S2.3c High-resolution digital DNA melting: a breakthrough in diagnosing IMI?
Authors: Goshia Tyler , Wiederhold Nathan , Jenks Jeffery , Mehta Sanjay , Hoenigl Martin , Goshia Tyler 
Published: September 2022

Abstract

S2.3 Novel diagnostic tools for invasive mold infection, September 21, 2022, 3:00 PM - 4:30 PM
Invasive mold infections (IMIs) such as Aspergillosis, Mucormycosis, Fusariosis, and Lomentosporiosis have emerged as important pathogens in immunocompromised patients, with mortality rates as high as 50% to nearly 80% for these infections. Outcomes can be substantially improved with early initiation of appropriate antifungal therapy, yet early diagnosis remains difficult to establish and often requires multidisciplinary teams evaluating clinical and radiological findings plus supportive microbiologic findings. Conventional fungal culture and PCR techniques are limited by low sensitivity, long turn-around time, and are insufficient for differentiating between infection and colonization. Other non-culture-based tests, such as the Galactomannan test, are available only for invasive aspergillosis and are limited by imperfect test performance. Better and more rapid diagnostics are needed to enable early diagnosis and targeted treatment of IMIs and improve survival.

We have developed a new technology called digital high resolution melting analysis (dHRM) to enable a rapid and robust diagnosis of IMI. This technology accomplishes fast genotyping of fungal genomic sequences in clinical samples by: (1) conducting broad-based amplification of fungal genes in a digital PCR format, and (2) conducting high-resolution melting of the DNA amplicons in each digital reaction. High resolution melting measures the fluorescence of a saturating intercalating dye as dPCR-amplified fungal DNA fragments are rapidly heated and disassociated, producing sequence-defined melt curves in a closed reaction format. These melt curves serve as unique fungal ‘fingerprints’, which allows us to correctly identify disease-causing pathogen(s) with an accuracy of 99%-100% through the use of machine learning methods trained to tolerate variations in reaction conditions. This approach provides a simple, low-cost, fast, and robust method for pathogen identification.

Here, we will present the performance of this new technology on clinical bronchoalveolar lavage (BAL) samples from patients with and without IMI. A total of 75 patient BAL samples were tested, coming from 30 patients with proven (n = 1), probable (n = 25), or putative (n = 4) invasive pulmonary aspergillosis (IPA) infections, and from 45 patients classified as not having IPA (n = 4 possible IPA and n = 41 no IPA). Our results show that dHRM was able to reliably differentiate between different Aspergillus spp. and was also able to identify Aspergillus spp. in BAL samples from patients with probable IPA where qPCR had resulted in negative. In a few instances, it also detected rare molds in culture-negative samples and multiple Aspergillus spp. within one BAL sample, a phenomenon that has been previously described in cultures. Interestingly, there was no clear correlation between dHRM results and BAL galactomannan levels, suggesting that dHRM may serve as an independent method allowing for a combined diagnostic approach. While further validation and optimization are needed, these results suggest that dHRM is a promising new diagnostic technology capable of providing clinically relevant information within 2-3 h.