microbially mediated transformation of dissolved nitrogen in aquatic environments a dissertation
TRANSCRIPT
Microbially Mediated Transformation of Dissolved Nitrogen in Aquatic Environments
A dissertation submitted
to Kent State University in partial
fulfillment of the requirements for the
degree of Doctor of Philosophy
by
Xinxin Lu (Lucy)
May 2015
© Copyright
All rights reserved
Except for previously published material
Dissertation written by
Xinxin Lu (Lucy)
B.S., Jimei University, 2005
M.S., Ocean University of China, 2008
Ph.D., Kent State University, 2015
Approved by
_______________________________________________________________
Xiaozhen Mou, Associate Professor, Ph.D., Department of Biological Sciences
_______________________________________________________________
Laura G. Leff, Professor, Ph.D., Department of Biological Sciences
_______________________________________________________________
Darren L. Bade, Assistant Professor, Ph.D., Department of Biological Sciences
_______________________________________________________________
Joseph D. Ortiz, Professor, Ph.D., Department of Geology
_______________________________________________________________
Scott Sheridan, Professor, Ph.D., Department of Geography
Accepted by
_______________________________________________________________
Laura G. Leff, Professor, Ph.D., Chair, Department of Biological Sciences
_______________________________________________________________
James L. Blank, Professor, Ph.D., Dean, College of Arts and Sciences
iii
TABLE OF CONTENTS
TABLE OF CONTENTS………………………………………………………………………...iii
LIST OF FIGURES……………………………………………………………………………....vi
LIST OF TABLES………………………………………………………………………………..xi
ACKNOWLEDGEMENTS……………………………………………………………………..xiv
CHAPTER
I. General Introduction ………………………………………..…………………………….1
References…………………………….………………..………………………...15
II. The Relative Importance of Anammox and Denitrification in Total N2 Production in
Offshore Bottom Seawater of the South Atlantic Bight………………………..…...…..28
Abstract……………………………………………………….…….…………...29
Introduction……………………………………………………….…..…………30
Methods…………………………………………………………....…………….31
Results and Discussion…………………………………………………………..35
Conclusion ………………………………………….…………………………...39
References…………………………….…………………………………………40
III. The Relative Importance of Anammox to Denitrification in Total N2 Production in Lake
Erie……………………………………………………………………………………….53
Abstract……………………………………………………….…….……………54
Introduction……………………………………………………….…..…………55
Methods…………………………………………………………....…………….57
Results and Discussion……………………………………………….…...……..59
iv
Conclusion…………………………………………….………………………....65
References…………………….…………………………………….....................66
IV. Temporal Dynamics and Depth Variations of Dissolved Free Amino Acids and
Polyamines in Coastal Seawater Determined by High-Performance Liquid
Chromatography............................................................................................................... .79
Abstract…………………………………………………………….……………80
Introduction……………………………………………………….…..………….81
Methods…………………………………………………………....……………..82
Results…………………………………………………….……………………...87
Discussion………………………………………………………………………..91
Conclusion……………………………………………………………………….95
References………………………………………………………………………..96
V. Identification of Polyamine-Responsive Bacterioplankton Taxa in the South Atlantic
Bight…………………………………………………………………..………………..115
Abstract……………………………………………………….…….…………..116
Introduction……………………………………………………….…..………...117
Methods…………………………………………………………....……………118
Results ……………………………………………….…………………………123
Discussion………………………………………………………………………127
Conclusion……………………………………………………………………...130
References………………………………………………………………………131
VI. Metagenomic and Metatranscriptomic Characterization of Polyamine-Transforming
Bacterioplankton in Marine Environments………………………………...……….......148
v
Abstract……………………………………………………….…….…………..149
Introduction……………………………………………………….…..………...150
Methods…………………………………………………………....……………151
Results.……………………………………………….…………………………157
Discussion………………………………………………………………………163
Conclusion……………………………………………………………………...168
References……………………………………………………………………....169
VII. Summary…………………………………………………………...…………………...199
References…………………………………………………...………………….206
vi
LIST OF FIGURES
Figure 1.1. The simplified diagram of N cycle in oxic and suboxic aquatic ecosystems………..25
Figure 1.2. The chemical structure of individual polyamine compounds, including putrescine,
cadaverine, norspermidine, spermidine, and spermine…………………………....................26
Figure 1.3. Polyamine degradation pathways and associated genes in bacteria…........................27
Figure 2.1. The sampling sites in the offshore bottom water of the SAB in spring (st1 and st2)
and fall (st2, st3, and st4) of 2011…………………………………………………………....45
Figure 2.2. Principal component analysis (PCA) biplot of environmental variables in bottom
water of st1and st2 in spring and st2, st3, and st4 in fall in the offshore of the SAB………..46
Figure 2.3. The N2 production rates through anammox and denitrification in offshore bottom
water of the SAB in (a) spring and (b) fall, 2011……………………………………………47
Figure S2.1. The depth profiles of oxygen saturation (%) in the water column at offshore SAB
sites in (a) spring and (b) fall, 2011…………………………………………….…………....48
Figure S2.2. The production of the 15
N-labeled N2 during (a) 15
NO3- incubation and (b)
15NH4
+
incubation in st1 and (c) 15
NO3- incubation and (d)
15NH4
+ incubation in st2 of the offshore
bottom water in the SAB in spring, 2011……………………………………………………49
Figure S2.3. The production of the 15
N-labeled N2 during 15
NO3- +
14NH4
+ incubations in (a) st1
and (b) st2 in the offshore bottom water of the SAB in spring, 2011………………………..50
Figure S2.4. The production of the 15
N-labeled N2 during (a) 15
NO3- incubation and (b)
15NH4
+
incubation in st2, (c) 15
NO3- incubation and (d)
15NH4
+ incubation in st3, and (e)
15NO3
-
incubation and (f) 15
NH4+ incubation in st4 of the offshore bottom water in the SAB in fall,
2011…………………………………………………………………………………………..51
vii
Figure S2.5. The production of the 15
N-labeled N2 during 15
NO3- +
14NH4
+ incubations in (a) st2,
(b) st3, and (c) st4 in the offshore bottom water of the SAB in fall, 2011………….....…….52
Figure 3.1. The sampling sites in SB, SS, CB1, and CB2 of Lake Erie in August of 2010, 2011,
and 2012………………………………………………………………………..……………73
Figure 3.2. The N2 production rates through anammox and denitrification in bottom water of SB,
SS, CB1, and CB2 in August of (a) 2010, (b) 2011, and (c) 2012 in Lake Erie…………..…74
Figure S3.1. Principal component analysis (PCA) biplot of environmental variables in bottom
water of SB, SS, CB1, and CB2 in Lake Erie in August of 2010, 2011, and 2012………….75
Figure S3.2. The production of the 15N-labeled N2 after incubation with (a) 15
NO3-, (b)
15NH4
+,
and (c) 15
NO3- +
14NH4
+ in SB, (d)
15NO3
-, (e)
15NH4
+, and (f)
15NO3
- +
14NH4
+ in SS, and (g)
15NO3
-, (h)
15NH4
+, and (i)
15NO3
- +
14NH4
+ in CB1 of bottom water in Lake Erie in August,
2010…………………………………………………………………………………………..76
Figure S3.3. The production of the 15
N-labeled N2 after incubation with (a) 15
NO3- and (b)
15NH4
+ in SB, (c)
15NO3
- and (d)
15NH4
+ in SS, (e)
15NO3
- and (f)
15NH4
+ in CB1, and (g)
15NO3
- and (h)
15NH4
+ in CB2 of bottom water in Lake Erie in August, 2011………….…...77
Figure S3.4. The production of the 15
N-labeled N2 after incubation with (a) 15
NO3- and (b)
15NH4
+ in SB, (c)
15NO3
- and (d)
15NH4
+ in SS, (e)
15NO3
- and (f)
15NH4
+ in CB1, and (g)
15NO3
- and (h)
15NH4
+ in CB2 of bottom water in Lake Erie in August, 2012………………78
Figure 4.1. Depth profiles of temperature and salinity at the GRNMS in (a) spring and (b) fall,
2011………………………………………………………………………………….……...107
Figure 4.2. HPLC chromatograms of (A) a standard mixture and (B) a seawater sample..........108
Figure 4.3. Temporal and depth dynamics of DFAAs and PAs………………………………..109
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Figure 4.4. The NMDS ordination based on individual DFAA concentrations at the GRNMS in
spring and fall, 2011………………………………………………………………………..110
Figure 4.5. Variations in the concentrations of major DFAAs in (a) surface, (b) mid-depth, and
(c) bottom water and major PAs in (d) surface, (e) mid-depth, and (f) bottom water within a
diurnal cycle at the GRNMS in spring……………………………………………………..111
Figure 4.6. Variations in the concentrations of major individual DFAAs in (a) surface and (b)
bottom water and major PAs in (c) surface and (d) bottom water within a diurnal cycle at the
GRNMS in fall……………………………………………………………………………..112
Figure S4.1. The NMDS ordination based on individual DFAA relative abundances at the
GRNMS in spring and fall, 2011…………………………………………………………...113
Figure S4.2. The NMDS ordination based on individual PA concentrations at the GRNMS in
spring and fall, 2011………………………………………………………………………..114
Figure 5.1. Sampling stations of st1 (nearshore), st2 (river-influenced nearshore), st3 (offshore),
and st4 (open ocean) in the South Atlantic Bight (SAB) in October, 2011………………...140
Figure 5.2. Principal component analysis (PCA) biplot of environmental variables measured in
water samples from st1, st2, st3, and st4……………………………………………………141
Figure 5.3. The relative abundance (%) of major bacterioplankton families in libraries of CTR,
PUT, and SPD treatments from (a) st1, (b) st2, (c) st3, and (d) st4………………………...142
Figure 5.4. Changes in putrescine and spemidine concentrations (bar graph; left axis) and cell
abundance (line graph; right axis) in the CTR, PUT, and SPD microcosms from (a) st1, (b)
st2, (c) st3, and (d) st4 after 48 h incubation………………………………………….…....143
ix
Figure 5.5. The non-metric multidimensional scaling (NMDS) ordination of samples from the
ORI, CTR, PUT, and SPD microcosms from stations st1 (nearshore; triangle), st2 (river-
influenced nearshore; hexagon), st3 (offshore; square), and st4 (open ocean; circle)……...144
Figure S5.1. The relative abundance (%) of major bacterioplankton at family level in libraries
generated from the original seawater samples (ORIs) collected for microcosm
experiments…………………………………………………………………………………145
Figure S5.2. Family-level rarefaction curves of bacterial 16S rRNA gene sequences in libraries
of original and incubated samples from (a) st1, (b) st2, (c) st3, and (d) st4………………..146
Figure S5.3. Non-metric multidimentional scaling (NMDS) ordination of the original seawater
samples from st1, st2, st3, and st4 based on the relative abundance of major bacterioplankton
families in libraries of each sample………………………………………...……………....147
Figure 6.1. The sampling sites of NS, OS, and OO in the Gulf of Mexico in May, 2013……...190
Figure 6.2. The non-metric multidimensional scaling (NMDS) ordination based on the relative
abundance of major COGs in (a) metagenomes and (b) metatranscriptomes of nearshore (NS;
triangle), offshore (OS; square), and open ocean (OO; star) in the Gulf of Mexico…………...191
Figure 6.3. Taxonomic binning of the protein-encoding sequences in significantly enriched
COGs at bacterial family levels in the PA libraries (PUT, SPD, and SPM) of metagenomes in
(a) NS, (b) OS, and (c) OO and metatranscriptomes in (d) NS, (e) OS, and (f) OO, in relative
to CTRs, in the Gulf of Mexico……………………………………..……………………...192
Figure 6.4. Significantly enriched PA diagnostic gene groups of transporter, γ-glutamylation,
transamination, spermidine cleavage in the PA libraries (PUT, SPD, and SPM) of
metagenomes in (a) NS, (b) OS, and (c) OO and metatranscriptomes in (d) NS, (e) OS, and (f)
OO, in relative to CTRs, in the Gulf of Mexico…………………….……..…………….....193
x
Figure 6.5. Relative abundance of diagnostic PA uptake/metabolism genes in CTR, PUT, SPD,
and SPM metagenomes of (a) NS, (b) OS, and (c) OO in the Gulf of Mexico by taxonomic
assignment…………………………………………………….............................................194
Figure 6.6. Relative abundance of diagnostic PA uptake/metabolism genes in CTR, PUT, SPD,
and SPM metatranscriptomes of (a) NS, (b) OS, and (c) OO in the Gulf of Mexico by
taxonomic assignment………………………………………………………………....…...195
Figure S6.1. Significantly enriched COG categories in the PA libraries (PUT, SPD, and SPM) of
metagenomes in (a) NS, (b) OS, and (c) OO and metatranscriptomes in (d) st1, (e) st2, and (f)
st3, in relative to CTRs, in the Gulf of Mexico…………………..………………..……….196
Figure S6.2. The NMDS ordination based on the relative abundance of major COGs in pooled
metagenomes and metatranscriptomes of nearshore (NS; triangle), offshore (OS; square), and
open ocean (OO; star) in the Gulf of Mexico………………………………………………197
Figure S6.3. The NMDS ordination based on the relative abundance of assigned enriched COGs
at bacterial family level in (a) metagenomes and (b) metatranscriptomes of nearshore (NS;
triangle), offshore (OS; square), and open ocean (OO; star) in the Gulf of Mexico……….198
xi
LIST OF TABLES
Table 1.1. Selected studies on the relative importance (%) of anammox in total N2 production in
aquatic ecosystems…………………………………………………...………………………24
Table 2.1. The environmental variables (average±standard error of the mean) in offshore bottom
water of the SAB in spring and fall, 2011…………………………………………………...44
Table 3.1: PCR primers sets used for both 16S rRNA and hzo gene amplification of
Planctomycetales and anammox bacteria……………………………………………………71
Table 3.2. The environmental variables (average±standard error of the mean) in bottom water of
Lake Erie in August of 2010, 2011, and 2012……………………………………………….72
Table 4.1. Optimized elution gradient program of amino acids and polyamines………………102
Table 4.2. Parameters for validation of HPLC method………………………………………...103
Table S4.1. Pair-wise correlation analysis among individual DFAAs in spring and fall based on
Pearson’s product-moment correlation coefficient…………………………………………104
Table S4.2. Correlations between DFAAs/PAs and environmental variables based on Pearson’s
product-moment correlation coefficient……………………………………………………105
Table S4.3. Correlations between individual DFAAs and PAs based on Pearson’s product-
moment correlation coefficient……………………………………………………………..106
Table 5.1. Results of ANOSIM analyses, with overall and pairwise differences between different
ecosystems in the SAB.....…………………...……………………………………………...136
Table S5.1. The biotic and abiotic variables (average±standard error of the mean) measured in
ORI samples of all four sampling sites……………………………………………………..137
Table S5.2. General statistics of 16S rRNA gene pyrotag sequence libraries of incubated
microcosms…………………………………………………………………………………138
xii
Table S5.3. Changes in concentrations of putrescine and spermidine that were added to sterilized
ORI-st4 seawater during 48 h incubation…………………………………………………..139
Table 6.1. In situ environmental variables (average±standard error of the mean) of in surface
water samples of NS, OS, and OO in the Gulf of Mexico in May, 2013……………..……173
Table 6.2. Statistics of experimental metagenomics and metatranscriptomics………………...174
Table 6.3. Selected major significantly enriched COG groups (OR > 1.5, P < 0.02) related to
metabolisms of amino acids, carbohydrates, energy production, and nucleotide production in
PUT, SPD, and SPM metagenomic libraries, based on OR calculated between the number of
putative gene sequences in the PA and CT metagenomes………………………………….175
Table 6.4. Selected major significantly enriched COG groups (OR > 1.5, P < 0.02) related to
metabolisms of amino acids, carbohydrates, energy production, and nucleotide production in
PUT, SPD, and SPM metatranscriptomic libraries, based on OR calculated between the
number of putative gene sequences in the PA and CT metatranscriptomes………………..177
Table S6.1. NCBI database accession numbers for reference sequences used to identify
homologs to PA functional genes…………………………………………………………..180
Table S6.2. Results of ANOSIM analyses, with pairwise differences between different PA
metagenomes (MG) and metatranscirptomes (MT)………………………………………...181
Table S6.3 Significantly enriched COG groups (OR > 1.5, P < 0.02) related to metabolisms of
amino acids, carbohydrates, energy production, coenzyme, inorganic ion, and nucleotide
production in PUT, SPD, and SPM metagenomic libraries, based on OR calculated between
the number of putative gene sequences in the PA and CT metagenomes………………….182
Table S6.3. Significantly enriched COG groups (OR > 1.5, P < 0.02) related to metabolisms of
amino acids, carbohydrates, energy production, coenzyme, inorganic ion, and nucleotide
xiii
production in PUT, SPD, and SPM metatranscriptomic libraries, based on OR calculated
between the number of putative gene sequences in the PA and CT metatranscriptoms……185
xiv
ACKNOWLEDGEMENTS
First of all, I would like express my special appreciation to my advisor, Dr. Xiaozhen (Jen) Mou,
for her valuable advice, guidance, and help on my academic, career, and personal matters
throughout my Ph. D. study at Kent State University. It has been my greatest pleasure to be her
student under her expertise.
I would also like to thank my committee members of Drs. Laura Leff, Darren Bade, Joseph Ortiz
for serving in my advisory committee and always giving me great support and valuable advice
during my study. Besides, I greatly appreciate my former M.S. advisor, Dr. Li Zou for her
constant encouragement, support, and advice to lead my step forward. I am grateful for the
funding support from the National Science Foundation Grants (OCE1029607 to X.M) and Kent
State University.
I appreciate my lab members and some undergraduates for their kind help and support towards
the completion of my research. Special thanks to Sarah Brower, Jisha Jacob, Steven Robbins,
Sumeda Madhuri, Anna Ormiston, Quangqin Xu, Curtis Clevinger, Mike Kelly, and Huan Bui.
I show my sincere thanks to all the faculties and staffs of Department of Biological Sciences for
giving wonderful courses and helping me out through the study in class and in research.
Lastly, I would like to thank my husband, my parents and sisters for their endless love and
encouragement.
1
Chapter 1
General Introduction
2
Nitrogen transformation in aquatic ecosystem
Nitrogen (N) serves as a fundamental building block of proteins and nucleic acids and its
biogeochemical transformation represents one of the most important nutrient cycles in
ecosystems. In aquatic environments, dominant N species include dinitrogen gas (N2), nitrate
(NO3-), ammonium (NH4
+), dissolved organic nitrogen (DON), and particulate organic nitrogen
(PON). Transformations among these N pools are mainly through a series of microbially
mediated processes, including nitrogen fixation, nitrification, ammonification, remineralization,
dissimilatory nitrate reduction to ammonium (DNRA), denitrification, and anaerobic ammonium
oxidation (anammox) (Figure 1.1). My study focuses on three of these processes in aquatic
environments, namely denitrification and anammox, both of which produce N2, and
transformation of DON, particularly polyamines (PAs).
Denitrification and anammox
Denitrification refers to dissimilatory reduction of NO3- through a sequence of reactions
to nitrite (NO2-), nitric oxide (NO), nitrous oxide (N2O), and then to N2. Microbes that carry out
the denitrification processes are called denitrifiers. Denitrifiers are ubiquitously distributed in a
variety of environments and mainly consist of heterotrophic bacteria that are widely distributed
among over 50 genera (Ward and Priscu, 1997). The great taxonomic diversity of denitrifiers
prevents reliable identification of them via widely used taxonomic biomarkers, such as 16S
rRNA genes. Instead, functional genes, such as nosZ, which encodes for nitrous oxide reductase
(Figure 1.1), are widely used to study denitrification (Scala and Kerkhof, 1999). For decades, N2
production through denitrification has been considered as the sole biological sink for fixed N,
until the discovery of anammox in waste water treatment systems in 1995 (Mulder et al., 1995).
During anammox, NH4+ is anaerobically oxidized by NO3
-/NO2
- to produce N2. In contrast to the
3
diverse taxonomic affiliations of denitrifiers, microorganisms that perform anammox are only
affiated with a group of autotrophic bacteria in the order Planctomycetales (Strous et al., 1999);
PCR primers for anammox specific 16S rRNA genes have been designed and widely applied to
study anammox bacteria in various environments (Woebken et al., 2008).
Since the discovery of anammox, many studies have sought to evaluate its contribution to
the removal of fixed N by N2 production. Most of these studies have been performed in marine
systems and they have demonstrated a great spatial variation in the relative importance of
anammox and denitrification (Thamdrup and Dalsgaard, 2002; Rysgaard et al., 2004; Humbert et
al., 2010). For example, anammox has been found to account for 2% to 67% of N2 production in
sites from a eutrophic coastal bay to the continental shelf (Thamdrup and Dalsgaard, 2002; Table
1.1). However, very few studies have examined potential temporal variations in the contribution
of anammox and denitrification to N2 production. Using 15
N isotope pairing technique, Hannig et
al. (2007) identified the temporal dynamics of anammox and denitrification in the water column
of central Baltic Sea, which was ascribed to the variations of physiochemical conditions.
Environmental factors that affect anammox and denitrification
A number of environmental factors can influence the activity of anammox and
denitrification in aquatic systems, including redox conditions (i.e., availability of O2 and
reductants), temperature, and supply of organic matter and inorganic nutrients (Dalsgaard and
Thamdrup, 2002; Rysgaard et al., 2004; Lam et al., 2009). Among these, O2 level, sulfide (H2S)
availability, and organic matter supply have differential impacts on anammox from
denitrification (Jensen et al., 2008; Lam et al., 2009; Ward et al., 2009).
Effects of O2 level. The presence of O2 inhibits both anammox and denitrification. The
influence of O2 on anammox is instantaneously reversible. In other words, anammox bacteria can
4
restore their full capacity of anaerobic N2 production once O2 is removed (Strous et al., 1997).
Moreover, anammox bacteria appear to be tolerant of oxygen at concentrations up to 13.5 µM
and may continue anammox activities at a low rate in suboxic (4-10 µM O2) marine environment
(Jensen et al., 2008). In contrast, after O2 shock, most denitrifying bacteria need at least 20 hrs to
regain their full capacity of denitrification (Baumann et al., 1996; Kuypers et al., 2005). In
addition, denitrifying bacteria are facultative anaerobes and prefer O2 as electron donors over
NO3-; they only perform denitrification when O2 concentrations drop below 2-4 µM (Devol
1978; Codispoti et al. 2005). Therefore, under suboxic conditions, anammox might dominate
over denitrification in N2 production (Jensen et al., 2008).
Effects of H2S. The presence of H2S has been found to alter the relative importance of
anammox and denitrification in aquatic ecosystems, although the exact underlying mechanism
remains unclear (Dalsgaard et al., 2003; Jensen et al., 2008; Wenk et al., 2013). In the anoxic
water column of Golfo Dulce, Costa Rica, the relative importance of anammox to total N2
production reduced with depth as the H2S concentration increased (Dalsgaard et al., 2003). A
direct inhibitory effect for anammox activity was observed using 15
N isotope pairing technique in
bottom water of Black Sea by adding H2S (Jensen et al., 2008). In contrary, a stimulation of H2S-
dependent chemolithotrophic denitrifcation has been observed in the anoxic water layer of Lake
Lugano (Wenk et al., 2013). Therefore, anammox might be less important in anaerobic
environments where H2S is present.
Effects of organic matter. Anammox and denitrifying bacteria have adopted distinct
trophic strategies on carbon demand. Anammox bacteria that have been identified so far are all
lithoautotrophs, i.e., requesting inorganic substrates as their carbon source and electron donor.
Denitrifiers, on the other hand, are mostly organoheterotrophic bacteria, which use organic
5
carbon as their carbon source and electron donor. Therefore, anammox bacteria maybe favored
over denitrifiers in anaerobic environments with low flux of organic substrates. Consistent with
this hypothesis, in the oxygen minimum zone (OMZ) of Eastern Tropical South Pacific, where
the supply of organic carbon was limited, anammox was found as the major N2 producers (Ward
et al., 2009). In the Arabian Sea, where high organic flux was found, denitrification dominated
the fixed N loss (Ward et al., 2009).
DON pool in marine ecosystem
DON is a major pool of labile N in aquatic environments (Bronk, 2002), particularly in
certain areas of the surface ocean that is characterized by low mineral nutrient concentrations
(McCarthy et al., 1998). DON accounts for up to 83% of total dissolved nitrogen in open ocean
surface water, 8% in open ocean bottom water, and 18% in coastal water (Berman and Bronk,
2003). The DON pool consists of a diverse mixture of compounds, but the structures of most
DON compounds cannot be readily characterized by conventional biochemical methods
(McCarthy et al., 1997). Consequently, the mechanism of DON cycling cannot be fully
elucidated (McCarthy et al., 1998).
Operationally, DON compounds are divided into two general categories based on their
molecular weight. High molecular weight (HMW; usually > 1 kDa) DON typically includes
proteins, nucleic acids (DNA and RNA), humic-like substances with a relatively low N content,
while low molecular weight (LMW) DON contains dissolved free amino acids (DFAAs), urea,
peptides, amino sugars, purines, pyrimidines, amides, and methyl amides (Berman and Bronk,
2003). Due to the analytical constraints, studies on DON biogeochemical transformation are
focused on only a few readily identified DON compounds, such as dissolved free amino acids
(DFAAs) and urea, although they only make up a small proportion of the DON pool.
6
The DON compounds in marine environments may be imported from terrestrial run-offs
and atmospheric inputs, or be released from phytoplankton and other marine organisms during
active growth (Scudlark et al., 1998), cell senesces and viral lysis (Agusti et al., 1998; Fuhrman,
1999), and heterotrophs (Bronk, 2002). Sinks for DON include photochemical degradation
(Bushaw-Newton and Moran, 1999; Kieber et al., 1999), abiotic adsorption (Schuster et al.,
1998), and uptakes of labile components by algae (Lewitus et al., 2000), cyanobacteria (Berman,
2001), bacteria (Antia et al., 1991; Bronk, 2002), archaea (Ouverney and Fuhrman, 2000),
protists (Tranvik et al., 1993), and animals (Jumars et al, 1989). Among these, bacterial uptake is
one of the main sinks for DON (Berman and Bronk, 2003). Bacterial transformations of DON
represent an important DON flux in marine systems (Berman and Bronk, 2003).
Polyamines
Short-chained aliphatic polyamines (PAs), such as putrescine, cadaverine, norspermidine,
spermidine, and spermine, are a class of LMW DON with multiple amino groups (Figure 1.2).
These compounds are ubiquitous in cells of all organisms and participate in many intracellular
processes, such as DNA, RNA, and protein syntheses (Tabor and Tabor, 1984; Igarashi and
Kashiwagi, 2000). Free PAs are widely distributed in seawater, typically at concentrations of a
few of nM (Nishibori et al. 2001, 2003). Putrescine and spermidine are usually dominant in the
PA pool in seawater (Badini et al., 1994; Nishibori et al., 2001, 2003).
PAs can serve as potentially important carbon, nitrogen, and/or energy sources to marine
bacterioplankton (Höfle, 1984; Lee and Jørgensen, 1995; Sowell et al., 2008; Mou et al., 2010,
2011; Liu et al., 2015). Bacteria take up exogenous PAs mainly through adenosine triphosphate
(ATP)-binding cassette (ABC) transporter (Pot) systems. A Pot system typically consists of 4
components, such as spermidine-preferential system of PotA (ATPase), PotB and PotC (channel-
7
forming permease proteins), and PotD (substrate binding protein) and putrescine-specific system
of PotF (a substrate binding protein), PotG (ATPase), and PotH and PotI (channel-forming
permease proteins) (Igarashi and Kashiwagi, 1999). Pot genes were found to constitute as much
as 0.6% of the total predicted genes of Ruegeria pomeroyi DSS-3 (Mou et al., 2010), a
representative of marine roseobacter which has been suggested as one of the numerically and
ecologically important heterotrophic bacterioplankton in marine systems (Hahnke et al., 2013).
This suggests that PAs may play an important role on marine bacterioplankton as nutrient
substrates.
Three catabolic pathways of PAs have been identified in bacterial systems (Figure 1.3).
Putrescine is degraded mainly through two pathways, namely transamination and the γ-
glutamylation (Chou et al., 2008; Mou et al., 2011). In both routes, putrescine is first broken
down to 4-aminobutyrate, which is then further deaminated and oxidized to produce succinic
acid, an intermediate for the tricarboxylic acid (TCA) cycle. Alternatively, putrescine can
convert to spermidine, and enter the 1,3-diaminopropane and γ-aminobutanal pathway for
spermidine (Dasu et al., 2006; Mou et al., 2011). Larger PA compounds, such as spermidine and
spermine, are mostly degraded into putrescine and enter putrescine degradation pathways.
Spermine can also be hydrolized into spermidine and 3-aminopropanaldehyde, which are further
degraded into intermediates that can enter the TCA cycle (Dasu et al., 2006). Similar as pot
genes, genes encoded for PA catabolic pathways have been found widely distributed among
marine bacterial genomes and metatranscriptomes (Mou et al., 2010, 2011, 2014), which again
indicates the potential importance of PAs as nutrient substrates for bacterioplankton in the ocean.
Compared with DFAAs, PAs are historically understudied and have rarely been included
in measurements of marine DON compounds. Therefore, the importance of PAs to the total
8
marine DON pool has not been established. This is partly due to the lack of effective analytical
methods that can simultaneously quantify PAs and other known DON compounds, particularly
DFAAs, in seawater. Besides, only a very limited number of studies have investigated the
bacterial genes and taxa that are involved in PA transformation in a few coastal and open ocean
environments (Sowell et al., 2008; Mou et al., 2010, 2011, 2014); little is known on the potential
variations of PA transformation-related bacterial genes and taxa in other marine systems.
Moreover, all existing PA studies in marine systems have predominantly focused on putrescine
and spermidine (Mou et al., 2010, 2011), while other PA compounds, such as spermine, which
often dominated the PA pools (Lu et al., 2014), might also be potentially important carbon and
nitrogen sources to marine bacterioplankton. However, genes and taxa of marine
bacterioplankton involved in transforming these PA compounds have not been studied.
Introduction to major techniques used
Isotope pairing technique. A number of methods have been developed to measure rates of
denitrification, such as mass balance and acetylene inhibition methods (Balderston et al., 1976;
Sørensen, 1978). Among them, the 15
N isotope pairing technique has later been applied to
simultaneously determine the anammox and denitrification potentials and rates in environmental
samples (Thamdrup and Dalsgaard, 2002; Dalsgaard et al, 2003). In this technique, three
incubations of different 15
N isotope compounds are performed in parallel, and anammox and
denitrification are quantified separately based on their biochemical reaction differences.
Specifically, in anoxic incubations amended with 15
NO3-,
15N
15N may be produced by only
denitrification while 14
N15
N can be generated by both anammox and denitrification. In the anoxic
incubations amended with 15
NH4+, only
14N
15N may be produced from anammox. Anammox and
9
denitrification N2 production rates can then be calculated from the linear regression of 15
N-N2
concentrations as a function of time.
Metagenomics and Metatranscriptomics. More than 99% of microorganisms cannot be
isolated with traditional culturing methods in the lab (Amann et al., 1995). Culture-independent
techniques, such as metagenomics and metatranscriptomics, provide us an avenue to explore the
uncultured microbial diversity and the biochemical functions contained within these uncultured
microorganisms (Kennedy et al., 2010). Metagenomics refers to the method that analyzes the
total genomic DNA and thus the potential metabolic functions carried within a microbial
community, by direct extracting and sequencing community DNA from environmental samples
(Warnecke and Hess, 2009). Unlike the analysis based on single taxonomic or functional genes,
this culture-independent method provides us not only the phylogenetic information but also
insights into energy, nutrient cycling, gene function, and population genetics within the
microbial community, without the PCR or cloning biases (Handelsman, 2004). With the
advances of next-generation sequencing, metagenomics has been widely employed to study the
complex assemblages of natural microbial communities and explore the biochemical pathways
that are present in the microbial communities (Kennedy et al., 2010).
Metatranscriptomics refers to the method that analyzes the total expressed genes within a
microbial community at a certain time, by randomly sequencing community mRNA from
environmental samples (Warnecke and Hess, 2009). Compared to metagenomics,
metatranscriptomics can provide us information on the actual microbial activities at a certain
time and place, as well as how the microbial activities change in response to environmental
forces or biotic interactions (Moran, 2010). Therefore, metatranscriptomics can establish a direct
link between the microbial communities in the environments and the metabolic functions they
10
are expressing at a certain time. Unlike the methods such as reverse transcription PCR and
microarray assays which target specific genes, metatranscriptomics can explore the taxonomy
and metabolic functions of active microbial communities without a prior knowledge of the
metabolisms present in the microbial communities (Vila-costa et al., 2012). A
metatranscriptomics study of PA-transforming bacterioplankton has been performed in an
inshore site at Sapelo Island, Georgia (Mou et al., 2011).
Research objectives and dissertation outlines
The general objective of my dissertation research is to study the bacterially mediated N
transformations in aquatic environments. Specifically, I studied two general processes: 1) the
nitrogen removal via anammox and denitrification in freshwater and marine systems and 2) PA
transformation in various marine systems. Following Chapter 1 (this chapter), I reported my
research findings in five chapters and provided a summary of the overall findings in Chapter 7. A
brief summary of the dissertation chapters is provided below.
Chapter 1: General Introduction
In this chapter, I gave readers detailed background information on anammox,
denitrification, DON, and PAs in aquatic systems and explained my research interests on them.
The hypothesis of each chapter was also described here.
Chapter 2: The Relative Importance of Anammox and Denitrification in Total N2 Production in
Offshore Bottom Seawater of the South Atlantic Bight
The relative contribution of anammox to total N2 production varies spatially in marine
environments, from 1% to 100% (Kuypers et al., 2005; Thamdrup et al., 2006; Hamersley et al.,
2007; Lam et al., 2009; Ward et al., 2009). In this chapter, I hypothesized that anammox
11
activities existed in the offshore bottom water of the South Atlantic bight (SAB), and its
contribution to fixed N removal was more than that of denitrification. Our results from 15
N
isotope pairing technique showed higher anammox potential rates than denitrification potential
rates in bottom water samples collected in April and October, 2011 from the SAB. Our study
suggests that anammox might play vital roles in fixed N removal in the bottom water of marine
systems.
Chapter 3: The Relative Importance of Anammox and Denitrification in Total N2 Production in
Lake Erie
Contribution of anammox to total N2 production in freshwater systems has only been
reported in a few lakes, with percent contribution ranging from 0% to 100% (Schubert et al.,
2006; Hamersley et al., 2009; Rissanen et al., 2011; Yoshinaga et al., 2011; Wenk et al., 2013).
In this chapter, I hypothesized that anammox and denitrification might occur during seasonal
hypoxia and post-phytoplankton bloom and contribute to fixed N removal from Lake Erie. 15
N
isotope pairing technique was used to measure the potential importance of anammox and
denitrification in total N2 production in samples collected from the bottom water of Sandusky
Bay, Sandusky Subbasin, and Central Basin in Lake Erie in summers of 2010, 2011, and 2012.
The results showed that the anammox contributed significantly (up to 99%) to the total N2
production. The anammox and denitrification rates varied greatly among sites and the 3 years we
studied. This underlines the importance of the studies of spatial and temporal dynamics of
anammox and denitrification, in order to establish the roles of the two nitrogen removal
processes and their contributions to nutrient balances in aquatic systems.
Chapter 4: Temporal Dynamics and Depth Variations of Dissolved Free Amino Acids and
Polyamines in Coastal Seawater Determined by High-Performance Liquid Chromatography
12
PAs are one group of labile DON that share many biogeochemical properties with
DFAAs. However, due to the lack of effective analytical methods that can simultaneously
quantify PAs and DFAAs in seawater, the PAs measurements are rarely included in marine DON
studies. A high-performance liquid chromatography (HPLC) method that uses pre-column
fluorometric derivatization with o-phthaldialdehyde, ethanethiol, and 9-fluorenylmethyl
chloroformate was optimized to determine 20 DFAAs and 5 PAs in seawater simultaneously.
This method was further used to examine the concentrations and distributions of DFAAs and
PAs and their temporal dynamics in water samples collected at different depths in Gray’s Reef
National Marine Sanctuary (GRNMS), a near-shore site on the continental shelf of the SAB.
Concentrations of PAs (tens to hundreds nM) were typically at least one order of magnitude
lower than DFAAs (a few nM), despite high concentration of PAs (159.0 nM) was observed in
fall surface water samples with the ratios of PAs to DFAAs closer to 2:3. Our result indicates
that, at least occasionally, PAs may serve as an important DON pool at the GRNMS. This view
is in accordance with recent molecular data but contrasts to measurements made in some other
marine environments.
Chapter 5: Identification of Polyamine-responsive Bacterioplankton taxa in the South Atlantic
Bight
Putrescine (C4H12N2) and spermidine (C7H19N3) are dominant short-chain PAs that are
widely distributed in seawater and in cells of marine organisms, such as phytoplankton,
microorganism, and animals (Tabor and Tabor, 1984; Lee and Jørgensen, 1995). In this chapter,
I hypothesized that the major bacterial taxa involved in putrescine and spermidine transformation
varied among different marine ecosystems. To test this hypothesis, microcosms of
bacterioplankton were set up using surface water collected from nearshore, offshore, and open
13
ocean sites in the SAB. Microcosms were incubated at in situ temperature with or without
amendments of putrescine or spermidine, and the taxonomic structures were tracked with 16S
rRNA gene pyrotag sequencing. Our results showed that the major PA-responsive bacterial taxa
varied significantly among different marine systems. In the nearshore site, Rhodobacteraceae
(Alphaproteobacteria) was the taxon most responsive to polyamine additions after incubation. In
the river-influenced nearshore, offshore, and open ocean sites, the most abundant PA-responsive
bacterioplankton were respectively Gammaproteobacteria of Piscirickettsiaceae, Vibrionaceae,
and Vibrionaceae and Pseudoalteromonadaceae. This indicates that Gammaproteobacteria
might play a more important role in PA transformations than previously thought in marine
ecosystems.
Chapter 6: Metagenomic and Metatranscriptomic Characterization of Polyamine-transforming
Bacteria in Marine Environments
PAs are ubiquitous components in cells and seawater, which are readily taken up by
marine bacterioplankton as carbon, nitrogen, and/or energy sources (Tabor and Tabor, 1984; Lee
and Jørgensen, 1995). In this chapter, I hypothesized that a diverse group of bacterioplankton
was involved in polyamine transformation, and their functional and compositional structures
varied among different marine systems and different polyamine compounds. To test this
hypothesis, microcosms of bacterioplankton were set up using surface water collected from
nearshore, offshore, and open ocean sites in Gulf of Mexico in May, 2013. Microcosms were
incubated onboard at in situ temperature with or without amendments of putrescine, spermidine,
or spermine. A total of 6700391 and 29039763 Illumina sequences were respectively recovered
for metagenomes and metatranscriptomes of incubated bacterioplankton. Our results showed that
γ-glutamylation and spermidine cleavage might be important PA degradation pathways in marine
14
bacterioplankton community. A diverse group of bacterial families were involved in PA
transformation, and were mainly affiliated with bacterial phyla of Actinobacteria, Bacteroidetes,
Cyanobacteria, Planctomycetes, and Proteobacteria. Both PA-transforming bacterioplankton
taxa and functional genes varied among different marine systems and different PA compounds.
Chapter 7: Summary
Biological N availability is an important factor that influences the organism composition,
diversity, and dynamics as well as ecosystem functioning in aquatic environments (Herbert,
1999; Rabalais, 2002). In this chapter, I synthesized the overall findings of my studies and
discussed the results of my dissertation in a broader context.
15
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24
Table 1.1. Selected studies on the relative importance (%) of anammox in total N2 production in
aquatic ecosystems.
Study sites Sample type % of total N2 production References
Baltic-North Sea Sediment 2-67% Thamdrup and Dalsgaard, 2002
Coastal bay of Costa Rica Water 19-35% Dalsgaard et al., 2003
Coasts of Greenland (Arctic) Sediment 1-35% Rysgaard et al., 2004
Benguela upwelling systems off Namibian Shelf
Water ~100% Kuypers et al., 2005
ETSP off Iquique, Chile Water ~100% Thamdrup et al., 2006
Peruvian OMZ Water ~100% Hamersley et al., 2007
Arabian Sea Water 1-13% Ward et al., 2009
Lake Tanganyika water ~13% Schubert et al., 2006
Lake Rassnitzer, Germany water 0-100% Hamersley et al., 2009
Lake Lugano, Switzerland water ~30% Wenk et al., 2013
AnammoxNH4
+
DNRADON
PON
N2
N2 N2
Rem
ineralization
Denitrification
NO2- NO3
-
NO2-
NH4+ NO2
- NO3-
NitrificationN2 fixation
N2
Oxic
Suboxic
Figure 1.1
Figure 1.1. The simplified diagram of N cycle in oxic and suboxic aquatic ecosystems. Modified
from Francis et al. (2007).
NO
N2OnosZ
nirK, nirS
napA, narG
25
Putrescine
Cadaverine
Norspermidine
Spermidine
Spermine
NH2NH2
NH2NH2
NH2 NH
NH2
NH2
HN NH2
NH2 NH
HN NH2
Figure 1.2
Figure 1.2. The chemical structure of individual polyamine compounds, including putrescine,
cadaverine, norspermidine, spermidine, and spermine.
26
Putrescine Spermidine Spermineγ-Glutamyl-putrescine
γ-Glutamyl-γ-aminobutyraldehyde 4-Aminobutyraldehyde
γ-Glutamyl-γ-aminobutyrate
4-Aminobutyrate
Succinate semialdehyde
1, 3-Diaminopropane
3, Aminopropanaldhyde
β-Alanine
Molonic semialdehyde
Acetyl-CoA
puuA speE
spdH
spdH
spdHspuC
kauB
puuB
puuC
puuD
gabT puuE
?
kauB
spdH
TCA cycle
Figure 1.3
Figure 1.3. Polyamine degradation pathways and associated genes in bacteria. Modified from Dasu et al. (2006), Chou et al. (2008), and Mou et al. (2011).
gltAgabD
?
27
28
Chapter 2
The Relative Importance of Anammox and Denitrification in Total N2 Production in
Offshore Bottom Seawater of the South Atlantic Bight
1(This chapter will be submitted to the journal of Marine Science and the author list is as follows: Lu, X.,
and Mou, X. Contributions: Lu, X. performed sampling, did all experimental and data analyses, and wrote
the manuscript; Mou, X. directed and supervised the study.)
29
Abstract
Anaerobic ammonium oxidation (anammox) and denitrification are two microbially mediated
processes that produce inert dinitrogen gas (N2) and lead to the removal of fixed nitrogen (N)
from natural environments. This study investigated the importance of anammox relative to
denitrification in total N2 production in offshore bottom water of the South Atlantic Bight (SAB).
Water samples were collected from 4 stations in spring (April) and fall (October) of 2011 and
analyzed using 15
N isotope pairing technique. The results show that anammox might be a
potentially important N removal process in the offshore bottom water of the SAB, whereas
denitrification might be a minor sink for fixed nitrogen. The potential anammox rates in the
offshore bottom water of the SAB reached up to 626 nM/d, which are comparable to rates
derived from other marine systems. Anammox and denitrification rates exhibited high spatial and
temporal variability in the offshore bottom water of the SAB, which may be ascribed to the
dynamics of in situ environmental variables.
30
Introduction
Nitrogen (N) serves as an important nutrient that often limits biological production in
marine environments (Hecky and Kilham, 1988; Falkowski et al., 1998). Although N2 comprises
79% of the air, most marine planktonic organisms can only utilize N in fixed (i.e., available)
forms, such as nitrate (NO3-) and ammonium (NH4
+). The supply of fixed N in marine systems is
largely regulated by microbially mediated N removal processes. In the conventional N cycle
paradigm, denitrification was considered as the sole microbially-mediated N removal process,
which reduces fixed N to inert N2 gas. This view has shifted since the discovery of anaerobic
ammonium oxidation or anammox in waste water treatment systems (Mulder et al., 1995).
Anammox, which combines nitrite and ammonium to produce N2, has been widely identified in
various oxygen limited marine environments (e.g. Thamdrup and Dalsgarrd, 2002; Rysgaard et
al., 2004; Kuypers et al., 2005; Trimmer et al., 2013).
The relative contribution of anammox vs. denitrification to N2 production varies spatially
in marine environments. In water samples from the oxygen minimum zone (OMZ) of the Eastern
Tropical South Pacific, anammox was responsible for nearly 100% of N2 production (Thamdrup
et al., 2006; Ward et al., 2009), while in the Arabian Sea OMZ, anammox only accounted for as
little as 1% of N2 production (Ward et al., 2009). In marine sediments, anammox contributed as
much as 67% of total N2 production in the Baltic-North Sea, but less than 2% of total N2
production in a eutrophic coastal bay (Thamdrup and Dalsgaard, 2002). Besides oxygen depleted
environments, anammox and denitrification potentials have also been identified in oxic and
suboxic environments. In the oxic and suboxic layers of sediments in a southeast England
estuary, the potential ratios of N2 produced by anammox and denitrification ranged between
31
1:100 to 1:10 (Nicholls and Trimmer, 2009). Temporal variability of anammox in marine
environments is largely unexplored.
This study investigated the presence and temporal variation of anammox activity in the
offshore bottom water of the SAB. Meanwhile, denitrification activity was also measured to
assess the potential importance of these two processes in N2 production. SAB refers to the US
southeast coastal ocean located between Cape Hatteras, North Carolina and Cape Canaveral,
Florida. In addition to coastal input, the continental shelf and slope of the SAB are intruded by
the warm, deep Gulf Stream, which supplies a significant amount of nutrients (Castelao, 2011).
The warm Gulf Stream water is lighter and overlies the cold and dense shelf water, which leads
to establishment of pycnocline and prevents vertical mixing of oxygen between the surface and
bottom water layers. In situ biological consumption in the bottom water beneath the Gulf Stream
often drives the system to be oxygen depleted (Atkinson et al., 1978; Atkinson and Blanton,
1986), a condition that may favor anammox and denitrification. The physicochemical properties
of the Gulf Stream vary seasonally and so does its impact on environmental conditions of the
SAB bottom water (Bishop et al., 1980). This creates opportunities to study temporal variation of
anammox and denitrification and environmental influence on them. I hypothesized that
anammox was a potentially important N removal process in the offshore bottom water of the
SAB, and its importance relative to denitrification in total N2 production might be affected by
environmental factors.
Methods
Sample collection and processing
Samplings were performed onboard the R/V Savannah in 2011, one in April from stations
st1 and st2 and the second one in October from stations st2, st3, and st4 (Figure 2.1). In fall
32
cruise, we planned to perform anammox and denitrification experiments on the same sampling
sites as those in spring, but we failed because of the bad weather conditions during sampling.
Bottom water samples were collected at about 1 m above the seafloor using Niskin bottles that
were mounted on a rosette sampling system (Sea-Bird Electronics, Bellevue, WA). In situ
environmental variables, namely temperature (T) and salinity (S), were determined with a
conductivity-temperature-depth (CTD) water column profiler (Sea-Bird Electronics, Bellevue,
WA, USA) mounted on the sampling system.
Bottom water was immediately transferred to three 250 mL acid washed BOD glass
bottles via Tygon tubing by placing the tubing at the bottom of the BOD bottles and filling from
the bottom with caution to avoid bubbles and minimize turbulence at the sample surface. After
the water overflowed for at least 3 folds of volume change, the BOD bottles were capped and
processed immediately using 15
N isotope pairing technique. Additional bottom water samples
were collected in carboys and immediately filtered by sequentially passing through 3 µm and 0.2
µm pore-size membrane filters (Millipore Inc., Cork, Ireland). The resulting filtrates were
collected in amber glass vials and stored at −80 °C before the analyses of nutrients including
dissolved organic carbon (DOC), dissolved nitrogen (DN), nitrate (NO3-), nitrite (NO2
-), and
ammonium (NH4+). Part of the water (1.8 mL) that passed only through the 3 µm pore size filters
was preserved in 1% (final concentration) freshly prepared paraformaldehyde, and incubated at
room temperature for 1 h before being stored at 4 °C until cell number enumeration using a
FACSAria flow cytometer (BD, Franklin Lakes, NJ, USA).
The anammox and denitrification potentials measured by 15
N isotope pairing technique
15N isotope pairing technique
was used to measure the potentials of anammox and
denitrification (Dalsgaard and Thamdrup, 2002; Thamdrup and Dalsgaard, 2002). Briefly, the
33
sealed bottom water samples, reagents and glassware were moved into a helium gas-filled
anaerobic glove box. Each sample was then divided into 3 subsamples and treated with 5 µM of
Na15
NO3, 2.5 µM of 15
NH4Cl, or 5 µM of Na15
NO3 and 2.5 µM of 14
NH4Cl. The treated water
subsamples were then flushed with helium for 20 min, dispensed into 6 Exetainers (12.6 mL,
leaving no headspace; Labco, High Wycombe, UK) and incubated for 48 hrs. At the beginning (0
h) and end (48 h) of the incubation, 5 mL of water was taken from each of 3 treatment Exetainers
for nutrient analyses and replaced with 5 mL helium, and the remaining water in Exetainers was
sacrificed with 50% ZnCl2 to stop biological activity. The isotope content of N2 in the Exetainers
was determined at the UC Davis Stable Isotope Facility, using a ThermoFinnigan GasBench +
PreCon trace gas concentration system interfaced to a ThermoScientific Delta V Plus isotope-
ratio mass spectrometer (ThermoScientific, Bremen, Germany). During denitrification, two NO3-
molecules combine and generate N2. Thus, 14
N15
N (one N atom from 14
NO3- in the original
water, one N atom from added 15
NO3-) and
15N
15N (both N atoms from added
15NO3
-) may be
both produced after 15
NO3- incubation. The theoretical ratio of
15N
15N to
14N
15N that are
produced by denitrification is equal to F/2(1-F), where F is the fraction of 15
N in NO3- pool
(Nielsen, 1992; Kuypers et al., 2006). Anammox produces N2 by combining one NO3- and one
NH4+, therefore,
14N
15N (one N atom from
14NH4
+ in the original water, one N atom from added
15NO3
-) is generated after
15NO3
- incubation. Accordingly, only
14N
15N may be produced from
anammox process in the incubation of 15
NH4+. Anammox and denitrification N2 production rates
were calculated from the linear regression of 15
N-N2 concentrations as a function of time,
whereas the concentrations of 15
N-N2 produced by anammox and denitrification were determined
based on the 15
NO3- incubation (Thamdrup and Dalsgaard, 2002). The calculation equations are
D total = P30×FN-2
,
34
A total = FN-1
[P29 + 2 × (1-FN-1
) × P30],
Where Dtotal represents the production of N2 by denitrification, Atotal represents the production of
N2 by anammox, FN represents the fraction of 15
N in NO3-, and P29 and P30 represent the
determined total mass of 29
N2 and 30
N2 production, respectively.
Environmental variable analysis
DOC and DN concentrations were determined with a Shimadzu TOC/TN analyzer (TOC-
VCPN; Shimadzu Corp., Tokyo, Japan) based on combustion oxidation/infrared detection and
combustion chemiluminescence detection methods, respectively (Clescerl et al., 1999).
Concentrations of NO3- were measured spectrometrically based on NO3
- reduction with cadmium
granules (Jones, 1984). Concentration of NO2- was determined based on colormetric methods,
which produced a chromophore measured at 540 nm by a microplate reader (BioTeck, Winooski,
VT, USA; Hernández-López and Vargas-Albores, 2003). Concentrations of NH4+ were
determined based on color reactions (Strickland and Parsons, 1968). Bacterioplankton were
stained with Sybr Green II (1:5000 dilution of the commercial stock) and enumerated using a
FACSAria flow cytometer (BD, Franklin Lakes, NJ, USA; Mou et al., 2013).
Statistical analysis
Statistical analyses were performed using the vegan package in R (Oksanen et al., 2007).
Principle component analysis (PCA) was performed on log transformed environmental variables,
including T, S, O2, DOC, DN, NO3-, NO2
-, NH4
+, and cell abundance to examine the variables
that contribute to the variances among study sites. The significance of differences of
environmental variables was tested using Student’s t test (for paired samples), or one-way
ANOVA (for multiple samples). Differences were deemed significant when P < 0.05. Potential
correlations between the anammox rate and the environmental variables were examined by
35
calculating Pearson’s product-moment correlation coefficients (r). Significant correlations were
reported when P < 0.05.
Results and discussion
In situ environmental conditions
PCA analysis of nutrient concentrations in the water samples showed both spatial and
temporal variations among the study sites (Figure 2.2). PCA1 explained 53.1% of the variance
and was mainly contributed by concentrations of DN and NH4+. Concentration of DN ranged
from 0.3 mg N/L to 3.1 mg N/L, with highest values found in water of st3 in fall (ANOVA, P <
0.05; Table 2.1). NH4+ concentration had highest value (8.7 µM; ANOVA, P < 0.05; Table 2.1)
in water of st2 in fall. PCA2 captured 31.6% of the variation and was mainly contributed by
concentrations of DOC and NOx-. Concentrations of DOC and NOx
- respectively varied from 0.7
to 7.2 mg C/L and from 0.04 to 0.5 mg N/L, with highest concentrations both determined in
water of st2 in spring (ANOVA, P < 0.05; Table 2.1). In contrast, T (7.3 to 8.7 °C), bacterial cell
abundance (7.1×105 to 9.3×10
5/mL), O2 (42.8% to 44.5%), and S (35.0 to 35.1 PSU) showed no
significant variation among sites in spring and fall (ANOVA, P > 0.05; Table 2.1).
The dynamics of nutrients observed in offshore bottom water of the SAB indicate a direct
influence of Gulf Stream, which physicochemical properties have been found vary seasonally
(Bishop et al., 1980). Besides, episodic sediment mixing and solute exchange by benthic
organisms may also contribute to the nutrient fluctuations in the overlying bottom water of the
SAB (Marinelli et al., 1998).
The dissolved oxygen saturation decreased with depth and reached at about 44% in the
bottom water of the study sites in April and October of 2011 (Figure S2.1), which suggests that
36
the studied environment was not strictly anoxic. Anammox bacteria have been found to be more
tolerant of oxygen than denitrifiers (Jensen et al., 2008). Using 15
N isotope pairing technique,
anammox potentials have been investigated in oxic and suboxic environments, where anammox
accounted for 1-10% of N2 production (Nicholls and Trimmer, 2009).
The anammox and denitrification potentials and rates
Anammox and denitrification potentials were determined from most of the offshore
bottom water samples at the SAB using 15
N isotope pairing technique, and both of them varied
spatially and temporally. In spring, there were significant increases in 14
N15
N after incubation
with 15
NO3- (2.5 and 0.3 µM, respectively) or
15NH4
+ (0.8 and 0.9 µM, respectively) at st1 and
st2 (t test, P < 0.05; Figure S2.2). No accumulation of 15
N15
N after incubation with 15
NO3- was
detected at either st1 or st2 (Figure S2.2). These indicate that anammox might be a more
potentially important N removal process than denitrification in offshore bottom water of the SAB
in spring (Dalsgaard et al., 2003). Ammonium availability has been suggested as an important
factor which might limit the anammox rate (Dalsgaard et al., 2003). However, stimulation of N2
through anammox was not observed in any spring water samples that received both 14
NH4+ and
15NO3
- (Figure S2.3), indicating that ammonium was not a limiting factor in water samples of the
SAB in spring. This is similar to the findings from the oxygen minimum zones (OMZ) of
Namibian, northern Chile, and Black sea (Kuypers et al., 2005; Thamdrup et al, 2006; Jensen et
al., 2008), but in contrast with that from the Dolfo Dulce, where the anammox activity increased
by 2 to 4 fold upon addition of unlabeled NH4Cl (Dalsgaard et al., 2003).
In fall, an increase of 14
N15
N and 15
N15
N after the incubation with 15
NO3- was detected
only at st3, from 0.4 µM to 0.6 µM and from 0.0005 µM to 0.0007 µM, respectively (Figure
S2.4). Consistently, incubation with 15
NH4+ also resulted in a production of
14N
15N in water of
37
st3 in fall. No obvious accumulation of 14
N15
N or 15
N15
N was observed in any fall st2 and st4
samples that received 15
NO3- or
15NH4
+. These results suggest that anammox and denitrification
might be only important in bottom water of st3 at the SAB in fall. Besides, a stimulation of
anammox was not observed in any water samples of fall based on the incubations with unlabeled
14NH4Cl and
15NO3
- (Figure S2.5), which reveals that the availability of ammonium did not limit
the anammox rate in the bottom water of the SAB in fall (Dalsgaard et al., 2003).
The potential N2 production rates through anammox and denitrification exhibited high
spatial and temporal variability (Figure 2.3). In spring, the anammox rates were respectively 626
nM/d and 199 nM/d at st1 and st2, which were significantly higher than their corresponding
denitrification rates (3 nM/d and undetectable, respectively).The anammox rates were much
lower in fall, which were undetectable at st2 and st4 and were 69 nM/d at st3. The anammox
rates in the offshore bottom water of the SAB in spring were comparable to the rates derived
from a coastal bay in Costa Rica (~500 nM/d; Dalsgaard et al., 2003), while the rates of
anammox in the offshore bottom water of the SAB in fall were similar to the rates in the OMZ of
northern Chile where maximal anammox rate was only at 16.8 nM/d (Thamdrup et al., 2006).
The denitrification activities in fall were only detected at st3 and st4, with the N2 production
rates ≤ 1.1 nM/d. These data suggest that anammox might be a more important N removal
process than denitrification in the offshore water column of the SAB. Similar findings have been
concluded in the water column of Namibian, northern Chile, Peruvian, and Black Sea OMZ as
well as sediments of the deepest sites in the Skagerrak (Kuypers et al., 2005; Thamdrup et al,
2006; Jensen et al., 2008; Trimmer et al., 2013).
The relationships of environmental variables with N2 potential production rate
38
Environmental factors, particularly redox conditions (i.e., availability of O2 and
reductants), temperature, and supply of organic matter and nutrients, play important roles in
regulating anammox and denitrification activities in many other marine systems (Dalsgaard and
Thandrup, 2002; Rysgaard et al., 2004; Lam et al., 2009). Here, Pearson’s product-moment
correlations did not revealed significant relationships (P > 0.05) between anammox potential
rates and the measured environmental variables, including T, DN, NOx-, NH4
+, and O2 saturation,
indicating these factors might play a minor role in regulating anammox activity in the offshore
bottom water of the SAB. However, it is possible that their relationship with anammox potential
N2 rates may be obscured by the complex physical dynamics in the SAB (Marinelli et al., 1998).
The in situ level of O2 is an important factor that affects anammox and denitrification
activities in aquatic environments (Kuypers et al., 2005; Jensen et al., 2008; Lam et al., 2009).
O2 availability has differential impacts on anammox from denitrification. After exposure to
oxygen, most denitrifying bacteria need at least 20 hrs to recover full denitrifying capacity from
enzyme depression/inhibition (Baumann et al., 1996; Zumft, 1997; Kuypers et al., 2005).
However, the effect of O2 on anammox is instantaneously reversible (Strous et al., 1997).
Therefore, once we established the anaerobic conditions during 15
N incubations, the anammox
bacteria would immediately produce N2 from combining NH4+ and NO3
-/NO2
-, but the
denitrifying bacteria could not. Moreover, recent studies have shown that anammox bacteria are
abundant in seawater with in situ O2 up to 20 µM (Hamersley et al., 2007) and active at O2
concentration up to 13.5 µM (Jensen et al., 2008), while denitrification is active only at ≤ 2 – 4
µM O2 (Devol, 1978; Codispoti et al. 2005). In the SAB bottom water, which had high O2
contents, the anammox bacteria might be dormant or convert NH4+
and NO3-/NO2
- to N2 in the
39
anaerobic microniche within the marine snow particles (Hamersley et al., 2007) while the
denitrification activity might be highly suppressed.
Conclusion
Potential activities of anammox and denitrification were detected in samples collected
from offshore bottom water of the SAB using 15
N isotope pairing technique. Our results indicate
that anammox might be a more important N removal process than denitrification in the offshore
water column of the SAB. The potential anammox rates in the offshore bottom water of the SAB
reached up to 626 nM/d, which were comparable to the rates observed from other marine
systems. Anammox and denitrification rates exhibited high spatial and temporal variability in the
offshore bottom water of the SAB, which may be ascribed to the dynamics of in situ
environmental variables.
40
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44
Table 2.1. The environmental variables (average ± standard error of the mean) in offshore
bottom water of the SAB in spring and fall, 2011. Standard errors are listed inside the
parentheses.
site S (PSU) T (°C) DOC (mg C/L) DN (mg N/L) NOx- (mg N/L) NH4
+ (µM) Cell (×105/mL)
st1-sp 35.1 8.7 0.8 (0.04) 0.3 (0.01) 0.3 (0.02) 0.9 (0.1) 7.2 (0.4)
st2-sp 35.0 7.8 7.2 (0.5) 2.0 (0.1). 0.4 (0.05) 1.5 (0.2) 8.6 (0.2)
st2-fa 35.0 7.3 1.0 (0.04) 1.9 (0.3) 0.5 (0.1) 8.7 (2.3) 7.1 (0.1)
st3-fa 35.0 8.1 0.9 (0.05) 3.1 (0.05) 0.04 (0.01) 6.3 (1.6) 9.3 (0.5)
st3-fa 35.1 7.8 0.7 (0.01) 0.9 (0.1) 0.1 (0.04) 1.1 (0.2) 8.5 (0.6)
31.5
31.0
30.5
30.5
Latit
ude
-81.5 -80.5 -79.5
Longitude
st1 (445 m)
st2 (501 m)
st3 (402 m)
st4 (503 m)
Figure 2.1
Figure 2.1. The sampling sites in the offshore bottom water of the SAB in spring
(st1 and st2) and fall (st2, st3, and st4) of 2011. The depth of water column at each
site is listed in the parentheses. Color is used to denote different sampling season
(blue, spring; red, fall; black, spring and fall).
45
Florida
Georgia
1.0
0.5
0.0
-0.5
-1.0
-1.0 -0.5 0.0 0.5 1.0
PC1 (53.1%)
PC2
(31.
6%)
DOC NOx-
DNNH4
+ Cell
TS
Figure 2.2
Figure 2.2. Principle component analysis (PCA) biplot of environmental variables in bottom waterof st1 and st2 in sping and st2, st3, and st4 in fall in the offshore of the SAB. Sample identifiers are based on site (st1, st2, st3, and st4) and sampling season (sp, spring; fa, fall).
st2-sp
st2-fa
st3-fast4-fa
st1-sp
46
O2
650
600
550
200
150
100
500
st1 st2
(a) spring DenitrificationAnammox
N2 p
rodu
ctio
n ra
te (n
M/d
)
(b) fall
N2 p
rodu
ctio
n ra
te (n
M/d
) 60
40
20
0st2 st3 st4
Figure 2.3
Figure 2.3. The N2 production rates through anammox and denitrification in offshore bottom
water of the SAB in (a) spring and (b) fall, 2011.
47
Dept
h (m
)
Dep
th (m
)
0
100
200
300
400
500
600
st1-sp
st2-sp
0 50 100 150
Oxygen saturation (%)
0
100
200
300
400
500
600
st2-fa
st3-fa
st4-fa
0 50 100 150
(a) (b)
Oxygen saturation (%)
Figure S2.1
Figure S2.1. The depth profiles of oxygen saturation (%) in the water column at offshore SAB
sites in (a) spring and (b) fall, 2011.
48
2.5
2.0
1.5
1.0
0.5
0.0
(a)29N230N2
0 2
29N
2 and
30N
2 pr
oduc
tion
(µM
)29
N2 a
nd 30
N2 pr
oduc
tion
(µM
)
29N
2 and
30N
2 pr
oduc
tion
(µM
)29
N2 a
nd 30
N2 pr
oduc
tion
(µM
)
1.0
0.8
0.6
0.4
0.2
0.0
0 2
(b)
0.4
0.3
0.2
0.1
0.0
0 2
(c)1.2
0.9
0.6
0.3
0.0
0 2
(d)
Time (d)
st1 15NO3- incubation st1 15NH4
+ incubation
st2 15NO3- incubation st2 15NH4
+ incubation
Figure S2.2
Figure S2.2. The production of the 15N-labeled N2 during (a) 15NO3- incubation and (b) 15NH4
+
incubation in st1 and (c) 15NO3- incubation and (d) 15NH4
+ incubation in st2 of the offshore
bottom water in the SAB in spring, 2011
49
29N
2 and
30N
2 pr
oduc
tion
(µM
)
0.3
0.2
0.1
0.0
(a)
0 2
29N230N2
(b)
29N
2 and
30N
2 pr
oduc
tion
(µM
)
0.8
0.7
0.6
0.5
0.2
0.1
0.0
0 2Time (d)
st1 15NO3- + 14NH4
+ incubation
st2 15NO3- + 14NH4
+ incubation
Figure S2.3
Figure S2.3. The production of the 15N-labeled N2 during 15NO3- + 14NH4
+ incubations in (a) st1
and (b) st2 in the offshore bottom water of the SAB in spring, 2011.
50
0.6
29N
2 and
30N
2 pr
oduc
tion
(µM
)
0.5
0.4
0.3
0.008
0.004
0.000
(a)
0 2
st2 15NO3- incubation
0.6
0.5
0.4
0.3
0.008
0.004
0.00029N
2 and
30N
2 pr
oduc
tion
(µM
) (b) st2 15NH4+ incubation
0 2
st3 15NO3- incubation(c)
29N
2 and
30N
2 pr
oduc
tion
(µM
)
0.6
0.4
0.008
0.004
0.000
0 2 0 2
0.6
0.50.4
0.3
0.008
29N
2 and
30N
2 pr
oduc
tion
(µM
)
0.004
0.000
0.6
0.5
0.4
0.3
29N
2 and
30N
2 pr
oduc
tion
(µM
)
0.001
0.000
0.6
0.5
0.4
0.3
0.001
0.000
29N
2 and
30N
2 pr
oduc
tion
(µM
)st4 15NO3- incubation(e)
st3 15NH4+ incubation(d)
(f) st4 15NH4+ incubation
Time (d)0 2 0 2
Figure S2.4
Figure S2.4. The production of the 15N-labeled N2 during (a) 15NO3- incubation and (b) 15NH4
+
incubation in st2, (c) 15NO3- incubation and (d) 15NH4
+ incubation in st3, and (e) 15NO3-
incubation and (f) 15NH4+ incubation in st4 of the offshore bottom water in the SAB
in fall, 2011.
51
(a)0.6
0.5
0.4
0.3
0.060.040.02
0.00
st2 15NO3- + 14NH4
+ incubation
29N230N2
0 2
0.7
29N
2 and
30N
2 pr
oduc
tion
(µM
)
0.6
0.5
0.4
0.1
0.05
0.00
(b)
0 2
29N
2 and
30N
2 pr
oduc
tion
(µM
)
0.50
0.45
0.40
0.350.10
0.05
0.00
0 2
(c)
st3 15NO3- + 14NH4
+ incubation
st4 15NO3- + 14NH4
+ incubation
Time (d)
29N
2 and
30N
2 pr
oduc
tion
(µM
)
Figure S2.5
Figure S2.5. The production of the 15N-labeled N2 during 15NO3- + 14NH4
+ incubations in (a) st2,
(b) st3, and (c) st4 in the offshore bottom water of the SAB in fall, 2011.
52
53
Chapter 3
The Relative Importance of Anammox and Denitrification in Total N2 Production in
Lake Erie
1(This chapter will be submitted to the journal of Great Lakes Research and the author list is as follows:
Lu, X., Bade, D.L., Leff, L.G., and Mou, X. Contributions: Lu, X. performed sampling, did all
experimental and data analyses, and wrote the manuscript; Bade, D.L. helped sampling and the design of
experiments; Leff, L.G helped in the study design; Mou, X. directed and supervised the study.)
54
Abstract
N2 production via microbially-mediated anaerobic ammonium oxidation (anammox) and
denitrification plays important roles in removing fixed N from natural environments. Here, we
investigated the anammox and denitrification potentials in the bottom water of Sandusky Bay,
Sandusky Subbasin, and Central Basin in Lake Erie in three consecutive summers of 2010, 2011,
and 2012. Results generated from 15
N isotope pairing technique showed that N2 production via
anammox was a potentially important process in removing fixed N from the bottom water of
Lake Erie, which contributed up to 99% of total N2 production. The potential rates of anammox
and denitrification varied largely among sites and the 3 years we studied, from undetectable to
922 nM/d and from 1 to 355 nM/d, respectively. PCR and sequencing analyses were performed
based on anammox-bacterial marker genes in attempts to identify anammox bacterial
communities. However, these tests were failed, likely due to the low relative abundance of
anammox bacteria in Lake Erie water samples. Nonetheless, our study represents the first effort
to report anammox and denitrification potential activities in water column of Lake Erie and
indicates that anammox might be a potentially important fixed N removal process in Lake Erie.
55
Introduction
Denitrification and anaerobic ammonium oxidation (anammox) are two important
anaerobic microbial-mediated processes that regenerate N2 from fixed N in natural environments.
Denitrification is mainly performed by heterotrophic bacteria, and produces N2 through a series
of reductions (NO3-→NO2
-→NO→N2O→N2). Anammox is carried out by autotrophic bacteria
that are restricted with the bacterial order of Planctomycetales (Strous et al., 1999). Anammox
bacteria produce N2 by combining ammonium and nitrite/nitrate (Dalsgaard et al., 2003). Since
its first discovery in bioreactors of waste water treatment systems in the 1990s (Mulder et al.,
1995; Van de Graaf et al., 1995), anammox has been identified in a variety of environments,
including marine environments, terrestrial ecosystems, estuary sediments, and freshwater
systems (e.g. Thamdrup and Dalsgarrd, 2002; Rysgaard et al., 2004; Schubert et al., 2006;
Humbert et al., 2010).
Due to the importance of fixed N availability to primary production in marine systems,
early anammox studies are mainly focused on marine environments. Anammox in freshwater
systems is relatively understudied. To date, the importance of anammox as a fixed N removal
process has only been examined in a few lakes (Schubert et al., 2006; Hamersley et al., 2009;
Yoshinaga et al., 2011; Wenk et al., 2013). Nonetheless, these existing studies indicate that
anammox may be ubiquitously distributed in freshwater systems and its importance to N2
production may vary spatially and temporally. For example, in a tropical lake (Lake
Tanganyika), up to 13% of N2 production was attributable to anammox (Schubert et al., 2006);
the value was 30% in a south-alpine lake (Lake Lugano) (Wenk et al., 2013). Temporal
variations of anammox activities were identified in a restored mining pit lake in Germany, where
56
anammox was the predominant N removal means in January and October but was less important
than denitrification in May (Hamersley et al., 2009).
To date, anammox has not been examined in Lake Erie or any other Laurentian Great
Lakes. The Laurentian Great Lakes are the largest freshwater lakes on Earth, supplying about
17% of the world surface freshwater (Reynolds, 1996; Ouellette et al., 2006). Lake Erie, the
smallest and shallowest of the Laurentian Great Lakes, serves as an important drinking-water
reservoir and recreational site for human and home to wildlife. In the past several decades,
phosphorus-centered management has been enforced in Lake Erie to control eutrophication and
eutrophication induced harmful algal blooms (HABs) (Dolan, 1993). Despite its initial success in
the 1970s, HABs, including those caused by cyanobacteria (blue-green algae), have returned in
Lake Erie since 1995 with increasing frequency, intensity, and more affected areas (Brittain et
al., 2000; Ouellette et al., 2006). One consequence of HABs is the formation of oxygen depletion
microzones in the usually oxygenated western basin of Lake Erie (Millie et al., 2009). HABs
have also invaded the Central Basin of Lake Erie (Ouellete et al., 2006), but oxygen limitation
there, especially, in its hypolimnion zone is caused by seasonal stratification of water column in
summer. These oxygen-limiting zones in Lake Erie may serve as incubating grounds for
anammox bacteria and denitrifiers.
Recent view on eutrophication issue in Lake Erie has slightly shifted. In addition to P,
recent observations have suggested that the primary productivity in Lake Erie is likely to be also
limited by N availability (North et al., 2007). N concentration in Lake Erie has decreased from
0.26 mg/L in 2005 to 0.18 mg/L in 2008 (USEPA Great Lakes Monitoring, http://www.epa.gov/
glnpo/monitoring/ limnology). However, according to the USEPA Great Lakes Monitoring
project (http://www.epa.gov/glnpo/monitoring/limnology), the N loading to Lake Erie is
57
consistently high, most likely due to the extensive use of N-rich fertilizers in the Lake Erie
watershed (Richards and Baker, 1993; Kumar et al., 2007). This indicates an increasing output of
N from Lake Erie, which we hypothesized to be partly through enhanced fixed N reduction to N2
by anammox and denitrification.
To test this hypothesis, we employed the 15
N isotope pairing technique to examine the
anammox and denitrification potentials in the bottom water of Sandusky Bay, Sandusky
Subbasin, and Central Basin in Lake Erie in summers of 2010, 2011, and 2012. Our results, for
the first time, showed that anammox and denitrification were potentially important fixed N
removal processes in the water column of Lake Erie.
Methods
Sample collection and processing
Water samples were collected from the bottom (~0.2 m above the sediment) of Lake Erie
in the Sandusky Bay (SB), Sandusky Sub-Basin (SS), and Central Basin (CB) (Figure 3.1) in the
summers of 2010, 2011, and 2012 by direct pumping water using a peristaltic pump.
Environmental variables including temperature (T) and oxygen concentration (O2) were
determined in situ with a Hydrolab H2O multidata Sonde (Hydrolab Corp., Austin, TX, USA).
For the 15
N isotope pairing technique, bottom water was immediately transferred to three
250 mL acid washed BOD glass bottles via Tygon tubing by placing the tubing at the bottom of
the BOD bottles. After the water overflowed for at least 3 folds of volume change, the BOD
bottles were capped and stored on ice in a cooler before returning to lab. Another 1 L of whole
water was subsequently filtered through 3 µm and 0.2 µm pore-size membrane filters (Millipore
Inc., Cork, Ireland). Cells collected on the 0.2 µm filters were frozen at −80 °C before DNA
58
extraction. The filtrates resulting from the double filtration were collected and stored at −20 °C
for the analyses of dissolved organic carbon (DOC), dissolved nitrogen (DN), nitrate/nitrite
(NOx-), and ammonium (NH4
+).
The anammox and denitrification potentials measured by 15
N incubations and analysis
The anammox and denitrification potentials were determined based on the 15
N isotope
pairing technique and the procedure was the same as in Chapter 2. Anammox and denitrification
N2 production rates were then calculated in the same way as in Chapter 2.
Molecular analysis of anammox bacteria
DNA was extracted from cells collected on the 0.2 µm filters with PowerSoil DNA
extraction kits (MoBio Laboratory Inc., Carlsbad, CA, USA). The anammox bacterial
communities were amplified from DNA samples with different combinations of primers,
including Brod541F/1260R, HzoF1/HzoR1, Amx368F/820R, and nested primers of first round
of Pla46F/1037R and second round of Amx368F/820R at optimized PCR conditions (Table 3.1).
In silico analysis was performed on these primers before PCR analysis to make sure they are able
to target for the anammox bacterial community during PCR amplification.
The PCR amplicons were examined by gel electrophoresis (1% agarose) to verify
amplicon length, and then excised from the gels and purified with the QIAquick gel extraction
kit (Qiagen, Chatsworth, CA, USA). The clone libraries were constructed for the amplicons of
Brod541F/1260R and Amx368F/820R using the TOPO®TA Cloning® Kit for Sequencing (Life
technologies, Carlsbad, NY, USA). Clones were screened for correct insert size and then their
plasmids DNA was extracted with the QIAprep Spin Miniprep Kit (Qiagen, Chatsworth, CA,
USA). The extracted plasmids DNA was quantified using the Quant-iT PicoGreen ds DNA
Assay Kit (Life technologies, Carlsbad, NY, USA) and sequenced with a 3730 DNA Analyzer
59
(Applied Biosystems, Darmstadt, Germany) based on the BigDye Terminator Cycle Sequencing
chemistry at the Plant-Microbe Genomic Facility of the Ohio State University. For phylogenetic
affiliations, the sequences were blasted with known anammox bacterial candidates in GenBank
and RDP databases.
Environmental variable analysis
Concentrations of DOC, DN, NOx-, and NH4
+ were determined and the procedures were
the same as in Chapter 2.
Statistical analysis
Statistical analyses were performed with the vegan package in R (Oksanen et al., 2007).
Principle component analysis (PCA) was performed on log transformed environmental variables,
including DOC, DN, NOx-, and NH4
+ to examine the variables that contribute to the variances
among study sites. The significance of differences in environmental variables, including DOC,
DN, NOx-, and NH4
+ between sampling sites was tested using Student’s t test (for paired
samples), or one-way ANOVA (for multiple samples). Significant differences were reported
when P < 0.05. Potential correlations between the anammox rates and the environmental
variables were examined by calculating Pearson’s product moment correlation coefficients (r).
Significant correlations were reported when P < 0.05.
Results and discussion
Environmental conditions of sampling sites
The measured nutrient concentrations showed variations among the bottom water of the
study sites in Lake Erie (Table 3.2 and Figure S3.1). PCA1 explained 69.7% of the variance and
was mainly contributed by concentration of NH4+. In all the years, NH4
+ concentrations were
higher in CB than in SB, and showed negatively correlations (r ≤ -0.91, P < 0.05) with the
oxygen saturation. PCA2 captured 24.9% of the variance and was mainly contributed by
60
concentrations of DOC and DN. In 2010, the DOC and DN concentrations in the bottom water of
SB (3.6 mg C/L and 0.9 mg N/L, respectively) was much higher than those in CB (2.4 mg C/L
and 0.2 mg N/L, respectively) (t test, P < 0.05). In 2011 and 2012, the highest concentrations of
DOC and DN were found in SS (20.8 mg C/L and 1.8 mg N/L) and CB (10.7 mg C/L and 0.9 mg
N/L), respectively (ANOVA, P < 0.05). In contrast, the NO3- concentrations did not varied
significantly (ANOVA, P > 0.05) among sampling sites, from 0.2 to 0.3 mg N/L in 2010, from
0.1 to 0.2 mg N/L in 2011, and from 0.0 to 0.1 mg N/L in 2012.
The dissolved oxygen saturation in bottom water of 2010 was 100.2 % in SB, 93.4 % in
SS, and 91.8 % in CB, demonstrating that the bottom water was aerobic (Table 3.2). In 2011,
dissolved oxygen in the bottom water of SS and CB was depleted based on DO measurement,
but oxygen saturation was high in Sandusky bay (Data not shown). In 2012, the oxygen
saturation in bottom water of the CB1 and CB2 was respectively 0.4 % and 1.3 %, showing the
bottom water was anoxic during sampling time (Table 3.2). In SB and SS bottom water of 2012,
the oxygen saturation was 89.1 % and 84.3 %, respectively (Table 3.2).
The anammox and denitrification potential in bottom water of lake Erie
N2 production potentials for anammox and denitrification were detected in the bottom
water of Lake Erie. In 2010, the 14
N15
N was produced in bottom water of SB after incubation
with 15
NO3-, with the concentration of
15N
15N remained constant at a low level during the
incubation (Figure S3.2a). Based on isotope paring, in anaerobic incubation of 15
NO3, 14
N15
N can
be produced by both anammox and denitrification, whereas 15
N15
N can only be generated by
denitrification. Therefore, our data indicate that fixed N loss might be mainly through anammox
rather than denitrification in SB once oxygen was depleted from the bottom water. Consistently,
significant amount of 14
N15
N (from 0.4 to 0.7 µM) was produced during the first 2 days of
61
incubation with 15
NH4+, which again suggests the importance of anammox over denitrification in
N removal in SB (Figure S3.2b). Similarly, in CB1 of 2010, incubation with 15
NO3- resulted in a
production of both 14
N15
N and 15
N15
N (Figure 3.2d), and incubation with 15
NH4+ resulted in an
accumulation of 14
N15
N (Figure S3.2e). This suggests that anammox and denitrification might
potentially occur in bottom water of CB1. In contrast, in SS of 2010, incubation with 15
NO3-
resulted in a production of both 14
N15
N and 15
N15
N (Figure S3.2g), but no 14
N15
N was produced
after incubation with 15
NH4+ (Figure S3.2h). This indicates that denitrification might be more
important than anammox in SS of Lake Erie in 2010. In the incubations with unlabeled 14
NH4Cl
and 15
NO3-, the production of
14N
15N was not stimulated through anammox in any of the 2010
Lake Erie samples (Figure S3.2c, S3.2f, and S3.2i), which suggests that ammonium availability
was not limiting anammox in bottom water of SB, CB1, and SS in Lake Erie in 2010 (Dalsgaard
et al., 2003).
In 2011, the anammox and denitrification potentials were determined in SB, SS, CB1,
and CB2 (Figure S3.3). After incubations with 15
NO3-,
14N
15N and
15N
15N were produced in
bottom water of SS, CB1, and CB2. In contrast, only 15
N15
N was produced in bottom water of
SB after the incubation with 15
NO3-. Consistently,
14N
15N was produced after incubations with
15NH4
+ in bottom water of SS, CB1, and CB2 but not SB. These data indicate that the fixed N
might be removed through anammox in SS, CB1, and CB2 in 2011. In contrast, in SB,
denitrification might dominate the nitrogen removal processes. No stimulation of 14
N15
N through
anammox was observed after the incubation with 14
NH4Cl and 15
NO3- in any of the 2011 Lake
Erie samples (Data not shown).
In 2012, incubations with 15
NO3- resulted in an accumulation of both
14N
15N and
15N
15N
in bottom water of SS and CB1, but produced only 15
N15
N in SB and CB2 (Figure S3.4).
62
Consistently, after the incubation with 15
NH4+, there were only significant increases of
14N
15N in
bottom water of SS and CB1 (t test, P < 0.05). These results together suggest that anammox
might be important in SS and CB1 of Lake Erie in 2012, while in SB and CB2, N2 production
was mostly attributable to denitrification. No stimulation of 14
N15
N through anammox was
determined after the incubations with 14
NH4Cl and 15
NO3- in Lake Erie samples of 2012 (Data
not shown).
Potential N2 production rates
The anammox and denitrification rates varied among sampling years. In 2010, the
anammox rate in bottom water of SB, SS, and CB1was each at 169, 0, and 46 nM/d, with the
corresponding denitrification rate of 1, 355, and 236 nM/d (Figure 3.2). In 2011, the anammox
and denitrification rates varied from 0 to 174 nM/d and from1 to 13 nM/d, respectively. In 2012,
the N2 production was dominated by anammox in bottom water of SS and CB1 (807 and 922
nM/d, respectively), but by denitrification in bottom water of SB and CB2 (13 and 48 nM/d,
respectively). The maximal anammox potential rates in our Lake Erie samples of 2010 and 2011
fell within the range of reported anammox rates in lakes, where the maximum are between 15
and 504 nM/d (Schubert et al., 2006; Hamersley et al., 2009; Wenk et al., 2013). High anammox
potential rates (807 and 922 nM/d) were found in our Lake Erie samples in Sandusky subbasin
and central Basin of 2012, which indicates that the anammox bacterial activity might be greatly
promoted in favorable environmental conditions and anammox play an important role in the
fixed N removal in Lake Erie. The maximum N2 production rates via denitrification in our Lake
Erie samples reached 355 nM/d, which were comparable to the reported denitrification rate
maxima in water columns of freshwater Lakes (74~480 nM/d; Schubert et al., 2006; Hamersley
et al., 2009; Wenk et al., 2013).
63
The relative importance of anammox in N2 production in Lake Erie ranged between 0%
and 99%, which is similar to those found in Lake Rassnitizer (~100%; Hamersley et al., 2009).
Similarly temporal shifts between denitrification and anammox have been observed in Baltic Sea
and Lake Rassnitzer (Hannig et al., 2007; Hamersley et al., 2009), where the authors attributed it
to the variations of the availability of reductants, such as reduced Fe and sulfide. Although we
did not measure the concentrations of reduced Fe and sulfide, it has been found that the
cyanobacterial blooms in freshwater lakes, which Lake Erie is known for, may cause a shift in
the productions of ferrous and FeS/FeS2 (Chen et al., 2014).
Anammox bacteria
In this study, we tested different combinations of primer sets to screen the bottom water
samples of Lake Erie for anammox bacterial genes. These primers are widely used in the study
of anammox bacterial in natural environments (i.e. Humbert et al., 2010; Li et al., 2010;
Yoshinaga et al., 2011; Wenk et al., 2013). Direct amplification with anammox 16S rRNA genes
of Amx368F/Amx820R and anammox functional genes of HzoF1/HzoR1 did not yield enough
PCR amplicons from any of our samples, but direct amplication with anammox 16S rRNA genes
of Brod540F/1260R produced high quality PCR amplicons for cloning and sequencing analyses.
Besides, a nested PCR approach, which used amplicons of Pla46F/1036R primer set as the
templates for the second PCR amplication with Amx368F/Amx820R, was also adopted and
provided with high quantity of PCR amplicons for subsequent cloning and sequencing analyses
(Schmid et al., 2005). A total of 480 sequences were recovered from the PCR amplicons of
Brod540F/1260R, and a total of 10 sequences were recovered from the PCR amplicons of
Pla46F/1036R nested with Amx368F/Amx820R. Unfortunately, none of the sequences showed
phylogenetic similarity with known anammox bacteria, which include Candidatus Brocadia,
64
Candidatus Kuenenia, Candidatus Scalindua, Candidatus Anammoxoglobus, and Candidatus
Jettenia (Mulder et al., 1995; Hamersley et al., 2007; Humbert et al., 2010). Our sequences were
affiliated with bacterial phyla of Proteobacteria or Firmicutes (Data not shown).
A few factors may prevent the successful identification of anammox bacteria from our
samples. First, the abundance of anammox bacteria was low in our samples. Using the average
anammox rate 208 nM/d in our samples and the single cell anammox rate 18 fmol/d calculated
for the Lake Tanganyika samples (Schubert et al., 2006), the estimated anammox cell number
was around 1.1×104/mL in Lake Erie, which was at the lower range of the reported anammox
bacterial numbers (1.3-5.2×104/mL) in aquatic environments (Kuypers et al., 2005; Schubert et
al., 2006; Hamersley et al., 2009). Second, it has been suggested that the anammox bacteria in
freshwater systems might be more diverse than those in marine and different freshwater lakes
may harbor varying anammox bacterial communities (Schubert et al., 2006; Hamersley et al.,
2009; Yoshinaga et al., 2011; Wu et al., 2012; Wenk et al., 2013). Therefore, it is likely that the
anammox primers we used were not targeting the anammox bacterial communities in Lake Erie.
Third, the primers for the identification of anammox bacteria had low specificity and produced
false positive results during PCR amplification of our Lake Erie samples. We examined the
primer specificity by using the probe match in RDP website
(https://rdp.cme.msu.edu/probematch/search.jsp). The primer set of Brod540F/1260R showed
540 hits to Planctomycetes, especially in the genus Candidatus Scalindua (538). However,
Brod540F/1260R primers were also found hits to bacterial phyla of Proteobacteria (5) and
Firmicutes (3). The primer set of Pla46F/1037R showed high hits to Planctomycetes (3691), but
also matched other 19 bacterial phyla such as Actinobacteria (4), Proteobacteria (28), and
Firmicutes (7). For the primer set of Amx368F/Amx820R, when allowed for three nucleotide
65
differences, there were high hits only to the bacterial order of Plantomycetales, including
Candidatus Brocadia (190), Candidatus Kuenenia (211), Candidatus Scalindua (177), and
unclassified Candidatus Brocadiaceae (330). The results of the in silico analyses of the
anammox primers demonstrate that false positive results might be produced through unspecific
PCR amplifications of anammox 16S rRNA genes in our lake samples.
Conclusion
This study was among one of the first investigations on anammox and its importance
relative to denitrification in fixed N loss through N2 production in Lake Erie and the Laurentian
Great Lakes. Using 15
N isotope pairing technique, we found that anammox and denitrification
might occur in bottom water of Lake Erie, and the N2 production via anammox might be more
important than denitrification in Lake Erie. The determined anammox and denitrification rates
varied among sites and the time of 2010, 2011, and 2012. This result illustrates the importance of
the studies on the temporal dynamics of anammox and denitrification for understanding the roles
of the two processes and their contributions to suboxic nutrient balances in aquatic ecosystems.
66
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70
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71
Table 3.1. PCR primers sets used for both 16S rRNA and hzo gene amplification of
Planctomycetales and anammox bacteria.
Primer sets Specific group Primer sequences (5’-3’) Annealing
temperature
Reference
Brod541F Scalindua sp. GAGCACGTAGGTGGGTTTGT 60 °C Penton et al.,
(2006)
Brod1260R Scalindua sp. GGATTCGCTTCACCTCTCGG 60 °C Penton et al.,
(2006)
Amx368F Anammox bacteria TTCGCAATGCCCGAAAGGAAAA
62 °C Schmid et al.,
2003
Amx820R Brocadia and Kuenenia
AAAACCCCTCTACTTAGTGCCC 62 °C Schmid et al., 2000
Pla46F Planctomycetes GGATTAGGCATGCAAGTC
62 °C Neef et al.,
1998
Univ1390R Bacteria GACGGGCGGTGTGTACAA 62 °C Zheng et al.,
1996
HzoF1 Anammox bacteria TGTGCATGGTCAATTGAAAG 53 °C Li et al., 2010
HaoR1 Anammox bacteria CAACCTCTTCWGCAGGTGCATG 53 °C Li et al., 2010
72
Table 3.2. The environmental variables (average± standard error of the mean) in bottom water of
Lake Erie in August of 2010, 2011, and 2012. The standards errors are listed inside the
parentheses.
site T
(°C) a
Oxygen
saturation (%)
DOC (mg
C/L)
DN (mg
N/L)
NOx- (mg
N/L)
NH4+
(µM)
2010
SB 18.7 100 3.6(0.4) 0.9(0.1) 0.3(0.1) 7.7(2.8)
SS 20.6 90.4 2.4(0.2) 0.8(0.1) 0.3(0.0) 8.4(1.1)
CB1 20.5 92.3 2.4(0.2) 0.2(0.0) 0.2(0.0) 8.6(3.0)
2011
SB 12.1(0.5) 0.9(0.0) 0.2(0.0) 0.3(0.1)
SS 20.8(2.1) 1.8(0.2) 0.1(0.0) 0.8(0.2) CB1 4.2(0.3) 0.6(0.1) 0.2(0.0) 2.8(0.3)
CB2 3.6(0.1) 0.7(0.0) 0.1(0.0) 3.2(0.2)
2012
SB 23.6 89.1 4.3(0.4) 0.3(0.0) 0.0(0.0) 0.3(0.0)
SS 23.8 84.1 4.1(0.3) 0.3(0.0) 0.0(0.0) 0.6(0.1)
CB1 14.4 0.4 5.6(0.5) 0.5(0.0) 0.1(0.0) 2.5(0.2)
CB2 15.2 1.3 10.7(1.5) 0.9(0.0) 0.1(0.0) 4.0(0.3)
42.0
Figure 3.1
41.8
41.6
41.4
-83.4 -83.0 -82.6 -82.2
SB (5 m) SS (13 m)
CB1 (19 m)
CB2 (17 m)
Longitude
Latit
ude
Figure 3.1. The sampling sites in SB, SS, CB1, and CB2 of Lake Erie in August of 2010,
2011, and 2012. The depth of water column at each site is listed in the parentheses.
Sandusky Bay
73
(a) 400DenitrificationAnammox
300
200
100
0SB SS CB1
SB SS CB1 CB2
200
N2 p
rodu
ctio
n ra
te (n
M/d
)N
2 pro
duct
ion
rate
(nM
/d)
N2 p
rodu
ctio
n ra
te (n
M/d
)
150
100
50
0
(b)
2010
2011
(c) 2012900
800
700
50
0SB SS CB1 CB2
Figure 3.2. The N2 production rates through anammox and denitrification in bottom water
of SB, SS, CB1, and CB2 in August of (a) 2010, (b) 2011, and (c) 2012 in the Lake Erie.
Figure 3.2
74
PC1 (69.7%)
PC2
(24.
9%)
-1.0 -0.5 0.0 0.5 1.0
1.0
0.5
0.0
-0.5
-1.0
CB1_11 SS_12SB_12
SB_11
SS_11
DOC
DN
CB2_12
CB1_12CB1_11CB2_11
SB_10SS_10
NH4+
NOx-
Figure S3.1
Figure S3.1. Principal component analysis (PCA) biplot of environmental variables in bottom
water of SB, SS, CB1, and CB2 in Lake Erie in August of 2010, 2011, and 2012. Sample identifiers
are based on site (SB, SS, CB1, and CB2) and sampling time (10, 2010; 11, 2011; 12, 2012).
75
29N
2 and
30N
2 pr
oduc
tion
(µM
) 0.8
0.6
0.4
0.2
0.04
0.02
0.00
0 2 4 6
(a) SB 15NO3- incubation
SB 15NH4+ incubation(b)
(c) SB 15NO3- + 14NH4
+ incubation
3.0
2.0
1.0
0.2
0.1
0.0
0 2 4 6
(d) SS 15NO3- incubation
29N
2 and
30N
2 pr
oduc
tion
(µM
) 0.8
0.6
0.4
0.060.040.020.00
0 2 4 6
0 2 4 6
SS 15NH4+ incubation(e)
(f) SS 15NO3- + 14NH4
+ incubation
29N
2 and
30N
2 pr
oduc
tion
(µM
)29
N2 a
nd 30
N2 pr
oduc
tion
(µM
)0 2 4 6
0.6
0.5
0.4
0.30.060.040.020.00
1.0
0.8
0.6
0.4
0.2
0.029N
2 and
30N
2 pr
oduc
tion
(µM
)
0.6
0.5
0.4
0.30.060.040.020.00
29N
2 and
30N
2 pr
oduc
tion
(µM
) CB1 15NH4+ incubation(h)
(g) CB1 15NO3- incubation
0 2 4 6
(i) CB1 15NO3- + 14NH4
+ incubation
29N
2 and
30N
2 pr
oduc
tion
(µM
) 0.6
0.5
0.4
0.30.060.040.020.00
0 2 4 6 0 2 4 6
29N
2 and
30N
2 pr
oduc
tion
(µM
)
29N
2 and
30N
2 pr
oduc
tion
(µM
)
2.0
1.5
1.0
0.50.2
0.1
0.0
0.6
0.5
0.4
0.3
0.2
0.1
0.0
0 2 4 6Time (d)
Figure S3.2
Figure S3.2. The production of the 15N-labeled N2 after incubation with (a) 15NO3-, (b) 15NH4
+,
and (c) 15NO3- + 14NH4
+ in SB, (d) 15NO3-, (e) 15NH4
+, and (f) 15NO3- + 14NH4
+ in SS, and
(g) 15NO3-, (h) 15NH4
+, and (i) 15NO3- + 14NH4
+ in CB1 of bottom water in Lake Erie in August, 2010.
29N230N2
76
29N
2 and
30N
2 pr
oduc
tion
(µM
)
2.5
2.0
1.5
0.40.20.0
0 2
(a) SB 15NO3- incubation SB 15NH4
+ incubation
29N
2 and
30N
2 pr
oduc
tion
(µM
)
2.5
2.0
1.5
0.40.20.0
(b)
0 2
(c)
29N
2 and
30N
2 pr
oduc
tion
(µM
)
2.0
1.5
0.005
0.000
0 2
(d)SS 15NO3- incubation SS 15NH4
+ incubation2.0
1.5
1.00.20.10.0
0 229N
2 and
30N
2 pr
oduc
tion
(µM
)
0.80.60.40.2
0.01
0.00
(e) CB1 15NO3- incubation
0 2 29N
2 and
30N
2 pr
oduc
tion
(µM
)
29N
2 and
30N
2 pr
oduc
tion
(µM
) (f) CB1 15NH4+ incubation0.6
0.4
0.2
0.0
0 2
29N
2 and
30N
2 pr
oduc
tion
(µM
) (g) CB2 15NO3- incubation
0.6
0.4
0.2
0.005
0.000
29N
2 and
30N
2 pr
oduc
tion
(µM
) (h) CB2 15NH4+ incubation
0.6
0.4
0.2
0.0
0 2 0 2
Time (d)
Figure S3.3. The production of the 15N-labeled N2 after incubation with (a) 15NO3- and
(b) 15NH4+ in SB, (c) 15NO3
- and (d) 15NH4+ in SS, (e) 15NO3
- and (f) 15NH4+ in CB1,
and (g) 15NO3- and (h) 15NH4
+ in CB2 of bottom water in Lake Erie in August, 2011.
Figure S3.3
29N230N2
77
29N
2 and
30N
2 pr
oduc
tion
(µM
)
(a) SB 15NO3- incubation
1.21.11.00.9
0.10
0.000.05
0 2
29N230N2
29N
2 and
30N
2 pr
oduc
tion
(µM
)
1.41.21.0
0.100.050.00
0 2
SB 15NH4+ incubation(b)
(c) SS 15NO3- incubation
29N
2 and
30N
2 pr
oduc
tion
(µM
)
3.02.52.01.5
0.10
0.000.05
0 2 29N
2 and
30N
2 pr
oduc
tion
(µM
)
1.4
1.2
0.150.100.050.00
0 2
(d) SS 15NH4+ incubation
(e) CB1 15NO3- incubation (f) CB1 15NH4
+ incubation
29N
2 and
30N
2 pr
oduc
tion
(µM
)
5.04.03.02.0
0.10
0.000.05
0 2 29N
2 and
30N
2 pr
oduc
tion
(µM
)
2.52.01.51.00.5
0.100.050.00
0 2
29N
2 and
30N
2 pr
oduc
tion
(µM
)
1.5
2.0
0.100.050.00
0 2 29N
2 and
30N
2 pr
oduc
tion
(µM
)
(g) CB2 15NO3- incubation (h) CB2 15NH4
+ incubation
0 2Time (d)
1.5
1.0
0.50.100.050.00
Figure S3.4
Figure S3.4. The production of the 15N-labeled N2 after incubation with (a) 15NO3- and
(b) 15NH4+ in SB, (c) 15NO3
- and (d) 15NH4+ in SS, (e) 15NO3
- and (f) 15NH4+ in CB1,
and (g) 15NO3- and (h) 15NH4
+ in CB2 of bottom water in Lake Erie in August, 2012.
78
79
Chapter 4
Temporal Dynamics and Depth Variations of Dissolved Free Amino Acids and Polyamines
in Coastal Seawater Determined by High-Performance Liquid Chromatography
1Lu, X., Zou, L., Clevinger, C., Liu, Q., Hollibaugh, J.T., and Mou, X. (2013) Marine Chemistry 163: 36–
44. Reprinted here with permission of the publisher. Contributions: Lu, X. performed sampling,
optimized the methodology, did all experimental and data analyses, and wrote the manuscript; Zou, L.
participated in the methodology optimization; Clevinger, C. helped in the sample collection and analysis;
Hollibaugh, J.T. helped in the study design; Mou, X. supervised the study. All authors contributed to the
final draft of the manuscript.
80
Abstract
Short-chained aliphatic polyamines (PAs) are a class of labile dissolved organic nitrogen (DON)
that have biogeochemical similarities to dissolved free amino acids (DFAAs). Here we
investigated the relative contributions of DFAAs and PAs to the total DON pool and their diurnal
dynamics at different depths at the Gray’s Reef National Marine Sanctuary (GRNMS) in the
spring and fall of 2011. A high-performance liquid chromatography (HPLC) method that uses
pre-column fluorometric derivatization with o-phthaldialdehyde, ethanethiol, and 9-
fluorenylmethyl chloroformate was optimized to measure 20 DFAAs and 5 PAs in seawater
simultaneously. The concentrations of DFAAs and PAs varied over 5-fold during individual
diurnal cycles and between seasons; and concentrations of the former (tens to hundreds nM)
were typically at least one order of magnitude higher than the latter (a few nM). An exception
was noted in fall surface water samples when the total PAs reached 159.0 nM and the ratio of
PAs to DFAAs was closer to 2:3. Compositions of individual DFAAs and PAs also exhibited
temporal dynamics, with glycine and spermidine consistently the most abundant compound in
each pool, respectively. DFAA concentration appeared to track chlorophyll a, whereas, total PA
concentrations were strongly correlated with bacterial cell abundance. Our results indicate that,
at least occasionally, PAs may serve as an important DON pool at the GRNMS. This view is in
accordance with recent molecular data but contrasts to measurements made in some other marine
environments.
81
Introduction
Dissolved organic nitrogen (DON) represents a major pool of fixed nitrogen in marine
systems and serves as an important nitrogen and carbon source for marine bacterioplankton
(Fuhrman and Ferguson, 1986; Bronk et al., 1994; Berman and Bronk, 2003). Dissolved free
amino acids (DFAAs) are recognized as an important component of labile marine DON that
originate primarily from phytoplankton cells via active exudation, during the process of cell
senescence, or upon sloppy feeding by zooplankton (Webb and Johannes, 1967; Carlucci et al.,
1984; Rosenstock and Simon, 2001). Once in seawater, DFAAs are rapidly transformed by
bacteria (Kirchman and Hodson, 1986), a process that can sustain over 100% of the estimated N
demand of marine bacteria (Keil and Kirchman, 1991; Jørgensen et al., 1993) and contributes to
the low DFAA concentrations (< 1-10 nM) that are typically found in seawater (Mopper and
Lindroth, 1982; Fuhrman and Ferguson, 1986). Therefore, although DFAAs only make up a
small proportion of the total DON pool, they contribute significantly to the DON flux (Lee and
Bada, 1975; Tada et al., 1998; Berman and Bronk, 2003).
Short-chained polyamines (PAs), such as putrescine, spermidine, and spermine, are
another group of ubiquitous, labile dissolved organic nitrogen compounds that share many
important biogeochemical features with DFAAs. First, PAs are also found in all living
organisms, with phytoplankton as their major source in marine ecosystems (Lee and Jørgensen,
1995). Second, concentrations of PAs inside phytoplankton cells (M to mM; Tabor and Tabor,
1984; Lu and Hwang, 2002) and in seawater (nM; Nishibori et al., 2003) are both comparable to
those of DFAAs. Finally, radiotracer experiments and recent gene-based studies have
consistently suggested that, like DFAAs, PAs may serve as an important source of C, N, and/or
energy to marine bacterioplankton (Höfle, 1984; Lee and Jørgensen, 1995; Poretsky et al., 2010;
82
Mou et al., 2011). However, PAs are historically understudied and have rarely been included in
measurements of marine DON compounds. Consequently, the importance of PAs relative to
DFAAs and to the total marine DON pool has not been rigorously established.
One factor contributing to this knowledge gap is the lack of effective analytical methods
that can simultaneously quantify DFAAs and PAs in seawater, even though methods specifically
targeting either marine DFAAs (Mopper and Lindroth, 1982) or PAs (Nishibori et al., 2003) are
available. Simultaneous analyses of DFAAs and PAs, using high-performance liquid
chromatography (HPLC), have been reported for samples of cheese, wine, beer, and vinegar
(Kutlán and Molnar-Perl, 2003; Körös et al., 2008). However, these methods were developed for
food extracts, which typically contain nearly 1000-fold higher concentrations of PAs and DFAAs
(M levels) than natural seawater (nM levels). Moreover, the effect of high salts in seawater
samples on the sensitivity and accuracy of these methods is unknown.
The objective of this study is two-fold: 1) to optimize current HPLC methods for
simultaneous and sensitive measurements of DFAAs and PAs in seawater, and 2) to compare the
abundance of DFAAs and PAs and examine their temporal dynamics at different depths in a
near-shore site on the continental shelf of the South Atlantic Bight.
Methods
Study site and sampling procedure
The sampling site is located off the coast of Georgia within the Gray’s Reef National
Marine Sanctuary (GRNMS; 31° 24.04′ N, 80° 51.51′ W). Two diurnal sampling series were
conducted on-board the R/V Savannah in 2011, one in spring (April 21-22) and the other in fall
(October 5-6). Water samples were collected every 3 h during a 24-hour period on each cruise (8
casts in each season) using Niskin bottles mounted on a rosette sampling system (Sea-Bird
83
Electronics, Bellevue, WA). Depth profiles of environmental variables including temperature,
salinity, and photosynthetically active radiation (PAR) were measured in situ with a
conductivity-temperature-depth (CTD) water column profiler (Sea-Bird Electronics, Bellevue,
WA) that was also mounted on the rosette sampling system. The water column was stratified in
spring, so samples were taken at nominal depths of 2 m (referred to as surface water hereafter), 4
m (within the thermocline, referred to as mid-depth hereafter) and 17 m (~2.5 m above the
sediment-water interface, referred to as bottom water hereafter) (Figure 4.1a). There was no
thermocline present in fall and samples were taken at depths of 2m (surface) and 17 m (bottom)
(Figure 4.1b).
Water samples were sequentially filtered through 3 and 0.2 µm diameter pore-size
membrane filters (Pall life sciences, Ann Arbor, MI) under low vacuum pressure (~10 mmHg)
immediately after collection. The filtrates were collected in amber glass vials and stored at −80
°C before measurements of the concentrations of DFAAs, PAs, dissolved organic carbon (DOC),
dissolved nitrogen (DN), nitrate/nitrite (NOx-), and soluble reactive phosphorus (SRP). Five
hundred milliliters of water were filtered through GF/F filters (Whatman International Ltd,
Maidstone, England), which were immediately wrapped in aluminum foil and stored at −20 °C
for chlorophyll a (Chl a) measurements. Bacterioplankton that passed 3 µm diameter pore-size
membrane filters were fixed with 1% freshly prepared paraformaldehyde and incubated at room
temperature for 1 h. Afterwards, fixed cells were collected onto 0.2 μm diameter pore-size
polycarbonate membrane filters and stored at 4 °C before cells were enumerated.
All samples were prepared in triplicate. Glassware, GF/F filters and aluminum foil were
combusted at 500 C for at least 6 h before use.
HPLC analysis
84
Simultaneous measurements of 20 individual DFAAs, 5 individual PAs, and ammonium
(Table 4.1) were performed on a Prominence 20A HPLC system (Shimadzu Corp., Tokyo,
Japan) consisting of a SIL-20A autosampler, an LC-20AD quaternary pump, a CTO-20A column
oven, and an RF-20Axs fluorescence detector, using a protocol modified from a procedure
developed for analysis of cheese (Körös et al., 2008). Briefly, standard solutions of DFAAs and
PAs were prepared using HPLC-grade water. 10 µL of α-aminobutyric acid (AABA) and 1,7-
diaminoheptane (DAH) mixture (5 µM each) were added as internal standards for the
quantification of DFAAs and PAs, respectively. A two-step derivatization procedure was
performed off-line using o-phthaldialdehyde (OPA), ethanethiol (ET) and 9-
fluorenylmethoxycarbonyl chloride (FMOC-Cl). First, the OPA-ET reagent was freshly prepared
by mixing 500 µL of OPA stock solution (0.22 g OPA in 10 mL methanol), 2 mL of 0.8 M
borate buffer (pH 11.0), 52 µL ET and 7.448 mL of methanol, and then aged in dark for 90 min
at 4 °C before use. Then, 15 µL of the OPA-ET reagent was added to 1 mL of sample and
allowed to react for 1 min at room temperature. Next, 1 µL of FMOC-Cl solution (0.11 g
FMOC-Cl in 10 ml acetonitrile; aged overnight at −20 °C) was added to the sample and
incubated at room temperature for another 1 min. One hundred microliters of the derivatized
sample was injected into the HPLC system immediately after reaction. The separation was
performed on a 250 mm × 4.6 mm i.d., 5 µm particle size, Phenomenex Gemini-NX C18 column
at 50 °C by a gradient elution (Table 4.2) at a flow rate of 1.8 mL min-1
. Excitation and emission
wavelengths of the detector were set at 330 and 460 nm, respectively. Typical HPLC
chromatograms of a standard solution at 10 nM and a seawater sample in spring were shown in
Figure 4.2.
85
DFAA and PA peaks in sample chromatograms were first identified with reference to the
retention times of standards and then confirmed by spiking samples with relevant standards. The
internal standard calibration curve was used to quantify DFAAs and PAs by plotting the area
ratio of analyte standard to internal standard (AABA or DAH) against the concentrations of
analyte standard. The linearity of the calibration curve was determined by least-squares linear
regression analysis. To evaluate the method accuracy and precision, a recovery study was
performed by analyzing five replicates of the seawater samples spiked with DFAA and PA
standards at three different levels (1, 10, and 20 nM). The recovery (%) was calculated using the
equation, auf 100Recovery CCC , where fC and uC are the amounts of determined
DFAA and PA compounds in amended and unamended samples, respectively. Ca is the amount
of DFAA and PA standard added to the test samples. The precision of the method was
determined by calculating the relative standard deviation (RSD, %) for the repeated
measurements. The limit of detection (LOD) and quantification (LOQ) were determined by
measuring dilutions of standards until the signal-to-noise (S/N) ratios were ≥ 3 and ≥ 10,
respectively.
Environmental variables
Nutrient concentrations were determined using standard procedures (Clescerl et al.,
1999). Briefly, DOC and DN concentrations were determined with a TOC-VCPN TOC/TN
analyzer (Shimadzu Corp., Tokyo, Japan) based on combustion oxidation/infrared detection and
combustion oxidation/chemiluminescence detection methods, respectively. Nitrate plus nitrite
(NOx-) concentrations were determined by the cadmium reduction method using a Lachat flow
injection analysis system (Lachat QuikChem FIA+ 8000Series, Loveland, CO). SRP
concentrations were measured based on the molybdenum blue colorimetric method using flow
86
injection protocols on the Lachat. Chl a was extracted from the GF/F filters with 90% acetone,
and measured spectrophotometrically (Tett et al., 1975). Bacterioplankton were stained with
4′,6-diamidine-2-phenylindole dihydrochloride (DAPI) and enumerated using a Zeiss Axioskop
epifluorescence microscope (Carl Zeiss, Jena, Germany) as described by Porter and Feig (1980).
Statistical analysis
All statistical calculations were performed using the PRIMER v5 software package
(Plymouth Marine Laboratory, Plymouth, UK) unless otherwise noted. Non-metric
multidimensional scaling (NMDS) analysis was used to examine similarity of DFAA and PA
profiles among samples based on a Euclidean distance matrix that was calculated based on log
transformed concentrations of individual compounds or their untransformed relative abundance.
The distance between two samples on NMDS plot reflects their similarity, i.e., the closer the
samples are on the plot, the more similar they are. The robustness of NMDS results was accessed
by analysis of similarity (ANOSIM). ANOSIM generates an index, rANOSIM, scaled from 0 to 1.
Sample groups were reported as well-separated when rANOSIM > 0.75, overlapping but clearly
different when 0.5 < rANOSIM < 0.75, or barely separable when rANOSIM < 0.25 (Clarke and
Warwick, 2001). Similarity percentages (SIMPER) analysis was then performed to identify
variables that contributed the most to the observed difference between sample groups.
Differences between samples in individual variables were tested for statistical
significance using ANOVA or t tests implemented within the R software package (R Core
Development Team, 2005), and differences were reported as significant when P < 0.05. The
significance of correlations between DFAAs or PAs and abiotic and biotic factors were tested
using Pearson’s product-moment correlation coefficient, with significant correlations reported
87
when P < 0.05 (R software package). Bonferroni corrections of p values were employed for
multiple tests.
Results
Optimization of HPLC method
The optimized HPLC method allowed simultaneous determination of 20 DFAAs, 5 short-
chain PAs and ammonium at high sensitivities. The LOD and LOQ of individual compounds
(except ammonium) ranged between 0.01-0.1 and 0.1-1 nM, respectively (Table 4.2). Regression
analyses of serially diluted standards showed good linear relationships (correlation coefficient,
R2 > 0.99) over the concentration range of 0.1 to 100 nM for most of the DFAA and PA
compounds (Table 4.2).
Our HPLC method also achieved good accuracy and repeatability precision for each
DFAA and PA compound (Table 4.2). At 1 nM, the recovery rates for spiked individual DFAAs
ranged from 82% to 114% with < 10.0% RSD; the values were 82% to 123% with 7.6-12.5%
RSD for spiked PAs. At 10 nM, the recovery rates for DFAAs ranged from 84% to 118% with <
10% RSD; the values were 90% to 102% with < 10.0% RSD for PAs, while at 20 nM, the
recovery rates and RSD of DFAAs and PAs were 84-132% and 0.4-8.3%, respectively.
Temporal variation and depth profiles of total DFAAs and PAs
Total DFAA and PA concentrations were positive correlated (r = 0.39, P < 0.05). In
spring, the concentrations of total DFAAs varied between 13.2 and 77.5 nM within a diurnal
cycle, with an overall average of 37.9 nM (Figure 4.3a). The total DFAA concentration showed
similar variation patterns among the three sampling depths. At the peak, the total DFAA
concentrations at the surface and bottom reached 77.5 nM and 59.8 nM, respectively, which were
about 3.5 times higher than their corresponding lowest values (21.7 and 13.2 nM, respectively).
88
No significant difference was found between light (after sunrise, before sunset) and dark (after
sunset, before sunrise) samples at any water depth (t test, P > 0.05). The concentrations of total
PAs ranged from undetectable to 9.4 nM (Figure 4.3b) with an overall daily average of 2.3 nM,
which was nearly 16-fold lower than the average concentrations of DFAAs. No significant
difference in total PA concentrations was identified among the three sampling depths (ANOVA,
P > 0.05). At any given depth, PA concentrations generally maintained at < 5.5 nM and peaked
once at 21:00 h to reach 7.4-9.5 nM. The same time point (21:00 h) also represented the
maximum ratio between PAs and DFAAs (total PAs/DFAAs = 0.2; Figure 4.3c). The relative
contributions of total PAs to DON varied from 0.05% to 0.4% in surface water, 0.05% to 0.4% at
mid-depth, and 0.001% to 0.6% in bottom water (Figure 4.3c).
In fall, total DFAA concentrations were measured between 54.3 and 420.0 nM, which
were about 5-fold higher than in spring (t test, P < 0.05) (Figure 4.3e). Total DFAA
concentrations at the surface were generally similar to or higher than those in the bottom water,
except at noon when bottom water DFAA concentrations peaked to reach 420.0 nM. Total PA
concentrations ranged from undetectable to 159.0 nM (Figure 4.3f), with an overall daily average
(15.7 nM) nearly 7-fold higher than in spring samples. PAs and DFAAs peaked at different times
(Figure 4.3e and 4.3f). The ratios between these two variables were lower than 0.04 for most of
the samples, but had maximal values of 0.7 and 0.6 in the surface and bottom water (both at
18:00 h), respectively (Figure 4.3g). Total PAs contributed from 0.03% to 0.3% of DON in the
surface water and from 0.001% to 0.4% of DON in the bottom water (Figure 4.3g).
Temporal and depth dynamics of individual DFAAs
Pair-wise correlation analysis showed that the concentrations of 13 of the 20 measured
DFAAs, including alanine, arginine, aspartic acid, γ-aminobutyric acid, glutamine, glutamic
89
acid, glycine, isoleucine, leucine, methionine, phenylalanine, serine, and taurine, were
significantly correlated with each other (r ≥ 0.55, P < 0.05 with Bonferroni correction; Table
S4.1). Most individual DFAAs (except asparagine, histidine, threonine, taurine, valine, ornithine,
and lysine) had significantly higher concentrations in fall (0.0-114.7 nM, with an overall average
of 7.9 nM) than in spring (0.0 to 29.4 nM, with an overall average of 1.9 nM) (t test, P < 0.05).
An NMDS plot based on concentrations of individual DFAAs grouped samples by seasons
(rANOSIM = 0.83, P < 0.05; Figure 4.4). No apparent grouping of samples was discovered when the
analysis was based on sampling depths or time (light vs. dark). NMDS analysis was also
performed based on relative abundances of individual DFAAs and the ordination plot (Figure
S4.1) showed similar grouping patterns as Figure 4.4.
Glycine, taurine, lysine, glutamic acid, asparagine, and histidine in descending order of
contribution accounted for most (~70%) of the total DFAAs concentration in spring samples
(Figure 4.5a, 4.5b, and 4.5c). Glycine and taurine dominated the surface and mid-depth samples
and peaked at different times (Figure 4.5a and 4.5b). Asparagine and lysine dominated the
bottom water (Figure 4.5c). Glycine, glutamine, alanine, aspartic acid, glutamic acid, and taurine
in descending order of contribution accounted for most (~70%) of the total DFAAs concentration
in fall samples (Figure 4.6a and 4.6b). Generally, glycine and glutamine dominated the surface
and bottom water.
Temporal and depth dynamics of individual PAs
No significant correlations were found between individual PA concentrations based on
Pearson correlation analysis. NMDS analysis based on individual PA concentrations ordinated
samples into two groups by season, although with overlap (Figure S4.2). ANOSIM analysis
confirmed the statistical significance of this ordination pattern (rANOSIM = 0.58, P < 0.05). Similar
90
to the DFAAs, NMDS and ANOSIM based on individual PAs did not group samples based on
sampling depths or time. Analyses based on the relative abundance of individual PAs produced
the same results.
Cadaverine and norspermidine were not detected in spring samples. In contrast,
spermidine was present in all of them (Figure 4.5d, 4.5e, and 4.5f), with concentrations between
0.1 and 4.1 nM. Putrescine and spermine were detected at all three depths but only in a quarter or
fewer of the samples. These two compounds had concentrations of 0.6-1.9 nM and 0.8-5.7 nM,
respectively. All 5 PAs were detected in fall samples (Figure 4.6c and 4.6d), but each was only
found in a few samples and concentrations were generally < 4.3 nM. Exceptionally high
concentrations of individual PAs, namely cadaverine, norspermidine, spermidine, and spermine,
were measured in surface samples taken at 18:00 h. At that time point, each of the 4 PA
compounds had concentrations (10.0-76.8 nM) comparable to major individual DFAAs (Figure
4.6a).
Potential correlation between DFAAs/PAs and other environmental variables
We calculated Pearson’s product-moment correlations between total DFAA or PA
concentrations and a number of ambient abiotic and biotic variables, including temperature,
salinity, PAR, Chl a, DOC, SRP, DN, NOx-, NH4
+, DON, and bacterial cell counts (Table S4.2).
Total DFAA concentrations were correlated with temperature, salinity, Chl a, DOC, SRP, and
DON, while total PA concentrations were correlated with DON and bacterial cell counts (r > 0.5,
P < 0.05 with Bonferroni correction). Significant correlations were also found between some
individual DFAAs and PAs; methionine, arginine, and threonine were each correlated with
spermidine, and methionine and arginine were each correlated with spermine (r ≥ 0.51, P < 0.05
with Bonferroni correction; Table S4.3).
91
Discussion
HPLC method development
Our optimized HPLC method allows simultaneous measurements of DFAAs and PAs
from seawater samples without desalting or concentration, which increases the sensitivity of the
parent method and reduces the chance of potential contamination during processing (Mopper and
Lindroth, 1982). We used pre-column derivatization with OPA-ET-FMOC to detect DFAAs and
PAs. OPA only reacts with primary amine groups and produces highly fluorescent isoindole
derivatives at alkaline pH when a thiol compound (ET) is present. FMOC reacts with both
primary and secondary (found in spermidine, spermine, and norspermidine) amine groups to
produce stable and highly fluorescent derivatives. This two-step derivatization has been shown to
yield maximal stability and reproducible results with DFAAs and PAs (Hanczkó et al., 2005).
Our optimized method had detection limits of DFAAs and PAs at 0.01-0.1 nM with high
accuracy and precision, which were similar to or lower than those of methods that are DFAA- or
PA-specific (Mopper and Lindroth, 1982; Nishibori et al., 2003). Individual DFAAs and PAs are
typically present in seawater from one to several nM (Fuhrman and Ferguson, 1986; Nishibori et
al., 2003), thus our method is sufficiently sensitive for their accurate measurement.
Some amino acids, such as cysteine, proline, and hydroxyproline, are generally difficult
to derivatize for HPLC measurement (Einarsson, 1985). Even with a second derivatization step
using FMOC, the detection limits of our method for these compounds were at 100 nM or above
(data not shown), which are much higher than their typical levels in seawater (Johnson et al.,
1982; van den Berg et al., 1988). Therefore, our method is not suitable for their measurements,
as is the case for other commonly used methods (Mopper and Lindroth, 1982). Nonetheless,
these compounds appear to be minor contributors to the DFAA pool (Chau and Riley, 1966) and
92
failure to measure them should not unduly affect our overall conclusions regarding the
distribution of total or individual DFAAs.
Liquid chromatography-mass spectrometry (LC-MS) quantification of amino acids and/or
peptides (Chaimbault et al., 1999; Petritis et al., 2000; Petritis et al., 2002; Qu et al., 2002;
Curtis-Jackson, 2009) can potentially quantify DFAAs and PAs simultaneously. This method
does not require derivatization but, compared to our method, suffers several important drawbacks
for measuring seawater samples. Firstly, seawater samples need to be desalted (e.g., by solid
phase extraction) prior to MS analysis, and this procedure may cause significant loss of DFAAs,
and likely PAs, and may lead to contamination (Dawson and Mopper, 1978; Dawson and
Liebezeit, 1981). Secondly, the detection limits of LC-MS methods (typically ≥ 50 nM for
individual DFAAs or PAs; Petritis et al., 2000; Byun et al., 2008) are too high to measure most
marine samples, where DFAAs and PAs concentrations are typically several nM or lower (this
study; Mopper and Lindroth, 1982; Nishibori et al., 2003). In addition, the LC-MS procedure
usually involves the addition of ion-pair reagents, such as perfluoroheptanoic acid (PFHA), to
improve the retention of polar amino acids (Chaimbault et al., 1999; Chaimbault et al., 2000).
However, these reagents can slowly accumulate on the HPLC column and affect the precision
and reliability of subsequent measurements. Frequent flushing of columns may solve this
problem, but this leads to increased analysis time and cost. Moreover, the molecular weights of
DFAAs and PAs are low (≤ 202), therefore, background ions in the mobile phase and sample
matrix may interfere with peaks from fragmented DFAAs and PAs (Chaimbault et al., 1999;
Petritis et al., 2002; Hou et al., 2009).
Temporal and depth variations of PAs and DFAAs
93
A unique aspect of this study is that we were able to quantify variations in PA
concentrations relative to DFAA concentrations in the same sample. We found that total PA
concentrations were significantly correlated with those of DFAAs, indicating these two DON
groups might be subject to similar transformation processes in seawater. The concentrations of
total PAs were consistently lower than those of DFAAs, with total PAs/DFAAs ratios below 0.05
for most of the samples. Therefore, compared with DFAAs, the contribution of total PAs to the
DON pool is minor, even though individual PA molecules contain multiple amine groups (2-4
N).
However, it should be noted that the above calculations were based on measurements of 5
individual PAs and 20 DFAAs. The concentrations of individual PAs were of the same order as
individual DFAAs in many cases. Significant correlations were also found between a few
individual PAs and DFAAs, suggesting that some DFAA and PA compounds might be
controlled by similar processes, likely the affinity of bacterioplankton transporters for these
compounds. Long-chain PAs, putrescine-based compounds with various degrees of methylation
and N-methyl propylamine repeat units, have been identified as important components of diatom
frustules (Kröger et al., 2000; Bridoux et al., 2012a) and are widely distributed in marine
sediments (Bridoux and Ingalls, 2010; Bridoux et al., 2012b). These compounds could not be
determined by our method, and their degradation products and pathways are currently unknown.
Based on these considerations, we argue that the importance of PAs to the total labile DON pool
may be more significant than we have estimated.
Phytoplankton are thought to be the major source of marine PAs (Lee and Jørgensen,
1995). However, we found no significant correlations between indicators of phytoplankton
abundance (Chl a concentrations) and PA concentrations. This does not disqualify phytoplankton
94
as major sources of PAs, since PAs concentrations were at the limit of detection, thus the
apparent dynamic range of PA concentrations was truncated by the detection limit. In addition,
their relationship with phytoplankton abundance may be obscured by the complex physical
dynamics at the GRNMS (NMSP, 2006). However, it is also possible that other organisms may
also be important sources of PAs in seawater. Indeed, we found strong correlations between
indicators of bacterial abundance (bacterial cell counts) and PA concentrations, suggesting
bacteria as an important PA producers (Tabor and Tabor, 1985) and/or degraders (Mou et al.,
2011).
We detected significant variation in the compositions of the PA pools between the spring
and fall cruises. This is likely due to differences between seasons in the composition of the
plankton community (phytoplankton, zooplankton and bacteria) that produce (Nishibori et al.,
2003; Hamana and Matsuzaki; 1992) and degrade (Sowell et al., 2009; Mou et al., 2011)
polyamines. Differences in the composition of the plankton community may also explain the
dominance of spermidine and/or spermine over putrescine in the PA pool. Putrescine has been
identified as the predominant PA in other marine environments (Badini et al., 1994; Nishibori et
al., 2003).
The concentration of total DFAAs at the GRNMS showed > 5-fold variation within each
of the two diurnal cycles we monitored, similar to the range observed in the Baltic Sea (Mopper
and Lindroth, 1982). In the latter study, the authors attributed these dynamics to diurnal variation
in rates of DFAA release by phytoplankton and uptake by bacteria. This interpretation was
supported by our findings that total DFAA and Chl a concentrations were significantly correlated
and is in accordance with the consensus view that phytoplankton are the major source of marine
DFAAs (Crawford et al., 1974; Carlucci et al., 1984). Different patterns of correlation among
95
individual DFAAs further indicated that they were subject to different transformation pathways,
as has been suggested previously (Webb and Johannes, 1967; Mopper and Lindroth, 1982; Shah
et al., 2002). For example, ornithine and lysine can be produced during bacterial degradation of
proteins and other N-rich organic compounds (Mopper and Lindroth 1982; Shah et al., 2002),
whereas glycine, taurine, and alanine are predominant DFAAs released by large marine
zooplankton (Webb and Johannes 1967). In our spring samples, glycine and taurine, dominated
the DFAA pool in samples of surface and mid-depth water, suggesting zooplankton as an
important source of DFAAs. In contrast, most of the bottom water samples contained high
concentrations of ornithine and lysine, suggesting that microbial activity associated with
sediments might be involved in DFAA production. Glycine and alanine dominated the DFAA
pool in both surface and bottom water at most sampling points in the fall, suggesting
zooplankton as primary source of DFAAs. This suggests that seasonal difference in the
composition of the DFAA pool may be largely driven by biological processes that produce
DFAAs in seawater.
Conclusion
We optimized an HPLC method to allow reliable and simultaneous measurements of 20
DFAAs and 5 PAs in seawater. Our results demonstrated that concentrations of individual PA
and DFAA compounds were often comparable. Both PAs and DFAAs generally represented a
small fraction of the labile DON pool; however, occasionally total PAs may reach concentrations
approaching total DFAAs. Our data suggest that PAs are occasionally an important component
of labile marine DON. Correlations between PA concentrations and bacterial abundance suggest
that PA is tightly coupled to the dynamics of bacterial communities in the ocean.
96
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101
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102
Table 4.1. Optimized elution gradient program of amino acids and polyamines. Eluents A:
acetonitrile; B (pH 7.0): methanol/0.36 M sodium acetate/water=55/8/37 (v/v/v); C (pH 8.0):
acetonitrile/0.36 M sodium acetate/water=55/5/40 (v/v/v); D (pH 7.0): acetonitrile/0.36 M
sodium acetate/water=10/5/85 (v/v/v).
Elution Time
(min)
Eluents
A (%) B (%) C (%) D (%)
0 0 15 0 85
2 0 15 0 85
6 0 20 0 80
9 0 40 0 60
17 0 75 0 25
20 0 85 0 15
25 0 100 0 0
27 0 100 0 0
33 0 20 80 0
37 0 20 80 0
53 100 0 0 0
56 100 0 0 0
56.1 0 15 0 85
62 0 15 0 85
65 0 15 0 85
103
Table 4.2. Parameters for validation of HPLC method.
Compound Linearity
(nM)
R2 a
LOD/LOQ
(nM)
Recovery/RSD (n = 5; %)
1 nM 10 nM 20 nM
Asp 0.1 -100 0.9997 0.05/0.1 82/5.7 100/4.6 91/3.2
Glu 0.1 -100 0.9999 0.05/0.1 108/3.7 108/8.2 94/8.3
Asn 0.1 -100 0.9999 0.05/0.1 86/8.2 118/4.4 117/2.4
Ser 1-100 0.9905 0.1/1 82/5.2 118/5.2 132/4.6
His 1-100 0.9999 0.1/1 86/4.8 115/5.2 123/0.7
Gln 0.1-100 0.9999 0.01/0.1 83/9.8 102/8.7 84/3.3
Thr 0.1-100 1.0000 0.05/0.1 84/6.1 118/5.5 116/2.7
Gly 0.1-100 0.9999 0.01/0.1 82/9.9 92/7.5 120/4.8
Arg 0.1-100 0.9998 0.05/0.1 89/6.4 101/5.5 98/2.2
Tyr 0.1-100 0.9998 0.05/0.1 101/5.6 105/4.9 103/2.0
Tau 0.1-100 1.0000 0.01/0.1 108/7.5 114/6.0 109/1.5
Ala 0.1-50 0.9981 0.01/0.1 83/9.9 108/4.4 110/2.8
GABA 1-100 0.9991 0.1/1 113/9.7 85/6.9 117/1.4
Val 1-100 1.0000 0.1/1 88/6.5 114/5.4 114/1.7
Met 0.1-100 1.0000 0.05/0.1 98/4.4 106/5.1 98/2.2
Ile 0.1-100 0.9994 0.05/0.1 114/5.7 114/4.7 107/1.1
Leu 0.1-100 1.0000 0.05/0.1 97/4.9 93/4.9 96/1.5
Phe 0.1-100 1.0000 0.01/0.1 105/5.4 99/5.2 100/1.1
Orn 1-100 0.9995 0.1/1 82/2.4 86/14 85/2.6
Lys 0.1-100 1.0000 0.01/0.1 86/4.2 84/11 86/1.1
Put 0.1-100 0.9994 0.05/0.1 108/11 102/8.4 113/3.4
Cad 0.1-100 0.9919 0.05/0.1 94/7.6 97/8.6 119/0.4
Norspd 1-100 0.9953 0.1/1 123/13 96/9.2 110/2.6
Spd 0.1-100 0.9945 0.01/0.1 119/11 96/7.8 110/2.6
Spm 1-100 0.9996 0.1/1 82/7.7 90/7.8 84/2.0
NH4+ 100-100,000 0.9998 50/100 96/0.2
b 92/8.0 97/1.5
a Abbreviations: R
2, correlation coefficient; LOD, limit of detection; LOQ, limit of
quantification; RSD, relative standard deviation. See Figure 4.2 for explanation of compound
abbreviations. b NH4
+ standards were spiked at three different levels of 1, 10, and 20 µM.
104
Table S4.1. Pair-wise correlation analysis among individual DFAAs in spring and fall based on
Pearson’s product-moment correlation coefficient. Amino acids with significant (P < 0.05 with
Bonferroni correction) correlations were shaded.
DFAA Asp a Glu Ser Gln Gly Arg Leu Phe GABA Ala Met Ile Tau Tyr Val Asn Lys His Thr
Glu 0.84
Ser 0.78 0.63
Gln 0.97 0.80 0.75
Gly 0.89 0.83 0.70 0.91
Arg 0.99 0.81 0.77 0.97 0.89
Leu 0.85 0.72 0.78 0.79 0.69 0.84
Phe 0.87 0.80 0.86 0.84 0.89 0.87 0.83
GABA 0.74 0.70 0.68 0.80 0.80 0.73 0.59 0.73
Ala 0.84 0.69 0.72 0.89 0.85 0.83 0.61 0.84 0.75
Met 0.69 0.65 0.42 0.73 0.75 0.72 0.49 0.71 0.62 0.61
Ile 0.70 0.73 0.48 0.68 0.74 0.71 0.71 0.80 0.53 0.57 0.76
Tau 0.55 0.72 0.34 0.61 0.63 0.53 0.37 0.45 0.55 0.56 0.59 0.43
Tyr 0.65 0.39 0.53 0.49 0.33 0.55 0.64 0.47 0.30 0.31 0.10 0.25 −0.03
Val 0.38 0.22 0.62 0.29 0.24 0.38 0.40 0.27 0.27 0.26 −0.13 0.08 0.04 0.47
Asn 0.24 0.34 −0.15 0.16 0.19 0.21 0.12 0.08 0.05 0.04 0.21 0.12 0.30 −0.05 −0.28
Lys 0.10 0.31 −0.14 0.14 0.24 0.09 0.01 0.15 −0.08 0.22 0.19 0.19 0.33 −0.18 −0.49 0.79
His −0.54 −0.24 −0.22 −0.61 −0.62 −0.57 −0.38 −0.62 −0.56 −0.62 −0.58 −0.61 −0.27 −0.16 −0.16 0.19 0.08
Thr −0.05 −0.02 0.02 −0.17 −0.22 −0.04 0.01 −0.24 −0.06 −0.34 −0.18 −0.24 −0.02 0.14 0.44 −0.08 −0.38 0.43
Orn −0.00 0.03 0.22 0.06 0.17 0.03 −0.06 −0.06 0.22 0.10 0.24 0.16 0.16 −0.06 0.02 −0.20 −0.18 −0.32 −0.31 a Abbreviations: See Figure 4.2 for explanation of DFAA abbreviations.
105
Table S4.2. Correlations between DFAAs/PAs and environmental variables based on Pearson’s
product-moment correlation coefficient. Variables with significant (P < 0.05 with Bonferroni
correction) correlations were shaded.
Variables PAR a S T Chl a DOC SRP DN NOx
- NH4
+ DON Cell
#
DFAAs 0.17 0.64 0.73 0.69 −0.72 0.53 0.55 −0.02 0.60 0.32 0.24
PAs 0.16 0.12 0.10 0.00 −0.01 −0.01 0.50 −0.12 0.16 0.04 0.49 a Abbreviations: PAR, photosynthetically active radiation; S, salinity; T, temperature; Chl a,
chlorophyll a; DOC, dissolved organic carbon; SRP, soluble reactive phosphorus; DN, dissolved
nitrogen; NOx-, nitrate/nitrite; DON, dissolved organic nitrogen; Cell
#, bacterial cell counts.
106
Table S4.3. Correlations between individual DFAAs and PAs based on Pearson’s product-
moment correlation coefficient. Variables without significant (P < 0.05 with Bonferroni
correction) correlations were blank or not shown.
DFAAs/PAs Spermidine Spermine
Methionine 0.75 0.64
Arginine 0.63 0.51
Threonine 0.61
0
3
6
9
12
15
18
Dep
th (m
)
0
3
6
9
12
15
18
Temperature (ºC)
Temperature
Salinity
20.0 21.0 22.0 23.0 24.0 25.5 26.0 26.5 27.0
33.0 34.0 35.0 35.0 35.5 36.0 36.5 37.0
SW
MW
BW
SW
BW
Figure 4.1
(a) (b)
Salinity (PSU)
Figure 4.1. Depth profiles of temperature and salinity at the GRNMS in (a) spring and
(b) fall, 2011. Abbreviation: SW, surface water; MW, mid-depth water; BW, bottom water.
107
Figure 4.2
50
40
30
20
10
0
Asp
(a)
GluAsn
Ser
His
GlnThr
GlyA
rgTy
r
Tau
AlaG
AB
A
AABA*
ValMet
IleLeu
Phe
Orn
Lys
Put
Cad
DAH*
Nor
spd
Spd
Spm
(b)60
50
40
30
20
10
0
5 15 25 35 45 55
5 15 25 35 45 55
Asp Glu
Asn Ser
Gln
Gly
Thr Arg
Tyr
Tau
Ala
GA
BA
AABA*
Met IleLeu
Phe OrnLys Put
DAH*
Spd
Retention time (min)
Fluo
resc
ence
inte
nsity
(mv)
Fluo
resc
ence
inte
nsity
(mv)
NH4+
NH4+
Figure 4.2. HPLC chromatograms of (a) a standard mixture and (b) a seawater sample. Peakes:
Asp, aspartic acid; Glu, glutamic acid; Asn, asparagine; Ser, serine; His, histidine; Gln,
glutamine; Thr, threonine; Gly, glycine; Arg, arginine; Tyr, tyrosine; Tau, taurine; Ala, alanine;
GABA, γ-aminobutyric acid; AABA, α-aminobutyric acid; Val, valine; Met, methionine; NH4+,
ammonium; Ile, isoleucine; Leu, leucine; Phe, phenylalanine; Orn, ornithine; Lys, lysine; Put,
putrescine; Cad, cadaverine; DAH, 1,7-diaminoheptane; Norspd, norspermidine; Spd, spermidine;
Spm, spermine. Internal standards were indicated by asteriskes.
108
12h 15h 18h 21h 24h 03h 06h 09h
10080
60
40
20
0
12
9
6
3
0
0.25
0.2
0.15
0.1
0.05
0.0
36
35
34
33
32
31
Tota
l DFA
As
(nM
)To
tal P
As
(nM
)To
tal P
As/
DFA
As
(bar
s)
Salin
ity (
PSU
)
SurfaceMid-depthBottom
(a)
(b)
(c)
(d)
SurfaceMid-depthBottom
HT LT HT LT
Day Night DayTime
0.8
0.6
0.4
0.2
0.0 Tota
l PA
s/D
ON
(%
, lin
es)
Salin
ity (
PSU
)
430420410400300200100
0
20015010050
42
0
(e)
(f)
(g)
(h)
0.80.70.60.5
0.030.020.010.00
36.6
36.4
36.2
36.0
35.8
0.6
0.4
0.2
0.0
21h 24h 03h 06h 09h 12h 15h 18h
LT HT LT HT
Night Day
Figure 4.3
Tota
l PA
s/D
ON
(%
, lin
es)
12h 15h 18h 21h 24h 03h 06h 09h
12h 15h 18h 21h 24h 03h 06h 09h
12h 15h 18h 21h 24h 03h 06h 09h
21h 24h 03h 06h 09h 12h 15h 18h
21h 24h 03h 06h 09h 12h 15h 18h
21h 24h 03h 06h 09h 12h 15h 18h
Tota
l DFA
As
(nM
)To
tal P
As
(nM
)To
tal P
As/
DFA
As
(bar
s)
Figure 4.3. Temporal and depth dynamics of DFAAs and PAs. Samples were organized into left
(spring) and right (fall) panels based on the sampling season, showing the variations in the (a; e)
concentrations of total DFAAs, (b; f) concentrations of total PAs, (c; g) ratios of total PAs/DFAAs,
and (d; h) salinity. Light availability and tidal cycles were schematically indicated in the bottom
panels. Abbreviation: HT, high tide; LT, low tide.
109
Fall
Fs06Fb18
Fb24
Fs03Fs24Fb06
Fb03Fs21
Fb21Fs18
Fs12
Fb12FS09
Fb09 Fb15
Fs15
Stress: 0.09
Spring
Sm21 Sb21Ss21
Sm03Sm06Sm18Sm24Sb03
Ss03 Sb24Ss24
Sb06
Ss06
Sb12
Ss12Sm12Sm15
Sb18Ss18
Ss15Sm09 Sb15
Ss09 Sb09
Blue: DayRed: Night
Surface Mid-depth
Unmarked: Bottom
Figure 4.4
Figure 4.4. The NMDS ordination based on individual DFAA concentrations at the GRNMS
in spring and fall, 2011. Sample notions were based on sampling season (S, spring; F, fall),
depth (s, surface; m, mid-depth; b, bottom), and time (in 24-hour format).
110
25201510
50C
once
ntra
tion
(nM
)
Con
cent
ratio
n (n
M)
4030
2010
0
Con
cent
ratio
n (n
M)
Con
cent
ratio
n (n
M)
25201510
50C
once
ntra
tion
(nM
)
6
4
2
0
7.5
5
2.5
0
6
4
2
0Con
cent
ratio
n (n
M)
GlyTauLysGluAsnHis
PutSpdSpm
(a)
(b)
(c)
(d)
(e)
(f)
Time
HT LT HT LT
Day Night Day
HT LT HT LT
Day Night Day
Figure 4.5
12h 15h 18h 21h 24h 03h 06h 09h
12h 15h 18h 21h 24h 03h 06h 09h
12h 15h 18h 21h 24h 03h 06h 09h
12h 15h 18h 21h 24h 03h 06h 09h
12h 15h 18h 21h 24h 03h 06h 09h
12h 15h 18h 21h 24h 03h 06h 09h
Figure 4.5. Variations in the concentrations of major DFAAs in (a) surface, (b) mid-depth,
and (c) bottom water and major PAs in (d) surface, (e) mid-depth, and (f) bottom water
within a diurnal cycle at the GRNMS in spring. Abbrevaiton: HT, high tide; LT, low tide;
See Fig. 4.2 for explanation of DFAA and PA abbreviations.
111
90
60
30
0
100
50
0
Con
cent
ratio
n (n
M)
Con
cent
ratio
n (n
M)
Con
cent
ratio
n (n
M)
Con
cent
ratio
n (n
M)
80604020
2
1
0
12
10
8
4
2
0
Time
GlyGlnAlaAspGluTau
PutCadNorspdSpdSpm
(a)
(b)
(c)
(d)
LT HT LT HT LT HT LT HT
Night Day Night Day
Figure 4.6
21h 24h 06h 09h 12h 15h 18h 21h 24h 03h 06h 09h 12h 15h 18h
21h 24h 03h 06h 09h 12h 15h 18h21h 24h 03h 06h 09h 12h 15h 18h
Figure 4.6. Variations in the concentrations of major DFAAs in (a) surface and (b) bottom
water and major PAs in (c) surface and (d) bottom water within a diurnal cycle at the
GRNMS in fall. Abbrevaiton: HT, high tide; LT, low tide; See Fig. 4.2 for explanation
of DFAA and PA abbreviations.
112
Fs09Fb18Fb12Fb15
Fs12Fb09
Fb24
Fs03 Fs18
Fs24
Fs21Fb06Fb03Fb21
Fs06
Sb09
Sm09
Ss09Ss15Sb15
Sb06Ss06
Ss18Sb18 Ss24
Sb12Sm15
Sm12
Sm21
Sb21
Ss21
Ss03
Sb24Ss12Sm18
Sm24
Sb03Sm03Sm06
Figure S4.1
Blue: DayRed: Night
Fall
SurfaceMid-depth
Unmarked: Bottom
Spring
Stress: 0.13
Fs15
Figure S4.1. The NMDS ordination based on individual DFAA relative abundances at the
GRNMS in spring and fall, 2011. See Figure 4.4 for explanation of samples notions.
113
Fs18Fb18
Stress: 0.11
Sb12
Fb12
Ss06Sm18Fs09
Fs21Fb06
Fb21
Fb03Fs24Fs06
Fs03Fb24
Fb15
Fs15
Fb09
Sb21
Sb18Ss21
Sm21
Sb24
Sb03Sb06Sm03
Ss18Sm12
Sm15Ss15
Sb15 Ss12Sb09Sm09
Ss03
Ss09Sm06
Ss24Sm24
Fs12
Figure S4.2
Blue: DayRed: Night
SurfaceMid-depth
Unmarked: Bottom
Figure S4.2. The NMDS ordination based on individual PA relative abundances at the
GRNMS in spring and fall, 2011. See Figure 4.4 for explanation of samples notions.
114
115
Chapter 5
Identification of Polyamine-responsive Bacterioplankton taxa in the South Atlantic Bight
1(This chapter will be submitted to the journal of Environmental Microbiology and the author list is as
follows: Lu, X., Sun, S., Hollibaugh, J.T., and Mou, X. Contributions: Lu, X. performed sampling, did all
experimental and data analyses, and wrote the manuscript; Sun, S. conducted bioinformatics analysis for
sequence data; Hollibaugh, J.T. helped in the study design; Mou, X. directed and supervised the study.)
116
Abstract
Putrescine and spermidine are short-chained aliphatic polyamines (PAs) that are
ubiquitously distributed in seawater. These compounds may be important sources of dissolved
organic carbon and nitrogen for marine bacterioplankton. However, our knowledge of the
taxonomic identity of PA-responsive bacteria is limited to inshore environments. We used
pyrotag sequencing to quantify the response of bacterioplankton to putrescine and spermidine
amendments of microcosms established using surface waters collected at the nearshore, offshore,
and open ocean stations in the South Atlantic Bight in October, 2011. Our analysis showed that
PA-responsive bacterioplankton consisted of bacterial taxa that are typically found as dominants
in marine systems. Rhodobacteraceae (Alphaproteobacteria) was the taxon most responsive to
polyamine additions at the nearshore site. Gammaproteobacteria of the families
Piscirickettsiaceae; Vibrionaceae; and Vibrionaceae and Pseudoalteromonadaceae, respectively,
were the dominant PA-responsive taxa in samples from a river-influenced nearshore station; an
offshore station; and an open ocean station. The spatial variability of PA-responsive taxa may be
attributed to differences in composition of the initial bacterial community which varied along the
in situ physiochemical gradient among sites. Our results also provided the first empirical
evidence that Gammaproteobacteria might play an important role in PA transformations in
marine systems.
117
Introduction
Putrescine (C4H12N2) and spermidine (C7H19N3) are short-chain polyamines (PAs) that
are widely distributed in the cells of marine organisms, such as bacteria, phytoplankton, and
zooplankton (Tabor and Tabor, 1984; Lee and Jørgensen, 1995). Intracellular PA concentrations
reach mM levels and they are vital to synthesis of DNAs, RNAs, and proteins (Tabor and Tabor,
1984; Igarashi and Kashiwagi, 2000). Seawater PA concentrations are typically at a few nM
(Nishibori et al., 2001, 2003; Lu et al., 2014; Liu et al., 2015), but in areas of high primary
productivity, PA concentrations can reach tens or even hundreds of nM (Lee and Jørgensen,
1995).
Multiple lines of evidence consistently suggest that PAs are important constituents of the
labile dissolved organic nitrogen (DON) pool in marine environments, and that they are actively
transformed by marine bacterioplankton. For example, radiotracer assays have demonstrated that
marine microbes can take up PAs at rates similar to those of amino acids (Höfle, 1984; Lee and
Jørgensen, 1995; Liu et al., 2015). In addition, genes and proteins involved in PA-
transformations are abundant in genomes (Mou et al., 2010), metatranscriptomes (Mou et al.,
2011), and metaproteomes (Sowell et al., 2008) of marine bacterioplankton.
Direct investigations of PA metabolizing bacterioplankton communities are just emerging.
So far, only two such studies have been reported and they both were conducted at the same
inshore site on Sapelo Island, Georgia (Mou et al., 2011, 2014). Both studies found that
putrescine and spermidine are used by similar bacterial taxa, with roseobacters as an important
functional lineage (Mou et al., 2011). However, these two studies yielded contrasting results
concerning the importance of SAR11 in PA transformation, with the one based on
metatranscriptomics suggesting they were important (Mou et al., 2011) and the one based on 16S
118
rRNA gene sequencing suggesting they were not (Mou et al., 2014). The present study aimed to
broaden the scope of communities analyzed and to compare them among marine systems. We
used microcosm experiments to identify PA-responsive bacterioplankton in the surface seawater
communities collected at nearshore, offshore, and open ocean stations in the South Atlantic
Bight (SAB) of the United States. These stations were chosen to represent marine systems
receiving varying influences of land and Gulf Stream waters. We hypothesized differences in PA-
responsive bacterioplankton community among these stations.
Methods
Sample collection, processing, and microcosm experiment setup
Surface water samples (~2 m below the air-water interface) were collected at the SAB
nearshore (st1 and st2), offshore (st3), and open ocean stations (st4) onboard of R/V Savannah in
October, 2011(Figure 5.1). Samples were collected in Niskin bottles that were mounted on a
rosette sampling system (Sea-Bird Electronics, Bellevue, WA). Temperature (T) and salinity (S)
were measured in situ with a conductivity-temperature-depth (CTD) water column profiler (Sea-
Bird Electronics, Bellevue, WA, USA) that was also mounted on the sampling system.
Immediately after collection, water samples were transferred from Niskin bottles into a
20 L carboy, mixed gently, then filtered through 3 μm pore-size membrane filters (Pall Life
Sciences, Ann Arbor, MI, USA) to exclude large particles and most bacterivores. Part of the
filtrate (~3.5 L each) was distributed into six 4 l amber carboys to establish bacterioplankton
microcosms. Another 1 L of whole water was sequentially filtered through 3 μm and 0.2 µm
pore-size membrane filters (Pall life sciences, Ann Arbor, MI, USA) to collect bacterioplankton
cells in initial samples (designated as ORI samples). The resulting filters were frozen
immediately in liquid nitrogen and stored at −80 °C until DNA extraction. Samples of the filtrate
119
from the 0.2 um filtration were collected in amber glass vials and stored at −80 °C prior to
measuring concentrations of a number of organic compounds and inorganic nutrients. In addition,
500 mL of whole seawater was passed through GF/F filters (Whatman International Ltd,
Maidstone, England) that had been combusted at 500 C for at least 6 h prior to use. The filters
were wrapped up in similarly combusted aluminum foil and immediately stored at −20 °C for
later chlorophyll a (Chl a) measurements.
Duplicate microcosms were amended with putrescine (~250 nM, final concentration;
PUT treatments), spermidine (~167 nM, final concentration; SPD treatments), or no amendments
(control; CTR treatments) and incubated onboard of the ship in the dark and at in situ
temperature in a flowing water bath. The stoichiometic C: N ratios of putrescine and spermidine
are respectively 4:2 and 7: 3, so the amended microcosms were equivalent in N additions. Based
on the amendments of PAs in microcosms, the expected new bacterial biomass after incubation
was calculated as: [PA] × NC × 12g/mol × Bacterial growth efficiency/per cell carbon biomass,
where [PA] is the added concentration of PAs, NC is the number of carbon in the PA compound.
In addition, duplicated microcosms containing 0.2 µm pore-size filter sterilized ORI-stn4 water
were mixed with 200 nM putrescine or spermidine (no cell controls; NCC treatment) and
incubated at the same conditions as the bacterioplankton microcosms. The NCCs were run to
determine whether the added PA compounds degraded abiotically.
At the beginning (0 h) and the end of the incubation (48 h), 1.8 mL water samples were
collected from each microcosm, mixed with freshly prepared paraformaldehyde (1%, final
concentration), and incubated at room temperatures for 1 h before storing at 4 °C for subsequent
enumeration of bacterial cells. After collecting samples for cell counts at the end of the
incubation, all water left in the microcosms was filtered through 0.2 µm pore-size polycarbonate
120
filter. The resulting filters were immediately frozen in liquid nitrogen and stored at −80 °C until
DNA analysis. The filtrates were collected in amber glass bottles and stored at −80 °C for later
analyses of putrescine and spermidine concentrations.
DNA extraction, PCR, and Pyrotag sequencing
DNA was extracted from frozen filters using the PowerSoil DNA extraction kits (MoBio
Laboratory Inc., Carlsbad, CA, USA). The V4-to-V6 region of the 16S rRNA genes was PCR
amplified using universal bacterial primers B530F (Vossbrinck et al., 1993) constructed with an
adaptor sequence and a barcode tag, and B1100R (modified from Turner et al., 1999)
constructed with an adaptor sequence. Five replicate PCR amplifications (25 µL each) were
performed for each sample and resulting amplicons were pooled and subsequently examined by
gel electrophoresis (1% agarose gel). Amplicons of the correct size were excised from the gels
and doubly purified, first with a QIAquick gel extraction kit (QIAGEN, Chatsworth, CA, USA)
and then with an AMpure XP systems kit (Beckman Coulter Genomics, Brea, CA, USA). Equal
molar concentrations of purified PCR amplicons from 13 random samples were pooled and
sequenced in one run with a 454 GS Junior System (Roche 454 Life Sciences, Branford, CT,
USA) using unidirectional Lib-L chemistry. A total of 26 samples were sequenced in two runs.
The pyrotag sequences we obtained were deposited in the NCBI Sequence Read Archive
(SRA) under the project accession no. SRR1602747 and SRR1602749.
16S rRNA gene pyrotag sequence annotation
Raw 16S rRNA gene pyrotag sequences were sorted based on their sample tag IDs, and
then primer and barcode sequences were removed. Quality control steps excluded reads that had
any incorrect base calls in the primer region, were shorter than 65 bp, or contained chimeras (as
121
detected by UCHIME; Edgar et al., 2001). The remaining sequences were clustered into
operational taxonomic units (OTUs) at the 97% identity cutoff level using the CD-HIT program
(Li and Godzik, 2006). OTUs containing single sequences were removed from the OTU list to
avoid potential overestimations on bacterial diversity (Kunin et al., 2010). The longest sequence
of each OTU was used for taxonomic annotation by BLAST against the SILVA SSU database
(Pruesse et al., 2007). Taxonomic compositions were summarized at the family and higher levels
whenever possible. Some sequences were affiliated with marine bacterial groups that do not have
official taxonomic standings at family level; these sequences were summarized at the clade level,
e.g. “SAR11.” For simplicity, the family and clade OTUs are referred as family hereafter in this
paper. Sequences that were assigned to chloroplasts were excluded from further analyses.
Nutrient analysis
Concentrations of dissolved organic carbon (DOC), dissolved nitrogen (DN),
nitrate/nitrite (NOx-), and soluble reactive phosphorus (SRP) were determined using standard
procedures (Clescerl et al., 1999). Briefly, DOC and DN concentrations were measured with a
TOC/TN analyzer (TOC-VCPN; Shimadzu Corp., Tokyo, Japan) using combustion-
oxidation/infrared detection and combustion/chemiluminescence detection methods, respectively.
NOx- and SRP concentrations were determined using flow injection protocols on a Lachat
(QuikChem FIA+ 8000Series, Loveland, CO, USA), following the cadmium reduction method
and the molybdenum blue colorimetric method, respectively.
Chl a was extracted from filters with 90% acetone and determined
spectrophotometrically following Tett et al. (1975). Ammonium (NH4+) concentrations were
determined using the indophenol colorimetric method (Solórzano, 1969). Putrescine and
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spermidine concentrations were measured fluorometrically using a Shimadzu 20A high-
performance liquid chromatography system (Shimadzu Corp., Tokyo, Japan) equipped with a
250 × 4.6 mm i.d., 5 µm particle size, Phenomenex Gemini-NX C18 column (Phenomenex,
Torrance, CA, USA) following pre-column derivatization with o-phthaldialdehyde, ethanethiol,
and 9-fluorenylmethyl chloroformate (Lu et al., 2014).
Bacterial cell counts.
Preserved bacterioplankton cells were enumerated using a FACSAria flow cytometer
(BD, Franklin Lakes, NJ, USA) (Mou et al., 2013). Cells were stained with Sybr Green II
(1:5000 dilution of the commercial stock) in the dark for 20 min and mixed with an internal
standard consisting of a known number of beads (5.2 µm diameter SPHEROTM
AccuCount
Fluorescence Microspheres; Spherotech Inc., Lake Forest, Illinois, USA). Cell counts were
calculated based on the ratios of counts of bacterial cells and beads.
Diversity calculation and statistical analyses
Diversity calculations and statistical analyses were performed using PRIMER v5
software (Plymouth Marine Laboratory, Plymouth, UK; Clark and Warwick, 2001) unless
otherwise noted. Shannon indices and rarefaction curves were calculated at the family level to
infer diversity and coverage, respectively.
Non-metric multidimensional scaling (NMDS) analysis based on Bray-Curtis
dissimilarities of the square-root transformed relative abundances of bacterial families (Clark and
Warwick, 2001) was performed to visualize differences in bacterioplankton community
composition. ANOSIM (analysis of similarity), an analogue of the standard univariate analysis of
variance (ANOVA), was employed to test the robustness of grouping patterns observed on the
123
NMDS plots. ANOSIM generated rANOSIM values on a scale of 0 to 1. Sample groups were
reported as well-separated when rANOSIM was more than 0.75, as clearly different but with some
overlap when rANOSIM was between 0.5 and 0.75, or as barely separable when rANOSIM was less
than 0.25 (P < 0.05; Clark and Warwick, 2001). Similarity of percentages (SIMPER) analysis
was performed to determine the contribution of individual bacterial families to the observed
variance between sample groups.
Principal components analysis (PCA) was preformed based on log-transformed variables
including, T, S; and the concentrations of cell, DOC, DN, NOx-, SRP, NH4
+, Chl a, putrescine,
and spermidine. Differences in individual variables between samples were tested for statistical
significance using t test or ANOVA implemented within the R software package (R Core
Development Team, 2005). Significant differences were reported when P < 0.05.
Results
Initial environmental conditions
PCA analysis based on measured variables showed a spatial variation among the four
initial water samples (ORI; Figure 5.2). PCA1 captured 62.5% of the variance and was mainly
contributed by concentrations of spermidine, DN, NOx-, and NH4
+. Spermidine concentrations
(0.4 to 5.3 nM) were highest at the nearshore station stn1 (ANOVA, P < 0.05; Table S5.1). In
contrast, Concentrations of DN (0.1 to 0.4 mg N/L), NOx- (8.6 to 44.0 µg N/L), and NH4
+ (0.2 to
2.9 µM) gradually increased as the distance increased from the shore and reached the maximum
at the open ocean site (stn4), which had significantly higher concentrations than at nearshore
sites (stn1 and stn2) (t test, P < 0.05; Table S5.1). PCA2 explained 25.0% of the variation among
samples and was driven mainly by concentration of Chl a (0.2 to 5.4 µg/L), which was greatest
at the nearshore station stn2 (ANOVA, P < 0.05; Table S5.1). Concentrations of DOC (1.1 to 2.1
124
mg C/L), SRP (39.4 to 54.6 µg P/L), and putrescine (undetectable to 0.9 nM) as well as bacterial
cell abundance (1.1×106 to 1.8×10
6/mL), T (25.5 to 28.9 °C), and S (35.8 to 36.4 PSU) showed
no significant differences among sites (ANOVA, P > 0.05; Table S5.1).
General statistics of 16S rRNA gene pyrotag sequences and initial bacterioplankton
communities
A total of 195202 partial 16S rRNA genes sequences of 561 bp average read length were
recovered from the ORIs and amended samples, with the number of sequences per sample
ranging from 1488 to 21608 (Table S5.2). Over 80% of these sequences were affiliated with 11-
13 bacterial families of the Actinobacteria, Bacteroidetes, Cyanobacteria, Deferribacteres,
Proteobacteria, and Verrucomicrobia phyla (Figure S5.1 and Figure 5.3). Rarefaction curves of
sequences grouped at the family level reached saturation for all libraries, indicating that
recovered reads were sufficient to represent bacterioplankton diversity at the family level in our
samples (Figure S5.2). The family-level Shannon index (H) values showed no significant
differences between any pair of microcosm libraries (t test, P > 0.05; Table S5.2).
ORI libraries from the four sampling sites contained the same dominant bacterial taxa
(families with > 2% sequences in at least one of the four ORI libraries), but the relative
abundance of each taxon varied significantly among sites (ANOVA, P < 0.05; Figure S5.1 and
Figure S5.3). Sequences representing the Pseudoalteromonadaceae (Gammaproteobacteria)
were the most abundant family (26.9% of sequences recovered) in the ORI library from st1
(ORI-st1), followed by sequences representing the Rhodobacteraceae (12.6%) and SAR11 (9.4%)
of Alphaproteobacteria (Figure S5.1). Sequences representing Family I Cyanobacteria
(Cyanobacteria; 25.1%), OCS155 marine group (Actinobacteria; 19.0%), and SAR11 (11.6%)
were the most abundant in sample ORI-st2 (Figure S5.1). Over 41% of the sequences recovered
125
from ORI-st3 were affiliated with Family I Cyanobacteria, followed by the OCS155 marine
group (23.4%) and SAR11 (14.6%) (Figure S5.1). Sequences representing SAR11 (19.9%),
SAR324 (Deltaproteobacteria; 18.6%), and SAR406 (Deferribacteres; 11.4%) dominated the
ORI-st4 sample (Figure S5.1).
Bacterial growth on PAs
PA additions to the microcosms only increased total DOC by < 1.1% and DON by <
8.3%, on average. Over 98% of the putrescine and spermidine added to the PUT and SPD
microcosms was consumed by bacterioplankton after 48 h of incubation at all four sites (Figure
5.4). In contrast, putrescine and spermidine concentrations in filter-sterilized seawater controls
(no-cell controls or NCCs) did not change significantly after 48 h of incubation (t test, P > 0.05;
Table S5.3). Furthermore, concentrations of putrescine and spermidine in CTR microcosms were
lower than 5.3 nM, and were only reduced significantly during incubation in CTR-GR
microcosms (t test, P > 0.05; Figure 5.4).
Total cell numbers increased significantly during the incubation in both PUT and SPD
microcosms from st4 (t test, P < 0.05), with doubling rates of 0.21 and 0.20 per day, respectively.
Bacterioplankton abundance did not change significant over the course of the incubation in any
of the other PA-amended microcosms (t test, P > 0.05; Figure 5.4); however, cell abundance was
significantly higher in PA-amended microcosms than in the CTR microcosms at the end of the
48 h incubation (t test, P < 0.05), due to the decreased cell abundance in CTR microcosms. The
observed changes in cell abundance (up to 0.5×106/mL) were consistent with the expected
increase in cell abundance (up to 0.8× 106/mL), which was calculated based on 10-30 fg C per
cell (Lee and Fuhrman, 1987; Fukuda et al., 1998) and 10%-60% growth efficiency (Kroer, 1993;
del Giorǵio et al., 1997; Church et al,. 2000).
126
PA-responsive bacterioplankton taxa
NMDS ordination grouped samples based on their sampling sites (Figure 5.5). ANOSIM
analysis showed that this separation was statistically significant (rANOSIM = 0.82, P < 0.05; Table
5.1). Bacterial community composition shifted relative to ORIs in all microcosms from all sites
after 48 h of incubation. Final samples generally grouped together by composition based on
treatments (rANOSIM 0.56, P < 0.05; Table 5.1). The relative abundances of only a few taxa
increased significantly in libraries from PA-amended microcosms compared to the corresponding
CTR libraries (t test, P < 0.05). These taxa were designated as PA-responsive bacterioplankton
and their composition varied among sites.
Rhodobacteraceae was the only PA (specifically, putrescine) responsive taxon identified
at st1 and their sequences were significantly overrepresented in the PUT-st1 treatment (average
22.6% of the sequences) compared to the CTR-st1 libraries (15.5%; Figure 5.3a; t test, P < 0.05).
SAR11 (21.1%-27.1%) and OCS155 marine group (17.1%-21.9%) sequences were also
abundant, but showed no significant increase relative to CTR-st1 libraries (t test, P > 0.05).
Piscirickettsiaceae (Gammaproteobacteria) responded to both putrescine and spermidine
amendments in st2 samples. Their relative abundances were 12.4% in PUT-st2 libraries and 62.0%
in SPD-st2 libraries, respectively, which were significantly greater than those in the CTR-st2
library (0.1%; t test, P < 0.05; Figure 5.3b). The relative abundances of Methylophilaceae
(Betaproteobacteria) sequences were also greater in PUT-st2 (7.0%) and SPD-st2 (6.8%)
libraries than in CTR-st2 (0.7%), but the differences were not statistically significant (t test; P >
0.05). Rhodobacteraceae and Vibrionaceae (Gammaproteobacteria) represented the second and
third most abundant taxa in PUT-st2 (23.8% and 19.8%, respectively) and SPD-st2 (12.5% and
127
7.9%, respectively) libraries; their relative abundances did not increased significantly compared
to CTR-st2 (28.7% and 16.9%, respectively; t test, P > 0.05)
Vibrionaceae in st3 samples responded to both PA compounds. Their relative abundances
were significantly greater in PUT-st3 (30.5%) and SPD-st3 (18.3%) libraries than in the CTR-st3
(8.2%) libraries (t test, P < 0.05; Figure 5.3c). Sequences assigned to Family I Cyanobacteria,
OCS155 marine group, and Rhodobacteraceae also had greater abundances in PUT-st3 (19.8%,
10.0%, and 21.2%, respectively) and SPD-st3 (22.1%, 18.1%, and 12.4%) libraries; however,
these values were not significantly higher (t test, P > 0.05) than their relative abundances in
CTR-st3 (26.6%, 20.7%, and 20.4%).
Vibrionaceae and Pseudoalteromonadaceae both responded to putrescine but not to
spermidine amendments in st4 samples. They each represented 31.4% and 26.5%, respectively,
of the sequences in PUT-st4 libraries and were significantly more abundant than in the CTR-st4
libraries (12.2% and 13.9%, respectively; Figure 5.3d). Alteromonadaceae
(Gammaproteobacteria) and SAR11 each accounted for ~18% of SPD-st4 sequences. These
values, however, were not significantly different (t test, P > 0.05 with Bonferroni correction)
from those of the CTR-stn12 libraries (14.8% and 20.9%, respectively).
Discussion
This study used perturbation experiments based on amending microcosms with test
substrates to identify bacterioplankton taxa that responded to PA additions. Putrescine and
spermidine amendments increased the supply of PAs by 2 to 3 orders of magnitude above
background PA concentrations in the study area (Table S5.1; Lu et al, 2014; Liu et al, 2015).
However, higher PA concentrations up to about 300 nM have been reported in natural marine
environments (Lee and Jørgensen, 1995; Lu et al., 2014).
128
PA additions sustained or stimulated the growth of bacterioplankton in all PUT and SPD
microcosms. This indicates that the added PAs were used as C, N and/or energy sources by
bacterioplankton (Höfle, 1984; Lee and Jørgensen, 1995). However, due to limitations of our
approach, we cannot rule out the possibility that some bacteria identified as responsive were
actually using PA metabolites released by other bacteria.
Bacterioplankton identified as PA-responsive were affiliated with bacterial families that
are typical to marine ecosystems, but their composition varied among nearshore, nearshore
(river-influenced), offshore, and open ocean sites. Rhodobacteraceae responded to putrescine
addition in nearshore seawater from st1, in agreement with prior coastal studies (Mou et al., 2011,
2014) and an in silico study of the distribution of PA metabolizing genes among marine bacterial
genomes (Mou et al., 2010). Rhodobacteraceae, especially the roseobacter clade, represents a
numerically (up to 25% of bacterioplankton) and ecologically important bacterial lineage with
broad capacity for processing plankton-derived DOC in coastal marine environments (Buchan et
al., 2005; Brinkhoff et al., 2008). The involvement of Rhodobacteraceae in PA processing may,
at least partly, explain the rapid turnover of PAs in coastal marine systems (Liu et al., 2015).
Rhodobacteraceae did not respond significantly to PA amendments at any of the other
three sampling sites, even though their relative abundances were among the highest of all taxa
detected in the PA-amended treatments at these sites. Therefore, compared to the PA- responsive
organisms identified in these experiments, Rhodobacteraceae might play a minor role in PA
removal at these stations. However, the abundance of bacterioplankton including
Rhodobacteraceae, was significantly higher in PA-amended samples than in the corresponding
CTRs after incubation at these stations; therefore, we cannot eliminate the possibility that the
Rhodobacteraceae was using added PAs at these three sites. The dominant PA-responsive
129
bacteria taxa of the st2, st3, and st4 were members of the Gammaproteobacteria:
Piscirickettsiaceae, Vibrionaceae, and Vibrionaceae and Pseudoalteromonadaceae, respectively.
The importance of Vibrionaceae, and of Gammaproteobacteria in general, in PA utilization
suggested by our results is in accordance with the common occurrence of PA metabolizing genes
among marine Gammaproteobacteria genomes (Mou et al., 2014).
Previous studies have suggested that putrescine and spermidine are transformed by
similar groups of bacterioplankton (Mou et al., 2011, 2014). Our results suggest the same for st2
(Piscirickettsiaceae) and st3 (Vibrionaceae) samples. However, PA-responsive taxa identified at
st1 (Rhodobacteraceae) and st4 (Vibrionaceae and Pseudoalteromonadaceae) only responded to
putrescine. This suggests that PA-transforming bacteria might specialize on specific compounds
and that their distribution varies spatially.
SAR11 bacteria (Alphaproteobacteria) have been repeatedly identified as an important
PA-metabolizing bacterial taxon in coastal and open ocean marine systems (Sowell et al., 2008;
Mou et al., 2011). They were identified as a major taxon at all sites in our studies. However, the
relative abundance of SAR11 did not increase significantly relative to CTRs in any of the PUT or
SPD treatments. Similarly, SAR11 did not respond strongly in a recent study of PA-responsive
bacterioplankton at an inshore site (Mou et al., 2014). These observations do not necessarily
exclude a role for SAR11 in PA utilization as the growth rate of SAR11 has been reported to be
between 0.13/day and 0.72/day (Eilers et al., 2000; Yokokawa et al., 2004; Malmstrom et al.,
2005). Therefore, the incubation time of 2 d in this and the previous study at the inshore site may
have been be too short to allow SAR11 and other slow-growing bacterioplankton taxa to
significantly increase their relative abundance in the PUT and SPD treatments.
130
Factors that regulate the composition of PA-responsive bacterioplankton communities
among marine systems are not fully understood. The original bacterioplankton community
compositions varied significantly among our study sites. As the type and copy number of genes
related to PA metabolism in bacterioplankton genomes differ among bacterial taxa (Mou et al.,
2010, 2014), variations in the composition of the PA-responsive bacterial community may be
ascribed to the overriding differences among the composition of the original bacterioplankton
communities. Furthermore, nearshore, offshore, and open ocean stations of the SAB represent a
natural gradient in many environmental variables, such as decreased Chl a supply and increased
concentrations of DN, NOx- , and NH4
+ from nearshore to open ocean
(Table S5.1; Liu et al. 2015),
which might also contribute to the observed difference among the SAB stations of PA-responsive
bacterial taxa.
Conclusions
The taxonomic composition of PA-responsive bacterioplankton assemblages varied
among our study sites, which is likely related to differences in the composition of the initial
bacterial communities along the gradient of physiochemical conditions in the SAB.
Rhodobacteraceae of the Alphaproteobacteria was major PA-responsive taxa at nearshore
coastal site, while families of Gammaproteobacteria were important at river-influenced
nearshore site and stations that are distant to shore. Bacteria responded to putrescine but not
spermidine at two of the study sites, indicating the two PA compounds may be transformed by
different taxa, which may distributed differently among sites.
131
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Turner, S., Pryer, K.M., Miao, V.P., and Palmer, J.D. (1999) Investigating deep phylogenetic
relationships among cyanobacteria and plastids by small subunit rRNA sequence analysis1. J
Eukaryot Microbiol 46: 327–338.
Vossbrinck, C.R., Baker, M.D., Didier, E.S., Debrunner-Vossbrinck, B.A., and Shadduck, J.A.
(1993) Ribosomal DNA sequences of Encephalitozoon hellem and Encephalitozoon cuniculi:
species identification and phylogenetic construction. J Eukaryot Microbiol 40: 354–362.
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Yokokawa, T., Nagata, T., Cottrell, M.T., and Kirchman, D.L.. (2004) Growth rate of the major
phylogenetic bacterial groups in the Delaware estuary. Limnol Oceanogr 49: 1620–1629.
136
Table 5.1. Results of ANOSIM analyses, with overall and pairwise differences between different
ecosystems in the SAB.
Group rANOSIM P
overall samples separated by station 0.82 0.001 st1 samples separated by treatment 0.56 0.03
st2 samples separated by treatment 0.78 0.01
st3 samples separated by treatment 0.55 0.02 st4 samples separated by treatment 0.70 0.02
137
Table S5.1. The biotic and abiotic variables (average±standard error of the mean) measured in ORI samples of all four sampling sites.
Site T
(°C)
S
(PSU)
DOC (mg
C/L)
DN (mg
N/L)
NOx- (µg
N/L)
NH4+
(uM)
SRP (µg
P/L)
Put
(nM)
Spd
(nM)
Chl a
(µg/L)
Cell
(×106/mL)
st1 26.8 36.4 1.5±0.04 0.1±0.01 8.6±1.5 0.2±0.07 39.4±4.8 N.D.a 5.3±1.3 1.1±0.3 1.5±0.2
st2 25.5 36.2 2.1±0.08 0.1±0.02 21.6±6.8 0.9±0.2 54.6±0.8 0.1±0.0 0.6±0.1 5.4±0.4 1.8±0.4
st3 27.6 36.4 1.4±0.04 0.4±0.03 31.3±14.2 0.2±0.1 46.9±1.6 N.D. 0.4±0.0 0.2±0.1 1.6±0.5 st4 28.9 35.8 1.1±0.07 0.3±0.00 44.0±16.3 2.9±0.2 46.1±1.5 N.D. 0.4±0.0 0.3±0.2 1.1±0.2 a N.D. stands for Not Detected. Abbreviations: Put, putrescine concentration; Spd, spermidine concentration.
138
Table S5.2. General statistics of 16S rRNA gene pyrotag sequence libraries of incubated
microcosms. Shannon index statistics were calculated at the family level.
Site Treatment # of Reads Shannon index
H
# of unique taxa
OTU Family Order Class Phylum
st1
ORI 3420 2.80 325 61 42 24 11
CTR1 4906 2.43 310 47 37 16 8
CTR2 6129 2.43 416 49 35 16 8
PUT1 7053 2.38 488 46 34 14 7
PUT2 8658 2.34 574 52 39 16 8
SPD1 10978 2.37 556 56 43 19 9
SPD2 21608 2.29 865 64 48 19 11
st2
ORI 6390 2.29 349 42 33 16 10
CTR1 6934 2.61 1134 58 41 20 10
CTR2 7052 2.33 1115 48 38 16 8
PUT1 6371 2.45 979 58 44 20 9
PUT2 3352 2.39 663 43 34 16 7
SPD1 2368 1.67 439 31 24 11 5
SPD2 1506 1.21 228 17 15 8 4
st3
ORI 1488 2.55 175 41 32 15 8
CTR1 5314 2.28 830 46 38 17 10
CTR2 3389 1.93 500 35 28 13 6
PUT1 8089 1.97 1072 47 35 17 10
PUT2 3570 2.10 581 41 35 17 10
SPD1 10530 2.30 1386 50 39 18 9
SPD2 9816 2.21 1376 54 40 18 9
st4
ORI 1987 2.61 190 40 32 18 10
CTR1 8084 2.33 487 50 39 17 10
CTR2 12100 2.47 633 55 41 18 10
PUT1 9051 2.50 556 48 36 19 10
PUT2 7480 2.41 413 51 36 17 9
SPD1 7318 2.50 453 50 38 21 12
SPD2 10261 2.32 550 52 39 20 11
139
Table S5.3. Changes in concentrations of putrescine and spermidine that were added to sterilized
ORI-st4 seawater during 48 h incubation.
Compound 0 h (nM) 48 h (nM)
Putrescine 200.0 ± 0.8 193.5 ± 9.0
Spermidine 204.1 ± 2.3 189.0 ± 19.8
st1 (17 m)
st2 (10 m)
st3 (33 m)
st4 (500 m)
31.5
31.0
30.5
30.0
-81.5 -80.5 -79.5
Longitude
Latit
ude
Georgia
Figure 5.1
St.Marys River
Figure 5.1. Sampling stations of st1 (nearshore), st2 (river-influenced nearshore), st3 (offshore),and st4 (open ocean) in the South Athantic Bight (SAB) in October, 2011. The water depth of each site is provided in the parentheses.
Florida
140
Spdst1
st3
st4
st2
DN
NOx-
NH4+
Put
Chl a
CellDOC SRP
TS
0.5
0.0
-0.5
-1.0
-1.0 -0.5 0.0 0.5 1.0
PC2
(25.
0%)
PC1 (62.5%)
Figure 5.2
Figure 5.2. Principle component analysis (PCA) biplot of environmental variables measured in water samples from st1, st2, st3, and st4. Abbreviation: Put, putrescine concentration; Spd, spermidine concentration.
141
OCS155 m
arine
grou
p
Cryomorp
hacea
e
Family
I Cya
noba
cteria
Rhodo
bacte
racea
e
Rhodo
spiril
lacea
e
SAR11 cl
ade
SAR116 c
lade
Vibrion
acea
e
Pseudo
altero
-
monad
acea
e
Alterom
onad
acea
e
SAR324 c
lade
ActinobacteriaBacteroidetes
CyanobacteriaAlpha- Delta- Gamma-
Proteobacteria
Perc
enta
ge (%
) of s
eque
nces
30
25
20
15
10
5
0
(a)
Perc
enta
ge (%
) of s
eque
nces
75
70
65
60
30
20
10
0
(b)
OCS155 m
arine
grou
p
Cryomorp
hacea
e
Family
I Cya
noba
cteria
Rhodo
bacte
racea
e
Rhodo
spiril
lacea
e
SAR11 cl
ade
Vibrion
acea
e
Pseudo
altero
-
monad
acea
e
Pisciri
cketts
iacea
e
Methylo
phila
ceae
Alterom
onad
acea
e
CTRPUTSPD
OCS155 m
arine
grou
p
Family
I Cya
noba
cteria
Cytoph
agia
Family
Incer
tae Sed
is
Vibrion
acea
e
Pseudo
altero
-
monad
acea
e
Alterom
onad
acea
e
SAR406 c
lade
Rhodo
bacte
racea
e
Rhodo
spiril
lacea
e
SAR11 cl
ade
SAR116 c
lade
35
30
25
20
15
10
5
0
Perc
enta
ge (%
) of s
eque
nces
(c)
40
35
30
25
20
15
10
5
0
Perc
enta
ge (%
) of s
eque
nces
(d)
OCS155 m
arine
grou
p
Family
I Cya
noba
cteria
SAR406 c
lade
Vibrion
acea
e
Pseudo
altero
-
monad
acea
e
Alterom
onad
acea
e
Kordiim
onad
acea
e
Colwell
acea
e
Rhodo
bacte
racea
e
Rhodo
spiril
lacea
e
SAR11 cl
ade
**
*
*
*
*
*
ActinobacteriaBacteroidetes
CyanobacteriaDeferribacteres Alpha- Gamma-
Proteobacteria
ActinobacteriaBacteroidetes
CyanobacteriaAlpha- Beta- Gamma-
Proteobacteria ActinobacteriaCyanobacteria
DeferribacteresAlpha- Gamma-
Proteobacteria
Figure 5.3
Figure 5.3. The relative abundance (%) of major bacterioplankton families in libraries of CTR, PUT, and SPD treatments from (a) st1, (b) st2, (c) st3, and (d) st4. Asterisks are used to indicate bacterial taxa showing a significantly higher relative abundance in libraries from the PUT or SPDtreatments relative to CTR libraries (t test, P < 0.05).
142
CTR PUT SPD
Con
cent
ratio
n (n
M)
Con
cent
ratio
n (n
M)
Con
cent
ratio
n (n
M)
Cel
l abu
ndan
ce (×
106 /m
L)
Cel
l abu
ndan
ce (×
106 /m
L)C
ell a
bund
ance
(×10
6 /mL)
2.52.01.51.0
0.00 h 48 h 0 h 48 h 0 h 48 h
CTR PUT SPD
(c) st3
400.0
300.0
200.0
8.0
6.0
4.0
2.0
0.0
Cell
putrescinespermidine
Con
cent
ratio
n (n
M)
400.0
300.0
200.0
8.06.04.02.00.0
0 h 48 h 0 h 48 h 0 h 48 h
(a) st1
400.0
300.0
200.08.06.04.02.00.0
2.52.01.51.0
0.0
2.52.01.51.0
0.0
2.52.01.51.0
0.0
500.0
400.0
300.0
200.0
8.0
6.0
4.0
2.0
0.0
(b) st2 (d) st4
Figure 5.4
Figure 5.4. Changes in putrescine and spermidine concentrations (bar graph; left axis) and cell abundance
(line graph; right axis ) in the CTR, PUT, and SPD microcosms from (a) st1, (b) st2, (c) st3, and
(d) st4 after 48 h incubations.
CTR PUT SPD0 h 48 h 0 h 48 h 0 h 48 h 0 h 48 h 0 h 48 h 0 h 48 h
CTR PUT SPD
143
Cel
l abu
ndan
ce (×
106 /m
L)
Figure 5.5
Figure 5.5. The non-metric multidimensional scaling (NMDS) ordination of samples from theORIs and CTR, PUT, and SPD microcosms from stations st1 (nearshore; triangle), st2 (river-influenced nearshore; hexagon), st3 (offshore; square), and st4 (open ocean; circle). Ordinations are based on the relative abundance of major bacterioplankton families inlibraries from each sample. Colors of shading are used to denote different treatments (white, ORIs; light gray, CTR; dark gray, PUT; black, SPD). Dashed lines group samples fromthe same station (black, st1; red, st2; blue, st3; green, st4).
Stress: 0.12
nearshore (st1)
river-influenced nearshore (st2)
offshore (st3)
open ocean (st4)
ORI CTR PUT SPD
144
Perc
enta
ge (%
) of s
eque
nces
OCS155 m
arine
grou
p
Cryomorp
hacea
e
Family
I Cya
noba
cteria
SAR406 c
lade
SAR11 cl
ade
Rhodo
bacte
racea
e
Phodo
spiril
lacea
e
SAR116 c
lade
SAR324 c
lade
Pseudo
altero
monad
acea
e
Vibrion
acea
e
Salin
ispha
eracea
e
MBC11C04
mari
ne gr
oup
40
30
20
10
0
st1st2st3st4
Actinobacteria
Bacteroidetes
Cyanobacteria
Deferribacteres
Alpha-
Delta-
Gamma-Verrucomicrobia
Proteobacteria
Figure S5.1
Figure S5.1. The relative abundance (%) of major bacterioplankton at family level in librariesgenerated from the original seawater samples (ORIs) collected for microcosm experiments.
145
0 5000 10000 21000 220000
20
40
60
Num
ber o
f bac
teria
l fam
ilies
Num
ber o
f bac
teria
l fam
ilies
ORI
CTR1
CTR2
PUT1
PUT2
SPD1
SPD2
Num
ber o
f bac
teria
l fam
ilies
0 2000 4000 8000 10000 120000
20
40
60
Num
ber o
f bac
teria
l fam
ilies
0 1000 2000 3000 6000 7000 80000
20
40
60
0 5000 10000 150000
20
40
60
Library size
80(a)
(b)
(c)
(d)
Figure S5.2
Figure S5.2. Family-level rarefaction curves of bacterial 16S rRNA gene sequences in librariesof original and incubated samples from (a) st1, (b) st2, (c) st3, and (d) st4.
146
st4
st1
st2
st3
stress: 0.0
Figure S5.3
Figure S5.3. Non-metric multidimentional scaling (NMDS) ordination of the original seawatersamples from st1, st2, st3, and st4 based on the relative abundance of major bacterioplankton families in libraries of each sample
147
148
Chapter 6
Metagenomic and Metatranscriptomic Characterization of Polyamine-transforming
Bacterioplankton in Marine Environments
1(This chapter will be submitted to The ISME journal and the author list is as follows: Lu, X., Sun, S.,
Hollibaugh, J.T., and Mou, X. Contributions: Lu, X. performed sampling, did all experimental and
data analyses, and wrote the manuscript; Sun, S. helped in the bioinformatics analysis for sequence
data; Hollibaugh, J.T. helped in the study design; Mou, X. directed and supervised the study.)
149
Abstract
Short-chained aliphatic polyamines (PAs) potentially serve as an important carbon, nitrogen,
and/or energy source to marine bacterioplankton. To study the genes and taxa involved in the
transformations of different PA compounds and their potential variations among marine
systems, we collected surface bacterioplankton from nearshore, offshore, and open ocean
stations in the Gulf of Mexico in May, 2013 and examined their metagenomic and
metatranscriptomic responses to additions of single PA model compounds (putrescine,
spermidine, or spermine). Our data showed an overrepresentation of genes affiliated with
putative γ-glutamylation and spermidine cleavage pathways in most PA-treated metagenomes
and metatranscriptomes, indicating they are important PA degradation routes by marine
bacterioplankton community. Identified PA-transforming taxa were affiliated with
Actinobacteria, Bacteroidetes, Cyanobacteria, Planctomycetes, and Proteobacteria,
indicating that PAs are nutrient substrates for a diversity of marine bacteria. The PA-
transforming bacterial genes and taxa showed strong spatial variations among nearshore,
offshore, and open ocean stations in the Gulf of Mexico. In contrast, model-compound
differences of PA-transforming genes and taxa were insignificant in metatranscriptomic
libraries and were only observed in some PA metagenomes.
150
Introduction
Short-chained aliphatic polyamines (PAs), such as putrescine, spermidine, and
spermine, are a group of nitrogen-rich, biologically active dissolved organic compounds.
They contribute to the growth of marine bacterioplankton as carbon, nitrogen, and/or energy
sources (Höfle, 1984; Lee and Jørgensen, 1995). PAs are ubiquitous in cells of organisms of
all three domains of life (Tabor and Tabor, 1984; Lee and Jørgensen, 1995) and widely
distributed in seawater (Nishibori et al. 2001, 2003). Concentrations of PAs in seawater range
from a few nM to about 200 nM (Lee and Jørgensen, 1995; Lu et al., 2014). Despite their low
concentrations, PA uptake by bacterioplankton may contribute up to 10% of bacterial N
demand and 5% of bacterial C demand in seawater (Liu et al., 2015).
Bacterial uptake of PAs is mainly facilitated by adenosine triphosphate (ATP)-binding
cassette (ABC) transporter (Pot) systems (Igarashi and Kashiwagi, 2010). Intracellular PAs
are degraded mainly through three pathways, namely the γ-glutamylation, transamination,
and spermidine cleavage (Lu et al., 2002; Dasu et al., 2006; Chou et al., 2008). PA
transporter and degrading genes have been identified in high abundance among marine
bacterioplankton genomes (Mou et al., 2010), metatranscriptomes (Mou et al., 2011), and
metaproteomes (Sowell et al., 2008). These studies consistently suggested that Roseobacter
lineage of Alphaproteobacteria are key PA transformers in coastal seawaters (Mou et al.,
2011, 2014), while SAR11 are important PA transformers in the open ocean (Sowell et al.,
2008).
However, few studies have examined the role of bacterioplankton in PA
transformation in offshore and open oceans, and their potential variations among different
marine systems. Moreover, all existing studies on marine PA-transforming bacteria have
focused only on putrescine and spermidine (Mou et al., 2010, 2011), even though other
151
polyamine compounds, such as spermine, can occasionally dominate the PA pools and
potentially overweigh the importance of putrescine and spermidine (Lu et al., 2014).
In this study, we investigated bacterial genes and taxa that might be involved in the
transformations of putrescine, spermidine, and spermine in surface water samples collected
from nearshore, offshore, and open ocean stations in the Gulf of Mexico by comparative
metagenomics and metatranscriptomics. We hypothesized that a diverse group of
bacterioplankton were involved in PA transformation, and the responsible bacterioplankton
genes and taxa would diverge among different marine ecosystems as well as different PA
compounds.
Methods
Sample collection, processing and microcosm experiment set up
The surface water were collected from one nearshore (NS), one offshore (OS), and
one open ocean (OO) station along a transect from the Louisiana coast into the Gulf of
Mexico aboard the R/V Pelican on 20-24 May of 2013 (Figure 6.1). Water samples were
collected in 12 L Niskin bottles mounted on a rosette sampling system (Sea-Bird Electronics,
Bellevue, WA). In situ environmental variables including temperature (T), salinity (S), and
relative fluorescence intensity (Chl) were measured by a conductivity-temperature-depth
(CTD) water column profiler (Sea-Bird Electronics, Bellevue, WA, USA) equipped with
sensors (Wet Labs, Philomath, OR, USA), which was also mounted on the rosette.
Immediately after collection, water was filtered through 3 μm pore-size membrane
filters (EMD Millipore Corp., Billerica, MA, USA). Part of the filtrates was used to fill up
sixteen carboys (18.9 L, each) to establish bacterioplankton microcosms. The remaining
filtrate (1 L) was further filtered through 0.2 μm pore-size polycarbonate membrane filters
(Pall life sciences, Ann Arbor, MI), and the resulting filtrate was immediately frozen at −20
152
°C onboard and stored at −80 °C after being transported back to the lab for determinations of
the concentrations of dissolved organic carbon (DOC), dissolved nitrogen (DN), nitrate (NO3-
), nitrite (NO2-), soluble reactive phosphorus (SRP), ammonium (NH4
+), and PAs.
Established microcosms were incubated onboard in duplicates at in situ temperature
in the dark, with amendments of 200 nM (final concentration) individual polyamine
compounds (putrescine treatment, PUT; spermidine treatment, SPD; spermine treatment,
SPM), or without amendments (control treatment, CTR). Microcosms for metatranscriptomic
analysis were incubated for 2 h, while microcosms for metagenomic analysis were incubated
for 48 h. The total filtering time of each sample was maintained to be less than 30 min. At the
end of the incubation, bacterial cells were collected onto 0.2 μm pore-size isopore membrane
filters (EMD Millipore Corp., Billerica, MA, USA) by filtration, and then immediately stored
in liquid nitrogen onboard and at −80 °C in lab until DNA or RNA extraction.
Samples for nutrient measurements were collected in triplicates. All plastic ware was
acid-washed, and all glassware was combusted at 500 C for at least 6 h before use.
Nutrient analysis
Concentrations of DOC and DN were determined with a TOC/TN analyzer (TOC-
VCPN; Shimadzu Corp., Tokyo, Japan) following methods of combustion oxidation/infrared
detection and combustion chemiluminescence detection, respectively (Clescerl et al., 1999).
Concentrations of NO3- were measured spectrometrically based on NO3
- reduction with
cadmium granules (Jones, 1984). Concentrations of NO2- were measured based on
colormetric methods, which generated a chromophore determined at 540 nm (Hernández-
López and Vargas-Albores, 2003). SPR concentrations were determined spectrometrically
using the ascorbic acid method (Murphy and Riley, 1962). Concentrations of NH4+ were
determined with a spectrophotometer based on color reactions (Strickland and Parsons,
1968).
153
Concentrations of putrescine, spermidine, and spermine were determined with a
Shimadzu 20A high-performance liquid chromatography (Shimadzu Corp., Tokyo, Japan)
equipped with a 250 × 4.6 mm i.d. 5 µm particle size, Phenomenex Gemini-NX C18 column
(Phenomenex, Torrance, CA, USA) following a protocol of pre-column fluorometric
derivatization with o-phthaldialdehyde, ethanethiol, and 9-fluorenylmethyl chloroformate (Lu
et al., 2014).
Bacterioplankton enumeration
Bacterioplankton cells were counted using a FACSAria flow cytometer (BD, Franklin
Lakes, NJ, USA) (Mou et al., 2013). Before counting, fixed bacterial cells were stained with
Sybr Green II (1:5000 dilution of the commercial stock) in the dark for 20 min. Afterwards,
cells were mixed with an internal bead standard with a known density (5.2 µm diameter
SPHEROTM
AccuCount Fluorescence Microspheres; Spherotech Inc., Lake Forest, Illinois,
USA). Cell abundances were calculated based on the ratios between the counts of bacterial
cells and the internal bead standard.
DNA preparation and sequencing
DNA was extracted from the bacterioplankton cells on the 0.2 μm pore-size
membrane filters using the Qiagen DNeasy DNA extraction kits (Qiagen, Chatsworth, CA,
USA). An addition step of bead beating with 0.1 mm size glass beads (0.2 g/filter; BioSpec,
Bartlesville, OK, USA) for 10 min at 3,000 rpm was added after enzymatic lysis with
lysozyme and proteinase K during DNA extraction (Hunt et al., 2013). The quantity of DNA
was determined with the Quant-iT PicoGreen ds DNA Assay Kits (Life technologies,
Carlsbad, NY, USA). DNA extracts of replicate treatments were pooled before sequencing.
The DNA of the PUT microcosms at OO was lost during processing. DNA library of each
treatment sample was prepared with TruSeq Nano DNA Sample Prep Kits and sequenced
154
using the Illumina MiSeq platforms (Illumina Inc., San Diego, CA, USA) at the University of
Minnesota Genomics Center.
cDNA preparation and sequencing
Total RNA was extracted from the bacterioplankton cells on the 0.2 μm pore-size
membrane filters using the Qiagen RNeasy RNA extraction kits (Qiagen, Chatsworth, CA,
USA), and a few modifications were made to increase RNA yields (Poretsky et al., 2009).
Briefly, frozen filters were shattered and vortexed for 10 min in a 50 mL Falcon tube with
lysis/binding solution and RNase-free beads from the RNA PowerSoil kits (MoBio
Laboratory Inc., Carlsbad, CA, USA). The extraction mixture was centrifuged (5000 rpm, 5
min), and the supernatant was then mixed with the same volume of 70% ethanol solution.
The mixture was drawn through a 23-gauge needle for 5 times, and then processed further
following the manufacturing instructions.
RNA extracts were treated with Ambion Turbo DNA-free kits (Life technologies,
Carlsbad, NY, USA) to remove DNA contamination. To remove rRNA, 1-5 µg of purified
RNA were treated with Ribo-Zero rRNA removal kits (Bacteria) (Epicentre, Madison, WI,
USA) according to the manufacturing protocols. The resulting mRNA was amplified using
AMBION MessageAMP II-Bacteria kits (Life technologies, Carlsbad, NY, USA). The
amplified antisense RNA (aRNA) was converted to double stranded cDNA with random
hexamers (Universal RiboClone cDNA Synthesis System; Promega, Madison, WI) following
the manufacturer’s instructions. cDNA was purified with QiaQuick PCR cleanup kits
(Qiagen, Valencia, CA, USA), and quantified with the Quant-iT PicoGreen ds DNA Assay
Kit. cDNA samples of replicated treatment were pooled for sequencing. cDNA library of
each treatment was prepared with Nextera XT DNA Sample Preparation Kits and sequenced
with the Illumina HiSeq 2000 v3 systems (Illumina Inc., San Diego, CA, USA) at the
University of Georgia Genomics Facility.
155
Sequence accession number
The raw DNA and cDNA sequences were deposited in the Sequence Read Archive of
NCBI under accession no. SRP049693.
Bioinformatic analysis
The raw paired-end Illumina reads were pre-processed by removing low quality bases
(Phred score < 30) and sequencing adapters. The resulting sequence reads were submitted to
Metagenome Rapid Annotation using Subsystem Technology (MG-RAST) v3 (Meyer et al.,
2008) for quality control and automated annotation. The putative protein-coding sequences
were identified and annotated using a sBLAT analysis against a protein database derived
from the M5NR, which integrates many nonredundant databases, including GenBank, SEED,
IMG, UniProt, KEGG, RefSeq, and eggNOGs. Similarity matches to a taxonomic group or a
metabolic subsystem were set at E-value ≤ 10-20
for metagenomic reads or E-value ≤ 10-10
for
metatranscirptomic reads, percent identity ≥ 40%, and alignment length ≥ 69 (Mou et al.,
2008), which is approximately corresponding to bit score ≥ 40.
Homologs to 30 known polyamine-transforming genes (Table S6.1), such as
polyamine transporter genes (potABCDEFGH) and polyamine-degrading genes
(puuABCDEPRT, spuABCI, aphAB, kau B, gabDT, gltA, gabT, and spdH), were putatively
identified in each of the RefSeq-annotated metagenomic and metatranscriptomic library using
tBLASTn with a cutoff value of bit score ≥ 40 (Mou et al., 2011).
Statistical analysis
A non-metric multidimensional scaling (NMDS) analysis was performed to ordinate
CTR, PUT, SPD, and SPM metagenomic or metatranscriptomic libraries using PRIMER v5
(Plymouth Marine Laboratory, Plymouth, UK; Clarke and Warwick, 2001) unless otherwise
noted. The similarity matrix was calculated based on normalized and square-root transformed
relative abundances of major COGs using the Bray-Curtis algorithm. The robustness of
156
NMDS grouping patterns was statistically evaluated by ANOSIM (analysis of similarity),
which is an analogue of the standard univariate ANOVA (analysis of variance). The
ANOSIM index rANOSIM was calculated on a scale of 0 to 1. When P < 0.05, the sample
groups were identified as well-separated when rANOSIM > 0.75, clearly different but
overlapping when 0.5 < rANOSIM 0.75, or barely separable when rANOSIM < 0.25 (Clarke and
Warwick, 2001).
Pair-wise comparisons were performed to compare the gene content of the COGs or
the putative PA uptake and degradation genes between PA amended (PUT, SPD, or SPM)
and CTR metagenomes or metatranscritptomes by calculating the odds ratios (OR) and
bionomical distribution probabilities (Gill et al., 2006) with Microsoft Excel. The OR was
calculated with the equation [np/(Np-np)]/[nc/(Nc-nc)], where np and nc were respectively the
number of targeted gene sequences in the PA (PUT, SPD, or SPM) and CTR metagenomes or
metatranscriptomes; Np and Nc represented the total number of sequences in the PA (PUT,
SPD, or SPM) and CTR metagenomes or metatranscriptomes, respectively. The binomial
distribution was presumed in each of the metagenomic or metatranscriptomic library. The
binomial distribution probability (P) was calculated with the [nc/ (Nc-nc)] as the expected
gene sequence frequency. COGs categories (level 2) or PA uptake and degradation genes
which were significantly enriched in PUT, SPD, or SPM metagenomes or metatranscriptomes
relative to CTRs were reported when the corresponding OR > 1and P < 0.02. COGs
significantly enriched in PUT, SPD, or SPM metagenomes or metatranscriptomes were
reported when the corresponding OR > 1.5 and P < 0.02
Variations of individual environmental variable between or among samples and
differences of assigned bacterial taxa of enriched COGs and PA diagnostic genes were
assessed for statistical significance using t test or ANOVA implemented within the R
157
software package (R Core Development Team, 2005). Significant differences were reported
when P < 0.05.
Results
Initial in situ environmental conditions
The measured in situ environmental variables varied among NS, OS, and OO (Table
6.1). As the distance to shore reduced, concentrations of NO3- plus NO2
- (NOX
-; 0.02 to 17.4
µM), DOC (1.95 to 3.17 mg C/L), DN (0.04 to 0.23 mg N/L), Chl (0.01 to 1.23 µg/L), and T
(24.6 to 26.2 °C) all increased and reached the highest values at NS (ANOVA, P < 0.05). The
trend was opposite for salinity, which had much lower value at NS (15.8 PSU) than at OS
(35.9 PSU) or OO (36.4 PSU) (ANOVA, P < 0.05). Concentrations of NH4+ (0.00 to 0.89
µM) and total PAs (8.4 to 24.3 nM) had the highest values at OS (ANOVA, P < 0.05). SRP
concentrations showed no significant differences among sites (ANOVA, P > 0.05), and were
consistently at 0.11 µM.
General structures of metagenomes and metatranscriptomes
A total of 6700391 Illumina MiSeq sequences with an average length of 363 bp and
29039763 Illumina HiSeq sequences with an average length of 137 bp were recovered for
metagenomic and metatranscriptomic libraries, respectively (Table 6.2). rRNA gene
sequences accounted for 0.7-1.5% and 7.3-39.1% of the metagenomic and
metatranscriptomic sequences, respectively.
Out of 6564670 of the putative protein-coding metagenomic sequences, 62.0%
received annotations to the gene level, 26.8-50.8% were assigned to 1742-2289 unique COGs,
and 19.2-33.6% were assigned to 150-184 unique KEGG pathways (Table 6.2). Sequences
with COG annotations distributed among 23 COG classes and about half (46.5-54.6%) were
affiliated with metabolism.
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Out of the 21576608 putative protein-coding sequences of metatranscriptomes, 59.7%
were annotated to the gene level, 29.2-50.1% were assigned to1461-2414 unique COGs, and
27.8-44.5% were assigned to 142-211unique KEGG pathways (Table 6.2). The identified
COGs belonged to 23 functional categories and were mostly affiliated with functional classes
of metabolism (38.6-54.0%).
PA responsive COGs in metagenomic libraries
NMDS and ANOSIM analyses were performed based on the relative abundance of
major COGs among metagenomic libraries. They consistently showed that metagenomes of
nearshore, offshore and open ocean bacterioplankton were well separated (rANOSIM = 0.65, P <
0.05; Figure 6.2b; Table S6.2).
At NS, ~7% of enriched COG categories (OR > 1, P < 0.02) in PA metagenomic
library were affiliated with metabolism of amino acids, carbohydrates, nucleotides, and
energy compared to CTR (Figure S6.1a). Among them, only 10 COGs were shared by
different PA libraries, such as COG0076 (Glutamate decarboxylase and related pyridoxal 5-
phosphate-dependent proteins) in PUT and SPD libraries, COG1166 [Arginine decarboxylase
(spermidine biosynthesis)] and COG1506 (Dipeptidyl aminopeptidases/acylaminoacyl-
peptidases) in SPD and SPM libraries (OR > 1.5, P < 0.02; Table 6.3 and Table S6.3). None
of enriched COGs were related to PA uptake and degradation.
At OS, ~5% of the enriched COG categories (OR > 1, P < 0.02) in PUT, SPD, and
SPM libraries were primarily affiliated with the functions of metabolism, such as
carbohydrate transport and metabolism (Figure S6.1b). Only 4 enriched COGs (OR > 1.5, P
< 0.02) related to amino acid, carbohydrate, and energy metabolism were shared by the PUT,
SPD, and SPM libraries of OS (Table 6.3 and Table S6.3), including COG0747 (ABC-type
dipeptide transport system, periplasmic component), COG2113 (ABC-type proline/glycine
betaine transport systems, periplasmic), COG4175 (ABC-type proline/glycine betaine
159
transport system, ATPase), and COG1018 [Flavodoxin reductases (ferredoxin-NADPH
reductases) family 1]. COG1177, which is an ABC-type spermidine/putrescine transport
system (permease), was found significantly enriched only in the SPM libraries of OS (0.07%
of annotated COG sequences, respectively).
At OO, ~4% of enriched COG categories (OR > 1, P < 0.02) in PA microcosms were
affiliated with metabolism, such as inorganic ion transport and metabolism (Figure S6.1c). A
total of 10 enriched COGs (OR > 1.5, P < 0.02) related to amino acid, carbohydrate,
nucleotide, and energy metabolisms was shared by the SPD and SPM libraries of OO, such as
COG2902 (NAD-specific glutamate dehydrogenase) (Table 6.3 and Table S6.3). PA uptake
and degradation related COGs were not found among enriched COGs in PA metagenomes of
OO.
PA responsive COGs in metatranscriptomic libraries
NMDS and ANOSIM analyses revealed that the major COGs of metatranscriptomes
were significantly different from those of metagenomes (rANOSIM = 0.99, P < 0.05; Figure
S6.2; Table S6.2), and were varying between NS (nearshore) and OS (offshore) or OO (open
ocean) (rANOSIM ≥ 0.58, P < 0.05; Figure 6.2b; Table S6.2). OR analysis identified more than 2
fold enriched COGs in PA metatranscriptomes than those of metagenomes, and the majority
of them (averagely 40%) were affiliated with functions of metabolism, particularly in amino
acid transport and metabolism (OR > 1, P < 0.02; Figure S6.1d, S6.1e, and S6.1f). Moreover,
compared to metagenomes where only COG1177 was found enriched, more enriched COGs
related to PA uptake and degradations were identified in the PA metatranscriptomes in
relative to corresponding CTR (Table 6.4 and Table S6.4).
At NS, PA uptake and degradation related COGs were found commonly enriched (OR
> 1.5, P < 0.02) in PUT, SPD, and SPM metatranscriptomes, including COG0686 (Alanine
dehydrogenase; 0.13%, 0.17%, and 0.12% of annotated COG sequences, respectively),
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COG1177 (ABC-type spermidine/putrescine transport permease; 0.07%, 0.08%, and 0.06%),
and COG3842 (ABC-type spermidine/putrescine transport ATPase; 0.13%, 0.12%, and
0.12%) (Tables 6.4 and Table S6.4).
At OS, 4 COGs (OR > 1.5, P < 0.02) related to PA uptake and degradation showed
enrichment and were shared by PUT, SPD, and SPM metatranscriptomes. Except COG0686
(0.06%, 0.07%, and 0.10%, respectively) which were responsive in metatranscriptomes of NS,
there were also COG0687 (Spermidine/putrescine-binding periplasmic protein; 0.49%, 0.48%,
and 0.25%, respectively), COG1176 (ABC-type spermidine/putrescine permease; 0.03%,
0.05%, and 0.03%, respectively), and COG1177 (0.02%, 0.03%, and 0.03%, respectively)
(Tables 6.4 and Table S6.4).
At OO, enriched COGs (OR > 1.5, P < 0.02) that were related to PA uptake and
degradation showed variance among PUT, SPD, and SPM metatranscriptomes. COG0686
was found significantly enriched only in PUT metatranscriptome (0.05%). COG1176 and
COG1177 were enriched only in SPD metatranscriptomes, in which each represented 0.05%
and 0.07% of the annotated COG sequences (Tables 6.4 and Table S6.4). COG1629
(Gaboriau et al., 2004; Chou et al., 2008), outer membrane receptor proteins (mostly Fe
transport), were significantly enriched in SPD (0.89%) and SPM (0.58%) metatranscriptomes.
Polyamine-responsive taxa in metagenomic and metatransciptomic libraries
The taxonomic affiliations of enriched COGs in metagenomic libraries were
significantly different among sites, as revealed by NMDS (Figure S6.3a; Table S6.2) and
ANOSIM (rANOSIM = 0.87, P < 0.05) analyses. In NS metagenomes, Rhodobacteraceae
(Alphaproteobacteria) was generally dominating bacterial families in PUT (14.4 %), SPD
(15.0%), and SPM (21.6%) libraries (Figure 6.3a). In OS metagenomes, sequences affiliated
with Alteromonadaceae, Pseudomonadaceae, and Alcanivoracaceae of
Gammaproteobacteria were the most abundant in PUT library, and each accounted for
161
14.6%, 13.6%, and 13.2% (Figure 6.3b). In SPD library of OS metagenomes, unclassified
Chroococcales (12.4%; Cyanobacteria) and Planctomycetaceae (7.6%; Planctomycetes)
were the most abundant families (Figure 6.3b). In SPM library of OS metagenomes,
Prochlorococcaceae (13.0%; Cyanobacteria), Rhodobacteraceae (9.2%), and
Comamonadaceae (9.0%) were predominant (Figure 6.3b). In OO metagenomes,
Idiomarinaceae (13.7%) and Shewanellaceae (13.0%) of Gammaproteobacteria were the
most abundant in the SPD library, while Pseudoalteromonadaceae (55.4%) dominated the
SPM library (Figure 6.3c).
Similarly, analyses of the taxonomic binning of the enriched COGs in the
metatranscriptomes of PUT, SPD, and SPM treatments using NMDS and ANOSIM revealed
significant differences among marine systems (rANOSIM = 0.90, P < 0.05; Figure S6.3b; Table
S6.2). In NS metatranscriptomes, enriched COGs were predominately affiliated with
Rhodobacteraceae (14.1%, 23.2%, and 19.0% of the enriched sequences, respectively) in the
PUT, SPD, and SPM libraries (Figure 6.3d), which was similar to its corresponding
metagenomes. In OS metatranscriptomes, enriched COGs were mostly affiliated with
Enterobacteriaceae (Gammaproteobacteria) in PUT (9.8%) and SPD (10.0%) libraries, and
with Propionibacteriaceae (14.9%; Actinobacteria) in SPM library (Figure 6.3e). In OO
metatranscriptomes, bacterial families were predominant by Rhodobacteraceae and
Alteromonadaceae (Gammaproteobacteria) in PUT library (10.0% and 9.9%, respectively),
and Rhodobacteraceae in SPD (21.1%) and SPM (20.2%) libraries (Figure 6.3f).
Polyamine uptake- and degradation-related genes and taxa
Major PA uptake- and degradation-related genes, including transporter genes
(potABCDEFGH), γ-glutamylation genes (puuABCDE), transamination genes (spuC, kauB,
and GabT), and spermidine cleavage genes (spdH and gltA) were compared among
metagenomic or metatranscriptomic libraries.
162
In metagenomic libraries, homologues of transporter genes were only significantly
enriched in SPM libraries of OS and OO in relative to those in the corresponding CTRs (OR
> 1, P < 0.02; Figure 6.4a, 6.4b, and 6.4c). The taxonomic binning of these transporter genes
were primarily assigned to Rhodobacteraceae (26.9% of total assigned putative PA genes)
and Alteromonadaceae (66.7%), respectively (Figure 6.5b and 6.5c). In metatranscriptomic
libraries, the putative transporter genes were only significantly enriched in the PUT, SPD,
and SPM libraries of NS and in the SPD library of OS relative to their corresponding CTRs
(OR > 1, P < 0.02; Figure 6.4d, 6.4e, and 6.4f). At NS, Rhodobacteraceae-affiliated
transporter genes were dominant in the PUT (15.3%), SPD (24.5%), and SPM (35.3%)
metatranscriptomes (Figure 6.6a). At OS, the transporter genes in SPD metatranscriptomes
were mainly affiliated with Rhodobacteraceae (21.9%) and SAR11 clade (17.4%) (Figure
6.6b).
The enriched putative PA-degrading genes (OR > 1, P < 0.02) in PA metagenomes
compared to corresponding CTRs, showed variations among different marine systems and PA
compounds. At NS, putative γ-glutamylation genes were enriched in SPD metagenomes (OR
> 1, P < 0.02; Figure 6.4a), and were mostly affiliated with Rhodobacteraceae (6.0%) (Figure
6.5a). In contrast, putative spermidine cleavage genes were enriched in the PUT and SPM
metagenomes of NS (OR > 1, P < 0.02; Figure 6.4a), and the taxonomic binning of these
genes was primarily assigned to Methylophilaceae (2.8%; Betaproteobacteria) and SAR11
clade (3.3%; Alphaproteobacteria), respectively (Figure 6.5a). At OS, putative γ-
glutamylation genes were enriched in PUT and SPD metagenomes (OR > 1, P < 0.02; Figure
6.4b), and were primarily binned to Alteromonadaceae (1.0%) and Plantomycetaceae (1.6%;
Planctomycetes), respectively (Figure 6.5b). Differently, putative spermidine cleavage genes
showed enrichment in SPD metagenomes at OS (OR > 1, P < 0.02; Figure 6.4b), and were
mainly assigned to Planctomycetaceae (1.8%) and Alteromonadaceae (1.4%) (Figure 6.5b).
163
At OO, putative γ-glutamylation genes were enriched in SPD metagenomes (OR > 1, P <
0.02; Figure 6.4c), and the majority of the sequences were affiliated with OMG group (2.0%)
(Figure 6.5c). Putative spermidine cleavage genes were enriched in SPM metagenomes of
OO (OR > 1, P < 0.02; Figure 6.4c), and were taxonomically binned to Rhizobiaceae (1.8%)
and Shewanellaceae (1.8%) (Figure 6.5c). Unlike NS and OS, putative transamination genes
were found enrichment in SPD metagenomes of OO (OR > 1, P < 0.02; Figure 6.4c), and
were mostly affiliated with Alteromonadaceae (3.2%) (Figure 6.5c).
As metagenomes, the putative polyamine-degrading genes (OR > 1, P < 0.02) showed
various enrichment patterns among different marine systems and PA compounds in PA-
treated metatranscriptomes. At NS, the γ-glutamylation, transamination, and spermidine
cleavage genes showed no significant enrichment in PA libraries in relative to CTR (Figure
6.4d). At OS, the putative γ-glutamylation genes were enriched in SPM metatranscriptomes
(OR > 1, P < 0.02; Figure 6.4e), with the taxonomic binning primarily assigned to
Phyllobacteriaceae (15.8%; Alphaproteobacteria) (Figure 6.6b). In contrast, the putative
spermidine cleavage genes were enriched in PUT metatranscriptomes at OS (OR > 1, P <
0.02; Figure 6.4e), and the majority of these sequences was affiliated with Rhodobacteraceae
(10.5%; Figure 6.6b). At OO, the putative γ-glutamylation and transamination genes showed
enrichment in all PA metatranscriptomes in relative to CTR (OR > 1, P < 0.02; Figure 6.4f):
putative γ-glutamylation genes were mostly affiliated with Vibrionaceae (2.6%;
Gammaproteobacteria) in the PUT, Rhodobacteraceae in the SPD(9.2%) and SPM (14.9%)
(Figure 6.6c); the majority of putative transamination genes were affiliated with
Pseudoalteromonadaceae (3.7%) and Vibrionaceae (2.9%) in the PUT, Alteromonadaceae
(12.3%) and Pseudoalteromonadaceae (6.2%) in the SPD, and Comamonadaceae (3.2%) in
the SPM (Figure 6.6c).
Discussion
164
Metagenomes and metatranscriptomes were compared between bacterioplankton that
received no and additions of single PA model compound in nearshore, offshore, and open
ocean sites in the Gulf of Mexico to examine bacterioplankton taxa and genes that are
involved in PA transformation in different marine systems. Using the Illumina sequencing
technique, more than 4 to 32 folds of DNA and cDNA reads were yielded from our
metagenomic (~7 million reads) and metatranscriptimic (~29 million reads) libraries than
those in previous PA studies (Mou et al., 2011), which greatly improved the sequence
coverage.
COGs related to the metabolisms of amino acids, carbohydrates, energy, and
nucleotide were highly enriched in PA metagenomes and metatranscriptomes, which is in
accordance with that PAs serve as carbon, nitrogen, and energy sources to marine
bacterioplankton and actively participate in nucleotide synthesis (Höfle, 1984; Tabor and
Tabor, 1984; Lee and Jørgensen, 1995). However, in either metagenomes or
metatranscirptomes, few of these COGs were commonly shared among sites, suggesting
various metabolism strategies might be adopted by marine bacterioplankton in processing
PAs when in situ environmental conditions were significantly different. Moreover, the
number of COGs that were commonly shared among PUT, SPD, and SPM libraries of the
same site in metagenomes or metatranscirptomes were also low, indicating that putrescine,
spermidine, and spermine may be metabolized via different pathways by marine
bacterioplankton (Dasu et al., 2006; Chou et al., 2008).
Three PA degradation pathways, including γ-glutamylation, transamination, and
spermidine cleavage, have been identified in bacterioplankton based on the studies of model
bacterial strain (Lu et al., 2002; Dasu et al., 2006; Chou et al., 2008). Putative genes encoded
the γ-glutamylation pathways were enriched in most PA metagenomes and
metatranscriptomes, which suggests its prevalence in marine bacterial PA degradation. This
165
is in consistent with the high abundance of γ-glutamylation genes in sequenced marine
genomes (~40%) and in global ocean sampling metagenomes (~10%) (Mou et al., 2010).
Enrichments of putative transamination genes were not found in nearshore and
offshore PA metagenomes and metatranscriptomes, suggesting a minor importance of this
pathway in PA transformation in the Gulf of Mexico. This result contrasts with the finding of
a previous metatranscriptomic study about coastal PA-transforming bacterioplankton, in
which transamination were dominating the putrescine and spermidine degradation (Mou et al.,
2011). This discrepancy may be partly due to the differences in the PA transforming
bacterioplankton communities between our and their study. When comparing the
metatranscriptomic data, Gammaproteobacteria (22%-35%) constituted as a major PA-
transforming bacterioplankton in the nearshore and offshore seawater of the Gulf of Mexico,
while Rhodobacterales (43-48%) and SAR11clade (26%-29%) of Alphaproteobacteria were
predominating the PA-transforming bacterioplankton in the inshore of Sapleo Island, Georgia
(Mou et al., 2011).
The spermidine cleavage genes were enriched in a number of PA metagenomes and
metatranscriptomes compared to corresponding CTRs, which provides the first empirical data
on the importance of spermidine cleavage in PA degradation by natural bacterioplankton
communities, including Planctomycetaceae, Rhodobacteraceae, SAR11 clade,
methylophilaceae, Alteromonadaceae, and Shewanellaceae. Key gene (spdH) of this pathway
has been in silico identified in genomes of six marine bacterioplankton including
Gammaproteobacteria and Bacteroidetes (Mou et al., 2011).
The major PA-degrading genes showed variations among different individual PA
compounds in seawater, which indicates the PA-transforming bacterial genes may diverge
among dominant PA compounds in seawater. For example, in metatagenomes of NS, γ-
glutamylation route was dominant in spermidine degradation while spermidine cleavage
166
pathway dominated putrescine and spermine degradation. This result disagrees with the
previous finding that PAs might be degraded by marine bacterioplankton in the similar
pathways (Mou et al., 2011). Surprisingly, none of the degrading genes were significantly
expressed in nearshore PA metatranscriptomes, which suggests that marine bacterioplankton
might not turn on the degradation genes when the intracellular PA contents are low (Igarashi
and Kashiwagi, 1999, 2000). The induction of the PA-degrading genes in cells is
accompanied by the inhibition of PA-uptake genes (Igarashi and Kashiwagi, 2000). However,
the diagnostic genes of PA pot transporters were all enriched in nearshore PA
metatranscriptomes, indicating that the bacterioplanktion were still taking up exogenous PAs
(~0.4% of DOC and 3.1% of DN) for the cell growth in a nutrient-rich coastal environment.
Enriched COGs and diagnostic PA genes in the metagenomic and metatranscriptomic
libraries were affiliated with a diverse group of bacterial families in the bacterial phyla of
Actinobacteria, Bacteroidetes, Cyanobacteria, Planctomycetes, and Proteobacteria (Alpha,
Beta, and Gamma), indicating that PAs can be utilized by a broad taxonomic lineage of
marine bacterioplankton. Variations of PA-transforming bacterioplankton community were
identified among our studying sites, which agree with the PA functional gene patterns.
At nearshore site, Rhodobacteraceae were the dominant PA-transforming bacterial
taxon in both metagenomes and metatranscriptomes, which suggests their significant role in
PA processing in coastal seawater. Rhodobacteraceae-affiliated roseobacters are known for
their strong ability in processing plankton-derived DOC compounds (González et al., 2000;
Hahnke et al., 2013), and their importance in PA transformations in nutrient rich coastal
seawater has been well documented (Mou et al., 2011, 2014; Lu et al., unpublished data). At
offshore and open ocean sites, bacterial families of Gammaproteobacteria showed
domination in PA-treated metagenomes and metatranscriptomes, indicating a key role that
Gammaproteobacteria might play in PA transformation in marine systems. Similar results
167
have been found in a PA responsive bacterioplankton study in seawater of the South Atlantic
Bight (SAB) that Gammaproteobacteria were the most responsive bacterial taxa to PA
additions at most of the studied sites (Lu et al., unpublished data).
Variations of the PA-transforming bacterioplankton were identified among different
individual PA compounds in metagenomic libraries (ANOVA, P < 0.05). This agrees with
the finding in the PA responsive bacterioplankton study in seawater of SAB, in which
different PA-responding bacterial families were identified in putrescine and spermidine
transformation (Lu et al., unpublished data). However, the assigned bacterial taxa of enriched
COGs and PA diagnostic genes were similar among different PA compounds in
metatranscriptomic libraries (ANOVA, P > 0.05), showing that the immediate shift-up
transcriptional response by marine bacterioplankton communities might be similar when
different PAs were amended to the microcosms (Mou et al., 2011).
Metagenomics is a method that analyzes the total genomic DNA and thus provides us
both phylogenetic information and the insights into the potential metabolic functions carried
within a microbial community (Warnecke and Hess, 2009). In contrast, metatranscriptomics
study the total expressed genes within a microbial community at a certain time, which
provide us information on the actual microbial activities at a certain time and place as well as
how the microbial activities respond to environmental stimuli shortly (Moran, 2010). Here,
the taxonomic and functional discrepancy between PA-responding metagenomes and
metatranscriptomes may be partly due to that some oligotrophic bacterial taxa might
transcriptionally respond slowly to an environmental stimulus (Vila-Costa et al., 2011). In
converse, some bacterioplankton responding rapidly to PA additions, such as
Rhodobacteraceae in PA metatranscriptomes, did not established dominance in offshore and
open ocean PA metagenomes after incubations. The assigned PA-transforming bacterial
families of the enriched COGs also showed variance with those assigned to diagnostic PA
168
genes in PA metagenomes or metatranscriptomes, which may indicate the differences of
marine bacterioplankton that utilized the byproducts of PAs and the true PA-degraders.
Conclusion
Using metagenomic and metatranscriptomic approaches, we identified the variations
of PA-transforming bacterioplankton genes and taxa among different marine systems in the
Gulf of Mexico. Genes of the γ-glutamylation and spermidine cleavage were enriched in most
of the PA-treated metagenomes and metatranscriptoms, indicating they might play key roles
in PA degradation in marine bacterioplankton community. In contrast, putative
transamination genes were only found important in PA degradation by bacterioplankton in
open ocean seawater. A diverse group of bacterial families in the bacterial phyla of
Actinobacteria, Bacteroidetes, Cyanobacteria, Planctomycetes, and Proteobacteria were
involved in PA transformation. At nearshore site, Rhodobacteraceae played a key role in
driving PA transformation, while at offshore and open ocean sites, bacterial families of
Gammaproteobacteria were the predominant PA-transforming bacterial taxa. Variations of
the PA-transforming bacterioplankton were identified among different individual PA
compounds in metagenomes but not metatranscriptomes, suggesting a necessity of using
combined metagenomics and metatranscriptomics for studying bacterial biogeochemistry.
169
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173
Table 6.1. In situ environmental variables (average±standard error of the mean) of in surface
water samples of NS, OS, and OO in the Gulf of Mexico in May, 2013.
Site SRP (µM) NOx- (µM) NH4
+ (µM) DOC (mg C/L) DN (mg N/L) PAs (nM) Chl (µg/L) T (°C) S (PSU)
NS 0.11±0.03 17.9±0.53 0.00±0.00 3.17±0.18 0.23±0.01 8.4±0.4 1.23 26.2 15.8
OS 0.11±0.03 0.02±0.01 0.89±0.13 1.95±0.17 0.06±0.01 24.3±4.2 0.06 24.6 35.9
OO 0.11±0.03 0.05±0.00 0.12±0.04 1.95±0.16 0.04±0.00 8.6±1.2 0.01 25.6 36.4
174
Table 6.2. Statistics of experimental metagenomics and metatranscriptomics.
Sample Treatment No. of total Reads
Ave. read length (bp)
No. (%) of rRNA genes
No. of functional genes
Number (%) of functional genes categorized *
COG KEGG SEED RefSeq
metagenomic libraries NS CTR 667,229 352 5,782(0.9) 651,430 195,916(30.1) 151,370(23.2) 278,781(42.8) 336,018(51.6)
PUT 690,505 368 5,172(0.7) 675,272 187,977(27.8) 144,906(21.5) 266,353(39.4) 326,610(48.4) SPD 572,549 364 8,652(1.5) 562,633 158,693(28.2) 122,385(21.8) 229,394(40.8) 272,623(48.5) SPM 730,564 363 7,220(1.0) 717,015 175,105(24.4) 142,492(19.9) 265,289(37.0) 331,892(46.3) OS CTR 627,823 375 6,185(1.0) 618,570 281,642(45.5) 200,375(32.4) 400,261(64.7) 450,690(72.9) PUT 541,964 364 7,153(1.3) 533,190 235,835(44.2) 164,547(30.9) 366,391(68.7) 401,489(75.3) SPD 417,359 356 4,926(1.2) 408,875 109,554(26.8) 786,44(19.2) 178,124(43.6) 202,107(49.4) SPM 619,496 369 5,071(0.8) 610,849 269,610(44.1) 201,170(32.9) 347,919(57.0) 412,019(67.5) OO CTR 446,460 371 3,716(0.8) 433,953 198,682(45.8) 136,013(31.3) 270,245(62.3) 298,374(68.8)
SPD 771,798 347 8,988(1.2) 749,701 344,920(46.0) 238,692(31.8) 511,034(68.2) 550,155(73.4) SPM 614,644 368 9,001(1.5) 603,182 306,355(50.8) 202,863(33.6) 462,950(76.8) 482,365(80.0)
metatranscriptomic libraries NS CTR 2,360,759 140 293,286 (12.4) 2,067,473 724,531(35.0) 741,283(35.9) 994,879(48.1) 1,161,920(56.2)
PUT 2,002,861 134 509,652(25.5) 1,493,209 548,629(36.7) 531,859(35.6) 783,403(52.5) 931,039(62.4) SPD 2,454,749 136 294,493(12.0) 2,160,256 631,739(29.2) 606,860(28.1) 902,016(41.8) 1,082,487(50.1) SPM 2,079,656 141 150,356(7.3) 1,929,300 622,236(32.2) 580,965(30.1) 862,679(44.7) 998,559(51.8)
OS CTR 2,657,869 139 1,029,667(38.7) 1,628,202 724,647(44.5) 596,630(36.6) 945,748(58.1) 1,073,485(65.9)
PUT 2,260,491 140 240,335(10.6) 2,020,156 858,533(42.5) 801,520(39.7) 1,055,081(52.2) 1,210,481(59.9) SPD 2,294,073 140 266,891(11.6) 2,027,182 848,002(41.8) 822,743(40.6) 1,060,235(52.3) 1,222,100(60.3) SPM 1,050,891 128 411,143(39.1) 639,748 202,262(31.6) 178,129(27.8) 281,705(44.0) 295,090(46.1)
OO CTR 2,032,099 137 404,275(19.9) 1,627,824 722,130(44.4) 616,363(37.9) 863,842(53.1) 1,023,499(62.9) PUT 3,266,221 140 736,311(27.9) 2,529,910 1,132,508(42.2) 979,983(37.0) 1,450,128(57.3) 1,674,293(66.2) SPD 1,567,641 134 306,161(19.5) 1,261,480 643,621(51.0) 492,913(39.1) 811,025(64.3) 912,896(72.4) SPM 2,388,368 137 196,500(8.2) 2,191,868 915,634(41.8) 815,063(37.2) 1,109,179(50.6) 1,277,930(58.3)
*% of total functional genes.
175
Table 6.3. Selected major significantly enriched COG groups (OR > 1.5, P < 0.02) related to metabolisms of amino acids,
carbohydrates, energy production, and nucleotide production in PUT, SPD, and SPM metagenomic libraries, based on OR calculated
between the number of putative gene sequences in the PA and CT metagenomes.
COG COG description NS OS OO
ORPUT/CT ORSPD/CT ORSPM/CT ORPUT/CT ORSPD/CT ORSPM/CT ORSPD/CT ORSPM/CTR
Amino acid transport and metabolism
0076 Glutamate decarboxylase and related PLP-dependent proteins 1.6 1.7 2.0 2.6
0308 Aminopeptidase N 1.6
0339 Zn-dependent oligopeptidases 1.7 1.5
0347 Nitrogen regulatory protein PII 1.8
0560 Phosphoserine phosphatase 2.1
0747 ABC-type dipeptide transport system, periplasmic 1.6 1.6 3.0
1115 Na+/alanine symporter 1.9
1166 Arginine decarboxylase (spermidine biosynthesis)* 1.5(0.03%) 1.8(0.04%) 1.7(0.04%) 3.5(0.08%)
1177 ABC-type spermidine/putrescine transport system, permease * 2.1(0.07%)
1506 Dipeptidyl aminopeptidases/acylaminoacyl-peptidases 124/0 132/0 1.9
1605 Chorismate mutase 2.0 2.4
1703 Putative periplasmic protein kinase ArgK and related G3E family 1.8 1.7 2.0 2.7
1770 Protease II 1.6
2021 Homoserine acetyltransferase 1.5
2113 ABC-type proline/glycine betaine transport systems, periplasmic 2.0 2.6 2.3
2902 NAD-specific glutamate dehydrogenase 1.9 2.3
4175 ABC-type proline/glycine betaine transport system, ATPase 1.7 1.5 2.1
4608 ABC-type oligopeptide transport system, ATPase 3.3 2.3
Carbohydrate transport and metabolism
0058 Glucan phosphorylase 1.5 3.0
0148 Enolase 1.5 1.5
0205 6-phosphofructokinase 1.5
0362 6-phosphogluconate dehydrogenase 3.9
0366 Glycosidases 2.1 2.7
0395 ABC-type sugar transport system, permease 2.1
0726 Predicted xylanase/chitin deacetylase 2.0
0738 Fucose permease 2.2
1175 ABC-type sugar transport systems, permease components 2.2
1523 pullulanase PulA and related glycosidases 2.5 1.6 2.1
1638 TRAP-type C4-dicarboxylate transport system, periplasmic 2.3
3250 Beta-galactosidase/beta-glucuronidase 164/0 2.1
3839 ABC-type sugar transport systems, ATPase components 2.1
4993 Glucose dehydrogenase 2.8 2.1 2.8
Energy production and conversion
5598 Trimethylamine:corrinoid methyltransferase 4.2
176
0022 Pyruvate/2-oxoglutarate dehydrogenase complex 2.0
0538 Isocitrate dehydrogenases 1.6
0654 2-polyprenyl-6-methoxyphenol hydroxylase and related FAD-dependent
oxidoreductases
260/0
0778 Nitroreductase 138/0 117/0
1018 Flavodoxin reductases (ferredoxin-NADPH reductases) family 1 2.0 1.6 4.2
1032 Fe-S oxidoreductase 2.5 1.9
1038 Pyruvate carboxylase 3.4 2.7
1048 Aconitase A 1.8
1071 Pyruvate/2-oxoglutarate dehydrogenase complex, dehydrogenase 2.3
1271 Cytochrome bd-type quinol oxidase 2.4 2.8
1301 Na+/H
+-dicarboxylate symporters 1.6 2.0 2.5 3.4
1757 Na+/H
+ antiporter 1.7 1.8 1.7 2.3
1805 Na+-transporting NADH: ubiquinone oxidoreductase, NqrB 1.7 1.6
2010 Cytochrome c, mono- and diheme variants 1.5 2.7
2710 Nitrogenase molybdenum-iron protein 1.8 2.7
3808 Inorganic pyrophosphatase 1.5
Nucleotide transport and metabolism
0047 Phosphoribosylformylglycinamidine (FGAM) synthase, glutamine
amidotransferase domain
1.8
0208 Ribonucleotide reductase, beta subunit 1.9 1.5
0209 Ribonucleotide reductase, alpha subunit 1.5
1816 Adenosine deaminase 2.2
*COG gene groups related to PA metabolisms inside the cell, and its relative percentage (%) of the total COG annotated sequences
were shown inside the parenthesis.
177
Table 6.4. Selected major significantly enriched COG groups (OR > 1.5, P < 0.02) related to metabolisms of amino acids,
carbohydrates, energy production, and nucleotide production in PUT, SPD, and SPM metatranscriptomic libraries, based on OR
calculated between the number of putative gene sequences in the PA and CT metatranscriptomes.
COG COG description NS OS OO
ORPUT/CT ORSPD/CT ORSPM/CT ORPUT/CT ORSPD/CT ORSPM/CT ORPUT/CT ORSPD/CT ORSPM/CTR
Amino acid transport and metabolism
0002 Acetylglutamate semialdehyde dehydrogenase 1.9 6.3
0014 Gamma-glutamyl phosphate reductase 3.5 2.9
0019 Diaminopimelate decarboxylase 2.0 1.8
0111 Phosphoglycerate dehydrogenase 2.4 3.0
0128 5-enolpyruvylshikimate-3-phosphate synthase 3.0 5.1
0133 Tryptophan synthase beta chain 2.9 2.8 2.6
0165 Argininosuccinate lyase 2.1 2.5
0263 Glutamate 5-kinase 1.6 3.2 2.5 3.6 2.4
0287 Prephenate dehydrogenase 1.9 2.4 3.5 6.8
0334 Glutamate dehydrogenase/leucine dehydrogenase 2.4 4.7 1.5
0339 Zn-dependent oligopeptidases 2.2 4.5
0347 Nitrogen regulatory protein PII 1.6 2.2 1.5
0367 Asparagine synthase (glutamine-hydrolyzing) 3.3 1.5
0404 Glycine cleavage system T protein 2.0 2.6 1.5
0460 Homoserine dehydrogenase 3.1 4.7
0462 Phosphoribosylpyrophosphate synthetase 3.3 2.7 2.0
0498 Threonine synthase 2.0 3.3
0520 Selenocysteine lyase 2.2 2.3 1.7
0559 Branched-chain amino acid ABC-type transport
system, permease
3.2 3.8 4.0 2.9
0626 Cystathionine beta-lyases/cystathionine gamma-
synthases
1.5 2.1 2.0 2.4 2.5 2.1
0665 Glycine/D-amino acid oxidases (deaminating) 1.5 2.5 1.9 2.0 2.5 1.8
0683 ABC-type branched-chain amino acid transport
systems, periplasmic
2.3 2.4
0685 5,10-methylenetetrahydrofolate reductase 3.0 2.5
0686 Alanine dehydrogenase* 1.6(0.13%) 2.1(0.17%) 1.5(0.12%) 1.6(0.06%) 1.8(0.07%) 2.4(0.10%) 1.3(0.05%)
0687 Spermidine/putrescine-binding periplasmic protein* 2.2(0.49%) 2.1(0.48%) 1.1(0.25%)
0747 ABC-type dipeptide transport system, periplasmic 1.7 3.8 3.1
0765 ABC-type amino acid transport system, permease 2.1 2.6 2.5 4.1
1045 Serine acetyltransferase 1.7 1.9 2.2
1115 Na+/alanine symporter 2.0 3.0 3.1
1176 ABC-type spermidine/putrescine transport permease* 1.4(0.03%) 2.2(0.05%) 1.5(0.03%) 1.3(0.05%)
1177 ABC-type spermidine/putrescine transport system,
permease *
2.5(0.07%) 3.0(0.08%) 2.3(0.06%) 1.6(0.02%) 2.7(0.03%) 2.4(0.03%) 2.7(0.07%)
1506 Dipeptidyl aminopeptidases/acylaminoacyl-peptidases 2.2 2.4
178
1932 Phosphoserine aminotransferase 1.5 2.5
3705 ATP phosphoribosyltransferase 1.9 10.1
3842 ABC-type spermidine/putrescine transport systems,
ATPase*
2.0(0.13%) 1.9(0.12%) 1.9(0.12%)
4166 ABC-type oligopeptide transport system, periplasmic 2.3
4176 ABC-type proline/glycine betaine transport system,
permease
2.1 1.8
4177 ABC-type branched-chain amino acid transport
system, permease
4.2 5.3 3.3 3.7
4597 ABC-type amino acid transport system, permease 2.2 3.3 2.9 1.6 2.6
Carbohydrate transport and metabolism
0149 Triosephosphate isomerase 704/0 3.8 5.0
0166 Glucose-6-phosphate isomerase 1.9 2.8
0235 Ribulose-5-phosphate 4-epimerase 2.4 1.8
0366 Glycosidases 2.6 1.9
0395 ABC-type sugar transport system, permease 2.2 3.2 2.2 14.1
0469 Pyruvate kinase 1.6 3.0
0574 Phosphoenolpyruvate synthase/pyruvate phosphate
dikinase
3.5 3.3
1086 Predicted nucleoside-diphosphate sugar epimerases 2.4
1175 ABC-type sugar transport systems, permease 2.1 3.6 3.7 6.2
1593 TRAP-type C4-dicarboxylate transport system 2.3 4.6
1638 TRAP-type C4-dicarboxylate transport system 2.3 2.3
1653 ABC-type sugar transport system, periplasmic 2.2 3.0
1850 Ribulose 1,5-bisphosphate carboxylase 8.5 2.8 4.1 3.5 5.5 2.4
1879 ABC-type sugar transport system, periplasmic 3.0 4.6
2513 PEP phosphonomutase and related enzymes 3.2 2.7 2.2
2721 Altronate dehydratase 3.7 2.8
Energy production and conversion
0045 Succinyl-CoA synthetase, beta subunit 2.8 4.7 1.5
0055 F0F1-type ATP synthase, beta subunit 2.0 2.3
0056 F0F1-type ATP synthase, alpha subunit 1.9 2.2 1.5
0074 Succinyl-CoA synthetase, alpha subunit 2.1 2.9
0224 F0F1-type ATP synthase, gamma subunit 3.6
0355 F0F1-type ATP synthase, epsilon subunit 1.7 3.5
0356 F0F1-type ATP synthase, subunit a 1.5 1.7 2.3
0437 Fe-S-cluster-containing hydrogenase 2.3
0538 Isocitrate dehydrogenases 3.0 2.7 1.9
0584 Glycerophosphoryl diester phosphodiesterase 500/0
0636 F0F1-type ATP synthase 1.5 2.4
0711 F0F1-type ATP synthase, subunit b 2.4 2.1 1.2
0712 F0F1-type ATP synthase, delta subunit 1.5 2.8
0838 NADH:ubiquinone oxidoreductase subunit 3 2.2
0839 NADH:ubiquinone oxidoreductase subunit 6 1.5 1.6 4.0
0843 Heme/copper-type cytochrome/quinol oxidases) 4.4
1005 NADH:ubiquinone oxidoreductase subunit 1 1.7 4.2
1007 NADH:ubiquinone oxidoreductase subunit 2 1.5 1.9 4.6
1008 NADH:ubiquinone oxidoreductase subunit 4 1.6 3.0
1018 Flavodoxin reductases 2.5 5.0 3.6 2.5 5.2 2.2
179
1038 Pyruvate carboxylase 1.9 4.5 3.1 2.1 17.8
1062 Zn-dependent alcohol dehydrogenases, class III 4.9 4.2 2.0
1141 Ferredoxin 14.0
1249 Pyruvate/2-oxoglutarate dehydrogenase complex 2.0 4.2
1251 NAD(P)H-nitrite reductase 9.9 32.0
1282 NAD/NADP transhydrogenase beta subunit 1.8 2.1 3.0
1290 Cytochrome b subunit of the bc complex 1.5 1.6 3.0
1347 Na+ transporting NADH:ubiquinone oxidoreductase 1.5 2.5 1.2
1622 Heme/copper-type cytochrome/quinol oxidases 1.9 1.7 2.0
1726 Na+ transporting NADH:ubiquinone oxidoreductase 3.3 5.9 1.5
1805 Na+ transporting NADH:ubiquinone oxidoreductase 3.2
1838 Tartrate dehydratase beta subunit/Fumarate hydratase 5.9 3.2 1.6
1845 Heme/copper-type cytochrome/quinol oxidase 1.9 1.8 3.2
1883 Na+ transporting methylmalonyl-CoA/oxaloacetate
decarboxylase
2.9 2.2 5.3 5.1 27.4
1894 NADH:ubiquinone oxidoreductase, NADH-binding 1.5 1.9 2.1
1902 NADH:flavin oxidoreductases, Old Yellow Enzyme 3.9 5.5
1951 Tartrate dehydratase alpha subunit/Fumarate hydratase 5.2 2.9 1.6
2010 Cytochrome c, mono- and diheme variants 2.4 1.9 2.4
2142 Succinate dehydrogenase, hydrophobic anchor 2.2 9.9 17.3 1.5
2209 Na+ transporting NADH:ubiquinone oxidoreductase 2.0 4.0 1.6
2838 Monomeric isocitrate dehydrogenase 4.4 5.5
2871 Na+ transporting NADH:ubiquinone oxidoreductase 1.6 2.1 1.5
2993 Cbb3-type cytochrome oxidase, cytochrome c 6.8 22.6 2.3
3288 NAD/NADP transhydrogenase alpha subunit 1.5 2.0 1.8
3794 Plastocyanin 2.2 2.9
3808 Inorganic pyrophosphatase 4.8 6.1 3.3
4231 Indolepyruvate ferredoxin oxidoreductase 3.0 3.4 1.6
4451 Ribulose bisphosphate carboxylase small subunit 3.6 2.1 1.7 2.1 2.5
4577 Carbon dioxide concentrating
mechanism/carboxysome shell protein
2.6 4.4 2.5
5016 Pyruvate/oxaloacetate carboxyltransferase 2.3 1.5 3.0 8.0
Nucleotide transport and metabolism
0041 Phosphoribosylcarboxyaminoimidazole mutase 1.6 2.9
0044 Dihydroorotase and related cyclic amidohydrolases 2.5 2.5 0.0
0046 Phosphoribosylformylglycinamidine synthase 2.2 3.0
0047 Phosphoribosylformylglycinamidine synthase 3.6 7.2
0104 Adenylosuccinate synthase 2.1 2.5
0458 Carbamoylphosphate synthase large subunit 2.1 1.6 2.0
0518 GMP synthase - Glutamine amidotransferase domain 2.2 2.1
0519 GMP synthase, PP-ATPase domain/subunit 2.1 1.9
0528 Uridylate kinase 2.7 2.6 2.8
0540 Aspartate carbamoyltransferase, catalytic chain 1.5 2.1
0563 Adenylate kinase and related kinases 1.5 3.0
01972 Nucleoside permease 3.2 417/0 159/0
2759 Formyltetrahydrofolate synthetase 2.1 2.9 1.8
*See Table 6.3 for explanation
180
Table S6.1. NCBI database accession numbers for reference sequences used to identify
homologs to PA functional genes.
Genes Description NCBI sequence accession
number
aphA acetylpolyamine aminohydrolase NP_250100.1
aphB acetylpolyamine aminohydrolase NP_249012.1
bltD spermine/spermidine acetyltransferase NP_390537.1
gabD succinate-semialdehyde dehydrogenase I NP_248956.1
gabT 4-aminobutyrate aminotransferase NP_248957.1
gltA type II citrate synthase NP_250271.1
kauB aldehyde dehydrogenase NP_253999.1
potA polyamine transporter subunit YP_489394.1
potB polyamine transporter subunit YP_489393.1
potC polyamine transporter subunit YP_489392.1
potD spermidine/putrescine ABC transporter periplasmic binding protein NP_415641.1
potE putrescine/proton symporter YP_488972.1
potF putrescine ABC transporter periplasmic binding protein NP_415375.1
potG utrescine transporter subunit YP_489128.1
potH putrescine transporter subunit YP_489129.1|
potI putrescine transporter subunit YP_489130.1
puuA glutamate--putrescine ligase NP_415813.4
puuB gamma-glutamylputrescine oxidoreductase NP_415817.1
puuC gamma-glutamyl-gamma-aminobutyraldehyde dehydrogenase; succinate semialdehyde dehydrogenase
NP_415816.1
puuD gamma-glutamyl-gamma-aminobutyrate hydrolase NP_415814.4
puuE 4-aminobutyrate aminotransferase, PLP-dependent NP_415818.1
puuR repressor for the divergent puu operons, putrescine inducible NP_415815.1
puuP putrescine importer YP_001730295.1
puuT putrescine transporter NP_752706.1
speG spermidine N(1)-acetyltransferase NP_416101.1
spuA glutamine amidotransferase NP_248988.1
spuB glutamine synthetase NP_248989.1
spuC aminotransferase NP_248990.1
spdH spermidine dehydrogenase NP_252402.1
spuI glutamine synthetase NP_248987.1
181
Table S6.2. Results of ANOSIM analyses, with pairwise differences between different PA
metagenomes (MG) and metatranscirptomes (MT).
Group rANOSIM P
major COGs between MG and MT 0.99 < 0.05
major COGs in MG by site 0.65 < 0.05
major COGs in MT between nearshore and offshore 0.58 < 0.05
major COGs in MT between nearshore and open ocean 0.67 < 0.05
taxonomic affiliations of the enriched COGs in MG by site 0.87 < 0.05
taxonomic affiliations of the enriched COGs in MT by site 0.90 < 0.05
182
Table S6.3. Significantly enriched COG groups (OR > 1.5, P < 0.02) related to metabolisms of amino acids, carbohydrates, energy production,
coenzyme, inorganic ion, and nucleotide production in PUT, SPD, and SPM metagenomic libraries, based on OR calculated between the number
of putative gene sequences in the PA and CT metagenomes.
COG COG description NS OS OO
ORPUT/CT ORSPD/CT ORSPM/CT ORPUT/CT ORSPD/CT ORSPM/CT ORSPD/CT ORSPM/CTR
Amino acid transport and metabolism
0076 Glutamate decarboxylase and related PLP-dependent proteins 1.6 1.7 2.0 2.6
0308 Aminopeptidase N 1.1 1.2 1.6
0339 Zn-dependent oligopeptidases 1.7 1.5
0347 Nitrogen regulatory protein PII 1.8
0560 Phosphoserine phosphatase 2.1
0747 ABC-type dipeptide transport system, periplasmic component 1.6 1.6 3.0
1115 Na+/alanine symporter 1.4 1.9
1176 Arginine decarboxylase (spermidine biosynthesis) 1.5 1.8 1.7 3.5 1177 ABC-type spermidine/putrescine transport system, permease component II 2.1
1506 Dipeptidyl aminopeptidases/acylaminoacyl-peptidases 124 132 1.9 1.2
1605 Chorismate mutase 2.0 2.4
1703 Putative periplasmic protein kinase ArgK and related GTPases of G3E family 1.2 1.8 1.7 2.0 2.7
1770 Protease II 1.4 1.6
2021 Homoserine acetyltransferase 1.3 1.3 1.5
2113 ABC-type proline/glycine betaine transport systems, periplasmic components 2.0 2.6 2.3
2902 NAD-specific glutamate dehydrogenase 1.9 2.3
4175 ABC-type proline/glycine betaine transport system, ATPase component 1.7 1.5 2.1
4608 ABC-type oligopeptide transport system, ATPase component 1.4 3.3 2.3
Carbohydrate transport and metabolism 0058 Glucan phosphorylase 1.5 3.0
0148 Enolase 1.5 1.2 1.5
0205 6-phosphofructokinase 1.5 1.3
0362 6-phosphogluconate dehydrogenase 1.3 3.9
0366 Glycosidases 2.1 2.7
0395 ABC-type sugar transport system, permease component 2.1
0726 Predicted xylanase/chitin deacetylase 1.2 2.0
0738 Fucose permease 1.4 2.2
1175 ABC-type sugar transport systems, permease components 2.2
1523 Type II secretory pathway, pullulanase PulA and related glycosidases 1.4 2.5 1.6 2.1
1638 TRAP-type C4-dicarboxylate transport system, periplasmic component 2.3
183
3250 Beta-galactosidase/beta-glucuronidase #DIV/0! 2.1 0.0
3839 ABC-type sugar transport systems, ATPase components 2.1
4993 Glucose dehydrogenase 1.3 1.4 2.8 2.1 2.8
Coenzyme transport and metabolism 0175 3'-phosphoadenosine 5'-phosphosulfate sulfotransferase (PAPS reductase)/FAD
synthetase and related enzymes
1.4 1.5 1.5 1.5 2.2
0413 Ketopantoate hydroxymethyltransferase 1.1 1.5
0422 Thiamine biosynthesis protein ThiC 1.4 2.5
0635 Coproporphyrinogen III oxidase and related Fe-S oxidoreductases 2.2 1.4
1239 Mg-chelatase subunit ChlI 1.7 1.8
Energy production and conversion
5598 Trimethylamine:corrinoid methyltransferase 1.4 4.2
0022 Pyruvate/2-oxoglutarate dehydrogenase complex, dehydrogenase (E1)
component, eukaryotic type, beta subunit
2.0
0538 Isocitrate dehydrogenases 1.1 1.6
0654 2-polyprenyl-6-methoxyphenol hydroxylase and related FAD-dependent
oxidoreductases
#DIV/0!
0778 Nitroreductase #DIV/0! #DIV/0!
1018 Flavodoxin reductases (ferredoxin-NADPH reductases) family 1 2.0 1.6 4.2
1032 Fe-S oxidoreductase 2.5 1.9
1038 Pyruvate carboxylase 1.2 3.4 2.7
1048 Aconitase A 1.4 1.8
1071 Pyruvate/2-oxoglutarate dehydrogenase complex, dehydrogenase (E1)
component, eukaryotic type, alpha subunit
1.4 2.3
1271 Cytochrome bd-type quinol oxidase, subunit 1 2.4 2.8
1301 Na+/H+-dicarboxylate symporters 1.4 1.6 2.0 2.5 3.4
1757 Na+/H+ antiporter 1.7 1.8 1.7 2.3
1805 Na+-transporting NADH:ubiquinone oxidoreductase, subunit NqrB 1.2 1.7 1.6
2010 Cytochrome c, mono- and diheme variants 1.5 2.7 1.2
2710 Nitrogenase molybdenum-iron protein, alpha and beta chains 1.2 1.8 1.1 2.7
3808 Inorganic pyrophosphatase 1.2 1.5
Inorganic ion transport and metabolism 0025 NhaP-type Na+/H+ and K+/H+ antiporters 1.7 2.8
0444 ABC-type dipeptide/oligopeptide/nickel transport system, ATPase component 1.2 2.3 2.6
0529 Adenylylsulfate kinase and related kinases 1.3 4.0 1.4
0659 Sulfate permease and related transporters (MFS superfamily) 1.4 2.0 1.1
0753 Catalase 2.6 3.4
1173 ABC-type dipeptide/oligopeptide/nickel transport systems, permease 1.4 2.4
184
components
1218 3'-Phosphoadenosine 5'-phosphosulfate (PAPS) 3'-phosphatase 1.1 2.2
1230 Co/Zn/Cd efflux system component 2.4 3.2
1629 Outer membrane receptor proteins, mostly Fe transport 1.8 2.7
1785 Alkaline phosphatase 2.7 2.9
2072 Predicted flavoprotein involved in K+ transport 1.7 1.4 1.8
2895 GTPases - Sulfate adenylate transferase subunit 1 1.6 3.5
3119 Arylsulfatase A and related enzymes 1.3 12.9 1.8
3696 Putative silver efflux pump 1.7 2.4 2.1 2.5
Lipid transport and metabolism 0511 Biotin carboxyl carrier protein 2.3 1.4 1.8
1257 Hydroxymethylglutaryl-CoA reductase 2.1 2.7
1884 Methylmalonyl-CoA mutase, N-terminal domain/subunit 1.7 2.5
2185 Methylmalonyl-CoA mutase, C-terminal domain/subunit (cobalamin-binding) 1.7 2.4
Nucleotide transport and metabolism 0047 Phosphoribosylformylglycinamidine (FGAM) synthase, glutamine
amidotransferase domain
1.4 1.4 1.8
0208 Ribonucleotide reductase, beta subunit 1.9 1.5
0209 Ribonucleotide reductase, alpha subunit 1.1 1.5 1.4
1816 Adenosine deaminase 1.3 2.2
Secondary metabolites biosynthesis, transport and catabolism 0146 N-methylhydantoinase B/acetone carboxylase, alpha subunit 1.8 1.8 1.5
1228 Imidazolonepropionase and related amidohydrolases 1.8 1.7 1.6 2.2
185
Table S6.4. Significantly enriched COG groups (OR > 1.5, P < 0.02) related to metabolisms of amino acids, carbohydrates, energy production,
coenzyme, inorganic ion, and nucleotide production in PUT, SPD, and SPM metatranscriptomic libraries, based on OR calculated between the
number of putative gene sequences in the PA and CT metatranscriptoms.
COG COG description NS OS OO
ORPUT/CT ORSPD/CT ORSPM/CT ORPUT/CT ORSPD/CT ORSPM/CT ORPUT/CT ORSPD/CT ORSPM/CTR
Amino acid transport and metabolism
0002 Acetylglutamate semialdehyde dehydrogenase 1.9 6.3
0014 Gamma-glutamyl phosphate reductase 3.5 2.9
0019 Diaminopimelate decarboxylase 2.0 1.8 0.0
0111 Phosphoglycerate dehydrogenase and related dehydrogenases 2.4 3.0
0128 5-enolpyruvylshikimate-3-phosphate synthase 3.0 5.1
0133 Tryptophan synthase beta chain 2.9 2.8 2.6
0165 Argininosuccinate lyase 2.1 2.5 1.1
0263 Glutamate 5-kinase 1.6 3.2 2.5 3.6 2.4
0287 Prephenate dehydrogenase 1.3 1.9 2.4 3.5 6.8 1.2
0334 Glutamate dehydrogenase/leucine dehydrogenase 2.4 4.7 1.5
0339 Zn-dependent oligopeptidases 2.2 4.5 1.2
0347 Nitrogen regulatory protein PII 1.6 2.2 1.5
0367 Asparagine synthase (glutamine-hydrolyzing) 3.3 1.5
0404 Glycine cleavage system T protein (aminomethyltransferase) 2.0 2.6 1.5
0460 Homoserine dehydrogenase 3.1 4.7 1.4
0462 Phosphoribosylpyrophosphate synthetase 3.3 2.7 2.0
0498 Threonine synthase 2.0 3.3 1.2
0520 Selenocysteine lyase 2.2 2.3 1.7
0559 Branched-chain amino acid ABC-type transport permease 3.2 3.8 4.0 0.0 2.9
0626 Cystathionine beta-lyases/cystathionine gamma-synthases 1.5 2.1 2.0 1.4 2.4 2.5 0.0 2.1
0665 Glycine/D-amino acid oxidases (deaminating) 1.5 2.5 1.9 2.0 2.5 1.8
0683 ABC-type branched-chain amino acid transport systems, periplasmic 2.3 2.4 1.4
0685 5,10-methylenetetrahydrofolate reductase 3.0 2.5 0.0
0686 Alanine dehydrogenase* 1.6
(0.13%)
2.1
(0.17%)
1.5
(0.12%)
1.6
(0.06%)
1.8
(0.07%)
2.4
(0.10%)
1.3
(0.05%)
0687 Spermidine/putrescine-binding periplasmic protein* 2.2
(0.49%)
2.1
(0.48%)
1.1
(0.25%)
0747 ABC-type dipeptide transport system, periplasmic 1.7 3.8 3.1 1.3
0765 ABC-type amino acid transport system, permease 2.1 2.6 2.5 1.1 4.1
1045 Serine acetyltransferase 1.7 1.9 2.2
1115 Na+/alanine symporter 2.0 3.0 3.1
1176 ABC-type spermidine/putrescine transport system, permease* 1.4
(0.03%)
2.2
(0.05%)
1.5
(0.03%)
1.3
(0.05%)
1177 ABC-type spermidine/putrescine transport system, permease component II* 2.5 3.0 2.3 1.6 2.7 2.4 2.7
186
(0.07%) (0.08%) (0.06%) (0.02%) (0.03%) (0.03%) (0.07%)
1506 Dipeptidyl aminopeptidases/acylaminoacyl-peptidases 2.2 2.4 1.4
1932 Phosphoserine aminotransferase 1.5 2.5 1.3
3705 ATP phosphoribosyltransferase involved in histidine biosynthesis 1.9 10.1
3842 ABC-type spermidine/putrescine transport systems, ATPase components* 2.0
(0.13%)
1.9
(0.12%)
1.9
(0.12%)
4166 ABC-type oligopeptide transport system, periplasmic 2.3
4176 ABC-type proline/glycine betaine transport system, permease component 1.4 2.1 1.8
4177 ABC-type branched-chain amino acid transport system, permease 4.2 5.3 3.3 3.7
4597 ABC-type amino acid transport system, permease 2.2 3.3 2.9 1.6 2.6
Carbohydrate transport and metabolism
0149 Triosephosphate isomerase 704/0 3.8 5.0
0166 Glucose-6-phosphate isomerase 1.9 2.8
0235 Ribulose-5-phosphate 4-epimerase and related epimerases and aldolases 2.4 1.8 1.4
0366 Glycosidases 2.6 1.9
0395 ABC-type sugar transport system, permease 1.2 2.2 3.2 2.2 14.1 1.2
0469 Pyruvate kinase 1.6 3.0
0574 Phosphoenolpyruvate synthase/pyruvate phosphate dikinase 3.5 3.3 1.2
1086 Predicted nucleoside-diphosphate sugar epimerases 2.4 0.0
1175 ABC-type sugar transport systems, permease 2.1 3.6 3.7 1.3 6.2
1593 TRAP-type C4-dicarboxylate transport system, permeaset 1.3 1.3 2.3 1.2 4.6 1.2
1638 TRAP-type C4-dicarboxylate transport system, periplasmic 2.3 2.3
1653 ABC-type sugar transport system, periplasmic 2.2 3.0 1.2
1850 Ribulose 1,5-bisphosphate carboxylase, 8.5 2.8 4.1 3.5 5.5 2.4
1879 ABC-type sugar transport system, periplasmic 3.0 4.6
2513 PEP phosphonomutase and related enzymes 3.2 2.7 2.2
2721 Altronate dehydratase 3.7 2.8
Coenzyme transport and metabolism
0007 Uroporphyrinogen-III methylase 3.3 6.6 4.9
0054 Riboflavin synthase beta-chain 2.5 3.2
0108 3,4-dihydroxy-2-butanone 4-phosphate synthase 2.1 1.8 1.7
147 Anthranilate/para-aminobenzoate synthases I 1.1 2.1 1.2
175 3'-phosphoadenosine 5'-phosphosulfate sulfotransferase (PAPS reductase)/FAD
synthetase
3.2 6.6
190 5,10-methylene-tetrahydrofolate dehydrogenase/Methenyl tetrahydrofolate
cyclohydrolase
2.1 2.2
192 S-adenosylmethionine synthetase 2.2 2.2
408 Coproporphyrinogen III oxidase 1.2 2.2 1.2
422 Thiamine biosynthesis protein ThiC 1.5 1.2 2.1
807 GTP cyclohydrolase II 2.1 1.9 1.8
1429 Cobalamin biosynthesis protein CobN and related Mg-chelatases 1.6 4.9 3.7 1.2 0.0 2.1
3572 Gamma-glutamylcysteine synthetase 2.2 2.0 1.8 1.5 4.1 1.3
5598 Trimethylamine:corrinoid methyltransferase 2.1 2.0 1.9 1.9 2.8 1.5 9.7 1.6
187
Energy production and conversion
0045 Succinyl-CoA synthetase, beta subunit 2.8 4.7 1.5
0055 F0F1-type ATP synthase, beta subunit 2.0 2.3 1.4
0056 F0F1-type ATP synthase, alpha subunit 1.9 2.2 1.5
0074 Succinyl-CoA synthetase, alpha subunit 2.1 2.9 1.1
0224 F0F1-type ATP synthase, gamma subunit 1.4 3.6 1.1
0355 F0F1-type ATP synthase, epsilon subunit (mitochondrial delta subunit) 1.7 3.5
0356 F0F1-type ATP synthase, subunit a 1.5 1.7 2.3
0437 Fe-S-cluster-containing hydrogenase components 1 1.2 2.3
0538 Isocitrate dehydrogenases 3.0 2.7 1.9
0584 Glycerophosphoryl diester phosphodiesterase 0.0 #DIV/0!
0636 F0F1-type ATP synthase, subunit c/Archaeal/vacuolar-type H+ATPase 1.5 1.2 2.4
0711 F0F1-type ATP synthase, subunit b 2.4 2.1 1.2
0712 F0F1-type ATP synthase, delta subunit (mitochondrial oligomycin sensitivity protein) 1.5 2.8
0838 NADH:ubiquinone oxidoreductase subunit 3 0.0 2.2
0839 NADH:ubiquinone oxidoreductase subunit 6 1.5 1.6 4.0
0843 Heme/copper-type cytochrome/quinol oxidases) 1.4 1.4 4.4
1005 NADH:ubiquinone oxidoreductase subunit 1 1.3 1.7 4.2
1007 NADH:ubiquinone oxidoreductase subunit 2 1.5 1.9 4.6
1008 NADH:ubiquinone oxidoreductase subunit 4 1.1 1.6 3.0
1018 Flavodoxin reductases (ferredoxin-NADPH reductases) family 1 2.5 5.0 3.6 2.5 5.2 2.2
1038 Pyruvate carboxylase 1.9 4.5 3.1 2.1 17.8
1062 Zn-dependent alcohol dehydrogenases, class III 4.9 4.2 2.0
1141 Ferredoxin 14.0
1249 Pyruvate/2-oxoglutarate dehydrogenase complex, dihydrolipoamide dehydrogenase (E3)
component
2.0 4.2 1.2
1251 NAD(P)H-nitrite reductase 9.9 32.0
1282 NAD/NADP transhydrogenase beta subunit 1.8 2.1 3.0
1290 Cytochrome b subunit of the bc complex 1.5 1.6 3.0
1347 Na+ transporting NADH:ubiquinone oxidoreductase, subunit NqrD 1.5 2.5 1.2
1622 Heme/copper-type cytochrome/quinol oxidases 1.9 1.7 2.0
1726 Na+ transporting NADH:ubiquinone oxidoreductase, subunit NqrA 3.3 5.9 1.5
1805 Na+
transporting NADH:ubiquinone oxidoreductase, subunit NqrB 1.2 1.2 3.2
1838 Tartrate dehydratase beta subunit/Fumarate hydratase class I, C-terminal 5.9 3.2 1.6
1845 Heme/copper-type cytochrome/quinol oxidase 1.9 1.8 3.2
1883 Na+ transporting methylmalonyl-CoA/oxaloacetate decarboxylase 2.9 2.2 5.3 5.1 27.4 1.4
1894 NADH:ubiquinone oxidoreductase, NADH-binding 1.5 1.9 2.1
1902 NADH:flavin oxidoreductases, Old Yellow Enzyme 3.9 5.5
1951 Tartrate dehydratase alpha subunit/Fumarate hydratase class I, N-terminal domain 5.2 2.9 1.6
2010 Cytochrome c, mono- and diheme variants 2.4 1.9 2.4
2142 Succinate dehydrogenase, hydrophobic anchor 1.4 1.2 2.2 9.9 17.3 1.5
2209 Na+ transporting NADH:ubiquinone oxidoreductase, subunit NqrE 2.0 4.0 1.6
2838 Monomeric isocitrate dehydrogenase 4.4 5.5 1.4
2871 Na+ transporting NADH:ubiquinone oxidoreductase, subunit NqrF 1.6 2.1 1.5
2993 Cbb3-type cytochrome oxidase, cytochrome c 6.8 22.6 2.3
3288 NAD/NADP transhydrogenase alpha subunit 1.5 2.0 1.8
3794 Plastocyanin 2.2 1.1 2.9
188
3808 Inorganic pyrophosphatase 4.8 6.1 3.3
4231 Indolepyruvate ferredoxin oxidoreductase, alpha and beta subunits 0.0 3.0 1.3 3.4 1.6
4451 Ribulose bisphosphate carboxylase small subunit 3.6 2.1 1.7 2.1 2.5
4577 Carbon dioxide concentrating mechanism/carboxysome shell protein 2.6 4.4 2.5
5016 Pyruvate/oxaloacetate carboxyltransferase 2.3 1.5 3.0 8.0 1.4
Inorganic ion transport and metabolism
0004 Ammonia permease 3.1 1.9 1.9
0025 NhaP-type Na+/H
+ and K
+/H
+ antiporters 3.0 3.1 2.0
0155 Sulfite reductase, beta subunit (hemoprotein) 2.3 1.8 2.7 4.8 1.1
0168 Trk-type K transport systems, membrane 1.8 2.3 3.2 1.2 2.5 1.1
0226 ABC-type phosphate transport system, periplasmic 1.9 2.4 1.3 2.8 0.0 2.3 10.2
0306 Phosphate/sulphate permeases 2.2 1.3 3.9
0530 Ca2+
/Na+ antiporter 2.3 2.6 3.4 3.0 12.0 1.9
0573 ABC-type phosphate transport system, permease 1.1 2.3 2.7 1.6 3.4 2.5 3.7 7.3
0581 ABC-type phosphate transport system, permease 1.2 2.3 2.6 1.2 1.9 2.8 0.0 9.5
0600 ABC-type nitrate/sulfonate/bicarbonate transport system, permeas 2.1 2.1 1.6 1.8 4.5 1.1
0601 ABC-type dipeptide/oligopeptide/nickel transport systems, permease 3.5 4.8 5.2 0.0 3.7 1.5
0605 Superoxide dismutase 2.4 3.2 1.3
0651 Formate hydrogenlyase subunit 3/Multisubunit Na+/H
+ antiporter 2.1 9.2 1.3
0659 Sulfate permease and related transporters (MFS superfamily) 1.7 2.4 2.5 2.3 2.3
0704 Phosphate uptake regulator 1.2 1.8 3.0 367/0 560/0
0715 ABC-type nitrate/sulfonate/bicarbonate transport systems, periplasmic 2.2 4.6 2.7
0798 Arsenite efflux pump ACR3 and related permeases 1.9 2.1 0.0 4.2 1.4 6.6
0803 ABC-type metal ion transport system, periplasmic component/surface adhesin 2.1 1.9 3.5 6.2 0.0 0.0 8.4 1.3
0855 Polyphosphate kinase 3.3 2.1 1.6 4.0
1009 NADH:ubiquinone oxidoreductase subunit 5 (chain L)/Multisubunit Na+ antiporter 1.1 1.6 3.6
1108 ABC-type Mn2+
/Zn2+
transport systems, permease 130/0 0.0 2.3 1.2 8.8 1.4
1116 ABC-type nitrate/sulfonate/bicarbonate transport system, ATPase 2.2 5.5 1.7
1117 ABC-type phosphate transport system, ATPase 1.1 1.9 2.6 1.5 4.1 1.1
1121 ABC-type Mn/Zn transport systems, ATPase 96/0 1.1 4.8 1.3
1173 ABC-type dipeptide/oligopeptide/nickel transport systems, permease 1.5 2.1 1.6 3.0 4.8 2.7 1.4 5.0 1.2
1226 Kef-type K+ transport systems, predicted NAD-binding component 184/0 402/0 3.4 13.9
1629 Outer membrane receptor proteins, mostly Fe transport* 10124/0
(0.89%)
3702/0
(0.58%)
1785 Alkaline phosphatase 3.7 5.2
2072 Predicted flavoprotein involved in K transport 2.2 2.0 1.3
2111 Multisubunit Na+/H
+ antiporter, MnhB subunit 6.6 58.7 2.1
2116 Formate/nitrite family of transporters 69.1 108.1
2217 Cation transport ATPase 2.8 1.8 3.4 3.9 18.8 2.2
2895 GTPases - Sulfate adenylate transferase subunit 1 5.3 5.7 1.3
3067 Na+/H
+ antiporter 3.2 2.2 2.5
3119 Arylsulfatase A and related enzymes 1.2 2.6 1.8 323/0 549/0 145/0
3221 ABC-type phosphate/phosphonate transport system, periplasmic 3.1 8.1 1.7 0.0 23.3 1.3
3639 ABC-type phosphate/phosphonate transport system, permease 1.2 2.1 7.1 21.7 0.0 0.0 35.8
3696 Putative silver efflux pump 1.2 2.3 8.4 20.2 2.6
4521 ABC-type taurine transport system, periplasmic 3.8 2.7 0.0
189
4985 ABC-type phosphate transport system, auxiliary 2.3 22.5 39.1
Lipid transport and metabolism
0332 3-oxoacyl-[acyl-carrier-protein] synthase III 1.5 2.0 1.1
0511 Biotin carboxyl carrier protein 1.7 3.4
0623 Enoyl-[acyl-carrier-protein] reductase (NADH) 1.9 2.0 1.6
0821 Enzyme involved in the deoxyxylulose pathway of isoprenoid biosynthesis 2.2 1.9 1.2
1024 Enoyl-CoA hydratase/carnithine racemase 1.9 3.9 1.2
1133 ABC-type long-chain fatty acid transport system, fused permease and ATPase
components
2.8 4.4 4.3
1154 Deoxyxylulose-5-phosphate synthase 1.4 1.5 2.2
1250 3-hydroxyacyl-CoA dehydrogenase 2.4 4.8 1.3
2030 Acyl dehydratase 3.2 1.6
3000 Sterol desaturase 4.0 3.5 3.5
3243 Poly(3-hydroxyalkanoate) synthetase 104/0 396/0
Nucleotide transport and metabolism
0041 Phosphoribosylcarboxyaminoimidazole (NCAIR) mutase 1.6 1.4 2.9
0044 Dihydroorotase and related cyclic amidohydrolases 2.5 2.5 0.0
0046 Phosphoribosylformylglycinamidine (FGAM) synthase, synthetase 2.2 3.0 1.1
0047 Phosphoribosylformylglycinamidine (FGAM) synthase, glutamine amidotransferase
domain
3.6 7.2 1.4
0104 Adenylosuccinate synthase 2.1 2.5 1.1
0458 Carbamoylphosphate synthase large subunit 2.1 1.6 2.0
0518 GMP synthase - Glutamine amidotransferase domain 2.2 2.1 1.1
0519 GMP synthase, PP-ATPase domain/subunit 2.1 1.9 1.1
0528 Uridylate kinase 2.7 2.6 2.8
0540 Aspartate carbamoyltransferase, catalytic chain 1.4 1.5 2.1
0563 Adenylate kinase and related kinases 1.5 3.0
01972 Nucleoside permease 3.2 417/0 159/0
2759 Formyltetrahydrofolate synthetase 2.1 2.9 1.8
Secondary metabolites biosynthesis, transport and catabolism
236 Acyl carrier protein 2.3 2.0 2.2
304 3-oxoacyl-(acyl-carrier-protein) synthase 2.1 2.4 1.7
318 Acyl-CoA synthetases (AMP-forming)/AMP-acid ligases II 2.2 2.1 0.0 1.1 2.3 1.3
767 ABC-type transport system involved in resistance to organic solvents, permease
component
2.6 4.8
1127 ABC-type transport system involved in resistance to organic solvents, ATPase
component
4.0 9.4
1228 Imidazolonepropionase and related amidohydrolases 3.4 3.0 0.0
4663 TRAP-type mannitol/chloroaromatic compound transport system, periplasmic
component
2.2 1.9 1.1
4664 TRAP-type mannitol/chloroaromatic compound transport system, large permease 3.0 3.3 4.4 0.0 2.1
31.0
30.0
29.0
28.0
27.0
NS (28 m)
OS (73 m)
OO (714 m)
Latit
ude
-93.0 -91.0 -89.0 -87.0
Longitude
Figure 6.1
Louisiana
Figure 6.1. The sampling sites of NS, OS, and OO in the Gulf of Mexico in May,
2013. The depth of water column at each site is listed in the parentheses.
190
OO_CTR
OS_SPM
OO_SPD
OS_CTR
OS_PUT
OO_SPM
OS_SPD
NS_SPM
NS_CTRNS_PUT
NS_SPD
nearshore offshore open ocean
Stress: 0.05
OO_SPD
(a) Metagenomes
OO_PUT
OO_SPMOO_CTR
OS_PUTOS_SPD
OS_CTR
OS_SPM
NS_CTR NS_PUTNS_SPD NS_SPM
Stress: 0.06(b) Metatranscriptomes
Figure 6.2
Figure 6.2. The non-metric multidimensional scaling (NMDS) ordination based on the relative abundance of major COGs in (a) metagenomes and (b) metatranscriptomes of nearshore (NS; triangle), offshore (OS; square), and open ocean (OO; star) in the Gulf of Mexico. Boxes are drawn to distinguish statistically separated groups.
191
Flavobacteria
ceae
Other Chroococca
les
Planctomyce
taceae
Bradyrhizo
biaceae
Rhizobiacea
e
Rhodobacteracea
e
Comamonadaceae
Alcanivo
racaceae
Alteromonadacea
e
Halomonadaceae
Pasteurell
aceae
Pseudoalter
o-
monadaceae
Pseudomonodacea
e
Shewanellacea
e
Prochlorococcacea
e
Mycobacte
riacea
e
Streptomyce
taceae
Flavobacteria
ceae
Other Chroococca
les
Prochlorococcacea
e
Rhodobacteracea
e
Alcanivo
racaceae
Alteromonadacea
e
Hahellacea
e
Halomonadaceae
Idiomarinacea
e
Pasteurell
aceae
Pseudoalter
o-
monadaceae
Shewanellacea
e
Vibrionacea
e
16
Rel
ativ
e ab
unda
nce
(%)
12
84
0
(b) OS
(c) OO
Rel
ativ
e ab
unda
nce
(%)
55
5012
840
Figure 6.3
Figure 6.3. Taxonomic binning of the protein-encoding sequences in significantly enriched
COGs at bacterial family levels in the PA libraries (PUT, SPD, and SPM) of metagenomes
in (a) NS, (b) OS, and (c) OO and metatranscriptomes in (d) NS, (e) OS, and (f) OO,
in relative to CTRs, in the Gulf of Mexico.
BacteroidetesCyanobacteria
PlanctomycetaceaeAlpha- Beta- Gamma-
Proteobacteria
ActinobacteriaBacteroidetes
CyanobacteriaAlpha- Gamma-
Proteobacteria
Microbacte
riacea
e
Pseudonocardiacea
e
Streptomyce
taceae
Flavobacteria
ceae
Bacillacea
e
Clostridiacea
e
Caulobacteracea
e
Bradyrhizo
biaceae
Rhizobiacea
e
Rhodobacteracea
e
Comamonadaceae
Rhodocyclacea
e
Alcanivo
racaceae
Halomonadaceae
Pseudoalter
o-
monadaceae
Rel
ativ
e ab
unda
nce
(%)
25201510
50
(a) NS
ActinobacteriaBacteroidetes
Firmicutes Alpha- Beta- Gamma-Proteobacteria
Propionibacteria
ceae
Flavobacteria
ceae
Bradyrhizo
biaceae
Brucellacea
e
Phyllobacte
riacea
e
Rhodobacteracea
e
SAR11 clade
Burkholderia
ceae
Comamonadaceae
Alteromonadacea
e
Enterobacte
riacea
e
Halomonadaceae
Pseudoalter
o-
monadaceae
Pseudomonadacea
e
Vibrionacea
eRel
ativ
e ab
unda
nce
(%)
25201510
50
(d) NS PUTSPDSPM
ActinobacteriaBacteroidetes Alpha- Beta- Gamma-
Proteobacteria
Rel
ativ
e ab
unda
nce
(%)
16
1284
0
Propionibacteria
ceae
Other Chroococca
les
Other Oscil
latoriales
Bradyrhizo
biaceae
Caulobacteracea
e
Hyphomonadaceae
Rhizobiacea
e
Rhodobacteracea
e
Burkholderia
ceae
Comamonadaceae
Aeromonadacea
e
Enterobacte
riacea
e
Pasteurell
aceae
Pseudoalter
o-
monadaceae
Vibrionacea
e
(e) OS
ActinobacteriaCyanobacteria Alpha-
ProteobacteriaBeta- Gamma-
Nostocacea
e
Other Chroococca
les
Prochlorococcacea
e
Other Oscil
latoriales
Bradyrhizo
biaceae
Caulobacteracea
e
Rhodobacteracea
e
Burkholderia
ceae
Alcanivo
racaceae
Alteromonadacea
e
Enterobacte
riacea
e
Halomonadaceae
Pseudoalter
o-
monadaceae
Shewanellacea
e
Vibrionacea
eRel
ativ
e ab
unda
nce
(%)
25201510
50
(f) OO
CyanobacteriaAlpha-
ProteobacteriaBeta- Gamma-
Metagenomes Metatranscriptomes
192
Odd
s rat
io (O
R)
PA/C
TR m
etag
enom
es
1
0
2
(a) NS
transporter γ-glutamylation transamination spermidine cleavage transporter γ-glutamylation transamination spermidine cleavage
transporter γ-glutamylation transamination spermidine cleavage transporter γ-glutamylation transamination spermidine cleavage
transporter γ-glutamylation transamination spermidine cleavage transporter γ-glutamylation transamination spermidine cleavage
Odd
s rat
io (O
R)
PA/C
TR m
etag
enom
es
6
5
2
1
0
Odd
s rat
io (O
R)
PA/C
TR m
etag
enom
es
(b) OS
(c) OO6
5
4
3
2
1
0
5
4
3
2
1
0
(d) NSPUTSPDSPM
Odd
s rat
io (O
R)
PA/C
TR m
etat
rans
crip
tom
sO
dds r
atio
(OR
) PA
/CTR
met
atra
nscr
ipto
ms
Odd
s rat
io (O
R)
PA/C
TR m
etat
rans
crip
tom
s
(e) OS
(f) OO
4
3
2
1
0
15
20
1043
21
0
Figure 6.4
Figure 6.4. Significantly enriched PA diagnostic gene groups of transporter,
transamination, spermidine cleavage in the PA libraries (PUT, SPD, and SPM) of
metagenomes in (a) NS, (b) OS, and (c) OO and metatranscriptomes in (d) NS, (e) OS, and
(f) OO, in relative to CRTs, in the Gulf of Mexico.
γ-glutamylation,
Metagenomes Metatranscriptomes
193
Microbacteriaceae
Micrococcaceae
Micromonosporaceae
Flavobacteriaceae
Rhizobiaceae
Rhodobacteraceae
SAR11 clade
Methylophilaceae
Alteromonadaceae
OMG group
Actinobacteria Bacteroidetes Alpha- Beta- Gamma-Proteobacteria
Perc
enta
ge (%
) of d
iagn
ostic
gen
esPe
rcen
tage
(%) o
f dia
gnos
tic g
enes
Perc
enta
ge (%
) of d
iagn
ostic
gen
es
(a) NS30
25
20
15
5
10
0
(b) OS
Other Chroococcales
Planctomycetaceae
Burkholderiaceae
Rhodobacteraceae
SAR11 clade
Comamonadaceae
Alteromonadaceae
Enterobacteriaceae
OMG group
Shewanellaceae
30
25
20
15
5
10
0
CyanobacteriaPlantomycetes Alpha- Beta- Gamma-
Proteobacteria70
65
3025201510
50
(c) OO
Rhizobiaceae
Rhodobacteraceae
SAR11 clade
Alteromonadaceae
Colwelliaceae
Enterobacteriaceae
Hahellaceae
OMG group
Pseudoaltero-
monadaceae
Shewanellaceae
Alpha- Gamma-Proteobacteria
Figure 6.5. Relative abundance of diagnostic PA uptake/metabolism genes in CTR,
PUT, SPD, and SPM metagenomes of (a) NS, (b) OS, and (c) OO in the Gulf of Mexico
by taxonomic assignment.
CTRPUTSPDSPM
spermidine cleavagetransaminationγ-glutamylationtransporter
Figure 6.5
194
Pe
rcen
tage
(%) o
f dia
gnos
tic g
enes
Perc
enta
ge (%
) of d
iagn
ostic
gen
esPe
rcen
tage
(%) o
f dia
gnos
tic g
enes
40
35
30
25
20
15
10
5
0
Rhizobiaceae
Rhodobacteraceae
SAR11 clade
Methylophilaceae
Alteromonadaceae
Pseudoaltero-
monadaceae
Brucellaceae
Rhizobiaceae
Rhodobacteraceae
SAR11 clade
Comamonadaceae
Methylophilaceae
Rhodocyclaceae
Alteromonadaceae
Pseudoaltero-
monadaceaeVibrionaceae
Thermotogaceae
Flavobacteriaceae
Bradyrhizobiaceae
Burkholderiaceae
Enterobacteriaceae
Hahellaceae
(a) NS
30
25
20
15
5
10
0
Corynebacteriaceae
Flavobacteriaceae
Bradyrhizobiaceae
Brucellaceae
Phyllobacteriaceae
Rhizobiaceae
Rhodobacteraceae
SAR11 clade
Alteromonadaceae
Enterobacteriaceae
Vibrionaceae
(b) OS
(c) OO
Bacteroidetes
ActinobacteriaBacteroidetes
Alpha- Beta- Gamma-Proteobacteria
Alpha- Gamma-Proteobacteria
Alpha- Beta- Gamma-Proteobacteria Thermotogae
45
40
35
20
15
10
5
0
CTRPUTSPDSPM
spermidine cleavagetransaminationγ-glutamylationtransporter
Figure 6.6. Relative abundance of diagnostic PA uptake/metabolism genes in CTR,
PUT, SPD, and SPM metatranscriptomes of (a) NS, (b) OS, and (c) OO in the Gulf
of Mexico by taxonomic assignment.
Figure 6.6
195
[D] Cell cycle control, cell division,
and chromosome partitioning
[F] Nucleotide transport and metabolism
Odd
s rat
io (O
R)
PA/C
TR m
etag
enom
es
(a) NS
[I] Lipid transport and metabolism
[N] Cell motilit
y
[O] Posttranslational modific
ation,
protein turnover and chaperones
[Q] Secondary metabolites biosynthesis,
transport and metabolism
[U] Intracellular tra
fficking, se
cretion,
and vesicular transport
[V] Defense metabolisms
2
1.5
1
0.5
0
Odd
s rat
io (O
R)
PA/C
TR m
etat
rans
crip
tom
s
[D] Cell cycle control, cell division,
and chromosome partitioning
[E] Amino acid transport and metabolism
[F] Nucleotide transport and metabolism
[H] Coenzyme transport and metabolism
[L] Replication, recombination and repair
[M] Cell wall/m
embrane/envelope biogenesis
[O] Posttranslational modific
ation,
protein turnover and chaperones
[P] Inorganic ion transport and metabolism
[S] Function unkown
[T] Signal transduction mechanism
s
[V] Defense metabolisms
[Z] Cytoskeleton
2
1.5
1
0.5
0
(d) NS
PUTSPDSPM
2
1.5
1
0.5
0
2
1.5
1
0.5
0
(b) OS (e) OS
[E] Amino acid transport and metabolism
[F] Nucleotide transport and metabolism
[G] Carbohydrate transport and metabolism
[H] Coenzyme transport and metabolism
[N] Cell motilit
y
[P] Inorganic ion transport and metabolism
[Q] Secondary metabolites biosynthesis,
transport and metabolism[S] Function unkown
[T] Signal transduction mechanism
s
[U] Intracellular tra
fficking, se
cretion,
and vesicular transport
[V] Defense metabolisms
[C] Energy production and conversion
[D] Cell cycle control, cell division,
and chromosome partitioning
[E] Amino acid transport and metabolism
[F] Nucleotide transport and metabolism
[G] Carbohydrate transport and metabolism
[H] Coenzyme transport and metabolism
[I] Lipid transport and metabolism
[J] Translation, rib
osomal structure
and biogenesis[K] Transcription
[Q] Secondary metabolites biosynthesis,
transport and metabolism
[U] Intracellular tra
fficking, se
cretion,
and vesicular transport
[V] Defense metabolisms
2
1.5
1
0.5
0
(c) OO (f) OO5.5
5
3
2
1
0
[L] Replication, recombination and repair
[N] Cell motilit
y
[P] Inorganic ion transport and metabolism
[T] Signal transduction mechanism
s
[U] Intracellular tra
fficking, se
cretion,
and vesicular transport
[V] Defense metabolisms
Odd
s rat
io (O
R)
PA/C
TR m
etag
enom
es
Odd
s rat
io (O
R)
PA/C
TR m
etat
rans
crip
tom
s
[D] Cell cycle control, cell division,
and chromosome partitioning
[F] Nucleotide transport and metabolism
[G] Carbohydrate transport and metabolism
[I] Lipid transport and metabolism
[M] Cell wall/m
embrane/envelope biogenesis
[N] Cell motilit
y
[O] Posttranslational modific
ation,
protein turnover and chaperones
[P] Inorganic ion transport and metabolism
[S] Function unkown
[T] Signal transduction mechanism
s
[U] Intracellular tra
fficking, se
cretion,
and vesicular transport
[V] Defense metabolisms
COG categories
Odd
s rat
io (O
R)
PA/C
TR m
etag
enom
es
Odd
s rat
io (O
R)
PA/C
TR m
etat
rans
crip
tom
s
Figure S6.1
Figure S6.1. Significantly enriched COG categories in the PA libraries (PUT, SPD,
and SPM) of metagenomes in (a) NS, (b) OS, and (c) OO and metatranscriptomes in (d) NS,
(e) OS and (f) OO, in realtive to CTRs, in the Gulf of Mexico.
Metagenomes Metatranscriptomes
196
NS_SPMNS_CTR
NS_PUTNS_SPD
OO_SPD
OO_CTROS_SPM
OS_PUT
OO_SPM
OS_SPD
OS_CTR
NS_CTR
NS_PUT
NS_SPMNS_SPD
OS_SPDOS_PUT OS_SPM
OS_CTROO_PUT
OO_SPD
OO_SPM
OO_CTR
Stress: 0.04
MetagenomesMetatranscriptomes
offshore open oceannearshore
Figure S6.2
Figure S6.2. The NMDS ordination based on the relative abundance of major COGs in
pooled metagenomes and metatranscriptomes of nearshore (NS; triangle), offshore
(OS; square), and open ocean (OO; star) in the Gulf of Mexico. Boxes are drawn to
distinguish statistically separated groups .
197
NS_SPMNS_PUTNS_SPD
OS_SPDOS_PUTOS_SPMOO_SPD
OO_SPM
Stress: 0.01
nearshore offshore open ocean
(a) Metagenomes
(b) Metatranscriptomes
NS_PUT
NS_SPM
NS_SPD
OO_SPM
OO_SPD
OO_PUT
OS_SPD
OS_PUT
OS_SPM
Stress: 0.09
Figure S6.3
Figure S6.3. The NMDS ordination based on the relative abundance of assigned enriched COGs at bacterial family level in (a) metagenomes and (b) metatranscriptomes of nearshore (NS; triangle), offshore (OS; square), and open ocean (OO; star) in the Gulf of Mexico. Boxes are drawn to distinguish statistically separated groups .
198
199
Chapter 7
Summary
200
Summary
Availability of labile nitrogen (N) is important in shaping composition, diversity, and
dynamics of organisms and may impact ecosystem functioning in aquatic environments (Herbert,
1999; Rabalais, 2002). Our knowledge on biogeochemical cycles of N and microorganisms that
mediate these processes has been significantly improved in the past few decades (Francis et al.,
2007). New findings, such as the discovery of anaerobic ammonium oxidation (anammox), an
alternative route to remove bioavailable N, have shifted the traditional paradigm of nitrogen
cycling (Francis et al., 2007). A new question, therefore, is raised on the contribution of
anammox, relative to denitrification, in N2 production. Moreover, some long-standing questions,
such as the chemical composition of labile dissolved organic nitrogen (DON) pool and the
transformation mechanism of labile DON are yet to be solved (McCarthy et al., 1998; Francis et
al., 2007; Gruber and Galloway, 2008). The overall objective of this research was to improve our
understanding of bacterially mediated N transformations in aquatic environments, specifically on
nitrogen removal by anammox and denitrification and on polyamine (a labile DON)
transformation.
Anammox and denitrification in aquatic environments
Anammox and denitrification are two microbially mediated processes that can both lead
to biological removal of fixed N. Because of their impacts on availability of labile N, the two
processes have been intensively investigated in marine environments (e.g. Thamdrup and
Dalsgaard, 2002; Dalsgaard et al. 2003). Both anammox and denitrification are found widely in
marine environments; but their relative importance in N removal (total N2 production) appeared
controversial (Thamdrup and Dalsgaard, 2002; Ward et al., 2009). The suggested relative
importance of anammox vs. denitrification in total N2 production in marine systems varies from
201
undetectable to 100% (Thamdrup and Dalsgaard, 2002; Rysgaard et al., 2004; Ward et al., 2009;
Humbert et al., 2010). Compared to marine environments, the importance of anammox to N2
production in freshwater environments is relatively unclear.
To fill this knowledge gap, investigations of the importance of anammox in total N2
production relative to denitrification using 15
N isotope pairing technique were performed in the
offshore bottom water of the South Atlantic bight (SAB) (Chapter 2) and in Lake Erie (Chapter
3). SAB is the southeastern United States seaboard located between Cape Hatteras, North
Carolina and Cape Canaveral, Florida. Due to the influences of Gulf Stream, the oxygen contents
in the offshore bottom water of the SAB are often depleted (Atkinson et al., 1978; Atkinson and
Blanton, 1986), a condition may favor the growth of anammox bacteria and denitrifiers. Lake
Erie was chosen as the counterpart site in freshwater systems. It is a part of the so-called “inland
sea”, i.e., the Laurentian Great Lakes, and plays important roles in serving people as a drinking-
water reservoir. Due to increased frequency and intensity of harmful algal blooms and seasonal
stratification (Brittain et al., 2000; Ouellette et al., 2006), the oxygen-limiting zones are often
formed in the water column of western basin and central basin in Lake Erie, which may serve as
incubating grounds for anammox bacteria and denitrifiers.
Our studies found that anammox might potentially be a more important N removal
process than denitrification in the studied marine and freshwater lakes ecosystems, and the
relative importance of anammox and denitrification in total N2 production may vary spatially and
temporally. This result contributes to our understanding on the role of anammox and
denitrification in labile N removal in aquatic ecosystems and reiterates the importance of the
studies on the temporal dynamics of anammox and denitrification for evaluation of their
contributions to suboxic nutrient balances. Besides, as anammox and denitrification have not
202
been studied in Lake Erie, or other Laurentian Great Lakes, our data also provide insights for the
effective management for addressing nutrient status and hypoxia in water column of Lake Erie
and other Laurentian Great Lakes.
A number of factors may influence the activity of anammox and denitrification, such as
O2, H2S, and organic matter (Dalsgaard and Thamdrup, 2002; Rysgaard et al., 2004; Lam et al.,
2009; Wenk et al., 2013), but the environmental variables that regulated anammox and
denitrification variability in aquatic systems are not clear yet. Here, potential correlations
between the anammox and denitrification rates and the in situ environmental variables, including
temperature, salinity, dissolved oxygen contents, dissolved organic carbon, dissolved nitrogen,
nitrate, nitrite, ammonium, and cell abundance, were assessed by calculating Pearson’s product-
moment correlation coefficients. However, none of the measured environmental variables were
found significantly correlated with the variability of anammox and denitrification rates in our
studied marine and freshwater lake ecosystems. In the future, more studies should be done to
illustrate the underlying mechanisms that relate to the variations of anammox and denitrification
activities in aquatic environments.
Polyamines (PAs) in marine systems
DON constitutes an important pool of labile N pool in marine environments (Bronk,
2002), especially in surface open oceans (McCarthy et al., 1998). Due to the analytical
constraints, biogeochemical studies of DON have been investigated only on a few compounds,
such as dissolved free amino acids (DFAAs). PAs are a class of labile DON that share many
important biogeochemical features with DFAAs. However, because of the lack of effective
analytical methods that can simultaneously quantify PAs and DFAAs in seawater, the
203
importance of PAs relative to DFAAs and to the labile DON pool has not been rigorously
established. In seawater, marine bacterioplankton may readily take up PAs as carbon, nitrogen,
and/or energy sources (Höfle, 1984; Lee and Jørgensen, 1995). However, investigations on the
bacterial PA-transformers have only been performed in inshore environments (Mou et al., 2011,
2014). Therefore, our knowledge on the bacterial genes and taxa that participate in PA
transformation of different PA compounds in different marine systems remains limited.
To fill these knowledge gaps, we first optimized a high-performance liquid
chromatography (HPLC) method that uses pre-column fluorometric derivatization with o-
phthaldialdehyde, ethanethiol, and 9-fluorenylmethyl chloroformate to determine 20 DFAAs and
5 PAs in seawater simultaneously (Chapter 4). The temporal dynamics and depth variations of
DFAAs and PAs were then examined in coastal seawater at the Grey’s Reef National Marine
Sanctuary in spring and fall, 2011 (Chapter 4). Our results showed that at least occasionally, PAs
may be provided as similar concentrations as dissolved free amino acids to marine
bacterioplankton communities and therefore is a non-negligible component of marine DON pool.
To identify PA-responsive bacterioplankton, we examined changes of bacterioplankton
community structures in microcosms incubated with additional putrescine or spermidine and in
no-addition controls using the surface seawater collected from the SAB (Chapter 5). The
continental shelf ecosystem off the Georgia coast, i.e., the SAB, is among the most productive
marine environments that host many hard or “live” bottom areas and is home to a large number of
phytoplankton, sponges, corals and many species of tropical and subtropical fishes (Marinelli et al.,
1998). From the Georgia bank seaward, the coastal, shelf, slope waters represent natural gradients
of many environmental parameters, including decreased nutrients and increased salinity. Our
results showed that the major bacterial taxa involved in putrescine and spermidine transformation
204
varied among different marine systems. Rhodobacteraceae (Alphaproteobacteria) was the taxon
most responsive to polyamine additions at the nearshore site. Gammaproteobacteria of the
Piscirickettsiaceae; Vibrionaceae; and Vibrionaceae and Pseudoalteromonadaceae, were the
dominant PA-responsive taxa in samples from the river-influenced nearshore station, offshore
station, and open ocean station，respectively.
To study the mechanisms that underlie the polyamine transformation by bacterioplankton,
an investigation of the gene contents of metagenomes and metatranscriptomes in
bacterioplankton that received no and additional supply of PA compounds (putrescine,
spermidine, or spermine) was performed in surface water collected from nearshore, offshore, and
open ocean sites in the Gulf of Mexico in May, 2013 using Illumina sequencing (Chapter 6). The
Gulf of Mexico refers to the ocean basin that is located among the northeast, north, and
northwest by the Gulf Coast of the United States, the southwest and south by Mexico, and the
southeast by Cuba. The water of the continental shelf on the northern Gulf of Mexico is subject
to the runoffs from Mississippi River and Atchafalaga River (Rabalais et al., 2002). Our results
showed that PA-responsive genes were mostly genes of γ-glutamylation and spermidine cleavage,
suggesting they are important PA degradation pathways in marine bacterioplankton community.
Identified PA-transforming taxa were affiliated with a diversity of marine bacteria, including
Actinobacteria, Bacteroidetes, Cyanobacteria, Planctomycetes, and Proteobacteria. Consistent
with the finding in the PA-responsive bacterioplankton study in the SAB (Chapter 5), the
bacterioplankton that involved in PA transformation varied spatially in seawater of the Gulf of
Mexico. Rhodobacteraceae (Alphaproteobacteria) was the dominant PA-transforming
bacterioplankton at the nearshore site, while bacterial families of Gammaproteobacteria became
important PA-transformers in offshore and open ocean sites.
205
Overall, our research on PA compounds and their transformation provides the first
empirical evidences on the bacterioplankton taxa and genes that involved in PA transformation
in offshore and open ocean marine systems. Both of our studies on PA transformation used
perturbation experiments based on amending microcosms with test substrates to identify
bacterioplankton taxa that responded to PA additions in seawater. In the future, in order to
investigate the in situ taxonomic and functional diversity of polyamine-metabolizing
bacterioplankton assemblages in diverse marine environments, studies should be performed on
the designing of functional gene primers which can target PA-transforming bacterial
communities.
206
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