Genome-Scale Characterization of Mycobacterium abscessus Complex Isolates from Portugal.
Sofia CarneiroMiguel PintoSónia SilvaAndrea SantosIrene RodriguesDaniela SantosSílvia DuarteLuís VieiraJoão Paulo GomesRita MacedoPublished in: International journal of molecular sciences (2023)
The Mycobacterium abscessus complex (MABC) is an emerging, difficult to treat, multidrug-resistant nontuberculous mycobacteria responsible for a wide spectrum of infections and associated with an increasing number of cases worldwide. Dominant circulating clones (DCCs) of MABC have been genetically identified as groups of strains associated with higher prevalence, higher levels of antimicrobial resistance, and worse clinical outcomes. To date, little is known about the genomic characteristics of MABC species circulating in Portugal. Here, we examined the genetic diversity and antimicrobial resistance profiles of 30 MABC strains isolated between 2014 and 2022 in Portugal. The genetic diversity of circulating MABC strains was assessed through a gene-by-gene approach (wgMLST), allowing their subspecies differentiation and the classification of isolates into DCCs. Antimicrobial resistance profiles were defined using phenotypic, molecular, and genomic approaches. The majority of isolates were resistant to at least two antimicrobials, although a poor correlation between phenotype and genotype data was observed. Portuguese genomes were highly diverse, and data suggest the existence of MABC lineages with potential international circulation or cross-border transmission. This study highlights the genetic diversity and antimicrobial resistance profile of circulating MABC isolates in Portugal while representing the first step towards the implementation of a genomic-based surveillance system for MABC at the Portuguese NIH.
Keyphrases
- antimicrobial resistance
- genetic diversity
- copy number
- multidrug resistant
- escherichia coli
- genome wide
- mycobacterium tuberculosis
- primary care
- electronic health record
- machine learning
- healthcare
- public health
- big data
- deep learning
- dna methylation
- drug resistant
- risk assessment
- quality improvement
- cystic fibrosis
- klebsiella pneumoniae
- psychometric properties