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Record W3210679426 · doi:10.5281/zenodo.3702901

Review of In Vivo Dual RNA-Seq Analysis Reveals the Basis for Differential Tissue Tropism of Clinical Isolates of Streptococcus pneumoniae

2020· article· en· W3210679426 on OpenAlexaboutno aff
Craig McCormick

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typearticle
Languageen
FieldMedicine
TopicInfective Endocarditis Diagnosis and Management
Canadian institutionsnot available
Fundersnot available
KeywordsTropismStreptococcus pneumoniaeTissue tropismIn vivoBiologyMicrobiologyDual (grammatical number)VirologyDifferential (mechanical device)VirusAntibioticsGenetics

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/3702901. 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: Vikrant Minhas, Rieza Aprianto, Lauren J. McAllister, Hui Wang, Shannon C. David, Kimberley T. McLean, Iain Comerford, Shaun R. McColl, James C. Paton, Jan-Willem Veening, Claudia Trappetti. In Vivo Dual RNA-Seq Analysis Reveals the Basis for Differential Tissue Tropism of Clinical Isolates of Streptococcus pneumoniae. BioRxiv 862755; doi: https://doi.org/10.1101/862755 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. https://doi.org/10.1016/j.cell.2019.11.029 SUMMARY: Previous work has shown that two genetically related clinical isolates (947-Ear RafRD249 isolate and 4559-Blood RafRG249 isolate) of Streptococcus pneumoniae serotype 14 ST15 display differences in virulence in the mouse lung following intranasal challenge. These different virulence phenotypes of the two clinical isolates correlated with non-conserved single nucleotide polymorphisms (SNPs) in the raffinose pathway regulatory gene rafR (RafRD249G). In this study, Minhas et al. investigated the effects of the RafRD249G SNP on bacterial and host transcriptomes in infected lungs using a dual RNA-seq approach. They observed differential expression of genes encoding sugar transporters that fine-tune carbohydrate metabolism, which were driven by the RafRD249G SNP. This contributed to distinct host niche specialization for the two clinical isolates. RNA-seq analysis of the host response to the S. pneumoniae infection suggested that the RafRD249G SNP in bacteria caused differential expression of host genes encoding for multiple cytokines, cytokine receptors, chemokines and chemokine ligands. Particularly, the expression of IL-17-related genes was enriched in the murine lungs infected with the 947-Ear isolate carrying the RafRD249 SNP compared to the 947-Ear isolate carrying the RafRG249 SNP. Using in vivo neutrophil depletion and IL-17A neutralization, the authors found that IL-17-induced neutrophil recruitment was partially responsible for the more efficient clearance of isolates of 947-Ear RafRD249 observed in the murine lungs. Overall, this study showed that the RafRD249G SNP had a significant impact on bacterial and host transcriptomes, leading to distinct virulence phenotypes and host response. The use of dual RNA-seq in this study provided a powerful approach for investigating in vivo host-pathogen interactions. OVERALL ASSESSMENT: STRENGTHS: The authors conducted a transcriptomic study on the impact of a non-conservative SNPs RafRD249G on host cells and two S. pneumoniae isolates (ear and blood) using a dual RNA-seq approach in murine lung infection. Their RNA-seq data strongly suggested that the RafRD249G SNP between the two isolates caused distinct patterns of gene expression. Moreover, the dual RNA-seq on the host side showed that the RafRD249G SNP also played a detrimental role in trigging differential host responses in infected lung. This study provides solid evidence that the dual RNA-seq approach is a useful tool for in vivo study of complex host-pathogen interactions. WEAKNESSES: There are a few weaknesses in the data analysis, data presentation, and experimental design. The lack of information in RNA-seq data analysis made it difficult for the reviewers to evaluate data quality. Some of the figure labels are difficult to interpret. In certain instances, methods were not described in sufficient detail to enable others to reproduce experiments. In this study, the RNA-seq data suggested an association between IL-17 expression and the RafRD249G SNP in bacteria. However, the conclusion that RafRD249G SNP was responsible for bacterial clearance via IL-17-mediated neutrophil recruitment was insufficiently supported. This could be improved by using appropriate controls, testing neutrophil recruitment in murine lung, and comparing data within each treatment. Moreover, we found that some of the writing in the manuscript was misleading and confusing. We believe that some of the weaknesses mentioned above could be solved by simplifying and clarifying the data presentation and interpretation. DETAILED U.P. ASSESSMENT: OBJECTIVE CRITERIA (QUALITY): 1. Quality: Experiments (1-3 scale) SCORE = 1.5 Figure by Figure, do experiments, as performed, have the proper controls? · Figures 2B and 2C: There appears to be an enrichment in higher fold changes on either side of the replication origin. Have the authors considered whether cell populations might be rapidly growing, which could provide multiple copies of the origin per cell, thereby clouding interpretation? · Figure 5: The gating strategy for multi-color flow cytometry analysis should be provided. · Figure 5 and 6A: We think adding a mock infected negative control or showing the data of time zero as the base line is necessary to interpret this data. Are specific analyses performed using methods that are consistent with answering the specific question? Is there the appropriate technical expertise in the collection and analysis of data presented? · In Figure 2&3, the meaning of "member genes" and "non-member genes" needs to be more clearly explained in the Methods and Figure Legends. · In Figure 2 panels D, E and F and Figure 3 panels D, E and F, we think it would be easier to follow the data presentation if only the differentially expressed genes were shown as the fold enrichment in a bar graph instead of the dot plot. The rest of the granular dot plot data can be presented in supplementary material. Do analyses use the best possible (most unambiguous) available methods, quantified via appropriate statistical comparisons? · OK 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 2 examples in the literature of such results. To address 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. · OK 2. Quality: Completeness (1-3 scale) SCORE = 2 Does the collection of experiments and associated analysis of data support the proposed title/abstract-level conclusions? Typically, the major (title or abstract level) conclusions are expected to be supported by at least two experimental systems. · It was clear that the bacterial clearance of both isolates was partially due to neutrophil recruitment to the infected lung. It was also clear that the different bacterial loads between the two isolates in lungs at 24h post infection was driven by the RafR SNP. However, the direct evidence to support the conclusion that the IL-17 expression and its downstream neutrophil recruitment in the infected lung contributed to the RafR SNP-mediated phenotypic differences between the two isolates was not convincing. We believe that there are few points in the data presentation and interpretation (as listed below) that need to be further clarified or explained. · There were two variables (different isolate and different neutrophil treatment) between "9-47-Ear isolate with anti-mouse Ly6G treatment" and "4559-Blood isolate with isotype control treatment", as well as between "4559M isolate with anti-mouse Ly6G treatment" and "4559-Blood isolate with isotype control treatment" in Figure 6C. We feel the authors need to address the rationale of comparing the bacterial burden of "9-47-Ear isolate with anti-mouse Ly6G treatment", "4559M isolate with anti-mouse Ly6G treatment" and "4559-Blood isolate with isotype control treatment" (lines 347-348). · The authors should clarify the comparison that was described on lines 340-342 since it was not shown in Figure 6C. · We would like to see the comparison of the bacterial loads between lungs infected with different isolates under each treatment, especially under anti-mouse Ly6G treatment and anti-IL17A treatment. We would also like to see whether the difference of bacterial loads between isolates changes when neutrophils are depleted or IL-17A is neutralized. · In line 262-266, the authors wrote that "Common differentially expressed genes of this function include genes encoding interleukin 17F (Il17f) and chemokine ligands (Cxcl2, Cxcl3 and Ccl20), with ascending expression level of response to 9-47M, 9-47-Ear and 4559-Blood". The authors need to indicate the corresponding figure. If it was Figure 3C, the comparison group B (9-47-Ear to 4559-Blood) did not show significant difference. · We think it is necessary to test the neutrophil recruitment in infected and mock-infected lung with/without anti-IL17A treatment. 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/abstract that is clearly defensible with the experiments as presented, and one solution to 'completeness' here should always be to temper an

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)0.018
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.002
Science and technology studies0.0020.002
Scholarly communication0.0050.002
Open science0.0030.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0100.010

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 head0.063
GPT teacher head0.340
Teacher spread0.277 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

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Published2020
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