Evaluating the Distribution of Bacterial Natural Product Biosynthetic Genes across Lake Huron Sediment

2021 ◽  
Author(s):  
Maryam Elfeki ◽  
Shrikant Mantri ◽  
Chase M. Clark ◽  
Stefan J. Green ◽  
Nadine Ziemert ◽  
...  
Keyword(s):  
Natural Product ◽  
Lake Huron ◽  
Biosynthetic Genes

Mind the mushroom: natural product biosynthetic genes and enzymes of Basidiomycota

2021 ◽  
Author(s):  
Markus Gressler ◽  
Nikolai A. Löhr ◽  
Tim Schäfer ◽  
Stefanie Lawrinowitz ◽  
Paula Sophie Seibold ◽  
...  
Keyword(s):  
Natural Product ◽  
Biosynthetic Genes ◽  
Natural Product Biosynthesis

This article comprehensively reviews basidiomycete enzymes and their genes involved in natural product biosynthesis and primarily focuses on typical pathways and enzymes, along with the methods applied to investigate mushroom metabolism.

Isolation and diversity of natural product biosynthetic genes of cultivable bacteria associated with marine sponge Mycale sp. from the coast of Fujian, China

2014 ◽  
Vol 60 (4) ◽  
pp. 217-225 ◽  
Author(s):  
Pei Su ◽  
De-Xiang Wang ◽  
Shao-Xiong Ding ◽  
Jing Zhao
Keyword(s):  
Phylogenetic Analysis ◽  
Natural Product ◽  
Marine Sponge ◽  
Antimicrobial Activities ◽  
Amino Acid Sequences ◽  
Rhodococcus Opacus ◽  
Rrna Gene ◽  
Type I ◽  
Biosynthetic Genes ◽  
A Domains

The marine sponge Mycale sp., a potential source of natural bioactive products, is widely distributed along the coast of Fujian, China. The cultivable bacterial community associated with Mycale sp., the antibacterial activities, and the PKS (polyketide synthase) and NRPS (nonribosomal peptide synthetase) gene diversity of these bacteria were investigated. Phylogenetic analysis of the 16S rRNA gene showed that the 51 isolates from Mycale sp. belonged to Actinobacteria, Bacteroidetes, Gammaproteobacteria, Alphaproteobacteria, and Firmicutes. Among them, some bacteria were first isolated from marine sponge. The 20 isolates with antimicrobial activities were primarily clustered within the groups Actinobacteria, Gammaproteobacteria, and Bacillus. Strain HNS054, which showed 99% similarity to Streptomyces labedae, exhibited the strongest antimicrobial activity against Gram-positive bacteria (Staphylococcus aureus MTCC 1430, Bacillus subtilis MTCC 441) and Vibrio species. The screening of natural product biosynthetic genes revealed that 8 Actinobacteria species with antimicrobial activities possessed PKS-KS (ketosynthase) or NRPS-A domains, and the Nocardiopsis species contained a hybrid or mixed PKS–NRPS system. The phylogenetic analysis of the amino acid sequences indicated that the identified KS domains clustered with those from diverse bacterial groups, including Actinobacteria, Alphaproteobacteria, Cyanobacteria, and Firmicutes. Most KS domain sequences had high homology (>80%) to type I KSs, but the KS domain of Nocardiopsis sp. strain HNS048 had 77% similarity to the type II KS domain of Burkholderia gladioli. The NRPS-A domains of the 8 isolates were grouped into the Gammaproteobacteria, Actinobacteria, and Firmicutes groups. The NRPS-A gene of strain HNS052, identified as Nocardiopsis cyriacigeorgica, showed only 54% similarity to Rhodococcus opacus. All results suggested that Mycale sp. harboured diverse bacteria that could contribute to the production of novel bioactive substances in the future.

scholarly journals A supervised fingerprint-based strategy to connect natural product mass spectrometry fragmentation data to their biosynthetic gene clusters

2021 ◽  
Author(s):  
Tiago F. Leao ◽  
Mingxun Wang ◽  
Ricardo da Silva ◽  
Justin J.J. van der Hooft ◽  
Anelize Bauermeister ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Natural Products ◽  
Natural Product ◽  
Nearest Neighbor ◽  
Gene Clusters ◽  
Supervised Machine Learning ◽  
Biosynthetic Gene ◽  
Biosynthetic Gene Clusters ◽  
Biosynthetic Genes ◽  
Natural Product Discovery

AbstractMicrobial natural products, in particular secondary or specialized metabolites, are an important source and inspiration for many pharmaceutical and biotechnological products. However, bioactivity-guided methods widely employed in natural product discovery programs do not explore the full biosynthetic potential of microorganisms, and they usually miss metabolites that are produced at low titer. As a complementary method, the use of genome-based mining in natural products research has facilitated the charting of many novel natural products in the form of predicted biosynthetic gene clusters that encode for their production. Linking the biosynthetic potential inferred from genomics to the specialized metabolome measured by metabolomics would accelerate natural product discovery programs. Here, we applied a supervised machine learning approach, the K-Nearest Neighbor (KNN) classifier, for systematically connecting metabolite mass spectrometry data to their biosynthetic gene clusters. This pipeline offers a method for annotating the biosynthetic genes for known, analogous to known and cryptic metabolites that are detected via mass spectrometry. We demonstrate this approach by automated linking of six different natural product mass spectra, and their analogs, to their corresponding biosynthetic genes. Our approach can be applied to bacterial, fungal, algal and plant systems where genomes are paired with corresponding MS/MS spectra. Additionally, an approach that connects known metabolites to their biosynthetic genes potentially allows for bulk production via heterologous expression and it is especially useful for cases where the metabolites are produced at low amounts in the original producer.SignificanceThe pace of natural products discovery has remained relatively constant over the last two decades. At the same time, there is an urgent need to find new therapeutics to fight antibiotic resistant bacteria, cancer, tropical parasites, pathogenic viruses, and other severe diseases. To spark the enhanced discovery of structurally novel and bioactive natural products, we here introduce a supervised learning algorithm (K-Nearest Neighbor) that can connect known and analogous to known, as well as MS/MS spectra of yet unknowns to their corresponding biosynthetic gene clusters. Our Natural Products Mixed Omics tool provides access to genomic information for bioactivity prediction, class prediction, substrate predictions, and stereochemistry predictions to prioritize relevant metabolite products and facilitate their structural elucidation.

Identifying Natural Product Biosynthetic Genes from a Soil Metagenome by Using T7 Phage Selection

ChemBioChem ◽  
2009 ◽  
Vol 10 (16) ◽  
pp. 2599-2606 ◽  
Author(s):  
Keya Zhang ◽  
Jing He ◽  
Min Yang ◽  
Michelle Yen ◽  
Jun Yin
Keyword(s):  
Natural Product ◽  
Biosynthetic Genes ◽  
Soil Metagenome ◽  
Phage Selection ◽  
T7 Phage

scholarly journals New Deoxyenhygrolides from Plesiocystis pacifica Provide Insights into Butenolide Core Biosynthesis

Marine Drugs ◽  
2022 ◽  
Vol 20 (1) ◽  
pp. 72
Author(s):  
Joachim J. Hug ◽  
Louise Kjaerulff ◽  
Ronald Garcia ◽  
Rolf Müller
Keyword(s):  
Natural Products ◽  
Natural Product ◽  
Benzyl Alcohol ◽  
In Silico ◽  
Structure Elucidation ◽  
Product Class ◽  
Biological Functions ◽  
Biosynthetic Genes

Marine myxobacteria present a virtually unexploited reservoir for the discovery of natural products with diverse biological functions and novel chemical scaffolds. We report here the isolation and structure elucidation of eight new deoxyenhygrolides (1–8) from the marine myxobacterium Plesiocystis pacifica DSM 14875T. The herein described deoxyenhygrolides C–J (1–8) feature a butenolide core with an ethyl residue at C-3 of the γ-lactone in contrast to the previously described derivatives, deoxyenhygrolides A and B, which feature an isobutyl residue at this position. The butenolide core is 2,4-substituted with a benzyl (1, 2 and 7), benzoyl (3 and 4) or benzyl alcohol (5, 6 and 8) moiety in the 2-position and a benzylidene (1–6) or benzylic hemiketal (7 and 8) in the 4-position. The description of these new deoxyenhygrolide derivatives, alongside genomic in silico investigation regarding putative biosynthetic genes, provides some new puzzle pieces on how this natural product class might be formed by marine myxobacteria.

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