Biology subjects

Loiseau, C.

Publications and source records attributed to Loiseau, C..

6 recordsLinked to original sources

bioRxiv2018

Transition bias influences the evolution of antibiotic resistance in Mycobacterium tuberculosis

Transition bias, an overabundance of transitions relative to transversions, has been widely reported among studies of mutations spreading under relaxed selection. However, demonstrating the role of transition bias in adaptive evolution remains challenging. We addressed this challenge by analyzing adaptive antibiotic-resistance mutations in the major human pathogen Mycobacterium tuberculosis. We found strong evidence for transition bias in two independently curated datasets 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 ribosmal 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.\n\nSignificance statementWhether and how transition bias influences adaptive evolution remain open questions. We studied 296 DNA mutations that confer antibiotic resistance to the human pathogen Mycobacterium tuberculosis. We uncovered strong transition bias among these mutations and also among the number of times each mutation has evolved in different strains or geographic locations, demonstrating that transition bias can influence adaptive evolution. For a subset of mutations, we were able to rule out an alternative selection-based hypothesis for this bias, indicating that transition bias can be caused by a biased mutation supply. By revealing this bias among M. Tuberculosis resistance mutations, our findings improve our ability to predict the mutational pathways by which pathogens overcome treatment.

evolutionary biology↗

bioRxiv2018

Whole genome sequencing for drug resistance profile prediction in Mycobacterium tuberculosis

Whole genome sequencing allows rapid detection of drug-resistant M. tuberculosis isolates. However, high-quality data linking quantitative phenotypic drug susceptibility testing (DST) and genomic data have thus far been lacking.\n\nWe determined drug resistance profiles of 176 genetically diverse clinical M. tuberculosis isolates from Democratic Republic of the Congo, Ivory Coast, Peru, Thailand and Switzerland by quantitative phenotypic DST for 11 antituberculous drugs using the BD BACTEC MGIT 960 system and 7H10 agar dilution to generate a cross-validated phenotypic DST readout. We compared phenotypic drug susceptibility results with predicted drug resistance profiles inferred by whole genome sequencing.\n\nBoth phenotypic DST methods identically classified the strains into resistant/susceptible in 73-99% of the cases, depending on the drug. Changes in minimal inhibitory concentrations were readily explained by mutations identified by whole genome sequencing. Using the whole genome sequences we were able to predict quantitative drug resistance levels where wild type and mutant MIC distributions did not overlap. The utility of genome sequences to predict quantitative levels of drug resistance was partially limited due to incompletely understood mechanisms influencing the expression of phenotypic drug resistance. The overall sensitivity and specificity of whole genome-based DST were 86.8% and 94.5%, respectively.\n\nDespite some limitations, whole genome sequencing has high predictive power to infer resistance profiles without the need for time-consuming phenotypic methods.\n\nOne sentence summaryWhole genome sequencing of clinical M. tuberculosis isolates accurately predicts drug resistance profiles and may replace culture-based drug susceptibility testing in the future.

microbiology↗

bioRxiv2018

Reference Set of Mycobacterium tuberculosis Clinical Strains: A tool for research and product development

The Mycobacterium tuberculosis complex (MTBC) causes tuberculosis (TB) in humans and various other mammals. The human-adapted members of the MTBC comprise seven phylogenetic lineages that differ in their geographical distribution. There is growing evidence that this phylogenetic diversity modulates the outcome of TB infection and disease. For decades, TB research and development has focused on the two canonical MTBC reference strains H37Rv and Erdman, both of which belong to Lineage 4. Relying on only a few laboratory-adapted strains can be misleading as study results might not be directly transferrable to clinical settings where patients are infected with a diverse array of strains, including drug-resistant variants. Here, we argue for the need to expand TB research and development by incorporating the phylogenetic diversity of the MTBC. To facilitate such work, we have assembled a group of 20 genetically well-characterized clinical strains representing the seven known human-adapted MTBC lineages. With the \"MTBC clinical strains reference set\" we aim to provide a standardized resource for the TB community. We hope it will enable more direct comparisons between studies that explore the physiology of MTBC beyond the lab strains used thus far. We anticipate that detailed phenotypic analyses of this reference strain set will increase our understanding of TB biology and assist in the development of new control tools that are universally effective.

microbiology↗

bioRxiv2018

A New Phylogenetic Framework for the Animal-adapted Mycobacterium tuberculosis Complex

Tuberculosis (TB) affects humans and other animals and is caused by bacteria from the Mycobacterium tuberculosis complex (MTBC). Previous studies have shown that there are at least nine members of the MTBC infecting animals other than humans; these have also been referred to as ecotypes. However, the ecology and the evolution of these animal-adapted MTBC ecotypes are poorly understood. Here we screened 12,886 publicly available MTBC genomes and newly sequenced 17 animal-adapted MTBC strains, gathering a total of 529 genomes of animal-adapted MTBC strains. Phylogenomic and comparative analyses confirm that the animal-adapted MTBC members are paraphyletic with some members more closely related to the human-adapted Mycobacterium africanum Lineage 6 than to other animal-adapted strains. Furthermore, we identified four main animal-adapted MTBC clades that might correspond to four main host shifts; two of these clades are proposed to reflect independent cattle domestication events. Contrary to what would be expected from an obligate pathogen, MTBC nucleotide diversity was not positively correlated with host phylogenetic distances, suggesting that host tropism in the animal-adapted MTBC seems to be driven more by contact rates and demographic aspects of the host population rather than host relatedness. By combining phylogenomics with ecological data, we propose an evolutionary scenario in which the ancestor of Lineage 6 and all animal-adapted MTBC ecotypes was a generalist pathogen that subsequently adapted to different host species. This study provides a new phylogenetic framework to better understand the evolution of the different ecotypes of the MTBC and guide future work aimed at elucidating the molecular mechanisms underlying host specificity.

evolutionary biology↗

bioRxiv2018

Treemmer: a tool to reduce large phylogenetic datasets with minimal loss of diversity

Large sequence datasets are difficult to visualize and handle. Additionally, they are often not an adequate representation of the natural diversity, but the result of uncoordinated and convenience sampling. Consequently, they can suffer from redundancy and sampling biases. Here we present Treemmer, a simple tool to evaluate the redundancy of phylogenetic trees and reduce their complexity by eliminating leaves that contribute the least to the tree diversity.\n\nTreemmer can reduce the size of datasets with different phylogenetic structures and levels of redundancy while maintaining a sub-sample that is representative of the original diversity.

bioinformatics↗

bioRxiv2017

Comparative genomics of Mycobacterium africanum Lineage 5 and Lineage 6 from Ghana suggests different ecological niches

Mycobacterium africanum (Maf) causes up to half of human tuberculosis in West Africa, but little is known on this pathogen. We compared the genomes of 253 Maf clinical isolates from Ghana, including both L5 and L6. We found that the genomic diversity of L6 was higher than in L5, and the selection pressures differed between both groups. Regulatory proteins appeared to evolve neutrally in L5 but under purifying selection in L6. Conversely, human T cell epitopes were under purifying selection in L5, but under positive selection in L6. Although only 10% of the T cell epitopes were variable, mutations were mostly lineage-specific. Our findings indicate that Maf L5 and L6 are genomically distinct, possibly reflecting different ecological niches.

genomics↗