Gene Bionetwork Analysis of Ovarian Primordial Follicle Development

Ovarian primordial follicles are critical for female reproduction and comprise a finite pool of gametes arrested in development. A systems biology approach was used to identify regulatory gene networks essential for primordial follicle development. Transcriptional responses to eight different growth factors known to influence primordial follicles were used to construct a bionetwork of regulatory genes involved in rat primordial follicle development. Over 1,500 genes were found to be regulated by the various growth factors and a network analysis identified critical gene modules involved in a number of signaling pathways and cellular processes. A set of 55 genes was identified as potential critical regulators of these gene modules, and a sub-network associated with development was determined. Within the network two previously identified regulatory genes were confirmed (i.e., Pdgfa and Fgfr2) and a new factor was identified, connective tissue growth factor (CTGF). CTGF was tested in ovarian organ cultures and found to stimulate primordial follicle development. Therefore, the relevant gene network associated with primordial follicle development was validated and the critical genes and pathways involved in this process were identified. This is one of the first applications of network analysis to a normal developmental process. These observations provide insights into potential therapeutic targets for preventing ovarian disease and promoting female reproduction

Moving toward a system genetics view of disease

Testing hundreds of thousands of DNA markers in human, mouse, and other species for association to complex traits like disease is now a reality. However, information on how variations in DNA impact complex physiologic processes flows through transcriptional and other molecular networks. In other words, DNA variations impact complex diseases through the perturbations they cause to transcriptional and other biological networks, and these molecular phenotypes are intermediate to clinically defined disease. Because it is also now possible to monitor transcript levels in a comprehensive fashion, integrating DNA variation, transcription, and phenotypic data has the potential to enhance identification of the associations between DNA variation and diseases like obesity and diabetes, as well as characterize those parts of the molecular networks that drive these diseases. Toward that end, we review methods for integrating expression quantitative trait loci (eQTLs), gene expression, and clinical data to infer causal relationships among gene expression traits and between expression and clinical traits. We further describe methods to integrate these data in a more comprehensive manner by constructing coexpression gene networks that leverage pairwise gene interaction data to represent more general relationships. To infer gene networks that capture causal information, we describe a Bayesian algorithm that further integrates eQTLs, expression, and clinical phenotype data to reconstruct whole-gene networks capable of representing causal relationships among genes and traits in the network. These emerging network approaches, aimed at processing high-dimensional biological data by integrating data from multiple sources, represent some of the first steps in statistical genetics to identify multiple genetic perturbations that alter the states of molecular networks and that in turn push systems into disease states. Evolving statistical procedures that operate on networks will be critical to extracting information related to complex phenotypes like disease, as research goes beyond a single-gene focus. The early successes achieved with the methods described herein suggest that these more integrative genomics approaches to dissecting disease traits will significantly enhance the identification of key drivers of disease beyond what could be achieved by genetic association studies alone

A Role for VEGFR2 Activation in Endothelial Responses Caused by Barrier Disruptive OxPAPC Concentrations

Introduction: Oxidation products of 1-palmitoyl-2-arachidonoyl-sn-glycero-3-phosphatidylcholine (OxPAPC) differentially modulate endothelial cell (EC) barrier function in a dose-dependent fashion. Vascular endothelial growth factor receptor-2 (VEGFR2) is involved in the OxPAPC-induced EC inflammatory activation. This study examined a role of VEGFR2 in barrier dysfunction caused by high concentrations of OxPAPC and evaluated downstream signaling mechanisms resulting from the effect of OxPAPC in EC from pulmonary and systemic circulation

<em>Enterococcus faecalis</em> Infection Causes Inflammation, Intracellular Oxphos-Independent ROS Production, and DNA Damage in Human Gastric Cancer Cells

Background: Achlorhydria caused by e.g. atrophic gastritis allows for bacterial overgrowth, which induces chronic inflammation and damage to the mucosal cells of infected individuals driving gastric malignancies and cancer. Enterococcus faecalis (E. faecalis) can colonize achlohydric stomachs and we therefore wanted to study the impact of E. faecalis infection on inflammatory response, reactive oxygen species (ROS) formation, mitochondrial respiration, and mitochondrial genetic stability in gastric mucosal cells. Methods: To separate the changes induced by bacteria from those of the inflammatory cells we established an in vitro E. faecalis infection model system using the gastric carcinoma cell line MKN74. Total ROS and superoxide was measured by fluorescence microscopy. Cellular oxygen consumption was characterized non-invasively using XF24 microplate based respirometry. Gene expression was examined by microarray, and response pathways were identified by Gene Set Analysis (GSA). Selected gene transcripts were verified by quantitative real-time polymerase chain reaction (qRT-PCR). Mitochondrial mutations were determined by sequencing. Results: Infection of MKN74 cells with E. faecalis induced intracellular ROS production through a pathway independent of oxidative phosphorylation (oxphos). Furthermore, E. faecalis infection induced mitochondrial DNA instability. Following infection, genes coding for inflammatory response proteins were transcriptionally up-regulated while DNA damage repair and cell cycle control genes were down-regulated. Cell growth slowed down when infected with viable E. faecalis and responded in a dose dependent manner to E. faecalis lysate. Conclusions: Infection by E. faecalis induced an oxphos-independent intracellular ROS response and damaged the mitochondrial genome in gastric cell culture. Finally the bacteria induced an NF-kappa B inflammatory response as well as impaired DNA damage response and cell cycle control gene expression

From endoplasmic-reticulum stress to the inflammatory response

The endoplasmic reticulum is responsible for much of a cell's protein synthesis and folding, but it also has an important role in sensing cellular stress. Recently, it has been shown that the endoplasmic reticulum mediates a specific set of intracellular signalling pathways in response to the accumulation of unfolded or misfolded proteins, and these pathways are collectively known as the unfolded-protein response. New observations suggest that the unfolded-protein response can initiate inflammation, and the coupling of these responses in specialized cells and tissues is now thought to be fundamental in the pathogenesis of inflammatory diseases. The knowledge gained from this emerging field will aid in the development of therapies for modulating cellular stress and inflammation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62741/1/nature07203.pd

Association between SNPs and gene expression in multiple regions of the human brain

Identifying the genetic cis associations between DNA variants (single-nucleotide polymorphisms (SNPs)) and gene expression in brain tissue may be a promising approach to find functionally relevant pathways that contribute to the etiology of psychiatric disorders. In this study, we examined the association between genetic variations and gene expression in prefrontal cortex, hippocampus, temporal cortex, thalamus and cerebellum in subjects with psychiatric disorders and in normal controls. We identified cis associations between 648 transcripts and 6725 SNPs in the various brain regions. Several SNPs showed brain regional-specific associations. The expression level of only one gene, PDE4DIP, was associated with a SNP, rs12124527, in all the brain regions tested here. From our data, we generated a list of brain cis expression quantitative trait loci (eQTL) genes that we compared with a list of schizophrenia candidate genes downloaded from the Schizophrenia Forum (SZgene) database (http://www.szgene.org/). Of the SZgene candidate genes, we found that the expression levels of four genes, HTR2A, PLXNA2, SRR and TCF4, were significantly associated with cis SNPs in at least one brain region tested. One gene, SRR, was also involved in a coexpression module that we found to be associated with disease status. In addition, a substantial number of cis eQTL genes were also involved in the module, suggesting eQTL analysis of brain tissue may identify more reliable susceptibility genes for schizophrenia than case–control genetic association analyses. In an attempt to facilitate the identification of genetic variations that may underlie the etiology of major psychiatric disorders, we have integrated the brain eQTL results into a public and online database, Stanley Neuropathology Consortium Integrative Database (SNCID; http://sncid.stanleyresearch.org)

Genetic network identifies novel pathways contributing to atherosclerosis susceptibility in the innominate artery

Abstract Background Atherosclerosis, the underlying cause of cardiovascular disease, results from both genetic and environmental factors. Methods In the current study we take a systems-based approach using weighted gene co-expression analysis to identify a candidate pathway of genes related to atherosclerosis. Bioinformatic analyses are performed to identify candidate genes and interactions and several novel genes are characterized using in-vitro studies. Results We identify 1 coexpression module associated with innominate artery atherosclerosis that is also enriched for inflammatory and macrophage gene signatures. Using a series of bioinformatics analysis, we further prioritize the genes in this pathway and identify Cd44 as a critical mediator of the atherosclerosis. We validate our predictions generated by the network analysis using Cd44 knockout mice. Conclusion These results indicate that alterations in Cd44 expression mediate inflammation through a complex transcriptional network involving a number of previously uncharacterized genes