CHARACTERIZING GENE EXPRESSION CHANGES AND GENE FAMILY EVOLUTION
IN THE CONTEXT OF STRESS RESPONSE IN MICRO-ALGAL SPECIES
By
Guangxi Wu

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Cell and Molecular Biology – Doctor of Philosophy
2013

ABSTRACT

CHARACTERIZING GENE EXPRESSION CHANGES AND GENE FAMILY EVOLUTION
IN THE CONTEXT OF STRESS RESPONSE IN MICRO-ALGAL SPECIES

By

Guangxi Wu

Algae are a large group of organisms that are of ecological and economical importance,
as they play an important role in the food web and the biogeochemical cycle, and some of them
are found to be suitable for biofuel production. Under stress conditions, they accumulate a large
amount of storage lipid. However, the mechanistic details of this phenomenon are not yet well
understood. Towards deciphering this phenomenon, I focused on characterizing gene expression
changes and gene family evolution in the context of stress response in two micro-algal species. I
first analyzed the global gene expression changes of the green alga model organism
Chlamydomonas reinhardtii under normal and nitrogen deprived conditions. I found that global
gene expression changes significantly under nitrogen deprivation; lipid metabolism was
associated with up-regulated genes while DNA replication and photosynthesis were downregulated. Second, I outlined global gene family evolution in the green algal lineage, particularly
in the extent of duplicate retention and loss, and stress response evolution among duplicates. I
found that stress responsive genes tend to be lineage-specifically retained and functional gains
occur frequently in gene duplicates, shaping the stress response gene repertoire in a speciesspecific manner. Finally, I assembled and annotated the genome of the stramenopile
Nannochloropsis oceanica CCMP1779, a phylogenetically distant species to C. reinhardtii

considered for biofuel production. I found that N. oceanica responds similarly to nitrogen
deprivation as C. reinhardtii, suggesting that general metabolic change is conserved across
distantly related species. In addition, we compared the N. oceanica genome to Nannochloropsis
gaditana to reveal its uniqueness in gene repertoire. We found that it is significantly different
from N. gaditana and this might reflect physiological and biochemical differences. Overall, I
reveal that though major metabolic changes under stress in micro-algae across diverse phylogeny
are similar, the particular genes involved in stress response could be significantly different as
they have been shaped by lineage-specific evolution. Thus, the species-specific mechanism in
stress response cannot be deciphered by studying one model organism. Therefore, it would be
beneficial to explore more micro-algal species, especially the ones considered for biofuel
production, to discover their uniqueness in stress response towards identifying and engineering
the ideal alga for biofuel production.

ACKNOWLEDGEMENTS

First, I would like to acknowledge my advisor Dr. Shin-Han Shiu for his continuous
support and excellent mentorship during my years in his laboratory as a graduate student. My
achievements are not possible without his efforts. I would also like to thank my former advisor
Dr. Thomas Whittam (1954-2008) for jumpstarting my interest in bioinformatics and molecular
evolution.

Next, I would like to thank my guidance committee members for sharing their insightful
perspective in their respective field and helpful opinions about my dissertation project.
Specifically I thank Dr. Christoph Benning for nurturing my interest in stress response in algae;
Dr. C. Robin Buell for discussions in genomics and bioinformatics; Dr. Barbara Sears for sharing
her knowledge in Chlamydomonas reinhardtii and scientific writing; and Dr. Barry Williams for
advice in evolutionary biology.

Last but not least, I would like to thank my coworkers, collaborators, the CMB program,
friends, and family for their endless support.

iv

TABLE OF CONTENTS

LIST OF TABLES……………………………………………………………………………viii
LIST OF FIGURES………………………………………………….……………………….…..ix
INTRODUCTION……………………………………………………………………………...1
REFERENCES……………………………………………………………………………………7
Contribution……………………………………………………………………………………...12
CHAPTER 1…………………………………………………………………………………..13
CHANGES IN TRANSCRIPT ABUNDANCE IN CHLAMYDOMONAS REINHARDTII
FOLLOWING
NITROGEN
DEPRIVATION
PREDICT
DIVERSION
OF
METABOLISM
Abstract…………………………………………………………………………………………..14
Introduction………………………………………………………………………………………15
Results…………………………………………………………………………………………....17
Defining conditions for N deprivation of C. reinhardtii………………………………..17
Global characteristics of C. reinhardtii gene expression following N deprivation .......20
Induction of gametogenesis and sexual reproduction………………………………….30
Effects on genes of N metabolism and protein biosynthesis…………………………...30
General changes in primary metabolism………………………………………………..32
Reduced transcript abundance for most photosynthetic genes......................................40
Specific changes in gene expression related to general lipid metabolism…………...41
Changes in RNA abundance for transcription factors....................................................46
Discussion………………………………………………………..…………………………..…..47
A role for PSBS following N deprivation……………………………………………....48
Recycling of membrane lipids or de novo synthesis of TAGs.......................................49
Conclusion…………………………………………………………………………………...…..52
Materials and methods…………………………………………………………………………..53
Strains and growth conditions..........................................................................................53
Sequencing read processing…………………………………………………………….53
Northern-blot analysis and RT-PCR…………………………………………………....55
Analysis of differential gene expression…………………………………………….....57
GC-MS analysis………………………………………………………………………....58
Sampling, extraction, and analysis of intracellular amino acid……………………….59
Extraction and analysis of Rib..........................................................................................59
Extraction and analysis of carbohydrate..........................................................................60
Acknowledgement……………………………………………………………………………….61
REFERENCES…………………………………………………………………………………..62
CHAPTER 2………………………………………………………………………………..…69
GREEN ALGAL RETAINED DUPLICATE GENES TEND TO BE STRESS RESPONSE
GENES AND EXPERIENCE FREQUENT RESPONSE GAINS
Abstract…………………………………………………………………………………….…….70
v

Introduction………………………………………………………………………………………71
Results and discussion…………………………………………………………………………...74
Variation in domain family sizes among land plants and green algae…………………...74
Gene gains and losses throughout green algal lineage…………………………………...78
Pseudogenes in green algae...............................................................................................82
Functions of retained C. reinhardtii genes duplicated after the C. reinhardtii-V. carteri
split……………………………………………………………………………………….88
Consistency of functional biases over the course of C. reinhardtii evolution…………...92
Functional categories enriched in conserved genes and genes associated with
pseudogenes……………………………………………………………………………...93
Retained duplicates and their stress responsiveness……………………………………..94
Stress response evolution post duplication in the C. reinhardtii lineage………………...95
Conclusion…………………………………………………………………………………101
Methods……………………………………………………………………..…………….……102
Protein domains in green algal species............................................................................102
Identification of missing domains in green algal genomes with variable annotation
qualities............................................................................................................................103
Defining green algal orthologous groups (OGs) and lineage-specific expansions
(LSEs)…………………………………………………………………………………..104
Pseudogenes in green algae.............................................................................................105
Inferring ancestral stress response state ..........................................................................106
Acknowledgement……………………………………………………………………………...108
APPENDIX……………………………………………………………..………………………109
REFERENCES…………………………………………………………………………………126
CHAPTER 3…………………………………………………………………………………132
GENOME AND FUNCTIONAL GENE ANNOTATION OF THE HETEROKONT
OLEAGINOUS ALGA NANNOCHLOROPSIS OCEANICA CCMP1779
Abstract………………………………………………………………………………………....133
Author summary………………………………………………………………………………..135
Introduction…………………………………………………………………………………….136
Results and discussion………………………………………………………………………….138
Strain selection—antibiotic sensitivity, growth and introduction of selectable
markers………………………………………………………………………………….138
Genome sequencing strategy, assembly, and annotation……………………………….148
Functional annotation based on protein domains, functional category assignments, and
expression……………………………………………………………………………....154
Comparison of N. oceanica and N. gaditana gene sets………………………………...160
Conclusion……………………………………………………………………………………..168
Materials and methods…………………………………………………………………………170
Strains and growth conditions…………………………………………………………..170
Nuclear transformation by electroporation......................................................................170
Southern analysis……………………………………………………………………….172
DNA and RNA preparation for sequencing and analysis……………………………....172
Assessment of RNA quantity and quality……………………………………………....173
Genome sequencing and hybrid genome assembly…………………………………….173
vi

Transcript assembly and differential expression analysis……………………………....175
Genome annotation..........................................................................................................176
Functional annotation and determination of differential expression…………………...177
Comparison of Nannochloropsis genomes......................................................................178
Database tools..................................................................................................................178
Acknowledgement…………………………………………………………………………...…179
REFERENCES………………………………………………………………………………....180
CONCLUDING REMARKS……………………………………………………………….186

vii

LIST OF TABLES

Table 1.1 Summary of expression tags generated using two different sequencing methods……21
Table 1.2 Gene Ontology categories significantly enriched in differentially regulated C.
reinhardtii genes………………………………………………………………………………....28
Table S2.1 Gene Ontology biological processes significantly enriched in C. reinhardtii retained
duplicates……………………………………………………………………………………121
Table S2.2 Gene Ontology categories significantly enriched in conserved genes or genes most
closely related to pseudogenes in C. reinhardtii……………………………………………122
Table S2.3 C. reinhardtii retained duplicate genes tend to be stress responsive……………….123
Table 3.1 Comparison of the effect of selected antibiotics on different Nannochloropsis
species…………………………………………………………………………………………..142
Table 3.2 Growth parameters of N. oceanica CCMP1779 in f/2 medium using different
supplements……………………………………………………………………………………144
Table 3.3 Number of resistant colonies achieved by electroporation of N. oceanica CCMP1779
cells in the presence of linearized pHyg3, pSelect100 plasmids per µg linearized plasmid DNA
and transformation rates………………………………………………………………………...146
Table 3.4 Genome summary……………………………………………………………………154
Table 3.5 Enriched GO categories in up- and down-regulated genes during N-deprived versus Nreplete conditions based on RNAseq data……………………………………………………158
Table 3.6 Enriched GO categories in conserved OGs and N. oceanica CCMP1779-specific and
N. gaditana-specific genes……………………………………………………………………165

viii

LIST OF FIGURES

Figure 1.1 Transcript levels of specific genes…………………………………………………19
Figure 1.2 Fold change correlation between Illumina and 454 data sets and impacts of Illumina
length bias on differential expression call………………………………………………………24
Figure 1.3 Regulation of genes involved in primary metabolism…………………….…….……34
Figure 1.4 Changes in labeling patterns reflecting changes in gene expression……………......37
Figure 1.5 Selected changes in glycerolipid metabolism transcript abundance…………………43
Figure 2.1 Domain families in green algae and land plants……………………………………...75
Figure 2.2 Gene gains and losses across land plants and green algae…………………………...80
Figure 2.3 Pseudogene counts in green algae and land plants…………………………………83
Figure 2.4 Functions and stress responsiveness of retained duplicates in the C. reinhardtii
lineage……………………………………………………………………………………………90
Figure 2.5 Stress response evolution scenarios of C. reinhardtii duplicates compared to their
ancestral genes………………………………………………………………………………….98
Figure S2.1 Analysis pipelines…………………………………………………………………110
Figure S2.2 Distribution of domains in un-annotated regions (DURs) in green algae………113
Figure S2.3 Bootstrap values of various branches……………………………………………116
Figure S2.4 Consistency between domain and species tree…………………………………….119
Figure S2.5 Green algal species tree based on 18s rRNA sequences………………………120
Figure 3.1 Rooted neighbor joining tree of 18s rRNA sequences of different Nannochloropsis
species using Eustigmatos vischeri as an outgroup……………………………………………139
Figure 3.2 Nuclear transformation……………………………………………………………..147
Figure 3.3 Hybrid assembly strategy using Illumina and 454 reads………………………….150
Figure 3.4 Gene annotation…………………………………………………………………….152

ix

Figure 3.5 Gene ontology………………………………………………………………………156
Figure 3.6 AED distributions of conserved and N. oceanica-specific genes…………………162

x

INTRODUCTION

1

INTRODUCTION

Algae are a group of heterogeneous organisms that are of ecological importance in the
food web, in symbiotic relationships with other organisms, and in the biogeochemical cycle as a
major generator of atmospheric oxygen and organic carbon (Round, 1984; Graham et al., 2009).
They are also a major contributor to biodiversity with an estimated number of 10 million species
(Graham et al., 2009). Moreover, algae are useful to humans as food sources for human and
aquaculture, sources of industrially useful products, pollutant removal agents, and model
organisms for scientific research (Graham et al., 2009). Recently, scientists have started
exploring the possibilities of using microalgae to generate biofuels (Hu et al., 2008).
Microalgae live in diverse habitats that have a wide range of salinity, temperatures, pH,
and nutrient availabilities (Graham et al., 2009). Some microalgae are considered suitable for
biofuel production due to their high oil content; when grown under various stress conditions,
they redirect metabolism towards the accumulation of neutral lipids such as triacylglycerol
(TAG) (Hu et al., 2008). Thus, studying stress responses in microalgae is of biological and
economical importance. Here, I focus on two single-celled microalgal species, the green alga
Chlamydomonas reinhardtii and the stramenopile Nannochloropsis oceanica CCMP1779. C.
reinhardtii is a soil-dwelling alga with one chloroplast, multiple mitochondria, and two anterior
flagella; it is in the green algal lineage that is more closely related to the ancestor of land plants
than any other protists (Merchant et al., 2007). C. reinhardtii has been used as model organism to
study photosynthesis and flagella and basal body functions, and its genome assembly is available
(Merchant et al., 2007). N. oceanica is a marine coccoid with a plastid derived from secondary
symbiosis (Reyes-Prieto et al., 2007; Graham et al., 2009); it is under the class of

2

eustigmatophyceae in heterokontophyta, which also includes diatoms and brown algae (Hoek,
1995). In addition, it is considered for biofuel production due to its high oil content (Gouveia and
Oliveira, 2009).
In microalgae, nitrogen (N) deprivation is among the most critical stress conditions that
affects lipid metabolism (Hu et al., 2008), and in C. reinhardtii, the formation of lipid droplets
high in TAG content after N deprivation has been well documented (Wang et al., 2009b;
Moellering and Benning, 2010). Though C. reinhardtii is not directly considered for biofuel
production, investigating its stress response mechanism could provide basic insights into other
algal species, especially green algae.

To investigate the mechanistic details of N deprivation

response on the gene expression level, the transcriptomes under normal and N deprived
conditions were determined. Microarrays have been used to characterize gene expression
changes in C. reinhardtii under stress conditions (Ledford et al., 2004; Jamers et al., 2006;
Ledford et al., 2007; Mus et al., 2007; Nguyen et al., 2008; Simon et al., 2008; Yamano et al.,
2008; Mustroph et al., 2010); however, they do not cover all genes in the genome and have other
limitations, such as reliance on existing knowledge, high background noise due to crosshybridization, and limited dynamic range of detection (Wang et al., 2009a). In this study, I
applied RNA-seq approaches to characterize the global gene expression changes in C. reinhardtii
under N deprivation. RNA-seq revealed the major metabolic changes after stress induction.
However, inferring metabolic fluxes from gene expression has its caveats as gene expression
changes do not directly translate into metabolic changes. Therefore, subsequent labeling
experiments were performed by the Shachar-Hill lab to investigate the meaningfulness of such
inference.

3

After determining the global gene expression changes under N deprivation in C.
reinhardtii, I next investigated how gene duplicate retention and subsequent functional gain at
the expression level have shaped the gene repertoire involved in stress response in green algae.
Gene duplication is an important source of generating raw genetic material for evolution to act
upon. The most common fate of gene duplicates is rapid loss following the duplication events
although a significant number of duplicates are retained (Lynch et al., 2001; Moore and
Purugganan, 2003; Moore and Purugganan, 2005). The subsequent gene family expansion due to
duplicate retention could contribute to organismal and regulatory complexity (Lespinet et al.,
2002; Vogel and Chothia, 2006). Among retained duplicates, functional bias exists, for example:
functional categories related to stress response tend to be associated with retained duplicates in a
wide range of organisms (Lespinet et al., 2002; Wapinski et al., 2007; Hanada et al., 2008). In
green algae, despite a number of studies on individual gene families (Porter et al., 1996;
Hallmann, 2006; Mariscal et al., 2006; Hua et al., 2011; Herrera-Valencia et al., 2012) and one
cross-species analysis focused on a limited number of functional categories (Blanc et al., 2012),
there is not yet a global analysis of all gene families. Here, I applied phylogenetic approaches to
investigate the gene family evolution in C. reinhardtii and eight other species with genome
sequences available on www.jgi.org. Further, I examined the functional evolution of gene
duplicates in the context of stress response by reconstructing the ancestral gene response state in
C. reinhardtii using above mentioned N deprivation and five other stress related RNA-seq
datasets. Such a study revealed how gene duplication and subsequent gain-of-function events
contributed to the diversity of stress responsive gene repertoires in the green algal lineage,
ultimately leading to the tremendous diversity in phenotypes, in aspects such as fatty acid
composition, the interaction of growth and lipid content, the time course of lipid accumulation,

4

and the threshold of stress required to stimulate lipid formation (Hu et al., 2008; Adams et al.,
2013).
Though C. reinhardtii has traditionally been used as a model organism for microalgae
(Merchant et al., 2007), it has caveats: it is not a direct candidate for biofuel production and has
limited tools for molecular manipulation. For example, targeted inactivation of genes by
homologous recombination is not available, and RNA interference is not efficient in generating
loss-of-function C. reinhardtii mutants. Therefore, there is a need to develop a new microalgal
model organism. The genus of Nannochloropsis in the supergroup of stramenopila is directly
considered for biofuel production and various species of this genus have been studied for their
lipid composition and accumulation (Sukenik and Carmeli, 1990; Schneider and Roessler, 1994;
Tonon et al., 2002; Danielewicz et al., 2011). Moreover, a number of studies have focused on the
biomass production under different conditions (Hu and Gao, 2003; Xu et al., 2004; Gouveia and
Oliveira, 2009; Rodolfi et al., 2009; Simionato et al., 2011; Srinivas and Ochs, 2012). Among
them, one study considered a Nannochloropsis species suitable for biofuel production due to a
high oil content (28.7% of dry weight); its oil quantity increases greatly when grown under N
deficient conditions (Gouveia and Oliveira, 2009). Recently, high-efficiency homologous
recombination was achieved in N. oceanica (Kilian et al., 2011), opening up possibilities of
developing an alternative model organism to interrogate the relationships between stress
response mechanism and lipid metabolism in microalgae. Here, I focus on the publicly available
N. oceanica CCMP1779, which was chosen for its growth in culture, antibiotic sensitivity, and
ease of nuclear transformation. I assembled its genome and used transcriptomes under normal
and N deprived conditions to facilitate its gene annotation. A consortium of scientists manually
annotated selected pathways in order to better understand the biology of N. oceanica. In addition,

5

a protocol for transformation was provided by the Benning lab. Next, I performed comparative
genomics analysis between N. oceanica and the recently sequenced genome of Nannochloropsis
gaditana (Radakovits et al., 2012) to reveal the difference in gene repertoire within the
Nannochloropsis genus. Overall, draft genome assembly and extensive annotation, coupled with
a transformation protocol, are valuable resources for the research community to further explore
the biology and the industrial potential of this species.
In conclusion, my research examined the mechanistic and evolutionary details of stress
response in two microalgal species towards the ultimate goal of deciphering the mechanisms of
stress response and subsequent lipid accumulation. My work sheds light on the impact of model
organism usage in this research field while providing an attractive new alternative. In addition,
my results will aid the identification of genes for further molecular characterization and
manipulation to optimize lipid production.

6

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7

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11

CONTRIBUTION

In Miller et al., I conducted sequencing read processing, differential RNA-seq analysis,
and global functional analysis using GeneOntology, and contributed to manuscript preparation.
In Vieler et al., I contributed to experimental design, conducted hybrid genome assembly, gene
annotation, transcript assembly and differential expression analysis, global functional analysis,
comparison of Nannochloropsis genomes, and manuscript preparation.

12

CHAPTER 1

CHANGES IN TRANSCRIPT ABUNDANCE IN CHLAMYDOMONAS REINHARDTII
FOLLOWING NITROGEN-DEPRIVATION PREDICT DIVERSION OF
METABOLISM

Note: The contents of this chapter have been published in Plant Physiology.

Rachel Miller*, Guangxi Wu*, Rahul R. Desphande, Astrid Vieler, Katrin Gaertner, Xiaobo Li,
Eric R. Moellering, Simone Zäuner, Adam Cornish, Bensheng Liu, Blair Bullard, Barbara B.
Sears, Min-Hao Kuo, Eric L. Hegg, Yair Shachar-Hill, Shin-Han Shiu, and Christoph Benning
(2010) Changes in Transcript Abundance in Chlamydomonas reinhardtii following Nitrogendeprivation Predict Diversion of Metabolism. Plant Physiol 154:1737-1752.

*These authors contributed equally.

13

ABSTRACT

Like many microalgae, Chlamydomonas reinhardtii forms lipid droplets rich in
triacylglycerols when nutrient deprived. To begin studying the mechanisms underlying this
process, nitrogen (N) deprivation was used to induce triacylglycerol accumulation and changes
in developmental programs such as gametogenesis. Comparative global analysis of transcripts
under induced and non-induced conditions was applied as a first approach to studying molecular
changes that promote or accompany triacylglycerol accumulation in cells encountering a new
nutrient environment. Towards this goal, high-throughput sequencing technology was employed
to generate large numbers of expressed sequence tags of eight biologically independent libraries,
four for each condition, N replete and N deprived, allowing a statistically sound comparison of
expression levels under the two tested conditions. As expected, N deprivation activated a subset
of control genes involved in gametogenesis while down-regulating protein biosynthesis. Genes
for components of photosynthesis were also down-regulated, with the exception of the PSBS
gene. N deprivation led to a marked redirection of metabolism: the primary carbon source,
acetate, was no longer converted to cell building blocks by the glyoxylate cycle and
gluconeogenesis but funneled directly into fatty acid biosynthesis. Additional fatty acids may be
produced by membrane remodeling, a process that is suggested by the changes observed in
transcript abundance of putative lipase genes. Inferences on metabolism based on transcriptional
analysis are indirect, but biochemical experiments supported some of these deductions. The data
provided here represent a rich source for the exploration of the mechanism of oil accumulation in
microalgae.

14

INTRODUCTION

The search for sustainable sources of biofuels has led to renewed interest in microalgae as
a potential feedstock and, consequently, a flurry of research has recently been initiated in
microalgae (Wijffels and Barbosa, 2010). Microalgae accumulate large quantities of oils in the
form of triacylglycerols (TAGs) when nutrient deprived, and a thorough analysis of the
underlying molecular mechanism is currently in its infancy (Hu et al., 2008). At this time,
Chlamydomonas reinhardtii is the premier microalgal molecular model for this analysis. As
such, the formation of lipid droplets following nitrogen (N) deprivation has recently been
documented in detail (Wang et al., 2009; Moellering and Benning, 2010). Although C.
reinhardtii is not under direct consideration for the production of biomass as a biofuel feedstock,
the analysis of its metabolism and physiology is expected to provide basic insights into
mechanisms of TAG accumulation relevant to other microalgae at least of the green algal
phylum.
The genome of C. reinhardtii is available (Merchant et al., 2007), and its annotation is
currently at version 4 (http://genome.jgi-psf.org/chlamy/chlamy.home.html). At this time, a
number of microarrays have been used to interrogate changes in response to environmental
factors (Ledford et al., 2004; Jamers et al., 2006; Ledford et al., 2007; Mus et al., 2007; Nguyen
et al., 2008; Simon et al., 2008; Yamano et al., 2008; Mustroph et al., 2010). These microarrays
could not cover all genes in the genome, but more recently, massively parallel cDNA sequencing
approaches were applied to C. reinhardtii, overcoming the shortcomings of microarrays
(González-Ballester et al., 2010). Likewise, we have chosen a cDNA sequencing-based approach

15

using 454 and Illumina technologies in parallel that allow the generation of large numbers of
ESTs of varying abundance, which can be counted to obtain a measure of gene expression
(Weber et al., 2007).
The goal of this study was to determine major changes in gene expression following N
deprivation, the nutrient condition established in our previous analysis of lipid droplet formation
and TAG accumulation in C. reinhardtii (Moellering and Benning, 2010). Comparison of the
transcript levels of induced, N-deprived C. reinhardtii cultures with those of un-induced, Nreplete cultures was expected to reflect the metabolic changes leading to TAG accumulation. Of
course, making inferences about metabolism based on gene expression levels has its caveats, as
gene expression does not necessarily directly translate into metabolic fluxes. To interrogate the
meaningfulness of some of the transcript-level changes we observed with regard to metabolism,
we also performed labeling experiments using acetate as the precursor. Acetate is a typical
carbon source provided to C. reinhardtii for photoheterotrophic growth enabling short doubling
times, and it is readily incorporated into fatty acids, the main constituents of TAGs. Keeping in
mind that these are clearly conditions optimized for an experimental laboratory system, we
nevertheless expect to be able to make basic inferences that will be relevant to a broader
understanding of the induction of TAG biosynthesis and lipid droplet accumulation in green
algae.

16

RESULTS

Defining Conditions for N Deprivation of C. reinhardtii

Ideally, one would like to use finely spaced time-course experiments to distinguish rapid
versus long-term changes in gene expression following N deprivation. However, because our
resources were limited, we decided to focus on two conditions, N replete and N deprived.
Independent biological replicates allowed for statistically sound interpretations of the data. To
determine the time point for N deprivation most likely to provide an accurate snapshot of
readjustment of transcript steady-state levels following N deprivation, we first used northern-blot
hybridization to compare the expression of genes known or expected to be regulated in C.
reinhardtii following N deprivation. An ammonium transporter, AMT4, which has been
previously shown to be activated by N deprivation (Mamedov et al., 2005), and two putative
diacylglycerol acyltransferases, tentatively designated DGTT2 and DGTT3 (protein identifiers
184281 and 400751, genome version 4), were monitored to test the various conditions. RNA was
+

+

isolated from cells grown in standard (10 mm NH4 ) and low-N (0.5 mm NH4 ) Tris-acetate
phosphate (TAP) medium to impose N deprivation as well as from cells grown to mid-log phase
+

in standard TAP medium, then transferred to either standard or no-N (0 mm NH4 ) TAP, with
samples taken at 24 and 48 h, to accomplish more drastic N deprivation (Figure 1.1A). For
standardization, equal amounts of RNA were loaded and the 18S rRNA abundance was
examined. Although C. reinhardtii ribosomes turn over following N deprivation (Siersma and
Chiang, 1971; Martin et al., 1976), their abundance drops no lower than 50% (see below). AMT4
17

mRNA was absent from the un-induced cells and present at a high level in N-limited or Ndeprived cells under the conditions tested. DGTT2 mRNA was present at low levels in all
conditions. DGTT3 mRNA was present at low levels and increased slightly following N
deprivation. N deprivation for 48 h showed the greatest difference in RNA levels compared with
the N-replete cultures. Based on this basic analysis and our previous time-course study of lipid
droplet formation and TAG accumulation (Moellering and Benning, 2010), a 48-h period of N
deprivation was chosen to compare global transcript levels in N-replete and N-deprived cells.

18

Figure 1.1. Transcript levels of specific genes. Cultures were grown in TAP medium that was
+

+

N replete (10 mm NH4 ; 10), continual N limited (0.5 mm NH4 ; 0.5), or N deprived (0 mm
+

NH4 ; 0) for 24 or 48 h. The expression levels of AMT4, DGTT2, and DGTT3 were measured by
RNA-DNA hybridization, and rRNA was visualized as a loading control. B, Cultures were
grown for 48 h in either N-replete or N-deprived conditions. The levels of PSBS1 and PSBS2
transcripts were measured using RT-PCR, and the constitutive IDA5 gene served as a control.

19

Global Characteristics of C. reinhardtii Gene Expression following N Deprivation

To determine the differential expression of genes in C. reinhardtii under N-replete and Ndeprived conditions, two sequencing approaches, 454 and Illumina, were applied. Read length is
longer for 454, but the number of reads per run is lower. As shown in Table 1.1, 60- to 85-fold
more sequence tags were generated with Illumina than with 454 sequencing. Among the 454
reads, 78% to 80% mapped to the C. reinhardtii genome. For the Illumina data, we mapped in
three different ways, with varying stringency depending on whether 3′-end read quality and
exon-spanning reads were considered. Without filtering reads, a substantially smaller proportion
of Illumina reads (63%–68%) were mapped compared with 454 reads. Trimming low-quality 3′
regions of reads resulted in a further 2.7% decrease in the number of mapped reads. Despite the
large number of unmapped Illumina reads, out of 16,710 C. reinhardtii gene models, 15,505
(92%) had one or more reads. In contrast, only 6,372 gene models (38.1%) were supported by
the 454 transcriptome data set. In addition, nearly all genes covered by 454 were also covered by
Illumina. Therefore, our sequencing data covered most annotated genes, enabling us to
interrogate differential expression under normal conditions and following N deprivation. In
addition, as expected, Illumina data provided a better coverage of the gene space than 454
sequences.

20

Table 1.1 Summary of expression tags generated using two different sequencing methods.
Sequencing
methods

Illumina

d

Genic

e

Intergenic

R1

2.15E5

1.69E7

1.68E5

1.08E5

c

Mapped

D

2.01E5

b

R
2.51E5

Treatment
Total

a

454

9.30E4

R2

R3

D1

D2

D3

1.83E7 1.78E7

1.77E7

1.79E7

1.52E7

1.09E7

1.24E7 1.15E7

1.07E7

1.13E7

9.86E6

1.32E5

8.96E6

9.43E6 1.02E7

8.33E6

8.82E6

7.81E6

3.61E4

1.93E6

2.97E6 1.31E6

2.38E6

2.48E6

2.05E6

a

Treatment types: (R) N-replete and (D) N-deprived media. For Illumina sequencing, there are
three replicates for each treatment.
b

Number of sequencing reads after filtering out low quality reads based on 454 and Illumina
base-calling methods.
c
d

Number of sequencing reads after mapping to C. reinhardtii v4.0 genome
Number of mapped sequencing reads overlapping with C. reinhardtii v4.0 filtered gene models

e

Number of mapped sequencing reads not overlapping with any C. reinhardtii v4.0 filtered gene
models

21

To determine differential gene expression following N deprivation, we modeled count
data with a moderated negative binomial distribution (see “Materials and Methods”). Using
thresholds of 5% or less false discovery rates and 2-fold or greater change for the Illumina data
set, 2,128 and 1,875 genes were categorized as up- and down-regulated, respectively, following
N deprivation. To see if fold changes inferred based on 454 and Illumina data sets were
consistent, we determined the statistical correlation in fold change between these two data sets
2

(Figure 1.2) and found that it was rather weak (Pearson’s correlation coefficient, r = 0.10, P <
2.2 × 10

−16

). There was an apparent anomaly, as 4,313 genes (out of 6,369 genes with one or

more reads from both data sets) had a high degree of up- and down-regulation, which was
observed with the 454 but not the Illumina data set (Figure 1.2A). Most of these genes with
extreme responses based on 454 had very low counts (less than 10 reads combined in both
conditions or zero read in one of the conditions; Figure 1.2A, red data points). As a result, high
and likely inaccurate fold change values were assigned to those genes. In fact, if we only
considered 2,056 genes with 10 or more reads combined and one or more reads in both
2

conditions, the correlation between Illumina and 454 data was substantially improved (r = 0.57,
−16

P < 2.2 × 10

; Figure 1.2A, blue data points).

One important consideration in identifying differentially regulated genes is that there is a
considerable transcript length bias in Illumina data. A longer transcript tends to have more reads
than a shorter transcript expressed at the same level (Oshlack and Wakefield, 2009; Bullard et
al., 2010). Consistent with earlier findings, we observed a significant correlation between the
number of reads assigned to a protein sequence and its length (Spearman’s rank ρ = 0.33, P <
2.2e-16; Figure 1.2B). Because of this length bias, longer transcripts may have more significant
22

differences in differential gene expression studies and in some cases will lead to false-positive
differential expression calls (Oshlack and Wakefield, 2009; Bullard et al., 2010). However,
unlike previously published studies, we did not find a significant correlation between the
percentage of genes differentially expressed and sequence length (ρ = 0.09, P = 0.7; Figure
1.2C). This finding indicates that, although length bias remains an issue, our differential
expression call may not be as significantly affected as reported previously.

When using Tophat 1.0.8 for read mapping, 64% - 73% of all reads are mapped to the
genome. When using Tophat 2.0.0 to re-run read mapping, 70%- 78% of all reads are mapped to
the genome. When comparing the differential expression calling between the previous results
and the results based on the second Tophat run (Figure 1.2D), a high correlation was observed
2

among differentially expressed genes (Pearson’s correlation coefficient, r = 0.89, P < 2.2 ×
−16

10

), suggests that the differential expression calling results from two mapping runs are highly

similar.

23

Figure 1.2. Fold change correlation between Illumina and 454 data sets and impacts of
Illumina length bias on differential expression call.
24

Figure 1.2 (cont’d)

25

Figure 1.2. (cont’d)
A, Only genes with one or more 454 and Illumina reads under either N-replete (+N) or Ndeprived (−N) conditions were plotted. Fold change is determined by the number of reads
following N deprivation divided by the number of reads under N-replete conditions for each
10

gene. For genes with 2 -fold or greater or 2

−10

-fold or lesser changes, the fold change values

were set to 10. Blue circles (“high” 454 read genes) indicate genes with 10 or greater 454 reads
(+N and –N combined) and one or more 454 reads in both +N and –N. Red circles (“low” 454
read genes) indicate genes that did not satisfy one or both of the above criteria.
B, Each box plot depicts the numbers of reads for protein-coding genes (log base 10) in a protein
sequence size bin (0–2,000 amino acids, bin size of 100 amino acids). All proteins of 2,000 or
more amino acids are classified as 2,000 amino acids. Outliers are shown in black circles.
C, Percentage of genes that are regarded as differentially expressed (DE) in each protein
sequence size bin. The line indicates the linear fit, and the equation for the line is shown as well.
D, Fold change of Illumina new mapping and old mapping, only genes that are differentially
expressed are included. For interpretation of the references to color in this and all other figures,
the reader is referred to the electronic version of this dissertation.

26

Approximately 7% to 14% of the Illumina reads mapped to the “intergenic regions”
(Table 1.1). We assembled Illumina reads into 42,574 transcribed fragments (transfrags). Among
them, 17,095 transfrags did not map with, or within the vicinity of (1,855 bases, 99th percentile
intron length), current gene models. With the same conservative criterion, transfrags were joined
into 1,828 “intergenic transcriptional units.” Most importantly, 287 of these intergenic
transcriptional units were up-regulated and 176 were down-regulated following N deprivation.
These transfrags are un-annotated genes that require further analysis to establish their
authenticity.

Gene Ontology (GO) annotation was used to coarsely identify major categories of genes
involved in particular biological processes to assess trends in their transcriptional regulation
following N deprivation. We found multiple GO categories with significant enrichment in their
numbers of differentially regulated genes (Table 1.2). Particularly, genes associated with lipid
metabolism tend to be up-regulated, while those involved in photosynthesis and DNA replication
initiation tend to be down-regulated.

27

Table 1.2. Gene Ontology categories significantly enriched in differentially regulated C.
reinhardtii genes
GO
a
R

No
GO
b
R

GO
c
U

No
GO
d
U

Reg

9

926

0

6633

8

927

0

6

929

22

photosynthesis

GO

Annotation

GO:0006270
h
bp
GO:0015995
bp
GO:0033014
bp
GO:0009765
bp
GO:0015979
bp
GO:0005576
i
cc
GO:0009522
cc
GO:0009538
cc
GO:0009654
cc
GO:0019898
cc

DNA replication
initiation
chlorophyll
biosynthetic process
tetrapyrrole
biosynthetic process
photosynthesis, light
harvesting

GO:0003755
j
mf
GO:0004600
mf
GO:0016851
mf
GO:0030051
mf
GO:0042027
mf
GO:0006006
bp
GO:0006468
bp

e

f

g

p

q

down

6.48E-9

4.51E-6

6633

down

5.29E-8

2.76E-5

0

6633

down

3.51E-6

7.32E-4

913

14

6619

down

23

912

0

6633

down

extracellular region

11

924

14

6619

down

8.28E-5

1.44E-2

photosystem I

4

931

0

6633

down

2.32E-4

3.23E-2

5

930

0

6633

down

2.85E-5

5.42E-3

6

929

0

6633

down

3.51E-6

7.32E-4

4

931

0

6633

down

2.32E-4

3.23E-2

21

914

32

6601

down

4.39E-7

1.83E-4

19

916

30

6603

down

2.29E-6

5.98E-4

4

931

0

6633

down

2.32E-4

3.23E-2

19

916

30

6603

down

2.29E-6

5.98E-4

19

916

30

6603

down

2.29E-6

5.98E-4

5

944

1

6618

up

1.65E-4

3.83E-2

117

832

501

6118

up

2.37E-6

2.47E-3

photosystem I
reaction center
oxygen evolving
complex
extrinsic to
membrane
peptidyl-prolyl cistrans isomerase
activity
cyclophilin
magnesium
chelatase activity
FK506-sensitive
peptidyl-prolyl cistrans isomerase
cyclophilin-type
peptidyl-prolyl cistrans isomerase
activity
glucose metabolic
process
protein amino acid
phosphorylation

28

5.72E12
1.02E21

5.98E-9
2.13E18

Table 1.2 (cont’d)
GO:0006629 lipid metabolic
18
931
38
6581 up
9.92E-5 2.59E-2
bp
process
GO:0004672 protein kinase
113 836
479 6140 up
2.08E-6 2.47E-3
mf
activity
GO:0004713 protein-tyrosine
85
864
342 6277 up
8.04E-6 4.20E-3
mf
kinase activity
protein
GO:0004674
serine/threonine
92
857
391 6228 up
2.50E-5 7.46E-3
mf
kinase activity
a
GO R indicates number of significantly up or down (R) regulated genes with the GO annotation
b

in question. No GO R, number of significantly up or down regulated genes without the GO
c

annotation. GO U, number of genes without significant expression change with the GO
d

annotation . No GO U, number of genes with no significant expression change that do not have
e

the GO annotation. Reg, direction of regulation (nitrogen deprived compared to nitrogen
f

g

h

replete). Fisher’s exact test p value. q value is calculated using R package qvalue. bp,
i

j

biological process. cc, cellular component. mf, molecular function.

29

Induction of Gametogenesis and Sexual Reproduction

Because N deprivation triggers gametogenesis (Martin and Goodenough, 1975; Kurvari
+

et al., 1998), we examined several genes known to be involved in mating-type plus (mt ) gamete
differentiation or sexual fusion in C. reinhardtii as internal controls for the induction state of the
cells following N deprivation. Following N deprivation, cells had substantial increases in the
abundance of transcripts of four of the six genes considered. These genes encode FUS1, which is
a glycoprotein required for sex recognition, SAG1 (the mt+agglutinin gene), peptidase M
gametolysin, which releases the gametes from the cell wall, and NSG13, which is a protein of
unidentified function known to be expressed in gametes, as summarized by (Harris, 2009). A
second gametolysin gene (encoding peptidase M11) and GSP1, which encodes a gamete-specific
transcription factor, did not show increased expression following N deprivation, but perhaps that
is because only a single time point was examined.

Effects on Genes of N Metabolism and Protein Biosynthesis

Many genes involved in N import and assimilation are known to be induced following N
deprivation (Schnell and Lefebvre, 1993; González-Ballester et al., 2004; Harris and Stern,
2009). Our analysis revealed greater than 2-fold up-regulation for several genes, including those
−

−

+

that encode NO3 NO2 transporters and reductases, as well as transport systems for NH4 and
+

organic N sources. Of the genes involved in assimilation of NH4 by the Gln synthetase-Glu
synthase cycle, only GLN3 was up-regulated. Similarly, most genes involved in amino acid
30

biosynthesis did not show a greater than 2-fold change. Thus, transcript abundance suggests that
following N deprivation, pathways for the acquisition of new N sources are strongly upregulated, whereas biosynthetic pathways that utilize the assimilated N remain relatively
unaffected.

Decades ago, N deprivation of C. reinhardtii was found to result in degradation and
resynthesis of both cytoplasmic and chloroplast ribosomes (Siersma and Chiang, 1971; Martin et
al., 1976). Both the rRNA and proteins of the ribosomes were turned over under the conditions
of N deprivation that also induce gamete differentiation. Hence, we expected that the mRNAs for
the ribosomal proteins might show different steady-state levels in the comparison of
logarithmically growing cells and cells that have been N deprived for 48 h. Indeed, following N
deprivation, the abundance of transcripts encoding proteins of the chloroplast ribosomes
consistently decreased to 30% to 50% of their levels of expression in logarithmically growing
cells.

A subset of the cytosolic 80S ribosomal protein genes has been identified in the version
4.0 genome data set. Among those that have been annotated, most are encoded by single-copy
genes, although a few have two copies (e.g. L7, L10, L13, and L23). As these gene products are
assembled into ribosomes, the respective genes have high levels of constitutive expression. The
abundance of the transcripts in vegetative cells and N-deprived cells was fairly similar.

The RPL22 ribosomal protein of the cytosolic ribosomes is encoded by a multigene
family in C. reinhardtii. Of the 37 RPL22 genes in version 4.0 of the genome data set, 13
appeared not to be expressed under either condition tested and six had barely detectable levels of
transcripts. Of the 18 remaining genes, two gave rise to the most predominant transcripts, and
31

their transcripts did not change markedly in abundance. Four of the 18 genes were moderately
expressed, and their transcript levels doubled in the N-deprived cells. Six of the 18 genes had
markedly lower levels of transcripts following N deprivation, while six others showed relatively
constant levels of expression. The RPL22 genes are scattered among at least six chromosomes,
and no correlation was found between location and level of gene expression.

General Changes in Primary Metabolism

Changes in transcript abundance of genes encoding enzymes of primary metabolism are
depicted in Figure 1.3. Transcripts encoding key enzymes of the glyoxylate cycle,
gluconeogenesis, and the photosynthetic carbon fixation cycle markedly decrease following N
deprivation. Transcript abundance for the glyoxylate cycle enzymes isocitrate lyase and malate
synthase decreased more than 16-fold. In addition, mRNA abundance of the cytosolic (predicted)
phosphoenolpyruvate

carboxykinase,

which

catalyzes

the

committed

reaction

of

gluconeogenesis, dropped to 25% of the levels in N-replete cells, as did transcripts encoding
enzymes involved in carbon fixation and reduction, ribulose-bisphosphate carboxylase,
sedoheptulose 1,7-bisphosphate aldolase, and sedoheptulose-bisphosphatase. In contrast, there
was a considerable increase in the transcript abundance of the cytosolic enzyme pyruvate
phosphate dikinase. This is a key enzyme in the C4 photosynthetic pathway and is regulated by
light. It has also been associated with suppressed phosphoenolpyruvate carboxykinase activity
(Magne et al., 1997) and salt stress (Fisslthaler et al., 1995). Recently, this enzyme has been
shown to play an important role in N remobilization (Taylor et al., 2010). Likewise, the

32

transcript abundances for enzymes of the pentose phosphate cycle predicted to be localized in the
cytosol, Glc-6-P 1-dehydrogenase and phosphogluconate dehydrogenase (decarboxylating), were
increased under those conditions. The mRNA encoding for one of the pyruvate decarboxylase
subunits represented in the data set was also increased in abundance following N deprivation.
The pyruvate decarboxylase complex converts pyruvate to acetyl-CoA, which is a precursor of
fatty acid biosynthesis. Genes for other enzymes of the glycolytic pathway, such as pyruvate
kinase, did not show very drastic changes in response to the N deprivation.
To verify whether the changes observed in RNA abundance actually reflect changes in
the activity of glyoxylate and gluconeogenic pathways, cells were grown in the presence of [U13

C] acetate. As the cells take up acetate as a carbon source, the distribution of the

13

C in the

cellular metabolites gives an insight into the activity of the pathways leading to them. The
intracellular amino acids as well as the sugar units of the carbohydrates and RNAs were analyzed
with gas chromatography-mass spectrometry (GC-MS) as described in “Materials and Methods”
(Figure 1.4). The natural abundance refers to the naturally occurring distribution of 13C in the
molecule. The mass isotopomers M0, M1, M2, etc. refer to molecules having, respectively, zero,
one, two, etc. atoms of 13C.

33

Figure 1.3. Regulation of genes involved in primary metabolism.
34

Figure 1.3. (cont’d)
The figure indicates the central metabolic pathways of C. reinhardtii and gives the differential
regulation of gene expression following N deprivation. Symbols represent log2 fold change as
follows: +++, greater than 5; ++, greater than 2 and less than 5; +, greater than 1; ±, less than 1
and greater than −1; −, less than −1; −−, less than −2 and greater than −5; −−−, less than −5.

35

Cells grown in N-replete medium showed a higher degree of labeling in Ser and Gly than
did N-deprived cells (Figure 1.4). The fully labeled fraction (M3) accounted for almost 80% of
the total Ser in the cells grown in N-replete medium. Hence, most of the Ser was derived from
the gluconeogenic pathway, which incorporates the labeling of acetate into the glycolytic
intermediate 3-phosphoglycerate, a precursor of Ser. There was also about 10% of M2 Ser. This
probably derived from the reaction catalyzed by the reversible Ser hydroxylmethyltransferase,
which favors the production of Ser from Gly (Mattingly et al., 1976). The Gly in this reaction
would mostly be fully labeled (M2; Fig. 4), largely from the glyoxylate cycle, hence giving rise
to M2 Ser. In cells grown in N-deprived medium, we observed a markedly lower incorporation
of the

13

C atoms into the amino acids. Almost 80% of Gly was unlabeled (M0), indicating a very

low activity of the glyoxylate cycle. Similarly, N-deprived cells had reduced label in
carbohydrates and Rib. Since these molecules were formed essentially by the gluconeogenic
pathway during growth in the medium employed, the N-deprived cells appeared to have much
lower gluconeogenic activity. Thus, these biochemical data corroborated the transcript
abundance data (Figure 1.3) that suggested a down-regulation of the glyoxylate and
gluconeogenic pathways in N-deprived cells.

36

Figure 1.4. Changes in labeling patterns reflecting changes in gene expression.

37

Figure 1.4 (cont’d)

38

Figure 1.4 (cont’d)
Labeling of metabolites after 24 h of N deprivation compared with N-replete cells is shown. The
metabolites give an indication of the pathway activity. Intracellular Ser and Gly were extracted
by quick quenching and extraction with cold methanol. Rib and Glc were obtained from the acid
hydrolysis of RNA and cellular polysaccharides, respectively. The labeling was analyzed by GCMS after derivatization. Natural labeling refers to the naturally occurring distribution of

13

C in

the molecule. The mass isotopomers M0, M1, M2, etc. refer to molecules having, respectively,
zero, one, two, etc. atoms of

13

C. White bars, Natural labeling; fine cross-hatched bars, N-replete

cells; coarse cross-hatched bars, N-deprived cells.

39

No appreciable change in transcripts for genes encoding components of the mitochondrial
respiratory pathway was noted following N deprivation. However, an 11-fold increase in the
transcript abundance of an alternative oxidase gene (AOX1) was observed, while
the AOX2 transcript was down-regulated 4-fold. These findings were consistent with previous
observations of changes in gene expression of AOX1 and AOX2 (Baurain et al., 2003).
The candidate genes for peroxisomal β-oxidation showed an overall decrease in their
transcript levels following N deprivation, with acyl-CoA oxidase and 3-oxoacyl-CoA thiolase
(ATO1) transcript abundance decreasing most drastically (greater than 3-fold). The only
exception was an enoyl-CoA oxidase/isomerase candidate gene (ECH1), which showed
increased transcript levels (greater than 2-fold). An apparent down-regulation of fatty acid
oxidation is in line with the accumulation of TAGs under these conditions.

Reduced Transcript Abundance for Most Photosynthetic Genes

In C. reinhardtii, photosynthetic efficiency decreases following N deprivation, at least
partially due to a reduction in the abundance of light-harvesting complexes (Plumley and
Schmidt, 1989; Peltier and Schmidt, 1991) and selective degradation of the cytochrome b6f
complex (Bulté and Wollman, 1992; Majeran et al., 2000). Likewise, the abundance of
transcripts encoding photosynthesis-related proteins was substantially reduced following N
deprivation. This regulation was not restricted to light-harvesting complexes and cytochromes
but extended to the two photosystems as well. Following N deprivation, the steady-state level of
all nucleus-encoded PSI genes decreased by at least 6-fold, while the abundance of transcripts
40

from genes encoding the corresponding light-harvesting proteins was decreased even further,
resulting in a 19- to 43-fold decrease relative to N-replete conditions. Only four of the
cytochrome subunits are encoded by the nuclear genome, and three of them showed a
considerable down-regulation (6-fold) following N deprivation. In contrast, the transcript levels
of PETO were weakly increased (2-fold). This observation supports the hypothesis that this
protein may have a regulatory role as opposed to being a functional cytochrome b6f subunit
(Hamel et al., 2000), because the PETO protein is only loosely bound to the complex and its
function is not required for the oxidoreductase activity. Expression of all nuclear genes encoding
PSII components also decreased following N deprivation, although the two least abundant
transcripts decreased only slightly. The PSII light-harvesting complex encoding transcripts
showed a comparable change in abundance. Most of the transcript levels were reduced, while the
weakly expressed LHCB7 gene showed no alteration in transcript levels.

The only two genes of the light-harvesting complex of PSII not following that pattern
were PSBS1 and PSBS2. Their transcript levels were strongly increased following N deprivation
(119- and 52-fold, respectively). This result was confirmed by reverse transcription (RT)-PCR
(Figreu 1.1B).

Specific Changes in Gene Expression Related to General Lipid Metabolism

N deprivation has been demonstrated to lead to the accumulation of TAG in specialized
organelles as well as to structural changes and breakdown of the intracellular membrane systems

41

such as the thylakoids and the endoplasmic reticulum (ER; Martin et al., 1976; Moellering and
Benning, 2010). Therefore, we expected this to be reflected in the expression of genes encoding
enzymes of lipid metabolic pathways. However, changes in transcript levels of genes encoding
fatty acid metabolism were modest (Figure 1.5). A 2-fold increase in transcript levels for
ketoacyl-acyl carrier protein (ACP) synthetase was observed. This enzyme is part of the fatty
acid synthase II complex that catalyzes the acyl-ACP-dependent elongation steps from C4 to
C14 in higher plants. The gene for acyl-ACP thioesterase (FAT1) also showed elevated transcript
levels following N deprivation (about 4-fold). Its reaction terminates fatty acid synthesis by
cleaving the acyl chain from ACP. This reaction competes with the direct transacylation of ACP
by glycerol-3-phosphate acyltransferases for the formation of phosphatidate. An increase in
FAT1 activity, therefore, could be indicative of increased fatty acid export from the chloroplast
to the ER, where TAG assembly occurs, as acyl-ACPs have to be hydrolyzed prior to export
(Pollard and Ohlrogge, 1999).

A strong increase in transcript levels was observed for the gene encoding the committing
step of TAG synthesis. Out of the five putative diacylglycerol acyltransferases genes identified in
the version 4.0 genome data set, only four were expressed under either or both growth
conditions. One of these genes (DGTT1, PID 285889) was almost completely suppressed under
N-replete conditions but showed a large increase in transcript abundance following N
deprivation. However, its overall transcript abundance was too low to be detected by northern
blot compared with other genes encoding putative diacylglycerol acyltransferases, which were
much less differentially expressed, consistent with the initial RNA-DNA hybridization analysis
(Figure 1.1A).

42

Figure 1.5. Selected changes in glycerolipid metabolism transcript abundance.
43

Figure 1.5 (cont’d)
Numbers indicate log2 fold change of transcript abundance following N deprivation. Enzymes
labeled with an asterisk cannot be unequivocally assigned to a specific step in the metabolic
pathway and are hypothetical.

44

Phosphatidic acid phosphatase takes part in the Kennedy pathway of glycerolipid and
TAG synthesis (Figure 1.5). Both of the two candidate genes for phosphatidic acid phosphatase
in C. reinhardtii annotated in the version 4.0 data set showed increased transcript levels
following N deprivation. Both are part of the PAP2 family, which is thought to have a broad
substrate specificity (Carman and Han, 2006). The increase in the expression of the presumed
phosphatidic acid phosphatase genes is consistent with the notion that DAG is generated from
phosphatidic acid for further TAG biosynthesis following N deprivation.

Out of a total of 16 putative membrane-bound desaturase- and hydroxylase-encoding
genes found in C. reinhardtii, only three showed a change in transcript abundance that is greater
than 2-fold. Transcript abundance for microsomal Δ12-desaturase was more than 3-fold higher
following N deprivation, as was that for the plastidic acyl-ACP-Δ9-desaturase, which introduces
the first double bond in an acyl chain. Other microsomal desaturase-encoding transcripts, such as
that encoding FAD13, an ω13/Δ5-desaturase, were also slightly increased in abundance, whereas
the plastid desaturase-encoding genes were not affected.

Of all lipid-related genes, those encoding putative lipases showed the strongest
differences in transcript abundance between the two conditions tested. By searching for “lipase,”
“phospholipase,” or “patatin” through the version 4.0 genome sequence data, 130 proteins
containing the GXSXG motif common to hydrolases were identified. Among the respective
genes, 35 (27%) showed increased and 11 (8.5%) showed decreased transcript levels by 2-fold or
more following N deprivation. In addition, many potential lipases may be considered
constitutively expressed. Seventy-four out of 130 (57%) lipase candidates were expressed at

45

slightly higher levels following N deprivation. Some of these genes may encode lipases that are
important for the turnover or replacement of membranes during cell growth or gamete fusion.

Changes in RNA Abundance for Transcription Factors

The Plant Transcription Factor Database (Pérez-Rodríguez et al., 2010) was used to
identify 386 genes encoding putative transcription factors and transcriptional regulators in the C.
reinhardtii transcript data set, which could be sorted into 53 families. Of the 368 genes, 83
showed a 2-fold or greater change in transcript abundance following N deprivation, with 46
being up-regulated and 37 being down-regulated.

To date, only a few of the putative transcription factors identified in the C. reinhardtii
genome have a known function. Transcript abundance for the gene encoding NIT2, a
transcription factor regulating nitrate metabolism, was increased 6-fold, while that for NAB1, a
transcription factor regulating light-harvesting proteins, was decreased 16-fold, consistent with
previously described physiological changes in response to N deprivation (Mussgnug et al., 2005;
+

Camargo et al., 2007). The transcript level for the GSP1 mt gamete-specific transcription factor
was decreased 3-fold at the 48-h sample point. When looking at the changes in RNA abundance
of putative transcription factor genes, no obvious trends emerged. However, transcripts falling
into the AP2-EREBP and bHLH families were generally more abundant following N
deprivation, while those of the FHA family were generally decreased.

46

DISSCUSSION

Microalgae such as C. reinhardtii undergo drastic changes in metabolism and ultimately
development when N deprived. Some of the most remarkable changes involve gametogenesis
(Harris, 2009) and metabolic changes that lead to the accumulation of TAGs (Hu et al., 2008).
The former aspect has been studied since the 1970s. However, focus on the latter has been
largely motivated by the renewed interest in microalgae as biofuel feedstocks (Wijffels and
Barbosa, 2010). As sequencing technology has become increasingly fast and affordable,
comparison of transcriptomic changes under different experimental conditions by massive
parallel sequencing of cDNA libraries is a viable first approach toward identifying genes that
define changes in response to N deprivation or other nutrient stresses (González-Ballester et al.,
2010). With the goal of gaining a better understanding of the factors underlying or even
controlling the process of TAG accumulation following N deprivation, the focus has to be on
metabolism and genes that encode enzymes of relevant pathways or regulatory factors.

The validity of making inferences on metabolism from transcriptome data in this study
has been verified in different ways. First, specific genes known to be induced following N
deprivation, such as genes involved in gametogenesis or ammonium transport, were found to be
expressed as described previously. Second, major metabolic changes predicted by transcript
analysis, such as the redirection of acetate from the glyoxylate cycle and gluconeogenesis to fatty
acid biosyntheses following N deprivation (Figure 1.3), were corroborated by labeling
experiments (Figure 1.4).

47

By and large, gross changes in transcript abundance in response to N deprivation follow
expected themes: genes encoding enzymes directly involved in N metabolism or N compound
uptake have to be induced, protein biosynthesis is reduced to adjust to the decreased availability
of amino acids, and photosynthesis is down-regulated to adjust to the altered metabolic state of
the cell. In cyanobacteria, N deprivation led to the degradation of the highly abundant phycobili
light-harvesting proteins so that they could be used as an N source for protein synthesis (Collier
and Grossman, 1992). In C. reinhardtii, there is evidence that the cytochrome subunits are
degraded not in response to the low concentrations of N per se but rather to the changed energy
content of the cell. Thus, one possible advantage for the cells to decrease photosynthesis
following N deprivation is to prevent the accumulation of reactive oxygen species (Bulté and
Wollman, 1992). At the same time, genes encoding proteins of the respiratory chain in
mitochondria were only moderately affected following N deprivation except for those encoding
alternative oxidases, which showed elevated transcript abundance. These enzymes are induced
under a number of stress conditions that affect the redox environment of the cell, but these
effects can be quite indirect and are often difficult to causally connect to the applied stress, in
this case N deprivation. It should also be pointed out here that only the expression of nuclear
genes is probed in this study; the expression levels of genes for organelle-encoded proteins
relevant to respiration or photosynthesis have not been examined.

A Role for PSBS following N Deprivation?

One particular surprise was the strong up-regulation of PSBS following N deprivation in
C. reinhardtii. In Arabidopsis (Arabidopsis thaliana), PSBS has been shown to play a critical
48

role in nonphotochemical quenching (Li et al., 2000; Li et al., 2002), but previous studies in C.
reinhardtii have not detected either of the two PSBS proteins in the thylakoids, and
nonphotochemical quenching was shown to be independent of these proteins (Bonente et al.,
2008). Only rarely have ESTs been found for PSBS transcripts, with the exception of the cDNA
stress collection II, which contains RNAs from different stress treatments, including the switch
from ammonium to nitrate (Shrager et al., 2003; Bonente et al., 2008). Our results indicate that
PSBS expression in C. reinhardtii is induced by ammonium deprivation, as was observed
previously for the expression of the gene for alternative oxidase, AOX1 (Baurain et al., 2003).

Recycling of Membrane Lipids or de Novo Synthesis of TAGs?

The elevation of synthesis and export of fatty acids from the chloroplast following N
deprivation could indicate that TAG is assembled from fatty acids that are synthesized de novo.
This step would require the activation of the fatty acids by a long-chain acyl-CoA synthetase. In
fact, increased abundance of RNA encoding a putative long-chain acyl-CoA synthetase was
observed, and the respective protein has been identified in the lipid droplet proteome (Moellering
and Benning, 2010). However, another enzyme that could contribute to the changing spectrum of
fatty acids is a putative phospholipid/glycerol acyltransferase, for which the transcript level
decreased during N deprivation. Long-chain acyl-CoA synthetases are likely to play a key role in
determining the fate of fatty acids in the cell (Shockey et al., 2002). Regulation of the respective
genes could be a major factor in controlling the flux of fatty acids toward glycerolipid synthesis
and their degradation by β-oxidation.

49

Major intracellular changes occur following N deprivation, and these are likely
accompanied by remodeling of membranes. Thus, fatty acids in membrane lipids might be
recycled into TAGs. Consistently, some of the transcripts whose abundance changes the most
were those encoding putative lipases. In general, lipases belong to a family of enzymes that
deesterify carboxyl esters, such as TAGs and phospholipids. As TAGs accumulate following N
deprivation, TAG lipases would be expected to be down-regulated. However, classifying lipases
with selective substrate specificity based solely on their primary sequences is challenging, a fact
that needs to be taken into consideration when interpreting our data set. A TAG lipase typically
contains a Ser-Asp/Glu-His catalytic triad, with the Ser catalytic center located in a GXSXG
motif (Brady et al., 1990; Winkler et al., 1990). Some recently characterized TAG lipases in
animals, yeast, and plants contain a patatin-like, iPLA2 family Ser-Asp catalytic dyad
(Zimmermann et al., 2004; Athenstaedt and Daum, 2005; Eastmond, 2006; Kurat et al., 2006).
Genes encoding lipases specific for membrane lipids would be expected to be up-regulated, as
they might mobilize fatty acids from membrane lipids into TAGs. Moreover, signaling pathways
involving lipid products generated by lipases, such as diacyglycerol, may also control steadystate TAG levels (Kanoh et al., 1993). Further biochemical characterization of some of the most
regulated lipase candidate genes will be necessary to determine their role in TAG accumulation
following N deprivation.

On the other hand, we recognize that lipase expression or activities may also be
controlled at the posttranscriptional level, including translational regulation and posttranslational
modifications of the encoded proteins. In mammals, a hormone-sensitive lipase is
phosphorylated by protein kinase A upon cAMP elevation and consequently exhibits better
accessibility to lipid droplets (Holm et al., 2000). Some of the C. reinhardtii TAG lipases may
50

have a similar regulatory pattern and hence not show significant transcriptional changes when
cells are N deprived. Reverse genetic studies on the lipase candidates and forward genetic
screens for mutants with TAG deficiency phenotypes will disclose the bona fide TAG lipases
and other lipases that impact TAG metabolism.

It should also be noted that during the analysis of the lipid gene data set, the annotation
ambiguities of several fatty acid desaturases became obvious in the version 4.0 genomic
sequence data set: the C. reinhardtii genome harbors four presumed paralogs of FAD5 (named
FAD5a–FAD5d). Based on our current prediction analysis (Emanuelsson et al., 1999) and
previous reports (Riekhof et al., 2005; Harris and Stern, 2009), FAD5a and FAD5b are presumed
to be targeted to the chloroplast, whereas FAD5c and FAD5d are likely located in the ER
membrane. However, experimental corroboration is still needed.

51

CONCLUSION

Our interpretation of this data set places emphasis on TAG metabolism and potential
regulatory factors, which are undoubtedly not yet completely identified. We expect that others
will be able to mine this data set, taking into account different biological processes pertaining to
N deprivation. Cross-querying this data set with a lipid droplet proteomics data set (Moellering
and Benning, 2010) should further narrow the possible candidates relevant for TAG
accumulation. Likewise, a meta-analysis of this and other data sets, including those from other
species, could facilitate the identification of genes most likely involved in TAG accumulation, as
was recently done for low-oxygen stress (Mustroph et al., 2010). Thus, this study represents only
a first step of many toward gaining a molecular understanding of TAG accumulation and other
cellular changes triggered by N deprivation in C. reinhardtii.

52

MATERIALS AND METHODS

Strains and Growth Conditions

+

The C. reinhardtii strain used was dw15.1 (cw15, nit1, mt ), kindly provided by Arthur
Grossman. The cells were grown in liquid cultures under continuous light (approximately 80
−2 −1

μmol photons m

+

s ). For N-replete growth, TAP medium (Harris, 2009a) with 10 mm NH4

(TAP + N) was used. For preliminary experiments, N deprivation was applied by two methods:
+

−1

6

continuous growth in TAP with 0.5 mm NH4 or growth in TAP + N to 5 × 10 cells mL ,
+

followed by transfer to TAP with no NH4 (TAP − N) for an additional 24 or 48 h. For further
6

−1

experiments, N deprivation was defined as growth in TAP + N to 5 × 10 cells mL , followed
by transfer to TAP − N for 48 h.
For labeling studies, the cells were grown in 500-mL shaker flasks with a culture volume
of 50 mL with continuous shaking. For the N deprivation experiment, cells were first grown in
TAP medium with unlabeled acetate with at least five cell doublings to mid logarithmic phase to
reach a biomass equivalent to 0.3 to 0.4 g cell dry weight L−1. The cells were divided up and
transferred to TAP medium containing [U-13C]acetate (Isotec), either TAP + N or TAP − N.

Sequencing Read Processing

To generate material for high-throughput sequencing, cells were grown in 100 mL of
6

−1

TAP + N to 5 × 10 cells mL . The cultures were split in half, and cells were collected by

53

centrifugation, with one pellet being re-suspended in 50 mL of TAP + N and the other in 50 mL
of TAP − N. After 48 h, the total RNA was harvested using a Qiagen RNeasy Plant Mini kit. The
RNA samples were treated with Qiagen RNase-free DNase I during extraction.

For 454 sequencing, full-length cDNA pools were generated with the Clontech SMART
cDNA library construction kit. cDNA was synthesized using a modified cDNA synthesis primer
(5′-TAGAGACCGAGGCGGCCGACATGTTTTGTTTTTTTTTCTTTTTTTTTTVN-3′).

Full-

length cDNAs were amplified by PCR and pooled to increase their concentration. An SfiI digest
was performed, followed by size fractionation. Fractions with the highest intensity and size
distribution were pooled and purified. The resulting cDNA pools were then submitted to the
Michigan State University-Research Technologies Service Facility for sequencing on a 454
GSFLX Titanium Sequencer (454 Life Sciences). For Illumina sequencing, total RNA was
submitted directly to the Michigan State University-Research Technologies Service Facility for
sequencing on an Illumina Genome Analyzer II (Illumina).

Default parameters were used to pass reads using 454 and Illumina quality-control tools.
The filtered sequence data were deposited in the National Center for Biotechnology Information
Short Read Archive with the reference series number GSE24367 and subseries numbers
GSE24365 and GSE24366 for the Illumina and the 454 data sets, respectively. The filtered 454
sequencing reads were mapped to the C. reinhardtii version 4.0 assembly from the Joint Genome
Institute with GMAP (Wu and Watanabe, 2005). In GMAP, the maximum intron length was set
at 980 bp, which is at the 95th percentile of annotated C. reinhardtii intron lengths. The Illumina
reads were mapped with Bowtie (Langmead et al., 2009) using parameters as follows: two or
fewer mismatches, sum of Phred quality values at all mismatched positions at 70 or less, and

54

excluding reads mapped to one or more locations. Because the sequence qualities of Illumina
reads degrade quickly toward the 3′ end, an alternative mapping data set was generated with
reads trimmed from the 3′ end (until the 3′-end-most position with Phred-equivalent score was 20
or greater). Trimmed reads of less than 30 bp were excluded from further analysis. In addition to
sequence quality issues, some reads may span two exons and would not be mapped by Bowtie
correctly. We used TopHat (Trapnell et al., 2009) to identify these exon-spanning reads to
generate another set of read mapping. The information from TopHat was used for assembling
mapped reads into transfrags with Cufflinks (Trapnell et al., 2010). In Cufflinks, the maximum
intron length was set at 1,855 bp (99th percentile of all the intron lengths), 5% minimum isoform
fraction, and 5% pre-mRNA fraction. Transfrags within 1,855 bp of an existing C. reinhardtii
version 4.0 gene model were regarded as potential missing exons of annotated genes. The rest
were regarded as intergenic exons, and adjacent transfrags less than 1,855 bp apart were joined
into “transcriptional units.”

Northern-Blot Analysis and RT-PCR

Total RNA was harvested from N-replete or N-deprived cells as described above, and 4
+

μg of each total RNA was separated on a 1% formaldehyde gel and transferred to a Hybond-N
nylon membrane (GE Healthcare). Probes were synthesized from cDNA and labeled with

32

P

using the Amersham Megaprime labeling kit (GE Healthcare). The blots were hybridized with
the labeled probes in Ambion ULTRAhybe (Applied Biosystems/Ambion) at 42°C overnight.
The blots were washed twice for 5 min with low-stringency buffer (1× SSC and 0.1% SDS) at
55

60°C and then twice for 5 min with high-stringency buffer (0.1× SSC and 0.1% SDS) at 60°C.
The blots were exposed to a Molecular Dynamics phosphor screen (GE Healthcare) overnight
and visualized with a Storm 820 phosphor imager (GE Healthcare). Probes were synthesized
from

cDNA

for

AMT4

(5′-GTATTGCGTCCGATCTGC-3′

and

5′-

CGTGGAAATGCTGTAGGG-3′), DGTT2 (5′-TAAAGCACCGACAAATGTGC-3′ and 5′CATGATCTGGCATTCTGTGG-3′), and DGTT3 (5′-GGTGGTGCTCTCCTACTGGA-3′ and
5′-CCATGTACATCTCGGCAATG-3′).

For RT-PCR, RNA was extracted from N-replete and N-deprived cultures using TRIzol
reagent (Invitrogen) and subjected to DNase treatment with the Turbo DNA-free kit from
Ambion. A total of 1 μg of DNA-free RNA was used for cDNA synthesis with the Invitrogen
Moloney murine leukemia virus reverse transcriptase. A total of 0.5 μg of oligo(dT)12–18 primer
(Invitrogen) and 0.5 μg of random hexamer primers (Promega) were added to the RNA, and the
volume was adjusted to 20 μL final volume. After heating the samples at 70°C for 10 min, they
were incubated on ice for 5 min. Twenty units of RNase inhibitor (Applied Biosystems), 20 nmol
of deoxyribonucleotide triphosphates (Invitrogen), 4 μL of first-strand buffer, and 0.2 μmol of
dithiothreitol were added to the reaction. The reaction mixture was incubated at 37°C for 10 min
for primer annealing. A total of 200 units of Moloney murine leukemia virus reverse
transcriptase was added, and the reaction was incubated at 37°C for 1 h followed by deactivation
at 70°C for 10 min. A total of 1 μL of a 1:10 dilution of the respective cDNA was used as
template for a PCR using GoTaq polymerase (Promega). The reaction mixture (25 μL) contained
1× buffer, 5 nmol of deoxyribonucleotide triphosphates, 12.5 pmol of each primer, and 1 unit of
polymerase. PCR cycle conditions were 3 min of initial denaturation at 94°C, following 40
cycles of 30 s of denaturation, 30 s of annealing at 60°C, and 3 min of elongation at 72°C. Final
56

elongation was performed at 72°C for 10 min. The PSBS-specific primers (5′ATGGCCATGACTCTGTCGAC-3′ and 5′-TTAGGCGGACTCCTCGTCC-3′) amplify both
PSBS1 and PSBS2. The IDA5 gene (5′-GCCAGGTCTCTGCTCTGGTG-3′ and 5′TACTCGGACTTGGCGATCCA-3′) served as a control.

Analysis of Differential Gene Expression

Differential expression between C. reinhardtii cultured in N-replete and N-depleted
medium was determined using the numbers of mapped reads overlapped with annotated C.
reinhardtii genes as inputs to EdgeR (Robinson et al., 2010). In the Joint Genome Institute
database, multiple sets of C. reinhardtii version 4 gene models are available. We used the
“filtered” gene models, which contain the best gene model for each locus. Genes were regarded
as differentially expressed if they have 2-fold or greater change between N-replete and Ndeprived samples and 5% or less false discovery rate. Differential expressed genes were regarded
as up-regulated if their expression levels in N-deprived samples were significantly higher than
those in N-replete samples. Conversely, down-regulated genes were those with significantly
lower levels of expression following N deprivation.

In addition to EdgeR, we used three other methods to evaluate differential expression:
Fisher’s exact test (Bloom et al., 2009), likelihood ratio test (Marioni et al., 2008), and a method
based on intensity ratio and average intensity (MARS; Wang et al., 2010). All three methods
were implemented in the DEGexp package (Wang et al., 2010). We found that among 4,004
differentially expressed genes called by EdgeR, 99.7% to 100% were regarded as differentially
expressed by the other three methods. On the other hand, EdgeR calls overlap with 96.6%,
57

94.7%, and 95.8% of calls by Fisher’s exact test, likelihood ratio test, and MARS, respectively.
Our findings indicate that EdgeR is more conservative than the other methods, but the overall
differential expression calls are highly similar among methods. We should note that methods
other than EdgeR did not explicitly consider variance between replicates and, as a result, will
likely have a higher false-positive differential expression call rate than that of EdgeR. Therefore,
in all subsequent analyses, we used only EdgeR-based differential expression calls.

GO annotation for the C. reinhardtii version 4.0 genome was acquired from the Joint
Genome Institute. Enrichment of differentially regulated genes in each GO category was
determined using Fisher’s exact test. To account for multiple testing, the P values from Fisher’s
exact tests were adjusted (Storey, 2003) and a false discovery rate of 5% was used as the
threshold for enriched GO terms.

GC-MS Analysis

To quantify

13

C-labeling patterns such as mass-isotopomer distributions and fractional

13

C enrichment, samples were analyzed using GC-MS using an HP 6890 GC apparatus

(Hewlett-Packard)

equipped

with

DB-5MS

column

(5%

phenyl-methyl-siloxan-

diphenylpolysiloxan; 30 m × 0.251 mm × 0.25 μm; Agilent) and a quadrupole mass spectrometer
(MS 5975; Agilent). Electron ionization was carried out at 70 eV. The obtained mass
spectrometric data were corrected for the natural abundance of the elements to give fractional
13

C labeling.

58

Sampling, Extraction, and Analysis of Intracellular Amino Acid

Cells from TAP + N and TAP − N cultures were harvested after 24 h. This time point was
chosen for these labeling experiments because at the required cell concentration (approximately
−1

0.2 g cell dry weight L ) for metabolite extraction, acetate depletion occurs in cells grown in
TAP + N at later time points due to the high initial inoculum.
The harvested cells (approximately 25 mg cell dry weight) were centrifuged at 3,000g for
1 min, and the supernatant was removed and quenched with 5 mL of cold 100% methanol
(Winder et al., 2008). The metabolites were harvested by vortexing the cells. A second extraction
was performed with 5 mL of chloroform:methanol (1:2). The extracts were then pooled. Water
was slowly added to the pooled extracts for phase separation. The polar metabolites, which
include the amino acids, were present in the aqueous phase. The aqueous phase was then dried
under N2 and converted to its t-butyldimethylsilyl derivative using N-methyl-N-(tbutyldimethylsilyl)trifluoroacetamide (Mawhinney et al., 1986). The GC and MS conditions for
this analysis were as described previously (Deshpande et al., 2009).

Extraction and Analysis of Rib

Rib for analysis was obtained from the RNA as described (Boren et al., 2003). RNA was
−1

extracted from cells (0.1–0.2 g L ) at the same time point as the intracellular amino acids using
the Tri reagent as described in the protocol (Molecular Research Center). The RNA was acid
hydrolyzed to its monomers and dried under N. It was further analyzed using GC-MS by
59

derivatizing it to its per-O-trimethylsilyl-O-ethyl oxime (MacLeod et al., 2001). The ions 481 to
486 (mass-to-charge ratio), corresponding to the whole carbon backbone of the Rib molecule
(C1–C5), were monitored using single ion monitoring of the MS data.

Extraction and Analysis of Carbohydrate

The carbohydrates in the cells were acid hydrolyzed by 2 n HCl at 102°C, and the
monomeric compounds were analyzed by GC-MS after the sample was dried under N2. The
sample was then converted to its di-O-isopropylidene acetate derivative for analysis by GC-MS
(Hachey et al., 1999). The ions 287 to 293 (mass-to-charge ratio), corresponding to the whole
carbon backbone of Glc (C1–C6), were monitored.
Sequence data from this article can be found in the National Center for Biotechnology
Information Gene Expression Omnibus under accession numbers GSE24367, GSE2466, and
GSE2465.

60

ACKNOWLEDGEMENT

We thank Titus Brown, Gaurav Moghe, and Cheng Zou for help with analysis of Illumina
data, James Bullard for help with comparing differential expression call methods, and the Joint
Genome Institute for providing C. reinhardtii sequences and gene annotation.

61

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CHAPTER 2

GREEN ALGAL RETAINED DUPLICATE GENES TEND TO BE STRESS RESPONSE
GENES AND EXPERIENCE FREQUENT RESPONSE GAINS

Note: The contents of this chapter are in a manuscript in preparation.

Guangxi Wu, David E. Hufnagel, Alisandra Denton, Shin-Han Shiu. Green algal retained
duplicate genes tend to be stress response genes and experience frequent response gains

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ABSTRACT

Green algae are being explored for biofuel production particularly because of their high
oil content under stress conditions. In addition, diversity in stress modulated oil content increase
exists among different species in response to stress. To investigate how gene families and gene
expression have evolved in the context of stress response that ultimately leads to the oil content
differences, we characterized the expansion patterns of gene families in nine green algal species,
and examined evolution of stress response among gene duplicates in Chlamydomonas
reinhardtii.
Substantial variation in domain family sizes exists among green algal species. Lineagespecific expansion of families occurred throughout the green algal lineage but inferred gene
losses occurred more often than gene gains, suggesting a continuous reduction of algal gene
repertoire. Retained duplicates tend to be involved in stress response, similar to land plant
species. However, stress response genes tend to be pseudogenized also. When comparing
ancestral and extant gene stress response state, we found that response gains occur in 13%
duplicate gene branches, much higher than 6% in Arabidopsis thaliana.
The frequent gains of stress response among green algal duplicates potentially reflect a
high rate of innovation, resulting in a species-specific gene repertoire contributed to adaptive
response to stress. Particularly, this species-specificity in gene family evolution and its
relationship to stress response are likely related to the oil content diversity among species, and
could be further explored towards identifying suitable green algal species for oil production.

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INTRODUCTION

Green algae are a group of photosynthetic organisms that are more closely related to land
plants than to other major eukaryotic groups (Graham et al., 2009). A number of micro-greenalgal species are suitable for biofuel production, and the lipid content of these algae increases
significantly under various stress conditions (Hu et al., 2008). For example, in the green algal
model Chlamydomonas reinhardtii, lipid droplets rich in triacylglycerol (TAG) form after
nitrogen (N) deprivation (Wang et al., 2009; Moellering and Benning, 2010). Other stress
conditions, such as salt stress and sulfur deprivation, also lead to increased TAG content in C.
reinhardtii (Siaut et al., 2011; Cakmak et al., 2012). In addition, stress response and subsequent
lipid accumulation exhibit tremendous diversity in green algae (Hu et al., 2008). For example, in
response to N deficiency several green algae showed substantial differences in the tradeoff
between growth and lipid content, the lipid accumulation time-course, and the stress level
required to stimulate lipid formation (Adams et al., 2013). Thus, a better understanding of the
mechanistic details of stress response in green algae will not only contribute to our knowledge
about adaptation to stressful environments but also will have the potential for improving
microalgal biofuel production.
To better understand how green algae respond to stress on a genomic level, we focused
on the retention and loss of gene family members. This is because gene duplications lead to raw
materials for evolution to act on (Ohno, 1970; Force et al., 1999) and they are a common feature
of most eukaryotic species (Lynch et al., 2001). Genes involved in stress response tend to be
retained in various eukaryotes (Lespinet et al., 2002; Hanada et al., 2008). Thus a thorough look
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at the retention and loss patterns among green algal gene families may provide an evolutionary
perspective on the connection between stress and green algal oil production. In general, the
majority of gene duplicates are rapidly lost following the duplication event, but a significant
number of duplicates are retained (Lynch et al., 2001; Moore and Purugganan, 2003; Moore and
Purugganan, 2005), contributing to organismal and regulatory complexity (Lespinet et al., 2002;
Vogel and Chothia, 2006). Gene retention occurs in a lineage-specific manner in a wide range of
organisms (Jordan et al., 2001; Lespinet et al., 2002; Hanada et al., 2008) and a significant
functional bias exists. In fungi, stress-related genes tend to undergo many duplications and losses
(Wapinski et al., 2007). Similarly, in Arabidopsis thaliana, stress responsive genes tend be
retained (Hanada et al., 2008) but also tend to be pseudogenized (Zou et al., 2009a). In a study of
duplicates in yeasts, nematode, fruit fly, and A. thaliana, genes involved in response to
environmental stress are prone to be retained in a lineage-specific manner (Lespinet et al., 2002).
There is not yet a global study summarizing the gene gain and loss patterns of known gene
families in green algae. In addition, it is not known if duplicate retention is correlated with their
stress responsiveness in green algae.
After duplication, gene duplicates may acquire a novel function that contributes to
adaptation (Ohno, 1970). Such neo-functionalization can be an important source of inter-specific
differences in stress response that could lay the foundation for diversity in stress-induced oil
production in algae. An earlier study in A. thaliana showed that, although the predominant fate
of duplicate was loss of stress response, in around 6% of the cases there was evidence of stress
responsive gain (Zou et al., 2009b). Examining evolution in gene expression among duplicates
could thus further elucidate how gene duplication and subsequent functional innovation shaped
the gene repertoire involved in stress response, and likely contributing to the diversity in stress
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response in green algae. There is no global study on functional evolution of duplicate genes in
green algae.
In this study, we integrated genomic and transcriptomic data to find out how gene
families and gene expression have evolved in the green algal lineage in the context of stress. We
first examined the variation of domain family composition in nine green algal species compared
to land plants. The nine green algal species included are Micromonas pusilla RCC299,
Micromonas pusilla CCMP1545, Ostreococcus sp.RCC809, Ostreococcus tauri, Ostreococcus
lucimarinus, Chlorella NC64A, Coccomyxa sp.C169, C. reinhardtii, and Volvox carteri (see
Methods). Then we investigated how gene gain and loss events occurred in the green algal
lineage and examined the functional bias in retained duplicates in C. reinhardtii using
phylogenetic approaches. We also examined the pseudogenization of gene duplicates and their
functional bias. Finally, we characterized the evolution of gene expression after duplication
events to find out how gene function evolved in the context of stress response. Our study reveals
the evolutionary trajectory of stress responsive gene families in the green algal lineage, and
because lipid production is sharply induced by stress (Hu et al., 2008), our results could help to
pinpoint candidate genes for further study on lipid metabolism.

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RESULTS AND DISCUSSION

Variation in domain family sizes among land plants and green algae

Our goal here was to define the overall gain and loss of duplicates in gene families over
the course of green algal evolution. Gene families can be defined in two ways. In an earlier
study, a protein sequence similarity based approach was used to identify protein families using
full length protein sequences in green algae (Blanc et al., 2012). Here, we used another approach
by defining a family as proteins having the same domain, using the Pfam database (Finn et al.,
2010); protein domains are well-defined regions of a protein that can perform a specific function
and form a structural unit (Graur, 2000). This approach was adopted because the sequence
similarity approach could group non-homologous genes/regions into the same family (A is
related to B, B to C, but not A and C). On the other hand, our approach only includes proteins
with known domains. On average 65.7% green algal genes have one or more recognized
domains, ranging from 56% to 76% among different species, while land plants have slightly
higher percentage (Figure 2.1A). This could reflect the fact that overall green algae are less well
studied than land plants and might have unknown algal-specific domains. It could also indicate a
discrepancy in annotation quality between different green algal species. Given that genes with
known domains are regarded as gene models with higher confidence (Cantarel et al., 2008),
using protein domains to establish family alleviates the annotation quality issue.

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Figure 2.1. Domain families in green algae and land plants.

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Figure 2.1 (cont’d)

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Figure 2.1 (cont’d)
(A). The coverage of Pfam domain annotation in green algal and land plant genomes. Blue dots
indicate green algal genomes, and red dots indicate land plant genomes. X axis indicates the
numbers of genes in each species, and Y axis indicates the proportion of genes with ≥1 Pfam
domains in each species.
(B). Correlation of domain family size profiles among green algae and land plants. The X and Y
axis indicate the green algal and land plant species included in this study. The color scale
indicates the range of PCC of domain family sizes between two species. PCC stands for
Pearson’s Correlation Coefficient. Only abbreviations of species names are given here. For full
species names, please refer to methods.

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Among 5,441 domain families found in nine green algae and eight land plants (see
Methods), 3,897 (71.6%) are shared between green algae and land plants, 759 (13.9%) are green
alga specific, and 785 (14.4%) are land plant specific. As expected, more closely related species
tend to have more highly correlated domain family sizes and there are clearly two distinct
clusters, one for green algae and the other for land plants (Figure 2.1B). Nonetheless, there is
large variation in family sizes among green algal species.

This is likely the results of lineage-

specific gene gain and loss events. Thus we next asked how lineage-specific gene gains and
losses have contributed to the extant domain family sizes in the green algal lineage.

Gene gains and losses throughout green algal lineage

The variation in domain family sizes among green algae suggests extensive lineagespecific evolution of gene families. To find out how gene gain and loss events over time have
shaped the domain family size differences among green algal species, we conducted
phylogenetic analysis on domain families present in green algae. To address the concern for gene
annotation quality in green algae, we examined current annotation and found that only a small
number of recognizable protein domains are represented in intergenic regions (11% of domains
found in annotated genes in V. carteri, 2-6% in other species, See methods). Therefore, we used
domain sequences in annotated genes for further analysis. Domain sequences from two land
plants, A. thaliana and Physcomitrella patens, were included as outgroups. Among 4,656 domain
families containing green algal genes, 4,207 with at least four sequences in green algae and the

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two land plants were further analyzed and a phylogenetic tree was built for each domain family.
After reconciliation of the domain trees with a species tree (Moreau et al., 2012), orthologous
groups (OGs) among the green algal and the land plant species were established for inferring
gene gain and loss events (Figure S2.1B). We have also generated another species tree based on
18s rRNA sequences (Figure S2.5) and the two trees are largely similar, except in the
Ostreococcus lineage. This ambiguity is likely due to the short branch length in this lineage that
is difficult to resolve. This tree-based approach is shown to be consistent with similarity-based
approach to identify OGs in land plants (Hanada et al., 2008). Overall, gene gain and loss events
were frequent in every branch of the phylogenetic tree (Figure 2.2). Interestingly, in the green
algal lineage, gene loss occurred more frequently than gene gain on every branch, suggesting
extensive net gene loss since the green algae-land plant common ancestor.

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Figure 2.2. Gene gains and losses across land plants and green algae. Blue and red bars
indicate number of the gene gain and loss events on each branch, respectively. Species names
and numbers of annotated genes with domain in each species are shown on the right. Branch 1, 2,
3, 4 indicate four different time periods in the C. reinhardtii lineage evolution. Refer to methods
for full species names.

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Another reason for an excess of loss events could be because there were errors in
phylogenetic inferences. To ascertain that our phylogenetic inference of orthologous groups was
robust, we conducted two tests. First, we randomly picked 1,500 gene families for bootstrap
studies. Among 50,690 branches, 46.5% have a bootstrap value of ≥ 80% (Figure S2.3A). We
found that 89.4% of the Volvocales branches (C. reinhardtii and V. carteri) have a bootstrap
value of ≥ 80% but only 47.1% in the Ostreococcus branches (O. sp.RCC809 and O.
lucimarinus). Our results showed that shorter branches in general have lower bootstrap values
than longer branches (Figure S2.3B). Second, we examined 129 domain families with only one
copy in each of the 9 green algal and 2 land plant species assuming that this group of domain
families has not undergone gene gain and loss. Thus, their gene trees should be identical to the
species tree in topology. Because the alternative scenario is possible (gene gains and losses have
occurred) in these families, this is a conservative estimate of the orthology inference accuracy.
Our results showed a varying degree of consistency in branching between gene trees and the
species tree on different branches on the species tree, ranging from 37.2% to 98.3% (Figure
S2.4). Longer branches on the species tree, indicating longer evolutionary distance, correspond
with higher consistency with gene trees. We should emphasize that in branches with high
consistency, for example the branch leading to the split of Micromonas and Ostreococcus with
84% consistent trees, there was a comparable gene loss to gain ratio compared to those with low
consistency, indicating that green algae have experienced extensive gene loss.
Several other observations are consistent with the extensive gene loss phenomenon we
have inferred in green algal species. First, a substantial number of gene families were lost on
lineages leading to C. reinhardtii and O. lucimarinus after the split from land plants (Cock et al.,
2010). Second, in Ostreococcus, downsizing of many gene families and gene losses were

81

observed (Derelle et al., 2006; Palenik et al., 2007). Third, a comparative analysis including
Micromonas and Ostreococcus revealed that the common ancestor of Mamiellales had already
experienced genome reduction (Worden et al., 2009). Therefore, although lineage-specific gene
gains took place, the lineage-specific losses contributed more significantly to the species-specific
gene repertoire in green algae.

Pseudogenes in green algae

We found that genes are frequently lost throughout the green algal lineage. Some of the
lost genes may still be present in the genome in the form of pseudogenes. Pseudogenes are
defined as defunct genomic regions that are fragments of functional genes some with in-frame
stops and/or frameshifts. To investigate pseudogenes in green algal genomes, we identified them
using a modified pipeline (Zou et al., 2009a). A total of 18,352 pseudogenes were identified in
all nine green algal species. In general, pseudogenes are less abundant in green algae than in land
plants, even when normalized against genome sizes or total number of genes (Figure 2.3A).
Among green algae and plants, the number of pseudogenes increases as the genome size and
number of genes increase (Figure 2.3B and C, Pearson’s correlation coefficient of 0.95 and
0.91). Larger domain families also tend to have more pseudogenes in green algae (Figure 2.3D),
although the correlation is weak (Pearson’s correlation coefficient of 0.20). This is possibly due
to their small genome sizes and evolutionary pressure towards a more compact genome (Derelle
et al., 2006; Palenik et al., 2007).

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Figure 2.3. Pseudogene counts in green algae and land plants.

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Figure 2.3 (cont’d)

84

Figure 2.3 (cont’d)

85

Figure 2.3 (cont’d)

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Figure 2.3 (cont’d)
(A). Pseudogenes identified in green algal and land plant genomes. Three numbers are given for
each species, including total pseudogene counts, pseudogene counts per Mb genome, and
pseudogene counts per 100 genes.
(B). Number of pseudogenes and genome sizes in green algae and land plants. Red dots indicate
green algae and blue dots indicate land platns.
(C). Number of pseudogenes and genes in green algae and land plants. Red dots indicate green
algae and blue dots indicate land platns.
(D). Number of genes and pseudogenes in each domain family in green algae. Each dot indicates
a domain family. X-axis indicates the number of genes in each domain family. Y-axis indicates
number of pseudogenes in each domain family.

87

Functions of retained C. reinhardtii genes duplicated after the C. reinhardtii-V. carteri split

Although gene losses appear to be more frequent, there are abundant retained duplicates
throughout the green algal lineage. However, it is not clear if there is a functional bias among
retained duplicates. To determine such a bias, we focused on C. reinhardtii since it is a green
algal model organism with a relatively well-annotated gene set (Merchant et al., 2007). After
annotating the C. reinhardtii proteome with GO categories based on sequence similarity (see
Methods), 5,725 of 17,114 proteins (33.5%) are in ≥1 GO categories. During reconciliation of
domain and species trees, the branches in the species tree where duplications took place were
inferred as well. Thus we can examine functional biases of retained duplicates in each of the
branches leading to C. reinhardtii. This information allows us to ask if functions of retained
genes were consistent over the course of C. reinhardtii evolution. Focusing on the C. reinhardtii
lineage after its split from the V. carteri lineage, 1,817 duplication events (involving 2,682
retained duplicates) took place in the C. reinhardtii lineage. Among 13 categories enriched in
retained duplicates, they can be classified into the following three types.
The first type of functional categories belong to those involved in stress response (Figure
2.4A, Table S2.3), similar to land plants and several other eukaryotes (Lespinet et al., 2002;
Hanada et al., 2008). This result is further corroborated by the results from stress expression
datasets (detailed in a later section), suggesting that these retained duplicates might have
contributed to the species-specific stress response in green algae. One example is the heat shock
protein Hsp20 family (PF00011): three duplication events took place in C. reinhardtii after the
split from V. carteri, creating four C. reinhardtii-specific duplicates that are responsive to stress
(at least one in six conditions mentioned below). The second type of retained duplicate enriched

88

categories is transport including ion, phosphate, and transmembrane transport. One potential
explanation is that functional divergence among duplicated transport genes allowed the
regulation, affinity, and subcellular location of transporters to be fine tuned. For example, the
potassium transporter family (PF02795) in C. reinhardtii experienced five duplication events
after the split from V. carteri and resulted in six C. reinhardtii-specific duplicates, all of which
are responsive to stress (at least one in six conditions mentioned below). These duplicates might
have contributed to the species-specificity of ion transport and stress response in C. reinhardtii.
In other green algal species, one example of such fine-tuning is the nitrate and ammonium
transporters in Micromonas (McDonald et al., 2010); however, it is not limited to nutrient
management: channelrhodopsins, ion-channels involved in light perception and phototaxis, are
also found to be diverse even within the Chlamydomonas genus (Hou et al., 2012). The third
type of enriched categories is signaling. For example, nine retained duplicates are involved in the
synthesis of cyclic nucleotides that are secondary messengers important for the regulation of
flagella formation (Hasegawa et al., 1987; Gaillard et al., 2006) and generally activation of ion
channels (Ward et al., 2009). Other examples of enriched signaling categories include protein
phosphorylation and signal transduction (Figure 2.4A, Table S2.3). Nucleosome assembly could
also be involved in signaling as nucleosome is proposed to be a signaling module in addition to
its function of DNA packaging (Turner, 2012). Together, these enrichments indicate duplicates
involved in complex environmental interactions and signaling system tend to be retained,
potentially because duplicates in these categories provide capacity for responding to new
environments and for new routes of regulation.

89

Figure 2.4. Functions and stress responsiveness of retained duplicates in the C. reinhardtii
lineage.
90

Figure 2.4 (cont’d)

(A) Functions of retained duplicates. Columns indicate four phylogenetic branches as in Figure
2.2 with the most recent on the far right. Rows indicate GO biological processes terms with over
or under-represented numbers of retained duplicate genes in at least one branch. Values shown
are -log10(p) for over-represented and log10(p) for under-represented categories, respectively.
(B) Stress responsiveness of retained duplicates. Stress conditions shown are N (nitrogen
deprivation), S (sulfur deprivation), Cu (copper deprivation), Fe (iron deprivation), CO2 (CO2
deprivation), oxidative (oxidative stress), and all (all six conditions combined). Values shown are
-log10(p) for over-representation and log10(p) for under-representation. Branches are the same as
(A).

91

Consistency of functional biases over the course of C. reinhardtii evolution
The above discussion was on the C. reinhardtii lineage after its split from the V. carteri
lineage. In the process of green algal evolution, some duplicates may be preferentially retained
throughout while others may be period-specific due to the ever-changing environment. In
addition, different kinds of genes may have different longevities thus resulting in differences in
enriched categories over time. To distinguish these possibilities, the timing of duplication of each
retained gene was pinpointed to an internal or external branch that led to C. reinhardtii. The
functions of ancestral genes at the time of duplication were assumed to be the same as those of
their descendants. First we focus on categories that are branch specific. The branches are
numbered as in Figure 2.2 and 2.4A. Stress response is enriched in just branch 4, while
nucleosome assembly is enriched in 3 and 4. The expansion of stress related gene families in
just the youngest branch may be because environmental condition would constantly change in
the evolutionary history of the C. reinhardtii lineage. Thus previously retained duplicates
conferring selective advantage may not be adaptive due to exposure of green algae to everchanging environments.
In contrast to stress, we found that signaling categories are consistently enriched for
retained duplicates over time. Further, retained duplicates in branch 3 and 4 tend to play
markedly similar roles as branch 1, particularly those in ion transport, signaling and cyclic
nucleotide synthesis. Retained duplicates in branch 2 were associated with several types of light
response and signal transduction. (Figure 2.4A, Table S2.3). This result suggests that gene
families involved in signaling and to a lesser degree transport are constantly expanded in a
lineage-specific manner, and more generally indicates consistent innovation in interaction with
the environment. Further, these enrichments are consistent with other eukaryotes (Lespinet et al.,
92

2002; Hanada et al., 2008). Three categories involved in double fertilization, pollen tube growth,
and embryo development are not relevant to the single-cellular C. reinhardtii. They are likely
annotated by the sequence similarity to plant proteins by Blast2go (Conesa et al., 2005).

Functional categories enriched in conserved genes and genes associated with pseudogenes

In addition to retained duplicates, we defined “conserved genes” as those with the same
copy number (1 to 3) in among green algal species. The categories enriched in conserved genes
in C. reinhardtii were few and largely involved in housekeeping functions, including GO terms
such as translation and two other ribosome-related terms. Other terms enriched in conserved
genes included tetrapyrrole synthesis, and photosystem I reaction center (Table S2.2). In
addition to functional bias in retention and conservation, we would also like to examine
functional bias in pseudogenization. To find out if there is such bias, we tested GO enrichment in
genes associated with pseudogenes, defined as genes that are the closest relatives to
pseudogenized duplicates. We found that stress response is the only biological process enriched
in genes associated with pseudogenes, indicating that, in addition to their high rate of retention,
these genes have frequently undergone gene loss, a finding similar to a pseudogene study in A.
thaliana (Zou et al., 2009a). This coupling between a high birth as well as high death rate is
likely because genes involved in responding to specific stress conditions could become
unnecessary when that condition does not persist.

93

Retained duplicates and their stress responsiveness

Multiple stress conditions lead to increased oil content in microalgae (Hu et al., 2008). In
addition, we showed that stress response categories were enriched in retained duplicates in the C.
reinhardtii lineage. However, GO annotation in C. reinhardtii is established solely based on
computational approaches and has only 33.5% of genes annotated. To address these issues, we
asked if retained genes tend to be stress responsive compared to singletons under six conditions:
deficiency in N, sulfur, iron, copper, and CO2, and oxidative stress (González-Ballester et al.,
2010; Miller et al., 2010; Castruita et al., 2011; Fang et al., 2012; Page et al., 2012; Urzica et al.,
2012). In all stress conditions except sulfur deprivation, stress responsiveness tends to be overrepresented among retained genes (p-value ≤0.01), (Figure 2.4B, Table S2.1). When combining
all six RNA-seq datasets and defining stress responsiveness of a gene as being responsive in ≥1
conditions, retained duplicates still tend to be stress responsive (Figure 2.4B, Table S2.1). This
result corroborates our conclusion that retained duplicates tend to be in the stress response
functional categories.
According to functional category analysis, stress response was not enriched among
retained duplicates except in branch 4, which is the most recent branch (Figure 2.4A).
Consistent with this finding, analysis of stress expression data also revealed that retained genes
tend to be stress responsive in more conditions if they were duplicated more recently (Figure
2.4B), with 5 conditions in branch 4 and gradually declining to none in branch 1. This is also
consistent with our finding that the closest relatives of pseudogenes tend to be involved in stress
response, again reinforcing the idea that, although stress response genes tend to be retained at a
higher rate compared to genome average, they tend to be more short-lived.

94

Stress response evolution post duplication in the C. reinhardtii lineage

Retained duplicates tend to be stress responsive (Figure 2.4 A and B), suggesting that
gene duplication might provide a source for innovations in response to stress. This possibility
then leads to the question how often innovation, defined as gain of stress responsiveness from a
non-responsive ancestral gene, occurred in green algae. To answer this question, we first
integrated phylogenetic data and stress related expression datasets to infer the ancestral response
state (Figure S2.1C). With ancestral states, the gain and loss events of duplicated genes can then
be distinguished (Zou et al., 2009b). Stress response states are defined as U (up-regulated by ≥2
fold, false discovery rate ≤ 5%), D (down-regulated by ≥2 fold, false discovery rate ≤ 5%), and N
(not significantly changed). Only the ancestral nodes leading immediately to extant genes were
included in our analysis to avoid complications from predicting responses in nested branches
(Zou et al., 2009b).
We define four “evolutionary events” (retention, gain, loss, or switch) based on a
comparison of stress response states between an extant gene and its most immediate ancestral
gene node for each of the six stress conditions. We found that 6,330 comparisons (10.9%) were
relevant because they involved either extant and/or ancestral stress responsive genes. Among the
six stress conditions, the median number of events involving retention of the ancestral stress
response is 48% (32% U -> U, and 16% D -> D). We also found that 35% were response loss
events (23% U -> N, and 12% D -> N). Meanwhile, comparatively fewer events (13%) involved
functional gain (9% N -> U, and 4% N -> D) and even fewer events (2%) involved functional
switch (1% D -> U, and 1% U -> D) (Figure 2.5A). To find out if younger duplicates tend to
retain their ancestral response state, we analyzed the relative frequencies of all four stress

95

response evolution scenarios against time, using synonymous substitution rate (Ks) as a proxy
for time (Figure 2.5B-E). Regardless of the Ks value, it is generally true that the rates of stress
response evolution scenarios are retention > loss > gain >> switch. However, the relative
abundance of each scenario changed overtime (Figure 2.5B-E). When 0 ≤ Ks ≤ 1.2, the rate of
retention decreased from 63.8% to 45.5% (median of all conditions), and it remains relatively
stable afterwards (Figure 2.5B). On the contrary, the rate of loss increased from 22.2% to 37.5%
when 0 ≤ Ks ≤ 1.2, and remains relatively stable after that (Figure 2.5C). These results show
that younger duplicate genes tend to retain the ancestral stress response state, similar to A.
thaliana (Zou et al., 2009b). The rate of functional gain peaked at Ks = 0.9, and decreased
thereafter, as similar pattern was observed in A. thaliana (Zou et al., 2009b).
Nonetheless, compared to the results of a similar study in A. thaliana (Zou et al., 2009b),
C. reinhardtii has comparable rates in response retention, loss, and switch, but a much higher
rate of functional gain (13% versus 6%). Note that we examined only six abiotic conditions in C.
reinhardtii compared to the 16 conditions encompassing biotic and abiotic stress environments in
the A. thaliana study (Zou et al., 2009b). Considering that the relative abundance of the
evolutionary scenarios is similar across divergent conditions (Zou et al., 2009b), having more
data will likely not contribute to significant changes in the gain rate estimate in either direction.
We should also emphasize that, regardless of the Ks value, C. reinhardtii consistently has a
higher rate of functional gain (Figure 2.5D) when compared to A. thaliana (Zou et al., 2009b).
Taken together, our findings indicate that innovation occurs more often in C. reinhardtii than in
A. thaliana in the context of stress response. The excess gain events could be due to the fact that
C. reinhardtii is a single cellular organism and has a shorter life cycle while encounters more
diverse environmental conditions, as one would expect that a shorter life cycle would lead to a

96

higher number of mutations per unit time compared to species with a longer generation time,
thus providing more raw material for adaptation to occur upon.

97

Figure 2.5. Stress response evolution scenarios of C. reinhardtii duplicates compared to
their ancestral genes.

98

Figure 2.5 (cont’d)

99

Figure 2.5 (cont’d)
(A). There are four scenarios as indicated on the far left column. The ancestral state column
shows the reconstructed ancestral response state and the extant state column shows the extant
gene response state. ‘U’, ‘D’, and ‘N’ indicate up-regulated, down-regulated, and not-regulated
under a stress condition, respectively. On the right, the preponderance (in percent total) of each
scenario for a given stress condition is shown. The percent of each scenario for six conditions are
summarized in a boxplot for each ancestral-extant combination.
(B-E). stress response evolution among C. reinhardtii paralogs using Ks as a proxy for time. Xaxis indicates Ks values, and Y-axis indicates the relative frequency of each stress evolution
scenario. (B). stress response retention. (C). loss. (D). gain. (E). switch.

100

CONCLUSION
Our analysis of gene family evolution, functional evolution and pseudogenization in the
green algal lineage complement previous studies in other eukaryotes, reinforcing that the
association between lineage-specific evolution and stress response is a common feature of
eukaryotes. This association is likely due to the selective pressure under ever-changing
environment. In this scenario, stress gene duplicates were frequently under positive selection. In
addition, the high rate of innovation in acquiring abilities to respond to stress in C. reinhardtii
duplicates contributes to a highly diverse stress responsive gene repertoire that can potentially be
adaptive.
The model organism C. reinhardtii is used to study the mechanism of stress response and
lipid accumulation in green algae although it is not a direct candidate for biofuel production
(Miller et al., 2010). As the general metabolic changes under stress might be similar across
divergent micro-algal species (Vieler et al., 2012), the particular genes involved in stress
response could be quite different as they are shaped by lineage-specific family expansion and
subsequent gain-of-function events. Such species-specificity cannot be deciphered when
focusing on one model organism. Therefore, in addition to focusing on the well-established
model organism of C. reinhardtii to discover the general biology of stress response in green
algae, it is necessary to investigate diverse green algal species considered for biofuel production
to discover their uniqueness in the relationships between stress response and lipid accumulation,
towards the ultimate goal of finding the perfect alga for biofuel production.

101

METHODS

Protein domains in green algal species

Genome and protein sequences of nine green algal species (see Introduction) and eight
land plant species were obtained from DOE Joint Genome Institute (www.jgi.doe.gov) and
Phytozome (www.phytozome.net, version 7.0). The land plant species are Populus trichocarpa,
Glycine max, A. thaliana, Vitis vinifera, Mimulus guttatus, Zea mays, Oryza sativa, and P.
patens. HMMER (Finn et al., 2011) was used with trusted cutoff to scan algal and plant protein
sequences for Pfam domains (Finn et al., 2010). Fisher’s exact test was used to test the
enrichment of GO categories in conserved domain families in green algae and expanded domain
families in green algae using GO annotation on Pfam domains (ftp.sanger.ac.uk).
To assess the completeness of gene annotation in green algae, we identified intergenic
regions with coding potential and domain presence. Green algal protein sequences were aligned
to green algal genome sequences with BLAST (tblastn, E-value threshold ≤ 1e-5, (Altschul et al.,
1990)). Matches with ≥30 amino acids long and ≥40% identity were kept and the matching
genomic sequences were translated into peptide sequences using Genewise (Birney et al., 2004).
After filtering out sequences with frameshifts and identical sequences, the rest were consolidated
by concatenating sequences with ≥5 amino acid overlaps. Protein domains in the concatenated
sequences were identified with HMMER using trusted cutoff. Domains overlapping with

102

domains identified in annotated genes or pseudogenes (see later section) were eliminated.
Overlapping domain sequences were merged and identical sequences removed (Figure S2.1A).

Identification of missing domains in green algal genomes with variable annotation qualities

The robustness of our domain family analysis is fundamentally dependent on the quality
of gene annotation in green algae. For example, an un-annotated domain would lead to a false
prediction of gene loss. Most of the green algal genome annotations are automatically generated
using computational approaches with various degrees of manual intervention. Thus, the quality
of the annotation is likely highly variable and some genes and thus protein domains may not be
annotated. Given our goal is to evaluate the gain and loss patterns of algal domain families, these
potentially missing domains are false negatives that can have a significant impact on our
subsequent studies. Thus, to identify domains in the genomes that are missed by current
annotation, we aligned all the protein sequences of nine green algal species to their genomes to
identify all sequences with coding potential.
A total of 810,578 matches were identified. The matching genomic sequences were
translated into peptide sequences using Genewise (Birney et al., 2004). After removing
redundant sequences that were identical in their entirety and merging overlapping sequences with
identical amino acids in the overlapping region, 474,350 sequences remained. Overlapping
sequences that were not identical in the overlapping regions were not removed since they might
contain different domains. We identified 238,446 domains from these non-redundant sequences.
103

After removing identical domain sequences, merging overlapping domain sequences, removing
domain sequences overlapping with domains in annotated genes and pseudogenes identified
using a published pipeline (Zou et al., 2009a), 6,432 domain sequences remained and are referred
to as domains in un-annotated regions, of which one-third are in V. Carteri (Figure S2.2B). They
are likely to be domains residing partially or completely in intronic or intergenic regions. Out of
the 1,985 V. carteri domains, 862 are retrotransposon related, while a total of 223 domains in all
eight other species are retrotransposon related. Most of the domains in un-annotated regions are
shorter than the average of annotated domains (Figure S2.2A), and the number is very small
compared to the annotated domains in each species (Figure S2.2B). For these reasons, we
conclude that despite the automated nature of the current green algal annotations they contain
most of the known Pfam domains in green algae and further analysis was done only using the
annotated domains.

Defining green algal orthologous groups (OGs) and lineage-specific expansions (LSEs)

To build a phylogeny for each protein domain X, sequences of domain X were extracted
from full length protein sequences of the green algae and land plants and aligned using MAFFT
(Katoh et al., 2002). Using the alignments generated, the phylogeny of each domain family was
inferred using RAxML (Stamatakis, 2006) with parameters -f d -m PROTGAMMAJTT. Domain
family trees were reconciled with a previously published species tree (Moreau et al., 2012) using
NOTUNG (Chen et al., 2000) to identify orthologous groups. We have also used the 18s rRNA
sequences of two land plants and all nine green algal species to built a species phylogeny using
RAxML (Stamatakis, 2006) with parameters -f a -x 12345 -p 12345 -# 1000 -m GTRGAMMA.
104

Given the topology is highly similar, only the previously published phylogeny was used. For
large domain families that RAxML run didn’t finish in 160 hours, a neighbor joining tree was
built with PHYLIP (Felsenstein, 2005) and the distance trees were broken down to smaller subclusters with ≥ 4 genes and ≤ 300 genes, and distance to root ≥ 0.05. Each sub-cluster was
regarded as a “sub-family”. Domain sequences of sub-family members were used to identify
orthologous groups with the same approach as above (Figure S2.1B). To test the robustness of
our orthology inference, bootstrap values were acquired on 1,500 randomly chosen domain
families using RAxML (-f a -# 400 -m PROTGAMMAJTT -x 12345). Bootstrap values and
branch length (from a species tree (Moreau et al., 2012)) are plotted on Figure S2.3B.
To functionally annotate the C. reinhardtii genome, protein sequences were first aligned
to the nr protein database (www.ncbi.nlm.nih.gov) to identify putative homologs with an e-value
cut-off of 1e-5. GO annotation was then inferred using blast2go based on genes with GO entries
in the nr database (Conesa et al., 2005). Fisher’s exact test was used to test the enrichment of
retained duplicates in GO categories and a significantly enriched category had false discovery
rate ≥5%.

Pseudogenes in green algae

Pseudogenes of green algae were predicted using a previously defined pipeline (Zou et
al., 2009a) with some modifications: 1) we used the whole genomes for the BLAST search and
filtered out hits overlapping with genes as opposed to using the intergenic sequences only; 2) we
105

made the pseudoexon merging recursive so that multiple pseudogenes can be derived from one
pseudoexon cluster provided that the merged pseudogenes do not overlap with each other; 3)
RepeatMasker (ver. 3.3.0) was run after the pseudogene pipeline on pseudogene sequences using
Viridiplantae repeats (Cutoff = 300, Divergence = 30) and pseudogenes with hits other than
“Simple_repeat”, “Low_complexity” and “Satellite” were removed from the dataset; 4) to
control for false positives due to proteins being split between contigs, we removed pseudogenes
that were within the 95th percentile genomic intron length from the end of a contig if they do not
have disabling mutations, defined as a frame shift or premature stop codon. A “high confidence
pseudogene” has one or more disabling mutations and has the following conditions satisfied: the
distance between the pseudogene and the ends of the contig larger than the distance between the
matching region and the end of the protein plus 95th percentile genomic intron length on both
ends.

Inferring ancestral stress response state

To identify genes responsive to various stress conditions, C. reinhardtii RNA-seq
datasets of N (Miller et al., 2010), S (González-Ballester et al., 2010), Fe (Page et al., 2012), Cu
(Castruita et al., 2011), and CO2 deficiency (Fang et al., 2012), and oxidative stress (Urzica et
al.,

2012),

were

obtained

from

the

Sequence

Read

Archive

at

NCBI

(http://www.ncbi.nlm.nih.gov/sra). Reads were aligned to C. reinhardtii genome using Tophat
(Trapnell et al., 2009), with following options: -i 13 -I 8712 -g 1. For each dataset, differential
expression was determined using EdgeR (Robinson et al., 2010) with a threshold of fold change

106

≥ 2 and false discovery rate ≤ 5%. Domain family phylogenies and extant stress response states
were combined to infer the ancestral stress response states of C. reinhardtii genes using
BayesTraits (Pagel, 1999). Three discrete functional states were defined as 1) up-regulation (u,
by ≥ 2 fold and FDR ≤ 0.05), 2) down-regulation (d, by ≥ 2 fold and FDR ≤ 0.05), and 3) noregulation (n). The ancestral states were estimated using multistate, maximum likelihood and
most recent common ancestor (MRCA) options for each family phylogeny and each stress
condition. Only ancestral states with a posterior probability > 0.5 were used for subsequent
analysis. BayesTraits cannot be used in cases that all genes in one tree had same state, and we
assumed in that case all ancestral genes had the same state as the extant ones. Ancestral gene
response states were compared to extant gene response states to infer innovations and losses in
stress response (Figure S2.1C).

107

ACKNOWLEDGEMENTS

This project was supported by National Science Foundation grants IOS-1126998 and
MCB-0929100 to SHS, Michigan State University College of Natural Science Dissertation
Continuation Fellowship to GW, and Deutsche Forschungsgemeinschaft grant IRTG-1525 to
AKD. We would also like to thank Andreas P.M. Weber for proof-reading the manuscript.

108

APPENDIX

109

APPENDIX

Figure S2.1. Analysis pipelines. (A). Pipeline used to identify missing domains. (B). Pipeline
for identifying retained duplicates in green algal lineage. (C). Pipeline for ancestral stress
response state inference in C. reinhardtii.

110

Figure S2.1 (cont’d)

111

Figure S2.1 (cont’d)

112

Figure S2.2. Distribution of domains in un-annotated regions (DURs) in green algae.

113

Figure S2.2 (cont’d)

114

Figure S2.2 (cont’d)
(A). DUR distribution by length. X-axis shows the length of DURs as fraction of the average
length of annotated domains in the same family. Y-axis indicates frequency.
(B). Preponderance of DURs among green algal species. Blue: the number of DURs in each
green algal species. Red: the number of DURs relative to the number of annotated domains.
Green: the number of DURs that are longer than half of the average lengths of annotated
domains in the same family relative to the number of annotated domains in each species.

115

Figure S2.3. Bootstrap values of various branches.

116

Figure S2.3 (cont’d)

117

Figure S2.3 (cont’d)
(A). Bootstrap value distribution of all branches in phylogenetic trees of 1,500 domain families
in green algae and land plants. X-axis indicates bootstrap values and Y-axis indicates number of
branches in each bootstrap value bin.
(B). Distributions of bootstrap values in the domain phylogenetic trees and branch length of the
species tree. X-axis indicates the branch length of each banch on the species tree. Y-axis
indicates the bootstrap value distribution of each branch.

118

Figure S2.4. Consistency between domain and species tree. The species tree topology is
shown. The number on each branch indicates the percentage of domain family trees with the
same branching pattern as the species tree on the branch in question.

119

Figure S2.5. Green algal species tree based on 18s rRNA sequences. Numbers indicate the
bootstrap values.

120

Table S2.1. C. reinhardtii retained duplicate genes tend to be stress responsive
a

b

Stress conditions

DR

DN

N deprivation
S deprivation
Cu deprivation
Fe deprivation
CO2 deprivation
Oxidative stress
all

654
383
48
41
106
438
1014

c

1156
1324
1703
1722
1679
1438
908

a

NDR

3934
3023
196
203
567
2919
7169

d

NDN

10838
11443
14367
14439
14024
12028
7858

p

e

8.71E-17
1.40E-01
3.72E-05
3.42E-03
8.50E-05
1.26E-04
3.37E-05

b

DR indicates retained duplicates responsive to stress; DN, retained duplicates not
c

responsive to stress; NDR, genes that are not retained duplicates (singletons) responsive
d

e

to stress; NDN, genes that are not retained duplicates not responsive to stress; p, pvalue of Fisher’s exact test.

121

Table S2.2. Gene Ontology categories significantly enriched in conserved genes or genes most
closely related to pseudogenes in C. reinhardtii
GO
b
N

NG
c
OG

NG
d
ON

p

FDR

Annotation

Conserved genes
GO:0006412 bp 27

66

131

4451

6.09E-19

9.29E-16

GO:0003735 mf 27

67

131

4450

8.31E-19

9.29E-16

GO:0005840 cc 26
GO:0005622 cc 23

63
108

132
135

4454
4409

2.44E-18
3.28E-11

1.82E-15
1.83E-08

GO:0009538 cc 4

1

154

4516

6.12E-06

2.74E-03

GO:0033014 bp 4

2

154

4515

1.79E-05

6.66E-03

translation
structural constituent
of ribosome
ribosome
intracellular
photosystem I
reaction center
tetrapyrrole
biosynthetic process

Genes associated with pseudogenes
GO:0006950 bp 34
106 72
5513
GO:0005524 mf 36
594 70
5025

3.77E-30
1.76E-10

1.36E-26
3.19E-07

GO

GO
a
G

e

f

response to stress
ATP binding

a

GOG indicates number of genes with a particularly GO annoation in the specified group
b

(conserved genes or genes most closely related to pseudogenes). GON, number of genes
c

with GO not in specified group. NGOG, number of genes without GO in specified group.
d

e

NGON, number of genes without GO not in specified group. p value is calculated using
f

Fisher’s exact test. FDR value is calculated using R package qvalue. bp, mf, and cc indicate
biological process, molecular function, and cellular components, respectively.

122

Table S2.3. Gene Ontology biological processes significantly enriched in C. reinhardtii retained
duplicates
GO

Annotation

C. reinhardtii lineage (4)
GO:0006950 response to stress
GO:0006334 nucleosome assembly
GO:0009617 response to bacterium
GO:0006468 protein phosphorylation
GO:0009611 response to wounding
GO:0007165 signal transduction
DNA mediated
GO:0009294 transformation
intracellular signal
GO:0035556 transduction
double fertilization
forming a zygote and
GO:0009567 endosperm
cGMP biosynthetic
GO:0006182 process
transmembrane
GO:0055085 transport
GO:0006817 phosphate ion transport
GO:0006811 ion transport
Volvocales lineage (3)
GO:0006334 nucleosome assembly
intracellular signal
GO:0035556 transduction
cyclic nucleotide
GO:0009190 biosynthetic process
double fertilization
forming a zygote and
GO:0009567 endosperm
GO:0006468 protein phosphorylation
DNA mediated
GO:0009294 transformation
GO:0009617 response to bacterium
GO:0007165 signal transduction
GO:0009611 response to wounding

GO
a
D

GO
b
N

NGO
c
D

NGO
d
N

e

f

p

FDR

109
94
23
57
22
27

31
32
15
108
18
33

770
785
856
822
857
852

4815
4814
4831
4738
4828
4813

6.23E-63
2.80E-51
2.26E-10
4.97E-10
6.96E-09
3.94E-08

2.25E-59
3.38E-48
1.02E-07
1.80E-07
1.94E-06
1.02E-05

20

18

859

4828

9.18E-08

2.08E-05

25

39

854

4807

3.19E-06

5.24E-04

15

19

864

4827

5.78E-05

8.71E-03

9

6

870

4840

9.50E-05

1.23E-02

28
6
10

61
2
11

851
873
869

4785
4844
4835

1.41E-04
2.73E-04
4.84E-04

1.70E-02
2.99E-02
4.61E-02

90

36

1094

4505

3.09E-35

5.58E-32

52

12

1132

4529

2.50E-25

3.01E-22

41

4

1143

4537

3.09E-24

2.24E-21

32
83

2
82

1152
1101

4539
4459

3.25E-20
9.12E-18

1.31E-17
3.00E-15

31
29
37
28

7
9
23
12

1153
1155
1147
1156

4534
4532
4518
4529

1.21E-15
2.47E-13
4.64E-12
2.17E-11

3.13E-13
5.25E-11
8.82E-10
3.74E-09

123

Table S2.3 (cont’d)
cGMP biosynthetic
GO:0006182 process
transmembrane
GO:0055085 transport
GO:0006811 ion transport
calcium ion
transmembrane
GO:0070588 transport
GO:0009987 cellular process
cAMP biosynthetic
GO:0006171 process
protein-chromophore
GO:0018298 linkage
Core chlorophyta lineage (2)
GO:0007165 signal transduction
GO:0006468 protein phosphorylation
GO:0008152 metabolic process
GO:0009637 response to blue light
GO:0010218 response to far red light
GO:0010114 response to red light
protein-chromophore
GO:0018298 linkage
Chlorophyta lineage (1)
intracellular signal
GO:0035556 transduction
cyclic nucleotide
GO:0009190 biosynthetic process
GO:0006468 protein phosphorylation
cGMP biosynthetic
GO:0006182 process
cAMP biosynthetic
GO:0006171 process
GO:0016310 phosphorylation
GO:0006811 ion transport
regulation of pollen
GO:0080092 tube growth
two-component signal
transduction system
GO:0000160 (phosphorelay)

15

0

1169

4541

5.05E-11

7.94E-09

45
14

44
7

1139
1170

4497
4534

2.80E-10
6.51E-06

4.05E-08
6.93E-04

7
52

1
103

1177
1132

4540
4438

1.05E-04
1.70E-04

8.34E-03
1.31E-02

8

3

1176

4538

2.96E-04

2.19E-02

6

1

1178

4540

4.46E-04

2.94E-02

39
83
86
13
14
15

21
82
102
3
4
5

1792
1748
1745
1818
1817
1816

3873
3812
3792
3891
3890
3889

1.69E-07
1.01E-06
6.40E-05
6.98E-05
8.51E-05
9.58E-05

8.74E-05
4.56E-04
1.35E-02
1.35E-02
1.40E-02
1.44E-02

7

0

1824

3894

3.40E-04

4.55E-02

61

3

1538

4123

1.15E-30

4.16E-27

44
94

1
71

1555
1505

4125
4055

9.07E-24
3.48E-15

1.09E-20
2.10E-12

15

0

1584

4126

4.68E-09

1.41E-06

11
59
16

0
63
5

1588
1540
1583

4126
4063
4121

7.87E-07
1.76E-06
5.85E-06

1.90E-04
3.98E-04
1.11E-03

9

0

1590

4126

1.02E-05

1.75E-03

8

1

1591

4125

2.48E-04

2.89E-02

124

Table S2.3 (cont’d)
ATP biosynthetic
GO:0006754 process
protein
GO:0046777 autophosphorylation
microtubule-based
GO:0007018 movement

9

2

1590

4124

3.15E-04

3.56E-02

11

4

1588

4122

3.33E-04

3.61E-02

19

14

1580

4112

3.44E-04

3.61E-02

a

b

GOD indicates number of retained duplicates with GO. GON, number of genes that are
c

not retained duplicates with GO. NGOD, number of retained duplicates without GO.
d

e

NGON, number of genes that are not retained duplicates without GO. p value is calculated
f

using Fisher’s exact test. FDR value is calculated using R package qvalue. Numbers in
parenthesis indicate branches as shown on Figure 2.2.

125

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126

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131

CHAPTER 3

GENOME AND FUNCTIONAL GENE ANNOTATION OF THE HETEROKONT
OLEAGINOUS ALGA NANNOCHLOROPSIS OCEANICA CCMP1779

Note: The contents of this chapter have been published in PLoS Genetics.
Astrid Vieler*, Guangxi Wu*, Chia-Hong Tsai, Blair Bullard, Adam J. Cornish, Christopher
Harvey, Ida-Barbara Reca, Chelsea Thornburg, Rujira Achawanantakun, Christopher J. Buehl,
Michael S. Campbell, David Cavalier, Kevin L. Childs, Teresa J. Clark, Rahul Deshpande, Erika
Erickson, Ann Armenia Ferguson, Witawas Handee, Que Kong, Xiaobo Li, Bensheng Liu,
Steven Lundback, Cheng Peng, Rebecca Roston, Sanjaya, Jeffrey P. Simpson, Allan TerBush,
Jaruswan Warakanont, Simone Zäuner, Eva M. Farre, Eric L. Hegg, Ning Jiang, Min-Hao Kuo,
Yan Lu, Krishna K. Niyogi, John Ohlrogge, Katherine W. Osteryoung, Yair Shachar-Hill,
Barbara B. Sears, Yanni Sun, Hideki Takahashi, Mark Yandell, Shin-Han Shiu, Christoph
Benning (2012) Genome, Functional Gene Annotation, and Nuclear Transformation of the
Heterokont Oleaginous Alga Nannochloropsis oceanica CCMP1779. PLoS Genet 8(11):
e1003064. doi:10.1371/journal.pgen.1003064
*These authors contributed equally.

132

ABSTRACT

Unicellular marine algae have promise for providing sustainable and scalable biofuel
feedstocks, although no single species has emerged as a preferred organism. Moreover, adequate
molecular and genetic resources prerequisite for the rational engineering of marine algal
feedstocks are lacking for most candidate species. Heterokonts of the genus Nannochloropsis
naturally have high cellular oil content and are already in use for industrial production of highvalue lipid products. First success in applying reverse genetics by targeted gene replacement
makes Nannochloropsis oceanica an attractive model to investigate the cell and molecular
biology and biochemistry of this fascinating organism group. Here, we present the assembly of
the 28.7 Mb genome of N. oceanica CCMP1779. RNA sequencing data from nitrogen-replete
and nitrogen-depleted growth conditions support a total of 11,973 genes, of which in addition to
automatic annotation some were manually inspected to predict the biochemical repertoire for this
organism. Among others, more than 100 genes putatively related to lipid metabolism, 114
predicted transcription factors, and 109 transcriptional regulators were annotated. Comparison of
the N. oceanica CCMP1779 gene repertoire with the recently published N. gaditana genome
identified 2,649 genes likely specific to N. oceanica CCMP1779. Many of these N. oceanica–
specific genes have putative orthologs in other species or are supported by transcriptional
evidence. However, because similarity-based annotations are limited, functions of most of these
species-specific genes remain unknown. Aside from the genome sequence and its analysis,
protocols for the transformation of N. oceanica CCMP1779 are provided. The availability of
genomic

and

transcriptomic

data

for N.

oceanica CCMP1779,

133

along

with

efficient

transformation protocols, provides a blueprint for future detailed gene functional analysis and
genetic engineering of Nannochloropsis species by a growing academic community focused on
this genus.

134

AUTHOR SUMMARY

Algae are a highly diverse group of organisms that have become the focus of renewed
interest due to their potential for producing biofuel feedstocks, nutraceuticals, and biomaterials.
Their high photosynthetic yields and ability to grow in areas unsuitable for agriculture provide a
potential sustainable alternative to using traditional agricultural crops for biofuels. Because none
of the algae currently in use have a history of domestication, and bioengineering of algae is still
in its infancy, there is a need to develop algal strains adapted to cultivation for industrial largescale production of desired compounds. Model organisms ranging from mice to baker's yeast
have been instrumental in providing insights into fundamental biological structures and
functions. The algal field needs versatile models to develop a fundamental understanding of
photosynthetic production of biomass and valuable compounds in unicellular, marine, oleaginous
algal species. To contribute to the development of such an algal model system for basic
discovery, we sequenced the genome and two sets of transcriptomes of N. oceanica CCMP1779,
assembled the genomic sequence, identified putative genes, and began to interpret the function of
selected genes. This species was chosen because it is readily transformable with foreign DNA
and grows well in culture.

135

INTRODUCTION

The search for sustainable sources of liquid transportation fuels has led to renewed
interest in microalgae as potential feedstocks and rising research activity focused on the basic
biology of algae. Microalgae can accumulate large quantities of oils (triacylglycerols) and
carbohydrates, particularly when nutrient-deprived (Dismukes et al., 2008; Wijffels and Barbosa,
2010). Recent estimates taking into account different locations predict that microalgal
−1

−1

photosynthesis can produce between 40,000 and 50,000 L ha year , which is 5-to-6 times the
yield observed for oil palm (Weyer et al., 2010). To realize this potential, it will be necessary to
understand photosynthetic growth and metabolism of specific model algae. Even though
genomic information and basic molecular tools are available for a range of organisms such as the
diatoms Phaeodactylum tricornutum (Siaut et al., 2007; Bowler et al., 2008), the brown
alga Ectocarpus siliculosus (Cock et

al.,

2010) or the tiny chlorophyte Ostreococcus

tauri (Derelle et al., 2006), the mechanistic study of microalgal gene functions is currently
lagging behind models such as Arabidopsis. Of all algae, Chlamydomonas reinhardtii is
currently the most thoroughly studied based on the number of entries in the Public Library of
Medicine

(http://www.ncbi.nlm.nih.gov/pubmed/).

Despite

its

proven

versatility,

Chlamydomonas is still somewhat limited with regard to available tools for its molecular
analysis. For example, efficient targeted inactivation of genes by gene disruption technology is
not available and loss-of-function mutants can be difficult to obtain by RNA interference and
related techniques. The recent achievement of homologous gene replacement in Nannochloropsis

136

oceanica (Kilian et al., 2011) opens up potential opportunities to develop this alga into an
alternate model organism representing marine, oleaginous microalgae.
Nannochloropsis

is

classified

under

the

class

Eustigmatophyceae

of

the

Heterokontophyta (Hoek, 1995), a diverse algal group that includes brown algae and diatoms.
The plastid of this alga is surrounded by four membranes derived from a secondary
endosymbiotic event (Reyes-Prieto et al., 2007). Strains from this genus have been investigated
for their lipid composition and lipid accumulation, e.g. (Sukenik and Carmeli, 1990; Schneider
and Roessler, 1994; Tonon et al., 2002; Danielewicz et al., 2011). In addition, the biomass
production by strains of Nannochloropsis grown under different conditions has been increasingly
studied in recent years, e.g. (Hu and Gao, 2003; Xu et al., 2004; Rodolfi et al., 2009; Simionato
et al., 2011; Srinivas and Ochs, 2012). Given the potential of this alga as an industrial feedstock
and the progress made in developing homologous gene replacement, several research groups
have set out to sequence the genome of different Nannochloropsis strains and draft genomes
of N. oceanica (Pan et al., 2011) and Nannochloropsis gaditana (Radakovits et al., 2012) have
recently become available.
Here, we focus on the publicly available strain N. oceanica CCMP1779, which we chose
based on its growth in culture, its sensitivity to antibiotics, and ease of integrating transformation
markers into its nuclear genome. We sequenced its genomic DNA and two sets of cDNAs
obtained from two different growth conditions to aid in the annotation of genes. Its genome has
been tentatively compared to that of N. gaditana. In addition a team of scientists has begun to
manually annotate and examine the gene repertoire for specific pathways and processes to better
understand the biology of this alga.

137

RESULTS AND DISCUSSION

Strain selection—antibiotic sensitivity, growth and introduction of selectable markers

Out of 20 axenic Nannochloropsis strains obtained from the Provasoli-Guillard National
Center for Marine Algae and Microbiota (NCMA, formerly CCMP), strains of the N. salina
(CCMP369), N. gaditana (CCMP1775 and 536) and N. granulata (CCMP529), as well as two
not further specified strains (CCMP1779 and CCMP531) were selected based on uniformly
dispersed, robust growth in enriched artificial sea water (16 g/L marine salt content) in batch
culture as well as on agar-solidified medium. Both unspecified Nannochloropsis sp. strains
cluster with strains of the N. oceanica species in a rooted tree (Saitou and Nei, 1987) based on 26
published 18S rRNA nucleotide sequences (Figure 3.1) using Eustigmatos vischeri
(Eustigmatophyceae) as an out-group (Andersen et al., 1998). For this reason, these strains are
hereafter referred to as N. oceanica. Because of poor growth under the conditions we have used,
N. oculata and the fresh water species N. limnetica were not further analyzed.

138

Figure 3.1. Rooted neighbor joining tree of 18s rRNA sequences of different
Nannochloropsis species using Eustigmatos vischeri as an outgroup.
139

Figure 3.1 (cont’d)
Labels refer to strain identification numbers from the respective culture collections, if applicable
the synonym is given as 2

nd

name. CCMP, Provasoli Guillard Culture Collection for Marine

Phytoplankton, USA; CCAP, Culture Collection of Algae and Protozoa, UK; MBIC, Marine
Biotechnology Institute Culture Collection, Japan, AS3-9 from (Fawley and Fawley, 2007).

140

The use of antibiotics is essential for eliminating contaminants from cultures and genes
conferring resistance to antibiotics are frequently used as markers for the introduction and
genomic insertion of foreign DNA. Therefore, we tested the Nannochloropsis strains for their
sensitivity to a range of antibiotics. Cells were plated at high density on agar-solidified medium
containing the antibiotics at high density to determine the appropriate dosage (Table 3.1). Zeocin
(5 µg/mL), and Hygromycin B (25 µg/mL) were chosen for use in subsequent selection marker
studies, because of the consistent inhibition of growth at low concentrations by these antibiotics.
Sensitivity to Paromomycin and Hygromycin B varied among the Nannochloropsis strains;
Paromomycin had promise as a selective agent for the two N. oceanica strains (CCMP1779 and
CCMP531), which were also the most sensitive to Hygromycin B. Of those four antibiotics,
plasmids with genes that confer resistance to Zeocin, Hygromycin B, or Paromomycin are
readily available and commonly used for transformation of Chlamydomonas as reviewed in
(Harris, 2009). Sensitivity to antibiotics is often determined by its rate of entry into the
respective cells, which may be determined by the cell membrane and its transporters and the
physical barrier provided by the cell wall. Differences in cell wall composition or thickness
allowing more efficient cell entry of antibiotics are possible explanations for increased sensitivity
in N. oceanica strains. Since efficient uptake of antibiotics or other supplemented molecules
(such as metabolic substrates, inhibitors or nucleic acids) is a desirable trait for a laboratory
model organism, we focused on N. oceanica.

141

Table 3.1. Comparison of the effect of selected antibiotics on different Nannochloropsis species.
Shown are the lethal doses in µg/mL determined by plating dilutions of cell suspensions on half
salinity f/2 agar plates. ‘>’ indicates the highest concentration of the respective antibiotic tested
and no detectable impact on cell growth observed. All of the Nannochloropsis strains listed here
were found to be resistant to the following antibiotics with the respective concentrations in
µg/mL given in parenthesis: Rifampicin (10), Benomyl (5), Nystatin (5), Spectinomycin (100),
Ampicillin (200), Chloramphenicol (100).
Species

Strain

Zeocin

Paromomycin

Hygromycin B

Spectinomycin

N. oceanica

ccmp1779

5

5

25

>100

ccmp531

5

10

50

>100

N. granulata

ccmp529

5

>200

100

>100

N. salina

ccmp369

5

>200

>100

>100

N. gaditana

ccmp1775

5

>100

100

>100

N. gaditana

ccmp536

5

>100

>100

>100

142

All Nannochloropsis strains were resistant to low concentrations of Rifampicin (10
µg/mL), Benomyl (5 µg/mL), Nystatin (5 µg/mL), and higher concentrations of Spectinomycin
(100 µg/ml), Ampicillin (200 µg/ml), and Chloramphenicol (100 µg/mL). Hence these
antibiotics can be useful for selecting against bacterial and other possible contaminants in
Nannochloropsis cultures.
Basic growth characteristics of N. oceanica CMP1779 were determined. The growth
curves were fitted to a sigmoidal curve and the averaged exponential growth rate k, maximum
cell density amax and time of half maximum cell density xc were determined (Table 3.2). Under
photoautotrophic conditions in enriched sea water the exponential growth rate of the population,
k, reached an average of 0.66±0.17 d
7

−1

6×10 cells mL

−1

and cultures grew to a cell density of approximately

(amax). The addition of vitamins did not enhance growth in liquid culture,

whereas the addition of an external carbon source drastically increased final cell densities in
stationary phase, reaching up to 8.7×10

7

or 1.5×10

8

−1

cells mL

when the medium was

supplemented with 30 mM glucose or fructose, respectively. The intrinsic growth rate did not
increase, indicating a positive effect of sugars on cell division only during the later log phase
and/or early stationary phase when self-shading limited growth in the photoautotrophic culture.

143

Table 3.2. Growth parameters of N. oceanica CCMP1779 in f/2 medium using different
supplements. V= f/2 Vitamine mix, Gl = Glucose, Fr=Fructose, curves have been determined in
triplicates based on cell density and fitted to a sigmoidal logistic function type 1 individually
using OriginPro software (y=a/1+exp(-k*(x*xc))). Parameters a (Amplitude, here: max. cell
density in cell/ml), xc (time of

½a

-1

in d) and k (coefficient, intrinsic growth rate d ) are

arithmetic means with standard deviation.
f/2

f/2+V

f/2+Gl

a

6.3E7 +/-8.5E6

6.4E7

+/-8.3E6

8.7E+07

+/-1.7E7

1.5E8

+/-7.4E6

xc

13.65

+/-1.50

11.47

+/-0.79

11.01

+/-0.90

12.86

+/-0.12

k

0.66

+/-0.17

0.63

+/-0.15

0.61

+/-0.07

0.45

+/-0.01

144

f/2+Fr

Introduction of foreign DNA and stable integration into the genome are crucial for many
reverse-genetics approaches. Recently, efficient protocols using an electroporation approach
have been published for N. oceanica sp. and N. gaditana (Kilian et al., 2011; Radakovits et al.,
2012). We tested the strain CCMP1779 for nuclear transformation using an endogenous
promoter region of a structural lipid droplet surface protein (Vieler et al., 2012) driving the
aphVII gene that confers resistance to Hygromycin B. Transformation was performed by
electroporation without prior enzymatic treatments (Li and Tsai, 2009), and selection on 50
µg/mL Hygromycin B, and resulted in a transformation rate of 1.25×10

−06

±0.6×10

−06

per µg

plasmid DNA (Table 3.3). This equals a more than 10-fold increase in transformation events
compared to plasmid pHyg3 (Berthold et al., 2002) that was engineered for C. reinhardtii. The
insertion of the transgene into the genome was confirmed for selected clones of both constructs
by Southern hybridization (Figure 3.2).

145

Table 3.3: Number of resistant colonies achieved by electroporation of N. oceanica CCMP1779
cells in the presence of linearized pHyg3, pSelect100 plasmids per µg linearized plasmid DNA
and transformation rates. Arithmetic means are given from three (pSelect100) or four (pHyg3
and no plasmid control) independent experiments with standard deviation. All transformation
reactions contained denatured salmon sperm DNA in 10-fold excess compared to plasmid DNA.
# resistant colonies per transformation rate (integrations per cell)
µg plasmid DNA
pHyg3

7.68

+/-6.38

1/13,029,316

+/-6.38E-08

pSelect100

125

+/-6.01

1/802,998

+/-6.01E-08

no plasmid 1

5.5

+/-8.41

1/160,000,000

+/-8.20E-09

1

for the no plasmid transformation, the average number of resistant colonies per 100 000 000

cells and the rate of spontaneous resistance occurring is shown.

146

Figure 3.2. Nuclear Transformation. Southern Hybridization of CCMP 1779 transgenic clones
transformed pSelect100 plasmid. C, DNA digested with BamHI restriction endonuclease, U,
DNA probed undigested. Lower panel depicts a schematic map of the SnaBI linearized plasmids
with the basic features indicated. P LDSP, Promoter region of LDSP (NannoCCMP1779_4188),
ORF aphVII, open reading frame of, T 35S, terminator sequence of 35S.

147

Genome sequencing strategy, assembly, and annotation

The N. oceanica CCMP1779 genome was sequenced with 454 and Illumina technology.
Both types of reads were used to generate a hybrid assembly with 3,731 contigs, an assembly
size of 28.7 Mb and an N50 of 24,152 bp (see Materials and Methods; Figure 3.3, NCBI/SRA
SRP013753). The coverage of the hybrid assembly was calculated to be ~116-fold (30-fold for
454, and 86-fold for Illumina data). In addition to genomic sequences, we conducted RNAsequencing (RNA-seq) and generated a de novo assembly of 65,321 transcripts. Using these
transcripts, we assessed the parameter choice for genome assembly (see Materials and Methods).
RNA-seq reads were also mapped to the final genome assembly and assembled into 35,756
transcripts to facilitate structural annotation.
Genome annotation was carried out using the MAKER pipeline (Cantarel et al., 2008). In
addition to ab initio gene predictions, transcripts from RNA-seq and protein sequences from six
other heterokonts (see Materials and Methods for species) were incorporated to generate a draft
gene annotation with evidence-based quality values (AED, Annotation Edit Distance) (Eilbeck et
al., 2009). Basic information about predicted genes and the genome is shown in Table 3.4. The
final annotation set contains 11,973 protein-coding genes: 6,362 gene models with transcript
and/or protein similarity support and an additional 5,611 ab initio predictions (NCBI/GEO
GSE36959). Protein domain search results showed that the percentage of proteins with InterPro
domains in CCMP1779 is comparable to but slightly lower than that of the other six sequenced
heterokonts (Figure 3.4C). We also found 83.4% of the proteins from the CEGMA database that
contain highly conserved eukaryotic proteins (Parra et al., 2007). For comparison, the
representation of CEGMA proteins in the green alga Chlamydomonas, the parasitic protozoan
148

Toxoplasma gondii, and the heterokont Ectocarpus siliculosus are 88.9%, 66.2% (Parra et al.,
2007), and 85.8% (Cock et al., 2010), respectively. These findings demonstrate that our
annotation is of similar quality as that for the other eukaryotes, particularly heterokont genome
annotations.

149

Figure 3.3. Hybrid assembly strategy using Illumina and 454 reads.
150

Figure 3.3 (cont’d)

N50 is used in reference to average contig length. The definition of N50 is the length N for
which 50% of all bases in the sequence assembly are in a sequence of length L < N. Kb means
kilobase.

151

Figure 3.4. Gene Annotation.
152

Figure 3.4 (cont’d)
(A) Annotation Edit Distance (AED) distribution of gene models in the first annotation set after
eliminating entries with AED = 1.
(B). AED distribution of gene models in the second annotation after eliminating entries with
AED = 1.
(C) Proportion of gene models with protein domain hits in different heterokonts (abbreviated as
indicated in Materials and Methods).

153

Table 3.4: Genome summary.

Functional annotation based on protein domains, functional category assignments, and
expression

To generate functional annotation, we first identified protein domains in annotated genes.
Of the 12,012 identified protein models in our first annotation run, 4,847 did not have a
significant match in the NCBI (http://www.ncbi.nlm.nih.gov) non-redundant protein database
(version 4, January 20, 2012). One potential explanation for this relatively high number of
putatively unique genes is that related sequences are not annotated in heterokonts. In addition,
we cannot rule out the possibility of false positive gene prediction. Of the 7,165 (59.6%) protein
models with matches, 721 protein sequences could not be mapped by Blast2GO (Conesa et al.,
154

2005) to retrieve GO (Gene Ontology) terms and annotation to select reliable functions. Manual
examination of a random selection of these proteins revealed that they matched mostly
uncharacterized proteins, usually from other heterokont genomes such as E. siliculosus or Albugo
laibachii. A total of 26,573 GO terms were assigned after augmented annex annotation (Myhre et
al., 2006) and merging primary GO annotations with the InterPro Scan results (Zdobnov and
Apweiler, 2001) (Figure 3.5). A total of 5,981 (49.8%) CCMP1779 genes had GO annotations.
Our RNA-seq runs were conducted with RNA samples obtained from cells grown under
nitrogen (N)-replete and N-deprived conditions that typically differ in the biosynthesis of storage
lipids among other metabolic functions, (see e.g. Miller et al., 2010). To assess whether
expression of genes in certain functional categories were particularly influenced by these
conditions, we determined the enrichment of GO terms in up- and down-regulated genes. At 1%
significance level (Fisher's Exact Test), genes with 7 and 27 GO terms were significantly
enriched in up- and down-regulated genes, respectively (Table 3.5). In particular, genes
associated with photosynthesis and DNA replication tended to be down-regulated following N
deprivation, but also genes for central carbon metabolism were affected, such as gluconeogenesis
and glycolysis. We previously observed similar effects for Chlamydomonas (Miller et al., 2010)
which is evolutionarily distant from Nannochloropsis.

155

Figure 3.5. Gene Ontology.
156

Figure 3.5 (cont’d)
(A) Overview of Blast2Go functional annotation results. No Blast, Number of sequences without
blast search performed; No Blast Hit, Number of sequences without blastp hits at the given
−5

threshold (e-value<10 ); No Mapping, Number of blast hits that did not map to the Blast2GO
database; No Annot., Number of mapped hits that did not retrieve GO annotations from the
Blast2GO database; Annot., Number of sequences that did retrieve one or more GO annotations
from the Blast2GO database; Total, Total number of analyzed sequences.
(B) The distribution of GO annotations by GO level shows the respective number of added GO
annotations in relation to their GO level for each category (P biological process, F molecular
function, C cellular component).
(C) Results distribution after implementation of InterProScan results. Before, Total number of
added GO terms after Blast2GO annotation; after, Total number of GO annotations after
implementation of InterProScan results; confirmed, Number of initial GO annotations confirmed
by InterProScan result; too general, Number of GO annotations removed after InterProScan
because of a lack of specificity.

157

Table 3.5. Enriched GO categories in up- and down-regulated genes during N-deprived versus
N-replete conditions based on RNAseq data.

GO
down-regulated
f

GO:0006096 bp
GO:0015979 bp

GO
a
R

No GO
b
R

GO
c
U

No GO
d
e
U
P

20
10

645
655

38
7

GO:0015995 bp
GO:0006094 bp

9
19

656
646

5
42

GO:0006012 bp

8

657

6

5

660

1

GO:0016851 mf

5

660

1

GO:0000084 bp
GO:0015976 bp

8
12

657
653

8
23

GO:0000216 bp
GO:0006098 bp

7
11

658
654

7
22

GO:0006888 bp

7

658

9

GO:0003980 mf

3

662

0

GO:0009773 bp

3

662

0

GO:0042132 mf

3

662

0

GO:0030604 mf

3

662

0

GO:0006000 bp

8

657

14

GO:0006271 bp

4

661

2

g

GO:0010007 cc

h

Annotation

5237 2.15E-06 glycolysis
5268 2.70E-06 photosynthesis
chlorophyll biosynthetic
5270 3.12E-06 process
5233 2.07E-05 gluconeogenesis
galactose metabolic
5269 3.84E-05 process
magnesium chelatase
5274 9.45E-05 complex
magnesium chelatase
5274 9.45E-05 activity
S phase of mitotic cell
5267 0.000134 cycle
5252 0.000256 carbon utilization
M/G1 transition of
5268 0.000361 mitotic cell cycle
5253 0.000608 pentose-phosphate shunt
ER to Golgi vesicle5266 0.000984 mediated transport
UDPglucose:glycoprotein
glucosyltransferase
5275 0.001398 activity
photosynthetic electron
transport in photosystem
5275 0.001398 I
fructose 1,6bisphosphate 15275 0.001398 phosphatase activity
1-deoxy-D-xylulose-5phosphate
reductoisomerase
5275 0.001398 activity
fructose metabolic
5261 0.001805 process
DNA strand elongation
involved in DNA
5273 0.00194 replication
158

Table 3.5 (cont’d)
GO:0005985 bp

6

659

8

GO:0006694 bp
GO:0030127 cc

7
4

658
661

12
3

5263 0.00321
5272 0.004127

GO:0003755 mf

9

656

21

5254 0.004272

GO:0006270 bp

5

660

6

5269 0.004493

GO:0007018 bp
GO:0070402 mf
GO:0042277 mf

10
3
3

655
662
662

26
1
1

5249 0.004883
5274 0.005123
5274 0.005123

GO:0019872 bp
up-regulated
GO:0009308 bp

5

660

7

5268

5

111

1

GO:0008131 mf
GO:0048038 mf
GO:0005507 mf
GO:0008146 mf

5
5
5
3

111
111
111
113

1
3
23
7

GO:0004190 mf
GO:0016887 mf
a

5267

0.00262

sucrose metabolic
process
steroid biosynthetic
process
COPII vesicle coat
peptidyl-prolyl cis-trans
isomerase activity
DNA-dependent DNA
replication initiation
microtubule-based
movement
NADPH binding
peptide binding
streptomycin
biosynthetic process

3
7

113
109

13
94

5823 1.54E-08 amine metabolic process
primary amine oxidase
5823 1.54E-08 activity
5821 1.39E-07 quinone binding
5801 0.000179 copper ion binding
5817 0.000788 sulfotransferase activity
aspartic-type
5811 0.003376 endopeptidase activity
5730 0.003423 ATPase activity

0.007

GO R, Number of significantly up- or down-regulated (R) genes with the GO annotation in

question.

b

annotation.

No GO R, Number of significantly up- or down-regulated genes without the GO
c

GO U, Number of genes without significant expression change with the GO

d

annotation. No GO U, Number of genes with no significant expression change that do not have
the GO annotation.

e

Fisher’s exact test P value.

h

component. mf, Molecular function.

159

f

bp, Biological process.

g

cc, Cellular

Comparison of N. oceanica and N. gaditana gene sets

Recently the genome sequence of a related species, N. gaditana (Ng, Radakovits et al.,
2012) has become available providing an opportunity for direct comparison. It was reported that
2,733 genes (30.2% of the total gene models) in the Ng genome were exclusive to the species
compared to E. siliculosus and other distantly related algae (Radakovits et al., 2012). The
assembly sizes were ~28.7 Mb for N. oceanica CCMP1779 (No) and ~29 Mb for Ng with a
larger protein number identified in No (12,012) compared to Ng (9,053). To identify unique and
conserved gene repertories between the two Nannochloropsis species, we first compiled
annotated protein coding sequences from both as well as E. siliculosus and defined orthologous
groups (OGs).
An OG contains a group of genes that were descendants of a single ancestral gene in the
most recent common ancestor of both Nannochloropsis species. Among 6,395 OGs identified,
5,048 OGs contain genes from both Nannochloropsis species. These “shared” OGs contain 5,324
No and 5,251 Ng genes, respectively. On the other hand, 6,688 No and 3,802 Ng genes are in
single species OGs (which is indicative of gene loss in the other species lineage) or are singleton
genes. To evaluate if any of the presumptive No-specific genes had a match in the Ng genome
and, thus, were not truly species-specific, a similarity search was carried out using No protein
sequences against Ng genome sequences. Of the 6,688 presumptive No-specific genes, 2,394 had
≥1 significant matches (see Materials and Methods) to the Ng genome, while 4,294 remain Nospecific. Among 3,802 presumptive Ng-specific genes, 1,153 of them have ≥1 significant
matches to the No genome and 2,649 remain Ng-specific.

160

Some of these species-specific genes may be relevant to biological differences between
the two species, perhaps related to their distinct life histories. However, they could also be false
positive predictions. Using three lines of evidence, we show that some of these species-specific
genes are likely authentic. The first is through examining their Annotation Edit Distance (AED),
a score that reflects the annotation quality with a range between 0 (perfect match to similar
sequences or transcript evidence) and 1 (no match) (Eilbeck et al., 2009). The AED distributions
of No genes in conserved OGs and those that are species-specific are shown in Figure 3.6. Here
conserved OGs refer to OGs with the same number of genes from both Nannochloropsis species.
Genes in conserved OGs have an average AED score of 0.35, significantly lower than that of
species-specific genes (0.73, Kolmogorov-Smirnov Test, p<2.2e-16). Given an AED closer to 1
indicating diminishing support, species-specific genes generally have less support based on
similarity or transcript evidence compared to conserved genes. Nonetheless, 34.8% of Nospecific genes have AED<0.5, indicating 50% of the annotated regions overlap with ≥1 similar
sequences and/or transcripts. Thus, some of these species-specific genes are likely not spurious.

161

Figure 3.6. AED distributions of conserved and N. oceanica-specific genes.
162

Figure 3.6 (cont’d)
(A) AED distribution of N. oceanica genes in a conserved OG. AED stands for annotation edit
distance.
(B) AED distribution of N. oceanica-specific genes.

163

The second line of evidence is that a number of No-specific and Ng-specific genes have
putative E. siliculosus orthologs. Among 1,040 OGs without Ng gene, 863 contain both No and
E. siliculosus genes. Similarly, in 307 OGs without No gene, 236 have genes from both Ng and
E. siliculosus. These findings indicate that a number of species-specific genes are authentic and
the reason they are species-specific is most likely due to gene loss and/or missing annotation in
one of the Nannochloropsis species. The third line of evidence is that 1,086 No- and 253 Ngspecific genes have a significant match to annotated proteins from other species that can be used
for functional category annotation based on sequence similarity (see previous section on
Blast2GO).
We conducted enrichment tests to examine which functional categories tend to be
associated with conserved genes or species-specific genes. Here conserved genes are defined as
genes that reside in OGs with the same number of genes from both Nannochloropsis species.
Species-specific genes on the other hand are defined as annotated genes from one species that do
not have a protein or genomic match from the second species. We found that conserved genes, as
expected, are involved in essential processes including translation, ribosome biogenesis,
photosynthesis, and central metabolism (Table 3.6). For species-specific genes, we also
identified multiple enriched categories (Table 3.6). However, the degree of enrichment is rather
marginal and the test statistics are not particularly robust. This is most likely because there is
extremely limited knowledge of gene functions among Heterokont species. One noteworthy
enriched GO category (acetyl-CoA carboxylase activity) may reflect subtle differences in fatty
acid biosynthesis, which is relevant for the use of the respective organism for the production of
biofuel feedstock.

164

Table 3.6: Enriched GO categories in conserved OGs and N. oceanica CCMP1779-specific and
N. gaditana-specific genes.
GO

GO
G

GO
No G

No
GO G

No GO p
No G

Annotation

Conserved OGs
GO:0006412 bp 89
GO:0009507 cc 112
GO:0003735 mf 90

21
33
24

3288
3265
3287

2582
2570
2579

8.6E-08
1.6E-07
4.8E-07

GO:0005739 cc
GO:0042254 bp
GO:0005840 cc
GO:0015979 bp
GO:0005743 cc

157
92
95
17
32

61
31
33
0
6

3220
3285
3282
3360
3345

2542
2572
2570
2603
2597

1.9E-06
2.9E-05
3.0E-05
7.6E-05
4.2E-04

GO:0005737 cc
GO:0006977 bp

206
11

113
0

3171
3366

2490
2603

3.0E-03
3.5E-03

GO:0006098 bp
GO:0055114 bp

27
285

6
170

3350
3092

2597
2433

4.1E-03
5.9E-03

GO:0015992 bp
GO:0051603 bp

25
9

6
0

3352
3368

2597
2603

6.1E-03
6.4E-03

GO:0016117 bp

9

0

3368

2603

6.4E-03

4
4

3357
3356

2599
2599

7.1E-03
7.2E-03

GO:0051536 mf 37
12
GO:0006118 bp 94
46
N. oceanica specific genes
GO:0004871 mf 15
18
GO:0008131 mf 5
1

3340
3283

2591
2557

8.5E-03
9.7E-03

translation
chloroplast
structural constituent of
ribosome
mitochondrion
ribosome biogenesis
ribosome
photosynthesis
mitochondrial inner
membrane
cytoplasm
DNA damage response,
signal transduction by p53
class mediator resulting in
cell cycle arrest
pentose-phosphate shunt
oxidation-reduction
process
proton transport
proteolysis involved in
cellular protein catabolic
process
carotenoid biosynthetic
process
protein transporter activity
threonine-type
endopeptidase activity
iron-sulfur cluster binding
electron transport

1071
1081

4876
4893

2.7E-04
1.0E-03

GO:0009308 bp 5
GO:0046429 mf 4

1081
1082

4893
4894

1.0E-03
1.1E-03

GO:0008565 mf 20
GO:0004298 mf 21

1
0

165

signal transducer activity
primary amine oxidase
activity
amine metabolic process
4-hydroxy-3-methylbut-2en-1-yl diphosphate
synthase activity

Table 3.6 (cont’d)
GO:0030604 mf 3

0

1083

4894

6.0E-03

GO:0004375 mf 3

0

1083

4894

6.0E-03

GO:0005622 cc 42
GO:0048038 mf 5
GO:0003989 mf 6

114
3
5

1044
1081
1080

4780
4891
4889

6.0E-03
6.7E-03
7.1E-03

GO:0003676 mf 64
197
N. gaditana specific genes
GO:0009507 cc 25
107
GO:0016791 mf 5
2
GO:0019843 mf 7
10

1022

4697

8.4E-03

228
248
246

2648
2753
2745

GO:0016730 mf 3

0

250

2755

GO:0019685 bp
GO:0005840 cc

3
11

0
38

250
242

2755
2717

GO:0036068 bp

3

1

250

2754

GO:0009535 cc

11

42

242

2713

GO:0031347 bp

2

0

251

2755

GO:0009539 cc

2

0

251

2755

GO:0030071 bp

2

0

251

2755

GO:0004722 mf 3

3

250

2752

GO:0009772 bp
GO:0030076 cc

3
3

3
3

250
250

2752
2752

GO:0009982 mf 3

3

250

2752

7.31E-05 chloroplast
7.39E-05 phosphatase activity
2.56E-04 rRNA binding
oxidoreductase activity,
acting on iron-sulfur
5.89E-04 proteins as donors
photosynthesis, dark
5.89E-04 reaction
1.95E-03 ribosome
light-independent
chlorophyll biosynthetic
2.21E-03 process
chloroplast thylakoid
3.76E-03 membrane
regulation of defense
7.05E-03 response
photosystem II reaction
7.05E-03 center
regulation of mitotic
metaphase/anaphase
7.05E-03 transition
protein serine/threonine
9.71E-03 phosphatase activity
photosynthetic electron
9.71E-03 transport in photosystem II
9.71E-03 light-harvesting complex
pseudouridine synthase
9.71 E-03 activity

166

1-deoxy-D-xylulose-5phosphate
reductoisomerase activity
glycine dehydrogenase
(decarboxylating) activity
intracellular
quinone binding
acetyl-CoA carboxylase
activity
nucleic acid binding

Table 3.6 (cont’d)
GO G, Number of genes with the GO annotation in the conserved OGs or species- specific
groups of genes. No GO G, Number of genes without the GO annotation in conserved OGs or
species- specific groups of genes. GO No G, Number of genes with the GO annotation not in
conserved OGs or species- specific groups of genes. No GO No G, Number of genes without the
GO annotation not in conserved OGs or species- specific groups of genes. p, Fisher’s exact test P
value. bp, biological process. mf, molecular function. cc, cellular component.

167

CONCLUSIONS

The N. oceanica CCMP1779 draft genome and its automated annotation reported here
provides a starting point for further exploration of the biology and utility of this species. At the
same time, a large number of experts in biochemistry and molecular biology manually annotated
selected genes mostly in pathways relevant for biofuel production, as well as other cellular and
regulatory aspects. However, the current annotation must be considered a work in progress and
we

would

like

to

encourage

the

reader

to

visit

the

project

website

at

www.bmb.msu.edu/Nannochloropsis.html for further exploration of the data. Comparison of the
gene repertoires between N. oceanica and N. gaditana has indicated that the differences between
these two species are comparable in magnitude to those observed between monocotyledonous
and dicotyledonous plant species, which diverged from each other 150–200 million years ago. A
substantial number of species-specific genes identified may reflect physiological and
biochemical differences, which can be explored in future comparative studies. Experimental
verification will likely provide insights into adaptations of the respective species to its specific
ecological niche, and may also reveal the need for considering species-specific characteristics
during genetic engineering for the purpose of biofuels feedstock production. For example
possible differences in sets of genes relevant to fatty acid biosynthesis (acetyl-CoA carboxylase)
may help us design strategies to maximize oil production in a given strain. Availability of
genome sequences of different Nannochloropsis species in combination with targeted gene
replacement by homologous recombination, which currently has only been documented for an N.
oceanica strain closely related to CCMP1779 (Kilian et al., 2011), will not only expedite the
functional analysis of individual genes in Nannochloropsis, but is a prerequisite for future
168

synthetic biology and engineering efforts focused on developing Nannochloropsis into a versatile
feedstock for different industrial purposes.

169

MATERIALS AND METHODS

Strains and growth conditions

The Nannochloropsis sp. strain used was CCMP1779, available from The ProvasoliGuillard National Center for Culture of Marine Phytoplankton (https://ncma.bigelow.org/). The
cells were grown in liquid cultures under continuous light (~80 µmole photons m −2 s−1). For Nreplete growth, f/2 medium with 2.5 mM nitrate (f/2+N) was used (Vieler et al., 2012). For
nitrogen-deprived experiments, N deprivation was applied by growth in f/2+N to 1×107 cells
mL−1, followed by transfer to f/2 without nitrogen source to 5×106 cells mL−1 for an additional
30 hours.

Nuclear transformation by electroporation

Initial transformation experiments were done with a construct described for nuclear
transformation of C. reinhardtii, pHyg3 (Berthold et al., 2002), containing a C. reinhardtii αtubulin promoter and the coding sequence of the Streptomyces hygroscopicus aph7 gene
conferring resistance to Hygromycin B. Subsequently, a plasmid custom made by DNA Cloning
Service (http://www.dna-cloning.com) 497pLC-Hpt-SfiI, which contains a 35S promoter region,
the aph7 coding sequence and a 35S terminator was digested with restriction endonucleases XbaI
and XhoI to eliminate the promoter region. Additionally, the plasmid contains two SfiI sites to
allow directional cloning of further expression cassettes. The native LDSP promoter was
170

amplified from CCMP1779 genomic DNA using the forward and reverse primers 5′and

GGCCTAGGTACGTA-GGTCTCTAAGATGGAGTGGATGG-3′

5′-TTCAGCTG-

TGTTGATGCGGGCTGAGATTGG-3′ and the resulting 790 bp PCR product cloned to the
pGEMteasy vector system (Promega, http://www.promega.com) for sequencing resulting in
pGEM-pLDSP. The promoter region was released from pGem-pLDSP by AvrII and PvuII digest
and blunt cloned to the dephosphorylated 497pLC-Hpt-SfiI backbone to result in the selection
plasmid pSELECT100.
7

−1

For transformation cells were harvested at a density of 1–2×10 cells mL , washed with
ice cold 375 mM sorbitol three times and resuspended in a final volume of 0.2 mL to a
8

−1

concentration of 5×10 cells mL . In addition to 2–10 µg SnaBI linearized Plasmid DNA, a
10fold excess of salmon sperm DNA (Invitrogen, http://www.invitrogen.com) was supplied into
the 2 mm electroporation cuvette. Electroporation was performed using a Bio-Rad
(http://www.bio-rad.com) GenePulser II set to 600 Ώ resistance at a field strength of 11 kV
cm−1leading to time constants of 20 to 25 ms. After the pulse the cells were resuspended in 5 mL
of f/2 media and allowed to recover for 48 h in continuous light with shaking before they were
spread on selection agar containing 50 µg ml

−1

Hygromycin B using warm top agar (f/2 media,

0.05% Phytoblend (Caisson Laboratories, http://www.caissonlabs.com) in 1:1 dilution (vol:vol).
Resistant colonies were observed as early as 10–14 days after electroporation; colonies were
usually transferred after about 3 weeks.

171

Southern analysis

For Southern analysis, 10 µg of DNA were digested with BamHI and BamHI/XbaI for
pHyg3, or BamHI only for the pSELECT100 and separated on an agarose gel (0.9% agarose, 75
Volts, 6 h runtime, 15 cm gel length) before blotting to a Hybond Nylon (Amersham, GE Health
Care, http://www.gelifesciences.com) positively charged membrane overnight. Hybridization
and detection was performed using the DIG labeling and detection system following the
manufacturer's instructions (Roche Applied Sciences, http://www.roche-applied-sciences.com).
Hybridization was done in 10 ml ULTRAhyb buffer (Invitrogen) at 68°C for pHyg3 or 42°C for
pSelect100.

The

oligonucleotides

for

the

probe

synthesis

by

PCR

were

5′-

ACCAACATCTTCGTGGACCT-3′ and 5-‘CTCCTCGAACACCTCGAAGT-3′ for pHyg3
transformed

cells

and

5′-CGCGCTACTTCGAGCGGAGG-3′

and

5′-

GCGCTTCTGCGGGCGATTTG-3′ for pSelect100 transformed cells using the respective
plasmid as a template.

DNA and RNA preparation for sequencing and analysis

For preparation of nuclear DNA a 50 mL cell culture (OD750 = 0.4 to 0.5) was harvested
by centrifugation (4,500× g, 5 min). The cell pellet was lysed in 2× cetyltrimethylammonium
bromide (CTAB) buffer (2% CTAB, 100 mM Tris-HCl pH 8.0, 1.4 M NaCl, and 20 mM EDTA)
and incubated at 60°C for 60 min. The lysate was mixed with 1 volume of phenol/chloroform
and centrifuged (13,000× g min). Transferred the supernatant to a new tube and repeated this

172

step at least once until there was no white interphase. The DNA was precipitated by 1 volume
isopropanol and 70% ethanol. High molecular weight DNA was examined by DNA gel
electrophoresis.
To generate material for RNA-sequencing, cells were grown in 200 ml f/2+N to 1×10

7

−1

cells mL . The cultures were split in half and cells were collected by centrifugation (4,500× g, 5
min), with one pellet being resuspended in 200 mL f/2+N, and the other in 200 mL f/2-N. After
30 hours, the total RNA was isolated using TRIzol Reagent (Invitrogen) according to
manufacturer's instructions. The RNA samples were cleaned up using RNeasy columns (Qiagen,
http://www.qiagen.com) following the manufacturer's instruction.

Assessment of RNA quantity and quality

The evaluation of RNA quantity and quality was done spectrophotometrically by UV
absorbance profile. Additional analysis was performed using an RNA 6000 Nano LabChip Kit
for microcapillary electrophoresis (Agilent 2100 Bioanalyzer, http://www.home.agilent.com).
This eukaryotic total RNA nano-assay generated information about RNA integrity through
electropherograms, gel picture, and RIN value (RNA Integrity Number) (Schroeder et al., 2006).

Genome sequencing and hybrid genome assembly

173

For genome sequencing, two approaches were employed. First, Illumina GS-II was used
to generate 55 bp paired-end reads with a 550 bp library and ~2.3 Gb sequences were generated.
The Illumina reads were filtered using FASTX (http://hannonlab.cshl.edu/fastx_toolkit/) with a
minimum Phred quality score of 20. Next, Velvet (Zerbino and Birney, 2008) was used to
assemble filtered Illumina reads, and a range of k-mer length were tested (31, 33, 35, 37, 39, 41,
43, 45, and 47). To determine an optimal k-mer length, 454 reads longer than 500 bp and de novo
assembled transcripts were mapped to the genome assemblies using GMAP (Wu and Watanabe,
2005). Based on how well the 454 reads and de novo transcripts mapped on to the Illumina
assemblies, as well as N50s, numbers of contigs, assembly sizes, and numbers of total reads
assembled, k-mer length of = 35 was chosen for generating the final Illumina assembly. Newbler
(454 Life Sciences) was used to assemble 454 reads (single-end reads, 449.9 MB sequences)
with the “Large Genome Option”.
Illumina and 454 assemblies were combined by iterative Minimus2 (Sommer et al.,
2007). Minimus2 was first run with a minimum identity of 98% among and between Illumina
and 454 contigs based on and all-against-all contig similarity searches with BLAST (Altschul et
al., 1997). If one contig had an alignment ≥200 bp and an identity ≥98% with ≥2 other contigs,
only the longest contig among the matching contigs was kept and the rest were set aside before
re-running Minimus2. This step was performed because such contigs may represent misassembled sequences and will confound Minimus2 as to which contigs it should assemble. A
similar procedure was used in the assembly of the A. laibachii genome (Kemen et al., 2011). In
the next iteration, the contigs set aside beforehand were added back to the assembly and
Minimus2 was run again. After another three iterations of Minimus2 run, an optimized assembly
was generated.

174

To assess assembly quality, long 454 reads with high Phred scores were mapped to the
genome assembly. First, 454 reads were trimmed from the 3′ end with a minimum Phred score of
20. Then, sequences longer than 200 bp were aligned to the genome using BLAST to determine
if a 454 read was broken up in >1 contigs. We also used de novo transcript assemblies (see next
section) to assess genome assembly quality. The genomic sequence data are deposited in NCBI
SRA (SRP013753).

Transcript assembly and differential expression analysis

De novo transcript assemblies were generated from 55 bp directional single-end Illumina
reads of N-replete and N-depleted conditions (NCBI/GEO GSE36959) using Oases
(http://www.ebi.ac.uk/~zerbino/oases/). First, Oases was run for k-mer lengths of 23, 25, 27, 29,
31, 33, 35, and 37, and the results were compiled. To identify a set of high confidence transcripts
from the de novo assemblies, proteins from six sequenced heterokont genomes, including
Ectocarpus siliculosus (Cock et al., 2010), Pythium ultimum (Lévesque et al., 2010),
Phytophthora sojae (Tyler et al., 2006), Phytophthora ramorum (Tyler et al., 2006),
Thalassiosira Pseudonana (Armbrust et al., 2004), and Phaeodactylum tricornutum (Bowler et
al., 2008), were aligned to the de novo transcripts and only those with significant matches to
known proteins were kept. These transcripts with cross-genome matches were mapped back to
the Illumina genome assemblies to evaluate genome assembly quality. In addition to de novo
transcript assembly, we generated a genome-based transcript assembly.

175

Transcriptomic reads from N-replete and N-depleted conditions were separately mapped
to the hybrid genome assembly using Tophat (Trapnell et al., 2009) (parameters: -I 10 –I 3000 –
library-type fr-unstranded –g 1). The mapped reads were assembled into transcripts using
Cufflinks (Trapnell et al., 2010) (-I 3000 –library-type fr-secondstrand) and a set of transcripts
was generated for each condition.

Genome annotation

The MAKER genome annotation pipeline (Cantarel et al., 2008) was used to annotate the
genome. The first run of MAKER was performed using the est2genome option in the absence of
a trained gene predictor. Transcripts from both N-replete and N-deprived growth conditions were
provided to MAKER along with protein sequences from the above mentioned six sequenced
heterokonts. Gene models obtained from the first run were used to train ab initio gene prediction
programs SNAP (Korf, 2004) and Augustus (Stanke and Waack, 2003). With the trained models,
MAKER was rerun. The gene models from the rerun were used for training SNAP and Augustus
again. The second round training models were provided to run MAKER for the third time to
generate the final annotations. The protein sequences were searched for Pfam domain Hidden
Markov Models using HMMER3 (Finn et al., 2010) with trusted cutoffs. CEGMA was run on
the genome assembly using default settings (Parra et al., 2007). A total of 11,973 genes (12,012
protein models considering alternative splice forms) were recovered with an average AED score
of 0.555. During the course of the study, a new version of MAKER was released. Thus we
conducted a second annotation run with the most recent MAKER version, a more recent repeat

176

library, and a larger protein evidence dataset. Given that the AED distributions were highly
similar between these two annotation datasets (Figure 3.4A and B) only annotation results from
the first set of analysis were used throughout.
InterProScan (Quevillon et al., 2005) was used to identify Pfam protein domains within
the predicted protein sets from N. oceanica CCMP1779 and six other heterokonts. Protein
families were identified by grouping proteins with identical protein domains, and the number of
proteins from each species that were classified into each protein family was tallied. Figure 3.4C
shows the percentages of proteins that have at least one InterPro domain, and those that have
none, of each species.

Functional annotation and determination of differential expression

Blast2GO (Conesa et al., 2005) (http://blast2go.com/b2ghome) was used for functional
annotation of predicted protein models with the default settings for the mapping and annotation
step. The initial BLAST (Altschul et al., 1997) search was performed with an e-value cut-off of
−5

10

and a maximum of 20 blast hits. This results in Gene Ontology (GO) annotations of 5,980

N. oceanica genes (in 4,012 GOs) and 3,008 N. gaditana genes (in 3,205 GOs). Fisher's exact
test was used to assess if either the number of conserved or species-specific genes are overrepresented in any GO category.
Cuffdiff from the Cufflinks package (Trapnell et al., 2010) was used to analyze the
differential gene expression under N-replete and N-deprived growth conditions. Fisher's exact

177

tests were performed to determine the enrichment of each GO category in up- and downregulated gene clusters and at the 1% significance level based on p-values.

Comparison of Nannochloropsis genomes

OrthoMCL (Li et al., 2003) was used to identify Orthologous Groups (OGs) of genes in
N. gaditana, N. oceanica CCMP1779, and E. siliculosus (run parameters: percentMatchCutoff =
50, evalueExponentCutoff = −5). BLAST (Altschul et al., 1990) was used to identify significant
matches of lineage-specific genes across species. A significant match was defined as identity
≥47.04% (5 percentile in the identity distribution of one-to-one orthologs between N. gaditana
−5

and N. oceanica), Expect value≤10 , alignment length ≥30 amino acids, and ≥50% of the
protein sequence covered in the alignment.

Database tools

To allow easy access to the CCMP1779 genome data, we released a public version of the
genome browser along with a basic BLAST tool to search nucleotide and protein databases,
accessible at www.bmb.msu.edu/nannochloropsis.html. The genome browser contains EST data
aligned to the latest genome assembly as well as alternative gene models in addition to the final
models retrieved from the MAKER gene annotation pipeline described above.

178

ACKNOWLEDGEMENT

We like to acknowledge John Johnston, Michigan State University, for help with the
database and server, and Brandon Guelette at Michigan State University for technical assistance
with the cell wall analysis. We thank Rebecca Hopkins and Tiffany Liu, Michigan State
University, for help with the annotation of light and circadian signaling genes.

179

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180

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CONCLUDING REMARKS

186

CONCLUDING REMARKS

The general metabolic changes under N deprivation are similar between phylogenetically
distant micro-algal species of the model oragnism C. reinhardtii and N. oceanica; biological
processes such as DNA replication and photosynthesis are down-regulated while lipid
metabolism is up-regulated (Chapter 1 and Chapter 3). However, I showed that genes involved in
stress response tend to be lineage-specifically retained and subsequent functional gains occur
frequently after gene duplication (Chapter 2). Therefore, the gene repertoire involved in stress
response in each micro-algal species could be highly species-specific, reflecting the phenotypic
diversity in stress response and lipid accumulation. This finding prompts the rethinking about the
use of a single model organism in the research of micro-algal stress response. An existing model
organism could offer insight into the general biology of stress response in microalgae, but
characterizing diverse micro-algal species, especially the ones linked to biofuel production, is
also necessary to discover their uniqueness in stress response and the ensuing lipid accumulation.
For example, obtaining the genome sequence of oil rich N. oceanica (Chapter 3) opened up the
possibility of developing it into an alternative model organism. Overall, my research serves as
one of the first steps towards the final goal of identifying and engineering the ideal alga for
biofuel production.

187