Microbially Mediated Transformation of Dissolved Nitrogen in Aquatic Environments a Dissertation Submitted to Kent State Univers

Home , Altronate dehydratase, Coproporphyrinogen III, Hydrazine oxidoreductase, Lithotroph, Spermidine dehydrogenase

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

Xinxin Lu (Lucy)

May 2015

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

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

iii

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

Abstract……………………………………………………….…….…………..149

Introduction……………………………………………………….…..………...150

Methods…………………………………………………………....……………151

Results.……………………………………………….…………………………157

Discussion………………………………………………………………………163

Conclusion……………………………………………………………………...168

References……………………………………………………………………....169

VII. Summary…………………………………………………………...…………………...199

References…………………………………………………...………………….206

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

15 15 - 15 + Figure S2.2. The production of the N-labeled N2 during (a) NO3 incubation and (b) NH4

15 - 15 + incubation in st1 and (c) NO3 incubation and (d) NH4 incubation in st2 of the offshore

bottom water in the SAB in spring, 2011……………………………………………………49

15 15 - 14 + Figure S2.3. The production of the N-labeled N2 during NO3 + NH4 incubations in (a) st1

and (b) st2 in the offshore bottom water of the SAB in spring, 2011………………………..50

15 15 - 15 + Figure S2.4. The production of the N-labeled N2 during (a) NO3 incubation and (b) NH4

15 - 15 + 15 - incubation in st2, (c) NO3 incubation and (d) NH4 incubation in st3, and (e) NO3

15 + incubation and (f) NH4 incubation in st4 of the offshore bottom water in the SAB in fall,

2011…………………………………………………………………………………………..51

15 15 - 14 + Figure S2.5. The production of the N-labeled N2 during NO3 + NH4 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,

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

15 - 15 + Figure S3.2. The production of the 15N-labeled N2 after incubation with (a) NO3 , (b) NH4 ,

15 - 14 + 15 - 15 + 15 - 14 + and (c) NO3 + NH4 in SB, (d) NO3 , (e) NH4 , and (f) NO3 + NH4 in SS, and (g)

15 - 15 + 15 - 14 + NO3 , (h) NH4 , and (i) NO3 + NH4 in CB1 of bottom water in Lake Erie in August,

2010…………………………………………………………………………………………..76

15 15 - Figure S3.3. The production of the N-labeled N2 after incubation with (a) NO3 and (b)

15 + 15 - 15 + 15 - 15 + NH4 in SB, (c) NO3 and (d) NH4 in SS, (e) NO3 and (f) NH4 in CB1, and (g)

15 - 15 + NO3 and (h) NH4 in CB2 of bottom water in Lake Erie in August, 2011………….…...77

15 15 - Figure S3.4. The production of the N-labeled N2 after incubation with (a) NO3 and (b)

15 + 15 - 15 + 15 - 15 + NH4 in SB, (c) NO3 and (d) NH4 in SS, (e) NO3 and (f) NH4 in CB1, and (g)

15 - 15 + NO3 and (h) NH4 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

vii

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

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

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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

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

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

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

xii 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

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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.

xiv

Chapter 1

General Introduction

1 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

2 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

15 of anammox and denitrification to N2 production. Using 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

3 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 15N 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

4 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.

5 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-

6 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

7 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 15N 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 15N isotope compounds are performed in parallel, and anammox and denitrification are quantified separately based on their biochemical reaction differences.

15 - 15 15 Specifically, in anoxic incubations amended with NO3 , N N may be produced by only denitrification while 14N15N can be generated by both anammox and denitrification. In the anoxic

15 + 14 15 incubations amended with NH4 , only N N may be produced from anammox. Anammox and

8 15 denitrification N2 production rates can then be calculated from the linear regression of 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

9 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

10 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 15N 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. 15N 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

11 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

12 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

13 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.

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23 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 Water ~100% Kuypers et al., 2005 off Namibian Shelf 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

24 Figure 1.1

N2 N2

N2 fixation Nitrification

+ - - PON NH4 NO2 NO3

Oxic

Suboxic - - NO2 NO3

Remineralization napA, narG

Denitrification - NO2 DON nirK, nirS

NO DNRA

N2O nosZ

+ NH4 N2 Anammox

Figure 1.1. The simplified diagram of N cycle in oxic and suboxic aquatic ecosystems. Modified from Francis et al. (2007).

25 Figure 1.2

NH 2 Putrescine NH2

Cadaverine NH2 NH2

Norspermidine NH2 N NH2 H H NH N 2 Spermidine NH2 H N NH 2 Spermine NH2 N H

Figure 1.2. The chemical structure of individual polyamine compounds, including putrescine, cadaverine, norspermidine, spermidine, and spermine.

26 Figure 1.3

puuA speE ? γ-Glutamyl-putrescine Putrescine Spermidine Spermine ? spdH

puuB spuC spdH

γ-Glutamyl-γ- spdH aminobutyraldehyde 4-Aminobutyraldehyde

1, 3-Diaminopropane puuC kauB

spdH 3, Aminopropanaldhyde γ-Glutamyl-γ- 4-Aminobutyrate aminobutyrate puuD kauB gabT puuE β-Alanine

Succinate semialdehyde Molonic semialdehyde

Acetyl-CoA gabD gltA TCA cycle

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).

27 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.)

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 15N 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.

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

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

31 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 15N 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 15N 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

32 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

15 15 15 14 Na NO3, 2.5 µM of NH4Cl, or 5 µM of Na NO3 and 2.5 µM of 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

14 15 14 - molecules combine and generate N2. Thus, N N (one N atom from NO3 in the original

15 - 15 15 15 - water, one N atom from added NO3 ) and N N (both N atoms from added NO3 ) may be

15 - 15 15 14 15 both produced after NO3 incubation. The theoretical ratio of N N to N N that are

15 - produced by denitrification is equal to F/2(1-F), where F is the fraction of N in NO3 pool

- (Nielsen, 1992; Kuypers et al., 2006). Anammox produces N2 by combining one NO3 and one

+ 14 15 14 + NH4 , therefore, N N (one N atom from NH4 in the original water, one N atom from added

15 - 15 - 14 15 NO3 ) is generated after NO3 incubation. Accordingly, only N N may be produced from

15 + anammox process in the incubation of NH4 . Anammox and denitrification N2 production rates

15 were calculated from the linear regression of N-N2 concentrations as a function of time,

15 whereas the concentrations of N-N2 produced by anammox and denitrification were determined

15 - based on the NO3 incubation (Thamdrup and Dalsgaard, 2002). The calculation equations are

-2 D total = P30×F N ,

-1 -1 A total = FN [P29 + 2 × (1-FN ) × P30],

Where Dtotal represents the production of N2 by denitrification, Atotal represents the production of

15 - N2 by anammox, FN represents the fraction of N in NO3 , and P29 and P30 represent the

29 30 determined total mass of N2 and 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

34 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

5 5 abundance (7.1×10 to 9.3×10 /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

35 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 15N 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 15N isotope pairing technique, and both of them varied spatially and temporally. In spring, there were significant increases in 14N15N after incubation

15 - 15 + with NO3 (2.5 and 0.3 µM, respectively) or NH4 (0.8 and 0.9 µM, respectively) at st1 and

15 15 15 - st2 (t test, P < 0.05; Figure S2.2). No accumulation of N N after incubation with 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

14 + through anammox was not observed in any spring water samples that received both NH4 and

15 - NO3 (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).

14 15 15 15 15 - In fall, an increase of N N and N N after the incubation with 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

15 + 14 15 S2.4). Consistently, incubation with NH4 also resulted in a production of N N in water of

36 st3 in fall. No obvious accumulation of 14N15N or 15N15N was observed in any fall st2 and st4

15 - 15 + samples that received NO3 or NH4 . 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

14 15 - NH4Cl and NO3 (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

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 15N 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

38 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 15N 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.

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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.

- + 5 site S (PSU) T (°C) DOC (mg C/L) DN (mg N/L) NOx (mg N/L) NH4 (µM) Cell (×10 /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)

Figure 2.1

31.5

st1 (445 m)

31.0 Georgia st4 (503 m) Latitude

Florida 30.5

st3 (402 m)

st2 (501 m) 30.5

-81.5 -80.5 -79.5

Longitude

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 Figure 2.2

1.0

st2-sp NO - DOC x 0.5

st2-fa DN NH + Cell 0.0 4 S O2 T

PC2 (31.6%) st1-sp

st4-fa st3-fa -0.5

-1.0

-1.0 -0.5 0.0 0.5 1.0 PC1 (53.1%)

Figure 2.2. Principle component analysis (PCA) biplot of environmental variables in bottom water of 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).

46 Figure 2.3

650 (a) spring Denitrification Anammox 600

550

200 150 100 production rate (nM/d) 2 N 50 0 st1 st2

(b) fall

20 production rate (nM/d) 2 N

0 st2 st3 st4

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 sites in(a)springand(b)fall,2011. Figure S2.1. The depthprofilesofoxygensaturation (%)inthewatercolumnatoffshore SAB Depth (m) 600 500 400 300 200 100 0 0 (a) Oxygen saturation(%) 50 100 st2-sp st1-sp 150 48 Depth (m) 600 500 400 300 200 100 0 0 (b) Oxygen saturation(%) 50 100 st4-fa st3-fa st2-fa 150 Figure S2.1 Figure S2.2

15 - 15 + (a) st1 NO3 incubation (b) st1 NH4 incubation 29 N 2 1.0 2.5 30 N2 2.0 0.8

1.5 0.6 production ( µ M) production ( µ M) 2 2

N 1.0 N 0.4 30 30 and and 2 0.5 2 0.2 N N 29 29 0.0 0.0 0 2 0 2

0.3 0.9

production ( µ M) 0.2 production ( µ M) 0.6

2 2 N N 30 30 0.3 and 0.1 and 2 2 N N 29 29

0.0 0.0 0 2 0 2 Time (d)

15 15 - 15 + Figure S2.2. The production of the N-labeled N2 during (a) NO3 incubation and (b) NH4 15 - 15 + incubation in st1 and (c) NO3 incubation and (d) NH4 incubation in st2 of the offshore bottom water in the SAB in spring, 2011

49 Figure S2.3. The productionofthe and (b)st2intheoffshore bottomwateroftheSABinspring,2011.

29 30 29 30 N2 and N2 production (µM) N2 and N2 production (µM) 0.0 0.1 0.2 0.5 0.6 0.7 0.8 0.0 0.1 0.2 0.3 0 (b) 0 (a)

30 29 N N 2 2 st2 st1 15 15 NO NO Time (d) 3 3 15 - - + + N-labeled N 14 14 NH NH 4 4 + + incubation incubation 50 2 during 15 NO 2 2 3 - + 14 NH 4 + incubationsin(a)st1 Figure S2.3 Figure S2.4

(a) st2 15NO - incubation (b) st2 15NH + incubation 0.6 3 0.6 4 0.5 0.5 0.4 0.4 0.3 0.3 production ( µ M) production ( µ M) 2 2 N N 0.008 0.008 30 30 and and 0.004 0.004 2 2 N N 29 29 0.000 0.000 0 2 0 2

15 - 15 + (c) st3 NO3 incubation (d) st3 NH4 incubation 0.6 0.6 0.5 0.4 0.4

production ( µ M) 0.3 production ( µ M) 2 2 N N 30

0.008 30 0.008 and and 2 0.004 2 0.004 N N 29 29 0.000 0.000 0 2 0 2

(e) st4 15NO - incubation (f) st4 15NH + incubation 0.6 3 0.6 4 0.5 0.5 0.4 0.4 0.3 0.3 production ( µ M) production ( µ M) 2 2 N N 30 30 0.001 0.001 and and 2 2 N N 29 29 0.000 0.000 0 2 0 2 Time (d)

15 15 - 15 + Figure S2.4. The production of the N-labeled N2 during (a) NO3 incubation and (b) NH4 15 - 15 + 15 - incubation in st2, (c) NO3 incubation and (d) NH4 incubation in st3, and (e) NO3 15 + incubation and (f) NH4 incubation in st4 of the offshore bottom water in the SAB in fall, 2011.

51 Figure S2.5

15 - 14 + (a) st2 NO3 + NH4 incubation 0.6 29 N2 0.5 30 N2 0.4 0.3 production ( µ M) 2

N 0.06 30 0.04 and 2 0.02 N 29 0.00

0 2 (b) st3 15NO - + 14NH + incubation 0.7 3 4 0.6 0.5 0.4 production ( µ M)

2 0.1 N 30 0.05 and 2 N

29 0.00

0 2 (c) st4 15NO - + 14NH + incubation 0.50 3 4 0.45 0.40 0.35 production ( µ M) 2

N 0.10 30

and 0.05 2 N 29 0.00

0 2 Time (d)

15 15 - 14 + Figure S2.5. The production of the N-labeled N2 during NO3 + NH4 incubations in (a) st2, (b) st3, and (c) st4 in the offshore bottom water of the SAB in fall, 2011.

52 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.)

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,

15 and 2012. Results generated from 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.

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

55 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

56 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 15N 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 15N 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

57 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 15N incubations and analysis

The anammox and denitrification potentials were determined based on the 15N 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

(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

59 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 14N15N was produced in bottom water of SB after incubation

15 - 15 15 with NO3 , with the concentration of N N remained constant at a low level during the

15 14 15 incubation (Figure S3.2a). Based on isotope paring, in anaerobic incubation of NO3, N N can be produced by both anammox and denitrification, whereas 15N15N 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 14N15N (from 0.4 to 0.7 µM) was produced during the first 2 days of

15 + incubation with NH4 , which again suggests the importance of anammox over denitrification in

15 - N removal in SB (Figure S3.2b). Similarly, in CB1 of 2010, incubation with NO3 resulted in a

14 15 15 15 15 + production of both N N and N N (Figure 3.2d), and incubation with NH4 resulted in an accumulation of 14N15N (Figure S3.2e). This suggests that anammox and denitrification might

15 - potentially occur in bottom water of CB1. In contrast, in SS of 2010, incubation with NO3 resulted in a production of both 14N15N and 15N15N (Figure S3.2g), but no 14N15N was produced

15 + after incubation with NH4 (Figure S3.2h). This indicates that denitrification might be more

14 important than anammox in SS of Lake Erie in 2010. In the incubations with unlabeled NH4Cl

15 - 14 15 and NO3 , the production of N N 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,

15 - 14 15 15 15 and CB2 (Figure S3.3). After incubations with NO3 , N N and N N were produced in bottom water of SS, CB1, and CB2. In contrast, only 15N15N was produced in bottom water of

15 - 14 15 SB after the incubation with NO3 . Consistently, N N was produced after incubations with

15 + NH4 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 14N15N through

14 15 - anammox was observed after the incubation with NH4Cl and NO3 in any of the 2011 Lake

Erie samples (Data not shown).

15 - 14 15 15 15 In 2012, incubations with NO3 resulted in an accumulation of both N N and N N in bottom water of SS and CB1, but produced only 15N15N in SB and CB2 (Figure S3.4).

15 + 14 15 Consistently, after the incubation with NH4 , there were only significant increases of N N 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 14N15N through anammox was

14 15 - determined after the incubations with NH4Cl and 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).

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,

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

64 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 15N 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.

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Wenk, C.B., Blees, J., Zopfi, J., Veronesi, M., Bourbonnais, A., and Schubert, C.J. et al. (2013)

Anaerobic ammonium oxidation (anammox) bacteria and sulfide-dependent denitrifiers

coexist in the water column of a meromictic south-alpine lake. Limnol Oceanogr 58: 1–12.

Wu, Y., Xiang, Y., Wang, J., and Wu, Q.L. (2012) Molecular detection of novel Anammox

bacterial clusters in the sediments of the shallow freshwater Lake Taihu. Geomicrobiol J 29:

852–859.

Yoshinaga, I., Amano, T., Yamagishi, T., Okada, K., Ueda, S., Sako, Y., and Suwa, Y. (2011)

Distribution and diversity of anaerobic ammonium oxidation (anammox) bacteria in the

sediment of a eutrophic freshwater lake, Lake Kitaura, Japan. Microbes Environ 26: 189–

197.

Zheng, D., Alm, E.W., Stahl, D.A., and Raskin, L. (1996) Characterization of universal small-

subunit rRNA hybridization probes for quantitative molecular microbial ecology studies.

Appl Environ Microbiol 62: 4504–4513.

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 Reference temperature 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 AAAACCCCTCTACTTAGTGCCC 62 °C Schmid et al., Kuenenia 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

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 Oxygen DOC (mg DN (mg NOx (mg NH4 (°C) a saturation (%) C/L) N/L) N/L) (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)

Figure 3.1

42.0

41.8 Latitude

41.6 CB1 (19 m)

CB2 (17 m)

Sandusky Bay SB (5 m) SS (13 m) 41.4

-83.4 -83.0 -82.6 -82.2 Longitude

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.

73 Figure 3.2

(a) 2010 Denitrification 400 Anammox 300

200

100 production rate (nM/d) 2

N 0 SB SS CB1

(b) 2011 200

150

100

50 production rate (nM/d) 2

N 0 SB SS CB1 CB2

50 production rate (nM/d) 2

N 0 SB SS CB1 CB2

74 Figure S3.1

1.0

DN SS_11 0.5 DOC - CB2_12 NOx + SB_11 NH4 SB_10 0.0 SS_10 CB1_11CB1_12 CB2_11 PC2 (24.9%)

CB1_11 SS_12 -0.5 SB_12

-1.0

-1.0 -0.5 0.0 0.5 1.0 PC1 (69.7%)

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 and (c) Figure S3.2. The productionofthe (g) 29 30 29 30 29 30 N2 and N2 production (µM) N2 and N2 production (µM) N2 and N2 production (µM) 0.00 0.02 0.04 0.06 0.00 0.02 0.04 0.06 0.00 0.02 0.04 15 0.3 0.4 0.5 0.6 0.4 0.6 0.8 0.2 0.4 0.6 0.8 NO 0 0 0 15 (a) (b) 3 (c) NO - , (h) SB SB SB 2 2 3 2 - + 15 15 15 15 NO NH NO NH 14 3 3 4 - - + NH + incubation 4 4 4 incubation 4 14 + NH , and(i) 4 +

4 inSB,(d) + 30 29 incubation 6 6 6 N N 2 2 15 NO

29 30 29 30 29 30 N2 and N2 production (µM) N2 and N2 production (µM) N2 and N2 production (µM) 15 3 0.00 0.02 0.04 0.06 15 - NO 0.2 0.3 0.4 0.5 0.6 0.0 0.2 0.4 0.6 0.8 1.0 0.3 0.4 0.5 0.6 0.0 0.1 + N-labeled N 14 0 0 0 3 - NH , (e) (d) (f) (e) 76 4 + Time (d) 2 15 SS SS inCB1ofbottomwaterLakeErie August, 2010. 2 2 SS NH 15 15 15 NH NO NO 2 afterincubationwith(a) 4 4 3 3 4 4 4 + - + - + incubation incubation , and(f) 14 NH 4 + 6 6 6 incubation 15 NO

29 30 29 30 29 30

3 N2 and N2 production (µM) N2 and N2 production (µM) N2 and N2 production (µM) 0.00 0.02 0.04 0.06 - + 0.3 0.4 0.5 0.6 0.0 0.1 0.2 0.5 1.0 1.5 2.0 0.0 0.1 0.2 1.0 2.0 3.0 14 0 0 0 NH (i) (h) (g) 4 + 15 inSS,and 2 2 2 CB1 CB1 CB1 NO 15 15 15 3 NH NO NO - , (b) 4 4 4 4 3 3 + - - + incubation incubation 14 NH 15 6 NH 6 6 Figure S3.2 4 + incubation 4 + , Figure S3.3

15 - 15 + (a) SB NO3 incubation (b) SB NH4 incubation 2.5 29 2.5 N2 30 2.0 N2 2.0 1.5 1.5 production ( µ M) production ( µ M)

2 0.4 2 0.4 N N 30 0.2 30 0.2

and 0.0 and 0.0 2 2 N N 29 0 2 29 0 2 (c) SS 15NO - incubation (d) SS 15NH + incubation 2.0 3 2.0 4

1.5 1.5 1.0 production ( µ M) production ( µ M) 2 2

N 0.005 N 0.2 30 30 0.1

and and 0.0 2 0.000 2 N N

29 0 2 29 0 2

(e) CB1 15NO - incubation (f) CB1 15NH + incubation 0.8 3 0.6 4 0.6 0.4 0.4 0.2

production ( µ M) production ( µ M) 0.2 2 2 N N

30 0.01 30 0.0 0.00 and and 2 2 N N

29 0 2 29 0 2 (g) CB2 15NO - incubation (h) CB2 15NH + incubation 0.6 3 0.6 4 0.4 0.4 0.2 production ( µ M) production ( µ M)

2 2 0.2 N N

30 0.005 30 0.000 0.0 and and 2 2 N N 29 0 2 29 0 2

Time (d)

15 15 - Figure S3.3. The production of the N-labeled N2 after incubation with (a) NO3 and 15 + 15 - 15 + 15 - 15 + (b) NH4 in SB, (c) NO3 and (d) NH4 in SS, (e) NO3 and (f) NH4 in CB1, 15 - 15 + and (g) NO3 and (h) NH4 in CB2 of bottom water in Lake Erie in August, 2011.

77 Figure S3.4

15 - 15 + (a) SB NO3 incubation 29 (b) SB NH4 incubation 1.2 N2 1.4 1.1 30 N2 1.2 1.0 0.9 1.0 production ( µ M) production ( µ M) 2 2

N 0.10 N 0.10 30 0.05 30 0.05

and 0.00 and 0.00 2 2 N N 29 0 2 29 0 2

2 0.15 0.10 2 N N 0.10 30 0.05 30 0.05 0.00 0.00 and and 2 2 N N 29 0 2 29 0 2

(e) CB1 15NO - incubation (f) CB1 15NH + incubation 5.0 3 2.5 4 4.0 2.0 3.0 1.5 2.0 1.0 0.5 production ( µ M) production ( µ M)

2 0.10 2 0.10 N N

30 0.05 30 0.05 0.00 0.00 and and 2 2 N N

29 0 2 29 0 2

(g) CB2 15NO - incubation (h) CB2 15NH + incubation 2.0 3 1.5 4

1.5 1.0

production ( µ M) production ( µ M) 0.5 2 2

N 0.10 N 0.10 30 0.05 30 0.05

and 0.00 and 0.00 2 2 N N 29 0 2 29 0 2 Time (d)

15 15 - Figure S3.4. The production of the N-labeled N2 after incubation with (a) NO3 and 15 + 15 - 15 + 15 - 15 + (b) NH4 in SB, (c) NO3 and (d) NH4 in SS, (e) NO3 and (f) NH4 in CB1, 15 - 15 + and (g) NO3 and (h) NH4 in CB2 of bottom water in Lake Erie in August, 2012.

78 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.

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.

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;

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

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

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.

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, Recovery  Cf Cu 100 Ca , where Cf and Cu 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

85 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

86 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).

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

88 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

89 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).

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

91 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

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

93 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

94 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.

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101

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 Eluents (min) 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

102

Table 4.2. Parameters for validation of HPLC method.

Compound Linearity R2 a LOD/LOQ Recovery/RSD (n = 5; %) (nM) (nM) 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: R2, 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.

103

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.

104

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.

a - + # Variables PAR 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.

105

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

106

Figure 4.1

Temperature (ºC) 20.0 21.0 22.0 23.0 24.0 25.5 26.0 26.5 27.0 0 0 SW SW

(a) (b) 3 3 MW

6 6

9 9 Depth (m)

12 12

Temperature 15 Salinity 15 BW BW

18 18 33.0 34.0 35.0 35.0 35.5 36.0 36.5 37.0 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 (a) AABA* DAH*

40 Tau Cad 30 Phe Gly Lys 20 GABA

Gln Leu Norspd Thr NH +

Tyr 4 Asp Glu Arg Ile Put Spd His Fluorescence intensity (mv) 10 Met Orn Asn Spm Ser Ala Val 0 5 15 25 35 45 55

+ (b) Tau NH4 60 Gly DAH*

50 Gln AABA* 40

20 GABA Lys Glu Phe Put 10 Asp Leu Orn Fluorescence intensity (mv) Thr Arg Met Spd Tyr Ala Ile

Asn Ser 0 5 15 25 35 45 55 Retention time (min)

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 Figure 4.3

Surface 100 (a) 430 (e) Mid-depth 80 Bottom 420 410 60 400

300 40 200 20 100 Total DFAAs (nM) DFAAs Total Total DFAAs (nM) DFAAs Total 0 0 12h 15h 18h 21h 24h 03h 06h 09h 21h 24h 03h 06h 09h 12h 15h 18h

12 (b) 200 (f) 150 9 100

6 50 4 3 Total PAs (nM) PAs Total Total PAs (nM) PAs Total 2 0 0 12h 15h 18h 21h 24h 03h 06h 09h 21h 24h 03h 06h 09h 12h 15h 18h

0.25 0.8 0.8 0.6 (c) Surface (g) 0.7 0.2 Mid-depth 0.6 Bottom 0.6 0.4 0.15 0.5 0.4 0.03 0.1 0.02 0.2 0.2 0.05 0.01 0.0 0.0

0.0 0.00 (%, lines) PAs/DON Total Total PAs/DFAAs (bars) PAs/DFAAs Total Total PAs/DON (%, lines) PAs/DON Total Total PAs/DFAAs (bars) PAs/DFAAs Total 12h 15h 18h 21h 24h 03h 06h 09h 21h 24h 03h 06h 09h 12h 15h 18h

36 (d) 36.6 (h) 35 36.4 34 36.2 33 36.0

32 Salinity (PSU) Salinity (PSU) 31 35.8 12h 15h 18h 21h 24h 03h 06h 09h 21h 24h 03h 06h 09h 12h 15h 18h HT LT HT LT LT HT LT HT

Day Night Day Night Day Time

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 Figure 4.4

Stress: 0.09 Fall Spring Fs06 Sm21 Sb21 Fb18 Ss21

Fb24 Sb12 Sm03 Fs18 Fs24 Fb03 Fb21 Sm18 Sm06 Sm24 Ss12 Fs03 Fb06 Fs21 Sm12 Sb03 Sm15 Ss03 Sb24 Fb09 Sb18 Ss24 Fs12 Fb15 Ss18 Sb06 Ss15 FS09 Sm09 Sb15 Fb12 Ss09 Ss06 Sb09 Fs15

Blue: Day Surface Unmarked: Bottom Red: Night Mid-depth

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 Figure 4.5

25 Gly (a) (d) 20 Tau 6 Lys Put 15 Glu Spd Asn 4 Spm 10 His 2 5 0

Concentration (nM) Concentration (nM) 0 12h 15h 18h 21h 24h 03h 06h 09h 12h 15h 18h 21h 24h 03h 06h 09h 40 (b) 7.5 (e) 30 5 20 10 2.5 0 0 Concentration (nM) Concentration (nM) 12h 15h 18h 21h 24h 03h 06h 09h 12h 15h 18h 21h 24h 03h 06h 09h 25 (c) (f) 20 6 15 4 10 2 5

Concentration (nM) 0 Concentration (nM) 0 12h 15h 18h 21h 24h 03h 06h 09h 12h 15h 18h 21h 24h 03h 06h 09h HT LT HT LT HT LT HT LT

Day Night Day Day Night Day Time

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 Figure 4.6

90 (a) Gly (c) 80 Gln 60 Put Ala 40 Cad Asp Norspd 60 Glu 20 Spd Tau Spm

30 2 1

Concentration (nM) 0 Concentration (nM) 0 21h 24h 06h 09h 12h 15h 18h 21h 24h 03h 06h 09h 12h 15h 18h

(b) 12 (d) 100 10 8 50 4

2 Concentration (nM) 0 Concentration (nM) 0 21h 24h 03h 06h 09h 12h 15h 18h 21h 24h 03h 06h 09h 12h 15h 18h

LT HT LT HT LT HT LT HT

Night Day Night Day Time

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 Figure S4.1

Stress: 0.13 Spring Sb09 Fs15 Fall Sm09 Sb06 Ss15 Ss09 Ss06 Sb15 Ss18 Fs09 Sb18 Ss24 Fb18 Fb12 Ss03 Fb09Fb15 Sb12 Sb03 Fs12 Fs21 Sm06 Sm15 Sm03 Fb06 Sm24 Sb24 Fs03 Fs18 Fb03Fb21 Sm18 Ss12

Fs24 Sm12

Fb24 Ss21

Sm21 Fs06 Sb21 Blue: Day Surface Unmarked: Bottom Red: Night Mid-depth

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 Figure S4.2

Stress: 0.11 Fs18 Fb18

Sb21 Sm21 Ss21 Fb12 Sb18 Sb12 Fs21 Ss06 Fb06 Fs09 Sm18 Fs03 Ss09 Fb21Fb24 Ss15 Fs06Fs24 Sb03Sb06 Sm06 Sm09 Sm03 Fb09 Sm24Sm15Sb09 Fb03 Sb24 Sb15 Ss12 Ss24 Sm12 Ss03 Ss18 Fs15 Fb15

Blue: Day Surface Fs12 Red: Night Mid-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 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.)

115

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.

116

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

117 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

118 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

119 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

120 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

121 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

122

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

123 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 1.8×106/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

124 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).

125

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

126

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).

127

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

128 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.

129

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.

130

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135

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

136

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 S DOC (mg DN (mg NOx (µg NH4 SRP (µg Put Spd Chl a Cell (°C) (PSU) C/L) N/L) N/L) (uM) P/L) (nM) (nM) (µg/L) (×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.

137

Table S5.2. General statistics of 16S rRNA gene pyrotag sequence libraries of incubated microcosms. Shannon index statistics were calculated at the family level.

Shannon index # of unique taxa Site Treatment # of Reads H OTU Family Order Class Phylum 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 st1 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 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 st2 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 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 st3 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 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 st4 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

138

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

139

Figure 5.1

31.5

st1 (17 m) Georgia

31.0

Latitude St.Marys River

st2 (10 m) 30.5 Florida

st3 (33 m) st4 (500 m) 30.0

-81.5 -80.5 -79.5

Longitude

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.

140 Figure 5.2

st3 0.5

st1 DN Spd

Cell T 0.0 S NO - st4 PC2 (25.0%) DOC SRP x Put

+ -0.5 NH4

Chl a st2 -1.0

-1.0 -0.5 0.0 0.5 1.0

PC1 (62.5%)

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 Figure 5.3

30 (a) 35 (c) CTR 30 * 25 PUT * SPD 25 * 20 20 15 15 10 10 Percentage (%) of sequences 5 Percentage (%) of sequences 5

0 0

SAR11 clade Pseudoaltero-Vibrionaceae SAR11 clade Pseudoaltero-Vibrionaceae SAR116SAR324 clade clade monadaceae SAR406 clade SAR116 clade monadaceae Cryomorphaceae RhodobacteraceaeRhodospirillaceae Alteromonadaceae Cytophagia Family RhodobacteraceaeRhodospirillaceae Alteromonadaceae Incertae Sedis OCS155 marine group OCS155 marine group Family I Cyanobacteria Family I Cyanobacteria Bacteroidetes Alpha- Delta- Gamma- Bacteroidetes Deferribacteres Alpha- Gamma- Actinobacteria Cyanobacteria Proteobacteria Actinobacteria Cyanobacteria Proteobacteria

40 75 (b) (d) * 70 * 35

65 30 * 60 25 20 30 15 20 * 10 Percentage (%) of sequences Percentage (%) of sequences 10 5 0 0

SAR11 clade Pseudoaltero-Vibrionaceae SAR11 cladeColwellaceaePseudoaltero-Vibrionaceae monadaceae SAR406 clade monadaceae Cryomorphaceae RhodobacteraceaeRhodospirillaceaeMethylophilaceaeAlteromonadaceaePiscirickettsiaceae KordiimonadaceaeRhodobacteraceaeRhodospirillaceaeAlteromonadaceae OCS155 marine group OCS155 marine group Family I Cyanobacteria Family I Cyanobacteria Bacteroidetes Alpha- Beta- Gamma- Cyanobacteria Alpha- Gamma- Actinobacteria Cyanobacteria Proteobacteria Actinobacteria Deferribacteres Proteobacteria

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 SPD treatments relative to CTR libraries (t test, P < 0.05).

142 Figure 5.4

(a) st1 (c) st3 2.5 2.5 Cell 2.0 400.0 2.0 400.0 1.5 1.5 /mL) /mL) 1.0 6 300.0 1.0 6 300.0 putrescine 200.0 spermidine 8.0 200.0 6.0 8.0 Concentration (nM) Cell abundance ( × 10 Concentration (nM) 6.0 4.0 Cell abundance ( × 10 4.0 2.0 2.0 0.0 0.0 0.0 0.0 0 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 CTR PUT SPD (b) st2 (d) st4 2.5 500.0 2.5 2.0 2.0 400.0 1.5 400.0 1.5 /mL) /mL) 1.0 6 1.0 6 300.0

300.0 200.0 8.0

200.0 6.0 Concentration (nM) Cell abundance ( × 10 8.0 Cell abundance ( × 10 Concentration (nM) 4.0 6.0 4.0 2.0 2.0 0.0 0.0 0.0 0.0 0 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 CTR PUT SPD

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.

143 Figure 5.5

Stress: 0.12 ORI CTR PUT SPD

nearshore (st1)

river-influenced nearshore (st2)

offshore (st3) open ocean (st4)

Figure 5.5. The non-metric multidimensional scaling (NMDS) ordination of samples from the ORIs 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 in libraries 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 from the same station (black, st1; red, st2; blue, st3; green, st4).

144 Figure S5.1

st1 st2 40 st3 st4

Percentage (%) of sequences 10

SAR11 clade Vibrionaceae SAR406 clade SAR116 cladeSAR324 clade Cryomorphaceae RhodobacteraceaePhodospirillaceae Salinisphaeraceae Delta- OCS155 marine groupFamily I Cyanobacteria Pseudoalteromonadaceae MBC11C04 marine group Bacteroidetes Deferribacteres Verrucomicrobia Actinobacteria Cyanobacteria Alpha- Gamma- Proteobacteria

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

ORI 60 (a) C TR1 (c) 80 C TR2 P UT1 s PUT2 s e e i i l l i S PD1 i m 60 m 40 a S PD2 a f f l l a a i i r r e e t t c 40 c b a b a o f o f r r 20 b e b e

u m 20 u m N N

0 0 0 5000 10000 21000 22000 0 2000 4000 8000 10000 12000

60 (b) (d) 60 s s e e i i l l i i m m a a 40 f f l l a a i 40 i r r e e t t c c b a b a o f o f r r 20 b e 20 b e u m u m N N

0 0 0 1000 2000 3000 6000 7000 8000 0 5000 10000 15000

Library size

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

stress: 0.0

st4 st3

st2

st1

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 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.)

148

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.

149

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

150 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

151

°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).

152

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

153 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.

154

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

155

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

156 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

158 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

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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.

161

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).

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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

163

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

164 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

165 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

166 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

167 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.

168

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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

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Table 6.2. Statistics of experimental metagenomics and metatranscriptomics.

No. of total Ave. read No. (%) of rRNA No. of functional Number (%) of functional genes categorized * Sample Treatment Reads length (bp) genes genes 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.

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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.

NS OS OO COG COG description 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 4.2 175

0022 Pyruvate/2-oxoglutarate dehydrogenase complex 2.0 0538 Isocitrate dehydrogenases 1.6 0654 2-polyprenyl-6-methoxyphenol hydroxylase and related FAD-dependent 260/0 oxidoreductases 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 1.8 amidotransferase domain 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.

176

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.

NS OS OO COG COG description 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 3.2 3.8 4.0 2.9 system, permease 0626 Cystathionine beta-lyases/cystathionine gamma- 1.5 2.1 2.0 2.4 2.5 2.1 synthases 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 2.3 2.4 systems, periplasmic 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, 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%) permease * 1506 Dipeptidyl aminopeptidases/acylaminoacyl-peptidases 2.2 2.4 177

1932 Phosphoserine aminotransferase 1.5 2.5 3705 ATP phosphoribosyltransferase 1.9 10.1 3842 ABC-type spermidine/putrescine transport systems, 2.0(0.13%) 1.9(0.12%) 1.9(0.12%) ATPase* 4166 ABC-type oligopeptide transport system, periplasmic 2.3 4176 ABC-type proline/glycine betaine transport system, 2.1 1.8 permease 4177 ABC-type branched-chain amino acid transport 4.2 5.3 3.3 3.7 system, permease 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 3.5 3.3 dikinase 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 2.2 178

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 2.9 2.2 5.3 5.1 27.4 decarboxylase 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 2.6 4.4 2.5 mechanism/carboxysome shell protein 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

179

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 NP_415816.1 semialdehyde dehydrogenase 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

180

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

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.

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 2.3 182

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 1.4 1.5 1.5 1.5 2.2 synthetase and related enzymes 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) 2.0 component, eukaryotic type, beta subunit 0538 Isocitrate dehydrogenases 1.1 1.6 0654 2-polyprenyl-6-methoxyphenol hydroxylase and related FAD-dependent #DIV/0! oxidoreductases 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) 1.4 2.3 component, eukaryotic type, alpha subunit 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 2.4 183

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 1.4 1.4 1.8 amidotransferase domain 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

184

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 2.1 1.5 1.6 1.8 2.4 1.3 (0.13%) (0.17%) (0.12%) (0.06%) (0.07%) (0.10%) (0.05%) 0687 Spermidine/putrescine-binding periplasmic protein* 2.2 2.1 1.1 (0.49%) (0.48%) (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 2.2 1.5 1.3 (0.03%) (0.05%) (0.03%) (0.05%) 1177 ABC-type spermidine/putrescine transport system, permease component II* 2.5 3.0 2.3 1.6 2.7 2.4 2.7 185

(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 1.9 1.9 (0.13%) (0.12%) (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 3.2 6.6 synthetase 190 5,10-methylene-tetrahydrofolate dehydrogenase/Methenyl tetrahydrofolate 2.1 2.2 cyclohydrolase 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 1.6 186

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) 2.0 4.2 1.2 component 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 2.9 187

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 3702/0 (0.89%) (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 0.0 188

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 2.8 4.4 4.3 components 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 3.6 7.2 1.4 domain 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 2.6 4.8 component 1127 ABC-type transport system involved in resistance to organic solvents, ATPase 4.0 9.4 component 1228 Imidazolonepropionase and related amidohydrolases 3.4 3.0 0.0 4663 TRAP-type mannitol/chloroaromatic compound transport system, periplasmic 2.2 1.9 1.1 component 4664 TRAP-type mannitol/chloroaromatic compound transport system, large permease 3.0 3.3 4.4 0.0 2.1

189

Figure 6.1

31.0

30.0 Louisiana

29.0

Latitude NS (28 m)

OS (73 m)

28.0

OO (714 m)

27.0

-93.0 -91.0 -89.0 -87.0 Longitude

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 Figure 6.2

(a) Metagenomes Stress: 0.05

OO_CTR

OS_SPM NS_SPM OO_SPM OS_CTR NS_CTR OO_SPD NS_PUT

OS_PUT NS_SPD

OS_SPD

nearshore offshore open ocean

Stress: 0.06 (b) Metatranscriptomes OO_CTR OS_SPM OS_PUT OO_SPM OS_SPD

NS_CTR NS_PUT OS_CTR NS_SPD NS_SPM OO_PUT

OO_SPD

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 Figure 6.3

(a) NS Metagenomes (d) NS Metatranscriptomes 25 25 PUT SPD 20 20 15 15 SPM 10 10 5 5 0 0 Relative abundance (%) Relative abundance (%)

Bacillaceae Clostridiaceae Rhizobiaceae Pseudoaltero- Brucellaceae SAR11 clade Pseudoaltero-Vibrionaceae Rhodocyclaceae monadaceae Burkholderiaceae Halomonadaceaemonadaceae MicrobacteriaceaeStreptomycetaceaeFlavobacteriaceaeCaulobacteraceaeBradyrhizobiaceaeRhodobacteraceaeComamonadaceaeAlcanivoracaceaeHalomonadaceae FlavobacteriaceaeBradyrhizobiaceaePhyllobacteriaceaeRhodobacteraceaeComamonadaceaeAlteromonadaceaeEnterobacteriaceae Pseudonocardiaceae Propionibacteriaceae Pseudomonadaceae Actinobacteria Firmicutes Alpha- Beta- Gamma- Bacteroidetes Alpha- Beta- Gamma- Bacteroidetes Proteobacteria Actinobacteria Proteobacteria

(b) OS (e) OS 16 16 12 12 8 8 4 4 0 0 Relative abundance (%) Relative abundance (%)

Rhizobiaceae Pseudoaltero- Pasteurellaceae Shewanellaceae Rhizobiaceae Pseudoaltero-Vibrionaceae AlcanivoracaceaeHalomonadaceaemonadaceae BurkholderiaceaeAeromonadaceaePasteurellaceaemonadaceae FlavobacteriaceaePlanctomycetaceaeBradyrhizobiaceaeRhodobacteraceaeComamonadaceaeAlteromonadaceae Pseudomonodaceae BradyrhizobiaceaeCaulobacteraceaeHyphomonadaceaeRhodobacteraceaeComamonadaceaeEnterobacteriaceae Other ChroococcalesProchlorococcaceae PropionibacteriaceaeOther ChroococcalesOther Oscillatoriales Cyanobacteria Alpha- Beta- Gamma- Cyanobacteria Alpha- Beta- Gamma- Bacteroidetes Planctomycetaceae Proteobacteria Actinobacteria Proteobacteria

Hahellaceae Pseudoaltero-Vibrionaceae Nostocaceae Pseudoaltero- Vibrionaceae IdiomarinaceaePasteurellaceaemonadaceaeShewanellaceae monadaceaeShewanellaceae MycobacteriaceaeStreptomycetaceaeFlavobacteriaceaeRhodobacteraceaeAlcanivoracaceaeAlteromonadaceaeHalomonadaceae CaulobacteraceaeRhodobacteraceaeBurkholderiaceaeAlcanivoracaceaeHalomonadaceae Other ProchlorococcaceaeChroococcales Other ChroococcalesProchlorococcaceaeOther OscillatorialesBradyrhizobiaceae AlteromonadaceaeEnterobacteriaceae Bacteroidetes Alpha- Gamma- Alpha- Beta- Gamma- Actinobacteria Cyanobacteria Proteobacteria Cyanobacteria Proteobacteria

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

Metagenomes Metatranscriptomes (a) NS 5 (d) NS PUT SPD 2 4 SPM 3

1 2

1 Odds ratio (OR) metagenomes PA/CTR Odds ratio (OR) metatranscriptoms PA/CTR 0 0 transporter γ-glutamylation transamination spermidine cleavage transporter γ-glutamylation transamination spermidine cleavage

6 (b) OS 4 (e) OS

5 3

2 2

1 1 Odds ratio (OR) metagenomes PA/CTR Odds ratio (OR) metatranscriptoms PA/CTR

0 0 transporter γ-glutamylation transamination spermidine cleavage transporter γ-glutamylation transamination spermidine cleavage

6 (c) OO 20 (f) OO

5 15 4 10 4 3 3 2 2 1 1 Odds ratio (OR) metatranscriptoms PA/CTR Odds ratio (OR) metagenomes PA/CTR 0 0 transporter γ-glutamylation transamination spermidine cleavage transporter γ-glutamylation transamination spermidine cleavage

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 CRTs, in the Gulf of Mexico.

193 Figure 6.5

(a) NS CTR spermidine cleavage 30 PUT transamination SPD γ-glutamylation 25 SPM transporter

Percentage (%) of diagnostic genes 0

Rhizobiaceae SAR11 clade OMG group Micrococcaceae Microbacteriaceae Flavobacteriaceae Rhodobacteraceae MethylophilaceaeAlteromonadaceae Micromonosporaceae Actinobacteria Bacteroidetes Alpha- Beta- Gamma- Proteobacteria 30 (b) OS

Percentage (%) of diagnostic genes 0

SAR11 clade OMG group Shewanellaceae Rhodobacteraceae BurkholderiaceaeComamonadaceaeAlteromonadaceae Other ChroococcalesPlanctomycetaceae Enterobacteriaceae Plantomycetes Alpha- Beta- Gamma- Cyanobacteria Proteobacteria 70 (c) OO

30 25 20 15 10 5 Percentage (%) of diagnostic genes 0

Rhizobiaceae SAR11 clade Colwelliaceae HahellaceaeOMG groupPseudoaltero- Shewanellaceae Rhodobacteraceae Alteromonadaceae Enterobacteriaceae monadaceae 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.

194 Actinobacteria of Mexicobytaxonomic assignment. PUT, SPD,andSPMmetatranscriptomesof (a)NS,(b)OS,and(c)OOintheGulf Figure 6.6.Relativeabundance ofdiagnosticPA uptake/metabolism genesinCTR, Bacteroidetes Corynebacteriaceae Flavobacteriaceae Percentage (%) of diagnostic genes Percentage (%) of diagnostic genes Percentage (%) of diagnostic genes

Brucellaceae Bacteroidetes 10 20 10 15 20 25 30 35 40 15 25 10 15 35 40 45 20 30 0 5 0 5 0 5

Flavobacteriaceae Bradyrhizobiaceae

Alpha- Rhizobiaceae (c) OO (b) OS (a) NS

Rhodobacteraceae Bradyrhizobiaceae

Rhizobiaceae Alpha-

Rhodobacteraceae

SAR11 clade Brucellaceae Proteobacteria Alpha- Comamonadaceae Phyllobacteriaceae

SAR11 clade

Beta- Methylophilaceae Burkholderiaceae Beta- Rhizobiaceae

Rhodobacteraceae Methylophilaceae Rhodocyclaceae Proteobacteria Proteobacteria 195 Alteromonadaceae Alteromonadaceae

SAR11 clade Gamma-

Alteromonadaceae Enterobacteriaceae

SPM SPD Pseudoaltero- PUT CTR monadaceae Gamma- Gamma- Enterobacteriaceae

Vibrionaceae Hahellaceae Thermotogae

transporter γ-glutamylation transamination Thermotogaceae spermidine cleavage

Vibrionaceae Pseudoaltero- monadaceae Figure 6.6 Figure S6.1

Metagenomes PUT Metatranscriptomes SPD 2 (a) NS 2 (d) NS SPM

1.5 1.5

1 1

0.5 0.5 Odds ratio (OR) metagenomes PA/CTR Odds ratio (OR) metatranscriptoms PA/CTR 0 0

[N] Cell motility [Z] Cytoskeleton [S] Function unkown [V] Defense metabolisms [V] Defense metabolisms

[D] Cell cycle control,[I] cell Lipid division, transport[O] andPosttranslational metabolism modification, [D] Cell cycle control, cell division,[O] Posttranslational modification, protein turnover[U] and Intracellular chaperones trafficking, secretion, protein turnover[T] and Signal chaperones transduction mechanisms and chromosome[F] Nucleotide partitioning transport and metabolism[Q] Secondary and metabolites vesicular transportbiosynthesis, and chromosome partitioning transport and metabolism [E] Amino[F] Nucleotide[H] acid Coenzyme[L] transport Replication, transport andtransport metabolismand recombination[P] metabolism and Inorganic metabolism andion transportrepair and metabolism [M] Cell wall/membrane/envelope biogenesis 2 (b) OS 2 (e) OS

1.5 1.5

1 1

0.5 0.5 Odds ratio (OR) metagenomes PA/CTR Odds ratio (OR) metatranscriptoms PA/CTR 0 0

[N] Cell motility [K] Transcription [S] Function unkown [V] Defense metabolisms [V] Defense metabolisms

[T] Signal transduction mechanisms [D] Cell cycle control, cell division,[I] Lipid[J] Translation, transport and ribosomal metabolism structure [U] Intracellular trafficking,[C] secretion, Energy production and conversion [U] Intracellular trafficking, secretion, [E] Amino[F] Nucleotide acid transport[H] transport Coenzyme and metabolism and transport metabolism[Q] Secondary and metabolism metabolites and biosynthesis, vesicular transport and chromosome[F] Nucleotide [H]partitioning Coenzymetransport and transport and metabolism biogenesis[Q] and Secondary metabolism metabolites biosynthesis, [G] Carbohydrate transport[P] Inorganic andtransport metabolism ion transport and metabolism and metabolism [E] Amino[G] acid Carbohydrate transport and transport metabolism and metabolismtransport and and metabolism vesicular transport 2 (c) OO 5.5 (f) OO 5 1.5 3

1 2

0.5 1

Odds ratio (OR) metagenomes PA/CTR 0 Odds ratio (OR) metatranscriptoms PA/CTR 0

[N] Cell motility [N] Cell motility [S] Function unkown [V] Defense metabolisms [V] Defense metabolisms

[I] Lipid transport[O] and Posttranslational metabolism modification, [T] Signal [U]transduction Intracellular mechanisms trafficking, secretion,[D] Cell cycle control, cell division, protein turnover[T] andSignal[U] chaperonesIntracellular transduction trafficking, mechanisms secretion, and chromosome partitioning and vesicular transport [L] Replication, recombination[P] Inorganic and ion repair transport and and metabolism vesicular transport [F] Nucleotide transport and metabolism[P] Inorganic ion transport and metabolism [G] Carbohydrate[M] Cell transportwall/membrane/envelope and metabolism biogenesis

COG categories

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.

196 Figure S6.2

Stress: 0.04

Metatranscriptomes Metagenomes

OO_CTR OS_SPM OO_SPM OO_SPD OS_CTR OS_CTR OO_SPD OO_PUT OO_SPM OS_PUT OS_SPD OO_CTR NS_SPM NS_SPM NS_SPD OS_SPM NS_CTR NS_PUT OS_PUT NS_PUT OS_SPD NS_SPD NS_CTR

nearshore offshore open ocean

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 Figure S6.3

(a) Metagenomes Stress: 0.01

NS_SPM NS_PUT OS_SPD NS_SPD OS_PUT OS_SPM OO_SPD

OO_SPM

nearshore offshore open ocean

(b) Metatranscriptomes Stress: 0.09 OO_SPD

OO_SPM

OO_PUT

NS_SPM NS_PUT NS_SPD OS_PUT OS_SPD

OS_SPM

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 Chapter 7

Summary

199

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

200 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 15N 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

201 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

202 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

203 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.

204

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.

205

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