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Authors: Gou et al

Link to paper:

Journal/ Pre-Print: bioRxiv

Key Words: microbiota, biomarker, proteomics, machine learning, metabolome

Research Highlights 

1. Construction of blood proteomic risk score for prediction of COVID-19 progression and link with pro-inflammatory cytokines

2. Identification of core gut microbiota features which predict blood proteomic biomarkers

3. Potential mechanism linking gut microbiota to COVID-19 susceptibility proposed


Gou et al aimed to identify biomarkers which predict susceptibility to severe COVID-19 infection and link them to the expression of pro-inflammatory cytokines. They investigated the association of a blood proteomic risk score (PRS) with inflammatory biomarkers as well as with core gut microbiota features using a machine-learning model. Their findings suggest that gut microbial features are predictive of the blood proteomic markers, marking a potential biological mechanism behind the diverse susceptibility among different groups of people.

Impact for SARS-CoV2/COVID19 research efforts

Understand the immune response to SARS-CoV2/COVID19: association of biomarkers that are linked to severe COVID-19 disease

Develop diagnostic tools for SARS-CoV2/COVID19: they link a core set of gut microbiota to previously identified blood proteomic biomarkers to predict susceptibility

Study Type

· In silico study / bioinformatics study

· Clinical Cohort study

Strengths and limitations of the paper

Novelty: Identification of a core microbiota which may influence blood risk proteomic markers

Standing in the field: needs replication in larger cohort

Appropriate statistics: Statistics described in method sections, but no power calculation

Viral model used: They used an original cohort of COVID-19 patients to set up their PRS, and then used this for further studies on healthy individuals

Translatability: The biomarkers need to be validated with a different method (more high-throughput and in a larger sample size) but could potentially be used in a diagnostic test. It is unlikely that the changes in the gut microbiome could be used as a biomarker as findings were correlative but not mechanistically proven.

Main limitations: Small sample size, adaptation of a proteome signature that was identified from another small study. They can only provide predictions and they don’t show microbiome of infected patients to confirm their findings. The sampling strategy was not very transparent. The gut microbiome work was relying on 16S (V3-V4) which has limited taxonomic resolution.