Dataset Information

Network motif analysis of a multi-mode genetic-interaction network.

ABSTRACT: Different modes of genetic interaction indicate different functional relationships between genes. The extraction of biological information from dense multi-mode genetic-interaction networks demands appropriate statistical and computational methods. We developed such methods and implemented them in open-source software. Motifs extracted from multi-mode genetic-interaction networks form functional subnetworks, highlight genes dominating these subnetworks, and reveal genetic reflections of the underlying biochemical system.

SUBMITTER: Taylor RJ

PROVIDER: S-EPMC2374991 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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Network motif analysis of a multi-mode genetic-interaction network.

Taylor R James RJ Siegel Andrew F AF Galitski Timothy T

Genome biology 20070101 8

Different modes of genetic interaction indicate different functional relationships between genes. The extraction of biological information from dense multi-mode genetic-interaction networks demands appropriate statistical and computational methods. We developed such methods and implemented them in open-source software. Motifs extracted from multi-mode genetic-interaction networks form functional subnetworks, highlight genes dominating these subnetworks, and reveal genetic reflections of the unde ...[more]

PMID: 17683534

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Project description:Chinese medicine is a complex system guided by traditional Chinese medicine (TCM) theories, which has proven to be especially effective in treating chronic and complex diseases. However, the underlying modes of action (MOA) are not always systematically investigated. Herein, a systematic study was designed to elucidate the multi-compound, multi-target and multi-pathway MOA of a Chinese medicine ,QSYQ, on myocardial infarction. Male Sprague Dawley rat model of myocardial infarction were administered QSYQ intragastrically for 7 days while the control group was not treated. The differentially expressed genes (DEGs) were identified from myocardial infarction rat model treated with QSYQ, followed by constructing a cardiovascular disease (CVD)-related multilevel compound-target-pathway network connecting main compounds to those DEGs supported by literature evidences and the pathways that are functionally enriched. Three conditions were compared with three replicates each: (1) sham, i.e. without left anterior descending coronary artery (LAD) ligation; (2) model, with LAD ligation; (3) QSYQ, with LAD ligation and treated with QSYQ intragastrically for 7 days, the dosage was 105.6 mg/kg once a day. Rats were sacrificed after 7 days of i.g. administration under 10% chloral hydrate anesthesia (300mg/kg). Three tissue samples on the border between infarct and non-infarct area were dissected from left ventricles of each group. The tissue samples were stored at -80M-bM-^DM-^C refrigerator. Total RNA was extracted using TRIZol Reagent (Invitrogen) and purified using RNeasy Mini kit (QIAGEN), following manufacturersM-bM-^@M-^Y protocols. RNA quality was evaluated using an Agilent 2100 Bioanalyzer and electrophoresis in 2% (w/v) agarose gels. Only RNA with RNA integrity numbers (RINs) greater than 7.0 and 28SrRNA/18S rRNA ratio greater than 0.7 was used for microarray analyses. Whole genome microarray analysis was performed using Affymetrix rat Genome 230 2.0 chips.

Dataset Information

Network motif analysis of a multi-mode genetic-interaction network.

Publications

Network motif analysis of a multi-mode genetic-interaction network.

Similar Datasets

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