Biodiversity affects the exometabolomes of four benthic functional groups in coral reefs

doi:10.21203/rs.3.rs-5083584/v1

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Biodiversity affects the exometabolomes of four benthic functional groups in coral reefs

https://doi.org/10.21203/rs.3.rs-5083584/v1

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Coral reef organisms associate with diverse microorganisms as holobionts. The microbial and biochemical properties of these holobionts extend beyond the physical boundary of the organisms into the surrounding environment. This dynamic zone called the aura-biome may mediate and be mediated by interactions between species in the reef. However, the factors such as the surrounding species that shape the biomolecules present in the aura-biome, remain largely unknown. Using LC-MS/MS of water samples in the aura-biome of the organisms, we show that biodiversity of neighboring species affects the exometabolome of species of stony corals, soft corals, macroalgae, and sponges. Exometabolomes were most distinct in organisms in high and low complexity polyculture, while exometabolomes of organisms in monoculture were indistinguishable from empty controls, indicating that surrounding reef species triggered the release of biomolecules. Exometabolomes were species- and organism-group specific with distinct metabolite patterns between the four functional groups. These differences between benthic reef species persisted under varying biodiversity treatments. We annotated 20 compounds from the exometabolomes, 15 of these belong to ten classes of natural products, with known effects ranging from competition to antifouling. Our data demonstrate that reef species have distinct metabolite auras, which are dynamically adapted to the surrounding species diversity, implicating them in the understudied water-mediated interactions between species. It is therefore essential to understand the composition of reef metabolites in aura-biomes and the factors shaping them to understand their role in mediating organismal interactions and nutrient cycling in coral reef ecosystems.

untargeted metabolomics

aura-biome

community diversity

biomolecules

coral reefs

Coral reefs are inhabited by a large variety of benthic organisms, including stony corals, soft corals, sponges, and macroalgae. These macroorganisms associate with diverse microorganisms as holobionts (Barott et al. 2011). The bacterial communities inside and on the surfaces of these organisms are distinct from those in the seawater (Rosenberg et al. 2007; Pollock et al. 2018). The seawater directly surrounding the organisms is yet another distinct microbial niche, which has been coined as aura-biome or coral ecosphere (Walsh et al. 2017; Weber et al. 2019). The aura-biome is a dynamic interaction zone between the organisms and the surrounding environment with species-specific microbial communities, functions, and chemical profiles composed of unique metabolites, the so-called exometabolome (Ochsenkühn et al. 2018). Such metabolites can mediate biocommunication between organisms (Witzany and Madl 2009) and likely affect holobiont productivity (Engelhardt et al. 2023), highlighting the sensitivity of reef organisms to shifts in the surrounding reef community. Given the rapid loss of biodiversity and shifts in benthic coral reef communities (Barnosky et al. 2011; Hughes et al. 2018) characterizing the metabolic profiles within the aura-biome of reef organisms is crucial to understand downstream effects of changing communities on species interactions and coral reef productivity.

The aura-biome surrounding holobionts is characterized by distinct metabolites, including specific nucleosides, amino acids, vitamins, and metabolic intermediates (Ducklow and Mitchell 1979; Fiore et al. 2017; Ochsenkühn et al. 2018; Wegley Kelly et al. 2022). These biomolecules are important as growth substrates, communication, and chemical defense and may act as antibacterial and chemoattracting substances and as indicators for diseases (Ochsenkühn et al. 2018). For example, pathogens and putative beneficial bacteria may be attracted to different metabolites excreted by stony corals (Garren et al. 2014; Pogoreutz et al. 2022). Soft corals, macroalgae, and sponges are also producers of rich metabolites and sources of bioactive compounds (Coll et al. 1982; Paul and Fenical 1986; Weber et al. 2022). However, most of the produced exudates are labile and only a few withstand microbial degradation (Wegley Kelly et al. 2022). Therefore, alterations in the composition of the associated microbiome, a fast response mechanism to environmental pressure (Voolstra and Ziegler 2020), coincide with changes in the metabolome of the holobiont (Sogin et al. 2017). The combination of release and consumption of the metabolites thus leads to a complex extracellular pool of metabolites on reefs (Weber et al. 2020).

The metabolites in the aura-biome mediate interactions within and between organisms on coral reefs. Within stony coral colonies, overlapping auras of branches can stop or redirect growth (Rinkevich and Loya 1985). Benthic reef organisms, in particular soft corals, macroalgae, and sponges also possess rich and distinct aura-biomes (Haas et al. 2011, 2013; Fiore et al. 2017; Weber et al. 2022), and these have been intensely studied for their allelopathic properties during competition between species (Sammarco et al. 1983; Bell 2008; Chadwick and Morrow 2011; Budzałek et al. 2021). We have recently shown that contact-free interactions with the aura-biomes of competing organisms directly affect coral productivity (Engelhardt et al. 2023), highlighting the urgent need to understand the factors shaping this dynamic seawater boundary between organisms.

Interactions between reef organisms alter tissue metabolomes and associated microbial communities (De Caralt et al. 2013; Quinn et al. 2016; Weber et al. 2022). However, it is still unclear if and how these organismal interactions, as well as ongoing biodiversity loss and community shifts, affect the exometabolomes in the auras of different functional groups in the reef. Here, we analyzed the exometabolome surrounding seven sessile reef species, including stony corals, soft corals, macroalgae, and a sponge in three biodiversity treatments, including monocultures and a low and high complexity polyculture. We used untargeted metabolomics via LC-MS/MS hypothesing that i) exometabolomes are species-specific, ii) exometabolomes are distinct between benthic organismal groups (i.e., stony corals, soft corals, macroalgae, sponges), iii) exometabolomes in the auras of reef organisms are affected by species diversity surrounding them, and iv) species-specific differences increase with species diversity in the environment.

To identify differences in the composition of the exometabolome of four functional benthic groups and their adaptation to different biodiversity treatments, we conducted a four-week experiment and sampled organism-surrounding seawater for untargeted metabolomics in June 2022 in the Ocean2100 coral aquarium facility of Justus Liebig University Giessen, Germany.

Species selection

To investigate the exometabolomes under different biodiversity scenarios for diverse benthic marine organismal groups, we studied two stony coral, two soft coral, two macroalgal, and one sponge species. We selected the species based on a series of productivity assays conducted in the Ocean2100 coral aquarium facility (Engelhardt et al. 2023; Vetter et al. 2024): Pocillopora verrucosa (Ellis & Solander, 1786) was found to increase photosynthetic productivity with increasing diversity of surrounding organisms (Engelhardt et al. 2023), while Montipora digitata (Dana, 1846) was less productive in polyculture (Engelhardt et al. 2023). The two soft coral species Sinularia sp. (May, 1899) and Xenia sp. (Lamarck, 1816) and the two macroalgal species Caulerpa sp. (Lamouroux, 1809) and Peyssonnelia sp. (Decaisne, 1841) were chosen based on their range of effects on the productivity of stony corals (Engelhardt et al. 2023). The sponge Haliclona cnidata (Schellenberg, Reichert, Hardt, Schmidtberg, Kämpfer, Glaeser, Schubert & Wilke, 2019) increased the photosynthetic productivity of stony corals (Engelhardt et al. 2023).

Fragmentation and acclimation

Stony coral fragments were produced with an angle grinder (Multitool 3000-15, Dremel, Netherlands). Soft corals and the macroalga Peysonnelia sp. were cut with a scalpel and glued to roughened glass slides. The macroalga Caulerpa sp. was tied together in small bundles with nylon string, and the sponge was threaded through the main tube with nylon string. All organisms were suspended in the water column. During the six weeks of healing and acclimation, we maintained all fragments in the main aquarium facility in controlled polyculture tanks (Fig. 1). At the start of the experiment, replicate fragments were distributed into three treatments: i) monoculture (each species held in isolation - no other species or sediments present), ii) controlled polyculture (all investigated species together in a clean tank), and iii) seminatural polyculture (all investigated species in a mesocosm tank with additional benthic organisms, fishes, shrimps, sea grass, and sediments). All organisms were cultured at 26 ˚C, a light intensity of 200 ± 20 µmol photons m^− 2 s^− 1, and salinity of 35. This set-up was maintained for four weeks until the incubations and sampling were performed (Fig. 1).

Water preparation and incubations

We conducted the incubations on four consecutive days. First, we sterile-filtered artificial seawater (ASW) from an empty tank within the same aquarium system: after pre-filtering the ASW through a fine-mesh net (65 µm), we used a peristaltic pump (MCP Process, Idex Health & Science GmbH) for serial filtration; first with an autoclaved 0.45 µm membrane filter (Qpore), then with an autoclaved 0.22 µm membrane filter (Qpore), and finally with a sterile 0.2 µm membrane filter (Whatman) using in-line filter holders (Sartorius, Göttingen).

We performed incubations in clean autoclaved 1-L glass jars filled with 625 ml of the sterile-filtered ASW. The organisms were rinsed with sterile-filtered ASW and separately incubated in the jars. On each measurement day, three control incubations without organisms were included to account for the metabolites already present in the incubation water. The incubations lasted 2 h on a multi-point stirring plate at 9 ± 1.5 cm s^− 1 flow and 200 ± 20 photons µmol m^− 2 s^− 1 (Rades et al. 2022).

Sampling and sample processing

After incubations, 50 ml of the seawater was transferred with an autoclaved syringe through a 0.22 µl Sterivex filter (EMD Millipore Corporation, Burlington, USA) and directly loaded on HLB solid-phase extraction columns (60 mg, Waters, Massachusetts, US) at a flow-rate of 1 ml min^− 1 (MCP Process, Idex Health & Science GmbH). Columns were preconditioned with 2 column volumes acetonitrile (HPLC grade, VWR, USA) and two column volumes ultra-pure water (Sartorius AG, Göttingen, Germany) on a SPE vacuum chamber (Macherey-Nagel, Düren, Germany). After sample loading, columns were rinsed with two column volumes of ultra-pure water, dried on a vacuum block for 15 minutes (Macherey-Nagel, Düren, Germany), and stored at 4°C until elution (max. 48 h). For elution, we used two column volumes of 100% methanol (LC-MS grade, Honeywell Riedel-de-Haën™, Germany), dried samples overnight in a vacuum centrifuge (GeneVac HT-12, SP Industries, USA), re-dissolved the dry metabolites with 75 µl methanol in a v-bottom 96-well microplate (Greiner bio-one, Germany), and closed the plate with a pierceable TPE capcluster (Micronic, Netherlands).

Mass spectrometric analysis

We used a quadrupole time-of-flight spectrometer (LC-QTOF maXis II Bruker Daltonik), which was equipped with an electrospray ionization source connected to an Agilent 1290 infinity LC system (Agilent) for all UHPLC-QTOF-HR-MS and MS/MS measurements (Brinkmann et al. 2022). At 45°C, we performed C18 RP-UHPLC (ACQUITY UPLC BEH C18 column [130 Å, 1.7µm, 2.1 x 100 mm]) with the following linear gradient: 0 min: 95% A; 0.30 min: 95% A; 18.00 min: 4.75% A; 18.10 min: 0% A; 22.50 min: 0% A; 22.60 min: 95% A; 25.00 min: 95% A (A: H2O, 0.1% formic acid; B: acetonitrile, 0.1% formic acid; flow rate: 0.6 mL/min). Scan range was 50-2000 m/z at 1 Hz for MS measurements. MS/MS analysis was carried out at 6 Hz with selection of top 5 intense ions in full spectrum for collisional induced dissociation (CID) using N₂. Precursors were excluded after two spectra and released after 0.5 min and if their intensity increased by ≥ 1.5-fold.

Metabolomics data processing

MS data were processed with DataAnalysis 5.3 (Bruker) with "Recalculate Line Spectra" (Threshold 10000) and "Find Molecular Features" (SN = 0), with features given as unique combination of mass to charge ratio (m/z) and retention time (s). By bucketing (12 s, 5 ppm), using ProfileAnalysis 2.3 (Bruker) we checked which features from different samples describe the same ion. Based on the chromatograms, three outliers could be identified and were removed from analyses (Caulerpa sp. monoculture 1, M. digitata monoculture 3, Xenia sp. controlled polyculture 3). Features found at least twice within each species and treatment (twice per quadruplet) were considered 'valid'. This reduced the number of features from 873 (MS raw data) to 338 (curated feature table (CFT)).

Molecular networking

For annotation and visualization of the MS/MS data, we used the Global Natural Products Social Molecular Networking (GNPS) database to create a molecular network. For this, we uploaded .mzML files from the untargeted data to the MassIVE server (https://massive.ucsd.edu/) and used molecular networking of the GNPS database with default parameters for high resolution mass spectrometers (version release 30; Wang et al. 2016). The results are available at:

https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=a356c65ad38249379d64a23e4f39c6ca . Most of the parent ions and molecular families had no matches (comparison to > 220 000 metabolites), even though the database includes a marine natural product library (Wang et al. 2016).

To check the quality of annotations, we generated an Extracted Ion Chromatogram (EIC) (based on sum formula ± 0.02 Da) for all samples containing the node. The highest full scan spectrum of the EIC at the relevant retention time (spectrum at side of highest peak if saturated) was checked for the entry of the annotated formula as well as for isotope patterns and mass precision using ‘Chem-Formula manually’. The retention time (RT) was limited to 18:00 min to exclude lipophilic compounds in the washing step as they show high signal intensities and therefore dominate statistical analyses (details see Online resource 1 Fig. 1–27 and Online resource Table 1). Cytoscape (Version 3.10.01) was used for visualization and to link known and unknown compounds that belong to the same molecular family to create chemical inventories of the organisms.

Statistical analysis

We performed statistical analyses and visualizations with the curated feature table (CFT) and MSMS node table from GNPS (M2NT) in R statistical environment (v.4.3.2, R Core Team 2023) using the packages tidyverse (Wickham et al. 2019), decontam (Davis et al. 2017), vegan (Oksanen et al. 2022), lme4 (Bates 2016) and IndicSpecies (Cáceres and Legendre 2009). In the M2NT, all nodes that only appeared in one sample were removed. The CFT were decontaminated with the decontam package using the prevalence method with the default threshold of 0.1.

To investigate effects of species, organismal group, and treatments, as well as between species within each treatment and between treatments within each species, we created general mixed effects models (glmer) with the lme4 package. For the M2NT we set family = binomial for glmer creation. With the CFT, glmers were performed on log + 1 transformed data with family = Gamma. In both, species, organismal group, and treatment were set as continuous fixed factor (depending on the target effect group) and day of sampling as random factor, followed by a Tukey post-hoc comparison and ‘holm’ adjustment for multiple testing. For visualization, we plotted Bray-Curtis dissimilarities of the CFT and Binary-Jaccard similarities of the M2NT (presence/absence) as non-metric Multi-Dimensional Scaling ordinations. To detect representative metabolites for treatments, species, or combinations thereof, we used the ‘multipatt’ function from IndicSpecies on peak intensity data from the CFT at p ≤ 0.01.

Exometabolomes by biodiversity treatments, organismal groups, and species

We investigated the influence of biodiversity on the exometabolome surrounding diverse benthic reef organisms. Across species, exometabolomes differed between treatments, with exometabolomes in controlled polyculture and seminatural polyculture being significantly different to monoculture and controls (p < 0.01, Fig. 2a, Online resource 3.1 Table 1). In contrast, exometabolomes were indistinguishable between monoculture and the control (p > 0.05, Fig. 2a). Exometabolomes of soft corals and macroalgae were significantly different to all organismal groups (all p < 0.001), except for macroalgae and the sponge (p > 0.05, Fig. 2b, Online resource 3.2 Table 2). In contrast, the exometabolomes of stony corals, the sponge, and the controls were indistinguishable (all p > 0.05, Online resource 3.2 Table 2). Differences between individual species also occurred. Exometabolomes were significantly different between all species (Fig. 2c, Online resource 3.3 Table 3, p < 0.05), except between P. verrucosa, Sinularia sp., and Xenia sp. and between Peyssonnelia sp., H. cnidata, and the control (p > 0.05). Analyses with the M2NT showed similar but stronger effects than analyses with the CFT (Online resource 2 Fig. 28–30).

Exometabolomes are distinct between species within treatments

Differences between species persisted within each biodiversity treatment (Fig. 3, Online resource 4). Exometabolomes differed between species in monoculture (Fig. 3a, Online resource 4.1 Table 4), with exometabolomes of Caulerpa sp. and P. verrucosa, Sinularia sp. and Xenia sp. being significantly different from each other and the control (p < 0.05), except for Caulerpa sp. and the control (p > 0.05). Exometabolomes of P. verrucosa and Xenia sp. differed to those of H. cnidata, M. digitata and Peyssonnelia sp. (all p < 0.05, Fig. 3a, Online resource 4.1 Table 4). Within controlled polyculture, exometabolomes of Caulerpa sp. and M. digitata differed from every species except H. cnidata (Online resource 4.2 Table 5). All other species had significantly different exometabolomes (p < 0.05, Online resource 4.2 Table 5), except for any combination of Peyssonnelia sp., Xenia sp., P. verrucosa, and the controls (p > 0.05). Within seminatural polyculture, exometabolomes of Caulerpa sp. were significantly different to those of all other species (p < 0.001), except M. digitata (p > 0.05, Fig. 3c, Online resource 4.3 Table 6). Exometabolomes of M. digitata and Xenia sp. were distinct from the control and all other species (p < 0.01, p < 0.001), except Peyssonnelia sp. (p > 0.05).

Exometabolomes of species differ between biodiversity settings

We also investigated effects of biodiversity on exometabolomes within individual species (Fig. 4, Online resource 5 Table 7). In Caulerpa sp. (Fig. 4a) and M. digitata (Fig. 4e) exometabolomes from both polyculture treatments differed from the control (p < 0.05, Fig. 4a) and between controlled polyculture and monoculture (p < 0.01). In Peyssonnelia sp. exometabolomes were different between the two polyculture treatments (p < 0.01, Fig. 4b, Online resource 5). Exometabolomes of Sinularia sp. in controlled polyculture differed to all treatments including the control (p < 0.05, Fig. 4c). Xenia sp. exometabolomes in controlled polyculture differed from those in monoculture and seminatural polyculture (all p < 0.001), but not the control (p > 0.05), while exometabolomes in monoculture and seminatural polyculture differed from those in the control (p < 0.001 Fig. 4d). Exometabolomes in P. verrucosa were similar in controlled polyculture and control, and seminatural polyculture and monoculture (p > 0.05), but significantly different in all other pairwise comparisons (p < 0.05, Fig. 4f). In the sponge H. cnidata, all exometabolomes differed (all p < 0.01), except between controlled polyculture and monoculture (p > 0.05, Fig. 4g, Online resource 5 Table 7).

Indicator features

We searched for features in CFT that were indicators for distinct treatments (Fig. 5), species (Fig. 6), and combinations thereof (Online resource 6 Table 8). Seven features were significantly associated with controlled polyculture, eight features were significantly associated with monoculture and seminatural polyculture, and three with controlled polyculture and monoculture (Fig. 5). No indicator features were shared between the two polyculture treatments (Fig. 5).

Caulerpa sp. (8 features) and Xenia sp. (3 features) were the only species with individual indicator features. The largest group of indicators was linked to all species except Caulerpa sp., with a total of 19 indicator features (Fig. 6). One feature each was significant for the groups Caulerpa sp., M. digitata, and Sinularia sp., for H. cnidata, Peyssonnelia sp., P. verrucosa, and Sinularia sp., and for Caulerpa sp., H. cnidata, Peyssonnelia sp., P. verrucosa, and Xenia sp., and two for H. cnidata, M. digitata, Peyssonnelia sp., P. verrucosa and Sinularia sp..

Feature Annotation

We could annotate and verify 23 of the 4,581 nodes of M2NT belonging to 20 substances. The substances Monactin, PC(P-18:0_20:4), and NCGC00384836-01_C34H20O12 were found in two nodes each. Most compounds were found within the controlled polyculture treatment, followed by monoculture, seminatural polyculture, and controls (Table 1, Online resource 7 Figs. 31 & 32, Online resource Table 1).

Of the annotated compounds, 15 were assigned by GNPS to ten classes of natural products (Online resource Table 1). Amyl amines were most abundant and found in all samples (n = 91). Rarely occurring compounds were C₂₁H₂₄O₆ (n = 1, no. 13 in Table 1) and C₄₇H₇₆O₁₇ (n = 3, no. 17 in Table 1). The annotated compounds soyasaponin (67% in pc), val-leu-pro-val-pro (64% in pc), C₄₃H₇₀O₁₂ (no. 18 in Table 1, 42% in pc), C₄₃H₇₀O₁₂ (no. 20 in Table 1, 38% in pc) and monactin (53% in pc) occurred more often in controlled polyculture than the other treatments. While docosenamide (30%) was connected to the seminatural polyculture treatment. Because values were under the threshold of the curated feature table, we could not pair the annotations with the results of the indicator analysis.

Here, we showed that the biomolecules surrounding reef organisms change in response to the diversity of neighboring organisms. While contact between organisms is known to affect organismal metabolomes (Quinn et al. 2016), our approach provides evidence of changes in exometabolomes at the level of the individual organism in response to the diversity of species surrounding them in a contact independent manner. Additionally, some species were characterized by signature features, which were always present independently of the biodiversity scenario.

Biodiversity affects exometabolomes of reef organisms

We showed that the diversity of neighboring organisms affects the exometabolomes of diverse coral reef organisms. While contact-based modulation of the metabolome has previously been shown (De Caralt et al. 2013), with interspecific interactions having a stronger modulation effect than intraspecific interactions (Quinn et al. 2016), the effects shown here were induced by water-mediated interactions between individual organisms, previously shown at the reef-scale (Becker et al. 2023). This supports the notion that benthic reef organisms are tightly connected with their surroundings through a dynamic metabolome. Modulations of the exometabolome likely mediate interspecific interactions (Engelhardt et al. 2023), which is supported by the differences in the exometabolomes we observed under different biodiversity settings in this study.

Changes in exometabolomes in response to shifting reef communities can be linked to interactions with other species. The exometabolomes of both polyculture treatments differed from the controls, but those of the monoculture treatment did not. Possibly, the monoculture treatment did not induce the organisms to release distinct metabolites because of a lack of neighboring organisms (and their metabolite auras; De Caralt et al. 2013; Januar et al. 2015). The production of secondary metabolites is costly, so the organisms may save the energy in the absence of triggering organisms (Cronin 2001; Dewick 2002). Thus, the absence of distinct metabolites in monocultures and their presence in polyculture highlights the role of exometabolites in mediating interactions between organisms on the reef.

Each of the biodiversity treatments produced a distinct metabolite pattern that was detectable across species, indicating similarities in the modulation of the exometabolomes. Even species within the same functional group have previously been shown to have distinct metabolomes (Lohr et al. 2019), but here species-boundary transcending responses to the biodiversity treatments were observed. Benthic community composition in reefs continues to shift rapidly (Bellwood et al. 2004), which will be accompanied by a changing pool of secondary metabolites and changes in function and structure of community metabolism on reefs (Weber et al. 2022). Our data demonstrate that changing benthic community composition on reefs causes shifts in reef water chemistry as a result of the community composition itself.

Specific indicator features associated with the controlled polyculture treatment could be detected. All organisms spent six weeks of healing and acclimation in controlled polyculture settings, after which they were distributed to the three treatments for four weeks. This might have given them more time to respond to the controlled polyculture conditions and develop specific metabolites that marked this treatment specifically. Due to the fast response of microbes, we expected the exometabolomes to be fully adjusted within three weeks (Webster and Reusch 2017 and literature cited therein; Gaubert et al. 2019).

Exometabolomes of coral reef organisms are species-specific

Exometabolomes of reef organisms are species-specific (Weber et al. 2022) and occurrence of metabolites is linked to ecological context, such as predation (Haber et al. 2011). Exometabolomes were species-specific and increased differences were found between different benthic functional groups. The exometabolome associated with the macroalgae Caulerpa sp. was most distinct from all other species, and additional differences were found within Caulerpa sp. between the polyculture treatments and the monoculture treatment and control. Members of the order Caulerpales are known producers of toxic secondary metabolites, mostly linear terpenoids such as Caulerpenyne, which may be triggered by predation or interspecific competition for space (Paul and Fenical 1986). Also, Caulerpa sp. is known to overgrow other organisms (Manikandan and Ravindran 2017), so that this distinct metabolite pattern matches its ecological strategy. In this study, eight indicator metabolites were identified for Caulerpa sp. and three for Xenia sp.. Xenia sp. demonstrated the highest abundance of indicator metabolites in seminatural polyculture, suggesting that the release of these metabolites might be triggered by the presence of other benthic organisms (Coll et al. 1982; Januar et al. 2015). Fast growing organisms, such as Xenia sp. and Caulerpa sp. may use specific exudates to claim new space within the reef (Atrigenio and Aliño 1996; Rasher et al. 2011). In contrast, Peyssonnelia sp. and H. cnidata were not distinguishable from the controls, implicating them as weak secondary metabolite producers. This was surprising, given that H. cnidata is a high microbial abundance sponge with a suspected ability to produce bioactive compounds (Schellenberg et al. 2020), and data which show that Peyssonnelia sp. can inhibit growth of Acropora species (Tanner 1996).

Differences in the exometabolomes between species also persisted within the biodiversity treatments. A high proportion of small compounds is probably produced and/or metabolized by associated microorganisms (Kujawinski 2011; Sang et al. 2019; Reddy et al. 2023), which have recently been shown to have enormous untapped biosynthetic potential (Paoli et al. 2024). Therefore, the observed differences in exometabolomes within biodiversity treatments are likely linked to the differences in associated microbial communities between species (Sunagawa et al. 2010; Pollock et al. 2018). The same filter-sterilized seawater was used for all incubations, indicating that microbes attached to the surface of the species and not environmental microbes in the incubation seawater were related to metabolite changes. Future efforts are needed to link patterns in microbial communities, exometabolomes, and biodiversity treatments. In addition, exometabolomes are also linked to life strategies (Quinn et al. 2016), which should be probed in future studies to understand the connection between ecological niche of a species and its exometabolome.

Annotated metabolites and their diverse functions within the reef ecosystem

The 15 annotated natural products of this study fulfill diverse functions for the organisms. The annotated compound monactin is a toxic macrotetrolides cyclic ester associated with the marine bacterial genus Streptomyces and has antibiotic properties (Graven et al. 1966; Tuttle et al. 2019). It could thus be released as defense against other organisms or as protection against diseases. Trinactin has two additional methyl groups compared to monactin (Meyers et al. 1965) and occurred in the same network as monactin. Their presence in controlled polyculture and absence from the controls suggests that the release is triggered by the presence of neighboring species. Similarly, 9-Octadecenamide was found in all treatments, but mainly in controlled polyculture. It is known to be produced by the seaweed associated epibiotic bacterium Streptomyces violaceoruber and has weak antifouling properties (Hong and Cho 2013). Soyasaponin belongs to the class of Oleanane triterpenoids, which are typical for soft corals (Yang et al. 2023 and literature cited therein) and macroalgae (Paul and Fenical 1986). Here, we found it in both groups and in the sponge H. cnidata.

Cytochalasin was found in all treatments, the least in controlled polyculture. Different cytochalasins have been produced by fungi associated with the soft coral Sarcophyton sp. and have antifouling properties (Zheng et al. 2013). The detection of cytochalasins in all treatments may indicate its role in health maintenance regardless of biodiversity setting. 13-Docosenamide was present and equally distributed across all treatments. This substance has been detected in the symbiont Symbiodinium psygmophilum (T.C. LaJeunesse, J.E. Parkinson & J.D. Reimer, 2012), but rarely in the coral tissue itself (Parkinson and Baums 2014), suggesting that it is produced and released by the associated microbes. Previously, hydroxy stearic acid has not been recorded from benthic reef organisms, but stearic acids can be produced by Symbiodiniaceae, and can be sensitive indicators for (thermal) stress (Kneeland et al. 2013). It occurred in stony corals, soft corals, and the sponge, all species living in symbiosis with Symbiodiniaceae. Valine, leucine, and proline are amino acids, which are commonly found in coral reefs (Weber et al. 2020) as important sources of dissolved organic nitrogen for corals (Grover et al. 2008). They are known to be contained in stony and soft coral mucus (Ducklow and Mitchell 1979), as well as sponges (Fiore et al. 2017). Similar to the abundance patterns of other compounds, their release was triggered by neighboring organisms.

Many of the annotated compounds were linked to Streptomycetes bacteria, which might be important sources of secondary metabolites, contributing to the pool of released natural products in coral reefs, but bacterial sequencing data would be needed to confirm their presence in our study. Additionally, studies investigating the effects of these annotated compounds on reef organisms are needed, however the majority of discovered nodes could not be annotated. This is not surprising, given that reference fragmentation spectra are still very few in public repositories and on average only 2–5% of MS2 spectra can be matched to known molecules (da Silva et al. 2015).

This study provides evidence that species diversity in the surrounding environment has an effect on the metabolome of individual species through water-mediated interactions. The benthic species had species-specific exometabolomes in their auras, within and across biodiversity scenarios. Sets of indicator features occurred in the distinct biodiversity treatments, which can be regarded as signatures for the community composition. The few annotated compounds belonged to a variety of natural product classes including fatty acids, terpenoides, amino acids, alkaloids, and polyketides. They potentially have species-specific effects including defense, competition, or antifouling. Benthic reef communities shift worldwide, with downstream effects on composition of biomolecules. These may be related to modifications in structure and function of community metabolism, highlighting the need to further study the organismic and metabolic feedback loops in coral reefs and to inform conservation efforts.

Acknowledgement

We thank Vanessa Tirpitz (JLU) for advice on statistics and Robert Quinn (MSU) and Sabrina Rosset (MSU) for useful dialogue during processing.

Funding declaration

This study is part of the ‘Ocean2100’ global change simulation project of the Colombian-German Center of Excellence in Marine Sciences (CEMarin) funded by the German Academic Exchange Service (DAAD). K.E.E. was supported by the Heinrich-Böll Foundation (P143008) and J.V. by the German Academic Scholarship Foundation.

Authorship contribution

K.E.E., F.W., J.V., S.S.., and M.Z. conceived the study and designed the experiments. K.E.E., J.V., and D.K. collected data, K.E.E. and C.H. analyzed and curated data. C.H. performed LC-MS/MS analysis. K.E.E. and M.Z. wrote and revised the manuscript. M.Z. and T.F.S. provided research materials and logistics. All authors read and approved the manuscript.

Data availability

Raw data generated and scripts for analysis during the current study are available in GitHub ‘Biodiversity-affects-the-exometabolomes-of-four-benthic-functional-groups-in-coral-reefs, [https://github.com/KaraEngelhardt/Biodiversity-affects-the-exometabolomes-of-four-benthic-functional-groups-in-coral-reefs]

Competing interests

The authors declare no competing interests.

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Table 1 is available in the Supplementary Files section.

No competing interests reported.

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Biodiversity affects the exometabolomes of four benthic functional groups in coral reefs

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Abstract

Figures

Introduction

Materials & Methods

Species selection

Fragmentation and acclimation

Water preparation and incubations

Sampling and sample processing

Mass spectrometric analysis

Metabolomics data processing

Molecular networking

Statistical analysis

Results

Exometabolomes by biodiversity treatments, organismal groups, and species

Exometabolomes are distinct between species within treatments

Exometabolomes of species differ between biodiversity settings

Indicator features

Feature Annotation

Discussion

Biodiversity affects exometabolomes of reef organisms

Exometabolomes of coral reef organisms are species-specific

Annotated metabolites and their diverse functions within the reef ecosystem

Conclusions

Declarations

References

Table 1

Additional Declarations

Supplementary Files

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