Metagenomic assessment of gut microbial communities and risk of severe COVID-19

From BugSigDB
Reviewed Marked as Reviewed by Peace Sandy on 2024-2-23
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

Curated date: 2024/02/23

Curator: Peace Sandy

Revision editor(s): Peace Sandy, ChiomaBlessing

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 ControlLinks
Enterococcus faecalis

Revision editor(s): Peace Sandy, ChiomaBlessing

Signature 2

Reviewed Marked as Reviewed by Peace Sandy on 2024-2-23

Curated date: 2024/02/23

Curator: Peace Sandy

Revision editor(s): Peace Sandy, ChiomaBlessing

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

NCBI Quality ControlLinks
Actinomyces oris
Actinomyces sp. HPA0247
Adlercreutzia equolifaciens
Adlercreutzia equolifaciens subsp. celatus
Agathobacter rectalis
Agathobaculum butyriciproducens
Anaerobutyricum hallii
Anaerostipes hadrus
Bifidobacterium adolescentis
Bifidobacterium longum
Bifidobacterium pseudocatenulatum
Blautia obeum
Blautia wexlerae
Collinsella aerofaciens
Collinsella stercoris
Coprococcus catus
Coprococcus comes
Dorea formicigenerans
Dorea longicatena
Eubacterium ramulus
Eubacterium ventriosum
Faecalibacterium prausnitzii
Firmicutes bacterium CAG:94
Firmicutes bacterium CAG:95
Fusicatenibacter saccharivorans
Gemella sanguinis
Gemmiger formicilis
Lachnospira eligens
Lactococcus lactis
Monoglobus pectinilyticus
Oscillibacter
Roseburia faecis
Roseburia hominis
Roseburia intestinalis
Roseburia inulinivorans
Rothia mucilaginosa
Ruminococcus bicirculans (ex Wegman et al. 2014)
Ruminococcus bromii
Schaalia odontolytica
Streptococcus gordonii
Streptococcus mitis
Streptococcus oralis
Streptococcus parasanguinis
Streptococcus salivarius
Streptococcus thermophilus
[Clostridium] leptum
[Eubacterium] siraeum
[Ruminococcus] torques

Revision editor(s): Peace Sandy, ChiomaBlessing