International Journal of Infectious Diseases (Apr 2022)

Molecular epidemiology and genetic diversity of Mycobacterium tuberculosis complex in referral health centers of Bamako, Mali: What is new?

  • Bourahima Kone,
  • Anou M. Somboro,
  • Mahamadou Kone,
  • Jane L. Holl,
  • Bocar Baya,
  • Djeneba Dabitao,
  • Dramane Diallo,
  • Bassirou Diarra,
  • Amadou Kone,
  • Yeya Dit Sadio Sarro,
  • Moumine Sanogo,
  • Antieme CG Togo,
  • Robert L. Murphy,
  • Souleymane Diallo,
  • Nadie Coulibaly,
  • Fatoumata Camara,
  • Seydou Samake,
  • Mahamadou Diakite,
  • Seydou Doumbia,
  • Mamoudou Maiga

Journal volume & issue
Vol. 117
pp. 204 – 211

Abstract

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Background and Aims: Tuberculosis (TB) remains an important global health issue worldwide. Despite this scourge threatening many human lives, especially in developing countries, thus far, no advanced molecular epidemiology study using recent and more accurate tools has been conducted in Mali. Therefore, this study aimed to use variable-number tandem repeats of mycobacterial interspersed repetitive units (MIRU-VNTR) technology coupled with the spoligotyping method to accurately determine the hot spots and establish the epidemiological transmission links of TB in Bamako, Mali. Methods: In a cross-sectional study, 245 isolates of Mycobacterium tuberculosis complex (MTBC) were characterized using spoligotyping and MIRU-VNTR, and an epidemiological investigation was conducted. Results: Of the 245 isolates, 184 (75.1%) were formally identified. The most widespread strain was the Cameroon strain (83; 45.1%). Eight major clusters were identified: Ghana (27; 14.7%), West African 2 (22; 12%), Haarlem (13; 7.1%), H37Rv (t) (8; 4.3%), Latin American Mediterranean (8; 4.3%), and Uganda I and II (6; 3.3%). Statistical analysis showed a significant difference between lineages from the respective referral health centers of Bamako, Mali (P = 0.01). Conclusion: This study establishes, for the first time, an accurate spatial distribution of circulating MTB strains in Bamako, Mali. The data was used to identify strains and “hot spots” causing TB infection and can also be used for more targeted public health responses, particularly for hot spots of drug-resistant strains.

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