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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.

 

The vast majority of bacteria require iron to grow. A significant iron acquisition strategy is the production of siderophores, which are secondary microbial metabolites synthesized to sequester iron(III). Siderophore structures encompass a variety of forms, of which highly modified peptidic siderophores are of interest herein. State‐of‐the‐art genome mining tools, such as antiSMASH (antibiotics & Secondary Metabolite Analysis SHell), hold the potential to predict and discover new peptidic siderophores, including a combinatoric suite of triscatechol siderophores framed on a triserine‐ester backbone of the general class, (DHB‐ l / d CAA‐ l Ser) 3 (CAA, cationic amino acid). Siderophores with l / d Arg, l / d Lys and l Orn, but not d Orn, were predicted in bacterial genomes. Fortuitously the d Orn siderophore was identified, yet its lack of prediction highlights the limitation of current genome mining tools. The full combinatoric suite of these siderophores, which form chiral iron(III) complexes, reveals stereospecific coordination chemistry encoded in microbial genomes. The chirality embedded in this suite of Fe(III)‐siderophores raises the question of whether the relevant siderophore‐mediated iron acquisition pathways are stereospecific and selective for ferric siderophore complexes of a defined configuration. 

Ferric complexes of triscatechol siderophores may assume one of two enantiomeric configurations at the iron site. Chirality is known to be important in the iron uptake process, however an understanding of the molecular features directing stereospecific coordination remains ambiguous. Synthesis of the full suite of (DHB L/D Lys L/D Ser) 3 macrolactone diastereomers, which includes the siderophore cyclic trichrysobactin (CTC), enables the effects that the chirality of Lys and Ser residues exert on the configuration of the Fe( iii ) complex to be defined. Computationally optimized geometries indicate that the Λ/Δ configurational preferences are set by steric interactions between the Lys sidechains and the peptide backbone. The ability of each (DHB L/D Lys L/D Ser) 3 diastereomer to form a stable Fe( iii ) complex prompted a genomic search for biosynthetic gene clusters (BGCs) encoding the synthesis of these diastereomers in microbes. The genome of the plant pathogen Dickeya chrysanthemi EC16 was sequenced and the genes responsible for the biosynthesis of CTC were identified. A related but distinct BGC was identified in the genome of the opportunistic pathogen Yersinia frederiksenii ATCC 33641; isolation of the siderophore from Y. frederiksenii ATCC 33641, named frederiksenibactin (FSB), revealed the triscatechol oligoester, linear -(DHB L Lys L Ser) 3 . Circular dichroism (CD) spectroscopy establishes that Fe( iii )–CTC and Fe( iii )–FSB are formed in opposite enantiomeric configuration, consistent with the results of the ferric complexes of the cyclic (DHB L/D Lys L/D Ser) 3 diastereomers. 

Genome mining for VibH homologs reveals several species of Acinetobacter with a gene cluster that putatively encodes the biosynthesis of catechol siderophores with an amine core. A. bouvetii DSM 14964 produces three novel biscatechol siderophores: propanochelin ( 1 ), butanochelin ( 2 ), and pentanochelin ( 3 ). This strain has a relaxed specificity for the amine substrate, allowing for the biosynthesis of a variety of non-natural siderophore analogs by precursor directed biosynthesis. Of potential synthetic utility, A. bouvetii DSM 14964 condenses 2,3-dihydroxybenzoic acid (2,3-DHB) to allylamine and propargylamine, producing catecholic compounds which bind iron( iii ) and may be further modified via thiol–ene or azide–alkyne click chemistry. 

Genome mining of biosynthetic pathways streamlines discovery of secondary metabolites but can leave ambiguities in the predicted structures, which must be rectified experimentally. Through coupling the reactivity predicted by biosynthetic gene clusters with verified structures, the origin of the β-hydroxyaspartic acid diastereomers in siderophores is reported herein. Two functional subtypes of nonheme Fe(II)/α-ketoglutarate–dependent aspartyl β-hydroxylases are identified in siderophore biosynthetic gene clusters, which differ in genomic organization—existing either as fused domains (IβH Asp ) at the carboxyl terminus of a nonribosomal peptide synthetase (NRPS) or as stand-alone enzymes (TβH Asp )—and each directs opposite stereoselectivity of Asp β-hydroxylation. The predictive power of this subtype delineation is confirmed by the stereochemical characterization of β-OHAsp residues in pyoverdine GB-1, delftibactin, histicorrugatin, and cupriachelin. The l - threo (2 S , 3 S ) β-OHAsp residues of alterobactin arise from hydroxylation by the β-hydroxylase domain integrated into NRPS AltH, while l - erythro (2 S , 3 R ) β-OHAsp in delftibactin arises from the stand-alone β-hydroxylase DelD. Cupriachelin contains both l - threo and l - erythro β-OHAsp, consistent with the presence of both types of β-hydroxylases in the biosynthetic gene cluster. A third subtype of nonheme Fe(II)/α-ketoglutarate–dependent enzymes (IβH His ) hydroxylates histidyl residues with l - threo stereospecificity. A previously undescribed, noncanonical member of the NRPS condensation domain superfamily is identified, named the interface domain, which is proposed to position the β-hydroxylase and the NRPS-bound amino acid prior to hydroxylation. Through mapping characterized β-OHAsp diastereomers to the phylogenetic tree of siderophore β-hydroxylases, methods to predict β-OHAsp stereochemistry in silico are realized. 

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/.