POLYAMINE TRANSFORMATION BY BACTERIOPLANKTON IN FRESHWATER ECOSYSTEMS A

POLYAMINE TRANSFORMATION BY BACTERIOPLANKTON IN FRESHWATER

ECOSYSTEMS

A thesis submitted

To Kent State University in partial

Fulfillment of the requirements for the

Degree of Masters of Science

By

Sumeda Madhuri

August 2017

© Copyright

All rights reserved

Thesis written by

Sumeda Madhuri

B.E., NMAMIT, Vishweshwaraya Technological University, 2010

M.S., Kent State University, 2017

Approved by:

Dr. Xiaozhen Mou, Advisor.

Dr. Laura G Leff, Chair, Department of Biological Sciences.

Dr. James L Blank, Dean, College of Arts and Sciences.

iii

TABLE OF CONTENT

LIST OF FIGURES………………………………………………………………………….…..V

LIST OF TABLES……………………………………………………………………………...VII

ACKNOWLEDGMENTS……………………………………………………………………....IX

CHAPTER-1: General Introduction……….………………………………….…………..1

References……………………………….……………….………………...……11

CHAPTER-2: Determination of Polyamine Concentrations, Turnover rates and, Fluxes in

Lake Erie Water Samples…..………………………………………………………..…..20

Abstract….…………………………………………………….………………...20

Introduction……….………………………………………………….………….21

Materials and Methods…............………………………….…………………….23

Results…………………………………………………………………………...29

Discussion……………………………………………………………………….34

Conclusion……………………………………………………………………….36

References……………………………………………………………………….36

CHAPTER-3: Effects of Exogenous Polyamines on Bacterioplankton Community

Structure in Lake Erie and Grand Lake St. Marys…………….………………………...55

Abstract………………………………………………………………………….55

Introduction……………………………………………………………………...56

Materials and Methods……..………………………………………….………...58

Results….………………………………………………………………………..63

iv

Discussion………………………………………………………………………..66

Conclusion……………………………………………………………………….69

References………………………………………………………….……………70

CHAPTER-4: Summary ………………………………………………………………...90

References………………………………………………………….……………92

APPENDICES…………………………………………………………………………...95

v

LIST OF FIGURES

Figure 1: Major polyamines found in natural environments and their structures…...…..............16

Figure 2: Map of Lake Erie (LE)......……..………..……………..……………………..............17

Figure 3: Map of Grand Lake St. Marys Celina Ohio (GLSM)…………………..……..............18

Figure 4: Sampling transects at Lake Erie (LE) in August 2012………………………..............40

Figure 5: Flow chart depicting the methods used for LE August 2012 samples………..............41

Figure 6: Concentrations (average ± SD) of (A) Chl a (B) NH4+, (C) NO3

-, and, (D) SRP among

WB, CB and, EB of LE August 2012……………………………………………………………42

Figure 7: Principle component analysis (PCA) biplot of physicochemical variables in LE August

2012 samples.……………….……………………………..…………………………………….43

Figure 8: Concentrations of individual PAs (average ± SD) in samples from LE including

(A) putrescine, (B) cadaverine, (C) norspermidine, (D) spermidine and (E)

spermine…..…………………………………………………………………….…..…………...44

Figure 9: Concentrations of DFAAs, PAs and, ratios between DFAAs/PAs; these two

measurements were for samples collected from LE August 2012.…..................…..…...............45

Figure 10: Figure 10: Bacterial cell counts of total bacterioplankton community (CCUF) and free

living bacterioplankton samples (CCF) collected in LE August 2012……..….............................46

Figure 11: Turnover rates (PTRUF; PTRF) and fluxes (PFUF; PFF) of putrescine in total and free

living bacterioplankton collected from LE August 2012….…..….…….…………......................47

Figure 12: RDA of turnover rates of putrescine (PTRUF, PTRF) and leucine (LTRUF, LTRF) versus

the physicochemical variables measured in LE August 2012…….…....……...…………………48

vi

Figure 13: Sampling sites at LE collected in July 2012…….................................……………..71

Figure 14: Sampling sites at GLSM collected in July 2012……………….……………………72

Figure 15: Flow chart depicting the methods used for LE and GLSM July 2012 samples……..73

Figure 16: Concentrations of Chl a from LE (A) and GLSM (B) for the samples collected in July

2012……………………………………………………………………………...........................74

Figure 17: Concentrations of PAs, DFAAs and ratio between the two measurements for

samples from LE (A) and GLSM (B) for the samples collected in July 2012..….…...................75

Figure 18: Turnover rates of putrescine in total bacterioplankton community and free

living bacterioplankton community from LE (A) and GLSM (B) in July 2012...........................76

Figure 19: Fluxes of putrescine in total bacterioplankton community and free living

bacterioplankton community from LE (A) and GLSM (B) in July 2012……...………………..77

Figure 20: Variation of bacterial cell counts and concentration of putrescine of LE1SB, LE2SSB

and, LE3CB in microcosms from LE July 2012…..…………….……………………………....78

Figure 21: Variation of bacterial cell counts and concentration of putrescine in GLSM1, GLSM2

and, GLSM3 microcosms from GLSM July2012……………………………………………….79

Figure 22: NMDS ordination plot for LE microcosms from July 2012…..……………………..80

Figure 23: NMDS ordination plot for GLSM microcosms from July 2012…....………..………81

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LIST OF TABLES

Table 1: Concentrations, turnover rates of PAs and DFAAs in marine environments and

corresponding references..………….……………………………………………………………19

Table 2: PCA analysis of the physicochemical variables LE August 2012….….………………49

Table 3: One-way ANOVA for the effects of basin on the environmental variables

individually in samples collected from LE August 2012……………...…...……………………50

Table 4: Percent contribution of putrescine and leucine to bacterial C and N demands...............51

Table 5: Pair-wise correlation analysis among individual environmental variables and

physicochemical variables of samples collected from LE August 2012 .……….………………52

Table 6: RDA analysis species scores for the physicochemical variables and turnover rates,

concentrations of PAs and DFAAs in the samples collected in LE August 2012……………….53

Table 7: Pair-wise correlation analysis among individual environmental variables from

LE and GLSM July 2012……………...……….…….……………………...…………………...82

Table 8: Percent contribution of putrescine and leucine to bacterial C and N demands of sample

from LE and GLSM collected in July 2012………………………………………………...…...83

Table 9: One-way ANOVA for the effects of basin on the environmental variables

individually in samples collected from LE and GLSM in July 2012.....…….……………..……84

Table 10: Repeated measure ANOVA analysis results………….…………………………...….85

Table 11: Shannon diversity indices for amendment study LE July 2012 samples…..………....86

Table 12: Shannon diversity indices for amendment study GLSM July 2012 samples…………87

viii

Dedication

To my dear parents, sister and my husband for believing in me.

ix

Acknowledgments

To begin, I would like to especially appreciate my advisor Dr. Xiaozhen Mou for her

valuable advice, guidance and help on academic, career and personal matters throughout my

journey here at Kent State University. I would also like to thank my committee members, Dr.

Darren Bade and Dr. Laura Leff. They have been an invaluable resource for me. Additionally, I

am also grateful for Kent State Graduate Student Senate and the Department of Biological

Sciences for giving me this great opportunity by allocating appropriate funds on my behalf.

I appreciate the help and support from my lab mates and undergraduate assistants during

my program. Especially thanking, Anna Ormiston, Anurag Sharma, Antony Nerris, Alecia

Roberts, Curtis Clevinger, Leigh Martin, Leighannah Atkins, Sarah Brower, Suhana

Chattopadhyay, Shorook Attar and Xinxin Lu, for their laboratory and field assistance. In

addition, I would like to thank Dr. Blackwood and Dr. Bade for helping in statistical analysis and

for their technical assistance and time.

Finally, I would like to thank my family for their constant support and encouragement.

1

Chapter 1 General Introduction

Nitrogen is a vital and abundant element, yet it is one of the limiting factors in aquatic

environments for microorganisms. Mostly nitrogen is available for aquatic bacterioplankton in

dissolved inorganic (such as ammonium, nitrate and nitrites) and organic (such as urea, proteins

and nucleic acids) forms. These nitrogen molecules are transformed mainly by bacterioplankton

communities (Azam et al., 1983).

As one of the major pool of labile nitrogen, dissolved organic nitrogen (DON) consists of

a versatile mixture of both, high molecular weight (HMW) molecules and low molecular weight

(LMW) molecules (Berman and Bronk, 2003). Nucleic acids, proteins, and, humic-like

substances are some common examples of HMWs, while dissolved free amino acids (DFAAs),

urea, and, methylamines are some common examples of LMWs (Berman and Bronk, 2003)

molecules. Yet, when compared with their inorganic counterparts, the composition and

distribution of natural DON compounds are relatively understudied (McCarthy et al., 1998;

Wiegner and Seitzinger, 2004). Available DON studies mainly focus on DFAAs and urea; two

DON compounds that are readily detected by established methods (Rosenstock and Simon, 1993;

Jorgensen et al., 1999). DFAAs and urea are suggested to account for 90 % of the labile DON

pool (Berman and Bronk, 2003; Keil and Kirchman, 1991). However, polyamines have recently

been proposed as another important component of labile DON by both biochemical (Nishibori et

al., 2001; Lee and Jorgensen, 1995; Liu et al., 2015; Lu et al., 2014) and metagenomic studies

(Mou et al., 2013b; Mou et al., 2011).

2

Polyamines

Polyamines (PAs) are a group of aliphatic organic compounds with multiple amine

groups and are ubiquitously present in all living organisms (Tabor and Tabor, 1984) and in

nature (Nishibori et al., 2003; Lee at al., 1992). Natural PA pool mainly includes cadaverine

(C5H12N2), norspermidine (C6H17N3), putrescine (C4H12N2), spermine (C10H26N4) and,

spermidine (C7H19N3; Figure 1). In marine environments, putrescine, spermine, and, spermidine

have been found to be most abundant among the common PA compounds (Nishibori et al.,

2003; Lu et al., 2014).

PAs are found at a few mmolL-1 concentrations within the bacterioplankton and

phytoplankton cell cytosol (Igarashi and Kashiwagi, 2000). These positively charged

intracellular PAs act as counter-ions to stabilize negatively charged RNA and DNA molecules

(Yoshida et al., 2004). Intracellular PAs are released into environments during viral lysis and

cell senescence of phytoplankton and zooplankton (Lee and Jorgensen, 1995; Nishibori et al.,

2003; Liu et al., 2014). In seawater, dissolved PAs are typically found at low nmol L-1

concentrations (Liu et al., 2014; Lu et al., 2014). Concentrations of PAs can range from

undetectable in oligotrophic seawaters to over 200 nmolL-1 in areas of high primary productivity

(Lee et al., 1992; Nishibori et al., 2001; Lee and Jorgensen, 1995).

Bacteria uptake exogenous PAs use an ATP-binding cassette (ABC) transporter (pot)

system (Igarashi and Kashiwagi, 1999). In the genome of Ruegeria pomeroyi DSS-3, a model

for abundant marine roseobacter, pot genes account for 0.6% of the total genome (Mou et al.,

2010). Genomes of many other abundant marine bacteria, including SAR11 have also shown the

presence of polyamine transporter genes (pot), further supporting the importance of PAs to DON

3

dynamics in aquatic environments (Mou et al., 2010). Once PAs are brought into the cell, they

can be broken down by transamination and/or glutamylation pathways and finally enter the tri-

carboxylic acid (TCA) cycle (Mou et al., 2011). Similar to PA transporter genes (pot genes), PA

degradation genes, i.e., puuB and spuC, also appear widely among marine microbial genomes

and metagenomes (Mou et al., 2010; Mou et al., 2011; Mou et al., 2013). A metatranscriptomic

study on coastal seawater further indicated that a diverse group of marine bacterial taxa may be

involved in transformation of PAs (Mou et al., 2011). These genomic and metagenomic studies

consistently suggest the importance of PAs to marine microorganisms (Mou et al., 2010; Mou et

al., 2011; Mou et al., 2013).

However, transformation of PAs (turnover rates, flux rates) has only been measured in a

small number of marine systems, including the oxic-layer of a eutrophic salt pond (Lee and

Jorgensen, 1995), a eutrophic stratified trench (Lee et al., 1992), and the South Atlantic Bight

(SAB) off the coast of Georgia (Liu et al., 2015; Table 1). On the other hand, these studies

consistently found that turnover rates of PAs were considerably higher in eutrophic waters than

in the oligotrophic open ocean, suggesting a possible correlation between turnover rates of PAs

and primary productivity (Nishibori et al., 2003; Liu et al., 2015).

Studies in freshwater environments related to PAs are yet to be reported. Due to

increased human activities and agricultural runoffs, many freshwater systems have become

eutrophic and are susceptible to harmful cyanobacterial algal blooms (CyanoHABs). Based on

the positive correlation between the primary productivity and turnover rates of PAs in marine

systems, we expect eutrophic freshwaters to potentially serve as hot spots for bacterially

mediated PA transformations. Supporting this idea, recent freshwater metagenomics studies

have identified PA transport and degradation related genes in bacterioplankton metagenomes of

4

various lakes, including Lake Erie (Mou et al., 2013a; K. McMahon, personal communication).

The general objective of this research is to empirically study the transformation of PAs in

freshwater environments. Specifically, the goals of this study were to (1) measure

concentrations, turnover rates, and fluxes of PAs in multiple freshwater environments and

examine their correlations with environmental variables and (2) investigate the impact of

elevated PAs on the structure of freshwater bacterioplankton communities.

Hypotheses

The general hypothesis for this thesis states that similar to marine environments, PAs are

ubiquitous in freshwaters and their transformation by bacterioplankton is affected by primary

productivity.

Hypothesis 1: In Lake Erie, the concentrations, turnover rates, and fluxes of PAs are

higher in the western basin than the central and eastern basins. Rationale: Primary productivity

and concentrations/dynamics of PAs have been found positively correlated in marine

environments (Lee and Jorgensen, 1995; Nishibori et al., 2001). We expected similar

relationship would be found in freshwater environments. In Lake Erie, a natural gradient of

decreasing nutrient concentrations and productivities has been observed from western basin to

central basin and/or eastern basin (Harked et al., 2015). Therefore, concentrations, turnover

rates, and fluxes of PAs would follow primary productivity by showing a declining trend from

the western basin to the eastern basin.

Hypothesis 2: The water from Grand Lake St Marys (GLSM) has higher values of PA

concentrations, turnover rates and fluxes of PAs than Lake Erie’s (LE) water samples.

Rationale: Both LE (especially in the western basin) and GLSM are facing eutrophication and

5

CyanoHABs. However, GLSM’s problem of eutrophication (>25µg L-1 of Chl a concentration)

is bigger than LE’s. Primary productivity at GLSM is higher than Lake Erie (OEPA, 2011; ~90

µg L-1 concentration of Chl a in GLSM). Based on the observed positive relationship between

primary productivity and PA dynamics, we predicted that GLSM would show higher PA

transformation rates than Lake Erie.

Hypothesis 3: A diverse group of bacterioplankton may be responsive to an elevated

supply of PAs in freshwater water lakes. Rationale: It has been suggested that labile organic

compounds that are commonly found in the environments are typically transformed by diverse

groups of generalist bacteria (Mou et al., 2008). PAs are common in all living organisms (Tabor

and Tabor, 1984; Incharoensakdi et al., 2010; Nishibori et al., 2001), thus are expected to be

ubiquitous in natural environments, including freshwater systems.

Important methods used

Radioactive uptake assay

In this study, radioactive uptake assay (Liu et al., 2015; Lee et al., 1993) was used to

determine the turnover rate of polyamines by freshwater microorganisms. Radioactive labeled

PA (14C-putrescine) and DFAA (H3-Leucine) model compounds were amended to water samples

and the decay of the radioisotope was detected to trace the assimilation/incorporation and

transformation of PAs and DFAAs by bacterial communities. Radioactive uptake assay is

relatively easy to perform and can provide fast, specific, and, sensitive measurement of the

turnover rate of tested compounds (Kirchman et al., 1985). However, radioactive substrates are

hazardous and require special handling and disposal procedure. More importantly, isotope tracer

labeling is based on the assumption that the radioisotope labeled compound undergo the same

6

chemical, physical and biological processes as the natural unlabeled compounds. However,

“isotope effects”, i.e., differential behaviors between radioisotopic labeled compounds and their

natural counterparts, have been observed, especially when the radioisotopic compounds are used

in high concentrations (Kirchman et al., 1985; Lee et al., 1992). Additionally, decomposition

correction and respiration correction associated with turnover rate should be calculated to avoid

over estimation of the turnover rates.

T-RFLP analysis

In this study, terminal restriction fragment length polymorphism (T-RFLP) is used to

track potential shifts in bacterial community structure. In T-RFLP, the bacterial community’s

16S rRNA partial genes are amplified by PCR using a fluorescently labeled primer as either the

forward or reverse primer to produce terminally labeled PCR amplicons.

After digestion with restriction enzymes, fluorescently labeled T-RFs of different

bacterial taxa (roughly at species level) will have variable lengths due to variations in 16S rRNA

gene sequences and be placed under different T-RF peaks. This length polymorphism of T-RFs

of a sample, therefore, can provide bacterial community fingerprints (Franklin et al., 1999). T-

RFLP analysis can provide a quick and cost-effective overview of microbial community

structure (Osborne et al., 2014), but these results are subjected to PCR amplification biases

(Brooks et al., 2015). Additionally, since multiple species can potentially generate the same

sized T-RFs, T-RFLP analysis may underestimate community diversity and overlook fine-scale

shifts of community structure (Blackwood et al., 2005; Buchan et al., 2010; Mou et al., 2008).

7

Sampling locations

Lake Erie (LE) and Grand Lake St. Marys (GLSM) were chosen as our study sites. Both

lake systems have eutrophication related problems and are often susceptible by cyanobacterial

harmful algal blooms (Michalak et al., 2013; OEPA, 2014). LE belongs to the North American

Laurentian Great Lakes (Superior, Michigan, Huron, Erie, and Ontario). LE is also the largest

system of surface freshwater on Earth. It has been estimated that approximately 18% of the

world’s supply of surface freshwater comes from these 5 Great Lakes (NRCS., 2005). LE is the

shallowest out of the five Laurentian Great Lakes. It is also an important source for drinking

water and recreation for the state of Ohio and other nearby states.

LE is naturally categorized into the western, central, and eastern basins (Figure 2); each

basin receives multiple river inflows. The western basin is the shallowest (7.4 m on an average)

and mainly receives water from the Detroit, Huron, Maumee, Ottawa, Portage, and, Raisin

Rivers (Michalak et al., 2013). The Maumee and Detroit Rivers have the largest nutrient loading

due to human impacts (Michalak et al., 2013). It has been estimated that 40% of LE’s

phosphorus and nitrogen inflow are from the sediment load and agricultural runoffs carried by

the Maumee and Detroit Rivers (Michalak et al., 2013). Due to the high nutrient inflow, the

western basin has become hyper eutrophic based on EPA standards (>25 µg/L of Chl a

concentration; OEPA, 2012; Michalak et al., 2013; Graham et al., 2008; Makarewicz and

Bertram, 2014). In WB, the high availability of nutrients and warm temperature have been

leading factors for cyanobacterial harmful algal blooms (CyanoHABs; Michalak et al., 2013).

CyanoHABs have become a major problem in freshwater environments leading to concerns

regarding the safety of drinking and recreational use of the water (Lyra et al., 2001; Okano et al.,

2009). Water contaminated by CyanoHABs and cyanotoxins have been known to cause skin

8

irritation, liver damage and cancerous conditions due to contact or consumption (Okano et al.,

2009). Local businesses around LE bring in a revenue of ~$7 billion annually, yet due to the

CyanoHABs revenue has decreased, thus negatively impacting all of the lake counties (NRCS,

2005).

Compared with WB, CB is less eutrophic in nature. CB (18.3 m depth on average) is

deeper than the WB. Its main source of water comes from the Ashtabula, Black, Cuyahoga,

Chagrin, Grand, and Rocky Rivers; among these, Grand River and Cuyahoga River are

considered to bring most phosphorus and nitrogen into the basin (OEPA, 2010).

The EB is the deepest among the other three basins in Lake Erie, having an average depth

of 24.4 m. Cattaraugus Creek contributes most of the nutrient inflow towards the eastern basin

(OEPA, 2010). Tributaries flowing towards CB and EB are comparatively less impacted by

human activities than WB tributaries. As a result, the concentration of nutrients is lower in CB

and EB than in WB; respectively, their trophic conditions are categorized as mesotrophic and

oligotrophic (OEPA, 2011; Michalak et al., 2013). WB is highly infested by CyanoHABs than

CB and EB, largely due to the nutrient gradient among the basins (OEPA, 2015).

Grand Lake St. Marys (GLSM) of Celina County, Ohio is the largest inland freshwater

reservoir in Ohio (Figure 3). This man-made reservoir has one tenth as much surface area as LE

(21.0 sq. m; 54.63 km2). It is a shallow (average depth 4.8 m) and also a well-mixed lake with

high concentrations of nutrients due to agricultural inflow (OEPA, 2012). GLSM is identified as

hyper-eutrophic by Ohio EPA (>25 µg/L of Chl a concentration; OEPA, 2012), with N and P

concentrations higher than those of LE’s eutrophic western basin. In the past, local businesses

around GLSM had average revenues of $150 million annually, however, due to the negative

9

impact of CyanoHABs on GLSM, the local businesses have dramatically shrunk with a steep

decrease in revenue by $250,000 each subsequent year (OEPA, 2014). Recently, lake restoration

measures, such as the addition of alum, have been taken up by collaborations between private

businesses like Battelle and Tetra Tech with the Celina County to work towards mediating the

water quality in GLSM (OEPA, 2012), however, post remediation, GLSM is still hyper eutrophic

in nature (OEPA, 2014).

Outline of the thesis

This study is reported in three chapters. They are briefly summarized below.

Chapter 1 General Introduction: This chapter introduces the importance and current

knowledge of polyamines in both freshwater and marine environments. It also briefly introduces

the study hypotheses, study sites and the major techniques associated with this study.

Chapter 2 Determination of Polyamine Concentrations, Turnover rates and Fluxes

in Lake Erie Water Samples: The main objective of this chapter was to determine the

concentration, turnover rates, and fluxes of PAs in water samples collected in summer 2012 from

21 coastal-to-offshore sites among the 8 transects along the south coast of LE. We found that the

average concentration, turnover rates, and fluxes of putrescine, for unfiltered and filtered

samples were significantly correlated with Chl a concentration of the lake. Putrescine accounted

for 9.9 % of the bacterial nitrogen demand and 4.8 % of the bacterial carbon demands (total

bacterioplankton community). Our measurements support the hypothesis by showing a higher

transformation of PAs in WB than CB and EB showing an association of transformation of PAs

with primary productivity. The data also suggest that turnover rates of putrescine can be

10

comparable to dissolved free amino acid in Lake Erie. Further work is needed to resolve the fate

of putrescine’s nitrogen.

Chapter 3 Effects of Exogenous Polyamines on Bacterioplankton Community

Structure in Lake Erie and Grand Lake St. Marys: The main objective of this chapter is to

evaluate the effects of exogenous polyamines on bacterioplankton community structure in both

LE and GLSM July 2012. Microcosms were established using free-living bacterioplankton from

each site. These bacterioplankton communities were incubated for 56 hours with or without

putrescine amendments. Community structure of bacterioplankton was tracked by 16S rRNA

gene-based PCR and terminal restriction fragment length polymorphism (T-RFLP). Our results

showed that for samples of both lakes, the bacterioplankton communities were responsive to an

elevated supply of PA, indicating that PAs are transformed by a diverse group of bacterial

communities.

Chapter 4 Summary: This chapter synthesizes the overall findings to discuss the results

in a broader context and provides directions for future studies by assessing the diverse

community capable of transforming PAs.

11

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Mou, X., Lu, X., Jacob, J., Sun, S., & Heath, R. (2013) Metagenomic identification of

bacterioplankton taxa and pathways involved in microcystin degradation in Lake Erie. PLoS

ONE 8(4): e 61890.

Mou, X., Moran, M. A., Stepanauskas, R., González, J. M., & Hodson, R. E. (2005) Flow-

cytometric cell sorting and subsequent molecular analyses for culture-independent identification

of bacterioplankton involved in dimethylsulfoniopropionate transformations. Applied and

environmental microbiology. 71(3):1405-1416.

Mou, X., Vila‐Costa, M., Sun, S., Zhao, W., Sharma, S., & Moran, M. A. (2011)

Metatranscriptomic signature of exogenous polyamine utilization by coastal

bacterioplankton. Environmental Microbiology Reports 3(6): 798-806.

Nishibori, N., Nishii, A., & Takayama, H. (2001) Detection of free polyamine in coastal

seawater using ion exchange chromatography. ICES Journal of Marine Science: Journal du

Conseil 58(6): 1201-1207.

Nishibori, N., Matuyama, Y., Uchida, T., Moriyama, T., Ogita, Y., Oda, M., & Hirota, H. (2003)

Spatial and temporal variations in free polyamine distributions in Uranouchi Inlet,

Japan. Marine Chemistry 82(3): 307-314.

Ohio, E. P. A. (2010). Ohio Lake Erie phosphorus task force final report. Ohio Environmental

Protection Agency, Columbus 1-90.

Ohio, E. P. A. (2012). Public water system harmful algal bloom response strategy. Draft (June

2013) Available online at. http://epa.ohio.gov/Portals/28/documents/HABs/PWS-

15

HABResponseStrategy 5-22-2013.pdf

Ohio, E. P. A. (2014). Ohio Lake Erie phosphorus task force II final report. Ohio Environmental

Protection Agency, Columbus 1-109.

Simon, M., & Azam, F. (1989) Protein content and protein synthesis rates of planktonic marine

bacteria. Marine Ecology Progress Series 51(3): 201-213.

Wiegner, T. N., & Seitzinger, S. P. (2004) Seasonal bioavailability of dissolved organic carbon

and nitrogen from pristine and polluted freshwater wetlands. Limnology and

Oceanography 49(5): 1703-1712.

16

Figure 1: Major polyamines found in natural environments and their structures.

Cadaverine

Putrescine

Spermidine

Spermine

17

Figure 2: A map of Lake Erie showing different basins (western, central and eastern basin;

courtesy: Google earth).

18

Figure 3: Map of Grand Lake St. Marys, Celina Ohio (GLSM, image courtesy: Google earth).

19

Table 1: Concentrations, turnover rates of PAs and DFAAs in marine environments and

corresponding references.

Marine

Environments

PA DFAA References

Concentrations

(nmol L-1)

Turnover

rates (d-1)

Concentrations

(nmol L-1)

Turnover

rates (d-1)

Eutrophic

stratified pond

0-250 2.4-16.8 200-1500 22-48 Lee and

Jorgensen,

1995

Oxic-stratified

trench

10 2 250 18 Lee et al.,

1992

Anoxic

stratified

trench

5 0.03 20 0.16 Lee et al.,

1992

Hiroshima Bay 1.3-18.4 NA 900-5400 NA Nishibori et

al., 2001

Georgia Bay 0.1-9.4 NA 13.2-77.5 NA Lu et al.,

2014

South Atlantic

Bight

0.02-4.4 0.005-0.94 60-77.8 0.04-12.2 Liu et al.,

2015

20

Chapter 2 Determination of Polyamine Concentrations, Turnover rates and Fluxes in Lake

Erie Water Samples

Abstract

Polyamines (PAs) are an important source of DON in marine systems but their role in

freshwater DON has yet to be assessed. As an initial step to fill this knowledge gap, this study

measured the concentrations, turnover rates, and, fluxes of PAs in samples taken from 21 sites

along 8 coastal transects of Lake Erie (LE) in the summer of 2012. The five commonly found

PA compounds, i.e., putrescine, spermidine, cadaverine, spermine and, norspermidine, were all

detected in Lake Erie samples, yet putrescine and spermidine (22.1 nmol L-1 and 36.2 nmol L-1 in

average, respectively) had the highest concentrations. The ratio of total dissolved and free amino

acids (DFAAs, a known important DON group in aquatic environments) vs. PA concentrations

were much lower in LE (2:1) than in marine environments (10:1). The cross-lake average

turnover rates and fluxes of putrescine were 2.2 d-1 and 58.6 nmol L-1 d-1, respectively; which

values were about ten folds higher than corresponding values in marine environments. Similar to

marine systems, PA concentrations, turnover rates, and, fluxes were correlated with

concentrations of Chl a, thus indicating the importance of primary producers in PA dynamics.

Putrescine alone potentially accounted for 9.7 % of the bacterial nitrogen demands and 4.8 % of

the bacterial carbon demands, indicating that putrescine can serve as an important N and C

sources for marine bacteria. Overall, this study provided the first empirical dataset on PA

dynamics in a large freshwater lake. The results suggest that PAs may be ubiquitous and

important DON to bacterial communities in freshwater lakes, similar to their role in marine

ecosystems.

21

Keywords: Polyamine (PAs), Putrescine (Put), Lake Erie (LE), Dissolved free amino acids

(DFAAs), Bacterioplankton.

Introduction

Polyamines (PAs) are polycationic compounds ubiquitously found in all living organisms

(Tabor and Tabor, 1976). Intracellular PAs are involved in many vital molecular functions of the

cell such as DNA and RNA synthesis and stabilization of the cell (Tabor and Tabor, 1985).

Intracellularly, PA concentrations range from µmol L-1 to mmol L-1 levels, with the low and high

concentrations associated with dormant and fast growing cells, respectively (Tabor and Tabor,

1985). Mostly in marine environments, concentrations of dissolved PAs (Table 1) range

between 0.1 to 50.0 nmol L-1. Concentrations of PAs peak up to 200.0 nmol L-1 in areas

impacted by algal blooms with high primary productivity (Nishibori et al., 2001; Lee and

Jorgensen, 1995).

Dissolved PA compounds that are most commonly found in the environment include

cadaverine (Cad, C5H17N2), putrescine (Put, C4H12N2), norspermidine (Nspd, C6H17N3),

spermine (Spm, C7H19N3), and, spermidine (Spd, C10H26N4; Table 1; Nishibori et al., 2001; Lu et

al., 2014; Liu et al., 2014). Among the five, putrescine, spermidine, and, spermine are the most

abundant in marine environments (Lu et al., 2014; Nishibori et al., 2001; Nishibori et al., 2003).

PAs are rich in nitrogen, analogous to dissolved free amino acids (DFAAs). Although the

concentrations of PAs are ten times lower than DFAAs in marine environments, PAs have been

suggested as an important carbon and/or nitrogen source to marine bacterioplankton (Lu et al.,

2014; Liu et al., 2015). The importance of PAs to bacterioplankton in freshwater environments

is largely unexplored. One recent metagenomic study in Lake Erie has identified PA

22

transformation related genes (Mou et al., 2013b), yet no direct measurement of PA

concentrations or turnover rates have been determined in freshwater. The aim of this study was

to address this knowledge gap by measuring concentrations, potential turnover rates, and, fluxes

of PAs in Lake Erie.

Lake Erie (LE) is the shallowest and the most southern lake of the five Laurentian Great

Lakes. It has been highly impacted by human activities, thus receives high loads of nutrients

(Michalak et al., 2013). The western basin (WB) of LE has the highest nutrient concentrations

among the three natural basins, due to the extensive nutrients discharge from the Maumee River

(Michalak et al., 2013; OEPA, 2014). As a result, it is categorized as a hyper eutrophic by the

EPA standards (Michalak et al., 2013; OEPA, 2014). In contrast, the central (CB) and eastern

(EB) basins receive less nutrient input, and their trophic statuses were mesotrophic and

oligotrophic, respectively (Michalak et al., 2013).

The direction of this study was to determine the concentrations, turnover rates, and,

fluxes of PAs in LE. Based on the positive correlation between primary productivity and PA

transformation rates that have been found in marine systems (Lee and Jorgenson, 1992, Liu et

al., 2015), we predicted that similar to marine environments, PA transformation may be

positively correlated with primary productivity in LE. Therefore, based on the known nutrients

and primary productivity gradient across the three LE basins, we hypothesize that in Lake Erie,

the concentrations, turnover rates, and, fluxes of PAs are higher in the western basin of Lake Erie

than the central and eastern basins.

23

Materials and methods

Sampling sites

From August 8th to August 23rd of 2012 (Figure 4; Appendix A1), water samples were

collected from 8 transects (21 coastal-to-offshore sites) along the southern shore of LE by Lake

Erie nearshore and offshore nutrient study (LENON). Each sampling transect corresponds to a

lake tributary where the water was collected at variable water column depths and homogenized.

The water samples were collected at Raisin River (WB-SSP) and Turtle Creek (WB-TC) in the

western basin; Huron River (CB-HUR), Grand River (CB-GRW), and, Ashtabula River (CB-

ASH) in central basin; Presque Isle River (CB-ERI), Chautauqua Creek (EB-WSF), and,

Cattaraugus Creek (EB-CCW) in the eastern basin. Water samples were collected from near the

surface (see below) at points along each transect corresponding to locations with water column

depths of 2, 5, 10, and, 20 m, where possible.

Water samples were collected at twice the in-situ Secchi disk depth (photosynthetic active

region-PAR) using a Niskin bottle and homogenized before transferring it into a 2-L sterile

bottle. Samples were transported back to the laboratory on ice for environmental testing and

some water was transported without ice specifically for radiological testing. These samples were

transported back to Kent State University within 3-4 hours of collection. At each sampling site,

temperature (T), conductivity (Con.), pH, dissolved oxygen (D.O.), and, Secchi disk depth

(Secchi) were measured by the National Center for Water Quality Research (NCWQR),

Heidelberg University (Tiffin, OH).

24

Sample processing and analysis

In the lab, 250 mL of the water sample was immediately filtered through 3.0 µm and then

0.2 µm membrane filters sequentially to separate particle-associated and free-living

bacterioplankton, respectively (MoBio Laboratories, Carlsbad, California). The final filtrates

were collected in amber glass vials and stored at -80 ˚C for nutrient analysis and measurement of

concentrations of DFAAs and PAs. The 3.0 µm and 0.2 µm filters were stored at -80 ˚C for DNA

extraction and analysis. Additionally, unfiltered water samples in triplicate (250 mL) were also

filtered through pre-combusted 0.45 µm GF/F filters (Whatman International Ltd, Maidstone,

United Kingdom) to collect phytoplankton for Chlorophyll-a (Chl a) analysis (Figure 5).

To prepare samples for turnover rate measurements of PAs, 100 mL of unfiltered water

samples was filtered through 3.0 µm membrane filters; the filtrates were then collected in glass

media bottles to obtain free-living bacterioplankton proportion. Filtrates (Free-living

bacterioplankton-F) and unfiltered water (whole bacterial community-UF) samples were further

processed to measure the turnover rates of PAs and DFAAs by using radioactive uptake studies.

For bacterial cell counts, 1.8 mL of unfiltered water and 1.8 mL of 3.0 µm-filtered water samples

were incubated with freshly made paraformaldehyde (PFA, 1 % final concentration) in triplicate

for 1 hour at room temperature as a cell fixative. Variability among the triplicate samples was

determined by standard deviations. These samples were then stored at 4 ˚C before bacterial cell

counts analysis by flow cytometry. All glassware and GF/F filters were combusted for 5 hours

prior to use.

Measurements of environmental variables

The cell-free filtrates (0.2 µm membrane filtered water) were thawed before analysis of

25

soluble reactive phosphate (SRP), nitrate plus nitrites (NOx-), and, ammonium (NH4+) using a

flow injection protocol with a Latchet (QuikChem FIA+ 8000series, Loveland, Colorado),

following a cadmium reduction method on the molybdenum blue colorimetric method,

respectively (APHA et al., 1999 ). Concentrations of Chl a were measured using the EPA

method 446, where the Chl a was extracted from a GF/F filter with 90 % acetone and measured

spectrophotometrically (USEPA, 1997). All measurements were performed in triplicate.

Variability between the triplicate samples was determined by standard deviations.

HPLC analysis to measure PAs and DFAAs

The cell-free filtrates (0.2 µm filtered water-filtrates) were thawed on ice.

Concentrations of 5 PAs and 20 DFAAs were measured simultaneously for each sample

following a high-pressure liquid chromatography (HPLC) protocol (Lu et al., 2014). This

protocol was performed fluorometrically on a Shimadzu 20A HPLC system (Shimadzu, Kyoto,

Japan) equipped with a 250 × 4.6 mm, 5µm particle size, Phenomenex Gemini-NX C-18 column

(Phenomenex Gemini-NX, Torrance, California) using pre-column fluorometric derivatization

with o-pthaldialdehyde/ethanethiol (OPA/ET) and 9-fluorenylmethylchloroformate (FMOC)

reagents (Lu et al., 2014).

Turnover rates of putrescine (PTRs) and leucine (LTRs)

Unfiltered whole water (whole bacterioplankton community-UF; 1.8 mL) and 3.0 µm

filtered water (free living bacterioplankton community-F; 1.8 mL) were incubated with 5 µCurie

mmol L-1 final concentration of radioactive 14C-putrescine (model for PAs; Perkin Elmer,

Waltham, Massachusetts) for one hour at in situ temperature (26 ˚C) in the dark. Biological

activities in controls were stopped immediately by addition of 1 mL of 40 % tricarboxylic acid

26

(TCA). Biological activities in other samples were terminated after one hour of incubation.

Standards were prepared from the 14C-putrescine to obtain a standard graph. Measurements were

taken in triplicates. Variability between the triplicate samples was determined by standard

deviation.

After the one-hour incubation period, the samples were centrifuged at 5000 × g for 10

minutes. The supernatant was discarded and the pellet was washed with 5 % cold TCA. The

pellet was then re-suspended in scintillation cocktail (Simon and Azam, 1989; Kirchman et al.,

1985). The amount of radioactivity retained by the cells was measured using a Beckman Coulter

c780 (Beckman Coulter, Inc., Brea, California). Blanks and standards were included in every

analysis run. The turnover rates of putrescine (PTRs/LTRs) were calculated by using the

following equation (1).

Turnover rates = DPM[experimental]-DPM[control]

DPM[added]× incubation time (1)

Where, DPM [experimental] is disintegration per minute (DPM) of radioactive chemicals

added to the experimental sample; DPM [control] is the DPM of radioactive material added to

control sample; DPM [added] is the DPM of radioactive material added to distilled water

(blank); incubation time is the lengths of the time the radioactive compounds were incubated

with water samples. The flux of putrescine (PFs) was calculated using the following equation

(2).

Flux = Turnover rate × Concentration (2)

Where, concentrations of the putrescine measured in nmol L-1.

27

The same protocol was used for determining the turnover rates (LTRs) and fluxes (LFs)

of leucine (model for DFAAs), except that radioactive 3H-leucine (the stock solution has 60

Curie mmol L-1; Perkin Elmer, Waltham, Massachusetts) was added to the water samples instead

of radioactive putrescine. Turnover rates of 3H-leucine were used to calculate the bacterial

protein production (BPP) using equation (3a) where the factor of 3565 g protein/mole leucine

incorporation was multiplied with BPP (3b; Kirchman, et al.,1985; Simon and Azam, 1989).

The contribution of putrescine and leucine towards the bacterial carbon demand (BCD)

and bacterial nitrogen demand (BND) were calculated by multiplying the BPP with the factor of

1.6 (Simon and Azam,1989; Liu et al., 2014), to estimate the dry weight bacterial protein

production (Simon and Azam,1989), based on BCD the BND calculated as below (Lee and

Jorgensen, 1995). BND was calculated using the BCD based on the C: N ratio.

Bacterial protein production (BPP)=Leucine incorporation × 3565 g (3a)

BCD=BPP × 1.6 × 0.54 (3b)

Where, 3565 g is a constant used for the grams of protein per mole of leucine, which is

incorporated by a bacterial cell (Liu et al., 2015). A conversion factor of 1.6 for bacterial protein

production (BPP) to obtain the bacterial dry weight is used. A conversion factor of 0.54 is used

to evaluate the (BCD) bacterial carbon demand (Liu et al., 2015).

Bacterial cell counts

Unfiltered water (whole bacterioplankton community-UF) and 3.0 µm-filtered (free living

bacterioplankton community-F) water samples were incubated with 4 % PFA (1 % final

concentration) at room temperature for 1 hour, afterwards bacterial cells in the samples were

stored at 4 °C or immediately enumerated using a BD-FACS Aria CaliburTM flow cytometer (BD,

28

Franklin lakes, New Jersey) following a protocol described previously (Mou et al., 2014). Prior

to running samples on the instrument, water samples were stained with SYBR green II (1:5000

dilution of the commercial stock; Molecular Probes Inc. Eugene, Oregon) in the dark for 3 hours

at room temperature and then mixed with an internal bead standard, i.e., 5.2 µm diameter

SPHEROTM Accu-Count Fluorescence Microspheres (Spherotech Inc., Lake Forest, Illinois).

Flow cytometric data acquisition was triggered by green fluorescence (FL1).

Statistical analysis

Statistical analyses were performed using the R statistics Vegan package (Oksanen et al.,

2011) unless otherwise specified. The pair-wise Pearsons product-moment correlation analysis

was performed to examine correlations between a biotic variable, including bacterial abundance

(BA), turnover rates of putrescine (PTRs) or turnover rates of leucine (LTRs), and, a abiotic

variable, including concentration of Chl a, NOx-, NH4

+, or SRP, using Microsoft Excel

(Microsoft Corp., Albuquerque, NM). Furthermore, an Analysis of Variance (ANOVA) was

performed using “basin” as a factor to determine potential differences in environmental and

biotic variables among basins (Oksanen et al., 2011). Significant results obtained for ANOVA

analysis was further tested by a post-hoc analysis (Tukey test). The T-test was used to analyze

potential pair-wise difference among individual environmental variable means. Statistical

significance was reported for above analyses when P<0.05.

Principle component analysis (PCA) was used to examine variations of physicochemical

variables among samples. PCA was performed based on a correlation matrix (Ramette, A. et al.,

2007), which was calculated using log-transformed physicochemical variables (except for pH,

which was not transformed). PCA results were represented as a biplot (Jolicoeur & Mosimann,

29

1960), where samples (dots) and physicochemical variables (vector/arrows) were ordinated along

axes correspond to the first two principal components (PCA1 and PCA2). The direction of a

vector indicates the maximum change of a specific variable and its length indicates the changing

rate of that variable (Ramette, A. et al., 2007). Angles between vectors reflect correlations

among physicochemical variables (Ramette, A. et al., 2007).

Redundancy analysis (RDA) was further performed based on the same data matrix of

physicochemical variables used in PCA analysis (independent variables) plus a data matrix of

biotic variables (dependent variables), including PA and DFAA turnover rates. RDA is a

constrained ordination analysis which helps to visualize variations of biotic variables directly in

relation to the physicochemical variables (Oksanen et al., 2011; Ramette, A. et al., 2007). The

fluxes of putrescine and leucine and their contribution towards the bacterial carbon demand and

nitrogen demand were not included in the RDA due to their mathematic association with

turnover rates of putrescine and leucine. RDA results were reported as a triplot of samples sites

(dots, based on variation of biotic variables), physicochemical variables (green arrows) and

biotic variables (black arrows). A RDA triplot can show variations of biotic variables among

sample sites and the extent of the variations can be explained by individual environmental

variables (green arrows). In addition, the triplot can display the correlations between individual

biotic variable and environmental variables. The significance of the RDA model was evaluated

by ANOVA with 999 permutations.

Results

General environmental conditions

The average concentrations of multiple environmental variables showed higher values in

WB than CB and EB. These included Chl a, NH4+, and, SRP (Figure 6). The average

30

concentrations of Chl a, NH4+, and, SRP in WB were 37.1 µg L-1, 0.05 mg L-1 and, 0.004 mg L-1,

respectively, while the values were 12.1 µg L-1, 0.03 mg L-1 and, 0.001 mg L-1, respectively in

the CB and 3.8 µg L-1, 0.03 mg L-1 and, 0.001 mg L-1, respectively in the EB (one-way ANOVA,

P<0.05). The environmental variables such as Con., D.O., T, NOx- and, pH were similar

throughout the lake (one-way ANOVA, P>0.05; Appendix A4).

PCA was performed to examine variations of environmental conditions among the LE

samples (Figure 7). PC1 represented 44 % of the total variance and was mostly contributed by

concentrations of Chl a, PAs, and, DFAAs (Table 2). PC2 represented 14 % of the total

variance, which was mainly contributed by conductivity, temperature, and, concentration of

nitrate/nitrite (Table 2). PCA generally separated samples based on basins. All WB samples

were clustered together and away from samples of the other two basins. The direction of vectors

and one-way ANOVA analysis showed that WB samples had higher concentrations of Chl a,

PAs, and, DFAAs and also showed lower pH, temperature, and, Secchi depth than the samples

from the CB and EB (P<0.05).

Polyamine and amino acid concentrations

The total average concentration of PAs in LE was (77.0 nmol L-1) higher in WB (171.4

nmol L-1) samples than the CB (31.9 nmol L-1) and the EB (27.8 nmol L-1; one-way ANOVA,

P<0.05; Figure 8 and 9). Among individual PAs, the average concentration of putrescine (22.1

nmol L-1), spermidine (36.2 nmol L-1), and, spermine (13.3 nmol L-1) in all LE samples were at

least 5 times higher than cadaverine (2.5 nmol L-1) and norspermidine (2.7 nmol L-1; one-way

ANOVA, P<0.05; Table 3 and Figure 8).

31

Concentrations of DFAAs followed the same trend among the basins and were higher in

WB (391.3 nmol L-1) than CB (86.6 nmol L-1) and EB (68.7 nmol L-1; one-way ANOVA,

P<0.05; Figure 9). The ratio of concentrations of PAs to DFAAs showed no significant

difference among basins and had an average ratio of ~1:2 (t-test, P<0.05; Figure 9).

Putrescine and leucine turnover rates and fluxes

Putrescine was used as a model for PAs in radioactive uptake assay (tracer labeling)

experiment to determine polyamine turnover rates (PTRs). For whole bacterial community

(unfiltered samples), the whole lake average PTRs was 2.2 d-1 and the corresponding putrescine

flux (PFs) was 58.6 nmol L-1d-1. Among individual basins, both PTRUF and PFUF were the

highest in WB (2.8 d-1, 142.8 nmol L-1 d-1) than CB (2.2 d-1 and 21.7 nmol L-1 d-1, respectively),

and, EB (1.5 d-1 and 11.1 nmol L-1 d-1, respectively; one-way ANOVA, P<0.05; Figure 11).

Free-living bacteria within the whole bacterial community averagely accounted for 54.5% (1.2 d-

1) of the PTRF and 56.9% (33.4 nmol L-1 d-1) of the PFF. The contribution of free-living bacteria

to PA transformation in WB accounted for 58.0% and in EB accounted for 56.0% of PA

transformation by free-living bacterial communities which were similar to one another, however,

they were higher than CB which accounted for 47.0% of PA transformation by the free-living

bacterial community; Figure 11).

The whole bacterial community (unfiltered samples), average turnover rates and fluxes of

leucine, the model compound for DFAAs, were 5.2 d-1 and 116.4 µmol L-1d-1, respectively.

Among individual basins, the LTRUF and LFUF were the fastest in the WB (9.1 d-1, 200.6 µmol L-

1 d-1, respectively) than the other basins (Table 2; Figure11). The corresponding values in CB

(3.7d-1 and 123.2 µmol L-1 d-1, respectively) were higher than EB (2.9 d-1 and 25 µmol L-1 d-1,

32

respectively) and both CB and EB were significantly lower than those in the WB (one-way

ANOVA, P<0.05; Figure 11).

Putrescine and leucine as a source of C and N

The cross-basin average of putrescine’s contribution to the BCD was significantly lower

than the BND which was estimated to be 4.8% to 9.9%, respectively (Table 4). The contribution

of putrescine towards bacterial carbon demand (BCDUF) was different whereas the contribution

of bacterial nitrogen demand (BNDUF) of the whole bacterial community was similar among WB

(BCDUF-2.7%, averagely BNDUF-7.5%, respectively) and CB (BCDUF-5.0% BNDUF-7.1%;

Table 4, respectively). However, the contribution of putrescine towards the BCDUF and BNDUF

in WB and CB for the total bacterioplankton community was lower than the contribution of

putrescine in EB’s bacterioplankton community (BCDUF-5.4%, BNDUF-15.3%, respectively;

Table 4; one-way ANOVA, P<0.05).

The average contribution of leucine, the DFAA model compound towards BNDUF and

BCDUF was 124.1 % and 58.7 % respectively for the total bacterioplankton community. The

contribution of leucine was 5-10 times higher than putrescine (t-test, P<0.05; Table 4). Similar

to putrescine, the leucine’s contribution towards BCDUF and BNDUF was higher in EB for the

total bacterioplankton community (BNDUF-135.7 %, BCDUF-67.6 %, respectively; Table 4; one-

way ANOVA, P<0.05) and CB (BNDUF-194.0 %, BCDUF-46.4 %, respectively; Table 4) than

WB (BNDUF-42.2 %, BCDUF-61.8 %, respectively; Table 4).

Relationship among environmental variables and turnover rates of PAs and DFAAs

RDA analysis was performed to identify the potential impact of each environmental

variable on variations of the turnover rates of PAs and DFAAs (Figure 12). In RDA, axes 1 and

33

2 together explained 34% of the variation of biotic data. RDA1 alone accounted for 27% of the

variation in biotic variables and 65% of the relationship between biotic and environmental

variables and mainly captured variations in Chl a, T, pH, concentrations of PAs and DFAAs,

LTRUF , LTRF, and, PTRUF. RDA 2 alone accounted for 7% of the variation among biotic

variable and 18% of the relationship between biotic and environmental variables and mainly

captured variations in NH4+ and LTRUF. The RDA triplot also revealed that the PTRUF showed

significant correlation with the concentrations of total PAs and DFAA, Chl a, and, SRP.

Pearson’s correlation analysis showed that PA turnover rates of whole bacterial community and

its free-living proportion were significantly correlated with PA concentrations (Pearsons

correlation analysis, r >0.43, P<0.05, respectively; Table 5). PAs turnover rates of the whole

bacterioplankton community were also significantly correlated with concentrations of Chl a,

DFAAs, PAs, bacterial cell counts, and, leucine turnover rates for whole bacterial community

(Pearson’s correlation analysis, r >0.43, P<0.05; Table 5). Additionally, similar to turnover rates

of PAs, turnover rates of DFAAs for whole bacterial community and its free-living bacterial

proportion both showed significant correlations (using pearsons pairwise correlation analysis)

with concentrations of SRP, NH4+, Chl a, DFAA, and, PA (Pearson’s correlation analysis,

coefficient r >0.43, P<0.05; Table 5). No significant correlation was seen between NH4+, NOx

-,

and, any other variable (Table 5). The bacterial cell count for free-living bacteria also showed

correlation with the concentrations of Chl a with Pearson’s pair wise correlation (Table 5).

Discussion

This study marked one of the first attempt to quantify transformation of PAs and its

importance to DON flux in freshwater environments. The results showed that PAs were found

throughout the southern shore of Lake Erie, which suggest that PAs are a common component of

34

the freshwater DON pool. The suggested pervasive nature of PAs in Lake Erie was comparable

to the results found in marine environments (Mou et al., 2015, Lu et al., 2014; Appendix 9).

Concentrations, turnover rates, and, fluxes of PAs were higher in WB than in CB and EB. Each

of them were significantly correlated with Chl a concentrations, as predicted by our hypothesis.

Similarly, the transformation of PAs in marine environments has been found to be closely

correlated with primary productivity (Nishibori et al., 2001; 2007; Lee et al., 1992; Liu et al.,

2015). The consistent correlation between PAs and primary productivity indicated that

phytoplankton may be a major source of PAs (Nishibori et al., 2001, 2007; Lu et al 2014; Liu et

al., 2015) in Lake Erie, and they release intracellular PAs to the lake water during cell

senescence and/or viral lysis (Lee et al., 1992).

All five commonly measured PAs were detected in LE samples, but concentrations of

putrescine and spermidine were higher than cadaverine, norspermidine or spermine. Similar

results have also been found in marine environments (Lu et al., 2014; Liu et al., 2015). Coastal

seawater studies and river associated marine studies have shown a dominance of putrescine and

spermidine concentration and its association with cyanobacterial blooms (Liu et al., 2015; Lee et

al., 1992; Nishibori et al., 2001; 2007). The cyanobacterial cytoplasm is found to contain high

concentrations of (Hamana et al., 1982; Hamana et al., 1992) putrescine and spermidine

suggesting cyanobacteria may most likely be the major sources of putrescine and cadaverine

(PAs) in the lake. In addition, the cell wall of eukaryotic algae, such as diatoms are known to

have spermine, thus they are also considered as one of the main sources of PAs (Hamana and

Matsuzaki 1982; 1985; Sumper et al., 2005), therefore supporting our results.

In Lake Erie, the concentrations of total PA was half that of DFAAs, while in marine

environments, PA concentrations were typically only one tenth of DFAAs (Lu et al., 2014; Liu et

35

al., 2015). The PA turnover rates and fluxes were higher in LE than marine environments.

Higher PA to DFAA ratio and higher transformation rates in LE suggests the importance of PA

dynamics in freshwaters than marine environments. The contribution of PA towards the BCD

and BND indicates that PAs might serve more as a nitrogen source than a carbon source. These

results were consistent with our hypothesis, that PAs might be an important DON source for

freshwater bacteria. Collectively, these results also suggest that PAs may play an important role

in the supply of N to bacterioplankton in productive environments than oligotrophic

environments. Nonetheless, further evaluation is required to address this hypothesis since

respiration and decomposition corrections were not measured in this study.

This study successfully measured the concentration, turnover rate, and, flux of PAs and

DFAAs in Lake Erie. However, some of the methods used have limitations. First, a single PA

(putrescine) was used as the model for PAs (Lee and Jorgenson et al., 1992; Lee et al., 1992;

Hofle et al., 1984). However, PAs are a mixed pool of compounds. Different PA compounds,

such as spermidine or spermine may follow different transformation mechanisms (Liu et al.,

2015). Second, in the radioactive uptake assay, putrescine and leucine were added at non-tracer

levels. Therefore, the obtained potential turnover rates of putrescine and leucine might have

been over estimated. However, this is nearly inevitable, because, prior to the study, the

concentrations of either compound in LE were unknown. Nonetheless, the chosen

concentrations of putrescine and leucine for radioactive uptake assay were able to give us

repeatable readings.

Conclusion

PAs were measured with high concentrations (77.0 nmol L-1) in Lake Erie samples. The

average concentrations, turnover rates, and, fluxes of putrescine in LE samples were significantly

36

correlated with concentrations of Chl a, thus indicating primary productivity may be a driving

factor for PA dynamics. Among the five PAs, putrescine and spermidine were found to be

abundant, suggesting an association of cyanobacterial blooms towards a production of PAs in the

lake. The concentrations, turnover rates, and, fluxes suggested that PAs might be used more as a

nitrogen source than a carbon source for Lake Erie bacterioplankton.

37

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Blackwood, C. B., Oaks, A., & Buyer, J. S. (2005) Phylum and class specific PCR primers for


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Charlton, M. N., & Milne J. E. (2004) Review of thirty years if Lake Erie water quality data

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Höfle, M. G. (1984) Degradation of putrescine and cadaverine in seawater cultures by marine

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39


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Poretsky, R. S., Sun, S., Mou, X., & Moran, M. A. (2010) Transporter genes expressed by

coastal bacterioplankton in response to dissolved organic carbon. Environmental


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Pegg, A. E., & McCann, P. P. (1982) Polyamine metabolism and function. American Journal of

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Rosenstock, B., & Simon, M. (1993) Use of dissolved combined and free amino acids by

planktonic bacteria in Lake Constance. Limnology and Oceanography 38(7): 1521-1531.

Schneider, J., & Wendisch, V. F. (2011) Biotechnological production of polyamines by bacteria:

recent achievements and future perspectives. Applied Microbiology and Biotechnology 91(1):

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Simon, M., & Azam, F. (1989) Protein content and protein synthesis rates of planktonic marine

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Tabor, C. W., & Tabor, H. (1985) Polyamines in microorganisms. Microbiological

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Yoshida, M., Kashiwagi, K., Shigemasa, A., Taniguchi, S., Yamamoto, K., Makinoshima, H., &

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41

Figure 4: Sampling transects at Lake Erie in August 2012. The sample transects are indicated

with a yellow diamond. The capital letters are the sample naming associated with the transects

(WB-SSP, WB-TC – samples were collected from Turtle creek and River Raisin respectively;

CB-HUR, CB-GRW, CB-ASH and CB-ERI – samples were collected from Huron River, Grand

river, Ashtabula River, and Presque Isle River respectively; EB-WSF, EB-CCW – samples were

collected from Chautauqua Creek and Cattaraugus creek respectively; Appendix A1).

42

Figure 5: Flow chart depicting the methodologies used to process for LE August 2012 samples.

43

Figure 6: Concentrations (average ± SD) of (A) Chl a (B) NH4+, (C) NOx

-, and, (D) SRP among WB, CB and, EB of LE August 2012.

0

10

20

30

40

50

WB CB EB

Conce

ntr

atio

n (

µg L

-1)

Chl a

0

0.02

0.04

0.06

0.08

WB CB EB

Conce

ntr

atio

n (

mg L

-1)

NH4+

0.000

0.010

0.020

0.030

0.040

0.050

WB CB EB

Conce

ntr

atio

n (

mg L

-1) NOx

-

0.000

0.001

0.002

0.003

0.004

0.005

0.006

WB CB EB

Conce

ntr

atio

n (

mg L

-1)

SRP

44

Figure 7: Principle component analysis (PCA) biplot of physicochemical variables in LE August

2012 samples. Green coloration represents WB, blue color represents central basin and orange

color represents eastern basin. Sample labeling is according to Appendix 1.

45

Figure 8: Concentrations (average ± SD) of individual PA compounds among WB, CB and EB of

LE August 2012, including (A) putrescine, (B) cadaverine, (C) norspermidine, (D) spermidine and,

(E) spermine. Sample labeling is as per basins (WB, CB and, EB).

0

20

40

60

80

WB CB EB

Co

nce

ntr

atio

n (

nm

ol

L-1

)

Putrescine

0

1

2

3

4

5

6

WB CB EB

Co

nce

ntr

atio

n (

nm

ol

L-1

)

Cadaverine

0

2

4

6

8

WB CB EB

Co

nce

ntr

atio

n (

nm

ol

L-1

)

NorspermidineC

0

10

20

30

40

50

60

WB CB EB

Co

nce

ntr

atio

n (

nm

ol

L-1

)

SpermineD

0

20

40

60

80

100

120

WB CB EB

Co

nce

ntr

atio

n (

nm

ol

L-1

)

SpermidineE

A B

46

Figure 9: Concentrations of DFAAs (average ± SD; gray bars), PAs (average ± SD; white bars) and,

ratios between DFAAs/PAs (black line); these two measurements were for samples collected from

LE August 2012. Sample labeling is as per basins (WB, CB and, EB).

0

0.5

1

1.5

2

2.5

3

0

50

100

150

200

250

300

350

400

450

WB CB EB

Rat

io (

DFA

As/

PA

s)

Conce

ntr

atio

n (

nm

ol

L-1

)DFAAs, PAs Concentration

DFAAs (nmol L-1) PAs (nmol L-1) Ratio (DFAAs/PAs)L-1) L-1)

47

Figure 10: Bacterial cell counts (average ± SD) of total bacterioplankton community (CCUF- gray

bars) and free living bacterioplankton samples (CCF-white bars) collected in LE August 2012.

Sample labeling is as per basins (WB, CB and, EB).

0

2

4

6

8

10

12

14

WB CB EB

Cel

l C

ou

nts

(×

10

6)

Cell Counts

CCUF CCFCCUF CCF

48

Figure 11: Turnover rates (PTRUF-white bars; PTRF-gray bars) and fluxes (PFUF-white bars; PFF-

gray bars) of putrescine in total bacterioplankton community (UF) and free living bacterioplankton (F)

collected from LE August 2012 (average ± SD). Sample labeling is according to basins (WB, CB

and, EB).

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

WB CB EB

Turn

over

rat

es (

d-1

)

Putrescine turnover rates

PTRUF PTRFPTRUFPTRF

0

50

100

150

200

WB CB EB

Flu

xes

(nm

ol

L-1

d-1

)

Putrescine Fluxes

PFUF PFFPFFPFUF

49

Figure 12: RDA analysis of turnover rates of putrescine (PTRUF, PTRF) and leucine (LTRUF,

LTRF) versus the physicochemical variables measured in LE August 2012. Green coloration

represents WB, blue color represents central basin and orange color represents eastern basin.

Sample labeling is according to Appendix 1.

50

Table 2: Principle component analysis displaying the factor loadings (Eigen vectors) of the

physicochemical variables and turnover rates, concentrations of PAs and DFAAs in the samples

collected in LE August 2012.

PC1 PC2 PC3

Eigen value 4.8 1.6 1.2

proportion explanation 0.44 0.14 0.11

cumulative explanation 0.44 0.59 0.7

Species scores (Eigen

vectors) PC1 PC2 PC3

NH4+ -0.54 0.28 -0.61

NOx- 0.09 -0.91 -0.29

SRP -0.57 0.44 -0.55

Chl a -1.00 -0.16 0.20

Secchi 0.81 -0.12 -0.39

T 0.86 0.63 0.09

Cond. -0.19 0.66 0.51

D.O. -0.52 0.40 -0.63

pH 0.84 0.21 0.02

PA -1.08 -0.10 0.20

DFAA -1.10 0.05 0.18

51

Table 3: One-way ANOVA for the effects of basin on individual environmental variables LE samples (* is to label significant

difference, P<0.05).

Between group

SS

Within group Between groups Within groups

SS DF DF F P

NH4+ 0.00093 0.00328 2 18 2.5 0.10

NOx- 0.00034 0.00084 2 18 3.6 0.049

SRP 0.00003 0.00002 2 18 12.2 0.00045*

Chl a 2980.9 527.5 2 18 50.9 3.93×10-8*

DFAA 24733.3 2328.7 2 18 95.6 2.59×10-10 *

Put 5525.5 998.1 2 18 49.8 4.59×10-8*

Cad 12.7 10.3 2 18 11.1 0.00074*

Nspd 25.0 19.5 2 18 11.5 0.00059*

Spd 15766.5 1378.9 2 18 102.9 1.41×10-8*

Spm 2233.8 1031.4 2 18 19.5 0.00003*

PA 64842.7 5959.4 2 18 0.0 3.55

CCUF 131.2 46.2 2 18 25.5 0.00001*

CCF 31.8 12.1 2 18 23.7 0.00001*

PTRUF 3.2 7.7 2 18 24.6 0.00001*

PTRF 23.5 384.3 2 18 3.7 0.043

52

Table 4: Percent contribution of putrescine and leucine to bacterial C and N demands (BCD, BND, respectively, average ± SD) of LE

samples. PA-CUF % and PA-CF % represent the percentage contribution of putrescine to BCD for the total and free-living

bacterioplankton community, respectively; PA-NUF%, PA-NF% represent the percentage contribution of putrescine to BND for the

total and free-living bacterioplankton community, respectively. DFAA-CUF %, DFAA-CF % represent the percentage contribution of

leucine to BCD for the total and free-living bacterioplankton community, respectively and DFAA-NUF %, DFAA-NF % represents the

percentage contribution of Leucine to BND for the total and free-living bacterioplankton community, respectively.

Basin Putrescine Leucine

PA-CUF% PA-NUF% PA-CF% PA-NF% DFAA-CUF% DFAA-NUF% DFAA-CF% DFAA-NF%

WB 2.7±1.2 7.5±1.0 2.6±0.8 5.7±1.0 61.8±12.5 42.2±6.4 24.3±3.6 34.8±14.8

CB 5.0±1.2 7.1±2.8 3.7±2.2 8.2±1.9 46.4±12.4 194±61.5 141.7±14.6 86.8±27.4

EB 5.4±1.8 15.3±2.1 4.9±1.7 9.0±1.3 67.6±26.0 135.7±34.7 85.2±3.7 55.9±33.8

53

Table 5: Pearsons pair wise product-moment correlation analysis among individual environmental variables of LE August 2012

samples. Significant correlations (P<0.05) are shaded in gray (with critical value of 0.43 for a P value of 0.05; DF= 19;

http://psystat.at.ua/Articles/Table_Pearson.PDF).

SRP NOx- NH4

+ Chl a DFAA TPA CCUF CCF LTRUF LTRF PTRUF

NOx- -0.04

NH4

+ 0.57 -0.09

Chl a 0.48 -0.13 0.35

DFAA 0.47 -0.27 0.35 0.88

TPA 0.44 -0.19 0.34 0.85 0.95

CCUF 0.26 -0.16 0.14 0.55 0.69 0.67

CCF 0.33 -0.17 0.23 0.47 0.51 0.49 0.76

LTRUF 0.48 0.09 0.49 0.65 0.64 0.65 0.38 0.40

LTRF 0.19 -0.40 -0.13 -0.37 -0.21 -0.24 -0.09 0.02 -0.30

PTRUF 0.18 0.24 0.00 0.73 0.61 0.56 0.48 0.47 0.48 -0.45

PTRF 0.18 0.09 0.38 0.37 0.53 0.53 0.53 0.37 0.53 -0.27 0.43

54

Table 6: RDA analysis tables with a) percent of variation (for biotic variable) and cumulative %

variation (for environmental and biotic variable), b) species scores for the environmental

variables, c) scores for the constraining variable and d) results of variation partitioning tests for

the physiochemical variables and turnover rates, concentrations of PAs and DFAAs in the

samples collected in LE August 2012.

a) percent of variation (for biotic variable) and cumulative % variation (for physicochemical

and biotic variable)

RDA1 RDA2 RDA3


cum% variation (*biotic data) 0.27 0.07 0.03

RDA1 RDA2 RDA3


cum% variation (*environmental and biotic

data) 0.65 0.18 0.09

b) species scores for the physicochemical variables

Species Scores RDA1 RDA2 RDA3

NH4+ -0.3 -0.76 0.14

NOx- -0.39 0.22 -0.09

SRP -0.27 -0.39 0.21

Chl a -0.88 0.15 0.15

Secchi 0.6 -0.37 -0.24

T 0.83 0.19 0.1

Cond. -0.03 -0.23 0.59

D.O. -0.11 -0.04 -0.02

pH 0.69 0.17 0.24

PA -0.79 0.19 0.009

DFAAs -0.84 0.02 0.05

55

c) scores for the constraining variable

Scores for constraining variables RDA1 RDA2 RDA3

LTRUF -0.81 -0.57 0.06

LTRF 0.73 -0.33 0.58

PTRUF -0.83 0.31 0.44

PTRF -0.54 -0.006 0.08

d) results of variation partitioning tests

RDA1

variation partitioning test None All

NH4+ 11.11 8.80

NOx- 14.44 11.40

SRP 10.00 7.90

Chl a 32.50 25.80

Secchi 22.20 17.60

T 30.70 24.40

Cond. 1.10 0.80

D.O. 4.07 3.20

pH 25.50 20.20

PA 29.20 23.20

DFAAs 31.10 24.70

(* none indicates an variation partitioning of the RDA analysis performed with a given variable

as a sole constraining variable and no covariables included and all indicates an analysis with

given variable as the sole constraining variable and all other variables as co variables).

56

Chapter 3 Effects of Exogenous Polyamines on Bacterioplankton Community Structure in

Lake Erie and Grand Lake St. Marys

Abstract

To study the dynamics of freshwater polyamine (PAs) and examine the effects of

exogenous PAs on the bacterioplankton community structure, surface water samples were

collected from Lake Erie (LE) and Grand Lake St Marys (GLSM) in July 2012. Concentrations

and turnover rates of PAs were measured using HPLC and radioactive trace uptake assay,

respectively. The response of bacterioplankton to elevated PA supplies in microcosms was

tracked by 16S rRNA gene-based terminal restriction fragment length polymorphism (T-RFLP).

Our results showed that concentration, turnover rates, and, fluxes of PAs were significantly

correlated with concentrations of chlorophyll-a (Chl a) and were much higher in GLSM samples

(235.7 nmol L-1, 6.08 d-1, and, 739.6 nmol L-1 d-1, respectively) than in the LE samples (44.8

nmol L-1, 3.1 d-1, and, 44.7 nmol L-1 d-1, respectively). Bacterial cell number in microcosms with

LE and GLSM water samples significantly increased after 56 hours of incubation when amended

with putrescine; a PA model compound. In contrast, the cell numbers in no-amendment controls

remained unchanged. Along with cell number increase, concentrations of added putrescine in

microcosms significantly decreased, indicating rapid consumption by bacterioplankton.

However, T-RFLP of 16S rRNA genes showed no significant changes between putrescine

treatments and controls in either LE or GLSM samples. These results indicated that a majority

of bacterioplankton taxa responded to putrescine with similar growth rates. Overall, our

57

chemical measurements and microcosm incubation experiment data consistently suggested that

PAs may be a common DON in freshwater systems, where they are used by a diverse group of

bacterioplankton.

Key Words: polyamines, bacterial transformation, Lake Erie (LE), Grand Lake St Marys

(GLSM).

Introduction

Marine environments studies have identified polyamines as potential sources of carbon,

nitrogen and/or energy for the marine bacterioplankton communities (Lu et al., 2015, Mou et al.,

2011, Lu et al., 2014). Our direct measurement of PAs in Lake Erie (LE) returned positive

results along the southern coast of LE; the PA concentrations were at an average of 77.0 nmol L-1,

which was ~10 times higher than previous measurements in marine environments (Chapter 2;

Liu et al., 2015; Lee et al., 1992; Table 1). These results indicate that PAs may be commonly

found in both freshwaters and marine environments where it serves as an important source of C

and/or N to bacterial communities. However, besides our previous study in Lake Erie (Chapter

2), further measurements in LE and other highly productive freshwater lakes (Grand Lake St

Marys) are needed to establish the importance of PA to DON flux in freshwaters. The objectives

of this study were twofold: (1) to examine concentrations, turnover rates and fluxes of PAs in

Lake Erie (LE) and the Grand Lake St Marys (GLSM) to evaluate the correlation of primary

productivity with transformation of PAs; and (2) to investigate the effect of PAs on

bacterioplankton structure in LE and GLSM to evaluate if a diverse group of bacterioplankton

uses PA in lakes.

58

GLSM is located in Celina OH, about 226 miles south of LE. GLSM (average width of

2.8 m and average depth of 6 m) is the largest man made reservoir in Ohio, although it is much

smaller (1/10th of LE in its surface area) and shallower (1/4th of LE in depth) than LE. Much like

the western basin of LE (Chl a concentation = 8-25µg L-1; OEPA 2011, 2014; OWEA, 2014), the

water of GLSM is identified as hyper-eutrophic by Ohio EPA (Chl a concentration >25µg L-1),

which leads to CyanoHABs in summer. Based on the suggested positive correlation between the

primary productivity and turnover rates of PAs, we expected GLSM to serve as another

freshwater hotspot for PA transformations.

Genomic and metagenomic studies in marine environments have shown the presence of

PA transforming genes in a diverse group of marine bacteria (Lu et al., 2015). However, the

effect of exogenous PAs on freshwater bacterial communities is yet to be examined. We

addressed this knowledge gap by amending LE and GLSM bacterioplankton with PAs and

examined the fingerprints of bacterioplankton communities using 16S rRNA gene-based terminal

restriction length polymorphism (T-RFLP).

Material and Methods

Sample collection and processing

Water samples were collected from the surface (0.5m below the air water interface) of LE

at Sandusky Bay (LE1SB), Sandusky sub basin (LE2SSB), and the central basin (LE3CB; Figure

12; Appendix A6) in July 2012. Similarly, surface water samples were also collected from the

Grand Lake St. Marys from the boating area (GLSM1), a beach area allocated for swimming

(GLSM2) and center of the lake, which, is noted as a fishing area (GLSM3) on July 2012 (Figure

13; Appendix A6).

59

At each sampling location, environmental variables including temperature (T),

conductivity (Con.), pH, dissolved oxygen (DO %), and, Secchi depth (Secchi) were measured

in-situ at the time of sampling using a Hydrolab H2O multi-data SONDE (Hydrolab Corporation,

Austin, Texas). Water samples were collected using Niskin bottles at 1 m depth and then

transferred into 10 L pre-washed polypropylene bottles (Figure 14).

Part of the water samples (500 ml) were filtered on site through 0.45 µm Whatman GF/F

filters (Whatman International Limited, Maidstone, England) to collect phytoplankton for

Chlorophyll-a (Chl a) analysis. Obtained filters and the rest of water samples were stored on ice

and transported to Kent State University (KSU) within 12 hours from sampling. At KSU, 2 L of

water samples from each site were filtered sequentially through 3.0 µm and then 0.2 µm

membrane filters (MoBio Laboratories, Carlsbad, California) to collect particle-associated and

free-living bacterioplankton, respectively. Filters were stored at -80˚C for molecular analysis.

The filtrates (10 ml for each sample) obtained after being filtered with 0.2 µm membranes were

collected in amber glass vials and immediately stored at -20˚C for nutrient analysis and

measurements of DFAAs and PAs using HPLC protocols described previously (Chapter 2).

Turnover rates and fluxes of leucine and putrescine

The turnover rates and fluxes of leucine (LTRs and LFs) and putrescine (PTRs and PFs)

were performed following the same protocol described in chapter 2.

Microcosm amendments of free-living bacterioplankton

Pre-filtered water samples (3.0 µm pore-size membranes) that were collected from each

site were amended with inorganic phosphorus (5 µmol L-1 NaH2PO4) and incubated in the dark at

60

room temperature with occasional mixing for 72 hours to create a carbon/nitrogen limited

condition (Figure 14). At the end of the three-day pre-incubation, 6 microcosms of 1 L were set

up in 2 L media bottles for samples from each sampling site. Three of these microcosms

received putrescine (PUT; 50 µmol L-1, final concentration) and the other three received no

amendments to serve as controls. In addition, no cell controls were set up in triplicate for each

sampling site by adding 50 µmol L-1 (final concentration) of putrescine to 250 mL distilled water.

All microcosms were incubated in a shaker at 150 rpm in dark at 25˚C for a total of 56 hours.

Five mL subsamples were taken from each microcosm every 12 hours (at 0, 12, 24, 48 and 56

hours of incubation) and processed for measuring the bacterial cell counts and concentrations of

PAs. At the end 56 hours’ incubation, bacterial cells in microcosms were collected onto a 0.2 µm

pore-size membrane filters (Millipore Inc., Cork, Ireland) and stored at -80˚C for subsequent

molecular studies. Variability between the triplicate samples was determined by standard

deviations.

Nutrient measurements

Concentrations of various nutrients, including nitrate/nitrite (NOx-), ammonium (NH4

+)

soluble reactive phosphorus (SRP), PAs (Put, Cad, Nspd, Spm, Spd; 5 individual compounds in

total, appendix 2), and DFAAs (20 individual compounds in total) and Chl a, were determined

following the same procedure described in Chapter 2.

Bacterial cell enumeration

Bacterial cell preservation and enumeration counts (CCs) were performed as described

previously (Mou et al., 2005; Chapter 2).

61

DNA extraction, PCR amplification and T-RFLP analysis

DNA were extracted from membrane filters using a Power-Soil DNA extraction kit

(MoBio Laboratories, Carlsbad, California) following the manufacture’s protocol. Amplification

of 16S rRNA genes was carried out using 27F (forward) and 1492R (reverse) primers (Delong et

al., 1989). The forward 27F primers were labeled with 6-carboxyflouroscein (FAM) at their 5’

end. A touchdown PCR program was used with the annealing temperature sequentially

decreasing from 62˚C to 52˚ by 1˚ C/cycle, followed by 15 cycles at 52˚ C. Each PCR cycle

included denaturing at 95˚C for 30 seconds, annealing at 62˚C to 52˚ C and extension at 72˚C for

50 seconds. An initial 3-minute denaturation and a final 8-minute extension step were also

included (Mou et al., 2013a). PCR amplification was performed in triplicate of 25 µL each,

which were pooled together for gel electrophoresis analysis. PCR amplicons were excised from

the gel and purified using the ultraclean Gel spin DNA Purification kit (MoBio laboratories,

Carlsbad, California). Purified amplicons were digested with 10 µL of HaeIII (New England

Biolabs, Ipswich, Massachusetts), 10 × buffer (2 µL) and bovine serum albumin (BSA; 0.2 µL)

for each sample for 4 hours at 37˚C and then purified by ethanol precipitation (Zeugin and

Hartley, 1985). The purified DNA was then re-suspended in 13 µL DI water before being

analyzed with a 3030 DNA analyzer (Applied Bio systems, Foster City, California) in the Plant-

Microbe Genomics Facility at Ohio State University.

Statistical analysis

All statistical analyses were performed using R statistics Vegan package (Oksanen et al.,

2011), unless otherwise mentioned. The pair-wise Pearson’s product-moment correlation

coefficient was performed to examine the correlation of biotic variables such as bacterial cell

62

count (CCs), turnover rates of putrescine (PTRs) and turnover rates of leucine (LTRs) with

environmental variables (concentration of Chl a, NOx-, NH4

+, and SRP) using Microsoft Excel

(Microsoft Corp., Albuquerque, NM). Pearsons correlation was performed to examine potential

correlation between individual variables. Furthermore, an analysis of variance (ANOVA) was

performed with the three basins as a factor to determine the differences in environmental and

biotic variables (Oksanen et al., 2011). Significant results from one-way ANOVA analysis was

further examined by a post-hoc analysis (Tukey test). Repeated measure ANOVA was

performed with the microcosms to determine the differences among amendments. Significance

was reported for statistical analysis with P<0.05.

T-RFLP output data was summarized based on relative peak areas of T-RFs, which was

used as a proxy for the relative abundance of bacterial species. These relative areas were square

root transformed before the analysis. The T-RF’s with lengths shorter than 600-bp and relative

peak areas less than 2 % of the total areas were excluded from further analysis. The relative

abundance data of T-RFs was used for the non-metric multi-dimensional scaling (NMDS)

analysis to examine potential variability in the bacterial community structure among basins,

based on Bray Curtis matrix (Primer v5, Quest Research Limited, Williamsburg, VA). The

robustness of NMDS analysis was verified by analysis of similarity (ANOSIM) analysis.

ANOSIM is a test performed to evaluate significant differences between groups based on

categorical variables. ANOSIM generates an rANOSIM value obtained in correlation scaled

from 0-1. When rANOSIM value was >0.75, sample groups were considered as well separated

when rANOSIM value was between 0.5-0.75, sample groups were considered to have minimum

overlapping; when rANOSIM <0.25, sample groups were considered with high similarity (Clark

63

and Warwick, 2001). Shannon-Weiner Index and Jaccard’s evenness index were calculated

based on the T-RF relative abundance data for each sample.

Result

General physical and chemical conditions

In LE, the average concentrations of Chl a, NH4+, and SRP (14.6 µg L-1, 27.2 µg L-1, and

9.2 µg L-1, respectively) was significantly higher in LE1SB for Chl a, NH4+ (24.0 µg L-1 and,

27.1 µg L-1, respectively) than the LE2SSB (13.3 µg L-1 and 22.2 µg L-1, respectively) and

LE3CB (6.7 µg L-1 and 32.5 µg L-1, respectively; one-way ANOVA, P<0.05; Figure 16).

GLSM samples had higher average concentrations of Chl a, NOx-, and, NH4

+ (92.0 µg L-

1, 26.5 µg L-1, and, 26.2 µg L-1, respectively), than LE (14.6 µg L-1, 28.2 µg L-1, and, 9.2 µg L-1,

respectively, respectively; Figure 16; t test, P<0.05). Within GLSM samples, the average

concentrations of Chl a, NH4+, and, SRP were significantly higher in GLSM 3 (96.2 µg L-1, 33.8

µg L-1, and, 6.8 µg L-1 respectively) and GLSM 2 (95.5 µg L-1, 28.7 µg L-1, and, 10.4 µg L-1,

respectively) than GLSM 1 (84.0 µg L-1, 16.2 µg L-1, and, 23.1 µg L-1; respectively; Figure 16;

Tukey test, P> 0.05; Table 9).

Concentrations, turnover rates and fluxes of PAs and DFAAs in LE and GLSM

In LE, the overall average concentration of PAs was 46.8 nmol L-1, which was about 1/4

of that of DFAAs (186.4 nmol L-1; t-test, P<0.05). Among individual PA compounds, putrescine

(14.4 nmol L-1), and spermidine (19.6 nmol L-1), were over 10-fold more abundant than the other

two PA compounds (Tukey test, P<0.05, Figure 15, Appendix A8). Turnover rates (Figure 18)

and fluxes (Figure 19) of PAs (‘Put’ as a model) in LE2SSB and LE3CB samples showed no

64

significant difference, and both were lower than that of LE1SB samples (one-way ANOVA,

P<0.05, Tukey test P>0.05). Pearson correlation analysis revealed that concentrations, turnover

rates and fluxes of PAs in Lake Erie were significantly correlated with the concentration of Chl a

(Table 7).

In GLSM, the overall average polyamine concentration was 235.7 nmol L-1, which was

about one fourth of DFAAs (909.1 µmol L-1; t-test, P<0.05). These PA and DFAA

concentrations in GLSM were over 6-fold higher than those in LE (t-test, P<0.05). Similar to LE,

putrescine (109.5 nmol L-1), and spermidine (90.4 nmol L-1) dominated the PA pool and

significantly more abundant than the other two PAs (one-way ANOVA, P<0.05; Appendix A6;

Tukey test, P<0.05; Figure 16; Appendix A6). Compared with LE samples, individual PA

concentrations in GLSM were higher (t-test, P<0.05; Appendix A6), except that concentrations

of spermine showed no significant difference between the lakes (t-test, P>0.05; Figure 17).

Turnover rates (Figure 18) and fluxes (Figure 19) of PAs among the 3 GLSM sites had no

significant difference and averagely reached 6.08 d-1 and 739.6 nmol L-1d-1, respectively (one-

way ANOVA, P>0.05). These values were significantly higher than their corresponding values

in LE (3.1 d-1 and 44.7 nmol L-1d-1, respectively; t-test, P>0.05). The turnover rates and fluxes

of leucine in GLSM (12.0 d-1 and 10.7 µmol L-1 d-1, respectively) were also significantly higher

than LE (7.4 d-1 and 3.7 µmol L-1d-1, respectively; t-test, P<0.05; Figure 17).

Contribution of PAs and DFAAs to bacterial carbon and nitrogen demand

In LE, putrescine contribution accounted for 0.4 to 1.5 % (1 % in average) of the BCD

and 2.0 to 7.5 % (4 % in average) of the BND (Table 8). Meanwhile, leucine contribution was

65

significantly higher and accounted for 13.2 to 22.0 % of BCD (15% in average) and 12.2 to 66.4

% (40 % in average) of the BND (t-test, P<0.05).

In GLSM, the potential contribution of putrescine to BCD and BND was much higher

than LE, with values of and 2.8 to 2.0 % (2 % in average) of the BCD and 10.0 to 14.5 % (12 %

in average) of the BND. The potential contribution of leucine accounted for 21.4 to 67.9 % (40

% in average) of the BCD and 137 % to 422 % (200 % in average) of the BND; both values were

significantly higher than those for putrescine (t-test, P<0.05; Table 8).

Response of bacterial growth to polyamine amendments

Bacterial communities responded to amended putrescine by increases in cell number for

all the three sites for both the lakes. The pre-incubated microcosms (with excessive inorganic

phosphate salts) were amended with 5 µmol L-1 of putrescine as a source of C and/or N (Figure

20 and 21). In all the tested LE microcosms, the added putrescine decreased by 95 % (from 5

µmol L-1 to 212.1 nmol L-1) within 56 hours (repeated measure ANOVA, P>0.05, and Figure 20,

Table 10). Along with the consumption of putrescine, there was a significant increase in

bacterial cell counts in all putrescine amended samples. Bacterial cell counts significantly

increased from 3.0 ×106 cells mL-1 to 12.0 ×106 cells mL-1 within 56 hours of incubation

(repeated measure ANOVA, P<0.05, and Figure 20). Meanwhile, bacterioplankton cell counts

in no-addition control microcosms showed no change for all 3 basins (repeated measure

ANOVA, P>0.05; Figure 20).

A similar pattern was observed for GLSM microcosm experiments. Added putrescine in

GLSM water was consumed by 90-96.5 % in 56 hours of incubation (repeated measure

ANOVA; P<0.05, Figure 21). Meanwhile, the putrescine amendment added to the distilled

66

water remained unchanged (repeated measure ANOVA, P>0.05, and Figure 21). Along with

putrescine consumption, bacterial cell counts increased by nearly three folds from 11.0 ×106 cells

mL-1 to 33.0 ×106 cells mL-1 in 56 hours (repeated measure ANOVA, P<0.05, and Figure 21). In

contrast, the bacterial cell counts in the control microcosms decreased by 65 % (repeated

measure ANOVA, P<0.05, Figure 21).

Response of bacterioplankton community structure to PA addition

Shannon index was calculated based on distribution and relative abundance of T-RF peak

area to reflect bacterial community diversity in tested samples. The results showed that the

initial diversity of LE (3.7 in average) and GLSM (3.5 in average) original bacterioplankton

community was similar (t-test, P>0.05; Figure 22; Table 11 and 12). In addition, the incubation

with PAs did not shift the diversity of bacterioplankton community in either LE or GLSM

samples (t-test, P>0.05; Figure 22; Table 11 and 12). NMDS analysis based on the relative

abundance of T-RF’s showed no obvious separation of bacterial community composition either

based on sites or sample treatments (Figure 22). ANOSIM analysis confirmed the similarity

among LE samples despite their location and treatment (rANOSIM<0.4; Figure 22; Table 10).

Similar to what had been found in LE samples, values of Shannon diversity index of

original samples (3.5) were similar to the putrescine amended samples (3.7 in average; t-test,

P>0.05; Figure 22). NMDS analysis and ANOSIM analysis further revealed that there was no

significant shift in GLSM bacterioplankton community composition between the original and PA

amended bacterioplankton communities (Figure 23; rANOSIM<0.1).

67

Discussion

Results from LE and GLSM in this chapter were consistent with our previous study

(Chapter 2). The results revealed higher concentrations, turnover rates, and fluxes of PAs in both

the eutrophic freshwater lakes than marine systems (Lee and Jorgenson, 1995; Lu et al., 2014;

Liu et al., 2015; Nishibori et al., 2003; Nishibori et al., 2001; Table 1; Appendix 9).

Additionally, positive correlation between primary productivity, concentrations, and, turnover

rates of PAs were noted in both LE and GLSM. Along with these findings from marine

environments (Lu et al., 2015; Lee and Jorgenson., 1995, Lu et al., 2014), our data suggested that

primary producers may be the major regulators of PA transformation in aquatic environments.

High concentrations of PAs have been measured from the cyanobacterial cytoplasm,

diatomic frustules, and the cell wall of eukaryotic algae (Hamana et al., 1982; 1985; Sumper et

al., 2005; Hamana et al., 1992), indicating primary producers may be a significant source of PAs.

Free-living bacterioplankton were found responsible for more PA transformation activities than

particle-associated bacterioplankton. Since most of the photosynthetic bacteria (i.e.,

cyanobacteria) were included in the particle-associated bacterioplankton fraction, the above

results suggest that degradation of PAs was mainly carried out by heterotrophic bacteria.

Consistent with the measurements in marine (Liu et al., 2015; Lee et al., 1992) and freshwater

systems (Chapter 2), PA concentrations and fluxes were significantly lower than those of DFAAs

in both LE and GLSM. However, the PA/DFAAs ratio in the lakes was significantly higher than

marine samples (Liu et al., 2015), indicating higher production and consumption rates in

freshwater than in marine environments.

68

Bacterioplankton in LE and GLSM showed a response to PA addition with a significant

increase in bacterial cell number (Figure 20 and 21). However, there was no shift in the diversity

or structure of the bacterial community (Figure 22 and 23). This indicates that dominant

bacterial taxa may respond to PAs with similar increases in growth rate (Mou et al., 2008), which

also suggests that PAs are common and labile substrates for LE and GLSM bacteria. Similar

results have been reported by studies on PA transforming bacteria in coastal seawater (Lu et al.,

2014; Nishibori et al., 2001).

We used microcosm incubation to study bacterioplankton response to elevated PA

supply. Microcosm-based experiments are known for easy replication and controlling abiotic

factors, yet due to their closed and confined systems, microcosms cannot fully represent the

conditions in nature. In addition, microcosm-based experiments also inevitably introduce

artifacts on responsive bacterioplankton taxa due to the “bottle effect” (Mou, et al., 2013). We

also pre-filtrated and incubated water samples before microcosm incubation, these additional

steps shifted the experimental systems from natural chemical conditions. However, previous

studies have shown that these preprocessing steps are necessary to remove impacts from

bacterivores and background organic compounds and this helps in obtaining detectable changes

in bacterial cell counts and community structure (Mou et al., 2013; Reed et al., 2016). T-RFLP

analysis was used to track bacterial community shift in samples. T-RFLP is fast, sensitive and

cost effective to reveal bacterial community fingerprints. However, T-RFLP may over simplify

the community structure and is ineffective in catching changes that do not involve producing

variations in T-RF lengths (Osborne, C. A., 2014). Further studies are needed to examine full-

length 16S rRNA gene sequences to solve the resolution limitation of T-RFLP analysis.

69

Conclusion

Measurements of Lake Erie and Grand Lake St Marys showed that our concentration,

turnover rates, and fluxes of PAs were significantly correlated with concentrations of

chlorophyll-a (Chl a), consistent with our findings in Chapter 2. The concentration, turnover

rates, and fluxes of PAs in GLSM were higher than LE and consistently these results were

higher than marine environment suggesting the importance of primary productivity towards the

dynamics of PAs. Microcosm incubation of bacterioplankton with elevated PAs stimulated the

growth of the majority of bacterial taxa in lake water samples suggesting diverse bacterial

community capable of transforming PAs in freshwaters.

70

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Metatranscriptomic signature of exogenous polyamine utilization by coastal bacterioplankton.

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73

Figure 13: Sampling sites at LE collected-on July 2012 (courtesy: Google images; co-ordinates:

Appendix: A5).

74

Figure 14: Sampling sites at GLSM collected in July 2012 (courtesy: Google images; co-

ordinates: Appendix A5).

75

Figure 15: Flow chart describing the methodology used for LE and GLSM sample analysis in

July 2012.

76

Figure 16: Concentrations (average ± SD) of Chl a in LE (A) and GLSM (B) for the samples

collected in July 2012. Sample labeling is as per basins (LE1SB, LE2SSB, and LE3CB;

GLSM1, GLSM2 and GLSM3).

0

5

10

15

20

25

30

LE1SB LE2SSB LE3CB

Conce

ntr

atio

n (

µg L

-1)

Chl a in LEA

0

20

40

60

80

100

120

GLSM1 GLSM2 GLSM3

Conce

ntr

atio

n (

µg L

-1)

Chl a in GLSMB

77

Figure 17: Concentrations (average ± SD) of DFAAs (white bars), PAs (gray bars) and

DFAA/PA ratios (black line) in LE (A) and GLSM (B) for the samples collected in July 2012.

Sample labelling is as per basins (LE1SB, LE2SSB and, LE3CB; GLSM1, GLSM2 and,

GLSM3).

0

2

4

6

8

10

12

14

16

0

100

200

300

400

500

600

700

800

900

LE1SB LE2SSB LE3CB

DFA

As/

PA

s (r

atio

)

Con

centr

atio

n (

nm

ol

L-1

)

PAs DFAAs DFAAs/PAs

A

0

2

4

6

8

10

12

0

200

400

600

800

1000

1200

1400

GLSM 1 GLSM 2 GLSM 3D

FA

As/

PA

s (r

atio

)

Conce

ntr

atio

n (

nm

ol

L-1

)

PAs DFAAs DFAAs/PAs

B

78

Figure 18: Turnover rates (average ± SD) of putrescine in the total bacterial community (white

bars - PTRUF) and free-living (gray bars - PTRF) bacterial community samples collected from LE

(A) and GLSM (B) in July 2012. Sample labeling is as per basins (LE1SB, LE2SSB and,

LE3CB; GLSM1, GLSM2 and, GLSM3).

0

1

2

3

4

5

6

LE1SB LE2SSB LE3CB

Turn

over

rat

e (d

-1)

PTR (U) PTR(F)PTRUF PTRF

A

0

1

2

3

4

5

6

7

8

9

GLSM1 GLSM2 GLSM2

Turn

over

rat

e (d

-1)

PTR(u) PTR(f)PTRUFPTRF

B

79

Figure 19: Fluxes (average ± SD) of putrescine in the total bacterial community (white bars -

PFUF) and free-living (gray bars - PFF) bacterial community samples collected from LE (A) and

GLSM (B) in LE and GLSM July 2012. Sample labeling is as per basins (LE1SB, LE2SSB and,

LE3CB; GLSM1, GLSM2 and, GLSM3).

0.0

20.0

40.0

60.0

80.0

LE1SB LE2SSB LE3CB

Flu

x (

nm

ol

L-1

d-1

)Putrescine Fluxes PFUF PFF

0.0

500.0

1000.0

1500.0

2000.0

2500.0

GLSM1 GLSM2 GLSM3

Flu

x (

nM

ol

L-1

d-1

)

Putrescine FluxesPFUF PFF

B

A

80

Figure 20: Variation of bacterial cell counts (average ± SD; A) and concentration of putrescine

(average ± SD; B) of LE1SB, LE2SSB and LE3CB in microcosms from July 2012. Horizontal

labels show: original: Ori, 0 and 56 h no amendment and with putrescine amendment (0-h

control, 0-h putrescine treated, 56-h control, and 56-h putrescine treated, respectively).

0

2

4

6

8

10

12

14

Ori 0h 12h 24h 48h 56h

Cel

l C

ounts

10

6m

l-1

LE3CB

Cell Counts

Putrescine Treated ControlA

A

A

B

B

B

LE3CB

81

Figure 21: Variation of bacterial cell counts (average ± SD; A) and concentration of putrescine

(average ± SD; B) in GLSM1, GLSM2 and GLSM3 microcosms from July 2012. Horizontal

labels show: original: Ori, 0 and 56 h no amendment and with putrescine amendment (0-h

control, 0-h putrescine treated, 56-h control, and 56-h putrescine treated, respectively).

0

5

10

15

20

25

30

35

40

Ori 0h 12h 24h 48h 56h

Cel

l C

ounts

10

6 m

l-1

GLSM 3 Cell counts

Putrescine Treated ControlA

B

B

B

A

A

82

Figure 22: NMDS ordination of bacterial community structures in LE bacterioplankton

microcosms based on T-RFLP data in LE July 2012. The NMDS plot shows the original water

samples as ‘Ori’, incubated with putrescine amendment as ‘Put’ and control without any

amendment as ‘C’. Green color indicates site 1: LE1SB, blue color indicates site 2: LE2SSB,

and red color indicates site 3: LE3CB.

83

Figure 23: NMDS ordination of samples collected GLSM July 2012. The NMDS plot shows the

original water samples as ‘Ori’, incubated with putrescine amendment as ‘Put’ and control

without any amendment as ‘C’. Green color indicates site 1: GLSM1, blue color indicates site 2:

GLSM2, and red color indicates site 3: GLSM3.

84

Table 7: Pair-wise correlation analysis among individual environmental variables and physiological variables of samples collected

from LE and GLSM samples from July 2012 based on Pearson’s product-moment correlation coefficient. Physiochemical variables

with significant (P<0.05) correlations were shaded in gray (critical value was 0.45 at P<0.05).

Chl a NH4

+ NO3- SRP

DO

(%) T Con. pH DOC CCF CCUF PTRUF PTRF

NH4+ 0.51

NO3- 0.23 0.65

SRP -0.19 0.51 0.86

DO (%) 0.20 -0.56 -0.76 -0.72

T 0.33 0.35 -0.36 -0.32 0.33

Con. 0.62 0.12 -0.43 -0.57 0.62 0.41

pH 0.42 0.31 0.33 0.38 0.26 0.28 0.15

DOC 0.14 0.17 0.04 -0.03 -0.12 -0.36 0.51 -0.33

CCUF 0.91 0.19 -0.19 -0.57 0.52 0.39 0.81 0.23 0.18

CCF 0.96 0.66 0.46 0.05 -0.05 0.18 0.51 0.40 0.26 0.77

PTRUF 0.89 0.28 -0.21 -0.58 0.44 0.55 0.75 0.17 0.07 0.97 0.75

PTRF 0.85 0.03 -0.28 -0.63 0.63 0.31 0.80 0.24 0.15 0.98 0.69 0.93

PA 0.41 -0.41 -0.14 -0.55 0.31 -0.29 0.12 -0.21 -0.07 0.54 0.29 0.47 0.61

85

Table 8: Percent contribution of putrescine and leucine to bacterial C and N demands of samples from LE and GLSM collected in July

2012. PA-CUF%, PA-CF%, respectively represent the percentage of BCD from putrescine and PA-NUF%, PA-NF%, respectively

represent the percentage of BND from putrescine. Similarly, DFAA-CUF%, DFAA-CF% represent the percentage of BCD from

leucine, respectively and DFAA-NUF%, DFAA-NF%, respectively represent the percentage of BND from leucine.

Putrescine Leucine

Lake PA-NUF% PA-CUF% PA-NF% PA-CF% DFAA-NUF% DFAA-CUF% DFAA-NF% DFAA-CF%

LE1SB 7.4±1.1 1.4±0.5 2.0±0.01 0.4±0.01 66.4±5.9 13.2±3.4 22.0±3.8 4.4±1.1

LE2SSB 8.0±2.2 1.6±0.4 2.2±0.5 0.4±0.01 69.6±3.8 13.9±4.1 60.9±9.2 12.1±0.9

LE3CB 7.9±2.1 1.5±0.7 2.7±0.8 0.5±0.01 59.4±7.1 11.8±2.8 20.3±4.1 4.0±0.5

GLSM1 14.0±2.8 2.8±0.9 13.8±2.9 2.7±0.7 339.8±33.9 67.9±3.6 137.2±11.7 27.4±3.7

GLSM2 2.3±0.05 0.4±0.02 1.8* 0.3±0.01 443.5±46.2 88.7±12.1 105.1±10.1 21.0±4.6

GLSM3 13.8±1.2 27.7±3.7 10.7±3.8 2.8±0.6 321.0±48.3 64.2±8.1 145.3±12.8 29.0±3.3

* indicates sample had no triplicate, due to loss/breakage of samples

86

Table 9: One-way ANOVA for the effects of basin on the environmental variables individually in samples collected from LE and

GLSM in July 2012 (* = P<0.05 was considered significant).

Between group

Within

group

Between

groups

Within

groups

SS SS DF DF F P

Chl a 8849.1 987.1 2 6 26.8 0.001*

NH4+ 1248.7 1134.5 2 6 3.0 0.1

NOx- 190.2 188.4 2 6 3.0 0.1

SRP 198.6 123.5 2 6 4.8 0.1

DPAA 1731.3 1665.1 2 6 3.1 0.1

T 0.07 12.3 2 6 0.017 1.3

Con. 0.04 15.2 2 6 0.007 1.6

CCUF 29.3 12.1 2 6 7.2 0.023*

CCF 89.2 77.4 2 6 3.4 0.1

PTRUF 63.1 63.2 2 6 2.9 0.3

PTRF 412.8 441.9 2 6 2.8 0.3

PA 352.1 52.3 2 6 20.1 0.001*

87

Table 10: Repeated measure ANOVA for the effect of microcosm amendment and time on the bacterioplankton cell count in the

microcosms for both LE and GLSM in July 2012 (* = P<0.05; DF=2,8).

Control Put Amendment

Time Concentration

Time ×

Concentration Time Concentration

Time ×

Concentration

P F P F P F P F P F P F

Cell Count

LE 0.04* 6.3 0.7 1.2 0.6 1.3 5.0×10-4* 44.1 6.7×10-7* 113.9 1.4×10-8* 175.4

Cell Count

GLSM 0.02* 7.5 0.2 2.6 0.9 0.8 4.0×10-4* 40.1 9.4×10-2* 32.5 3.1×10-4* 42.1

88

Table 11: Shannon diversity index for July 2012 LE samples.

Sites #TRFs Evenness H'

LE1SB ORI 31.67 0.97 3.70

LE2SSB ORI 64.33 0.97 3.74

LE3CB ORI 92.00 0.98 3.87

LE1SB Put 80.00 0.99 3.56

LE2SSB Put 77.33 0.98 3.33

LE3CB Put 47.00 0.96 2.37

LE1SB C 57.67 0.98 3.86

LE2SSB C 65.67 0.97 2.69

LE3CB C 59.50 0.96 3.60

89

Table 12: Shannon diversity index for July 2012 GLSM samples.

Sites #TRFs Evenness H'

GLSM1 ORI 72.33 0.97 3.89

GLSM2 ORI 47.67 0.98 3.20

GLSM3 ORI 82.67 0.97 3.59

GLSM1 Put 64.33 0.98 3.78

GLSM2 Put 64.33 0.97 3.74

GLSM3 Put 91.67 0.98 3.86

GLSM1 C 81.33 0.99 3.59

GLSM2 C 78.67 0.97 3.37

GLSM3 C 63.67 0.98 3.67

90

Chapter 4 Summary

Polyamines (PAs) are a group of DON compounds that are similar in C: N ratio to DFAAs

(Tabor and Tabor 1984; Lu et al., 2015). The importance of DFAAs to bacterioplankton and

nitrogen biogeochemistry in aquatic environments have been well established (Berman and

Bronk, 2003; Keil and Kirchman, 1991); however, the importance of PAs is largely unexplored.

PA studies in marine environments consistently suggest that PAs are an important source of C, N

and/or energy for the marine bacterioplankton communities (Poretsky et al. 2010; Höfle 1984;

Lee and Jorgensen 1995; Liu et al. 2015; Lu, et al. 2014). Direct measurement of PAs in

freshwaters is still lacking. A recent freshwater metagenomic study has identified PA

transforming genes in Lake Erie (Mou et al., 2013), indicating the importance of PAs to

freshwater bacterioplankton. In this study, dynamics of PAs and response of bacterioplankton

communities towards elevated PAs were evaluated empirically in freshwater systems for the first

time.

We found concentrations, turnover rates, and fluxes of PAs and DFAAs in two

eutrophic freshwater environments, i.e., LE and GLSM, that were higher than reported

corresponding values in marine environments. This can be partly explained by consistently

identified positive correlations between concentrations, turnover rates, and fluxes of PAs and

DFAAs with primary productivities. These correlations also held by samples between and within

the two lakes tested. Collectively, these results suggest that in aquatic environments, primary

producers are one of the major drivers for PA dynamics.

91

Contributions of PAs towards the BCD and BND in LE (5-10%) and GLSM samples (7-

15 %) were higher than marine systems (4-8 %; Liu et al., 2015), even though they were still

significantly lower than contributions of DFAAs to BCD and BND in the lake samples (90-120

%). Further, our results showed that PAs and DFAAs contributed more to BND than to BCD,

indicating that PAs and DFAAs may be more important as a source of N than of C, which is

consistent with findings from marine environments. However, further research is needed to

make more accurate estimations of the contribution of PAs and DFAAs, since decomposition

correction and respiration correction associated with turnover rate were not calculated in this

study.

Microbial communities are the major contributors for cycling carbon, nitrogen and

phosphorous in aquatic environments. These biogeochemical processes are made possible by

tight collaboration among the abiotic and biotic components (Azam at al., 1983). We found that

elevated supply of putrescine, as a model of PAs, stimulated bacterial growth in lake water

samples but did not alter bacterioplankton community structure. This indicates that dominant

groups of local bacterial taxa might be involved in PA transformation and their growth rates

under conditions of PA enrichment were similar.

Overall, our study demonstrated that PAs may be a common component of freshwater

DON pools and they are a potentially important source of carbon and/or nitrogen to

microorganisms in freshwater environments. Future studies, such as metagenomic analysis of

the bacterial community and their PA transformation genes, are required to elucidate biotic and

abiotic factors that regulate PA transformations in freshwater environments.

92

References

Azam, F., Fenchel, T., Field, J. G., Gray, J. S., Meyer-Reil, L. A., & Thingstad, F. (1983) The

ecological role of water-column microbes in the sea. Marine Ecology Progress Series.

Oldendorf 10(3): 257-263.

Berman, T., & Bronk, D. A. (2003) Dissolved organic nitrogen: a dynamic participant in aquatic

ecosystems. Aquatic microbial ecology 31(3), 279-305.

Charlton, M. N., & Milne J. E. (2004) Review of thirty years if Lake Erie water quality data

NWRI contribution. National Water Reserve Institute 37(4): 72-81.

Graham, J L., & Loftin., K A., (2008) Cyanobacteria in lakes and reservoits: toxin and taste and

odor samplibng guidelines. Biochemical and Biophysical Research Communications 16(1): 62-

75.







Lee, C., & Jørgensen, N. O. (1995) Seasonal cycling of putrescine and amino acids in relation to




93

2): 117-133.

Lu, X., Sun, S., Zhang, Y. Q., Hollibaugh, J. T., & Mou, X. (2015) Temporal and vertical

distributions of bacterioplankton at the Gray's Reef National Marine Sanctuary. Applied and





McCarthy, M. D., Benner, R., Lee, C., & Fogel, M. L. (2007) Amino acid nitrogen isotopic

fractionation patterns as indicators of heterotrophy in plankton, particulate, and dissolved organic

matter. Geochimica et Cosmochimica Acta 71(19), 4727-4744.





Mou, X., Jacob, J., Lu, X., Robbins, S., Sun, S., & Ortiz, J. D. (2013) Diversity and distribution

of free-living and particle-associated bacterioplankton in Sandusky Bay and adjacent waters of

Lake Erie Western Basin. Journal of Great Lakes Research 39(2): 352-357.

Mou, X., Moran, M. A., Stepanauskas, R., González, J. M., & Hodson, R. E. (2005) Flow-

cytometric cell sorting and subsequent molecular analyses for culture-independent identification

of bacterioplankton involved in dimethylsulfoniopropionate transformations. Applied and

environmental microbiology. 71(3):1405-1416.


94

Bromodeoxyuridine labelling and fluorescence‐activated cell sorting of polyamine‐

transforming bacterioplankton in coastal seawater. Environmental Microbiology 17(3): 876-888.



ONE 8(4): e 61890.



Conseil 58(6): 1201-1207.

Poretsky, R. S., Sun, S., Mou, X., & Moran, M. A. (2010) Transporter genes expressed by

coastal bacterioplankton in response to dissolved organic carbon. Environmental

microbiology 12(3): 616-627.


Reviews 49(1): 81.

95

APPENDICES

Appendix 1: Lake Erie transect locations for samples from LE August 2012.

SI# Basin Site Transect Water

Column

Depth

Latitude Longitude

1 Western WB-SSP-2 River Raisin 2 41.86449 -83.3736

2 WB-SSP-5 5 41.85631 -83.3342

3 WB-TC-2 Turtle Creek 2 41.62611 -83.3425

4 WB-TC-5 5 41.85528 -83.2281

5 Central CB-HUR-5 Huron River 5 41.46683 -82.6503

6 CB-HUR-10 10 41.48346 -82.6334

7 CB-HUR-20 15 41.51694 -82.5502

8 CB-GRE-10 Grand river 10 41.78336 -81.2168

9 CB-GRE-20 20 41.80016 -81.4168

10 CB-ASH-10 Ashtabula river 10 41.91036 -80.8123

11 CB-ASH-20 20 41.96683 -80.8002

12 Eastern EB-ERI-10 Presque Isle river 10 42.33348 -80.0342

13 EB-ERI-20 20 42.36673 -80.1

14 EB-WSF-2 Chautauqua creek 2 42.34845 -79.5854

15 EB-WSF-5 5 42.33338 -79.6002

16 EB-WSF-10 10 42.33348 -79.6002

17 EB-WSF-20 20 42.36673 -79.6002

18 EB-CCW-2 Cattaraugus creek 2 42.56821 -79.1429

19 EB-CCW-5 5 42.57226 -79.158

20 EB-CCW-10 10 42.57473 -79.1831

21 EB-CCW-20 20 42.58355 -79.2169

96

Appendix 2: Concentrations of ammonium, nitrate/nitrite, Soluble reactive phosphate and Chlorophyll a from LE samples from

August 2012.

Site site naming basin NH4+(mg L-1) NOx

-(mg L-1) SRP (mg L-1) Chl a (µg L-1)

site 1 WB-SSP-2 WB 0.0054 0.0204 0.044 41.4

site 2 WB-SSP-5 WB 0.0055 0.0366 0.05 40.3

site 3 WB-TC-2 WB 0.0045 0.0263 0.051 34.5

site 4 WB-TC-5 WB 0.0037 0.0258 0.04 32.3

site 5 CB-HUR-5 CB 0.0038 0.0279 0.036 27.9

site 6 CB-HUR-10 CB 0.0023 0.0295 0.032 20.0

site 7 CB-HUR-20 CB 0.0006 0.0301 0.05 14.9

site 8 CB-GRW-10 CB 0.0007 0.0327 0.036 11.3

site 9 CB-GRW-20 CB 0.0015 0.0351 0.016 10.0

site 10 CB-ASH-10 CB 0.0019 0.0351 0.02 8.7

site 11 CB-ASH-20 CB 0.0012 0.0351 0.008 7.0

site 12 CB-ERI-10 CB 0.0082 0.0351 0.056 5.1

site 13 CB-ERI-20 CB 0.0035 0.0443 0.04 4.6

site 14 EB-WSF-2 EB 0.0025 0.0143 0.033 3.8

site 15 EB-WSF-5 EB 0.0033 0.0153 0.03 3.7

site 16 EB-WSF-10 EB 0.0019 0.0245 0.059 3.5

site 17 EB-WSF-20 EB 0.0032 0.0345 0.048 4.2

site 18 EB-CCW-2 EB 0.0001 0.0332 0.025 4.2

site 19 EB-CCW-5 EB 0.0001 0.0319 0.026 4.5

site 20 EB-CCW-10 EB 0.0012 0.0287 0.031 3.8

site 21 EB-CCW-20 EB 0.0015 0.0283 0.015 3.5

97

Appendix 3: Environmental variables measured from LE August 2012.

Site site naming basin Secchi (m) T °C Cond. (mS m-1) D.O. (mg L-1) pH

site 1 WB-SSP-2 WB 1.7 23.45 0.349 8.57 8.21

site 2 WB-SSP-5 WB 1.8 21.5 0.257 8.7 8.03

site 3 WB-TC-2 WB 1.5 21.6 0.271 8.6 8.1

site 4 WB-TC-5 WB 1.8 22.5 0.284 8.56 8.13

site 5 CB-HUR-5 CB 1.5 24.77 0.276 9.07 8.56

site 6 CB-HUR-10 CB 1.5 24.43 0.274 8.77 8.45

site 7 CB-HUR-20 CB 1.5 24.49 0.274 7.88 8.52

site 8 CB-GRW-10 CB 2.5 23.55 0.285 8.34 8.35

site 9 CB-GRW-20 CB 3.5 23.6 0.282 7.59 8.42

site 10 CB-ASH-10 CB 3.5 23.71 0.277 8.41 8.35

site 11 CB-ASH-20 CB 3.5 23.3 0.276 7.94 8.47

site 12 CB-ERI-10 CB 4 24.2 0.281 8.78 8.43

site 13 CB-ERI-20 CB 7.5 23.53 0.282 8.52 8.35

site 14 EB-WSF-2 EB 2 25.16 0.282 8.53 8.4

site 15 EB-WSF-5 EB 4 25.18 0.282 8.22 8.37

site 16 EB-WSF-10 EB 5.5 24.87 0.282 8.29 8.35

site 17 EB-WSF-20 EB 5 24.57 0.28 8.54 8.41

site 18 EB-CCW-2 EB 2 25.25 0.281 8.53 8.33

site 19 EB-CCW-5 EB 5 24.95 0.281 8.24 8.33

site 20 EB-CCW-10 EB 6.75 24.79 0.28 8.24 8.33

site 21 EB-CCW-20 EB 6.5 24.4 0.277 8.53 8.57

98

Appendix 4: Concentrations of putrescine (Put), cadaverine (Cad), norspermidine (Nspd), spermidine (Spd) and spermine (Spm) from

LE August 2012.

Site site naming basin Put (nmol L-1) Cad (nmol L-1) Nspd (nmol L-1) Spd (nmol L-1) Spm (nmol L-1)

site 1 WB-SSP-2 WB 57.24 4.68 4.93 90.51 32.9

site 2 WB-SSP-5 WB 59.75 4.57 4.32 69.53 53.3

site 3 WB-TC-2 WB 50.4 4.02 3.48 101.5 26.67

site 4 WB-TC-5 WB 31.35 2.08 4.61 69.41 10.49

site 5 CB-HUR-5 CB 25.85 1.6 4.97 25.84 2.08

site 6 CB-HUR-10 CB 16.35 2 2.84 17.76 8.16

site 7 CB-HUR-20 CB 12.63 3.11 2.77 11.94 7.38

site 8 CB-GRW-10 CB 7.03 2.06 1.39 16.11 3.64

site 9 CB-GRW-20 CB 6.45 1.25 0.46 10.22 3.65

site 10 CB-ASH-10 CB 8.11 1.24 2.24 4.94 8.74

site 11 CB-ASH-20 CB 5.49 1.22 3.61 7.93 10.06

site 12 CB-ERI-10 CB 2.64 1.24 1.78 9.35 4.66

site 13 CB-ERI-20 CB 1.38 1.65 3.01 8.8 2.06

site 14 EB-WSF-2 EB 3.19 2.41 3.22 7.07 2.8

site 15 EB-WSF-5 EB 6.58 3.46 1.35 3.76 2.52

site 16 EB-WSF-10 EB 7.11 2.69 1.01 8.5 2.46

site 17 EB-WSF-20 EB 8.72 1.82 0.63 17.96 5.61

site 18 EB-CCW-2 EB 10.89 2.11 1.23 21.51 6.28

site 19 EB-CCW-5 EB 7.16 2 1.06 19.56 4.54

site 20 EB-CCW-10 EB 7.05 1.66 0.83 15.41 2.28

site 21 EB-CCW-20 EB 7.18 1.47 1.02 14.03 2.54

99

’

Appendix 5: Sampling location for LE and GLSM in July 2012.

Lake Transect Depth Latitude Longitude

Lake Erie Site-1 Surface 41°28’7.64”N 82°47’21.24”W

Site-2 Surface 41°31’58.36”N 82°36’28.36”W

Site-3 Surface 41°41’12.03”N 82°8’35.26”W

Grand lake St. Mary Site-1 Surface 40°30'29.74”N 84°32'24.07”W

Site-2 Surface 40°30'37.57”N 84°32'25.39”W

Site-3 Surface 40°31'0.31”N 84°32'0.33”W

100

Appendix 6: Concentrations of ammonium, nitrate/nitrite, Soluble reactive phosphate from LE and GLSM July 2012.

NH4+(µg L-1) NO3

-(µg L-1) SRP (µg L-1) Chl a (µg L-1)

Site site naming Average ±SD Average ±SD Average ±SD Average ±SD

site 1 LE1SB 27.1 4.6 33.00 12.7 2.5 0.35 24.0 2.6

site 2 LE2SSB 22.2 4.8 26.7 7.00 9.1 0.73 13.3 3.0

site 3 LE3CB 32.5 14.1 57.0 4.53 16.2 0.20 6.7 3.2

site 1 GLSM1 33.8 5.5 23.5 3.2 10.4 1.0 84.0 12.8

site 2 GLSM2 28.7 1.4 32.9 4.1 10.5 2.4 95.8 13.2

site 3 GLSM3 16.2 2.8 23.1 2.5 6.8 1.3 96.2 14.9

101

Appendix 7: Environmental variables measured from Lake Erie and GLSM July 2012.

D.O % T ˚C Con. (mS m-1) pH

Site site naming Average ±SD Average ±SD Average ±SD Average ±SD

site 1 LE1SB 105.60 1.56 26.38 0.45 0.38 0.01 9.35 0.21

site 2 LE2SSB 106.27 13.44 26.53 0.23 0.29 0.07 8.99 0.20

site 3 LE3CB 101.07 7.18 25.94 0.40 0.24 0.03 7.72 0.52

site 1 GLSM1 97.03 2.22 25.57 0.05 0.26 0.02 8.56 0.23

site 2 GLSM2 84.93 4.60 25.61 0.23 0.03 0.28 8.31 0.30

site 3 GLSM3 100.03 3.40 21.74 4.30 0.24 0.01 7.53 0.10

102

Appendix 8: Concentrations of putrescine (Put), cadaverine (Cad), norspermidine (Nspd), spermidine (Spd) and spermine (Spm) from

LE and GLSM July 2012.

*indicates sample had no triplicate, due to loss/breakage of samples.

Put (nmol L-1) Cad (nmol L-1) Nspd (nmol L-1) Spd (nmol L-1) Spm (nmol L-1)

Site Site naming average ±SD Average ±SD average ±SD average ±SD average ±SD

site 1 LE1SB 13.75 0.21 1.38 0.25 1.88 0.01 21.19 0.45 2.24 0.02

site 2 LE2SSB 14.92 0.76 0.10 * 1.75 0.11 20.33 2.20 2.44 0.11

site 3 LE3CB 14.79 0.67 3.75 0.08 1.53 0.18 17.40 2.72 2.39 0.21

site 1 GLSM1 26.03 4.87 2.06 0.58 3.69 0.40 3.69 0.40 29.70 8.98

site 2 GLSM2 45.47 4.33 1.71 0.09 4.49 0.80 4.49 0.80 35.47 2.70

site 3 GLSM3 257.23 35.25 2.29 0.55 2.64 0.61 2.64 0.61 206.30 60.19

103

Appendix 9: Concentrations, turnover rates of PAs and DFAAs in marine environments and freshwater environments.

Marine

Environments

PA DFAA

Concentrations

(nmol L-1)

Turnover

rates (d-1)

Concentrations

(nmol L-1)

Turnover

rates (d-1)

Eutrophic

stratified pond

0-250 2.4-16.8 200-1500 22-48

Oxic-stratified

trench

10 2 250 18

Anoxic

stratified trench

5 0.03 20 0.16

Hiroshima Bay 1.3-18.4 NA 900-5400 NA

Georgia Bay 0.1-9.4 NA 13.2-77.5 NA

South Atlantic

bight

0.02-4.4 0.005-0.94 60-77.8 0.04-12.2

Lake Erie 77.0 2.2 182.7 5.2

Grand Lake St

Marys

235.7 6.8 909.0 12.0

Documents

POLYAMINE TRANSFORMATION BY BACTERIOPLANKTON IN FRESHWATER ECOSYSTEMS A