Literature DB >> 14747287

Gene expression profiles define a key checkpoint for type 1 diabetes in NOD mice.

Sarah E Eckenrode1, Qingguo Ruan, Ping Yang, Weipeng Zheng, Richard A McIndoe, Jin-Xiong She.   

Abstract

cDNA microarrays with >11,000 cDNA clones from an NOD spleen cDNA library were used to identify temporal gene expression changes in NOD mice (1-10 weeks), which spontaneously develop type 1 diabetes, and changes between NOD and NOD congenic mice (NOD.Idd3/Idd10 and NOD.B10Sn-H2(b)), which have near zero incidence of insulitis and diabetes. The expression profiles identified two distinct groups of mice corresponding to an immature (1-4 weeks) and mature (6-10 weeks) state. The rapid switch of gene expression occurring around 5 weeks of age defines a key immunological checkpoint. Sixty-two known genes are upregulated, and 18 are downregulated at this checkpoint in the NOD. The expression profiles are consistent with increased antibody production, antigen presentation, and cell proliferation associated with an active autoimmune response. Seven of these genes map to confirmed diabetes susceptibility regions. Of these seven, three are excellent candidate genes not previously implicated in type 1 diabetes. Ten genes are differentially expressed between the NOD and congenic NOD at the immature stage (Hspa8, Hif1a, and several involved in cellular functions), while the other 70 genes exhibit expression differences during the mature (6-10 week) stage, suggesting that the expression differences of a small number of genes before onset of insulitis determine the disease progression.

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Year:  2004        PMID: 14747287     DOI: 10.2337/diabetes.53.2.366

Source DB:  PubMed          Journal:  Diabetes        ISSN: 0012-1797            Impact factor:   9.461


  14 in total

Review 1.  Is insulin signaling molecules misguided in diabetes for ubiquitin-proteasome mediated degradation?

Authors:  Muthuswamy Balasubramanyam; Rangasamy Sampathkumar; Viswanathan Mohan
Journal:  Mol Cell Biochem       Date:  2005-07       Impact factor: 3.396

2.  Advances and challenges in biomarker development for type 1 diabetes prediction and prevention using omic technologies.

Authors:  Colleen Carey; Sharad Purohit; Jin-Xiong She
Journal:  Expert Opin Med Diagn       Date:  2010-09-01

3.  Mapping of the murine type 1 diabetes locus Idd20 by genetic interaction.

Authors:  Joëlle Morin; Christian Boitard; David Vallois; Philip Avner; Ute Christine Rogner
Journal:  Mamm Genome       Date:  2006-11-07       Impact factor: 2.957

4.  Genome-wide transcriptional analyses of islet-specific CD4+ T cells identify Idd9 genes controlling diabetogenic T cell function.

Authors:  Gregory J Berry; Christine Frielle; Thaiphi Luu; Anna C Salzberg; Daniel B Rainbow; Linda S Wicker; Hanspeter Waldner
Journal:  J Immunol       Date:  2015-02-11       Impact factor: 5.422

5.  Biomarkers for type 1 diabetes.

Authors:  Sharad Purohit; Jin-Xiong She
Journal:  Int J Clin Exp Med       Date:  2008-02-29

6.  Parallel multiplicity and error discovery rate (EDR) in microarray experiments.

Authors:  Wayne Wenzhong Xu; Clay J Carter
Journal:  BMC Bioinformatics       Date:  2010-09-16       Impact factor: 3.169

7.  IFN regulatory factors 4 and 8 expression in the NOD mouse.

Authors:  Gilles Besin; Simon Gaudreau; Emilie Dumont-Blanchette; Michael Ménard; Chantal Guindi; Gilles Dupuis; Abdelaziz Amrani
Journal:  Clin Dev Immunol       Date:  2011-05-15

8.  Gene expression profiling in the type 1 diabetes rat diaphragm.

Authors:  Erik van Lunteren; Michelle Moyer
Journal:  PLoS One       Date:  2009-11-13       Impact factor: 3.240

9.  Transcriptome analysis of epigenetically modulated genome indicates signature genes in manifestation of type 1 diabetes and its prevention in NOD mice.

Authors:  Sundararajan Jayaraman; Akshay Patel; Arathi Jayaraman; Vasu Patel; Mark Holterman; Bellur Prabhakar
Journal:  PLoS One       Date:  2013-01-30       Impact factor: 3.240

10.  A statistical framework for integrating two microarray data sets in differential expression analysis.

Authors:  Yinglei Lai; Sarah E Eckenrode; Jin-Xiong She
Journal:  BMC Bioinformatics       Date:  2009-01-30       Impact factor: 3.169

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