Skip to content
1887

Abstract

Species belonging to the complex (MKC) are frequently isolated from humans and the environment and can cause serious diseases. The most common MKC infections are caused by the species (), leading to tuberculosis-like disease. However, a broad spectrum of virulence, antimicrobial resistance and pathogenicity of these non-tuberculous mycobacteria (NTM) are observed across the MKC. Many genomic aspects of the MKC that relate to these broad phenotypes are not well elucidated. Here, we performed genomic analyses from a collection of 665 MKC strains, isolated from environmental, animal and human sources. We inferred the MKC pangenome, mobilome, resistome, virulome and defence systems and show that the MKC species harbours unique and shared genomic signatures. High frequency of presence of prophages and different types of defence systems were observed. We found that the species splits into four lineages, of which three are lowly represented and mainly in Brazil, while one lineage is dominant and globally spread. Moreover, we show that four sub-lineages of this most distributed lineage emerged during the twentieth century. Further analysis of the genomes revealed almost 300 regions of difference contributing to genomic diversity, as well as fixed mutations that may explain the ’s increased virulence and drug resistance.

Funding
This study was supported by the:
  • Deutsches Zentrum für Infektionsforschung
    • Principle Award Recipient: NotApplicable
  • Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (Award 26/210.877/2019)
    • Principle Award Recipient: PhilipSuffys
  • Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (Award 26/204.546/2021)
    • Principle Award Recipient: PhilipSuffys
  • Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (Award 26/201.115/2022)
    • Principle Award Recipient: PhilipSuffys
  • Conselho Nacional de Desenvolvimento Científico e Tecnológico (Award 207422/2014-1)
    • Principle Award Recipient: PhilipSuffys
  • Conselho Nacional de Desenvolvimento Científico e Tecnológico (Award 310418/2016-0)
    • Principle Award Recipient: PhilipSuffys
  • Conselho Nacional de Desenvolvimento Científico e Tecnológico (Award 307474/2020-8)
    • Principle Award Recipient: PhilipSuffys
  • Conselho Nacional de Desenvolvimento Científico e Tecnológico (Award 306344/2021-1)
    • Principle Award Recipient: ElenaLasunskaia
  • Academy of Medical Sciences (Award SBF006\1090)
    • Principle Award Recipient: ConorJ Meehan
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License.
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.001266
2024-07-17
2025-06-24
Loading full text...

Full text loading...

/deliver/fulltext/mgen/10/7/mgen001266.html?itemId=/content/journal/mgen/10.1099/mgen.0.001266&mimeType=html&fmt=ahah

References

  1. Machado E, Vasconcellos S, Gomes L, Catanho M, Ramos J et al. Online resource Microbiology Society; 2024 https://doi.org/10.6084/m9.figshare.26014465.v1
    [Google Scholar]
  2. Tagini F et al. Phylogenomics reveal that Mycobacterium Kansasii subtypes are species-level lineages. Description of Mycobacterium pseudokansasii sp. nov., Mycobacterium innocens sp. nov. and mycobacterium attenuatum sp. nov. Int J Syst Evol Microbiol 2019 [View Article]
    [Google Scholar]
  3. Jagielski T, Borówka P, Bakuła Z, Lach J, Marciniak B et al. Genomic insights into the Mycobacterium kansasii complex: an update. Front Microbiol 2020; 10:2918 [View Article] [PubMed]
    [Google Scholar]
  4. Taillard C, Greub G, Weber R, Pfyffer GE, Bodmer T et al. Clinical implications of Mycobacterium kansasii species heterogeneity: Swiss National Survey. J Clin Microbiol 2003; 41:1240–1244 [View Article] [PubMed]
    [Google Scholar]
  5. Mussi VO, Simão TLBV, Almeida FM, Machado E, de Carvalho LD et al. A murine model of Mycobacterium kansasii infection reproducing necrotic lung pathology reveals considerable heterogeneity in virulence of clinical isolates. Front Microbiol 2021; 12:718477 [View Article] [PubMed]
    [Google Scholar]
  6. Tagini F, Pillonel T, Bertelli C, Jaton K, Greub G. Pathogenic determinants of the Mycobacterium kansasii complex: an unsuspected role for distributive conjugal transfer. Microorganisms 2021; 9:348 [View Article] [PubMed]
    [Google Scholar]
  7. Luo T, Xu P, Zhang Y, Porter JL, Ghanem M et al. Population genomics provides insights into the evolution and adaptation to humans of the waterborne pathogen Mycobacterium kansasii. Nat Commun 2021; 12:2491 [View Article] [PubMed]
    [Google Scholar]
  8. Wang J, McIntosh F, Radomski N, Dewar K, Simeone R et al. Insights on the emergence of Mycobacterium tuberculosis from the analysis of Mycobacterium kansasii. Genome Biol Evol 2015; 7:856–870 [View Article] [PubMed]
    [Google Scholar]
  9. Guo Y, Cao Y, Liu H, Yang J, Wang W et al. Clinical and microbiological characteristics of Mycobacterium kansasii pulmonary infections in China. Microbiol Spectr 2022; 10:e0147521 [View Article] [PubMed]
    [Google Scholar]
  10. Goldenberg T, Gayoso R, Mogami R, Lourenço MC, Ramos JP et al. Clinical and epidemiological characteristics of M. kansasii pulmonary infections from Rio de Janeiro, Brazil, between 2006 and 2016. J Bras Pneumol 2020; 46:1–7 [View Article] [PubMed]
    [Google Scholar]
  11. Bakuła Z, Modrzejewska M, Pennings L, Proboszcz M, Safianowska A et al. Drug susceptibility profiling and genetic determinants of drug resistance in Mycobacterium kansasii. Antimicrob Agents Chemother 2018; 62:e01788-17 [View Article] [PubMed]
    [Google Scholar]
  12. Machado E, Vasconcellos SEG, Cerdeira C, Gomes LL, Junqueira R et al. Whole genome sequence of Mycobacterium kansasii isolates of the genotype 1 from Brazilian patients with pulmonary disease demonstrates considerable heterogeneity. Mem Inst Oswaldo Cruz 2018; 113:e180085 [View Article] [PubMed]
    [Google Scholar]
  13. Borówka P, Lach J, Bakuła Z, van Ingen J, Safianowska A et al. Draft genome sequences of Mycobacterium kansasii clinical strains. Genome Announc 2017; 5:1–3 [View Article] [PubMed]
    [Google Scholar]
  14. Baym M, Kryazhimskiy S, Lieberman TD, Chung H, Desai MM et al. Inexpensive multiplexed library preparation for megabase-sized genomes. PLoS One 2015; 10:e0128036 [View Article] [PubMed]
    [Google Scholar]
  15. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014; 30:2114–2120 [View Article] [PubMed]
    [Google Scholar]
  16. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 2012; 19:455–477 [View Article] [PubMed]
    [Google Scholar]
  17. Koren S, Walenz BP, Berlin K, Miller JR, Bergman NH et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res 2017; 27:722–736 [View Article] [PubMed]
    [Google Scholar]
  18. Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 2015; 25:1043–1055 [View Article] [PubMed]
    [Google Scholar]
  19. Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun 2018; 9:1–8 [View Article] [PubMed]
    [Google Scholar]
  20. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 2014; 30:2068–2069 [View Article] [PubMed]
    [Google Scholar]
  21. Page AJ, Cummins` CA, Hunt M, Wong VK, Reuter S et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics 2015; 31:3691–3693 [View Article] [PubMed]
    [Google Scholar]
  22. Tonkin-Hill G, MacAlasdair N, Ruis C, Weimann A, Horesh G et al. Producing polished prokaryotic pangenomes with the Panaroo pipeline. Genome Biol 2020; 21:180 [View Article] [PubMed]
    [Google Scholar]
  23. Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol 2013; 30:772–780 [View Article] [PubMed]
    [Google Scholar]
  24. Hadfield J, Croucher NJ, Goater RJ, Abudahab K, Aanensen DM et al. Phandango: an interactive viewer for bacterial population genomics. Bioinformatics 2018; 34:292–293 [View Article] [PubMed]
    [Google Scholar]
  25. Tonkin-Hill G, Gladstone RA, Pöntinen AK, Arredondo-Alonso S, Bentley SD et al. Robust analysis of prokaryotic pangenome gene gain and loss rates with Panstripe. Genome Res 2023; 33:129–140 [View Article] [PubMed]
    [Google Scholar]
  26. Kozlov AM, Darriba D, Flouri T, Morel B, Stamatakis A. RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 2019; 35:4453–4455 [View Article] [PubMed]
    [Google Scholar]
  27. Mostowy R, Croucher NJ, Andam CP, Corander J, Hanage WP et al. Efficient inference of recent and ancestral recombination within bacterial populations. Mol Biol Evol 2017; 34:1167–1182 [View Article] [PubMed]
    [Google Scholar]
  28. Letunic I, Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res 2021; 49:W293–W296 [View Article] [PubMed]
    [Google Scholar]
  29. Page AJ, Taylor B, Delaney AJ, Soares J, Seemann T et al. SNP-sites: rapid efficient extraction of SNPs from multi-FASTA alignments. Microb Genom 2016; 2:e000056 [View Article] [PubMed]
    [Google Scholar]
  30. Croucher NJ, Page AJ, Connor TR, Delaney AJ, Keane JA et al. Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins. Nucleic Acids Res 2015; 43:e15 [View Article] [PubMed]
    [Google Scholar]
  31. Tonkin-Hill G, Lees JA, Bentley SD, Frost SDW, Corander JR. RhierBAPS: an R implementation of the population clustering algorithm hierBAPS. Wellcome Open Res 2018; 3:93 [View Article] [PubMed]
    [Google Scholar]
  32. Didelot X, Croucher NJ, Bentley SD, Harris SR, Wilson DJ. Bayesian inference of ancestral dates on bacterial phylogenetic trees. Nucleic Acids Res 2018; 46:e134 [View Article] [PubMed]
    [Google Scholar]
  33. Schwengers O, Barth P, Falgenhauer L, Hain T, Chakraborty T et al. Platon: identification and characterization of bacterial plasmid contigs in short-read draft assemblies exploiting protein sequence-based replicon distribution scores. Microb Genom 2020; 6:1–12 [View Article] [PubMed]
    [Google Scholar]
  34. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol 1990; 215:403–410 [View Article] [PubMed]
    [Google Scholar]
  35. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J et al. BLAST+: architecture and applications. BMC Bioinformatics 2009; 10:1–9 [View Article] [PubMed]
    [Google Scholar]
  36. Garcillán-Barcia MP, Redondo-Salvo S, Vielva L, Cruz F. MOBscan: automated annotation of MOB relaxases. In Methods in Molecular Biology (Clifton, N.J.) vol. 2075 2020 pp 295–308
    [Google Scholar]
  37. Guo J, Bolduc B, Zayed AA, Varsani A, Dominguez-Huerta G et al. VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. Microbiome 2021; 9:1–13 [View Article] [PubMed]
    [Google Scholar]
  38. Nayfach S, Camargo AP, Schulz F, Eloe-Fadrosh E, Roux S et al. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat Biotechnol 2021; 39:578–585 [View Article] [PubMed]
    [Google Scholar]
  39. Shaffer M, Borton MA, McGivern BB, Zayed AA, La Rosa SL et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res 2020; 48:8883–8900 [View Article] [PubMed]
    [Google Scholar]
  40. Lanza VF, Baquero F, de la Cruz F, Coque TM. AcCNET (Accessory Genome Constellation Network): comparative genomics software for accessory genome analysis using bipartite networks. Bioinformatics 2017; 33:283–285 [View Article] [PubMed]
    [Google Scholar]
  41. Russell DA, Hatfull GF. PhagesDB: the actinobacteriophage database. Bioinformatics 2017; 33:784–786 [View Article] [PubMed]
    [Google Scholar]
  42. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13:2498 [View Article] [PubMed]
    [Google Scholar]
  43. Gibson MK, Forsberg KJ, Dantas G. Improved annotation of antibiotic resistance determinants reveals microbial resistomes cluster by ecology. ISME J 2015; 9:207–216 [View Article] [PubMed]
    [Google Scholar]
  44. Eddy SR. Accelerated Profile HMM Searches. PLoS Comput Biol 2011; 7:e1002195 [View Article] [PubMed]
    [Google Scholar]
  45. Payne LJ, Todeschini TC, Wu Y, Perry BJ, Ronson CW et al. Identification and classification of antiviral defence systems in bacteria and archaea with PADLOC reveals new system types. Nucleic Acids Res 2021; 49:10868–10878 [View Article] [PubMed]
    [Google Scholar]
  46. Tesson F, Hervé A, Touchon M, d’Humières C, Cury J et al. Systematic and quantitative view of the antiviral arsenal of prokaryotes. bioRxiv 2021 [View Article]
    [Google Scholar]
  47. Abby SS, Néron B, Ménager H, Touchon M, Rocha EPC. MacSyFinder: a program to mine genomes for molecular systems with an application to CRISPR-Cas systems. PLoS One 2014; 9:e110726 [View Article] [PubMed]
    [Google Scholar]
  48. Grissa I, Vergnaud G, Pourcel C. CRISPRFinder: a web tool to identify clustered regularly interspaced short palindromic repeats. Nucleic Acids Res 2007; 35:W52–W57 [View Article] [PubMed]
    [Google Scholar]
  49. Bespiatykh D, Bespyatykh J, Mokrousov I, Shitikov E. A comprehensive map of Mycobacterium tuberculosis complex regions of difference. mSphere 2021; 6:e0053521 [View Article] [PubMed]
    [Google Scholar]
  50. McElroy KE, Luciani F, Thomas T. GemSIM: general, error-model based simulator of next-generation sequencing data. BMC Genomics 2012; 13:74 [View Article] [PubMed]
    [Google Scholar]
  51. Engel HWB, Berwald LG, Grange JM, Kubin M. Phage typing of Mycobacterium kansasii. Tubercle 1980; 61:11–19 [View Article]
    [Google Scholar]
  52. Zhang Y, Yu C, Jiang Y, Zheng X, Wang L et al. Drug resistance profile of Mycobacterium kansasii clinical isolates before and after 2-month empirical antimycobacterial treatment. Clin Microbiol Infect 2023; 29:353–359 [View Article] [PubMed]
    [Google Scholar]
  53. Rajendran P, Padmapriyadarsini C, Nagarajan N, Samyuktha R, Govindaraju V et al. Molecular characterisation of M. kansasii isolates by whole-genome sequencing. Pathogens 2023; 12:1249 [View Article] [PubMed]
    [Google Scholar]
  54. Marin MG, Wippel C, Quinones-Olvera N, Behruznia M, Jeffrey BM et al. Analysis of the limited M. tuberculosis accessory genome reveals potential pitfalls of pan-genome analysis approaches. bioRxiv 20242024.03.21.586149 [View Article] [PubMed]
    [Google Scholar]
  55. Guan Q, Ummels R, Ben-Rached F, Alzahid Y, Amini MS et al. Comparative genomic and transcriptomic analyses of Mycobacterium kansasii subtypes provide new insights into their pathogenicity and taxonomy. Front Cell Infect Microbiol 2020; 10:122 [View Article] [PubMed]
    [Google Scholar]
  56. Rivera-Calzada A, Famelis N, Llorca O, Geibel S. Type VII secretion systems: structure, functions and transport models. Nat Rev Microbiol 2021; 19:567–584 [View Article] [PubMed]
    [Google Scholar]
  57. Ates LS, Brosch R. Discovery of the type VII ESX-1 secretion needle?. Mol Microbiol 2017; 103:7–12 [View Article] [PubMed]
    [Google Scholar]
  58. DeStefano MS, Shoen CM, Cynamon MH. Therapy for Mycobacterium kansasii infection: beyond 2018. Front Microbiol 2018; 9:2271 [View Article] [PubMed]
    [Google Scholar]
  59. Bohr LL, Youngblom MA, Eldholm V, Pepperell CS. Genome reorganization during emergence of host-associated Mycobacterium abscessus. Microb Genom 2021; 7:000706 [View Article] [PubMed]
    [Google Scholar]
  60. Doron S, Melamed S, Ofir G, Leavitt A, Lopatina A et al. Systematic discovery of antiphage defense systems in the microbial pangenome. Science 2018; 359:eaar4120 [View Article] [PubMed]
    [Google Scholar]
  61. Tock MR, Dryden DT. The biology of restriction and anti-restriction. Curr Opin Microbiol 2005; 8:466–472 [View Article] [PubMed]
    [Google Scholar]
  62. Molineux IJ. Host-parasite interactions: recent developments in the genetics of abortive phage infections. New Biol 1991; 3:230–236 [PubMed]
    [Google Scholar]
  63. Kamerbeek J, Schouls L, Kolk A, van Agterveld M, van Soolingen D et al. Simultaneous detection and strain differentiation of Mycobacterium tuberculosis for diagnosis and epidemiology. J Clin Microbiol 1997; 35:907–914 [View Article] [PubMed]
    [Google Scholar]
  64. Zhang Q, Ye Y. Not all predicted CRISPR-Cas systems are equal: isolated cas genes and classes of CRISPR like elements. BMC Bioinformatics 2017; 18:92 [View Article] [PubMed]
    [Google Scholar]
  65. Shi S, Ehrt S. Dihydrolipoamide acyltransferase is critical for Mycobacterium tuberculosis pathogenesis. Infect Immun 2006; 74:56–63 [View Article] [PubMed]
    [Google Scholar]
  66. Forrellad MA, Klepp LI, Gioffré A, Sabio y García J, Morbidoni HR et al. Virulence factors of the Mycobacterium tuberculosis complex. Virulence 2013; 4:3–66 [View Article] [PubMed]
    [Google Scholar]
  67. Nair J, Rouse DA, Bai GH, Morris SL. The rpsL gene and streptomycin resistance in single and multiple drug-resistant strains of Mycobacterium tuberculosis. Mol Microbiol 1993; 10:521–527 [View Article] [PubMed]
    [Google Scholar]
  68. Sreevatsan S, Stockbauer KE, Pan X, Kreiswirth BN, Moghazeh SL et al. Ethambutol resistance in Mycobacterium tuberculosis: critical role of embB mutations. Antimicrob Agents Chemother 1997; 41:1677–1681 [View Article] [PubMed]
    [Google Scholar]
  69. Martini MC, Hicks ND, Xiao J, Alonso MN, Barbier T et al. Loss of RNase J leads to multi-drug tolerance and accumulation of highly structured mRNA fragments in Mycobacterium tuberculosis. PLoS Pathog 2022; 18:e1010705 [View Article] [PubMed]
    [Google Scholar]
  70. Kreutzfeldt KM, Jansen RS, Hartman TE, Gouzy A, Wang R et al. CinA mediates multidrug tolerance in Mycobacterium tuberculosis. Nat Commun 2022; 13:2203 [View Article] [PubMed]
    [Google Scholar]
/content/journal/mgen/10.1099/mgen.0.001266
Loading
/content/journal/mgen/10.1099/mgen.0.001266
Loading

Data & Media loading...

Supplements

Loading data from figshare Loading data from figshare
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error