Dataset Information

Characterization of chemically induced liver injuries using gene co-expression modules.

ABSTRACT: Liver injuries due to ingestion or exposure to chemicals and industrial toxicants pose a serious health risk that may be hard to assess due to a lack of non-invasive diagnostic tests. Mapping chemical injuries to organ-specific damage and clinical outcomes via biomarkers or biomarker panels will provide the foundation for highly specific and robust diagnostic tests. Here, we have used DrugMatrix, a toxicogenomics database containing organ-specific gene expression data matched to dose-dependent chemical exposures and adverse clinical pathology assessments in Sprague Dawley rats, to identify groups of co-expressed genes (modules) specific to injury endpoints in the liver. We identified 78 such gene co-expression modules associated with 25 diverse injury endpoints categorized from clinical pathology, organ weight changes, and histopathology. Using gene expression data associated with an injury condition, we showed that these modules exhibited different patterns of activation characteristic of each injury. We further showed that specific module genes mapped to 1) known biochemical pathways associated with liver injuries and 2) clinically used diagnostic tests for liver fibrosis. As such, the gene modules have characteristics of both generalized and specific toxic response pathways. Using these results, we proposed three gene signature sets characteristic of liver fibrosis, steatosis, and general liver injury based on genes from the co-expression modules. Out of all 92 identified genes, 18 (20%) genes have well-documented relationships with liver disease, whereas the rest are novel and have not previously been associated with liver disease. In conclusion, identifying gene co-expression modules associated with chemically induced liver injuries aids in generating testable hypotheses and has the potential to identify putative biomarkers of adverse health effects.

SUBMITTER: Tawa GJ

PROVIDER: S-EPMC4165895 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Characterization of chemically induced liver injuries using gene co-expression modules.

Tawa Gregory J GJ AbdulHameed Mohamed Diwan M MD Yu Xueping X Kumar Kamal K Ippolito Danielle L DL Lewis John A JA Stallings Jonathan D JD Wallqvist Anders A

PloS one 20140916 9

Liver injuries due to ingestion or exposure to chemicals and industrial toxicants pose a serious health risk that may be hard to assess due to a lack of non-invasive diagnostic tests. Mapping chemical injuries to organ-specific damage and clinical outcomes via biomarkers or biomarker panels will provide the foundation for highly specific and robust diagnostic tests. Here, we have used DrugMatrix, a toxicogenomics database containing organ-specific gene expression data matched to dose-dependent c ...[more]

PMID: 25226513

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Project description:BACKGROUND: Biological networks characterize the interactions of biomolecules at a systems-level. One important property of biological networks is the modular structure, in which nodes are densely connected with each other, but between which there are only sparse connections. In this report, we attempted to find the relationship between the network topology and formation of modular structure by comparing gene co-expression networks with random networks. The organization of gene functional modules was also investigated. RESULTS: We constructed a genome-wide Arabidopsis gene co-expression network (AGCN) by using 1094 microarrays. We then analyzed the topological properties of AGCN and partitioned the network into modules by using an efficient graph clustering algorithm. In the AGCN, 382 hub genes formed a clique, and they were densely connected only to a small subset of the network. At the module level, the network clustering results provide a systems-level understanding of the gene modules that coordinate multiple biological processes to carry out specific biological functions. For instance, the photosynthesis module in AGCN involves a very large number (> 1000) of genes which participate in various biological processes including photosynthesis, electron transport, pigment metabolism, chloroplast organization and biogenesis, cofactor metabolism, protein biosynthesis, and vitamin metabolism. The cell cycle module orchestrated the coordinated expression of hundreds of genes involved in cell cycle, DNA metabolism, and cytoskeleton organization and biogenesis. We also compared the AGCN constructed in this study with a graphical Gaussian model (GGM) based Arabidopsis gene network. The photosynthesis, protein biosynthesis, and cell cycle modules identified from the GGM network had much smaller module sizes compared with the modules found in the AGCN, respectively. CONCLUSION: This study reveals new insight into the topological properties of biological networks. The preferential hub-hub connections might be necessary for the formation of modular structure in gene co-expression networks. The study also reveals new insight into the organization of gene functional modules.

Project description:Because of similarities in histopathology and tumor progression stages between mouse and human lung adenocarcinomas, the mouse lung tumor model with lung adenomas as the endpoint has been used extensively to evaluate the efficacy of putative lung cancer chemopreventive agents. In this study, a competitive cDNA library screening (CCLS) was employed to determine changes in the expression of mRNA in chemically induced lung adenomas compared with paired normal lung tissues. A total of 2555 clones having altered expression in tumors were observed following competitive hybridization between normal lung and lung adenomas after primary screening of over 160,000 clones from a mouse lung cDNA library. Among the 755 clones confirmed by dot blot hybridization, 240 clones were underexpressed, whereas 515 clones were overexpressed in tumors. Sixty-five clones with the most frequently altered expression in six individual tumors were confirmed by semiquantitative RT-PCR. When examining the 58 known genes, 39 clones had increased expression and 19 had decreased expression, whereas the 7 novel genes showed overexpression. A high percentage (>60%) of overexpressed or underexpressed genes was observed in at least two or three of the lesions. Reproducibly overexpressed genes included ERK-1, JAK-1, surfactant proteins A, B, and C, NFAT1, alpha-1 protease inhibitor, helix-loop-helix ubiquitous kinase (CHUK), alpha-adaptin, alpha-1 PI2, thioether S-methyltransferase, and CYP2C40. Reproducibly underexpressed genes included paroxanase, ALDH II, CC10, von Ebner salivary gland protein, and alpha- and beta-globin. In addition, CCLS identified several novel genes or genes not previously associated with lung carcinogenesis, including a hypothetical protein (FLJ11240) and a guanine nucleotide exchange factor homologue. This study shows the efficacy of this methodology for identifying genes with altered expression. These genes may prove to be helpful in our understanding of the genetic basis of lung carcinogenesis and in developing biomarkers for lung cancer chemoprevention studies in mice.

Dataset Information

Characterization of chemically induced liver injuries using gene co-expression modules.

Publications

Characterization of chemically induced liver injuries using gene co-expression modules.

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