Submitted 21 June 2016Accepted 6 December 2016Published 11 January 2017

Corresponding authorCara L Fiore fiorecappstateedu

Academic editorJoseph Pawlik

Additional Information andDeclarations can be found onpage 16

DOI 107717peerj2870

Copyright2017 Fiore et al

Distributed underCreative Commons CC-BY 40

OPEN ACCESS

Sponge exhalent seawater contains aunique chemical profile of dissolvedorganic matterCara L Fiore1 Christopher J Freeman2 and Elizabeth B Kujawinski1

1Marine Chemistry amp Geochemistry Woods Hole Oceanographic Institution Woods Hole MAUnited States

2 Smithsonian Marine Station Smithsonian Institution Fort Pierce FL United States

ABSTRACTSponges are efficient filter feeders removing significant portions of particulate anddissolved organic matter (POM DOM) from the water column While the assimilationand respiration of POM and DOM by sponges and their abundant microbial symbiontcommunities have received much attention there is virtually no information on theimpact of sponge holobiont metabolism on the composition of DOM at a molecular-level We applied untargeted and targeted metabolomics techniques to characterizeDOM in seawater samples prior to entering the sponge (inhalant reef water) insamples exiting the sponge (exhalent seawater) and in samples collected just outsidethe reef area (off reef seawater) Samples were collected from two sponge speciesIrcinia campana and Spheciospongia vesparium on a near-shore hard bottom reef inthe Florida Keys Metabolic profiles generated from untargeted metabolomics analysisindicated that many more compounds were enhanced in the exhalent samples than inthe inhalant samples Targeted metabolomics analysis revealed differences in diversityand concentration of metabolites between exhalent and off reef seawater For examplemost of the nucleosides were enriched in the exhalent seawater while the aromaticamino acids caffeine and the nucleoside xanthosine were elevated in the off reef watersamples Although themetabolic profile of the exhalent seawater was unique the impactof spongemetabolism on the overall reef DOMprofile was spatially limited in our studyThere were also no significant differences in the metabolic profiles of exhalent waterbetween the two sponge species potentially indicating that there is a characteristicDOMprofile in the exhalent seawater of Caribbean sponges Additional work is needed todetermine whether the impact of sponge DOM is greater in habitats with higher spongecover and diversity This work provides the first insight into the molecular-level impactof sponge holobiont metabolism on reef DOM and establishes a foundation for futureexperimental studies addressing the influence of sponge-derived DOM on chemicaland ecological processes in coral reef ecosystems

Subjects Biochemistry Marine Biology MicrobiologyKeywords Dissolved organic matter Sponge Coral reef Metabolomics

INTRODUCTIONCoral reefs exist in relatively oligotrophic environments where there is tight couplingbetween benthic and pelagic nutrient cycles (Wild et al 2008 Naumann et al 2012) As

How to cite this article Fiore et al (2017) Sponge exhalent seawater contains a unique chemical profile of dissolved organic matterPeerJ 5e2870 DOI 107717peerj2870

both filter feeders and hosts to abundant symbiont communities sponges can have a majorrole in coral reef biogeochemistry (Southwell et al 2008) through the removal of particles(Ribes et al 2005) and the transfer of dissolved organic matter (DOM) to higher trophiclevels in the benthos (De Goeij et al 2013 Rix et al 2016) Sponge holobiont (spongeplus associated microbes) metabolism has been shown to influence the composition ofinorganic nitrogen (eg Southwell et al 2008) and the concentration of dissolved organiccarbon (DOC Ribes Coma amp Gili 1999 Yahel et al 2003) However the impact on thecomposition of DOM is not well characterized DOM comprises dissolved organic carbonnitrogen phosphorus and other elements (Hedges 2002) but the carbon component isthe largest Recent studies have begun to examine the sources and major componentsof DOM on coral reefs For example macroalgae generally produce exudates that arehigher in neutral sugars and in DOC concentration than coral exudates (Haas et al 2011Nelson et al 2013) Studies comparing DOC from different sources and their effects onecosystem function have begun to elucidate ecologically important connections betweenDOC microbial community composition and coral reef biogeochemical cycling (egWildet al 2008 Haas et al 2013)

The composition of DOM can influence the population structure of microbialcommunities (Landa et al 2013) including those in coral reef seawater and sediment(Haas et al 2013 Nelson et al 2013) DOM composition also influences coral health(Morrow et al 2012) and ecosystem-wide nutrient cycling (Wild et al 2004) Corals aswell as algae contribute to DOM by releasing neutral sugars such as fucose and galactosealthough the proportion of these components is different between the two sources (Nelsonet al 2013) To date however our understanding of the composition of coral reef DOMhas been limited by methods that cannot fully resolve the complex nature of DOMparticularly within the low-molecular weight component (LMW DOM lt1000 Da) Thisfraction could be particularly important to reef ecosystems as it contains molecules thatare produced during metabolism and that may be labile to other organisms (egMazur ampHomme 1993 Amin et al 2015)

Sponges are typically considered to be a sink for DOM releasing exhalent water thathas lower concentrations of carbon relative to water entering the sponge (Wilkinson 1987Yahel et al 2003 De Goeij et al 2008b McMurray et al 2016) It is thus not surprisingthat DOM is an important carbon source for many sponges contributing up to 10 of totalcarbon assimilated by two deep water sponges (Van Duyl et al 2008) and contributingover an order of magnitude more carbon to the biomass of coral reef sponges than POM(Yahel et al 2003) The cryptic reef framework where sponges are common has alsobeen shown to be a major sink for DOM (Van Duyl amp Gast 2001 De Goeij amp Van Duyl2007) most of which is likely assimilated by sponges (De Goeij et al 2008b) Additionallycoral-excavating sponges rely largely on DOM to meet their carbon demand and may beable to take advantage of nutrient-rich environments (Mueller et al 2014) highlightingthe pivotal role of DOM in these ecosystems

While the removal of DOM by sponges has been documented for some time (Stephensamp Schinske 1961 Reiswig 1990 Alber amp Valiela 1995 Ribes Coma amp Gili 1999) it wasunclear until recently whether sponge cells could assimilate DOM directly or only via

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their microbial symbionts (sensu De Bary 1879) An experiment with isotopically labeledDOM and particulate organic matter (POM) demonstrated that both sponge-associatedmicrobes and the sponge itself are capable of assimilating dissolved material (De Goeij etal 2008a De Goeij et al 2013) although the specific components of DOM removed bythe sponge holobiont could not be determined A few studies have provided preliminaryinsight into these components where the uptake of several amino acids but only aminimal amount of glucose by sponges was observed (Stephens amp Schinske 1961 DeGoeij et al 2008a) The difference in uptake between the amino acids and glucose mayindicate some discrimination in the removal of metabolites from seawater by the spongeholobiont consistent with previous observations of differential metabolite removal bymarine free-living microbes (Malmstrom et al 2004 Nelson amp Craig 2012)

Sponges with their high abundance on many reefs particularly in the Caribbean(McMurray Finelli amp Pawlik 2015) their large filtration capacity (upwards of 30 Lminus1 hrminus1

per L of sponge (Weisz Lindquist amp Martens 2008)) and microbial symbiont diversity(eg Easson amp Thacker 2014) likely influence the composition and availability of DOM oncoral reefs Here we use high resolution untargeted and targeted metabolomics techniquesto probe the composition of DOM on a near shore reef in the Florida Keys (USA) andto characterize the influence of the sponge holobiont metabolism on DOM compositionUntargeted or discovery-based analysis allows for a semi-quantitative profile-view of lowmolecular weight molecules within a sample while the targeted metabolomics approachprovides a quantitative comparison of molecules relevant to the growth of many organisms(Patti Yanes amp Siuzdak 2012 Kido Soule et al 2015) In the present study two spongespecies were selected for analysis that are known to harbor distinct symbiont communitiesIrcinia campana a highmicrobial abundance (HMA) sponge and Spheciospongia vespariuma low microbial abundance (LMA) sponge (Poppell et al 2014) By applying metabolomicsanalysis to inhalant and exhalent water of these two sponge species and to water collectedaway from the reef we demonstrate a complex dynamic of removal and addition of labileDOM to the surrounding seawater that would be difficult to predict based solely on bulkcarbon analyses

MATERIALS AND METHODSSample collectionSeawater samples were collected near Long Key State Park FL (248169N 808200W)by scuba divers (4ndash5 m depth Fig 1) Abundance data on sponge populations were notobtained but in general sponges were in relatively low abundance compared to populationstypical of offshore coral reefs in the Florida Keys Acid-washed polypropylene syringes (100ml) attached to 1 L FlexFoil Plus sample bags (SKC Pittsburgh PA USA) via a three-wayvalve with Teflon tubing were used to collect ambient reef water and exhalent water fromthe sponge The valve allowed the syringe to be filled and then pumped into the bag tentimes to collect 1L of seawater Ambient reef water (herein referred to as inhalant) wascollected near each sponge but away from the direction of the exhalent plume Exhalentwater from each sponge was collected by drawing water into the syringe slowly (1 ml sminus1)

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Figure 1 Schematic of sampling location and sample types The three sample types are shown inhalantexhalent and off reef seawater The red vase-like sponges represent Ircinia campana and the gray spongesrepresent Spheciospongia vesparium The numbers of samples collected for each sample type are shownFor inhalant and exhalent samples one sample type was collected from each of three individuals Reefseawater samples were collected near the surface approximately 300 m from the reef area Arrows repre-sent direction of water flow and color is associated with samples representing each sponge species in laterfigures

through tubing placed just above the osculum for Spheciospongia vesparium and just abovethe base of the spongeocoel for Ircinia campana All sponges (three individuals per species)were checked for pumping activity using fluorescein dye and sampled once the dye hadvisibly disappeared The time the dye front took to move from the base of the spongeocoelto the osculum was recorded in order to obtain the centerline fluid velocity and to estimatepumping rates While this method can be confounded by sponge morphology (McMurrayPawlik amp Finelli 2014) it has been shown to provide a rough estimate of pumping rate atleast within species (Weisz Lindquist amp Martens 2008 Southwell et al 2008 Fiore Bakeramp Lesser 2013) Thus we used pumping rates to provide an order of magnitude estimatefor the release of metabolites to the reef Volume measurements described previously(McMurray Blum amp Pawlik 2008) were recorded for each sponge using measuring tape(to plusmn10 mm) to calculate sponge volumes (McMurray Blum amp Pawlik 2008 WeiszLindquist amp Martens 2008) The height of the sponge was assumed to be the height ofthe spongeocoel for S vesparium Off reef samples were collected in polycarbonate bottles(n= 2 15 L total) by hand near the surface and approximately 300 m seaward from thereef over a mix of sand and hard substrate

The seawater was put on ice immediately upon end of the dive Upon return to theSmithsonian Marine Station (Fort Pierce FL) subsamples (40 ml) of each water samplewere added to combusted vials and acidified to pH = 2 using 12 N hydrochloric acid andstored frozen for total organic carbon (TOC) analysis TOC concentrations were used tocalculate the extraction efficiency for a subset of samples The rest of the seawater wasfiltered through a combusted glass fiber filter (GFF 07 microm Whatman) and a 02 micromPTFE filter (Omnipore Millipore MA USA) The filtrate was frozen and shipped toWoods Hole Oceanographic Institution (WHOI Woods Hole MA USA) for extractionof dissolved organic matter

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Extraction of low molecular weight dissolved organic matter andinstrument methodsExtractions of filtrate samples and the instrument methods used here were performed asdescribed in Kido Soule et al (2015) and Fiore et al (2015) Briefly the seawater filtrate wasacidified to pH 3 (Longnecker 2015) and then de-salted and extracted with1g6 cc PPLsolid phase extraction cartridges (BondElut Agilent Santa Clara CA USA) as describedpreviously DOM was eluted from the column using 100 methanol prior to storageat minus20 C until analysis Just prior to mass spectrometry analysis on the instrumentsdescribed below the methanol extract was dried down and redissolved in 500 microl of 955wateracetonitrile Deuterated biotin was added to each sample as a high-performanceliquid chromatography (HPLC) injection standard (final concentration 005 microg mlminus1)At this stage 100 and 200 microl of the extract was removed for targeted and untargetedmetabolomics analysis respectively Equal amounts of each experimental extract werecombined to create a pooled sample

Samples for TOC analysis once reaching WHOI were stored at 4 C until analysis witha Shimadzu TOC-VCSH total organic carbon analyzer (Hansell amp Carlson 2001) MilliQwater blanks standard curves using potassium hydrogen phthalate and certified standardreference material (the latter provided by Prof D Hansell University of Miami) were usedfor instrument calibration and were made fresh each day Targeted metabolomics analysiswas performed using high-performance liquid chromatography (Thermo PAL autosamplerand Accela pump) coupled to a triple stage quadrupole mass spectrometer (TSQ VantageThermo Fisher Scientific MA USA) via a heated electrospray ionization (H-ESI) sourceoperated in both positive and negative ion modes A set of standards consisting of aminoacids vitamins nucleosides and other metabolites (Kido Soule et al 2015) was used in thetargeted method to identify and quantify these compounds in the experimental samplesSome metabolites have low recovery yields following SPE (WM Johnson MC Kido SouleEB Kujawinski 2016 unpublished data) thus a subset of 23 standards that have highrecovery rates with SPE were analyzed in the experimental samples Selected reactionmonitoring (SRM) conditions were optimized for each standard

Untargeted analysis of extracted metabolites was performed using high-performanceliquid chromatography (Micro AS autosampler and Surveyor MS pump plus) coupled viaESI to a hybrid linear ion trap-Fourier transform-ion cyclotron resonance (FT-ICR) massspectrometer (7T LTQ FT Ultra Thermo Fisher Scientific) Electrospray ionization is asoft ionization technique and as such minimizes molecular fragmentation in the sourceThus each mass feature likely represents an individual compound The pooled sample wasanalyzed every seven injections in both the targeted and untargeted metabolomics methodsfor quality control More details on instrument methods have been previously described(Kido Soule et al 2015 Fiore et al 2015) with the exception that only positive ion modefor the untargeted metabolomics method was analyzed in the current study

Metabolomics data analysisUntargeted metabolomics data were manually inspected in XCalibur (20) for issues withinjection contamination or ion suppression by comparing peak intensities of the injection

Fiore et al (2017) PeerJ DOI 107717peerj2870 522

standard in each sample and peak intensities of the pooled samples prior to performinganalysis in XCMS Peak intensities did not change substantially over the time course of theanalytical run so it is unlikely that there was any systematic decrease in instrument responseover time Manual inspection of chromatograms in XCalibur did reveal contaminationby plastics (eg polyethylene glycol) in all samples easily distinguished by regularlyspaced mass features (44 mz) These mass features were present from 93 to 113 mintherefore all features within this time frame were removed (263 mass features 8)The untargeted metabolomics data were processed with XCMS 1380 (Smith et al 2006)and CAMERA 1180 programs (Kuhl et al 2012) in R The resulting peak table fromXCMS and CAMERA was further qualitychecked by retaining only mass features thatwere present in at least two out of the three pooled samples and removing features within10 ppm of fluorescein (as [M + H]+ 4 features) Typical mass errors are 1ndash2 ppmmaking this a very conservative criterion for fluorescein Mass features are defined hereas a unique combination of mass-to-charge (mz) value and retention time Peak areasof mass features are proportional to metabolite concentration (eg Fiore et al 2015) andthus were normalized to the filtrate volume for analysis Mass features of interest that hadan associated fragmentation spectrum were processed using the xcmsFragment function inXCMS (Benton et al 2008) and the spectrawere queried against theMETLINmetabolomicsdatabase (Smith et al 2005) and in the program MetFrag (Wolf et al 2010) as describedin Fiore et al (2015) All metabolomics data are available through the MetaboLights onlinedatabase (httpwwwebiacukmetabolights) by accession number MTBLS281

Statistical analyses were performed with R statistical software (v302 R Core Team)The function metaMDS (vegan package Oksanen 2016) was used to perform non-metric multidimensional scaling (nMDS) of volume-normalized untargeted data Thedimensionality was assessed by Monte Carlo analysis using 20 iterations with real data and20 iterations with randomized data for each of several dimensions (n= 2ndash5) Mantel testswere performed to determine the coefficient of determination (r2) between ordinal distanceof the nMDS and the Bray-Curtis dissimilarity for both axes and each axis separatelyAnalysis of similarity (ANOSIM) was performed to test for significant differences in thediversity and abundance of mass features between groups detected in the nMDS A pairedT -test was used to compare peak areas between all inhalant (n= 6) and all exhalent (n= 6)seawater samples with log transformation to meet test assumptions (peaks required to be infive out of six replicates any of which could have a missing sample) A Welchrsquos T -test wasused to compare exhalent seawater samples by species (log transformed peaks requiredto be in all three replicates per species) The generated p-values were adjusted for multiplecomparisons using false discovery rate (FDR Benjamin amp Hochberg 1995)

Targeted metabolomics analysis was performed using five-to-seven point manually-curated external calibration curves for each standard (05ndash500 ng mlminus1) to determinerelative metabolite concentrations (XCalibur 20) The concentrations were then exportedto Microsoft Excel A paired T -test followed by FDR p-value corrections was also usedto test for significant differences in log-transformed metabolite concentrations betweenthe exhalent and inhalant reef seawater samples within each species (n= 3 per species)Additionally metabolite concentration data for the two sponge species were pooled to test

Fiore et al (2017) PeerJ DOI 107717peerj2870 622

for differences in concentration between all exhalent and all inhalant seawater samples(n= 6 inhalant and n= 6 exhalent) using a paired T -test (log transformed) and p-valueswere adjusted as described above NMDS and ANOSIM were also used as described forthe untargeted metabolomics dataset to test the effect of sample type (inhalant exhalentand off reef) on metabolite composition from the targeted metabolomics dataset Vectorsrepresenting correlations (n= 1000 permutations) between metabolite concentrationsand sample type (inhalant exhalent off reef) were added to the plot using the functionenvfit part of the Vegan package in R The length of the vector corresponds to the strengthof the correlation with a longer vector indicating a stronger correlation

The amount (pmol) of a givenmetabolite added to the reef seawater from a sponge over a24-hour day was calculated bymultiplying the difference in concentration of the metabolitein the exhalent and inhalant seawater (pmol Lminus1) by the minimum and maximum rangeof the volumetric flow rate of the sponges (average = 03 L sminus1plusmn 028) then multipliedby the conversion of seconds to 24 h We assume that the sponge continues to pumpover a 24-hour period While sponges do cease pumping this is highly sporadic occurringanywhere from 2 to 21 days and lasting from 10 min to multiple days depending on thespecies (Reiswig 1971 Vogel 1974 Pile et al 1997 McMurray Pawlik amp Finelli 2014)Thus in one 24-hour period it is likely that these sponges would continue to filter andpump water

RESULTSMetabolic profiles of ambient reef water sponge exhalent water andoff reef waterVolume normalized pumping rates were 017 L sminus1 Lminus1 sponge (plusmn011) for S vespariumwith an average volume of 24 L (plusmn07) and 008 L sminus1 Lminus1 sponge (plusmn012) for I campanawith an average volume of 156 L (plusmn15) Extraction efficiency for the SPE-extracted DOMwas calculated for several representative samples (IC-A1 SV-A2 SV-E1 SV-E2 Off reef 1Off reef 2) and ranged from 23 to 59

Untargeted metabolomics analysis yielded a total of 2973 mass features across samplesThere was a large overlap in features across all samples with about half of the features sharedbetween the three sample types (58) The sponge exhalent water had the highest numberof unique mass features (334 features 12) followed by off-reef water (195 features 7)and inhalant reef water (73 features 3) The exhalent seawater and off reef seawatershared the most features (347 12) while the exhalent and inhalant seawater samplesshared the fewest features (42 2) Analysis of similarity following nMDS visualizationindicated a significant difference between the sample groups of inhalant and off reef watercombined relative to exhalent water (Fig 2 ANOSIM R= 0398 p= 0016)

Of the 2003 mass features present in at least five out of six replicates of sponge exhalentand inhalant water 243 (12) were significantly more abundant in the exhalent water(T -test FDR adjusted plt 005) In contrast 12 features (lt1) were significantly moreabundant in inhalant seawater than in exhalent water (T -test FDR adjusted plt 005)

Because the off reef samples (n= 2) could not be statistically compared to the othersample types a threshold of 20times the average peak area was delineated to determine

Fiore et al (2017) PeerJ DOI 107717peerj2870 722

Figure 2 Non-metric multidimensional scaling (nMDS) plot of dissolved organic matter profiles fromuntargeted metabolomics analysis The strength of the correlation between the distance matrix and thenMDS plot distance are given for each axis and the overall plot Samples are Ircinia campana (IC) Sphe-ciospongia vesparium (SV) and off reef Two groups were distinguished based on analysis of similarity(ANOSIM R = 0398 p = 0016) which are highlighted by the black ovals Off reef and inhalant (I) sea-water samples grouped together on the left and the exhalent (E) seawater samples grouped together on theright

whether a peak was higher in a particular sample type and then used for comparativeanalyses (Table 1) A similar number of differential mass features were yielded using athreshold of 20times difference in peak area and statistical analysis (T -test plt 005 430features greater in the exhalent than inhalant samples 52 greater in the inhalant samples)to compare the inhalant and exhalent seawater samples Using this threshold the offreef samples were first compared to all of the inhalant water samples (n= 6) Manyfeatures (n= 106) were elevated in the inhalant seawater relative to the off reef samplesInterestingly within these features that are abundant in the ambient reef water nearly half(n= 42) were significantly higher in the exhalent than in the inhalant water (T -test FDRadjusted plt 005) In contrast only three features were significantly higher in the inhalantseawater (T -test FDR adjusted plt 005) There were also 71 mass features that werelower in the inhalant seawater compared to the off reef samples Of these features 32 weresignificantly different between the exhalent and inhalant seawater all of which were higherin the exhalent seawater samples The off reef samples were then compared to the exhalentsamples yielding many features that were more abundant in the exhalent seawater samplesparticularly from S vesparium (Table 1) Mass features of interest (ie those that weremore abundant in a certain sample type as described above) with associated fragmentation

Fiore et al (2017) PeerJ DOI 107717peerj2870 822

Table 1 Mass features that differentiate different sample types (off reef inhalant exhalent) for eachsponge species (Ircinia campana (IC) and Spheciospongia vesparium (SV)) To determine whether apeak was higher in a particular sample type a threshold of 20times (average peak area) was used for com-parison because the off reef seawater samples have two replicates and thus could not be compared withconventional statistics Numbers represent features that are greater in the row sample than in the columnsample (for instance there were 164 mass features that were on average at least 20times higher in the IC in-halant samples than in the off reef samples) lsquondrsquo not determined

Off reef IC-Inhalant SV-Inhalant IC-Exhalent SV-Exhalent

Off reef ndash 156 164 81 55IC-Inhalant 164 ndash nd 27 ndSV-Inhalant 155 nd ndash nd 56IC-Exhalent 601 584 nd ndash 93SV-Exhalent 587 nd 608 26 ndash

spectra were used to query metabolomics databases None of the mass features investigatedhere revealed a significant match to any characterized metabolites

The overwhelming majority of mass features (n= 2243 85) were detected in bothexhalent sample groups (I campana exhalent seawater IC-E and S vesparium exhalentseawater SV-E) Of these shared features T -tests revealed no significant differences(plt 005) between the two species following p-value adjustment When all features presentin the exhalent seawater samples in each species were compared (n= 2623 features)121 features had p-values below 005 however none of these were significantly differentbetween inhalant and exhalent waters following p-value corrections A number of featureswere unique to IC-E and SV-E (156 and 227 respectively) however some of these featureswere highly variable in their corresponding samples yielding high p-values in statisticalcomparisons

Targeted metabolomics analysisSixteen (of 23 possible) metabolites were detected and quantified in these samplesincluding several amino acids B vitamins and nucleosides (Figs 3 and 4) Most ofthe 16 metabolites were detected in all sample types however the nucleoside xanthosinewas only detected in the off-reef samples Seven of the 16 metabolites were highest in theoff reef samples including some amino acids and caffeine but also the glycerol derivativeglycerol-3-phosphate (Fig 3)Within the reef samples (inhalant and exhalent) the exhalentwater contained higher concentrations of metabolites than inhalant seawater for severalmetabolites including most of the nucleosides and the amino acid tryptophan (Fig 4)Statistical comparison of metabolite concentrations in inhalant and exhalent samplesyielded no significant differences after p-value corrections (T test pgt 005 for all) Sampletypes (inhalant exhalent off reef) were significantly different from each other as detectedby ANOSIM (R= 042 p= 0004) The nMDS visualization and vector overlays showedsignificant correlations between off reef seawater samples and aromatic amino acidscaffeine and xanthosine while most of the nucleosides and riboflavin were significantlycorrelated with the exhalent seawater samples (Fig 5 Table S1 all p-values lt 005) Theinhalant samples however were not significantly correlated to any of the metabolites

Fiore et al (2017) PeerJ DOI 107717peerj2870 922

Figure 3 Average (plusmnSD) ratios of metabolite concentrations of off reef and inhalant seawater samples(off reefinhalant)Note that the y-axis is presented as log2 scale to highlight equivalent concentrations(ratio= 1 red dashed line) The ratio of each metabolite was calculated by averaging individual ratios ofeach of the two off reef and each inhalant sample (12 ratios) MTA 5prime-methylthioadenosine Xanthosine isnot shown as it was only detected in the off reef samples

Figure 4 Average (plusmnSD) ratio of metabolite concentration of exhalent and inhalant seawater samples(exhalentinhalant) for each sponge species (IC Ircinia campana SV Spheciospongia vesparium)Note that the y-axis is presented as a log2 scale to highlight equivalent concentrations (ratio= 1 reddashed line) No significant differences were observed between the concentrations of metabolites in theinhalant and exhalent seawater samples Asterisks indicate metabolites that were detected in only one ortwo replicates of the inhalant samples resulting in no standard deviation for that metabolite ratio MTA5prime-methylthioadenosine n= 3

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Figure 5 Non-metric multidimensional scaling (nMDS) plot of metabolite concentrations based ontargeted metabolomics analysis The strength of the correlation between the distance matrix and thenMDS plot distance are given for each axis and the overall plot Samples are exhalent (for both spongespecies) inhalant (for both sponge species) and off reef These three groups were distinguished based onanalysis of similarity (ANOSIM R = 042 p = 0004) Vector overlays are based on correlations betweenmetabolite concentration and sample type (inhalant exhalent off reef) with a longer vector indicatinga stronger correlation Correlations with pantothenic acid MTA and 4-aminobenzoic acid were notsignificant G3P glycerol-3-phosphate MTA 5prime-methylthioadenosine

including pantothenic acid in the broader nMDS analysis (Fig 5 Table S1 plt 005) Over24 h with constant sponge pumpingsim2 pmol tosim1 nmol of the metabolites quantified inthe targeted method would be released to the water column from each sponge Howeverfour metabolites (4-hydroxybenzoic acid glycerol-3-phosphate 5prime-methylthioadenosineand pantothenic acid) were likely removed by the sponge holobiont as they were depletedin concentration in the exhalent seawater relative to the surrounding seawater (Table 2)One metabolite the folate precursor 4-aminobenzoic acid yielded no net addition orremoval to the surrounding seawater as the average concentration in the exhalent andinhalant samples were approximately equal (Table 2)

DISCUSSIONThe present study supports three main conclusions regarding the alteration of DOM bythe sponge holobiont (1) the profile of DOM in the exhalent water is unique from thesurrounding seawater and seawater further away from the reef (2) the influence of sponge-derived DOMon the DOMprofile of surrounding seawater is localized close to the exhalentplume and (3) theDOMprofiles of two sponge species with distinctmicrobial communitiesare highly similar The latter conclusion may indicate a characteristic lsquolsquosponge holobiontsignalrsquorsquo for sponge-derived DOM and provides a testable hypothesis for future work

Fiore et al (2017) PeerJ DOI 107717peerj2870 1122

Table 2 Estimated release of metabolites from sponges based on targeted metabolomics analysis The average concentration of each metabo-lite in the inhalant and exhalent seawater with standard deviation is shown The average concentration in the exhalent water and the minimum andmaximum pumping rate (average= 03 Lminus1 plusmn 028) was used to estimate the amount of metabolite released per sponge over 24 hours MTA 5prime-methylthioadenosine

Metabolite Inhalant (fmol Lminus1 plusmn SD) Exhalent (fmol Lminus1 plusmn SD) Min (pmol dayminus1) Max (pmol dayminus1)

Tryptophan 18 (plusmn9) 50 (plusmn30) 55 1604Kynurenine 7 (plusmn2) 8 (plusmn3) 2 50Tryptamine 15 (plusmn3) 18 (plusmn5) 5 130Phenylalanine 70 (plusmn21) 95 (plusmn57) 43 1253Tyrosine 4 (plusmn4) 12 (plusmn12) 14 4004-hydroxybenzoic acid 65 (plusmn36) 53 (plusmn26) minus21 minus6014-aminobenzoic acid 2 (plusmn05) 2 (plusmn06) 0 0Glycerol-3-phosphate 13 (plusmn6) 10 (plusmn12) minus5 minus150MTA 7 (plusmn2) 4 (plusmn08) minus5 minus150Adenosine 6 (plusmn2) 10 (plusmn12) 7 200Guanosine 3 (plusmn2) 19 (plusmn25) 28 802Inosine 6 (plusmn2) 20 (plusmn16) 24 702Caffeine 8 (plusmn3) 17 (plusmn20) 16 451Pantothenic acid 23 (plusmn5) 9 (plusmn6) minus24 minus702Riboflavin 2 (plusmn1) 3 (plusmn2) 2 50

Several lines of evidence support the distinction of sponge-derived DOM profiles fromthose of surrounding seawater The analysis of similarity based on the untargeted metabolicprofile of the seawater samples indicated that the exhalent water samples were significantlydifferent from both the inhalant seawater and off reef seawater DOM profiles Because themajority of mass features were common to all sample types this suggests that differences inthe concentration of individual compounds are driving the observed statistical separation insamples However it is likely to be a combination of diversity and concentration differencesin each sample type that are responsible for the statistical observations For example thereare 340 unique features in the exhalent samples a small portion of the overall numberof features but these unique features may have a role in driving differences betweensample types Similarly pairwise comparisons between the shared features of exhalentand inhalant seawater indicated that over 400 metabolites exhibited significantly differentconcentrations between these two water types The majority of these differential featureswere elevated in concentration in the exhalent water indicating significant contributionof metabolites to the seawater within the immediate vicinity of the sponge Similarlymany mass features were enriched in the exhalent samples relative to the off reef seawatersamples However the lack of separation of inhalant and off reef seawater samples basedon DOM composition implies that the impact of the distinct exhalent profile is spatiallylimited as it may be rapidly utilized or diluted

While there is not a strong impact of sponge-derived DOM on the overall reef DOMprofile the distinct profile of DOM in the exhalent plume of the sponges may influencethe metabolism and potentially community structure of the nearby pelagic microbialcommunity (Judd Crump amp Kling 2006 Landa et al 2013) Given the connections

Fiore et al (2017) PeerJ DOI 107717peerj2870 1222

between the microbial community composition and activity biogeochemical cycling andcoral health (egDinsdale et al 2008Kline et al 2006Haas et al 2011) it is possible thatsponge-derived DOM could also impact the coral reef community as a whole This workprovides another avenue for investigating the dynamics of coral reefmicrobial communitiesand the interactive effects of microbial and benthic invertebrate metabolism on overlyingseawater chemistry

Part of the impact of sponge-derived DOM on seawater chemistry was estimated basedon the quantified metabolites Our calculations indicated that over a 24 hr period thesesponges would release between sim2 picomoles to about a nanomole or more of manyof the metabolites quantified using targeted metabolomics analysis In some cases thesponges were a net sink for compounds including 4-hydroxybenzoic acid and pantothenicacid supporting the idea that sponges are removing certain compounds and adding othercompounds to the seawater as it passes through the sponge As a comparison total dissolvedfree amino acids (DFAA) have been quantified previously in Biscayne Bay FL (USA) wheretheir concentrations ranged from sim15 to 50 nM in the surface waters between 15 and 10m from shore (Lee amp Bada 1977) DFAA are typically a small portion of the total dissolvedamino acid pool (Lee amp Bada 1977 Sommerville amp Preston 2001) but are likely to be labileto the nearby microbial community (Joslashrgensen 1987Middelboe Borch amp Kirchman 1995)We estimate that up to a nanomole each of tryptophan and phenylalanine would be releasedfrom each sponge each day potentially making these sponges an important source of thesetwo amino acids to the DFAA pool These results support a unique profile of DOM derivedfrom sponge exhalent seawater with some components reduced in concentration and othercomponents added or elevated in concentration relative to the surrounding seawater

The metabolic profiles of the exhalent seawater from the two sponge species were notsignificantly different and the overwhelming majority of mass features were present inthe exhalent seawater from both species Further work is needed to determine the extentto which DOM derived from other sponge species might be similar to our observationsNevertheless the degree of similarity of the exhalent seawater profiles between I campanaand S vesparium was surprising given that these species have been characterized as HMAand LMA species with different density and diversity of symbionts (Poppell et al 2014)The dichotomy between HMA and LMA sponges is not always straightforward (Gloeckneret al 2014) but several studies have documented functional differences relevant tobiogeochemical cycling between the two categories of sponges (Weisz et al 2007 FreemanEasson amp Baker 2014) In the case of the species used in this study there is a fairly wellcharacterized dichotomy between the diverse and abundant community in I campana(HMA) and the notably low density of microbes in S vesparium (LMA Hardoim amp Costa2014 Poppell et al 2014) While the exhalent seawater DOM profiles exhibited highoverall similarity more unique mass features were present in the exhalent seawater fromS vesparium than from I campana In addition there was a higher number of features thatwere more abundant in the exhalent seawater of S vesparium than of I campana comparedto the off reef samples Further work is needed to ascertain the degree to which the overallDOM profile of seawater exiting from sponges represents general metabolic waste eitherfrom the sponges or their microbial symbionts Previous work with marine eukaryotic

Fiore et al (2017) PeerJ DOI 107717peerj2870 1322

phytoplankton observed differences in DOM exudate composition among phylogeneticgroups (Romera-Castillo et al 2011 Becker et al 2014) consistent with the hypothesis thatDOM exudate profiles could differ among closely related organisms such as sponge speciesas well as between different organism types Future work should focus on whether the massfeatures observed in this study particularly those unique to a sponge species or elevatedin one of the two species are the result of symbiont or host metabolism and whether suchfeatures are ecologically significant

Both the targeted and untargeted metabolomics analyses suggest that sponges arereducing the concentrations of some DOM components and increasing the concentrationsof others This is expected because while the sponge holobiont would remove somecompounds from thewater for assimilation andor energy waste products frommetabolismare also likely to be added to the seawater by the sponge Consequently sponge metabolismand filtration could have a significant impact on the pelagic microbial community andon the success of organisms that rely on DOM as a carbon source directly or indirectlyvia microbial symbionts For example boring sponges rely on dissolved organic carbon(DOC) to meet their energy demands (at least 80 of TOC they remove is DOCMueller etal 2014) The impact of sponge-derived DOC or DOM in general on the metabolism ofboring sponges has not yet been investigated In fact to our knowledge there are no studiesthat have examined the influence of sponge-derived DOM on the growth and metabolismof any coral reef organism

The ecological relevance of sponge-derived DOM in coral reef ecosystems would intheory be proportional to the impact that sponge metabolism has on the composition ofDOM in the surrounding seawater Of the 106 mass features that were higher in all of theinhalant seawater samples than the off reef samples many (n= 42) were also higher in theexhalent relative to inhalant seawater This suggests that the surrounding reef DOM profilemay be partially derived from sponge exhalent seawater Similarly of the 71 features thatwere lower in the inhalant compared to off reef samples 32 were significantly elevated inthe exhalent samples relative to the inhalant samples further distinguishing the inhalantand exhalent seawater samples from each other The impact of these sponge-derivedmetabolites on coral reef nutrient dynamics is likely to be multifaceted with individualcompounds having different activity in the environment Further experiments are necessaryto determine the full impact of sponge-derived DOM on the reef DOM profile

A portion of the local impact of sponge-derivedDOMon seawater chemical compositionhowever could be elucidated in the targeted metabolomics analysis The targetedmetabolomics dataset is a small subset of metabolites compared to the metabolic profilegenerated by the untargeted metabolomics analysis Thus it is not surprising to seedifferences between the two datasets For example the untargeted metabolomics analysisindicated a clear difference between the inhalant and exhalent seawater samples whilenone of the quantified metabolites were significantly different in concentration betweenthese two sample types There was a general trend towards higher concentrations ofsome quantified metabolites including several amino acids nucleosides and the vitaminriboflavin (B2) in the exhalent water relative to inhalant seawater suggesting that spongescould be a source of these compounds in the reef ecosystem Meanwhile in comparison to

Fiore et al (2017) PeerJ DOI 107717peerj2870 1422

the reef samples the two off reef samples contained higher concentrations of xanthosinecaffeine and aromatic amino acids indicating a non-sponge source for these compoundsor rapid consumption of these compounds by the reef sponge holobiont These preliminarydata further support holobiont metabolism as a factor influencing reef DOM compositionand suggest that to some extent there are differences in the composition of reef DOMrelative to that of nearby off reef seawater

Differences in metabolite concentration and composition between the reef samples andthe off reef samples can be illustrated by caffeine (137-trimethylxanthine) and the purinenucleoside xanthosine Caffeine exhibited similar behavior to the amino acids with highconcentrations in the exhalent seawater relative to inhalant seawater and particularly highconcentrations in the off reef water compared to both the inhalant and exhalent seawaterCaffeine is a purine alkaloid found in plants and is considered a contaminant in freshwaterand marine systems (Weigel Bester amp Huumlhnerfuss 2001 Gardinali amp Zhao 2002 Singhet al 2010 Del Rey Granek amp Sylvester 2012) with the potential to negatively impactcoral-algal symbiosis (Pollack Balazs amp Ogunseitan 2009) Caffeine concentrations in theoff reef samples were approximately 12 times higher than in the reef seawater even thoughthe sponges examined here were a net source of caffeine There is likely a non-spongesource for caffeine off reef given the concentration differences between on reef and off reefsamples Meanwhile the nucleoside xanthosine was detected only in the off reef seawatersamples suggesting that either there is no major source of xanthosine within the reefenvironment or that there is a high uptake of xanthosine within the reef resulting in a lowstanding concentration that may be below our detection levels

The nearly constant source of DOM from sponges and the differences in DOMcomposition between the reef (inhalant and exhalent) and off reef samples may influencethe planktonic community structure in these two habitats In a four-year study in Morsquooreasuch differences were observed in the composition and abundance of bacterioplanktoncommunity members between water overlying coral reefs and seawater away from the reef(Nelson et al 2011) The presence of particular sponge species on the reef or overall spongecommunity composition could also influence reef DOC composition and concentrationand ultimately planktonic community structure For instance the Caribbean spongeXestospongia mutawas found to feed selectively on live plankton yetDOCwhich comprisedmost of the TOC (84) in the inhalant seawater accounted for most of the sponge diet(70) and the sponge retained DOC at rates of approximately minus9 to 46 exhibitingboth release and uptake of DOC (McMurray et al 2016) Although evidence exists thatTOCDOC concentrations are highly variable across sites within the Caribbean and acrossocean basins (Nelson et al 2011 Haas et al 2016) no studies to date have investigated thepotential link to sponge community structure If there are inherent differences betweenlocations in terms of carbon concentration and composition future work characterizingthe DOM profiles and the benthic communities of geographically disparate sites may helpto link the flux of individual metabolites with specific members of these communities

The fact that none of the metabolites driving the differences between sample types couldbe identified using metabolomics databases highlights the gaps in our knowledge of thecomposition of coral reef DOM and thus in the flow of biologically relevant nutrients in

Fiore et al (2017) PeerJ DOI 107717peerj2870 1522

this system This is not surprising given that metabolomics databases are still growingand are generally biased towards human-associated metabolites Even recent humanmetabolomics studies however can only identify lt2 of mass features generated byuntargeted metabolomics analysis (eg Bouslimani et al 2015 Quinn et al 2016)

Classic examples of the impact of sponge holobiont metabolism on seawater chemistryinclude the addition of inorganic nitrogen and removal of DOC Previous studiesaddressing such phenomena have demonstrated the complex roles that sponges playin coral reef ecosystems The results presented here build on our understanding of theimpact of sponges on the composition of coral reef DOM by demonstrating that spongesremove certain components of low molecular weight DOM and add other componentsThese actions resulted in a distinct profile of DOM in the exhalent seawater which mayinfluence the metabolic activity of the nearby pelagic microbial community with potentialimplications for coral reef biogeochemical cycling In areas with a high density of spongessuch as many of the off shore reefs in the Caribbean sponge-derived DOM may have agreater impact on the overall profile of coral reef DOM Additionally the similarity ofDOM profiles from sponges with distinct prokaryotic communities provides preliminaryevidence for a common sponge signal in seawater DOM While this present study waslimited in size it is the first high-resolution assessment of the impacts of sponge filtrationand holobiont metabolism on coral reef DOM an important reservoir of organic nutrientsin this ecosystem Finally this work establishes a foundation for future hypothesis-drivenstudies aimed at better understanding the role of sponges in ecological and biogeochemicalprocesses on coral reefs

ACKNOWLEDGEMENTSThe authors would like to thank Kenan Matterson and Cole Easson for assistance in thefield Michael Lesser for syringes used in sampling Krista Longnecker for TOC analyses andMelissa Kido Soule for help with metabolomics analyses and K Longnecker M Kido Souleand Winn Johnson for comments that improved an earlier version of this manuscriptThe authors would also like to thank Krin Grande for artwork assistance The manuscriptwas improved by constructive reviews from L Aluwihare and C Nelson This representscontribution number 1047 at the Smithsonian Marine Station in Fort Pierce Florida

ADDITIONAL INFORMATION AND DECLARATIONS

FundingThis workwas funded by 54 backers through the crowdfunding platformExperiment (httpsexperimentcomprojectshow-do-sponges-influence-the-availability-of-nutrients-on-coral-reefs) The funders had no role in study design data collection and analysis decisionto publish or preparation of the manuscript

Grant DisclosuresThe following grant information was disclosed by the authorsExperiment (54 backers)

Fiore et al (2017) PeerJ DOI 107717peerj2870 1622

Competing InterestsThe authors declare there are no competing interests

Author Contributionsbull Cara L Fiore and Elizabeth B Kujawinski conceived and designed the experimentsperformed the experiments analyzed the data contributed reagentsmaterialsanalysistools wrote the paper prepared figures andor tables reviewed drafts of the paperbull Christopher J Freeman performed the experiments analyzed the data contributedreagentsmaterialsanalysis tools wrote the paper prepared figures andor tablesreviewed drafts of the paper

Data AvailabilityThe following information was supplied regarding data availability

MetaboLights httpwwwebiacukmetabolightsMTBLS281

Supplemental InformationSupplemental information for this article can be found online at httpdxdoiorg107717peerj2870supplemental-information

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