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Focuses on the use of OrthoMCL method for identification of orthologous groups for eukaryotic genomes. Non-application of prokaryotic genome analysis method to eukaryotes; Features of OrthoMCL.

Many thousands of proteins encoded by the genome of Plasmodium falciparum, the causal organism of the deadliest form of human malaria, are of unknown function. It is of utmost importance that these proteins be characterized if we are to develop combative strategies against malaria based on the biology of the parasite. In an attempt to infer protein function on a genome-wide scale, we computationally modeled the P. falciparum interactome, elucidating local and global functional relationships between gene products. The resulting interaction network, reconstructed by integrating in silico and experimental functional genomics data within a Bayesian framework, covers ∼68% of the parasite genome and provides functional inferences for more than 2000 uncharacterized proteins, based on their associations. Network reconstruction involved the use of a novel strategy, where we incorporated continuously updated, uniform reference priors in our Bayesian model. This method for generating interaction maps is thus also well suited for application to other genomes, where pre-existing interactome knowledge is sparse. Additionally, we superimposed this map on genomes of three apicomplexan pathogens--Plasmodium yoelii, Toxoplasma gondii, and Cryptosporidium parvum--describing relationships between these organisms based on retained functional linkages. This comparison provided a glimpse of the highly evolved nature of P. falciparum; for instance, a deficit of nearly 26% in terms of predicted interactions is observed against P. yoelii, because of missing ortholog partners in pairs of functionally linked proteins. [ABSTRACT FROM AUTHOR]

Serum response factor (SRF) binds a 1216-fold degenerate cis element known as the CArG box. CArG boxes are found primarily in muscle- and growth-factor-associated genes although the full spectrum of functional CArG elements in the genome (the CArGome) has yet to be defined. Here we describe a genome-wide screen to further define the functional mammalian CArGome. A computational approach involving comparative genomic analyses of human and mouse orthologous genes uncovered >100 hypothetical SRF-dependent genes, including 10 previously identified SRF targets, harboring a conserved CArG element within 4000 bp of the annotated transcription start site (TSS). We PCR-cloned 89 hypothetical SRF targets and subjected each of them to at least two of several validations including luciferase reporter, gel shift, chromatin immunoprecipitation, and mRNA expression following RNAi knockdown of SRF; 60189 (67%) of the targets were validated. Interestingly, 26 of the validated SRF target genes encode for cytoskeletal/contractile or adhesion proteins. RNAi knockdown of SRF diminishes expression of several SRF-dependent cytoskeletal genes and elicits an attending perturbation in the cytoarchitecture of both human and rodent cells. These data illustrate the power of integrating existing algorithms to interrogate the genome in a relatively unbiased fashion for cis-regulatory element discovery. In this manner, we have further expanded the mammalian CArGome with the discovery of an array of cyto-contractile genes that coordinate normal cytoskeletal homeostasis. We suggest one function of SRF is that of an ancient master regulator of the actin cytoskeleton. [ABSTRACT FROM AUTHOR]

Peroxisome proliferator-activated receptor γ(PPARγ), a nuclear receptor and the target of anti-diabetic thiazolinedione drugs, is known as the master regulator of adipocyte biology. Although it regulates hundreds of adipocyte genes, PPARγ binding to endogenous genes has rarely been demonstrated. Here, utilizing chromatin immunoprecipitation (ChIP) coupled with whole genome tiling arrays, we identified 5299 genomic regions of PPARγ binding in mouse 3T3-L1 adipocytes. The consensus PPARγ/RXRα "DR-1"-binding motif was found at most of the sites, and ChIP for RXRα showed colocalization at nearly all locations tested. Bioinformatics analysis also revealed CCAAT/enhancer-binding protein (C/EBP)-binding motifs in the vicinity of most PPARγ-binding sites, and genome-wide analysis of C/EBPα binding demonstrated that it localized to 3350 of the locations bound by PPARγ. Importantly, most genes induced in adipogenesis were bound by both PPARγ and C/EBPα, while very few were PPARγ-specific. C/EBPβ also plays a role at many of these genes, such that both C/EBPα and β are required along with PPARγ for robust adipocyte-specific gene expression. Thus, PPARγ and C/EBP factors cooperatively orchestrate adipocyte biology by adjacent binding on an unanticipated scale. [ABSTRACT FROM AUTHOR]

The failure to expand functional pancreatic β-cell mass in response to increased metabolic demand is a hallmark of type 2 diabetes. Lineage tracing studies indicate that replication of existing β-cells is the principle mechanism for β-cell expansion in adult mice. Here we demonstrate that the proliferative response of β-cells is dependent on the orphan nuclear receptor hepatocyte nuclear factor-4α (HNF-4α), the gene that is mutated in Maturity-Onset Diabetes of the Young 1 (MODY1). Computational analysis of microarray expression profiles from isolated islets of mice lacking HNF-4α in pancreatic β-cells reveals that HNF-4α regulates selected genes in the β-cell, many of which are involved in proliferation. Using a physiological model of β-cell expansion, we show that HNF-4α is required for β-cell replication and the activation of the Ras/ERK signaling cascade in islets. This phenotype correlates with the down-regulation of suppression of tumorigenicity 5 (ST5) in HNF-4α mutants, which we identify as a novel regulator of ERK phosphorylation in β-cells and a direct transcriptional target of HNF-4α in vivo. Together, these results indicate that HNF-4α is essential for the physiological expansion of adult β-cell mass in response to increased metabolic demand. [ABSTRACT FROM AUTHOR]

Regions of gain and loss of genomic DNA occur in many cancers and can drive the genesis and progression of disease. These copy number aberrations (CNAs) can be detected at high resolution by using microarray-based techniques. However, robust statistical approaches are needed to identify nonrandom gains and losses across multiple experiments/samples. We have developed a method called Significance Testing for Aberrant Copy number (STAC) to address this need. STAC utilizes two complementary statistics in combination with a novel search strategy. The significance of both statistics is assessed, and P-values are assigned to each location on the genome by using a multiple testing corrected permutation approach. We validate our method by using two published cancer data sets. STAC identifies genomic alterations known to be of clinical and biological significance and provides statistical support for 85% of previously reported regions. Moreover, STAC identifies numerous additional regions of significant gain/loss in these data that warrant further investigation. The P-values provided by STAC can be used to prioritize regions for follow-up study in an unbiased fashion. We conclude that STAC is a powerful tool for identifying nonrandom genomic amplifications and deletions across multiple experiments. A Java version of STAC is freely available for download at http://cbil.upenn.edu/STAC. [ABSTRACT FROM AUTHOR]