Improved lanthipeptide detection and prediction for antiSMASH

Embleporicin: A Novel Class I Lanthipeptide from the Actinobacteria Embleya sp. NF3

Genome mining has emerged as a revolutionary tool for discovering new ribosomally synthesized and post-translationally modified peptides (RiPPs) in various genomes. Recently, these approaches have been used to detect and explore unique environments as sources of RiPP-producing microorganisms, particularly focusing on endophytic microorganisms found in medicinal plants. Some endophytic actinobacteria, especially strains of Streptomyces, are notable examples of peptide producers, as specific biosynthetic clusters encode them. To uncover the genetic potential of these organisms, we analyzed the genome of the endophytic actinobacterium Embleya sp. NF3 using genome mining and bioinformatics tools. Our analysis led to the identification of a putative class I lanthipeptide. We cloned the core biosynthetic genes of this putative lanthipeptide, named embleporicin, and expressed them in vitro using a cell-free protein system (CFPS). The resulting product demonstrated antimicrobial activity against Micrococcus luteus ATCC 9341. This represents the first RiPP reported in the genus Embleya and the first actinobacterial lanthipeptide produced through cell-free technology.

BPAGS: a web application for bacteriocin prediction via feature evaluation using alternating decision tree, genetic algorithm, and linear support vector classifier

The use of bacteriocins has emerged as a propitious strategy in the development of new drugs to combat antibiotic resistance, given their ability to kill bacteria with both broad and narrow natural spectra. Hence, a compelling requirement arises for a precise and efficient computational model that can accurately predict novel bacteriocins. Machine learning's ability to learn patterns and features from bacteriocin sequences that are difficult to capture using sequence matching-based methods makes it a potentially superior choice for accurate prediction. A web application for predicting bacteriocin was created in this study, utilizing a machine learning approach. The feature sets employed in the application were chosen using alternating decision tree (ADTree), genetic algorithm (GA), and linear support vector classifier (linear SVC)-based feature evaluation methods. Initially, potential features were extracted from the physicochemical, structural, and sequence-profile attributes of both bacteriocin and non-bacteriocin protein sequences. We assessed the candidate features first using the Pearson correlation coefficient, followed by separate evaluations with ADTree, GA, and linear SVC to eliminate unnecessary features. Finally, we constructed random forest (RF), support vector machine (SVM), decision tree (DT), logistic regression (LR), k-nearest neighbors (KNN), and Gaussian naïve Bayes (GNB) models using reduced feature sets. We obtained the overall top performing model using SVM with ADTree-reduced features, achieving an accuracy of 99.11% and an AUC value of 0.9984 on the testing dataset. We also assessed the predictive capabilities of our best-performing models for each reduced feature set relative to our previously developed software solution, a sequence alignment-based tool, and a deep-learning approach. A web application, titled BPAGS (Bacteriocin Prediction based on ADTree, GA, and linear SVC), was developed to incorporate the predictive models built using ADTree, GA, and linear SVC-based feature sets. Currently, the web-based tool provides classification results with associated probability values and has options to add new samples in the training data to improve the predictive efficacy. BPAGS is freely accessible at https://shiny.tricities.wsu.edu/bacteriocin-prediction/.

Systematic mining of the human microbiome identifies antimicrobial peptides with diverse activity spectra

Human-associated bacteria secrete modified peptides to control host physiology and remodel the microbiota species composition. Here we scanned 2,229 Human Microbiome Project genomes of species colonizing skin, gastrointestinal tract, urogenital tract, mouth and trachea for gene clusters encoding RiPPs (ribosomally synthesized and post-translationally modified peptides). We found 218 lanthipeptides and 25 lasso peptides, 70 of which were synthesized and expressed in E. coli and 23 could be purified and functionally characterized. They were tested for activity against bacteria associated with healthy human flora and pathogens. New antibiotics were identified against strains implicated in skin, nasal and vaginal dysbiosis as well as from oral strains selectively targeting those in the gut. Extended- and narrow-spectrum antibiotics were found against methicillin-resistant Staphylococcus aureus and vancomycin-resistant Enterococci. Mining natural products produced by human-associated microbes will enable the elucidation of ecological relationships and may be a rich resource for antimicrobial discovery.

Machine Learning Prediction of Antimicrobial Peptides

Antibiotic resistance constitutes a global threat and could lead to a future pandemic. One strategy is to develop a new generation of antimicrobials. Naturally occurring antimicrobial peptides (AMPs) are recognized templates and some are already in clinical use. To accelerate the discovery of new antibiotics, it is useful to predict novel AMPs from the sequenced genomes of various organisms. The antimicrobial peptide database (APD) provided the first empirical peptide prediction program. It also facilitated the testing of the first machine-learning algorithms. This chapter provides an overview of machine-learning predictions of AMPs. Most of the predictors, such as AntiBP, CAMP, and iAMPpred, involve a single-label prediction of antimicrobial activity. This type of prediction has been expanded to antifungal, antiviral, antibiofilm, anti-TB, hemolytic, and anti-inflammatory peptides. The multiple functional roles of AMPs annotated in the APD also enabled multi-label predictions (iAMP-2L, MLAMP, and AMAP), which include antibacterial, antiviral, antifungal, antiparasitic, antibiofilm, anticancer, anti-HIV, antimalarial, insecticidal, antioxidant, chemotactic, spermicidal activities, and protease inhibiting activities. Also considered in predictions are peptide posttranslational modification, 3D structure, and microbial species-specific information. We compare important amino acids of AMPs implied from machine learning with the frequently occurring residues of the major classes of natural peptides. Finally, we discuss advances, limitations, and future directions of machine-learning predictions of antimicrobial peptides. Ultimately, we may assemble a pipeline of such predictions beyond antimicrobial activity to accelerate the discovery of novel AMP-based antimicrobials.

Secondary metabolite biosynthetic diversity in Arctic Ocean metagenomes

Polyketide synthases (PKSs) and non-ribosomal peptide synthetases (NRPSs) are mega enzymes responsible for the biosynthesis of a large fraction of natural products (NPs). Molecular markers for biosynthetic genes, such as the ketosynthase (KS) domain of PKSs, have been used to assess the diversity and distribution of biosynthetic genes in complex microbial communities. More recently, metagenomic studies have complemented and enhanced this approach by allowing the recovery of complete biosynthetic gene clusters (BGCs) from environmental DNA. In this study, the distribution and diversity of biosynthetic genes and clusters from Arctic Ocean samples (NICE-2015 expedition), was assessed using PCR-based strategies coupled with high-throughput sequencing and metagenomic analysis. In total, 149 KS domain OTU sequences were recovered, 36 % of which could not be assigned to any known BGC. In addition, 74 bacterial metagenome-assembled genomes were recovered, from which 179 BGCs were extracted. A network analysis identified potential new NP families, including non-ribosomal peptides and polyketides. Complete or near-complete BGCs were recovered, which will enable future heterologous expression efforts to uncover the respective NPs. Our study represents the first report of biosynthetic diversity assessed for Arctic Ocean metagenomes and highlights the potential of Arctic Ocean planktonic microbiomes for the discovery of novel secondary metabolites. The strategy employed in this study will enable future bioprospection, by identifying promising samples for bacterial isolation efforts, while providing also full-length BGCs for heterologous expression.

Mining and Biosynthesis of Bioactive Lanthipeptides From Microorganisms

Antimicrobial resistance is one of the most serious public health issues in the worldwide and only a few new antimicrobial drugs have been discovered in recent decades. To overcome the ever-increasing emergence of multidrug-resistant (MDR) pathogens, discovery of new natural products (NPs) against MDR pathogens with new technologies is in great demands. Lanthipeptides which are ribosomally synthesized and post-translationally modified peptides (RiPPs) display high diversity in their chemical structures and mechanisms of action. Genome mining and biosynthetic engineering have also yielded new lanthipeptides, which are a valuable source of drug candidates. In this review we cover the recent advances in the field of microbial derived lanthipeptide discovery and development.

A Regulator Based "Semi-Targeted" Approach to Activate Silent Biosynthetic Gene Clusters

By culturing microorganisms under standard laboratory conditions, most biosynthetic gene clusters (BGCs) are not expressed, and thus, the products are not produced. To explore this biosynthetic potential, we developed a novel "semi-targeted" approach focusing on activating "silent" BGCs by concurrently introducing a group of regulator genes into streptomycetes of the Tübingen strain collection. We constructed integrative plasmids containing two classes of regulatory genes under the control of the constitutive promoter ermE*p (cluster situated regulators (CSR) and Streptomyces antibiotic regulatory proteins (SARPs)). These plasmids were introduced into Streptomyces sp. TÜ17, Streptomyces sp. TÜ10 and Streptomyces sp. TÜ102. Introduction of the CSRs-plasmid into strain S. sp. TÜ17 activated the production of mayamycin A. By using the individual regulator genes, we proved that Aur1P, was responsible for the activation. In strain S. sp. TÜ102, the introduction of the SARP-plasmid triggered the production of a chartreusin-like compound. Insertion of the CSRs-plasmid into strain S. sp. TÜ10 resulted in activating the warkmycin-BGC. In both recombinants, activation of the BGCs was only possible through the simultaneous expression of aur1PR3 and griR in S. sp. TÜ102 and aur1P and pntR in of S. sp. TÜ10.

Precursor peptide-targeted mining of more than one hundred thousand genomes expands the lanthipeptide natural product family

Background

Lanthipeptides belong to the ribosomally synthesized and post-translationally modified peptide group of natural products and have a variety of biological activities ranging from antibiotics to antinociceptives. These peptides are cyclized through thioether crosslinks and can bear other secondary post-translational modifications. While lanthipeptide biosynthetic gene clusters can be identified by the presence of genes encoding characteristic enzymes involved in the post-translational modification process, locating the precursor peptides encoded within these clusters is challenging due to their short length and high sequence variability, which limits the high-throughput exploration of lanthipeptide biosynthesis. To address this challenge, we enhanced the predictive capabilities of Rapid ORF Description & Evaluation Online (RODEO) to identify members of all four known classes of lanthipeptides.

Results

Using RODEO, we mined over 100,000 bacterial and archaeal genomes in the RefSeq database. We identified nearly 8500 lanthipeptide precursor peptides. These precursor peptides were identified in a broad range of bacterial phyla as well as the Euryarchaeota phylum of archaea. Bacteroidetes were found to encode a large number of these biosynthetic gene clusters, despite making up a relatively small portion of the genomes in this dataset. A number of these precursor peptides are similar to those of previously characterized lanthipeptides, but even more were not, including potential antibiotics. One such new antimicrobial lanthipeptide was purified and characterized. Additionally, examination of the biosynthetic gene clusters revealed that enzymes installing secondary post-translational modifications are more widespread than initially thought.

Conclusion

Lanthipeptide biosynthetic gene clusters are more widely distributed and the precursor peptides encoded within these clusters are more diverse than previously appreciated, demonstrating that the lanthipeptide sequence-function space remains largely underexplored.

Recent development of antiSMASH and other computational approaches to mine secondary metabolite biosynthetic gene clusters

Many drugs are derived from small molecules produced by microorganisms and plants, so-called natural products. Natural products have diverse chemical structures, but the biosynthetic pathways producing those compounds are often organized as biosynthetic gene clusters (BGCs) and follow a highly conserved biosynthetic logic. This allows for the identification of core biosynthetic enzymes using genome mining strategies that are based on the sequence similarity of the involved enzymes/genes. However, mining for a variety of BGCs quickly approaches a complexity level where manual analyses are no longer possible and require the use of automated genome mining pipelines, such as the antiSMASH software. In this review, we discuss the principles underlying the predictions of antiSMASH and other tools and provide practical advice for their application. Furthermore, we discuss important caveats such as rule-based BGC detection, sequence and annotation quality and cluster boundary prediction, which all have to be considered while planning for, performing and analyzing the results of genome mining studies.

MetaMiner: A Scalable Peptidogenomics Approach for Discovery of Ribosomal Peptide Natural Products with Blind Modifications from Microbial Communities

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are an important class of natural products that contain antibiotics and a variety of other bioactive compounds. The existing methods for discovery of RiPPs by combining genome mining and computational mass spectrometry are limited to discovering specific classes of RiPPs from small datasets, and these methods fail to handle unknown post-translational modifications. Here, we present MetaMiner, a software tool for addressing these challenges that is compatible with large-scale screening platforms for natural product discovery. After searching millions of spectra in the Global Natural Products Social (GNPS) molecular networking infrastructure against just eight genomic and metagenomic datasets, MetaMiner discovered 31 known and seven unknown RiPPs from diverse microbial communities, including human microbiome and lichen microbiome, and microorganisms isolated from the International Space Station.

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.

RiPPMiner: a bioinformatics resource for deciphering chemical structures of RiPPs based on prediction of cleavage and cross-links

Ribosomally synthesized and post-translationally modified peptides (RiPPs) constitute a rapidly growing class of natural products with diverse structures and bioactivities. We have developed RiPPMiner, a novel bioinformatics resource for deciphering chemical structures of RiPPs by genome mining. RiPPMiner derives its predictive power from machine learning based classifiers, trained using a well curated database of more than 500 experimentally characterized RiPPs. RiPPMiner uses Support Vector Machine to distinguish RiPP precursors from other small proteins and classify the precursors into 12 sub-classes of RiPPs. For classes like lanthipeptide, cyanobactin, lasso peptide and thiopeptide, RiPPMiner can predict leader cleavage site and complex cross-links between post-translationally modified residues starting from genome sequences. RiPPMiner can identify correct cross-link pattern in a core peptide from among a very large number of combinatorial possibilities. Benchmarking of prediction accuracy of RiPPMiner on a large lanthipeptide dataset indicated high sensitivity, specificity, accuracy and precision. RiPPMiner also provides interfaces for visualization of the chemical structure, downloading of simplified molecular-input line-entry system and searching for RiPPs having similar sequences or chemical structures. The backend database of RiPPMiner provides information about modification system, precursor sequence, leader and core sequence, modified residues, cross-links and gene cluster for more than 500 experimentally characterized RiPPs. RiPPMiner is available at http://www.nii.ac.in/rippminer.html.

Computational Opportunities and Challenges in Finding Cyclic Peptide Modulators of Protein-Protein Interactions

Peptide cyclization can improve stability, conformational constraint, and compactness. However, apart from beta-turn structures, which are well incorporated into cyclic peptides (CPs), many primary peptide structures and functions are markedly altered by cyclization. Accordingly, to mimic linear peptide interfaces with cyclic peptides, it can be beneficial to screen combinatorial cyclic peptide libraries. Computational methods have been developed to screen CPs, but face a number of challenges. Here, we review methods to develop in silico computational libraries, and the potential for screening naturally occurring libraries of CPs. The simplest and most rapid computational pharmacophore methods that estimate peptide three-dimensional structures to be screened versus targets are relatively easy to implement, and while the constraint on structure imposed by cyclization makes them more effective than the same approaches with linear peptides, there are a large number of limiting assumptions. In contrast, full molecular dynamics simulations of cyclic peptide structures not only are costly to implement, but also require careful attention to interpretation, so that not only is the computation time rate limiting, but the interpretation time is also rate limiting due to the analysis of the typically complex underlying conformational space of CPs. A challenge for the field of computational cyclic peptide screening is to bridge this gap effectively. Natural compound libraries of short cyclic peptides, and short cyclized regions of proteins, encoded in the genomes of many organisms present a potential treasure trove of novel functionality which may be screened via combined computational and experimental screening approaches.

Identification of Putative Novel Class-I Lanthipeptides in Firmicutes: A Combinatorial In Silico Analysis Approach Performed on Genome Sequenced Bacteria and a Close Inspection of Z-Geobacillin Lanthipeptide Biosynthesis Gene Cluster of the Thermophilic Geobacillus sp. Strain ZGt-1

Lanthipeptides are ribosomally synthesized and post-translationally modified polycyclic peptides. Lanthipeptides that have antimicrobial activity are known as lantibiotics. Accordingly, the discovery of novel lantibiotics constitutes a possible solution for the problem of antibiotic resistance. We utilized the publicly available genome sequences and the bioinformatic tools tailored for the detection of lanthipeptides. We designed our strategy for screening of 252 firmicute genomes and detecting class-I lanthipeptide-coding gene clusters. The designed strategy resulted in identifying 69 class-I lanthipeptide sequences, of which more than 10% were putative novel. The identified putative novel lanthipeptides have not been annotated on the original or the RefSeq genomes, or have been annotated merely as coding for hypothetical proteins. Additionally, we identified bacterial strains that have not been previously recognized as lanthipeptide-producers. Moreover, we suggest corrections for certain firmicute genome annotations, and recommend lanthipeptide records for enriching the bacteriocin genome mining tool (BAGEL) databases. Furthermore, we propose Z-geobacillin, a putative class-I lanthipeptide coded on the genome of the thermophilic strain Geobacillus sp. ZGt-1. We provide lists of putative novel lanthipeptide sequences and of the previously unrecognized lanthipeptide-producing bacterial strains, so they can be prioritized for experimental investigation. Our results are expected to benefit researchers interested in the in vitro production of lanthipeptides.

Concepts and Methods to Access Novel Antibiotics from Actinomycetes

Actinomycetes have been proven to be an excellent source of secondary metabolites for more than half a century. Exhibiting various bioactivities, they provide valuable approved drugs in clinical use. Most microorganisms are still untapped in terms of their capacity to produce secondary metabolites, since only a small fraction can be cultured in the laboratory. Thus, improving cultivation techniques to extend the range of secondary metabolite producers accessible under laboratory conditions is an important first step in prospecting underexplored sources for the isolation of novel antibiotics. Currently uncultured actinobacteria can be made available by bioprospecting extreme or simply habitats other than soil. Furthermore, bioinformatic analysis of genomes reveals most producers to harbour many more biosynthetic gene clusters than compounds identified from any single strain, which translates into a silent biosynthetic potential of the microbial world for the production of yet unknown natural products. This review covers discovery strategies and innovative methods recently employed to access the untapped reservoir of natural products. The focus is the order of actinomycetes although most approaches are similarly applicable to other microbes. Advanced cultivation methods, genomics- and metagenomics-based approaches, as well as modern metabolomics-inspired methods are highlighted to emphasise the interplay of different disciplines to improve access to novel natural products.

Improving the attrition rate of Lanthipeptide discovery for commercial applications

INTRODUCTION

Lanthipeptides are a class of ribosomally synthesized and post-translationally modified peptides. Lanthipeptides with antimicrobial activity are referred to as lantibiotics. Lantibiotics are generally active against Gram-positive bacteria. However, some modifications have expanded their activity toward Gram-negative bacteria. Furthermore, additional functions aside from antibacterial activities have been reported for lanthipeptides. Areas covered: This review provides a synopsis of current anthipeptide research for potential therapeutics. The review highlights the current tools used for identifying lanthipeptides from genomic sequencing data. It also describes the current approaches that have been used to overcome the limitations in the purification and isolation of lanthipeptides. The status of lanthipeptides in terms of potential applications and approaches that are currently being done to promote the development of lanthipeptides as novel therapeutics are also discussed. Expert opinion: Significant improvements have been made to promote the discovery of new lanthipeptides, while, simultaneously, tools have been developed to promote their production and isolation. Lanthipeptides are showing significant promise for treating bacterial infections, as well as for new applications as anticancer and antiviral agents, or as a novel treatment for pain management. At the current rate of lanthipeptide discovery and isolation of the products, it is likely several new applications will be discovered.

Genome database mining for the discovery of novel lantibiotics

INTRODUCTION

The effectiveness of lantibiotics against MDR pathogens and the progression of agents MU1140, NAI-107, NVB302 and duramycin into pre-clinical and clinical trials have highlighted their potential in the fight against bacterial resistance. The number of known lantibiotics and knowledge of their biosynthetic pathways has increased in recent years due to higher quality genomic data being delivered by next generation sequencing technologies combined with the development of specific genome mining tools, enabling the prediction of lantibiotic clusters. Areas covered: In this review, the author describes how the increase of high quality genomic data has increased the discovery of novel lantibiotics. Expert opinion: Novel apparatus such as the iChip enabling the isolation of uncultable bacteria will undoubtedly increase the identification rate of novel antimicrobial peptides including lantibiotics. The ability to then assess the lantibiotic clusters via recombinant production or synthesis using a high throughput method is one of the next challenges for developing these agents into the clinical environment.

Using natural products for drug discovery: the impact of the genomics era

INTRODUCTION

Evolutionarily selected over billions of years for their interactions with biomolecules, natural products have been and continue to be a major source of pharmaceuticals. In the 1990s, pharmaceutical companies scaled down their natural product discovery programs in favor of synthetic chemical libraries due to major challenges such as high rediscovery rates, challenging isolation, and low production titers. Propelled by advances in DNA sequencing and synthetic biology technologies, insights into microbial secondary metabolism provided have inspired a number of strategies to address these challenges. Areas covered: This review highlights the importance of genomics and metagenomics in natural product discovery, and provides an overview of the technical and conceptual advances that offer unprecedented access to molecules encoded by biosynthetic gene clusters. Expert opinion: Genomics and metagenomics revealed nature's remarkable biosynthetic potential and her vast chemical inventory that we can now prioritize and systematically mine for novel chemical scaffolds with desirable bioactivities. Coupled with synthetic biology and genome engineering technologies, significant progress has been made in identifying and predicting the chemical output of biosynthetic gene clusters, as well as in optimizing cluster expression in native and heterologous host systems for the production of pharmaceutically relevant metabolites and their derivatives.

The sil Locus in Streptococcus Anginosus Group: Interspecies Competition and a Hotspot of Genetic Diversity

The Streptococcus Invasion Locus (Sil) was first described in Streptococcus pyogenes and Streptococcus pneumoniae, where it has been implicated in virulence. The two-component peptide signaling system consists of the SilA response regulator and SilB histidine kinase along with the SilCR signaling peptide and SilD/E export/processing proteins. The presence of an associated bacteriocin region suggests this system may play a role in competitive interactions with other microbes. Comparative analysis of 42 Streptococcus Anginosus/Milleri Group (SAG) genomes reveals this to be a hot spot for genomic variability. A cluster of bacteriocin/immunity genes is found adjacent to the sil system in most SAG isolates (typically 6–10 per strain). In addition, there were two distinct SilCR peptides identified in this group, denoted here as SilCR_SAG-A and SilCR_SAG-B, with corresponding alleles in silB. Our analysis of the 42 sil loci showed that SilCR_SAG-A is only found in Streptococcus intermedius while all three species can carry SilCR_SAG-B. In S. intermedius B196, a putative SilA operator is located upstream of bacteriocin gene clusters, implicating the sil system in regulation of microbe–microbe interactions at mucosal surfaces where the group resides. We demonstrate that S. intermedius B196 responds to its cognate SilCR_SAG-A, and, less effectively, to SilCR_SAG-B released by other Anginosus group members, to produce putative bacteriocins and inhibit the growth of a sensitive strain of S. constellatus.

Burkholderia paludis sp. nov., an Antibiotic-Siderophore Producing Novel Burkholderia cepacia Complex Species, Isolated from Malaysian Tropical Peat Swamp Soil

A novel Gram negative rod-shaped bacterium, designated strain MSh1T, was isolated from Southeast Pahang tropical peat swamp forest soil in Malaysia and characterized using a polyphasic taxonomy approach. The predominant cellular fatty acids (>10.0%) were C16:0 (31.7%), C17:0 cyclo (26.6%), and C19:0 cyclo ω8c (16.1%). The polar lipids detected were phosphatidylglycerol, phosphatidylethanolamine, and diphosphatidylglycerol. The predominant ubiquinone was Q-8. This revealed that strain MSh1T belongs to the genus Burkholderia. The type strain MSh1T can be differentiated from other Burkholderia cepacia complex (Bcc) species by phylogenetic analysis of 16S rRNA gene sequence, multilocus sequence analysis (MLSA), average nucleotide identity (ANI) and biochemical tests. DNA-DNA relatedness values between strain MSh1T and closely related type strains were below the 70% threshold value. Based on this polyphasic study of MSh1T, it can be concluded that this strain represents a novel species within the Bcc, for which the name Burkholderia paludis sp. nov. is proposed. The type strain is MSh1T (= DSM 100703T = MCCC 1K01245T). The dichloromethane extract of MSh1T exhibited antimicrobial activity against four Gram positive bacteria (Enterococcus faecalis ATCC 29212, E. faecalis ATCC 700802, Staphylococcus aureus ATCC 29213, S. aureus ATCC 700699) and a Gram negative bacteria (Escherichia coli ATCC 25922). Further purification work has led to the isolation of Compound 1, pyochelin. Pyochelin demonstrated antimicrobial activity against four S. aureus strains and three E. faecalis strains with MIC-values of 3.13 μg/ml and 6.26 μg/ml, respectively. SEM analysis showed that the cellular morphology of E. faecalis ATCC 700802 was not affected by pyochelin; suggesting that it might target the intracellular components. Pyochelin, a siderophore with antimicrobial activity might be useful in treating bacterial infections caused by S. aureus and E. faecalis, however further work has to be done.

Linking Biosynthetic Gene Clusters to their Metabolites via Pathway- Targeted Molecular Networking

The connection of microbial biosynthetic gene clusters to the small molecule metabolites they encode is central to the discovery and characterization of new metabolic pathways with ecological and pharmacological potential. With increasing microbial genome sequence information being deposited into publicly available databases, it is clear that microbes have the coding capacity for many more biologically active small molecules than previously realized. Of increasing interest are the small molecules encoded by the human microbiome, as these metabolites likely mediate a variety of currently uncharacterized human-microbe interactions that influence health and disease. In this mini-review, we describe the ongoing biosynthetic, structural, and functional characterizations of the genotoxic colibactin pathway in gut bacteria as a thematic example of linking biosynthetic gene clusters to their metabolites. We also highlight other natural products that are produced through analogous biosynthetic logic and comment on some current disconnects between bioinformatics predictions and experimental structural characterizations. Lastly, we describe the use of pathway-targeted molecular networking as a tool to characterize secondary metabolic pathways within complex metabolomes and to aid in downstream metabolite structural elucidation efforts.

One-pot synthesis of class II lanthipeptide bovicin HJ50 via an engineered lanthipeptide synthetase

Lanthipeptides are a large class of bacteria-produced, ribosomally-synthesized and post-translationally modified peptides. They are recognized as peptide antibiotics because most of them exhibit potent antimicrobial activities against Gram-positive bacteria especially those that are phylogenetically related to producers. Maturation of class II lanthipeptide like bovicin HJ50 undergoes precursor modification by LanM and a subsequent leader peptide cleavage by LanT. Herein, via co-expression of precursor gene bovA, modification gene bovM and transporter gene bovT in Escherichia coli C43 (DE3), bioactive bovicin HJ50 was successfully produced and secreted. To further achieve in vitro one-pot synthesis of bovicin HJ50, an engineered bovicin HJ50 synthetase BovT150M was obtained by fusing the peptidase domain of BovT (BovT150) to the N-terminus of BovM. BovT150M exhibited dual functions of precursor modification and leader peptide cleavage to release mature bovicin HJ50. Under the guidance of BovA leader peptide, BovT150M exhibited substrate tolerance to modify non-native substrates including suicin and lacticin 481. This work exemplifies the feasibility of enzyme chimera of peptidase domain (LanT150) and modification enzyme (LanM) as a one-pot lanthipeptide synthetase.

Synthetic Biology of Natural Products

The diversity and natural modularity of their biosynthetic pathways has turned natural products into attractive, but challenging, targets for synthetic biology approaches. Here, we discuss the current state of the field, highlighting recent advances and remaining bottlenecks. Global genomic assessments of natural product biosynthetic capacities across large parts of microbial diversity provide a first survey of the available natural parts libraries and identify evolutionary design rules for further engineering. Methods for compound and pathway detection and characterization are developed increasingly on the basis of synthetic biology tools, contributing to an accelerated translation of genomic information into usable building blocks for pathway assembly. A wide range of methods is also becoming available for accessing ever larger parts of chemical space by rational diversification of natural products, guided by rapid progress in our understanding of the underlying biochemistry and enzymatic mechanisms. Enhanced genome assembly and editing tools, adapted to the needs of natural products research, facilitate the realization of ambitious engineering strategies, ranging from combinatorial library generation to high-throughput optimization of product titers. Together, these tools and concepts contribute to the emergence of a new generation of revitalized natural product research.

Evolutionary architecture of the infant-adapted group of Bifidobacterium species associated with the probiotic function

Bifidobacteria, often associated with the gastrointestinal tract of animals, are well known for their roles as probiotics. Among the dozens of Bifidobacterium species, Bifidobacterium bifidum, B. breve, and B. longum are the ones most frequently isolated from the feces of infants and known to help the digestion of human milk oligosaccharides. To investigate the correlation between the metabolic properties of bifidobacteria and their phylogeny, we performed a phylogenomic analysis based on 452 core genes of forty-four completely sequenced Bifidobacterium species. Results show that a major evolutionary event leading to the clade of the infant-adapted species is linked to carbohydrate metabolism, but it is not the only factor responsible for the adaptation of bifidobacteria to the gut. The genome of B. longum subsp. infantis, a typical bifidobacterium in the gut of breast-fed infants, encodes proteins associated with several kinds of species-specific metabolic pathways, including urea metabolism and biosynthesis of riboflavin and lantibiotics. Our results demonstrate that these metabolic features, which are associated with the probiotic function of bifidobacteria, are species-specific and highly correlate with their phylogeny.

Antibiotic drug discovery

Due to the threat posed by the increase of highly resistant pathogenic bacteria, there is an urgent need for new antibiotics; all the more so since in the last 20 years, the approval for new antibacterial agents had decreased. The field of natural product discovery has undergone a tremendous development over the past few years. This has been the consequence of several new and revolutionizing drug discovery and development techniques, which is initiating a ‘New Age of Antibiotic Discovery’. In this review, we concentrate on the most significant discovery approaches during the last and present years and comment on the challenges facing the community in the coming years.

The secondary metabolite bioinformatics portal: Computational tools to facilitate synthetic biology of secondary metabolite production

Natural products are among the most important sources of lead molecules for drug discovery. With the development of affordable whole-genome sequencing technologies and other ‘omics tools, the field of natural products research is currently undergoing a shift in paradigms. While, for decades, mainly analytical and chemical methods gave access to this group of compounds, nowadays genomics-based methods offer complementary approaches to find, identify and characterize such molecules. This paradigm shift also resulted in a high demand for computational tools to assist researchers in their daily work. In this context, this review gives a summary of tools and databases that currently are available to mine, identify and characterize natural product biosynthesis pathways and their producers based on ‘omics data. A web portal called Secondary Metabolite Bioinformatics Portal (SMBP at http://www.secondarymetabolites.org) is introduced to provide a one-stop catalog and links to these bioinformatics resources. In addition, an outlook is presented how the existing tools and those to be developed will influence synthetic biology approaches in the natural products field.

Dereplication, sequencing and identification of peptidic natural products: from genome mining to peptidogenomics to spectral networks

Covering: 2000 to 2015. While recent breakthroughs in the discovery of peptide antibiotics and other Peptidic Natural Products (PNPs) raise a challenge for developing new algorithms for their analyses, the computational technologies for high-throughput PNP discovery are still lacking. We discuss the computational bottlenecks in analyzing PNPs and review recent advances in genome mining, peptidogenomics, and spectral networks that are now enabling the discovery of new PNPs via mass spectrometry. We further describe the connections between these advances and the new generation of software tools for PNP dereplication, de novo sequencing, and identification.

Streptocollin, a Type IV Lanthipeptide Produced by Streptomyces collinus Tü 365

Lanthipeptides are ribosomally synthesized and post-translationally modified microbial secondary metabolites. Here, we report the identification and isolation of streptocollin from Streptomyces collinus Tü 365, a new member of class IV lanthipeptides. Insertion of the constitutive ermE* promoter upstream of the lanthipeptide synthetase gene stcL resulted in peptide production. The streptocollin gene cluster was heterologously expressed in S. coelicolor M1146 and M1152 with 3.5- and 5.5-fold increased yields, respectively. The structure and ring topology of streptocollin were determined by high resolution MS/MS analysis. Streptocollin contains four macrocyclic rings, with one lanthionine and three methyllanthionine residues. To the best of our knowledge, this is the first report on the isolation of a class IV lanthipeptide in preparative amounts, and on the successful heterologous expression of a class IV lanthipeptide gene cluster.

Computational approaches to natural product discovery

Starting with the earliest Streptomyces genome sequences, the promise of natural product genome mining has been captivating: genomics and bioinformatics would transform compound discovery from an ad hoc pursuit to a high-throughput endeavor. Until recently, however, genome mining has advanced natural product discovery only modestly. Here, we argue that the development of algorithms to mine the continuously increasing amounts of (meta)genomic data will enable the promise of genome mining to be realized. We review computational strategies that have been developed to identify biosynthetic gene clusters in genome sequences and predict the chemical structures of their products. We then discuss networking strategies that can systematize large volumes of genetic and chemical data and connect genomic information to metabolomic and phenotypic data. Finally, we provide a vision of what natural product discovery might look like in the future, specifically considering longstanding questions in microbial ecology regarding the roles of metabolites in interspecies interactions.

Distribution and Genetic Diversity of Bacteriocin Gene Clusters in Rumen Microbial Genomes

Some species of ruminal bacteria are known to produce antimicrobial peptides, but the screening procedures have mostly been based on in vitro assays using standardized methods. Recent sequencing efforts have made available the genome sequences of hundreds of ruminal microorganisms. In this work, we performed genome mining of the complete and partial genome sequences of 224 ruminal bacteria and 5 ruminal archaea to determine the distribution and diversity of bacteriocin gene clusters. A total of 46 bacteriocin gene clusters were identified in 33 strains of ruminal bacteria. Twenty gene clusters were related to lanthipeptide biosynthesis, while 11 gene clusters were associated with sactipeptide production, 7 gene clusters were associated with class II bacteriocin production, and 8 gene clusters were associated with class III bacteriocin production. The frequency of strains whose genomes encode putative antimicrobial peptide precursors was 14.4%. Clusters related to the production of sactipeptides were identified for the first time among ruminal bacteria. BLAST analysis indicated that the majority of the gene clusters (88%) encoding putative lanthipeptides contained all the essential genes required for lanthipeptide biosynthesis. Most strains of Streptococcus (66.6%) harbored complete lanthipeptide gene clusters, in addition to an open reading frame encoding a putative class II bacteriocin. Albusin B-like proteins were found in 100% of the Ruminococcus albus strains screened in this study. The in silico analysis provided evidence of novel biosynthetic gene clusters in bacterial species not previously related to bacteriocin production, suggesting that the rumen microbiota represents an underexplored source of antimicrobial peptides.

Discovery of microbial natural products by activation of silent biosynthetic gene clusters

Microorganisms produce a wealth of structurally diverse specialized metabolites with a remarkable range of biological activities and a wide variety of applications in medicine and agriculture, such as the treatment of infectious diseases and cancer, and the prevention of crop damage. Genomics has revealed that many microorganisms have far greater potential to produce specialized metabolites than was thought from classic bioactivity screens; however, realizing this potential has been hampered by the fact that many specialized metabolite biosynthetic gene clusters (BGCs) are not expressed in laboratory cultures. In this Review, we discuss the strategies that have been developed in bacteria and fungi to identify and induce the expression of such silent BGCs, and we briefly summarize methods for the isolation and structural characterization of their metabolic products.

(Post-)genomics approaches in fungal research

To date, hundreds of fungal genomes have been sequenced and many more are in progress. This wealth of genomic information has provided new directions to study fungal biodiversity. However, to further dissect and understand the complicated biological mechanisms involved in fungal life styles, functional studies beyond genomes are required. Thanks to the developments of current -omics techniques, it is possible to produce large amounts of fungal functional data in a high-throughput fashion (e.g. transcriptome, proteome, etc.). The increasing ease of creating -omics data has also created a major challenge for downstream data handling and analysis. Numerous databases, tools and software have been created to meet this challenge. Facing such a richness of techniques and information, hereby we provide a brief roadmap on current wet-lab and bioinformatics approaches to study functional genomics in fungi.