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

• Published in: Tuberculosis (Edinburgh, Scotland) Vol. 95; no. 4; pp. 421 - 425
• Main Authors: Laux da Costa, Lucas, Delcroix, Melaine, Dalla Costa, Elis R, Prestes, Isaías V, Milano, Mariana, Francis, Steve S, Unis, Gisela, Silva, Denise R, Riley, Lee W, Rossetti, Maria L.R
• Format: Journal Article
• Language: English
• Published: Scotland Elsevier Ltd 01.07.2015
• Subjects: Adolescent; Adult; Aged; Area Under Curve; Asthma - diagnosis; Asthma - genetics; Biomarkers; Brazil; CD64; Decision Trees; Diagnosis, Differential; Diagnostics; Female; GBP5; Gene Expression Profiling - methods; Genetic Markers; Granzymes - genetics; GTP-Binding Proteins - genetics; GZMA; Host-Pathogen Interactions; Humans; Infectious Disease; Male; Middle Aged; Mycobacterium tuberculosis - pathogenicity; Pneumonia - diagnosis; Pneumonia - genetics; Predictive Value of Tests; Pulmonary/Respiratory; Real-Time Polymerase Chain Reaction; Receptors, IgG - genetics; ROC Curve; Tuberculosis; Tuberculosis, Pulmonary - diagnosis; Tuberculosis, Pulmonary - genetics; Tuberculosis, Pulmonary - microbiology; Young Adult; Brazil; CD64; granzyme A; GBP5; OPD; healthy non-infected donor; Diagnostics; GZMA; NID; guanylate binding protein 5; LTBI; TB; Tuberculosis; pneumonia; Fc gamma receptor 1A; Fc gamma receptor 1B; Biomarkers; lactotransferrin; healthy latently infected; other pulmonary diseases; FCGR1B; PN; LTF
• ISSN: 1472-9792; 1873-281X
• DOI: 10.1016/j.tube.2015.04.008

Summary 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.