The metaproteome of the gut microbiota in pediatric patients affected by COVID-19

Abstract

Introduction: The gut microbiota (GM) play a significant role in the infectivity and severity of COVID-19 infection. However, the available literature primarily focuses on adult patients and it is known that the microbiota undergoes changes throughout the lifespan, with significant alterations occurring during infancy and subsequently stabilizing during adulthood. Moreover, children have exhibited milder symptoms of COVID-19 disease, which has been associated with the abundance of certain protective bacteria. Here, we examine the metaproteome of pediatric patients to uncover the biological mechanisms that underlie this protective effect of the GM.

Methods: We performed nanoliquid chromatography coupled with tandem mass spectrometry on a high resolution analytical platform, resulting in label free quantification of bacterial protein groups (PGs), along with functional annotations via COG and KEGG databases by MetaLab-MAG. Additionally, taxonomic assignment was possible through the use of the lowest common ancestor algorithm provided by Unipept software.

Results: A COVID-19 GM functional dissimilarity respect to healthy subjects was identified by univariate analysis. The alteration in COVID-19 GM function is primarily based on bacterial pathways that predominantly involve metabolic processes, such as those related to tryptophan, butanoate, fatty acid, and bile acid biosynthesis, as well as antibiotic resistance and virulence.

Discussion: These findings highlight the mechanisms by which the pediatric GM could contribute to protection against the more severe manifestations of the disease in children. Uncovering these mechanisms can, therefore, have important implications in the discovery of novel adjuvant therapies for severe COVID-19.

Keywords: COVID-19; SARS-CoV-2; functional patterns; gut microbiota; mass spectrometry; metaproteomics; pediatrics.

Figure 1

Illustration of the workflow applied for the metaproteomic analysis starting from faces of COVID-19 patients and age-matched healthy control subjects.

Figure 2

Hierarchical clustering of the differentially expressed protein groups (PGs). A heat map based on LFQ PGs’ intensity abundances and subject to a z-score transformation was used to visualize color-coded hierarchical cluster analysis from stool samples of patients (COVID-19, red color) and age-matched healthy subjects (CTRL, green color). The analysis was performed for all PGs (A, 698 differentially expressed PGs), bacterial PGs (675 differentially expressed PGs, B), and human PGs (23 differentially expressed PGs, C).

Figure 3

Graphical representation of the differentially expressed bacterial protein groups (PGs) comparing COVID-19 to CTRLs. Dashed gray lines indicate the set limits of log₁₀(abundances’ ratio value) ≥ |0.3| and statistically significance values (–log₁₀ p-value ≤ 0.05 established by t-test). Red and green circles indicate the changes for significant protein groups (over-expression and under-expression in COVID-19, respectively). Some PGs are highlighted by their acronym. For associations with the full name, protein group ID, as well as KEGG pathway and COG identifier, please refer to the Supplementary file 2 (i.e., tnaA, Tryptophanase 1, PG 3319, associated to tryptophan metabolism, COG3033).

Figure 4

Graphical representation of COG categories modulated in differentially expressed bacterial protein groups (PGs) comparing COVID-19 with CTRLs. The bars represent the frequency of differentially expressed bacterial PGs associated with their functional annotation. Over-expressed and under-expressed PGs in COVID-19 are denoted by the color coding of red and green, respectively.

Figure 5

Graphical representation of the distribution of Lowest Common Ancestor (LCA) taxa within statistically over-expressed KEGG pathways in COVID-19. A selection of KEGG pathways belonging to metabolic pathways were displayed (A), as well as processes of bacterial antibiotic resistance and virulence (B).

Figure 6

Box plots of a selection of statistically significant differential KEGG pathways in COVID-19 patients, classified according to disease severity, based on Kruskal-Wallis test. (A) displays KEGG pathways related to metabolic pathways, (B) shows antibiotic resistance and virulence processes, and (C) shows the Mismatch repair pathway. Significance of pair-wise comparisons (Mann-Whitney test, Benjamini-Hochberg adjusted p-value) were displayed: * p-value ≤ 0.05; ** p-value ≤ 0.01; *** p-value ≤ 0.001; and ns, no statistical significance (p-value > 0.05).

Figure 7

Heatmap showing Spearman’s correlations between KEGG pathways’ intensity and blood clinical features for the entire COVID-19 cohort. Red boxes indicate positive correlations, blue boxes show negative correlations. Benjamini-Hochberg adjusted p-value were displayed: * p-value ≤ 0.05 and ** p-value ≤ 0.01. KEGG pathways’ clusterization was computed by Euclidean distance. WBC, White Blood Cells; N, Neutrophils, L, Lymphocytes, and CRP, C-reactive protein.