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Genetic Screens

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...

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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

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

Túlio L Campos^1,2, Pasi K Korhonen¹, Neil D Young¹

¹Department of Biosciences, Melbourne Veterinary School, Faculty of Science, The University of Melbourne, Parkville, VIC 3010, Australia.

International Journal of Molecular Sciences

|October 29, 2025

Summary

This summary is machine-generated.

Machine learning identified 406 high-priority essential genes in Dirofilaria immitis, the heartworm parasite. These genes are crucial for parasite survival and represent promising new targets for developing novel anthelmintic drugs.

Keywords:

Dirofilaria immitis dirofilariasis essential genes heartworm disease machine learning

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Area of Science:

Parasitology
Genomics
Bioinformatics

Background:

* *Dirofilaria immitis* causes heartworm disease in various animals and humans.
* Current treatments rely on macrocyclic lactones, but drug resistance is a growing concern.
* Novel intervention strategies are urgently needed to combat heartworm infections.

Purpose of the Study:

* To utilize a machine learning (ML) framework to predict and prioritize essential genes in *D. immitis*.
* To identify potential new therapeutic targets for heartworm disease.
* To explore the genomic distribution and functional roles of these essential genes.

Main Methods:

* Applied an ML-based framework using genomic, transcriptomic, and functional data from model organisms.
* Trained and evaluated ML models with 26 predictive features.
* Analyzed transcriptomic data and performed chromosomal mapping of predicted essential genes.

Main Results:

* Identified 406 'high-priority' essential genes in *D. immitis*.
* These genes are highly transcribed across developmental stages and enriched in vital pathways like ribosome biogenesis and translation.
* Genes are associated with reproductive and neural tissues, with a distinct genomic distribution compared to model organisms.

Conclusions:

* ML-guided approaches are effective for discovering essential genes in parasitic nematodes.
* The identified genes offer promising targets for developing new anthelmintic therapies against *D. immitis*.
* Further research integrating advanced sequencing and mapping techniques is recommended for comprehensive genomic analysis.