Single Nucleotide Polymorphisms in IL17A and IL6 Are Associated with Decreased Risk for Pulmonary Tuberculosis in Southern Brazilian Population.

In Mycobacterium tuberculosis (MTB) infection, the complex interaction of host immune system and the mycobacteria is associated with levels of cytokines production that play a major role in determining the outcome of the disease. Several single-nucleotide polymorphisms (SNPs) in cytokine genes have been associated with tuberculosis (TB) outcome. The aim of this study was to evaluate the association between previously reported SNPs IL2-330 T>G (rs2069762); IL4-590 C>T (rs2243250); IL6-174 G>C (rs1800795); IL10-592 A>C (rs1800872); IL10-1082 G>A (rs1800896); IL17A -692 C>T (rs8193036); IL17A -197 G>A (rs2275913); TNF -238 G>A (rs361525); TNF -308 G>A (rs1800629) and IFNG +874 T>A (rs2430561) and pulmonary TB (PTB) susceptibility. We conducted a case-control study in individuals from Southern Brazil who were recruited between February 2012 and October 2013 in a high incidence TB city. We performed a multiplex genotyping assay in 191 patients with PTB and 175 healthy subjects. Our results suggest a decreased risk for PTB development associated with the IL17A -197A allele (OR = 0.29; p = 0.04), AA genotype (OR = 0.12; p = 0.04) and A carrier (AG/AA) (OR = 0.29; p = 0.004) and IL6 -174C carrier (CC/CG) (OR = 0.46; p = 0.04). We could not properly analyze IL17A -692 C>T (rs8193036) and IFNG +874T>A due to genotypic inconsistencies and found no evidence of association for the IL2, IL4, IL10 and TNF polymorphisms and PTB. In conclusion, our results show a protective effect of IL17 and IL6 polymorphisms on PTB outcome in Southern Brazilian population.

A real-time PCR signature to discriminate between tuberculosis and other pulmonary diseases.

The goal of this study was to identify a host gene signature that can distinguish tuberculosis (TB) from other pulmonary diseases (OPD). We conducted real-time PCR on whole blood samples from patients in Brazil. TB and OPD patients (asthma and non-TB pneumonia) differentially expressed granzyme A (GZMA), guanylate binding protein 5 (GBP5) and Fc gamma receptor 1A (CD64). Receiver operating characteristic, tree classification and random forest analyses were applied to evaluate the discriminatory power of the three genes and find the gene panel most predictive of patients' disease classification. Tree classification produced a model based on GBP5 and CD64 expression. In random forest analysis, the combination of the three genes provided a robust biosignature to distinguish TB from OPD with 95% specificity and 93% sensitivity. Our results suggest that GBP5 and CD64 in tandem may be the most predictive combination. However, GZMA contribution to the prediction model requires further investigation. Regardless, these three genes show promise as a rapid diagnostic marker separating TB from OPD.