Unknown,Transcriptomics,Genomics,Proteomics

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RNA-sequencing of healthy human skeletal myocytes

ABSTRACT: Skeletal myocytes are metabolically active and susceptible to insulin resistance, thus implicated in type 2 diabetes (T2D). This complex disease involves systemic metabolic changes and their elucidation at the systems level requires genome-wide data and biological networks. Genome-scale metabolic models (GEMs) provide a network-context to integrate high-throughput data. We generated myocyte-specific RNA-seq data and investigated their correlation with proteome data. These data were then used to reconstruct a comprehensive myocyte GEM. Next, we performed a meta-analysis of six studies comparing muscle transcription in T2D versus healthy subjects. Transcriptional changes were mapped on the myocyte GEM, revealing extensive transcriptional regulation in T2D, particularly around pyruvate oxidation, branched-chain amino acid catabolism, and tetrahydrofolate metabolism, connected through the down-regulated dihydrolipoamide dehydrogenase. Strikingly, the gene signature underlying this metabolic regulation successfully classifies the disease state of individual samples, suggesting that regulation of these pathways is a ubiquitous feature of myocytes in response to T2D. Isolated skeletal muscle precursor cells from six normal glucose tolerant and non-obese males and females were differentiated in vitro. RNA from fully differentiated myotubes was sequenced using RNA-seq.

ORGANISM(S): Homo sapiens

SUBMITTER: Leif Väremo

PROVIDER: E-GEOD-63887 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:The obese people with abnormal BMI are predisposed to insulin resistance and diabetes. At the same time, human subjects with obesity and high BMI that are otherwise insulin sensitive are an interesting group to study the underlying gene expression patterns which provide them with such protective phenotype. Objective: Insulin resistance (IR) is one of the earliest predictors of type 2 diabetes. However, diagnosis of IR is limited. High fat fed mouse models provide key insights into IR. We hypothesized that early features of IR are associated with persistent changes in gene expression (GE) and endeavoured to (a) develop novel methods for improving signal:noise in analysis of human GE using mouse models; (b) identify a GE motif that accurately diagnoses IR in humans; and (c) identify novel biology associated with IR in humans. Methods: We integrated human muscle GE data with longitudinal mouse GE data and developed an unbiased three-level cross-species analysis platform (single-gene, gene-set and networks) to generate a gene expression motif (GEM) indicative of IR. A logistic regression classification model validated GEM in 3 independent human datasets (n =115). Results: This GEM of 93 genes substantially improved diagnosis of IR compared to routine clinical measures across multiple independent datasets. Individuals misclassified by GEM possessed other metabolic features raising the possibility that they represent a separate metabolic subclass. The GEM was enriched in pathways previously implicated in insulin action and revealed novel associations between Î²-catenin and Jak1 and IR. Functional analyses using small molecule inhibitors showed an important role for these proteins in insulin action. Conclusions: This study shows that systems approaches for identifying molecular signatures provides a powerful way to stratify individuals into discrete metabolic groups. Moreover, we speculate that the Î²-catenin pathway may represent a novel biomarker for IR in humans that warrant future investigation. Gene expression from muscle biopsies of Lean, Obese insulin sensitive (OIS), Obese insulin resistant (OIR) and obese T2D patients (T2D) were compared for differential expression between the groups (n=7 in each group)

Project description:Background: Insulin's effect on protein synthesis (translation of transcripts) and post-translational modifications, especially those involving reversible modifications such as phosphorylation of various signaling proteins, are extensively studied. On the other hand, insulin's effect on the transcription of genes, especially of transcriptional temporal patterns, is not well investigated in the literature. Methods: To identify significant transcriptional temporal patterns, primary differentiated rat skeletal muscle myotubes were treated with insulin and samples were collected every 10 min for 8 hours. Pooled samples at every hour were analyzed by a gene array approach to measure transcript levels. The pattern of transcript levels were analyzed based on a novel method that integrates selection, clustering, and functional annotation to find the main temporal patterns associated with functional groups of differentially expressed genes. Conclusion: In response to insulin, 326 genes were selected as differentially expressed in response to in vitro insulin treatment in skeletal muscle myotubes. Approximately 20% of the genes that were differentially expressed were identified as belonging to the insulin signaling pathway. 12 different clusters of gene expression profiles are identified which shows patterns that includes a slow and gradual decrease in gene expression, a gradual increase in gene expression reaching a peak at about 5 hours and then reaching a plateau or an initial decrease and other different variable patterns of increase in gene expression over time. As far as we know, this is the first systematic study in the literature monitoring transcriptional response to insulin in endothelial cells, in a time series microarray experiment. The purpose of this investigation was to examine the transcriptional response of skeletal muscle cells during acute insulin stimulation. Samples were collected at times 0, 20, 40, 60, . . . , 480 minutes (every 20 minutes, for 8 hours) from both insulin treated and control cultures, for a total of 50 biological samples. Samples (20, 40, 60), (80, 100, 120), (140, 160, 180), (200, 220, 240), (260, 280, 300), (320, 340, 360), (380, 400, 420) and (440, 460, 480) from insulin-treated and control cultures were pooled together, obtaining eight joint samples. Consistently, sample 0’ was harvested and collected in triplicates and pooled together (0a, 0b, 0c) for each culture and two targets for the hybridization were prepared separately. All of the culturing and sampling procedures described above were repeated two additional times on different days, using the same identical cell line, to obtain a complete triplicate of the experiment. Sample 'Insulin_3_rep_a' (pool of 140', 160', 180') was excluded from the analysis since it had quite poor MvA plots compared to the median microarray signal.

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RNA-sequencing of healthy human skeletal myocytes

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