Anti-IFNG monoclonal antibody, clone 5T.C4 [APC/Cy7®]

Hepatitis C virus infection causes chronic liver disease that leads to cancer-related mortality. Presently around 30% of the HCV (infected) affected population get rid of the infection through spontaneous disease clearance. This phenomenon is conducted by a set of reported immune candidate genes. Hence, this study focuses only on these immune-response related genes with aid of network approach, where the idea is to disseminate the network for better understanding of key functional genes and their transcription control activity. Based on the network analysis the IFNG, TNF, IFNB1, STAT1, NFKB1, STAT3, SOCS1, and MYD88 genes are prioritized as hub genes along with their common transcription factors (TFs), IRF9, NFKB1, and STAT1. The dinucleotide frequency of TF binding elements indicated GG-rich motifs in these regulatory elements. On the other hand, gene enrichment report suggests the regulation of response to interferon gamma signaling pathway, which plays central role in the spontaneous HCV clearance. Therefore, our study tends to prioritize the genes, TFs, and their regulatory pathway towards HCV clearance. Even so, the resultant hub genes and their TFs and TF binding elements could be crucial in underscoring the clearance activity in specific populations.

Mycobacterium tuberculosis (M. tuberculosis) infection in humans can cause active disease or latent infection. However, the factors contributing to the maintenance of latent infection vs. disease progression are poorly understood. In this study, we used a genome-wide RNA sequencing (RNA-seq) approach to identify host factors associated with M. tuberculosis infection status and a novel gene signature that can distinguish active disease from latent infection. By RNA-seq, we characterized transcriptional differences in purified protein derivative (PPD)-stimulated peripheral blood mononuclear cells (PBMCs) among three groups: patients with active tuberculosis (ATB), individuals with latent TB infection (LTBI), and TB-uninfected controls (CON). A total of 401 differentially expressed genes enabled grouping of individuals into three clusters. A validation study by quantitative real-time PCR (qRT-PCR) confirmed the differential expression of TNFRSF10C, IFNG, PGM5, EBF3, and A2ML1 between the ATB and LTBI groups. Additional clinical validation was performed to evaluate the diagnostic performance of these five biomarkers using 130 subjects. The 3-gene signature set of TNFRSF10C, EBF3, and A2ML1 enabled correct classification of 91.5% of individuals, with a high sensitivity of 86.2% and specificity of 94.9%. Diagnostic performance of the 3-gene signature set was validated using a clinical cohort of 147 subjects with suspected ATB. The sensitivity and specificity of the 3-gene set for ATB were 82.4 and 92.4%, respectively. In conclusion, we detected distinct gene expression patterns in PBMCs stimulated by PPD depending on the status of M. tuberculosis infection. Furthermore, we identified a 3-gene signature set that could distinguish ATB from LTBI, which may facilitate rapid diagnosis and treatment for more effective disease control.