Microbial and Small Eukaryotes Associated With Reefs in the Upper Gulf of Thailand

Introduction

Coral reefs provide habitat where microbes and small eukaryotes (e.g., fungi, diatoms and copepods) live in potentially symbiotic relationships with corals, and these organisms play important roles in food and energy networks. These eukaryotic assemblages have been denoted “coral holobionts.” Similar to bacteria, microbial and small eukaryotes serve as primary producers and recyclers, for examples, microalgae Symbiodinium photosynthesizes (Mydlarz et al., 2010), copepods and fungi degrade toxic nitrate and nitrite compounds, and fungi fix nitrogen gas in seawater to forms that can be used by other organisms (Wegley et al., 2007). These activities support colonization, reproduction, growth, and resistance to stress (i.e., against coral bleaching, pathogen infection and pollution) for corals (Massana and Pedrós-Alió, 2008; Ainsworth et al., 2010; Mydlarz et al., 2010; Pootakham et al., 2018).

The upper Gulf of Thailand (GoT) harbored pristine reefs where corals remain abundant and healthy, including reefs at Ko Samae San (S), Khao Ma Cho (K) and Ko Tao Mo (T). These sites also harbor sone of the world’s most diverse assemblages (Phongsuwan et al., 2013; Ramírez et al., 2017). These sites are part of the Sea Natural History Museum of Thailand, thus they are protected from many anthropogenic activities. These fringing reefs include numerous colonies of Acropora, Platygyra, and Porites (Phongsuwan et al., 2013; Latypov, 2015). The microbes and small eukaryotes of these relatively pristine sites have not been studied in detail.

Our study provides the first data on microbial and small eukaryotic assemblages at the S, K, and T sites, using fungal ribosomal intergenic spacer analysis (F-RISA) and sequencing of the V9 region of 18S rRNA gene. F-RISA measures a DNA length that is characteristic for fungal species, so different communities yield different combinations of lengths (Ranjard et al., 2001; Gillevet et al., 2009). The next generation sequencing (NGS) provides more power to detect and identify natural microbial diversity, without limitations due to media or other conditions associated with laboratory cultures (Riesenfeld et al., 2004; Rusch et al., 2007; Somboonna et al., 2014; Mahé et al., 2015; Khitmoh et al., 2017). Our study primarily characterized and compared the eukaryotic assemblages of the prevalent coral species at the three sites (Porites lutea, Platygyra sinensis, Acropora humilis and Acropora millepora), and the surrounding niches (sediment and seawater) during the wet and dry seasons in 1 year (Figure 1A). Independent replicates were collected during all sampling events (3–10 replicates). Our study also correlated data on the relative abundances of representative genera of eukaryotes with parameters describing water quality. These initial data serve as a reference, and they may advance our understanding of similar reefs where data on microbial and small eukaryotes are inadequate.

FIGURE 1

FIGURE 1. Samples collected (A), unweighted pair group with arithmetic mean (UPGMA) clustering to differentiate communities (B), relative abundances at the phylum (C) and genus (D) levels, and Pearson’s correlations for genera (E) and variables characterizing water quality (F). Unclassified phyla were not displayed in (C,D). In (D), organisms with relative abundances <1% were omitted. In (F), the vector direction and length represent the direction and strength of the correlation of that variable with the relative abundances of organisms. Red vectors represent significant correlations (p < 0.01), and gray vectors represent non-significant correlations (p ≥ 0.01).

Materials and Methods

Sampling

Samples were collected at S, K, and T sites during April-May (dry season) and September-October (wet season) in 2014. The coral reefs at these sites appeared healthy during both sampling events. Samples included 3–10 independent replicates for seawater, sediments, and prevalent coral species (P. lutea, P. sinensis, A. Humilis, and A. millepora). We collected coral colonies that were 5 cm in diameter, 5 L of seawater within 1 m above a colony, and 50 g of sediment within 1 m below a colony. All samples were placed in sterile bottles or bags, transported on ice, and analyzed within 7–14 days. Water quality was characterized by measuring temperature, pH, salinity, dissolved oxygen (DO) and density of water on-site (Figure 1A).

Metagenomic Extraction

Metagenomic DNA were extracted using Power Water DNA Isolation Kit for seawater and Power Soil DNA Isolation Kit for corals and sediments (MoBio, Carlsbad, CA, United States) according to the manufacturer’s instructions. Microorganisms in replicate 2.5 L aliquots of seawater were captured on a sterile 0.22-micron filter using a vacuum filtration system (Merck Millipore, Massachusetts, MA, United States). For coral and sediment samples, 1 g of ground coral and 1 g of sediment were used. Extractions were performed for individual replicates and checked for DNA quality and concentration by agarose gel electrophoresis and nanodrop spectrophotometry, respectively.

Fungal Ribosomal Intergenic Spacer Analysis for Subgrouping of Independent Sampled Replicates

Fungal ribosomal intergenic spacer analysis was performed according to established protocols, using universal primers 18S-2234C (5′-GTTTCCGTAGGTGAACCTGC-3′) and 28S-3126T (5′-ATATGCTTAAGTTCAGCGGGT-3′) (Ranjard et al., 2001; Gillevet et al., 2009). To prevent potential bias in PCR, for each replicate the minimum of two PCRs were performed and pooled. Each PCR comprised 1 × EmeraldAmp^® GT PCR Master Mix (TaKaRa, Shiga, Japan), 0.3 μM forward primer, 0.3 μM reverse primer, and the metagenome. The thermocycling conditions were 95°C for 4 min, followed by 30–35 cycles of 94°C for 1 min, 55∘C for 1 min and 72∘C for 2 min, and ended by 72∘C for 10 min. Different communities or subgroups were identified by different banding patterns upon agarose gel electrophoresis.

18S rRNA Gene Library Construction and NGS

The 18S rRNA V9 gene libraries were constructed following Caporaso et al. (2012), using universal eukaryotic primers Illumina_Euk_1391F (5′-GTACACACCGCCCGTC-3′) and Illumina_EukBr (5′-TGATCCTTCTGCAGGTTCACCTAC-3′), appended with 5′ Illumina adapter and 3′ Golay barcode sequences. PCR comprised 1 × EmeraldAmp^® GT PCR Master Mix (TaKaRa), 0.2 μM of each primer, and the metagenome. Triplicate PCRs were performed and pooled for each sample or subgroup to address potential bias. The thermocycling conditions were 94°C for 3 min, and 28–30 cycles of 94°C for 45 s, 50°C for 60 s and 72°C for 90 s, followed by 72°C for 10 min. The amplicons were purified using GenepHlow^TM Gel Extraction Kit (Geneaid Biotech Ltd., New Taipei City, Taiwan), and quantified with Picogreen (Invitrogen, Eugene, Oregon, OR, United States). 200 ng of each sample or subgroup was pooled for NGS, along the sequencing primers and index sequence (Caporaso et al., 2012), using Miseq300 platform (Illumina, San Diego, CA, United States) housed at the Chulalongkorn Medical Research Center, Chulalongkorn University (Bangkok, Thailand).

Bioinformatic Analyses

Raw sequences were quality screened following Mothur’s standard operating procedures for MiSeq (Schloss et al., 2009) that included removal of sequences that have (i) ambiguous bases, (ii) >1 mismatch base in the primer region, (iii) ≥1 mismatch base in the barcode, (iv) >10 homopolymers, (v) a minimum sequencing quality score of <35 over a 50-bp window, (vi) a read length of <350 bases, and (vii) chimera sequence (Huse et al., 2010; Edgar et al., 2011). Sequences were aligned against Silva database (Schloss et al., 2009). Operational taxonomic units (OTUs) were assigned using Naïve Bayesian method (Wang et al., 2007). Data for all samples (and subgroups) were normalized to an equal sequencing depth (78,068 sequences). Mothur was used to compute Good’s coverage to estimate a sequencing coverage, rarefaction curve, alpha diversity (species richness and evenness in a sample: Chao1 richness, Shannon diversity and inverse Simpson index), beta diversity (thetayc dissimilarity coefficients, unweighted pair group with arithmetic mean (UPGMA) clustering, and principal coordinates analysis (PCoA)), and AMOVA (Schloss et al., 2009). For sample with 2 subgroups, a student’s t-test was computed, and the subgroups were merged if p > 0.01. Correlations between the relative abundances of common genera and water quality variables were determined using Pearson’s correlation (Schloss et al., 2009) and XLSTAT-Ecology software¹.

Availability of Supporting Data

Nucleic acid sequences were deposited in an open access Sequence Read Archive database of NCBI, accession number SRP095762.

Results

Fungal Ribosomal Intergenic Spacer Analysis for Subgrouping of Independent Sampling Replicates

The 3–10 replicate samples exhibited similar F- RISA banding patterns, suggesting limited diversity among replicates (no subgroup classification). Nonetheless, we analyzed bacterial ribosomal intergenic spacer analysis (B-RISA), and some samples showed two B-RISA patterns (e.g., SCD1 and SCD2 represent 2 B-RISA subgroups in Figure 1A and SCW1 represents one B-RISA subgroup). To ensure we did not miss any eukaryotic subgroup, we constructed and sequenced the 18S rRNA gene libraries based on the B-RISA subgroups.

18S rRNA Gene Sequencing

Libraries of 18S rRNA genes were constructed successfully for all subgroups, because the numbers of quality sequences per subgroup were high (avg. 218,885 sequences/subgroup). This sequencing depth was statistically sufficient, as indicated by Good’s coverage of 99.99–100% at genus-level OTUs (Supplementary Table S1A). These results are consistent with estimates of richness at the genus level derived from rarefaction curves that plateaued at this sequencing depth. The 18S rDNA data for fungi were analyzed separately, and they yielded Good’s coverage values of 87.33–97.91% (Supplementary Table S1B).

Shannon diversity indices indicated greater taxonomic richness in sediment and seawater (Supplementary Table S1A). These results highlighted the specificity of the community on corals. Eukaryotes in the Kingdoms Chromista (88%: Dinoflagellata 66% and Bacillariophyta 22%) and Animalia (mostly worms and molluscs) were more common than fungi (Supplementary Figures S1A,B). Among the four fungal phyla, Ascomycota was the most dominant (>90%), followed by Basidiomycota (5.609%), an unclassified phylum (3.741%) and Glomeromycota (0.003%; Supplementary Figure S1C).

Comparison of Communities From Species of Corals, Sites, and Times

In the UPGMA, the communities from corals clearly clustered separately from the communities from seawater and sediment across all sites and the two periods of time (Figure 1B). These results further highlight the unique community associated with corals, and AMOVA also identified a statistically significant difference between these communities (p < 0.001). However, the communities at the three sites and during the wet and dry sampled months were not statistically different (S vs. K vs. T, p = 0.082; months, p = 0.707). Moreover, pairwise comparisons between F-RISA subgroups supported the F-RISA results, because all corresponding subgroups were statistically similar (avg. p = 0.33; Supplementary Table S2). The corresponding subgroups were thereby merged.

Figures 1C,D exhibited phyla and genera that dominated and differentiated the communities. The genus Symbiodinium in phylum Dinoflagellata dominated samples of corals, whereas the genus Navicula in Bacillariophyta dominated in sediments and seawater. Fungi, although relatively rare, were 25–96 times more abundance in samples of corals (avg. 1.67%) than in sediments (0.017%) and seawater (0.066%; Figure 1C), and helped differentiate the communities on corals (phylum Ascomycota: genus Geomyces; Figure 1E). Additional genera specific to corals were Symbiodinium and Rathbunaster (Figure 1E).

To better understand the community associated with corals, genera that were reported to affect coral health were assessed. Symbiodinium, a well-known coral holobiont, was most abundant on all species of coral and more abundant in the dry (66%) months. In contrast, the relative abundance of Symbiodinium in sediment and seawater was only 1% (Figure 2). The presence and abundance of Geomyces on corals in the dry months raises some concern, because Geomyces can cause a skin disease called “white-nose syndrome” (Work and Meteyer, 2014). However, the corals we collected appeared healthy, so Symbiodinium and echinoderm Rathbunaster might limit the effects of Geomyces.

FIGURE 2

FIGURE 2. Relative abundance of Symbiodinium, Geomyces, Navicula, and Calanus across types of samples and the dry and wet months. Relative abundances <9% are not noted.

The diatom Navicula was relatively abundant in sediment, and the free-living copepod Calanus was relatively abundant in seawater (Figure 2). These organisms may play important roles in the system. Navicula has been reported to play major role in cycling of nitrogen and phosphate (Kwon et al., 2013; Stock et al., 2014), and it is a food source for Calanus, which itself is a food for corals and other animals.

Correlation With Water Quality

Dissolved oxygen, density of water, salinity, pH, and temperature were correlated in the similar vector direction to the coral communities (p < 0.01; Figure 1F). The community diversity were not significantly correlated with numbers of OTUs and water depth. Given a sufficient sequencing depth, additional OTUs should not alter relative abundances substantially, and all samples were collected the similar depth (2–3 m) so the communities would not be expected to exhibit substantial variation.

Conclusion

This study provided initial data on the diversity of microbes and small eukaryotes on coral reefs in the upper GoT. The community on corals was consistently different from the communities in seawater and sediments, and all communities were similar at the three sites (S, K, and T) and in the two periods of sampling. Pearson’s correlations indicated that the relative abundances of various genera were correlated in different vector directions with several metrics characterizing water quality.

Author Contributions

DB did molecular biology experiments and performed the data analysis. AW helped the data analysis. ST and VV conceived the study. SC provided the samples and conceived the study. NS conceived the study, coordinated the experiments and data analysis, and wrote the manuscript. All authors read and approved the final manuscript.

Funding

This research was supported by the 90th Anniversary of Chulalongkorn University Fund, and Chulalongkorn Academic Advancement into its 2nd Century Project, Thailand Research Fund (RSA6080087), Thailand Research Fund (RSA6180046), and EU-Horizon 2020 Project TASCMAR (634674).

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors acknowledged the Plant Genetic Conservation Project under the Royal Initiative of Her Royal Highness Princess Maha Chakri Sirindhorn, and the Naval Special Warfare Command, Royal Thai Navy for their assistance in the field; and the National Center for Genetic Engineering and Biotechnology (BIOTEC), and the National Science and Technology Development Agency (NSTDA) for allowing us to compute the data on the server. The authors also thank P. Sodsai and W. Poomipak for sequencing advice.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmars.2018.00436/full#supplementary-material

FIGURE S1 | Overall eukaryotic distribution by kingdoms (A) and phylum (B,C). In (C), only fungal phyla were displayed.

TABLE S1 | Estimates of sample coverage and diversity indices at the phylum and genus levels for all 18S rDNA profiles (A) and for fungal profiles (B).

TABLE S2 | Student’s t-test to evaluate statistically significant differences in the community structures of genus-level operational taxonomic unit (OTU) compositions between corresponding subgroups.

Footnotes

1. ^ www.xlstat.com/en/solutions/ecology

References

Ainsworth, T. D., Thurber, R. V., and Gates, R. D. (2010). The future of coral reefs: a microbial perspective. Trends Ecol. Evol. 25, 233–240. doi: 10.1016/j.tree.2009.11.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Caporaso, J. G., Lauber, C. L., Walters, W. A., Berg-Lyons, D., Huntley, J., Fierer, N., et al. (2012). Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624. doi: 10.1038/ismej.2012.8

PubMed Abstract | CrossRef Full Text | Google Scholar

Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C., and Knight, R. (2011). UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200. doi: 10.1093/bioinformatics/btr381

PubMed Abstract | CrossRef Full Text | Google Scholar

Gillevet, P. M., Sikaroodi, M., and Torzilli, A. P. (2009). Analyzing salt-marsh fungal diversity: comparing ARISA fingerprinting with clone sequencing and pyrosequencing. Fungal Ecol. 2, 160–167.

Google Scholar

Huse, S. M., Welch, D. M., Morrison, H. G., and Sogin, M. L. (2010). Ironing out the wrinkles in the rare biosphere through improved OTU clustering. Environ. Microbiol. 12, 1889–1898. doi: 10.1111/j.1462-2920.2010.02193.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Khitmoh, K., Wilantho, A., Wongsawaeng, D., Tongsima, S., and Somboonna, N. (2017). 16S rRNA gene sequencing assessment of the prokaryotic communities in the Southeast Andaman Sea, Thailand and potential environmental alerts. Chiang Mai J. Sci. 44, 15–28.

Google Scholar

Kwon, H. K., Oh, S. J., and Yang, H. S. (2013). Growth and uptake kinetics of nitrate and phosphate by benthic microalgae for phytoremediation of eutrophic coastal sediments. Bioresour. Technol. 129, 387–395. doi: 10.1016/j.biortech.2012.11.078

PubMed Abstract | CrossRef Full Text | Google Scholar

Latypov, Y. Y. (2015). Particular qualities of forming of reefs and coral communities in the Gulf of Thailand. Nat. Rev. Microbiol. 5, 1–8.

Google Scholar

Mahé, F., Mayor, J., Bunge, J., Chi, J., Siemensmeyer, T., Stoeck, T., et al. (2015). Comparing high-throughput platforms for sequencing the V4 region of SSU-rDNA in environmental microbial eukaryotic diversity surveys. J. Eukaryot. Microbiol. 62, 338–345. doi: 10.1111/jeu.12187

PubMed Abstract | CrossRef Full Text | Google Scholar

Massana, R., and Pedrós-Alió, C. (2008). Unveiling new microbial eukaryotes in the surface ocean. Curr. Opin. Microbiol. 11, 213–218. doi: 10.1016/j.mib.2008.04.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Mydlarz, L. D., McGinty, E. S., and Harvell, C. D. (2010). What are the physiological and immunological responses of coral to climate warming and disease? J. Exp. Biol. 213, 934–945. doi: 10.1242/jeb.037580

PubMed Abstract | CrossRef Full Text | Google Scholar

Phongsuwan, N., Chankong, A., Yamarunpatthana, C., Chansang, H., Boonprakob, R., Petchkumnerd, P., et al. (2013). Status and changing patterns on coral reefs in Thailand during the last two decades. Deep Sea Res. Pt. 2 96, 19–24.

Google Scholar

Pootakham, W., Mhuantong, W., Putchin, L., Yocha, T., Sonthirod, C., Kongkachana, W., et al. (2018). Dynamics of coral-associated microbiomes during a thermal bleaching event. Microbiologyopen 23:e00604. doi: 10.1002/mbo3.604

PubMed Abstract | CrossRef Full Text | Google Scholar

Ramírez, F., Afán, I., Davis, L. S., and Chiaradia, A. (2017). Climate impacts on global hot spots of marine biodiversity. Sci. Adv. 3:e1601198. doi: 10.1126/sciadv.1601198

PubMed Abstract | CrossRef Full Text | Google Scholar

Ranjard, L., Poly, F., Lata, J. C., Mougel, C., Thioulouse, J., and Nazaret, S. (2001). Characterization of bacterial and fungal soil communities by automated ribosomal intergenic spacer analysis fingerprints: biological and methodological variability. Appl. Environ. Microbiol. 67, 4479–4487.

PubMed Abstract | Google Scholar

Riesenfeld, C. S., Schloss, P. D., and Handelsman, J. (2004). Metagenomics: genomic analysis of microbial communities. Ann. Rev. Genet. 38, 525–552.

Google Scholar

Rusch, D. B., Halpern, A. L., Sutton, G., Heidelberg, K. B., Williamson, S., Yooseph, S., et al. (2007). The Sorcerer II global ocean sampling expedition: northwest Atlantic through eastern tropical Pacific. PLoS Biol. 5:e77. doi: 10.1371/journal/pbio.0050077

PubMed Abstract | CrossRef Full Text | Google Scholar

Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., et al. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541. doi: 10.1128/AEM.01541-09

PubMed Abstract | CrossRef Full Text | Google Scholar

Somboonna, N., Wilantho, A., Assawamakin, A., Monanunsap, S., Sangsrakru, D., Tangphatsornruang, S., et al. (2014). Structural and functional diversity of free-living microorganisms in reef surface. Kra island, Thailand. BMC Genomics 15:607. doi: 10.1186/1471-2164-15-607

PubMed Abstract | CrossRef Full Text | Google Scholar

Stock, W., Heylen, K., Sabbe, K., Willems, A., and De Troch, M. (2014). Interactions between benthic copepods, bacteria and diatoms promote nitrogen retention in intertidal marine sediments. PLoS One 9:e111001. doi: 10.1371/journal.pone.0111001

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, Q., Garrity, G. M., Tiedje, J. M., and Cole, J. R. (2007). Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267.

PubMed Abstract | Google Scholar

Wegley, L., Edwards, R., Rodriguez-Brito, B., Liu, H., and Rohwer, F. (2007). Metagenomic analysis of the microbial community associated with the coral Porites astreoides. Environ. Microbiol. 9, 2707–2719.

PubMed Abstract | Google Scholar

Work, T., and Meteyer, C. (2014). To understand coral disease, look at coral cells. Ecohealth 11, 610–618. doi: 10.1007/s10393-014-0931-1

PubMed Abstract | CrossRef Full Text | Google Scholar