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Thanks for addressing all comments!
[# PeerJ Staff Note - this decision was reviewed and approved by Valeria Souza, a PeerJ Section Editor covering this Section #]
Seem to revise following the other reviewer's comments already.
no comment
no comment
The manuscript is now satisfactory after a second revision. The authors addressed my concerns.
The manuscript is now satisfactory after a second revision.
The manuscript is now satisfactory after a second revision.
Please properly address comments of reviewer 3.
no comment
no comment
no comment
The authors have well addressed all of my concerns and I have no further comments.
This is a re-review so please see the attached pdf which explains the parts of the manuscript where the authors did not address my concerns.
see attachment
see attachment
see attachment
Please address all reviewers' comments.
**PeerJ Staff Note:** Please ensure that all review, editorial, and staff comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
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1. The study is generally lacking in a clear experimental design or key research questions. The approach is highly exploratory and descriptive, lacking a hypothesis, and does not represent a clear advance to the field. It is not clear why or what benefit would be gained based on the monitoring strategy. Several broad, unsubstantiated statements and assumptions are made throughout the manuscript.
2. Tables 1–2 are clear but could be moved to supplementary materials to improve readability.
3. Line 166: Welch’s t-test/Wilcoxon are appropriate, but clarify why both were used.
There is some merit in the results; however, the authors fail to write a starting sentence after the abstract. I request that the authors rewrite the entire manuscript, reduce some redundant images (i.e., too many supplemental figures), and resubmit, or else the current reporting does not meet the academic writing standard.
For example,
In abstract:
-Background and Methods writings are poor. Need to improve both the writing details and the writing style.
-Results: Rewrite to add statistics, i.e., AMOVA, P-value = xx. Must improve the result writing to show meaningful result description, not just narration.
-Conclusion: Rewrite to conclude drawn from the results.
OK.
Improve impact and novelty in writing. Again, the data are ok, but the writing in each section requires significant improvement and adds more details and critical discussions.
Will reconsider after the authors significantly improve the writing of the entire manuscript. Please rewrite and resubmit.
This is a review of the manuscript “Effect of rainfall on metagenomics in a sewage environment in Hongta District, Yuxi City, Yunnan Province.”
The goals of this study were clear, the data and topic quite interesting, and the paper was easy to follow. However, to be accepted for publication, several items will need to be addressed, including adding details, metadata, and, if available, code used for analysis. The figures can also be improved. But if this is done, this could be a valuable contribution to the literature.
The sampling design and general approach are good, though I have some issues with the lack of detail presented in the method, which I discuss below.
KEGG Analyses: The KEGG analysis results of Figure 5 are very strange. I know it is a commonly used database, but the results make no sense and don’t seem to have any relevance to the system. Microbial communities do not have immune, cardiovascular, digestive, or neurodegenerative systems. The authors say that these matches indicate the functions they found are indicative of “disease” (I presume they mean functions that cause disease in humans), but I don’t think this is the case. These are matches of genes in bacteria to similar genes involved in animal systems, like the immune system, not a bacterial disease that affects the immune system.
It would be much better to match these microbial genes with the bacterial KEGG pathways, which I think they do in the supplemental?
But it would be much more interesting and relevant if the authors showed the results of their VFPB virulence factors (pathogenicity genes) analysis, which they say they do in the methods. The VFPB database is a good source of virulence factors, and it would be great to see if there were differences in virulence genes between the urban and rural runoff.
Bottom line: The authors should replace Figure 5 KEGG analysis with something more relevant, with a focus on the virulence factors per se. And they should test whether the number of virulence factors is higher during Plow than in Phigh. (Replace figure 5 and discuss the new results, Lines 264-270.
Also, the authors saw that they used a lot of databases (not sure how they were matched), but I only see KEGG analysis results.
Corrections for multiple comparisons: The other thing that needs to be addressed is the lack of correction for the number of tests. Figures 5 and 7 have over 25 independent tests, and I do not see any evidence that these have been adjusted for multiple comparisons in the manuscript. This is easy to correct in R using FDR adjustment (False discovery rate) for the number of comparisons. With a P-value of 0.05, every 20th test will be significant by random chance, which is why you need to adjust the P-values.
Compositional data analysis:
Data availability: I was pleased to see that the authors uploaded the metagenomes. However, to make the analyses reproducible, readers need to know more information about the codenames. The SRA entries have the code names that are in the paper, but no information about the metadata associated with this codename. Urban or Rural? High or low rainfall? Sample data and location?
The authors need to add a text file (.tsv or .csv) that gives us information as a supplement. It should look something like this, but have the relevant data.
The metadata/mapping file should look something like this, with as much metadata as they have.
Sample_ID Accession Location Date Rainfall Type
24HT0101 SRX25422668 Hongta ?? High Urban
23HT1201 SRX25422667 Hongta ?? Low Rural
Code Availability: If the authors have programming notebooks, these should be deposited along with the mapping/metadata file. I would prefer that both be put in an open repository like Zenodo.org, which offers a DOI. Python and R code.
Also, I was curious why the authors didn't try to assemble the contigs into MAGs using MaxBin or similar software? Was that attempted, and it failed? The diversity is very high in these systems...
Figure 1 and Figure 2: The text at the bottom of the legends is very small and should be increased. To save vertical space, it could be written horizontally. The other thing about these figures is that they have no information on the types of samples collected. Can the tree be color-coded by rainfall or urban/rural, or something? Not clear what we are supposed to learn from these figures.
Figure Legends: In general, the figure legends do not have very much detail. They are part of the results, and they should explain the figures or something about the figures. The different parts should be explained and described. Figure 3, for example, doesn’t say what a and b are. In figures 1 and 2, what is g_p_? Are there any interesting clusters? The short descriptions are fine for the supplemental figures, but not the main ones.
Figure 3: The text is very hard to read. I suggest changing the white text on gray to black on gray. Also, the numbers on the axes are hard to read – change them to black too. For the legend on the side.
It would also be better, instead of a group for part a, you used “Location,” and the legend was:
Location
North
Central
For part b, use Precipitation instead of group
Precipitation
High
Low
Figures 4, 6, and 8: The text is small and hard to read. Increase font size and make the text darker.
How were the taxonomic analyses completed? I do not see a description of how this was done in the methods. Was this done using Metamark? Against what database? I’m not sure what the authors mean by unigenes. There is a UniGene database (NCBI). Was this used? More detail is needed in the methods.
How were the ARGs found? It looks like you used the CARD database. Was all of it used? Or only parts of it? This database has different sections.
Labeling: Plow1 and Phigh1: Why does this have a number after it? I think there is only one group of each (There is no Plow2)
Maybe just use PLow and PHigh.
Two papers that were not mentioned that have to do with metagenomics, sewage, ARGs, and rainfall that should be cited and discussed: There were a surprising number of similar taxa (Acinetobacter, Comamonas, Pseudomonas) found.
https://www.mdpi.com/1660-4601/20/1/600
https://www-sciencedirect-com.libproxy.sdsu.edu/science/article/pii/S0269749123020699
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