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Updated: Mar 19, 2026

Investigating Bacterial-Fungal Interactions using Fungal Highway Columns in Diverse Environments and Substrates
Published on: January 24, 2025
Large-scale multi-omics profiling reveals environmental and evolutionary drivers of fungal phylogeographic and
Huali Xie1,2,3, Jie Hu4, Xiulan Zhao1
1Key Laboratory of Biology and Genetic Improvement of Oil Crops, Key Laboratory of Detection for Mycotoxins, Ministry of Agriculture and Rural Affairs, Hubei Hongshan Laboratory, Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, China.
Abstract:
Chemical innovation is essential for fungi to adapt to ever-changing ecological environments. However, the environmental and evolutionary drivers of fungal metabolic differentiation remain ambiguous. Here, we show the phylogeographic diversity of 1052 Aspergillus flavus strains across four continents, as conducted through phylogenetic and biogeographical analysis, including 544 newly sequenced strains from China. These strains exhibit varying levels of population-specific mycotoxin production, as determined by population metabolomics analysis. We report a toxigenic subpopulation from China, identified through comparative population genomics analysis. Pan-metabolome analysis reveals strong phylogeographic metabolic patterns associated with specific ecological niches. Low-mycotoxin production clades harbor distinct uncharacterized biosynthetic gene clusters and produce different specialized metabolites instead. This discrepancy is only partially explained by variation in biosynthetic pathway genes, and changes in regulation and primary metabolism appear to mainly drive differentiation of specialized metabolite profiles across fungal populations, as indicated by pangenome profiling, metabolites-genome-wide association study, genotype-environment association study, pan-transcriptome analysis, and gene knockout experiments. Altogether, our results reveal how environmental shifts drive the fungal metabolic evolution, and provide insights for predicting the risk of harmful fungal outbreaks and for biogeographically-informed, precise control measures.
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