Mining kidney toxicogenomic data by using gene co-expression modules

Abstract

BackgroundAcute 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.ResultsWe 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.ConclusionsOur 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3143-y) contains supplementary material, which is available to authorized users.

Proteomics and Metabolomics for AKI Diagnosis

“…As proof for the validity of our molecular interaction model, many of the (patho)physiological processes mediated by these cell surface-receptor complexes were described also in the context of AKI or its underlying disease conditions. [245][246][247][248][249][250][251][252] This also accounts for all other proteins included in the molecular interaction graph not involved in receptor signaling such as the flavin adenine dinucleotide-linked sulfhydryl oxidase ALR Q37 , for which renoprotective effects were described in ischemia/reperfusion, 253 the peptidylprolyl isomerase F, a mitochondrial protein found to be involved in ischemia/reperfusion-induced cell death, 254 the calcitonin gene-related peptide 2, a vasodilator that increases susceptibility to AKI, 255 and superoxide dismutase 3, an extracellular oxidoreductase that decreases oxidative stress and injury after ischemia/ reperfusion-induced AKI. 256 The connection of all gap-bridging proteins to AKI was interpreted as a sign for the high integrity of the molecular interaction graph.…”

Identification of the Toxicity Pathways Associated With Thioacetamide-Induced Injuries in Rat Liver and Kidney

“…Computational methods, such as bi-clustering ( Pontes et al, 2015 ), are used to create co-expressed gene sets, which consist of genes whose expression pattern is correlated across a set of chemical exposure conditions. In our initial efforts, we used the Iterative Signature Algorithm ( Bergmann et al, 2003 ) to identify co-expressed gene sets (modules) associated with molecular toxicity pathways and link them to specific injuries in the liver and kidney ( Tawa et al, 2014 ; AbdulHameed et al, 2016 ). Our injury modules were selectively activated by chemical insults.…”

Transcriptional Responses Reveal Similarities Between Preclinical Rat Liver Testing Systems

“…Other studies have addressed various questions of applying TGx including optimal treatment duration and sample size for a better predictive performance ( Liu et al, 2011 ; Gusenleitner et al, 2014 ; Matsumoto et al, 2015 ; Liu S. et al, 2017 ). TGx has also been used in semi-quantitative risk assessment such as defining points of departure and benchmark dosing ( Yang et al, 2007 ; AbdulHameed et al, 2016 ; Chauhan et al, 2016 ; Dean et al, 2017 ; Farmahin et al, 2017 ; Kawamoto et al, 2017 ). Most widely used application of TGx approaches is to understand the molecular mechanisms of different toxicological endpoints ( Ellinger-Ziegelbauer et al, 2008 ; Blomme et al, 2009 ; Rodrigues et al, 2016 ; Hendrickx et al, 2017 ; Rueda-Zárate et al, 2017 ).…”