Large-scale genomic analyses with machine learning uncover predictive patterns associated with fungal phytopathogenic lifestyles and traits

Aug 4, 2026

Invasive plant pathogenic fungi drive major economic and environmental losses in crops and forests, but biosurveillance often struggles to infer risk from fungal lifestyles and traits when pathogens are not yet taxonomically identified. This study tests whether phytopathogenic genomic patterns generalize across Ascomycota and Basidiomycota and whether supervised machine learning can predict lifestyles and traits using 387 fungal genomes. Feature sets derived from CAZyme, peptidase, secondary metabolite cluster, transporter, and transcription factor annotations showed strong cross-taxonomic prediction, with the best performance using CAZyme, peptidase, and SMC data.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Introduction

Biosurveillance challenges for invasive fungal pathogens

Methods

Supervised machine learning with phylogenetic and genomic integration

Genomic feature sets from functional annotations

Results

Cross-taxonomic prediction of lifestyles and traits

Importance of phylogeny and added genomic data

Trait highlights, including plant pathogenicity

Discussion

Evolutionary insight from gene family expansions

Implications for predicting risks of unknown fungi in biosurveillance

UC Berkeley UC Berkeley Previously Published Works Title Large-scale genomic analyses with machine learning uncover predictive patterns associated with fungal phytopathogenic lifestyles and traits Permalink [https://escholarship.org/uc/item/04g8v45s](https://escholarship.org/uc/item/04g8v45s) Journal Scientific Reports, 13(1) ISSN 2045-2322 Authors Dort, EN Layne, EFeau, Net al. Publication Date 2023-10-01 DOI 10.1038/s41598-023-44005-w Copyright Information This work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/) Peer reviewed [eScholarship.org](eScholarship.org) Powered by the California Digital Library University of California [www. nature.com/scientificreports](www. nature.com/scientificreports) OPEN Large‑scale genomic analyses with machine learning uncover predictive patterns associated with fungal phytopathogenic lifestyles and traits E. N. Dort1, E. Layne2, N. Feau 3, A. Butyaev2, B. Henrissat4,5, F. M. Martin6, S. Haridas7, A. Salamov7, I. V. Grigoriev7,8, M. Blanchette2 & R. C. Hamelin1,9,10* Invasive plant pathogenic fungi have a global impact, with devastating economic and environmental effects on crops and forests. Biosurveillance, a critical component of threat mitigation, requires risk prediction based on fungal lifestyles and traits. Recent studies have revealed distinct genomic patterns associated with specific groups of plant pathogenic fungi. We sought to establish whether these phytopathogenic genomic patterns hold across diverse taxonomic and ecological groups from the Ascomycota and Basidiomycota, and furthermore, if those patterns can be used in a predictive capacity for biosurveillance. Using a supervised machine learning approach that integrates phylogenetic and genomic data, we analyzed 387 fungal genomes totest a proof‑of‑concept for the use of genomic signatures in predicting fungal phytopathogenic lifestyles and traits during biosurveillance activities. Our machine learning feature sets were derived from genome annotation data of carbohydrate‑active enzymes (CAZymes), peptidases, secondary metabolite clusters (SMCs), transporters, and transcription factors. We found that machine learning could successfully predict fungal lifestyles and traits across taxonomic groups, with the best predictive performance coming from feature sets comprising CAZyme, peptidase, and SMC data. While phylogeny was an important component in most predictions, the inclusion of genomic data improved prediction performance for every lifestyle and trait tested. Plant pathogenicity was one of the best‑predicted traits, showing the promise of predictive genomics for biosurveillance applications. Furthermore, our machine learning approach revealed expansions in the number of genes from specific CAZyme and peptidase families in the genomes of plant pathogens compared to non‑phytopathogenic genomes (saprotrophs, endo‑ and ectomycorrhizal fungi). Such genomic feature profiles give insight into the evolution of fungal phytopathogenicity and could be useful to predict the risks of unknown fungi in future biosurveillance activities. The health of many natural and managed plant ecosystems is threatened by fungal plant pathogens, which often cause emerging infectious diseases that are difficult to mitigate once established1–3. Due to their perennial nature, trees are particularly vulnerable to non-native pathogens known as forest invasive alien species (FIAS), which 1Department of Forest and Conservation Sciences, Faculty of Forestry, University of British Columbia, Vancouver, BC, Canada. 2School of Computer Science, McGill University, Montreal, QC, Canada. 3Pacific Forestry Centre, Canadian Forest Service, Natural Resources Canada, Victoria, BC, Canada. 4Department of Biotechnology and Biomedicine (DTU Bioengineering), Technical University of Denm

FAQs

What is the main goal of the study?

To determine whether genomic patterns linked to fungal phytopathogenic lifestyles and traits generalize across diverse fungal lineages and can be used for predictive biosurveillance.

How is machine learning applied in this work?

A supervised machine learning framework integrates phylogenetic information with genomic feature annotations to predict fungal lifestyles and traits from 387 fungal genomes.

Which genomic feature sets produced the best predictive performance?

Feature sets combining CAZyme, peptidase, and secondary metabolite cluster (SMC) information achieved the strongest overall performance across tested lifestyles and traits.

Aug 4, 2026

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