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PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes

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TLDR
An analytical strategy is introduced, Gene Set Enrichment Analysis, designed to detect modest but coordinate changes in the expression of groups of functionally related genes, which identifies a set of genes involved in oxidative phosphorylation whose expression is coordinately decreased in human diabetic muscle.
Abstract
DNA microarrays can be used to identify gene expression changes characteristic of human disease. This is challenging, however, when relevant differences are subtle at the level of individual genes. We introduce an analytical strategy, Gene Set Enrichment Analysis, designed to detect modest but coordinate changes in the expression of groups of functionally related genes. Using this approach, we identify a set of genes involved in oxidative phosphorylation whose expression is coordinately decreased in human diabetic muscle. Expression of these genes is high at sites of insulin-mediated glucose disposal, activated by PGC-1α and correlated with total-body aerobic capacity. Our results associate this gene set with clinically important variation in human metabolism and illustrate the value of pathway relationships in the analysis of genomic profiling experiments.

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Activation of nuclear receptor coactivator PGC-1α by arginine methylation

TL;DR: Peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α), a tissue-specific and inducible transcriptional coActivator for several nuclear receptors, plays a key role in energy metabolism and is potentiated by arginine methylation by protein arginin methyltransferase 1 (PRMT1), another nuclear receptor coactivators.
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Increased lipid availability impairs insulin-stimulated ATP synthesis in human skeletal muscle

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Decoding insulin resistance and metabolic syndrome for promising therapeutic intervention.

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Targeting VEGF-B as a novel treatment for insulin resistance and type 2 diabetes

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References
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Journal ArticleDOI

Cluster analysis and display of genome-wide expression patterns

TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
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Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

TL;DR: A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case and suggests a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
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Significance analysis of microarrays applied to the ionizing radiation response

TL;DR: A method that assigns a score to each gene on the basis of change in gene expression relative to the standard deviation of repeated measurements is described, suggesting that this repair pathway for UV-damaged DNA might play a previously unrecognized role in repairing DNA damaged by ionizing radiation.
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A direct approach to false discovery rates

TL;DR: The calculation of the q‐value is discussed, the pFDR analogue of the p‐value, which eliminates the need to set the error rate beforehand as is traditionally done, and can yield an increase of over eight times in power compared with the Benjamini–Hochberg FDR method.
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Mechanisms Controlling Mitochondrial Biogenesis and Respiration through the Thermogenic Coactivator PGC-1

TL;DR: PGC-1, a cold-inducible coactivator of nuclear receptors, stimulates mitochondrial biogenesis and respiration in muscle cells through an induction of uncoupling protein 2 (UCP-2) and through regulation of the nuclear respiratory factors (NRFs).
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