A blood RNA signature for tuberculosis disease risk: a prospective cohort study

Abstract

Background Identification of blood biomarkers that prospectively predict progression of Mycobacterium tuberculosis infection to tuberculosis disease may lead to interventions that impact the epidemic. Methods Healthy, M. tuberculosis infected South African adolescents were followed for 2 years; blood was collected every 6 months. A prospective signature of risk was derived from whole blood RNA-Sequencing data by comparing participants who ultimately developed active tuberculosis disease (progressors) with those who remained healthy (matched controls). After adaptation to multiplex qRT-PCR, the signature was used to predict tuberculosis disease in untouched adolescent samples and in samples from independent cohorts of South African and Gambian adult progressors and controls. The latter participants were household contacts of adults with active pulmonary tuberculosis disease. Findings Of 6,363 adolescents screened, 46 progressors and 107 matched controls were identified. A 16 gene signature of risk was identified. The signature predicted tuberculosis progression with a sensitivity of 66·1% (95% confidence interval, 63·2–68·9) and a specificity of 80·6% (79·2–82·0) in the 12 months preceding tuberculosis diagnosis. The risk signature was validated in an untouched group of adolescents (p=0·018 for RNA-Seq and p=0·0095 for qRT-PCR) and in the independent South African and Gambian cohorts (p values <0·0001 by qRT-PCR) with a sensitivity of 53·7% (42·6–64·3) and a specificity of 82·8% (76·7–86) in 12 months preceding tuberculosis. Interpretation The whole blood tuberculosis risk signature prospectively identified persons at risk of developing active tuberculosis, opening the possibility for targeted intervention to prevent the disease. Funding Bill and Melinda Gates Foundation, the National Institutes of Health, Aeras, the European Union and the South African Medical Research Council (detail at end of text).

Latent tuberculosis infection: An overview

“…A more sophisticated understanding of the immunology of LTBI and the underlying factors associated with progression to active disease is critical to identifying new biomarkers or immune signatures that will form the basis of new LTBI diagnostics and tools to monitor the success of therapeutic regimens. A promising advance in this direction is a 16-gene messenger RNA transcript-signature in blood that predicts subsequent disease progression and is able to distinguish between latent infection and active TB disease (37). In addition, it seems to be associated with bacterial burden as the immune transcript-signature was lost following TB treatment.…”

Protein kinase C-delta (PKCδ), a marker of inflammation and tuberculosis disease progression in humans, is important for optimal macrophage killing effector functions and survival in mice

“…For whole-blood transcriptomics, we analyzed the publicly available RNA-sequencing data set from the Adolescent Cohort Study. 19 We determined expression profiles of all PKC isoforms from 800 days before diagnosis of TB disease. A detailed description of the study and analysis is mentioned in Supplementary Methods.…”

“…This included 46 adolescents with latent Mtb infection who progressed to active disease (progressors) and 107 adolescents with Mtb infection who remained healthy. 19 PKCd was significantly upregulated in progressors when compared with latently infected healthy controls ( Figure 1a). Although two PKC isoforms (y and Z) exhibited downregulation during progression to active TB disease, the expression levels of other PKC isoforms (a, b, g, i, e, and z) remained constant (Supplementary Figure S1 online).…”

“…19 Given the pivotal role of PKCd in signal transduction, we asked whether expression of this kinase changes during the transition from Mtb infection to TB disease. We analyzed gene expression data in whole blood from a nested control study correlating risk of TB from 800 days before diagnosis.…”

Host-Based Peripheral Blood Gene Expression Analysis for Diagnosis of Infectious Diseases

“…As interest in host transcriptomic technologies increases, the capabilities of this type of analysis are being explored for a number of additional important bacterial diseases, including the development of classifiers that can identify tuberculosis (TB) based on the extent of disease, predict the response to treatment (21,22), and distinguish TB from other similar pulmonary diseases such as lung cancer, sarcoidosis, and communityacquired pneumonia with 88% sensitivity and 94% specificity (23). There are also preliminary data suggesting a role for gene expression-based classifiers in identifying cases of infection by less common bacterial pathogens such as Bacillus anthracis, Yersinia pestis, Francisella tularensis, Brucella melitensis, and many others (24, 25), and we anticipate that the number of potential clinical uses will continue to grow.…”