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Ingestion of the cycad toxins β-methylamino-L-alanine (BMAA) and azoxyglycosides is harmful to diverse organisms. However, some insects are specialized to feed on toxin-rich cycads with apparent immunity. Some cycad-feeding insects possess a common set of gut bacteria, which might play a role in detoxifying cycad toxins. Here, we investigated the composition of gut microbiota from a worldwide sample of cycadivorous insects and characterized the biosynthetic potential of selected bacteria. Cycadivorous insects shared a core gut microbiome consisting of six bacterial taxa, mainly belonging to the Proteobacteria, which we were able to isolate. To further investigate selected taxa from diverging lineages, we performed shotgun metagenomic sequencing of co-cultured bacterial sub-communities. We characterized the biosynthetic potential of four bacteria from Serratia, Pantoea, and two different Stenotrophomonas lineages, and discovered a suite of biosynthetic gene clusters notably rich in siderophores. Siderophore semi-untargeted metabolomics revealed a broad range of chemically related yet diverse iron-chelating metabolites, including desferrioxamine B, suggesting the occurrence of an unprecedented desferrioxamine-like biosynthetic pathway that remains to be identified. These results provide a foundation for future investigations into how cycadivorous insects tolerate diets rich in azoxyglycosides, BMAA, and other cycad toxins, including a possible role for bacterial siderophores.

 

Abstract With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/.