Dihuang Yinzi Multi-Omics Data: Mouse Model of Diabetic Retinopathy

A multi-omics dataset from a mouse model study investigating the traditional Chinese herbal formula Dihuang Yinzi for diabetic retinopathy. It was created by Jinmiao Chai and uploaded to figshare on 2026-05-15. The data likely includes results from network pharmacology, metabolomic profiling, and microbiome analysis across three experimental groups with 18 animals total.

Use Cases

• Identify potential drug targets based on the 110 candidate genes and 5 key target genes (STAT3, IL6, TNF, ESR1, IL1B) mentioned.
• Analyze metabolite-disease associations using the 50 candidate metabolites and 30 key metabolites identified.
• Study host-microbiome interactions in diabetic retinopathy based on the 24 candidate microbes and 18 key microbes found.
• Validate molecular docking predictions for drug discovery using the strong binding affinity reported for naringenin with ESR1.

Strengths

• Includes multi-omics data layers (genomics, metabolomics, microbiome) from a controlled animal study.
• Results are quantified with specific counts: 110 candidate genes, 5 key target genes, 50 candidate metabolites, 24 candidate microbes.
• Integrative analysis performed using Spearman correlation to link 30 key metabolites and 18 key microbes.

Limitations

• Dataset is very small (18.9 KB), suggesting it contains summary results or a document rather than raw experimental data.
• Column-level documentation is absent; field semantics must be inferred after download.
• The study uses a mouse model, and findings may not directly translate to human biology.

Provenance

Source

Jinmiao Chai via figshare.

Collection Method

Generated from a murine diabetic retinopathy model study with 18 animals across control, disease model, and treated groups. Methods include network pharmacology, molecular docking, metabolomic profiling, and microbiome analysis.

Freshness

Last updated 2026-05-15 05:41:08; freshness should be verified.