SARS-CoV-2 infection in nonhuman primates alters the composition and functional activity of the gut microbiota

Abstract

The current pandemic of coronavirus disease (COVID) 2019 constitutes a global public health issue. Regarding the emerging importance of the gut-lung axis in viral respiratory infections, analysis of the gut microbiota's composition and functional activity during a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection might be instrumental in understanding and controling COVID 19. We used a nonhuman primate model (the macaque), that recapitulates mild COVID-19 symptoms, to analyze the effects of a SARS-CoV-2 infection on dynamic changes of the gut microbiota. 16S rRNA gene profiling and analysis of β diversity indicated significant changes in the composition of the gut microbiota with a peak at 10-13 days post-infection (dpi). Analysis of bacterial abundance correlation networks confirmed disruption of the bacterial community at 10-13 dpi. Some alterations in microbiota persisted after the resolution of the infection until day 26. Some changes in the relative bacterial taxon abundance associated with infectious parameters. Interestingly, the relative abundance of Acinetobacter (Proteobacteria) and some genera of the Ruminococcaceae family (Firmicutes) was positively correlated with the presence of SARS-CoV-2 in the upper respiratory tract. Targeted quantitative metabolomics indicated a drop in short-chain fatty acids (SCFAs) and changes in several bile acids and tryptophan metabolites in infected animals. The relative abundance of several taxa known to be SCFA producers (mostly from the Ruminococcaceae family) was negatively correlated with systemic inflammatory markers while the opposite correlation was seen with several members of the genus Streptococcus. Collectively, SARS-CoV-2 infection in a nonhuman primate is associated with changes in the gut microbiota's composition and functional activity.

Keywords: Gut microbiota; SARS-CoV-2; gut dysbiosis; metabolic output; nonhuman primates.

Figure 1.

A, Infection of rhesus (Rh) macaques and cynomolgus (Cyno) macaques by SARS-CoV-2, and sample collection. Viral loads and systemic factors were quantified before infection (day −9 represents the basal line) and at different time points post-infection. Feces samples, but not other samples, were collected the day of infection (day 0). B, Viral loads (measured in an RT-qPCR assay) in nasopharyngeal swabs, tracheal swabs, and rectal swabs. The estimated limit of detection was 2.3 log10 copies of SARS-CoV-2 RNA per ml, and the estimated limit of quantification was 3.9 log10 copies per ml (dotted horizontal line); C, The plasma CRP concentration. D, Cytokine and chemokine concentrations in plasma. B-D, The time course analysis for each animal (n = 4). Basal line was adjusted to day 0 on the graphs

Figure 1.

A, Infection of rhesus (Rh) macaques and cynomolgus (Cyno) macaques by SARS-CoV-2, and sample collection. Viral loads and systemic factors were quantified before infection (day −9 represents the basal line) and at different time points post-infection. Feces samples, but not other samples, were collected the day of infection (day 0). B, Viral loads (measured in an RT-qPCR assay) in nasopharyngeal swabs, tracheal swabs, and rectal swabs. The estimated limit of detection was 2.3 log10 copies of SARS-CoV-2 RNA per ml, and the estimated limit of quantification was 3.9 log10 copies per ml (dotted horizontal line); C, The plasma CRP concentration. D, Cytokine and chemokine concentrations in plasma. B-D, The time course analysis for each animal (n = 4). Basal line was adjusted to day 0 on the graphs

Figure 2.

Changes over time in the composition of the bacterial gut microbiota. A, Beta diversity was analyzed. Bray Curtis distance between indicated time point and Day 0. *p < .05. The overall composition of the bacterial microbiota at the phylum (b) and class (c) levels was determined for each animal and each time points during the infection (n = 4)

Figure 2.

Changes over time in the composition of the bacterial gut microbiota. A, Beta diversity was analyzed. Bray Curtis distance between indicated time point and Day 0. *p < .05. The overall composition of the bacterial microbiota at the phylum (b) and class (c) levels was determined for each animal and each time points during the infection (n = 4)

Figure 3.

Alterations in the fecal microbiota’s composition over the course of a SARS-CoV-2 infection. A linear discriminant analysis effect size (LEfSe) analysis shows that the representation of the various bacterial taxa changed over the course of the infection. Only taxa with a statistically significant LDA score (log10) > 2 (compared with day −9 & 0) are shown. The heat map on the left shows the relative abundance of the taxa, and the heat map on the right shows the LDA scores. The taxa are clustered by abundance pattern by day compared to D-9 and D0

Figure 3.

Alterations in the fecal microbiota’s composition over the course of a SARS-CoV-2 infection. A linear discriminant analysis effect size (LEfSe) analysis shows that the representation of the various bacterial taxa changed over the course of the infection. Only taxa with a statistically significant LDA score (log10) > 2 (compared with day −9 & 0) are shown. The heat map on the left shows the relative abundance of the taxa, and the heat map on the right shows the LDA scores. The taxa are clustered by abundance pattern by day compared to D-9 and D0

Figure 4.

Alteration of the fecal microbiota interaction network during a SARS-CoV-2 infection. Genus-level correlation networks for bacterial abundance were built using Spearman’s correlation test for four time periods. A, days −9 & 0, before infection. B, days 3 & 5 post-infection. C, days 10 & 13 post-infection. D, day 20 & 26 post-infection. Each circle (node) represents a genus, the color represents the phylum (blue: Firmicutes; green: Bacteroidetes; yellow: Actinobacteria; orange: Proteobacteria; gray: other), and the size increases with the number of direct edges. The edges color indicates the direction of the correlation (green for positive and red for negative). Only significant correlations (p < .05 and q < 0.25 after correction for false discovery rate using the Benjamini-Hochberg procedure) are shown. E, The mean ± standard error of the mean degree of connectivity and neighborhood connectivity are indicated. *p < .05; ***p < .001; ****p < .0001

Figure 4.

Alteration of the fecal microbiota interaction network during a SARS-CoV-2 infection. Genus-level correlation networks for bacterial abundance were built using Spearman’s correlation test for four time periods. A, days −9 & 0, before infection. B, days 3 & 5 post-infection. C, days 10 & 13 post-infection. D, day 20 & 26 post-infection. Each circle (node) represents a genus, the color represents the phylum (blue: Firmicutes; green: Bacteroidetes; yellow: Actinobacteria; orange: Proteobacteria; gray: other), and the size increases with the number of direct edges. The edges color indicates the direction of the correlation (green for positive and red for negative). Only significant correlations (p < .05 and q < 0.25 after correction for false discovery rate using the Benjamini-Hochberg procedure) are shown. E, The mean ± standard error of the mean degree of connectivity and neighborhood connectivity are indicated. *p < .05; ***p < .001; ****p < .0001

Figure 5.

Correlations between bacterial taxa and infection-related variables. Correlation networks were built using Spearman’s correlation and figures were built using Cytoscape V.3.8.0. Only significant correlations (p < .05 and q < 0.15 after correction for the false discovery rate, using the Benjamini-Hochberg procedure) are shown

Figure 5.

Correlations between bacterial taxa and infection-related variables. Correlation networks were built using Spearman’s correlation and figures were built using Cytoscape V.3.8.0. Only significant correlations (p < .05 and q < 0.15 after correction for the false discovery rate, using the Benjamini-Hochberg procedure) are shown

Figure 6.

Fecal metabolite production is altered during a SARS-CoV-2 infection. A, SCFAs, BAs and tryptophan metabolites were measured in fecal samples from each animal and at each time point, using targeted quantitative metabolomics. Values for individual animals are presented. B, Correlations between fecal concentrations of tryptamine and serotonin. C, Correlations between fecal metabolites concentrations and plasma cytokine concentrations. Only significant correlations (p < .05) are shown. Correlations with q < 0.15 (after correction for the false discovery rate, using the Benjamini and Hochberg procedure) are indicated by an asterisk (*). TCA, taurocholic acid; GCDCA, glycochenodeoxycholic acid; LCA, lithocholic acid; CDCA, chenodeoxycholic acid; GDCA, glycodeoxycholic acid; GCA, glycocholic acid; TLCA, taurocholic acid; UDCA, ursodeoxycholic acid; CA, cholic acid; HCA, hyocholic acid; DCA, deoxycholic acid; TDCA, taurodeoxycholic acid; TCDCA, taurochenodeoxycholic acid; TRP, tryptophan; KYN, kynurenine; KA, kynurenic acid; XA, xanthurenic acid; QA, quinolinic acid; PICO, picolinic acid; 3-HAA, 3-hydroxyanthranilic Acid; CNB, cinnabarinic acid; 3-IPA, 3-Indole propionic acid; 3IS, 3-Indole sulfate; I-3 CA, Indole-3-carboxaldehyde; IAM, indole-3-acetamide; IAA, indole-3-acetic acid; ILA, indole-3-lactic acid; 5HIAA, 5-hydroxyindole acetic acid; 5HT, serotonin; 5HTP, 5-hydroxytryptophan; NasSerotonin, N-Acetylserotonin; sum_indoles, sum of all the metabolites from the indole pathway; sum_IDO, sum of all the metabolites from the IDO pathway; sum_indoles_ratio, sum of all the metabolites from the indole pathway divided by the amount of all the trypophan metabolites; sum_IDO_ratio, sum of all the metabolites from the IDO pathway divided by the amount of all the trypophan metabolites

Figure 6.

Fecal metabolite production is altered during a SARS-CoV-2 infection. A, SCFAs, BAs and tryptophan metabolites were measured in fecal samples from each animal and at each time point, using targeted quantitative metabolomics. Values for individual animals are presented. B, Correlations between fecal concentrations of tryptamine and serotonin. C, Correlations between fecal metabolites concentrations and plasma cytokine concentrations. Only significant correlations (p < .05) are shown. Correlations with q < 0.15 (after correction for the false discovery rate, using the Benjamini and Hochberg procedure) are indicated by an asterisk (*). TCA, taurocholic acid; GCDCA, glycochenodeoxycholic acid; LCA, lithocholic acid; CDCA, chenodeoxycholic acid; GDCA, glycodeoxycholic acid; GCA, glycocholic acid; TLCA, taurocholic acid; UDCA, ursodeoxycholic acid; CA, cholic acid; HCA, hyocholic acid; DCA, deoxycholic acid; TDCA, taurodeoxycholic acid; TCDCA, taurochenodeoxycholic acid; TRP, tryptophan; KYN, kynurenine; KA, kynurenic acid; XA, xanthurenic acid; QA, quinolinic acid; PICO, picolinic acid; 3-HAA, 3-hydroxyanthranilic Acid; CNB, cinnabarinic acid; 3-IPA, 3-Indole propionic acid; 3IS, 3-Indole sulfate; I-3 CA, Indole-3-carboxaldehyde; IAM, indole-3-acetamide; IAA, indole-3-acetic acid; ILA, indole-3-lactic acid; 5HIAA, 5-hydroxyindole acetic acid; 5HT, serotonin; 5HTP, 5-hydroxytryptophan; NasSerotonin, N-Acetylserotonin; sum_indoles, sum of all the metabolites from the indole pathway; sum_IDO, sum of all the metabolites from the IDO pathway; sum_indoles_ratio, sum of all the metabolites from the indole pathway divided by the amount of all the trypophan metabolites; sum_IDO_ratio, sum of all the metabolites from the IDO pathway divided by the amount of all the trypophan metabolites