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Record W6967573254 · doi:10.5281/zenodo.10926278

PREreview of "Global diversity and evolution ofSalmonellaPanama, an understudied serovar causing gastrointestinal and invasive disease worldwide: a genomic epidemiology study"

2024· peer-review· en· W6967573254 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2024
Typepeer-review
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsSalmonella entericaSerotypeEpidemiologySalmonellaGlobal healthPopulationDiversity (politics)Molecular epidemiologyGenomics

Abstract

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This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/10926278. We, the students of MICI5029/5049, a Graduate Level Molecular Pathogenesis Journal Club at Dalhousie University in Halifax, NS, Canada, hereby submit a review of the following BioRxiv preprint: Global diversity and evolution of Salmonella Panama, an understudied serovar causing gastrointestinal and invasive disease worldwide: a genomic epidemiology study Caisey V. Pulford, Blanca M. Perez-Sepulveda, Danielle J. Ingle, Rebecca J. Bengtsson, Rebecca J. Bennett, Ella V. Rodwell, Maria Pardos de la Gandara, Charlotte Chong, P. Malaka De Silva, Magali Ravel, Véronique Guibert, Elisabeth Njamkepo, Neil Hall, Marie A. Chattaway, Benjamin P. Howden, Deborah A Williamson, Jay C. D. Hinton, François-Xavier Weill, Kate S. Baker bioRxiv 2024.02.09.579599; doi: https://doi.org/10.1101/2024.02.09.579599 We will adhere to the Universal Principled (UP) Review guidelines proposed in: Universal Principled Review: A Community-Driven Method to Improve Peer Review. Krummel M, Blish C, Kuhns M, Cadwell K, Oberst A, Goldrath A, Ansel KM, Chi H, O'Connell R, Wherry EJ, Pepper M; Future Immunology Consortium. Cell. 2019 Dec 12;179(7):1441-1445. doi: 10.1016/j.cell.2019.11.029 SUMMARY: Nontyphoidal Salmonella (NTS) disease caused by Salmonella enterica serovars represents a global health burden associated with gastrointestinal disease. Most research is focused on serovars Typhimurium and Enteritidis that cause NTS in sub-Saharan Africa; however, the relatively understudied S. enterica serovar Panama (S. Panama) causes NTS cases globally. Pulford, et al. (2024), present a genomic epidemiology study investigating antimicrobial resistance (AMR) trends across defined population groups (clades) of S. Panama. Through this work they also speculated on the evolution of the serovar and proposed assessing genetic markers for invasiveness found within the clades of S. Panama. To complete this research, they complied a robust dataset of 836 sequenced genomes of S. Panama isolates collected over 88 years from 45 countries across 6 continents. Four population groupings or clades of the isolates were defined using phylogenetic relationships and Bayesian Analysis of population structure. This analysis revealed regional clustering in the phylogenetic tree, as demonstrated by the high number of isolates in each clade from the same geographic area (Latin America & Caribbean, Martinique, Europe or Asia & Oceania). Next, the genomes were scanned for genes and mutations known to result in reduced antimicrobial susceptibility to determine AMR trends across the clades. Only 14.5% of isolates were identified as having resistance, and the majority of those fell within either the European or Asia/Oceania clades (C2 & C4). It was found that AMR genes clustered on plasmids within the genomes suggesting plasmid mediated resistance within S. Panama. To address the evolutionary history of the serovar, they determined the most recent common ancestor (MRCA) of all the clades and each clade individually to show when the serovar emerged (1500s) and when it was more recently introduced to various regions (1870-1890s). Finally, they used an 'invasiveness index' (based upon the assumption mutations in certain genes correspond to increased invasiveness) to demonstrate that the European clade (C2) had a significantly higher invasiveness index than the other clades. OVERALL ASSESSMENT: This study provides an excellent example of the application of genomic epidemiology techniques to an understudied serovar of S. enterica. The authors generated and thoroughly investigated a large dataset to provide meaningful insights into AMR within the S.Panama serovar. The data presented will be very helpful in public health monitoring of this serovar in the future. Here, we provide the authors with feedback to shift how some data is presented/discussed to strengthen the paper for both a public health audience and researchers looking to build upon this study. STRENGTHS: The authors have completed a solid genomic epidemiology study into S. Panama using the standard bioinformatic methodologies in the field. The phylogenic grouping into clades corresponding to geographic groups is well done. The prevalence of AMR in the serovar (specifically located on plasmids) and certain clades is convincing. Overall, the paper is well written, sufficient background information and rationale is provided, and the results are easily understood, all of which make the study more accessible to a generalist audience. WEAKNESSES: There seems to be an assumption that S. Panama originated in Panama made throughout this paper; providing evidence of this specific origin would lend credence to their phylodynamic analysis. The main take away points regarding plasmid-mediated AMR are underemphasized compared to the less convincing invasiveness index findings. DETAILED U.P. ASSESSMENT: OBJECTIVE CRITERIA (QUALITY) 1. Quality: Experiments (1–3 scale; note: 1 is best on this scale) SCORE = 1.5 Figure by figure, do experiments, as performed, have the proper controls? [note: we use this 'figure-by-figure' section for broader detailed critiques, rather than only focusing on controls]. · Fig 2: Overall, the figure is very informative, but the use of different scales for each clade makes it more challenging to compare between clades. Potentially, making a condensed version of the figure using stacked bar chart for main text and moving the original to supplemental could aid comprehension. · Fig S2: This figure was well done and effectively demonstrates how AMR spread through the European clade. Perhaps this figure could be moved from supplementals to main text. · Fig 3: It appears that the tool used for data visualization for this figure is not properly cited. Any other tools used for data visualization should also be cited. · Fig S3: When discussing this figure within the text, including the distinction between significance difference and meaningful difference would be beneficial. Also, including the machine learning model (cited in this results section) features/criteria used to make choices would be helpful (further mentioned in a comment below). Are specific analyses performed using methods that are consistent with answering the specific question? · Is there appropriate technical expertise in the collection and analysis of data presented? · Yes, appropriate technical expertise is demonstrated throughout. · Do analyses use the best-possible (most unambiguous) available methods quantified via appropriate statistical comparisons? · Yes, the analyses were well done throughout the paper. · Are controls or experimental foundations consistent with established findings in the field? A review that raises concerns regarding inconsistency with widely reproduced observations should list at least two examples in the literature of such results. Addressing this question may occasionally require a supplemental figure that, for example, re-graphs multi-axis data from the primary figure using established axes or gating strategies to demonstrate how results in this paper line up with established understandings. It should not be necessary to defend exactly why these may be different from established truths, although doing so may increase the impact of the study and discussion of discrepancies is an important aspect of scholarship. · Generally strong throughout the manuscript. 2. Quality: Completeness (1–3 scale) SCORE = 1.5 · Does the collection of experiments and associated analysis of data support the proposed title- and abstract-level conclusions? Typically, the major (title- or abstract-level) conclusions are expected to be supported by at least two experimental systems. Within the evolutionary timescales section of the results, a concluding point is that the predicted timeframes of MRCA of C3 and C4 coincide with the construction of the Panama Canal. However, it appears that the MRCA timeframes of C1 and C2 also overlap with the construction of the Panama Canal. Also, there is no additional discussion or speculation on the implications of this finding. Explanation or justification as to why the only the MRCAs of C3 and C4 but not C1 and C2 were mentioned would be helpful. Additional discussion or speculation on the significance of finding the MRCAs coinciding with the construction of the Panama Canal would strengthen the concluding the statement of this section. The conclusions made regarding the AMR data and genes found on plasmids and the implications for how these genes are being acquired was quite convincing. In comparison, the conclusions from the invasiveness index are less convincing (see comment on Fig S3 above and comment below). As such, by putting more emphasis on the AMR data and a little less on the invasiveness index would strengthen the paper. · Are there experiments or analyses that have not been performed but if ''true'' would disprove the conclusion (sometimes considered a fatal flaw in the study)? In some cases, a reviewer may propose an alternative conclusion and abstract that is clearly defensible with the experiments as presented, and one solution to ''completeness'' here should always be to temper an abstract or remove a conclusion and to discuss this alternative in the discussion section. · N/A 3. Quality: Reproducibility (1–3 scale) SCORE = 1 · Figure by figure, were experiments repeated per a standard of 3 repeats or 5 mice per cohort, etc.? · NA · Is there sufficient raw data presented to assess the rigor of the analysis? · Yes · Are methods for experimentation and analysis adequately outlined to permit reproducibility? · Yes. However, see comment on transparency of machine learning model (below). · If a ''discovery' dataset is used, has a ''validation' cohort been assessed and/or has the issue of false discovery

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.996
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.002
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0670.036

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.121
GPT teacher head0.302
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainEvaluation
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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