Does Global Gene Expression Analysis in Type 2 Diabetes Provide an Opportunity to Identify Highly Promising Drug Targets?

Abstract

The recent technological advances in high-throughput gene expression analysis allow the simultaneous investigation of thousands of genes. These technologies represent promising tools for the identification of new drug targets and considerable progress has been achieved in cancer research where microarray data provide a basis to design new drugs and to predict adverse reactions and the efficacy of chemotherapy. The metabolic syndrome represents a cluster of disorders including high blood pressure, insulin resistance/type 2 diabetes mellitus, visceral obesity and dyslipidaemia with fatty liver disease being a common associated complication. High-throughput gene expression analyses using GeneChips, microarrays and serial analysis of gene expression (SAGE) have been applied to study global gene expression in insulin resistance/type 2 diabetes mellitus. Type 2 diabetes mellitus is a multifactorial and polygenic disease by which several organs are affected. Therefore, the identification of both, disease causing and therapeutically relevant target genes is an ambitious challenge. In the present review we focus on genomic approaches that used biopsies from human skeletal muscle, liver and adipose tissue, the main organs affected by insulin resistance. Members of the PPARgamma coactivator-1 (PGC-1) family of transcriptional coactivators are decreased in skeletal muscle in insulin resistance accounting for the reduced expression of genes involved in mitochondrial oxidative phosphorylation. Hepatic steatosis is also linked to alterations in mitochondrial phosphorylation and oxidative metabolism. An up regulation of pro-inflammatory genes can be detected in early stages of fatty liver disease without histological signs of inflammation. Impaired adipogenesis, intra-adipose accumulation of macrophages and a sustained release of inflammatory and acute phase proteins are characteristic features of adipose tissue in obesity and may aggravate systemic insulin resistance.

Adipokines in Liver Cirrhosis

“…Adipokines that are expressed and secreted in higher quantity from visceral fat may be increased in the portal vein when compared to systemic levels [ 16 ]. These proteins are supposed to contribute to the metabolically-harmful effects of visceral adiposity [ 14 ]. Resistin is not raised in the portal vein, arguing against higher release from macrophages in intra-abdominal adipose tissues of humans [ 16 ].…”

“…Shifting lipid storage from subcutaneous to visceral fat tissues is well known to be associated with liver damages [ 13 ]. Functional differences of subcutaneous and visceral adipose tissue are associated with changes in their adipokine profiles [ 14 , 15 ] and possibly distinct levels of these adipokines in the portal and systemic blood [ 16 ]. We are, however, unaware of studies showing adverse effects of excess visceral fat mass on hepatic function in patients with liver cirrhosis.…”

Chemerin-156 is the Active Isoform in Human Hepatic Stellate Cells

“…In murine peritoneal exudate cells, GPR1 mRNA was 0.02 to 0.03% of CMKLR1 mRNA levels [ 7 ]. In adipose tissues, GPR1 mRNA was highly abundant in the stromal vascular cell fraction, which is composed of fibroblasts, mesenchymal stem cells, endothelial cells, as well as smooth muscle cells and macrophages [ 8 , 9 ]. Chemerin is a well described chemoattractant and can act via both receptors to stimulate chemotaxis of immune or non-immune cells [ 1 , 10 , 11 , 12 ].…”

Hepatic Monoacylglycerol O-acyltransferase 1 as a Promising Therapeutic Target for Steatosis, Obesity, and Type 2 Diabetes

“…1,2 Numerous attempts have been made to identify the candidate genes by genome-wide association studies and using DNA microarray analysis. 3,4,5 Understanding the identity of potential drug targets in the early stages of biomedical research is of great importance for the development of drugs with a high efficacy. 6,7,8 The advent of promising biotechnologies such as microarray, proteomics, next-generation sequencing, and other platforms made it possible to identify candidate genes that show marked differences in disease states.…”