Affiliation:
1. Department of Bioinformatics Stella Maris College (Autonomous) Chennai India
2. Centre for Advanced Study in Crystallography and Biophysics University of Madras Chennai India
Abstract
Abstract
Respiratory infections are the leading causes of mortality and the current pandemic COVID-19 is one such trauma that imposed catastrophic devastation to the health and economy of the world. Unravelling the correlations and interplay of the human microbiota in the gut–lung axis would offer incredible solutions to the underlying mystery of the disease progression. The study compared the microbiota profiles of six samples namely healthy gut, healthy lung, COVID-19 infected gut, COVID-19 infected lungs, Clostridium difficile infected gut and community-acquired pneumonia infected lungs. The metagenome data sets were processed, normalized, classified and the rarefaction curves were plotted. The microbial biomarkers for COVID-19 infections were identified as the abundance of Candida and Escherichia in lungs with Ruminococcus in the gut. Candida and Staphylococcus could play a vital role as putative prognostic biomarkers of community-acquired pneumonia whereas abundance of Faecalibacterium and Clostridium is associated with the C. difficile infections in gut. A machine learning random forest classifier applied to the data sets efficiently classified the biomarkers. The study offers an extensive and incredible understanding of the existence of gut–lung axis during dysbiosis of two anatomically different organs.
Publisher
Oxford University Press (OUP)
Subject
Applied Microbiology and Biotechnology
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献