Identifying a predictive gene signature and signaling networks/pathways associated with acute kidney injury

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

Logistic regression analysis of clinical outcomes in women categorized as vitamin D sufficient compared to vitamin D insufficient or deficient women.

<p>*, note: none of the differences were p<0.05 by logistic regression (chi-square analysis)</p><p>**, Adjusted for BMI, season, ethnicity, and tobacco use</p><p>OR, odds ratio</p><p>IUGR, intrauterine growth restriction; GDM, gestational diabetes mellitus.</p><p>Logistic regression analysis of clinical outcomes in women categorized as vitamin D sufficient compared to vitamin D insufficient or deficient women.</p

Distribution of clinical outcomes in women categorized as Vitamin D deficient, insufficient, or sufficient.

<p>IUGR, intrauterine growth restriction; GDM, gestational diabetes mellitus.</p><p>Distribution of clinical outcomes in women categorized as Vitamin D deficient, insufficient, or sufficient.</p

Plasma vitamin D concentrations in women according to clinical outcome.

<p>IUGR, intrauterine growth restriction; GDM, gestational diabetes mellitus.</p><p>Plasma vitamin D concentrations in women according to clinical outcome.</p

Systems Level Analysis and Identification of Pathways and Networks Associated with Liver Fibrosis

<div><p>Toxic liver injury causes necrosis and fibrosis, which may lead to cirrhosis and liver failure. Despite recent progress in understanding the mechanism of liver fibrosis, our knowledge of the molecular-level details of this disease is still incomplete. The elucidation of networks and pathways associated with liver fibrosis can provide insight into the underlying molecular mechanisms of the disease, as well as identify potential diagnostic or prognostic biomarkers. Towards this end, we analyzed rat gene expression data from a range of chemical exposures that produced observable periportal liver fibrosis as documented in DrugMatrix, a publicly available toxicogenomics database. We identified genes relevant to liver fibrosis using standard differential expression and co-expression analyses, and then used these genes in pathway enrichment and protein-protein interaction (PPI) network analyses. We identified a PPI network module associated with liver fibrosis that includes known liver fibrosis-relevant genes, such as tissue inhibitor of metalloproteinase-1, galectin-3, connective tissue growth factor, and lipocalin-2. We also identified several new genes, such as perilipin-3, legumain, and myocilin, which were associated with liver fibrosis. We further analyzed the expression pattern of the genes in the PPI network module across a wide range of 640 chemical exposure conditions in DrugMatrix and identified early indications of liver fibrosis for carbon tetrachloride and lipopolysaccharide exposures. Although it is well known that carbon tetrachloride and lipopolysaccharide can cause liver fibrosis, our network analysis was able to link these compounds to potential fibrotic damage before histopathological changes associated with liver fibrosis appeared. These results demonstrated that our approach is capable of identifying early-stage indicators of liver fibrosis and underscore its potential to aid in predictive toxicity, biomarker identification, and to generally identify disease-relevant pathways.</p></div

Mapping toxicity pathways of liver fibrosis using integrated gene expression and protein-protein interaction network analysis

<p>Liver fibrosis is a common pathologic feature observed in a wide spectrum of liver injuries. Identifying toxicity pathways associated with liver fibrosis can provide insight into the underlying molecular mechanisms of the disease, as well as identify potential biomarkers. Towards this end, we analyzed DrugMatrix, a toxicogenomics database with gene expression data from rats after exposure to diverse chemicals and drugs. We used differential expression and co-expression analyses to identify liver fibrosis-relevant genes. These genes were then mapped to protein-protein interaction (PPI) networks to identify network modules associated with liver fibrosis. We identified a network module that was enriched with known liver fibrosis genes such as Timp1 and Lgals3. The network module also supports the published disease mechanism and identified potential new genes associated with liver fibrosis. Using our network analysis we were able to link compounds such as carbon tetrachloride to potential fibrotic damage before histopathological changes associated with liver fibrosis appeared. The gene expression pattern of genes in the network module was in agreement with external liver fibrosis data from Gene Expression Omnibus (GEO). These results demonstrated that our integrated gene expression and PPI network analysis approach has the potential to aid in predictive toxicity, biomarker identification, and to identify toxicity pathways. </p

Validation with external datasets.

<p>M5 gene expression compared with external datasets. A) GSE13747 represents liver fibrosis produced by bile duct ligation. B) GSE6929 represents sunitinib (SU11248) treatment in liver cirrhosis.</p

Number of fibrosis-relevant genes from differential and co-expression analysis.

<p>Number of genes in the liver fibrosis-relevant differentially expressed gene list and liver fibrosis-relevant co-expressed gene list and the overlap between them.</p

Chemical exposure conditions that produced periportal liver fibrosis.

<p>Chemical exposure conditions that produced periportal liver fibrosis.</p

Workflow used in this study to identify pathways and networks associated with liver fibrosis.

<p>Workflow used in this study to identify pathways and networks associated with liver fibrosis.</p