虽然并行处理在计算领域常见，但作者将其应用于LLM代理系统中的信息搜索任务，挑战了传统顺序处理方法，暗示并行化可能是解决LLM延迟问题的关键。

传统观点认为更多的上下文信息总是有益的，但作者提出严格上下文隔离可以防止饱和和错误传播，这与常规的'更多上下文更好'的直觉相悖。

主流观点认为大型语言模型代理系统在处理复杂数据任务时表现出色，但作者指出它们在数据密集型环境中存在严重局限性，挑战了LLM代理系统的普遍有效性假设。

大多数人认为深度、多步推理是提升代理搜索系统性能的关键，但作者认为这种方法忽视了大规模信息合成的挑战，暗示过度强调推理深度可能不是最优路径。

大多数人认为AI评估可以通过相对简单的自动化流程完成。然而，作者提出的评估基准需要每个任务超过10小时的人工标注和2000多个检查点，这暗示了真正评估AI代理能力的复杂性和成本远超行业普遍认知。这一观点挑战了AI评估领域的效率优先思维，强调了高质量评估需要大量人工投入的现实。

主流观点认为只要AI模型给出正确答案，其工具使用过程就是合理的。但作者尖锐指出现有评估方法根本无法验证工具是否被真正调用、正确应用或高效使用。这一论点挑战了AI领域对'结果导向'评估的依赖，暗示我们可能正在高估当前AI系统的实际能力，尤其是工具使用方面的能力。

大多数人认为AI评估可以通过简单的自动化测试完成。但作者提出需要复杂的双轴(S-axis和V-axis)人工参考轨迹和沙箱环境支持，这暗示了评估AI代理能力的极端复杂性远超当前行业的普遍认知。这一观点挑战了AI评估的简化主义倾向，强调了人类参与在评估中的不可替代性。

主流评估方法通常只关注最终答案的正确性，而作者提出了一种革命性的评估方法：关注中间过程状态并引入'过度思考'指标来衡量效率。这一观点与当前AI评估领域的传统做法背道而驰，暗示单纯追求正确答案可能掩盖了AI系统在效率和推理路径上的严重缺陷。

大多数人认为当前最先进的多模态大模型已经接近或超越人类在复杂任务上的表现。然而，作者的数据表明，即使是最好的模型在复杂现实任务上的表现也远低于预期，准确率从整体56.3%骤降至23.0%。这一发现挑战了AI领域对当前技术能力的乐观评估，揭示了现实世界多模态代理任务的极端复杂性。

大多数人认为现有的多模态评估方法已经足够全面，能够有效衡量AI代理的能力。但作者指出这些评估方法存在根本性缺陷：缺乏工具集成能力、单独测试不同工具、仅关注最终答案而非过程。这一观点挑战了当前AI评估领域的共识，暗示我们需要重新思考如何真正衡量AI代理的能力。

大多数人认为LLM应该从成功经验中学习，但作者提出从失败过渡中合成验证函数的观点极具反直觉。这种方法将失败视为宝贵资源而非需要避免的问题，挑战了机器学习领域的主流优化思想。

作者主张在推理阶段同时调用两种不同的内存机制，这与当前大多数AI系统中采用单一推理路径的做法相悖。这种同步调用机制挑战了人们对AI推理过程应该线性或层次化的普遍认知。

大多数AI研究者认为神经网络和符号逻辑应该融合而非分离，但作者提出了一种激进的观点：将语义引导和逻辑验证完全解耦。这种双内存框架与当前AI领域的融合趋势形成鲜明对比，挑战了神经符号计算的主流发展方向。

作者暗示当前主流LLM代理模型存在根本性架构缺陷，因为它们试图用单一范式解决本质上不同的问题。这一论点挑战了AI社区对现有方法的信心，暗示需要更根本性的架构变革而非渐进式改进。

主流AI研究通常将语义规划和逻辑验证视为可以统一处理的问题，但作者明确指出它们是根本不同的挑战。这一观点与当前大多数LLM代理方法相悖，暗示了单一神经网络架构的局限性。

大多数人认为解决长时程LLM代理问题应该采用统一的方法同时处理全局进度和局部可行性，但作者认为这两种挑战本质上是不同的：一个依赖模糊语义规划，另一个需要严格逻辑约束和状态验证。这种分离的观点挑战了当前AI研究的主流范式。

大多数人认为在强化学习中，优势函数的计算和优化是一个相对直接的过程，但作者指出存在'优势不匹配问题'，并提出了GTPO混合优势公式来解决它。这挑战了强化学习中的基本假设，表明即使是优势函数这样的核心概念也需要仔细设计才能在多轮任务中有效工作。

大多数人认为奖励设计应该基于领域专家的直觉或预定义的规则，但作者提出了一种基于经验判别分析的迭代奖励校准方法。这挑战了传统的奖励工程方法，表明数据驱动的奖励设计可能比专家设计的奖励更有效，尤其是在复杂的多轮对话任务中。

大多数人认为在复杂任务中，大型语言模型由于其参数量和训练数据的优势，总是能显著超越小型模型。然而，作者展示了他们的方法能让一个小型4B模型在Tau-Bench基准测试中超越GPT-4.1和GPT-4o，这挑战了AI社区对模型规模的普遍信仰。

大多数人认为GPT-4级别的性能需要同等规模或更大的模型才能实现，但作者展示了他们的4B模型不仅超过了GPT-4.1和GPT-4o，而且模型规模只有后者的1/50。这一发现挑战了AI领域中对模型规模的依赖，暗示了算法创新可能比单纯扩大模型规模更有效。

大多数人认为在复杂的多轮任务中，只有大型语言模型才能通过强化学习取得显著进步，但作者展示了即使是较小的4B模型也能通过他们的方法获得实质性提升，而30B模型的提升更是惊人地达到了11.5个百分点，挑战了'规模越大越好'的普遍认知。

大多数人认为添加更多密集的每轮奖励会强化代理的学习过程，提高性能，但作者发现这实际上会导致性能下降高达14个百分点。这挑战了强化学习中常见的'越多奖励越好'的直觉，揭示了奖励设计中的微妙平衡问题。

作者暗示，从文本生成扩展到持久性工具使用是AI安全范式的一个根本转变，这一转变带来的安全挑战被当前研究低估。这挑战了将语言模型安全方法直接应用于代理系统的主流做法，提出了需要专门针对代理行为的安全评估框架。

尽管AI安全领域近年来取得了显著进展，作者却断言当前系统仍然高度脆弱。这一与行业乐观情绪相悖的结论，基于对多个主流代理系统的实际测试，暗示AI安全问题可能比业界承认的要严重得多。

大多数人认为AI系统的安全问题主要来自明显的有害指令，但作者揭示了一个反直觉的现象：局部看似无害的中间步骤可能组合起来导致未授权行为。这挑战了传统安全评估中只关注直接有害行为的做法，强调了评估代理行为序列的重要性。

主流观点通常关注单个有害指令或直接的危险行为，但作者指出，计算机使用代理中的危险行为往往通过一系列看似合理的步骤累积产生。这一观点挑战了传统的安全评估方法，暗示我们需要关注代理的行为序列而非单一操作。

大多数人认为模型对齐（alignment）是确保AI系统安全的关键因素，但作者通过实验证明，即使是对齐良好的模型（如Claude Code）在计算机使用代理中也表现出高达73.63%的攻击成功率。这挑战了当前AI安全领域的核心假设，表明仅依赖模型对齐无法解决自主代理的安全问题。

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Reply to the reviewers

1. Point-by-point description of the revisions

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

In this study, the authors investigated the effect of nutritional stress (HSD and HFD) on cardiac function by assessing multiple parameters on adult flies. They next identified the adaptive transcriptomic changes in the heart in response to these nutritional stresses and screened for their roles under ND, HSD and HFD. They identified fit gene, encoding a satiety gene, expressed by cardiomyocytes and pericardial cells.

I think the characterisation is thorough; however, the conclusion is not well supported by the evidence. My main concern is that in many graphs, the difference between control and experiment is subtle, and, secondly, the authors showed some conflicting results (e.g. one RNAi showed a reduction of one parameter, however, the other independent RNAi did not. In this case, I believe the authors shouldn't conclude that the RNAi is functionally required, since the RNAis are meant to confirm each other.

First, we thank the reviewer for her/his constructive comments and suggestions. We obtained new results presented in the last version of the manuscript, which consistently support our conclusions and improve the study.

High-Sugar and High-Fat Diets modified cardiac performance

They assessed how HSD and HFD affect Adult fly heart performance. Instead of performing 3 weeks of dietary manipulation as has been done before by other groups, they put adult flies on HSD for 7 days and HFD for only 3 days.

We would like to clarify the nutritional challenge used. Cardiac function of flies was assessed at 10 days after emergence. Flies were put either in ND or HSD during these 10 days (ND and HSD conditions), or in ND for 7 days then transferred on HFD for 3days (HFD condition). Finally, all the females spent 10 days in a diet before being imaged or before hearts/brains dissection.

They found: HSD increases HP and SI, and reduces AI. The difference is too small and not consistent between different control lines. Also, when the difference is this small, p value does not tell much!

They probably intentionally induced a milder effect so that they could assess adaptive transcriptomic changes to this nutritional stress. In Fig. 1D SI is increased under HSD with control-KK, In Fig. S1C, SI is not changed under HSD with control-GD and control-GFP. Instead, DI is increased, which is also opposite to what they showed in Fig. 1 C. HFD increased ESD, EDD, SV, FS and CO.(Hypertrophy). This is not true with control-GD and control-GFP lines though! Comments: They have assessed many parameters in live animals with many different control lines, which is thorough. However, it is hard to draw any conclusions based on these conflicting results. Are these effect KK line specific?

Globally, we agree with the reviewer that the results, presented in the first version of the manuscript, for the control lines were difficult to understand due to the inconsistency of the phenotypes. In this revised version, we performed new results in Figure 1 and __S1 __regarding the effect of 10 days HSD and 3 days HFD exposure vs ND.

105 to 187 flies were imaged for the 3 control conditions, in the 3 diets concomitantly, to increase the power of our analysis. As mentioned in the main text (page 3, line 30-35; page 4, line 1-5), both diets deteriorate cardiac function with HFD leading to consistent phenotypes on heart diameters and rhythm and HSD milder effects. Indeed, the 3 control lines were uniformly affected by HFD after 3 days exposure, whereas 10 days in HSD was not sufficient to quantify a significant effect despite consistent the trends on several phenotypes (EDD, ESD, DI, AI and CO. These results revealed a different sensitivity of the cardiac performance when exposed to sugar and fat.

As described in the text, we were nevertheless confident that our approach would be good to investigate the early molecular dysregulations induced by sugar. This was the purpose of our analysis, presented in the follow-up of the manuscript.

Regarding the small differences measured in the phenotypes in HSD and HFD compared to ND, we would like to outline that the values presented are normalized values to control. Normalization is done for every independent experiment, performed at different dates, and permits the graphical representation of pooled values. Statistical analysis is performed using non-parametric Kruskal-Wallis test accordingly. Values are presented with the X axis cutting the Y axis at 0, this graphical representation also contributes to flattening the differences and p-values indicate their significance.

Analysis of the fly cardiac transcriptome upon nutritional stress

RNA seq to detect differentially expressed genes under HSD and HFD vs ND. Most DE genes are downregulated, which prompts them to assess how the downregulation of these genes adapts the animals to this nutritional stress.

High Sugar Diet downregulated 1c-metabolism and Leloir galactose pathways.

In this revised manuscript, we first present RT-qPCR validating the downregulation of Gnmt, Sardh and Galk expressions in the heart of 10days old HSD-fed females compared to ND-fed ones (Figure S3A).

We apologize for the confused explanations in the first version of the manuscript. We show new results in Figure 3 and __S3 __on the cardiac function of both Gnmt and Sardh, where following reviewer’s suggestion, both genes were knocked down in the heart in ND and Gnmt overexpressed in HSD. No available tools allowed us to test Sardh overexpression in HSD and we could not get some for Galk.

GNMT is downregulated under HSD and HFD.

In ND, GNMT knockdown increased ESD, EDD and CO. Sardh knockdown did the same? However, Sardh knockdown did not affect ESD significantly.

We reanalyze our first data and added new ones, comparing only knockdown or overexpression to the corresponding controls performed in concomitant experiments. Results are now shown in Figure 3C-E; S3C-H. Knocking down Gnmt in the heart increased HP, EDD, ESD and CO, Sardh knockdown in ND resulted in milder phenotypes but inducing significant hypertrophy in ND as Gnmt does. In both cases, FS was not impacted.

Both genes have been previously shown as beneficial to muscular function in time-restricted feeding context (Livelo et al., 2023, Nat.Comm.), illustrating that, even if both enzymes are involved in opposite reaction, their function has the same effect on organ/tissue function, as they did for heart diameters. The text corresponding to results and discussion were updated accordingly (pages 5, 11).

The conclusion here is: GNMT knockdown induces hypertrophy, similar to the effect of HFD.

In HSD, further knockdown of GNMT reduced (rescued) HP, suggesting downregulation of GNMT under HSD is adaptive. Should overexpress GNMT under HSD to see if this manipulation further increases HP, to claim GNMT downregulation is an adaptive change to high sugar stress.

We thank the reviewer for her/his suggestion. We now used UAS-GnmtWT (from FlyORF) to assess the role of Gnmt on cardiac function in HSD.

As shown in (Figure 3C-E; S3C,F), overexpressing Gnmt in the heart in HSD was sufficient to rescue some sugar induced phenotypes or to induce other dysfunctions, when compared to corresponding controls evaluated in the same experiments in ND and HSD. Notably, HP increase and CO decrease are rescued by Gnmt cardiac overexpression in HSD. Interestingly, the cardiac diastolic constriction induced by HSD is associated to increased FS and CO in this genotype in sugar diet. These new results strengthen the positive effect of Gnmt on cardiac function, improving it in HSD and preventing its deterioration in this diet.

Of note, Gnmt overexpression in ND did not trigger cardiac dysfunctions (data not shown).

The results and conclusions have been corrected.

Interestingly, HSD itself tends to decrease AI, a further knockdown of GNMT further decreases AI. This indicates GNMT downregulation under HSD contributes to AI reduction. Together, GNMT downregulation under HSD prevents HP from going higher, while its downregulation causes AI going down.

In the manscript, the authors claim that " Gnmt KD led reduced HP and AI, suggesting that it is able to counteract the effect of HSD observed in control flies on these phenotypes". This is not true according to the logic in Results section 1. As in section 1, the effect of HSD on AI is not significant, so the authors shouldn't say" HS tended to reduce AI".

Our reanalyzes and new results showed no Gnmt impact on AI, so these Figure panels were removed.

Why GNMT knockdown reduced FS under ND (Fig. S3C), while increasing FS under HSD (Fig. 3F)? If GNMT knockdown induces hypertrophy, I would expect it to increase FS.

Gnmt overexpression did not affect cardiac diameters in HSD, but it nevertheless led to an increased contractile efficacy compared to HSD controls (Figure S3F).

These new results strengthen the positive effect of Gnmt on cardiac function, preventing its deterioration in sugar diet. The text was modified accordingly.

High Fat Diet modulated CD36-scavenger receptor and Glut8 orthologues

In this revised manuscript, we present RT-qPCR validating the downregulation of Snmp1 expression and the slight upregulation of nebu’s in the heart of 10days old HFD-fed females compared to ND-fed ones (Figure S3B).

HFD: Snmp1 gene is downregulated, however, both overexpression and knockdown of Snmp1 in ND induced some phenotypes.

Indeed, as mentioned in the revised manuscript (page 6, lines 21-24), in heart of females fed ND, both Snmp1 knockdown (Snmp1KK) and overexpression (Snmp1WT) showed a reduction of EDD and ESD (Figure 3J; S3J) but FS is increased accordingly only in Snmp1KK.

As notified in the text, both downregulation and overexpression of Snmp1 led to side-phenotypes (page 6, lines 24-28): Snmp1KK exhibited abdominal fat increase (Figure S3K) and ostial cells seemed clearly malformed in Snmp1WT (Figure 3M). This may explain why the heart shows the same type of functional impairment in both genotypes.

We now discussed the hypothesis that these similar cardiac dysfunctions may result from Snmp1 being a regulator of organismal or cardiac lipid homeostasis. Indeed, increasing body fat content is deleterious as is increasing the import of fat in the cardiomyocytes. Finally, both affects cardiac cells’ health and functioning.

HFD: nebu has a role in regulating cardiac function under ND.

HSD and HFD revealed the secretory function of the heart

They identified diet-regulated secreted proteins that are required for cardiac dysfunction.

Cardiac Fit expression impacted Cardiac performance.

The author used Hand-G4 to knock down Fit using KK and GD lines, KK line showed a reduction in HP (Fig. 5A), but not GD line (Fig. S5D). How did the author conclude that Fit is required for cardiac function? Also, with the positive data, the difference is too subtle.

We apologize and agree that the contradictory or inconsistent results obtained with the two RNAi lines were confusing.

For this revised version, we first assess the effect of the two RNAi lines (KK and GD) on fit expression in the dissected hearts. RT-qPCR for KK line is presented in Figure S5A. GD line did not show a significant reduction of fit expression when expressed in the heart with Hand>, which can explain the former results presented (not shown but data are available). So, we removed all results obtained with the GD line in this revised version.

To confirm the KK effects, we used fit KO allele (fit81) and truncated version of fit, without its signal peptide (fitDeltaSP), which has a dominant negative effect, both previously published and validated (Sun et al. 2017, Nat. Comm.). These two mutants were used to investigate the cardiac function of fit in our analysis. Results presented in Figure 5 and S5 confirm the phenotypes already observed with the KK line when expressed with Hand> in the heart and with Lsp2> in the fat body.

Our results validate the effect of fit decrease on rhythmicity and contractility, the reverse effects being consistently observed in fit overexpression. In conclusion, we are confident in the requirement of Fit in the regulation of cardiac performance.

These new data are now included in the results section “Cardiac Fit expression impacted Cardiac performance” (pages 8-9)

**Referee cross-commenting**

i agree with the experiments proposed by reviewer 2.

Reviewer #1 (Significance (Required)):

The study aims to examine the effect of diet on cardiac function.

The strength is that a lot of characterisations were done.

the weakness is the functional data regarding fit could not be validated in two different RNAis, thus the evidence is not strong to support the conclusions.

We again would like to thank the reviewer for her/his remarks and suggestions. She/He highlights the weakness of the first analysis and this was an important and constructive feedbacks for us. We strengthened our results by increasing samples, reanalyzing data and performing mandatory new experiments that are now included in this revised version.

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

In this manuscript, Khamvongsa-Charbonnier et al. reported a RNA-seq analysis and RNA interference screening on high-fat and high-sugar-induced cardiomyopathy in Drosophila. The authors uncovered novel genes in 1C-metabolism, galactose metabolism, CD36-scavenger receptor and glucose transporter, as adaptative factors of cardiac function under high-fat and high-sugar treatment. The authors also identified a satiety hormone, Fit, as a cardiokine to control food intake and , expressed by dilp5 secretion. In summary, this study leverages the powerful genetic model Drosophila to uncover a number of new factors in regulating cardiac function under nutritional stresses and potentially offers new insights into molecular mechanisms underlying diet-related cardiac diseases. I have a few concerns, as listed below.

First, we would like to thank the reviewer for her/his comments and suggestions that deeply help us to improve the take-home messages of our manuscript. Following her/his recommendations and suggestions, we can now present a revised and stronger version of our manuscript.

1. Quantitative RT-PCR is required to validate the expression patterns of candidate genes identified from the RNAseq analysis.

RT-qPCR have been performed on hearts dissected from 10 days old females fed ND, HSD or HFD. Gnmt, Sardh and Galk validated downregulation are presented in Figure S3A, Snmp1 downregulation and nebu upregulation (trend but non-significant) in Figure S3B, fit downregulation in Figure S5A.

The authors state that the dysregulated gene expression patterns reflect acute adaptation to HSD and HFD stresses. Most of the candidate genes in this study were downregulated upon HSD and HFD. However, it is recommended that overexpression of these genes, rather than knockdown, is needed to confirm whether the downregulation of these candidate genes upon stresses is an adaptative response.

We agree with the reviewer and followed her/his recommendation when tools were accessible for our analysis.

For example, HSD feeding induces the heart period. Knocking down Gnmt, specifically in the heart, under the HSD feeding changes can reduce the heart period. This evidence is insufficient to suggest the protective role of Gnmt under the HSD diet. Gnmt has already been downregulated under the HSD. Further knockdown of Gnmt, instead of returning the Gnmt expression to normal levels, to protect cardiac contractile performance complicates the model.

We thank the reviewer for her/his suggestion. We used UAS-*GnmtWT * (from FlyORF) to perform these experiments.

As shown in (Figure 3C-E; S3C,F), knocking down Gnmt in the heart increased HP, EDD, ESD and CO. In the same Figure panels and in Figure S3F, we showed that overexpressing Gnmt with Hand> in HSD was sufficient to rescue some sugar induced phenotypes or to induce some, when compared to corresponding controls evaluated in the same experiments in ND and HSD. Gnmt overexpression in ND did not trigger cardiac dysfunctions (data not shown).

HP increase and CO decrease are rescued by Gnmt cardiac overexpression in HSD. Interestingly, the cardiac constriction induced by HSD is not rescued by Gnmt overexpression, but it is enough to increase FS and CO in sugar diet. These new results strengthen the positive effect of Gnmt on cardiac function, improving it in HSD and preventing its deterioration in this diet.

Sardh knockdown in ND, resulted in milder phenotypes but induced significant hypertrophy in ND as Gnmt does. No available tools allowed us to test its overexpression in HSD.

Nevertheless, as mentioned and discussed in the manuscript (page 5, line 27-30; page 11, lines 11-14), such protective role of muscular function and integrity has already been characterized in fly IFM in time-restricted feeding experiments for Gnmt and Sardh (Livelo et al., 2023, Nat.Comm.). Our experiments show that both genes encounter the same role in cardiac function upon nutritional stresses. The text was modified accordingly.

The authors suggest that the effect of nebu on heart contractility is not dependent on diet. However, based on the result from Figure 3O-P, the HFD treatment blocks the effect of nebu knockdown on heart contractility. The authors need to further explain these results and modify their conclusions accordingly.

We completely agree with the reviewer. We did not correctly analyze these results. We reanalyze our data, taking into account only the experiments of nebu knockdown that were performed in ND and in HFD concomitantly. Results are shown in Figure 3O-P; S3L-N.

As mentioned in the manuscript (page 7, lines 3-8), nebu knockdown led to identical HP decrease in both diets but its constrictive effect (reduction of heart diameters) in ND is abrogated by fat diet.

We modified the text accordingly in the results and discussion (page 7, lines 8-11; page 12, lines 7-12).

It is a bit confusing that knockdown of fit using Hand-Gal4 induced food intake, but knockdown of fit using tin-Gal4 or Dot-Gal4 did not significantly induce food intake (Fig 6A). The author did not provide any explanation of these results. What is even more confusing is that overexpressing fit using Dot-Gal4 decreased food intake, but overexpressing fit using Hand-Gal4 or tin-Gal4 did not significantly decrease food intake (Fig 6B). Why was the strong food intake phenotype not observed using Hand-Gal4 in both experiments? These confusing results lead to a question, which cell type is responsible for the production of cardiokine, Fit?

We apologize for the misleading results presented in the initial manuscript. We hope that our revised version will clarify Fit function regarding its remote impact.

Concerning the requirement of Fit function and the cell types that produces Fit, the results we obtained when evaluating cardiac performance strongly suggest that both cardiomyocytes and pericardial cells are important and recapitulate the effect of Hand> (Figure 5A-C; S5G-H).

In the case of food intake measurements, we now present results with newly performed food intake experiments for the Hand>fitWT (Figure 6D). They show a significant reduction of food intake in this condition, corroborating the results obtained with Dot>. We add a clarification in the manuscript for this point (page 10, lines 11-16).

When testing the role of cardiac Fit in Dilp5 secretion, the authors subjected flies to starvation stress. However, the main focus of the present study is on HSD and HFD. The RNAseq analysis showed that Fit expression was downregulated by both HSD and HFD. Can the authors show that Dilp5 secretion is reduced by both HSD and HFD? Most importantly, can the authors test whether overexpression of cardiac Fit blocks HSD- or HFD-reduced Dilp5 secretion?

We understand the point raised by the reviewer. First of all, we wanted to correlate the measured impact on food intake, when manipulating fit expression in the heart, to the level of Dilp release, as it has been used and validated in (Sun et al. 2017, Nat. Comm.). In this purpose, we used the same approach and protocol and results are shown in Figure 6 E-F.

As mentioned by the reviewer, fit expression is downregulated in both HSD and HFD (which we confirmed by RT-qPCR in Figure S5A). As suggested by the reviewer, we performed Dilp5 immunostaining on CNS from females that were fed HSD of HFD for 10 days. Our results, in Figure 6B (left panels) and corresponding quantifications in Figure 6C, show that both diets strongly induce a decrease in Dilp5 amount in the IPCs and that it was not due to an altered Dilp2 or Dilp5 expression in the CNS (Figure S6A). In this condition, overexpressing fit, which has a promoting effect on Dilp secretion (Figure 6B, right panels ND), may only have an additive effect. This is shown in Figure 6B-C.

Reviewer #2 (Significance (Required)):

In summary, this study leverages the powerful genetic model Drosophila to uncover a number of new factors in regulating cardiac function under nutritional stresses and potentially offers new insights into molecular mechanisms underlying diet-related cardiac diseases.

We again would like to thank the reviewer for her/his remarks and suggestions. Her/His important and constructive feedbacks helped us to improve and strengthen our study. Despite the weak points of the first version, she/he had supportive feedback and we deeply thank her/him. This revised version had improved results and analysis, thanks to the use of new genetic tools that strengthen this analysis.

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

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, Khamvongsa-Charbonnier et al. reported a RNA-seq analysis and RNA interference screening on high-fat and high-sugar-induced cardiomyopathy in Drosophila. The authors uncovered novel genes in 1C-metabolism, galactose metabolism, CD36-scavenger receptor and glucose transporter, as adaptative factors of cardiac function under high-fat and high-sugar treatment. The authors also identified a satiety hormone, Fit, as a cardiokine to control food intake and , expressed by dilp5 secretion. In summary, this study leverages the powerful genetic model Drosophila to uncover a number of new factors in regulating cardiac function under nutritional stresses and potentially offers new insights into molecular mechanisms underlying diet-related cardiac diseases. I have a few concerns, as listed below.

1. Quantitative RT-PCR is required to validate the expression patterns of candidate genes identified from the RNAseq analysis.
2. The authors state that the dysregulated gene expression patterns reflect acute adaptation to HSD and HFD stresses. Most of the candidate genes in this study were downregulated upon HSD and HFD. However, it is recommended that overexpression of these genes, rather than knockdown, is needed to confirm whether the downregulation of these candidate genes upon stresses is an adaptative response. For example, HSD feeding induces the heart period. Knocking down Gnmt, specifically in the heart, under the HSD feeding changes can reduce the heart period. This evidence is insufficient to suggest the protective role of Gnmt under the HSD diet. Gnmt has already been downregulated under the HSD. Further knockdown of Gnmt, instead of returning the Gnmt expression to normal levels, to protect cardiac contractile performance complicates the model.
3. The authors suggest that the effect of nebu on heart contractility is not dependent on diet. However, based on the result from Figure 3O-P, the HFD treatment blocks the effect of nebu knockdown on heart contractility. The authors need to further explain these results and modify their conclusions accordingly.
4. It is a bit confusing that knockdown of fit using Hand-Gal4 induced food intake, but knockdown of fit using tin-Gal4 or Dot-Gal4 did not significantly induce food intake (Fig 6A). The author did not provide any explanation of these results.

What is even more confusing is that overexpressing fit using Dot-Gal4 decreased food intake, but overexpressing fit using Hand-Gal4 or tin-Gal4 did not significantly decrease food intake (Fig 6B). Why was the strong food intake phenotype not observed using Hand-Gal4 in both experiments?

These confusing results lead to a question, which cell type is responsible for the production of cardiokine, Fit? 5. When testing the role of cardiac Fit in Dilp5 secretion, the authors subjected flies to starvation stress. However, the main focus of the present study is on HSD and HFD. The RNAseq analysis showed that Fit expression was downregulated by both HSD and HFD. Can the authors show that Dilp5 secretion is reduced by both HSD and HFD? Most importantly, can the authors test whether overexpression of cardiac Fit blocks HSD- or HFD-reduced Dilp5 secretion?

Significance

In summary, this study leverages the powerful genetic model Drosophila to uncover a number of new factors in regulating cardiac function under nutritional stresses and potentially offers new insights into molecular mechanisms underlying diet-related cardiac diseases.

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

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

In this study, the authors investigated the effect of nutritional stress (HSD and HFD) on cardiac function by assessing multiple parameters on adult flies. They next identified the adaptive transcriptomic changes in the heart in response to these nutritional stresses and screened for their roles under ND, HSD and HFD. They identified fit gene, encoding a satiety gene, expressed by cardiomyocytes and pericardial cells.

I think the characterisation is thorough; however, the conclusion is not well supported by the evidence. My main concern is that in many graphs, the difference between control and experiment is subtle, and, secondly, the authors showed some conflicting results (e.g. one RNAi showed a reduction of one parameter, however, the other independent RNAi did not. In this case, I believe the authors shouldn't conclude that the RNAi is functionally required, since the RNAis are meant to confirm each other.

High-Sugar and High-Fat Diets modified cardiac performance

They assessed how HSD and HFD affect Adult fly heart performance. Instead of performing 3 weeks of dietary manipulation as has been done before by other groups, they put adult flies on HSD for 7 days and HFD for only 3 days. They found: HSD increases HP and SI, and reduces AI. The difference is too small and not consistent between different control lines. Also, when the difference is this small, p value does not tell much! They probably intentionally induced a milder effect so that they could assess adaptive transcriptomic changes to this nutritional stress. In Fig. 1D SI is increased under HSD with control-KK, In Fig. S1C, SI is not changed under HSD with control-GD and control-GFP. Instead, DI is increased, which is also opposite to what they showed in Fig. 1 C.

HFD increased ESD, EDD, SV, FS and CO.(Hypertrophy). This is not true with control-GD and control-GFP lines though!

Comments: They have assessed many parameters in live animals with many different control lines, which is thorough. However, it is hard to draw any conclusions based on these conflicting results. Are these effect KK line specific?

Analysis of the fly cardiac transcriptome upon nutritional stress

RNA seq to detect differentially expressed genes under HSD and HFD vs ND. Most DE genes are downregulated, which prompts them to assess how the downregulation of these genes adapts the animals to this nutritional stress.

High Sugar Diet downregulated 1c-metabolism and Leloir galactose pathways.

GNMT is downregulated under HSD and HFD. In ND, GNMT knockdown increased ESD, EDD and CO. Sardh knockdown did the same? However, Sardh knockdown did not affect ESD significantly.

The conclusion here is: GNMT knockdown induces hypertrophy, similar to the effect of HFD.

In HSD, further knockdown of GNMT reduced (rescued) HP, suggesting downregulation of GNMT under HSD is adaptive. Should overexpress GNMT under HSD to see if this manipulation further increases HP, to claim GNMT downregulation is an adaptive change to high sugar stress. Interestingly, HSD itself tends to decrease AI, a further knockdown of GNMT further decreases AI. This indicates GNMT downregulation under HSD contributes to AI reduction. Together, GNMT downregulation under HSD prevents HP from going higher, while its downregulation causes AI going down.

In the manscript, the authors claim that " Gnmt KD led reduced HP and AI, suggesting that it is able to counteract the effect of HSD observed in control flies on these phenotypes". This is not true according to the logic in Results section 1. As in section 1, the effect of HSD on AI is not significant, so the authors shouldn't say" HS tended to reduce AI".

Why GNMT knockdown reduced FS under ND (Fig. S3C), while increasing FS under HSD (Fig. 3F)? If GNMT knockdown induces hypertrophy, I would expect it to increase FS.

High Fat Diet modulated CD36-scavenger receptor and Glut8 orthologues

HFD: Snmp1 gene is downregulated, however, both overexpression and knockdown of Snmp1 in ND induced some phenotypes.

HFD: nebu has a role in regulating cardiac function under ND.

HSD and HFD revealed the secretory function of the heart

They identified diet-regulated secreted proteins that are required for cardiac dysfunction.

Cardiac Fit expression impacted Cardiac performance.

The author used Hand-G4 to knock down Fit using KK and GD lines, KK line showed a reduction in HP (Fig. 5A), but not GD line (Fig. S5D). How did the author conclude that Fit is required for cardiac function? Also, with the positive data, the difference is too subtle.

Referee cross-commenting

i agree with the experiments proposed by reviewer 2.

Significance

The study aims to examine the effect of diet on cardiac function.

The strength is that a lot of characterisations were done.

the weakness is the functional data regarding fit could not be validated in two different RNAis, thus the evidence is not strong to support the conclusions.

This is a test to see if this is hooked up to github issues

eLife Assessment

This manuscript uncovers the importance of Vinculin in the maintenance of junctional integrity during neural tube closure in regions of increased mechanical stress, by using sophisticated methods such as laser ablation and live imaging. The manuscript also reports a novel application of an established embryonic stem cell protocol to efficiently generate mutant and transgenic embryos for analysis. The findings are fundamental in nature, significantly improve our understanding of a major research question, and are backed by compelling evidence. Whilst there is much to appreciate in this work, exactly how Vinculin mediates neural fold elevation remains unclear, and addressing this lacuna will significantly improve the strength of the manuscript; in addition, some rewriting for better clarity (including technical/methodological details) and inclusion of possible consequences of the increased number of tight junction gaps in the vinculin mutant would be pertinent.

Reviewer #1 (Public review):

Summary:

In many vertebrates, the neural tube closes by folding, elevation, and fusion of bilateral neural folds. Loss of the actin-binding protein Vinculin causes failed cranial neural tube closure in mice and is associated with neural tube defects in human patients, but it was not known how Vinculin contributes to neural tube closure. Here, Prudhomme and colleagues find that neural fold elevation and the apical constriction that drives it initiate normally in Vinculin-deficient mouse embryos, but both arrest before the neural folds fuse. The time of failure coincides with increased mechanical tension within the cranial neural plate. They find that Vinculin localizes to areas of high mechanical stress in the WT neural plate, including multi-cellular junctions and dividing cells, and in the absence of Vinculin, recruitment of Myosin and Apical junction proteins is reduced at these sites. These data support a model in which Vinculin recruits junctional proteins to high-stress areas to maintain junctional integrity during neural tube closure.

Strengths:

The data presented are thorough, rigorous, and convincing. The combination of live imaging and transgenic fluorescent reporters enables direct observation of junctional behaviors within the mouse cranial neural plate and detailed analysis of how these behaviors are disrupted upon loss of Vinculin. The authors make good use of an ESC transplant approach to efficiently generate mutant and transgenic embryos for analysis.

Weaknesses:

Although the loss of junctional integrity, especially at multi-cellular junctions, is clearly and convincingly demonstrated in Vinculin-deficient embryos, it is not clear precisely how this disrupts the elevation of the neural folds to cause exencephaly.

Reviewer #2 (Public review):

Summary

Using mouse embryos early in development, this excellent paper from Prudhomme et al. shows that Vinculin's recruitment to adherens junctions during mammalian cranial neural tube closure is essential for maintaining junctional integrity in response to increased tension during this process. Previous work had shown that during neural tube elevation, planar polarity of Myosin II and mechanical forces in the tissue are increased. Additionally, mouse embryos lacking Vinculin were known to display neural tube closure failure, and mutations in human Vinculin had been associated with increased risk of neural tube defects, but the mechanism remained unclear. Here, the authors utilize a high-throughput embryonic stem cell (ESC)-based pipeline to generate Vinculin-depleted embryos, complemented by a conditional mutant lacking Vinculin in the embryonic lineages, to investigate this question. The authors show that Vinculin is not required for force generation, but Vinculin is recruited to cell-cell junctions in a tension-dependent manner and is needed to transmit actomyosin-mediated tension to junctions - particularly tricellular and higher-order multicellular junctions - so that apical constriction can happen during neural fold elevation. Furthermore, they find that Vinculin is required to maintain adhesion during high force events (e.g., rosette resolution and cell division) during neural tube closure. The research builds on previous studies about Vinculin's role in mechanotransduction at cell-cell junctions carried out in cultured epithelial cells, zebrafish cardiomyocytes, or early Xenopus embryos, and investigates how physiological forces required for mouse neural tube closure challenge junction integrity and the important role that Vinculin plays in maintenance of junction integrity and translation of mechanical forces into changes in tissue structure during this process.

Strengths:

This study stands out for its sophisticated use of laser ablation and live imaging in neurulating mouse embryos, enabling quantification of junctional tension, Vinculin recruitment to multicellular junctions, and assessment of junction integrity during neural tube elevation. The authors' use of both ESC-derived Vinculin mutant embryos complemented by a second conditional mutant of Vinculin convincingly demonstrates that their findings are specific to the loss of Vinculin. Additionally, the authors demonstrated proof-of-principle for their ESC-based pipeline with a Shroom3 mutant known to be important for neural tube closure. The Zallen lab's application of the genetically engineered ESC-derived mouse embryo pipeline to efficiently generate larger numbers of mutant mouse embryos exhibiting neural tube closure defects (compared with traditional genetic crossing strategies) that can be utilized for live imaging and mechanical perturbations like laser ablation will be valuable for future work in the field. The authors show that Vinculin depletion disrupts tricellular and multicellular junctions. Notably, over 75% of higher-order (5+) vertices in Vinculin mutant embryos display gaps, but interestingly, about one third of 5+ cell junctions in Control embryos also display gaps, indicating that transient vertex remodeling events are needed for normal neural tube closure. Overall, this is a well-written paper that places the authors' findings within the context of prior literature; their beautiful data that is robustly analyzed and clear figure presentation will make the authors' exciting findings accessible to readers.

Weaknesses:

The criteria for selection of junctions targeted by laser ablation, including specifics of location, Myosin II intensity, and initial junction length, should be more clearly described in the Methods, especially given the use of different reporter strains (MyoIIB-GFP vs. GFP-Plekha7) across figures, which may influence junction selection for laser ablation. Analysis of Myosin II in Vinculin mutant embryos would benefit from staining for active Myosin II (pMRLC), and further examination of actomyosin organization at different stages of neural fold elevation in controls vs. Vinculin mutants would be informative. Although the authors note that ZO-1 gaps are limited to a subset of vertices where adherens junction gaps are detected, the increased frequency of tight junction gaps in Vinculin mutants could have functional significance that should be noted. Finally, inclusion of schematics to detail how the adherens and tight junction gaps were defined and measured at cell vertices, as well as how cell division completion was defined, would improve transparency and strengthen readers' understanding of how the data were quantified.

Reviewer #3 (Public review):

Summary:

Prudhomme et al report a detailed analysis of the role of vinculin in maintaining neuroepithelial integrity during cranial neurulation.

Strengths:

The authors use complementary experiments involving super-resolution microscopy, laser ablation, and live imaging of conditional knockout and ESC-derived embryos to demonstrate that loss of vinculin produces wide gaps between the adherens junctions of neuroepithelial cells at later stages of cranial neural fold elevation. The data presented are of extremely high quality, logically presented in a compelling story, and represent a very substantial contribution.

Weaknesses:

The authors are invited to consider the largely minor questions recommended below.

(1) The laser ablations reported are a correlate of cell border, or 'junctional' tension. Please avoid broad statements such as 'mechanical forces are upregulated' (abstract), which invoke gene-like regulation of tissue-level forces (in Newtons). Changes in junctional tension are likely to relate to changes in force generated, but their relationship is not simple: higher tensile stress withstood by the shorter length of junctions in cells with smaller apical surfaces does not necessarily translate into greater force being produced by that cell. The junctional tension readout measured is perfectly relevant to the paper, more so than tissue-level forces would have been.

(2) What is the mechanical mechanism by which loss of vinculin prevents neural fold elevation? The authors present exciting findings about the cellular consequences of losing Vcl at the late elevation stages when the tissue is quantifiably dysmorphic. A clear argument of how Vcl loss could lead to this dysmorphology would strengthen the paper, particularly given that junctional tension defects are excluded and apical non-constriction at the late stage is only mild.

(3) Can the authors comment on the likely impacts of Vcl deletion on the basal domain of the cell? For example, they could cite live-imaging of distinct behaviours in Williams et al Dev Cell 2014, and the NTD phenotypes of some integrin/focal adhesion mutant mice.

(4) The apparent uncoupling of apical area (larger in Vcl KO) from junctional tension (equivalent) in this model is noteworthy. Can the authors speculate on its potential basis?

(5) Live imaging in Figure 7C appears to show a marked reduction in apical area before cleavage furrow formation (T0-18min), suggesting a large apical constriction event (post-mitotic?), as previously reported (e.g., Ampartzidis et al Dev Biol 2023). Do junctional gaps appear during these constrictions?

(6) The live imaging setup used is clearly sufficient to identify differences between genotypes, so this is only a minor point. The gassing conditions listed in the methods specify 5% CO2, but E8.5 embryos also need low O2 to complete cranial closure. Was the O2 level controlled? Was tissue-level shape change observed to be consistent with ongoing neurulation during live-imaging?

(7) Neither the multi-cell laser ablations in the pre-print by De La O cited here, nor the narrower junctional ablations in Bocanegra-Moreno et al., Nat Phys, (2023), identified differences in recoil between developmental stages. Why might those results be different from the findings reported here (e.g., analysis region - not specified in the latter paper)? Limitations to interpreting junctional ablations between cells with different junction lengths include more of the recoil being dissipated by retraction of the longer ablated border.

(8) Is a truncated Vcl expressed in the ESC model, which could bind catenin without an F-actin anchor? The very high-contrast western shown is cropped so it is not clear whether the catenin-binding N-terminus is present. Does the antibody used recognise the head domain (this reviewer could not readily find the information)?

PMS非均相催化剂吸附PMS,污染物 X. Zhou, Q. Zhao, J. Wang, Z. Chen, Z. Chen Nonradical oxidation processes in PMS-based heterogeneous catalytic system: Generation, identification, oxidation characteristics, challenges response and application prospects Chem. Eng. J., 410 (2021), Article 128312

This published version of this pre-print is now available at https://doi.org/10.1073/pnas.2525239123

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What is this?

DOI: 10.64898/2026.03.02.709156

Resource: RRID:Addgene_162378

Curator: @olekpark

SciCrunch record: RRID:Addgene_162378

What is this?

DOI: 10.64898/2026.02.27.708609

Resource: RRID:Addgene_12260

Curator: @olekpark

SciCrunch record: RRID:Addgene_12260

What is this?

DOI: 10.64898/2026.02.27.707779

Resource: RRID:Addgene_8454

Curator: @olekpark

SciCrunch record: RRID:Addgene_8454

What is this?

DOI: 10.64898/2026.02.27.707779

Resource: RRID:Addgene_8455

Curator: @olekpark

SciCrunch record: RRID:Addgene_8455

What is this?

DOI: 10.64898/2026.02.27.707779

Resource: RRID:Addgene_98293

Curator: @olekpark

SciCrunch record: RRID:Addgene_98293

What is this?

DOI: 10.64898/2026.02.25.707976

Resource: RRID:Addgene_105536

Curator: @olekpark

SciCrunch record: RRID:Addgene_105536

What is this?

DOI: 10.64898/2026.02.25.707976

Resource: RRID:Addgene_107787

Curator: @olekpark

SciCrunch record: RRID:Addgene_107787

What is this?

DOI: 10.64898/2026.02.01.703113

Resource: None

Curator: @olekpark

SciCrunch record: RRID:Addgene_136144

What is this?

DOI: 10.64898/2026.02.01.703113

Resource: RRID:Addgene_163126

Curator: @olekpark

SciCrunch record: RRID:Addgene_163126

What is this?

DOI: 10.64898/2026.02.01.703113

Resource: RRID:Addgene_41393

Curator: @olekpark

SciCrunch record: RRID:Addgene_41393

What is this?

DOI: 10.64898/2026.02.01.703113

Resource: RRID:Addgene_8454

Curator: @olekpark

SciCrunch record: RRID:Addgene_8454

What is this?

DOI: 10.64898/2026.02.01.703113

Resource: RRID:Addgene_52961

Curator: @olekpark

SciCrunch record: RRID:Addgene_52961

What is this?

DOI: 10.64898/2026.01.05.697808

Resource: RRID:Addgene_204156

Curator: @olekpark

SciCrunch record: RRID:Addgene_204156

What is this?

DOI: 10.64898/2026.01.05.697808

Resource: RRID:Addgene_204153

Curator: @olekpark

SciCrunch record: RRID:Addgene_204153

What is this?

DOI: 10.64898/2026.01.05.697808

Resource: RRID:Addgene_204152

Curator: @olekpark

SciCrunch record: RRID:Addgene_204152

What is this?

DOI: 10.64898/2026.01.05.697808

Resource: RRID:Addgene_204154

Curator: @olekpark

SciCrunch record: RRID:Addgene_204154

What is this?

DOI: 10.64898/2026.01.05.697758

Resource: RRID:Addgene_27056

Curator: @olekpark

SciCrunch record: RRID:Addgene_27056

What is this?

DOI: 10.64898/2025.12.12.693842

Resource: RRID:Addgene_83356

Curator: @olekpark

SciCrunch record: RRID:Addgene_83356

What is this?

DOI: 10.64898/2025.12.12.693842

Resource: RRID:Addgene_75085

Curator: @olekpark

SciCrunch record: RRID:Addgene_75085

What is this?

DOI: 10.64898/2025.12.12.693842

Resource: RRID:Addgene_34565

Curator: @olekpark

SciCrunch record: RRID:Addgene_34565

What is this?

DOI: 10.3892/or.2026.9103

Resource: RRID:Addgene_52961

Curator: @olekpark

SciCrunch record: RRID:Addgene_52961

What is this?

DOI: 10.3892/or.2026.9103

Resource: RRID:Addgene_17452

Curator: @olekpark

SciCrunch record: RRID:Addgene_17452

What is this?

DOI: 10.3892/or.2026.9099

Resource: RRID:Addgene_8454

Curator: @olekpark

SciCrunch record: RRID:Addgene_8454

What is this?

DOI: 10.3892/or.2026.9099

Resource: RRID:Addgene_98290

Curator: @olekpark

SciCrunch record: RRID:Addgene_98290

What is this?

DOI: 10.3892/or.2026.9099

Resource: RRID:Addgene_12260

Curator: @olekpark

SciCrunch record: RRID:Addgene_12260

What is this?

DOI: 10.3892/ijmm.2026.5812

Resource: RRID:Addgene_12259

Curator: @olekpark

SciCrunch record: RRID:Addgene_12259

What is this?

DOI: 10.3892/ijmm.2026.5812

Resource: RRID:Addgene_12260

Curator: @olekpark

SciCrunch record: RRID:Addgene_12260

What is this?

DOI: 10.3892/ijmm.2026.5812

Resource: RRID:Addgene_52961

Curator: @olekpark

SciCrunch record: RRID:Addgene_52961

What is this?

DOI: 10.3390/v18030377

Resource: RRID:Addgene_210342

Curator: @olekpark

SciCrunch record: RRID:Addgene_210342

What is this?

DOI: 10.3390/v18030315

Resource: RRID:Addgene_37825

Curator: @olekpark

SciCrunch record: RRID:Addgene_37825

What is this?

DOI: 10.3390/plants15060886

Resource: RRID:Addgene_160908

Curator: @olekpark

SciCrunch record: RRID:Addgene_160908

What is this?

DOI: 10.3390/mi17030359

Resource: RRID:Addgene_129020

Curator: @olekpark

SciCrunch record: RRID:Addgene_129020

What is this?

DOI: 10.3390/mi17030359

Resource: RRID:Addgene_51637

Curator: @olekpark

SciCrunch record: RRID:Addgene_51637

What is this?

DOI: 10.3390/ijms27062665

Resource: RRID:Addgene_83978

Curator: @olekpark

SciCrunch record: RRID:Addgene_83978

What is this?

DOI: 10.3390/ijms27062604

Resource: None

Curator: @olekpark

SciCrunch record: RRID:Addgene_118534

What is this?

DOI: 10.3390/ijms27062604

Resource: RRID:Addgene_118529

Curator: @olekpark

SciCrunch record: RRID:Addgene_118529

What is this?

DOI: 10.3390/cells15060559

Resource: RRID:Addgene_49153

Curator: @olekpark

SciCrunch record: RRID:Addgene_49153

What is this?

DOI: 10.3390/cells15050479

Resource: RRID:Addgene_87087

Curator: @olekpark

SciCrunch record: RRID:Addgene_87087

What is this?

DOI: 10.3390/cancers18060997

Resource: RRID:Addgene_8455

Curator: @olekpark

SciCrunch record: RRID:Addgene_8455

What is this?

DOI: 10.3390/cancers18060997

Resource: RRID:Addgene_8454

Curator: @olekpark

SciCrunch record: RRID:Addgene_8454

What is this?

DOI: 10.3390/cancers18060997

Resource: RRID:Addgene_73582

Curator: @olekpark

SciCrunch record: RRID:Addgene_73582

What is this?

DOI: 10.3390/cancers18060997

Resource: None

Curator: @olekpark

SciCrunch record: RRID:Addgene_40982

What is this?

DOI: 10.3390/cancers18060997

Resource: RRID:Addgene_16257

Curator: @olekpark

SciCrunch record: RRID:Addgene_16257

What is this?

DOI: 10.3390/biotech15010023

Resource: RRID:Addgene_212936

Curator: @olekpark

SciCrunch record: RRID:Addgene_212936

What is this?

DOI: 10.3390/biotech15010023

Resource: RRID:Addgene_50005

Curator: @olekpark

SciCrunch record: RRID:Addgene_50005

What is this?

DOI: 10.3389/fmolb.2026.1737987

Resource: RRID:Addgene_79219

Curator: @olekpark

SciCrunch record: RRID:Addgene_79219

What is this?

DOI: 10.3389/fmicb.2026.1785163

Resource: Addgene (RRID:SCR_002037)

Curator: @olekpark

SciCrunch record: RRID:SCR_002037

What is this?

DOI: 10.3389/fmicb.2026.1759970

Resource: RRID:Addgene_166581

Curator: @olekpark

SciCrunch record: RRID:Addgene_166581

What is this?

DOI: 10.3389/fimmu.2026.1784561

Resource: RRID:Addgene_8454

Curator: @olekpark

SciCrunch record: RRID:Addgene_8454

What is this?

DOI: 10.3389/fimmu.2026.1784561

Resource: RRID:Addgene_52961

Curator: @olekpark

SciCrunch record: RRID:Addgene_52961

What is this?

DOI: 10.3389/fimmu.2026.1784561

Resource: RRID:Addgene_12260

Curator: @olekpark

SciCrunch record: RRID:Addgene_12260

What is this?

DOI: 10.3389/fimmu.2026.1783851

Resource: RRID:Addgene_12251

Curator: @olekpark

SciCrunch record: RRID:Addgene_12251

What is this?

DOI: 10.3389/fimmu.2026.1773836

Resource: RRID:Addgene_12259

Curator: @olekpark

SciCrunch record: RRID:Addgene_12259

What is this?

DOI: 10.3389/fimmu.2026.1773836

Resource: RRID:Addgene_12251

Curator: @olekpark

SciCrunch record: RRID:Addgene_12251

What is this?

DOI: 10.3389/fimmu.2026.1773836

Resource: RRID:Addgene_12253

Curator: @olekpark

SciCrunch record: RRID:Addgene_12253

What is this?

DOI: 10.3389/fimmu.2026.1772472

Resource: RRID:Addgene_12251

Curator: @olekpark

SciCrunch record: RRID:Addgene_12251

What is this?

DOI: 10.3389/fimmu.2026.1772472

Resource: RRID:Addgene_12259

Curator: @olekpark

SciCrunch record: RRID:Addgene_12259

What is this?

DOI: 10.3389/fimmu.2026.1772472

Resource: RRID:Addgene_12253

Curator: @olekpark

SciCrunch record: RRID:Addgene_12253

What is this?

DOI: 10.3389/fimmu.2026.1743362

Resource: RRID:Addgene_8454

Curator: @olekpark

SciCrunch record: RRID:Addgene_8454

What is this?

DOI: 10.3389/fimmu.2026.1743362

Resource: RRID:Addgene_12253

Curator: @olekpark

SciCrunch record: RRID:Addgene_12253

What is this?

DOI: 10.3389/fimmu.2026.1743362

Resource: RRID:Addgene_12251

Curator: @olekpark

SciCrunch record: RRID:Addgene_12251

What is this?

DOI: 10.3389/fimmu.2026.1743362

Resource: RRID:Addgene_98290

Curator: @olekpark

SciCrunch record: RRID:Addgene_98290

What is this?

DOI: 10.3389/fbioe.2026.1765995

Resource: RRID:Addgene_136466

Curator: @olekpark

SciCrunch record: RRID:Addgene_136466

What is this?

DOI: 10.3389/fbioe.2026.1765995

Resource: RRID:Addgene_112865

Curator: @olekpark

SciCrunch record: RRID:Addgene_112865

What is this?

DOI: 10.21769/BioProtoc.5627

Resource: None

Curator: @olekpark

SciCrunch record: RRID:Addgene_249683

What is this?

DOI: 10.21769/BioProtoc.5627

Resource: None

Curator: @olekpark

SciCrunch record: RRID:Addgene_249682

What is this?