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Characterization of the stromatolite microbiome fromLittle Darby Island, The Bahamas using predictive andwhole shotgun metagenomic analysis

Giorgio Casaburi,1 Alexandrea A. Duscher,1

R. Pamela Reid2 and Jamie S. Foster1*1Department of Microbiology and Cell Science,University of Florida, Space Life Science Lab, MerrittIsland, FL, USA.2Rosenstiel School of Marine Sciences, University ofMiami, Miami, FL, USA.

Summary

Modern stromatolites represent ideal ecosystems tounderstand the biological processes required for theprecipitation of carbonate due to their long evolution-ary history and occurrence in a wide range of habi-tats. However, most of the prior molecular work onstromatolites has focused on understanding the taxo-nomic complexity and not fully elucidating the func-tional capabilities of these systems. Here, we begin tocharacterize the microbiome associated withstromatolites of Little Darby Island, The Bahamasusing predictive metagenomics of the 16S rRNA genecoupled with direct whole shotgun sequencing. Themetagenomic analysis of the Little Darbystromatolites revealed many shared taxa and corepathways associated with biologically induced car-bonate precipitation, suggesting functional conver-gence within Bahamian stromatolites. A comparisonof the Little Darby stromatolites with other lithifyingmicrobial ecosystems also revealed that althoughfactors, such as geographic location and salinity, dodrive some differences within the population, thereare extensive similarities within the microbial popula-tions. These results suggest that for stromatolite for-mation, ‘who’ is in the community is not as critical asmetabolic activities and environmental interactions.Together, these analyses help improve our under-standing of the similarities among lithifying ecosys-tems and provide an important first step incharacterizing the shared microbiome of modernstromatolites.

Introduction

Stromatolites are quintessential geomicrobiological eco-systems with a fossil record that extends back 3.5 billionyears (Grotzinger and Knoll, 1999). These long-lived fea-tures are sedimentary structures formed as a result of thesynergy between the metabolisms of microbial mats andthe environment (Reid et al., 2000). Ancient stromatolitesformed massive carbonate reefs comparable in size tomodern coral reefs, dominating life on our planet for 80%of Earth’s history (Awramik, 1984). Modern stromatolites,in contrast, are relatively rare, but have been found indiverse habitats including freshwater, marine andhypersaline environments (Playford and Cockbain, 1976;Reid et al., 1995; Reid et al., 2003; Laval et al., 2000;Breitbart et al., 2009; Santos et al., 2010; Farías et al.,2013; Schneider et al., 2013; Perissinotto et al., 2014).One of the most well-studied stromatolite systems hasbeen the island of Highborne Cay located in the NorthernExuma Cays, The Bahamas, which for more than 20years has served as a model to understand the geologi-cal, biochemical and biological processes associated withstromatolite formation and accretion under normal seawater conditions (e.g. Reid et al., 1995; Visscher et al.,1998; Reid et al., 2000; Baumgartner et al., 2009a; Stolzet al., 2009; Khodadad and Foster, 2012).

These previous studies have identified several keyguilds of microbes that together, through their metabolicactivities, generate steep geochemical gradients through-out the microbial mat community that can influence theprecipitation potential of calcium carbonate (Paerl et al.,2001; Dupraz and Visscher, 2005; Dupraz et al., 2009).These functional groups have been identified throughbiogeochemical and 16S rRNA gene analyses andinclude: oxygenic and anoxygenic phototrophs, aerobicheterotrophs, sulfate reducers, sulfide oxidizers andfermenters (Visscher et al., 1998; 2000; Steppe et al.,2001; Baumgartner et al., 2009a,b; Foster et al., 2009;Khodadad and Foster, 2012). It is the net activity of thesedifferent functional groups that influence carbonate pre-cipitation by altering the pH within the microbial mat. Forexample, some metabolisms, such as photosynthesis andsome types of sulfate reduction, promote carbonate pre-cipitation through an increase in pH, whereas other

Received 16 August, 2015; revised 13 October, 2015; accepted 13October, 2015. *For correspondence. E-mail [email protected]; Tel.321-525-1047.

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Environmental Microbiology (2015) doi:10.1111/1462-2920.13094

© 2015 Society for Applied Microbiology and John Wiley & Sons Ltd

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metabolisms, such as sulfide oxidation and aerobic res-piration, can decrease the pH, thereby promoting disso-lution of calcium carbonate (Dupraz and Visscher, 2005;Visscher and Stolz, 2005; Gallagher et al., 2012).

Although considerable progress has been made indelineating key metabolisms and microbes associatedwith modern stromatolites of Highborne Cay, the underly-ing molecular mechanisms and cellular interactions thatdrive these geochemical gradients are not well character-ized (Khodadad and Foster, 2012). Additionally, very littleis known regarding underlying microbial and functionalgene diversity of other stromatolites found throughout TheBahamas. Stromatolites have also been observed in theSouthern Exuma Cays including Bock Cay, Iguana Cay,Lee Stocking Island and Little Darby Island (Dill et al.,1986; Pinckney et al., 1995b; Reid et al., 1995; 2010;Feldmann and McKenzie, 1998) and along the easternmargin of Exuma Sound in Schooner Cays (Dravis, 1983);however, studies of these sites have been limited tobiogeochemical and morphological analyses.

To more fully understand the mechanisms ofstromatolite formation and accretion it is necessary tobegin to characterize the communities beyondbiogeochemistry and taxonomic diversity and assess theassociated stromatolite microbiome, that is, the totality ofthe microbes, functional genes and environmental inter-actions that result in the deposition of calcium carbonate.Not all microbial mats are conducive to the formation ofstromatolite structures (Dupraz et al., 2009; Foster andGreen, 2011), and characterizing the microbiome repre-sents a holistic approach to understanding the connectiv-ity and microbial interactions that lead to stromatoliteformation. In this study, we begin to characterize themicrobiome of stromatolites from Little Darby Island,located in the Southern Exuma Cays by assessing themicrobial and functional gene diversity using two comple-mentary approaches, 16S rRNA gene and shotgunmetagenomic sequencing analysis. Much like thestromatolites of Highborne Cay, the stromatolites of LittleDarby Island are sub-tidal, forming under normal seawa-

ter conditions and experiencing frequent burial events.Additionally, these ecosystems accrete through the trap-ping and binding of sediments and carbonate precipitationby the surface microbial mat community (Fig. 1; Reidet al., 2010). By expanding the analysis of stromatolite-forming communities beyond Highborne Cay, we canbegin to assess those taxa and pathways shared in Baha-mian stromatolites and determine whether these similari-ties extend beyond The Bahamas to other lithifyingmicrobial habitats. Together, these results may help delin-eate the essential biological processes and pathwaysneeded for modern stromatolite formation.

Results and discussion

Overview of stromatolite environment of Little DarbyIsland, The Bahamas

The stromatolites of Little Darby Island are located on thenortheast side of the island and are distributed parallel tothe shore in a narrow band approximately 300 m in lengthand 100 m wide. The stromatolites occur in the sub-tidalzone at depths ranging from 1 m to 3 m and vary in heightfrom 2 cm to 1 m (Fig. 1A). The Little Darby stromatolitesundergo extensive sand transport through wave actionand experience partial and/or full burial events throughoutthe year. Microbial mats occur on the surfaces of thestromatolite build-ups and typically exceed 2 cm in thick-ness. The upper 2 mm of the microbial mats are enrichedin exopolymeric substances (EPS) giving the surface mata caramel colour (Fig. 1B). Microscopic analysis of thecaramel surface layer revealed a layer of vertically orien-tated cyanobacterial filaments morphologically identifiedas primarily Schizothrix spp. and Phormidium spp.(Fig. 1C). Additional pronounced layers of cyanobacteriawere visible in cross-sections of the stromatolite-formingmats at a depth of 5 mm (Fig. 1B).

Taxonomic diversity of Little Darby stromatolites

To assess the microbial communities associated withLittle Darby stromatolites, a two-pronged approach was

A B C

o

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Fig. 1. Overview of stromatolites of Little Darby Island, The Bahamas.A. Stromatolites located in the shallow subtidal zone. Bar, 50 cm.B. Cross-section of microbial mat from the surface of stromatolites. Red box represents area visualized in C. Arrow points to subsurface greenlayer of enriched cyanobacteria. Bar, 5 mm.C. Cyanobacteria-rich area with extensive exopolymeric substances and trapped white oolitic sand grains (o). Bar, 500 μm.

2 G. Casaburi, A. A. Duscher, R. P. Reid and J. S. Foster

© 2015 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology

used that included targeted bacterial 16S rRNA geneamplicon and whole shotgun metagenomic sequencing.Recent studies have shown numerous limitations whenrelying on a single marker gene for assessing microbialdiversity, including primer bias, lower taxonomic resolu-tion and stability issues regarding operational taxonomicunits (OTU) clustering (Yousef et al., 2009; He et al.,2015). Whole genome shotgun metagenomics over-comes the limitations of targeting a single gene and canprovide a comprehensive assessment of communitydiversity (Gilbert et al., 2010). However, in complex com-munities, such as lithifying microbial mats, genomic data-bases can be far less representative and sometimestaxonomically skewed compared with some well-curated16S rRNA gene databases (Poretsky et al., 2014). There-fore, we used a dual approach to assess the overallcommunity complexity of the Little Darby stromatolites.For the amplicon library, recovered reads were qualityfiltered in QIIME resulting in 2 305 124 high quality filteredpaired-end reads (see experimental procedures for addi-tional detail). To help improve the diversity estimates, acut-off filter of 0.005% as the minimum total observationcount of an OTU was applied in QIIME, as previouslydescribed (Bokulich et al., 2013). The stringent filteringresulted in 2157 remaining OTUs that represented 13phyla within microbial mats (Fig. 2A). Of these phyla, theProteobacteria (62.2%) and the Cyanobacteria (31.6%)were dominant with the mats. Bacteroidetes andSpirochetes represented only 3.4% and 1.26% of thecommunity, respectively, whereas the other phyla eachcomprised less than 1% of the community (Fig. 2A). Otherphyla were observed within the 16S rRNA gene librariesbut at levels below 0.005% abundance. These resultsstrongly correlate with previous 16S rRNA gene sequenc-ing of Highborne Cay stromatolites (Baumgartner et al.,2009a; Foster and Green, 2011).

Within the Proteobacteria, the most representedclasses were the Alphaproteobacteria (61.5%)and Deltaproteobacteria (3.2%) (Fig. 2A). TheAlphaproteobacteria in the Little Darby stromatolites wereenriched in taxa known to be anoxygenic phototrophs,specifically purple non-sulfur bacteria derived fromthe orders Rhodobacterales (15.3%), Rhizobiales(11.3%) and Rhodospirillales (7.8%), whereas theDeltaproteobacteria were primarily comprised ofsulfate-reducing bacteria associated with the taxaDesulfobacterales (1.7%) and Desulfovibrionales (0.2%).The cyanobacterial population within the stromatolite-forming mats was dominated by the subclassOscillatoriophycideae (39%) primarily with the ordersChroococcales (9%) and Oscillatoriales (0.6%). Othercyanobacterial orders were recovered but at much lowerabundance, such as the Nostocales (0.2%) andPleurocapsales (0.3%); however, it is important to note

that most of the recovered cyanobacterial sequenceswere unable to be classified beyond the phylum level andthat many of the cyanobacterial taxa previously identifiedin Little Darby Island stromatolites using morphologicalapproaches, such as Schizothrix spp. and Solentia spp.(Reid et al., 2010), have no representative genomeavailable.

Whole genome shotgun metagenomic sequencing ofDNA derived from the seven distinct stromatolite headsgenerated a total of 7 927 495 quality-filtered, paired-endreads. The high-quality reads were processed using bothMetagenome Composition Vector (METACV), whichtargets primarily prokaryotes and the MG-RAST pipeline,which targets all three domains. The majority of themetagenomic reads were derived from Bacteria (>70%).Archaea constituted 10% of the total data set, whereasEukarya were represented by less than 3% of the recov-ered sequences with 16% of the total sequencesreported as unclassified in both analysis programs.Analysis of the metagenomic data revealed a much morecomplex bacterial community within the stromatolite-forming mats compared with the 16S rRNA gene libraries(Fig. 2B) and provided additional insight into the archaealand eukaryotic community (Figs S1–2). Based onMETACV analysis, there was a total of 2883 OTUs, a33% increase compared with the 16S rRNA gene library,representing a total of 27 bacterial phyla in the shotgunmetagenomic library. Similarly to the amplicon library, theProteobacteria (50.0%) and Cyanobacteria (28.4%) werethe dominant phyla within the community. Themetagenomic sequencing analysis provided a muchhigher taxonomic resolution of the Bacteria with over 232families observed in the data set (Fig. 2B). The resultsalso revealed a much more complex Alphaproteobacteriapopulation specifically in the Rhizobiales with recoveredsequences sharing similarity to the facultativemethyltrophs of the families Xanthobacteraceae andMethylobacteriaceae, which have the ability to grow onmethylamine, fructose, fucose, xylose, arabinose andsome organic acids (Green, 2006). Substrates, such asthese, have been found in high abundance in the EPSthat comprise stromatolite-forming mats (Kawaguchi andDecho, 2002). However, as in the 16S rRNA gene analy-sis, the dominant Alphaproteobacteria in the shotgunlibrary were the phototrophic purple non-sulfur bacteriaRhodobacteraceae (10.5%) and Rhodospirallaceae(6.5%), which correlates with the alphaproteobacterialpopulations found in other lithifying microbial matsystems such as the thrombolites of Highborne Cay, TheBahamas; hypersaline stromatolites of Hamelin Pool,Shark Bay; Kiritimati Atoll, Kiribati; and Socampa,Argentina (Foster and Green, 2011; Khodadad andFoster, 2012; Farías et al., 2013; Schneider et al.,2013).

Microbiome of Little Darby Island stromatolites 3


Metagenomic sequencing analysis of thecyanobacterial population strongly correlated with the 16SrRNA gene analysis indicating that the microbial commu-nity was primarily comprised of Cyanobacteria derivedfrom the subclass Oscillatoriophycideae and sharedsequence similarity to genera such as Cyanothece,Synechococcus and Acaryochloris (Fig. S3). Shotgunmetagenomic sequencing also indicated that Nostocales

(5.2%) occur at a higher relative abundance comparedwith the 16S rRNA gene analysis. However, one majorcaveat to taxonomic identification of Cyanobacteria is therelatively low diversity of sequenced reference genomesfor data comparison. This under-representation in theMETACV database may account for the lack ofPleurocapsales and representation of the Oscillatorialesfamily by a single genus Trichodesmium (Fig. S3). These

Fig. 2. Taxonomic abundance and diversity of Little Darby stromatolites. Krona plot visualizing the taxonomic hierarchies of (A) the 16S rRNAgene analysis performed with QIIME and (B) whole shotgun metagenome sequencing analysed with METACV. The inner ring represents thedifferent phyla associated with the mats, and as the plot progresses outwards, there is an increasing taxonomic resolution for each ring (i.e.class, order, family respectively).Sequences have been filtered and only those OTUs with relative abundance greater than 0.1% are reported.



results reinforce the need for a greater diversity ofcyanobacterial cultivars to be sequenced.

The shotgun metagenomic library also provided infor-mation on the archaeal and eukaryotic populations withinthe Little Darby stromatolites. Methanogens dominatedthe archaeal population and were diverse, containing rep-resentatives of all known orders, Methanobacteriales,Methanococcales, Methanopyrales, Methanosarcinales,Methanomicrobiales and the recently recognizedMethanocellales (Fig. S1). Of the different orders ofmethanogens, the metabolically versatile Methano-sarcinales were the most abundant. Members of this

order can grow and generate methane from a wide rangeof compounds including hydrogen, acetate, methanol andmany other single carbon molecules (Anderson et al.,2009; Sakai et al., 2011). Genes associated with archaealmethane metabolism accounted for approximately 5% ofthe recovered archaeal functional genes (data notshown).

Another enriched archaeal taxa were the Nitroso-pumilales, an ammonia-oxidizing Thaumarchaeota, whichhas been shown to be prominent in unlaminated,thrombolite-forming microbial mats from Highborne Cay,The Bahamas (Mobberley et al., 2012; 2013; 2015).

Fig. 2. Continued



Additionally, the Little Darby stromatolite communitieswere enriched with Halobacteria, which are obligateheterotrophs. Halobacteria have been observed in a widerange of lithifying microbial mat systems, such as thehypersaline stromatolites of Shark Bay (Burns et al.,2004; Goh et al., 2006; Leuko et al., 2007; Ruvindy et al.,2015). Although their precise ecological role instromatolite formation has not been delineated, theseHaloarchaea are metabolically diverse, known to formbiofilms (Fröls et al., 2012), metabolize carbohydrates(e.g. pentoses, hexoses; Andrei et al., 2012) and growphotoheterotrophically (Hartmann et al., 1980); all ofwhich are important elements in lithifying microbial matecosystems (Dupraz et al., 2009).

The shotgun metagenomic sequencing revealed thatdiatoms dominated the eukaryotic population within theLittle Darby stromatolite-forming mats, and were enrichedin the orders Bacillariophyceae, Fragilariophyceae,Coscinodiscophyceae and Mediophyceae (Fig. S2). Pre-vious studies on the stromatolites of Highborne Cay haveidentified several dominant species of diatoms includingthe stalked diatoms Striatella unipunctata and Licmorpharemulus (class Fragilariophyceae) as well as severalNaviculid (class Bacillariophyceae) tube diatoms (Stolzet al., 2009). Diatoms have been shown to play an impor-tant role in the accretion of Bahamian stromatolites (Reidet al., 2000; Stolz et al., 2009; Bowlin et al., 2012). Boththe tube and stalked diatoms can form networks that trapsediment grains and become bound into the stromatoliteframework by filamentous Cyanobacteria formingunlithified grain layers (Bowlin et al., 2012). The highabundance of diatoms within the eukaryotic populationlikely reflects the collection of samples in August, wherewater temperatures were > 27°C. Previous work hasshown a positive correlation of the relative abundance ofstalked and tube diatoms under elevated temperatures(Bowlin et al., 2012). The other dominant eukaryotictaxa within the Little Darby stromatolite ecosystemsincluding Rhodophyta, Chlorophyta, Streptophyta andHeterokontophyta (Stramenopiles) have all been welldocumented in other lithifying microbial ecosystemsincluding Highborne Cay, Cuatro Ciénegas, Mexico andHamelin Pool, Shark Bay Western Australia (Breitbartet al., 2009; Myshrall et al., 2010; Mobberley et al., 2012;2013; Edgcomb et al., 2014). Functional genes associ-ated with eukaryotic taxa were primarily associated withgenetic information processing and cellular processes,such as cell growth, transport and death (data notshown). However, there were numerous carbohydratemetabolisms genes associated with sugar metabolism(e.g. fructose, xylose, galactose), suggesting theeukaryotes may be contributing to the heterotrophic deg-radation of EPS material within the stromatolite formingmats.

An overlay of the 16S rRNA gene amplicon andmetagenomic libraries is visualized in Fig. 3. The radarplot depicts the relative abundance of recovered genesassociated with the 92 dominant shared bacterial familieswithin the stromatolite-forming community and areexpressed as a percentage on a log scale. There is a highcorrelation (r = 0.91, Pearson) between the two data setsregarding the relative abundance of the dominant bacte-rial families, suggesting that both approaches capture theoverall diversity of the dominant taxa within the commu-nity. For example, in some phyla, such as Cyanobacteriaand Firmicutes, there was a strong correlation betweenthe relative abundances of families between the 16SrRNA gene and metagenomic data sets (Fig. 3). However,for some taxa there was extensive variability in the rela-tive abundance observed between the data sets. In theProteobacteria, there were several examples of families,such as the alphaproteobacterial families Brucellaceaeand Bradyrhizobiaceae, which had a higher relative abun-dance in the shotgun metagenomic library. These differ-ences between the data sets may reflect issues regardingmany of the clustering programs associated with 16SrRNA gene analysis. Several recent studies have identi-fied that OTU clustering of sequences can be impacted bythe number of sequences being clustered (He et al.,2015). Although there was a strong correlation betweenthe two data sets, assigning a taxonomic classification tometagenomic reads provided a more comprehensiveassessment of the complexity of the stromatolite formingmat community structure than with 16S rRNA gene analy-sis alone.

Functional gene complexity of stromatolite-formingmicrobial mats using both predictive and whole shotgunsequencing analyses

For decades ribosomal RNA gene analysis has provided arobust mechanism to survey the diversity of complexmicrobial communities (e.g. Woese et al., 1990); however,it provided little direct evidence of the metabolic capabili-ties. With the recent, rapid expansion of sequenced ref-erence genomes, it has now become possible to use thisdata to help predict the functional composition of commu-nities metagenome (Zaneveld et al., 2010; Collins andHiggs, 2012; Langille et al., 2013). Using the programPhylogenetic Investigation of Communities by Recon-struction of Unobserved States (PICRUST), the genecontent of the target ecosystem can be inferred using thereference genomes of one or more of its closest relativesas well as a reconstruction of the organisms’ ancestralgenome (Langille et al., 2013). This program has beeneffectively used to predict accurately the functional com-plexity of the metagenomes of the human microbiome,soils and the hypersaline, non-lithifying microbial mats



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(Langille et al., 2013). However, some environmentalmetagenomes, such as stromatolite ecosystems, havethe potential problem that there may not be enough ref-erence genomes available to make accurate predictions.To assess the effectiveness of using the 16S rRNA geneas a prognostic tool for metagenomic analyses instromatolites, we generated a simulated prediction of themetabolic functions of the Little Darby stromatolites from16S rRNA gene sequences with PICRUST. This predictivemetagenomic approach utilized the QIIME output for taxo-nomic profiling and directly compared it with the wholeshotgun sequenced metagenome library.

For the 16S rRNA gene library, we identified a total of6909 KEGG (Kyoto Encyclopedia of Genes andGenomes) functions, corresponding to 328 level 3 KEGGorthology (KO) entries, whereas the post-quality filteredwhole metagenome shotgun reads analysed in METACVshowed a total of only 2944 KEGG functions, correspond-ing to 257 level 3 KO entries. We filtered out those entriesoccurring at a low relative abundance (<1%) and com-pared the two data sets. In total, we identified 88 dominantlevel 3 KOs in both data sets, mostly related to metabo-lism (Fig. 4). There was a moderate to strong correlationbetween the 16S rRNA gene PICRUST prediction and thewhole metagenome sequencing (r = 0.78, Pearson) andthe two data sets never varied more than 4% in each KOpathway (Fig. 4). Photosynthesis, chlorophyll metabolismand carbon fixation pathways in photosynthetic organismswere dominant within the metagenome with a higher rep-resentation of genes associated with these metabolismsin the whole shotgun library compared with the 16S rRNAgene predicted libraries. The underestimation of genesrelated to photosynthetic pathways in the 16S rRNA genepredictions further confirms the need for additionalcyanobacterial reference genomes to be targeted forsequencing to fill phylogenetic gaps in the databases.

Other metabolic pathways that were enriched in theLittle Darby stromatolite metagenome included genesassociated with aerobic oxidative phosphorylation, nitro-gen fixation, glycolysis, as well as methane metabolism.Together, the metagenome reflected all of the major func-tional groups, in varying relative abundances, typicallyassociated with lithifying microbial mat systems (Duprazand Visscher, 2005; Visscher and Stolz, 2005) andoverlap with the dominant pathways in the stromatolitesand thrombolites of Highborne Cay (Khodadad andFoster, 2012; Mobberley et al., 2013; 2015).

In addition to the various microbial metabolisms, therewere also a high abundance of genes associated withtwo-component signalling systems (Fig. 4), includinggenes associated with quorum sensing and its regulation(e.g. luxQ, luxU, luxO), as well as numerous genes asso-ciated with OmpR family that respond to a range of envi-ronmental stress conditions, such as high light, nutrient

limitation (e.g. phosphate, nitrogen, Mg2+, Mn2+) andosmotic stress suggesting a strong ability of stromatolite-forming microorganisms to sense and consequentlyrespond to changes in different environmental conditions.Environmental factors, such as sand burial events, tem-perature and photosynthetic active radiation have beenshown to have a profound impact on the rates of meta-bolic activity within lithifying microbial ecosystems, as wellas provide control of the cycling and development of thedominant microbial mat communities (Dupraz et al., 2009;Bowlin et al., 2012). Moreover, lithifying microbial matecosystems are usually oligotrophic and must scavengenutrients from the surrounding water. A high number ofsequences associated with sensing and responding tonutrient limitations, such as the phosphate regulon (phoR-phoP), were observed in the metagenome. The resultscorresponded with other microbialite ecosystems, such asthe alkaline lake Alchichica and fresh water springs ofCuatro Ciénegas in Mexico (Breitbart et al., 2009;Valdespino-Castillo et al., 2014). Much like the Mexicanmicrobialites, the phosphate regulon and assimilationgenes from the Little Darby stromatolites were derivedfrom a diverse group of organisms, primarily theProteobacteria classes Alphaproteobacteria, Gamma-proteobacteria and Deltaproteobacteria, Cyanobacteriaclass Oscillatoriophycideae and Actinobacteria(Fig. S4).

Together, the PICRUST prediction and shotgun libraryapproaches suggest a strong correlation between phylog-eny and function, further supporting that, even for envi-ronmental metagenomes, such as stromatolites, 16SrRNA gene libraries can be used to provide insight into themetabolic capabilities of complex microbial ecosystems.Although whole metagenome shotgun sequencing isbecoming increasingly more affordable, providing thenecessary sequencing and sampling depth for all environ-ments may still be cost-prohibitive. Therefore, theseresults suggest that using phylogenic markers, such asthe 16S rRNA gene, can provide an important first lookinto the functional complexity of lithifying mat ecosystemsas well as the microbial diversity.

To delineate the taxa associated with most highly rep-resented genes of the stromatolite shotgun metagenomelibrary, genomic reads were matched with metabolic func-tion in METACV and then grouped by phylogenetic rela-tionships in MEGAN 4 (see Experimental procedures). Aheat map correlating the relative abundance of eachtaxon with the specific gene is shown in Fig. 5. Of thedifferent metabolic KO groups, the most abundant geneswere associated with photosynthesis, carbohydrate syn-thesis and degradation, as well as nitrogen and sulfurmetabolism. Within the photosynthesis KO, the mostabundant reads shared sequence similarity to genesassociated with photosystem I and II, chlorophyll and



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accessory pigment synthesis, and the electron acceptorferredoxin (Fig. 5). Most of these genes were assigned tothe cyanobacterial order Chroococcales, with a few genesassociated with the filamentous order Oscillatoriales. Highrates of photosynthesis have been previously shown toincrease the localized alkalinity by depleting CO2 resultingin disassociation of bicarbonate into CO2 and OH−

(Dupraz et al., 2009). The role of photosynthesis, as amajor driver in the precipitation of carbonate throughchanges in the carbonate alkalinity, has been well docu-

mented (Chafetz, 1986; Paerl et al., 2001; Dupraz andVisscher, 2005; Dupraz et al., 2009).

Another group of highly represented genes within thestromatolite metagenome were associated with the syn-thesis and degradation of sugars. Several genes associ-ated with xylose fucose, mannose metabolism, as well asgenes associated with the production of alginate (e.g.phosphomannomutase), were highly represented withinthe stromatolite metagenome. The taxa associated withthese genes varied but were primarily derived from the

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[K11181] sulfite reductase, dissimilatory−type beta subunit [K00395] adenylylsulfate reductase, subunit B[K11180] sulfite reductase, dissimilatory−type alpha subunit[K00394] adenylylsulfate reductase, subunit A[K00958] sulfate adenylyltransferase[K02586] nitrogenase molybdenum−iron protein alpha chain[K02591] nitrogenase molybdenum−iron protein beta chain[K02588] nitrogenase iron protein IF4[K15864] nitrite reductase (NO−forming)[K00371] nitrate reductase 1, beta subunit[K02567] periplasmic nitrate reductase NapA[K04561] nitric−oxide reductase, cytochrome b−containing subunit I[K00362] nitrite reductase (NAD(P)H) large subunit[K00370] nitrate reductase 1, alpha subunit[K00376] nitrous−oxide reductase[K01711] GDPmannose 4,6−dehydratase [K01805] xylose isomerase [K00971] mannose−1−phosphate guanylyltransferase[K10112] maltose/maltodextrin transport system ATP−binding protein[K07246] D−malate dehydrogenase[K03780] L(+)−tartrate dehydratase beta subunit[K01812] glucuronate isomerase[K15778] phosphomannomutase[K02377] GDP−L−fucose synthase[K03779] L(+)−tartrate dehydratase alpha subunit[K01840] phosphomannomutase[K00128] aldehyde dehydrogenase (NAD+)[K02706] photosystem II P680 reaction center D2 protein[K02705] photosystem II CP43 chlorophyll apoprotein[K02704] photosystem II CP47 chlorophyll apoprotein[K02703] photosystem II P680 reaction center D1 protein[K02692] photosystem I subunit II[K02691] photosystem I subunit VII[K02690] photosystem I P700 chlorophyll a apoprotein A2[K02689] photosystem I P700 chlorophyll a apoprotein A1[K05378] phycoerythrin−associated linker protein[K05377] phycoerythrin beta chain[K05376] phycoerythrin alpha chain[K02290] phycobilisome rod−core linker protein[K02286] phycocyanin−associated rod linker protein[K02285] phycocyanin beta chain[K02284] phycocyanin alpha chain[K02097] phycobilisome core component[K02096] phycobilisome core−membrane linker protein[K02095] allophycocyanin−B[K02094] phycobilisome core linker protein[K02093] allophycocyanin beta subunit[K02092] allophycocyanin alpha subunit[K02641] ferredoxin−−NADP+ reductase[K02639] ferredoxin[K02112] F−type H+−transporting ATPase subunit beta[K02111] F−type H+−transporting ATPase subunit alpha[K02110] F−type H+−transporting ATPase subunit c[K02108] F−type H+−transporting ATPase subunit a[K02637] cytochrome b6−f complex subunit 4[K02636] cytochrome b6−f complex iron−sulfur subunit[K02635] cytochrome b6[K02634] apocytochrome f

Fig. 5. Relative contribution bacterial taxa to selected gene pathways. Heat map showing the relative contribution (%) of bacterial orders toselected metabolic pathways expressed as KO entries and associated products derived from metagenomic shotgun sequencing library.Different colours cluster genes belonging to the same pathway; photosynthesis (green), carbohydrate (pink), nitrogen (blue) and sulfur (purple)metabolisms. Unclassified bacterial orders (U) are reported as bacterial classes.



Alphaproteobacteria orders Rhodobacteriales; however,some genes associated with fucose synthesis were asso-ciated with the cyanobacterial orders Chroococcales andNostocales. Previous studies have characterized the EPSwithin Highborne Cay stromatolites and have shown thatmore than 50% of the material is comprised of carbohy-drates such as galactose, xylose and fucose (Kawaguchiand Decho, 2000). Additionally, lithified stromatoliteforming mats (i.e. Type 3 mats) of Highborne Cay, exhib-ited a high propensity to utilize hexose (e.g. galactose,mannose), deoxy sugars (fucose) and acidic sugars (e.g.ketoglutaric acid) (Khodadad and Foster, 2012). Together,these results suggest that Bahamian stromatolites havean increased metabolic capacity for the heterotrophicdegradation and rearrangement of EPS material, whichhas the potential to alter the Ca2+ binding affinity of theEPS, thereby promoting carbonate precipitation (Duprazet al., 2009).

The predominant genes associated with nitrogenmetabolism were nitrogenases primarily associated withthe Cyanobacteria, including heterocystous formingNostocales and the non-heterocystous Chroococcaleswith similarity to Cyanothece and Synechococcus.However, genes were also recovered from numerousother taxa including the Firmicutes, Proteobacteria andAcidobacteria. Similar results were observed in themetagenome and metatranscriptome of the thrombolitesof Highborne Cay, where there was an enrichment of nifgenes expressed at midday from a diversity group ofmicrobes (Mobberley et al., 2015) as well as from nifHsurveys of Mexican microbialites in Cuatro Ciénegas,Lake Alchichica and the Bacalar costal lagoon (Falcónet al., 2007; Beltrán et al., 2012). However, in addition tonitrogen fixing genes, there was also a high representa-tion of genes in the stromatolite metagenome associatedwith denitrification, including reductases for nitrate,nitrite, nitric oxide and nitrous oxide from a wide rangeof taxa. Although primarily associated with thealphaproteobacteria orders Rhodobacteriales andRhizobiales, a high relative abundance of denitrificationgenes were also recovered from other Proteobacteria(Beta-, Delta- and Gammaproteobacteria) as well as thecyanobacterial order Chroococcales. The dissimilatoryreduction of nitrate to N2 has the potential to result in a netloss of carbonate thereby having a negative impact oncarbonate precipitation within the stromatolites (Visscherand Stolz, 2005).

Although sulfur metabolism was not a dominant level 3KO pathway in either of the shotgun or 16S rRNA genePICRUST predictive metagenome (Fig. 4), genes encod-ing the three key enzymes associated with dissimilatorysulfate reduction and sulfide oxidation were representedwithin the stromatolite metagenome. Specifically, genesencoding sulfate adenylytransferase (ATP sulfurylase),

adenylysulfate reductase (APS reductase) and sulfitereductase were found primarily in the Deltapro-teobacteria, Gammaproteobacteria and Chromatialeswithin the Little Darby stromatolites. Sulfate-reducing bac-teria are a major functional group in microbial matssystems and have been identified as having a major rolein the precipitation of calcium carbonate (Visscher et al.,1998; 2000; Dupraz et al., 2009), although more recentstudies have shown that the net precipitation potential ofsulfate reduction is highly dependent on the type of elec-tron donor (Gallagher et al., 2012). Together, these resultsfurther confirm that lithifying microbial ecosystems, suchas the Little Darby stromatolites, utilize a diverse networkof microbes and functional genes to aid in the influx andcycling of nutrients within the stromatolites. Further,through the coordinated activities of these microbialmetabolisms, the geochemical microenvironment withinthe stromatolites can be altered, thereby potentially gen-erating conditions (e.g. changes in localized pH) thateither promote the precipitation or dissolution of carbon-ate (Visscher and Stolz, 2005; Dupraz et al., 2009).

Comparison of Bahamian stromatolites to the globalmicrobialite population from varying environments

In addition to profiling the taxa and functional complexityof the Little Darby stromatolite microbiome, we comparedthis system with other carbonate-based microbialitesthroughout the globe. Publically available data sets thatused next generation sequencing (454 and Illumina plat-forms) for 16S rRNA gene analysis within lithifying micro-bial mat ecosystems were mined for the comparison(Table 1). As stromatolites are highly novel communities,a subsampled open-reference OTU picking approach inQIIME to be inclusive of unique taxa was needed (Rideoutet al., 2014); therefore, we targeted only 16S rRNA genelibraries that had overlapping amplicons (V2-V4 regions).Unfortunately, this approach prohibited the inclusion ofseveral prominent microbialite studies, such as the 16SrRNA gene analyses of microbialites collected throughoutMexico (Cuatros Ciénegas, Alchichica, Sian Ka’an andBacalar; Centeno et al., 2012), which targeted the V5–6region. Additionally, several earlier studies that used clonelibraries to survey stromatolites from across the globe(e.g. Papineau et al., 2005; Allen et al., 2009;Baumgartner et al., 2009b; Foster et al., 2009; Goh et al.,2009; Santos et al., 2010; Foster and Green, 2011) werenot included due to their limited sequencing depth.

A total of 10 different study sites were included in thecomparison for a total of 171 samples (Table 1). The sitesincluded a wide range of microbialite habitats includingseveral freshwater (Pavilion Lake, British Columbia;South African estuaries), marine (Highborne Cay andLittle Darby Island, The Bahamas), hypersaline (Hamelin



Pool, Shark Bay, Western Australia; Socompa Lake,Argentina; Storr’s Lake, San Salvador, The Bahamas) andultrahypersaline (Kiritimati Atoll, Kiribati) locations (Limet al., 2009; Mobberley et al., 2012; Dupraz et al., 2013;Farías et al., 2013; Schneider et al., 2013; Perissinottoet al., 2014; Russell et al., 2014; Paul et al., 2015). Twonon-lithifying microbial mat sites were also included toassess whether there where major differences betweenmat communities that undergo lithification; the hydrother-mal vent mats from Santorini Crater in the MediterraneanSea and the hypersaline microbial mats of GuerreroNegro (Table 1). All the sequences from each data setwere merged, normalized and analysed in QIIME (seeExperimental procedures).

The alpha diversity of each community was assessedusing rarefaction curves derived from both the ShannonDiversity and the Faith’s Phylogenetic Diversity Index(Fig. S5). The results indicated that the highest levels ofmicrobial diversity were within the hypersalinestromatolites of Shark Bay Australia, whereas the lowestlevels of diversity were observed in the microbialites of theSwartkops estuaries and the non-lithifying hydrothermalvent communities of Santorini Caldera, which may reflectthe lower sample number and sequencing depth of thetwo sites. A beta diversity analysis on the different com-munities was completed using Principal CoordinateAnalysis (PCoA) of the unweighted UniFrac rarefied dis-tance matrix (Fig. 6). Some distinctive clustering wasapparent in the PCoA plot indicating that geography doesplays a role in driving some of the microbial diversitywithin lithifying microbial mat ecosystems [Fig. 6A;P = 0.001; R = 0.93, analysis of similarity (ANOSIM)]. Themost isolated system appeared to be the Hamelin Poolstromatolites located in Shark Bay Western Australia.Hamelin Pool is home to the most extensive and diversecommunities of stromatolites in the world and is thought tohave been isolated from the Shark Bay system approxi-mately 6000 years ago (Logan et al., 1974; Playford and

Cockbain, 1976). The extended geographic isolation ofthese stromatolites may be a major factor in distinctiveclustering of the Hamelin Pool community from the othertargeted habitats.

Another environmental factor that appeared to drivedifferences in the microbial diversity between habitatswas salinity (Fig. 6B). Salinity has been long known to bea contributor to driving differences in diversity within ter-restrial and aquatic-based microbial communities (e.g.Pinckney et al., 1995a; Lozupone and Knight, 2007;Dupont et al., 2014). Although the correlation was not asstrong as geography, differences between the populationsdid appear to be linked to the salinity of the surroundingenvironment (P = 0.001; R = 0.59 ANOSIM). For example,the two most distant microbialite communities were theultrahypersaline Kiritimati Atoll (up to 170 ppt) and fresh-water Pavilion Lake (<1 ppt) samples (Fig. 6B). Samplesfrom the marine (Little Darby Island and Highborne Cay;35–38 ppt) and moderately hypersaline (Guerrero Negro,Storr’s Lake; Socampo Lake; 66–100 ppt) sites clusteredtogether. Interestingly, sites such as the SwartkopsEstuary in South Africa (5–35 ppt) and Storr’s Lake, SanSalvador, The Bahamas (26–> 90 ppt), which undergoextensive transitions in their salinity throughout the yearclustered with the open marine Bahamian microbialites ofLittle Darby and Highborne Cay (Perissinotto et al., 2014;Paul et al., 2015). Although metagenomic analyses do notcurrently exist for most of these sites, including theSwartkops Estuary and Storr’s Lake, the results dosuggest that the communities may harbour key taxa orclades that may have a large variation in gene contentand metabolic capabilities, thereby enabling a quickresponse to the regular freshening events that occur inthese habitats. Similar results have been seen alongsalinity gradients in the Baltic Sea where the complexity ofpan-genome of some taxa may enable organisms tothrive under a wide range of salinities (Dupont et al.,2014).

Table 1. Metadata associated with metagenomic sequence data collected from stromatolite ecosystems.

Sample location Habitat SRA #aSequencingPlatform Sample #b

Pavilion Lake, British Columbia Freshwater SRP035880 454 GS FLX Titanium 13Swartkops Estuary, South Africa Freshwater SRP039055 454 GS Junior 1Highborne Cay, Bahamas Marine SRP004035 454 GS FLX Titanium 8Little Darby Island, Bahamas Marine SRS101923 Illumina MiSeq 2Hamelin Pool, Western Australia Hypersaline SRP055055 Illumina GAIIx 75Socompa Lake, Argentina Hypersaline SRP007748 454 GS FLX Titanium 1Storrs Lake, San Salvador Island Hypersaline SRP031628 454 GS FLX Titanium 5Lake 21, Kiritimati Atoll Ultrahypersaline SRP015407 454 GS FLX Titanium 50Santorini Caldera, Mediterranean Marine NLc SRP019274 454 GS FLX Titanium 10Guerrero Negro, Mexico Hypersaline NL SRP030038 454 GS FLX Titanium 8

a. Accession number of the National Center for Biotechnology Information Short Read Archive.b. Sample number reflects the number of sequencing runs.c. Non-lithifying (NL) microbial mat systems.



Although geography and salinity appear to be importantfactors driving differences between the communities, theoverall variation was low (Fig. 6; PC1 17%; PC2 5%; PC34%), suggesting that there were extensive taxonomicsimilarities within the different lithifying and non-lithifyingmat systems. For example, the non-lithifying microbialmats from Santorini Crater and Guerrero Negro clusteredclosely with several lithifying communities, including allthe Bahamian sites, Socompa Lake and the SwartkopsEstuary stromatolites. These results suggest that the taxathemselves are not the distinguishing characteristic ofwhy some microbial mat systems undergo lithification andothers do not, suggesting that ‘who’ in the community isnot as critical as their metabolic capabilities or interactionswith their environment to promote microbialite formation.

Conclusions

Taken together, the results of this study expand our under-standing of the stromatolite microbiome by characterizingnot only the taxa associated with these systems but alsothe full range of metabolic capabilities. Metagenomic

sequencing of the Little Darby stromatolites revealed therelative abundances and gene products associated withseveral key phototrophic (e.g. photosynthesis, EPS pro-duction) and heterotrophic (e.g. sulfate reduction, EPSdegradation) metabolisms typically associated with car-bonate precipitation. Carbonate precipitation instromatolites relies on the coupling of both phototrophicand heterotrophic metabolic processes, and it is thebalance between these processes that determines the netprecipitation potential. Additionally, the direct comparisonof whole shotgun sequencing with 16S rRNA gene-basedanalyses demonstrated the effectiveness of using predic-tive metagenomic analyses in environmental ecosystems,such as stromatolites, to help provide a first look at thefunctional capabilities of the community. However, thesemetagenomic approaches have also revealed pro-nounced gaps in the database for key taxa, such asCyanobacteria, indicating the importance of continuing torefine and expand the catalogue of reference genomesfrom environmental systems. Lastly, a comparison of theLittle Darby Island stromatolites with both lithifying andnon-lithifying microbial ecosystems worldwide revealed

PC2 (5%)

PC1 (17%)

PC3 (4%)Swartkops Estuary, South Africa Socompa Lake, Argentina Hamelin Pool, Western Australia

Storrs Lake, San Salvador Island

Pavilion Lake, British Columbia Lake 21, Kiritimati Atoll, Kiribati Sanortini Caldera, Mediterranean Guerrero Negro, Mexico

R=0.93; p=0.001

Highborne Cay, The Bahamas Little Darby Island, The Bahamas

> 100 ppt

Salinity Level

35 - 100 ppt < 35 ppt

R = 0.59; p=0.001

BA

PC1 (17%)

PC2 (5%)

PC3 (4%)Location

Fig. 6. Comparative diversity analyses of lithifying and non-lithifying microbial mats across geographical locations. Principal coordinatesanalysis plotting the unweighted UniFrac distance matrix generated from rarefied taxon abundances clustering according to geographical area.Analysis of similarity indicated that (A) geography (R = 0.93) and (B) salinity (R = 0.59) played a role in significant driving differences betweenthe microbial mat communities (P = 0.001). Salinity was divided into three major categories based on the habitat and included: > 100 ppt(Kiritimati Atoll); 35–100 ppt (Guerrero Negro, Hamelin Pool; Highborne Cay; Little Darby Island; Santorini Caldera; Socompa Lake; Storr’sLake); and <35 ppt (Swartkops Estuary; Pavilion Lake).



that although geographic location and salinity do drivesome differences within the microbial communities, thetargeted ecosystems were highly similar. These resultssuggest that the taxonomic composition or ‘who’ is in thecommunity is not as important as their functional capabili-ties and their interactions with their environment. Asmetagenomic and metatranscriptomic analyses ofstromatolites become increasingly available, it willbecome critical to provide more comprehensive functionalcomparisons to begin to more fully characterize theshared microbiome of modern stromatolites and assessthe mechanisms and interactions that lead to carbonateprecipitation in microbial ecosystems.

Experimental procedures

Sample collection and DNA extraction

Samples of stromatolites were collected from waters aroundLittle Darby Island located in the southern Exuma Cays, TheBahamas (76°49′ W, 115 24°43′N) in August 2012. The upper1 cm of living stromatolite-forming microbial mat material wascollected from seven different stromatolite heads and imme-diately placed in RNA later (Life Technologies, Grand Island,NY). The samples were transported to the Space Life Sci-ences lab and stored at −80°C until processed. Eachstromatolite sample was vertically sectioned and genomicDNA was extracted using a modified xanthogenate methodas previously described and used for either 16S rRNA geneamplification or metagenomic analysis (Foster et al., 2009).

16S rRNA gene amplicon library construction

To generate the amplicon libraries, DNA was PCR amplifiedusing a bacterial domain-specific primers that targeted theV1–2 region of the 16S rRNA gene (27F and 338R; Lane,1991; Liu et al., 2007). The PCR reactions were conducted intriplicate for each sample and contained the following finalconcentrations: 1 x Pfu reaction buffer (Stratagene, La Jolla,CA), 280 μM dNTPs, 2.5 μg of BSA, 600 nM each primer,0.75 ng of genomic DNA, 1.25 U of Pfu DNA polymerase(Stratagene) and nuclease free water (Sigma, St Louis, MO)in a volume of 25 μl. The reactions were initially denatured at95°C for 5 min, followed by 30 cycles of 95°C for 30 s, 64°Cfor 1 min, 72°C for 1 min and a final extension at 72°C for7 min. Negative controls were conducted for all reactions.PCR products were gel extracted and concentrated using anUltraClean Purification kit (MoBio, Carlsbad, CA), and repli-cate amplicons were pooled in equimolar ratios. A subset ofgenomic DNA consisting of a minimum of three extractionsfrom each head was pooled and concentrated using a DNAClean & Concentrator-25 kit (Zymo Research, Irvine, CA).The pooled stromatolite DNA was spiked into the ampliconlibraries and sequenced using the ILLUMINA MISEQ platformgenerating two libraries of paired-end reads (2 × 250 bp).

Bioinformatics analyses of 16S rRNA amplicon libraries

The 16S rRNA amplicon libraries were analysed using theQuantitative Insights Into Microbial Ecology tool (QIMME v. 1.8;

Caporaso et al., 2010). A pre-quality filtering step was con-ducted using the default parameters in QIMME. For asequence to be retained the following criteria had to be met:(i) a minimum average quality Phred score of 25; (ii) aminimum and maximum sequence length (200–1000, 454data set); (iii) no more than six ambiguous bases (N) orhomopolymers.

In addition to the amplicon libraries generated in this study,the NCBI database was mined for all 16S rRNA genesequences derived from microbialite (e.g. stromatolites andthrombolites) ecosystems generated using next-generationsequencing (Table 1). Two additional non-lithifying microbialmat systems were included as outgroups (Table 1). A total of171 samples were recovered from short read archive (SRA)database and merged with the data sets from this study andanalysed in QIMME using a subsampled open-reference OTUpicking approach. The OTU picking procedure was computedwith UCLUST (at 97% identity) using the 16S rRNAGREENGENES database (v. 13_8) as a reference database(DeSantis et al., 2006; Edgar, 2010). Sequences that did notmatch the GREENGENES database were clustered as de novoas to not lose the overall novel diversity. Taxonomic assign-ment was also computed using UCLUST with a 0.9% similarityagainst a representative set of 16S rRNA gene sequencesfrom the GREENGENES database, obtaining a final BiologicalObservation Matrix (McDonald et al., 2012). The observa-tions are reported as OTUs and the matrix contained countscorresponding to the number of times each OTU wasobserved in each sample. For visualization purposes, thetaxonomic composition data were transported in Krona(Ondov et al., 2011) showing only the most abundant taxa(>0.1%).

The alpha and beta diversity analyses were computed inQIIME using the script core_diversity_analyses.py, at a rar-efaction depth of 450 sequences/sample. Alpha diversity wascomputed for each rarefied OTU table, using the ShannonDiversity index and the Faith’s Phylogenetic Diversity Index.Beta diversity was performed using unweighted UniFrac dis-tance matrices and plotted as principal coordinate analysis(PCoA) in Emperor (Vazquez-Baeza et al., 2013) in QIIME. Anonparametric test and the ANOSIM (Clarke, 1993) methodwere used to determine the statistical significance related tothe diversity analyses.

Metabolic profile prediction of Little Darby Island with16S rRNA gene analysis

To predict the functional gene content of the 16S rRNAgene amplicon libraries, PICRUST v.1 .0 (Langille et al.,2013) was used, and the results were then compared withactual metagenomic data as described below. An OTU tablegenerated in QIIME was normalized by dividing each OTU bythe predicted 16S rRNA gene copy number abundanceusing the script normalize_by_copy_number.py. Themetagenome functional prediction was then computed bymultiplying each normalized OTU abundance by each pre-dicted functional trait abundance, producing a table of KOpredictions using the script predict_metagenomes.py. Theresulting table was collapsed at KO level 3 withinthe pathway hierarchy of KEGG using the scriptcategorize_by_function.py.



Metagenome sequencing and analysis

Genomic DNA derived from the Little Darby stromatolites wassequenced using the Illumina MISEQ platform and the paired-end reads were quality filtered considering the Phred qualityscores and read length, using SICKLE (v. 1.2; Joshi and Fass,2011) with default parameters. High-quality filtered readswere assigned for functionality at different KEGG levels,using METACV v. 2.3.0 (Liu et al., 2013) with default param-eters. Metagenome Composition Vector classifies shortmetagenomic reads into specific taxonomic and functionalgroups using a composition and phylogeny-based algorithm.The reads were not assembled due to the variable relativeabundance of different taxa within the stromatolite-formingmats, which may result in potential chimeric contigs (Liuet al., 2013; Casaburi, 2015; Howe and Chain, 2015). Theoutput was loaded into the statistical program R and initiallyfiltered at a correlation score > 50 to retain only the topmatched genes and then filtered for abundance and for dif-ferent KEGG classification levels. Taxonomic information wasextracted using METAGENOME ANALYZER V.5.6.5 to highlightphylogenetic relationships within the stromatolite community(Huson et al., 2007). To compare the predicted metabolicprofile with the 16S rRNA gene sequences with themetagenome data set a Pearson Correlation was performedcorrelating the KEGG entries identified at level 3 of KOclassification.

Acknowledgements

The authors would like to thank Christina Khodadad andArtemis Louyakis for their technical assistance on the projectas well as the owners of Little Darby Island for access to thestudy site. Permits were provided by The Bahamas Ministryof Agriculture, Marine Resources and Local Government, andlogistical support was provided by the Little Darby IslandResearch Station. The project was supported by the NASAAstrobiology: Exobiology and Evolutionary Biology ProgramElement (NNX14AK14G) and the Florida Space GrantConsortium.

References

Allen, M.A., Goh, F., Burns, B.P., and Neilan, B.A. (2009)Bacterial, archaeal and eukaryotic diversity of smooth andpustular microbial mat communities in the hypersalinelagoon of Shark Bay. Geobiology 7: 82–96.

Anderson, I., Ulrich, L.E., Lupa, B., Susanti, D., Porat, I.,Hooper, S.D., et al. (2009) Genomic characterization ofmethanomicrobiales reveals three classes ofmethanogens. PLoS ONE 4: e5797.

Andrei, A.S., Banciu, H.L., and Oren, A. (2012) Living withsalt: metabolic and phylogenetic diversity of archaeainhabiting saline ecosystems. FEMS Microbiol Lett 330:1–9.

Awramik, S.A. (1984) Ancient stromatolites and microbialmats. In Microbial Mats: Stromatolites. Cohen, Y.,Castenholz, R.W., and Halvorson, H.O. (eds). New York:Alan R. Liss, pp. 1–22.

Baumgartner, L.K., Spear, J.R., Buckley, D.H., Pace, N.R.,Reid, R.P., and Visscher, P.T. (2009a) Microbial diversity inmodern marine stromatolites, Highborne Cay, Bahamas.Environ Microbiol 11: 2710–2719.

Baumgartner, L.K., Dupraz, C., Buckley, D.H., Spear, J.R.,Pace, N.R., and Visscher, P.T. (2009b) Microbial speciesrichness and metabolic activities in hypersaline microbialmats: insight into biosignature formation throughlithification. Astrobiology 9: 861–874.

Beltrán;, Y., Centeno, C.M., Garcia-Oliva, F., Legendre, P.,and Falcón, L.I. (2012) N2 fixation rates and associateddiversity (nifH) of microbialite and mat-forming consortiafrom different aquatic environments in Mexico. AquatMicrob Ecol 67: 15–24.

Bokulich, N.A., Subramanian, S., Faith, J.J., Gevers, D.,Gordon, J.I., Knight, R., et al. (2013) Quality-filtering vastlyimproves diversity estimates from Illumina ampliconsequencing. Nat Methods 10: 57–59.

Bowlin, E.M., Klaus, J., Foster, J.S., Andres, M., Custals, L.,and Reid, R.P. (2012) Environmental controls on microbialcommunity cycling in modern marine stromatolites. Sedi-ment Geol 263-264: 45–55.

Breitbart, M., Hoare, A., Nitti, A., Siefert, J., Haynes, M.,Dinsdale, E., et al. (2009) Metagenomic and stable isotopicanalyses of modern freshwater microbialites in CuatroCiénegas, Mexico. Environ Microbiol 11: 16–34.

Burns, B.P., Goh, F., Allen, M., and Neilan, B.A. (2004) Micro-bial diversity of extant stromatolites in the hypersalinemarine environment of Shark Bay, Australia. EnvironMicrobiol 6: 1096–1101.

Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K.,Bushman, F.D., Costello, E.K., et al. (2010) QIIME allowsanalysis of high-throughput community sequencing data.Nat Methods 7: 335–336.

Casaburi, G. (2015) Bioinformatics Approaches for the Studyof the Human Gut Microbiome. Saarbrücken, Germany:Edizioni Accademiche Italiane.

Centeno, C.M., Legendre, P., Beltran, Y.,Alcantara-Hernandez, R.J., Lidstrom, U.E., Ashby, M.N.,and Falcon, L.I. (2012) Microbialite genetic diversity andcomposition relate to environmental variables. FEMSMicrobiol Ecol 82: 724–735.

Chafetz, H.S. (1986) Marine peloids – a product of bacteriallyinduced precipitation of calcite. J Sediment Petrol 56: 812–817.

Clarke, K.R. (1993) Nonparamentric multivariate analyses ofchanges in community structure. Aust J Ecol 18: 117–143.

Collins, R.E., and Higgs, P.G. (2012) Testing the infinitelymany genes model for the evolution of the bacterial coregenome and pangenome. Mol Biol Evol 29: 3413–3425.

DeSantis, T.Z., Hugenholtz, P., Larsen, N., Rojas, M., Brodie,E.L., Keller, K., et al. (2006) Greengenes, a chimera-checked 16S rRNA gene database and workbench com-patible with ARB. Appl Environ Microbiol 72: 5069–5072.

Dill, R.F., Shinn, E.A., Jones, A.T., Kelly, K., and Steinen, R.P.(1986) Giant subtidal stromatolites forming in normal salin-ity water. Nature 324: 55–58.

Dravis, J.J. (1983) Hardened subtidal stromatolites,Bahamas. Science 219: 385–386.

Dupont, C.L., Larsson, J., Yooseph, S., Ininbergs, K., Goll, J.,Asplund-Samuelsson, J., et al. (2014) Functional tradeoffs



underpin salinity-driven divergence in microbial communitycomposition. PLoS ONE 9: e89549.

Dupraz, C., and Visscher, P.T. (2005) Microbial lithification inmarine stromatolites and hypersaline mats. TrendsMicrobiol 13: 429–438.

Dupraz, C., Reid, R.P., Braissant, O., Decho, A.W., Norman,R.S., and Visscher, P.T. (2009) Processes of carbonateprecipitation in modern microbial mats. Earth Sci Rev 96:141–162.

Dupraz, C., Fowler, A., Tobias, C., and Visscher, P.T. (2013)Stromatolitic knobs in Storr’s Lake (San Salvador,Bahamas): a model system for formation and alteration oflaminae. Geobiology 11: 527–548.

Edgar, R.C. (2010) Search and clustering orders of magni-tude faster than BLAST. Bioinformatics 26: 2460–2461.

Edgcomb, V.P., Bernhard, J.M., Summons, R.E., Orsi, W.,Beaudoin, D., and Visscher, P.T. (2014) Active eukaryotesin microbialites from Highborne Cay, Bahamas, andHamelin Pool (Shark Bay), Australia. ISME J 8: 418–429.

Falcón, L.I., Cerritos, R., Eguiarte, L.E., and Souza, V. (2007)Nitrogen fixation in microbial mat and stromatolite commu-nities from Cuatro Cienegas, Mexico. Microb Ecol 54: 363–373.

Farías, M.E., Rascovan, N., Toneatti, D.M., Albarracin, V.H.,Flores, M.R., Poire, D.G., et al. (2013) The discovery ofstromatolites developing at 3570 m above sea level in ahigh-altitude volcanic lake Socompa, Argentinean Andes.PLoS ONE 8: e53497.

Feldmann, M., and McKenzie, J.A. (1998) Stromatolite-thrombolite associations in a modern environment, LeeStocking Island, Bahamas. Palaios 13: 201–212.

Foster, J.S., and Green, S.J. (2011) Microbial diversity inmodern stromatolites. In Cellular Origin, Life in ExtremeHabitats and Astrobiology: Interactions with Sediments.Seckbach, J., and Tewari, V. (eds). Netherlands: Springer,pp. 385–405.

Foster, J.S., Green, S.J., Ahrendt, S.R., Hetherington, K.L.,Golubic, S., Reid, R.P., and Bebout, L. (2009) Molecularand morphological characterization of cyanobacterialdiversity in the marine stromatolites of Highborne Cay,Bahamas. ISME J 3: 573–587.

Fröls, S., Dyall-Smith, M., and Pfeifer, F. (2012) Biofilm for-mation by haloarchaea. Environ Microbiol 14: 3159–3174.

Gallagher, K.L., Kading, T.J., Braissant, O., Dupraz, C., andVisscher, P.T. (2012) Inside the alkalinity engine: the role ofelectron donors in the organomineralization potential ofsulfate-reducing bacteria. Geobiology 10: 518–530.

Gilbert, J.A., Field, D., Swift, P., Thomas, S., Cummings, D.,Temperton, B., et al. (2010) The taxonomic and functionaldiversity of microbes at a temperate coastal site: a ‘multi-omic’ study of seasonal and diel temporal variation. PLoSONE 5: e15545.

Goh, F., Leuko, S., Allen, M.A., Bowman, J.P., Kamekura, M.,Neilan, B.A., & Burns, B.P. (2006). Halococcushamelinensis sp. nov., a novel halophilic archaeon isolatedfrom stromatolites in Shark Bay, Australia. InternationalJournal of Systematic and Evolutionary Microbiology, 56(6): 1323–1329.

Goh, F., Allen, M.A., Leuko, S., Kawaguchi, T., Decho, A.W.,Burns, B.P., and Neilan, B.A. (2009) Determining the spe-cific microbial populations and their spatial distribution

within the stromatolite ecosystem of Shark Bay. ISME J 3:383–396.

Green, P.N. (2006). Methylobacterium. In The prokaryotes(pp. 257–265). Springer New York.

Grotzinger, J.P., and Knoll, A.H. (1999) Stromatolites in Pre-cambrian carbonates: evolutionary mileposts or environ-mental dipsticks? Annu Rev Earth Planet Sci 27: 313–358.

Hartmann, R., Sickinger, H.D., and Oesterhelt, D. (1980)Anaerobic growth of halobacteria. Proc Natl Acad Sci USA77: 3821–3825.

He, Y., Caporaso, J.G., Jiang, X.T., Sheng, H.F., Huse, S.M.,Rideout, J.R., et al. (2015) Stability of operational taxo-nomic units: an important but neglected property foranalyzing microbial diversity. Microbiome 3: 20.

Howe, A., and Chain, P.S. (2015) Challenges and opportuni-ties in understanding microbial communities withmetagenome assembly (accompanied by IPython Note-book tutorial). Front Microbiol 6: 678.

Huson, D.H., Auch, A.F., Qi, J., and Schuster, S.C. (2007)MEGAN analysis of metagenomic data. Genome Res 17:377–386.

Joshi, N.A., and Fass, J.N. (2011) Sickle: a sliding-window,adaptive, quality-based trimming tool for FASTQ files(Version 1.21). Software.

Kawaguchi, T., and Decho, A.W. (2000) Biochemical charac-terization of cyanobacterial extracellular polymers (EPS)from modern marine stromatolites (Bahamas). PrepBiochem Biotech 30: 321–330.

Kawaguchi, T., and Decho, A.W. (2002) Isolation and bio-chemical characterization of extracellular polymeric secre-tions (EPS) from modern soft marine stromatolites(Bahamas) and its inhibitory effect on CaCO3 precipitation.Prep Biochem Biotech 32: 51–63.

Khodadad, C.L., and Foster, J.S. (2012) Metagenomic andmetabolic profiling of nonlithifying and lithifyingstromatolitic mats of Highborne Cay, The Bahamas. PLoSONE 7: e38229.

Lane, D.J. (1991) 16S/23S rRNA sequencing. In Nucleic AcidTechniques in Bacterial Systematics. Stackebrandt, E., andGoodfellow, M. (eds). Chichester, England: Wiley, pp. 115–175.

Langille, M.G., Zaneveld, J., Caporaso, J.G., McDonald, D.,Knights, D., Reyes, J.A., et al. (2013) Predictive functionalprofiling of microbial communities using 16S rRNA markergene sequences. Nat Biotechnol 31: 814–821.

Laval, B., Cady, S.L., Pollack, J.C., McKay, C.P., Bird, J.S.,Grotzinger, J.P., et al. (2000) Modern freshwatermicrobialite analogues for ancient dendritic reef structures.Nature 407: 626–629.

Leuko, S., Goh, F., Allen, M.A., Burns, B.P., Walter, M.R., andNeilan, B.A. (2007) Analysis of intergenic spacer regionlength polymorphisms to investigate the halophilic archaealdiversity of stromatolites and microbial mats.Extremophiles 11: 203–210.

Lim, D.S., Laval, B.E., Slater, G., Antoniades, D., Forrest,A.L., Pike, W., et al. (2009) Limnology of Pavilion Lake, BC,Canada – characterization of a microbialite forming envi-ronment. Fund Appl Limnol 173: 329–351.

Liu, J., Wang, H., Yang, H., Zhang, Y., Wang, J., Zhao, F., andQi, J. (2013) Composition-based classification of shortmetagenomic sequences elucidates the landscapes of



taxonomic and functional enrichment of microorganisms.Nucleic Acids Res 41: e3.

Liu, Z., Lozupone, C., Hamady, M., Bushman, F.D., andKnight, R. (2007) Short pyrosequencing reads suffice foraccurate microbial community analysis. Nucleic Acids Res35: e120.

Logan, B.W., Hoffman, P., and Gebelein, C.D. (1974) Algamats, cryptalgal fabrics and structures, Hamelin Pool,Western Australia. In Evolution and Diagenesis of Quater-nary Carbonate Sequences, Shark Bay, Western Australia.Logan, B.W. (ed.). Tulsa, Oklahoma: American Associationof Petrology, Geology and Mineralogy, pp. 140–194.

Lozupone, C.A., and Knight, R. (2007) Global patterns inbacterial diversity. Proc Natl Acad Sci USA 104: 11436–11440.

McDonald, D., Clemente, J.C., Kuczynski, J., Rideout, J.R.,Stombaugh, J., Wendel, D., et al. (2012) The BiologicalObservation Matrix (BIOM) format or: how I learned to stopworrying and love the ome-ome. GigaScience 1: 7.

Mobberley, J.M., Ortega, M.C., and Foster, J.S. (2012) Com-parative microbial diversity analyses of modern marinethrombolitic mats by barcoded pyrosequencing. EnvironMicrobiol 14: 82–100.

Mobberley, J.M., Khodadad, C.L., and Foster, J.S. (2013)Metabolic potential of lithifying cyanobacteria-dominatedthrombolitic mats. Photosynth Res 118: 125–140.

Mobberley, J.M., Khodadad, C.L., Visscher, P.T., Reid, R.P.,Hagan, P., and Foster, J.S. (2015) Inner workings ofthrombolites: spatial gradients of metabolic activity asrevealed by metatranscriptome profiling. Sci Rep 5: 12601.

Myshrall, K., Mobberley, J.M., Green, S.J., Visscher, P.T.,Havemann, S.A., Reid, R.P., and Foster, J.S. (2010)Biogeochemical cycling and microbial diversity in themodern marine thrombolites of Highborne Cay, Bahamas.Geobiology 8: 337–354.

Ondov, B.D., Bergman, N.H., and Phillippy, A.M. (2011) Inter-active metagenomic visualization in a web browser. BMCBioinform 12: 385.

Paerl, H.W., Steppe, T.F., and Reid, R.P. (2001) Bacteriallymediated precipitation in marine stromatolites. EnvironMicrobiol 3: 123–130.

Papineau, D., Walker, J.J., Mojzsis, S.J., and Pace, N.R.(2005) Composition and structure of microbial communitiesfrom stromatolites of Hamelin Pool in Shark Bay, WesternAustralia. Appl Environ Microbiol 71: 4822–4832.

Paul, V.G., Wronkiewicz, D.J., Mormile, M.R., and Foster,J.S. (2015) Mineralogy and microbial diversity of themicrobialites in the hypersaline Storr’s Lake, TheBahamas. Astrobiology: in review.

Perissinotto, R., Bornman, T.G., Steyn, R.P., Miranda, N.F.,Dorrington, R.A., Matcher, G.F., et al. (2014) Tufastromatolite ecosystems on the South African south coast.S Afr J Sci 110: 1–8.

Pinckney, J., Paerl, H.W., and Bebout, B.M. (1995a) Salinitycontrol of benthic microbial mat community production in aBahamian hypersaline lagoon. J Exp Mar Biol Ecol 187:223–237.

Pinckney, J., Paerl, H.W., Reid, R.P., and Bebout, B.M.(1995b) Ecophysiology of stromatolitic microbial mats, LeeStocking Island, Exuma Cays, Bahamas. Microb Ecol 29:19–37.

Playford, P.E., and Cockbain, A.E. (1976) Modern algalstromatolites at Hamelin Pool, a hypersaline barred basinin Shark Bay, Western Australia. In Stromatolites – Devel-opments in Sedimentology. Walter, M.R. (ed.). Amsterdam,the Netherlands: Elsevier, pp. 389–411.

Poretsky, R., Rodriguez, R.L., Luo, C., Tsementzi, D., andKonstantinidis, K.T. (2014) Strengths and limitations of 16SrRNA gene amplicon sequencing in revealing temporalmicrobial community dynamics. PLoS ONE 9: e93827.

Reid, P., Foster, J.S., Radtke, G., and Golubic, S. (2010)Modern marine stromatolites of Little Darby Island, ExumaArchipelago, Bahamas: environmental setting, accretionmechanisms and role of euendoliths. In Advances inStromatolite Geology. Reitner, J., Thrauth, M.H., Stüwe,K., and Yuen, D. (eds). Berlin, Germany: Springer, pp.77–90.

Reid, R.P., Macintyre, I.G., Browne, K.M., Steneck, R.S., andMiller, T. (1995) Modern marine stromatolites in the ExumaCays, Bahamas – uncommonly common. FACIES 33:1–17.

Reid, R.P., Visscher, P.T., Decho, A.W., Stolz, J.F., Bebout,B.M., Dupraz, C., et al. (2000) The role of microbes inaccretion, lamination and early lithification of modernmarine stromatolites. Nature 406: 989–992.

Reid, R.P., James, N.P., Macintyre, I.G., and Dupraz, C.P.(2003) Shark Bay stromatolites: microfabrics and reinter-pretation of origins. FACIES 49: 299–324.

Rideout, J.R., He, Y., Navas-Molina, J.A., Walters, W.A.,Ursell, L.K., Gibbons, S.M., et al. (2014) Subsampledopen-reference clustering creates consistent, comprehen-sive OTU definitions and scales to billions of sequences.PeerJ 2: e545.

Russell, J.A., Brady, A.L., Cardman, Z., Slater, G.F., Lim,D.S., and Biddle, J.F. (2014) Prokaryote populations ofextant microbialites along a depth gradient in PavilionLake, British Columbia, Canada. Geobiology 12: 250–264.

Ruvindy, R., White Iii, R.A., Neilan, B.A., and Burns, B.P.(2015) Unravelling core microbial metabolisms in thehypersaline microbial mats of Shark Bay using high-throughput metagenomics. ISME J 2015/05/30.

Sakai, S., Takaki, Y., Shimamura, S., Sekine, M., Tajima, T.,Kosugi, H., et al. (2011) Genome sequence of a mesophilichydrogenotrophic methanogen Methanocella paludicola,the first cultivated representative of the orderMethanocellales. PLoS ONE 6: e22898.

Santos, F., Pena, A., Nogales, B., Soria-Soria, E., Del Cura,M.A., Gonzalez-Martin, J.A., and Anton, J. (2010) Bacterialdiversity in dry modern freshwater stromatolites fromRuidera Pools Natural Park, Spain. Syst Appl Microbiol 33:209–221.

Schneider, D., Arp, G., Reimer, A., Reitner, J., and Daniel, R.(2013) Phylogenetic analysis of a microbialite-formingmicrobial mat from a hypersaline lake of the Kiritimati atoll,Central Pacific. PLoS ONE 8: e66662.

Steppe, T.F., Pinckney, J.L., Dyble, J., and Paerl, H.W. (2001)Diazotrophy in modern marine Bahamian stromatolites.Microb Ecol 41: 36–44.

Stolz, J.F., Reid, R.P., Visscher, P.T., Decho, A.W., Norman,R.S., Aspden, R.J., et al. (2009) The microbial communi-ties of the modern marine stromatolites at Highborne Cay,Bahamas. Atoll Res Bull 567: 1–29.



Valdespino-Castillo, P.M., Alcantara-Hernandez, R.J.,Alcocer, J., Merino-Ibarra, M., Macek, M., and Falcon, L.I.(2014) Alkaline phosphatases in microbialites andbacterioplankton from Alchichica soda lake, Mexico. FEMSMicrobiol Ecol 90: 504–519.

Vazquez-Baeza, Y., Pirrung, M., Gonzalez, A., and Knight, R.(2013) EMPeror: a tool for visualizing high-throughputmicrobial community data. GigaScience 2: 16.

Visscher, P.T., and Stolz, J.F. (2005) Microbial mats asbioreactors: populations, processes and products.Palaeogeogr Palaeoclimatol Palaeoecol 219: 87–100.

Visscher, P.T., Reid, R.P., Bebout, B.M., Hoeft, S.E.,Macintyre, I.G., and Thompson, J.A. (1998) Formation oflithified micritic laminae in modern marine stromatolites(Bahamas): the role of sulfur cycling. Amer Mineral 83:1482–1493.

Visscher, P.T., Reid, R.P., and Bebout, B.M. (2000)Microscale observations of sulfate reduction: correlation ofmicrobial activity with lithified micritic laminae in modernmarine stromatolites. Geology 28: 919–922.

Woese, C.R., Winker, S., and Gutell, R.R. (1990) Architectureof ribosomal RNA: constraints on the sequence of ‘tetra-loops. Proc Natl Acad Sci USA 87: 8467–8471.

Yousef, M., Ketany, M., Manevitz, L., Showe, L.C., andShowe, M.K. (2009) Classification and biomarker identifi-cation using gene network modules and support vectormachines. BMC Bioinform 10: 337.

Zaneveld, J.R., Lozupone, C., Gordon, J.I., and Knight, R.(2010) Ribosomal RNA diversity predicts genome diversityin gut bacteria and their relatives. Nucleic Acids Res 38:3869–3879.

Supporting information

Additional Supporting Information may be found in the onlineversion of this article at the publisher’s website:

Fig. S1. Krona plot depicting the taxonomic composition ofArchaea based on analysis of the whole shotgunmetagenome sequences. Rings are associated with differenttaxonomy ranks (phylum, class, order and family respec-tively). Sequences have been filtered and only those OTUswith relative abundance greater than 0.1% are reported.Fig. S2. Krona plot depicting the taxonomic composition ofEukaryota based on analysis of the whole shotgunmetagenome sequences. Rings are associated with differenttaxonomy ranks (phylum, class, order and family respec-tively). Sequences have been filtered, and only those OTUswith relative abundance greater than 0.1% are reported.Fig. S3. Phylogenetic affiliation of metagenomic reads asso-ciated with the Cyanobacteria phylum collapsed at genuslevel in Little Darby stromatolites. Absolute abundances (inparentheses) are reported as the total number of reads asso-ciated at each taxonomic level, including unclassified taxon.Fig. S4. Genus level distribution of reads associated withphosphate sensing, scavenging, and regulation based onMETACV analysis. KEGG orthology groups included in thetree were as follows: K01077, K01113, K02040, K07636,K07657, K07658, K07659, K07638. Tree created in MEGAN

using the annotated read abundance, which is given in paren-theses for each taxa.Fig. S5. Changes in alpha diversity within rarefaction curvescomputed as Shannon diversity (A) and Faith’s phylogeneticdiversity (B) metrics comparing microbial mats derived fromdifferent locations. Bars represent standard deviation. Diver-sity analyses were computed at a rarefaction depth of 450sequences/sample.



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