Genetic diversity of Mycobacterium tuberculosis affects immune responses and clinical outcomes of tuberculosis (TB). However, how bacterial diversity orchestrates immune responses to direct distinct TB severities is unknown. Here we study 681 patients with pulmonary TB and show that M. tuberculosis isolates from cases with mild disease consistently induce robust cytokine responses in macrophages across multiple donors. By contrast, bacteria from patients with severe TB do not do so. Secretion of IL-1β is a good surrogate of the differences observed, and thus to classify strains as probable drivers of different TB severities. Furthermore, we demonstrate that M. tuberculosis isolates that induce low levels of IL-1β production can evade macrophage cytosolic surveillance systems, including cGAS and the inflammasome. Isolates exhibiting this evasion strategy carry candidate mutations, generating sigA recognition boxes or affecting components of the ESX-1 secretion system. Therefore, we provide evidence that M. tuberculosis strains manipulate host-pathogen interactions to drive variable TB severities.

Over 10 million people developed tuberculosis (TB) in 2016, and over 1.8 million individuals succumbed to the disease. These numbers make TB the ninth leading cause of death worldwide and the leading cause from a single infectious agent. Therefore, finding novel therapeutic targets in Mycobacterium tuberculosis, the pathogen that causes most cases of human TB, is critical. In this study, we reveal a novel virulence factor in M. tuberculosis, the nrp gene. The lack of nrp highly attenuates the course of M. tuberculosis infection in the mouse model, which is particularly relevant in immune-deficient hosts. This is very relevant as TB is particularly incident in immune-suppressed individuals, such as HIV patients.

Widespread and frequent resistance to the second-line tuberculosis (TB) medicine streptomycin, suggests ongoing transmission of low fitness cost streptomycin resistance mutations. To investigate this hypothesis, we studied a cohort of 681 individuals from a TB epidemic in Portugal. Whole-genome sequencing (WGS) analyses were combined with phenotypic growth studies in culture media and in mouse bone marrow derived macrophages. Streptomycin resistance was the most frequent resistance in the cohort accounting for 82.7% (n = 67) of the resistant Mycobacterium tuberculosis isolates. WGS of 149 clinical isolates identified 13 transmission clusters, including three clusters containing only streptomycin resistant isolates. The biggest cluster was formed by eight streptomycin resistant isolates with a maximum of five pairwise single nucleotide polymorphisms of difference. Interestingly, despite their genetic similarity, these isolates displayed different resistance levels to streptomycin, as measured both in culture media and in infected mouse bone marrow derived macrophages. The genetic bases underlying this phenotype are a combination of mutations in gid and other genes. This study suggests that specific streptomycin resistance mutations were transmitted in the cohort, with the resistant isolates evolving at the cluster level to allow low-to-high streptomycin resistance levels without a significative fitness cost. This is relevant not only to better understand transmission of streptomycin resistance in a clinical setting dominated by Lineage 4 M. tuberculosis infections, but mainly because it opens new prospects for the investigation of selection and spread of drug resistance in general.

The advent of whole-genome sequencing has provided an unprecedented detail about the evolution and genetic significance of species-specific variations across the whole Mycobacterium tuberculosis Complex. However, little attention has been focused on understanding the functional roles of these variations in the protein coding sequences. In this work, we compare the coding sequences from 74 sequenced mycobacterial species including M. africanum, M. bovis, M. canettii, M. caprae, M. orygis, and M. tuberculosis. Results show that albeit protein variations affect all functional classes, those proteins involved in lipid and intermediary metabolism and respiration have accumulated mutations during evolution. To understand the impact of these mutations on protein functionality, we explored their implications on protein ductility/disorder, a yet unexplored feature of mycobacterial proteomes. In agreement with previous studies, we found that a Gly71Ile substitution in the PhoPR virulence system severely affects the ductility of its nearby region in M. africanum and animal-adapted species. In the same line of evidence, the SmtB transcriptional regulator shows amino acid variations specific to the Beijing lineage, which affects the flexibility of the N-terminal trans-activation domain. Furthermore, despite the fact that MTBC epitopes are evolutionary hyperconserved, we identify strain- and lineage-specific amino acid mutations affecting previously known T-cell epitopes such as EsxH and FbpA (Ag85A). Interestingly, in silico studies reveal that these variations result in differential interaction of epitopes with the main HLA haplogroups.

Immune responses mediated by T cells are aimed at specific peptides, designated T cell epitopes, that are recognized when bound to human leukocyte antigen (HLA) molecules. The HLA genes are remarkably polymorphic in the human population allowing a broad and fine-tuned capacity to bind a wide array of peptide sequences. Polymorphisms might generate neoepitopes by impacting the HLA-peptide interaction and potentially alter the level and type of generated T cell responses. Multiple algorithms and tools based on machine learning (ML) have been implemented and are able to predict HLA-peptide binding affinity with considerable accuracy. Challenges in this field include the availability of adequate epitope datasets for training and benchmarking and the development of fully integrated pipelines going from next-generation sequencing to neoepitope prediction and quality analysis metrics. Effectively predicting neoepitopes from in silico data is a demanding task that has been facilitated by ML and will be of great value for the future of personalized immunotherapies against cancer and other diseases.

The already enormous burden caused by Mycobacterium tuberculosis and Human Immunodeficiency Virus type 1 (HIV-1) alone is aggravated by co-infection. Despite obvious differences in the rate of evolution comparing these two human pathogens, genetic diversity plays an important role in the success of both. The extreme evolutionary dynamics of HIV-1 is in the basis of a robust capacity to evade immune responses, to generate drug-resistance and to diversify the population-level reservoir of M group viral subtypes. Compared to HIV-1 and other retroviruses, M. tuberculosis generates minute levels of genetic diversity within the host. However, emerging whole-genome sequencing data show that the M. tuberculosis complex contains at least nine human-adapted phylogenetic lineages. This level of genetic diversity results in differences in M. tuberculosis interactions with the host immune system, virulence and drug resistance propensity. In co-infected individuals, HIV-1 and M. tuberculosis are likely to co-colonize host cells. However, the evolutionary impact of the interaction between the host, the slowly evolving M. tuberculosis bacteria and the HIV-1 viral “mutant cloud” is poorly understood. These evolutionary dynamics, at the cellular niche of monocytes/macrophages, are also discussed and proposed as a relevant future research topic in the context of single-cell sequencing.

Neoepitopes generated by amino acid variants specifically found in pathogens or cancer cells are gaining momentum in immunotherapy development. HABIT (HLA Binding InTelligence) is a web platform designed to generate and analyse machine learning-based T cell epitope predictions for improved neoepitope discovery. Availability and Implementation:HABIT is available at http://habit.evobiomed.com. Peptide-HLA binding prediction software were implemented in a web application for interactive data exploration using shiny package powered by RStudio.