Deducing corticotropin-releasing hormone receptor type 1 signaling networks from gene expression data by usage of genetic algorithms and graphical Gaussian models

Abstract

BackgroundDysregulation 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.ResultsWe 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.ConclusionsThe 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 using different optimization algorithms based on linear classifiers as well as the nonlinear random forest method resulted in congruent candidate genes. The calculated interacting network connecting these new target genes was bioinformatically mapped to known CRHR1-dependent signaling pathways. Additionally, the differential expression of the identified target genes was confirmed experimentally.

Corticotropin-releasing hormone regulates common target genes with divergent functions in corticotrope and neuronal cells

“…1A). The regulation of Crem and Nf2 by CRH in AtT-20 cells was validated in agreement with previous studies (Trümbach et al, 2010).…”

“…The signaling mechanisms of CRHR1 in AtT-20 cells have been extensively studied, whereas detailed analyses of neuronal CRHR1 signal transduction are missing (Peeters et al, 2004; Kronsbein Trümbach et al, 2010). As the selected five candidates regulated by CRH in AtT-20 cells show a functional effect on neuronal CRHR1 signaling we were interested if these genes are also regulated by CRH in the neuronal cell line HN9.…”

“…Cells were maintained in a humidified 5% CO 2 atmosphere at 37°C. After FCS deprivation for 18 h cells were treated in each experiment with human/rat CRH (100 nM in 0.01 M acetic acid) (Bachem, Heidelberg, Germany) for 1, 3, 6, 12 and 24 h, respectively (Trümbach et al, 2010). The efficacy of the CRH treatment in AtT-20 cells was approved by increased levels of ACTH in the cell culture medium and by increased Pomc mRNA expression as determined by qRT-PCR (Supplementary Fig.…”

“…The gene expression data were deposited in NCBI's Gene Expression Omnibus (GEO) (Edgar et al, 2002) and are accessible by the GEO Series accession numbers GSE13156 and GPL7467 (Trümbach et al, 2010).…”

“…Therefore, mouse pituitary corticotrope AtT-20 cells are an established cell culture model for CRHR1 signaling research. Peeters et al (2004) as well as Trüm-bach et al (2010) identified new target genes of CRHR1 signaling in AtT-20 cells by using microarray technology, but never investigated the functional role of these target genes in CRHR1-controled signal transduction itself (Peeters et al, 2004;Trümbach et al, 2010). Furthermore, the precise signaling mechanisms of CRHR1 in neuronal cell types are not fully understood yet.…”