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通过胰岛特异性CD4 + T细胞的DNA微阵列分析鉴定1型糖尿病候选基因。

Identifying type 1 diabetes candidate genes by DNA microarray analysis of islet-specific CD4 + T cells.

作者信息

Berry Gregory J, Frielle Christine, Brucklacher Robert M, Salzberg Anna C, Waldner Hanspeter

机构信息

Department of Microbiology & Immunology, College of Medicine, Pennsylvania State University, Hershey, PA 17033, USA.

Genome Sciences Facility, College of Medicine, Pennsylvania State University, Hershey, PA 17033, USA.

出版信息

Genom Data. 2015 Jun 14;5:184-8. doi: 10.1016/j.gdata.2015.05.041. eCollection 2015 Sep.

Abstract

Type 1 diabetes (T1D) is a T cell-mediated autoimmune disease resulting from the destruction of insulin-producing pancreatic beta cells and is fatal unless treated with insulin. During the last four decades, multiple insulin-dependent diabetes (Idd) susceptibility/resistance loci that regulate T1D development have been identified in humans and non-obese diabetic (NOD) mice, an established animal model for T1D. However, the exact mechanisms by which these loci confer diabetes risk and the identity of the causative genes remain largely elusive. To identify genes and molecular mechanisms that control the function of diabetogenic T cells, we conducted DNA microarray analysis in islet-specific CD4 + T cells from BDC2.5 TCR transgenic NOD mice that contain the Idd9 locus from T1D-susceptible NOD mice or T1D-resistant C57BL/10 mice. Here we describe in detail the contents and analyses for these gene expression data associated with our previous study [1]. Gene expression data are available at the Gene Expression Omnibus (GEO) repository from the National Center for Biotechnology Information (accession number GSE64674).

摘要

1型糖尿病(T1D)是一种由T细胞介导的自身免疫性疾病,由产生胰岛素的胰腺β细胞被破坏所致,若不使用胰岛素治疗则会致命。在过去的四十年里,已经在人类和非肥胖糖尿病(NOD)小鼠(一种已确立的T1D动物模型)中鉴定出多个调节T1D发生的胰岛素依赖型糖尿病(Idd)易感/抗性基因座。然而,这些基因座赋予糖尿病风险的确切机制以及致病基因的身份在很大程度上仍然未知。为了鉴定控制致糖尿病T细胞功能的基因和分子机制,我们对来自BDC2.5 TCR转基因NOD小鼠的胰岛特异性CD4 + T细胞进行了DNA微阵列分析,这些小鼠含有来自T1D易感NOD小鼠或T1D抗性C57BL/10小鼠的Idd9基因座。在此,我们详细描述了与我们之前的研究[1]相关的这些基因表达数据的内容和分析。基因表达数据可从美国国立生物技术信息中心的基因表达综合数据库(GEO)获得(登录号GSE64674)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c95c/4583664/038c5d070e56/gr1.jpg

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