34 research outputs found

    A probabilistic generative model for GO enrichment analysis

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    The Gene Ontology (GO) is extensively used to analyze all types of high-throughput experiments. However, researchers still face several challenges when using GO and other functional annotation databases. One problem is the large number of multiple hypotheses that are being tested for each study. In addition, categories often overlap with both direct parents/descendents and other distant categories in the hierarchical structure. This makes it hard to determine if the identified significant categories represent different functional outcomes or rather a redundant view of the same biological processes. To overcome these problems we developed a generative probabilistic model which identifies a (small) subset of categories that, together, explain the selected gene set. Our model accommodates noise and errors in the selected gene set and GO. Using controlled GO data our method correctly recovered most of the selected categories, leading to dramatic improvements over current methods for GO analysis. When used with microarray expression data and ChIP-chip data from yeast and human our method was able to correctly identify both general and specific enriched categories which were overlooked by other methods

    Preterm birth associated with maternal fine particulate matter exposure : A global, regional and national assessment

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    Reduction of preterm births (< 37 completed weeks of gestation) would substantially reduce neonatal and infant mortality, and deleterious health effects in survivors. Maternal fine particulate matter (PM2.5) exposure has been identified as a possible risk factor contributing to preterm birth. The aim of this study was to produce the first estimates of ambient PM2.5-associated preterm births for 183 individual countries and globally. To do this, national, population-weighted, annual average ambient PM2.5 concentration, preterm birth rate and number of livebirths were combined to calculate the number of PM2.5-associated preterm births in 2010 for 183 countries. Uncertainty was quantified using Monte-Carlo simulations, and analyses were undertaken to investigate the sensitivity of PM2.5-associated preterm birth estimates to assumptions about the shape of the concentration-response function at low and high PM2.5 exposures, inclusion of provider-initiated preterm births, and exposure to indoor air pollution. Globally, in 2010, the number of PM2.5-associated preterm births was estimated as 2.7 million (1.8–3.5 million, 18% (12–24%) of total preterm births globally) with a low concentration cut-off (LCC) set at 10 ÎŒg m− 3, and 3.4 million (2.4–4.2 million, 23% (16–28%)) with a LCC of 4.3 ÎŒg m− 3. South and East Asia, North Africa/Middle East and West sub-Saharan Africa had the largest contribution to the global total, and the largest percentage of preterm births associated with PM2.5. Sensitivity analyses showed that PM2.5-associated preterm birth estimates were 24% lower when provider-initiated preterm births were excluded, 38–51% lower when risk was confined to the PM2.5 exposure range in the studies used to derive the effect estimate, and 56% lower when mothers who live in households that cook with solid fuels (and whose personal PM2.5 exposure is likely dominated by indoor air pollution) were excluded. The concentration-response function applied here derives from a meta-analysis of studies, most of which were conducted in the US and Europe, and its application to the areas of the world where we estimate the greatest effects on preterm births remains uncertain. Nevertheless, the substantial percentage of preterm births estimated to be associated with anthropogenic PM2.5 (18% (13%–24%) of total preterm births globally) indicates that reduction of maternal PM2.5 exposure through emission reduction strategies should be considered alongside mitigation of other risk factors associated with preterm births

    Proteomic Characterization of Plasmid pLA1 for Biodegradation of Polycyclic Aromatic Hydrocarbons in the Marine Bacterium, <i>Novosphingobium pentaromativorans</i> US6-1

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    <div><p><i>Novosphingobium pentaromativorans</i> US6-1 is a halophilic marine bacterium able to degrade polycyclic aromatic hydrocarbons (PAHs). Genome sequence analysis revealed that the large plasmid pLA1 present in <i>N. pentaromativorans</i> US6-1 consists of 199 ORFs and possess putative biodegradation genes that may be involved in PAH degradation. 1-DE/LC-MS/MS analysis of <i>N. pentaromativorans</i> US6-1 cultured in the presence of different PAHs and monocyclic aromatic hydrocarbons (MAHs) identified approximately 1,000 and 1,400 proteins, respectively. Up-regulated biodegradation enzymes, including those belonging to pLA1, were quantitatively compared. Among the PAHs, phenanthrene induced the strongest up-regulation of extradiol cleavage pathway enzymes such as ring-hydroxylating dioxygenase, putative biphenyl-2,3-diol 1,2-dioxygenase, and catechol 2,3-dioxygenase in pLA1. These enzymes lead the initial step of the lower catabolic pathway of aromatic hydrocarbons through the extradiol cleavage pathway and participate in the attack of PAH ring cleavage, respectively. However, <i>N. pentaromativorans</i> US6-1 cultured with <i>p</i>-hydroxybenzoate induced activation of another extradiol cleavage pathway, the protocatechuate 4,5-dioxygenase pathway, that originated from chromosomal genes. These results suggest that <i>N. pentaromativorans</i> US6-1 utilizes two different extradiol pathways and plasmid pLA1 might play a key role in the biodegradation of PAH in <i>N. pentaromativorans</i> US6-1.</p></div
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