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MethylSense: high accuracy machine learning-based diagnostics for Aspergillus fumigatus infection in chickens using
Markus Hodal Drag1,2,3, Christina Hvilsom1, Louise Ladefoged Poulsen2
1Copenhagen Zoo, Frederiksberg, Denmark.
Abstract:
Avian aspergillosis, caused by Aspergillus fumigatus (Af), lacks sensitive antemortem diagnostics. Existing microbial cell-free DNA (cfDNA) tests are prone to contamination and require a high pathogen load. We hypothesized that infection-induced tissue damage in chickens creates differentially methylated regions (DMRs) in host cfDNA, enabling machine learning (ML) diagnostics. Serum cfDNA samples (n = 124) were obtained from broiler chickens (n = 76) with Af and non-Af infections (Escherichia coli or Gallibacterium anatis) and controls. Oxford Nanopore sequencing enabled DMR detection and ML training. Performance was evaluated using an independent set (n = 49) and 10-repeat Monte Carlo cross-validation (CV) (n = 490 evaluations per test) as quality control. A High Accuracy test (93 DMRs, neural network) achieved 98.0% accuracy (sensitivity 95%, specificity 100%, AUC 0.974, PR-AUC 0.928) in the independent set, with CV accuracy 92.0% [95% CI: 89.7%-94.4%]. A Fast test (35 DMRs, SVM) achieved 81.6% accuracy and CV accuracy 79.6% [74.9%-84.3%]. An In Situ test (5 DMRs, random forest) designed for field deployment achieved 71.4% accuracy and CV accuracy 62.9% [58.7%-67.0%]. Stratified CV accuracy showed 84.6% [65.1%-95.6%] correct classifications for E. coli and 100% [80.5%-100%] for G. anatis. Markers showed high bootstrap stability and predominantly overlapped EMARs and enhancers. In conclusion, we present MethylSense (https://github.com/markusdrag/MethylSense), an automated open-source software. The High Accuracy test achieved 92.0% [89.7%-94.4%] CV accuracy (CV sensitivity 94.5% [91.4%-97.6%], CV specificity 90.3% [87.8%-92.9%]). While validated in chickens, MethylSense is adaptable to other species and pathogens, offering scalable, contamination-resilient diagnostics for veterinary and conservation applications.IMPORTANCEMethylSense is an automated software for training machine learning diagnostics using differentially methylated regions (DMRs) in cell-free DNA from Oxford Nanopore sequencing. We applied MethylSense to develop three Aspergillus fumigatus tests for chickens, each optimized for different clinical scenarios. The High Accuracy test (93 DMRs, neural network) demonstrated 98.0% accuracy, in a blinded test set (n = 49) with sensitivity 95%, specificity 100%, ROC-AUC 0.974, and PR-AUC 0.928. Stratified 10-repeat Monte Carlo cross-validation (n = 490) showed correct classifications of 84.6% [CI: 65.1%-95.6%] Escherichia coli and 100% [80.5%-100%] Gallibacterium anatis infected specificity samples. A Fast test for rapid <1 h sequencing (35 DMRs, support vector machine) achieved 81.6% accuracy (sensitivity 80%, specificity 82.8%). An In Situ test (5 DMRs, random forest) for field deployment via methylation-specific PCR achieved 71.4% accuracy (sensitivity 45%, specificity 89.7%). Bootstrap analysis demonstrated exceptional marker stability (80.6%-100%) with minimal batch effects, confirming robust host-based diagnostics.
Insights
This study introduces MethylSense, an automated software for diagnosing avian aspergillosis using host cell-free DNA methylation patterns. MethylSense enables accurate, contamination-resilient diagnostics for chickens, adaptable to other species and pathogens.
Area of Science:
- Veterinary Diagnostics
- Genomics
- Bioinformatics
Background:
- Avian aspergillosis, caused by Aspergillus fumigatus, lacks sensitive antemortem diagnostic methods.
- Current cell-free DNA (cfDNA) tests are susceptible to contamination and require high pathogen loads.
- Host-derived cfDNA methylation patterns offer a potential alternative for diagnostics.
Purpose of the Study:
- To develop and validate machine learning (ML)-based diagnostic tests for avian aspergillosis using host cfDNA methylation.
- To create an automated open-source software (MethylSense) for detecting differentially methylated regions (DMRs) in cfDNA.
- To evaluate the performance of ML models trained on cfDNA DMRs for diagnosing Aspergillus fumigatus infections in chickens.
Main Methods:
- Serum cfDNA samples were collected from broiler chickens with Aspergillus fumigatus, Escherichia coli, Gallibacterium anatis infections, and controls.
- Oxford Nanopore sequencing was employed for cfDNA analysis and DMR detection.
- Machine learning models (neural network, SVM, random forest) were trained using DMRs for diagnostic classification.
Main Results:
- A High Accuracy test (93 DMRs, neural network) achieved 98.0% accuracy in an independent set and 92.0% cross-validation accuracy.
- A Fast test (35 DMRs, SVM) achieved 81.6% accuracy, and an In Situ test (5 DMRs, random forest) achieved 71.4% accuracy.
- Stratified cross-validation demonstrated high accuracy in differentiating specific bacterial infections (E. coli, G. anatis).
Conclusions:
- MethylSense software enables the development of scalable, contamination-resilient, host-based cfDNA methylation diagnostics.
- The developed diagnostic tests show high accuracy for avian aspergillosis and are adaptable to other species and pathogens.
- This approach offers a promising advancement for veterinary and conservation diagnostics.
