SARS-CoV-2 triggered oxidative stress and abnormal energy metabolism in gut microbiota

Abstract

Specific roles of gut microbes in COVID-19 progression are critical. However, the circumstantial mechanism remains elusive. In this study, shotgun metagenomic or metatranscriptomic sequencing was performed on fecal samples collected from 13 COVID-19 patients and controls. We analyzed the structure of gut microbiota, identified the characteristic bacteria, and selected biomarkers. Further, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations were employed to correlate the taxon alterations and corresponding functions. The gut microbiota of COVID-19 patients was characterized by the enrichment of opportunistic pathogens and depletion of commensals. The abundance of Bacteroides spp. displayed an inverse relationship with COVID-19 severity, whereas Actinomyces oris, Escherichia coli, and Streptococcus parasanguini were positively correlated with disease severity. The genes encoding oxidoreductase were significantly enriched in gut microbiome of COVID-19 group. KEGG annotation indicated that the expression of ABC transporter was upregulated, while the synthesis pathway of butyrate was aberrantly reduced. Furthermore, increased metabolism of lipopolysaccharide, polyketide sugar, sphingolipids, and neutral amino acids were found. These results suggested the gut microbiome of COVID-19 patients was in a state of oxidative stress. Healthy gut microbiota may enhance antiviral defenses via butyrate metabolism, whereas the accumulation of opportunistic and inflammatory bacteria may exacerbate COVID-19 progression.

Keywords: COVID‐19; SARS‐CoV‐2; gut microbiota; metagenome; metatranscriptome.

FIGURE 1

Schematic diagram of fecal specimen collection in COVID‐19 patients. “CoV” indicates COVID‐19 patients. “0” represents the baseline date of the first feces collection; “+ve stool sample”: the positive qRT‐PCR test result for SARS‐CoV‐2 in stool specimen; “−ve stool sample”: the negative qRT‐PCR test result for SARS‐CoV‐2 in stool specimen. “+ve throat swab”: the positive qRT‐PCR test result for SARS‐CoV‐2 in throat swab; “−ve throat swab”: the negative qRT‐PCR test result for SARS‐CoV‐2 in throat swab test. Fecal specimens sequenced by both shotgun metagenome and metatranscriptome sequencing were marked with asterisk symbols

FIGURE 2

Alteration in gut microbial diversity and community structures in COVID‐19 (n = 13), health (n = 13) and community‐acquired pneumonia (CAP) (n = 8) groups. Alpha diversity of the gut microbiota among the three groups based on the (A) Shannon index and (B) Chao index. (C) Microbiome communities were assessed by principal coordinate analysis (PCoA) of Bray–Curtis distances. (D) Venn diagram presenting the overlap of operational taxonomic units (OTUs) of the fecal microbiome across all groups. Significance was marked as *p < 0.05, **p < 0.01, ***p < 0.001

FIGURE 3

Taxonomic differences in the stool microbiota between COVID‐19 and control groups. (A) Comparison of the relative abundance at the species levels across all groups. Specific to box figure, each box corresponds to an interquartile range of taxa abundance, and the black line represents to median abundance. Vertical lines indicate the variability in the abundance of each taxon. Significance was marked as *p < 0.05, **p < 0.01, ***p < 0.001. (B) LEfSe analysis conducted to reveal the significant differences in microbiota composition between COVID‐19 (orange) and health (blue) groups. (C) Pearson correlation of associated species in COVID‐19 and health groups. The degree of correlation is indicated by a color gradient from red (positive correlation) to blue (negative correlation)

FIGURE 4

(A) Functional classification of differential genes upregulated in the COVID‐19 group according to gene ontology (GO) terms in the domains “molecular function” (MF), “cellular component” (CC), and “biological process” (BP). (B) Statistics of KEGG annotation of differential genes upregulated in the COVID‐19 group. The size of each circle represents the number of significant unigenes upregulated in the corresponding pathway (the significant threshold of differential genes as an absolute value of log2 (fold change) ≥1, p < 0.05). The upregulation factor was calculated with the number of upregulated gene divided by the total number of background genes in the corresponding pathway. A pathway with a p value <0.03 is considered significantly over‐represented. (C) Circos plot showing the information of most enriched pathways among microbiota in metagenome. Circos plots were divided into two parts. Leftmost part showed the pathway entry of gut microbiota based on annotation from KEGG database, while rightmost part represented four different cohorts. The leftmost part and rays (links) of circos are divided into 20 different colors according to the enrichment degree. The thickness of each ribbon represents the abundance from each cohort

FIGURE 5

Gut microbiome composition of metagenome (MG) and metatranscriptome (MT) in COVID‐19 patients. (A–C) Bacterial composition at phylum, genus, and species levels (relative abundance ≥1%), respectively. Microbiota composition indicates the average relative abundance of bacterial presence in all samples (n = 10). (D) Ratio of mean relative abundance of microbes in MT to that in MG (MT/MG). Log2 is used for data normalization

FIGURE 6

KEGG annotations of the intestinal metagenome and metatranscriptome in COVID‐19 patients (n = 10). (A) KEGG level 2 annotations of fecal metagenome (MG) and metatranscriptome (MT) data (Top 10 in abundance). (B) Ratio of mean relative abundance of KEGG level 2 annotations in MG to that in MT (MT/MG). The assignment of KEGG pathway entry annotations of MG (C) and MT (D) statistics