Literature DB >> 18855692

SAGE application in the study of diabetes.

Toshinari Takamura1, Hirofumi Misu, Taro Yamashita, Shuichi Kaneko.   

Abstract

Type 2 diabetes is a multifactorial disease that is caused by the disruption of inter-organ networks. These disruptions lead to absolute and/or relative deficiencies in the actions of insulin due to either a genetic disposition or environmental factors. Specifically, the liver plays a central role in energy homeostasis and is a major source of bioactive secretory proteins that contribute to the pathophysiology of diabetes and subsequent complications. Therefore, comprehensive gene expression analyses of critical tissues, including the liver, are important steps for understanding the molecular signature of type 2 diabetes. Serial analysis of gene expression (SAGE) techniques have made it possible to compare tag levels among independent libraries and to identify previously unrecognized genes with novel functions that may be important in the development of diseases. Here, we review possible applications of SAGE to the study of diabetes from the following perspectives: (1) to understand and quantify normal gene expression profiles in the liver with respect to both a single gene and gene ontology of cellular components; (2) to identify biological pathways or co-regulated gene sets associated with the pathophysiology of diabetes to gain a more comprehensive understanding of genetic and environmental alterations; and (3) to identify novel functional hepatic genes that may regulate the pathophysiology of diabetes by comparing independent SAGE libraries in combination with DNA chip analyses. Such SAGE-based approaches may lead to the identification of novel therapeutic targets for the treatment of type 2 diabetes and its complications.

Entities:  

Mesh:

Substances:

Year:  2008        PMID: 18855692     DOI: 10.2174/138920108785915184

Source DB:  PubMed          Journal:  Curr Pharm Biotechnol        ISSN: 1389-2010            Impact factor:   2.837


  2 in total

Review 1.  Application of serial analysis of gene expression to the study of human genetic disease.

Authors:  Martin P Horan
Journal:  Hum Genet       Date:  2009-07-10       Impact factor: 4.132

Review 2.  Uncovering the complexity of transcriptomes with RNA-Seq.

Authors:  Valerio Costa; Claudia Angelini; Italia De Feis; Alfredo Ciccodicola
Journal:  J Biomed Biotechnol       Date:  2010-06-27
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.