Back

Bacillus velezensis EU07 suppresses Fusarium graminearum via transcriptomic reprogramming

Baysal, O.; Jimenez-quiros, C.; Cevher-Keskin, B.; Tor, M.

2025-10-10 plant biology
10.1101/2025.10.10.681611 bioRxiv
Show abstract

Fusarium graminearum, the causal agent of Fusarium head blight, is a devastating pathogen of cereals worldwide. Biological control using Bacillus species has emerged as a sustainable strategy to suppress this pathogen, but the molecular basis of antagonism remains poorly understood. Here, we investigated the interaction between Bacillus velezensis EU07 and F. graminearum strain K1-4 through morphological assays and RNA-seq profiling. Microscopy revealed severe hyphal distortions including swelling and branching abnormalities, following exposure to EU07 cell pellets. Transcriptomic analysis after 6 h of treatment identified 1,264 differentially expressed genes (DEGs), with 732 downregulated and 532 upregulated. Genes encoding secondary metabolite biosynthesis enzymes, including trichothecene (TRI) cluster genes, cytochrome P450s, and transporters, were strongly repressed. Key metabolic pathways, such as amino acid catabolism and mitochondrial transporters (e.g., 2-oxoglutarate/malate carrier protein), also showed reduced expression. Conversely, genes associated with oxidative stress responses, detoxification, and membrane transport were induced, reflecting a compensatory survival strategy. These results demonstrate that EU07 disrupts F. graminearum both morphologically and at the transcriptional level, suppressing virulence-associated pathways while triggering stress adaptation. This dual impact highlights B. velezensis EU07 as a promising biocontrol agent and provides candidate fungal genes for targeted RNAi-based crop protection strategies.

Published in Applied Microbiology and Biotechnology (predicted rank #20) · training set

Matching journals

The top 9 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.