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Secondary metabolite biosynthetic diversity in the fungal family Hypoxylaceae and Xylaria hypoxylon.

ABSTRACT: To date little is known about the genetic background that drives the production and diversification of secondary metabolites in the Hypoxylaceae. With the recent availability of high-quality genome sequences for 13 representative species and one relative (Xylaria hypoxylon) we attempted to survey the diversity of biosynthetic pathways in these organisms to investigate their true potential as secondary metabolite producers. Manual search strategies based on the accumulated knowledge on biosynthesis in fungi enabled us to identify 783 biosynthetic pathways across 14 studied species, the majority of which were arranged in biosynthetic gene clusters (BGC). The similarity of BGCs was analysed with the BiG-SCAPE engine which organised the BGCs into 375 gene cluster families (GCF). Only ten GCFs were conserved across all of these fungi indicating that speciation is accompanied by changes in secondary metabolism. From the known compounds produced by the family members some can be directly correlated with identified BGCs which is highlighted herein by the azaphilone, dihydroxynaphthalene, tropolone, cytochalasan, terrequinone, terphenyl and brasilane pathways giving insights into the evolution and diversification of those compound classes. Vice versa, products of various BGCs can be predicted through homology analysis with known pathways from other fungi as shown for the identified ergot alkaloid, trigazaphilone, curvupallide, viridicatumtoxin and swainsonine BGCs. However, the majority of BGCs had no obvious links to known products from the Hypoxylaceae or other well-studied biosynthetic pathways from fungi. These findings highlight that the number of known compounds strongly underrepresents the biosynthetic potential in these fungi and that a tremendous number of unidentified secondary metabolites is still hidden. Moreover, with increasing numbers of genomes for further Hypoxylaceae species becoming available, the likelihood of revealing new biosynthetic pathways that encode new, potentially useful compounds will significantly improve. Reaching a better understanding of the biology of these producers, and further development of genetic methods for their manipulation, will be crucial to access their treasures.

SUBMITTER: Kuhnert E

PROVIDER: S-EPMC8403587 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Fungal secondary metabolites (SMs) contribute to the diversity of fungal ecological communities, niches, and lifestyles. Many fungal SMs have one or more medically and industrially important activities (e.g., antifungal, antibacterial, and antitumor). The genes necessary for fungal SM biosynthesis are typically located right next to each other in the genome and are known as biosynthetic gene clusters (BGCs). However, whether fungal SM bioactivity can be predicted from specific attributes of genes in BGCs remains an open question. We adapted machine learning models that predicted SM bioactivity from bacterial BGC data with accuracies as high as 80% to fungal BGC data. We trained our models to predict the antibacterial, antifungal, and cytotoxic/antitumor bioactivity of fungal SMs on two data sets: (i) fungal BGCs (data set comprised of 314 BGCs) and (ii) fungal (314 BGCs) and bacterial BGCs (1,003 BGCs). We found that models trained on fungal BGCs had balanced accuracies between 51% and 68%, whereas training on bacterial and fungal BGCs had balanced accuracies between 56% and 68%. The low prediction accuracy of fungal SM bioactivities likely stems from the small size of the data set; this lack of data, coupled with our finding that including bacterial BGC data in the training data did not substantially change accuracies currently limits the application of machine learning approaches to fungal SM studies. With >15,000 characterized fungal SMs, millions of putative BGCs in fungal genomes, and increased demand for novel drugs, efforts that systematically link fungal SM bioactivity to BGCs are urgently needed.IMPORTANCEFungi are key sources of natural products and iconic drugs, including penicillin and statins. DNA sequencing has revealed that there are likely millions of biosynthetic pathways in fungal genomes, but the chemical structures and bioactivities of >99% of natural products produced by these pathways remain unknown. We used artificial intelligence to predict the bioactivities of diverse fungal biosynthetic pathways. We found that the accuracies of our predictions were generally low, between 51% and 68%, likely because the natural products and bioactivities of only very few fungal pathways are known. With >15,000 characterized fungal natural products, millions of putative biosynthetic pathways present in fungal genomes, and increased demand for novel drugs, our study suggests that there is an urgent need for efforts that systematically identify fungal biosynthetic pathways, their natural products, and their bioactivities.

Project description:Recent reviews have reinforced sponge-associated bacteria as a valuable source of structurally diverse secondary metabolites with potent biological properties, which makes these microbial communities promising sources of new drug candidates. However, the overall diversity of secondary metabolite biosynthetic potential present in bacteria is difficult to access due to the fact that the majority of bacteria are not readily cultured in the laboratory. Thus, use of cultivation-independent approaches may allow accessing "silent" and "cryptic" secondary metabolite biosynthetic gene clusters present in bacteria that cannot yet be cultured. In the present study, we investigated the diversity of secondary metabolite biosynthetic gene clusters (BGCs) in metagenomes of bacterial communities associated with three sponge species: Clathria reinwardti, Rhabdastrella globostellata, and Spheciospongia sp. The results reveal that the three metagenomes contain a high number of predicted BGCs, ranging from 282 to 463 BGCs per metagenome. The types of BGCs were diverse and represented 12 different cluster types. Clusters predicted to encode fatty acid synthases and polyketide synthases (PKS) were the most dominant BGC types, followed by clusters encoding synthesis of terpenes and bacteriocins. Based on BGC sequence similarity analysis, 363 gene cluster families (GCFs) were identified. Interestingly, no GCFs were assigned to pathways responsible for the production of known compounds, implying that the clusters detected might be responsible for production of several novel compounds. The KS gene sequences from PKS clusters were used to predict the taxonomic origin of the clusters involved. The KS sequences were related to 12 bacterial phyla with Actinobacteria, Proteobacteria, and Firmicutes as the most predominant. At the genus level, the KSs were most related to those found in the genera Mycolicibacterium, Mycobacterium, Burkholderia, and Streptomyces. Phylogenetic analysis of KS sequences resulted in detection of two known 'sponge-specific' BGCs, i.e., SupA and SwfA, as well as a new 'sponge-specific' cluster related to fatty acid synthesis in the phylum Candidatus Poribacteria and composed only by KS sequences of the three sponge-associated bacterial communities assessed here.

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Project description:BackgroundThere is little information about the DNA sequence variation within and between closely related plant species. The combination of re-sequencing technologies, large-scale DNA pools and availability of reference gene sequences allowed the extensive characterisation of single nucleotide polymorphisms (SNPs) in genes of four biosynthetic pathways leading to the formation of ecologically relevant secondary metabolites in Eucalyptus. With this approach the occurrence and patterns of SNP variation for a set of genes can be compared across different species from the same genus.ResultsIn a single GS-FLX run, we sequenced over 103 Mbp and assembled them to approximately 50 kbp of reference sequences. An average sequencing depth of 315 reads per nucleotide site was achieved for all four eucalypt species, Eucalyptus globulus, E. nitens, E. camaldulensis and E. loxophleba. We sequenced 23 genes from 1,764 individuals and discovered 8,631 SNPs across the species, with about 1.5 times as many SNPs per kbp in the introns compared to exons. The exons of the two closely related species (E. globulus and E. nitens) had similar numbers of SNPs at synonymous and non-synonymous sites. These species also had similar levels of SNP diversity, whereas E. camaldulensis and E. loxophleba had much higher SNP diversity. Neither the pathway nor the position in the pathway influenced gene diversity. The four species share between 20 and 43% of the SNPs in these genes.ConclusionBy using conservative statistical detection methods, we were confident about the validity of each SNP. With numerous individuals sampled over the geographical range of each species, we discovered one SNP in every 33 bp for E. nitens and one in every 31 bp in E. globulus. In contrast, the more distantly related species contained more SNPs: one in every 16 bp for E. camaldulensis and one in 17 bp for E. loxophleba, which is, to the best of our knowledge, the highest frequency of SNPs described in woody plant species.

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Secondary metabolite biosynthetic diversity in the fungal family Hypoxylaceae and Xylaria hypoxylon.

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