Immunometabolic Signatures Predict Risk of Progression to Active Tuberculosis and Disease Outcome.

Fergal J Duffy,Gerhard Walzl,Jeroen Maertzdorf,Jayne S Sutherland,Hazel M Dockrell,Thomas J Scriba,January Weiner,Daniel E Zak,Gerard Tromp,Louis J Picker,Drew Dover,Tom H M Ottenhoff,Stefan H E Kaufmann,Sara Suliman,Ethan Thompson,David L Tabb,Michael K Axthelm,Shreemanta K Parida ,Steven G Smith ,Scott G Hansen

doi:10.3389/fimmu.2019.00527

Abstract

There remains a pressing need for biomarkers that can predict who will progress to active tuberculosis (TB) after exposure to Mycobacterium tuberculosis (MTB) bacterium. By analyzing cohorts of household contacts of TB index cases (HHCs) and a stringent non-human primate (NHP) challenge model, we evaluated whether integration of blood transcriptional profiling with serum metabolomic profiling can provide new understanding of disease processes and enable improved prediction of TB progression. Compared to either alone, the combined application of pre-existing transcriptome- and metabolome-based signatures more accurately predicted TB progression in the HHC cohorts and more accurately predicted disease severity in the NHPs. Pathway and data-driven correlation analyses of the integrated transcriptional and metabolomic datasets further identified novel immunometabolomic signatures significantly associated with TB progression in HHCs and NHPs, implicating cortisol, tryptophan, glutathione, and tRNA acylation networks. These results demonstrate the power of multi-omics analysis to provide new insights into complex disease processes.

Highlights

Tuberculosis (TB) is an infectious disease caused by the bacterial pathogen Mycobacterium tuberculosis (M. tb), which is spread via aerosolized droplets that originate from the expectorations of diseased individuals
By employing a multi-step analytical strategy (Figure S1), we tested whether integration of blood transcriptional profiling with serum metabolomic profiling can provide new understanding of disease processes and enable improved prediction of TB progression
GC6-74 comprised HIV-negative household contacts of active TB cases that were recruited from four African study sites, in South Africa (Stellenbosch University/SUN), The Gambia (Medical Research Council Unit The Gambia/MRC), Uganda (Makerere University/MAK), and Ethiopia (Armauer Hansen Research Institute/AHRI)

Summary

Introduction

Tuberculosis (TB) is an infectious disease caused by the bacterial pathogen Mycobacterium tuberculosis (M. tb), which is spread via aerosolized droplets that originate from the expectorations of diseased individuals. About 10% of individuals latently infected with M.tb will. Immunometabolomic Signatures of TB Risk progress to active disease at some point in their lives [1]. Major obstacles to fighting TB are the lack of effective TB diagnostics and the extremely large number of latently infected individuals, estimated at 23% of the world’s population [13]. Due to the impracticality of effectively treating all latently infected individuals and the accompanying possible side effects of such treatments, an effective method for identifying individuals at high risk of progression to active TB disease is highly desirable. Since M. tb is spread by individuals with active TB, early identification and treatment of high-risk individuals could break the chain of transmission and facilitate control of the TB epidemic

Methods

Results

Conclusion