Insights into the Antimicrobial Resistance Profile of a Next Generation Probiotic Akkermansia muciniphila DSM 22959.
Daniela MachadoJoana Cristina BarbosaDiana AlmeidaJosé Carlos AndradeAna Cristina FreitasAna Maria Pereira GomesPublished in: International journal of environmental research and public health (2022)
Akkermansia muciniphila is a Gram-negative intestinal anaerobic bacterium recently proposed as a novel probiotic candidate to be incorporated in food and pharmaceutical forms. Despite its multiple health benefits, the data addressing its antimicrobial susceptibility profile remain scarce. However, the absence of acquired resistance in probiotic strains is a compulsory criterion for its approval in the qualified presumption of safety list. This study aimed at characterizing the A. muciniphila DSM 22959 strain's antimicrobial susceptibility profile using phenotypic and in silico approaches. To establish the phenotypic antimicrobial susceptibility profile of this strain, minimum inhibitory concentrations of eight antimicrobials were determined using broth microdilution and E-test methods. Additionally, the A. muciniphila DSM 22959 genome was screened using available databases and bioinformatics tools to identify putative antimicrobial resistance genes (ARG), virulence factors (VF), genomic islands (GI), and mobile genetic elements (MGE). The same categorization was obtained for both phenotypic methods. Resistance phenotype was observed for gentamicin, kanamycin, streptomycin, and ciprofloxacin, which was supported by the genomic context. No evidence was found of horizontal acquisition or potential transferability of the identified ARG and VF. Thus, this study provides new insights regarding the phenotypic and genotypic antimicrobial susceptibility profiles of the probiotic candidate A. muciniphila DSM 22959.
Keyphrases
- antimicrobial resistance
- gram negative
- multidrug resistant
- escherichia coli
- genome wide
- pseudomonas aeruginosa
- healthcare
- copy number
- public health
- microbial community
- lactic acid
- staphylococcus aureus
- risk assessment
- machine learning
- human health
- big data
- artificial intelligence
- electronic health record
- candida albicans