1,277 research outputs found

    Error margin analysis for feature gene extraction

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    <p>Abstract</p> <p>Background</p> <p>Feature gene extraction is a fundamental issue in microarray-based biomarker discovery. It is normally treated as an optimization problem of finding the best predictive feature genes that can effectively and stably discriminate distinct types of disease conditions, e.g. tumors and normals. Since gene microarray data normally involves thousands of genes at, tens or hundreds of samples, the gene extraction process may fall into local optimums if the gene set is optimized according to the maximization of classification accuracy of the classifier built from it.</p> <p>Results</p> <p>In this paper, we propose a novel gene extraction method of error margin analysis to optimize the feature genes. The proposed algorithm has been tested upon one synthetic dataset and two real microarray datasets. Meanwhile, it has been compared with five existing gene extraction algorithms on each dataset. On the synthetic dataset, the results show that the feature set extracted by our algorithm is the closest to the actual gene set. For the two real datasets, our algorithm is superior in terms of balancing the size and the validation accuracy of the resultant gene set when comparing to other algorithms.</p> <p>Conclusion</p> <p>Because of its distinct features, error margin analysis method can stably extract the relevant feature genes from microarray data for high-performance classification.</p

    Validation of oligoarrays for quantitative exploration of the transcriptome

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    <p>Abstract</p> <p>Background</p> <p>Oligoarrays have become an accessible technique for exploring the transcriptome, but it is presently unclear how absolute transcript data from this technique compare to the data achieved with tag-based quantitative techniques, such as massively parallel signature sequencing (MPSS) and serial analysis of gene expression (SAGE). By use of the TransCount method we calculated absolute transcript concentrations from spotted oligoarray intensities, enabling direct comparisons with tag counts obtained with MPSS and SAGE. The tag counts were converted to number of transcripts per cell by assuming that the sum of all transcripts in a single cell was 5·10<sup>5</sup>. Our aim was to investigate whether the less resource demanding and more widespread oligoarray technique could provide data that were correlated to and had the same absolute scale as those obtained with MPSS and SAGE.</p> <p>Results</p> <p>A number of 1,777 unique transcripts were detected in common for the three technologies and served as the basis for our analyses. The correlations involving the oligoarray data were not weaker than, but, similar to the correlation between the MPSS and SAGE data, both when the entire concentration range was considered and at high concentrations. The data sets were more strongly correlated at high transcript concentrations than at low concentrations. On an absolute scale, the number of transcripts per cell and gene was generally higher based on oligoarrays than on MPSS and SAGE, and ranged from 1.6 to 9,705 for the 1,777 overlapping genes. The MPSS data were on same scale as the SAGE data, ranging from 0.5 to 3,180 (MPSS) and 9 to1,268 (SAGE) transcripts per cell and gene. The sum of all transcripts per cell for these genes was 3.8·10<sup>5 </sup>(oligoarrays), 1.1·10<sup>5 </sup>(MPSS) and 7.6·10<sup>4 </sup>(SAGE), whereas the corresponding sum for all detected transcripts was 1.1·10<sup>6 </sup>(oligoarrays), 2.8·10<sup>5 </sup>(MPSS) and 3.8·10<sup>5 </sup>(SAGE).</p> <p>Conclusion</p> <p>The oligoarrays and TransCount provide quantitative transcript concentrations that are correlated to MPSS and SAGE data, but, the absolute scale of the measurements differs across the technologies. The discrepancy questions whether the sum of all transcripts within a single cell might be higher than the number of 5·10<sup>5 </sup>suggested in the literature and used to convert tag counts to transcripts per cell. If so, this may explain the apparent higher transcript detection efficiency of the oligoarrays, and has to be clarified before absolute transcript concentrations can be interchanged across the technologies. The ability to obtain transcript concentrations from oligoarrays opens up the possibility of efficient generation of universal transcript databases with low resource demands.</p

    NAD+ protects against EAE by regulating CD4+ T-cell differentiation

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    CD4+ T cells are involved in the development of autoimmunity, including multiple sclerosis (MS). Here we show that nicotinamide adenine dinucleotide (NAD+) blocks experimental autoimmune encephalomyelitis (EAE), a mouse model of MS, by inducing immune homeostasis through CD4+IFNγ+IL-10+ T cells and reverses disease progression by restoring tissue integrity via remyelination and neuroregeneration. We show that NAD+ regulates CD4+ T-cell differentiation through tryptophan hydroxylase-1 (Tph1), independently of well-established transcription factors. In the presence of NAD+, the frequency of T-bet−/− CD4+IFNγ+ T cells was twofold higher than wild-type CD4+ T cells cultured in conventional T helper 1 polarizing conditions. Our findings unravel a new pathway orchestrating CD4+ T-cell differentiation and demonstrate that NAD+ may serve as a powerful therapeutic agent for the treatment of autoimmune and other diseases

    Induced pseudoscalar coupling of the proton weak interaction

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    The induced pseudoscalar coupling gpg_p is the least well known of the weak coupling constants of the proton's charged--current interaction. Its size is dictated by chiral symmetry arguments, and its measurement represents an important test of quantum chromodynamics at low energies. During the past decade a large body of new data relevant to the coupling gpg_p has been accumulated. This data includes measurements of radiative and non radiative muon capture on targets ranging from hydrogen and few--nucleon systems to complex nuclei. Herein the authors review the theoretical underpinnings of gpg_p, the experimental studies of gpg_p, and the procedures and uncertainties in extracting the coupling from data. Current puzzles are highlighted and future opportunities are discussed.Comment: 58 pages, Latex, Revtex4, prepared for Reviews of Modern Physic

    Microarray profiling reveals the integrated stress response is activated by halofuginone in mammary epithelial cells

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    <p>Abstract</p> <p>Background</p> <p>The small molecule Halofuginone (HF) is a potent regulator of extracellular matrix (ECM ) gene expression and is unique in its therapeutic potential. While the basis for HF effects is unknown, inhibition of TGFβ signaling and activation of the amino acid restriction response (AAR) have been linked to HF transcriptional control of a number of ECM components and amelioration of fibrosis and alleviation of autoimmune disease by regulation of Th17 cell differentiation, respectively. The aim of this study was to generate a global expression profile of HF targets in epithelial cells to identify potential mediators of HF function in this cell type.</p> <p>Results</p> <p>We report that HF modulation of the expression of the ECM remodeling protein Mmp13 in epithelial cells is separable from previously reported effects of HF on TGFβ signal inhibition, and use microarray expression analysis to correlate this with transcriptional responses characteristic of the Integrated Stress Response (ISR).</p> <p>Conclusions</p> <p>Our findings suggest activation of the ISR may be a common mechanism underlying HF biological effects.</p

    Complex SUMO-1 Regulation of Cardiac Transcription Factor Nkx2-5

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    Reversible post-translational protein modifications such as SUMOylation add complexity to cardiac transcriptional regulation. The homeodomain transcription factor Nkx2-5/Csx is essential for heart specification and morphogenesis. It has been previously suggested that SUMOylation of lysine 51 (K51) of Nkx2-5 is essential for its DNA binding and transcriptional activation. Here, we confirm that SUMOylation strongly enhances Nkx2-5 transcriptional activity and that residue K51 of Nkx2-5 is a SUMOylation target. However, in a range of cultured cell lines we find that a point mutation of K51 to arginine (K51R) does not affect Nkx2-5 activity or DNA binding, suggesting the existence of additional Nkx2-5 SUMOylated residues. Using biochemical assays, we demonstrate that Nkx2-5 is SUMOylated on at least one additional site, and this is the predominant site in cardiac cells. The second site is either non-canonical or a “shifting” site, as mutation of predicted consensus sites and indeed every individual lysine in the context of the K51R mutation failed to impair Nkx2-5 transcriptional synergism with SUMO, or its nuclear localization and DNA binding. We also observe SUMOylation of Nkx2-5 cofactors, which may be critical to Nkx2-5 regulation. Our data reveal highly complex regulatory mechanisms driven by SUMOylation to modulate Nkx2-5 activity

    Uncovering Ubiquitin and Ubiquitin-like Signaling Networks

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    Microscopic imaging and technolog
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