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Mass spectrometry methods for predicting antibiotic resistance.

Yannick Charretier1, Jacques Schrenzel2

  • 1Genomic Research Laboratory, Division of Infectious Diseases, Geneva University Hospitals. yannick.charretier@genomic.ch.

Proteomics. Clinical Applications
|June 18, 2016
PubMed
Summary

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) shows promise for predicting microbial antibiotic resistance. While effective for some resistance mechanisms, it struggles with target mutation-based resistance.

Keywords:
Antimicrobial resistance predictionBacteriaMass spectrometryProteomics

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Area of Science:

  • Clinical Microbiology
  • Analytical Chemistry
  • Infectious Diseases

Background:

  • Whole-cell MALDI-TOF MS is a rapid, cost-effective method for microorganism identification, replacing traditional phenotypic methods.
  • Emerging antibiotic resistance in microorganisms presents significant clinical challenges, particularly with Gram-negative bacteria.
  • Current antimicrobial susceptibility testing relies on culture-based methods, leading to delays in therapy.

Purpose of the Study:

  • To evaluate the potential of mass spectrometry (MS) for predicting microbial antibiotic resistance.
  • To review technological advances in MS relevant to antibiotic resistance prediction.
  • To illustrate MS-based antibiotic resistance prediction for Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa.

Main Methods:

  • Review of technological advancements in mass spectrometry for microbial analysis.
  • Evaluation of MS capabilities in identifying antibiotic resistance mechanisms.
  • Case studies focusing on Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa.

Main Results:

  • MS can identify antibiotic resistance mediated by horizontal gene transfer and changes in gene product quantity.
  • MS shows potential for growth-independent antibiotic resistance prediction, reducing turnaround time.
  • Detection of antimicrobial resistance mediated by target mutations remains a challenge for MS methods.

Conclusions:

  • Mass spectrometry holds significant potential for rapid antibiotic resistance prediction, complementing traditional methods.
  • MS offers a growth-independent approach to identify certain resistance mechanisms, aiding timely therapeutic decisions.
  • Further development is needed to address MS limitations in detecting target mutation-based antimicrobial resistance.