Document Detail

Analysis of stomach and gut microbiomes of the eastern oyster (Crassostrea virginica) from coastal Louisiana, USA.
Jump to Full Text
MedLine Citation:
PMID:  23251548     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
We used high throughput pyrosequencing to characterize stomach and gut content microbiomes of Crassostrea virginica, the Easter oyster, obtained from two sites, one in Barataria Bay (Hackberry Bay) and the other in Terrebonne Bay (Lake Caillou), Louisiana, USA. Stomach microbiomes in oysters from Hackberry Bay were overwhelmingly dominated by Mollicutes most closely related to Mycoplasma; a more rich community dominated by Planctomyctes occurred in Lake Caillou oyster stomachs. Gut communities for oysters from both sites differed from stomach communities, and harbored a relatively diverse assemblage of phylotypes. Phylotypes most closely related to Shewanella and a Chloroflexi strain dominated the Lake Caillou and Hackberry Bay gut microbiota, respectively. While many members of the stomach and gut microbiomes appeared to be transients or opportunists, a putative core microbiome was identified based on phylotypes that occurred in all stomach or gut samples only. The putative core stomach microbiome comprised 5 OTUs in 3 phyla, while the putative core gut microbiome contained 44 OTUs in 12 phyla. These results collectively revealed novel microbial communities within the oyster digestive system, the functions of the oyster microbiome are largely unknown. A comparison of microbiomes from Louisiana oysters with bacterial communities reported for other marine invertebrates and fish indicated that molluscan microbiomes were more similar to each other than to microbiomes of polychaetes, decapods and fish.
Authors:
Gary M King; Craig Judd; Cheryl R Kuske; Conor Smith
Related Documents :
23896668 - International survey of perceived barriers to admission and discharge from spinal cord ...
21513198 - Long-term comparison of the fish community in a costa rican rocky shore marine reserve.
25271418 - Dance communication affects consistency, but not breadth, of resource use in pollen-for...
23035858 - Leaving a spinal unit and returning to the wider community: an interpretative phenomeno...
23683168 - Problems and hopes perceived by mothers, fathers and physicians of children receiving p...
3086918 - Suicide and homicide among hispanics in the southwest.
Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.     Date:  2012-12-12
Journal Detail:
Title:  PloS one     Volume:  7     ISSN:  1932-6203     ISO Abbreviation:  PLoS ONE     Publication Date:  2012  
Date Detail:
Created Date:  2012-12-19     Completed Date:  2013-06-03     Revised Date:  2013-07-11    
Medline Journal Info:
Nlm Unique ID:  101285081     Medline TA:  PLoS One     Country:  United States    
Other Details:
Languages:  eng     Pagination:  e51475     Citation Subset:  IM    
Affiliation:
Department of Biological Sciences, Louisiana State University, Baton Rouge, Louisiana, USA. gkingme@gmail.com
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Animals
Bacteria / classification,  genetics
Cluster Analysis
Crassostrea / microbiology*
Ecosystem*
Louisiana
Metagenome* / genetics
Phylogeny
Principal Component Analysis
RNA, Ribosomal, 16S / genetics
Seawater*
Stomach / microbiology*
Chemical
Reg. No./Substance:
0/RNA, Ribosomal, 16S
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Full Text
Journal Information
Journal ID (nlm-ta): PLoS One
Journal ID (iso-abbrev): PLoS ONE
Journal ID (publisher-id): plos
Journal ID (pmc): plosone
ISSN: 1932-6203
Publisher: Public Library of Science, San Francisco, USA
Article Information
Download PDF
Copyright: 2012
License:
Received Day: 8 Month: 6 Year: 2012
Accepted Day: 5 Month: 11 Year: 2012
collection publication date: Year: 2012
Electronic publication date: Day: 12 Month: 12 Year: 2012
Volume: 7 Issue: 12
E-location ID: e51475
PubMed Id: 23251548
ID: 3520802
Publisher Id: PONE-D-12-16625
DOI: 10.1371/journal.pone.0051475

Analysis of Stomach and Gut Microbiomes of the Eastern Oyster (Crassostrea virginica) from Coastal Louisiana, USA Alternate Title:Stomach and Gut Microbiomes of the Eastern Oyster
Gary M. King1*
Craig Judd1
Cheryl R. Kuske2
Conor Smith1
Josh Neufeldedit1 Role: Editor
1Department of Biological Sciences, Louisiana State University, Baton Rouge, Louisiana, United States of America
2Bioscience Division, MS 888, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
University of Waterloo, Canada
Correspondence: * E-mail: gkingme@gmail.com
[conflict] Competing Interests: The authors have declared that no competing interests exist.
Contributed by footnote: Conceived and designed the experiments: GK. Performed the experiments: GK CJ. Analyzed the data: GK CJ CS. Contributed reagents/materials/analysis tools: GK CK. Wrote the paper: GK CJ CK CS.

Introduction

The Eastern oyster, Crassostrea virginica, is well known for its commercial value and importance as an “ecosystem engineer” [1][3]. Volumes have been written about its biology and ecology, including interactions with bacteria and other microbes. Much of this literature has emphasized diseases [4], [5] and the presence of human pathogens, especially Vibrio parahaemolyticus and V. vulnificus[6][9].

Many studies have addressed other aspects of oyster-bacteria interactions. Cristispira has been identified as a symbiont associated with the crystalline style, a molluscan digestive structure [10]. Stappia (now Labrenzia) has been isolated from C. gigas and C. virginica, and in the latter implicated as an antagonist for the etiological agent of Juvenile Oyster Disease [11]. Culture-dependent studies have characterized Vibrio and other genera associated with bulk animals and specific tissues [6][8], [12], [13] including identification of “indigenous” bacteria in C. gigas haemolymph [14], [15]. Such studies have also shown that an Eastern Mediterranean oil spill did not affect oyster-associated bacteria [16]. Culture-independent studies have documented patterns of diversity among different populations and tissues, compared hatchery-raised and wild animals, and identified the ε-Proteobacterium, Arcobacter, as a major contributor to the microbial community of the Chilean oyster, Tiostrea chiliensis[17].

Despite the pathogen-associated and fingerprinting studies summarized above, and the potential importance of bacteria for oyster nutrient acquisition, surprisingly little information exists on oyster stomach and gut microbiome diversity. Although pH values of stomach and gut tissues are similar, and particle transit times relatively short (about 1–2 h) during active feeding [18], it is unclear whether characteristic communities exist in the contents of these tissues; it is equally uncertain how microbiomes might vary within a population or across populations. To address these questions, we obtained two sets of triplicate animals, one set each from Hackberry Bay and Lake Caillou in coastal Louisiana during summer, 2010. These two geographically distinct sites (Barataria Bay and Terrebonne Bay, respectively) represent economically important sources of oysters, and experience similar salinity regimes and variability [19]. We separately collected stomach and gut contents, and sequenced PCR-amplified 16S rRNA genes using a pyrosequencing platform (Roche Diagnostics 454 Titanium). The results revealed substantial differentiation between stomach and gut microbiomes of animals from one site (Lake Caillou), but somewhat less differentiation for the second site (Hackberry Bay). Notably, Mollicutes accounted for >80% of all bacterial sequences in the stomach microbiomes of Lake Caillou oysters, but <10% of Hackberry Bay oysters. Stomach OTUs also included Actinobacteria, Chloroflexi, Firmicutes, Planctomycetes, Proteobacteria, and Spartobacteria. Chloroflexi, Mollicutes, Planctomycetes and Spartobacteria might comprise a putative core stomach microbiome, while Chloroflexi, Firmicutes, α-Proteobacteria and Verrucomicrobia might contribute to a putative core gut microbiome.


Materials and Methods
Sample Collection

Oysters were collected on August 4, 2010 from Hackberry Bay, a small bay adjoining Barataria Bay, Louisiana, USA. This site was unaffected by oil from the Deepwater Horizon oil spill [20]. Triplicate oysters were held on ice (<6 h) for initial processing at the Louisiana Sea Grant Oyster Hatchery, Grand Isle, LA, USA. The external valves were thoroughly cleaned to remove surface contamination, and then carefully opened leaving the animal intact. Stomach contents of individual animals were sampled using 23-gauge needles and 1-cm3 syringes, yielding about 0.2 cm3 of fluid, which was transferred to sterile 1.5 cm3 microfuge tubes. Gut contents were obtained by locating the intestine of individual animals and then carefully extruding hindgut material from the anus into sterile 1.5 cm3 microfuge tubes. Stomach and gut contents were transported on ice to a laboratory at Louisiana State University (LSU) where DNA was extracted using a MoBio PowerMax soil extraction kit (MoBio Laboratories, Inc., Carlsbad, CA) following the manufacturer’s instructions with the addition of a freeze (at −80°C, 10 min)/thaw (at 60°C, 5 min) cycle repeated three times. A second set of oysters collected on September 1, 2010 from Caillou Bay (Caillou Lake), Louisiana, USA were processed similarly with the exception that animals were transported on ice to the LSU laboratory prior to sampling stomach and gut contents. This site was also unaffected by the Deepwater Horizon oil spill. Sampling permits were not required for either site.

DNA Analysis

DNA extracts from all samples were amplified by PCR with Platinum high-fidelity DNA polymerase (Life Technologies Corp, La Jolla, CA) in 25 µl reactions using standard protocols with the exception of a 68°C extension temperature, and primers 515f and 806r modified with barcodes and adaptors for sequencing using the Roche 454 pyrosequencing platform with titanium chemistry [21]. Each reaction mixture contained 11.5 µl water, 2.5 µl 10X high-fidelity buffer (Life Technologies Corp, La Jolla, CA), 0.75 µl of 100 mM dNTPs, 1 µl MgSO4, 5 µl of 0.5 mg ml−1 BSA, 1.5 µl for each of 515f and 806r primers, 0.2 µl high-fidelity DNA polymerase (Life Technologies Corp, La Jolla, CA), and 1 µl of extracted DNA. Reaction mixtures were denatured for 3 min at 94°C, followed by 26 cycles of 94°C for 1 min, 1 min at 54°C, and 2 min at 68°C, with a 10 min extension step at 68°C after the cycles were complete. Triplicate reactions for each sample were pooled, and then a final mixture was prepared for sequencing by adding amplicons from each sample in equal masses. Pyrosequencing was conducted by the Los Alamos National Laboratory sequencing facility, resulting in a total of 237,842 raw reads with an average length of 295 bp. Sequences have been submitted to the MG-RAST server as 4501864.3-4501873.3 (http://metagenomics.anl.gov/linkin.cgi?project=1994).

Sequence Analysis

Raw sequences with quality scores were processed using three pipelines. PANGEA [22] was used to compare the phylogenetic composition of samples for which OTUs were classified using MEGABLAST with a reference database containing 170,273 full-length 16S rRNA gene sequences from Bacteria and Archaea isolates. Raw reads were screened using default values (average quality score, 20; minimum length, 100 bp) [22]. Reads were binned based on barcodes, which were trimmed prior to MEGABLAST. Sequences were assigned to domain/phylum, class/order/family and genus and species levels, respectively, using similarity threshold values of 0.8, 0.9, 0.95 and 0.99 for [22]. Sequences not classified by MEGABLAST were clustered into OTUs based on the same similarity thresholds. PANGEA also created a second analysis in which all samples consisted of an equal number of reads; these normalized sample datasets were constructed using sequences randomly chosen without replacement from the original screened sample files. The compositions of stomach and gut samples were compared using principal components analysis after eliminating singletons (sequences represented only once in the full dataset), and after removing cyanobacterial and eukaryotic sequences (chloroplast and mitochondrial 16S rRNA from algal cells in stomachs and guts). Of the remaining sequences identified at a phylum level or lower, representative sequences for OTUs accounting for ≥0.1% of the total were curated manually using MEGABLAST in GenBank. Any sequences misidentified by PANGEA were reclassified as necessary.

The CloVR pipeline [23] was used with its default settings (e.g., average quality score, 25; minimum length, 100 bp) to create analyses based on taxonomic affiliations (i.e., sample composition) and sequence phylogeny. For this purpose, CloVR used a hybrid pipeline consisting of Mothur sub-routines that classified sequences with the RDP database, and QIIME sub-routines for various statistical analyses. After removing cyanobacterial and eukaryotic sequences, OTUs classified by CloVR that accounted for ≥0.1% of the total remaining reads were subjected to manual curation as above. Aligned representative sequences for classified, curated OTUs were then used for a Fast UniFrac analysis (http://bmf2.colorado.edu/fastunifrac/) based on a neighbor-joining tree as input.

The Mothur pipeline [24] was used with more stringent values than the other platforms for sequence trimming (i.e., a moving window of 50 bp with an average quality score of 35; minimum length, 100 bp). The “classify” function of the Mothur pipeline was used to identify sample composition for OTUs representing ≥0.1% of the database after removing cyanobacterial and eukaryotic sequences. The remaining sequences were curated as above. These curated sequences plus the minor OTUs excluding singletons were used to generate diversity indices for the samples independent of taxonomic identifications (e.g., Shannon, inverse Simpson’s and evenness indices).


Results

The pre-processing routines of the three pipelines employed in this study resulted in markedly different sequence numbers for analysis (Table S1). PANGEA yielded the greatest read number (199,592), and Mothur yielded the least (45,626). Sequences most closely related to cyanobacteria and eukaryotes (chloroplasts and mitochondrial 16S rRNA genes) dominated the trimmed data sets (>70%) irrespective of their size (Table S1). These sequences were eliminated from further analyses. Singleton sequences represented from 0.5% (CloVR) to 5.9% (PANGEA) of the data sets after pre-processing; these sequences were also eliminated to minimize impacts of sequencing error. Chimeric sequences were not identified in by PANGEA, but appeared to constitute only a small fraction (<0.2%) of the total sequence set (Table S1) based on results from CloVR and Mothur.

Several patterns appeared consistently. The relative abundance of OTUs as a percentage of the number of sequences analyzed showed that Lake Caillou oyster stomach and gut microbiome compositions differed substantially (Fig. 1a; Tables 1, 2). A small number of Mollicute OTUs dominated the former, while Chloroflexi (mostly Caldilineae), Firmicutes, γ-Proteobacteria and Verrucomicrobia (Spartobacteria) dominated the later. All three pipelines also revealed differences between Hackberry Bay oyster stomach and gut microbiomes (Fig. 1a; Tables 1, 2), but the differences were less pronounced than those for Lake Caillou oysters. Differences among the Hackberry Bay stomach and gut microbiomes resulted primarily from modest changes in multiple lineages (e.g., Chloroflexi, Firmicutes, α-Proteobacteria, δ-Proteobacteria, Planctomycetes and Spartobacteria). In addition, each of the pipelines revealed distinct differences between the microbiomes of the two populations from Hackberry Bay and Lake Caillou. The most notable differences occurred between the two sets of stomach microbiomes, with somewhat less differentiation between the gut microbiomes (Fig. 1a, Tables 1, 2).

In spite of many similarities, PANGEA, CloVR and Mothur output differed in important respects. Relative to CloVR and Mothur, PANGEA identified fewer Proteobacteria, Mollicutes and Verrucomicrobia in Hackberry Bay oyster stomach microbiomes, and fewer Actinobacteria, Chloroflexi, Planctomycetes, and Verrucomicrobia in gut microbiomes. PANGEA also consistently recorded a larger percentage of “unclassified” sequences than did CloVR or Mothur; PANGEA did not identify 60% of the Hackberry Bay oyster stomach sequences beyond the domain level (Tables 1, 2).

Differences were also observed in the taxonomic affiliations of the most abundant OTUs (Table 3). PANGEA, CloVR and Mothur all reported Planctomycetes as one of two equally most abundant OTUs in Hackberry Bay oyster stomach microbiomes, but the specific affiliations within the Planctomycetes differed. The affiliations of the second OTU also differed, including a Firmicute (PANGEA), Spartobacteria (CloVR) and Mollicute (Mothur). In addition, PANGEA reported a sequence related to Mycoplasma mobile as the most abundant OTU for Hackberry Bay oyster gut microbiomes, while the other pipelines reported a Chloroflexi strain (Table 3). In contrast, the three pipelines showed much closer agreement for Lake Caillou samples: all found that an OTU closely related to M. mobile was most abundant in stomach microbiomes, and an OTU closely related to a Shewanella sp. was most abundant in gut microbiomes. The two Shewanella isolates reported, MOLA 59 (PANGEA) and THt8-1 (CloVR and Mothur), were identical over the nucleotide positions analyzed. However, Shewanella sp. THt8-1 and Shewanella sp. MOLA 59 were isolated from terrestrial plant and marine sources, respectively.

Analyses of the composition (phyla and classes) of the 284 classified OTUs (Fig. 1b) revealed patterns that diverged somewhat from those based on relative abundance of phyla and classes among all sequences (Fig. 1a). First, differences between stomach and gut microbiomes within a site and across sites based on OTU composition were less pronounced than those based on frequencies of occurrence (Fig. 1a vs. 1b). This was evident for major (e.g., Chloroflexi, Firmicutes, γ-Proteobacteria, δ-Proteobacteria and Planctomyces) and minor (e.g., Archaea, β-Proteobacteria, and Spartobacteria) contributors to OTU composition (Fig. 1b). Second, the percentage contribution of some phyla and classes to the classified OTUs was substantially overrepresented relative to their abundance in the sequence data set, while other phyla and classes were substantially underrepresented (Fig. 1a, b). Mollicutes were greatly overrepresented in Hackberry Bay and Lake Caillou stomach microbiomes, but underrepresented in gut microbiomes. Chloroflexi and Planctomyces were also overrepresented in Lake Caillou oyster gut and Hackberry Bay oyster stomach and gut microbiomes, while α-, and β-Proteobacteria were underrepresented in all microbiomes (Fig. 1a, b).

An analogous pattern was observed when the phylogenetic composition of all OTUs that occurred in pooled Hackberry Bay and Lake Caillou stomach microbiomes was compared with the composition of OTUs that occurred in or were shared (SHR-S) across both sites. In particular, Chloroflexi, Mollicutes, Planctomyces and Spartobacteria were overrepresented among the SHR-S OTUs (Fig. 2). Similarly, a comparison of OTUs occurring in pooled Hackberry Bay and Lake Caillou gut microbiomes with the shared gut OTUs (SHR-G) showed that Chloroflexi, Firmicutes, α-Proteobacteria, Planctomyces and Verrucomicrobia were overrepresented (Fig. 2). The number of SHR-S OTUs (44) was much smaller than the number of SHR-G OTUs (112), the latter of which accounted for almost 40% of all classified OTUs, and an even larger percentage of those found in the gut microbiomes (Table 4).

OTUs that occurred uniquely in stomach or gut microbiomes of both Hackberry Bay and Lake Caillou oyster populations (SHRU-S, SHRU-G) represented another distinct sub-group. The SHRU-S microbiome was represented by just 5 of the 44 SHR-S OTUs in only 3 phyla/classes, and accounted for only 2.1% of the 284 total OTUs identified in the collective stomach and gut microbiomes (Table 4). In contrast, the SHRU-G microbiome were represented by 44 of the 112 SHR-G OTUs in 12 phyla/classes, and accounted for 15.5% of all identified OTUs (Table 4). The composition of SHR-S and SHRU-S microbiome OTUs differed markedly, while differences between the SHR-G and SHRU-G microbiomes were confined to fewer phyla and classes (Fig. 2).

In addition to variability between stomach and gut phylogenetic composition, the microbiomes varied among the replicate oysters from each site. For some phyla and classes, relative abundances were similar among replicates, and variability (expressed as the standard error of the mean) was similar for each of the three pipelines (see for example Mollicutes and α-Proteobacteria in stomach and gut microbiomes, respectively; Table 1, 2). However, in many cases replicates varied substantially, and the extent of variability differed among pipelines. Mollicutes in the Hackberry Bay gut microbiome, for example, were observed in only 1 of 3 replicates by CloVR and Mothur, and were disproportionately abundant in one replicate according to PANGEA (Table 2).

Variability among replicates was captured by cluster analysis (Fig. 3) and principal components analysis (PCA) of CloVR results using UniFrac distances (Fig. 4a, b), and also by PCA of the relative abundances of classified OTUs (Fig. S1). Results from a cluster analysis using the weighted UniFrac metric showed that the Lake Caillou stomach replicates and Hackberry Bay gut replicates each formed distinct clusters, and that the individual replicates were relatively close in distance. The remaining stomach and gut samples were much less coherent, with replicates resolved at greater distances. Unweighted UniFrac PCA showed that Lake Caillou gut microbiomes clustered together on axis one and two, but that replicates for the other microbiomes were much more dispersed, even though the distinctions between sites and between gut and stomach remained evident (Fig. 4a). Weighted UniFrac PCA, which considered the relative abundances of OTUs, showed that Lake Caillou stomach and Hackberry Bay gut replicates each formed relatively tight clusters on both axes, while replicates for the other microbiomes were dispersed (Fig. 4b). The two stomach microbiomes remained well separated on PCA axis 1, but the gut microbiomes clustered together (Fig. 4b).


Discussion

We present here the first detailed analyses of Crassostrea virginica stomach and gut microbiome compositions. The sample size (triplicate animals for each of two sites) and single sampling time limit extrapolation of the results, but provide a number of new insights. Previous studies have emphasized cultivable members of the gut community, whole animals, pathogens (human and oyster), or specific groups that might contribute to digestion, e.g., Cristispira[5], [8][11], [13], [25]. Cultivation-free approaches have revealed Arcobacter (ε-Proteobacteria) as a major contributor to microbial communities of whole Chilean oysters, Tiostrea chilensis, but whole tissue specific associations have not been reported [17]. Hernádez-Zárate and Olmos-Soto [26] have used group-specific FISH and PCR to identify bacteria in C. gigas tissues, but they did not sequence PCR amplicons or report relative abundances of specific phylogenetic groups. Recently, a PCR and DGGE study of C. virginica has reported spatial and seasonal differences of whole animal microbiomes for two populations from Maine (USA), but phylogenetic composition has not been assessed qualitatively or quantitatively [27], nor have variations among individual animals been described.

Partial 16S rRNA gene sequences derived from high throughput pyrosequencing as used in this study reveal differences in oyster microbiome composition at several levels, although some of the details of composition vary with the pipeline chosen for sequence analysis (e.g., Tables 1, 2). See Supporting Information S1 for additional discussion of these differences, which do not affect the patterns of variation between stomach and gut microbiomes or variations between sites.

Overall, the results show substantial differences between stomach and gut microbiomes, and between the stomach microbiomes of animals from Hackberry Bay and Lake Caillou (e.g., Fig. 1a, b and 2; Tables 1, 2). In addition, the microbiome compositions of individual replicate animals vary (Fig. 3, 4). Variations between stomach and gut microbiomes likely reflect details of the digestive system, but differences between sites and among replicates suggest that microbiome composition might respond to local factors, and perhaps to genetic differences among individuals. Analogous variability has been reported for other animals [28], [29].

Oyster Stomach Microbiome

Based on the frequency of OTU occurrence, the stomach microbiome of oysters from Louisiana can exist in at least two states. Mollicutes most closely related to Mycoplasma overwhelmingly dominate the classified sequences (>80%) of a state represented by Lake Caillou oysters (Fig. 1a; Tables 1, 3). No other class contributes more than about 2%. Planctomycetes dominate (23%–33%) the alternate state- that of Hackberry Bay oysters (Fig. 1a; Tables 1, 3)- but several other groups also occur in the stomachs of these oysters at modest abundances, e.g., Chloroflexi (8%), Firmicutes (9%–11), Mollicutes (5%–9%), Proteobacteria (5%–12%), and Verrucomicrobia (3%–14%). In addition, two similarly abundant OTUs that belong to different phyla (Planctomyces and either Firmicutes, Tenericutes or Verrucomicrobia) dominate Hackberry Bay oyster stomachs at a species level (evolutionary distance = 0.03; Table 3). The proportion of classified OTUs accounted for by various phyla and classes is also consistent with two distinct states for the stomach microbiome (Fig. 2), although the differences are less pronounced for this metric than for frequency-based estimates of composition. UniFrac PCA (weighted and unweighted) and cluster analyses provide additional support for the “two state” concept (Fig. 3, 4).

The physiological and ecological significance of these oyster stomach microbiomes is uncertain. Dominance by Mollicutes or Planctomycetes is somewhat unusual relative to other microbiomes [28], [30][32], although Mollicutes appear abundant in the digestive gland of the Sydney rock oyster (Saccostrea glomerata) and in the intestine of the abalone, Haliotis discus hannai[33], [34]. Mollicutes have also been reported in oyster gut goblet cells based on microscopic evidence [25], and documented for other invertebrates and fish guts by culture-based and molecular ecological methods [30], [35][42]. Otherwise relatively little is known about their associations with invertebrate digestive systems. Indeed, the ecological roles of Mollicutes more generally remain uncertain, with some reports of pathogenesis in selected fish and invertebrates [43][46], but other reports indicating some form of commensalism [30], [39].

Thus far, genomic evidence offers few insights, since the genetic repertoire of Mycoplasma mobile, the taxon most closely related to the oyster OTUs, is limited in its scope [47]. M. mobile congenerics in oyster stomachs might simply proliferate using substrate produced by the host or other microbes during digestion; similar suggestions have been made to account for Mollicute associations with cold-water corals [39]. Nonetheless, the possibility that Mollicutes might contribute symbiotically to their hosts cannot be dismissed.

The role of Planctomyces in digestive systems is also uncertain. Although they are ecologically important members of the marine bacterioplankton, functionally diverse and associated with algae, invertebrates and vertebrates [48], [49], they usually occur at relatively low abundances in gut microbiomes (< about 5%) [30][32]. However, results from this study suggest that unknown conditions in the Hackberry Bay oyster stomach favor Planctomycete proliferation (Table 1).

It is tempting to speculate here, as others have elsewhere [50], that Pirellula-like members of the oyster microbiome exploit sulfated algal polysaccharides for growth, since numerous genes putatively coding for sulfohydrolase enzymes have been observed in the Rhodopirellula baltica genome [51], and since sulfated polysaccharides might be commonly ingested by oysters as a consequence of phytoplankton consumption. The ability to use sulfated polysaccharides would thus provide an explanation for Planctomycete abundance. Unfortunately, the phylogenetic relationships between R. baltica and planctomycete OTUs identified in this study are insufficient to support such inferences. Nonetheless, all Blastopirellula, Pirellula, and Rhodopirellula isolates characterized to date use a wide range of simple non-sulfated sugars [48], [49], [52], at least some of which are likely to occur in the oyster digestive tract as algal biomass is hydrolyzed.

Oyster Gut Microbiome

The oyster gut microbiome harbors a more speciose or OTU-rich community than does the stomach microbiome based on observed species (Sobs) and ACE and Chao1 diversity estimators (Table 5). These indices also indicate that stomachs and guts of Lake Caillou oysters harbor fewer OTUs than Hackberry Bay oysters. Thus, OTU richness varies between oyster tissues (e.g., stomach and gut) as has been well documented for the human microbiome [53], but also appears to vary among populations. The source of variations in richness among oyster populations is unknown.

Variations in richness notwithstanding, the gut microbiome is not necessarily more diverse than the stomach microbiome based on Shannon and inverse Simpson’s indices and the evenness estimator, each of which are similar for the Lake Caillou gut microbiome and the two Hackberry Bay microbiomes (Table 5). This similarity indicates that in some cases the structure of oyster microbiome diversity (richness and evenness) is independent of the digestive system and phylotype composition. In contrast, all diversity indices for the Lake Caillou stomach microbiome are substantially lower than for Hackberry Bay stomachs, and lower than for both gut microbiomes as well. This can be attributed to the dominance in Lake Caillou oyster stomachs of Mollicute OTUs (e.g., Table 5 and Fig. 1a).

The composition of gut microbiomes from Louisiana oysters differs from that of other mollusks and from that of other marine and non-marine animals (Fig. 5). Gruenthal [54] has shown that Proteobacteria dominate (>80%) the gut microbiomes of California black (Haliotis cracherodii) and white abalone (H. sorenseni); Actinobacteria, Chloroflexi, Planctomyces and Verrucomicrobia appear to be absent from both. Huang et al. [41] indicate that Mollicutes and δ-Proteobacteria dominate the intestine of the small abalone. Cardoso et al. [55] report that Bacteroidetes and Firmicutes dominate the gut of the gastropod snail, Achatina fulica. Firmicutes along with Bacteroidetes, Proteobacteria and Actinobacteria dominate the guts of other invertebrates (e.g., soil-feeding termites [56] and cockroaches [57]) and vertebrates (e.g., herbivorous marine fishes [58]; grass carp, [31], [59] and primates [60]), while Mollicutes dominate the guts of some fish [30], [36]. In contrast, Proteobacteria account for only about 20% of the gut composition of the oysters in this study, Chloroflexi, Planctomyces and Verrucomicrobia are each relatively abundant, and Actinobacteria, Bacteroidetes, Firmicutes, and Mollicutes each contribute about 10% or less (Fig. 1a; Table 3).

These differences in composition among gut systems arise from the effects of multiple interacting variables, including gut architecture, digestive physiology, diet, and the extent to which hosts and microbiomes have evolved symbiotically [61][63]. While the effects of some variables, e.g., diet, have clear impacts on some microbiomes [36], [64], [65] the variables that most affect oyster microbiome composition have not been identified. Other than general contributions to heterotrophic metabolism and polymer hydrolysis, the functions of the major members of the oyster gut microbiome are also largely unknown. However, as Mouchet et al. [66] have noted, some functions are likely conserved across microbiomes independent of phylogenetic composition. Thus, the relatively unique composition of the oyster gut microbiome might be functionally similar to the microbiomes of other animal guts.

Putative Core Microbiomes

Stomachs and guts of oysters in Louisiana harbor a diverse community of bacteria. However, much of this diversity might be due to transient populations that arrive with food sources. During passage through the digestive system OTU abundances might change, in part due to digestion [67], but such changes need not produce or reflect a core community. Although the concept of phylogenetically distinct core microbiomes is controversial [63], to explore its applicability to oysters, we have identified OTUs that are shared among all replicate stomach or gut microbiomes (SHR-S, SHR-G). Note that SHR-S OTUs can occur in some or all of the gut microbiomes, and SHR-G OTUs can occur in some or all stomachs. In addition, we have identified OTUs that are shared among all replicate stomachs or guts and occur uniquely in one type of microbiome or the other (SHRU-S, SHRU-G).

A comparison of SHR and SHRU OTUs reveals that the putative core stomach microbiome consists of only a small fraction of all OTUs (about 2%) representing just three phyla (Firmicutes, Planctomycetes and γ-Proteobacteria). A larger fraction of all OTUs (about 16%) occurs in the putative core gut microbiome, which encompassing 12 phyla and classes (Fig. 2). Mollicutes are notably absent from the putative core microbiome based on SHRU OTUs, which reflects the fact that Mollicutes occur in both the stomach and gut.

The differences between core stomach and gut microbiomes suggest that the stomach might support fewer specific symbiotic interactions, while the gut appears suitable for more phylogenetically and presumably physiologically divergent groups, e.g., Chloroflexi, Crenarchaea, Proteobacteria and Spirochaeta (Fig. 2). Greater niche differentiation in the gut than the stomach might reflect a decrease in bacterial digestion by the host and an increase in the availability and diversity of heterotrophic substrates subsequent to the initial processing of phytoplankton cells in the stomach and diverticula.

The richness of the putative core gut microbiome contrasts with the more limited core microbiomes proposed for zebrafish, an herbivorous bird (the hoatzin, Opisthocomus hoazin) and humans [28], [29], [63]. For example, the core zebrafish gut microbiome [28] is comprised of half the number of major phyla and classes that occur in the putative oyster gut microbiome (e.g., 5 versus 10). The core microbiomes of the human gut and hoatzin crop are even more limited, with some arguing that a core gut microbiome for humans might not exist [63]. These observations suggest the possibility that the diversity of core microbiomes might vary systematically among host phyla (e.g. vertebrates and invertebrates), and between terrestrial and aquatic hosts.

Summary and Conclusions

Relatively deep sequencing of Louisiana oyster stomach and gut contents revealed novel microbiomes that differ from those of other mollusks and other invertebrates and vertebrates. Microbiome composition varied at three levels: between stomach and gut, among replicates at a site, and between sites. These results provide a basis for developing future biogeographically informed analyses based on more extensive temporal and spatial sampling, and comparisons among bivalves and gastropods. Roles for some of the more prominent phylotypes observed, including Chloroflexi, Mollicutes, Planctomycetes and Spartobacteria are unknown, but warrant attention, as some of these taxa appear to contribute to a core microbiome that might be conserved within C. virginica and perhaps other shellfish. Additional effort should also be directed towards understanding the roles of environment variables (e.g., temperature salinity, phytoplankton and bacterioplankton regimes) as factors that shape stomach and gut microbiomes.


Supporting Information Figure S1

Principal components analysis of percent composition of each of the replicate Hackberry Bay (HB) and Lake Caillou (LC) stomach (S) and gut microbiomes (G) for sequences derived from the CloVR pipeline; percentages were analyzed using an arcsin transform.

(DOC)


Click here for additional data file (pone.0051475.s001.doc)

Table S1

Composition of trimmed data sets for three sequence processing pipelines using trim variable values as defined in the text. The percentages of cyanobacterial and eukaryotic sequences reflect removal of singletons and chimeras.

(DOC)


Click here for additional data file (pone.0051475.s002.doc)

Supporting information S1

(DOC)


Click here for additional data file (pone.0051475.s003.doc)


References
1. Dame RF,, Spurrier JD,, Zingmark RG, (Year: 1992) In situ metabolism of an oyster reef. J Exp Mar Biol Ecol164: 147–159
2. Dame RF,, Wolaver TG,, Libes SM, (Year: 1985) The summer uptake and release of nitrogen by an intertidal oyster reef. Neth J Sea Res19: 265–268
3. Wetz MS,, Lewitus AJ,, Koepfler ET,, Hayes KC, (Year: 2002) Impact of the Eastern Oyster Crassostrea virginica on microbial community structure in a salt marsh estuary. Aquat Microb Ecol28: 87–97
4. Douillet P,, Langdon CJ, (Year: 1993) Effects of marine bacteria on the culture of axenic oyster Crassostrea gigas (Thunberg) larvae. Biol Bull184: 36–51
5. Boettcher KJ,, Barber BJ,, Singer JT, (Year: 2000) Additional evidence that Juvenile Oyster Disease is caused by a member of the Roseobacter group and colonization of non-affected animals by Stappia stellulata-like strains. Appl Environ Microbiol66: 3924–393010966410
6. DePaola A,, Hopkins LH,, Peller JT, (Year: 1990) Incidence of Vibrio parahaemolyticus in U.S. coastal waters and oysters. Appl Environ Microbiol56: 2299–23022403249
7. Harwood VJ,, Gandhi JP,, Wright A, (Year: 2004) Methods for isolation and confirmation of Vibrio vulnificus from oysters and environmental sources: a review. J Microbiol Methods59: 301–31615488274
8. Johnson CN,, Flowers AR,, Noriea NF,, Zimmerman AM,, Bowers JC,, et al. (Year: 2010) Relationships between environmental factors and pathogenic vibrios in the Northern Gulf of Mexico. Appl Environ Microbiol76: 7076–8420817802
9. Sobrinho PS,, Destro MT,, Franco BD,, Landgraf M, (Year: 2010) Correlation between environmental factors and prevalence of Vibrio parahaemolyticus in oysters harvested in the southern coastal area of Sao Paulo State, Brazil. Appl Environ Microbiol76: 1290–129320023076
10. Mayasich SA,, Smucker RA, (Year: 1987) Role of Cristispira sp. and other bacteria in the chitinase and chitobiase activities of the crystalline style of Crassostrea virginica (Gmelin). Microb Ecol14: 157–166
11. Boettcher KJ,, Geaghan KK,, Maloy AP,, Barber BJ, (Year: 2005) Roseovarius crassostreae sp. nov., a member of the Roseobacter clade and the apparent cause of juvenile oyster disease (JOD) in cultured Eastern oysters. Int J Syst Evol Microbiol55: 1531–153716014477
12. Kueh CSW,, Chan K-Y, (Year: 1985) Bacteria in bivalve shellfish with special reference to the oyster. J Appl Bacteriol59: 41–474030530
13. Pujalte MJ,, Ortigosa M,, Macián MC,, Garay E, (Year: 1999) Aerobic and facultative anaerobic heterotrophic bacteria associated to Mediterranean oysters and seawater. Internatl Microbiol2: 259–266
14. Olafsen JA,, Mikkelsen HV,, Giæver, Hansen GH, (Year: 1993) Indigenous bacteria in hemolymph and tissues of marine bivalves at low temperatures. Appl Environ Microbiol59: 1848–185416348962
15. Faury N,, Saulnier D,, Thompson FL,, Gay M,, Le Roux F, (Year: 2004) Vibrio crassostreae sp. nov., isolated from the haemolymph of oysters (Crassostrea gigas). Int J Syst Evol Microbiol54: 2137–214015545447
16. Kassaify ZG,, El Hajj RH,, Hamadeh SK,, Zurayk R,, Barbour EK, (Year: 2009) Impact of oil spill in the Mediterranean Sea on biodiversified bacteria in oysters. J Coastal Res252: 469–473
17. Romero J,, Garcia-Varela M,, Laclette JP,, Espejo RT, (Year: 2002) Bacterial 16S rRNA gene analysis revealed that bacteria related to Arcobacter spp. constitute an abundant and common component of the oyster microbiota (Tiostrea chilensis). Microb Ecol44: 365–37112399898
18. Galtsoff PS (1964) The American oyster Crassostrea virginica Gmelin. Fishery Bull Fish Wildlife Service, Vol. 64.
19. Wiseman WJ,, Swenson EM,, Power J, (Year: 1990) Salinity trends in Louisiana estuaries. Estuaries13: 265–271
20. Bianchi TS,, Cook RL,, Perdue EM,, Kolic PE,, Green N,, et al. (Year: 2011) Impacts of diverted freshwater on dissolved organic matter and microbial communities in Barataria Bay, Louisiana, U.S.A. Mar Environ Res. 72: 248–257
21. Fierer N,, Hamady M,, Lauber CL,, Knight R, (Year: 2008) The influence of sex, handedness, and washing on the diversity of hand surface bacteria. Proc Natl Acad Sci USA105: 17994–1799919004758
22. Giongo A,, Crabb DB,, Davis-Richardson AG,, Chauliac D,, Mobberley JM,, et al. (Year: 2010) PANGEA: pipeline for analysis of next generation amplicons. ISME J4: 852–86120182525
23. Angiuoli SV,, Matalka M,, Gussman A,, Galens K,, Vangala M,, et al. (Year: 2011) CloVR: A virtual machine for automated and portable sequence analysis from the desktop using cloud computing. BMC Bioinformatics12: 35621878105
24. Schloss PD,, Wescott SL,, Ryabin T,, Hall JR,, Hartmann M,, et al. (Year: 2009) Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol75: 7537–754119801464
25. Harshbarger JC,, Chang SC,, Otto SV, (Year: 1977) Chlamydiae (wth phages), Mycoplasmas, and Rickettsiae in Chesapeake Bay bivalves. Science196: 666–668193184
26. Hernandez-Zarate G,, Olmos-Soto J, (Year: 2006) Identification of bacterial diversity in the oyster Crassostrea gigas by fluorescent in situ hybridization and polymerase chain reaction. J Appl Microbiol100: 664–67216553721
27. La Valley KJ,, Jones S,, Gomez-Chiarri M,, Dealteris J,, Rice M, (Year: 2009) Bacterial community profiling of the Eastern Oyster (Crassostrea virginica): comparison of culture-dependent and culture-independent outcomes. J Shellfish Res28: 827–835
28. Roeselers G, Mittge EK, Stephens WZ, Parichy DM, Cavanaugh CM, et al. . (2011) Evidence for a core gut microbiota in the zebrafish. ISME J 10.1038/ismeJ2011.38.
29. Godoy-Vitorino F,, Ley RE,, Gao Z,, Pei Z,, Ortiz-Zuazaga H,, et al. (Year: 2008) Bacterial community in the crop of the hoatzin, a neotropical folivorous flying bird. Appl Environ Microbiol74: 5905–591218689523
30. Bano N,, DeRae Smith A,, Bennett W,, Vasquez L,, Hollibaugh JT, (Year: 2007) Dominance of Mycoplasma in the guts of the long-jawed mudsucker, Gillichthys mirabilis, from five California salt marshes. Environ Microbiol9: 2636–264117803786
31. Wu S,, Wang G,, Angert ER,, Wang W,, Li W,, et al. (Year: 2012) Composition, diversity, and origin of the bacterial community in grass carp intestine. PloS one7: 10.1371/journal.pone.0030440.g001
32. Godoy-Vitorino F,, Goldfarb KC,, Karaoz U,, Leal S,, Garcia-Amado MA,, et al. (Year: 2012) Comparative analyses of foregut and hindgut bacterial communities in hoatzins and cows. ISME-J6: 531–54121938024
33. Green TJ,, Barnes AC, (Year: 2010) Bacterial diversity of the digestive gland of Sydney rock oysters, Saccostrea glomerata infected with the paramyxean parasite, Marteilia sydneyi. 109: 613–622
34. Tanaka R,, Ootsubo M,, Sawabe T,, Ezura Y,, Tajima K, (Year: 2004) Biodiversity and in situ abundance of gut microflora of abalone (Haliotis discus hannai) determined by culture-independent techniques. Aquaculture241: 453–463
35. Boyle PJ,, Maki JS,, Mitchell R, (Year: 1987) Mollicute identified in novel association with aquatic invertebrate. Curr. Microbiol15: 85–89
36. Holben WE,, Williams P,, Saarinen M,, Särkilahti LK,, Apajalahti JHA, (Year: 2002) Phylogenetic analysis of intestinal microflora indicates a novel Mycoplasma phylotype in farmed and wild salmon. Microb Ecol44: 175–18512082453
37. Moran D,, Turner SJ,, Clements KD, (Year: 2005) Ontogenetic development of the gastrointestinal microbiota in the marine herbivorous fish Kyphosus sydneyanus. Microb Ecol49: 590–59716041474
38. Kostanjsek R,, Strus J,, Avgustin G, (Year: 2007) “Candidatus Bacilloplasma,” a novel lineage of Mollicutes associated with the hindgut wall of the terrestrial isopod Porcellio scaber (Crustacea: Isopoda). Appl Environ Microbiol73: 5566–557317630315
39. Kellogg CA,, Lisle JT,, Galkiewicz JP, (Year: 2009) Culture independent characterization of bacterial communities associated with the cold-water coral Lophelia pertusa in the northern Gulf of Mexico. Appl Environ Microbiol75: 2294–230319233949
40. Nechitaylo TY,, Timmis KN,, Golyshin PN, (Year: 2009) Candidatus Lumbricincola, a novel lineage of uncultured Mollicutes from earthworms of family Lumbricidae. Environ Microbiol11: 1016–102619396950
41. Huang Z-B,, Guo F,, Zhao J,, Li W-D,, Ke C-H, (Year: 2010) Molecular analysis of the intestinal bacterial flora in cage-cultured adult small abalone, Haliotis diversicolor. Aqualture Res. 41: e760–e769 doi:10.1111/j.1365-2109.2010.02577.x.
42. Meziti A,, Ramette A,, Mente E,, Kormas KA, (Year: 2010) Temporal shifts of the Norway lobster (Nephrops norvegicus) gut bacterial communities. FEMS Microbiol Ecol74: 472–48420831590
43. Azevedo C, (Year: 1993) Occurrence of an unusual branchial mycoplasma-like infection in cockle Cerastoderma edule (Mollusca, Bivalvia) Dis aquat Org. 16: 55–59
44. Kirchhoff K,, Beyene P,, Fischer M,, Flossdorf J,, Heitmann J,, et al. (Year: 1987) Mycoplasma mobile sp. nov., a new species from fish. Int. J Syst Bacteriol37: 192–197
45. Krol RM,, Hawkins WE,, Overstreet RM, (Year: 1991) Rickettsial and mollicute infections in hepatopancreatic cells of cultured Pacific white shrimp (Penaeus vannamei). J Invert Path57: 362–370
46. Chen J-G, Lou D, Yang J-F (2011) Isolation and identification of Acholeplasma sp. from the mud crab, Scylla serrata Evidence-based Comp Alt Med doi:10.1155/2011/209406.
47. Jaffe JD,, Stang-Thomann N,, Smith C,, DeCaprio D,, Fisher S,, et al. (Year: 2004) The complete genome and proteome of Mycoplasma mobile. Genome Res14: 1447–146115289470
48. Ward N,, Rainey FA,, Stackebrandt E,, Schlesner H, (Year: 1995) Unravelling the extent of diversity within the order Planctomycetales. Appl Environ Microbiol61: 2270–22757793948
49. Ward N,, Staley JT,, Fuerst JA,, Giovannoni S,, Schlesner H,, et al. (Year: 2006) The Order Planctomycetales, including the genera Planctomyces, Pirellula, Gemmata and Isosphaera and the candidatus genera Brocadia, Kuenenia and Scalindua. Prokaryotes7: 757–793
50. van Kessel MAHJ,, Dutilh BE,, Neveling K,, Kwint MP,, Veltman JA,, et al. (Year: 2011) Pyrosequencing of 16s rRNA gene amplicons to study the microbiota in the gastrointestinal tract of carp. AMB Express 141: 10.1186/2191–0855-1-41
51. Glockner FO,, Kube M,, Bauer M,, Teeling H,, Lombardot T,, et al. (Year: 2003) Complete genome sequence of the marine planctomycete Pirellula sp. strain 1. Proc Natl Acad Sci USA100: 8298–830312835416
52. Schlesner H,, Rensmann C,, Tindall BJ,, Gade D,, Rabus R,, et al. (Year: 2004) Taxonomic heterogeneity within the Planctomycetales as derived by DNA-DNA hybridization, description of Rhodopirellula baltica gen. nov., sp. nov., transfer of Pirellula marina to the genus Blastopirellula gen. nov. as Blastopirellula marina comb. nov. and emended description of the genus Pirellula. Int J Syst Evol Microbiol54: 1567–158015388712
53. Stearns JC, Lynch MDJ, Senadheera DB, Tenenbaum HC, Goldberg MB, et al. . (2011) Bacterial biogeography of the human digestive tract. Scientific reports 1: 170 10.1038/srep00170.
54. Gruenthal KM (2007) Conservation genetics of California abalone species. University of California-San Diego, San Diego. 228 pp.
55. Cardoso AM, Cavalcane JJV, Vieira RP, Lima JL, Grieco MAB, et al. . (2012) Gut bacterial communities in the giant land snail Achatina fulica and their modification by sugarcane-based diet. PloS one 10.1371/journal.pone.0033440.
56. Fall S,, Hamelin J,, Ndiaye F,, Assigbetse K,, Aragno M,, et al. (Year: 2007) Differences between bacterial communities in the gut of a soil-feeding termite (Cubitermes niokoloensis) and its mounds. Appl Environ Microbiol73: 5199–20817574999
57. Schauer C,, Thompson CL,, Brune A, (Year: 2012) The bacterial community in the gut of the cockroach Shelfordella lateralis reflects the close evolutionary relatedness of cockroaches and termites. Appl Environ Microbiol78: 2758–276722327579
58. Clements KD,, Pasch IBY,, Moran D,, Turner SJ, (Year: 2006) Clostridia dominate 16S rRNA gene libraries prepared from the hindgut of temperate marine herbivorous fishes. Mar Biol150: 1431–1440
59. Han S,, Liu Y,, Zhou Z,, He S,, Cao Y,, et al. (Year: 2010) Analysis of bacterial diversity in the intestine of grass carp (Ctenopharyngodon idellus) based on 16S rDNA gene sequences. Aquaculture Res42: 47–56
60. Ochman H,, Worobey M,, Kuo C-H,, Ndjango J-BN,, Peeters M,, et al. (Year: 2010) Evolutionary relationships of wild hominids recapitulated by gut microbial communities. PLoS Biol8(11): e1000546 doi:10.1371/journal.pbio.1000546. 21103409
61. Ley RE,, Peterson DA,, Gordon JI, (Year: 2006) Ecological and evolutionary forces shaping microbial diversity in the human intestine. Cell124: 837–84816497592
62. Bevins CL,, Salzman NH, (Year: 2011) The potter’s wheel: the host’s role in sculpting its microbiota. Cellular Molecular Life Sci68: 3675–3685
63. Karasov WH,, del Rio CM,, Caviedes-Vidal E, (Year: 2011) Ecological physiology of diet and digestive systems. Ann Rev Physiol73: 69–9321314432
64. Turnbaugh PJ,, Ley RE,, Mahowald MA,, Magrini V,, Mardis ER,, et al. (Year: 2006) An obesity-associated gut microbiome with increased capacity for energy harvest. Nature444: 1027–103117183312
65. Wang Y,, Gilbreath TM III,, Kukutla P,, Yan G,, Xu J, (Year: 2011) Dynamic gut microbiome across life history of the malaria mosquito Anopheles gambiae in Kenya. PLoS ONE6(9): e24767 doi:10.1371/journal.pone.0024767. 21957459
66. Mouchet MA,, Bouvier C,, Bouvier T,, Troussellier M,, Escalas A,, et al. (Year: 2012) Genetic difference but functional similarity among fish gut bacterial communities through molecular and biochemical fingerprints. FEMS Microbiol Ecol79: 568–58022092438
67. Crosby MP,, Newell RIE,, Langdon CJ, (Year: 1990) Bacterial mediation in the utilization of carbon and nitrogen from detrital complexes by Crassostrea virginica. Limnol Oceanogr35: 625–639
68. Smriga S,, Sandin SA,, Azam F, (Year: 2010) Abundance, diversity and activity of microbial assemblages associated with coral reef fish guts and feces. FEMS Microbiol Ecol73: 31–4220455942
69. Li K,, Guan W,, Wei G,, Liu B,, Xu J,, et al. (Year: 2007) Phylogenetic analysis of intestinal bacteria in the Chinese mitten crab (Eriocheir sinensis). J Appl Microbiol103: 675–68217714401
70. Li M,, Y Hong,, Gu J-D, (Year: 2009) Phylogenetic diversity and axial distribution of microbes in the intestinal tract of the polychaete Neanthes glandicincta. Microb Ecol58: 892–90219572164

Figures

[Figure ID: pone-0051475-g001]
doi: 10.1371/journal.pone.0051475.g001.
Figure 1  Composition of oyster stomach and gut microbiomes.

A. Phylogenetic composition (phyla and classes) of Hackberry Bay (HB) and Lake Caillou (LC) stomach and gut microbiomes based on frequencies of occurrence within the set of all classified sequences. B. As for A, but composition is based on the unweighted abundance of phyla and classes with the 284 classified OTUs (all results from CloVR analysis).



[Figure ID: pone-0051475-g002]
doi: 10.1371/journal.pone.0051475.g002.
Figure 2  Phylogenetic composition (phyla and classes) of all OTUs found in pooled Hackberry Bay (HB) and Lake Caillou (LC) stomach or gut microbiomes; OTUs found in (shared among) all stomach replicates or all gut replicates for both sites (SHR-S and SHR-G, respectively); OTUs found exclusively in all stomach or gut replicates (SHRU-S and SHRU-G, respectively).

See text for further details.



[Figure ID: pone-0051475-g003]
doi: 10.1371/journal.pone.0051475.g003.
Figure 3  Cluster analysis based on UniFrac distances for sequences derived from the CloVR pipeline for each of the replicate Hackberry Bay (HB) and Lake Caillou (LC) stomach (S) and gut microbiomes (G).

[Figure ID: pone-0051475-g004]
doi: 10.1371/journal.pone.0051475.g004.
Figure 4  Principal components analysis of unweighted (A) and weighted (B) UniFrac distances for sequences derived from the CloVR pipeline for each of the replicate Hackberry Bay (HB) and Lake Caillou (LC) stomach (S) and gut microbiomes (G).

[Figure ID: pone-0051475-g005]
doi: 10.1371/journal.pone.0051475.g005.
Figure 5  Cluster analysis based on unweighted (A) and weighted (B) UniFrac distances obtained for C. virginica microbiomes (HB-S, HB-G, LC-S, LC-G as above; this study) and microbiome compositions derived from 16S rRNA gene clone libraries (accession numbers in parenthesis) of an herbivorous coral reef surgeon fish, Acanthurus nigrificans (gut sequences FJ653927–FJ65392774 [68]); the carnivorous mitten crab, Eriocheir sinensis (gut sequences DQ856498–DQ856562 [69]); a detritivorous/phytophagus polychaete, Neanthes glandicincta (gut sequences FJ618851–FJ618896 [70]); the macroalgae-consumng gastropod small abalone, Haliotis diversicolor (gut sequences GU070680–GU070693 [41]); and the filter-feeding Sydney rock oyster, Saccostrea glomerata (digestive gland sequences FM995169–FM995191 [33]).

All sequences were aligned with the NAST Aligner; a BioNJ tree was used as input for UniFrac. Weighted UniFrac was conducted with normalized sequence abundances. Note that oyster microbiomes form a distinct cluster based on the unweighted analysis (A), while in a weighted analysis (B) all mollusk microbiomes are distinct from crab, fish and polychaete microbiomes; also Lake Caillou oyster stomach microbiomes cluster with the Sydney rock oyster and small abalone microbiomes. Mollicutes dominate all of the latter [Table 1; 33, 41] and account for the observed association.



Tables
[TableWrap ID: pone-0051475-t001] doi: 10.1371/journal.pone.0051475.t001.
Table 1  Stomach microbiome compositions of C. virginica from Hackberry Bay and Lake Caillou, Louisiana determined by three pipelines.
HB-S LC-S
Phylum/Class PANGEA CloVR Mothur PANGEA CloVR Mothur
Actinobacteria 2.65±2.12 1.52±0.25 1.29±0.14 0.15±0.04 0.36±0.18 0.30±0.17
Bacteroidetes 0.09±0.04 0.15±0.08 0.04±0.02 0.07±0.07 0.04±0.04
Total Firmicutes 11.41 8.86 11.37 1.15 1.90 2.09
Bacilli 9.62±6.80 8.02±1.19 9.17±1.81 1.11±0.52 1.84±0.35 1.96±0.45
Clostridia 1.79±1.21 0.84±0.42 2.20±0.92 0.04±0.02 0.06±0.02 0.13±0.07
Mollicutes 5.37±3.05 8.37±7.83 9.11±7.97 79.07±7.47 86.09±4.34 88.12±3.76
Total Proteobacteria 5.11 10.35 11.97 1.91 1.53 1.71
alpha-Proteobacteria 1.79±0.66 5.83±3.44 6.04±4.32 1.30±0.56 0.40±0.14 0.40±0.15
beta-Proteobacteria 1.19±0.67 1.23±0.68 1.03±0.84 0.06±0.03 0.09±0.02 0.05±0.01
delta-Proteobacteria 1.14±0.32 1.81±0.73 2.58±0.35 0.17±0.03 0.28±0.14 0.53±0.22
gamma-Proteobacteria 0.99±0.31 1.48±0.36 2.32±0.44 0.38±0.09 0.76±0.38 0.73±0.35
Total Chloroflexi 8.03 8.02 7.74 0.23 0.68 0.54
Anaerolineae 0.11±0.04 0.01±0.00 0.04±0.02
Caldilineae 0.12±0.10 8.02±1.34 7.74±0.56 0.02±0.02 0.68±0.29 0.50±0.19
Planctomycetes 23.29±18.08 32.74±9.51 29.31±9.27 1.65±0.43 2.07±0.52 1.76±0.42
Total Verrucomicrobia 3.49 12.53 13.56 0.43 1.12 0.97
Spartobacteria 3.15±1.01 12.78±1.70 0.32±0.06 0.87±0.33
Verrucomicrobiae 0.33±0.11 12.53±1.37 0.65±0.34 0.11±0.03 1.12±0.45 0.06±0.03
Crenarchaea 0.15±0.14 0.13±0.13 0.02±0.02 0.02±0.02
Euryarchaea 0.21±0.11 0.39±0.00 0.08±0.04
Deinococci 0.11±0.03 0.52±0.20 0.65±0.34 0.03±0.02 0.04±0.02 0.09±0.07
Fusobacteria 0.23±0.23
Unclassified 39.65±10.54 16.58±2.18 14.49±3.97 15.30±7.70 5.97±2.53 4.25±2.07
Total 99.58 99.85 99.87 99.96 99.92 99.84

Values for each class or phylum are percentages of the total non-eukaryotic sequences for triplicate samples with standard errors for taxa represented at more than 0.1% in at least one pipeline. These taxa account for >99.5% of all bacterial sequences. Rare occurrences were noted for Chlamydiae, Chlorobi, Dictoglomia, Lentisphaerae, Spirochaeta, Synergistes and Thermomicrobia. Bold indicates that comparisons of HB and LC for the CloVR pipeline are statistically different at p<0.05.


[TableWrap ID: pone-0051475-t002] doi: 10.1371/journal.pone.0051475.t002.
Table 2  Hackberry Bay and Lake Caillou oyster gut microbiome compositions and values as in Table 1.
HB-G LC-G
Phylum/Class PANGEA CloVR Mothur PANGEA CloVR Mothur
Acidobacteria 0.03±0.02 0.32±0.08 0.21±0.03 0.02±0.01 0.40±0.10# 0.06±0.03
Actinobacteria 1.00±0.69 2.32±0.68 2.27±0.46 2.51±0.55 2.96±0.57# 3.37±0.81
Bacteroidetes 0.29±0.13 0.20±0.10 0.60±0.04 0.62±0.51 0.69±0.47 1.01±0.71
Chlamydiae 0.07±0.03 0.19±0.15 0.19±0.02 0.13±0.03# 0.08±0.08
Ktedonobacteria 0.12±0.06
Lentisphaerae 0.06±0.06 0.36±0.09
Spirochaeta 0.20±0.20 0.38±0.27 0.66±0.50 1.17±0.76
Synergistes 0.93±0.79
Total Firmicutes* 3.16 3.17 7.11 4.78 6.40 9.93
Bacilli 1.82±1.48 1.74±1.11# 1.71±0.28 4.25±2.64 6.09±4.04 7.51±0.45
Clostridia 1.34±0.65 1.43±0.18 5.40±0.17 0.53±0.06 0.31±0.08# 0.86±0.09
Mollicutes 5.04±4.80 0.03±0.03 0.07±0.07 1.48±0.43 1.57±0.44# 0.75±0.44
Total Proteobacteria 11.26 14.44 17.86 21.12 23.57 19.10
alpha-Proteobacteria 3.57±0.88 5.36±0.58 4.99±0.70 2.64±0.49 2.86±0.22# 3.74±0.09
beta-Proteobacteria 0.23±0.07 0.84±0.04 0.71±0.15 0.73±0.23 0.86±0.20# 0.60±0.16
delta-Proteobacteria 2.74±0.51 6.45±0.57# 8.52±0.64 2.46±0.33 1.55±0.63 2.95±0.82
gamma-Proteobacteria 4.60±3.01 1.79±0.33 3.64±0.77 15.20±9.36 17.97±11.03 11.41±6.43
epsilon-Proteobacteria 0.02±0.02 0.09±0.07 0.33±0.17 0.40±0.21
Total Chloroflexi* 5.90 22.13 23.96 6.68 9.79 10.92
Anaerolineae 0.06±0.02 0.38±0.15 0.57±0.29 0.60±0.16
Caldilineae 0.06±0.03 22.13±1.90# 23.58±1.77 0.22±0.13 9.79±1.81# 10.26±1.94
Planctomycetes 7.19±2.28 22.48±0.29 21.39±0.83 15.42±1.76 19.98±2.35# 22.77±2.67
Total Verrucomicrobia 5.55 9.31 10.80 7.22 9.47 10.96
Spartobacteria 4.93±1.35 9.34±1.37 5.98±1.23 8.93±1.58
Verrucomicrobiae 0.62±0.17 9.31±0.82 0.59±0.34 1.24±0.12 9.47±1.98# 0.80±0.40
Crenarchaea 0.01±0.01 0.44±0.12 0.05±0.04 0.08±0.08
Euryarchaea 0.03±0.03 0.20±0.20 0.14±0.05 0.21±0.02
Deinococci 0.05±0.05 0.03±0.03 0.09±0.06 0.11±0.07 0.21±0.16
Fusobacteria 0.02±0.01 0.16±0.12 0.20±0.20 0.29±0.11 0.30±0.09# 0.31±0.17
Unclassified 60.43±3.34 25.18±1.21# 14.69±0.12 38.89±8.07 23.82±6.37 17.52±2.42
Total 100.00 99.99 99.99 99.64 99.99 99.86

Taxa shown account for >99.6% of all bacterial sequences. Rare occurrences were noted for Chlorobi, Dictoglomia, Desferribacters, Nitrospirae, Thermobaculum, TM7 and WS3. Bold indicates statistical significance as in Table 1. Asterisks indicate that totals include unclassified members of a phylum. A superscript # indicates a statistically significant difference (p<0.05) between stomach and gut compositions using the CloVR pipeline.


[TableWrap ID: pone-0051475-t003] doi: 10.1371/journal.pone.0051475.t003.
Table 3  Taxonomic affiliation of the most abundant OTUs (evolutionary distance = 0.03) in Crassostrea virginica stomach (S) and gut (G) microbiomes for Hackberry Bay (HB) and Lake Caillou (LC) as determined by three different sequence analysis pipelines.
Group PANGEA CloVR Mothur
HBS Pirellula sp. Schlesner 139 (96%) Planctomycete MS1316 (93%) Planctomycete str. 116 (90%)
Falklamia sp. H119 (89%) Bacterium Ellin507 (92%) Mycoplasma mobile (92%)
HBG M. mobile (93%) Chloroflexi str. ET-1 (93%) Chloroflexi str. ET-1 (93%)
LCS M. mobile (93%) M. mobile (93%) M. mobile (93%)
LCG Shewanella sp. MOLA 59 (99%) Shewanella sp. THt8-1 (99%) Shewanella sp. THt8-1 (99%)

Bacterium Ellin507 belongs to the Spartobacteria and Falklamia sp. H119 to the Firmicutes (Bacilli).


[TableWrap ID: pone-0051475-t004] doi: 10.1371/journal.pone.0051475.t004.
Table 4  Numbers of OTUs observed for Hackberry Bay (HB) and Lake Caillou (LC) stomach and gut (S, G) microbiomes, and numbers of OTUs shared among samples based on sequences classified through the Mothur pipeline.
HBS HBG LCS LCG
OTU total 138 243 172 304
Classified OTUs 95 166 121 214
% Unclassified 31.2 31.7 29.7 29.6
HBS-HBG LCS-LCG HBS-LCS HBG-LCG
Total Shared 64 95 44 112
% of Total Classifieda 22.5 33.5 15.5 39.4

aTotal classified refers to the sum for all gut and stomach samples (284).

Among all samples, 401 bacterial OTUs were observed at an evolutionary distance of 0.05–0.10, of which 284 were classified to at least the level of class. Values for HBS-HBG, LCS-LCG, HBS-LCS, and HBG-LCG represent classified OTUs shared between HB stomach and gut microbiomes, Lake Caillou stomach and gut microbiomes, HB and LC stomach microbiomes, and HB and LC gut microbiomes, respectively.


[TableWrap ID: pone-0051475-t005] doi: 10.1371/journal.pone.0051475.t005.
Table 5  Diversity indices for Hackberry Bay and Lake Caillou stomach and gut microbiomes at an evolutionary distance D = 0.03.
Hackberry Bay Lake Caillou
Variable Stomach Gut Stomach Gut
Sobs 79±10 121±20 18±5 98±18
Ace 148±27 442±140 36±14 252±87
Chao1 232±41 948±315 54±27 411±168
Shannon 3.631±0.222 4.052±0.255 1.269±0.240 3.957±0.268
1/D 24.91±7.92 29.40±7.77 2.32±0.62 35.56±16.47
Evenness 0.40±0.08 0.34±0.05 0.16±0.04 0.49±0.13
Coverage 0.83±0.02 0.68±0.04 0.96±0.01 0.74±0.05

Values are means and standard errors derived from triplicates for each sample, based on analyses by Mothur (excluding singleton and eukaryotic sequences).



Article Categories:
  • Research Article
Article Categories:
  • Biology
    • Ecology
      • Biodiversity
      • Biogeography
      • Coastal Ecology
      • Microbial Ecology
    • Genomics
      • Metagenomics
    • Microbiology
      • Microbial Ecology
    • Population Biology
      • Population Ecology
    • Zoology
      • Malacology


Previous Document:  Creativity in the wild: improving creative reasoning through immersion in natural settings.
Next Document:  Dynamics of tumor hypoxia in response to patupilone and ionizing radiation.