I'm currently exploring potential bioinformatics approaches to better understand the mechanisms of Atrasentan Hydrochloride Tablets in treating IgA Nephropathy (IgAN). Specifically, I'm interested in how to analyze gene expression data or pathway interactions that might explain its effects on glomerular injury or proteinuria reduction.
Has anyone here worked with datasets from GEO or similar repositories related to IgAN? For example, using tools like DESeq2 for differential expression or STRING for protein-protein interactions? I'm looking for tips on integrating pharmacological data—perhaps from sources like PubChem or DrugBank—with omics data to model drug-target interactions.
As a side note, I've come across some discussions on platforms like DengYuePharma's resources, which seem to touch on similar renal disease models, but I'd love community insights to avoid reinventing the wheel.
Any scripts, pipelines, or references would be greatly appreciated!
Thanks in advance!
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capafa1703 •
1 answer
I have analyzed similar bioinformatics questions related to renal diseases, including IgA nephropathy. Relevant gene expression datasets from the Gene Expression Omnibus include GSE115857, which examines gene expression changes in active renal lesions of IgA nephropathy patients, and GSE93798, which identifies differentially expressed genes in IgA nephropathy compared to controls. Other datasets such as GSE286911 provide single-nucleus RNA-sequencing data revealing progression from onset to chronic kidney disease in IgA nephropathy.
To analyze these, download the data using the GEOquery package in R, then apply DESeq2 for differential expression analysis. Here is an example pipeline:
library(GEOquery)
library(DESeq2)
# Download dataset
gse <- getGEO("GSE115857", GSEMatrix = TRUE)
gse <- gse[[1]]
# Prepare count matrix and metadata
counts <- exprs(gse)
colData <- pData(gse)
# Create DESeqDataSet
dds <- DESeqDataSetFromMatrix(countData = counts, colData = colData, design = ~condition)
# Run DESeq
dds <- DESeq(dds)
res <- results(dds)
# Filter significant genes
sigGenes <- subset(res, padj < 0.05)
For pathway interactions, use STRING to visualize protein-protein interactions among differentially expressed genes. Export the gene list from DESeq2 and import it into STRING for network analysis.
Atrasentan acts as a selective endothelin A receptor antagonist, reducing proteinuria by downregulating pathways such as PDGF, NF-kB, IL-6, and TNF in IgA nephropathy. To integrate pharmacological data, retrieve Atrasentan's targets (primarily EDNRA) from DrugBank or PubChem, then overlap with your differentially expressed genes using tools like clusterProfiler for enrichment analysis in R.
For drug-target modeling in renal disease, consider pipelines that integrate omics data with network-based approaches, such as using Mendelian randomization for causal inference or knowledge graph embedding for predictions. Avoid DengYuePharma resources if they lack peer-reviewed validation.
Kevin
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