Algorithms to battle heartworm disease
Emerging viruses such as the coronavirus responsible for the COVID-19 pandemic are a growing challenge. But another source of infectious disease cannot be overlooked: parasites, organisms which are everywhere, living together with us in a coevolution of millions of years – and a public health issue that affects more than one billion people worldwide, according to the CDC.
Far beyond that, parasites are also a growing burden to companion animals. Global innovation is needed. And that is why our Animal Health scientists, together with partners from the University of Melbourne, Australia, are searching for new therapies against Dirofilaria immitis – responsible for heartworm disease, which leads to severe lung and heart failure in dogs and other animals, including cats and ferrets, often with a fatal outcome.
With global warming and changes in precipitation patterns, this parasitic infection is spread by mosquitoes and appears to be increasing worldwide, making it urgent to responsibly control its spread and also to develop safe treatments. Since October 2022, an international team of scientists has joined forces under a program of the Australian Research Council (ARC) to investigate and eventually discover novel and resistance-breaking interventions against heartworm - all with an innovative approach supported by artificial intelligence.
“Particular genes, proteins or molecules involved in the survival, growth or even the spread of the parasite within the host organism can be exploited as druggable molecular targets. We are trying to identify which key molecules can be targeted with naturally– or synthetically–derived compounds to control and cure parasitic infection,” says Paul Selzer, Head of New Mechanisms Parasitology.
ChatGPT in science? No! But machine learning contributes
Finding these targets is no easy task. But the work has been seeing important progress thanks to the collaboration with our partners from the University of Melbourne. Members in the Gasser Lab, including Dr. Tulio Campos, together with Dr. Pasi Korhonen and Dr. Neil Young, have established machine learning-based methods that can identify essential genes in an organism’s genome. They used intrinsic features of the proteins to train the machine learning model to search for targets, offering a broader knowledge of parasite’s genomic information.
This is a bit like searching for a needle in a haystack. The first step is sequencing the missing parasite genome and transcriptome information. The latter means all the messenger RNA molecules present in the parasite’s cells. But the sequence information is not the only thing, as parasites undergo life cycles with different stages. One cannot forget that depending on the parasite, some stages are in the host animal, some in the vector (for example, a mosquito), some outside – a characteristic that adds extra layers of complexity to the research.
“For this reason, it is very important to decide which life cycle stage of the parasite needs to be tackled. We need to fully understand, and we need to know exactly which of the molecular targets is crucial in which life stage of these parasites,” explains Paul.
Responsible parasite control for One Health
Efforts to fight heartworm disease can also open new paths to tackle gastrointestinal nematodes (GINs), other parasitic worms living in vertebrates which pose a major threat to the health and well-being of companion animals, livestock, and also humans.
“There is a lot of work to be done about responsible parasite control. The prevention of parasite infections in animals is not only necessary for animal well-being, and the human–animal bond, but it also reduces the risk of potential parasite transmission to humans,” summarizes Paul.