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PONE-D-23-05789Identifying Essential Genes in Genome-Scale Metabolic Models of Consensus Molecular Subtypes of Colorectal CancerPLOS ONE

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Reviewers' comments:

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors investigate acquired metabolic sensitivity in different types of colorectal cancer. They use a published definition to assign samples retrieved from the TCGA database into 4 specific and 1 unknown molecular subtype, based on gene expression. They then use this data to reconstruct 6 genome scale metabolic models (5 disease and 1 healthy) and to score their metabolic reactions into 4 confidence levels. They set up a complex hierarchical multi-objective optimization problem to simultaneously estimate the flux in the cells and their acquired sensitivity to knock outs of reactions. This involves solving a flux balance analysis (FBA) problem while minimizing the L2 norm of fluxes weighted by the calculated confidence. The effects of simulated knockouts are graded by their effects on 3 metrics corresponding to 1) the difference in flux between healthy and diseased; 2) the effect on growth and ATP synthesis in the cancer model; 3) the same for healthy model. While the work seems technically sound, it is not clear if its assumptions can be biologically justified and thereby if it has relevance for addressing sensitivity in cancer. Its advantages over current approaches are not quantified.

Major comments

The approach relies on gene expression data to decide on reaction inclusion. While this is commonly done, it should be noted that genes must be translated to proteins for reactions to occur and differences in protein stability and other effects may influence protein levels so that mRNA levels do not accurately reflect them. Furthermore, an expression level below the limit of detection does not necessarily imply that a protein is inactive, since catalytic efficiency varies by orders of magnitude. For tumor samples, genes expression levels may be strongly influenced by the presence of infiltrating immune cells, so observed differences between healthy- and diseased samples may not necessarily reflect changes in the colon cells. It may be appropriate to discuss these limitations in the data used.

Genome scale metabolic models are underdetermined and there is a large range of flux distributions that may satisfy an objective function. The predicted fluxes are therefore dependent on the modeling choices. Often minimization of the L1 norm (parsimonious FBA) is used to select a unique solution. In this work a weighted L2 norm is used. It is based on the 4 confidence levels derived from gene expression data and they are weighted with factors (0.25, 0.5, 0.75, 1). This weighing is a modeling decision, and it could be of interest to show how the results would be affected by different choices. In particular since the difference between healthy and diseased fluxes, is used in the evaluation and may be affected. One alternative to quantized confidence scores would be a continuous value.

The objective function for healthy cells is set to maximize ATP synthesis. It is unclear if this is a realistic assumption about what constitutes a healthy state. Furthermore, in absence of limitations on uptake reactions and other constraints, FBA will increase the flux through the objective function until it reaches one of the artificial bounds on internal fluxes (often -1000 or 1000). Is this occurring in these simulations or is this prevented somehow. TAre the predicted fluxes reasonable for human cells? Fluxes of around 13.649 mmol/gDW h, are reported, is this level in line with published values for healthy colon cancer cells? It could also be interesting to comment on the difference between culture medium and the in vivo microenvironment, where perhaps metabolites, such as cholesterol, may be present.

The abstract discusses that gene essentiality prediction is a time-consuming process. However, a brute force method is used in this study to validate the essentiality predictions. It would be useful to quantify the speed up that can be achieved with the proposed approach.

In table 4 the cell mortality grade is reported. It appears to be strikingly similar for all genes within each molecular subtype. Is there an explanation to this, e.g. how the objective function is set up. For the metabolic deviation grade metric, the fluxes of the cancer cell model is compared with the healthy flux distribution. Is it always the same flux distribution for the healthy or does it vary between models? It is stated that one objective is to maximize the cell viability of perturbed healthy cells during gene knockout or deletion. Why is it assumed that healthy cells will be resistant to perturbations?

The model makes predictions about gene essentiality and the predictions are compared with observations in DepMap. It would be useful to quantify the prediction performance, e.g. in the form of a confusion matrix (with true and false positives) and to apply an appropriate statistical tests. It could also be interesting to speculate on what may be the reason that some predicted essential genes were not essential in DepMap.

What was the rational for perturbing essential genes together with non-essential?

Minor comments

It is hard to tell from the methods if any work was done in this study to classify the TCGA samples. If the classification from Guinney 2015 was used without modification, then the corresponding methods section could be simplified.

Table 3 does not appear to be showing what was intended, since the first 2 lines for each species show the exact same value, even though there seems to be multiple unique entities for each model.

It implied that cells with 0 growth rate are dead, however cells can also enter a state of senescence where they neither grow nor die.

It is mentioned that “inhibition of formate production reduced intracellular cholesterol synthesis and thereby eliminated the growth of cancer cells”. Why is growth eliminated by a mere reduction in biosynthesis rate?

The use of reaction identifiers specific to the recon model, such as ‘R_OMPDC’ may perhaps not be very informative to most readers.

Perhaps the sentence "Formate is an intermediate metabolite in one-carbon metabolism, and acts as the source and sink in mammalian cell metabolism" may need further clarification about what is considered source and sink.

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Reviewer #1: No

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Identifying Essential Genes in Genome-Scale Metabolic Models of Consensus Molecular Subtypes of Colorectal Cancer

PONE-D-23-05789R1

Dear Dr. wang,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Bashir Sajo Mienda, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have addressed the comments. It should perhaps be noted that while the language of the manuscript is generally clear, the revised sections would benefit from proofreading by a native speaker.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

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