Characterization of the Stromatolite Microbiome from Little Darby Island, the Bahamas Using Predictive and Whole Shotgun Metagenomic Analysis

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

Characterization of the stromatolite microbiome from Little Darby Island, The Bahamas using predictive and whole shotgun metagenomic analysis

Giorgio Casaburi,1 Alexandrea A. Duscher,1 Introduction R. Pamela Reid2 and Jamie S. Foster1* Stromatolites are quintessential geomicrobiological eco- 1Department of Microbiology and Cell Science, systems with a fossil record that extends back 3.5 billion University of Florida, Space Life Science Lab, Merritt years (Grotzinger and Knoll, 1999). These long-lived fea- Island, FL, USA. tures are sedimentary structures formed as a result of the 2Rosenstiel School of Marine Sciences, University of synergy between the metabolisms of microbial mats and Miami, Miami, FL, USA. the environment (Reid et al., 2000). Ancient stromatolites formed massive carbonate reefs comparable in size to Summary modern coral reefs, dominating life on our planet for 80% of Earth’s history (Awramik, 1984). Modern stromatolites, Modern stromatolites represent ideal ecosystems to in contrast, are relatively rare, but have been found in understand the biological processes required for the diverse habitats including freshwater, marine and precipitation of carbonate due to their long evolution- hypersaline environments (Playford and Cockbain, 1976; ary history and occurrence in a wide range of habi- Reid et al., 1995; Reid et al., 2003; Laval et al., 2000; tats. However, most of the prior molecular work on Breitbart et al., 2009; Santos et al., 2010; Farías et al., stromatolites has focused on understanding the taxo- 2013; Schneider et al., 2013; Perissinotto et al., 2014). nomic complexity and not fully elucidating the func- One of the most well-studied stromatolite systems has tional capabilities of these systems. Here, we begin to been the island of Highborne Cay located in the Northern characterize the microbiome associated with Exuma Cays, The Bahamas, which for more than 20 stromatolites of Little Darby Island, The Bahamas years has served as a model to understand the geologi- using predictive metagenomics of the 16S rRNA gene cal, biochemical and biological processes associated with coupled with direct whole shotgun sequencing. The stromatolite formation and accretion under normal sea metagenomic analysis of the Little Darby water conditions (e.g. Reid et al., 1995; Visscher et al., stromatolites revealed many shared taxa and core 1998; Reid et al., 2000; Baumgartner et al., 2009a; Stolz pathways associated with biologically induced car- et al., 2009; Khodadad and Foster, 2012). bonate precipitation, suggesting functional conver- These previous studies have identified several key gence within Bahamian stromatolites. A comparison guilds of microbes that together, through their metabolic of the Little Darby stromatolites with other lithifying activities, generate steep geochemical gradients through- microbial ecosystems also revealed that although out the microbial mat community that can influence the factors, such as geographic location and salinity, do precipitation potential of calcium carbonate (Paerl et al., drive some differences within the population, there 2001; Dupraz and Visscher, 2005; Dupraz et al., 2009). are extensive similarities within the microbial popula- These functional groups have been identified through tions. These results suggest that for stromatolite for- biogeochemical and 16S rRNA gene analyses and mation, ‘who’ is in the community is not as critical as include: oxygenic and anoxygenic phototrophs, aerobic metabolic activities and environmental interactions. heterotrophs, sulfate reducers, sulfide oxidizers and Together, these analyses help improve our under- fermenters (Visscher et al., 1998; 2000; Steppe et al., standing of the similarities among lithifying ecosys- 2001; Baumgartner et al., 2009a,b; Foster et al., 2009; tems and provide an important first step in Khodadad and Foster, 2012). It is the net activity of these characterizing the shared microbiome of modern different functional groups that influence carbonate pre- stromatolites. cipitation by altering the pH within the microbial mat. For example, some metabolisms, such as photosynthesis and Received 16 August, 2015; revised 13 October, 2015; accepted 13 October, 2015. *For correspondence. E-mail jfoster@ufl.edu; Tel. some types of sulfate reduction, promote carbonate pre- 321-525-1047. cipitation through an increase in pH, whereas other

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 green layer of enriched cyanobacteria. Bar, 5 mm. C. Cyanobacteria-rich area with extensive exopolymeric substances and trapped white oolitic sand grains (o). Bar, 500 μm. metabolisms, such as sulfide oxidation and aerobic res- ter conditions and experiencing frequent burial events. piration, can decrease the pH, thereby promoting disso- Additionally, these ecosystems accrete through the trap- lution of calcium carbonate (Dupraz and Visscher, 2005; ping and binding of sediments and carbonate precipitation Visscher and Stolz, 2005; Gallagher et al., 2012). by the surface microbial mat community (Fig. 1; Reid Although considerable progress has been made in et al., 2010). By expanding the analysis of stromatolite- delineating key metabolisms and microbes associated forming communities beyond Highborne Cay, we can with modern stromatolites of Highborne Cay, the underly- begin to assess those taxa and pathways shared in Baha- ing molecular mechanisms and cellular interactions that mian stromatolites and determine whether these similari- drive these geochemical gradients are not well character- ties extend beyond The Bahamas to other lithifying ized (Khodadad and Foster, 2012). Additionally, very little microbial habitats. Together, these results may help delin- is known regarding underlying microbial and functional eate the essential biological processes and pathways gene diversity of other stromatolites found throughout The needed for modern stromatolite formation. Bahamas. Stromatolites have also been observed in the Southern Exuma Cays including Bock Cay, Iguana Cay, Results and discussion Lee Stocking Island and Little Darby Island (Dill et al., Overview of stromatolite environment of Little Darby 1986; Pinckney et al., 1995b; Reid et al., 1995; 2010; Island, The Bahamas Feldmann and McKenzie, 1998) and along the eastern margin of Exuma Sound in Schooner Cays (Dravis, 1983); The stromatolites of Little Darby Island are located on the however, studies of these sites have been limited to northeast side of the island and are distributed parallel to biogeochemical and morphological analyses. the shore in a narrow band approximately 300 m in length To more fully understand the mechanisms of and 100 m wide. The stromatolites occur in the sub-tidal stromatolite formation and accretion it is necessary to zone at depths ranging from1mto3mandvary in height begin to characterize the communities beyond from 2 cm to 1 m (Fig. 1A). The Little Darby stromatolites biogeochemistry and taxonomic diversity and assess the undergo extensive sand transport through wave action associated stromatolite microbiome, that is, the totality of and experience partial and/or full burial events throughout the microbes, functional genes and environmental inter- the year. Microbial mats occur on the surfaces of the actions that result in the deposition of calcium carbonate. stromatolite build-ups and typically exceed 2 cm in thick- Not all microbial mats are conducive to the formation of ness. The upper 2 mm of the microbial mats are enriched stromatolite structures (Dupraz et al., 2009; Foster and in exopolymeric substances (EPS) giving the surface mat Green, 2011), and characterizing the microbiome repre- a caramel colour (Fig. 1B). Microscopic analysis of the sents a holistic approach to understanding the connectiv- caramel surface layer revealed a layer of vertically orien- ity and microbial interactions that lead to stromatolite tated cyanobacterial filaments morphologically identified formation. In this study, we begin to characterize the as primarily Schizothrix spp. and Phormidium spp. microbiome of stromatolites from Little Darby Island, (Fig. 1C). Additional pronounced layers of cyanobacteria located in the Southern Exuma Cays by assessing the were visible in cross-sections of the stromatolite-forming microbial and functional gene diversity using two comple- mats at a depth of 5 mm (Fig. 1B). mentary approaches, 16S rRNA gene and shotgun Taxonomic diversity of Little Darby stromatolites metagenomic sequencing analysis. Much like the stromatolites of Highborne Cay, the stromatolites of Little To assess the microbial communities associated with Darby Island are sub-tidal, forming under normal seawa- Little Darby stromatolites, a two-pronged approach was

© 2015 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology Microbiome of Little Darby Island stromatolites 3 used that included targeted bacterial 16S rRNA gene that most of the recovered cyanobacterial sequences amplicon and whole shotgun metagenomic sequencing. were unable to be classified beyond the phylum level and Recent studies have shown numerous limitations when that many of the cyanobacterial taxa previously identified relying on a single marker gene for assessing microbial in Little Darby Island stromatolites using morphological diversity, including primer bias, lower taxonomic resolu- approaches, such as Schizothrix spp. and Solentia spp. tion and stability issues regarding operational taxonomic (Reid et al., 2010), have no representative genome units (OTU) clustering (Yousef et al., 2009; He et al., available. 2015). Whole genome shotgun metagenomics over- Whole genome shotgun metagenomic sequencing of comes the limitations of targeting a single gene and can DNA derived from the seven distinct stromatolite heads provide a comprehensive assessment of community generated a total of 7 927 495 quality-filtered, paired-end diversity (Gilbert et al., 2010). However, in complex com- reads. The high-quality reads were processed using both munities, such as lithifying microbial mats, genomic data- Metagenome Composition Vector (METACV), which bases can be far less representative and sometimes targets primarily prokaryotes and the MG-RAST pipeline, taxonomically skewed compared with some well-curated which targets all three domains. The majority of the 16S rRNA gene databases (Poretsky et al., 2014). There- metagenomic reads were derived from Bacteria (>70%). fore, we used a dual approach to assess the overall Archaea constituted 10% of the total data set, whereas community complexity of the Little Darby stromatolites. Eukarya were represented by less than 3% of the recov- For the amplicon library, recovered reads were quality ered sequences with 16% of the total sequences filtered in QIIME resulting in 2 305 124 high quality filtered reported as unclassified in both analysis programs. paired-end reads (see experimental procedures for addi- Analysis of the metagenomic data revealed a much more tional detail). To help improve the diversity estimates, a complex bacterial community within the stromatolite- cut-off filter of 0.005% as the minimum total observation forming mats compared with the 16S rRNA gene libraries count of an OTU was applied in QIIME, as previously (Fig. 2B) and provided additional insight into the archaeal described (Bokulich et al., 2013). The stringent filtering and eukaryotic community (Figs S1–2). Based on resulted in 2157 remaining OTUs that represented 13 METACV analysis, there was a total of 2883 OTUs, a phyla within microbial mats (Fig. 2A). Of these phyla, the 33% increase compared with the 16S rRNA gene library, Proteobacteria (62.2%) and the Cyanobacteria (31.6%) representing a total of 27 bacterial phyla in the shotgun were dominant with the mats. Bacteroidetes and metagenomic library. Similarly to the amplicon library, the Spirochetes represented only 3.4% and 1.26% of the Proteobacteria (50.0%) and Cyanobacteria (28.4%) were community, respectively, whereas the other phyla each the dominant phyla within the community. The comprised less than 1% of the community (Fig. 2A). Other metagenomic sequencing analysis provided a much phyla were observed within the 16S rRNA gene libraries higher taxonomic resolution of the Bacteria with over 232 but at levels below 0.005% abundance. These results families observed in the data set (Fig. 2B). The results strongly correlate with previous 16S rRNA gene sequenc- also revealed a much more complex Alphaproteobacteria ing of Highborne Cay stromatolites (Baumgartner et al., population specifically in the Rhizobiales with recovered 2009a; Foster and Green, 2011). sequences sharing similarity to the facultative Within the Proteobacteria, the most represented methyltrophs of the families Xanthobacteraceae and classes were the Alphaproteobacteria (61.5%) Methylobacteriaceae, which have the ability to grow on and Deltaproteobacteria (3.2%) (Fig. 2A). The methylamine, fructose, fucose, xylose, arabinose and Alphaproteobacteria in the Little Darby stromatolites were some organic acids (Green, 2006). Substrates, such as enriched in taxa known to be anoxygenic phototrophs, these, have been found in high abundance in the EPS specifically purple non-sulfur bacteria derived from that comprise stromatolite-forming mats (Kawaguchi and the orders Rhodobacterales (15.3%), Rhizobiales Decho, 2002). However, as in the 16S rRNA gene analy- (11.3%) and Rhodospirillales (7.8%), whereas the sis, the dominant Alphaproteobacteria in the shotgun Deltaproteobacteria were primarily comprised of library were the phototrophic purple non-sulfur bacteria sulfate-reducing bacteria associated with the taxa Rhodobacteraceae (10.5%) and Rhodospirallaceae Desulfobacterales (1.7%) and Desulfovibrionales (0.2%). (6.5%), which correlates with the alphaproteobacterial The cyanobacterial population within the stromatolite- populations found in other lithifying microbial mat forming mats was dominated by the subclass systems such as the thrombolites of Highborne Cay, The Oscillatoriophycideae (39%) primarily with the orders Bahamas; hypersaline stromatolites of Hamelin Pool, Chroococcales (9%) and Oscillatoriales (0.6%). Other Shark Bay; Kiritimati Atoll, Kiribati; and Socampa, cyanobacterial orders were recovered but at much lower Argentina (Foster and Green, 2011; Khodadad and abundance, such as the Nostocales (0.2%) and Foster, 2012; Farías et al., 2013; Schneider et al., Pleurocapsales (0.3%); however, it is important to note 2013).

Fig. 2. Taxonomic abundance and diversity of Little Darby stromatolites. Krona plot visualizing the taxonomic hierarchies of (A) the 16S rRNA gene analysis performed with QIIME and (B) whole shotgun metagenome sequencing analysed with METACV. The inner ring represents the different 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.

Metagenomic sequencing analysis of the (5.2%) occur at a higher relative abundance compared cyanobacterial population strongly correlated with the 16S with the 16S rRNA gene analysis. However, one major rRNA gene analysis indicating that the microbial commu- caveat to taxonomic identiﬁcation of Cyanobacteria is the nity was primarily comprised of Cyanobacteria derived relatively low diversity of sequenced reference genomes from the subclass Oscillatoriophycideae and shared for data comparison. This under-representation in the sequence similarity to genera such as Cyanothece, METACV database may account for the lack of Synechococcus and Acaryochloris (Fig. S3). Shotgun Pleurocapsales and representation of the Oscillatoriales metagenomic sequencing also indicated that Nostocales family by a single genus Trichodesmium (Fig. S3). These

Fig. 2. Continued results reinforce the need for a greater diversity of order can grow and generate methane from a wide range cyanobacterial cultivars to be sequenced. of compounds including hydrogen, acetate, methanol and The shotgun metagenomic library also provided infor- many other single carbon molecules (Anderson et al., mation on the archaeal and eukaryotic populations within 2009; Sakai et al., 2011). Genes associated with archaeal the Little Darby stromatolites. Methanogens dominated methane metabolism accounted for approximately 5% of the archaeal population and were diverse, containing rep- the recovered archaeal functional genes (data not resentatives of all known orders, Methanobacteriales, shown). Methanococcales, Methanopyrales, Methanosarcinales, Another enriched archaeal taxa were the Nitroso- Methanomicrobiales and the recently recognized pumilales, an ammonia-oxidizing Thaumarchaeota, which Methanocellales (Fig. S1). Of the different orders of has been shown to be prominent in unlaminated, methanogens, the metabolically versatile Methano- thrombolite-forming microbial mats from Highborne Cay, sarcinales were the most abundant. Members of this The Bahamas (Mobberley et al., 2012; 2013; 2015).

Additionally, the Little Darby stromatolite communities An overlay of the 16S rRNA gene amplicon and were enriched with Halobacteria, which are obligate metagenomic libraries is visualized in Fig. 3. The radar heterotrophs. Halobacteria have been observed in a wide plot depicts the relative abundance of recovered genes range of lithifying microbial mat systems, such as the associated with the 92 dominant shared bacterial families hypersaline stromatolites of Shark Bay (Burns et al., within the stromatolite-forming community and are 2004; Goh et al., 2006; Leuko et al., 2007; Ruvindy et al., expressed as a percentage on a log scale. There is a high 2015). Although their precise ecological role in correlation (r = 0.91, Pearson) between the two data sets stromatolite formation has not been delineated, these regarding the relative abundance of the dominant bacte- Haloarchaea are metabolically diverse, known to form rial families, suggesting that both approaches capture the biofilms (Fröls et al., 2012), metabolize carbohydrates overall diversity of the dominant taxa within the commu- (e.g. pentoses, hexoses; Andrei et al., 2012) and grow nity. For example, in some phyla, such as Cyanobacteria photoheterotrophically (Hartmann et al., 1980); all of and Firmicutes, there was a strong correlation between which are important elements in lithifying microbial mat the relative abundances of families between the 16S ecosystems (Dupraz et al., 2009). rRNA gene and metagenomic data sets (Fig. 3). However, The shotgun metagenomic sequencing revealed that for some taxa there was extensive variability in the rela- diatoms dominated the eukaryotic population within the tive abundance observed between the data sets. In the Little Darby stromatolite-forming mats, and were enriched Proteobacteria, there were several examples of families, in the orders Bacillariophyceae, Fragilariophyceae, such as the alphaproteobacterial families Brucellaceae Coscinodiscophyceae and Mediophyceae (Fig. S2). Pre- and Bradyrhizobiaceae, which had a higher relative abun- vious studies on the stromatolites of Highborne Cay have dance in the shotgun metagenomic library. These differ- identified several dominant species of diatoms including ences between the data sets may reflect issues regarding the stalked diatoms Striatella unipunctata and Licmorpha many of the clustering programs associated with 16S remulus (class Fragilariophyceae) as well as several rRNA gene analysis. Several recent studies have identi- Naviculid (class Bacillariophyceae) tube diatoms (Stolz fied that OTU clustering of sequences can be impacted by et al., 2009). Diatoms have been shown to play an impor- the number of sequences being clustered (He et al., tant role in the accretion of Bahamian stromatolites (Reid 2015). Although there was a strong correlation between et al., 2000; Stolz et al., 2009; Bowlin et al., 2012). Both the two data sets, assigning a taxonomic classification to the tube and stalked diatoms can form networks that trap metagenomic reads provided a more comprehensive sediment grains and become bound into the stromatolite assessment of the complexity of the stromatolite forming framework by filamentous Cyanobacteria forming mat community structure than with 16S rRNA gene analy- unlithified grain layers (Bowlin et al., 2012). The high sis alone. abundance of diatoms within the eukaryotic population likely reflects the collection of samples in August, where Functional gene complexity of stromatolite-forming water temperatures were > 27°C. Previous work has microbial mats using both predictive and whole shotgun shown a positive correlation of the relative abundance of sequencing analyses stalked and tube diatoms under elevated temperatures (Bowlin et al., 2012). The other dominant eukaryotic For decades ribosomal RNA gene analysis has provided a taxa within the Little Darby stromatolite ecosystems robust mechanism to survey the diversity of complex including Rhodophyta, Chlorophyta, Streptophyta and microbial communities (e.g. Woese et al., 1990); however, Heterokontophyta (Stramenopiles) have all been well it provided little direct evidence of the metabolic capabili- documented in other lithifying microbial ecosystems ties. With the recent, rapid expansion of sequenced ref- including Highborne Cay, Cuatro Ciénegas, Mexico and erence genomes, it has now become possible to use this Hamelin Pool, Shark Bay Western Australia (Breitbart data to help predict the functional composition of commu- et al., 2009; Myshrall et al., 2010; Mobberley et al., 2012; nities metagenome (Zaneveld et al., 2010; Collins and 2013; Edgcomb et al., 2014). Functional genes associ- Higgs, 2012; Langille et al., 2013). Using the program ated with eukaryotic taxa were primarily associated with Phylogenetic Investigation of Communities by Recon- genetic information processing and cellular processes, struction of Unobserved States (PICRUST), the gene such as cell growth, transport and death (data not content of the target ecosystem can be inferred using the shown). However, there were numerous carbohydrate reference genomes of one or more of its closest relatives metabolisms genes associated with sugar metabolism as well as a reconstruction of the organisms’ ancestral (e.g. fructose, xylose, galactose), suggesting the genome (Langille et al., 2013). This program has been eukaryotes may be contributing to the heterotrophic deg- effectively used to predict accurately the functional com- radation of EPS material within the stromatolite forming plexity of the metagenomes of the human microbiome, mats. soils and the hypersaline, non-lithifying microbial mats

Fig. 3. Radar plot depicting dominant bacterial families shared between 16S and metagenomic data sets. Relative abundance of bacterial families in 16S rRNA gene (blue line) and whole

shotgun (red line) metagenome libraries expressed as a percentage on a log scale. 7 Colours denote families within different phyla. 8 G. Casaburi, A. A. Duscher, R. P. Reid and J. S. Foster

(Langille et al., 2013). However, some environmental limitation (e.g. phosphate, nitrogen, Mg2+,Mn2+) and metagenomes, such as stromatolite ecosystems, have osmotic stress suggesting a strong ability of stromatolite- the potential problem that there may not be enough ref- forming microorganisms to sense and consequently erence genomes available to make accurate predictions. respond to changes in different environmental conditions. To assess the effectiveness of using the 16S rRNA gene Environmental factors, such as sand burial events, tem- as a prognostic tool for metagenomic analyses in perature and photosynthetic active radiation have been stromatolites, we generated a simulated prediction of the shown to have a profound impact on the rates of meta- metabolic functions of the Little Darby stromatolites from bolic activity within lithifying microbial ecosystems, as well 16S rRNA gene sequences with PICRUST. This predictive as provide control of the cycling and development of the metagenomic approach utilized the QIIME output for taxo- dominant microbial mat communities (Dupraz et al., 2009; nomic profiling and directly compared it with the whole Bowlin et al., 2012). Moreover, lithifying microbial mat shotgun sequenced metagenome library. ecosystems are usually oligotrophic and must scavenge For the 16S rRNA gene library, we identified a total of nutrients from the surrounding water. A high number of 6909 KEGG (Kyoto Encyclopedia of Genes and sequences associated with sensing and responding to Genomes) functions, corresponding to 328 level 3 KEGG nutrient limitations, such as the phosphate regulon (phoR- orthology (KO) entries, whereas the post-quality filtered phoP), were observed in the metagenome. The results whole metagenome shotgun reads analysed in METACV corresponded with other microbialite ecosystems, such as showed a total of only 2944 KEGG functions, correspond- the alkaline lake Alchichica and fresh water springs of ing to 257 level 3 KO entries. We filtered out those entries Cuatro Ciénegas in Mexico (Breitbart et al., 2009; occurring at a low relative abundance (<1%) and com- Valdespino-Castillo et al., 2014). Much like the Mexican pared the two data sets. In total, we identified 88 dominant microbialites, the phosphate regulon and assimilation level 3 KOs in both data sets, mostly related to metabo- genes from the Little Darby stromatolites were derived lism (Fig. 4). There was a moderate to strong correlation from a diverse group of organisms, primarily the between the 16S rRNA gene PICRUST prediction and the Proteobacteria classes Alphaproteobacteria, Gamma- whole metagenome sequencing (r = 0.78, Pearson) and proteobacteria and Deltaproteobacteria, Cyanobacteria the two data sets never varied more than 4% in each KO class Oscillatoriophycideae and Actinobacteria pathway (Fig. 4). Photosynthesis, chlorophyll metabolism (Fig. S4). and carbon fixation pathways in photosynthetic organisms Together, the PICRUST prediction and shotgun library were dominant within the metagenome with a higher rep- approaches suggest a strong correlation between phylog- resentation of genes associated with these metabolisms eny and function, further supporting that, even for envi- in the whole shotgun library compared with the 16S rRNA ronmental metagenomes, such as stromatolites, 16S gene predicted libraries. The underestimation of genes rRNA gene libraries can be used to provide insight into the related to photosynthetic pathways in the 16S rRNA gene metabolic capabilities of complex microbial ecosystems. predictions further confirms the need for additional Although whole metagenome shotgun sequencing is cyanobacterial reference genomes to be targeted for becoming increasingly more affordable, providing the sequencing to fill phylogenetic gaps in the databases. necessary sequencing and sampling depth for all environ- Other metabolic pathways that were enriched in the ments may still be cost-prohibitive. Therefore, these Little Darby stromatolite metagenome included genes results suggest that using phylogenic markers, such as associated with aerobic oxidative phosphorylation, nitro- the 16S rRNA gene, can provide an important first look gen fixation, glycolysis, as well as methane metabolism. into the functional complexity of lithifying mat ecosystems Together, the metagenome reflected all of the major func- as well as the microbial diversity. tional groups, in varying relative abundances, typically To delineate the taxa associated with most highly rep- associated with lithifying microbial mat systems (Dupraz resented genes of the stromatolite shotgun metagenome and Visscher, 2005; Visscher and Stolz, 2005) and library, genomic reads were matched with metabolic func- overlap with the dominant pathways in the stromatolites tion in METACV and then grouped by phylogenetic rela- and thrombolites of Highborne Cay (Khodadad and tionships in MEGAN 4 (see Experimental procedures). A Foster, 2012; Mobberley et al., 2013; 2015). heat map correlating the relative abundance of each In addition to the various microbial metabolisms, there taxon with the specific gene is shown in Fig. 5. Of the were also a high abundance of genes associated with different metabolic KO groups, the most abundant genes two-component signalling systems (Fig. 4), including were associated with photosynthesis, carbohydrate syn- genes associated with quorum sensing and its regulation thesis and degradation, as well as nitrogen and sulfur (e.g. luxQ, luxU, luxO), as well as numerous genes asso- metabolism. Within the photosynthesis KO, the most ciated with OmpR family that respond to a range of envi- abundant reads shared sequence similarity to genes ronmental stress conditions, such as high light, nutrient associated with photosystem I and II, chlorophyll and

© 2015 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology 05SceyfrApidMcoilg n onWly&Sn Ltd, Sons & Wiley John and Microbiology Applied for Society 2015 © niomna Microbiology Environmental irboeo iteDryIln stromatolites Island Darby Little of Microbiome 9 Fig. 4. Functional comparison of Little Darby stromatolite metagenome from 16S rRNA metabolic prediction and whole shotgun sequencing. Comparison of relative abundances (%) of level 3 KEGG pathways from shotgun metagenomic sequences and 16S rRNA metabolic prediction (PICRUST). Gene pathways within the same colour belong to the same level 1 of KEGG orthology classiﬁcation. Pearson correlation value (r) is shown to estimate functional similarities derived from the two data sets. 10 G. Casaburi, A. A. Duscher, R. P. Reid and J. S. Foster

Proteobacteria Cyanobacteria Bacteroidetes Firmicutes Plancotomycetes Verrucomicrobia Verrucomicrobia Alphaproteobacteria Acidobacteria KEGG Genes KO#

[K02634] apocytochrome f [K02635] cytochrome b6 [K02636] cytochrome b6−f complex iron−sulfur subunit [K02637] cytochrome b6−f complex subunit 4 [K02108] F−type H+−transporting ATPase subunit a [K02110] F−type H+−transporting ATPase subunit c [K02111] F−type H+−transporting ATPase subunit alpha [K02112] F−type H+−transporting ATPase subunit beta [K02639] ferredoxin [K02641] ferredoxin−−NADP+ reductase [K02092] allophycocyanin alpha subunit [K02093] allophycocyanin beta subunit [K02094] phycobilisome core linker protein [K02095] allophycocyanin−B [K02096] phycobilisome core−membrane linker protein [K02097] phycobilisome core component [K02284] phycocyanin alpha chain [K02285] phycocyanin beta chain [K02286] phycocyanin−associated rod linker protein [K02290] phycobilisome rod−core linker protein [K05376] phycoerythrin alpha chain [K05377] phycoerythrin beta chain [K05378] phycoerythrin−associated linker protein [K02689] photosystem I P700 chlorophyll a apoprotein A1 [K02690] photosystem I P700 chlorophyll a apoprotein A2 [K02691] photosystem I subunit VII [K02692] photosystem I subunit II [K02703] photosystem II P680 reaction center D1 protein [K02704] photosystem II CP47 chlorophyll apoprotein [K02705] photosystem II CP43 chlorophyll apoprotein [K02706] photosystem II P680 reaction center D2 protein [K00128] aldehyde dehydrogenase (NAD+) [K01840] phosphomannomutase [K03779] L(+)−tartrate dehydratase alpha subunit [K02377] GDP−L−fucose synthase [K15778] phosphomannomutase [K01812] glucuronate isomerase [K03780] L(+)−tartrate dehydratase beta subunit [K07246] D−malate dehydrogenase [K10112] maltose/maltodextrin transport system ATP−binding protein [K00971] mannose−1−phosphate guanylyltransferase [K01805] xylose isomerase [K01711] GDPmannose 4,6−dehydratase [K00376] nitrous−oxide reductase [K00370] nitrate reductase 1, alpha subunit [K00362] nitrite reductase (NAD(P)H) large subunit [K04561] nitric−oxide reductase, cytochrome b−containing subunit I [K02567] periplasmic nitrate reductase NapA [K00371] nitrate reductase 1, beta subunit [K15864] nitrite reductase (NO−forming) [K02588] nitrogenase iron protein IF4 [K02591] nitrogenase molybdenum−iron protein beta chain [K02586] nitrogenase molybdenum−iron protein alpha chain [K00958] sulfate adenylyltransferase [K00394] adenylylsulfate reductase, subunit A [K11180] sulfite reductase, dissimilatory−type alpha subunit [K00395] adenylylsulfate reductase, subunit B [K11181] sulfite reductase, dissimilatory−type beta subunit ia(U) riales r ria(U) idiales fovibrio r Nostocales Vibrionales Rhizobiales vularculales Clost Desul r Chromatiales Bacteroidetes Cytophagales Bacteroidales Oscillatoriales Chroococcales ucomicrobia(U) Pa r Burkholderiales Rhodospirillales Flavobacteriales Caulobacterales Gloeobacterales 020406080100 Acidobacteriales Alteromonadales Bacteroidetes (U) Oceanospirillales Rhodobacteriales Planctomycetales Nitrosomonadales Relative (%) contribution Hydrogenophilales Ver Sphingobacte Sphingomonadales Deltaproteobacteria Betaproteobacteria(U) Deltaproteobacte

Alphaproteobacteria(U) Gammaproteobacte

Fig. 5. Relative contribution bacterial taxa to selected gene pathways. Heat map showing the relative contribution (%) of bacterial orders to selected 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. accessory pigment synthesis, and the electron acceptor mented (Chafetz, 1986; Paerl et al., 2001; Dupraz and ferredoxin (Fig. 5). Most of these genes were assigned to Visscher, 2005; Dupraz et al., 2009). the cyanobacterial order Chroococcales, with a few genes Another group of highly represented genes within the associated with the ﬁlamentous order Oscillatoriales. High stromatolite metagenome were associated with the syn- rates of photosynthesis have been previously shown to thesis and degradation of sugars. Several genes associ- increase the localized alkalinity by depleting CO2 resulting ated with xylose fucose, mannose metabolism, as well as − in disassociation of bicarbonate into CO2 and OH genes associated with the production of alginate (e.g. (Dupraz et al., 2009). The role of photosynthesis, as a phosphomannomutase), were highly represented within major driver in the precipitation of carbonate through the stromatolite metagenome. The taxa associated with changes in the carbonate alkalinity, has been well docu- these genes varied but were primarily derived from the

Alphaproteobacteria orders Rhodobacteriales; however, adenylysulfate reductase (APS reductase) and sulfite some genes associated with fucose synthesis were asso- reductase were found primarily in the Deltapro- ciated with the cyanobacterial orders Chroococcales and teobacteria, Gammaproteobacteria and Chromatiales Nostocales. Previous studies have characterized the EPS within the Little Darby stromatolites. Sulfate-reducing bac- within Highborne Cay stromatolites and have shown that teria are a major functional group in microbial mats more than 50% of the material is comprised of carbohy- systems and have been identified as having a major role drates such as galactose, xylose and fucose (Kawaguchi in the precipitation of calcium carbonate (Visscher et al., and Decho, 2000). Additionally, lithified stromatolite 1998; 2000; Dupraz et al., 2009), although more recent forming mats (i.e. Type 3 mats) of Highborne Cay, exhib- studies have shown that the net precipitation potential of ited a high propensity to utilize hexose (e.g. galactose, sulfate reduction is highly dependent on the type of elec- mannose), deoxy sugars (fucose) and acidic sugars (e.g. tron donor (Gallagher et al., 2012). Together, these results ketoglutaric acid) (Khodadad and Foster, 2012). Together, further confirm that lithifying microbial ecosystems, such these results suggest that Bahamian stromatolites have as the Little Darby stromatolites, utilize a diverse network an increased metabolic capacity for the heterotrophic of microbes and functional genes to aid in the influx and degradation and rearrangement of EPS material, which cycling of nutrients within the stromatolites. Further, has the potential to alter the Ca2+ binding affinity of the through the coordinated activities of these microbial EPS, thereby promoting carbonate precipitation (Dupraz metabolisms, the geochemical microenvironment within et al., 2009). the stromatolites can be altered, thereby potentially gen- The predominant genes associated with nitrogen erating conditions (e.g. changes in localized pH) that metabolism were nitrogenases primarily associated with either promote the precipitation or dissolution of carbon- the Cyanobacteria, including heterocystous forming ate (Visscher and Stolz, 2005; Dupraz et al., 2009). Nostocales and the non-heterocystous Chroococcales with similarity to Cyanothece and Synechococcus. Comparison of Bahamian stromatolites to the global However, genes were also recovered from numerous microbialite population from varying environments other taxa including the Firmicutes, Proteobacteria and Acidobacteria. Similar results were observed in the In addition to profiling the taxa and functional complexity metagenome and metatranscriptome of the thrombolites of the Little Darby stromatolite microbiome, we compared of Highborne Cay, where there was an enrichment of nif this system with other carbonate-based microbialites genes expressed at midday from a diversity group of throughout the globe. Publically available data sets that microbes (Mobberley et al., 2015) as well as from nifH used next generation sequencing (454 and Illumina plat- surveys of Mexican microbialites in Cuatro Ciénegas, forms) for 16S rRNA gene analysis within lithifying micro- Lake Alchichica and the Bacalar costal lagoon (Falcón bial mat ecosystems were mined for the comparison et al., 2007; Beltrán et al., 2012). However, in addition to (Table 1). As stromatolites are highly novel communities, nitrogen fixing genes, there was also a high representa- a subsampled open-reference OTU picking approach in tion of genes in the stromatolite metagenome associated QIIME to be inclusive of unique taxa was needed (Rideout with denitrification, including reductases for nitrate, et al., 2014); therefore, we targeted only 16S rRNA gene nitrite, nitric oxide and nitrous oxide from a wide range libraries that had overlapping amplicons (V2-V4 regions). of taxa. Although primarily associated with the Unfortunately, this approach prohibited the inclusion of alphaproteobacteria orders Rhodobacteriales and several prominent microbialite studies, such as the 16S Rhizobiales, a high relative abundance of denitrification rRNA gene analyses of microbialites collected throughout genes were also recovered from other Proteobacteria Mexico (Cuatros Ciénegas, Alchichica, Sian Ka’an and (Beta-, Delta- and Gammaproteobacteria) as well as the Bacalar; Centeno et al., 2012), which targeted the V5–6 cyanobacterial order Chroococcales. The dissimilatory region. Additionally, several earlier studies that used clone reduction of nitrate to N2 has the potential to result in a net libraries to survey stromatolites from across the globe loss of carbonate thereby having a negative impact on (e.g. Papineau et al., 2005; Allen et al., 2009; carbonate precipitation within the stromatolites (Visscher Baumgartner et al., 2009b; Foster et al., 2009; Goh et al., and Stolz, 2005). 2009; Santos et al., 2010; Foster and Green, 2011) were Although sulfur metabolism was not a dominant level 3 not included due to their limited sequencing depth. KO pathway in either of the shotgun or 16S rRNA gene A total of 10 different study sites were included in the PICRUST predictive metagenome (Fig. 4), genes encod- comparison for a total of 171 samples (Table 1). The sites ing the three key enzymes associated with dissimilatory included a wide range of microbialite habitats including sulfate reduction and sulfide oxidation were represented several freshwater (Pavilion Lake, British Columbia; within the stromatolite metagenome. Specifically, genes South African estuaries), marine (Highborne Cay and encoding sulfate adenylytransferase (ATP sulfurylase), Little Darby Island, The Bahamas), hypersaline (Hamelin

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

Sequencing Sample location Habitat SRA #a Platform Sample #b

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

Pool, Shark Bay, Western Australia; Socompa Lake, Cockbain, 1976). The extended geographic isolation of Argentina; Storr’s Lake, San Salvador, The Bahamas) and these stromatolites may be a major factor in distinctive ultrahypersaline (Kiritimati Atoll, Kiribati) locations (Lim clustering of the Hamelin Pool community from the other et al., 2009; Mobberley et al., 2012; Dupraz et al., 2013; targeted habitats. Farías et al., 2013; Schneider et al., 2013; Perissinotto Another environmental factor that appeared to drive et al., 2014; Russell et al., 2014; Paul et al., 2015). Two differences in the microbial diversity between habitats non-lithifying microbial mat sites were also included to was salinity (Fig. 6B). Salinity has been long known to be assess whether there where major differences between a contributor to driving differences in diversity within ter- mat communities that undergo lithification; the hydrother- restrial and aquatic-based microbial communities (e.g. mal vent mats from Santorini Crater in the Mediterranean Pinckney et al., 1995a; Lozupone and Knight, 2007; Sea and the hypersaline microbial mats of Guerrero Dupont et al., 2014). Although the correlation was not as Negro (Table 1). All the sequences from each data set strong as geography, differences between the populations were merged, normalized and analysed in QIIME (see did appear to be linked to the salinity of the surrounding Experimental procedures). environment (P = 0.001; R = 0.59 ANOSIM). For example, The alpha diversity of each community was assessed the two most distant microbialite communities were the using rarefaction curves derived from both the Shannon ultrahypersaline Kiritimati Atoll (up to 170 ppt) and fresh- Diversity and the Faith’s Phylogenetic Diversity Index water Pavilion Lake (<1 ppt) samples (Fig. 6B). Samples (Fig. S5). The results indicated that the highest levels of from the marine (Little Darby Island and Highborne Cay; microbial diversity were within the hypersaline 35–38 ppt) and moderately hypersaline (Guerrero Negro, stromatolites of Shark Bay Australia, whereas the lowest Storr’s Lake; Socampo Lake; 66–100 ppt) sites clustered levels of diversity were observed in the microbialites of the together. Interestingly, sites such as the Swartkops Swartkops estuaries and the non-lithifying hydrothermal Estuary in South Africa (5–35 ppt) and Storr’s Lake, San vent communities of Santorini Caldera, which may reflect Salvador, The Bahamas (26–> 90 ppt), which undergo the lower sample number and sequencing depth of the extensive transitions in their salinity throughout the year two sites. A beta diversity analysis on the different com- clustered with the open marine Bahamian microbialites of munities was completed using Principal Coordinate Little Darby and Highborne Cay (Perissinotto et al., 2014; Analysis (PCoA) of the unweighted UniFrac rarefied dis- Paul et al., 2015). Although metagenomic analyses do not tance matrix (Fig. 6). Some distinctive clustering was currently exist for most of these sites, including the apparent in the PCoA plot indicating that geography does Swartkops Estuary and Storr’s Lake, the results do plays a role in driving some of the microbial diversity suggest that the communities may harbour key taxa or within lithifying microbial mat ecosystems [Fig. 6A; clades that may have a large variation in gene content P = 0.001; R = 0.93, analysis of similarity (ANOSIM)]. The and metabolic capabilities, thereby enabling a quick most isolated system appeared to be the Hamelin Pool response to the regular freshening events that occur in stromatolites located in Shark Bay Western Australia. these habitats. Similar results have been seen along Hamelin Pool is home to the most extensive and diverse salinity gradients in the Baltic Sea where the complexity of communities of stromatolites in the world and is thought to pan-genome of some taxa may enable organisms to have been isolated from the Shark Bay system approxi- thrive under a wide range of salinities (Dupont et al., mately 6000 years ago (Logan et al., 1974; Playford and 2014).

R=0.93; p=0.001 R = 0.59; p=0.001

PC1 (17%) PC1 (17%)

Location PC3 (4%) PC3 (4%) Swartkops Estuary, South Africa Socompa Lake, Argentina Salinity Level Hamelin Pool, Western Australia > 100 ppt Storrs Lake, San Salvador Island 35 - 100 ppt Highborne Cay, The Bahamas Little Darby Island, The Bahamas < 35 ppt

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

Fig. 6. Comparative diversity analyses of lithifying and non-lithifying microbial mats across geographical locations. Principal coordinates analysis 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 between the 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’s Lake); and <35 ppt (Swartkops Estuary; Pavilion Lake).

Although geography and salinity appear to be important sequencing of the Little Darby stromatolites revealed the factors driving differences between the communities, the relative abundances and gene products associated with overall variation was low (Fig. 6; PC1 17%; PC2 5%; PC3 several key phototrophic (e.g. photosynthesis, EPS pro- 4%), suggesting that there were extensive taxonomic duction) and heterotrophic (e.g. sulfate reduction, EPS similarities within the different lithifying and non-lithifying degradation) metabolisms typically associated with car- mat systems. For example, the non-lithifying microbial bonate precipitation. Carbonate precipitation in mats from Santorini Crater and Guerrero Negro clustered stromatolites relies on the coupling of both phototrophic closely with several lithifying communities, including all and heterotrophic metabolic processes, and it is the the Bahamian sites, Socompa Lake and the Swartkops balance between these processes that determines the net Estuary stromatolites. These results suggest that the taxa precipitation potential. Additionally, the direct comparison themselves are not the distinguishing characteristic of of whole shotgun sequencing with 16S rRNA gene-based why some microbial mat systems undergo lithification and analyses demonstrated the effectiveness of using predic- others do not, suggesting that ‘who’ in the community is tive metagenomic analyses in environmental ecosystems, not as critical as their metabolic capabilities or interactions such as stromatolites, to help provide a first look at the with their environment to promote microbialite formation. functional capabilities of the community. However, these metagenomic approaches have also revealed pronounced gaps in the database for key taxa, such as Conclusions Cyanobacteria, indicating the importance of continuing to Taken together, the results of this study expand our under- refine and expand the catalogue of reference genomes standing of the stromatolite microbiome by characterizing from environmental systems. Lastly, a comparison of the not only the taxa associated with these systems but also Little Darby Island stromatolites with both lithifying and the full range of metabolic capabilities. Metagenomic non-lithifying microbial ecosystems worldwide revealed

© 2015 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology 14 G. Casaburi, A. A. Duscher, R. P. Reid and J. S. Foster that although geographic location and salinity do drive Caporaso et al., 2010). A pre-quality filtering step was con- some differences within the microbial communities, the ducted using the default parameters in QIMME. For a targeted ecosystems were highly similar. These results sequence to be retained the following criteria had to be met: (i) a minimum average quality Phred score of 25; (ii) a suggest that the taxonomic composition or ‘who’ is in the minimum and maximum sequence length (200–1000, 454 community is not as important as their functional capabili- data set); (iii) no more than six ambiguous bases (N) or ties and their interactions with their environment. As homopolymers. metagenomic and metatranscriptomic analyses of In addition to the amplicon libraries generated in this study, stromatolites become increasingly available, it will the NCBI database was mined for all 16S rRNA gene become critical to provide more comprehensive functional sequences derived from microbialite (e.g. stromatolites and comparisons to begin to more fully characterize the thrombolites) ecosystems generated using next-generation sequencing (Table 1). Two additional non-lithifying microbial shared microbiome of modern stromatolites and assess mat systems were included as outgroups (Table 1). A total of the mechanisms and interactions that lead to carbonate 171 samples were recovered from short read archive (SRA) precipitation in microbial ecosystems. database and merged with the data sets from this study and analysed in QIMME using a subsampled open-reference OTU Experimental procedures picking approach. The OTU picking procedure was computed with UCLUST (at 97% identity) using the 16S rRNA Sample collection and DNA extraction GREENGENES database (v. 13_8) as a reference database (DeSantis et al., 2006; Edgar, 2010). Sequences that did not Samples of stromatolites were collected from waters around match the GREENGENES database were clustered as de novo Little Darby Island located in the southern Exuma Cays, The as to not lose the overall novel diversity. Taxonomic assign- Bahamas (76°49′ W, 115 24°43′N) in August 2012. The upper ment was also computed using UCLUST with a 0.9% similarity 1 cm of living stromatolite-forming microbial mat material was against a representative set of 16S rRNA gene sequences collected from seven different stromatolite heads and imme- from the GREENGENES database, obtaining a final Biological diately placed in RNA later (Life Technologies, Grand Island, Observation Matrix (McDonald et al., 2012). The observa- NY). The samples were transported to the Space Life Sci- tions are reported as OTUs and the matrix contained counts − ences lab and stored at 80°C until processed. Each corresponding to the number of times each OTU was stromatolite sample was vertically sectioned and genomic observed in each sample. For visualization purposes, the DNA was extracted using a modified xanthogenate method taxonomic composition data were transported in Krona as previously described and used for either 16S rRNA gene (Ondov et al., 2011) showing only the most abundant taxa amplification or metagenomic analysis (Foster et al., 2009). (>0.1%). The alpha and beta diversity analyses were computed in 16S rRNA gene amplicon library construction QIIME using the script core_diversity_analyses.py, at a rarefaction depth of 450 sequences/sample. Alpha diversity was To generate the amplicon libraries, DNA was PCR amplified computed for each rarefied OTU table, using the Shannon using a bacterial domain-specific primers that targeted the Diversity index and the Faith’s Phylogenetic Diversity Index. V1–2 region of the 16S rRNA gene (27F and 338R; Lane, Beta diversity was performed using unweighted UniFrac dis- 1991; Liu et al., 2007). The PCR reactions were conducted in tance matrices and plotted as principal coordinate analysis triplicate for each sample and contained the following final (PCoA) in Emperor (Vazquez-Baeza et al., 2013) in QIIME.A concentrations: 1 x Pfu reaction buffer (Stratagene, La Jolla, nonparametric test and the ANOSIM (Clarke, 1993) method μ μ CA), 280 M dNTPs, 2.5 g of BSA, 600 nM each primer, were used to determine the statistical significance related to 0.75 ng of genomic DNA, 1.25 U of Pfu DNA polymerase the diversity analyses. (Stratagene) and nuclease free water (Sigma, St Louis, MO) in a volume of 25 μl. The reactions were initially denatured at 95°C for 5 min, followed by 30 cycles of 95°C for 30 s, 64°C Metabolic profile prediction of Little Darby Island with for 1 min, 72°C for 1 min and a final extension at 72°C for 16S rRNA gene analysis 7 min. Negative controls were conducted for all reactions. PCR products were gel extracted and concentrated using an To predict the functional gene content of the 16S rRNA UltraClean Purification kit (MoBio, Carlsbad, CA), and repli- gene amplicon libraries, PICRUST v.1 .0 (Langille et al., cate amplicons were pooled in equimolar ratios. A subset of 2013) was used, and the results were then compared with genomic DNA consisting of a minimum of three extractions actual metagenomic data as described below. An OTU table from each head was pooled and concentrated using a DNA generated in QIIME was normalized by dividing each OTU by Clean & Concentrator-25 kit (Zymo Research, Irvine, CA). the predicted 16S rRNA gene copy number abundance The pooled stromatolite DNA was spiked into the amplicon using the script normalize_by_copy_number.py. The libraries and sequenced using the ILLUMINA MISEQ platform metagenome functional prediction was then computed by generating two libraries of paired-end reads (2 × 250 bp). multiplying each normalized OTU abundance by each predicted functional trait abundance, producing a table of KO Bioinformatics analyses of 16S rRNA amplicon libraries predictions using the script predict_metagenomes.py. The resulting table was collapsed at KO level 3 within The 16S rRNA amplicon libraries were analysed using the the pathway hierarchy of KEGG using the script Quantitative Insights Into Microbial Ecology tool (QIMME v. 1.8; categorize_by_function.py.

Metagenome sequencing and analysis Baumgartner, L.K., Spear, J.R., Buckley, D.H., Pace, N.R., Reid, R.P., and Visscher, P.T. (2009a) Microbial diversity in Genomic DNA derived from the Little Darby stromatolites was modern marine stromatolites, Highborne Cay, Bahamas. sequenced using the Illumina MISEQ platform and the paired- Environ Microbiol 11: 2710–2719. end reads were quality filtered considering the Phred quality Baumgartner, L.K., Dupraz, C., Buckley, D.H., Spear, J.R., scores and read length, using SICKLE (v. 1.2; Joshi and Fass, Pace, N.R., and Visscher, P.T. (2009b) Microbial species 2011) with default parameters. High-quality filtered reads richness and metabolic activities in hypersaline microbial were assigned for functionality at different KEGG levels, mats: insight into biosignature formation through using METACV v. 2.3.0 (Liu et al., 2013) with default param- lithification. Astrobiology 9: 861–874. eters. Metagenome Composition Vector classifies short Beltrán;, Y., Centeno, C.M., Garcia-Oliva, F., Legendre, P., metagenomic reads into specific taxonomic and functional and Falcón, L.I. (2012) N2 fixation rates and associated groups using a composition and phylogeny-based algorithm. diversity (nifH) of microbialite and mat-forming consortia The reads were not assembled due to the variable relative from different aquatic environments in Mexico. Aquat abundance of different taxa within the stromatolite-forming Microb Ecol 67: 15–24. mats, which may result in potential chimeric contigs (Liu Bokulich, N.A., Subramanian, S., Faith, J.J., Gevers, D., et al., 2013; Casaburi, 2015; Howe and Chain, 2015). The Gordon, J.I., Knight, R., et al. (2013) Quality-filtering vastly output was loaded into the statistical program R and initially improves diversity estimates from Illumina amplicon > filtered at a correlation score 50 to retain only the top sequencing. Nat Methods 10: 57–59. matched genes and then filtered for abundance and for dif- Bowlin, E.M., Klaus, J., Foster, J.S., Andres, M., Custals, L., ferent KEGG classification levels. Taxonomic information was and Reid, R.P. (2012) Environmental controls on microbial extracted using METAGENOME ANALYZER V.5.6.5 to highlight community cycling in modern marine stromatolites. Sedi- phylogenetic relationships within the stromatolite community ment Geol 263-264: 45–55. (Huson et al., 2007). To compare the predicted metabolic Breitbart, M., Hoare, A., Nitti, A., Siefert, J., Haynes, M., profile with the 16S rRNA gene sequences with the Dinsdale, E., et al. (2009) Metagenomic and stable isotopic metagenome data set a Pearson Correlation was performed analyses of modern freshwater microbialites in Cuatro correlating the KEGG entries identified at level 3 of KO Ciénegas, Mexico. Environ Microbiol 11: 16–34. classification. 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