Poster presented in SOT 2016<div><br></div><div>Understanding the molecular mechanisms
and signaling networks of acute kidney injury (AKI) will aid in biomarker
development. In this study, we carried out co-expression-based analyses of
DrugMatrix, a toxicogenomics database with kidney gene expression data from
rats after exposure to diverse chemicals. We used the iterative signature
algorithm and exhaustively generated modules using 50 different parameter
combinations. We clustered the modules using gene and condition overlap scores
and obtained 16 module clusters. Two of the module clusters showed activation
in chemical exposures causing kidney injury and mapped well-known AKI marker
genes such as <i>Havcr1</i>, <i>Tff3,</i> and <i>Clu</i>. 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 <em>Havcr1</em>, we determined genes
within the modules that frequently co-express with <em>Havcr1</em>,
including <em>Cd44</em>, <em>Plk2</em>, <em>Mdm2</em>, <em>Hnmt</em>, <em>Macrod1</em>,
and <em>Gtpbp4</em>. In this gene set,
CD44 is a potential non-invasive biomarker candidate as it is up-regulated
during AKI, undergoes cleavage of its ectodomain, and is secreted in urine. Overall, our analysis shows data mining of toxicological big data and
identification of new insights/biomarker candidates for acute kidney injury.</div
