Acta Oceanol. Sin., 2014, Vol. 33, No. 2, P. 1–12
DOI: 10.1007/s13131-014-0435-4
http://www.hyxb.org.cn
E-mail: [email protected]
Transcriptome sequencing of essential marine brown and red
algal species in China and its significance in algal biology and
phylogeny
WU Shuangxiu1,3†, SUN Jing1,3,4†, CHI Shan2†, WANG Liang1,3,4†, WANG Xumin1,3, LIU Cui2,
LI Xingang1,3, YIN Jinlong1, LIU Tao2*, YU Jun1,3*
1
CAS Key Laboratory of Genome Sciences and Information, Beijing Key Laboratory of Genome and
Precision Medicine Technologies, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing
100101, China
2 College of Marine Life Science, Ocean University of China, Qingdao 266003, China
3 Beijing Key Laboratory of Functional Genomics for Dao-di Herbs, Beijing Institute of Genomics, Chinese
Academy of Sciences, Beijing 100101, China
4 University of Chinese Academy of Sciences, Beijing 100049, China
Received 3 April 2013; accepted 26 July 2013
©The Chinese Society of Oceanography and Springer-Verlag Berlin Heidelberg 2014
Abstract
Most phaeophytes (brown algae) and rhodophytes (red algae) dwell exclusively in marine habitats and play
important roles in marine ecology and biodiversity. Many of these brown and red algae are also important
resources for industries such as food, medicine and materials due to their unique metabolisms and metabolites. However, many fundamental questions surrounding their origins, early diversification, taxonomy,
and special metabolisms remain unsolved because of poor molecular bases in brown and red algal study.
As part of the 1 000 Plant Project, the marine macroalgal transcriptomes of 19 Phaeophyceae species and 21
Rhodophyta species from China's coast were sequenced, covering a total of 2 phyla, 3 classes, 11 orders, and
19 families. An average of 2 Gb per sample and a total 87.3 Gb of RNA-seq raw data were generated. Approximately 15 000 to 25 000 unigenes for each brown algal sample and 5 000 to 10 000 unigenes for each red algal
sample were annotated and analyzed. The annotation results showed obvious differences in gene expression and genome characteristics between red algae and brown algae; these differences could even be seen
between multicellular and unicellular red algae. The results elucidate some fundamental questions about
the phylogenetic taxonomy within phaeophytes and rhodophytes, and also reveal many novel metabolic
pathways. These pathways include algal CO2 fixation and particular carbohydrate metabolisms, and related
gene/gene family characteristics and evolution in brown and red algae. These findings build on known algal
genetic information and significantly improve our understanding of algal biology, biodiversity, evolution,
and potential utilization of these marine algae.
Key words: Phaeophyceae, brown algae, Rhodophyta, red algae, marine macroalgae, transcriptome
sequencing, secondary generation sequencing
Citation: Wu Shuangxiu, Sun Jing, Chi Shan, Wang Liang, Wang Xumin, Liu Cui, Li Xingang, Yin Jinlong, Liu Tao, Yu Jun. 2014. Transcriptome sequencing of essential marine brown and red algal species in China and its significance in algal biology and phylogeny.
Acta Oceanologica Sinica, 33(2): 1–12, doi: 10.1007/s13131-014-0435-4
1 Introduction
Algae are a highly diverse group of organisms that live in
a range of aquatic and terrestrial environments (Grossman,
2007). Dwelling exclusively in particular marine habitats, including some harsh environments, are the phaeophytes, known
as brown algae belonging to Class Phaeophyceae of Phylum
Ochrophyta, and rhodophytes, known as red algae of Phylum
Rhodophyta. These organisms are morphologically diverse,
varying from unicells about 1 µm in diameter, such as Cyanidioschyzon merolae (Matsuzaki et al., 2004) and Galdieria sulphuraria (Schönknecht et al., 2013), to complex multicellular forms
reaching lengths of more than 30 m, such as Macrocystis Pyrifera (Tirichine and Bowler, 2011); though, most phaeophytes are
multicellular (Grosberg and Strathmann, 2007).
Both brown and red algae exhibit a range of different haploid-diploid life cycles, house a variety of novel metabolic pathways, and synthesize various unique chemical compounds of
both ecological and commercial importance (Grossman, 2007).
These marine algae serve as major carbon-fixation producers
and play essential roles in stabilizing different marine ecosystems, forming submerged forests or creating niches for a broad
range of other marine organisms (Cock et al., 2012). As a result,
these environmental tolerance characteristics make brown and
red algae ideal candidates for mechanism study and novel gene
discovery (Misumi et al., 2008).
In particular, special polysaccharides, such as alginates and
Foundation item: The National Natural Science Foundation of China under contract Nos 31140070, 31271397 and 41206116; the algal transcriptome sequencing was supported by 1KP Project (www.onekp.com).
*Corresponding author, E-mail: [email protected], [email protected]
†Contributed equally.
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WU Shuangxiu et al. Acta Oceanol. Sin., 2014, Vol. 33, No. 2, P. 1–12
fucoids in brown algae and agars in red algae, as well as their
numerous and various derivatives, are valuable resources in the
production of antitumours, anticoagulants, solid matrices in
medicines, and additives for foods and cosmetics (Berteau and
Mulloy, 2003; Drury et al., 2003; Matsubara, 2004; Grossman,
2007). Recently, red and brown algae have also attracted growing interest as potential resources for biofuel production due to
their huge biomass storages (Bartsch et al., 2008). Therefore, the
corresponding novel carbohydrate metabolism pathways have
become long-term areas of focus in research. In addition, there
is a long-standing debate on the existence of a C4 photosynthetic pathway during CO2-fixation in marine phytoplankton
(Falkowski and Raven, 1997). However, so far only a few carbohydrate metabolism genes, such as the genes encoding GDPmannose dehydrogenase of Ectocarpus silicuiosus (Tenhaken et
al., 2011) and mannuronan C-5-epimerase of Laminaria digitata (Nyvall et al., 2003) in the alginate biosynthesis pathway, and
one gene encoding the first enzyme, mannitol-1-phosphate dehydrogenase in the mannitol biosynthesis pathway (Rousvoal
et al., 2011), have been characterized by molecular biological
experiments.
The origin and evolution of phaeophytes and rhodophytes
is also a research hotspot. Rhodophytes are believed to have
originated from a non-photosynthetic unicellular eukaryote
engulfing a photosynthetic cyanobacterium 1.5–1.8 billion
years ago (Gould et al., 2008; Kutschera and Niklas, 2005; Parker
et al., 2008). Termed the primary endosymbiosis, this event
gave rise to the extant Plantae (or Archaeplastida), consisting
of three photosynthetic lineages: Glaucophyta, Rhodophyta
(red algae), and a collective group of Chlorophyta (green algae)
and land plants, whose chloroplasts have double layered membranes (Simon et al., 2009). After the primary endosymbiosis,
a second heterotrophic eukaryote engulfed a unicellular green
or red photosynthetic eukaryote, resulting in a variety of secondary-endosymbiosis photosynthetic eukaryotes. These secondary-endosymbiosis photosynthetic eukaryotes have three
or four membraned chloroplasts and include cryptophytes,
haptophytes, heterokonts (also known as Stramenopiles), and
dinoflagellates (Delwiche and Palmer, 1996; Kutschera and
Niklas, 2005, 2008; Baldauf, 2008). Phaeophytes are believed
to have arisen with diatoms and golden algae (all belonging to
Ochrophyta), as well as Oomycetes via the secondary endosymbiotic event (Reyes-Prieto et al., 2007). These organisms all belong to heterokonts, which are characterized by the occurrence
of cells with two unequal flagella in their life history (Ben Ali et
al., 2001). There continues to be heated debate over the origin of
eukaryotic algae as monophyletic or polyphyletic.
Even within Phaeophyceae and Rhodophyta, there exists
controversy over the taxonomic classifications of some species.
For example, for brown algae, there were arguments on whether
Sargassum fusiforme, which was nominated as Hizikia fusiformis before, belongs to genus Sargassum or Hizikia (Cho et al.,
2006), whether Saccharina sculpera, which was nominated as
Kjellmaniella crassifolia previously, belongs to genus Saccharina or Kjellmaniella in taxonomy (Lane et al., 2006), and what is
the exact molecular evidence on phylogenetic positions of Orders Ishigeales and Dictyotales (Kawai et al., 2005; Silberfeld et
al., 2010). For red algae, there were the arguments on what is the
relationship between Gracilariopsis lemaneiformis and Genus
Gracilaria, which are commonly cultured together (Zhang and
Xia, 1992), and what is the relationship between Grateloupia
chiangii, which was nominated as Prionitis divaricata previously, and genera Grateloupia and Gracilaria (Wang et al., 2001).
Algal evolution study is complicated and difficult given currently available genome data because of multiple methods of
gene acquisition by algae. Nuclear genomes are mosaics of
genes acquired over long periods of time, not only by vertical
descent but also by endosymbiotic gene transfer (EGT) and
horizontal gene transfer (HGT) during both the primary and
the secondary endosymbiosis processes (Green, 2011; Tirichine and Bowler, 2011). Algal evolution study is further complicated by the dearth of existing sequenced red and brown algal
genomes. Within red algae, C. merolae and G. sulphuraria are
the only unicellular species that have been sequenced, and Pyropia yezoensis and Chondrus crispus are the only multicellular
species that have been sequenced. For brown algae, a comprehensive view of genetic characteristics was not available until
2010, when the complete genome sequence of E. silicilosus, a
small multicellular brown alga from the order Ectocarpales,
was published (Cock et al., 2010). In addition, expressed sequence tag (EST) libraries of G. sulphuraria (Weber et al., 2004)
and RNA-seq data of Pyropia yezoensis of Rhodophyta (Liang
et al., 2010), Saccharina japonica (Deng et al., 2012), S. latissima (Heinrich et al., 2012) and E. siliculosus (Dittami et al.,
2009) of Phaeophyceae were the only molecular data available
for studies in brown algae and red algae until now. Therefore,
more genome information on more species is needed to solve
these questions.
In November 2009, a NESCent/iPlant-sponsored 1 000 Plant
(1KP) Analysis Workshop was held in Phoenix to initiate the 1 000
Plant Transcriptome Sequencing Project (1KP Project, www.
onekp.com). The project aimed to resolve relationships across
the green plant phylogeny and elucidate processes contributing
to diversification and biological innovations, including origins
of multicellularity, colonization of land, the evolution of vascular systems, and the origins of seeds and flowers. The 1KP Project will generate unparalleled plant sequence databases for investigating the evolution of gene families, regulatory networks
and biosynthetic pathways. In this program, our group provided
21 Rhodophyta species and 19 Phaeophyceae species, with the
genome size ranging from about 107 Mb to 782 Mb (http://data.
kew.org/cvalues/CvalServlet? querytype=6), covering a total of
2 phyla, 3 classes, 11 orders and 19 families. All of these algae are
macroalgae and represent various unique and important biological characteristics and commercial values; most have never
been subjected to large-scale gene sequencing. For each sample, an average of 2 Gb of raw RNA-seq data were generated on
the Illumina sequencing platform HiSeq 2000. Paired-end data
were assembled by SOAPdenovo-trans to typically yield 10 000
scaffolds with lengths of greater than 1 kb for each sample. The
results were released on password-protected repositories at
Westgrid (http://206.12.25.82/1kp-data) and TACC (http://web.
corral.tacc.utexas.edu/OneKP). Using these data, we annotated
genes, classified unigene functions, analyzed pathways, and
compared differences between all brown and red algal samples.
Coupled with public algal genomic and transcriptomic data,
further analyses for determining the origins, early diversification, evolution, taxonomy, special metabolisms, and genes/
gene families of brown algae and red algae will make significant
contributions to our understanding of algal gene characteristics, phylogenetic evolution, important biological processes,
and algae-based biotechnologies.
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2 Material and methods
2.1 Algal sample collection
Algal samples were collected from field conditions along the
coast of China during October, 2010 to March, 2012 (Table 1).
Some of these samples were sterilized with KI-I2 buffer (containing 0.15% IK, 0.05% I2, weight/volume) for 3 s, washed with
sterile seawater, and stored in liquid nitrogen immediately
for total RNA extraction. Others were taken back and cultured
in the laboratory of the Culture Collection of Seaweed in the
Ocean University of China, in a modified seawater medium,
supplemented with nutrients of 4 mg/L of NaNO3 and 0.4 mg/L
of KH2PO4 under 10°C and 30 μmol photons m−2 s−1 of irradiance.
2.2 Total algal RNA extraction
The algal samples were first immersed in liquid nitrogen and
ground to a fine powder using a chilled mortar and pestle. Total
RNA was extracted using an improved CTAB method (Li et al.,
2012; Johnson et al., 2012; Yao et al., 2009; Ghangal et al., 2009;
Xu et al., 2010) for brown algal samples and using an improved
Trizol method (Li et al., 2012; Johnson et al., 2012) for red algal
samples. The quality and quantity of extracted RNA were assessed using a Nanodrop ND 1000 spectrophotometer (Labtech
International Ltd, Lewes, UK) and Agilent 2100 bioanalyzer (Agilent Biotechnolgies. Santa Clara, USA).
2.3 Transcriptome sequencing
cDNA library construction and sequencing were performed
Table 1. Species information of 18 brown algae and 21 red algae for transcriptome sequencing
Phylum
Ochrophyta
Class
Order
Family
Species
Tissue
Date
Desmarestiaceae
Desmarestia viridis
branches/ leaves
2012-03-20
Dictyotales
Dictyotaceae
Dictyopteris undulata
leaves
2012-03-20
Ishigeales
Ishigeaceae
Ishige okamurai
branches
2012-02-29
Laminariales
Laminariaceae
Saccharina japonica
leaves
2011-04-16
Saccharina sculpera
leaves
2011-07-27
Alariaceae
Chordariaceae
Undaria pinnatifida
Punctaria latifolia
leaves
leaves
2012-03-07
2012-03-20
Scytosiphonaceae
Colpomenia sinuosa
leaves
2012-02-28
Petalonia fascia
leaves
2012-03-09
Scytosiphon lomentaria
leaves
2012-02-28
Scytosiphon dotyi
leaves
2012-03-09
Sargassum fusiforme
branches/ leaves
2011-04-02
branches/ leaves
2011-12-13
Sargassum henslowianum
branches/ leaves
2011-12-13
Sargassum horneri
branches/ leaves
2011-05-07
Sargassum integerrimum
branches/ leaves
2011-12-13
Sargassum muticum
branches/ leaves
2012-03-07
2011-04-16
Phaeophyceae Desmarestiales
Ectocarpales
Fucales
Sargassaceae
Sargassum hemiphyllum var.
chinense
Rhodophyta
Sargassum thunbergii
branches/ leaves
Sargassum vachellianum
branches/ leaves
2011-02-22
Pyropia yezoensis
leaves
2011-04-16
Bangiophyceae
Bangiales
Bangiaceae
Florideophyceae
Ceramiales
Ceramiaceae
Ceramium kondoi
branches
2012-02-28
Dasyaceae
Heterosiphonia pulchra
branches
2012-03-18
Rhodomelaceae
Symphyocladia latiuscul
branches/ leaves
2011-08-30
Neosiphonia japonica
branches/ leaves
2011-04-16
Dumontiaceae
Dumontia simplex
leaves
2012-03-18
Endocladiaceae
Gloiopeltis furcata
branche/ leaves
2011-04-28
Gigartinaceae
Mazzaella japonica
leaves
2012-03-18
Chondrus crispus
leaves
2011-04-16
Phyllophoraceae
Ahnfeltiopsis flabelliformis
branches/ leaves
2011-08-24
Solieriaceae
Eucheuma denticulatum
branches
2010-10-22
Betaphycus philippinensis
branches
2012-02-20
Kappaphycus alvarezii
branches
2011-03-24
Gracilaria vermiculophylla
branches
2011-04-18
Gigartinales
Gracilariales
Halymeniales
Gracilariaceae
Halymeniaceae
Gracilaria chouae
branches
2011-04-28
Gracilaria blodgettii
branches
2011-07-14
Gracilariopsis lemaneiformis
branches
2011-05-20
Grateloupia livida
leaves
2011-04-18
Grateloupia turuturu
leaves
2011-04-28
Grateloupia catenata
branches/ leaves
2011-08-23
Grateloupia chiangii
branches/ leaves
2011-04-18
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by the BGI (Shenzhen, China) on Illumina (San Diego, USA)
HiSeq instruments in accordance with the manufacturer's instructions. Briefly, mRNA was isolated from total RNA with Sera-mag Magnetic Oligo (dT) Beads. The mRNA with fragment
buffer was sheared into short fragments of about 200 bp. Using these mRNA fragments as templates, first-strand cDNAs
were synthesized by random hexamers-primers and reverse
transcriptase. The second-strand cDNA was synthesized using
DNA polymerase I, together with RNase H and dNTPs, and was
purified by QiaQuick PCR purification kit (Qiagen). The doublestranded cDNA was subjected to end-repair and phosphorylation using T4 DNA polymerase, Klenow DNA polymerase, and
T4 PNK. PE adapter was added to the repaired cDNA fragments
by T4 DNA ligase. Fragment size selection was performed using
agarose gel, from which fragments of 200–250 bp were extracted. The selected cDNA fragments were amplified by PCR. The
constructed cDNA library was sequenced by Illumina HiSeq
2000.
2.4 De novo assembly
Strict reads filtering was performed before the assembly.
Pair-end reads with primer or adaptor sequences were removed. Reads with more than 10% of bases below Q20 quality
or more than 5% of bases as unknown nucleotides (Ns) were filtered from total reads. De novo assembly was carried out using
SOAPdenovo-Trans (Li et al., 2010) (http://soap.genomics.org.
cn/SOAPdenovo-Trans.html). Gapcloser was then used for gap
filling of the scaffolds.
2.5 Transcriptome analysis
To identify gene expression patterns in our target species,
the BLASTX homology search was conducted against the NCBI
non-redundant (nr) protein database (of July 2012, http://www.
ncbi.nlm.nih.gov) with E-value less than 10-5. Functional classification of the unigenes' Gene Orthology (GO) (http://www.geneontology.org/) was performed by the InterProScan program
2.0
(Zdobnov and Apweiler, 2001). The Clusters of Orthologous
Groups (COG) classification was performed against the COG
database (http://www.ncbi.nlm.nih.gov/COG) using BLASTX
(E-value<10−5) (Tatusov et al., 2003). Pathway analysis was performed using the Kyoto Encyclopedia of Genes and Genomes
(KEGG) annotation service KAAS (Moriya et al., 2007).
3 Results and discussion
3.1 Sample collecting
In order to discover more expressed gene information
on phylogenetic evolution, essential metabolism pathways,
and relatively important biological characteristics in species of brown and red algae, we collected 19 species of brown
algae and 21 species of red algae during their growing season
(Table 1). These brown algal samples covered 6 orders, Desmarestiales, Dictyotales, Ishigeales, Laminariales, Ectocarpales
and Fucales, and 8 families. These red algal samples covered 5
orders, Bangiales, Ceramiales, Gigartinales, Gracilariales and
Halymeniales, and 11 families. These species not only represented major taxonomic and biodiversity units for comprehensive phylogenetic study of brown and red algae but also stood
for significant ecologically important or commercially valuable
species for marine resource utilization or protection.
3.2 Transcriptome sequencing and assembly
We sequenced approximately 2 Gb per algal sample and total 87.3 Gb of RNA-seq raw data using the Illumina Hiseq 2000
platform. After strict reads filtering, we generated a total of 514
million 90 bp paired-end reads with high-quality because these
reads had a quality value more than 20 (error rate 0.01) and accounted for more than 97.48% bases of the whole sequences. On
average about 10–16 millions paired-end reads were obtained
for most algal libraries, except Gracilaria blodgettii, Grateloupia chiangii, Symphyocladia latiuscul and Ceramium kondoi,
whose reads numbered less than 10 million (Fig. 1).
brown algae
red algae
Number of reads (107)
1.5
1.0
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Fig.1. Statistics of paired-end reads numbers of transcriptome sequencing data of all the red and brown algal libraries.
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WU Shuangxiu et al. Acta Oceanol. Sin., 2014, Vol. 33, No. 2, P. 1–12
These clean paired-end reads were assembled into scaffolds
using SOAPdenovo-trans and Gapcloser (Li et al., 2010). For red
algal samples, 10 000 to 50 000 scaffolds were obtained in each
library, and N50 of each library was between 2 000 bp and 3 000
bp mostly. For brown algal samples, 50 000 to 100 000 scaffolds
were obtained and the N50 of each library ranged from 756 bp
to 1 709 bp (Fig. 2).
On the whole, the assembly quality of red algal samples was
much better than that of brown algal samples. This discrepancy
might be a result of poorer RNA quality of brown algal samples
due to their high contents of viscous polysaccharides (data not
shown); or, particular brown algal genome characteristics could
have lead to difficult assembly.
3.3 Gene annotation
To understand the expressed genes in these red and brown
algae, we annotated the transcripts after assembly by sequence
alignment against the databases of nr, Swissprot, and GO using
BLASTX. For red algal samples, approximately 5 000 to 10 000
unigenes were annotated in each library while about 15 000 to
25 000 unigenes were annotated to each brown algal sample
(Fig. 3).
The annotated unigenes in brown algal samples numbered
far more than those in red algal samples. This discrepancy could
be attributed to an existing sequenced brown algae genome for
E. siliculosus, which allowed for the alignment of more than
90% of unigenes in each library. In contrast, no sequenced multicellular red algal genome existed at the time of our annotation
blast, leading to difficulties in the annotation of marine red algae transcripts. The results proved that there are obvious differences in gene and genome characteristics between multicellular and unicellular red algae, as well as between red algae and
brown algae. Therefore, to better understand red algal genetic
characteristics, it is necessary to carry out a genomic study on
4 000
a
average length
median length
n50
multicellular red algal species
3.4 Gene ontology (GO) classification
To understand the function of the unigenes we found in
brown and red algal samples, GO assignment was applied using the Interproscan program (Zdobnov and Apweiler, 2001).
Approximately 50%–70% unigenes were assigned to at least one
GO term among all the algal libraries. These unigenes were further classified into functional categories in Level 2 to Level 4.
In GO Level 2 (Fig. 4), the GO classifications of all libraries were
mostly consistent with each other, and there was no distinct difference between brown and red algae. Unigenes that classified
in GO Category “biological process” were divided into 12 subcategories, while Categories “cellular component” and “molecular function” were divided into 9 and 12 subcategories respectively, with the percentage of unigenes over 0.01%. “Cellular
process” and “metabolic process” were the largest two subcategories in the “biological process” group that comprised about
40% unigenes. Moreover, “cell part” and “binding” were the
largest subcategories in the “cellular component” and “molecular function” groups, comprising 12%–20% and 47%–55% of all
unigenes, respectively. Based on GO results in level 3 and level 4,
the largest category in the “biological process” group was “cellular macromolecule metabolic process,” which is a subgroup
of both “cellular process” and “metabolic process”; “intracellular part” and “nucleotide binding” were the largest categories in
“cellular component” and “molecular function” groups.
In addition, unigenes assigned to categories of “developmental process,” “signaling,” and “localization” in brown algal
samples numbered more than those in red algal samples, indicating potential differences in function between brown and
red algae. However, these unigenes consisted of less than 0.01%
of all unigenes, necessitating further investigation for related
genes and their expression levels, and functional effects on algal
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Sa
Sa
c
Length/bp
3 000
Fig.2. Statistics of the assembly quality of transcriptome sequencing data of all the red algal (a) and brown algal (b) libraries.
ar
llu
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nt
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Ratio of unigenes to all unigenes
Sa
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ter ho a ja sc
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ram ye lch
ium zoen ra
ko sis
nd
oi
Number
6
WU Shuangxiu et al. Acta Oceanol. Sin., 2014, Vol. 33, No. 2, P. 1–12
30 000
brown algae
red algae
25 000
20 000
15 000
10 000
5 000
0
Fig.3. Statistics of unigene number of all the red and brown algal libraries.
1
Biological process
brown algae
red algae
0.1
0.01
0.001
Cellular component
biological process.
3.5 Clusters of orthologous groups (COG) classification
To further confirm the function role of the unigenes we
found, COG classification was applied by sequence alignment
Molecular function
Fig.4. GO classification in Level 2 for all the red and brown algal samples.
against the COG database, which contains 112 920 proteins
from 7 eukaryotic complete genomes. Among all 25 categories
of COG classification (Fig. 5), “translation, ribosomal structure
and biogenesis” (J), “posttranslational modification, protein
turnover, chaperones” (O) and “general function prediction
WU Shuangxiu et al. Acta Oceanol. Sin., 2014, Vol. 33, No. 2, P. 1–12
0.14
brown algae
red algae
Ratio of unigenes to all unigenes
0.12
0.10
0.08
0.06
0.04
0.02
0.00
7
A: RNA processing and modification
B: Chromatin structure and dynamics
C: Energy production and conversion
D: Cell cycle control, cell division, chromosome partitioning
E: Amino acid transport and metabolism
F: Nucleotide transport and metabolism
G: Carbohydrate transport and metabolism
H: Coenzyme transport and metabolism
I: Lipid transport and metabolism
J: Translation, ribosomal structure and biogenesis
K: Transcription
L: Replication, recombination and repair
M: Cell wall/membrane/envelope biogenesis
N: Cell motility
O: Posttranslational modification, protein turnover, chaperones
P: Inorganic ion transport and metabolism
Q: Secondary metabolites biosynthesis, transport and catabolism
R: General function prediction only
S: Function unknown
T: Signal transduction mechanisms
U: Intracellular trafficking, secretion, and vesicular transport
V: Defense mechanisms
W: Extracellular structures
Y: Nuclear structure
Z: Cytoskeleton
Fig.5. COG classification for all the red and brown algal samples.
only” (R) were the most abundant categories in all algal libraries, comprising more than 10% of unigenes. For red algal samples, Category J, “translation, ribosomal structure and biogenesis,” included the most genes, while for brown algal samples,
Category R, “general function prediction only” included the
most genes. This discrepancy indicated that there were differences in gene characteristics or expression profiles between red
algae and brown algae.
The high proportion of unigenes in Categeory R, “general
function prediction only,” for both brown algal and red algal
samples indicated unknown function for numerous genes despite the available annotations based on the genomes of the
brown alga, E. siliculosus (Cock et al., 2010), and the unicellular
red alga, C. merolae (Matsuzaki et al., 2004). Therefore, further
physiological, biochemical and molecular studies are important to reveal these gene functions.
3.6 KEGG pathway analysis
In order to understand the high-level functions and utilities
of the transcripts in biological systems, scaffolds were searched
on the KEGG database to reconstruct the metabolic pathways
(Fig. 6). On average, approximately 577 unigenes of red algal
samples and 660 unigenes of brown algal samples were assigned
to 287 KEGG pathways, respectively. Sequence comparisons revealed a similar distribution of genes among most categories
for both algal samples. Most genes, about 37.23% for red algae
and 36.41% for brown algae, were assigned to the function of
“metabolism”, followed by the function of “genetic information
processing”, and the group relevant to “human diseases”. The
highest two numbers of scaffolds were assigned to pathways
of “translation” in Category “genetic information processing”
and “carbohydrate metabolism” in Category “metabolism”, followed by two pathways on “amino acid metabolism in Category
‘metabolism’ and ‘folding, sorting and degradation’ in Category
‘genetic information processing’”. The abundance of scaffolds
assigned to metabolism pathways, especially to carbohydrate
metabolism pathways, indicated that the active physiological
process was focusing on the metabolic process, especially on
the carbohydrate metabolism and relative processes both for
brown algae and red algae in the growth season when we collected them.
4 Discussion
4.1 Contribution to algal phylogenetic study
Phylogenetic studies, particularly on ancient events of the
different origins of extant Kingdoms Plantae (or Archaeplastida) (Simon et al., 2009) and Chromista (Cavalier-Smith, 2010),
really depended on the development of molecular bases of
representative species. With more and more complete-genome sequencing and EST data of model algal species released
(Table 2), many fundamental questions about origins of different algal lineages, origins of multicellularity, and occurrences
of primary and secondary endosymbiosis have become clearer.
Especially, the development of high-throughput sequencing
technology and reduced sequencing cost has allowed for such
phylogenetic studies on diverse groups of algae.
The first algal genome sequencing on C. merolae, a unicellular red alga that lives in the high temperatures and strongly acidic habitat, supported the hypothesis of a single primary plastid
endosymbiosis of red algae and green plants by analyzing the
Calvin cycle enzymes (Matsuzaki et al., 2004). Genomic and
cDNA sequence information of Chlamydomonas reinhardtii,
a model species of unicellular green algae, helped to advance
our understanding of the last common ancestor of plants and
animals (Merchant et al., 2007). Phylogenetic analyses on the
draft genome and transcriptome data, as well as concatenated
multi-proteins of plastid of Cyanophora paradoxa, a “living fossil” glaucophyte, showed further evidence for a single origin of
Plantae plastids and also placed glaucophytes very close to the
divergence point of red and green algae (Price et al., 2012).
Cryptophytes and chlorarachniophytes are termed as sec-
8
WU Shuangxiu et al. Acta Oceanol. Sin., 2014, Vol. 33, No. 2, P. 1–12
350
Number of unigenes
300
A: Metabolism
B: Genetic Information Processing
C: Environmental Information Processing
D: Cellular Processes
E: Organismal Systems
F: Human Diseases
red algae
brown algae
250
200
150
100
50
Ca
rbo
hy
d
En rate
e
m
Nu L rgy eta
Gl Me
m b
i
t
Glycan abol Amcleotpid metabolis
M Me ycan bio ism ino ide eta olism
Bi eta tab b syn of aci me bo m
o
Xe syn bolis olis iosy thes othed metabolism
no the m m o nth is r a tab lis
bio sis of f esi and mi ol m
tic of ter cof s a m no ism
s b ot pen act nd eta ac
iod her oi ors me bo ids
eg se ds a an tab lism
rad co nd d ol
ati nda po vita ism
on ry ly m
Fo
ldi
an me ket ins
d m ta id
ng
b e
,s
Tr etab olites
ort
a
ing
Re a Tnscr olisms
Sig
pli nd ra ipt
na
c
d ns io
lin
g m Memation egra lation
ole Sig bra and dati n
Tr cu na ne re on
an les l tr tra pa
sp a an ns ir
ort nd sd po
an int uct rt
Ce
d e i
ll g C cat ract on
Ce row ell abol ion
ll c th m ism
o a ot
I mm nd ilit
Enmmu uni deaty
Ci doc ne cati h
rc ri sy on
Diulatone system
Exgesti ry s stem
c v ys
Neretor e system
y
En
Servou system
vir
ns s s tem
on
o
r
me De y system
nta vel yst
Ne
l a opmem
uro
da
I
d
m
pt ent
e
En
g
m
e
S
do
ne un Ca ation
u
b
cri Ca st rat e d nc
a
Infne a rdio nce ive disea ers
ec nd vas de ise ses
tio m cu pe as
Inf Infeus d etabolar dnden es
ec cti ise lic ise ce
tio ou as
us s d es: diseases
dis is ba ase
ea eas cte s
ses es ria
: p : vi l
ara ral
sit
ic
0
A
B
C
D
E
F
Fig.6. Metabolic pathway analysis on the unigenes using KEGG for all the brown and red algal samples.
ondary plastid-bearing eukaryotic algae, which distinctively
own the endosymbiont nuclei (nucleomorphs) persisting in
the cytoplasm. The nuclear genomes of the cryptophyte Guillardia theta and the chlorarachniophyte Bigelowiella natans
were recently sequenced to investigate the pattern and process
of host–endosymbiont integration, and the reason for the persistence of nucleomorphs in cryptophytes (Curtis et al., 2012).
So far, the genome of E. silicilosus, a model organism of brown
algae, is the largest one (approximately 214 Mb) among all sequenced algae, and its sequencing analysis provided important
clues for the emergence of multicellularity in this group (Cock
et al., 2010). However, more gene information about muticellular brown and red algae is needed to prove the findings about
the origin and evolution in plant kingdom.
In this study, we selected a broad range of 19 Phaeophyceae
species and 21 Rhodophyta species, covering a total of 3 classes,
11 orders and 19 families. These species typically represented
main taxonomic units for phylogenetic studies on marine Phaeophyceae and Rhodophyta. Each algal sample yielded 2 Gb
raw data on average; approximately 10 000 to 50 000 scaffolds
and 5 000 to 10 000 unigenes were obtained for red algae, and
about 50 000 to 100 000 scaffolds and 15 000 to 25 000 unigenes
were obtained for brown algae (Figs 3 and 4). Based on these
transcripts' assembly and annotation results, we could see clear
differences in brown and red algal genomes or gene expression characteristics. Further using these transcriptome data for
nuclear-oriented, mitochondrial-oriented and plastid-oriented
genes, we not only addressed phylogenetic taxonomy of phaeophytes and rhodophytes as a whole, but also confirmed that
of taxonomy-contentious species, such as Sargassum fusiforme
and Saccharina sculpera of brown algae, as well as Gracilariopsis lemaneiformis and Grateloupia chiangii of red algae (Sun et
al., 2014; Jia et al., 2014a, b).
Moreover, such a broad range of algal transcriptome information enabled us to do phylogenetic analyses on many important genes and gene families related to the novel and unique
metabolic pathways of brown and red algae, such as unique
and complex carbohydrate metabolisms, light harvesting and
transporting systems, special amino acid/iodine metabolisms,
and stress adaptation systems, among others. These phylogenetic analyses helped to distinguish between host cells, engulfing cells, or horizontal gene transformation (HGT) as origins of
genes or pathways, and elucidated the mosaic characteristics
of algal genome. All these analyses will be reported in separate
articles in this journal.
4.2 Contribution to algal genetic and biological study
All of our selected brown and red algal species in this study
were of great ecological or commercial importance and most
were studied for the first time in this transcriptome sequencing
study. Therefore, these transcriptome data, especially for the
unigene annotation and functional analysis on GO, COG and
KEGG, definitely provided valuable information not only to discover genome characteristics of these marine algae, but also to
help reveal the unique characteristics and mechanisms of marine algae in adapting to ocean environments.
For example, numerous marine macroalgae, including the
Ishige species, Sargassum species, Saccharina species and
Gracilaria species, inhabit the harsh and extreme environments
of upper intertidal zones. The transctiptome analysis on I. oka-
9
WU Shuangxiu et al. Acta Oceanol. Sin., 2014, Vol. 33, No. 2, P. 1–12
Table 2. Update information of publicly available algal genome sequences
Phylum
Order
Family
Species
Cercozoa
Chlorarachniales
Chlorarachniaceae
Bigelowiella natans
Chlorophyta Chlamydomonadales Chlamydomonadaceae Chlamydomonas reinhardtii
Chlorellales
Chlorellaceae
Chlorella variabilis
Chlorococcales
Coccomyxaceae
Coccomyxa subellipsoidea
Mamiellales
Bathycoccaceae
Ostreococcus lucimarinus
Ostreococcus tauri
Bathycoccus prasinos
Cryptophyta
Glaucophyta
Haptophyta
Ochrophyta
Rhodophyta
Strain
CCMP2755
CC-503
NC64A
c-169
CCE9901
OTH95
RCC1105
CCMP1545
Genome
Reference
size/Mb
91.4
Curtis et al. (2012)
105.4 Merchant et al. (2007)
42.2
Blanc et al. (2010)
48.8
Blanc et al. (2012)
13.2
Palenik et al. (2007)
12.6
Derelle et al. (2002)
15
Moreau et al. (2012)
21.8
Worden et al. (2009)
21
125.5 Prochnik et al. (2010)
83.5
Curtis et al. (2012)
70
Price et al. (2012)
167.7
Read et al. (2013)
214
Cock et al. (2010)
Radakovits et al.
30.4
(2012)
27.6
Pan et al.(2011)
Mamiellaceae
Micromonas pusilla
Volvocales
Cryptomonadales
Glaucocystales
Isochrysidales
Ectocarpales
Volvocaceae
Cryptomonadaceae
Glaucocystaceae
Noëlaerhabdaceae
Ectocarpaceae
Volvox carteri
Guillardia theta
Cyanophora paradoxa
Emiliania huxleyi
Ectocarpus siliculosus
RCC 299
UTEX2908
CCMP2712
CCMP329
CCMP1516
Ec32
Eustigmatales
Monodopsidaceae
Nannochloropsis gaditana
CCMP526
Nannochloropsis oceanica
LAMB0001
Naviculales
Phaeodactylaceae
Phaeodactylum tricornutum
CCP1055/1
27.5
Bowler et al. (2008)
Pelagomonadales
Pelagomonadaceae
Aureococcus anophagefferens CCMP1984
50.9
Thalassiosirales
Thalassiosiraceae
Gobler et al. (2011)
Armbrust et al.
Thalassiosira pseudonana
CCMP1335
32.4
Thalassiosira oceanica
CCMP1005
69.4
Cyanidiaceae
Cyanidioschyzon merolae
10D
16.6
Galdieriaceae
Galdieria sulphuraria
074W
13.7
Bangiales
Bangiaceae
Pyropia yezoensis
U-51
43
Gigartinales
Gigartinaceae
Chondrus crispus
Cyanidiales
murae found ample kinds of Rab proteins, which were thought
to play an important role in adaptating to environmental stress
(Bolte et al., 2000; Agarwal et al., 2008). These Rab proteins provided clues about how algae cope with the highly variable environment of the intertidal zone. In addition, numerous genes
for trehalose metabolism were annotated in all these sequenced
transcriptomes, showing this resistance-adaptation oligosaccharide widely distributed in brown and red algae (Qu et al.,
2014). The phylogenetic analysis on algal trehalose-metabolism
genes has been done and will be reported in a separate article.
The analysis of the gene family of heat shock protein (HSP), an
adaptation protein, is also ongoing.
Red algal species of genus Gracilaria not only play vital roles
in the recycling and maintenance of nitrogen and phosphorus
balance in seawater (Huovinen et al., 2006), but also are major
resources for phycobiloprotein production as coloring materials (Dufosséa et al., 2005), fluorescent probes (Glazer, 1997),
and even anti-inflammatory and anti-hyperalgesic agents (Shih
et al., 2009). In this study we annotated all three types of phycobiloproteins in Gracilaria species and all red algal species
and constructed phylogenetic trees based on phycobiloprotein
sequences. These analyses verify the contentious taxonomic
classifications among red algae and also provide valuable gene
information for the study of phycobiloprotein biosynthetic
pathways (Xu et al., 2014).
In the genome study of E. silicilosus, a large integrated vi-
105
(2004)
Lommer et al. (2012)
Matsuzaki et al.
(2004)
Schönknecht et al.
(2013)
Nakamura et al.
(2013)
Collén et al. (2013)
ral sequence fragment about 335 kb in length was found in the
genome of E. silicilosus (Cock et al., 2010). We screened endogenous viral elements (EVEs), the host genomic fragments originated from viral genomes, in all publicly available algal genome
sequences and transcriptome/EST data, including the transcriptome data of this study, and found EVEs existing universally in algal genomes. Their distribution and expression characteristics in different algal species are also analyzed (Wang et
al., 2014).
The most unique biological characteristics of brown and red
algae are their special carbohydrate metabolisms and products,
such as alginates and fucoidins of brown algal cell wall (Drury
et al., 2003; Berteau and Mulloy, 2003), and agars in red algae
(Matsubara, 2004). These carbohydrate products are valuable
resources for medicines, food and material industry. In particular, the Saccharina species, the famous seaweed food known as
kelps, are important algal resources for the production of iodine, alginate, mannitol and fucoids (Raj and Sharma, 2003). In
this study, we analyzed transcriptome data of S. japonica and
characterized the gene structure, duplication, and phylogenetic
evolution of the gene family (algG), which encodes alginate-c5mannuronan-epimerase and is involved in algal alginate biosynthesis. In addition, a large number of vanadium-dependent
haloperoxidases (vHPO) involved in iodine metabolism (Küpper et al., 1998; Butler and Carter-Franklin, 2004) were predicted by using bioinformatics analysis, which enhanced the un-
10
WU Shuangxiu et al. Acta Oceanol. Sin., 2014, Vol. 33, No. 2, P. 1–12
derstanding of biological characteristics and economic value of
this organism (Wang et al., 2014).
For red algae in Family Solieriaceae, Betaphycus gelatinus,
Kappaphycus alvarezii and Eucheuma denticulatum produce carrgeenans, including beta (β)-, kappa (κ)- and iota (ι)carrageenans, that are important for their uses in food and
medicine industry (Rudolph, 2000). However, the biosynthesis
pathway of carrageenan is not clear. The comparative analyses of transcriptomes of three species found 30 differentiallyexpressed KEGG orthologs related to the carbohydrate metabolism, including 21 orthologs in glycolysis/gluconeogenesis
pathway, 16 orthologs in carbon fixation of photosynthetic organisms, 5 orthologs of galactose metabolism and 9 orthologs
in fructose and mannose metabolism, and 8 orthologs in sulfur
metabolism, giving clues on the biosynthesis pathways of different types of carrgeenans of red algae (Song et al., 2014).
In KEGG analysis, we for the first time annotated the entire
C4-pathway genes in this study's transcriptome data, including ones encoding phosphoenolpyruvate carboxylase (PEPC),
pyruvate,orthophosphate dikinase (PPDK), alanine transaminase (ALT), malate dehydrogenase (MDH), malic enzyme (ME),
aspartate aminotransferase (ATT), and pyruvate kinase (PK).
We also for the first time verified these genes universally distributed among all algal lineages (unpublished data). More detailed analyses on CO2 fixation and carbohydrate metabolism
and other essential metabolism pathways among brown and
red algae based on these transcriptomes and biological experiments are ongoing in our laboratory.
5 Conclusions
The analyses of transcriptome sequencing results of such
broad range of marine brown and red algae for the first time
not only helped to verify the taxonomy-contentious species of
Phaeophyceae and Rhodophyta, and elucidate genome and
metabolism characteristics of marine brown and red algae, but
also provided many new insights into marine algal adaptation
mechanisms to seashore environments, as well as the origins
and evolutions of eukaryotic and multicellular algae. With the
increasing impact of industrial development on the changing
atmospheric, aquatic, and terrestrial ecosystems, more genetic
information needs to be deeply explored in marine algae in
order to protect and utilize the vast amount of marine bioresources.
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