Genome-wide transcriptional profiling identifies potential signatures in discriminating active tuberculosis from latent infection

Abstract

// Liping Pan 1 , Na Wei 2 , Hongyan Jia 1 , Mengqiu Gao 3 , Xiaoyou Chen 3 , Rongrong Wei 1 , Qi Sun 1 , Shuxiang Gu 1 , Boping Du 1 , Aiying Xing 1 and Zongde Zhang 1 1 Beijing Chest Hospital, Capital Medical University, Beijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, China 2 Medical Laboratory, Linyi Chest Hospital, Linyi 276000, China 3 Tuberculosis Department, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, China Correspondence to: Zongde Zhang, email: zzd417@163.com Keywords: genome-wide transcriptional profiling; signature; tuberculosis; latent tuberculosis infection Received: May 10, 2017      Accepted: November 14, 2017      Published: December 04, 2017 ABSTRACT To better understand the host immune response involved in the progression from latent tuberculosis infection (LTBI) to active tuberculosis (TB) and identify the potential signatures for discriminating TB from LTBI, we performed a genome-wide transcriptional profile of Mycobacterium tuberculosis (M.TB)–specific antigens-stimulated peripheral blood mononuclear cells (PBMCs) from patients with TB, LTBI individuals and healthy controls (HCs). A total of 209 and 234 differentially expressed genes were detected in TB vs. LTBI and TB vs. HCs, respectively. Nineteen differentially expressed genes with top fold change between TB and the other 2 groups were validated using quantitative real-time PCR (qPCR), and showed 94.7% consistent expression pattern with microarray test. Six genes were selected for further validation in an independent sample set of 230 samples. Expression of the resistin (RETN) and kallikrein 1 (KLK1) genes showed the greatest difference between the TB and LTBI or HC groups ( P < 0.0001). Receiver operating characteristic curve (ROC) analysis showed that the areas under the curve (AUC) for RETN and KLK1 were 0.844 (0.783–0.904) and 0.833 (0.769–0.897), respectively, when discriminating TB from LTBI. The combination of these two genes achieved the best discriminative capacity [AUC = 0.916 (0.872–0.961)], with a sensitivity of 71.2% (58.7%–81.7%) and a specificity of 93.6% (85.7%–97.9%). Our results provide a new potentially diagnostic signature for discriminating TB and LTBI and have important implications for better understanding the pathogenesis involved in the transition from latent infection to TB activation.