Shift to more quiet tone

Coleridge plays between moments of stasis and intense movement. It relates to his creative process by saying that sometimes you don't know what to do, while others, it is clear what direction you should be heading.

Contradiction between his words illustrates how his creativity is not straightforward and even he can't fully describe the ways his mind works.

Represents the mystery of this place and how it can't be fully understood. Could be mapped to the subconscious part of his imagination.

emphasis on the idyllic nature of this place.

I think, especially if someone was exposed to such thought processes in their formative years, it becomes almost an instinct to question, doubt, and perceive actions/thoughts as shameful or "wrong," which, even after leaving the practice, still impacts an individual especially when gone untreated/managed.

This brings me back to my statement about spiritual harm and the possibility that environments in which harm is done may return to being a safe or comfortable space because there is space granted to talking about it to either troubleshoot or soothe/protect an individual. In religious spaces, this is often not the case and may even cause more shaming to take place.

True, however, is this type of harm as widely accepted as religious teachings which may result in harm, and is support more accessible within that space which allows the space to feel safe enough to still engage in?

The concept and definition we have so far of "spiritual harm," I would argue, is integral in understanding how religious trauma comes about from the complex and chronic repeat exposure to religious teachings and because we are a spiritual being with psyche vulnerable to harm on a level other than physical.

Do we need to approach treating mental illness in those with religious trauma any differently than those with mental health problems from a different psychological cause? What treatment is shown to be the most effective, CBT, DBT, or other therapies?

The limited research may be because, historically, this behavior and type of teaching were accepted, and only recently have people started to vocalize the negative aspects of participating in a religion so widely accepted and touted as "the right way."

I wonder how shame and fear are made more pernicious with an added element of physical and sexual abuse by church leaders. However, it is important to understand how Christianity creates an environment fostering shameful feelings at a very basic level without those added elements.

for - definition - production system ('hardware') - and organizing system ('software') - Arbib and Seba

definition Arbib and Seba - human civilization can be broken down into the interaction between two complimentary systems - the production system - by which we meet fundamamental material needs for food, energy, transportation, water, materials - also called 'hardware' - the organizing system - by which how the production system is governed and managed and includes the economy, polity, security, culture, ideology and values - also called 'software'

comment - A transformation is required in both the hardware and the software to mitigate the worst impacts of our current polycrisis

The wind changes and so too does the speaker's internal feelings in that it now has a more manageable as if paying attention to the tumultuous sounds of the wind was akin to a release of some sort.

Here, the speaker realizes that they must not rely on nature to lift his spirits and instead needs to gain real passion and life from within.

Serves as foreshadowing for the "deadly storm" about to take place which correlates to the speaker's future explanation of their emotional distress.

Emphasizes the speaker's disconnect with nature as the speaker can see the moon, yet can not feel its presence. This contributes to the speakers overwhelming sense of isolation.

These lines have a contrast in that Coleridge first explains how there were once positive feelings associated with hope and imagination; however, now, these feelings have morphed into despair and a lack of an ability to connect with nature.

Discussion questions: - What role does nature play in the poem? - What is the significance of the epigraph? - What is the relationship between imagination and emotion?

Because of the injury the Buccaneers may try to make a trade which would impact many people in both in the NFL, fans, and fantasy football players

This event just happened on Monday night so it is an example of timeliness

This comment shows prominence as Chris Godwin is a star player for the Tampa Bay Buccaneers.

for - stats - green growth - 2024 - Global South vs Global North

stats - green growth - 2024 - Global South has - 60% of world population - 20% of fossil fuel production - fossil fuel production in decline - 70% of global renewable resource potential - In 2024, 87% of capex of electricity generation is renewable - From 2019 to 2024, renewable energy has grown 23% annually and now supplies 9% of its electricity - 17% of Global South has already overtaken Global North in % of renewable electricty generation

for - Report - Powering Up the Global South - Rocky Mountain Institute - RMI - 2024 - Vikram Singh - Kingsmill Bond

Summary - This report shows that the Global South is adopting cleantech faster than the Global North

Its giving ghost ship

No captain spells a very bad time

as in the ship?

From where?

Bad at steering

What?

Is Herman Melville racist?"

rich kid went on an adventure and got into some trouble

Explains why Babo is so content to be a servant

that took a turn

Like the early americas

What the ship wanted? Melville has taken personification and really ran with it

suffering has made the captain mute

the people ahve kind of turned to anarchy

Is Babo the chained up guy?

loss of supplies=loss of power

I think every slave ever would disagree

woah... racism just jumped a few notches there

religious ritual from the POV of an outsider maybe?

more racism

I want to analyze again how this characterizes Americans

The quick pacing makes you almost feel his heart rate accelerating!

i hate how hes comparing people to animals.

OOP

Ominous! Also the Freemason reference is fascinating

Love the spooky imagery!

the boats hasn't been maintained properly the "woolly" look is barnacles and peeling paint

Hmm there's a lot of characterization to unpack here

This line is powerful

Christmas at his house must suck

Uh oh

This feels off putting

The stranger hid their intentions?

I think this means he is ignorant of, and not experienced in the "evil" ways of man.

Tossing this back to "what is the author saying about being American" because I think you could analyze this in that lens

Uhh this reminds me of that racist stereotype that black people are violent. Yikes

What is he trying to say about being American here?

Bro leave disabled people alone. And stop being racist. And-

Again - what is the author trying to say about being American here?

Interestingly symbolic...

Maybe the author should think more about privilege lol

Style of storytelling reminds me again of Frankenstein

Again, slave ship, no need to "pretty it up" by calling it an emigrant ship

A slave ship you mean

Again, what is the author saying about being American here?

man poor babo

More racism...ugh

Sounds like depression

What is the author trying to say about American mentality/culture/demeanor?

...correct me if I'm wrong, but is the captain here the only white person? If so, ughh

RACISM

This feels racist

Ghosts!

In-human, creepy description of black people...yikes

more racism

As far as I know, I don't think Spanish people are particularly susceptible to scurvy?? What is the author trying to say with this?

Yay death! Yay ghost ship!

Tell me you're scared of paganism without telling me you're scared of paganism

racism

Personification of the ship - genuinely if I heard this out of context and ship-specific stuff wasn't mentioned, I would've just thought of a woman. It's either really pervading personification or I'm just too sleepy lol

this line feels really questionable considering how similar it is to things that have been said to justify slavery

YIKES...

Ghost ship?

Underscores? Also thanks to the person who commented what this is!

the spanish once again being brought up in relation to the slave trade

Why am I reminded of the Creature's appearance on the ship in Frankenstein?

What did I just say about eerie lol?

I feel like there's an interesting, eerie tone in this.

oh...

Religious simile!

character is an unbelievably good person apparently

Very descriptive language.

Super mysterious writing, lots of imagery.

Radyografideki patoloji bu faktörlerden dolayı görülebilir

Enfeksiyonu ortadan kaldırma yeteneğinin olmaması

Periodontal hastalıkların doğrudan etkisiyle PDL'de iltihaplanma meydana gelir. Travmalar da iltihabi sonuçlar doğurabilir

Pulp dokusundaki iltihap, apikal foramen veya lateral kanallar aracılığıyla periodontal ligamente (PDL) iletilir

main topic

Our relationship with technology and our environment is interconnected and always evolving and changing.

I think that the concept of 'shadow profiles' presents a significant challenge to privacy regulations and the ability to monitor whether data on media is kept as secure as possible. It may also in some ways be categorised as a result of misinformation. For example, a user may decline services on a media keeping in mind that they don't want to be a part of the company's data collecting practices however their data is still gathered in the system which can be deemed unethical.

I have come across multiple research papers regarding de-identification of personal information and what is so interesting as well as a bit concerning is the idea that any individual can be identified by only 3 pieces of information including zip code, birthday and gender. This discovery did lead to a change in policies regarding privacy research and regulations on various media; however this leads me to think that as privacy protections evolve, so will the methods to breach them.

eLife Assessment

The authors analyze a comprehensive cohort of human plasma samples to identify an extracellular vesicles protein signature for early diagnosis of pancreatic cancer. The application of liquid biopsies is valuable, and the work addresses a key clinical problem as pancreas cancer is often diagnosed in late stages. The strength of evidence is solid. Altogether, this work supports the potential use of extracellular vesicles in clinical settings, with promising value to scientists and clinicians.

Reviewer #1 (Public review):

This study presents a large cohort of plasma-derived extracellular vesicle samples from 124 individuals, including patients with PDAC, benign pancreatic diseases and controls. The authors identified a panel of protein markers for the early detection of pancreatic cancer and validated in an external cohort.

Reviewer #2 (Public review):

This work investigates the use of extracellular vesicles (EVs) in blood as a noninvasive 'liquid biopsy' to aid in differentiation of patients with pancreatic cancer (PDAC) from those with benign pancreatic disease and healthy controls, an important clinical question where biopsies are frequently non-diagnostic. The use of extracellular vesicles as biomarkers of disease has been gaining interest in recent history, with a variety of published methods and techniques, looking at a variety of different compositions ('the molecular cargo') of EVs particularly in cancer diagnosis (Shah R, et al, N Engl J Med 2018; 379:958-966).

This study adds to the growing body of evidence in using EVs for earlier detection of pancreatic cancer, identifying both new and known proteins of interest. Limitations in studying EVs in general include dealing with low concentrations in circulation and identifying the most relevant molecular cargo. This study provides validation of assaying EVs using the novel EVtrap method (Extracellular Vesicles Total Recovery And Purification), which the authors show to be more efficient than current standard techniques and potentially more scalable for larger clinical studies.

The strength of this study is in its numbers - the authors worked with a cohort of 124 cases, 93 of them which were PDAC samples, which considered large for an EV study (Jia, E et al. BMC Cancer 22, 573 (2022)). The benign disease group (n=20, between chronic pancreatitis and IPMNs) and healthy control groups (n=11) were relatively small, but the authors were not only able to identify candidate biomarkers for diagnosis that clearly stood out in the PDAC cohort, but also validate it in an independent cohort of 36 new subjects. Proteins they've identified as associated with pancreatic cancer over benign disease included PDCD6IP, SERPINA12 and RUVBL2. They were even able to identify a set of EV proteins associated with metastasis and poorer prognosis , which include the proteins PSMB4, RUVBL2 and ANKAR and CRP, RALB and CD55. Their 7-EV protein signature yielded an 89% prediction accuracy for the diagnosis of PDAC against a background of benign pancreatic diseases that is compelling and comparable to other studies in the literature (Jia, E. et al. BMC Cancer 22, 573 (2022)).

The limitations of this study are its containment within a single institution - further studies are warranted to apply the authors' 7-EV protein PRAC panel to multiple other cases at other institutions in a larger cohort.

Author response:

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

Reviewer #1 (Public Review):

In this manuscript, Bockorny, Muthuswamy, and Huang et al. performed proteomics analysis of plasma extracellular vesicles (EVs) from pancreatic ductal adenocarcinoma (PDAC) patients and patients with benign pancreatic diseases (chronic pancreatitis and intraductal papillary mucinous neoplasm, IPMN) to develop a 7-EV protein signature that predicts PDAC. Moreover, the authors identified PSMB4, RUVBL2, and ANKAR as being associated with metastasis. These studies provide important insight into alterations of EVs during PDAC progression and the data supporting predict PDAC with EV protein signatures are solid. However, there are certain concerns regarding the rigor and novelty of the data analysis and interpretation, as well as the clinical implications, as detailed below.

(1) Plasma EVs were characterized by transmission electron microscopy and nanoparticle tracking analysis to confirm their morphology and size. The authors should also include an analysis of putative EV markers (e.g., tetraspanins, syntenin, ALIX, etc.) to confirm that the analyzed particles are EVs.

We thank the reviewer for this comment. In the previous study from our co-authors who developed EVtrap method (PMID:32396726), they used electron microscopy and NTA , as well as quantification of typical EV protein markers, such as CD9, to confirm that particles isolated using EVtrap had typical characteristics of the extracellular vesicles. As such, these experiments were not replicated here. We added the following statement to the manuscript:

“Previous analyses using electron microscopy and nanoparticle tracking also confirmed that the vast majority of particles isolated by EVtrap had diameters between 100-200 nm, consistent with exosomes (PMID:32396726). In addition, EVtrap isolates demonstrates higher abundance of CD9, a common exosome marker, as compared to isolates from other traditional EV isolation methods such as size exclusion chromatography and ultracentrifugation (PMID:32396726)”

(2) The authors identified multiple over-expressed proteins in PDAC based on their foldchange and p-value; however, due to the heterogeneity of PDAC, it is necessary to show a heatmap displaying their abundance in all samples. High fold change does not necessarily indicate consistently high abundance in all PDAC samples.

We thank the reviewer for this suggestion. We have now included the heatmap in the new Supplementary Figure 3.

(3) PSMB4, RUVBL2, and ANKAR were identified as being associated with metastasis. The authors state that they intended to distinguish early and late-stage cancer samples, but it is unclear why they chose to compare metastatic and non-metastatic samples, as the non-metastatic group also includes late-stage cancer samples. This sentence should be rephrased to more accurately reflect the sample types profiled.

We thank the reviewer for pointing this out. We would like to clarify that this analyses shown in Figures 3B and 3C pertain to patients with Metastatic vs Non-Metastatic disease, not early versus late stage. We edited the text to ensure this information is clear.

(4) Non-metastatic and metastatic patients were separated based on global protein abundance. The samples within each group display significant heterogeneity, with some samples displaying similar patterns although they were classified into different groups (Figure 3A), and the samples within the same group, particularly the metastasis group, did not consistently exhibit similar patterns of protein abundance. The authors should clarify this point.

We thank the reviewer for this comment. The EV proteomic expression is anticipated not to show the exact pattern across of samples of each group. The purpose of this experiment depicted in Figure 3 heatmap is to show the enrichment for pattern of expressions, but we acknowledge that not all samples from the same group have the exact proteome pattern.

We added this statement in the discussion section:

“As expected, the EV proteomic profiles of PDAC patients exhibited significant heterogeneity. While the above mentioned markers exhibited strong association with disease states at population levels, their abundances in individual patients varied significantly. Those observations highlight the need to develop multi-protein panels for pancreatic cancer diagnosis and prognosis.”

(5) The authors performed the survival analysis on a set of EV proteins but did not specify the origin of these markers or how many markers were examined. The authors should show their abundances across different groups, such as different stages and metastasis status.

We thank the reviewer for the comments. The goal of this experiment was not to identify EV proteins that performed similarly well for diagnosing and prognostication. In Figure 3A, 3B and 3C, we identified EV proteins that had better performance for diagnosis of metastatic disease. In these experiments we made  comparative analysis between patients with metastasis versus non-metastasis. In the experiment depicted in Figure 3D, the goal was to identify EV markers that had better performance is prognosticating outcomes as measured by overall survival, out of the markers identified in the previous experiments from Figure 3A. We would like to further clarify that based on our observation and others, it has become clear that EV profiles from cancer patients are highly heterogenous and we do not anticipate that a single marker will have sufficient test performance for cancer diagnosis or prognosis assessment when measured isolated. Rather, we anticipate that a panel of markers may yield better performance for diagnosis while a different combination of EV markers may have better performance for prognosis assessment.

(6) The classification model yielded a 100% accuracy, which may refer to AUC, in their discovery cohort, but it decreased to 89% in the independent cohort. This suggests that the authors have encountered overfitting issues with their model, where it performed well on the discovery cohort but did not generalize well to the independent cohort. The authors should clarify this point. The AUC score of the 7-EV signature is 0.89 and is not equivalent to prediction accuracy. In order to demonstrate prediction accuracy, the authors should show the confusion matrix of training and testing data as well as other evaluation metrics, such as accuracy, precision, and recall.

We thank the reviewer for providing these insightful comments. As you noted, the 7-biomarker signature machine learning model attained an impressive 100% accuracy within the internal Discovery Cohort, raising concerns about potential overfitting in the external validation dataset. Acknowledging the noted difference in AUROC of 0.11 in the external validation cohort, which surpasses the typical reported range of ~0.06-0.09, the model demonstrated a commendable AUROC of 0.89 in an independent patient cohort. Moreover, the utilization of an alternate technology to measure protein abundance in the validation dataset, underscores the model’s reproducibility and validity. We have provided the model metrics for both internal- and external-validation cohort. For these, please see updated Supplementary Figure 7, as well as the new Supplementary Figure 6 and Supplementary Figure 8. We also amended the discussion section to acknowledge that the validation cohort had limited sample size and proteins were measured in using a different method. Those factors likely contributed to the lower accuracy of predictions in the validation cohort. We addressed these limitations in the discussion section of the manuscript.

(7) The authors should include more details of their model and the process of selection of signatures to enhance the reproducibility and transparency of their methods.

We thank the reviewer for their valuable comments. To enhance clarity, we have incorporated additional information regarding the method employed for biomarker signature identification into the ‘Methods Section’ in page 23.  We note that Supplementary Table 7a provides details on ‘Sensitivity, Specificity, Precision, and AUC’ for the 16 markers included in the external validation study. Additionally, Supplementary Table 7b presents the contingency table for 7-biomarker signature, offering insights into model accuracy for both the Internal-Discovery and External Validation cohorts.  

Reviewer #2 (Public Review):

The authors intended to identify a protein signature in extracellular vesicles of serum to distinguish pancreatic ductal adenocarcinoma from benign pancreatic diseases.

A major strength of the work presented is the valuable profiling of a significant number of patient samples, with a rich cohort of patients with pancreatic cancer, benign pancreatic diseases, and healthy controls. However, despite the strong cohorts presented, the numbers of patient samples for benign pancreatic diseases as well as controls were very limited.

Also, the method used to isolate vesicles, EVTrap, recognizes double bilayers, which means that it can detect cellular debris and apoptotic bodies, which are very common in the circulation of patients that are undergoing chemotherapy. It would be important to identify the patients that are therapy naïve and the ones that are not because of this possible bias.

We thank the Reviewer for these comments. We want to point out that the experiments presented in Supplementary Figure 1 (Transmission electron microscopy images and Nanoparticle tracking analysis) confirm that the vesicles isolated with EVTrap are not cellular debris and apoptotic bodies. Rather, these structures are in the nano range expected for exosomes. This is further supported by the additional work from our co-author and collaborator describing the development of EVtrap and its performance in isolating exosomes when compared to other traditional methods such as ultracentrifugation and size exclusion chromatography (PMID:32396726).

As per the Reviewer’s request, we have provided an additional heatmap figure depicting whose patients are treatment naïve to differentiate from those who have received treatment (revised Figure 2C).

Additionally, the transmission electron microscopy data reflect this heterogeneity of the samples, also with little identification of double bilayered vesicles. It would be important to identify some extracellular vesicles markers in those preparations to strengthen the quality of the samples analyzed.

We appreciate the comment from the Reviewer and acknowledge the importance of identifying exosome markers on the isolate from EVtrap. These experiments have already been done and are reported in the original paper describing the development of this method by our co-authors in a separate work. In the manuscript PMID: 30080416, our collaborators demonstrated the detection of CD9, a well-known exosome marker, using Western Blot from isolates using EVtrap or ultra-centrifugation, a traditional technique to isolate exosomes. This work showed that EVtrap yielded much higher recovery rate of exosomes with lower contamination from soluble proteins. We did not repeat these already published experiments, but we amended our manuscript to reference these results.

What is more, previously published work with this same methodology identifies around 2000 proteins per sample. It would be important to explain why in this study there seems to be a reduction in more than 50% of the amount of proteins identified in the vesicles.

We thank the Reviewer for pointing out this important detail. In the previous work in which EVtrap was developed by our co-authors, the blood samples were processed using a different protocol, with shorter centrifugation (2,500g for 10 min) (PMID: 32396726). In the current work, we employed three centrifugation steps. As detailed in the Methods section of the manuscript, blood samples were centrifuged at 1,300g for 15 min. Then  plasma was removed from the top carefully avoiding cell pellet;  Repeat centrifugation of plasma at 2,500g for 15 min;  Again, plasma was removed from the top carefully avoiding cell pellet;  Third centrifugation at 2,500g for 15 min. This more extensive centrifugation process was intended to further increase the removal of platelets, apoptotic bodies, and other large particles and aggregates. Accordingly, we anticipate that the additional centrifugation steps decreased the contamination of our isolates but may have also decreased the amount of exosome proteins, hence the lower amount of exosome proteins identified in our study as compared to the original study from our co-authors (PMID: 32396726).

One of the proteins that constantly surges on the analysis is KRT20. It would be important to proceed with the analysis by first filtering out possible contaminants of the proteomics, of which keratins are the most common ones.

We thank the Reviewer for this comment. We would like to point out that we do believe that KRT20 is, in fact, cancer related and a not a contaminant. This is supported by our results presented in this manuscript showing enrichment or KRT20 in PDAC cases, and lower expression in benign samples. If this protein was a contaminant, its expression would be found uniformly in all samples, there would be no apparent reason for different expression between malignant vs benign cases, as all samples were processed following the same procedures. In addition, increased expression of KRT20 in PDAC tissues has also been reported by others. For instance, in a study by Schmiz-Winnthal  (PMID: 16364723), the authors showed that Cytokeratin 20 (KRT20) were expressed in 76% of PDAC patients and expression of KRT20 was associated with poor survival after surgical resection. Based on these observations, we believe that the KRT20 identified in our study is indeed a tumor associated EV protein rather than contamination.

Finally, none of the 7-extracellular vesicle protein signatures has been validated by other techniques, such as western blot, in extracellular vesicles isolated by other, standard, methods, such as size exclusion chromatography.

A distinct technique for protein analysis was done but not a different method of isolation of these vesicles. This would strengthen the results and the origin of the proteins.

We appreciate the Reviewer’s comment. We would like to again emphasize that the goal of this manuscript was not to compare the performance of EVtrap with other traditional EV isolation approaches such as ultracentrifugation and size exclusion chromatography.  The main goal of study is to determine proteomic profiles of EVs isolated from clinical samples and provide such information to research community for further studies. As the Reviewer points out, proteins in EVs are highly heterogeneous which highlight the complexity of EV biology and interpatient heterogeneity of pancreatic cancer.  We do not anticipate the development of EV-based markers for pancreatic diagnosis can be achieved by a single team, but by a community of researchers. We hope information presented in the current study will help other researchers identify additional candidates for validation in future work. Nonetheless, we edited the manuscript to discuss the limitation of not doing cross-validation of protein detection using a different method.

The conclusions that are reached do not fully meet the proposed aims of the identification of a protein signature in circulating extracellular vesicles that could improve early detection of the disease. The authors did not demonstrate the superiority of detection of these proteins in extracellular vesicles versus simply performing an ELISA, nor their superiority with respect to the current standard procedure for diagnosis.

We would like to clarify to the Reviewer that the goal of this manuscript was not to prove superiority of the EV signature biomarker in diagnosing pancreatic cancer as compared to current standard of care (SOC) practice, i.e., CT scans, endoscopic ultrasound and CA19-9. In order to prove such superiority, one would require a large, randomized phase III trial with several hundred patients. This was not the pursue of our discovery EV proteomics study and we double checked our manuscript to ensure no such claim was made. Rather, we aimed at developing a new pipeline for discovery of new EV biomarkers and we believe we were able to prove that this approach was successful in discovering a new class of biomarkers based on proteins expressed on extra-cellular vesicles that have predominant expression on patients with pancreatic cancer. Future studies should continue to advance this field with goals of improving on the current standard of care diagnostic methods.

The authors also suggest that profiling of circulating extracellular vesicles provides unique insights into systemic immune changes during pancreatic cancer development. How is this better than a regular hemogram is not clear.

We would like to clarify that the overall goal of this study is to provide patient-relevant information for the research community to further investigate biology of extracellular vesicles. For the state 'unique insights into systemic immune changes' we referred to the fact that we discovered EVs carrying proteins involved in immune responses. Previous studies have shown that EVs play important roles in cell-cell communication, discoveries from our study provide candidates for future studies on cellular mechanisms underlying immune regulation during pancreatic cancer development.

Finally, it would be important to determine how this signature compares with many others described in the literature that have the exact same aim. Why and how would this one be better?

We would like to again clarify that comparing the diagnostic performance of the EV biomarkers discovered in the study against standard of care methods (CA19-9, ctDNA, CT scan) was beyond the scope of this discovery EV proteomics work. We reviewed the manuscript to ensure that no claims were made as far as superiority against point-of-care tests available in clinic.

Reviewer #3 (Public Review):

This work investigates the use of extracellular vesicles (EVs) in blood as a noninvasive 'liquid biopsy' to aid in the differentiation of patients with pancreatic cancer (PDAC) from those with benign pancreatic disease and healthy controls, an important clinical question where biopsies are frequently non-diagnostic. The use of extracellular vesicles as biomarkers of disease has been gaining interest in recent history, with a variety of published methods and techniques, looking at a variety of different compositions ('the molecular cargo') of EVs particularly in cancer diagnosis (Shah R, et al, N Engl J Med 2018; 379:958-966).

This study adds to the growing body of evidence in using EVs for earlier detection of pancreatic cancer, identifying both new and known proteins of interest. Limitations in studying EVs, in general, include dealing with low concentrations in circulation and identifying the most relevant molecular cargo. This study provides validation of assaying EVs using the novel EVtrap method (Extracellular Vesicles Total Recovery And Purification),which the authors show to be more efficient than current standard techniques and potentially more scalable for larger clinical studies.

The strength of this study is in its numbers - the authors worked with a cohort of 124 cases,93 of them which were PDAC samples, which are considered large for an EV study (Jia, E etal. BMC Cancer 22, 573 (2022)). The benign disease group (n=20, between chronic pancreatitis and IPMNs) and healthy control groups (n=11) were relatively small, but the authors were not only able to identify candidate biomarkers for diagnosis that clearly stood out in the PDAC cohort, but also validate it in an independent cohort of 36 new subjects.

Proteins they have identified as associated with pancreatic cancer over benign disease included PDCD6IP, SERPINA12, and RUVBL2. They were even able to identify a set of EV proteins associated with metastasis and poorer prognosis, which include the proteins PSMB4, RUVBL2 and ANKAR and CRP, RALB and CD55. Their 7-EV protein signature yielded an 89% prediction accuracy for the diagnosis of PDAC against a background of benign pancreatic diseases that is compelling and comparable to other studies in the literature (Jia,E. et al. BMC Cancer 22, 573 (2022)).

The limitations of this study are its containment within a single institution - further studies are warranted to apply the authors' 7-EV protein PRAC panel to multiple other cases at other institutions in a larger cohort.

We are very thankful to the Reviewer for the positive feedback. We are similarly optimistic that EV-based biomarkers will assist future researchers to develop better diagnostic assays for patients with pancreatic cancer, as well as other tumor types lacking accurate blood-based tests.

Author response:

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

Reviewer #1 (Public review):

Summary:

In this manuscript, Herrmannova et al explore changes in translation upon individual depletion of three subunits of the eIF3 complex (d, e and h) in mammalian cells. The authors provide a detailed analysis of regulated transcripts, followed by validation by RT-qPCR and/or Western blot of targets of interest, as well as GO and KKEG pathway analysis. The authors confirm prior observations that eIF3, despite being a general translation initiation factor, functions in mRNA-specific regulation, and that eIF3 is important for translation re-initiation. They show that global effects of eIF3e and eIF3d depletion on translation and cell growth are concordant. Their results support and extend previous reports suggesting that both factors control translation of 5'TOP mRNAs. Interestingly, they identify MAPK pathway components as a group of targets coordinately regulated by eIF3 d/e. The authors also discuss discrepancies with other reports analyzing eIF3e function.

Strengths:

Altogether, a solid analysis of eIF3 d/e/h-mediated translation regulation of specific transcripts. The data will be useful for scientists working in the Translation field.

Weaknesses:

The authors could have explored in more detail some of their novel observations, as well as their impact on cell behavior.

The manuscript has improved with the new corrections. I appreciate the authors' attention to the minor comments, which have been fully solved. The authors have not, however, provided additional experimental evidence that uORF-mediated translation of Raf-1 mRNA depends on an intact eIF3 complex, nor have they addressed the consequences of such regulation for cell physiology. While I understand that this is a subject of follow-up research, the authors could have at least included their explanations/ speculations regarding major comments 2-4, which in my opinion could have been useful for the reader.

Our explanations/speculations regarding major comments 2 and 3 were included in the Discussion. We apologize for this misunderstanding as we thought that we were supposed to explain our ideas only in the responses. We did not discuss the comment 4, however, as we are really not sure what is the true effect and did not want to go into wild speculations in our manuscript. We thank this reviewer for his insightful comments and understanding.

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

Reviewer #1 (Recommendations For The Authors):

Major comments:

(1) The authors report the potential translational regulation of Raf kinase by re-initiation. It would be interesting to show that Raf is indeed regulated by uORF-mediated translation, and that this is dependent on an intact eIF3 complex. Analyzing the potential consequences of Raf1 regulation for cancer cell proliferation or apoptosis would be a plus.

We agree that this is an interesting and likely possibility. In fact, another clue that translation of Raf1 is regulated by uORFs comes from Bohlen et al. 2023 (PMID: 36869665) where they showed that RAF1 translation is dependent on PRRC2 proteins (that promote leaky scanning through these uORFs). We noted in the discussion that our results from eIF3d/e/hKD and the PRRC2A/B/CKD partly overlap. It is a subject of our follow-up research to investigate whether eIF3 and PRRC2 co-operate together to regulate translation of this important mRNA. 

(2) The authors show that eIF3 d/e -but not 3h- has an effect on cell proliferation. First, this indicates that proliferation does not fully correlate with eIF3 integrity. Depletion of eIF3d does not affect the integrity of eIF3, yet the effects on proliferation are similar to those of eIF3e. What is the possibility that changes in proliferation reflect functions of eIF3d outside the eIF3 complex? What could be the real consequences of disturbing eIF3 integrity for the mammalian cell? Please, discuss.

Yes, proliferation does not fully correlate with eIF3 integrity. Downregulation of eIF3 subunits that lead to disintegration of eIF3 YLC core (a, b, c, g, i) have more detrimental effect on growth and translation than downregulation of the peripheral subunits (e, k, l, f, h, m). Our previous studies (Wagner et al. 2016, PMID: 27924037 and Herrmannová et al. 2020, PMID: 31863585) indicate that the YLC core of eIF3 can partially support translation even without its peripheral subunits. In this respect eIF3d (as a peripheral subunit) is an amazing exception, suggesting it may have some specialized function(s). Whether this function resides outside of the eIF3 complex or not we do not know, but do not think so. Mainly because in the absence of eIF3e – its interaction partner, eIF3d gets rapidly degraded. Therefore, it is not very likely that eIF3d exists alone outside of eIF3 complex with moonlighting functions elsewhere. We think that eIF3d, as a head-interacting subunit close to an important head ribosomal protein RACK1 (a landing pad for regulatory proteins), is a target of signaling pathways, which may make it important for translation of specific mRNAs. In support is these thoughts, eIF3d (in the context of entire eIF3) together with DAP5 were shown to promote translation by an alternate capdependent (eIF4F-independent) mechanism (Lee et al. 2016, PMID: 27462815; de la Parra et al. 2018, PMID:30076308). In addition, the eIF3d function (also in the context of entire eIF3) was proved to be regulated by stress-triggered phosphorylation (Lamper et al. 2020, PMID: 33184215). 

(3) Figure 6D: Surprisingly, reduced levels of ERK1/2 upon eIF3d/e-KD are compensated by increased phosphorylation of ERK1/2 and net activation of c-Jun. Please comment on the functional consequences of buffering mechanisms that the cell deploys in order to counteract compromised eIF3 function. Why would the cell activate precisely the MAPK pathway to compensate for a compromised eIF3 function?

This we do not know. We can only speculate that when translation is compromised, cells try to counteract it in two ways: 1) they produce more ribosomes to increase translational rates and 2) activate MAPK signaling to send pro-growth signals, which can in the end further boost ribosome biogenesis.

(4) Regarding DAP-sensitive transcripts, can the authors discuss in more detail the role of eIF3d in alternative cap-dependent translation versus re-initiation? Are these transcripts being translated by a canonical cap- and uORF-dependent mechanism or by an alternative capdependent mechanism?

This is indeed not an easy question. On one hand, it was shown that DAP5 facilitates translation re-initiation after uORF translation in a canonical cap-dependent manner. This mechanism is essential for translation of the main coding sequence (CDS) in mRNAs with structured 5' leaders and multiple uORFs. (Weber et al. 2022, PMID: 36473845; David et al., 2022, PMID: 35961752). On the other hand, DAP5 was proposed to promote alternative, eIF4F-independent but cap-dependent translation, as it can substitute the function of the eIF4F complex in cooperation with eIF3d (de la Parra et al., 2018, PMID: 30076308; Volta et al., 2021 34848685). Overall, these observations paint a very complex picture for us to propose a clear scenario of what is going on between these two proteins on individual mRNAs. We speculate that both mechanisms are taking place and that the specific mechanism of translation initiation differs for differently arranged mRNAs.

Minor comments:

(5) Figure S2C: why is there a strong reduction of the stop codon peak for 3d and 3h KDs?

We have checked the Ribowaltz profiles of all replicates (in the Supplementary data we are showing only a representative replicate I) and the stop codon peak differs a lot among the replicates. We think that this way of plotting was optimized for calculation and visualization of P-sites and triplet periodicity and thus is not suitable for this type of comparison among samples. Therefore, we have performed our own analysis where the 5’ ends of reads are used instead of P-sites and triplicates are averaged and normalized to CDS (see below please), so that all samples can be compared directly in one plot (same as Fig. S13A but for stop codon). We can see that the stop codon peak really differs and is the smallest for eIF3hKD. However, these changes are in the range of 20% and we are not sure about their biological significance. We therefore refrain from drawing any conclusions. In general, reduced stop codon peak may signal faster termination or increased stop codon readthrough, but the latter should be accompanied by an increased ribosome density in the 3’UTR, which is not the case. A defect in termination efficiency would be manifested by an increased stop codon peak, instead.

Author response image 1.

(6) Figures 5 and S8: Adding a vertical line at 'zero' in all cumulative plots will help the reader understand the author's interpretation of the data. 

We have added a dashed grey vertical line at zero as requested. However, for interpretation of these plots, the reader should focus on the colored curve and whether it is shifted in respect to the grey curve (background) or not. Shift to the right indicates increased expression, while shift to the left indicates decreased expression. The reported p-value then indicates the statistical significance of the shift.

(7) The entire Figure 2 are controls that can go to Supplementary Material. The clustering of Figure S3B could be shown in the main Figure, as it is a very easy read-out of the consistent effects of the KDs of the different eIF3 subunits under analysis.

We have moved the entire Figure 2 to Supplementary Material as suggested (the original panels can be found as Supplementary Figures 1B, 1C and 3A). Figure S3B is now the main Figure 2E. 

(8) There are 3 replicates for Ribo-Seq and four for RNA-Seq. Were these not carried out in parallel, as it is usually done in Ribo-seq experiments? Why is there an extra replicate for RNASeq?

Yes, the three replicates were carried out in parallel. We have decided to add the fourth replicate in RNA-Seq to increase the data robustness as the RNA-Seq is used for normalization of FP to calculate the TE, which was our main analyzed metrics in this article. We had the option to add the fourth replicate as we originally prepared five biological replicates for all samples, but after performing the control experiments, we selected only the 3 best replicates for the Ribo-Seq library preparation and sequencing.  

(9) Please, add another sheet in Table S2 with the names of all genes that change only at the translation (RPF) levels.

As requested, we have added three extra sheets (one for each downregulation) for differential FP with Padjusted <0.05 in the Spreadsheet S2. We also provide a complete unfiltered differential expression data (sheet named “all data”), so that readers can filter out any relevant data based on their interest.

(10) Page 5, bottom: ' ...we showed that the expression of all 12 eIF3 subunits is interconnected such that perturbance of the expression of one subunit results in the down-regulation of entire modules...'. This is not true for eIF3d, as shown in Fig1B and mentioned in Results.

This reviewer is correct. By this generalized statement, we were trying to summarize our previous results from Wagner et al., 2014, PMID: 24912683; Wagner et al.,2016, PMID: 27924037 and Herrmannova et al.,2020, PMID: 31863585. The eIF3d downregulation is the only exception that does not affect expression of any other eIF3 subunit. Therefore, we have rewritten this paragraph accordingly: “We recently reported a comprehensive in vivo analysis of the modular dynamics of the human eIF3 complex (Wagner et al, 2020; Wagner et al, 2014; Wagner et al., 2016). Using a systematic individual downregulation strategy, we showed that the expression of all 12 eIF3 subunits is interconnected such that perturbance of the expression of one subunit results in the down-regulation of entire modules leading to the formation of partial eIF3 subcomplexes with limited functionality (Herrmannova et al, 2020). eIF3d is the only exception in this respect, as its downregulation does not influence expression of any other eIF3 subunit.”

(11) Page 10, bottom: ' The PCA plot and hierarchical clustering... These results suggest that eIF3h depletion impacts the translatome differentially than depletion of eIF3e or eIF3d.' This is already obvious in the polysome profiles of Figure S2C.

We agree that this result is surely not surprising given the polysome profile and growth phenotype analyses of eIF3hKD. But still, we think that the PCA plot and hierarchical clustering results represent valuable controls. Nonetheless, we rephrased this section to note that this result agrees with the polysome profiles analysis: “The PCA plot and hierarchical clustering (Figure 2A and Supplementary Figure 4A) showed clustering of the samples into two main groups: Ribo-Seq and RNA-seq, and also into two subgroups; NT and eIF3hKD samples clustered on one side and eIF3eKD and eIF3dKD samples on the other. These results suggest that the eIF3h depletion has a much milder impact on the translatome than depletion of eIF3e or eIF3d, which agrees with the growth phenotype and polysome profile analyses (Supplementary Figure 1A and 1D).”

(12) Page 12: ' As for the eIF3dKD "unique upregulated" DTEGs, we identified one interesting and unique KEGG pathway, the ABC transporters (Supplementary Figure 5A, in green).' This sentence is confusing, as there are more pathways that are significant in this group, so it is unclear why the authors consider it 'unique'.

The eIF3dKD “unique upregulated” group comprises genes with increased TE only in eIF3dKD but not in eIF3eKD or eIF3hKD (500 genes, Fig 2G). All these 500 genes were examined for enrichment in the KEGG pathways, and the top 10 significant pathways were reported (Fig S6A). However, 8 out of these 10 pathways were also significantly enriched in other gene groups examined (e.g. eIF3d/eIF3e common). Therefore, the two remaining pathways (“ABC transporters” and “Other types of O-glycan biosynthesis”) are truly unique for eIF3dKD. We wanted to highlight the ABC transporters group in particular because we find it rather interesting (for the reasons mentioned in the article). We have corrected the sentence in question to avoid confusion: “Among the eIF3dKD “unique upregulated” DTEGs, we identified one interesting KEGG pathway, the ABC transporters, which did not show up in other gene groups (Supplementary Figure 6A, in green). A total of 12 different ABC transporters had elevated TE (9 of them are unique to eIF3dKD, while 3 were also found in eIF3eKD), 6 of which (ABCC1-5, ABCC10) belong to the C subfamily, known to confer multidrug resistance with alternative designation as multidrug resistance protein (MRP1-5, MRP7) (Sodani et al, 2012).

Interestingly, all six of these ABCC transporters were upregulated solely at the translational level (Supplementary Spreadsheet S2).”    

(13) Note typo ('Various') in Figure 4A.

Corrected

(14) The introduction could be shortened.

This is a very subjective requirement. In fact, when this manuscript was reviewed in NAR, we were asked by two reviewers to expand it substantially. Because a number of various research topics come together in this work, e.g. translational regulation, the eIF3 structure and function, MAPK/ERK signaling, we are convinced that all of them demand a comprehensive introduction for non-experts in each of these topics. Therefore, with all due respect to this reviewer, we did not ultimately shorten it.

Reviewer #2 (Recommendations For The Authors):

- In Figure 2, it would be useful to know why eIF3d is destabilized by eIF3e knockdown - is it protein degradation and why do the eIF3d/e knockdowns not more completely phenocopy each other when there is the same reduction to eIF3d as in the eIF3d knockdown sample?

Yes, we do think that protein degradation lies behind the eIF3d destabilization in the eIF3eKD, but we have not yet directly demonstrated this. However, we have shown that eIF3d mRNA levels are not altered in eIF3eKD and that Ribo-Seq data indicate no change in TE or FP for eIF3d-encoding mRNA in eIF3eKD. Nonetheless, it is important to note (and we discuss it in the article) that eIF3d levels in eIF3dKD are lower than eIF3d levels in eIF3eKD (please see Supplementary Figure 1C). In fact, we believe that this is one of the main reasons for the eIF3d/e knockdowns differences.

- The western blots in Figures 4 and 6 show modest changes to target protein levels and would be strengthened by quantification.

We have added the quantifications as requested by this reviewer and the reviewer 3.

- For Figure 4, this figure would be strengthened by experiments showing if the increase in ribosomal protein levels is correlated with actual changes to ribosome biogenesis.

As suggested, we performed polysome profiling in the presence of EDTA to monitor changes in the 60S/40S ratio, indicating a potential imbalance in the biogenesis of individual ribosome subunits. We found that it was not affected (Figure 3G). In addition, we performed the same experiment, normalizing all samples to the same number of cells (cells were carefully counted before lysis). In this way, we confirmed that eIF3dKD and eIF3eKD cells indeed contain a significantly increased number of ribosomes, in agreement with the western blot analysis (Figure 3H).

- In Figure 6, there needs to be a nuclear loading control.

This experiment was repeated with Lamin B1 used as a nuclear loading control – it is now shown as Fig. 5F.

- For Figure 8, these findings would be strengthened using luciferase reporter assays where the various RNA determinants are experimentally tested. Similarly, 5′ TOP RNA reporters would have been appreciated in Figure 4.

This is indeed a logical continuation of our work, which represents the current work in progress of one of the PhD students. We apologize, but we consider this time- and resource-demanding analysis out of scope of this article.

Reviewer #3 (Recommendations For The Authors):

(1) Within the many effects observed, it is mentioned that eIF3d is known to be overexpressed while eIF3e is underexpressed in many cancers, but knockdown of either subunit decreases MDM2 levels, which would be expected to increase P53 activity and decrease tumor cell transformation. In contrast, they also report that 3e/3d knockdown dramatically increases levels of cJUN, presumably due to increased MAPK activity, and is expected to increase protumor gene expression. Additional discussion is needed to clarify the significance of the findings, which are a bit confusing.

This is indeed true. However, considering the complexity of eIF3, the largest initiation factor among all, as well as the broad portfolio of its functions, it is perhaps not so surprising that the observed effects are complex and may seem even contradictory in respect to cancer. To acknowledge that, we expanded the corresponding part of discussion as follows: “Here, we demonstrate that alterations in the eIF3 subunit stoichiometry and/or eIF3 subcomplexes have distinct effects on the translatome; for example, they affect factors that play a prominent (either positive or negative) role in cancer biology (e.g., MDM2 and cJUN), but the resulting impact is unclear so far. Considering the complex interactions between these factors as well as the complexity of the eIF3 complex per se, future studies are required to delineate the specific oncogenic and tumor suppressive pathways that play a predominant role in mediating the effects of perturbations in the eIF3 complex in the context of neoplasia.”

(2) There are places in the text where the authors refer to changes in transcriptional control when RNA levels differ, but transcription versus RNA turnover wasn't tested, e.g. page 16 and Figure S10, qPCR does not confirm "transcriptional upregulation in all three knockdowns" and page 19 "despite apparent compensatory mechanisms that increase their transcription."

This is indeed true, the sentences in question were corrected. The term “increased mRNA levels” was used instead of transcriptional upregulation (increased mRNA stabilization is also possible).

(3) Similarly, the authors suggest that steady-state LARP1 protein levels are unaffected based on ribosome footprint counts (page 21). It is incorrect to assume this, because ribosome footprints can be elevated due to stalling on RNA that isn't being translated and doesn't yield more protein, and because levels of translated RNA/synthesized proteins do not always reflect steady-state protein levels, especially in mutants that could affect lysosome levels and protein turnover. Also page 12, 1st paragraph suggests protein production is down when ribosome footprints are changed.

Yes, we are well-aware of this known limitation of Ribo-seq analysis. Therefore, the steadystate protein levels of our key hits were verified by western blotting. In addition, we have removed the sentence about LARP1 because it was based on Ribo-Seq data only without experimental evaluation of the steady-state LARP1 protein levels.

(4) The translation buffering effect is not clear in some Figures, e.g. S6, S8, 8A, and B. The authors show a scheme for translationally buffered RNAs being clustered in the upper right and lower left quadrants in S4H (translation up with transcript level down and v.v.), but in the FP versus RNA plots, the non-TOP RNAs and 4E-P-regulated RNAs don't show this behavior, and appear to show a similar distribution to the global changes. Some of the right panels in these figures show modest shifts, but it's not clear how these were determined to be significant. More information is needed to clarify, or a different presentation, such as displaying the RNA subsets in the left panels with heat map coloring to reveal whether RNAs show the buffered translation pattern defined in purple in Figure S4H, or by reporting a statistical parameter or number of RNAs that show behavior out of total for significance. Currently the conclusion that these RNAs are translationally buffered seems subjective since there are clearly many RNAs that don't show changes, or show translation-only or RNA-only changes.

We would like to clarify that S4H does not indicate a necessity for changes in FPs in the buffered subsets. Although opposing changes in total mRNA and FPs are classified as buffering, often we also consider the scenario where there are changes to the total mRNA levels not accompanied by changes in ribosome association.

In figure S6, the scatterplots indicate a high density of genes shifted towards negative fold changes on the x-axis (total mRNA). This is also reflected in the empirical cumulative distribution functions (ecdfs) for the log2 fold changes in total mRNA in the far right panels of A and B, and the lack of changes in log2 fold change for FPs (middle panels). Similarly, in figure S8, the scatterplots indicate a density of genes shifted towards positive fold changes on the x-axis for total mRNA. The ecdfs also demonstrate that there is a significant directional shift in log2 fold changes in the total mRNA that is not present to a similar degree in the FPs, consistent with translational offsetting. It is rightly pointed out that not all genes in these sets follow the same pattern of regulation. We have revised the title of Supplementary Figure S6 (now S7) to reflect this. However, we would like to emphasize that these figures are not intended to communicate that all genes within these sets of interest are regulated in the same manner, but rather that when considered as a whole, the predominant effect seen is that of translational offsetting (directional shifts in the log2 fold change distribution of total mRNA that are not accompanied by similar shifts in FP mRNA log2 fold changes).

The significance of these differences was determined by comparing the ecdfs of the log2 fold changes for the genes belonging to a particular set (e.g. non-TOP mTOR-sensitive, p-eIF4E-sensitive) against all other expressed genes (background) using a Wilcoxan rank sum test. This allows identification of significant shifts in the distributions that have a clear directionality (if there is an overall increase, or decrease in fold changes of FPs or total mRNA compared to background). If log2 fold changes are different from background, but without a clear directionality (equally likely to be increased or decreased), the test will not yield a significant result. This approach allows assessment of the overall behavior of gene signatures within a given dataset in a manner that is completely threshold-independent, such that it does not rely on classification of genes into different regulatory categories (translation only, buffering, etc.) based on significance or fold-change cut-offs (as in S4H). Therefore, we believe that this unbiased approach is well-suited for identifying cases when there are many genes that follow similar patterns of regulation within a given dataset.

(5) Page 10-"These results suggest that eIF3h depletion impacts the translatome differentially than depletion of eIF3e or eIF3d" ...These results suggest that eIF3h has less impact on the translatome, not that it does so differently. If it were changing translation by a different mechanism, I would not expect it to cluster with control.

This sentence was rewritten as follows: “The PCA plot and hierarchical clustering (Figure 2A and Supplementary Figure 4A) showed clustering of the samples into two main groups: RiboSeq and RNA-seq, and also into two subgroups; NT and eIF3hKD samples clustered on one side and eIF3eKD and eIF3dKD samples on the other. These results suggest that the eIF3h depletion has a much milder impact on the translatome than depletion of eIF3e or eIF3d, which agrees with the growth phenotype and polysome profile analyses (Supplementary Figure 1A and 1D).”

Other minor issues:

(1) There are some typos: Figure 2 leves, Figure 4 variou,

Corrected.

(2) Figure 3, font for genes on volcano plot too small

Yes, maybe, however the resolution of this image is high enough to enlarge a certain part of it at will. In our opinion, a larger font would take up too much space, which would reduce the informativeness of this graph.

(3) Figure S5, highlighting isn't defined.

The figure legend for S5A (now S6A) states: “Less significant terms ranking 11 and below are in grey. Terms specifically discussed in the main text are highlighted in green.” Perhaps it was overlooked by this reviewer.

(4) At several points the authors refer to "the MAPK signaling pathway", suggesting there is a single MAPK that is affected, e.g in the title, page 3, and other places when it seems they mean "MAPK signaling pathways" since several MAPK pathways appear to be affected.

We apologize for any terminological inaccuracies. There are indeed several MAPK pathways operating in cells. In our study, we focused mainly on the MAPK/ERK pathway. The confusion probably stems from the fact that the corresponding term in the KEGG pathway database is labeled "MAPK signaling pathway" and this term, although singular, includes all MAPK pathways. We have carefully reviewed the entire article and have corrected the term used accordingly to either: 1) MAPK pathways in general, 2) the MAPK/ERK pathway for this particular pathway, or 3) "MAPK signaling pathway", where the KEGG term is meant.

(5) Some eIF3 subunit RNAs have TOP motifs. One might expect 3e and 3h levels to change as a function of 3d knockdown due to TOP motifs but this is not observed. Can the authors speculate why the eIF3 subunit levels don't change but other TOP RNAs show TE changes? Is this true for other translation factors, or just for eIF3, or just for these subunits? Could the Western blot be out of linear range for the antibody or is there feedback affecting eIF3 levels differently than the other TOP RNAs, or a protein turnover mechanism to maintain eIF3 levels?

This is indeed a very interesting question. In addition to the mRNAs encoding ribosomal proteins, we examined all TOP mRNAs and added an additional sheet to the S2 supplemental spreadsheet with all TOP RNAs listed in (Philippe et al., 2020, PMID: 32094190). According to our Ribo-Seq data, we could expect to see increased protein levels of eIF3a and eIF3f in eIF3dKD and eIF3eKD, but this is not the case, as judged from extensive western blot analysis performed in (Wagner et. al 2016, PMID: 27924037). Indeed, we cannot rule out the involvement of a compensatory mechanism monitoring and maintaining the levels of eIF3 subunits at steady-state – increasing or decreasing them if necessary, which could depend on the TOP motif-mediated regulation. However, we think that in our KDs, all non-targeted subunits that lose their direct binding partner in eIF3 due to siRNA treatment become rapidly degraded. For example, co-downregulation of subunits d, k and l in eIF3eKD is very likely caused by protein degradation as a result of a loss of their direct binding partner – eIF3e. Since we showed that the yeast eIF3 complex assembles co-translationally (Wagner et. al 2020, PMID: 32589964), and there is no reason to think that mammalian eIF3 differs in this regard, our working hypothesis is that free subunits that are not promptly incorporated into the eIF3 complex are rapidly degraded, and the presence or absence of the TOP motif in the 5’ UTR of their mRNAs has no effect. As for the other TOP mRNAs, translation factors eEF1B2, eEF1D, eEF1G, eEF2 have significantly increased FPs in both eIF3dKD and eIF3eKD, but we did not check their protein levels by western blotting to conclude anything specific.

Maybe by this the author implies that the process is more important than the goal, so by following the process "the master" automatically reaches grateness.

This means that one seemingly very hard or impossible task/goal still can be reached if it is divided into smaller; hence easier tasks.

By saying this the author means that we should treat even small things as something important, because it can grow into something bigger over time.

The authors wanted to start by directly impairing ubiquitylation of Mcm7. However this was not simple, because Mcm7 has multiple sites for possible ubiquitylation.

Figure 2EV shows the process of broadly finding regions of ubiquitylation sites on Mcm7.

Pannel A:

Their previous findings from 2020 show that the first lysine residue #29 on Mcm7 was the sole residue for ubiquitylation by SCF-Dia2 in vitro, even with Mrc1 present, which stimulates the formation of long ubiquitin chains on Mcm7.

However,

The other programs (3) are missing from the carousel. Also, the title should be one line (if possible).

X (twitter) icon to the footer besides the LinkedIn icon.

the song is made for the individual person

Bro this is the ol' reliable SpongeBob meme. But also, the idea of a cliche song that still works every time is not very common.

This basically saying more about how important the song is to them and the spirit and the hope of the less doubt.

This annotation is referring to the serin song that everybody is willing to do anything to hear it

The author introduces this song as irresistible to give an information about the poem.

This could be saying that the song is so beautiful that all the sailors try to help the beautiful voice.

It only activates in the targetted area. This makes it less addictive. The words used in this source is also more complex. There are terms in the study that I do not entirely understand. I had to look up some meanings of them.

example of how the atom was replaced

the changing of the atoms makes the opioid less addictive. The new opioid works in the targetted area instead of like a traditional opioid. The traditional opioid just releases everywhere, giving the brain that feeling of dopamine.

to stop the harmful side effects of opioids

S1 = ventricles contract; SL valves open, AV valves close S2 = atrial contract; AV valves open, SL valves close S3 = dilated cardiomyopathy (rapid filling of excess volume of blood into dilated ventricles; overfilled water balloon) S4 = hypertrophied cardiomyopathy (ventricle is stiff, so atria has to push extra hard to get blood into stiffed ventricles)

All of these countries are currently experiencing population crisis, so it makes sense that they are competing for influence on how the future of care will look

It feels like this did not happen, at least not the the extent they expected. 18 years also seems a bit unrealistic for this change, especially considering that the first iphone came out the same year this was created.

I wonder if something similar will happen in the US eventually if birth rates drop lower than the sustainability rate of 2.1.

This part implies that the future aid devices will need some level of advanced AI, as it "predicts" timings.

This feels like a lot compared to how little I had heard about this previously.

uses music to enjoy doing work and finish work more efficiently

the ability to be aware of one's own thought processes and to regulate them

This sentence reminds me of "You Can Learn to Write In General" and how it talks about how unalike different kinds of writings / writing styles can be

What i'm interpreting from this is that students delay their time with work by procrastinating. When they see that they're running out of time, it motivates them to get the work done (?).

"Lose our health" could be referring to the passage of time and how we slowly lose our health over time. "love of colour" evokes a positive feeling around the idea of love, maybe suggesting that as we grow older we experience many different types of love, in people, things, activities.

formatting allows for emphasis on emotions, impacts and even the consequences

A literal understanding of this term could be someone who doesn't belong within a fraternity because of their background but still participates in the activities behind the scenes.

could it relate to the soul of both the children and the mother? it is healthy for the children to leave the house, go to the movies, but it can also be for the mother to get the children out her space for a period of time

Almost feels like it could be either an internal rant or someone stressing to a new mother how they should allow their children to experience life in a certain way as to ensure they will not ruin the family

separating this line from the previous adds intense dramatic emphasis

It almost feels like the poet is having a conversation with themselves, is an observer of the environment around them, invites the reader into the mind of the poet

O’Hara runs through his whole day, mundane points and all, before his day ends with the shock of finding out Billie Holiday has died. I don’t know if the details surrounding her death were that public back then, but it’s ironic that he goes through his routine, start to finish, accomplishing all these tasks, mean while Holiday was locked in a hotel room dying. There’s a pointed contrast there. His day was full of events (monotonous ones) while hers was spent essentially in a prison before it ends with her death.

unpunctuated poem, this line implies a breath being taken between the sentences and a separation of ideas but is still unpunctuated - partially but not entirely separated, interconnectivity of passages and words

-immersion in the environment, people, places, art, culture, life around the speaker goes on continuously and is not able to be viewed in a vacuum despite the attempts to separate

-throughout there is constant reference to things happening <-- jam-packed with action, but none of the actions try to introduce this separation either; thematically life goes on despite the trauma of the speaker

yan kanalların açığa çıkma olasılığı artar.

cep epitelinin apikal yönde çoğaldığında

Hastanın problemli dişi dışında periodontal sorunların olmaması.

Klinik belirtiler periodontal hastalığa çok benzediğinden, doğru teşhis için dikkatli bir muayene gerekmektedir.

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review):

This study delineates an important set of uninjured and injured periosteal snRNAseq data that provides an overview of periosteal cell responses to fracture healing. The authors also took additional steps to validate some of the findings using immunohistochemistry and transplantation assays. This study will provide a valuable publicly accessible dataset to reexamine the expression of the reported periosteal stem and progenitor cell markers.

Strengths: 

(1) This is the first single-nuclei atlas of periosteal cells that are obtained without enzymatic cell dissociation or targeted cell purification by FACS. This integrated snRNAseq dataset will provide additional opportunities for the community to revisit the expression of many periosteal cell markers that have been reported to date.

(2) The authors delved further into the dataset using cutting-edge algorithms, including CytoTrace, SCENIC, Monocle, STRING, and CellChat, to define the potential roles of identified cell populations in the context of fracture healing. These additional computation analyses generate many new hypotheses regarding periosteal cell reactions.

(3) The authors also sought to validate some of the computational findings using immunohistochemistry and transplantation assays to support the conclusion.

Weaknesses: 

(1) The current snRNAseq datasets contain only a small number of nuclei (1,189 nuclei at day 0, 6,213 nuclei on day 0-7 combined). It is unclear if the number is sufficient to discern subtle biological processes such as stem cell differentiation. 

We analyzed a total of 6,213 nuclei from uninjured periosteum and fracture calluses at 3 stages of bone healing. We were able to describe 11 distinct cell populations, revealing the diversity of cell populations in uninjured periosteum and post-injury, including rare cell types in the fracture environment such Schwann cells, adipocytes and pericytes. The number of nuclei was sufficient to perform extensive analysis using a combination of cutting-edge algorithms. We agree that more nuclei would allow more in-depth analyses of cell fate transitions and rare populations, such as pericytes and Schwann cells. However, we concentrated here on SSPC/fibrogenic cells that are well represented in our dataset. Our study robustness is also reinforced by the analysis of 4 successive time points to define the SSPC/fibrogenic cell trajectories. Our validations using immunohistochemistry and transplantation assays also confirmed that our dataset is sufficient to define cell trajectories. There is no clear consensus on the number of cells needed to perform sc/snRNAseq analyses, as it depends on the cell types analyzed and the fold changes in gene expression. Previously reported single cell datasets containing a lower number of cells reached major conclusions including SSPC identification, cell differentiation trajectories and differential gene expression (658 cells in (Debnath et al. 2018), 300 in (Ambrosi et al. 2021), around 175 in (Remark et al. 2023).)

(2) The authors' designation of Sca1+CD34+ cells as SSPCs is not sufficiently supported by experimental evidence. It will be essential to demonstrate stem/progenitor properties of Sca1+CD34+ cells using independent biological approaches such as CFU-F assays. In addition, the putative lineage trajectory of SSPCs toward IIFCs, osteoblasts, and chondrocytes remains highly speculative without concrete supporting data. 

We performed additional analyses to further support that Sca1+ SSPCs display stem/progenitor properties. We performed CFU assays with Prx1-GFP+ SCA1+ and Prx1-GFP+ SCA1- periosteal cells (Figure 2F-G). We showed that Prx1-GFP+ SCA1+ display significant increased CFU potential compared to Prx1-GFP+ SCA1- cells. In addition, we isolated and transplanted Prx1-GFP+ Sca1+ and Prx1-GFP+ Sca1- periosteal cells at the fracture site of wild-type mice (Figure 2H). Only Sca1+ cells contributed to the callus formation, reinforcing that Sca1+ cells are the SSPC population mediating bone repair. 

The differentiation trajectory of SSPCs presented in our study is supported by a combination of bioinformatic analyses and in vivo validation:

- snRNAseq allowed us to identify the different populations in the uninjured periosteum. In silico, in vitro and in vivo analyses all point to Sca1+ cells as the SSPC population (Fig 2EG).

- At day 3 post-fracture, we did not detect Sca1+ cells in the callus (Fig 4 – Supplementary figure 2). Instead, we observed the appearance of a new population, IIFCs. This population clustered along SSPCs and pseudotime analyses indicate that SSPCs can differentiate into IIFCs (Fig 5B). We confirmed the ability of Sca1+ pSSPCs to form IIFCs, by grafting them in the fracture callus and assessing their fibrogenic fate at day 5 post-fracture (Fig 6B).

- In silico, we observed that IIFCs clustered along osteogenic and chondrogenic cells. The pseudotime trajectory suggests that IIFCs can differentiate into both lineages (Fig 5B-C). This is coherent with the progressive expression of osteochondrogenic genes observed in IIFCs (Fig 5C, Fig 8A, C, E). In vivo, we observed the progressive expression of Runx2 and Sox9 by IIFCs undergoing differentiation (Fig 6A). We now show that IIFCs are not undergoing apoptosis, indicating that these cells further differentiate (Fig 7 – Supplementary figure 2). To functionally assess the osteochondrogenic potential of IIFCs, we used transplantation assay and showed that Prx1-GFP+ IIFCs isolated from day 3 post-fracture form cartilage and bone when transplanted at the fracture site of wild-type mice (Fig 6C). 

We would like to insist on the robustness of the bioinformatic analyses performed in our study. First, we used datasets from different time points post-fracture to capture the true temporal progression of cell populations in the fracture callus. We used a large combination of tools shown to be reliable in many studies (Julien et al. 2021; Matsushita et al. 2020; Debnath et al. 2018; Baccin et al. 2020; Junyue Cao et al. 2019; Zhong et al. 2020), and all tools converge in the same trajectory. To further show the relevance of pseudotime in our model, we illustrated the distribution of the cell populations by time point (Fig. 5D). We can observe a parallel between the time points and the pseudotime, reinforcing that the pseudotime trajectory reflects the timing of SSPC differentiation. Overall, the combined in silico, in vitro and in vivo analyses support that Sca1+ Pi16+ cells are the periosteal SSPC population, specifically represented in the uninjured dataset. In response to bone fracture, these SSPCs give rise to IIFCs that are specifically represented in the intermediate stages (days 3 and 5) prior to osteochondrogenic differentiation.

(3) The designation of POSTN+ clusters as injury-induced fibrogenic cells (IIFCs) is not fully supported by the presented data. The authors' snRNAseq datasets (Figure 1d) demonstrate that there are many POSTN+ cells prior to injury, indicating that POSTN+ cells are not specifically induced in response to injury. It has been widely recognized that POSTN is expressed in the periosteum without fracture. This raises a possibility that the main responder of fracture healing is POSTN+ cells, not SSPCs as they postulate. The authors cannot exclude the possibility that Sca1+CD34+ cells are mere bystanders and do not participate in fracture healing. 

IIFCs are a population of cells that express high levels of ECM related genes, including Postn, Aspn and collagens. We did not claim that Postn expression is specific to IIFCs. While Postn is detected in the uninjured periosteum, snRNAseq analyses and RNAscope experiments showed that the expression of Postn is limited to a small number of cells in the cambium layer of the periosteum (Fig 4B , Figure 4 – Supplementary figure 1B). These Postn-expressing cells in the uninjured periosteum are not SSPCs, as they do not co-express/co-localize with Pi16+ and Sca1+ cells detected in the fibrous layer (Fig4, Figure 4– Supplementary figure 1A, Figure 6-Supplementary figure 1). These Postn-expressing cells are undergoing osteogenic differentiation as shown by the correlation between Runx2 and Postn expression (Fig. 4 – Supplementary Figure 1C). After fracture, we observed a strong increase in ECM-related gene expression and specifically in the IIFC population. We now show the strong increase of Postn expression after injury (Fig. 4 – Supplementary Figure 1D-E, Figure 6-Supplementary figure 1E). 

As mentioned in our response above, we now show that SCA1+ cells form cartilage and bone after fracture, while SCA1- cells (including the POSTN+ population) from the uninjured periosteum did not contribute. These data reveal that Sca1+ CD34+ cells are the main SSPC population mediating bone healing and that POSTN+ IIFCs are a transient stage of SSPC differentiation. We added the following text to the result section: “Pi16-expressing SSPCs are located within the fibrous layer, while we observed few POSTN+ cells in the cambium layer (Fig. 4 – Supplementary Fig. 1A). Postn expression is weak in uninjured periosteum and is limited to differentiating cells. Postn expression is strongly increased in response to fracture, specifically in IIFCs (Fig. 4 – Supplementary Fig. 1B-E). “

(4) Detailed spatial organization of Sca1+CD34+ cells and POSTN+ cells in the uninjured periosteum with respect to the cambium layer and the fibrous layer is not demonstrated. 

We performed RNAscope experiments to locate Pi16-expressing and Postn-expressing cells in the uninjured periosteum. We observed that Pi16-expressing cells are in the external fibrous layer of the periosteum while Postn-expressing cells are located along the cortex in the cambium layer. The data are added in Fig 4B and Fig. 4- Supplementary Figure 1 and mentioned in the result section “Pi16-expressing SSPCs were located within the fibrous layer, while Postn-expressing cells were found in the cambium layer and corresponded to Runx2-expressing osteogenic cells (Fig. 4 – Supplementary Fig. 1A-C).”.

(5) Interpretation of transplantation experiments in Figure 5 is not straightforward, as the authors did not demonstrate the purity of Prx1Cre-GFP+SCA1+ cells and Prx1Cre-GFP+CD146- cells to pSSPCs and IIFCs, respectively. It is possible that these populations contain much broader cell types beyond SSPCs or IIFCs.  

We agree with the reviewer that our methodology for cell transplantation required more justification and validation. We decided to use a transgenic mouse line to be able to trace the cells in vivo after grafting. Prx1 marks limb mesenchyme during development and the Prx1Cre mouse model allows to label all SSPCs contributing to callus formation. Therefore, we used Prx1Cre, R26mTmG mice as donors for SSPCs and IIFCs isolation (Duchamp de Lageneste et al. 2018; Logan et al. 2002). Prx1 does not mark immune and endothelial cells but can label pericytes and fibroblastic populations (Duchamp de Lageneste et al. 2018; Logan et al. 2002; Julien et al. 2021). In the uninjured periosteum, Sca1 (Ly6a) is only expressed by SSPCs and endothelial cells (Fig 3-Supplementary figure 2, Fig 6-Supplementary figure 1). We sorted GFP+ Sca1+ cells from uninjured periosteum of Prx1Cre, R26mTmG mice to isolate only SSPCs and excluding endothelial cells and pericytes. For IIFCs, we isolated cells at day 3 post-fracture, as in our snRNAseq data, we detected IIFCs but no SSPCs, chondrocytes or osteoblasts at this stage of repair. To eliminate Prx1-derived pericytes, we sorted GFP+CD146- cells, as CD146 is specifically expressed by pericytes. We added Figure 6-supplementary Figure 1 to better illustrate the expression of Prx1, SCA1 (Ly6a) and CD146 (Mcam) in the uninjured and day 3 post-fracture datasets. We further demonstrate the purity of SSPCs and IIFCs isolation by qPCR on sorted GFP+ Sca1+ cells from uninjured periosteum and GFP+ CD146- cells from day 3 post-fracture periosteum and hematoma and confirmed the absence of contamination by other cell populations (Figure 6-Supplementary figure 1E). We made the following changes in the text: “To functionally validate the steps of pSSPC activation, we isolated SCA1+ GFP+ pSSPCs from Prx1Cre; R26mTmG mice, excluding endothelial cells, and grafted them at the fracture site of wild-type hosts” and “we isolated GFP+ CD146- from the fracture callus of Prx1Cre; R26mTmG mice at day 3 post fracture, that correspond to IIFCs without contamination by pericytes (CD146+ cells) (Fig. 6C, Figure 6 – Supplementary Fig.1).

Reviewer #2 (Public Review):

Summary: 

The authors described cell type mapping was conducted for both WT and fracture types. Through this, unique cell populations specific to fracture conditions were identified. To determine these, the most undifferentiated cells were initially targeted using stemness-related markers and CytoTrace scoring. This led to the identification of SSPC differentiating into fibroblasts. It was observed that the fibroblast cell type significantly increased under fracture conditions, followed by subsequent increases in chondrocytes and osteoblasts.

Strengths: 

This study presented the injury-induced fibrogenic cell (IIFC) as a characteristic cell type appearing in the bone regeneration process and proposed that the IIFC is a progenitor undergoing osteochondrogenic differentiation. 

Weaknesses: 

This study endeavored to elucidate the role of IIFC through snRNAseq analysis and in vivo observation. However, such validation alone is insufficient to confirm that IIFC is an osteochondrogenic progenitor, and additional data presentation is required.  

As mentioned in the response to Reviewer 1, the differentiation trajectory of SSPCs presented in our study is supported by a combination of bioinformatic analyses and in vivo validation:

- snRNAseq allowed us to identify the different populations in the uninjured periosteum. In silico, in vitro and in vivo analyses altogether showed that Sca1+ cells are the SSPC population (Fig 2E-G).

- At day 3 post-fracture, we did not detect Sca1+ cells in the callus (Fig 4 – Supplementary figure 2). Instead, we observed the appearance of a new population, IIFCs. This population clustered along SSPCs and pseudotime analyses indicate that SSPCs can differentiate into IIFCs (Fig 5B). We confirmed the ability of Sca1+ SSPCs to form IIFCs, by grafting them in the fracture callus and assessing their fate at day 5 post-fracture (Fig 6B).

- In silico, we observed that IIFCs clustered along osteogenic and chondrogenic cells. The pseudotime trajectory suggests that IIFCs can differentiate into both lineages (Fig 5B-C). This is coherent with the progressive expression of osteochondrogenic genes observed in IIFCs (Fig 5C, Fig 8A, C, E). In vivo, we observed the progressive expression of Runx2 and Sox9 by IIFCs undergoing differentiation (Fig 6A). We now show that IIFCs are not undergoing apoptosis, indicating that these cells further differentiate (Fig 7 – Supp 2). To functionally assess the osteochondrogenic potential of IIFCs, we used transplantation assay and showed that Prx1-GFP+ IIFCs from day 3 post-fracture form cartilage and bone when transplanted at the fracture site of wild-type mice (Fig 6C). 

We would like to insist on the robustness of the bioinformatic analyses performed in our study. First, we used datasets from different time points post-fracture to capture the true temporal progression of cell populations in the fracture callus. We used a large combination of tools shown to be reliable in many studies (Julien et al. 2021; Matsushita et al. 2020; Debnath et al. 2018; Baccin et al. 2020; Junyue Cao et al. 2019; Zhong et al. 2020), and all tools converge in the same trajectory. To further show the relevance of pseudotime in our model, we illustrate the distribution of the cell populations by time point (Fig. 5D). We can observe a parallel between the time points and the pseudotime, reinforcing that the pseudotime trajectory reflects the timing of SSPC differentiation. Overall, the combined in silico, in vitro and in vivo analyses strongly support that Sca1+ Pi16+ cells are the periosteal SSPC population, specifically represented in the uninjured dataset. In response to bone fracture, these SSPCs give rise to IIFCs that are specifically represented in the intermediate stages (days 3 and 5) prior to osteochondrogenic differentiation.

We made the following changes in the text:

- Line 81-87: “We performed in vitro CFU assays with sorted GFP+SCA1+  and GFP+SCA1- cells isolated from the periosteum of Prx1Cre; R26mTmG mice, as Prx1 labels all SSPCs contributing to the callus formation1. Prx1-GFP+ SCA1+ showed increased CFU potential, confirming their stem/progenitor property (Fig 2F-G).  Then, we grafted Prx1GFP+ SCA1+ et Prx1-GFP+ SCA1- periosteal cells at the fracture site of wild-type mice. Only SCA1+ cells formed cartilage and bone after fracture indicating that SCA1+ cells correspond to periosteal SSPCs with osteochondrogenic potential (Fig 2H).”

- Line 120-122: “We did not detect Pi16-expressing SPPCs, consistent with the absence of cells expressing SSPC markers in day 3 snRNAseq dataset compared to uninjured periosteum (Fig. 4 – Supplementary Figure 2).”

- Line 170-172: “Only a small subset of IIFCs undergo apoptosis, further supporting that IIFCs are maintained in the fracture environment giving rise to osteoblasts and chondrocytes (Fig. 7 – Supplementary Figure 2).”

- Line 277-278: “Following this unique fibrogenic step, IIFCs do not undergo cell death but undergo either osteogenesis or chondrogenesis”

- Line 281-283: “During bone repair, this initial fibrogenic process is an integral part of the SSPC differentiation process, and a transitional step prior to osteogenesis and chondrogenesis.”

Reviewer #3 (Public Review): 

In this manuscript, the authors explored the transcriptional heterogeneity of the periosteum with single nuclei RNA sequencing. Without prior enrichment of specific populations, this dataset serves as an unbiased representation of the cellular components potentially relevant to bone regeneration. By describing single-cell cluster profiles, the authors characterized over 10 different populations in combined steady state and post-fracture periosteum, including stem cells (SSPC), fibroblast, osteoblast, chondrocyte, immune cells, and so on. Specifically, a developmental trajectory was computationally inferred using the continuum of gene expression to connect SSPC, injury-induced fibrogenic cells (IIFC), chondrocyte, and osteoblast, showcasing the bipotentials of periosteal SSPCs during injury repair. Additional computational pipelines were performed to describe the possible gene regulatory network and the expected pathways involved in bone regeneration. Overall, the authors provided valuable insights into the cell state transitions during bone repair and proposed sets of genes with possible involvements in injury response. 

While the highlights of the manuscript are the unbiased characterization of periosteal composition, and the trajectory of SSPC response in bone fracture response, many of the conclusions can be more strongly supported with additional clarifications or extensions of the analysis.  

(1) As described in the method section, both the steady-state data and full dataset underwent integration before dimensional reduction and clustering. It would be appreciated if the authors could compare the post-integration landscapes of uninjured cells between steady state and full dataset analysis. Specifically, fibroblasts were shown in Figure 1C and 1E, and such annotations did not exist in Figure 2B. Will it be possible that the original 'fibroblasts' were part of the IIFC population? 

As suggested, we now identified the fibroblast population from the uninjured periosteum in the integration of datasets from all time points (Figure 5B and Fig. 5 – Supplementary Figure 2). We identified 4 fibroblast populations in the uninjured periosteum: Luzp2+, Cldn1+, Hsd11b1+ and Csmd1+ fibroblasts. Luzp2+ and Cldn1+ fibroblasts are clustering distinctly from the other populations in the integrated dataset. Hsd11b1+ fibroblasts blend with SSPCs and IIFCs in the integrated dataset probably due to the low cell number. Finally, Csmd1+ fibroblasts are clustering at the interface between SSPCs and IIFCs likely because they correspond to differentiating cells both in the uninjured periosteum and in response to fracture. We modified the resolution of clustering in our subset dataset, in order to represent Luzp2+ and Cldn1+ fibroblasts as an isolated cluster (Figure 5B, cluster 10). In addition, both pseudotime (Fig. 5B) and gene regulatory network analyses (Fig. 7D), show that the fibroblast populations are distinct from the activation trajectory of SSPCs. We added the following sentence to the text “Fibroblasts from uninjured periosteum (Hsd11b1+, Cldn1+ and Luzp2+ cells corresponding to cluster 10 of Fig. 5B) clustered separately from the other populations, suggesting the absence of their contribution to bone healing.”

(2) According to Figure 2, immune cells were taking a significant abundance within the dataset, specifically during days 3 & 5 post-fracture. It will be interesting to see the potential roles that immune cells play during bone repair. For example, what are the biological annotations of the immune clusters (B, T, NK, myeloid cells)? Are there any inflammatory genes or related signals unregulated in these immune cells? Do they interact with SSPC or IIFC during the transition?   

In this manuscript, we report the overall dataset and focused our analyses on the response of SSPCs to injury and their differentiation trajectories. We did not include detailed analyses of the immune cell populations, that are out of scope of this manuscript and are part of another study (Hachemi et al, biorxiv, 2024)

(3) The conclusion of Notch and Wnt signaling in IIFC transition was not sufficiently supported by the analysis presented in the manuscript, which was based on computational inferences. It will be great to add in references supporting these claims or provide experimental validations examining selected members of these pathways.

The role of Wnt and Notch in bone repair has been widely studied and both signaling pathways are known to be regulators of SSPCs differentiation (Lee et al. 2021; Matthews et al. 2014; Novak et al. 2020; Wang et al. 2016; Kraus et al. 2022; Dishowitz et al. 2012; Junjie Cao et al. 2017; Matsushita et al. 2020; Steven Minear et al. 2010; Steve Minear et al. 2010; Kang et al. 2007; Komatsu et al. 2010). It was previously shown that Notch inactivation at early stages of repair leads to bone non-union while Notch inactivation in chondrocytes and osteoblasts does not significantly affect healing, confirming its role in SSPC differentiation before osteochondral commitment (Wang et al. 2016). Wnt was shown to be a critical driver of osteogenesis (Matsushita et al. 2020; Steve Minear et al. 2010; Steven Minear et al. 2010; Kang et al. 2007; Komatsu et al. 2010), as Wnt inhibition alters bone formation and Wnt overactivation increases bone formation (Pinzone et al. 2009; Balemans et Van Hul 2007). The role of Wnt is specific to osteogenic engagement as Wnt inhibition promotes chondrogenesis (Hsieh et al. 2023; C.-L. Wu et al. 2021; Ruscitto et al. 2023). A study by Lee et al. recently confirmed the successive activation and crosstalk of Notch and Wnt pathways during osteogenic differentiation of SSPCs during bone healing (Lee et al. 2021). They showed a peak of Notch activation at day 3 post-injury followed by a progressive decrease that parallels an increase of Wnt signaling inducing osteogenic differentiation. These studies correlate with the sequential activation of Notch and Wnt observed in our snRNAseq analyses. Our analyses now reveal how this sequential activation of Notch and Wnt relates to the fibrogenic and osteogenic phase of SSPC differentiation respectively. We clarified this in the discussion and added the references above to support our claims. 

Recommendations for the authors: 

Reviewer #1 (Recommendations For The Authors): 

(1) The manuscript is well-written overall. However, the authors often oversimplify outcomes and overstate the results. Some of the statements (delineated below) need to be recalibrated to be in line with the presented data. 

In addition to the suggested conclusions, we also toned down the following ones to avoid overstating our results :

Line 24: suggesting a crucial paracrine role of this transient IIFC population

Line 227: suggesting their central role in mediating cell interactions after fracture

line 243: IIFCs produce paracrine factors that can regulate SSPCs

- Line 77 (86): The authors should add "might" before "correspond to". 

We provided new sets of data including CFU experiments and transplantation assay to reinforce our conclusion. We replaced “correspond to” by “encompass”

- Line 102: SSPCs are obviously not "absent" in day 3 snRNAseq (Figure 2d). The percentage dropped (only) 75%, according to Figure 2e, which is far from disappearance. Overall, immunohistochemical staining is often dichotomous with snRNAseq designations. The authors should more carefully describe the results. 

We agree that this comment may not reflect the data shown as we observe a strong decrease in the percentage of cells in SSPC clusters, but still detect few cells in the SSPC clusters. However, when we looked at the presence of Sca1+ Pi16+ cells at different time points, we confirmed the absence of cells expressing SSPC signature genes (Sca1, Pi16, Cd34) at day 3 injury. Due to the clustering resolution of the combined integration, some cells in the SSPC clusters might not be Sca1+ Pi16+. We now show these results in Fig. 4 – Supplementary Figure 2. We changed the text accordingly (line 120): “We did not detect Pi16-expressing SPPCs, consistent with the absence of cells expressing SSPC markers in the day 3 snRNAseq dataset compared to uninjured periosteum (Fig. 4 – Supplementary Figure 2)”.

- Line 134: The authors need to clearly state that GFP+IIFCs were isolated based on Prx1CreGFP+CD146-. The authors did not clearly demonstrate the relationship between POSTN+ cells and CD146- cells, which poses concerns about the interpretation of transplantation experiments. 

As mentioned above in response to reviewer 1-public review, we have clarified and provided additional information on our strategy to isolate SSPCs and IIFCs. We used the Prx1Cre; R26mTmG mice to mark all SSPCs and their derivatives with the GFP reporter in order to trace these populations after cell grafting. In the uninjured periosteum, Sca1 (Ly6a) is only expressed by SSPCs and endothelial cells. We sorted GFP+Sca1+ cells to exclude endothelial cells. For IIFCs, we isolated cells at day 3 post-fracture, as in our snRNAseq data, we detect IIFCs but no SSPCs, chondrocytes or osteoblasts at this time point. However, we also detected pericytes that can be Prx1-derived. To eliminate potential pericyte contamination, we sorted GFP+ CD146- cells, as CD146 is specifically expressed by pericytes. We added Figure 6-supplementary Figure 1 to better illustrate the expression of Prx1, SCA1 (Ly6a) and CD146 (Mcam) in the uninjured and day 3 post-fracture datasets. We further demonstrate the purity of SSPCs and IIFCs isolation by qPCR on sorted GFP+ Sca1+ cells from uninjured periosteum and GFP+ CD146- cells from day 3 postfracture periosteum and hematoma and confirmed the absence of contamination by other cell populations (Figure 6-Supplementary figure 1E). We made the following changes in the text (line 153): “To functionally validate the steps of pSSPC activation, we isolated SCA1+ GFP+ pSSPCs from Prx1Cre; R26mTmG mice, excluding endothelial cells, and grafted them at the fracture site of wild-type hosts” and “we isolated GFP+ CD146- from the fracture callus of Prx1Cre; R26mTmG mice at day 3 post fracture, that correspond to IIFCs without contamination by pericytes (CD146+ cells) (Fig. 6C, Figure 6 – Supplementary Fig.1).

- Line 211: It is obvious from Figure 8F that ligand expression was not "specific" to the IIFC phase.

The data only shows a slight enrichment of ligand score. 

We corrected the text by “ligand expression was increased during the IIFC phase”.

(2) Some of the computational predictions are incongruent with the known lineage trajectory. For example, in vivo lineage tracing experiments, including but not limited to, PLoS Genet. 2014. 10:e1004820, demonstrate that some of the chondrocytes within fracture callus can differentiate into osteoblasts. This is incompatible with the authors' conclusion that osteoblasts and chondrocytes represent two different terminal stages of cell differentiation in fracture healing. How do the authors reconcile this apparent inconsistency? 

In this manuscript, we generated datasets corresponding to the initial stages of bone repair until day 7 post-injury. Therefore, our analyses encompass SSPC activation stages and engagement into osteogenesis and chondrogenesis. The results show that a portion of osteoblasts in the fracture callus are differentiating directly from IIFC via intramembranous ossification. The reviewer is correct to mention that osteoblasts have also been shown to derive from transdifferentiation of chondrocytes, which occurs at later stages of repair during the active phase of endochondral ossification (Julien et al. 2020; Aghajanian et Mohan 2018; Zhou et al. 2014; Hu et al. 2017). This process of chondrocyte to osteoblast transdifferentiation is not represented in our integrated dataset and may require adding later time points. However, when we analyzed the days 5 and 7 datasets independent of days 0 and 3, we were able to identify a cluster of hypertrophic chondrocytes (expressing Col10a1) connecting the clusters of chondrocytes and osteoblasts. This suggests that in this cluster, hypertrophic chondrocytes are undergoing transdifferentiation into osteoblasts as shown in the Author response image 1. Additional time points are needed in a future study to perform in depth analyses of chondrocyte transdifferentiation. 

Author response image 1.

Periosteum-derived chondrocytes undergo cartilage to bone transformation. A. UMAP projection of the subset of SSPCs, IIFCs, osteoblasts and chondrocytes in the integration of days 5 and 7 post-fracture datasets. B. Feature plots of Acan, Col10a1 and Ibsp expression.  C. UMAP projection separated by time points. D. Percentage of cells in the hypertrophic/differentiating chondrocyte cluster.

(3) The authors did not cite some of the studies that described the roles of Notch signaling in fracture healing, for example, J Bone Miner Res. 2014. 29:1283-94. The authors should test the specificity of Notch signaling activities to IIFCs (POSTN+ cells) in vivo. 

The role of Notch in the activation of SSPCs during bone repair has been investigated in several studies (Lee et al. 2021; Matthews et al. 2014; Novak et al. 2020; Wang et al. 2016; Kraus et al. 2022; Dishowitz et al. 2012; Junjie Cao et al. 2017). Notch dynamic was previously described with a peak at day 3 post-injury before a reduction when cells engage in osteogenesis and chondrogenesis (Lee et al. 2021; Dishowitz et al. 2012; Matthews et al. 2014). Notch plays a role in the early steps of SSPC activation prior to osteochondral differentiation as Notch inactivation in chondrocytes and osteoblasts does not affect bone repair (Wang et al. 2016). We added the references listed above to emphasize the correlation between our results and previous reports on the role of Notch and made changes in the discussion.

Reviewer #2 (Recommendations For The Authors): 

Suggestions 

(1) This research utilized snRNA seq for the basic hypothesis formation; however, the number of nuclei acquired was quite limited. Therefore, please explain the rationale for employing snRNA seq instead of scRNA seq, which includes cytoplasm, and additionally provide the markers used for cell type mapping in the scRNA analysis.  

As mentioned in our response to reviewer #1 above, we analyzed a total of 6,213 nuclei from uninjured periosteum and fracture calluses at 3 stages of bone healing. We were able to describe 11 distinct cell populations including rare cell types in the fracture environment such Schwann cells, adipocytes and pericytes. The number of nuclei was sufficient to perform extensive analysis using a combination of cutting-edge algorithms. We agree that more nuclei would allow more indepth analyses of cell fate transitions and rare populations, such as pericytes and Schwann cells. However, we concentrated here on SSPC/fibrogenic cell that are well represented in our dataset. Our study robustness is also reinforced by the analysis of 4 successive time points to define the SSPC/fibrogenic cell trajectories. Our validations using immunohistochemistry and transplantation assays also confirmed that our dataset is sufficient to define cell trajectories. There is no clear consensus on the number of cells needed to perform scRNAseq analyses, as it depends on the cell types analyzed and the fold changes in gene expression. Previously reported single cell datasets containing a lower number of cells reached major conclusions including SSPC identification, cell differentiation trajectories and differential gene expression (658 cells in(Debnath et al. 2018), 300 in (Ambrosi et al. 2021) around 175 in(Remark et al. 2023))

Several studies have shown that snRNAseq provide data quality equivalent to scRNAseq in terms of cell type identification, number of detected genes and downstream analyses (Selewa et al. 2020; Wen et al. 2022; Ding et al. 2020; H. Wu et al. 2019; Machado et al. 2021). While, snRNAseq do not allow the detection of cytoplasm RNA, there is several advantages in using this technique: 

(1) better representation of the cell types. To perform scRNAseq, a step of enzymatic digestion is needed. This usually leads to an overrepresentation of some cell types loosely attached to the ECM (immune cells, endothelial cells) and a reduced representation of cell types strongly attached to the ECM, such as chondrocytes and osteoblasts. In addition, large or multinucleated cells like hypertrophic chondrocytes and osteoclasts are too big to be sorted and encapsidated using 10X technology. Here, we optimized a protocol to mechanically isolate nuclei from dissected tissues that allows us to capture the diversity of cell types in periosteum and fracture callus.

(2) higher recovery of nuclei. We performed both isolation of cells and nuclei from periosteum in our study and observed that nuclei extraction is the most efficient way to isolate cells from the periosteum and the fracture callus.

(3) reduction of isolation time and cell stress. Previous studies showed that enzymatic digestion causes cell stress and induces stem cell activation (Machado et al. 2021; van den Brink et al. 2017). Therefore, we decided to perform snRNAseq to analyze the transcriptome of the intact periosteum without digestion induced-biais.

We added this sentence in the result section: “Single nuclei transcriptomics was shown to provide results equivalent to single cell transcriptomics, but with better cell type representation and reduced digestion-induced stress response (Selewa et al. 2020; Wen et al. 2022; Ding et al. 2020; H. Wu et al. 2019; Machado et al. 2021)”.

The list of genes used for cell type mapping are presented in Figure 3 – Supplementary figure 1. We added a detailed dot plot as Figure 3 – Supplementary figure 2.

(2) During the fracture healing process of long bones, the influx of fibroblasts is a relatively common occurrence, and the fibrous callus that forms during bone repair and regeneration is reported to disappear over time. Therefore, inferring that IIFC differentiates into osteo- and chondrogenic cells based solely on their simultaneous appearance in the same time and space is challenging. More detailed validation is necessary, beyond what is supported by bioinformatics analysis. 

The first step of bone repair is the formation of a fibrous callus, before cartilage and bone formation. There are no data in the literature demonstrating that an influx of fibroblasts occurs at the fracture site. Several studies now show that cells involved in callus formation are recruited locally (i.e. from the bone marrow, the periosteum and the skeletal muscle surrounding the fracture site) (Duchamp de Lageneste et al. 2018; Julien et al. 2021; Colnot 2009; Jeffery et al. 2022; Debnath et al. 2018; Matsushita et al. 2020; Julien et al. 2022; Matthews et al. 2021). The contribution of locally activated SSPCs to the fibrous callus is less well understood. Lineage tracing shows that GFP+ cell populations traced in Prx1Cre-GFP mice include SSPCs, IIFCs, chondrocytes and osteoblasts.

The timing of the cell trajectories observed in our dataset correlates with the timing of callus formation previously described in the literature as the day 3 post-fracture mostly contains IIFCs while chondrocytes and osteoblasts appear from day 5 post-fracture. We conclude that IIFCs differentiate into osteochondrogenic cells based on multiple evidence beside the simultaneous appearance in time and space:

- In silico trajectory analyses identify a trajectory from SSPCs to osteochondrogenic cells via IIFCs. We added an analysis to show that our pseudotime trajectory parallels the timepoints of the dataset, confirming that the differentiation trajectory follows the timing of cell differentiation (Figure 5D).

- We show that IIFCs start to express chondrogenic and osteogenic genes prior to engaging into chondrogenesis and osteogenesis. In addition, we detected activation of osteo- and chondrogenic specific transcription factors in IIFCs. This shows a differentiation continuum between SSPCs, IIFCS, and osteochondrogenic cells (Figures 6-8).

- Using transplantation assay, we showed that IIFCs form cartilage and bone, therefore reinforcing the osteochondrogenic potential of this population (Figure 6B).

- IIFCs do not undergo apoptosis. We assessed the expression of apoptosis-related genes by IIFCs and did not detect expression. This was confirmed by cleaved caspase 3 immunostaining showing that a very low percentage of cells in the early fibrotic tissue undergo apoptosis. 

Therefore, the idea that the initial fibrous callus is replaced by a new influx of SSPCs or committed progenitors is not supported by recent literature and is not observed in our dataset containing all cell types from the periosteum and fracture site. Overall, our bioinformatic analyses combined with our in vivo validation strongly support that IIFCs are differentiating into chondrocytes and osteoblasts during bone repair. Additional in vivo functional studies will aim to further validate the trajectory and investigate the critical factors regulating this process.

(3) The influx of most osteogenic progenitors to the bone fracture site typically appears after postfracture day 7. It's essential to ascertain whether the osteogenic cells observed at the time of this study differentiated from IIFC or migrated from surrounding mesenchymal stem cells. 

As mentioned above, there is not clear evidence in the literature indicating an influx of osteoprogenitors. Cells involved in callus formation are recruited locally and predominantly from the periosteum (Duchamp de Lageneste et al. 2018; Julien et al. 2021; Colnot 2009; Jeffery et al. 2022; Debnath et al. 2018; Matsushita et al. 2020; Matthews et al. 2021; Julien et al. 2022). Our datasets therefore include all cell populations that form the callus. Other sources of SSPCs include the surrounding muscle that contributes mostly to cartilage, and bone marrow that contributes to a low percentage of the callus osteoblasts in the medullary cavity (Julien et al. 2021; Jeffery et al. 2022). We provide evidence that IIFCs give rise to osteogenic cells using our bioinformatic analyses and in vivo transplantation assay (listed in the response above). As indicated in our response to reviewer #1, the steps leading to osteogenic differentiation observed in our dataset reflect the first step of callus ossification and correspond to the process of intramembranous ossification (up to day 7 post-injury). Endochondral ossification also contributes to osteoblasts including the transdifferentiation of chondrocytes into osteoblasts (Julien et al. 2020; Zhou et al. 2014; Hu et al. 2017). While this process mostly occurs around day 14 postfracture, we begin to detect this transition in our integrated day 5-day 7 dataset as shown in Author response image 1. 

(4) It's crucial to determine whether the IIFC appearing at the fracture site contributes to the formation of the callus matrix or undergoes apoptosis during the fracture healing process. In the early steps of bone repair, the callus is mostly composed of an extracellular matrix (ECM). IIFCs are expressing high levels of ECM genes, including Postn, Aspn and collagens (Col3a1, Col5a1, Col8a1, Col12a1) (Figure 3 – Supplementary Figures 1-2 and Fig. 7 – Supplementary Figure 1B). IIFCs are the cells expressing the highest levels of matrix-related genes compared to the other cell types in the fracture environment (i.e. immune cells, endothelial cells, Schwann cells, pericytes, …) as shown now in Fig. 7 – Supplementary Figure 1A. Therefore, IIFCs are the main contributors to the callus matrix.

We investigated if IIFCs undergo apoptosis. We observed that only a low percentage of IIFCs express apoptosis-related genes and are positive for cleaved caspase 3 immunostaining at days 3, 5 and 7 of bone repair. This shows that IIFCs do not undergo apoptosis and reinforces our model in which IIFCs further differentiate into osteoblasts and chondrocytes. We added these data in Fig. 7 – Supplementary Figure 2 and added the sentence in the results section “Only a small subset of IIFCs undergo apoptosis, further supporting that IIFCs are maintained in the fracture environment giving rise to osteoblasts and chondrocytes (Fig. 7 – Supplementary Figure 2).” 

(5) Results from the snRNA seq highlight the paracrine role of IIFC, and verification is needed to ensure that the effect this has on surrounding osteogenic lineages is not misinterpreted.  

To assess cell-cell interactions, we used tools such as Connectome and CellChat to infer and quantify intercellular communication networks between cell types. Studies showed the robustness of these tools combined with in vivo validation (Sinha et al. 2022; Alečković et al. 2022; Li et al. 2023). Here we used these tools to illustrate the paracrine profile of IIFCs, but in vivo validation would be required using gene inactivation to assess the requirement of individual paracrine factors. We performed extensive analyses of the crosstalk between immune cells and SSPCs using our dataset in another study combined with in vivo validation, showing the robustness of the tool and the dataset (Hachemi et al. 2024). We adjusted our conclusions to reflect our analyses: “suggesting a crucial paracrine role of this transient IIFC population during fracture healing”, “suggesting their central role in mediating cell interactions after fracture”, “suggesting that SSPCs can receive signals from IIFC”. 

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eLife Assessment

This fundamental study generated a single cell atlas of mouse periosteal cells under both steady-state and fracture healing conditions to address the knowledge gap regarding cellular composition of the periosteum and their responses to injury. Based on convincing transcriptome analyses and experimental validation, the authors identified the injury induced fibrogenic cell (IIFC) as a characteristic cell type appearing in the bone regeneration process and proposed that the IIFC is a progenitor undergoing osteochondrogenic differentiation. This study will provide a significant publicly accessible dataset to reexamine the expression of the reported periosteal stem and progenitor cell markers.

Reviewer #1 (Public review):

This study delineates an important set of uninjured and injured periosteal snRNAseq data that provides an overview of periosteal cell responses to fracture healing. The authors also took additional steps to validate some of the findings using immunohistochemistry and transplantation assays. This study will provide a valuable publicly accessible dataset to reexamine the expression of the reported periosteal stem and progenitor cell markers.

Strengths:

(1) This is the first single-nuclei atlas of periosteal cells that are obtained without enzymatic cell dissociation or targeted cell purification by FACS. This integrated snRNAseq dataset will provide additional opportunities for the community to revisit the expression of many periosteal cell markers that have been reported to date.<br /> (2) The authors delved further into the dataset using cutting-edge algorithms, including CytoTrace, SCENIC, Monocle, STRING and CellChat, to define potential roles of identified cell populations in the context of fracture healing. These additional computation analyses generate many new hypotheses regarding periosteal cell reactions.<br /> (3) The authors also sought to validate some of the computational findings using immunohistochemistry and transplantation assays to support the conclusion.

Weaknesses:

(1) The current snRNAseq datasets contain only a small number of nuclei (1,189 nuclei at day 0, 6,213 nuclei day 0-7 combined). It is possible that these datasets are underpowered to discern subtle biological changes in skeletal stem/progenitor cell populations during fracture healing.<br /> (2) POSTN is expressed in the cambium layer of the periosteum without fracture. The current data do not exclude the possibility that these pre-existing POSTN+ cells are the main responder of fracture healing.

Reviewer #2 (Public review):

Summary:

The authors described cell type mapping was conducted for both WT and fracture types. Through this, unique cell populations specific to fracture conditions were identified. To determine these, the most undifferentiated cells were initially targeted using stemness-related markers and CytoTrace scoring. This led to the identification of SSPC differentiating into fibroblasts. It was observed that the fibroblast cell type significantly increased under fracture conditions, followed by subsequent increases in chondrocytes and osteoblasts.

Strengths:

This study presented the injury-induced fibrogenic cell (IIFC) as a characteristic cell type appearing in the bone regeneration process and proposed that the IIFC is a progenitor undergoing osteochondrogenic differentiation.

Comments on revised version:

The authors have thoroughly addressed the reviewer's comments and have conducted additional experiments.

form: short, scattered, variable lengths of stanzas with visual cues cutting itself off at varied points

form gives a sense of exaggerated speech ('uncontrollable!' 'what good? what worth? dying!') and sets a strong tone for the speaker

stanzas like to use repetition, alliteration, common themes in the stanzas to accentuate the tone and importance; sounds very realistic and grounded in how we speak and over-emphasize ideas or questions or words that matter most to us (stanza one: everything else, anything else, i have nothing, shall have nothing, immediate, inescapable, invaluable) (stanza 2/3/7 refer to pink/orange/green) (stanza 5/7 use multiple questions)

for - meme - abolish philanthropy - to - critique - Andrew Carnegie essay - The Gospel of Wealth

meme - abolish philanthropy - Agree. Corporations, through externalizing social and ecological impacts, have created a majority of the problems of the polycrisis, that non-profits are created to solve - It would be far more efficient to NOT create those problems to begin with - see my annotations on Andrew Carnegie's "Gospel of Wealth" - where I critique Carnegie's philosophy

to - critique - Andrew Carnegie - essay - The Gospel of Wealth - https://hyp.is/go?url=https%3A%2F%2Fwww.carnegie.org%2Fabout%2Four-history%2Fgospelofwealth%2F&group=world

for - group dynamics of expanding and converging groups

group dynamics of expanding and converging groups - It is natural for groups to expand and grow and when they do, it changes the dynamics of the social interactions - Effort is required to know each other. It requires time to share and absorb what is shared - That legacy knowledge becomes the unspoken and implicit ground for future discourse - When new people are introduced to a group, or new groups are introduced to each other, - a minimum amount of sharing is required to establish common ground, common understanding - When members of a group have unique ideas to share, - a standardized, shareable documentation may become necessary for greater efficacy of sharing - the constitutions that are often at the heart of institutions became necessary for the same reasons

for - similarity - not just talk, make an impact

similarity - not just talk, make an impact - I think many of us are of like-mind. Surveying the precarity of the current polycrisis, there is immense complexity and very little time - Given these challenging circumstances, it behooves us to perform very careful sense-making to identify both the individual and the collective leverage points that will have the greatest impact in the shortest time - This also means we have to be careful of which groups we choose to work with as an optimal set of synergies is required if the group is to have possibility of reaching the greatest impact collectively

for - example - people- centered, interpersonal network

example - people-centered, interpersonal network - This is the scenario that innovators find themselves in always - you are at the center of multiple networks, each exploring an idea of interest to you - By its very nature, we often form silos in these groups, as they are sometimes mutually exclusive - for instance, our family group does not often overlap with this group - Sometimes we feel there is enough synergy to pursue de-siloing and introduce members of one group to other groups - If we have a people-centered software system that locates ourselves precisely at the center of all our groups, - then at least we have a uniform information system that can allow us to associate ideas across group silos without friction - As Gyuri says: - https://hyp.is/RVVayCOKEe2OJnff8kssaA/iopcommunity.com/what-is-the-internet-of-people-iop/ - - All financially stable organizations begin as an idea between people, with uncertainty of whether it will succeed

the entire piece, specifically this sentence, is an example of the overall theme of the piece, which is also the repeated refrain of "clearly expressing something" and the consideration of the content expressed. The structure of this sentence presents concrete ideas but in a difficult-to-digest form (long run-on sentence, basic/filler words repeated often) Is the content about clearly expressing something being clearly expressed? if someone is clearly expressing something in a way that is not clear, does that take away the something of the thing expressed?

the humanity/the struggle makes him more human, more convincingly great

very abstract imagery, very intuitively written. Reminds me of poetry I've written that made sense to me but I've scrapped because it would be gibberish to others, but it's interesting to be a reader and to try and unpack the speaker's garbled thoughts.

Google is able to track your phone and see where you have been. By seeing what stores you visit, they are able to send ads by places you visit.

This case ruled that the Sentencing Reform Act of 1984 and the U.S. Sentencing Commission were constitutional. The court's decision was based on delegation of powers. Congress can delegate authority to other branches of government under broad guidelines.

All of these together provide a comprehensive picture of parasitism in a population.

This would make sense since they most likely had increased exposure.

I never thought about how something so subtle could have such a significant effect on bacterial infections.

So where the defendant is legally responsible for having caused injury to the plaintiff, corrective justice insists that the defendant is answerable to the plaintiff for this result

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study evaluates whether species can shift geographically, temporally, or both ways in response to climate change. It also teases out the relative importance of geographic context, temperature variability, and functional traits in predicting the shifts. The study system is large occurrence datasets for dragonflies and damselflies split between two time periods and two continents. Results indicate that more species exhibited both shifts than one or the other or neither, and that geographic context and temp variability were more influential than traits. The results have implications for future analyses (e.g. incorporating habitat availability) and for choosing winner and loser species under climate change. The methodology would be useful for other taxa and study regions with strong community/citizen science and extensive occurrence data.

We thank Reviewer 1 for their time and expertise in reviewing our study. The suggestions are very helpful and will improve the quality of our manuscript.

Strengths:

This is an organized and well-written paper that builds on a popular topic and moves it forward. It has the right idea and approach, and the results are useful answers to the predictions and for conservation planning (i.e. identifying climate winners and losers). There is technical proficiency and analytical rigor driven by an understanding of the data and its limitations.

We thank Reviewer 1 for this assessment.

Weaknesses:

(1) The habitat classifications (Table S3) are often wrong. "Both" is overused. In North America, for example, Anax junius, Cordulia shurtleffii, Epitheca cynosura, Erythemis simplicicollis, Libellula pulchella, Pachydiplax longipennis, Pantala flavescens, Perithemis tenera, Ischnura posita, the Lestes species, and several Enallagma species are not lotic breeding. These species rarely occur let alone successfully reproduce at lotic sites. Other species are arguably "both", like Rhionaeschna multicolor which is mostly lentic. Not saying this would have altered the conclusions, but it may have exacerbated the weak trait effects.

We thank the reviewer for their expertise on this topic. We obtained these habitat classifications from field guides and trait databases, and we will review our primary sources to clarify the trait classifications. We will also reclassify the species according to the expertise of this reviewer and perform our analysis again. 

(2) The conservative spatial resolution (100 x 100 km) limits the analysis to wide- ranging and generalist species. There's no rationale given, so not sure if this was by design or necessity, but it limits the number of analyzable species and potentially changes the inference.

It is really helpful to have the opportunity to contextualize study design decisions like this one, and we thank the reviewer for the query. Sampling intensity is always a meaningful issue in research conducted at this scale, and we addressed it head-on in this work.

Very small quadrats covering massive geographical areas will be critically and increasingly afflicted by sampling weaknesses, as well as creating a potentially large problem with pseudoreplication. There is no simple solution to this problem. It would be possible to create interpolated predictions of species’ distributions using Species Distribution Models, Joint Species Distribution Models, or various kinds of Occupancy Models. None of these approaches then leads to analyses that rely on directly observed patterns. Instead, they are extrapolations, and those extrapolations typically fail when tested, (for example, papers by Lee-Yaw demonstrate that it is rare for SDMs to predict things well; occupancy models often perform less well than SDMs and do not capture how things change over time - Briscoe et al. 2021, Global Change Biology). The result of employing such techniques would certainly be to make all conclusions speculative, rather than directly observable. 

Rather than employing extrapolative models, we relied on transparent techniques that are used successfully in the core macroecology literature that address spatial variation in sampling explicitly and simply. Moreover, we constructed extensive null models that show that range and phenology changes, respectively, are contrary to expectations that arise from sampling difference. 100km quadrats make for a reasonable “middle-ground” in terms of the effects of sampling, and we will add a reference to the methods section to clarify this.

(3) The objective includes a prediction about generalists vs specialists (L99-103) yet there is no further mention of this dichotomy in the abstract, methods, results, or discussion.

Thank you for pointing this out - it is an editing error that should have been resolved prior to submission. We will replace the terms specialist and generalist with specific predictions based on traits.

(4) Key references were overlooked or dismissed, like in the new edition of Dragonflies & Damselflies model organisms book, especially chapters 24 and 27.

We thank Reviewer 1 for making us aware of this excellent reference. We will review this text and include it as a reference, in addition to other references recommended by Reviewer 1 and other reviewers.

Reviewer #2 (Public review):

Summary:

This paper explores a highly interesting question regarding how species migration success relates to phenology shifts, and it finds a positive relationship. The findings are significant, and the strength of the evidence is solid. However, there are substantial issues with the writing, presentation, and analyses that need to be addressed. First, I disagree with the conclusion that species that don't migrate are "losers" - some species might not migrate simply because they have broad climatic niches and are less sensitive to climate change. Second, the results concerning species' southern range limits could provide valuable insights. These could be used to assess whether sampling bias has influenced the results. If species are truly migrating, we should observe northward shifts in their southern range limits. However, if this is an artifact of increased sampling over time, we would expect broader distributions both north and south. Finally, Figure 1 is missed panel B, which needs to be addressed.

We thank Reviewer 2 for their time and expertise in reviewing our study.

It is possible that some species with broad niches may not need to migrate, although in general failing to move with climate change is considered an indicator of “climate debt”, signaling that a species may be of concern for conservation (ex. Duchenne et al. 2021, Ecology Letters). We will revise the discussion to acknowledge potential differences in outcomes.

We used null models to test whether our results regarding range shifts were robust, and if they varied due to increased sampling over time. We found that observed northern range limit shifts are not consistent with expectations derived from changes in sampling intensity (Figure S1, S2). 

We thank Reviewer 2 for pointing out this error in Figure 1. This conceptual figure was a challenge to construct, as it must illustrate how phenology and range shifts can occur simultaneously or uniquely to enable a hypothetic odonate to track its thermal niche over time. In a previous version of the figure, we had a second panel and we failed to remove the reference to that panel when we simplified the figure. 

Reviewer #3 (Public review):

Summary:

In their article "Range geographies, not functional traits, explain convergent range and phenology shifts under climate change," the authors rigorously investigate the temporal shifts in odonate species and their potential predictors. Specifically, they examine whether species shift their geographic ranges poleward or alter their phenology to avoid extreme conditions. Leveraging opportunistic observations of European and North American odonates, they find that species showing significant range shifts also exhibited earlier phenological shifts. Considering a broad range of potential predictors, their results reveal that geographical factors, but not functional traits, are associated with these shifts.

We thank Reviewer 3 for their expertise and the time they spent reviewing our study. Their suggestions are very helpful and will improve the quality of our manuscript.

Strengths:

The article addresses an important topic in ecology and conservation that is particularly timely in the face of reports of substantial insect declines in North America and Europe over the past decades. Through data integration the authors leverage the rich natural history record for odonates, broadening the taxonomic scope of analyses of temporal trends in phenology and distribution to this taxon. The combination of phenological and range shifts in one framework presents an elegant way to reconcile previous findings improving our understanding of the drivers of biodiversity loss.

We thank Reviewer 3 for this assessment.

Weaknesses:

The introduction and discussion of the article would benefit from a stronger contextualization of recent studies on biological responses to climate change and the underpinning mechanism.

The presentation of the results (particularly in figures) should be improved to address the integrative character of the work and help readers extract the main results. While the writing of the article is generally good, particularly the captions and results contain many inconsistencies and lack important detail. With the multitude of the relationships that were tested (the influence of traits) the article needs more coherence.

We thank Reviewer 3 for these suggestions. We will revise the introduction and discussion to better contextualize species’ responses to climate change and the mechanisms behind them. We will carefully review all figures and captions, and we will make changes to improve the clarity of the text and the presentation of results.

eLife Assessment

This fundamental work by Mäkelä et al. presents compelling experimental evidence supported by a theoretical model that the amount of chromosomal DNA can become limiting for the total rate of mRNA transcription and consequently protein production in the model bacterium Escherichia coli. The work is based on a mutant that allows inhibition of DNA replication while following growth at the single-cell level due to cell filamentation. The work significantly advances our understanding of growth and of the central dogma, and will be of considerable interest within both systems biology and microbial physiology.

Reviewer #1 (Public review):

Summary:

The manuscript by Mäkelä et al. presents compelling experimental evidence that the amount of chromosomal DNA can become limiting for the total rate of mRNA transcription and consequently protein production in the model bacterium Escherichia coli. Specifically, the authors demonstrate that upon inhibition of DNA replication the rate of RNA transcription and the single-cell growth rate continuously decrease, the latter in direct proportion to the concentration of active ribosomes, as measured indirectly by single-particle tracking. The decrease of ribosomal activity with filamentation is likely caused by a decrease of the concentration of mRNAs, as suggested by an observed plateau of the total number of active RNA polymerases. These observations are compatible with the hypothesis that DNA limits the total rate of transcription and thus, indirectly, translation.

The authors also demonstrate that the decrease of RNAp activity is independent of two candidate stress response pathways, the SOS stress response and the stringent response, as well as an anti-sigma factor previously implicated in variations of RNAp activity upon variations of nutrient sources.

Remarkably, the reduction of growth rate is observed soon after the inhibition of DNA replication, suggesting that the amount of DNA in wild-type cells is tuned to provide just as much substrate for RNA polymerase as needed to saturate most ribosomes with mRNAs. While previous studies of bacterial growth have most often focused on ribosomes and metabolic proteins, this study provides important evidence that chromosomal DNA has a previously underestimated important and potentially rate-limiting role for growth.

Strengths:

This article links the growth of single cells to the amount of DNA, the number of active ribosomes and to the number of RNA polymerases, combining quantitative experiments with theory. The correlations observed during depletion of DNA, notably in M9gluCAA medium, are compelling and point towards a limiting role of DNA for transcription and subsequently for protein production soon after reduction of the amount of DNA in the cell. The article also contains a theoretical model of transcription-translation that contains a Michaelis-Menten type dependency of transcription on DNA availability and is fit to the data.

At a technical level, single-cell growth experiments and single-particle tracking experiments are well described, suggesting that different diffusive states of molecules represent different states of RNAp/ribosome activities, which reflect the reduction of growth.

Apart from correlations in DNA-deplete cells, the article also investigates the role of candidate stress response pathways for reduced transcription, demonstrating that neither the SOS nor the stringent response are responsible for the reduced rate of growth. Equally, the anti-sigma factor Rsd recently described for its role in controlling RNA polymerase activity in nutrient-poor growth media, seems also not involved according to mass-spec data. While other (unknown) pathways might still be involved in reducing the number of active RNA polymerases, the proposed hypothesis of the DNA substrate itself being limiting for the total rate of transcription is appealing.

Finally, the authors confirm the reduction of growth in the distant Caulobacter crescentus, which lacks overlapping rounds of replication and could thus have shown a different dependency on DNA concentration.

Weaknesses:

The study has no apparent weaknesses after review.

Reviewer #2 (Public review):

In this work, the authors uncovered the effects of DNA dilution on E. coli, including a decrease in growth rate and a significant change in proteome composition. The authors demonstrated that the decline in growth rate is due to the reduction of active ribosomes and active RNA polymerases because of the limited DNA copy numbers. They further showed that the change in the DNA-to-volume ratio leads to concentration changes in almost 60% of proteins, and these changes mainly stem from the change in the mRNA levels.

Comments on revised version:

The authors have satisfyingly answered all of our questions.

Reviewer #3 (Public review):

Mäkelä et al. here investigate genome concentration as a limiting factor on growth. Previous work has identified key roles for transcription (RNA polymerase) and translation (ribosomes) as limiting factors on growth, which enable an exponential increase in cell mass. While a potential limiting role of genome concentration under certain conditions has been explored theoretically, Mäkelä et al. here present direct evidence that when replication is inhibited, genome concentration emerges as a limiting factor.

A major strength of this paper is the diligent and compelling combination of experiment and modeling used to address this core question. The use of origin- and ftsZ-targeted CRISPRi is a very nice approach that enables dissection of the specific effects of limiting genome dosage in the context of a growing cytoplasm. While it might be expected that genome concentration eventually becomes a limiting factor, what is surprising and novel here is that this happens very rapidly, with growth transitioning even for cells within the normal length distribution for E. coli. Fundamentally, it demonstrates the fine balance of bacterial physiology, where the concentration of the genome itself (at least under rapid growth conditions) is no higher than it needs to be. A further surprising finding of this study is that susceptibility to this genome-limiting effect is felt differently by different genes, with unstable transcripts more affected and rRNA and many essential genes being more robust to it.

It should be noted that the authors do not identify a "smoking gun" - a gene or small number of genes that mediate the effects of genome concentration-dependent growth limitation. However, what they do achieve is to develop plausible criteria for identifying such a gene - through investigating essential genes that decrease in their abundance more rapidly than others.

Overall, this study provides a fundamental contribution to bacterial physiology by illuminating the relationship between DNA, mRNA, and protein in determining growth rate. While coarse-grained, the work invites exciting questions about how the composition of major cellular components is fine-tuned to a cell's needs and which specific gene products mediate this connection. The work also suggests the presence of buffering mechanisms that allow essential proteins such as RNA polymerase to be robust to fluctuations in genome concentration, which is an exciting area for future exploration. This work has implications not only for biotechnology, as the authors discuss, but potentially also for our understanding of how DNA-targeted antibiotics limit bacterial growth.

Comments on revised version:

Nothing left to add - the authors did a fantastic job addressing my points. In some ways doing so opened up even more interesting questions, but I happily accept that those are best left to future investigations.

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

The manuscript by Mäkelä et al. presents compelling experimental evidence that the amount of chromosomal DNA can become limiting for the total rate of mRNA transcription and consequently protein production in the model bacterium Escherichia coli. Specifically, the authors demonstrate that upon inhibition of DNA replication the single-cell growth rate continuously decreases, in direct proportion to the concentration of active ribosomes, as measured indirectly by single-particle tracking. The decrease of ribosomal activity with filamentation, in turn, is likely caused by a decrease of the concentration of mRNAs, as suggested by an observed plateau of the total number of active RNA polymerases. These observations are compatible with the hypothesis that DNA limits the total rate of transcription and thus translation. The authors also demonstrate that the decrease of RNAp activity is independent of two candidate stress response pathways, the SOS stress response and the stringent response, as well as an anti-sigma factor previously implicated in variations of RNAp activity upon variations of nutrient sources.

Remarkably, the reduction of growth rate is observed soon after the inhibition of DNA replication, suggesting that the amount of DNA in wild-type cells is tuned to provide just as much substrate for RNA polymerase as needed to saturate most ribosomes with mRNAs. While previous studies of bacterial growth have most often focused on ribosomes and metabolic proteins, this study provides important evidence that chromosomal DNA has a previously underestimated important and potentially rate-limiting role for growth. 

Thank you for the excellent summary of our work.

Strengths: 

This article links the growth of single cells to the amount of DNA, the number of active ribosomes and to the number of RNA polymerases, combining quantitative experiments with theory. The correlations observed during depletion of DNA, notably in M9gluCAA medium, are compelling and point towards a limiting role of DNA for transcription and subsequently for protein production soon after reduction of the amount of DNA in the cell. The article also contains a theoretical model of transcription-translation that contains a Michaelis-Menten type dependency of transcription on DNA availability and is fit to the data. While the model fits well with the continuous reduction of relative growth rate in rich medium (M9gluCAA), the behavior in minimal media without casamino acids is a bit less clear (see comments below). 

At a technical level, single-cell growth experiments and single-particle tracking experiments are well described, suggesting that different diffusive states of molecules represent different states of RNAp/ribosome activities, which reflect the reduction of growth. However, I still have a few points about the interpretation of the data and the measured fractions of active ribosomes (see below). 

Apart from correlations in DNA-deplete cells, the article also investigates the role of candidate stress response pathways for reduced transcription, demonstrating that neither the SOS nor the stringent response are responsible for the reduced rate of growth. Equally, the anti-sigma factor Rsd recently described for its role in controlling RNA polymerase activity in nutrient-poor growth media, seems also not involved according to mass-spec data. While other (unknown) pathways might still be involved in reducing the number of active RNA polymerases, the proposed hypothesis of the DNA substrate itself being limiting for the total rate of transcription is appealing. 

Finally, the authors confirm the reduction of growth in the distant Caulobacter crescentus, which lacks overlapping rounds of replication and could thus have shown a different dependency on DNA concentration. 

Weaknesses: 

There are a range of points that should be clarified or addressed, either by additional experiments/analyses or by explanations or clear disclaimers. 

First, the continuous reduction of growth rate upon arrest of DNA replication initiation observed in rich growth medium (M9gluCAA) is not equally observed in poor media. Instead, the relative growth rate is immediately/quickly reduced by about 10-20% and then maintained for long times, as if the arrest of replication initiation had an immediate effect but would then not lead to saturation of the DNA substrate. In particular, the long plateau of a constant relative growth rate in M9ala is difficult to reconcile with the model fit in Fig 4S2. Is it possible that DNA is not limiting in poor media (at least not for the cell sizes studied here) while replication arrest still elicits a reduction of growth rate in a different way? Might this have something to do with the naturally much higher oscillations of DNA concentration in minimal medium?

The reviewer is correct that there are interesting differences between nutrient-rich and -poor conditions. They were originally noted in the discussion, but we understand how our original presentation made it confusing. We reorganized the text and figures to better explain our results and interpretations. In the revised manuscript, the data related to the poor media are now presented separately (new Figure 6) from the data related to the rich medium (Figures 1-3).  The total RNAP activity (abundance x active fraction) is significantly reduced in poor media (Figure 6A-B) similarly to rich medium (Figure 3H). Thus, DNA is limiting for transcription across conditions. However, the total ribosome activity in poor media (Figure 6C-D) and thus the growth rate (Figure 6EF) was less affected in comparison to rich media (Figure 2H and 1C). Our interpretation of these results is that while DNA is limiting for transcription in all tested nutrient conditions (as shown by the total active RNAP data), post-transcriptional buffering activities compensate for the reduction in transcription in poor media, thereby maintaining a better scaling of growth rates under DNA limitation. 

The authors argue that DNA becomes limiting in the range of physiological cell sizes, in particular for M9glCAA (Fig. 1BC). It would be helpful to know by how much (fold-change) the DNA concentration is reduced below wild-type (or multi-N) levels at t=0 in Fig 1B and how DNA concentration decays with time or cell area, to get a sense by how many-fold DNA is essentially 'overexpressed/overprovided' in wild-type cells. 

We now provide crude estimates in the Discussion section. The revised text reads: “Crude estimations suggest that ≤ 40% DNA dilution is sufficient to negatively affect transcription (total RNAP activity) in M9glyCAAT, whereas the same effect was observed after less than 10% dilution in nutrient-poor media (M9gly or M9ala) (see Materials and Methods).” We obtained these numbers based on calculations and estimates described in the Materials and Methods section and Appendix 1 (Appendix 1 – Table 1).

Fig. 2: The distribution of diffusion coefficients of RpsB is fit to Gaussians on the log scale. Is this based on a model or on previous work or simply an empirical fit to the data? An exact analytical model for the distribution of diffusion constants can be found in the tool anaDDA by Vink, ..., Hohlbein Biophys J 2020. Alternatively, distributions of displacements are expressed analytically in other tools (e.g., in SpotOn). 

We use an empirical fit of Gaussian mixture model (GMM) of three states to the data and extract the fractions of molecules in each state. This avoids making too many assumptions on the underlying processes, e.g. a Markovian system with Brownian diffusion. The model in anaDDA (Vink et al.) is currently limited to two-transitioning states with a maximal step number of 8 steps per track for a computationally efficient solution (longer tracks are truncated). Using a short subset of the trajectories is less accurate than using the entire trajectory and because of this, we consider full tracks with at least 9 displacements. Meanwhile, Spot-On supports a three-state model but it is still based on a semi-analytical model with a pre-calculated library of parameters created by fitting of simulated data. Neither of these models considers the effect of cell confinement, which plays a major role in single-molecule diffusion in small-sized cells such as bacteria. For these reasons, we opted to use an empirical fit to the data. We note that the fractions of active ribosomes in WT cells, which we extracted from these diffusion measurements, are consistent with the range of estimates obtained by others using similar or different approaches (Forchhammer and Lindhal 1971; Mohapatra and Weisshaar, 2018; Sanamrad et al., 2014). 

The estimated fraction of active ribosomes in wild-type cells shows a very strong reduction with decreasing growth rate (down from 75% to 30%), twice as strong as measured in bulk experiments (Dai et al Nat Microbiology 2016; decrease from 90% to 60% for the same growth rate range) and probably incompatible with measurements of growth rate, ribosome concentrations, and almost constant translation elongation rate in this regime of growth rates. Might the different diffusive fractions of RpsB not represent active/inactive ribosomes? See also the problem of quantification above. The authors should explain and compare their results to previous work. 

We agree that our measured range is somewhat larger than the estimated range from Dai et al, 2016. However, they use different media, strains, and growth conditions. We also note that Dai et al did not make actual measurements of the active ribosome fraction. Instead, they calculate the “active ribosome equivalent” based on a model that includes growth rate, protein synthesis rate, RNA/protein abundance, and the total number of amino acids in all proteins in the cell. Importantly, our measurements show the same overall trend (a ~30% decrease) as Dai et al, 2016. Furthermore, our results are within the range of previous experimental estimates from ribosome profiling (Forchhammer and Lindhal 1971) or single-ribosome tracking (Mohapatra and Weisshaar, 2018; Sanamrad et al., 2014). We clarified this point in the revised manuscript. 

To measure the reduction of mRNA transcripts in the cell, the authors rely on the fluorescent dye SYTO RNAselect. They argue that 70% of the dye signal represents mRNA. The argument is based on the previously observed reduction of the total signal by 70% upon treatment with rifampicin, an RNA polymerase inhibitor (Bakshi et al 2014). The idea here is presumably that mRNA should undergo rapid degradation upon rif treatment while rRNA or tRNA are stable. However, work from Hamouche et al. RNA (2021) 27:946 demonstrates that rifampicin treatment also leads to a rapid degradation of rRNA. Furthermore, the timescale of fluorescent-signal decay in the paper by Bakshi et al. (half life about 10min) is not compatible with the previously reported rapid decay of mRNA (24min) but rather compatible with the slower, still somewhat rapid, decay of rRNA reported by Hamouche et al.. A bulk method to measure total mRNA as in the cited Balakrishnan et al. (Science 2022) would thus be a preferred method to quantify mRNA. Alternatively, the authors could also test whether the mass contribution of total RNA remains constant, which would suggest that rRNA decay does not contribute to signal loss. However, since rRNA dominates total RNA, this measurement requires high accuracy. The authors might thus tone down their conclusions on mRNA concentration changes while still highlighting the compelling data on RNAp diffusion. 

Thank you for bringing the Hamouche et al 2021 paper to our attention. To address this potential issue, we have performed fluorescence in situ hybridization (FISH) microscopy using a 16S rRNA probe (EUB338) to quantify rRNA concentration in 1N cells. We found that the rRNA signal only slightly decreases with cell size (i.e., genome dilution) compared to the RNASelect signal (e.g., a ~5% decrease for rRNA signal vs. 50% for RNASelect for a cell size range of 4 to 10 µm2). We have revised the text and added a figure to include the new rRNA FISH data (Figure 4). In addition, as a control, we validated our rRNA FISH method by comparing the intracellular concentration of 16S rRNA in poor vs. rich media (new Figure 4 – Figure supplement 3).

The proteomics experiments are a great addition to the single-cell studies, and the correlations between distance from ori and protein abundance is compelling. However, I was missing a different test, the authors might have already done but not put in the manuscript: If DNA is indeed limiting the initiation of transcription, genes that are already highly transcribed in non-perturbed conditions might saturate fastest upon replication inhibition, while genes rarely transcribed should have no problem to accommodate additional RNA polymerases. One might thus want to test, whether the (unperturbed) transcription initiation rate is a predictor of changes in protein composition. This is just a suggestion the authors may also ignore, but since it is an easy analysis, I chose to mention it here. 

We did not find any correlation when we examined the potential relation between RNA slopes and mRNA abundance (from our first CRISPRi oriC time point) or the transcription initiation rate (from Balakrishnan et al., 2022, PMID: 36480614) across genes. These new plots are presented in Figure 7 – Figure supplement 2B. In contrast, we found a small but significant correlation between RNA slopes and mRNA decay rates (from Balakrishnan et al., 2022, PMID: 36480614), specifically for genes with short mRNA lifetimes (new Figure 7F). This effect is consistent with our model prediction (Figure 5 – Figure supplement 2). 

Related to the proteomics, in l. 380 the authors write that the reduced expression close to the ori might reflect a gene-dosage compensatory mechanism. I don't understand this argument. Can the authors add a sentence to explain their hypothesis? 

We apologize for the confusion. While performing additional analyses for the revisions, we realized that while the proteins encoded by genes close to oriC tend to display subscaling behavior, this is not true at the mRNA level (new Figure 7 – Figure supplement 3B). In light of this result, we no longer have a hypothesis for the observed negative correlation at the protein level (originally Figure 5D, now Figure 7 – Figure supplement 3A). The text was revised accordingly.  

In Fig. 1E the authors show evidence that growth rate increases with cell length/area. While this is not a main point of the paper it might be cited by others in the future. There are two possible artifacts that could influence this experiment: a) segmentation: an overestimation of the physical length of the cell based on phase-contrast images (e.g., 200 nm would cause a 10% error in the relative rate of 2 um cells, but not of longer cells). b) timedependent changes of growth rate, e.g., due to change from liquid to solid or other perturbations. To test for the latter, one could measure growth rate as a function of time, restricting the analysis to short or long cells, or measuring growth rate for short/long cells at selected time points. For the former, I recommend comparison of phase-contrast segmentation with FM4-64-stained cell boundaries.

As the reviewer notes, the small increase in relative growth was just a minor observation that does not affect our story whether it is biologically meaningful or the result of a technical artefact. But we agree with the reviewer that others might cite it in future works and thus should be interpreted with caution.

An artefact associated with time-dependent changes (e.g. changing from liquid cultures to more solid agarose pads) is unlikely for two reasons. 1. We show that varying the time that cells spend on agarose pads relative to liquid cultures does not affect the cell size-dependent growth rate results (Figure 1 – supplement 5A). 2. We show that the growth rate is stable from the beginning of the time-lapse with no transient effects upon cell placement on agarose pads for imaging (Figure 1 – supplement 1). These results were described in the Methods section where they could easily be missed. We revised the text to discuss these controls more prominently in the Results section.

As for cell segmentation, we have run simulations and agree with the reviewer that a small overestimation of cell area (which is possible with any cell segmentation methods including ours) could lead to a small increase in relative growth with increasing cell areas (new Figure 1 – Figure supplement 3). Since the finding is not important to our story, we simply revised the text and added the simulation results to alert the readers to the possibility that the observation may be due to a small cell segmentation bias.

Reviewer #2 (Public Review): 

In this work, the authors uncovered the effects of DNA dilution on E. coli, including a decrease in growth rate and a significant change in proteome composition. The authors demonstrated that the decline in growth rate is due to the reduction of active ribosomes and active RNA polymerases because of the limited DNA copy numbers. They further showed that the change in the DNA-to-volume ratio leads to concentration changes in almost 60% of proteins, and these changes mainly stem from the change in the mRNA levels. 

Thank you for the support and accurate summary!

Reviewer #3 (Public Review): 

Summary: 

Mäkelä et al. here investigate genome concentration as a limiting factor on growth.

Previous work has identified key roles for transcription (RNA polymerase) and translation (ribosomes) as limiting factors on growth, which enable an exponential increase in cell mass. While a potential limiting role of genome concentration under certain conditions has been explored theoretically, Mäkelä et al. here present direct evidence that when replication is inhibited, genome concentration emerges as a limiting factor. 

Strengths: 

A major strength of this paper is the diligent and compelling combination of experiment and modeling used to address this core question. The use of origin- and ftsZ-targeted CRISPRi is a very nice approach that enables dissection of the specific effects of limiting genome dosage in the context of a growing cytoplasm. While it might be expected that genome concentration eventually becomes a limiting factor, what is surprising and novel here is that this happens very rapidly, with growth transitioning even for cells within the normal length distribution for E. coli. Fundamentally, it demonstrates the fine balance of bacterial physiology, where the concentration of the genome itself (at least under rapid growth conditions) is no higher than it needs to be. 

Thank you!

Weaknesses: 

One limitation of the study is that genome concentration is largely treated as a single commodity. While this facilitates their modeling approach, one would expect that the growth phenotypes observed arise due to copy number limitation in a relatively small number of rate-limiting genes. The authors do report shifts in the composition of both the proteome and the transcriptome in response to replication inhibition, but while they report a positional effect of distance from the replication origin (reflecting loss of high-copy, origin-proximal genes), other factors shaping compositional shifts and their functional effects on growth are not extensively explored. This is particularly true for ribosomal RNA itself, which the authors assume to grow proportionately with protein. More generally, understanding which genes exert the greatest copy number-dependent influence on growth may aid both efforts to enhance (biotechnology) and inhibit (infection) bacterial growth. 

We agree but feel that identifying the specific limiting genes is beyond the scope of the study. This said, we carried out additional experiments and analyses to address the reviewer’s comment and identify potential contributing factors and limiting gene candidates. First, we examined the intracellular concentration of 16S ribosomal RNA (rRNA) by rRNA FISH microscopy and found that it decays much slower than the bulk of mRNAs as measured using RNASelect staining (new Figure 4 and Figure 4 – Figure supplements 1 and 3). We found that the rRNA signal is far more stable in 1N cells than the RNASelect signal, the former decreasing by only ~5% versus ~50% for the later in response to the same range of genome dilution (Figure 4C).  Second,  we carried out new correlation analyses between our proteomic/transcriptomic datasets and published genome-wide datasets that report various variables under unperturbed conditions (e.g., mRNA abundance, mRNA degradation rates, fitness cost, transcription initiation rates, essentiality for viability); see new Figure 7E-G and Figure 7 – Figure supplement 2. In the process, we found that genes essential for viability tend, on average, to display superscaling behavior (Figure 7G). This suggests that cells have evolved mechanisms that prioritize expression of essential genes over nonessential ones during DNA-limited growth. Furthermore, this analysis identified a small number of essential genes that display strong negative RNA slopes (Figure 7C, Datasets 1 and 2), indicating that the concentration of their mRNA decreases rapidly relative to the rest of the transcriptome upon genome dilution. These essential genes with strong subscaling behavior are candidates for being growth-limiting. 

The text and figures were revised to include these new results.

Overall, this study provides a fundamental contribution to bacterial physiology by illuminating the relationship between DNA, mRNA, and protein in determining growth rate. While coarse-grained, the work invites exciting questions about how the composition of major cellular components is fine-tuned to a cell's needs and which specific gene products mediate this connection. This work has implications not only for biotechnology, as the authors discuss, but potentially also for our understanding of how DNA-targeted antibiotics limit bacterial growth. 

Thank you!

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors): 

Below are my comments. 

(1) I noticed that a paper by Li et al. on biorxiv has found similar results as this work ("Scaling between DNA and cell size governs bacterial growth homeostasis and resource allocation," https://doi.org/10.1101/2021.11.12.468234), including the linear growth of E. coli when the DNA concentration is low. This relevant reference was not cited or discussed in the current manuscript. 

We agree that authors should cite and discuss relevant peer-reviewed literature. But broadly speaking, we feel that extending this responsibility to all preprints (and by extension any online material) that have not been reviewed is a bit dangerous. It would effectively legitimize unreviewed claims and risk their propagation in future publications. We think that while imperfect, the peer-reviewing process still plays an important role. 

Regarding the specific 2021 preprint that the reviewer pointed out, we think that the presented growth rate data are quite noisy and that the experiments lack a critical control (multi-N cells), making interpretation difficult. Their report that plasmid-borne expression is enhanced when DNA is severely diluted is certainly interesting and makes sense in light of our measurements that the activities, but not the concentrations, of RNA polymerases and ribosomes are reduced in 1N cells. However, we do not know why this preprint has not yet been published since 2021. There could be many possible reasons for this. Therefore, we feel that it is safer to limit our discussion to peer-reviewed literature.

(2) I think the kinetic Model B in the Appendix has been studied in previous works, such as Klump & Hwa, PNAS 2008, https://doi.org/10.1073/pnas.0804953105

Indeed, Klumpp & Hwa 2008 modeled the kinetics of RNA polymerase and promoter association prior to our study. But there is a difference between their model and ours. Their model is based on Michaelis Menten-type (MM) functions in which the RNAP is analogous to the “substrate” and the promoter to the “enzyme” in the MM equation. In contrast, our model uses functions based on the law of mass action (instead of MMtype of function). We have revised the text, included the Klumpp & Hwa 2008 reference, and revised the Materials & Methods section to clarify these points. 

(3) On lines 284-285, if I understand correctly, the fractions of active RNAPs and active ribosomes are relative to the total protein number. It would be helpful if the authors could mention this explicitly to avoid confusion. 

The fractions of active RNAPs and active ribosomes are expressed as the percentage of the total RNAPs and ribosomes. We have revised the text to be more explicit. Thank you.

(4) On line 835, I am not sure what the bulk transcription/translation rate means. I guess it is the maximum transcription/translation rate if all RNAPs/ribosomes are working according to Eq. (1,2). It would be helpful if the authors could explain the meaning of r_1 and r_2 more explicitly. 

Our apology for the lack of clarity. We have added the following equations:

(5) Regarding the changes in protein concentrations due to genome dilution, a recent theoretical paper showed that it may come from the heterogeneity in promoter strengths (Wang & Lin, Nature Communications 2021). 

In the Wang and Lin model, the heterogeneity in promoter strength predicts that the “mRNA production rate equivalent”, which is the mRNA abundance multiplied by the mRNA decay rate, will correlate the RNA slopes. However, we found these two variables to be uncorrelated (see below, The Spearman correlation coefficient ρ was 0.02 with a p-value of 0.24, indicating non-significance (NS).

Author response image 1.

The mRNA production rate equivalent (mRNA abundance at the first time point after CRISPRi oriC induction multiplied by the mRNA degradation rate measured by Balakrishnan et al., 2022, PMID: 36480614, expressed in transcript counts per minute) does not correlate (Spearman correlation’s p-value = 0.24) with the RNA slope in 1N-rich cells.  Data from 2570 genes are shown (grey markers, Gaussian kernel density estimation - KDE), and their binned statistics (mean +/- SEM, ~280 genes per bin, orange markers). 

In addition, we found no significant correlation between RNA slopes and mRNA abundance or transcription initiation rate. These plots are now included in Figure 7E and Figure 7 –Figure supplement 2B. Thus, the promoter strength does not appear to be a predictor of the RNA (and protein) scaling behavior under DNA limitation. 

Reviewer #3 (Recommendations For The Authors): 

One general area that could be developed further is analysis of changes in the proteome/transcriptome composition, given that there may be specific clues here as to the phenotypic effects of genome concentration limitation. Specifically: 

• In Figure 5D, the authors demonstrate an effect of origin distance on sensitivity to replication inhibition, presumably as a copy number effect. However, the authors note that the effect was only slight and postulated a compensatory mechanism. Due to the stability of proteins, one should expect relatively small effects - even if synthesis of a protein stopped completely, its concentration would only decrease twofold with a doubling of cell area (slope = -1, if I'm interpreting things correctly). It would be helpful to display the same information shown in Figure 5D at the mRNA level, since I would anticipate that higher mRNA turnover rates mean that effects on transcription rate should be felt more rapidly. 

We thank the reviewer for this suggestion. To our surprise, we found that there is no correlation between gene location relative to the origin and RNA slope across genes. This suggests that the observed correlation between gene location and protein slopes does not occur at the mRNA level. Given that we do not have an explanation for the underlying mechanism, we decided to present these data (the original data in Figure 5D and the new data for the RNA slope) in a supplementary figure (Figure 7 – Figure supplement 3).

• Related to this, did the authors see any other general trends? For example, do highly expressed genes hit saturation faster, making them more sensitive to limited genome concentration? 

We found that the RNA slopes do not correlate with mRNA abundance or transcription initiation rates. However, they do correlate with mRNA decay. That is, short-lived mRNAs tend to have negative RNA slopes. The new analyses have been added as Figure 7E-F and Figure 7 – Figure supplement 2B. The text has been revised to incorporate this information. 

• Presumably loss of growth is primarily driven by a subset of genes whose copy number becomes limiting. Previously, it has been reported that there is a wide variety among "essential" genes in their expression-fitness relationship - i.e. how much of a reduction in expression you need before growth is reduced (e.g. PMID 33080209). It would be interesting to explore the shifts in proteome/transcriptome composition to see whether any genes particularly affected by restricted genome concentration are also especially sensitive to reduced expression - overlap in these datasets may reveal which genes drive the loss of growth. 

This is a very interesting idea – thank you! We did not find a correlation between the protein/RNA slope and the relative gene fitness as previously calculated (PMID 33080209), as shown below.

Author response image 2.

The relative fitness of each gene (data by Hawkins et al., 2020, PMID: 33080209, median fitness from the highest sgRNA activity bin) plotted versus the gene-specific RNA and protein slopes that we measured in 1Nrich cells after CRISPRi oriC induction. More than 260 essential genes are shown (262 RNA slopes and 270 protein slopes, grey markers), and their binned statistics (mean +/- SEM, 43-45 essential genes per bin, orange markers). The spearman correlations (ρ) with p-values above 10-3 are considered not significant (NS). In our analyses, we only considered correlations significant if they have a Spearman correlation p-value below 10-10.

However, while doing this suggested analysis, we noticed that the essential genes that were included in the forementioned study have RNA slopes above zero on average. This led us to compare the RNA slope distributions of essential genes relative to all genes (now included in Figure 7G). We found that they tend to display superscaling behavior (positive RNA slopes), suggesting the existence of regulatory mechanisms that prioritize the expression of essential genes over less important ones when genome concentration becomes limiting for growth.  The text has been revised to include this new information.

Other suggestions: 

• In Figure 3 the authors report that total RNAP concentration increases with increasing cytoplasmic volume. This is in itself an interesting finding as it may imply a compensatory mechanism - can the authors offer an explanation for this? 

We do not have a straightforward explanation. But we agree that it is very interesting and should be investigated in future studies given that this superscaling behavior is common among essential genes. 

• The explanation of the modeling within the main text could be improved. Specifically, equations 1 and 2, as well as a discussion of models A and B (lines 290-301), do not explicitly relate DNA concentration to downstream effects. The authors provide the key information in Appendix 1, but for a general reader, it would be helpful to provide some intuition within the main text about how genome concentration influences transcription rate (i.e. via 𝛼RNAP).  

We apologize for the lack of clarity. We have added information that hopefully improves clarity.

eLife Assessment

This valuable study uses dynamic metabolic models to compare perturbation responses in a bacterial system, analyzing whether they return to their steady state or amplify beyond the initial perturbation. The evidence supporting the emergent properties of perturbed metabolic systems to network topology and sensitivity to specific metabolites is solid, although the authors do not explain the origin of some significant inconsistencies between models.

Reviewer #1 (Public review):

(1a) Summary:

The author studied metabolic networks for central metabolism, focusing on how system trajectories returned to their steady state. To quantify the response, systematic perturbation was performed in simulation and the maximal destabilization away from steady state (compared with initial perturbation distance) was characterized. The author analyzed the perturbation response and found that sparse network and networks with more cofactors are more "stable", in the sense that the perturbed trajectories have smaller deviation along the path back to the steady state.

(1b) Strengths and major contributions:

The author compared three metabolic models and performed systematic perturbation analysis in simulation. This is the first work characterized how perturbed trajectories deviate from equilibrium in large biochemical systems and illustrated interesting findings about the difference between sparse biological systems and randomly simulated reaction networks.

(1c) Weaknesses:

There are two main weaknesses in this study:

First, the metabolic network in this study is incomplete. For example, amino acid synthesis and lipid synthesis are important for biomass and growth, but they are not included in the three models used in this study. NADH and NADPH are as important as ATP/ADP/AMP, but they are not included in the models. In the future, a more comprehensive metabolic and biosynthesis model is required.

Second, this work does not provide mathematics explanation on the perturbation response χ. Since the perturbation analysis are performed closed to steady state (or at least belongs to the attractor of single steady state), local linear analysis would provide useful information. By complement with other analysis in dynamical systems (described in below) we can gain more logical insights about perturbation response.

(1d) Discussion and impact for the field:

Metabolic perturbation is an important topic in cell biology and has important clinical implication in pharmacodynamics. The computational analysis in this study provides an initiative for future quantitative analysis on metabolism and homeostasis.

Comments on revised version:

The revised version of this manuscript made some clarifications, while I think the analysis of response coefficients is still numerical and model-specific, being unclear under dynamical systems of views.

Reviewer #2 (Public review):

The authors have conducted a valuable comparative analysis of perturbation responses in three nonlinear kinetic models of E. coli central carbon metabolism found in the literature. They aimed to uncover commonalities and emergent properties in the perturbation responses of bacterial metabolism. They discovered that perturbations in the initial concentrations of specific metabolites, such as adenylate cofactors and pyruvate, significantly affect the maximal deviation of the responses from steady-state values. Furthermore, they explored whether the network connectivity (sparse versus dense connections) influences these perturbation responses. The manuscript is reasonably well written.

Comments on revised version:

The authors have addressed my concerns to a large extent. However, a few minor issues remain, as listed below:

(1) The authors identified key metabolites affecting responses to perturbations in two ways: (i) by fixing a metabolite's value and (ii) by performing a sensitivity analysis. It would be helpful for the modeling community to understand better the differences and similarities in the obtained results. Do both methods identify substrate-level regulators? Is freezing a metabolite's dynamics dramatically changing the metabolic response (and if yes, which ones are so different in the two cases)? Does the scope of the network affect these differences and similarities?

(2) Regarding the issues the authors encountered when performing the sensitivity analysis, they can be approached in two ways. First, the authors can check the methods for computing conserved moieties nicely explained by Sauro's group (doi:10.1093/bioinformatics/bti800) and compute them for large-scale networks (but beware of metabolites that belong to several conserved pools). Otherwise, the conserved pools of metabolites can be considered as variables in the sensitivity analysis-grouping multiple parameters is a common approach in sensitivity analysis.

Author response:

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

Reviewer #1 (Public Reviews):  

First, the metabolic network in this study is incomplete. For example, amino acid synthesis and lipid synthesis are important for biomass and growth, but they4 are not included in the three models used in this study. NADH and NADPH are as important as ATP/ADP/AMP, but they are not included in the models. In the future, a more comprehensive metabolic and biosynthesis model is required.  

Thank you for the critical comment on the weakness of the present study. We actually tried to study a larger model like Turnborg et al (2021), which is a model of JCVI-syn3A, but we give up to include it in our model list to study in depth. This is because we noticed that the concentration of ATP in the model can be negative (we confirmed this with one of the authors of the paper). Another "big" kinetic model of metabolism that we could list would be Khodayari et al (2017). However, we could not find the models to compare the dynamics of this big model with. Therefore, we decided to use the model only for the central carbon metabolism for now. We would like to leave a more extended study for the near future.  

We would like to mention that NADH and NADPH are included in Khodayari model and Boecker model, while NADH and NADPH are ramped up to NADH in the latter model.  

Second, this work does not provide a mathematical explanation of the perturbation response χ. Since the perturbation analysis is performed close to the steady state (or at least belongs to the attractor of single-steady-state), local linear analysis would provide useful information. By complementing with other analysis in dynamical systems (described below) we can gain more logical insights about perturbation response.  

We tried a linear stability analysis. However, with the perturbation strength we used here, the linearization of the model is no longer valid, in the sense that the linearized model

leads to negative concentrations of the metabolites (xst+Δx < 0 for some metabolites). We have added a scatter plot of the response coefficient of trajectories sharing the initial condition, while the dynamics are computed by the original model and the linearized model, respectively. (Fig. S1). 

Since the response coefficient is based on the logarithm of the concentrations, as the metabolite concentrations approach zero, the response coefficient becomes larger. The high response coefficient in the Boecker and Chassagnole model would be explained by this artifact.  The linearized Khodayari model shows either χ~1 or χ = 0 (one or more metabolite concentrations become negative). This could be due to the number of variables in the model. For the response coefficient to have a larger value, the perturbation should be along the eigenvector that leads to oscillatory dynamics with long relaxation time (i.e., the corresponding eigenvalue has a small real part in terms of absolute value and a non-zero imaginary part). However, since the Khodayari model has about 800 variables, if perturbations are along such directions, there is a high probability that one or more metabolite concentrations will become negative.

We fully agree that if the perturbations on the metabolite concentrations are in the linear regime, the response to the perturbations can be estimated by checking the eigenvalues and eigenvectors. However, we would say that the relationship between the linearized model (and thus the spectrum of eigenvalues) and the original model is unclear in this regime.  We remarked this in Lines 158160.

Recommendations for the authors:

My major suggestion is about understanding the key quantity in this study: the response coefficient χ. When the perturbed state is close to the fixed point, one could adopt local stability analysis and consider the linearized system. For a linear system with one stable fixed point P, we consider the Jacobian matrix M on P. If all eigenvalues of M are real and negative, the perturbed trajectory will return to P with each component monotonically varies. If some eigenvalues have negative real part and nonzero imaginary part, then the perturbed trajectory will spiral inward to the fixed point. Depending on the spiral trajectory and the initially perturbed state, some components would deviate furthermore (transiently) from the fixed point on the spiral trajectory. This explains why the response coefficient χ can be greater than 1. 

Mathematically, a locally linearized system has similar behavior to the linear system, and the examples in this study can be analyzed in the similar way. Specifically, if a system has many complex eigenvalues, then the perturbed trajectory is more likely to have further deviation. The metabolic network models investigated in this work are not extremely large, and hence the author could analyze its spectrum of the Jacobian matrix at the steady state. Since the steady state is stable, I expect the spectrum located in the left half of the complex plane. If the spectrum spread out away from the real axis, we expect to see more spiral trajectories under perturbation. I think the spectrum analysis will provide a complementary view with respect to analysis on χ.  The authors' major findings, about the network sparsity and cofactors, can also be investigated under the framework of the spectrum analysis.  

Of course, when the nonlinear system is perturbed far away from the fixed point, there are other geometrical properties of the vector field that can cause the response coefficient χ to be greater than 1. This could also be investigated in the future by testing the behavior of small and large perturbations and observing if the systems have signatures of nonlinearity.  

Since all perturbed states return to the steady state, the eigenvalues of the Jacobi matrix accompanying the linearized system around the steady state are in the left half complex plane (negative real value). Also, some eigenvalues have non-zero imaginary parts.    

The reason we emphasize the "nonlinear regime" is that the linearization is no longer valid in this regime, i.e. the metabolite concentrations can be negative when we calculate the linearized system. Certainly, there are complex eigenvalues in the Jacobi matrix of any model. However, we would say that there is no clear relationship between the eigenvalues and the response coefficient.      

Minor suggestions:  

Line 127: Regarding the source of perturbation, cell division also generates unequal concentration of proteins and metabolites for two daughter cells, and it is an interesting mechanism to create metabolic perturbation. 

Thank you for the insightful suggestion. We mentioned the cell division as another source of perturbation (Lines 130-131).

Line 175: I do not quite understand the statement "fixing each metabolite concentration...", since the metabolite concentration in the ODE simulation would change immediately after this fixing.  

We meant in the sentence that we fixed the concentration of the selected metabolite as the steady state concentration and set the dx/dt of that metabolite to zero. We have rewritten the sentences to avoid confusion (Lines 180-181).

Figure 2: There are a lot of inconsistencies between the three models. Could we learn which model is more reasonable, or the conclusion here is that the cellular response under perturbation is model-specific? The latter explanation may not be quite satisfactory since we expect the overall cellular property should not be sensitive to the model details. 

Ideally, the overall cellular property should be insensitive to model details. However, the reality is that the behavior of the models (e.g., steady-state properties, relaxation dynamics, etc.) depends on the specific parameter choices, including what regulation is implemented. I think this situation is part of the motivation for the ensemble modeling (by J. Liao and colleague) that has been developed.  

Detailed responsiveness would be model specific. For example, FBP has a fairly strong effect in the Boecker model, but less so in the Khodayari model, and the opposite effect in the Chassagnole model (Fig. 2). Our question was whether there are common tendencies among kinetic models that tend to show model-specific behavior.  

Reviewer 2 (Public Review):

(1) In the study on determining key metabolites affecting responses to perturbations (starting from line 171), the authors fix the values of individual concentrations to their steady-state values and observe the responses. Such a procedure adds artificial constraints to the network because, in the natural responses of cells (and models) to perturbations, it is highly unlikely that metabolites will not evolve in time. By fixing the values of specific metabolites, the authors prohibit the metabolic network from evolving in the most optimal way to compensate for the perturbation. Instead of this procedure, have the authors considered for this task applying techniques from variance-based sensitivity analysis (Sobol, global sensitivity analysis), where they can calculate the first-order sensitivity index and total effect index? Using this technique, the authors would be able to determine the key metabolites while allowing for metabolic responses to perturbations without unnatural constraints. 

Thank you for the useful suggestion for studying the roles of each metabolite for responsiveness. We have computed the total sensitivity index (Homma and Salteli, 1996) for each metabolite of each model (Fig.S5). The total sensitivity indices of ATP are high-ranked in Khodayari- and Chassagnole model, while it is middle-ranked in the Boecker model. We believe that the importance of the adenyl cofactors is highlighted also in terms of the Sobol’ sensitivity analysis (the figure is referred in Lines 193-195). 

We have encountered a minor difficulty for computing the sensitivity index. For the computation of the sensitivity index, we need to carry out the following Monte Carlo integral, 

where the superscript (m) is the sample number index. The subscript i represents the ith element of the vector x, and ~i represents the vector x except for the ith element. The tilde stands for resampling.  

There are several conserved quantities in each model. For independent resampling, we need to deal with the conserved quantities. For the Boecker and Chassagnole models, we picked a single metabolite from each conservation law and solved its concentration algebraically to make the metabolite concentration the dependent variable. Then, we can resample the metabolite concentration of one metabolite without changing the concentrations of other metabolites, which are independent variables.  

However, in the Khodayari model, it was difficult to solve the dependent variables because the model has about 800 variables. Therefore, we gave up the computations of the sensitivity indices of the metabolites whose concentration is part of any conserved quantities, namely NAD, NADH, NADP, NADPH, Q8, and Q8H2.

(2) To follow up on the previous remark, the authors state that the metabolites that augment the response coefficient when their concentration is fixed tend to be allosteric regulators. The authors should report which allosteric regulations are implemented in each of the models so that one can compare against Figure 2. Again, the effect of allosteric regulation by a specific metabolite that is quantified the way the authors did is biased by fixing the concentration value - it is true that negative feedback is broken when the metabolite concentration is fixed, however, in the rate law, there is still the fixed inhibition term with its value corresponding to the inhibition at the steady state. To see the effect of allosteric regulation by a metabolite, one can change the inhibition constants instead of constraining the responses with fixed concentrations.  

We have listed the substrate-level regulations (Table S1-3). Also, we re-ran the simulation with reduced the effect of the substrate-level regulations for the reactions that are suspected to influence the change of the response coefficient. Instead of fixing the concentrations (Fig. S6). 

The impact of substrate-level regulations is discussed in Lines 203-212.   

We replaced "allosteric regulation" with "substrate-level regulation" because we noticed that some regulations are not necessarily allosteric.

(3) Given the role of ATP in metabolic processes, the authors' finding of the sensitivity of the three networks' responses to perturbations in the AXP concentrations seems reasonable. However, drawing such firm conclusions from only three models, with each of them built around one steady state and having one kinetic parameter set despite that they were built for different physiologies, raises some questions. It is well-known in studies related to basins of attraction of the steady states that the nonlinear responses also depend on the actual steady states, the values of kinetic parameters, and implemented kinetic rate law, i.e., not only on the topology of the underlying systems. In the population of only three models, we cannot exclude the possibility of overlaps and strong similarities in the values of kinetic parameters, steady states, and enzyme saturations that all affect and might bias the observed responses. Ideally, to eliminate the possibility of such biases, one should simulate responses of a large population of models for multiple physiologies (and the corresponding steady states) and multiple parameter sets per physiology. This can be a difficult task, but having more kinetic models in this work would go a long way toward more convincing results. Recently, E. coli nonlinear kinetic models from several groups appeared that might help in this task, e.g., Haiman et al., PLoS Comput Biol, 17(1): e1008208, (2021), Choudhury et al., Nat Mach Intell, 4, 710-719, (2022); Hu et al., Metab Eng, 82, 123-133 (2024), Narayanan et al., Nat Commun, 15:723, (2024). 

We have computed the responsiveness of 215 models generated by the MASSpy package (Haiman et al, 2021). Several model realizations showed a strong responsiveness, i.e. a broader distribution of the response coefficient (Fig.S8), and mentioned in Lines 339-341.

We would like to mention that the three models studied in the present manuscript have limited overlap in terms of kinetic rate law and, accordingly, parameter values. In the Khodayari model, all reactions are bi-uni or uni-uni reactions implemented by mass-action kinetics, while the Boecker and Chassagnole models use the generalized Michaelis-Menten type rate laws. Also, the relationship between the response coefficients of the original model and the linearized model highlights the differences between the models (Fig. S1). If the models were somewhat effectively similar, the scatter plots of the response coefficient of the original- and linearized model should look similar among the three models. However, the three panels show completely different trends. Thus, the three models have less similarity even when they are linearized around the steady states. 

(4) Can the authors share their insights on what could be the underlying reasons for the bimodal distribution in Figure 1E? Even after adding random reactions, the distribution still has two modes - why is that?  

We have not yet resolved why only the Khodayari model shows the bimodal distribution of the response coefficient. However, by examining the time courses, the dynamics of the Khodayari model look like those of the excitable systems. This feature may contribute to the bimodal distribution of the response coefficient. In the future, we would like to show whether the system is indeed the excitable system and whcih reactions contribute to such dynamics.

(5) Considering the effects of the sparsity of the networks on the perturbation responses (from line 223 onwards), when we compare the three analyzed models, it is clear that the Khodayari et al. model is a superset of the other two models. Therefore, this model can be considered as, e.g., Chassagnole model with Nadd reactions (though not randomly added). Based on Figures 1b and S2, one can observe that the responses of the Khodayari models have stronger responses, which is exactly opposite to the authors' conclusion that adding the reactions weakens the responses.

The authors should comment on this.  

The sparsity of the network is defined by the ratio of the number of metabolites to the number of reactions. Note that the Khodayari model is a superset of the Boecker and Chassagnole models in terms of the number of reactions, but also in terms of the number of metabolites (Boecker does not have the pentose phosphate pathway, Chassagnole does not have the TCA cycle, and neither has oxyative phosphorylation). Thus, even if we manually add reactions to the Boecker model, for example, we cannot obtain a network that is equivalent to the Khodayari model.  We added one sentence to clarify the point (Lines 254-255).

Recommendations for the authors: 

(1) Some typos: Line 57, remove ?; Line 134, correct "relaxation". 

Thank you for pointing out. We fixed the typos.

(2) Lines 510-515, please rewrite/clarify, it is confusing what are you doing. 

We rewrote the sentences (Lines 529-532). We are sorry for the confusion.

(3) Line 522, where are the expressions above Leq and K*? 

Leq appears in the original paper of the Boecker model, but we decided not to use Leq. We apologize for not removing Leq from the present manuscript. The * in K* is the wildcard for representing the subscripts. We added a description for the role of “*”. 

(4) Lines 525-530, based on the wording, it seems like you test first for 128 initial concentrations if the models converge back to the steady state and then you generate another set of 128 initial concentrations - is this what you are doing, or you simply use the 128 initial concentrations that have passed the test? 

We apologize for the confusion. We did the first thing. We have rewritten the sentence to make it clearer. 

(5) Figure 3, caption, by "broken line," did the authors mean "dashed line"? 

We meant dashed line. We changed “broken line” to “dashed line”.

Why was marijuana so popular and mainstream in the past???

Diaspora: the jews living outside Israel (https://www.merriam-webster.com/dictionary/diaspora)

Interesting musical roots for Reggae... Wonder if this is still present?

Mystical roots.

(Note, I give this the fiction tag because I might want to look into this mystical religion for fiction writing as inspiration)

Logical that marijuana (a drug) is correlated with the mystical concept of communicating with diseased spirits for marijuana makes you hallucinate (or perhaps it's demonic in nature?)

Main early pioneers: - Toots and the Maytals (band) - Wailers (band)

Notable members of these bands: Toots and Maytals - Paul Douglas - Radcliffe "Dougie" Bryan - Jackie Jackson - Carl Harvey - Marie "Twiggi" Gitten - Stephen Stewart - Charles Farquarson - Frederick "Toots" Hibbert - Henry "Raleigh" Gordon - Nathaniel "Jerry" Matthias - Hux Brown - Harold Butler - Michelle Eugene - Winston Wright - Winston Grennan - Andy Bassford - Leba Hibbert - Thomas Copied from: https://en.wikipedia.org/wiki/Toots_and_the_Maytals

Wailers: * Aston Barrett Jr. * Owen "Dreadie" Reid * Josh David Barrett * Glen DaCosta * Andres Lopez * Junior Jazz * Aston "Familyman" Barrett * Donald Kinsey * Junior Marvin * Carlton Barrett * Alvin "Seeco" Patterson * Tyrone Downie * Earl "Wire" Lindo * Al Anderson * Gary "Nesta" Pine * Joe Yamanaka * Elan Atias * Anthony Watson * Chico Chin * Everald Gayle * Irvin "Carrot" Jarrett * Brady Walters * Basil Creary * Keith Sterling * Kevin "Yvad" Davy * Ras Mel Glover * "Drummie Zeb" Williams * Audley Chisholm * Koolant Brown * Dwayne Anglin * Ceegee Victory * Javaughn Bond * Shema McGregor Copied from: https://en.wikipedia.org/wiki/The_Wailers_Band

Other notable pioneers: - Bob Marley

Reggae is known to have depth and meaning to its tracks due to tackling of social and economic issue as well as injustice in general.

Reggae came to be during a time of subjugation to Britain?

Emerged in Jamaica

she would not be able to see this

do they though?

somewhere outside the shop, a person screamed.

why is she with him?

'the shop was eerily quiet except for its ambient music'

syntax is off here - 'the great cruise ships, that had once polluted the city's harbours, docked elsewhere'

this is very interesting but too expositional here i think

you use this tense a lot and you don't need to. just say 'the tram arrived'

'with her index finger, she slowly traced the outline of her features'

this seems inconsistent with her character

inconsistent voice here

this is funny but it takes away from the tension in the scene

how would she know this?

Jamaica became independent in 1962