Mass spectrometry methods for predicting antibiotic resistance.
Developing elaborate techniques for clinical applications can be a complicated process. Whole-cell MALDI-TOF MS revolutionized reliable microorganism identification in clinical microbiology laboratories and is now replacing phenotypic microbial identification. This technique is a generic, accurate, rapid, and cost-effective growth-based method. Antibiotic resistance keeps emerging in environmental and clinical microorganisms, leading to clinical therapeutic challenges, especially for Gram-negative bacteria. Antimicrobial susceptibility testing is used to reliably predict antimicrobial success in treating infection, but it is inherently limited by the need to isolate and grow cultures, delaying the application of appropriate therapies. Antibiotic resistance prediction by growth-independent methods is expected to reduce the turnaround time. Recently, the potential of next-generation sequencing and microarrays in predicting microbial resistance has been demonstrated, and this review evaluates the potential of MS in this field. First, technological advances are described, and the possibility of predicting antibiotic resistance by MS is then illustrated for three prototypical human pathogens: Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa. Clearly, MS methods can identify antimicrobial resistance mediated by horizontal gene transfers or by mutations that affect the quantity of a gene product, whereas antimicrobial resistance mediated by target mutations remains difficult to detect.
Genomic Research Laboratory, Division of Infectious Diseases, Geneva University Hospitals. yannick.charretier@genomic.ch.
Yannick
Y
Charretier
Genomic Research Laboratory, Division of Infectious Diseases, Geneva University Hospitals.
Jacques
J
Schrenzel
D000818:Animals; D001419:Bacteria / Q000187:drug effects / Q000378:metabolism; D004352:Drug Resistance, Microbial; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D040901:Proteomics
Antimicrobial resistance prediction; Bacteria; Mass spectrometry; Proteomics