Bmc Genomics
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Systemic administration of beta-adrenoceptor (beta-AR) agonists has been found to induce skeletal muscle hypertrophy and significant metabolic changes. In the context of energy homeostasis, the importance of beta-AR signaling has been highlighted by the inability of beta(1-3)-AR-deficient mice to regulate energy expenditure and susceptibility to diet induced obesity. However, the molecular pathways and gene expression changes that initiate and maintain these phenotypic modulations are poorly understood. Therefore, the aim of this study was to identify differential changes in gene expression in murine skeletal muscle associated with systemic (acute and chronic) administration of the beta(2)-AR agonist formoterol. ⋯ This is the first study to utilize gene expression profiling to examine global gene expression in response to acute beta(2)-AR agonist treatment of skeletal muscle. In summary, systemic administration of a beta(2)-AR agonist had a profound effect on global gene expression in skeletal muscle. In terms of hypertrophy, beta(2)-AR agonist treatment altered the expression of several genes associated with myostatin signaling, a previously unreported effect of beta-AR signaling in skeletal muscle. This study also demonstrates a beta(2)-AR agonist regulation of circadian rhythm genes, indicating crosstalk between beta-AR signaling and circadian cycling in skeletal muscle. Gene expression alterations discovered in this study provides insight into many of the underlying changes in gene expression that mediate beta-AR induced skeletal muscle hypertrophy and altered metabolism.
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Comparative Study
Comparison of feature selection and classification for MALDI-MS data.
In the classification of Mass Spectrometry (MS) proteomics data, peak detection, feature selection, and learning classifiers are critical to classification accuracy. To better understand which methods are more accurate when classifying data, some publicly available peak detection algorithms for Matrix assisted Laser Desorption Ionization Mass Spectrometry (MALDI-MS) data were recently compared; however, the issue of different feature selection methods and different classification models as they relate to classification performance has not been addressed. With the application of intelligent computing, much progress has been made in the development of feature selection methods and learning classifiers for the analysis of high-throughput biological data. The main objective of this paper is to compare the methods of feature selection and different learning classifiers when applied to MALDI-MS data and to provide a subsequent reference for the analysis of MS proteomics data. ⋯ Regarding feature selection, SVMRFE outperformed GLGS in classification. As for the learning classifiers, when classification models derived from the best training were compared, SVMs performed the best with respect to the expected testing accuracy. However, the distance metric learning LMNN outperformed SVMs and other classifiers on evaluating the best testing. In such cases, the optimum classification model based on LMNN is worth investigating for future study.
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Epigenetic modifications of histones and regulation of chromatin structure have been implicated in regulation of virulence gene families in P. falciparum. To better understand chromatin-mediated gene regulation, we used a high-density oligonucleotide microarray to map the position and enrichment of nucleosomes across the entire genome of P. falciparum at three time points of the intra-erythrocytic developmental cycle (IDC) in vitro. We used an unmodified histone H4 antibody for chromatin immunoprecipitation of nucleosome-bound DNA. ⋯ These data on nucleosome enrichment changes add to our understanding of the influence of chromatin structure on the regulation of gene expression. Histones are generally enriched in coding regions, and relatively poor in intergenic regions. Histone enrichment patterns allow for identification of new putative gene-coding regions. Most genes do not show correlation between chromatin structure and steady-state mRNA levels, indicating the dominant roles of other regulatory mechanisms. We present a genome-wide nucleosomal occupancy map, which can be used as a reference for future experiments of histone modification mapping.
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Interstitial cystitis (IC), a chronic bladder disease with an increasing incidence, is diagnosed using subjective symptoms in combination with cystoscopic and histological evidence. By cystoscopic examination, IC can be classified into an ulcerative and a non-ulcerative subtype. To better understand this debilitating disease on a molecular level, a comparative gene expression profile of bladder biopsies from patients with ulcerative IC and control patients has been performed. ⋯ GeneChip expression arrays present a global picture of ulcerative IC and provide us with a series of marker genes characteristic for this subtype of the disease. Evaluation of biopsies from other bladder patients with similar symptoms (e.g. patients with non-ulcerative IC) will further indicate whether the data presented here will be valuable for the diagnosis of IC.
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Human protein-protein interaction (PPIs) data are the foundation for understanding molecular signalling networks and the functional roles of biomolecules. Several human PPI databases have become available; however, comparisons of these datasets have suggested limited data coverage and poor data quality. Ongoing collection and integration of human PPIs from different sources, both experimentally and computationally, can enable disease-specific network biology modelling in translational bioinformatics studies. ⋯ HAPPI is by far the most comprehensive public compilation of human protein interaction information. It enables its users to fully explore PPI data with quality measures and annotated information necessary for emerging network biology studies.