An RNA-seq Based Machine Learning Approach Identifies Latent Tuberculosis Patients With an Active Tuberculosis Profile

A better understanding of the response against Tuberculosis (TB) infection is required to accurately identify the individuals with an active or a latent TB infection (LTBI) and also those LTBI patients at higher risk of developing active TB. In this work, we have used the information obtained from studying the gene expression profile of active TB patients and their infected –LTBI- or uninfected –NoTBI- contacts, recruited in Spain and Mozambique, to build a class-prediction model that identifies individuals with a TB infection profile. Following this approach, we have identified several genes and metabolic pathways that provide important information of the immune mechanisms triggered against TB infection. As a novelty of our work, a combination of this class-prediction model and the direct measurement of different immunological parameters, was used to identify a subset of LTBI contacts (called TB-like) whose transcriptional and immunological profiles are suggestive of infection with a higher probability of developing active TB. Validation of this novel approach to identifying LTBI individuals with the highest risk of active TB disease merits further longitudinal studies on larger cohorts in TB endemic areas.

Multi-parameter flow cytometry immunophenotyping distinguishes different stages of tuberculosis infection

OBJECTIVES

To identify new potential host biomarkers in blood to discriminate between active TB patients, uninfected (NoTBI) and latently infected contacts (LTBI).

METHODS

A blood cell count was performed to study parent leukocyte populations. Peripheral blood mononuclear cells (PBMCs) were isolated and a multi-parameter flow cytometry assay was conducted to study the distribution of basal and Mycobacterium tuberculosis (Mtb)-stimulated lymphocytes. Differences between groups and the area under the ROC curve (AUC) were investigated to assess the diagnostic accuracy.

RESULTS

Active TB patients presented higher Monocyte-to-lymphocyte and Neutrophil-to-lymphocyte ratios than LTBI and NoTBI contacts (p<0.0001; AUC>0.8). Lymphocyte subsets with differences (p >0.05; AUC >0.7) between active TB and both contact groups include the basal distribution of Th1/Th2 ratio, Th1-Th17, CD4⁺ Central Memory (T_CM) or MAIT cells. Expression of CD154 is increased in Mtb-activated CD4⁺ T_CM and Effector Memory T cells in active TB and LTBI compared to NoTBI. In CD4⁺T cells, expression of CD154 showed a higher accuracy than IFNγ to discriminate Mtb-specific activation.

CONCLUSIONS

We identified different cell subsets with potential use in tuberculosis diagnosis. Among them, distribution of CD4 T_CM cells and their expression of CD154 after Mtb-activation are the most promising candidates.