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

<p>Abstract</p> <p>Background</p> <p>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 <it>in vitro </it>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.</p> <p>Results</p> <p>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.</p> <p>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.</p> <p>Conclusions</p> <p>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 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.</p

Gene response profiles for Daphnia pulex exposed to the environmental stressor cadmium reveals novel crustacean metallothioneins

<p>Abstract</p> <p>Background</p> <p>Genomic research tools such as microarrays are proving to be important resources to study the complex regulation of genes that respond to environmental perturbations. A first generation cDNA microarray was developed for the environmental indicator species <it>Daphnia pulex</it>, to identify genes whose regulation is modulated following exposure to the metal stressor cadmium. Our experiments revealed interesting changes in gene transcription that suggest their biological roles and their potentially toxicological features in responding to this important environmental contaminant.</p> <p>Results</p> <p>Our microarray identified genes reported in the literature to be regulated in response to cadmium exposure, suggested functional attributes for genes that share no sequence similarity to proteins in the public databases, and pointed to genes that are likely members of expanded gene families in the <it>Daphnia </it>genome. Genes identified on the microarray also were associated with cadmium induced phenotypes and population-level outcomes that we experimentally determined. A subset of genes regulated in response to cadmium exposure was independently validated using quantitative-realtime (Q-RT)-PCR. These microarray studies led to the discovery of three genes coding for the metal detoxication protein metallothionein (MT). The gene structures and predicted translated sequences of <it>D. pulex </it>MTs clearly place them in this gene family. Yet, they share little homology with previously characterized MTs.</p> <p>Conclusion</p> <p>The genomic information obtained from this study represents an important first step in characterizing microarray patterns that may be diagnostic to specific environmental contaminants and give insights into their toxicological mechanisms, while also providing a practical tool for evolutionary, ecological, and toxicological functional gene discovery studies. Advances in <it>Daphnia </it>genomics will enable the further development of this species as a model organism for the environmental sciences.</p

Nutritional Systems Biology of Fat : integration and modeling of transcriptomics datasets related to lipid homeostasis

Fatty acids, in the form of triglycerides, are the main constituent of the class of dietary lipids. They not only serve as a source of energy but can also act as potent regulators of gene transcription. It is well accepted that an energy rich diet characterized by high intakes of dietary fat is linked to the dramatic increase in the prevalence of obesity in both developed and developing countries in the last several decades. Obese individuals are at increased risk of developing the metabolic syndrome, a cluster of metabolic abnormalities that ultimately increase the risk of developing vascular diseases and type 2 diabetes. Many studies have been performed to uncover the role of fatty acids on gene expression in different organs, but integrative studies in different organs over time driven by high throughput data are lacking. Therefore, we first aimed to develop integrative approaches on the level of individual genes but also pathways using genome-wide transcriptomics datasets of mouse liver and small intestine that are related to fatty acid sensing transcription factor peroxisome proliferator activated receptor alpha (PPARα). We also aimed to uncover the behavior of PPARαtarget genes and their corresponding biological functions in a short time series experiment, and integrated and modeled the influence of different levels of dietary fat and the time dependency on transcriptomics datasets obtained from several organs by developing system level approaches. We developed an integrative statistical approach that properly adjusted for multiple testing while integrating data from two experiments, and was driven by biological inference. By quantifying pathway activities in different mouse tissues over time and subsequent integration by partial least squares path model, we found that the induced pathways at early time points are the main drivers for the induced pathways at late time points. In addition, using a time course microarray study of rat hepatocytes, we found that most of the PPARα target genes at early stage are involved in lipid metabolism-related processes and their expression level could be modeled using a quadratic regression function. In this study, we also found that the transcription factorsNR2F, CREB, EREF and RXR might work together with PPARα in the regulation of genes involved in lipid metabolism. By integrating time and dose dependent gene expression data of mouse liver and white adipose tissue (WAT), we found a set of time-dose dependent genes in liver and WAT including potential signaling proteinssecreted from WAT that may induce metabolic changes in liver, thereby contributing to the pathogenesis of obesity. Taken together, in this thesis integrative statistical approaches are presented that were applied to a variety of datasets related to metabolism of fatty acids. Results that were obtained provide a better understanding of the function of the fatty acid-sensor PPARa, and identified a set of secreted proteins that may be important for organ cross talk during the development of diet induced obesity. </p