GlucoGenes®, a database of genes and proteins associated with glucose metabolism disorders, its description and applications in bioinformatics research

TL;DR

GlucoGenes® is a database of genes and proteins related to glucose metabolism disorders, offering tools for bioinformatics research and analysis.

Contribution

The novel contribution is the creation of GlucoGenes®, a freely accessible database and portal for genes and proteins linked to glucose metabolism disorders.

Findings

Evolutionary analysis showed a 40% increase in genes with phylostratigraphic age index values from multicellular organisms.

Most genes in GlucoGenes® are highly conserved with divergence index values below 0.6 or 1.

181 SNP markers in promoter regions were identified, affecting gene expression related to glucose metabolism.

Abstract

Data on the genetics and molecular biology of diabetes are accumulating rapidly. This poses the challenge of creating research tools for a rapid search for, structuring and analysis of information in this field. We have developed a web resource, GlucoGenes®, which includes a database and an Internet portal of genes and proteins associated with high glucose (hyperglycemia), low glucose (hypoglycemia), and both metabolic disorders. The data were collected using text mining of the publications indexed in PubMed and PubMed Central and analysis of gene networks associated with hyperglycemia, hypoglycemia and glucose variability performed with ANDSystems, a bioinformatics tool. GlucoGenes® is freely available at: https://glucogenes.sysbio.ru/genes/main. GlucoGenes® enables users to access and download information about genes and proteins associated with the risk of hyperglycemia and hypoglycemia, molecular regulators with hyperglycemic and antihyperglycemic activity, genes up-regulated by high glucose and/or low glucose, genes down-regulated by high glucose and/or low glucose, and molecules otherwise associated with the glucose metabolism disorders. With GlucoGenes®, an evolutionary analysis of genes associated with glucose metabolism disorders was performed. The results of the analysis revealed a significant increase (up to 40 %) in the proportion of genes with phylostratigraphic age index (PAI) values corresponding to the time of origin of multicellular organisms. Analysis of sequence conservation using the divergence index (DI) showed that most of the corresponding genes are highly conserved (DI < 0.6) or conservative (DI < 1). When analyzing single nucleotide polymorphism (SNP) in the proximal regions of promoters affecting the affinity of the TATA-binding protein, 181 SNP markers were found in the GlucoGenes® database, which can reduce (45 SNP markers) or increase (136 SNP markers) the expression of 52 genes. We believe that this resource will be a useful tool for further research in the field of molecular biology of diabetes.

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