Chemical Nose Strategy with Metabolic Labeling and “Antibiotic-Responsive Spectrum” Enables Accurate and Rapid Pathogen Identification

The worldwide antimicrobial resistance (AMR) dilemma urgently requires rapid and accurate pathogen phenotype discrimination and antibiotic resistance identification. The conventional protocols are either time-consuming or depend on expensive instrumentations. Herein, we demonstrate a metabolic-label...

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Bibliographic Details
Published in:Analytical chemistry (Washington) 2024-01, Vol.96 (1), p.427-436
Main Authors: Wang, Xin, Li, Huida, Yang, Jianyu, Wu, Chengxin, Chen, Mingli, Wang, Jianhua, Yang, Ting
Format: Article
Language:English
Subjects:
Algorithms
Amino acids
Antibiotic resistance
Antibiotics
Antimicrobial resistance
Cell walls
D-Amino acids
Discrimination
Drug resistance
Labeling
Machine learning
Metabolism
Microorganisms
Nanoparticles
Nose
Pathogens
Phenotypes
Phenotyping
Taxonomy
Urinary tract
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Summary:The worldwide antimicrobial resistance (AMR) dilemma urgently requires rapid and accurate pathogen phenotype discrimination and antibiotic resistance identification. The conventional protocols are either time-consuming or depend on expensive instrumentations. Herein, we demonstrate a metabolic-labeling-assisted chemical nose strategy for phenotyping classification and antibiotic resistance identification of pathogens based on the “antibiotic-responsive spectrum” of different pathogens. d-Amino acids with click handles were metabolically incorporated into the cell wall of pathogens for further clicking with dibenzocyclooctyne-functionalized upconversion nanoparticles (DBCO-UCNPs) in the presence/absence of six types of antibiotics, which generates seven-channel sensing responses. With the assistance of machine learning algorithms, eight types of pathogens, including three types of antibiotic-resistant bacteria, can be well classified and discriminated in terms of microbial taxonomies, Gram phenotypes, and antibiotic resistance. The present metabolic-labeling-assisted strategy exhibits good anti-interference capability and improved discrimination ability rooted in the unique sensing mechanism. Sensitive identification of pathogens with 100% accuracy from artificial urinary tract infection samples at a concentration as low as 105 CFU/mL was achieved. Pathogens outside of the training set can also be discriminated well. This clearly demonstrated the potential of the present strategy in the identification of unknown pathogens in clinical samples.
ISSN:0003-2700
1520-6882
DOI:10.1021/acs.analchem.3c04469