Review 2: "Guild-level microbiome signature associated with COVID-19 severity and prognosis"

Teresa Nogueira

doi:doi:10.1162/2e3983f5.f4c160b1

Review 2: "Guild-level microbiome signature associated with COVID-19 severity and prognosis"

This study identifies microbial guilds associated with mild or severe cases of COVID-19, and uses ML to predict the clinical outcome of SARS-COV-2 infection in other cohorts based on the 2 guilds. Reviewers encourage decision-makers to consider the claims potentially actionable.

by Teresa Nogueira

Published onOct 24, 2022

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This Pub is a Review of

Guild-level microbiome signature associated with COVID-19 severity and prognosis

by Mingquan Guo, Guojun Wu, Yun Tan, Yan Li, Xin Jin, Weiqiang Qi, XiaoKui Guo, Chenhong Zhang, Zhaoqin Zhu, and Liping Zhao

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dx.doi.org

Description

AbstractCOVID-19 severity has been associated with alterations of the gut microbiota. However, the relationship between gut microbiome alterations and COVID-19 prognosis remains elusive. Here, we performed a genome-resolved metagenomic analysis on fecal samples collected from 300 in-hospital COVID-19 patients at time of admission. Among the 2,568 high quality metagenome-assembled genomes (HQMAGs), Redundancy Analysis identified 33 HQMAGs which showed differential distribution among mild, moderate, and severe/critical severity groups. Random Forest model based on these 33 HQMAGs classified patients from different severity groups (average AUC = 0.79). Co-abundance network analysis found that the 33 HQMAGs were organized as two competing guilds. Guild 1 harbored more genes for short-chain fatty acid biosynthesis, and fewer genes for virulence and antibiotic resistance, compared with Guild 2. Random Forest regression showed that these 33 HQMAGs at admission had the capacity to predict 8 clinical parameters, which are predictors for COVID-19 prognosis, at Day 7 in hospital. Moreover, the dominance of Guild 1 over Guild 2 at admission predicted the death/discharge outcome of the critical patients (AUC = 0.92). Random Forest models based on these 33 HQMAGs classified patients with different COVID-19 symptom severity, and differentiated COVID-19 patients from healthy subjects, non-COVID-19, and pneumonia controls in three independent datasets. Thus, this genome-based guild-level signature may facilitate early identification of hospitalized COVID-19 patients with high risk of more severe outcomes at time of admission.

RR:C19 Evidence Scale rating by reviewer:

Reliable. The main study claims are generally justified by its methods and data. The results and conclusions are likely to be similar to the hypothetical ideal study. There are some minor caveats or limitations, but they would/do not change the major claims of the study. The study provides sufficient strength of evidence on its own that its main claims should be considered actionable, with some room for future revision.

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Review:

The authors of the present study have identified two statistically significant different microbiome signatures, as enterotypes, which they called guilds, in the human gut microbiomes of 330 Chinese individuals (using stool samples) collected on admission and during hospitalization. They report that the prognosis for COVID-19 correlates with the enrichment of the patient's gut microbiome with each of these guilds. The generation of high-quality Microbiome Generated Genomes (MAGs) by read-recruitment allowed them to confirm their previous results at the level of genome composition. The distribution of MAGs is different between different levels of COVID-19 severity. Finally, they tested the MAGs results using another dataset composed of 33 reference genomes, thus validating this signature in the patients under study.

The claims are generally supported by the data and methods used. The results and conclusions are likely similar to the results of other authors who have suggested that human gut dysbiosis is linked to COVID-19 prognosis.

There are some minor caveats or limitations, related to the fact that the study used only samples from Chinese individuals, which does not allow the conclusions to be generalized. However, it does not alter the study's main claims about the microbiological signature on the severity and prognosis of COVID-19. Decision makers should consider the claims of this study actionable with limitations based on the methods and data.

The manuscript confirms previous work and the results contribute to a broader understanding of investigating the effect of human gut microbiota composition on the outcome of COVID-19 patients requiring hospitalization and thus support the advancement of the understanding of COVID-19 in society. It frames the work in the current literature/understanding, and cites some current literature, but does not properly discuss limitations. I would thus recommend this paper for publication with some improvements in the Introduction section.

The paper is clearly and accurately presented, well-structured and well-written, with the ability to speak to key audiences.

The issues regarding diversity and inclusion do not apply to this study as all samples are of Chinese individuals—albeit randomly chosen—of different age groups, and there is no information on gender.

Review 2: "Guild-level microbiome signature associated with COVID-19 severity and prognosis"

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