Gene Association Networks Research Articles

AB34-6: Transcriptomic remodeling correlates with cardiac function remodeling in tachycardia-induced heart failure development

Understanding of the molecular systems responsible for heart failure (HF) progression remains a major challenge in identification of therapeutic targets for slowing and reversing the HF phenotype. To assess the hypothesis that HF progression involves transcriptional remodeling, we have used the canine tachycardia pacing-induced HF model to perform transcriptional profiling at 0 day, 3 days, 1week, 2 weeks and 3 weeks following onset of rapid pacing. We have developed a gene association network for determinants of action potential prolongation, a hallmark of HF, based on the temporal profile of gene expression changes during development of HF.

CFinder: locating cliques and overlapping modules in biological networks

Most cellular tasks are performed not by individual proteins, but by groups of functionally associated proteins, often referred to as modules. In a protein association network modules appear as groups of densely interconnected nodes, also called communities or clusters. These modules often overlap with each other and form a network of their own, in which nodes (links) represent the modules (overlaps). We introduce CFinder, a fast program locating and visualizing overlapping, densely interconnected groups of nodes in undirected graphs, and allowing the user to easily navigate between the original graph and the web of these groups. We show that in gene (protein) association networks CFinder can be used to predict the function(s) of a single protein and to discover novel modules. CFinder is also very efficient for locating the cliques of large sparse graphs. CFinder (for Windows, Linux and Macintosh) and its manual can be downloaded from http://angel.elte.hu/clustering. Supplementary data are available on Bioinformatics online.

Human laminin-5 and laminin-10 mediated gene expression of prostate carcinoma cells

In prostate cancer progression, the basal lamina switches from predominantly laminin-5 to laminin-10. DU-145 prostate cancer cells were treated with either soluble laminin-5 (20 ng/ml) or laminin-10 (1 microg/ml) for 6, 24, and 48 hr. Total RNA was harvested for a 7,500 human cDNA microarray. Hybridizations were carried out in accordance with a 10 sample analysis of variance (ANOVA) statistical model. One thousand one hundred sixteen genes had measurable expression 2 standard deviations above background and 50% of spots for any given sample for all hybridizations were positive. Expression values of significantly varying genes were clustered and a list of 408 genes (P < 0.05) with a 1.5 or greater fold change in at least one time point were chosen for further analysis. Seventy eight changed in a time-dependent manner with laminin-10 treatment, 85 changed with laminin-5, and 13 showed changes with both treatments. The 408 genes that passed a paired t-test in at least one time-dependent category were further analyzed using Pathway Miner. One of the largest gene association networks involved signal transduction in the growth factor-MAP kinase pathways. EGFR was validated by real-time PCR and laminin-10 mediated cell adhesion activated EGFR in DU-145 cells. Both laminins appear to be important signal transducers in prostate cancer.

A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics

Inferring large-scale covariance matrices from sparse genomic data is an ubiquitous problem in bioinformatics. Clearly, the widely used standard covariance and correlation estimators are ill-suited for this purpose. As statistically efficient and computationally fast alternative we propose a novel shrinkage covariance estimator that exploits the Ledoit-Wolf (2003) lemma for analytic calculation of the optimal shrinkage intensity. Subsequently, we apply this improved covariance estimator (which has guaranteed minimum mean squared error, is well-conditioned, and is always positive definite even for small sample sizes) to the problem of inferring large-scale gene association networks. We show that it performs very favorably compared to competing approaches both in simulations as well as in application to real expression data.

Pathway Miner: extracting gene association networks from molecular pathways for predicting the biological significance of gene expression microarray data.

We have developed a web-based system (Pathway Miner) for visualizing gene expression profiles in the context of biological pathways. Pathway Miner catalogs genes based on their role in metabolic, cellular and regulatory pathways. A Fisher exact test is provided as an option to rank pathways. The genes are mapped onto pathways and gene product association networks are extracted for genes that co-occur in pathways. The networks can be filtered for analysis based on user-selected options. Pathway Miner is a freely available web accessible tool at http://www.biorag.org/pathway.html