MitoP2: the mitochondrial proteome database—now including mouse data

The MitoP2 database () integrates information on mitochondrial proteins, their molecular functions and associated diseases. The central database features are manually annotated reference proteins localized or functionally associated with mitochondria supplied for yeast, human and mouse. MitoP2 enables (i) the identification of putative orthologous proteins between these species to study evolutionarily conserved functions and pathways; (ii) the integration of data from systematic genome-wide studies such as proteomics and deletion phenotype screening; (iii) the prediction of novel mitochondrial proteins using data integration and the assignment of evidence scores; and (iv) systematic searches that aim to find the genes that underlie common and rare mitochondrial diseases. The data and analysis files are referenced to data sources in PubMed and other online databases and can be easily downloaded. MitoP2 users can explore the relationship between mitochondrial dysfunctions and disease and utilize this information to conduct systems biology approaches on mitochondria

Universality classes in nonequilibrium lattice systems

This work is designed to overview our present knowledge about universality classes occurring in nonequilibrium systems defined on regular lattices. In the first section I summarize the most important critical exponents, relations and the field theoretical formalism used in the text. In the second section I briefly address the question of scaling behavior at first order phase transitions. In section three I review dynamical extensions of basic static classes, show the effect of mixing dynamics and the percolation behavior. The main body of this work is given in section four where genuine, dynamical universality classes specific to nonequilibrium systems are introduced. In section five I continue overviewing such nonequilibrium classes but in coupled, multi-component systems. Most of the known nonequilibrium transition classes are explored in low dimensions between active and absorbing states of reaction-diffusion type of systems. However by mapping they can be related to universal behavior of interface growth models, which I overview in section six. Finally in section seven I summarize families of absorbing state system classes, mean-field classes and give an outlook for further directions of research.Comment: Updated comprehensive review, 62 pages (two column), 29 figs included. Scheduled for publication in Reviews of Modern Physics in April 200

Stochastic processes with finite correlation time: modeling and application to the generalized Langevin equation

The kangaroo process (KP) is characterized by various forms of the covariance and can serve as a useful model of random noises. We discuss properties of that process for the exponential, stretched exponential and algebraic (power-law) covariances. Then we apply the KP as a model of noise in the generalized Langevin equation and simulate solutions by a Monte Carlo method. Some results appear to be incompatible with requirements of the fluctuation-dissipation theorem because probability distributions change when the process is inserted into the equation. We demonstrate how one can construct a model of noise free of that difficulty. This form of the KP is especially suitable for physical applications.Comment: 22 pages (RevTeX) and 4 figure

Mapping gene associations in human mitochondria using clinical disease phenotypes

Nuclear genes encode most mitochondrial proteins, and their mutations cause diverse and debilitating clinical disorders. To date, 1,200 of these mitochondrial genes have been recorded, while no standardized catalog exists of the associated clinical phenotypes. Such a catalog would be useful to develop methods to analyze human phenotypic data, to determine genotype-phenotype relations among many genes and diseases, and to support the clinical diagnosis of mitochondrial disorders. Here we establish a clinical phenotype catalog of 174 mitochondrial disease genes and study associations of diseases and genes. Phenotypic features such as clinical signs and symptoms were manually annotated from full-text medical articles and classified based on the hierarchical MeSH ontology. This classification of phenotypic features of each gene allowed for the comparison of diseases between different genes. In turn, we were then able to measure the phenotypic associations of disease genes for which we calculated a quantitative value that is based on their shared phenotypic features. The results showed that genes sharing more similar phenotypes have a stronger tendency for functional interactions, proving the usefulness of phenotype similarity values in disease gene network analysis. We then constructed a functional network of mitochondrial genes and discovered a higher connectivity for non-disease than for disease genes, and a tendency of disease genes to interact with each other. Utilizing these differences, we propose 168 candidate genes that resemble the characteristic interaction patterns of mitochondrial disease genes. Through their network associations, the candidates are further prioritized for the study of specific disorders such as optic neuropathies and Parkinson disease. Most mitochondrial disease phenotypes involve several clinical categories including neurologic, metabolic, and gastrointestinal disorders, which might indicate the effects of gene defects within the mitochondrial system. The accompanying knowledgebase (http://www.mitophenome.org/) supports the study of clinical diseases and associated genes

LINT, a Novel dL(3)mbt-Containing Complex, Represses Malignant Brain Tumour Signature Genes

Mutations in the l(3)mbt tumour suppressor result in overproliferation of Drosophila larval brains. Recently, the derepression of different gene classes in l(3)mbt mutants was shown to be causal for transformation. However, the molecular mechanisms of dL(3)mbt-mediated gene repression are not understood. Here, we identify LINT, the major dL(3)mbt complex of Drosophila. LINT has three core subunits—dL(3)mbt, dCoREST, and dLint-1—and is expressed in cell lines, embryos, and larval brain. Using genome-wide ChIP–Seq analysis, we show that dLint-1 binds close to the TSS of tumour-relevant target genes. Depletion of the LINT core subunits results in derepression of these genes. By contrast, histone deacetylase, histone methylase, and histone demethylase activities are not required to maintain repression. Our results support a direct role of LINT in the repression of brain tumour-relevant target genes by restricting promoter access

Genomewide Analyses Define Different Modes of Transcriptional Regulation by Peroxisome Proliferator-Activated Receptor-β/δ (PPARβ/δ)

Peroxisome proliferator-activated receptors (PPARs) are nuclear receptors with essential functions in lipid, glucose and energy homeostasis, cell differentiation, inflammation and metabolic disorders, and represent important drug targets. PPARs heterodimerize with retinoid X receptors (RXRs) and can form transcriptional activator or repressor complexes at specific DNA elements (PPREs). It is believed that the decision between repression and activation is generally governed by a ligand-mediated switch. We have performed genomewide analyses of agonist-treated and PPARβ/δ-depleted human myofibroblasts to test this hypothesis and to identify global principles of PPARβ/δ-mediated gene regulation. Chromatin immunoprecipitation sequencing (ChIP-Seq) of PPARβ/δ, H3K4me3 and RNA polymerase II enrichment sites combined with transcriptional profiling enabled the definition of 112 bona fide PPARβ/δ target genes showing either of three distinct types of transcriptional response: (I) ligand-independent repression by PPARβ/δ; (II) ligand-induced activation and/or derepression by PPARβ/δ; and (III) ligand-independent activation by PPARβ/δ. These data identify PPRE-mediated repression as a major mechanism of transcriptional regulation by PPARβ/δ, but, unexpectedly, also show that only a subset of repressed genes are activated by a ligand-mediated switch. Our results also suggest that the type of transcriptional response by a given target gene is connected to the structure of its associated PPRE(s) and the biological function of its encoded protein. These observations have important implications for understanding the regulatory PPAR network and PPARβ/δ ligand-based drugs

Mining for genotype-phenotype relations in Saccharomyces using partial least squares

<p>Abstract</p> <p>Background</p> <p>Multivariate approaches are important due to their versatility and applications in many fields as it provides decisive advantages over univariate analysis in many ways. Genome wide association studies are rapidly emerging, but approaches in hand pay less attention to multivariate relation between genotype and phenotype. We introduce a methodology based on a BLAST approach for extracting information from genomic sequences and Soft- Thresholding Partial Least Squares (ST-PLS) for mapping genotype-phenotype relations.</p> <p>Results</p> <p>Applying this methodology to an extensive data set for the model yeast <it>Saccharomyces cerevisiae</it>, we found that the relationship between genotype-phenotype involves surprisingly few genes in the sense that an overwhelmingly large fraction of the phenotypic variation can be explained by variation in less than 1% of the full gene reference set containing 5791 genes. These phenotype influencing genes were evolving 20% faster than non-influential genes and were unevenly distributed over cellular functions, with strong enrichments in functions such as cellular respiration and transposition. These genes were also enriched with known paralogs, stop codon variations and copy number variations, suggesting that such molecular adjustments have had a disproportionate influence on <it>Saccharomyces </it>yeasts recent adaptation to environmental changes in its ecological niche.</p> <p>Conclusions</p> <p>BLAST and PLS based multivariate approach derived results that adhere to the known yeast phylogeny and gene ontology and thus verify that the methodology extracts a set of fast evolving genes that capture the phylogeny of the yeast strains. The approach is worth pursuing, and future investigations should be made to improve the computations of genotype signals as well as variable selection procedure within the PLS framework.</p