Suppressor of cytokine signaling-3 is affected in T-cells from tuberculosisTB patients.

T-cells and T-cell-derived cytokines are crucial mediators of protection against Mycobacterium tuberculosis infection, but these factors are insufficient as biomarkers for disease susceptibility. In order to define T-cell molecules involved in tuberculosis (TB), we compared gene expression profiles of T-cells from patients with active TB, healthy donors with latent M. tuberculosis infection (LTBIs) and non-infected healthy donors (NIDs) by microarray analysis. Pathway-focused analyses identified a prevalent subset of candidate genes involved in the Janus kinase (JAK)-signal transducer and activator of transcription signalling pathway, including those encoding suppressor of cytokine signalling (SOCS) molecules, in the subset of protection-associated genes. Differential expression was verified by quantitative PCR analysis for the cytokine-inducible SH2-containing protein (CISH), SOCS3, JAK3, interleukin-2 receptor α-chain (IL2RA), and the proto-oncogene serine/threonine protein kinase (PIM1). Classification analyses revealed that this set of molecules was able to discriminate efficiently between T-cells from TB patients and those from LTBIs, and, notably, to achieve optimal discrimination between LTBIs and NIDs. Further characterization by quantitative PCR revealed highly variable candidate gene expression in CD4(+) and CD8(+) T-cells from TB patients and only minor differences between CD4(+) and CD8(+) T-cell subpopulations. These results point to a role of cytokine receptor signalling regulation in T-cells in susceptibility to TB.

Short-term reproducibility of a commercial interferon gamma release assay.

Interferon gamma release assays (IGRAs) have been shown to be sensitive and highly specific for the detection of immune memory against Mycobacterium tuberculosis. Little is known about the reproducibility and within-person variability of these assays. Various aspects of short-term reproducibility of a commercial IGRA, the QuantiFERON-TB Gold In-Tube (QFT-IT) assay, were assessed. The QFT-IT assay was performed twice within 3 days in 27 health care workers in Cape Town, South Africa. Two sets of tests were performed by different operators on day 1, and one set was performed on day 3. Aspects such as interoperator, intraoperator, day-to-day variability, and test-retest variability as well as different the storage methods of plasma were investigated. Seventeen of 27 (63%) of participants had at least one positive QFT-IT text; six had discordant results. The agreement of all aspects studied was high, with kappa values between 0.82 and 1.00 for dichotomous measures, and interclass correlations (ICC) of 0.809 to 0.965 were observed for continuous gamma interferon (IFN-gamma) measures. The variability of the magnitude of response was highest comparing measures obtained from individuals on different days (ICC of 0.809). The magnitude of the IFN-gamma responses between assays performed for individual participants was variable, with ranges from 0.03 to 11 IU/ml, resulting is discordant results for five participants. The results indicate that the QFT-IT assay is a robust and highly reproducible assay. Considerable intraindividual variability occurs in the magnitude of IFN-gamma responses, which may influence the interpretation of serial measures.

Deconfounding microarray analysis - independent measurements of cell type proportions used in a regression model to resolve tissue heterogeneity bias.

OBJECTIVES: Microarray analysis requires standardized specimens and evaluation procedures to achieve acceptable results. A major limitation of this method is caused by heterogeneity in the cellular composition of tissue specimens, which frequently confounds data analysis. We introduce a linear model to deconfound gene expression data from tissue heterogeneity for genes exclusively expressed by a single cell type. METHODS: Gene expression data are deconfounded from tissue heterogeneity effects by analyzing them using an appropriate linear regression model. In our illustrating data set tissue heterogeneity is being measured using flow cytometry. Gene expression data are determined in parallel by real time quantitative polymerase chain reaction (qPCR) and microarray analyses. Verification of deconfounding is enabled using protein quantification for the respective marker genes. RESULTS: For our illustrating dataset, quantification of cell type proportions for peripheral blood mononuclear cells (PBMC) from tuberculosis patients and controls revealed differences in B cell and monocyte proportions between both study groups, and thus heterogeneity for the tissue under investigation. Gene expression analyses reflected these differences in celltype distribution. Fitting an appropriate linear model allowed us to deconfound measured transcriptome levels from tissue heterogeneity effects. In the case of monocytes, additional differential expression on the single cell level could be proposed. Protein quantification verified these deconfounded results. CONCLUSIONS: Deconfounding of transcriptome analyses for cellular heterogeneity greatly improves interpretability, and hence the validity of transcriptome profiling results.