MethylSense: high accuracy machine learning-based diagnostics for <i>Aspergillus fumigatus</i> infection in chickens using host cell-free DNA methylation and Nanopore sequencing

Avian aspergillosis, caused by <i>Aspergillus fumigatus</i> (<i>Af</i>), 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 (<i>n</i> = 124) were obtained from broiler chickens (<i>n</i> = 76) with <i>Af</i> and non-<i>Af</i> infections (<i>Escherichia coli</i> or <i>Gallibacterium anatis</i>) and controls. Oxford Nanopore sequencing enabled DMR detection and ML training. Performance was evaluated using an independent set (<i>n</i> = 49) and 10-repeat Monte Carlo cross-validation (CV) (<i>n</i> = 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 <i>In Situ</i> 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 <i>E. coli</i> and 100% [80.5%-100%] for <i>G. anatis</i>. 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 <i>Aspergillus fumigatus</i> 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 (<i>n</i> = 49) with sensitivity 95%, specificity 100%, ROC-AUC 0.974, and PR-AUC 0.928. Stratified 10-repeat Monte Carlo cross-validation (<i>n</i> = 490) showed correct classifications of 84.6% [CI: 65.1%-95.6%] <i>Escherichia coli</i> and 100% [80.5%-100%] <i>Gallibacterium anatis</i> 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 <i>In Situ</i> 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.