Changes in the blood transcriptome following prophylactic treatment reflect latent tuberculosis heterogeneity
Julie G Burel1, Akul Singhania1, Paige Dubelko1, Julius Muller2, Rachel Tanner2, Eneida Parizotto2, Martin Dedicoat3, Thomas E. Fletcher4,5, James Dunbar6, Adam F. Cunningham7, Cecilia S. Lindestam Arlehamn1, Donald G Catanzaro8, Antonino Catanzaro9, Timothy C Rodwell9, Helen McShane2, Matthew K. O’Shea3,4,7 and Bjoern Peters1,9
1 Vaccine Discovery Division, La Jolla Institute for Immunology, La Jolla, CA, USA; 2 The Jenner Institute, University of Oxford, Oxford, UK; 3 University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; 4 Royal Centre for Defence Medicine, Joint Medical Command, Birmingham, UK; 5 Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
6 Department of Infectious Diseases, The Friarage Hospital, Northallerton, UK; 7 Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK; 8 Department of Biological Sciences, University of Arkansas, Fayetteville, AR, USA
9 Department of Medicine, University of California San Diego, CA, USA
About 2 billion people are infected with Mycobacterium tuberculosis (Mtb) worldwide, representing a considerable potential transmission reservoir which hampers eradication efforts. Prioritizing latent tuberculosis (LTBI) patients who would benefit most from chemoprophylaxis treatment would improve TB control strategies. We hypothesized that LTBI individuals at risk of developing active TB (ATB) display immunological profiles more similar to those with ATB than the LTBI cohort at large, and thus will exhibit a similar signature in response to treatment. We studied changes in the blood transcriptome in a cohort of 42 LTBI patients who received anti-TB therapy. Before starting treatment, the LTBI cohort was divided into high, intermediate and low risk of progression to active disease based on the expression of previously published gene signatures of ‘risk of progression to active TB’ (Riskhigh, Riskint and Risklow groups, respectively). We found that LTBI Riskhigh and LTBI Risklow groups were associated with two distinct transcriptomic signatures after treatment, with LTBI Riskhigh resembling ATB and associating with IFNg signaling and activated T cells. Importantly, some of the treatment signatures genes were already differentially expressed between LTBI Riskhigh and LTBI Risklow groups prior to treatment, suggesting the transcriptomic reprogramming following treatment in LTBI can be predicted. We conclude that pre-treatment blood RNA signatures in individuals with LTBI can be used to predict immune profiles following therapy, which represents a promising approach to identify individuals who would most likely benefit from therapeutic intervention.