Yang, X., Pratley, R.E., Tokraks, S., Bogardus, C. & Permana, P.A. Microarray profiling of skeletal muscle tissues from equally obese, non-diabetic insulin-sensitive and insulin-resistant Pima Indians. Diabetologia 45, 1584-1593

Lundberg Laboratory for Diabetes Research, Sahlgrenska University Hospital, Göteborg, Sweden.
Diabetologia (Impact Factor: 6.67). 12/2002; 45(11):1584-93. DOI: 10.1007/s00125-002-0905-7
Source: PubMed


We carried out global transcript profiling to identify differentially expressed skeletal muscle genes in insulin resistance, a major risk factor for Type II (non-insulin-dependent) diabetes mellitus. This approach also complemented the ongoing genomic linkage analyses to identify genes linked to insulin resistance and diabetes in Pima Indians.
We compared gene expression profiles of skeletal muscle tissues from 18 insulin-sensitive versus 17 insulin-resistant equally obese, non-diabetic Pima Indians using oligonucleotide arrays consisting of about 40,600 transcripts of known genes and expressed sequence tags, and analysed the results with the Wilcoxon rank sum test. We verified the mRNA expression of ten differentially (best-ranked) and ten similarly (worst-ranked) genes using quantitative Real Time PCR.
There were 185 differentially expressed transcripts by the rank sum test. The differential expressions of two out of the ten best-ranked genes were confirmed and the similar expressions of all ten worst-ranked genes were reproduced.
Of the 185 differentially expressed transcripts, 20 per cent were true positives and some could generate new hypotheses about the aetiology or pathophysiology of insulin resistance. Furthermore, differentially expressed genes in chromosomal regions with linkage to diabetes and insulin resistance serve as new diabetes susceptibility genes.

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    • "Microarray technology is a powerful tool for analyzing mRNA abundance in cells or tissues on a genome-wide scale. Microarray-based transcription profiling analysis has been applied to many types of biological inquiry (Spellman et al., 1998; Yang et al., 2002; Oberthuer et al., 2006). Identification of candidate genes is an important inquiry. "
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    • "The microarray technology has been extensively used to identify differentially expressed genes in skeletal muscle cells under different physiological states, e.g., non-diabetic vs. diabetic subjects during poor glycemic control and following insulin treatment [15], insulin treated versus insulin deprived type 1 diabetic patients [16], and [15], normal vs. impaired glucose tolerant individuals [17], and basal state vs. euglycemic hyperinsulinemic clamp [18]. Further information on transcriptional regulation can be gained by monitoring gene transcripts related to time following insulin administration. "
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    ABSTRACT: Insulin action on protein synthesis (translation of transcripts) and post-translational modifications, especially of those involving the reversible modifications such as phosphorylation of various signaling proteins, are extensively studied but insulin effect on transcription of genes, especially of transcriptional temporal patterns remains to be fully defined. To identify significant transcriptional temporal patterns we utilized primary differentiated rat skeletal muscle myotubes which were treated with insulin and samples were collected every 20 min for 8 hours. Pooled samples at every hour were analyzed by gene array approach to measure transcript levels. The patterns of transcript levels were analyzed based on a novel method that integrates selection, clustering, and functional annotation to find the main temporal patterns associated to functional groups of differentially expressed genes. 326 genes were found to be differentially expressed in response to in vitro insulin administration in skeletal muscle myotubes. Approximately 20% of the genes that were differentially expressed were identified as belonging to the insulin signaling pathway. Characteristic transcriptional temporal patterns include: (a) a slow and gradual decrease in gene expression, (b) 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 pattern of increase in gene expression over time. The new method allows identifying characteristic dynamic responses to insulin stimulus, common to a number of genes and associated to the same functional group. The results demonstrate that insulin treatment elicited different clusters of gene transcript profile supporting a temporal regulation of gene expression by insulin in skeletal muscle cells.
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    • "Yang et al. [179] compared muscle transcription profiles of equally obese high- and low-insulin-resistant individuals. The authors concluded that 185 differentially expressed transcripts, 20 per cent were true positives and some could generate new hypotheses about the aetiology or pathophysiology of insulin-resistance. "
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