this is using both narrative bias and status quo bias. Narrative because it is a dramatic story that is happening right now. Status quo bias because it is something that many people believe in and support, so they are more inclined to read this article.

Article says that higher-ups probably do not believe in the movement, but their beliefs don't matter. They're just creating an environment in which people (fanatics) are motivated to destroy supposedly Satanic forces

It's the tickle in the nose when something really hurts or really gives joy. I wonder what comes after the tickle in the nose except for tears.

you want to prove that a large enough learning rate stops the algorithm from converging... try a larger value for alpha? if step size is too large you'll just end up with large oscillations that stops the model from converging

Author Response

Reviewer #1 (Public Review):

Rosas et al studied the mechanism/s that enabled carbapenems resistance of a Klebsiella isolate, FK688, which was isolated from an infected patient. To identify and characterize this mechanism, they used a combination of multiple methods. They started by sequencing the genome of this strain by a combination of short and long read sequencing. They show that Klebsiella FK688 does not encode a carbapenemase, and thus looked for other mechanisms that can explain this resistance. They discover that both DHA-1 (located on the mega-plasmid) and an inactivation of the porin OmpK36, are required for carbapenem resistance in this strain. By using experimental evolution, it was shown that resistance is lost rapidly in the absence of antibiotics selection, by a deletion in pNAR1 that removed blaDHA-1. Moreover, their results suggested that it is likely that exposure to other antibiotics selected for the acquisition of the mega-plasmid that carries DHA-1, which then enabled this strain to gain resistance to carbapenemase by a single deletion.

The major strength of this study is the use of various approaches, to tackle an important and interesting problem.

The conclusions of this paper are mostly well supported by data, but one aspect is not clear enough. The description of the evolutionary experiment is not clear. I could not find a clear description of the names of the evolved populations. However, the authors describe strains B3 and A2, but their source is not clear. The legends of the relevant figure (Figure 5) are confusing. For example, the text describing panel B is not related to the image shown in this panel. Moreover, it is shown in panel C (and written in the main text) that the OmpK36+ evolved populations had only translucent colonies, so what is the source of B3(o)?

We appreciate the point and in response have added a panel to Figure 5 (in the revised paper this is now Fig. 5A) to illustrate the evolutionary experiment and specify that there are two lineages (A and B) with 20 replicates each that, after 200 generations of evolution, give rise to populations of which A2 and B3 are the exemplars characterized.

We have corrected the legends in Figure 5.

We now explain (sentence starting on Line 197) that the B3 (o) is the single isolate of an opaque colony from lineage B3, it is the only colony that we identified from out of 595 colonies observed in the B3 population. B3(o) was sequenced and analysed as a comparator and has some value in that regard, despite being an anomaly.

Reviewer #2 (Public Review):

The authors sequenced a clinical pathogen, Klebsiella FK688, and definitively establish the genetic basis of the carbapenem-resistance phenotype of this strain. They also show that the causal mutations confer reduced fitness under laboratory conditions, and that carbapenem sensitivity readily re-evolves in the lab due to the fitness costs associated with the resistance mutations in the clinical isolate. They also establish that subinhibitory concentrations of ceftazidime select for the otherwise deleterious blaDHA-1 gene. Based on this finding the authors speculate that prior beta-lactam selection faced by the ancestors of Klebsiella FK688 potentiated the evolution of the carbapenem-resistance phenotype of this strain. If this hypothesis is true, then prior history of beta-lactam exposure may generally potentiate the evolution of carbapenem resistance.

Strengths:

From a technical perspective, the findings in this paper are solid. In addition, the authors establish a simple genetic basis for carbapenem resistance in a clinical strain, which is a valuable and non-trivial finding (i.e. they show that the CRE phenotype in this strain is not an omnigenic trait distributed over hundreds of loci).

Weaknesses:

The main weakness of this paper is that the authors draw overly broad conclusions of a conceptual nature from narrow experimental findings. This could be addressed by drawing more modest and narrow implications from the findings.

1) The title of this paper is "Treatment history shapes the evolution of complex carbapenem-resistant phenotypes in Klebsiella spp." But they provide no data on the treatment history of the patient from whom this strain was isolated from. Therefore, the authors have no evidence to support their central claim. Indeed, it is completely possible that this strain never faced beta-lactam selection in the past, or that the patient's hypothetical history of betalactamase was irrelevant for the evolution of FK688. First, it is completely possible that this is a hospital-acquired infection, such that the history of this strain is due to selection in other contexts in the hospital that have little to do with the patient's treatment history. Second, it is completely possible that this strain (the chromosome anyway) has no prior history of beta-lactamase selection, and that it acquired the megaplasmid containing blaDHA-1 via conjugation from some other strain. In this second hypothetical scenario, it is possible that the fitness cost of the blaDHA-1 gene is not particularly high in a different source strain, but that it has some cost in the FK688 strain that it was isolated from. And of course, fitness costs in the human host could be very different than fitness costs in the laboratory, where strains are evolving under strong selection for fast growth. And given the benefit of resistance, it's clear that this strain clearly has a strong fitness advantage over faster-growing sensitive strains in the context of the source patient under antibiotic treatment.

My general point here is that the broad claims made about patient history or prior history shaping the evolution of this strain are largely indefensible because there is no data here to make solid inferences about how prior history shaped the evolution of this strain.

We appreciate the point and have changed our title and scaled back the strength of our conclusions regarding patient treatment history.

2) Historical contingency. The authors claim that their work shows how historical contingency shapes the evolution of resistance. One problem with this claim is that it is trivial- this is only a significant claim if the reader believes that prior history is not important in the evolution of antibiotic resistance, which is a straw-man null hypothesis, to mix a couple metaphors. To be more concrete, clearly strain background (prior history) matters-eliminating the plasmid with the resistance gene eliminates resistance. But that is not particularly surprising, given the past 50 years of evolutionary microbiology literature on plasmids and resistance. By contrast to this work, the major contribution of papers that examine the role of historical contingency in evolution (i.e. various Lenski papers) is that those works quantitatively measure the role of history in comparison to other factors (chance, adaptation). Since this work is a deep dive into a single clinical isolate, the data presented here do not and cannot shed light on the role of historical contingency in the emergence of this strain. The authors' claims about the prior history that led to the CRE phenotype are reasonable- but are fundamentally speculative. I have nothing against speculation, as long as it is clear what claims are speculative, and what are concrete implications. But the authors frame these speculative claims as concrete implications of their findings.

This is a fair point. We have reframed the study to not focus on historical contingency.

As the reviewer points out, any discussion about historical contingency in the context of evolution is trivial in one sense. One of the reasons that the studies of Lenski and Blount provide new insights into the role of historical evolution because they knew the history of their populations (at, least for the number of generations since the LTEE began), and had a high degree of control and understanding of the growth conditions where the trait evolved. As such, they could go back to time points before the trait evolved, and then repeat the evolution experiment many times, in the exact same environment where the trait originally evolved, and then count how often they observed the evolution of that trait.

Here we study a clinical isolate, and have less understanding of the evolutionary history of our strain. While we cannot re-evolve carbapenem resistant in the exact same environment experienced by the FK688 strain, we did test the capacity for the wild type, and two possible intermediate genotypes genotypes, to evolve carbapenem resistance in growth media with carbapenem.

Altogether- we have comprehensive evidence for the genetic cause of carbapenem resistance: the BLA1 plasmid + OmpK36. We showed, by experiment, that it is much more likely for carbapenem resistance to evolve in a FK688 strain that carries the BLA1 plasmid, than in an FK688 strain that did not carry the plasmid even if it had acquired the OmpK36 mutation. We think this not trivial because a significant proportion of all of the carbapenem resistant Klebsiella that have been isolated are non-carbapenemase CRE. Our reconstruction provides a plausible explanation for why non-carbapenemase CRE evolve – because they are evolving from strains that have already been treated with a non-carbapenem beta-lactam drug and have thereby selected for the presence of a beta-lactamase (that is not a carbapenemase).

So, while we have scaled back the strength of our claims, we do think that our results can provide some insight into how the evolutionary history of a pathogen can shape the molecular path to antibiotic resistance.

3) The authors claim that "[This work] suggests that the strategic combinations of antibiotics could direct the evolution of low-fitness, drug-resistant genotypes". I suppose this is true, but I also think this is a stretch of an implication given these findings. To be blunt, while I suppose it's better to have costly resistance variants that re-evolve sensitivity than to have low-cost high-resistance strains circulating, I think the patient's family would probably disagree that the evolution of a low-fitness drug-resistant genotype was good or strategic in the clinical context, even if better from a public health perspective. Low-fitness drug-resistant strains are just as lethal under clinical antibiotic concentrations!

Thank you for the comment, we see how this sentence could be seen as too strong a conclusion and have rewritten the last sentence of the DISCUSSION (line 351):

“These results show how an individual’s treatment history might shape the evolution of AMR, and should be taken into consideration in order to explain the evolution of non-carbapenemase CRE”

The authors do show the plausibility of their hypothesis/model that prior beta-lactam selection is sufficient to potentiate the evolution of carbapenem-resistance (by the additional ompK loss-of-function mutation). I think those findings are very nice. But the authors undermine their results by extrapolating too far from their data. Hence, I think narrowing the scope of the implications would improve this paper.

In addition to narrowing the scope of the implications as written, I also would like to add that there may be other ways of framing this paper (other than historical contingency) that may make the significance of this work more apparent to a broader audience. This may be worth considering during the revision process.

We have taken these suggestions on board and have re-framed the final sentences of the ABSTRACT, INTRODUCTION and DISCUSSION accordingly. Specifically, we have removed reference to historical contingency and instead have reframed our experiments as providing a genetic and evolutionary explanation for an interesting and concerning cause of antibiotic resistance – non-carbapenemase CRE.

Reviewer #2 (Public Review):

The authors sequenced a clinical pathogen, Klebsiella FK688, and definitively establish the genetic basis of the carbapenem-resistance phenotype of this strain. They also show that the causal mutations confer reduced fitness under laboratory conditions, and that carbapenem sensitivity readily re-evolves in the lab due to the fitness costs associated with the resistance mutations in the clinical isolate. They also establish that subinhibitory concentrations of ceftazidime select for the otherwise deleterious blaDHA-1 gene. Based on this finding the authors speculate that prior beta-lactam selection faced by the ancestors of Klebsiella FK688 potentiated the evolution of the carbapenem-resistance phenotype of this strain. If this hypothesis is true, then prior history of beta-lactam exposure may generally potentiate the evolution of carbapenem resistance.

Strengths:

From a technical perspective, the findings in this paper are solid. In addition, the authors establish a simple genetic basis for carbapenem resistance in a clinical strain, which is a valuable and non-trivial finding (i.e. they show that the CRE phenotype in this strain is not an omnigenic trait distributed over hundreds of loci).

Weaknesses:

The main weakness of this paper is that the authors draw overly broad conclusions of a conceptual nature from narrow experimental findings. This could be addressed by drawing more modest and narrow implications from the findings.

1) The title of this paper is "Treatment history shapes the evolution of complex carbapenem-resistant phenotypes in Klebsiella spp." But they provide no data on the treatment history of the patient from whom this strain was isolated from. Therefore, the authors have no evidence to support their central claim. Indeed, it is completely possible that this strain never faced beta-lactam selection in the past, or that the patient's hypothetical history of betalactamase was irrelevant for the evolution of FK688. First, it is completely possible that this is a hospital-acquired infection, such that the history of this strain is due to selection in other contexts in the hospital that have little to do with the patient's treatment history. Second, it is completely possible that this strain (the chromosome anyway) has no prior history of beta-lactamase selection, and that it acquired the megaplasmid containing blaDHA-1 via conjugation from some other strain. In this second hypothetical scenario, it is possible that the fitness cost of the blaDHA-1 gene is not particularly high in a different source strain, but that it has some cost in the FK688 strain that it was isolated from. And of course, fitness costs in the human host could be very different than fitness costs in the laboratory, where strains are evolving under strong selection for fast growth. And given the benefit of resistance, it's clear that this strain clearly has a strong fitness advantage over faster-growing sensitive strains in the context of the source patient under antibiotic treatment.

My general point here is that the broad claims made about patient history or prior history shaping the evolution of this strain are largely indefensible because there is no data here to make solid inferences about *how* prior history shaped the evolution of this strain.

2) Historical contingency. The authors claim that their work shows how historical contingency shapes the evolution of resistance. One problem with this claim is that it is trivial- this is only a significant claim if the reader believes that prior history is not important in the evolution of antibiotic resistance, which is a straw-man null hypothesis, to mix a couple metaphors. To be more concrete, clearly strain background (prior history) matters-eliminating the plasmid with the resistance gene eliminates resistance. But that is not particularly surprising, given the past 50 years of evolutionary microbiology literature on plasmids and resistance. By contrast to this work, the major contribution of papers that examine the role of historical contingency in evolution (i.e. various Lenski papers) is that those works *quantitatively* measure the role of history in comparison to other factors (chance, adaptation). Since this work is a deep dive into a single clinical isolate, the data presented here do not and cannot shed light on the role of historical contingency in the emergence of this strain. The authors' claims about the prior history that led to the CRE phenotype are reasonable- but are fundamentally speculative. I have nothing against speculation, as long as it is clear what claims are speculative, and what are concrete implications. But the authors frame these speculative claims as concrete implications of their findings.

3) The authors claim that "[This work] suggests that the strategic combinations of antibiotics could direct the evolution of low-fitness, drug-resistant genotypes". I suppose this is true, but I also think this is a stretch of an implication given these findings. To be blunt, while I suppose it's better to have costly resistance variants that re-evolve sensitivity than to have low-cost high-resistance strains circulating, I think the patient's family would probably disagree that the evolution of a low-fitness drug-resistant genotype was good or strategic in the clinical context, even if better from a public health perspective. Low-fitness drug-resistant strains are just as lethal under clinical antibiotic concentrations!

The authors do show the plausibility of their hypothesis/model that prior beta-lactam selection is sufficient to potentiate the evolution of carbapenem-resistance (by the additional ompK loss-of-function mutation). I think those findings are very nice. But the authors undermine their results by extrapolating too far from their data. Hence, I think narrowing the scope of the implications would improve this paper.

In addition to narrowing the scope of the implications as written, I also would like to add that there may be other ways of framing this paper (other than historical contingency) that may make the significance of this work more apparent to a broader audience. This may be worth considering during the revision process.

• We don't have everything we need, e.g., information, resources, etc.

• We're tired, overwhelmed, frustrated, and/or burnt out.

• We're waiting until we have more motivation. Future me will be brimming with motivation and get so much done

• We lack urgency. This can wait until tomorrow.

• We lack a big enough why. Why does this project even matter?

• We've hit the project's boring/tedious/difficult bits.

• We're surrounded by tantalizing distractions

• We struggle to get started because we'll just be interrupted in a few minutes, breaking our flow.

• We feel overwhelmed by the enormity of the project.

• We struggle with perfectionism

• We get sucked into distraction spirals. A 5-minute Reddit break turns into two hours.

• We do many urgent but unimportant tasks, so there's no time/energy for the important stuff.

• We seek distractions to quiet the monkey brain. (If we could snap our fingers and silence the mental chatter, we'd be more productive.)

• We dream of creating something amazing and perfect. Once we start the work, the ability to create something perfect disappears. As long as we don't start, perfection feels possible.

• We talk about projects and daydream a lot. These feel good and feel like progress. But these good feelings diminish our motivation to actually get started.

• We dream of doing so many things. It's hard to commit to one project because it means saying No to a dozen others. Not starting on a project means we're keeping our options open.

• We seek out distractions to soothe negative emotions.

• We fear judgment and criticism.

• We fear making mistakes.

• We fear failing

• We have imposter syndrome.

• We have too many concurrent projects/responsibilities. This causes us to constantly switch contexts and/or feel overwhelmed. This kills any chance of getting into a flow state.

• We feel like other people don't have to work as hard, and it's not fair that things take so much effort for us.

• We don't have anyone to cheer us on, celebrate tiny wins and small victories, etc

• We don't have anyone holding us accountable.

• We're surrounded by people who procrastinate, so it feels normal to put things off. Who wants to be the only person not procrastinating?

• We feel lonely, so it's hard to find the motivation to do stuff.

via u/clumsyAmeba on https://www.reddit.com/r/productivity/comments/121pe2x/ive_identified_27_causes_of_procrastinationwhich/

It's interesting how both of these philosophers both mentioned psychology. What I find more surprising though is just the fact that psychology wasn't as well known of a term as philosophy and similar terms. I've always known psychology was a newer idea but it's crazy how long ago they were bringing it up even though they didn't have much of meaning to it

I know in mexican culture leaving home is difficult. I have a friends who still live with family, but what this piece shows is that leaving home isn’t just leaving the people you grew up with, it’s leaving the land you’ve walked on for years. The author shows this in the next line with, “But I didn’t leave all the parts of; I kept the ground of my own being. On it I walked taking with the land of the Vaelly, Texas

I love this because it's so relatable, maybe a form of imposter syndrome? Sometimes you just take the jump

ever since I moved for uni people have been shortening my name to jen instead of saying jennifer which never happened to me before and I don't entirely mind it but my family I've warned not to start calling me this because I don't want to feel like a Jen just because I'm in university. I would much rather be a better version of the old Jennifer and just grow on that which is similar to what the author writes here which I really like.

often times in mexican culture, one of the traditions associated with it that I never really respected was respecting your elders. so many times I would see the mistreat members of the family or say such awful things and personally I never learned to let them talk to me or people I cared about in such ways which of course led to getting in trouble for "rebelling" or not being respectful but it's true that tradition expects you to put others before yourself always which just isn't always right.

this word is crazy but also just sounds like borderline personality disorder but I didn't know it was it's own thing referred to as caught between dimensions I really like that.

there's a book I've read that mentions this persona of a woman as an actual spirit or deity typically by the name of wild woman or for one culture it's just mother earth I believe

What a strong use of metaphor here, you can really feel and picture what he is saying, just how much poetry and writing meant to him. It's sad that he couldn't find this in school as a child where theoretically you are supposed to have access to these kinds of things.

I think in cases where individuals are released from doing a sentence that they should be able to vote just like everyone else since they do in fact have their freedom. There are different laws for different states and it's not the same everywhere but for the states that is in effective in I think it's something to think about.

It's so interest to see that we have always been pulled toward this idea. It almost make me feel like that this idea were not invented by discovered cause it's had always been there in us, it's something we have always been seeking and to find it just now.

• merger of symbolic
• actually compute
• statistical approach

real life examples that are visceral to the reader to show it's not just talk. These perceptions are toxic and deadly

When you're a kid you can't wait to grow up but instead of focusing on the freedom of aging focus on the freedom of being young. You will never as young as you are right now. Have fun, live bright, make a few bad choices, live how YOU want. It's your life and it'll be worth it.

she'll continue on in life and will continue to do things even if they remind her of their memories and moments they shared. She hopes one day he'll think of her and the moments they had together and that he'll regret leaving and making him wish he had stayed.

If the addition person doesn't want to work on their self it's impossible to help. As a family member or seeing someone we love going through that we can't accept the fact we can't help them. The freedom starts when you try to help and you see you don't have success, then you just should stop.

More ammunition for the microservices crowd?

addiction has such a negative stigma behind it, it seems to be better to hide how they died then just say they overdosed. being ridiculed for something that you never wanted or you had no chance of knowing will happen is sad.

Here we see The Weeknd spiraling in his mind and is apathetic to himself and to life. He feels as though he is continuing to make the same mistakes and is facing this darker side of himself "...The human soul, because of its karmic baggage, is a place of ambiguity. It is a place where sinner and saint, the sacred and the profane, the divine and the diabolical coexist in seed form. When we get in touch with this part of ourselves and accept it for what it is, we simultaneously lose the need to judge others. Christ said, "He who is without sin shall cast the first stone." I believe what he was saying was that self-acceptance makes us compassionate, forgiving and nonjudgmental of others. This is the first stage of liberation." (Phipps, 2000)

This frames the beginning journey into The Weeknds "ego-death" as he is high on what we can assume to be psychedelics and is confessing to this women that he is losing who he thought he was and is trying to keep his faith in re-learning who he is again. As Deepak Chopra put it "...When people get in touch with themselves, they become aware that the inner core of their being contains opposing energies." (Phipps, 2000). This woman is someone he trust to watch over him as he journeys through himself.

He loves her so much he's enchanted by her, and even if she put a spell it defintely worked

Many people have one night stands or just simply flings that don't last them. She wants love that will last, someone that will actually stay

I didn't realize the many aspects that go into the creation of architecture. Before reading this WP, I thought architects just designed buildings that fit theme of the location that it's in. It's also interesting to me how the history of the area affects the designs as well.

I'm also a kinesthetic learner, for me it's difficult to grasp knowledge if it's just readings on what we're supposed to be learning. I believe engaging physically does help you become an active learner in that subject.

This part is what really got me. At first, I thought that maybe Grace's twins put makeup on him or something to coverup the bruises and sores. But to find out it is his own tribe's magic and healing powers just made me so happy that he isnʻt in pain anymore. And to have healing powers is so cool. I like that these healing powers is magical and fantasy...but it is also their beliefs. So I guess it is borderline fantasy/magic/religious beliefs.

What a beautiful introduction.

• Start with a personal anecdote, providing us with a mood; elaboration on an environment in which to place the essay. I feel calm before even digging into the meat and empathize with the subject - who doesn't like observing nature?

• A character in the story provides dialogue relevant to the text, easing into the thesis through the eyes of someone else. This feels far less forceful than the position paper - it's just something someone else is saying - and yet "all animals play" is the thesis of the work. That brief, fun assertion stays with us throughout the piece!

This is the core argument for claiming moral duty in avoiding epistemic harm, that is, not fighting wrong beliefs.

This seems like such a real problem anybody has, there are absolutely some fonts I find that I just prefer over others, it's really unfortunate more people can't see that, especially when there's a disability at play.

trying to rise above

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

Learn more at Review Commons

Reply to the reviewers

We would like to thank the reviewers for their extensive review of our manuscript and constructive criticism. We have attempted to address the points raised in the reviewer's comments and have performed additional experiments and have edited the text of the manuscript to explain these points. Please see below, our point-by-point response to the reviewer’s comments. In the submitted revised manuscript, some figure numbers have changed from the prior reviewed version.

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

In this MS, Mrj - a member of the JDP family of Hsp70 co-chaperones was identified as a regulator of the conversion of Orb2A (the Dm ortholog of CPEB) to its prion-like form.

In drosophila, Mrj deletion does not cause any gross neurodevelopmental defect nor leads to detectable alterations in protein homeostasis. Loss of Mrj, however, does lead to altered Orb2 oligomerization. Consistent with a role of prion-like characteristics of Orb2 in memory consolidation, loss of Mrj results in a deficit in long-term memory.

Aside from the fact that there are some unclarities related to the physicochemical properties of Orb2 and how Mrj affects this precisely, the finding that a chaperone could be important for memory is an interesting observation, albeit not entirely novel.

In addition, there are several minor technical concerns and questions I have that I feel the authors should address, including a major one related to the actual approach used to demonstrate memory deficits upon loss of Mrj.

Reviewer #1 (Significance (Required)):

Figure 1 (plus related Supplemental figures): • There seem to be two isoforms of Mrj (like what has been found for human DNAJB6). I find it striking to see that only (preferentially?) the shorter isoform interacts with Orb2. For DNAJB6, the long isoform is mainly related to an NLS and the presumed substrate binding is identical for both isoforms. If this is true for Dm-Mrj too, the authors could actually use this to demonstrate the specificity of their IPs where Orb2 is exclusively non-nuclear?

According to Flybase, Mrj has 8 predicted isoforms of which four are of 259 amino acids (PA, PB, PC, and PD), 3 are of 346 amino acids (PE, PG, and PH) and one is of 208 amino acids (PF) length (Supplementary data 1). We isolated RNA from flyheads and used this in RT-PCR experiments to check which Mrj isoforms express in the brain. Since both the 346 amino acid (1038 nucleotide long) and 259 amino acids (777 nucleotides long) form, which we refer to as the long and middle isoform, has the same N and C terminal sequences we used the same primer pair for this, but on RT-PCR the only amplicon we got corresponds to the 259 amino acid form. For the 208 amino acids (624 nucleotides long) form we designed a separate forward primer and attempted to amplify this using RT-PCR but were unable to detect this isoform also. This data is now presented in Supplemental Figure 4B. Since the only isoform detected from fly head cDNA corresponded to the 259 amino acid form, we think this is the predominant isoform of Mrj expressing in Drosophila and this is what is in our DnaJ library and what we have used in all our experiments here. This is also the same isoform described in previous papers on Drosophila Mrj (Fayazi et al, 2006; Li et al, 2016b). For this 259 amino acid Mrj isoform, we see its expression in both the nucleus and cytoplasm (Supplemental Figure 4C). As the long 346 AA fragment was undetectable in the brain, it was not feasible to address the reviewer’s point of using the long and short forms of Mrj for IP with Orb2. However, we have performed IP of human CPEB2 (hCPEB2) with the long and short isoforms of human DnaJB6 and have detected interaction of hCPEB2 with both the long and short isoforms of DnaJB6 (Supplemental Figure 6E).

• I would be interested to know a bit more about the other 5 JDPs that are interactors with Orb2: are the human orthologs of those known? It is striking that these other 5 JDPs interact with Orb2 in Dm (in IPs) but have no impact on Sup35 prion behavior. Importantly, this does not imply they may not have impact on the prion-like behavior of other Dm substrates, including Dm-Orb2.

We have performed BlastP analysis of CG4164, CG9828, CG7130, DroJ2, and Tpr2 protein sequences against Human proteins. Based on this we have listed the highest-ranking candidate identified here for each of these genes.

Drosophila Gene

Human gene

Query cover

Percent identity

E value

CG4164

dnaJ homolog subfamily B member 11 isoform 1

98 %

62.96%

2e-150

CG9828

dnaJ homolog subfamily A member 2

92%

39.41%

3e-84

CG7130

dnaJ homolog subfamily B member 4 isoform d

56%

69.44%

2e-30

Tpr2

dnaJ homolog subfamily C member 7 isoform 1

93%

46.22%

6e-139

DroJ2

dnaJ homolog subfamily A member 4 isoform 2

98%

60.60%

2e-169

In the context of the chimeric Sup35-based assay where Orb2A’s Prion-like domain (PrD) is coupled with the C-terminal domain of Sup35, the only protein which could convert Orb2A PrD-Sup35 C from its non-prion state to prion state was Mrj. Within the limitations of this heterologous-system based assay, the other 5 DnaJ domain proteins as well as the Hsp70’s were unable to convert the Orb2A PrD from its non-prion to prion-like state. What these other 5 interacting JDP proteins are doing through their interaction with Orb2A and if they are even expressing in the Orb2 relevant neurons will need to be tested separately and will be the subject of our future studies.

• The data in panels H, I indeed suggest that Mrj1 alters the (size of) the oligomers. It would be important to know what is the actual physicochemical change that is occurring here. The observed species are insoluble in 0.1 % TX100 but soluble in 0.1% SDS, which suggest they could be gels, but not real amyloids such as formed by the polyQ proteins that require much higher SDS concentrations (~2%) to be solubilized. This is relevant as Mrj1 reduces polyQ amyloidogenesis whereas is here is shown to enhance Orb2A oligomerization/gelidification. In the same context, it is striking to see that without Mrj the amount of Orb2A seems drastically reduced and I wonder whether this might be due to the fact that in the absence of Mrj a part of Orb2A is not recovered/solubilized due to its conversion for a gel to a solid/amyloid state? In other words: Mrj1 may not promote the prion state, but prevents that state to become an irreversible, non-functional amyloid?

On the reviewer’s point to address what is the actual physicochemical change occurring here, we will need to develop methods to purify the Orb2 oligomers in significant quantities to examine and distinguish if they are of gel or real amyloid-like nature. Currently, within the limitations of our ongoing work, this has not been possible for us to do and we can attempt to address this in our future work. Cryo-EM derived structure of endogenous Orb2 oligomers purified from a fly head extract from 3 million fly heads, made in the TritonX-100 and NP-40 containing buffer, the same buffer as what we have used here for the first soluble fraction, showed these oligomers as amyloids (Hervas et al, 2020). If the oligomers extracted using 0.1% and 2% SDS are structurally and physicochemically different, within the limitations of our current work, had not been possible to address.

The other point raised by the reviewer is, if in the absence of Mrj (in the context of Figure 4 of our previously submitted manuscript), a part of Orb2 is not solubilized due to us using a lower 0.1% SDS for extraction. To address this, we attempted to see how much of leftover Orb2 is remaining in the pellet after extraction with 0.1 % SDS. Towards this, according to the reviewers’ suggestion, we used a higher 2% SDS containing buffer to resuspend the leftover pellet after 0.1% SDS extraction, and post solubilisation ran all the fractions in SDD-AGE. We did this experiment with both wild-type and Mrj knockout fly heads. Under these different extractions, we first observed while there is more Orb2 in the soluble fraction (Triton X-100 extracted) of Mrj knockout, this amount is reduced in both the 0.1% SDS solubilized and 2% SDS solubilized fractions. So, even though there is leftover Orb2 after 0.1% SDS extraction, which can be extracted using 2% SDS, this amount is reduced in Mrj knockout. The other observation here is the Orb2 extracted using 2% SDS shows a longer smear in comparison to the 0.1% SDS extracted form suggesting a possibility of more and higher-sized oligomers present in this fraction. Since we do not have the exact physicochemical characterization of these oligomers detected with 0.1% and 2% SDS-containing buffer, we are not differentiating them by using the terms gels and real amyloids, but refer to them as 0.1% SDS soluble Orb2 oligomers and 2% SDS soluble Orb2 oligomers. Overall, our observations here suggest in absence of Mrj, both of these kinds of Orb2 oligomers are decreased and so Mrj is most likely promoting the formation of Orb2 oligomers. It is possible that the 0.1% SDS soluble Orb2 oligomers gradually accumulate and undergo a further transition to the 2% SDS soluble Orb2 oligomers, so if in absence of Mrj, the formation of the 0.1% SDS soluble Orb2 oligomers is decreased, the next step of formation of 2% SDS soluble Orb2 oligomers also be decreased. This data is now presented in Figure 5H, I and J).

On the other possibility raised by the reviewer that Mrj can prevent the oligomeric state of Orb2 to become an irreversible non-functional amyloid, we think it is still possible for Mrj to do this but this could not be tested under the present conditions.

• It may be good for clarity to refer to the human Mrj as DNAJB6 according to the HUGO nomenclature. Also, the first evidence for its oligomerization was by Hageman et al 2010.

We have now changed mentions of human Mrj to DNAJB6. We apologize for missing the Hageman et al 2010 reference and have now cited this reference in the context of Mrj oligomerization.

• It is striking to see that Mrj co-Ips with Hsp70AA, Hsp70-4 but not Hsp70Cb. The fact that interactions were detected without using crosslinking is also striking given the reported transient nature of J-domain-Hsp70 interactions Together, this may even suggest that Mrj-1 is recognized as a Hsp70 substrate (for Hsp70AA, Hsp70-4 but not Hsp70Cb) rather than as a co-chaperone. In fact, a variant of Mrj-1 with a mutation in the HPD motif should be used to exclude this option.

In IP experiments we notice Mrj interacts with Hsp70Aa and Hsc70-4 but not with Hsc70-1 and Hsc70Cb. In our previously submitted manuscript, we realized we made a typo on the figure, where we referred to Hsp70Aa as Hsc70Aa. We have corrected this in the current revised manuscript. On the crosslinking point raised by the reviewer, we reviewed the published literature for studies of immunoprecipitation experiments which showed an interaction between DnaJB6 and Hsp70. We noted while one of the papers (Kakkar et al, 2016) report the use of a crosslinker in the experiment which showed an interaction between GFP-Hsp70 and V5-DnaJB6, in another two papers the interaction between endogenous Mrj and endogenous Hsp/c70 (Izawa et al, 2000) and Flag-Hsp70 and GFP-DnaJB6 (Bengoechea et al, 2020) could be detected without using any crosslinker. Our observations of detecting the interaction of Mrj with Hsp70Aa and Hsc70-4 in the absence of a crosslinker are thus similar to the observations reported by (Izawa et al, 2000; Bengoechea et al, 2020).

On the point of if Mrj is a substrate for Hsp70aa and Hsc70-4 and not a co-chaperone, we feel in the context of this manuscript, since we are focussing on the role of Mrj in the regulation of oligomerization of Orb2 and in memory, the experiment with HPD motif mutant is probably not necessary here. However, if the reviewers suggest this experiment to be essential, we can attempt this experiment by making this HPD motif mutant.

• The rest of these data reconfirm nicely that Mrj/DNAJB6 can suppress polyQ-Htt aggregation. Yet note that in this case the oligomers that enter the agarose gel are smaller, not bigger. This argues that Mrj is not an enhancer of oligomerization, but rather an inhibitor of the conversion of oligomers to a more amyloid like state.

Figure 2 and Supplemental Figure 4 discuss the effect of Mrj on Htt aggregation. We have used 2 different Htt constructs here. For Figure 2I, we used only Exon1 of Htt with the poly Q repeats. Here in SDD-AGE, for the control lane, we see the Htt oligomers as a smear for the control. For Mrj, we see only a small band at the bottom which can be interpreted most likely as either a monomer or as small oligomers since we do not observe any smear here. However, for the 588 amino acid fragment of HttQ138 in the SDD-AGE we do not see a difference in the length of the smear but in the intensity of the smear of the Htt oligomers (Supplemental Figure 4E). Based on this we are suggesting in presence of Mrj, there are lesser Htt oligomers. On the point of Mrj is not an enhancer of oligomerization, but rather an inhibitor of the conversion of oligomers to a more amyloid-like state, our experiments with the Mrj knockout show reduced Orb2 oligomers (both for 0.1% and 2% SDS soluble forms), suggesting Mrj plays a role in the conversion of Orb2 to the oligomeric state. If Mrj inhibits the conversion of oligomers to a more amyloid-like state, this is possible but we couldn’t test this hypothesis here. However, for Htt amyloid aggregates, previous works done by other labs with DnaJB6 as well as our experiments with Mrj suggest this as a likely possibility.

Figure 3: • The finding that knockout of DNAJB6 in mice is embryonic lethal is related to a problem with placental development and not embryonic development (Hunter et al, 1999; Watson et al, 2007, 2009, 2011) as well recognized by the authors. Therefore, the finding that deletion of Dm-Mrj has no developmental phenotype in Drosophila may not be that surprising.

We agree with the reviewer’s point that DNAJB6 mutant mice have a problem with placental development. However, one of the papers cited here (Watson et al, 2009) suggests DNAJB6 also plays a crucial role in the development of the embryo independent of the placenta development defect. The mammalian DNAJB6 mutant embryos generated using the tetraploid complementation method show severe neural defects including exencephaly, defect in neural tube closure, reduced neural tube size, and thinner neuroepithelium. Due to these features seen in the mice knockout, and the lack of such developmental defects in the Drosophila knockout, we interpreted our findings in Drosophila as significantly different from the mammals.

• It is a bit more surprising that Mrj knockout flies showed no aggregation phenotype or muscle phenotype, especially knowing that DNAJB6 mutations are linked to human diseases associated with aggregation (again well recognized by the authors). However, most of these diseases are late-onset and the phenotype may require stress to be revealed. So, while important to this MS in terms of not being a confounder for the memory test, I would like to ask the authors to add a note of caution that their data do not exclude that loss of Mrj activity still may cause a protein aggregation-related disease phenotype. Yet, I also do think that for the main message of this MS, it is not required to further test this experimentally.

We agree with the reviewer and have added this suggestion in the discussion that loss of Mrj may still result in a protein aggregation-related disease phenotype, probably under a sensitized condition of certain stresses which is not tested in this manuscript.

Figure 4:

• IPs were done with Orb2A as bite and clearly pulled down substantial amounts of GFP-tagged Mrj. For interactions with Orb2B, a V5-tagged Mrj was use and only a minor fraction was pulled down. Why were two different Mrj constructs used for Arb2A and Orb2b?

In the previously submitted manuscript, we have used HA-tagged Mrj (not V5) for checking the interaction with full-length Orb2B tagged with GFP. This was done with the goal of using the same Mrj-HA construct as that used in the initial Orb2A immunoprecipitation experiment. Since this has raised some concern as in the IPs to check for interaction between truncated Orb2A constructs (Orb2A325-GFP and Orb2AD162-GFP) and Mrj (Mrj-RFP), we used a different GFP and RFP tag combination. To address this, we have now added the same tag combinations for the IPs (Mrj-RFP with Orb2A-GFP and Orb2B-GFP). In these immunoprecipitation experiments where Mrj-RFP was pulled down using RFP Trap beads, we were able to detect positive interaction with GFP-tagged Orb2A and Orb2B. This data is now added in Figure 4F and 4I. We also have added the IP data for interaction between Mrj-HA and untagged Orb2B in Figure 4K, similar to the combination of initial experiment between Mrj-HA and untagged Orb2A.

• In addition, I think it would be important what one would see when pulling on Mrj1, especially under non-denaturing conditions and what is the status of the Orb2 that is than found to be associated with Mrj (monomeric, oligomeric and what size).

We have now performed IP from wild-type fly heads using anti Mrj antibody and ran the immunoprecipitate in SDS-PAGE and SDD-AGE followed by immunoblotting them with anti-Orb2 antibody. Our experiments suggest that immunoprecipitating endogenous Mrj brings down both the monomeric and oligomeric forms of Orb2. This data is now added in Figure 4L, M and N.

• This also relates to my remark at figure 1 and the subsequent fractionation experiments they show here in which there is a slight (not very convincing) increase in the ratio of TX100-soluble and insoluble (0.1% SDS soluble) material. My question would be if there is a remaining fraction of 0.1% insoluble (2% soluble) Orb2 and how Mrj affects that? As stated before, this is (only) mechanistically relevant to understanding why there is less oligomers of Orb2 in terms of Mrj either promoting it or by preventing it to transfer from a gel to a solid state. The link to the memory data remains intriguing, irrespective of what is going on (but also on those data I do have several comments: see below).

We have addressed this in response to the reviewer’s comments on Figure 1. We find in both wild type and Mrj knockout fly heads, there are Orb2 oligomers that can be detected using 0.1% SDS extraction and with further extraction with 2% SDS. The 2% SDS soluble Orb2 oligomers were previously insoluble during 0.1% SDS-based extraction. However, the amounts of both of these oligomers are reduced in Mrj knockout fly heads. Since we do not have the physicochemical characterization of both of these kinds of oligomers, we are not using the terms gel or solid state here but referring to these oligomers as 0.1% SDS soluble Orb2 oligomers and 2% SDS soluble Orb2 oligomers. We speculate that the 0.1% SDS soluble Orb2 oligomers over time transition to the 2% SDS soluble Orb2 oligomers. As in the absence of Mrj in the knockout flies, both the 0.1% SDS soluble and 2% SDS soluble Orb2 oligomers are decreased, this suggests Mrj is promoting the formation of Orb2 oligomers. On the reviewer’s point, if Mrj can prevent the transition from 0.1% SDS soluble to 2% SDS soluble Orb2 oligomers, we think it is possible for Mrj to both promote oligomerization of Orb2 as well as prevent it from forming bigger non-functional oligomers, but the second point is not tested here. The relevant data is now presented in Figure 5H, I and J.

• I also find the sentence that "Mrj is probably regulating the oligomerization of endogenous Orb2 in the brain" somewhat an overstatement. I would rather prefer to say that the data show that Mrj1 affects the oligomeric behavior/status of Orb2.

Based on the reviewer’s suggestion we have now changed the sentence to Mrj is probably regulating the oligomeric status of Orb2

Figure 5:

• To my knowledge, the Elav driver regulates expression in all neurons, but not in glial cells that comprise a significant part of the fly heads/brain. The complete absence of Mrj in the fly-heads when using this driver is therefore somewhat surprising. Or do we need to conclude from this that glial cells normally already lack Mrj expression?

On driving Mrj RNAi with Elav Gal4, we did not detect any Mrj in the western. We attempted to address the glial contribution towards Mrj’s expression we used a Glia-specific driver Repo Gal4 line to drive the control and Mrj RNAi line and performed a western blot using fly head lysate with anti-Mrj antibody. In this experiment, we did not observe any difference in Mrj levels between the two sets. As the Mrj antibody raised by us works in western blots but not in immunostainings, we could not do a colocalization analysis with a glial marker. However, we used the Mrj knockout Gal4 line to drive NLS-GFP and performed immunostainings of these flies with a glial marker anti-Repo antibody. Here we see two kinds of cells in the brain, one which have GFP but no Repo and the other where both are present together. This suggest that while Glial cells have Mrj but probably majority of Mrj in the brain comes from the neurons. We also found a reference where it was shown that Elav protein as well as Elav Gal4 at earlier stages of development expresses in neuroblasts and in all Glia (Berger et al, 2007). So, another possibility is when we are driving Mrj RNAi using Elav Gal4, this knocks down Mrj in both the neurons as well as in the glia. This coupled with the catalytic nature of RNAi probably creates an effective knockdown of Mrj as seen in the western blot. This data is now added in Supplementary Figure 5G and H.

• Why not use these lines also for the memory test for confirmation? I understand the concerns of putative confounding effects of a full knockdown (which were however not reported), but now data rely only on the mushroom body-specific knockdown for the 201Y Gal4 line, for which the knockdown efficiency is not provided. But even more so, here a temperature shift (22oC-30oC) was required to activate the expression of the siRNA. For the effects of this shift alone no controls were provided. The functional memory data are nice and consistent with the hypothesis, but can it be excluded that the temperature shift (rather than the Mrj) knockdown has caused the memory defects? I think it is crucial to include the proper controls or use a different knockdown approach that does not require temperature shifts or even use the knockout flies.

We have now performed the memory experiments with Mrj knockout flies. Our experiments show at 16 and 24-hour time points Mrj knockout flies have significantly reduced memory in comparison to the control wildtype. This data is now added in Figure 6B.

Figure 6:

The finding of a co-IP between Rpl18 and Mrj (one-directional only) by no means suffices to conclude that Mrj may interact with nascent Orb2 chains here (which would be the relevant finding here). The fact that Mrj is a self-oligomerising protein (also in vitro, so irrespective of ribosomal associations!), and hence is found in all fractions in a sucrose gradient, also is not a very strong case for its specific interaction with polysomes. The finding that there is just more self-oligomerizing Orb2A co-sedimenting with polysomes in sucrose gradients neither is evidence for a direct effect of Mrj enhancing association of Orb2A with the translating ribosomes even though it would fit the hypothesis. So all in all, I think the data in this figure and non-conclusive and the related conclusions should be deleted.

We have now performed the reverse co-IP between Rpl18-Flag and Mrj-HA using anti-HA antibody and could detect an interaction between the two. This data is now added in Supplementary Figure 6A.

To address if Mrj is a self-oligomerizing protein that can migrate to heavier polysome fractions due to its size, we have loaded recombinant Mrj on an identical sucrose gradient as we use for polysome analysis. Post ultra-centrifugation we fractionated the gradients and checked if Mrj can be detected in the fraction numbers where polysomes are present. In this experiment, we could not detect recombinant Mrj in the heavier polysome fractions (data presented in Supplementary Figure 6B). Overall, our observations of Mrj-Rpl18 IPs, the presence of cellularly expressed Mrj in polysome fractions, and the absence of recombinant Mrj from these fractions, suggest a likelihood of Mrj’s association with the translating ribosomes.

On the reviewer’s point of us concluding Mrj may interact with nascent Orb2 chains, we have not mentioned this possibility in the manuscript as we don’t have any evidence to suggest this. We have also added a sentence: This indicates that in presence of Mrj, the association of Orb2A with the translating ribosomes is enhanced, however, if this is a consequence of increased Orb2A oligomers due to Mrj or caused by interaction between polysome-associated Orb2A and Mrj will need to be tested in future.

Based on these above-mentioned points, we hope we can keep the data and conclusions of this section.

Overall, provided that proper controls/additional data can be provided for the key experiments of memory consolidation, I find this an intriguing study that would point towards a role of a molecular chaperone in controlling memory functions via regulating the oligomeric status of a prion-like protein and that is worthwhile publishing in a good journal.

However, in terms of mechanistical interpretations, several points have to be reconsidered (see remarks on figure 1,4); this pertains especially to what is discussed on page 13. In addition, I'd like the authors to put their data into the perspective of the findings that in differentiated neurons DNAJB6 levels actually decline, not incline (Thiruvalluvan et al, 2020), which would be counterintuitive if these proteins are playing a role as suggested here in memory consolidation.

We have addressed the comments on Figures 1 and 4 earlier. We have also added new memory experiment’s data with the Mrj knockout in Figure 6.

We have attempted to put the observations with Drosophila Mrj in perspective to observations in Thiruvalluvan et al, on human DnaJB6 in the discussions as follows:

Can our observation in Drosophila also be relevant for higher mammals? We tested this with human DnaJB6 and CPEB2. In mice CPEB2 knockout exhibited impaired hippocampus-dependent memory (Lu et al, 2017), so like Drosophila Orb2, its mammalian homolog CPEB2 is also a regulator of long-term memory. In immunoprecipitation assay we could detect an interaction between human CPEB2 and human DnaJB6, suggesting the feasibility for DnaJB6 to play a similar role to Drosophila Mrj in mammals. However, as the human DnaJB6 level was observed to undergo a reduction in transitioning from ES cells to neurons, (Thiruvalluvan et al, 2020) how this can be reconciled with its possible role in the regulation of memory. We speculate, such a reduction if is happening in the brain will occur in a highly regulatable manner to allow precise control over CPEB2 oligomerization only in specific neurons where it is needed and the reduced levels of DnaJB6 is probably sufficient to aid CPEB oligomerization Alternatively, there may be additional chaperones that may function in a stage-specific manner and be able to compensate for the decline in levels of DNAJB6.

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

Summary: The manuscript describes the role of the Hsp40 family protein Mrj in the prion-like oligomerization of Orb2. The authors demonstrate that Mrj promotes the oligomerization of Orb2, while a loss in Mrj diminishes the extent of Orb2 oligomerization. They observe that while Mrj is not an essential gene, a loss in Mrj causes deficiencies in the consolidation of long-term memory. Further, they demonstrate that Mrj associates with polysomes and increases the association of Orb2 with polysomes.

Major comments: None

Minor comments:

1. In the section describing the chaperone properties of Mrj in clearing Htt aggregates (Fig 2), the legend describes that "Mrj-HA constructs are more efficient in decreasing Htt aggregation compared to Mrj-RFP". It would be helpful to add Mrj-RFP to the quantification in Fig 2G to know exactly the difference in efficiency. Is there an explanation for why the 2 constructs behave differently?

We have added the quantitation of Htt aggregates in presence of Mrj-RFP in the revised version (Data presented in Figure 2G). While the efficiency of Mrj-RFP to decrease Htt aggregates is significantly less in comparison to Mrj-HA, it is still significantly better in comparison to the control CG7133-HA construct. It is possible, due to the tagging of Mrj with a larger tag (RFP), this reduces its ability to decrease the Htt aggregates in comparison to the construct where Mrj is tagged with a much smaller HA tag.

Figs A, B, C, G need to have quantification of the percentage of colocalization with details about the number of cells quantified for each experiment.

We have now added the intensity profile images and colocalization quantitation (pearson’s coefficient) in the Supplemental Figure 5A and B. This quantitation is done from multiple ROI’s taken from at 4-6 cells.

In Fig 6 B, C, F, G it would be helpful to label the 40S, 60S and 80S peaks in the A 254 trace.

We have now labeled the 80S, and polysome peaks in the Figure 7B, C, F and G. We could not separate the 40S and 60S peaks in the A254 trace.

It's interesting that Mrj has opposing functions with regard to aggregation when comparing huntingtin with Orb2. From the literature presented in the discussion, it appears as though chaperones including Mrj have an anti-aggregation role for prions. It would be helpful to have more discussion around why, in the case of Orb2, this is different. The discussion states that "The only Hsp40 chaperone which was found similar to Mrj in increasing Orb2's oligomerization is the yeast Jjj2 protein" - this point needs elaboration, as well as a reference.

In the discussions section we have now added the following speculations on this:

One question here is why Mrj behaves differently with Orb2 in comparison to other amyloids. Orb2 differs from other pathogenic amyloids in its extremely transient existence in the toxic intermediate form (Hervás et al, 2016). For the pathogenic amyloids, since they exist in the toxic intermediate form for longer, Mrj probably gets more time to act and prevent or delay them from forming larger aggregates. For Orb2, Mrj may help to quickly transition it from the toxic intermediate state, thereby helping this state to be transient instead of longer. An alternate possibility is post-transition from the toxic intermediate state, Mrj stabilizes these Orb2 oligomers and prevents them from forming larger aggregates. This can be through Mrj interacting with Orb2 oligomers and blocking its surface thereby preventing more Orb2 from assembling over it. Another difference between the Orb2 oligomeric amyloids and the pathogenic amyloids is in the nature of their amyloid core. For the pathogenic amyloids, this core is hydrophobic devoid of any water molecules, however for Orb2, the core is hydrophilic. This raises another possibility that if the Orb2 oligomers go beyond a certain critical size, Mrj can destabilize these larger Orb2 aggregates by targeting its hydrophilic core.

On the Jjj2 point raised by the reviewer, we have added the (Li et al, 2016a) reference now and elaborated as:

The only Hsp40 chaperone which was found similar to Mrj in increasing Orb2’s oligomerization is the yeast Jjj2 protein. In Jjj2 knockout yeast strain, Orb2A mainly exists in the non-prion state, whereas on Jjj2 overexpression the non-prion state could be converted to a prion-like state. In S2 cells coexpression of Jjj2 with Orb2A lead to an increase in Orb2 oligomerization (Li et al, 2016a). However, Jjj2 differs from Mrj, as when it is expressed in S2 cells, we do not detect it to be present in the polysome fractions.

The Jjj2 polysome data is now presented in Supplementary Figure 6C.

Reviewer #2 (Significance (Required)):

General assessment:

Overall, the work is clearly described and the manuscript is very well-written. The motivation behind the study and its importance are well-explained. I only have minor comments and suggestions to improve the clarity of the work. The study newly describes the interaction between the chaperone Mrj and the translation regulator Orb2. The experiments that the screen for proteins that interact with Orb2 and promote its oligomerization are very thorough. The experiments that delve into the role of Mrj in protein synthesis are a good start, and need to be explored further, but that is beyond the scope of this study.

Advance: The study describes a new interaction between the chaperone Mrj and the translation regulator Orb2. The study is helpful in expanding our knowledge of prion regulators as well factors that affect memory acquisition and consolidation.

Audience: This paper will be of most interest to basic researchers.

My expertise is in Drosophila genetics and neuronal injury.

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

The manuscript submitted by Desai et al. identifies a chaperone of the Hsp40 family (Mrj) that binds Orb2 and modulates its oligomerization, which is critical for Orb2 function in learning and memory in Drosophila. Orb2 are proteins with prion-like properties whose oligomerization is critical for their function in the storage of memories. The main contribution of the article is the screen of Hsp40 and Hsp70-family proteins that bind Orb2. The authors show IP results for all the candidates tested, including those that bind Fig. 1) and those that don't (Supp Fig 3). There is also a figure devoted to examining the interaction of Mrj with polyglutamine models (Htt). They also generate a KO mutant that is viable and shows no gross defects or protein aggregation. Lastly, they show that the silencing of Mrj in the mushroom body gamma neurons results in weaker memories in a courtship paradigm. Although the data is consistent and generally supportive of the hypothesis, key details are missing in several areas, including controls. Additionally, the interpretation of some results leaves room for debate. Overall, this is an ambitious article that needs additional work before publication.

Specific comments:

1. General concern over the interpretation of IP experiments and colocalization. These experiments don't necessarily reflect direct interactions. They are consistent with direct interaction but not the only explanation for a positive IP or colocalization.

This paper is centred on the interaction between Orb2A and Mrj, which we have detected using immunoprecipitation. The reviewer’s concern here is, this experiment is not able to distinguish if this can be a direct protein-protein interaction or if the two proteins are part of a complex.

To address this concern we have used purified recombinant protein-based pulldowns. Our experiments with purified protein pulldowns (GST tagged Mrj from E.coli with Orb2A from E.coli or Orb2A-GFP from Sf9 cells) suggest Orb2A and Mrj can directly interact amongst themselves. This data is now presented in Figure 1J and K.

The Huntingtin section has a few concerns. The IF doesn't show all controls and the quantification is not well done in terms of what is relevant. A major problem is the interpretation of Fig 2F. The idea is that Mrj prevents the aggregation of Htt, which is the opposite of what is observed with Orb2. The panel actually shows a large Htt aggregate instead of multiple small aggregates. This has been reported before in Drosophila and other systems with different polyQ models. Mrj and other Hsp40 and Hsp70 proteins modify Htt aggregation, but in an unexpected way. This affects the model shown in Fig. 6H. Lastly, Fig 2H and 2I show very different level of total Htt.

In Figure 2F of the previously submitted manuscript, we have shown representative images of HttQ103-GFP cells coexpressing with a control DnaJ protein CG7133-HA and Mrj-HA. In Figure 2G we quantitated the number of cells showing aggregates within the population of doubly transfected cells. On the reviewer’s point of figure 2F showing large Htt aggregates instead of multiple small aggregates, we do not see a large Htt aggregate in presence of Mrj in this figure, the pattern looks diffused here and very different from the control CG7133 where the aggregates are seen. We have performed the same experiment with a different Htt construct (588 amino acids long fragment) tagged with RFP, and here also we notice in presence of Mrj, the aggregates are decreased and the expression pattern looks diffused (Supplementary Figure 4E, 4F).

If the comment on large Htt aggregates in presence of Mrj is concerning figure 2E, here we show Mrj-RFP to colocalize with the Htt aggregates. Here, even though Mrj-RFP colocalizes with Htt aggregates, it rescues the Htt aggregation phenotype as in comparison to the control CG7133, the number of cells with Htt aggregates is still significantly less here. We have added this quantitation of rescue by Mrj-RFP in the revised manuscript now. The observation of colocalization of Mrj-RFP with Htt aggregates is similar to previous reports of chaperones rescuing Htt aggregation and yet showing colocalization with the aggregates. Both Hdj-2 and Hsc70 suppress Htt aggregation and yet were observed to colocalize with Htt aggregates in the cell line model as well as in nuclear inclusions in the brain (Jana et al, 2000). In a nematode model of Htt aggregation, DNJ-13 (DnaJB-1), HSP-1 (Hsc70), and HSP-11 (Apg-2) were shown to colocalize with Htt aggregates and yet decrease the Htt aggregation (Scior et al, 2018). Hsp70 was also found to colocalize with Htt aggregates in Hela cells (Kim et al, 2002).

Regarding Figures 2H and 2I, while figure 2H is of an SDS-PAGE to show no difference in the levels of monomeric HttQ103 (marked with *) in presence of Mrj and the control CG7133, figure 2I is for the same samples ran in an SDD-AGE where reduced amount of Htt oligomers as seen with the absence of a smear in presence of Mrj. The apparent difference in Htt levels between 2H and 2I is due to the detection of Htt aggregates/oligomers in the SDD-AGE which are unable to enter the SDS-PAGE and hence undetected. In Supplementary Figure 4E, similar experiments were done with the longer Htt588 fragment and here we notice in the SDD-AGE reduced intensity of the smear made up of Htt oligomers, again suggesting a reduction in Htt aggregates. Thus our results are not in contradiction to previous studies where Mrj was found to rescue Htt aggregate-associated toxicity.

Endogenous expression of Mrj using Gal4 line: where else is it expressed in the brain / head and in muscle. Fig 3G shows no muscle abnormalities but no evidence is shown for muscle expression. It is nice that Fig 3E and F show no abnormal aggregates in the Mrj mutant, but this would be maybe more interesting if flies were subjected to some form of stress.

We have now added images of the brain and muscles to show the expression pattern of Mrj. Using Mrj Gal4 line and UAS- CD8GFP, we noticed enriched expression in the optic lobes, mushroom body, and olfactory lobes. We also noticed GFP expression in the larval muscles and neuromuscular junction synaptic boutons. This data is now presented in Supplementary Figure 5C, D, E and F.

On the reviewer’s point of subjecting the Mrj KO flies to some form of stress, we have not performed this. We have added in the discussions a note of caution, that loss of Mrj may still result in a protein aggregation-related disease phenotype, probably under a sensitized condition of certain stresses which is not tested in this manuscript.

Fig. 5B shows no Mrj detectable from head homogenates in flies silencing Mrj in neurons with Elav-Gal4. It would be nice if they could show that ONLY neurons express Mrj in the head. Also noted, Elav-Gal4 is a weak driver, so it is surprising that it can generate such robust loss of Mrj protein

We have used an X chromosome Elav Gal4 driver to drive the UAS-Mrj RNAi line and here we could not detect Mrj in the western. To address the reviewer’s point on the glial contribution towards expression of Mrj, we used a Glial driver Repo Gal4 to drive Mrj RNAi. In this experiment, we did not detect any difference in Mrj levels between the control and the Mrj RNAi line (presented now in Supplementary Figure 5G). We also used the Mrj knockout Gal4 line to drive NLS-GFP and immunostained these using a glial marker anti-Repo antibody. Here, we were able to detect cells colabelled by GFP as well as Repo, suggesting Mrj is likely to be present in the glial cells (presented now in Supplementary Figure 5H). We also looked in the literature and found a reference where it was shown that Elav protein as well as Elav Gal4 at earlier stages of development expresses in neuroblasts and in all Glia (Berger et al, 2007). So, another possibility is when we are driving Mrj RNAi using Elav Gal4, this knocks down Mrj in both the neurons as well as in the glia.

Fig 4-Colocalization of Orb2 with Mrj lacks controls. The quantification could describe other phenomena because the colocalization is robust but the numbers shown describe something else.

We have now added the intensity profile and colocalization quantitation (pearson’s coefficient) in Supplemental Figure 5A and B. This quantitation is done from multiple ROI’s taken from 4-6 cells. Also, to suggest the interaction of Orb2 isoforms with Mrj, we are not depending on colocalization alone and have used immunoprecipitation experiments to support our observations.

Fly behavior. The results shown for Mrj RNAi alleles is fine but it would be more robust if this was validated with the KO line AND rescued with Mrj overexpression.

We have now performed memory assays with the Mrj knockout. Our experiments showed Mrj knockouts to show significantly decreased memory in comparison to wild-type flies at 16 and 24-hour time points (presented in Figure 6B). We have not been able to make an Mrj Knockout-UAS Mrj recombinant fly, most likely due to the closeness of the two with respect to their genomic location in second chromosome.

Minor comments:

Please, revise minor errors, there are several examples of words together without a space.

We have identified the words without space and have corrected them now.

Intro: describe the use of functional prions. Starting the paragraph with this sentence and then explaining what prion diseases are is a little confusing. Also "prion proteins" can be confusing because the term refers to PrP, the protein found in prions.

We have now altered the introduction and have described functional prions.

Results, second subtitle in page 5. This sentence is quite confusing using prion-like twice

We have now changed the heading to “Drosophila Mrj converts Orb2A from non-prion to a prion-like state.”

Page 6: "conversion from non-prion to prion-like form...". This can be presented differently. Prion-like properties are intrinsic, proteins don't change from non-prion to prion-like. They may be oligomeric or monomeric or highly aggregated but the prion-like property doesn't change

We agree with the reviewer's point of Prion-like properties are intrinsic, but the protein might or might not exist in the prion-like state or confirmation. When we are using the term conversion from non-prion to prion-like form we mean to suggest a conformational conversion leading to the eventual formation of the oligomeric species. We also noted the terminology of non-prion to prion-like state change is used in several papers (Satpute-Krishnan & Serio, 2005; Sw & Yo, 2012; Uptain et al, 2001).

Scale bars and text are too small in several figures

We have now mentioned in the figure legends the size of the scale bars. For several images we have made the scale bars also larger.

Not sure why Fig 4C is supplemental, seems like an important piece of data.

We have kept this data in the supplemental data as we performed this experiment with recombinant protein which is tagged with 6X His and we are not sure if this high degree of oligomerization/aggregation of recombinant Mrj and further precipitation over time, happens inside the cells/ brain.

Intro to Mrj KO in page 7 is too long. Most of it belongs in the discussion

We have now moved the portions on mammalian DNAJB6 which were earlier in the results section to the discussions section.

Change red panels in IF to other color to make it easier for colorblind readers.

We have now changed the red panels to magenta. We apologize for our figures not being colorblind friendly earlier.

The discussion is a little diffuse by trying to compare Orb2 with mammalian prions and amyloids and yeast prions.

We looked into the functional prion data and couldn’t find much on chaperone mediated regulation of these. Also, we felt comparing with the amyloids and yeast prions brings out the contrast with respect to the Mrj mediated regulatory differences between the two.

Reviewer #3 (Significance (Required)):

This is a paper with a broad scope and approaches. The paper describes the role of Mrj in the oligomerization of Orb2 by protein biochemistry techniques and determine the role of loss of Mrj in the mushroom bodies in fly behavior.

The audience for this content is basic research and specialized. The role of Mrj in Orb2 aggregation and function sheds new light on the mechanisms regulating the function of this protein involved in a novel mechanism of learning and memory.

References:

Bengoechea R, Findlay AR, Bhadra AK, Shao H, Stein KC, Pittman SK, Daw JA, Gestwicki JE, True HL & Weihl CC (2020) Inhibition of DNAJ-HSP70 interaction improves strength in muscular dystrophy. J Clin Invest 130: 4470–4485

Berger C, Renner S, Lüer K & Technau GM (2007) The commonly used marker ELAV is transiently expressed in neuroblasts and glial cells in the Drosophila embryonic CNS. Dev Dyn 236: 3562–3568

Fayazi Z, Ghosh S, Marion S, Bao X, Shero M & Kazemi-Esfarjani P (2006) A Drosophila ortholog of the human MRJ modulates polyglutamine toxicity and aggregation. Neurobiol Dis 24: 226–244

Heinrich SU & Lindquist S (2011) Protein-only mechanism induces self-perpetuating changes in the activity of neuronal Aplysia cytoplasmic polyadenylation element binding protein (CPEB). Proc Natl Acad Sci U S A 108: 2999–3004

Hervás R, Li L, Majumdar A, Fernández-Ramírez MDC, Unruh JR, Slaughter BD, Galera-Prat A, Santana E, Suzuki M, Nagai Y, et al (2016) Molecular Basis of Orb2 Amyloidogenesis and Blockade of Memory Consolidation. PLoS Biol 14: e1002361

Hervas R, Rau MJ, Park Y, Zhang W, Murzin AG, Fitzpatrick JAJ, Scheres SHW & Si K (2020) Cryo-EM structure of a neuronal functional amyloid implicated in memory persistence in Drosophila. Science 367: 1230–1234

Izawa I, Nishizawa M, Ohtakara K, Ohtsuka K, Inada H & Inagaki M (2000) Identification of Mrj, a DnaJ/Hsp40 family protein, as a keratin 8/18 filament regulatory protein. J Biol Chem 275: 34521–34527

Jana NR, Tanaka M, Wang G h & Nukina N (2000) Polyglutamine length-dependent interaction of Hsp40 and Hsp70 family chaperones with truncated N-terminal huntingtin: their role in suppression of aggregation and cellular toxicity. Hum Mol Genet 9: 2009–2018

Kakkar V, Månsson C, de Mattos EP, Bergink S, van der Zwaag M, van Waarde MAWH, Kloosterhuis NJ, Melki R, van Cruchten RTP, Al-Karadaghi S, et al (2016) The S/T-Rich Motif in the DNAJB6 Chaperone Delays Polyglutamine Aggregation and the Onset of Disease in a Mouse Model. Mol Cell 62: 272–283

Kim S, Nollen EAA, Kitagawa K, Bindokas VP & Morimoto RI (2002) Polyglutamine protein aggregates are dynamic. Nat Cell Biol 4: 826–831

Li L, Sanchez CP, Slaughter BD, Zhao Y, Khan MR, Unruh JR, Rubinstein B & Si K (2016a) A Putative Biochemical Engram of Long-Term Memory. Curr Biol 26: 3143–3156

Li S, Zhang P, Freibaum BD, Kim NC, Kolaitis R-M, Molliex A, Kanagaraj AP, Yabe I, Tanino M, Tanaka S, et al (2016b) Genetic interaction of hnRNPA2B1 and DNAJB6 in a Drosophila model of multisystem proteinopathy. Hum Mol Genet 25: 936–950

Liebman SW & Chernoff YO (2012) Prions in yeast. Genetics 191: 1041–1072

Lu W-H, Yeh N-H & Huang Y-S (2017) CPEB2 Activates GRASP1 mRNA Translation and Promotes AMPA Receptor Surface Expression, Long-Term Potentiation, and Memory. Cell Rep 21: 1783–1794

Prusiner SB (2001) Neurodegenerative Diseases and Prions. New England Journal of Medicine 344: 1516–1526

Satpute-Krishnan P & Serio TR (2005) Prion protein remodelling confers an immediate phenotypic switch. Nature 437: 262–265

Scior A, Buntru A, Arnsburg K, Ast A, Iburg M, Juenemann K, Pigazzini ML, Mlody B, Puchkov D, Priller J, et al (2018) Complete suppression of Htt fibrilization and disaggregation of Htt fibrils by a trimeric chaperone complex. EMBO J 37: 282–299

Si K (2015) Prions: what are they good for? Annu Rev Cell Dev Biol 31: 149–169

Si K, Choi Y-B, White-Grindley E, Majumdar A & Kandel ER (2010) Aplysia CPEB can form prion-like multimers in sensory neurons that contribute to long-term facilitation. Cell 140: 421–435

Si K, Lindquist S & Kandel ER (2003) A neuronal isoform of the aplysia CPEB has prion-like properties. Cell 115: 879–891

Sw L & Yo C (2012) Prions in yeast. Genetics 191

Thiruvalluvan A, de Mattos EP, Brunsting JF, Bakels R, Serlidaki D, Barazzuol L, Conforti P, Fatima A, Koyuncu S, Cattaneo E, et al (2020) DNAJB6, a Key Factor in Neuronal Sensitivity to Amyloidogenesis. Mol Cell 78: 346-358.e9

Uptain SM & Lindquist S (2002) Prions as protein-based genetic elements. Annu Rev Microbiol 56: 703–741

Uptain SM, Sawicki GJ, Caughey B & Lindquist S (2001) Strains of [PSI(+)] are distinguished by their efficiencies of prion-mediated conformational conversion. EMBO J 20: 6236–6245

Watson ED, Mattar P, Schuurmans C & Cross JC (2009) Neural stem cell self-renewal requires the Mrj co-chaperone. Dev Dyn 238: 2564–2574

Wickner RB (2016) Yeast and Fungal Prions. Cold Spring Harb Perspect Biol 8: a023531

Wickner RB, Edskes HK, Maddelein ML, Taylor KL & Moriyama H (1999) Prions of yeast and fungi. Proteins as genetic material. J Biol Chem 274: 555–558

Wickner RB, Masison DC, Edskes HK & Maddelein ML (1996) Prions of yeast, [PSI] and [URE3], as models for neurodegenerative diseases. Cold Spring Harb Symp Quant Biol 61: 541–550

This is something that has happened to me before. When I go to class and the technology I need to complete my project isn't working I feel like I wasn't able to fully complete what I needed to. This can happen to teachers too when they're trying to teach a lesson or class. It's not a good feeling to have when you wanted to use your technology, but couldn't because it wasn't working. We live in a world that uses a lot of technology. We run the risk of having it break or not work when we need it the most. That feeling of having your technology not work is something we'll have to deal with until possibly someone finds a way to change that. The feeling of not having my technology work is something I don't want to have to randomly experience without a real warning.

The film spawned a cottage industry of sequels, including Bride of Frankenstein (1935) and Son of Frankenstein (1939), as well as multiple remakes. Scenes that were originally cut or censored from the film, such as the prologue and the drowning scene with the young girl, have since been restored. Makeup artist Jack Pierce, who was responsible for the monster’s distinctive look, went on to create the costumes for several other famous Universal Pictures creatures, including the title characters in The Mummy (1932) and The Wolf Man (1941). https://www.britannica.com/topic/Frankenstein-film-by-Whale It is interesting to note how in the novel the creature compares itself to Satan but not in the way as being alike but more of envy as even the great evil that is Satan had allies. Be it other devils but at the very least he had someone on his side to admire and encourage him yet still the creature had nothing just pure loneliness. It's also interesting to see that in the films based on the book the creature would have a wive and even a son be created and then on other monsters would come into exist such as the mummy and the wolf man.

This is fascinating. Not disputing what they're saying here, but react's whole suspense system kinda works in this way. When it suspends it's actually just throwing a promise that, when resolved, lets react's scheduler know it's okay to continue. Internally it's probably doing some crazy rehydration of the internal state of the component but that's effectively how I understand it to work.

It's cool that unison has a this mechanism first classed.

This is really fascinating! In some ways this makes me think of React's context which enables passing data deeply down a component tree.

It's notable while this is true for internal system concerns it may not necessarily be true for external system concerns. It does avoid the whole left-pad situation though.

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

Learn more at Review Commons

Reply to the reviewers

We thank the reviewers for their time and for the helpful comments. We felt the reviews overall were fair and quite positive. All three reviewers felt the manuscript could be of broad, general interest, especially given the relevance of the protein (Pbp1/ataxin-2) to neurodegenerative conditions and stress granule biology. Reviewer 3 seems to have some doubt there could be specificity for the types of transcripts regulated by Pbp1, given prior studies of mammalian ataxin-2 which implicated 16,000+ mRNAs that could bind via PAR-CLIP experiments! However, our study shows the power of utilizing a simpler model organism and thinking about the metabolic state of cells for elucidating the function of this interesting protein. Although our demonstration of the specificity of Pbp1 for regulating Puf3-target mRNAs involved in mitochondrial biogenesis and mitochondrial function may be surprising to this reviewer, we have the utmost confidence in our data and feel the study represents a highly significant finding that will be of interest to many researchers.

1. Point-by-point description of the revisions

This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

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

*In this manuscript, van de Poll et al. aim to further establish the function of poly(A) binding protein-binding protein 1 (Pbp1) and its relationship to the RNA-binding protein Puf3 in regulating the expression of mitochondrial proteins. This work builds upon a solid body of previous studies from this group regarding the function of Puf3 and the role of Pbp1 in regulating TORC1 signaling. Here, the authors show that Pbp1 has a physical and functional interaction with Puf3 and that ablation or disruption of Pbp1 eliminates this interaction and reduces the expression of some Puf3 target proteins. This work provides reliable data supporting a role for Pbp1 in the regulation of mitochondrial protein expression and that there is a clear functional interaction between Puf3 and Pbp1. Nonetheless, there are several issues that should be addressed before the manuscript is suitable for publication. *

*1.) The model put forward suggests that Pbp1 works to recruit Puf3 to the vicinity of mitochondria, where Puf3 can then promote the expression of its target mRNAs. In Figure 3A, however, deletion of Pbp1 has a stronger affect on the expression of COX2 than deletion of Puf3 alone, which would not be expected if the role of Pbp1 is to modulate Puf3 function. Similarly, the expression of COX2 is higher in the double deletion versus the Pbp1 single deletion. The authors should attempt to clarify this experimentally, or at least make mention of alternative mechanisms for Pbp1 which may be causing this. *

We have mentioned alternative mechanisms in the text. We suggest that Puf3-target mRNAs also can be translated through Tom20, in the absence of Puf3 (p. 8, ref 18). In single deletion strains lacking Pbp1 (but with Puf3 present), Puf3 may direct some of its target mRNAs to decay pathways, leading to lower Cox2 expression compared to double deletion strains that also lack Puf3. We performed qPCR analysis of several Puf3-target and other mRNAs in pbp1∆puf3∆ double deletion strains and some transcripts (e.g., COX17) would support this possibility (Fig S4).

* 2.) The authors mention that the increased pull-down of Puf3 with Pbp1 in respiratory conditions suggests that the Pbp1-Puf3 interaction is responsive to the cellular metabolic state (Figure 3B). However, the increase in Puf3 expression makes it difficult to compare the interactions between the two conditions. *

Yes this is correct, we stated this in the legend of Fig 4B as: “Increased amounts of Puf3 are associated with Pbp1 in respiratory conditions.” We clarified in the text that Puf3 expression increases in respiratory conditions and this likely explains the increased pull-down of Puf3 with Pbp1 (p. 10).

* 3.) The authors only look at a very small subset of Puf3 target mRNAs using qPCR when it would be much more informative and overall convincing to examine a larger amount using RNA-seq experiments. *

We conducted an RNA-seq experiment to compare transcriptomes of WT vs pbp1∆ cells in fermentative (YPD) vs respiratory (YPL) conditions and observed mRNAs with functions associated with mito-translation and mito-respiration (i.e., Puf3-targets) to be most differentially expressed – and majority of these are lower in abundance in pbp1∆ cells (new Fig 2).

* 4.) The authors consistently mention that Pbp1 function is helping to stabilize Puf3 target mRNAs. However, if the authors wish to prove this particular mode-of-action, more direct evidence should be provided, such as a pulse-chase experiment. Otherwise, other models allowing for increased mRNA abundance should be noted. *

Using thiolutin which is the standard for such expts, we measured mRNA half-lives of several Puf3-target and other mRNAs (COX17, COX10, POR1, ACT1) by qPCR in WT vs pbp1∆ cells. However, these data turned out to be difficult to interpret, as several “control” mRNAs exhibited different decay profiles in pbp1∆ vs WT cells, and their behavior was different in respiratory conditions compared to what was reported in common glucose media. Nonetheless, the data are included as Fig S2, and the important observation is that each of the Puf3-target mRNAs tested behaves similarly following thiolutin treatment, compared to non Puf3-target mRNAs. Given that Puf3-target mRNAs were more stable in pbp1∆ cells (compared to PGK1) following thiolutin treatment, we have deleted the term “stabilize” throughout the text. The exact fate of these mRNAs in normal vs pbp1∆ mutant cells will require more sophisticated investigation in future studies.

* 5.) Given the proposed model, one would expect Puf3 to have reduced mRNA binding upon deletion of Pbp1. It would be interesting to examine Puf3 mRNA binding, perhaps through cross-linking immunoprecipitation (CLIP), to see if this indeed is the case. This would provide further direct evidence that Pbp1 is functioning through Puf3 and facilitating its function. Similarly, the authors mention that Pbp1 contains putative RNA binding domains, however, they make no mention if these domains may contribute to its function in mitochondrial protein expression. *

We performed an RNA-IP experiment to test whether Puf3 has reduced binding to its target mRNAs in the absence of Pbp1. In new Fig 7, Puf3 is still able to bind its mRNA targets in the absence of Pbp1. However, the association of Pbp1 with these mRNAs is reduced in puf3∆ knockouts. Such results are perhaps expected as Puf3 has been shown to bind in a sequence-specific manner to a ~8 nt motif in the 3’UTR of its target mRNAs. However, it is unclear whether the Lsm / LsmAD domains of Pbp1 actually bind RNAs directly (hence our use of the term “putative”) - they may be involved in protein-protein interactions. Moreover, Fig 5 shows that deletion of both domains has no apparent effect on mitochondrial protein expression. We prefer to address the role of the Lsm and LsmAD domains of Pbp1 in a future study.

* Reviewer #1 (Significance (Required)):

Overall, this manuscript provides a modest advance, but one that could prove to have important implications for the field-especially if the Pbp1 findings prove relevant to its human ortholog, ataxin-2. The advance is limited by the robustness of the specific molecular model proposed and the extent to which the Pbp1-Puf3 relationship is examined on the gene-expression level.

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

In this manuscript, the authors found that the pbp1∆ mutant grew poorly on nonfermentable carbon source medium (YP Lactate medium) and that the pbp1∆ mutant had decreased amount of Cox2 protein, a cytochrome c oxidase. The pbp1∆ mutant also had decreased amounts of COX17, COX10, and MRP51 mRNAs. Since these mRNAs are target mRNAs of the RNA-binding protein Puf3, the authors next investigated the relationship between Pbp1 and Puf3. The analysis of the GFP reporter gene containing Puf3-binding sites in the 3' UTR showed that the levels of GFP reporter mRNA and protein were decreased in the pbp1∆ mutant strain. This reduction was dependent on the Puf3-binding site in the 3' UTR. Next, the authors examined the genetic interaction between the pbp1∆ and puf3∆ mutants, and found that the levels of Cox2 protein were reduced in the two mutants. Finally, the authors showed the interaction between Pbp1 and Puf3 by co-immunoprecipitation and determined the regions of Pbp1 and Puf3 required for the interaction. They also showed that these regions in Pbp1 and Puf3 proteins are also important for the regulation of Cox2 protein levels. The story is very clear and the data is reliable. However, the data from Western blotting should be shown quantitatively to make the results more reliable. Also, although the story is based on the reduction of Cox2 protein level, it would be better to discuss whether other proteins or mRNAs should be considered as well.

Major comments:

Figure 2C. Protein levels of GFP should be quantified and the data should be shown. *

These data have been quantified (now Fig 3C).

* Figure 3. Not only the Cox2 protein level but also mRNA levels of COX2, COX17, COX10, MRP51, etc in pbp1∆ mutant, puf3∆ mutant and pbp1∆ puf3∆ double mutant should be shown. Then the point of action of Pbp1 and Puf3 would become clearer. *

The mRNA levels have been determined by qPCR and are now in Fig S4.

Figure 4. * For the domain analysis of Pbp1 protein, showing differences in cell proliferation as in Figure 1B would indicate the physiological importance of the domain. *

Growth curves of the various Pbp1 domain deletions have been performed and shown in Fig 5D. They do support the physiological importance of the domain(s).

* Figure 4B. Quantification of the amount of co-immunoprecipitated proteins would indicate the strength of binding. *

These data have been quantified (now Fig 5B).

* Figure 4C. Protein levels should be quantified and the data presented. *

These data have been quantified (now Fig 5C).

* Line 153-8 The description of Line 153-8 is not appropriate for this position because it breaks up the flow of the story before and after. *

These text have been moved as requested.

* Minor comments:

Line 153 Isn't the following the first reference cited for Pbp1 is a negative regulator of TORC1? Transient sequestration of TORC1 into stress granules during heat stress Terunao Takahara 1, Tatsuya Maeda Mol Cell. 2012 Jul 27;47(2):242-52. doi: 10.1016/j.molcel.2012.05.019. Epub 2012 Jun 21.

Ref19. Ref 19 also shows that the pbp1∆ mutant strains grow poorly on the medium containing glycerol and lactate as carbon sources.

Overall, the gene is not italicized. *

These requested edits to the references and text have been made in the revised version of the manuscript.

* Reviewer #2 (Significance (Required)):

This manuscript analyzes the relationship between Pbp1 and Puf3 in yeast. Since these proteins are evolutionarily conserved from yeast to humans and are also associated with disease in humans, this reviewer believes this manuscript will be of interest to a wide audience. *

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

In this study, van de Poll et al. describe the involvement of a cytosolic RNA-binding protein (Pbp1) in the transcriptional regulation of mitochondrial biogenesis during the shift from fermentative to respiratory growth in budding yeast. Using the advantage of yeast genetics and molecular biology, they show that a number of mitochondrial transcripts and proteins are downregulated in pbp1Δ cells both under normal growth conditions and during respiratory shift. Since Puf3, another RNA-binding protein, is known to regulate the fate of these transcripts, they further investigate the interaction between Pbp1 and Puf3. Through a series of biochemical assays, they characterize the interaction between Pbp1 and Puf3 taking place through their low-complexity domains, which is suggested by authors to stabilize and promote the translation of Puf3-target transcripts.

As stated in the manuscript, Pbp1 is an evolutionarily conserved protein, encoded by ATXN2 gene in humans. Due to its involvement in multiple neurological disorders, it has been widely studied in mouse models and patient samples. The transcriptome profiles of Atxn2-KO mouse liver and cerebellum have been published (albeit having used a relatively older microarray technology for today's standards), revealing a prominent dysregulation of global translational machinery, and the ER-protein secretion pathway (Fittschen et al., 2015). These findings were followed by numerous studies showing dysregulations of distinct transcript pools under examination. Moreover, a PAR-CLIP study showed ATXN2 to associate with ~16.000 transcripts, 8000 of which depended on its interaction with PABPC1 (Yokoshi et al., 2014). In that study, ATXN2 was shown to preferentially bind to target transcripts on 3'-UTR AU-rich sequences. In addition to PABPC1, some other RNA-binding proteins, like TDP-43, were also shown to modulate the indirect interaction of ATXN2 with transcripts, while it also maintained direct interactions through its Lsm and LsmAD domains. Altogether, the mammalian data on ATXN2 thus far depicts it as an avid interactor of numerous RNA-binding proteins and countless transcripts, including microRNAs. The authors however have not cited or compared their findings with this vast array of mammalian literature (with the exception of two properly discussed papers in Discussion).

Considering its stress-responsive nature and association with RNP granules, it is plausible to assume that ATXN2/Pbp1 could regulate certain groups of transcripts in terms of their stability (in stress granules and p-bodies) or active translation under given environmental conditions and cellular state. The work of van de Poll et al. in this regard is an important step in expanding our knowledge about the many downstream effects of ATXN2/Pbp1. Yet, the following issues should be solved:

1) The pre-eminence of the proposed group of affected transcripts (i.e. those associated with mitochondrial biogenesis) has to be empirically established. Among all its interactions with other RNA-binding proteins, how important or dominant is the interaction of Pbp1 with Puf3 for mitochondrial biogenesis during the shift towards respiratory growth? Line 74 in the Results says "Analysis of a panel of nuclear- and mitochondrial-encoded mRNAs..."; how broad was this panel? how were the genes selected? Considering the fact that ATXN2/Pbp1 is associated with an immense number of transcripts, hand-picking a number of Puf3 targets and selectively analyzing their expression will surely give some significant dysregulations. Therefore, an unbiased transcriptomic survey is necessary to see all Pbp1-dependent dysregulations during respiratory shift. Since it's a process that requires heavy mitogenesis, one can assume that many dysregulations will concern mitochondrial factors. Indeed, proteome surveys in Atxn2-KO mouse tissues and pbp1Δ yeast (under fermentative growth and stress) point out to a strong mitochondrial dysregulation, so it raises hopes to see an even stronger Pbp1 impact during the respiratory shift in yeast. Then, bioinformatic analyses can reveal what proportion of those are Puf3 targets. If the authors' premise is valid, then the Puf3-targets will stand out in the transcriptome data, and give them an unbiased, solid and much stronger base for the following interaction analyses. They can then compare this dataset to the readily available mouse transcriptome from Atxn2-KO or polyQ-disease models, and strengthen their hypothesis about ATXN2/Pbp1 regulating mitochondrial biogenesis in association with Puf3.

The Results text about Figure 1 in its current state is an overstatement of the available data. Line 81-84 suggests mitochondrially encoded Cox2 levels are reduced because some mitoribosome subunits are Puf3 targets, but pbp1Δ itself is known to have altered mitochondrial membrane potential which negatively impacts mitochondrial import. So, the reduction of Cox2 levels (and potentially other mitochondrial-encoded proteins, it was never checked) may have nothing to do with Puf3, but rather be a direct consequence of reduced mitoribosome import in pbp1Δ. In order to make this statement, the total mitochondrial translation rates of WT and pbp1Δ strains would have to be compared, and if they are found the same, a selective effect on Puf3-target proteins has to be shown among many tested candidates. Same applies to line 86 "the specific decrease in Puf3-target mRNAs in pbp1Δ cells" referring to Figure 3C. This statement cannot be made without analyzing a larger group of transcripts including the targets of other RNA-binding proteins. The current data does not support any specific dysregulation of Puf3-targets, it just shows some Puf3 targets to be dysregulated, however without the knowledge of how many significant among all Puf3-targets, or how significant are Puf3-targets compared to others. An unbiased, high-throughput transcriptome data, and detailed bioinformatic analyses should replace Figure 1C. The high variation among replicates at 1h-3h-5h time points is also alarming, and puts the reproducibility of these experiments under question. *

As mentioned in the response to Reviewer 1, we performed an unbiased RNA-seq experiment comparing transcriptomes of WT vs pbp1∆ cells. The data are quite striking and strongly support our hypothesis (new Fig 2). To address the reviewer’s concern that pbp1∆ phenotypes may be due to altered mito membrane potential and mito protein import, we have performed an experiment to examine import of Cox4, which is a protein substrate that is commonly used for this purpose (note COX4 is not a Puf3-target mRNA). Steady-state Cox4 protein amounts are similar in WT vs pbp1∆ cells. Moreover, following treatment of cells with the uncoupler CCCP, there is more of the “pre”-processed Cox4 form in WT cells compared to pbp1∆ cells. These data would argue against the reviewer’s hypothesis and are included in the revised manuscript as Fig S1. Since every mitochondrial ribosomal subunit gene transcript is a target of Puf3 (PMID: 16254148) and therefore subject to regulation by Pbp1, we would argue that a defect in mito biogenesis (due to compromised translation of these mRNAs) precedes and may explain any subsequent defect in mito membrane potential. *

2) Stabilization of mRNAs The basal reduction in the mRNA levels of the reporter construct in pbp1Δ is a strong but not necessarily direct evidence of stabilization by Pbp1. mRNA half-life analyses (i.e. degradation curves) should be performed with desired targets to measure stability in WT and pbp1Δ strains. *

As mentioned above in the response to Reviewer 1, we performed mRNA half-life analyses for several transcripts in WT vs pbp1∆ strains using thiolutin treatment. The results are not straightforward to interpret as loss of Pbp1 led to a stabilization of Puf3-target mRNAs (relative to PGK1), however all Puf3-target mRNAs that we examined exhibited similar decay profiles. Thus, we deleted the term “stabilize” and further determination of the fate of these mRNAs in the absence of new transcription will require more careful and sophisticated experiments.*

3) Cox2 levels Regarding Line 125: "Cox2 protein levels, which are dependent on the translation of Puf3-target mRNAs": This may be generally true, but the data here suggests otherwise. pbp1Δ cells have completely diminished Cox2 levels, whereas puf3Δ have approx. 50% reduction. This means that Pbp1 is more important to maintain normal Cox2 levels, and does so independent of Puf3. In contrast, puf3Δ "rescues" some of the defect in pbp1Δ cells and increases Cox2 abundance to ~50%. As stated above, Cox2 reduction in pbp1Δ could be a direct consequence of mitochondrial membrane depolarization unrelated to Puf3, and could be accompanied by many other non-Puf3-targets being downregulated. Therefore, the authors should refrain from "Cox2 levels are dependent on the translation of Puf3-target mRNAs" statements throughout the text without an experimental proof in the context of pbp1Δ strain. *

See response to Reviewer 1. We believe that in the absence of Pbp1, Puf3 may now preferentially promote decay of various mito biogenesis transcripts, leading to apparently lower Cox2 levels. Moreover, per the results of our Cox4 experiment (Fig S1), we would respectfully disagree with the reviewer’s hypothesis. Nonetheless, we included an additional statement that mitochondrial membrane depolarization, as a consequence of reduced expression of numerous Puf3-targets, could also contribute to lower Cox2 abundance (p. 6 and 15). *

4) Pbp1-Puf3 interaction The authors state that Pbp1-Puf3 interaction is required for Puf3-target mRNA stabilization and translation. This suggests that Pbp1 stabilizes this pool of mRNAs because of its interaction with Puf3 primarily, not the mRNAs themselves. One general question while studying the interaction between two RNA-binding proteins is whether they interact in an RNA-dependent manner in vivo. The co-IP analyses show the interaction between Pbp1 and Puf3 to increase under respiratory shift as expected. However, in co-IPs from cell lysates, many RNA-binding proteins may seemingly interact due to their association with translation machinery at that given time. But this does not mean "direct" protein-protein interaction, just a co-existence around actively translating ribosomes. In order to ensure the direct interaction of these two proteins, the same co-IPs should be performed with/out RNase treatment of the lysate (many protocols available online). Only if Pbp1-Puf3 interaction persists in RNase+ samples, they can conclude a direct interaction. In addition, RNA-immunoprecipitation analyses should be performed in Pbp1-Flag and Pbp1-Flag/ puf3Δ strains to check if the association of Pbp1 with Puf3-target mRNAs indeed depends on Puf3. *

This is a good suggestion. We performed an experiment to test whether RNase treatment alters interaction between Pbp1 and Puf3 and there was minimal effect, supporting the hypothesis that the interaction may be direct. We also performed RNA-IP of Pbp1 in the presence of absence of Puf3 (new Fig 7). As the reviewer predicted, the RNA-IP enrichment of Puf3-target mRNAs was reduced in puf3∆ strains, suggesting that the association of Pbp1 with such transcripts depends on Puf3. It is known from work by others that Puf3 contains a PUF domain that enables sequence-specific binding to a motif in the 3’UTR of its target mRNAs, so these results are quite sensible.

* Minor comments: • Second sentence of the Abstract ("How mutations in its mammalian ortholog ataxin-2 are linked to neurodegenerative conditions remains unclear") is semantically incorrect. The term "linked" suggests an observed but uncharacterized effect of a genetic variation on a certain syndrome. Diseases can be linked to a chromosome or a locus without knowing the exact causative mutation. How the CAG repeat expansion mutations in ATXN2 are causative of SCA2, ALS or Parkinson-plus syndromes are very well known. One should also be careful with using "mutations" as a general term in the context of ATXN2, because there are certain variations in and around ATXN2 locus leading to a decrease in its activity and metabolic problems, which is far from its neurodegeneration-causing mutations. If the authors meant to state that the pathological mechanism is unclear by this sentence, that would also be a negligence of the extensive literature around this topic. Multiple disease models in mouse, Drosophila and C. elegans collectively point out to an RNA metabolism deficit, caused by toxic ATXN2 aggregates that sequestrate other RNA-binding proteins and their target transcripts. The specific downstream effects involve synaptic strength, Calcium-related action potentials, ER stress and cholesterol/sphingolipid synthesis. Therefore, this sentence could be rephrased to "PolyQ expansion mutations in its mammalian ortholog ataxin-2 lead to spinocerebellar dysfunction due to toxic protein aggregation." and simply avoid going into mechanistic details as it is not necessary for this manuscript.

• Lines 241-243: "Human sequencing" is also an incorrect term. Can be rephrased to "PolyQ expansion mutations in ATXN2 are associated with SCA2 and ALS". The references 22-24 are the first association of polyQ mutations with SCA2, however a reference for ALS is missing. Elden et al. Nature 2010 should be cited here.

• The authors should discuss the relevance of these findings to the mammalian ortholog of Pum3, namely PUM1/2. Afterall, it is also a very important conserved protein and well-studied in mammalian literature. *

These changes to the text and references have been made.

* Following a better characterization of the transcript pools selectively affected by Pbp1 (meaning a transcriptome survey), a graphical abstract sort of scheme could be useful in putting the findings in perspective and conveying the message.*

We decided not to include a graphical abstract at this time, since it is difficult for us to “picture” what is going on inside an Pbp1-containing RNP granule at this time. * Reviewer #3 (Significance (Required)):

The intricate experiments characterizing the nature of interaction between Pbp1 and Puf3 (Figures 3B, 4, 5, 6) are quite convincing. However, some fundamental questions remain especially regarding the primary rationale of studying Pbp1-Puf3 relationship and the breadth of some conclusions. The data are of general interest to a broad audience. The statistical tests in Figure 1 are of concern. The reviewer(s) has experience both with yeast molecular biology and with mammalian Atxn2 function. *

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

Learn more at Review Commons

Referee #3

Evidence, reproducibility and clarity

In this study, van de Poll et al. describe the involvement of a cytosolic RNA-binding protein (Pbp1) in the transcriptional regulation of mitochondrial biogenesis during the shift from fermentative to respiratory growth in budding yeast. Using the advantage of yeast genetics and molecular biology, they show that a number of mitochondrial transcripts and proteins are downregulated in pbp1Δ cells both under normal growth conditions and during respiratory shift. Since Puf3, another RNA-binding protein, is known to regulate the fate of these transcripts, they further investigate the interaction between Pbp1 and Puf3. Through a series of biochemical assays, they characterize the interaction between Pbp1 and Puf3 taking place through their low-complexity domains, which is suggested by authors to stabilize and promote the translation of Puf3-target transcripts.

As stated in the manuscript, Pbp1 is an evolutionarily conserved protein, encoded by ATXN2 gene in humans. Due to its involvement in multiple neurological disorders, it has been widely studied in mouse models and patient samples. The transcriptome profiles of Atxn2-KO mouse liver and cerebellum have been published (albeit having used a relatively older microarray technology for today's standards), revealing a prominent dysregulation of global translational machinery, and the ER-protein secretion pathway (Fittschen et al., 2015). These findings were followed by numerous studies showing dysregulations of distinct transcript pools under examination. Moreover, a PAR-CLIP study showed ATXN2 to associate with ~16.000 transcripts, 8000 of which depended on its interaction with PABPC1 (Yokoshi et al., 2014). In that study, ATXN2 was shown to preferentially bind to target transcripts on 3'-UTR AU-rich sequences. In addition to PABPC1, some other RNA-binding proteins, like TDP-43, were also shown to modulate the indirect interaction of ATXN2 with transcripts, while it also maintained direct interactions through its Lsm and LsmAD domains. Altogether, the mammalian data on ATXN2 thus far depicts it as an avid interactor of numerous RNA-binding proteins and countless transcripts, including microRNAs. The authors however have not cited or compared their findings with this vast array of mammalian literature (with the exception of two properly discussed papers in Discussion).

Considering its stress-responsive nature and association with RNP granules, it is plausible to assume that ATXN2/Pbp1 could regulate certain groups of transcripts in terms of their stability (in stress granules and p-bodies) or active translation under given environmental conditions and cellular state. The work of van de Poll et al. in this regard is an important step in expanding our knowledge about the many downstream effects of ATXN2/Pbp1. Yet, the following issues should be solved:

1. The pre-eminence of the proposed group of affected transcripts (i.e. those associated with mitochondrial biogenesis) has to be empirically established. Among all its interactions with other RNA-binding proteins, how important or dominant is the interaction of Pbp1 with Puf3 for mitochondrial biogenesis during the shift towards respiratory growth? Line 74 in the Results says "Analysis of a panel of nuclear- and mitochondrial-encoded mRNAs..."; how broad was this panel? how were the genes selected? Considering the fact that ATXN2/Pbp1 is associated with an immense number of transcripts, hand-picking a number of Puf3 targets and selectively analyzing their expression will surely give some significant dysregulations. Therefore, an unbiased transcriptomic survey is necessary to see all Pbp1-dependent dysregulations during respiratory shift. Since it's a process that requires heavy mitogenesis, one can assume that many dysregulations will concern mitochondrial factors. Indeed, proteome surveys in Atxn2-KO mouse tissues and pbp1Δ yeast (under fermentative growth and stress) point out to a strong mitochondrial dysregulation, so it raises hopes to see an even stronger Pbp1 impact during the respiratory shift in yeast. Then, bioinformatic analyses can reveal what proportion of those are Puf3 targets. If the authors' premise is valid, then the Puf3-targets will stand out in the transcriptome data, and give them an unbiased, solid and much stronger base for the following interaction analyses. They can then compare this dataset to the readily available mouse transcriptome from Atxn2-KO or polyQ-disease models, and strengthen their hypothesis about ATXN2/Pbp1 regulating mitochondrial biogenesis in association with Puf3.

The Results text about Figure 1 in its current state is an overstatement of the available data. Line 81-84 suggests mitochondrially encoded Cox2 levels are reduced because some mitoribosome subunits are Puf3 targets, but pbp1Δ itself is known to have altered mitochondrial membrane potential which negatively impacts mitochondrial import. So, the reduction of Cox2 levels (and potentially other mitochondrial-encoded proteins, it was never checked) may have nothing to do with Puf3, but rather be a direct consequence of reduced mitoribosome import in pbp1Δ. In order to make this statement, the total mitochondrial translation rates of WT and pbp1Δ strains would have to be compared, and if they are found the same, a selective effect on Puf3-target proteins has to be shown among many tested candidates. Same applies to line 86 "the specific decrease in Puf3-target mRNAs in pbp1Δ cells" referring to Figure 3C. This statement cannot be made without analyzing a larger group of transcripts including the targets of other RNA-binding proteins. The current data does not support any specific dysregulation of Puf3-targets, it just shows some Puf3 targets to be dysregulated, however without the knowledge of how many significant among all Puf3-targets, or how significant are Puf3-targets compared to others. An unbiased, high-throughput transcriptome data, and detailed bioinformatic analyses should replace Figure 1C. The high variation among replicates at 1h-3h-5h time points is also alarming, and puts the reproducibility of these experiments under question. 2. Stabilization of mRNAs The basal reduction in the mRNA levels of the reporter construct in pbp1Δ is a strong but not necessarily direct evidence of stabilization by Pbp1. mRNA half-life analyses (i.e. degradation curves) should be performed with desired targets to measure stability in WT and pbp1Δ strains. 3. Cox2 levels Regarding Line 125: "Cox2 protein levels, which are dependent on the translation of Puf3-target mRNAs": This may be generally true, but the data here suggests otherwise. pbp1Δ cells have completely diminished Cox2 levels, whereas puf3Δ have approx. 50% reduction. This means that Pbp1 is more important to maintain normal Cox2 levels, and does so independent of Puf3. In contrast, puf3Δ "rescues" some of the defect in pbp1Δ cells and increases Cox2 abundance to ~50%. As stated above, Cox2 reduction in pbp1Δ could be a direct consequence of mitochondrial membrane depolarization unrelated to Puf3, and could be accompanied by many other non-Puf3-targets being downregulated. Therefore, the authors should refrain from "Cox2 levels are dependent on the translation of Puf3-target mRNAs" statements throughout the text without an experimental proof in the context of pbp1Δ strain. 4. Pbp1-Puf3 interaction The authors state that Pbp1-Puf3 interaction is required for Puf3-target mRNA stabilization and translation. This suggests that Pbp1 stabilizes this pool of mRNAs because of its interaction with Puf3 primarily, not the mRNAs themselves. One general question while studying the interaction between two RNA-binding proteins is whether they interact in an RNA-dependent manner in vivo. The co-IP analyses show the interaction between Pbp1 and Puf3 to increase under respiratory shift as expected. However, in co-IPs from cell lysates, many RNA-binding proteins may seemingly interact due to their association with translation machinery at that given time. But this does not mean "direct" protein-protein interaction, just a co-existence around actively translating ribosomes. In order to ensure the direct interaction of these two proteins, the same co-IPs should be performed with/out RNase treatment of the lysate (many protocols available online). Only if Pbp1-Puf3 interaction persists in RNase+ samples, they can conclude a direct interaction. In addition, RNA-immunoprecipitation analyses should be performed in Pbp1-Flag and Pbp1-Flag/ puf3Δ strains to check if the association of Pbp1 with Puf3-target mRNAs indeed depends on Puf3.

Minor comments:

• Second sentence of the Abstract ("How mutations in its mammalian ortholog ataxin-2 are linked to neurodegenerative conditions remains unclear") is semantically incorrect. The term "linked" suggests an observed but uncharacterized effect of a genetic variation on a certain syndrome. Diseases can be linked to a chromosome or a locus without knowing the exact causative mutation. How the CAG repeat expansion mutations in ATXN2 are causative of SCA2, ALS or Parkinson-plus syndromes are very well known. One should also be careful with using "mutations" as a general term in the context of ATXN2, because there are certain variations in and around ATXN2 locus leading to a decrease in its activity and metabolic problems, which is far from its neurodegeneration-causing mutations. If the authors meant to state that the pathological mechanism is unclear by this sentence, that would also be a negligence of the extensive literature around this topic. Multiple disease models in mouse, Drosophila and C. elegans collectively point out to an RNA metabolism deficit, caused by toxic ATXN2 aggregates that sequestrate other RNA-binding proteins and their target transcripts. The specific downstream effects involve synaptic strength, Calcium-related action potentials, ER stress and cholesterol/sphingolipid synthesis. Therefore, this sentence could be rephrased to "PolyQ expansion mutations in its mammalian ortholog ataxin-2 lead to spinocerebellar dysfunction due to toxic protein aggregation." and simply avoid going into mechanistic details as it is not necessary for this manuscript.
• Lines 241-243: "Human sequencing" is also an incorrect term. Can be rephrased to "PolyQ expansion mutations in ATXN2 are associated with SCA2 and ALS". The references 22-24 are the first association of polyQ mutations with SCA2, however a reference for ALS is missing. Elden et al. Nature 2010 should be cited here.
• The authors should discuss the relevance of these findings to the mammalian ortholog of Pum3, namely PUM1/2. Afterall, it is also a very important conserved protein and well-studied in mammalian literature.
• Following a better characterization of the transcript pools selectively affected by Pbp1 (meaning a transcriptome survey), a graphical abstract sort of scheme could be useful in putting the findings in perspective and conveying the message.

Significance

The intricate experiments characterizing the nature of interaction between Pbp1 and Puf3 (Figures 3B, 4, 5, 6) are quite convincing. However, some fundamental questions remain especially regarding the primary rationale of studying Pbp1-Puf3 relationship and the breadth of some conclusions. The data are of general interest to a broad audience.<br /> The statistical tests in Figure 1 are of concern. The reviewer(s) has experience both with yeast molecular biology and with mammalian Atxn2 function.

reply to jameslongley at https://forum.zettelkasten.de/discussion/2532/personal-knowledge-management-is-bullshit

I find that these sorts of articles against the variety of practices have one thing in common: the writer fails to state a solid and realistic reason for why they got into it in the first place. They either have no reason "why" or, perhaps, just as often have all-the-reasons "why", which may be worse. Much of this is bound up in the sort of signaling and consumption which @Sascha outlines in point C (above).

Perhaps of interest, there are a large number of Hypothes.is annotations on that original article written by a variety of sense-makers with whom I am familiar. See: https://via.hypothes.is/https://www.otherlife.co/pkm/ Of note, many come from various note making traditions including: commonplace books, bloggers, writers, wiki creators, zettelkasten, digital gardening, writers, thinkers, etc., so they give a broader and relatively diverse perspective. If I were pressed to say what most of them have in common philosophically, I'd say it was ownership of their thought.

Perhaps it's just a point of anecdotal evidence, but I've been noticing that who write about or use the phrase "personal knowledge management" are ones who come at the space without an actual practice or point of view on what they're doing and why—they are either (trying to be) influencers or influencees.

Fortunately it is entirely possible to "fake it until you make it" here, but it helps to have an idea of what you're trying to make.

it's interesting how a lot of marginalized authors would blend the lines between academia and art. I guess when your community has just started to be studied in a serious and honest way, you'd need to create the knowledge that undergirds the art instead of building upon knowledge that already exists within the discourse.

Just because the it follows the law, doesn't mean that it's misleading

Review coordinated by Life Science Editors. Reviewed by: Dr. Helen Pickersgill, Life Science Editors Potential Conflicts of Interest: None

Main point of the paper: The authors discover a new function for a nucleolar protein, a hydroxylase called MINA (a Myc target gene) involved in ribosome biogenesis and linked with lots of diseases (cancer, allergy), at the plasma membrane of epithelial cells. Here, it promotes epithelial tight junction integrity and barrier function, likely together with a guanylate kinase, MPP6.

Why this is interesting: This may be the first link specifically between ribosome biogenesis and adhesion, which occur in two different subcompartments of the cell, and it is mediated by one protein, MINA. The linking of these processes would enable the careful coordination that is needed between cell division and adhesion, and could thereby help cells transition between two states (i.e., from quiescence to proliferation). So, it’s the multifunctional property of MINA in two different parts of the cell that could help promote an important state transition, which may be why it’s linked to different diseases.

Background: MINA is primarily known as a nucleolar-localized protein that promotes ribosome biogenesis/function. It catalyzes histidine hydroxylation of the large ribosomal subunit protein RPL27A thereby promoting its activity. Consistent with this, MINA is upregulated in rapidly growing cells, including tumors. However, it is expressed also in normal human tissues, and has also been linked with seemingly ribosome-independent functions, specifically in suppressing cell migration, invasion, and EMT. This suggests it has other, unknown functions, which may or may not be related to each other.

Transitioning between distinct cell states such as differentiation and proliferation requires extensive coordination of diverse processes. For example, transitioning from quiescence to growth requires increasing metabolic activity and involves coupling processes like the cell cycle and adhesion to enable the cell to successfully divide.

Results: • MINA depletion in intestinal epithelial cells (Caco-2 cells stably expressing MINA-targeting shRNA) caused flattening and other phenotypes indicative of a role in tight junction integrity and barrier function. This pointed to an unexpected role for a nucleolar protein in epithelial membrane biology. • Pull-downs with a MINA mutant that was unable to localize to the nucleolus in three cell lines (Caco-2, U2OS, and MEFs) together with proteomic screens in Caco-2 cells of the WT and nucleolar-interacting mutant identified MPP6, a member of the MAGUK superfamily of cell adhesion and polarity proteins, as a candidate interacting partner of MINA. • In vitro interaction assays with endogenous and tagged proteins verified the MINA-MPP6 interaction in the nucleoplasm, and showed that it was promoted by RNApol1 inhibition (which increases the pool of extra-nucleolar MINA in subconfluent cells).<br /> • MINA knockdown redistributes HA-tagged-MPP6 from the plasma membrane to the nucleus, suggesting a functional consequence of this interaction. • shRNA knockdown of MPP6 phenocopied MINA depletion with respect to altered barrier function. i.e., both are implicated in tight junction integrity and epithelial barrier function.

Remaining questions/points: • It’s not yet demonstrated definitively that MINA works via MPP6 in tight junction integrity and epithelial barrier function. It would help to have more insight into this mechanism – it appears to not be related to MINA’s catalytic activity, and may ‘just’ involve MINA promoting MPP6 membrane localization, but whether this is direct or indirect (and if/how it’s regulated) is unclear. • Whether there is a regulatory link between ribosomal biogenesis and membrane biology via MINA is unclear – i.e., if/how MINA’s two functions are physiologically regulated to coordinate proliferation and adhesion (this may require going beyond cell lines). It could be that these two functions are normally independent (but if you experimentally interfere in vitro, then you see a dependency).

Student engagement is necessary, often students feel disconnected or discontented because of a teachers inability to effectively reach the whole classroom. Something that connects with this concept is the incredible job some professors do in having poll everywhere's in which they ask questions to see what the class is thing, and then expanding on which answer was right and which was wrong. Also, apps such as poll everywhere in which students answer polls give insight to educators on possible things students need review on and how they might possible want to learn. The line, "It is important you know what you are ding as a teacher and also as a learner" stuck out to me because teachers need to learn how to connect with each student to incentivize them to become active in the subject.

I agree to a certain extent. Yes there are people outside of school that can teach us life lessons but at the same time, not all those lessons may be school oriented. Then again, this brings to light the organized education the school system brings. Just because we don't learn certain things in school doesn't mean it's any less valuable than algebra or essay rhetorics. Intelligence is not solely based on a student's reading level or their report card, that's why we see schools incorporating SEL lessons as well.

That's what's tough about being a teacher. It doesn't matter how high of an education you have to the kids, or where you went to school. What matters is how your experience and training translates into the classroom. Again, teachers are there for the students, and if the students don't feel supported, challenged, acknowledged, then has the teacher really done their job? It's one of the fields that depends immensely on the personality of the teacher, not just their credentials.

search : human attention flows

I completely agree. It's not just something that should be taught to students but also adults. Maybe that's why there are so many adults who still struggle with their emotions because this type of teaching wasn't implemented. There are some friends of mine who I'll use the same SEL strategies on them that I use on the kids.

Author Response

Reviewer #2 (Public Review):

In this manuscript, the authors use an embedding of human olfactory perceptual data within a graph neural network (which they term principal odor map, or POM). This embedding is a better predictor of a diverse set of olfactory neural and behavior data than methods that use chemical features as a starting point to create embeddings. The embedding is also seen to be better for comparison of pairwise similarities (distances of various sorts) - the claim is that proximity of pairs of odors in the POM is predictive of their similarity in neural data from olfactory receptor neurons.

A major strength of the paper is the conceptualization of the problem. The authors have previously described a graph neural net (GNN) to predict verbal odor descriptors from molecular features (here, a 2019 preprint is cited, but a newer related one in 2022 describing the POM is not cited). They now use the embedding created by that GNN to predict similarities in large and diverse datasets in olfactory neuroscience (which the authors have curated from published work). They show that predictions from POM are better than just generic chemical features. The authors also present an interesting hypothesis that the underlying latent structure discovered by the GNN relates to metabolic pathway proximity, which they claim accounts for the success in the prediction of a wide range of data (insect sensory neuron responses to human behavior). In addition to the creativity of the project, the technical aspects, are sound and thorough.

There are some questions about the ideas, and the size of the effects observed.

1) The authors frame the manuscript by invoking an analogy to other senses, and how naturalstatistics affect what's represented (and how similarity is defined). However, in vision or audition, the part of the world that different animals "look at" can be very different (different wavelengths, different textures and spatial frequencies, etc). It is still unresolved why any given animal has the particular range of reception it has. Each animal is presumably adapted for its ecological niche, which can have different salient sensory features. In vision, different animals pick different sound bandwidths or EM spectra. Therefore, it is puzzling to think that all animals will somehow treat chemicals the same way.

Our assumption (an assumption of the broader interpretation, not of the analyses themselves) that all terrestrial animals have a correlated odor environment is certainly only true for some values of “correlated”. One could imagine, for example, that some animals are able to exploit food energy sources that humans cannot (for example, plants with high cellulose content), and that they might therefore be adapted to smell metabolic signatures of such plants, whereas humans would not be so adapted. This seems quite reasonable and there are probably many such examples. In future work they might be used to test the theory directly: representations might be more likely to differ across species on tasks when the relevant ecological niches are non-overlapping. We have updated the discussion to propose such future tests. However, it is also apparent that the odor environment overall is nonetheless highly correlated across species. Recent work (Mayhew et al, PNAS) showed that nearly all molecules that pass simple mass transport requirements (that should apply to all mammals, at the least) are likely to have an odor to humans, so it seems unlikely that the “olfactory blind spots” are intrinsically large.

2) The performance index could be made clearer, and perhaps raw numbers shown beforeshowing the differences from the benchmark (Mordred molecular descriptor). For example, can we get a sense of how much variance in the data does it explain, what percent of the hold-out tests does it fit well, etc.?

The performance index in Figure 1 is required to compare across different types of tasks, which are in turn dictated by the nature of the data (e.g. continuous vs categorical). Regression tasks yields an R2 value and categorical tasks yield an AUROC. We normalized and placed these on a single scale in order to show all of the tasks clearly together. We have added a table to the shared code (from link in Methods section, go to predictive_performance/data/dataset_performance_index_raw.csv) that shows the original (non-normalized) values, for both the POM and the benchmark(s) across multiple seeds and various metrics with the model hyper-parameters that generate the best performance.

3) The "fitting" and predictions are in line with how ML is used for classification and regression inlots of applications. The end result is a better fit (prediction), but it's not actually clear whether there are any fundamental regularities or orders identified. The metabolic angle is very intriguing, but it looks like Mordred descriptor does a very good job as well (extended figure 5 [now Figure 2-figure supplement 5]). Is it possible to show the relation between metabolic distance and Mordred distance in Figure 2c? In fact, even there, cFP distance looks very well correlated with metabolic distance (we are talking about r= 0.9 vs r = 0.8). This could simply be due to a slightly nonlinear mapping between chemical similarity and perceptual similarity (which was used to get POM distance).

We show additional “showdown” comparisons between metabolic distance, POM distance, and alternative distance metrics in the new Figure 2-figure supplement 3 and Figure 2-figure supplement 4. Indeed, the Mordred descriptors perform well; after all, metabolic reactants and products must be at least somewhat structurally related. But POM (derived only from human perceptual data) outperforms it significantly. Visual inspection of Figure 2c also reveals that the dispersion of structural distances (at a given metabolic distance) is just much higher than the dispersion of POM distances. This won’t change if one uses a non-linear curve fit, as it is a property of the data itself.

It’s also worth noting while r=0.8 and r=0.9 might seem close, in terms of variance unexplained (1 - r2) they are approximately two-fold different. Reducing the unexplained variance by half seems like a meaningful difference. Alternatively, if one simulates scatter plots with correlation r=0.8 vs r=0.9, it is apparent that the latter is simply a much tighter relationship.

4) How frequent are such examples shown in Fig 2d? Pentenal and pentenol are actually verysimilar in many ways, and it may be that Tanimoto distance is not a great descriptor of chemical similarity. cFP edit distance is quite small, just like metabolic distance. The thiol example on the right is much better. Also, even in Fig 2C POM vs metabolic distance, the lowest metabolic distances have large variations in the POM values - so there too, metabolic reactions that create very different molecules in 1 step can vary widely in POM distance as well.

We agree that Tanimoto distance is not perfect. We were unable to find a measure of structural distance that agreed with human intuitions about “structural distance” in all cases; indeed that intuition is often generated by an understanding of odor/flavor characteristics of function in metabolic networks, which would beg the question! To answer the question about the frequency of examples like the ones shown in Figure 2d, we created a new density map (Figure 2-figure supplement 4) showing the number of one-step metabolite pairs for a given range of POM vs cFP edit/Tanimoto distance. We found >25 pairs of metabolites in the same “small POM distance” and “large structural distance” quadrant from which we found the original examples shown in Figure 2d..

5) A major worry is that Mordred descriptors are doing fine, and POM offers only a smallimprovement (but statistically significant of course). Another way to ask this question is this: if you plot pairwise correlation/distance of pairs of odors from POM against that for Mordred, how correlated does this look? My suspicion is that it will be highly correlated.

It will look highly correlated (as shown in the new Figure 2-figure supplement 3). The reason is that metabolic reactions cannot make arbitrary transformations to molecules (the reactants must have some structural relationship to the products) or similarly that olfactory receptors (in any species) cannot have arbitrary tuning – at the end of the day receptors mostly bind to similar-looking classes of molecules. As stated above, we believe that the improvement here is not just statistically significant but meaningful – a 2-fold drop in unexplained variance is large – and that it is important to identify principles by which the nervous system can be tuned, above and beyond the physical constraints imposed by basic rules of chemistry.

Also, the metabolic distances that we constructed from available data are themselves noisy, since not all metabolic pathways and the compounds that compose them are known, which places an upper bound on the correlation that we could have obtained. Despite that, we still found a correlation of r>0.9.

6) The co-occurrence in mixtures and close POM distance may arise from the way theembedding was done - with perceptual descriptors used as a key variable. Humans may just classify molecules that occur in a mixture as similar just from experiencing them together. Can the authors show that these same molecules in Fig 4d,e have very similar representations in neural data from insects or mice?

We have added a new Figure 4-figure supplement 1 to show this. One constraint is that the neural datasets must contain molecules that are also in the natural substance datasets used in Figure 4. In all cases where the data is sufficient to be powered to test the hypothesis (i.e. more than five co-occuring pairs of molecules in essential oil), we observe an effect in the predicted direction.

Reviewer #2 (Public Review):

In this manuscript, the authors use an embedding of human olfactory perceptual data within a graph neural network (which they term principal odor map, or POM). This embedding is a better predictor of a diverse set of olfactory neural and behavior data than methods that use chemical features as a starting point to create embeddings. The embedding is also seen to be better for comparison of pairwise similarities (distances of various sorts) - the claim is that proximity of pairs of odors in the POM is predictive of their similarity in neural data from olfactory receptor neurons.

A major strength of the paper is the conceptualization of the problem. The authors have previously described a graph neural net (GNN) to predict verbal odor descriptors from molecular features (here, a 2019 preprint is cited, but a newer related one in 2022 describing the POM is not cited). They now use the embedding created by that GNN to predict similarities in large and diverse datasets in olfactory neuroscience (which the authors have curated from published work). They show that predictions from POM are better than just generic chemical features. The authors also present an interesting hypothesis that the underlying latent structure discovered by the GNN relates to metabolic pathway proximity, which they claim accounts for the success in the prediction of a wide range of data (insect sensory neuron responses to human behavior). In addition to the creativity of the project, the technical aspects, are sound and thorough.

There are some questions about the ideas, and the size of the effects observed.

1. The authors frame the manuscript by invoking an analogy to other senses, and how natural statistics affect what's represented (and how similarity is defined). However, in vision or audition, the part of the world that different animals "look at" can be very different (different wavelengths, different textures and spatial frequencies, etc). It is still unresolved why any given animal has the particular range of reception it has. Each animal is presumably adapted for its ecological niche, which can have different salient sensory features. In vision, different animals pick different sound bandwidths or EM spectra. Therefore, it is puzzling to think that all animals will somehow treat chemicals the same way.

2. The performance index could be made clearer, and perhaps raw numbers shown before showing the differences from the benchmark (Mordred molecular descriptor). For example, can we get a sense of how much variance in the data does it explain, what percent of the hold-out tests does it fit well, etc.?

3. The "fitting" and predictions are in line with how ML is used for classification and regression in lots of applications. The end result is a better fit (prediction), but it's not actually clear whether there are any fundamental regularities or orders identified. The metabolic angle is very intriguing, but it looks like Mordred descriptor does a very good job as well (extended figure 5). Is it possible to show the relation between metabolic distance and Mordred distance in Figure 2c? In fact, even there, cFP distance looks very well correlated with metabolic distance (we are talking about r= 0.9 vs r = 0.8). This could simply be due to a slightly nonlinear mapping between chemical similarity and perceptual similarity (which was used to get POM distance).

4. How frequent are such examples shown in Fig 2d? Pentenal and pentenol are actually very similar in many ways, and it may be that Tanimoto distance is not a great descriptor of chemical similarity. cFFP edit distance is quite small, just like metabolic distance. The thiol example on the right is much better. Also, even in Fig 2C POM vs metabolic distance, the lowest metabolic distances have large variations in the POM values - so there too, metabolic reactions that create very different molecules in 1 step can vary widely in POM distance as well.

5. A major worry is that Mordred descriptors are doing fine, and POM offers only a small improvement (but statistically significant of course). Another way to ask this question is this: if you plot pairwise correlation/distance of pairs of odors from POM against that for Mordred, how correlated does this look? My suspicion is that it will be highly correlated.

6. The co-occurrence in mixtures and close POM distance may arise from the way the embedding was done - with perceptual descriptors used as a key variable. Humans may just classify molecules that occur in a mixture as similar just from experiencing them together. Can the authors show that these same molecules in Fig 4d,e have very similar representations in neural data from insects or mice?

It's one of the interesting research objects for me, because I've never thought of that as a research topic. Also, I agree that it's important and has value to be studied.

Reading this example of how our perspectives change as we gain knowledge enlightens many things for me. It helped me reflect on everything that changes our perspectives as we learn more about it. For example looking at a math problem that seems difficult at first but once you learn how to solve it, it's not so difficult with the knowledge you learned and eventually your perspective on the math problem has changed to viewing it as an easy problem.

this escalates fast, reads a bit like exposition and does a lot of the heavy lifting. what is nev doing whilst she's talking? pacing? what do we see through the lowered phone?

Lulu Wang talks about her family's reactions when they knew that she was shooting this movie, a story based on her real family story.

Yes, maybe it's another paper, but it is interesting to consider GPT-n and the massive web scale data extraction that took place to create these LLM models that are gate kept behind APIs, which people are binding their services too with abandon.

Maybe there's a connection to be made here to the debate around whether these models just represent surface level statistics (Markov chains) or if some sort underlying alien representational model: https://thegradient.pub/othello/

The elders are the ones that hold the most knowledge of the most recent past. It's lingers in their expressions just as much as it does in their minds.

He isn't lusting after her or in love with her. In social class, she is beneath him. However, the king will benefit from this, so he will marry her if the task is preformed correctly. This could be considered deceit #8 because all the other deceits lead to him deceiving the girl into believing she was worthy to be queen. However, it was because of all the other deceit and lies that he thinks he has something to gain from the girl. So he deceives her to believe she's valuable to him. The only value is the gold. Is she this blind? Maybe #8 or even #9 if we count the King's deceit, is the deception to the reader again as they try to figure this out. Was this deceit too? Was the girl so blind that she couldn't see the king didn't want her, he wanted her for what she could "do". And she can't even do that, so in reality does he want the manikin for his skill instead? Which would he benefit more from? A king does need a Queen and a Queen in his eyes doesn't have to come from royalty, it just has to come from riches untold.

This quote speaks to the heart of Bonds' activism and her deep connection to her Appalachian roots. She recognizes that the destruction of the mountains is not only an environmental issue but a cultural one as well.

It's not just me who's noticed this.

Interesting that for someone propounding Luhmann's zettelkasten system that Scheper has done this. Was it because he did it himself and then didn't want to change (likely) or because he spent time seeing others' problems with Luhmann's numbering system and designed a better way (less likely)?

Reality actively resists modeling

I see the need for students to be exposed to these higher order thinking skills. It's unfortunate these projects and assignments are usually reserved for the advanced and honor students who happen to be in the right school or class. Regular and at-risk students need them just as much if not more because they may not be headed to college but their careers will still require them to think critically, be creative, and think on their feet.

Therefore aside from serving as a motivation to get through the class, higher order thinking assignments may also change their outlook on how relevant the class is to their future job. So while I meet certain kinds of technology with pessimism because I feel it lowers the cognitive processing required of students, I am also forced to acknowledge that said technology also forces educators to move past the lower level skills of remembering and understanding. Hopefully my school can move beyond the stringent teaching to tests and allow us greater space to explore with these high order thinking activities.

Father's name is Ben is working for walker brothers is like a door to door snake oil sales man

As someone who consistently does this, it's an important skill to make sure students understand what operation is represented by what symbol. Beyond simply giving the appropriate operation, they should also be aware of what words represent it and how they will visualize it. This is especially important for students learning algebra, as they will be using multiple symbols for some operations like multiplication and division. Beyond just understanding how to do the operations, it is key that they understand how we visually represent some of these concepts to help them use context clues.

I chose this chapter for my discussion because I find the concept of the Maillard reaction very interesting. The Maillard reaction occurs with the interaction and reactions between two key substances: amino acids and sugar during the heating processes opposed to the process of caramelization that this process is commonly mistaken for. The differences is that caramelization occurs between sugars only. The Maillard reaction is responsible for our 5 senses springing to life in the cooking process. It is what gives our favorite foods, such as steak, the smell that attracts us to it and makes us hungry. it is what gives its appetizing color. It is also what gives it it's great taste that makes you crave more. this reading was so interesting to me because I always knew how big of a role food played on our 5 senses, but I didn't know there was a name for something that is the cause for the way food tastes, smells, and looks. This completely blew my mind because before learning about this in this class, I believed food was the way it was…because it simply was. I had never thought about the scientific chemical reactions that went into every aspect of it during the cooking, baking, grilling, etc. processes. This explains why things like meat smell. look, and taste completely different when they transition stages between raw, perfectly cooked, and burnt. an example stated in this reading talked about how adding certain elements can even speed up the Maillard process. For example, when i bake any type of bread, I always brush the top of it with an egg wash. I didn't know why I did this, all the baking recipes just told me to do. No explanation to back up why this extra step did what it did. All I knew was that it gave me the desired effect of having my bread an appealing, beautiful golden brown color. I now know there is a whole scientific name and process that explains why this occurs.

I chose this chapter for my discussion because I find the concept of the Maillard reaction very interesting. The Maillard reaction occurs with the interaction and reactions between two key substances: amino acids and sugar during the heating processas opposed to the process of caramelization that this process is commonly mistaken for. The differences is that caramelization occurs between sugars only. The Maillard reaction is responsible for our 5 senses springing to life in the cooking process. It is what gives our favorite foods, such as steak, the smell that attracts us to it and makes us hungry. it is what gives it it's appetizing color. It is also what gives it it's great taste that makes you crave more. this reading was so interesting to me because I always knew how big of a role food played on our 5 senses, but I didn't know there was a name for something that is the cause for the way food tastes, smells, and looks. This completely blew my mind because before learning about this in this class, I believed food was the way it was...because it simply was. I had never thought about the scientific chemical reactions that went into every aspect of it during the cooking, baking, grilling, etc. processes. This explains why things like meat smell. look, and taste completely different when they transition stages between raw, perfectly cooked, and burnt. an example stated in this reading talked about how adding certain elements can even speed up the Maillard process. For example, when i bake any type of bread, I always brush the top of it with an egg wash. I didn't know why I did this, all the baking recipes just told me to do. No explanation to back up why this extra step did what it did. All I knew was that it gave me the desired effect of having my bread an appealing, beautiful golden brown color. I now know there is a whole scientific name and process that explains why this occurs.

Many of the specifics you address aren't well covered in much of the literature, and as a result often cause a lot of confusion.

The use of the / or the . in these numbers is broadly only to improve readability.

One of the major benefits of Luhmann's particular numbering method was specifically to cut way back on the overcrowding of his index in comparison with other commonplacing book indexing schemes (like that of John Locke in particular). If you look at Luhmann's index it will usually only have a 1-3 entries for each word as related material will be found in neighboring cards within a particular branch.

In point 1, it would appear that your issue is mentally equating the "top level" number with a category/topic in the first place. It's just an idea and the number is a location. Start by separating the two. You manage to do this in your own dating system by creating an abstract number, but you're simultaneously requiring yourself (or a computer) to build up a date-based number which requires additional, unnecessary work. Your system is equivalent to all the others if you cut off the date-based root.

Perhaps the following two articles may be of some help in thinking through what you're doing: - On The Interdisciplinarity of Zettelkasten: Card Numbering, Topical Headings, and Indices https://boffosocko.com/2023/01/19/on-the-interdisciplinarity-of-zettelkasten-card-numbering-topical-headings-and-indices/ - Thoughts on Zettelkasten numbering systems https://boffosocko.com/2022/10/27/thoughts-on-zettelkasten-numbering-systems/

Of course at the end of the day, it's the system that works for you and the way you think that works best, so if none of it makes sense, then definitely use your own method.

The idea that creating a college-going culture requires a system-wide effort is both daunting and inspiring. It's daunting to think that it's not just up to the teachers or counselors, but it's also inspiring to know that everyone can contribute to this effort.

This is so true, not just for district officials but for anyone. Empathy is so difficult to find in those in power because often times, they were able to reach their position starting at a higher advantage. We see it as adults but kids see it too. I remember working with this one student who was telling another student that it's hard for people to understand her depression because not everyone is depressed, that's why she feels so alone.

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

Learn more at Review Commons

Reply to the reviewers

1. General Statements [optional]

We are grateful to the reviewers for highlighting the value and power of our 3D chimeric dataset to explore cancer/stellate interactions in pancreatic cancer invasion. We also appreciate their support of our findings identifying divergent roles for the two related enzymes ADAMTS2 and ADAMTS14. We thank the reviewers for their detailed comments, which have allowed us to prepare a significantly stronger and clearer manuscript.

Following the reviewers comments we have made three major changes to the manuscript, which we will outline here in addition to the point-by-point rebuttal.

1. i) Revised manuscript structure. We have modified the structure of the manuscript, which we hope improves the clarity and accessibility of the work.

Figure 1 remains the description of our 3D invasion model and our approach to identify stellate cell and cancer cell transcriptomic information from this context.

Figure 2 describes our focus on proteases and now includes concordance of our data with clinical data sets. This is also now where we describe the strikingly opposing roles for ADAMTS2 and ADAMTS14 in regulating invasion.

Figure 3 is now the figure demonstrating that ADAMTS2 and ADAMTS14 have an equal contribution to collagen processing from stellate cells. This is an important experiment given that the main physiological roles for these enzymes are in the processing of collagen, and the importance of collagen for cancer progression. It was therefore reasonable to hypothesise that the effect of these enzymes on invasion could be due to differences in their collagen processing in this context. The finding that both have an equal effect on collagen processing points towards a wider, and more diverse, role for these enzymes in regulating biology.

Figure 4 describes the divergent roles of these two enzymes on myofibroblast differentiation, and by extension TGFβ bioavailability. In this figure we now include experiments with TGFβ reporter constructs, which demonstrate an increase in active TGFβ following loss of ADAMTS14 and a reduction in TGFβ activity following loss of ADAMTS2.

Figure 5 is our matrisomic experiment to identify enriched enzyme-specific substrates following knockdown of either ADAMTS2 or ADAMTS14.

Figure 6 details our investigation into the substrate responsible for the reduction in invasion following loss of ADAMTS2. As the previous matrisomic experiment identified only two enriched ADAMTS2 substrates, we investigated both in our 3D assays, identifying SERPINE2 as the responsible substrate. Further analysis identified a reduction in plasmin activity in ADAMTS2 deficient cells. This was rescued with co-knockdown of SERPINE2, implicating this pathway as being crucial for mediating the effect of ADAMTS2. Additionally, we now include experiments demonstrating that concomitant knockdown of SERPINE2 alongside ADAMTS2 rescues the reduction in TGFβ activity observed with ADAMTS2 loss alone.

Figure 7 describes our analysis of ADAMTS14 substrates. As the matrisomics identified a large change in proteins following ADAMTS14 knockdown, we performed an siRNA screen of candidates to identify those responsible for ADAMTS14 phenotype. This, followed by further validation in our 3D invasive assay, revealed Fibulin2 as the responsible substrate. Fibulin2 has a well-established role in regulating TGFβ release from the matrix. In accordance with this we present new data using TGFβ reporter constructs, which demonstrate that the increase in active TGFβ following ADAMTS14 knockdown can be reversed with co-knockdown of Fibulin2.

1. ii) Improvement of the clinical significance of our chimeric data set and ADAMTS proteins. Ideally, we would like to present IHC images of ADAMTS2 and ADAMTS14 expression in PDAC tissue samples to corroborate our in vitro findings. However as these enzymes are secreted, this precludes antibody based imaging, as it would not provide cell type specific information. RNA scope presents an alternative, however we have experienced technical issues with this technique due to RNA degradation in PDAC tissue and unavailability of ADAMTS2/14 specific probes. In place of this we have used a range of publically available resources.

We have compared our chimeric data set with human clinical data using the resource published by Maurer and colleagues (PMID: 30658994). This paper presents transcriptomic data from PDAC tumour and stromal compartments using laser microdissection of clinical tissue. In accordance with our data set, the majority of metzincins, including ADAMTS2 and ADAMTS14, are expressed in the stromal compartment. These data are presented in updated figure 2.

We have also examined ADAMTS2 and ADAMTS14 expression in PDAC and CAF subtypes using publically available data sets. Using the TCGA dataset, we identified that ADAMTS2 and ADAMTS14 are highly expressed in PDAC tumours compared to normal counterparts. As the majority of PDAC is comprised of stroma, the bulk transcriptomic data from TCGA, combined with the results from the Maurer publication, lead us to conclude that this expression reflects the stromal origin of these proteases. In addition, using publically available single cell RNA sequencing data published by Luo and colleagues (PMID: 36333338), we identified ADAMTS2 and ADAMTS14 expression in the prominent PDAC CAF subtypes, inflammatory and myofibroblastic CAFs. Together these data demonstrate that these enzymes are enriched in clinical disease, which when combined with our mechanistic 3D studies implies a greater role for these enzymes in disease progression than previously appreciated.

iii) Improved mechanistic link between ADAMTS2 and ADAMTS14 with TGFβ bioavailability

To strengthen the association between ADAMTS2 and ADAMTS14 function, their substrates SERPINE2 and Fibulin2, and TGFβ bioavailability, we have performed the following experiments using TGFβ reporter constructs:

We have taken conditioned media from stellate cells lacking either ADAMTS2 or ADAMTS14, along with co-knockdown of their substrate, and stimulated a recipient cell line expressing a SMAD Luciferase reporter. These cells express luciferase in response to TGFβ stimulation. In accordance with a role for ADAMTS14 and Fibulin2 in regulating TGFβ, we demonstrate that following ADAMTS14 knockdown there is a strong increase in active TGFβ in the media (Figure 4I), which is abrogated with co-knockdown of Fibulin2 (Figure 7F).

We have also obtained a fluorescent reporter, CAGA-eGFP, which expresses GFP in response to TGFβ stimulation in order to examine TGFβ activity in 3D cultures. Stellate cells expressing this construct were embedded in collagen: Matrigel hydrogels following knockdown of either ADAMTS2 or ADAMTS14 and CAGA fluorescence recorded after 72 hours of culture. In accordance with our data, stellate cells deficient in ADAMTS14 showed increased fluorescence in 3D, indicative of increased TGFβ activity, which was abrogated with co-knockdown of Fibulin2 (Figure 4J, K and 7G, H). Equally, loss of ADAMTS2 reduced TGFβ activity in 3D culture, which was rescued with co-knockdown of SERPINE2 (Figure 4J, K and 6 D, E).

These experiments confirm a link between the ADAMTS enzyme, its relevant substrate, and TGFβ bioavailability. Together with extensive published work linking SERPINE2 and Fibulin2 with TGFβ release we are confident in our proposed mechanism for the dichotomic relationship of ADAMTS2 and ADAMTS14 in regulating TGFβ and thus myofibroblast action.

2. Point-by-point description of the revisions

This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

• *

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

• *

This study aims to explain the opposing contributions of stromal stellate cells/CAFs to PDAC. By first identifying stroma-specific proteases, followed by a process of candidate selection and elimination, the authors find that two specific metalloproteases that share enzymatic activity against collagen in fact have differential activity on TGFb availability. This could be interpreted as a way of shaping the CAF population and tumor-promotin or -restricting properties of the stroma.

There are several flaws that the authors could address to improve the manuscript:

1. In the flow of experiments and analyses, there is a strange mix of fully unbiased discovery phases followed by functional experiments that do not consider all possible candidates to test, and vice versa. For instance, from the mixed-species transcript analysis, ADAMTS2 and -14 are chosen based on their shared collagenase activity based on literature. However, the authors then perform again a proteomics analysis to identify things from the entire matrisome that are cleaved by these enzymes? Then, for ADAMTS2 a co-silencing approach is done on one selected candidate (Serpine2), but for ADAMTS14 an siRNA screen is performed? The problem of this approach is that the rationale for some studied enzymes is very strong, where as for others it is not.

We thank the reviewer for their comment and trust the revised manuscript provides more clarity for the rationale of our approach. We performed the chimera sequencing as a discovery experiment to reveal the communication between cancer and stellate cells in a 3D, invasive context. We present the chimera experiment and data here as a resource for the community, with our analysis of ADAMTS2 and ADAMTS14 function serving as a first example of the biological insight this data set can reveal. Other insights revealed from this dataset are active avenues of research in our group.

Our finding that ADAMTS2 and ADAMTS14 have dramatically opposing roles in regulating invasion was especially striking given their equal contribution to collagen processing in this context. This led us to conclude that the divergent nature of these enzymes must be due to enzyme-specific substrates. A substrate repertoire for these enzymes has been previously published (PMID: 26740262) and we reasoned that the responsible substrate would be enriched following knockdown of the relevant enzyme. Thus we preformed matrisomics on cells lacking either of these enzymes, which did indeed reveal enrichment of known, enzyme-specific substrates that we could use for further analysis.

The matrisome following ADAMTS2 knockdown was minimally changed and only presented enrichment of two ADAMTS2 substrates. As there was only a minimal cellular phenotype in 2D following loss of ADAMTS2, we decided to concentrate our studies on the two identified substrates in our 3D assay. Conversely as the matrisome following ADAMTS14 knockdown was dramatically different from control cells, and ADAMTS14 knockdown presented a clear phenotype in αSMA expression, we decided to perform a screen of all matrisome hits. This highlighted the role of IL-1β in mediating myofibroblast differentiation, which has been reported elsewhere and validated our approach. Further, this refined the number of enriched ADAMTS14 substrates to two, MMP1 and Fibulin2, with Fibulin2 being identified as the responsible candidate in our 3D assays.

The ECM is more than just collagen. Choosing these two metalloproteases based on their shared collagen substrate is an approach that perhaps oversimplifies the ECM a bit, and again, does not provide the strongest rationale that these metalloproteases are most likely to explain counteracting stromal activities on tumor growth and progression.

We fully agree with the reviewers comment and feel our work acutely demonstrates this point. Loss of either ADAMTS2 or ADAMTS14 had similar effects on collagen processing; implicating their divergent roles on invasion was independent of their effects on collagen regulation. This work therefore showcases the incredible complexity of ECM regulation in tumour progression. As discussed in the manuscript, collagen along with other elements of the ECM can regulate tumour progression and we believe our work adds an additional facet to this.

Related to the above: How were the stellate cells used for the matrisome analysis grown? In the suspension setup or adherent? This will have a large impact on the outcome. Is there for instance hyaluronic acid in this matrix?

The matrisome analysis was conducted on cells cultured in 2D. Vitamin C was added to the media to promote matrix production. We agree that this is not truly reflective of the in vivo situation but as a discovery tool this led us to identify the ADAMTS2 and ADAMTS14 substrates responsible for the function observed in 3D.

1. Performing the species-specific transcript analysis both ways is a neat approach, but why did the authors ignore the opportunity to formally overlay/compare the two stromal gene sets to define likely candidates based on statistics?

We primarily used this approach as a discovery tool to identify key differences between cancer and stellate cell compartments. Comparing the two species data sets is problematic as the murine cancer cells express many elements found in the stellate cells, while the human data set presents a cleaner comparison. This is evident from comparing metzincin expression in the two data sets. The human data set (Figure 2A) shows clear separation between cancer and stellate compartments, which is less evident in the murine data set (Supp figure 2A). As noted in supplementary figure 1A, unlike the human cancer cells used in this study, the murine cancer cells are capable of invading without stellate support (although when cultured with stellate cells invasive projections are always stellate led). Nevertheless the murine data set matches the human, although with less clarity.

Minor comments: The bioinformatics Methods need more details on how reads were mapped to the different genomes. How many mismatches were allowed and was the mapping done separately or using for instance Xenofilter?

We have improved the methodology section to include more detail for this separation. Using STAR aligner, reads were mapped to host species using a combined human and mouse genome. Ambiguous reads were subsequently discarded from the analysis. While there are bioinformatic packages that seek to match ambiguous reads to parent species we did not use these for our analysis.

The authors use the knowledge on the activities of both ADAMTS2 and -14 on collagen as a rationale to choose these two. Is there really a need for the paragraph (and associated figures) from line 102 on?

Given the prominent role collagen has been shown to have in regulating PDAC progression and the primary role for ADAMTS2 and ADAMTS14 being collagen processing, we initially hypothesised that the divergent role for these enzymes on invasion could be due to differences in collagen processing in this context. The fact that both equally contribute to collagen processing is surprising and adds to the novelty of our findings that these enzymes have a more complex role in regulating stromal biology.

We have altered the structure of the manuscript to emphasise this point. The divergent roles of ADAMTS2 and ADAMTS14 on invasion are now presented in Figure 2, with their equal role in collagen processing now presented after in Figure 3. Figure 4 onwards now details the opposing roles of these enzymes in myofibroblast differentiation and our investigation into the enzyme-specific substrates responsible for this.

Abstract, line 21; some words are missing?

We thank the reviewer for bringing this to our attention and have now amended the abstract.

Were the siRNA screen hits validated?

Yes, hits relevant for our further investigations, MMP1 and Fibulin2, are presented in the manuscript.

What is the genotype of the mouse cancer cells? KPC-derived?

DT6066 are KPC derived while R254 are derived from KPF mice. This has been added to the methods with relevant reference.

Reviewer #1 (Significance (Required)):

The trick of dissecting tumor from stromal signals in spheroid cocultures by RNA-Seq is a cool trick, but not new and the authors should probably cite some prior work.

We have included reference to other work where researchers have used species deconvolution to explore heterocellular interactions (Lines 68-72). However, we believe our work is one of the first to use this approach to explore cellular interactions in an in vitro, 3D, invasive context.

What this all means for patients (or in vivo tumors even) remains unclear. There is some debate on whether highly activated CAFs (ACTA2/aSMA+ cells, some call them myCAFs) are indeed tumor-restrictive or whether they promote invasion. The authors appear to argue the latter (which I can agree with) but without any translational work to show what the net outcome of this mechanism is, the study remains descriptive and perhaps of limited interest.

We contend that our 3D invasion model is a powerful tool to understand the role of stellate cells in leading invasion. We have shown the utility of this model in several studies to dissect the biology of this cell type, revealing the importance of the nuclear translocation of FGFR1 in stellate invasion (PMID: 36357571), the role of the kinase PKN2 in regulating stellate heterogeneity (PMID: 35081338) and the influence of cancer cell-derived exosomes on stellate invasion (PMID: 33592190).

CAFs within PDAC stroma are highly plastic and can adopt multiple functions depending on distinct environmental cues. Thus, identifying how they are regulated is of paramount importance if they are to be therapeutically targeted. We contend that our mechanistic studies using heterocellular 3D models can aid in the dissection of the biology of these cells with more granularity than offered by clinical or in vivo studies, particularly in the context of secreted proteases. To add clinical relevance for our findings we have compared our chimera data set with previously published laser microdissected tumour and stroma PDAC tissue (Figure 2B), and identified ADAMTS2 and ADAMTS14 expression in prominent CAF subtypes (inflammatory and myofibroblastic) from published single cell RNA seq data taken from tumours (Supp figure 2C). As these enzymes are produced in multiple CAF subtypes, genetically targeting them in vivo appears prohibitive. The generation of ADAMTS2 and ADAMTS14 specific inhibitors would be required to assess their roles in vivo.

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

The manuscript by Carter and colleagues examines that role of cancer-associated fibroblasts (CAFs) in regulation of invasion in a 3D co-culture assay with epithelial cells. The authors propose that invasive chains of cancer cells are led by fibroblasts. The authors utilise a system of co-culture to create chimeric human-mouse fibroblast-cancer cell spheroids (both directions utilised, to eliminate species bias) to allow for in situ sequencing of the co-operating transcriptional programmes of each cell type during 3D invasion. From this powerful approach, this allowed the authors to identify two key two collagen-processing enzymes, ADAMTS2 and ADAMTS14, as contributing to CAF function in their system. The authors identify that these two enzymes have opposing roles in invasion, and map some of their key substrates in invasion, which extend beyond collagen-processing. The authors propose that one key function is to control the processing of TGFbeta, the latter of which is a regulator of the myofibroblast subpopulation of CAF. Overall, the findings of the manuscript are interesting, but need some further proof-of-principal demonstrations to extend their findings to support the claims within.

• The authors demonstrate a clear role of ADAMTS2 and ADAMTS14 in stellate function during differentiation and invasion. Is there any evidence of such changes in patient materials? Could the authors query publicly available databases of micro-dissected stroma vs epithelium to validate the translational relevance of their findings?

We thank the reviewer for their suggestion; we have now explored clinical relevance of ADAMTS2 and ADAMTS14 expression in two ways. We have used previously published work by Maurer and colleagues (PMID: 30658994), which descibes transcriptomic analysis of laser microdissected tumour and stroma from pancreatic cancer tissue. In accordance with our chimeric data set the majority of metzincins, including ADAMTS2 and ADAMTS14, are expressed in the sromal compartment (Figure 2B). We have also used publically available scRNA seq data to examine ADAMTS2 and ADAMTS14 expression in distinct CAF subtypes (Supp Figure 2C). Both ADAMTS2 and ADAMTS14 are expressed in inflammatory and myofibroblastic CAFs, with ADAMTS14 expression lower than that of ADAMTS2. Given the complexity of CAF heterogeneity it is possible that ADAMTS2/14 secretion by one population regulates the resulting phenotype of surrounding CAFs, however this hypothesis if beyond the scope of our current work.

Major comments: - Page no. 4, Line 71, The authors conclude that the invasion in the chimeric spheroids is "led by" stellate cells. This is a key concept in the manuscript. How do the authors define the "led by" phenomena? What is the frequency that this occurs?

In our experience all invasive projections are stellate led, defined as a stellate-labelled nucleus present at the tip of invasive projections. Indeed the human cancer cells used in this study are incapable of invading in the absence of stellate cells (Supp figure 1 A). We have previously reported this model where we demonstrated FGFR1 activity in the stellate cells is crucial for invasion (PMID: 36357571). Others have demonstrated the general importance for fibroblasts in leading invasion (PMID: 18037882, 28218910). Interestingly in our study, mouse cancer cells were capable of invading in the absence of stellate cells. However, when cultured with stellate cells, projections were predominantly stellate led.

• For Figure 2A and S2A, the text suggests that the heatmap represents the stellate vs cancer cell expression (as shown in Figure 1B and S1B) in the respective species but the labelling below the heatmap suggests they are all cancer cells (Mia, Pan, R2 and DT). Is this a typo? Could the authors clarify this?

We use Mia, Pan, R2 and DT to define the sphere combination from which the data originated. We have improved the clarity of the heatmaps by colour coding the different cell types within each sphere, and matching it with the cell type data presented in the heat map. We hope this improved labelling makes the heatmaps more accessible.

• The text and the figures are lacking information about the cell line names used in the experiment, e.g, Figure 2C, 2D, 2SB, 2SC and 2SD does not indicate what cell line was used in the study. This is the same with other figures as well. Please indicate in all instances exactly which samples are queried.

We have now included reference to the cell type and stellate cell species used in each experiment in relevant figure legends. Key 3D invasive experiments were conducted with both human and mouse stellate cells.

• It's mentioned in the text that the authors have used the cancer and stellate cells in a 1:2 ratio but the numbers of stellate cells look different between different spheroids confocal images. e.g. The numbers look very different between the Miapaca2:PS1 vs Miapaca2:mPSC spheroids. Is this simply the representative images, or are their bona fide differences. This, in turn, would impact on claims of cells being 'led' by stellate cells. Can the authors clarify?

This is a consequence of the method by which the stellate cells were immortalised. Human PS1 stellate cells were immortalised with hTERT, while mouse stellate cells were immortalised with SV40. A consequence of this is that the mouse stellate cells proliferate faster in 3D than the human stellate cells, with both proliferating slower than the cancer cell compartment. So while spheroids start at 1000 cells (666 stellate, 333 cancer) with stellate cells as the prominent component they are quickly overtaken by the cancer cells. Despite this difference in proliferation we find no difference in the invasive capacity of the stellate cells, with invasive projections always stellate led irrespective of whether they are human or mouse.

• While for most of the experiments the authors generated the chimeric spheroids first and then performed the respective experiments, it appears that for the invasion assay simply co-culture of Cancer cells and stellate cells was done. Is this correct? Have the authors tried performing the assay with the chimeric spheroids to see if the stellate cells still invade?

The Boyden chamber migration assay was conducted by seeding a co-culture of stellate and cancer cells in the apical compartment then imaging their migration to the basolateral side. This provided a second method to predominantly showcase the enhanced migration of cells lacking ADAMTS14 in a manner that could be quantified over time. We have not tried placing spheroids in the apical compartment and imaging invasion through the pores.

• The authors claim that ADAMTS2 and ADAMTS14 regulate the bioavailability of TGFB, and this is a key reason that these regulate CAF differentiation. However, there is no direct demonstration of this concept, which is conspicuous by absence. Could the authors either directly demonstrate this, or remove such notions from the results, and explicitly state that this is an untested speculation in discussion? Examples of this are:

o Line 173, authors state "ADAMTS2 facilitates TGFβ release through degradation of the plasmin inhibitor, SERPINE2 (Figure 5D)"

o Line 196 authors conclude "Together these data implicate 197 ADAMTS14 as a key regulator of TGFβ bioavailability (Figure 6F)."

o Line 240 states "This reduces the activation of Plasmin, preventing the release of TGFβ (Figure 5C)." Since this is just a model without detailed experiments, It will be better to propose rather than conclude.

We appreciate the reviewer’s concern and have now added additional experiments to strengthen the association of ADAMTS enzymes and TGFβ bioavailability.

Using a TGFβ-responsive luciferase reporter we demonstrate that the media from stellate cells lacking ADAMTS14 has greatly increased amounts of active TGFβ (Figure 4), which is abrogated when Fibulin2 is knocked down alongside (Figure 7). This links ADAMTS14 and Fibulin2 to TGFβ activity. Given the extensive literature detailing a role for Fibulin2 in regulating matrix TGFβ release through interactions with fibrillin (e.g, PMID: 19349279, 12598898, 12429738) we believe this is how ADAMTS14 is regulating myofibroblast differentiation. As we do not directly examine the association of Fibulin2 with fibrillin in this manuscript we have amended the associated statements to reflect this.

We have also used a TGFβ-responsive fluorescent reporter to examine TGFβ activity of stellate cells in 3D. Consistent with our results, loss of ADAMTS2 reduces, while loss of ADAMTS14 enhances, TGFβ activity (Figure 4), which can be reversed with concomitant knockdown of their respective substrates SERPINE2 (Figure 6) and Fibulin2 (Figure 7).

• Figure S5C shows a less invasive phenotype in the NTCsi + ADAMTS14si spheroids compared to the NTCsi + NTCsi control. However, there appears no appreciable difference between NTCsi + ADAMTS14si and NTCsi + NTCsi spheroids' brightfield images in Figure 5SD.

Could the authors comment on this?

We thank the reviewer for bringing this to our attention and apologise for our mistake. The images were positioned erroneously. This has now been corrected and the images reflect the quantification that demonstrates a clear increase in invasion following loss of ADAMTS14, which is abrogated with co-knockdown of Fibulin2.

Minor Comments: - Page no. 2, Line 20 has an incomplete sentence "Crosstalk between cancer and stellate cells is pivotal in pancreatic cancer, resulting in differentiation 21 of stellate cells into myofibroblasts that drive."

Apologies for the error. This has been rectified.

• Figure 2C; Figure S2C and Figure S5E lack quantification for the western blots.

We have now included densitometry for all western blots, presenting values relative to the respective loading control and normalised to the experimental control. Values are averages taken from all biological repeats with significance indicated where relevant.

• Why did the authors choose to investigate the Metzincin family? Could the authors provide their reasoning to investigate these proteins, to the exclusion of other candidates?

We focused on the metzincin family, as they are best known for their involvement in cancer invasion. A goal for this manuscript is to present our chimera data set as a discovery tool for the community. While this initial manuscript focuses on protease activity, we have further projects on-going that have used this data set to identify important elements of cancer/stellate communication.

• Info about the number of fields imaged per sample for the microscopy data is missing in the figure legends (e.g. Figure 2F and 2I, Figure 5SF).

We have now included a statement in each relevant figure legend to indicate that quantification was performed on at least five fields of view per biological repeat.

• Any particular reason why the ADAMTS2 expression was not checked through Western blotting like ADAMTS14 in Figure S2B.

We attempted to examine ADAMTS2 by western blotting but were unable to find an antibody that produced consistent results with our samples, and corroborated consistent knockdown by PCR.

• The legends for Figure 3SC and 5SF mention that "Images are representative of at least two biological replicates". How many technical replicates were used? It would be useful if the relative intensity of the images is measured and plotted in a graph.

We have now moved these images to the main figure alongside quantification of αSMA intensity. Images are collected from two biological repeats with quantification obtained from at least five fields of view per image. Together these data strongly demonstrate that loss of ADAMTS14 increases αSMA fibre intensity, which is blocked by either an inhibitor of TGFβ signalling (Figure 4), or co-knockdown of Fibulin2 (Figure 7).

Reviewer #2 (Significance (Required)):

This work provides an examination of the cross talk between fibroblasts and cancer cells in a 3-Dimensional culture model of pancreatic tumour cell invasion. By using chimeric human-mouse spheroids, the authors are able to identify cell-type specific transcripts by bulk RNA sequencing in situ. This advance is not to be underestimated as a number of existing approaches for cell type-specific profiling (eg. single-cell sequencing) relies upon dissociation of cell communities prior to sequencing. It is very likely that transcriptional programmes change during this isolation process. This approach allows the authors to identify transcriptional co-operating programmes in situ. This data provides a resource to understand this key co-operation of these two cell types during tumourigenesis, and will be of interest to the pancreatic cancer field. In addition, the mapping of the key substrate of these enzymes provides further insights that may be useful in understanding the expanded target repertoire of these enzymes beyond collagen processing.

We thank the reviewer for their strong support of our chimeric spheroid approach and resulting investigation into the dichotomic roles of ADAMTS2 and ADAMTS14.

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

AMADTS2 and ADAMTS14 belong to the disintegrin and metalloproteinase with thrombospondin motif protein family, mainly produced by pancreatic stellate cells (PSC) and are related to cancer cell invasion. This study reveals that ADAMTS2 and ADAMTS14 have opposite roles in myofibroblast differentiation based on experiments testing HSC driven cancer cell invasion, variant expression of HSC activation makers and the related downstream targets analysed upon RNA sequencing analyses. The authors (TA) established PSC/cancer cell chimeric spheroids for investigating the crosstalk between these cell types in 3D in vitro. Based on their findings, they claim that ADAMTS2 and ADAMTS14 have different functions regarding PSC and TGF-β activation. However, their conclusions mainly rely on quantitatve data of invasion and mechanistic details are completely lacking.

Comments: Typos, even in the abstract, e.g. first sentence incomplete

We apologise for the error in the abstract and have rectified this in the revised manuscript.

Introduction is rather sparce with one third of the text repeating the results of the study

Our manuscript details a discovery experiment using chimeric spheroids to identify cancer cell and stellate cell transcriptomes in a 3D invasive context. We then showcase the power of this data set by using it to identify and then describe divergent roles for ADAMTS2 and ADAMTS14 in shaping stellate cell biology. Given this two-tiered approach we incorporated text that would normally be placed in the introduction into the results section (e.g. our description of the importance of collagen processing in PDAC, presented as a prelude to the results from figure 3). We feel this improves the flow of the manuscript, rather than having information that isn’t necessarily relevant to the reader at the outset.

Some citations do not at all fit with the position where they are placed; needs approval

We have examined this in detail and are confident in our use of appropriate references throughout.

In this study, it is said that TS2 (AMADTS2) and TS14 (ADAMTS14) have opposite functions on myofibroblast differentiation, with individual depletion leading to distinct matrisomal phenotypes in PSC. However, both similarly contribute to collagen processing. As we know, collagen is increased in response to TGFβ signaling, since TS2 depletion (knock down, kd) inhibits and TS14 kd is suggested to promote TGFβ activation, it is expected that this has impact on the available collagen levels. How do the authors explain that nevertheless the kd effect on collagen is very similar?

The primary effects of these enzymes are on the processing of pro-collagen to its mature form, rather than on the production of collagen. This is evidenced in figure 3B where collagen expression in the whole cell lysate is the same following ADAMTS2 knockdown, and slightly reduced with loss of ADAMTS14, but the mature form is lost in the cell culture supernatant.

While myofibroblast differentiation is associated with increased collagen production, it is possible that this is perturbed in a situation where the cell is surrounded by collagen that is incompletely processed (e.g. through biomechanical feedback). Given that our results clearly indicated that the effect of ADAMTS2 and ADAMTS14 on invasion is independent of their roles in collagen processing, this avenue is beyond the scope of the current manuscript.

The authors claim that TS2 facilitates TGFβ release and TS14 is a key regulator of TGFβ bioavailability. However, throughout the whole data, there is no experimental evidence for this conclusion. TGFβ activation, LAP concentration and downstream effects should be provided.

Most of the conclusions in the manuscript are based on effects to invasion and the estimated quantification histograms. "Black boxes in between the treatment, e.g. knockdown and readout, that relate to the signals and mechanisms remain black boxes throughout. For example, the impact of the treatments on stellate cell activation markers, the cancer cells invasion signaling, the SERPINE2- and Fibulin2-dependent myofibroblast differentiation pathways should be mechanistically investigated.

We disagree with this comment. Our invasive model shows a clear role for ADAMTS2 and ADAMTS14 in regulating invasion, which is mitigated by disrupting their substrates SERPINE2 and Fibulin2.

ADAMTS2 loss is associated with a reduction in plasmin activity, which again is mitigated with concurrent loss of SERPINE2. Equally, inhibition of plasmin activity with Aprotinin matches the loss of invasion observed with loss of ADAMTS2. Plasmin has a well-established role in mediating TGFβ release from the matrix. We have now included additional experiments using a TGFβ fluorescent reporter in 3D culture. This demonstrates that loss of ADAMTS2 reduces TGFβ activity, which can be rescued with co-knockdown of SERPINE2 (Figure 6). Our data therefore support a mechanism where ADAMTS2 blocks TGFβ release from the matrix, and therefore myofibroblast differentiation, through its regulation of SERPINE2 activity.

We have strengthened our proposed mechanism for ADAMTS14 regulation of TGFβ through Fibulin2 with the use of both luciferase and fluorescent TGFβ reporter constructs. Using these reporters, we demonstrate that stellate cells lacking ADAMTS14 exhibit increased TGFβ activity (Figure 4), which is mitigated with co-knockdown of Fibulin2 (Figure 7). Combined with the effects on αSMA expression and 3D invasion, our data fit with a model where ADAMTS14 regulates TGFβ bioavailability through Fibulin2.

The authors investigate one cell line each for their conclusions; we know that different cell lines behave differently; can they confirm that the finding they present is of general validity or a finding that is specific for the tested cancer/PSC cell lines. Can the principle findings also be proven in primary cells. More importantly, the authors should proof their findings in PaCa tissue of patients as follows: Expression of the proteases in the tissue, related variation of matrisome signatures, e.g. by snRNASeq, to confirm relevance of the finding.

All our key 3D invasive experiments are repeated with both human and mouse stellate cells, adding strength to our proposed association with ADAMTS2 and SERPINE2, and ADAMTS14 and Fibulin2, on the invasive capacity of stellate cells. As detailed above we have explored the clinical relevance of our findings by examining laser dissected tumour and stromal data from PDAC tissue, and scRNA fibroblast data. These data confirm that ADAMTS2 and ADAMTS14 are predominantly expressed in the stromal compartment of the tumour and are associated with key CAF subtypes present in the PDAC environment, inflammatory and myofibroblastic CAFs.

Details related to the figures: Figure 1: Are the numbers of PSC and PaCa cells integrated in the spheres related to the numbers found in patients?

The 2:1 ratio of stellate to cancer cells used to produce spheres is a technical requirement and reflects the numbers in patients (PMID: 23359139). Cancer cells will proliferate substantially faster than the stellate cells so at the end of the experiment (day 3) the spheres are predominantly cancer cells. Nevertheless the stellate cells are able to drive invasion of the cancer cells, which can be quantitatively assessed in this model.

B, it seems that the PSC in the spheroid are not equally distributed but instead are all located in close vicinity to eachother in a cloud; is that the representative situation for the spheres and is this similar in the PaCa cancer tissue? Does this have influence on the results?

We have replaced this image with a more representative image that shows mouse stellate cells dispersed throughout the sphere.

Figure 2: It is interesting to hear that BMP1, which is actually a ligand for BMP signaling is a protease for Collagen. How does this work?

While the BMP family generally belong to the TGFβ superfamily, BMP1 is the exception in that it is a C-terminal collagenase. Please refer to reference 21 in the manuscript (PMID: 33879793), which details the role of BMP1 on collagen processing and the resulting effect on PDAC progression.

C, Quantification of all blots should be presented.

We have now included densitometry for all western blots, presenting values relative to the loading control and normalised to the experimental control. Values are averages taken from all biological repeats with significance indicated by stars.

Figure S2: TS2 kd and TS14 kd should be confirmed and provided by both qrt PCR and WB data.

We were unable to assess ADAMTS2 knockdown by western blot due to the quality of available antibodies. We are confident that either western or PCR confirmation of knockdown is sufficient, especially given the strong phenotype observed with the resulting knockdown.

Figure 3: F; this result is arguing against the conclusion that TGFb bioavailability is a function of the ADAMs, since the kd impacts on the treatment result with exogenous TGFb. This suggests an effect downstream of ligand activation by proteasomal cleavage, e.g. receptor activation or signal transduction; this needs clarification. H, I: TGFβR inhibitor reduces TS14kd enhanced αSMA expression. How is unclear and needs clarification, since from F we know that already activated TGFβ needs TS2 to fully induce αSMA expression.

SupplFig.3: B, C, as above!

αSMA expression in stellate cells requires continuous exposure to TGFβ over 48 hours. Active TGFβ has an incredibly short half-life (minutes) and so requires positive feedback to maintain signalling. We propose that following ADAMTS2 knockdown the cells are incapable of releasing further TGFβ to maintain the phenotype. Equally following ADAMTS14 knockdown the cells are able to release more TGFβ, which is incapable of initiating signalling when the receptor is blocked.

Figure 4: TIMP1 is a canonical TGFb signaling target gene in fibrosis. How the authors explain that TIMP1 is upregulated in both knockdowns, when they claim that TS2 and 14 have opposing functions on TGFb activation. This result as well puts their conclusions as regards TGFb and also the myofibroblast phenotype into question. Especially, since TIMP1 signifies stellate cell activation not only in the pancreas, but also in the liver and kidney. C, D, E should be explained in more detail and all details of the results should be presented.

TIMP1 is a substrate for both ADAMTS2 and ADAMTS14, so its enrichment following knockdown of either is unsurprising, reflective of reduced cleavage of TIMP1. Both our 3D invasive assessment in Figure 6 and αSMA imaging in supplementary figure 5 demonstrate that TIMP1 is not responsible for the effect observed as a consequence from loss of either ADAMTS2 or ADAMTS14.

This holds also for the different myofibroblast phenotypes. All data should be included. From recent scRNASeq investigations, several myofibroblast populations were described and compared, e.g my-stellate cells vs i-stellate cells. To which of these phenotypes the identified populations belong?

As mentioned above, we have interrogated publically available data sets and identified ADAMTS2 and ADAMTS14 expression in multiple CAF subtypes. As these proteases are secreted it is probable that one CAF subtype can control the phenotype of surrounding CAFs through ADAMTS2 and ADAMTS14 production. While intriguing, this hypotheses is beyond the scope of the current work.

Figure 5: C, Only brightfield images are provided, confocal images are suggested for comparison of +/- Aprotinin treatment.

We do not think the addition of confocal images will add to the comparison. Aprotinin clearly reduces invasion, which coupled with the action of stellate-derived SERPINE2 on invasion, and reduced plasmin activity following ADAMTS2 knockdown, suggests that plasmin is important for regulating the effects of ADAMTS2 on invasion.

The efficiency of TS2 and Serpine2 kd should be provided by qrt PCR and WB.

TS2 kd promoted SERPINE2 expression should also be presented by qrt PCR and WB.

We are confident that either western or PCR confirmation of knockdown is sufficient. Of note is that following ADAMTS2 knockdown, SERPINE2 expression is unchanged (sup figure 4C). This would indicate that the enrichment of SERPINE2 observed in the matrisome following loss of ADAMTS2 is reflective of reduced cleavage, rather than a change in expression.

Figure 6: A, why ta use aSMA and not invasive activity as a readout here?

Increased αSMA expression following ADAMTS14 knockdown provides a strong, clear, 2D phenotype to act as a readout for an siRNA screen with high-content imaging. Performing such a screen with our 3D invasive model is currently impractical.

There are many parameters leading to decreased aSMA expression upon kd; (1) why only MMP1 and Fibulin were selected as candidates?

From our αSMA screen, MMP1 and Fibulin2 knockdown were the only candidates that were able to both prevent an increase in αSMA seen with ADAMTS14 loss alone, and are known ADAMTS14 substrates. Further validation in our 3D invasive model demonstrated that Fibulin2 and not MMP1 was responsible for the effect of ADAMTS14 loss on invasion.

(2) the single kd control of the screen candidates is missing!

We feel this control is not needed, as the goal of the experiment was to establish which candidate was responsible for mediating the effects brought about by ADAMTS14 knockdown. Increased αSMA expression with IL-1β loss validates our approach, as this is a known negative regulator of TGFβ signalling.

(3) Can it be expected that all these matrisomal proteins are involved in aSMA expression regulation? I have doubts.

We agree with the reviewers comment, from the siRNA screen (sup figure 5B) it is clear that the majority of the identified matrisome proteins have a minimal effect on αSMA expression following loss of ADAMTS14.

C, D, E, why MMP1 was not also tested in these assays?

Our spheroid assay clearly demonstrated that invasion was enhanced following ADAMTS14 knockdown even with co-knockdown of MMP1. Given the strong rescue observed with co-knockdown of Fibulin2 we proceeded to further analyse this candidate over MMP1.

F, Fibrillin is shown in the figure but not described in the text. It would be quite interesting to see whether Fibrillin kd has the same effect as TS14 kd on LTGF-β activation (which of course need to be shown experimentally).

The association of fibrillin with TGFβ release is well established as it underpins the biology behind Marfan syndrome. Loss of fibrillin, or mutations to its TGFβ binding sites results in a phenotype consistent with super active TGFβ signalling.

E, what is the meaning of αSMA intensity quantification? By IF staining of αSMA? PSC αSMA expression should be quantified by qrt PCR and WB.

We have now incorporated the confocal images analysing αSMA expression into the main figure and labelled the quantification accordingly. We feel this improves the clarity of the figures. Every western blot is now presented with quantification.

Also here, kd efficiency of TS14 and Fibulin2 should be provided by qrt PCR and WB.

Figure S5E should be part of figure 6, qrt PCR of Fibulin2 should be added.

We have moved this western blot to the main figure (Fig 7C). We feel additional PCR validation of Fibulin 2 knockdown is not necessary.

Figure 5/6 and throughout: It is claimed that ADAMTS2 and ADAMTS14 regulate TGFβ bioavailability through SREPINE2-Plasmin and Fibulin2. As mentioned above, TGFβ activation is only mentioned in the schemes, but no experimental evidence is given. In addition, according to previous studies, ADAMTSs can activate latent TGFβ directly by interaction with the LAP of latent TGFβ. .

We have now included extra experimental evidence to support an association of ADAMTS proteins with TGFβ bioavailability. Using a TGFβ luciferase reporter construct, we demonstrate that active TGFβ is increased following loss of ADAMTS14, which is abrogated with concomitant loss of Fibulin2. This provides further evidence that ADAMTS14 is mediating its effects on myofibroblast differentiation / invasion through TGFβ release.

Figure 3B, C, and 6D: We are confused from the migration/invasion assays. Invasion should be based on migration of tumor cells, whereas in the migration assays only stellate cells seem to be active? Can you explain this to us? According to Figure 3B, stellate and cancer cells are cocultured in the chamber. Is this the same condition as for the experiment presented as figure 6D?

In our migration assay, stellate and cancer cells are co-cultured in the apical chamber and cell migration imaged over time. We pooled data of both cancer and stellate cell migration following stellate specific knockdown of either ADAMTS2 or ADAMTS14, which showed an increase in cell migration following loss of ADAMTS14. In figure 7, we again use this assay to demonstrate that Fibulin2 expression accounts for the phenotype observed from loss of ADAMTS14.

In summary, this study for the first time found that ADAMTS2 and ADAMTS14 have opposite roles on myofibroblast differentiation, which is shown by using chimeric spheroids of stellate and pancreatic cancer cells. The authors claim a therapeutic potential for pancreatic cancer by regulating ADAMTS2/14-mediated stellate cell activation, which should avoid cancer cell invasion. The approach is interesting and there is preliminary evidence, however the study has many gaps and requires substantive workload.

We thank the reviewer for their support of our findings. We hope the additional data, combined with the known role for these substrates in the regulation of TGFβ, strengthens the clarity of our manuscript.

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

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

The manuscript by Carter and colleagues examines that role of cancer-associated fibroblasts (CAFs) in regulation of invasion in a 3D co-culture assay with epithelial cells. The authors propose that invasive chains of cancer cells are led by fibroblasts. The authors utilise a system of co-culture to create chimeric human-mouse fibroblast-cancer cell spheroids (both directions utilised, to eliminate species bias) to allow for in situ sequencing of the co-operating transcriptional programmes of each cell type during 3D invasion. From this powerful approach, this allowed the authors to identify two key two collagen-processing enzymes, ADAMTS2 and ADAMTS14, as contributing to CAF function in their system. The authors identify that these two enzymes have opposing roles in invasion, and map some of their key substrates in invasion, which extend beyond collagen-processing. The authors propose that one key function is to control the processing of TGFbeta, the latter of which is a regulator of the myofibroblast subpopulation of CAF. Overall, the findings of the manuscript are interesting, but need some further proof-of-principal demonstrations to extend their findings to support the claims within.

• The authors demonstrate a clear role of ADAMTS2 and ADAMTS14 in stellate function during differentiation and invasion. Is there any evidence of such changes in patient materials? Could the authors query publicly available databases of micro-dissected stroma vs epithelium to validate the translational relevance of their findings?

Major comments:

• Page no. 4, Line 71, The authors conclude that the invasion in the chimeric spheroids is "led by" stellate cells. This is a key concept in the manuscript. How do the authors define the "led by" phenomena? What is the frequency that this occurs?
• For Figure 2A and S2A, the text suggests that the heatmap represents the stellate vs cancer cell expression (as shown in Figure 1B and S1B) in the respective species but the labelling below the heatmap suggests they are all cancer cells (Mia, Pan, R2 and DT). Is this a typo? Could the authors clarify this?
• The text and the figures are lacking information about the cell line names used in the experiment, e.g, Figure 2C, 2D, 2SB, 2SC and 2SD does not indicate what cell line was used in the study. This is the same with other figures as well. Please indicate in all instances exactly which samples are queried.
• It's mentioned in the text that the authors have used the cancer and stellate cells in a 1:2 ratio but the numbers of stellate cells look different between different spheroids confocal images. e.g. The numbers look very different between the Miapaca2:PS1 vs Miapaca2:mPSC spheroids. Is this simply the representative images, or are their bona fide differences. This, in turn, would impact on claims of cells being 'led' by stellate cells. Can the authors clarify?
• While for most of the experiments the authors generated the chimeric spheroids first and then performed the respective experiments, it appears that for the invasion assay simply co-culture of Cancer cells and stellate cells was done. Is this correct? Have the authors tried performing the assay with the chimeric spheroids to see if the stellate cells still invade?

• The authors claim that ADAMTS2 and ADAMTS14 regulate the bioavailability of TGFB, and this is a key reason that these regulate CAF differentiation. However, there is no direct demonstration of this concept, which is conspicuous by absence. Could the authors either directly demonstrate this, or remove such notions from the results, and explicitly state that this is an untested speculation in discussion? Examples of this are:

  • Line 173, authors state "ADAMTS2 facilitates TGFβ release through degradation of the plasmin inhibitor, SERPINE2 (Figure 5D)"
  • Line 196 authors conclude "Together these data implicate 197 ADAMTS14 as a key regulator of TGFβ bioavailability (Figure 6F)."
  • Line 240 states "This reduces the activation of Plasmin, preventing the release of TGFβ (Figure 5C)." Since this is just a model without detailed experiments, It will be better to propose rather than conclude.

• Figure S5C shows a less invasive phenotype in the NTCsi + ADAMTS14si spheroids compared to the NTCsi + NTCsi control. However, there appears no appreciable difference between NTCsi + ADAMTS14si and NTCsi + NTCsi spheroids' brightfield images in Figure 5SD. Could the authors comment on this?

Minor Comments:

• Page no. 2, Line 20 has an incomplete sentence "Crosstalk between cancer and stellate cells is pivotal in pancreatic cancer, resulting in differentiation 21 of stellate cells into myofibroblasts that drive."
• Figure 2C; Figure S2C and Figure S5E lack quantification for the western blots.
• Why did the authors choose to investigate the Metzincin family? Could the authors provide their reasoning to investigate these proteins, to the exclusion of other candidates?
• Info about the number of fields imaged per sample for the microscopy data is missing in the figure legends (e.g. Figure 2F and 2I, Figure 5SF).
• Any particular reason why the ADAMTS2 expression was not checked through Western blotting like ADAMTS14 in Figure S2B.
• The legends for Figure 3SC and 5SF mention that "Images are representative of at least two biological replicates". How many technical replicates were used? It would be useful if the relative intensity of the images is measured and plotted in a graph.

Significance

This work provides an examination of the cross talk between fibroblasts and cancer cells in a 3-Dimensional culture model of pancreatic tumour cell invasion. By using chimeric human-mouse spheroids, the authors are able to identify cell-type specific transcripts by bulk RNA sequencing in situ. This advance is not to be underestimated as a number of existing approaches for cell type-specific profiling (eg. single-cell sequencing) relies upon dissociation of cell communities prior to sequencing. It is very likely that transcriptional programmes change during this isolation process. This approach allows the authors to identify transcriptional co-operating programmes in situ. This data provides a resource to understand this key co-operation of these two cell types during tumourigenesis, and will be of interest to the pancreatic cancer field. In addition, the mapping of the key substrate of these enzymes provides further insights that may be useful in understanding the expanded target repertoire of these enzymes beyond collagen processing.

In chapter 17, Ice cream, sorbet, and other frozen products are discussed. Making ice-cream is not a simple process, but instead, a complex one. As we know, sugar is one main ingredient in the making of ice cream. However, it's actually more important to not that the ice crystals and air bubbles play just as of an important role. I chose this annotation to dive into because the temperature of ice cream matters. Ice cream absorbs heat when human tongues come into contact with it. The temp increases as the larger surface area melts. That is, the 'ice chrystals'. Some ice creams feel colder than others because of the way they were made, however, the temperature is typically the same below -320 degrees F. Air bubbles must be prevented from colliding together because the ice cream has to be stabilized. There are multiple important elements and steps that make for a quality ice cream, but maintaining the right temperature can make the process easier.

yea this is an intriguing point, it's definitely weird that the emperor just disregarded them.

I have thought this, seen this and believe that this is real. It can be very hard to feel accepted when you don't fit the standards that everyone says are what make you beautiful and I think that is why so many girls feel the need to alter their appearance.

This take is also striking to me, because I both relate and disagree.

As a south asian woman, I am often reminded that I am the great-grand-daughter of colonized Indian/Pakistani people. Like Hurston, it does not make me sad or pitiful, but at times, makes me slightly angry and often informed. It allows me to remind myself why success as a woman of color is so important to me. It also allows me to remind myself that discrimination and stereotypes are out of my control, and we're working towards a brighter future, keeping our colonial past always in our hearts and minds.

I understand Hurston's take to be slightly more cynical, yet very similar. However, I believe where we differ is that I choose to remember the history as a reminder to move forward. It seems through this passage that Hurston's ideology is more to disregard it because it was so long ago and never her choice or suffering.

This ideology makes complete sense, I was just struck by it because it's not something I often hear from people of color.

the fact that the cops tried to stop people from showing who had done the graffiti is so funny bc the artists aren't even trying to hide who they are and the cops don't seem to be doing anything about tracking ppl down based on these photos. It's like tweeting that you just robbed a bank when your profile has your full name

It's just ASSUMED that all graffiti is ugly when??? that's not how art works, you can't just say an entire medium is ugly

He wishes she could see herself through his eyes so she could see how beautiful she truly is

This comparison is very good and impactful because it's very easy for us to kill a small creature but it's just as easy for God to send someone down to hell.

• Content
• Means lumps leftist politics in South conservative European politics. He doesn't see them as fundamentally different, both follow the same exploitative and destructive logic

• In Other Words...
• white guilt

This makes so much sense, object based file system, can I do that on linux?

In Spider-Man, Peter Parker is a man of responsibility, caution, friendship, and a sense of undying loyalty to those who are close to him. His Aunt, May, lived by her own motto: "When you help someone, you help everyone". This quote confirms her thoughts are just as similar as that of Aunt May, alongside myself. I think as long as one can spare a second of good, it's a second truly not wasted and benefitable.

Principios del freewriting dados por Peter Elbow en Writing without Teachers

I just think this is cool because I was just doing some readings for data science that focused on how models can be used for basically everything and how all of science really has do with models. I think it's cool that this is one of the main ways this paper suggests using CT in education.

Analysis: I just don't understand what's so special about "pro-Trump people" that only they are privileged to be protected. Is it because they were mainly white? Of course. It is just like every school shooter being protected, every white cop being protected for harming others. That is not normal, everyone has the right to feel safe. We have had too many incidents where authority protects those who harm the most regardless if it's physical or verbal.

This is a super important thing to bring up. In whatever classroom you teach in there will be all kinds of people in it. No two people are exactly the same, so you will have different personalities and attitudes in your classroom. It can be challenging teaching so many different kinds of people, but having resources to ask people if they’ve been through something familiar is beneficial. You never know who has been through a similar experience as you. It’s nice to get some advice for a situation you’re going through. It can make you feel like you aren’t alone, and what you’re going through isn’t just happening to you. Someone may have had the exact experience that you are having. As a teacher you might feel like what you’re going through is unique to just you, but having a place to talk to people and see that they went through the same or similar thing as you is so nice to have. It can be difficult being a teacher, but there are other teachers out there who might understand exactly what you are going through. They could have advice for what to do in the situation you’re going through.

I really like this idea that everyone is involved in a PLN space even if it's just for spectating or commenting. A YouTube video or a TikTok video has content to it, but the comments open up a whole new world of perspectives and wouldn't gain attention unless people like and share the video. When you comment your experience and thoughts, it adds new light to the content and turns it from just a resource to a space and network. I don't just watch a TikTok for the video itself, but half of what I get from it is people in the comments agreeing/disagreeing and adding for example how this activity worked out in their own classroom.

The common perception of the Web as a sui generis medium is also harmful. Conceptually, the most applicable relevant standard for Web content are just the classic standards of written works, generally. But because it's embodied in a computer people end up applying the standards of have in mind for e.g. apps.

You check out a book from the library. You read it and have a conversation about it. Your conversation partner later asks you to tell them the name of the book, so you do. Then they go to the library and try to check it out, but the book they find under that name has completely different content from what you read.

Again, reviving a species - regardless of feasibility or importance - is always going to come at a great cost. This cost can always be put towards conservation instead.

It's interesting that the co-founder's rebuttal is that recently extinct species would never be a serious consideration because there are living species with similar (but not exactly the same, especially given a relatively short timeframe) ecological niches. Yet the mammoth - which has no current ecosystem to play a role in, and would have to pretty much single-handedly change the environment so much as to reverse and continuously work against the effects of natural and on-going, human-accelerated climate change just to have a habitat - is one of the main focuses of the project. I find this rather absurd, to put it lightly.

Something to try out. What would the best Spanish translation be?

I dont know if it's just me, but there is something about this learning journey that feels illegal. I think I am feeling the way I feel due to the influence of centralization and the corporate world we live in. This knowledge should be open to all and more people should know about this.

this is a very binary way of thinking: whether there is suffering or not. also the fact that he doesn't say much about the peaceful times is interesting. I think it's similar to the ideas of utopia where not everyone will understand it because it's such a subjective idea, but there is common suffering that people can relate to. It could also just be emphasizing the times of war is greater than these times of peace.

From this statement, I got that countries are always "at the ready" for war, and have their reasoning be that they are just trying to protect their citizens. I think that this is untrue. While there are plenty of extreme nationalists that support this behavior, I still do think that it's rare that you would be able to find an average citizen in a given country that feels protected by the thought of their country going to war or even supportive of the idea in general. With that being said, now that "wars/conflict" is so normalized, I do think that there are plenty of people who are more willing to cheer on their countries in this game of violence.

Woah. That hit. I think "Just in case" or "Some people are crazy". It's justified to think this way despite laws in place people criminals exist.

I think the diagram could be a little clearer and maybe it would help the students understand better. For example, making the DNA and RNA strands different colors would make them easier to quickly distinguish. Same for the Cas9 protein vs. the DNA-modifying enzyme. You could also put a box around the target base location just to make it very clear where that empty label box is pointing.

I would make sure that however you depict the system in the worksheet, it's identical to how it's shown in the slides.

Why Cauchy when we already know that the sequence of the function is just the heaviside function at \(x = 1/2\)?????

Because convering to some limit in the spaces never meant that the metric of the sequence can be Cauchy. The metric. To show that it's incomplete, we need to start with the \(d(x, y)\) metric first, and then show that the convergence of the sequence of piecwise linear function converges in metric and then show that the limit is not in the space. Just showing thta it's not in the space is not entirely valid. But we sure can make use of the fact that a converging sequence in metric should also be Cauchy yes.

Prompt engineering is not science

the most valuable bit of this page

Chat GPT "Continue Writing"

Our current moment is just a fraction of that. From the explosion of independent music platforms like Bandcamp, the media industry has been made much easier to participate in than it’s ever been before. We are, however, still no closer to the ideal of a utopian future, the perfect combination of egalitarianism and technology that was once so tantalizingly within our grasp.

But something has changed. We are seeing a shift in the way we access, discover and consume music and art, and an awareness among both audiences and creators as to how these tools can be leveraged to achieve something greater. Bandcamp, for instance, has acted as something of a unifying force, connecting creatives from all walks of life, from all backgrounds and across all genres. Almost any musical genre imaginable is represented on the platform and, whether a rap connoisseur or a jazz enthusiast, there is a slice of the musical pie for everyone.

What’s more, the platform doesn’t just cater to the needs of fans, but also provides a space where producers, engineers and aspiring music entrepreneurs alike can create and collaborate, allowing them to access the tools they need to build up their careers. The barrier to entry has been lowered and this gives us something to believe in--that perhaps the music industry isn’t as fractured and disconnected as it once was.

It’s not just Bandcamp that has been doing this. Services such as SoundCloud and YouTube have been hugely instrumental in changing the way we discover and consume music and, in turn, how artists are perceived and distributed. By unifying audiences, providing much needed access to resources and providing a platform for collaboration, these services have helped level the playing field for independent artists, making it possible for them to reach wider and more diverse audiences than ever before.

It may be some time before the utopian vision of a world united by music becomes a reality, but the proliferation of independent music platforms and the tools they provide have made a significant step towards this ideal. We may never know what’s around the corner, but right now, all we can do is appreciate the progress that’s been made and continue to support platforms such as Bandcamp, SoundCloud and YouTube as they endeavor to change the music industry and make it accessible to everyone.

Author Response

Reviewer #1 (Public Review):

This is a very interesting paper showing that during amino acid starvation of Neurospora, the general amino acid control factors CPC-1 and CPC-3 are crucial to maintaining circadian rhythm at the levels of rhythmic growth and transcription of the FRQ gene. They show that deleting both genes leads to reduced and arrhythmic cell growth and FRQ transcription that can be accounted for by severely reduced occupancy of the FRQ promoter by the key transcription factor WCC. This defect in turn appears to result from diminished H3 acetylation of the FRQ promoter that was observed at least in the cpc-1 mutant, which is mediated by Gcn5. Thus, they show that Gcn5 occupancy at FRQ is rhythmic and impaired by cpc-1 knock-out, that CPC-1 occupies the FRQ promoter, and provide coIP evidence that Cpc-1 interacts with Gcn5 and Ada2 and, hence, could act directly to recruit these cofactors to the FRQ promoter. Importantly, they show that knock out of GCN5 eliminates rhythmic cell growth and FRQ expression (although surprisingly not FRQ mRNA abundance), as well as reducing H3ac levels and WCC binding at FRQ. They further show that TSA treatment can reverse the effects of histidine starvation on the circadian period in WT cells, and can partially restore rhythmic growth to histidine-starved cpc-3 cells, and that elimination of HDAC Hda1 increases H3ac at FRQ in WT cells. They provide some evidence that transcriptional activation of certain aa biosynthetic genes by CPC-1 is also rhythmic, although the evidence for this is not strong and it's unclear whether CPC-1 occupancy or its activation function would be periodic. They also did not address whether CPC-1 occupancy at FRQ is rhythmic.

This work is important in providing convincing evidence that CPC-1-mediated induction of transcription factor CPC-3 in starved Neurospora cells mediates CPC-1-mediated recruitment of Gcn5 and acetylation of the FRQ promoter, which counteracts the function of histone deacetylase HDA1 to maintain high occupancy of the transcription factor WCC and attendant circadian rhythm of FRQ transcription. Although the work does not identify new regulatory circuits, such as rhythmic transcription of FRQ, the role of Gcn5, Hda1, and promoter histone acetylation in supporting transcriptional activation, and the general amino acid control response to amino acid starvation are all well-established mechanisms, the work is significant in showing how these pathways and mechanisms are integrated to maintain circadian rhythm in the face of amino acid limitation.

There is an abundance of convincing experimental evidence provided to support the key claims just summarized above. However, there are a few instances in which additional experiments might be required to resolve a discrepancy in the data or provide stronger evidence to support a claim.

Thanks for the comments. We have revised the manuscript as suggested.

Reviewer #2 (Public Review):

This study by Liu et al. investigates the mechanism that enables the Neurospora circadian clock to maintain robust molecular and physiological rhythms under conditions of nutrient stress. The authors showed that the nutrient-sensing GCN2 signaling pathway is required to maintain robust circadian clock function and output rhythms under amino acid starvation in the filamentous fungus Neurospora. Specifically, they observed that under amino acid starvation conditions, knocking out GCN2 pathway components GCN4 (CPC-1) and GCN2 (CPC-3) severely disrupts rhythmic transcription of core clock gene frequency (frq) and clock-regulated conidiation rhythm. They provided data to indicate that the observed disruptions are due to reduced binding of the White Collar (WC) complex to the frq promoter stemming from lower histone H3 acetylation levels. This prompted the authors to propose a model in which GCN2 (CPC-3) and GCN4 (CPC-1) are activated upon sensing amino acid starvation, recruit GCN-5 containing SAGA acetyltransferase complex to maintain robust histone acetylation rhythm at the frq promoter. They then performed a battery of assays to show that both GCN-5 and ADA-2 are necessary for maintaining robust H3ac, frq mRNA, and conidiation rhythms under normal conditions. To support that low H3ac level at the frq promoter is the cause for impaired WC binding and frq transcription, they demonstrated they can partially rescue the observed rhythm defects of the knockout mutants under amino acid starvation using an HDAC inhibitor. Finally, the authors used RNA-seq to identify genes and pathways that are differentially activated by GCN4 (CPC-1) under amino acid starvation conditions. Many of these genes are involved in amino acid metabolism and they showed that 3 of them exhibit rhythmic expression in WT but low and non-rhythmic expression in the CPC-1 KO strain.

Strength: The 24-hour period length of the circadian clock is known to be stable over a range of environmental and metabolic conditions because of circadian compensation mechanisms. Whereas temperature compensation (maintenance of circadian period length over a physiological range of temperature) has been studied extensively in multiple model organisms, the phenomenon of nutritional compensation and its underlying mechanisms are poorly understood. This study provides new insights into this important yet understudied area of research in chronobiology. In addition to advancing our understanding of fundamental mechanisms governing clock compensation mechanisms, this study also adds to our understanding of metabolic regulation of rhythmic biology and the relationship between nutrition and healthy biological rhythms. Given that the GCN2 nutrient-sensing pathway is broadly conserved beyond Neurospora, findings from this study will likely be relevant to other eukaryotic systems.

The authors provided strong evidence supporting their claims that the GCN2 signaling pathway is important for maintaining the robustness of the Neurospora clock under conditions of amino acid starvation. The authors performed parallel experiments in normal (no 3-AT) vs amino acid-starved conditions (+3-AT). Their observations of relatively minor disruptions of molecular and conidiation rhythms in cpc-3 and cpc-1 KO strains in normal nutrient conditions compared to starvation conditions support their model that sensing of amino acid starvation by GCN2 pathway-induced changes at the chromatin and transcriptional level that are necessary to maintain a robust frq oscillator. Without the comparison between normal vs amino acid starved conditions, this part of their model will not be as strong.

Previously Karki et al. (2020) showed that rhythmic activation of GCN2 kinase is regulated by the clock, resulting in clock-control rhythmic translation initiation. This study uncovers an additional mechanism through which GCN2 pathway modulates circadian rhythms by regulating histone acetylation of rhythmic genes. RNA-seq as described in Figure 7 provides some potential targets.

Thanks for the comments and suggestions. We have revised the manuscript as suggested.

Weakness:

(1) The authors propose a model (Figure 8) in which the GCN2 pathway is ,activated by amino acid starvation and recruits the SAGA complex to promote histone acetylation level at the frq promoter. There is however no data in this study showing that the GCN2 pathway is activated in amino acid-starved conditions, only that it is required to maintain robust frq and conidiation rhythms. The authors should clarify how they are defining "activation of the GCN2 pathway" in this study. For example, is it recruitment of GCN-5 and SAGA complex to frq promoter?

Thanks for the question. CPC-3, the GCN2 homolog in Neurospora, is the only eIF2α kinase responsible for eIF2α phosphorylation at serine 51(Karki S et al. 2020, PMID: 32355000). As shown in the revised Figure 1-figure supplement 1A, the eIF2α phosphorylation and CPC-1 were induced by 3-AT treatment in the WT but not in the cpc-3KO strain. These results demonstrate that the GCN2 pathway is activated by amino acid starvation, and as a result, the CPC-1 expression is activated to recruit the SAGA complex to the frq promoter.

(2) The experiments to examine the involvement of GCN-5 and ADA-2 were performed in normal conditions (no amino acid starvation). Unlike cpc-1 and cpc-3 KO strains, gcn-5 and ada-2 KO strains showed severely disrupted frq rhythms in normal nutrient conditions, suggesting they are normally required for robust circadian rhythms. If GCN-5 and the SAGA complex are normally involved in regulating H3ac rhythms in the frq loci, how does GCN2 pathway modulates the activity of GCN-5 and SAGA complex in conditions of amino acid starvation? Are the interactions between GCN2/4 with GCN-5 and SAGA complex different in normal vs amino acid starved conditions? The authors should clarify their model.

As mentioned above, our data suggested that GCN-5 and ADA-2 are required for robust circadian rhythms under normal conditions. As suggested, we did detect dampened rhythmic expression of frq in the gcn-5KO and ada-2KO strains under amino acid starvation (Figure 5D and 5E and Figure 5–figure supplement 1E and 1F). We also performed Co-IP to compare the difference of interactions between CPC-1 with ADA-2 and GCN5 with ADA-2 under normal and amino acid starved conditions. The results showed that although the Myc.GCN-5, MYC.CPC-1 or Flag.ADA-2 protein level was repressed by 3 mM 3-AT treatment (likely due to global translational inhibition by induced eIF2α phosphorylation) (Karki S et al. 2020, PMID: 32355000), the interactions between CPC-1 with ADA-2 and GCN-5 with ADA-2 were almost the same under normal and amino acid starved conditions (IP was normalized with Input) (Figure 4B and 4C). These results indicated that amino acid starved conditions had little impact on the protein interactions between CPC-1 with GCN-5 and SAGA complex.

In our model, we proposed that amino acid starvation resulted in compact chromatin structure (due to decreased H3ac) in the frq promoter in the WT strain (Figure 3B), likely due to activation of histone deacetylases or inhibition of histone acetyltransferases. Amino acid starvation activates GCN2 pathway and induces CPC-1 expression. The induced CPC-1 can recruit GCN5-containing SAGA complex to the frq promoter to loosen the chromatin structure, promoting frq rhythmic transcription under starvation conditions. However, in the cpc-3KO mutants, CPC-1 could not effectively recruit GCN5 containing SAGA complex to frq promoter, resulting in arrhythmic frq transcription. We have now clarified our model in the revised discussion.

(3) Given that the GCN2 pathway is important for nutrient sensing, the authors should not disregard the alternative hypothesis that the GCN2 pathway may be important for nutrient compensation and plays a role in maintaining the robustness of rhythms in a range of nutrient conditions.

Thanks for the suggestion. We now discussed the alternative hypothesis in the revised manuscript. “Because GCN2 signaling pathway is important for nutrient sensing, it may be important for nutrient compensation and plays a role in maintaining the robustness of rhythms in a range of nutrient conditions”.

(4) The authors should use circadian statistics to compute the phase and amplitude of the mRNA, DNA binding of the WC complex, and H3Ac rhythms. This will allow them to compare between rhythms and provide statistical significance values, rather than just providing qualitative descriptions. This will be valuable when comparing rhythms between strains and between nutrient conditions.

As suggested, we used CircaCompare to analyze our data.

Reviewer #3 (Public Review):

This is an important paper anchored by the observation that cultures of Neurospora undergoing amino acid starvation lose circadian rhythmicity if orthologs in the classic GCN2/CPC-3 cross-pathway control system are absent. Data convincingly show that Neurospora orthologs of Saccharomyces GCN2 and GCN4 (CPC-3 and CPC-1 respectively) are needed to promote histone acetylation at the core clock gene frequency to facilitate rhythmicity. While the binding of CPC-1 and thereby GCN-5 are plainly rhythmic, the explanation of exactly where rhythmicity enters the pathway is incomplete.

Figure 1 shows that inhibition of the HIS-3 activity affected by 3-AT, which should trigger cross-pathway control, is correlated with a graded reduction in the amplitude of the rhythm, and eventually to arrhythmicity at 3 mM 3-AT. While normalized data are shown in Figure 1B, raw data should also be provided in the Supplement as sometimes normalization hides aspects of the data. Ideally, this would be on the same scale in wt and in mutant strains.

We revised as suggested and added the raw data. The results are now shown in Figure 1–figure supplement 2A and 2B and Figure 5–figure supplement 1B and 1C.

Figure 2. The logical conclusion from Fig 1 is that circadian frq expression driven by the WCC has been impacted by amino acid starvation in the mutants. If so, either WC-1/WC-2 levels might be low, or else they might not be able to bind to DNA. When this was assessed, ChIP assays showed a loss of DNA binding. Although documented, an interesting result is that WCC protein amounts are sharply increased, especially for WC-1. The authors could comment on possible causes for this.

Line 176, "hypophosphorylation of WC-1 and WC-2 (which is normally associated with WC activation . . . )". While the authors are correct that this is often the case it is not always the case and this introduces a potentially interesting caveat. That is, the overall phosphorylation status of WCC does not always reflect its activity in driving frq transcription. This was first noticed by Zhou et al., (2018 PLOS Genetics) who reported that even though WCC is always hyperphosphorylated in ∆csp-6, the core clock maintains a normal circadian period with only minor amplitude reduction. This should be noted, cited, and discussed.

Thanks for the suggestion. We revised the manuscript as suggested, “It should be noted that the overall phosphorylation status of WCC does not always reflect its activity in driving frq transcription, possibly due to the unknown function of multiple key phosphosites on WCC (Wang et al., 2019; X. Zhou et al., 2018)”.

Figure 2 and Figure 2 Suppl. report different gel conditions and show that the sharply increased WC1/WC-2 levels seen in Fig 2 resulting from 3-AT treatment of the cpc pathway mutants are due to the accumulation of hypophosphorylated WC-1/2. The conclusion would be stronger if the gels in the Supplement showed the same degree of difference between wt and mutants as seen in Fig 2. In any case, these hypophosphorylated WC should be active and able to bind DNA but plainly are not based on Fig 2.

Thanks for the comments. It’s correct that WC-1/WC-2 were hypo-phosphorylated and their protein levels were increased (Figure 2 and Figure 2-figure supplement 1). However, the reduced binding of WC-1/WC-2 at the frq promoter explains for the reduced frq transcription in the cpc-1KO or cpc-3KO mutants under amino acid starvation.

Figure 3 correlates the unexpected loss of DNA binding by hypophosphorylated WCC with reduced histone H3 acetylation at frq. The 3 mM 3-AT reported to result in arrhythmicity in cpc mutants in Figures 1 and 2 results in a small (~20%?) and not statistically significant reduction in H3 acetylation in wt, compatible with the sustained rhythms seen in wt in Figure 1, but in a substantial (~5 fold) loss of binding in the ∆cpc-1 background; so CPC-1 is needed for H3 acetylation at frq to sustain the rhythm during amino acid starvation. The simplest explanation here then is that the hypophosphorylated WCC cannot bind to DNA because the chromatin is closed due to decreased AcH3.

Thanks for the comments.

Figure 4. Title:" Figure 4. CPC-1 recruits GCN-5 to activate frq transcription in response to amino acid starvation"; the conditions of amino acid starvation should be mentioned here for the reader's benefit. (In the unlikely case that there was no amino acid starvation here then many things about the manuscript need to be reconsidered.)

Based on the model from yeast where amino acid starvation activates GCN2 (aka CPC-3 in Neurospora) kinase which activates the transcriptional activator GCN4 (aka CPC-1) which recruits the SAGA complex containing the histone acetylase GCN5 to regulated promoters, CPC-1 was tagged and shown by ChIP to bind rhythmically at frq. Co-IP experiments establish the interaction of components of the SAGA complex in Neurospora and Neurospora GCN-5 indeed is bound to frq, likely recruited by CPC-1. This part all follows the Saccharomyces model with the interesting twist that the binding CPC-1 is weakly rhythmic and GCN-5 strongly rhythmic in a CPC-1-dependent manner. Based on the figure legend title, these cultures should always be starved for amino acids (although as noted this should be made explicit in the figure legend). In any case, given this, from where does the rhythmicity in GCN-5-binding arise? This question is developed more below.

Line 224, "low in the cpc-1KO strain, suggesting that CPC-1 rhythmically recruit GCN-5". Because ChIP was done only for a half circadian cycle (DD10-22), it is hard to conclude "rhythmically". The statement should be modified.

To address the concern, we performed the ChIP assay using the CPC-1 antibody instead of Myc antibody (revised Figure 4A). Analysis of the ChIP results with CircaCompare showed that CPC-1 binding at the frq promoter was rhythmic without 3-AT (Figure 4A) or with 3 mM 3-AT treatment (Figure 4-figure supplement 1A). Due to the ADA-2-GCN5 and CPC-1-ADA-2 interactions with/without 3-AT treatment (Revised Figure 4B-C), CPC-1 should be able to recruit GCN-5-containing SAGA complex to activate frq transcription in response to amino acid starvation. We have now clarified this model in the revised manuscript. Please also see response to Reviewer 2/point 5.

It was previously reported that the CPC-3/CPC-1 signaling pathway was rhythmically controlled by circadian clock, as indicated by CPC-3-mediated rhythmic eIF2α phosphorylation at serine 51 (Karki S et al. 2020, PMID: 32355000). Our data showed rhythmic CPC-1 and GCN-5 binding at the frq promoter in the WT strain and decreased GCN-5 binding in the cpc-1KO mutant (Figure 4A and 4D). These results suggested that the circadian clock controlled the CPC-3/CPC-1 signaling pathway rhythmically, which in turn promoted the rhythmic frq transcription through recruiting GCN5 containing SAGA complex under amino acid starvation. We clarified the model and description in the discussion.

As suggested by the reviewer, we modified the statement "suggesting that CPC-1 recruits GCN-5-containing SAGA complex to the frq promoter".

Figure 5 shows that rhythmicity in general and of frq/FRQ specifically requires GCN-5 even under conditions of normal amino acid sufficiency, and that normal levels of H3 acetylation and its rhythm at frq require GCN-5. Not surprisingly, high H3 acetylation at frq correlated with high WC-2 DNA binding, and ADA-2 is required for SAGA functions.

As earlier, raw bioluminescence data corresponding to panel B should be provided in the figure or Supplement.

Also, if CPC-3 and CPC-1 regulate frq transcription through GCN-5, why is the frq level extremely low in the cpc-3KO or cpc-1KO(Fig.1D) but remains normal in gcn-5KO (Fig. 5D)?

Raw bioluminescence data are listed in Figure 5–figure supplement 1B and 1C. For frq transcription in the WT and gcn-5KO mutant, please see response to Essential Revisions point 4.

Figure 6 documents the counter effects of TSA which inhibits histone deacetylation and shortens the period versus 3-AT which decreases (via CPC-3 to CPC-1 to GCN-5) histone acetylation and causes period lengthening or arrhythmicity. HDA-1 is necessary for histone deacetylation at frq.

Thanks for the comments.

Figure 7 documents extensive changes in gene expression associated with 3-AT-induced amino acid starvation and the CPC-3 to CPC-1 pathway. How do these results compare with other previously studied systems, particularly Saccharomyces, where similar experiments have been done? Are the same genes regulated to the same extent or are there some interesting differences?

Thanks for the suggestion. We revised our manuscript by comparing the difference of these genes in Saccharomyces. GCN4/CPC-1 targets are similar. “Similar to Saccharomyces cerevisiae (Natarajan et al., 2001), genes in amino acid biosynthetic pathways, vitamin biosynthetic enzymes, peroxisomal components, and mitochondrial carrier proteins were also identified as CPC-1 targets”.

Figure 8 provides a model consistent with the role of the CPC-3/GCN2 pathway in regulating genes in response to amino acid starvation. It seems this could be any gene responding to amino acid starvation.

Not accounted for in the model is the data from Fig 4 which show the rhythmic binding of CPC-1 and stronger rhythmic binding of GCN-5 to frq, both under amino acid starvation. In the presence of 3-AT, amino acid starvation is constant, which should mean that CPC-3 and CPC-1 would always be "on". Why doesn't CPC-1 recruit GCN5 at the same level at all times leading to constant high H3 acetylation rather than rhythmic H3 acetylation as seen in Figure 3? Perhaps, unlike the statement in lines 345-34, it is WCC that regulates rhythmic GCN-5 binding and facilitates rhythmic histone acetylation at frq. Or perhaps the clock introduces rhythmicity upstream from GCN5. Without an answer to the question of where rhythmicity comes into the pathway, the story is only about how the CPC-3/GCN2 pathway in regulating genes in response to amino acid starvation; without explaining the rhythmicity the story seems incomplete.

It was previously reported that the CPC-3/CPC-1 signaling pathway was rhythmically controlled by circadian clock, as indicated by CPC-3-mediated rhythmic eIF2α phosphorylation at serine 51 (Karki S et al. 2020, PMID: 32355000). Our data showed rhythmic CPC-1 and GCN-5 binding at the frq promoter in the WT strain and decreased GCN-5 binding in the cpc-1KO mutant (Figure 4A and 4D). These results suggested that the circadian clock controlled the CPC-3/CPC-1 signaling pathway rhythmically, which in turn promoted the rhythmic frq transcription through recruiting GCN5 containing SAGA complex under amino acid starvation. We clarified the model and description in the discussion.

I love that WIDA philosophy, so many teachers look at MLLs and focus on what they are unable to do, class them as struggling students, 'slow learners' 'EAL kids'. They set their expectations lower yet don't give them the supports to access the content and learn the language that they need to be successful. WIDA's framework is changing more than just mindsets at the school I work for. Its taking some time, but it's happening!

why? I'm not sure I understand the connection

force replication

Sehr gute Idee, Werbung durch klink

Evaluation 1 (Seth Benzell)

Editors' note (David Reinstein): I converted math and Greek letters into latex format, mainly to demonstrate this capacity. I made no other changes.

Thanks to the Unjournal for their invitation to review “Artificial Intelligence and Economic Growth”. In this essay the authors have three announced goals: Help set an agenda for research on the impact of AI on growth, refine research questions on the subject, and summarise and recontextualize key previous findings with an emphasis on Baumol’s cost disease. This is an ambitious task, but the authors largely succeed!

In the first four sections of the paper, the authors do a wonderful job of outlining a general neoclassical model of automation. They explain how the key parameters of the model determine the impact of automation. They distinguish between two types of economic singularity, and show how the more extreme variety emerges naturally from some parameterizations of their model – something which I believe is an important innovation of this paper (including above Nordhaus (2015) a direct antecedent paper). These models stimulate the reading researcher to ask how these parameters could be estimated, opening a door to applied economists to contribute to the macroeconomic question of growth and AI. After this, section 5 is a bit of a disappointment. It lists several additional economic phenomena that might be caused by AI and automation, and occasionally ties these ideas back to economic growth, but in a less organised way without the assistance of a model. The essay closes with empirical evidence on capital shares and automation, which was adequate for the time empirically, but is somewhat lacking in its interpretation of the data.

Let me start by going into detail about what I liked about the first several sections, including some complementary thoughts it inspired in me. Then I’ll explain what I consider the main factor omitted in these sections: the impact of automation and AI on saving and investment. I’ll close with some thoughts on the limitations of sections 5 and 6, and how they might be improved.

Section 2 of the paper lays out a general, neoclassical, model of automation, drawing on Zeira (1998) and Acemoglu and Restrepo (2016). The key equations are clearly presented. The authors highlight Baumol’s "cost disease" -- the phenomenon that an increase in output of one sector of the economy will make goods in a complementary sector more expensive -- as a key phenomenon to be understood for projecting AI and automation-led growth. '\(\rho\)' is the parameter in the model that governs how substitutable different goods (for example, automatable and non-automatable ones) are in the economy. When \(\rho\) is smaller, the economy is relatively more limited by its scarce labor than it is boosted by automation. It is more likely for interest rates and the capital share to even decline because of greater automation. This effect is exacerbated by capital accumulation over time, in contrast to labor which is inelastically supplied. The way I once heard this phenomenon described is "You can always have more capital per-capita, but you can't have more capita per-capita", and the authors do a good job of explaining this theme from the previous literature.

The authors do a great job of highlighting the importance of “\(\rho\)” to economic growth. Implicitly the authors are suggesting to applied researchers to go out and measure this elasticity! Between automated and non-automated tasks, or between relatively capital intensive and labor intensive sectors, for example.

The authors explain several special cases of their model, to explain how other parameters balance against each other as well. They focus on the role of “\(\beta\)”, the share of sectors which are automated. I think the authors are correct in taking a narrative approach to possible paths ‘beta’ can take, rather than following Acemoglu and Restrepo (2016) and trying to endogenize it to the decisions of scientists. It's the right level of detail to stop at, given their more general concerns.

Sections 3 and 4 go farther beyond the current state of the literature, introducing AI as an input to technology production functions and considering versions of an economic singularity. Section 3's formalization is clear, but I might have appreciated a note from the author that other approaches to modelling "AI in the idea production function" might be better -- whereas I think the model in section 2 is more solidly paradigmatic. The key parameter here turns out to be "\(\phi\)", the rate at which knowledge growth is increasing/decreasing in the stock of knowledge.

In section 4, the authors layout what I think are the best taxonomy of economic singularities I've seen (I think the best alternative that would have been in the literature at the time would have been Nordhaus 2015's). While these are somewhat extreme scenarios, they immediately ground themselves by showing how a type I case is the natural result of the oldest economic model of automation -- the AK growth model. I would make the connection between the AK growth model and the "\(\rho=\infty\)" (i.e. all goods are perfect substitutes) case of the general model in section 3 more explicit. The authors then show that the key parameter determining whether type-2 singularity is \(\phi\). In the simpler model (example 2), \(\phi\) being greater than 0 is enough to create an infinite-economic-output singularity. In the third example, the condition is a slightly more complicated function of \(\phi\). The section closes with an ok discussion of some more general related concerns regarding an economic singularity, returning again to \(\rho\) and the role of 'scarce bottlenecks' in output.

I really appreciated these sections, and feel they do a generally good job at agenda setting for both theorists and applied researchers. For applied researchers, I think the way the paper identifies "rho", "phi", and "beta" as especially important serves as a useful directive towards what they should attempt to measure. What might have made the paper even better is a small table with empirical evidence on these parameters so far, to give the applied researcher inspired by this paper a starting point.

For the theorist, the mind swims with possible extensions to and variations on the approaches presented. Obviously a paper like this can't cover or even suggest every possibility. One might imagine variations of a growth model that allows for "\(\rho\)" - which can be interpreted as a taste parameter - to be endogenous in some way. In section 5, the authors hint that markups changing over time could be important. They do the same, in referencing Acemoglu and Restrepo (2016) about making "\(\beta\)" endogenous. Another natural extension makes labor supply endogenous, or might explore an automation -->politics-->growth public choice mechanism. I don't think it's a problem that the authors failed to mention all these possibilities, but some of these I do think are more interesting and directly connect AI and growth than some of the other epiphenomena discussed in section 5 (some of which are less clearly reasoned -- for example, isn't it just as plausible to think that AI will increase centralization and superstar firms as it is to decrease it?) .

Still I do think that the authors fall down in not focusing more heavily on the role of saving in the model. Throughout the paper, the saving rate in the model is assumed to be constant -- a hypothesis that isn't well grounded in either a representative agent model (which achieves a constant interest rate in the long run) or an OLG model (in which saving will be a function of many other considerations). I think this is an important oversight for a document that wants to set the agenda.

I’ll admit I’m a bit of a partisan for this issue, having considered it in (Benzell et. al. 2015) and (Benzell et. al. 2022). In the first paper, we show how in OLG models automation technologies can actually lower output and welfare for future generations. The reason is that savings are made by the young out of their labor incomes, for consumption in their retirements. When automation accumulates, the share of income going to young and laboring savers decreases, and the share going to old spenders increases. This reduces the amount which is saved and reinvested. In certain cases, the reduced saving effect is large enough to more than offset the productivity growth effect of automation. The possibility that a new technology could lower long-run output is not admitted for in the authors' model – ruling out certain conceptually coherent scenarios such as the one imagined in Asimov’s “The Caves of Steel” – where highly productive AGIs and automation exists, but a low saving and reinvestment rate by a socialist government keeps society impoverished.

More generally, the exogenous saving framework pursued by the authors doesn't allow for any inter-generational analysis of the impact of automation. On a more practical level, interpreting the decrease in the global interest rate as telling us something about automation (for example, see the recent "cite") needs to account for global demographic and distributional factors that have created a "global saving glut" (cite). In (Benzell et. al. 2021), we find that even a rate of automation at 5x the historical rate would fail to overcome this headwind and increase interest rates.

This brings me to the final section of the paper, on the evidence to date on automation and capital shares. Karabarbounis and Neiman (2014) is correctly taken as the starting point, and I think the discussion is ok for the time overall. My main quibble is with the characterization of Autor et. al. (2017) and Barkai (2017). These are presented as 'alternative theories of capital share's increase' but they're more like alternate theories of what K+N are measuring. These papers and Barkai and Benzell (2018) claim it is the profit share of income which is increasing, not the capital share, a theory which is consistent with the microevidence on markups (for example, De Loecker et al 2020). . That has tremendous implications for its interpretation in a model of automation. For example Benzell et al (2022) theorise that the profit share has increased because certain inelastically supplied inputs in the economy are complements to automation and measured as profits. Why do I mention this? Well, because it has dramatic implications for whether the rho<1 or rho>1 case is true: If rho<1 then "capital share" shouldn't be increasing, especially if interest rates and growth are low. On the other hand, rho>1 implies an AK world asymptotically, which also seems unlikely. We think it more likely that \(\rho\) is \(\lt 1\), but physical capital's share is actually decreasing, which is how Benzell et al (2022) reconciles this riddle.

Works Cited:

Acemoğlu, D., & Restrepo, P. (2016). The race between machines and humans: Implications for growth, factor shares and jobs.

Autor, D., Dorn, D., Katz, L. F., Patterson, C., & Van Reenen, J. (2020). The fall of the labor share and the rise of superstar firms. The Quarterly Journal of Economics, 135(2), 645-709.

Barkai, S. (2020). Declining labor and capital shares. The Journal of Finance, 75(5), 2421-2463.

Barkai, S., & Benzell, S. G. (2018). 70 years of US corporate profits (No. 277). Working Paper.

Benzell, S. G., Brynjolfsson, E., & Saint-Jacques, G. (2022b). Digital Abundance Meets Scarce Architects: Implications for Wages, Interest Rates, and Growth.

Benzell, S. G., Kotlikoff, L. J., LaGarda, G., & Sachs, J. D. (2015). Robots are us: Some economics of human replacement (No. w20941). National Bureau of Economic Research.

Benzell, S. G., Kotlikoff, L. J., LaGarda, G., & Ye, V. Y. (2021). Simulating Endogenous Global Automation (No. w29220). National Bureau of Economic Research.

De Loecker, J., Eeckhout, J., & Unger, G. (2020). The rise of market power and the macroeconomic implications. The Quarterly Journal of Economics, 135(2), 561-644.

Karabarbounis, L., & Neiman, B. (2014). The global decline of the labor share. The Quarterly journal of economics, 129(1), 61-103.

Nordhaus, W. D. (2015). Are we approaching an economic singularity? information technology and the future of economic growth (No. w21547). National Bureau of Economic Research.

Zeira, J. (1998). Workers, machines, and economic growth. The Quarterly Journal of Economics, 113(4), 1091-1117.

Evaluator details

How long have you been in this field?

I started my PhD in Economics in 2012. I became interested in the impact of automation on economic growth shortly after, so about 10 years.

How many proposals and papers have you evaluated?

I have reviewed about 30 papers. I’d say about ⅓ to ½ of these are broadly on the subject of automation.

Evaluation 2 (Phil Trammell)

Written report

This piece is the chapter on AI and economic growth in Agrawal et al.’s 2019 Economics of Artificial Intelligence: An Agenda. In introducing their chapter, Aghion et al. write that their “primary goal” with it “is to help shape an agenda for future research.” In total, the piece seems to have three goals. First: section 2 contributes to the theory of bread-and-butter automation and industrial growth, supported in part by empirical observations presented in section 6. Second: sections 3 and 4 contribute to the theory of AI and economic growth, in the setting of an R&D-based growth model. (An appendix does so in the setting of a Schumpeterian growth model.) Finally: section 5 informally discusses the implications of AI for growth within models that give firm incentives a central role, and topics for future research in this area.

It is a shame that the authors felt compelled to pack so much in. Each of these three components could easily have generated an excellent piece of its own. Indeed, in my judgment, both of the paper’s original contributions far outshine its commentary on future research directions. Some compression of this kind was probably warranted in context, given the rest of the Agenda’s relative neglect of growth, and how much on the subject there is to say. Nevertheless, the result is a document that both abounds with a truly remarkable array of important new insights about AI and growth, and has somewhat more than the usual share of mistakes and awkward inclusions or omissions.

The outright mistakes are perhaps the more minor flaws, since they are easily corrected on a close reading. Indeed, the PDF on one author’s website when this review was being written already corrected five, in red, from the version published in the Agenda. Writing this review uncovered five more (now incorporated in a further edited PDF, mostly in blue). Of course, some mistakes are understandable, and none so far identified overturn the paper’s central conclusions. Still, they make it harder for a reader to trust any results he has not checked.

The greater flaws, in my view, are the scattered inclusions and important-seeming omissions. As discussed further below, furthermore, these decisions on both counts tend to steer the paper away from scenarios in which AI produces a departure from the “Kaldor Facts” of constant growth rates and factor shares.

The body of the paper opens by exploring how AI might come to replace human labor in every task yet fail to produce any break in economic trends. It does so by introducing in section 2 a simple model in which, over time, asymptotically 100% of tasks are automated, yet the stylized facts of historical growth all asymptotically obtain. In particular, the model asymptotically yields a constant and positive labor share, growth rate, level of capital-augmenting technology, and growth rate in labor-augmenting technology.

Though the model is presented as a baseline from which to explore AI and growth further, it is a brilliant insight on its own. Uzawa’s (1961) Theorem teaches us that to match the broad strokes of industrialized growth, all technology growth can—and sometimes must—be modeled as labor-augmenting. This result offers a valuable guide to closed-form modeling, but no intuition about how technology develops “under the hood”. The image it most directly invokes—of workers buzzing about their work ever faster, and capital accumulating unchanged beside them—is absurd. But more realistic models, in which technological progress consists primarily in the creation of more capable machinery (and leaves workers’ flesh and bones largely untouched), had proved difficult to reconcile with the stylized facts above. Zeira’s (1998) model of automation, for instance, predicts an ever-increasing growth rate and capital share. For offering such an elegant, tractable, and intuitive reconciliation of automation with the stylized facts, I would say that the model of section 2 deserves a place in all but the most elementary introductions to growth theory.

Its quality as a contribution to the theory of historical growth, in turn, strengthens it as a contribution to the theory of growth under AI. The insight that, in the long run, an arbitrarily high fraction of human jobs may be automated without changing the labor share or growth rate is valuable, and at odds with much of the public conversation around automation and work. But after reflecting briefly on the implications of low substitutability across tasks, it is not very surprising that one can write down some model in which this occurs. The surprise, at least to me, is that arguably the most reasonable stylized account of historical automation to date turns out to be just such a model. This observation constitutes a powerful argument for the classic view that, for the foreseeable future, AI advances will amount only to “more of the same”.

The case for this model, or at least this view, is bolstered by the observation in section 6 that in industries with more automation, labor productivity rises but not the capital share.

Having delivered this excellent contribution, section 2 closes with a rather ad hoc simulation in which automation proceeds not continuously but on and off in 30-year spurts. The simulation reveals that exogenous fluctuations in automation can produce fluctuations in growth rates and factor shares, and can generate a capital share that rises, falls, or stays constant over the longer run. The motivation for this flourish is evidently that the simple model generates constant growth and a capital share that rises over time (albeit asymptotically, to a value below 1), whereas the received wisdom is that growth rates and factor shares fluctuate but exhibit no trend at all.

On its own, the fact that fluctuations in make fluctuations out is no surprise. Moreover, the fact that the simpler model produces an asymptotically rising capital share is to my mind not a weakness but yet another strength. The capital share hasrisen over time, both recently and over the longer run, as documented by e.g. Piketty (2014). This trend has coincided with a rise in the capital-to-output ratio: a coincidence that, given a conventional CES production function, would imply that labor and capital are already gross substitutes.

Piketty famously accepts this conclusion, despite extensive evidence against it from other domains, and makes it the cornerstone of his policy agenda. The Aghion et al. model of automation, meanwhile, departing only slightly from conventional CES, manages to reconcile the evidence of a historically (but not boundlessly) rising capital share with the evidence that labor and capital are still gross complements. Any reflections on the significance of this reconciliation, however, are seemingly crowded out of the paper by an awkward model-tweak that eliminates the reconciliation so as to hew to the “stylized facts” even more closely.

With this foundation, sections 3 and 4 explore conditions under which even more thorough automation does produce more extreme consequences. In particular, it explores a Jones (1995)-style R&D-based growth model in which both a “final goods” sector and a “research” sector may be automated. Unfortunately, the results are presented in a way that somewhat deemphasizes the most radical growth possibilities. Still, they are taken more seriously than in any other economics publication to date.

The paper’s first contribution in this direction is a labeling of explosive growth scenarios. Those in which the time-path of output has a vertical asymptote—a time before which output exceeds any finite level—are termed “Type II” growth explosions. (These vertical asymptotes are the mathematical singularities for which techno-accelerationist views are sometimes called “singularitarian”.) Growth scenarios in which the exponential growth rate of output rises boundlessly without producing a vertical asymptote are termed “Type I” growth explosions. Objections that either scenario is physically impossible miss the point. Eternal exponential growth, and even eternally constant output, are presumably impossible as well. What a taxonomy of this kind gives us is a guide to the circumstances under which AI developments should be expected to accelerate growth, and, at least in qualitative terms, how dramatically.

Section 3 explains that asymptotic automation of research tasks, along the lines of section 2’s asymptotic automation of good production tasks, can allow for exponential growth in research inputs, technology, and thus output, even without population growth or any automation of final good production. Absent research automation, one of the latter two processes would be necessary for exponential output growth.

The discussion here feels incomplete. Since the automation of research tasks is presumably itself the result of technological development, one wonders under what conditions this process can sustain itself. Here, however, the automation of research is simply presented as exogenous.

Section 4.1 gives four examples of scenarios in which automation, within the frameworks introduced so far, can yield a growth explosion. Again, the discussion feels incomplete, now for two reasons. First, the examples are not systematic. Indeed, the scenario that would follow most straightforwardly from section 3—it turns out that, for some parameter values, growth is not only sustained but explosive when research automation is modeled as the output of technological development—is not discussed at all. Second, the discussion of the scenarios themselves is sometimes patchy, as outlined below.

Example 1 notes that full automation of final good production generates an “AK” economy. Output thus grows exponentially absent growth in technology (“A”), and double-exponentially given exponential growth in A. Not discussed is that output exhibits a Type I growth explosion even if we don’t simply stipulate exponential growth in A, but instead maintain the standard Jones idea production function and a constant population. In this case, A rises subexponentially but still unboundedly, and the exponential growth rate of output accordingly does the same.

Examples 2 and 4 find that full automation of idea production alone suffices to produce a Type II growth explosion as long as ideas do not “get harder to find” too quickly. (That said, as noted in section 4.2, recent estimates suggest they do.)

Example 3 finds that sufficient automation of good and idea production together produce a Type II growth explosion. A fortiori, it thus finds that the full automation of good and idea production—i.e., simply general AI—always produces a Type II growth explosion, whatever the rate at which ideas get harder to find and whatever values any other parameters take on. This could have been the paper’s headline result, but it is not even quite stated, let alone emphasized.

Section 4’s discussion of explosive growth scenarios concludes by giving various roadblocks to them the “last word”. Some tasks may be near-impossible to automate, for instance, or near-impossible to make more productive even once automated. In the face of bottlenecks like these, singularitarian dynamics might break down.

Finally, section 5 informally explores the growth implications of AI on a variety of views in which growth depends centrally on firm incentives. An appendix then delves formally into one such view: a Schumpterian model in which AI can slow growth by making it easier for actors to steal or replace each other’s innovations, thus disincentivizing their development.

The three classes of considerations discussed in sections 5.1, 5.2, and 5.3 are AI’s growth implications via impacts on market structure, resource allocation across sectors, and firm organization respectively. In short, AI could increase or decrease an industry’s competitiveness, by making it easier to overcome barriers to entry (say, verifying quality in the absence of reputation) or to erect them (say, with closed networks). Models like that of Aghion and Howitt (1992), in turn, teach us that increases in competitiveness can increase or decrease innovation incentives. AI could also affect growth in other ways (say, by facilitating adjudication in the face of incomplete contracts). Collectively, these considerations render AI’s growth implications complex and ambiguous. They allow for the paper’s most explicit calls for follow-up research, and its most wide-ranging.

It is not clear why this broad and open-ended discussion is reserved for firm-centric growth considerations in particular. As noted earlier, one can imagine a version of this paper that remains focused on formal results within the Jones-style R&D-based framework. But a paper surveying AI’s growth possibilities more broadly would ideally explore these implications from something closer to the full range of mainstream growth perspectives, including e.g. those with a central role for institutions or for human (and given AI, presumably machine) capital accumulation. Also, even within the firm-centric discussion, a singularity-sympathetic reader will again find something of a de-emphasis of AI’s radical potential. The singularities of section 4.1 are followed in 4.2 by the point that automation could face bottlenecks, for instance; the observation that attempts at growth-slowing idea theft could face bottlenecks too is left to the reader.

The conclusion reinforces this slant. The only paragraph on explosive growth opens by noting it as a “(theoretical) possibility”, and goes on primarily to summarize why the possibility may fail.

In fairness, this reticence may be due to a perception that many economists, jaded by the Luddite track record, would react poorly to models in which capital ever thoroughly substitutes for labor. For what it’s worth, Patrick Francois’s comment on the paper, published just after it in the Agenda, offers at least some evidence to the contrary: he quickly accepts the plausibility of near-term general AI, but muses on its implications for political economy rather than growth.

All that said, in sum, Aghion et al. provide excellent and wide-ranging analyses of automation and of AI-driven growth. They take several valuable steps beyond prior work in either area (such as Nordhaus’s 2020-published exploration of a model with high substitutability in final goods but mere exogenous growth in technology). In effect, they synthesize and rigorize a number of observations about AI’s growth potential from the likes of Solomonoff (1985)—formerly perhaps best summarized by Sandberg (2010)—and bring them to economists’ attention. They then augment these observations with powerful new results and framings of their own. The result is the best economics paper published to date on what has as good a claim as anything to being the most important subject in the history of the world.

Link to corrected proof.

Evaluator details

How long have you been in this field? \ ~4 years, if you mean doing original research in economic theory with a large proportion of my time; ~2.5 years, if you mean having some particular focus on growth theory or the economics of AI. \

How many proposals and papers have you evaluated? \ I’m not sure how to interpret this.

1. I’ve peer-reviewed one paper on the economics of AI.
2. I’ve “evaluated” around 30 papers on the economics of AI in the course of writing a literature review on the subject.
3. More generally, I’ve given informal feedback on many research ideas and papers in progress by fellow researchers at GPI, fellow economics graduate students, and people (usually undergraduates) interested in doing EA-relevant economics work who reach out or are put in touch with me in some way.

I also just tried this and I cannot believe that I didn't know about this. It's so simple!

NOW I SAY, "OKAY, THIS IS DONE." I READ IT. IT FEELS LIKE A MOVIE. AND I'M GONNA PUT IT IN THE BOX. AND I'M GONNA PUT IT IN THE BOX IN THIS ORDER, AND THIS IS THE ORDER THAT I'M GONNA WRITE IT IN IN MY FIRST DRAFT. OVER THE NEXT NUMBER OF WEEKS, I START WRITING. WHAT I'VE BEEN DOING, I'VE BEEN SEEING THE SCENES IN MY HEAD FOR SO LONG AT THIS POINT THAT IT'S ALMOST LIKE JUST REGURGITATION. LIKE, I'M JUST GETTING OUT. AND I CALL IT MY VOMIT DRAFT. #

Dustin Lance Black's "vomit draft" is similar to Mozart's peeing his music out like a cow. His method is also similar to Victor Margolin who's gone over the material several times by the time he's finally writing out his draft.

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

Learn more at Review Commons

Reply to the reviewers

We apologize for the delay in resubmitting this revised manuscript. We faced a number of challenges over the previous year unrelated to this project that slowed progress on completing the necessary revisions. However, we are happy to report we have addressed all of the reviewer’s valuable comments in this revised submission through the including of 27 new or improved figure panels and significant adaptations to the text. We highlight these changes below.

REVIEWER #1.

Reviewer #1 General Comments. “This study investigates changes in mitochondrial morphology in response to ER stress due to pharmacological inhibition or genetic dysfunction in vitro via two different cell models (MEFs and HeLa cells). The authors specifically implicate the PERK branch of the ER-stress induced pathway in this process based on the observation that mitochondria elongate in response to thapsigargin (Tg) treatment which is blocked by the pathway inhibitors GSK and ISRIB or by genetic ablation of Perk/PERK. Homozygous knockout cells lacking PERK exhibit a fragmented mitochondrial phenotype even in the absence of Tg, which is rescued by expression of the wildtype but not a hypomorphic allele (PERKPSP). One of the more interesting suppositions of this manuscript is that mitochondrial elongation is dependent on the abundance of phosphatidic acid (PA); treatment with Tg provokes an increase in mitochondrial PA, but PA does not accumulate in mitochondria from cells co-treated with GSK, an inhibitor of PERK. This correlation suggests that increased mitochondrial PA accumulation is PERK-dependent. In addition, predicted manipulation of PA levels achieved by a gain of function expression of the lipase Lipin diminished mitochondrial elongation in response to ER stress. Similar results were obtained by PA-PLA1 overexpression, a cytosolic lipase that converts PA into lysophosphatidic acid (LPA). To further describe the mechanistic link between ER stress and mitochondrial morphology, the authors found that PRELID1, which transports PA from the OMM to the intermembrane space, and TIM17A, a component of the protein translocation machinery, were stabilized by loss of PERK or YME1L [and possibly an effect of ATF4], regardless of ER stress via Tg treatment. The authors also report that Tg treatment prevents OPA1 cleavage in cells treated with CCCP, an uncoupler of the proton gradient, suggesting that the effect due to Tg treatment is not through ER stress but decreased mitochondrial fusion via mito-stress induced OPA1 cleavage. To address this, cells were treated with ionomycin which induces mitochondrial fragmentation independent of DRP1. The authors observed an increase in mitochondrial fragmentation in the presence of ionomycin. However, co-treatment with Tg prevented fragmentation, as did overexpression of mitoPLDGFP, which converts cardiolipin to PA on the OMM. These results support a model in which, under ER stress conditions, PERK activation leads to translational attenuation, which leads to a decrease in the steady state levels of PRELID1 via YME1L-dependent degradation and to the accumulation of PA on the OMM. Based on published work this PA accumulation is expected to inhibit the mitochondrial division dynamin, DRP1. The authors tested this by examining the dependence of mitochondrial elongation on PRELID1.”

“Perturbances in PERK signaling evoke an alteration in mitochondrial morphology and have been extensively reported on, due to their clinical implications on neurodegenerative disorders such as Alzheimer's disease. The present work provides insight into the molecular basis for Stress Induced Mitochondrial Hyperfusion (SIMH) which can be triggered by ER stress. The authors find that this process occurs downstream of PERK and proceeds through accumulation of PA in the OMM by stabilization of Prelid, a mitochondrial resident protein that transports PA from the OMM to IMM for cardiolipin synthesis. The evidence of this work represents a substantial addition to the field of mitochondrial dynamics/SIMH and the Unfolded Protein Response”

“The novelty of this work is in the inclusion of PRELID1 downstream of PERK signaling pathway for transmission of ER stress to the mitochondria, a process that involves phosphatidic acid (PA). Some studies have addressed how phosphatidic acid is a modulator and a signal in mitochondrial physiology. The role of the lipids in mitochondrial dynamics represent an important and emerging field that needs to be explored in order to understand how metabolites control mitochondrial fusion/fission.”

__Our Response to Reviewer #1 General Comments. __We thank the reviewer for the positive comments related to our manuscript. We address specific comments brought up by the reviewer in our revised manuscript as highlighted below. We combined specific comments related to the same point in this response to best manage the various points brought up by the reviewer.

Reviewer #1 Comment #1. __The Reviewer__ brought up the quality of our images numerous times in their review. A few examples are included below.

“Image quality of mitochondria is sub par and the images do not always appear representative of/match the accompanying histograms. When using a single fluorescent marker (mito-GFP), the images should be in grey scale.”

“In several images there is substantial background GFP signal resulting in images that are fuzzy on the high quality PDF (printout is unintelligible). Example: Figure 2, Mock+veh. Example: Figure S2I, Mock+veh, +PA-PLA Tg. Example: Figure 3C mock+veh”

“Images from prior paper (Lebeau J, et al. 2018) are of much higher quality and is much easier to discern mitochondrial”

“Mitochondrial morphology doesn't appear uniform even within the same cell so how is this accounted for in scoring of mitochondrial morphology? Also, how are authors scoring mitochondrial morphology? Due to the inconsistencies in the chosen images, we feel this manuscript would benefit from addition of a supplementary figure showing examples for each cell model expressing mtGFP (i.e. HeLa and MEFs) depicting the fragmented, tubular and elongated mitochondria. This should be able to be constructed from images already collected for these analyses that weren't already used in the paper.”

__Our Response to Reviewer #1 Comment #1. __In the revised manuscript, we improved the quality of the images and converted all images to greyscale, as suggested by the reviewer.

As described in Materials and Methods, we quantified mitochondrial morphology by cell, scoring whether a cell has primarily fragmented, tubular, or elongated mitochondria morphology. This scoring was performed by at least two blinded researchers for at least 3 independent experiments with a total of >60 cells/condition counted across all experiments. Scores for individual experiments were then combined and averaged. Statistics were calculated from these averaged scores. In the revised manuscript, the images presented are representative of each individual condition. In addition, we now include new panels showing the quantification of total cells counted/condition across all individual replicates by a representative researcher for the main text figures (e.g., see Fig. S1C). This provides an alternative representation of the observed phenotype across the individual experiments for these key figures.

As suggested by the reviewer, in the revised submission we also now provide representative images of cells with primarily fragmented, tubular, and elongated mitochondria for both MEF and HeLa cells (Fig. S1A,B). We appreciate this suggestion as we feel it improves the clarity of our manuscript.

Reviewer #1 Comment #2. ____“Mitochondria in Perk-/- MEFs are highly fragmented, which is potentially inconsistent with previous work (Lebeau J, et al. 2018) performed by the same research group. Can the authors comments on this discrepancy? Also, do the authors interpret this fragmentation to mean that Perk is required to maintain mitochondrial elongation in the absence of exogenous ER stress (Tg)? If so, the authors should test whether expression of a dominant negative version of DRP1 rescues this fragmented morphology. This would be an additional critical test of the authors' model.”

“Vehicle treated Perk-/- cells have fragmented morphology which is different from Figure 2F in above publication by same group. Can the authors explain this discrepancy?”

Our Response to Reviewer #1 Comment #2. In our previous publication, we did not quantify mitochondrial morphology in Perk-deficient cells. However, as reported in this current manuscript, we find that Perk-deficient cells display higher amounts of fragmented mitochondria, as compared to Perk+/+ MEFs (Fig. 1B,C). We quantified this result across 5 independent experiments. Moreover, we found that reconstitution with PERKWT restored tubular mitochondrial morphology in Perk-deficient cells, demonstrating that this effect can be attributed to loss of PERK.

With respect to the increase in mitochondrial fragmentation observed in Perk-deficient MEFs, we attribute this to reduced mitochondrial membrane potential observed in these cells. We now show that Perk-deficient MEFs show 50% reductions in TMRE staining, as compared to controls. We include this data in the revised manuscript as __Fig. S1D __and the accompanying text below.

Line 91. “*Perk-/- MEFs showed increases in fragmented mitochondria in the absence of treatment (Fig. 1B,C and Fig. S1C). This corresponds with reductions in the mitochondrial membrane potential in Perk-deficient cells, as measured by tetramethylrhodamine ethyl ester (TMRE) staining (Fig. S1D). This suggests that the increase of fragmentation in these cells can be attributed to mitochondrial depolarization. Tg-induced mitochondrial elongation was also impaired in Perk-deficient cells (Fig. 1B,C and Fig. S1C).”. *

Reviewer #1 Comment #3. “The authors postulate that mitochondrial elongation in response to Perk activation is specifically outer membrane PA-dependent negative regulation of DRP1. However, PA is readily convertible to other phospholipids, notably CL and LPA, both of which positively regulate mitochondrial fusion. The authors do not measure abundance of other phospholipids, particularly LPA or CL in their targeted lipidomics experiments, only PC. The authors need to consider this alternate possibility.”

Reviewer #1 Comment #3. __Overexpression of PA-PLA1 (which converts PA to LPA) blocks ER stress induced mitochondrial elongation (__Fig. S2L-O). This indicates that the observed Tg-dependent increase in mitochondrial elongation are unlikely to be attributed to increases in LPA. mitoPLD converts CL to PA at the outer mitochondrial membrane. Since mitoPLD overexpression increases mitochondrial elongation (Fig. 3A,B), this again suggests that CL is not a major driver of mitochondrial elongation. These results combined with the sensitivity of ER stress induced mitochondrial elongation to two different PA lipases strongly support a model whereby increases in PA contribute to ER stress induced mitochondrial elongation.

In the revised manuscript, we include measurements of CL in mitochondria isolated from MEFmtGFP cells treated with Tg and/or depleted of Prelid1. As expected, reductions of PRELID1 decrease CL in isolated mitochondria (Fig. S5C). Treatment with Tg reduced CL to similar extents in mitochondria isolated from MEFmtGFP cells expressing non-silencing shRNA. However, we did not observe further reductions of CL in Prelid1-depleted cells. This is consistent with a model whereby ER stress-dependent reductions in PRELID1 decrease PA trafficking across the IMS and lead to reductions in CL synthesis. These results are discussed in the revised manuscript as below:

Line 214. “PRELID1 traffics PA from the outer to inner mitochondrial membrane, where it serves as a precursor to the formation of cardiolipin.56,66,67 Thus, reductions in PRELID1 should decrease cardiolipin. To test this, we shRNA-depleted Prelid1 from MEFmGFP cells and monitored cardiolipin in isolated mitochondria in the presence or absence of ER stress. We confirmed efficient PRELID1 knockdown by immunoblotting (Fig. S5A). Importantly, Prelid1 depletion did not alter Tg-induced reductions of TIM17A or increases of ATF4. Further, Tg-dependent increases in PA were observed in Prelid1-depleted MEFmtGFP cells (Fig. S5B). These results indicate that loss of PRELID1 does not impair PERK signaling in these cells. Prelid1 depletion reduced cardiolipin in mitochondria isolated from MEFmtGFP cells (Fig. S5B). Treatment of MEFmtGFP cells expressing non-silencing shRNA with Tg for 3 h also reduced cardiolipin to levels similar to those observed in Prelid1-deficient cells. However, Tg did not further reduce cardiolipin in Prelid-depleted cells. These results are consistent with a model whereby ER stress-dependent reductions in PRELID1 limit PA trafficking across the inner mitochondrial membrane and contribute to reductions in cardiolipin during acute ER stress”

Reviewer #1 Comment #4. “In Figure 5, the authors found very little difference in mitochondrial elongation following knockdown of Prelid1 (comparison between vehicle only conditions), which is potentially inconsistent with their model as decreased PRELID1 should lead to increased OMM PA [and subsequently mitochondrial fusion/elongation]. Therefore, these findings do not adequately support the authors' main model.”

Our Response to Reviewer #1 Comment #4. __Our model predicts that ER stress induced mitochondrial elongation is mediated through a process involving both PERK kinase-dependent increases in total PA and YME1L-dependent PRELID1 degradation induced downstream of PERK-dependent translation attenuation (see __Fig. 6). Thus, we predict that PRELID1 degradation is required, but not sufficient, to promote mitochondrial elongation. Our results showing that PRELID1 depletion does not basally disrupt mitochondrial morphology or inhibit Tg-induced mitochondrial elongation are consistent with this model. Moreover, we show that genetic Prelid1 depletion rescues Tg-induced mitochondrial elongation in cells co-treated with the PERK signaling inhibitor ISRIB – a compound that blocks PERK-dependent PRELID1 degradation (Fig. 4D), but not increases in PA (Fig. 2B, S2E,F) – in both MEFmtGFP and HeLa cells (Fig. 5A-D). This is consistent with our proposed model whereby PRELID1 degradation is required but not sufficient for promoting mitochondrial elongation. We make this point clearer in the revised manuscript.

Line 259: “Interestingly, Prelid1 depletion did not basally influence mitochondrial morphology or inhibit Tg-induced mitochondrial elongation (Fig. 5A,B and Fig. S5E). This indicates that reduction of PRELID1, on its own, is not sufficient to increase mitochondrial elongation, likely reflecting the importance of PERK kinase-dependent increases in PA in this process.53”

Reviewer #1 Comment #5. “The manuscript requires more careful editing - there were grammatical and punctuation errors.”

“… the text needs considerable editing to make the language clearer and formal whereas the figures are not always presented in a manner that is easily absorbed by the reader. Representative microscopy images chosen do not always match the corresponding graphical summary and are not clear even on PDF version compared to (Lebeau J, et al. 2018 - full citation above).”

__Our Response to Reviewer #1 Comment #5. __We carefully edited the revised manuscript.We also confirmed that representative images match the observed quantifications.

Reviewer #1 Comment #6. “In order to further investigate the contribution PRELID1-dependent accumulation of PA in the OMM and its role in mitochondrial elongation, the authors should investigate the abundance of PA (and other lipids) in Perk, Prelid, Yme1l KO mutants. These experiments should quantitatively complement the results in Figure 5. KD of Prelid would be expected to increase mitochondrial elongation but there is no difference compared to WT in Figure 5.”

Our Response to Reviewer #1 Comment #6. __We thank the reviewer for this comment and now include new data to further demonstrate that co-treatment with the PERK kinase inhibitor GSK2656157 inhibits Tg-dependent increases in PA, while the PERK signaling inhibitor ISRIB does not (__Fig. 2B __and __Fig. S2A-E). Further, it is published that Perk-deletion inhibits ER stress-induced increases in PA, while knockin cells expressing the non-phosphorylatable eIF2a S51A mutant do not (Bobrovnikova-Marjon et al (2012) Mol Cell Biol). This is consistent with a model whereby PERK-dependent increases in PA are attributed to a PERK kinase-dependent, yet eIF2a phosphorylation-independent, mechanism. In the revised manuscript, we include additional quantification of PA across other genetic manipulations, as requested. Notably, we confirm that Lipin1 overexpression reduced basal PA and prevents Tg-dependent increases in PA (Fig. 2C, Fig. S2F,G). Further, we show that PRELID1 depletion does not significantly impact Tg-dependent increases of PA (Fig. S5B).

However, it is important to highlight that our work is specifically monitoring how acute ER stress-dependent PERK activation impacts mitochondria. Genetic manipulations that target many of the core components of these pathways are well established to globally disrupt many aspects of mitochondrial biology. Thus, these types of genetic manipulations often confound our ability to accurately monitor the contribution of specific stress-responsive signaling pathways in adapting mitochondria in response to acute insults. For example, a recent publication demonstrates that deletion of Perk impairs ER-mitochondrial phospholipid transport through mechanism independent of PERK kinase activity (Sassano et al (2023) J Cell Biol). While this problem can be limited if specific perturbations do not basally disrupt the phenotype being monitored (e.g., PRELID1 depletion does not significantly impact basal mitochondrial morphology; Fig. 5), our ability to evaluate how stress-responsive signaling regulates mitochondria in response to acute insults (e.g., ER stress) still requires temporal control to properly evaluate how these pathways impact aspects of mitochondrial biology. It is for this reason that we paired PRELID1 depletion with pharmacologic interventions that can be used to temporally inhibit PERK signaling (e.g., ISRIB, GSK), allowing us to best define the specific role for PERK-dependent reductions PRELID1 in promoting mitochondrial elongation in response to ER stress.

Reviewer #1 Comment #7. “Title of the subsection: "hypomorphic PERK variants inhibit ER..." is inappropriate since authors only investigated a single hypomorphic variant (PSP). KO mutant is a null not hypomorphic mutant”

__Our Response to Reviewer #1 Comment #7. __We agree and have made the suggested change in the revised manuscript.

Reviewer #1 Comment #8. “Can the authors elaborate on the possible biological relevance for the inhibition of OPA1 cleavage via Tg treatment?”

Our Response to Reviewer #1 Comment #8. __We show that Tg pretreatment inhibits mitochondrial depolarization induced by CCCP (__Fig. S3G). Thus, the impaired CCCP-induced, OMA1-dependent OPA1 processing observed in response to pretreatment with Tg likely reflects disruptions in mitochondrial uncoupling afforded by this treatment. We make this point clearer in the revised manuscript.

Line 170: “However, Tg pretreatment inhibited CCCP-induced proteolytic cleavage of the inner membrane GTPase OPA1 (Fig. 3C) – a biological process upstream of DRP1 in mitochondrial fragmentation induced by membrane uncoupling.43-47,64 This appears to result from Tg-dependent increases in mitochondrial membrane polarity (Fig. S3G), preventing efficient uncoupling in CCCP-treated cells and precluding our ability to determine whether Tg pretreatment directly impairs DRP1 activity under these conditions..”

Reviewer #1 Comment #9. “PRELID is a known short-lived protein; can the authors elaborate on possible additional impact due to 3-6 hr Tg treatment which is sufficient to induce expression of ATF4 target genes (Figure S2G).”

Our Response to Reviewer #1 Comment #9. PRELID1 is a short-lived mitochondrial protein that is rapidly degraded in response to acute ER insults. As demonstrated in Fig. 4 of our manuscript, this reduction is mediated by the IMM protease YME1L downstream of PERK-regulated translation attenuation. This 3-6 h timecourse corresponds with the translational attenuation induced downstream of PERK-dependent eIF2a phosphorylation following treatment with Tg and corresponds with the loss of PRELID1 observed in Tg-treated cells.

Note that the increase in ATF4 noted by the reviewer reflects the fact that ATF4 (and related proteins) are preferentially translated following eIF2a phosphorylation due to the presence of uORFs in their promoter. Thus, while global protein translation (including PRELID1 translation) is reduced by eIF2a phosphorylation, proteins like ATF4 are selectively translated.

Reviewer #1 Comment #10. “Thapsigargin induced ER stress does not only activate PERK arm of the ISR, correct? Could the authors comment on this?”

__Our Response to Reviewer #1 Comment #10. __I believe the reviewer is asking whether Tg treatment activates other arms of the integrated stress response (ISR). At the short timepoints used in this work (3-6 h), Tg-dependent increases in ISR signaling can be fully attributed to PERK signaling. This is evident as Perk deletion or inhibition blocks markers of ISR signaling in cells treated with Tg for these shorter timepoints (e.g., __Fig. 4E __of this paper; Harding et al (2002) Mol Cell and Lebeau et al (2018) Cell Reports). While other ISR kinases can be activated in response to more prolonged ER stress, the ISR activation observed in these shorter treatments with Tg are well established to be attributed to PERK activity.

Tg does induce all three arms of the unfolded protein response (i.e., ATF6, IRE1, and PERK) in the 3-6 h timeframe used in this manuscript. We previously showed that pharmacologic inhibition of ATF6 and IRE1 activity does not influence Tg-induced mitochondrial elongation (Lebeau et al (2018) Cell Reports). However, as reproduced in this manuscript, inhibition of PERK signaling blocks ER stress induced mitochondrial elongation. We make this point clearer in the revised manuscript.

Line 86. “Pharmacologic inhibition of PERK signaling, but not other arms of the UPR, blocks mitochondrial elongation induced by ER stress.39”

Reviewer #1 Comment #11. “*Addition of drugs and duration (3-6 hrs) likely very toxic to cells; how does this treatment affect viability? Unhealthy cells will have unhealthy mitochondria so it's hard to be confident that subtle morphological differences are specific. Why do authors use 3 hrs Tg-treatment after initially using 6 hrs in Figure 1? Would be helpful to assay toxicity and mitochondrial morphology of thapsigargin and other drugs in WT vs. Perk KO MEFs over time.” *

__Our Response to Reviewer #1 Comment #11. __Thapsigargin (Tg) is toxic to cells, but apoptosis is observed in cell culture models only after much longer treatments 24-72 h. We are using Tg to monitor how cells respond to acute ER stress. We chose the short 3-6 h timecourse because this is sufficient to induce PERK-dependent translation attenuation independent of cell death. Consistent with this, we observe no reductions in cellular viability or death in the short 3-6 h treatments used in this study. This timecourse is standard in the field when monitoring cellular changes induced by acute ER stress.

Reviewer #1 Comment #12. “Previously, an increase in fragmentation was observed at 0.5 hours but this subsided by 6 hours in WT (Lebeau J, et al. 2018) but is this the same for Perk KO MEFs?”

Our Response to Reviewer #1 Comment #12. __The increase in mitochondrial fragmentation observed following Tg treatment results from the rapid increase of mitochondrial Ca2+ induced by this treatment (Hom et al (2007) J Cell Phy). Consistent with this, we have found that pharmacologic inhibition of PERK signaling using the compound ISRIB, does not inhibit mitochondrial fragmentation in MEFmtGFP cells treated for 30 min with Tg. Since Perk-deficient MEFs already show increased fragmentation (__Fig. 1B,C), monitoring mitochondrial morphology in Perk-deficient cells treated with Tg for 30 min is unlikely to reveal additional insights into the mechanism outlined in this manuscript.

Reviewer #1 Comment #13. “How much protein was loaded per lane and what was the percentage of polyacrylamide gel? Please clarify details in methodology.”

Our Response to Reviewer #1 Comment #13. We loaded 100 µg of protein for our immunoblotting experiments. We used 10% or 12% SDS-PAGE gels. We included this information in the revised Materials and Methods.

Reviewer #1 Comment #14. “Figure 1A is virtually identical to Figure 2A (with exception of "MEF A/A") from previous publication: Lebeau J, Saunders JM, Moraes VWR, Madhavan A, Madrazo N, Anthony MC, Wiseman RL. The PERK Arm of the Unfolded Protein Response Regulates Mitochondrial Morphology during Acute Endoplasmic Reticulum Stress. Cell Rep. 2018 Mar 13;22(11):2827-2836. doi: 10.1016/j.celrep.2018.02.055. PMID: 29539413; PMCID: PMC5870888.”

Our Response to Reviewer #1 Comment #14. __Yes. __Fig. 1A is a cartoon showing PERK-dependent regulation of mitochondria and the specific pharmacologic and genetic manipulations used in this paper to alter this pathway. This is adapted from our previous manuscript (Lebeau et al (2018) Cell Reports). We properly reference this adaptation in the revised manuscript. We feel it is important to show this figure to specifically highlight how different manipulations influence this signaling pathway.

Reviewer #1 Comment #15. “If the authors' hypothesis is correct, overexpression of PRELID1 should have same effect as overexpression of Lipin”

Our Response to Reviewer #1 Comment #15. Overexpressed PRELID1 will be sensitive to the same rapid YME1L-dependent degradation observed for the endogenous protein. Thus, overexpressing PRELID1 would be expected to have no effect (or a very minor effect) on mitochondrial morphology in Tg-treated cells. We show that Lipin1 overexpression basally increases mitochondrial fragmentation and blocks Tg-induced mitochondrial elongation (Fig. 2). Identical results were observed in cells overexpressing the alternative PA lipase PA-PLA1 (Fig. S2). We feel that these data, in combination with others shown in our manuscript, strongly support the dependence of this process on PA levels and localization.

Reviewer #1 Comment #16. “What is the selective marker used for HeLa cells expressing mitoPLDGFP since the HeLa parental cell background already expressed a mitochondrial targeted GFP, we assume it was puromycin but this was not clear in the Figure legend or methods? If so, it would be helpful to clarify this. If not, how can the authors observe a difference in morphology if the selectable marker is the same? Indeed, mitoPLDGFP is expressed, detectable by immunoblot, but this is on a cell population level so no way of knowing whether the specific cells scored expressed mitoPLDGFP unless another selectable marker was used (i.e. should have used CFP, RFP, etc.).”

“The authors state "Note the expression of mitoPLDGFP did not impair our ability to accurately monitor mitochondrial morphology in these cells." in Figure 3 legend and again basically the same in S3: "Note that the expression of the mitoPLDGFP did not impair our ability to monitor mitochondrial morphology in these cells." Could the authors explain their reasoning here?”

__Our Response to Reviewer #1 Comment #16. __We co-transfected the mitochondrial localized mitoPLD-GFP with mtGFP in HeLa cells using calcium phosphate transfection. In using this approach, we (and others) have consistently found that this method leads to the efficient transfection of cells with both plasmids. Thus, cells will express both mitoPLD-GFP and mtGFP. We used mitoPLD-GFP because we were reproducing published experiments (Adachi et al (2016) Mol Cell) and we wanted to use the same overexpression plasmid used in these previous studies. It is clear from our images that the presence of GFP-tagged mitoPLD did not influence our ability to accurately monitor mitochondrial elongation in these cells. Further, the robust increase in mitochondrial elongation observed in cells overexpressing mitoPLD-GFP and the further increase in elongation observed upon co-treatment with Tg demonstrate the effectiveness of this assay. This is consistent with published results (Adachi et al (2016) Mol Cell).

Reviewer #1 Comment #17. ____“Figure S4C: the authors show that Tg treatment on MEF mtGFP cells for distinct hours to determine PRELID levels. However, in the Results section states that this treatment was with CHX, could the authors please check this and correct?”

Our Response to Reviewer #1 Comment #17. __The data shown in __Fig. S4C from the previous version is in Tg-treated cells. We corrected this in the revised manuscript.

Reviewer #1 Comment #18. “Figure 6: A schematic representation should be a graphic summary of all findings reported in the text with no text except where absolutely essential. A good model should be easily understood without reading any description since all concepts were supported in the main text and by experimentation.”

*“The model also contains some inaccuracies. The suggestion is that the authors re-do the model and clarify some aspects such as: *

*The model suggests that ISRIB inhibits PRELID1 directly but there is no evidence for this whereas PRELID is directly regulated by YME1L (also typo here in figure: "Yme1" no "l"). *

*This model incorrectly uses inhibition symbols; for example, mutation of Perk does not inhibit its activity as GSK does. The KO does not have Perk so cannot perform its function. These are not the same. *

Similarly, the lipases (Lipin and PA-PLA1) should be depicted instead as altering flux of PA away from OMM not as inhibition.

The authors should connect PA accumulation in the OMM graphically to mitochondrial elongation [instead of through text]. If the authors consider the numbered labels convenient, please use just the number and place the description in the figure legend instead.”

Our Response to Reviewer #1 Comment #18. __We have adapted our model shown in __Fig. 6 and the accompanying legend to address points brought up by the reviewer. In particular, because the reviewer found it difficult to follow how specific manipulations impacted specific steps, we removed those parts from the revised figure for clarity.

__Reviewer #1 Comment #19. __The reviewer made many suggestions to improve the Materials and Methods section of this manuscript in their review, which we do not include here for space considerations.

__Our Response to Reviewer #1 Comment #19. __We have addressed all of the reviewer’s comments regarding the Materials and Methods section in our revised manuscript.

Reviewer #1 Comment #20. The reviewer made many suggestions about the presentation of our figures that we do not include here for space considerations.

Our Response to Reviewer #1 Comment #20. __We have addressed all of the reviewer’s comments regarding the Figures__ in our revised manuscript.

REVIEWER #2.

Reviewer #2 General Comments. “Previous studies have shown that ER stress increases amounts of phosphatidic acid (PA) (PMID: 22493067) and induces elongation of mitochondria through the protein and lipid kinase PERK (PMID: 29539413, work by Wiseman's lab). The current work reports that ER stress by thapsigargin promotes the degradation of a mitochondrial protein PRELID1, which transfers PA from the outer membrane to the inner membrane. An inner membrane protease, YME1L, was identified as responsible for this degradation of PRELID1. Consistent with the notion that PA is required for the morphological change, overexpression of a PA phosphatase (Lipin) or a PA phospholipase (PA-PLA1) decreased ER-stress-induced mitochondrial elongation.”

“Overall, this manuscript is a nice extension of the authors' previous work and investigates the molecular mechanism underlying the regulation of mitochondrial elongation induced by ER stress. However, the current data do not strongly support the role of PRELID1 in either ER-stress-mediated PA level elevation or mitochondrial elongation, as described in Specific comments. The authors should address these points.”

__Our Response to Reviewer #2 General Comments. __We thank the reviewer for the thorough and careful read of our manuscript. We address the specific points brought up by the reviewer in our revised manuscript, as described below.

Reviewer #2 Comment #1. “The authors report that PRELID1 knockdown did not promote mitochondrial elongation under either normal or ER-stress conditions (Fig. 5). If PRELID1 plays a vital role in mitochondrial elongation, PRELID1 depletion will restore elongation. Therefore, the presented data argue against the authors' conclusion. Since PRELID1 has multiple homologs, including PRELID3B, which is also a short-lived protein like PRELID1, these homologs might redundantly function in PA transport, especially when PRELID1 is absent. Therefore, the authors need to knock them down simultaneously. This possibility is consistent with the previous authors' data that YME1L depletion decreases ER-stress-induced mitochondrial elongation (PMID: 29539413). YME1L knockdown may rescue multiple short-lived PRELID1 homologs.”

Our Response to Reviewer #2 Comment #1. __Our model indicates that ER stress-dependent increases in mitochondrial elongation require two steps: 1) PERK kinase-dependent increases in total PA and 2) YME1L-dependent degradation of PRELID1 downstream of PERK-dependent translation attenuation. Thus, it is not surprising that PRELID1 depletion did not induce mitochondrial elongation on its own. However, we do demonstrate that depletion of PRELID1 rescues Tg-induced mitochondrial elongation in cells co-treated with the PERK signaling inhibitor ISRIB – a compound that specifically blocks Tg-dependent PRELID1 degradation, but not PERK kinase dependent increases in total PA (__Fig. 6). This demonstrates that PRELID1 reductions are required, but not sufficient for promoting mitochondrial elongation. We make this point more clear in the revised manuscript.

Line 259: “Interestingly, Prelid1 depletion did not basally influence mitochondrial morphology or inhibit Tg-induced mitochondrial elongation (Fig. 5A,B and Fig. S5E). This indicates that reduction of PRELID1, on its own, is not sufficient to increase mitochondrial elongation, likely reflecting the importance of PERK kinase-dependent increases in PA in this process.53”

With respect to PRELID3B/SLMO2. This lipid transporter is primarily associated with trafficking phosphatidylserine (PS) from the outer to the inner mitochondrial membrane, where it is then converted to phosphatidylethanolamine (PE). As alluded to by the reviewer, we found that SLMO2, like PRELID1, is also a short-lived mitochondrial protein that is rapidly degraded by YME1L downstream of PERK-dependent translation attenuation. We have also found that Tg treatment disrupts mitochondrial PE levels through a PERK-dependent mechanism on a similar timescale to that observed for PA changes. However, shRNA depletion of SLMO2 in HeLa cells – a condition that mimics the reductions in SLMO2 observed during ER stress – increases basal mitochondrial fragmentation and inhibits Tg-induced mitochondrial elongation. Since chronic, genetic reductions in SLMO2 (which mirror the acute reduction in SLMO2 observed during ER stress) show opposite impacts on mitochondrial morphology to that observed upon Tg treatment, we interpreted this result to indicate that SLMO2 reductions are likely not involved in PERK-dependent regulation of mitochondrial elongation during acute ER stress. In contrast, depletion of PRELID1 is sufficient to rescue Tg-induced mitochondrial elongation in cells co-treated with ISRIB (Fig. 5A-D) – a compound that selectively blocks ER stress-dependent reductions in PRELID1. This implicates reductions in PRELID1 in this process. We are continuing to define the specific impact of PERK-dependent regulation of SLMO2 on mitochondrial morphology, ultrastructure, and/or function in work outside the scope of this current manuscript, but we felt it most appropriate to focus this manuscript on PA-dependent morphology remodeling based on the presented data.

Reviewer #2 Comment #2. “Another possibility is that since a previous study has shown that PERK-produced PA activates the mTOR-AKT pathway (PMID: 22493067), this signaling pathway may contribute to mitochondrial morphology in addition to PRELID1. The authors should test the combined effects of mTOR-AKT inhibition in ER-stress-induced mitochondrial elongation.”

Our Response to Reviewer #2 Comment #2. __As highlighted by the reviewer, PERK-dependent increases in PA can influence mTOR and AKT activity. To test this, we monitored mTOR-dependent S6K phosphorylation and AKT phosphorylation in MEFmtGFP and HeLa cells treated with Tg for 3 h. While we did observe increases in S6K phosphorylation in Tg-treated MEFmtGFP cells, mTOR activity was not changed in Tg-treated HeLa cells. AKT phosphorylation was not affected in MEFmtGFP or HeLa cells (not shown). We include these mTOR data in the revised manuscript (see __Fig. S3C,D). Since we observe PERK-dependent mitochondrial elongation in both MEFmtGFP and HeLa cells, we interpret these results to indicate that PA-dependent increases in mTOR activity is not primarily responsible for ER stress dependent increases in mitochondrial elongation across cell types. We describe these results in the revised manuscript.

Line 156: “In contrast, PERK-dependent increases in PA can activate mTOR during ER stress.53 Consistent with this, we observe Tg-dependent increases in mTOR-dependent S6K phosphorylation in MEFmtGFP cells (Fig. S3C). However, despite increasing PA and promoting mitochondrial elongation, Tg did not increase S6K phosphorylation in HeLa cells (Fig. S3D). These results suggest that PERK-dependent alterations in mTOR activity are unlikely to be primary contributors to ER stress induced mitochondrial elongation across cell types.”

Reviewer #2 Comment #3. “The authors' model suggests the loss of PRELID1 increases PA levels in the mitochondrial outer membrane (Fig. 6). The authors should test PA levels in mitochondria isolated from cells depleted for PRELID1 and its homologs (simultaneously). Since PA that is transported to the inner membrane is actively converted to other phospholipids, such as CDP-DAG, elevated levels of PA are likely seen if the outer membrane to inner membrane transport is blocked.

Our Response to Reviewer #2 Comment #3. __We agree with the reviewer it is important to evaluate how PERK-dependent degradation of PRELID1 impacts other phospholipids dependent on PA trafficking to the IM where it can be converted to other lipids, most notably cardiolipin (CL). In the revised manuscript, we now show measurements of CL in MEFmtGFP cells treated with Tg and/or depleted of Prelid1. As expected, reductions in PRELID1 decrease CL in isolated mitochondria (__Fig. S5C). Treatment with Tg reduced CL to similar extents in MEFmtGFP-treated cells expressing non-silencing shRNA. However, we did not observe further reductions of CL in Prelid1-depleted cells. This is consistent with a model whereby ER stress-dependent reductions in PRELID1 decrease PA trafficking across the IMS and lead to reductions in CL synthesis. These results are discussed in the revised manuscript as below:

Line 214. “PRELID1 traffics PA from the outer to inner mitochondrial membrane, where it serves as a precursor to the formation of cardiolipin.56,66,67* Thus, reductions in PRELID1 should decrease cardiolipin. To test this, we shRNA-depleted Prelid1 from MEFmGFP cells and monitored cardiolipin in isolated mitochondria in the presence or absence of ER stress. We confirmed efficient PRELID1 knockdown by immunoblotting (Fig. S5A). Importantly, Prelid1 depletion did not alter Tg-induced reductions of TIM17A or increases of ATF4. Further, Tg-dependent increases in PA were observed in Prelid1-depleted MEFmtGFP cells (Fig. S5B). These results indicate that loss of PRELID1 does not impair PERK signaling in these cells. Prelid1 depletion reduced cardiolipin in mitochondria isolated from MEFmtGFP cells (Fig. S5C). Treatment of MEFmtGFP cells expressing non-silencing shRNA with Tg for 3 h also reduced cardiolipin to levels similar to those observed in Prelid1-deficient cells. However, Tg did not further reduce cardiolipin in Prelid-depleted cells. These results are consistent with a model whereby ER stress-dependent reductions in PRELID1 limit PA trafficking across the inner mitochondrial membrane and contribute to reductions in cardiolipin during acute ER stress.” *

We are continuing to define how PERK signaling influences other mitochondrial phospholipids during conditions of ER stress in work outside the scope of this manuscript. Notably, we are continuing to evaluate how ER stress and PERK signaling influences aspects of cardiolipin synthesis in response to both acute and chronic ER stress. Further, as discussed above, we are determining how PERK-dependent reductions in PRELID3B/SLMO2 influence PS trafficking and subsequent PE synthesis at the IM and the implications of these changes on mitochondrial biology. Initial experiments indicate that PERK signaling reduces PE during ER stress, indicating that other phospholipids can be influenced by this pathway. However, we view this work as being outside the scope of the current manuscript focused specifically on defining the impact of PA remodeling on mitochondrial morphology.

Reviewer #2 Comment #4. “The authors need to test whether Lipin and PA-PLA1 overexpression decreased PA levels in mitochondria treated with thapsigargin. The current manuscript only shows the effect of Lipin and PA-PLA1 on PA levels in whole-cell lysate without ER stress (Fig. S2F).”

Our Response to Reviewer #2 Comment #4. __We agree. In the revised manuscript, we now show that Lipin overexpression blocks Tg-dependent increases in PA (__Fig. 2C). Identical experiments are also shown for Prelid1-depleted cells (Fig. S5B).

Reviewer #2 Comment #5. “The authors propose that PA inhibits DRP1 in mitochondrial division under ER stress. It has been shown that PA blocks DRP1 after recruitment to mitochondria (PMID: 27635761). Does thapsigargin induce mitochondrial accumulation of DRP1?”

Our Response to Reviewer #2 Comment #5. __The reviewer is correct that our results suggest that ER stress promotes mitochondrial elongation through a model involving PA-dependent inhibition of mitochondrial fission at the outer membrane. In the revised manuscript, we now show that Tg treatment does not significantly influence the recovery of DRP1 in mitochondrial fractions (__Fig. S3A). Further, we recapitulate results from previous publications showing that Tg does not significantly influence DRP1 phosphorylation at either S637 or S616 (Fig. S3B). This indicates that DRP1 localization and posttranslational modification does not appear affected by Tg treatment. However, we do show that Tg pretreatment inhibits DRP1-dependent mitochondrial fission induced by ionomycin (Fig. 3D,E). Combined with other results, our data are consistent with a model whereby PERK-dependent increases in PA and PRELID1 degradation leads to the accumulation of PA on the OM where it can inhibit DRP1 activity (Fig. 6). We make this point clearer in the revised manuscript.

Line 154: “However, as reported previously39, Tg did not influence DRP1 phosphorylation at either S637 or S616 (Fig. S3A) or alter the amount of DRP1 enriched in mitochondrial fractions from MEFmtGFP cells (Fig. S3B).”

REVIEWER #3.

Reviewer #3 General Comments. “The authors investigated signaling pathways and molecular mechanisms leading to mitochondrial dysfunction after ER stress. This study extends their previous publication (Lebeau et al., 2018) by providing evidence on how PERK regulates mitochondrial structure and function in response to ER stress. Some key findings are that PERK induces mitochondrial elongation by increasing and retaining phosphatidic acid (PA) in the outer mitochondrial membrane which is important for cell adaptation and survival. This process requires PERK-dependent translational attenuation through YME1L-PRELID dependent mechanism. This is a very strong study with compelling evidence.”

“This study adds to our current knowledge on how ER stress affects mitochondria adaptation and proteostasis, which may contribute to the pathogenesis and progression of numerous neurodegenerative diseases. Specifically, this study establishes a new role for PERK in mitochondrial adaptive remodeling focused on trafficking and accumulation of phospholipids. Identifying molecular markers like PERK and its involvement with PRELID, YME1L, and PA to regulate mitochondrial remodeling during ER stress is important to understand the effects of drug-targeting this ER stress-responsive factor.”

__Our Response to Reviewer #3 General Comments. __We thank the reviewer for the enthusiastic comments about our manuscript. We address the reviewers remaining concerns as outlined below.

Reviewer #3 Comment #1. “Only one minor point should be addressed: In Fig S2G & H, the authors indicate that "Lipin1 overexpression did not significantly influence increases of ATF4 protein". The blots show a decrease in ATF4 in Tg-treated HeLa cells. The same effect is observed in Fig. S3F showing reduction in ATF4, but the authors described it as the "overexpression of mitoPLD did not significantly impact other aspects of PERK signaling in Tg-treated cells". The quantification of the blots or indication that the blots were quantified should be clarified and noted (at least in the legend).”

Our Response to Reviewer #3 Comment #1. __We agree. We now include quantification of ATF4 in immunoblots from HeLA cells overexpressing lipin1 and treated with Tg (__Fig. S2J). As we suggested, these results confirm that Tg treatment does not significantly influence ATF4 expression in these cells. In addition, we now include additional data showing that lipin overexpression does not significantly reduce Tg-dependent expression of ISR target genes including Asns or Chop (Fig. S2I). This further supports other findings in the manuscript showing that different manipulations do not significantly impact ISR signaling (evident by ATF4 expression or TIM17A or PRELID1 degradation).

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

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

Summary:

This study investigates changes in mitochondrial morphology in response to ER stress due to pharmacological inhibition or genetic dysfunction in vitro via two different cell models (MEFs and HeLa cells). The authors specifically implicate the PERK branch of the ER-stress induced pathway in this process based on the observation that mitochondria elongate in response to thapsigargin (Tg) treatment which is blocked by the pathway inhibitors GSK and ISRIB or by genetic ablation of Perk/PERK. Homozygous knockout cells lacking PERK exhibit a fragmented mitochondrial phenotype even in the absence of Tg, which is rescued by expression of the wildtype but not a hypomorphic allele (PERKPSP). One of the more interesting suppositions of this manuscript is that mitochondrial elongation is dependent on the abundance of phosphatidic acid (PA); treatment with Tg provokes an increase in mitochondrial PA, but PA does not accumulate in mitochondria from cells co-treated with GSK, an inhibitor of PERK. This correlation suggests that increased mitochondrial PA accumulation is PERK-dependent. In addition, predicted manipulation of PA levels achieved by a gain of function expression of the lipase Lipin diminished mitochondrial elongation in response to ER stress. Similar results were obtained by PA-PLA1 overexpression, a cytosolic lipase that converts PA into lysophosphatidic acid (LPA). To further describe the mechanistic link between ER stress and mitochondrial morphology, the authors found that PRELID1, which transports PA from the OMM to the intermembrane space, and TIM17A, a component of the protein translocation machinery, were stabilized by loss of PERK or YME1L [and possibly an effect of ATF4], regardless of ER stress via Tg treatment. The authors also report that Tg treatment prevents OPA1 cleavage in cells treated with CCCP, an uncoupler of the proton gradient, suggesting that the effect due to Tg treatment is not through ER stress but decreased mitochondrial fusion via mito-stress induced OPA1 cleavage. To address this, cells were treated with ionomycin which induces mitochondrial fragmentation independent of DRP1. The authors observed an increase in mitochondrial fragmentation in the presence of ionomycin. However, co-treatment with Tg prevented fragmentation, as did overexpression of mitoPLDGFP, which converts cardiolipin to PA on the OMM. These results support a model in which, under ER stress conditions, PERK activation leads to translational attenuation, which leads to a decrease in the steady state levels of PRELID1 via YME1L-dependent degradation and to the accumulation of PA on the OMM. Based on published work this PA accumulation is expected to inhibit the mitochondrial division dynamin, DRP1. The authors tested this by examining the dependence of mitochondrial elongation on PRELID1.

Major comments:

1. Are the key conclusions convincing? A considerable amount of work was performed by the authors in preparation of this manuscript and while we find the model exciting, there are several issues that need to be addressed in order for the model to be sufficiently supported.
   1. Image quality of mitochondria is sub par and the images do not always appear representative of/match the accompanying histograms. When using a single fluorescent marker (mito-GFP), the images should be in grey scale.
   2. Mitochondria in Perk-/- MEFs are highly fragmented, which is potentially inconsistent with previous work (Lebeau J, et al. 2018) performed by the same research group. Can the authors comments on this discrepancy? Also, do the authors interpret this fragmentation to mean that Perk is required to maintain mitochondrial elongation in the absence of exogenous ER stress (Tg)? If so, the authors should test whether expression of a dominant negative version of DRP1 rescues this fragmented morphology. This would be an additional critical test of the authors' model.
   3. The authors postulate that mitochondrial elongation in response to Perk activation is specifically outer membrane PA-dependent negative regulation of DRP1. However, PA is readily convertible to other phospholipids, notably CL and LPA, both of which positively regulate mitochondrial fusion. The authors do not measure abundance of other phospholipids, particularly LPA or CL in their targeted lipidomics experiments, only PC. The authors need to consider this alternate possibility.
   4. In Figure 5, the authors found very little difference in mitochondrial elongation following knockdown of Prelid1 (comparison between vehicle only conditions), which is potentially inconsistent with their model as decreased PRELID1 should lead to increased OMM PA [and subsequently mitochondrial fusion/elongation].
   5. The manuscript requires more careful editing - there were grammatical and punctuation errors.
2. Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? In Figure 5, the authors found very little difference in mitochondrial elongation following knockdown of Prelid1 (comparison between vehicle only conditions), which is potentially inconsistent with their model as decreased PRELID1 should lead to increased OMM PA [and subsequently mitochondrial fusion/elongation]. Therefore, these findings do not adequately support the authors' main model.
3. Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.
   • a. In order to further investigate the contribution PRELID1-dependent accumulation of PA in the OMM and its role in mitochondrial elongation, the authors should investigate the abundance of PA (and other lipids) in Perk, Prelid, Yme1l KO mutants. These experiments should quantitatively complement the results in Figure 5. KD of Prelid would be expected to increase mitochondrial elongation but there is no difference compared to WT in Figure 5.
   • b. The main premise is that ER-stress activates PERK which in turn leads to increased abundance of PA at the OMM in a PRELID1-dependent manner. PA has been shown to inactivate DRP1, resulting in decreased fission (and mitochondrial elongation). The authors should test their model by expressing a dominant negative allele of DRP1 to see if it rescues the fragmented morphology of Perk KO mutant.
4. Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.
   • a. The authors have all the necessary cell line and methods in hand, so we consider these experiments to be doable.
5. Are the data and the methods presented in such a way that they can be reproduced?
   • a. Not all are described in a way that could be easily reproduced (see specific comments below).
6. Are the experiments adequately replicated and statistical analysis adequate?
   • a. The foundation of this paper is based on qualitative analysis of confocal fluorescence microscopy images, but the chosen images are often not of high quality so performing statistical analysis in these cases is misleading. Also, each imaging-based experiment was performed three times, but with only 20 cells for each replicate. Does this represent sufficient statistical power?

Specific major comments by section

Introduction - No additional major comments.

Results - Title of the subsection: "hypomorphic PERK variants inhibit ER..." is inappropriate since authors only investigated a single hypomorphic variant (PSP). KO mutant is a null not hypomorphic mutant.

Discussion - Can the authors elaborate on the possible biological relevance for the inhibition of OPA1 cleavage via Tg treatment? - PRELID is a known short-lived protein; can the authors elaborate on possible additional impact due to 3-6 hr Tg treatment which is sufficient to induce expression of ATF4 target genes (Figure S2G). - Thapsigargin induced ER stress does not only activate PERK arm of the ISR, correct? Could the authors comment on this?

Methods - Addition of drugs and duration (3-6 hrs) likely very toxic to cells; how does this treatment affect viability? Unhealthy cells will have unhealthy mitochondria so it's hard to be confident that subtle morphological differences are specific. Why do authors use 3 hrs Tg-treatment after initially using 6 hrs in Figure 1? Would be helpful to assay toxicity and mitochondrial morphology of thapsigargin and other drugs in WT vs. Perk KO MEFs over time. Previously, an increase in fragmentation was observed at 0.5 hours but this subsided by 6 hours in WT (Lebeau J, et al. 2018) but is this the same for Perk KO MEFs? Figures/supplementary figures - General: - In several images there is substantial background GFP signal resulting in images that are fuzzy on the high quality PDF (printout is unintelligible). - Example: Figure 2, Mock+veh. - Example: Figure S2I, Mock+veh, +PA-PLA Tg. - Example: Figure 3C mock+veh. - Mitochondrial morphology doesn't appear uniform even within the same cell so how is this accounted for in scoring of mitochondrial morphology? Also, how are authors scoring mitochondrial morphology? Due to the inconsistencies in the chosen images, we feel this manuscript would benefit from addition of a supplementary figure showing examples for each cell model expressing mtGFP (i.e. HeLa and MEFs) depicting the fragmented, tubular and elongated mitochondria. This should be able to be constructed from images already collected for these analyses that weren't already used in the paper. - Images from prior paper (Lebeau J, et al. 2018) are of much higher quality and is much easier to discern mitochondrial phenotype. - How much protein was loaded per lane and what was the percentage of polyacrylamide gel? Please clarify details in methodology. - Figure 1: - See general comments. - Figure 1A is virtually identical to Figure 2A (with exception of "MEF A/A") from previous publication: Lebeau J, Saunders JM, Moraes VWR, Madhavan A, Madrazo N, Anthony MC, Wiseman RL. The PERK Arm of the Unfolded Protein Response Regulates Mitochondrial Morphology during Acute Endoplasmic Reticulum Stress. Cell Rep. 2018 Mar 13;22(11):2827-2836. doi: 10.1016/j.celrep.2018.02.055. PMID: 29539413; PMCID: PMC5870888. - Figure 1B: the complemented Perk KO + vehicle should be similar to WT vehicle, but those images look quite different, even so, the respective bars are equal. - Vehicle treated Perk-/- cells have fragmented morphology which is different from Figure 2F in above publication by same group. Can the authors explain this discrepancy? - Figure S1: - No additional major comments. - Figure 2: - See general comments. - If the authors' hypothesis is correct, overexpression of PRELID1 should have same effect as overexpression of Lipin. ● Figure S2: - Images in Figure S2I are not representative of corresponding bars in Figure S2J (specifically vehicle treated panels). The "+PA-PLA1+Tg" panel instead appears fragmented (in comparison with other images). - Do authors have clearer images to substitute for CHX-treated panels? ● Figure 3: - What is the selective marker used for HeLa cells expressing mitoPLDGFP since the HeLa parental cell background already expressed a mitochondrial targeted GFP, we assume it was puromycin but this was not clear in the Figure legend or methods? If so, it would be helpful to clarify this. If not, how can the authors observe a difference in morphology if the selectable marker is the same? Indeed, mitoPLDGFP is expressed, detectable by immunoblot, but this is on a cell population level so no way of knowing whether the specific cells scored expressed mitoPLDGFP unless another selectable marker was used (i.e. should have used CFP, RFP, etc.). - The authors state "Note the expression of mitoPLDGFP did not impair our ability to accurately monitor mitochondrial morphology in these cells." in Figure 3 legend and again basically the same in S3: "Note that the expression of the mitoPLDGFP did not impair our ability to monitor mitochondrial morphology in these cells." Could the authors explain their reasoning here? - Figure S3: - Same as in Figure 3; "mock+Veh" appears more fragmented than tubular so is there a more representative image that the authors can show? - Figure 4: - No major comments. - Figure S4: - Figure S4C: the authors show that Tg treatment on MEF mtGFP cells for distinct hours to determine PRELID levels. However, in the Results section states that this treatment was with CHX, could the authors please check this and correct? - Figure 5: - 5C: PLKO NS shRNA +Tg appears more fragmented than tubular; do the authors have a more representative image? - Figure S5: - No major comments. - Figure 6: - A schematic representation should be a graphic summary of all findings reported in the text with no text except where absolutely essential. A good model should be easily understood without reading any description since all concepts were supported in the main text and by experimentation. - The model also contains some inaccuracies. The suggestion is that the authors re-do the model and clarify some aspects such as: - The model suggests that ISRIB inhibits PRELID1 directly but there is no evidence for this whereas PRELID is directly regulated by YME1L (also typo here in figure: "Yme1" no "l"). - This model incorrectly uses inhibition symbols; for example, mutation of Perk does not inhibit its activity as GSK does. The KO does not have Perk so cannot perform its function. These are not the same. Similarly, the lipases (Lipin and PA-PLA1) should be depicted instead as altering flux of PA away from OMM not as inhibition. - The authors should connect PA accumulation in the OMM graphically to mitochondrial elongation [instead of through text]. If the authors consider the numbered labels convenient, please use just the number and place the description in the figure legend instead.

Minor comments:

1. Specific experimental issues that are easily addressable.
   • a. Yes, please see specific examples below.
2. Are prior studies referenced appropriately?
   • a. References appeared adequate except in the Materials and Methods section (see specific examples below).
3. Are the text and figures clear and accurate?
   • a. No, the text needs considerable editing to make the language clearer and formal whereas the figures are not always presented in a manner that is easily absorbed by the reader. Representative microscopy images chosen do not always match the corresponding graphical summary and are not clear even on PDF version compared to (Lebeau J, et al. 2018 - full citation above).
4. Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
   • a. Yes, please see specific examples below.

Specific minor comments by section

Introduction - This section contains minor grammatical errors and awkward writing which should be rephrased to be more concise. For example: - Incorrect use of commas (ex: absence of commas on page 3, bottom of paragraph 3).

Results - Overall, this section contains many grammatical errors and awkward language but these are unevenly distributed as some subsections are well written and thoroughly edited whereas others need closer inspection. For example: - No period at end of first subsection title; this should be consistent throughout. - Text not consistently written in past tense/passive voice. - Post-translational should be hyphenated (page 5, 2x on bottom of page). - The use of dashes to conjoin thoughts is too casual and sentences should be restructured with the aid of parentheses or semicolons only when necessary (ex: page 6, paragraph 2 through page 7). - Homogenize the use of hyphens in all sentences such as: ER stress-induced, ER stress-dependent.

Discussion

• Minor grammatical errors and awkward wording throughout; description of ideas should be more concisely written. For example:
  • Page 13, paragraph 1: "Thus, an improved understanding of how different PERK-dependent alterations to mitochondrial morphology and function integrate will provide additional insight to the critical importance of this pathway in regulating mitochondria during conditions of ER stress."
  • Page 13, paragraph 2: "Further investigations will be required to determine the specific impact of altered PERK signaling on mitochondria morphology and function in the context of these diseases to reveal both the pathologic and potentially therapeutic implications of PERK activity on the mitochondrial dysfunction observed in the pathogenesis of these disorders."
• Awkward/oxymoronic word choices. For example:
  • Page 11, paragraph 2: "...GSK2606414 reduces Tg-dependent increases of PA..." could be written as "... blocks/limits Tg-dependent increase of PA..." instead.
• What is evidence that ionomycin is completely independent of DRP1?

Methods

• Please provide more description or a reference for the method used for CRISPR/Cas9 gene editing (page 15, paragraph 1).
• Since different versions of chemicals are often available from the same company (for example in solution vs. powder, as a salt, different purities, etc.) it would be helpful for the authors to also include the catalog number for the purchased drugs and analytical standards (page 16, paragraph 1).
• The authors did an excellent job of blinding these images and utilizing several researchers to score each. However, we feel that 20 cells per biological replicate (~60 total per condition) is insufficient when mitochondrial morphology in chosen representative images is unclear. We think it is reasonable to request the authors to score additional images they collected as part of this investigation.
• The below two sentences contain some redundancies and should be combined/rephrased (page 16, paragraph 2).
  • "Three different researchers scored each set of images and these scores were averaged for each individual experiment. All quantifications shown were performed for at least 3 independent experiments, where averages in morphology quantified from each individual experiment were then combined."
• Incorrect units, for example: "500g" should be "500 x g" on page 16, paragraph 3 and "g" should be italicized. Same for "200g" on page 17, paragraph 1.
• Inconsistent abbreviation of chemicals, for example:
  • Chloroform and hydrochloric acid but not methanol in methods on page 17, paragraph 1. Also, the "l" in "HCL" should be lowercase.
• "Solvents" (2x) on page 17, paragraph 2 should be singular not plural.
• What does RT stand for on page 17, paragraph 2?
• Tris buffered saline is abbreviated incorrectly as "TB" then correctly later in the same paragraph as "TBS" on page 18, paragraph 3.
• Paragraph 4 on page 18 should be indented to be consistent with formatting of previous methods sections.
• To remove any ambiguity, catalog numbers should be included for antibodies (also consider including the lot number as there can be lot to lot variability).
• What percentage of tween v/v was supplemented in TBS buffer? Different concentrations of tween can impact antibody binding and would beneficial to include for reproducibility.
• Please indicate the incubation time and conditions for the secondary antibodies.
• The abbreviation for phosphate buffered saline is "PBS" not "PBD" (page 19, paragraph 1).
• Could the authors state clearly the reference transcript used for RT-qPCR (assumed is RIBOP)?
• Sometimes GIBCO is capitalized, sometimes not (Gibco), which should also be consistent.
• Who is the supplier for CCCP and what is the catalog number? Similarly, what is the catalog number for TMRE (both on page 19, paragraph 3)?
• Student's t-test is capitalized and possessive (similar to Tukey's) on page 19, paragraph 4.

Figures/supplementary figures

• General:
  • With respect to the lines overlaying histograms scoring mitochondrial morphology for designating statistical significance [with color-coded asterisks]:
    • It is assumed that the bars of the histogram being compared are those at the ends of each line but these aren't aligned perfectly. Please tidy up the figure by shifting these and consider capping lines to make more clear.
    • It appears that the authors provide these lines at all instances of statistically significant differences whether the comparison is important to their conclusions or not; including only the necessary comparisons will reduce the noise of these figures and make them easier to absorb and interpret. For example:
    • Figure 1C: why is comparison being made only for KO vs. complemented (+veh) - difference between KO and WT not statistically significant? Also, wouldn't the difference between WT and KO +Tg percent fragmented be statistically significant? The comparisons being made appear arbitrary or if not, was not clearly stated (same criticism for 2D, 3B, 3D, etc.).
  • The authors appear to use "transfection" and "transduction" interchangeably such that it is unclear whether expression of transgenes or shRNA is stably vs. transiently expressed. It would help if the authors could clarify their language here as well.
• Figure 1:
  • Figure 1A - PERK is membrane bound not soluble; should this not be represented in the model? Model colors are not easily distinguishable from each other on printout and should be upgraded.
  • Figure 1C - phenotypic scoring is not easy to interpret; perhaps authors could rearrange the figure such that each treatment is adjacent since that is the more interesting comparison? All cells in figure 1 are MEFs so delete "MEFs" below Perk+/+ and Perk -/-.
• Figure S1:
  • How much protein was loaded per lane and what percentage of polyacrylamide gel was used?
• Figure 2:
  • See general comments.
  • Figure 2A - extra letter/typo in "Fold Change."
  • Why do authors switch to HeLa cells after measuring PA content in MEFs?
• Figure S2:
  • Authors are now including ns for "not significant" and the p value where before they were not before. The intent for including the p-value in S2B appears to be because it suggests a trend towards statistical significance (actually a bit surprised it is not based on SEM error bars; authors should recheck their calculations) which is inappropriate. Either provide all the p-values, possibly as a separate table or none at all.
  • Now including double headed error bars for S2D-E which is inconsistent with rest of manuscript.
  • What is standard error for vehicle treated cells in 3B, 3D, and 3E? Given the above mistake it's reasonable to suspect that the error bars were omitted by accident.
• Figure 3:
  • Title should have hyphen for "stress-induced" and ionomycin shouldn't be capitalized.
  • Now using double headed error bars for 3B which is inconsistent with majority of other figures.
• Figure S3:
  • Title should have hyphen for "stress-induced" and ionomycin shouldn't be capitalized.
• Figure 4:
  • What is the purpose of including 4A? This depicts a concept which is not particularly difficult to grasp, was not experimentally shown in this manuscript, and is somewhat redundant with Figure 6. We recommend removing from Figure 4 and combining with Figure 6.
  • Since all cells used in Figure 4 were MEFs, the authors can remove "MEFs" from figure and just include genotype.
  • Figure 4C: typo in Yme1l - has two 1's.
• Figure S4:
  • See general comments.
• Figure 5:
  • Figure 5C: What does PLKO abbreviation stand for in the control line? pLKO.1 vector (see methods but not explained further).
• Figure S5:
  • Figure S5A-B: KD clearly worked but how efficient is unclear (quantitatively, i.e. 50, 90%, etc.?). The authors could perform serial dilutions of protein (i.e. 5, 10, 20 ug of the same samples for SDS-PAGE/immunoblot) or RT-qPCR. If knockdown is incomplete, this could explain the discrepancy in Figure 5 where depletion of Prelid should result in elongation [via OMM depletion of PA].
• Figure 6:
  • This is a more appropriate location for panel 4A.

Significance

1. Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.
   • a. Perturbances in PERK signaling evoke an alteration in mitochondrial morphology and have been extensively reported on, due to their clinical implications on neurodegenerative disorders such as Alzheimer's disease. The present work provides insight into the molecular basis for Stress Induced Mitochondrial Hyperfusion (SIMH) which can be triggered by ER stress. The authors find that this process occurs downstream of PERK and proceeds through accumulation of PA in the OMM by stabilization of Prelid, a mitochondrial resident protein that transports PA from the OMM to IMM for cardiolipin synthesis. The evidence of this work represents a substantial addition to the field of mitochondrial dynamics/SIMH and the Unfolded Protein Response.
2. Place the work in the context of the existing literature (provide references, where appropriate).
   • a. The novelty of this work is in the inclusion of PRELID1 downstream of PERK signaling pathway for transmission of ER stress to the mitochondria, a process that involves phosphatidic acid (PA). Some studies have addressed how phosphatidic acid is a modulator and a signal in mitochondrial physiology. The role of the lipids in mitochondrial dynamics represent an important and emerging field that needs to be explored in order to understand how metabolites control mitochondrial fusion/fission.

References

Yoshihiro Adachi, Kie Itoh, Tatsuya Yamada, Kara L. Cerveny, Takamichi L. Suzuki, Patrick Macdonald, Michael A. Frohman, Rajesh Ramachandran, Miho Iijima, Hiromi Sesaki. Coincident Phosphatidic Acid Interaction Restrains Drp1 in Mitochondrial Division. Molecular Cell. Volume 63, Issue 6. 2016. Pages 1034-1043. https://doi.org/10.1016/j.molcel.2016.08.013

Huang H, Gao Q, Peng X, Choi SY, Sarma K, Ren H, Morris AJ, Frohman MA. piRNA-associated germline nuage formation and spermatogenesis require MitoPLD profusogenic mitochondrial-surface lipid signaling. Dev Cell. 2011 Mar 15;20(3):376-87. https://doi.org/10.1016/j.devcel.2011.01.004 3. State what audience might be interested in and influenced by the reported findings. - a. Audiences of the fields such as Mitochondrial dynamics, UPR, lipid metabolism, neurodegenerative diseases, ER-stress response, Integrated Stress Response. 4. Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. - a. Mitochondrial morphology, mtDNA inheritance, mitochondrial metabolism, fluorescence/indirect immunofluorescence microscopy

I was trying to figure out why it wasn't working for me. This was probably the reason.

So if you just have gem 'rack', it just silently has no effect.

It seems like it should just work either way. Or at least give a warning/error if you try to use this config and it's not going to use that config!