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.

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.

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

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

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

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 infrastructure (Horai et al., 2010; Johnson and Lange, 2015; Sawada et al., 2012; Wang et al., 2016; Wishart et al., 2013). This is currently the largest molecular network generated and only possible through the GNPS interface (Wang et al., 2016). From the more than 15.6 million spectra collected, 6249 MS/MS spectra were dereplicated to existing publicly accessible reference data, with 91 of the matches to previously characterized cyanobacterial and algal derived natural products from 30 molecular families (Nguyen et al., 2013). The spectral library matches from the marine cyanobacteria/algae collection included: 74 matches to the GNPS library, 7 matches to the NIH pharmacologically active compounds library, 3 matches to the NIH natural product library, 1 match to the NIH clinical collection, 1 match to the FDA natural products library and 5 matches to the Faulkner legacy library. This is a match rate of about 0.04% which is much lower than database matches of the average untargeted metabolomics experiments (1.8%, [Wang et al., 2016]) and metabolomics analyses of model samples such as E. coli, human cell lines, mice or humans (da Silva et al., 2015). The low match rate to existing natural product libraries within GNPS highlights that these marine cyanobacterial/algal communities are significantly underexplored from a chemical perspective.

The hundreds of unannotated molecular families from these samples suggest that they contain a significant number of natural products that remain uncharacterized. Molecular networking allows putative structural propagation through a molecular family because most molecules of similar structure fragment similarly. By investigating selected molecular families, we identified previously characterized metabolites and previously undescribed derivatives (Supplementary file 2 and Figure 3—figure supplements 2 and 3). For example, we identified the spectral cluster representing the apratoxin molecular family by a spectral match to the apratoxin B library standard. Further dereplication of this molecular family using literature information and manual analysis of MS/MS spectra revealed several known apratoxin derivatives (A, D, F, G, H) (Figure 3—figure supplement 2) (Luesch et al., 2002; Tidgewell et al., 2010; Watrous et al., 2012; Yang et al., 2013). Additional apratoxin derivatives are suggested on the basis of mass differences of common functional groups (Figure 3—figure supplement 2). Further, GNPS identified a molecular family comprised of the barbamides (Orjala and Gerwick, 1996). Within this molecular family, we were able to deduce several candidate derivatives based on fragmentation patterns and presence of chlorine isotopes (Figure 3—figure supplement 1 and Figure 3—figure supplement 3 [1]) (Orjala and Gerwick, 1996; Yang et al., 2013). For example, a 14 Da decrease from the y ion of barbamide most likely results from loss of the amide methyl group, leading to N-desmethyl barbamide (Figure 3—figure supplement 3 [2]). O-desmethyl barbamide was previously published and is identifiable by the loss of 14 Da from the characteristic MS/MS fragment containing the three chlorine atoms (m/z 242.98) (Kim et al., 2012). The 34 Da reduction from the y ion appears to be associated with the substitution of the Phe group by Leu, a conservative hydrophobic replacement for A-domains that accept Phe in the NRPS-based biosynthesis of barbamide. Such a conservative substitution has previously been observed in other NRPS biosynthetic systems (Figure 3—figure supplement 3 [3]) (Flatt et al., 2006). This exchange of amino acids gives rise to a new molecular entity; however, it is reminiscent of related compounds, such as dysidin, which was isolated from the cyanobacteria-harboring sponge Dysidea herbacea (Ilardi and Zakarian, 2011).

Because a majority (55%, Figure 3D) of molecules detected in this dataset were found in more than one sampling location, we explored the spatial distribution and abundance of specific natural products using binning networks. For this visualization, the abundance/presence of a metabolite was correlated to the eight collection sites. In the network, each node represents a distribution pattern of molecules, where the node size and color correspond to the relative number of features that share the same distribution (Figure 4). Of the 256 conceivable regiospecific patterns (based on the eight collection sites) some molecules share similar distribution patterns, while some distribution patterns are absent or very minor in their abundance (light colored nodes). To visualize the distribution of select molecules identified via molecular networking, in our eight major sampling sites, MS/MS feature intensity values were projected onto a 2D geographical map based on GPS coordinates using the open-source tool ‘ili (an open-source tool for 2D and 3D data visualization created by our laboratories, https://github.com/ili-toolbox/ili [Protsyuk et al., 2017], available as a Google Chrome app; a copy is archived at https://github.com/elifesciences-publications/ili) (see legend of Figure 4 for more information). For example, out of the 33,481 detected features, dolastatin 10 along with 72 other chemical features share the same widespread distribution pattern (Curaçao (NAC), PAB, Panama-Coiba (PAC), PAL, PAP, PNG), while palmyramide A and 3033 other chemical features are endemic to PAL, at least within the scope of this data set, and are not found in any of the other sampling locations. Additionally, while barbamide is widely distributed, putative analogs are encountered relatively infrequently (Figure 4B and Figure 3—figure supplement 3).

Figure 4

Download asset Open asset

Discovery of new natural products

To emphasize the application of MS-based geographic network analyses for the discovery of uncharacterized natural products, we examined several chemical features from locations of high chemical diversity, namely the PAP, PAL and PNG sites. One sample was chosen from PAP and targeted for the isolation of an m/z 536.333 molecule from a cluster with no described family members (For molecular network see Materials and methods, molecular family #483). This specific molecular feature was observed in several cyanobacterial collections of Lyngbya from PAP obtained in different years from 2007 to 2013 (collections in Dec 2007, Sept 2010 and Jan 2013). The MS/MS data indicated that this compound was peptidic and contained the amino acids glycine, valine (or N-methyl glycine), leucine or isoleucine, and 2-hydroxyvaleric acid (Hiv). The presence of this latter residue suggested it was the product of a non-ribosomal peptide synthetase (NRPS) (Kehr et al., 2011). An additional residue was observed corresponding to the fragment mass m/z 149.094, compatible with the formula C₁₀H₁₃O⁺. Further analysis of the sequential fragment losses suggested a putative structure of [Gly-Ile/Leu-Hiv-Val-C₁₀H₁₃O]⁺. A search for a putative peptide with a molecular mass m/z 536.333 in various chemical databases (Blunt et al., 2015, Blunt et al., 2012; Luzzatto-Knaan et al., 2015; http://pubs.rsc.org/marinlit/) was unsuccessful, suggesting that this natural product was uncharacterized.

In order to confirm the mass spectrometry-based partial structural assignment, we isolated and structurally characterized this natural product of mass m/z 536.333 (Figure 5, Figure 5—figure supplements 1 and 2, Supplementary file 3 [4]). Isolation of compound 4, given the common name of ‘yuvalamide A’, was guided by LC-MS analyses. NMR analysis (DMSO-d₆) of the pure compound confirmed the presence of Gly, Val, Ile, and hydroxyisovaleric acid, and revealed the presence of 2,2-dimethyl-3-hydroxy-7-octynoic (Dhoya) as the C₁₀H₁₃O⁺ fragment (Figure 5, Figure 5—figure supplements 1 and 2, Supplementary file 3). These NMR-based determinations correlate well with the structural predictions obtained by mass spectrometry.

Figure 5 with 3 supplements see all

Download asset Open asset

The other chemical entities in the yuvalamide A molecular family were present at very low abundance, preventing their isolation and structural analysis by NMR. However, based on the structure of yuvalamide A and its fragmentation pattern by LC-ESI-MS/MS, we deduced putative structures of these co-metabolites. An analog of yuvalamide A (Figure 5—figure supplement 3 compound 5, yuvalamide B, [M+H]⁺ m/z 538.3487, [M+Na]⁺ m/z 560.3320) was detected by HRMS. Its fragmentation was nearly identical to compound 4, differing only in the fragment belonging to Dhoya, suggesting this to be the alkene analog 2,2-dimethyl-3-hydroxy-7-octenoic acid (Dhoea). Co-occurring analogs possessing the Dhoya or Dhoea unit have been observed previously in marine cyanobacterial natural extracts. The Dhoya subunit is usually the more abundant of the two, as observed in this case for yuvalamide A (Boudreau et al., 2012; Han et al., 2005, 2011; Luesch et al., 2001; Sitachitta et al., 2000; Wan and Erickson, 2001). Moreover, the geographical distribution of yuvalamide B is the same as yuvalamide A (Figure 5), supporting the proposal that these share a common biosynthetic origin. Two additional yuvalamide analogs were observed and tentative structures were assigned based on MS (Figure 5—figure supplement 3).

Over the last 40 years, a number of academic natural product discovery programs have generated unique and significant collections from a variety of sources. However, the samples as well as the associated collection and characterization information is often buried in individual laboratory materials and documentation, and frequently this disappears with academic turnovers. To explore a new paradigm for characterizing and preserving such information over a longer time period for the community at large, we harnessed recent advances and metabolomics approaches to deeply annotate the metabolomes of these collections. We then evaluated this rich dataset along with collection information for its diversity, distribution and discovery of new natural products.

To date, biodiversity studies have mostly relied on available genomic information (Bowen et al., 2013; Passarini et al., 2015). While organisms might be phylogenetically related, their metabolomic repertoire may vary greatly. The detection of biosynthetic gene clusters indicates natural product potential, and has been successfully employed to predict new molecules, especially of the core structure (Medema and Fischbach, 2015; Medema et al., 2015; Owen et al., 2015; Walsh, 2015). However, post assembly modifications (e.g. halogenations, oxidations, methylations) are often times difficult to predict without the structures in hand, and therefore genomic analyses do not provide a complete assessment of the secondary metabolomes. Chemical diversity represents the expression of that genetic potential and offers a new strategy to explore differences and to evaluate the diversity encrypted in complex samples and various environments. Herein, we broadly inventoried the molecules that are present in a large library of field collected marine algae and cyanobacteria using LC-MS/MS. Using the mass spectrometry detectable natural products from each sample, the chemodiversity and the regional patterns of these samples were characterized.

It is reported in the literature that marine cyanobacteria are an exceptional source for diverse new natural products (Blunt et al., 2015; Burja et al., 2001; Engene et al., 2011; Kiuru et al., 2014; Tan, 2007). However, this perception is difficult to assess quantitatively in comparison to other marine or terrestrial life forms. To gain insights into how unique are the secondary metabolites of marine cyanobacteria and algae, we compared these MS data with other metabolomic data sets that are available from the public domain.

The mass spectrometry data from marine cyanobacteria/algae collections obtained over the past 30 years indicate that, at a global level, this group is rich in metabolite features that do not overlap with other available metabolomic data sets (Figure 1C). Common multivariate analyses used for metabolomics were successful in distinguishing the different datasets based on their unique metabolomic inventories.

Key to a chemical pattern analysis is the ability to organize molecules on the basis of structural features along with their associated metadata attributes. GNPS molecular networking generates molecular features that are based upon fragmentation similarity, thereby providing resolution at the individual molecular level. This approach also enables the rapid matching of spectra to known molecules by comparison with various natural product MS/MS libraries (Nguyen et al., 2016; Wang et al., 2016; Yang et al., 2013). In this regard, the MS/MS spectrum of a molecule is a unique molecular signature or barcode. When structurally similar molecules are fragmented, the resulting MS/MS spectra are very often quite similar because they contain similarly positioned labile bonds (Wang et al., 2016). Using molecular networking, a visual representation of these ‘barcodes’ is produced by grouping similar patterns. Molecular networking enables the evaluation of the MS/MS based detectable chemical space, and groups molecules that have similar biosynthetic origins and resultant structures. At the organism level (e.g. invertebrates, plants, corals), marine environments such as the Papua New Guinea archipelago (PNG) are considered to be biodiversity ‘hotspots’ (Bowen et al., 2013). However, not only is PNG a biodiversity hotspot, molecular networking quantitatively revealed that this region is rich in the presence of unique natural products compared to other sampled locations in this study (Figure 3) (Bowen et al., 2013; La Barre, 2014). Although this is demonstrated here for these marine algal and cyanobacterial collections, it would be interesting to evaluate if high biodiversity always correlates with high chemical diversity, and as a corollary, how chemical diversity changes upon a shift in biodiversity. The geographical patterns of molecule distribution could be further investigated via the tools presented here, given the appropriate sampling strategy. While molecular networking revealed the locations with lowest and highest chemical diversity, visualizing these data on a world map enabled assessment of the distribution patterns of specific molecules within the eight sampled regions. This analysis also enabled targeting samples with higher abundance of molecules of interest, and may therefore assist in characterizing exceptionally high-producers of natural products. We have demonstrated that some chemical entities were found in common to multiple locations (e.g. barbamide, dolastatin 10) whereas others appeared to be unique and endemic to specific sampling sites (e.g. hoiamide B, palmyramide) (Figure 4). This observation is supported by site resampling over several years, an insight gained as a distinctive attribute of this unique collection of marine samples.

Additional tracing of the metadata associated with a given molecular family can assist in identification of the producing organism, and facilitate future biosynthetic and ecological investigations (Coates et al., 2014; Harvey et al., 2015; Kleigrewe et al., 2015; Leal et al., 2016; Owen et al., 2013). Finally, we demonstrated the utility of this overall approach through the discovery and subsequent structural elucidation and geographical distribution of yuvalamide A (4), reported here for the first time.

1. 1. Tal Luzzatto-Knaan

   (2015) 150717_Marine_lichen_actino_freshW_coral

   Publicly available at Global Natural Products Social Molecular Networking website (http://gnps.ucsd.edu/).

   http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=e69c25e3fbcf4c09b6e0c75633565f95
2. 1. Tal Luzzatto-Knaan

   (2014) 141021_BigData_06_MCS3_MMP4_p31

   Publicly available at Global Natural Products Social Molecular Networking website (http://gnps.ucsd.edu/).

   http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=0837888b3dab43efa5bf9a50254c7c8f
3. 1. Tal Luzzatto-Knaan
   2. Neha Garg
   3. Mingxun Wang
   4. Evgenia Glukhov
   5. Yao Peng
   6. Gail Ackermann
   7. Amnon Amir
   8. Brendan M Duggan
   9. Sergey Ryazanov
   10. Lena Gerwick
   11. Rob Knight
   12. Theodore Alexandrov
   13. Nuno Bandeira
   14. William H Gerwick
   15. Pieter C Dorrestein

   (2017) GNPS_Cyanobacterial_collection_Plate31

   Publicly available via the Mass spectrometry Interactive Virtual Environment (MassIVE) (accession no: MSV000078892).

   ftp://massive.ucsd.edu/MSV000078892
4. 1. Tal Luzzatto-Knaan
   2. Neha Garg
   3. Mingxun Wang
   4. Evgenia Glukhov
   5. Yao Peng
   6. Gail Ackermann
   7. Amnon Amir
   8. Brendan M Duggan
   9. Sergey Ryazanov
   10. Lena Gerwick
   11. Rob Knight
   12. Theodore Alexandrov
   13. Nuno Bandeira
   14. William H Gerwick
   15. Pieter C Dorrestein

   (2016) GNPS_Dorrestein_Gerwick_Cyanobacteria

   Publicly available via the Mass spectrometry Interactive Virtual Environment (MassIVE) (accession no: MSV000078568).

   ftp://massive.ucsd.edu/MSV000078568/other/

1. 1. Bai R
   2. Pettit GR
   3. Hamel E

   (1990) Dolastatin 10, a powerful cytostatic peptide derived from a marine animal. inhibition of tubulin polymerization mediated through the Vinca alkaloid binding domain

   Biochemical Pharmacology 39:1941–1949.

   https://doi.org/10.1016/0006-2952(90)90613-P

   • PubMed
   • Google Scholar
2. 1. Blokhin AV
   2. Yoo HD
   3. Geralds RS
   4. Nagle DG
   5. Gerwick WH
   6. Hamel E

   (1995)

   Characterization of the interaction of the marine cyanobacterial natural product curacin A with the colchicine site of tubulin and initial structure-activity studies with analogues

   Molecular Pharmacology 48:523–531.

   • PubMed
   • Google Scholar
3. 1. Blunt JW
   2. Copp BR
   3. Keyzers RA
   4. Munro MH
   5. Prinsep MR

   (2012) Marine natural products

   Natural Product Reports 29:144–222.

   https://doi.org/10.1039/C2NP00090C

   • PubMed
   • Google Scholar
4. 1. Blunt JW
   2. Copp BR
   3. Keyzers RA
   4. Munro MH
   5. Prinsep MR

   (2015) Marine natural products

   Natural Product Reports 32:116–211.

   https://doi.org/10.1039/C4NP00144C

   • PubMed
   • Google Scholar
5. 1. Boeuf G
   2. Kornprobst JM

   (2009)

   Biodiversity and marine chemodiversity

   Biofutur pp. 28–32.

   • Google Scholar
6. 1. Boudreau PD
   2. Byrum T
   3. Liu WT
   4. Dorrestein PC
   5. Gerwick WH

   (2012) Viequeamide A, a cytotoxic member of the kulolide superfamily of cyclic depsipeptides from a marine button Cyanobacterium

   Journal of Natural Products 75:1560–1570.

   https://doi.org/10.1021/np300321b

   • PubMed
   • Google Scholar
7. 1. Bouslimani A
   2. Sanchez LM
   3. Garg N
   4. Dorrestein PC

   (2014) Mass spectrometry of natural products: current, emerging and future technologies

   Natural Product Reports 31:718.

   https://doi.org/10.1039/c4np00044g

   • PubMed
   • Google Scholar
8. 1. Bowen BW
   2. Rocha LA
   3. Toonen RJ
   4. Karl SA
   5. Lab T
   6. ToBo Laboratory

   (2013) The origins of tropical marine biodiversity

   Trends in Ecology & Evolution 28:359–366.

   https://doi.org/10.1016/j.tree.2013.01.018

   • PubMed
   • Google Scholar
9. 1. Bugni TS
   2. Richards B
   3. Bhoite L
   4. Cimbora D
   5. Harper MK
   6. Ireland CM

   (2008) Marine natural product libraries for high-throughput screening and rapid drug discovery

   Journal of Natural Products 71:1095–1098.

   https://doi.org/10.1021/np800184g

   • PubMed
   • Google Scholar
10. 1. Burja AM
    2. Banaigs B
    3. Abou-Mansour E
    4. Grant Burgess J
    5. Wright PC

    (2001) Marine cyanobacteria—a prolific source of natural products

    Tetrahedron 57:9347–9377.

    https://doi.org/10.1016/S0040-4020(01)00931-0

    • Google Scholar
11. 1. Caporaso JG
    2. Kuczynski J
    3. Stombaugh J
    4. Bittinger K
    5. Bushman FD
    6. Costello EK
    7. Fierer N
    8. Peña AG
    9. Goodrich JK
    10. Gordon JI
    11. Huttley GA
    12. Kelley ST
    13. Knights D
    14. Koenig JE
    15. Ley RE
    16. Lozupone CA
    17. McDonald D
    18. Muegge BD
    19. Pirrung M
    20. Reeder J
    21. Sevinsky JR
    22. Turnbaugh PJ
    23. Walters WA
    24. Widmann J
    25. Yatsunenko T
    26. Zaneveld J
    27. Knight R

    (2010) QIIME allows analysis of high-throughput community sequencing data

    Nature Methods 7:335–336.

    https://doi.org/10.1038/nmeth.f.303

    • PubMed
    • Google Scholar
12. 1. Charlop-Powers Z
    2. Owen JG
    3. Reddy BV
    4. Ternei MA
    5. Guimarães DO
    6. de Frias UA
    7. Pupo MT
    8. Seepe P
    9. Feng Z
    10. Brady SF

    (2015) Global biogeographic sampling of bacterial secondary metabolism

    eLife 4:e05048.

    https://doi.org/10.7554/eLife.05048

    • PubMed
    • Google Scholar
13. 1. Chlipala GE
    2. Mo S
    3. Orjala J

    (2011) Chemodiversity in freshwater and terrestrial cyanobacteria - a source for drug discovery

    Current Drug Targets 12:1654–1673.

    https://doi.org/10.2174/138945011798109455

    • PubMed
    • Google Scholar
14. 1. Coates RC
    2. Podell S
    3. Korobeynikov A
    4. Lapidus A
    5. Pevzner P
    6. Sherman DH
    7. Allen EE
    8. Gerwick L
    9. Gerwick WH

    (2014) Characterization of cyanobacterial hydrocarbon composition and distribution of biosynthetic pathways

    PLoS One 9:e85140.

    https://doi.org/10.1371/journal.pone.0085140

    • PubMed
    • Google Scholar
15. 1. Crawford JM
    2. Portmann C
    3. Kontnik R
    4. Walsh CT
    5. Clardy J

    (2011) NRPS substrate promiscuity diversifies the xenematides

    Organic Letters 13:5144–5147.

    https://doi.org/10.1021/ol2020237

    • PubMed
    • Google Scholar
16. 1. da Silva RR
    2. Dorrestein PC
    3. Quinn RA

    (2015) Illuminating the dark matter in metabolomics

    PNAS 112:12549–12550.

    https://doi.org/10.1073/pnas.1516878112

    • PubMed
    • Google Scholar
17. 1. Engene N
    2. Choi H
    3. Esquenazi E
    4. Rottacker EC
    5. Ellisman MH
    6. Dorrestein PC
    7. Gerwick WH

    (2011) Underestimated biodiversity as a Major explanation for the perceived rich secondary metabolite capacity of the cyanobacterial genus Lyngbya

    Environmental Microbiology 13:1601–1610.

    https://doi.org/10.1111/j.1462-2920.2011.02472.x

    • PubMed
    • Google Scholar
18. 1. Engene N
    2. Rottacker EC
    3. Kaštovský J
    4. Byrum T
    5. Choi H
    6. Ellisman MH
    7. Komárek J
    8. Gerwick WH

    (2012) Moorea producens gen. nov., sp. nov. and Moorea bouillonii comb. nov., tropical marine cyanobacteria rich in bioactive secondary metabolites

    International Journal of Systematic and Evolutionary Microbiology 62:1171–1178.

    https://doi.org/10.1099/ijs.0.033761-0

    • PubMed
    • Google Scholar
19. 1. Flatt PM
    2. O'Connell SJ
    3. McPhail KL
    4. Zeller G
    5. Willis CL
    6. Sherman DH
    7. Gerwick WH

    (2006) Characterization of the initial enzymatic steps of barbamide biosynthesis

    Journal of Natural Products 69:938–944.

    https://doi.org/10.1021/np050523q

    • PubMed
    • Google Scholar
20. 1. Garg N
    2. Kapono C
    3. Lim YW
    4. Koyama N
    5. Vermeij MJ
    6. Conrad D
    7. Rohwer F
    8. Dorrestein PC

    (2015) Mass spectral similarity for untargeted metabolomics data analysis of complex mixtures

    International Journal of Mass Spectrometry 377:719-717.

    https://doi.org/10.1016/j.ijms.2014.06.005

    • PubMed
    • Google Scholar
21. 1. Gerwick WH
    2. Moore BS

    (2012) Lessons from the past and charting the future of marine natural products drug discovery and chemical biology

    Chemistry & Biology 19:85–98.

    https://doi.org/10.1016/j.chembiol.2011.12.014

    • PubMed
    • Google Scholar
22. Conference

    1. Hachul S
    2. Junger M

    (2004) Drawing large graphs with a potential-field-based multilevel algorithm

    Proceedings of the 12th International Conference. pp. 285–295.

    https://doi.org/10.1007/978-3-540-31843-9_29

    • Google Scholar
23. 1. Han B
    2. Goeger D
    3. Maier CS
    4. Gerwick WH

    (2005) The wewakpeptins, cyclic depsipeptides from a Papua New Guinea collection of the marine Cyanobacterium lyngbya semiplena

    The Journal of Organic Chemistry 70:3133–3139.

    https://doi.org/10.1021/jo0478858

    • PubMed
    • Google Scholar
24. 1. Han B
    2. Gross H
    3. McPhail KL
    4. Goeger D
    5. Maier CS
    6. Gerwick WH

    (2011) Wewakamide A and guineamide G, cyclic depsipeptides from the marine cyanobacteria Lyngbya semiplena and Lyngbya majuscula

    Journal of Microbiology and Biotechnology 21:930–936.

    https://doi.org/10.4014/jmb.1105.05011

    • PubMed
    • Google Scholar
25. 1. Harvey AL
    2. Edrada-Ebel R
    3. Quinn RJ

    (2015) The re-emergence of natural products for drug discovery in the genomics era

    Nature Reviews Drug Discovery 14:111–129.

    https://doi.org/10.1038/nrd4510

    • PubMed
    • Google Scholar
26. 1. He Y
    2. Caporaso JG
    3. Jiang XT
    4. Sheng HF
    5. Huse SM
    6. Rideout JR
    7. Edgar RC
    8. Kopylova E
    9. Walters WA
    10. Knight R
    11. Zhou HW

    (2015) Stability of operational taxonomic units: an important but neglected property for analyzing microbial diversity

    Microbiome 3:20.

    https://doi.org/10.1186/s40168-015-0081-x

    • PubMed
    • Google Scholar
27. 1. Horai H
    2. Arita M
    3. Kanaya S
    4. Nihei Y
    5. Ikeda T
    6. Suwa K
    7. Ojima Y
    8. Tanaka K
    9. Tanaka S
    10. Aoshima K
    11. Oda Y
    12. Kakazu Y
    13. Kusano M
    14. Tohge T
    15. Matsuda F
    16. Sawada Y
    17. Hirai MY
    18. Nakanishi H
    19. Ikeda K
    20. Akimoto N
    21. Maoka T
    22. Takahashi H
    23. Ara T
    24. Sakurai N
    25. Suzuki H
    26. Shibata D
    27. Neumann S
    28. Iida T
    29. Tanaka K
    30. Funatsu K
    31. Matsuura F
    32. Soga T
    33. Taguchi R
    34. Saito K
    35. Nishioka T

    (2010) MassBank: a public repository for sharing mass spectral data for life sciences

    Journal of Mass Spectrometry 45:703–714.

    https://doi.org/10.1002/jms.1777

    • PubMed
    • Google Scholar
28. 1. Ilardi EA
    2. Zakarian A

    (2011) Efficient total synthesis of dysidenin, dysidin, and barbamide

    Chemistry - an Asian Journal 6:2260–2263.

    https://doi.org/10.1002/asia.201100338

    • PubMed
    • Google Scholar
29. 1. Johnson SR
    2. Lange BM

    (2015) Open-access metabolomics databases for natural product research: present capabilities and future potential

    Frontiers in Bioengineering and Biotechnology 3:22.

    https://doi.org/10.3389/fbioe.2015.00022

    • PubMed
    • Google Scholar
30. 1. Kehr JC
    2. Gatte Picchi D
    3. Dittmann E

    (2011) Natural product biosyntheses in cyanobacteria: a treasure trove of unique enzymes

    Beilstein Journal of Organic Chemistry 7:1622–1635.

    https://doi.org/10.3762/bjoc.7.191

    • PubMed
    • Google Scholar
31. 1. Kim EJ
    2. Lee JH
    3. Choi H
    4. Pereira AR
    5. Ban YH
    6. Yoo YJ
    7. Kim E
    8. Park JW
    9. Sherman DH
    10. Gerwick WH
    11. Yoon YJ

    (2012) Heterologous production of 4-O-demethylbarbamide, a marine cyanobacterial natural product

    Organic Letters 14:5824–5827.

    https://doi.org/10.1021/ol302575h

    • PubMed
    • Google Scholar
32. 1. Kiuru P
    2. D′Auria MV
    3. Muller CD
    4. Tammela P
    5. Vuorela H
    6. Yli-Kauhaluoma J

    (2014) Exploring marine resources for bioactive compounds

    Planta Medica 80:1234–1246.

    https://doi.org/10.1055/s-0034-1383001

    • PubMed
    • Google Scholar
33. 1. Kleigrewe K
    2. Almaliti J
    3. Tian IY
    4. Kinnel RB
    5. Korobeynikov A
    6. Monroe EA
    7. Duggan BM
    8. Di Marzo V
    9. Sherman DH
    10. Dorrestein PC
    11. Gerwick L
    12. Gerwick WH

    (2015) Combining mass spectrometric metabolic profiling with genomic analysis: a powerful approach for discovering natural Products from Cyanobacteria

    Journal of Natural Products 78:1671–1682.

    https://doi.org/10.1021/acs.jnatprod.5b00301

    • PubMed
    • Google Scholar
34. Book

    1. La Barre S

    (2014) Marine biodiversity and chemodiversity — the treasure troves of the future

    In: Grillo O, editors. Biodiversity – The Dynamic Balance of the Planet. InTech. pp. 1–26.

    https://doi.org/10.5772/57394

    • Google Scholar
35. 1. Leal MC
    2. Hilário A
    3. Munro MH
    4. Blunt JW
    5. Calado R

    (2016) Natural products discovery needs improved taxonomic and geographic information

    Nat. Prod. Rep. 33:747–750.

    https://doi.org/10.1039/C5NP00130G

    • PubMed
    • Google Scholar
36. 1. Luesch H
    2. Moore RE
    3. Paul VJ
    4. Mooberry SL
    5. Corbett TH

    (2001a) Isolation of dolastatin 10 from the marine Cyanobacterium symploca species VP642 and total stereochemistry and biological evaluation of its analogue symplostatin 1

    Journal of Natural Products 64:907–910.

    https://doi.org/10.1021/np010049y

    • PubMed
    • Google Scholar
37. 1. Luesch H
    2. Yoshida WY
    3. Moore RE
    4. Paul VJ
    5. Corbett TH

    (2001b) Total structure determination of apratoxin A, a potent novel cytotoxin from the marine Cyanobacterium lyngbya majuscula

    Journal of the American Chemical Society 123:5418–5423.

    https://doi.org/10.1021/ja010453j

    • PubMed
    • Google Scholar
38. 1. Luesch H
    2. Yoshida WY
    3. Moore RE
    4. Paul VJ

    (2002) New apratoxins of marine cyanobacterial origin from guam and palau

    Bioorganic & Medicinal Chemistry 10:1973–1978.

    https://doi.org/10.1016/S0968-0896(02)00014-7

    • PubMed
    • Google Scholar
39. 1. Luzzatto-Knaan T
    2. Melnik AV
    3. Dorrestein PC

    (2015) Mass spectrometry tools and workflows for revealing microbial chemistry

    The Analyst 140:4949–4966.

    https://doi.org/10.1039/C5AN00171D

    • PubMed
    • Google Scholar
40. 1. McDonald D
    2. Clemente JC
    3. Kuczynski J
    4. Rideout JR
    5. Stombaugh J
    6. Wendel D
    7. Wilke A
    8. Huse S
    9. Hufnagle J
    10. Meyer F
    11. Knight R
    12. Caporaso JG

    (2012) The biological observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome

    GigaScience 1:7.

    https://doi.org/10.1186/2047-217X-1-7

    • PubMed
    • Google Scholar
41. 1. Medema MH
    2. Fischbach MA

    (2015) Computational approaches to natural product discovery

    Nature Chemical Biology 11:639–648.

    https://doi.org/10.1038/nchembio.1884

    • PubMed
    • Google Scholar
42. 1. Medema MH
    2. Kottmann R
    3. Yilmaz P
    4. Cummings M
    5. Biggins JB
    6. Blin K
    7. de Bruijn I
    8. Chooi YH
    9. Claesen J
    10. Coates RC
    11. Cruz-Morales P
    12. Duddela S
    13. Düsterhus S
    14. Edwards DJ
    15. Fewer DP
    16. Garg N
    17. Geiger C
    18. Gomez-Escribano JP
    19. Greule A
    20. Hadjithomas M
    21. Haines AS
    22. Helfrich EJ
    23. Hillwig ML
    24. Ishida K
    25. Jones AC
    26. Jones CS
    27. Jungmann K
    28. Kegler C
    29. Kim HU
    30. Kötter P
    31. Krug D
    32. Masschelein J
    33. Melnik AV
    34. Mantovani SM
    35. Monroe EA
    36. Moore M
    37. Moss N
    38. Nützmann HW
    39. Pan G
    40. Pati A
    41. Petras D
    42. Reen FJ
    43. Rosconi F
    44. Rui Z
    45. Tian Z
    46. Tobias NJ
    47. Tsunematsu Y
    48. Wiemann P
    49. Wyckoff E
    50. Yan X
    51. Yim G
    52. Yu F
    53. Xie Y
    54. Aigle B
    55. Apel AK
    56. Balibar CJ
    57. Balskus EP
    58. Barona-Gómez F
    59. Bechthold A
    60. Bode HB
    61. Borriss R
    62. Brady SF
    63. Brakhage AA
    64. Caffrey P
    65. Cheng YQ
    66. Clardy J
    67. Cox RJ
    68. De Mot R
    69. Donadio S
    70. Donia MS
    71. van der Donk WA
    72. Dorrestein PC
    73. Doyle S
    74. Driessen AJ
    75. Ehling-Schulz M
    76. Entian KD
    77. Fischbach MA
    78. Gerwick L
    79. Gerwick WH
    80. Gross H
    81. Gust B
    82. Hertweck C
    83. Höfte M
    84. Jensen SE
    85. Ju J
    86. Katz L
    87. Kaysser L
    88. Klassen JL
    89. Keller NP
    90. Kormanec J
    91. Kuipers OP
    92. Kuzuyama T
    93. Kyrpides NC
    94. Kwon HJ
    95. Lautru S
    96. Lavigne R
    97. Lee CY
    98. Linquan B
    99. Liu X
    100. Liu W
    101. Luzhetskyy A
    102. Mahmud T
    103. Mast Y
    104. Méndez C
    105. Metsä-Ketelä M
    106. Micklefield J
    107. Mitchell DA
    108. Moore BS
    109. Moreira LM
    110. Müller R
    111. Neilan BA
    112. Nett M
    113. Nielsen J
    114. O'Gara F
    115. Oikawa H
    116. Osbourn A
    117. Osburne MS
    118. Ostash B
    119. Payne SM
    120. Pernodet JL
    121. Petricek M
    122. Piel J
    123. Ploux O
    124. Raaijmakers JM
    125. Salas JA
    126. Schmitt EK
    127. Scott B
    128. Seipke RF
    129. Shen B
    130. Sherman DH
    131. Sivonen K
    132. Smanski MJ
    133. Sosio M
    134. Stegmann E
    135. Süssmuth RD
    136. Tahlan K
    137. Thomas CM
    138. Tang Y
    139. Truman AW
    140. Viaud M
    141. Walton JD
    142. Walsh CT
    143. Weber T
    144. van Wezel GP
    145. Wilkinson B
    146. Willey JM
    147. Wohlleben W
    148. Wright GD
    149. Ziemert N
    150. Zhang C
    151. Zotchev SB
    152. Breitling R
    153. Takano E
    154. Glöckner FO

    (2015) Minimum Information about a biosynthetic gene cluster

    Nature Chemical Biology 11:625–631.

    https://doi.org/10.1038/nchembio.1890

    • PubMed
    • Google Scholar
43. 1. Mevers E
    2. Matainaho T
    3. Allara' M
    4. Di Marzo V
    5. Gerwick WH

    (2014) Mooreamide A: a cannabinomimetic lipid from the marine Cyanobacterium moorea bouillonii

    Lipids 49:1127–1132.

    https://doi.org/10.1007/s11745-014-3949-9

    • PubMed
    • Google Scholar
44. 1. Mushegian AA
    2. Peterson CN
    3. Baker CC
    4. Pringle A

    (2011) Bacterial diversity across individual lichens

    Applied and Environmental Microbiology 77:4249–4252.

    https://doi.org/10.1128/AEM.02850-10

    • PubMed
    • Google Scholar
45. 1. Nguyen DD
    2. Melnik AV
    3. Koyama N
    4. Lu X
    5. Schorn M
    6. Fang J
    7. Aguinaldo K
    8. Lincecum TL
    9. Ghequire MG
    10. Carrion VJ
    11. Cheng TL
    12. Duggan BM
    13. Malone JG
    14. Mauchline TH
    15. Sanchez LM
    16. Kilpatrick AM
    17. Raaijmakers JM
    18. Mot R
    19. Moore BS
    20. Medema MH
    21. Dorrestein PC

    (2016) Indexing the Pseudomonas specialized metabolome enabled the discovery of poaeamide B and the bananamides

    Nature Microbiology 2:16197.

    https://doi.org/10.1038/nmicrobiol.2016.197

    • PubMed
    • Google Scholar
46. 1. Nguyen DD
    2. Wu CH
    3. Moree WJ
    4. Lamsa A
    5. Medema MH
    6. Zhao X
    7. Gavilan RG
    8. Aparicio M
    9. Atencio L
    10. Jackson C
    11. Ballesteros J
    12. Sanchez J
    13. Watrous JD
    14. Phelan VV
    15. van de Wiel C
    16. Kersten RD
    17. Mehnaz S
    18. De Mot R
    19. Shank EA
    20. Charusanti P
    21. Nagarajan H
    22. Duggan BM
    23. Moore BS
    24. Bandeira N
    25. Palsson BØ
    26. Pogliano K
    27. Gutiérrez M
    28. Dorrestein PC

    (2013) MS/MS networking guided analysis of molecule and gene cluster families

    PNAS 110:E2611–E2620.

    https://doi.org/10.1073/pnas.1303471110

    • PubMed
    • Google Scholar
47. 1. Nunnery JK
    2. Mevers E
    3. Gerwick WH

    (2010) Biologically active secondary metabolites from marine cyanobacteria

    Current Opinion in Biotechnology 21:787–793.

    https://doi.org/10.1016/j.copbio.2010.09.019

    • PubMed
    • Google Scholar
48. 1. Orjala J
    2. Gerwick WH

    (1996) Barbamide, a chlorinated metabolite with molluscicidal activity from the Caribbean Cyanobacterium lyngbya majuscula

    Journal of Natural Products 59:427–430.

    https://doi.org/10.1021/np960085a

    • PubMed
    • Google Scholar
49. 1. Owen JG
    2. Charlop-Powers Z
    3. Smith AG
    4. Ternei MA
    5. Calle PY
    6. Reddy BV
    7. Montiel D
    8. Brady SF

    (2015) Multiplexed metagenome mining using short DNA sequence tags facilitates targeted discovery of epoxyketone proteasome inhibitors

    PNAS 112:4221–4226.

    https://doi.org/10.1073/pnas.1501124112

    • PubMed
    • Google Scholar
50. 1. Owen JG
    2. Reddy BV
    3. Ternei MA
    4. Charlop-Powers Z
    5. Calle PY
    6. Kim JH
    7. Brady SF

    (2013) Mapping gene clusters within arrayed metagenomic libraries to expand the structural diversity of biomedically relevant natural products

    PNAS 110:11797–11802.

    https://doi.org/10.1073/pnas.1222159110

    • PubMed
    • Google Scholar
51. 1. Passarini MR
    2. Miqueletto PB
    3. de Oliveira VM
    4. Sette LD

    (2015) Molecular diversity of fungal and bacterial communities in the marine sponge Dragmacidon reticulatum

    Journal of Basic Microbiology 55:207–220.

    https://doi.org/10.1002/jobm.201400466

    • PubMed
    • Google Scholar
52. 1. Pereira AR
    2. Kale AJ
    3. Fenley AT
    4. Byrum T
    5. Debonsi HM
    6. Gilson MK
    7. Valeriote FA
    8. Moore BS
    9. Gerwick WH

    (2012) The carmaphycins: new proteasome inhibitors exhibiting an α,β-epoxyketone warhead from a marine Cyanobacterium

    ChemBioChem 13:810–817.

    https://doi.org/10.1002/cbic.201200007

    • PubMed
    • Google Scholar
53. 1. Pereira AR
    2. McCue CF
    3. Gerwick WH

    (2010) Cyanolide A, a glycosidic macrolide with potent molluscicidal activity from the Papua New Guinea Cyanobacterium lyngbya bouillonii

    Journal of Natural Products 73:217–220.

    https://doi.org/10.1021/np9008128

    • PubMed
    • Google Scholar
54. Software

    1. Protsyuk I
    2. Ryazanov S
    3. Alexandrov T

    (2017) Web-based software for visualization of molecular maps in 2D and 3D., version 95490e5a453dcdefe4627d6a8dfc32ec5b1ce92c

    Web-based software for visualization of molecular maps in 2D and 3D..

    https://github.com/MolecularCartography/ili
55. 1. Purves K
    2. Macintyre L
    3. Brennan D
    4. Hreggviðsson GÓ
    5. Kuttner E
    6. Ásgeirsdóttir ME
    7. Young LC
    8. Green DH
    9. Edrada-Ebel R
    10. Duncan KR

    (2016) Using molecular networking for Microbial secondary metabolite bioprospecting

    Metabolites 6:2.

    https://doi.org/10.3390/metabo6010002

    • PubMed
    • Google Scholar
56. 1. Rastogi RP
    2. Sinha RP

    (2009) Biotechnological and industrial significance of cyanobacterial secondary metabolites

    Biotechnology Advances 27:521–539.

    https://doi.org/10.1016/j.biotechadv.2009.04.009

    • PubMed
    • Google Scholar
57. 1. Sawada Y
    2. Nakabayashi R
    3. Yamada Y
    4. Suzuki M
    5. Sato M
    6. Sakata A
    7. Akiyama K
    8. Sakurai T
    9. Matsuda F
    10. Aoki T
    11. Hirai MY
    12. Saito K

    (2012) RIKEN tandem mass spectral database (ReSpect) for phytochemicals: a plant-specific MS/MS-based data resource and database

    Phytochemistry 82:38–45.

    https://doi.org/10.1016/j.phytochem.2012.07.007

    • PubMed
    • Google Scholar
58. Book

    1. Schopf JW

    (2012) The Fossil Record of Cyanobacteria

    In: Whitton B. A, editors. Ecology of Cyanobacteria II: Their Diversity in Space and Time. London, United Kingdom: Springer. pp. 15–38.

    https://doi.org/10.1007/978-94-007-3855-3_2

    • Google Scholar
59. 1. Shannon P
    2. Markiel A
    3. Ozier O
    4. Baliga NS
    5. Wang JT
    6. Ramage D
    7. Amin N
    8. Schwikowski B
    9. Ideker T

    (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks

    Genome Research 13:2498–2504.

    https://doi.org/10.1101/gr.1239303

    • PubMed
    • Google Scholar
60. 1. Sitachitta N
    2. Williamson RT
    3. Gerwick WH

    (2000) Yanucamides A and B, two new depsipeptides from an assemblage of the marine cyanobacteria Lyngbya majuscula and Schizothrix species

    Journal of Natural Products 63:197–200.

    https://doi.org/10.1021/np990466z

    • PubMed
    • Google Scholar
61. 1. Tan LT
    2. Goh BP
    3. Tripathi A
    4. Lim MG
    5. Dickinson GH
    6. Lee SS
    7. Teo SL

    (2010) Natural antifoulants from the marine Cyanobacterium lyngbya majuscula

    Biofouling 26:685–695.

    https://doi.org/10.1080/08927014.2010.508343

    • PubMed
    • Google Scholar
62. 1. Tan LT

    (2007) Bioactive natural products from marine cyanobacteria for drug discovery

    Phytochemistry 68:954–979.

    https://doi.org/10.1016/j.phytochem.2007.01.012

    • PubMed
    • Google Scholar
63. 1. Tidgewell K
    2. Engene N
    3. Byrum T
    4. Media J
    5. Doi T
    6. Valeriote FA
    7. Gerwick WH

    (2010) Evolved diversification of a modular natural product pathway: apratoxins F and G, two cytotoxic cyclic depsipeptides from a Palmyra collection of Lyngbya bouillonii

    ChemBioChem 11:1458–1466.

    https://doi.org/10.1002/cbic.201000070

    • PubMed
    • Google Scholar
64. 1. Uzair B
    2. Tabassum S
    3. Rasheed M
    4. Rehman SF

    (2012) Exploring marine cyanobacteria for lead compounds of pharmaceutical importance

    The Scientific World Journal 2012:179782–10.

    https://doi.org/10.1100/2012/179782

    • PubMed
    • Google Scholar
65. 1. Vázquez-Baeza Y
    2. Pirrung M
    3. Gonzalez A
    4. Knight R

    (2013) EMPeror: a tool for visualizing high-throughput microbial community data

    GigaScience 2:16.

    https://doi.org/10.1186/2047-217X-2-16

    • PubMed
    • Google Scholar
66. 1. Villa FA
    2. Gerwick L

    (2010) Marine natural product drug discovery: leads for treatment of inflammation, Cancer, infections, and neurological disorders

    Immunopharmacology and Immunotoxicology 32:228–237.

    https://doi.org/10.3109/08923970903296136

    • PubMed
    • Google Scholar
67. 1. Walsh CT

    (2015) A chemocentric view of the natural product inventory

    Nature Chemical Biology 11:620–624.

    https://doi.org/10.1038/nchembio.1894

    • PubMed
    • Google Scholar
68. 1. Wan F
    2. Erickson KL

    (2001) Georgamide, a new cyclic depsipeptide with an alkynoic acid residue from an australian Cyanobacterium

    Journal of Natural Products 64:143–146.

    https://doi.org/10.1021/np0003802

    • PubMed
    • Google Scholar
69. 1. Wang M
    2. Carver JJ
    3. Phelan VV
    4. Sanchez LM
    5. Garg N
    6. Peng Y
    7. Nguyen DD
    8. Watrous J
    9. Kapono CA
    10. Luzzatto-Knaan T
    11. Porto C
    12. Bouslimani A
    13. Melnik AV
    14. Meehan MJ
    15. Liu WT
    16. Crüsemann M
    17. Boudreau PD
    18. Esquenazi E
    19. Sandoval-Calderón M
    20. Kersten RD
    21. Pace LA
    22. Quinn RA
    23. Duncan KR
    24. Hsu CC
    25. Floros DJ
    26. Gavilan RG
    27. Kleigrewe K
    28. Northen T
    29. Dutton RJ
    30. Parrot D
    31. Carlson EE
    32. Aigle B
    33. Michelsen CF
    34. Jelsbak L
    35. Sohlenkamp C
    36. Pevzner P
    37. Edlund A
    38. McLean J
    39. Piel J
    40. Murphy BT
    41. Gerwick L
    42. Liaw CC
    43. Yang YL
    44. Humpf HU
    45. Maansson M
    46. Keyzers RA
    47. Sims AC
    48. Johnson AR
    49. Sidebottom AM
    50. Sedio BE
    51. Klitgaard A
    52. Larson CB
    53. Boya P CA
    54. Torres-Mendoza D
    55. Gonzalez DJ
    56. Silva DB
    57. Marques LM
    58. Demarque DP
    59. Pociute E
    60. O'Neill EC
    61. Briand E
    62. Helfrich EJ
    63. Granatosky EA
    64. Glukhov E
    65. Ryffel F
    66. Houson H
    67. Mohimani H
    68. Kharbush JJ
    69. Zeng Y
    70. Vorholt JA
    71. Kurita KL
    72. Charusanti P
    73. McPhail KL
    74. Nielsen KF
    75. Vuong L
    76. Elfeki M
    77. Traxler MF
    78. Engene N
    79. Koyama N
    80. Vining OB
    81. Baric R
    82. Silva RR
    83. Mascuch SJ
    84. Tomasi S
    85. Jenkins S
    86. Macherla V
    87. Hoffman T
    88. Agarwal V
    89. Williams PG
    90. Dai J
    91. Neupane R
    92. Gurr J
    93. Rodríguez AM
    94. Lamsa A
    95. Zhang C
    96. Dorrestein K
    97. Duggan BM
    98. Almaliti J
    99. Allard PM
    100. Phapale P
    101. Nothias LF
    102. Alexandrov T
    103. Litaudon M
    104. Wolfender JL
    105. Kyle JE
    106. Metz TO
    107. Peryea T
    108. Nguyen DT
    109. VanLeer D
    110. Shinn P
    111. Jadhav A
    112. Müller R
    113. Waters KM
    114. Shi W
    115. Liu X
    116. Zhang L
    117. Knight R
    118. Jensen PR
    119. Palsson BØ
    120. Pogliano K
    121. Linington RG
    122. Gutiérrez M
    123. Lopes NP
    124. Gerwick WH
    125. Moore BS
    126. Dorrestein PC
    127. Bandeira N

    (2016) Sharing and community curation of mass spectrometry data with global natural Products Social molecular networking

    Nature Biotechnology 34:828–837.

    https://doi.org/10.1038/nbt.3597

    • PubMed
    • Google Scholar
70. 1. Watrous J
    2. Roach P
    3. Alexandrov T
    4. Heath BS
    5. Yang JY
    6. Kersten RD
    7. van der Voort M
    8. Pogliano K
    9. Gross H
    10. Raaijmakers JM
    11. Moore BS
    12. Laskin J
    13. Bandeira N
    14. Dorrestein PC

    (2012) Mass spectral molecular networking of living microbial colonies

    PNAS 109:E1743–E1752.

    https://doi.org/10.1073/pnas.1203689109

    • PubMed
    • Google Scholar
71. Book

    1. Whitton BA
    2. Potts M

    (2012) Introduction to the Cyanobacteria

    In: Whitton B. A, editors. Ecology of Cyanobacteria II: Their Diversity in Space and Time. London, United Kingdom: Springer. pp. 1–13.

    https://doi.org/10.1007/978-94-007-3855-3_1

    • Google Scholar
72. 1. Williamson RT
    2. Boulanger A
    3. Vulpanovici A
    4. Roberts MA
    5. Gerwick WH

    (2002) Structure and absolute stereochemistry of phormidolide, a new toxic metabolite from the marine Cyanobacterium phormidium sp

    The Journal of Organic Chemistry 67:7927–7936.

    https://doi.org/10.1021/jo020240s

    • PubMed
    • Google Scholar
73. 1. Wishart DS
    2. Jewison T
    3. Guo AC
    4. Wilson M
    5. Knox C
    6. Liu Y
    7. Djoumbou Y
    8. Mandal R
    9. Aziat F
    10. Dong E
    11. Bouatra S
    12. Sinelnikov I
    13. Arndt D
    14. Xia J
    15. Liu P
    16. Yallou F
    17. Bjorndahl T
    18. Perez-Pineiro R
    19. Eisner R
    20. Allen F
    21. Neveu V
    22. Greiner R
    23. Scalbert A

    (2013) HMDB 3.0--the Human Metabolome database in 2013

    Nucleic Acids Research 41:D801–D807.

    https://doi.org/10.1093/nar/gks1065

    • PubMed
    • Google Scholar
74. 1. Worley B
    2. Powers R

    (2013) Multivariate analysis in Metabolomics

    Current Metabolomics 1:92–107.

    https://doi.org/10.2174/2213235X11301010092

    • PubMed
    • Google Scholar
75. 1. Yang JY
    2. Sanchez LM
    3. Rath CM
    4. Liu X
    5. Boudreau PD
    6. Bruns N
    7. Glukhov E
    8. Wodtke A
    9. de Felicio R
    10. Fenner A
    11. Wong WR
    12. Linington RG
    13. Zhang L
    14. Debonsi HM
    15. Gerwick WH
    16. Dorrestein PC

    (2013) Molecular networking as a dereplication strategy

    Journal of Natural Products 76:1686–1699.

    https://doi.org/10.1021/np400413s

    • PubMed
    • Google Scholar

Author details

1. Tal Luzzatto-Knaan

   Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, United States

   Contribution

   TL-K, Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing—original draft, Project administration, Writing—review and editing

   Contributed equally with

   Neha Garg

   For correspondence

   tal.luzzatto@mail.huji.ac.il

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-8392-0501

2. Neha Garg

   Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, United States

   Contribution

   NG, Data curation, Formal analysis, Methodology, Writing—review and editing

   Contributed equally with

   Tal Luzzatto-Knaan

   Competing interests

   The authors declare that no competing interests exist.

3. Mingxun Wang

   Center for Computational Mass Spectrometry and Department of Computer Science and Engineering, University of California San Diego, San Diego, United States

   Contribution

   MW, Software

   Competing interests

   The authors declare that no competing interests exist.

4. Evgenia Glukhov

   Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, San Diego, United States

   Contribution

   EG, Methodology, Project administration

   Competing interests

   The authors declare that no competing interests exist.

5. Yao Peng

   Department of Chemistry and Biochemistry, University of California San Diego, San Diego, United States

   Contribution

   YP, Methodology

   Competing interests

   The authors declare that no competing interests exist.

6. Gail Ackermann

   Departments of Pediatrics and Computer Science and Engineering, University of California San Diego, San Diego, United States

   Contribution

   GA, Formal analysis, Methodology, Project administration

   Competing interests

   The authors declare that no competing interests exist.

7. Amnon Amir

   Departments of Pediatrics and Computer Science and Engineering, University of California San Diego, San Diego, United States

   Contribution

   AA, Formal analysis, Validation, Project administration

   Competing interests

   The authors declare that no competing interests exist.

8. Brendan M Duggan

   Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, United States

   Contribution

   BMD, Formal analysis, Validation, Methodology

   Competing interests

   The authors declare that no competing interests exist.

9. Sergey Ryazanov

   European Molecular Biology Laboratory, Heidelberg, Germany

   Contribution

   SR, Software, Visualization

   Competing interests

   The authors declare that no competing interests exist.

10. Lena Gerwick

    Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, San Diego, United States

    Contribution

    LG, Resources, Funding acquisition, Methodology

    Competing interests

    The authors declare that no competing interests exist.

11. Rob Knight

    Departments of Pediatrics and Computer Science and Engineering, University of California San Diego, San Diego, United States

    Contribution

    RK, Software, Validation, Writing—review and editing

    Competing interests

    The authors declare that no competing interests exist.

12. Theodore Alexandrov

    1. Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, United States
    2. European Molecular Biology Laboratory, Heidelberg, Germany

    Contribution

    TA, Software, Visualization, Methodology

    Competing interests

    The authors declare that no competing interests exist.

13. Nuno Bandeira

    1. Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, United States
    2. Center for Computational Mass Spectrometry and Department of Computer Science and Engineering, University of California San Diego, San Diego, United States

    Contribution

    NB, Software, Validation, Visualization

    Competing interests

    The authors declare that no competing interests exist.

14. William H Gerwick

    1. Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, United States
    2. Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, San Diego, United States

    Contribution

    WHG, Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing—review and editing

    For correspondence

    wgerwick@ucsd.edu

    Competing interests

    The authors declare that no competing interests exist.

15. Pieter C Dorrestein

    1. Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, United States
    2. Center for Computational Mass Spectrometry and Department of Computer Science and Engineering, University of California San Diego, San Diego, United States
    3. Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, San Diego, United States

    Contribution

    PCD, Conceptualization, Resources, Supervision, Funding acquisition, Writing—original draft, Writing—review and editing

    For correspondence

    pdorrestein@ucsd.edu

    Competing interests

    The authors declare that no competing interests exist.

• 3,380

  views

• 666

  downloads

• 34

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.