Antibiotic biosynthesis pathways from endophytic streptomyces SUK 48 through metabolomics and genomics approaches

Streptomyces sp. has been known to be a major antibiotic producer since the 1940s. As the number of cases related to resistance pathogens infection increases yearly, discovering the biosynthesis pathways of antibiotic has become important. In this study, we present the streamline of a project report summary; the genome data and metabolome data of newly isolated Streptomyces SUK 48 strain are also analyzed. The antibacterial activity of its crude extract is also determined. To obtain genome data, the genomic DNA of SUK 48 was extracted using a commercial kit (Promega) and sent for sequencing (Pac Biosciences technology platform, Menlo Park, CA, USA). The raw data were assembled and polished using Hierarchical Genome Assembly Process 4.0 (HGAP 4.0). The assembled data were structurally predicted using tRNAscan-SE and rnammer. Then, the data were analyzed using Kyoto Encyclopedia of Genes and Genomes (KEGG) database and antiSMASH analysis. Meanwhile, the metabolite profile of SUK 48 was determined using liquid chromatographymass spectrophotometry (LC-MS) for both negative and positive modes. The results showed that the presence of kanamycin and gentamicin, as well as the other 11 antibiotics. Nevertheless, the biosynthesis pathways of aurantioclavine were also found. The cytotoxicity activity showed IC50 value was at 0.35 � 1.35 mg/mL on the cell viability of HEK 293. In conclusion, Streptomyces sp. SUK 48 has proven to be a non-toxic antibiotic producer such as auranticlavine and gentamicin

Survey of biosynthetic gene clusters from sequenced myxobacteria reveals unexplored biosynthetic potential

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Eleutherococcus senticosus Maxim. belongs to the Araliaceae family. Phytochemical studies reveal that E. senticosus leaves contain triterpene glycosides along with organic acid derivatives and flavonoid compounds. It is believed that E. senticosus is similar to ginseng because they come from same family and both contain triterpene saponins. E. senticosus leaves have been developed as a functional beverage called ci-wu-jia tea in recent years. Triterpene glycosides are difficult to identify by ultraviolet (UV) detection and contents of these compounds are low in E. senticosus leaves. In this study, a sensitive ultra-high performance liquid chromatographic (UHPLC) method combining UV and tandem mass spectrometry (MS/MS) was developed to characterize the triterpene glycosides from E. senticosus leaves and related commercial products. Fragmentation patterns of three sub-groups of triterpene glycosides in E. senticosus leaves were investigated. Additionally, fragmentation pathways and UV characteristics of organic acid derivatives and flavonoids were also characterized. A compound screening library, including 241 compounds reported in the literature, was created and used to confirm the compounds in the samples. In this study, a total of 24 samples, including 13 plant samples of E. senticosus and 11 ci-wu-jia tea products, were analyzed. Out of the 11 commercial products, three products were discovered to contain green tea (Camellia sinensis) that was considered to be an adulterant since it was not an ingredient on the labels. The developed UHPLC-UV-MS/MS analytical method combined with the UNIFI processing method can simultaneously characterize organic acid derivatives, flavonoids, and triterpene saponins from E. senticosus. It provides a simple and sensitive way to perform quality control of E. senticosus and related ci-wu-jia tea products

MIBiG 2.0: a repository for biosynthetic gene clusters of known function

Fueled by the explosion of (meta)genomic data, genome mining of specialized metabolites has become a major technology for drug discovery and studying microbiome ecology. In these efforts, computational tools like antiSMASH have played a central role through the analysis of Biosynthetic Gene Clusters (BGCs). Thousands of candidate BGCs from microbial genomes have been identified and stored in public databases. Interpreting the function and novelty of these predicted BGCs requires comparison with a well-documented set of BGCs of known function. The MIBiG (Minimum Information about a Biosynthetic Gene Cluster) Data Standard and Repository was established in 2015 to enable curation and storage of known BGCs. Here, we present MIBiG 2.0, which encompasses major updates to the schema, the data, and the online repository itself. Over the past five years, 851 new BGCs have been added. Additionally, we performed extensive manual data curation of all entries to improve the annotation quality of our repository. We also redesigned the data schema to ensure the compliance of future annotations. Finally, we improved the user experience by adding new features such as query searches and a statistics page, and enabled direct link-outs to chemical structure databases. The repository is accessible online at https://mibig.secondarymetabolites.org/

NeuRiPP : neural network identification of RiPP precursor peptides

Significant progress has been made in the past few years on the computational identification of biosynthetic gene clusters (BGCs) that encode ribosomally synthesized and post-translationally modified peptides (RiPPs). This is done by identifying both RiPP tailoring enzymes (RTEs) and RiPP precursor peptides (PPs). However, identification of PPs, particularly for novel RiPP classes remains challenging. To address this, machine learning has been used to accurately identify PP sequences. Current machine learning tools have limitations, since they are specific to the RiPPclass they are trained for and are context-dependent, requiring information about the surrounding genetic environment of the putative PP sequences. NeuRiPP overcomes these limitations. It does this by leveraging the rich data set of high-confidence putative PP sequences from existing programs, along with experimentally verified PPs from RiPP databases. NeuRiPP uses neural network archictectures that are suitable for peptide classification with weights trained on PP datasets. It is able to identify known PP sequences, and sequences that are likely PPs. When tested on existing RiPP BGC datasets, NeuRiPP was able to identify PP sequences in significantly more putative RiPP clusters than current tools while maintaining the same HMM hit accuracy. Finally, NeuRiPP was able to successfully identify PP sequences from novel RiPP classes that were recently characterized experimentally, highlighting its utility in complementing existing bioinformatics tools

antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline

Secondary metabolites produced by bacteria and fungi are an important source of antimicrobials and other bioactive compounds. In recent years, genome mining has seen broad applications in identifying and characterizing new compounds as well as in metabolic engineering. Since 2011, the 'antibiotics and secondary metabolite analysis shell-antiSMASH' (https://antismash.secondarymetabolites.org) has assisted researchers in this, both as a web server and a standalone tool. It has established itself as the most widely used tool for identifying and analysing biosynthetic gene clusters (BGCs) in bacterial and fungal genome sequences. Here, we present an entirely redesigned and extended version 5 of antiSMASH. antiSMASH 5 adds detection rules for clusters encoding the biosynthesis of acyl-amino acids, β-lactones, fungal RiPPs, RaS-RiPPs, polybrominated diphenyl ethers, C-nucleosides, PPY-like ketones and lipolanthines. For type II polyketide synthase-encoding gene clusters, antiSMASH 5 now offers more detailed predictions. The HTML output visualization has been redesigned to improve the navigation and visual representation of annotations. We have again improved the runtime of analysis steps, making it possible to deliver comprehensive annotations for bacterial genomes within a few minutes. A new output file in the standard JavaScript object notation (JSON) format is aimed at downstream tools that process antiSMASH results programmatically.</p

Genome Mining Revealed a High Biosynthetic Potential for Antifungal Streptomyces sp. S-2 Isolated from Black Soot

The increasing resistance of fungal pathogens has heightened the necessity of searching for new organisms and compounds to combat their spread. Streptomyces are bacteria that are well-known for the production of many antibiotics. To find novel antibiotic agents, researchers have turned to previously neglected and extreme environments. Here, we isolated a new strain, Streptomyces sp. S-2, for the first time, from black soot after hard coal combustion (collected from an in-use household chimney). We examined its antifungal properties against plant pathogens and against fungi that potentially pose threat to human health (Fusarium avenaceum, Aspergillus niger and the environmental isolates Trichoderma citrinoviridae Cin-9, Nigrospora oryzae sp. roseF7, and Curvularia coatesieae sp. junF9). Furthermore, we obtained the genome sequence of S-2 and examined its potential for secondary metabolites production using anti-SMASH software. The S-2 strain shows activity against all of the tested fungi. Genome mining elucidated a vast number of biosynthetic gene clusters (55), which distinguish this strain from closely related strains. The majority of the predicted clusters were assigned to non-ribosomal peptide synthetases or type 1 polyketide synthetases, groups known to produce compounds with antimicrobial activity