Phylogenetics is the study of the evolutionary relatedness among groups of organisms. Molecular phylogenetics uses sequence data to infer these relationships for both organisms and the genes they maintain. With the large amount of publicly available sequence data, phylogenetic inference has become increasingly important in all fields of biology. In the case of natural product research, phylogenetic relationships are proving to be highly informative in terms of delineating the architecture and function of the genes involved in secondary metabolite biosynthesis. Polyketide synthases and nonribosomal peptide synthetases provide model examples in which individual domain phylogenies display different predictive capacities, resolving features ranging from substrate specificity to structural motifs associated with the final metabolic product. This chapter provides examples in which phylogeny has proven effective in terms of predicting functional or structural aspects of secondary metabolism. The basics of how to build a reliable phylogenetic tree are explained along with information about programs and tools that can be used for this purpose. Furthermore, it introduces the Natural Product Domain Seeker, a recently developed Web tool that employs phylogenetic logic to classify ketosynthase and condensation domains based on established enzyme architecture and biochemical function.

Access to genome sequence data has challenged traditional natural product discovery paradigms by revealing that the products of most bacterial biosynthetic pathways have yet to be discovered. Despite the insight afforded by this technology, little is known about the diversity and distributions of natural product biosynthetic pathways among bacteria and how they evolve to generate structural diversity. Here we analyze genome sequence data derived from 75 strains of the marine actinomycete genus Salinispora for pathways associated with polyketide and nonribosomal peptide biosynthesis, the products of which account for some of today's most important medicines. The results reveal high levels of diversity, with a total of 124 pathways identified and 229 predicted with continued sequencing. Recent horizontal gene transfer accounts for the majority of pathways, which occur in only one or two strains. Acquired pathways are incorporated into genomic islands and are commonly exchanged within and between species. Acquisition and transfer events largely involve complete pathways, which subsequently evolve by gene gain, loss, and duplication followed by divergence. The exchange of similar pathway types at the precise chromosomal locations in different strains suggests that the mechanisms of integration include pathway-level homologous recombination. Despite extensive horizontal gene transfer there is clear evidence of species-level vertical inheritance, supporting the concept that secondary metabolites represent functional traits that help define Salinispora species. The plasticity of the Salinispora secondary metabolome provides an effective mechanism to maximize population-level secondary metabolite diversity while limiting the number of pathways maintained within any individual genome.

Genome sequencing is rapidly changing the field of natural products research by providing opportunities to assess the biosynthetic potential of strains prior to chemical analysis or biological testing. Ready access to sequence data is driving the development of new bioinformatic tools and methods to identify the products of silent or cryptic pathways. While genome mining has fast become a useful approach to natural product discovery, it has also become clear that identifying pathways of interest is much easier than finding the associated products. This has led to bottlenecks in the discovery process that must be overcome for the potential of genomics-based natural product discovery to be fully realized. In this perspective, we address some of these challenges in the context of our work with the marine actinomycete genus Salinispora, which is proving to be a useful model with which to apply genome mining as an approach to natural product discovery.

New bioinformatic tools are needed to analyze the growing volume of DNA sequence data. This is especially true in the case of secondary metabolite biosynthesis, where the highly repetitive nature of the associated genes creates major challenges for accurate sequence assembly and analysis. Here we introduce the web tool Natural Product Domain Seeker (NaPDoS), which provides an automated method to assess the secondary metabolite biosynthetic gene diversity and novelty of strains or environments. NaPDoS analyses are based on the phylogenetic relationships of sequence tags derived from polyketide synthase (PKS) and non-ribosomal peptide synthetase (NRPS) genes, respectively. The sequence tags correspond to PKS-derived ketosynthase domains and NRPS-derived condensation domains and are compared to an internal database of experimentally characterized biosynthetic genes. NaPDoS provides a rapid mechanism to extract and classify ketosynthase and condensation domains from PCR products, genomes, and metagenomic datasets. Close database matches provide a mechanism to infer the generalized structures of secondary metabolites while new phylogenetic lineages provide targets for the discovery of new enzyme architectures or mechanisms of secondary metabolite assembly. Here we outline the main features of NaPDoS and test it on four draft genome sequences and two metagenomic datasets. The results provide a rapid method to assess secondary metabolite biosynthetic gene diversity and richness in organisms or environments and a mechanism to identify genes that may be associated with uncharacterized biochemistry.

Genome sequencing has revealed that bacteria contain many more biosynthetic gene clusters than predicted based on the number of secondary metabolites discovered to date. While this biosynthetic reservoir has fostered interest in new tools for natural product discovery, there remains a gap between gene cluster detection and compound discovery. Here we apply molecular networking and the new concept of pattern-based genome mining to 35 Salinispora strains, including 30 for which draft genome sequences were either available or obtained for this study. The results provide a method to simultaneously compare large numbers of complex microbial extracts, which facilitated the identification of media components, known compounds and their derivatives, and new compounds that could be prioritized for structure elucidation. These efforts revealed considerable metabolite diversity and led to several molecular family-gene cluster pairings, of which the quinomycin-type depsipeptide retimycin A was characterized and linked to gene cluster NRPS40 using pattern-based bioinformatic approaches.

Comparative genomics is providing new opportunities to address the diversity and distributions of genes encoding the biosynthesis of specialized metabolites. An analysis of 119 genome sequences representing three closely related species of the marine actinomycete genus Salinispora reveals extraordinary biosynthetic diversity in the form of 176 distinct biosynthetic gene clusters (BGCs) of which only 24 have been linked to their products. Remarkably, more than half of the BGCs were observed in only one or two strains, suggesting they were acquired relatively recently in the evolutionary history of the genus. These acquired gene clusters are concentrated in specific genomic islands, which represent hot spots for BGC acquisition. While most BGCs are stable in terms of their chromosomal position, others migrated to different locations or were exchanged with unrelated gene clusters suggesting a plug and play type model of evolution that provides a mechanism to test the relative fitness effects of specialized metabolites. Transcriptome analyses were used to address the relationships between BGC abundance, chromosomal position and product discovery. The results indicate that recently acquired BGCs can be functional and that complex evolutionary processes shape the micro-diversity of specialized metabolism observed in closely related environmental bacteria.

Bacterial genomes are rife with orphan biosynthetic gene clusters (BGCs) associated with secondary metabolism of unrealized natural product molecules. Often up to a tenth of the genome is predicted to code for the biosynthesis of diverse metabolites with mostly unknown structures and functions. This phenomenal diversity of BGCs coupled with their high rates of horizontal transfer raise questions about whether they are really active and beneficial, whether they are neutral and confer no advantage, or whether they are carried in genomes because they are parasitic or addictive. We previously reported that Salinispora bacteria broadly use the desferrioxamine family of siderophores for iron acquisition. Herein we describe a new and unrelated group of peptidic siderophores called salinichelins from a restricted number of Salinispora strains in which the desferrioxamine biosynthesis genes have been lost. We have reconstructed the evolutionary history of these two different siderophore families and show that the acquisition and retention of the new salinichelin siderophores co-occurs with the loss of the more ancient desferrioxamine pathway. This identical event occurred at least three times independently during the evolution of the genus. We surmise that certain BGCs may be extraneous because of their functional redundancy and demonstrate that the relative evolutionary pace of natural pathway replacement shows high selective pressure against retention of functionally superfluous gene clusters.

The Natural Product Domain Seeker (NaPDoS) webtool detects and classifies ketosynthase (KS) and condensation domains from genomic, metagenomic, and amplicon sequence data. Unlike other tools, a phylogeny-based classification scheme is used to make broader predictions about the polyketide synthase (PKS) and nonribosomal peptide synthetase (NRPS) genes in which these domains are found. NaPDoS is particularly useful for the analysis of incomplete biosynthetic genes or gene clusters, as are often observed in poorly assembled genomes and metagenomes, or when loci are not clustered, as in eukaryotic genomes. To help support the growing interest in sequence-based analyses of natural product biosynthetic diversity, here we introduce version 2 of the webtool, NaPDoS2, available at http://napdos.ucsd.edu/napdos2. This update includes the addition of 1417 KS sequences, representing a major expansion of the taxonomic and functional diversity represented in the webtool database. The phylogeny-based KS classification scheme now recognizes 41 class and subclass assignments, including new type II PKS subclasses. Workflow modifications accelerate run times, allowing larger datasets to be analyzed. In addition, default parameters were established using statistical validation tests to maximize KS detection and classification accuracy while minimizing false positives. We further demonstrate the applications of NaPDoS2 to assess PKS biosynthetic potential using genomic, metagenomic, and PCR amplicon datasets. These examples illustrate how NaPDoS2 can be used to predict biosynthetic potential and detect genes involved in the biosynthesis of specific structure classes or new biosynthetic mechanisms.

Using automated genome analysis tools, it is often unclear to what degree genetic variability in homologous biosynthetic pathways relates to structural variation. This hampers strain prioritization and compound identification and can lead to overinterpretation of chemical diversity. Here, we assessed the metabolic potential of Nocardia, an underinvestigated actinobacterial genus that is known to comprise opportunistic human pathogens. Our analysis revealed a plethora of putative biosynthetic gene clusters of various classes, including polyketide, nonribosomal peptide, and terpenoid pathways. Furthermore, we used the highly conserved biosynthetic pathway for nocobactin-like siderophores to investigate how gene cluster differences correlate to structural differences in the produced compounds. Sequence similarity networks generated by BiG-SCAPE (Biosynthetic Gene Similarity Clustering and Prospecting Engine) showed the presence of several distinct gene cluster families. Metabolic profiling of selected Nocardia strains using liquid chromatography-mass spectrometry (LC-MS) metabolomics data, nuclear magnetic resonance (NMR) spectroscopy, and GNPS (Global Natural Product Social molecular networking) revealed that nocobactin-like biosynthetic gene cluster (BGC) families above a BiG-SCAPE threshold of 70% can be assigned to distinct structural types of nocobactin-like siderophores.IMPORTANCE Our work emphasizes that Nocardia represent a prolific source for natural products rivaling better-characterized genera such as Streptomyces or Amycolatopsis Furthermore, we showed that large-scale analysis of biosynthetic gene clusters using similarity networks with high stringency allows the distinction and prediction of natural product structural variations. This will facilitate future genomics-driven drug discovery campaigns.

Microorganisms are a rich source of bioactives; however, chemical identification is a major bottleneck. Strategies that can prioritize the most prolific microbial strains and novel compounds are of great interest. Here, we present an integrated approach to evaluate the biosynthetic richness in bacteria and mine the associated chemical diversity. Thirteen strains closely related to Pseudoalteromonas luteoviolacea isolated from all over the Earth were analyzed using an untargeted metabolomics strategy, and metabolomic profiles were correlated with whole-genome sequences of the strains. We found considerable diversity: only 2% of the chemical features and 7% of the biosynthetic genes were common to all strains, while 30% of all features and 24% of the genes were unique to single strains. The list of chemical features was reduced to 50 discriminating features using a genetic algorithm and support vector machines. Features were dereplicated by tandem mass spectrometry (MS/MS) networking to identify molecular families of the same biosynthetic origin, and the associated pathways were probed using comparative genomics. Most of the discriminating features were related to antibacterial compounds, including the thiomarinols that were reported from P. luteoviolacea here for the first time. By comparative genomics, we identified the biosynthetic cluster responsible for the production of the antibiotic indolmycin, which could not be predicted with standard methods. In conclusion, we present an efficient, integrative strategy for elucidating the chemical richness of a given set of bacteria and link the chemistry to biosynthetic genes. IMPORTANCE We here combine chemical analysis and genomics to probe for new bioactive secondary metabolites based on their pattern of distribution within bacterial species. We demonstrate the usefulness of this combined approach in a group of marine Gram-negative bacteria closely related to Pseudoalteromonas luteoviolacea, which is a species known to produce a broad spectrum of chemicals. The approach allowed us to identify new antibiotics and their associated biosynthetic pathways. Combining chemical analysis and genetics is an efficient "mining" workflow for identifying diverse pharmaceutical candidates in a broad range of microorganisms and therefore of great use in bioprospecting.