Biomarkers to diagnose Latent Tuberculosis Infection (LTBI) and predict those who would progress to active Tuberculosis (ATB) disease

Identification: Singh-Prashant


Biomarkers to diagnose Latent Tuberculosis Infection (LTBI) and predict those who would progress to active Tuberculosis (ATB) disease
Prashant Singh1 , Vivek Yadav1 , Sandeep Namburi1 , Ramakrishna Boyanapalli1 
1Celleome Biosciences LLP, Gurgaon Haryana, 122001 
Tuberculosis (TB), an infection caused by the bacterium Mycobacterium tuberculosis (MTB), is recognized as the world’s number one cause of death from an infectious disease.  A quarter to third of the human population is believed to be infected, with about 10 million active cases and 1.6 million deaths each year, and the highest caseload in India. Majority of people infected with the bacterium are neither symptomatic nor infectious LTBI,but it is estimated that approximately 5 - 10% of LTBI subjects develop ATB within three years. While the World Health Organization (WHO) recommends identifying LTBI subjects and actively treating the patients, countries with the largest TB burden are lagging due to primarily focusing on treating ATB and drug resistant TB (DR-TB). Although rapid molecular screening tools to diagnose LTBI (GeneXpert®, Cepheid USA) and ATB (TruNat™, Molbio® Diagnostics, India) are available, no tool is currently available to predict the subset of LTBI individuals at risk to developing ATB. In order to discover biomarkers predicting LTBI subjects at risk of progressing to ATB, we identified publicly available transcriptomes sequenced from whole blood of LTBI, ATB, and control human subjects. Besides the LTBI (n = 139) and ATB (n = 74) subjects, the dataset included 9 LTBI subjects who subsequently developed ATB, and 16 who did not. Using published software packages and in-house scripts to analyze differential gene expression, transcript expression, and alternative splicing (AS), we built a computational pipeline. Using our pipeline, we identified AS mRNA transcripts differentially expressed in the LTBI subjects who progressed to ATB compared to those who did not. Biomarkers for developing a screening tool to predict LTBI subjects progressing to ATB were discovered in silico using splice junction (SJ) analyses of our data. We have also developed novel biomarkers, which identify LTBI subjects compared to healthy or no MTB infected individuals. Since our datasets comprise of individuals native to the Southeast Asian subcontinent, and our biomarkers exhibit differential expression unique to Southeast Asians, the two sets of biomarkers can be used to develop molecular diagnostic kits as screening tools for use in South Asia.



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