Changes in dimethylsulfoniopropionate demethylase gene assemblages in response to an induced phytoplankton bloom.

Abstract

Over half of the bacterioplankton cells in ocean surface waters are capable of carrying out a demethylation of the phytoplankton metabolite dimethylsulfoniopropionate (DMSP) that routes the sulfur moiety away from the climatically active gas dimethylsulfide (DMS). In this study, we tracked changes in dmdA, the gene responsible for DMSP demethylation, over the course of an induced phytoplankton bloom in Gulf of Mexico seawater microcosms. Analysis of >91,000 amplicon sequences indicated 578 different dmdA sequence clusters at a conservative clustering criterion of ≥90% nucleotide sequence identity over the 6-day study. The representation of the major clades of dmdA, several of which are linked to specific taxa through genomes of cultured marine bacterioplankton, remained fairly constant. However, the representation of clusters within these major clades shifted significantly in response to the bloom, including two Roseobacter-like clusters and a SAR11-like cluster, and the best correlate with shifts of the dominant dmdA clades was chlorophyll a concentration. Concurrent 16S rRNA amplification and sequencing indicated the presence of Roseobacter, SAR11, OM60, and marine Rhodospirillales populations, all of which are known to harbor dmdA genes, although the largest taxonomic change was an increase in Flavobacteriaceae, a group not yet demonstrated to have DMSP-demethylating capabilities. Sequence heterogeneity in dmdA and other functional gene populations is becoming increasingly evident with the advent of high-throughput sequencing technologies, and understanding the ecological implications of this heterogeneity is a major challenge for marine microbial ecology.

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APPLIED AND ENVIRONMENTAL MICROBIOLOGY, Jan. 2011, p. 524–531
0099-2240/11/$12.00doi:10.1128/AEM.01457-10
Copyright © 2011, American Society for Microbiology. All Rights Reserved.
Vol. 77, No. 2
Changes in Dimethylsulfoniopropionate Demethylase Gene
Assemblages in Response to an Induced
Phytoplankton Bloom?
Erinn C. Howard,1‡ Shulei Sun,1§ Christopher R. Reisch,2Daniela A. del Valle,3†
Helmut Bu ¨rgmann,4Ronald P. Kiene,3and Mary Ann Moran1*
Department of Marine Sciences1and Department of Microbiology,2University of Georgia, Athens, Georgia 30602; Department of
Marine Sciences, University of South Alabama, Mobile, Alabama 366883; and Eawag, Swiss Federal Institute of
Aquatic Science and Technology, Department of Surface Waters—Research and Management,
CH-6047 Kastanienbaum, Switzerland4
Received 18 June 2010/Accepted 12 November 2010
Over half of the bacterioplankton cells in ocean surface waters are capable of carrying out a demethylation
of the phytoplankton metabolite dimethylsulfoniopropionate (DMSP) that routes the sulfur moiety away from
the climatically active gas dimethylsulfide (DMS). In this study, we tracked changes in dmdA, the gene
responsible for DMSP demethylation, over the course of an induced phytoplankton bloom in Gulf of Mexico
seawater microcosms. Analysis of >91,000 amplicon sequences indicated 578 different dmdA sequence clusters
at a conservative clustering criterion of >90% nucleotide sequence identity over the 6-day study. The repre-
sentation of the major clades of dmdA, several of which are linked to specific taxa through genomes of cultured
marine bacterioplankton, remained fairly constant. However, the representation of clusters within these major
clades shifted significantly in response to the bloom, including two Roseobacter-like clusters and a SAR11-like
cluster, and the best correlate with shifts of the dominant dmdA clades was chlorophyll a concentration.
Concurrent 16S rRNA amplification and sequencing indicated the presence of Roseobacter, SAR11, OM60, and
marine Rhodospirillales populations, all of which are known to harbor dmdA genes, although the largest
taxonomic change was an increase in Flavobacteriaceae, a group not yet demonstrated to have DMSP-de-
methylating capabilities. Sequence heterogeneity in dmdA and other functional gene populations is becoming
increasingly evident with the advent of high-throughput sequencing technologies, and understanding the
ecological implications of this heterogeneity is a major challenge for marine microbial ecology.
Dimethylsulfoniopropionate (DMSP) is a ubiquitous phyto-
plankton metabolite that is degraded by marine microorgan-
isms by at least two major pathways. The cleavage pathway
involves degradation of DMSP by phytoplankton or bacteria to
produce dimethylsulfide (DMS). DMS is the largest natural
source of sulfur from the ocean to the atmosphere (2) and,
upon oxidation, forms cloud condensation nuclei, hypothesized
to affect climate on a global scale (1, 4, 19, 20). The alternative
DMSP degradation pathway is carried out by bacteria alone
and involves an initial demethylation to methylmercaptopro-
pionate (MMPA), with some portion of the sulfur subse-
quently shunted to sulfur-containing amino acids (12, 14, 26).
The genes involved in the initial step of both DMSP degra-
dation pathways have recently been discovered (7, 9, 31).
Three different genes that each encode the first step of the
DMSP cleavage pathway (dddD, dddL, and dddP) have been
identified (7, 31, 32), although they are in low abundance in
bacteria from surface ocean waters; an estimated 0.1% (10),
3.0% (10), and 3.2% (31) of cells contain these genes, respec-
tively. Conversely, the only gene found thus far that encodes
the first step in the DMSP demethylation pathway (dmdA) is
highly abundant in surface waters; an estimated 58% of cells
contain this gene (9, 10).
Five clades of DmdA, designated A through E, have been
found in cultured marine bacteria or marine metagenomic
sequences thus far. Clade A sequences include genes from
various marine Roseobacter and Rhodospirillales species. Clade
B dmdA sequences are represented by the only sequenced
marine SAR116 group member, “Candidatus Puniceispirillum
marinum” (22). Clade C sequences include a dmdA gene from
SAR11 isolate Pelagibacter ubique HTCC7211 (34). Clade D
sequences include genes from SAR11 isolates P. ubique strains
HTCC1002 and HTCC1062, as well as a second homolog in P.
ubique HTCC7211. Clade E sequences include a gene from
marine gammaproteobacterium HTCC2080 in the OM60
clade (9, 10, 34). Each of these recognized clades has been
further grouped into up to four distinct subclades that contain
clusters of closely related sequences, including A1 to A3, B1 to
B4, C1 and C2, D1 to D3, and E1 and E2 (34).
Across many open and coastal ocean surface water habitats,
the distribution of genes across dmdA clades is surprisingly
consistent (10). Generally, clade D sequences are found in the
* Corresponding author. Mailing address: Department of Marine
Sciences, University of Georgia, Athens, GA 30602. Phone: (706)
542-6481. Fax: (706) 542-5888. E-mail: mmoran@uga.edu.
‡ Present address: U.S. Naval Research Laboratory, 4555 Overlook
Ave. SW, Washington, DC 20375.
§ Present address: Center for Research in Biological Systems, Uni-
versity of California San Diego, 9500 Gilman Drive #0446, La Jolla,
CA 92093.
† Present address: School of Ocean and Earth Science and Tech-
nology (SOEST) and Center for Microbial Oceanography: Re-
search and Education (C-MORE), University of Hawaii, Honolulu,
HI 96822.
?Published ahead of print on 19 November 2010.
524

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highest abundance (?40% of cells) and clade E sequences are
found in the lowest abundance (?0.6% of cells) (9, 10). These
clade and subclade distributions, however, have not been stud-
ied over time in a single environment, particularly as ecological
conditions shift. Because phytoplankton blooms typically cre-
ate substantial changes in DMSP concentration and flux (8, 33,
35, 39), and these might favor certain DmdA proteins or the
organisms that carry them, this study tracked the composition
and diversity of the dmdA gene pool in an experimental phy-
toplankton bloom induced by nutrient additions to Gulf of
Mexico seawater. Relative changes in dmdA clade abundance
were tracked by deep sequencing of amplicons using a univer-
sal dmdA primer set and interpreted in the context of simul-
taneous changes in DMSP concentration and fate, bacterial
production, chlorophyll a (Chl a) concentration, and bacterial
and archaeal community composition.
MATERIALS AND METHODS
Sample collection. Seawater was collected from oligotrophic surface waters
(?1 m deep) in the Gulf of Mexico (30° [03.041], 87° [59.708]; 27°C, salinity ?
34) in October 2006. Water was filtered through a 200-?m mesh into six 20-liter
extensively preleached Cubitainers (Fold-A-Carrier; Reliance Products, Ltd.)
leaving negligible headspace to minimize volatile organic sulfur partitioning.
Three experimental Cubitainer microcosms were amended with sodium nitrate
(10 ?M) and potassium phosphate (0.6 ?M) to induce bloom conditions; three
control microcosms received no nutrient amendments. The microcosms were
incubated at 27°C with a 12-h-on/12-h-off light cycle (200 ?mol quanta m?2s?1)
for the duration of the experiment.
Chemical analysis and bacterial activity measurements. At the initial time
point (T ? 0), the full contents of one control and one experimental microcosm
(20 liters each) were sacrificed for sampling. Thereafter, samples were collected
daily from each of the other four microcosms (two control and two experimental)
over the next 6 days. The Cubitainers collapsed as water volume was removed,
resulting in relatively constant headspace volume throughout the experiment.
For chemical and activity measurements, ?500 ml of water was collected and
immediately subdivided as follows. Chlorophyll a (Chl a) samples were collected
by filtration (50 ml) on Whatman GF/F filters. The filters were extracted in 90%
acetone for 24 h at ?20°C, and Chl a in the extracts was quantified by fluorom-
etry (23). Samples for dissolved DMSP (DMSPd) were collected by small-volume
drip filtration through GF/F filters (15). Total DMSP (dissolved plus particulate)
was collected by acidifying whole seawater with 5 ?l ml?1of 50% H2SO4, similar
to the method described by Curran et al. (6). Dissolved and total DMSP were
quantified as DMS after alkaline hydrolysis. Bacterial production was measured
by [3H]leucine incorporation using the method developed by Kirchman et al. (17)
and modified by Smith and Azam (28); live and killed (5% trichloroacetic acid
[TCA]) controls were incubated with [3H]leucine (20 nM final concentration) for
1 h in the dark. [35S]DMSP was synthesized from L-[35S]methionine by enzymatic
decarboxylation/deamination with L-amino acid oxidase and subsequent methyl-
ation of 3-methiolpropionate with acidic methanol. Purification of the
[35S]DMSP was by high-performance liquid chromatography (HPLC) and ion
exchange using the procedures outlined in the work of Kiene et al. (13). Rate
constants for DMSPd consumption were obtained by measuring the loss of tracer
[35S]DMSP from the dissolved pool over time. At each time point, subsamples of
seawater were filtered though 0.2-?m nylon membranes and the filtrate was
immediately acidified for preservation. [35S]DMSP remaining in the filtrate was
assayed as described in the work of Slezak et al. (27). The rate of DMSPd
consumption (in nM day?1) was obtained by multiplying the DMSPd concen-
tration by the rate constant. The fraction of [35S]DMSP assimilated into micro-
bial macromolecules was determined after 24 h of incubation by filtering water
samples through 0.2-?m nylon filters, rinsing the filters with 5% TCA, and
counting the35S activity remaining on the filter.
DNA extraction. For DNA samples, water from each time point was sequen-
tially filtered through 8-?m (293-mm-diameter) and 3-?m (47-mm-diameter)
prefilters, and then particles were collected on 0.2-?m (47-mm)-pore-size poly-
carbonate filters (Poretics) until the filters clogged (?250- to 500-ml volume,
depending on the sample). The 0.2-?m filters were flash frozen in liquid
nitrogen and then stored at ?20°C until extraction. DNA was extracted using
the PowerMax soil DNA isolation kit (MoBio Laboratories, Inc.) following
manufacturer’s instructions, and concentrations were estimated by absor-
bance on a NanoDrop spectrophotometer (Thermo Scientific).
Primer design. The universal dmdA primer set, dmdAUF160 (3?-GTICARIT
ITGGGAYGT-5?) and dmdAUR697 (3?-TCIATICKITCIATIAIRTTDGG-5?),
was designed as described previously (34). Briefly, dmdA sequences from cul-
tured organisms and the Global Ocean Sampling (GOS) metagenome (10) were
aligned in Geneious Pro 3.5.6 (Geneious v4.7; A. J. Drummond et al., 2009)
using the ClustalW algorithm. The primer set was modified with degenerate and
inosine bases to target as many dmdA sequences as possible while excluding
related gcvT and aminomethyltransferase gene sequences (34).
PCRs. DNA extracted from duplicate control and experimental samples (2
treatments ? 2 replicates ? 7 time points; 28 total samples) was used as template
for PCRs, followed by pyrosequencing of amplicons (21). PCRs were carried out
with Platinum High-Fidelity Taq DNA polymerase (1/2 U; Invitrogen) in the
native 10? PCR buffer with 8 ng (for dmdA amplifications), 5 ng (for bacterial
16S rRNA amplifications), or 10 ng (for archaeal 16S rRNA amplifications) of
template DNA. Universal dmdA primers were modified from the work of Varal-
jay et al. (34) to include 454 Life Sciences A or B adaptor sequences and 14
tetranucleotide “key” sequences for sample differentiation during sequence anal-
ysis (11, 29). The dmdA cycling conditions were as follows: 94°C for 2 min,
followed by 40 cycles of 94°C for 35 s, 41°C for 35 s, and 68°C for 60 s, with a final
extension step of 68°C for 10 min (34). 16S rRNA bacterial and archaeal primers
covering the V6 hypervariable region (developed by Sogin et al. [29] and mod-
ified by Huber et al. [11]; these are a combination of 5 forward and 4 reverse
bacterial 16S rRNA primers and 1 forward and 2 reverse archaeal 16S rRNA
primers) were made with 14 distinct tetranucleotide keys, as described above.
Bacterial or archaeal primers were mixed equally from 10 ?M stock solutions,
and 2 ?l of this pooled solution was used in each 25-?l PCR mixture (11). The
16S rRNA gene cycling conditions were as follows: 94°C for 2 min, followed by
30 cycles of 94°C for 30 s, 58°C for 30 s, and 68°C for 30 s, with a final extension
step of 68°C for 10 min.
Sequencing preparation. PCR products were run on 1% (dmdA amplicons) or
2% (bacterial and archaeal 16S rRNA amplicons) agarose gels, followed by
purification using the QIAquick gel extraction kit (Qiagen). As a final DNA
purification, products were cleaned using the Ampure system (Agencourt Bio-
science Corporation), with modifications to the volume of purified PCR products
(30 ?l) and AMPure beads (50.4 ?l). After final purification, DNA concentra-
tions were determined as described above and products (61 control and 60
experimental samples) were pooled in equal amounts, except that dmdA ampli-
cons were added to the mixtures at double the amount of the 16S rRNA
amplicons. Two separate pools were assembled, each of which used the distinct
identification keys only once, and these were sequenced on separate halves of the
PicoTiterPlate (454 Life Sciences). A total of ?1,300 ng DNA was used in Roche
GS FLX LR70 pyrosequencing at the University of South Carolina Environmen-
tal Genomics Facility (Columbia, SC).
Sequence analysis. For reads of high quality (those that contained full and
correct forward primer sequences and no uncalled bases and had average quality
scores of ?21), adaptor and key sequences were removed and sequences were
clustered using the CD-HIT program (18) with ?90% similarity for dmdA
clusters (34) and ?99% similarity for 16S rRNA clusters. The average read
length was 190 bp for dmdA amplicons, 70 bp for bacterial rRNA amplicons, and
72 bp for archaeal rRNA amplicons after adaptor, key, and primer sequences
were trimmed. For analysis of dmdA amplicons, reference sequences (defined as
the longest read in the cluster) were analyzed via BLASTX against an in-house
DmdA database assembled from cultured marine bacteria and the Global Ocean
Sampling (25) metagenomic sequences (http://roseobase.org/DmdApaper) and
augmented with a number of paralogous, non-DmdA sequences (10). Sequences
were considered to be valid dmdA sequences if the top hit in the database had a
bit score of ?30. The sequences were assigned to the clade of the top hit (either
A, B, C, D, or E or unclassified). These dmdA sequences were further assigned
to protein subclades via a tree-building method using known DmdA subclade
sequences as anchors (34). For taxonomic analysis of bacterial 16S rRNA am-
plicons, reference sequences were aligned against a marine bacterial 16S rRNA
database with representatives of all major marine taxa (3). Amplicons with
?90% similarity and an overlap of ?70% with one of the representative marine
sequences were assigned to that taxon. For taxonomic analysis of archaeal 16S
rRNA amplicons, the SIMO RDP Agent (www.simo.marsci.uga.edu) was used to
compare amplicon sequences against archaeal type species housed in the Ribo-
somal Database Project (RDP) database (5), using similarity cutoffs of 100% for
species, ?95% for genus, ?92% for family, ?91% for order, ?85% for class, and
?75% for phylum assignments. Because of the poor coverage of archaeal diver-
sity among type species, most sequences were identified only to the phylum level
or remained unclassified.
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Statistical analysis. Clusters were analyzed via multidimensional scaling
(MDS) using Primer 5 for Windows software (Plymouth Marine Laboratory,
Plymouth, United Kingdom). A Bray-Curtis similarity matrix (4th root trans-
formed to deemphasize the contribution of any one particular dominant cluster)
was constructed from the clusters from each replicate microcosm (two control
and two nutrient amended) over time (6 days). For dmdA sequences, only the top
100 clusters (which contained 70% of the amplicons) were included in the
statistical analysis. For bacterial 16S rRNA sequences, all clusters were analyzed
(9,588 clusters representing 55 marine taxa). dmdA and 16S rRNA clusters were
also evaluated with a SIMPER analysis (Primer 5), which determines the clusters
contributing most to the differences between samples. Reference sequences from
the dmdA clusters that cumulatively created 50% of the dissimilarity between
control and bloom microcosms were analyzed in a maximum-likelihood tree
created using RAxML 7.0 with 100 bootstrap samplings. dmdA and 16S rRNA
clusters were evaluated with both BIO-ENV and BVSTEP routines (Primer 5) to
determine the measured environmental variables that correlated with changes in
these clusters (BIO-ENV does a full search while BVSTEP does a stepwise
search to determine the variable[s] best explaining the cluster structure changes).
A rarefaction curve of all dmdA sequences clustered at the 90% similarity level
was created using EcoSim 7.0 (EcoSim: null models software for ecology, version
7; N. J. Gotelli and G. L. Entsminger, 2008, Acquired Intelligence Inc. and
Kesey-Bear, Jericho, VT) with 1,000 resamplings.
Nucleotide sequence accession numbers. rRNA sequences and accompanying
metadata (compiled according to Minimum Information for Metagenomic Se-
quences [MIMS] standards) have been deposited in the Community Cyberinfra-
structure for Advanced Marine Microbial Ecology Research and Analysis
(CAMERA) database with project identification (ID) CAM-PROJ_DICE (sam-
ple CAM_S_A001 for experimental microcosms and CAM_S_A002 for control
microcosms). dmdA amplicon sequences have been deposited in the NCBI Short
Read Archive with project ID 49967.
RESULTS
Phytoplankton bloom dynamics. While control microcosms
maintained low Chl a levels throughout the experiment (?1 ?g
liter?1), the nutrient-amended experimental microcosms had
variable and higher Chl a concentrations that peaked at day 4
(6 ?g liter?1[Fig. 1A]), indicating a phytoplankton bloom.
Changes in rates of bacterial secondary production suggested
increased substrate availability after day 3 in the experimental
microcosms, consistent with bloom conditions (Fig. 1C).
The fraction of consumed dissolved DMSP (DMSPd) de-
graded to DMS (maximally 14% [data not shown]) and the
fraction of DMSPd-sulfur assimilated into macromolecules
(maximally 55% [Fig. 1E]) followed the same trend in both
control and experimental microcosms. However, the ratio of
particulate DMSP (DMSPp) to Chl a, a proxy for the relative
amount of DMSP produced per phytoplankton cell (35), de-
creased over the course of the induced bloom in the experi-
mental microcosms (Fig. 1B). This pattern suggests that the
phytoplankton dominating the bloom were poor DMSP pro-
ducers relative to those in the initial and control communities,
although their higher numbers nonetheless caused substantial
increases in DMSP flux. For all environmental variables except
NO3
experimentally manipulated), the conditions and processes in
the experimental microcosms were most dissimilar to the initial
samples and the control microcosms on days 4 to 6 (Fig. 2A).
DMSP demethylase gene (dmdA) dynamics. The 91,418
dmdA sequences obtained (from 28 samples representing two
replicates per treatment for days 0 to 6) formed 578 clusters at
90% nucleotide sequence identity (average of 158 sequences
per cluster). At this similarity level, the experiment-wide rich-
ness had not yet reached a plateau (Fig. 3). Given the error
rate for FLX pyrosequencing (?0.5% [21]) compared to the
?and PO4
3?(which were excluded because they were
10% sequence divergence within a cluster, sequencing artifacts
played little or no role in cluster assignments. Non-dmdA se-
quences captured by the universal dmdA primer set were also
found (20,761 sequences); these formed 647 clusters (average
of 32 sequences per cluster) and were not considered further.
Overall, 82% of the amplicons were identified as dmdA se-
quences, while 18% were identified as encoding GcvT proteins
or other related sequences.
dmdA cluster richness was highest in both treatments 24 h
after the initiation of the experiment (day 1 [Table 1]), with
240 and 238 clusters, respectively, in experimental and control
microcosms. Richness generally decreased after this point
(normalized cluster richness in Table 1), but overall the tem-
poral shifts were not large.
To track the temporal dynamics of the community dmdA
pool at a finer scale, the 100 largest clusters (averaging 784
sequences per cluster and accounting for 70% of all dmdA
sequences) were analyzed in more detail. This analysis assumes
FIG. 1. Biological and chemical properties of control and experi-
mental microcosms during the 6-day study. Panels show concentrations
of chlorophyll a (A), the ratio of particulate DMSP concentration to
chlorophyll a concentration (B), rates of heterotrophic bacterial pro-
tein production (C), the concentration of dissolved DMSP (D), the
fraction of the dissolved DMSP consumed by bacteria that was assim-
ilated into macromolecules (E), and the concentration of DMS (F).
Each point represents the mean and standard deviation of three inde-
pendent determinations from two replicate microcosms.
526HOWARD ET AL.APPL. ENVIRON. MICROBIOL.

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that PCR biases, to whatever extent they occurred, were con-
sistent across samples. A multidimensional scaling (MDS) plot
showed that cluster compositions on days 4 to 6 in the exper-
imental microcosms were more similar to each other than to
the control samples or the other experimental samples (Fig.
2B). Clusters contributing most to this pattern were from
clades A, B, and D (24, 4, and 4 clusters, respectively) (Fig. 4).
A group of 6 clusters related to Dinoroseobacter shibae dmdA
(clade A) contained only sequences from the experimental
microcosms, while 4 clusters related to SAR11 dmdA se-
quences (clade D) contained only sequences from the control
microcosms (Fig. 4).
At higher-order groupings, the percentage of clade A se-
quences in the amplicon pools increased in experimental mi-
crocosms from 47% to 70% of the total dmdA sequences be-
tween days 3 and 4, while clade E sequences increased from 9
to 20% over the same time period. Sequences from clades C
and D decreased in relative abundance (from 18 to 4% and
from 26 to 6% of dmdA sequences, respectively; data not
shown). There was evidence for shifts in relative abundance of
sequences in the control microcosms over this same time in-
terval, but to a smaller extent (clade A sequences decreased
from 68 to 65% of total dmdA sequences; clades C, D, and E
sequences increased from 4 to 6%, 10 to 20%, and 15 to 18%
of total dmdA sequences, respectively). A BIO-ENV/BVSTEP
analysis indicated that the observed changes in relative abun-
dance of the 100 largest DmdA clusters were best correlated in
both control and bloom microcosms with the change in Chl a
concentrations (R ? 0.813).
Bacterial and archaeal communities. The 180,801 bacterial
16S rRNA sequences obtained from control and experimental
microcosms formed 9,588 taxonomic clusters at 99% nucleo-
tide sequence identity (all time points considered together).
Given the ?0.5% error rate typical for 454 sequencing (21),
some clusters may be influenced by sequencing artifacts. How-
ever, the 1% sequence divergence allowance means that am-
plicons with a single miscalled base will be placed in the same
cluster as otherwise identical amplicons. The major bacterial
groups represented by these clusters were Roseobacter, SAR11,
and Flavobacteria (Fig. 5A). The relative abundance of Fla-
vobacteria amplicons increased over time in the experimental
microcosms (from 12% to 34% of total sequences), while
SAR11 abundance decreased (from 31% to 12% [Fig. 5A]).
These changes were larger than those in the control micro-
cosms, suggesting that they were linked to conditions associ-
FIG. 2. Multidimensional scaling of the Bray-Curtis similarity matrix generated from measures of environmental variables (principal-compo-
nent analysis) (A), the relative abundance of the 100 largest dmdA clusters (B), the relative abundance of bacterial 16S rRNA gene clusters (C),
and the relative abundance of archaeal 16S rRNA gene clusters (D) over time in control and experimental microcosms. Arrows show the time
progression for control “C” (closed circles) and experimental “E” (open triangles) microcosms.
FIG. 3. Rarefaction curve of dmdA sequences (all treatments and
samples combined) clustered at the 90% similarity level.
VOL. 77, 2011 DYNAMICS OF DMSP DEMETHYLASE GENE ASSEMBLAGES527

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ated with the development and decline of the phytoplankton
bloom. Roseobacters accounted for a significant percentage of
the 16S rRNA amplicons from the microcosm bacterial com-
munities (17% ? 7% [Fig. 5A]) but showed no dynamics re-
lated to time point or treatment.
The distribution of 16S rRNA sequences among bacterial
clusters diverged over days 4 to 6 in the experimental micro-
cosms compared to the control microcosms and the initial
sample (Fig. 2C). A SIMPER analysis indicated that the in-
creases in Flavobacteria amplicons were most responsible for
this difference. The environmental variable best correlated
with the observed changes in bacterial community in control
microcosms was DMS concentration (R ? 0.796), while a com-
bination of environmental variables were correlated in the
experimental microcosms (DMSPp-to-Chl a ratio, DMSPp,
and DMS; R ? 0.886).
Archaeal 16S rRNA genes could be amplified from micro-
cosm DNA only for days 0 to 3 for both control and experi-
mental treatments, suggesting that Archaea abundance de-
clined significantly after 3 days of incubation. The 6,578
archaeal 16S rRNA sequences formed 519 clusters at the 99%
sequence identity level. The majority of archaeal 16S rRNA
amplicons (?83%) could not be classified to the phylum level
since they were ?75% identical to any type species (likely
reflecting an underrepresentation of mesophilic Archaea
among characterized isolates). Of the sequences that could be
categorized to the phylum level, the majority were Euryarcha-
eota (averaging 16.6% of all archaeal sequences) regardless of
treatment or time point (Fig. 5B), and a small number were
Crenarchaeota (averaging 0.2% of archaeal sequences). Anal-
ysis of archaeal clusters, including those composed of unclas-
sified sequences, showed a change in composition over the
limited time course in which their 16S rRNA genes could be
amplified (Fig. 2D). These observed changes in the archaeal
community are best correlated in experimental microcosms
with Chl a and DMSPd concentrations (R ? 0.829). The con-
trol microcosms had too few sample points for statistical anal-
ysis.
DISCUSSION
In the context of an experimentally induced phytoplankton
bloom, we asked whether shifting ecological conditions, includ-
ing DMSP supply, influenced the relative composition of the
community dmdA sequence pool. At least five clades and 14
subclades of DmdA are found in marine bacterioplankton
communities (10, 34), yet little information is currently avail-
able on whether or not these groups represent proteins with
differing kinetic properties or ecological roles. Because some
of the sequence groups are associated with specific taxa,
changes in relative DmdA abundance might alternatively be
linked to changes in taxonomic composition.
Overall, 578 dmdA clusters were identified at a conservative
definition of 90% nucleotide sequence similarity (34). Clusters
unique to either experimental or control microcosms (29% of
the total) averaged only 1.5 sequences each, indicating that a
significant amount of rare diversity that would not have been
captured in a shallower sequencing effort was present. None-
theless, we still likely underestimated richness, based both on
evidence from rarefaction analysis (Fig. 3) and on recognition
that some dmdA sequences likely have mismatches to the uni-
versal primers (34).
Several dmdA subclades shifted in relative abundance in
response to the phytoplankton bloom, with two groups in clade
A (Roseobacter-like) and one in clade D (SAR11-like) showing
the most significant changes (Fig. 4). All members of one of the
Roseobacter-like groups showed a positive response to bloom
conditions (Fig. 4), suggesting a “bloom” subclade. Conversely,
the SAR11-like group members all showed a negative response
to bloom conditions, suggesting a “nonbloom” subclade.
The DmdA composition at the clade level had smaller over-
all changes. Reisch et al. (24) found that purified proteins
representing clade A (from Roseobacter isolate Ruegeria
pomeroyi DSS-3) and clade D (from SAR11 isolate Pelagibacter
ubique HTCC1062) had similar catalytic efficiencies, pH op-
tima, and Kmvalues. Some bacterioplankton may accumulate
DMSP to high intracellular concentrations (e.g., 70 mM in R.
pomeroyi [24]), which is typical for compounds used as an
osmoprotectant and in agreement with field studies of natural
bacterioplankton communities (30, 37). Accumulation of
DMSP to high intracellular concentrations would reduce se-
lective pressures for enzymes optimized to the low external
DMSP concentrations (low nM), an idea consistent with the
relatively invariant clade representation found previously
across the GOS metagenome sites (10). While PCR amplifi-
cations are not quantitative in the same way as metagenomic
data sets, the current study supports the idea that dmdA rela-
tive abundance is largely consistent at the clade level but dy-
namic at the subclade and cluster level (Fig. 2).
Biogeochemical data collected during the microcosm exper-
TABLE 1. dmdA sequence number, cluster number (90% sequence identity), diversity, and coveragea
Day
Control Experimental
No. of
reads
No. of
clusters
Normalized no.
of clusters
H?
Coverage
(%)
No. of
reads
No. of
clusters
Normalized no.
of clusters
H?
Coverage
(%)
0
1
2
3
4
5
6
6,222
6,632
6,385
7,854
9,130
15,882
7,394
208
238
194
181
216
211
164
168 (152–185)
192 (175–206)
163 (149–176)
143 (125–162)
160 (141–175)
162 (148–175)
135 (118–148)
3.85
4.01
3.86
3.58
3.69
3.81
3.49
66
68
76
70
67
86
76
4,842
5,254
6,348
6,765
7,202
6,851
6,167
206
240
201
197
205
245
185
178 (161–191)
203 (183–217)
170 (154–184)
158 (141–176)
161 (143–178)
193 (176–211)
158 (143–169)
3.92
4.12
4.01
3.64
3.81
4.17
4.02
69
69
75
69
66
67
76
aNormalized no. of clusters, cluster numbers estimated for a 4,800-sequence library to normalize for differences in number of reads across samples; the range of 1,000
bootstraps is given in parentheses. H?, Shannon-Wiener diversity index. Coverage, Good’s coverage.
528HOWARD ET AL.APPL. ENVIRON. MICROBIOL.

Page 6

iment provided a framework for interpreting the observed clus-
ter-level shifts in relative abundance of dmdA. Measurements
of DMSP and DMS concentrations and flux confirmed that the
induced phytoplankton bloom resulted in higher concentra-
tions of dissolved DMSP and DMS compared to the controls
(Fig. 1D and F). However, the ratio of DMSPp to Chl a
indicated that the phytoplankton responsible for the bloom
were poor producers of DMSP on a per-cell basis (Fig. 1B).
FIG. 4. Maximum-likelihood tree of the translated dmdA clusters most responsible for the dissimilarity between control and experimental
microcosms on days 4 to 6, as determined by SIMPER analysis (sequences named “Cluster xxxx”), along with representative dmdA sequences from
the GOS metagenome (sequences named “xx JCVI PEP”) and cultured bacteria. Cluster sequences in bold are the five creating the greatest
dissimilarity (9.5% of the total). Filled and open stars indicate clusters with more sequences in control or experimental microcosms, respectively,
and numbers in parentheses indicate the number of sequences in that cluster. Gray shading indicates clade C, which is internal to clade B in this
tree. The tree was created using RAxML 7.0, and values at the nodes show the number of times that the node appeared in 100 resamplings.
VOL. 77, 2011 DYNAMICS OF DMSP DEMETHYLASE GENE ASSEMBLAGES529

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Microscopic analysis and removal of dissolved H3SiO4(data
not shown) indicated that diatoms, which are not strong DMSP
producers, dominated the bloom. Thus, although higher in an
absolute sense, DMSP was relatively less important as a bac-
terial substrate in the experimental microcosms compared to
the controls. This more minor role for DMSP as a source of
reduced carbon and sulfur is consistent with the increase in
relative abundance of Flavobacteriaceae over the course of the
bloom (Fig. 5A), a group which may not metabolize DMSP
based on the absence of demethylation and cleavage genes in
the existing genome sequences (10; however, see reference 36).
Flavobacteriaceae are known to make use of plankton-derived
extracellular polymers (polysaccharides and other high-molec-
ular-weight compounds excreted from cells [16]), which may be
more abundant toward the end of the bloom. Thus, the data
are suggestive of a reduced bacterioplankton dependence on
DMSP within the bloom community compared to the control.
In an ecosystem context, it is interesting that despite sub-
stantial differences between bloom and nonbloom conditions
in the concentration and consumption of dissolved DMSP (Fig.
1B and D), there was little effect on the ratio of products from
the two competing pathways for DMSP degradation (DMS
production averaged 20% of DMSP assimilation in the exper-
imental microcosms and 16% in the controls). Future experi-
mental studies can address the important interplay between
functional gene sequence heterogeneity and the pathways and
rates of microbially mediated sulfur transformations in the
ocean.
The dynamics of five clades and multiple subclades of dmdA
would not have been detected without the deep coverage li-
braries made possible by high-throughput amplicon sequenc-
ing. Such dynamics were likely driven by a collection of factors
that caused differential growth rates among bacterioplankton
taxa (Fig. 1C) and shifts in taxonomic composition (Fig. 5).
Factors driving these changes may have been directly related to
DMSP dynamics: for example, if an ecological advantage was
conferred by certain variants in translated demethylase se-
quences as environmental conditions changed. Alternatively,
they may have been driven by factors not explicitly related to
DMSP supply, such as bloom-related changes in organic car-
bon or nutrient availability or food web interactions. Among
the suite of environmental factors measured in this study, the
best correlate with shifts of the dominant dmdA clades was Chl
a concentration, not concentration or flux of DMSP or its
metabolites (Fig. 2). The dmdA population changes occurring
in this case, therefore, may be an outcome of phytoplankton-
driven taxonomic shifts rather than specifically linked to
DMSP cycling. There have been few exhaustive analyses of the
diversity of functional genes (34, 38). Even in this study, we are
still scratching the surface of characterizing the diversity and
understanding the ecological relevance of functional gene se-
quence variation.
ACKNOWLEDGMENTS
We thank B. Tolar, E. Fichot, M. Coll-Llado ´, A. Rellinger, C. Smith,
S. Gifford, X. Mou, and A. Spaulding for assistance in sample collect-
ing and processing; H. Luo and V. Varaljay for expertise in phyloge-
netic tree construction; and the captain of the R/V E. O. Wilson
(Dauphin Island, AL) for help in sample collection.
This research was supported by NSF grant OCE-0724017 (to
M.A.M. and R.P.K.) and by a grant from the Gordon and Betty Moore
Foundation (to M.A.M.).
FIG. 5. Relative abundances of bacterial (A and B) and archaeal (C and D) 16S rRNA gene sequences over time in control and experimental
microcosms.
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