Dynamic associations between the respiratory tract and gut antibiotic resistome of patients with COVID-19 and its prediction power for disease severity

ABSTRACT

The antibiotic resistome is the collection of all antibiotic resistance genes (ARGs) present in an individual. Whether an individual’s susceptibility to infection and the eventual severity of coronavirus disease 2019 (COVID-19) is influenced by their respiratory tract antibiotic resistome is unknown. Additionally, whether a relationship exists between the respiratory tract and gut ARGs composition has not been fully explored. We recruited 66 patients with COVID-19 at three disease stages (admission, progression, and recovery) and conducted a metagenome sequencing analysis of 143 sputum and 97 fecal samples obtained from them. Respiratory tract, gut metagenomes, and peripheral blood mononuclear cell (PBMC) transcriptomes are analyzed to compare the gut and respiratory tract ARGs of intensive care unit (ICU) and non-ICU (nICU) patients and determine relationships between ARGs and immune response. Among the respiratory tract ARGs, we found that Aminoglycoside, Multidrug, and Vancomycin are increased in ICU patients compared with nICU patients. In the gut, we found that Multidrug, Vancomycin, and Fosmidomycin were increased in ICU patients. We discovered that the relative abundances of Multidrug were significantly correlated with clinical indices, and there was a significantly positive correlation between ARGs and microbiota in the respiratory tract and gut. We found that immune-related pathways in PBMC were enhanced, and they were correlated with Multidrug, Vancomycin, and Tetracycline ARGs. Based on the ARG types, we built a respiratory tract-gut ARG combined random-forest classifier to distinguish ICU COVID-19 patients from nICU patients with an AUC of 0.969. Cumulatively, our findings provide some of the first insights into the dynamic alterations of respiratory tract and gut antibiotic resistome in the progression of COVID-19 and disease severity. They also provide a better understanding of how this disease affects different cohorts of patients. As such, these findings should contribute to better diagnosis and treatment scenarios.

KEYWORDS:

• Dynamic association
• COVID-19
• antibiotic resistome
• respiratory tract
• gut
• disease severity
• prediction

Author contributions

YC was the principal investigator who designed and supervised the study. SZ, FY, DZ, QZ, MX, LY, BL, GX, XY, WC, QW, YT, YD, LH, and WW had roles in recruitment, data collection, and clinical management. YS, WQ, SZ, and YC did bioinformatics, statistical analysis, and data interpretation. YS, WQ, SZ, YC, XC, MT, JB, DH, CL, and MT had roles in results discussion. YS, WQ, SZ, YC, and MT drafted the article, and all authors contributed to review and revision and have seen and approved the final version. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Acknowledgments

We acknowledge the contributions of other clinical and technical staff of the First Affiliated Hospital, College of Medicine, Zhejiang University.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data sharing

The raw metagenomics sequencing dataset was deposited in the Sequence Read Archive (SRA) under BioProject accession PRJNA950577. Access will be granted after an appropriate request to corresponding author.

Ethical approval

The study protocol was approved by the Ethics Committee of the First Affiliated Hospital, Zhejiang University School of Medicine, China (2021IITA0239). The authors gratefully acknowledge the support of the high-performance computing cluster and the help of Enhui Shen from Center for Bioinformatics and Big data Technology, Zhejiang University.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/19490976.2023.2223340

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant numbers 81971919 and 82072377] and Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholar [LR23H200002].