Don D. Nguyen

MS/MS networking guided analysis of molecule and gene cluster families

Significance The paper introduces the concepts of molecular families (MFs) and gene cluster families (GCFs). We define MFs as structurally related molecules based on their mass spectral fragmentation patterns, whereas GCFs are biosynthetic gene clusters that show similar gene cluster organization with a high degree of sequence similarity. We use MS/MS networking as a tool to map the molecular network of more than 60 organisms, most of which are unsequenced, and locate their nonribosomal peptide MFs. These MFs from unsequenced organisms are then connected to GCFs of publicly available genome sequences of closely related organisms. The ability to correlate the production of specialized metabolites to the genetic capacity of the organism that produces such molecules has become an invaluable tool in aiding the discovery of biotechnologically applicable molecules. Here, we accomplish this task by matching molecular families with gene cluster families, making these correlations to 60 microbes at one time instead of connecting one molecule to one organism at a time, such as how it is traditionally done. We can correlate these families through the use of nanospray desorption electrospray ionization MS/MS, an ambient pressure MS technique, in conjunction with MS/MS networking and peptidogenomics. We matched the molecular families of peptide natural products produced by 42 bacilli and 18 pseudomonads through the generation of amino acid sequence tags from MS/MS data of specific clusters found in the MS/MS network. These sequence tags were then linked to biosynthetic gene clusters in publicly accessible genomes, providing us with the ability to link particular molecules with the genes that produced them. As an example of its use, this approach was applied to two unsequenced Pseudoalteromonas species, leading to the discovery of the gene cluster for a molecular family, the bromoalterochromides, in the previously sequenced strain P. piscicida JCM 20779T. The approach itself is not limited to 60 related strains, because spectral networking can be readily adopted to look at molecular family–gene cluster families of hundreds or more diverse organisms in one single MS/MS network.

Pep2Path: automated mass spectrometry-guided genome mining of peptidic natural products

Nonribosomally and ribosomally synthesized bioactive peptides constitute a source of molecules of great biomedical importance, including antibiotics such as penicillin, immunosuppressants such as cyclosporine, and cytostatics such as bleomycin. Recently, an innovative mass-spectrometry-based strategy, peptidogenomics, has been pioneered to effectively mine microbial strains for novel peptidic metabolites. Even though mass-spectrometric peptide detection can be performed quite fast, true high-throughput natural product discovery approaches have still been limited by the inability to rapidly match the identified tandem mass spectra to the gene clusters responsible for the biosynthesis of the corresponding compounds. With Pep2Path, we introduce a software package to fully automate the peptidogenomics approach through the rapid Bayesian probabilistic matching of mass spectra to their corresponding biosynthetic gene clusters. Detailed benchmarking of the method shows that the approach is powerful enough to correctly identify gene clusters even in data sets that consist of hundreds of genomes, which also makes it possible to match compounds from unsequenced organisms to closely related biosynthetic gene clusters in other genomes. Applying Pep2Path to a data set of compounds without known biosynthesis routes, we were able to identify candidate gene clusters for the biosynthesis of five important compounds. Notably, one of these clusters was detected in a genome from a different subphylum of Proteobacteria than that in which the molecule had first been identified. All in all, our approach paves the way towards high-throughput discovery of novel peptidic natural products. Pep2Path is freely available from http://pep2path.sourceforge.net/, implemented in Python, licensed under the GNU General Public License v3 and supported on MS Windows, Linux and Mac OS X.

Indexing the Pseudomonas specialized metabolome enabled the discovery of poaeamide B and the bananamides

Pseudomonads are cosmopolitan microorganisms able to produce a wide array of specialized metabolites. These molecules allow Pseudomonas to scavenge nutrients, sense population density and enhance or inhibit growth of competing microorganisms. However, these valuable metabolites are typically characterized one-molecule–one-microbe at a time, instead of being inventoried in large numbers. To index and map the diversity of molecules detected from these organisms, 260 strains of ecologically diverse origins were subjected to mass-spectrometry-based molecular networking. Molecular networking not only enables dereplication of molecules, but also sheds light on their structural relationships. Moreover, it accelerates the discovery of new molecules. Here, by indexing the Pseudomonas specialized metabolome, we report the molecular-networking-based discovery of four molecules and their evolutionary relationships: a poaeamide analogue and a molecular subfamily of cyclic lipopeptides, bananamides 1, 2 and 3. Analysis of their biosynthetic gene cluster shows that it constitutes a distinct evolutionary branch of the Pseudomonas cyclic lipopeptides. Through analysis of an additional 370 extracts of wheat-associated Pseudomonas, we demonstrate how the detailed knowledge from our reference index can be efficiently propagated to annotate complex metabolomic data from other studies, akin to the way in which newly generated genomic information can be compared to data from public databases.

Meta-omics uncover temporal regulation of pathways across oral microbiome genera during in vitro sugar metabolism

Dental caries, one of the most globally widespread infectious diseases, is intimately linked to pH dynamics. In supragingival plaque, after the addition of a carbohydrate source, bacterial metabolism decreases the pH which then subsequently recovers. Molecular mechanisms supporting this important homeostasis are poorly characterized in part due to the fact that there are hundreds of active species in dental plaque. Only a few mechanisms (for example, lactate fermentation, the arginine deiminase system) have been identified and studied in detail. Here, we conducted what is to our knowledge, the first full transcriptome and metabolome analysis of a diverse oral plaque community by using a functionally and taxonomically robust in vitro model system greater than 100 species. Differential gene expression analyses from the complete transcriptome of 14 key community members revealed highly varied regulation of both known and previously unassociated pH-neutralizing pathways as a response to the pH drop. Unique expression and metabolite signatures from 400 detected metabolites were found for each stage along the pH curve suggesting it may be possible to define healthy and diseased states of activity. Importantly, for the maintenance of healthy plaque pH, gene transcription activity of known and previously unrecognized pH-neutralizing pathways was associated with the genera Lactobacillus, Veillonella and Streptococcus during the pH recovery phase. Our in vitro study provides a baseline for defining healthy and disease-like states and highlights the power of moving beyond single and dual species applications to capture key players and their orchestrated metabolic activities within a complex human oral microbiome model.

Microbiota of Healthy Corals Are Active against Fungi in a Light-Dependent Manner

Coral reefs are intricate ecosystems that harbor diverse organisms, including 25% of all marine fish. Healthy corals exhibit a complex symbiosis between coral polyps, endosymbiotic alga, and an array of microorganisms, called the coral holobiont. Secretion of specialized metabolites by coral microbiota is thought to contribute to the defense of this sessile organism against harmful biotic and abiotic factors. While few causative agents of coral diseases have been unequivocally identified, fungi have been implicated in the massive destruction of some soft corals worldwide. Because corals are nocturnal feeders, they may be more vulnerable to fungal infection at night, and we hypothesized that the coral microbiota would have the capability to enhance their defenses against fungi in the dark. A Pseudoalteromonas sp. isolated from a healthy octocoral displayed light-dependent antifungal properties when grown adjacent to Penicilliumcitrinum (P. citrinum) isolated from a diseased Gorgonian octocoral. Microbial MALDI-imaging mass spectrometry (IMS) coupled with molecular network analyses revealed that Pseudoalteromonas produced higher levels of antifungal polyketide alteramides in the dark than in the light. The alteramides were inactivated by light through a photoinduced intramolecular cyclization. Further NMR studies led to a revision of the stereochemical structure of the alteramides. Alteramide A exhibited antifungal properties and elicited changes in fungal metabolite distributions of mycotoxin citrinin and citrinadins. These data support the hypothesis that coral microbiota use abiotic factors such as light to regulate the production of metabolites with specialized functions to combat opportunistic pathogens at night.

An Integrated Metabolomic and Genomic Mining Workflow To Uncover the Biosynthetic Potential of Bacteria

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. ABSTRACT 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.

Plasticity of streptomyces coelicolor membrane composition under different growth conditions and during development

Streptomyces coelicolor is a model actinomycete that is well known for the diversity of its secondary metabolism and its complex life cycle. As a soil inhabitant, it is exposed to heterogeneous and frequently changing environmental circumstances. In the present work, we studied the effect of diverse growth conditions and phosphate depletion on its lipid profile and the relationship between membrane lipid composition and development in S. coelicolor. The lipid profile from cultures grown on solid media, which is closer to the natural habitat of this microorganism, does not resemble the previously reported lipid composition from liquid grown cultures of S. coelicolor. Wide variations were also observed across different media, growth phases, and developmental stages indicating active membrane remodeling. Ornithine lipids (OL) are phosphorus-free polar lipids that were accumulated mainly during sporulation stages, but were also major components of the membrane under phosphorus limitation. In contrast, phosphatidylethanolamine, which had been reported as one of the major polar lipids in the genus Streptomyces, is almost absent under these conditions. We identified one of the genes responsible for the synthesis of OL (SCO0921) and found that its inactivation causes the absence of OL, precocious morphological development and actinorhodin production. Our observations indicate a remarkable plasticity of the membrane composition in this bacterial species, reveal a higher metabolic complexity than expected, and suggest a relationship between cytoplasmic membrane components and the differentiation programs in S. coelicolor.

Analysis of the Ketosynthase-Chain Length Factor Heterodimer from the Fredericamycin Polyketide Synthase

The pentadecaketide fredericamycin has the longest carbon chain backbone among polycyclic aromatic polyketide antibiotics whose biosynthetic genes have been sequenced. This backbone is synthesized by the bimodular fdm polyketide synthase (PKS). Here, we demonstrate that the bimodular fdm PKS as well as its elongation module alone synthesize undecaketides and dodecaketides. Thus, unlike other homologs, the fdm ketosynthase-chain length factor (KS-CLF) heterodimer does not exclusively control the backbone length of its natural product. Using sequence- and structure-based approaches, 48 CLF multiple mutants were engineered and analyzed. Unexpectedly, the I134F mutant was unable to turn over but could initiate and partially elongate the polyketide chain. This unprecedented mutant suggests that the KS-CLF heterodimer harbors an as yet uncharacterized chain termination mechanism. Together, our findings reveal fundamental mechanistic differences between the fdm PKS and its well-studied homologs.

Discovery of tanshinone derivatives with anti-MRSA activity via targeted bio-transformation

Two potent anti-MRSA tanshinone glycosides (1 and 2) were discovered by targeted microbial biotransformation, along with rapid identification via MS/MS networking. Serial reactions including dehydrogenation, demethylations, reduction, glycosylation and methylation have been observed after incubation of tanshinone IIA and fungus Mucor rouxianus AS 3.3447. In addition, tanshinosides B (2) showed potent activities against serial clinical isolates of oxacillin-resistant Staphylococcus aureus with MIC values of 0.78 μg/mL. This is the first study that shows a significant increase in the level and activities of tanshinone glycosides relative to the substrate tanshinone IIA.