Spleen leukocytes were collected, as described previously [25,26]

Spleen leukocytes were collected, as described previously [25,26] from female NOD mice at 2, 3 and 4 weeks of age (representing the period prior to overt insulitis) and from two matched control strains, NOR and C57BL/6 (C57); n = 5 for each strain and age group, except NOD 2 week, where n = 4. CD4 T-cells were then negatively separated by magnetic beads according to the manufacturer’s protocol (Miltenyi Biotec). Purity was assessed by flow cytometry using FITC-conjugated anti-mouse CD4 monoclonal antibodies (Becton Dickinson); only samples of > 90% purity were used in the study. Total

RNA was extracted from untreated, whole CD4 T-cells, as described previously

[ 26, 27]. A total of 1–1.5 µg of total RNA was processed with Two-Cycle Target labeling protocol and hybridized on Affymetrix, Mouse430_2 expression arrays, GW 572016 according to the manufacturer’s instructions. Normalization, scaling, and basic evaluation of the quality of the expression data from GDC-0199 solubility dmso each chip were conducted using the GCOS software (Affymetrix), as described previously [ 25, 26]. The microarray data sets are available in the gene expression omnibus repository [GSE46600]. Microarray results were validated by quantitative Real-time PCR, as described previously [ 25] ( Fig. S1). Expression values were normalized to glyceraldehyde-3-phosphate dehydrogenase (Gapdh). The target genes, primers and probes are listed in Table S1. Statistical analysis of the microarray data was conducted as previously described [25,26]. Filtration of the probe sets present on the chip array (∼60,000) identified ∼31,000 probe sets that had a present/marginal expression flag in at least one of the samples. We then performed one-way ANOVA (at various statistical stringencies) on the filtered probe sets for each age separately in order to define lists of age-specific genes

that were differentially expressed between strains. Lists generated at either p < 0.005 Branched chain aminotransferase (“smaller” list) or p < 0.05 (“larger” list) adjusted with Benjamini–Hochberg multiple test correction (corresponding to a false discovery rate (FDR) of 0.5% or 5%, respectively) were used in further analysis. Finally, we conducted hierarchical clustering on these lists as previously described [ 27] to identify genes that were uniquely differentially expressed in NOD mice relative to both control strains. These genes are herein referred to as NOD altered genes. We subjected the lists of NOD altered genes to data mining using a suite of modern bioinformatics tools. Enriched gene ontology (GO) categories and KEGGs pathways were determined using WebGestalt Gene Set Analysis Toolkit (http://bioinfo.vanderbilt.edu/webgestalt [28]), as described previously [25].

Leave a Reply

Your email address will not be published. Required fields are marked *


You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <strike> <strong>