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- Title
Deducing corticotropin-releasing hormone receptor type 1 signaling networks from gene expression data by usage of genetic algorithmsand graphical Gaussian models.
- Authors
Trümbach, Dietrich; Graf, Cornelia; Pütz, Benno; Kühne, Claudia; Panhuysen, Marcus; Weber, Peter; Holsboer, Florian; Wurst, Wolfgang; Welzl, Gerhard; Deussing, Jan M.
- Abstract
Background: Dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis is a hallmark of complex and multifactorial psychiatric diseases such as anxiety and mood disorders. About 50-60% of patients with major depression show HPA axis dysfunction, i.e. hyperactivity and impaired negative feedback regulation. The neuropeptide corticotropin-releasing hormone (CRH) and its receptor type 1 (CRHR1) are key regulators of this neuroendocrine stress axis. Therefore, we analyzed CRH/CRHR1-dependent gene expression data obtained from the pituitary corticotrope cell line AtT-20, a well-established in vitro model for CRHR1-mediated signal transduction. To extract significantly regulated genes from a genome-wide microarray data set and to deduce underlying CRHR1- dependent signaling networks, we combined supervised and unsupervised algorithms. Results: We present an efficient variable selection strategy by consecutively applying univariate as well as multivariate methods followed by graphical models. First, feature preselection was used to exclude genes not differentially regulated over time from the dataset. For multivariate variable selection a maximum likelihood (MLHD) discriminant function within GALGO, an R package based on a genetic algorithm (GA), was chosen. The topmost genes representing major nodes in the expression network were ranked to find highly separating candidate genes. By using groups of five genes (chromosome size) in the discriminant function and repeating the genetic algorithm separately four times we found eleven genes occurring at least in three of the top ranked result lists of the four repetitions. In addition, we compared the results of GA/MLHD with the alternative optimization algorithms greedy selection and simulated annealing as well as with the state-of-the-art method random forest. In every case we obtained a clear overlap of the selected genes independently confirming the results of MLHD in combination with a genetic algorithm. With two unsupervised algorithms, principal component analysis and graphical Gaussian models, putative interactions of the candidate genes were determined and reconstructed by literature mining. Differential regulation of six candidate genes was validated by qRT-PCR. Conclusions: The combination of supervised and unsupervised algorithms in this study allowed extracting a small subset of meaningful candidate genes from the genome-wide expression data set. Thereby, variable selection
- Subjects
GENETIC algorithms; GENETIC regulation; HORMONE receptors; GENE expression; MENTAL depression; MICROBIAL genetics
- Publication
BMC Systems Biology, 2010, Vol 4, p159
- ISSN
1752-0509
- Publication type
Article
- DOI
10.1186/1752-0509-4-159