Chloé Loiseau

Insertion sequences (IS) are the most common type of transposable element in prokaryotes and shape the structure of genomes through transposition and by providing a substrate for recombination. Despite the mutational impact of IS, the evolutionary dynamics of most elements in most species remain unknown. Here we study the dynamics of IS6110 in 10,000 strains of the Mycobacterium tuberculosis complex (MTBC). We developed a tool that allows the detection and comparison of IS insertions from short reads without using a reference genome. Using ancestral state reconstruction (ASR) on presence-absence patterns of IS6110, we describe the distribution of copy numbers (CNs) in the MTBC, infer birth rates of the element, and identify genomic regions with large numbers of parallel IS6110 insertions. Copy numbers in the MTBC range from 1 in some clades to more than 30 in strains of La3 (M. orygis). IS6110 birth rates scale approximately linearly with copy number and are elevated on terminal branches, consistent with the delayed action of purifying selection. A key characteristic of IS6110 is its occurrence in hotspots: the 5% most frequently targeted regions account for half of all independent insertion events. The motif 5’-TCTCAAAW-3’ is enriched around target sites and in hotspots, suggesting that the accumulation of insertions in these regions results through a combination of non-random insertion and purifying selection in other regions. To conclude the study, we propose a niche constraints model according to which the distribution of IS6110 in the MTBC is governed by the rarity of regions that have both suitable DNA properties and little functional value for the host.

Even though our meta-analysis ranks Mycobacterium tuberculosis genomes among the bacterial pathogens that are most straightforward to assemble, most available assemblies rely on short-read sequencing and contain genomic blind spots that miss functionally important genes. Complete genomes are essential for functional genomics, particularly for identifying small ORF-encoded proteins (SEPs; ≤100 amino acids), which can play critical biological roles yet are frequently missed by standard annotations. Here, we generated complete long-read assemblies for six clinical reference strains representing lineage 1 and the more pathogenic lineage 2, followed by comparative genomic and proteogenomic analyses. We additionally provide software to predict comprehensive sets of mycobacteria-specific PE and PPE family genes, including lineage-specific variants. Using parallel accumulation–serial fragmentation mass spectrometry, we detected approximately two-thirds of each strain’s annotated proteome from unfractionated cell extracts. Extending our proteogenomic framework across related strains, and rigorously controlling proteogenomic discovery rates using entrapment strategies, we revealed 12–24 previously unannotated proteins per strain, predominantly SEPs, 56–60 alternative translation start sites, and 9–17 expressed pseudogenes. Newly identified proteins included conserved and lineage-specific SEPs, an antitoxin, candidate antimicrobial peptides, and novel proteins under purifying selection. Overall, applying proteogenomics to phylogenomically selected clinical reference strains provides a valuable approach for discovering candidate diagnostics or therapeutics, as illustrated here for a WHO-listed critical bacterial pathogen.

Studies in model organisms show that mutations conferring resistance to different antibiotics can interact epistatically, but the biological and epidemiological consequences of such interactions are unknown. Here we show that in Mycobacterium tuberculosis (Mtb) , positive sign epistasis between RpoB and GyrA mutations causing resistance to rifampicin and fluoroquinolone, respectively, can lead to double-resistant strains with high in vitro fitness. Two of these RpoB-GyrA mutation combinations account for 53% in a global collection of highly drug-resistant Mtb clinical isolates, compared to in vitro fitness. Moreover, the two high-fitness RpoB-GyrA combinations are associated with a more benign and idiosyncratic proteome perturbation compared to low-fitness combinations. Our findings highlight the relevance of epistasis for the emergence and spread of antimicrobial resistance.

Tuberculosis (TB) remains a major public health concern. Improving TB control programmes and treatment success rates requires a deeper understanding of the factors that determine disease presentation and patient treatment outcomes. While the demographic and clinical factors influencing treatment outcomes are well documented, the role of bacterial genetics remains limited. In this study, we analyzed the Mycobacterium tuberculosis complex (MTBC) genomes and the associated clinical data from 4,536 TB patients in the country of Georgia covering a period of 13 years. Multivariable modelling confirmed the role of known demographic and clinical factors such as sex, age, body mass index (BMI) and comorbidities in determining treatment outcomes, as well as the efficacy of novel TB treatments containing bedaquiline. In addition, we found that some bacterial variables, including the MTBC lineage, the specific mutations conferring resistance to rifampicin and fluoroquinolones, as well as a high bacterial burden were associated with unfavorable outcomes. GWAS analyses revealed no genetic mutations in the bacteria other than known drug resistance-conferring mutations to be associated with treatment outcomes. However, we found that mutations in the bacterial gene sufD were linked to cavitary disease. Additionally, we observed that mutations in sufD, mutations conferring resistance to rifampicin and fitness compensatory mutations were associated with the bacterial burden within patients. We conclude that both patient and bacterial factors determine disease presentation and clinical outcomes in TB.

Mycobacterium tuberculosis complex (MTBC) lineages show substantial variability in virulence, but the epidemiological consequences of this variability have not been studied in detail. Here, we aimed for a lineage-specific epidemiological characterization by applying phylodynamic models to genomic data from different countries, representing the most abundant MTBC lineages. Our results show that all lineages are associated with similar durations and levels of infectiousness, resulting in similar reproductive numbers. However, L1 and L6 are associated with a delayed onset of infectiousness, leading to longer periods between subsequent transmission events. Together, our findings highlight the role of MTBC phylogenetic diversity in tuberculosis disease progression and transmission.

Multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant (XDR) TB are threatening global TB control. The World Health Organization has recently endorsed new regimens for the treatment of MDR-TB that rely on the new and repurposed drugs bedaquiline, pretomanid and linezolid with or without moxifloxacin (BPaL(M)). As BPaL(M) is being rolled-out, resistance to these new drugs is already emerging, leading to acquired XDR-TB. Importantly, instances of transmitted XDR-TB have been reported. The spread of highly drug-resistant M. tuberculosis (MTB) strains pose at risk novel TB treatments that took decades to develop. In this study, we analyzed 6,926 MTB genomes from a 13-year nationwide study in Georgia, a known geographical hotspot of MDR-TB, together with more than 80,000 MTB genomes from public sources to estimate the relative contribution of transmission to the burden of XDR-TB. We show that XDR-TB is already geographically widespread, occurring in at least 27 countries across four continents. Moreover, we estimated that a quarter of the XDR-TB cases identified are likely the consequence of transmission. Our findings call for urgent improvements in the global diagnostic capacity, infection control, and surveillance of XDR-TB.

Human tuberculosis (TB) is caused by members of the Mycobacterium tuberculosis complex (MTBC). The MTBC comprises several human-adapted lineages known as M. tuberculosis sensu stricto, as well as two lineages (L5 and L6) traditionally referred to as Mycobacterium africanum . Strains of L5 and L6 are largely limited to West Africa for reasons unknown, and little is known of their genomic diversity, phylogeography and evolution. Here, we analysed the genomes of 350 L5 and 320 L6 strains, isolated from patients from 21 African countries, plus 5 related genomes that had not been classified into any of the known MTBC lineages. Our population genomic and phylogeographical analyses showed that the unclassified genomes belonged to a new group that we propose to name MTBC lineage 9 (L9). While the most likely ancestral distribution of L9 was predicted to be East Africa, the most likely ancestral distribution for both L5 and L6 was the Eastern part of West Africa. Moreover, we found important differences between L5 and L6 strains with respect to their phylogeographical substructure and genetic diversity. Finally, we could not confirm the previous association of drug-resistance markers with lineage and sublineages. Instead, our results indicate that the association of drug resistance with lineage is most likely driven by sample bias or geography. In conclusion, our study sheds new light onto the genomic diversity and evolutionary history of M. africanum , and highlights the need to consider the particularities of each MTBC lineage for understanding the ecology and epidemiology of TB in Africa and globally.

Transition bias, an overabundance of transitions relative to transversions, has been widely reported among studies of the rates and spectra of spontaneous mutations. However, demonstrating the role of transition bias in adaptive evolution remains challenging. In particular, it is unclear whether such biases direct the evolution of bacterial pathogens adapting to treatment. We addressed this challenge by analyzing adaptive antibiotic-resistance mutations in the major human pathogen Mycobacterium tuberculosis (MTB). We found strong evidence for transition bias in two independently curated data sets comprising 152 and 208 antibiotic-resistance mutations. This was true at the level of mutational paths (distinct adaptive DNA sequence changes) and events (individual instances of the adaptive DNA sequence changes) and across different genes and gene promoters conferring resistance to a diversity of antibiotics. It was also true for mutations that do not code for amino acid changes (in gene promoters and the 16S ribosomal RNA gene rrs) and for mutations that are synonymous to each other and are therefore likely to have similar fitness effects, suggesting that transition bias can be caused by a bias in mutation supply. These results point to a central role for transition bias in determining which mutations drive adaptive antibiotic resistance evolution in a key pathogen.