadvertently calculated in the opposite orientation. All other values remain unchanged.

Responding further to this point, to strengthen the data here we have also conducted an additional trial, and Figure 3D and Table S7 have been updated accordingly. The more robust data still supports the conclusion that E. coli infection does not mask a life extending effect of atg-13 RNAi. However, in the new, summed data, atg-13 RNAi on no Kan does not shorten lifespan at all, in contrast to our previous trials (conducted several years earlier), but consistent with several other instances of variability in the study. Moreover, the modest life-shortening effect of atg-13 RNAi on Kan (-6.7%) is now statistically significant. The manuscript has been updated accordingly.

Experimental interpretation

(9) On page 9, the authors report testing N2 and daf-2(e1370) lifespan at 15˚C and 20˚C, but only the daf-2 results are discussed in the text. The N2 results at 15˚C appear in Table S5 but are never addressed. Notably, bec-1 knockdown significantly suppressed N2 lifespan at 20˚C in Table S2 but appears to significantly extend it at both 15˚C and 20˚C in Table S5. These discrepancies should be discussed.

The issue of inter-trial variability is discussed in our response to point 1 in the public review. More specifically: here it may be significant that the trials listed in Table S5 were performed several years after those in Table S2. The discrepancy is now noted and discussed as follows. “In these trials bec-1 RNAi also modestly increased N2 lifespan at both temperatures (Table S5), surprisingly given that in previous trials (performed several years earlier) bec-1 RNAi shortened N2 lifespan (Table S2). The reason for this discrepancy is unknown.”

(10) Regarding the CPH analysis of bec-1 in daf-2(e1368) (Figure 1), the authors state that bec-1 knockdown does not have a significantly greater effect in daf-2(e1368) relative to N2, and then note that this is consistent with the earlier observation by Hansen et al. that bec-1 shortens daf-2 lifespan without affecting N2. However, in the cumulative dataset, bec-1 does significantly suppress N2 lifespan. A more precise statement here would prevent readers from drawing an incorrect conclusion.

The point here is that our study and the Hansen et al study both point to atg RNAi suppression of daf-2 Age being limited to class 1 mutants, not that there are no effects on N2. To try to improve clarity we have rephrased as follows. “These findings are broadly consistent with the earlier observation that bec-1 and vps-34 RNAi shortened the lifespan of the daf-2(mu150) class 1 mutant but not of N2 at 20˚C (CPH analysis not performed) (Hansen et al., 2008).”

(11) The reference to Hashimoto et al. (2009) on page 10 states that atg-9 and atg-13 RNAi increased lifespan, but that study does not include data for atg-13. The lifespan extension reported by Hashimoto et al. was for atg-7, atg-9, bec-1, and unc-51. Please correct this citation.

Done. It now reads “where atg-9 (and also atg-7, bec-1 and unc-51) RNAi increased lifespan”.

(12) In a previous publication from this group, atg-2 and atg-13 knockdown with 15 µM FUDR led to significant lifespan extension, whereas in the current study, the same treatments significantly suppressed lifespan. Although this discrepancy is briefly mentioned in the Discussion, a more thorough discussion of possible explanations would strengthen the manuscript's value as a reference dataset for future studies.

A more detailed discussion has been added, as follows. “Regarding the causes of variability between results of ostensibly identical tests performed under ostensibly identical conditions: one clue is provided by a study comparing results of lifespan assays performed across three sites under similar conditions. This revealed that inter-trial variation occurred mainly at each site over time, rather than between sites (Lucanic et al., 2017). One possibility is that this reflects batch variation in media components, such as the BactoPeptone constituent of nematode growth medium (Petrascheck, 2014).”

(13) The N2 mean lifespan on GFP RNAi with 0 µM FUDR at 20˚C is approximately 15 days, whereas the N2 lifespan at 20˚C in Table S2 is approximately 20 days. While inter-experiment variability is expected, a difference of this magnitude warrants acknowledgement, as it could influence the interpretation of subsequent comparisons.

We have now acknowledged this in the legend to Figure 3, as follows. “We note that in (A) the lifespan of the gfp RNAi control is somewhat lower than in other experiments (mean 14.96 days, Table S6); see Discussion for consideration of possible reasons for inter-trial variability.”

(14) Regarding the glp-1 experiments (page 11 and Table S9), the N2 lifespan values in these experiments differ from earlier N2 results at 20˚C for several knockdowns (e.g., bec-1 knockdown appears to increase N2 lifespan in Figure 4). Additionally, the glp-1 lifespan results are not consistent between the two replicates for most genes except atg-2 and atg-18. The developmental shift (raised at 25˚C, then moved to 20˚C to obtain the glp-1 phenotype) could plausibly account for some of this variation compared to animals raised continuously at 20˚C. If so, this should be explicitly discussed.

Agreed. The following has been added to the Figure 4 legend. “That bec-1 RNAi increases N2 lifespan in (A) (+16.7%, p < 0.0001) but not (B) could imply an interaction with temperature during development, or merely variability of atg RNAi effects (see Discussion).”

(15) At 25˚C, N2 lifespan shows significant suppression upon atg-13 and atg-18 knockdown in Table S9, while these same effects were non-significant in Table S2. These and other interexperiment discrepancies should be noted and, if there are identifiable experimental differences, those should be specified.

This discrepancy has now been noted on page 9, immediately after the description of the data in Table S2, as follows. “(although in later tests at 25˚C, life-shortening effects of atg-13 and atg-18 RNAi were seen; Table S9)”

(16) If autophagy is already regulated by heat stress at 25˚C, this could explain the diminished effects of autophagy gene knockdown at higher temperatures. Measuring autophagy gene expression by qPCR across different temperatures and genetic backgrounds could provide useful mechanistic insight.

Agreed. However, more informative will be to measure effects of temperature and genotype on autophagy more directly, using fluorescent reporters of autophagic flux. We are addressing this as part of an ongoing study using improved and fully validated autophagic flux measurement methodologies (please see our response to reviewer 2, public review). 

(17) The authors report that atg-18 knockdown upregulates other autophagy genes (supplementary data). This is intriguing given that atg-18 shows the strongest phenotype. Whether this reflects a compensatory mechanism and why it does not rescue the lifespan suppression deserves further discussion.

On reflection we decided to remove from the manuscript the data relating to effects of atg-18 RNAi on mRNA levels of other atg genes due to concerns about data quality. 

(18) Regarding the FUDR and infection hypothesis: the logic that reduced bacterial infection upon FUDR treatment explains the loss of lifespan suppression is reasonable, but it does not account for why atg-13 knockdown actively extends lifespan in the presence of FUDR. This point could benefit from further discussion. In the kanamycin experiment, they further see that life-shortening effects of atg RNAi are not solely attributable to infection, but the question of lifespan extension remains unanswered.

Good point. We have addressed this as follows. “We also conclude the increase in lifespan upon atg-13 RNAi in the presence of 800 μM FUDR (Figure 3C) is not attributable to suppression of E. coli infection, but rather to some other, unidentified mechanism.”

(19) The timing of when lifespan assays are initiated relative to other experimental treatments is another potential source of variability (as seen from earlier reported data) that could be acknowledged as a consideration for future studies.

Good point. We have added the following to the section of the discussion about tackling condition dependency issues. “Another factor to take into account is the apparent tendency of results of C. elegans lifespan assays to vary over time (Lucanic et al., 2017).”

Reviewer #2 (Recommendations for the authors):

Major Comments:

(1) Assessment of autophagic activity

The authors demonstrate by qPCR that feeding RNAi reduces mRNA levels of autophagy-related genes. However, reduced transcript levels do not necessarily confirm functional inhibition of autophagy. Incorporating established assays of autophagic flux, such as Western blot analysis of lipidated ATG-8 (LGG-1/ATG-8-II) or validated fluorescence-based reporters, would substantially strengthen the mechanistic conclusions.

We agree with the reviewer that direct assessment of autophagic flux would provide additional functional insight. We are currently performing complementary assays to directly assess autophagic flux using reporter-based approaches, with a focus on standardizing reporter-based measurements and developing a quantitative image analysis pipeline to improve reproducibility and interpretation across studies. These analyses will be presented in a separate manuscript focused specifically on methodological clarity and quantitative assessment of autophagic flux.

(2) Use of genetic mutants

Several C. elegans loss-of-function mutants for autophagy genes are available. Validation of key findings using selected genetic mutants, where feasible, would provide complementary evidence and enhance confidence in the RNAi-based results.

In principle is this a good idea. In practice many loss-of-function mutants in core autophagy genes exhibit developmental defects or impaired viability, which can confound interpretation in aging studies. As our aim was to examine the effects of autophagy gene perturbation specifically during adulthood, RNAi provided a practical approach that allowed post-developmental knockdown while minimising disruption of normal development.

Minor Comments:

(1) Context-dependent effects of autophagy

The findings are conceptually consistent with prior work demonstrating dual roles of autophagy in C. elegans survival during starvation, where physiological levels promote survival but insufficient or excessive autophagy contributes to mortality (Kang et al., Genes & Development, 2007). Including a discussion of this study would help frame the present results within a broader biological context.

Good idea to cite this study, and we have now done so in the introduction, as follows. “It is by now clear that autophagy can enhance as well as inhibit the development of pathologies in C. elegans, including senescent ones (Kang et al., 2007).”

(2) Relevance beyond C. elegans

It would be helpful for the authors to clarify whether similar context-dependent effects of autophagy on lifespan have been reported in other organisms. Briefly referencing comparable findings in additional model systems would broaden the relevance and impact of the study.

This is a good idea, but our search for similar cases in other model organisms failed to identify clear examples. Perhaps more to the point here is that context dependent effects and, perhaps, condition selection bias, are a serious issue in scientific research in general. To emphasize this, the following has been added as the last line of the discussion. “More widely, condition dependency and conditional selection bias risk diminishing the reliability of research findings in many scientific disciplines.”

Hashimoto, Y., Ookuma, S. and Nishida, E., 2009. Lifespan extension by suppression of autophagy genes in Caenorhabditis elegans. Genes Cells. 14, 717-726.

Lucanic, M., Plummer, W., Chen, E., Harke, J., Foulger, A., Onken, B., Coleman-Hulbert, A., Dumas, K., Guo, S., Johnson, E., Bhaumik, D., Xue, J., Crist, A., Presley, M., Harinath, G., Sedore, C., Chamoli, M., Kamat, S., Chen, M., Angeli, S., Chang, C., Willis, J., Edgar, D., Royal, M., Chao, E., Patel, S., Garrett, T., Ibanez-Ventoso, C., Hope, J., Kish, J., Guo, M., Lithgow, G., Driscoll, M. and Phillips, P., 2017. Impact of genetic background and experimental reproducibility on identifying chemical compounds with robust longevity effects. Nat Commun. 8, 14256.

Minnerly, J., Zhang, J., Parker, T., Kaul, T. and Jia, K., 2017. The cell non-autonomous function of ATG-18 is essential for neuroendocrine regulation of Caenorhabditis elegans lifespan. PLoS Genet. 13, e1006764.

Schmauck-Medina, T., Anisimov, A., Meyer, D.H., Hu, Y., Huang, Z., Wu, Y., Taylor, S., Takla, M., MacArthur, M.R., Mitchell, S.J., Ai, R., Simonsen, A., Jensen, V., Labbadia, J., Shen, H.-M., Hansen, M., Schumacher, B., Rubinsztein, D., Lautrup, S., Lu, G. and Fang, E.F., 2026. ATG-18/WIPI2 drives longevity in an HLH-30/TFEB-dependent manner. bioRxiv.

Wang, D. and Ruvkun, G., 2004. Regulation of Caenorhabditis elegans RNA interference by the daf-2 insulin stress and longevity signaling pathway. Cold Spring Harb Symp Quant Biol. 69, 429-31.

Wilhelm, T., Byrne, J., Medina, R., Geisinger, J., Hajduskova, M., Tursun, B. and Richly, H., 2017. Neuronal inhibition of the autophagy nucleation complex extends life span in postreproductive C. elegans Genes and Development. 31, 1561–1572.

Yang, Y., Arnold, M.L., Lange, C.M., Sun, L.H., Broussalian, M., Doroodian, S., Ebata, H., Choy, E.H., Poon, K., Moreno, T.M., Singh, A., Driscoll, M., Kumsta, C. and Hansen, M., 2024. Autophagy protein ATG-16.2 and its WD40 domain mediate the beneficial effects of inhibiting early-acting autophagy genes in C. elegans neurons. Nat Aging. 4, 198-212.

de akoestische signalen die onze hersenen gebruiken om te bepalen waar een geluid vandaan komt.

het punt waar lichtstralen samenkomen nadat ze zijn gebroken door het hoornvlies en de ooglens

and even then even if it ws per email send, sendig to fake is stilla waste

should we tag some intresting reasons why an email might disappear or not exist?

aniation is cool, but doensT' last long enough. or like on empty page i guess make it look mor basic.?

what picture?

no animation here also not interesintg. change or add more intelligence

the list hsoul dappear last and it dons'T look like a list, more like bullet points.

also this isridimentary right? no the actual house designed? also we can be more interesting with color, like teal is good but geez... looks at our websit ewe have brading or real right?

should the first thign be like intro page, picture name of preentation, logo, etc. like and the animation after when she says subject line should see writing, story telling newsletter temaplte segmentaton presseing send., you knwo the steps of an esp no?

eLife Assessment

This is an important study utilizing innovative CRISPR based approaches demonstrating the role of the KLF family of transcription factors in the post-natal maturation of cortical projection neurons. The strength of evidence overall is compelling, and the study is well executed. The screen data presented provides a number of interesting candidates for future analyses into the mechanism of action of KLF family members in neuronal maturation.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The original reviews were generally positive. In their revision, the authors partially address reviewers concerns, but mention that a number of requested experiments are for future studies.]

Summary:

This is an interesting manuscript by Kirk and colleagues describing a highly valuable knock-down system that leverages CRISPRi in order to further elucidate the role of the Kruppel-Like Factor (KLF) transcription factor family in regulating the maturation of postnatal cortical projection neurons. The authors firstly use RNA-Seq and ATAC-Seq data in order to identify the KLF TF family as a potential regulator of cortical neuron maturation in the postnatal brain and subsequently knock down four KLF family members; KLF9, KL13, KLF6 and KLF7, in order to ascertain the functions of specific KLF genes in the developing cortex. The described CRISPRi knock down strategy is highly robust and penetrant as evidenced by a KD efficiency > 95% (assessed by both qPCR and single molecule FISH) and demonstrates that KLF6 and KLF7 play an activating role in driving the expression of target genes relating to axonal growth whereas KLF9 and 13 play a repressive role that inhibits the expression of overlapping gene targets. Together, the authors propose a model where the KLF TF family acts as a regulatory "switch" from activation to repression in the postnatal cortex as a mechanism to control a shift in projection neuron function from axonal growth to circuit refinement. The findings and conclusions of the manuscript offer a valuable contribution to the field of postnatal cortical development and further our understanding of the regulatory mechanisms that govern neuron maturation.

Reviewer #2 (Public review):

Summary:

Kirk et al. use RNA-Seq and CRISPRi to provide evidence that KLF family transcription factors regulate postnatal neuronal maturation of pyramidal neurons. The genetic programs regulating postnatal neuronal maturation are not well understood. The authors first analyzed chromatin accessibility and gene expression data from layer 4 and 6 pyramidal neurons and found that KLF TFs are predicted regulators of postnatal neuronal maturation. They then use CRISPRi knockdown and find that KLF activators first activate genes and then this is followed by KLF repressors repressing genes. Interestingly, some genes, such as those with cytoskeletal functions, are shared targets of KLF activators and repressors.

Strengths:

The study is well-executed and the paper is well-written. A major strength of this study is the application of state-of-the-art transgenic approaches. The CRISPRi approach used to knock down multiple KLFs is compelling. The genomic data generated appears to be high quality and is carefully analyzed. The presented findings provide important insights into the genetic programs that regulate postnatal maturation in cortical pyramidal neurons. The discovery that KLF family activators/repressors regulate gene expression changes during this critical step of neuronal development fills an important gap in the field.

Weaknesses:

While beyond the scope of the current study, future studies should investigate the contributions of KLFs on postnatal morphological and physiological changes.

Reviewer #3 (Public review):

Summary:

In their manuscript "Multiplexed CRISPRi Reveals a Transcriptional Switch Between KLF Activators and Repressors in the Maturing Neocortex", Kirk and colleagues seek to dissect the developmentally regulated pan-neuronal gene programs that control the postnatal maturation of cortical neurons. For this, the authors analyzed newly generated and existing RNA-seq and ATAC-seq of Layer 4 and Layer 6 cortical pyramidal neurons at postnatal day 2 (P2) and day 30 (P30) and identified thousands of shared developmentally regulated genes and genomic (promoter) regions, including genes involved in axon growth (tend to be downregulated) and synaptic function (tend to be upregulated). Motif enrichment analysis of promoters of differentially regulated genes revealed a strong presence of KLF/Sp family binding motifs, pointing to Krüppel-Like Factors (KLFs) as key transcriptional regulators of cortical maturation. Expression profiling showed a developmental switch from activating KLFs (Klf6, Klf7) expressed neonatally to repressive KLFs (Klf9, Klf13) upregulated during maturation. Using an elegant in vivo multiplexed CRISPR interference (CRISPRi) system, the authors achieved efficient, cell-type-specific knockdown of these TFs and showed that Klf9 and Klf13 repress a set of genes that includes cytoskeletal regulators such as Tubb2b, Dpysl3, and Rac3. Conversely, Klf6 and Klf7 promoted the expression of these same genes in the early postnatal period, and their knockdown led to reduced expression of these genes, particularly at P10 when their activating influence is strongest. Since promoters of shared KLF targets were enriched for KLF/Sp motifs but showed little change in chromatin accessibility, the authors propose a model in which distinct KLF family members function either as transcriptional repressors and activators that compete at constitutively accessible promoters and thereby act as a developmental transcriptional switch that coordinates the downregulation of axon growth programs and upregulation of synaptic maturation genes during cortical development.

Strengths:

The study addresses an interesting question and advances our understanding of the transcriptional regulation underlying postnatal cortical development. A major strength of the study lies in the innovative use of in vivo multiplexed CRISPR interference (CRISPRi), which allows for cell-type-specific, combinatorial knockdown of redundant TFs - this an elegant solution to a long-standing challenge in transcription factor research and should be useful also for other neuroscience studies that require local and cell-type-specific gene loss-of-function. Also, the integration of RNA-seq and ATAC-seq across developmental time points provides a robust foundation for identifying direct targets of the KLF family, and the findings are reinforced by cross-species conservation and the identification of targets with clear neurodevelopmental relevance.

Weaknesses:

The major weakness of the study lies in its relatively narrow scope: the study focuses primarily on transcriptional mechanisms and largely lacks functional validation of the neuronal phenotypes that are predicted by the gene expression data (e.g. axonal morphology). Despite these limitations, the paper offers an interesting model for a transcriptional switch during neuronal maturation in the cortex and establishes a powerful methodological framework for dissecting redundant gene networks in vivo.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

This is an interesting manuscript by Kirk and colleagues describing a highly valuable knock-down system that leverages CRISPRi in order to further elucidate the role of the Kruppel-Like Factor (KLF) transcription factor family in regulating the maturation of postnatal cortical projection neurons. The authors firstly use RNA-Seq and ATAC-Seq data in order to identify the KLF TF family as a potential regulator of cortical neuron maturation in the postnatal brain and subsequently knock down four KLF family members; KLF9, KL13, KLF6 and KLF7, in order to ascertain the functions of specific KLF genes in the developing cortex. The described CRISPRi knock down strategy is highly robust and penetrant as evidenced by a KD efficiency > 95% (assessed by both qPCR and single molecule FISH) and demonstrates that KLF6 and KLF7 play an activating role in driving the expression of target genes relating to axonal growth whereas KLF9 and 13 play a repressive role that inhibits the expression of overlapping gene targets. Together, the authors propose a model where the KLF TF family acts as a regulatory "switch" from activation to repression in the postnatal cortex as a mechanism to control a shift in projection neuron function from axonal growth to circuit refinement. The findings and conclusions of the manuscript offer a valuable contribution to the field of postnatal cortical development and further our understanding of the regulatory mechanisms that govern neuron maturation.

The conclusions of this manuscript are generally supported by the data, but some aspects of the data collection and analysis require some further clarification. Specifically:

(1) The authors comprehensively assess the molecular effects of KLF TF knock-down, however, the authors do not deeply address the cellular effects of these knock-downs. The authors conclude that knockdown of KLF6/7 and KLF9/13 cause downregulation and upregulation, respectively, of a common set of genes involved in cytoskeletal or axon regulation such as Tubb2 and Dpysl3. How is the morphology of the cells affected by these knockdowns? For example, does KLF9/13 knockdown cause neurite/axonal outgrowth? The authors should perform some basic experiments to assess changes in cell morphology following KLF TF KD. This is the one key point that needs addressing, in my opinion.

We appreciate this comment and agree that the cellular effects of KLF activator and/or repressor KD are not addressed by the experiments in this manuscript. However, the effects of KLF9, KLF13, and KLF9/13 knockdown on neurite outgrowth have been previously examined in vitro (Avci et al., 2012; Avila-Mendoza et al., 2020) and in vivo (Apara et al., 2017). The collective findings of these papers (enhanced neurite outgrowth and axon regeneration following KLF repressor KD) align with the predicted outcome of upregulating the set of cytoskeletal remodeling genes identified as putative KLF family targets in this manuscript. Furthermore, the in vitro results demonstrate that the partial redundancy between KLF9 and KLF13 we identified at the transcriptional level is relevant for their roles in repressing neurite outgrowth. Our results suggest that these previous findings are likely to hold true in cortical neurons in vivo while offering a molecular explanation for these effects at the level of gene expression. Other groups have examined the effect of KLF6 or KLF7 overexpression on corticospinal axon regeneration in vivo and found that these transcription factors can individually promote axon regrowth after injury (Blackmore et al., 2012; Wang et al., 2018). Similarly, this earlier work did not identify transcriptional mechanisms underlying the observed effect so our results also offer a plausible set of targets that could mediate the link between KLF activator overexpression and axon regeneration.

(2) The authors identify 374 DEGs in P10 Klf6/7 KD neurons and 115 DEGs at P20 (figure 6B). Have the authors looked to see what proportion of these DEGs are upregulated in the KLF9/13 KDs in order to get a more global understanding of the degree of overlap in the genes regulated by the KLF family members? [MOU2] Along similar lines, the authors later indicate that there are 144 shared targets between the KLF activator and repressor pairs (Figure 7C). What percentage does this represent of the total number of DEGs between the KLF pairs. This could further illustrate the degree to which the KLF pairs regulate the same set of genes. If it is already indicated in the manuscript, it should be made a bit more clear to the reader.

We thank the reviewer for the suggestion to include more comprehensive quantitative measures of the degree of overlap between targets of KLF activator and repressor pairs. We have included the exact number and percentage of P10 and P20 KLF6/7 targets that are differentially expressed (adj. p-value <= 0.05) in KLF9/13 KD neurons and vice versa in the text in the section associated with Figure 7, which is devoted to describing this class of overlapping targets. Furthermore, figures 7D and S7.2B both show how the full set of KLF6/7 targets are affected by Klf9/13 KD and vice versa. Collectively, this demonstrates that both KLF activator and repressor targets trend towards opposite regulation by the opposing pair.

(3) Figures 5B and 6D2 are very interesting as they relate the changes in gene expression over time in neurons from P2 to P30 to the functions of KLF9/13 and KLF6/7, respectively. I would be curious to see how these two forms of analyses overlap with one another. For example, in Figure 6D2, where would the KLF9/13 upregulated genes fall on the plot shown in Figure 6D2? And would those overlapping genes fit a similar correlation?

We agree with the reviewer that this is a powerful way to visually demonstrate the overlap between KLF6/7 and KLF9/13 targets in a more unbiased way and within a developmental context. The suggested analysis has been included in the supplement to Figure 7 (Fig S7.3).

(4) Figure 7E shows expression levels of shared KLF TF targets in control or KD conditions. Interestingly, the expression of Tubb2b, shows higher expression in ScrGFP P10 when compared to KLF9/13 P20, suggesting that derepression of KLF9/13 does not fully restore the expression level of Tubb2b seen at P10. This may suggest that other repressive regulators may be involved in the downregulation of Tubb2b from P10 to P20[MOU4] . Can the authors further comment on this, perhaps in the discussion, and speculate if there are other regulatory factors at play that may be controlling some of the shared targets by KLF6/7 and KLF9/13?

We thank the reviewer for this keen observation. We have included a comment on this within the text associated with Figure 7. While other regulatory factors are plausible, this is easily explained by low expression of Klf6 and Klf7 in the P20 cortex, which cannot drive transcription of Tubb2b and other targets to the same level as what is observed at P10 when the KLF activators are still relatively abundant, even after repression by Klf9/13 is removed. Within this framework, overexpression of KLF activators on a KLF repressor background would be the only way to restore expression of Tubb2b to its P10 expression levels. This is wholly compatible with our model of ‘push-pull’ regulation of KLF targets outlined in Figure 9. The finding could also be affected by the addition of repressive histone marks by the KLF9/13-associated SID complex in early neonatal development that may persist following the knockdown of these repressors, preventing complete restoration of neonatal expression patterns.

Reviewer #2 (Public review):

Summary:

Kirk et al. use RNA-Seq and CRISPRi to provide evidence that KLF family transcription factors regulate postnatal neuronal maturation of pyramidal neurons. The genetic programs regulating postnatal neuronal maturation are not well understood. The authors first analyzed chromatin accessibility and gene expression data from layer 4 and 6 pyramidal neurons and found that KLF TFs are predicted regulators of postnatal neuronal maturation. They then use CRISPRi knockdown and find that KLF activators first activate genes and then this is followed by KLF repressors repressing genes. Interestingly, some genes, such as those with cytoskeletal functions, are shared targets of KLF activators and repressors.

Strengths:

The study is well-executed and the paper is well-written. A major strength of this study is the application of state-of-the-art transgenic approaches. The CRISPRi approach used to knock down multiple KLFs is compelling. The genomic data generated appears to be high quality and is carefully analyzed. The presented findings provide important insights into the genetic programs that regulate postnatal maturation in cortical pyramidal neurons. The discovery that KLF family activators/repressors regulate gene expression changes during this critical step of neuronal development fills an important gap in the field.

Weaknesses:

A limitation of the current study is that the functional importance of KLF for postnatal neuronal maturation is unclear. Although the authors find that KLFs regulate some of the gene expression changes during postnatal neuronal maturation, it is still unclear whether such gene expression changes mediate the postnatal changes in morphology and physiology. While beyond the scope of the current study, future studies should investigate the contributions of KLFs on postnatal morphological and physiological changes.

We thank the reviewer for their helpful comments on this manuscript. We agree that the effects of KLF knockdown identified in this study are primarily descriptive, but – as noted - a detailed analysis of the morphological and physiological consequences of KLF knockdown are beyond the scope of this paper. However, we believe that a more mechanistic model of KLF function during neuronal maturation can be obtained by considering our findings on the bidirectional regulation of core cytoskeletal genes by KLF activators and repressors alongside published in vivo and in vitro data on the opposing roles of KLF family members on axon outgrowth/regrowth (Apara et al., 2017; Avila-Mendoza et al., 2020; Blackmore et al., 2012; Moore et al., 2009; Wang et al., 2018). Thus, we offer a set of transcriptional targets that likely mediate these opposing effects and a developmental context within which they might operate.

Reviewer #3 (Public review):

Summary:

In their manuscript "Multiplexed CRISPRi Reveals a Transcriptional Switch Between KLF Activators and Repressors in the Maturing Neocortex", Kirk and colleagues seek to dissect the developmentally regulated pan-neuronal gene programs that control the postnatal maturation of cortical neurons. For this, the authors analyzed newly generated and existing RNA-seq and ATAC-seq of Layer 4 and Layer 6 cortical pyramidal neurons at postnatal day 2 (P2) and day 30 (P30), and identified thousands of shared developmentally regulated genes and genomic (promoter) regions, including genes involved in axon growth (tend to be downregulated) and synaptic function (tend to be upregulated). Motif enrichment analysis of promoters of differentially regulated genes revealed a strong presence of KLF/Sp family binding motifs, pointing to Krüppel-Like Factors (KLFs) as key transcriptional regulators of cortical maturation. Expression profiling showed a developmental switch from activating KLFs (Klf6, Klf7) expressed neonatally to repressive KLFs (Klf9, Klf13) upregulated during maturation. Using an elegant in vivo multiplexed CRISPR interference (CRISPRi) system, the authors achieved efficient, cell-type-specific knockdown of these TFs and showed that Klf9 and Klf13 repress a set of genes that includes cytoskeletal regulators such as Tubb2b, Dpysl3, and Rac3. Conversely, Klf6 and Klf7 promoted the expression of these same genes in the early postnatal period, and their knockdown led to reduced expression of these genes, particularly at P10 when their activating influence is strongest. Since promoters of shared KLF targets were enriched for KLF/Sp motifs but showed little change in chromatin accessibility, the authors propose a model in which distinct KLF family members function either as transcriptional repressors and activators that compete at constitutively accessible promoters and thereby act as a developmental transcriptional switch that coordinates the downregulation of axon growth programs and upregulation of synaptic maturation genes during cortical development.

Strengths:

The study addresses an interesting question and advances our understanding of the transcriptional regulation underlying postnatal cortical development. A major strength of the study lies in the innovative use of in vivo multiplexed CRISPR interference (CRISPRi), which allows for cell-type-specific, combinatorial knockdown of redundant TFs - this an elegant solution to a long-standing challenge in transcription factor research, and should be useful also for other neuroscience studies that require local and cell-type-specific gene loss-of-function. Also, the integration of RNA-seq and ATAC-seq across developmental time points provides a robust foundation for identifying direct targets of the KLF family, and the findings are reinforced by cross-species conservation and the identification of targets with clear neurodevelopmental relevance.

Weaknesses:

The major weakness of the study lies in its relatively narrow scope: the study focuses primarily on transcriptional mechanisms and largely lacks functional validation of the neuronal phenotypes that are predicted by the gene expression data (e.g. axonal morphology). For example, the authors analyzed the effects of KLF9/13 KD on the neurons' excitability and excitatory inputs, but did not assess the effects on inhibitory inputs and E/I-ratio or morphological parameters such as axonal length and axonal target fields - the manuscript would be strengthened considerably by such analyses (axonal projections could be analyzed e.g. via local injections of the gRNA AAVs and subsequent immunolabeling of brain sections). Similarly, the chromatin-based mechanisms underlying KLF activity remain relatively speculative, and the transcriptional mechanisms upstream of the KLFs remain unexplored (this could be addressed by analyzing existing datasets; see "Additional Point 1" below). Finally, the manuscript is too long (e.g., nearly five pages in the Discussion section are devoted to discussing various misregulated genes) and would benefit from presenting the Results and Discussion sections more concisely. However, despite these limitations, the paper offers an interesting model for a transcriptional switch during neuronal maturation in the cortex and establishes a powerful methodological framework for dissecting redundant gene networks in vivo.

Shorten discussion (possibly results also).

We appreciate the reviewer’s feedback and agree that linking transcriptional perturbations to cellular phenotypes of KLF activator and/or repressor KD would significantly strengthen this manuscript. However, we believe this is beyond the scope of this current manuscript and the effects of individual KLF activators and repressors on axon outgrowth/regrowth are known from prior in vitro and in vivo studies (Apara et al., 2017; Avila-Mendoza et al., 2020; Blackmore et al., 2012; Moore et al., 2009; Wang et al., 2018). Since there was no detectable effect of KLF repressor KD on excitatory synaptic transmission (Fig. S4.2), we did not believe it was likely that our excitatory neuron-specific knockdown would affect inhibitory synapses under our model where KLF targets have primarily axonal functions. In the absence of ATAC-seq from Klf9/13 KD neurons, the relationship between chromatin accessibility and KLF binding is limited to descriptions of chromatin accessibility around KLF targets in neonatal and mature neurons. However, we do offer two testable hypotheses in our Discussion of early developmental transitions driving the KLF switch (Thyroid Hormone and structured activity input). Finally, we recognize that the manuscript is too long and have edited or removed parts of the Discussion.

Recommendations for the authors:

Summary of Recommendations from Reviewing Editor: While the screen data is important and novel, there is consensus among the reviewers that the current study would greatly benefit by additional analyses as to the consequences of KLF knockdown on neuronal morphology and neurite outgrowth. It is recommended that this major open question be experimentally addressed. It is also recommended that additional comments/points below are addressed with changes to the figures/text as appropriate.

Reviewer #1 (Recommendations for the authors):

Minor points:

(1) In Figure 2C2, it can be assumed that green represents KLF7 and red represents KLF9 but a legend indicating this would be helpful here. The Y axis is labeled as "Binned D-V Axis". Can the authors further clarify what this means? Is it referring to the different layers of the cortex? It looks like the expression pattern of KLF7 and 19 is not consistent throughout the entire dorsal-ventral axis at P7 and there is a region where KLF19 expression is higher than KLF7. Could this suggest that KLF family members may act on different types of neurons in distinct layers at different timepoints? Could the authors expand on this at all?

We thank the reviewer for pointing this out and the appropriate legend has been added. A detailed description of our method for binning counts in the Dorsal-Ventral axis (y-axis) to normalize for differences in cortical thickness across ages is included in the methods section (see RNAScope Image Acquisition and Analysis), but text has been added to the results to clarify this. The non-uniform distribution of Klf7 across cortical layers at P2 and P7 likely indicates that that KLF switch, while conserved, occurs with variable kinetics across cortical cell types which could reflect their distinct rates of maturation in vivo (Gao et al., 2025). Minor edits have been made to the text associated with Figure 2 to call attention to this detail.

(2) Figure 6C1 describes three clusters of DEGs. If I understand correctly, cluster 1 represents downregulated DEGs by KLF6/7 that are also developmentally downregulated between P10 and P20 and cluster 2 represents downregulated DEGs by KLF6/7 that do not change between P10 and P20. It could be interesting to see how these gene sets compare with one another. For example, are the genes in cluster 1 that decrease from P10 to P20 more related to axon regulation/cytoskeleton when compared to cluster 2 which consist of genes that do not change from P10 to P20 and perhaps may reflect other regulatory functions of KLF6/7.

We appreciate this suggestion and we have updated Figure 6C1 to reflect this observation by highlighting genes that are part of significant enriched GO terms to allow the reader to see which cluster they belong to. Separate GO analyses performed for each downregulated gene cluster did not yield significant results and were therefore not included.

Reviewer #3 (Recommendations for the authors):

See above

Additional points:

(1) Upstream transcriptional regulation of KLFs:

The idea that KLFs are regulated by Thyroid hormone (T3) is interesting, and the manuscript would be strengthened by exploring this idea further. This could be done, e.g. by analyzing existing snRNA-seq on genes regulated in cortical neurons by T3 (see PMID: 39178853). Similarly, KLFs were suggested to act together with AP1, and many KLFs were previously found to be activity-regulated - hence, the manuscript would be strengthened by analyzing the connection between KLFs and AP1. This could be done e.g. by re-analyzing transcriptomic and proteomic data generated e.g. by the Greenberg laboratory.

We agree with the reviewer that the role of T3 in driving the KLF switch is intriguing and is an active area of investigation in our lab. However, these experiments are ongoing and are beyond the scope of this current manuscript. A preliminary analysis of the T3-treated snRNA-seq dataset present in Hochbaum et al., 2024 found that all major excitatory cortical cell types upregulate Klf9 upon T3 treatment while the expression of selected shared targets including Dpysl3 and Gap43 are downregulated in several cell types, supporting our hypothesis. These findings have been included in Figure S9.

It was not our intention to suggest that there may be cooperation between AP1 and the KLF family, as AP1 motif enrichment was detected in shared upregulated genes while the KLF/Sp motif was found in shared downregulated genes. Furthermore, DEGs from both KLF knockdown experiments had exclusive enrichment for promoter KLF/Sp motifs, making it unlikely that there is widespread co-regulation of these genes by the AP1 family.

(2) Figure 5D and Page 27, regarding Rac3:

The authors state that " no change in accessibility in motif-positive peaks upstream of Plppr1 and Rac3 (Figures 5D and S5C)." This statement seems incorrect for Rac3 in L6 neurons where the ATAC signal is strongly reduced at P30 relative to P2. The authors might want to explain this or choose a better example.

We acknowledge that Rac3 was a poor choice to represent a developmentally regulated Klf9/13 target with no change in promoter accessibility, and have updated Figure 5 with Atat1, a tubulin acetylase and shared KLF target, as our exemplar for this category of transcript.

(3) Supplemental Figure 2:

There seems to be a discrepancy between the expression levels in Panels A and C: the data in Panel A were generated from adult mouse cortex, and the levels of six KLFs are indicated as rather high - however, in Panel C, the adult levels of KLF6, 7 and 13 are rather low. How can this be explained?

We thank the reviewer for detecting this apparent discrepancy. Most of this can be attributed to the log scale used in Figure S2A, which flattens expression differences in the moderate to high expression range. We elected to use a log scale in Figure S2A to highlight KLF family members with higher cortical expression relative to those with low or no expression. In addition, the counts displayed in this figure were batch-corrected to account for significant batch effects between libraries from additional excitatory cortical cell types included in Figure S2A, which were prepared and sequenced at different locations (Brandeis University v. Janelia Research Campus, as in Sugino et al., 2019). The additional processing step has been included in our methods section.

(4) IGV plots in Figure 5D and Supplemental Figure 5B:

It might be worth highlighting/shading the promoter region to see the position of the peaks relative to the promoters and TSSs.

The TSS is indicated on all IGV plots by a dark arrow indicating the direction of transcription, so the promoter can be inferred to be immediately upstream.

(5) Middle of page 20:

Remove "saw"; this seems like a typo.

(6) Page 27, reference to the plot for Plppr1:

The plot for Plppr1 is in Supplemental Figure 5B2, not in S5C.

(7) Page 28:

There seems to be a typo/omission before "...general applicability of CRISPRi"

not how we show research. tooltip next to number throughout presentaiton animation

why are wea sking, we are telling them there is 100% caus eever cleaned etc if they didnT'

this is also hard to understand. imagine all of this as an actual video to learn about your list

we can show this better like how much they spend per wtv, how mcuh we woul dhave remove, how muc is left, how much less money they spend and how much we cost vs that.

adnt ehn how much they spent overall (bt in a amanner where if the list was smalelr before than they weter't paying the 400$ plan right?so price per email or contact righ?)

this only makes sens eif you read, not if you look at animation

this is out of context no one knwos what it means the animation woul dbe 100 people, emails boes

then a line saying ofor every 100 contact :shows 100 icons 41 have gone quiet, and 59 still exist.

and from the emails that exist on averga 80 somethign percent actualyl inboxes:then some ixon greyed ot to who how muc is left getting an email, adn then 30% of them on average open an email how much is left., and then how many of hte open rates re fake remove that slice.. you end up with a small piece of the real lsit, waste noice, etc, etc

we want people to feelwhat it means. maybe my idea isnT' great, but you get it right?

For this one, we can just have a input and they can just type the number whatever the number is they can just put it

OK, SO THE ANIMATION FOR THIS ONE THE WAY IT SHOULD WORK IS THAT IT STARTS AND SLOWLY GROWS AND WE SEE HOW MANY PEOPLE THEY'VE GOT ON THE LIST SO LET'S SAY 40,000 SO I SEE THE SCALE OVER THE YEARS GO TO 40,000 AND THEN SLOWLY WE SAY DID YOU KNOW THAT THIS IS THE PERCENTAGE OF EMAILS I DON'T EXIST ANYMORE AND THEN OVERTIME WE CAN SEE THE SLIDE OVERTIME THAT GIRLS AND GIRLS AND GIRLS ACCUMULATIVELY overtime and then we had you know because it's a ratio here is a line of the ratio and that way they can see how much of the list is still valid, but an animated manner for just animating how things are showing up that's not very valuable animation.

can we change the animation into this: C:/Users/ASUS/OneDrive/Documents/GitHub/rme-marketing/.claude/worktrees/slack-yt-runner-4d7637/02-projects/review-my-emails/review-playground/storyboard-anim-tests.html

What about calling these marine microbes "sea ice algae"? Otherwise they could be confused with freshwater "glacier ice algae".

As the final episode of the first release, this one doesn't build much suspense. My suggestion is to include some scenes of Nora's everyday college life in the first half—for example, when she's studying quietly in her room and her roommate bursts in excitedly, asking if Nora wants to go to a party that night. Nora declines, as usual. Then the episode ends with Julian saying, "It's time for you to gain more privileges. I trust you won't let me down."

delete the contract

delete the contract, reason as above

delete the contract, reason as above

Let's just delete the contract. Personally, I'd prefer that the exclusive service arrangement remain just a verbal agreement between Nora and Adrian.

The last third of this episode and Episode 9 follow a similar plotline, which might lead to viewer fatigue. My suggestion is that Episode 8 should end right when Nora shouts, “Get down on your knees!” at the very end; this would leave viewers wanting more and encourage them to keep watching.

I don't think this line fits the logic of the story very well. It would be better to change it to, "So this is what you meant when you said you wouldn't fall for him."

This scene is unnecessary and adds nothing to the plot.

Here, Nora’s tone doesn’t sound much like that of a new assistant speaking to her boss, and she has always presented herself to Julian as capable yet well-behaved; her tone needs to be gentler, but not weak.

The pace is a bit slow here. After Nora says she did it on purpose, she could say, “He let me borrow his clothes,” and then the last two sentences can be omitted entirely—they’re too redundant.

I think this line sounds a little odd. At first glance, the audience might not quite understand what Nora is saying. It might be better to change it to something like, “I’ve done my best.”

eLife Assessment

This study shows that partial cone photoreceptor loss induces pathway-specific circuit remodeling in the mouse retina, with alpha OFF-sustained and OFF-transient retinal ganglion cells adapting differently through changes in their pre- and postsynaptic circuits. The results are valuable because they provide a key understanding of the diversity of circuit remodeling in retinal degeneration. The data are convincing, although clearer reporting of the numbers of independent animals and retinas, a more rigorous distinction between compensation and circuit change, and discussion of the functional consequences would strengthen the mechanistic insights.

Reviewer #1 (Public review):

Summary:

Lee et al. investigate how parallel retinal pathways respond to a common loss of photoreceptor input. The authors induce partial cone loss in adult mice and compare the functional responses of sustained OFF alpha (sOFFa) and transient OFF alpha (tOFFa) ganglion cells, together with changes in their presynaptic circuits. Using targeted patch-clamp recordings, linear-nonlinear analyses, pharmacological dissection of inhibitory inputs, and quantitative synaptic imaging, they show that the two pathways do not respond uniformly to cone loss. tOFFa ganglion cells exhibit more extensive changes in spatiotemporal receptive fields than sOFFa ganglion cells, with contributions from excitatory transmission, presynaptic glycinergic inhibition, direct GABAergic and glycinergic inhibition, and intrinsic properties. At the same time, transformations between synaptic input and spike output partially preserve ganglion cell signaling despite the loss of cones.

Strengths:

This is a technically careful and high-quality study. The comparison of two well-defined ganglion cell types and their dominant bipolar-cell pathways provides an unusually detailed view of where circuit modifications arise following a shared perturbation. The combination of recordings at successive stages of signal processing, pharmacological manipulations, and synaptic imaging is a particular strength. The use of partial stimulation in control retina also helps distinguish the immediate consequence of reduced input from subsequent circuit changes. The resulting conclusion that common photoreceptor loss produces pathway-specific forms of remodeling rather than a uniform retinal response is interesting and well supported. The work adds to our understanding of the diversity and circuit specificity of responses to retinal degeneration.

Weaknesses:

The principal limitations concern the precision of some mechanistic interpretations rather than the central observation of pathway-specific remodeling. First, the framework used to classify effects as compensation or circuit change sometimes treats the absence of a statistically significant difference as evidence that two conditions are equivalent. Second, the numbers of animals and retinas contributing to the main physiological and anatomical comparisons are not consistently reported, making it difficult to evaluate the independence of measurements obtained from multiple cells, images, or synaptic puncta. Finally, the consequences of the observed remodeling for the visual signals carried by these pathways remain unclear. This is particularly relevant for tOFFa ganglion cells, which have been implicated in responses to looming or approaching dark objects. The altered temporal filtering, center-surround organization, and input-output transformation could preserve, degrade, or otherwise transform such signals. These issues qualify the mechanistic and functional interpretation but do not substantially weaken the main conclusion that the two pathways respond differently to partial cone loss.

Reviewer #2 (Public review):

Summary:

This is an elegant, rigorous, and thought-provoking study that examines how different neural circuits are altered in response to loss of a common sensory input. To study this question, the authors use the mouse retina as a model system to investigate how downstream retinal circuits undergo modifications following a well-controlled partial loss of cone photoreceptors.

Strengths:

The experiments were conducted with a high degree of rigor, and the authors carefully considered and implemented appropriate controls throughout the study. Multiple parameters were tested, including pharmacological approaches to assess responses from different ganglion cell types. In addition, the authors complemented their functional data with confocal imaging to further support their findings. Overall, this is a well-written paper that provides a thorough analysis demonstrating how two similar ganglion cell types undergo distinct adaptations (i.e., compensation versus remodeling) in response to the loss of the same sensory input.

Weaknesses:

No additional experiments are needed. However, the authors may wish to consider the following points:

(1) Do the differences in compensation versus remodeling observed in ganglion cells reflect changes in the OPL? Different bipolar types may remodel their dendrites and form aberrant contacts with rods in the absence of cones. However, this would be challenging to test because there are currently no good markers for different bipolar types.

(2) It would be interesting to determine whether these functional changes can be detected at the transcriptomic level or whether they are mediated primarily through post-translational modifications.

Author response:

Reviewer #1 (Recommendations for the authors):

(1) Please report effect sizes and confidence intervals for the key comparisons in Figure 1. Where equality between conditions is central to the interpretation, use an equivalence test or soften the equal signs and associated mechanistic language. Please also clarify the permutation analysis: the Methods state that the null distributions were centered on the observed difference of deltas, whereas a permutation null would ordinarily test relative to zero.

We thank the reviewer for these suggestions. We will report effect sizes and associated 95% confidence intervals for the key comparisons in Figure 1. We will also evaluate equivalence using defined margins based on control variability (e.g., standard deviation) and assess how conclusions depend on the margin definition. We will soften the associated mechanistic language and replace equal signs with approximate-equality symbols, clarifying that these indicate similarity without implying statistical equivalence. Finally, we will clarify the description of the permutation analysis in the Methods, including how the null distributions were constructed and centered.

(2) Please report, for each experimental group, the numbers of cells or images, retinas, and animals, and indicate when multiple observations came from the same animal. Where observations are nested, the analysis should account for this structure using an animal-level or hierarchical bootstrap, a mixed-effects analysis, or animal-level summaries. It would be reassuring to confirm that the main pathway-specific conclusions remain robust when the animal determines the independent sample size.

We thank the reviewer for highlighting the importance of accounting for nested observations. We will report the numbers of cells and images in the main figures. To supplementary table (Dataset S1), which already reports animal numbers, we will add the number of cells obtained from each animal. Finally, we will assess the robustness of our conclusions using animal-level summaries and mixed-effects analyses that account for observations nested within animals.

(3) Please expand the discussion of how the changes in tOFFa temporal filtering, spatial organization, and input-output transformation are expected to affect visual coding, including responses to looming or approaching dark objects. If readily feasible, direct electrophysiological recordings of responses to an expanding dark stimulus would be informative. I do not consider these recordings necessary, however; predictions from the existing linear-nonlinear models or a more developed discussion of the expected coding consequences would be sufficient.

We appreciate the suggestion and will use a linear-nonlinear model to predict how responses to a looming stimulus change following cone loss. We will use an expanding stimulus based on published looming protocols and apply the measured spatial and temporal filters and pass the resulting linear output through nonlinearities to predict the response. We will compare how the response changes over time in control and cone-DTR.

Reviewer #2:

Weaknesses:

(1) Do the differences in compensation versus remodeling observed in ganglion cells reflect changes in the OPL? Different bipolar types may remodel their dendrites and form aberrant contacts with rods in the absence of cones. However, this would be challenging to test because there are currently no good markers for different bipolar types.

We agree that bipolar cell remodeling could contribute to the differences in functional changes at ganglion cell level. Our functional measurements do not establish whether these differences originate in the OPL. We will address this possibility in the Discussion.

(2) It would be interesting to determine whether these functional changes can be detected at the transcriptomic level or whether they are mediated primarily through post-translational modifications.

We agree that determining the molecular mechanisms underlying these functional changes would be informative. As our current experiments do not distinguish between changes in gene expression and post-translational modifications, we will acknowledge the limitation and address these possibilities as directions for future work in the Discussion.

Swiss pilot moving 7% of laptops away from Ms by end of 27. Swiss army fully moving to open desk by Oct 2026. Mentions [[Matthias Stürmer p]] as now prof at Bern

nach dummy-codierung: exploratory hypotheses

H1

de werking van de menselijke geest en het onbewuste systematisch wordt beschreven

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Resource: (IMSR Cat# JAX_002216,RRID:IMSR_JAX:002216)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:002216

What is this?

DOI: 10.1136/jitc-2026-015127

Resource: GraphPad Prism (RRID:SCR_002798)

Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_002798

What is this?

DOI: 10.1136/jitc-2026-015127

Resource: ImageJ (RRID:SCR_003070)

Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_003070

What is this?

DOI: 10.1136/jitc-2026-015127

Resource: FlowJo (RRID:SCR_008520)

Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_008520

What is this?

DOI: 10.1136/jitc-2026-015127

Resource: (KCLB Cat# 10223, RRID:CVCL_1986)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_1986

What is this?

DOI: 10.1136/jitc-2026-015127

Resource: (IMSR Cat# JAX_003751,RRID:IMSR_JAX:003751)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:003751

What is this?

DOI: 10.1136/jitc-2026-015127

Resource: RRID:IMSR_JAX:007594

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:007594

What is this?

DOI: 10.1136/jitc-2026-015127

Resource: (IMSR Cat# JAX_002288,RRID:IMSR_JAX:002288)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:002288

What is this?

DOI: 10.1136/jitc-2026-015127

Resource: RRID:IMSR_JAX:000664

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:000664

What is this?

DOI: 10.1126/sciadv.aef3538

Resource: RRID:Addgene_54568

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_54568

What is this?

DOI: 10.1126/sciadv.aef3538

Resource: RRID:Addgene_105802

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_105802

What is this?

DOI: 10.1126/sciadv.aef3538

Resource: RRID:Addgene_58170

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_58170

What is this?

DOI: 10.1126/sciadv.aef3538

Resource: RRID:Addgene_29725

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_29725

What is this?

DOI: 10.1126/sciadv.aef3538

Resource: RRID:Addgene_54586

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_54586

What is this?

DOI: 10.1210/endocr/bqag065

Resource: RRID:MMRRC_076250-JAX

Curator: @AleksanderDrozdz

SciCrunch record: RRID:MMRRC_076250-JAX

What is this?

DOI: 10.1126/sciadv.aec0722

Resource: RRID:Addgene_14883

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_14883

What is this?

DOI: 10.1111/jnc.70552

Resource: (Abcam Cat# ab150075, RRID:AB_2752244)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2752244

What is this?

DOI: 10.1096/fj.202602506R

Resource: RRID:AB_3750924

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_3750924

What is this?

DOI: 10.1096/fj.202602506R

Resource: RRID:AB_3750925

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_3750925

What is this?

DOI: 10.1096/fj.202602506R

Resource: (Cell Signaling Technology Cat# 2138, RRID:AB_2167955)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2167955

What is this?

DOI: 10.1096/fj.202602170R

Resource: (CLS Cat# 300408/p608_AGS, RRID:CVCL_0139)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0139

What is this?

DOI: 10.1080/19490976.2026.2731687

Resource: RRID:AB_3105781

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_3105781

What is this?

DOI: 10.1080/19490976.2026.2731687

Resource: RRID:AB_2769104

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2769104

What is this?

DOI: 10.1080/19490976.2026.2728329

Resource: phyloseq (RRID:SCR_013080)

Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_013080

What is this?

DOI: 10.1080/19490976.2026.2728329

Resource: RRID:IMSR_ENV:HSD-956

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_ENV:HSD-956

What is this?

DOI: 10.1080/19490976.2026.2724175

Resource: Mutant Mouse Regional Resource Center (RRID:SCR_002953)

Curator: @AleksanderDrozdz

SciCrunch record: RRID:SCR_002953

What is this?

DOI: 10.1038/s44321-026-00516-3

Resource: (RCB Cat# RCB0988, RRID:CVCL_0025)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0025

What is this?

DOI: 10.1038/s42255-026-01591-z

Resource: (ATCC Cat# CRL-2934, RRID:CVCL_B036)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_B036

What is this?

DOI: 10.1038/s42003-026-10884-8

Resource: RRID:AB_2108625

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2108625

What is this?

DOI: 10.1038/s41598-026-45137-5

Resource: RRID:BDSC_31701

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_31701

What is this?

DOI: 10.1038/s41598-026-45137-5

Resource: RRID:BDSC_8191

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_8191

What is this?

DOI: 10.1038/s41598-026-45137-5

Resource: RRID:BDSC_60084

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_60084

What is this?

DOI: 10.1038/s41598-026-45137-5

Resource: RRID:BDSC_8263

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_8263

What is this?

DOI: 10.1038/s41598-026-45137-5

Resource: RRID:BDSC_8263

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_8263

What is this?

DOI: 10.1038/s41598-026-45137-5

Resource: RRID:BDSC_9306

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_9306

What is this?

DOI: 10.1038/s41598-026-45137-5

Resource: RRID:BDSC_58312

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_58312

What is this?

DOI: 10.1038/s41598-026-45137-5

Resource: RRID:BDSC_64196

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_64196

What is this?

DOI: 10.1038/s41598-026-45137-5

Resource: RRID:BDSC_51518

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_51518

What is this?

DOI: 10.1038/s41598-026-45137-5

Resource: RRID:BDSC_5072

Curator: @bdscstockkeepers

SciCrunch record: RRID:BDSC_5072

What is this?

DOI: 10.1038/s41586-026-11030-4

Resource: RRID:Addgene_237350

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_237350

What is this?

DOI: 10.1038/s41586-026-11030-4

Resource: RRID:Addgene_204152

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_204152

What is this?

DOI: 10.1038/s41586-026-11030-4

Resource: RRID:Addgene_204154

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_204154

What is this?

DOI: 10.1038/s41586-026-11030-4

Resource: RRID:Addgene_12259

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_12259

What is this?

DOI: 10.1038/s41586-026-11030-4

Resource: RRID:Addgene_204154

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_204154

What is this?

DOI: 10.1038/s41586-026-11026-0

Resource: RRID:Addgene_130996

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_130996

What is this?

DOI: 10.1038/s41586-026-11026-0

Resource: RRID:Addgene_191207

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_191207

What is this?

DOI: 10.1038/s41416-026-03608-6

Resource: RRID:Addgene_66800

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_66800

What is this?

DOI: 10.1038/s41416-026-03608-6

Resource: (RRID:CVCL_0063)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0063

What is this?

DOI: 10.1038/s41416-026-03608-6

Resource: RRID:Addgene_21074

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_21074

What is this?

DOI: 10.1038/s41416-026-03608-6

Resource: RRID:Addgene_19010

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_19010

What is this?

DOI: 10.1038/s41388-026-03975-7

Resource: (IMSR Cat# JAX_006440,RRID:IMSR_JAX:006440)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:006440

What is this?

DOI: 10.1038/s41388-026-03975-7

Resource: (IMSR Cat# JAX_026563,RRID:IMSR_JAX:026563)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:026563

What is this?

DOI: 10.1038/s41388-026-03975-7

Resource: (IMSR Cat# JAX_008462,RRID:IMSR_JAX:008462)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:008462

What is this?

DOI: 10.1038/s41380-026-03876-x

Resource: (IMSR Cat# JAX_028532,RRID:IMSR_JAX:028532)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:028532

What is this?

DOI: 10.1021/acsptsci.6c00419

Resource: (Abcam Cat# ab97260, RRID:AB_10680425)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_10680425

What is this?

DOI: 10.1021/acsptsci.6c00419

Resource: (Abcam Cat# ab97245, RRID:AB_10680049)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_10680049

What is this?

DOI: 10.1016/j.xpro.2026.104837

Resource: (RRID:CVCL_0063)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_0063

What is this?

DOI: 10.1016/j.xcrm.2026.103056

Resource: (RRID:CVCL_1926)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_1926

What is this?

DOI: 10.1016/j.tice.2026.103959

Resource: (BCRC Cat# 60343, RRID:CVCL_0547)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_0547

What is this?

DOI: 10.1016/j.scr.2026.104108

Resource: (BioLegend Cat# 901301, RRID:AB_2565003)

Curator: @nmaralla

SciCrunch record: RRID:AB_2565003

What is this?

DOI: 10.1016/j.scr.2026.104108

Resource: (R and D Systems Cat# AF2018, RRID:AB_355110)

Curator: @nmaralla

SciCrunch record: RRID:AB_355110

What is this?

DOI: 10.1016/j.neuron.2026.08.015

Resource: OlyVIA (RRID:SCR_016167)

Curator: @nmaralla

SciCrunch record: RRID:SCR_016167

What is this?

DOI: 10.1038/s41467-026-76702-1

Resource: (MMRRC Cat# 032045-JAX,RRID:MMRRC_032045-JAX)

Curator: @AleksanderDrozdz

SciCrunch record: RRID:MMRRC_032045-JAX

What is this?

DOI: 10.1016/j.molcel.2026.08.016

Resource: (ATCC Cat# CRL-5985, RRID:CVCL_1531)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_1531

What is this?

DOI: 10.1016/j.molcel.2026.08.016

Resource: (RRID:CVCL_A4BQ)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_A4BQ

What is this?

DOI: 10.1016/j.molcel.2026.08.016

Resource: (ATCC Cat# CRL-4000, RRID:CVCL_4388)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_4388

What is this?

DOI: 10.1016/j.isci.2026.117600

Resource: (Proteintech Cat# 55192-1-AP, RRID:AB_11183034)

Curator: @nmaralla

SciCrunch record: RRID:AB_11183034

What is this?

DOI: 10.1016/j.isci.2026.117600

Resource: (KCB Cat# KCB 200970YJ, RRID:CVCL_0336)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_0336

What is this?

DOI: 10.1016/j.isci.2026.117600

Resource: (RRID:CVCL_0063)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_0063

What is this?

DOI: 10.1016/j.isci.2026.117600

Resource: DESeq2 (RRID:SCR_015687)

Curator: @nmaralla

SciCrunch record: RRID:SCR_015687

What is this?

DOI: 10.1016/j.isci.2026.117600

Resource: (Novus Cat# NBP1-91810, RRID:AB_11037820)

Curator: @nmaralla

SciCrunch record: RRID:AB_11037820

What is this?

DOI: 10.1016/j.xpro.2026.104836

Resource: ZEISS ZEN Microscopy Software (RRID:SCR_013672)

Curator: @nmaralla

SciCrunch record: RRID:SCR_013672

What is this?

DOI: 10.1016/j.isci.2026.117585

Resource: Olympus FV10-ASW Viewer (RRID:SCR_024433)

Curator: @nmaralla

SciCrunch record: RRID:SCR_024433

What is this?

DOI: 10.1016/j.isci.2026.117580

Resource: (Cell Signaling Technology Cat# 8661, RRID:AB_11217435)

Curator: @nmaralla

SciCrunch record: RRID:AB_11217435

What is this?

DOI: 10.1016/j.isci.2026.117558

Resource: Seurat (RRID:SCR_016341)

Curator: @nmaralla

SciCrunch record: RRID:SCR_016341

What is this?

DOI: 10.1016/j.isci.2026.117551

Resource: Epik (RRID:SCR_016745)

Curator: @nmaralla

SciCrunch record: RRID:SCR_016745

What is this?

DOI: 10.1016/j.isci.2026.117551

Resource: AlphaFold (RRID:SCR_025454)

Curator: @nmaralla

SciCrunch record: RRID:SCR_025454

What is this?

DOI: 10.1016/j.isci.2026.117551

Resource: Biorender (RRID:SCR_018361)

Curator: @nmaralla

SciCrunch record: RRID:SCR_018361

What is this?

DOI: 10.1016/j.immuni.2026.08.012

Resource: RRID:IMSR_JAX:038428

Curator: @nmaralla

SciCrunch record: RRID:IMSR_JAX:038428

What is this?

DOI: 10.1016/j.immuni.2026.08.012

Resource: (IMSR Cat# JAX_013188,RRID:IMSR_JAX:013188)

Curator: @nmaralla

SciCrunch record: RRID:IMSR_JAX:013188

What is this?

DOI: 10.1016/j.immuni.2026.08.012

Resource: RRID:IMSR_JAX:036564

Curator: @nmaralla

SciCrunch record: RRID:IMSR_JAX:036564

What is this?

DOI: 10.1016/j.immuni.2026.08.012

Resource: (IMSR Cat# JAX_002288,RRID:IMSR_JAX:002288)

Curator: @nmaralla

SciCrunch record: RRID:IMSR_JAX:002288

What is this?

DOI: 10.1016/j.immuni.2026.08.012

Resource: (IMSR Cat# JAX_002116,RRID:IMSR_JAX:002116)

Curator: @nmaralla

SciCrunch record: RRID:IMSR_JAX:002116

What is this?

DOI: 10.1016/j.immuni.2026.08.012

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

Curator: @nmaralla

SciCrunch record: RRID:IMSR_JAX:008449

What is this?

DOI: 10.1016/j.immuni.2026.08.012

Resource: (IMSR Cat# JAX_004103,RRID:IMSR_JAX:004103)

Curator: @nmaralla

SciCrunch record: RRID:IMSR_JAX:004103

What is this?

DOI: 10.1016/j.immuni.2026.08.012

Resource: (IMSR Cat# JAX_020649,RRID:IMSR_JAX:020649)

Curator: @nmaralla

SciCrunch record: RRID:IMSR_JAX:020649

What is this?

DOI: 10.1016/j.immuni.2026.08.012

Resource: (IMSR Cat# JAX_026123,RRID:IMSR_JAX:026123)

Curator: @nmaralla

SciCrunch record: RRID:IMSR_JAX:026123

What is this?

DOI: 10.1016/j.immuni.2026.08.011

Resource: (ATCC Cat# CRL-5935, RRID:CVCL_1543)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_1543

What is this?

DOI: 10.1016/j.immuni.2026.08.011

Resource: (IMSR Cat# CRL_028,RRID:IMSR_CRL:028)

Curator: @nmaralla

SciCrunch record: RRID:IMSR_CRL:028

What is this?

DOI: 10.1016/j.immuni.2026.08.011

Resource: (RRID:CVCL_UR31)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_UR31

What is this?

DOI: 10.1016/j.immuni.2026.08.011

Resource: (DSMZ Cat# ACC-598, RRID:CVCL_2211)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_2211

What is this?

DOI: 10.1016/j.immuni.2026.08.011

Resource: (RRID:CVCL_0063)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_0063

What is this?

DOI: 10.1016/j.immuni.2026.08.011

Resource: (NCI-DTP Cat# NCI-H1395, RRID:CVCL_1467)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_1467

What is this?

DOI: 10.1016/j.immuni.2026.08.011

Resource: RRID:Addgene_12259

Curator: @nmaralla

SciCrunch record: RRID:Addgene_12259

What is this?

DOI: 10.1016/j.immuni.2026.08.011

Resource: (ATCC Cat# CRL-2638, RRID:CVCL_7256)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_7256

What is this?

DOI: 10.1016/j.immuni.2026.08.011

Resource: RRID:Addgene_12260

Curator: @nmaralla

SciCrunch record: RRID:Addgene_12260

What is this?

DOI: 10.1016/j.eclinm.2026.104242

Resource: RStudio (RRID:SCR_000432)

Curator: @nmaralla

SciCrunch record: RRID:SCR_000432

What is this?

DOI: 10.1016/j.cub.2026.08.061

Resource: (Thermo Fisher Scientific Cat# A-21449, RRID:AB_2535866)

Curator: @nmaralla

SciCrunch record: RRID:AB_2535866

What is this?

DOI: 10.1016/j.cub.2026.08.061

Resource: (Molecular Probes Cat# A-11077, RRID:AB_141874)

Curator: @nmaralla

SciCrunch record: RRID:AB_141874

What is this?

DOI: 10.1016/j.cub.2026.08.061

Resource: (Molecular Probes Cat# A-11006, RRID:AB_141373)

Curator: @nmaralla

SciCrunch record: RRID:AB_141373

What is this?