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HRA011076
   HRA011076.xlsx
Title:
Integrative Multi-Omics Analysis Reveals Microbiota Alterations and Clinical Indicators Predictive of Pulmonary Fibrosis Progression Following SARS-CoV-2 Infection
Release date:
2025-04-21
Description:
Pulmonary fibrosis (PF) following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a life-threatening complication, characterized by irreversible respiratory dysfunction. 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. This study employs a multi-omics approach to investigate alterations in both microbiota profiles and clinical indicators in patients with PF following SARS-CoV-2 infection. The aim is to develop a predictive model for the onset and progression of PF, incorporating risk stratification to enable early targeted therapeutic interventions and facilitate timely clinical decision-making, ultimately mitigating irreversible parenchymal remodeling and improving long-term respiratory outcomes.
Data Accessibility:   
Controlled access Request Data
BioProject:
PRJCA038245
Study type:
Human metagenome
Data Access Committee

For each controlled access study, there is a corresponding Data Access Committee(DAC) to determine the access permissions. Access to actual data files is not managed by NGDC.


DAC NO.:
HDAC005761
DAC name:
Metagenome of sputum and fecal
Contact person:
Shen Yifei
Email:
yifeishen@zju.edu.cn
Description:
A total of 68 patients with confirmed SARS-CoV-2 infection were included in the study, divided into two subgroups: patients with PF and patients without PF. Metagenomic sequencing of sputum and fecal specimens was performed to profile respiratory and intestinal microbiota. Peripheral blood mononuclear cells (PBMCs) were collected for transcriptome sequencing, and gene expression data were analyzed using Gene Set Enrichment Analysis to identify pathway alterations associated with PF. Spearman correlation analysis was used to examine relationships between clinical indicators and microbial composition. A random forest classifier was developed to predict PF risk based on integrated respiratory-intestinal microbiota profiles as well as clinical indicators.
Individuals & samples
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Submitter:   Shen Yifei / yifeishen@zju.edu.cn
Organization:   The First Affiliated Hospital, Zhejiang University School of Medicine
Submission date:   2025-04-11
Requests:   -