Ohio State Researchers Build Simulator to Track Jumping Genes in Fungi

Researchers at The Ohio State University have developed a computational tool to trace the history of transposable elements, or transposons, which are segments of DNA that can copy themselves into different parts of a genome.

These mobile genetic elements are often called jumping genes because they insert and delete themselves within a host's DNA. This activity can significantly expand the size of a genome over time. Shujun Ou, an assistant professor of molecular genetics at Ohio State and the senior author of the study, noted that transposon activity is dense over millions of years, involving many insertions and deletions.

The team created the simulator, named PrinTE, to address the lack of tools that could evolve a genome forward from an ancestral point to the modern day. Previous tools only provided a snapshot of transposon infection at a single point in time. Ou and his colleagues, including postdoctoral scholar Christopher Benson, designed PrinTE to test hypotheses about how these elements alter gene functions and genome size over evolutionary time.

The study, published in Nature Communications on Nov. 2, 2026, focused on a case study involving a fungal subdivision that includes myrtle rust. This fungus has a genome of 1 billion base pairs, which is unusually large for a fungus. Benson explained that transposons often insert as a copy-and-paste mechanism, leading to enormous genome sizes even if the elements do not have an immediately noticeable functional impact.

This phenomenon contributes to the genome size paradox, where plants and fungi with vastly different genome sizes can function similarly. Ou pointed out that if genome size were directly tied to function, larger organisms would need to have more complex structures, which is not always observed in nature.

The simulations revealed that in the myrtle rust lineage, the rate of transposon insertion was four to five times higher than the deletion rate. This imbalance caused the genome to expand. However, the data also showed that bursts of insertions were followed by a drop in activity, suggesting the host organism eventually suppresses further insertions. Benson compared this process to an immune system catching up with an infection.

PrinTE uses machine learning to guide the simulations, helping researchers avoid trajectories that do not fit the observed present-day genome. Ou described the process as walking in the dark, where the goal is to find the evolutionary path that best matches the current genetic data. The tool is publicly available and has already been used by scientists studying other biological systems.