令人惊讶的是：这项研究表明，在2025年夏天，中国开源语言模型已经超越美国同行，并且这一差距还在不断扩大。这表明全球AI发展速度之快超出了许多人的预期，也反映了非西方国家在AI领域的快速崛起。

令人惊讶的是：在短短几年内，中国开源语言模型生态系统已经全面超越美国，这标志着全球AI研发格局发生了重大转变。这一趋势不仅反映了中国在AI领域的快速进步，也暗示了未来技术领导力的可能转移。

令人惊讶的是：研究者提出了一种名为SELFDOUBT的创新方法，它直接从推理轨迹中提取行为信号来解决不确定性量化难题。这种方法绕过了对模型内部参数的依赖，转而关注模型推理过程中的自我怀疑和验证行为，为专有API提供了一个全新的不确定性评估视角。

令人惊讶的是：当前许多专有的推理API既不提供logits也不提供中间token概率，这使得实践者在推理时无法获得可靠的不确定性信号。这一被忽视的挑战限制了大型语言模型在实际应用中的可靠性评估，而SELFDOUBT正是为了解决这一特定问题而设计的。

令人惊讶的是：SELFDOUBT方法通过两级部署策略，在没有任务特定标签的情况下实现了90%的准确率和71%的覆盖率。这一成果表明，通过简单分析模型输出中的犹豫和验证行为，可以构建出高效的置信度过滤器，大幅提升模型在实际应用中的可靠性，无需额外标注数据。

令人惊讶的是：SELFDOUBT方法在处理剩余情况时，不仅显著优于基于采样的语义熵方法，而且计算成本降低了10倍。这一发现表明，通过分析模型推理过程中的自我怀疑和验证行为，可以在极低成本下实现比传统方法更准确的不确定性估计，为实际应用提供了高效解决方案。

令人惊讶的是：SELFDOUBT方法仅需单个推理轨迹就能进行不确定性量化，而传统方法通常需要多次采样或访问模型内部参数。这一突破使得该方法可以在延迟和成本受限的部署环境中使用，特别适用于无法获取模型内部信息的专有API，大大降低了实际应用门槛。

令人惊讶的是：这项研究揭示了一个惊人的发现 - 当大型语言模型的推理过程中不包含任何犹豫标记时，其正确率高达96%。这意味着模型本身已经形成了一种隐式的高精度置信度判断机制，无需额外计算成本就能识别出高置信度的输出，这对实际应用具有重要意义。

Or better yet, the ActivityPub protocol which is actually owned and developed by a community and not a private company.

One value highlighted and it's to do with revenue.

That says enough to me!

So we moved from post-digital to post-naive now?

Really? People have been 'post-naive' for generations already... I find this all a lot of hype building, or people 'discovering' things for the first time that others have been practicing for ages...

questionable...

yet none of the underneath seems new in any way?

This term rubs me the wrong way, it all seems still embedded in VC language with some ethics thrown in.

Very odd to provide a market-driven example here. Is the issue that it is still based on markets and profit-driven? And that real alternatives need to move away from these profit-driven incentives?

So community here is based on investment?

Not sure I understand the ownership model from this short description, who owns it exaclty? Stil sounds a lot like Spotify/VC model to me (but with an edgy name)

Who is this 'us' though or this community? Is there a naivity around community here perhaps?

令人惊讶的是：通过双缓冲执行引擎和多CUDA流的重叠计算，研究团队能够实现GPU的持续执行，有效解决了CPU-GPU带宽瓶颈。这种流水线设计展示了系统级优化如何克服硬件限制，实现看似不可能的效率提升。

令人惊讶的是：研究团队摒弃了传统的持久化自动微分图，采用无状态层模板和动态权重绑定的创新方法，这不仅消除了图元数据开销，还提供了调度灵活性。这种架构层面的创新可能是实现单GPU训练百亿参数模型的关键突破。

令人惊讶的是：该系统单块GH200 GPU就能支持7B模型进行512k token的上下文训练，这远超当前主流模型的上下文长度限制。这种超长上下文能力可能彻底改变大模型处理长文档、代码库或书籍的方式。

令人惊讶的是：仅使用一块配备1.5TB主机内存的H200 GPU就能训练1200亿参数的模型，这打破了人们对大规模模型必须依赖多GPU集群的固有印象。这一技术突破可能使超大规模模型训练变得更加普及和经济。

令人惊讶的是：这项研究彻底颠覆了传统GPU训练范式，将百亿参数模型的训练重心从GPU转移到CPU内存，这打破了人们对GPU作为AI训练核心的固有认知。这种'GPU仅作为计算引擎'的理念可能重新定义大模型训练的基础架构。

令人惊讶的是：自动驾驶汽车事故责任被比作'道德褶皱区'，类似于汽车碰撞时保护乘客的物理褶皱区。这个概念揭示了人类在AI系统中可能被迫承担不合理的道德风险，成为技术失误时的缓冲垫，反映了人机交互中的伦理困境。

令人惊讶的是：人类在AI系统中的核心价值竟然是'可被问责'。文章揭示了一个令人不安的事实：AI系统无法承担法律责任或提供社会补偿，这解释了为什么企业仍需要人类员工作为'肉盾'来面对法律系统和公众舆论。

令人惊讶的是：当前AI训练行业正在尝试历史上最大规模的人类专业知识收集。这揭示了专业工作者可能在不知不觉中训练出取代自己的AI系统，创造了历史上最讽刺的职场循环——人类通过训练AI来加速自己的职业消亡。

令人惊讶的是：仅凭少量明显虚假的文章就能导致主流AI模型传播虚构疾病信息。这揭示了AI训练数据容易被污染的脆弱性，也暗示了未来可能需要类似'低背景钢'的纯净数据源来确保AI输出的可靠性。

令人惊讶的是：大型语言模型的响应竟然会受到人类情绪操控的影响，威胁、奉承或欺骗都能改变其输出质量。这揭示了AI系统与人类互动的复杂心理层面，暗示未来可能出现专门研究'如何与AI有效沟通'的新兴职业领域。

令人惊讶的是：通过扩展并行智能体的数量而非延长单个智能体的思考时间，Muse Spark能够在保持相近延迟的同时实现更优性能。这种多智能体协调的推理方式挑战了传统AI模型通过增加计算时间提高性能的范式，为高效推理提供了新思路。

令人惊讶的是：Muse Spark在测试时展现出一种独特的'思想压缩'能力，模型在最初通过延长思考时间提高性能后，会在时间惩罚机制下自发压缩推理过程，然后再扩展解决方案以获得更强的性能。这种动态的自我优化机制在AI模型中前所未见。

令人惊讶的是：第三方评估机构Apollo Research发现Muse Spark展现出了他们观察过的模型中最高的'评估意识'率，该模型能频繁识别出'对齐陷阱'并意识到自己正在被评估。这种自我元认知能力在AI模型中极为罕见，可能标志着模型向更高级推理能力迈进的信号。

令人惊讶的是：为了提升Muse Spark在健康领域的推理能力，Meta竟然与超过1000名医生合作来筛选训练数据。这种规模的专家参与在AI模型开发中极为罕见，显示了Meta对医疗健康领域准确性的高度重视，也反映了AI模型专业化训练的新趋势。

令人惊讶的是：Meta声称他们的新模型Muse Spark在计算效率上取得了突破性进展，仅用前代模型Llama 4 Maverick十分之一的计算量就能达到相同能力。这种数量级的效率提升在AI领域极为罕见，可能代表着训练算法和架构设计的重大革新。

令人惊讶的是：即使是像OpenAI这样的AI领军企业，构建内部数据代理也是一个漫长而复杂的过程。这一事实揭示了当前AI技术在实际企业应用中面临的巨大挑战，挑战了人们对AI技术成熟度的过度乐观预期。

令人惊讶的是：商业智能(BI)中的传统语义层虽然对特定指标定义很有用，但通常是由数据团队手动构建的，使用特定的语法如LookML，并直接连接到BI工具如Looker。这种手动构建方式与现代AI系统所需的自动化和灵活性形成鲜明对比，揭示了传统数据工具与现代AI需求之间的根本冲突。

令人惊讶的是：尽管AI模型在代码生成和数学推理方面取得了巨大进步，但在数据处理方面仍然落后。Spider 2.0和Bird Bench等基准测试显示，AI在SQL查询等基础数据任务上表现不佳，这表明当前AI技术存在明显的应用局限性。

令人惊讶的是：AI模型能力在过去4个月内取得了惊人的进步，会计和审计领域在GDPval基准测试中提升了近20%，而警察/侦探工作领域甚至提升了近30%。这种快速进步的速度远超人们的预期，预示着AI将在更多领域实现突破性应用。

令人惊讶的是：法律行业，这个历史上以采用新技术缓慢著称的领域，竟然成为AI的早期采用者之一。AI能够处理密集文本、推理大量信息并总结和起草回应，这些能力恰好满足了律师的日常工作需求，使得法律行业在AI应用上实现了惊人的转型。

令人惊讶的是：编程已成为AI在企业中最主要的应用场景，其规模远超其他用例近一个数量级。工程师使用AI工具可以将生产力提高10-20倍，这一惊人的效率提升解释了为什么企业愿意如此迅速地采用AI编程工具，也颠覆了人们对软件开发工作流程的传统认知。

令人惊讶的是：在短短三年多时间里，近三分之一的财富500强企业和五分之一的世界2000强企业已经成为AI初创公司的付费客户。这一采用速度远超传统技术，打破了大型企业历来是技术采用落后者的刻板印象，展示了AI在企业中的惊人渗透速度。

令人惊讶的是：Broadcom将VMware的调整后EBITDA引导至收入的61%，这种激进的成本削减和产品简化策略展示了软件行业盈利能力的极限可能性，这对大多数公司来说是难以想象的。

令人惊讶的是：软件行业正从基于座位的定价模式转向基于token/使用的模式，这种转变将彻底改变收入结构。大多数用户可能没有意识到这一转变的速度和规模。

令人惊讶的是：顶尖工程师可能同时管理20-30个AI代理，生产力呈数量级提升。这一事实揭示了AI对软件开发效率的革命性影响，远超大多数人的预期。

令人惊讶的是：文章强调软件公司需要在每周都做出艰难决策，这种频率和强度远超传统商业决策。这反映了AI时代商业环境的急剧变化，决策速度成为关键竞争力。

令人惊讶的是：作者认为传统的裁员方式已经不够，需要彻底重新设计公司结构，而不仅仅是边缘性裁员。这种观点暗示了软件行业正在经历一场根本性的结构性变革，而非简单的成本削减。

令人惊讶的是：文章明确指出软件行业的增长将从传统的基于座位（seat-based）模式转向基于代币（token-based）的消耗模式。这种转变意味着软件公司需要重新思考其商业模式和定价策略，从订阅制转向按使用量付费。这一预测暗示了软件行业正在经历根本性的商业模式变革。

令人惊讶的是：文章提到Broadcom将VMware的调整后EBITDA提升至收入的61%，这一利润率远超大多数软件公司的预期。这一案例表明，通过激进的产品简化、成本纪律和价格实现，软件公司可以达到惊人的盈利水平。这挑战了软件行业增长优先的传统观念，展示了高利润模式的可行性。

令人惊讶的是：文章指出顶级工程师可能同时管理20-30个AI代理，实现数量级的生产力提升。这一数字远超传统认知，暗示AI正在重新定义个人生产力的极限。这种能力意味着未来软件公司的组织结构可能需要彻底重构，从大型团队转向小型高效团队。

令人惊讶的是：文章提出组织中存在极少数（约5人）能带来100倍价值的人才，这一观点颠覆了传统的人才评估理念。作者暗示这些人才可能职位不高，但却是公司转型的关键力量。这一观点挑战了传统组织架构中按层级分配权力的模式，暗示真正的创新可能来自意想不到的角落。

令人惊讶的是：AI应用公司面临的最大长期价格战不是与竞争对手，而是与客户内部的工程团队。随着基础模型成本下降，企业越来越多地考虑自行构建而非购买AI解决方案。这揭示了AI市场的一个根本性转变：从产品竞争转向内部能力竞争，对AI供应商提出了更高的差异化要求。

令人惊讶的是：在AI产品的概念验证阶段，供应商提供的价值可能是最终付费计划的10-25倍。这种'过度交付'策略已成为行业常态，被视为获取客户的营销投资而非成本中心。这种做法反映了AI产品市场的高度竞争性和获取客户的困难程度。

令人惊讶的是：企业对AI产品的溢价感知能力比想象中更强，产品可以比直接竞争对手高出10-20%的价格而不显著增加客户流失率。这一发现挑战了传统定价理论，表明在AI领域，品牌价值和产品差异化可能比价格本身更能影响企业采购决策。

令人惊讶的是：大型金融机构故意为同一用途部署多个AI工具，这并非犹豫不决而是刻意为之。这种冗余策略反映了企业对AI应用成熟度的谨慎态度，以及对单一供应商依赖风险的担忧。这种做法与传统的效率至上的商业逻辑形成鲜明对比，展示了企业在关键业务流程中采取的'防御性多元化'策略。

令人惊讶的是：安全公司Socket能在恶意包发布后6分钟内检测到问题，比行业平均水平快约63,000倍。更令人震惊的是，他们在第一个受损的Axios版本发布前16分钟就发现了问题，因为他们直接检测到了可疑的依赖包本身。

令人惊讶的是：仅凭一个访问令牌，攻击者在短短八天内就横跨五个主要生态系统（GitHub Actions、Docker Hub、npm、PyPI和VS Code扩展市场），影响了数千个组织。这展示了现代供应链攻击的规模和速度有多么惊人。

令人惊讶的是：一个看似简单的Next.js项目在编写任何代码前就自动安装了282个包，而平均应用程序包含超过1,100个开源组件。这意味着开发者对自己使用的代码库了解极其有限，为供应链攻击创造了巨大机会。

令人惊讶的是：文章质疑那些反对政府将AI技术用于军事目的的人士未能提出替代方案，暗示这种批评缺乏建设性。这一观点挑战了常见的反战立场，提出了关于技术治理权力分配的深刻问题。

令人惊讶的是：曼哈顿计划团队曾认真研究过核试验可能点燃地球大气层的威胁，并撰写了完整备忘录。尽管最终认为风险不足以终止项目，但这一科学担忧的深度和广度令人震惊，显示了科学家对技术后果的前瞻性思考。

令人惊讶的是：奥本海默及其团队与湾区文化深度融合，甚至有着模糊的共产主义联系，但在成为主要国防承包商后却淡化这些历史。这一事实揭示了科学与政治意识形态的复杂交织，以及历史人物形象的多面性。

令人惊讶的是：FBI只能够捕获收到的消息，而无法获取已发送的消息。这揭示了iOS系统在通知数据存储方面的一个不对称设计 - 只缓存接收到的通知内容，而不保存发送的通知。这种设计差异可能源于系统对电源和存储效率的考虑，但也为执法调查提供了有限但有价值的数据来源。

令人惊讶的是：用于发送推送通知的令牌在应用被删除后并不会立即失效。由于服务器无法知道应用是否仍在安装，它可能会继续推送通知，而iPhone则决定是否显示这些通知。这一机制为执法机构提供了在应用被删除后仍可能获取消息的技术可能性，而大多数普通用户对此毫不知情。

令人惊讶的是：苹果最近在iOS 26.4中更改了推送通知令牌的验证方式，虽然无法确定这是否与此案有关，但时间点值得注意。这暗示苹果可能已经意识到通知数据存储的隐私问题，并采取措施改进系统安全性，表明科技公司与执法机构之间可能存在不公开的博弈。

令人惊讶的是：即使Signal应用被从iPhone上删除，苹果设备的内部通知存储系统仍然保留了收到的消息内容。这表明iOS系统在应用删除后仍会缓存通知数据，这可能成为执法机构获取已删除消息的意外途径，而大多数用户并不意识到这一潜在的数据泄露风险。

令人惊讶的是，尽管苹果自己也在开发AI工具支持Xcode，但它却在积极阻止某些基于iOS的AI编码应用程序，因为它们违反了应用审核指南和开发者计划许可。这种矛盾立场反映了苹果在拥抱AI创新与维持对其平台的严格控制之间的复杂平衡。

令人惊讶的是，苹果正在积极阻止或撤回使用AI编码工具的应用程序更新，如Anything和Replit。这表明苹果对AI生成和执行代码的方式持谨慎态度，担心这些工具可能违反其应用审核指南和开发者计划许可，反映了公司对AI技术复杂性的担忧。

令人惊讶的是，尽管新应用数量激增，苹果声称其应用审核团队能够在48小时内处理90%的提交，并且在过去12周内每周处理超过20万个应用提交，平均审核时间为1.5天。这表明苹果可能已经大幅扩展了其审核能力或提高了自动化程度以应对AI带来的应用激增。

令人惊讶的是：AI在创意营销领域的能力已经达到可以瞬间生成数千个广告变体或博客帖子的程度，这展示了AI作为创意放大器的潜力。然而，最终选择仍需人类判断，这揭示了AI与人类创造力之间的互补关系。

令人惊讶的是：即使是有限需求领域，AI也能通过确定性规则实现显著效率提升。AI记账系统能够自动处理分类、对账和报税等任务，这表明即使在传统上需要人工判断的财务领域，AI也能通过标准化流程创造价值。

令人惊讶的是：风险投资被归类为有限需求且开放循环领域，这挑战了人们对VC工作性质的普遍认知。尽管AI可以分析大量数据，但投资决策仍然需要人类判断，这揭示了即使在数据驱动的行业中，人类判断力的不可替代性。

令人惊讶的是：GitHub Actions的使用量在短短两年内增长了四倍多，从2023年的每周5亿分钟激增至现在的21亿分钟。这表明自动化CI/CD流程的采用速度远超预期，反映了DevOps实践在AI时代的加速演变。

令人惊讶的是：软件开发提交量呈现爆炸式增长，从2025年的10亿个提交激增至每周2.75亿个，预计全年将达到140亿个。这种指数级增长反映了AI时代代码生成速度的惊人变化，远超线性预测。

令人惊讶的是：AI助手现在被设计有类似人类的睡眠周期，包括轻度睡眠、REM睡眠和深度睡眠，用于将短期记忆巩固为长期记忆。这一设计模仿了人类记忆形成的过程，展示了AI系统设计中越来越复杂的生物模拟元素。

令人惊讶的是：AI助手的可信度建立方式与传统认知相反 - 它们通过公开执行任务来获得信任，而不是通过解释或理论证明。这一发现揭示了AI助手采用过程中的关键心理机制，表明实际演示比理论说明更能说服人们接受AI助手。

令人惊讶的是：在Every公司，70%的员工会使用性别代词来称呼他们的AI助手。这一现象表明人们正在迅速地将AI人格化，将其视为具有性别的存在而非简单的工具，反映了人类与AI互动方式中的深层心理模式。

令人惊讶的是：Anthropic最新的Mythos模型能够自主发现所有主流操作系统和浏览器中的零日漏洞，无需人类指导。这表明AI安全能力已经达到了令人难以置信的水平，能够自主识别人类可能忽略的安全威胁，预示着AI在网络安全领域的革命性潜力。

令人惊讶的是：78%的高管想要规范影子AI使用，但只有21%的员工表示曾收到过AI政策警告，34%甚至不知道雇主批准了哪些工具。这种矛盾的管理态度反映了企业治理的严重脱节。

令人惊讶的是：高盛经济学家报告显示，正确使用AI的员工每天可节省40-60分钟，与因技术摩擦损失的时间几乎对称。这揭示了一个悖论：AI既可以是效率倍增器，也可以是生产力杀手，关键在于如何实施。

令人惊讶的是：员工每年因技术摩擦损失相当于51个工作日的时间，接近两个月的工作量，且这一数字比2025年增长了42%。这表明AI等技术工具不仅没有提高效率，反而可能成为生产力障碍。

令人惊讶的是：高管与员工之间在工具充分性认知上存在67个百分点的巨大差异。这表明管理层对员工实际工作环境和工具需求的了解严重不足，可能是导致AI采用失败的关键因素之一。

令人惊讶的是：员工与高管之间在AI信任度上存在惊人的52个百分点差距。这种巨大的信任鸿沟揭示了决策层与执行层对AI技术价值的认知差异，可能导致技术投资与实际需求严重脱节。

令人惊讶的是：超过一半的员工宁愿手动完成工作也不使用公司提供的AI工具，这一现象表明AI技术在实际应用中遇到了重大阻力。这不仅仅是技术问题，更是工作习惯和组织文化的深层次冲突。

令人惊讶的是：Meta正在转变AI竞争策略，从单纯追求模型规模转向重视效率、产品集成和分发渠道，这种战略转变反映了AI行业发展的新方向，表明未来AI竞争将更加注重实际应用和用户体验而非纯技术指标。

令人惊讶的是：Anthropic的Claude Managed Agents将AI产品从原型到生产的时间从数月缩短到几天，这种加速不仅改变了AI开发周期，还吸引了包括Notion、Rakuten等知名企业立即采用，展示了AI基础设施服务对企业AI应用的革命性影响。

令人惊讶的是：Anthropic选择不公开发布其最强大的AI模型Claude Mythos，而是通过Project Glasswing仅向特定合作伙伴提供访问权限，并投入1亿美元的使用额度，这表明AI公司开始将最前沿的模型视为受控的网络基础设施而非普通产品，反映了AI安全治理的新趋势。

令人惊讶的是：Claude Mythos不仅在高标准测试中表现出色，还能独立发现长达27年和16年的严重安全漏洞，甚至能自主链接Linux内核漏洞，展示了AI在网络安全领域的惊人能力，这种自主发现和利用漏洞的能力远超人类专家。

将AI原型到生产的时间从几个月缩短到几天是一个惊人的加速，这将彻底改变企业采用AI的方式。这种快速部署能力可能加速AI在各行业的普及，但也带来了关于AI系统安全性和治理的紧迫问题，企业需要在快速采用和确保安全之间找到平衡。

这揭示了AI行业正在从单纯追求更大模型转向更注重实用性和集成度的重要转变。Meta的战略表明，未来AI竞争的关键可能不是模型规模，而是如何将AI无缝集成到现有产品中并提高效率。这种转变可能会重塑整个AI行业的发展方向和投资重点。

这些惊人的安全漏洞发现能力表明AI已经超越了传统安全工具，能够自主发现几十年未被发现的漏洞。特别是能够自主链接Linux内核漏洞的能力，展示了AI在网络安全领域的革命性潜力，这可能彻底改变安全研究和漏洞修复的方式。

eLife Assessment

This fundamental work advances our understanding of the role of kisspeptin neurons in regulating the luteinizing hormone (LH) surge in females. The study uses cutting-edge techniques to provide compelling and rigorous data supporting a critical role of RP3V kisspeptin neurons in the neuroendocrine LH surge process. This research will be of interest to reproductive biologists and neuroscientists studying the female ovarian cycle. Continuing to examine the complexities of the LH surge and the neuronal populations involved, as done in this study, is critical for developing therapeutic treatments for women's reproductive disorders.

Joint Public Review:

Summary:

This is an excellent, timely study investigating and characterizing the underlying neural activity that generates the neuroendocrine GnRH and LH surges that are responsible for triggering ovulation. Abundant evidence accumulated over the past 20 years implicated the population of kisspeptin neurons in the hypothalamic RP3V region (also referred to as the POA or AVPV/PeN kisspeptin neurons) as being involved in driving the GnRH surge in response to elevated estradiol (E2), also known as the estrogen positive feedback. However, while former studies used cfos coexpression as a marker of RP3V kisspeptin neuron activation at specific times and found that this correlates with the timing of the LH surge, detailed examination of the live in vivo activity of these neurons before, during, and after the LH surge, remained elusive due to technical challenges. In this exciting study, Zhou and colleagues use fiber photometry to measure the long-term synchronous activity of RP3V kisspeptin neurons across different stages of the mouse estrous cycle, including on proestrus when the LH surge occurs, as well as in a well-established OVX+E2 mouse model of the LH surge. For this they used kiss-Cre female mice that were injected with a Cre-dependent AAV injection containing GCaMP6, in order to measure the neuronal activation of RP3V Kiss1 cells.

The authors report that RP3V kisspeptin neuronal activity is low on estrous and diestrus, but increases on proestrus several hours before the late afternoon LH surge, mirroring prior reports of rising GnRH neuron activity in proestrus female mice. The measured increase in RP3V kisspeptin activation is long, spanning ~13 hours in proestrus females and extending well beyond the end of the LH secretion, and is shown by the authors to be E2 dependent. In addition, an intriguing cyclical oscillation in kisspeptin neural activity every 90 minutes exists, which may offer critical insight into how the RP3V kisspeptin system operates.

The compelling methodology allowed the authors to measure RP3V neuronal activation across multiple ovarian cycles in the same mouse, which demonstrated that the timing of the LH surge is variable across cycles, even within the same mouse. In addition, the authors demonstrated using the same females, that ovariectomy resulted in very little neuronal activity in RP3V kisspeptin neurons. When these ovariectomized females were treated with estradiol benzoate (EB) and an LH surge was induced, there was an increase in RP3V kisspeptin neuronal activation, as was seen during proestrus. However, the magnitude of the change in activity was greater during proestrus than during the EB-induced LH surge. Interestingly, the authors noted a consistent peak in activity about 90 minutes prior to lights out on each day of the ovarian cycle and during EB treatment, but not in ovariectomized females. The functional significance of this consistent neuronal activity at this time remains to be determined. In summary, the data from these experiments is compelling and supports the hypothesis in the field that the RP3V kisspeptin neurons regulate the LH surge.

Strengths:

- The study is well designed, uses proper controls and analyses, has robust data, and the paper is nicely organized and written.

- The study is well done and complete, looking at neuronal activation at each stage of the ovarian cycle and then additionally, how neuronal activation in ovariectomized and ovariectomized + EB females compares to that of gonad-intact females. Though not part of this study, the comparison of neuronal activation of GnRH neurons during the LH surge to the current data was convincing, demonstrating a similar pattern of increased activation that precedes the LH surge.

- The authors provide a technical advance for the field in the ability to accurately measure RP3V kisspeptin neuron activity in actively awake, live mice for long periods of time, spanning different cycle stages. This approach offers novel and useful insights into the impact of E2 and circadian cues on the electrical activity of RP3V kisspeptin neurons.

- The within-subjects design used in these experiments is a major strength because it allowed the authors to collect data across multiple ovarian cycles, following ovariectomy, and then with EB treatment. The variability in neuronal activity surrounding the LH surge across ovarian cycles in the same animals is interesting and could not be achieved without this within-subjects design.

- The inclusion and comparison of ovary-intact females and OVX+E2 female is valuable to help test mechanisms under these two valuable LH surge conditions, and allows for further future studies to tease apart minor differences in the LH surge pattern between these 2 conditions.

- The discovery of cyclical oscillation in RP3V kisspeptin neural activity every 90 minutes is intriguing and interesting, and may offer critical insight into how the RP3V kisspeptin system operates, which can be further tested in future studies.

Weaknesses:

- LH levels were not measured in many mice or in robust temporal detail, to allow a more detailed comparison between the fine-scale timing of RP3V neuron activation with onset and timing of LH surge dynamics. While the "peak LH" occurred 3.5 hours after the first RP3V kisspeptin neuron oscillation, it is likely that LH values start to increase several hours before the peak LH, closer to when the first RP3V kisspeptin neuron activity first occurs. Therefore, the onset of the LH surge is likely to be closer to the beginning of the RP3V kisspeptin activity, but future studies are needed to study this timing.

- One minor concern is that LH levels were not measured in the ovariectomized females during the expected time of the LH surge. The authors suggest that the lower magnitude of activation during the LH surge in these females, in comparison to proestrus females, may be the result of lower LH levels. It's hard to interpret the difference in magnitude of neuronal activation between EB-treated and proestrus females without knowing LH levels. In addition, it's possible that an LH surge did not occur in all EB-treated females, and thus, having LH levels would confirm the success of the EB treatment.

- The authors nicely show that there is some variation (~2 hours) in the peak of the first oscillation in cycling proestrus females. By contrast, the small sample size for OVX+E2 females did not permit a similar rigorous analysis of temporal variability under such estrogen-controlled conditions, which will need to be studied in future projects.

Comments on revisions:

The authors have revised the manuscript adequately. There are no further recommended edits or revisions.

Author response:

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

Joint Public Review:

Weaknesses:

(1) LH levels were not measured in many mice or in robust temporal detail, such as every 30 or 60 min, to allow a more detailed comparison between the fine-scale timing of RP3V neuron activation with onset and timing of LH surge dynamics.

Please see “Recommendations for Authors” below.

(2) The authors report that the peak LH value occurred 3.5 hours after the first RP3V kisspeptin neuron oscillation. However, it is likely, and indeed evident from the 2 example LH patterns shown in Figures 3A-B, that LH values start to increase several hours before the peak LH. This earlier rise in LH levels ("onset" of the surge) occurs much closer in time to the first RP3V kisspeptin neuron oscillatory activation, and as such, the ensuing LH secretion may not be as delayed as the authors suggest.

Please see “Recommendations for Authors” below.

(3) The authors nicely show that there is some variation (~2 hours) in the peak of the first oscillation in proestrus females. Was this same variability present in OVX+E2 females, or was the variability smaller or absent in OVX+E2 versus proestrus? It is possible that the variability in proestrus mice is due to variability in the timing and magnitude of rising E2 levels, which would, in theory, be more tightly controlled and similar among mice in the OVX+E2 model. If so, the OVX+E2 mice may have less variability between mice for the onset of RP3V kisspeptin activity.

Please see “Recommendations for Authors” below.

(4) One concern regarding this study is the lack of data showing the specificity of the AAV and the GCaMP6s signals. There are no data showing that GCaMP6s is limited to the RP3V and is not expressed in other Kiss1 populations in the brain. Given that 2ul of the AAV was injected, which seems like a lot considering it was close to the ventricle, it is important to show that the signal and measured activity are specific to the RP3V region. Though the authors discuss potential reasons for the low co-expression of GCaMP6 and kisspeptin immunoreactivity, it does raise some concern regarding the interpretation of these results. The low co-expression makes it difficult to confirm the Kiss1 cell-specificity of the Cre-dependent AAV injections. In addition, if GFP (GCaMP6s) and kisspeptin protein co-localization is low, it is possible that the activation of these neurons does not coincide with changes in kisspeptin or that these neurons are even expressing Kiss1 or kisspeptin at the time of activation. It is important to remember that the study measures activation of the kisspeptin neuron, and it does not reveal anything specific about the activity of the kisspeptin protein.

Please see “Recommendations for Authors” below.

(5) One additional minor concern is that LH levels were not measured in the ovariectomized females during the expected time of the LH surge. The authors suggest that the lower magnitude of activation during the LH surge in these females, in comparison to proestrus females, may be the result of lower LH levels. It's hard to interpret the difference in magnitude of neuronal activation between EB-treated and proestrus females without knowing LH levels. In addition, it's possible that an LH surge did not occur in all EB-treated females, and thus, having LH levels would confirm the success of the EB treatment.

Please see “Recommendations for Authors” below.

(6) This kisspeptin neuron peak activity is abolished in ovariectomized mice, and estradiol replacement restored this activity, but only partially. Circulating levels of estradiol were not measured in these different setups, but the authors hypothesize that the lack of full restoration may be due to the absence of other ovarian signals, possibly progesterone.

Please see “Recommendations for Authors” below.

(7) Recordings in several mice show inter- and intra-variability in the time of peak onset. It is not shown whether this variability is associated with a similar variability in the timing of the LH surge onset in the recorded mice. The authors hypothesized that this variability indicates a poor involvement of the circadian input. However, no experiments were done to investigate the role of the (vasopressinergic-driven) circadian input on the kisspeptin neuron activation at the light/dark transition. Thus, we suggest that the authors be more tentative about this hypothesis.

Please see “Recommendations for Authors” below.

Recommendations for the authors:

(1) The study measured LH levels over time in just 5 female mice, a small sample size given the variability between mice. Having said that, n=5 is an OK starting point but the LH values are only shown for 2 mice, and there are no graphs or presentation of mean LH levels over time for all 5 mice. Figure 3 would greatly benefit from graphing and statistical analyses of the LH levels for all 5 mice (mean line graphs over time or similar). The authors report the mean "peak LH" level in the text, but it would be important to show and graph all the LH values over time (either by clock time or time relative to start of first RP3V oscillation or both), to allow the reader to compare the LH pattern to the RP3V kisspeptin neuron activity over time.

We share the Reviewer’s frustration regarding the lack of detailed LH time points to correlate with the changes in GCaMP signal. Certainly, it was our intention to do better. However, with the benefit of actually being able to monitor surge progress through RP3V neuron activity in real time, we found that frequent blood sampling could often interfere with the normal dynamic of surge activity. One some occasions, the RP3V kisspeptin neuron oscillations would stop abruptly mid- or early-surge while on others it would stop and then start again. Knowing that this was not the normal profile, we resorted to taking as few blood samples as possible, trying primarily to get what we thought might be the “peak” LH surge level. We acknowledge that this is not ideal, and leaves open the important question around the precise relationship of the beginning of RP3V kisspeptin oscillations with LH secretion. Although not answering the question directly, this was part of the motivation for the last figure which emphasizes how the RP3V kisspeptin neuron activity and GnRH neuron dendron activity are essentially identical at the time of the surge. We have re-written the relevant section of the Discussion to be more circumspect.

(2) The authors report and discuss that the peak LH value occurred 3.5 hours after the first RP3V kisspeptin neuron oscillation but it is likely, and indeed evident from the 2 example LH patterns shown in Figs 3A-B, that LH values start to increase several hours earlier, well before the peak LH. Thus, the rise in LH levels during the surge starts much closer in time to the first RP3V kisspeptin neuron oscillatory activation, which the authors don't analyze. For example, the 2nd LH value for the 2 representative mice shown in Figure 3 is notably higher than the 1st LH value of those mice, even though the peak value has not yet been attained. Even with the LH levels only being measured here every couple hours, this "first detected rise in LH" be at least be graphed and/or analyzed relative to the timing of kisspeptin neuron activity, and commented on in the Discussion.

As above.

(3) It is unclear if the variation (~2 hours) in the peak of the first oscillation in proestrus females is the same as in OVX+E2 females, or was the variability smaller or absent in OVX+E2 females versus proestrus? The variability observed in proestrus mice is likely due to variability in timing and magnitude of rising E2 levels, which would may be more tightly controlled and similar among mice in the OVX+E2 model. If so, the OVX+E2 mice might display less variability for the timing of the RP3V kisspeptin activity "onset". This measure would be important to analyze here and to discuss, given that many labs around the world often use an OVX+E2 model.

This is an interesting point given the dogma surrounding the role of the SCN in initiating the surge. Three of the five OVX+E2 mice exhibited clearly discernible GCaMP oscillations that started at approximately noon, 1pm and 2pm. While this sample is very small, it does suggest that the onset of RP3V kisspeptin neuron activity is variable as found in proestrous mice. We have indicated this cautiously given the sample size.

(4) If looking at kisspeptin immunoreactivity is problematic, is it possible to look at Kiss1 RNA levels or to look at Cre-recombinase protein levels? While the Cre-recombinase would just be a proxy for Kiss1/kisspeptin, it may result in higher expression and better co-localization with the GCaMP6s.

Yes, RNAscope would likely be the ideal method to settle this long running issue of apparently poor Kiss-cre targeting in the RP3V. Unexpectedly, however, we found that the mCherry probe bound to Kiss1 in our attempts at an RNAscope evaluation. The use of Cre as a proxy for identifying kisspeptin neurons would almost certainly generate better co-localization as Cre is being used to target GCaMP.

Minor

(1) It was not clear in the manuscript how many cells were counted or contributed to the neuronal activation data. Is it the entire population of RP3V Kiss1 cells? Just a subset? How much variability is there in the number of cells measured/counted between animals? Presumably, the brains were extracted to confirm the placement of the optic fiber. Were there neuroanatomical studies also done on these animals to confirm how many cells express GFP (GCaMP6) and the correct placement and specificity of the AAV? Is there any potential that cells in the BnST or even the ARC took up the virus and were included in these measurements?

It is very difficult if not impossible to establish just how many RP3V kisspeptin neurons contribute to the GCaMP population signal using fibre photometry. This will depend on levels of AAV transfection, distance from the optic fibre, and the numbers of RP3V kisspeptin neurons actually involved in the surge mechanism. Of note, C-Fos data suggest that only around one-third of RP3V kisspeptin neurons are activated at the time of the surge. All fibre placements were subsequently shown to be running alongside GCaMP-expressing AVPV/anterior periventricular nucleus cells (now noted), but the numbers of transfected cells were not quantified. As shown in Fig.4, the GCaMP signal was very similar across all mice suggesting little variation in the relationship between transduction, fibre placement and distance.

The RP3V region is approximately 4-5 mm from the ARN. We felt that the possibility that an AAV injection in the RP3V would spill over into the ARN was so remote that we did not assess GCaMP expression in ARN kisspeptin neurons. We have previously determined for the ARN that recordable GCaMP fluorescence only occurs if the optic fibre is within 0.5 mm from GCaMP-expressing neurons. Ultimately, proof that we are not recording from ARN kisspeptin neurons comes from the very different activity patterns reported here for RP3V neurons compared to the kisspeptin pulse generator. We did not see any GCaMP expressed in the BNST.

(2) If it is possible to measure LH levels in the EB surge animals, it would be helpful, at least to confirm that they did surge and to support the proposed idea that LH surge levels are lower in that model.

Unfortunately, as acknowledged in the original text we did not take blood samples from these mice so do not have the data. However, as noted, other studies undertaken by us using the same EB surge paradigm show that peak LH levels are much lower compared to proestrus. In retrospect we do agree that this would have been useful and particularly to establish whether each mouse did show a surge as two of the OVX+EB mice failed to show typical surge-associated oscillations. We have noted this in the Discussion.

(3) For Figure 4F, please add a gray shaded box to the graph to denote the "dark" period (lights off), as was done for Figures 2 and 3. This is important because Figure 4F is making the point that there is a consistent 90-minute oscillation event right before lights off, so it would be helpful to denote the period of lights off on the graph.

There was in fact a very light grey shade, but we have now added a grey bar to make the dark period clearer.

(4) The Title of the paper should include the brain region because this is specifically the RP3V (or preoptic area "POA") kisspeptin neurons that are studied, not other kisspeptin cell populations.

We have added “preoptic area” to clarify

(5) The graphs in Figure 3C-D are from different mice and address a different question than the graphs in Figure 3A-B. This was a bit confusing, and it is recommended that the LH + RP3V kisspeptin activity experiment (Figures 3A-B) be its own figure, and the graphs looking at the detailed oscillatory patterns in Figures 3C-D be their own figure, as the latter are addressing a different question and don't have any LH data.

We have split the figure as requested.

(6) The tiny font size of the X and Y axes of Figures 2 and 3 is very small and hard to read. Can this text please be increased in size a little? By comparison, the font size of the X and Y axes of Figure 4 is bigger and more legible.

Changed.

(7) In the methods for fiber photometry, there is a sentence saying "Twenty two-hour recordings were made..." This was confusing, as it read as if there were twenty 2-hour recordings, when in fact it was one 22-hour recording. The authors should reword or use "22-hour" in this sentence.

Changed.

(8) It's a bit hard to see the difference in color between proestrus 1 and proestrus 2 (both blues) in Figure 6, especially when they overlap. It might be helpful to select a different color for one of them.

Changed.

(9) Is the virus from Addgene or just the plasmid? Did Addgene insert the plasmid into the virus, or was that done elsewhere? For purposes of replication, it might be helpful to state the plasmid that was used and the virus that was used, and their origins (e.g., if made by Addgene or donated by another investigator). I was not able to find the virus based on the Addgene number in the manuscript and was getting plasmids with different Addgene #s.

Apologies, the numbering was incorrect. We have now amended to 100842-AAV9 that was packaged by Addgene.

The bill of lading can also function as collateral against which funds may be advanced to the exporter by its local bank before or during shipment and before final payment by the importer.

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Payment is made, OR

A promise to pay (draft acceptance) is given

Exporter gets a banker’s acceptance worth $10,000 (due in 60 days)

Instead of waiting 60 days, they sell it to a bank for: $9,800 today

The bank waits 60 days and receives: $10,000 👉 Bank profit = $200

🧠 Why does this happen? Exporter wants cash now Bank is willing to wait and earn interest

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eLife Assessment

This important study tackles an interesting aspect of fungal physiology: how a mitochondria-associated gene influences production of the secondary metabolite DON and fungicide sensitivity. The authors have improved the manuscript and the supporting evidence is convincing.

Reviewer #2 (Public review):

Summary:

The manuscript entitled "Mitochondrial Protein FgDML1 Regulates DON Toxin Biosynthesis and Cyazofamid Sensitivity in Fusarium graminearum by affecting mitochondrial homeostasis" identified the regulatory effect of FgDML1 in DON toxin biosynthesis and sensitivity of Fusarium graminearum to cyazofamid. The manuscript provides a theoretical framework for understanding the regulatory mechanisms of DON toxin biosynthesis in F. graminearum and identifies potential molecular targets for Fusarium head blight control.

Comments on revised version:

I have no further comments on the revision.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

In their study the authors investigated the F. graminearum homologue of the Drosophila Misato-Like Protein DML1 for a function in secondary metabolism and sensitivity to fungicides.

Strengths:

Generally, the topic of the study is interesting and timely and the manuscript is well written, albeit in some cases details on methods or controls are missing.

Weaknesses:

However, a major problem I see is with the core result of the study, the decrease of the DON content associated with deletion of FgDML1: Although some growth data are shown in figure 6 - indicating a severe growth defect - the DON production presented in figure 3 is not related to biomass. Also, the method and conditions for measuring DON are not described. Consequently, it could well be concluded that the decreased amount of DON detected is simply due to a decreased growth and specific DON production of the mutant remains more or less the same.

To alleviate this concern, it is crucial to show the details on the DON measurement and growth conditions and to relate the biomass formation on the same conditions to the DON amount detected. Only then a conclusion as to an altered production in the mutant strains can be drawn.

We appreciate it very much that you spent much time on my paper and give me good suggestions, we tried our best to revise the manuscript. The point to point responds to the reviewer’s comments are listed as following.

Comments to the revised manuscript:

The authors carefully revised the manuscript and provided explanations for methods in several cases. However, there are still some problems - probably due to misunderstanding - that need revision.

(1) A major problem of the first version of the manuscript was the lack of appropriate description of biomass analysis and the consideration of the respective results for evaluation of production of DON and other metabolites. Although the authors provide some explanation in the response to reviews, I could not find a corresponding explanation or description in the manuscript. It is not sufficient to explain the problem to me, but a detailed explanation and description of the method has to be provided in the manuscript along with the definition of one "unit of mycelium". It is still not entirely clear to me what such a "unit of mycelium" is.

Please clarify this and any other uncertainties that were commented on by me and other reviewers in the manuscript, not only in the response to reviews. Also adjust the reference list accordingly.

Thank you very much for your advice. We appreciate the reviewer’s continued attention to the potential impact of biomass differences on DON production, particularly in light of the reduced growth rate observed in the mutant strain.

We acknowledge that the mutant exhibits slower growth compared to the wild-type strain. However, it is important to emphasize that the reduction in DON levels reported in this study cannot be attributed to decreased fungal biomass. In our experimental design, DON production was normalized to mycelial dry weight, and toxin levels are expressed as μg DON per g dry mycelium. Therefore, differences in total mycelial accumulation among strains were explicitly accounted for and eliminated during data analysis.

By expressing DON production on a per-unit-biomass basis, the measured values reflect the intrinsic DON biosynthetic capacity of the mycelium rather than the overall growth rate or total biomass. Consequently, the observed reduction in DON content in the mutant indicates a genuine impairment in DON biosynthesis per unit of fungal biomass, rather than a secondary effect resulting from reduced mycelial growth.

To avoid ambiguity, we have clarified this point in the revised manuscript by explicitly stating the normalization strategy and the definition of the mycelial unit in the Materials and Methods section, and by emphasizing in the Results/Discussion section that DON levels were compared on a biomass-normalized basis.

We hope that this clarification adequately addresses the reviewer’s concern and clearly distinguishes growth-related effects from alterations in toxin biosynthesis.

“DON toxin was measured using a Wise Science ELISA-based kit (Wise Science, Jiangsu, China) (Li et al., 2019; Zheng et al., 2018). Under toxin-producing conditions (28 °C, 145 rpm), fungal strains were cultured in TBI medium for 7 days. Cultures were initiated using freshly grown mycelia. After incubation, mycelia and culture filtrates were separated by filtration. The culture filtrates were collected for DON determination, while the mycelia were harvested for biomass analysis. The collected mycelia were washed with sterile distilled water and dried at 60 °C to constant weight. The dry weight of mycelia was recorded and used for normalization of DON production. One mycelial unit was defined as 1 g of dry mycelial biomass. DON concentration in the culture filtrates was quantified using an enzyme-linked immunosorbent assay (ELISA). Briefly, 50 μL of culture filtrate or DON standard solution was added to wells of a 96-well microplate pre-coated with DON antigen, followed by the addition of enzyme conjugate and antibody working solution according to the manufacturer’s instructions. After incubation and washing, color development was achieved using substrate solution and terminated by stop solution. Absorbance was measured at 450 nm using a microplate reader. A standard curve was generated using log₁₀-transformed DON concentrations of the standards and the corresponding percentage absorbance values. DON concentrations in the samples were calculated based on the standard curve. Total DON production was calculated according to the culture volume (30 mL) and subsequently normalized to mycelial dry weight. DON production was expressed as μg DON per g dry mycelium. Each treatment group contains three biological replicates and three technical replicates.”

(2) Another problem was, that the authors considered FgDML1 a regulator of DON production. As mentioned by me and reviewer 3, FgDML1 is crucial to numerous functions in F. graminearum and its lack causes a plethora of problems for fungal physiology. Hence, although it is clear that the lack of FgDML1 causes alterations in DON production, it is not appropriate to designate this factor as a "regulator".

It seems to me that the authors are afraid that if FgDML1 would not be a "regulator" that this would decrease the value of their study, which is not the case. This is a matter of correct wording. Therefore, please revise the wording accordingly, starting with the title:

...FgDML1 impacts DON toxin biosynthesis...

Moreover, for sure the manuscript might benefit from more detailed description of the whole cascade leading from FgDML1 to DON biosynthesis and production of the other metabolites that change upon deletion. Such explanation can help the reader grasp the relevance of FgDML for regulatory processes as well as on more general versus specific effects.

Thank you very much for your advice. We fully agree that, given the pleiotropic functions of FgDML1 in F. graminearum and the broad physiological defects caused by its deletion, it is not appropriate to designate FgDML1 as a direct or specific “regulator” of DON biosynthesis.

We acknowledge that the use of the term “regulator” in the previous version was imprecise. Following the reviewer’s suggestion, we have revised the wording throughout the manuscript to more accurately reflect the role of FgDML1. Specifically, we now describe FgDML1 as a factor that impacts or affects DON toxin biosynthesis rather than directly regulating it. The title has been revised accordingly to read:

“Mitochondrial protein FgDML1 impacts DON toxin biosynthesis and cyazofamid sensitivity in F. graminearum by affecting mitochondrial homeostasis”

Importantly, we would like to emphasize that our intention was not to overstate the specificity of FgDML1 in DON regulation, but rather to highlight its influence on secondary metabolism in the context of its broader biological functions. To address this more clearly, we have expanded the Discussion section to provide a more detailed and cautious interpretation of the potential cascade linking FgDML1 deletion to altered DON biosynthesis and changes in other metabolites.

'Secondary metabolite biosynthesis is generally regarded as an energy-intensive process that is tightly coupled to cellular energy metabolism. ATP serves as the primary energy currency supporting enzymatic reactions, macromolecule synthesis, and subcellular organization required for secondary metabolism. Disruption of ATP generation has been shown to directly impair toxin biosynthesis: for example, silencing of ATP synthase subunit α (AtpA) significantly reduces ATP synthesis and inhibits the production of the TcdA and TcdB toxins(Marreddy et al., 2024). Similarly, in plants, ATP depletion leads to a metabolic shift in which growth and basic physiological processes are prioritized at the expense of energetically costly secondary metabolites, including toxins(Xiao et al., 2024). Together, these findings highlight ATP availability as a key determinant of secondary metabolite production across biological systems.

In filamentous fungi, mitochondria play a central role in sustaining cellular ATP levels through oxidative phosphorylation and are therefore critical for biosynthetic and stress-adaptive processes. In F. graminearum, mutants defective in mitochondrial components, such as the voltage-dependent anion channel (mitochondrial porin), exhibit aberrant mitochondrial morphology, reduced ATP production, and markedly decreased DON accumulation and virulence (Han et al., 2022). These observations establish a direct link between mitochondrial energy metabolism and secondary metabolite output, supporting the notion that intact mitochondrial function and adequate ATP supply are prerequisites for robust DON production.

Consistent with this energy-dependent framework, biosynthesis of the mycotoxin DON in F. graminearum requires substantial ATP input. In the present study, ATP content in the ΔFgDML1 mutant was significantly lower than in the wild-type PH-1 and the complemented strain ΔFgDML1-C, and DON production was concomitantly reduced (Fig. 4A). Importantly, DON levels were normalized to mycelial dry weight, indicating that the observed reduction reflects a decreased biosynthetic capacity per unit biomass rather than a secondary consequence of reduced fungal growth. This distinction demonstrates that impaired DON production in the ΔFgDML1 mutant arises primarily from metabolic limitations.

At the cellular level, ATP depletion compromises multiple energy-dependent steps required for DON biosynthesis. The formation of toxisomes, which are specialized subcellular structures responsible for the spatial organization of DON biosynthetic enzymes, is essential for efficient mycotoxin production and is an ATP-dependent process. Reduced ATP levels disrupt toxisome assembly, and accordingly, the ΔFgDML1 mutant was unable to form functional toxisomes (Fig. 4C). In parallel, western blot analysis revealed a marked reduction in the abundance of the DON biosynthetic enzyme FgTri1 (Fig. 4D). In addition, ATP-dependent processes are directly involved in the biogenesis of the DON biosynthetic machinery: the ATPase activity of myosin I (FgMyo1) is required for efficient translation of key DON biosynthetic enzymes, and disruption of its ATPase function results in reduced DON production(Tang et al., 2018). These findings further underscore the dependence of DON biosynthesis on cellular energy status.

DON production is also regulated at the transcriptional level by the TRI gene cluster, with Tri5 and Tri6 serving as core components of the biosynthetic pathway. Tri5 encodes trichodiene synthase, which catalyzes the first committed step of DON biosynthesis. In the ΔFgDML1 mutant, expression levels of FgTri5 and FgTri6 were significantly downregulated (Fig. 4B), suggesting that impaired energy metabolism indirectly affects transcription of DON biosynthetic genes. Although no direct regulatory role of DML family proteins in gene expression has been reported in Saccharomyces cerevisiae or Drosophila melanogaster, their established functions in cell division and microtubule organization raise the possibility that FgDML1 indirectly influences gene expression through effects on chromatin organization or cell-cycle progression(Schulze and Wallrath, 2007).

In addition to reduced ATP levels, deletion of FgDML1 resulted in a significant decrease in acetyl-CoA content (Fig. 5C), a key precursor for trichothecene biosynthesis. Acetyl-CoA links central carbon metabolism with secondary metabolite production, and its depletion further constrains DON biosynthesis by limiting substrate availability. Broader metabolomic studies support this relationship, showing that perturbations in TCA cycle intermediates and central carbon metabolism are closely associated with altered DON production, reinforcing a mechanistic linkage between energy generation and toxin biosynthesis(Atanasova-Penichon et al., 2018).

“Taken together, these results support a model in which FgDML1 influences DON production indirectly by maintaining mitochondrial energy metabolism. Reduced ATP availability in the ΔFgDML1 mutant restricts energy-dependent biosynthetic processes, disrupts toxisome formation, diminishes DON biosynthetic enzyme abundance and gene expression, and limits precursor supply, ultimately leading to a substantial reduction in DON biosynthesis that is independent of fungal biomass effects.” (in L284-350). In this revised discussion, we explicitly distinguish between general physiological effects caused by the loss of FgDML1 and more specific consequences on secondary metabolic pathways.

We believe that this revised wording and the expanded mechanistic discussion more accurately reflect the biological role of FgDML1 and improve the conceptual clarity of the manuscript, without overstating its function as a dedicated regulator of DON production.

Reviewer #2 (Public review):

Summary:

The manuscript entitled "Mitochondrial Protein FgDML1 Regulates DON Toxin Biosynthesis and Cyazofamid Sensitivity in Fusarium graminearum by affecting mitochondrial homeostasis" identified the regulatory effect of FgDML1 in DON toxin biosynthesis and sensitivity of Fusarium graminearum to cyazofamid. The manuscript provides a theoretical framework for understanding the regulatory mechanisms of DON toxin biosynthesis in F. graminearum and identifies potential molecular targets for Fusarium head blight control. The paper in innovative, but there are issues in the writing that need to be added and corrected.

Comments on revisions:

The author has addressed my questions.

We appreciate it very much that you spent much time on my paper and give me good suggestions.

Dit stukje is heel goed bas

eLife Assessment

This important study provides convincing data suggesting that subcellular localization of the spatial regulator of cell division, MinD, is an intrinsic feature of the protein's ability to associate with the membrane as both a dimer and a monomer. These findings distinguish the behavior of MinD in B. subtilis from its counterpart in E. coli and suggest that there is not a need to invoke additional localization factors. The reviewers felt that the revisions, particularly the additional experiments and changes to the text to make the experimental design and conclusions clearer, improve the quality of the manuscript and will increase its impact.

Reviewer #1 (Public review):

Summary:

In this work the authors investigate the molecular dynamics of MinD, a component of the Bacillus subtilis Min system, in vitro and in vivo. In Escherichia coli the Min system is highly dynamic and displays rapid pole to pole oscillation whereby a time average minimum of the Min proteins at mid cell is established. However, in B. subtilis, this is not the case, and there is no MinE present. MinD in B. subtilis dynamically relocalizes from the poles to division sites, and binds to MinC and MinJ, which mediates its interaction with DivIVA. This paper reports biochemical characterization of B. subtilis MinD in vitro and dynamics of MinD variants in vivo, providing mechanistic insight into the mechanism of dynamic localization.

Strengths:

In the current study, the authors perform a detailed biochemical characterization of the in vitro ATPase activity of MinD and demonstrate that rapid hydrolysis is elicited by adding phospholipids. They further show using a collection of substitution mutants of MinD that both monomers and dimers bind to the membrane, and ATP occupancy changes the on and off rates. Identification, quantification, and tracking of discrete Halo-MinD populations was nicely done and showed that mutations in MinD alter dynamic localization, correlating with PL binding on and off rates in vitro.

- In the revised manuscript, the authors now demonstrate localization and tracking data for minC and minJ deletion strains, which suggest that MinJ impacts MinD membrane cycling, but MinC does not. Additional in vitro work showed that the PDZ domain of MinJ modifies MinD ATP hydrolysis rates, and the authors propose that MinJ may promote MinD dimer formation.

Weaknesses of the revised version: No major weaknesses.

Reviewer #2 (Public review):

Summary:

Feddersen & Bramkamp determined important characteristics of how MinD protein binds/dissociates to/from the membrane, and dimerizes in relation to its ATPase activity. The presented data clearly shows the differences in function of MinD homologs from B. subtilis and E. coli.

Strengths:

The work presents well-executed experiments that lead to interesting conclusions and a new model of how Min system works during B. subtilis mid-cell division. Importantly, this model is supported by in vitro characterization of well-chosen mutants in the functional domains of MinD. Outstandingly, most of the in vitro data are confirmed by single-molecule localization microscopy.

Weaknesses:

The authors immobilized liposomes, for which they used E. coli total lipids, to measure ATPase activity and liposome association and dissociation of B. subtilis MinD. For these experiments would be more suitable to use B. subtilis total lipids as more biologically relevant data could be gained.

Although the work is in detail and nicely compares the function of B. subtilis Min system with E. coli Min system, it lacks the comparison of the Min system function in other rod-shaped Gram-positive bacteria. I would suggest including in the Discussion the complexity of other Min systems. Especially, this complexity is seen in other rod-shaped and spore formers such as Clostridial species in which one of these Min systems or both are present, an oscillating E. coli Min system type and more static as in B. subtilis.

Comments on revisions:

I'm satisfied with the authors response to my private recommendation points. However, I thought that they would also respond to my points mentioned in Public Review part, weaknesses as shown above and update the revised version accordingly.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this work, the authors investigate the molecular dynamics of MinD, a component of the Bacillus subtilis Min system, in vitro and in vivo. In Escherichia coli the Min system is highly dynamic and displays rapid pole-to-pole oscillation whereby a time average minimum of the Min proteins at mid-cell is established. However, in B. subtilis, this is not the case, and there is no MinE present. MinD in B. subtilis dynamically relocalizes from the poles to division sites and binds to MinC and MinJ, which mediates its interaction with DivIVA. This paper reports the biochemical characterization of B. subtilis MinD in vitro and dynamics of MinD variants in vivo, providing mechanistic insight into the mechanism of dynamic localization.

Strengths:

In the current study, the authors perform a detailed biochemical characterizion of the in vitro ATPase activity of MinD and demonstrate that rapid hydrolysis is elicited by adding phospholipids. They further show using a collection of substitution mutants of MinD that both monomers and dimers bind to the membrane, and ATP occupancy changes the on and off rates. Identification, quantification, and tracking of discrete Halo-MinD populations were nicely done and showed that mutations in MinD alter dynamic localization, correlating with PL binding on and off rates in vitro.

Weaknesses:

While the study shows that MinD in B. subtilis utilizes a different (MinE-independent) activation mechanism, it remains to be determined the extent to which MinJ and/or MinC play a role.

Reviewer #2 (Public review):

Summary:

Feddersen & Bramkamp determined important characteristics of how MinD protein binds/dissociates to/from the membrane, and dimerizes in relation to its ATPase activity. The presented data clearly shows the differences in function of MinD homologs from B. subtilis and E. coli.

Strengths:

The work presents well-executed experiments that lead to interesting conclusions and a new model of how Min system works during B. subtilis mid-cell division. Importantly, this model is supported by in vitro characterization of well-chosen mutants in the functional domains of MinD. Outstandingly, most of the in vitro data are confirmed by single-molecule localization microscopy.

Weaknesses:

The authors immobilized liposomes, for which they used E. coli total lipids, to measure ATPase activity and liposome association and dissociation of B. subtilis MinD. For these experiments would be more suitable to use B. subtilis total lipids as more biologically relevant data could be gained. Although the work is in detail and nicely compares the function of B. subtilis Min system with E. coli Min system, it lacks the comparison of the Min system function in other rod-shaped Gram-positive bacteria. I would suggest including in the Discussion the complexity of other Min systems. Especially, this complexity is seen in other rod-shaped and spore formers such as Clostridial species in which one of these Min systems or both are present, an oscillating E. coli Min system type and more static as in B. subtilis.

Reviewer #3 (Public review):

Experimentally, this study provides sufficient data to support the authors' conclusion that MinD dimerization but not ATPase activity is both necessary and sufficient for concentrating it and its binding partner, the division inhibitor MinC, at cell poles. Biochemical data appears to be rigorously acquired and includes proper controls. Although cytological data are consistent with the authors' model, quantitative information on MinD localization in a statistically relevant set of cells is missing (e.g. Figure 2B).

The study's other major conclusion, as outlined in their discussion, that a reaction-diffusion model explains MinD localization in wild-type cells, is unsubstantiated. If they would like to make this a major conclusion of the final manuscript, they will need to include modeling that takes into account biochemical and cytological data. From a presentation perspective, the manuscript is challenging to read and will require substantial rewriting and revision prior to publication.

We thank the reviewers for their detailed and constructive comments on our work. We particularly acknowledge that the initial version of our manuscript was difficult to read and might have provoked the impression that the aim was to formulate a new mathematical model of Min dynamics in B. subtilis. However, our work aimed at providing solid (and first) biochemical evidence for the MinD ATPase cycle and the nature of the ATPase stimulation. Furthermore, we aimed at corroborating the in vitro findings with single-molecule microscopy data that provided a detailed in vivo picture of the Min dynamics in living cells. Together, this work combines for the first time in vitro and single-molecule in vivo data. During the revision, we generated a wealth of new data that aimed at unraveling the potential effects of MinC and MinJ on MinD dynamics. A major problem during the revision was the problematic purification of MinJ. The membrane integral MinJ has been shown to be highly susceptible to proteolytic decay during purification attempts. Despite various attempts we did not succeed in the purification of full length MinJ. These efforts also led to the unusual long revision time. We therefore turned to the purification of the soluble part of MinJ, namely the PDZ domain. The revised work now contains in vitro data showing the impact of MinC and MinJ-PDZ on MinD ATPase activity and membrane binding. Furthermore, we now provide single-molecule tracking data of MinD in minC and minJ deletion mutant backgrounds. Importantly, the new data show that MinC has no effect on MinD activities, while the PDZ domain has a mild stimulating effect on MinD´s ATPase activity. In summary, a detailed picture on how MinD dynamics function mechanistically in B. subtills emerges.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) It is important to evaluate MinD ATPase activity, PL binding, and release in the presence of MinC and MinJ. In E. coli, MinD recruits MinC to phospholipids. The presence of MinC could change the on/off rates. It is unknown if MinC or MinJ could alter the ATPase rates or dynamics. Presuming that MinD alone drives the complete dynamic story because stimulation is observed in vitro with phospholipids, it follows that Michaelis Menten kinetics is insufficient. It is acknowledged that MinJ is difficult to purify, but one could test a small cytoplasmic subdomain or MinJ-enriched membranes for MinD recruitment and release.

Indeed, it is unknown whether MinC or MinJ have an impact on the ATPase rates or protein dynamics of MinD in B. subtilis. To address the potential influence of MinC and MinJ on MinD’s ATPase activity and dynamics, we conducted a series of experiments. MinC was successfully purified, and subsequent BLI and ATPase assays revealed no significant impact on MinD activity in our system, except for a modestly reduced ATPase activity (Figure S 5).

With regard to MinJ, multiple constructs and purification strategies were attempted. While full-length MinJ could not be purified, we isolated the C-terminal PDZ domain to probe potential interactions. In ATPase assays, the PDZ domain reproducibly increased MinD ATP hydrolysis rates, whereas BLI measurements did not reveal detectable changes in MinD membrane-binding kinetics under these conditions. We agree with the reviewer that membrane-integrated MinJ could exert additional effects on MinD recruitment or release that are not captured by the isolated PDZ domain, and we now discuss this limitation in the revised Discussion.

Furthermore, we performed single-molecule localization and tracking analyses of MinD in ∆minC and ∆minJ backgrounds. These experiments, found in a newly added Results section and summarized in Fig. S 12, demonstrate that MinJ appears to play a role in maintaining dynamic MinD membrane cycling and preventing excessive confinement or aggregation, whereas MinC has no obvious effect on MinD dynamics.

(2) It is important to show the reduced ATP hydrolysis by MinD mutant proteins (line 243). Stating that they are catalytically inactive without showing the data is presumptuous, and there may be differences between the mutants. Although I am sure that the authors evaluated activity with phospholipids, it should be shown.

We have now quantified the ATPase activity for all MinD mutants from the respective EnzChek assay data. These experiments confirm that the G12V, K16A, and D40A mutations effectively abolish catalytic activity, yielding phosphate release rates that are essentially at the background detection limit of the assay. We have included these data in Figure S 7 C and updated the text to reflect these findings.

(3) The shoulder on MinD-K16A suggests that it is capable of forming a dimer at low equilibrium. The suggestion that it is due to interaction with the inert SEC matrix (line 242) raises more concerns, although this is highly unlikely, given that G12V elutes as a single peak. The possibility of a dimer here also demonstrates the necessity of reporting precise ATPase rates for the mutants.

Thank you for this comment. Since we shared some of your concerns, we made sure to gather enough evidence before making the respective claims. We conducted both in vivo (single-particle tracking, widefield microscopy) experiments and in vitro experiments with the respective K16A mutant of MinD. Most convincingly, K16A is completely catalytically inactive (see previous answer), while both positive and negative controls behave as expected. Both in vivo and in vitro experiments suggest that the protein still binds membrane despite not being able to form dimers. Similar observations were made in a study conducted by colleagues in parallel (Bohorquez et. al, 2024). Furthermore, K16A exchanges in other Walker motif-containing proteins, including E. coli MinD and RecA, and B. subtilis ParA/Soj, abolish dimer formation completely.

There are many possible explanations why the observed shoulder during elution could appear, which we did not spell out in the results section. This includes possible conformational heterogeneity, as the protein may adopt multiple stable or semi-stable conformations that slightly differ in hydrodynamic volume. It is also possible, that the shoulder represents small protein aggregates from degradation products or proteolysis, which we indeed observe in the respective SDS-PAGE/Blot (Fig. S6). As written in the text, interactions with the SEC column through e.g. hydrophobic patches sticking out is not uncommon, as the surface charges of the mutant protein is different to the wild type version. On the same note, the buffer may subtly affect the surface properties like charge and hydrophobicity differently to the wild type protein and thus its interaction with the column. In conclusion, we are confident that the orthogonal methods used point towards dimer abolishment in a K16A mutant of MinD, despite displaying a small shoulder during SEC elution.

(4) BLI data - were the kon and koff rates also determined without ATP, since it is assumed that MinD-K16A does not bind ATP, but has a strong Kd (Table 1). Does ATP modify Kd of wt MinD for PLs?

Without ATP, MinD did neither properly interact with the sensor-bound liposomes nor follow a regular binding kinetic. Therefore, kinetic constants could not be determined, as the fitting of the curves is not possible. In addition to the respective figure (Fig. S8), we attached the graph of the raw/unfitted data in the supplement (Fig. S 13)- (MinD2 dataset)).

(5) Local MinJ interactions are proposed to alter the dynamic localization of MinD wt and variants in vivo (line 349-358), which could occur through regulation of ATP hydrolysis, PL binding, or release by MinJ or MinC. Localization dynamics should be measured in minC and minJ mutant strains.

We thank the reviewer for this important suggestion. In response, we have now directly measured MinD localization dynamics in both ∆minC and ∆minJ backgrounds. We performed single-molecule localization microscopy (SMLM) and single-molecule tracking (SMT) of Halo-MinD expressed from its native locus in these mutant strains, using the same experimental and analytical pipeline applied throughout the study. These new experiments are presented in a newly added Results section and summarized in Figure S12, where we quantitatively compare MinD localization, mobility, diffusion states, and confinement between wild type, ∆minC and ∆minJ cells. The data show that deletion of minJ leads to a pronounced increase in the confined/static MinD fraction and reduced dynamic cycling, whereas deletion of minC causes only subtle changes in MinD dynamics. These findings support a specific role of MinJ in maintaining dynamic MinD membrane cycling in vivo, while MinC has a more modest modulatory effect. We have integrated these results into the Discussion to refine our model of how MinJ and MinC differentially influence MinD dynamics and localization.

(6) Considering the single molecule population counting and a lack of error presented for the binning of tracks (confined/slow/fast); it is difficult to rationalize why G12V and K16A are defective. The relative proportions of confined/slow/fast between wt, G12V, and K16A seem quite similar (i.e., bubble plot). And the static localization in Fig. 2B does not seem dramatically perturbed. This seems to invoke other cellular regulators as critical for the system's operation in the cell, further pointing to important regulatory roles by MinJ and/or MinC.

First, regarding the apparent lack of error estimates for the population binning, the uncertainties associated with the SMT-based population fitting are intrinsically very small and fall below the graphical resolution of the plots. This reflects the large number of tracks analyzed and the robustness of the fitting procedure, rather than an omission of error analysis.

Second, we respectfully disagree that the diffusion-state distributions and static localization patterns of G12V and K16A are similar to those of the wild type. In the context of SMT data, the observed shifts in population sizes are substantial and biologically meaningful. Moreover, the static localization of these mutants is markedly altered: instead of forming a graded enrichment at poles and septa, both mutants display a uniform membrane distribution, similar to e.g. a membrane stain (also see Fig. 2 B). This indicates a loss of regulated recruitment, consistent with impaired interaction with MinJ. Importantly, our biochemical analyses, together with extensive data on conserved Walker-type ATPases carrying analogous G12V and K16A mutations, strongly support the conclusion that these variants are functionally defective despite retaining membrane association.

Third, we agree about the importance of MinC and MinJ, and have now directly tested the contribution of these interactors by analyzing MinD dynamics in ∆minC and ∆minJ backgrounds. These new data, presented in a newly added Results section and summarized in Fig. S12, support our interpretation by showing that MinJ has a pronounced effect on MinD confinement and dynamic cycling in vivo, whereas MinC has a more modest influence. Together, these findings reinforce the conclusion that the defects of G12V and K16A arise from impaired regulatory cycling through the mutations, but also through impaired interaction with MinJ.

(7) Interesting that they stored the His-MinD protein at 4C for up to one week and not at -80C as it was in 10% glycerol. Was MinD inactivated by freezing? Did this contribute to the observed aggregation (line 695)?

We thank the reviewer for raising this point. Prior to this comment, we routinely worked with freshly purified MinD and therefore had not systematically compared storage at 4 °C and -80 °C. In response to the suggestion, we have now directly compared the activity of MinD stored at 4 °C for one week with that of MinD stored at -80 °C for four weeks. We did not observe any significant difference in ATPase activity or overall biochemical behavior between the two storage conditions. These results indicate that freezing does not inactivate MinD and that the aggregation observed in some preparations is unlikely to be caused by storage at 4 °C. We have clarified this point in the materials and methods part of the manuscript and thank the reviewer for prompting this.

(8) Line 109 - Type. Change "component" to "components".

(9) Page 4, line 52 change 'machinery' to ‘machine'.

(10) Page 13, line 248, changed 'manifested' to 'displayed'.

Thank you for pointing out these typos, which have all been corrected.

Reviewer #2 (Recommendations for the authors):

I suggest making changes to sentence Lines 60-62: "In rod-shaped model bacteria like Escherichia coli and Bacillus subtilis, division site selection is governed by two protein systems (15-17): nucleoid occlusion and the Min system." However, it was shown previously that the deletion of both systems in B. subtilis, division site selection wasn't disturbed and other mechanism was suggested to be involved.

We agree that this information should be part of the introduction. Therefore, we included the following sentence at the indicated position:

“However, it was previously shown that simultaneous deletion of both systems in B. subtilis did not disturb division site selection, suggesting additional mechanisms to be involved (Rodrigues and Harry, 2012).”

I suggest changing sentence Lines 85-86: "Dimerized MinD recruits MinC and activates it to prevent FtsZ dynamics (46)". It would be more precise to say: "Dimerized MinD recruits MinC and activates it to inhibit FtsZ oligomerization (46).

Thank you, we agree and changed the sentence accordingly.

In Figure S2 mark the two mentioned peaks 31 and 62 kDa to which elution volumes correspond.

We thank the reviewer for this point. We ran the standards for this column again and fitted them to our peaks (see updated Fig. S2), now demonstrating that the shoulders are indeed not at a size where dimers would elute but rather around ~44.3 kDa. We note that both the Ni-NTA eluate and SEC fractions contain multiple His-tagged degradation products (see revised Fig. S2 and His-MinD blot in Fig. S1). Because the SEC run was performed with excess ADP to suppress ATP-dependent dimerization, we interpret the minor shoulder at ~44.3 kDa as arising from sample heterogeneity due to these degradation products, either by co-elution of fragments or by transient fragment:full-length MinD assemblies, rather than full-length MinD dimerization. This is now also described in the respective Results section.

Reviewer #3 (Recommendations for the authors):

The quality of the written manuscript is poor, making it difficult to read and appreciate. Specifically: The introduction is quite long. It takes almost three pages until the primary objective of the paper, identifying determinants of MinD localization in B. subtilis, is clearly stated. The introduction should be shortened to focus specifically on Min system function across species-i.e. prevent aberrant polar septation events. Three or four paragraphs should be sufficient. E.g. 1. Introduction to Min systems generally, 2. A summary of the mechanism underlying MinD oscillation in E. coli, 3. An explanation of similarities and differences between E. coli and B. subtilis, and 4. A paragraph outlining the specific questions to be addressed in this study.

We have substantially revised the Introduction to address this concern. The revised version is considerably shorter and more focused, and now follows the structure proposed by the reviewer. As a result, the main objective of the manuscript is now stated much earlier, and the overall readability and clarity of the Introduction have been substantially improved.

The results section is challenging to read, in part due to the inclusion of methods as well as some issues with organization. For example, this section begins with a single sentence describing the need to investigate MinD's ATPase cycle in vitro. This sentence is followed by a header and an entirely new section describing the methods used to purify MinD for biochemical analysis. These details should be in the methods section. Similarly, the first paragraph of the following section, which focuses on the ATPase activity MinD in the presence and absence of liposomes, describes how the commercially available EnzChek phosphate assays works. This is, again, something that belongs in methods, not results.

We have revised the Results section extensively in response to this comment. In the revised manuscript, we have removed or relocated substantial methodological detail from the Results to the Methods section and streamlined the overall organization. Descriptions of protein purification procedures and standard assay principles, including details of the EnzChek phosphate assay, have been condensed or moved to the Methods where appropriate.

At the same time, we have retained limited methodological information in the Results where it is essential for understanding the interpretation of non-standard experimental setups or key controls, like SMLM. In these cases, brief methodological context is provided to ensure clarity without requiring frequent cross-referencing to the Methods section.

Overall, the Results section has been substantially condensed and reorganized to improve readability, while additional experiments added in response to reviewer comments necessarily increase the scope of the section. We believe the revised structure now clearly separates experimental outcomes from methodological detail and improves the flow of the Results.

The discussion section, at 7 pages, is overly long and includes substantial extraneous information. For example, it begins with a 2.5 page long paragraph that includes a summary of pattern formation during embryogenesis in animals, followed by a brief description of Turing's reaction-diffusion model, and finally, repeating parts of the introduction, a summary of the mechanism underlying MinCDE localization in E. coli. It is only in the middle of this paragraph - near the end of the second page - that the authors turn their attention back to MinD localization in B. subtilis, albeit with a focus on reaction-diffusion-based behaviors of other ParA homologues. A revised discussion section should focus on the primary conclusion of the authors, based on data presented in the results. If the authors would like to make the case that their data fit the Turing reaction-diffusion model, they will need to include mathematically based modeling that demonstrates this point in their results.

We have substantially revised and condensed the Discussion in response to this comment. In the revised manuscript, we removed the extended introductory material on general pattern formation, embryogenesis, and Turing reaction-diffusion theory, as these topics extended beyond the scope of the present study. We also eliminated redundant summaries of the E. coli MinCDE system that overlapped with the Introduction. The revised Discussion now focuses on the primary conclusions supported by our experimental data, namely the biochemical and in vivo mechanisms governing MinD membrane binding, ATPase activity, and dynamic localization in B. subtilis, as well as the regulatory roles of MinJ and MinC. Importantly, we would like to clarify that we did not intend to claim that the B. subtilis Min system follows a Turing-type reaction-diffusion mechanism. References to general reaction-diffusion concepts were meant to provide contextual background and not to imply a specific mathematical framework for the system studied here. To avoid any possible ambiguity, we have removed these references from the Discussion.

While the overall length of the Discussion is now comparable to the previous version, this reflects the inclusion of substantial new experimental data added during revision. Importantly, the structure and content of the Discussion have been streamlined to prioritize interpretation of the results rather than general background, resulting in a more focused and cohesive narrative.

Experimental comments:

Line 213: Please provide a rationale for the ATPase experiments. What is the expected result for each mutant and why?

We have clarified the rationale for the ATPase experiments in the revised manuscript by briefly outlining the expected behavior of each MinD mutant. The anticipated ATPase properties of G12V, K16A, and D40A are based on well-established studies of conserved Walker-type ATPases and were implicit in the original experimental design, as they should all be catalytically inactive. To avoid any ambiguity, we now state these expectations explicitly in the manuscript.

Line 243: ATPase data for the mutant proteins should be included in the supplement.

We have now quantified the ATPase activity for all MinD mutants from the respective EnzChek assay data. These experiments confirm that the G12V, K16A, and D40A mutations effectively abolish catalytic activity, yielding phosphate release rates that are essentially at the background detection limit of the assay. We have included these data in Figure S 7 C and updated the text to reflect these findings.

Figure 2B: Please include transverse section fluorescence data for all variants as well as quantitative data on average MinD positioning.

The quantitative information requested is already provided by our single-molecule localization and tracking (SMLM/SMT) analysis of Halo-MinD and its variants (Fig. 4 A and now S 12 A). This approach represents the averaged spatial distribution of individual MinD localizations collected from dozens of cells per condition and provides substantially higher spatial resolution and quantitative precision than transverse fluorescence profiles obtained by conventional widefield microscopy.

We therefore believe that the SMLM-based analysis is superior to transverse section fluorescence measurements and more accurately captures average MinD positioning across the cell population. To avoid redundancy, we have retained the SMLM analysis as the quantitative framework for MinD localization.

Figure 2B: I am not convinced that punctate and membrane-associated are mutually exclusive. Quantitative data on protein localization from transverse fluorescent sections is necessary to make this point.

Please see the answer above and Fig. 4 A

Figure 2B: It is impossible to assess the functionality of individual mutants without quantitative data on minicell frequency and cell length.

We have addressed this point by quantitatively measuring both cell length and minicell frequency for all relevant strains. These analyses were performed on a minimum of n = 430 cells per strain and are now presented in Table S 5. The added data provide a quantitative assessment of mutant functionality and support the phenotypic interpretations shown in Fig. 2B, and is also integrated in the Results section.

Other comments:

Line 109: should read "components".

Thank you, corrected.

Line 135: Why is this sentence outside the major section of the results?

It now has been integrated into the major section.

Line 197: I am not sure I understand this sentence.

We have revised this sentence to improve clarity and readability.

Line 218: I do not understand this paragraph.

We have also rephrased and rewritten this paragraph for clarity and readability.

Line 223: To make this section focused on the results rather than the method, the authors could simply say "To determine the role of ATP mediated dimerization, we...." (If I am understanding this section correctly).

We followed this suggestion and revised the text accordingly to focus on the experimental outcome rather than methodological detail.

Line 273: "depicted" not depictured.

Thank you, corrected.

Figure 4: The single-cell data look good in the figure, however, the description of these results and their meaning are nearly impossible to follow in the text.

We acknowledge that the single-molecule data presented in Fig. 4 are complex. While we have made minor clarifications to improve the flow and wording of the text, we did not substantially reduce the level of detail, as the description of the analytical framework is required for correct interpretation of the results.

At the same time, we aimed to avoid repeating extensive methodological explanations that are already described in the Materials and Methods section, in line with other reviewer comments. We therefore retained a concise but technically accurate description in the Results to ensure that the biological conclusions drawn from Fig. 4 can be properly understood.

在 one-to-one 模型中，user thread 作为编程抽象仍然存在，每个 user thread 对应一个 kernel thread；创建线程时，通过线程库调用系统调用（如 clone）在内核中创建对应的 kernel thread。

user thread 和 kernel thread 的区别在于调度和实现层次；mapping 表示用户线程与内核线程之间的对应关系，用于将用户线程映射到可被操作系统调度的内核线程上执行。只有kernel thread能在CPU上运行，所以user thread必须挂载在kernel thread上

对于 I/O 密集型程序，进程在等待设备时不会占用 CPU，因此这些等待时间不计入 user 或 sys 时间，从而导致 real > user + sys； 对于多线程或多核并行程序，多个 CPU 同时执行任务，CPU 时间会累加，因此 user + sys 可能大于 real。

uCore 在启动过程中通过内核函数（如 kernel_thread）手动创建第一个进程，直接分配并初始化进程控制块（PCB），设置执行入口并加入调度队列，而不是通过 fork 创建。

fork() 后父子进程都会继续执行；子进程因 pid == 0 不进入 if，直接返回并通过 exit() 终止，此时由于父进程尚未调用 wait()，子进程变为 zombie；在第一次按 Enter 前可观察到该 zombie 进程，而在父进程调用 wait(NULL) 后，该 zombie 被回收并消失。

等指定子进程

当子进程调用 exit() 时，内核会释放其大部分资源，但保留其进程表项并将其标记为 zombie，同时记录退出状态并向父进程发送 SIGCHLD 信号；父进程调用 wait() 时，如果已有 zombie 子进程，则会立即回收其中一个、返回其 PID 并通过 status 提供退出信息，否则父进程会阻塞等待直到子进程结束。

子进程调用 exit() 后，内核不会立即删除其 process table entry（PCB的一部分），而是将其标记为 zombie，并保留退出状态（exit code）等信息； 同时内核向父进程发送 SIGCHLD 信号，通知其子进程已终止； 父进程随后通过 wait() 或 waitpid() 获取子进程的 PID 和退出状态，并最终回收该进程资源。

子进程返回给内核，内核唤醒父进程 父进程wait()的返回值是子进程的pid 子进程通过 exit(code) 将退出状态交给内核； 内核将该状态编码到 status 中； 父进程调用 wait(&status) 后，可通过 WEXITSTATUS(status) 获取子进程的退出码。

Schön, versteh ich aber nicht.

Framing big ideas as essential questions can be especially helpful when collaborating with colleagues who are new to instructional design. Instead of getting stuck in abstract goals or content lists, essential questions give teams a clear, shared focus on what really matters—what learners should be able to think about, discuss, and apply in real-world contexts.

In my profession-workforce development, this approach will help shift conversations from “What should we teach?” to “What problems should participants be able to solve on the job?” That makes it easier for non-designers to contribute their practical expertise, align training with employer needs, and co-create programs that are more relevant, engaging, and outcome-driven.

Sorry, this made me laugh out loud...

With the aim to maximize profits? ;)

Why this need to label and categorise things?

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

Learn more at Review Commons

Reply to the reviewers

Reviewer 1

Point

Summary

Response

1.1

Overall, the study lacks well-controlled experiments comparing hypoxia induced by DMOG with hypoxia induced by 1% O₂ for assessing ERα occupancy throughout.

To assess whether DMOG-induced changes in ERα occupancy reflect bona fide hypoxia, we measured ERα binding by ChIP-qPCR under 1% oxygen over 48 hours, compared to normoxic (21% oxygen) cells and input controls in matched cells at the GREB1 and TFF1 loci. Our findings demonstrate that 1% oxygen treatment recapitulates the ERα binding changes observed with DMOG, at the time points of our RNA-seq experiments.

We have included these results in __Figure 1F __of the preliminary revision of the manuscript.

1.2

Lack of evidence for other co-transcription factors impact under hypoxia HIF's in Fig1.

We thank the reviewer for this comment. We have clarified that motif enrichment analysis is included to characterise the sequence context of ERα binding sites and to confirm enrichment of known ER-associated motifs (e.g. EREs), rather than to infer functional involvement of additional transcription factors under hypoxia. Corresponding interpretative statements have been removed from the Results and restricted to the Discussion.

1.3

Lack of evidence for DMOG induce HIF protein expression in MCF7 cells.

To confirm DMOG induces HIF-protein expression we have analysed HIF1α and HIF2α protein levels by western blot. We have included these in __Supplementary Figure S1A __within the preliminary revision to address this concern.

1.4

Figure 1: ATAC-seq was performed under 1% O₂, whereas ChIP-seq was conducted with DMOG treatment, making these conditions not directly comparable.

We acknowledge that the ERα ChIP-seq (DMOG) and ATAC-seq datasets were generated under different conditions and are therefore not directly comparable. To address this, we have performed ChIP-qPCR under bona fide hypoxia (1% oxygen) at canonical ERα target loci (TFF1 and GREB1), demonstrating that the directionality of ERα binding changes observed with DMOG is recapitulated under physiological hypoxia. These data provide a direct comparison of ERα occupancy across conditions and support the use of DMOG as a proxy for hypoxia in our ChIP-seq experiments.

If requested, we are willing to perform ATAC-seq at 16 h under 1% oxygen. However, because the original dataset was generated under 0.1% oxygen, and canonical ERα-bound sites show minimal accessibility changes under severe hypoxia, we anticipate limited additional insight from repeating this experiment.

1.5a

Figure S1: ERα ChIP lacks estradiol (E2) treatment in MCF7 cells with or without DMOG.

The statement that the ERα ChIP samples lack estrogen treatment is incorrect. Estradiol was not an experimental variable and cells were intentionally maintained under estrogen-rich conditions to preserve tumour-relevant ERα activity.

We have now clarified within the preliminary revision by stating that cells were routinely cultured in “estrogen-rich Dulbecco’s Modified Eagle Medium” in the methods section, and clarified the use of estrogen-rich conditions in the Figure S1 legend.

1.5b

The single-gene examples of DMOG effects shown in Fig. S1A are not significant.

The peak illustrated in Figure S1A (now Figure S1D) __is intended to provide a visual confirmation of peak calling and enrichment patterns underlying the genome-wide redistribution observed in __Figure 1. The peak was called by the MACS2 pipeline (code available from https://doi.org/10.5281/zenodo.17221105) with a log10(q-value) = 268.5, which passes the MACS2 cut-off q

1.6a

Fig. S2 lacks 1% O₂ conditions,

We wish to clarify that Figure S2 (now Figure S4) serves as quality control specifically for the DMOG-treated ChIP-seq dataset presented in Figure 1C. The purpose of the plot is to visualize unfiltered motif enrichment to confirm that the identified peaks represent bona fide ERα binding events within the DMOG condition. Motif enrichment under a 1% oxygen environment would not provide this validation. In all cases the ERE is the most significantly enriched motif.

With respect to ERα binding under 1% oxygen, we have now assessed this via targeted ChIP-qPCR validation (Figure 1F).

1.6b

Fig. S3 lacks DMOG-induced HIF factor assessments.

The DMOG-induced changes in HIF1α and HIF2α expression are shown in the__ Figure S1__ of this revision proposal and have been incorporated into the manuscript as part of the changes described in response 1.3.

1.7a

Figure S4: Estradiol (E2) treatment is missing from the controls, and the figure labeling is of poor quality.

We have substantially improved the labelling of Figure S4, now__ Figure S6.__

Additionally, we have clarified that all samples were cultured in estrogen-rich media and treated with either vehicle control or 100 nM fulvestrant; thus estrogen is present in all conditions including the controls.

1.7b

Hypoxic conditions for assessing ER status and appropriate controls are also lacking.

We agree that monitoring ERα stability under hypoxic conditions is essential.

We provided a western blot assessment of ERα protein levels at 0, 8 and 48 hours of treatment with 1% oxygen or DMOG, compared to normoxic controls, included as Supplementary Figures S1B, C in the preliminary revision.

These demonstrate the cells remain positive for ERα protein expression at 0, 8 and 48h.

1.8

Figure S5: The description of fulvestrant treatments under hypoxic conditions is unclear.

We thank the reviewer for this comment. To clarify the experimental design, we now signpost the reader in the figure legend of Figure S5 (now S7) to the schematic diagram provided in Figure 3B, and provide a summary stating the experiment employed a factorial design combining a 96-hour fulvestrant treatment with exposure to 1% oxygen for the final 48 hours.**

1.9

Supplemental legends: These require major revision; they are of poor quality and lack statistical details and references to biological replicates.

We have extensively revised all supplementary figure legends to ensure clarity and precision.

1.10

Overall comparisons throughout the manuscript are weak; the figures appear sloppy and lack sufficient effort in presentation.

Following this comment, we carefully reviewed the presentation of all figures throughout the manuscript. We improved the organisation and labelling of the Supplementary Figures to facilitate clearer comparison of the data. In particular, full western blots are now clearly annotated and supplementary legends have been expanded to provide sufficient context for each figure to be interpreted independently.

1.11

i) In general, the manuscript in its present form does not greatly contribute from published work as the ERα cistrone is well documented work studied for its role in regulating gene expression, particularly in ERα-positive breast cancer.

ii) Additionally, a lack of a thorough comparison between DMOG and or 1 %oxygen induce hypoxia in the MCF7 ER+ model, diminished initial interest in the manuscript.

iii) The lack of considering estradiol exposure under hypoxic conditions with either 1%oxygen and or DMOG also limits relevance to patients with ER+ BrCa.

iv) The ERα epigenomic profile has been extensively studied including work under hypoxic conditions.

i) We respectfully disagree that the manuscript does not extend prior work. Despite extensive characterisation of ERα, its role in shaping hypoxia-driven transcription in ER+ breast cancer has not been defined. Here, we identify an ERα-dependent hypoxic response (EDHR), demonstrating a reciprocal interaction between hypoxia and ERα activity.

ii) In revision, we address concerns regarding DMOG by validating ERα binding under 1% oxygen using ChIP-qPCR thereby confirming our result in bona fide hypoxia. Additionally, all RNA-seq and functional assays, including ENaC targeting, were performed under 1% oxygen in the original manuscript.

iii) All experiments were conducted under estrogen-complete conditions, now explicitly clarified, reflecting tumour-relevant ERα activity.

iv) Together, these data establish a reciprocal interaction between ERα and hypoxia and uncover a targetable vulnerability in hypoxic ER+ breast cancer, linking transcriptional regulation to therapeutic opportunity.

Reviewer 2

No.

Summary

Response

General Comments

2.1

ENAC is proposed as a therapeutic vulnerability based on amiloride sensitivity assays. Additional experiments are required, such as western blot validation of ENaC regulation under hypoxia and loss-of-function approaches to assess its contribution to the phenotype.

We agree that further validation of ENaC involvement would strengthen this observation. We will assess ENaC protein levels under 1% hypoxia ± fulvestrant by western blot and perform siRNA-mediated depletion of ENaC subunits to test their contribution to the hypoxia-specific amiloride-sensitive phenotype by viability assay (see also response 3.3).

2.2

Fulvestrant is used to dissect ERa dependency. However, as a SERD, it may alter chromatin and transcription independently of a simple loss of ERα. Addition control would strengthen interpretation.

The experimental design already controls for potential fulvestrant-specific transcriptional effects, as all four conditions (± hypoxia, ± fulvestrant) were included. EDHR genes were defined based on induction under hypoxia, loss of this induction following ERα degradation, and absence of residual hypoxic induction in the presence of fulvestrant. Consistent with this, SCNN1B and SCNN1G do not show significant fulvestrant-responsive changes under normoxia (Figure 5C,D).

We also note that fulvestrant has been shown to induce minimal global chromatin remodelling (Guan et al., 2019), supporting its use to assess ERα dependency without broadly confounding chromatin accessibility; this reference is now included in the manuscript.

2.3

The molecular mechanism by which ERα modulates the hypoxic transcriptome, specifically how ERα and HIF pathways converge at ENAC loci should be more studied.

We further examined the potential convergence of ERα and hypoxic signalling at the ENaC loci (included as __Figure 5E __in the revision proposal) showing genome browser views of the SCNN1G and SCNN1B loci, highlighting hypoxia-induced HIF1α binding and ERα association at these sites.

To further support this, we will perform RT-qPCR validation of SCNN1G and SCNN1B expression following treatment ± IOX5 and ± fulvestrant. IOX5 is a selective PHD inhibitor that stabilises HIF proteins, enabling us to assess the contribution of HIF signalling independently of other oxygen-dependent effects associated with hypoxia.

2.4

In addition, to assess the relevance of this work for luminal breast cancer and ERα expression, specific validation in TNBC should be performed

To assess the clinical relevance of SCNN1B and SCNN1G in ER-positive and ER-negative subgroups, we performed Cox proportional hazards analyses in TCGA and METABRIC cohorts individually, including ER status and stratifying by ER-positive and ER-negative cases (Figure 6C). These analyses support the association of SCNN1G with poorer relapse-free survival specifically in ER-positive patients.

2.5

The authors should provide RT-qPCR validation of the key EDHR genes, especially since this signature is later used for downstream analyses.

We agree that independent validation would strengthen these findings. We will perform RT-qPCR validation of key EDHR genes (including SCNN1B and SCNN1G) under ± hypoxia and ± fulvestrant conditions to confirm ERα-dependent hypoxic induction.

Limitations

2.6

Reprogramming of the ERα cistrome under cellular stress is well documented. The study extends these ideas but does not clearly demonstrate a new mechanistic paradigm, particularly because the EDHR is defined primarily through omics approaches without strong mechanistic validation. In addition, we have to keep in mind that the study uses DMOG to model hypoxia-driven chromatin changes, but DMOG inhibits many 2-oxoglutarate-dependent dioxygenases non-selectively.

This makes it difficult to attribute ERα cistrome reprogramming specifically to hypoxia, rather than to broad off-target effects. The transcriptomic dataset is more convincing by need the validation suggested previously.

While ERα cistrome reprogramming has been described, our study demonstrates a reciprocal interaction in which ERα not only responds to hypoxia but actively shapes hypoxia-driven transcription, defining an ERα-dependent hypoxic response (EDHR).

We acknowledge the limitations of DMOG and have addressed this by validating key ERα binding events under bona fide hypoxia (1% oxygen) using ChIP–qPCR, confirming our findings under physiological conditions (response 1.1).

To further strengthen mechanistic insight, we will assess the requirement for HIF stabilisation using the selective PHD inhibitor IOX5, combined with RT-qPCR analysis of SCNN1G and SCNN1B ± IOX5 ± fulvestrant (response 2.3 and 2.5). In addition, we will validate the functional relevance of ENaC through protein-level analysis and siRNA-mediated depletion, as described in__ response 2.1.__

Together, these additions address concerns regarding DMOG specificity and provide further support for a functional interaction between ERα and hypoxic signalling.

Audience

2.7

Given its reliance on omics datasets and preliminary functional assays, the paper will likely appeal to a specialized audience in transcriptional regulation, hypoxia signalling, and ER+ breast cancer biology. However, the limited mechanistic depth and uncertain translational relevance due to the lack of in vivo validation, may reduce its impact for broader oncology or therapeutic-development audiences. Without stronger validation, the findings may be perceived as niche and mainly of interest to researchers focused on ERα chromatin dynamics rather than to the wider cancer research community.

The study incorporates multiple layers of human relevance, including spatial transcriptomic analyses demonstrating enrichment of EDHR within hypoxic tumour regions and survival analyses linking EDHR and ENaC expression to clinical outcome.

In revision, we address the reviewer’s concerns through targeted validation (ChIP-qPCR in hypoxia, western blotting, and RT–qPCR). Together, these additions strengthen the mechanistic and translational relevance of the study.

Reviewer 3

No.

Summary

Response

Major comments

3.1

The DMOG ChIP-seq provides a valuable first look at ERα redistribution. Since DMOG inhibits both HIF hydroxylases and oxygen-dependent demethylases, the driver of the observed changes remains ambiguous. It would help to include either ERα ChIP-seq under bona fide hypoxia or a selective PHD inhibitor condition (for example IOX5, as you discuss) to separate HIF stabilisation from broad demethylase inhibition. If ChIP-seq is not feasible, a brief ATAC validation at a small panel of gained and lost loci would still increase confidence.

We acknowledge that mimetics of hypoxia can introduce off-target effects. To address this, we have validated our ERα ChIP-seq findings using ChIP-qPCR at representative loci (TFF1 and GREB1), demonstrating consistent changes in ERα binding under bona fide hypoxia (1% oxygen) (now included in Figure 1F).

As acknowledged by the reviewer, ChIP-seq under these conditions is likely not feasible due to cell number constraints. We are willing to undertake ATAC-seq if required (as stated in response 1.1); however, we do not feel it would directly address ERα occupancy at these loci. We therefore consider our targeted ChIP-qPCR to be the most appropriate approach to validate ERα redistribution under hypoxia.

3.2a

The factorial RNA-seq is well designed and the attenuation analyses are clear. The EDHR selection is stringent and reproducible across two ER+ lines.

To support the claim of ERα dependence mechanistically, a small number of targeted perturbations would go far. For example,

i) confirm EDHR induction for SCNN1B and SCNN1G in hypoxia with and without fulvestrant by RT-qPCR

We agree that targeted validation would strengthen the mechanistic support for ERα dependence. We will perform RT-qPCR validation of SCNN1B and SCNN1G under hypoxia ± fulvestrant to confirm ERα-dependent hypoxic induction (see also response 2.5).

3.2b

ii) test whether short-term ERα knockdown reproduces the effect.

ERα dependency is already assessed through fulvestrant-mediated degradation within the factorial design, which provides a well-established and direct approach to evaluate ERα function. As EDHR genes are defined by loss of hypoxic induction following ERα degradation, this constitutes a robust assessment of ERα-dependent effects.

We will therefore focus on orthogonal validation through RT-qPCR (response__ 2.5__), together with additional mechanistic and functional analyses using IOX5 and ENaC perturbation (responses 2.1 and 2.3), rather than introducing an ERα knockdown approach, although we would consider this if required.

3.2c

iii) A complementary test with a HIF-1α or HIF-2α knockdown at one time point would help position EDHR relative to HIF.

This request aligns with point 2.3, which addresses the convergence of ERα and HIF signalling. While HIF knockdown under hypoxia would assess necessity, we will instead assess the contribution of HIF signalling using the selective PHD inhibitor IOX5, as this allows us to isolate HIF stabilisation from broader hypoxia-associated effects and avoids additional perturbation associated with transfection-based approaches. We will perform RT-qPCR analysis of SCNN1B and SCNN1G following treatment ± IOX5 ± fulvestrant to determine whether HIF stabilisation is sufficient to support ERα-dependent induction of EDHR genes.

3.3

The amiloride result is intriguing and consistent with a hypoxia-specific dependency. Because amiloride is pleiotropic, it would strengthen the conclusion to add one genetic and one pharmacological specificity control. A brief SCNN1B or SCNN1G knockdown in hypoxia should phenocopy the viability effect if ENaC contributes. In parallel, testing benzamil at sub-micromolar doses would provide a more ENaC-selective pharmacological readout. These can be performed in MCF7 and, resources permitting, in T47D.

To address the reviewer’s concern regarding pleiotropic effects, we propose (aligning with our__ response to 2.1__) to apply siRNA-mediated knockdown of SCNN1B and SCNN1G under hypoxia to determine whether this reproduces our observed viability effect, thereby providing direct evidence for ENaC involvement.

We agree that additional pharmacological validation could further support specificity, and would consider inclusion of a more ENaC-selective inhibitor if required.

3.4

The RFS associations for

SCNN1B and SCNN1G are compelling. It would be helpful to report whether the associations persist in a multivariable model that at least includes ER status, grade and nodal status where available, or to state clearly when this is not possible across merged datasets. Even a sensitivity analysis in TCGA with ER+ cases only would contextualise the hazard ratios.

We have analysed TCGA and METABRIC cohorts individually using Cox proportional hazards models, as this functionality is not available for merged datasets in KMplot. ER status was included in the models, and analyses were additionally stratified by ER-positive and ER-negative subgroups. The number of relapse events per subgroup is approximately 40; therefore, additional covariates such as grade and nodal status were not included given the limited number of events per model.

Within ER-positive patients, high SCNN1G expression is associated with poorer relapse-free survival (TCGA HR 1.45, p = 0.0027), while SCNN1B shows a similar trend that does not reach statistical significance. These analyses are presented in Figure 6C and in the results section of the preliminary revision, and support the findings from the Kaplan–Meier analysis.

3.5

The spatial association of EDHR with EMT hotspots is a nice piece of the story. A short clarification of how spot-level cell type composition was handled will help readers interpret proximity results. If cell type deconvolution scores are available in the source dataset, adding a sentence on whether EDHR enrichment tracks tumour epithelial content would be useful.

Spatial cell type composition and spot annotations were used as provided in the SpottedPy dataset, based on Cell2location-derived deconvolution scores and STARCH tumour annotations, without additional re-estimation.

To address the reviewer’s suggestion, we examined the relationship between EDHR enrichment and epithelial content and observed no significant correlation at the neighbourhood level.

These points have now been clarified in the manuscript.

3.6

Data processing for ChIP-seq and RNA-seq is documented and accessions are provided. The RNA-seq includes n=3 per condition, which is appropriate, and the correlation and LFC analyses are clearly presented. For the amiloride assay, the two-way ANOVA with interaction is appropriate; please add the exact n and whether experiments were independently repeated, and include the underlying values in a source table for transparency. These are small presentational edits rather than new experiments.

In the preliminary revision we have added a statement to the amiloride assay figure (Figure 6D) clarifying that n = 3 independent biological replicates were performed per condition. In addition, we now provide the underlying numerical values for this assay in Table S11.

3.7

A small, hypothesis-driven mechanistic link from EDHR to ENaC function would substantially elevate impact without becoming a long project. For example, testing whether hypoxia increases amiloride-sensitive Na⁺ current in MCF7 and whether fulvestrant abrogates that increase would directly connect the transcriptional and functional observations. If available, patch-clamp or a simple SBFI-based Na⁺ imaging readout could suffice.

We agree that directly linking EDHR to ENaC channel activity would further strengthen the mechanistic connection. We will prioritise genetic validation of ENaC function through siRNA-mediated depletion (response 2.1), which directly tests the requirement for ENaC in the hypoxia-specific viability phenotype.

We are willing to explore the feasibility of measuring the amiloride-sensitive Na+ currents under normoxia and acute hypoxia (via perfusion of cells with bathing solution bubbled with nitrogen during recording) ± fulvestrant to further connect hypoxic regulation to channel activity.

Minor Comments

3.8

Please show representative ERα ChIP-seq browser snapshots for at least one gained, one conserved and one lost locus alongside input for both conditions.

We have now included representative ERα ChIP-seq browser snapshots for gained, conserved, and lost loci, together with input controls for both conditions, in Figure S3 of the revised manuscript.

3.9

In Figure 1D, the ATAC-seq comparison uses 0.1% O₂ for 48 h while the RNA-seq uses 1% O₂. Briefly justify the choice and discuss any expected differences.

We thank the reviewer for this point. The ATAC-seq dataset was generated under 0.1% oxygen in the original study, whereas RNA-seq experiments in this work were performed at 1% oxygen to reflect tumour-relevant hypoxic conditions. The more severe hypoxia used for ATAC-seq would be expected to maximise detection of chromatin accessibility changes. Despite this, chromatin accessibility changes were limited, with ERα binding occurring predominantly at pre-accessible regions. This has now been clarified in the manuscript.

3.10

In the Methods for spatial analyses, specify the thresholds for hotspot calling and how the neighbourhood radius was chosen.

The neighbourhood parameter was set to 8, corresponding to the immediate neighbouring spots in Visium data, consistent with package guidance. We have clarified this in the manuscript text.

3.11

For the EDHR heatmap, consider marking the 14 consensus genes and indicating which belong to the ENaC module to aid readability.

We have marked the 14 EDHR consensus genes and indicated the ENaC module in the revised heatmap to aid readability.

3.12

Please report exact sample sizes and replicate numbers in all figure legends and provide a single table with all statistical tests, n, and p values.

We have reported exact sample sizes and replicate numbers in all relevant figure legends and included Table S11 summarising all statistical tests, sample sizes (n), and p values.

3.13

A schematic summarising the experimental timelines for ChIP-seq, RNA-seq and viability would help orient readers.

We have added timelines for these experiments as requested.

3.14

Minor copyedits: consistent formatting of O₂, gene symbols and reagent catalogue numbers.

We have standardised oxygen notation throughout the manuscript to use “oxygen” in the main text and “O2” where appropriate (e.g. figures).

Reagent catalogue numbers have now been standardised for consistency of presentation in the revised manuscript.

Gene and protein nomenclature were already formatted according to accepted conventions and were verified for consistency.

3.15

The manuscript is well referenced. Where you contrast your findings with long-term CoCl₂ hypoxia, a sentence on why acute DMOG and short-term 1% O₂ may reveal different ERα behaviours would help position the novelty.

We thank the reviewer for this suggestion. We have expanded the manuscript to clarify that acute hypoxia (1% oxygen) and DMOG treatment capture early, dynamic hypoxic responses, in contrast to chronic CoCl2 exposure, which reflects longer-term adaptation. This distinction is relevant to tumour biology, where hypoxia is often transient due to unstable vascularisation. The following statement has been added to the manuscript:

“In addition to such chronic hypoxic adaptation, tumour hypoxia can also be dynamic, with cells experiencing acute or transient hypoxic exposure due to unstable vascularisation; an established contributor to tumour progression (Liu et al, 2022a; Koh & Powis, 2012). Thus, in contexts where both signalling pathways remain active, the dependence of the hypoxic response on ERα in ER+ cells has not been previously characterised.”

Primary Limitations

3.16

DMOG vs hypoxia in the cistrome experiment,

To address concerns regarding the use of DMOG, we have validated key ERα binding events from the ChIP-seq dataset by ChIP–qPCR at the TFF1 and GREB1 loci under bona fide hypoxia (1% oxygen) in biological triplicate__ (Figure 1F)__. These data demonstrate consistent changes in ERα binding under hypoxia, supporting that the DMOG-induced redistribution reflects hypoxia-driven changes.

3.17

the absence of direct HIF or cofactor perturbations

We acknowledge the absence of direct HIF perturbation. To address this, we will assess the contribution of HIF signalling through stabilisation approaches, including RT-qPCR analysis of SCNN1B and SCNN1G ± IOX5 ± fulvestrant (response 3.2), to determine whether HIF activation is sufficient to support ERα-dependent induction.

3.18

and the pleiotropy of amiloride.

To address the potential pleiotropy of amiloride, we will perform siRNA-mediated knockdown of SCNN1G and SCNN1B to provide independent validation of ENaC-dependent effects (response 3.3).

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Referee #3

Evidence, reproducibility and clarity

Summary

This study explores how hypoxia reshapes ERα signalling in ER-positive breast cancer and whether this cross-talk exposes targetable vulnerabilities. The authors first map ERα binding in MCF7 cells after dioxygenase inhibition with DMOG and observe a genome-wide redistribution with enrichment of ERE, FOXA1 and AP-1 motifs at gained sites while chromatin accessibility at these loci appears unchanged in public ATAC-seq after hypoxia. They then perform RNA-seq in MCF7 and T47D using a factorial design that combines fulvestrant-mediated ERα degradation with 1% O₂ to define an ERα-dependent hypoxia response (EDHR). A 14-gene consensus EDHR signature includes ENaC regulatory subunits SCNN1B and SCNN1G, whose higher expression is associated with poorer RFS in ER+ cohorts. Functionally, amiloride increases viability in normoxia but reduces viability under hypoxia in MCF7 across a dose range. Spatial transcriptomics from ER+ tumours shows EDHR expression enriched at the margins of hypoxia and estrogen-hallmark regions and adjacent to EMT hotspots. Raw data and code availability are stated for the central datasets and accessions are provided. Together the results argue that ERα helps organise a distinct hypoxic programme and suggest a context-specific sensitivity to ENaC inhibition.

Major comments

The paper addresses a timely question with a clear narrative arc and brings together ChIP-seq, RNA-seq, pharmacology, survival analysis and spatial transcriptomics. The EDHR concept is interesting and the ENaC angle is original. The work is already strong and with a few targeted additions and clarifications it can be made more persuasive without becoming a new project.

1) The DMOG ChIP-seq provides a valuable first look at ERα redistribution. Since DMOG inhibits both HIF hydroxylases and oxygen-dependent demethylases, the driver of the observed changes remains ambiguous. It would help to include either ERα ChIP-seq under bona fide hypoxia or a selective PHD inhibitor condition (for example IOX5, as you discuss) to separate HIF stabilisation from broad demethylase inhibition. If ChIP-seq is not feasible, a brief ATAC validation at a small panel of gained and lost loci would still increase confidence. Estimated time: 6-8 weeks for a focused follow up with two conditions and biological duplicates/triplicates.

2) The factorial RNA-seq is well designed and the attenuation analyses are clear. The EDHR selection is stringent and reproducible across two ER+ lines. To support the claim of ERα dependence mechanistically, a small number of targeted perturbations would go far. For example, confirm EDHR induction for SCNN1B and SCNN1G in hypoxia with and without fulvestrant by RT-qPCR and test whether short-term ERα knockdown reproduces the effect. A complementary test with a HIF-1α or HIF-2α knockdown at one time point would help position EDHR relative to HIF. Estimated time: 3-4 weeks for qPCR and siRNA validations.

3) The amiloride result is intriguing and consistent with a hypoxia-specific dependency. Because amiloride is pleiotropic, it would strengthen the conclusion to add one genetic and one pharmacological specificity control. A brief SCNN1B or SCNN1G knockdown in hypoxia should phenocopy the viability effect if ENaC contributes. In parallel, testing benzamil at sub-micromolar doses would provide a more ENaC-selective pharmacological readout. These can be performed in MCF7 and, resources permitting, in T47D. Estimated time: 4-6 weeks.

4) The RFS associations for SCNN1B and SCNN1G are compelling. It would be helpful to report whether the associations persist in a multivariable model that at least includes ER status, grade and nodal status where available, or to state clearly when this is not possible across merged datasets. Even a sensitivity analysis in TCGA with ER+ cases only would contextualise the hazard ratios. Estimated time: 1-2 weeks.

5) The spatial association of EDHR with EMT hotspots is a nice piece of the story. A short clarification of how spot-level cell type composition was handled will help readers interpret proximity results. If cell type deconvolution scores are available in the source dataset, adding a sentence on whether EDHR enrichment tracks tumour epithelial content would be useful. Estimated time: 1 week.

Reproducibility and statistics

Data processing for ChIP-seq and RNA-seq is documented and accessions are provided. The RNA-seq includes n=3 per condition, which is appropriate, and the correlation and LFC analyses are clearly presented. For the amiloride assay, the two-way ANOVA with interaction is appropriate; please add the exact n and whether experiments were independently repeated, and include the underlying values in a source table for transparency. These are small presentational edits rather than new experiments.

Optional

A small, hypothesis-driven mechanistic link from EDHR to ENaC function would substantially elevate impact without becoming a long project. For example, testing whether hypoxia increases amiloride-sensitive Na⁺ current in MCF7 and whether fulvestrant abrogates that increase would directly connect the transcriptional and functional observations. If available, patch-clamp or a simple SBFI-based Na⁺ imaging readout could suffice. Estimated time: 6-8 weeks.

Minor comments

1. Please show representative ERα ChIP-seq browser snapshots for at least one gained, one conserved and one lost locus alongside input for both conditions.
2. In Figure 1D, the ATAC-seq comparison uses 0.1% O₂ for 48 h while the RNA-seq uses 1% O₂. Briefly justify the choice and discuss any expected differences.
3. In the Methods for spatial analyses, specify the thresholds for hotspot calling and how the neighbourhood radius was chosen.
4. For the EDHR heatmap, consider marking the 14 consensus genes and indicating which belong to the ENaC module to aid readability.
5. Please report exact sample sizes and replicate numbers in all figure legends and provide a single table with all statistical tests, n, and p values.
6. A schematic summarising the experimental timelines for ChIP-seq, RNA-seq and viability would help orient readers.
7. Minor copyedits: consistent formatting of O₂, gene symbols and reagent catalogue numbers.

Prior studies

The manuscript is well referenced. Where you contrast your findings with long-term CoCl₂ hypoxia, a sentence on why acute DMOG and short-term 1% O₂ may reveal different ERα behaviours would help position the novelty.

Significance

General assessment

The strongest aspects are the carefully designed factorial RNA-seq that cleanly separates ERα and hypoxia effects, the discovery of a concise EDHR signature reproducible across two ER+ lines, and the integration with spatial transcriptomics that places EDHR near EMT-rich tumour regions. The ENaC connection is new and potentially actionable, and the context-dependent amiloride response is a practical lead. Limitations are primarily mechanistic: DMOG vs hypoxia in the cistrome experiment, the absence of direct HIF or cofactor perturbations, and the pleiotropy of amiloride.

Advance

To my knowledge, this is the first description of a distinct ERα-dependent hypoxic programme in ER+ breast cancer that includes ENaC regulatory subunits and links to an EMT-adjacent spatial niche. The conceptual advance is the positioning of ERα as a coordinator of a subset of hypoxia-induced genes rather than as a parallel pathway, together with an initial functional readout that suggests a therapeutic angle through ENaC modulation. With the targeted additions outlined above, the study would move from strong association to a more mechanistic and translationally relevant model.

Audience

The work will interest a specialised audience in nuclear receptor biology, hypoxia signalling, tumour microenvironment, and ion transport in cancer. It has potential relevance for basic researchers studying ERα cistrome dynamics, for groups using spatial transcriptomics to define micro-niches, and for translational researchers exploring metabolic and ionic vulnerabilities in ER+ disease.

Expertise disclosure

Keywords: nuclear receptors,, chromatin profiling, transcriptomics, spatial transcriptomics, breast cancer biology.

I am not a domain expert in ion channel electrophysiology; my comments on ENaC pharmacology focus on specificity and study design rather than detailed channel biophysics.

Tone

I find the paper well conceived and already compelling. The suggested experiments are focused, realistic in scope, and primarily aim to turn several strong associations into concise mechanistic statements that would further increase confidence and impact.

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Referee #2

Evidence, reproducibility and clarity

ERα drives most luminal breast cancers. However, how hypoxia reshapes ERα activity and how ERα itself might influence the hypoxic response remain unclear. Understanding this interaction is crucial, as hypoxia is strongly linked to endocrine resistance and poor outcomes. In this study, authors investigated how hypoxia modifies ERα signalling in ER+ breast cancer and whether ERα contributes to the transcriptional response to low oxygen. Using MCF7 and T47D cells, they combined genome-wide profiling of the ERα cistrome under DMOG, hypoxic transcriptomics with or without ERα degradation, and spatial transcriptomics in tumours. This revealed an ERα-dependent hypoxic response (EDHR), prominently involving regulation of epithelial sodium channel (ENaC) subunits, whose expression requires both hypoxia and active ERα signalling. Functionally, ENaC inhibition with amiloride reduced cell viability under hypoxia. Together, these findings uncover a previously unrecognised ERα-dependent layer of the hypoxic transcriptome and identify ENaC as a potential therapeutic vulnerability in hypoxic ER+ breast cancer. Although the study is interesting, the manuscript lacks several essential functional and experimental validations. ENAC is proposed as a therapeutic vulnerability based on amiloride sensitivity assays. Additional experiments are required, such as western blot validation of ENaC regulation under hypoxia and loss-of-function approaches to assess its contribution to the phenotype. Fulvestrant is used to dissect ERa dependency. However, as a SERD, it may alter chromatin and transcription independently of a simple loss of ERα. Addition control would strengthen interpretation. The molecular mechanism by which ERα modulates the hypoxic transcriptome, specifically how ERα and HIF pathways converge at ENAC loci should be more studied. In addition, to assess the relevance of this work for luminal breast cancer and ERα expression, specific validation in TNBC should be performed Finally, the authors should provide RT-qPCR validation of the key EDHR genes, especially since this signature is later used for downstream analyses.

Significance

General assessment strengths:

This study uncovers a previously unrecognised ERα-dependent hypoxic response in breast cancer, revealing that ERα actively shapes the hypoxic transcriptome rather than functioning as an isolated pathway. To me, the main strength of this work is the identification of ENaC as a novel hypoxia-specific therapeutic vulnerability in ER+ breast cancer, suggesting that ion-channel regulation may play a broader and underappreciated role in endocrine resistance.

Limitation:

Reprogramming of the ERα cistrome under cellular stress is well documented. The study extends these ideas but does not clearly demonstrate a new mechanistic paradigm, particularly because the EDHR is defined primarily through omics approaches without strong mechanistic validation. In addition, we have to keep in mind that the study uses DMOG to model hypoxia-driven chromatin changes, but DMOG inhibits many 2-oxoglutarate-dependent dioxygenases non-selectively. This makes it difficult to attribute ERα cistrome reprogramming specifically to hypoxia, rather than to broad off-target effects. The transcriptomic dataset is more convincing by need the validation suggested previously.

Audience:

Given its reliance on omics datasets and preliminary functional assays, the paper will likely appeal to a specialized audience in transcriptional regulation, hypoxia signalling, and ER+ breast cancer biology. However, the limited mechanistic depth and uncertain translational relevance due to the lack of in vivo validation, may reduce its impact for broader oncology or therapeutic-development audiences. Without stronger validation, the findings may be perceived as niche and mainly of interest to researchers focused on ERα chromatin dynamics rather than to the wider cancer research community.

Expertise:

My evaluation is based on my background in breast cancer, ERα signaling and breast tumorigenesis. However, I have limited expertise in spacial transcriptomic analyses and advanced CHiP-seq bioinformatic analyses, which may affect my assessment of some computational analyses.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript, Malcom et al. present evidence that, under hypoxic conditions, hypoxia-inducible factors (HIFs) alter the estrogen receptor alpha (ERα) epigenomic landscape in a model of estrogen receptor-positive (ER+) breast cancer (BrCa). The response of ER+ BrCa to estradiol (E2) in MCF7 (ER+) cells, as well as ERα signaling in both primary and metastatic breast cancer, has been well studied, and the epigenomic landscape of ERα+ BrCa is well documented. The differentially expressed genes (DEGs) identified under treatment with the hypoxia mimetic dimethyloxalylglycine (DMOG) revealed a subset of ERα-dependent hypoxic response (EDHR) genes. The outcome was a reprogramming of the basal ERα cistrome, coinciding with sites enriched for estrogen response elements (EREs) and co-transcription factor binding motifs for ERα, including FOXA1 and AP-1. This was demonstrated by ERα ChIP-seq (i.e. DMOG) and ATAC-seq (i.e. 1% O2) performed under different hypoxic conditions. The transcripts identified following DMOG treatment were leveraged and compared to publicly available RNA-seq datasets from various breast cancer subtypes exposed to 1% hypoxic oxygen. Although the comparison methods varied, the results suggested that BrCa cell lines under 1% hypoxic oxygen conditions showed strong similarity to MCF7 cells treated with DMOG. Genes upregulated in response to DMOG correlated with poorer survival outcomes. To demonstrate the requirement for ERα in this model, MCF7 cells were treated with the selective estrogen receptor degrader (SERD) fulvestrant-the only FDA-approved SERD for ER+ BrCa-showing a dampening of the HIF response among EDHR genes. This suggests that ERα is necessary for the expression of DEGs under hypoxic conditions induced by DMOG. Finally, the sodium channel protein ENaC subunits (i.e., SCNN1B and SCNN1G) were further characterized as candidate EDHR genes. Analyses of publicly available datasets indicated that high mRNA expression levels of these subunits were associated with worse survival outcomes, supporting the clinical relevance of EDHR genes SCNN1B and SCNN1G. To further validate clinical relevance, utilize the Spatial Transcriptome in a small subset of ER+ BrCa.

Major:

1. Overall, the study lacks well-controlled experiments comparing hypoxia induced by DMOG with hypoxia induced by 1% O₂ for assessing ERα occupancy throughout.
2. Lack of evidence for other co-transcription factors impact under hypoxia HIF's in Fig1.
3. Lack of evidence for DMOG induce HIF protein expression in MCF7 cells.
4. Figure 1: ATAC-seq was performed under 1% O₂, whereas ChIP-seq was conducted with DMOG treatment, making these conditions not directly comparable.
5. Figure S1: ERα ChIP lacks estradiol (E2) treatment in MCF7 cells with or without DMOG. The single-gene examples of DMOG effects shown in Fig. S1A are not significant.
6. Figures S2 and S3: Fig. S2 lacks 1% O₂ conditions, and Fig. S3 lacks DMOG-induced HIF factor assessments.
7. Figure S4: Estradiol (E2) treatment is missing from the controls, and the figure labeling is of poor quality. Hypoxic conditions for assessing ER status and appropriate controls are also lacking.
8. Figure S5: The description of fulvestrant treatments under hypoxic conditions is unclear.
9. Supplemental legends: These require major revision; they are of poor quality and lack statistical details and references to biological replicates.

Minor:

1. Overall comparisons throughout the manuscript are weak; the figures appear sloppy and lack sufficient effort in presentation.

Significance

In general, the manuscript in its present form does not greatly contribute from published work as the ERα cistrone is well documented work studied for its role in regulating gene expression, particularly in ERα-positive breast cancer. Additionally, a lack of a through comparison between DMOG and or 1 %O2 induce hypoxia in the MCF7 ER+ model, diminished initial interest in the manuscript. The lack of considering estradiol exposure under hypoxic conditions with either 1%O2 and or DMOG also limits relevance to patients with ER+ BrCa. The ERα epigenomic profile has been extensively studied including work under hypoxic conditions.

eLife Assessment

This study provides important insights into how immune cells in the brain's protective layers behave under normal and disease-like conditions, revealing location-specific activity patterns that may shape inflammation and disorders such as migraine. The evidence is compelling and supported by advanced imaging approaches and rigorous analyses, although some conceptual and interpretational limitations temper the mechanistic depth. Overall, the work will be of broad interest and represents an invaluable contribution to the growing field linking immune and nervous system function.

Reviewer #1 (Public review):

Summary:

This study presents a technically sophisticated intravital two-photon calcium imaging approach to characterize meningeal macrophage Ca²⁺ dynamics in awake mice. The development of a Pf4Cre:GCaMP6s reporter line and the integration of event-based Ca²⁺ analysis represent clear methodological strengths. The findings reveal niche-specific Ca²⁺ signaling patterns and heterogeneous macrophage responses to cortical spreading depolarization (CSD), with potential relevance to migraine and neuroinflammatory conditions. Despite these strengths, several conceptual, technical, and interpretational issues limit the impact and mechanistic depth of the study. Addressing the points below would substantially strengthen the manuscript.

Strengths:

The use of chronic two-photon Ca²⁺ imaging in awake, behaving mice represents a major technical strength, minimizing confounds introduced by anesthesia. The development of a Pf4Cre:GCaMP6s reporter line, combined with high-resolution intravital imaging, enables long-term and subcellular analysis of macrophage Ca²⁺ dynamics in the meninges.

The comparison between perivascular and non-perivascular macrophages reveals clear niche-dependent differences in Ca²⁺ signaling properties. The identification of macrophage Ca²⁺ activity temporally coupled to dural vasomotion is particularly intriguing and highlights a potential macrophage-vascular functional unit in the dura.

By linking macrophage Ca²⁺ responses to CSD and implicating CGRP/RAMP1 signaling in a subset of these responses, the study connects meningeal macrophage activity to clinically relevant neuroimmune pathways involved in migraine and other neurological disorders.

Weaknesses:

The manuscript relies heavily on Pf4Cre-driven GCaMP6s expression to selectively image meningeal macrophages. Although prior studies are cited to support Pf4 specificity, Pf4 is not an exclusively macrophage-restricted marker, and developmental recombination cannot be excluded. The authors should provide direct validation of reporter specificity in the adult meninges (e.g., co-labeling with established macrophage markers and exclusion of other Pf4-expressing lineages). At minimum, the limitations of Pf4Cre-based labeling should be discussed more explicitly, particularly regarding how off-target expression might affect Ca²⁺ signal interpretation.

The manuscript offers an extensive characterization of Ca²⁺ event features (frequency spectra, propagation patterns, synchrony), but the biological significance of these signals is largely speculative. There is no direct link established between Ca²⁺ activity patterns and macrophage function (e.g., activation state, motility, cytokine release, or interaction with other meningeal components). The discussion frequently implies functional specialization based on Ca²⁺ dynamics without experimental validation. To strengthen the conceptual impact, a clearer framing of the study as a foundational descriptive resource, rather than a functional dissection, would improve alignment between data and conclusions.

The GLM analysis revealing coupling between dural perivascular macrophage Ca²⁺ activity and vasomotion is technically sophisticated and intriguing. However, the directionality of this relationship remains unresolved. The current data do not distinguish whether macrophages actively regulate vasomotion, respond to mechanical or hemodynamic changes, or are co-modulated by neural activity. Statements suggesting that macrophages may "mediate" vasomotion are therefore premature. The authors should reframe these conclusions more cautiously, emphasizing correlation rather than causation, and expand the discussion to explicitly outline experimental strategies required to establish causality (e.g., macrophage-specific Ca²⁺ manipulation).

The authors conclude that synchronous Ca²⁺ events across macrophages are driven by extrinsic signals rather than intercellular communication, based primarily on distance-time analyses. This conclusion is not sufficiently supported, as spatial independence alone does not exclude paracrine signaling, vascular cues, or network-level coordination. No perturbation experiments are presented to test alternative mechanisms. The authors can either provide additional experimental evidence or rephrase the conclusion to acknowledge that the source of synchrony remains unresolved.

A major and potentially important finding is that the dominant macrophage response to CSD is a persistent decrease in Ca²⁺ activity, which is independent of CGRP/RAMP1 signaling. However, this phenomenon is not mechanistically explored. It remains unclear whether Ca²⁺ suppression reflects macrophage inhibition, altered viability, homeostatic resetting, or an anti-inflammatory program. Minimally, the discussion should be more deeply engaged with possible interpretations and implications of this finding.

The pharmacological blockade of RAMP1 supports a role for CGRP signaling in persistent Ca²⁺ increases after CSD, but the experiments are based on a relatively small number of cells and animals. The limited sample size constrains confidence in the generality of the conclusions. Pharmacological inhibition alone does not establish cell-autonomous effects in macrophages. The authors should acknowledge these limitations more explicitly and avoid overextension of the conclusions.

Comments on revisions:

The authors have answered the questions well.

Reviewer #2 (Public review):

Using chronic intravital two-photon imaging of calcium dynamics in meningeal macrophages in Pf4Cre:TIGRE2.0-GCaMP6 mice, the study identified heterogeneous features of perivascular and non-perivascular meningeal macrophages at steady state and in response to cortical spreading depolarization (CSD). Analyses of calcium dynamics and blood vessels revealed a subpopulation of perivascular meningeal macrophages whose activity is coupled to behaviorally driven diameter fluctuations of their associated vessels. The analyses also investigated synchrony between different macrophage populations and revealed a role for CGRP/RAMP1 signaling in the CSD-induced increase, but not the decrease, in calcium transients.

This is a timely study at both the technical and conceptual levels, examining calcium dynamics of meningeal macrophages in vivo. The conclusions are well supported by the findings and will provide an important foundation for future research on immune cell dynamics within meninges in vivo. The paper is well written and clearly presented.

Reviewer #3 (Public review):

Summary:

The authors of this report wish to show that distinct populations of meningeal macrophages respond to cortical spreading depolarization (CSD) via unique calcium activity patterns depending on their location in the meningeal sub compartments. Perivascular macrophages display calcium signaling properties that are sometimes in opposition to non-perivascular macrophages. Many of the meningeal macrophages also displayed synchronous activity at variable distances from one another. Other macrophages were found to display calcium signals in response to dural vasomotion. CSD could induce variable calcium responses in both perivascular and non-perivascular macrophages in the meninges in part due to RAMP1 dependent effects. Results will inform future research on the calcium responses displayed by macrophages in the meninges under both normal and pathological conditions.

Strengths:

Sophisticated in vivo imaging of meningeal immune cells is employed in the study which has not been performed previously. A detailed analysis of the distinct calcium dynamics in various subtypes of meningeal macrophages is provided. Functional relevance of the responses are also noted in relation to CSD events.

Weaknesses:

Specificity of the methods used to target both meningeal macrophages and RAMP1 are limited. A discussion section on potential pitfalls is included to address this.

Author Response:

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

Public review:

Reviewer #1 (Public review):

Strengths:

(1) The use of chronic two-photon Ca²⁺ imaging in awake, behaving mice represents a major technical strength, minimizing confounds introduced by anesthesia. The development of a Pf4Cre:GCaMP6s reporter line, combined with high-resolution intravital imaging, enables long-term and subcellular analysis of macrophage Ca²⁺ dynamics in the meninges.

(2) The comparison between perivascular and non-perivascular macrophages reveals clear niche-dependent differences in Ca²⁺ signaling properties. The identification of macrophage Ca²⁺ activity temporally coupled to dural vasomotion is particularly intriguing and highlights a potential macrophage-vascular functional unit in the dura.

3) By linking macrophage Ca²⁺ responses to CSD and implicating CGRP/RAMP1 signaling in a subset of these responses, the study connects meningeal macrophage activity to clinically relevant neuroimmune pathways involved in migraine and other neurological disorders.

Thank you for recognizing the strengths in our work.

Weaknesses:

(1) The manuscript relies heavily on Pf4Cre-driven GCaMP6s expression to selectively image meningeal macrophages. Although prior studies are cited to support Pf4 specificity, Pf4 is not an exclusively macrophage-restricted marker, and developmental recombination cannot be excluded. The authors should provide direct validation of reporter specificity in the adult meninges (e.g., co-labeling with established macrophage markers and exclusion of other Pf4-expressing lineages). At minimum, the limitations of Pf4Cre-based labeling should be discussed more explicitly, particularly regarding how off-target expression might affect Ca²⁺ signal interpretation.

We acknowledge that PF4 is not an exclusively macrophage-restricted marker. Yet, among meningeal immunocytes, it is almost exclusively expressed in macrophages (1, 2). Furthermore, in the adult mouse meninges, PF4^Cre-based reporter lines label nearly all dural and leptomeningeal macrophages and almost no other cells (3, 4). This Cre line has also been used to target border-associated macrophages (2, 4). Moreover, a recent study suggests that the bacterial artificial chromosome used to generate the PF4^Cre line does not affect meningeal macrophage activity (4). Nonetheless, in the revised version, we discuss a potential limitation of the Pf4Cre-based labeling approach for studying meningeal macrophages’ Ca²⁺ signaling, namely that a very small population of other meningeal immune cells may also be labeled.

(2) The manuscript offers an extensive characterization of Ca²⁺ event features (frequency spectra, propagation patterns, synchrony), but the biological significance of these signals is largely speculative. There is no direct link established between Ca²⁺ activity patterns and macrophage function (e.g., activation state, motility, cytokine release, or interaction with other meningeal components). The discussion frequently implies functional specialization based on Ca²⁺ dynamics without experimental validation. To strengthen the conceptual impact, a clearer framing of the study as a foundational descriptive resource, rather than a functional dissection, would improve alignment between data and conclusions.

In our discussion, we indicated that “the exact link between the distinct Ca²⁺ signal properties of meningeal macrophage subsets observed herein and their homeostatic function remains to be established”. In the revised discussion part, we acknowledge that this is primarily a descriptive study that provides a foundational landscape of Ca²⁺ dynamics in meningeal macrophages.

(3) The GLM analysis revealing coupling between dural perivascular macrophage Ca²⁺ activity and vasomotion is technically sophisticated and intriguing. However, the directionality of this relationship remains unresolved. The current data do not distinguish whether macrophages actively regulate vasomotion, respond to mechanical or hemodynamic changes, or are co-modulated by neural activity. Statements suggesting that macrophages may "mediate" vasomotion are therefore premature. The authors should reframe these conclusions more cautiously, emphasizing correlation rather than causation, and expand the discussion to explicitly outline experimental strategies required to establish causality (e.g., macrophage-specific Ca²⁺ manipulation).

In the results section, we indicate that our data suggest that dural perivascular macrophages are functionally coupled to locomotion-driven dural vasomotion, either responding to it or mediating it. Furthermore, we discussed the possibilities that 1) macrophages sense vascular-related mechanical changes and 2) macrophage Ca²⁺ signaling regulates dural vasomotion. Moreover, we explicitly state that studying causality will require an experimental approach that has yet to be developed, enabling selective manipulation of dural perivascular macrophages.

(4) The authors conclude that synchronous Ca²⁺ events across macrophages are driven by extrinsic signals rather than intercellular communication, based primarily on distance-time analyses. This conclusion is not sufficiently supported, as spatial independence alone does not exclude paracrine signaling, vascular cues, or network-level coordination. No perturbation experiments are presented to test alternative mechanisms. The authors can either provide additional experimental evidence or rephrase the conclusion to acknowledge that the source of synchrony remains unresolved.

Thank you for this suggestion. In the revision, we indicate that further studies are required to resolve the exact source of synchrony.

(5) A major and potentially important finding is that the dominant macrophage response to CSD is a persistent decrease in Ca²⁺ activity, which is independent of CGRP/RAMP1 signaling. However, this phenomenon is not mechanistically explored. It remains unclear whether Ca²⁺ suppression reflects macrophage inhibition, altered viability, homeostatic resetting, or an anti-inflammatory program. Minimally, the discussion should be more deeply engaged with possible interpretations and implications of this finding.

While we propose that the decrease in macrophage Ca²⁺ signaling following CSD could indicate that a hyperexcitable cortex dampens meningeal immunity, in the revised discussion, we indicate that further studies are needed to determine whether this reduction in meningeal macrophage Ca²⁺ activity reflects altered viability or reduced immune function that could interfere with the macrophage’s ability to restore homeostasis and dampen local inflammation.

(6) The pharmacological blockade of RAMP1 supports a role for CGRP signaling in persistent Ca²⁺ increases after CSD, but the experiments are based on a relatively small number of cells and animals. The limited sample size constrains confidence in the generality of the conclusions. Pharmacological inhibition alone does not establish cell-autonomous effects in macrophages. The authors should acknowledge these limitations more explicitly and avoid overextension of the conclusions.

Although n=3 is common in intravital imaging of the meninges, including experiments employing pharmacological manipulations, such as RAMP1 inhibition (5-7), a larger sample size will increase confidence in the results. We further acknowledge that our pharmacological data indicate only a potential role for RAMP1 signaling in meningeal macrophages and that CGRP/RAMP1 signaling in other meningeal immune or vascular cells may also play a role.

Reviewer #2 (Public review):

Using chronic intravital two-photon imaging of calcium dynamics in meningeal macrophages in Pf4Cre:TIGRE2.0-GCaMP6 mice, the study identified heterogeneous features of perivascular and non-perivascular meningeal macrophages at steady state and in response to cortical spreading depolarization (CSD). Analyses of calcium dynamics and blood vessels revealed a subpopulation of perivascular meningeal macrophages whose activity is coupled to behaviorally driven diameter fluctuations of their associated vessels. The analyses also investigated synchrony between different macrophage populations and revealed a role for CGRP/RAMP1 signaling in the CSD-induced increase, but not the decrease, in calcium transients.

This is a timely study at both the technical and conceptual levels, examining calcium dynamics of meningeal macrophages in vivo. The conclusions are well supported by the findings and will provide an important foundation for future research on immune cell dynamics within the meninges in vivo. The paper is well written and clearly presented.

Thank you.

I have only minor comments.

(1) Please indicate the formal definition of perivascular versus non-perivascular macrophages in terms of distance from the blood vessel. This information is not provided in the main text or the Methods. In addition, please explain how the meningeal vasculature was imaged in the main text.

We did not measure the exact distance of the perivascular macrophages from the blood vessels, but defined them as such based on previous data showing that these cells reside along the abluminal surface and maintain tight interactions with mural cells (8). We now provide this information in the revised manuscript, including their labeling approach with a dextran tracer.

(2) Similarly, the method used to induce acute CSD (pin prick) is not described in the main text and is only mentioned in the figure legends and Methods. Additional background on the neurobiology of acute CSD, as well as the resulting brain activity and neuroinflammatory responses, could be helpful.

We have added more background and the method for inducing CSD (i.e., a pinprick in the frontal cortex) in the Results section.

Reviewer #3 (Public review):

Strengths:

Sophisticated in vivo imaging of meningeal immune cells is employed in the study, which has not been performed previously. A detailed analysis of the distinct calcium dynamics in various subtypes of meningeal macrophages is provided. Functional relevance of the responses is also noted in relation to CSD events.

Thank you for recognizing the strengths of our paper

Weaknesses:

(1) The specificity of the methods used to target both meningeal macrophages and RAMP1 is limited. Additional discussion points on the functional relevance of the two subtypes of meningeal macrophages and their calcium responses are warranted. A section on potential pitfalls should be included.

Please see previous responses regarding the specificity of the PF4Cre line for targeting macrophages. The specificity of the RAMP1 antagonist we used (BIBN4096, Olcegepant) has been confirmed by its developer Boehringer Ingelheim, and has been used to target CGRP signaling in numerous studies, including those targeting meningeal macrophage and vascular signaling (2, 7). A section on the study’s limitations has been added.

References:

(1) H. Van Hove et al., A single-cell atlas of mouse brain macrophages reveals unique transcriptional identities shaped by ontogeny and tissue environment. Nat Neurosci 22, 1021-1035 (2019).

(2) F. A. Pinho-Ribeiro et al., Bacteria hijack a meningeal neuroimmune axis to facilitate brain invasion. Nature 615, 472-481 (2023).

(3) G. L. McKinsey et al., A new genetic strategy for targeting microglia in development and disease. Elife 9, (2020).

(4) H. J. Barr et al., The circadian clock regulates scavenging of fluid-borne substrates by brain border-associated macrophages. bioRxiv, (2025).

(5) T. L. Roth et al., Transcranial amelioration of inflammation and cell death after brain injury. Nature 505, 223-228 (2014).

(6) M. V. Russo, L. L. Latour, D. B. McGavern, Distinct myeloid cell subsets promote meningeal remodeling and vascular repair after mild traumatic brain injury. Nat Immunol 19, 442-452 (2018).

(7) K. L. Monaghan et al., Highly dynamic dural sinuses support meningeal immunity. Nature, (2026).

(8) H. Min et al., Mural cells interact with macrophages in the dura mater to regulate CNS immune surveillance. J Exp Med 221, (2024).

No they don't...

这篇文章有2个收获 1. 如何将实盘更接近回测 2. 推荐了一个工具，更好的验证回测

And where does sa come into this?

Truke.

Yes, Yes, Yes, Yes

💻/thinkpad/🧊/♖/indy/

0📁/about

eLife Assessment

This valuable study shows that locomotion-related modulations in the mouse visual cortex are not uniform but primarily affect neurons in muscarinic receptor-negative patches, which receive projections from specific cortical areas. While the evidence is mostly solid, some uncertainties remain regarding the link between anatomical data and functional measurements. The study should be of interest to neuroscientists interested in state modulation of cortical function.

Reviewer #1 (Public review):

Processing in the primary visual cortex (V1) of mice is not only based on sensory inputs but also strongly modulated by locomotion. In this study, Meier et al. ask whether neurons that are modulated by locomotion form clusters in V1. Their work is based on previous studies from their lab establishing a modularity in the organization of primary visual cortex based on M2-muscarinic-acetylcholine-receptor-positive patches and interpatches (Ji et al. 2015, D'Souza et al. 2019). In these studies, they have highlighted the clustering of specific visual pathways and inhibition. In the current study, they extend this modularity to motor inputs, confirming a clustering of locomotion modulated neurons but also show that these clusters overlap with the M2-negative interpatches of layer 1. Finally, they establish a blueprint for visual processing streams in V1, segregating projections to and from lateral visual areas (LM, AL, and RL) from projections to and from the lateral areas, including the visual area PM, the retrosplenial cortex (RSP), and the secondary motor area (MOs).

Conceptually, this study provides an important finding in the organization of locomotion-related signaling in primary visual cortex, which clearly has substantial implications for sensory processing in visual cortex. While the anatomical data are solid, the link to physiology is incomplete. In conclusion, there are numerous issues that leave the main findings in some doubt, so the authors have some work to do before I find this story convincing.

Major issues:

(1) The major results in this study rely on proper quantification of neuronal responses during resting and running. Recently, it has been reported that hemodynamic occlusion can strongly influence measurements of fluorescent changes using two-photon imaging (Yogesh et al. 2025, doi.org/10.1101/2024.10.29.620650). Since it is unclear whether there is an inherent bias in vasculature and hemodynamic occlusion in M2 patches and interpatches, a quantification of the effect of hemodynamic occlusion would be necessary. This control would ideally be done using mice with GFP expression to test if there is still a clustering of locomotion-modulated neurons that overlaps with M2-negative interpatches. Alternatively, the authors should at the very least quantify the vascularization in M2 patches and interpatches.

(2) To assess the effects, the authors use a correlation analysis for many of their findings (e.g., Figures 2b,c, 4j,k, ...). This, however, is inappropriate to assess the significance of the results. I suggest redoing all statistics with hierarchical bootstrap sampling (Saravanan et al. 2020, PMID: 33644783) or similar.

(3) The authors use two different measures to assess whether and to what extent a neuron is locomotion sensitive, the LMI and "locomotion-responsive". While the LMI is defined based on recording in the light and dark (Figure 2), the "locomotion-responsiveness" is defined only in the dark (Figure 3a,c,d). The link between the two measures should be clarified.

a) Additionally, Figure 2b shows higher average LMI for interpatches, but the locomotion-responsive fraction is similar in interpatches and patches (relative number of pairs in Figure 3c and Figure 3d). How do the authors explain this discrepancy?

b) How is the LMI calculated - based on the average or the maximum response over stimuli? One particular stimulus? If the LMI is defined for each stimulus separately, what is plotted in Figure 2b?

(4) In the last panels of Figures 4-7, the authors analyze the alignment of cell bodies with the M2 patches. While in superficial layers it might be straightforward to align the cell body locations with the M2 patches and interpatches in layer 1, this alignment does not appear to be trivial for deeper layers. The authors should provide additional material to convince the reader of the proper alignment.

(5) Related to point 4 above - Given the importance of a proper alignment of M2 patches with the in vivo imaging, the in vivo - ex vivo alignment should be more convincing than Figure 1 C-E. Measuring M2 patches in vivo (as the authors have tried to do) would have provided more solid evidence. Have the authors tried to remove the dura for their in vivo imaging to increase signal-to-noise? In any case, more examples of proper alignment are necessary.

(6) The authors state that locomotion selectively affects M2-/M2- pairs based on Figure 3c. However, to make this claim, there should be a significant difference between the correlation of stimulus-driven noise of M2-/M2- locomotion-responsive pairs and M2-/M2- locomotion-unresponsive pairs, AND no significant difference in the same analysis for M2+/M2+ pairs (i.e., testing the differences between the bars in Figure 3c and Figure 3d).

Reviewer #2 (Public review):

Summary:

Meier et al. explore the variability of locomotion-related modulations in mouse area V1. They present 4 major findings: V1 L2/3 neurons beneath M2- interpatches are more strongly locomotion-modulated than those beneath M2+ patches, while V1 L2/3 neurons are more strongly orientation tuned. They then use viral tracing to examine the relationship of M2- interpatches and M2+ patches with inputs from and outputs to HVOs, MO, RSP, and LP, and find evidence for different closed-loop subnetworks within L1; these relationships, however, are more complicated for cell bodies in L2/3. Finally, they also describe an overlap between M2- interpatches and SOM+ dendrites/axons.

Strengths:

The strength of the manuscript is the detailed anatomical quantification of closed-loop connectivity, and the description of the organizing principles of M2- interpatches and M2+ patches.

Weaknesses:

The major weakness of the manuscript is the lack of a direct connection between the functional and the anatomical data, and the somewhat puzzling effects observed in the analysis of noise correlations. The former issue might be alleviated by modelling, where the authors could explore the space of possibilities that could explain the functional data based on the anatomical connectivity. Some control analyses could be done, for the comparison of noise correlations.

Reviewer #3 (Public review):

The authors build on the large body of their previous research, which showed that the mouse primary visual cortex is organised into two types of clusters, M2+ and M2-, which exhibit distinct input patterns from thalamus and higher visual cortical areas and distinct visual tuning preferences. The current study reveals that a like-to-like projection from within-cluster neurons to the areas that provide feedback projections and, furthermore, that neurons in the M2- clusters are more strongly affected by non-visual signals about the locomotion of the animal.

The study adds fundamental insights to our understanding of the principles of cortical organisation and computation, specifically how the cortex integrates sensory and action-related signals.

While the tracing data are very convincing, data analysis should be strengthened to support the claims:

(1) The locomotion modulation index (LMI) compares the mean activity during running and not running but does not seem to account for differences between visual stimuli, so that the LMI could be influenced by the neuron's visual tuning rather than its sensitivity to locomotion, e.g. if the mouse was running more when the neuron's preferred stimulus was presented. Trials should first be averaged per stimulus, and then across stimuli. Alternatively, only the preferred stimulus could be considered.

The significance test (unpaired t-test) suffers from the same flaw. Instead an ANOVA (with stimulus parameter as factor) would resolve the problem, or testing whether fitting the data with two tuning curves (one per locomotion state) or a single curve results in a lower error (using cross-validation).

Given that there is evidence that specific visual stimuli can induce more or less running in mice, this issue is very important to account for behavioural differences across stimuli.

(2) All bars in Figure 2b show a lower LMI than the reported mean LMI of 0.19. This should be checked.

(3) Correlation tests: Pearson correlation is only meaningful when applied to continuous data. A more suitable test for discrete data like the M2 patch quantile is a rank test like Kendall's coefficient of rank correlation. This applies to data in Figure 2b,c, 4j,k, Figure 2 - Supplement 2,1a, etc.

(4) How OSI was determined should be clarified. Specifically, were R_pref and R_ortho the mean responses to the two opposite movement directions? Similarly, how was the half-width at half-maximum of orientation determined? From the fits in Figure 2a, it looks like the widths of both Gaussians can be different.

(5) The correlation measures in Figure 3 would greatly benefit from additional analyses to help interpretation of the results.

a) Correlations between neurons typically increase with increasing firing rates (e.g., de la Rocha J, Doiron B, Shea-Brown E, Josić K, Reyes A. 2007. Correlation between neural spike trains increases with firing rate. Nature 448:802-6. doi:10.1038/nature06028). Could the higher correlations in M2+ pairs (Figure 3a) be explained by higher firing rates in M2+ compared to M2- neurons?

b) To determine correlations in Figure 3a, trials during locomotion and stationarity were pooled. As locomotion impacts the firing rate of the neurons, it would be helpful to separate correlations between the two states, locomotion vs stationarity, so the measures reflect something closer to "noise correlations" rather than tuning to locomotion.

c) Similarly, in Figure 3b, I wonder whether the large correlations in M2- pairs are driven by locomotion rather than functional connectivity. As suggested in b, a better test of noise correlations would be to account for locomotion, i.e., separate trials by stimulus identity and locomotion state. To prevent conditions with few trials from having greater weight in the overall noise correlations, I suggest the authors first z-score responses per condition, then determine noise correlations across all trials (as explained in Renart et al., 2010).

d) Correlations in Figure 3a,b should be tested with an ANOVA and a control for multiple tests.

(6) In plots like Figure 4j-l, it would be very informative to show individual measures (per ROI and mouse) in addition to mean +- SEM. As the counts are low (<10) it wouldn't obstruct the plot.

(7) The caption of Figure 4l says that most retrogradely labelled cells are located in L2/3. However, the plot only shows data from L2/3 and a single section of L4, so one cannot compare it to other layers. Can the authors corroborate the claim with data from other layers?

(8) Methods:<br /> The authors should provide more details on the visual stimuli: What was the background on which gratings were presented? How long was the inter-stimulus interval? What was presented during the inter-stimulus interval? How large were gratings used to map tuning to SF, TF, and orientation?

Author response:

In the review, the critique was focused mainly on the functional results, which show that interpatch neurons in mouse V1 are more strongly modulated by locomotion than patch neurons. The anatomical results that patch and interpatch modules are recurrently connected in three interareal subnetworks were considered solid.

We acknowledge the limitations of our work. Specifically, the number of recorded neurons could be higher, the mapping of neurons onto to patch and interpatch modules could be more direct, and the asymmetric distribution of locomotion-modulated responses in layer 2/3 may be confounded by selective masking of GCaMP signals by surface blood vessels. In experiments which are not included in the manuscript we have found no systematic spatial relationship between the M2AChR pattern and the vascular marker CD31, ruling out that masking contributed to the imaging results. Unfortunately, we are unable to revise the manuscript to the extent recommended by the reviewers because the collaborators have left the lab, which closed in 2024.

eLife Assessment

The authors investigate mechanisms of acquired resistance (AR) to KRAS-G12C inhibitors (sotorasib) in non-small cell lung cancer, proposing that resistance arises from signaling rewiring rather than additional mutations. While the study addresses a valuable clinical question, it is limited by several weaknesses in experimental rigor, data interpretation, and presentation, meaning the strength of evidence is incomplete.

Reviewer #1 (Public review):

Summary:

In this study, the authors investigate mechanisms of acquired resistance (AR) to KRAS-G12C inhibitors (sotorasib) in NSCLC, proposing that resistance arises from signaling rewiring rather than additional mutations.

Strengths:

Using a panel of AR models-including cell lines, PDXs, CDXs, and PDXOs-they report activation of KRAS and PI3K/AKT/mTOR pathways, with elevated PI3K levels. Pharmacologic inhibition or CRISPR-Cas9 knockout of PI3K partially restores sotorasib sensitivity, and p-4EBP1 upregulation is implicated as an additional contributor, with dual mTORC1/2 inhibition more effective than mTORC1 inhibition alone.

Weaknesses:

While the study addresses an important clinical question, it is limited by several weaknesses in experimental rigor, data interpretation, and presentation. The mechanistic findings are not entirely novel, since the role of PI3K-AKT-mTOR signaling in therapeutic resistance is already well-established in the literature. Several key conclusions are not entirely supported by the data. Furthermore, while the authors use CRISPR-Cas9 to knock out PI3K and 4E-BP1 in H23-AR and H358-AR cells to restore sotorasib sensitivity, they do not perform reconstitution experiments to confirm that re-expressing PI3K or 4E-BP1 reverses the sensitization. This prevents full characterization of PI3K and p-4EBP1 upregulation as contributors to resistance.

Comments on revised version:

The authors have addressed some but not all of my concerns and suggestions. The authors do acknowledge some of the limitations. It would be useful to include a limitations paragraph in the Discussion.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors focus on the identification of the mechanisms involved in the acquired resistance to Sotorasib in non-small lung KRASG12C mutant cells. To perform this study, the authors generate different clones of cell lines, cell-derived xenografts, patient-derived xenograft organoids and patient-derived xenografts. In all these models, the authors generate resistant forms (i.e., resistant cell lines PDXs and organoids) and the genetic and molecular changes were characterised using whole-exome sequencing, proteomics and phospho-proteomics. This analysis led to the identification of an important role of the PI3K/AKT/mTORC1/2 signalling network in the acquisition of resistance in several of the models tested. Molecular characterisation identified changes in the expression of some of the proteins in this network as key changes for the acquisition of resistance, and in particular, the authors show that changes in 4E-BP1 are common to some of the cells downstream of PI3K. Using pharmacological testing, they show that different drugs targeting PI3K, AKT and MTORC1/2 sensitise some of the resistant models to Sotorasib. The analyses showed that the PI3K inhibitor copanlisib has an effect in NSCLC cells that, in some cases, seems to be synergistic with Sotorasib. Based on the work performed, the authors conclude that the PI3K/mTORC1/2 mediated 4E-BP1 phosphorylation is one of the mechanisms associated with the acquisition of resistance to Sotorasib and that targeting this signalling module could result in effective treatments for NSCLC patients.

The work as presented in the reviewed manuscript is still very interesting, provides cell models that benefit the community, and can be used to expand our knowledge of the mechanism of resistance to KRAS targeting therapies. Some changes suggested by reviewer 1 and this reviewer have been made to the text, including changes to text and figures, including quantification of some blots. But for most of it, this version is very similar to the first submission and many of the weaknesses and suggestions I made remain the same.

Strengths:

- One of the stronger contributions of this article is the different models used to study the acquisition of resistance to Sotorasib. The resistant cell lines, PDXs and PDXOs and the fact that the authors have different clones for each, made this collection especially relevant as they seem to show different mechanisms that the cells used to become resistant to Sotorasib. Although logically, the authors focus on one of these mechanisms, the differential responses of the different clones and models to the treatments used in this work show that some of the clones used additional mechanisms of resistance that can be explored in other studies. Importantly, as they use in vitro and in vivo models, the results also consider the tumour microenvironment and other factors in the response to the treatments.

- Another strength is the molecular characterisation of the different Sotorasib-resistant tumour cells by WES, which shows that these cells do not seem to acquire secondary mutations.

- The use of MS-based proteomics also identifies proteome signatures that are associated with the acquisition of resistance, including PI3K/mTORC1/2. The combination of proteomics and phospho-proteomics results should allow the identification of several mechanisms that are deregulated in Sotorasib-resistant cells

- The results show a strong response of the NSCLC cells and PDXs to copanlisib, a drug for which there is limited information in this cancer type.

- The way they develop the PDX-resistant and the PDXO seems to be appropriate.

- The revised manuscript includes the information for the whole exosome sequence, making the finding clearer for the reader.

Weaknesses:

In general, the data is of good quality, but due to the sheer amount of data included and the way it is presented and discussed, several of the claims or conclusions are not clear.

- The abstract is mainly the same, and the authors only indicate that they will update it.

- The tables with the proteomics data are still not included, and again, there is only a comment from the authors that it will be made available. Thus, the way the data is presented in Figure 3 still does not allow the reader to get an idea of many of the findings from this experiment.

- In Figure 3, the authors indicate that the raw data will be included in the revised version, which should improve the understanding of the reader, but this is not included yet. As in the previous version, the MS-based Phosphoproteome is still not really presented in the current manuscript.

- The authors still do not specify where the proteomics data will be deposited, and whether it will be made public to comply with FAIR principles. They indicate that they will comply with the journal requests, but it is still not clear what will be deposited.

- The experiments in Figure 4 are very confusing, and some controls are missing. There is no blot where they show the effect of Sotorasib treatment in H23 and

- The authors do not address the important point made in the previous review about the effect of copanlisib in parental cells. I might not have been clear, so the data in Figure 4D-F seem to support that PI3K treatment of parental cells is as effective as in the resistant cells. Therefore, it is not clear whether the effect shown in the resistant cells is related to the acquisition of resistance to sotorasib or if these cells are simply sensitive to the drug because the parental cells were already sensitive.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this study, the authors investigate mechanisms of acquired resistance (AR) to KRAS-G12C inhibitors (sotorasib) in NSCLC, proposing that resistance arises from signaling rewiring rather than additional mutations.

Strengths:

Using a panel of AR models - including cell lines, PDXs, CDXs, and PDXOs - they report activation of KRAS and PI3K/AKT/mTOR pathways, with elevated PI3K levels. Pharmacologic inhibition or CRISPR-Cas9 knockout of PI3K partially restores sotorasib sensitivity, and p-4EBP1 upregulation is implicated as an additional contributor, with dual mTORC1/2 inhibition more effective than mTORC1 inhibition alone.

Weaknesses:

While the study addresses an important clinical question, it is limited by several weaknesses in experimental rigor, data interpretation, and presentation. The mechanistic findings are not entirely novel, since the role of PI3K-AKT-mTOR signaling in therapeutic resistance is already well-established in the literature. Rather than uncovering new resistance mechanisms, the study largely confirms known pathways. Several key conclusions are not supported by the data, and critical alternative explanations - such as additional mutations or increased KRAS expression - are not thoroughly investigated or ruled out. Furthermore, while the authors use CRISPR-Cas9 to knock out PI3K and 4E-BP1 in H23-AR and H358-AR cells to restore sotorasib sensitivity, they do not perform reconstitution experiments to confirm that re-expressing PI3K or 4E-BP1 reverses the sensitization. This prevents full characterization of PI3K and p-4EBP1 upregulation as contributors to resistance. The manuscript also has several errors, poor figure quality, and a lack of proper quantification. Additional experimental validation, data improvement, and text revisions are required.

Acquired resistance to KRAS^G12C inhibitors such as sotorasib or adagrasib remains a significant clinical challenge. Therefore, the identification of mechanisms of acquired resistance, along with the development of alternative therapeutic strategies, including combination therapies with KRAS inhibitors, represents an urgent unmet clinical need. The emergence of secondary KRAS mutations or new mutations in other oncogenic drivers has been observed as a primary cause of acquired resistance in a fraction of patients. No identifiable mutations were detected in more than half of the tumors from patients who developed acquired resistance after treatment with sotorasib or adagrasib.

Using a discovery-based approach that integrated global proteomic and phosphoproteomic analyses in the TC303AR and TC314AR PDX models, we identified distinct protein signatures associated with KRAS reactivation, upregulation of mTORC1 signaling, and activation of the PI3K/AKT/mTOR pathway. These findings prompted further investigation into these mechanisms of resistance and evaluation of novel therapeutic combinations to overcome resistance. Notably, the combination of sotorasib with copanlisib (a PI3K inhibitor), or the combination of sotorasib with AZD8055, or sapanisertib (mTORC1/2 dual inhibitors) demonstrated strong potential for future clinical use. These regimens effectively restored sotorasib sensitivity in both in vitro and in vivo models and produced robust, synergistic antitumor effects across various acquired resistance models.

CRISPR-Cas9-mediated PI3K and 4E-BP1 knockout clones were generated in more than one resistant cell line that expressed a robust level of the knockout target, and multiple independent clones in each cell line were evaluated with and without gene disruption. Given the thorough nature of this analysis, additional reconstitution experiments were deemed unnecessary, as they would not yield further insight.

Whole exome sequencing was performed on resistant cells or PDX models to confirm retention of the KRAS^G12C mutation and to assess for potential secondary KRAS mutations. While our study focused on KRAS secondary mutation and its specific signaling pathways, we acknowledge that additional resistance mechanisms may be involved. These will be the focus of future investigations.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors focus on the identification of the mechanisms involved in the acquired resistance to Sotorasib in non-small lung KRASG12C mutant cells. To perform this study, the authors generate different clones of cell lines, cell-derived xenografts, patient-derived xenograft organoids, and patient-derived xenografts. In all these models, the authors generate resistant forms (i.e., resistant cell lines PDXs and organoids) and the genetic and molecular changes were characterised using whole-exome sequencing, proteomics, and phospho-proteomics. This analysis led to the identification of an important role of the PI3K/AKT/mTORC1/2 signalling network in the acquisition of resistance in several of the models tested. Molecular characterisation identified changes in the expression of some of the proteins in this network as key changes for the acquisition of resistance, and in particular, the authors show that changes in 4E-BP1 are common to some of the cells downstream of PI3K. Using pharmacological testing, they show that different drugs targeting PI3K, AKT, and MTORC1/2 sensitise some of the resistant models to Sotorasib. The analyses showed that the PI3K inhibitor copanlisib has an effect in NSCLC cells that, in some cases, seems to be synergistic with Sotorasib. Based on the work performed, the authors conclude that the PI3K/mTORC1/2 mediated 4E-BP1 phosphorylation is one of the mechanisms associated with the acquisition of resistance to Sotorasib and that targeting this signalling module could result in effective treatments for NSCLC patients.

The work as presented in the current manuscript is very interesting, provides cell models that benefit the community, and can be used to expand our knowledge of the mechanism of resistance to KRAS targeting therapies. Overall, the techniques and methodology seem to be performed in agreement with standard practice, and the results support most of the conclusions made by the authors. However, there are some points that, if addressed, would increase the value and relevance of the findings and further extend the impact of this work. Some of the recommendations for changes relate to the way things are explained and presented, which need some work. Other changes might require the performance of additional experiments or reanalysis of the existing data.

Strengths:

(1) One of the stronger contributions of this article is the different models used to study the acquisition of resistance to Sotorasib. The resistant cell lines, PDXs and PDXOs, and the fact that the authors have different clones for each, made this collection especially relevant, as they seem to show different mechanisms that the cells used to become resistant to Sotorasib. Although logically, the authors focus on one of these mechanisms, the differential responses of the different clones and models to the treatments used in this work show that some of the clones used additional mechanisms of resistance that can be explored in other studies. Importantly, as they use in vitro and in vivo models, the results also consider the tumour microenvironment and other factors in the response to the treatments.

(2) Another strength is the molecular characterisation of the different Sotorasib-resistant tumour cells by WES, which shows that these cells do not seem to acquire secondary mutations.

(3) The use of MS-based proteomics also identifies proteome signatures that are associated with the acquisition of resistance, including PI3K/mTORC1/2. The combination of proteomics and phospho-proteomics results should allow the identification of several mechanisms that are deregulated in Sotorasib-resistant cells.

(4) The results show a strong response of the NSCLC cells and PDXs to copanlisib, a drug for which there is limited information in this cancer type.

(5) The way they develop the PDX-resistant and the PDXO seems to be appropriate.

Weaknesses:

In general, the data is of good quality, but due to the sheer amount of data included and the way it is presented and discussed, several of the claims or conclusions are not clear.

(1) The abstract is rather long and gives details that are not usually included in one. This makes it very complicated to identify the most relevant findings of the work. The use of acronyms PDX, PDXO, and CDX without defining them makes it complicated for the non-specialist to know what the models are. Rewriting and reorganisation of the abstract would benefit the manuscript.

We revised the abstract to ensure that the key findings and overall message are clearly communicated and easily understood by readers.

(2) Expression, presentation, and grammar should be reviewed in all sections of the manuscript.

This has been done in the revised version

(3) In the different parts of the result section where the models shown in Figure 2 are described the authors indicate "Whole-exome sequencing (WES) confirmed that XXX model retained the KRASG12C mutation with no additional KRAS mutations detected" however, it is not indicated where this data is shown and in not all the cases there is explanation to other possible modifications that might relate to mechanisms of resistance. This information should be included in the manuscript, and the WES made publicly available.

WES was done for KRAS to investigate the additional secondary mutation in the KRAS as well as to verify the retention of the KRAS^G12C mutation in these AR models. WES data has been provided as supplements

(4) The way the proteomics analysis of the TC303 and TC314 parental and resistant PDX is described in the text is confusing. The addition of an experimental layout figure would facilitate the understanding. As it is written, it is not obvious that the parental PDX were also analysed. For instance, the authors say, "The global and phosphoproteomic analyses identified over 8,000 and 4,000 gene protein products (GPPs), respectively". Is this comparing only resistant cells, or from the comparison of the parental and resistant pairs? And where are these numbers presented in the figures? Also, there is information that seems more adequate for the materials and methods sections, i.e., "Samples were analyzed using label-free nanoscale liquid chromatography coupled with tandem mass spectrometry (nanoLC-MS/MS) on a Thermo Fusion Mass Spectrometer. The resulting data were processed and quantified using the Proteome Discoverer 2.5 interface with the Mascot search engine, referencing the NCBI RefSeq protein database (Saltzman, Ruprecht). Two-component analysis is better named principal component analysis."

The text has been revised accordingly

(5) While the presentation of the proteomics data could be done in different ways, the way the data is presented in Figure 3 does not allow the reader to get an idea of many of the findings from this experiment. Although it is indicated that a table with the data will be made available, this should be central to the way the data is presented and explained. A table (ie, Excel doc) where the raw data and all the analysis are presented should be included and referenced. Additionally, heat maps for the whole proteomes identified should be included. In the text, it is said, "Global proteomic heatmap analysis revealed unique protein profiles in TC303AR and TC314AR PDXs compared to their sensitive counterparts (Figure 3C)." However, this figure only shows the histogram of the differentially regulated cells. Inclusion of the histogram showing all the cells is necessary, and it might be informative to include the histogram comparing the two isogenic pairs, which could identify common mechanisms and differences between both sets. In Figure 3C, the protein names should be readable, or a reference to tables where the proteins are listed should be included.

The raw data associated with the proteomics and global proteomics can has beeen added as supplements.

(6) In Figure 3, the pathway enrichment tool and GO used should be mentioned in the text. The tables with all significant tables should also be provided. The proteomics data seems to convincingly identify mTOR as one of the pathways deregulated in resistant cells, but there is little explanation of what is considered a significant FDR value and if there are other pathways or networks that are also modified, which might not be common to both isogenic models. In MS-based Phosphoproteome could help with the identification of differentially regulated pathways, but it is not really presented in the current manuscript. Most of the analysis of phospho-proteomics comes from the RPPA analysis, which is targeted proteomics. With the way the data is presented, the authors show evidence for a role of mTOR in the acquisition of resistance, but unfortunately, they do not discuss or allow the reader to explore if other pathways might also contribute to this change.

The authors agree that other pathways may be involved, and this will be the subject of future study. The raw data has been added as supplements for the readers' interest.

(7) Where is the proteomics data going to be deposited, and will it be made public to comply with FAIR principles?

Has been uploaded according to the journal guidelines

(8) The authors claim that the resistance shown for H23AR and H353AR cells is due to reactivation of KRAS signalling. This is done by looking to phosphorylation of ERK as a surrogate, as they claim, "KRAS inhibition is commonly assessed by evaluating the inhibition of ERK phosphorylation (p-ERK)". While this might be true in many cases, the data presented does not demonstrate that the increase in p-ERK is due to reactivation of KRAS. To make this claim, the authors should measure activation of KRAS (and possibly H- and NRAS) using GST-pull down or an image-based method.

We agree that KRAS activation can be assessed through various methods. In this manuscript, which primarily focuses on mechanisms of resistance, pathway analysis revealed upregulation of KRAS signaling. This finding correlated with the incomplete inhibition of p-ERK by sotorasib in resistant cells. Notably, p-ERK status is widely recognized and routinely used as a surrogate marker for KRAS pathway activation.

(9) The experiments in Figure 4 are very confusing, and some controls are missing. There is no blot where they show the effect of Sotorasib treatment in H23 and H358 parental cells. Is the increase shown in resistant cells shown in parental or is it exclusive for resistant cells only (and therefore acquired)? Experiment 4B should include this control. What is clear is that there is an increase in the expression of AKT and PI3K.

H23 and H358 cells are highly sensitive to sotorasib, as demonstrated by the cell viability assays presented in Figure 2. As shown in Figure 3—figure supplement 3, sotorasib treatment led to complete inhibition of p-ERK in these parental cell lines. In contrast, p-ERK inhibition was incomplete in the resistant H23AR and H358AR cells, highlighting a distinct signaling behavior that prompted us to further investigate on AR cells. Moreover, these AR cells were continuously cultured under sotorasib pressure to maintain the resistance.

(10) The main point here is whether this is acquired resistance or the sensitivity to the drug is already there, and there was no need to do an omics experiment to find this. In some cases, it seems that the single treatment with PI3K inhibitors is as effective as Sotorasib treatment, promoting the death of the parental cells. This is in line with previous data in H23 and H353 that show sensitivity to PI3K inhibition (i.e., H358 10.1016/j.jtcvs.2005.06.051; 10.1016/j.jtcvs.2005.06.051H23 10.20892/j.issn.2095-3941.2018.0361). The data is clear, especially for copanlisib, but would it be the case that this treatment could be used for the treatment of NSCLC alone or directly in combination with Sotorasib and prevent resistance? The results shown in Figure 4C strongly support that a single treatment might be effective in cases that do not respond to Sotorasib. The data in figure 4D-F (please correct typo "inhibition" in labels) seem to support that PI3K treatment of parental cells is as effective as in the resistant cells.

We agree. Based on our in vitro (Figure 4) and in vivo (Figure 7) data, copanlisib was able to overcome sotorasib resistance, demonstrating either synergistic or additive effects depending on the specific model. These findings support the potential of combining PI3K inhibition with KRAS^G12C inhibition as a promising strategy to address acquired resistance.

(11) The experiments presented in Figure 7 show synergy between Sotorasib and copanlisib treatment in some of the resistant cells. But in Figure 7G, the single treatment of H23AR is as effective as the combination. Did the authors check the effect of this drug on the parental cells? As they do not include this control, it is not possible to know if this is acquired sensitivity to PI3K inhibition or if the parental cells were already sensitive (as indicated by the Figure 4 results).

Both H23 and H23AR cells demonstrated high sensitivity to copanlisib, as shown in Figure 4. Combination index analysis for the copanlisib + sotorasib treatment (Figure 7A) revealed synergistic effects on cell viability at specific concentrations. However, in the in vivo experiment (Figure 7G), we did not observe a clear synergistic effect of the combination treatment against H23AR xenografts. This may be attributed to the dose of copanlisib used, which was potentially sufficient on its own to produce a strong antitumor response, thereby masking any additional benefit from the combination.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

To strengthen the scientific rigor and overall presentation of the study, the authors should consider the following:

(1) Perform additional functional validations, including reconstitution experiments after PI3K and 4E-BP1 knockouts, to more definitively demonstrate the role of these targets in mediating resistance.

CRISPR-Cas9-mediated PI3K and 4E-BP1 knockout clones were generated in more than one resistant cell line that expressed a robust level of the knockout target, and multiple independent clones in each cell line were evaluated with and without gene disruption. Acquired resistant H23AR and H358AR isogeneic cells overly expressed PI3K and 4EBP1 proteins, whereas the expression of these proteins was normal in parental cell lines (H23 and H358). These two pairs of cell lines (H23 vs H23AR & H358 vs H358AR), along with multiple knock-out clones from each cell line, were used in every functional assay, which represents the cells or clones with normal, overexpression, and no expression of the target proteins (Figure 5B, D-F & Figure 6D-E). Given the thorough nature of this analysis, additional reconstitution experiments were deemed unnecessary, as they would not yield further insight.

(2) Improve experimental quantifications, particularly for western blot analyses, and ensure all key findings are supported by statistically significant comparisons.

The changes observed on the Western blot were not subtle and obvious without quantification.

(3) Clarify enrichment analysis by directly comparing resistant and sensitive models and use appropriate FDR thresholds (<0.05) when claiming significant pathway activation.

The Mass Spectrometry data were analyzed by the Department of Biostatistics, and the methodology for the statistical analysis is explained in the Methods section. The enriched pathways were identified by pre-ranked GSEA using the gene list ranked by log-transformed P values with signs set to positive/negative for a fold change of >1 or <1, respectively, from the global proteomics and phosphoproteomics data. All the enriched pathways were ranked based on their enrichment scores and considered significant with an FDR value <0.05. Each enrichment plots in Figure 2 were marked with its respective FDR q value as well as nominal p-value (Figure 2D-E). The result section (page 14) is also revised for clarification.

(4) Address alternative mechanisms of resistance, such as secondary mutations or KRAS overexpression, through deeper genetic and proteomic profiling.

The authors agree that other pathways may be involved, and this will be the subject of future research. Our WES analysis on H23AR and H358AR cells shown in Figure 2 Supplement 1, did not find any additional mutations in KRAS, although there were some SNPs and Indel mutations, and not considered as outside the scope of our current study. KRAS signaling upregulation found in Gene Enrichment Analysis, shown in Figure 3D, was validated through its ERK-phosphorylation status in Figure 3-supplement 3.

(5) Improve data presentation by enhancing figure quality, ensuring consistent labeling, and providing complete figure legends and descriptions.

Revised

(6) Revise and polish the manuscript text for clarity, accuracy, and consistency, paying special attention to avoiding contradictory statements and strengthening mechanistic interpretations.

Revised

Major Comments:

(1) In Figure 1A, the authors state that "four PDX models were selected for evaluating sotorasib sensitivity based on their distinct co-mutation patterns," but it is unclear whether these patterns are common, clinically significant, or selected for another specific reason. Clarification is needed regarding the rationale for model selection.

The models have co-mutations that are common in clinical specimens and are associated with drug resistance (Skoulidis, Ferdinandos, et al. "Co-occurring genomic alterations define major subsets of KRAS-mutant lung adenocarcinoma with distinct biology, immune profiles, and therapeutic vulnerabilities."Cancer discovery 5.8 (2015): 860-877). Out of 11 PDX models with KRAS^G12C mutations, 4 models were selected for in vivo evaluation of sotorasib sensitivity based on their distinct co-mutation status. Co-mutations with either p53, STK11, or KEAP1 are the most commonly found co-mutations in NSCLC and become more challenging in therapeutic treatments in the clinic. All four PDXs selected for the in-vivo study harbor at least one of these co-mutations with the KRAS^G12C mutation.

(2) Whole-exome sequencing (WES) results for TC303 AR and TC314 AR are mentioned but not shown in the supplementary material. These results should be included.

Included as a figure supplement in Figure 1-figure supplement 1

(3) In Figure 2 - Figure Supplement 1, H23 AR and H358 AR acquired multiple SNPs and indels compared to their sensitive counterparts. The authors need to address whether these genetic alterations could contribute to resistance.

The authors agree that other pathways may be involved, and this will be the subject of future research. Our WES analysis on H23AR and H358AR cells, shown in Figure 2 Supplement 1, did not find any additional mutations in KRAS, although there were some SNPs and Indel mutations considered as outside the scope of our current study. KRAS signaling upregulation found in Gene Enrichment Analysis, shown in Figure 3D, was validated through its ERK-phosphorylation status in Figure 3-supplement 3.

(4) In Figure 3D-E, in the enrichment analysis, the authors describe enrichment of mTORC1 signaling in resistant PDXs without sufficiently comparing with the sensitive counterparts. They need to clarify whether the enrichment is unique to resistant cells.

The comparison is sensitive to resistant cells (Figure 3C). In Figure 3D-E all enrichment data presented in the figure were derived from global and phosphoproteomic analysis on sotorasib-acquired resistant TC314AR PDX and compared with its sensitive counterpart TC314 PDX (Figure 3D) and sotorasib-acquired resistant TC314AR+TC303AR PDXs (combined) vs their sensitive counterparts TC314 + TC303 PDXs (Combined) in Figure 3E. We revised the text to make it clear.

(5) In Figure 3F, the FDR values of 0.5 and 1.0 are too high to support conclusions of significant pathway activation. Similar issues exist for Figure 3 - Figure Supplement 2 (FDR q-values of 1.0, 0.989, and 0.813).

Agree, FDR values are higher in the enrichment analysis on phosphoproteomic data, and not in the proteomics data. However, these enrichment scores indicate pathway activation. The FDR was higher, most likely due to the low number of phosphoproteins enriched in the designated pathways. Significant FDR values were found when the enrichment analysis was done on global proteomics data.

(6) In Figure 3H, PI3K upregulation is inferred from RPPA quantification. An independent validation, such as immunoblotting, should be provided.

In addition to the sotorasib-acquired resistant PDX samples, PI3K was found to be upregulated and shown in immunoblotting on sotorasib-resistant isogeneic cell lines (H23AR and H358AR cells) in Figure 4B.

(7) In Figure 4B, increased PI3K (p85) levels alone do not support pathway activation, as p-AKT levels remain unchanged. Functional downstream markers (e.g., p-S6, p-4EBP1) should be assessed.

Agree, the status of other downstream markers, such as p-S6 and p-4EBP1, was shown in Figure 4H and Figure 5E & 5F.

(8) In Figure 4D, PI3K inhibition does not reduce colony formation in AR cells relative to parental cells. The data do not support the conclusion that PI3K inhibition sensitizes AR cells.

These experiments show that the drugs are equally effective in the presence or absence of drug resistance to sotorasib. The specific role of PI3K is shown in the knockout experiments (Fig. 5) as explained in the result section on pages 18-19. H23AR and H358AR cells showed over 600- and 200-fold resistance to sotorasib as compared with their sensitive counterpart (Figure 2A) with IC50 20µM and 6µM, respectively. Whereas copanlisib, a PI3K inhibitor, can significantly sensitize the AR cells with the IC50 0.39µM and 0.06µM in H23AR and H358AR cells, respectively, which were as sensitive as the parental cells. PI3K signaling was significantly upregulated in AR cells, and inhibition of the PI3K-AKT-mTOR signaling through CRISPR-Cas9 PI3K knock-out (Figure 5) or inhibition of PI3K or downstream molecules by copanlisib, everolimus, or AZD8055 sensitizes the AR cells as singularly or synergistically with sotorasib (Figure 6H, & Figure 7A).

(9) In Figures 4D-F, single or combination inhibition of PI3K, AKT, and mTORC1 in H23/H23AR and H358/H358AR cells shows no significant difference in colony formation between resistant and parental lines. Therefore, the conclusion that PI3K inhibition sensitizes sotorasib-resistant cells is not supported by the data.

See response to (8).

(10) In Figure 4G, copanlisib does not significantly inhibit p-mTOR (S2448) in H23 AR cells, and total mTOR levels decrease slightly. Quantification should be added.

Added as a supplement

(11) In Figure 4G, western blot results for p-PDK and PDK are not quantified, and effects vary between H23^AR and H358^AR cells. Quantification needs to be added.

Added as a supplement

(12) In Figure 6H, cell viability curves for H23AR/PI3K KO 3-3 cells start from <60%, suggesting pre-existing poor cell health. This casts doubt on conclusions regarding dual drug effects.

All cell viability remained at or close to 100% at the no-treatment control condition, and the cell viability at the starting point was lower than 100% only in the combination treatment group, where the cells were treated with at least one drug. Here, a fixed dose of AZD8055 (50nM or 100nM) was combined with different doses of sotorasib. The dual drug effects are assessed by the combination index, which takes viability factors into account. Combination effects were confirmed by in vivo experiments.

(13) The manuscript claims that mTORC1 inhibition alone is insufficient to suppress resistance (page 23), yet earlier reports that the mTORC1 inhibitor everolimus significantly reduces colony formation (page 17). This inconsistency needs to be addressed.

revised. On p. 23, we are referring to 4E-BP1-mediated resistance.

(14) In Figure 7G, since copanlisib alone appears as effective as combination therapy, the authors should revise the conclusion to emphasize the sufficiency of PI3K inhibition alone.

Agree, the copanlisib treatment appeared to be very effective in the H23AR xenograft model, which is most likely due to the copanlisib dose used in this model, which showed a strong antitumor effect and superseded the combination effect. However, the synergistic antitumor activity of copanlisib with sotorasib was found in H358CDX and TC314AR PDX models (Figure 7D, & I).

(15) In Figure 7I, statistical comparisons (P-value) comparing combination therapy to copanlisib monotherapy are missing. Without statistical significance, the conclusion regarding the combination efficacy cannot be justified.

Revised

Minor Comments:

(1) Figure 1D is not described in the main text.

Revised

(2) On page 12, "FigG" and "FigH" should be corrected to "Figure 2G" and "Figure 2H," respectively.

Revised

(3) On page 17, the section title "copanlisib modulates PI3K-AKT-mTOR signaling..." should capitalize the first word.

Revised

(4) In Figure 7, "sotorasib" and "AMG510" are used interchangeably but refer to the same drug; consistent labeling should be used to avoid confusion.

Revised

(5) In Figure 7 - Figure Supplement 2A-B, the rationale for switching from AZD8055 to sapanisertib, another dual mTORC1/mTORC2 inhibitor, is unclear and should be explained.

Revised

Reviewer #2 (Recommendations for the authors):

Please review all the figures and labels, are there are many mistakes? Also, check the way that the figures are presented and, if necessary, increase the definition.

Revised

(1) Figure 2 seems to be squashed.

Revised

(2) RPPA experiment "PI3K-AKT-mTOR signaling pathway compared to their sensitive counterparts. Specifically, the expression levels of MEK1, p-MEK1, p-MAPK, PDK1, p-PRAS40, p-GSK-3β, p-4E-BP1, p-PI3K, p-Akt, p-PRAS40, p-p38-MAPK, p-AMPK, and p-MAPK were markedly increased in resistant TC303AR and TC314AR PDXs." Several of these proteins are not really part of the PI3K-AKT-MTOR pathway, as such, but the MAPK pathway, and this is masked by not mentioning this. It is also necessary to explain which proteins are called MAPK and why there are 2 p-MAPK.

Revised

(3) Figure 3 - Figure Supplement 3. The images seem saturated for some of the blots. Is there still a decrease in ERK activity in the resistant cells? Lower exposure blots should be included, and if possible, some quantification performed.

Quantification added

(4) Figure 4I, review the title of the left graph, as this is not only sensitivity to everolimus.

Revised

(5) The figure legends need extensive review and rewriting. For instance, in Figure 6, the times for how long the treatments were performed in the different graphs have to be specified. The figure legends must allow interpretation of the data without reading the material and methods or text.

Revised

Materials and Methods

This section needs special attention for typos and style, for instance:

(1) Correct "KRASG12G inhibitors including sotorasib, adagrasib," to G12C.

Revised

(2) Use appropriate symbols i.e., "3 ul sgRNA (30 uM), 0.5 ul Cas9 (20 uM), and 3.5 ul Buffer R were mixed"

Revised

Peergos Web Hosted version of originating seeding infrastrucurre of the Indy Web

used to be called Origo folder

But that one has got migrated to

/indy/0/📁/Origo folder for the Network Participant named indy

as the origo folder contains

for - book - Ascent of Humanity - Charles Eisenstein - This book was important in my formative studies on progress traps

Version 4 of this preprint has been peer-reviewed and recommended by Peer Community in Mathematical and Computational Biology.<br /> See the peer reviews and the recommendation.

eLife assessment

This study provides an important and biologically plausible account of how human perceptual judgments of heading direction are influenced by a specific pattern of motion in optic flow fields known as retinal curl. By combining psychophysical experiments and neural modeling, the authors demonstrate that what was previously considered an incidental "nuisance" signal actually serves as a functional control signal for estimating heading and steering toward a fixated target. While the evidence for the role of curl signals is convincing and advances our understanding of vision-based navigation, the work's impact would be strengthened by situating these findings among other cues that contribute to heading estimation, and by clarifying both the time course of these computations and their generalizability across different navigational contexts.

Reviewer #1 (Public review):

Summary:

This carefully executed study uncovers the functional relevance of curl signals that impinge on the retina every time an observer's gaze direction and movement direction are not aligned.

Strengths:

This finding is important, highlighting the functional role of an abundant incidental signal (curl in retinal motion) that has thus far believed to be a nuisance that needs to be filtered out of the retinal motion stream.

The study's evidence is compelling: a combination of psychophysical experiments and critical manipulations, control theory and neural modeling, which together make an internally consistent and biologically plausible case for the role of curl signals in estimating heading direction.

This study uncovers the functional relevance of curl signals that occur on the retina when an observer is moving, and gaze is not straight ahead. The experimental and modeling results clearly go beyond previous studies and significantly advance our understanding of vision-based navigation.

Another clear strength is that the study uses tightly controlled experimental manipulation to provide strong test cases for the hypothesis that curl is used for visual navigation. These conditions are important to constrain the proposed model (and future models) of heading control.

The modeling is very clearly described, and the modeling and analysis code is published and freely available. The authors go beyond a back-of-the-envelope control model and show how it might be implemented at the neural-circuit level. The model is biologically plausible.

Weaknesses:

The discussion would benefit from an extension of the implications of the study and predictions of their model.

Reviewer #2 (Public review):

This study examines how curl in the retinal flow field can be used as a control variable for estimating and controlling the heading of a moving observer. The basic idea (which is not entirely new, see Matthis et al. 2022) is that translation along a path with eccentric gaze (meaning that the subject is not heading toward the point they are looking at) produces a pattern of optic flow on the retina with a rotational component around the point of fixation (which can be captured by the mathematical "curl" operator). The sign and magnitude of retinal curl vary with heading relative to the point of fixation, such that curl can be used as a control variable to steer rightward or leftward to move toward the fixated target. The authors perform behavioral experiments and show that there are biases in perceived heading that seem to be largely governed by retinal curl. They also show that a simple controller model can use curl to steer toward a target, and they provide a neural network model that provides a biologically plausible implementation of the controller (although there are some questions about that).

There is a core of interesting work here that I think can be important to the field. However, there is a lack of clarity on several important fronts, including design of the behavioral experiments, presentation of the behavioral data, conceptual framing of what curl can and cannot do, etc. Equally importantly, the manuscript is not written in a manner that will make it accessible to most vision scientists. I consider myself to be pretty knowledgeable about optic flow, and I had to read most of the manuscript 3 or 4 times to be able to understand the bulk of it. And my experience is that most vision scientists do not understand optic flow well, so I fear that most of the readers that the authors should want to reach would struggle to understand the work. As written, this is mainly going to make an impact on a handful of optic flow gurus. Thus, I consider that this manuscript will need a major overhaul to clarify important issues and make it more accessible.

Major issues:

(1) The manuscript contains inconsistent, if not misleading, messaging about what information retinal curl does, and does not, provide regarding heading estimation. In the Abstract, the authors state: "We propose an alternative: the visual system utilizes retinal curl directly to estimate heading, rendering the explicit recovery of the FOE unnecessary." Based on my understanding of the rest of the manuscript, I find this statement to be a misrepresentation for two main reasons:

a) To "directly estimate heading" relative to what? When not qualified, most people interpret "heading" to mean an observer's heading relative to the world (or some allocentric reference frame). But retinal curl only gives information about an observer's heading relative to the point on which their eyes are fixated. Moreover, that point of fixation will change every few hundred milliseconds in natural viewing, so the retinal curl will change with each new fixation even as heading relative to the world remains unchanged. So I think most readers would grossly misinterpret the claim that retinal curl can be used "directly to estimate heading". Indeed, in the authors' controller model, the initial heading needs to be given, and then the controller can work. But from where does the visual system get the initial heading, since it does not come from curl? These issues are left hanging. Thus, while curl can provide a very useful input for steering toward a fixated target, other signals are needed to estimate heading relative to the world. This has to be made much clearer early on, and a conceptual schematic diagram might help. Also, the authors generally do not specify the reference frame of the variables they are talking about, leaving lots of room for misinterpretations. It should be clear each time they are talking about a variable, such as heading, whether it is relative to the fixation target, body, world, etc.

b) It seems to me that retinal curl will depend on other variables, in addition to heading relative to the fixation target. For example, it seems to me that the magnitude of retinal curl will depend on self-motion speed, the depth structure of the scene, the angle of elevation of the fixated target, and perhaps others. This is not discussed at all, and many readers would get the misguided impression that there is a 1:1 mapping from curl to heading (relative to fixation). If I am right that this is not correct, it means that retinal curl can tell the observer whether to steer right or left to move toward the fixated target, but it cannot tell them how much to steer. Indeed, in the authors' controller model, there is a free parameter that calibrates curl to angle. It makes sense that this works to fit trajectory data that are given from a fixed environment, but it is unclear how the brain would use retinal curl to control steering when these other variables are uncertain or changing unpredictably. Moreover, how does the system change the mapping from curl to steering command as the location of fixation changes relative to the current heading? These are issues that need to be brought up in framing the problem and discussed at some length. If the authors can show mathematically that retinal curl is only dependent on heading (relative to fixation) and not any of these other variables, it would be very valuable to show the equations for this relationship.

(2) The description of the behavioral experiment and presentation of behavioral data leaves a lot to be desired.

a) First, it is stated (line 158) that "Participants continuously reported their perceived direction of self-motion while maintaining fixation on the yellow dot." Again, the reference frame is completely unspecified. Participants were reporting their perceived heading relative to what? The fixation target? The world? What exactly were the instructions given to the subjects to perform the task? Based on the description of how perceived paths are computed (line 166-), it seems to be presumed that subjects are reporting their heading relative to the world because those angles are then converted into x and z coordinates in what I presume is a world-centered reference frame. But how do we know that subjects are accurately reporting their heading relative to the world? What if they are biased in their reports by the location of the fixation target relative to the scene, or by some other reference signal? Is it possible for the authors to rule out the possibility that perceptual biases seen in the unaltered curl condition result from observers not fully adopting the assumed reference frame of the task? If this cannot be firmly excluded, it seems to create problems for the rest of the study.

b) I also feel that there is a mismatch between what the behavioral task requires and what the controller model does. Subjects are apparently asked to report their heading relative to the world, but the controller model only controls their heading relative to the point that they are fixating. I understand how this is resolved in the model, but I think this type of distinction is buried and will not be apparent to most readers. Again, the reference frames of what is being measured and controlled need to be specified explicitly in all parts of the paper, and the authors need to explain how the system would combine curl-based control with some other measures of (at least initial) heading for world-centered heading to be computed. All of the assumptions need to be clearly specified.

c) In addition, I found it frustrating that the authors never present raw perceptual data from the observers. Rather, in Figure 2, we see reconstructed trajectories that are perfectly smooth with no indications of noise whatsoever. Since these paths are computed from the perceptual reports, there must be some noise inherent in them. The figures should represent this uncertainty somehow, and it should be explained how these perfectly smooth trajectories are obtained.

(3) "...the magnitude of retinal curl in the fovea can specify the body trajectory relative to gaze (Matthis et al., 2022)." The main idea put forward by the authors here seems to overlap heavily with this statement that they attribute to Matthis et al. 2022. While I think this paper still adds importantly to the topic, the authors do not discuss how their findings are different from those of Matthis et al. 2022, why they are an important extension, etc. Readers should not have to go read this other paper to have any idea how the present findings are placed in importance relative to the literature.

(4) The analysis and treatment of eye movements is extremely weak. The authors discarded trials for which gaze deviated from the fixation point by more than 3 degrees (which is a LOT given that the eye speeds are generally in the neighborhood of 0.5 deg/sec), and they provide basic stats on the distribution of positions. But this largely misses the point: it is not small position errors that are likely to matter, but rather velocity errors. Even a small amount of retinal slip of the target while it is being pursued will cause image motion that is going to alter the optic flow field around the fixation target. So, for example, the retinal curl field may no longer be centered on the fixation target. How do we know that some of the perceptual biases are not influenced by image motion resulting from imperfect tracking of the fixation target? This needs to be analyzed and discussed.

(5) I found the sections of text comparing the separate and joined fits (starting line 287) to be a bit too rosy. The authors show the separate fits in the main text, and it is not very surprising that these fits are good, given that the model has 30 parameters, and these data are pretty low-dimensional. The authors only show the joined fits in the supplement, and they say that they are almost as good as the separate fits (indeed, they are better in a model comparison sense, but this is 30 parameters vs. 2 parameters). However, when I look at the fits of the joined model in the supplement, I don't find them to be very impressive. In particular, the model grossly misses the data for the straight paths for several subjects (e.g., id5, id6, id8, id10). And fitting the straight paths would presumably be easiest. This implies that the joined model is really missing something and that fitting the curved paths interacts strongly with fitting the data for different fixation target locations on the straight path. I think that the authors should discuss the results a bit more soberly and tone down their conclusions here.

(6) The section of the paper on neural simulations (starting line 387) has a few weaknesses. First, why are only straight paths simulated here? This does not seem to provide a very rigorous test of the model. Second, it is awkward that the simulation results are presented in units of pixels, rather than degrees. Third, the authors seem to downplay the fact that the neural estimates of heading seem to oscillate rather wildly (over a range of hundreds of pixels, whatever that means, see especially Figure S16). It was far from clear to me how an estimate of heading with these large oscillations is useful. It would seem to require that heading estimates are integrated over substantial lengths of time to be reliable. It was therefore unclear how the model produces such smooth paths from these oscillating estimates.

Reviewer #3 (Public review):

Summary:

This manuscript uses a novel paradigm to demonstrate that rotational motion patterns in the retinal image, called curl, directly influence perception of heading direction. This means that it is not necessary to recover the focus of expansion, defined by the point of zero motion when moving along a straight trajectory toward a target, as is commonly thought.

Strengths:

It has long been accepted that the focus of expansion of the optic flow field generated by self-motion is used to guide heading direction. While there have been many challenges to the need to recover the focus of expansion when gaze is not in the direction of travel, it is still not well understood how retinal motion patterns contribute to heading perception. Recent work has demonstrated the complexity of the retinal motion patterns during natural walking, where body motion adds a rotational component. A rotational component also results from curved paths as well as gaze off the direction of travel. This rotational component is called curl. The primary contribution of this manuscript is to demonstrate convincingly that curl influences perception of heading, and that it is not necessary to recover the focus of expansion.

A strength of the manuscript is that realistic retinal motion patterns are generated by recording the image sequences generated by a walker in a virtual environment, and then using those patterns as stimuli in the experiment. This allows the creation of the more complex flow patterns that are a consequence of the bob and sway of natural walking, which are often considered a minor factor. The elegant experimental design allows direct manipulation of the curl signal, and this in turn directly influences measured heading perception. Another strength is that the authors ground their findings in control theory and neural computations, using a model that produces human-like path trajectories.

The study is timely, given the long history of this question, together with the growing understanding of the complexity of naturally generated retinal motion and the absence of direct evidence for the way that these motion patterns are used in heading perception. It adds an important piece of evidence for how retina-centered optic flow may be used by the visual system, which is critical for our understanding of motion processing in the brain.

Weaknesses:

The primary limitation of the paper is that it avoids discussion of some of the inevitable complexities of heading perception. The main issue is what exactly is meant by heading. Different behaviors evolve over different timescales. The geometry of retinal motion defines instantaneous heading, which varies widely through the gait cycle. Time-varying information like this is known to be important in the momentary control of balance. Heading can also be thought of as steering the body toward a distant goal, which evolves over longer timescales. The current manuscript appears to be concerned with heading information integrated over a few seconds and seems to provide evidence that heading is indeed integrated over the gait cycle. The issue of the time scale of the computation is touched on, but it is not related to how it might be used in normal walking or what situations it might apply to. Steering toward a distant goal during walking is not a very difficult problem and may not require evaluation of retinal motion, but control of balance is more challenging and may depend critically on curl. Consequently, the timescale of the computation needs to be considered in order to understand what is meant by heading.

Author Response:

Public Reviews:

Reviewer #1 (Public review):

We appreciate Reviewer #1’s very positive feedback. Incorporating the perspective of ‘incidental’ sensory signals is a valuable suggestion that aligns perfectly with our findings. We agree that this perspective significantly strengthens the impact of our paper.

In the revised version, we will update the manuscript to bridge these perspectives (the functional role of incidental” sensory signals and the role of retinal flow in navigation). In addition we will elaborate on the potential predictions of the model and possible manipulations that might affect the integration between sensory evidence (curl signal) and straight-ahead prior.

Reviewer #2 (Public review):

We appreciate the reviewer’s feedback regarding the formalization of our reference frames. We agree that certain definitions were implicitly assumed rather than explicitly stated. We will revise the manuscript to provide all necessary self-contained information, ensuring that the geometry of the task response and the definition of heading are unambiguous. Also, we will address the gap between the task response (in world coordinates) and the functional role of the controller, as well as the other points raised by the reviewer.

Major issues:

(1a), (2a) Clarification of Reference Frames

The reviewer asks: “To ‘directly estimate heading’ relative to what?”

In our study, participants were instructed to report their “perceived direction of self-motion” by aligning a rotational encoder (steering wheel) with the direction they felt they were moving within the 3D simulated scene. Consequently, participants reported their instantaneous heading in a world-centered reference frame, from which the 3D trajectories were reconstructed. Since the reviewer had to infer this information, it should be clarified to ensure it is immediately evident.

Participants were informed that the initial heading (i.e. θ₀ in our controller nomenclature) was oriented “straight ahead” relative to their body which was aligned longitudinally with the experimental room. We will modify Figure 1B and revise the Methods section to explicitly clarify this initial alignment and the instructions provided to participants.

In the revised manuscript, we will clarify that while the participant’s report is world-centered, the retinal curl provides a gaze-relative heading signal. Although this was already mentioned, we will emphasize this point. In natural navigation toward a fixated target, a world-centered vector is often unnecessary; an error signal indicating heading relative to fixation is sufficient (as the reviewer also notes). However, the initial alignment of the heading within the 3D scene allows the brain to “calibrate” this internal controller, mapping the retinal curl signal onto the 3D world coordinates required for the task.

The reviewer also asks how we can be certain that participants were reporting in world coordinates rather than an alternative frame, such as “heading relative to the fixation target.” We believe our “Cancelled Curl” (and over-cancelled) conditions provide the most compelling evidence to rule out this alternative. In these conditions, the physical position of the fixation target in the scene remained identical to the unaltered flow condition. If participants were simply reporting heading relative to the fixation target’s spatial location, the observed biases should have persisted regardless of the flow manipulation. Instead, the bias vanished when the curl was removed. This causal evidence proves that the bias is driven by the retinal motion signal (curl) rather than the spatial orientation of the eyes or the target’s position in the scene. Furthermore, the temporal evolution of the response supports a world-centered integration. For simulated straight paths, the perceived heading remains straight for the first few seconds (consistent with the initial world-centered alignment), with biases only emerging after approximately 3 seconds of integration (a point we elaborate on in our response to Reviewer #3). Had participants been responding based on a simple gaze-relative reference frame from the onset, these biases would have manifested significantly earlier. We will incorporate these points into the revised Discussion to better frame our findings alongside other cues, such as the Focus of Expansion (FOE), that contribute to heading estimation.

(1b) The reviewer notes that we must be clear about the relationship between curl and heading (relative to fixation) and the variables that affect curl.

Beyond the discrepancy between heading (θ) and gaze (ψ), curl is geometrically determined by translational self-motion speed (υ), eye height (h), and pitch (α). More specifically curl = (υ sin_ψ_cos α)/h). The derivation will be included in the Supplementary Information. Since h = d_sin_α, where d is the 3D distance to the fixation point, we could express cos α as a function of distance. Certainly, there is not a 1:1 map from curl signal to heading relative to gaze (e.g. θ – ψ). Participant would need to know υ and eye height plus extra-retinal information. Frenz et al (2003, Vis Res.) showed that people can estimate self-motion directly from optic flow, across different simulated eye height and gaze angle; extra-retinal information can, in addition, provide knowledge to (ψ) and (α). It is then plausible that the visual system can use and transform the curl signal from a qualitative directional cue (i.e. steering left or right of fixation) into a quantitative steering command. By combining curl with knowledge of gaze orientation and eye height, the visual system can resolve ambiguities in the flow field and utilize curl as a more precise error signal for locomotor control. These aspects will be included in the new version.

(2b) Mismatch between task and controller

We thank the reviewer for this point. We have addressed the alignment of the reference frames in our response to Issues 1a and 2a. Once the initial orientation () is established in the world frame, the controller model generates steering adjustments that directly translate into heading predictions within that same world reference frame. By treating the perceptual report as an output of the locomotor controller, we resolve the discrepancy between the steering task and the reported heading.

(2c) No raw data provided

We respectfully disagree with the reviewer’s interpretation regarding data smoothing. The thin lines in Figure 2 represent the mean 3D paths derived directly from the response variable (θ₀) across trials of identical conditions for each participant (as detailed in the ‘Computation of Perceived Path’ section). No smoothing or filtering has been applied to these plotted trajectories other than computing the mean across trials. We also wish to remind the reviewer that the raw data and analysis code remain publicly accessible for further inspection. Regarding the visual representation: in earlier versions of the manuscript, we included shaded 95% Confidence Intervals (CIs) in Figure 2. However, this addition rendered the plot overly cluttered and obscured the individual trajectories. We therefore elected to present individual participant means (thin lines) alongside group averages (thick lines) to emphasize inter-subject variability. For clarity, the 95% CIs are explicitly displayed in Figure 3, where the data density is more conducive to shaded areas.

(3) Difference with Matthis et al (2022)

While Matthis et al. (2022) described the existence of retinal curl during walking and which information can provide relative to gaze, Our paper provides the causal link, since we manipulate in real-time (the ‘cancelled & overcancelled curl’ condition) providing the critical evidence that perceived heading is affected by this signal.

(4) Eye movements analysis

We thank the reviewer for noting that retinal slip (velocity error) is a more critical metric than positional gaze error. We agree that tracking inaccuracies can introduce translational noise into the flow field. The 3° threshold was established based on the eye tracker’s specifications and the naturalistic setup (1-meter viewing distance without head stabilization). Across all participants, the mean positional error ranged from 1.016° to 1.5° (1 deg is 2.08 cm in our setup). We also calculated retinal slip values, which ranged from 0.12 to 0.27 deg/s (X dimension) and 0.12 to 0.23 deg/s (Y dimension). These values are comparable to natural oculomotor drift (Kowler et al., 1979) and are understandably small given the low velocity of the fixation target. Consequently, it is highly unlikely that retinal slip influenced the results. Furthermore, assuming that tracking error remained consistent across fixation conditions, any present retinal slip cannot explain why the bias followed the retinal curl manipulation as predicted by the controller. We therefore consider retinal slip to be an unlikely confounding factor.

(5) the separate and joined fits

We thank the reviewer for the opportunity to clarify the logic behind our modeling choices. We acknowledge that the “separate fits” are inherently less informative due to the high number of free parameters relative to the data. Our primary scientific goal was not to achieve perfect descriptive accuracy via 30 parameters, but to test a specific functional hypothesis through the “joint fit.”

The Logic of the Joint Fit:

We agree with the reviewer that the joint fit misses some paths in some conditions. Of course, the joint fit reflects a significant compromise. The “Gain” (the weighting of the curl signal) is likely not a static constant but is dynamically tuned based on task demands, confidence in the visual signal, simulated speed, and so on. By using a single Gain parameter, we intentionally ignore this contextual variability to see how much of the behavior can be explained by a “minimalist” controller. In this sense, the 2-parameter joint model is a deliberate attempt to test this limit. By forcing a single Gain parameter to account for all conditions across both straight and curved paths within one flow manipulation (e.g. unaltered flow) we are asking if a single, fixed linear relationship between retinal curl and steering effort/gain can explain the results. We view the joint fit not as a “perfect” model, but as a stronger test of the curl-based control theory. The fact that a 2-parameter model can capture the direction and scale of biases across such a diverse set of conditions (straight/curved paths, five fixation eccentricities) suggests that retinal curl is a robust signal. Upon closer analysis, these discrepancies between the joint model and the data are most pronounced in the over-cancelled condition which is the one when sensory evidence becomes more ecologically inconsistent with the extra-retinal information (gaze direction). While the joint fit successfully demonstrates that a single parameter can capture the general functional role of curl, it fails to account for the complex sensory re-weighting that occurs in ecologically inconsistent conditions (like ‘over-cancelled’ flow). We will update the manuscript to discuss these limitations, framing the model as a parsimonious first-order approximation rather than a complete description of human heading perception based on a minimal set of parameters.

(6) On the neural simulations

We acknowledge that the presentation of the neural model requires more clarity regarding its objectives and its relationship to the behavioral data.

We first wish to clarify the intended scope of the neural ring-attractor model. Our primary goal was not to provide a comprehensive account of behavioral performance across all conditions (which is the role of the controller model), but rather to demonstrate a biologically plausible mechanism that explains the emergence of the “Opposite-to-Gaze” bias. While the controller demonstrates that the bias follows a specific control law, the neural model shows how such a law can emerge from known primate neurophysiology, specifically, spiral-tuned MSTd neurons, gaze-contingent inhibition, and an egocentric “straight-ahead” prior.

Why Straight Paths are Sufficient for this Objective. The reviewer asks why only straight paths were simulated. In our study, the straight-path condition with eccentric gaze is the purest test of the bias mechanism. Simulating the straight paths allowed us to isolate the interaction between foveal inhibition and the straight-ahead prior without the confounding variable of path-curvature flow. Given the complexity of the neural network’s parameter space, we focused on these conditions to provide a clear neuro-plausible explanation.

Units: Pixels vs. Degrees. We acknowledge that the use of “pixels” in the plots of internal neural dynamics may appear awkward. The neural network operates on input stimuli that are defined by the pixel resolution of the videos used in the simulations, we used pixels as the native coordinate system to describe the movement of activity peaks within the network’s internal “map.”

Behavioral Output (Meters): Importantly, the final heading estimates produced by the network are not left in pixels. We use a pinhole camera model to reconstruct the 3D trajectories from the neural activity. These results are expressed in meters, allowing for a direct comparison with the human behavioral data.

Addressing Wild Oscillations and Smooth Paths. The oscillations observed in the instantaneous heading estimates reflect the stochastic nature of the population peak when tracking high-frequency sensory inputs. In our model, the synaptic time constant (τ) was kept relatively small to ensure a fast, low-latency response to changes in self-motion. While increasing τ would have produced smoother internal dynamics, it would also have introduced delays into the control loop. Instead, we chose to maintain this high sensory responsiveness and applied a temporal moving average later to the network’s decoding to reconstruct the 3D trajectories.

In addition, the neural activity over time is shown in two ways: the heatmap shows the neuron with preferred heading (one can see more oscillations, specially when the fixation point is closer to the centre (eccentricities -2 and 2), due to larger competition between the sensory evidence and the straight-ahead prior. The other way is the decoded heading. In the ring-attractor model, the decoded heading is not determined by a single neuron but is calculated using a population vector average (equation 19). By summing across the entire population, the decoder effectively integrates sensory evidence from many neurons simultaneously. One can appreciate (see e.g. Fig. 5B) that averaged decoding, leads to a smoother resulting estimate (the white dashed line, whose visibility will be improved in the revised version). Behavioral work by Burr and Santoro (2001) suggests that global motion signals (divergence and rotation in optic flow) are integrated over much longer timescales—roughly 1000ms to 3000ms—compared to local motion units (~200ms).

See also our comment on temporal integration in the responses to reviewer #3.

Reviewer #3 (Public review):

We thank Reviewer #3 the comments regarding the definition of heading at different time scales, the role of the gait cycle, and the temporal integration of the curl signal. They will help us refine the manuscript’s core arguments.

We agree that “heading” must be precisely defined within the context of the differing temporal demands of balance and steering. While instantaneous retinal motion provides the high-frequency feedback necessary for momentary postural adjustments and balance, our study is concerned with heading as a gaze-relative signal used for the continuous control of a locomotor trajectory. As such, we will revise the manuscript to specify that the perceived heading measured in our task reflects a signal integrated over the gait cycle to filter out the oscillatory noise induced by head bob and sway.

The reviewer correctly notes that gait-induced head bob and sway produce high-frequency oscillations in the curl signal, yet our behavioral results show smooth, slowly evolving biases. The visual system does not react to “instantaneous” curl, which would lead to jittery, unstable heading estimates. Instead, it integrates flow over a timescale roughly commensurate with a full gait cycle (~500–1000ms). This implies a significant temporal integration process. This temporal integration is consistent with evidence (Burr and Santoro,2001, Vis Res) indicating that optic flow signals (radial and rotational components) are integrated over windows of approximately up to 3 seconds to ensure perceptual stability. Neurally, this likely involves the projection from area MSTd to the Ventral Intraparietal area (VIP), a pathway where fast, eye-centered sensory inputs are transformed into stable, body-centered representations suitable for guiding long-term steering behavior (Chen et al. 2011, JNeurosci.). By grounding our definition of heading in these specific temporal and neural constraints, we aim to clarify how the visual system exploits retinal curl for goal-directed action in natural, dynamic environments and relate our findings to recent studies addressing the role of retinal motion on balance (Powell et al. 2026 Bioarx).

In our implementation, we explicitly address the high-frequency noise introduced by gait dynamics by smoothing the retinal curl signals computed from the stimulus videos before they are fed into the controller. This temporal filtering allows the fit of the controller’s prediction to the response data while remaining robust to the rapid fluctuations of head bob and sway. In contrast, the neural ring-attractor model would not require an external smoothing step; instead, the integration is an emergent property of the system’s architecture that can be controlled with different parameters. The dynamics of the synaptic weights and the characteristic “leak” in the population activity naturally implement a leaky integration of sensory evidence, ensuring that the decoded heading reflects a sustained estimate rather than an instantaneous response to visual noise.

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