Genome-Wide Inference of Essential Genes in Dirofilaria immitis Using Machine Learning

The filarioid nematode <i>Dirofilaria immitis</i> is the causative agent of heartworm disease, a major parasitic infection of canids, felids and occasionally humans. Current prevention relies on macrocyclic lactone-based chemoprophylaxis, but the emergence of drug resistance highlights the need for new intervention strategies. Here, we applied a machine learning (ML)-based framework to predict and prioritise essential genes in <i>D. immitis</i> in silico, using genomic, transcriptomic and functional datasets from the model organisms <i>Caenorhabditis elegans</i> and <i>Drosophila melanogaster</i>. With a curated set of 26 predictive features, we trained and evaluated multiple ML models and, using a defined threshold, we predicted 406 'high-priority' essential genes. These genes showed strong transcriptional activity across developmental stages and were inferred to be enriched in pathways related to ribosome biogenesis, translation, RNA processing and signalling, underscoring their potential as anthelmintic targets. Transcriptomic analyses suggested that these genes are associated with key reproductive and neural tissues, while chromosomal mapping revealed a relatively even genomic distribution, in contrast to patterns observed in <i>C. elegans</i> and <i>Dr. melanogaster</i>. In addition, initial evidence suggested structural variation in the X chromosome compared with a recently published <i>D. immitis</i> assembly, indicating the importance of integrating long-read sequencing with high-throughput chromosome conformation capture (Hi-C) mapping. Overall, this study reinforces the potential of ML-guided approaches for essential gene discovery in parasitic nematodes and provides a foundation for downstream validation and therapeutic target development.