Bioinformatic and experimental tools for identification of single‐nucleotide polymorphisms in genes with a potential role for the development of the insulin resistance syndrome

Abstract

Objectives. Genes with a possible role for the development of the insulin resistance syndrome (IRS) were scanned for novel single-nucleotide polymorphisms (SNPs) using bioinformatics. Methods. GenBank mRNA sequences were compared to the human EST database using gapped BLAST BLAST, software that is available on the internet. Mismatches between the search and the EST sequences indicated potential SNPs. Thirty-two SNPs in 13 genes were randomly chosen for experimental veri®cation. PCR and direct sequencing were used to determine the`true' SNPs. A random sample of 30 Swedish men with slightly elevated diastolic blood pressure (85±94 mmHg) obtained from a population-based study was selected for the sequencing.After completion of these stages, the potential SNPs were checked against the large and rapidly expanding SNP databases HGBASE and NCBI. Results. EST searches of 146 genes revealed 106 potential SNPs in 44 genes. Experimental analysis of 32 of these potential SNPs veri®ed two SNPs; endothelin receptor A 1471 G/C (3¢ UTR) and PAI-1 Trp514Arg from a T/C exchange. These two SNPs were also identi®ed in the NCBI and HGBASE databases together with two polymorphisms that were not experimentally identi®ed in our homogenous Swedish population. Overall, the HGBASE and NCBI databases contained entries of 22% (23 out of 106) of the SNPs identi®ed through our EST searches. Conclusions. In the search for genetic variations causing complex diseases like IRS in homogenous populations (such as the Swedish one used here), important information can be obtained through bioinformatic searches of human genome databases and experimental veri®cation.

A Simple Analysis of Gene Expression and Variability in Gene Arrays Based on Repeated Observations

“…[4][5][6] Spanning these different techniques, however, are considerations related to sample preparation, which the current study demonstrates are important. While still relatively expensive, the up-front use of multiple gene array analysis lends inherent efficiency to a screening process, whose subsequent steps and implications are costly and time consuming.…”

“…[3] In fact, a variety of different technical approaches are currently available, including sequencing of expressed genes, gene arrays (including both fluorescence-based as well as radioactive label-based hybridization formats), and serial analysis of gene expression (SAGE). [4][5][6] Individual approaches are associated with certain advantages and disadvantages. Importantly, however, each approach is labor intensive, time consuming and expensive.…”

In silico whole-genome screening for cancer-related single-nucleotide polymorphisms located in human mRNA untranslated regions

“…Transcriptome analysis not only gives information about gene expression levels in normal versus cancer cells, but also about genetic variations. In that respect, large-scale scanning of EST databases have previously been used for identification of SNPs in genes involved in a various number of disorders [49-51]. As noted elsewhere [8,9,15,52], EST-based strategies have inherent limitations, including poor sequencing depth, variations in library sizes, poor quality annotation and differences in transcript sampling.…”

The Role of Single Nucleotide Polymorphisms of Untranslated Regions (Utrs) in Insulin Resistance Pathogenesis in Patients with Type 2 Diabetes

“…Using bioinformatics and experimental approaches they revealed the association of nineteen SNPs located in 3'UTR with IR and T2DM. Nine SNPs were located in UTRs of genes implicated in insulin function and regulation pathway, nine SNPs were placed in UTRs of genes regulating cytokines synthesis and inflammation processes and one SNP in 3'UTR of gene classified into regulation of glucose metabolism and glucose transport (Bennet et al, 2001). …”