Collaborative Research: RUI: Identifying mechanisms of insect repellence in polypore fungi

Kean University, Union NJ

Investigators

Abstract

This project explores a unique type of fungus that parasitizes aspen trees. Known as the aspen bracket, this fungus has a remarkable trait: insects tend to avoid it. This behavior suggests that the fungus — either on its own or in partnership with the aspen tree — may produce substances that are toxic or unappealing to insects. By studying how the aspen bracket functions, researchers hope to discover new insect-repelling or insect-killing compounds that are both effective and environmentally friendly. These natural substances may work in ways not previously seen in fungi, offering new approaches to pest control. The findings could have valuable applications in agriculture, forestry, and public health. In addition to its scientific goals, the project has a strong educational mission: it will involve undergraduate and high school students in hands-on research. By engaging students and sharing results with both the public and the scientific community, the project aims to inspire broader participation in science, technology, engineering, and math (STEM) fields. The metabolic profile of the aspen bracket (Phellinus tremulae) will be characterized with respect to the content and origin of insecticidal or repellent compounds. First, the project will identify and characterize metabolites in the fungus. Both untargeted and targeted metabolomics will be conducted using liquid chromatography–mass spectrometry (LC-MS), and compounds of interest will be purified and tested on insects to confirm their repellent or toxic activity. Second, the origin of these compounds in P. tremulae will be investigated. Comparative analyses will assess the presence of these compounds in wild-collected fungal specimens, host aspen tissues, and cultured fungal samples to determine whether the compounds are biosynthesized by the fungus or accumulated from the host. Lastly, the project will explore the mode of action of these compounds using computational modeling. A suite of in silico approaches will be applied, including molecular docking, molecular dynamics simulations, and free binding energy calculations, to evaluate interactions between the compounds and known insecticidal protein targets. Additionally, chemometric models such as quantitative structure-activity relationship (QSAR), read-across, and q-RASAR, combined with machine learning (ML) and artificial intelligence (AI) algorithms, will be used to predict insecticidal efficacy, potency, and chemical stability. Environmental and human safety assessments will also be conducted computationally to evaluate toxicity and regulatory viability. Together, these approaches may uncover new natural insecticides, reveal previously unknown biosynthetic pathways, and provide a model for integrating metabolomics, pest management, and predictive computational toxicology. The results of this project can be translated into new biotechology, i.e., new pesticides. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.