Exploration of the diversity of multi-drug resistant Mycobacterium tuberculosis complex in Lagos, Nigeria using WGS: Distribution of lineages, drug resistance patterns and genetic mutations.
Mohd Nur Fakhruzzaman NoorizhabNorzuliana Zainal AbidinLay Kek TehThean Hock TangNneka OnyejepuChioma Kunle-OpeNwanneka E TochukwuMichael A SheshiTimothy NwaforOlaoluwa P AkinwaleAhmad Izuanuddin IsmailNorazmi Mohd NorMohd Zaki SallehPublished in: Tuberculosis (Edinburgh, Scotland) (2023)
Multidrug-resistant (MDR) or extensively drug-resistant (XDR) Tuberculosis (TB) is a major challenge to global TB control. Therefore, accurate tracing of in-country MDR-TB transmission are crucial for the development of optimal TB management strategies. This study aimed to investigate the diversity of MTBC in Nigeria. The lineage and drug-resistance patterns of the clinical MTBC isolates of TB patients in Southwestern region of Nigeria were determined using the WGS approach. The phenotypic DST of the isolates was determined for nine anti-TB drugs. The sequencing achieved average genome coverage of 65.99X. The most represented lineages were L4 (n = 52, 83%), L1 (n = 8, 12%), L2 (n = 2, 3%) and L5 (n = 1, 2%), suggesting a diversified MTB population. In term of detection of M/XDR-TB, while mutations in katG and rpoB genes are the strong predictors for the presence of M/XDR-TB, the current study also found the lack of good genetic markers for drug resistance amongst the MTBC in Nigeria which may pose greater problems on local tuberculosis management efforts. This high-resolution molecular epidemiological data provides valuable insights into the mechanistic for M/XDR TB in Lagos, Nigeria.
Keyphrases
- mycobacterium tuberculosis
- drug resistant
- multidrug resistant
- acinetobacter baumannii
- gram negative
- pulmonary tuberculosis
- high resolution
- genome wide
- klebsiella pneumoniae
- mass spectrometry
- gene expression
- human immunodeficiency virus
- dna methylation
- cystic fibrosis
- healthcare
- prognostic factors
- newly diagnosed
- copy number
- emergency department
- hepatitis c virus
- single cell
- machine learning
- end stage renal disease
- deep learning
- preterm infants
- peritoneal dialysis
- big data