Back

Wheat infection by Fusarium graminearum species complex members is facilitated by a transcriptionally conserved non-ribosomal peptide synthetase gene cluster

Tralamazza, S. M.; Nielsen, M. R.; Abraham, L. N.; Atanasoff-Kardjalieff, A. K.; Glauser, G.; Sorensen, J. L.; Studt-Reinhold, L.; Correa, B.; Croll, D.

2025-01-25 microbiology
10.1101/2025.01.24.634825 bioRxiv
Show abstract

Biosynthetic gene clusters (BGCs) are often found in fungal pathogens and encode secondary (or specialized) metabolites that play crucial roles in host and niche adaptation. However, the regulatory dynamics and functions of these BGCs during host infection remain largely unknown. To address this gap, we employed interspecies comparative transcriptomics to identify BGCs involved in host-pathogen interactions mediated by the Fusarium graminearum species complex (FGSC). We conducted joint transcriptomic and metabolomic analyses during wheat infection and in vitro experiments with five members of the FGSC to understand gene regulation during host infection. Our findings revealed that expression regulation was predominantly species-specific, but we also identified a set of shared upregulated genes that were common to all five FGSC species showing enrichment for metabolic and pathogenicity-associated functions. We focused on jointly upregulated BGC during infection and identified a NRPS-like gene cluster named SM3. The cluster was highly conserved within FGSC and shared by the more distant F. sambucinum species complex. We show that inactivation of the BGC core gene significantly impairs F. graminearum sensu stricto (s.s.) spore production, reduces fungal spread and Fusarium Head Blight symptoms on wheat kernels. Our results highlight that comparative infection transcriptomes can reveal conserved metabolic functions mediating the host infection process of a plant pathogen.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.