Tryptophan-Driven Metabolomic Shift in Acidobacteriaceae Reveals Phytohormones and Antifungal Metabolites
Zumkeller, C. M.; Hartwig, C.; Lanzalonga, W.; Patras, M. A.; Liu, Y.; Marner, M.; Mihajlovic, S.; Spohn, M. S.; Schaeberle, T. F.
Show abstract
Acidobacteriota is one of the most abundant phyla in soils and has recently attracted attention for its potential role in promoting phytosanitary benefits. The metabolomic capabilities of this phylum remain poorly characterised, with few experimentally confirmed metabolites described. To address these gaps, we combined metabolomic profiling with comparative genomic analysis to explore the functional potential of novel strains within the Acidobacteriaceae family. Genome mining across the phylum for plant-growth-promoting traits revealed the presence and taxon-specific enrichment of genes related to phytohormone production, as well as other genes associated with plant-beneficial microorganisms. When tryptophan was added to the cultivation medium, it triggered a strong metabolic response in the strains, resulting in measurable changes in the production levels of phytohormones such as indole-3-acetic acid and indole-3-pyruvate. Building on these findings, we examined the metabolic reprogramming caused by tryptophan supplementation, which suppressed the growth of phytopathogenic fungi, leading to the identification of malassezindoles and pityriacitrins as active agents. This was confirmed by isolating and elucidating the structure of pityriacitrin B and one of its methyl esters through NMR studies. Overall, these findings shed light on the previously unexplored metabolic potential of the Acidobacteriota phylum, emphasising its ecological importance for phytosanitary applications. ImportanceDespite their ubiquity and genomic diversity, the functional metabolism of members of the Acidobacteriota has largely remained uncharacterised. This study links genomic predictions to experimentally verified metabolomic outputs of Acidobacteriaceae, demonstrating tryptophan-responsive metabolic shifts translating to phytohormones and metabolites suppressing fungal growth. Our work underscores the emerging role of Acidobacteriota as important contributors to soil ecosystem functioning and plant-microbe interactions.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A cyclic dipeptide for salinity stress alleviation and the trophic flexibility of an endophyte reveal niches in salt marsh plant-microbe interactions 96%
- Selective enrichment of high-affinity clade II N2O-reducers in a mixed culture 96%
- Strong pairwise Interactions do not Drive Interactions in a Plant Leaf Associated Microbial Community 95%
Similar papers in this journal
- Exploring the interspecific interactions and the metabolome of the soil isolate Hylemonella gracilis 97%
- Genome-guided discovery of natural products through multiplexed low coverage whole-genome sequencing of soil Actinomycetes on Oxford Nanopore Flongle 96%
- Commensal oral Rothia mucilaginosa produces enterobactin - a metal chelating siderophore 96%
Similar papers in this journal
- Lydicamycins Induce Morphological Differentiation in Actinobacterial Interactions 97%
- Bacterial-like nonribosomal peptide synthetases produce cyclopeptides in the zygomycetous fungus Mortierella alpina 96%
- Thermophilic carboxylesterases from hydrothermal vents of the volcanic island of Ischia active on synthetic and biobased polymers and mycotoxins 96%
Similar papers in this journal
Similar papers in this journal
- Genomic and chemical decryption of the Bacteroidetes phylum for its potential to biosynthesize natural products 97%
- Thermal endurance by a hot-spring-dwelling phylogenetic relative of the mesophilic Paracoccus 95%
- Genome-Scale Modeling of Rothia mucilaginosa Reveals Insights into Metabolic Capabilities and Therapeutic Strategies for Cystic Fibrosis 95%
"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.