Back

Impact of SARS-CoV-2 Infection on Antimicrobial Resistance Gene Profiles in the Upper Respiratory Tract: A Cross-Sectional Study

Tomar, S. S.; Khairnar, K.

2024-12-05 epidemiology
10.1101/2024.11.14.24317312 medRxiv
Show abstract

ObjectivesTo investigate the impact of SARS-CoV-2 infection on the antimicrobial resistance (AMR) gene profiles in the upper respiratory tract (URT) and To evaluate variations in AMR gene diversity, abundance, and ESKAPE-associated AMR in URT. By comparing SARS-CoV-2-positive patients to healthy controls. Methods95 URT swab samples from SARS-CoV-2-positive (n=48) and RTPCR-negative control participants (n=47) collected from central India. Metagenomic DNA was extracted, and metagenomic sequencing was performed using the Illumina NextSeq550 platform. Sequencing data were analysed using the Chan Zuckerberg ID pipeline for Antimicrobial resistance (AMR) gene detection and taxonomic profiling. Chao1, Shannon and Simpson diversity indices, Bray-Curtis dissimilarity, and Bayesian regression, were used to identify significant differences in AMR gene abundance and microbial associations. ResultsThe Chao1 index (p=0.01651) of SARS-CoV-2 samples indicated significantly higher AMR gene richness than the controls. Resistance genes, such as mecA, blaOXA-48, and blaNDM-1, showed higher abundance in SARS-CoV-2 samples. These genes were found to be linked to high-priority pathogens like Klebsiella pneumoniae, Escherichia coli, and Staphylococcus aureus. Bayesian regression demonstrated that SARS-CoV-2 infection is a significant factor in elevated AMR gene abundance ({beta} = 1.549, HDI [1.409, 1.691]). Females showed higher AMR levels than males ({beta} = 0.261, HDI [0.167, 0.350]), and the model outputs showed no significant age correlation. Sankey diagrams and heatmaps showed higher AMR gene diversity and abundance in the SARS-CoV-2 group. ConclusionsSARS-CoV-2 infection alters the URTs AMR gene profile and increases the resistance genes abundance and diversity. The results indicate a requirement to enhance AMR surveillance of COVID-19 patients to adapt antimicrobial stewardship strategies and reduce the chances of secondary infections. It is, therefore, essential to carry out more extensive studies to analyze temporal variations and the effects of antibiotic overuse on AMR evolution.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.