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Universal Digital High Resolution Melt for the detection of pulmonary mold infections
Tyler Goshia1, April Aralar1, Nathan Wiederhold2
1Department of Bioengineering, University of California San Diego, San Diego, CA, USA.
Background:
Invasive mold infections (IMIs) such as aspergillosis, mucormycosis, fusariosis, and lomentosporiosis are associated with high morbidity and mortality, particularly in immunocompromised patients, with mortality rates as high as 40% to 80%. Outcomes could 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 mycological findings. Universal digital high resolution melting analysis (U-dHRM) may enable rapid and robust diagnosis of IMI. This technology aims to accomplish timely pathogen detection at the single genome level by conducting broad-based amplification of microbial barcoding genes in a digital polymerase chain reaction (dPCR) format, followed by high-resolution melting of the DNA amplicons in each digital reaction to generate organism-specific melt curve signatures that are identified by machine learning.
Methods:
A universal fungal assay was developed for U-dHRM and used to generate a database of melt curve signatures for 19 clinically relevant fungal pathogens. A machine learning algorithm (ML) was trained to automatically classify these 19 fungal melt curves and detect novel melt curves. Performance was assessed on 73 clinical bronchoalveolar lavage (BAL) samples from patients suspected of IMI. Novel curves were identified by micropipetting U-dHRM reactions and Sanger sequencing amplicons.
Results:
U-dHRM achieved an average of 97% fungal organism identification accuracy and a turn-around-time of 4hrs. Pathogenic molds (Aspergillus, Mucorales, Lomentospora and Fusarium) were detected by U-dHRM in 73% of BALF samples suspected of IMI. Mixtures of pathogenic molds were detected in 19%. U-dHRM demonstrated good sensitivity for IMI, as defined by current diagnostic criteria, when clinical findings were also considered.
Conclusions:
U-dHRM showed promising performance as a separate or combination diagnostic approach to standard mycological tests. The speed of U-dHRM and its ability to simultaneously identify and quantify clinically relevant mold pathogens in polymicrobial samples as well as detect emerging opportunistic pathogens may provide information that could aid in treatment decisions and improve patient outcomes.
Insights
Universal digital high-resolution melting analysis (U-dHRM) offers rapid and accurate diagnosis of invasive mold infections (IMIs). This new method can identify multiple fungal pathogens in hours, improving patient outcomes.
Area of Science:
- Mycology
- Molecular Diagnostics
- Infectious Diseases
Background:
- Invasive mold infections (IMIs) pose a significant threat, especially to immunocompromised individuals, with high mortality rates.
- Early diagnosis is crucial for effective antifungal therapy but remains challenging, often requiring multidisciplinary evaluation.
- Universal digital high-resolution melting analysis (U-dHRM) presents a potential solution for rapid and robust IMI diagnosis.
Approach:
- A universal fungal assay for U-dHRM was developed, creating a database of melt curve signatures for 19 fungal pathogens.
- A machine learning algorithm was trained to classify fungal melt curves and detect novel ones.
- The assay's performance was evaluated on 73 clinical bronchoalveolar lavage samples, with novel curves identified via Sanger sequencing.
Key Points:
- U-dHRM achieved 97% accuracy in fungal organism identification with a 4-hour turnaround time.
- Pathogenic molds like Aspergillus, Mucorales, Lomentospora, and Fusarium were detected in 73% of suspected IMI BALF samples.
- The technique successfully identified mixed mold infections in 19% of samples and showed good sensitivity for IMI.
Conclusions:
- U-dHRM demonstrates potential as a standalone or complementary diagnostic tool for standard mycological tests.
- Its speed and ability to identify and quantify multiple mold pathogens, including emerging ones, can aid treatment decisions.
- This technology may significantly improve patient outcomes for invasive mold infections.
