Bet on the people, not just projects... gets students to commit to outcomes early, rather than coming at it like passive employees. Still, that ends up faculty-steered, and despite that proposed more vaguely. Anyway, a good idea on its own terms

clifton refuses to minimize herself and her hips to fit inot a standard that society has of her.

it seems like clifton is using her hips as a metaphor. her hips are the largest part of her that she refuses to diminish. her hips reflect her sentiments about her self

In figure 2(a) why are the Gastric points so disparate/spread out for HistoMethylGigaPath? Same for Lung ADC?

I think either this is a t-SNE artifact or implies something abt the model.

Then again for 2(a) one could argue with the four panels => four separate times get embeddings => four independent optimizations with their own initialization, seed, and convergence. I wouldn't expect there to be a shared coordinate system. Even the same embedding re-run at a different seed will redistribute cluster compactness.... If so though the figure doesn't imply much at all / is lowkey useless.

Important contribution!

Methodologically its really smart to split the WSI branch from the DNAm branch and then use a contrastive alignment on the embeddings. What does the joint space look like? Any structure that falls out? Have the authors investigated any patterns in the joint embedding space? I would expect some rather clear boundaries given the inputs.

Sounds too much like AI. Better:

Auch auf TikTok taucht der Cicaplast Baume B5+ immer wieder auf. Videos über den Balsam wurden bereits millionenfach angesehen und erscheinen in vielen verschiedenen Sprachen.

Somehow this sentence sounds very technical. Better:

Die Formel enthält 5 % Panthenol sowie Madecassoside, Zink und Mangan. Ergänzt wird sie durch das Thermalwasser von La Roche-Posay. Auf Alkohol wurde bewusst verzichtet.

Sounds more natural:

Der Cicaplast Baume B5+ gehört zu den Produkten, die ich besonders empfehle. Er eignet sich für trockene und beanspruchte Haut sehr gut. Man kann sagen, dass er eine gute Wahl ist, wenn du noch nicht weißt, welches Produkt am besten zu deiner Haut passt.

noch

lol, four feature streams, no ablation. Do the topological fingerprints add anything over the molecular embeddings; both substrate-side, presumably overlapping Id guess? And do the geometric descriptors actually survive/lead to signal gain once ESM3 features are present? If ESM3's structure track is used, those two streams overlap by construction, since both descend from the stage-1 co-folded pose; that's also a leakage concern. Separately: stage 1 is MSA-guided, so the pipeline draws on evolutionary information twice. What does the PLM contribute then?

Performance claims here are uninterpretable without the evaluation design. How are negatives sampled at each EC depth?

Beautiful figure!

This is true. Good context!

Stage 2 being MSA-free is presumably to avoid the consensus/apo bias that deep MSAs impose on side-chain placement. But does stage 2 condition on stage-1 coordinates? If so, evolutionary information persists as a geometric prior, and the ablation that matters is MSA-free stage 2 without the stage-1 pose, otherwise you can't attribute the induced-fit gains to the MSA-free regime.

Interesting idea / choice of FM.

What are possible ways to force their FCNN to learn epistasis (that is, if there is any signal in this dataset)? Perhaps their FCNN architecture (or a reduced parameter variant) could be trained on the residual error signal from your additive linear model (with appropriate cross-validation structure), where the loss function further penalizes any component of its predictions that could be from a linear model? Double mutants seem obviously like the easiest setting for testing this, subject to experimental noise. But given my colleague George's caution about the single measurement per double mutant pair on the community review page, and your points about how few double mutants were incorporated overall, this seems unlikely to work out.

Needs a proper title

Needs a proper title

This is a test comment

How exactly would the authors go about tackling this given the approach presented in the manuscript?

Important application

Surprising to see a continuous manifold.

ML for operational meteorologists

I already know with time constraints I'm going to mostly focus on my initial thoughts, but I think reading this alongside Cronon's "Only Connect" essay would be a great exercise.

This is such a great basic skill for (I think) anyone to have, whether it's while working, talking with others, raising kids, etc.

Understanding other people have mental models you don't have, and that they're ignorant of your own mental models (or that what you have is a personal model, a version of truth, not "the" truth) is helpful in treating humanity like it's human.

I have ranted a lot, while I was teaching and after, about the various "coding for Kindergarteners" initiatives I saw back in the day. And primarily my rants were about how no one knew whether we'd need developers in 20 years (I still think we do, but I know plenty of folks in our current LLM-based world who think we don't, or that day is coming) and maybe what we'd really need then is expert qualitative decision making and we'd wish all those Kindergarteners went and became Art History majors or something.

Anyway, I often think of education as helping make you a more whole person, for (what I believe is) a similar reason to the author's point. You don't know what's coming, and being the most complete version of yourself is probably the best way to be prepared.

Absolutely. If your goal is to make a good product, it's not enough to build it to spec (even if the specs are very thorough). Understanding why someone wants something a specific way helps you design the thing they actually want, not the thing they've described. Maybe if they're thinking if perfectly clear and well thought out, you'll find the thing they've written does in fact match their vision, but I'd bet that at least some times it does not.

Maybe less ephemeral if you find yourself somewhat immediately and repeatedly use that same acquired knowledge over and over again, but generally I agree.

We used to call some of this the differences between "skills and knowledges" and "ways of thinking". Do you know how to "thinking like a scientist", "think like a historian", "this like a musician", etc. It was meant to be a high level way of acknowledging both that the ways of thinking in different disciplines are different (to use some of the author's words, I'd say how you go about exploring, questioning, examining, etc., is different between disciplines) and also that part of a student's education is to learn more than the skills and knowledges, but also these many other approaches and tools which make up discipline-specific reasoning.

As a teacher, my goal was to make sure my students would no longer need me. That meant giving them specific knowledge when they needed it, but also giving them the tools they'd need to grow in that knowledge, use it, outgrow it, etc.

I can know I got a "A" on a paper, but do I understand why I got that A? If not, can I hope to replicate that level of work consistently? Can I even understand why getting an "A" was important if I didn't understand (and thus get to agree with) why it was given?

What long-term education did I get from writing that paper. I believe almost every paper I've written had long term benefits; an obvious take for at least some of them would be helping me learn the research and then analysis skills I use now all the time.

I've see a regression in some public schools with this, where High School students especially are asked earlier and earlier to specify a "track" they want to be on that limits their choices in which classes they get to take. So if you choose a science track you might not get space in your schedule for, say, a photography class.

In High School! Who knows at 14 or 15 or 16 what the rest of their life is going to be like!

I get that parents want to feel some security in their child's future (even though I think, and this article I feel strongly indicates, that this is a false security, as those parents also don't really know what kinds of skills or ways of thinking will benefit their children in the longer term) and I think businesses in particular would like to pass off their training needs on to schools (college, high school, whatever, just to save themselves time and money; though again, I think this is disadvantageous to the worker, who's bargaining power against a business either comes with collectivism (a dirty word in the USA) or having the flexibility to move to new employers (something a holistic education greatly helps with)) and I think there are even some political figures who'd prefer citizens are, say, less well-rounded, less critical, and less civically informed.

These "tracks" and tight and tighter specificity in education only constrain the futures of students, they don't brighten it, and as the world moves on a constrained future has a greater chance of being squeezed out.

Absolutely! Whole students for their whole lives. I'd bet that software engineers who are well-read, thoughtful, engaged in their community, etc, make better software engineers.

I'd double bet that they'd certainly make for more empathetic and thoughtful software engineers who will, say, care enough about user privacy that they'd push back again bad practices bad business goals.

For the folks who haven't read this yet: https://www.bu.edu/cas/files/2015/06/Cronon_The-Goals-of-a-Liberal-Education.pdf.

And in case that link goes dead one day, it's Cronon's essay entitled "Only Connect...".

In fact, I'd bet the people doing the enslaving don't want them having anything other than vocational skills.

I sometimes get stuck on what appears to be a societal preference for things like radical individualism, and also a denigration of education. As if individualism isn't supported by more education!

I don't think society is fixated on college as an educational path, I think society is fixated on a college education as a form of credentialling to divide workers into more and less skilled groups. Which we've seen falling apart, and which I think is part of the reason we're seeing huge pressure on educational institutions (both from lower enrollment as the credential no longer shows as much worth, and from government and business as a way of making sure these intuitions are measure as credentialing institutions with the purpose of aligning with business needs).

Higher Ed is one way, but not the only way, to fulfill the need to grow ourselves, become more curious, become more thoughtful. I think it's a very good way, if you have the means and if your curiosity can be developed in that environment. But i also think for too long schools accepted they would continue to have enrollment based on that credentialling scheme, and as its falling apart I haven't seen much push from them to identify themselves as places where students will be challenged, mentored, and ultimately grow as complete people.

Bang on slogan.

Man, if nothing else, I'd love every student to view every course they take (higher ed and not) though that lens. And realize that the unknown encompasses both known unknowns, and unknown unknowns.

When I taught in a less economically advantaged north shore MA city, a step rep at our of our neighbor (and much more advantaged) cities was trying to sell his constituents on some kind of state-wide program that would benefit people outside of their city. And he did it by explaining that his constituents' children would be "captains of industry" and they'd do better if the future works from my little city had had sufficient education to be "good employees" (with the implication that the education we were giving them now wouldn't even get them to "good").

Most of us in the "city of sin" didn't really love that line of reasoning.

Yeah. Both of these. Because even chromebooks and programming classes for kids isn't necessarily "access". It's more than some of them would have had on their own, yes, but it's a lot less than the kids who get to try everything get. And it's another form of restriction, when other people (with their dubious wisdom - again, a serious person cannot believe that in twenty years the payscales of the software industry vs other industries will still be the same as it is now) have picked just what advantages the children with less or no access get to have, and which access they don't get to have.

That could be its own essay, and I'd throw in what structures that thwart curiously are intentionally difficult to undermine, and are designed to throw blame at the party struggling to participate in education as opposed to the ones who structure the opportunities for education.

This makes me think about...

This is a really nice series of investigations and a surprising set of findings. If I understand correctly you suggest the bli-3/H2O2 arm of hsp-6p::GFP activation in the two hit model is an active immune response to the E. faecalis. I'm curious why that response occurs for E. faecalis, but not for similar Gram-positive colonizing bacterium such as S. aureus in figure S2A, which might also be expected to draw increased H2O2 secretion.

You mentioned a screen of 500 bacterial strains that showed 18% activate the mtUPR. Do you know how many of those strains are heme auxotrophs? It would be interesting to see if the heme auxotroph arm ever exists without the bli-3/H2O2 arm, or if they are in some way linked

selenium treatment

@jallspaw Would you happen to have a source for the Dekker quotes? I don't find 2004 in the references, and they are not present in the 2003 source, either.

Was it maybe in

• Ten Questions About Human Error | A New View of Human Factors and Syst

or

• The hindsigth bias is not a bias and not about history
• BMJDekk.doc - Sid Dekker The Hindsight Bias.pdf

?

tooltips being obscured above

tables/T3_power_per_domain_trueauc080.csv 我希望能把这个 table 补全，也就是补充一些我们现在已经计算好的指标，整理成一张表放在这里。具体要求如下：

1. 每一个数据集原始文献中的 AUC： 这是一整列。下面已经列出了一些，不确定的部分先列“未确定”就好。

2. 我们现在真实的 AUC： 按照下面的值填进去，其他不用管。

3. 我们过去 report 的 AUC： 按照下面的值填进去，其他不用管。

4. 新增三行 in-domain 的训练结果： 每一行的 title 分别是模型的名字：CellPA、SpaceGM 和 CELTA Community。在这五个数据集上 in-domain 的结果，底下的 report 中已经有相关的数据了，需要汇总一下。如果没有的，就先写“不清楚”，后续再补充。

我希望把这个 table 补全：

1. 首先，把这每一篇文章中他们原始预测 response 的 AUC 添加到这里面，只要是能加的都加上。如果有什么加不上的，可以单独再列一个 table（比如你搜不到文章正文，或者有什么不确定的，需要一个 table 让我来确认，在下面进行一个补充）
2. 第二，把我们之前测试中最好的、无论是不是准确真实的版本的 AUC，加到中间一列
3. 第三，把现在比较真实的最好的 AUC，添加到另外一列

Tom Hansen interested in typewriter repair workshop https://www.facebook.com/groups/705152958470148/posts/1333610698957701/

valuesGke.yaml is insufficient for the chart. Also need values which are specific for this installation

``` gcp: project: opensearch-migration serviceAccountEmail: osmigration5c7e5d4a-node-sa@opensearch-migration.iam.gserviceaccount.com

gcsBucketConfiguration: bucketName: os-migration-5c7e5d4a

images: migrationConsole: repository: us-central1-docker.pkg.dev/opensearch-migration/migrations/migration-console

installer:
  repository: us-central1-docker.pkg.dev/opensearch-migration/migrations/migration-console

reindexFromSnapshot:
  repository: us-central1-docker.pkg.dev/opensearch-migration/migrations/reindex-from-snapshot

captureProxy:
  repository: us-central1-docker.pkg.dev/opensearch-migration/migrations/traffic-capture-proxy

trafficReplayer:
  repository: us-central1-docker.pkg.dev/opensearch-migration/migrations/traffic-replayer

```

Missing step to add repo. There is no public URL for the Helm repo.

curl -fL -o migration-assistant-3.3.5.tgz \ https://github.com/opensearch-project/opensearch-migrations/releases/download/3.3.5/migration-assistant-3.3.5.tgz

helm upgrade --install ma ./migration-assistant-3.3.5.tgz \ --namespace ma \ --create-namespace

The Artifact Registry API is enabled and the GKE node service account already has roles/artifactregistry.reader. What’s missing is the actual Docker repository and images.

You need four things:

1. Create the migrations repository:

gcloud artifacts repositories create migrations \ --project=opensearch-migration \ --location=us-central1 \ --repository-format=docker \ --description="OpenSearch Migration Assistant images"

1. Authenticate Docker for pushes:

gcloud auth configure-docker us-central1-docker.pkg.dev

1. Populate it with these release 3.3.5 images:

opensearch-migrations-console opensearch-migrations-reindex-from-snapshot opensearch-migrations-traffic-capture-proxy opensearch-migrations-traffic-replayer

The quickest approach is to mirror the published images rather than build them locally. For example:

docker pull public.ecr.aws/opensearchproject/opensearch-migrations-console:3.3.5

docker tag \ public.ecr.aws/opensearchproject/opensearch-migrations-console:3.3.5 \ us-central1-docker.pkg.dev/opensearch-migration/migrations/migration-console:3.3.5

docker push \ us-central1-docker.pkg.dev/opensearch-migration/migrations/migration-console:3.3.5

Repeat that mapping for:

opensearch-migrations-reindex-from-snapshot → migrations/reindex-from-snapshot:3.3.5

opensearch-migrations-traffic-capture-proxy → migrations/traffic-capture-proxy:3.3.5

opensearch-migrations-traffic-replayer → migrations/traffic-replayer:3.3.5

1. Update Terraform to reference tag 3.3.5, including capture proxy and traffic replayer. Currently deployment/ terraform/gcp/main.tf:382 only fully configures the console, installer, and reindex images, and uses latest.

Must ensure project has sufficient quotas set

Missing the following APIs in this list

compute.googleapis.com storage.googleapis.com

Possibly provide command for creating project?

terraform init must be completed first before terraform apply

Might want to provide specific commands using git and gh cli to checkout the release tag

Using Git:

git clone --branch 3.3.5 --depth 1 --single-branch \ https://github.com/opensearch-project/opensearch-migrations.git

GitHub CLI:

gh repo clone opensearch-project/opensearch-migrations \ -- --branch 3.3.5 --depth 1 --single-branch

Add download link for Helm

https://helm.sh/docs/intro/install/

Add download link for kubectl

https://kubernetes.io/docs/tasks/tools/

eLife Assessment

This valuable study presents a comparative analysis of the transcriptomic features underlying C. elegans longevity, providing insights into how different changes in gene expression can promote longevity. The authors present solid evidence with analysis and selected functional validation showing that some long-lived animals share common changes while others appear to use opposing strategies. The datasets and analyses contained within and the user-friendly website developed will be of interest to researchers interested in complicated transcriptomic analyses and/or the biology of aging.

[Editors' note: this paper was reviewed by Review Commons.]

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers.]

This manuscript by Rudich ZD et al. systematically profiled the transcriptomic changes in nine long-lived C. elegans mutants and presented a careful and informative comparative analysis of these aging-related changes. In addition to these valuable datasets and bioinformatics analyses, the authors performed a large-scale RNAi screen to assess the role of the differentially expressed genes (DEGs) in these mutants and identify several potential targets to promote healthy aging. Moreover, the authors have provided a user-friendly website to examine genes of interest in those longevity mutants from their datasets.

Strengths:

Compared to previous transcriptomic analyses of these mutants in different reports, this study minimized the technical variations and benefitted from the advances in RNA-Seq technology and bioinformatics tools. Therefore, it should provide a more consistent and comprehensive view of the molecular mechanisms underlying the longevity of these mutants. The datasets in this manuscript are valuable to other researchers in the biology of aging.

Weaknesses:

Meanwhile, since these mutants have been extensively studied, the advance of this study in unknown ageing mechanisms remains limited.

Comments on revised version.

The authors addressed the concerns successfully.

Reviewer #2 (Public review):

Summary:

In the manuscript titled "Multiple Molecular Pathways to Longevity: Opposing Gene Expression Programs Define Distinct Aging Strategies", the authors investigated diverse genetic pathways that contribute to lifespan extension in Caenorhabditis elegans and aimed to identify shared and distinct molecular mechanisms among various longevity mutants. Through comprehensive RNA sequencing of different longevity mutants representing seven distinct pathways, the authors showed that these mutants cluster into three primary groups based on their gene expression profiles. This transcriptomic analysis revealed that while some longevity genes are commonly regulated across multiple pathways, others exhibit opposing expression patterns, suggesting that distinct molecular strategies can lead to increased lifespan. Specifically, they identified a set of 196 genes that are consistently upregulated in most longevity mutants, many of which are involved in innate immunity and stress defense. By performing RNAi-based screening, the authors further validated the functional roles of several candidates, including C08F11.7, ugt-62, and K05C4.9, supporting their contributions to longevity and stress resistance. The authors conclude that longevity is mediated through multiple molecular pathways and provide a public online tool to study these complex transcriptomic landscapes.

Significance:

This study provides a systematic, side-by-side transcriptomic comparison of nine genetically distinct long-lived C. elegans mutants, revealing that lifespan extension arises from both shared and opposing gene expression programs. By identifying three distinct longevity groups and demonstrating that key pathways can be modulated in opposite directions to achieve long life, the work challenges the notion of a single universal transcriptional signature of aging. Importantly, functional validation shows that select commonly regulated genes can directly modulate lifespan and stress resistance, highlighting actionable molecular targets for promoting healthy aging.

Comments on revised version:

The authors addressed my concerns successfully.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

In the revised manuscript, the authors have addressed most of my concerns. In the text of this manuscript, the authors should still include more discussion on why osm-5 and daf-2 are categorized into two different groups. 

According to this suggestion, we have expanded our discussion to discuss why osm-5 and daf-2 worms fall into different longevity groups despite the fact that disruption of DAF-16 decreases both of the their lifespans. Please see lines 363-376.

Test

eLife Assessment

This important study uses an elegant visual-anagram approach to test whether perceived animacy shapes visual working memory and guides visual attention while tightly controlling for lower- and mid-level image properties. The evidence is convincing and provides a rigorous demonstration that perceived animacy influences visual cognition beyond the contribution of its typical visual correlates. The findings will be of broad interest to researchers studying high-level vision, attention, and working memory.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the comments raised in the previous round of review.]

This is a very cool paper that casts light on a persistent problem in the psychology and philosophy of visual representation: is there high-level perception? Every vision scientist agrees that low-level features such as shape, color, texture, motion and spatial frequency are represented in visual perception, but there is a great deal of controversy about the representation of high-level properties such as causation, faces, agency and animacy. Animacy is especially problematic because there are large differences in line curvature between stimuli that represent animate and inanimate items.

This article uses a novel approach-visual "anagrams" that are exactly the same image, except one is rotated 90 degrees relative to the other. They found persistent differences in visual processing between animate and inanimate stimuli. (Of course, the stimuli aren't animate-they represent animate items.). For example, there were processing differences between changes between animate and inanimate items (rabbit to boot) that were not present in rabbit to dog. They also showed such differences in two kinds of visual search tasks.

Of course, there are feature differences that exploit orientation. A classic example is the difference between a square and a diamond that is produced from the square by rotating it 45 degrees.

They addressed an aspect of this challenge having to do with some features using silhouettes. There was no search advantage for silhouetted stimuli.

Reviewer #2 (Public review):

Summary:

The authors present a creative approach using visual anagrams matched on low-level image statistics to isolate animacy from low-level visual features and report consistent effects of animacy on visual working memory and attention.

Strengths:

(1) An important methodological advance in controlling low-level confounds that have historically complicated the study of animacy.

(2) The converging effects across multiple experiments, together with the pre-registered design, strengthen the reliability of the reported findings.

Reviewer #3 (Public review):

This study makes clever use of generative AI to create stimuli that are pixel-for-pixel identical but which have radically different meanings depending on their orientation, to investigate the perception of animacy while retaining control over low-level image features (so-called 'anagram' stimuli).

The authors present seven elegantly designed experiments in a commendably compact format.

Experiments 1 and 2 involved a working memory paradigm in which participants had to spot which of five objects in an array changed after a pause. Importantly, the changed object was an anagram stimulus that in one orientation matched the animacy/inanimacy of the changed object, and in the other orientation was the opposite (e.g., a rabbit is replaced by either a dog or a boot, where the dog and boot stimuli are actually identical, just rotated by 90 degrees). They found a difference in accuracy depending on whether the animacy of the objects matched.

Experiments 3 and 4 used a visual search task in which the participants had to localize the target, and the distractors were anagrams that either matched the target in terms of animacy or did not. There was a significant cost in terms of response time when the animacy of the target was the same as that of the distractors. Experiments 5 and 6 also used a similar visual search design, except that the task was to determine if the target was present or absent from the display, and the distractors again either matched or differed from the target in terms of animacy. Again, the authors found slower responses when the distractor arrays matched the animacy of the target than when they differed.

An obvious potential concern about the studies is addressed by Experiment 7. It is unclear if the observed effects are related to the specific orientations of the target and distractor stimuli selected in each condition. For example, it could be that all the animate versions of the anagrams involved tall and skinny shapes, while all the inanimate versions involved wide and short objects, due to the 90-degree rotational difference between the two versions of the stimuli. To control for this, the authors repeated the visual search experiment but with convex-hull silhouettes of each of the stimuli. In other words, all targets and distractors from each trial were replaced by a black splotch with approximately the same overall outline (envelope) as the corresponding stimulus. Importantly, in contrast to the anagram stimuli, the silhouettes had had no meaningful semantic interpretation, and their animacy did not change depending on their orientation.

Author response:

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

eLife Assessment

This valuable study uses an elegant visual-anagram approach to test whether perceived animacy structures visual working memory and attention while controlling for many low-level image properties. The evidence is solid, with converging results across seven preregistered experiments, but the central claim that animacy itself is represented independently of visual features should be tempered, as residual mid-level configural cues, ensemble or category structure, and broader semantic differences may also contribute to the effects. The work will be of interest to researchers studying high-level visual representation, attention, and working memory.

We thank the Editors and Reviewers for this careful and informed assessment. We appreciate that every Reviewer found our approach to be elegant, our findings to be solid, and our question to be of broad interest. We respond to each Reviewer’s specific comments in more detail below; but we thought to summarize some of the highlights - especially the specific comments that come up in this Assessment - here.

(1) The Reviewers make the insightful point that, even if our stimuli effectively control for many low-level features, there may be other high-level features that explain performance in our experiments (R2: “Although the anagram paradigm effectively controls low-level visual features […] these stimuli differ not only in animacy but also along other semantic dimensions such as natural versus manmade categories.”). We are happy to embrace this possibility. If our results were explained by high-level visual representation of the natural vs. manmade distinction, rather than the animate vs. inanimate distinction, this would still be an appeal to a (not altogether unrelated) high-level property being represented independently from its lower-level features, which was the primary motivation for our study. We framed our work specifically around animacy given the persistent debates regarding perceived animacy, as well as the fact that our stimuli do quite saliently vary along that dimension; but we are certainly open to other nearby high-level categories being at play. We also think this is an empirical question that could be tested in future work. For example, objects like rocks and lakes are natural but inanimate. If they behave more like dogs than like boots in our paradigms, then Reviewer #2 may be right that naturalness was the relevant property all along; but if they behave more like boots than like dogs, then perhaps it really was animacy doing the work. We now discuss this explicitly in our paper, and we appreciate the opportunity to not only clarify our claims but also spur discussion for future work.

(2) Multiple Reviewers raise the question of whether semantic factors that go beyond the images themselves may be driving our effects. Reviewer #3 raises a particularly interesting question along these lines: “if all the stimuli in the experiments were replaced with the verbal names of the depicted objects instead of pictures, would we expect different results?” We have now taken this question quite literally and run this experiment exactly as described. Of course, much research already explores cognitive processing of animate/inanimate words, finding (for example) stronger memory for animate objects than inanimate ones (e.g., Nairne et al., 2013; Nairne et al., 2017). However, such tasks do not invoke effects of visual processing, whereas the question at issue here is specifically whether the visual system prioritizes animacy independent of its lower-level features. To this end, we conducted a new, pre-registered experiment (now Experiment 8) where participants search for animate/inanimate words on some trials, and animate/inanimate pictures on others. Given the nature of visual search tasks, we should expect to find no search advantage for words (as their meanings are not processed in vision per se) — and we should also expect to replicate (once again) our search advantage for pictures. This is exactly what we found. In other words, linguistic stimuli alone failed to produce the effect, while anagrams did produce the effect. We believe this rules out the strongest form of the semantic labeling account.

(3) Finally, Reviewers #2 and #4 raise some concerns regarding residual mid-level features such as configural shape and ensemble statistics, which lie somewhere between animacy itself and more basic properties like contrast or spatial frequency. In our paper, we now clarify each of these concerns in greater detail. In short: We think that our stimuli and experiments indeed control for these residual cues. For example, rotating an image preserves its configural shape; and, as we argue below, the specific ensemble statistics argument fails to get off the ground without appeal to animacy itself.

Public Reviews:

Reviewer #1 (Public review):

Summary:

Evidence for visual representation of animacy.

Strengths:

This is a very cool paper that casts light on a persistent problem in the psychology and philosophy of visual representation: is there high-level perception? Every vision scientist agrees that low-level features such as shape, color, texture, motion and spatial frequency are represented in visual perception, but there is a great deal of controversy about the representation of high-level properties such as causation, faces, agency and animacy. Animacy is especially problematic because there are large differences in line curvature between stimuli that represent animate and inanimate items.

This article uses a novel approach-visual "anagrams" that are exactly the same image, except one is rotated 90 degrees relative to the other. They found persistent differences in visual processing between animate and inanimate stimuli. (Of course, the stimuli aren't animate-they represent animate items). For example, there were processing differences between changes between animate and inanimate items (rabbit to boot) that were not present in rabbit to dog. They also showed such differences in two kinds of visual search tasks.

Of course, there are feature differences that exploit orientation. A classic example is the difference between a square and a diamond that is produced from the square by rotating it 45 degrees.

They addressed an aspect of this challenge having to do with some features using silhouettes. There was no search advantage for silhouetted stimuli.

Weaknesses:

I thought this was an excellent submission. I have two suggestions for revision:

We are glad to hear this Reviewer recognizes the broad challenge we are tackling in this work (separating high-level from low-level visual features) and found our submission to be “excellent”.

(1) I thought that experiment 7 should have been described in more detail, with the upshot explained better. What exactly do the authors take it to show?

Sorry for the lack of clarity here. We think the Reviewer actually gets this right earlier in their review; many feature differences exploit orientation, and our silhouettes control (Experiment 7) shows that those differences alone fail to explain our effects. For example, one might worry that our search effects merely reflect oddities in the aspect ratio or center of mass of the images. Converting the anagrams into silhouettes preserves these features. Thus, the fact that we found no search advantage with silhouettes suggests that these features on their own fail to produce the relevant effects; put the other way around, the effects we observed earlier must go beyond those features. We now discuss this in greater depth in our paper.

(2) There should be a candid discussion of what the loose ends are and how they might be addressed. It would be good to have some examples like the square/diamond case with some indication of what would address such challenges.

We agree with this, though we are somewhat limited by the space constraints of the Short Report format. A primary loose end we see is the possibility that high-level properties other than animacy explain our results (as raised by other Reviewers). We have added some discussion of this possibility to the paper.

We would like to thank this Reviewer for their thoughtful feedback.

Reviewer #2 (Public review):

Summary:

The authors present a creative approach using visual anagrams matched on low-level image statistics to isolate animacy from low-level visual features and report consistent effects of animacy on visual working memory and attention. While this is a thoughtful design and is well executed across seven pre-registered experiments, it remains unclear whether the reported effect is truly driven by animacy, as opposed to broader differences in ensemble statistics or semantic structure across the "mixed animacy" versus "uniform animacy" conditions. As such, the interpretation of a "pure" animacy effect may be overstated.

Strengths:

(1) An important methodological advance in controlling low-level confounds that have historically complicated the study of animacy.

(2) The converging effects across multiple experiments, together with the pre-registered design, strengthen the reliability of the reported findings.

We are glad to hear this Reviewer found our work to be “creative” and believes it offers an “important methodological advance”.

Weaknesses:

(1) Specificity of the animacy effect vs. category-level ensemble structure

The central claim is that animacy itself drives the observed effects. However, the key manipulation ("mixed animacy" versus "uniform animacy") also introduces differences in category-level ensemble structure. For example, in Experiments 1-2, cross-category change detection (e.g., dog to chair) may be easier not because of animacy per se, but because of a change in overall ensemble statistics (Brady & Alvarez, 2011, 2015). In addition, since each display contains five objects (two in one category and three in the other category), cross-category changes may also alter category balance in a way that further facilitates detection. In contrast, within-category changes preserve both ensemble structure and category composition, making them more difficult to detect.

Brady, T. F., & Alvarez, G. A. (2011). Hierarchical encoding in visual working memory: Ensemble statistics bias memory for individual items. Psychological Science.

Brady, T. F., & Alvarez, G. A. (2015). Contextual effects in visual working memory reveal hierarchically structured memory representations. Journal of Vision.

We appreciate the opportunity to clarify our claims and the support for them. Our claim is indeed that animacy (or a closely related high-level property; see below) drives our effects, over and above its lower-level correlates — i.e., that the explanation for differences in change detection or search across conditions will invoke a high-level property of the images. As we understand the Reviewer’s concern(s), they either (a) are already addressed by our novel methodology, or (b) would still fall perfectly in line with our claim as stated above.

Consider the Reviewer’s concern that cross-category change detection “may be easier not because of animacy per se, but because of a change in overall ensemble statistics”. Which ensemble statistics change across categories in our stimulus set? Take as an example the case depicted in our figure, where a rabbit changes into either a dog (within-category) or a boot (cross-category). The dog and the boot are the very same image, just rotated; thus, they have the same luminance, curvature, area, spatial frequency, and so on. So if the change from rabbit to dog changes the array’s ensemble statistics with respect to any of those properties, it does so in the very same way as the change from rabbit to boot — and yet detection is still better for rabbit → boot than for rabbit → dog. Indeed, for nearly any ensemble statistic, the difference between the rabbit-display and the dog-display will be identical to the difference between the rabbit-display and the boot-display. To engage with the specific cases discussed in the two cited papers (Brady & Alvarez, 2011, 2015): The dog and the boot are the same size (because they are the same image), so average size is identical (just as average luminance, curvature, area, and spatial frequency are identical). And the very few properties left over (e.g., aspect-ratio) are addressed by later experiments.

To be clear: We are not saying that there are no differences in ensemble statistics between the rabbit-display and the dog-display; across those displays, we replace one image with a different image, so there are likely all kinds of corresponding differences in ensemble statistics. The key question is whether that change in ensemble statistics differs across trial types in ways that might explain our effect - i.e., whether there is any difference between the rabbit-display and dog-display that is not also present between the rabbit-display and the boot-display. We don’t see how the answer could be yes, at least with respect to the statistics typically considered. A similar logic applies to the search tasks, with the silhouette control (Experiment 7) providing especially strong evidence that certain ensemble statistics or lower-level features cannot explain our effect.

Now, it’s possible the Reviewer is referring to properties other than the low-/mid-level properties we mention above. Perhaps, for example, many animate stimuli on a display at one time have a striking collective appearance (all these animals are looking at me!) that lots of inanimate stimuli do not (this might be related to the Reviewer’s concern about “category balance”). But as we see it, this explanation just invokes animacy all over again, and so is the sort of explanation we would embrace.

We now say more about this concern in the paper to be as clear as possible about our claims.

(2) Limited stimulus set and potential learning effects

The relatively small stimulus set (six anagram pairs) and repeated exposure raise the possibility of learning or familiarity effects. Does performance change over time? e.g., are there meaningful differences between early and late trials (e.g., first 10% vs. last 10%)? If such differences are present, they could suggest the development of task-specific strategies or increased efficiency with repeated exposure, rather than stable effects driven by the experimental manipulation itself.

This is an interesting question, and we recognize this analysis absent from our initial submission. To be fair, stimulus sets of this size are not unusual in change-detection and search tasks, which often involve red, green, and blue squares repeated over the course of several hundred trials. Still, we certainly take the Reviewer’s point here and also embrace their analytical approach to addressing it. We’ve now run the “familiarity effects” analyses the Reviewer suggests (as well as some they did not suggest). The top-level headline is that learning or familiarity effects cannot explain our results, and if anything most of these analyses not only fail to support this alternative account but actively point against it. Below are more details.

First, we worry that the Reviewer’s concern about “the development of task-specific strategies … rather than stable effects driven by the experimental manipulation itself” isn’t actually addressed by the suggested analysis of comparing the last 10% of trials to the first 10%. One reason for this is simply that it’s possible that both mechanisms are at play - i.e., that there is a baseline difference even without any familiarity that is then enhanced by some learning mechanism. (There are other issues as well: For example, one might imagine that participants get quite good at the task during the middle 80% of trials, but then get fatigued at the end. If this were true, then comparing the first 10% to the last 10% of trials could make it seem like there is no learning or familiarity, even if there were such effects. And on top of all this there is just the issue of statistical power, since far fewer trials go into these analyses than into our primary, pre-registered analyses). Nevertheless, we ran the Reviewer’s proposed analyses (using the first and last 10 trials of each type, which offers the best chance to find the pattern the Reviewer is concerned about). If anything, this analysis points in the opposite direction to the Reviewer’s prediction: 4/6 experiments (Experiments 1, 3, 4, and 5) revealed numerically weaker effects at the end of the task than the start, while only 2/6 experiments (Experiments 2 and 6) revealed numerically stronger effects at the end of the task than the start. Moreover, most of these results were non-significant, with only one marginal result (Experiment 5, p< = 0.08) and one significant result (Experiment 2, p = 0.01), and this is before any correction for multiple comparisons, which would make all of these results non-significant. So even though our account could easily accommodate learning effects, it’s not clear that they even exist here in any consistent or reliable way.

Second, however, we think a more informative way to answer the Reviewer’s question is to ask not about learning over the course of the experiment but rather whether the key effects arise very early in the task. If they do, then any learning effects arising later couldn’t fully account for our results. Now, again, these tests are underpowered and only exploratory (to do this analysis properly, we would want to run entirely new experiments designed for this purpose), but we in fact did find evidence that our key effects arise early. In 5/6 experiments (Experiments 1, 3, 4, 5, and 6), the key effect was significantly (or in one case marginally) present even at the beginning of the experiment (Experiment 1, p = 0.07; Experiment 3, p = 0.01; Experiment 4, p < 0.001; Experiment 5, p < 0.001; Experiment 6, p < 0.01), and most of these results would survive correction for multiple comparisons. (In only one experiment, Experiment 2, was there a numerical disadvantage, but it was not significant; p = 0.34.) So even though our experiments were not designed or powered for this purpose, they do seem to suggest that the effects arise even without much familiarity at all.

All told, we think these analyses suggest quite strongly that learning alone fails to explain our key effects. There is no evidence that the effects in general are stronger at the end of the experiment than the beginning (if anything it is the opposite); and there is evidence that most of the effects we investigated can be detected even very early in the experimental sessions. We have added discussion of these new analyses to our manuscript.

(3) Role of semantics

Although the anagram paradigm effectively controls low-level visual features, it still relies on high-level semantics (e.g., "dog" vs. "boot"). These stimuli differ not only in animacy but also along other semantic dimensions such as natural versus manmade categories. From a semantic standpoint, it remains unclear whether the observed effects can be uniquely attributed to animacy or whether they reflect broader conceptual distinctions.

We agree with the Reviewer here. While we feel comfortable interpreting our effects in terms of a high-level property like animacy as opposed to a lower-level property like curvature, it remains possible that the observed effects reflect some other, closely related high-level distinction (like natural vs. manmade). Our primary concern was to tease apart high-level properties from low-level features, which the Reviewer’s question does not threaten — if attention and memory are sensitive to the natural/artificial distinction, that’s interesting too, and a near neighbor of our actual claim. Still, we agree that this could be addressed, and we even see it as an empirical question testable in future work. Perhaps the most relevant departures between animate/inanimate and natural/manmade include objects like clouds, plants, and rocks — objects that are natural but not “animate” in the sense often used in this literature. If something like our paradigm revealed that rocks behave more like dogs than like boots, that would suggest that naturalness, rather than animacy, was driving the effects; but if rocks behave more like boots than like dogs, that would point to animacy even more strongly. We remain open-minded about this possibility, but it would of course require multiple new experiments with a brand new stimulus set and so goes beyond the present contribution. In any case, we have added a discussion of this issue to the paper and have adjusted our claims accordingly.

Reviewer #3 (Public review):

Summary:

This study makes clever use of generative AI to create stimuli that are pixel-for-pixel identical but which have radically different meanings depending on their orientation, to investigate the perception of animacy while retaining control over low-level image features (so-called 'anagram' stimuli).

The authors present seven elegantly designed experiments in a commendably compact format.

Experiments 1 and 2 involved a working memory paradigm in which participants had to spot which of five objects in an array changed after a pause. Importantly, the changed object was an anagram stimulus that in one orientation matched the animacy/inanimacy of the changed object, and in the other orientation was the opposite (e.g., a rabbit is replaced by either a dog or a boot, where the dog and boot stimuli are actually identical, just rotated by 90 degrees). They found a difference in accuracy depending on whether the animacy of the objects matched.

Experiments 3 and 4 used a visual search task in which the participants had to localize the target, and the distractors were anagrams that either matched the target in terms of animacy or did not. There was a significant cost in terms of response time when the animacy of the target was the same as that of the distractors. Experiments 5 and 6 also used a similar visual search design, except that the task was to determine if the target was present or absent from the display, and the distractors again either matched or differed from the target in terms of animacy. Again, the authors found slower responses when the distractor arrays matched the animacy of the target than when they differed.

An obvious potential concern about the studies is addressed by Experiment 7. It is unclear if the observed effects are related to the specific orientations of the target and distractor stimuli selected in each condition. For example, it could be that all the animate versions of the anagrams involved tall and skinny shapes, while all the inanimate versions involved wide and short objects, due to the 90-degree rotational difference between the two versions of the stimuli. To control for this, the authors repeated the visual search experiment but with convex-hull silhouettes of each of the stimuli. In other words, all targets and distractors from each trial were replaced by a black splotch with approximately the same overall outline (envelope) as the corresponding stimulus. Importantly, in contrast to the anagram stimuli, the silhouettes had had no meaningful semantic interpretation, and their animacy did not change depending on their orientation.

Strengths:

The main strength is the elegant use of stimuli that control almost perfectly for low-level image features.

Thank you for this kind feedback. This summary perfectly captures both our empirical contribution and the claims we are making.

Weaknesses:

My only real concern about the study is whether the findings truly provide evidence for a high-level visual representation of animacy independent of the low-level stimulus characteristics, or whether, instead, the effects are essentially semantic priming, which is independent of visual processing per se. For example, if all the stimuli in the experiments were replaced with the verbal names of the depicted objects instead of pictures, would we expect different results? Words can also access semantic representations of the animacy of objects, and also don't suffer from low-level visual confounds. It would be helpful to add a discussion of this possibility to the article.

Wow, we love this question! And so we’ve now conducted exactly the experiment the Reviewer suggests here. In a new pre-registered study (Experiment 8), we presented participants with a present/absent search task (as in Experiments 5–7). One half of trials consisted of the anagram stimuli (such that we could, once again, replicate the mixed-animacy search advantage); but the other half of trials consisted of the words describing the anagrams (e.g., “dog”, “boot”, “sheep”, “car”, etc.). The experiment worked beautifully: We found no effect with the words, but replicated the search advantage with the pictures — and also found a significant difference between the effects elicited by the two stimulus types.

We agree with the Reviewer that this now rules out the possibility that semantic representations alone explain these visual effects. Thank you! 

Reviewer #4 (Public review):

In this article, the authors investigate whether perceived animacy influences visual processing independently of lower-level visual features by using "visual anagrams." Across seven experiments, they test whether animacy, isolated from many lower-level visual properties, structures visual working memory and guides visual attention. The central claim is that the visual system may represent animacy itself, rather than animacy emerging solely from associations among low-level visual properties.

I find this investigation compelling. The experiments described provide strong control over several lower-level visual features, including curvature, texture, and related image properties. However, the visual anagrams are not pixelwise-identical across orientations. Because the images are rotated, the retinal configuration of pixels and the spatial organization of some low- to mid-level shape features also change. As a result, the configural arrangement of mid-level visual features may still contribute to perceived animacy.

We are glad to hear the Reviewer finds our investigation “compelling”.

I encourage the authors to discuss how independent perceived animacy is in this context from the contribution of mid-level visual features, such as configural shape cues that are diagnostic of animacy. This distinction would help sharpen the interpretation of the results and more precisely define the level of visual representation isolated by the visual-anagram approach.

This is a helpful point, and it also echoes a sentiment expressed by Reviewer #2. While configural shape is diagnostic of animacy writ large, it can’t account for our observed effects here because rotating an image does not vary its configural shape. We now mention this in our work, and we agree that it helps sharpen the interpretation of our studies.

Additionally, previous studies have argued that low- and mid-level curvilinear features may contribute to animate/inanimate categorization, and may in some cases be sufficient to support such distinctions (e.g., PMID: 33798259; PMID: 28654965). I encourage the authors to clarify how these previous findings on curvilinearity and rectilinearity fit with the overarching claim of the current study, namely that the visual system may represent animacy itself rather than animacy emerging solely from associations among lower-level visual properties.

Yes, many studies from exactly that corner of the field actually motivated the present work, which is why we cited them in our submission. In a way, we are approaching this issue from the other side of the equation. Whereas the papers the Reviewer points to (along with many others) ask whether mid-level features (such as curvilinearity and rectilinearity) are sufficient to support perceived animacy, we ask whether these and other features are necessary to support perceived animacy. Prior work is relatively split on this issue, leaving the question wide open. We take our work to show that differences in curvature are not necessary for differences in perceived animacy, because our anagrams have identical curvature yet differ in animacy — and the visual system capitalizes on that difference. Put the other way around, representation of animacy can and does go beyond representation of its low- and mid-level correlates. Thank you!

se explicara a detalle en los preliminares y de alli se cita aca

citar esto

en los preliminares hay que explicar a detalles que significa cola pesada hacia la derecha

esplicar que es la canicula

eLife Assessment

This study provides important insights into the neural mechanisms linking sleep and long-term memory consolidation. By combining behavioural, genetic, imaging, and connectomic approaches in Drosophila, it identifies a target neural circuit that will be of broad interest to researchers studying sleep, memory, and neural circuits. The evidence supporting the involvement of the identified circuit in the regulation of sleep and memory is solid and represents a substantial advance in the field. Nevertheless, there is limited evidence to support the mechanistic claim that this circuit directly links sleep and memory consolidation within the available data, and some results should therefore be interpreted with appropriate caution.

Reviewer #1 (Public review):

Summary:

The authors aim to use state-of-the art behaviour, imaging and connectome techniques to identify the neural interaction between sleep and long-term memory consolidation in the PAM-DPM circuits, a well-known dopaminergic pathway within Drosophila Mushroom Body.

Strengths:

The investigation follows a logical strategy to collect huge dataset of sleep, appetitive memory and live imaging. The authors identified and showed that activation of a PAM subset: alpha-1 reduces sleep quality and memory consolidation in a starvation dependant manner. The author also convincingly demonstrated the corresponding neuronal responses of DPM neurons following PAM alpha-1 activation, and the positive role of DPM neural activity in sleep and memory consolidation. Moreover, the new data provide TRIC-LUC provided better temporal resolution of neural activity correlates for PAMalpha1-DPM inhibition. Importantly, the author demonstrated that memory loss derived from PAM alpha 1 activation can be partly restored by ectopic sleep enhancement via feeding THIP at the memory consolidation period after training.

Weaknesses:

Although the revised version carries arguments to satisfy the reviewers' concern, the writing is now less cohesive. Crucially an explanation however remains required for the following experimental contradiction: the central observation of the study indicates that PAM alpha1 activation cause DPM inhibition which disrupt sleep and memory consolidation. Therefore, one would expect a reduced PAMalpha1 and increased DPM activities after memory training, but the authors found the opposite is true from now enhanced TRIC-LUC dataset. The authors indicate this data reinforce the inhibitory nature of PAM-alph1-DPM, but it does not explain why such a reduced DPM activity is observed after training.

Reviewer #2 (Public review):

Summary:

Sleep plays a critical role in memory consolidation, but the neural mechanisms underlying this relationship remain incompletely understood. The authors examined a specific subset of PAM dopaminergic neurons, PAM-α1, and DPM neurons in Drosophila. These neurons have previously been implicated in memory, and DPM neurons have also been linked to sleep. The study explores whether this circuit provides a mechanistic link between sleep and memory consolidation.

Strengths:

The authors report several novel findings. Brief activation or inhibition of PAM-α1 neurons, or brief inhibition of DPM neurons during the first few hours after training, impairs 24-hour LTM. Notably, these brief manipulations disrupt sleep for many hours afterward, particularly during the night. The authors further show that perturbation of PAM-α1 and DPM neurons impairs sleep and appetitive memory consolidation under starvation conditions, and that pharmacological sleep induction during the night rescues the LTM defects. Together, these findings suggest that PAM-α1 and DPM neurons are involved in sleep regulation and LTM consolidation under starvation. These are important observations that advance our understanding of the circuits regulating sleep and memory consolidation.

Weaknesses:

Some claims require additional evidence or clarification.

(1) Previous studies linking impaired memory to reduced sleep have primarily examined conditions involving severe sleep deprivation. In contrast, this manuscript argues that relatively modest decreases in total sleep, accompanied by sleep fragmentation, are sufficient to impair memory consolidation. It remains unclear whether sleep fragmentation of this magnitude is itself critical for LTM consolidation. An independent method for inducing comparably mild sleep loss and fragmentation would be needed to directly test this interpretation.

(2) It is unclear why both activation and inactivation of PAM-α1 neurons produce similar effects on sleep and memory. In addition, MB299B-labeled neurons exert stronger effects on memory than MB043B-labeled neurons, whereas MB043B-labeled neurons have stronger effects on sleep. If sleep disruption is the primary driver of impaired memory consolidation, a stronger correspondence between the sleep and memory phenotypes might be expected. The authors speculate that MB043B may affect sleep through non-PAM neurons, but without identifying the relevant neurons, this remains speculative.

(3) The complex schematic model (Fig. 12), with parallel circuits and unidentified neuronal groups, underscores the difficulty of interpreting the current data. In the "less activity" arm of the model, distinct circuits are proposed to regulate sleep and LTM, respectively, and DPM neurons are not included. This makes it difficult to reconcile the model with the central claim that the PAM-α1-to-DPM microcircuit links sleep and LTM consolidation.

(4) The TRIC-LUC reporter system is not ideal for resolving dynamic changes in neuronal activity. Activity-dependent Ca²⁺ signaling must first reconstitute the TRIC transcriptional system, which then drives luciferase transcription, translation, and accumulation. The original characterization of TRIC indicates that TRIC signals accumulate and decay over several hours. Thus, the kinetics of the TRIC-LUC reporter should be interpreted cautiously, particularly when inferring transient or precisely timed changes in neuronal activity.

(5) Including data from training under fed conditions would provide a more complete understanding of state-dependent neural activity and would help distinguish starvation-specific effects from more general circuit mechanisms.

Reviewer #3 (Public review):

Summary:

Understanding the neural circuits that link sleep and memory remains a fundamental challenge in neuroscience. In this study, Lin Yan and colleagues investigate how dopamine signaling in Drosophila regulates long-term memory (LTM) formation in the context of sleep. They identify a specific microcircuit between protocerebral anterior medial dopamine neurons (PAM-DANs) and dorsal paired medial (GABAergic DPM) neurons that modulates memory consolidation. Their findings suggest that disrupting the basal activity of PAM-α1 neurons during early consolidation impairs LTM, with particularly pronounced effects under starvation conditions. Notably, sleep fragmentation caused by this disruption can be pharmacologically rescued, restoring LTM. These results provide compelling evidence how dopamine signaling plays a crucial role in linking sleep and memory, offering new insights into the underlying mechanisms.

Strength:

This study presents a well-executed investigation into sleep-memory interactions, utilizing a combination of connectomics, behavioral assays, functional imaging, and pharmacological manipulations. The authors convincingly demonstrate that the PAM-α1 and DPM circuit interact, highlighting a potential mechanism by which sleep influences memory consolidation. The anatomical and functional dissection of this circuit is of high interest to the field, and the study's integration of sleep and memory processes contributes significantly to our understanding of the role of dopamine in cognitive functions. Additional experiments investigating the contribution of MBON-α1 to the circuit, connectomic analysis together with a dissection of dopamine receptor function further strengthen the proposed circuit motif and its biological relevance.

Weaknesses:

While the study is well designed, presents compelling findings and has been further strengthened by additional experiments, some aspects remain unclear. The role of DPM neurons in memory consolidation seems not yet fully resolved, as different genetic approaches yield variable results. Furthermore, some manipulations impair memory without affecting sleep fragmentation - or vice versa, suggesting that the observed memory deficits cannot be explained solely by impaired sleep-dependent consolidation. It would also have been interesting to discuss potential mechanisms by which dopamine receptor-mediated cAMP signaling could lead to a reduction in Ca²⁺ signals. I am confident that these questions can be addressed in future studies.

Conclusion:

Overall, this study provides valuable new insights into how sleep and dopaminergic circuits interact to regulate memory consolidation in Drosophila and may reveal general principles underlying the neural regulation of memory.

Author response:

The following is the authors’ response to the original reviews

eLife Assessment

This study approaches an important topic providing insight into the neuronal circuitry that interconnects memory consolidation and sleep. The data were collected and analysed using a solid methodology, contributing new findings for neurobiologists working on how memories are stored and the roles of sleep. However, the data is incomplete to support the proposed role of the PAM-DPM circuits as the link between sleep state and long-term memory consolidation.

We sincerely appreciate the editor and reviewers’ thoughtful and constructive comments on our study. Your insightful feedback has not only affirmed the significance of our work on the interplay between memory consolidation and sleep, but also provided valuable inputs for improving the clarity, rigour, and impact of our study.

We have carefully addressed all the comments raised by the reviewers and revised the manuscript accordingly. We have also streamlined the paper with the goal of making it more accessible to readers. We feel this revised version strengthens our conclusion that the PAM-DPM circuits as the link between sleep and memory consolidation.

The main improvements in terms of data addition are three complementary sets of circuit-specific experiments:

(1) To better characterize the dynamics of the PAM-DPM circuit following associative memory training, we performed 3-hour continuous neural activity recording in freely behaving flies. This experiment addresses the activity of the microcircuit in a much more relevant time frame than the CRTC data in the previous version of the paper which looked only at the first hour after training. Specifically, we expressed the Tric-LUC reporter gene, a calcium-responsive tool that harnesses the interaction between calmodulin and its cognate binding peptides to drive rapid luciferase transcription in a calcium-dependent manner (Gao et al., 2015; Guo et al., 2017), in PAM-α1 and DPM neurons, respectively. Flies were then subjected to either associative memory training or a no-training control condition, with real-time luciferase levels monitored throughout the recording window.

In the absence of training, both PAM-α1 and DPM neurons displayed similar neural activity over the 3-hour recording period. The first hour was characterized by a synchronous decrease in activity for both neuron types, with hours 2 and 3 achieving a stable baseline. Since the decrease in the first hour is also seen in the trained condition, we think it is likely a reflection of the animals becoming acclimated to the recording tubes.

Notably, associative memory training profoundly reshaped the activity profile of the PAM-DPM circuit in the LTM consolidation time window. Training induced a mild yet statistically significant elevation in PAM-α1 neural activity specifically during the third hour of recording, while concurrently eliciting a robust reduction in DPM neuron activity over the last two hours (revised Figure 8C-F). These findings not only support the hypothesized role of the inhibitory PAM-α1-DPM circuit in sleep and memory consolidation, but also advance our mechanistic understanding of underlying neural dynamics.

(2) To further support the functional connectivity of the PAM-DPM microcircuit, we conducted in vivo experiments to complement the dissected brain prep P2X2 data. Optogenetic activation of PAM neurons in intact flies via the red light-gated cation channel CsChrimson (Klapoetke NC et al., 2014) resulted in a significant reduction in GCaMP signals within DPM neurons (revised Figure 2B). These findings strongly confirm that PAM neurons exert direct inhibitory control over DPM neurons in the intact brain.

(3) Further, we investigated how dopamine signaling to the DPM inhibits its activity, and issue which has not been investigated previously. We conducted a series of experiments:

Firstly, we verified which dopamine receptors (Dop1R1, Dop1R2, DopEcR, and Dop2R) express on the DPM neurons via double-labeling with gene-embedded GAL4 lines. We found that DPM neurons have expression of both Dop1R1 and Dop1R2 (revised Figure 10A).

Secondly, to clarify which receptors on DPM neurons respond to dopamine and how they signal, in addition to EPAC experiments in the first submission, we recorded neural activity changes when we knocked down Dop1R1 and Dop1R2 in DPM neurons. DPM neurons exhibited a significantly reduced GCaMP level with DA application, regardless of whether Dop1R1 or Dop1R2 was intact or knocked down knockdown in comparison to the no-DA control condition (revised Supplemental Figure 3C-E). These data suggest that either residual Dop1R1 and Dop1R2 remaining in the RNAi condition is sufficient or that the two receptors may coordinate to mediate the inhibition of neural activity.

Finally, we investigated the behavioral contributions of Dop1R1 and Dop1R2 in DPM neurons to sleep and memory processes (revised Figure 10C-H). Dop1R1 knockdown resulted in a marked reduction in daytime sleep and a significant impairment of 24 h memory expression. In contrast, Dop1R2 knockdown selectively compromised 24 h memory without affecting sleep.

When integrated with our EPAC assay findings from the initial submission, which demonstrated, that Dop1R1 is the primary receptor mediating dopamine-induced cAMP elevation, these new data collectively delineate a more complex mechanistic framework: dopamine signaling in DPM neurons coordinates the dual regulation of sleep and memory predominantly via Dop1R1. Meanwhile, Dop1R2 are engaged in the selective modulation of memory.

All newly generated experimental datasets, comprehensive statistical analyses, and their corresponding figure panels (revised Figures 2B, 10, 11 and Supplemental Figure 3) have been fully incorporated into the revised manuscript.

In addition to adding the experiments described above, we have reorganized and streamlined the paper. First, the CRTC data have been replaced by the Tric-luc data. The CRTC data were taken in the first hour after training and do not shed light on the bulk of the consolidation window. Since the behavioral and sleep effects we see with manipulation of the PAM/DPM microcircuit all occur with a time delay, examining later times in consolidation is more relevant. Additionally, the first hour post-training is quite complex since there are sensory changes and STM processes overlaid on the processes we want to study. Second, we have moved the data in Figure 8 to supplemental (revised Supplemental Figure 2) since they are basically a control for the experiments in Figure 7 validating known requirements for appetitive LTM.

We have also substantially expanded the Discussion section to contextualize the PAM-DPM circuit within the broader framework of well-characterized memory-regulatory pathways, such as the intrinsic circuits of the mushroom body, and to explicitly delineate the hierarchical interplay between sleep-dependent synaptic plasticity and LTM consolidation.

We contend that these complementary experimental assays and targeted revisions markedly strengthen the causal evidence underscoring the role of the PAM-DPM circuit as a pivotal regulatory node bridging sleep states and LTM consolidation. We are confident that these revisions essentially address the concerns raised by the reviewers.

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors aim to use state-of-the art behavior, imaging, and connectome techniques to identify the neural interaction between sleep and long-term memory consolidation in the PAM-DPM circuits, a well-known dopaminergic pathway within Drosophila Mushroom Body.

Strengths:

From a Drosophila sleep researcher's perspective, the investigation follows a clear and logical strategy to collect a huge dataset of sleep, appetitive memory, and live imaging. The authors clearly identified and showed that activation of a PAM subset: alpha-1 reduces sleep quality and memory consolidation in a starvation-dependent manner. The authors also convincingly demonstrated the corresponding neuronal responses of DPM neurons following PAM alpha-1 activation, and the positive role of DPM neural activity in sleep and memory consolidation. Moreover, the authors applied a new way of sleep statistics to demonstrate hour-by-hour changes between treatment and genotypes. Importantly, the authors demonstrated that memory loss derived from PAM alpha 1 activation can be partly restored by ectopic sleep enhancement via feeding THIP during the memory consolidation period after training.

Weaknesses:

Two investigatory gaps relate to the misalignment between circuital activity and behaviors, due to the nature of large circuital functional analysis like this. Firstly, the central observation of the study indicates that PAM alpha1 activation causes DPM inhibition which disrupts sleep and memory consolidation. Therefore one would expect a reduced PAMalpha1 and increased DPM activities after memory training, but the authors found that the endogenous CRTC::GFP reported neuronal activity for PAMalpha1 and DPM are both increased after memory training (Figure 9). This can be due to the difficult functional demarcation among the 14 PAMalpha1 projections. Secondly, the authors acknowledged the contradicting finding that memory defect is detected in PAMalpha1 inactivation (Figure 7C), yet suggested a tight link between sleep and memory consolidation; it is clear loss of PAM subset activity can disrupt memory consolidation without affecting sleep (cf Figure 7C and 7I).

Thank you for your insightful analysis and the relevant possibilities you've raised. We agree that given that memory consolidation and sleep are time-dependent processes, the 1-hour window employed to capture neural activity changes via the CRTC::GFP reporter may not fully reflect the overall dynamics of neural activity in this microcircuit. To better characterize the dynamics of the PAM-DPM circuit in the consolidation window following associative memory training, we performed 3-hour continuous neural activity recording in freely behaving flies. Specifically, we expressed the Tric-LUC reporter gene, a calcium-responsive tool that harnesses the interaction between calmodulin and its cognate binding peptides to drive rapid luciferase transcription in a calcium-dependent manner (Gao et al., 2015; Guo et al., 2017), in PAM-α1 and DPM neurons, respectively. Flies were then subjected to either associative memory training or a no-training control condition, with real-time luciferase levels monitored throughout the recording window.

In the absence of training, PAM-α1 neurons displayed stable neural activity over the entire 3-hour recording period. However, associative memory training profoundly reshaped the activity profile of the PAM-DPM circuit. Training induced a mild yet statistically significant elevation in PAM-α1 neural activity specifically during the third hour of recording, while concurrently eliciting a robust reduction in DPM neuron activity over the last two hours (revised Figure 8C-F). These findings not only support to the hypothesized role of inhibitory PAM-α1-DPM circuit in sleep and memory consolidation, but also advance our mechanistic understanding of underlying neural dynamics.

Regarding the second question, the core finding underlying the link between sleep and memory elucidated in the present study lies in the whole PAM-α1-DPM microcircuit rather than the specific DANs alone. MB299B and MB043B, the two split-GAL4 drivers employed to target PAM-α1 neurons, were originally characterized previously (Aso et al., 2014). However, these drivers also exhibit non-specific labeling of additional cells, and we can not rule out the possibility that such off-target labeling may have masked the subtype-specific necessity in sleep or memory processes.

Reviewer #2 (Public review):

Summary:

Sleep plays a critical role in memory consolidation, but the neural mechanisms underlying this relationship remain poorly understood. The authors present novel findings implicating two small neuronal groups with inhibitory connections, PAM-a1 to DPM, in sleep regulation and LTM consolidation. However, whether the PAM-a1 to DPM microcircuit promotes LTM consolidation through sleep regulation requires further investigation.

Strengths:

The authors report several novel findings. Brief activation or inhibition of PAM-a1 neurons, or brief inhibition of DPM neurons during the first few hours after training, impairs 24-hour LTM. Notably, these brief manipulations disrupt sleep for many hours afterward, particularly at night. Interestingly, disruption of PAM-a1 and DPM neurons impairs sleep and appetitive memory consolidation only under starvation conditions, and pharmacological induction of sleep during the night rescues the LTM defects. These findings suggest that PAM-a1 and DPM neurons are involved in sleep regulation and LTM consolidation under starvation. These are important findings that advance our understanding of the link between sleep and memory consolidation.

Weaknesses

Some claims lack sufficient evidence or clarity:

(1) All sleep experiments are conducted under the "training" (temperature-change) condition. While genotypic controls are helpful, additional no-training controls are required to confirm that the observed differences are due to training rather than unknown genotype-related factors. The fact that experimental genotypes exhibit significantly altered sleep even before "training" (e.g., Figs. 7H, J, K, 8A, B, D) highlights the necessity of these controls.

Thank you for raising this important question. We have re-examined the sleep profiles recorded over two acclimation days and one day of baseline sleep, which preceded the implementation of the “training” paradigm (temperature manipulation) and thus served as a valid no-training control. As shown in Author response images 1-4, subtle yet discernible genotype-dependent differences were indeed observed under baseline conditions. However, when animals were subjected to starvation, the experimental manipulations (activation or inactivation of the target cells) elicited marked, statistically significant alterations in sleep patterns that cannot be accounted for by the baseline genotype differences. Collectively, these data confirm that the observed sleep phenotypes are attributable to the “training” intervention, rather than to confounding, pre-existing genotype-related factors.

Author response image 1.

Baseline and manipulation day sleep profiles following PAM activation and PAM/DPM inactivation under starvation conditions.

Author response image 2.

Baseline and manipulation day sleep profiles following PAM activation and PAM/DPM inactivation under non-starvation conditions.

Author response image 3.

Baseline and manipulation day sleep profiles following PAM- α1 activation and inactivation under starvation conditions.

Author response image 4.

Baseline and manipulation day sleep profiles following PAM- α1 activation and inactivation under non-starvation conditions.

(2) Previous studies on disrupted memory due to sleep reduction have primarily examined conditions with severe sleep deprivation. In contrast, this report claims that relatively small decreases in total sleep accompanied by sleep fragmentation are responsible for impaired memory consolidation. It remains unclear whether sleep fragmentation at this level is truly critical for memory consolidation. The authors should cause sleep loss and fragmentation of similar magnitude through other means and determine whether it can impair LTM.

We appreciate the reviewer’s insightful suggestion. While alternative assays for inducing sleep loss or sleep fragmentation are indeed available, this line of investigation lies beyond the core scope of the present study. We will certainly take this valuable suggestion into consideration for the future studies.

(3) The authors employed a neural activity reporter to show that starvation increases the basal activity of PAM-a1 but not DPM neurons in untrained flies (Figures 9C-E). They observed small increases in the activity of both neuron groups immediately after training but not one hour later. Given the inhibitory connection from PAM-a1 to DPM, it is unclear why both neuron groups show increased activity after training. Additionally, as the authors acknowledge, it is puzzling how the inactivation of PAM-a1 produces similar effects on sleep and memory as DPM inhibition and PAM-a1 activation. Further experiments are needed to clarify these findings, such as manipulating PAM-a1 activity during the one-hour post-training period and evaluating the effect on DPM activity. Including data from training under fed conditions would provide a more comprehensive understanding of state-dependent neural activity. Even if certain experiments are not feasible, these issues warrant further discussion. It is also important to clarify that the term "synchronized" does not imply single-spike-level synchrony.

Thank you for raising these critical questions. To deepen our understanding of these issues, we have conducted additional experiments and have incorporated them into the revised manuscript. Below are our specific responses to each of your points:

(1) Regarding the contradiction between "PAM-α1 inhibition of DPM" and a transient increase in the activity of both neurons immediately after training:

PAM/PAM-α1 neurons are well-documented to respond to reward signals (Liu et al., 2012, Ichinose et al., 2015), while DPM neurons have been shown to respond to both olfactory stimuli and electric shocks, and to form delayed olfactory memory traces (Yu et al., 2005). Thus, the concurrent increase in the activity of PAM-α1 and DPM neurons immediately following training is likely a response to the olfactory and/or sucrose stimuli in the assay. Given that memory consolidation and sleep are time-dependent processes, the 1-hour window employed to capture neural activity changes via the CRTC::GFP reporter likely does not fully reflect the overall dynamics of neural activity in this microcircuit. Additionally, this time window overlaps with the period in which the animals are adapting to the new tubes and is likely contaminated with other sensory information.

To better characterize the dynamics of the PAM-DPM circuit following associative memory training, we performed 3-hour continuous neural activity recording in freely behaving flies. Specifically, we expressed the Tric-LUC reporter gene, a calcium-responsive tool that harnesses the interaction between calmodulin and its cognate binding peptides to drive rapid luciferase transcription in a calcium-dependent manner (Gao et al., 2015; Guo et al., 2017), in PAM-α1 and DPM neurons, respectively. Flies were then subjected to either associative memory training or a no-training control condition, with real-time luciferase levels monitored throughout the recording window.

In the absence of training, PAM-α1 neurons displayed stable neural activity over the entire 3-hour recording period. Notably, associative memory training profoundly reshaped the activity profile of the PAM-DPM circuit. Training induced a mild yet statistically significant elevation in PAM-α1 neural activity specifically during the third hour of recording, while concurrently eliciting a robust reduction in DPM neuron activity over the last two hours (revised Figure 9F-I). These findings not only support to the hypothesized role of inhibitory PAM-α1-DPM circuit in sleep and memory consolidation, but also advance our mechanistic understanding of underlying neural dynamics post-training. We have replaced the CRTC data with this more relevant data set.

(2) Regarding the state-dependent neural activity:

We agree that investigating state-dependent neural activity would be an interesting extension of our study. However, this falls beyond the scope of the current study and will be considered in future research. Our primary findings, including sleep disruptions and the associated memory impairments, were specifically observed under starvation conditions, which align with the appetitive memory paradigm employed here. Delving into neural activity changes under non-starvation state would not yield direct evidence to support the core conclusions of the present work, as the study’s focus is on the starvation-dependent interplay between sleep, neural circuitry, and appetitive memory consolidation.

(3) Regarding the terminology of “synchronization”:

We believe that the use of the term “synchronization” in our study is appropriate. In the context of neural circuitry, synchronization refers to the process by which distinct neurons or neural populations achieve temporal alignment of their activity, a phenomenon that supports neural communication and information integration. In the present work, this specifically describes how PAM-α1 and DPM neurons exhibit phase-related temporal coordination of their activity to regulate the interplay between sleep and memory consolidation.

(4) The authors considered that PAM-a1 and DPM might function in parallel, independent pathways for sleep and LTM. They rejected this possibility based on the lack of additive effects when both neuronal groups were simultaneously inactivated. However, they found that MB299B-labelled neurons exert stronger memory effects than MB043B-labelled neurons, while MB043B neurons have stronger sleep effects. If sleep is a primary driver of memory consolidation, a stronger correlation between memory and sleep effects would be expected. This observation merits further discussion.

We appreciate the reviewer’s constructive suggestions. We have performed additional experiments to explore a well-characterized memory-related PAM-α1 recurrent loop in sleep regulation. The new data, along with further discussion, have been incorporated into the revised manuscript.

The two split-GAL4 drivers (MB299B and MB043B) used to target PAM-α1 neurons were originally characterized previously (Aso et al., 2014). However, these drivers exhibit non-specific labeling of additional neuronal populations, a technical limitation that may have masked the subtype-specific functional requirements of PAM-α1 in sleep and memory processes.

In addition, we assessed sleep and LTM following the thermoactivation of DPM neurons (revised Supplemental Figure 1), and no significant changes were observed in either phenotype.

PAM-α1 has previously been demonstrated to drive appetitive LTM formation and consolidation via a recurrent loop with MBON-α1 (Ichinose et al., 2015). To investigate whether MBON-α1 also participates in sleep regulation, we activated or inactivated MBON-α1 neurons under both starvation and non-starvation conditions. Our results revealed that inhibition of MBON-α1 under both starvation and non-starvation conditions resulted in a significant reduction in sleep and a reduced arousal threshold (revised Figure 11B, D), suggesting that MBON-α1 participates in regulating sleep in a state-independent manner. However, no significant changes were observed upon activation of MBON-α1 neurons (revised Figure 11A, C). Combined with our observation that inhibition of MBON-α1 during the memory consolidation phase also impaired 24 h LTM, these new data indicate that MBON-α1-mediated sleep is necessary for effective memory consolidation. Notably, while activation of MBON-α1 during consolidation phase similarly impaired LTM, this manipulation did not alter the sleep profile, suggesting a dissociation between MBON-α1’s mechanistic roles in sleep regulation and LTM processing.

Taken together (see Author response table 1 and the new schematic diagram of revised Figure 12), these findings reveal a dedicated hierarchical, modular regulatory network that mediates sleep-LTM coupling via an activity-dependent mechanism. Within this network, activation of PAM-α1 acts as an upstream modulator to inhibit the activity of DPM, a downstream integrative hub that coordinates the execution of sleep and memory processes via recruiting different signaling cascades mediated by distinct dopamine receptors. MBON-α1, which is likely inhibited by PAM-α1, serves as parallel pathway to suppress sleep and impair LTM. Conversely, inactivation of PAM-α1 relieves its inhibitory control over MBON-α1, leading to MBON-α1 activation; MBON-α1 then functions as a signal amplifier that further exacerbates the reduced activity of PAM-α1, ultimately resulting in LTM impairment. Inactivation of PAM-α1, together with non-PAM-α1 neurons labeled by MB043B, contributes to the regulation of sleep. Sleep and memory are highly intertwined within this circuit, where distinct neuronal populations exhibit specialized yet interdependent functional roles, with overlapping and divergent regulatory contributions to sleep and LTM. The inherent complexity of this regulatory network thus merits further dedicated investigation in future studies.

Author response table 1.

(5) Given prior knowledge that PAM neurons are heterogeneous and that the R58E02 driver is broadly expressed, data in Figures 1-5 concerning PAM are outdated. The use of more restricted PAM-a1 drivers from the outset would make the manuscript easier to read and interpret.

We sincerely appreciate the reviewer’s point of view regarding the selection of PAM drivers. While we acknowledge the well-characterized heterogeneity of PAM neurons and the broad expression profile of the R58E02 driver, and fully agree that employing subtype-restricted drivers enhances the precision of functional interpretation, this set of experiments serves as an essential foundational step and logical basis for subsequent subtype-specific investigations and thus merits retention in the manuscript. As detailed above, the more specific drivers also have some drawbacks in terms of additional expression, making the broad driver critical for setting the stage.

(6) Some figures lack relevant data, certain experiments are missing necessary controls, and anomalies are present in some data sets.

We sincerely appreciate the reviewer’s detailed suggestions, and we have revised the manuscript comprehensively in accordance with them.

Reviewer #3 (Public review):

Summary:

Understanding the neural circuits that link sleep and memory remains a fundamental challenge in neuroscience. In this study, Lin Yan and colleagues investigate how dopamine signaling in Drosophila regulates long-term memory (LTM) formation in the context of sleep. They identify a specific microcircuit between protocerebral anterior medial dopamine neurons (PAM-DANs) and dorsal paired medial (GABAergic DPM) neurons that modulates memory consolidation. Their findings suggest that disrupting the basal activity of PAM-α1 neurons during early consolidation impairs LTM, with particularly pronounced effects under starvation conditions. Notably, sleep fragmentation caused by this disruption can be pharmacologically rescued, restoring LTM. These results provide compelling evidence that dopamine signaling plays a crucial role in linking sleep and memory, offering new insights into the underlying mechanisms.

Strengths:

This study presents a well-executed investigation into sleep-memory interactions, utilizing a combination of connectomics, behavioral assays, functional imaging, and pharmacological manipulations. The authors convincingly demonstrate that the PAM-α1 and DPM circuits interact, highlighting a potential mechanism by which sleep influences memory consolidation. The anatomical and functional dissection of this circuit is of high interest to the field, and the study's integration of sleep and memory processes contributes significantly to our understanding of dopamine's role in cognitive functions.

Weaknesses:

While the study is well-designed and presents compelling findings, some aspects require further clarification. The interpretation of dopamine receptor signaling remains incomplete, particularly regarding inhibitory pathways. The role of DPM in memory consolidation is not entirely conclusive, as different genetic approaches yield variable results. Additionally, some inconsistencies in neuronal activity patterns and experimental variability, especially regarding sleep patterns or pharmacological rescue, should be addressed to strengthen the mechanistic framework.

Conclusion:

Overall, this study provides valuable new insights into how sleep and dopamine circuits interact to regulate memory consolidation. While the findings are compelling, addressing the points above-particularly receptor signaling and the specific role of DPM and its activity patterns within the microcircuit would further solidify the study's conclusions.

We sincerely appreciate the reviewer’s constructive feedback and useful suggestions, which have been instrumental in enhancing the rigour and completeness of our study.

To address these points, we have performed a series of additional experiments that we believe strengthen the mechanistic framework of our work. The key new findings are summarized below:

(1) Regarding the dopamine receptor signaling

To define the dopamine receptor (DAR) signaling mechanisms underlying DPM neuron activity and its regulatory roles in sleep and memory, we first characterized DAR expression profile of the DPM neurons. Using double-labeling assay, we detected robust expression of Dop1R1 and Dop1R2 in DPM neurons, whereas no detectable colocalization was observed for DopEcR and Dop2R (revised Figure 10A). Accordingly, we refined our FRET-based EPAC data by removing the DopEcR knockdown group, and now present cAMP changes in DPM neurons following Dop1R1 and Dop1R2 knockdown, in direct comparison with the intact receptor control group (revised Figure 10B). These data conform that Gαs-coupled Dop1R1 is the primary receptor mediating DA-dependent cAMP elevation in DPM neurons.

To further identify the DARs responsible for transducing DA-induced inhibitory effect on DPM neural activity, we quantified GCaMP levels in DPM neurons with targeted knockdown of individual DARs. Knockdown of either Dop1R1 or Dop1R2 failed to abolish DA-induced Ca²⁺ decrease; only Dop1R2 knockdown exhibited a trend toward attenuating this Ca²⁺ decrease (revised Supplemental Figure 3C-E), suggesting that the two receptors cooperate to modulate DPM neural activity.

Finally, to dissect the specific contributions of DARs in DPM neurons to sleep and/or memory regulation, we performed sleep monitoring and memory assays in animals with DPM-specific knockdown of distinct DARs (revised Figure 10C-H). Knockdown Dop1R1 in DPM neurons resulted in statistically significant sleep reduction, decreased arousal threshold, and impaired 24 h LTM memory (revised Figure 10C-E). In contrast, knockdown Dop1R2 in DPM neuron selectively impaired 24 h LTM memory with no effect on sleep (revised Figure 10F-H). Collectively, these findings demonstrate that coupling sleep and LTM requires Dop1R1 in DPM neurons through the modulation of both cAMP signaling and neuronal activity, while Dop1R2 specifically mediates LTM regulation, likely through modulating DPM neural activity alone.

(2) We have additionally characterized the role of MBON-α1 in sleep, which has been previously shown as a PAM-α1-related recurrent feedback loop in the regulation of memory formation and consolidation (Ichinose et al., 2015).

To investigate whether MBON-α1 also participates in sleep regulation, we activated or inactivated MBON-α1 neurons under both starvation and non-starvation conditions (revised Figure 11A-D). Our results revealed that inhibition of MBON-α1 under both starvation and non-starvation conditions resulted in a significant reduction in sleep and a reduced arousal threshold (revised Figure 11B, D), suggesting that MBON-α1 participates in regulating sleep in a state-independent manner. However, no significant changes were observed upon activation of MBON-α1 neurons (revised Figure 11A, C). Moreover, inhibition of MBON-α1 during the memory consolidation phase significantly impaired 24 h LTM (revised Figure 11E-F). These results indicate that MBON-α1mediated sleep is necessary for effective memory consolidation. Notably, while activation of MBON-α1 during consolidation phase similarly impaired LTM, this manipulation did not alter the sleep profile, suggesting a dissociation between MBON-α1’s mechanistic roles in sleep regulation and LTM processing.

Taken together (see Author response table 1 and the new schematic diagram of revised Figure 12), these findings reveal a dedicated hierarchical, modular regulatory network that mediates sleep-LTM coupling via an activity-dependent mechanism. Within this network, activation of PAM-α1 acts as an upstream modulator to inhibit the activity of DPM, a downstream integrative hub that coordinates the execution of sleep and memory processes via recruiting different signaling cascades mediated by distinct dopamine receptors. MBON-α1, which is likely inhibited by PAM-α1, serves as parallel pathway to suppress sleep and impair LTM. Conversely, inactivation of PAM-α1 relieves its inhibitory control over MBON-α1, leading to MBON-α1 activation; MBON-α1 then functions as a signal amplifier that further exacerbates the reduced activity of PAM-α1, ultimately resulting in LTM impairment. Inactivation of PAM-α1, together with non-PAM-α1 neurons labeled by MB043B, contributes to the regulation of sleep. Sleep and memory are highly intertwined within this circuit, where distinct neuronal populations exhibit specialized yet interdependent functional roles, with overlapping and divergent regulatory contributions to sleep and LTM. The inherent complexity of this regulatory network thus merits further dedicated investigation in future studies (See Author response table 1).

We have modified the schematic diagram in the revised manuscript to illustrate the mechanistic framework (revised Figure 12).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Here I listed details for potential clarification or further investigation related to the weaknesses:

(1) Line 145-147: I suspected the authors used previously verified RNAi lines, but it would be informative to include a citation or their own validation for the effectiveness of these RNAi lines.

We sincerely appreciate the reviewer’s suggestion. As our double-labeling assays confirmed that only Dop1R1 and Dop1R2 are colocalized with DPM neurons (see our responses to the public review from Reviewer #2 and #3), we refined the revised data to focus exclusively on these two DARs. Corresponding revisions have been made to the Materials and Methods, Results and Discussion sections. Additionally, we conducted qPCR analysis to verify the knockdown efficiency of these DARs, providing further support for our findings that Dop1R1 and Dop1R2 are functionally required in DPM neurons for the regulation of sleep and memory (revised Supplemental Figure 3).

(2) Line 169: Moving from describing Figure 3A/B to Figure 3C, it is not immediately clear from 3C-H, the authors follow the training paradigm of 3B?

To enhance clarity, we have added the referenced figure citations in the “Memory assay” section: “For all 24 h sucrose-odour memory, a single training session of sucrose paired with an odour for 2 min was employed (Figure 3A).”

(3) Line 179: before the PAM inactivation data are shown in Figure 7, the authors seem to be getting ahead of themselves by stating "suggesting that activity of PAM neurons is necessary for the consolidation window or that heterogeneity in the subsets of PAM neurons masks any phenotype." when the Figure 3 data collectively indicate that "suppression" of PAM is necessary.

This statement is based on our observation that inactivation of the majority of PAM neurons labeled by R58E02 results in sleep disruption but leaves memory intact, and we stand by this conclusion.

(4) Line 186-188: The labelling of DP1 is not entirely aligned between the figures and the text for a reader to follow which time period is described, as DP1 is embedded within the dark phase in the figures.

These experiments spanned two consecutive days. LP1 and DP1 denote the light and dark periods on the first day, respectively, whereas LP2 designates the light period on the second day. As only one full dark phase was monitored across the experimental interval, we characterized the relevant phenotype using the general terms dark phase or nighttime, rather than specifying DP1. We thank the reviewer for this thoughtful observation; nonetheless, we consider the original description correct and unambiguous, and thus appropriate for inclusion in the manuscript.

(5) Line 321: The statistics for Figure 9 CRTC::GFP measurement is crucial for interpretation but the referring and labelling for this on Figure 9 is poor: it is not apparent which comparisons are indicated. There is inconsistency between Table 1 Figure 9D and Table 3 Figure 9D entries: no significant between train and untrain indicated in Table 1 but it is described as significant in the text and Table 3?

Thank you for this observation. As described above, we have removed these data from the paper and replaced them with Tric-Luc data that capture the consolidation window more completely.

(6) Line 423: The starvation-mediated sleep suppression is not clear in this manuscript, can the author comment on this? The response to this may also alter the summary concept cartoon.

This is an important point. To directly address the reviewer’s question regarding starvation-mediated sleep suppression, we have generated a representative response figure comparing sleep duration under starvation versus non-starvation conditions (Author response image 5). This figure clearly demonstrates that sleep is suppressed under starvation, providing straightforward evidence to address this concern.

Author response image 5.

Examples of starvation-mediated sleep suppression.

However, the key focus of our study is that changes in neuronal activity disrupt sleep under starvation conditions but not under non-starvation conditions. To emphasize this critical distinction, we have incorporated additional discussion focused specifically on this point.

“It is well established that starvation induces sleep suppression (MacFadyen, 1973; Thimgan et al., 2010; Melnattur and Shaw, 2019; Keene et al., 2010; He et al., 2020; Yangkyun et al., 2022), and our results are consistent with these previous findings: all genotypes exhibited less sleep under starvation than under fed conditions (i.e. Figures 4A-B, 5A-B and 11). Under normal appetitive memory training, starvation-induced sleep loss does not necessarily impair memory processing (Thimgan et al., 2010; Chouhan et al., 2021). PAM-α1 neuronal activity is higher in starved, trained flies than in fed or untrained flies (data not shown), suggesting that these neurons act as a critical node for integrating internal motivational and arousal states, as well as conveying positive valence for the normal appetitive memory process, independently of starvation-induced sleep loss. While DPM neurons are less sensitive to starvation, they still exhibit training-induced elevated activity (data not shown), indicating coherent responsiveness to upstream signaling. In the present study, we found that under fed conditions, sleep remained intact even when excessive changes in neural activity occurred within the PAM(-α1)-DPM circuit; in contrast, under starvation conditions, significant sleep reduction and fragmentation were observed. These observations indicate that starvation may trigger a transition from a physiologically normal brain state to an unstable, abnormally active state, which consequently elicits negative behavioral outputs.”

(7) Line 1121: The data points for Figure 9 D-E are surprisingly low considering there are 14 PAMalpha1 labelled, the data presented here indicated potentially only 1-2 neurons were counted per fly brain. Can this contribute to the large variation and the contradiction of PAM's memory-suppressing role?

We sincerely appreciate the reviewer’s critical comments regarding the sample size of labeled PAM-α1 neurons in Figure 9D–E. We have revisited our raw data, incorporated additional brain samples, and reanalyzed the dataset. For this updated analysis, we included all clearly distinguished neurons, excluded overlapping ones, and calculated a single NLI per brain for statistical analysis. The key conclusions remain consistent with those in the original submission, confirming the robustness of the observed phenotype.

Memory consolidation is a time-dependent process. To further elucidate the link between neural activity and behavioral outputs, we performed additional experiments with an extended recording period. A detailed response to this point is provided in the response to public review, and we therefore do not reiterate the details here.

(8) Line 345: the effect size and data spread of THIP restored memory is different from the controls in Figure 10, perhaps warranting a more conservative interpretation of the role of sleep in memory consolidation.

We appreciate this critical comment. We fully agree that the role of sleep in memory consolidation requires cautious interpretation, a point we have integrated into the revised manuscript.

Drug treatment in Drosophila, particularly for group-based assays, can introduce substantial variability at both the individual and group levels. To account for this, we employed a statistically valid sample size for our analyses to ensure robust conclusions. While minor quantitative discrepancies exist in the data, this technical consideration does not significantly alter the core conclusions of the study.

Reviewer #2 (Recommendations for the authors):

(1) As mentioned in the public review, all data using the broad PAM-DAN driver should be removed. Concerns regarding the experiments involving the broad driver are not included here.

A detailed response to this point is provided in the response to public review, and we therefore do not reiterate the details here.

(2) In GCaMP experiments (Figure 9B), the ΔF/F traces for the AHL and AHL+ATP conditions start diverging before the addition of ATP. The quantification shows they are not significantly different in the first 30 seconds, but the fact that in two separate experiments (2A and 9B), they diverge in the same direction makes me wonder whether the AHL condition is different from the +ATP condition even before the ATP treatment. Also, the traces should include standard errors.

We observed the same diverging trend in the first 30-second baseline as the reviewer. We reviewed the raw data for each sample and found that this divergence is likely attributable a small number of outliers. Given the absence of a statistically significant difference, this divergence does not affect our conclusions.

We have also added standard errors to the revised figures.

(3) Figure 9B. The authors need to show data for a control genotype. +>P2X2; VT064246-LexA > GCaMP6f that does not include MB299B-Gal4 is crucial to demonstrate that expression of P2X2 in PAM-α1 is responsible for the inhibitor effect, as LexA-P2X2 may be leaky.

One of the UAS-P2X2 lines was found to exhibit leaky expression, so we instead used a non-leaky UAS-P2X2 line for all related experiments. To address the reviewer’s comments and further validate our findings, we have added complementary experiments with a control genotype. In addition, we also added a control to confirm the non-leaky expression of LexA-P2X2 under the driver of R58E02-LexA. As shown in revised Figures 8B, application of ATP in the absence of MB299B-GAL4 failed to induce a significant inhibitory effect. These data strongly and convincingly support our conclusion.

(4) Figure 9B. Some of the individual data show values lower than -100% ΔF/F0. By definition, ΔF/F cannot be less than -100%, as this would require negative fluorescence, which is physically impossible. The calculation of fluorescence changes using ΔF/F should be carefully reconsidered.

We thank the reviewer pointing out this potential confusion. We used a standard method of calculating the change in fluorescence over time using △F/F = (Fn-F₀) / F₀×100% as we previously described (Liu et al., 2019). Changes of greater than +100% of △F/F would not be unusual, since the reported value is a ratio to the initial level of fluorescence, not a subtraction of the baseline value from the signal (which obviously could not go below 100%). We have included a sentence in the results explaining this (page 7): “As previously described, we used the percent change in fluorescence over time as a ratio to the initial level, △F/F = (Fn-F0)/F0×100% for quantification (Liu et al., 2019).” And we have carefully reviewed our raw and processed data and confirmed that our analysis was correct.

(5) Figure 2B. The number of UAS transgenes should be controlled, as Gal4 could be diluted with 3 UAS constructs in experimental conditions compared to only 1 UAS construct in controls. Are Dop1R2 and DopEcR significantly different from wt? Why do they present an average ΔF/F in 2A and a maximum in 2B?

We appreciate the reviewer’s careful observations and valuable comments.

As the reviewer noted, the EPAC imaging experiment utilizes three UAS transgenes, which enable Gal4 enhancement via Dicer, targeted manipulation of dopamine receptor expression levels, and neural activity monitoring in DPM neurons. All other imaging experiments in the study employ only one or two UAS transgenes. Given the robustness of the observed phenotypes, the potential dilution effect is not a major concern. Knockdown of Dop1R2 and DopEcR showed no significant differences relative to the WT control group; the maximum values presented in Fig. 2B are included solely to illustrate statistical significance. While the EPAC (CFP/YPF) signal reflects an obvious cAMP elevation, no differences were detected in the averaged signal across groups.

Notably, in the revised manuscript, our double-labeling assays confirmed that only Dop1R1 and Dop1R2 are colocalized with DPM neurons (see our responses to the public review from Reviewer #2). Accordingly, we have refined our data analysis to focus exclusively on these two DARs.

(6) Figures 7H, J. Why is almost every MB299B>TrpA1 fly sleeping at ZT0?

To align the starvation protocol for sleep analysis with that used in the memory assay, MB299B>TrpA1 flies and their genetic controls were transferred to fresh sleep tubes containing starvation food during the ZT0–1 time window. This transfer resulted in no detectable locomotor activity during this period, a pattern indicative of sleep in all flies.

(7) The number of episodes and P(wake) should be presented for all sleep data.

We have added these two parameters as new panels to all relevant sleep figures. The corresponding statistical analyses have also been included in the supplemental tables.

Reviewer #3 (Recommendations for the authors):

The study's findings provide compelling insights into the neural circuits connecting sleep and memory and the role of dopamine in general. While the anatomic dissection of the microcircuit and its overall involvement in sleep and memory is convincing and of high interest to the field and beyond, some statements of the study need further clarification, particularly the interpretation of receptor signaling and the role of DPM.

Major Points

(1) Figure 2: cAMP Imaging and Dopamine Receptor Involvement

The authors present calcium and cAMP imaging to support the inhibitory connection between PAM and DPM neurons. While using both sensors is a robust approach, I am not entirely convinced that cAMP imaging is the ideal approach for identifying the dopamine receptors involved. To my knowledge, only Dop1R1 is classically linked to Gs-mediated cAMP signaling. Dop1R2 is typically coupled to Gq (PLC and DAG), while DopEcR is non-canonical and can engage both pathways. Additionally, these receptors are classically excitatory, yet the authors did not analyze Dop2R, the primary inhibitory dopamine receptor - which would represent the most relevant candidate for an inhibitory PAM-DPM connection.

We have addressed this point in our response to the public comments, so will not reiterate here.

While dopamine receptor functions can vary by neuronal context, I would appreciate clarification on the following points:

(a) Why was Dop2R not tested? Was it omitted or found to have no effect?

We sincerely appreciate the reviewer’s critical questions. This point has been addressed in our response to the public comments. Briefly, Dop2R is not colocalized with DPM neurons; instead, only Dop1R1 and Dop1R2 are detected in DPM neurons, which is why we focused exclusively on these two receptors in the revised manuscript.

(b) Why was cAMP imaging chosen for receptor identification? Was calcium imaging performed, and if so, what were the results?

This is an excellent point, and we sincerely appreciate the reviewer’s valuable input, which has helped to strengthen the logical framework of our analysis on receptor-mediated neural activity. These dopamine receptors are well-characterized as members of the Gas-coupled protein receptor family, and cAMP signaling serves as a reliable readout of their functional activity. To strengthen the logic flow of our analysis on the target inhibitory circuit, we have made the following key revisions to the manuscript: 1) defined the expression profile of dopamine receptors in DPM neurons; 2) refined our cAMP imaging data analyses based on specific receptor subtypes; and 3) assessed DPM neural activity via calcium imaging under conditions of targeted receptor knockdown. For further details, please refer to our response to the public comments.

(c) Since the data suggest multiple receptor involvements and complex interactions, I encourage a more detailed discussion of the working hypothesis, particularly regarding the unexpected finding that classically excitatory receptors contribute to an inhibitory connection.

We appreciate the suggestion to elaborate on our working model. Accordingly, we have revised the schematic diagram and refined the manuscript to clearly illustrate the underlying mechanistic framework. For further details, please refer to our response to the public comments.

(2) Figure 3: DPM Involvement in Memory Consolidation

The authors show that PAM activation and DPM inhibition during consolidation impair appetitive LTM. However, the role of DPM is critical. While the c316-GAL4 driver yields strong effects, VT064246 inhibition shows only slight significance, requiring more than twice the sample size of other experiments. Given that c316-GAL4 is not DPM-specific and also labels MB Kenyon cells, I suggest using MB-GAL80 to restrict expression - or commenting on the possibility that other neurons like MB-KCs could directly participate in the phenotype. This is particularly relevant since VT064246 efficiently modulates sleep, indicating that it is generally effective in altering behavior. These issues weaken the claim that DPM plays a crucial role in linking sleep and memory, and should be addressed. Minor comment on this Figure: In the Figure legend, the driver and "n" are not mentioned for 3C, while this is the case for all other panels. Moreover, the DPM schematic only depicts the MB, making it somewhat confusing. DPM innervates the entire MB, still, it would be helpful to shade the DPM projections more distinctly within the MB for clarity.

We thank the reviewer for the suggestion to improve the precision of our figures.

Regarding the expression specificity concern, in all experiments using c316-GAL4, we had eyeless-GAL80 and MB-GAL80 co-expressed to restrict GAL4-driven expression to DPMs. While complete suppression of expression of MB-KCs was not achievable, we largely eliminated the potential confounding effects from majority of these cells. VT064246-GAL4 is known to exhibit weak expression (Jenett et al., 2011; Haynes et al., 2015), but high relative specificity. Importantly, the overall conclusion derived from experiments using c316-GAL4 with GAL80s and VT064246-GAL4 are consistent, which strongly supports the role of DPM neurons in mediating the link between sleep and memory.

As suggested, we have added sample sizes for all panels and refined the depiction of DPM projections in revised Figure 3C.

Minor Comments

(1) Introduction:

The authors introduce dopamine's role in forgetting but focus on aversive rather than appetitive memories. To avoid confusion, this distinction should be mentioned explicitly (likewise in the discussion). Regarding references: Zhang et al. (line 95) do not discuss DPM or APL. Donlea et al. (line 97) do not cover dopamine - I think Pimentel et al. (2016) would be a more appropriate citation.

This is a good point. We have removed Zhang et al. (2013) and replaced Donlea et al with Pimentel et al. 2016 as suggested.

(2) Figure 9: DPM Activation During Consolidation:

The authors show that PAM neurons are activated by starvation and further enhanced by appetitive training. Surprisingly, DPM neurons also increase activity post-training, despite the proposed inhibitory connection between PAM and DPM. The authors state that "PAM-α1-DPM microcircuit exhibits synchronized neural activity changes during the consolidation window" (line 326), yet they do not address this apparent contradiction. If I have not overlooked key information, this should be clarified/addressed e.g. in the discussion.

This is an excellent point. We have addressed this in our response to point (3) from Reviewer #2 in the public comments, so we will not reiterate it here.

(3) Figure 10D/E: THIP Rescue of LTM Deficits:

Some inconsistencies in the THIP rescue experiments need clarification:

(a) In Figure 10D, MB299B activation with THIP appears not to significantly restore memory relative to zero, nor to differ from untreated conditions in Figures 7A or 10E.

(b) In Figure 10E, MB299B activation +/- THIP shows a much clearer effect.

(c) Are Figures 7A, 10E, and 10D independent experiments, or were they conducted together?

(d) Should the left bar in 10D and the right bar in 10E be identical? If not, I do not fully understand the discrepancy and suggest discussing the variation.

Upon revisiting the raw datasets and conducting a one-sample t-test to analyze the group differences, the experimental group in Figure 7A showed no significant difference from the theoretical mean (set at zero). This group also did not differ from the two genetic controls, indicating that the restored memory was comparable to control levels. In Figure 10E, the group with MB299B activation plus THIP treatment exhibited a significant difference from the theoretical mean (one-sample t-test) and from the non-THIP control group, confirming a significant restoration of memory function. Owing to our laboratory relocation, the starvation duration at the new facility was adjusted based on a recalibrated starvation curve; the higher overall 24 h memory index in Figure 10E is likely attributable to a relatively longer starvation period. However, this experimental parameter variation does not alter the study’s overall conclusions.

(4) Sleep Phenotypes and Starvation Effects:

Sleep scores are shown under starvation/fed conditions but not under baseline conditions (without inhibition/activation). Could the authors indicate whether they observe basal starvation-induced sleep changes? The authors frequently state that PAM-DPM effects on sleep are context-dependent, yet mild but significant changes occur under fed conditions. I suggest rewording to clarify that the effect is enhanced in a context-dependent manner rather than strictly context-dependent.

Starvation-induced sleep reduction is a well-characterised phenotype. Our study focused on the key question of whether altered neuronal activity modulates sleep under innate starvation conditions. Accordingly, all comparisons were made between the experimental and control groups under both starvation and fed conditions. We appreciate the reviewer’s suggestion to improve clarity and have revised the text as suggested.

(5) Starvation Duration in Methods:

The authors use 30-46h of starvation, which is longer than the ~20h typically used in appetitive memory studies. Could the authors explain why such extended starvation times were necessary?

Determining starvation levels via survival curves is a well-established and relatively objective method, one that has been widely adopted in prior studies. For the memory test, we standardized the total starvation duration for each genotype to the time point at which mortality reached 20%. Owing to inherent differences in to starvation resistance across distinct genotypes, the final starvation durations ranged from 20 hours to 46 hours.

(6) Variability in PAM-α1 Sleep Effects:

(a) The extent and timing of sleep effects differ across PAM-α1 drivers (e.g. night vs. light-period effects). Could MBON co-targeting by these drivers contribute to the variability?

We have supplemented additional experiments to investigate the effects of MBON-α1 neurons on 24h memory and sleep. For detailed findings, please refer to our response to your public comments.

(b) Even within the same driver, results differ (e.g., Figure 7H vs. 10A). A general comment on these differences would be important, e.g. regarding the relevance of day and night sleep for memory consolidation.

We sincerely appreciate the reviewer’s incisive observation regarding these details. The discrepancy stems from the timing of neuronal activity inhibition, during which a laboratory relocation led to adjustments in starvation duration for memory experiments, which in turn indirectly altered sleep patterns.

(c) Technical note: Similar y-axis scales for sleep plots (Figures 10A and B) would make comparison easier.

We have unified the y-axis scales to the same range.

(7) Discussion, Line 376:

The phrase "sleep deprivation is important for memory consolidation" is misleading, as it could imply that deprivation aids memory formation. Please clarify.

We appreciate the reviewer’s suggestion. We have revised the text to: “These results demonstrate that preserving unperturbed sleep during the critical memory consolidation window is essential for stabilizing appetitive long-term memory.

eLife Assessment

This important work examines the effects of gaze on valuation signals in the human brain as participants choose between bundles of sequentially presented items food items. The paper provides convincing analyses of how gaze affects participants choice behaviour and how this varies across time. The work will be of interest to neuroscientists working on attention and decision-making.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have updated the labelling of Figure 7. As all of the reviewer comments have now been addressed, I believe that this version of the manuscript can now be put forward as the Version of Record.]

Summary:

This study builds upon a major theoretical account of value-based choice, the 'attentional drift diffusion model' (aDDM), and examines whether and how this might be implemented in the human brain using functional magnetic resonance imaging (fMRI). The aDDM states that the process of internal evidence accumulation across time should be weighted by the decision maker's gaze, with more weight being assigned to the currently fixated item. The present study aims to test whether there are (a) regions of the brain where signals related to the currently presented value are affected by the participant's gaze; (b) regions of the brain where previously accumulated information is weighted by gaze.

To examine this, the authors developed a novel paradigm that allowed them to dissociate currently and previously presented evidence, at a timescale amenable to measuring neural responses with fMRI. They asked participants to choose between bundles or 'lotteries' of food times, which they revealed sequentially and slowly to the participant across time. This allowed modelling of the haemodynamic response to each new observation in the lottery, separately for previously accumulated and currently presented evidence.

Using this approach, they find that regions of the brain supporting valuation (vmPFC and ventral striatum) have responses reflecting gaze-weighted valuation of the currently presented item, where as regions previously associated with evidence accumulation (preSMA and IPS) have responses reflected gaze-weighted modulation of previously accumulated evidence.

A major strength of the current paper is the design of the task, nicely allowing the researchers to examine evidence accumulation across time despite using a technique with poor temporal resolution. The dissociation between currently presented and previously accumulated evidence in different brain regions in GLM1 (before gaze-weighting), as presented in Figure 5, is already compelling. The result that regions such as preSMA response positively to |AV| (absolute difference in accumulated value) is particularly interesting, as it would seem that the 'decision conflict' account of this region's activity might predict the exact opposite result. Additionally, the behaviour has been well modelled at the end of the paper when examining temporal weighting functions across the multiple samples.

In response to reviewer comments, the authors have explicitly tested for the effects of gaze-weighting over and above any main effect of value, and convincingly shown that these effects are both present in the main regions of interest - namely |SV| and gaze-weighted |SV| in the vmPFC, alongside |AV| and |AV_gaze| in the pre-SMA. This provides clear evidence in support of the notion of gaze-weighting of value signals in these regions.

Reviewer #2 (Public review):

Summary:

In this paper the authors seek to disentangle brain areas that encode the subjective value of individual stimuli/items (input regions) from those that accumulate those values into decision variables (integrators) for value-based choice. The authors used a novel task in which stimulus presentation was slowed down to ensure that such a dissociation was possible using fMRI despite its relatively low temporal resolution. In addition, the authors leveraged the fact that gaze increases item value, providing a means of distinguishing brain regions that encode decision variables from those that encode other quantities such as conflict or time-on-task. The authors adopt a region-of-interest approach based on an extensive previous literature and found that the ventral striatum and vmPFC correlated with the item values and not their accumulation whereas the pre-SMA, IPS and dlPFC correlated more strongly with their accumulation. Further analysis revealed that the pre-SMA was the only one of the three integrator regions to also exhibit gaze modulation.

The study uses a highly innovative design and addresses an important and timely topic. The manuscript is well-written and engaging, while the data analysis appears highly rigorous.

Weaknesses:

With 23 subjects the study has relatively low statistical power for fMRI although the within-subjects design and relatively high trial count reduces these concerns.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study builds upon a major theoretical account of value-based choice, the 'attentional drift diffusion model' (aDDM), and examines whether and how this might be implemented in the human brain using functional magnetic resonance imaging (fMRI). The aDDM states that the process of internal evidence accumulation across time should be weighted by the decision maker's gaze, with more weight being assigned to the currently fixated item. The present study aims to test whether there are (a) regions of the brain where signals related to the currently presented value are affected by the participant's gaze; (b) regions of the brain where previously accumulated information is weighted by gaze.

To examine this, the authors developed a novel paradigm that allowed them to dissociate currently and previously presented evidence, at a timescale amenable to measuring neural responses with fMRI. They asked participants to choose between bundles or 'lotteries' of food times, which they revealed sequentially and slowly to the participant across time. This allowed modelling of the haemodynamic response to each new observation in the lottery, separately for previously accumulated and currently presented evidence.

Using this approach, they find that regions of the brain supporting valuation (vmPFC and ventral striatum) have responses reflecting gaze-weighted valuation of the currently presented item, where as regions previously associated with evidence accumulation (preSMA and IPS) have responses reflected gaze-weighted modulation of previously accumulated evidence.

A major strength of the current paper is the design of the task, nicely allowing the researchers to examine evidence accumulation across time despite using a technique with poor temporal resolution. The dissociation between currently presented and previously accumulated evidence in different brain regions in GLM1 (before gazeweighting), as presented in Figure 5, is already compelling. The result that regions such as preSMA response positively to |AV| (absolute difference in accumulated value) is particularly interesting, as it would seem that the 'decision conflict' account of this region's activity might predict the exact opposite result. Additionally, the behaviour has been well modelled at the end of the paper when examining temporal weighting functions across the multiple samples.

In response to reviewer comments, the authors have explicitly tested for the effects of gaze-weighting over and above any main effect of value, and convincingly shown that these effects are both present in the main regions of interest - namely |SV| and gazeweighted |SV| in the vmPFC, alongside |AV| and |AV_gaze| in the pre-SMA. This provides clear evidence in support of the notion of gaze-weighting of value signals in these regions.

We thank the reviewer for their comments.

Reviewer #2 (Public review):

Summary:

In this paper the authors seek to disentangle brain areas that encode the subjective value of individual stimuli/items (input regions) from those that accumulate those values into decision variables (integrators) for value-based choice. The authors used a novel task in which stimulus presentation was slowed down to ensure that such a dissociation was possible using fMRI despite its relatively low temporal resolution. In addition, the authors leveraged the fact that gaze increases item value, providing a means of distinguishing brain regions that encode decision variables from those that encode other quantities such as conflict or time-on-task. The authors adopt a region-of-interest approach based on an extensive previous literature and found that the ventral striatum and vmPFC correlated with the item values and not their accumulation whereas the preSMA, IPS and dlPFC correlated more strongly with their accumulation. Further analysis revealed that the pre-SMA was the only one of the three integrator regions to also exhibit gaze modulation.

The study uses a highly innovative design and addresses an important and timely topic. The manuscript is well-written and engaging, while the data analysis appears highly rigorous.

Weaknesses:

With 23 subjects the study has relatively low statistical power for fMRI although the within-subjects design and relatively high trial count reduces these concerns.

We thank the reviewer for their comments.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Something seems to have gone slightly wrong in (I think) the labelling of new figure 7 (the correlation matrix between the different regressors). There are five variables on both the x- and y-axes of the figure, and they are the same five variables - meaning the diagonal of the matrix would be all equal to 1 (being the correlation of a regressor with itself - e.g. |AVgaze| with |AVgaze|). In the figure legend, six variables are mentioned, including lagged |deltaAVgaze| - but this doesn't appear on the x or y-axes. I suspect that the authors may need to check that this matrix has been calculated correctly, and isn't being mislabelled?

We thank the reviewer for noticing this issue. We have now corrected Figure 7.

eLife Assessment

This important study reports the development of the first tankyrase degrader and demonstrates its enhanced ability to inhibit β-catenin signaling compared to conventional tankyrase inhibitors. The evidence supporting the conclusions is comprehensive and convincing, based on rigorous biochemical and cellular analyses. The findings will be of broad interest to researchers studying Wnt signaling, protein degradation, and cancer biology.

Reviewer #1 (Public review):

[Editors' note: the second round of revision has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the minor comments raised in the previous round of review.]

Summary:

This manuscript reports the discovery and characterization of the first bifunctional degrader of tankyrase. Notably, the tankyrase degrader exhibits stronger β-catenin inhibition and tumor growth suppression compared to conventional tankyrase inhibitors. Mechanistically, while tankyrase inhibitors stabilize tankyrase and promote Axin puncta formation-thereby impairing β-catenin degradation-the degrader avoids this effect, resulting in deeper suppression of β-catenin signaling. These findings suggest that targeted degradation of tankyrase offers a novel therapeutic strategy for β-catenin-driven cancers. Overall, this is a compelling study with significant translational potential.

Strengths:

(1) The manuscript presents a rigorous and well-executed study on a timely and impactful topic.

(2) The biochemical and cellular characterization of the tankyrase degrader is thorough, and the comparative analysis with tankyrase inhibitors is insightful.

(3) The finding that tankyrase stabilization by inhibitors may interfere with Axin function is novel and significant. It aligns with earlier observations (e.g., Huang 2009) that transient tankyrase overexpression can stabilize β-catenin independently of PAR domain activity.

(4) The use of TNKS1/2 knockout cells expressing catalytically inactive tankyrase to demonstrate β-catenin inhibitory activity of the tankyrase degrader is elegant.

(5) The finding that the tankyrase degrader has superior anti-proliferative effects in colorectal cancer models has important therapeutic implications.

Comments on previous version:

I had a favorable opinion of the manuscript in the first round of review. I don't have additional comments on the revised manuscript. The manuscript looks fine to me.

Reviewer #2 (Public review):

Summary:

The ADP-ribosyltransferase tankyrase controls many biological processes, many of which are relevant to human disease. This includes Wnt/beta-catenin signalling, which is dysregulated in many cancers, most notably colorectal cancer. Tankyrase is a positive regulator of Wnt/beta-catenin signalling in that it counters the activity of the beta-catenin destruction complex (DC). Catalytic inhibition of tankyrase not only blocks PAR-dependent ubiquitylation and degradation of AXIN1/2, the central scaffolding protein in the DC, but also tankyrase itself. As a result, blocking tankyrase gives rise to tankyrase accumulation, which may accentuate its non-catalytic functions, which have been proposed to drive Wnt/beta-catenin signalling. Most tankyrase catalytic inhibitors have shown limited efficacy and substantial toxicity in vivo. By developing tankyrase-directed PROTACs, the authors aim to block both catalytic and non-catalytic functions of tankyrase, aspiring to achieve a more complete inhibition of Wnt/beta-catenin signalling. The successfully developed PROTAC, based on the existing catalytic inhibitor IWR1, IWR1-POMA, induces the degradation of both TNKS and TNKS2, blocks beta-catenin-dependent transcription without stabilising the DC in puncta/degradasomes, and inhibits cancer cell growth in vitro. Mechanistically, this points to a scaffolding role of tankyrase in the DC, at least under conditions of tankyrase catalytic inhibition, in line with previous proposals.

Strengths:

The study clearly illustrates the incentive for developing a tankyrase degrader, namely, to abolish both catalytic and non-catalytic functions of tankyrase. By and large, the study achieves these ambitions, and the findings support the main conclusions, although the statement that a more complete inhibition of the pathway is achieved requires corroboration. The proteomics studies are powerful. IWR1-POMA constitutes a very useful tool to re-evaluate targeting of tankyrase in oncogenic Wnt/beta-catenin signalling. The paired compounds will benefit investigations of tankyrase scaffolding functions across many different biological systems controlled by tankyrase. The findings are exciting.

Comments on previous version:

I thank the authors for responding to the queries raised in the original review, most of which have now been addressed. This further strengthens this well-conducted study and well-presented manuscript. I congratulate the authors for this interesting and insightful work.

Reviewer #3 (Public review):

In this manuscript, Wang et al employ a chemical biology approach to investigate the differences between the enzymatic and scaffolding roles of tankyrase during Wnt β-catenin signalling. It was previously established that, in addition to its enzymatic activity, tankyrase 1/2 also plays a scaffolding function within the destruction complex, a property conferred by SAM-domain-dependent polymerization (PMID: 27494558). It is also known that TNKS1/2 is an autoregulated protein and that its enzymatic inhibition leads to accumulation of total TNKS proteins and stabilization of Axin punctae (through the scaffolding function of TNKS1/2), leading to rigidification of the DC and decreased β-catenin turnover. The authors surmised that this could, in part, explain the limited efficacy of TNKS1/2 catalytic inhibition for the treatment of colorectal cancers. To test this hypothesis, they evaluated a series of PROTAC molecules promoting the degradation of TNKS1/2 to block both the catalytic and scaffolding activities. They show that IWR1-POMA (their most active molecule) promotes more efficient suppression of beta-catenin-mediated transcription and is more active in inhibiting colorectal cancer cell and CRC patient-derived organoids growth. Mechanistically, the authors used FRAP to demonstrate that catalytic inhibitors of TNKS led to a reduced dynamic assembly of the DC (rigidification), whereas IWR1-POMA did not affect the dynamics.

Overall, this is an interesting study describing the design and development of a PROTAC for TNKS1/2 that could have increased efficacy where catalytic inhibitors have displayed limited activity. Knowing the importance of the scaffolding role of TNKS1/2 within the destruction complex, targeting both the catalytic and scaffolding roles certainly makes sense. The manuscript contains convincing evidence of the different mechanisms of the PROTAC vs catalytic inhibitors. Some additional efforts to quantify several of the experiments and to indicate the reproducibility and statistical analysis would strengthen the manuscript. Ultimately, it would have been great to evaluate the in vivo efficacy of IWR1-POMA in an in vivo CRC assay (APCmin mice or using PDX models); however, I realize that this is likely beyond the scope of this manuscript.

Reviewer #4 (Public review):

From the Reviewing Editor:

This important study reports the development of the first PROTACs targeting the ADP-ribosyltransferases tankyrase 1 and 2, with the goal of inhibiting Wnt/β-catenin signaling more completely than is possible with catalytic tankyrase inhibitors. The work addresses a significant limitation of existing tankyrase inhibitors: although catalytic inhibition stabilizes AXIN1/2 and suppresses Wnt signaling, it also stabilizes tankyrase itself, potentially enhancing non-catalytic scaffolding functions and promoting accumulation of degradasome-like puncta.

The evidence is convincing. The authors use appropriate and well-validated approaches, including chemical biology, cellular assays, and proteomic profiling, to show that PROTAC-mediated degradation of tankyrase avoids tankyrase accumulation while still stabilizing AXIN and inhibiting Wnt/β-catenin signaling. The data support the conclusion that degradation of tankyrase can separate pathway inhibition from the confounding effects of stabilized tankyrase protein and may therefore offer advantages over conventional catalytic inhibitors.

A strength of the study is the clear mechanistic comparison between tankyrase degradation and catalytic inhibition. The manuscript provides convincing evidence that the PROTAC and catalytic inhibitors act through distinct mechanisms, with the PROTAC targeting both catalytic and scaffolding roles of tankyrase. The study is well conducted and clearly presented, and the authors have addressed most concerns raised during review.

A remaining limitation is that the therapeutic potential of the compound is not tested in vivo, for example in APC-mutant colorectal cancer models, APCmin mice, or patient-derived xenografts. Such experiments would strengthen claims about practical efficacy, although they are not essential for the main mechanistic conclusions of the manuscript.

Overall, this is an important and insightful contribution. It advances the tankyrase and Wnt signaling fields by providing a new chemical strategy to suppress tankyrase function more completely than catalytic inhibition alone, and it offers a useful framework for future therapeutic exploration of tankyrase degradation.

Author response:

The following is the authors’ response to the previous reviews

We thank the Reviewers for the favourable feedback. There is no additional comments from Reviewers 1, 3, and 4, and we address the minor concerns from Reviewer 2 as follows.

(1) I appreciate the authors acknowledge that testing the physical properties of the degradasome puncta is necessary to explore whether they indeed represent condensates. The term "condensates" implies liquid-liquid phase separation (rightly or wrongly). However, this question has not yet been resolved in the case of degradasomes. I therefore suggest the term "condensates" to be avoided. A simple morphological description as "puncta" may suffice.

We have revised our manuscript to state: “The DC has been proposed to exist as biomolecular condensates.” Additionally, we have included a time-lapse image showing that AXIN1-GFP puncta exhibit dynamic fusion behaviour in cells (Fig. S8), suggesting that the DC may be liquid-like, at least with AXIN1 overexpression.

(2) I thank the authors for including the additional data comparing tankyrase binding by IWR and IWRPOMA. I agree that using the BRET signal of IWR-POMA is informative. Adding the IC₅₀ values directly to the figure panels (S3E, S3G) would help the reader to quickly assess binding. The comparison between these two panels is insightful.

Added.

(3) Regarding the use of the terms TNKS, TNKS1 and TNKS2, if the authors would like to use the name "TNKS" to refer to both paralogues collectively, can this please be specified early in the manuscript to limit confusion with the official gene name "TNKS", which of course only refers to one paralogue? Regarding the use of the terms TNKS, TNKS1 and TNKS2, if the authors would like to use the name "TNKS" to refer to both paralogues collectively, can this please be specified early in the manuscript to limit confusion with the official gene name "TNKS", which of course only refers to one paralogue?

We now specify in the Introduction that TNKS1/2 are encoded by TNKS/TNKS2, and are collectively referred to as TNKS in this manuscript.

eLife Assessment

This revised paper provides solid evidence for HGF-induced trafficking of the HGF receptor, MET, together with metalloprotease MT1-MMP into invadopodia and the role of this trafficking in triple-negative breast cancer (TNBC) cell invasion in vitro. The evidence could be improved with additional experimental controls and analysis, but the findings will be valuable for cancer cell biologists investigating TNBC.

Reviewer #1 (Public review):

Summary:

This study identifies a mechanism responsible for the accumulation of the MET receptor in invadopodia, following stimulation of Triple-negative breast cancer (TNBC) cells with HGF. HGF-driven accumulation and activation of MET in invadopodia causes the degradation of the extracellular matrix promoting cancer cell invasion, a process here investigated using gelatine-degradation and spheroid invasion assays.

Mechanistically, HGF stimulates the recycling of MET from RAB14-positive endodomes to invadopodia, increasing their formation. At invadopodia, MET induces matrix degradation via direct binding with the metallo protease MT1-MMP.

The delivery of MET from the recycling compartment to invadopodia is mediated by RCP which facilitates the colocalization of MET to RAB14 endosomes. On this compartment, HGF induces the recruitment of the motor protein KIF16B promoting the tubulation of the RAB14-MET recycling endosomes to the cell surface.

This pathway is critical for the HGF-driven invasive properties of TNBC cells as it is impaired upon silencing of RAB14.

Strengths:

The study is well organized and executed using state of the art technology. The effects of MET recycling in the formation of functional invadopodia are carefully studied taking advantage of mutant forms of the receptor that are degradation-resistant or endocytosis-defective.

Data analyses are rigorous and appropriate controls are used in most of the assays to assess the specificity of the scored effects. Overall, the quality of the research is high.<br /> The conclusions are well supported by the results and the data and methodology are of interest for a wide audience of cell biologists.

Previous Weaknesses:

The role of the MET receptor in invadopodia formation and cancer cell dissemination has been intensively studied in many settings including Triple Negative breast cancer cells. The novelty of the present study mostly consists in the detailed molecular description of the underlying mechanism based on HGF-driven MET recycling. The question of whether the identified pathway is specific for TNBC cells or represents a general mechanism of HGF-mediated invasion detectable in other cancer cells is not addressed or at least discussed.

Comments on revised version:

The authors have partially replied to my previous concerns.

Reviewer #2 (Public review):

Summary:

In this manuscript, Khamari and colleagues investigate how HGF-MET signaling and the intracellular trafficking of the MET receptor tyrosine kinase influence invadopodia formation and invasion in triple-negative breast cancer (TNBC) cells. They show that HGF stimulation enhances both the number of invadopodia and their proteolytic activity. Mechanistically, the authors demonstrate that HGF-induced, RAB4- and RCP-RAB14-KIF16B-dependent recycling routes deliver MET to the cell surface specifically at sites where invadopodia form. Moreover, they report that MET physically interacts with MT1-MMP - a key transmembrane metalloproteinase required for invadopodia function- and that these two proteins co-traffic to invadopodia upon HGF stimulation.

Although the HGF-MET axis has previously been implicated in invadopodia regulation (e.g., by Rajadurai et al., Journal of Cell Science 2012), studies directly linking ligand-induced MET trafficking with the spatial regulation of MT1-MMP localization and activity have been lacking.

Overall, the manuscript addresses a relevant and timely topic and provides several novel insights.

Comments on revised version:

I appreciate the authors' efforts to revise the manuscript and address the reviewers' comments. While the revised version includes additional experiments and several improvements in data presentation, the major methodological and conceptual concerns raised in the initial review remain largely unresolved. In my opinion, these issues critically undermine the central mechanistic conclusions of the study.

(1) Inappropriate experimental design for studying MET trafficking

A major concern remains the use of prolonged HGF stimulation times (2-6 hours) to study MET endocytosis and recycling. This is not an appropriate experimental design for investigating receptor tyrosine kinase trafficking dynamics. Ligand-induced internalization of MET occurs within minutes, with maximal endosomal accumulation typically observed within 5-15 minutes, whereas recycling occurs over approximately 15-60 minutes.

Importantly, the authors have not included short stimulation time points or any kinetic analysis that would allow a proper assessment of MET internalization or recycling. The additional surface biotinylation experiment does not address this issue, as it still does not provide temporal information regarding receptor trafficking.

Therefore, the current data do not support the conclusions regarding MET endocytosis or recycling, and this major methodological concern has not been adequately addressed in the revised manuscript.

(2) Insufficient validation of antibody specificity in immunofluorescence

The validation of antibody specificity for MET, phospho-MET, and MT1-MMP in immunofluorescence experiments remains insufficient. While the authors demonstrate knockdown efficiency by immunoblotting and show some reduction in fluorescence signal, they do not provide rigorous evidence that the immunofluorescence signal is specifically abolished upon gene silencing under identical imaging conditions. Such validation is essential, particularly because the manuscript relies heavily on imaging-based localization and colocalization analyses. Without these controls, it cannot be excluded that the observed signal represents non-specific staining.

Importantly, the authors attempt to justify antibody specificity primarily by citing previous publications that used the same antibodies. However, this is not an adequate substitute for experimental validation within the current study. Previous reports do not guarantee specificity under the present experimental conditions, particularly in immunofluorescence, where staining patterns can be strongly influenced by fixation procedures, antibody concentrations, imaging settings, and cell type. Moreover, those studies may themselves lack sufficiently rigorous validation of antibody specificity. Therefore, antibody specificity should be demonstrated directly in the experimental system used in this manuscript, especially given that the principal conclusions rely extensively on the subcellular localization of MET, phospho-MET, and MT1-MMP.

(3) Questionable MET localization in TIRF microscopy

The presence of punctate MET signal in TIRF microscopy under unstimulated conditions raises additional concerns. Under basal conditions, MET is generally expected to exhibit a predominantly diffuse distribution at the plasma membrane, whereas prominent punctate structures are typically associated with ligand-induced clustering, endocytosis, or trafficking events.

The observation of numerous MET-positive puncta in unstimulated cells, together with the insufficient validation of antibody specificity, raises the possibility that at least part of the observed signal represents non-specific staining or imaging artefacts rather than bona fide MET localization. This concern is further compounded by the lack of rigorous immunofluorescence antibody validation discussed above and significantly undermines the interpretation of all TIRF-based trafficking analyses presented in the manuscript.

(4) The evidence supporting a MET-specific role in invadopodia remains unconvincing

The authors argue that the role of MET in invadopodia formation is validated using three independent approaches: shRNA-mediated knockdown, SMARTpool siRNA-mediated knockdown, and pharmacological inhibition with PHA665752. However, I do not agree that these constitute three independent orthogonal validations of MET function.

First, the shRNA-mediated knockdown presented in this study achieves only modest depletion of MET protein. The authors themselves acknowledge this limitation and therefore selected cells with visibly reduced MET staining for imaging. Consequently, the shRNA experiments cannot be considered a robust or independent validation of MET function.

Second, although pooled SMARTpool siRNAs are widely used to improve knockdown efficiency, they cannot exclude off-target effects, as each individual guide RNA contributes its own potential off-target profile. Therefore, pooled siRNAs cannot by themselves establish that an observed phenotype is specifically attributable to depletion of the intended target and do not replace validation using independent individual siRNAs or rescue experiments.

Third, the pharmacological data should also be interpreted with caution. Throughout the manuscript, PHA665752 is presented as a MET inhibitor supporting the specificity of the observed phenotype. However, there is essentially no such thing as a truly selective receptor tyrosine kinase inhibitor. PHA665752 inhibits multiple kinases in addition to MET, particularly at concentrations commonly used in cell-based assays. Consequently, the inhibitor cannot be considered an independent validation of MET-specific function.

Importantly, the newly added siRNA experiments do not resolve my original concern regarding the role of MET in invadopodia formation. Although siRNA-mediated MET depletion is substantially more efficient than the shRNA-mediated knockdown presented in the original manuscript, this marked difference in MET depletion is not accompanied by a correspondingly stronger inhibition of invadopodia formation or ECM degradation. If MET were indeed the principal driver of the observed phenotype, one would expect the magnitude of the biological effect to correlate with the efficiency of MET depletion. This inconsistency raises the possibility that the observed phenotype is not solely attributable to MET depletion and calls into question the specificity of the proposed mechanism.

Taken together, the three perturbation approaches used by the authors cannot be regarded as independent orthogonal validation of MET function. One approach provides only modest target depletion, another relies on pooled RNAi reagents that cannot exclude off-target effects, and the third employs a multi-kinase inhibitor rather than a MET-specific compound. Collectively, these limitations substantially weaken the conclusion that the reduction in invadopodia formation is specifically attributable to loss of MET. A convincing demonstration of MET-specific function would require rescue experiments or another truly orthogonal validation strategy.

(5) Weak evidence for MET-MT1-MMP interaction

The evidence supporting a physical interaction between MET and MT1-MMP remains unconvincing. The newly added co-immunoprecipitation experiment does not reveal a convincing MET-MT1-MMP interaction, and I am unable to appreciate a specific co-immunoprecipitated MT1-MMP signal in the presented blot. As presented, these data do not convincingly demonstrate a specific or functionally relevant interaction. Given that this interaction constitutes a central component of the proposed mechanistic model, this remains a major weakness of the study.

(6) Overinterpretation of the data

Taken together, the study proposes a mechanistic model linking MET trafficking to MT1-MMP localization and invadopodia function. However, the experimental evidence largely supports correlative observations rather than demonstrating a direct mechanistic relationship.

Specifically, MET endocytosis and recycling are not properly demonstrated because of the inappropriate temporal resolution of the trafficking experiments; the localization data remain uncertain owing to insufficient validation of the immunofluorescence reagents; and the proposed interaction between MET and MT1-MMP is not convincingly demonstrated. Consequently, the manuscript establishes correlation rather than causality, and the central mechanistic conclusions appear to be substantially overstated relative to the presented data.

Conclusion:

While the manuscript addresses an interesting and biologically relevant question, the current experimental evidence does not adequately support the proposed mechanistic model. The combination of inappropriate experimental design for trafficking studies, insufficient validation of key imaging reagents, questionable interpretation of the localization data, lack of convincing evidence for the proposed MET-MT1-MMP interaction, and the absence of a clear relationship between the degree of MET depletion and the biological phenotype substantially limits the reliability of the conclusions.

In my opinion, these issues cannot be addressed by further revision of the current manuscript, as they require substantial additional experimentation, including appropriately designed trafficking assays with short kinetic time points, rigorous validation of antibody specificity for immunofluorescence, and stronger mechanistic evidence linking MET trafficking to MT1-MMP-dependent invadopodia function.

Author response:

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

We thank the editor and the reviewers for their comments on the revised manuscript. Based on the comments, we decided to go for a minor revision which will address all the comments of reviewer 1.

Towards the comments of the reviewer 2, we would like to state that we already provided the results from the new experiments and the reasons why we did not perform some of the suggested ones. Interestingly, we have not deviated from standard practices in the field in our approaches. Yet, the reviewer is not convinced and raised concern about the robustness of our observations. We therefore decided to carry out a few more control experiments which in our opinion are redundant as they already were carried out multiple times by us as well as by the other field experts under identical conditions and using the identical cell lines.

Reviewer #1 (Public review):

Summary:

This study identifies a mechanism responsible for the accumulation of the MET receptor in invadopodia, following stimulation of Triple-negative breast cancer (TNBC) cells with HGF. HGF-driven accumulation and activation of MET in invadopodia causes the degradation of the extracellular matrix promoting cancer cell invasion, a process here investigated using gelatine-degradation and spheroid invasion assays.

Mechanistically, HGF stimulates the recycling of MET from RAB14-positive endodomes to invadopodia, increasing their formation. At invadopodia, MET induces matrix degradation via direct binding with the metallo protease MT1-MMP.

The delivery of MET from the recycling compartment to invadopodia is mediated by RCP which facilitates the colocalization of MET to RAB14 endosomes. On this compartment, HGF induces the recruitment of the motor protein KIF16B promoting the tubulation of the RAB14-MET recycling endosomes to the cell surface.

This pathway is critical for the HGF-driven invasive properties of TNBC cells as it is impaired upon silencing of RAB14.

Strengths:

The study is well organized and executed using state of the art technology. The effects of MET recycling in the formation of functional invadopodia are carefully studied taking advantage of mutant forms of the receptor that are degradation-resistant or endocytosisdefective.

Data analyses are rigorous and appropriate controls are used in most of the assays to assess the specificity of the scored effects. Overall, the quality of the research is high.

The conclusions are well supported by the results and the data and methodology are of interest for a wide audience of cell biologists.

Previous Weaknesses:

The role of the MET receptor in invadopodia formation and cancer cell dissemination has been intensively studied in many settings including Triple Negative breast cancer cells. The novelty of the present study mostly consists in the detailed molecular description of the underlying mechanism based on HGF-driven MET recycling. The question of whether the identified pathway is specific for TNBC cells or represents a general mechanism of HGFmediated invasion detectable in other cancer cells is not addressed or at least discussed.

Comments on revised version:

The authors have partially replied to my previous concerns.

We sincerely thank the reviewer for careful evaluation of our manuscript and recognizing the strength of our study. We are grateful for the positive assessment about the well-executed methods, rigorous data analysis, usage of appropriate controls, and for acknowledging that we have addressed, at least in part, the concerns raised in the previous round of review. We appreciate the reviewer’s constructive comments, which have helped us further clarify the scope and significance of our findings.

Reviewer #1 (Recommendations for the authors):

The authors have partially replied to my previous concerns. The following points still have to be addressed.

(1) Despite many TNBC tumours present high expression of the EGFR, trials with EGFR inhibitors have been very disappointing (please read PMID: 41651315). EGFR inhibition has been extensively attempted with negative outcome, and this is well known.

In the clinical practise, Triple Negative breast cancer patients are commonly treated with chemotherapy, not with EGFR or MET inhibitors.

Line 31-38 are misleading at best and should be removed and the incipit of the study modified. The biology of this study is sound there is no need to push the clinical relevance with instances that are notoriously not applicable.

We are thankful to the reviewer for bringing up this point. We will modify the manuscript as per the reviewer’s suggestion.

(2) In reply to point 4, the authors claim that they checked MMP2 and the experiment is shown in FigS5I, which is not......

We sincerely apologise for uploading an incorrect file. The correct file will be uploaded.

(3) I noticed that, in the previous version of the manuscript in Fig. S4A the panel showing the mutation frequency was erroneously indicated in the legend as referring to MET.

The authors replied that they fixed this but actually the legend is still wrong...

We thank the reviewer for bringing this error into our attention. We will attentively correct the legend in the revised version of the manuscript.

Reviewer #2 (Public review):

Summary:

In this manuscript, Khamari and colleagues investigate how HGF-MET signaling and the intracellular trafficking of the MET receptor tyrosine kinase influence invadopodia formation and invasion in triple-negative breast cancer (TNBC) cells. They show that HGF stimulation enhances both the number of invadopodia and their proteolytic activity. Mechanistically, the authors demonstrate that HGF-induced, RAB4- and RCP-RAB14KIF16B-dependent recycling routes deliver MET to the cell surface specifically at sites where invadopodia form. Moreover, they report that MET physically interacts with MT1MMP - a key transmembrane metalloproteinase required for invadopodia function- and that these two proteins co-traffic to invadopodia upon HGF stimulation.

Although the HGF-MET axis has previously been implicated in invadopodia regulation (e.g., by Rajadurai et al., Journal of Cell Science 2012), studies directly linking ligandinduced MET trafficking with the spatial regulation of MT1-MMP localization and activity have been lacking.

Overall, the manuscript addresses a relevant and timely topic and provides several novel insights.

Comments on revised version:

I appreciate the authors' efforts to revise the manuscript and address the reviewers' comments. While the revised version includes additional experiments and several improvements in data presentation, the major methodological and conceptual concerns raised in the initial review remain largely unresolved. In my opinion, these issues critically undermine the central mechanistic conclusions of the study.

We thank the reviewer for critically re-evaluating our manuscript and acknowledging the novel insights and relevance of our study.

We thank the reviewer for pointing out the study by Rajadurai et al., Journal of Cell Science, 2012, which provided crucial evidence about the role of MET signaling in invadopodia formation [1]. However, the experimental system largely used by Rajadurai et al. is fundamentally different from the receptor trafficking mechanism investigated in the present study. The group have mostly used overexpression of Tpr-MET, which is a cytosolic MET mutant, that does not undergo the canonical ligand-induced RTK endocytosis and subsequent degradation or recycling. Our study did not only establish another link between MET signaling and invadopodia formation; rather, we identified a trafficking-dependent mechanism whereby HGF stimulation regulates the spatial redistribution and recycling of full-length MET to invadopodia, thus providing more physiologically relevant insights.

We also respectfully disagree that the methodological concerns raised critically undermine our mechanistic conclusions. Although other approaches as suggested by the reviewer could provide complementary information, we believe that the methods used in our study are appropriate for assessing invasive behaviour of the TNBC cells. These methods have been used by us and other research groups in the filed as reflected from the existing literature [2–6].

In this context, we would also like to add that the study referred by the reviewer above, has used a more off target prone approach (SiGenome Smartpool) compared to ONTARGETplus (chemically modified SiRNA pool for minimizing off target effect) in addition to the same small molecule inhibitor used in our study. Additionally, we also used a shRNA-based silencing to verify the phenotype. Since the silencing was not as pronounced as the siRNA-mediated knockdown, the reviewer has expressed concern.

We would like to point out that the antibody we used in IF for MT1-MMP (MMP14) have been published in multiple peer-reviewed journals by various research groups using the identical cell lines (MDA-MB-231, ATCC- HTB-26) [6–8]. MET antibodies used in the study (CST, D1C2 XP & L6E7) has been validated in MDA-MB-231 and MET-depleted cells [9-12]. Since these antibodies have been utilized for IF since a long time across various research groups under identical laboratory/experimental conditions, the exercise of validation suggested by the reviewer is surprising.

(1) Inappropriate experimental design for studying MET trafficking

A major concern remains the use of prolonged HGF stimulation times (2-6 hours) to study MET endocytosis and recycling. This is not an appropriate experimental design for investigating receptor tyrosine kinase trafficking dynamics. Ligand-induced internalization of MET occurs within minutes, with maximal endosomal accumulation typically observed within 5-15 minutes, whereas recycling occurs over approximately 15-60 minutes.

Importantly, the authors have not included short stimulation time points or any kinetic analysis that would allow a proper assessment of MET internalization or recycling. The additional surface biotinylation experiment does not address this issue, as it still does not provide temporal information regarding receptor trafficking.

Therefore, the current data do not support the conclusions regarding MET endocytosis or recycling, and this major methodological concern has not been adequately addressed in the revised manuscript.

We want to clarify that, our prime objective is to determine how MET trafficking is regulated at the time points at which we observe the functional effects of HGF on invadopodia formation and matrix degradation. Since our functional assays were performed following 2-3 h of HGF stimulation, we specifically examined MET localization and trafficking at these same time points.

Though shorter time points could provide information on the kinetics of MET trafficking, but their absence does not invalidate our conclusions regarding the role of MET trafficking in HGF-induced invasive function. In other words, our conclusions are made for the time points for which we have conducted the experiments. It is needless to add that different cargo molecule will show different kinetics.

In summary, we wanted to study the MET trafficking at the late hours in accordance with our functional assays and accordingly designed our experiments. Also, we have supported our results through biochemical methods which is considered to be one of the gold standards in the field.

(2) Insufficient validation of antibody specificity in immunofluorescence

The validation of antibody specificity for MET, phospho-MET, and MT1-MMP in immunofluorescence experiments remains insufficient. While the authors demonstrate knockdown efficiency by immunoblotting and show some reduction in fluorescence signal, they do not provide rigorous evidence that the immunofluorescence signal is specifically abolished upon gene silencing under identical imaging conditions. Such validation is essential, particularly because the manuscript relies heavily on imaging-based localization and colocalization analyses. Without these controls, it cannot be excluded that the observed signal represents non-specific staining.

Importantly, the authors attempt to justify antibody specificity primarily by citing previous publications that used the same antibodies. However, this is not an adequate substitute for experimental validation within the current study. Previous reports do not guarantee specificity under the present experimental conditions, particularly in immunofluorescence, where staining patterns can be strongly influenced by fixation procedures, antibody concentrations, imaging settings, and cell type. Moreover, those studies may themselves lack sufficiently rigorous validation of antibody specificity. Therefore, antibody specificity should be demonstrated directly in the experimental system used in this manuscript, especially given that the principal conclusions rely extensively on the subcellular localization of MET, phospho-MET, and MT1-MMP.

We understand the reviewer’s concern regarding antibody specificity and agree that appropriate validation is important for imaging-based analyses. However, we strongly disagree with the statement that the MT1-MMP, MET antibody were not adequately validated. The specificity of the MT1-MMP antibody was independently validated by both siRNA- and sgRNA-mediated gene silencing, where we observed a substantially diminished MT1-MMP signal by immunoblotting (Fig S5I, M’). Moreover, the antibody has been used for IF in the same cell line by multiple research groups [6–8]. So, in our opinion, this validation is completely redundant.

We have validated the MET staining/ signal using the antibody in gene-silenced cells by immunoblotting and immunofluorescence (Fig S1I, K, L), as also acknowledged by the reviewer in comment-4. In addition, we would also like to clarify that the references cited in support of antibody specificity were not selected simply because they used the same antibodies. They include studies that provide experimental validation of the antibodies by gene silencing.

To further confirm the antibody specificity, we will add immunofluorescence images of MET or MT1-MMP silenced cells stained with respective antibodies. However, we may not want to add these data to the manuscript as they do not carry any additional values to the manuscript.

(3) Questionable MET localization in TIRF microscopy

The presence of punctate MET signal in TIRF microscopy under unstimulated conditions raises additional concerns. Under basal conditions, MET is generally expected to exhibit a predominantly diffuse distribution at the plasma membrane, whereas prominent punctate structures are typically associated with ligand-induced clustering, endocytosis, or trafficking events.

The observation of numerous MET-positive puncta in unstimulated cells, together with the insufficient validation of antibody specificity, raises the possibility that at least part of the observed signal represents non-specific staining or imaging artefacts rather than bona fide MET localization. This concern is further compounded by the lack of rigorous immunofluorescence antibody validation discussed above and significantly undermines the interpretation of all TIRF-based trafficking analyses presented in the manuscript.

We would like to highlight the apparent similarities between Fig 2A, B and the unstimulated condition in Fig. 2H. In figure 2A, B, MET is detected using an anti-MET antibody, whereas in Figure 2H, GFP-MET is imaged under live cell condition. We believe the reviewer would agree that imaging GFP-MET in live cells avoids fixation- and antibody-related artifacts. The comparable localization observed using these two independent approaches therefore provides additional support that the MET distribution shown in Fig. 2A, B reflects genuine receptor localization rather than an imaging or staining artefact.

(4) The evidence supporting a MET-specific role in invadopodia remains unconvincing

The authors argue that the role of MET in invadopodia formation is validated using three independent approaches: shRNA-mediated knockdown, SMARTpool siRNA-mediated knockdown, and pharmacological inhibition with PHA665752. However, I do not agree that these constitute three independent orthogonal validations of MET function.

First, the shRNA-mediated knockdown presented in this study achieves only modest depletion of MET protein. The authors themselves acknowledge this limitation and therefore selected cells with visibly reduced MET staining for imaging. Consequently, the shRNA experiments cannot be considered a robust or independent validation of MET function.

Second, although pooled SMARTpool siRNAs are widely used to improve knockdown efficiency, they cannot exclude off-target effects, as each individual guide RNA contributes its own potential off-target profile. Therefore, pooled siRNAs cannot by themselves establish that an observed phenotype is specifically attributable to depletion of the intended target and do not replace validation using independent individual siRNAs or rescue experiments.

Third, the pharmacological data should also be interpreted with caution. Throughout the manuscript, PHA665752 is presented as a MET inhibitor supporting the specificity of the observed phenotype. However, there is essentially no such thing as a truly selective receptor tyrosine kinase inhibitor. PHA665752 inhibits multiple kinases in addition to MET, particularly at concentrations commonly used in cell-based assays. Consequently, the inhibitor cannot be considered an independent validation of MET-specific function.

Importantly, the newly added siRNA experiments do not resolve my original concern regarding the role of MET in invadopodia formation. Although siRNA-mediated MET depletion is substantially more efficient than the shRNA-mediated knockdown presented in the original manuscript, this marked difference in MET depletion is not accompanied by a correspondingly stronger inhibition of invadopodia formation or ECM degradation. If MET were indeed the principal driver of the observed phenotype, one would expect the magnitude of the biological effect to correlate with the efficiency of MET depletion. This inconsistency raises the possibility that the observed phenotype is not solely attributable to MET depletion and calls into question the specificity of the proposed mechanism.

Taken together, the three perturbation approaches used by the authors cannot be regarded as independent orthogonal validation of MET function. One approach provides only modest target depletion, another relies on pooled RNAi reagents that cannot exclude off-target effects, and the third employs a multi-kinase inhibitor rather than a MET-specific compound. Collectively, these limitations substantially weaken the conclusion that the reduction in invadopodia formation is specifically attributable to loss of MET. A convincing demonstration of MET-specific function would require rescue experiments or another truly orthogonal validation strategy.

We had adopted three independent approaches to validate the phenotype. All three approaches are well practiced in the field. The small molecule inhibitor has been used in the study by Rajadurai et al, J Cell Science, 2012 and it is very much accepted in studying cellular kinases [1].

We agree that even though the smart pool has always chance of off-target effects, the OnTargetPlus Smart pool has the minimum chance of off-target effects because of the patented chemical modifications, compared to the individual oligos and SiGenome SMARTpool, which was used by Rajadurai, et. al. in their study [1].

The shRNA mediated silencing resulted in less reduction in the MET level (~50-60%) but is it scientifically not acceptable, particularly when it showed similar phenotype over n=3 sets of experiments?

The arguments made by the reviewer in this context seems to be harsh. However, we decided to carry out MET silencing using two independent oligos from the SMART pool.

(5) Weak evidence for MET-MT1-MMP interaction

The evidence supporting a physical interaction between MET and MT1-MMP remains unconvincing. The newly added co-immunoprecipitation experiment does not reveal a convincing MET-MT1-MMP interaction, and I am unable to appreciate a specific coimmunoprecipitated MT1-MMP signal in the presented blot. As presented, these data do not convincingly demonstrate a specific or functionally relevant interaction. Given that this interaction constitutes a central component of the proposed mechanistic model, this remains a major weakness of the study.

We have detected the interaction in both GFP pulldown assay in 4 different cell lines and the corresponding reverse His-Ni-NTA pulldown assay, providing complementary evidence for their physical association (Fig 6E, S5F, G). We have also clearly stated in the manuscript that this interaction is weak in nature and have not claimed it to be a strong interaction. While we acknowledge that the signal is modest, disregarding reproducible positive results would not be an appropriate interpretation of the pulldown assays. We believe the reproducibility of these findings supports a genuine MET and MT1-MMP association, and we have reported it accordingly.

(6) Overinterpretation of the data

Taken together, the study proposes a mechanistic model linking MET trafficking to MT1MMP localization and invadopodia function. However, the experimental evidence largely supports correlative observations rather than demonstrating a direct mechanistic relationship.

Specifically, MET endocytosis and recycling are not properly demonstrated because of the inappropriate temporal resolution of the trafficking experiments; the localization data remain uncertain owing to insufficient validation of the immunofluorescence reagents; and the proposed interaction between MET and MT1-MMP is not convincingly demonstrated. Consequently, the manuscript establishes correlation rather than causality, and the central mechanistic conclusions appear to be substantially overstated relative to the presented data.

We agree that there is scope for further investigation of MET and MT1-MMP cotrafficking, however, we have provided preliminary evidence demonstrating the cotrafficking of MET and MT1-MMP at the cell surface (Fig 6F). Importantly, MET and MT1-MMP co-trafficking represents only one component of the manuscript and not a central mechanistic conclusion. As the title of the study indicates, the major component of the study is focused on MET trafficking and its implication in invadopodia-associated TNBC invasion, for which we have provided direct experimental evidence. Therefore, we believe that describing the overall study as primarily overstated and correlative underestimates the extent of the experimental evidence supporting our mechanistic conclusions.

Conclusion:

While the manuscript addresses an interesting and biologically relevant question, the current experimental evidence does not adequately support the proposed mechanistic model. The combination of inappropriate experimental design for trafficking studies, insufficient validation of key imaging reagents, questionable interpretation of the localization data, lack of convincing evidence for the proposed MET-MT1-MMP interaction, and the absence of a clear relationship between the degree of MET depletion and the biological phenotype substantially limits the reliability of the conclusions.

In my opinion, these issues cannot be addressed by further revision of the current manuscript, as they require substantial additional experimentation, including appropriately designed trafficking assays with short kinetic time points, rigorous validation of antibody specificity for immunofluorescence, and stronger mechanistic evidence linking MET trafficking to MT1-MMP-dependent invadopodia function.

We thank the reviewer for outlining the remaining concerns. We will address the points raised by performing additional antibody validation and independent oligo-mediated MET silencing experiment, providing further support for the specificity and robustness of our findings.

However, we respectfully disagree, that the conclusions require the extensive additional experimentation suggested by the reviewer. As clarified above, our trafficking experiments were designed around the time points at which the functional invasive phenotype is observed, rather than to define the kinetics of MET internalization.

In conclusion, we would expect that the views of the reviewer 2 towards the manuscript should change and the reliability of our manuscript to the public should improve.

References:

(1) Rajadurai CV, Havrylov S, Zaoui K, Vaillancourt R, Stuible M, Naujokas M, Zuo D, Tremblay ML, Park M. Met receptor tyrosine kinase signals through a cortactinGab1 scaffold complex, to mediate invadopodia. J Cell Sci. 2012 Jun 15;125(Pt 12):2940-53. doi: 10.1242/jcs.100834. Epub 2012 Feb 24. PMID: 22366451; PMCID: PMC3434810.

(2) Sharma P, Parveen S, Vinod Shah L, Mukherjee M, Kalaidzidis Y, Joseph Kozielski A, et al. Title: SNX27-retromer assembly directs MT1-MMP trafficking to invadopodia and promotes breast cancer metastasis.

(3) Mader CC, Oser M, Magalhaes MAO, Bravo-Cordero JJ, Condeelis J, Koleske AJ, et al. Molecular and Cellular Pathobiology An EGFR-Src-Arg-Cortactin Pathway Mediates Functional Maturation of Invadopodia and Breast Cancer Cell Invasion [Internet]. doi:10.1158/0008-5472.CAN-10-1432

(4) Parveen S, Khamari A, Raju J, Coppolino MG, Datta S. Syntaxin 7 contributes to breast cancer cell invasion by promoting invadopodia formation. J Cell Sci. 2022 Jun 15;135(12). doi:10.1242/jcs.259576 PubMed PMID: 35762511.

(5) Joffre C, Barrow R, Ménard L, Calleja V, Hart IR, Kermorgant S. A direct role for Met endocytosis in tumorigenesis. Nat Cell Biol. 2011 Jun 5;13(7):827–37. doi:10.1038/ncb2257 PubMed PMID: 21642981.

(6) Monteiro P, Rossé C, Castro-Castro A, Irondelle M, Lagoutte E, Paul-Gilloteaux P, et al. Endosomal WASH and exocyst complexes control exocytosis of MT1-MMP at invadopodia. J Cell Biol. 2013 Dec 23;203(6):1063–79. doi:10.1083/jcb.201306162 PubMed PMID: 24344185.

(7) Wenzel EM, Pedersen NM, Elfmark LA, Wang L, Kjos I, Stang E, et al. Intercellular transfer of cancer cell invasiveness via endosome-mediated protease shedding. Nat Commun. 2024 Feb 10;15(1):1277. doi:10.1038/s41467-024-45558-8

(8) Pedersen NM, Wenzel EM, Wang L, Antoine S, Chavrier P, Stenmark H, Raiborg C. Protrudin-mediated ER-endosome contact sites promote MT1-MMP exocytosis and cell invasion. J Cell Biol. 2020 Aug 3;219(8):e202003063. doi: 10.1083/jcb.202003063. PMID: 32479595; PMCID: PMC7401796.

(9) Duan Q, Jia HR, Chen W, Qin C, Zhang K, Jia F, Fu T, Wei Y, Fan M, Wu Q, Tan W. Multivalent Aptamer-Based Lysosome-Targeting Chimeras (LYTACs) Platform for Mono- or Dual-Targeted Proteins Degradation on Cell Surface. Adv Sci (Weinh). 2024 May;11(17):e2308924. doi: 10.1002/advs.202308924. Epub 2024 Feb 29. PMID: 38425146; PMCID: PMC11077639.

(10) Wei J, Wang J, Guan W, Li J, Pu T, Corey E, Lin TP, Gao AC, Wu BJ. PlexinD1 is a driver and a therapeutic target in advanced prostate cancer. EMBO Mol Med. 2025 Feb;17(2):336-364. doi: 10.1038/s44321-024-00186-z. Epub 2025 Jan 2. PMID: 39748059; PMCID: PMC11822115.

(11) Yamasaki A, Miyake R, Hara Y, Okuno H, Imaida T, Okita K, Okazaki S, Akiyama Y, Hirotani K, Endo Y, Masuko K, Masuko T, Tomioka Y. Dual-targeting therapy against HER3/MET in human colorectal cancers. Cancer Med. 2023 Apr;12(8):9684-9696. doi: 10.1002/cam4.5673. Epub 2023 Feb 7. PMID: 36751113; PMCID: PMC10166911.

(12) Soonnarong R, Putra ID, Sriratanasak N, Sritularak B, Chanvorachote P. Artonin F Induces the Ubiquitin-Proteasomal Degradation of c-Met and Decreases AktmTOR Signaling. Pharmaceuticals (Basel). 2022 May 21;15(5):633. doi: 10.3390/ph15050633. PMID: 35631459; PMCID: PMC9145792.

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

We sincerely thank the editor and reviewers for thoroughly evaluating the manuscript. Following the comments from the reviewers we caried out four major sets of experiments and added the results and the conclusions derived from them in the revised manuscript. We also modified the abstract and the introduction. As suggested by the reviewers, we have rewritten the discussion. All the mislabelling and typing errors have been corrected, and representative graphs has been replaced as suggested. The list of the newly carried out experiments are -

(i) In the original submission, we carried out a microscopy-based study to investigate the recycling of MET. We now added surface biotinylation approach to show the RCP or KIF16B-mediated surface delivery of MET (Fig. 5J).

(ii) To demonstrate the functional effect of KIF16B silencing on TNBC invasion we have performed ECM degradation assay with depleted KIF16B cells (Fig. S4K-L). Further, the MET degradation in KIF16B-silenced cells has also been investigated using immunoblotting (Fig. S4H).

(iii) To rule out the off-target effect of the siRNA used in this study, we have validated the invadopodia-associated function using 2 individual siRNAs for RAB14 and RCP (Fig. S4K-L).

(iv) We have now introduced MMP2 as a positive control as a substrate of MT1-MMP to show the effect of silencing of the protease on its cleavage (Fig. S5I).

We also incorporated following changes, largely additions of new plots, data in the revised manuscript.

(i) RAB4 and RAB14 colocalization with MET in BT-549 cell line has also been added in Fig. 4 (A, B, C).

(ii) Data showing MET silencing using siRNA and its effect on invadopodia has been added to Fig 1D.

(iii) Graph showing percentage of cells forming invadopodia has added to Fig. S1G.

(iv) Line intensity plots of MET-containing invadopodia has been added in Fig. 2G’.

(v) We have added quantification of all the blots to the figures.

(vi) A graph representing MET degradation kinetics with HGF over 3 experiments has been added to Fig S2F.

(vii) The full field of view of Fig 1F has been added in the Fig S1L. A quantification of the gelatin degradation has been added to Fig 1F.

(viii) The blot for loading control of Fig S1K has been changed from Actin to Vinculin.

(ix) The blot showing expression of MT1-MMP in the SCR and knockout cells has been added to Fig S5M’.

(x) The survival plot for patients with altered or unaltered MET and RCP has been removed. The graph showing frequency alteration of MET has also been removed.

(xi) Additional immunoblots associated with all the figures are now provided in a newly added supplementary figure (Fig S6).

Reviewer #1 (Public review):

Summary:

This study identifies a mechanism responsible for the accumulation of the MET receptor in invadopodia, following stimulation of Triple-negative breast cancer (TNBC) cells with HGF. HGF-driven accumulation and activation of MET in invadopodia causes the degradation of the extracellular matrix, promoting cancer cell invasion, a process here investigated using gelatin-degradation and spheroid invasion assays.

Mechanistically, HGF stimulates the recycling of MET from RAB14-positive endosomes to invadopodia, increasing their formation. At invadopodia, MET induces matrix degradation via direct binding with the metalloprotease MT1-MMP. The delivery of MET from the recycling compartment to invadopodia is mediated by RCP, which facilitates the colocalization of MET to RAB14 endosomes. In this compartment, HGF induces the recruitment of the motor protein KIF16B, promoting the tubulation of the RAB14-MET recycling endosomes to the cell surface. This pathway is critical for the HGF-driven invasive properties of TNBC cells, as it is impaired upon silencing of RAB14.

Strengths:

The study is well-organized and executed using state-of-the-art technology. The effects of MET recycling in the formation of functional invadopodia are carefully studied, taking advantage of mutant forms of the receptor that are degradation-resistant or endocytosis-defective.

Data analyses are rigorous, and appropriate controls are used in most of the assays to assess the specificity of the scored effects. Overall, the quality of the research is high.

The conclusions are well-supported by the results, and the data and methodology are of interest for a wide audience of cell biologists.

We sincerely thank the reviewer for the positive feedback and for considering our study to be well executed and rigorous. The valuable suggestions and comments certainly improved the understanding of the role of the RAB14-RCP-KIF16B axis in MET trafficking and breast cancer invasion.

Weakness

The role of the MET receptor in invadopodia formation and cancer cell dissemination has been intensively studied in many settings, including triple-negative breast cancer cells. The novelty of the present study mostly consists of the detailed molecular description of the underlying mechanism based on HGF-driven MET recycling. The question of whether the identified pathway is specific for TNBC cells or represents a general mechanism of HGF-mediated invasion detectable in other cancer cells is not addressed or at least discussed

We thank the reviewer for raising this point. We would like to clarify that in TNBCs, the overexpression of EGFR and MET in the null background of the hormonal receptors; progesterone receptor, estrogen receptor, and HER2 is considered to be very crucial in terms of prognosis and treatment (PMID: 27655711, 25368674). Hence study of MET signalling and trafficking is more relevant for TNBCs compared to other cancer cells. In the current study, we therefore focused on two TNBC cell lines. We have added this in the first paragraph of introduction. Line no: 31-38.

Reviewer #1 (Recommendations for the authors):

Major points

(1) My major concern refers to the clinical data presented in this study. Different from the mechanistic findings, the quality of these analyses is too low, the description in the figure legends is scant, and is absent in the Method section. The results concerning the prognostic value of RCP are the most problematic. What is shown in the Kaplan Meier? Is it the correlation between the RCP mRNA levels and the patient's survival? More importantly, to study the correlation between genetic alteration and prognostic outcome, multivariable analyses should be performed comparing the genetic alteration with known prognostic factors (sex, age, tumor size, node status, ER/PrR, HER2, Ki67 if available, tumor grade). This is because breast cancer prognosis depends on multiple interrelated factors, and only multivariate models can adjust for confounding and identify which variables independently predict outcome-providing far more accurate and clinically useful prognostic information than univariate analysis. Furthermore, overexpression of RTKs has been extensively reported and studied in TNBCs. Similarly, the relevance of MET in cancer cell invasion has been firmly established. Therefore, the data presented here, whose quality does not match the mechanistic part of the study, can be considered unnecessary. I would, therefore, recommend removing the "clinical" data. The introduction should be modified accordingly.

We thank the reviewer for this insightful comment. To generate the graphs, we selected studies available in the publicly accessible database cBioportal (cbioportal.org/). The graphs generated by the database, representing the genetic alterations of genes has added in the Fig. S4A. However, as suggested by the reviewer, we have removed the survival plots for MET and RCP, the gene alteration frequency of MET and modified the text accordingly.

(2) Overexpression of KIF16B has been shown to inhibit the degradative pathway, stimulating the recycling one. In agreement, silencing of KIF16B accelerates EGFR degradation (PMID: 15882625). Does this also apply to the MET receptor? In the present setting, does the overexpression of KIF16B result in prolonged MET expression?

We thank the reviewer for raising this question. Although we did not analyze the expression level of MET in the KIF16B overexpressed cells, we have analyzed the total MET levels in the control and the KIF16B silenced cells using immunoblotting and did not observe any significant changes in the MET protein levels (Fig S4H). Line no: 390-391. This led us to believe that the depletion or overexpression of KIF16B may not have any direct effect on MET expression.

(3) The contribution of MMP2 and MMP9 to the degradative properties of HGFstimulated TNBC cells should be investigated and compared to MT1-MMP.

We believe that this is a relevant note from the reviewer. However, MT1-MMP is one of the most well-established metalloproteases, known till date for its role in invadopodia-associated activities in breast cancer (PMID: 35008569, 27501444). Moreover, it is the best-known candidate protease, which is a transmembrane metalloprotease could be the model in studying membrane recycling to invadopodia (PMID: 20605060, 19692588). However, as pointed out by the reviewer, we completely agree that MMP2 and MMP9 also contribute significantly to invadopodia-associated functions in TNBCs (PMID: 23902685, 25699257). HGF is also known to promote the expression and activity of MMP2 and MMP9 (PMID: 23320110, 26259977). Interestingly, the cellular machineries involved in their enhanced activity due to HGF stimulation may be distinct from what was observed in the current study and may require a distinct, elaborated study, which is beyond the scope of the current one.

(4) The authors appropriately tested the possible shedding effect of MT1-MMP on MET. They should repeat the experiments, adding a positive control of shedding. Furthermore, the legends referring to these experiments, shown in Supplementary Figure S6, seem to be wrong (or mislabeled).

We thank the reviewer for the suggestion. MT1-MMP is known to proteolytically cleave and initiate the activation of MMP2 (PMID: 11161720, 15095267). We now carried out the experiment with MMP2 as a control (Fig S5I). We observe an increase in the unprocessed MMP2 level in the MT1-MMP silenced cells, whereas the MET levels are unaltered. Line no: 467-468.

We sincerely apologize for the oversight in the mislabelling. We have now corrected it in the revised manuscript.

(5) Does altered expression of RAB14 and/or KIF16B affect MT1-MMP delivery to invadopodia in the TNBC cell lines? Does KIF16B silencing affect invasion?

The role of RAB14 and KIF16B in MT1-MMP delivery to podosomes, a structure similar to invadopodia in macrophages has been studied by Hey S. et al. (PMID: 37696580). The study suggests that KIF16B silencing reduces the invasion of macrophages. However, the effect is not known for TNBCs. Thus, we have conducted the ECM degradation assay in TNBC cell lines to show the effect of KIF16B gene silencing on breast cancer invasion to the revised manuscript (Fig S4K-L). In both MDA-MB-231 and BT-549 cells we observed reduced ECM degradation activity upon KIF16B depletion, corroborating the observation from Hey S. et al. Line no: 392-400.

Minor points

(1) I recommend authenticating cell lines and stable populations by STR profiling.

All the cell lines used in the study have been purchased from ATCC, and STR is a standard practice followed by ATCC. Further, to avoid any alteration, cells were discontinued after 20 passages. We have added this statement to the methods section in the revised manuscript. Line no: 643-644.

(2) In the legend to Supplementary Figure S4A, the panel is described as the frequency of alterations of MET, while, if I correctly interpret it, the bar graph refers to RCP. As mentioned above, these data could be removed.

We thank the reviewer for pointing out the mistake. We have rectified this in the revised version.

(3) Check for typos. Sometimes invadopodia is written with the capital: "Invadopodia", in other instances it is not. The authors should be consistent throughout the manuscript. English language editing would help.

We sincerely apologize for the inconsistency in the writing. We have removed the unnecessary capitalization of invadopodia in the revised manuscript. Line no: 142, 159, 280, 436.

Reviewer #2 (Public review):

Summary:

In this manuscript, Khamari and colleagues investigate how HGF-MET signaling and the intracellular trafficking of the MET receptor tyrosine kinase influence invadopodia formation and invasion in triple-negative breast cancer (TNBC) cells. They show that HGF stimulation enhances both the number of invadopodia and their proteolytic activity. Mechanistically, the authors demonstrate that HGF-induced, RAB4- and RCP-RAB14-KIF16B-dependent recycling routes deliver MET to the cell surface specifically at sites where invadopodia form. Moreover, they report that MET physically interacts with MT1-MMP - a key transmembrane metalloproteinase required for invadopodia function- and that these two proteins co-traffic to invadopodia upon HGF stimulation.

Although the HGF-MET axis has previously been implicated in invadopodia regulation (e.g., by Rajadurai et al., Journal of Cell Science 2012), studies directly linking ligand-induced MET trafficking with the spatial regulation of MT1-MMP localization and activity have been lacking.

Overall, the manuscript addresses a relevant and timely topic and provides several novel insights. However, some sections require clearer and more concise writing (details below). In addition, the quality, reliability, and robustness of several data sets need to be improved.

Strengths:

A key strength of the study is the novel demonstration that HGF-mediated, RAB4- and RAB14-dependent recycling of MET delivers this receptor, together with MT1MMP, to invadopodia -highlighting a previously unrecognized mechanism, regulating the formation and proteolytic function of these invasive structures. Another strong point is the breadth of experimental approaches used and the substantial amount of supporting data. The authors also include an appropriate number of biological replicates and analyze a sufficiently large number of cells in their imaging experiments, as clearly described in the figure legends.

We greatly appreciate the positive assessment from the reviewer, who also acknowledged the novelty and relevance of our study. Below, we have carefully addressed the comments/concerns raised regarding this study and that have strengthened the reliability and robustness by revisiting the data, providing additional analyses where required, and clarifying methodological details.

Weakness

(1) Inappropriate stimulation times for endocytosis and recycling assays. The experiments examining MET endocytosis and recycling following HGF stimulation appear to use inappropriate incubation times. After ligand binding, RTKs typically undergo endocytosis within minutes and reach maximal endosomal accumulation within 5-15 minutes. Although continuous stimulation allows repeated rounds of internalization, the temporal dynamics of MET trafficking should be examined across shorter time points, ideally up to 1 hour (e.g., 15, 30, and 60 minutes). The authors used 2-, 3-, or 6-hour HGF stimulation, which, in my opinion, is far too long to study ligandinduced RTK trafficking.

We understand the reviewer’s concern regarding the HGF stimulation time point for endocytosis and recycling. We want to highlight that to study the recycling/surface delivery of MET in response to HGF, we performed TIRF microscopy-based imaging, where images were taken within 1h of HGF addition (Fig. 2I). Additionally, we have incorporated surface biotinylation to show the recycling of MET as suggested in comment-7 (Fig. 5J). For this experiment we have used 30 min of HGF stimulation. Line no: 382-391.

Moreover, we have observed the functional effect of HGF on ECM (gelatin) degradation and invadopodia formation after 3 h of HGF stimulation. We were curious to know where does the MET localises with prolonged ligand stimulation. Hence, to study the localization of MET to invadopodia or the endocytic markers, the cells were stimulated with HGF for 2-3 hours.

(2) Low efficiency of MET silencing in Figure S1I. The very low MET knockdown efficiency shown in Figure S1I raises concerns. Given the potential off-target effects of a single shRNA and the insufficient silencing level, it is difficult to conclude whether the reduction in invadopodia number in Figure 1F is genuinely MET-dependent. The authors later used siRNA-mediated silencing (Figure S5C), which was more effective. Why was this siRNA not used to generate the data in Figure 1F? Why did the authors rely on the inefficient shRNA C#3?

We understand the concern raised by the reviewer. We want to emphasize that we have employed three different approaches to investigate the effect of MET silencing/inhibition on invadopodia formation. (i) A MET kinase inhibitor, PHA665752, which shows reduced invadopodia formation (Fig. 1E, E’). (PMID: 21973114, 41009793) (ii) Silencing with shRNA: Since the level of silencing of MET with the shRNA was not sufficient, cells were stained with MET as a readout for MET silencing, and images of the cells with reduced MET expression were captured. ECM degradation activity and invadopodia numbers were found to be reduced in the MET-depleted cells (Fig. 1F). (iii) We have now added the data showing the effect of siRNA-mediated MET depletion on invadopodia formation to the revised figure 1D. Line no: 123-125. To draw a robust conclusion regarding the role of MET on invadopodia-associated TNBC invasion, we have integrated all three complementary approaches.

(3) Missing information on incubation times and inconsistencies in MET protein levels. The figure legends do not indicate how long the cells were incubated with HGF or the MET inhibitor PHA665752 before immunoblotting. This information is crucial, particularly because both HGF and PHA665752 cause a substantial decrease in the total MET protein level. Notably, such a decrease is absent in MDA-MB-231 cells treated with HGF in the presence of cycloheximide (Figure S2F). The authors should comment on these inconsistencies. Additionally, the MET bands in Figure S1J appear different from those in Figure S1C, and MET phosphorylation seems already high under basal conditions, with no further increase upon stimulation (Figure S1J). The authors should address these issues.

We apologise for the unintentional omission of experimental detailing about HGF or drug incubation time, which we have incorporated into the figure legend appropriately. Regarding the decreased MET level in the drug-treated condition: literature suggests that the MET inhibitor PHA665752 also promotes MET degradation, corroborating our result shown in Fig. S1J (PMID: 15788682, 18327775). Further in Fig. S1J, the relative phosphorylation of MET when compared to the total MET level in the HGF-treated condition is higher (~2-fold). Quantification of the blot has been added now.

Further, addition of HGF for 3 h leads to 40±15% reduction in the MET protein levels as seen in the Author response image 1 representing quantification of different immunoblot. The degradation of MET in the Fig S1J is 65% which nearly fall in the range for HGF-mediated MET degradation.

Author response image 1.

Quantification of immunoblots showing MET signal intensity in the presence or absence of HGF normalized with the loading control. N=6.

Next, in the fig. S1A, K the rabbit anti-MET (CST, D1C2 XP) antibody has been used, which binds to a C-terminal motif of MET and identifies both the 170kDa as well as 140kDa protein representing the uncleaved and cleaved form of MET. In Fig. S1J, the mouse antiMET (CST, L6E7) antibody has been used, which binds to an N-terminal motif of MET and recognizes only the 140kDa protein.

(4) Insufficient representation and randomization of microscopic data. For microscopy, only single representative cells are shown, rather than full fields containing multiple cells. This is particularly problematic for invadopodia analysis, as only a subset of cells forms these structures. The authors should explain how they ensured that image acquisition and quantification were randomized and unbiased. The graphs should also include the percentage of cells forming invadopodia, a standard metric in the field. Furthermore, some images include altered cells - for example, multinucleated cells - which do not accurately represent the general cell population.

We thank the reviewer for raising this point. The single-cell images are shown for clarity and to visualize the subcellular features; however, the conclusions are made based on the quantitative analysis of multiple cells collected from multiple fields of view (Frames). At least 30 such frames per condition having 4-7 cells/ frame has been acquired and analysed for the quantification throughout this manuscript. We would like to highlight that the image acquisition has been done over random fields on a coverslip. In the revised manuscript, for a better representation of the population of cell-forming invadopodia, a graph showing the percentage of cells forming invadopodia have been added (Fig S1G). Line no: 118-119. The percentage of cells forming invadopodia increased upon HGF stimulation in MDAMB-231.

(5) Use of a single siRNA/shRNA per target. As noted earlier, using only one siRNA or shRNA carries the risk of off-target effects. For every experiment involving gene silencing (MET, RAB4, RAB14, RCP, MT1-MMP), at least two independent siRNAs/shRNAs should be used to validate the phenotype.

We would like to clarify that we are using SMARTPool siRNA, which contains 4 individual siRNAs for the target gene. Literature suggests that using a pool of siRNA has reduced off-target effects compared to using single oligos for gene silencing (PMID: 14681580, 33584737, 24875475).

While SMARTpool siRNA minimizes the off-target effect, it does not eliminate the possibility of it. To confirm that the observed phenotypes are specifically attributable to the genes investigated in this study, we now performed functional experiments using two independent siRNAs targeting RCP and RAB14. The results have been added to figure S4K-L. Silencing of RCP or RAB14 using single oligos resulted in decrease in the degradation index comparable to SMARTpool siRNA, thus phenocopied the SMARTpool siRNA. Line no: 392-400.

Further, RAB4 is well established to be associated with MET trafficking and it served as a positive control in our study (PMID: 21664574, 30537020). Additionally, a recent study by Hey et al. have used individual oligos for KIF16B to demonstrate the effect of KIF16B silencing on gelatin degradation, which corroborate with our observation from the KIF16B silencing using the SMARTpool siRNA (PMID: 37696580).

For MET, we used siRNA, shRNA and an inhibitor to show the effect of MET inhibition/perturbation in the invadopodia-associated activity, which validates the observations of siRNA-mediated gene silencing (detailed in point 2).

We did not perform any experiments using single oligos targeting MT1-MMP, since in our MT1-MMP siRNA-based study, now we have taken an appropriate positive control to validate the efficacy of MT1-MMP silencing (Fig. S1I). In addition, we have shown the effect of MT1-MMP depletion on invadopodia formation using a CRISPR-based gene knock-out study, and another study from our group has shown a similar effect using siRNA (PMID: 31820782), which supports our MT1-MMP KO cell observation.

(6) Insufficient controls for antibody specificity. The specificity of MET, p-MET, and MT1-MMP staining should be demonstrated in cells with effective gene silencing. This is an essential control for immunofluorescence assays.

The anti-MET antibody (CST, D1C2 XP) has been used in several studied (PMID: 41166312, 41152910, 39748059). The CST L6E7 anti-MET antibody has been used in studied by Radke et al, Wang et el., Kong et al. (PMID: 36435874, 38262412, 32214092). In our study, immunoblots demonstrating depletion of MET in the siRNA or shRNA-treated cells has been provided in Fig. S1I, K respectively. Further, we have demonstrated MET silencing using immunofluorescence. We also have now added the entire field of view in Fig S1L of showing cells treated with control or shRNA against MET. In the shRNA-treated condition, the cell at the centre shows low MET fluorescence intensity indicating depletion of the RTK, while the surrounding cells have MET staining similar to control.

Tyr 1234/35 are present in the active site of MET kinase domain and upon binding of the ligand promotes their autophosphorylation (PMID: 17667909). Earlier studies have established the specificity of the phosphor-MET antibody by immunoblotting and immunofluorescence using MET inhibitors (PMID: 21973114, 41009793). In our study we have shown that the inhibition of MET kinase activity using PHA665752 abolished the MET phosphorylation at the Tyr 1234/35, as shown in Fig S1J which revalidates the specificity of the antibody.

Additionally, in a previous study Joffre et al. have shown that an oncogenic mutant form of MET, M1250T is highly phosphorylated at the Tyr 1234/1235 (PMID: 21642981). Using the phospho-MET antibody, we have shown in Fig 3C, S2I the increased Tyr phosphorylation of M1250T MET mutant as reported by Joffre et al.

The anti-MT1-MMP antibody is also a very well-established antibody reported in multiple studies (PMID:32479595, 31820782, 35762511). In our study, we have shown the specificity of the antibody using immunoblot analysis. Immunoblots showing significant depletion of MT1-MMP protein level following the SMARTpool siRNA and sgRNA-mediated gene silencing has been provided in Fig. S5I, M’, respectively. Further MT1MMP silencing has been also validated by immunofluorescence in the following studies. PMID: 22291036, 21571860, 20505159.

(7) Inadequate demonstration of MET recycling. MET recycling should be directly demonstrated using the same approaches applied to study MT1-MMP recycling. The current analysis - based solely on vesicles near the plasma membrane - is insufficient to conclude that MET is recycled back to the cell surface.

We appreciate the reviewer’s suggestion for an alternative approach to show MET trafficking. We have demonstrated MET trafficking using surface biotinylation, where we have shown that the RCP and KIF16B depletion affect the surface delivery of MET (Fig 5J). Line no: 382-391.

In addition, to study the surface delivery of cargo, TIRF is a widely used reliable approach and it is highly sensitive technique for detection of surface delivery events (PMID: 24344185, 20971701). We have also tried to investigate the trafficking of MET using antibody uptake approach; however, it could not be established as the binding of the antibody hindered the ligand binding and vice versa.

(8) Insufficient evidence for MET-MT1-MMP interaction. The interaction between MET and MT1-MMP should be validated by immunoprecipitation of endogenous proteins, particularly since both are endogenously expressed in the studied cell lines.

We thank the reviewer for pointing out the insufficient evidence for MET-MT1-MMP interaction at the endogenous level. We now carried out the immunoprecipitation of endogenous MET to validate the interaction with MT1-MMP (Fig S5H). A light (low intensity) band corresponding to MT1-MMP was detected in the anti-MT1-MMP immunoblot for the immunoprecipitated sample. We believe that the interaction between MT1-MMP and MET may be weak in nature, resulting in limited co-immunoprecipitation of the endogenous MT1-MMP by MET. The immunoblot is now added to the revised manuscript. Line no: 460-461.

(9) Inconsistent use of cell lines and lack of justification. The authors use two TNBC cell lines: MDA-MB-231 and BT-549, without providing a rationale for this choice. Some assays are performed in MDA-MB-231 and shown in the main figures, whereas others use BT-549, creating unnecessary inconsistency. A clearer, more coherent strategy is needed (e.g., present all main findings in MDA-MB-231 and confirm key results in BT549 in supplementary figures).

MDA-MB-231 and BT-549 are two well-characterized TNBC cell lines that readily form invadopodia. These cell lines have been extensively used to study invadopodia-associated breast cancer cell invasion (PMID: 32697977, 35915226, 31533971). These two cell lines also show overexpression of MET, making them suitable model cell lines for our study (PMID: 36139568, 20687930, 27502396).

Overall, most of the conclusions reported in this manuscript are derived from multiple experimental approaches using two TNBC cell lines for generalization.

We agree with the reviewer that showing the results from one type of cell line in the main figure would have been better, and wherever possible, we now provided the observations from a single cell line in the main figures and the data from the other cell line in the supplementary figures. However, some of the overexpression studies were performed in BT-549 cells to derive robust statistically meaningful conclusions. Therefore, we could not avoid adding results from both the cell lines in some of the figures. We would like to add that the legends for these figures have been edited to clearly mention the cell lines associated with each of the figure panels to avoid any confusion or inconsistency.

(10) Inconsistency in invadopodia numbers under identical conditions. The number of invadopodia formed in Figure 1E is markedly lower than in Figure 1C, despite identical conditions. The authors should explain this discrepancy.

We sincerely thank the reviewer for pointing out the inconsistency in invadopodia numbers across 2 experiments. Fig. 1C has 2 conditions: UT and the HGF-treated condition. The Untreated condition has the serum-free media without any stimulation. Whereas we have added vehicle (DMSO) in Fig. 1E, E’, since the drug is resuspended in DMSO. This difference in the treatment is likely to be responsible for the decreased numbers of invadopodia in Fig. 1E. In different studies it has been shown that DMSO is not biologically inert and can affect invasive properties of cells by perturbing actin dynamics and metalloprotease activity (PMID: 33552397, 22529897, 7188610).

(11) Questionable colocalization in some images. In some figures - for example, Figure 2G - the dots indicated by arrows do not convincingly show colocalization. The authors should clarify or reanalyze these data.

As suggested by the reviewer, we have now re-analyzed the data for figure 2G. The apparent visual lack of colocalization is likely due to the relatively lower fluorescence intensity of MET at these structures. We have now added the line intensity plots for the indicated puncta to show the intensity of both channels at the ‘dots’ in the figure 2G’ and they show correlation in their intensity distribution.

We would also like to elaborate that to quantify the colocalization of two channels, we have used the automated image analysis software Motiontracking (motiontracking.mpi-cbg.de) (PMID: 16143105), which has been detailed in the method section. Briefly, the algorithm works on object-based co-localization. If the fluorescence intensity distributions at a given object corresponding to any two different channels (fluorophores) show 35% or more overlap (Author response image 2), the object is considered as a multi-colour object and the overlapped area value is used to calculate the degree of co-localization. The calculation is carried out over all the objects in a given field of view (frame) and over all the field of views (frames) acquired for a given condition. Also, the apparent colocalization is corrected for random colocalization, which is the random permutation of object colocalization. This makes object-based colocalization more reliable than intensity-based colocalization.

Author response image 2.

Image showing the object identification and contour of the multicolour object identified by Motiontracking. The plot shows the intensity distribution of these two objects as analyzed by Motiontracking.

(12) Abstract, Introduction, and Discussion require substantial rewriting.

(a) The abstract should be accessible to a broader audience and should avoid using abbreviations and protein names without context.

(b) The introduction should better describe the cellular processes and proteins investigated in this study.

(c) The discussion currently reads more like an extended summary of results. It lacks deeper interpretation, comparison with existing literature, and consideration of the broader implications of the findings.

We thank the reviewer for this suggestion. We have substantially modified the abstract, and the introduction following the reviewer’s suggestion. The introduction has been edited to describe the cellular processes investigated in this study and some of the key associated molecular machineries. In the discussion section, we have avoided redundant descriptions of the results but retained some of them wherever necessary for interpretation and relevant discussion in the light of existing literature.

Reviewer #2 (Recommendations for the authors):

(1) Quality of charts. Several charts (e.g., Figure 1B, 1C, 1F) are of poor visual quality. The authors should provide higher-resolution graphs with clearer axis labels, consistent formatting, and properly scaled data.

We thank the reviewer for pointing out the insufficient visual quality of some of the charts. We believe the resolution of the charts/graphs have changed while converting to PDF, due to image compression. We will provide the uncompressed charts with much improved visual quality, provided they are not restricted by file size limitation.

(2) Full protein names on first mention. Whenever a protein appears for the first time in the manuscript, its full name should be provided, if possible, before using the abbreviation.

We have incorporated the full name of the protein while reporting for the first time in the manuscript.

(3) Correct use of "invadopodium" vs. "invadopodia." Invadopodia is the plural form; the singular is invadopodium. The sentence "Invadopodia, an actin-rich membrane protrusion decorated with proteases, is a tool for ECM and basement membrane degradation during cancer cell invasion" should be corrected accordingly.

We are thankful to the reviewer for pointing out the grammatical error. We corrected the error in the revised version. Line no: 44.

(4) Unclear sentence about resistance and invasion.

The sentence "However, often patients develop resistance to EGFR-targeted therapies due to overexpression of MET; yet, the mechanistic understanding of MET-dependent cancer invasion is unclear" is confusing because the shift from drug resistance to invasion is abrupt. The authors should revise this sentence for clarity and logical flow.

We are thankful to the reviewer for highlighting this sentence. We have rewritten the sentence as follows “Since one of the receptor tyrosine kinases (RTK), EGFR is often amplified in TNBC patients, they are usually targeted for its treatment. However, often patients develop resistance to EGFR-targeted therapies due to overexpression of another RTK MET”. Line no: 35-38.

(5) Incorrect figure reference. In the paragraph describing the results related to RAB proteins, there is an incorrect reference to Figure 3 instead of Figure 4. This should be corrected.

We sincerely apologize to the reviewer for the incorrect figure reference. We have corrected the reference to figures in the revised manuscript. Line no: 251, 261.

(6) Ambiguous sentence regarding MET activation. The sentence "MET, upon activation by HGF, triggers the activation of the RTK that induces cancer cell invasion" is unclear and should be rewritten for precision and clarity.

We are thankful to the reviewer for highlighting the unintentional mistake. We have now added a clearer sentence “MET-HGF signalling axis are reported to promotes invasion in gastric cancer cells and melanoma cells”. Line no: 96-97.

(7) Questionable wording of figure legend. The phrase "Immunoblotting of indicated cell lines with MET and Tubulin" is an informal shortcut. The authors should rephrase it.

We are thankful to the reviewer for highlighting this sentence. We have modified the figure legend with appropriate text. The modified text is as follows: Lysates of MDA-MB-231, BT-549 and MCF10A DCIS were separated by SDS-PAGE and analyzed by Western blot. Membranes were probed with anti-MET and anti-Vinculin antibody.

(8) Unnecessary capitalization. Terms such as invadopodia and cortactin should not be capitalized. The authors should correct capitalization throughout the manuscript.

We are thankful to the reviewer for raising this point. We have modified this accordingly in the revised version. Line no: 142, 159, 280, 436.

eLife Assessment

This work establishes a valuable theoretical finding about how the spike timing dependence of inhibitory plasticity shapes recurrent network connectivity. The combination of theoretical analysis and simulations provides convincing evidence that effective inhibitory connectivity forms a so-called Mexican-hat profile when multiple inhibitory neuron types follow distinct learning rules. These mechanisms are thought to be implicated in the contextual modulation of neuronal responses to stimuli.

Reviewer #1 (Public review):

Summary:

Festa et al. provide a detailed analysis of the outcome of spike-timing-dependent plasticity acting on inhibitory synapses for distinct shapes of the kernel that governs how pre- and postsynaptic spike times induce synaptic changes. The authors investigate symmetric and asymmetric kernels, providing a theoretical description of the ingredients that give rise to rate- or covariance-dominated plasticity based on a simplified two-neuron circuit. These analyses are confirmed via simulations of large recurrent networks with random excitatory connectivity. For excitatory connections arranged in a one-dimensional ring, the authors show that two distinct classes of inhibitory neurons (distinguished by their plasticity rules) form an effective Mexican-hat weight profile. Furthermore, the authors show that external inhibition of one of the inhibitory neuron types gives rise to the phenomenon of surround modulation.

Strengths:

The analytical description of the two-neuron circuit is robust and accurately captures the qualitative evolution of inhibitory weights in the recurrent network with random excitatory connectivity. The emergence of the Mexican hat from the combination of distinct inhibitory synaptic plasticity rules acting on different neuron types is an important result that reveals how such connectivity can be learned in biologically plausible networks. All the analyses are well done, and the simulation results are convincing, which supports a robust interpretation of the findings.

Weaknesses:

The two-neuron circuit model is a good choice for the analytics, but it may have hidden a covariance effect of the "rate-dominated" symmetric spike-based kernel that would appear when several inhibitory neurons, each sharing a different spike correlation with the postsynaptic neuron, converge onto it. The rate homeostasis achieved by the rate-dominated model arises from adjusting inhibitory weights according to their initial correlation with the output neuron, so that after learning, the weights are distributed such that these correlations are cancelled out (Vogels et al., 2011). In other words, even the rate-dominated rule is covariance-driven under the hood: with a single inhibitory input, the two-neuron circuit cannot expose this, but with several differently correlated inputs, the covariance dependence should reappear.

It is unclear whether the distribution of inhibitory weights has stabilised after 25 minutes of simulation time (Figure 3C), given that a considerable proportion of (mutual) weights reach the maximum allowed weight while (unidirectional) weights appear to vanish. Without a maximum-weight bound, and given sufficiently long simulations, the weights might diverge to infinity or decay to zero, so the apparent stationarity may be imposed by the bound rather than reflecting a true steady state. This could also be a finite-size effect, given the small number of excitatory connections per neuron.

The connections from excitatory neurons to the two inhibitory populations are different in the ring model (exc to PV is wider than exc to SST according to Table 3), and it is not clear whether this width difference, rather than the plasticity rules themselves, is responsible for the emergence of the Mexican hat.

Reviewer #2 (Public review):

Summary:

This study investigates how inhibitory synaptic plasticity can stabilize recurrent neural circuits while also shaping their functional connectivity. The authors analyze inhibitory spike-timing-dependent plasticity rules and show that different temporal kernels promote distinct E/I motifs, including reciprocal E/I connectivity and lateral inhibition. Using reduced circuit analyses and larger spiking network simulations, they demonstrate that inhibitory plasticity can generate structured effective connectivity, including Mexican-hat-like interactions in ring networks, while maintaining stable activity. The work therefore extends the view of inhibitory plasticity from a primarily homeostatic mechanism to one that may contribute to computationally useful circuit organization.

Strengths:

A major strength of the study is that it identifies a concrete mechanism by which the temporal shape of iSTDP rules determines the structure of learned inhibitory connectivity. The comparison between rules favoring reciprocal E/I motifs and those favoring "lateral" inhibition is shown across both reduced circuit models and larger spiking networks. The ring-network simulations further connect these learned motifs to circuit-level outcomes, including Mexican-hat-like effective connectivity, surround-suppression, and modular spontaneous activity.

Weaknesses:

The main limitations concern the extent to which the learned motifs are fully self-organized and how broadly the results generalize. In particular, the ring-network results rely on a pre-specified ring-like excitatory architecture and on two inhibitory populations with distinct plasticity rules, making it important to clarify which aspects of the Mexican-hat effective connectivity emerge from iSTDP itself. The conclusions would also be strengthened by intermediate plasticity rules. Finally, the ring-network simulations provide an interpretable proof of principle, but the authors should clarify whether the PV/SST effects depend on this specific architecture or would also arise in a more generic recurrent or cortex-like connectivity motif.

The authors largely achieve their aim of showing that inhibitory synaptic plasticity can provide structured stabilization of recurrent circuits. The results support this claim within the model framework by demonstrating that different temporal forms of iSTDP lead to distinct learned E/I motifs and can shape effective connectivity and cortical-like response patterns. However, the broader biological interpretation remains more suggestive because some results depend on specific assumptions for the network architecture and plasticity rules.

The work is likely to be valuable for researchers studying inhibitory plasticity, E/I balance, cortical circuit development, and biologically plausible learning because it provides a clear theoretical link between local inhibitory learning rules and circuit-level organization. The combination of analytically tractable motifs, spiking network simulations, and publicly available code makes the framework useful for future research.

The significance of the work lies not in showing that inhibitory plasticity can have functions beyond homeostatic stabilization, which has been established by previous theoretical and experimental studies, but in formalizing how the temporal form of iSTDP rules can bias the emergence of distinct E/I motifs. At present, the work identifies rules that are sufficient to generate these motifs in model networks, while the mapping of these rules onto specific interneuron types remains for future experimental testing.

Author response:

We thank the editors for sending our work for review and the reviewers for their thorough and constructive evaluations. In response to their feedback, we will submit a revised version of the manuscript soon. Below, we provide clarification on several of the concerns raised and outline the changes planned for the revised manuscript.

Reviewer 1, weaknesses

(1) The two-neuron circuit model is a good choice for the analytics, but it may have hidden a covariance effect of the "rate-dominated" symmetric spike-based kernel that would appear when several inhibitory neurons, each sharing a different spike correlation with the postsynaptic neuron, converge onto it. The rate homeostasis achieved by the rate-dominated model arises from adjusting inhibitory weights according to their initial correlation with the output neuron, so that after learning, the weights are distributed such that these correlations are cancelled out (Vogels et al., 2011). In other words, even the rate-dominated rule is covariance-driven under the hood: with a single inhibitory input, the two-neuron circuit cannot expose this, but with several differently correlated inputs, the covariance dependence should reappear.

Reviewer 1  highlights that the rate-homeostatic rule by Vogels et al. (2011) also includes a covariance-dependent component. Thus, in a network with multiple excitatory units, differences in pre-postsynaptic correlations can drive a redistribution of weights, resulting in stronger inhibitory weights for higher correlations. Our two-neuron circuit, which contains only one plastic inhibitory input, cannot reveal this competitive effect. We note, however, that although in the Vogels rule the covariance-dependent term is non-zero, it is typically much smaller than the rate-dependent term (see Methods, Section 4.3). Consequently, covariance-dependent organization may emerge on a slower timescale (a similar effect was also shown by Lagzi and Fairhall 2024; https://doi.org/10.1126/sciadv.adi4350). In the revised manuscript, we will clarify this point and analyze small motifs with multiple excitatory units and heterogeneous correlations, focusing on both the timescale of weight redistribution and the resulting steady-state weights. We will also revise our interpretation of Supplementary Figure S9: within the simulated time window, the rate-dominated rule produces uniform inhibitory connectivity, but this does not exclude slower covariance-dependent reorganization.

(2) It is unclear whether the distribution of inhibitory weights has stabilised after 25 minutes of simulation time (Figure 3C), given that a considerable proportion of (mutual) weights reach the maximum allowed weight while (unidirectional) weights appear to vanish. Without a maximum-weight bound, and given sufficiently long simulations, the weights might diverge to infinity or decay to zero, so the apparent stationarity may be imposed by the bound rather than reflecting a true steady state. This could also be a finite-size effect, given the small number of excitatory connections per neuron.

The reviewer raises the possibility that the apparent stationarity in Figure 3C is influenced by the imposed weight bounds. In the revised manuscript, we will discuss this point and present extended versions of the simulations in Figure 3 where we remove the weight bounds. We will also test whether the observed behavior depends on network size or excitatory connection density. We note that, under different external input regimes, the weights stabilize without reaching the hard bound (Figure S11), suggesting that saturation is not a necessary outcome.

(3) The connections from excitatory neurons to the two inhibitory populations are different in the ring model (exc to PV is wider than exc to SST according to Table 3), and it is not clear whether this width difference, rather than the plasticity rules themselves, is responsible for the emergence of the Mexican hat.

We recognize that we did not fully justify the parametrization used in the ring model. The pre-existing ring architecture determines the spatial correlations available to iSTDP and therefore contributes to the learned connectivity. In the revised manuscript, we will add control simulations in which the excitatory inputs to the PV and SST populations have either identical or markedly different widths, while the plasticity rules are kept fixed. These controls will allow us to assess the relative contributions of these factors to the formation of a Mexican-hat effective-connectivity profile.

Reviewer 2, weaknesses

(1) The main limitations concern the extent to which the learned motifs are fully self-organized and how broadly the results generalize. In particular, the ring-network results rely on a pre-specified ring-like excitatory architecture and on two inhibitory populations with distinct plasticity rules, making it important to clarify which aspects of the Mexican-hat effective connectivity emerge from iSTDP itself. The conclusions would also be strengthened by intermediate plasticity rules. Finally, the ring-network simulations provide an interpretable proof of principle, but the authors should clarify whether the PV/SST effects depend on this specific architecture or would also arise in a more generic recurrent or cortex-like connectivity motif.

This comment raises an important distinction between the components that are specified and those that emerge through plasticity. In our simulations, the excitatory architecture is fixed, whereas the initially weak and unstructured inhibitory-to-excitatory connections are learned through iSTDP. Thus, in the ring network, the spatial organization of excitation is prescribed, but the inhibitory connectivity and resulting Mexican hat-like effective connectivity emerge from the interaction of this architecture with the two iSTDP rules. Importantly, the central result that symmetric and antisymmetric rules promote distinct reciprocal and lateral E/I motifs is not restricted to the ring network, but is also observed in sparse randomly connected spiking networks and in networks with intrinsically generated irregular activity. The ring network is therefore used to demonstrate how these general motif-forming mechanisms can support specific circuit computations.

Our framework parametrizes a broader family of pairwise iSTDP rules rather than relying exclusively on isolated, preselected rules, allowing the contribution of rule shape and rate-dependent terms to be understood analytically. Intermediate or “mixed” plasticity rules, including those considered by Yang and Doiron (2026) https://doi.org/10.1103/9nv2-y63v, as well as additional network architectures, are valuable directions for extending the framework. We will revise the Discussion to clarify which components are prescribed, which emerge through plasticity, and which conclusions apply beyond the ring-network implementation.

(2) The authors largely achieve their aim [...] by demonstrating that different temporal forms of iSTDP lead to distinct learned E/I motifs and can shape effective connectivity and cortical-like response patterns. However, the broader biological interpretation remains more suggestive because some results depend on specific assumptions for the network architecture and plasticity rules.

This comment points to an important distinction between biological generality and mechanistic insight. Our models are deliberately simplified to isolate how the temporal structure of iSTDP interacts with internally generated correlations to select distinct E/I connectivity motifs, and to make this relationship analytically tractable. The resulting predictions are then reproduced in large conductance-based spiking networks with different connectivity structures and sources of irregular activity. Thus, although we do not claim that the specific biological implementations considered here capture the full diversity of cortical circuits, the conclusions are not restricted to a single minimal model or network architecture. Adding further biological detail would introduce additional parameters and architecture-specific assumptions, but would not by itself establish greater generality or provide the same mechanistic understanding. In the revised Discussion, we will clarify the distinction between the general mechanistic principles established by our framework and the more specific biological interpretations that remain to be tested.

(3) [...] At present, the work identifies rules that are sufficient to generate these motifs in model networks, while the mapping of these rules onto specific interneuron types remains for future experimental testing.

Our use of the PV and SST labels is intended as a biologically motivated implementation rather than as a universal assignment of plasticity rules to these interneuron classes. The symmetric and antisymmetric kernels were motivated by in vitro measurements from PV and SST interneurons in mouse orbitofrontal cortex, respectively (Lagzi et al., 2021; https://doi.org/10.1101/2021.09.06.459211). Because inhibitory plasticity can vary across brain regions, developmental stages, and experimental conditions (Feldman, 2012; https://doi.org/10.1016/j.neuron.2012.08.001), the general conclusion of our work concerns the mapping from the temporal structure of an iSTDP rule to the E/I motif it promotes, rather than a fixed correspondence between a particular rule and an interneuron identity.

Our models therefore establish more than the sufficiency of two isolated rules: the analytical framework explains how features of the plasticity kernel and rate-dependent terms determine whether reciprocal, lateral, or blanket inhibitory connectivity emerges. The specific association of these mechanisms with PV and SST interneurons in other circuits remains an experimentally testable prediction. We will clarify this distinction in the revised manuscript and emphasize that, once cell-type-specific plasticity rules are measured in a given circuit, the framework can predict the E/I connectivity motifs that those rules are expected to promote.

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Hardest meltdown moments?

And how to replace them with flexible anchors

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this sounds like your house

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The

Trying to keep it together.

When it's time to put them into practice

The strategies you KNOW just aren't happening

And dinner is burning while you try not to lose your mind.

Learn how to

Regulate Your Home? Free live workshop for moms of neurodivergent kids

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Normally, participating in a program with live coaching with me would cost hundreds more. This is your chance to get personal guidance, answers, and encouragement - included with your enrolment in Motif Building Academy.

All for $397 $297 includes the full program + live meet ups (at no extra charge)

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Inside you'll find video lessons that guide you step by step through the entire process, from building your foundations to quilting them as custom motifs or edge to edge, so that you can finish your tops with fun, original quilting patterns.

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Learn how to create beautiful custom motifs for your quilts with a simple, repeatable, step-by-step framework inside the Motif Builder Academy

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to design your own motifs

Struggling with low pickup rates? Explore cold calling mistakes that reduce morning call performance and learn how to improve connect rates with smarter telesales strategies.

这个可以作为信号解读重要性，这样可以强调企业可以通过互文性增强企业减少信息差。

What a wonderful and nice note to end on. All of these people calling her opinion "controversy" need to calm the fuck down.

Literally. Like, what did you expect bro??? Aren't you supposed to be a warrior??

Bro was basically Simba, he just couldn't wait to be king.

Damn, I kind of fucked with his armor ☹️

True SHIT

Xenia is an ancient Greek concept of hospitality, guest-friendship, and a sacred rule for treating strangers. Thanks, Google.

PERFECT description for him

SEE I could have sworn it was supposed to have wheels and was left at the front gate of Troy. PMO.

Forgot he did that. Lets talk about Barbehniemer..........

The lack of depth in Polyphemus' character was very disappointing.

HEHEHEHE I LOVE THIS WOMAN

I had not noticed this when I watched the film, but yk what yeah. But I guess the attempt at still including somewhat beautiful speech is appreciated.

Roasted.

LITERALLY WHY DID THEY MAKE HER AN UGLY WITCH LADY I'M PISSED

But I feel like there doesn't HAVE to be a Zeus, you know? These gods/deities basically rule these [most of, if not all of these] people's lives, they drive their decisions. I feel like presence isn't required when you control an element, and hence, can convey your wrath with that element.

True shit, because I'm sure that less than half the people who watched this film know who/what Scylla was.

True, but isn't that somewhat historically accurate? I'm not sure of Penelope and Odysseus' story, but I can't imagine that their marriage was out of pure love. It actually probably wasn't even voluntary for Penelope, because she probably didn't have a choice.

SAY IT LOUDER FOR THE PPL IN THE BACK

Not surprising tbh

True shit omg, we needed more of her. I NEED to read Clytemnestra (Constanza Casati).

Awesome sauce.

Straight up (imagine the autotune)

Tea.

Only nice thing she's said so far, I think.

Damn. Got his ass.

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