Supplementary Materials for “Coral and macroalgal exudates vary in neutral sugar
composition and differentially enrich bacterioplankton populations in a tropical reef
ecosystem” by Craig E. Nelson, Stuart J. Goldberg, Linda Wegley Kelly, Andreas F. Haas,
Jennifer E. Smith, Forest Rohwer, and Craig A. Carlson
Supplementary Methods:
Compositional analysis of dissolved combined neutral sugars – The concentrations of DCNS
were determined using established methods (Goldberg et al. 2009, 2010). Surface reference
waters (SRW; collected in the Santa Barbara Channel) and Nanopure® blanks were analyzed in
triplicate, and unknown samples were analyzed in duplicate (see Goldberg et al. 2009 for details
about the use of SRW). Four mL aliquots of Nanopure® water blanks and unknown samples
were transferred into individual 5 mL pre-combusted (450 °C for ~4 hrs) glass ampoules
(Wheaton). All samples were acidified with H2SO4 (Cf=0.85 M) and hydrolyzed for 24 h at 100
°C. At the completion of hydrolysis, the samples were cooled to room temperature and
neutralized with CaCO3 in clean polycarbonate (PC) tubes. These PC tubes were vortexed
vigorously prior to a 30-minute centrifugation at 28,760 x g. The sample supernatants were
collected and then transferred into combusted glass 20 mL scintillation vials. Neutralized
samples were desalted using an equal mixture of anion (AG 2X-8, 20-50 mesh; Bio-Rad) and
cation (AG 50 WX-8, 100-200 mesh; Bio-Rad) exchange resin. The DCNS were analyzed by
high performance liquid chromatography and pulsed amperometric detection (HPLC-PAD).
Chromatographic conditions for the separation of monosaccharides followed the procedures of
Goldberg et al., (2009) after slight modification. After the time period of peak integration (20
minutes at 12 mM NaOH), there were 10 min and 30 min washes with 200 mM 12 mM NaOH,
respectively. Fucose, rhamnose, arabinose, galactose, glucose, and mannose + xylose (co-elute
due to column chemical characteristics) were separated using a CarboPac PA-10 analytical and
guard columns (Dionex; Sunnyvale, CA). Within any given sequence of samples, a known
monostandard mix of 6 monosaccharides (Dionex; Sunnyvale, CA) was analyzed after every 6th
sample to monitor the performance variability of the working electrode, reference electrode, and
the CarboPac columns. A quantification standard consisting of a mix of the 7 target
monosaccharides was used, and was also hydrolyzed and desalted. High (3000, 2000, 1000, and
600 nM) and low (500, 250, 100, 50, and 20 nM) concentration standard curves were analyzed 2
to 3 times during each sequence. The recovery of individual monosaccarides after desalting and
acid hydrolysis was 70-90% and ~55-60%, respectively. The coefficient of variability for the
concentrations of DCNS and individual monosaccharides within SRW were comparable to
historical values (see Goldberg et al., 2009). The coefficients of variability for the concentrations
of DCNS within SRW ranged between 3 and 5% while those for fucose, rhamnose, arabinose,
galactose, glucose, and mannose + xylose were ~6%, 15%, 16%, 3%, 5%, and 4%, respectively.
DNA extraction, amplification, and pyrosequencing - Frozen Sterivex (n=3 replicates from each
treatment, n=4 controls, and n=6 ambient samples) were thawed 30 minutes at 37°C and lysis
proceeded by 12 h 55°C digestion with sodium dodecyl sulfate (1%) and Proteinase K (0.2 g L-1)
and genomic DNA extraction from 15% of the total lysate using silica microcentrifuge columns
(Qiagen DNEasy – Tissue protocol). The polymerase chain reaction (PCR) was used to construct
multiplex amplicon pyrosequencing libraries of the 16S ribosomal subunit gene using primers 8f
and 338r as described previously (Nelson and Carlson 2012). PCR reaction conditions were 270s
94°C hotstart, 35 cycles of 30s 94°C, 45s 57°C, 90s 72°C, 600s 72°C extension and 4°C storage.
PCR products were pooled at equimolar quantities and pyrosequenced on a Roche/454 GS FLX
using Titanium Chemistry (laboratory of Stefan Schuster, Pennsylvania State University).
Phylogenetic community analysis – Amplicons of community 16S rRNA gene sequences were
dereplicated, aligned, clustered into operational taxonomic units (OTUs), and analyzed for
diversity and phylogenetic similarity among samples as within the software environment
MOTHUR v20 (Schloss et al. 2009) as previously described (Nelson and Carlson 2012). Median
read lengths were ~350bp and most reads spanned the full amplicon. An average of 607 quality
sequences were acquired from each sample (range 154-1439). A distance matrix was built and
sequences were average-neighbor hierarchically clustered into 95% sequence identity OTUs
(95% similary in the V1-V2 amplicon region is estimated to roughly approximate 97% similarity
over the entire 16S subunit gene; Schloss 2010). Each OTU was consensus-classified (70%
minimum confidence) and a consensus sequence for each OTU was built to represent 95% of all
sequences in the OTU cluster. Rare OTUs with less than 5 sequences among the 22 samples
were removed from further analysis.
Weighted Unifrac analysis (Lozupone and Knight 2005) was used to calculate
community similarity among samples. A maximum-likelihood phylogenetic tree was constructed
from 180 consensus sequences (one representative of each OTU) using a gamma-distributed
generalized time-reversible nucleotide evolution model with 100X rapid-bootstrapping (RAxML;
Stamatakis 2006). These final weighted Unifrac distance matrices were verified to have strong
correlation (rMantel > 0.85, p < 0.01) with distance matrices derived from alternate analytical
frameworks: 1) Unifrac matrices generated either unweighted (147,230 sequences), weighted by
unique sequences (31,539 sequences) or sequences clustered at the 95% OTU level (3,138
sequences) or from 2) Bray-Curtis distances among samples calculated from relative abundances
of either OTUs or clade- and family-level consensus-classified phylotypes. Moreover, these
correlations persisted under either SINA or MOTHUR alignment algorithms and using either
relaxed neighbor-joining or maximum likelihood tree-building programs (Clearcut or RaxML),
emphasizing that dominant patterns in community differentiation persisted regardless of
community distance metric (Unifrac vs. Bray-Curtis), phylogenetic resolution (sequence
aggregation at unique, 95%, or classification-based phylotyping levels), alignment algorithm
(SINA or MOTHUR), or phylogeny algorithm (Clearcut or RAxML).
In order to more accurately classify sequences according to phylogenetic position (rather
than assigning phylotypes solely based on the consensus SILVA Bayesian taxonomic
classification described above) we built a maximum likelihood tree (as above) scaffolded with
the nearest full-length 16S SINA-aligned neighbors (172) to each OTU consensus sequence
(180) downloaded from the SILVA v106 SSU Ref database. Using the consensus taxonomy as a
guide, clades were identified within the tree according to high bootstrap confidence areas (>0.7)
and visual assessment of tree structure, branch length, and consistency of Bayesian consensus
classification described above. Clades were named according to the lowest common identifier in
consensus classifications of OTUs and scaffold reference sequences. All clade-level phylotype
analyses were conducted by summing relative abundances of common OTUs grouped manually
according to clades in this scaffolded maximum-likelihood phylogeny.
Supplementary Results:
Bacterial populations selected against in exudate treatments - Of the 27 OTUs with significant
ANOVA’s indicating differences in relative abundance among treatments, a suite of 6 OTUs
belonging to families commonly abundant in oligotrophic oceanic waters showed steady and
significant declines from the ambient to the control to the various exudate treatments, including
Chloroplasts (OTU85, removed from analysis), Synechococcus (OTU22) and four abundant
OTUs of common marine oligotrophs belonging to the clades SAR11 (OTU9 and OTU43),
SAR116 (OTU15), and the NS9 clade of Flavobacteria (OTU38; Table 5). Notably, two OTUs
which were among the most common in ambient waters remained relatively abundant in all of
the dilution cultures, one belonging to the Alteromonadaceae NOR5 clade (OTU5; 1.95% in
ambient and 1.17 to 3.72% in cultures) and another belonging to the Flavobacterial NS5 clade
(OTU7; 2.15% in ambient and 3.47-8.22% in cultures, with the exception of the Porites culture –
0.10%). All of the incubation cultures, including the controls, were dominated (20.2% to 33.5%)
by a single OTU belonging to the Alphaproteobacterial family Rhodobacteraceae (OTU1, 96.7%
identity to Phaeobacter) which was also common in ambient waters but at significantly lower
relative abundance (8.0%). In addition, there were seven less abundant taxa that were
significantly enriched in multiple incubation bottles (including the control) relative to the
ambient waters (Supplementary Table S7; none of these taxa showed statistically significant
differences among treatments).
Diversity patterns in bacterioplankton communities – Diversity indices were calculated from
OTU abundance data (Supplementary Table S8) using MOTHUR to calculate Shannon diversity
index, Simpson evenness index, Chao1 nonparametric richness estimates, and to implement the
mixed-model parametric richness estimation routine CatchAll (Bunge 2011; McCliment et al.
2011). Treatments did not differ significantly in mean sequence reads acquired per sample
(ANOVA p = 0.17, Supplementary Figure S9). However, although reads per sample did not
covary with Shannon or Simpson diversity indices (p > 0.05), both observed and estimated
richness of OTUs covaried strongly with reads per sample (p < 0.001); we therefore included
reads per sample as a random-effect variable in ANOVA models comparing Richness among
treatments. The number of OTUs observed differed significantly among treatments (p = 0.011),
with highest counts in the Porites exudate treatment and ambient water and significantly lower
counts in the Amansia treatment (Tukey α = 0.05). Richness was also significantly different
among treatments using either nonparametric (Chao1) or parametric (CatchAll) estimators
(ANOVA p = 0.033 and 0.038, respectively). The Chao1 estimator only found a significantly
higher richness in the Ambient water (p = 0.011) while the CatchAll estimator only found a
significantly higher richness in the Porites amendment treatment (p = 0.015), and all other
treatments did not differ significantly (p > 0.05). Shannon and Simpsons indices of diversity
differed strongly among treatments (p < 0.001), with both showing lower diversity and evenness
in the Amansia treatment relative to all other treatments (Tukey α = 0.05). We found no
significant differences in diversity metrics among control, Turbinaria, and Halimeda treatments
(Supplementary Figure S9).
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Goldberg, S. J., C. A. Carlson, D. A. Hansell, N. B. Nelson, and D. A. Siegel. 2009. Temporal
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Supplementary Figure and Table Legends:
Supplementary Figure S1. Example photographs of the four benthic primary producers
found in lagoonal reefs on the north shore of Moorea, French Polynesia used to produce
exudates in this study. Species are as follows: a)Turbinaria ornata – Ochrophyta; b) Halimeda
opuntia – Chlorophyta; c) Amansia rhodantha – Rhodophyta; d) Porites lobata - Cnidaria.
Photos a-c sourced from AlgaeBase: Guiry, M.D. & Guiry, G.M. 2011.
http://www.algaebase.org, National University of Ireland, Galway; photo d sourced from MCRLTER at http://mcr.lternet.edu/education with respective copyrights: a) Eric
Coppejans(eric.coppejans@ugent.be); b) John Huisman (j.huisman@murdoch.edu.au); c)
Heroen Verbruggen (heroen.verbruggen@gmail.com); d) Matthew Meier
(matt@matthewmeierphoto.com).
Supplementary Figure S2. Replicate bacterioplankton growth curves over time in each
treatment.
Supplementary Figures S3. Complete phylogeney of all OTUs analyzed in this study and
nearest neighbors. Bars at right of each OTU give relative abundance of each taxa in each
treatment.
Supplementary Table S4. Fifteen most common OTUs in each treatment, with mean
relative abundances. A set of six OTUs which were common to most treatments are colorcoded for ease of visualization of shifts in dominance from Ambient Waters to experimental
treatments.
Supplementary Table S5. OTU mean relative abundances among treatments with ANOVA
and Dunnet’s tests for significant differences among treatments. ANOVA p-values in bold
italic are significant after multiple comparison corrections. Mean relative abundances
significantly different from Control treatments (Dunnet’s p < 0.05) are underlined; those in bold
are higher while those in italics are lower than Control cultures. *Note that for OTUs statistical
tests did not include Ambient comparisons.
Supplementary Figure S6. Maximum-likelihood phylogeny of selected OTUs differing
significantly among treatments and their nearest cultured isolate neighbors.
Supplementary Table S7. Eight taxa which were enriched in all dilution culture
incubations (exudate-amendments and controls) relative to ambient waters.
Supplementary Table S8. Sequence reads, observed OTUs, diversity indices, and richness
estimates for each sample. Multiplex barcodes are listed to associate each sequence set with the
SRA Accession. *Simpson Diversity here is calculated as 1-D.
Supplementary Figure S9: Mean OTU diversity indices among treatments. Whiskers
represent one standard error of the mean. Data are listed in Table S8.