Metagenomic assessment of gut microbial communities and risk of severe COVID-19
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Study information
-
Quality control
- Retracted paper
- Contamination issues suspected
- Batch effect issues suspected
- Uncontrolled confounding suspected
- Results are suspect (various reasons)
- Tags applied
study design
Citation
PMID PubMed identifier for scientific articles.
DOI Digital object identifier for electronic documents.
URI
Authors
Nguyen LH, Okin D, Drew DA, Battista VM, Jesudasen SJ, Kuntz TM, Bhosle A, Thompson KN, Reinicke T, Lo CH, Woo JE, Caraballo A, Berra L, Vieira J, Huang CY, Das Adhikari U, Kim M, Sui HY, Magicheva-Gupta M, McIver L, Goldberg MB, Kwon DS, Huttenhower C, Chan AT, Lai PS
Journal
Genome medicine
Year
2023
Keywords:
Machine learning, Microbiome, SARS-CoV-2
BACKGROUND: The gut microbiome is a critical modulator of host immunity and is linked to the immune response to respiratory viral infections. However, few studies have gone beyond describing broad compositional alterations in severe COVID-19, defined as acute respiratory or other organ failure. METHODS: We profiled 127 hospitalized patients with COVID-19 (n = 79 with severe COVID-19 and 48 with moderate) who collectively provided 241 stool samples from April 2020 to May 2021 to identify links between COVID-19 severity and gut microbial taxa, their biochemical pathways, and stool metabolites. RESULTS: Forty-eight species were associated with severe disease after accounting for antibiotic use, age, sex, and various comorbidities. These included significant in-hospital depletions of Fusicatenibacter saccharivorans and Roseburia hominis, each previously linked to post-acute COVID syndrome or "long COVID," suggesting these microbes may serve as early biomarkers for the eventual development of long COVID. A random forest classifier achieved excellent performance when tasked with classifying whether stool was obtained from patients with severe vs. moderate COVID-19, a finding that was externally validated in an independent cohort. Dedicated network analyses demonstrated fragile microbial ecology in severe disease, characterized by fracturing of clusters and reduced negative selection. We also observed shifts in predicted stool metabolite pools, implicating perturbed bile acid metabolism in severe disease. CONCLUSIONS: Here, we show that the gut microbiome differentiates individuals with a more severe disease course after infection with COVID-19 and offer several tractable and biologically plausible mechanisms through which gut microbial communities may influence COVID-19 disease course. Further studies are needed to expand upon these observations to better leverage the gut microbiome as a potential biomarker for disease severity and as a target for therapeutic intervention.
Experiment 1
Reviewed Marked as Reviewed by Peace Sandy on 2024-2-23
Curated date: 2023/12/19
Curator: OdigiriGreat
Revision editor(s): OdigiriGreat, Peace Sandy, ChiomaBlessing
Subjects
- Location of subjects
- United States of America
- Host species Species from which microbiome was sampled. Contact us to have more species added.
- Homo sapiens
- Body site Anatomical site where microbial samples were extracted from according to the Uber Anatomy Ontology
- Feces Cow dung,Cow pat,Droppings,Dung,Excrement,Excreta,Faeces,Fecal material,Fecal matter,Fewmet,Frass,Guano,Matières fécales@fr,Merde@fr,Ordure,Partie de la merde@fr,Piece of shit,Porción de mierda@es,Portion of dung,Portion of excrement,Portion of faeces,Portion of fecal material,Portion of fecal matter,Portion of feces,Portion of guano,Portion of scat,Portionem cacas,Scat,Spoor,Spraint,Stool,Teil der fäkalien@de,Feces,feces
- Condition The experimental condition / phenotype studied according to the Experimental Factor Ontology
- Gut microbiome measurement Gut microbiome measurement,gut microbiome measurement
- Group 0 name Corresponds to the control (unexposed) group for case-control studies
- Moderate COVID-19
- Group 1 name Corresponds to the case (exposed) group for case-control studies
- Severe COVID-19
- Group 1 definition Diagnostic criteria applied to define the specific condition / phenotype represented in the case (exposed) group
- Hospitalized patients with severe Covid-19
- Group 0 sample size Number of subjects in the control (unexposed) group
- 48
- Group 1 sample size Number of subjects in the case (exposed) group
- 79
- Antibiotics exclusion Number of days without antibiotics usage (if applicable) and other antibiotics-related criteria used to exclude participants (if any)
- NIL
Lab analysis
- Sequencing type
- PCR
- 16S variable region One or more hypervariable region(s) of the bacterial 16S gene
- Not specified
- Sequencing platform Manufacturer and experimental platform used for quantifying microbial abundance
- Illumina
Statistical Analysis
- Data transformation Data transformation applied to microbial abundance measurements prior to differential abundance testing (if any).
- relative abundances
- Statistical test
- Random Forest Analysis
- Significance threshold p-value or FDR threshold used for differential abundance testing (if any)
- 0.05
- MHT correction Have statistical tests be corrected for multiple hypothesis testing (MHT)?
- Yes
- Confounders controlled for Confounding factors that have been accounted for by stratification or model adjustment
- age, body mass index, ethnic group, antibiotic exposure, comorbidity, race, sequence read depth, Confounders controlled for: "use of corticosteroids or remdesivir" is not in the list (abnormal glucose tolerance, acetaldehyde, acute graft vs. host disease, acute lymphoblastic leukemia, acute myeloid leukemia, adenoma, age, AIDS, alcohol consumption measurement, alcohol drinking, ...) of allowed values.use of corticosteroids or remdesivir, Confounders controlled for: "days since admission" is not in the list (abnormal glucose tolerance, acetaldehyde, acute graft vs. host disease, acute lymphoblastic leukemia, acute myeloid leukemia, adenoma, age, AIDS, alcohol consumption measurement, alcohol drinking, ...) of allowed values.days since admission, Confounders controlled for: "SARS-CoV-2 stool viral load" is not in the list (abnormal glucose tolerance, acetaldehyde, acute graft vs. host disease, acute lymphoblastic leukemia, acute myeloid leukemia, adenoma, age, AIDS, alcohol consumption measurement, alcohol drinking, ...) of allowed values.SARS-CoV-2 stool viral load, Confounders controlled for: "participant-level random effect" is not in the list (abnormal glucose tolerance, acetaldehyde, acute graft vs. host disease, acute lymphoblastic leukemia, acute myeloid leukemia, adenoma, age, AIDS, alcohol consumption measurement, alcohol drinking, ...) of allowed values.participant-level random effect
Alpha Diversity
- Inverse Simpson Modification of Simpsons index D as 1/D to obtain high values in datasets of high diversity and vice versa
- decreased
Signature 1
Reviewed Marked as Reviewed by Peace Sandy on 2024-2-23
Source: Fig. 3a
Description: Stool-based classifier for COVID-19 disease severity showing the differential abundance among patients with severe/critical COVID-19 compared to patients with mild/moderate COVID-19
Abundance in Group 1: increased abundance in Severe COVID-19
| NCBI | Quality Control | Links |
|---|---|---|
| Enterococcus faecalis |
Revision editor(s): Peace Sandy, ChiomaBlessing
Signature 2
Reviewed Marked as Reviewed by Peace Sandy on 2024-2-23
Source: Fig. 3a
Description: Stool-based classifier for COVID-19 disease severity showing the differential abundance among patients with severe/critical COVID-19 compared to patients with mild/moderate COVID-19
Abundance in Group 1: decreased abundance in Severe COVID-19
Revision editor(s): Peace Sandy, ChiomaBlessing
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