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Natural products (NPs) from microorganisms have been important sources for discovering new therapeutic and chemical entities. While their corresponding biosynthetic gene clusters (BGCs) can be easily identified by gene-sequence-similarity-based bioinformatics strategies, the actual access to these NPs for structure elucidation and bioactivity testing remains difficult. Deletion of the gene encoding the RNA chaperone, Hfq, results in strains losing the production of most NPs. By exchanging the native promoter of a desired BGC against an inducible promoter in Δhfq mutants, almost exclusive production of the corresponding NP from the targeted BGC in Photorhabdus, Xenorhabdus and Pseudomonas was observed including the production of several new NPs derived from previously uncharacterized non-ribosomal peptide synthetases (NRPS). This easyPACId approach (easy Promoter Activated Compound Identification) facilitates NP identification due to low interference from other NPs. Moreover, it allows direct bioactivity testing of supernatants containing secreted NPs, without laborious purification.
Biosynthetic gene content of the "Perfume Lichens" Evernia prunastri and Pseudevernia furfuracea
(2019)
Lichen-forming fungi produce a vast number of unique natural products with a wide variety of biological activities and human uses. Although lichens have remarkable potential in natural product research and industry, the molecular mechanisms underlying the biosynthesis of lichen metabolites are poorly understood. Here we use genome mining and comparative genomics to assess biosynthetic gene clusters and their putative regulators in the genomes of two lichen-forming fungi, which have substantial commercial value in the perfume industry, Evernia prunastri and Pseudevernia furfuracea. We report a total of 80 biosynthetic gene clusters (polyketide synthases (PKS), non-ribosomal peptide synthetases and terpene synthases) in E. prunastri and 51 in P. furfuracea. We present an in-depth comparison of 11 clusters, which show high homology between the two species. A ketosynthase (KS) phylogeny shows that biosynthetic gene clusters from E. prunastri and P. furfuracea are widespread across the Fungi. The phylogeny includes 15 genomes of lichenized fungi and all fungal PKSs with known functions from the MIBiG database. Phylogenetically closely related KS domains predict not only similar PKS architecture but also similar cluster architecture. Our study highlights the untapped biosynthetic richness of lichen-forming fungi, provides new insights into lichen biosynthetic pathways and facilitates heterologous expression of lichen biosynthetic gene clusters.
Background: Downy mildews are the most speciose group of oomycetes and affect crops of great economic importance. So far, there is only a single deeply-sequenced downy mildew genome available, from Hyaloperonospora arabidopsidis. Further genomic resources for downy mildews are required to study their evolution, including pathogenicity effector proteins, such as RxLR effectors. Plasmopara halstedii is a devastating pathogen of sunflower and a potential pathosystem model to study downy mildews, as several Avr-genes and R-genes have been predicted and unlike Arabidopsis downy mildew, large quantities of almost contamination-free material can be obtained easily.
Results: Here a high-quality draft genome of Plasmopara halstedii is reported and analysed with respect to various aspects, including genome organisation, secondary metabolism, effector proteins and comparative genomics with other sequenced oomycetes. Interestingly, the present analyses revealed further variation of the RxLR motif, suggesting an important role of the conservation of the dEER-motif. Orthology analyses revealed the conservation of 28 RxLR-like core effectors among Phytophthora species. Only six putative RxLR-like effectors were shared by the two sequenced downy mildews, highlighting the fast and largely independent evolution of two of the three major downy mildew lineages. This is seemingly supported by phylogenomic results, in which downy mildews did not appear to be monophyletic.
Conclusions: The genome resource will be useful for developing markers for monitoring the pathogen population and might provide the basis for new approaches to fight Phytophthora and downy mildew pathogens by targeting core pathogenicity effectors.
A new cyclic lipopeptide, phototemtide A (1), was isolated from Escherichia coli expressing the biosynthetic gene cluster pttABC from Photorhabdus temperata Meg1. The structure of 1 was elucidated by HR‐ESI‐MS and NMR experiments. The absolute configurations of amino acids and 3‐hydroxyoctanoic acid in 1 were determined by using the advanced Marfey's method and comparison after total synthesis of 1, respectively. Additionally, three new minor derivatives, phototemtides B–D (2–4), were identified by detailed HPLC–MS analysis. Phototemtide A (1) showed weak antiprotozoal activity against Plasmodium falciparum, with an IC50 value of 9.8 μm. The biosynthesis of phototemtides A–D (1–4) was also proposed.
Synthesis and SAR of the antistaphylococcal natural product nematophin from Xenorhabdus nematophila
(2019)
The repeated and improper use of antibiotics had led to an increased number of multiresistant bacteria. Therefore, new lead structures are needed. Here, the synthesis and an expanded structure–activity relationship of the simple and antistaphylococcal amide nematophin from Xenorhabdus nematophila and synthetic derivatives are described. Moreover, the synthesis of intrinsic fluorescent derivatives, incorporating azaindole moieties was achieved for the first time.
Bacteria of the genera Photorhabdus and Xenorhabdus produce a plethora of natural products to support their similar symbiotic lifecycles. For many of these compounds, the specific bioactivities are unknown. One common challenge in natural product research when trying to prioritize research efforts is the rediscovery of identical (or highly similar) compounds from different strains. Linking genome sequence to metabolite production can help in overcoming this problem. However, sequences are typically not available for entire collections of organisms. Here we perform a comprehensive metabolic screening using HPLC-MS data associated with a 114-strain collection (58 Photorhabdus and 56 Xenorhabdus) from across Thailand and explore the metabolic variation among the strains, matched with several abiotic factors. We utilize machine learning in order to rank the importance of individual metabolites in determining all given metadata. With this approach, we were able to prioritize metabolites in the context of natural product investigations, leading to the identification of previously unknown compounds. The top three highest-ranking features were associated with Xenorhabdus and attributed to the same chemical entity, cyclo(tetrahydroxybutyrate). This work addresses the need for prioritization in high-throughput metabolomic studies and demonstrates the viability of such an approach in future research.