The Rat Genome Database 2013--data, tools and users

Laulederkind, Stanley J. F.; Hayman, G. Thomas; Wang, Shur-Jen; Smith, Jennifer; Lowry, T. F.; Nigam, Rajni; Petri, Victoria; Pons, Jeff De; Dwinell, Melinda R.; Shimoyama, Mary; Munzenmaier, Diane H.; Worthey, Elizabeth A.; Jacob, Howard J.

doi:10.1093/bib/bbt007

Cited by 61 publications

(59 citation statements)

References 19 publications

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“…18 'Cluster' of genes, known to be involved in inflammation, were identified using Rat Genome database and Netage database. 64,65 Functional gene-gene network interaction was inferred using STRING tool and literature search. 66,67 STRING uses diverse method to predict relationship among genes which include neighborhood, gene fusion, co-occurrence co-expression, experiments, databases and text-mining.…”

Section: Gene Expression By Qpcrmentioning

confidence: 99%

Islet adaptation to obesity and insulin resistance in WNIN/GR-Ob rats

Singh¹,

Ganneru²,

Malakapalli³

et al. 2014

Islets

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WNIN/GR-Ob mutant rat is a novel animal model to study metabolic syndrome (obesity, insulin resistance, hyperinsulinemia, impaired glucose tolerance and cardiovascular diseases). We have investigated the islet characteristics of obese mutants at different age groups (1, 6 and 12 months) to assess the islet changes in response to early and chronic metabolic stress. Our data demonstrates altered islet cell morphology and function (hypertrophy, fibrotic lesions, vacuolation, decreased stimulation index, increased TNFa, ROS and TBARS levels) in mutants as compared to controls. Furthermore, network analysis (gene-gene interaction) studied in pancreas demonstrated increased inflammation as a key factor underlying obesity/metabolic syndrome in mutants. These observations pave way to explore this model to understand islet adaptation in response to metabolic syndrome.

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Section: Gene Expression By Qpcrmentioning

confidence: 99%

Islet adaptation to obesity and insulin resistance in WNIN/GR-Ob rats

Singh¹,

Ganneru²,

Malakapalli³

et al. 2014

Islets

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“…To find genes that were broadly characteristic of these modules we identified the center genes, as described in the Methods, derived from all modules that showed an activation of greater than 1.5. Tables 4 and 5 list the characteristic genes for the two injury indicators, as well as functional gene annotations from the Rat Genome Database (RGD) [56].…”

Section: Resultsmentioning

confidence: 99%

Characterization of Chemically Induced Liver Injuries Using Gene Co-Expression Modules

Tawa

AbdulHameed

et al. 2014

PLoS ONE

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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.

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“…The potential influence of 4 covariates on SBP was regressed out and the residuals were then used as the phenotype vector for genomic variable selection. Bioinformatics analysis [13] was performed to study the association of the selected markers/genes to BP-related disease.…”

Section: Discussionmentioning

confidence: 99%

“…The bioinformatics analysis was based on Human Genome Assembly GRCh37 (Genome Reference Consortium Human genome build 37) [13]. The input into RGD are the genes selected (the selected SNP/expression corresponded genes).…”

Section: Methodsmentioning

confidence: 99%

Integrating multiple genomic data: sparse representation based biomarker selection for blood pressure

et al. 2016

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BackgroundAlthough many genes have been implicated as hypertension candidates, to date, few studies have integrated different types of genomic data for the purpose of biomarker selection.MethodsApplying a newly proposed sparse representation based variable selection (SRVS) method to the Genetic Analysis Workshop19 data, we analyzed a combined data set consisting of 11522 gene expressions and 354893 single-nucleotide polymorphisms (SNPs) from 397 subjects (case/control: 151/246), with the aim to identify potential biomarkers for blood pressure using both gene expression measures and SNP data.ResultsAmong the top 1000 variables (SNPs/gene expressions = 575/425) selected, the bioinformatics analysis showed that 302 were plausibly associated with blood pressure. In addition, we identified 173 variables that were associated with body weight and 84 associated with left ventricular contractility. Together, 55.9 % of the top 1000 variables showed associations with blood pressure related phenotypes(SNP/gene expression =348/211).ConclusionsOur results support the feasibility of the SRVS algorithm in integrating multiple data sets of different structure for comprehensive analysis.

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The Rat Genome Database 2013--data, tools and users

Cited by 61 publications

References 19 publications

Islet adaptation to obesity and insulin resistance in WNIN/GR-Ob rats

Islet adaptation to obesity and insulin resistance in WNIN/GR-Ob rats

Characterization of Chemically Induced Liver Injuries Using Gene Co-Expression Modules

Integrating multiple genomic data: sparse representation based biomarker selection for blood pressure

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