Gene set class comparison identifies biological pathways that are over-represented in the experimental data by comparing the number of differentially expressed genes for a given BioCarta pathway with that expected by random chance alone. The significance threshold for this test was p = 0.005 using a univariate F-test to define differentially AZD8055 solubility dmso expressed genes (as above) with an LS permutation test used to identify BioCarta gene sets having more genes differentially expressed among the phenotype classes than expected by chance. Of the 218 BioCarta gene lists tested, 107 gene lists contained

one or more differentially expressed genes, and of these BioCarta gene lists, two were identified as significantly enriched for differentially expressed genes: “Adhesion Molecules on Lymphocytes” and “Monocyte and its Surface Molecules,” containing 11 and 12 genes, respectively. When examined, these two gene sets contained 11 of 12 identical genes. Hierarchical clustering of genes was used to survey the differentially expressed genes to identify global patterns of expression. To perform this analysis, the genes were centered and scaled, using one-minus correlation with average linkage computed. Differences between

the means of experimental groups were analyzed using the two-tailed Student’s t-test or ANOVA as appropriate. Differences were considered significant where p ≤ 0.05. Inherently logarithmic data from bacterial growth were transformed for statistical analysis. This work was supported by the Trudeau Institute, Inc., NIH grants AI46530 and AI069121 and an American Lung Association DeSouza Award to AMC.; PTDC/SAU-MII/099102/2008 from the I-BET-762 mw FCT (Fundação para a Ciência e a Tecnologia) to RA. The Authors would like to thank Flow Cytometry Core and the Imaging Core at Trudeau Institute and Phyllis Spatrick at the Genomic

Core Facility at UMASS Medical School for excellent technical support. The authors declare no financial or commercial conflict of interest. Disclaimer: Supplementary materials have been peer-reviewed but not copyedited. Figure S1. Live CD4+ T-cell populations in M. avium infected mice. WT and nos2−/− mice were either left uninfected (UnInf) or infected (Inf) intravenously with 106 M. unless avium 25291 and the spleens, lungs and livers harvested. The organs were processed for flow cytometry and the (A, C) frequency and (B, D) number of live lymphocytes (LO) (A, B) and CD4+ T cells (C, D) within the organs determined. Cells were gated on live lymphocytes, doublet discrimination, and CD3+, CD4+ (n = 4–22, *p < 0.05, **p < 0.01, ***p < 0.001, by ANOVA). Figure S2. Gating scheme for flow cytometric analysis and cell sorting. (A) The gating scheme for the detection of live, single cell, CD3+CD4+CD44+ T cells is shown in sequence. (B) Representative purity of the live, single cell (i) CD4+CD44+CD69hi and (ii) CD4+CD44+CD69lo cells sorted prior to RNA extraction.