AEM Accepted Manuscript Posted Online 10 April 2015 Appl. Environ. Microbiol. doi:10.1128/AEM.03873-14 Copyright © 2015, American Society for Microbiology. All Rights Reserved.
1
Abundance and distribution of dimethylsulfoniopropionate-degrading
2
genes and the corresponding bacterial community structure at
3
dimethyl sulfide hotspots in the tropical and subtropical Pacific Ocean
4
Yingshun Cui,a* Shotaro Suzuki,a Yuko Omori,b Shu-Kuan Wong,a Minoru Ijichi,a Ryo Kaneko,a Sohiko
5
Kameyama,c Hiroshi Tanimotob, and Koji Hamasakia
6 7
Marine Microbiology, Department of Marine Ecosystem Dynamics, Division of Marine life Science,
8
Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba
9
277-8564, Japana; Center for Global Environmental Research, National Institute for Environmental
10
Studies, Ibaraki 305-8506, Japanb; Faculty of Environmental Earth Science, Hokkaido University, North
11
10 West 5, Kita-ku, Sapporo, 060-0810, Japanc
12 13
Running head: DMSP-degrading genes in the Pacific Ocean
14 15
*Address correspondence to Yingshun Cui,
[email protected] 16 17 18
1
19
Abstract
20
Dimethylsulfoniopropionate (DMSP) is mainly produced by marine phytoplankton, but is released into
21
the microbial food web and degraded by marine bacteria to dimethyl sulfide (DMS) and other products.
22
To reveal the abundance and distribution of bacterial DMSP-degrading genes and the corresponding
23
bacterial communities in relation to DMS and DMSP concentrations in seawater, we collected surface
24
seawater samples from DMS hotspot sites during a cruise across the Pacific Ocean. We analyzed the
25
genes coding DMSP lyase (dddP) and DMSP demethylase (dmdA), which are responsible for DMSP
26
transformation to DMS and DMSP assimilation, respectively. Averaged relative abundance of these
27
DMSP-degrading genes to the 16S rRNA genes was 33 ± 12%. The abundances of these genes showed
28
large spatial variations. dddP genes showed more variable abundance than dmdA genes. Multi-
29
dimensional analysis based on the abundance of DMSP-degrading genes and environmental factors
30
revealed that the distribution pattern of these genes was influenced by chlorophyll a concentrations and
31
temperatures. dddP genes, dmdA subclade C/2, and dmdA subclade D exhibited significant correlations
32
with the marine Roseobacter clade, SAR11 subgroup Ib, and SAR11 subgroup Ia, respectively. SAR11
33
subgroup Ia and Ib which possessed dmdA genes were suggested to be the main potential DMSP
34
consumers. The Roseobacter clade members possessing dddP genes in oligotrophic subtropical regions
35
were possible DMS producers. These results suggest that DMSP-degrading genes are abundant and
36
widely distributed in the surface seawater and the marine bacteria possessing these genes influence the
37
DMSP degradation and regulate the DMS emission in subtropical gyres of the Pacific Ocean.
38 39
Introduction
40
Dimethylsulfoniopropionate (DMSP), the precursor of dimethylsulfide (DMS), is mainly produced
41
by marine phytoplankton, marine macroalgae, and a few angiosperms in the ocean (1-3), and is an
42
important carbon and sulfur source for marine bacteria (4). After DMSP has been released, it is mainly
43
assimilated and degraded by marine bacteria (5, 6). Phytoplankton and their predators also degrade DMSP
44
to a certain extent (7, 8). Once incorporated into bacterial cells, DMSP is degraded via two major
45
pathways: a demethylation pathway involving DMSP demethylase, encoded by dmdA (9) and a cleavage
46
pathway involving several different ddd+ (DMSP-dependent DMS) (dddD, dddL, dddP, dddQ, dddY, and 2
47
dddW) genes (10-15). dmdA, the first DMSP degradation gene identified, is the most widely distributed
48
DMSP-degrading gene. It was reported that approximately 60% of marine bacteria in the open ocean and
49
coastal waters contain this gene (16). The dmdA gene, which is found mainly in members of the SAR11,
50
SAR116, Gammaproteobacteria, and Roseobacter clade (16-19), can be grouped into five clades and 14
51
subclades based on their nucleotide sequences (16, 20).
52
In the cleavage pathway, bacteria transform DMSP to DMS. Aerosols formed from the oxidation of
53
DMS increase the cloud cover in the ocean, thus, creating albedo that indirectly controls the global heat
54
fluxes (8, 21-23). DMS was found to be universally present in seawater and made up a large proportion of
55
organic sulfur compounds emitted from the sea surface to the atmosphere (5, 23). This sea-to-air DMS
56
flux represents about a half of the global biogenic sulfur flux (24). dddP and dddQ genes are the most
57
frequently detected ddd+ genes in marine bacteria and are mainly found in the Roseobacter clade (12, 13,
58
25).
59
Recent metagenomic and metatranscriptomic analyses have increased our understanding of DMSP
60
cycling. These studies showed that the temporal variability in the abundance of DMSP-degrading genes in
61
the Sargasso Sea (18) and the North Pacific Ocean (17) was strongly influenced by seasonal changes in
62
primary production, UV radiation, and depth-related environmental parameters such as particulate DMSP
63
(DMSPp) and DMS concentrations (17, 18, 26). However, their findings were limited to a few, small,
64
localized sampling points in the study area. The main objectives of this study are: 1) to describe the
65
distributions and abundance of DMSP-degrading genes in relation to surface water DMS concentrations
66
along a large-scale Pacific Ocean transect, 2) to identify the bacterial communities potentially associated
67
with these genes, 3) to identify the environmental factors influencing the distribution of
68
possessing DMSP-degrading genes, and 4) to explore the relationship between DMS concentration and
69
DMSP-degrading gene abundance on a larger spatial scale. In this study, we targeted dmdA and dddP
70
genes and collected 20 seawater samples corresponding to an increase of DMS concentrations recorded
71
with the use of an on-board real-time DMS monitoring system during a cruise across the Pacific Ocean.
bacteria
72
Materials and Methods
73
Real-time DMS monitoring and seawater sample collection. In order to monitor the real-time
74
dissolved DMS concentration on board, we continuously drew seawater from 5-m depth with the use of a 3
75
shipboard built-in pumping system. The outlet of the pumping system was directly connected to the
76
equilibrator inlet-proton transfer reaction-mass spectrometry (EI-PTR-MS) system throughout the cruise,
77
which allowed us to continuously measure seawater DMS concentration and pinpoint “DMS hotspots”
78
(27). Linear regression analysis showed that the dissolved DMS concentrations obtained using EI-PTR-
79
MS were consistent and within the range of dissolved DMS concentrations measured using gas
80
chromatography/mass spectrophotometry during the R/V Hakuho-maru cruise from July to August 2008
81
(slope: 0.90 ± 0.02, intercept: −0.03 ± 0.30, and R2=0.99) (27). Seawater samples for DNA extraction
82
were collected from an alternative outlet of the pumping system when we detected any significant
83
increase (>1 nM) in DMS concentrations. Once collected, the samples were immediately filtered onto 47-
84
mm diameter 3.0-µm pore size Nuclepore™ filters (Whatman®, Clifton, NJ, USA) and sequentially
85
filtered onto 0.22-µm pore size Sterivex™ filter units (Millipore™, Bedford, MA, USA) to collect
86
particle-associated (>3 µm) and free-living (0.22 to 3 µm) bacteria, respectively. The filters were
87
immediately stored at −80 °C until further analysis. We collected 20 different surface seawater samples
88
from DMS hotspot sites, which matched to known DMS concentrations, from different sites during the
89
cruise of R/V Hakuho-maru from December 2011 to March 2012 (Table 1 and Supplementary materials
90
S1). In total, there were eight samples from the subtropical North Pacific Ocean (NP), seven samples
91
from the subtropical South Pacific Ocean (SP), and five samples from the equatorial Pacific Ocean (EP).
92
Among the samples from the subtropical South Pacific Ocean, three samples (S3p1, S3b, and S3p2) were
93
collected from the Peruvian upwelling area.
94
DMS and DMSP quantification by gas chromatography. The DMS and DMSP (dissolved DMSP:
95
DMSPd; particle DMSP: DMSPp; total DMSP = DMSPd + DMSPp: DMSPt) concentrations were
96
measured by gas chromatography at the same locations as where DNA samples were collected during the
97
latter half of the cruise (at E1p to E2b and N3b1 to N3b2). The method was previously described in detail
98
(28). For DMS measurement, 30 ml seawater collected from the outlet of the shipboard built-in pumping
99
system was directly filtered through a GF/F (47-mm) filter (Whatman®, USA). As soon as possible after
100
sampling, the DMS concentration was measured by a gas chromatography-flame photometric detector
101
(GC-FPD, Shimadzu GC-2014, Kyoto, Japan) with a 25% TCEP packed column (GL Science, Tokyo,
102
Japan) combining a purge & trap extraction/pre-concentration system. For DMS + DMSPt measurement, 4
103
30 ml seawater was collected directly into a glass vial without prior filtration, and was subjected to an
104
alkali treatment to permit cleavage into gaseous DMS. After the alkali treatment, the sample bottles were
105
stored at 4°C for at least 24 h to allow completion of the cleavage process before further analysis. For
106
DMS + DMSPd measurements, 30 ml seawater was directly filtered through a GF/F (47 mm) filter
107
(Whatman®) and subjected to an alkali treatment to cleave DMS in a similar manner to that used for DMS
108
+ DMSPt measurement. The DMSPp concentration was calculated as follows: DMSPp = (DMS + DMSPt)
109
– (DMS + DMSPd).
110
Environmental factors. Salinity, temperature, and chlorophyll a concentration were continuously
111
monitored with the Automated Environmental Monitor for Biological Oceanography (AMEMBO) system.
112
The system consisted of a bubble trap, a Seabird SBE-21 thermosalinograph, and a Turner Design 10-R in
113
vivo fluorometer. An outlet of the shipboard built-in pumping system was directly connected to the
114
AMEMBO system. The chlorophyll a concentrations determined by the AMEMBO system were
115
calibrated against the chlorophyll a extracted from filter samples.
116
DNA extraction. Genomic DNA was extracted using ChargeSwitch® Forensic DNA Purification Kit
117
(Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions, with slight modifications.
118
After breaking the Sterivex™ filter unit which was used to collected free-living bacteria (0.22 µm to 3
119
µm), the membrane was cut into pieces with sterile surgical blades. The membrane pieces were crushed
120
by bead-beating in a Micro Smash™ MS-100 (TOMY, Tokyo, Japan) at 5000 rpm for 30 s to physically
121
break the cell walls of bacteria on the membrane. After the bead-beating, we followed the manufacturer’s
122
instructions of the ChargeSwitch® Forensic DNA Purification Kit (Invitrogen). For each sample, initial
123
steps of bead-beating until elution were repeated twice to increase yield. The DNA concentration was
124
determined using a Quant-iT™ PicoGreen® dsDNA Kit (Invitrogen). Nuclepore™ filters (3 µm) were also
125
subjected to the same extraction steps as with the Sterivex™ filter units to collect DNA from particle-
126
associated bacteria (>3 µm).
127
Abundance of dddP, dmdA, and 16S rRNA genes. The copy numbers of DMSP-degrading genes
128
were analyzed by qPCR on a Bio-Rad Chromo4™ System. Eight dmdA primer sets designed to target the
129
different subclades A/1, A/2, B/3, B/4, C/2, D/1, D/3, and E/2 (20) and a dddP primer set targeting the
130
Roseobacter clade (18) were used to detect DMSP-degrading genes. Among the dmdA primer sets, A/1 5
131
and A/2 target the Roseobacter clade (dmdA A clade), B/3 and B/4 target dmdA clade B (SAR116
132
member “Candidatus Puniceispirillum marinum” IMCC1322-like marine bacteria clade), C/2 targets
133
dmdA clade C (Pelagibacter ubique HTCC7211(PB7211-1421)-like clade), D/1 and D/3 target dmdA
134
clade D (Pelagibacter ubique HTCC7211 (PB7211-770) and Pelagibacter ubique HTCC1062 (SAR-
135
0264)-like clade), and E/2 targets dmdA clade E (Marine gammaproteobacterium HTCC2080-like clade)
136
(9, 16, 20). Environmental samples (one sample each from the North, South, and equatorial Pacific Ocean)
137
were subjected to Sanger sequencing to confirm dmdA and dddP gene specificity. The annealing
138
temperatures of the dmdA primer sets, except for those targeting B/4 and E/2 subclades, were as described
139
by Varaljay et al. (2010). The annealing temperature of the dddP primer set was as described by Levine et
140
al. (2010). In this study, the annealing temperature of the primers targeting the E/2 subclade was
141
optimized to 61°C, because PCR amplifications with the previously described annealing temperature
142
(57°C) yielded multiple bands from our environmental samples. PCR amplification of the B/4 subclade
143
also yielded multiple bands, and so we omitted this primer set from this study. The abundance of 16S
144
rRNA genes was determined using the BACT1369F and PROK1492R primer set (29). qPCR standards
145
were made from PCR products amplified from environmental samples using the TOPO TA Cloning Kit
146
(Invitrogen) and plasmid DNA was purified using a PureLink® Quick Plasmid Miniprep Kit (Invitrogen).
147
The purified plasmid DNA was digested with Not1 and subjected to agarose gel insert purification
148
(Qiagen, Hilden, Germany). The concentration of the qPCR standards was determined using a Quant-iT™
149
PicoGreen® dsDNA Kit (Invitrogen).
150
We used SYBR® Premix Ex Taq™ (Tli RNaseH Plus) kit (TaKaRa Bio Inc., Otsu, Japan) for qPCR
151
detection. qPCR was performed in 20-µl mixtures containing 10 µl 2× SYBR Premix Ex Taq, 0.4 µl 50×
152
ROX reference dye, 0.2 mM each primer, 2 µl 1/10-diluted template DNA, and 6.8 µl water. All qPCR
153
reactions were run in triplicate for each sample. The qPCR conditions were as follows: initial
154
denaturation for 30 s at 95°C, followed by 35 cycles of denaturation at 95°C for 5 s, annealing at the
155
primer-specific annealing temperature for 30 s, and extension at 72°C for 30 s. The fluorescence intensity
156
was read, and then the temperature was raised from 65°C to 95°C and the fluorescence intensity read 2 s
157
after every 0.2°C increase in temperature. A ten-fold serially diluted standard and no-template control
6
158
were run in triplicates for each reaction. The presence of a single band was verified by agarose gel
159
electrophoresis.
160
Bacterial 16S rRNA gene analysis. DNA from free-living fraction of each environmental sample
161
was targeted to determine the bacterial 16S rRNA gene community compositions. Bacterial 16S rRNA
162
genes
163
CCATCTCATCCCTGCGTGTCTCCGACTCAGxxxxAGAGTTTGATCMTGGCTCAG-3’
164
reverse primer 519R: 5’-GWATTACCGCGGCKGCTG-3’; where x’s represent the barcode sequences,
165
adapter sequences are double underlined while primer sequences are underlined. This primer set targets
166
the V1-V3 region of the bacterial 16S rRNA gene (30, 31). PCR products were prepared in triplicate and
167
pooled for purification. The PCR products were purified using the Ampure system (Agencourt Bioscience
168
Corporation), with modifications to the volume of purified PCR products (22.5 μl) and AMPure beads (72
169
μl). The prepared DNA libraries were sequenced on using 454 GS Junior (Roche Applied Science,
170
Indianapolis, IN) according to the manufacturer’s instructions.
were
amplified
for
20
cycles
using
the
barcoded
primer
set
27F:
5’-
and
the
171
A total of 369,855 16S rRNA gene sequences for taxonomic analysis were processed using mothur v.
172
1.33.3 (32) following the standard operating procedure (SOP) proposed by Schloss et al. (33). Sequences
173
were removed from the analysis if they had a read quality score under 25, contained ambiguous characters,
174
contained more than two mismatches to the forward primer or one mismatches to the barcode, or were
175
under 150bp or over 550 bp. Sequencing noise was further reduced through the pre-cluster method (34).
176
Chimeras were identified and removed using chimera.uchime. The average read length was 186 bp after
177
barcode and primer sequences were trimmed. Greengene database (gg-13-5-99) was used to align and
178
classify the sequences. The similarity cutoff of >97% was used to assigning the same OTUs.
179
dmdA genes detected in this study were from six different subclades, and previous studies showed
180
that they were mainly found in the SAR11 clade (the Pelagibacteraceae). OTUs classified as the
181
Pelagibacteraceae and with relative abundance >0.5% were used for phylogenetic tree construction using
182
the neighbor-joining method for further analysis.
183
Nucleotide sequence accession numbers. Bacterial 16S rRNA gene sequences and accompanying
184
metadata have been deposited in the DDBJ (http://www.ddbj.nig.ac.jp/) Sequence Read Archive under the
185
project number DRA002967. 7
186
Statistical analysis. To investigate potential environmental drivers of DMSP degradation, principal
187
component analysis (PCA) and redundancy analysis (RDA) were performed using R’s BiodiversityR
188
package (35, 36). The relative abundance of DMSP-degrading gene data and environmental factor data
189
were used to perform PCA and RDA analysis. The environmental factors used in RDA were standardized
190
by subtracting the mean value and then divided by the standard deviation of the variable. Hierarchical
191
clustering was performed using R’s Vegan package (37). Spearman’s rank correlation coefficient (ρ) and
192
significance (P) values were calculated using R’s Hmisc package (38) and the PerformanceAnalytics
193
packages (39).
194
The DMSP-degrading gene abundance data used in this study were copy numbers of DMSP-
195
degrading genes normalized to that of the bacterial 16S rRNA gene, unless stated otherwise. DMSP
196
(DMSPp, DMSPd, and DMSPt) data were only available for eight stations in this study; therefore, these
197
variables were not included in the RDA analysis. Chlorophyll a samples were not collected from
198
northernmost stations (N1p to N1b2), and so these three stations were also excluded from the analysis.
199
We used the Bray-Curtis dissimilarity distance matrix based on the relative abundance of 16S rRNA data
200
to construct the dendrogram. The dendrogram was constructed by using the UPGMA algorithm.
201
RESULTS
202
DMS and DMSP concentrations. The concentrations of DMS and DMSP in seawater samples were
203
listed in Table 1 together with environmental data and sampling site information. In this study, we focused
204
on samples corresponding to DMS peaks (i.e., those collected when increase in DMS concentration were
205
detected) and DMS baselines (i.e., those collected when the DMS concentration was at least 1 nM lower
206
than the corresponding peak DMS concentration) (Supplementary materials S1). The average DMS
207
concentration was 3.95 ± 1.37 nM, with the lowest concentration detected in the North Pacific Ocean
208
(N1b2: 1.00 nM) and the highest was detected in the South Pacific Ocean (S2p2: 6.31 nM). Regionally,
209
the highest average DMS concentration was in the samples from the SP region (4.92 ± 1.16 nM) and the
210
lowest was in the samples from the NP region (3.14 ± 1.29 nM). The average DMS concentration in the
211
samples from the Equatorial Pacific Ocean was 3.90 ± 0.78 nM.
212
DMSP concentrations were measured in all EP and three NP (N3b1 to N3b2) samples. DMSP
213
(DMSPd and DMSPp) concentrations in the samples from the EP region were higher than those from the 8
214
NP region. The average DMSPt (total DMSP, combined value of DMSPd and DMSPp) concentration in
215
the samples from EP region was about three times higher than that from the NP region (EP: 23.26 ± 1.27
216
nM, NP: 7.86 ± 1.16 nM). Most of the DMSPt (>81%) in seawater was in the form of DMSPp in both
217
regions. DMSPp concentrations in the Pacific Ocean were positively correlated with chlorophyll a
218
concentrations (ρ = 0.75, P < 0.05), which agreed with the previous results that marine phytoplankton
219
were the main source of DMSPp production (3, 40).
220
Bacterial dmdA and dddP gene abundance. Seven subclades (A/1, A/2, B/3, C/2, D/1, D/3, and E/2)
221
of dmdA genes, dddP genes, and bacterial 16S rRNA genes were quantified using qPCR. The bacterial
222
16S rRNA gene copy numbers were used to normalize the copy numbers of dmdA subclade genes and the
223
dddP genes to evaluate the ratios of bacteria possessing DMSP-degrading genes in the total marine
224
bacterial community. The values obtained after normalization of each gene copy number against that of
225
the bacterial 16S rRNA gene copy numbers were indicated as the relative abundance.
226
Both free-living (0.22 µm to 3 µm) and particle-associated (>3 µm) bacterial DNA were used to
227
quantify DMSP-degrading genes. However, particle associated bacteria possessing dddP, dmdA subclade
228
C/2 and D/1 genes which were the most abundant DMSP-degrading genes detected in this study
229
accounted for less than 3% of those of free-living bacteria (Supplementary material S3). Therefore, in this
230
paper, we mainly focused on the results from free-living bacteria.
231
The copy numbers of dmdA and dddP genes and their distributions in Pacific Ocean varied greatly
232
among sampling sites. Some sites showed particularly high copy numbers of dddP genes (sites around the
233
upwelling area in the SP region and a few sites in the EP region). At most sites, the copy numbers of dddP
234
genes was less than that of the total dmdA genes (the combined value of all dmdA subclades); however,
235
around the upwelling area in the SP region, the copy numbers of dddP genes exceeded those of total
236
dmdA genes (Supplementary material S2). The copy numbers ratio of these genes (dddP / total dmdA)
237
ranged from 2% to 140%. The copy numbers of dddP genes in the samples from NP region, especially the
238
northernmost stations (average of N1p to N1b2: 9.45 ± 0.65 × 105 copies l-1), were lower than those
239
samples from SP and EP regions. In the samples from the SP region, the copy numbers of dddP genes
240
were higher in areas closer to the Peruvian upwelling area. Samples from these stations (S3p1, S3b, and
241
S3p2) showed the highest copy numbers of dddP genes, with an average of 1.03 ± 0.44 × 108 copies l-1. 9
242
The dmdA subclade D/1 (SAR11 clade) was the most abundant subclade throughout the collected samples
243
(3.49 ± 1.70 × 107 copies l-1) except those from the northernmost stations (N1p, N1b1, and N1b2). In
244
these northernmost stations, the dominant dmdA subclade was C/2 (SAR11 clade), with an average of
245
1.35 ± 0.79 × 107 copies l-1. The copy numbers of the dmdA subclades B/3 and E/2 genes were highly
246
variable among samples, and accounted for only about 5% of total dmdA genes.
247
The relative abundance of DMSP-degrading genes, the values after normalizing each gene copy
248
numbers by that of the bacterial 16S rRNA gene copy numbers, was an average 33 ± 12% (ranging from
249
16% to 60%) (Fig. 1). The averaged relative abundances of dddP genes in the samples from NP, SP, and
250
EP regions were 3% ± 2%, 13% ± 11%, and 15% ± 6%, respectively. Unusually high relative abundances
251
of dddP were found in the samples from around the upwelling area of the SP region (S3p1: 23%, S3p2:
252
31%), and also in the samples from EP region (E1p: 22%, E1b: 22%). An averaged relative abundance of
253
total dmdA was 23% ± 5%; the maximum value was detected around the SP upwelling area (32%).
254
Among all of the dmdA subclades, C/2, D/1, and D/3 subclades from the SAR11 clades made up the
255
greatest proportion in total dmdA genes, with an average of 88% ± 6%. Bacteria possessing the dmdA
256
subclade A/1 and A/2 in the northernmost sites N1p and N1b1 exceeded those possessing dddP, while at
257
other sites the bacteria with these genes made up to about one fifth of the bacteria possessing dddP genes.
258
Bacterial community structure analysis. After removing low quality sequences, a total 150,170
259
sequences were obtained from pyrosequencing, which were divided into 2112 different OTUs (97%
260
identity), and maximum 431 different OTUs were from DMS hot spot samples (Supplementary material
261
S4).
262
The bacterial communities in DMS hot spot samples were dominated by Alphaproteobacteria (52 ±
263
9%), followed by Synechococcophycideae (24 ± 11%), Gammaproteobacteria (11 ± 3%) and
264
Flavobacteriia (4 ± 1%). OTUs belonging to these four classes made up >80% of all observed OTUs.
265
Generally, samples from NP regions showed lower relative abundance of Alphaproteobacteria and higher
266
relative abundance of Synechococcophycideae compared with the other two regions. In the two samples
267
from the northernmost station N1b1 and N1b2, the relative abundance of Synechococcophycideae (N1b1:
268
41%, N1b2: 40%) were higher than Alphaproteobacteria (N1b1: 38%, N1b2: 36%). Within the
269
Synechococcophycideae class, over 99.9% of OTUs concentrated in the Synechococcaceae; the relative 10
270
abundance of this family showed higher values in the samples from NP regions (35 ± 8%) and lower
271
values in the samples from EP (16 ± 4%)
272
70-95% of total Gammaproteobacteria abundance in all collected samples. The samples from EP regions
273
showed higher relative abundance of this family than the samples from the other two regions. Within the
274
Alphaproteobacteria, Pelagibacteraceae (or SAR11 clade) was the most abundant family followed by the
275
Rhodobacteraceae. OTUs belonging to these two families made up to 94 ± 2% of total
276
Alphaproteobateria relative abundance. Pelagibacteraceae was the most abundant family in all the
277
samples. This family was most abundant in the samples from SP regions (53 ± 2% in total OTUs)
278
followed by the samples from EP (50 ± 4%) and NP (37 ± 5%) regions. Compared with
279
Pelagibacteraceae, Rhodobacteraceae exhibited much lower relative abundance in total OTUs ranging
280
from 1 to 8%.
and SP (17 ± 6%) regions (Fig. 2). Halomonadaceae made up
281
The phylogenetic tree of Pelagibacteraceae constructed from the OTUs with relative abundance
282
more than 0.5% yielded four different clades (Supplementary material S5). OTU0001, the dominant OTU
283
in all samples, was found to be confined to the SAR11 subclade Ia. OTU0003, OTU0032, and OTU0106
284
belonged to the SAR11 subclade Ib; OTU00005, OTU00016, and OTU00039 belonged to SAR11
285
subclade II; OTU00028 belonged to SAR11 subclade IV. The combined relative abundances of SAR11
286
subgroup Ia and Ib were about 70-90% of total relative abundance of SAR11 clade (Pelagibacteraceae),
287
and 25-50% of total bacterial OTUs detected.
288
To compare the bacterial communities of different sampling stations, the UPGMA cluster analysis
289
was performed with the OTUs with relative abundance more than 0.5% (Supplementary material S6). The
290
dendrogram separated the samples into three different clusters: the first cluster consisted of the three
291
northernmost area samples (N1p, N1b1, and N1b2), the second cluster consisted of the remaining NP
292
region samples and one SP region sample (S1b) and the final cluster consisted of all EP region samples
293
and the remaining SP region samples. More specifically, most of the grouped samples (samples from the
294
DMS peak site and corresponding baseline sites) clustered together except for S1p-S1b and E2p1-E2p2-
295
E2b.
296
Correlation analysis among bacterial 16S rRNA gene abundance, DMSP-degrading gene
297
abundance, and environmental factors. dmdA subclade C/2, D/1, and D/3 (dmdA SAR11 clade) 11
298
exhibited the highest abundance within the tested dmdA subclades. dddP genes quantified in this study
299
were specific for the marine Roseobacter clade. Therefore, a correlation analysis of clade-specific dmdA
300
and dddP genes abundance relative to bacterial 16S rRNA genes abundance for Pelagibacteraceae and
301
Rhodobacteraceae was carried out for all DMS hotspot samples. The relative abundance of the
302
Rhodobacteraceae correlated significantly with the relative abundance of dddP genes (ρ = 0.92, P
0.1).
304
The dmdA subclade D (the sum of dmdA subclade D/1 and D/3) correlated significantly with the relative
305
abundance of the SAR11 subclade Ia (ρ = 0.57, P < 0.01); the dmdA subclade C/2 with the SAR11
306
subclade Ib (ρ = 0.78, P < 0.001) (Supplementary material S7). The relative abundance of
307
Rhodocacteraceae correlated negatively with the relative abundance of SAR11 subclade Ib (ρ = −0.91, P
308
< 0.001) but correlated positively with SAR11 subclade Ia (ρ = 0.74, P < 0.01). The relative abundance of
309
SAR11 subclade Ia correlated negatively with SAR11 subclade Ib (ρ = −0.87, P < 0.001). The relative
310
abundance of dddP genes negatively correlated with the relative abundance of dmdA subclade C/2 (ρ =
311
−0.75, P < 0.01) but positively correlated with dmdA subclade A/1 (ρ = 0.95, P < 0.001). The relative
312
abundance of dddP genes increased with dmdA subclade D, but this was not statistically significant (ρ =
313
0.6, P < 0.1).
314
The DMSP-degrading gene abundance was also correlated to some environmental factors. The
315
relative abundance of total dmdA genes were positively correlated with DMSPd concentration (ρ = 0.71,
316
P < 0.01), and also increased with DMSPt concentrations (ρ = 0.71, P < 0.1), although not in significant
317
correlation. Among all of the dmdA subclades, the relative abundance of SAR11 clade (combined value of
318
relative abundance of dmdA subclade C/2, D/1, and D/3) was correlated with DMSPd concentration (ρ =
319
0.6, P < 0.05). The relative abundance of dddP was correlated with DMSPp concentration (ρ = 0.9, P
chlorophyll a
338
(µg l-1) > salinity (ppt) > DMS (nM); for axis RDA2: chlorophyll a (µg l-1) > temperature (°C) > DMS
339
(nM) > salinity (ppt). ANOVA test showed that axis RDA1, chlorophyll a, and temperature were
340
statistically significant in the RDA model (P < 0.01) while axis RDA2, DMS, and salinity were not (P >
341
0.1). The patterns in the RDA ordination, which showed the relationships between DMSP-degrading gene
342
structures and the four environmental factors, were consistent with the patterns in the PCA ordination.
343
The axis RDA1 represented 52% of the variation in bacterial communities possessing DMSP-degrading
344
genes, and most of the variation was explained by chlorophyll a and temperature. This axis (RDA1)
345
correlated significantly with chlorophyll a (ρ = 0.62, P < 0.01) and temperature (ρ = 0.62, P < 0.01), but
346
not with DMS (ρ = 0.11, P >0.1) or salinity (ρ = 0.12, P > 0.1). RDA1 was positively correlated with the
347
samples from the Group 2 sites, and negatively correlated with the samples from the Group 1 sites. RDA
348
constructed with the relative abundance of total dmdA (indicated as dmdAt) relative and dddP also
349
showed similar results with the above RDA (Supplementary material S8).
350
dddP gene abundance and DMS concentration. The relative abundance of dddP genes collected
351
from the DMS peak site was higher than that of the corresponding DMS baseline site in the same
352
sampling region, except the samples from the northern most area N1p-N1b2 and one sample E2b from EP
353
regions (Fig. 4). When all samples were evaluated, these relationship between the relative abundance of 13
354
dddP genes and DMS concentrations did not meet the criteria for statistical significance (ρ = 0.32, P >
355
0.1).
356
PCA results indicated that samples from the NP region and non-upwelling area in the SP region (the
357
Group 1 sites) shared similar DMSP-degrading gene structure (Fig. 3a) which was strongly influenced by
358
the chlorophyll a concentrations and temperature (Fig. 3b). Therefore, samples from the Group 1 sites and
359
Group 2 sites were analyzed separately. When data from Group 1 sites were pooled, the relative
360
abundance of dddP was positively correlated with the seawater DMS concentration (ρ= 0.70, P < 0.05)
361
(Supplementary material S9). In addition, the proportion of the relative abundance of dddP to the relative
362
abundance of total DMSP-degrading genes (dddP / (dddP + total dmdA)) was also positively correlated
363
with DMS concentrations (ρ = 0.69, P < 0.05). For Group 2 sites, the DMS concentration increased with
364
the relative abundance of dddP genes, but this was not statistically significant (ρ = 0.44, P > 0.1).
365
DISCUSSION
366
We obtained four key results in this study. First, the distribution of both dddP and dmdA genes in the
367
Pacific Ocean varied greatly among the sampling sites. In most sites, total dmdA genes were more
368
abundant than dddP genes, however, in some sites, dddP gene abundance exceeded total dmdA (Fig. 1).
369
Second, distribution patterns of DMSP-degrading genes in the oligotrophic North and South Pacific
370
Ocean differed from the equatorial and South Pacific upwelling area. This distribution patterns were
371
influenced by the chlorophyll a concentrations and temperature (Fig. 3). Third, SAR11 subgroup I (Ia and
372
Ib) possessing dmdA genes were the major potential DMSP consumers in the surface seawater. Four, the
373
DMS concentrations increased with the relative abundance of marine Roseobacter clade possessing dddP
374
genes (Fig. 4). In the samples from NP and non-upwelling SP regions, there were statistically significant
375
positive correlations between the relative abundance of dddP genes as well as the proportion of the dddP
376
genes in the total DMSP-degrading genes (dddP / dddP + total dmdA) and DMS concentrations
377
(Supplementary material S9).
378
The range of average DMS concentrations in the subtropical and equatorial Pacific Ocean is
379
moderate to low throughout the year, with higher concentrations during summer (41). Our data possibly
380
reflected this seasonal shift in DMS concentrations in both hemispheres. The average DMS concentration
381
in the samples from NP region (winter) was lower than that in the samples from SP region (summer). The 14
382
highest value was found at S2p2, which was higher than the average DMS concentration in summer in
383
this region (South Pacific Subtropical Gyre: SPSG). This high DMS concentration might be influenced by
384
the permanent upwelling area of the Peru Current System (42).
385
The per-cell uptake of amino acid and/or dissolved adenosine triphosphate by particle-associated
386
bacteria is faster than uptake by free-living bacteria (43, 44). However, due to the low abundance of
387
particle-associated bacteria fraction (3 µm) was 10-15% and less
391
than 5%, respectively (S. Suzuki, personal communication). Our results indicated that particle-associated
392
bacteria possessing DMSP-degrading genes were less than 3% of those of free-living bacteria
393
(Supplementary material S3). Therefore, based on these results (the low abundance of particle-associated
394
bacteria and DMSP-degrading genes), we assumed that SAR11 and Rhodobacteraceae populations which
395
are the main possessor of dmdA and dddP genes, respectively, contribute less in DMSP metabolism in
396
particle-associated bacteria fraction than in free-living bacteria fraction.
397
The relative abundance of total dmdA ranged from 15% to 32% in the free-living bacteria fraction
398
within each sample. This value was lower than the values calculated from the (2007) Global Ocean
399
Sampling (GOS) metagenomic database, normalized using well-known single-copy genes, which showed
400
that more than half of marine bacteria possessed dmdA genes (16). We assumed several reasons to explain
401
this discrepancy: first, the primer sets used in this study might not cover all known dmdA gene diversity,
402
since the primer nucleotide sequences have to avoid redundancies for accurate qPCR amplification (20).
403
Another possible reason was that the bacterial 16S rRNA gene was used as the normalizer in this study
404
since the single-copy genes used in the Global Ocean Sampling Survey were hard to target. We predicted
405
the 16S rRNA gene copy numbers within each sample according to the 16S rRNA gene community
406
structures against strains with known genomic sequences using PICRUSt (48). The sequences used to
407
predict 16S rRNA gene copy numbers covered over 80% of total sequences obtained from bacterial
408
community structures. The predicted 16S rRNA gene copy numbers ranged from 1.1 to 1.4 copies, with
409
an average value of 1.3 ± 0.1 copies. This result agreed with the previous results which showed that the 15
410
surface seawater free-living bacteria contained on average 1.8 copies of the 16S rRNA genes, with higher
411
average 16S rRNA genes in coastal ocean (2.8 copies) compared to open ocean (1.3 copies) (49). The
412
multi-copy numbers of 16S rRNA gene contained in a bacterial cell could cause underestimation of the
413
proportion of dmdA gene possessing cells in the samples. Further, non-bacterial 16S rRNA genes could
414
have decreased the relative abundance of dmdA genes, as they contributed on average 4 ± 2% (range 1 to
415
8%) to the total small subunit rRNA pool.
416
The average relative abundance of dddP obtained in this study (10%) was about two times higher
417
than those in the GOS metagenomic data (8, 12) and also about three times higher than the values showed
418
in the station ALOHA and BATS (17, 18). The relatively high values (c.a. 20-30%) were recorded in
419
several EP and SP (around the Peruvian upwelling area) samples such as S3p1, S3p2, E1p, and E1b.
420
These values contributed to the relatively high average values obtained in this study. dddP gene
421
distributions in the Pacific Ocean varied significantly. This unevenness was also appeared in GOS
422
metagenomic data with several higher values in coastal and hypersaline sites (12-70%) and low values in
423
other coastal and open ocean (0%) (12).
424
The relative abundance of SAR11 subgroup Ia was positively correlated with dmdA subclade D, and
425
SAR11 subgroup Ib was positively correlated with dmdA subclade C/2, implying that SAR11 subgroup Ia
426
and subgroup Ib possessed dmdA subclade D and subclade C/2 genes, respectively. Genomic sequences
427
for SAR11 subgroup Ia bacteria such as Pelagibacter ubique HTCC 1062 and Pelagibacter ubique HTCC
428
1002 contained dmdA subclade D genes which supported the hypothesis above. However, no such data
429
from SAR11sugroup Ib were available to confirm the presence of dmdA subclade C/2 genes in this group.
430
From our data, SAR11 subgroup Ia and Ib made up an average 38% of the total bacteria (based on 16S
431
rRNA gene sequences) (Fig. 2), while dmdA subclade C/2 and D genes made up an average 20% of the
432
total bacteria (based on 16S rRNA gene copy numbers) (Fig. 1a). Therefore, over half of SAR11 subgroup
433
Ia and Ib possessed either dmdA subclade C/2 or subclade D genes. Previous studies revealed that roughly
434
half of SAR11 bacteria were responsible to assimilate dissolved DMSP (50). Therefore, it can be inferred
435
that SAR11 subgroup Ia and Ib bacteria possessing dmdA subclade C/2 and D genes could play an
436
important role in DMSP assimilation pathways. In addition, based on the relatively high correlations
437
between DMSPd concentrations with SAR11 subgroup Ia and Ib and dmdA SAR11 clade, as well as the 16
438
abundance of dmdA SAR11 clade in the total DMSP related genes, we believed that DMSP was mainly
439
assimilated by the SAR11 subgroup Ia and Ib which possessed dmdA genes. Previous studies also
440
reported that SAR11 bacteria would compete for labile DOM compounds, including DMSP, and one-
441
carbon (C1) compounds (51). This might explain why the relative abundance of SAR11 Ia was negatively
442
correlated with SAR11 Ib. Competition within SAR11 clades could also appear in the dmdA gene
443
abundance data (the negative correlation of dmdA subclade C/2 with subclade D), although it was not
444
statistically significant.
445
Rhodobacteraceae showed much lower abundance in all samples compared to SAR11. Samples
446
from upwelling area of SP regions and few samples from the EP regions exhibited relatively high
447
abundance of Rhodobacteraceae. The relative abundance of Rhodobacteraceae were highly correlated
448
with the relative abundance of dddP genes, this was reasonable because the Roseobacter specific primer
449
set was used to quantify dddP gene abundance. The slope of this correlation lines was 4.3, suggesting that
450
each marine Roseobacter cell possessed about 4 copies of dddP genes. This amount does not agree with
451
the previous studies reporting a single copy of dddP per cell (7, 8). One of the reasons causing this bias
452
might be the overestimation of dddP genes with previously designed primer set. The marine Roseobacters
453
were the main populations possessing dddP genes, however other organisms (e.g. Marinomonas,
454
Burkholderia, and Aspergillus) were also reported to possess dddP genes (12). Therefore, non-
455
Roseobacters-dddP genes might be amplified during qPCR quantification and cause the high copy
456
numbers of dddP per cell. The primer set used to quantify 16S rRNA genes could also cause the
457
overestimation of dddP gene ratios in total bacterial populations with the possible underestimation of total
458
bacterial abundance.
459
Multi-dimensional analysis separated the DMS hotspot samples into Group 1 and Group 2. Based on
460
this separation, we assumed that the different oceanographic province consisted of different DMSP-
461
degrading gene structures. RDA analysis revealed that one of the most explainable environmental
462
parameters influencing these DMSP-degrading gene structures was chlorophyll a concentrations, which
463
was highly correlated with the RDA1. Phytoplanktons were the main DMSP producers (3, 40), thus their
464
product DMSP might influence the bacterial DMSP-degrading gene structure in that area.
17
465
At Group 1 sites, the DMS concentrations were positively correlated with both the relative
466
abundance of dddP and the proportion of the dddP to the total DMSP-degrading genes (dddP / (dddP +
467
total dmdA)). These results indicated a possible coupling between the presence of bacterial DMSP
468
cleavage pathways and DMS emission in these sites. In addition, DMS concentrations, to some degree
469
were connected with the relative abundance of dddP in Group 2 samples. We assumed that one of the
470
possible reasons for this weak connection was the small sample size, because it was only composed of
471
eight samples in Group 2 sites, compared with twelve samples in Group 1 sites. Another possible reason
472
was that the marine bacteria possessing other ddd+ genes (or other undiscovered genes) were responsible
473
for DMS production in this group.
474
Overall, the distributions of dmdA and dddP genes in the Pacific Ocean showed great spatial
475
variations. These distribution patterns were possibly influenced by chlorophyll a concentrations and
476
temperatures. SAR11 which possessed dmdA genes were suggested to be the main potential consumers of
477
DMSP. The marine Roseobacter clade which possessed dddP genes was responsible for DMS productions
478
in the North and South pacific oligotrophic ocean. Our study implies that the shift in bacterial community
479
structures in response to changes in environmental factors can influence the extent and ability of DMSP
480
metabolism by bacterial assemblages and regulate the DMS emission in subtropical gyres of the Pacific
481
Ocean.
482 483
Acknowledgments
484
We are grateful to the captain and crew of the R/V Hakuho-maru (Japan Agency for Marine-Earth
485
Science and Technology: JAMSTEC), cruise KH11-10, 12-01, for assistance with sample collection. We
486
are grateful to T. Miki of National Taiwan University for the comments about the statistical analysis. We
487
are also grateful to H.-G. Lee and L. Jin of Korea Research Institute of Bioscience & Biotechnology for
488
useful comments and discussions.
489
This research was supported by the Japan Society for the Promotion of Science (JSPS) Research
490
Fellowships for Foreign Scientists (No. P 11389) to C. Y. and grants-in-aid (No. 21014005, 2301389,
491
24121004) from JSPS to K. H.
492 18
References
493 494
1.
495 496
Phytoplankton. Acs Sym Ser 393:167-182. 2.
497 498
Keller MD, Bellows WK, Guillard RRL. 1989. Dimethyl Sulfide Production in Marine-
Stefels J. 2000. Physiological aspects of the production and conversion of DMSP in marine algae and higher plants. J Sea Res 43:183-197.
3.
Stefels J, Steinke M, Turner S, Malin G, Belviso S. 2007. Environmental constraints on the
499
production and removal of the climatically active gas dimethylsulphide (DMS) and implications
500
for ecosystem modelling. Biogeochemistry 83:245-275.
501
4.
Kiene RP, Linn LJ, Gonzalez J, Moran MA, Bruton JA. 1999. Dimethylsulfoniopropionate
502
and methanethiol are important precursors of methionine and protein-sulfur in marine
503
bacterioplankton. Appl Environ Microb 65:4549-4558.
504
5.
505 506
Yoch DC. 2002. Dimethylsulfoniopropionate: Its sources, role in the marine food web, and biological degradation to dimethylsulfide. Appl Environ Microb 68:5804-5815.
6.
Kiene RP, Linn LJ. 2000. Distribution and turnover of dissolved DMSP and its relationship
507
with bacterial production and dimethylsulfide in the Gulf of Mexico. Limnol Oceanogr 45:849-
508
861.
509
7.
510 511
seawater: Tracer studies using S-35-DMSP. Geochim Cosmochim Ac 64:2797-2810. 8.
512 513
Kiene RP, Linn LJ. 2000. The fate of dissolved dimethylsulfoniopropionate (DMSP) in
Moran MA, Reisch CR, Kiene RP, Whitman WB. 2012. Genomic Insights into Bacterial DMSP Transformations. Annu Rev Mar Sci 4:523-542.
9.
Howard EC, Henriksen JR, Buchan A, Reisch CR, Buergmann H, Welsh R, Ye WY,
514
Gonzalez JM, Mace K, Joye SB, Kiene RP, Whitman WB, Moran MA. 2006. Bacterial taxa
515
that limit sulfur flux from the ocean. Science 314:649-652.
516
10.
Todd JD, Rogers R, Li YG, Wexler M, Bond PL, Sun L, Curson ARJ, Malin G, Steinke M,
517
Johnston AWB. 2007. Structural and regulatory genes required to make the gas dimethyl sulfide
518
in bacteria. Science 315:666-669.
519 520
11.
Curson ARJ, Rogers R, Todd JD, Brearley CA, Johnston AWB. 2008. Molecular genetic analysis of a dimethylsulfoniopropionate lyase that liberates the climate-changing gas 19
521
dimethylsulfide in several marine alpha-proteobacteria and Rhodobacter sphaeroides.
522
Environmental Microbiology 10:1099-1099.
523
12.
Todd JD, Curson ARJ, Dupont CL, Nicholson P, Johnston AWB. 2009. The dddP gene,
524
encoding a novel enzyme that converts dimethylsulfoniopropionate into dimethyl sulfide, is
525
widespread in ocean metagenomes and marine bacteria and also occurs in some Ascomycete
526
fungi. Environmental Microbiology 11:1624-1625.
527
13.
Todd JD, Curson ARJ, Kirkwood M, Sullivan MJ, Green RT, Johnston AWB. 2011. DddQ,
528
a novel, cupin-containing, dimethylsulfoniopropionate lyase in marine roseobacters and in
529
uncultured marine bacteria. Environmental Microbiology 13:427-438.
530
14.
Curson ARJ, Sullivan MJ, Todd JD, Johnston AWB. 2011. DddY, a periplasmic
531
dimethylsulfoniopropionate lyase found in taxonomically diverse species of Proteobacteria. Isme
532
J 5:1191-1200.
533
15.
534 535
in a model Roseobacter marine bacterium, Ruegeria pomeroyi DSS-3. Isme J 6:223-226. 16.
536 537
Todd JD, Kirkwood M, Newton-Payne S, Johnston AWB. 2012. DddW, a third DMSP lyase
Howard EC, Sun SL, Biers EJ, Moran MA. 2008. Abundant and diverse bacteria involved in DMSP degradation in marine surface waters. Environmental Microbiology 10:2397-2410.
17.
Varaljay VA, Gifford SM, Wilson ST, Sharma S, Karl DM, Moran MA. 2012. Bacterial
538
Dimethylsulfoniopropionate Degradation Genes in the Oligotrophic North Pacific Subtropical
539
Gyre. Appl Environ Microb 78:2775-2782.
540
18.
Levine NM, Varaljay VA, Toole DA, Dacey JW, Doney SC, Moran MA. 2012. Environmental,
541
biochemical and genetic drivers of DMSP degradation and DMS production in the Sargasso Sea.
542
Environ Microbiol 14:1210-1223.
543
19.
Howard EC, Sun SL, Reisch CR, del Valle DA, Burgmann H, Kiene RP, Moran MA. 2011.
544
Changes in Dimethylsulfoniopropionate Demethylase Gene Assemblages in Response to an
545
Induced Phytoplankton Bloom. Appl Environ Microb 77:524-531.
546
20.
Varaljay VA, Howard EC, Sun S, Moran MA. 2010. Deep sequencing of a
547
dimethylsulfoniopropionate-degrading gene (dmdA) by using PCR primer pairs designed on the
548
basis of marine metagenomic data. Appl Environ Microbiol 76:609-617. 20
549
21.
550 551
Atmospheric Sulfur, Cloud Albedo and Climate. Nature 326:655-661. 22.
552 553
23.
Lovelock JE, Maggs RJ, Rasmusse.Ra. 1972. Atmospheric Dimethyl Sulfide and Natural Sulfur Cycle. Nature 237:452-453.
24.
556 557
Vallina SM, Simo R. 2007. Strong relationship between DMS and the solar radiation dose over the global surface ocean. Science 315:506-508.
554 555
Charlson RJ, Lovelock JE, Andreae MO, Warren SG. 1987. Oceanic Phytoplankton,
Andreae MO. 1990. Ocean-Atmosphere Interactions in the Global Biogeochemical Sulfur Cycle. Mar Chem 30:1-29.
25.
Peng MJ, Xie QY, Hu H, Hong K, Todd JD, Johnston AWB, Li YG. 2012. Phylogenetic
558
diversity of the dddP gene for dimethylsulfoniopropionate-dependent dimethyl sulfide synthesis
559
in mangrove soils. Can J Microbiol 58:523-530.
560
26.
Bell TG, Poulton AJ, Malin G. 2010. Strong linkages between dimethylsulphoniopropionate
561
(DMSP) and phytoplankton community physiology in a large subtropical and tropical Atlantic
562
Ocean data set. Global Biogeochem Cy 24:1-12.
563
27.
Kameyama S, Tanimoto H, Inomata S, Tsunogai U, Ooki A, Yokouchi Y, Takeda S, Obata
564
H, Uematsu M. 2009. Equilibrator inlet-proton transfer reaction-mass spectrometry (EI-PTR-
565
MS) for sensitive, high-resolution measurement of dimethyl sulfide dissolved in seawater.
566
Analytical Chemistry 81:9021-9026.
567
28.
Kasamatsu N, Kawaguchi S, Watanabe S, Odate T, Fukuchi M. 2004. Possible impacts of
568
zooplankton grazing on dimethylsulfide production in the Antarctic Ocean. Can J Fish Aquat Sci
569
61:736-743.
570
29.
571 572
in mixed microbial populations via 5'-nuclease assays. Appl Environ Microbiol 66:4605-4614. 30.
573 574
Suzuki MT, Taylor LT, DeLong EF. 2000. Quantitative analysis of small-subunit rRNA genes
Kim M, Morrison M, Yu ZT. 2011. Evaluation of different partial 16S rRNA gene sequence regions for phylogenetic analysis of microbiomes. J Microbiol Meth 84:81-87.
31.
Lane D. 1991. 16S/23S rRNA sequencing, p. 115-175. In E. Stackebrandt and M. Goodfellow
575
(ed.), Nucleic acid techniques in bacterial systematics. John Wile & Sons Ltd., West Sussex,
576
United Kingdom. . Nucleic acid techniques in bacterial systematics. 21
577
32.
Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA,
578
Oakley BB, Parks DH, Robinson CJ, Sahl JW, Stres B, Thallinger GG, Van Horn DJ,
579
Weber CF. 2009. Introducing mothur: Open-Source, Platform-Independent, Community-
580
Supported Software for Describing and Comparing Microbial Communities. Appl Environ
581
Microb 75:7537-7541.
582
33.
583 584
Schloss PD, Gevers D, Westcott SL. 2011. Reducing the Effects of PCR Amplification and Sequencing Artifacts on 16S rRNA-Based Studies. Plos One 6:e27310.
34.
585
Huse SM, Welch DM, Morrison HG, Sogin ML. 2010. Ironing out the wrinkles in the rare biosphere through improved OTU clustering. Environmental Microbiology 12:1889-1898.
586
35.
Kindt R, Kindt MR. 2014. Package ‘BiodiversityR’. R package. Version:2.5-1.
587
36.
Dixon P. 2003. VEGAN, a package of R functions for community ecology. J Veg Sci 14:927-
588 589
930. 37.
590
Oksanen J, Kindt R, Legendre P, O’Hara B, Stevens MHH, Oksanen MJ, Suggests M. 2007. The vegan package. Community ecology package. Version:1.8-5.
591
38.
Harrell Jr FE, Dupont MC, Hmisc D. 2007. The design package. R package version:2.1-1.
592
39.
Carl P, Peterson BG, Boudt K, Zivot E. 2008. PerformanceAnalytics: Econometric tools for
593 594
performance and risk analysis. R package. Version:0.9-7. 40.
Curson
ARJ,
Todd
JD,
Sullivan
MJ,
Johnston
AWB.
2011.
Catabolism
of
595
dimethylsulphoniopropionate: microorganisms, enzymes and genes. Nat Rev Microbiol 9:849-
596
859.
597
41.
Lana A, Bell TG, Simo R, Vallina SM, Ballabrera-Poy J, Kettle AJ, Dachs J, Bopp L,
598
Saltzman ES, Stefels J, Johnson JE, Liss PS. 2011. An updated climatology of surface
599
dimethlysulfide concentrations and emission fluxes in the global ocean. Global Biogeochem Cy
600
25:GB1004.
601
42.
Penven P, Echevin V, Pasapera J, Colas F, Tam J. 2005. Average circulation, seasonal cycle,
602
and mesoscale dynamics of the Peru Current System: A modeling approach. J Geophys Res-
603
Oceans 110:C10021.
22
604
43.
605 606
Utilization by Free-Living and Attached Bacterioplankton. Mar Biol 64:43-51. 44.
607 608
45.
Azam F, Hodson RE. 1977. Size Distribution and Activity of Marine Microheterotrophs. Limnol Oceanogr 22:492-501.
46.
611 612
Simon M. 1985. Specific Uptake Rates of Amino-Acids by Attached and Free-Living Bacteria in a Mesotrophic Lake. Appl Environ Microb 49:1254-1259.
609 610
Hodson RE, Maccubbin AE, Pomeroy LR. 1981. Dissolved Adenosine-Triphosphate
Ducklow HW, Kirchman DL. 1983. Bacterial Dynamics and Distribution during a Spring Diatom Bloom in the Hudson River Plume, USA. J Plankton Res 5:333-355.
47.
Alldredge AL, Youngbluth MJ. 1985. The Significance of Macroscopic Aggregates (Marine
613
Snow) as Sites for Heterotrophic Bacterial Production in the Mesopelagic Zone of the
614
Subtropical Atlantic. Deep-Sea Res 32:1445-1456.
615
48.
Langille MGI, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC,
616
Burkepile DE, Thurber RLV, Knight R, Beiko RG, Huttenhower C. 2013. Predictive
617
functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat
618
Biotechnol 31:814-821.
619
49.
Biers EJ, Sun SL, Howard EC. 2009. Prokaryotic Genomes and Diversity in Surface Ocean
620
Waters: Interrogating the Global Ocean Sampling Metagenome. Appl Environ Microb 75:2221-
621
2229.
622
50.
Malmstrom RR, Kiene RP, Cottrell MT, Kirchman DL. 2004. Contribution of SAR11
623
bacteria to dissolved dimethylsulfoniopropionate and amino acid uptake in the North Atlantic
624
ocean. Appl Environ Microb 70:4129-4135.
625 626
51.
Tripp HJ, Kitner JB, Schwalbach MS, Dacey JWH, Wilhelm LJ, Giovannoni SJ. 2008. SAR11 marine bacteria require exogenous reduced sulphur for growth. Nature 452:741-744.
627
23
Figures
629
FIG. 1. The relative abundance of DMSP-degrading genes in the Pacific Ocean. a) Relative
630
abundance of seven different subclades of dmdA (bacterial 16S rRNA copy numbers were used as
631
normalizer). b) Relative abundance of dddP (bacterial 16S rRNA copy numbers were used as normalizer).
632
Samples from the North Pacific Ocean are indicated with NP, South Pacific Ocean with SP, and
633
Equatorial Pacific Ocean with EP. Arabic numbers correspond to sampling points at each water mass. 35 30
E/2
25
D/3
20
D/1
15
634
E2b
E2p2
E1b
E2p1
E1p
S3b
S3p2
S3p1
S2p2
S1b
S2p1
S1p
N3p
N3b2
N2b
N3b1
A/2 N2p
B/3
0 N1b2
C/2
5 N1p
10
N1b1
dmdA gene abundance (%)
628
A/1
Sampling Sites
a
dddP gene abundance (%)
35 30 25 20 15 dddP
10 5
b
E2b
E2p2
E2p1
E1b
E1p
S3p2
S3b
S3p1
S2p2
S2p1
S1b
S1p
N3p
N3b2
N3b1
N2b
N2p
N1b2
N1p
635
N1b1
0
Sampling Sites
24
636
FIG. 2. Bacterial community composition of DMS hotspot samples, classified on the family level
637
based on 16S rRNA gene sequences. SAR11 groups were separated into SAR11 subgroup Ia, Ib, and
638
SAR11 others. 100 Relative abundance (%)
others Alteromonadaceae
80
Cryomorphaceae Flammeovirgaceae
60
Flavobacteriaceae 40
Rhodobacteraceae Halomonadaceae
20
Synechococcaceae SAR11 others E2b
E2p2
E1b
E2p1
E1p
S3b
S3p2
S3p1
S2p2
S1b
S2p1
S1p
N3p
N3b2
N2b
N3b1
N2p
N1b2
N1p
639
N1b1
0
SAR11 subgroup Ib SAR11 subgroup Ia
Sampling Sites
640
25
641
FIG. 3. Ordination plot of principal component analysis (PCA) and redundancy analysis (RDA). a)
642
PCA results, b) RDA results. PCA and RDA were constructed using relative abundance of dmdA
643
subclades and dddP. Sampling sites were indicated in black, and the relative abundance of dmdA
644
subclades and dddP genes were indicated in red. Group 1 and Group 2 sites were shaded in green and
645
yellow, respectively. Environmental factors used in RDA were indicated in blue arrows. The two factors
646
explaining the highest proportion of variability were shown in parentheses in RDA. a
0.2
0.3
S3p1
N1b2
0.1
S2p1 S2p2
E1b
S3p2
0.0
N2p
C/2
dddP B/3/ A2E/2 A/1
S1b
D/3
N2b S1p
-0.1
PC2 16% Variance
N1p N1b1
E1p S3b
-0.2
N3b1 E2p2 N3b2 N3p E2p1
D/1
-0.3
E2b
-0.2
647
0.0
0.2
0.4
0.6
PC1 80% Variance
26
b
1 0.2
S2p1 S2p2 N2p
N2b S1p
DMS salinity E1b
S3p2
dddP
A/1 A/2 E/2 C/2 B/3 D/3
0
0.0
S1b
N3b1 N3b2
-0.2
RDA2 9% (chlorophyll a & temperature)
0.4
S3p1
E1p N3p
E2p2 E2p1
D/1
S3b
temperature
chlorophyll a
-0.2
-1
E2b
0.0
0.2
0.4
0.6
RDA1 52% (temperature & chlorophyll a)
648
650
of dddP genes, filled circle: DMS concentration. The relative abundance of dddP genes in each grouped
651
samples were marked with similar patterns and separated with vertical dotted lines.
652
E2b
E2p2
E2p1
E1b
E1p
S3p2
S3b
S3p1
S2p2
S1b
S2p1
S1p
0 N3p
1
0 N3b2
2
5 N3b1
3
10
N2b
4
15
N2p
5
20
N1b2
6
25
N1p
7
30
N1b1
35
DMS (nM)
FIG. 4. The relative abundance of dddP genes and DMS concentration. Bar: the relative abundance
Relative abundance of dddP (%)
649
Sampling sites
27
653
TABLES
654
TABLE 1. Sampling locations and environmental data. Location
Environ. Parameters chlorophyll a DMS* (μg l-1) (nM)
ID
Description
Sample Location
GMT mm/dd/yyyy
Time
Latitude
Longitude
Sample depth
DMS (nM)
Temperature (°C)
Salinity (ppt)
DMSPd (nM)
DMSPp (nM)
N1p
peak1
North Pacific Ocean
12/12/2011
10:36
23°0'6'' N
177°48'22'' E
5m
4.49
26.76
35.32
NA
NA
NA
NA
DMSPt (nM) NA
N1b1
baseline1-1
North Pacific Ocean
12/12/2011
14:04
22°59'56'' N
178°43'11'' E
5m
1.39
26.82
35.33
NA
NA
NA
NA
NA
N1b2
baseline1-2
North Pacific Ocean
12/13/2011
20:30
22°59'54'' N
179°59'54'' E
5m
1.00
26.50
35.33
NA
NA
NA
NA
NA
N2p
peak2
North Pacific Ocean
12/28/2011
19:45
11°56'49'' N
146°55'35'' W
5m
4.25
25.35
33.89
0.07
NA
NA
NA
NA
N2b
baseline2
North Pacific Ocean
12/28/2011
22:03
11°32'47'' N
146°31'54'' W
5m
3.14
25.36
33.58
0.07
NA
NA
NA
NA
S1p
peak3
South Pacific Ocean
01/06/2012
20:26
19°59'58'' S
120°0'51'' W
5m
5.66
25.82
36.21
0.08
NA
NA
NA
NA
S1b
baseline3
South Pacific Ocean
01/07/2012
10:29
22°31'14'' S
119°59'59'' W
5m
3.37
25.66
36.35
0.10
NA
NA
NA
NA
S2p1
peak4-1
South Pacific Ocean
01/20/2012
11:26
20°0'2'' S
100°0'3'' W
5m
6.08
24.48
35.86
0.11
NA
NA
NA
NA
S2p2
peak4-2
South Pacific Ocean
01/20/2012
11:26
20°0'0'' S
100°0'6'' W
5m
6.31
24.73
35.86
0.07
NA
NA
NA
NA
S3p1
peak5-1
South Pacific Ocean
01/23/2012
23:13
15°24'16'' S
86°43'20'' W
5m
4.50
24.04
35.49
0.07
NA
NA
NA
NA
S3b
baseline5-1
South Pacific Ocean
01/23/2012
18:09
15°0'36'' S
85°35'54'' W
5m
3.21
24.51
35.42
0.12
S3p2
peak5-2
South Pacific Ocean
01/24/2012
01:25
14°45'15'' S
84°52'41'' W
5m
5.29
24.10
35.37
0.19
NA
NA
NA
NA
E1p
peak6
Equatorial Pacific Ocean
02/07/2012
03:25
0°3'22'' N
115°2'2'' W
5m
4.80
24.60
34.88
0.22
3.56
NA
4.37
NA
18.79
NA
23.16
NA
E1b
baseline6
Equatorial Pacific Ocean
02/08/2012
15:05
0°0'4'' N
115°0'4'' W
5m
2.86
24.27
34.90
0.19
2.38
2.05
20.20
22.25
E2p1
peak7-1
Equatorial Pacific Ocean
02/09/2012
21:31
0°0'8'' S
121°5'35'' W
5m
4.16
26.43
34.24
0.16
5.14
6.84
14.80
21.63
E2p2
peak7-2
Equatorial Pacific Ocean
02/10/2012
02:59
0°0'10'' S
122°43'34'' W
5m
4.55
26.73
34.12
0.21
5.80
6.37
17.70
24.07
E2b
baseline7
Equatorial Pacific Ocean
02/10/2012
09:25
0°0'9'' S
124°39'30'' W
5m
3.11
23.86
34.81
0.22
3.70
5.35
19.86
25.21
N3b1
baseline8-1
North Pacific Ocean
02/16/2012
18:30
8°25'11'' N
147°3'13'' W
5m
2.91
26.33
34.79
0.15
3.23
1.02
6.00
7.02
N3p
peak8-1
North Pacific Ocean
02/17/2012
09:05
10°53'13'' N
149°8'19'' W
5m
4.71
25.35
34.02
0.19
5.34
0.72
8.78
9.49
N3b2
baseline8-2
North Pacific Ocean
02/18/2012
00:17
14°3'23'' N
151°50'54'' W
5m
3.19
25.36
34.22
0.11
3.42
0.47
6.59
7.06
655 656
*DMS concentrations were detected by gas chromatography for several stations; therefore, two sets of DMS concentration data were available for these stations
28
657
29