Author response: Digitizing mass spectrometry data to explore the chemical diversity and distribution of marine cyanobacteria and algae (2017)

Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Natural product screening programs have uncovered molecules from diverse natural sources with various biological activities and unique structures. However, much is yet underexplored and additional information is hidden in these exceptional collections. We applied untargeted mass spectrometry approaches to capture the chemical space and dispersal patterns of metabolites from an in-house library of marine cyanobacterial and algal collections. Remarkably, 86% of the metabolomics signals detected were not found in other available datasets of similar nature, supporting the hypothesis that marine cyanobacteria and algae possess distinctive metabolomes. The data were plotted onto a world map representing eight major sampling sites, and revealed potential geographic locations with high chemical diversity. We demonstrate the use of these inventories as a tool to explore the diversity and distribution of natural products. Finally, we utilized this tool to guide the isolation of a new cyclic lipopeptide, yuvalamide A, from a marine cyanobacterium. https://doi.org/10.7554/eLife.24214.001 eLife digest Cyanobacteria and algae are found in all oceans around the globe. Like plants, they can use sunlight as a source of energy in a process called photosynthesis. As a result, these organisms are important sources of oxygen and another vital nutrient called nitrogen for other marine organisms. Many of these organisms also produce a variety of other chemicals known as “natural products” to help them to survive in their environments. Some of these natural products have shown potential as medicinal drugs. The search for new chemicals with useful medicinal properties has led researchers to collect samples of algae and cyanobacteria from various locations around the world. An approach called mass spectrometry is often used to identify new chemicals because it can provide information about the structure of a molecule based on how much its fragments weigh. Luzzatto-Knaan et al. used mass spectrometry to search for new chemicals in samples of algae and cyanobacteria that had been collected by diving and snorkeling in a wide variety of tropical marine environments over several decades. The experiments reveal that the organisms in these samples produce a diverse range of chemicals, most of which were previously unknown and have not been found in other similar environmental collections. The data were grouped together into eight major collection areas covering different parts of the tropics. The samples from some areas contained a wider variety of chemicals than others. Within each collection area, some molecules were found to be very common whereas others were only present at specific locations. To highlight the distribution of these natural products, Luzzatto-Knaan et al. display the data on a world map. Further experiments used this approach as a guide to extract a previously unknown chemical called yuvalamide A from a marine cyanobacterium. The next challenge would be to associate the geographical patterns of chemicals to their potential ecological roles. This approach offers a new way to explore large-scale collections of environmental samples to discover and study new natural products. https://doi.org/10.7554/eLife.24214.002 Introduction The marine environment is extraordinarily rich in both biological and chemical diversity. It is estimated that nearly 90% of all organisms living on earth inhabit marine and coastal environments (Blunt et al., 2012; Kiuru et al., 2014). Moreover, marine organisms are recognized as an extremely rich source of novel chemical entities that have potential utility in medicine, personal health care, cosmetics, biotechnology and agriculture (Gerwick and Moore, 2012; Kiuru et al., 2014; La Barre, 2014; Rastogi and Sinha, 2009). Marine cyanobacteria (‘blue-green algae’) are considered the most ancient group of oxygenic photosynthetic organisms, having appeared on earth some three billion years ago. It is thought that their extensive evolutionary history explains, at least in part, the extensive nature of their structurally-unique and biologically-active natural products (Burja et al., 2001; Chlipala et al., 2011; Schopf, 2012; Whitton and Potts, 2012). Indeed, since the 1970s, several hundred unique chemical entities have been discovered from marine cyanobacteria, and these have been found to possess a remarkable spectrum of biological activities including anticancer, anti-inflammatory, antibacterial, anti-parasitic, neuromodulatory, and antiviral, among others (Blunt et al., 2015; Nunnery et al., 2010; Tan, 2007; Villa and Gerwick, 2010). Examples of marine cyanobacterial natural products that are under investigation for anticancer applications include the cyclodepsipeptides apratoxin A and F, the nitrogen-containing lipid curacin A and the lipopeptides dolastatin 10 and carmaphycin B (Bai et al., 1990; Blokhin et al., 1995; Luesch et al., 2001a, 2001b; Pereira et al., 2012; Tidgewell et al., 2010). These natural product templates have inspired the synthesis of lead compounds using medicinal chemistry approaches, and have resulted in one clinically approved drug (Brentuximab vedotin) for the treatment of cancer; several others are in various stages of clinical and preclinical evaluation (Gerwick and Moore, 2012; Luesch et al., 2001a; 2001b; Tan et al., 2010; Tidgewell et al., 2010; Uzair et al., 2012). Unfortunately, such fruitful natural product materials, rich with so much unexplored potential, oftentimes disappear upon completion of academic careers and closing of laboratories. In this study, we used a mass spectrometry based approach to digitize (convert to data format that can be stored, shared and analyzed by computational tools) the chemical inventory of an established marine cyanobacteria and algae collection in order to better evaluate its diversity and probe for novel natural products. Additionally, while a number of biologically potent natural products have been isolated from marine cyanobacteria and algae, the overall molecular profile of these organisms has not been compared to other classes of bacteria or terrestrial/freshwater cyanobacteria (Chlipala et al., 2011). To address this, we analyzed the crude extracts of a relatively large number of algae and cyanobacteria, as well as their derived chromatographic fractions, by high resolution liquid chromatography tandem mass spectrometry (HR-LC-MS/MS). We then compared this metabolomics dataset to four existing public datasets [Mass spectrometry Interactive Virtual Environment (MassIVE) repository] within the Global Natural Product Social (GNPS) platform (Wang et al., 2016). The marine samples used in this study were derived from over 300 field collections from locations in the Indian, Indo-Pacific, Central and South Pacific, and Western Atlantic (Caribbean) oceans with initial field taxonomic identifications as predominantly cyanobacteria and macro-algae. It should be noted that these are environmental samples, and therefore are inherently communities of organisms and are not single axenic cultures. Additionally, three samples were from non-axenic laboratory cultures of the cyanobacteria Phormidium (No.1646) and Lyngbya (No. 1933, 1963); the latter two were recently classified as Moorea (Supplementary file 1) (Engene et al., 2012). From both a chemical and biosynthetic perspective, these three cultures represent the most intensively studied organisms in this dataset (Engene et al., 2012; Kleigrewe et al., 2015; Mevers et al., 2014; Pereira et al., 2010; Williamson et al., 2002). While these and other of our cultured filamentous cyanobacteria cannot yet be grown axenically, their MS-derived molecular data represent marine cyanobacterial metabolomics markers that aid in data analysis. Furthermore, in exploring the chemical diversity of marine cyanobacterial and algal assemblages, we established a cartographic platform that combines LC-MS data and geographic locations that facilitates the discovery of new chemical scaffolds as well as identifies geographical areas of high chemical diversity (Boeuf and Kornprobst, 2009). The discovery of such ‘hotspots’ may reveal new patterns of phylogenetic diversity as well as identify geographical areas with enhanced bioprospecting opportunities. Assessment of the chemical diversity of these marine cyanobacterial and algal collections involved four major steps: (1) Collection - including permits, field collection, transport, extraction, fractionation and metadata recording; (2) Data acquisition and digitization in public repository - by LC-HRMS/MS to generate molecular fingerprints; (3) Data analysis and visualization - clustering similarly structured compounds as molecular families within the GNPS platform, identification of known molecules and assessing the richness and diversity between as well as within samples; (4) Discovery - identification of geographical patterns of distribution, differentiating common from regiospecific natural products, dereplication of new derivatives and the discovery of previously uncharacterized natural products. Results Over the past 30 years, a significant number of cyanobacterial and algal collections were obtained under the appropriate governmental permits using scuba diving and snorkeling in the Caribbean (Puerto Rico, Grenada, Panama), Central and South Pacific (Hawaii, Palmyra Atoll, French Polynesia, Fiji), Indo-Pacific (Indonesia, Papua New Guinea), and Indian Oceans (Madagascar, South Africa). These collections represent natural assemblages of benthic filamentous marine cyanobacteria and various classes of macro-algae (Rhodophyta, Chlorophyta and Phaeophyceae). Each collection was extracted (CH2Cl2/MeOH, 2:1) and fractionated using a standardized vacuum liquid chromatography protocol (Supplementary file 1). Approximately 2600 fractions originating from 317 marine collections, including the unfractionated crude samples, were analyzed by reversed phase ultra-performance liquid chromatography (RP-UPLC) coupled with high resolution quadrupole time-of-flight mass spectrometry (HR-qTOF-MS) to obtain retention times and MS and tandem MS/MS fragmentation spectra (LC-MS/MS). Nearly 6000 spectra were collected for each LC-MS/MS run, generating in excess of 15.6 million spectra for the samples in this study. These data were analyzed using the GNPS platform enabling the extensive organization of the LC-MS/MS data (Wang et al., 2016). GNPS detected features (i.e. molecules) based on MS/MS spectra and MS intensities. Further, some of these MS/MS spectra were identified by matching to spectral libraries available on GNPS. Even in the absence of MS/MS spectral matching to known reference MS/MS spectra, GNPS molecular networking can associate structurally related molecules that exhibit similar MS/MS fragmentation patterns into molecular families (Watrous et al., 2012; Yang et al., 2013). Comparative metabolomics and chemical diversity of large scale datasets Few tools allow assessment of the chemical diversity within large and diverse MS datasets such as those in the current study (Bouslimani et al., 2014; Charlop-Powers et al., 2015; Luzzatto-Knaan et al., 2015; Purves et al., 2016). To determine whether this collection of marine cyanobacterial and algal communities possessed unique chemical diversity, the LC-MS/MS data were analyzed with publicly available data sets accessed via the GNPS-MassIVE database (For datasets see methods) (Wang et al., 2016). These datasets include LC-MS/MS data from terrestrial and marine actinobacteria (1000 samples), lichens (132 samples), marine sponges and corals (260 samples) and freshwater cyanobacteria (535 samples). Collection of all datasets was acquired using tandem mass spectrometry to obtain unique fragmentation fingerprints for each detected molecule. To evaluate the chemical diversity of these source materials, we employed diverse MS-based informatic approaches, including multivariate analyses and molecular networking. Multivariate approaches, such as principal component analysis (PCA) and partial least squares-discriminant analysis, (PLS-DA), have been widely employed to assess the chemical diversity of metabolomics data (Worley and Powers, 2013). Here, for comprehensive metabolomics analysis, we applied principal coordinate analysis (PCoA), a statistical method that provides an overview of the dissimilarities between samples based on the metabolome of an individual sample relative to all other samples in the analysis (He et al., 2015) The relatedness between samples is calculated by the dissimilarity distance matrix and displayed in a three-dimensional plot where each sphere represents a sample with specific MS/MS features. We utilized the Bray-Curtis distance matrix applied to all MS/MS features, as exported from GNPS, using the QIIME platform (Caporaso et al., 2010). The separation of the samples in PCoA space highlights the differences between the various origins of the samples (Figure 1A). The lichen samples separate from the marine associated samples, although they overlap the spatial distributions of some cyanobacteria populations, perhaps because most lichens contain cyanobacteria, bacteria and fungi (Mushegian et al., 2011). Interestingly, among the five environmental samples, the marine cyanobacteria/algae and freshwater cyanobacteria are more similar in their metabolomics profiles than any other of the source materials, even though the two aquatic environments are quite distinct (Figure 1A). Nevertheless, while presenting the highest similarity compared to the other datasets, the marine cyanobacteria/algae and freshwater cyanobacteria samples show a clear differentiation in their chemical inventories (Figure 1B). To quantify the common and differential chemical features driving these distances, we generated a Venn diagram using the MS/MS features. (Figure 1C,). This analysis revealed that only 13.7% of the marine cyanobacteria/algae molecular features overlapped with other datasets, highlighting that 86.3% were features unique to these collections. Among the molecular commonalities, a number of the mass spectrometry signals were identified as matches to reference spectra in GNPS. These included a mixture of primary metabolites such as amino acids, fatty acids, structural lipids, pheophytin and pheophorbide A (chlorophyll derived products), as well as common mass spectrometry background signals such as formate clusters, plasticizers and polymers. Remarkably, despite being most similar by PCoA analysis, no reference MS/MS spectra of the known freshwater cyanobacterial natural products matched with our marine cyanobacteria and algal collection dataset. Figure 1 Download asset Open asset MS/MS features as generated in GNPS are shown for marine cyanobacteria and algae (green), marine and terrestrial actinobacteria (blue), lichens (red), freshwater cyanobacteria (yellow) and corals (pink). (A) PCoA plots of 300 samples randomly selected from each dataset display the distance between samples based on molecular features using Bray-Curtis dissimilarity matrix. (B) Marine cyanobacteria and algae and freshwater cyanobacteria. Laboratory cultures of Phormidium 1646, Lyngbya 1933 are highlighted in red circles. Each sphere represents the full sample metabolome (C) Venn diagram display of overlapping MS/MS features. Percentage of overlapping features with the marine cyanobacteria and algae dataset are given in parenthesis in their respective colors (Supplementary file 1). https://doi.org/10.7554/eLife.24214.003 In exploring the chemical diversity of the marine cyanobacteria and algae communities, we hypothesized that either the organism collected (cyanobacterial or algal) or the geographical location was responsible for sample-to-sample metabolomics differences (Figure 2). Our PCoA analysis did not indicate differentiation based on field annotations as algal versus cyanobacterial (Figure 2A), and the non-significant clustering based on geographical origin, highlights the vast chemical diversity of samples within the same region (Figure 2B). PCA, the most common dimensionality reduction and visualization method used for metabolomics analysis, displayed a parallel outcome with minor trends being observed (Supplementary file 1, Figure 2—figure supplement 1). Figure 2 with 1 supplement see all Download asset Open asset MS/MS feature diversity between and within sampling sites. PCoA plots of crude extract molecular features using Bray-Curtis, dissimilarity matrix. Each point represents a single sample and points are colored by metadata. (A) PCoA plot shows the distance between samples based on field identified classification. (B) PCoAs color coded by geographical origin shows the distance of all locations together (B: upper panel) and the individual PCoA plots of the four collection sites with most samples (B: lower panel). Laboratory cultures of Phormidium 1646, Lyngbya 1933 and Lyngbya 1963 are highlighted in orange circles. https://doi.org/10.7554/eLife.24214.004 Construction of fraction libraries has become a common strategy to make more efficient the discovery of bioactive molecules in natural products studies so as to reduce sample complexity for biological screening as well as increase the titer of minor constituents (Bugni et al., 2008; Harvey et al., 2015). In the current analysis, the fraction library yielded over five times the number of molecular features compared to the crude extracts, thereby enabling a more thorough assessment of chemical richness and diversity (Figure 3A). The fraction library resulted in 1094 molecular families compared to 132 in the crude extract analysis. This tenfold difference highlights the advantage of using fraction libraries for chemical and biological assay screens. By comparing the number of features as well as the number of underlying spectra for a given molecular family (Nguyen et al., 2013), we could identify particular chemical scaffolds that are more diverse in their structural variations and are more abundant in our collections. For example, the barbamide molecular family (Figure 3B) is comprised of 40 molecular features represented in 985 individual spectra and found in 75 collections. By contrast, the palmyrolide A molecular family has only two associated features and consists of 32 spectra that are found in five collections (Figure 3B). Using a rarefaction analysis, both the crude extracts and the fraction library were found to level off under the current experimental conditions of analysis, indicating that the sample collections are saturating the chemical diversity of these materials (Figure 3A). Figure 3 with 3 supplements see all Download asset Open asset Chemodiversity and richness of molecular features based on MS/MS data. (A) Rarefaction curve of cyanobacteria collection library showing the chemical richness of crude extracts vs. the fraction library. (B) Abundance of molecular families: Bars represent the number of features and spectra comprising each molecular family clustered by GNPS molecular networking. (C) Bar chart depicting both the total number of extracts from a given location and the percent contribution of unique features to the entire dataset. (D) Pie chart representing the percentage of unique molecular features attributed to origin of the sample. HI-Hawaii, NAC-Curacao, PAB-Panama Bocas del Toro, PAC-Panama Coiba, PAG-Panama Gulf of Chiriqui, PAP-Panama Portobelo, PAL-Palmyra Atoll, PNG-Papua New Guinea. https://doi.org/10.7554/eLife.24214.006 Mapping the distribution of marine natural products To identify locations of high chemical diversity among sampling sites represented in this study (locations with more unique natural products compared to other regions), we compared the MS/MS features found only in a single location to the overall diversity of the entire dataset (Figure 3C and D). To do this, we normalized the contribution of features from a single location to a percentage contribution as some locations were sampled more frequently than others based on metadata information collected across three decades (Figure 3C). For example, approximately 4% of all MS/MS features contributing to the overall diversity are from Panama-Bocas del Toro (PAB) whereas about 7–8% are each contributed from Panama-Portobelo (PAP) and Palmyra Atoll (PAL), and 20% are from Papua New Guinea (PNG) (Figure 3D). To explore the chemical nature of differentially produced metabolites in the marine cyanobacterial and algal sample collections, GNPS based molecular networking was used and the network was visualized in Cytoscape (For molecular network and Cytoscape file see Materials and methods, Figure 3—figure supplement 1) (Purves et al., 2016; Shannon et al., 2003; Watrous et al., 2012). As MS/MS spectral alignment detects molecular features, a set of structurally related features connected by edges is termed a ‘molecular family’. Experimental MS/MS spectra were dereplicated (also described as ‘finding known unknowns’ by the metabolomics community) against the community contributed mass spectral ‘GNPS library’ and third party spectral libraries including MassBank, HMDB, ReSpect and NIST 2014 housed within GNPS analysis et al., 2010; and 2015; et al., 2012; et al., 2016; et al., 2013). This is the molecular network generated and only the GNPS (Wang et al., 2016). From the more than 15.6 million spectra MS/MS spectra were dereplicated to existing publicly reference with of the matches to previously cyanobacterial and algal derived natural products from 30 molecular families (Nguyen et al., 2013). The spectral library matches from the marine cyanobacteria/algae collection matches to the GNPS matches to the compounds 3 matches to the natural product 1 to the clinical collection, 1 to the natural products library and matches to the library. This is a of about which is much lower than database matches of the untargeted metabolomics experiments et al., and metabolomics analyses of samples such as or et al., 2015). The to existing natural product libraries within GNPS highlights that these marine communities are underexplored from a chemical The of molecular families from these samples that they contain a significant number of natural products that networking structural a molecular family because most molecules of similar structure By selected molecular we identified previously metabolites and previously derivatives (Supplementary file 2 and Figure 3—figure supplements 2 and For example, we identified the spectral representing the apratoxin molecular family by a spectral to the apratoxin B library Further dereplication of this molecular family using information and analysis of MS/MS spectra revealed several known apratoxin derivatives F, (Figure 3—figure supplement et al., Tidgewell et al., 2010; Watrous et al., 2012; Yang et al., 2013). apratoxin derivatives are on the of mass differences of common (Figure 3—figure supplement 2). Further, GNPS identified a molecular family comprised of the and Gerwick, Within this molecular we were to several derivatives based on fragmentation patterns and of (Figure 3—figure supplement 1 and Figure 3—figure supplement 3 and Gerwick, Yang et al., 2013). For example, a from the of barbamide most from of the to barbamide (Figure 3—figure supplement 3 barbamide was previously and is by the of from the MS/MS the three et al., 2012). The reduction from the to be associated with the of the group by a for that in the of a has previously been observed in other biosynthetic (Figure 3—figure supplement 3 et al., This of amino to a new molecular it is of related such as which was isolated from the and 2011). a Figure of molecules detected in this dataset were found in more than one sampling we the spatial distribution and of specific natural products using For this the of a was to the eight collection sites. In the each represents a distribution of where the and color to the relative number of features that the same distribution (Figure the regiospecific patterns on the eight collection some molecules similar distribution while some distribution patterns are or very minor in their colored To the distribution of molecules identified via molecular in our eight major sampling sites, MS/MS feature were onto a geographical map based on using the tool tool for and data visualization by our et al., available as a a is at of Figure for more For example, of the detected features, dolastatin 10 with other chemical features the same distribution while A and other chemical features are to at least within the of this data and are not found in any of the other sampling locations. Additionally, while barbamide is widely are relatively (Figure and Figure 3—figure supplement Figure Download asset Open asset showing the geographic distribution of selected cyanobacterial natural products. (A) were by their distribution patterns across the eight geographical each represented by a with edges by one of features in each is to and color scale from to red as by the scale on the patterns are represented for selected natural products within these in each map display of and Papua New Guinea. Each sample is to a specific coordinate based on of the collection samples are represented by around the collection the (B) display distribution and of barbamide (1) and two barbamide is by color scale from to high HI-Hawaii, PAB-Panama Bocas del Toro, PAC-Panama Coiba, PAG-Panama Gulf of Chiriqui, PAP-Panama Portobelo, PAL-Palmyra Atoll, PNG-Papua New Guinea. use the source tool the in the data is and then on the Mapping and the scale to and map to For to different on the of the molecule shown the and another molecule either with a or with can to the and cyanobacteria natural products. any Discovery of new natural products To the of MS-based geographic network analyses for the discovery of uncharacterized natural products, we several chemical features from locations of high chemical diversity, the and sites. sample was from and for the isolation of an molecule from a with no described family (For molecular network see Materials and methods, molecular family This specific molecular feature was observed in several cyanobacterial collections of Lyngbya from obtained in