ArticleCommunications biology2022
Non-obligate pairwise metabolite cross-feeding suggests ammensalic interactions between Bacillus amyloliquefaciens and Aspergillus oryzae.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Microbial Interactions in Food Fermentation: Interactions, Analysis Strategies, and Quality Enhancement.Foods (Basel, Switzerland) · 2025Review
- Metabolomic Insights into Cross-Feeding Interactions Between Priestia megaterium PM and Pseudomonas fluorescens NO4: Unveiling Microbial Communication in Plant Growth-Promoting Rhizobacteria.Microbial ecology · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bacterial-fungal metabolite trade-offs determine their ecological interactions. We designed a non-obligate pairwise metabolite cross-feeding (MCF) between Bacillus amyloliquefaciens and Aspergillus oryzae. Cross-feeding Aspergillus metabolites (MCF-1) affected higher growth and biofilm formation in Bacillus. LC-MS/MS-based multivariate analyses (MVA) showed variations in the endogenous metabolite profiles between the cross-fed and control Bacillus. We observed and validated that Aspergillus-derived oxylipins were rapidly depleted in Bacillus cultures concomitant with lowered secretion of cyclic lipopeptides (CLPs). Conversely, Bacillus extracts cross-fed to Aspergillus (MCF-2) diminished its mycelial growth and conidiation. Fungistatic effects of Bacillus-derived cyclic surfactins were temporally reduced following their hydrolytic linearization. MVA highlighted disparity between the cross-fed (MCF-2) and control Aspergillus cultures with marked variations in the oxylipin levels. We conclude that the pairwise MCF selectively benefitted Bacillus while suppressing Aspergillus, which suggests their ammensalic interaction. Widening this experimental pipeline across tailored communities may help model and simulate BFIs in more complex microbiomes.
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