The Relationship Between Pediatric Gut Microbiota and SARS-CoV-2 Infection

Abstract

This is the first study on gut microbiota (GM) in children affected by coronavirus disease 2019 (COVID-19). Stool samples from 88 patients with suspected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and 95 healthy subjects were collected (admission: 3-7 days, discharge) to study GM profile by 16S rRNA gene sequencing and relationship to disease severity. The study group was divided in COVID-19 (68), Non-COVID-19 (16), and MIS-C (multisystem inflammatory syndrome in children) (4). Correlations among GM ecology, predicted functions, multiple machine learning (ML) models, and inflammatory response were provided for COVID-19 and Non-COVID-19 cohorts. The GM of COVID-19 cohort resulted as dysbiotic, with the lowest α-diversity compared with Non-COVID-19 and CTRLs and by a specific β-diversity. Its profile appeared enriched in Faecalibacterium, Fusobacterium, and Neisseria and reduced in Bifidobacterium, Blautia, Ruminococcus, Collinsella, Coprococcus, Eggerthella, and Akkermansia, compared with CTRLs (p < 0.05). All GM paired-comparisons disclosed comparable results through all time points. The comparison between COVID-19 and Non-COVID-19 cohorts highlighted a reduction of Abiotrophia in the COVID-19 cohort (p < 0.05). The GM of MIS-C cohort was characterized by an increase of Veillonella, Clostridium, Dialister, Ruminococcus, and Streptococcus and a decrease of Bifidobacterium, Blautia, Granulicatella, and Prevotella, compared with CTRLs. Stratifying for disease severity, the GM associated to "moderate" COVID-19 was characterized by lower α-diversity compared with "mild" and "asymptomatic" and by a GM profile deprived in Neisseria, Lachnospira, Streptococcus, and Prevotella and enriched in Dialister, Acidaminococcus, Oscillospora, Ruminococcus, Clostridium, Alistipes, and Bacteroides. The ML models identified Staphylococcus, Anaerostipes, Faecalibacterium, Dorea, Dialister, Streptococcus, Roseburia, Haemophilus, Granulicatella, Gemmiger, Lachnospira, Corynebacterium, Prevotella, Bilophila, Phascolarctobacterium, Oscillospira, and Veillonella as microbial markers of COVID-19. The KEGG ortholog (KO)-based prediction of GM functional profile highlighted 28 and 39 KO-associated pathways to COVID-19 and CTRLs, respectively. Finally, Bacteroides and Sutterella correlated with proinflammatory cytokines regardless disease severity. Unlike adult GM profiles, Faecalibacterium was a specific marker of pediatric COVID-19 GM. The durable modification of patients' GM profile suggested a prompt GM quenching response to SARS-CoV-2 infection since the first symptoms. Faecalibacterium and reduced fatty acid and amino acid degradation were proposed as specific COVID-19 disease traits, possibly associated to restrained severity of SARS-CoV-2-infected children. Altogether, this evidence provides a characterization of the pediatric COVID-19-related GM.

Keywords: COVID-19; SARS-CoV-2; diversity index; dysbiosis; gut microbiota; immunology.

Figure 1

Graphical representation of hierarchical analysis of global ASV distribution at L6 (genus) for COVID-19 and CTRL subjects filtered by a t-test between classes with p-value < 0.05 at T₀. In the heatmap, the hierarchical complete linkage dendrogram is based on the ASVs Pearson’s correlation coefficient. The color scale characterizes the Z-score for each variable: red, high level; blue, low level. The column color labels represent respectively: patient’s class (red, COVID-19; orange, Non–COVID-19; green, CTRLs), gender (blue, male; pink, female), severity (green, asymptomatic; orange, mild; red, moderate), and antibiotic [green, absent (0); red, present (1)].

Figure 2

Gut microbiota ecology evaluation. Alpha-diversity of COVID-19 and Non–COVID-19 cohorts and CTRLs based on Chao-1 (A), Shannon (B), observed species (C), Faith PD (D), Simpson (E), and Good’s coverage (F) indices. Statistically significant (p-value < 0.05) comparisons by ANOVA test are indicated by asterisk. Beta-diversity of COVID-19 and Non–COVID-19 cohorts and CTRLs, performed by Bray–Curtis (G), Euclidean distance (H), and unweighted (I) and weighted (J) UniFrac algorithms.

Figure 3

ASV distributions of COVID-19 and Non–COVID-19 cohorts and CTRLs at L2 (A), L5 (B), and L6 (C), filtered by statistically significance based on the Kruskal–Wallis test. *p-value < 0.001; p-value **FDR < 0.05; ***p-value FDR < 0.001.

Figure 4

ASV distributions at L2 (A), L5 (B), and L6 (C), filtered by statistically significance based on the Kruskal–Wallis test. *p-value < 0.001; p-value **FDR < 0.05; ***p-value FDR < 0.001. ASV distributions were reported in the comparisons among COVID-19 and CTRL subjects.

Figure 5

ASV distributions at L2 (A), L5 (B), and L6 (C) filtered by statistically significance based on the Kruskal–Wallis test. *p-value < 0.001; p-value **FDR < 0.05; ***p-value FDR < 0.001. ASV distributions were reported in the comparisons among COVID-19 and CTRL subjects at the admission time.

Figure 6

Important ASVs selected by model classification analysis. The bars represent the importance scores of each ASV in the prediction of models.

Figure 7

Functional profile of microbial communities of COVID-19 and CTRL subgroups. (A) Graphical representation of hierarchical analysis of the KO-based prediction of functional profile of microbial communities of COVID-19 and CTRL subgroups. The top 200 features with lowest p-value (p < 0.05). The color scale characterizes the Z- score for each variable: red, high level; blue, low level. The column color labels represent respectively: patient’s class (red, COVID-19; green, CTRLs), time (green, T₀), gender (blue, male; pink, female), severity (green, asymptomatic; orange, mild; red, moderate), and antibiotic [green, absent (0); red, present (1)]. (B, C) Principal component analysis (PCA) and partial least-squares regression (PLS) interpretable visualizations, based on of KO feature projection for COVID-19 and CTRLs.