Review of Exploring the interaction network of a synthetic gut bacterial community

This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/4768664. 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: Exploring the interaction network of a synthetic gut bacterial community. Anna S. Weiss, Anna G. Burrichter, Abilash Chakravarthy Durai Raj, Alexandra von Strempel, Chen Meng, Karin Kleigrewe, Philipp C. Münch, Luis Rössler, Claudia Huber, Wolfgang Eisenreich, Lara M. Jochum, Stephanie Göing, Kirsten Jung, Alvaro Sanchez, Bärbel Stecher. bioRxiv 2021.02.25.432904; doi: https://doi.org/10.1101/2021.02.25.432904 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: Weiss et al., examine the growth rates, metabolic usage, and interaction patterns of the Oligo-Mouse-Microbiota synthetic community in an in vitro setting. They use this bottom-up approach to investigate the different metabolites used by various strains in the community, and the overlap of these metabolites between strains. They also show that some microbes like Enterococcus faecalis have strong impacts on the growth rates of other members within the community. Examination of E. faecalis specifically goes on to show that it could be due to cross-feeding events as well as the production of bacteriocins. Overall, the work shows that metabolite usage profiles along with the production of antimicrobials by community members can have a large impact on the observed community abundances in an in vitrosetting. OVERALL ASSESSMENT: STRENGTHS: -The authors provide several interesting interactions in their in vitro study that can be further investigated in vivo. -The authors do a good job at explaining the methods used in the paper. -The authors investigation into E. faecalis specifically was interesting and might give some information on how this microbe blooms after antibiotic use. WEAKNESSES: -Metabolic models were not validated. -While many of these findings are interesting it is unclear how well they will translate to the complex environment of the murine gut. -It would have been helpful to relate in vitro results to observations in vivo. DETAILED U.P. ASSESSMENT: OBJECTIVE CRITERIA (QUALITY) 1. Quality: Experiments (1–3 scale) SCORE = 2 · Figure by figure, do experiments, as performed, have the proper controls? [Note: in this section, the class discusses proper controls, but also uses the 'figure-by-figure' opportunity to discuss rationale and approaches] · Figure S1: Is it possible to look at these growth curves in different media types? A. muciniphila is dependent on mucin in the gut and may have different growth dynamics in media that contains mucin. Overall, we think that by examining multiple media types (or at least the addition of mucin) would improve the ability to translate these in vitro results to an in vivo setting. If this is not possible the authors should at least discuss the implications of using different media types and the lack of mucin in the media. · Figure S5: We found this figure difficult to read given the number of different shapes and colors displayed. Despite this, we believe that it conveys important information. Would it be possible to create a multiple plot panel showing the profiles of each strain separately? This would help with readability and interpretation of the figure. It may also be possible to highlight the area that shows key differences in metabolic activity for the reader to interpret. Finally, depending on the journal, would it be possible to include an interactive version of the plot? (Using an R library such as plotly) · Figure 1: Is there any phylogenetic relationship between how pH changes in DSM? For example, do organisms show similar pH changes when grown in SM from taxa in the same phyla, but then show different pH changes when grown in SM from different phyla? Is it possible to show whether these pH changes correspond with the overlap if metabolic usage between strains? o While not required it would be interesting to see whether the use of multiple bacteria in the lawn during the spot assay show the expression of the lanthibiotic from B. coccoides YL58. Alternatively, the B. coccoidesYL58 co-culture or its supernatant could be used for the spot assay instead of spotting the monoculture of YL58. o Minor point: it would be interesting to see if using SM instead of bacterial culture in the spot assay would inhibit bacterial growth. o These experiments rely heavily on spent media, but the gut is a somewhat nutrient-rich environment. While the spent media pairs nicely with the metabolomic work, we believe that the authors should mention this difference within the Discussion. o Color consistency between figures is really appreciated and helpful. We were wondering if more easily discernible colors should be used to represent the bacterial strains (the greens are quite similar to one another). o We believe the manuscript could benefit from the use of a colorblind friendly palette. · Figure 2: Are there metabolites depleted by all bacterial strains tested? If so we think this would be interesting to highlight. o Is the conclusion that A. muciniphila using a lower number of metabolites due to it using metabolites that are "novel" and not in the databases used to analyze the mass spec data? Its not clear whether annotated compounds were used in this analysis or all features with a mass:charge ratio and retention time were used. Clearing this up should address the above question. · Figure 3: We were confused about the description of the "draft metabolic model" in the manuscript. The authors should clarify whether this is a purely computational model. · Figure 4: We were wondering if the relative abundance of each strain in any pair at time 0 would be at least close to 50% (the blue and red bar) because the authors started with same OD of two strains and did 1:1 ratio mixing. We realize this could be due to many reasons such as different DNA extraction efficiencies, 16S rRNA copy number and different primer efficacies for different taxa. However, the authors should comment and clarify this result as we would expect some of them to be closer to 50:50 than shown in the figure. o The abundance of these microbes is referred to as "absolute abundance", which is clearly not true given the differences at time 0. Perhaps they should replace this with 16S rRNA gene copy number of 16S copy number throughout the text. · Figure 5: (Fig. 5B) Authors mention that strains YL2 and I49 did not grow in the co-culture, but provide no possible explanation as to why these strains did not grow. (Figs. 5B and 5C): strains YL44 and KB18 seem to be absent from co-cultures in both B and C, but this is not mentioned. Also, is KB18 present in the original inoculum? The authors mention they co-cultured all 12 strains, but the colored abundance representation for the "inoculum" does not include KB18. o While not necessary, it would have been interesting to see whether the compounds that contain C14 malate are those that the metabolic models from Figure 3) would have predicted. o The authors mention that compositional analysis was used in line 316 although the meaning of this statement is unclear. Furthermore, the authors are examining relative abundance data and therefore should not use terms such as "increased/decreased abundance". Instead, terms that refer to relative changes should be used instead. o We think Figure 5F may be missing a negative control for C14 (a different sugar that is not taken up by bacteria). Are specific analyses performed using methods that are consistent with answering the specific question? · The analyses performed in this study directly address the research question. Is there the appropriate technical expertise in the collection and analysis of data presented? · Yes Do analyses use the best-possible (most unambiguous) available methods quantified via appropriate statistical comparisons? · Is a t-test appropriate for the analysis on metabolomic data? Perhaps the authors should use a non-distribution based tested such as a Wilcoxon ranked sum. 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. · For the most part, the authors do make use of controls when possible, however, the analysis of C14 malate uptake would be strengthened by comparison to a negative control. 2. Quality: Completeness (1–3 scale) SCORE = 2 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.

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