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Polymorphisms in miRNA binding sites involved in metabolic diseases in mice and humans.


ABSTRACT: Type 2 diabetes and obesity are well-studied metabolic diseases, which are based on genetic and epigenetic alterations in combination with an obesogenic lifestyle. The aim of this study was to test whether SNPs in miRNA-mRNA binding sites that potentially disrupt binding, elevate the expression of miRNA targets, which participate in the development of metabolic diseases. A computational approach was developed that integrates transcriptomics, linkage analysis, miRNA-target prediction data, and sequence information of a mouse model of obesity and diabetes. A statistical analysis demonstrated a significant enrichment of 566 genes for a location in obesity- and diabetes-related QTL. They are expressed at higher levels in metabolically relevant tissues presumably due to altered miRNA-mRNA binding sites. Of these, 51 genes harbor conserved and impaired miRNA-mRNA-interactions in human. Among these, 38 genes have been associated to metabolic diseases according to the phenotypes of corresponding knockout mice or other results described in the literature. The remaining 13 genes (e.g. Jrk, Megf9, Slfn8 and Tmem132e) could be interesting candidates and will be investigated in the future.

SUBMITTER: Gottmann P 

PROVIDER: S-EPMC7190857 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Polymorphisms in miRNA binding sites involved in metabolic diseases in mice and humans.

Gottmann Pascal P   Ouni Meriem M   Zellner Lisa L   Jähnert Markus M   Rittig Kilian K   Walther Dirk D   Schürmann Annette A  

Scientific reports 20200429 1


Type 2 diabetes and obesity are well-studied metabolic diseases, which are based on genetic and epigenetic alterations in combination with an obesogenic lifestyle. The aim of this study was to test whether SNPs in miRNA-mRNA binding sites that potentially disrupt binding, elevate the expression of miRNA targets, which participate in the development of metabolic diseases. A computational approach was developed that integrates transcriptomics, linkage analysis, miRNA-target prediction data, and se  ...[more]

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