Dr Chris Brosnan (left) and Stephen Fletcher at the University of Queensland. Credit: Megan Pope
Scientists have created a freely available software that will allow them to better design RNAi biopesticides, a type of agricultural spray which relies on genetic processes to combat pests and pathogens.
RNA interference (RNAi) works by introducing double-stranded RNA (dsRNA) to switch off genes essential for the survival of organisms and viruses.
Unlike broad-acting pesticides, it is possible to design dsRNAs so that they work only on specific organisms while otherwise degrading harmlessly in the environment.
The algorithm, dsRNAmax, allows researchers to predict whether their dsRNA design will target several closely related pests while leaving other species unharmed.
The team has published the freely available software and demonstrated its safety and efficacy in a new study, where used it to develop a dsRNA which killed 3 pest nematodes while sparing another.
Stephen Fletcher, a PhD student at the University of Queensland (UQ), Australia, is lead author of the paper and the creator of dsRNAmax. In an interview with Cosmos, Fletcher said that RNAi-based biopesticides have existed for nearly 25 years and have lots of advantages over conventional, broad spectrum crop protection.
“If you have an insecticide, it will kill a lot of insects – not only the pests but all the beneficial insects in the field. You can have toxicity issues with the environment and also to human health.
“The big advantage of [RNAi biopesticides] is that dsRNA is natural. It is in the environment, most living things have it, so it’s completely safe and non-toxic. It’s only toxic if it’s designed that way to the pest it’s targeting.”
Co-author of the study Dr Chris Brosnan, also from UQ, adds that RNAi exists in basically all eukaryotes from single cell yeast to humans: “We all have the ability to process this dsRNA into something called small interfering RNAs (siRNAs).”
siRNA is a type of non-coding RNA approximately 20–30 nucleotides long. Unlike messenger RNA (mRNA), it doesn’t carry the codes to make proteins. Instead, siRNA targets mRNA to stop the production of the protein it encodes.
By hijacking this pathway, scientists can silence any gene in viruses, fungi and invertebrates such as worms and insects. Fletcher stresses that this is not genetic modification: “There’s no trait modification, nothing is heritable.”
When dsRNA is sprayed onto crops it sits on the surface of the plant or remains in the space between its cells.
Importantly, the dsRNAs have no effect on plants or vertebrates, like humans. “You have all these barriers, like in your digestive system, in circulation and in the cells themselves,” says Brosnan.
A dsRNA sequence must strike a fine balance between being general enough to stop the expression of a gene – which might have multiple different copies in the same or related species – but specific enough to avoid affecting unwanted targets.
“People had kind of been trying to guess at sequences, but there was no formal way to do it,” says Fletcher, whose software provides this service by designing dsRNA in a way that maximises the number of possible siRNAs matching the target gene sequences and avoiding off-target species.
“I wanted the software to be really efficient, so that we could add hundreds of species into this list if we wanted to,” says Fletcher.
“Theoretically it works perfectly,” he adds. “But the big issue we had is that we couldn’t prove it.”
Nematodes under the microscope. Credit: UQ
That was until the team started collaborating with the Queensland Government Department of Primary Industries (DPI). DPI works on root-knot nematodes in the genus Meloidogyne which cause damage to crops by feeding on root cells, disrupting a plant’s ability to take up water and nutrients.
The software designed a dsRNA to target a gene present in 3 closely related root-knot nematodes supplied by DPI. The dsRNA was designed to simultaneously avoid matching with the same evolutionary conserved gene in another nematode – Caenorhabditis elegans.
Experiments revealed that while the dsRNA killed the 3 pest species, it had no impact on C. elegans. “We also showed that if you target the same gene in C. elegans with something that does perfectly match, it stops it reproducing,” says Fletcher.
He now plans to use a machine learning approach to further improve dsRNAmax’s ability to predict the most effective dsRNA sequences: “These exist naturally in all different organisms … we look at the natural ones that exist and how they’re processed within the organism and then use that to inform and predict how well our designs will work.”
Brosnan and his team are also working to refine and expand the number of genes, and nematode species, they can target with the approach.
“It’ll be of a lot of use to the field … the idea is that we’re designing things that go to market, that are actually used. It’s not a research tool, it’s [going] to have real world impacts,” says Fletcher.
The research is presented in the journal NAR Genomics & Bioinformatics.