Small molecule in situ resin capture provides a compound first approach to natural product discovery

Domain: earth_science
Published: 2024-06-19
Editorial outcome: accept

Abstract
Culture-based microbial natural product discovery strategies fail to realize the extraordinary biosynthetic potential detected across earth’s microbiomes. Here we introduce Small Molecule In situ Resin Capture (SMIRC), a culture-independent method to obtain natural products directly from the environments in which they are produced. We use SMIRC to capture numerous compounds including two new carbon skeletons that were characterized using NMR and contain structural features that are, to the best of our knowledge, unprecedented among natural products. Applications across diverse marine habitats reveal biome-specific metabolomic signatures and levels of chemical diversity in concordance with sequence-based predictions. Expanded deployments, in situ cultivation, and metagenomics facilitate compound discovery, enhance yields, and link compounds to candidate producing organisms, although microbial community complexity creates challenges for the later. This compound-first approach to natural product discovery provides access to poorly explored chemical space and has implications for drug discovery and the detection of chemically mediated biotic interactions.

Introduction
Microbial natural products account for many of today’s essential medicines, including most antibiotics1,2. These compounds are traditionally discovered using a ‘microbe-first’ approach, where individual strains are cultured in the laboratory and bioassays used to guide the isolation of active compounds from culture extracts. While new compounds are regularly discovered using this approach, it has become increasingly difficult to find fundamentally new chemical scaffolds, a critical resource for drug discovery and a driving force behind subsequent synthetic and biosynthetic studies. Recent analyses of genomic, metagenomic, and amplicon data all indicate the existence of extensive and yet to be realized natural product biosynthetic diversity across Earth’s microbiomes3–7. Despite this realization, our best discovery efforts have failed to access the vast majority of predicted chemical space, and thus new approaches to natural product discovery are needed.

The challenges associated with microbial natural product discovery are well documented8 and include the re-isolation of known compounds9, the recognition that only a small percentage of bacterial diversity has been cultured10, and observations that most of the genes associated with natural product biosynthesis are silent under laboratory growth conditions11. Efforts to address these challenges include improved cultivation methods12, co-cultivation or the addition of elicitors to activate silent biosynthetic gene clusters (BGCs)13,14, and genome mining15,16, where BGCs are activated via genetic manipulation17 or heterologous expression18. While these methods have shown promise, they remain time consuming, are primarily limited to the small percentage of microbial diversity that can be cultured19, and fail to capture the rich natural product diversity predicted to be encoded by the Earth’s collective microbiome, which is estimated to encompass as many as one trillion microbial species20.

Here we report a new approach to microbial natural product discovery that bypasses the need for laboratory cultivation. Rather than relying on cultured strains to drive discoveries, compounds are captured directly from the environments in which they are produced using an adsorbent resin without consideration of the producing organism. This culture-independent approach, which we call Small Molecule In Situ Resin Capture (SMIRC), builds upon solid-phase methods developed to monitor marine toxins21, detect known microbial products in marine sediments22, probe the biosynthetic origins of marine natural products23, and detect soft coral24 and sponge metabolites in seawater25. Unlike traditional discovery approaches, SMIRC can capture novel natural products originating from bacteria to phytoplankton in a single deployment and, as we show, some that were previously reported from marine plants and invertebrates. As such, it requires no knowledge of cultivation conditions, or the factors required to induce biosynthetic gene expression, two significant obstacles to current discovery strategies. SMIRC was further modified to enhance in situ microbial growth with the aim of capitalizing on nature to provide the cues needed to trigger natural product biosynthesis. We demonstrate the potential of SMIRC for natural product discovery with the isolation and characterization of two new carbon skeletons, one of which possesses biological activity, and the detection of ten other new compounds, all from a single deployment site.

Results
SMIRC deployments and compound dereplication
Microbial natural products are generally secreted into the environment where they play important ecological roles as allelochemicals, siderophores, signaling molecules, and more26–28. SMIRC employs the adsorbent resin HP-20, which has proven effective for the adsorption of marine toxins across a wide range of polarities29, to capture these small organic molecules after they are released and thus provides a mechanism to access environmental metabolomes. This technique avoids the extraction of media and intracellular components associated with traditional culture-based approaches, which can mask low-concentration compounds of interest. We first tested SMIRC in a Zostera marina seagrass meadow (<2m depth) in Mission Bay, San Diego (Fig. 1A) and obtained salt-free, dark brown extracts averaging 54.2±6.1mg per replicate (n=4). A microfractionation antibiotic assay targeting the outer membrane deficient E. coli strain lptD412330 revealed a large active peak with a UV maximum at 360nm (Fig. 1B).

Major compounds in the active fraction (m/z 381.0906 and 301.1396) were detected using high-resolution mass spectrometry (MS) with the mass difference of 79.951 Da attributed to the loss of SO3 (calcd. 79.9568 Da). Bioassay-guided C18 fractionation from one SMIRC replicate extract followed by HPLC purification yielded 2mg of the major compound (m/z 301.1396), which was identified as the flavonoid chrysoeriol based on the comparison of MS and NMR data with published reports (Fig. 1C). Sulfated flavonoids with antibacterial activity have been reported from Z. marina31, providing a candidate producer for these compounds. While our goal was to capture novel microbial products, this first deployment demonstrated that compound yields sufficient for NMR-based structure elucidation could be obtained using SMIRC. In addition, it supports the use of resin capture techniques to address questions in chemical ecology25, in this case the impact of seagrass metabolites on microbial community structure. The effectiveness of HP-20 to concentrate compounds from seawater was also evident as the 2mg chrysoeriol captured using 100g resin was equivalent to the yield calculated from >1000L of seawater (<1mg/L chrysoeriol).

In a follow-up deployment at the same site, the SMIRC technique was modified by embedding the resin in an agar matrix with the aim of increasing the yields of microbial-derived compounds by facilitating in situ microbial growth. The addition of agar led to a different chemical profile and the detection of a major new peak in the UV chromatogram (Fig. 1D), which we isolated (0.4mg) and identified as the α-pyrone aplysiopsene A (Fig. 1E) using NMR and tandem MS/MS (Supplementary Information). Aplysiopsene A was initially reported from the sacoglossan mollusk Aplysiopsis formosa32 while structurally related compounds have been reported from a sea fan-derived fungus33 and the actinobacterium Nocardiopsis dassonvillei34. Given that sacoglossans were not observed at the deployment site, the isolation of aplysiopsene A suggests that it may be of microbial origin and that SMIRC can provide new insights into the roles of microbes in the production of natural products reported from marine invertebrates35,36. Of note, we observed pink bacterial-like colonies (Fig. 1F) on the agar-resin matrix, indicating that this approach can support in situ cultivation and that the extract chemical profiles may be influenced by microbes growing on the agar surface. While efforts to cultivate aplysiopsene A producing bacteria from these samples have yet to be successful, these results provide a second example in which SMIRC deployments yielded sufficient compound for NMR-based structural elucidation and evidence that embedding the resin in agar can enhance compound discovery.

We next deployed SMIRC in the littoral zone at Cabrillo State Marine Reserve (CSMR, Fig. 2A), a relatively pristine habitat comprised of sand patches and rocky reef dominated by the seagrass Phyllospadix sp. and inhabited by a diverse assemblage of macroalgae and invertebrates.

Deployments (without agar) spanned 2–8 days depending on tides and swells, yielding extract masses averaging 177.1mg (±87.6mg, n=17), with some exceeding 300mg per 100g resin. While no antibiotic activity was detected in the initial screen, LCMS analyses revealed extensive chemical complexity, visualized as congested peaks in the LC (UV, 254nm) and MS (total ion count) chromatograms (Supplementary Fig. 1). This level of complexity was common to the crude extracts, which required extensive fractionation. The large number of molecules that could not be identified in these extracts suggested a high degree of chemical novelty and opportunities for natural product discovery guided by the unusual masses observed in the LCMS data.

Natural product discovery
We first targeted a potentially novel compound from the CSMR extracts that had a relatively high-intensity molecular peak (m/z 328.2485 [M+H]+ Fig. 2B), suggesting it could be isolated. The calculated molecular formula of C18H33NO4 (calcd [M+H]+ 328.2483, 0.61ppm) with three degrees of unsaturation matched three compounds in the Dictionary of Natural Products (https://dnp.chemnetbase.com): 3-hydroxy-C14-homoserine lactone and curvularides B and E, initially reported from various bacteria and an endophytic fungus37, respectively. We subjected two extracts with the highest estimated abundance of the compound to HPLC purification guided by UV and evaporative light-scattering (ELSD) detection given the absence of a strong chromophore. Repeated rounds of HPLC purification, supported by LCMS analyses, yielded ~40μg of a pure compound as a white solid. The 1H NMR spectrum (600MHz, CD3OD, 1.7mm microcryoprobe) revealed three doublet resonances attributable to three methyl groups (δ 0.90, J=6.5Hz; 0.96, J=6.7; 1.20, J=6.3Hz) positioned on methine carbons (Fig. 2C), thus indicating that our targeted compound is neither 3-hydroxy-C14-homoserine lactone, which possesses only one methyl group, nor one of the curvularides, which have five methyl groups. The complete structure elucidation of this compound (Supplementary Notes, Supplementary Table 1, Supplementary Figs. 2–4), which we have called cabrillostatin (1, Fig. 3), was accomplished with 2D NMR experiments (1H-1H COSY, HSQC, HMBC). Cabrillostatin represents a new carbon skeleton comprised of the uncommon, non-proteinogenic gamma-amino acid statine N-acylated to a 9-hydroxydecanoate ester to form a 15-membered macrocycle. The characterization of this compound by NMR validates the application of SMIRC for natural product discovery.

While isolating cabrillostatin (1), a second compound of interest was detected that displayed an isotopic pattern in the MS spectrum indicative of a dihalogenated molecule bearing one Cl and one Br (Fig. 2B). The molecular formula C29H40BrClO9, with nine degrees of unsaturation, was calculated from the highest intensity isotopologue (Supplementary Fig. 5) at m/z 629.1510 (calcd for m/z [M-H2O+H]+ 629.1512, 0.32ppm). Database searches returned no matches for this formula suggesting it represented a new natural product. Multiple rounds of HPLC purification (Methods) yielded 50μg of pure compound from a resin extract (100mg). A panel of NMR experiments (1H, 1H-1H COSY, HSQC, HMBC, NOESY, 600MHz, 1.7mm microcryoprobe, CD3CN) conducted over long acquisition times (>72h for HMBC) allowed assembly of most of the molecular structure but left several important substructures undefined due to missing H-C correlations in the HMBC spectrum. Pooling samples from two SMIRC extracts yielded ~100μg of compound that was subjected to NMR experiments (1H, COSY, and HSQC), repeated in CD3OD, that improved line shapes, dispersion of signals and S/N. An LR-HSQMBC (optimized for J=8Hz) experiment revealed additional correlations not observed in CD3CN38. Extensive analysis of the NMR data recorded in both solvents and MS/MS fragmentation patterns (Supplementary Tables 2 and 3, Supplementary Figs. 6 and 7A) allowed us to establish the structure as a new bromo-chloro-polyketide that we have named cabrillospiral A (2) (Fig. 3).

The structure of cabrillospiral A (2) exhibits several rare features including an α-halogenated bicyclic [6,5]-spiroketal and an α-β unsaturated aldehyde appended to a C=C double bond that is exocyclic to a tetrahydrofuran. The latter, a vinylogous formate ester, is to the best of our knowledge, unprecedented among compounds in both the natural product and synthetic literature. Cabrillospiral A (2) has 11 chiral centers distributed across four independent stereogenic substructures, making stereochemical elucidation a challenge. Interpretation of the NOESY data, 1H-homonuclear scalar coupling (2,3J), electronic circular dichroism (ECD) experiments, and DFT calculated ECD spectra led to the assignment of the Δ2 double bond geometry, the absolute configuration at the ring A as 5S, 6S and the relative configuration of four of these isomeric α,β-unsaturated carbonyl compounds, may arise from a facile, non-enzymatic β-elimination of H2O from precursor compounds (Supplementary Fig. 7C). Detailed analysis of the LCMS data revealed no evidence of the hydrated congeners of 2 or 3 with predicated MW higher by 18 amu, nor O-Me analogs with MW incremented by 30 amu.

Notably, compounds 1, 2, and unidentified compound m/z 757.4630 [M+H]+ (Supplementary Information) were consistently detected in three separate deployments spanning a period of seven months, suggesting that repeat deployments can provide a path forward to address supply bottlenecks for some target compounds (Supplementary Fig. 12). Evidence that the highest compound yields were obtained from the longest deployments justifies more detailed studies of the time course of compound recovery.

Bioactivity testing
Multiple SMIRC deployments at the CSMR site allowed us to isolate additional amounts of cabrillostatin (1, 26μg) and cabrillospirals A (2, 75μg) and B (3, 27μg) for biological testing. Given that the amino acid statine is the pharmacophore for some protease inhibitors39, we tested cabrillostatin (1) for activity against the aspartic acid protease cathepsin D. Surprisingly, 1 was not inhibitory but instead potentiated protease activity in a concentration-dependent manner from 1.5-fold at 100nM to almost 2-fold at 10μM (Supplementary Fig. 13A). Testing of 1 in an expanded panel of aspartic acid, serine, and cysteine proteases revealed no additional inhibitory activity (Supplementary Fig. 13B). We next tested compounds (1-3) against a panel of nine diverse cancer cell lines (Supplementary Table 5) using cell painting40 and high-content imaging to maximize the detection of activity and infer mechanism of action41. Phenotypic profiles generated using a customized image analysis pipeline provided in-depth quantification of cell morphology and biomarker staining in comparison with 28 reference compounds across six diverse drug categories with distinguishable phenotypes42 (Supplementary Fig. 14A, Supplementary Table 6). Principal component analysis (PCA) clearly separated 1 from the DMSO controls in most cell lines (Fig. 4A). Quantifying the separation showed that 1 was significantly bioactive (p<10−6) at 10μM against six of the nine cell lines (Fig. 4B, Supplementary Table 7) but not against OVCAR4, HepG2, and 786-O cells indicating a degree of selectivity. The phenotypic profiles of 1 relative to the reference compounds were unique (confidence score >0.1, Supplementary Table 8), indicating that it possesses a distinct mechanism of action. A dose-response experiment in A549 cells validated the bioactivity of 1 at 10μM but did not detect activity at lower concentrations (Supplementary Fig. 14B). In contrast to 1, cabrillospirals A and B (2-3) induced no significant phenotypic changes in any of the cell lines at 10μM.

To compliment the high-content phenotypic profiling assays, we tested compounds 1–3 in an induced pluripotent stem cell-derived cardiomyocyte (iPSC-CM) model43 that can detect changes in the frequency and amplitude of cardiomyocyte beating induced by anti-arrhythmic or anti-cancer drugs (Supplementary Figs. 15A, B, Supplementary Table 9). Concordant with the phenotypic profiling results, compound 1, but not 2 or 3, showed a clear alteration of iPSC-CM contractions (Fig. 4C, Supplementary Fig. 15C) in the form of increased peak amplitude and a small decrease of peak frequency (Fig. 4D), both of which were validated using an independent batch of iPSC-derived cardiomyocytes (Supplementary Fig. 15D). The observed changes in the iPSC-CM Ca2+ transients warrant further investigation given the suggested positive inotropic effect. The intriguing set of biological activities detected for cabrillostatin support the potential applications of SMIRC for natural product drug discovery, although it should be noted that special methods are required to generate defined test concentrations when dealing with small compound quantities (see Methods).

Environmental metabolomes
We deployed SMIRC at three additional locations including a second seagrass meadow, a hypersaline lake (the Salton Sea), and a sand slope (6m depth) off the Scripps Pier. We analyzed the extracts from all sites using GNPS classic molecular networking (CMN, Supplementary Figs. 16, 17)44 and compared the metabolomes using FBMN45. A PCoA plot (Fig. 5A) revealed dramatic differences in metabolomes across sites. While more study is required to determine what drives these differences and why the CSMR extracts appeared enriched in novel compounds, these observations support deployments in diverse habitats to further explore the discovery potential of the resin capture approach. We also identified a variety of known natural products and anthropogenic contaminants, including triazine and carbamate pesticides (Supplementary Fig. 18) using GNPS molecular networking and manual database searches. Applying strict GNPS criteria (precursor ion tolerance 0.02 Da, fragment ion tolerance 0.05), we identified library matches for 325 out of 5137 (6.33%) parent ions (nodes) representing 230 unique compounds after the removal of redundant matches. The diverse compounds captured by HP-20 included fatty acids, peptides, pseudoalkaloids, tryptophan alkaloids, flavonoids, and terpenoids (Supplementary Table 10, Supplementary Fig. 17). From the Mission Bay seagrass deployment, we identified the microbial natural product apratoxin A (Supplementary Fig. 19), thus expanding the geographic distribution of this potent cyanobacterial toxin to temperate waters46,47. We also identified okadaic acid and pectenotoxin-2, two polyether toxins associated with diarrhetic shellfish poisoning48,49, as well as the macrocyclic imine toxin 13-desmethylspirolide C50 (Supplementary Figs. 20–22). While these matches can be considered “level 2” since they are based on comparisons with literature values or MS/MS spectral libraries51, numerous compounds detected using the DEREPLICATOR+ algorithm52 including callipeltin B, cyanopeptolin 880, amphidinolides, kahalalide Y, and kabiramide D, could not be validated in this manner, highlighting the need for stringent manual curation of environmental metabolomes53.

In addition to cabrillostatin (1) and cabrillospirals A-B (2-3), 10 compounds isolated from the CSMR extracts were considered novel based on the absence of isobaric MS matches in natural product databases. Novelty was supported by HRMS/MS data and complemented by 1H NMR for two compounds (Fig. 5, Supplementary Table 5, Supplementary Figs. 23–32, 60, 61). Among these compounds, six display isotopic signatures indicative of chlorination (Table 1), while MS/MS fragmentation spectra indicate structural diversity that includes polyether toxins (Supplementary Figs. 23–25), alkaloids (Supplementary Figs. 26–29), peptides (Supplementary Fig. 30), and polyketides (Supplementary Figs. 31–32). Some of these compounds are associated with large molecular families (Fig. 5B), suggesting that many derivatives could also be discovered. This represents an extraordinary level of chemical novelty from a single deployment site and hints at the discovery potential of the technique.

Environmental distributions of cabrillostatin (1) and cabrillospirals A-B (2-3)
The GNPS platform was recently expanded to include the web-enabled search engine MASST (Mass Spectrometry Search Tool)54, the equivalent of NCBI BLAST for MS/MS datasets in the MassIVE repository. Using MASST, we parsed ~2700 untargeted, small-molecule datasets to identify the environmental distributions and potential biotic origins of cabrillostatin (1), cabrillospirals A-B (2-3), aplysiopsene A, and 15 other compounds (Table 1, Supplementary Tables 11, 12) detected in the SMIRC extracts. Surprisingly, 1 was identified in 11 datasets collected from 2019 to 2021 and largely associated with the study of dissolved organic matter (DOM) in nearshore55 and deep ocean seawater samples (Supplementary Table 12). A match to a cultured dinoflagellate metabolome led us to suspect Ostreopsis sp. as the biogenic source. However, 1 was also detected in the medium controls, which were prepared using seawater collected from the Scripps pier56. Cabrillostatin clustered with several related compounds in the molecular network55 allowing us to assign putative structures for four derivatives (cabrillostatins B, B1, C, and D) based on comparative MS/MS analyses (Fig. 5C, Supplementary Figs. 33–37). Among these, cabrillostatin B is cyclized with 11-hydroxydodecanoic acid, and cabrillostatin B1 bears an additional keto group. In cabrillostatins C and D, the statine moiety is replaced with either 4-amino-3-hydroxydeca-4-en-7,9-diynoic acid or 4-amino-3-hydroxy-5-phenylpentanoic acid (Ahppa), respectively (Supplementary Figs. 31–34). While a compound with the Ahppa unit was reported from a marine α-proteobacterium57, the proposed diynoic amino acid is, to the best of our knowledge, unprecedented among natural products.

Determining the structure of 1 has improved our broader understanding of DOM, which has largely defied characterization55,58, and raises questions about the ecological roles of a widespread and biologically active natural product in seawater ecosystems. Some of the compounds detected in the SMIRC extracts (Fig. 5B) are members of large and yet-to-be-characterized compound families. Annotating these families will further advance our understanding of marine DOM.

In contrast to the cabrillostatins, the cabrillospirals were not detected in any of the MassIVE datasets, suggesting that this compound is highly site-specific and demonstrating the value of deploying SMIRC across diverse habitats.

Metagenomic analyses
In an attempt to link compounds identified in the CSMR extracts to the producing organisms, we generated untargeted shotgun metagenomes from marine sediment and bulk seawater samples (average 76.4±12M paired-end reads per sample) collected at the SMIRC deployment sites. Both the sediment and seawater communities were dominated by Proteobacteria and Bacteroidetes, with higher relative abundances of Actinobacteria and Planctomycetes in the sediment samples (Supplementary Fig. 38A). In total, we recovered 1843 BGCs (N50=18kbp), with many (52%) of the larger BGCs (>10 genes) predicted to be complete (i.e., not on a contig edge). The predicted products of these BGCs are largely nonribosomal peptides (28%), terpenes (25.7%), and ribosomally synthesized and post-translationally modified peptides (RiPPs, 17.7%). They could be clustered into 631 gene cluster families (GCFs, Supplementary Fig. 38B), of which only four (those encoding the alkaloid osmolyte ectoine, the peptide natural product anabaenopeptin, and the pigments flexirubin and zeaxanthin-like carotenoids) shared similarity to BGCs in the MIBiG reference database59, emphasizing both the biosynthetic complexity and the potential novelty of these environments.

Based on the structure of cabrillostatin (1), we expected a hybrid PKS-NRPS BGC with the starter unit adenylation (A) domain selective for leucine, a ketosynthase (KS) domain that could account for the C2 elongation of leucine to yield the statine moiety, coupled with highly reducing PKS modules to account for the saturated fatty acyl component. However, none of the 43 PKS-NRPS hybrid BGCs recovered from the two sample types encoded the predicted enzymes. Given that a low abundance organism could account for production, we compared KS domains from the unassembled reads with those associated with statine biosynthesis in the didemnin B and burkholdac A BGCs36,60, however none clustered in a KS phylogeny. Interestingly, the two reference KSs shared <60% amino-acid identity with a database of metagenome-extracted KSs, supporting the rarity of statine incorporation into natural products.

For cabrillospirals (2-3), potential biosynthetic hooks included a modular type I PKS (T1PKS), halogenases, a p-hydroxybenzoic acid (p-HBA) starter unit, and methyltransferases to account for the C-2 hydroxymethyl and C-8 methyl groups. Among the 68 T1PKS BGCs identified, we detected two candidates (Supplementary Fig. 38C) in a high-quality MAG (4.96 Mbp; 98.2% complete, 3.6% contamination) in the phylum Planctomycetes, which was recently recognized as a rich and poorly explored source of specialized metabolites61. While lacking key biosynthetic genes, the planctomycete MAG was nonetheless the best candidate identified. Unfortunately, long-read Oxford Nanopore sequencing failed to cover this BGC and efforts to culture Planctomycetes were unsuccessful35. While genetic linkages between compounds and producers have been established in the context of symbioses35,62, the results presented here reinforce the challenges associated with making these connections in complex microbial communities.

Methods
Resin processing and extraction
HP-20 resin was soaked in MeOH, gently shaken at 40 rpm for 1h, then filtered (Büchner funnel-filter membrane, 200μm) under reduced pressure and washed with MeOH (x3) then H2O (x3). HPLC grade MeOH and MilliQ grade H2O were used throughout, and all glassware was acid washed and rinsed with MeOH before use. Bulk-washed resin could be stored in MilliQ water at room temperature for several weeks before deployment, at which time 100g was packaged between two layers of Nitex™ nylon mesh (120μm, Genesee Scientific, El Cajon, CA) and framed within a 30cm wooden embroidery hoop (Fig. 1A). Packaged resin was secured in the field using aluminum tent stakes, rocks, or attached to ropes ~30cm above the seafloor using SCUBA. Upon recovery, individual resin units were washed with deionized water and the resin removed from the nylon mesh and transferred into a glass fritted Büchner funnel (200μm pore size), where it was rinsed with H2O and eluted three times with 200mL MeOH under vacuum. The solvent was removed on a rotary evaporator to yield a salt-free crude extract. Agar-resin matrix was prepared by adding 250g of washed HP-20 resin to 33g Instant Ocean salt mixture and 12g agar in 1L of deionized H2O, which was autoclaved at 121°C for 20mins. After cooling to 50°C, the liquid agar-resin was swirled and distributed into ten 15cm Petri dishes. Two solidified agar/resin plates were aseptically removed from the dishes and enclosed between two layers of Nitex™ nylon mesh as described above. The Nitex™ mesh and embroidery hoops were treated with 70% isopropanol and allowed to dry before packaging. Packaged containers were wrapped with 70% isopropanol-treated aluminum foil prior to deployment. Upon recovery, agar-resin from each container was extracted three times with 150mL of dichloromethane-MeOH (1:1) and the solvents removed on a rotary evaporator.

LCMS analyses
LC-HRMS was performed on an Agilent 6530 Accurate-Mass QToF with ESI-source coupled with an Agilent 1260 Infinity HPLC equipped with a degasser, binary pump, autosampler, DAD detector, and a 150×4.6mm Kinetex C18 5μm column (Phenomenex, Torrance, CA) calibrated using the Agilent Reference Calibration Mix. A total number of 108 samples including crude extracts (n=51), SPE fractions (n=34) and controls (n=23) were analyzed. Controls included non-deployed HP-20 resin and Nitex mesh MeOH extracts and the solvents used for extraction and treatment of the SMIRC containers (MeOH, DCM, isopropanol). The samples were dissolved, when applicable, in MeOH (5μL, 1mg/mL) and eluted (1mL/min) using an isocratic mobile phase (20:80 acetonitrile (ACN):H2O and 0.1% formic acid (FA)) for 2min followed by a gradient elution to 95:5 ACN:H2O over 18min, isocratic for 2min, then increasing to 100% ACN over 1min, and finishing with 100% ACN for 2min. For feature-based molecular networking, a 100×3mm Kinetex C18 2.6μm particle size column (Phenomenex, Torrance, CA) was used. Crude extracts (n=24) and controls (n=7) were eluted using an isocratic mobile phase 10:90 ACN:H2O and 0.1% FA for 2min followed by a gradient elution to 98:2 ACN:H2O over 20min, then increasing to 100% ACN over 3min and finishing with 100% ACN for 1min. The flow was set to 0.5mL/min.

MS data were acquired over the range 135–1700 m/z in positive mode using the following parameters: MS scan rate, 2/s; MS/MS scan rate, 3/s; gradient collision energy (slope 2.6, offset 15 eV); source gas temperature, 300°C; gas flow, 11L/min. All solvents were LCMS grade.

The data was manually analyzed using the Masshunter Qualitative Analysis Software B.05.00.

Molecular networking and feature detection
CMN and automated library searches were performed using the online workflow (https://ccms-ucsd.github.io/GNPSDocumentation/) on the GNPS website (http://gnps.ucsd.edu)64 and visualized using Cytoscape65 and the GNPS network visualizer. For CMN, the data was filtered by removing all MS/MS fragment ions within ±17 Da of the precursor m/z. MS/MS spectra were filtered by choosing the top 6 fragment ions in the ±50 Da window throughout the spectrum. The following settings were used: precursor ion mass tolerance (PIMT) 0.02 Da, MS/MS fragment mass tolerance (FMT) 0.05 Da, and cosine score cutoff 0.7 with more than three matched fragments. The maximum size of a molecular family was set to 100. The library spectra were similarly filtered with matches requiring a cosine score >0.7 and at least four matched fragment peaks. Spectra of interest were queried in MASST via the GNPS platform54 and publicly available metabolomic datasets were searched with default parameters (https://masst.ucsd.edu/). For the FBMN, LCMS raw data was pre-processed with MZmine 2.5366 using MS1 feature detection: retention time (rt) 2–25 min, MS signal intensity 1E3, centroid mass detector; MS2 feature detection: rt 2–25 min, MS signal intensity 1E2, centroid mass detector. MS1 features were transformed to chromatograms with the minimum number of scans 2, group intensity threshold 1E3, minimum highest intensity 2E3, m/z tolerance 0.05 Da or 20 ppm. Chromatograms were deconvoluted using the baseline cutoff algorithm (min peak height 4E3, peak duration 0.02–1.0 min, baseline level 1E3), m/z center calculation was set to MEDIAN, m/z range for MS2 scan pairing was set to 0.02 Da and rt range to 0.1 min. The chromatograms were deisotoped with m/z tolerance of 0.02 Da or 10 ppm, absolute rt tolerance of 0.1 min, maximum charge 2, and the representative isotope was set to lowest m/z. Aligned feature lists were built with the join aligner, m/z tolerance of 0.02 Da, rt tolerance of 0.2 min, and weight for m/z and rt set to 75 and 25, respectively. The aligned feature list was exported to FBMN. The network was created with a PIMT 0.1 Da, FMT 0.1 Da, cosine cutoff 0.7, and 4 matched peaks, the maximum size of a molecular family 200, while other settings were the same as for CMN above. Both FBMN and CMN jobs including detailed settings description can be accessed online under https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=26bfbaebdd7245c182579769ee258822 (CMN) and (https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=8130a98b878c4c6fa55c8ef3e412c648 (FBMN).

Molecular formula annotation
The molecular formulas for the compounds of interest were determined using the online tool ChemCalc67. MS/MS spectra of targeted compounds were exported as .mgf files from Masshunter Qualitative Analysis Software B.05.00 and manual curation applied to remove noise and 13C and 37Cl isotopic mass peaks. The .mgf files were then subjected to the SIRIUS 5.8.3 analysis platform, which includes CSI:FingerID substructure prediction68, and CANOPUS69, followed by further manual analysis.

Antibiotic screening
Crude extracts were screened for antibiotic activity using an HPLC microfractionation assay. In brief, 1mg (i.e., 33μL at 30mg/mL) of crude extract was injected into an analytical HPLC (reversed-phase 150×4.6mm Kinetex C18 5μm column, flow rate 0.8mL/min) using a gradient from 10–100% ACN (with 0.1% FA) over 16 min then isocratic for 4 min. Fractions (200μL) were collected every 15s starting at 1.8 min (system dead time) into flat bottom 96-well micro-titer plates using a Gilson FC204 fraction collector, yielding 80 fractions per extract. The fractions were dried under vacuum (4h), and the content of each well dissolved in 10μL DMSO and 190μL of an overnight culture of outer membrane defective E. coli LptD421330,70. Positive (10μM chloramphenicol) and negative (10μM streptomycin and solvent/media) controls were tested for each plate. The plates were incubated for 16h with shaking (120 rpm, 30°C) and turbidity (OD650) measured before and after incubation on a Molecular Devices Emax Precision Plate Reader (Molecular Devices, San Jose, CA). Growth inhibition was calculated as the reduction in OD relative to solvent/media controls.

Compound isolation
For chrysoeriol, crude extracts were separated into five fractions using 2g C18 reversed-phase silica gel 90Å pore size (Sigma-Aldrich, St. Louis, MO) and a 20mL MeOH:H2O step gradient (25:75, 50:50, 75:25, 90:10, and 100:0 MeOH:H2O). HPLC was performed using Agilent’s 1100 G1312A binary pump, 1100 G1315A DAD UV/Vis detector, 1100 G1313A autosampler, and 1100 G1322A degasser (Agilent Technologies, Santa Clara, CA) coupled to a Shimadzu (Kyoto, Japan) low-temperature ELSD through a Hewlett Packard 35900E (Spring, TX) interface. Antibiotically active fractions were further separated by HPLC (10×250mm C18 column, isocratic MeOH:H2O (69:31) with 0.1% FA) and 2.5mL/min flow. For aplysiopsene A, crude agar-resin extract was fractionated following the same procedure and fraction 3 (75:25 MeOH:H2O) further separated by HPLC (4.6×150mm C18 column, isocratic ACN:H2O (50:50) with 0.05% FA, 1mL/min flow). For cabrillostatin (1) and cabrillospirals A-B (2-3), crude extracts from four resin containers were separately fractionated as described above and respective fractions 3 (75:25 MeOH:H2O) were then submitted to HPLC (4.6×150mm C18 column, isocratic ACN:H2O (35:65) with 0.05% FA, 1mL/min flow) for isolation of 1-3.

NMR and ECD spectroscopy
NMR spectra (1D and 2D) were measured at 23°C on a JEOL ECZ spectrometer (500MHz) equipped with a 5mm 1H room temperature probe (JEOL, Akishima, Tokyo, Japan) or on a Bruker Avance III (600MHz) NMR spectrometer with a 1.7mm 1H [13C/15N] microcryoprobe (Billerica, MA). Quantitation of sub-milligram samples was achieved using the ‘quantitative solvent 13C-satellite’ method, which is estimated to have 5–10% accuracy, and cholesterol as the external standard71. NMR spectra were referenced to the solvent signals (CHD2OD, δ 3.31, CD3OD, δ 49.00 ppm; CHD2CN, δ 1.94, CD3CN, δ 1.74 ppm). The NMR spectra were processed using MestReNova (Mnova 12.0, Mestrelab Research). ECD spectra were measured on a JASCO J-810 spectropolarimeter (JASCO, Tokyo, Japan) at 23°C in CH3OH and 5mm path length quartz cells. Minimal energy conformers of model compounds were constructed with Spartan ‘20 or ‘24 (Wavefunction, Inc., Irvine, CA, USA), and the ECD spectra were calculated with Gaussian 09 Revision-C.01-SMP (Gaussian, Inc., Wallingford, CT, USA).

Detailed structural elucidation of compounds 1-3 is provided in the Supplementary Notes.

Quantification of flavonoids in seawater
One liter of seawater was collected from the seagrass meadow, passed through a Waters Oasis HLB SPE column (Waters Corporation, Milford, MA), eluted with 3mL MeOH, and analyzed by HPLC (UV detection, 360nm) using chromatography conditions described above for antibiotic screening (microfractionation). A methanolic solution of purified chrysoeriol (1mg/mL) was used as a standard. AUC values were used to calculate flavonoid concentrations.

Cell lines
Lung (A549, #CCL-185), prostatic (DU145, #HTB-81), renal (786-O, #CRL-1932), and pancreatic (AsPC1, #CRL-1682) carcinoma cell lines were purchased from ATCC (Manassas, VA). Hepatocellular (HEPG2, #CCLZR209), glioblastoma (U87MG, #CCLZR406), and colorectal (HCT116, #CCLZR253) carcinoma cell lines were purchased from the UCSF cell line core facility. Ovarian (OVCAR4, #SCC258) carcinoma cell lines were purchased from Sigma-Aldrich (St. Louis, MO). WM164 was a gift from Dr. Helmut Schaider. A549, OVCAR4, 786-O, WM164, and AsPC1 were maintained in RPMI-1640 (Invitrogen, Waltham, MA). DU145, HEPG2, U87MG, and HCT116 were maintained in DMEM with 4.5g/dL glucose (Invitrogen, Waltham, MA). Both media were supplemented with 10% fetal bovine serum (Gemini Bio, West Sacramento, CA) and 1% penicillin/streptomycin (Thermo Fisher Scientific, Waltham, MA). Cell lines were maintained in culture in a humidified 37°C incubator with 5% CO2 for a maximum of 6 weeks. Frozen cell lines were stored in liquid nitrogen with 10% DMSO. All cell lines were routinely tested by PCR for mycoplasma72,73. No known misidentified cell lines were used, and cell lines were authenticated in 2024 using Short Tandem Repeat profiling.

Cell painting
Reference compounds were purchased from Selleck (Houston, TX) and/or MedChemExpress (Monmouth Junction, NJ) and tested at 2 doses (1:1000 and 1:10,000 dilutions) in duplicate. Natural products were screened at 10µM in triplicate. Cells were seeded into 384-well PhenoPlates (Perkin Elmer, Waltham, MA) at an empirically determined density in 75mL of the respective media per well and treated with compounds for 48h. Compounds were added directly to the well using the ECHO 650 liquid handling system (Beckman Coulter, Brea, CA) integrated into a Perkin Elmer EXPLORER G3 WORKSTATION. Transfers and plate handling were established using Perkin Elmer’s plate::works™ (v6.2) software. Staining with cell painting dyes were adjusted from prior studies40. Briefly, 10µL of an 8× Mitotracker deep red (Invitrogen, Waltham, MA) master mix (in DMEM at a concentration of 5mM) was added to each well, incubated for 30min, then fixed by adding 30µL 16% paraformaldehyde (final concentration 4%) for 30min at room temperature (RT). Plates were then washed with 1× HBSS (Invitrogen, Waltham, MA), permeabilized with 1× HBSS + 0.5% (vol/vol) Triton X-100 solution (30min), and washed twice with 1× HBSS. Thirty ml of a staining master mix in 1× HBSS + 1% (wt/vol) BSA solution (Supplementary Table 1) was added and incubated in the dark at RT for 30min. All dyes were purchased from Life Technologies. Finally, cells were washed three times with 1× HBSS, covered with aluminum foil seals, and light-protected until imaging. All pipetting steps were automated using the Perkin Elmer EXPLORER G3 WORKSTATION and performed by MultiFlo FX and 405TS washer (BioTek, Agilent Technologies).

High-content imaging and data analysis
Imaging was performed on the Operetta CLS confocal spinning-disk high-content analysis system (Perkin Elmer, Waltham, MA) using a ×20 water-immersion objective. Each well was imaged at 5 fields of view, in 5 channels (filters according to wavelength information in Supplementary Table 13) in 3 z-planes. Cell segmentation and single-cell feature extraction were performed using the Harmony™ software (v4.9, PerkinElmer, Waltham, MA) from maximum intensity projections of each field of view. In total, ~1100 features were calculated for each cell, including intensity, morphology, and texture features. Phenotypic profiles were calculated using the Kolmogorov–Smirnov (KS) statistic and bioactivity defined as cellular responses distinct from DMSO controls42. The Mahalanobis metric was used to calculate distances between DMSO replicates and between DMSO and test compounds. A Gaussian kernel estimation was then applied to the DMSO-DMSO distances to establish an null distribution, which was compared to the DMSO-compound distance. Compound distances ≥2X the 99th percentile of the null distribution (p≤10−6) were defined as bioactive. Reference compound classification and novel compound class prediction were calculated per well based on phenotypic profiles42. All computations were performed using Python (v3.9).

iPSC-derived cardiomyocyte differentiation
Induced pluripotent stem cell (iPSC) derived cardiomyocytes were generated from human iPSCs (WTC background genetically modified to inducibly express dCas9-KRAB fusion protein and constitutively express GCaMP6, purchased from the Gladstone Institute Stem Cell Core facility) using previously published differentiation, expansion, and maturation protocols with modifications43,74,75. Briefly, human iPSCs were dissociated into a single-cell suspension using TrypLE™ Express (Thermo Fisher Scientific, Waltham, MA) and seeded into Geltrex™ LDEV-Free Reduced Growth Factor Basement Membrane Matrix-coated 12 well-plates (Thermo Fisher Scientific, Waltham, MA) at 75k cells per well in E8 media supplemented with 10μM Y-27632. Cells were fed daily with E8 media and differentiation was initiated once the iPSCs reached 80–90% confluence (~48h) by addition of RPMI-1640 supplemented with B27 without insulin and 7.5μM CHIR99021. After 48h, the media was replaced with RPMI-1640 supplemented with B27 without insulin and 7.5μM IWP2. After an additional 48h, the media was replaced with RPMI-1640 supplemented with B27 without insulin. Two days later, the media was changed to RPMI-1640 supplemented with B27 containing insulin and 1% P/S that was replenished every 2-3 days. Spontaneous beating was generally observed 8–10 days after the addition of CHIR99021. iPSC-derived cardiomyocytes (iCMs) from wells that showed spontaneous beating in >80% of the surface area were exposed to lactate enrichment medium. After 6 days of enrichment, iCMs were seeded into Geltrex coated 72cm2 flasks and cultured in RPMI-1640 with B27, insulin, and 2μM CHIR99021 until reaching confluence. iCMs were then replated into Geltrex coated 384-well PhenoPlates (Perkin Elmer, Waltham, MA) and switched to maturation medium for 10 days prior to use.

iPSC-derived cardiomyocyte Ca2+ transient imaging and analysis
Anti-arrhythmic and anti-cancer drugs were purchased from Selleck (Houston, TX) or MedChemExpress (Monmouth Junction, NJ) and tested at 2 doses (1:1000 and 1:10,000 dilutions) in duplicate. Natural products were screened at 10µM in triplicate. After 24h or 48h of drug treatment (primary and validation experiments, respectively), iPSC-derived cardiomyocyte Ca2+ transients were monitored using an EVOS M7000 Imaging System (Thermo Fisher Scientific, Waltham, MA) with atmospheric control set at 37°C, 80% humidity, 5% CO2, for 20s at a frame rate of 30hz in the GFP channel (Excitation max 488nm) using a ×20 objective (one FoV). Mean fluorescence intensities for each field of view were extracted using the scikit-image (v1.2.2) module. The signal was baseline-corrected using the Python package BaselineRemoval (v0.1.3), and the mean signal intensity of frames 200–600 (~13 sec) was plotted for each compound.

Protease inhibition studies
SARS-CoV-2 Mpro was expressed as outlined previously76. SARS-CoV-2 PLpro was purchased from Acro Biosystems (PAE-C518), and human TMPRSS2 was purchased from Cusabio Technology (CSB-YP023924HU). Other recombinant proteases were purchased from R&D Systems, which included SARS-CoV-1 3CL/Mpro (E-718), MERS-CoV 3CL/Mpro (E-719), human cathepsin B (953-CY), human cathepsin L (952-CY), human cathepsin D (1014-AS), and human cathepsin E (1294-AS). The final enzyme concentration in each assay was 50nM of SARS-CoV-1, SARS-CoV-2 Mpro, MERS-CoV Mpro, and SARS-CoV-2 PLpro; 0.5nM of human cathepsin L and B, 1nM human cathepsin D and E, and 3nM TMPRSS277. Fluorogenic peptide substrates were purchased from a variety of vendors and used at the following concentrations: 10μM MCA-Ala-Val-Leu-Gln-Ser-Gly-Phe-Arg-Lys(DNP)-Lys-NH2 (a gift from Charles Craik, University of California San Francisco)78, 50μM Mu-His-Ser-Ser-Lys-Leu-Gln-AMC (Sigma SCP0224), 50μM z-Arg-Leu-Arg-Gly-Gly-AMC (Bachem I1690), 5μM Mca-Gly-Lys-Pro-Ile-Leu-Phe-Phe-Arg-Leu-Lys(DNP)-DArg-NH2 (CPC Scientific, SUBS-017A), 85.5μM Boc-Gln-Ala-Arg-AMC (Biosynth, MQR-3135-v), 5μM z-Phe-Arg-AMC (R&D Systems, ES009) for cathepsin L, and 100μM of the same substrate for cathepsin B. Assay buffers for protease assays were as follows: Assay Buffer 1 for SARS-CoV-1 Mpro and MERS-CoV Mpro: 50mM HEPES pH 7.5, 150mM NaCl, 1mM EDTA, 1mM DTT, 0.01% Tween-20; Assay Buffer 2 for SARS-CoV-2 Mpro: 50mM Tris-HCl, pH 7.5, 150mM NaCl, 1mM EDTA, 0.01% Tween-20; Assay Buffer 3 for SARS-CoV-2 PLpro: 50mM HEPES pH 6.5, 150mM NaCl, 0.01% Tween-20, 0.1mM DTT; Assay Buffer 4 for Cathepsin B: 50mM Na-Acetate pH 5.5, 5mM DTT, 1mM EDTA, 0.01% BSA; Assay Buffer 5 for Cathepsin L: 50mM Na-Acetate pH 5.5, 5mM DTT, 1mM EDTA, 0.01% BSA, 100mM NaCl; Assay Buffer 6 for Cathepsin D and E: 50mM Citrate phosphate buffer, and Assay Buffer 7 for TMPRSS2: 25mM Tris-HCl, pH 8.0, 150mM NaCl, 5mM CaCl2, 0.01% Triton X-100. Cabrillostatin (1) was diluted into the appropriate assay buffer to a concentration of 40μM and then pre-incubated with each protease for 15min at 25°C. Separately, the fluorogenic substrates were diluted in the appropriate assay buffers at twice the target concentration and then added to the enzyme/inhibitor mixture. All assays were performed at 25°C in triplicate wells, and DMSO was used as vehicle control. The final test concentration of cabrillostatin was 10μM. Control inhibition assays were conducted with 10μM GC373 for all 3CL/Mpro enzymes (a gift from John Vederas, University of Alberta), 10μM of compound 159, an in-house discovered PLpro inhibitor78, 10μM E-64 for cathepsin L and B (Sigma E3132), 10μM Pepstatin for cathepsin D and E (Sigma P5318), and 1μM Camostat (a gift from James Janetka, Washington University St. Louis). The final volume of each reaction was 30μL in a 384-well plate, and fluorescence was measured at 360/460nm (ex/em) for peptide-AMC substrates or 320/400nm (ex/em) for internally quenched MCA-peptide-DNP substrates in a Biotek Synergy HTX fluorescence plate reader. The reaction velocity was calculated as relative fluorescent units per second. The activity was normalized to wells lacking inhibitor but containing the vehicle (DMSO) in assay buffer.

Metagenomic analyses
Sediment (February and March 2021) and bulk seawater samples (August 2021) were collected in parallel with the SMIRC deployments. Sediment samples (~80g) were collected in sterile WhirlPak™ bags and transported on ice to Scripps Institution of Oceanography for storage. Bulk seawater samples (2L each) were immediately filtered (0.2μm Sterivex). All samples were stored (−20°C) until further processing. DNA was extracted using a phenol-chloroform protocol79 and assessed using a Nanodrop for quality and concentration estimates. When needed, low-concentration extracts from the same sediment/water samples were pooled prior to sequencing. Libraries for all samples were prepared using the Nextera XT DNA library preparation kit (Illumina, San Diego, CA) for sequencing on an Illumina NovaSeq 6000 system with 150-bp paired-end reads.

Raw reads were quality trimmed, and adapters were removed using the BBMap package. For taxonomic and biosynthetic analyses, samples were processed using a read-based approach to limit assembly biases80. Briefly, single-copy phylogenetic marker genes were extracted with hidden Markov models (HMMs)81 from each sample and classified against a reference genomic database using phylogenetic inferences with pplacer (v1.1.alpha17)82. Translated reads were searched against the NaPDoS283 database for ketosynthase domains. Filtered reads were normalized with BBNorm (target=40, min depth=5) and assembled with the IDBA-UD assembler using the pre-correction flag (mink=30, maxk=200, step=10)84. Assembled contigs (>2000bp) were searched for BGCs with antiSMASH (v5.1.2; both fungal and bacterial versions)85. BGCs were clustered into gene cluster families (GCFs) based on relatedness using BiG-SCAPE (v1.1.2)86. Contigs were binned into metagenome-assembled genomes (MAGs) by calculating coverage profiles with bowtie287 followed by an unsupervised binning approach with Metabat2 across individual samples88. All MAGs were checked for quality (i.e., completeness and contamination) with checkM89, dereplicated with dRep90, and assigned taxonomy with GTDB-Tk91.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript describes an interesting method that captures natural products directly\nfrom marine environments at amounts that can be suitable for NMR-based structure\nelucidation. This was demonstrated with several technical setups and habitats and resulted\nin two series of new polyketides, named cabrillostatin and the cabrillospirals, in addition to\nsome known natural products. While ample work exists on natural product discovery from\nextracted individual organisms, knowledge on natural roles of metabolites and their\noccurrence in complex communities is scarce. As a method that can be readily implemented\nin research labs, “SMIRC” could facilitate such studies. In addition to the methodology, the\nmanuscript features some impressive structure elucidation work from small substance\namounts as well as metagenomic studies that attempted to identify compound sources. The\nlatter were unfortunately not successful and the section might be shortened and parts of it\nmoved to the SI.\nFurther comments/questions:\n1) Abstract: “Isolated two novel carbon skeletons” should be rephrased to “isolate\ncompounds with two novel carbon skeletons” or similar.\n2) Page 3: “Despite this realization, our best discovery efforts have failed to access this\npredicted novelty...”: This should be rephrased. It reads as if nobody was able to obtain\nnovel natural products based on bioinformatic predictions.\n3) Page 10: Could the vinylogous formate ester be an artifact arising from, e.g., dehydration\nof an unstable and structurally less unusual cyclic hemiacetal? This might explain why no\nactivity was found. Did the authors detect a candidate for a congener by LC-MS?\n4) Page 14: ...and complemented by...\n5) Page 18: Explain the specialized term “hybrid KS”.\n6) Page 7: It seems unlikely that the cabrillospiral PKS would contain >10 almost identical KS\ndomains. Can the authors explain the rationale?", "reviewer_2": "The manuscript by Bogdanov and colleagues describes a culture-independent method to\ndiscover novel natural products based on the deployment of adsorbent resin in the\nenvironment to capture natural product in situ. While previous work from this group already\ndescribed deployment of resin in the environment to study microbial products\n(https://doi.org/10.1128/AEM.02830-18), it was thus far followed only by metabolomics\npaired with metagenomics. Here the authors add on top of these approaches the isolation\nof purified molecules and their characterization by NMR, MS and in terms of bioactivity.\nNotably, the authors describe the discovery of two new carbon skeletons. This achievement\ndemonstrates that it is in fact possible to obtain sufficient amount of purified compounds\ndirectly from the environment for structural elucidation by NMR which is impressive and of\ngreat interest to the natural products community. In addition to offering a way to address\nquestions in chemical ecology, as a culture-independent approach it should also provide\naccess to molecules that are not produced by microorganisms that are readily cultured or\nare not expressed in standard laboratory conditions.\nAccording to the authors, this paper describes a method but the potential future users\namong the readers will regret the lack of a critical analysis by the authors of the limitations\nof the approach. A lot of attention is brought to the validation of particular abilities\n(structure resolution, isolation of bioactive molecules, linking molecules to their producing\ngene clusters) with various degrees of success but it remains hard to forge an opinion on the\nscalability of this approach as a viable new paradigm in natural product discovery. The\nauthors should address the key question of scalability by sharing the main bottleneck of the\napproach, according to them, and provide insights from their experience regarding its\nlimitations.\nWhile some minor variations are introduced (embedding the resin in agar) their impact does\nnot seem clear as the most successful part of the work, which led to the discovery of two\nnew carbon skeletons does not make use of it and appears identical in terms of\nmethodology to the previous use of this approach by this group\n(https://doi.org/10.1128/AEM.02830-18). What enabled this success for these molecules ?\nWas it to attempt the approach in multiple sites to find one of suitable chemical complexity\n? Was it a question of scale ? If so, would using more resin be desirable and practical ?\nProviding such answers and setting clear expectations in terms of practical difficulty in the\nmain text will most likely impact a lot the potential for use of the approach by the\ncommunity.\nSpecific comments:\nA brief explanation of why HP-20 is the best resin to use for SMIRC would be nice, would\nthere be interesting alternatives?\nMethod section could indicate what type of precision scale and method was used to\nprecisely weight such low amount of material (a few dozens of g). It would be good to\nstate the error range expected since this could impact a lot the concentrations reported for\nthe bioactivities.\nIt is a bit confusing that the chromatograms in Figure 1 are UV with microfractionation\nbioassays while they are mass chromatogram without bioassay data in Figure 2. For a\npotential user of the approach, it would be easier to gain an understanding of what to\nexpect from a typical SMIRC trace if the format was consistent.\nDFT calculation was carried out, but the calculated data is not provided by the authors. Only\nexperimental ECD spectra is shown in Fig. S11, calculated ECD spectra should be included to\ncompare it with the experimental ones. Calculated chemical shifts and 3D Cartesian\ncoordinates of the structures in Fig. S12, Fig. S13 and Fig. S14 should be provided as well.\nThe authors could provide more context around the claim of bioactivity of cabrillostatin. In\nits current format, the description of bioactivity data (line 203-231) makes it hard for a non-\nexpert to understand why this panel of assays was chosen in particular. Each assay reported\nhere seem to lead to some signal for cabrillostatin, were more assays conducted but not\nreported ? If yes, including the full list would be of great interest to the readers wishing to\nattempt SMIRC in the future and the negative data on the other assays would make a\nstronger case for specific cabrillostatin activities. Currently, the formulation “intriguing\nbiological activities\" used by the authors line 372 is indeed the only conclusion a non-expert\nwould reach and does not appear sufficient to “demonstrate the applications of this\napproach to natural product drug discovery » (line 374). To be clear, I do think there is\npotential to discover novel active compounds with SMIRC but unless this part in made\nclearer by the authors, I suggest removing “broad” in the abstract line 39, removing\n“promising” line 76, and adding the word “potential” line 374.\nMetagenomics:\nThe metagenomic analyses part describes an ambitious attempt at identifying the\ncabrillostatin and cabrillospirals BGCs which failed to reach a solid conclusion but\nnevertheless should be applauded.\nSince the demonstration of identifying the BGC is not achieved in this work, it is unclear in\nthe abstract why metagenomic are “introduced” in this work line 36 since an attempt at\nmetagenomics was already present in the group’s previous work with resin deployment in\nsitu. Similarly, “link compounds to producing organisms » line 37 of the abstract is very\nspeculative and should be removed unless a more solid demonstration is provided. Such\noverclaims will diminish the perceived novelty of a truly successful demonstration in the\nfuture by these authors or others.\nThe assembled sequences of the two candidate Type1PKS BGCs proposed by the authors are\ntoo small for the cabrillospirals, yet the authors showcase these BGCs and the associated\nplanctomycte MAG as their top candidates because they consider that the assembled\nsequence they report is truncated due to assembly challenges commonly associated with\nhighly repetitive modular PKSs. While this effect is indeed sometimes reported it is not a\nrule. In any case, this effect should not affect the rest of the contig which is not as repetitive\nas the PKS and the decorating genes should be faithfully represented. Is there any indication\nto be found there ? In a recent paper (https://doi.org/10.1038/s41396-023-01410-3), the\nsame authors made the case of a BGC as the origin of an halogenated compound based on\nthe presence of halogenase genes in it. Their absence on the proposed BGCs of a\ndihalogenated molecule seem to contradict the authors’ conviction in their top candidate.", "reviewer_3": "In “Small Molecule In Situ Resin Capture – A Compound First Approach to Natural Product\nDiscovery,” Bogdanov et al. report on the natural product isolation and structure elucidation\ndirectly from the environment. Their approach makes use of a hydrophobic resin which is\ndeployed in the environment to capture natural products directly where they are produced\nand act. They then use a combination of state-of-the-art mass spectrometry and NMR\nmethods to dereplicate and elucidate a range of compounds, including so far unknown\ncompounds with new carbon backbones.\nTheir approach tackles a central bottleneck in common culture-based NP discovery\napproaches, which is the difficulty of culturing microbes and the often encountered silence\nof the genetic potential in culturing conditions. While the approach of resin-based\nenvironmental sampling is not necessarily novel, the authors bring its capabilities for NP\ndiscovery, purification, and de novo structure elucidation to a new level, which I found quite\nimpressive. In addition to solving new structures out of ultra-complex environmental\nsamples, it is great to see that the authors used public MS/MS data and state-of-the-art\ncomputational metabolomics tools to compare their own data to other public datasets to\ncheck for the occurrence of their newly identified molecules in other marine environments,\nmainly from the Pacific.\nThe connection of environmental sampling, de novo structure elucidation, and repository\nscale data analysis, is quite innovative in my opinion and could be a prime example for\nfuture NP discovery studies. In my opinion, this paper should be of broad interest for both\nthe natural product, marine chemical ecology, as well as the metabolomics community, and\nI think that Nature Communications would be a great place for it.\nWhile I am in principle very enthusiastic about the paper, there are a couple of points the\nauthors may want to consider and revise before the paper gets published.\nBesides your own metagenome data, is there any evidence or hints to other ocean\nmetagenomes and putative gene clusters thereof, that could be linked to the biosynthesis of\ncabrillostatins?\nFor cabrillospirals, are Planctomycetes accessible by current culturing methods? If yes, did\nyou attempt to cultivate and verify the production from Planctomycetes?\nAs for the comparison of your data against the repository, I am wondering to what degree\necological insights can be drawn from these dataset matches?\nOther specific comments:\nLine 61: Can you provide some examples/numbers for the richness of the Earth’s\nmicrobiome? Perhaps from the Earth Microbiome Project or other initiatives?\nLine 125: How likely is it that the compounds discovered are made by the bacteria growing\non the resin? Did you do any cultivation and MS experiments of the pink colony-forming\nbacteria?\nLine 147: Why did you target specifically this compound?\nLine 152: What is “tandem UV”? Or do you mean coupled UV and ELSD detection?\nLine 191: What is the reason/bottleneck to assign the stereochemistry? Will this be feasible\nwith the amounts of compound you isolate? Or is this a limitation of the approach? As you\nstated, it sounds a little vague. Especially as the paper has been published a while ago, I\nwould assume you could estimate whether this will be feasible by now.\nLine 250: You describe the identification of a wide range of compounds using MS/MS\nmatching. What confidence level (according to the Sumner et al. levels) were these? Did you\nconfirm any of them with authentic standards?\nLine 436: Why did you use a 4.6 mm column (semi-prep scale) at such a high flow rate?\nTypically, a 2 mm column and lower flow rates are more appropriate for ESI, or did you do\nflow splitting?\nLine 449: Please provide the GNPS settings.\nSI Fig. S3: Change the title to “HR-ESIMS/MS”."}, "author_response": "We thank the reviewers for their helpful comments, which we have addressed point by point below. Our\nresponses are in italics.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThe manuscript describes an interesting method that captures natural products directly from marine\nenvironments at amounts that can be suitable for NMR-based structure elucidation. This was\ndemonstrated with several technical setups and habitats and resulted in two series of new polyketides,\nnamed cabrillostatin and the cabrillospirals, in addition to some known natural products. While ample\nwork exists on natural product discovery from extracted individual organisms, knowledge on natural roles\nof metabolites and their occurrence in complex communities is scarce. As a method that can be readily\nimplemented in research labs, “SMIRC” could facilitate such studies. In addition to the methodology, the\nmanuscript features some impressive structure elucidation work from small substance amounts as well as\nmetagenomic studies that attempted to identify compound sources. The latter were unfortunately not\nsuccessful and the section might be shortened and parts of it moved to the SI.\nResponse: We reduced the metagenomic section of the paper without the need to move any text to the SI.\nFurther comments/questions:\n1) Abstract: “Isolated two novel carbon skeletons” should be rephrased to “isolate compounds with two\nnovel carbon skeletons” or similar.\nResponse: change made as suggested.\n2) Page 3: “Despite this realization, our best discovery efforts have failed to access this predicted\nnovelty...”: This should be rephrased. It reads as if nobody was able to obtain novel natural products\nbased on bioinformatic predictions.\nResponse: the text has been changed to “Despite this realization, our best discovery efforts have failed to\naccess the vast majority of this predicted chemical space and thus new approaches to natural product\ndiscovery are needed.”\n3) Page 10: Could the vinylogous formate ester be an artifact arising from, e.g., dehydration of an\nunstable and structurally less unusual cyclic hemiacetal? This might explain why no activity was found.\nDid the authors detect a candidate for a congener by LC-MS?\nResponse: As for many natural products, it’s possible that the compound isolated (2-3, vinylogous\nformate esters) are artifacts of subsequent chemical (or enzymatic) reactions that occur outside the\nproducing organism. However, a detailed search of the LCMS data did not find any candidate congeners\nthat would support this possibility. We also did not see a methoxy analog of the hemiacetal, which might\nbe expected given the use of methanol in the extraction. We added text to clarify this point. Even if 2-3 are\nnot natural products, the novelty of the structures is not diminished. Finding the associated BGC could\nhelp resolve this question.\n4) Page 14: ...and complemented by...\nResponse: change made as suggested\n5) Page 18: Explain the specialized term “hybrid KS”.\ntheir first use and revised the text to clarify that it is a hybrid PKS-NRPS BGC that was predicted.\n6) Page 7: It seems unlikely that the cabrillospiral PKS would contain >10 almost identical KS\ndomains. Can the authors explain the rationale?\nResponse: We have removed this text in the process of shortening this section in response to a prior\ncomment.\nReviewer #2 (Remarks to the Author):\nThe manuscript by Bogdanov and colleagues describes a culture-independent method to discover novel\nnatural products based on the deployment of adsorbent resin in the environment to capture natural product\nin situ. While previous work from this group already described deployment of resin in the environment to\nstudy microbial products (https://doi.org/10.1128/AEM.02830-18), it was thus far followed only by\nmetabolomics paired with metagenomics. Here the authors add on top of these approaches the isolation\nof purified molecules and their characterization by NMR, MS and in terms of bioactivity. Notably, the\nauthors describe the discovery of two new carbon skeletons. This achievement demonstrates that it is in\nfact possible to obtain sufficient amount of purified compounds directly from the environment for\nstructural elucidation by NMR which is impressive and of great interest to the natural products\ncommunity. In addition to offering a way to address questions in chemical ecology, as a culture-\nindependent approach it should also provide access to molecules that are not produced by microorganisms\nthat are readily cultured or are not expressed in standard laboratory conditions.\nAccording to the authors, this paper describes a method but the potential future users among the readers\nwill regret the lack of a critical analysis by the authors of the limitations of the approach. A lot of\nattention is brought to the validation of particular abilities (structure resolution, isolation of bioactive\nmolecules, linking molecules to their producing gene clusters) with various degrees of success but it\nremains hard to forge an opinion on the scalability of this approach as a viable new paradigm in natural\nproduct discovery. The authors should address the key question of scalability by sharing the main\nbottleneck of the approach, according to them, and provide insights from their experience regarding its\nlimitations.\nResponse: We have expanded on our analysis of the limitation of the approach throughout the text, which\nincludes the following:\n“although challenges in these areas remain”.\n“Notably, compounds 1, 2, and unidentified compound m/z 757.4630 [M+H]+ were consistently detected\nin three separate deployments spanning seven months, suggesting that repeat deployments can provide a\npath forward to overcome supply bottlenecks for some target compounds (Fig. S11).”\n“While our efforts to link these compounds to their biological origins remain ongoing, the challenges\nassociated with linking compounds with gene clusters in complex systems such as the ones sampled here\nremain clear.”\n“Yet supply issues remain the major bottleneck with this approach, which in the case of cabrillostatin\nshould be resolvable via synthetic methods. More complex structures such as the cabrillospirals will require\na larger investment from the synthetic community or beyond to meet supply challenge. Ultimately,\ncompound supply will need to be addressed on a case-by-case basis and the upper limits of scale-up\n2\ndeployments vetted. Regardless of the approaches taken to address resupply challenges, the discovery of\nnew natural product chemotypes remains a top priority for natural products research and a driving force\nin drug discovery.”\n“While our efforts to link these compounds to their biological origins remain ongoing, it is clear that\nestablishing these types of linkages in complex systems such as the ones sampled here remain a major\nchallenge.“\nWhile some minor variations are introduced (embedding the resin in agar) their impact does not seem\nclear as the most successful part of the work, which led to the discovery of two new carbon skeletons does\nnot make use of it and appears identical in terms of methodology to the previous use of this approach by\nthis group (https://doi.org/10.1128/AEM.02830-18).\nResponse: While embedding the resin in agar did not lead to the discovery of a new compound, it had a\ndramatic effect on the chemical profile of the extracts (Figure 1) and yielded a known compound that was\nnot observed when resin was used alone. We think this is a particularly important modification to the\ntechnique that warrants further study. We did not use the agar modification in the initial AEM paper cited\nin this comment. The agar modification was new to the present study.\nWhat enabled this success for these molecules? Was it to attempt the approach in multiple sites to find\none of suitable chemical complexity? Was it a question of scale? If so, would using more resin be\ndesirable and practical? Providing such answers and setting clear expectations in terms of practical\ndifficulty in the main text will most likely impact a lot the potential for use of the approach by the\ncommunity.\nResponse: We deployed the resins at 4 sites and the resulting metabolomes were very different from each\nother (Fig. 5A). It’s not clear why the CSMR was more productive. We revised the text to address this\npoint: “While determining what drives these differences, or why the CSMR extracts appeared enriched in\nnovel compounds, will require more study, these observations support deployments in diverse habitats to\nfurther explore the discovery potential of the resin capture approach.” We also added a sentence to the\ndiscussion: “Why this site appeared particularly rich in novel compounds remains unknown, but the\npotential benefits of increased biodiversity within a marine protected area may have played a role.” The\nscale of the deployments can certainly play a role and is something that needs to be considered for initial\ndeployments. We have added a sentence to address this: “Yet others were not, indicating that temporal\nvariability is an important consideration when selecting the quantity of resin to deploy.”\nSpecific comments:\nA brief explanation of why HP-20 is the best resin to use for SMIRC would be nice, would there be\ninteresting alternatives?\nResponse: We selected HP20 based on literature reports that it was effective and superior to other\nadsorbent resins. We modified the following sentence and added a reference to address this comment:\n“SMIRC employs the adsorbent resin HP-20, which has proven effective for the adsorption of lipophilic\nmarine toxins 26, to capture these small organic molecules after they are released and thus provides a\nmechanism to access environmental metabolomes.”\nMethod section could indicate what type of precision scale and method was used to\nprecisely weight such low amount of material (a few dozens of g). It would be good to state the error range expected since this could impact a lot the concentrations reported for\nthe bioactivities.\nquantitative NMR method is related to the electronic integration of the NMR signals, which is typically 5-\n10% (ref 72) and has been added to the text. While this error was unlikely to have affected the results, we\nhave nonetheless added to the text that special quantification methods are required to determine test\nconcentrations.\nIt is a bit confusing that the chromatograms in Figure 1 are UV with microfractionation\nbioassays while they are mass chromatogram without bioassay data in Figure 2. For a\npotential user of the approach, it would be easier to gain an understanding of what to\nexpect from a typical SMIRC trace if the format was consistent.\nResponse: Figure 1 describes the bioassay guided isolation of an active compound. Figure 2 describes\nextracts from the CSMR site that were not active, so we instead used the MS data to guide the isolation of\nnew compounds. We have added text to better clarify this. Figure S1 provides a typical UV trace and MS\nchromatogram from a crude extract. We have modified the legend to indicate that this is a typical extract\nand that the lack of resolution reflects the complexity of the extract. We have also added the following\nsentence to better inform users of what to expect. “This level of complexity was common to the crude\nextracts, which required extensive purification.”\nDFT calculation was carried out, but the calculated data is not provided by the authors. Only\nexperimental ECD spectra is shown in Fig. S11, calculated ECD spectra should be included to\ncompare it with the experimental ones. Calculated chemical shifts and 3D Cartesian\ncoordinates of the structures in Fig. S12, Fig. S13 and Fig. S14 should be provided as well.\nResponse: We expanded the DFT calculations for the structures shown in the Figures S12-S14 and\ncalculated the ECD spectra for 5S,6S, 5S,6R, 5R,6S and 5R,6R diastereomers. We established the\nabsolute configuration of the A ring in cabrillospirals A and B based on the comparison of the\nexperimental and calculated ECD spectra.We also corrected the configuration putatively assigned to\ncabrillospiral B (changed Figure 3 and added text to the main manuscript page 10, paragraph 2 and to\nthe structure elucidation part of supplementary information). We added a new figure (Fig. S10) with the\ncalculated ECD spectra and updated figures S7-S9. We included tables with 3D cartesian coordinates to\nthe supplementary information (Tables S14-S18).\nWe have assigned the absolute configuration of a substantial part of molecules 2 and 3. The complexities\nof the final and complete stereochemical determinations, including DFT calculated NMR chemical shifts\nfor the whole molecule, demand deeper and more sophisticated investigations that we are beyond the\nscope of this Communications paper.\nThe authors could provide more context around the claim of bioactivity of cabrillostatin. In\nits current format, the description of bioactivity data (line 203-231) makes it hard for a non-expert to understand\nwhy this panel of assays was chosen in particular. Each assay reported here seem to lead to some signal\nfor cabrillostatin, were more assays conducted but not reported ? If yes, including the full list would be of\ngreat interest to the readers wishing to attempt SMIRC in the future and the negative data on the other\nassays would make a stronger case for specific cabrillostatin activities.\nResponse: Our choice of assays for cabrillostatin was first driven by reports that the amino acid statine\nwas a phamacophore for protease activity. Here we report results from all proteases tested including\nthose with no activity. To obtain additional bioactivity data, we employed cell painting and high content\nimaging using cancer cells in a manner that would be more informative than simple cytotoxicity assays.\nWe reasoned that testing a diverse set of cancer cell lines using this approach would improve the chances\nof capturing cellular responses and provide information about potential mechanisms of action. These\nassays demonstrated significant activity and some degree of selectivity. We have modified the following\n4\ntext to better clarify this point: “Quantifying the separation showed that 1 was significantly bioactive\n(p<10-6) at 10 mM against six of the nine cell lines (Fig. 4B, Table S7) but not against OVCAR4, HepG2,\nand 786-O cells indicating a degree of selectivity.” The final assay employed an induced pluripotent stem\ncell-derived cardiomyocyte (IPSC-CM) model. This offered a readout of cell functionality instead of\nbiomarkers, which provided a complementing assay to the cancer cell lines phenotypic profiling. It’s\ncorrect that cabrillostatin displayed activity in all of the assays (but not against all proteases or cell\nlines), raising the interesting question as to why this biologically active compound appears to be so\nwidely distributed.\nCurrently, the formulation “intriguing biological activities\" used by the authors line 372 is indeed the\nonly conclusion a non-expert would reach and does not appear sufficient to “demonstrate the applications\nof this approach to natural product drug discovery » (line 374). To be clear, I do think there is\npotential to discover novel active compounds with SMIRC but unless this part in made\nclearer by the authors, I suggest removing “broad” in the abstract line 39, removing\n“promising” line 76, and adding the word “potential” line 374.\nResponse: Changes made as suggested.\nMetagenomics:\nThe metagenomic analyses part describes an ambitious attempt at identifying the\ncabrillostatin and cabrillospirals BGCs which failed to reach a solid conclusion but\nnevertheless should be applauded. Since the demonstration of identifying the BGC is not achieved in this work, it is unclear in the abstract why metagenomic are “introduced” in this work line 36 since an attempt at metagenomics was already present in the group’s previous work with resin deployment in situ. Similarly, “link compounds to producing organisms » line 37 of the abstract is very speculative and should be removed unless a more solid demonstration is provided. Such overclaims will diminish the perceived novelty of a truly successful demonstration in the future by these authors or others.\nResponse: We appreciate this comment and have revised the relevant sentence in the abstract as follows:\n“Expanded deployments, in situ cultivation, and metagenomics were employed to facilitate compound\ndiscovery, enhance yields, and attempt to link compounds to producing organisms, although the\ncomplexity of the microbial communities created challenges for the later.” We have also modified the\ndiscussion to better address this comment: “While efforts to link compounds to producers have been\nsuccessful in the context of symbioses 33,65, the results presented here reinforce the challenges associated\nwith linking compounds to producers in complex microbial communities.”\nThe assembled sequences of the two candidate Type1PKS BGCs proposed by the authors are too small\nfor the cabrillospirals, yet the authors showcase these BGCs and the associated planctomycte MAG as\ntheir top candidates because they consider that the assembled sequence they report is truncated due to\nassembly challenges commonly associated with highly repetitive modular PKSs. While this effect is\nindeed sometimes reported it is not a rule. In any case, this effect should not affect the rest of the contig\nwhich is not as repetitive as the PKS and the decorating genes should be faithfully represented. Is there\nany indication to be found there ? In a recent paper (https://doi.org/10.1038/s41396-023-01410-3), the\nsame authors made the case of a BGC as the origin of an halogenated compound based on\nthe presence of halogenase genes in it. Their absence on the proposed BGCs of a\ndihalogenated molecule seem to contradict the authors’ conviction in their top candidate.\nResponse: We have reduced this section of the paper in response to a comment by reviewer 1 and to\naddress the concerns raised here. This includes the following modified text: “While lacking key\nbiosynthetic genes, the planctomycete MAG was nonetheless the best candidate identified.”\nIn “Small Molecule In Situ Resin Capture – A Compound First Approach to Natural Product\nDiscovery,” Bogdanov et al. report on the natural product isolation and structure elucidation\ndirectly from the environment. Their approach makes use of a hydrophobic resin which is\ndeployed in the environment to capture natural products directly where they are produced\nand act. They then use a combination of state-of-the-art mass spectrometry and NMR\nmethods to dereplicate and elucidate a range of compounds, including so far unknown\ncompounds with new carbon backbones.\nTheir approach tackles a central bottleneck in common culture-based NP discovery approaches, which is\nthe difficulty of culturing microbes and the often encountered silence of the genetic potential in culturing\nconditions. While the approach of resin-based environmental sampling is not necessarily novel, the\nauthors bring its capabilities for NP discovery, purification, and de novo structure elucidation to a new\nlevel, which I found quite impressive. In addition to solving new structures out of ultra-complex\nenvironmental samples, it is great to see that the authors used public MS/MS data and state-of-the-art\ncomputational metabolomics tools to compare their own data to other public datasets to check for the\noccurrence of their newly identified molecules in other marine environments, mainly from the Pacific.\nThe connection of environmental sampling, de novo structure elucidation, and repository scale data\nanalysis, is quite innovative in my opinion and could be a prime example for future NP discovery studies.\nIn my opinion, this paper should be of broad interest for both the natural product, marine chemical\necology, as well as the metabolomics community, and I think that Nature Communications would be a\ngreat place for it.\nWhile I am in principle very enthusiastic about the paper, there are a couple of points the authors may\nwant to consider and revise before the paper gets published.\nBesides your own metagenome data, is there any evidence or hints to other ocean metagenomes and\nputative gene clusters thereof, that could be linked to the biosynthesis of cabrillostatins?\nResponse: This is a great question. Our best biosynthetic hook for cabrillostain is the hybrid KS predicted\nto extend leucine to yield the non-proteogenic amino acid statine. In response to this question, we blasted\nthe two reference KSs linked to statine biosynthesis against an extensive KS library derived from\nmetagenomes, and we found no matches. We have added the following text: “Interestingly, the two\nreference KSs shared <60% amino acid identity with a database of metagenome-extracted KSs 59,\nsupporting the rarity of statine incorporation into natural products.\nFor cabrillospirals, are Planctomycetes accessible by current culturing methods? If yes, did you attempt to\ncultivate and verify the production from Planctomycetes?\nResponse: We tried to cultivate Planctomycetes from the resin and from seagrass and algae samples\ncollected at the site but these efforts were not successful. We have added mention of this to the text.\nAs for the comparison of your data against the repository, I am wondering to what degree\necological insights can be drawn from these dataset matches?\nResponse: While few ecological insights can be drawn from our study, this approach will hopefully\ninspire future studies in chemical ecology. E.g., the detection of antimicrobial seagrass flavonoids in\nseawater suggests these compounds could shape community structure in seagrass beds. It’s also\nintriguing that the biologically active cabrillostatin and related compounds were commonly observed in\nseawater metabolome datasets.\nLine 61: Can you provide some examples/numbers for the richness of the Earth’s microbiome? Perhaps\nfrom the Earth Microbiome Project or other initiatives?\nResponse: An estimate for the richness of Earth’s microbiome and the associated referene has been\nadded.\nLine 125: How likely is it that the compounds discovered are made by the bacteria growing on the resin?\nDid you do any cultivation and MS experiments of the pink colony-forming bacteria?\nResponse: It can't be ruled out that bacteria growing on the resin are a source of some of the compounds\ncaptured. This concept is what inspired us to embed the resin in agar to encourage “in situ microbial\ngrowth”. Our efforts to obtain compound producers in pure culture have not been successful to date. We\nadded this information to the text.\nLine 147: Why did you target specifically this compound?\nResponse: Cabrillostatin was targeted because it appeared to be novel and had a relatively high-intensity\nmolecular peak, which suggested it could be recoverable. We modified the text accordingly.\nLine 152: What is “tandem UV”? Or do you mean coupled UV and ELSD detection?\nResponse: Yes, we meant coupled UV and ELSD detection and have changed the text accordingly.\nLine 191: What is the reason/bottleneck to assign the stereochemistry? Will this be feasible with the\namounts of compound you isolate? Or is this a limitation of the approach? As you stated, it sounds a little\nvague. Especially as the paper has been published a while ago, I would assume you could estimate\nwhether this will be feasible by now.\nResponse: Determination of the absolute configuration of cabrillospirals A/B is independent of compound\navailability and cannot be done with spectroscopic methods alone. Given its three independent\nstereoclusters and a high degree of free rotation, a partial or total chemical synthesis will likely be\nrequired. We have modified the text to clarify this: “The full assignment of the relative and absolute\nconfigurations remains challenging and will likely require synthetic efforts.”\nLine 250: You describe the identification of a wide range of compounds using MS/MS matching. What\nconfidence level (according to the Sumner et al. levels) were these? Did you confirm any of them with\nauthentic standards?\nResponse: We have added that these matches were level 2 along with the Sumner reference. None were\nmade by comparison with authentic standards, which can prove extremely difficult to acquire.\nLine 436: Why did you use a 4.6 mm column (semi-prep scale) at such a high flow rate? Typically, a 2\nmm column and lower flow rates are more appropriate for ESI, or did you do flow splitting?\nResponse: While not ideal, the 4.6 mm column is our standard protocol (without flow splitting) and was\nused to provide a general overview of extract composition and to guide compound isolation. However, we\nhave added to the Methods section that a smaller column was used for the feature-based analyses.\nResponse: The settings are provided in detail in the GNPS documentation as cited. Given the length of the\nMethods section, we have elected not to repeat them here. We have clarified in the text where the settings\ncan be found. “Classic Molecular networking and automated library searches were performed using the\nonline workflow and the settings detailed in the GNPS documentation (https://ccms-\nucsd.github.io/GNPSDocumentation/)”.\nSI Fig. S3: Change the title to “HR-ESIMS/MS”.\nResponse: Change made as suggested.\nReviewer #2 (Remarks to the Author):\nThe authors have addressed the points I raised in my initial review. The new text provides\nthe readers with more details regarding the limitations of the approach. I believe this work\nshows the potential of in situ resin capture and this paper will be a good guide for\nresearchers interested in replicating the strategy.\nReviewer #3 (Remarks to the Author):\nI would like to thank the authors for addressing my comments and in principle I would be\nhappy to endorse the publication of their manuscript.\nThe only thing I would suggest them to still modify before the paper gets published is the\nreference to the GNPS settings. As the authors are probably well aware, settings such ass\nmass tolerance, cosine cut-off and filtering options are user defined and have to be selected\nfor each networking job. Hence, referring to the general documentation of GNPS is not\nproviding these information.\nResponse to REVIEWERS' COMMENTS\nReviewer #2 (Remarks to the Author):\nThe authors have addressed the points I raised in my initial review. The new text provides the\nreaders with more details regarding the limitations of the approach. I believe this work shows the\npotential of in situ resin capture and this paper will be a good guide for researchers interested in\nreplicating the strategy.\nReviewer #3 (Remarks to the Author):\nI would like to thank the authors for addressing my comments and in principle I would be happy\nto endorse the publication of their manuscript.\nThe only thing I would suggest them to still modify before the paper gets published is the\nreference to the GNPS settings. As the authors are probably well aware, settings such ass mass\ntolerance, cosine cut-off and filtering options are user defined and have to be selected for each\nnetworking job. Hence, referring to the general documentation of GNPS is not providing these\ninformation.\nResponse: the GNPS settings have been added to the main manuscript as suggested."}]

Decision letter