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

Reviewers: Olaf Larsen (Vrije University) | 📘📘📘📘📘 • Teresa Nogueira (INIAV) | 📗📗📗📗◻️

Published onOct 24, 2022
Reviews of "Guild-level microbiome signature associated with COVID-19 severity and prognosis"
key-enterThis Pub is a Review of
Guild-level microbiome signature associated with COVID-19 severity and prognosis
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.

To read the original manuscript, click the link above.

Summary of Reviews: 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.

Reviewer 1 (Olaf Larsen) | 📘📘📘📘📘

Reviewer 2 (Teresa Nogueira) | 📗📗📗📗◻️

RR:C19 Strength of Evidence Scale Key

📕 ◻️◻️◻️◻️ = Misleading

📙📙 ◻️◻️◻️ = Not Informative

📒📒📒 ◻️◻️ = Potentially Informative

📗📗📗📗◻️ = Reliable

📘📘📘📘📘 = Strong

To read the reviews, click the links below. 

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