ArticleBriefings in bioinformatics2026
Integrative multi-omics analysis reveals microbiota alterations and clinical indicators predictive of pulmonary fibrosis progression following SARS-CoV-2 infection.
Article in Briefings in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Preventing ventilator-associated pneumonia via the gut-lung axis: the Shenling Baizhu San hypothesis.Frontiers in medicine · 2026Article
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Authors and funding
10 authors.
Funding
Abstract
Pulmonary fibrosis (PF) following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a life-threatening complication. Despite growing concerns about PF after SARS-CoV-2 infection, early recognition remains challenging. Additionally, the role of changes in respiratory and intestinal microbiota in PF progression remains insufficiently understood. To address this gap, this study uses a multi-omics approach to analyze microbiota and clinical changes in PF patients following SARS-CoV-2 infection, developing a predictive model for PF progression with risk stratification to enable early interventions and improve outcomes. A total of 68 patients with confirmed SARS-CoV-2 infection were included in the study, divided into two subgroups: patients with PF (COVID-PF) and patients without PF (COVID-non PF). Metagenomic sequencing of bronchoalveolar lavage fluid (BALF) and fecal specimens was performed to profile respiratory and intestinal microbiota. Peripheral blood mononuclear cells (PBMCs) were collected for transcriptome sequencing. A random forest classifier was developed to predict PF risk based on integrated respiratory-intestinal microbiota profiles as well as clinical indicators. Our findings suggest that there are significant differences in the respiratory and intestinal microbiota between COVID-non PF and COVID-PF patients. Transcriptomic analysis of PBMCs revealed significant activation of immunomodulatory pathways associated with PF development. The machine learning model further allowed early PF risk stratification, demonstrating that changes in both microbiomes, along with clinical indicators, can predict the progression and prognosis of PF. Overall, these results offer new insights into disease and suggest options for early detection and personalized treatment strategies for PF in SARS-CoV-2-infected patients.
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