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Mining kidney toxicogenomic data by using gene co-expression modules
Mohamed Diwan M AbdulHameed1, Danielle L Ippolito2, Jonathan D Stallings2
1Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, 504 Scott Street, Fort Detrick, MD, 21702, USA.
Background:
Acute kidney injury (AKI) caused by drug and toxicant ingestion is a serious clinical condition associated with high mortality rates. We currently lack detailed knowledge of the underlying molecular mechanisms and biological networks associated with AKI. In this study, we carried out gene co-expression analyses using DrugMatrix-a large toxicogenomics database with gene expression data from rats exposed to diverse chemicals-and identified gene modules associated with kidney injury to probe the molecular-level details of this disease.
Results:
We generated a comprehensive set of gene co-expression modules by using the Iterative Signature Algorithm and found distinct clusters of modules that shared genes and were associated with similar chemical exposure conditions. We identified two module clusters that showed specificity for kidney injury in that they 1) were activated by chemical exposures causing kidney injury, 2) were not activated by other chemical exposures, and 3) contained known AKI-relevant genes such as Havcr1, Clu, and Tff3. We used the genes in these AKI-relevant module clusters to develop a signature of 30 genes that could assess the potential of a chemical to cause kidney injury well before injury actually occurs. We integrated AKI-relevant module cluster genes with protein-protein interaction networks and identified the involvement of immunoproteasomes in AKI. To identify biological networks and processes linked to Havcr1, we determined genes within the modules that frequently co-express with Havcr1, including Cd44, Plk2, Mdm2, Hnmt, Macrod1, and Gtpbp4. We verified this procedure by showing that randomized data did not identify Havcr1 co-expression genes and that excluding up to 10 % of the data caused only minimal degradation of the gene set. Finally, by using an external dataset from a rat kidney ischemic study, we showed that the frequently co-expressed genes of Havcr1 behaved similarly in a model of non-chemically induced kidney injury.
Conclusions:
Our study demonstrated that co-expression modules and co-expressed genes contain rich information for generating novel biomarker hypotheses and constructing mechanism-based molecular networks associated with kidney injury.
Insights
Researchers identified gene co-expression modules linked to acute kidney injury (AKI) using toxicogenomics data. This approach reveals molecular mechanisms and aids in developing biomarkers for early detection of drug-induced kidney damage.
Area of Science:
- Toxicogenomics
- Bioinformatics
- Molecular Biology
- Nephrology
Background:
- Drug and toxicant ingestion causes acute kidney injury (AKI), a condition with high mortality.
- Current understanding of AKI's molecular mechanisms and biological networks is limited.
- Investigating gene expression patterns in response to chemical exposure is crucial for understanding AKI.
Purpose of the Study:
- To identify molecular mechanisms and biological networks underlying AKI.
- To analyze gene co-expression patterns in response to chemical exposure using the DrugMatrix database.
- To develop a gene signature for predicting chemical-induced kidney injury.
Main Methods:
- Utilized the Iterative Signature Algorithm to generate gene co-expression modules from DrugMatrix data.
- Performed gene co-expression analyses on rat gene expression data following chemical exposure.
- Integrated gene modules with protein-protein interaction networks and external datasets for validation.
Main Results:
- Identified specific gene module clusters associated with kidney injury, containing known AKI-related genes (e.g., Havcr1, Clu, Tff3).
- Developed a 30-gene signature capable of predicting kidney injury potential before its occurrence.
- Discovered the involvement of immunoproteasomes in AKI and identified key co-expressed genes with Havcr1.
Conclusions:
- Gene co-expression modules provide valuable information for generating biomarker hypotheses.
- Co-expressed genes facilitate the construction of mechanism-based molecular networks for kidney injury.
- This approach enhances understanding of AKI's molecular underpinnings and aids in biomarker discovery.
