Mouse Model Multi-Omics Data on Lung Injury and Gut Disruption

Longitudinal multi-omics data from a mouse model study investigating lung injury, gut microbiome, and serum metabolism. Zhihang Liu published this dataset in April 2026, integrating lung pathology, lung function, 16S rRNA sequencing, and untargeted metabolomics from day 1 to day 15 post-infection.

Use Cases

• Identify metabolic biomarkers like 1-Methylnicotinamide (1-MNA) for acute exacerbation using random forest modeling on serum metabolome data.
• Analyze correlations between gut microbiota abundance (e.g., Intestinimonas) and pulmonary inflammatory markers across the 15-day time series.
• Model the temporal progression of lung pathology features, including immune cell recruitment, collagen deposition, and emphysema.
• Investigate the sustained downregulation of serum unsaturated fatty acid biosynthesis pathways and their correlation with lung inflammation metrics.

Strengths

• Integrates four distinct data modalities: lung pathology/function, gut microbiome, and serum/fecal metabolomes.
• Provides longitudinal data with measurements from day 1 to day 15 post-infection.
• Includes results from random forest analysis identifying a biomarker with an AUC of 1.0.

Limitations

• Data is derived from a controlled mouse model, limiting direct applicability to human clinical research.
• The 11.4 MB file size suggests a relatively small sample size, though exact row count is unknown.
• Specific column names and data structure within the Excel file are not provided.

Provenance

Source

figshare, authored by Zhihang Liu.

Collection Method

Experimental data from a mouse model study involving cigarette smoke exposure, influenza A virus infection, and subsequent multi-omics analysis.

Time Range

Longitudinal measurements from day 1 to day我们发现15 post-infection.

Freshness

Last updated April 2026.

Geography

Laboratory study; geographic coverage is null.