Biological characteristics of SARS-CoV-2 resistant populations by integrated gut microbiota sequencing, metabolomics, and proteomics: a cohort comparison study

Abstract

Objective: Most research reports on COVID-19 infections have focused on the correlation between the severity of the disease symptoms and immune deficits, while the mechanisms affecting the susceptibility to SARS-CoV-2 remain largely unknown. The study aimed to comprehensively analyze the differences in immunity, gut microbiota, metabolism, and proteomics between the SARS-CoV-2 resistant population and the susceptible population.

Methods and results: In this cohort comparison study, participants were rigorously selected based on inclusion and exclusion criteria in a continuous enrollment manner using combined questionnaires and clinical data, ultimately including 25 SARS-CoV-2 resistant volunteers versus 16 SARS-CoV-2 infected patients. The clinical information of the participants was recorded in detail, and fecal and blood samples were collected in a standardized manner for subsequent multi-omics analysis, including gut microbiota sequencing, metabolomics, and proteomics. This study has preliminarily elucidated the characteristics of the gut microbiota, serum metabolites, and serum proteins in the SARS-CoV-2 resistant population. It exhibits a unique metabolic signature characterized by elevated levels of serum phosphatidylinositol and the abundance of Prevotella, which may serve as a potential predictive biomarker for resistance to SARS-CoV-2.

Conclusion: Given the crucial role of phosphatidylinositol in cell membrane architecture and viral infectivity, this study provides a promising entry point for further research into the pathogenesis and prevention strategies of COVID-19.

Keywords: COVID-19; SARS-CoV-2; gut microbiota; metabolomics; multiomics; phosphatidylinositols; proteomics; resistant population.

FIGURE 1

Participant flow diagram.

FIGURE 2

Clinical immune indexes and complete blood count analysis. “*” indicates P < 0.05 for comparison between two groups. “**” indicates P < 0.01 for comparison between two groups. “***” indicates P < 0.001 for comparison between two groups.

FIGURE 3

Differences in gut microbiota between the SARS-CoV-2 resistance group and the infection group. (A) Violin diagram of alpha diversity; (B) partial least squares discriminant analysis of gut microbiota; (C) the result chart of differential bacterial genera screening at the genus level between the COVID-19 infection group and the COVID-19 resistant group; (D) linear discriminant analysis effect size analysis; microbial features were classified based on both statistical significance and biological effect size.

FIGURE 4

Between-group comparative analysis of serum metabolites. (A) Partial least squares discriminant analysis of metabolites; (B) volcano plot for differential metabolites; (C) metabolomics cluster analysis plot; (D) Kyoto Encyclopedia of Genes and Genomes enrichment pathway of metabolites.

FIGURE 5

Correlation between the gut microbiota and metabolites. (A) Heatmap of joint analysis of metabolomics and gut microbiota; (B) diagram of differential metabolites and differential microbiota network regulation. “*” indicates P < 0.05 for comparison between two groups. “**” indicates P < 0.01 for comparison between two groups.

FIGURE 6

Proteomics and its correlation between metabolites. (A) Principal component analysis of proteomics; (B) proteomics cluster analysis plot; (C) volcano plot of differential protein; (D) differential protein enrichment pathway plot; (E) heatmap of joint analysis of differential metabolites and differential proteins. “*” indicates P < 0.05 for comparison between two groups. “**” indicates P < 0.01 for comparison between two groups. Protein identifiers and their functional descriptions are provided in Supplementary Table 1.