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Whole blood RNA-seq based Gene biomarkers differentiates active TB from latent TB

Kadar Moideen 1, Nathella P. Kumar 1, 2, Ramalingam Bethunaickan 2 and Subash Babu 1, 3

1 National Institutes of Health—NIRT— International Center for Excellence in Research, Chennai, India and

2 National Institute for Research in Tuberculosis, Chennai, India

3 LPD, NIAID, NIH, Bethesda, MD

Background: Tuberculosis (TB) is one of the infectious diseases having the highest rate of mortality. Various biomarkers are being studied around the world to diagnose TB, and molecular biomarkers like the transcriptomic biomarkers may serve as useful tools for TB diagnosis. Transcriptomic analysis reveals the molecular functions and modifications within the cells upon infection or treatment with specificity. RNA sequencing (RNA-seq) is a high throughput next-generation sequencing (NGS) technology developed in the last decade in the field of transcriptomics with the characteristics of lower detection time, higher accuracy data and cheaper cost.

Aim & Objective: To identify gene biomarkers that can differentiate latent infection from active TB disease.

Methodology: 3mL of whole blood in RNA Tempus tube was collected from 20 pulmonary TB patients at baseline (PTB BL) and after 6 months of anti TB treatment (PTB PT) and from 20 latently infected people (LTB) positive for Interferon gamma test in South India. RNA was isolated from the blood sample using the kit method by Applied Biosystems and RNA sequencing was performed on an Illumina platform1. Differential Gene expression and pathway enrichment analysis were done using bioinformatics pipelines available at CSI NGS Portal.

Results: From our preliminary differential gene expression analysis, identified a top 20 gene set out of 15501 genes that was significantly different (P=0.0000 to P<0.0001) with higher the log fold change between pulmonary TB patients both at baseline and at 6 months of post-treatment (Group 1), and those with latent TB (Group 2) (Fig.Volcano plot). Canonical pathway analysis revealed that genes such as TMEM120B, C1orf56 and PXN-AS1 involved in translation enrichment, rRNA processing, and mitochondrial translation were among the top 20 genes that were down-regulated, while SCARNA9, SKA3 and PTP4A1 involved in Neutrophil degranulation, Platelet activation and Toll-like receptor cascade pathways were up-regulated in the PTB group at both pre-treatment and post-treatment time points, as compared to the LTB group. Further validation of the identified gene set is necessary in a larger population to confirm its utility as a biomarker for diagnosis and therapeutic monitoring.

Conclusion: The transcriptomic signature identified in this study can differentiate active TB from latent infection and may serve as a potential gene biomarker for diagnosis of active TB disease.