About this Research Topic
Tuberculosis (TB), caused by the Mycobacterium tuberculosis complex (MTBC) bacteria, is a devastating, chronic infectious disease of humankind, killing more than any other pathogen, and adding 10 million cases annually. Globally, livestock and wildlife are also significantly affected, leading to impacts as diverse as food insecurity, economic losses and threats to the conservation status of important fauna. In addition, a neglected risk of zoonotic transmission of animal TB to humans hampers disease eradication, making a cogent argument for a ‘One-Health’ approach. The MTBC is composed of 11 species/ecotypes which evolve clonally. Their genomes are highly similar; homologous regions are >99.99% identical, horizontal gene transfer (HGT) is absent, and gene synteny is predominantly conserved. This homology facilitates studies on population structure and epidemiology, but has proven to be a challenge for the identification of genetic mechanisms underpinning distinct phenotypes like host tropism, virulence, transmissibility, and propensity to acquire drug resistance.
In the absence of HGT, members of MTBC evolve mostly through single nucleotide polymorphisms (SNPs), short indels, large deletions, transposition of insertion sequence (IS) elements, and duplication of a few paralogous gene families. Whole-genome sequencing (WGS) has greatly improved our ability to study TB epidemics. In particular, WGS-based SNP detection has served as the primary basis for evaluating MTBC population structure, evolution, transmission, and antibiotic resistance. However, many challenges remain in our use of WGS to further understand MTBC biology, including: i)
standardization of bioinformatic pipelines for genomic epidemiology of all MTBC species and second-line antibiotic resistance detection in M. tuberculosis; (ii) the role of repetitive genomic areas, indels, and IS elements in MTBC evolution, (iii) the identification of molecular mechanisms driving the distinct phenotypes observed among MTBC lineages; and (iv) the evolutionary history of non-M. tuberculosis species of MTBC. Recent work has also shown that the full ecological and genetic diversity of MTBC is underestimated, warranting studies in less well-sampled locales to determine a more representative MTBC evolutionary history. Therefore, the goal of this research topic is to further decipher the evolution of the MTBC and to fill the gaps in our knowledge around the application of genomics to TB disease control.
This Research Topic may include studies that investigate and discuss, but are not limited to, the following:
• The evolution, genetic diversity, population structure and phylogeography of MTBC lineages and species.
• The functional genetic underpinnings of distinct MTBC phenotypes.
• The role of repetitive genomic regions, indels, and IS elements in the evolution of MTBC.
• Intra-host genetic diversity of MTBC and its impact on disease development, contact tracing, and antibiotic resistance.
• WGS-based outbreak tracing and antibiotic resistance detection.
• New bioinformatic tools and sequencing techniques (e.g. new bioinformatic tools or studies addressing technical challenges related to outbreak racing, antibiotic resistance detection and evolution studies of MTBC, direct sequencing from clinical or environmental samples).
The Topic welcomes original research, reviews, mini-reviews and perspectives.
In the absence of HGT, members of MTBC evolve mostly through single nucleotide polymorphisms (SNPs), short indels, large deletions, transposition of insertion sequence (IS) elements, and duplication of a few paralogous gene families. Whole-genome sequencing (WGS) has greatly improved our ability to study TB epidemics. In particular, WGS-based SNP detection has served as the primary basis for evaluating MTBC population structure, evolution, transmission, and antibiotic resistance. However, many challenges remain in our use of WGS to further understand MTBC biology, including: i)
standardization of bioinformatic pipelines for genomic epidemiology of all MTBC species and second-line antibiotic resistance detection in M. tuberculosis; (ii) the role of repetitive genomic areas, indels, and IS elements in MTBC evolution, (iii) the identification of molecular mechanisms driving the distinct phenotypes observed among MTBC lineages; and (iv) the evolutionary history of non-M. tuberculosis species of MTBC. Recent work has also shown that the full ecological and genetic diversity of MTBC is underestimated, warranting studies in less well-sampled locales to determine a more representative MTBC evolutionary history. Therefore, the goal of this research topic is to further decipher the evolution of the MTBC and to fill the gaps in our knowledge around the application of genomics to TB disease control.
This Research Topic may include studies that investigate and discuss, but are not limited to, the following:
• The evolution, genetic diversity, population structure and phylogeography of MTBC lineages and species.
• The functional genetic underpinnings of distinct MTBC phenotypes.
• The role of repetitive genomic regions, indels, and IS elements in the evolution of MTBC.
• Intra-host genetic diversity of MTBC and its impact on disease development, contact tracing, and antibiotic resistance.
• WGS-based outbreak tracing and antibiotic resistance detection.
• New bioinformatic tools and sequencing techniques (e.g. new bioinformatic tools or studies addressing technical challenges related to outbreak racing, antibiotic resistance detection and evolution studies of MTBC, direct sequencing from clinical or environmental samples).
The Topic welcomes original research, reviews, mini-reviews and perspectives.
Keywords: genomics, evolution, tuberculosis, Mycobacterium tuberculosis complex
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
