Clemson UniversityTigerPrints
All Theses Theses
8-2015
Relationships between Growth Rate and GeneExpression in Ruegeria pomeroyi DSS-3, a ModelMarine AlphaproteobacteriumNattasha VinasClemson University, [email protected]
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Recommended CitationVinas, Nattasha, "Relationships between Growth Rate and Gene Expression in Ruegeria pomeroyi DSS-3, a Model MarineAlphaproteobacterium" (2015). All Theses. 2184.https://tigerprints.clemson.edu/all_theses/2184
RELATIONSHIPS BETWEEN GROWTH RATE AND GENE EXPRESSION IN Ruegeria pomeroyi DSS-3, A MODEL MARINE ALPHAPROTEOBACTERIUM
A Thesis Presented to
the Graduate School of Clemson University
In Partial Fulfillment of the Requirements for the Degree
Master of Science Microbiology
by Nattasha Viñas August 2015
Accepted by: Dr. Barbara J. Campbell, Committee Chair
Dr. J. Michael Henson Dr. Harry D. Kurtz, Jr.
ii
ABSTRACT
Microbes are important contributors to ecosystem processes such as
biogeochemical cycling. Their activities vary, depending on environmental parameters
such as carbon type and concentration, temperature, and salinity. Current estimates of in
situ microbial growth is limited to gross estimates or rely on incubation based methods
that disturb the natural state of the community. The goal of this master’s thesis is to
develop molecular methods to directly assess microbial growth rates in the environment
at the level of a taxonomic group. Here we grew Ruegeria pomeroyi DSS-3 under
different temperatures (15 °C or 30 °C) and different carbon sources (yeast
extract/tryptone, glucose, or acetate). We then characterized differences in growth rates
and gene expression either with select growth-related genes (rpoD, rpoB, rpoS, rplB, and
ftsI) or of the whole transcriptome. Ratios of rpoB and rplB mRNA:mRNA genes were
significantly upregulated in log versus stationary phase as measured by qPCR for all
three experiments utilizing a minimal media. Acetate-grown cells exhibited significant
differences between growth phases for all five genes. These results indicate expression
of the selected genes depend on growth phase as well carbon source availability.
Conversely, a negative correlation between growth-related mRNA per cell and specific
growth rate was observed. We also examined changes in other genes in log vs. stationary
phases of growth using a transcriptomics approach. The bulk of differentially expressed
genes were involved in amino acid transport and metabolism as well as translation and
ribosomal structure. Our cultivation-based results indicate that monitoring differences in
specific growth-related transcripts levels is a viable option for determining growth-related
iii
activity changes in microbial taxa in marine environments. Future experiments should
include growth in a mixed culture and with other bacteria with different ecological
strategies in order to generalize our results to the total marine microbial community.
iv
DEDICATION
To my mother
v
ACKNOWLEDGMENTS
I would firstly like to thank my advisor, Dr. Barbara Campbell, and my committee
members Dr. J. Michael Henson and Dr. Harry Kurtz for their support and guidance
throughout this project. Thanks are also in order for Dr. Matthew Cottrell of the
University of Delaware for performing the RNA-seq experiment.
Thank you to undergraduates Tara Brown, Tianna Gore, and Brenton Davis for
undertaking the burden of cell counting. Thank you to Dr. Abhiney Jain for thought-
provoking conversations about my science. Thanks also to my fellow graduate students
and lab members for listening to me complain, sometimes loudly, about failed
experiments and also for strange discourse in the graduate office. Special thanks to my
labmate Marco Valera for driving me home during unsavory weather after late night time
points. I would also like to acknowledge the NSF Grant OCE-1261359. Finally, I would
like to thank the friends I have made over the years that have stuck by my side, supported
me though my lowest points, and celebrated my accomplishments. Truly, I could not
have done this without your love.
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TABLE OF CONTENTS
Page
TITLE PAGE .................................................................................................................... i ABSTRACT ..................................................................................................................... ii DEDICATION ................................................................................................................ iv ACKNOWLEDGMENTS ............................................................................................... v LIST OF TABLES ........................................................................................................ viii LIST OF FIGURES ........................................................................................................ ix CHAPTER I. INTRODUCTION ......................................................................................... 1 Background .............................................................................................. 1 Marine Microbial Inhabitants .................................................................. 2 Current Modes of Measuring Activity ..................................................... 4 RNA-seq .................................................................................................. 5 Cellular Components that Vary with Growth Rate .................................. 6 Growth-related Genes .............................................................................. 8 II. RESEARCH OBJECTIVES ........................................................................ 10 Project Motivation ................................................................................. 10 Experimental Goals ................................................................................ 11 III. EXPERIMENTAL METHODS ................................................................... 13 Primer Design ........................................................................................ 13 Culture Conditions ................................................................................. 13 DNA/RNA Extractions .......................................................................... 14 cDNA Synthesis and qPCR ................................................................... 14 Cell Enumeration ................................................................................... 15 Protein Extractions and Analysis ........................................................... 15 RNA-seq Sample Collection, Processing, and Analysis ........................ 16 Data Analysis ......................................................................................... 16
vii
Table of Contents (Continued)
Page IV. RESULTS .................................................................................................... 18 Ratio Changes Between Growth Phases ................................................ 18 Expression Level Changes Between Growth Rates ............................... 22 RNA-seq Analysis Between Log and Stationary Phase ........................ 30 V. DISCUSSION .............................................................................................. 35 APPENDICES ............................................................................................................... 43 A: Supplementary Tables .................................................................................. 44 REFERENCES .............................................................................................................. 45
viii
LIST OF TABLES
Table Page 1. R. pomeroyi DSS-3 Primers Designed for qPCR ......................................... 13 2. qPCR and Melt Curve Conditions for All Primer Sets ................................ 15
ix
LIST OF FIGURES
Figure Page 1. Simplified schematic of a marine microbial food web ................................ 10 2. Representaive growth curve of acetate-grown cells .................................... 19 3. Ratios of cDNA/DNA in acetate-grown cells .............................................. 20 4. Relationship between protein and cell counts in acetate-grown cells ........................................................................................................ 22 5. Representative growth curves of glucose-growth cells ............................... 24 6. Ratios of cDNA/DNA in glucose-grown cells ............................................. 25 7. Relationship between protein and cell counts in glucose-grown cells ........................................................................................................ 27 8. Comparisons of gene expression and protein concentration between specific growth rates at mid log ............................................... 29 9. Breakdown of differentially expressed genes (p < 0.000001) by COG category ................................................................................... 31 10. Differential expression confidence of select growth-related genes as a means of comparison to qPCR ............................................. 32 11. Differential expression confidence of ribosomal proteins that have a p-value < 0.000001 .............................................................. 33
1
CHAPTER ONE
INTRODUCTION
BACKGROUND
Aquatic habitats, in particular marine ecosystems, make up over 70% of the
Earth’s surface. Because aquatic regions are an important resource and habitat for
humans, animals, and microbes, their exposure to multiple sources of stresses such as
pollution, nutrient run-off, climate change, and more are of concern. Microbes are key
players in biogeochemical cycling such as the carbon and nitrogen cycles in these
habitats. Bacteria have been shown to be responsible for the bulk of abundance and
activity in the ocean (Azam and Malfatti, 2007). Furthermore, aquatic bacterial
communities are changing in response to the above mentioned environmental factors
(Lafferty et al. 2004). For instance, top down factors such as viral grazing may shape the
abundance of both rare and dominant bacterial groups in marine systems (Bouvier and
del Giorgio, 2007). Other biotic features such as the presence of different predatory
bacteria may also select for the survival of certain bacterial species (Pineiro et al., 2013).
Abiotic factors such as salinity, temperature, and oxygen contribute to the level of
predictability in reoccurring bacterial groups (Fuhrman et al., 2006). Tracking the
changes in microbial composition and activity in marine environments is important as
variability can change the flux of biogeochemical processes (Azam and Malfatti, 2007).
The overall goal of marine microbial ecologists is to study which microbes live in
marine systems, how they vary in response to the variety of environmental fluxes and
factors, and what they contribute to these systems. Determining which taxa are active in
2
various biogeochemical cycles is necessary to guide researchers in deciding which
microbes to further characterize and monitor in depth. Furthermore, contributions of
individual taxa to community processes are largely unknown. Developing a method to
directly measure environmental growth rates of individual taxa would help elucidate their
role in the marine environment.
MARINE MICROBIAL INHABITANTS
A diverse array of microbes live in various habitats within marine systems.
Microbial community composition has repeatedly been shown to change across salinity
gradients from freshwater rivers feeding estuaries down to the open ocean (Bouvier and
del Giorgio, 2002; Campbell and Kirchman, 2013; Kirchman et al. 2005). The open
ocean is oligotrophic with very little influx of nutrients. In turn, microbial abundances
tend to be low, with about half a million to a million cells per milliliter in the upper ocean
(Whitman et al.1998). A cultured representative of SAR11, one of the most abundant
bacterial clades in the oceans, has slow growth rates and a streamlined genome (Rappé et
al., 2002). These two traits are indicative of specialists, which tend to be selected for in
homogenous environments such as the open ocean (Kassen, 2002). In contrast,
heterogeneous environments, consisting of a diverse range of bottom up factors such as
resource availability, tend to favor ecological generalists, those bacteria whose genome
has evolved to be highly adaptable to a variety of environments (Kassen, 2002). The
coastal ocean is an example of a heterogeneous environment because it experiences
fluctuating levels of nutrients which results in a more variable microbial community
3
(Zinger et al., 2011). A bacterial group representing both an abundant marine resident
and a generalist species is the Roseobacter clade.
Roseobacters are found in most marine environmental samples, ranging from
polar sea ice (Brinkmeyer et al., 2003), coastal biofilms (Dang and Lovell, 2000), as
symbionts with dinoflagettes and sponges (Miller and Belas, 2004; Taylor et al. 2003).
Certain areas contain a higher abundance of Roseobacters than others. For example,
using community DNA hybridization, a pure culture of Roseovarius tolerans (ISM) was
shown to be representative of 20% of the coastal community it was isolated from, while
composing less than 1% of other marine communities (Fuhrman et al. 1994).
Roseobacter abundance peaks at ten meters below the surface and declines as depth
increases (González et al., 2000). The clade itself contains a diverse range of
physiologies including the types of metabolism (and thus biogeochemical processes) they
perform. There are currently at least 54 Roseobacter isolates (see www.roseobase.org).
The isolate used in this study, Ruegeria pomeroyi DSS-3, has been shown to be involved
in dimethylsulfoniopropionate (DMSP) degradation (J. M. Gonzalez, 2003) and carbon
monoxide oxidation (Cunliffe, 2013; Moran et al., 2004). Another isolate, Roseovarius
sp. TM 1035, is also involved in DMSP degradation (Miller and Belas, 2004) but is an
aerobic anoxygenic phototroph (AAnP) (Newton et al., 2010). Genomic analysis has
further confirmed the vast metabolic range of these microbes, and that trophic strategy is
the best predictor for genomic content, though even that is not terribly strong (Newton et
al., 2010). While studying pure cultures gives in depth analysis into the physiology and
4
genomic studies give us an idea of the metabolic potential, it is still crucial to determine
the actual activity of these microbes in their natural environment.
CURRENT MODES OF MEASURING ACTIVITY
A few different techniques have been developed over the years to measure
microbial activities, from general measurements such as community growth rates,
respiration, and bacterial production to more specific measures such as determining
specific substrate uptake, enzyme activity, and the use of microautoradioactivity (MAR)
with fluorescence in situ hybridization (FISH, collectively known as MAR-FISH) (del
Giorgio and Gasol, 2008; Staley and Konopka, 1985). These different techniques can be
sorted into two groups, depending on whether an incubation period is needed for the
experiment or if data is collected directly from the environment, such as with proteomic
techniques.
Bacterial biomass production can be measured by thymidine/leucine incorporation
(Fuhrman et al. 1982). Their assimilation is evidence of heterotrophic production.
Production by specific bacterial groups (and thus inferred contribution to geochemical
cycling) can be measured using MAR-FISH (Cottrell and Kirchman, 2003). In one such
study, it was found that about 50% of the variation of activity as measured by MAR-
FISH can be explained by abundance (Cottrell and Kirchman, 2003). While specific
substrate incorporation is very useful in determining activity, disadvantages are present.
For example, a bacterium may not take up thymidine but could still be metabolically
active (Pollard and Moriarty, 1984). Also, whether or not bacteria take up leucine (and
other compounds) may depend on the concentration of the substrate (Alonso and
5
Pernthaler, 2006). Another drawback to incubation based experiments is potentially
inducing artificial spikes and declines in growth that may not reflect natural activity in
the environment (LaRock et al. 1988).
RNA-seq
Transcriptomics is the study of all the RNA transcripts produced under a certain
condition or in a certain bacterial taxa. Microarrays were initially used to quantify gene
expression (Schena et al. 1995). Development of high throughput technologies now
allows researchers to probe environmental systems to collect data that can be used to
reveal patterns in microbial activity. For example, two strains of Prochlorococcus
adapted to either high-light or low-light conditions show highly diverse transcriptomes
when grown in the same conditions (Voigt et al., 2014).
RNA sequencing (also called RNA-seq) is a form of transcriptomic analysis,
giving researchers a snapshot of RNA present at a particular time without need of an
incubation. In addition, the technology provides quantifiable data, allowing comparisons
of differential gene expression between experiments (Creecy and Conway, 2015). RNA-
seq can also give researchers valuable insight into how bacteria react to stressed
conditions (Harke and Gobler, 2013; Pinto et al., 2014). This potentially allows
researchers to use those differentially expressed genes as stress indicators in the
environment. Similarly, transcriptomics can be used to determine genes expressed
during utilization of specific substrates (Bullerjahn and Green, 2013). These particular
RNAs may then be used as general activity indicators.
6
CELLULAR COMPONENTS THAT VARY WITH GROWTH RATE
Determining how certain macromolecular components change with growth has
been of interest since the 1950’s with early studies conducted on Salmonella typhimurium
and later with Escherichia coli B/r (Churchward et al. 1982; Schaechter et al. 1958).
Certain parameters change with growth, with some positively correlating (RNA/protein
for example) and others negatively correlating (DNA/protein) with growth rate (for
instance, Figure 2a and b in (Bremer and Dennis, 1987)). Each of these macromolecules
provides information with DNA used to evaluate the functional potential of a community,
while RNA and protein are used to evaluate the actual metabolic activity at a specific
time and environmental condition. In a study where the proteome of R. pomeroyi DSS-3
was analyzed under 30 different environmental conditions, the protein profile grouping
was mostly related to the growth phase the cells were harvested – exponential/early
stationary versus mid-late stationary – because ribosomal proteins represented over 26%
and 14% of the normalized spectral abundance factor of all proteins, respectively
(Christie-Oleza, 2012). RNA is typically less stable than protein, therefore it allows us a
snapshot of what is happening in the cell at a particular period without risk of
contamination from a previous time point.
Measuring cell-specific RNA content has shown to be useful in determining
whether cells are active or not. Cell counts measured by 16S rRNA probes correlate with
cells that were found to uptake 3H-labeled amino acids via autoradiography, suggesting
these methods count the same type of cells (Karner and Fuhrman, 1997). This is
probably because cells must be at least minimally active in order to have enough rRNA
7
be detected by probes. Past studies exploring the use of 16S rRNA to rRNA gene ratios
as indicators of growth in marine environments have uncovered interesting and somewhat
unexpected results. For example, in coastal ocean samples it was found that while
abundance follows activity for the majority of bacterial taxa, many rare bacteria exhibited
higher activity at low abundance levels (Campbell et al., 2011). Differences in the
relationship between 16S rRNA and rDNA in some marine taxa also reflect differences in
light, nutrient concentrations, and other environmental factors (Campbell and Kirchman,
2013).
Issues encountered with the use of rRNA as an activity indicator include rRNA
concentration not always linearly correlating with growth rate across taxa and dormant
cells having a high number of ribosomes (Blazewicz, Barnard, Daly, and Firestone,
2013); more issues and studies cited can be found summarized in Box 1 of the Blazewicz
review. That said, since many other studies have found a correlation with growth rate
and RNA concentration (Fegatella, et al., 1998; Kemp et al. 1993; Kerkhof and Kemp,
1999), it is worth studying how other growth related genes (besides rRNA) and their
corresponding transcripts change with growth rate and across taxa. In this project, six
genes (rpoB, rpoD, rpoS, rplB, and ftsI) were studied and their transcript abundance
measured across growth phases and growth rates of a Roseobacter strain.
GROWTH-RELATED GENES
RNA polymerase, which transcribes DNA into RNA in bacteria, is made up of
several subunits. There are two α subunits and single β, β’, and ω subunits in the core
enzyme. This core enzyme has a weak affinity to DNA and needs the σ subunit to bind
8
specifically to DNA promoters; this complete structure is called the holoenzyme. The
rpoB gene encodes the β-subunit of RNA polymerase. rpoD encodes the σ70 subunit,
which is responsible for the binding and transcriptional initiation of housekeeping genes.
The genes that are turned on during the transition to stationary phase and during various
stressed conditions are managed by σ38, encoded by rpoS. σ70 has been shown in E. coli
to have constant concentrations between exponential and stationary phase, while σ38 has
been shown to increase from exponential to stationary phase (Piper, et al., 2009; Sharma
and Chatterji, 2010). There is also some evidence that the concentration of constitutively
expressed genes may change as growth rate increases (Klumpp et al. 2009). The number
of RNA polymerases has also been shown to remain relatively constant between growth
phases, yet the number of RNA polymerases per cell seem to increase with growth rate
(Sharma and Chatterji, 2010; Bremer and Dennis, 1987).
Two other genes were selected because of their potential for varying with growth
rate. The penicillin-binding protein encoded by ftsI is involved in the septal
peptidoglycan synthesis that occurs during cell division (Errington et al. 2003). Since
ribosome abundance is growth-rate dependent, the ribosomal protein encoding gene rplB
was chosen to monitor as well (Klumpp et al., 2009). While some of the above mentioned
proteins have remained constant during different physiological conditions, it still remains
to be seen how their corresponding transcript levels change. These growth-related genes
were chosen for this study because they or their close relatives may be found in nearly all
bacterial and archaeal genomes (Gil et al., 2004). The five selected genes occur only
once per genome, and rpoB, rpoD, and rplB are conserved between taxa, which may be
9
beneficial when applying the results of this work to other microorganisms (Gil et al.,
2004; Raes et al., 2007).
10
CHAPTER TWO
RESEARCH OBJECTIVES
PROJECT MOTIVATION
Figure 1. Simplified schematic of a marine microbial food web. POM, particular organic matter; DOM, dissolved organic matter. Adapted from Figure 1 of Azam and Malfatti, 2007.
Abundance of bacteria can only partially explain variations of activity in a given
sample since some rare bacteria have been found to be more active than abundant taxa
(Campbell and Kirchman, 2013; Cottrell and Kirchman, 2003). This discrepancy
between abundance and perceived activity may be explained by the growth rate of
individual taxa. We are interested in the growth rates of heterotrophic bacteria in
particular because their uptake of organic matter can considerably change the overall
flow of carbon in the ocean (Azam and Malfatti, 2007). The role of these heterotrophs in
marine ecosystems can be seen in Figure 1, but in short, the growth of these bacteria is
positively correlated to the amount of CO2 respired into the atmosphere.
11
The model organism used in this study, Ruegeria pomeroyi DSS-3, is in the
Roseobacter clade. One important attribute of this organism and clade is its ability to
metabolize DMSP. This metabolism sometimes results in production of dimethyl sulfide
(DMS), a compound which comprises the bulk of the sulfur flux into the atmosphere
(Andreae, 1997). This flux can subsequently affect the climate system through the
formation of aerosols and alterations in how much solar energy is reflected back into
space (Charlson et al. 1987). In one study, the abundance of Roseobacter cells was found
to be highly correlated to dissolved DMSP consumption and bacterioplankton production,
while this correlation was not seen with the abundance of other taxa (Zubkov et al.,
2001). This observation suggests a few bacterial species may at times dominate the
production of compounds important to global climate change. The objectives of this
study revolve around developing a method that utilizes transcriptional changes associated
with different levels of growth in order to gauge environmental growth rates of marine
bacterial taxa.
EXPERIMENTAL GOALS We hypothesized ratios of specific mRNA:mRNA gene copy number can be used
as an indicator of marine microbial activity. The overarching goal of this master’s thesis
was to determine how expression levels of select genes vary between growth phases and
specific growth rates in R. pomeroyi DSS-3. To address this goal, three objectives were
created.
Objective 1: Determine whether expression levels change between growth phases
in batch culture-grown cells. The chosen conditions for this experiment consisted of
12
cultures grown at 30 °C with minimal marine media supplemented with acetate and a
vitamin solution. Samples from early and mid log, stationary, and death phase were
analyzed. cDNA counts of specific growth-related genes were normalized to their
respective DNA counterpart in a given sample. We hypothesized ratios of the transcripts
to genes would correlate positively with log phase growth and negatively with stationary
and death phase.
Objective 2: Determine if expression levels of the selected genes would differ
when growth conditions (and thus rates) were changed. This is important to address
since microbial communities are subjected to varying environmental conditions. We
expected to see a positive correlation between ratios and growth rates. Samples were
taken from mid log and stationary phase. Transcripts and corresponding genes were
quantified via qPCR for the first two goals.
Objective 3: Analyze transcriptome data for differentially expressed genes
between mid log and stationary phase in R. pomeroyi DSS-3. This allows comparisons
between RNAseq and qPCR analysis and also provides a general view of which genes are
associated with changes in growth in this organism.
13
CHAPTER THREE
EXPERIMENTAL METHODS
PRIMER DESIGN
Primers used in all qPCR experiments for this project were designed either with
the NCBI primer designing tool or in Geneious with Primer3 and checked for the correct
product amplification using BLAST (Table 1). Optimized annealing temperature was
determined using a temperature gradient with four different temperatures in a qPCR test.
Table 1. R. pomeroyi DSS-3 Primers Designed for qPCR
Gene Forward Reverse
Optimized Annealing Temp (°C)
rpoB CGGATGAACGTCGGTCAGAT CGTCCATGCCAGAGATACCC 60 rpoD GCGGTGGACAAGTTCGAGTA CAGCGGCATCTGCAGTTTTT 56.7 rpoS CGAAAGCCTGACCCATTGCG TTTGACCCTCGCCGGTTG 59 ftsI CACATTTGCCAGCCTGTTCC CGAGGTGAGCGTATAGCCAG 57 rplb ATGGCGGCTATGCTCAGATC TTGCCGTAGTTCTGGTTGCT 57
16S rRNA (1369F and
1492R)
CGGTGAATACGTTCYCGG
GGWTACCTTGTTACGACTT
54
CULTURE CONDITIONS
Strains were prepared for growth curve experiments by recovering from half-
strength YTSS glycerol stocks into half-strength YTSS broth (8 g yeast extract, 3 g
tryptone, and 20 grams sea salt per liter for full strength). The sea salt concentration was
the same in full and half-strength YTSS. Subsequent passages were performed in marine
minimal basal media (250 mL Basal Medium, 50 mL FeEDTA Stock, and 699 mL DI
H2O Sea Salt Solution per 1 L mixture). The ingredients for each component of the
minimal media are as follows: 699 mL DI H2O and 20g Sigma Sea Salts made up the sea
14
salt solution, 150 ml 1M Tris HCl pH 7.5, 87 mg K2HPO4, 1.5 g NH4Cl, and 375 ml DI
H2O made up the basal medium stock, and 50 mg FeEDTA (ethylenediamine tetraacetic
acid; ferric-sodium salt) plus 100 ml DI H2O made up the FeEDTA stock. Each
component was mixed together after autoclaving and carbon substrates were added at a
final concentration of 10 mM. The minimal media was also supplemented with a vitamin
solution (0.1% final volume) (Gonzalez et al., 1997). Biological replicates were created
during the third passage in three separate 125 mL flasks. Growth curves were conducted
on the third passage. Unless otherwise noted, strains were grown in the dark, at 30 °C,
and in a shaker at 250 RPM. Other culture conditions (intended to change specific
growth rate) include 1) growth in minimal media supplemented with either acetate or
glucose and 2) an incubation temperature of 15 °C.
DNA/RNA EXTRACTIONS
Two milliliters of culture per time point were taken and spun down at 16,000 x g
for 10 minutes. The supernatant was discarded and 600 µL of RLT buffer (provided by
QIAGEN) and 6 µL of beta-mercaptoethanol was added and allowed to incubate on a
rotator at 60 °C for 10 minutes. The protocol for the QIAGEN AllPrep DNA/RNA Mini
Kit was then followed for both DNA and RNA extraction and purification. There was an
extra elution step at the end of each RNA/DNA extraction with the same elute to increase
the respective nucleic acid yield.
cDNA SYNTHESIS AND qPCR
RNA samples were treated with Turbo DNase from Ambion according to the
manufacturer’s instructions. Samples were checked for complete DNA digestion by
15
performing a PCR test for 16S rRNA genes. RNA was diluted to concentrations
demonstrated to be in the linear range of qPCR (data not shown). The diluted RNA was
then reverse transcribed into cDNA using the High-Capacity cDNA Reverse
Transcription Kit from Applied Biosystems. The manufacturer’s provided protocol was
followed. Samples were amplified in triplicate with qPCR. A melt curve was performed
after each reaction to determine if there was any contaminating DNA or nonspecific
amplification. Conditions for qPCR are listed in Table 2 with the variations in annealing
temperature dependent on the primer pair as indicated in Table 1.
Table 2. qPCR and Melt Curve Conditions for All Primer Sets Step 1 2 3 4 Repeat
from Step 2
39x
5 6 7 Repeat Step 7 150x
Temp. (°C)
95 95 57-60 72 72 65 65 + 0.2/cycle
Time (min)
5:00 00:15 00:30 00:30 Plateread
5:00 0:30 0:05 Plateread
CELL ENUMERATION
Samples for cell counts were diluted with paraformaldehyde (2% final
concentration) buffered with sodium phosphate buffer (pH 7.2). After filtering and
staining with 4’,6-diamidino-2-phenylindole (DAPI) for 5-10 minutes, cells were imaged
with an epifluorescence microscope in at least ten fields per filter and counted with the
aid of ImageJ software.
For repeat growth curves of the same conditions, cell counts from previous
experiments were correlated with OD and fitted with an exponential trendline from lag to
late-log time points. The resulting equation was used for enumeration. Samples taken
16
for OD580 were diluted to or below 0.50 when necessary. Cell counts were always taken
during stationary and death phases.
PROTEIN EXTRACTIONS AND ANALYSIS
One milliliter of culture collected for protein analysis was spun down at 15,800 x
g at 4 °C for 10 minutes. After discarding the supernatant, the pellets were frozen at -80
°C until further analysis. For the extraction, protein pellets were thawed on ice and lysis
buffer (20 mM Tris, 100 mM NaCl, 1 mM EDTA, and 0.5% Triton X-100) was added.
After suspending cells, they were boiled in buffer for 5 minutes. Samples were then
cooled on ice, and the instructions for the Bio-Rad Protein Assay Standard Procedure for
Microtiter Plates was followed with the use of BSA standards.
RNA-seq SAMPLE COLLECTION, PROCESSING, AND ANALYSIS
RNA-seq samples were collected by Dr. Matt Cottrell of the University of
Delaware from biological triplicates of cultures grown in a modified YTSS medium (0.4
grams yeast extract, 0.25 grams tryptone, and 20 grams sea salts per liter) at 18 °C.
rRNA was partially removed prior to sequencing on an Illumina HiSeq machine at the
University of Delaware. Sequences were processed and analyzed in Geneious version R8
(http://www.geneious.com, Kearse et al., 2012). Leftover rRNA reads were removed by
mapping reads to a FASTA file containing the 5S, 16S, and 23S sequences of the
respective reference genome. Sequences were trimmed from the 3’ end, resulting in an
average read length of 75 bp. Reads were then mapped to the reference genome and
megaplasmid using TopHat. The Bowtie2 preset was used at the lowest sensitivity
(which is the same as -D 5 -R 1 -N 0 -L 22 -i S,0,2.50 in --end-to-end mode). Expression
17
levels were calculated with ambiguously mapped reads counted as partial matches.
Transcript expression levels were compared and normalized by the median of gene
expression ratios. Expression levels were calculated and compared between each of the
biological replicates and also between pooled triplicates. Any resulting differentially
expressed genes between replicates were treated as cellular noise and ignored when
analyzing pooled replicates.
DATA ANALYSIS
After data collection, the original concentrations (in copies/µL) of each transcript
and gene in extracted RNA and DNA were calculated. The ratios of mRNA:DNA were
created and compared to see if there were any significant differences between growth
phases. The mRNA:DNA ratios from mid log were compared to corresponding ratios at
different growth rates to determine if there were any significant differences. The
Student’s t-test was used when comparing means of ratios between growth phases.
Linear regression analysis was used to compare mid log ratios with specific growth rate.
Specific mRNA/cell and protein/cell were also calculated.
18
CHAPTER FOUR
RESULTS
RATIO CHANGES BETWEEN GROWTH PHASES
Two types of growth rates were analyzed in this study: specific growth rate and
changes in growth from one time point to another. Growth in different culture conditions
addressed specific growth rates changes and comparing different growth phases
addressed the growth rate changes cultures experience during a single growth curve
experiment. For the latter analysis, readings for optical density at 580 nm were taken as a
general indicator of the progression of cells through the four growth phases: lag,
logarithmic, stationary, and death. Cell counts were later processed and their relationship
with OD was calculated. The growth rate for each growth curve conducted in this study
was calculated by determining the slope of the line of best fit through three points in
exponential phase using cells counts. This process is demonstrated in Figure 2, where the
slope corresponds to a growth rate of 0.402 hour-1 or 9.648 day-1 for cells grown at 30 °C
in minimal media supplemented with acetate as a sole carbon source.
19
Figure 2. Representative growth curve of acetate-grown cells. OD580 is included for comparison. Arrows indicate where samples were taken for qPCR and protein analysis. Error bars indicate standard error (n=3). Since the primary objective of this project was to identify which, if any,
transcripts correspond to an increased activity level, expression levels of the selected
growth-related genes from early log, mid log, stationary, and death phase were analyzed.
Lag samples were not included because it didn’t seem there was enough cell mass for
accurate transcript quantification, though it may be useful to study in future projects.
Ratios of transcript per gene in copies/µL of extracted RNA and DNA for rpoB, rpoD,
rpoS, rplB, and ftsI as determined by qPCR are shown in Figure 3. rpoB had significant
differences (p < 0.05) in expression between samples taken at log, stationary, and death
phases. Differences in expression of rpoB seemed to be most pronounced between mid
log and stationary (p = 0.00643) and mid log and death (p = 0.00619). Similarly, rplB
expression was significantly different (p < 0.01) in the two log phase samples versus
death and stationary. rpoD expression was different between early log and stationary,
20
early log and death, and mid log and stationary. The difference in rpoD expression
between mid log and death was close to significant (p = 0.0522). rpoS ratios showed
significant differences (p <0.05) in early log and mid log versus death, mid log versus
stationary, and stationary versus death. ftsI differences were significant (p < 0.05) in mid
log versus stationary and death. There were also differences (p <0.01) in early log versus
stationary and death. There were no significant differences between early log and mid
log, though the lowest p-values were associated with rpoB and rplB expression, p =
0.0966 and 0.0852, respectively. In all cases with significant differences for all five
genes, each were upregulated in log phase growth when compared to stationary or death.
Figure 3. Ratios of cDNA/DNA in acetate-grown cells. Ratios are specific transcripts per gene copy number as determined by qPCR. Error bars indicate standard deviation.
We were interested in how protein per cell changed between growth phases and
growth rates, since protein per cell has been shown to positively correlate with growth
rate in E. coli B/r (Bremer and Dennis, 1987). After collecting and centrifuging the cells,
the resulting pellet did not undergo a wash step before lysis in buffer. Any residual
21
proteins in the media and from the extracellular matrix are thus included in the analysis.
Since each protein pellet was treated the same way, we believed it would still be valuable
to perform comparisons of protein levels. We first focused on comparing the protein
content between growth phases. The general trend of the soluble protein quantified from
mid log, stationary, and death phase followed cell counts, as shown in Figure 4a. There
seemed to be a bit more protein in relation to cell counts in death phase when compared
to mid log or stationary, and this discrepensy is made more apparent in Figure 4b when
protein was normalized by cell counts. Though the differences between mid log and
death (p = 0.0781) and stationary and death (p = 0.0950) do not quite reach significance,
it may indicate an issue with protein quantification in death phase. Also, the protein
quantified in mid log was outside of the standard curve, so this may have skewed results
from that phase of growth.
22
Figure 4. Relationship between protein and cell counts in acetate-grown cells. a) Protein concentration compared to cell concentration in three growth phases and b) amount of protein per cell. Error bars indicate standard deviation.
EXPRESSION LEVEL CHANGES BETWEEN GROWTH RATES
The second objective of this project addressed how expression levels of growth-
related genes change with specific growth rate. Specific growth rates were manipulated
by growing cells in glucose at 30 °C or at 15 °C (Figure 5). Specific growth rates were
23
11.225 and 0.4193 day-1 for glucose-grown cells at 30 °C and 15 °C, respectively. The
order of fastest to slowest growing cells were those grown in minimal media plus glucose
at 30 °C, then acetate at 30 °C, and finally glucose at 15 °C. This order is expected since
glucose is a richer carbon source than acetate and cells typically grow slower in colder
temperatures. Previous growth rates for Ruegeria pomeroyi DSS-3 ranged from 0.42
day-1 to 2.78 day-1, though culture conditions varied from what was used in this study
(Cunliffe, 2013; J. M. Gonzalez, 2003). The inclusion of a vitamin solution in the media
and rigorous shaking at 250 RPM may have contributed to the higher growth rates
measured in this study.
24
Figure 5. Representative growth curves of glucose-grown cells. a) Growth at 30 °C and b) at 15 °C. Arrows indicate where samples were taken for qPCR and protein analysis. Error bars indicate standard error where n=3, except for days 10 and 12 where n=2.
Along with comparing ratios of cDNA/DNA of various genes between growth
curve experiments, we also wanted to determine whether expression ratios had consistent
differences between growth phases as with the acetate-grown cells. Mid log and
stationary phase ratios were chosen to compare because we assumed cells were in the
25
peak of growth in mid log while cells in stationary phase were not actively growing. It
should be noted that samples taken from cells grown in glucose at 15 °C had two
replicates instead of the typical three for day 10 and 12. Interestingly, for both growth
curve experiments with cells grown in glucose, rpoB and rplB were the only genes with
ratios that were significantly different (p < 0.01) between growth phases (Figure 6).
rpoD was the only other gene whose expression to gene copy number ratios seemed to
approach significance at p = 0.0543 and p = 0.0939 for the 30 °C and 15 °C experiments,
respectively.
26
Figure 6. Ratios of cDNA/DNA in glucose-grown cells. Ratios are specific transcripts per gene copy number as determined by qPCR in cells grown a) at 30 °C and b) at 15 °C. Error bars indicate standard deviation. Note differences in y-axis scale. Despite missing a wash step during protein processing, we were still interested in
determining if protein concentration in the cell varied between growth phases of differing
culture conditions. Cells grown in glucose at 30 °C exhibited a highly significant protein
difference per cell (p < 0.001) between mid log and stationary (Figure 7b). This result is
what was originally expected for all growth curve experiments. Conversely, cells grown
27
in glucose at 15 °C showed no difference between mid log and stationary (Figure 7c). A
hypothesis for the unexpected protein levels in stationary and death (see Figure 4b and
7c) has two parts: 1) DNA of recently lysed cells had already degraded and cells were
thus not counted after DAPI staining, but 2) the cell wall and protein content was still
intact enough to be picked up in the protein assay. To test this, a linear line of best fit,
seen in Figure 7b, was created to estimate the hypothetical amount of cells that may have
contributed to the total measured protein in stationary phase. Figure 7c includes the
following hypothetical protein/cell average if this hypothesis is true. The difference
between mid log and this hypothetical average is not significant (p = 0.3124), but it does
exhibit how residual proteins from nonviable cells could contribute to total measured
protein.
28
Figure 7. Relationship between protein and cell counts in glucose-grown cells. a) Amount of protein per cell for cells grown at 30 °C. b) Linear trendline to determine hypothetical cell counts for day 10 for cells grown at 15°C. c) Amount of protein per cells grown at 15 °C, including hypothetical amount. Error bars indicate standard deviation. Three stars represent p < 0.001.
29
While cDNA/DNA ratios were consistently higher in log phase versus stationary
or death phase, indicating higher ratios at higher growth rates, most ratios from mid log
had no correlation to specific growth rates from other growth curve experiements (Figure
8a). The only gene that showed a positive correlation, with an R2 of 0.9938, is rpoB (p =
0.05). The rest of the ratios had R2 values ranging from 0.03587 to 0.5164.
Conflictingly, there was a negative correlation of copies of specific transcripts per cell in
mid log phase to specific growth rates (Figure 8b). R2 values ranged from 0.9982 for
rpoB and 0.9944 for rpoS to the lowest value of 0.9569 for rplB. Only rpoB and rpoS
had a significant correlation (p < 0.05).There was not a strong correlation between
protein concentration per cell in mid log and specific growth rate (Figure 8c).
30
Figure 8. Comparisons of gene expression and protein concentration between specific growth rates at mid log. a) Ratios of cDNA/DNA and b) copies of transcripts per cell and c) amount of protein per cell. Error bars represent standard deviation. A single star represents p < 0.05
31
RNAseq ANALYSIS BETWEEN LOG AND STATIONARY PHASE
In order to compare our qPCR results to relative mRNA levels between growth
phases, we utilized a transcriptomic approach to identify differentially expressed genes
between log and stationary phase. Cells were grown in a rich medium containing yeast
extract and tryptone and incubated at 18 °C. The Geneious software program was used to
map reads to the R. pomeroyi DSS-3 genome and megaplasmid. Differentially expressed
genes between log and stationary phase time points were then identified. To be confident
the genes analyzed in detail were differentially expressed, a p-value filter of 0.000001
was chosen. A total of 441 differentially expressed genes were identified and 306 had a
Clusters of Orthologous Groups (COG) ID associated with them. The most abundant
functional categories assigned to these 306 genes are those associated with Amino Acid
Transport and Metabolism and also Translation, Ribosomal structure, and Biogenesis
(Figure 9). The combined categories Function Unknown and General Functional
Prediction Only make up 20% of the assigned genes. Gene product names of those genes
without an assigned COG ID can be seen in Table A-1.
32
Figure 9. Breakdown of differentially expressed genes (p < 0.000001) by COG category.
33
In order to determine if the transcripts analyzed by qPCR showed similar
differential expression patterns using RNA-seq analysis, we compiled the differential
expression confidences of rpoB, rpoD, and rplB (Figure 10). The differential expression
confidence was calculated and defined in the Geneious software program as the negative
base 10 log of the p-value. Values were adjusted to be negative for genes that were
underexpressed in stationary compared to log and positive for overexpressed genes.
Results for rpoA, rpoN, rpoH-2, and Rne/Rng were also included as qPCR primers were
designed for these genes and could be useful in future experiments. DSS-3 genes rpoA,
rpoN, and Rne/Rge showed significant downregulation in stationary phase compared to
log with p-values of 0.00027, 2.40x10-13, and 0.0067, respectively. rpoD was
downregulated at stationary with a p-value of 0.0026. rplB was upregulated in stationary
with a p-value of 0.0012.
Figure 10. Differential expression confidence of select growth-related genes as a means of comparison to qPCR. One star represents p < 0.05, two stars represent p < 0.01, and three stars represent p < 0.001.
34
In the interest of identifying genes that could be used in similar qPCR
experiments in the future, 16 differentially expressed ribosomal proteins (p < 0.000001)
were identified (Figure 11). 87.5% of these genes were downregulated in stationary
compared to mid log phase.
Figure 11. Differential expression confidence of ribosomal proteins that have a p-value < 0.000001.
35
CHAPTER FIVE
DISCUSSION
Microbes are essential to biogeochemical cycling. Variations in the activity of
marine heterotrophic bacteria can have a global effect on the patterns of carbon flux
(Azam and Malfatti, 2007). Many Roseobacters, including the marine model organism
Ruegeria pomeroyi DSS-3, have the ability to metabolize DMSP (Buchan et al. 2005;
Gonzalez, 2003). The metabolism of this compound can sometimes lead to the
production of DMS which is responsible for the bulk of sulfur influx into the atmosphere
(Andreae, 1997). Tracking the microbial activities responsible for regulating various
greenhouse gases may provide insight to climate change models and could potentially aid
in managing those microbial processes (Singh et al. 2010).
Many studies have found RNA content to be positively correlated with growth rate
(Bremer and Dennis, 1987; Fegatella et al., 1998; Kemp et al., 1993; Kerkhof and Kemp,
1999). Researchers have also had success with using 16S rRNA:rDNA gene ratios as an
indicator of growth (Campbell et al., 2011; Muttray and Mohn, 1999). However, there
are substantial limitations to using these ratios as an activity indicator, as reviewed in
Blazewicz et al., 2013. Because of these limitations, developing alternative measures of
microbial activity in the environment is of great interest and importance. This study
sought to determine if using ratios of growth-related mRNA and their corresponding
genes are a viable index for estimating growth rates in the environment.
Our data indicate select growth-related gene expression is different between growth
phases when normalized by the corresponding gene. In acetate-grown cells, expression
36
of all five genes was significantly different when comparing at least one log phase growth
point to a point in stationary or death phase. The lack of a significant difference between
early log and mid log time points supports the hypothesis that expression of these growth-
related genes is correlated with actively growing cells. Differences in gene expression
may also depend on available carbon sources. Only rpoB and rplB were significantly
different between log and stationary phase in glucose-grown cells, despite having very
different growth rates at 15 °C and 30 °C. The gene expression patterns observed in this
study follow the substrate metabolism patterns found in other bacteria (Kovárová-Kovar
and Egli, 1998). Our results are also similar to oxygenase mRNA copies per cell
obtained with Pseudomonas putida G7 grown in rich media that was extracted with a
similar QIAGEN RNA/DNA extraction kit (Kong and Nakatsu, 2010).
In a study of nine marine Proteobacteria, seven of the nine strains exhibited a peak in
16S rRNA/cell in mid to late exponential phase (Kerkhof and Kemp, 1999) Because of
this positive correlation, it was hypothesized all genes except rpoS would be upregulated
in log versus stationary phase. rpoS was the exception as transcription has been shown to
increase five- to ten-fold when entering stationary phase in E. coli in rich medium
(Khmel’, 2005). Previous studies have found an increase in rpoS expression and
abundance in exponential phase when comparing minimal versus rich media (Dong and
Schellhorn, 2009; Tao et al. 1999). Since cells grown in glucose showed no significant
differences in rpoS expression, there must be another explanation besides the lack of a
rich medium for the upregulation of rpoS in cells grown in acetate. A simple explanation
for the elevated levels of rpoS in log compared to stationary for acetate-grown cells is
37
that cells were highly stressed due to the low carbon and energy availability, prompting
the “turning on” of genes regulated by rpoS. It has been shown previously in E. coli that
acetate induces expression of rpoS and plays a role in acetate-induced acid tolerance
(Arnold et al. 2001; Schellhorn and Stones, 1992). These higher stress levels may also be
at least partly responsible for the differences in gene expression for ftsI and rpoD in
acetate-grown cells.
Comparing gene expression ratios between specific growth rates is less
straightforward than comparing between growth phases. rpoB was the only gene whose
expression showed a positive correlation with specific growth rate when normalized by
gene copy number. When transcripts were normalized by cell counts, the expression of
all genes exhibited a negative correlation with growth rate, but only two of the
correlations were significant. When transcripts were normalized by cell counts and
compared between growth phases, similar patterns of differential expression were seen as
when normalized by gene copy number (data not shown). It is possible there was an
error in the quantification of cDNA in the 15 ºC cells (either due to an error in the qPCR
standards or nonspecific amplification) and it was compensated for by the normalization
of the similarly overamplified gene copies. An alternative explanation is these genes are
actually negatively correlated with specific growth rate. Because of the previous
literature supporting the opposite result and because of our own results comparing gene
expression between growth phases, this seems the less likely option and needs further
support. This result sparks an interesting question – which of the two scenarios, specific
growth rates or the changes between growth phases, most resembles what is actually
38
occurring in the environment? When we compare between specific growth rates, we are
making the assumption that bacteria in the environment are behaving similarly to cells in
exponential phase growth. More likely, cells in the environment go through miniature
growth phases – starting from dormancy when nutrient availability is poor and adjusting
to nutrient influx (as from lag to log phase) or quickly switching gene expression to
utilize a different substrate or combat a stressor (as from exponential to stationary). If
cells in the environment indeed behave more closely to the phases in batch culture,
perhaps it would be prudent to focus on the changes in expression between growth phases
rather than specific growth rate.
The results of our protein analysis did not meet our expectations. We hypothesized
there would be higher protein levels per cell in log phase growth when compared to
stationary. Only glucose-grown cells incubated at 30 °C exhibited this difference. As
already addressed, the protocol for protein analysis was missing a wash step before cell
lysis, which resulted in including any contaminating protein from the extracellular matrix
and media. However, I do not believe this would account for the high protein
concentrations found in stationary phase for acetate-grown cells and cells grown at 15 °C.
More likely, recently lysed cells that were not counted because of their loss of DNA had
intact protein that contributed to the total protein quantified in stationary. It is also
possible cell counts were incorrect for that time point. Additionally, there were no
correlation between protein per cell and specific growth rate. In the past, protein content
has been shown to increase with growth rate (Bremer and Dennis, 1987). There is also
recent evidence that any increase in the abundance of some proteins results in an equal
39
decrease in others, resulting in a relatively constant protein mass per cell (Schmidt et al.,
2011). Future experiments should be performed with an optimized protein extraction
protocol.
Our trancriptome data provided additional insight into the differential gene expression
of R. pomeroyi DSS-3 that could be helpful in selecting which genes to monitor via qPCR
in the future. Amino acid transport and metabolism as well as translation and ribosomal
structure made up the bulk of genes that were differentially expressed in DSS-3. Both of
these functional groups are involved in changes in growth and growth rates in some
bacteria. Under low nitrogen conditions, significant increases in transcripts for amino
acid transporters were found in a cyanobacterium using RNA-seq analysis (Harke and
Gobler, 2013). In a proteomic study performed on DSS-3 ribosomal proteins comprised
26% of the total proteins in exponential phase but only 14% in stationary (Christie-Oleza
et al., 2012). Additionally, increases of rRNA and tRNA transcripts were correlated with
an increase of growth rate in E. coli (Bremer and Dennis, 1987).
It is interesting to note which of the specific growth-related genes were up or
downregulated in log or stationary phase according to RNA-seq analysis. While most
highly significant ribosomal protein genes were downregulated in stationary phase, rpsR
and rpsQ were upregulated. rpsR encodes protein S18 in the ribosomal 30S subunit in E.
coli (Isono and Kitakawa, 1978). rpsQ has been shown to be nonessential in E. coli since
rpsQ-knockout mutants were viable (Bubunenko et al. 2007). There is not much
information on these genes that would suggest why these two genes would have high
expression levels in stationary. rpoD only showed significant differences in acetate-
40
grown cells, the p-value was close to significance in glucose-grown cells at 30 °C, so to
see a downregulation of rpoD in stationary phase is not unexpected. It was surprising to
see rplB upregulated in stationary as we saw rplB upregulated in log phase compared to
stationary for three specific growth rates. rpoB showed no differential expression. We
perceive three possibilities for the discrepancy between qPCR and RNA-seq results.
Firstly, the type of normalization of any transcriptome data will affect the subsequent
differential expression values. Data in this transcriptome study was normalized using the
median of gene expression ratios, recommended by the Geneious software and
specifically Dillies et al., 2013. It is also the same type of normalization utilized in the
popular R package DeSeq (Anders and Huber, 2010). It thus seems unlikely the
normalization type is cause for variations in rplB and rpoB gene differentiation as
compared to qPCR analysis.
Another possible explanation for the discrepancy between the qPCR and RNA-seq
results is an issue with the RNA-seq experiment itself. After removing leftover rRNA
reads in Geneious, an average of about 42% and 26% of reads from the log and stationary
samples, respectively, mapped to the genome and megaplasmid indicating an issue in
either the processing or sequencing of the reads or with the culture. Lastly, it is possible
gene expression of rpoB and rplB changes in a rich medium. We have seen in this study
that gene expression between growth phases can change depending on the carbon source.
It is not unreasonable to then hypothesize gene expression may not be differentiated
between growth phases for rpoB in a rich medium. It seems less likely that rplB would
have opposite overexpression in minimal versus rich media. Regardless of which
41
explanation is favored, it is clear more supporting information is needed, either in the
form of performing a qPCR analysis from cultures grown in a rich media, optimizing
RNA-seq processing, and/or performing dual qPCR and RNA-seq analysis on the same
growth curve conditions.
Although the cost-reduction of sequencing leads to cheaper projects yielding much
data directly from environmental sources, these results indicate the importance of culture-
dependent studies. More culture and isolation studies should be performed to elucidate
the functions of the many differentially expressed hypothetical proteins found in this
study. Along with the hypothetical proteins are genes that have been annotated but have
unknown functions. Once these hypothetical proteins have assigned functions, we will
have a clearer picture of what is happening in the cell between various conditions of
growth and stress.
We found that rpoB and rplB are the top candidates for use in future qPCR
experiments as they were significantly upregulated in conditions of high versus low
activity across the three growth curves conducted with minimal media. In order to verify
if there is a similar pattern of gene expression rich media experiments, qPCR analysis
should be performed on these genes after cells have grown in half-strength YTSS media.
Another possible growth curve experiment that could provide environmentally relevant
information would be growing cells using DMSP as a sole or secondary carbon source. It
would also be interesting to perform experiments with continuous cultures. This would
eliminate any variations due to sampling at slightly different phases of growth between
growth curves. Performing experiments with a mixed culture will eventually be
42
necessary. An ideal experimental set-up would be to perform RNA-seq analysis on a few
different growth conditions, identify differentially expressed genes, and verify
differentiation with qPCR analysis. An alternative to this would be to design and test
primers on those transcripts we already found differentially expressed through RNA-seq.
Since we ultimately aim to measure growth rates of individual taxa, it would be prudent
to perform similar experiments with other microorganisms to see if 1) similar genes are
differentially expressed and 2) if the amount of differentially expressed genes is
dependent on ecological strategy.
43
APPENDICES
44
Appendix A
Supplementary Tables
Table A-1. Breakdown of Differentially Expressed Genes Differentially
Expressed Genes, p <
10-7
Total Genes Identified by Locus Tag*
Total Genes with COG ID
Assignment Total Genes Unassigned and Gene Product
Names 441 439 306 133
acetyltransferase, GNAT family alkane 1-monooxygenase (EC 1.14.15.3) ATP synthase F0 subcomplex C subunit
CAAX amino terminal protease family
protein coenzyme PQQ biosynthesis protein A cytochrome c550, putative
dTDP-4-dehydrorhamnose reductase (EC
1.1.1.133) helicase, ATP-dependent, putative hemolysin, putative Hpt domain protein ISSpo2, transposase 4 lipoproteins, putative MAPEG family protein
monofunctional biosynthetic
peptidoglycan transglycosylase nitrile hydratase beta subunit nitrile hydratase subunit alpha PaxA, putative R body protein RebB homolog response regulator SapC protein, putative serine protease, subtilase family tellurite resistance protein TPR domain protein 2 transcriptional regulators, LuxR family
twin-arginine translocation pathway signal
sequence domain protein
2 twin-arginine translocation pathway
signal sequence domain proteins, putative universal stress family protein universal stress protein family protein 3 YeeE/YedE family proteins 97 hypothetical proteins
*Identified using Find Genes function in IMG/ER at http://img.jgi.doe.gov/
45
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