ABUNDANCE, SIZE, AND SINGLE-CELL ACTIVITY
OF BACTERIAL GROUPS IN POLAR AND
TEMPERATE WATERS
by
Tiffany R. A. Straza
A dissertation submitted to the Faculty of the University of Delaware in
partial fulfillment of the requirements for the degree of Doctor of Philosophy in
Oceanography
Winter 2010
c 2010 Tiffany R. A. Straza
°
All Rights Reserved
ABUNDANCE, SIZE, AND SINGLE-CELL ACTIVITY
OF BACTERIAL GROUPS IN POLAR AND
TEMPERATE WATERS
by
Tiffany R. A. Straza
Approved:
Charles E. Epifanio, Ph.D.
Director of the School of Marine Science and Policy
Approved:
Nancy M. Targett, Ph.D.
Dean of the College of Earth, Ocean, and Environment
Approved:
Debra Hess Norris, M.S.
Vice Provost for Graduate and Professional Education
I certify that I have read this dissertation and that in my opinion it meets
the academic and professional standard required by the University as a
dissertation for the degree of Doctor of Philosophy.
Signed:
David L. Kirchman, Ph.D.
Professor in charge of dissertation
I certify that I have read this dissertation and that in my opinion it meets
the academic and professional standard required by the University as a
dissertation for the degree of Doctor of Philosophy.
Signed:
Byron C. Crump, Ph.D.
Member of dissertation committee
I certify that I have read this dissertation and that in my opinion it meets
the academic and professional standard required by the University as a
dissertation for the degree of Doctor of Philosophy.
Signed:
Thomas E. Hanson, Ph.D.
Member of dissertation committee
I certify that I have read this dissertation and that in my opinion it meets
the academic and professional standard required by the University as a
dissertation for the degree of Doctor of Philosophy.
Signed:
Jonathan H. Sharp, Ph.D.
Member of dissertation committee
ACKNOWLEDGEMENTS
The process to the PhD has been productive and enjoyable. I wish to especially thank my adviser, David Kirchman, for his support, critique, and attention
to detail. The more I know about science, and observe other scientists, the more
pleased I am to have been a member of “Kirchman lab”. Committee members
Byron Crump, Thomas Hanson, and Jonathan Sharp provided encouragement and
excellent feedback on project ideas, protocols, proposal writing, and the dissertation. Over the years, both Dave and Jon have welcomed me into their homes several
times as well; Ana and Gwyneth, thank you for the recipes!
Within the “Kirchman lab”, Matt Cottrell and Liying Yu were always helpful
with advice, training, and sometimes just helping me find the right equipment.
Liying and her husband Ming Xu also generously welcomed me into their home many
times. I greatly value the time shared, including the effort Li spent teaching me to
prepare Chinese food. Fellow lab students Vanessa Michelou, Sharon Grim, Katrina
Twing, Karen Rossmassler, Glenn Christman, Hila Elifantz, Rex Malmstrom, Lisa
Waidner, Mrina Nikrad, and lab visitor Helene Hodal taught me techniques, troubleshot protocols, packed tips, helped with sampling, and in general helped keep work
in the lab possible and enjoyable. I value their friendship, scientific expertise, and
attention to keeping the lab drawers shut completely.
The crew of the R/V Sharp made sampling possible and enjoyable. Staff
Peggy Conlon and Connie Edwards were helpful, empathetic, and hilarious, and I
do not know which characteristic deserves more thanks. Librarians Ellen Erbe and
Debbie Booth were very gracious in their help acquiring the many inter-library loans
iv
I wanted. Ryan Dale introduced me to Python coding and other applications that
made the work more efficient, or at least more technologically involved. Friendships
within and outside of the CMS → CMES → CEOE community, while impossible
to list completely here, are much appreciated. The Possum Point Players, Deni
Robinson, and especially Lynne VanHauter helped to keep me creative and sane.
My family provided the more nebulous, but nonetheless requisite, supports
to survival. My parents have continuously been very supportive of me. While in
Lewes and from afar, Tommy Moore has been a sounding board and problem-solver
I very much needed. I thank him and his roommates for their unbridled support of
my cooking.
v
TABLE OF CONTENTS
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii
ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv
Chapter
1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.1
1.2
1.3
1.4
1.5
Bacterial diversity. . . . . . . . . . . . . .
Bacterial biovolume. . . . . . . . . . . . .
Use of organic compounds. . . . . . . . . .
Environmental controls of marine bacteria.
Bibliography . . . . . . . . . . . . . . . . .
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1
1
3
4
6
9
2 SEASONAL, GEOGRAPHIC AND PHYLOGENETIC
VARIATION IN BACTERIAL BIOVOLUME USING PROTEIN
AND NUCLEIC ACID STAINING . . . . . . . . . . . . . . . . . . . 15
2.1
2.2
2.3
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.3.1
2.3.2
2.3.3
2.4
2.5
2.6
Protein staining. . . . . . . . . . . . . . . . . . . . . . . . . . 18
Abundance of bacterial groups. . . . . . . . . . . . . . . . . . 19
Microscopy and data analyses. . . . . . . . . . . . . . . . . . . 19
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
vi
3 SINGLE-CELL ACTIVITY OF BACTERIAL GROUPS
RELATED TO ENVIRONMENTAL CONDITIONS IN THE
DELAWARE BAY, USA . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.1
3.2
3.3
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Methods and Materials . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.3.1
3.3.2
3.4
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.4.1
3.4.2
3.4.3
3.5
3.6
Identification of bacterial groups. . . . . . . . . . . . . . . . . 44
Bacterial incorporation of substrates. . . . . . . . . . . . . . . 44
Abundance of bacterial groups. . . . . . . . . . . . . . . . . . 46
Uptake of organic material. . . . . . . . . . . . . . . . . . . . 47
Effects of light exposure. . . . . . . . . . . . . . . . . . . . . . 48
Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4 ABUNDANCE AND SINGLE-CELL ACTIVITY OF
BACTERIAL GROUPS IN ANTARCTIC COASTAL WATERS
4.1
4.2
4.3
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
Methods and Materials . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.3.1
4.3.2
4.3.3
4.4
Sample collection. . . . . . . . . . . . . . . . . . . . . . . . . . 75
Identification of bacterial groups. . . . . . . . . . . . . . . . . 75
Incorporation of substrates by specific bacterial groups. . . . . 76
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.4.1
4.4.2
4.4.3
4.5
4.6
71
Bacterial group abundance. . . . . . . . . . . . . . . . . . . . 78
Total community activity. . . . . . . . . . . . . . . . . . . . . 79
Use of compounds by bacterial groups. . . . . . . . . . . . . . 80
Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
vii
5 CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.1
5.2
5.3
5.4
Bacterial biovolume. . . . . . . .
Use of organic compounds. . . . .
Biogeography of marine microbes.
Bibliography . . . . . . . . . . . .
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101
102
103
105
Appendix
A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
viii
LIST OF FIGURES
2.1
Epifluorescence micrograph of cells stained with DAPI (A) and
Sypro Ruby (B). . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.2
Seasonal variation in biovolume based on protein and DNA stains in
Delaware Bay. Lines are means over the entire period. . . . . . . . 36
2.3
Geographic variation in biovolume based on protein and DNA stains
(A) and protein/DNA ratio (B). Delaware Bay samples are
represented by the mean ± standard deviation. . . . . . . . . . . . 37
2.4
Geographic variation in biovolumes of Alphaproteobacteria,
Gammaproteobacteria, and the SF group. Error bars are one
standard error. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.5
Biovolumes of SAR11 and total bacteria (EUB338) based on protein
(A) and DNA (B). Within each site, the latitude for the means were
offset slightly to clarify. Error bars are one standard error. . . . . . 39
3.1
Temperature (A), bulk 3 H-leucine incorporation (B), microbial
abundance (C), and concentrations of chlorophyll a (D) in Delaware
Bay and coastal waters. Arrows indicate times of Micro-FISH
sampling. Error bars are one standard error. . . . . . . . . . . . . . 66
3.2
Uptake of leucine and an amino acid mixture (A) and protein (B) by
the bacterial community in the dark. Horizontal lines indicate mean
uptake of a compound. Error bars are one standard error. ND = no
data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.3
Uptake of leucine and protein by Alpha- and Gammaproteobacteria
compared to the total bacterial community (EUB338-labeled cells)
in the dark in experiments conducted from 2006 to 2008. Line is the
1:1 line. The mean standard errors are indicated for leucine (+) and
protein (X). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
ix
3.4
Bulk 3 H-leucine incorporation in light or dark incubations in
experiments conducted from 2006 to 2008. Line is the 1:1 line. Error
bars are one standard error. . . . . . . . . . . . . . . . . . . . . . . 69
3.5
Incorporation of leucine and protein by SAR11 bacteria compared to
the total community in the light in experiments conducted from
2006 to 2008. Line is the 1:1 line. The mean standard errors are
indicated for leucine (+) and protein (X). . . . . . . . . . . . . . . 70
4.1
Sampling locations (white dots) on transects off the west Antarctic
Peninsula. An: Anvers Island; R: Renaud Island; L: Lavoisier Island;
Ad: Adelaide Island; MB: Marguerite Bay. Solid line denotes coastal
domain (inshore); dashed line denotes continental shelf break; area
between the lines is the continental shelf domain and area beyond
shelf break is continental slope/deep ocean domain. Most of the
coastal and shelf domain area is covered by seasonal sea ice. . . . . 94
4.2
Microbial abundance and bulk 3 H-leucine incorporation at sampling
sites. Horizontal axis is the transect line as defined by Church et al.
(2003) and shown in Figure 4.1. . . . . . . . . . . . . . . . . . . . . 95
4.3
Abundances of three major bacterial groups. Error bars are one
standard error. Horizontal axis is explained in Figure 4.2. . . . . . 96
4.4
Abundances of three bacterial sub-groups: SAR11
(Alphaproteobacteria), Polaribacter (SF group), and Ant4D3
(Gammaproteobacteria). Error bars are one standard error.
Horizontal axis is explained in Figure 4.2. . . . . . . . . . . . . . . 97
4.5
Fractions of the total bacterial community actively using selected
compounds in coastal and offshore waters. Error bars are one
standard error. Horizontal axis is explained in Figure 4.2. . . . . . 98
4.6
Fractions of bacterial groups actively using selected compounds.
Samples from coastal stations are grouped together followed by
offshore stations. Within the location groups, samples are arranged
left to right from transect 200 to transect 600 (Church et al. 2003).
Error bars are one standard error. . . . . . . . . . . . . . . . . . . . 99
x
A.1
Uptake of leucine and an amino acid mixture (A) and protein (B) by
the bacterial community in the light. Horizontal lines indicate mean
uptake of a compound. Error bars are one standard error. ND = no
data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
A.2
Alpha- (A) and gammaproteobacterial (B) uptake of leucine.
Asterisks indicate significant difference between light and dark
treatments. Error bars are one standard error based on ten fields of
view. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
A.3
Alpha- and gammaproteobacterial uptake of protein. Asterisks
indicate significant difference between light and dark treatments.
Error bars are one standard error. . . . . . . . . . . . . . . . . . . . 112
A.4
Alpha- and gammaproteobacterial uptake of a mixture of 15 amino
acids. Asterisks indicate significant difference between light and
dark treatments. Error bars are one standard error. . . . . . . . . . 113
xi
LIST OF TABLES
2.1
Abundance of total prokaryotes and bacterial groups. Mean ±
standard error. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.2
Mean protein volume (µm3 ) of bacterial groups, ± one standard
error. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.1
Correlations between relative abundances of three bacterial groups
and environmental variables. N is the number of dates examined
over two years. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.2
Mean fraction of groups incorporating the indicated compounds in
light and dark incubations. Mean % ± standard error of 11-14
samples of total community or 16-30 samples for bacterial groups. . 62
3.3
Pearson correlation coefficients of environmental variables compared
with the fraction of the total community incorporating a given
compound. N is number of samples. . . . . . . . . . . . . . . . . . . 63
3.4
Response to light by the total community and bacterial groups. # is
the number of experiments with a significant difference (ANOVA, p
< 0.05) between dark and light incubations. Light effect, %
stimulation or inhibition, was calculated as (ActivityLight ActivityDark )/ActivityDark , and the mean reported here. . . . . . . 64
3.5
Correlations between the light effect on uptake of a given compound
(Light - Dark) and photosynthetically active radiation (PAR) prior
to sampling. N = 11-13. . . . . . . . . . . . . . . . . . . . . . . . . 65
4.1
Basic biogeochemical properties in waters off the west Antarctic
peninsula. Biovolume of prokaryotic cells was determined by protein
staining. Mean ± standard error. . . . . . . . . . . . . . . . . . . . 91
xii
4.2
Abundance of bacterial groups in the three regions studied. Mean ±
standard error. N = 14-15 for each group. . . . . . . . . . . . . . . 92
4.3
Contribution of each group to the total fraction of cells taking up a
given compound. Mean ± standard error. N = 14-15 for each group. 93
B.1
Locations of stations sampled during January 2007 Palmer LTER
cruise. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
B.2
Fraction of bacterial groups actively using leucine. Mean ± standard
error from ten fields. . . . . . . . . . . . . . . . . . . . . . . . . . . 116
xiii
ABSTRACT
Microbial communities dominate the fluxes of organic material in the ocean,
in part due to their high abundance. To determine the amount of carbon processed by bacteria, bulk properties, such as production, abundance, biomass, and
respiration, are measured for the total community. Phylogenetic analyses of bacteria are used to describe the structure within microbial communities. However,
neither bulk activity measurements nor phylogenetic identification alone can determine which bacterial groups respond to certain environmental conditions or which
bacterial groups use certain organic compounds. The goal of this dissertation was
to assess the responses of different bacterial taxa to environmental conditions and
available substrates.
A basic characteristic of microbes is cell size. The size of microbial cells affects
ecological interactions with other organisms and may be related to rates of biomass
production. Using a protein stain, I analyzed the biovolume of microbial communities in Arctic, Antarctic, and temperate waters. Microbes in higher latitudes were
on average 30% larger than cells from temperate waters. The abundance of bacterial
taxa varied among geographic regions, and the size of some bacterial groups also
differed among regions. Gammaproteobacteria and members of the SphingobacteriaFlavobacteria (SF) group were larger in high latitude waters. In each environment,
SF cells were larger than other bacteria by about 15%, while Gammaproteobacteria
were intermediate in size and Alphaproteobacteria did not differ in size from the
average bacterial cell.
xiv
In addition to varying in size, bacterial taxa differ in the use of organic
material. I used microautoradiography and fluorescent in situ hybridization to identify bacteria incorporating organic compounds. In the Delaware estuary and midAtlantic bight, about 30% of all cells incorporated leucine and other amino acids,
while only 10% incorporated protein. Using light and dark treatments, I found that
light affected single-cell activity in about 20% of cases, but there was no net effect
of light on bulk bacterial production. Light did not affect Gamma- and Alphaproteobacteria differently. However, 25% more bacteria in the SAR11 clade used leucine
in the light than the total community. Other environmental conditions besides light
also correlated with the abundance and activity of bacterial groups. Gammaproteobacterial abundance correlated with bacterial production and concentrations of
dissolved organic carbon and nitrogen, and a higher fraction of Gammaproteobacteria used leucine in the summer than in the fall.
There is also geographic variation in abundance and activity of specific bacterial taxa. I examined the abundance and single-cell activity of dominant bacterial
clades in waters off the west Antarctic peninsula. More bacteria used leucine (40%)
than used a mixture of amino acids or protein (12-22%). Gammaproteobacteria were
a large fraction (20%) of the community in this region, and using a new probe I assessed the ecological role of the Ant4D3 gammaproteobacterial clade. The Ant4D3
clade constituted 10% of the total community, and while the active fraction of this
clade did not differ among various compounds, Ant4D3 dominated the incorporation
of amino acids. The use of organic material varied among the Polaribacter, SAR11,
and Ant4D3 clades. Polaribacter contributed the most to protein uptake. Though
dominated by different bacterial taxa, the activity of this Antarctic microbial community was comparable to that of temperate communities. The research presented
in Chapter 4 is the first description of the single-cell activity of bacterial groups in
coastal Antarctic waters.
xv
The research described in this dissertation details the abundance of specific
bacterial groups along with bacterial cell size (Chapter 2), light effects on bacterial
activity (Chapter 3), and bacterial activity in polar waters (Chapter 4). Generally
the approach taken was to divide the “black box” of all microbes into broad phylogenetic groups, which display characteristic differences yet are abundant as cohesive
units in the microbial community. Assessing microbial communities at this scale,
I found variation of broad bacterial taxa in size, activity, and response to environmental factors. The combination of single-cell methods with genomic approaches
will enable us to move toward quantifying bacterial contribution to global processes
and predicting the response of bacterial groups to environmental change.
xvi
Chapter 1
INTRODUCTION
Microbes are abundant members of the marine ecosystem. Together, autotrophic and heterotrophic microbes dominate the global cycles of carbon and of
other elements and drive fluxes of dissolved organic material in the ocean. Prokaryotes form the largest proportion of the total biomass in marine systems (Ducklow
2000, Whitman et al. 1998). Identifying the microbes contributing to marine geochemical cycles is an important focus of microbial ecology and biogeochemistry
(Pernthaler and Amann 2005). In this dissertation, I will focus on the abundance
and activity of marine bacteria. With advances in metagenomics and other cultureindependent methods, the phylogenetic structure of microbial communities can readily be described (DeLong and Karl 2005). Microbial ecologists are left with the
question of function: What are these communities doing in the environment? Much
of modern microbial ecology centers around a search for meaningful ecological units,
related to genetic sequence, on a scale at which we can see patterns between bacterial
diversity and functions such as the cycling of organic material.
1.1
Bacterial diversity.
Historically, bacteria have been studied as a “black box” with no differentia-
tion among bacterial groups. However, current evidence suggests that specific phylogenetic groups vary in abundance and activity, and contribute differently to the use
of different substrates (Alonso-Saez and Gasol 2007, Malmstrom et al. 2007). The
distinction between cyanobacteria and heterotrophic bacteria is an obvious one, but
1
other groupings may be just as ecologically relevant. Broad phylogenetic groupings,
such as the common class-level distinctions used by fluorescent in situ hybridization
(FISH) probes, may be useful if differences in abundance or activity are apparent at
that scale (Amann and Fuchs 2008, Amann et al. 1990). To identify and quantify
groups of bacteria in environmental samples, single-cell methods such as FISH are
used (Amann et al. 1990, DeLong et al. 1989).
Bacteria in several broad phylogenetic clusters are found widespread throughout the ocean. Dominant bacterial groups in marine systems include the Proteobacteria and members of the Bacteroidetes, which in marine waters is mostly represented
by the Flavobacteria and Sphingobacteria (including the genus Cytophaga) classes
and will be referred to as “SF group” in this dissertation (Cottrell and Kirchman
2000, Glöckner et al. 1999, Kirchman 2002, Kirchman et al. 2005). Betaproteobacteria typically dominate lacustrine and other low-salinity environments, while Alphaand Gammaproteobacteria dominate saline regimes. There is some evidence pointing toward a greater importance of Gammaproteobacteria in polar waters (Glöckner
et al. 1999). The SF group is important in estuaries and may play a role in polar
ecosystems (Kirchman 2002, Kirchman et al. 2003). Primary evidence for bacterial
biogeographical variation has come from comparing bacterial communities between
locations with different environmental conditions (Kirchman et al. 2005, Pommier
et al. 2005).
Estuaries are important systems of high productivity and strong environmental gradients, making estuaries ideal sites for examining bacterial response to environmental factors. In estuaries, the salinity gradient and mixing in the transitional
zone shape patterns of bacterial abundance (Bouvier and del Giorgio 2002, Crump
et al. 2004, Kirchman et al. 2005). In the Delaware estuary, the most abundant bacterial groups as determined using FISH are Alphaproteobacteria, Betaproteobacteria,
2
Sphingobacteria-Flavobacteria (SF group), and Actinobacteria, with Gammaproteobacteria usually present but in low abundance (Cottrell and Kirchman 2004,
Kirchman et al. 2005). Alphaproteobacterial abundance increases with increasing
salinity, forming a greater proportion of the total community at the mouth of the
bay. The opposite trend is seen for Betaproteobacteria and Actinobacteria. These
patterns have been seen in other systems, including the Chesapeake Bay and the
Ria de Aveiro estuary in Portugal (Bouvier and del Giorgio 2002, Henriques et al.
2006, Kan et al. 2006). A unique estuarine community may develop at intermediate salinities if the bacterial community doubling time is shorter than the residence
time, which can vary seasonally (Crump et al. 2004). Chapter 3 of this dissertation
focuses on the single-cell activity of bacterial groups in the Delaware estuary and
coastal waters.
1.2
Bacterial biovolume.
Bacterial biomass depends on both the abundance and the size of individ-
ual cells. Bacterial abundance in surface waters is often quite stable in spite of
seasonal shifts or differences between geographic regions (Ducklow 2000, La Ferla
and Leonardi 2005). Conversion factors are used to estimate elemental contents or
biomass in terms of carbon, often relying on cell abundance and an assumed carbon
content per cell. However, cellular carbon content can vary greatly among regions
and perhaps among types of bacteria (Fukuda et al. 1998, Gundersen et al. 2002,
Lee and Fuhrman 1987). Also, many ecological interactions, such as grazing, are
affected not only by total biomass but also by individual cell size (Hahn and Höfle
2001). One key issue in measurements of bacterial biovolume is obtaining accurate cell sizes from natural samples. The common stains such as 4’-6-diamidino-2phenylindole (DAPI) and acridine orange may misrepresent the true cell size (Porter
and Feig 1980, Suzuki et al. 1993). DAPI is specific to nucleic acids and may stain
only the nucleoid, missing the cell wall and other cellular components and therefore
3
underestimating cell size. Acridine orange is less specific and is known to stain detritus as well as live cells, along with other problems preventing its use for accurate
measurements of pelagic bacterial volumes.
The size variation among bacterial groups is not well characterized. Typically
cells cultured in rich media are larger than cells found in marine environments, but
the range of possible size for different bacterial strains is not well characterized
(Lebaron and Joux 1994, Schaechter et al. 1958). Pelagibacter ubique, when grown
on minimal media, has been suggested to be among the smallest bacterial cells
(Rappé et al. 2002). However, environmental analyses have found that SAR11 cells
may not be any smaller than the average bacteria (Malmstrom et al. 2005). The
research presented in Chapter 2 also examines the size of this important group. In
the Delaware Estuary, total SF group bacteria and the SF sub-group DE Cluster
2 both had average cell volumes significantly larger than the average bacterium
(Kirchman et al. 2003). The authors also found that the average biovolume of SF
group bacteria in the Arctic was significantly smaller than the average bacterium,
but DE Cluster 2 bacteria were significantly larger than the average bacterium.
These results indicate biovolume variation within groups, with patterns potentially
masked when samples are analyzed in bulk or at the class level. While marine
bacteria are often smaller and grow more slowly than their cultured counterparts,
bacteria in all parts of the size spectrum are active in marine environments (Cottrell
and Kirchman 2004). In chapter 2, I discuss the variation in bacterial biovolume
among geographic regions, and point to differences between bacterial groups.
1.3
Use of organic compounds.
Many types of organic material, differing by characteristics such as elemental
content or molecular weight, are available in the marine environment and may be
used for bacterial growth (Crump et al. 2003, Kaiser and Benner 2009). Marine
dissolved organic material is dominated by low molecular weight, refractory material
4
(Amon and Benner 1994). Low molecular weight material may be transported
directly across the cell membrane, while material larger than about 500 Da must be
broken down (Weiss et al. 1991). However, bacterial growth and respiration were
higher in treatments with high molecular weight material than with low molecular
weight (Amon and Benner 1994).
While marine bacteria are known to be active and contribute to biogeochemical cycles, our understanding of the distribution of activity within bacterial communities is still incomplete (del Giorgio and Gasol 2008). Initial attempts to quantify
marine bacteria using solid rich media culturing techniques determined that the
number of colonies that would grow from a volume of seawater was quite low (Daley
1979). Advances in epifluorescent microscopy demonstrated high numbers of cells
present, but there was still uncertainty over what fraction of the cells were actually alive (Daley 1979, Porter and Feig 1980, Zweifel and Hagström 1995). Initially
some researchers suggested that most oceanic bacteria are inactive because bacteria
in the marine environment are generally smaller than cultured bacteria (Stevenson
1978). We now know that environmental bacteria are growing. In situ bacterial
production rates are now commonly estimated by the assimilation of radiolabeled
leucine or thymidine (Kirchman et al. 1985). Bulk measurements give community
average activity rates, while cell-specific techniques such as microautoradiography
describe the assimilation by individual bacteria (Lee et al. 1999).
Functional roles of different types of bacteria in fluxes of organic material depend on both the abundance of the bacterial group and the rate and type of substrate
use by that group (Pernthaler and Amann 2005). Because of the different metabolic
capacity required to transform and incorporate different types of organic material,
the use of different types of organic material may vary among phylogenetic clusters
of bacteria. There appear to be differences in substrate selection among the broad
5
groups commonly examined using FISH. In some experiments, the Alphaproteobacteria form the largest fraction of any group using low molecular weight compounds
such as amino acids (Elifantz et al. 2007, Longnecker et al. 2006). A high fraction of
the ubiquitous SAR11 clade of Alphaproteobacteria has been seen to incorporate low
molecular weight material such as amino acids, while few SAR11 cells incorporate
high molecular weight compounds (Malmstrom et al. 2005). It has been suggested
that the use of high molecular weight material is dominated by members of the
SF group (Cottrell and Kirchman 2000, Kirchman 2002, Malmstrom et al. 2007).
More data are needed to characterize the use of organic compounds by bacterial
groups, and with this goal in mind the research presented in Chapters 3 and 4 was
undertaken.
1.4
Environmental controls of marine bacteria.
The abundance of bacterial groups may be driven by bottom-up factors such
as substrate availability, light, or temperature. While some seasonal patterns in
group abundance have been observed (Crump and Hobbie 2005, Fuhrman et al.
2006, Henriques et al. 2006), the factors controlling the shifts are still not well
understood. The total bacterial community and bacterial groups respond to phytoplankton blooms, which often follow predictable seasonal patterns (Alderkamp
et al. 2006, Crump et al. 2003). Members of the Alphaproteobacteria and the SF
group are known to vary in abundance in response to phytoplankton blooms. The
Roseobacter clade of Alphaproteobacteria often forms a minor part of the total bacterial community, but has been seen to increase in abundance in association with
phytoplankton blooms (Alderkamp et al. 2006). SF group members increased in
abundance during blooms of different kinds of phytoplankton (Fandino et al. 2005).
In the North Sea, SF group abundance increased from about 20% to 63% of the
total community during a prymnesiophyte bloom (Alderkamp et al. 2006).
6
Recent findings have suggested that light may also directly affect heterotrophic
bacterial activity. The discoveries of aerobic anoxygenic phototrophic bacteria and
the presence of proteorhodopsin genes in many divisions of bacteria necessitate reconsideration of environmental pressures, bacterial responses, and the subsequent
impact on DOM cycling patterns (Béjà et al. 2000, de la Torre et al. 2003, Kolber et al. 2000). Aerobic anoxygenic phototrophic bacteria may play an important
role in estuarine systems (Waidner and Kirchman 2005, 2007), and more studies
are needed to determine the role of light in shaping their contribution to production. Marine proteorhodopsins appear to function as light-dependent proton pumps
when expressed in E. coli (Friedrich et al. 2002, Walter et al. 2007). One cultured
Flavobacteria strain, MED134, containing the proteorhodopsin gene suite responded
to light with an increased growth rate (Gómez-Consarnau et al. 2007). This indicates
that proteorhodopsins can be used for light-reactive processes, and more generally
that light can have an impact on bacterial growth (Gómez-Consarnau et al. 2007).
However, to date MED134 is the only tested strain which has responded to light,
and a gammaproteobacterium and a SAR11 strain with the proteorhodopsin gene
did not respond to light treatment (Giovannoni et al. 2005, Stingl et al. 2007).
Bulk growth rates measured by incorporation of leucine and methionine by
the bacterial community responded positively to light treatment in some experiments (Church et al. 2004, Mary et al. 2008, Moran et al. 2001). However, repeated
experiments show the response is not always present (Michelou et al. 2007). Exposure to sunlight, and particularly UV radiation, may actually inhibit bacterial
activity (Alonso-Saez et al. 2006). When single-cell activity was measured after exposure to UV radiation, some groups of bacteria such as the Gammaproteobacteria
were more resistant while others were more susceptible to damage, particularly the
Alphaproteobacteria (Alonso-Saez et al. 2006). Different responses to light have the
7
potential to shape bacterial communities seasonally and with depth. The high UVresistance of Gammaproteobacteria may explain the high abundance of Gammaproteobacteria in the sea-surface microlayer (Alonso-Saez et al. 2006, Franklin et al.
2005). Specialization for different light conditions has been seen in genomic studies
of Prochlorococcus and proteorhodopsin-containing organisms (Béjà et al. 2001, Field
et al. 1997, Man et al. 2003, Moore et al. 1995). Light appears to have a complex
affect on bacterial communities, and more data are required to describe bacterial
response to light. The research presented in Chapter 3 explores the impact of light
on organic material uptake by heterotrophic bacteria.
The main goal of this dissertation was to compare the ecological roles of bacterial groups in different environments. I compared bacterial communities from high
and low latitudes, and examined the uptake of different substrates. This work will
help to place marine bacteria in the larger ecological context by considering the size
distribution of cells, contribution to substrate use, and response to environmental
conditions. Experimental studies measuring ecological functions, such as the assimilation of dissolved organic material, along with community structure provide further
insight into the ecological and biogeochemical role of bacteria.
8
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14
Chapter 2
SEASONAL, GEOGRAPHIC AND PHYLOGENETIC
VARIATION IN BACTERIAL BIOVOLUME USING
PROTEIN AND NUCLEIC ACID STAINING
2.1
Abstract
Biovolume is an important characteristic of cells that shapes the contribution
of microbes to total biomass and biogeochemical cycling. Most studies of bacterial
cell volumes use 4’-6-diamidino-2-phenylindole (DAPI), which stains nucleic acids
and therefore only a portion of the cell. I used SYPRO Ruby protein stain combined
with fluorescence in situ hybridization to examine biovolumes of bacteria in the total
community as well as in phylogenetic sub-groups. Protein-based volumes varied
more and were consistently larger than DNA-based volumes by 3.3-fold on average.
Bacterial cells were about 30% larger in the Arctic Ocean and Antarctic coastal
waters than in temperate regimes. I hypothesized that geographic differences in the
abundance of specific bacterial groups drove the observed patterns in biovolume.
In support of this hypothesis, I found that Gammaproteobacteria and members
of the Sphingobacteria-Flavobacteria group were larger in higher latitude waters
and that the mean volumes of both groups were larger than the mean bacterial
volume in all environments tested. Cell size of SAR11 bacteria was larger than the
mean size of the total bacterial community on average, though this varied. Protein
staining increases the accuracy of biovolume measurements and gives insights into
how biomass of marine microbial communities varies over time and space.
15
2.2
Introduction
Size is an important characteristic of microbes that affects cell physiology
and trophic interactions. Cell size in combination with abundance can be used to
estimate microbial biomass (Gundersen et al. 2002, Lee and Fuhrman 1987). The
size and shape of a cell affects the efficiency of nutrient uptake by determining
the ratio of surface area to volume (Button 1994). Size-dependent mortality also
contributes to the structuring of microbial assemblages (Hahn and Höfle 2001).
Larger cells are grazed more, and cells too small or too large cannot be ingested
by protists (Gonzalez et al. 1990, Hahn and Höfle 2001). Small cells may also be
less susceptible to viral lysis (Weinbauer and Höfle 1998). An accurate description
of microbial cell volumes is important for understanding microbial interactions and
the contribution of different cells to total microbial biomass.
In spite of its importance, biovolume of cells in microbial communities has not
been extensively examined, although some data indicate that size varies by season
and among geographic locations due to variation in environmental conditions. The
size of bacterial cells varied daily in an artificial lake, peaking in the afternoon
(Jugnia et al. 1998). Mean bacterial cell volume in the Sargasso Sea was smallest in
the winter, with high day-to-day variation (Carlson et al. 1996). Acridine orangestained cells in the northern Adriatic Sea were larger in June than in February
for two consecutive years, and varied with environmental parameters (La Ferla and
Leonardi 2005). Cell volume varied twenty-fold whereas cell abundance ranged
tenfold (La Ferla and Leonardi 2005). Other studies are necessary to determine
seasonal and geographic changes in cell volumes.
Biovolume may also vary among microbial groups. Cultured representatives
of the ubiquitous SAR11 clade of Alphaproteobacteria measured using transmission
electron microscopy have a mean size of 0.01 µm3 , among the smallest of cultured
bacteria (Rappé et al. 2002). The opposite is seen in the Bacteroidetes phylum,
16
which in marine waters is mostly represented by the Flavobacteria and Sphingobacteria classes (including the genus Cytophaga), referred to as the “SF group” (Amann
and Fuchs 2008). These cells are often larger than cells in other bacterial groups
(Kirchman 2002), although this varies. While the average size of SF group bacteria
was the same as total bacteria in the Delaware Bay, SF group members were smaller
than the average prokaryote in the Arctic Ocean (Kirchman et al. 2003). Bacteria in
a SF sub-group, DE Cluster 2, were larger than other bacteria in both environments.
Because biomass depends on cell size, not just cell abundance, the distribution of
biovolume within bacterial groups is as important as abundance.
Most epifluorescence microscopic studies of natural bacteria use 4’-6-diamidino2-phenylindole (DAPI), a stain for nucleic acids that fluoresces blue under UV excitation when bound to double-stranded DNA (Porter and Feig 1980). Because DAPI
is specific for DNA, DAPI images may contain only the nucleoid, not the whole cell.
Cell volumes determined with DAPI are about 60% smaller than volumes based on
acridine orange, another common nucleic acid stain (Suzuki et al. 1993). Acridine
orange is less specific than DAPI and stains detrital particles and dead cells (Bölter
et al. 2002, Suzuki et al. 1993). Also, when bound to RNA, acridine orange fluoresces
the same color as cyanobacteria and as bacteria identified with probes commonly
used in fluorescent in situ hybridization (FISH, Bölter et al. 2002). DAPI can be
used to estimate cell abundances, but a different stain for measuring cell volumes is
necessary.
Because protein comprises about 60% of cell mass (Simon and Azam 1989),
I selected SYPRO Ruby protein stain (Berggren et al. 2000) for determining cell
volume by epifluorescence microscopy. SYPRO Ruby (Molecular Probes) is specific
to proteins and stains most classes of proteins (Berggren et al. 2000). It fluoresces
at a wavelength distinguishable from cyanobacterial auto-fluorescence and cyanine3-labeled FISH probes. In this study, I measured cell volumes with DAPI DNA
17
staining and SYPRO Ruby protein staining of bacteria from a range of seasons
and geographic locations. I observed greater variation in size based on protein
staining than on nucleic acids, and describe here biovolumes of bacteria from several
environments and bacterial groups.
2.3
Materials and Methods
Samples were taken from several locations to examine geographical variation
in bacterial biovolume. Surface waters were sampled monthly beginning in February
2006 at the mouth of the Delaware Bay and 18.5 km offshore (http://www.ocean.udel
.edu/cms/dkirchman/MOPE). Antarctic samples were collected in coastal shelf waters off the West Antarctic peninsula in January 2007 (http://pal.lternet.edu). Arctic (Chukchi Sea) sampling was described by Malmstrom et al. (2007). Samples
from the North Atlantic (Michelou et al. 2007) and central North Pacific near Hawaii
(Cottrell et al. 2006) were also examined. Briefly, water samples were fixed with
paraformaldehyde (2% final concentration) and filtered onto 0.22 µm polycarbonate
filters, rinsed with 0.22 µm-sterilized deionized water, then stored at -20 ◦ C.
2.3.1
Protein staining.
For protein staining, filter pieces were dipped in 0.3% low-melt agarose (Meta-
phor) and placed cell-side down on glass slides. After drying, the filter piece was
wetted with 95% ethanol then carefully peeled away, leaving the exposed cells embedded in agarose on the slide. Cells on the slides were stained with SYPRO Ruby
protein stain (Invitrogen; diluted 1:1 with deionized water) for 30 minutes, then
rinsed with deionized water. After drying, samples were mounted with DAPI (0.5
ng µL-1 ) in a 4:1 mixture of Citifluor (Ted Pella) and Vectashield (Vector Labs)
anti-fading mountants.
18
2.3.2
Abundance of bacterial groups.
To estimate abundance and biomass of specific bacterial groups, samples were
analyzed by catalyzed reporter deposition fluorescent in situ hybridization (CARDFISH; Pernthaler et al. 2002) prior to Sypro Ruby staining. Probes for Alphaproteobacteria (Alf968), Gammaproteobacteria (Gam42a), the SF group (CF319a),
SAR11 clade (SAR11-441r) of Alphaproteobacteria, all bacteria (EUB338) and a
negative control were used (Amann et al. 1990, Glöckner et al. 1999, Karner and
Fuhrman 1997, Manz et al. 1996, 1992, Morris et al. 2002). Cells were embedded on
the filter using 0.1% agarose, then treated with lysozyme to increase permeability.
Filter pieces were hybridized with horseradish peroxidase-labeled oligonucleotide
probes, and subsequently stained with cyanine-3 (Cy3)-labeled tyramides (TSA kit,
Perkin Elmer). Following this staining, the filter pieces were transferred onto slides
and stained with SYPRO Ruby and DAPI as described above.
To test the impact of the protocols on biovolume measurements, I compared
cell volumes from the same sample with only protein staining, FISH with protein
staining, and CARD-FISH with protein staining. There was no significant impact of
protocol on size of the protein-stained image (independent Student’s t-test, n=6, p
> 0.05). Mean cell volumes (± SE) based on protein were 0.08 ± 0.01 µm3 for Ruby
staining only, 0.10 ± 0.02 µm3 after FISH, and 0.09 ± 0.02 µm3 after CARD-FISH.
2.3.3
Microscopy and data analyses.
Samples were examined using a modification of the semi-automated mi-
croscopy system described by Cottrell and Kirchman (2003). Ten fields of view
were counted per sample, using a constant exposure time of 300 msec for SYPRO
Ruby images and 75-100 msec for DAPI images (Fig. 2.1). The Cy3 image exposure
time for CARD-FISH analyses was set using the negative control, and ranged 200300 msec. Objects in the SYPRO Ruby and Cy3 images were only considered to
be cells if there was a corresponding object in the DAPI image. Group abundances
19
from CARD-FISH were determined as the percentage of the total DAPI objects
appearing in both the Cy3 and DAPI images. Non-specific probe binding was below
5% for all samples.
The volumes of the DNA- and protein-stained images were calculated assuming cells were cylinders with hemispherical caps (Baldwin and Bankston 1988) and
were compared using a paired Student’s t-test. Objects with volume less than 0.0042
µm3 (equivalent to the pore size of the filter) or greater than 0.344 µm3 (equivalent to 0.87 µm diameter) in any stain image were not considered to be prokaryotic
cells, and were excluded from all calculations (Gasol et al. 1995). Volume data
were log-transformed and community percentage data were arcsin-transformed for
all statistical tests.
2.4
Results
I tested the application of SYPRO Ruby protein stain for measuring the
biovolume of environmental bacteria, and compared protein volumes to DNA volumes. In the Delaware Bay from 2006-2007, the protein-stained cell images were
always larger than the DNA-stained images (Fig. 2.2). The mean protein/DNA
ratio was always greater than one and ranged from 1.5-5.6 (mean 3.2 ± 1.0). Protein volume (CV = 0.74) varied more than DNA volume (CV = 0.58), but there
was no clear seasonal trend (Fig. 2.2). No significant correlations of cell volume or
protein/DNA ratio were observed with other parameters, such as chlorophyll a concentrations, 3 H-leucine incorporation, temperature, and photosynthetically active
radiation (data not shown).
I analyzed geographic variation in cell volume in “high” (Arctic and Antarctic) and “low” latitudes (Delaware Bay, North Atlantic, and North Pacific). Cells
were significantly larger in high latitude than in low latitude waters based on protein (independent t-test, p=0.00001) and DNA (p=0.007) volumes, as well as on
the protein/DNA ratio (p=0.01) (Fig. 2.3). The mean difference for protein-based
20
volume was 0.02 µm3 or 32%, whereas for DNA the difference was only 0.003 µm3
(16%). The protein/DNA ratio in these samples ranged from 1-6.6, with a mean of
3.3 ± 1.2. Biovolume based on both DAPI (CV = 0.58) and SYPRO Ruby (CV =
0.74) varied more than cell abundance (CV = 0.18).
To further explore this geographic difference in volume, I examined the average cell size of major bacterial groups in the Arctic, Antarctic, and Delaware Bay
using CARD-FISH combined with SYPRO Ruby staining. Alpha- and Gammaproteobacteria were less abundant in Delaware Bay than in the high latitude samples
(t-test, p=0.02 and 0.0002, respectively), whereas the abundance of the SF group
was not significantly different (Table 2.1). Protein volumes within the bacterial
groups across the latitudes varied for some groups (Table 2.2). SF cells were significantly smaller (mean difference 0.04 µm3 , or 25%) in the Delaware Bay than
in the Arctic and Antarctic, which did not significantly differ (ANOVA, p > 0.05)
from each other (Fig. 2.4). Gammaproteobacteria were also smaller in the Delaware
Bay than in the Arctic (40% larger in the Arctic), and largest in Antarctic waters
(64% larger than in the Delaware Bay). The volume of Alphaproteobacteria did not
significantly differ among the three locations (ANOVA, p>0.05).
With data from all latitudes pooled together, SF group cells were larger than
cells of all other groups (Table 2.2). Gammaproteobacteria were 10% smaller than
the SF, but 15% larger than Alphaproteobacteria and the average bacterial (EUB338positive) cell. Alphaproteobacteria did not differ from the average bacterial cell.
This was the pattern observed in Arctic samples. In the Antarctic, cell volumes of
SF and Gammaproteobacteria were not significantly different, and both were larger
than Alphaproteobacteria and the average bacterial cell, which were the same. In
the Delaware Bay, SF cells were larger than all the other groups by 15% (ANOVA,
p < 0.05), and cell sizes of Gammaproteobacteria, Alphaproteobacteria, and average
bacteria were not significantly different from each other (ANOVA, p > 0.05).
21
To determine if it is possible to account for the mean bacterial biovolume
based on the examined bacterial groups, I calculated the sum of the biovolumes of
the groups weighted by their abundances. In the Arctic and Antarctic, the sum of the
measured group volumes was similar to the average volume of the total community.
The sum of Arctic group volumes was 0.15 ± 0.02 µm3 , versus the EUB338 cell mean
of 0.10 ± 0.01 µm3 , while the sum of Antarctic group volumes was 0.11 ± 0.02 µm3 ,
versus 0.09 ± 0.01 µm3 for the EUB338 cells (Table 2.2). However, in the Delaware
Bay this calculation differed greatly from measured EUB338 cell volumes. The sum
of group biovolumes was 0.03 ± 0.01 µm3 , substantially less than the EUB338 cell
mean volume of 0.11 ± 0.01 µm3 , suggesting that another abundant group is driving
the mean cell volume in the estuary. The presence of another abundant group is
implied by the low percentage (29 ± 10%) of the community accounted for by Alphaand Gammaproteobacteria along with the SF group (Table 2.1).
Because of the suggestion that SAR11 cells may be smaller than other bacteria (Rappé et al. 2002), I examined this clade in the same regions discussed above.
When all samples were pooled, the protein biovolumes of SAR11 cells were larger
than the mean of all bacteria (EUB338-positive), while there was no significant difference between DNA-based volumes (ANOVA; Fig. 2.5). The protein/DNA ratio
was also higher for SAR11 than for all bacteria. However, the mean difference in
protein volume between SAR11 and total bacterial cells was only 0.003 µm3 or 3%.
In the Arctic samples, SAR11 cells were larger than cells in the total bacterial community and in total Alphaproteobacteria (Table 2.2). In the Delaware Bay, SAR11
cells were larger than the bacterial mean, as well as the mean size of Alpha- and
Gammaproteobacteria. However, in the Antarctic, SAR11 cells were smaller than
cells in the total assemblage, although they were not different from cells in the
alphaproteobacterial group (ANOVA, p>0.05).
22
2.5
Discussion
Bacterial abundance and biovolume are commonly examined using DAPI
stain for DNA. When using nucleic acid stains like DAPI, the localization of the
fluorescent signal due to concentration of DNA in the nucleoid can lead to underestimation of cell sizes (Suzuki et al. 1993, Zweifel and Hagström 1995). I tested
the application of the SYPRO Ruby protein stain for epifluorescence microscopy
to measure bacterial biovolumes. Biovolumes based on protein-stained cells were
larger and varied more than DNA-based biovolume estimates in the marine systems
examined here.
Protein forms a large portion of bacterial cells, but may also be part of
marine detritus (Long and Azam 1996). Long and Azam (1996) used Coomassie
Blue staining to show that non-cellular protein particles may be highly abundant
in coastal systems, ranging in abundance from 106 -108 L-1 in coastal waters with
20-40% colonized by bacteria. In the present study, all objects I considered cells
were those present in the DAPI-stained image, with a volume within a set size
range (0.0042-0.344 µm3 ). This excludes objects like Coomassie-stained particles,
which would be present only in the Ruby-stained image. Additionally, using the
two images together allows us to compare protein- and DNA-based volumes.
I observed a mean protein/DNA volume ratio of 3.3 but with much variation,
perhaps due to the dependence of the cellular protein/DNA ratio on growth rates
and phases during growth cycles. The expected ratio of protein to DNA based
on weight is on the order of 4.8-5.8 (Simon and Azam 1989), larger than what I
observed. Because DAPI staining may be non-specific (Zweifel and Hagström 1995),
the observed DAPI volume may be larger than the actual nucleoid size, leading to
the slightly lower protein/DNA ratio that I observed.
Classic studies of pure cultures found that rapidly-growing cells in high nutrient conditions have high protein content and large volumes (Baker et al. 1983,
23
Lebaron and Joux 1994, Moyer and Morita 1989, Schaechter et al. 1958). These
same cells when starved can decrease their total size and may condense their nucleoids into a small portion of the cell (Lebaron and Joux 1994). Greater plasticity
in protein content explains why I observed more variability in protein-based volumes
than in DNA-based estimates, although there were no significant relationships with
environmental factors. Zubkov et al. (1999) compared Hoechst DNA-stained cells
with SYPRO Red protein-stained cells using flow cytometry and found that protein biomass varies with growth conditions for cells with similar DNA content. The
observations of low variation in DAPI-based volumes agree with this consistency in
DNA content, which may be explained by the presence of cells growing at different
rates in a complex microbial community responding to different environmental pressures. I observed no relationship of mean cell size with mean community growth
rates, suggesting that factors other than growth rate alone control cell volume in
natural microbial communities.
I observed larger cells at higher latitudes, even with DNA staining, but especially using the protein stain, where cell volumes differed by 32% between high and
low latitudes. The most obvious difference between the high and low latitude sites is
temperature. Cell volumes determined using acridine orange staining of four facultatively psychrophilic strains grown by Wiebe et al. (1992) were largest in the lowest
temperatures at a given substrate concentration. The mechanism is unknown, but
was suggested to be related to generation time, i.e. cells were largest when growing
slowly at low temperatures (Wiebe et al. 1992). However, an increase in cell volume
with slower growth rate is opposite to the classic model (Schaechter et al. 1958). A
different mechanism must be applicable in these environmental conditions.
In addition to temperature and other bottom-up controls of bacterial cell volume, the size distribution of natural assemblages is also affected by top-down factors
of cell mortality. Perhaps the prevalence of large cells in high latitudes is a result
24
of changes in grazing pressure or viral mortality, as has been proposed to explain
the larger volumes of bacteria in sea ice than in the surrounding water (Mock et al.
1997). Viral mortality in the Chukchi Sea varies, however the measured grazing and
viral lysis in Arctic waters leaves much cell mortality unexplained (Steward et al.
1996, 2007). Either changes in rates of mortality or differences in the functional
response could alter the size structure of bacterial communities. The observed variation in size suggests that the pressures or responses differ between high and low
latitude environments.
I hypothesized that differences in size among latitudes may be explained
by shifts in community structure, known to occur among aquatic regimes (Biers
et al. 2009, Glöckner et al. 1999, Pommier et al. 2007). The groups targeted by
our FISH probes are phylogenetically diverse but have been shown to have distinct
biogeographical patterns (Kirchman et al. 2005). There were no differences in the
abundance of the SF group among latitudes, but both Alpha- and Gammaproteobacteria were more abundant in the high latitude samples. Additionally, in the Arctic
and Antarctic, Gammaproteobacteria and the SF group cells were on average significantly larger than their counterparts in the Delaware Bay. The high numbers of
large Gammaproteobacteria in high latitude waters may drive the changes observed
in the mean biovolume of the total bacterial community.
The group-level probes used here were designed based on a smaller database
of bacterial sequences than is available now, and as such may miss some bacteria
(Amann and Fuchs 2008). However, the probes appear to match to a sufficiently
large fraction of the targeted groups for the purpose of this study. The probes
GAM42a and ALF968 cover 76-80% of Gamma- and Alphaproteobacteria respectively, with few mis-matches (Amann and Fuchs 2008). CF319a matches 90% of
both Flavobacteria and Sphingobacteria, including Cytophaga (Amann and Fuchs
25
2008). Better group-level probes that capture the true diversity of the groups, without being compromised by mismatches, have not yet been developed (Amann and
Fuchs 2008). However, problems with the probes do not affect our general conclusion that variation in bacterial biovolume is related to the abundance and size of
cells in different bacterial groups.
The broad phylogenetic groups examined here can be further divided into
subgroups. I found no difference in SAR11 abundance among regions, indicating
another alphaproteobacterial sub-group contributed to the higher total alphaproteobacterial abundance in higher latitudes. The mean size of SAR11 cells was the
same as the mean size of alphaproteobacterial cells in Antarctic waters, but SAR11
cells were larger in the Delaware Bay and Arctic waters. Similarly, using DAPI,
Kirchman et al. (2003) found that biovolumes of the DE Cluster 2 SF sub-group
were greater than biovolumes of total SF bacteria in both the Delaware Bay and the
Arctic. Variation in the average cell size of a bacterial group may be due to variation in the cell size of its subgroups and the relative abundance of those subgroups.
The use of a single biovolume for a microbial group can hide ecologically-relevant
variation within that group.
Bacteria of the SAR11 sub-group of Alphaproteobacteria are ubiquitous in
marine environments, and are hypothesized to have a compressed genome and small
cell size (Rappé et al. 2002). In contrast to expectations (Rappé et al. 2002), SAR11
cells were not always smaller than the mean volume of bacteria in our samples, and
with all samples pooled together SAR11 cells were actually slightly larger than the
average bacterium. Malmstrom et al. (2004) obtained similar results using DAPI
and found that members of the SAR11 clade in the North Atlantic and Sargasso Sea
were at least as large as other bacteria. The small size of cultured SAR11 strains
may have been influenced by the fixation and transmission electron microscopy
preparation, which can cause cell shrinkage (Rappé et al. 2002). In addition, SAR11
26
is a very diverse clade (Field et al. 1997), with some members perhaps smaller than
others. A better understanding of SAR11 biovolumes will improve our measure of
their contributions to the biomass and size structure of the total assemblage.
Cell volume can be used along with conversion factors of carbon per unit biovolume to estimate total bacterial biomass (Gundersen et al. 2002, Lee and Fuhrman
1987). If the size of the DAPI image is used to calculate both the volume and the
conversion factor, then the inaccuracy in the resulting volume estimate could be
canceled out for the average bacterium. However, the use of DAPI may lead to
a perceived lack of variation in biovolume and biomass. While the abundance of
bacterial cells usually does not vary greatly (Ducklow 2000), cell size can vary substantially even using DAPI (Carlson et al. 1996, La Ferla and Leonardi 2005), but
especially using a protein-based biovolume measurement (this study). Using DNAstained images to calculate biomass masks the true variation in total biomass of
microbial assemblages.
In this study, I described seasonal and geographic variation in cell biovolumes
of bacterial groups. Importantly, I observed large variations in cell-specific biovolumes based on protein staining that were not apparent with DNA staining. Bacterial
groups differed both in abundance and in biovolume for different geographic regions.
Some, such as the SF group, displayed biovolume characteristics that were expected
while others, such as the SAR11 clade, did not. A simple calculation showed that
mean cell volume does depend on the groups present. Larger mean cell volumes in
higher latitudes may be explained by the presence of more Gammaproteobacteria.
The combination of biovolume measurements such as SYPRO Ruby staining with
group-specific identification furthers our understanding of variability in complex
natural assemblages.
27
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Table 2.1: Abundance of total prokaryotes and bacterial groups. Mean ± standard
error.
(105 cells mL−1 )
Area
Arctic
Antarctic
Delaware Bay
a Cells
% of total
Total Abundance
Bacteriaa
Alpha
Gamma
SF Group
SAR11
9.5 ±2.2
7.2 ±1.9
4.8 ±0.69
74 ± 4
81 ± 4
50 ± 9
11 ± 4
12 ± 3
3±1
71 ± 8
50 ± 12
11 ± 2
39 ± 11
20 ± 5
16 ± 10
30 ± 12
10 ± 4
5±4
identified with EUB338 CARD-FISH probe.
33
Table 2.2: Mean protein volume (µm3 ) of bacterial groups, ± one standard error.
Region
Arctic
Antarctic
Delaware
All
a Based
Bacteriaa
0.100
0.106
0.087
0.097
±
±
±
±
0.04
0.04
0.06
0.04
Alpha
0.123
0.094
0.090
0.103
±
±
±
±
0.06
0.06
0.03
0.05
Gamma
0.120
0.142
0.086
0.118
±
±
±
±
0.04
0.03
0.04
0.04
SF Group
0.138
0.152
0.109
0.134
on cells identified with EUB338 CARD-FISH probe.
34
±
±
±
±
0.03
0.05
0.04
0.04
SAR11
0.107
0.082
0.114
0.100
±
±
±
±
0.05
0.07
0.06
0.06
Figure 2.1: Epifluorescence micrograph of cells stained with DAPI (A) and Sypro
Ruby (B).
35
Figure 2.2: Seasonal variation in biovolume based on protein and DNA stains in
Delaware Bay. Lines are means over the entire period.
36
Figure 2.3: Geographic variation in biovolume based on protein and DNA stains (A)
and protein/DNA ratio (B). Delaware Bay samples are represented by the mean ±
standard deviation.
37
Figure 2.4: Geographic variation in biovolumes of Alphaproteobacteria, Gammaproteobacteria, and the SF group. Error bars are one standard error.
38
Figure 2.5: Biovolumes of SAR11 and total bacteria (EUB338) based on protein
(A) and DNA (B). Within each site, the latitude for the means were offset slightly
to clarify. Error bars are one standard error.
39
Chapter 3
SINGLE-CELL ACTIVITY OF BACTERIAL GROUPS
RELATED TO ENVIRONMENTAL CONDITIONS IN
THE DELAWARE BAY, USA
3.1
Abstract
Bacteria in temperate coastal environments may respond to the substantial
variation in environmental conditions with changes in abundance or activity. I examined the uptake of leucine, protein, and a mixture of 15 amino acids by selected
phylogenetic groups in light and dark treatments. In coastal waters of the Delaware
Bay and mid-Atlantic bight, single-cell activity of different bacterial groups varied
with molecular weight of the substrate, light availability, and other environmental
conditions. About 30% of all cells incorporated leucine and the amino acid mixture,
while only 10% incorporated protein. More Gamma- and Alphaproteobacteria used
leucine than used protein. While the active fraction of the total bacterial community
did not change between summer and fall, more Gammaproteobacteria actively used
leucine in the summer. Light availability affected single-cell activity in 20% of experiments, but there was neither an effect on bulk bacterial production nor differences
between the two proteobacterial classes. However, about 25% more bacteria in the
SAR11 clade than in the total community used leucine in the light. The complex
effects of light on heterotrophic bacterial communities included both stimulation
and inhibition, and bacterial groups responded differently to light availability.
40
3.2
Introduction
Use of dissolved organic material (DOM) by bacteria depends on many phys-
iological and environmental factors, including metabolic capacity, substrate type,
and light availability (Pernthaler and Amann 2005). Bottom-up control by these
environmental factors can shape the contribution by bacteria to fluxes of organic
material. In temperate marine environments, bacteria are exposed to different environmental conditions within seasonal cycles and across geographic space. Identifying
responses to those conditions requires the use of practical but meaningful phylogenetic units within the bacterial community. Broad phylogenetic groupings, such as
those recognized by commonly-used fluorescent in situ hybridization (FISH) probes,
may be useful if differences in abundance or activity are apparent at that scale
(Amann and Fuchs 2008, Amann et al. 1990b). Linking patterns of bacterial group
abundance and the activity of those groups with factors controlling biomass and
activity has proved challenging.
There is evidence that broad groups of bacteria vary in their use of organic
material (Cottrell and Kirchman 2000, Elifantz et al. 2007, Longnecker et al. 2006).
Some studies have shown that more cells in the Alphaproteobacteria class use low
molecular weight compounds like amino acids or glucose than high molecular weight
compounds such as protein or polysaccharides (Alonso-Saez et al. 2009, Cottrell and
Kirchman 2000, Elifantz et al. 2005). Within the Alphaproteobacteria, more than
half of the ubiquitious SAR11 clade incorporated amino acids and dimethylsulfoniopropionate (DMSP) in the North Atlantic (Malmstrom et al. 2004). However,
a smaller fraction of SAR11 used polysaccharides and protein than low molecular
weight material such as leucine and other amino acids (Elifantz et al. 2005, Malmstrom et al. 2005). The single-cell activity of Gammaproteobacteria has not been as
well-studied, but the fraction of this group using different compounds did not vary
overall in western Arctic waters (Elifantz et al. 2007). In contrast, a smaller fraction
41
of Gammaproteobacteria were actively using glucose than other compounds in both
the Mediterranean Sea and Delaware estuary (Alonso-Saez and Gasol 2007, Elifantz
et al. 2005).
The activity of these broad bacterial groups may shift in response to many environmental factors, some of which vary seasonally (Fuhrman et al. 2006, Henriques
et al. 2006). In Blanes Bay in the northwest Mediterranean, more Gammaproteobacteria were actively using amino acids in winter than in summer (Alonso-Saez and
Gasol 2007). Active fractions of Gammaproteobacteria and the SphingobacteriaFlavobacteria increased with increasing concentrations of glucose during a spring
phytoplankton bloom in the North Sea (Alonso and Pernthaler 2006b). In the California current system, the fraction of active cells in the whole bacterial community
responded to changes in temperature and salinity but there were no differences in the
responses of the bacterial groups studied, including Sphingobacteria-Flavobacteria
and the Alpha-, Beta-, and Gammaproteobacteria (Longnecker et al. 2006). The
specific environmental factors driving bacterial community structure are not well
characterized.
The ability of some heterotrophic bacteria to use light as an additional source
of energy may drive variation in growth and abundance (Béjà et al. 2001, Kolber
et al. 2000). Exposure to light can stimulate the total community uptake of compounds such as leucine and methionine (Church et al. 2004, Mary et al. 2008, Moran
et al. 2001), but the effect may not always be present or the same (Michelou et al.
2007). Some of the light effect may be due to the proteorhodopsin gene, a potential
light-harvesting mechanism found in many bacterial taxa (Béjà et al. 2000, de la
Torre et al. 2003). A cultured Flavobacteria strain containing proteorhodopsin grew
faster in the light than in the dark (Gómez-Consarnau et al. 2007). However, cultured strains containing the gene for proteorhodopsin of Pelagibacter ubique and a
gammaproteobacterium did not respond to light (Giovannoni et al. 2005, Stingl et al.
42
2007). In the environment, stimulation of bacterial activity by light exposure may
be offset by light inhibition. Alonso-Saez et al. (2006) observed higher sensitivity
to UV light in the Alphaproteobacteria, with other groups such as the Gammaproteobacteria and Bacteroidetes more resistant to photo-damage. The effects of light
vary, and more work is necessary to determine the responses of different bacterial
groups.
The goal of this study was to identify the variation in single-cell activity of
different bacterial groups in the lower Delaware Bay in response to environmental properties. I hypothesized that substrate use by bacterial groups varies with
molecular weight of the substrate, related to light exposure. I tested the uptake of
leucine, protein, and a mixture of 15 amino acids in both light and dark conditions
by selected phylogenetic groups in summer and fall. As expected, single-cell activity
varied among the compounds. However, while the effect of light did vary among
bacterial groups, I observed both stimulation and inhibition of bacterial activity by
light.
3.3
Methods and Materials
Surface waters were sampled monthly at the mouth of the Delaware Bay
and 18.5 km offshore starting in 2006 (http://www.ocean.udel.edu/cms/dkirchman/
MOPE). Abundances of all prokaryotes was measured using epifluorescence microscopy with 4’-6-diamidino-2-phenylindole (DAPI) staining (Cottrell et al. 2006,
Porter and Feig 1980). Concentrations of chlorophyll a, dissolved organic carbon
(DOC) and dissolved organic nitrogen (DON) were estimated by standard methods (Benner and Strom 1993, Parsons et al. 1984, Sharp et al. 1995). Bacterial
production was estimated using 3 H-leucine incorporation with the microcentrifuge
method, using a 30 min incubation at in situ temperatures in the dark (Kirchman
2001). Bulk growth rates were calculated by dividing bacterial production (mg C
m−3 d−1 ) by biomass (mg C m−3 ), assuming a conversion factor of 20 fg C cell−1
43
and 1.5 kg C per mole of leucine (Kirchman et al. 2009, Lee and Fuhrman 1987).
Additionally, bulk incorporation of 3 H-leucine in the light and dark was measured
using 6h incubations with light from a metal halide lamp (1 x1016 quanta sec−1
cm−2 ; Hydrofarm).
3.3.1
Identification of bacterial groups.
The abundance of bacterial groups was estimated using catalyzed reporter
deposition fluorescent in situ hybridization (CARD-FISH; Pernthaler et al. 2002).
Water samples were fixed overnight at 4 ◦ C with paraformaldehyde (PFA, 2% final concentration). Samples were filtered through 0.22 µm polycarbonate filters
(Millipore) with a 0.45 µm nitrocellulose support filter (Millipore), rinsed with 0.22
µm filter-sterilized deionized water, and stored at -20 ◦ C until further processing.
Probes for Alphaproteobacteria (Alf968), Gammaproteobacteria (Gam42a), SAR11
clade (SAR11-441r), all bacteria (EUB338) and a negative control were used (Amann
et al. 1990a, Karner and Fuhrman 1997, Manz et al. 1992, Morris et al. 2002, Neef
1997). Cells were embedded on the filter using 0.1% agarose, then treated with
lysozyme to increase permeability. Filter pieces were hybridized with horseradish
peroxidase-labeled oligonucleotide probes, and subsequently stained with cyanine-3
(Cy3)-labeled tyramides (TSA kit, Perkin Elmer). Following this staining, the filter pieces were either taken on to microautoradiography or transferred onto slides
and stained with DAPI (0.5 ng µL-1 ) in a 4:1 mixture of Citifluor (Ted Pella) and
Vectashield (Vector Labs) anti-fading mountants.
3.3.2
Bacterial incorporation of substrates.
The uptake of selected compounds by specific bacterial groups was examined
using microautoradiography combined with CARD-FISH (Micro-FISH) on samples
taken in the summer and fall (Fig. 3.1). Water samples for Micro-FISH were incubated with 20 nM 3 H-leucine (Perkin Elmer), a mixture of 15 tritiated amino acids
44
(0.5 nM, American Radiolabeled Chemicals), or 0.4 µg mL-1 3 H-protein. 3 H-protein
was made using a Vibrio alginolyticus culture (Nagata et al. 1998). Incubations
with leucine or amino acid mixture lasted 2 h and protein incubations lasted 6 h
at in situ temperatures in the dark or exposed to light from the metal halide lamp.
After incubation, samples were fixed with PFA (2% final concentration) overnight at
4 ◦ C and filtered as for FISH. PFA was added to killed controls prior to the addition
of labeled compounds.
Microautoradiography was carried out as described by Cottrell and Kirchman
(2003). Slides were dipped in film emulsion (Amersham Hypercoat EM-1), then filter
pieces were placed cell-side down onto the emulsion. A time series of exposure in
the emulsion before developing was used to select the shortest time at which the
number of active cells identified reached a maximum. The exposure time for leucine
samples was 24 h, for amino acids 3-4 days, and for protein samples 6 days. At the
end of the exposure time, slides were developed and fixed (Dektol developer and
fixer, Kodak). After drying overnight, the filter pieces were carefully peeled away
and the cells stained with DAPI in the 4:1 mountant as described above.
Cells were counted using a semi-automated microscope system previously
described (Cottrell and Kirchman 2003). Ten fields of view were counted for each
sample, with a constant exposure time of 100 msec for silver grain images. DAPI
image exposure times ranged 75-100 msec, and Cy3 image exposure times were set
by the negative control and ranged 300-500 msec. Group abundances are presented
as the percentage of the total number of DAPI-stained objects that were also Cy3labeled. The proportion of the total community that was active was calculated as
the number of DAPI-stained objects with associated silver grain clusters divided
by the total number of DAPI-stained objects. Similarly, the number of probe- and
DAPI-stained objects with silver grains was divided by the total number of probeand DAPI-positive cells to calculate the percentage of a group that was actively
45
taking up a given compound. Non-specific probe binding was below 5% for all
samples. All percentage data were arcsine-tranformed for statistical analyses.
3.4
Results
I examined the variation in single-cell activity of different bacterial groups in
the lower Delaware Bay in comparison to environmental changes over three years.
Environmental properties at two sites varied over time. Bulk 3 H-leucine incorporation and temperature peaked in the fall, whereas total bacterial abundance averaged
4.76 ± 0.83 x 106 cells mL−1 and peaked in late summer (Fig. 3.1). Chlorophyll a
concentrations correlated positively with bacterial abundance (r = 0.70, N = 78, p
< 0.05) and temperature (r = 0.78, N = 78, p < 0.05).
3
H-leucine incorporation
correlated with temperature (r = 0.74, N = 70, p < 0.05; Fig. 3.1) and bacterial
abundance (r = 0.53, N = 70, p < 0.05). The two sites were similar and the results
from both stations were combined for analyses.
3.4.1
Abundance of bacterial groups.
I analyzed the abundance of selected bacterial groups using CARD-FISH.
Gammaproteobacteria and Alphaproteobacteria were equally abundant, each accounting for around 25% of the community. SAR11 abundance averaged 15% (CV =
0.78) of the total microbial abundance. Relative Gammaproteobacteria abundance
varied more than the other groups (CV = 0.98) and positively correlated with bulk
3
H-leucine incorporation, temperature, and concentrations of DOC and DON (Table
3.1). Alphaproteobacterial abundance did not vary with environmental conditions,
but SAR11 abundance was negatively correlated with temperature and chlorophyll
a concentration (Table 3.1). The abundance of the Sphingobacteria-Flavobacteria
averaged 9.8% in initial samples (n = 7, data not shown), and this group was not
examined further.
46
3.4.2
Uptake of organic material.
Incorporation by bacteria differed among the substrates examined, and varied
with environmental properties. Leucine and the amino acid mixture were incorporated by 30 ± 3 % of the bacterial community (Table 3.2, Fig. 3.2). In contrast, on
average only 10 ± 1 % of cells incorporated protein. The percentage of cells taking
up leucine decreased with total bacterial abundance (Table 3.3). The fractions of
the community incorporating leucine and amino acids negatively correlated with
chlorophyll a concentration (r = -0.47 and -0.33, respectively), while the fraction
using protein positively correlated with chlorophyll a concentration (r = 0.42, Table 3.3). The proportion of the bacterial community using protein also positively
correlated with temperature and bacterial abundance (Table 3.3).
Incorporation of organic material varied within groups and between the different groups (Table 3.2). Fewer EUB338-positive cells incorporated protein (10%)
than incorporated leucine and amino acids (both 30%). Greater proportions of
Alpha- and Gammaproteobacteria actively used leucine than protein, but the fraction using the amino acid mixture did not differ from that using either leucine or
protein for both groups (Table 3.2, Appendix A). In leucine experiments, more Alphaproteobacteria were active than Gammaproteobacteria and the total community;
Gammaproteobacteria and the total community did not differ (Fig. 3.3). The fraction of SAR11 incorporating leucine did not differ from the fraction of the other
groups. Although alphaproteobacterial abundance did not vary significantly with
environmental conditions (Table 3.1), the fraction of the Alphaproteobacteria that
were active in using all compounds negatively correlated with bacterial production
(r = -0.25, N = 69, p = 0.038) and the concentration of DOC (r = -0.24, p = 0.045).
I compared the activity of bacterial groups between summer (late May - August) and fall (September - November) in dark incubations (Fig. 3.2, see Appendix
A for light incubations). Bulk growth rates did not differ between summer and
47
fall, averaging 0.11 ± 0.02 d−1 . The total fraction of cells incorporating protein
was higher in the fall than in the summer, while the incorporation of leucine and
amino acid mixture did not differ between the two seasons. A larger proportion
of Gammaproteobacteria incorporated leucine in the summer (45%) than in the fall
(23%). More Alphaproteobacteria and SAR11 incorporated amino acids in the summer (50 and 28%, respectively) than in the fall (23 and 16%). Likewise, more SAR11
used protein in the summer (10%) than in the fall (2%).
3.4.3
Effects of light exposure.
Overall, bulk 3 H-leucine incorporation did not differ between light and dark
in 6h experiments conducted over two years, but in individual cases light did have an
effect (Fig. 3.4). However, light did not always stimulate bacterial production and in
some cases leucine incorporation was inhibited (Fig. 3.4). Single-cell activity of the
total community in 6 out of 35 experiments was affected by light; 3 were stimulated
by light while 3 had higher activity in the dark (Table 3.4). In leucine assays with
significant differences, only stimulation by light was observed. The opposite was the
case for protein, although only one incubation was affected. For amino acid uptake,
both stimulation and inhibition by light occurred (Table 3.4). Single-cell activity in
about 25% of protein and amino acid mixture experiments responded to light, while
only 11% of leucine experiments were affected.
Light conditions prior to sampling may affect the bacterial response to light in
incubations (Alonso-Saez et al. 2006). The fraction of the total bacterial community
using the amino acid mixture correlated with the mean PAR during three days
preceding sampling (0.66, p < 0.05). The fraction of cells using leucine or protein
did not vary with environmental light conditions. The effect of light, determined
as the difference in the fraction of cells actively incorporating a given compound in
the light versus in the dark, in sample incubations was related to the PAR prior to
sampling (Table 3.5). Light-affected incorporation of leucine negatively correlated
48
with the PAR at sampling and the sum of the PAR during the sampling day. In
contrast, the light effect on incorporation of the amino acid mixture correlated
negatively with the mean and peak PAR during the three days prior to sampling.
Light-affected protein incorporation did not correlate with light conditions prior to
sampling.
Single-cell activity of bacterial groups responded to light in a similar fraction
(20%) of cases as the total community. Alpha- and Gammaproteobacteria did not
differ in response to light (Table 3.4). Gammaproteobacteria responded to light in
9 cases, while Alphaproteobacteria responded in 6 cases. For protein and leucine,
only Gammaproteobacteria and SAR11 cells had higher fractions of cells active in response to light. However, both groups also had cases in which more cells were active
in the dark. Light exposure led to more cells using amino acids only for Alphaproteobacteria (Table 3.4). There were similar numbers of cases of light stimulation and
light inhibition, but the magnitude of response was approximately two-fold greater
in cases where cells were stimulated by light.
About 25% more SAR11 cells were active than the average bacterial cell
in using leucine when exposed to light (ANOVA p<0.05, Fig. 3.5). Pooling light
and dark experiments together, 14% more SAR11 cells actively used leucine than
gammaproteobacterial cells. In light incubations, more Gammaproteobacteria used
protein than SAR11 cells, though the difference was only about 7%, and neither
group differed significantly from the total community or from Alphaproteobacteria.
3.5
Discussion
The composition of bacterial communities differs among locations and varies
with environmental changes (Eilers et al. 2001, Kirchman et al. 2005, Longnecker
et al. 2006, Pinhassi et al. 2004, Schauer et al. 2003). Causes for this variation
in abundance and activity, including differences in availability and use of organic
material, are still uncertain. The aim of this study was to assess the abundance
49
of broad bacterial groups and their use of dissolved organic material in relation to
environmental factors. Temperate estuaries like the Delaware Bay are useful model
systems with strong seasonal changes (Pennock and Sharp 1986). I found variation
in abundance and single-cell activity with environmental conditions, and differences
in the use of organic material among bacterial groups.
The abundance of bacterial groups may change between environments, but
also within one location as seasons change. In this study, I did find differences
between the Alpha- and Gammaproteobacteria in abundance. Alphaproteobacteria
are abundant in many environments (Alonso-Saez and Gasol 2007, Elifantz et al.
2007), and are also a dominant group in the Delaware estuary. While the abundance of total Alphaproteobacteria did not vary with environmental properties, the
abundance of both Gammaproteobacteria and the alphaproteobacterial clade SAR11
changed with environmental variables. SAR11 abundance varied seasonally in the
mid-Atlantic Bight and the northwest Mediterranean Sea (Alonso-Saez and Gasol
2007, Campbell et al. 2009). The abundance of Gammaproteobacteria has been
seen to change in response to changing environmental conditions, including phytoplankton blooms (Alderkamp et al. 2006, Alonso-Saez and Gasol 2007, Teira et al.
2008). Whereas Gammaproteobacteria are a small fraction of the total community
in other marine systems (Alonso-Saez et al. 2007, Kirchman et al. 2005), I found
high gammaproteobacterial abundance in the estuary and coastal waters.
The Gammaproteobacteria provide an example of how broad bacterial groups
can have identifiable characteristics.
The fraction of Gammaproteobacteria us-
ing amino acids and protein was as high as the fraction of Alphaproteobacteria.
Gammaproteobacteria have often been found to be less abundant in marine systems,
and are thought to be specialized to DOM-rich conditions (Alonso-Saez et al. 2007,
Kirchman et al. 2005). The total abundance and active fraction of the SAR86 cluster of Gammaproteobacteria increased during a phytoplankton bloom (Alderkamp
50
et al. 2006), and the largest fraction of SAR86 was active in experiments with the
highest concentrations of leucine (Alonso and Pernthaler 2006a). In this study, the
correlation between gammaproteobacterial abundance and total bacterial production, as well as concentrations of DOC and DON, supports the hypothesis that this
group is specialized for highly productive conditions (Eilers et al. 2000, Rehnstam
et al. 1993).
The results presented here add to data showing differences in substrate use
by broad phylogenetic groups in marine environments (Cottrell and Kirchman 2000,
2003, Elifantz et al. 2007). High fractions of Alphaproteobacteria have been seen
to use low molecular weight substrates (Alonso-Saez and Gasol 2007, Longnecker
et al. 2006). My results agree with these findings, as more Alphaproteobacteria
were actively using leucine than Gammaproteobacteria in my samples. Also, more
cells of both Gammaproteobacteria and Alphaproteobacteria used leucine than protein. Members of the Bacteroidetes, predominantly Sphingobacteria and Flavobacteria in marine systems, use high molecular weight material (Cottrell and Kirchman 2000, Kirchman et al. 2005, Malmstrom et al. 2007). The low abundance of
Sphingobacteria-Flavobacteria in these samples may explain in part the low fraction
of cells using protein.
The activity of the bacterial community may change with or without proportional differences of the bacterial taxa present (Longnecker et al. 2006). Some studies have tracked the single-cell activity of bacterial groups over seasons (Alderkamp
et al. 2006, Alonso-Saez et al. 2007, 2008) or over environmental gradients (Cottrell and Kirchman 2003, Kirchman et al. 2005, Longnecker et al. 2006). Yet few
identify the factors resulting in changes in the abundance of broad bacterial groups
(Alderkamp et al. 2006, Cottrell and Kirchman 2003, Longnecker et al. 2006). The
differences I observed in activity between summer and fall varied among both bacterial groups and compounds. More Gammaproteobacteria were actively using leucine
51
in the summer, although the use of leucine by the total community did not change
between summer and fall. A higher fraction of the total community incorporated
protein in the fall, but more SAR11 used protein in the summer. More data are
needed to determine the environmental factors controlling the growth of specific
bacterial groups (Alderkamp et al. 2006, Kirchman et al. 2005).
The SAR11 clade is ubiquitous in many marine environments, but the fraction
of the group active in DOM uptake varies (Alonso and Pernthaler 2006b, Rappé
et al. 2002). SAR11 in the North Atlantic contributed to about half of the total
community leucine and glucose incorporation, but SAR11 were not dominant in the
assimilation of protein (Malmstrom et al. 2005). Alonso and Pernthaler (2006b)
observed a higher proportion of Roseobacter cells using glucose than SAR11 cells.
The data presented here provide further evidence that SAR11 cells are active in
the ocean and respond to environmental changes. The abundance of SAR11 varied,
and the fraction using amino acids and protein changed between summer and fall.
Overall, SAR11 cells were sometimes stimulated by light when using leucine, in
agreement with other results suggesting SAR11 can use light to supplement energy
requirements (Campbell et al. 2009, Lami et al. 2009).
The use of sunlight for energy has been identified as a factor potentially
influencing heterotrophic bacterial growth (Béjà et al. 2001, Kolber et al. 2000).
The discoveries of aerobic anoxygenic phototrophic bacteria and the presence of
proteorhodopsin genes in many divisions of bacteria necessitate reconsideration of
environmental pressures, bacterial responses, and the subsequent impact on DOM
cycling (Béjà et al. 2001, Kolber et al. 2000). The light responses of a few bacterial
groups have been analyzed, including the incorporation of leucine by Prochlorococcus and Synechococcus in the light (Michelou et al. 2007). Expression of the
proteorhodopsin gene was upregulated in the light by Flavobacteria and the SAR11
52
clade during microcosm experiments, though continuous light did not stimulate population growth (Lami et al. 2009). To test inhibition by light, Alonso-Saez et al.
(2006) examined the single-cell activity of bacterial groups in the dark after treatment with UV light. Gammaproteobacteria was one of the groups most resistant
to solar radiation, and this group also increased in abundance in surface following
stratification of the water column in the Mediterranean (Alonso-Saez et al. 2006,
Franklin et al. 2005). I observed a decrease in the light effect on incorporation of
leucine and the amino acid mixture with an increase in PAR prior to sampling, supporting the hypothesis of light inhibition due to photodamage (Alonso-Saez et al.
2006).
I found that light had a direct effect in about 20% of my experiments. As has
been seen in other studies, however, there was a large amount of variability in response to light, with some stimulation and some inhibition. The lack of a consistent,
observable light effect may be due in part to methodological limitations. Because of
long doubling times, the environmental bacterial community may take several hours
or more to respond to changes. However, I had to keep these whole-water sample
incubations short to avoid “bottle effects” and limitation of labeled substrate, as
well as indirect effects of light through production of DOM by photoautotrophs and
grazers. The determination of the active fraction of a group by microautoradiography, without measurements of changes in the rate of activity within the active
fraction, may fail to identify some effects of light. Also, the bacteria analyzed in this
study were broad phylogenetic groups, and even the clade of SAR11 is very diverse
(Carlson et al. 2009). The average response of a broad group may have masked
differences in response by sub-groups.
In this study, I identified the factors controlling bacterial abundance and activity in a temperate estuary and coastal waters. Environmental changes resulted
53
in characteristic differences in the single-cell activity and abundance of broad phylogenetic groups of bacteria. In the Delaware Bay and other environments, light
can be an important controlling factor on bacterial abundance and activity. Broad
groups of bacteria responded differently to light, providing impetus for determining
the responses of more specific groups. Like other environmental factors, light has
a complex effect on bacterial communities. Characterizing the effects of light is an
important component of predicting bacterial ecology based on environmental conditions.
54
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60
Table 3.1: Correlations between relative abundances of three bacterial groups and
environmental variables. N is the number of dates examined over two years.
Property
Temperature
Chl a concentration
Bacterial Abundance
Bacterial Production
Dissolved Organic Carbon
Dissolved Organic Nitrogen
N
*
Gammaproteobacteria
Alphaproteobacteria
SAR11
0.31*
0.08
0.15
0.38**
0.25*
0.26*
64
0.001
-0.12
-0.07
0.09
0.22
0.18
71
-0.37**
-0.36**
-0.32*
-0.12
0.22
0.20
63
p < 0.05
p< 0.01
**
61
Table 3.2: Mean fraction of groups incorporating the indicated compounds in light
and dark incubations. Mean % ± standard error of 11-14 samples of total
community or 16-30 samples for bacterial groups.
Bacterial Group
Leucine
Light
Leucine
Dark
AA mix
Light
AA mix
Dark
Protein
Light
Protein
Dark
All cells
Bacteriaa
Gammaproteobacteria
Alphaproteobacteria
SAR11
28
25
36
59
51
32
30
34
60
46
28 ± 4
27 ± 7
21 ± 6
36 ± 11
20 ± 6
33
26
25
33
21
7±1
8±1
11 ± 3
10 ± 3
4±2
11 ± 3
13 ± 4
10 ± 3
13 ± 4
7±2
a Cells
±
±
±
±
±
3
2
5
6
6
±
±
±
±
±
4
3
5
3
6
identified with EUB338 CARD-FISH probe.
62
±
±
±
±
±
5
4
4
9
5
Table 3.3: Pearson correlation coefficients of environmental variables compared
with the fraction of the total community incorporating a given compound. N is
number of samples.
Compound
Leucine
Amino acids
Protein
*
Temperature
Secchi Depth
Chl a
concentration
Bacterial
abundance
N
-0.33
-0.28
0.40∗
0.42*
0.18
-0.17
-0.47**
-0.33∗
0.42∗∗
-0.41∗
-0.24
0.45∗∗
31
37
39
p < 0.05
p < 0.01
**
63
Table 3.4: Response to light by the total community and bacterial groups. # is the
number of experiments with a significant difference (ANOVA, p < 0.05) between
dark and light incubations. Light effect, % stimulation or inhibition, was calculated
as (ActivityLight - ActivityDark )/ActivityDark , and the mean reported here.
Group
Compound
Stimulated
#
%a
Inhibited
#
%
All cells
All cells
All cells
AA mix
Leucine
Protein
1
2
0
149
113
–
2
0
1
-68
–
-67
8
9
12
Bacteriab
Bacteria
Bacteria
AA mix
Leucine
Protein
1
1
0
251
276
–
1
0
4
-97
–
-72
6
14
5
Alphaproteobacteria
Alphaproteobacteria
Alphaproteobacteria
AA mix
Leucine
Protein
2
0
0
218
–
–
2
0
2
-84
–
-77
4
15
7
Gammaproteobacteria
Gammaproteobacteria
Gammaproteobacteria
AA mix
Leucine
Protein
0
3
2
–
271
171
2
1
1
-91
-100
-95
6
11
6
SAR11
SAR11
SAR11
AA mix
Leucine
Protein
0
1
1
–
353
100
0
0
2
–
–
-95
8
14
6
a The
# unaffected
effect of light (mean 148%) was greater than the mean variance in single-cell activity
within a sample as determined from replicate counts (32%, n=23).
b Cells
identified with EUB338 CARD-FISH probe.
64
Table 3.5: Correlations between the light effect on uptake of a given compound
(Light - Dark) and photosynthetically active radiation (PAR) prior to sampling. N
= 11-13.
ActivityLight - ActivityDark
Property
PAR at sampling
Mean PARa
Peak PARb
Sum daily PARc
Leucine
AA mixture
Protein
-0.66*
-0.01
-0.06
-0.59*
-0.24
-0.66*
-0.69*
-0.42
-0.40
0.26
0.02
-0.26
*
p < 0.05
Mean from 8am - 4pm during 3 days preceding sampling
b Mean peak from 8am - 4pm for 3 days preceding sampling
c Sum from 3am - time of sampling
a
65
Figure 3.1: Temperature (A), bulk 3 H-leucine incorporation (B), microbial abundance (C), and concentrations of chlorophyll a (D) in Delaware Bay and coastal
waters. Arrows indicate times of Micro-FISH sampling. Error bars are one standard error.
66
Figure 3.2: Uptake of leucine and an amino acid mixture (A) and protein (B) by
the bacterial community in the dark. Horizontal lines indicate mean uptake of a
compound. Error bars are one standard error. ND = no data.
67
Figure 3.3: Uptake of leucine and protein by Alpha- and Gammaproteobacteria
compared to the total bacterial community (EUB338-labeled cells) in the dark in
experiments conducted from 2006 to 2008. Line is the 1:1 line. The mean standard
errors are indicated for leucine (+) and protein (X).
68
Figure 3.4: Bulk 3 H-leucine incorporation in light or dark incubations in experiments
conducted from 2006 to 2008. Line is the 1:1 line. Error bars are one standard error.
69
Figure 3.5: Incorporation of leucine and protein by SAR11 bacteria compared to
the total community in the light in experiments conducted from 2006 to 2008. Line
is the 1:1 line. The mean standard errors are indicated for leucine (+) and protein
(X).
70
Chapter 4
ABUNDANCE AND SINGLE-CELL ACTIVITY OF
BACTERIAL GROUPS IN ANTARCTIC COASTAL
WATERS
4.1
Abstract
I estimated the abundance and single-cell activity of bacterial groups in wa-
ters off the west Antarctic Peninsula using a combination of microautoradiography
and fluorescent in situ hybridization. A new probe demonstrated the abundance
of the Ant4D3 sub-group of Gammaproteobacteria in Antarctic waters to be 10%
of the total community and half of the Gammaproteobacteria population. The
Ant4D3, Polaribacter, and SAR11 sub-groups accounted for the majority of the
Gammaproteobacteria, Sphingobacteria-Flavobacteria, and Alphaproteobacteria, respectively. Approximately 40% of the microbial community actively incorporated
leucine (added at 20 nM), while a smaller fraction (12-22%) used protein and an
amino acid mixture (added at 0.5 nM). The fractions of SAR11, Polaribacter, and
Ant4D3 that were active differed from each other and varied among substrates.
While among the three groups the SAR11 had the largest fraction of active cells
incorporating leucine, five-fold fewer SAR11 cells used protein or the amino acid
mixture than used leucine. Polaribacter dominated the community using protein.
The active fraction of Ant4D3 did not vary among compounds, but this group dominated the incorporation of amino acids, and was an abundant and active component
71
of the bacterial community. Though dominated by different bacterial taxa, the activity of this Antarctic bacterial community was comparable to that of temperate
communities.
72
4.2
Introduction
Polar regions are important but under-studied biomes with unique ecological
pressures (Kirchman et al. 2009b). Both Arctic and Antarctic marine waters are
characterized by relatively constant low temperatures and high seasonal variability
in light flux. One major difference between the two is that the Arctic Ocean receives
terrestrial inputs of organic material while the coastal waters of Antarctica do not
(Bano and Hollibaugh 2002, Wheeler et al. 1996). Microbes are abundant in these
cold pelagic zones as well as in sea ice (Brown and Bowman 2001). Understanding
polar microbial ecology is important as these environments form a major part of
the global ocean, and are susceptible to rapid change in varying climate (Kirchman
et al. 2009b, Smetacek and Nicol 2005).
A primary question is whether bacteria in polar regions are active even in
low temperatures and seasonal darkness (Ducklow et al. 2001, Kirchman et al. 2005,
Simon et al. 1999, Smith and del Giorgio 2003). Early work suggested that bacterial
growth was limited in cold temperatures, potentially requiring higher concentrations
of substrate to achieve rates similar to temperate bacterial growth (Pomeroy and
Deibel 1986, Pomeroy et al. 1991). Bulk 3 H-leucine incorporation rates in the Ross
Sea of Antarctica are low compared to rates in temperate regions (Ducklow et al.
2001, Kirchman et al. 2009b). In spite of low bulk growth rates, the fraction of
individual cells that are active in the Arctic Ocean is high (Elifantz et al. 2007,
Malmstrom et al. 2007, Vila-Costa et al. 2008). Similar fractions of cells use leucine
in the Weddell Sea as in temperate waters (Grossman 1994). The fraction of active cells may vary among bacterial groups, and may be a factor shaping bacterial
biogeography.
Many groups examined in lower latitudes are also present in polar waters,
though the broad taxa may be dominated by different clades (Bano and Hollibaugh
2002, Elifantz et al. 2007, Murray and Grzymski 2007). The bacterial community
73
structure varies seasonally in the Arctic (Bano and Hollibaugh 2002). There is some
evidence from culture-independent studies that while gammaproteobacterial abundance is often low in temperate waters, this group may be abundant in polar waters
(Alonso-Saez et al. 2008a, Glöckner et al. 1999, Pommier et al. 2007, Schattenhofer et al. 2009). The Gammaproteobacteria are third-most abundant members of
the communityin the western Arctic following the Sphingobacteria-Flavobacteria (SF
group) and Alphaproteobacteria (Alonso-Saez et al. 2008b, Elifantz et al. 2007). The
Ant4D3 clade of Gammaproteobacteria is abundant in clone libraries from waters
off the west Antarctic peninsula (Murray and Grzymski 2007). Antarctic bacterial
communities have not been characterized as extensively as those in Arctic waters,
and much remains to be explored in both polar systems (DeLong et al. 1994, Murray
and Grzymski 2007).
The alphaproteobacterial clade SAR11 is abundant in many marine environments and may play a role in polar systems as well (Giebel et al. 2009, Malmstrom
et al. 2007, Schattenhofer et al. 2009). SAR11 is the most abundant group in Arctic
surface waters, comprising about 20% of the bacterial community, with 15-40% of the
group actively incorporating leucine (Malmstrom et al. 2007). SAR11 is present in
Antarctic waters, but quantitative estimates of abundance are still needed (Murray
and Grzymski 2007). Though lower in abundance, high fractions of Roseobacter, the
Polaribacter clade of the SF group, and the SAR86 cluster of Gammaproteobacteria
were active in the North Sea and Chukchi Sea (Alderkamp et al. 2006, Alonso-Saez
et al. 2008b, Malmstrom et al. 2007).
The goal of this study was to characterize the abundance and activity of
bacterial groups in waters off the west Antarctic peninsula. Surface waters were
sampled on transects up to 200km offshore and 900km along the west Antarctic
Peninsula, including the shallow water column on the shelf and deep open ocean
waters offshore (Church et al. 2003, Waters and Smith 1992). I used fluorescent in
74
situ hybridization (FISH) and microscopy to examine the abundance of numerically
important bacterial groups and their uptake of selected substrates. I found that
the use of leucine, a mixture of amino acids, and protein differed within the total
community and among dominant sub-groups SAR11, Polaribacter, and the Ant4D3
clade of Gammaproteobacteria.
4.3
4.3.1
Methods and Materials
Sample collection.
Surface waters were sampled in January 2007 along transects off the west
coast of Antarctica (http://pal.lternet.edu, Church et al. 2003, Fig. 4.1). Abundance of all prokaryotes was measured using flow cytometry with SYBR green
staining (Knap et al. 1994). Concentrations of chlorophyll a were measured by
standard methods (Holm-Hansen et al. 1965). Bacterial production was estimated
using 3 H-leucine incorporation (20 nM additions) with the microcentrifuge method,
using 3-4h incubations in the dark (Ducklow 2000, Kirchman 2001). SYPRO Ruby
protein staining to estimate cell volumes was carried out as described in Chapter 2.
4.3.2
Identification of bacterial groups.
The abundance of bacterial groups was estimated using fluorescent in situ
hybridization (Amann et al. 1990b, DeLong et al. 1989). Water samples were
fixed overnight at 4 ◦ C with paraformaldehyde (PFA, 2% final concentration). The
water was filtered through 0.22 µm polycarbonate filters (Millipore) with a 0.45
µm nitrocellulose support filter (Millipore), rinsed with 0.22 µm-sterilized deionized water, and stored at -20 ◦ C until further processing. Probes for Alphaproteobacteria (Alf968, Neef 1997), Gammaproteobacteria (Gam42a, Manz et al. 1992),
Sphingobacteria-Flavobacteria group (CF319a, Manz et al. 1996), Polaribacter (mixture of 3 probes described in Malmstrom et al. 2007), SAR11 clade (mixtureof 4
75
probes, Morris et al. 2002), all bacteria (EUB338, Amann et al. 1990a) and a negative control were used (Karner and Fuhrman 1997). Two probe sets were used separately for Roseobacter : RSB67 with helper probes RSB67h3 and RBS67h5 (Zubkov
et al. 2001, 2002) and a probe mixture designed for Arctic Roseobacter cells (Malmstrom et al. 2007).
I designed probe Ant4D3a (5’-CAA GCC AGG GCG TCG CCT-3’) for a
sub-group of Gammaproteobacteria based on the Ant4D3 fosmid clone (Grzymski
et al. 2006). The probe was tested in silico using ARB with SILVA database release
98 (March 2009; Ludwig et al. 2004, Pruesse et al. 2007). An Ant4D3 cultured strain
was not available as a positive control, but I tested probe specificity, with formamide
concentrations from 10-35%, on cultures of Alteromonas, Vibrio alginolyticus, and
a Cytophaga species strain DB362. Probe binding was below 2% in all culture tests.
Filter pieces were hybridized with cyanine-3 (Cy3)-labeled oligonucleotide probes in
30% formamide. Following a final washing step, the filter pieces were either taken on
to microautoradiography or transferred onto slides and stained with 4’-6-diamidino2-phenylindole (DAPI; 0.5 ng µL-1 ) in a 4:1 mixture of Citifluor (Ted Pella) and
Vectashield (Vector Labs) anti-fading mountants.
4.3.3
Incorporation of substrates by specific bacterial groups.
The uptake of selected compounds by specific bacterial groups was exam-
ined using microautoradiography combined with FISH (Micro-FISH). Water samples
for Micro-FISH were incubated with 20nM 3 H-leucine (Perkin Elmer), 3 H-glucose
(0.5nM, Perkin Elmer), a mixture of 15 tritiated amino acids (0.5 nM, American
Radiolabeled Chemicals), or 0.4 µg mL-1 3 H-protein. Tritiated protein was made
using a Vibrio alginolyticus culture (Nagata et al. 1998). Incubations with leucine,
glucose and the amino acid mixture lasted 4 h while protein incubations lasted 8 h
at in situ temperatures in the dark. After incubation, samples were fixed with PFA
76
(2% final concentration) overnight at 4 ◦ C and filtered as for FISH. PFA was added
to killed controls prior to the addition of labelled compounds.
Live and killed control filters from selected glucose experiments (n = 5) and
amino acid experiments (n= 3) were used to calculate bulk uptake by adding 5ml of
scintillation cocktail and counting. The turnover rate constant was calculated as the
radioactivity taken up divided by the radioactivity added. Bulk growth rates were
calculated from leucine incorporation, assuming a conversion factor of 1.5 kg C per
mole of leucine and 20 fg C cell−1 (Lee and Fuhrman 1987) to calculate production
and biomass, by dividing bacterial production (mg C m−3 d−1 ) by biomass (mg C
m−3 ) (Kirchman et al. 2009a).
Microautoradiography was carried out as described by Cottrell and Kirchman
(2003). Slides were dipped in film emulsion (Amersham Hypercoat EM-1), then filter
pieces were placed cell-side down onto the emulsion. A time series of exposure in
the emulsion before developing was used to select the shortest time at which the
number of active cells identified reached a maximum. The exposure time for leucine
and amino acids samples was 2 days, and for protein samples 4 days. At the end
of the exposure time, slides were developed and fixed (Dektol developer and fixer,
Kodak). After drying overnight, the filter pieces were carefully peeled away and the
cells stained with DAPI in the 4:1 mountant as described above.
Cells were counted using a semi-automated microscope system previously
described (Cottrell and Kirchman 2003). Ten fields of view were counted from each
sample, with a constant exposure time of 100 msec for silver grain images. DAPI
image exposure times ranged 75-100 msec, and Cy3 image exposure times were set
by the negative control and ranged 300-500 msec. Group abundances are presented
as the percentage of the total number of DAPI objects that were also Cy3-labeled.
The proportion of the total community that was active was calculated as the number
of DAPI objects with associated silver grain clusters divided by the total number of
77
DAPI-stained objects. Similarly, the number of probe- and DAPI-stained objects
with silver grains was divided by the number of probe-positive cells to calculate
the percentage of a group that was actively taking up a given compound. The
contribution of each group to the total active cells using a given compound was
calculated using the abundance of the group multiplied by the fraction of the group
with silver grains, divided by the fraction of total cells with silver grains. Nonspecific binding of probes tested using the negative control probe was below 5% for
all samples. Percentages were arcsine-transformed prior to statistical tests.
4.4
Results
I examined the single-cell activity of selected bacterial groups off the west
coast of the Antarctic peninsula. The transects were divided into three regions:
“coast” for stations up to 100 km offshore, “shelf” for the mid-shelf region 100 to
200 km from shore, and “offshore”. Bulk uptake of 3 H-leucine and growth rates
were highest in coastal waters (ANOVA p = 0.001; Fig. 4.2). While chlorophyll a
concentrations varied among regions (p = 0.04), this effect appeared to be driven by a
very high concentration at one coastal station (Fig. 4.2). Total bacterial abundance
and bacterial biovolume based on protein staining did not differ significantly among
the three regions (Table 4.1; Straza et al. 2009).
4.4.1
Bacterial group abundance.
The abundance of bacterial groups was examined using FISH. The EUB338
probe for all bacteria labeled about 80% of all DAPI-positive objects. The three major groups examined accounted for 70% of the microbial cells and 90% of probed bacterial cells (EUB338-positive). The abundances of Alphaproteobacteria, Gammaproteobacteria, and the SF group averaged 20-30% and did not differ significantly from
each other or among locations (Table 4.2). Alphaproteobacterial abundance (CV =
78
0.36) varied more than the abundance of the SF group (CV = 0.21) and Gammaproteobacteria (CV = 0.21, Fig. 4.3).
The clades within the broad phylogenetic groups varied in relative abundance
among locations (Fig. 4.4). All sub-groups were equally abundant offshore, ranging
from 8-18% of the community (Fig. 4.4). In coastal samples, the gammaproteobacterial clade Ant4D3 was half as abundant as Polaribacter, but neither differed from
the abundance of SAR11. SAR11 was the most abundant group (27%) in shelf
samples. Only cells identified by the Arctic Roseobacter probe mixture varied in
abundance by location. These Roseobacter cells were about 3-fold more abundant
offshore than in coastal or shelf waters (Table 4.2). The Ant4D3, Polaribacter, and
SAR11 sub-groups accounted for most of the Gammaproteobacteria, SF group, and
Alphaproteobacteria, respectively (Table 4.2). Ant4D3 averaged 10% of the bacterial
community, accounting for about half of the Gammaproteobacteria (Table 4.2). The
Polaribacter comprised 57% of the SF group, while SAR11 made up over 80% of
the Alphaproteobacteria. The abundance of Ant4D3 cells (CV = 0.25) varied less
than that of SAR11 (CV = 0.47) or Polaribacter (CV = 0.54).
4.4.2
Total community activity.
Single-cell activity differed among the selected compounds. Overall a greater
fraction (41 ± 3%) of cells used leucine than the fraction that used either the amino
acid mixture (12 ± 1%) or protein (22 ± 2%; Fig. 4.5). The fraction of cells using
glucose was <5% (data not shown), and glucose samples were not further analyzed.
In coastal samples, a greater fraction of cells used leucine than used the amino acid
mixture (ANOVA, p = 0.036), but the fractions using protein and leucine did not
differ from each other (Fig. 4.5). In offshore samples, the fraction using leucine
was 3-fold greater than the fractions using amino acids and protein (p = 0.006; Fig.
4.5). However, the relative number of cells using these compounds did not differ
significantly among regions.
79
4.4.3
Use of compounds by bacterial groups.
The bacterial groups differed in use of the substrates examined. About 24%
of Polaribacter cells used leucine, less than half of the active fractions of Ant4D3
and SAR11 (ca. 55%, p = 0.022). In contrast, roughly four-fold more Polaribacter
and Ant4D3 (32-40%) used protein than SAR11 (9.5%, p = 0.002). The use of the
amino acid mixure was different among the groups, with 30% of Ant4D3 cells active
versus only 10% of SAR11 and Polaribacter (p = 0.046; Fig. 4.6). Within each subgroup, the fraction active varied by compound. The fraction of Polaribacter using
protein was four times the fraction using amino acids (p = 0.001; Fig. 4.6). Fivefold more SAR11 used leucine than used either protein or amino acids (p < 0.001;
Fig. 4.6). The fraction of active cells within the Ant4D3 group or cells identified
with the EUB338 probe did not vary among compounds (Fig. 4.6). Because of the
low abundance of Roseobacter cells, the single-cell activity of this group was not
measured.
Previous sections analyzed the active fraction of cells within each bacterial
group. Another, separate question is the contribution of groups to the total uptake of
a given compound, which takes into account the abundance and the active fraction of
a group. The contribution to the use of each compound by different groups mirrored
the patterns seen within each group (Table 4.3). SAR11 accounted for the largest
fraction of cells using leucine (22%), while Ant4D3 and Polaribacter contributed
equal fractions (12-15%). Around 68% of the cells that incorporated the amino
acid mixture were Ant4D3. The majority of cells that incorporated protein were
Polaribacter (Table 4.3).
4.5
Discussion
I assessed the abundance and single-cell activity of bacterial groups in waters
off the west coast of Antarctica. Previous studies of single-cell activity have focused
on broad phylogenetic groups on the class or phylum level (Cottrell and Kirchman
80
2003, Zhang et al. 2006), with some recent studies examining clades of the Alphaproteobacteria (Alonso and Pernthaler 2006a,b, Alonso-Saez and Gasol 2007). In this
study, I examined clades within the Gammaproteobacteria and SF group, and the
use of leucine, amino acids, and protein by the SAR11, Polaribacter, and Ant4D3
clades. Bacterial groups differed in abundance and substrate use between the groups
and among different compounds.
Members of the broad phylogenetic groups commonly found in temperate
waters were also abundant in the Southern Ocean, although dominated by different
sub-clades. While the abundance of Gammaproteobacteria often averages 10% or less
in other areas (Alonso-Saez et al. 2008a, Glöckner et al. 1999, Pommier et al. 2007),
in this study the group was as abundant as the Alphaproteobacteria and the SF
group, in agreement with other evidence of higher Gammaproteobacteria abundance
in polar regions (Elifantz et al. 2007, Glöckner et al. 1999, Schattenhofer et al.
2009). The Alphaproteobacteria, Gammaproteobacteria, and SF group dominated
the Antarctic bacterial community, as has been seen in waters off eastern Antarctica
(Abell and Bowman 2005) as well as in clone libraries from my study region (Murray
and Grzymski 2007). These three groups are also the dominant members in Arctic
waters (Elifantz et al. 2007, Vila-Costa et al. 2008).
Some of the clades within the larger groups appear to be specialized to polar
regions, and perhaps there are differences between the Arctic and Antarctic. Using
probes designed for Roseobacter representatives in Arctic waters, Malmstrom et al.
(2007) found low abundance but a large active fraction of the group in the Arctic.
Using the same probe set, I found related bacteria in Antarctic waters again in
low abundance (< 5%), though reaching ca. 10% offshore. These Roseobacter
representatives may be specialized to polar regions, and it remains to be seen if
the Antarctic Roseobacter are as active (Selje et al. 2004). Similarly, the members
of the Polaribacter clade of the SF group have only been found in polar oceans
81
(Malmstrom et al. 2007, Pommier et al. 2005). Using the most recent ARB database,
bacteria targeted by the Polaribacter probes were 95 ± 2.8% similar on average
(81% minimum), based on 98 sequences within the group. The number of available
sequences from the SAR11 clade of Alphaproteobacteria is greater (3001 sequences),
but the clade diversity was approximately the same as that of Polaribacter (93
± 4.3% similarity, 74% minimum). SAR11 was about 4-fold more abundant than
Polaribacter in the Chukchi Sea (Malmstrom et al. 2007), but in waters off the west
Antarctic Peninsula I found equal abundance of SAR11 and Polaribacter cells.
A striking finding is the abundance of the Ant4D3 cluster and the Gammaproteobacteria as a whole. Characterization of clades within the Gammaproteobacteria,
especially by FISH, has been limited in part due to the low or moderate abundance
of the whole group in many regions (Alonso-Saez et al. 2008a,b, Eilers et al. 2000,
Glöckner et al. 1999). In the North Sea, each of nine clades within the Gammaproteobacteria formed <1% of the community, with the exception of the SAR86 cluster
which peaked at 10% in one sample (Eilers et al. 2000). Malmstrom et al. (2007)
found three gammaproteobacterial clades that averaged <10% abundance in the
Chukchi Sea. In contrast, the Ant4D3 cluster dominated Gammaproteobacteria in
Antarctic waters (Grzymski et al. 2006, this study). The Ant4D3 clade appears to
be specialized to cold environments and may play an important role in polar microbial communities (Murray and Grzymski 2007). Members of the Ant4D3 clade
share 98 ± 2.6% similarity (minimum 86% similar) based on 43 sequences matched
exactly by the probe (Murray and Grzymski 2007).
Previous work has suggested a paradox between low bulk bacterial production rates and high fractions of active cells in polar regions. This study suggests
that paradox may not exist in waters off the west Antarctic peninsula, in the austral summer. I found that single-cell activity differed among the tested compounds,
but in general the fraction of bacteria active was comparable to temperate regimes
82
(Cottrell and Kirchman 2003, Elifantz et al. 2005). The fraction of the community
incorporating leucine was 40% in my samples, similar to what other microautoradiographic studies found in other marine regions (Smith and del Giorgio 2003). Bulk
growth rates were comparable to those of bacterial communities in the Arctic, and
bacterial communities closest to the coast reached growth rates similar to those in
temperate waters (Kirchman et al. 2009a). The microautoradiography results are
consistent with the bulk rates. After microautoradiography, the area of the silver
grain clusters around a cell is proportional to the activity of the cell (Sintes and
Herndl 2006). Silver grain areas around cells taking up leucine in these Antarctic
samples (mean 1.18 ± 0.18 µm2 ) were larger than areas around cells in the midAtlantic bight (0.65 ± 0.06 µm2 , N = 17).
One apparent difference between the waters of the coastal west Antarctic
Peninsula and other regions is the use of other compounds besides leucine. The
fraction of cells using glucose was negligible in my samples, and the turnover rate
constant for glucose was 17-fold lower than the rate constant for amino acids. In
the North Sea and western Arctic a smaller fraction of bacteria used glucose than
all other compounds, but the fraction using glucose still ranged 10-30% (Alonso and
Pernthaler 2006b, Alonso-Saez et al. 2008b, Elifantz et al. 2007, 2005). In contrast,
the fraction of cells using protein in these Antarctic waters was comparable to that
seen in the North Atlantic (Malmstrom et al. 2005), and higher than the fraction
incorporating protein in estuaries such as the Delaware Bay (Chapter 3, Cottrell
and Kirchman 2000).
Bacterial groups vary in the use of different organic compounds (Wagner
et al. 2006). Large fractions of gammaproteobacterial clade Arctic96B-16 and Polaribacter actively incorporated leucine in the Arctic despite the low abundances of
the clades (Malmstrom et al. 2007). While the fractions of Ant4D3 and Polaribacter using leucine in my samples were lower, the Ant4D3 group dominated the
83
incorporation of the amino acid mixture, and the Polaribacter were very important in the use of protein. My observations of Polaribacter are similar to those of
other studies in which high fractions of the SF group used high molecular weight
compounds (Cottrell and Kirchman 2000, Kirchman et al. 2005, Malmstrom et al.
2007). In contrast, the ubiquitous SAR11 clade has been suggested to specialize on
low molecular weight compounds (Malmstrom et al. 2005, 2004), supported by my
observations here. Malmstrom et al. (2005) found lower fractions of SAR11 incorporating protein than other low molecular weight compounds in the North Atlantic.
Importantly, the SAR11 clade accounted for the largest fraction of cells using leucine
in this study. While SAR11 cells are often abundant, a high fraction is not active in
all regions (Alonso-Saez et al. 2008b). My results suggest that SAR11 cells are actively contributing to the flux of dissolved organic material in the coastal Southern
Ocean.
The polar oceans are important regions susceptible to extreme climate pressures and potential climate change (Kirchman et al. 2009b, Smetacek and Nicol
2005). Even in these cold and seasonally dark waters, bacteria are abundant and
active. Studies of polar microbes have pointed out a potential paradox between
low bulk growth rates and high cell-specific activity. In contrast, I observed a high
fraction of the bacterial community taking up organic compounds, and growth rates
in coastal samples comparable to those from temperate systems (Kirchman et al.
2009a). The bacteria present in polar waters may be specialized to grow in polar
conditions, and while many of the bacterial groups present in temperate waters are
also important in polar regions, there are some key differences in abundance and
potentially activity (Murray and Grzymski 2007, Pommier et al. 2005, this study).
Bacterial groups differed in abundance and in response to different substrates. The
analysis of clades within the common broad phylogenetic groups allows for the identification of more specific ecological niches.
84
4.6
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90
Table 4.1: Basic biogeochemical properties in waters off the west Antarctic
peninsula. Biovolume of prokaryotic cells was determined by protein staining.
Mean ± standard error.
Property
Total Abundance (105 cells mL−1 )
Chl a concentration (mg m−3 )
Leucine Incorporation (pM h−1 )
Bulk Growth Rate (d−1 )
Prokaryote Biovolume (µm3 )
Coast
Shelf
Offshore
Total
7.4 ± 0.9
4.2 ± 1.8
42 ± 6.3
0.11 ± 0.02
0.13 ± 0.03
7.0 ± 1.6
0.7 ± 0.16
14 ± 1.2
0.04 ± 0.01
0.13 ± 0.04
4.3 ± 0.3
0.5 ± 0.1
13 ± 2.5
0.05 ± 0.01
0.13 ± 0.06
6.2 ± 1.2
1.9 ± 1.3
23 ± 7.3
0.07 ± 0.02
0.13 ± 0.01
91
Table 4.2: Abundance of bacterial groups in the three regions studied. Mean ±
standard error. N = 14-15 for each group.
Group
Bacteria∗
SF Group
Gammaproteobacteria
Alphaproteobacteria
Polaribacter (SF)
Ant4D3 (Gamma)
SAR11 (Alpha)
Roseobacter∗∗ (Alpha)
Arctic Roseobacter (Alpha)+
% of Total Abundance
Coast
Shelf
Offshore
84.8 ± 0.8
32.5 ± 1.0
19.7 ± 0.8
24.9 ± 0.8
21.9 ± 0.8
10.6 ± 0.8
17.3 ± 0.8
9.6 ± 0.4
3.4 ± 0.3
75.1 ± 1.1
26.8 ± 0.6
17.2 ± 0.4
22.8 ± 0.9
10.2 ± 0.4
9.7 ± 0.4
26.5 ± 0.9
4.5 ± 0.3
5.3 ± 0.3
∗
79.5 ± 1.0
29.0 ± 0.6
22.5 ± 0.6
20.1 ± 0.7
18.5 ± 0.4
10.8 ± 0.4
15.4 ± 0.8
6.6 ± 0.3
9.8 ± 0.4
Eub338-positive cells
Ros67-positive cells
+ Cells positive after probing with Arctic Roseo probe mix
∗∗
92
Total
79.5 ± 0.6
29.4 ± 0.4
19.8 ± 0.4
22.6 ± 0.5
16.9 ± 0.3
10.4 ± 0.3
19.7 ± 0.5
6.9 ± 0.2
6.2 ± 0.2
Table 4.3: Contribution of each group to the total fraction of cells taking up a
given compound. Mean ± standard error. N = 14-15 for each group.
Leucine
Ant4D3
Polaribacter
SAR11
Coast
8.9 ± 4.0
13.5 ± 2.9
27.2 ± 4.0
Offshore
18.8 ± 4.3
10.0 ± 1.8
15.7 ± 3.3
Total
15.4 ± 4.1
120 ± 2.7
22.2 ± 5.1
Amino Acids
Ant4D3
Polaribacter
SAR11
Coast
76.9 ± 45.9
38.9 ± 15.1
28.9 ± 15.4
Offshore
33.5 ± 11.4
6.3 ± 2.2
10.5 ± 4.1
Total
68.0 ± 28.7
24.9 ± 9.5
21.0 ± 8.8
Protein
Ant4D3
Polaribacter
SAR11
Coast
11.0 ± 4.6
40.3 ± 9.4
6.1 ± 2.2
Offshore
44.3 ± 17.8
125.3 ± 48.2
9.6 ± 5.0
Total
29.5 ± 11.2
76.7 ± 25.3
7.8 ± 3.5
93
Figure 4.1: Sampling locations (white dots) on transects off the west Antarctic
Peninsula. An: Anvers Island; R: Renaud Island; L: Lavoisier Island; Ad: Adelaide
Island; MB: Marguerite Bay. Solid line denotes coastal domain (inshore); dashed
line denotes continental shelf break; area between the lines is the continental shelf
domain and area beyond shelf break is continental slope/deep ocean domain. Most
of the coastal and shelf domain area is covered by seasonal sea ice.
94
Figure 4.2: Microbial abundance and bulk 3 H-leucine incorporation at sampling
sites. Horizontal axis is the transect line as defined by Church et al. (2003) and
shown in Figure 4.1.
95
Figure 4.3: Abundances of three major bacterial groups. Error bars are one standard
error. Horizontal axis is explained in Figure 4.2.
96
Figure 4.4: Abundances of three bacterial sub-groups: SAR11 (Alphaproteobacteria), Polaribacter (SF group), and Ant4D3 (Gammaproteobacteria). Error bars are
one standard error. Horizontal axis is explained in Figure 4.2.
97
Figure 4.5: Fractions of the total bacterial community actively using selected compounds in coastal and offshore waters. Error bars are one standard error. Horizontal
axis is explained in Figure 4.2.
98
Figure 4.6: Fractions of bacterial groups actively using selected compounds. Samples
from coastal stations are grouped together followed by offshore stations. Within the
location groups, samples are arranged left to right from transect 200 to transect 600
(Church et al. 2003). Error bars are one standard error.
99
Chapter 5
CONCLUSIONS
Linking the activity and identity of microbes is a key task in microbial ecology and is necessary for moving from cataloging diversity to modeling microbial
responses and global fluxes of organic matter. The wealth of phylogenetic information available or readily attainable through molecular techniques can be combined
with methods of measuring microbial activity to more fully understand the role of
microbes in biogeochemical cycles. An important problem, however, is determining
the phylogenetic scale at which meaningful physiological differences occur. Currently
it is impractical to consider each unique strain’s activity in a whole ecosystem. Yet
one of the major goals of microbiology is to open up the “black box” of the total
community and identify functional ecological groups of bacteria.
Cohan (2002) argues that bacteria form ecotypes, groups with the same ecological niche, and that ecotype-specific selection generates species differentiation.
This differentiation would be reflected in genetic sequence (Cohan 2002). A 3%
similarity of genetic sequence is often used as a cut-off for a bacterial “species”.
Bacteria with 16S rRNA gene sequence divergence over 3% are never members of
the same species, while strains with <3% sequence divergence may or may not be
the same species (Cohan 2002, Stackebrandt and Goebel 1994). However, the 3%
divergence definition does not always distinguish ecologically different groups. For
example, Prochlorococcus is known to be split into distinct ecotypes with different ecological strategies (Rocap et al. 2003). Ecotypes displaying <3% sequence
100
divergence have different nitrogen assimilation strategies, optimal light conditions,
and many other variations, including cyanophage specificities (Moore and Chisholm
1999, Rocap et al. 2003, Sullivan et al. 2003). This problem remains unsolved. To
approach the answer, we may go back to one goal behind identifying species, which
is to identify ecological processes carried out by different bacteria and determine
how these processes are affected by changes in the environment.
5.1
Bacterial biovolume.
A basic parameter of bacterial ecology is cell volume. Bacteria in the marine
environment are generally smaller than cultured bacteria, and this initially led some
to suggest that most oceanic bacteria are inactive (Stevenson 1978). Gasol et al.
(1995) used reduction of 5-cyano-2,3-ditolyl tetrazolium chloride (CTC) dye to indicate activity, and found that the average size of active bacteria was always larger
than the average size of inactive bacteria. In some studies, bacterial size positively
correlates with activity (Bernard et al. 2000, Gasol et al. 1995), but this relationship
is not always linear (Bernard et al. 2000) nor always true (Cottrell and Kirchman
2004). Certainly, activity is seen across all size classes (Cottrell and Kirchman 2004),
as is inactivity (Gasol et al. 1995), and it remains to be seen whether some bacteria
have broader ranges of possible sizes than others.
The research presented in Chapter 2 demonstrated differences in bacterial
cell volume among geographic locations and between bacterial groups. However,
the causes for this variation are still unknown, and may include temperature effects,
selection by grazers, growth rate, and differences in morphological plasticity of bacterial groups. While determining the effects of each factor is most readily done using
bacterial cultures, the culture conditions should be matched as closely as possible to
environmental conditions. For example, our understanding of temperature effects
on bacteria is confounded by the fact that most of the past experimental work using
cultures has been done in moderate conditions, not temperatures as low as those
101
experienced by bacteria in polar waters. A study carried out by Wiebe et al. (1992)
is one of the very few using temperatures as low as -1.5 ◦ C. The cell volumes of four
facultatively psychrophilic strains were largest in the lowest temperatures at a given
substrate concentration (Wiebe et al. 1992). The authors suggest a slow growth
rate as the cause, but this would contradict the classic model of quickly growing
cells being large (Schaechter et al. 1958, Wiebe et al. 1992). Classic studies of pure
cultures found that rapidly-growing cells in high nutrient conditions have high protein content and large volumes (Baker et al. 1983, Lebaron and Joux 1994, Moyer
and Morita 1989, Schaechter et al. 1958). When cultured cells are starved, they
can decrease their total size (Lebaron and Joux 1994). The responses of bacteria
in the environment are not understood, perhaps due in part to the fact that the
controlling factors may not act independently. Temperature and growth substrate
concentrations are linked in their effect on growth rate, and potentially on cell volume as well (Pomeroy and Wiebe 2001, Wiebe et al. 1992). Careful culture-based
experimental work describing the effects of individual factors but also testing combinations of factors is necessary to better describe changes in bacterial growth rate
and cell volume, and link bacterial size to activity.
5.2
Use of organic compounds.
An important function of marine bacteria is biomass production and res-
piration fueled by assimilation of dissolved organic matter (DOM; Pernthaler and
Amann 2005). The different growth responses of bacterial groups may drive the
observed changes in bacterial community composition among locations, as we saw
in Chapter 2, and with environmental changes of seasonality (Eilers et al. 2001,
Schauer et al. 2003), phytoplankton blooms (Pinhassi et al. 2004), and salinity
(Kirchman et al. 2005). Chapters 3 and 4 described the use of different types of organic material by bacterial groups. The differences in uptake shown by this research
demonstrate that the broad phylogenetic groups used commonly in fluorescent in
102
situ hybridization studies are useful ecological units (Amann and Fuchs 2008, Amann
et al. 1990). An abundant group not included in my study of the Delaware Bay was
the Sphingobacteria-Flavobacteria (SF). The SF group seems to be important in the
use of high molecular weight material (Kirchman et al. 2005), and their low abundance in the Delaware Bay during the seasons I sampled may explain in part the
low fraction of the community using protein. In Antarctic waters, members of the
SF group were both abundant and dominated the use of protein (Chapter 4). More
work relating the abundance of certain groups to the use of specific kinds of organic
substrates is necessary to determine whether the use of some compounds is specific
to certain groups or if other factors control competition for ecological niches.
A recently described factor potentially shaping competition for resources is
the use of light for energy by heterotrophic bacteria (Béjà et al. 2001, Kolber et al.
2000). However, while genomic data shows the potential for light use by many
different bacterial groups, there is still a paucity of evidence for a response by heterotrophic bacterial to light (Giovannoni et al. 2005, Michelou et al. 2007, Stingl
et al. 2007). My research demonstrated mixed responses to light by the total community and bacterial groups (Chapter 3). Gene expression-based approaches will
allow researchers to identify the groups actively expressing light-use genes and to
measure their contribution to the total community metabolism (Campbell et al.
2009, Lami et al. 2009). Experimental studies over longer time scales, long enough
for bacteria to respond to the altered light condition, are needed to estimate the
potential impacts of light use on bacterial growth rates and biomass production.
5.3
Biogeography of marine microbes.
My research also contributed to the understanding of polar microbial commu-
nities, and demonstrated some key differences between polar and temperate regions.
Polar communities live in unique environments with extreme conditions, potentially
leading to ecological specialization to those conditions. There is seemingly a paradox
103
between low bulk rates of productivity and high fractions of cells in the community
that are active in polar regions. There is a pronounced seasonality in bacterial
production, as evidenced by a tenfold difference in leucine incorporation between
summer and winter in the central Arctic Ocean (Sherr and Sherr 2003). Certainly
at the individual cell level microbes in polar regions are alive and growing (Elifantz
et al. 2007, Kirchman et al. 2009, Malmstrom et al. 2007). Up to 60% of cells in the
water column of polar oceans are viable (Ortega-Retuerta et al. 2008). In Chapter 4,
I described high fractions of the bacterial community actively using leucine, amino
acids, and even high molecular weight protein in Antarctic coastal waters. More
extensive sampling of these unique polar biomes, including the abundant sea-ice
microbial communities, is necessary particularly because these regions are sensitive
to potential temperature shifts in a changing climate (Kirchman et al. 2009, Murray
and Grzymski 2007, Smetacek and Nicol 2005, Staley and Gosink 1999).
The use of culture-independent approaches is a major part of modern microbial ecology. However, direct experimentation with environmental microbial communities is still an important part of current research. The metagenome of a microbial
community may indicate potential activity, with certain genes present, but does not
indicate which genes are being actively expressed. Single-cell approaches can be used
to assess the activity of individual microbes, and of groups at different scales. The
research presented in this dissertation demonstrates the use of phylogenetic groups
of bacteria as units useful for describing marine bacterial ecology. Broad phylogenetic groups of bacteria differed in key physiological characteristics, including cell
volume, and there were differences between bacterial communities in high and low
latitudes. Combining single-cell methods with genomic approaches will enable us
to move from only characterizing snapshots of microbial communities to predicting
their responses and quantifying their contribution to global processes.
104
5.4
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Appendix A
The uptake of leucine, amino acids, and protein were the same in the light as
in the dark (Fig. A.1, Chapter 3). No patterns were distinguishable within a group
or between the Alphaproteobacteria and Gammaproteobacteria. However, there were
light responses in some individual cases in leucine (Fig. A.2), protein (Fig. A.3),
and the amino acid mixture (Fig. A.4).
109
Figure A.1: Uptake of leucine and an amino acid mixture (A) and protein (B) by
the bacterial community in the light. Horizontal lines indicate mean uptake of a
compound. Error bars are one standard error. ND = no data.
110
Figure A.2: Alpha- (A) and gammaproteobacterial (B) uptake of leucine. Asterisks
indicate significant difference between light and dark treatments. Error bars are one
standard error based on ten fields of view.
111
Figure A.3: Alpha- and gammaproteobacterial uptake of protein. Asterisks indicate
significant difference between light and dark treatments. Error bars are one standard
error.
112
Figure A.4: Alpha- and gammaproteobacterial uptake of a mixture of 15 amino
acids. Asterisks indicate significant difference between light and dark treatments.
Error bars are one standard error.
113
Appendix B
Stations sampled during the January 2007 Palmer LTER cruise were located
on the regular LTER cruise transects (Table B.1). The stations closest to shore and
furthest offshore from transects 2, 3, 4, and 6 were analyzed for the use of compounds
by three bacterial groups (Chapter 4). The uptake of leucine by bacterial groups at
four other stations is presented in Table B.2.
114
Table B.1: Locations of stations sampled during January 2007 Palmer LTER
cruise.
Station
Latitude (◦ S)
Longitude (◦ W)
200.-060
200.020
200.100
200.200
200.260
300.040
300.120
400.040
400.120
400.200
500.120
500.200
600.040
600.120
600.220
68.16
67.64
67.12
66.44
66.03
66.89
66.37
66.25
65.75
65.24
65.11
64.61
64.93
64.45
63.84
68.95
70.29
71.55
73.03
73.92
68.92
70.16
67.34
68.59
69.80
67.08
68.29
64.41
65.65
67.15
115
Table B.2: Fraction of bacterial groups actively using leucine. Mean ± standard
error from ten fields.
Group
Station
% of group active
Bacteria∗
200.-060
400.120
500.120
500.200
200.-060
400.120
500.120
500.200
200.-060
400.120
500.120
500.200
200.-060
400.120
500.120
500.200
50.0 ± 4.5
52.4 ± 2.7
38.5 ± 4.0
65.2 ± 2.2
33.3 ± 18.3
66.7 ± 18.3
83.3 ± 7.5
78.8 ± 11.2
70.3 ± 6.1
19.7 ± 5.0
22.5 ± 5.5
56.4 ± 3.5
64.1 ± 7.9
71.4 ± 5.0
52.9 ± 6.5
55.7 ± 5.1
Ant4D3
Polaribacter
SAR11
∗
Eub338-positive cells
116