Cross-kingdom pathogen detection via duplex universal PCR and high-resolution melt

Pei-Wei Lee1, Marissa Totten2, Amelia Traylor1

  • 1Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, United States.

Biosensors & Bioelectronics
|November 23, 2024
PubMed

Insights

A new duplex PCR-HRM method rapidly detects bacterial and fungal pathogens, including co-infections. This advanced diagnostic tool offers high accuracy for infectious disease identification.

Area of Science:

  • Microbiology
  • Molecular Diagnostics
  • Infectious Diseases

Background:

  • Polymicrobial infections, involving bacteria and fungi, present diagnostic and treatment challenges.
  • Current culture-based methods for pathogen detection are slow and inefficient.
  • There is a critical need for rapid, broad-spectrum diagnostic approaches for infectious agents.

Purpose of the Study:

  • To develop a single, rapid diagnostic method for detecting both bacterial and fungal pathogens.
  • To address the limitations of existing culture-based diagnostic techniques.
  • To improve the identification of monomicrobial and polymicrobial infections.

Main Methods:

  • A duplex universal PCR and high-resolution melt (HRM) assay was developed.
  • The method utilizes two universal primer sets targeting bacterial and fungal genomic DNA.
  • The assay was adapted to a microfluidic-based digital format with machine learning analysis.

Main Results:

  • The duplex PCR-HRM method accurately detected 16 WHO-flagged pathogens.
  • Digital duplex PCR-HRM achieved over 99.9% detection accuracy.
  • The method quantitatively detected co-infections and identified pathogens in clinical bronchoalveolar lavage samples.

Conclusions:

  • The digital duplex PCR-HRM assay is a rapid, accurate, and comprehensive diagnostic tool.
  • This method significantly advances infectious disease diagnostics for bacterial and fungal pathogens.
  • The technology holds potential for improved patient management and outcomes in polymicrobial infections.