Hori et al. BMC Genomics 2010, 11:72
http://www.biomedcentral.com/1471-2164/11/72
RESEARCH ARTICLE
Open Access
Heat-shock responsive genes identified and
validated in Atlantic cod (Gadus morhua) liver,
head kidney and skeletal muscle using genomic
techniques
Tiago S Hori1, A Kurt Gamperl1, Luis OB Afonso2, Stewart C Johnson3, Sophie Hubert4, Jennifer Kimball5,
Sharen Bowman4, Matthew L Rise1*
Abstract
Background: Daily and seasonal changes in temperature are challenges that fish within aquaculture settings
cannot completely avoid, and are known to elicit complex organismal and cellular stress responses. We conducted
a large-scale gene discovery and transcript expression study in order to better understand the genes that are
potentially involved in the physiological and cellular aspects of stress caused by heat-shock. We used suppression
subtractive hybridization (SSH) cDNA library construction and characterization to identify transcripts that were
dysregulated by heat-shock in liver, skeletal muscle and head kidney of Atlantic cod. These tissues were selected
due to their roles in metabolic regulation, locomotion and growth, and immune function, respectively. Fish were
exposed for 3 hours to an 8°C elevation in temperature, and then allowed to recover for 24 hours at the original
temperature (i.e. 10°C). Tissue samples obtained before heat-shock (BHS), at the cessation of heat-shock (CS), and 3,
12, and 24 hours after the cessation of heat-shock (ACS), were used for reciprocal SSH library construction and
quantitative reverse transcription - polymerase chain reaction (QPCR) analysis of gene expression using samples
from a group that was transferred but not heat-shocked (CT) as controls.
Results: We sequenced and characterized 4394 ESTs (1524 from liver, 1451 from head kidney and 1419 from
skeletal muscle) from three “forward subtracted” libraries (enriched for genes up-regulated by heat-shock) and 1586
from the liver “reverse subtracted” library (enriched for genes down-regulated by heat-shock), for a total of 5980
ESTs. Several cDNAs encoding putative chaperones belonging to the heat-shock protein (HSP) family were found in
these libraries, and “protein folding” was among the gene ontology (GO) terms with the highest proportion in the
libraries. QPCR analysis of HSP90a and HSP70-1 (synonym: HSPA1A) mRNA expression showed significant upregulation in all three tissues studied. These transcripts were more than 100-fold up-regulated in liver following
heat-shock. We also identified HSP47, GRP78 and GRP94-like transcripts, which were significantly up-regulated in all
3 tissues studied. Toll-like receptor 22 (TLR22) transcript, found in the liver reverse SSH library, was shown by QPCR
to be significantly down-regulated in the head kidney after heat-shock.
Conclusion: Chaperones are an important part of the cellular response to stress, and genes identified in this work
may play important roles in resistance to thermal-stress. Moreover, the transcript for one key immune response
gene (TLR22) was down-regulated by heat-shock, and this down-regulation may be a component of heat-induced
immunosuppression.
* Correspondence: mrise@mun.ca
1
Ocean Sciences Centre, Memorial University of Newfoundland, St. John’s,
NL, A1C 5S7, Canada
© 2010 Hori et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
Hori et al. BMC Genomics 2010, 11:72
http://www.biomedcentral.com/1471-2164/11/72
Background
Temperatures are known to vary considerably at aquaculture cage-sites [1] and can approach upper critical temperatures (i.e. temperatures that are lethal) for Atlantic
cod (Gadus morhua). These changes can occur both
rapidly [e.g. increase of ~8°C in less than 12 hours during
thermocline inversions, especially at depths where Atlantic
cod tend to congregate (≥ 5 m)] [1] and seasonally. Fish
confined to cages cannot completely avoid these temperatures and therefore are likely to be exposed to stressful
conditions [1]. The stress response consists of numerous
modifications to an organism’s physiology and behaviour
that are necessary to regain and maintain homeostasis
once it has been challenged by changes in the environment, e.g. changes in temperature [2]. The cellular
response to stress is the coordinated reaction to a threat of
macromolecular damage and protects the cell against the
potentially hazardous consequences of such events [3].
Cortisol is often regarded as a suitable indicator of stress,
and one of the key hormones regulating the stress
response [4-6]. Most actions of cortisol are thought to be
mediated by the glucocorticoid receptor (GR), which upon
binding to the hormone, moves into the nucleus and acts
as a transcription factor that interacts with specific promoter regions known as glucocorticoid responsive elements
(GREs) [4]. Stress can therefore have a significant impact
on the transcription of specific genes. Thermal stress is
also known to alter the transcription of a variety of genes
including those encoding proteins that are involved in the
response to oxidative stress, apoptosis, protein folding,
energy metabolism, protein synthesis, membrane fluidity
and immune function [7-12]. The proteins encoded by
these transcripts include some of the elements that comprise and/or regulate both the organismal and cellular
stress responses, and may help to protect the animal
against the deleterious effects of stress. Among these are
chaperones (e.g. members of the heat-shock protein gene
family), anti-oxidative enzymes [e.g. catalase, superoxide
dismutases (SODs), glutathione-S-transferases (GSTs)]
and enzymes of carbohydrate metabolism (e.g. glycogen
phosphorylase and phosphofructokinase).
Transcriptomic studies have been used to investigate
the impacts of environmental stress on several organisms
including fish [13-19]. Specifically, suppression subtractive hybridization (SSH) libraries have been used to identify fish genes that are responsive to diverse stimuli such
as polyriboinosinic polyribocytidylic acid (pIC, a viral
mimic) injection [20], formalin-killed atypical Aeromonas
salmonicida injection [21], osmotic stress [22], cadmium
[23], and pesticide exposure [24]. In order to better characterize the genes and molecular pathways involved in
the Atlantic cod response to heat-shock, we constructed,
sequenced, and characterized reciprocal SSH libraries
Page 2 of 22
enriched for transcripts dysregulated by heat-shock. Candidate heat-shock responsive cod cDNAs identified in the
libraries were further investigated using real-time quantitative reverse transcription - polymerase chain reaction
(QPCR). This report is the first to use high-throughput
genomic techniques [SSH library construction; sequencing of expressed sequence tags (ESTs); cDNA sequence
assembly, identification, and functional annotation of
assembled sequences in an EST database; and QPCR for
several SSH-identified genes in different tissues and at
different time points post-stress] to characterize the transcriptomic response of Atlantic cod to thermal stress.
This study is part of the Genome Canada funded Atlantic
Cod Genomics and Broodstock Development Project
(CGP, http://www.codgene.ca), which is developing many
tools to study stress physiology in this species. A better
understanding of changes in Atlantic cod gene transcription in response to heat-shock will potentially lead to
new tools and techniques [e.g. molecular biomarkers,
QPCR assays, and single nucleotide polymorphism (SNP)
genotyping assays] for studying the impacts of environmental stress on cod, and for selecting individuals
(broodstock) that can tolerate higher water temperatures.
Results
Plasma cortisol levels
Plasma cortisol did not change significantly in the
undisturbed control (C) group over the course of the
experiment, averaging 30.4 - 41.2 ng ml -1 (Fig. 1). In
contrast, plasma cortisol levels were significantly (p <
0.05) increased in the “transferred but not heat-shocked
control” (CT) and heat-shocked (HS) groups at the cessation of the 3 hour heat-shock (CS) when compared to
their levels at the before heat-shock (BHS) time point
(Fig. 1). At this time point (CS), average plasma cortisol
levels in the heat-shocked group (HS: 132.4 ng ml -1 )
were 2.3-fold higher than for the CT group (57.8 ng ml1
) and 5.7-fold higher than for the HS BHS values (23.4
ng ml-1) (Fig. 1). Plasma cortisol in fish that were only
transferred to a new tank at 10°C (CT) returned to basal
levels (29.9 ng ml -1 ) at 3 hours after the cessation of
heat-shock (3ACS). In contrast, cortisol in the heatshocked fish reached its highest level at this time-point
(164.8 ng ml-1), and remained significantly elevated at
12 (12ACS) and 24 (24ACS) hours after the cessation of
heat-shock; values at these time-points were 3.5-fold
and 2.8-fold higher than those measured in BHS fish,
respectively.
Characterization of SSH libraries and identification of
candidate heat-shock responsive transcripts
We constructed and characterized 3 forward SSH
libraries (liver, head kidney and skeletal muscle) and 1
reverse SSH library (liver). The SSH library method
Hori et al. BMC Genomics 2010, 11:72
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Figure 1 Average (± SEM) plasma cortisol (ng/ml) levels in cod before and following a 3 hour heat-shock (transfer from 10°C to 18°C).
Plasma cortisol levels in undisturbed control (C), control transferred (CT) and heat-shocked (HS) groups are shown before heat-shock (BHS), at
the cessation of heat-shock (CS) and 3 h after the cessation of heat-shock (3ACS), 12 h after the cessation of heat-shock (12ACS) and 24 h after
the cessation of heat-shock (24ACS). Different letters indicate significant differences between sampling points within the same group (p < 0.05).
* indicates a significant difference between groups within a sampling point (p < 0.05). Cortisol levels in the C group did not change significantly
during the experiment.
requires 2 μg of poly (A) + RNA (mRNA) [25], and
mRNA usually only represents 1-5% of total RNA. In
order to obtain adequate quantities of mRNA for SSH
library construction, we isolated mRNA from pooled
total RNA samples (see Methods). For SSH library construction, mRNA from heat-shocked (HS) (i.e. netted
and transferred to a new tank at 18°C) fish were subtracted against mRNA from tissues of control transferred (CT) fish that were subjected to a handling stress
(i.e. netted and transferred to a new tank at the same
temperature) but without heat-shock. The mRNA used
for SSH library construction consisted of samples taken
at the cessation of the stressor and throughout the
recovery period (up to 24 hours after the cessation of
heat-shock). The resulting SSH libraries were enriched
for transcripts that were dysregulated by the combined
handling and heat-shock, but not those transcripts that
were dysregulated by handling stress alone. We used
three tissues to facilitate the identification of cod transcripts that were stress responsive across tissues, and
therefore likely to be linked to the organism’s overall
sensitivity or resistance to thermal stress. We built reciprocal SSH libraries to identify transcripts that were upregulated by heat-shock (i.e. “forward subtracted”
libraries) as well as transcripts that were down-regulated
by heat-shock (i.e. “reverse subtracted” libraries). We
sequenced a total of 5980 expressed sequence tags
(ESTs): 1524 from the liver forward library, 1451 from
the head kidney forward library, 1419 from the skeletal
muscle forward library, and 1586 from the liver reverse
library. Although reverse SSH libraries were also made
for the head kidney and skeletal muscle, initial complexity evaluation and/or sequencing showed that they were
of low complexity (i.e. dominated by a few highly abundant transcripts), and they were therefore not subjected
to deeper sequencing (Table 1). The ESTs were
assembled into contiguous sequences (contigs) using
Paracel Transcript Assembler (PTA) and annotated
using AutoFACT [26]. Selected defense (i.e. stress and
immune) relevant sequences are presented in Tables 2
and 3. In these tables, we do not include contigs and
singletons with BLASTx hit E-values above 10-5, as well
as many sequences that are unclassified (i.e. no BLAST
hit), redundant, or with BLAST hits that have non-stress
related functional annotations. A complete list of
assembled sequences (i.e. contigs and singletons), ESTs
contributing to contigs, EST accession numbers, associated AutoFACT hit descriptions (including BLASTx
statistics) and functional annotations, can be found in
Additional file 1 (Table S1) and Additional file 2 (Table
S2).
Haemoglobin subunit alpha-1 was the largest contig
(17 contributing sequences) in the head kidney forward
library (Additional file 1, Table S1A), and haemoglobin
subunit beta-1 was the largest contig (25 contributing
sequences) in the liver forward library (Additional file 1,
Table S1B). In the skeletal muscle forward library the
largest contig was parvalbumin (66 contributing
Hori et al. BMC Genomics 2010, 11:72
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Table 1 Statistics for ESTs generated for all SSH libraries1
HK_F
HK_R6
SM_F
L_F
L_R
Tissue
Head Kidney
Head Kidney
Skeletal Muscle
Liver
Liver
Direction2
CGP ID3
forward
sb_gmnlkfta
reverse
sb_gmnlkrta
forward
sb_gmnlmfta
forward
sb_gmnllfta
reverse
sb_gmnllrta
Library Name
# of ESTs
Average EST length4
1451
93
1419
1524
1586
297 bp
400 bp
340 bp
241 bp
227 bp
# of contigs5
212
8
159
200
178
# of singletons
746
35
483
612
668
# of non- redundant ESTs7
% redundancy8
958
43
642
812
846
33.9%
53.7%
54.7%
46.7%
46.6%
1
Agarose gel electrophoresis of the muscle reverse library revealed the presence of 5 clear bands, which indicated a low-complexity library. Therefore,
sequencing of this library was not attempted.
2
The forward SSH libraries were constructed to be enriched for genes that were up-regulated by heat-shock, and the reverse SSH libraries were constructed to be
enriched for genes that were down-regulated by heat-shock.
3
The identifiers (ID) or names of the SSH libraries in the CGP EST database: http://www.codgene.ca.
4
The ESTs were trimmed with Phred [6263] with the trim_alt and trim_cutoff fixed at 0.06, followed by the removal of known contaminant sequences and short
sequences (< 75 bp), and the average EST length was calculated based on edited sequences.
5
Sequences generated were then clustered using Paracel Transcript Assembler (PTA), with the cluster threshold set at 100 for relatively stringent clustering.
6
The 4 largest contigs in this library (Additional file 2, Table S2A) were annotated as haemoglobins and contained 45 sequences, which represented almost 50%
of the good sequences obtained for it. Thus, this library was deemed low-complexity and not sequenced further.
7
The number of non-redundant ESTs is the sum of the number of contigs plus the number of singletons.
8
Percent redundancy is the proportion of redundant ESTs in each library, calculated as [1 - (Number of non-redundant ESTs/total number of ESTs)] multiplied by
100.
sequences) (Additional file 1, Table S1C). In order to
identify and validate transcripts that were up-regulated
in response to heat-shock, we looked in the forward
libraries for cod cDNAs that: a) had significant BLASTx
hits (i.e. E-values lower than 10-5) against proteins with
stress-relevant functional annotations; b) were in contigs
in more than one forward library; and/or c) had a relatively large number of contributing sequences (more
than 5). A list of selected contigs from the forward
libraries, with detailed BLASTx statistics and functional
annotations, is presented in Table 2. These include several cod cDNAs with BLASTx hits against putative chaperone or chaperone-related proteins, including the
following: the translationally-controlled tumor protein
(TCTP) (found in skeletal muscle, head kidney and
liver); heat shock protein 47 (HSP47) (skeletal muscle);
several putative members of the T-complex containing
chaperonin system (CCT) including chaperonin containing TCP1, subunit 5 (synonym: CCT 5) (skeletal muscle
and liver), T-complex protein 1 subunit beta (synonym:
CCT 2) (liver), T complex 1 (synonym: CCT 1) (liver,
skeletal muscle and head kidney) and TCP1-theta (synonym: CCT 8) (head kidney); heat shock protein 90 alpha
(HSP90a) (head kidney and skeletal muscle); heat shock
protein 90 kDa beta, member 1 (synonym: glucose regulated protein 94, GRP94) (liver and head kidney);
HSP70-1 (synonym: HSPA1A) (skeletal muscle); and the
78 kDa glucose-regulated protein (synonym: GRP78)
(liver).
We also identified several cod transcripts with significant BLASTx hits against genes and proteins involved in
carbohydrate metabolism (Table 2 and Additional file 1,
Table S1). These include enolase 3 (skeletal muscle),
phosphofructokinase 1 (PFK) (skeletal muscle), transaldolase 1 (Taldo1) (head kidney and skeletal muscle),
aldolase B (liver), phosphoglucomutase 1 (PGM1) (liver
and skeletal muscle), glycogen phosphorylase (head kidney) and phosphoenolpyruvate carboxykinase (PEPCK)
(liver).
In the liver reverse SSH library (i.e. enriched for transcripts down-regulated by heat-shock) the 3 largest contigs were also haemoglobins (subunits alpha-1, beta-1
and beta-2) (Additional file 2, Table S2B). Some of the
defence-relevant transcripts identified in this library
were: tetraspanin-6, nuclear factor interleukin-3 regulated protein, alpha 1-microglobulin/bikunin (bikunin),
lectin, Immunoglobulin (Ig) heavy chain (synonym:
IgM), hepcidin, interleukin-8 (IL-8), Toll-like receptor
22 (TLR22) and TNFAIP3 interacting protein 1. We
also identified the cell cycle regulator nuclear protein 1
(NUPR1, synonym: p8). Stress has been shown to influence fish immune function [27], and therefore for
QPCR studies we chose primarily cod cDNAs representing acute phase and immune-relevant genes as heatshock responsive candidate transcripts from the liver
reverse SSH library. Detailed information on these
selected cod transcripts is presented in Table 3. For a
more comprehensive list (i.e. containing all contigs and
singletons identified in the reverse libraries along with
AutoFACT hit descriptions, BLASTx statistics and functional annotations), refer to Additional file 2, Table S2.
Hori et al. BMC Genomics 2010, 11:72
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Table 2 Selected cDNAs1 from all 3 forward (enriched for genes up-regulated by heat-shock) SSH libraries
representing stress response related genes
BLASTx identification4 of selected contigs
#
Gene Name [Species of best BLASTx
%ID (aa
ESTs Hit]
length of
align.)
Contig ID
(Accession
Number)
Tissue
(Library)
QPCR
16.C1
(ES784003)
76.C1
(ES784315)
Muscle
(gmnlmfta)
Muscle
(gmnlmfta)
Fig.5D2
13
Enolase 3 (beta muscle) [Danio rerio]
Fig.5C2
8
Translationally-controlled tumor
protein5 [Cyprinus carpio]
91% (159/
174)
68% (116/
170)
45.C1
(ES783752)
Muscle
(gmnlmfta)
Fig.4E2
7
Heat Shock Protein 47 [Onchorhynchus
mykiss]
31.C1
(ES783410)
Muscle
(gmnlmfta)
Fig.5B2
7
27.C1
(ES783916)
Muscle
(gmnlmfta)
Not
sig.3
79.C1
(ES781823)
Liver
(gmnllfta)
37.C1
(ES780395)
Evalue
Gene Ontology or function of
putative orthologue7
2e-81
Glycolysis (BP), Cytoplasm (CC)
9e-62
Calcium binding and apoptosis
regulation [58]
75% (132/
174)
2e-76
Serine-type endopeptidase
inhibitor activity (MF)
Chaperonin containing TCP1, subunit 5
(epsilon) (synonym: CCT 5) [Danio
rerio]
93% (149/
159)
2e-98
Protein folding (BP)*
5
Phosphofructokinase, muscle a [Danio
rerio]
88% (173/
195)
4e-98
Glycolysis (BP)*
Not
done
5
Cyclophilin A6 [Argopecten irradians]
80% (132/
164)
2e-75
Protein folding (BP)*
H. Kidney
(gmnlkfta)
Not
Done
5
Taldo1 protein5,6 (synonym:
transaldolase) [Danio rerio]
82% (120/
145)
6e-63
Pentose-phosphate shunt (BP)*
65.C1
(EY973473)
H. Kidney
(gmnlkfta)
Fig.4A2
4
Heat shock protein 90 alpha
[Paralichthys olivaceus]
93% (93/99)
5e-47
Protein folding (BP)*, Response to
stress (BP)*
62.C1
(ES781078)
Liver
(gmnllfta)
Not
done
4
Glutathione S-transferase pi5 [Carassius
auratus]
69% (46/66)
9e-14
Metabolic process (BP)*
24.C1
(ES781741)
Liver
(gmnllfta)
Not
done
4
T-complex protein 1 subunit beta6
[Salmo salar]
88% (174/
196)
2e-77
Protein folding (BP)*
41.C1
(EY973602)
H. Kidney
(gmnlkfta)
Not
done
3
Copper/zinc superoxide dismutase
[Epinephelus coioides]
82% (125/
152)
2e-71
Superoxide metabolic process (BP),
Superoxide dismutase activity (MF)
113.C1
(ES781350)
Liver
(gmnllfta)
Fig.4B2
3
Heat shock protein 90 kDa beta,
member 15,6 (synonym: GRP94) [Danio
rerio]
91% (122/
134)
4e-54
Protein folding (BP), Response to
stress (BP)*
146.C1
(ES781990)
Liver
(gmnllfta)
Fig.5A2
2
T-complex 16 (synonym: CCT 1) [Pan
troglodytes]
98% (59/60)
3e-28
Assists folding of tubulin and
other cytoskeleton proteins [56]
128.C1
(ES783784)
Muscle
(gmnlmfta)
Fig.4C2
2
HSP70-1 protein6 (synonym: HSPA1A)
[Oryzias latipes]
92% (170/
183)
7e-76
Response to stress (BP)*, ATP
binding (MF)
5.C1 (EX190083)
Not
done
Fig.4D2
2
TCP1-theta6 [Notothenia coriiceps]
90% (56/62)
2e-25
10.C1
(ES781650)
27.C1
(ES780987)
84.C1
(ES781112)
172.C1
(ES781772)
H. Kidney
(gmnlkfta)
Liver
(gmnllfta)
Liver
(gmnllfta)
Liver
(gmnllfta)
Liver
(gmnllfta)
2
98% (60/61)
1e-28
Not
sig.3
Not
done
Not
done
2
78 kDa glucose-regulated protein6
(synonym: GRP78) [Salmo salar]
Aldolase B5 [Poecilia reticulata]
91% (45/49)
8e-20
Protein folding (BP), Protein
binding (MF)9
ATP binding (MF), Ig chain folding
[39]10
Glycolysis (BP)11
2
Pdia4 protein6 [Danio rerio]
77% (71/92)
5e-36
Cell redox homeostasis (BP)*
2
Phosphoglucomutase 1 [Danio rerio]
96% (38/41)
1e-14
Carbohydrate metabolic process
(BP)*
174.C1
(EX190172)
H. Kidney
(gmnlkfta)
Not
done
2
Glycogen phosphorylase [Oreochromis
mossambicus]
84% (69/82)
4e-34
Carbohydrate metabolic process
(BP)*
8
1
ESTs from each individual forward library (e.g. liver) were assembled separately. Contigs identified in the forward library that were also found in the liver reverse
library were included in this table if present as one contig of 2 or more ESTs in at least 2 forward libraries or if validated by QPCR. For genes represented by
multiple contigs in a given library or across tissues (i.e. in different forward libraries), the contig with the highest number of contributing sequences is shown.
Possible reasons for the presence of multiple contigs with the same gene name within a given library are provided in the discussion. All SSH contigs and
singletons for the 3 libraries with all BLASTx statistics and GenBank accession numbers are presented in Additional file 1, Table S1. The GenBank accession
number of one representative EST from each contig is given in this table. The names of these SSH libraries in the CGP EST database are gmnllfta (liver), gmnlkfta
(head kidney), gmnlmfta (muscle).
2
These genes showed at least one statistically significant difference between 2 groups and/or time points (e.g. CT at BHS versus CT at CS, or CT at CS versus HS
at CS) in at least 1 tissue.
3
These 2 genes were analyzed with QPCR at the individual level, but the data is not presented because there were no statistically significant differences between
groups and/or time points.
4
The BLASTx hit with the lowest E-value and a gene name (e.g. not predicted or hypothetical) is shown. BLAST statistics were collected on the 4th of December,
2008 and reflect the entries in the nr protein database up to that date. aa = amino acids.
5
These cDNAs were represented by a contig or a singleton in the liver reverse library.
Hori et al. BMC Genomics 2010, 11:72
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6
Synonyms for protein names were retrieved from the Swiss-Prot Protein Knowledgebase Database http://ca.expasy.org/sprot/: Cyclophilin A: Peptidyl-prolylisomerase A, PPIase.
Taldo1 protein: Transaldolase 1.
T-complex protein 1 subunit beta: CCT2.
Heat shock protein 90 kDa beta, member 1: HSPB1, Chaperone protein GP96, Tumor rejection antigen (gp96) 1.
T-complex 1: Chaperonin-containing T-complex polypeptide alpha subunit.
HSP70-1: HSP70, Heat Shock Protein 70 kDa 1, HSP72.
TCP1-theta: T-complex protein 1 subunit theta, CCT8.
Glucose-regulated protein 78 kDa: Heat Shock Protein 70 kDa protein 5.
Pdia4 protein: Protein disulfide isomerase-associated 4.
7
Functional annotation associated with the cod cDNA’s best BLAST hit or an annotated putative orthologue from Homo sapiens or Mus musculus (* or reference
in []). Gene ontology (GO) categories: Biological Process (BP), Molecular Function (MF) and Cellular Component (CC).
8
Other functional annotations associated with this cod EST are: Endoplasmic reticulum (CC) and Nucleotide binding (MF).
9
Other functional annotations associated with this cod EST are: Oxireductase activity (MF), Zinc ion binding (MF) and Copper ion binding (MF).
10
Other functional annotations associated with this cod EST are: Cellular protein (CC), Cytoplasm (CC), Metabolism (BP), ATP binding (MF), Nucleotide binding
(MF).
11
Other functional annotations associated with this cod EST are: Catalytic activity (MF), Fructose biphosphate aldolase activity (MF) and Metabolic process (BP).
Functional (Gene Ontology) annotation of assembled
ESTs from SSH libraries
The ESTs from the 3 forward SSH libraries represented
958 (head kidney: 212 contigs and 746 singletons), 812
(liver: 200 contigs and 612 singletons) and 642 (skeletal
muscle: 159 contigs and 483 singletons) putative transcripts, and ESTs from the liver reverse library represented 846 putative transcripts (178 contigs and 668
singletons) (Table 1). Using AutoFACT [26] and GOblet
[28], these assembled sequences were assigned to 143,
157, 165 and 125 gene ontology (GO) terms (belonging
to all 3 GO categories), respectively. A comprehensive
list of assembled ESTs, and detailed BLAST statistics
and functional annotations (e.g. associated GO terms)
can be found at the Cod Genomics Project website
http://www.codgene.ca and in Additional files 1, 2 and 3
(Tables S1-S3). Protein biosynthesis and transport were
among the GO terms with the highest proportion of
associated assembled ESTs in all 4 libraries (Figs. 2 and
3). We identified many sequences that were assigned to
GO terms relevant to stress or immune responses (e.g.
metabolism, protein folding, immune response, proteolysis, glycolysis, response to stress and signal transduction). The proportions of assembled ESTs associated
with GO annotations belonging to specific biological
process categories relative to the total number of
assembled ESTs with biological process GO annotations
within each SSH library are shown in Figs. 2 and 3.
Expression of candidate heat-shock responsive transcripts
We used QPCR to validate and further study the effects
of heat-shock on 16 SSH-identified Atlantic cod transcripts. While SSH libraries were constructed using
pooled mRNA samples, QPCR was conducted using
individual RNA templates to assess biological variability.
Eight cod cDNA sequences selected for QPCR had significant BLASTx hits against proteins with chaperone
functions (TCTP, HSP47, CCT 5, HSP90a, GRP94,
CCT 1, HSP70-1 and GRP78), 3 had significant BLASTx
hits against proteins involved in carbohydrate metabolism (enolase, PFK and aldolase), 3 were identified as
immune-relevant transcripts (IgM, TLR22 and IL-8), 1
was most similar at the predicted amino acid level to an
acute phase protein (bikunin) and 1 cDNA was identified as the transcript for a gene involved in regulation of
the cell cycle (NUPR1). The numbers of contributing
sequences and libraries where these transcripts were
found are detailed in Tables 2 and 3.
Of the 11 cDNAs arising from the forward libraries
and analyzed with QPCR (Table 2), all of those encoding proteins with putative chaperone function with the
exception of TCTP were shown to be significantly upregulated at the mRNA level by heat-shock in at least
one tissue and at least one time point post heat-shock
(Fig. 4A-E and Fig. 5A-C). TCTP mRNA levels were
responsive to handling alone (Fig. 5C) in the head kidney, with an average fold down-regulation of 3.0 and 1.6
at CS and at 3ACS, respectively. We also found that
HSP70-1 (Fig. 4C) and GRP78 (Fig. 4D) transcripts
responded significantly to handling alone in the liver.
HSP70-1 mRNA was significantly up-regulated by an
average of 47.1-fold at 12ACS in the CT group when
compared to the values for BHS fish, while GRP78
mRNA was significantly down-regulated by an average
of ~3.0-fold at all time points in the same group. These
two transcripts showed no response to handling alone
in the other tissues studied. Of the cDNAs with significant BLASTx hits against proteins involved in carbohydrate metabolism, enolase mRNA expression was
significantly responsive to handling alone in only a single tissue (i.e. average down-regulation of 1.9-fold in the
head kidney of the CT group at the CS time point, Fig.
5D), but was not significantly responsive to heat-shock.
PFK and aldolase presented no significant differences
between groups or time points (data not shown).
The genes with the highest significant mRNA up-regulation in response to heat-shock were HSP90a [Fig. 4A
- average fold-changes of 168.7 (liver - CS) and 61.3
(skeletal muscle - 3ACS)]; HSP70-1 [Fig. 4C - average
fold-changes of 100.2 (liver - 12ACS) and 17.8 (head
kidney - 12ACS)]; GRP94 [Fig. 4B - average fold-
Hori et al. BMC Genomics 2010, 11:72
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Page 7 of 22
Table 3 Selected cDNAs1 from the liver reverse (enriched for genes down-regulated by heat-shock) SSH library
representing immune/stress related genes
BLASTx identification4 of selected transcripts
Contig or Sequence
ID (Accession
Number)
QPCR
154.C1 (ES783183)
Not
done
Not
done
Not
done
Not
done
Not
done
111.C1 (FL634330)
121.C1 (ES782223)
129.C1 (ES782343)
143.C1 (ES782977)
#
Gene Name [Species of best
ESTs BLASTx Hit]
%ID (aa
length of
align.)
Evalue
Gene Ontology or function of putative
orthologue7
3
Map4k45,6 [Mus musculus]
92% (24/26)
4e-05
Protein amino acid phosphorylation (BP)*
2
Glutathione peroxidase [Xenopus
tropicalis]
Tetraspanin-6 [Salmo salar]
55% (30/54)
1e-11
Response to oxidative stress (BP)*
96% (28/29)
9e-09
Nuclear factor interleukin-3
regulated [Danio rerio]
Chaperonin containing TCP1subunit 34,6 [Salmo salar]
73% (31/42)
6e-08
G-protein coupled receptor protein signaling
pathway (BP)*
Immune response (BP)*
94% (127/
134)
7e-67
Protein folding (BP)*
2
2
2
128.C1 (ES782638)
Fig.6B2
2
Nuclear protein 15,6 [Salmo
salar]
50% (35/70)
4e-11
Cell growth (BP)*, Acute inflammatory response
(BP)*
168.C1 (ES782218)
Fig.6C2
2
Alpha-1-microglobulin/bikunin
precursor [Oncorhynchus mykiss]
55% (21/38)
1e-04
Serine-type endopeptidase inhibitor activity
(MF), Endopepitidase (MF), Transporter activity
(MF)
69.C1 (ES783083)
Fig.6D2
2
Immunoglobulin heavy chain,
secretory form5 [Gadus morhua]
97% (137/
140)
1e-70
Immune response (BP)*
91.C1 (ES782456)
Not
done
2
Lectin [Oncorhynchus mykiss]
48% (25/52)
2e-13
Sugar binding (MF)*
1e08 (ES782565)
Not
done
1
Hepcidin precursor5 [Gadus
morhua]
98% (78/79)
1e-26
Innate immune response (BP)*
2i08 (ES782607)
Fig.6A2
1
TLR224,6 [Takifugu rubripes]
88% (39/44)
3e-29
Immune response (BP), Inflammatory response
(BP)8
7m20 (FL634530)
Not
sig.3
1
Interleukin-8 [Melanogrammus
aeglefinus]
82% (63/76)
4e-36
Immune response (BP), Cytokine activity (MF)9
7p13 (FL634573)
Not
done
1
TNFAIP3 interacting protein 1
[Danio rerio]
86% (74/86)
1e-34
Negative regulation of viral genome duplication
(BP)
1
ESTs from the liver reverse library were assembled separately. These cDNAs were not represented by any contig of 2 or more ESTs in any of the 3 forward
libraries. For genes represented by multiple contigs in this library, the contig with the highest number of contributing sequences is shown. Possible reasons for
the presence of multiple contigs with the same gene name within a given library are provided in the discussion. All SSH contigs and singletons for the liver
reverse library, with all BLASTx statistics and GenBank accession numbers, are presented in the Additional file 2, Table S2. The GenBank accession number of one
representative EST from each contig is given in this table. The name of this SSH library in the CGP EST database is gmnllrta. Sequence names reflect the plate
number and the well number (e.g. 7p13 - plate 7, well p13).
2
These genes showed at least one statistically significant difference between 2 groups and/or time points (e.g. CT at BHS versus CT at CS, or CT at CS versus HS
at CS) in at least 1 tissue.
3
This gene was analyzed with QPCR, but the data is not presented because there were no statistically significant differences between groups and/or time-points.
4
Refer to footnote 4 in Table 2.
5
These cDNAs were each represented by one singleton in the liver forward library.
6
Synonyms for protein names were retrieved from the Swiss-Prot Protein Knowledgebase Database http://ca.expasy.org/sprot/: Map4k4: Mitogen-activated protein
kinase kinase kinase kinase 4. Chaperonin containing TCP1- subunit 3: CCT3. Nuclear protein 1: Protein p8, Candidate of metastasis 1. TLR22: Toll-like receptor 22.
7
Refer to footnote 7 in Table 2.
8
Other functional annotations associated with this cod EST are: Innate immune response (BP), Receptor activity (MF), Integral part to membrane (CC).
9
Other functional annotations associated with this cod EST are: Extracellular region (CC), Extracellular space (CC).
changes of 25.1 (head kidney - 3ACS) and 17.5 (liver 3ACS)]; and HSP47 [Fig. 4E - average fold-changes of
20.4 (liver - 3ACS) and 16.9 (skeletal muscle - 12ACS)].
Of the 7 transcripts significantly up-regulated by heatshock that were identified in the forward libraries (i.e.
HSP90a, GRP94, HSP70-1, GRP78, HSP47, CCT1 and
CCT5 - Fig. 4A-E and Fig. 5A-B), only two transcripts
(CCT-1 and CCT-5, Fig. 5A and 5B) were not significantly up-regulated (i.e. in at least one heat-shock time
point relative to the BHS time point) in all 3 tissues
tested. Within the tissues, HSP90a mRNA presented
the greatest fold up-regulation in liver (average 168.7fold up-regulation at CS) and skeletal muscle (average
61.3-fold up-regulation at 3ACS), while GRP94 mRNA
presented the highest fold up-regulation in the head kidney (average 25.1-fold up-regulation at 3ACS). The transcripts with significant up-regulation in response to
heat-shock also showed differences in timing of expression in the three tissues studied. As previously stated,
HSP90a mRNA expression in the liver peaked relative
to the BHS time point at CS (Fig. 4A). However, many
stress-relevant transcripts had significant maximum fold
Hori et al. BMC Genomics 2010, 11:72
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Page 8 of 22
Figure 2 Summary of Gene Ontology (GO) functional annotation of assembled ESTs identified in the forward SSH libraries. GO
annotations were obtained using AutoFACT [26] and GOblet [28] analysis of clusters. Numbers represent the percentage of ESTs with a particular
GO annotation relative to the total number of sequences with GO annotation (all belonging to the Biological Process GO category). Light grey:
liver. Dark grey: skeletal muscle. Black: head kidney.
up-regulation at 3ACS relative the BHS time point.
These were: HSP90a in the skeletal muscle (Fig. 4A,
61.3 fold-change); GRP94 in all tissues [Fig. 4B - average
fold-changes of 17.5 (liver), 25.1 (head kidney), and 11.9
(skeletal muscle)]; GRP78 in all tissues [Fig. 4D - average fold-changes of 5.2 (liver), 4.4 (head kidney), and 3.8
(skeletal muscle)]; HSP47 in the liver (Fig. 4E - average
fold-change of 20.4); and CCT1 (Fig. 5A - average foldchange of 9.3) and CCT5 (Fig. 5B - average fold-change
of 10.8) both in the liver. Transcripts for HSP90a in the
head kidney (Fig. 4A - average fold-change of 6.3),
HSP47 in the skeletal muscle and head kidney [Fig. 4E average fold-changes of 16.9 (skeletal muscle) and 1.6
(head kidney)], and HSP70-1 in all tissues [Fig. 4C average fold-change of 100.2 (liver), 17.8 (head kidney)
and 10.1 (skeletal muscle)] had maximum significant
mRNA fold up-regulation at 12ACS relative to the BHS
time point. The timing and magnitude of the changes in
expression of transcripts for HSP90a (Fig. 4A), HSP47
(Fig. 4E), and GRP78 (Fig. 4D) also varied between different tissues. For example, HSP90a transcript was
maximally up-regulated at CS in the liver but at 3ACS
in the skeletal muscle relative to the BHS time point.
From the 5 cDNAs selected for QPCR studies from
the reverse liver library (Table 3), NUPR1 and TLR22
were the only cod transcripts that responded significantly to heat-shock. The transcript levels for TLR22
were down-regulated by an average of 2.2- and 2.6-fold
in the HS group in the head kidney at CS and 3ACS,
respectively, when compared to the levels at the BHS
time point (Fig. 6A). Interestingly, NUPR1 transcripts
were significantly up-regulated by heat-shock at CS in
Hori et al. BMC Genomics 2010, 11:72
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Page 9 of 22
Figure 3 Summary of Gene Ontology (GO) functional annotation of assembled ESTs identified in the liver reverse SSH library. GO
annotations were obtained using AutoFACT [26] and GOblet [28] analysis of clusters. Numbers represent the percentage of ESTs with a particular
GO annotation relative to the total number of sequences with GO annotation (all belonging to the Biological Process GO category).
both liver (average 2.7-fold change) and head kidney
(average 3.0-fold change), and at 3ACS the HS group
still showed significantly higher levels of NUPR1 transcript than in the CT group in both tissues (Fig. 6B).
Bikunin and IgM mRNAs displayed similar responses in
CT and HS groups (Fig. 6C and 6D) and IL-8 mRNA
showed no significant changes (data not shown). Bikunin transcripts in the liver and head kidney were
reduced by both stressors. They were significantly lower
in the CT group in the liver at 3ACS when compared to
levels at the BHS time point, and in the head kidney
from HS fish when compared to those from the CT
group at the CS time-point (Fig. 6C).
Discussion
Thermal stress can pose a significant challenge to cod at
aquaculture cage sites as summer temperatures may
approach the upper critical thermal limit for this species
and/or change rapidly during the day (due to thermocline inversions, i.e. when bays “turn over”)[1]. Heatstress is known to cause many physiological changes in
cod including the release of stress hormones (e.g. cortisol), changes in the expression of immune relevant transcripts, alterations in oxygen consumption and heart
rate, and increased mortality [1,7,29]. Therefore, a better
understanding of the mechanisms mediating the
response to thermal stress should provide insights into
how to mitigate and/or avoid the deleterious effect of
such environmental challenges. We observed a significant elevation in average plasma cortisol in both control
transferred (CT) and heat-shocked (HS) cod at CS;
plasma cortisol in HS fish was 2.3-fold higher than in
CT fish at the CS time point, and 5.5-fold higher in HS
when compared to CT at 3ACS. Further, cortisol levels
in HS fish remained elevated during the recovery period.
In contrast, average plasma cortisol levels in the CT
group had returned to basal levels (i.e. not significantly
different from the CT group at BHS) by 3ACS. These
results confirm that 3 hours of exposure to 18°C was a
severe stressor for these juvenile cod.
Some of the terms more frequently represented
amongst the GO annotated ESTs derived from the
Hori et al. BMC Genomics 2010, 11:72
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Page 10 of 22
Figure 4 QPCR of selected genes with stress relevant functional annotations identified in the forward SSH libraries (designed to be
enriched for transcripts up-regulated by heat-shock): Part I. The RQs (relative quantities), normalized to 18S ribosomal RNA expression and
calibrated to the individual with the lowest expression of each gene of interest (see methods section), are presented as averages ± SEM. The
levels of gene expression of heat-shock protein 90 alpha (HSP90a: A), heat shock protein 90kDa beta, member 1 (GRP94: B), HSP70-1 protein
(HSP70-1: C), 78 kDa glucose regulated protein (GRP78: D), heat-shock protein 47 (HSP47: E) are shown for the control transferred (CT) and heatshocked (HS) groups before heat-shock (BHS), at the cessation of heat-shock (CS), 3h after the cessation of heat-shock (3ACS), and 12h after the
cessation of heat-shock (12ACS). Different letters indicate significant differences between sampling points within the same treatment (p<0.05). *
indicates significant differences between CT and HS groups at a given sampling point (p<0.05). Numbers in the boxes represent overall foldchanges. For each treatment, overall fold upregulation was calculated relative to the appropriate before heat-shock (BHS) value as (average RQ of
time point)/(average RQ of appropriate group at the BHS time point), and overall fold down-regulation was calculated when necessary (i.e. if
overall fold upregulation was < 1) as the inverse of overall fold up-regulation. Each panel shows the expression for a given gene of interest in all
3 tissues studied. L: liver. HK: head kidney. M: skeletal muscle.
Hori et al. BMC Genomics 2010, 11:72
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Figure 5 QPCR of selected genes with stress relevant functional annotations identified in the forward SSH libraries (designed to be
enriched for transcripts up-regulated by heat-shock): Part II. The RQs (relative quantities), normalized to 18S ribosomal RNA expression and
calibrated to the individual with the lowest expression of each gene of interest (see methods section), are presented as averages ± SEM. The
levels of gene expression of T-complex 1 (CCT1: A), chaperonin containing TCP1, subunit 5 (CCT5 : B), translationally-controlled tumor protein
(TCTP: C) and enolase 3 (Enolase: D) are shown for the control transferred (CT) and heat-shocked (HS) groups before heat-shock (BHS), at the
cessation of heat-shock (CS), 3h after the cessation of heat-shock (3ACS), and 12h after the cessation of heat-shock (12ACS). Different letters
indicate significant differences between sampling points within the same treatment (p<0.05). * indicates significant differences between CT and
HS groups at a given sampling point (p<0.05). Numbers in the boxes represent overall fold-changes. For each treatment, overall fold upregulation was calculated relative to the appropriate before heat-shock (BHS) value as (average RQ of time point)/(average RQ of appropriate
group at the BHS time point), and overall fold down-regulation was calculated when necessary (i.e. if overall fold up-regulation was < 1) as the
inverse of overall fold up-regulation. Each panel shows the expression for a given gene of interest in all 3 tissues studied. L: liver. HK: head
kidney. M: skeletal muscle.
Hori et al. BMC Genomics 2010, 11:72
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forward libraries were protein folding, signal transduction, immune response, and response to stress (Fig. 2).
The genes associated with these terms may be important
in the strategies involved with coping with stress, and
variability in their sequences (e.g. exonic, intronic, or
regulatory region SNPs) and/or timing and magnitude
of mRNA expression (i.e. expression profiles) could
reveal markers for increased resistance to thermal and
other stressors (research ongoing). This is the first study
to use high-throughput genomic techniques to investigate the response to heat-shock in cod, and to provide
expression profiles of a wide range of transcripts encoding putative chaperone proteins in tissues of fish that
have distinct physiological roles. Many of the transcripts
validated at the individual level using QPCR in this
study had BLASTx hits that were associated with the
GO terms mentioned above and exhibited differences in
expression profiles between tissues. These findings indicate that the cellular response to heat-shock in cod is
complex, involves several genes, and may be controlled
by different cues and/or transcription regulation
mechanisms in different tissues, as has been observed in
human cells [30]. In all three tissues studied we showed
an increase in transcript levels of HSP70-1 (a putative
orthologue of the human HSPA1A gene as per the
nomenclature proposed by Kampinga et al. [31]). Previous reports on cod and haddock (Melanogrammus
aeglefinus) [29,32,33] did not detect an increase in
HSC71/HSP70-1 protein expression in the gills and liver
of cod or haddock in response to thermal stress. It
could be that an elevation in HSC71/HSP70-1 protein
was not detected in cod or haddock in the aforementioned studies due to the fact that the antibodies [i.e.
polyclonal anti-rainbow trout (Onchorynchus mykiss)
HSP70 (Agrisera, Sweden) and monoclonal anti-mouse
(Mus musculus) HSP70/HSC71 (Sigma Co., St. Louis)]
used were not generated against cod or haddock
HSP70-1 and may not have recognized the HSP70-1
protein in these species.
Several of the cDNAs identified in this study were
represented by more than one contig in a single library.
There are several possible reasons for the presence of
more than one contig with the same annotation in a
given library. Multiple, same-named contigs may represent: a) different paralogues; b) different alleles at a
given locus; or c) non-contiguous segments of a given
cDNA (the last-mentioned potential cause of multiple
same-named contigs may be the most likely since SSH
library construction includes a restriction digest with
RsaI, potentially resulting in more than one contig from
a given full mRNA). For example, we report two contigs
that were annotated as HSP90a in the head kidney forward library (Additional file 1, Table S1A). Further analysis of these contigs using nucleotide alignments
Page 12 of 22
against a full-length sequence obtained from Chinook
salmon (Onchorynchus tshawytscha) [GenBank: U89945]
[34] suggests that these are likely to represent non-contiguous regions of the same cod cDNA (data not
shown). However, the HSP family provides important
examples of differential expression (i.e. constitutive and
induced expression profiles) between distinct paralogues
(e.g. HSC71/HSP70-1) [35], and further studies addressing this question will be needed to determine the roles
of different Atlantic cod chaperone paralogues in thermal tolerance.
Molecular chaperones play important roles in cell
physiology in both unstressed and stressed situations.
These proteins assist with the folding of nascent peptides and the de-novo folding of denatured proteins, the
transport of unfolded proteins across membranes, quality control and conformational changes that affect function [36]. Sudden or chronic increases in temperature
are known to induce both mRNA and protein expression of several chaperones, such as those belonging to
the HSP family [35].
Among the clients that these proteins bind to are
physiologically relevant proteins such as the glucocorticoid [4] and aryl hydrocarbon [37] receptors (clients of
HSP90), heat-shock factor 1 [38] (client of HSP70),
Immunoglobulin (Ig) heavy chain [39] (client of GRP78)
and the Toll-like receptors [40] (clients of GRP94).
Transcripts encoding putative orthologues of all of
these chaperones were identified in our libraries, and all
of them were confirmed to be heat-shock responsive
mRNAs. HSP90a mRNA expression was up-regulated
in the liver more than 150-fold at CS relative to the
before heat-shock (BHS) time point (Fig 4A). The upregulation of HSP90s in response to heat-shock has
been demonstrated at both mRNA and protein levels in
different species of fish [41,42]. For example, Cara et al.
[41] detected a ~6000% increase in HSP90 proteins and
a ~600% increase in HSP70 protein in fasted +10°C
heat-shocked rainbow trout larvae. In our study, there
was a higher maximum fold-induction of HSP90a transcripts compared to HSP70-1 transcripts in both liver
and muscle following heat-shock. We observed a significant increase in HSP70-1 mRNA expression in the liver
of CT fish at 12ACS, which may have been a result of
fasting. Cara et al. [41] observed increased HSP70 protein expression in fasted non heat-shocked rainbow
trout larvae. Among the many clients of these chaperones are heat-shock factor 1 (client of HSP70) and the
glucocorticoid receptor (client of HSP90). Therefore,
the increase in the levels of mRNAs encoding these
chaperones may indicate that their products are essential in maintaining signal transduction during stress and
are likely to be proteins involved in heat-stress
tolerance.
Hori et al. BMC Genomics 2010, 11:72
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Page 13 of 22
Figure 6 QPCR of selected transcripts with immune relevant functional annotations identified in the reverse liver SSH library
(designed to be enriched for transcipts down-regulated by heat-shock). The RQs (relative quantities), normalized to 18S ribosomal RNA
expression and calibrated to the individual with the lowest expression of each gene of interest (see methods section), are presented as averages
± SEM. The levels of gene expression of TLR22 (Toll-like receptor 22: A), nuclear protein 1 (NUPR1: B), alpha 1-microglobulin/bikunin (Bikunin: C)
and immunoglobulin heavy chain, secretory form (IgM: D) are shown for the control transferred (CT) and heat-shocked (HS) groups before heatshock (BHS), at the cessation of heat-shock (CS), 3h after the cessation of heat-shock (3ACS), and 12h after the cessation of heat-shock (12ACS).
Different letters indicate significant differences between sampling points within the same treatment (p< 0.05). * indicates significant differences
between CT and HS groups within a given sampling point (p< 0.05). Numbers in the boxes represent overall fold-changes. For each treatment,
overall fold up-regulation was calculated relative to the appropriate before heat-shock (BHS) values as (average RQ for a time point)/(average RQ
of the appropriate group at the BHS time point), and overall fold down-regulation was calculated when necessary (i.e. if overall fold
upregulation was < 1) as the inverse of overall fold up-regulation. Each panel shows the expression for a given gene of interest in both tissues
studied. L: liver. HK: head kidney.
Hori et al. BMC Genomics 2010, 11:72
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Stress also has an impact on the fish’s immune system,
and temperature stress has been shown to decrease
serum IgM content and increase the susceptibility of sea
bass (Dicentrachus labrax) to nodavirus [27]. Nodaviruses belong to the family Nodaviridae, and are the
causative agents of viral nervous necrosis (VNN). These
viral pathogens also infect cod, and can cause high levels
of morbidity and mortality [43]. GRP78 is essential for
the appropriate folding and secretion of immunoglobulin light and heavy chains from the endoplasmic reticulum (ER) [39,44]. IgM heavy chain transcripts in liver
were significantly up-regulated by handling stress but
not by heat-shock in our study (Fig. 6D), and thus,
GRP78 may be important for the proper folding of this
immune relevant protein following exposure to only
some types of stressor. GRP78 mRNA, which encodes
the ER-resident member of the HSP70 family, was significantly up-regulated by heat-shock in all tissues studied. GRP94 (synonym: Gp96), the ER-resident member
of the HSP90 family, is the major chaperone for the
Toll-like receptors (TLRs) [40]. Yang et al. [40] demonstrated that Gp96 null mice were also macrophage-TLR
null and highly susceptible to Listeria infections. In our
study, GRP94 transcripts were significantly up-regulated
in all tissues after heat-shock, with the head kidney presenting the highest up-regulation (25.1-fold) at 3ACS
(Fig. 4B) relative to GRP94 mRNA levels before heatshock. TLRs may play an important role in the defence
against viral infections and have been shown to be upregulated by the viral mimic pIC in fugu (Takifugu
rubripes) [45].
Therefore, divergent forms of GRP gene sequences or
different expression profiles of the mRNAs encoding
these proteins between families and/or populations
could play an important role in temperature-related
immunosuppression. Given that TLR22 mRNA was significantly down-regulated in the head kidney of heatshocked cod (Fig. 6A) when compared to its levels
before heat-shock, and that this receptor in fish recognizes double-stranded RNA and induces genes of the
interferon pathway [45], it is possible that its down-regulation following thermal stress results in reduced protein levels and is linked to decreased resistance to
viruses in stressed fish [27] (a hypothesis we are currently testing). However, stress does not always correlate
negatively with disease resistance. Weber et al. [46] have
shown that a single 3 hour crowding event does not
affect rainbow trout survival following a challenge with
Yersinia ruckeri (the causative agent of enteric redmouth
disease). On the other hand, the work of Fevolden et al.
[47] indicates that the impact of stress on immune competence may be pathogen-specific. These authors have
shown that rainbow trout strains selected for high cortisol response had lower survival rate when challenged
Page 14 of 22
with A. salmonicida (the causative agent of furunculosis), but higher survival rates when challenged with
Vibrio anguillarum (the causative agent of vibriosis),
when compared to strains selected for low cortisol
response. Fast et al. [48] showed that Atlantic salmon
(Salmo salar) subjected to long-term handling stress (i.e.
once a day for 4 weeks) had reduced up-regulation of
LPS (lipopolysaccharide)-induced macrophage IL-1b
mRNA expression compared to control fish. In this
study, chronic handling stress appeared to cause
reduced immune competence as evidenced by the
decreased survival of isolated macrophages from stressed
fish (compared with macrophages from control, nonstressed fish) following incubation with A. salmonicida
[48]. Clearly, the relationships between stress and
immune responses in fish are complex and require
further investigation.
Other cod transcripts encoding molecular chaperonelike proteins were identified in this work including several putative members of the T-complex-containing chaperones (CCT), prolyl-peptidyl-isomerase (PPIase,
synonym: cyclophilin A), protein disulfide isomerase
(PDI) (the two latter being classified as foldases,
enzymes that catalyze reactions which accelerate protein
folding and are an important part of the ER chaperone
machinery) [44], and an ER-resident chaperone (HSP47)
that is essential for the normal synthesis of procollagen
and its stabilization during stress [49]. Collagen is an
essential and ubiquitous component of the extracellular
matrix and a potential target for denaturation and
aggregation. We found that maximum up-regulation of
HSP47 mRNA by heat-shock was at 3ACS in liver
(20.4-fold), and at 12ACS in both skeletal muscle (16.9fold) and head kidney (1.6-fold) when compared to its
levels before heat-shock (Fig. 4E).
In mammals apoptosis induced by the denaturation
and aggregation of proteins is one of the causes of death
in heat-shocked cells [50]. Over-expression of the
HSP70-1 protein (synonym: HSPA1A) plays an important role in protecting cells from apoptosis, presumably
by preventing protein aggregation and inactivating the
c-jun N-terminal kinase (JNK) pro-apoptotic pathway
[50]. In our study, the mRNA encoding the putative cod
orthologue of this particular chaperone was one of the
most highly induced transcripts in the liver, with a
100.2-fold up-regulation at 12ACS (Fig. 4C) relative to
the BHS time point. Although HSP70s have been shown
to be anti-apoptotic in sea bream (Sparus auratus) primary macrophage cultures [51], previous studies have
reported that HSP70-1 protein is not responsive to heatstress in cod [29,32]. However as previously mentioned,
these studies relied on anti-mouse HSC71/HSP70 or
anti-rainbow trout HSP70 protein commercial antibodies, which may not efficiently cross-react with the
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orthologous HSP70 protein in cod. We have demonstrated that, at least at the transcriptional level, there
was a significant up-regulation of a HSP70-1-like transcript in response to heat-shock. Moreover, the maximum-fold up-regulation of HSP70-1 mRNA at 12ACS
in all tissues (Fig. 4C), is consistent with its reported
role in acquired thermal tolerance during the recovery
of mildly heat-shocked mammalian cells [52]. GRP78 is
also known to protect cells against apoptosis, since it
interacts with the key players in the ER stress signalling
system (e.g. ATF6 and PERK) in non-stressed cells, preventing pro-apoptotic signalling [53]. Misfolded proteins
in the ER interact with GRP78, which causes the activation of the pro-apoptotic ER stress signalling cascades
[53]. Thus, up-regulation of the GRP78 transcripts may
lead to elevated levels of this protein that would still be
able to silence the pro-apoptotic ER stress signalling
pathway. The up-regulation of NUPR1 (synonym: p8)
mRNA may also lead to increased expression of this
protein, and be an indication of increased levels of antiapoptotic factors in both liver and head kidney. This
protein has been correlated with reduced apoptosis in
pancreatic cancer cells [54]. Protein aggregation is
known to trigger apoptosis [3], and therefore, cell viability under thermal stress may depend on the ability to
elicit a significant anti-apoptotic response through the
expression of transcripts such as those encoding HSP701 and NUPR1. However, up-regulation of NUPR1 has
also been linked to the acute phase response to pancreatitis in mammals [55]. We found that bikunin transcript,
which also encodes an acute phase protein, was downregulated by handling stress in the liver and by heatshock in the head kidney. Thus, heat-shock may also
affect the inflammatory response. In the spleen of Atlantic cod, bikunin transcript levels were not affected by
saline control injection (which includes general handling
stress), but were significantly suppressed by viral mimic
(pIC) injection at 2 and 6 h post-injection, and significantly induced by the viral mimic at 24 h post-injection
(these data relative to saline injected controls at these
time points) [20]. Finally, it is worth noting that while
NUPR1 was identified as a contig of 2 sequences in the
reverse liver library (enriched for genes down-regulated
by heat-shock), QPCR showed that this transcript was
up-regulated by heat-shock in the liver and head kidney
of cod. This was not surprising however, as in our
hands, the SSH technique sometimes appears to be less
effective at enriching for genes that are down-regulated
by a stressor (i.e. in reverse subtractions) than at enriching for genes that are up-regulated by a stressor (i.e. in
forward subtractions). As evidence of this, three out of
four transcripts identified in a reverse spleen SSH library
designed to be enriched for cod transcripts that were
down-regulated by exposure to a stressor (viral mimic)
Page 15 of 22
could not be confirmed by QPCR as significantly downregulated by the stressor (i.e. no statistically significant
differences were detected) [20]. Therefore, the presence
of NUPR1 as a contig of 2 sequences in the reverse liver
library in the current study could be an artifact of the
SSH technique.
The timing of up-regulation of some transcripts
encoding putative chaperone proteins suggests that
HSP90a may be a first line of defence against heatstress, while HSP70-1 may be more important during
recovery. Moreover, given that the mRNA expression of
most of the studied chaperone genes peaked either at
CS or 3ACS, we hypothesize that early time points may
be crucial in the process of recovery and repair of
damaged proteins. Two other transcripts, CCT 1 and
CCT 5, putative members of the TCP1 complex, were
significantly up-regulated by thermal stress in the liver
at 3ACS (Fig. 5A and 5B). Of the 8 known mammalian
members of this complex, we identified cDNAs for 6
putative orthologues in cod: CCT 1, 2, 3, 5, 6 and 8
[Tables 2 and 3, and Additional files 1 (Table S1) and 2
(Table S2)]. These chaperonins are known to form heterologous polymers that assist in the folding of actins
and tubulins [56], important components of the cytoskeleton. Structural proteins seem to be among the most
heat-labile proteins, and their misfolding and/or denaturation contributes greatly to protein aggregation [57].
Although we only saw a small, albeit significant, downregulation (3.0 fold; CS-Fig. 5C) of TCTP mRNA expression in the head kidney in the CT group relative to the
BHS time point, this transcript may still represent an
important component of the molecular mechanism
involved in thermal resistance. The product of the TCTP
gene, a ubiquitously expressed protein in most mammalian cells, is known to bind to calcium and tubulin and to
be responsive to stressors such as starvation and heatstress [58]. In addition, Bonnet et al. [59] have shown
that in yeast cells exposed to heat-shock there is a downregulation of TCTP mRNA, and in rat (Rattus norvegicus) C6.9 glioma cells TCTP mRNA is up-regulated in
response to induced programmed cell-death [60]. Downregulation of TCTP in response to heat-shock in yeast
may be one of the mechanisms that prevent heat-induced
apoptosis, and it is possible that this down-regulation,
which was detected in our experiments (e.g. 1.5 fold
down-regulation in HS head kidney at CS), was not significant due to high variance between biological replicates (Fig. 5C). Down-regulation of TCTP may also play
a role in preventing apoptosis triggered by other stressors
(i.e. handling) since we found it to be significantly downregulated by 3.0 fold at CS in the head kidney.
We saw little change in the mRNA expression of genes
with carbohydrate metabolism related functional annotations (enolase, aldolase, PFK) (Fig. 5D, Table 2) with heat-
Hori et al. BMC Genomics 2010, 11:72
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shock at 18°C. However, this finding does not preclude the
possibility that carbohydrate metabolism is increased when
Atlantic cod are acutely exposed to elevated temperatures.
This is because glycolysis had a relatively high prevalence
(3.13%) amongst the biological process GO terms in the
muscle forward library (Fig. 2). PFK and glycogen phosphorylase (identified in the head kidney forward library)
are the rate limiting enzymes of glycolysis, and allosteric
regulation of these enzymes is likely to be the main
mechanism through which carbohydrate metabolism is reorganized during acute stress. Finally, the results of PerezCasanova et al. [29] suggest that carbohydrate metabolism
(based on measurements of plasma glucose) is not upregulated significantly in cod until temperature reaches at
least 20°C during acute thermal stress.
Interestingly, enolase transcript was significantly
down-regulated (by 1.9-fold) in the head kidney of control transferred (CT) fish at the CS time point relative
to the CT BHS time point, and GRP78 mRNA was significantly down-regulated in the liver in the CT group
at all time points relative to the BHS time point. These
results indicate that, even though there is a conserved
general stress response, some responses at the transcriptome level are stressor specific (i.e. responsive to either
heat-shock or handling stress).
Conclusions
In conclusion, the present work adds significantly to the
available data on the stress physiology of cod. We have
contributed a total of 5980 ESTs (derived from all 4
SSH libraries) from three important stress-responsive
tissues. Among these are several cDNAs encoding putative chaperones, which we have demonstrated to be
responsive to heat-shock. Apoptosis and the aggregation
of denatured proteins are likely to play a major role in
heat-induced cell death in fish cells. SSH-identified transcripts (e.g. HSP90a, GRP94, GRP78, HSP70-1, HSP47)
that were not only highly responsive to heat-shock, but
also dysregulated in all three tissues studied, encode
proteins that are known to prevent both programmed
cell-death and aggregation. The functional genomics
research reported herein may lead to the development
of molecular markers (e.g. exonic, intronic, or regulatory
SNPs associated with heat-stress responsive genes, and
mRNA/protein expression profiles that correlate with
thermal tolerance) that could be used for the selection
of heat-resistant Atlantic cod broodstock for the aquaculture industry.
Methods
Heat-shock and sampling
One hundred and fifty juvenile cod (~35 g) from a single CGP family (06NL04) were divided equally into 3 ×
250 L saltwater flow-through tanks (10°C, dissolved
Page 16 of 22
oxygen > 90% of air saturation). The tanks were then
randomly assigned as control (C), control + handling
stress (CT) and heat-shocked (HS), and fish were
allowed to acclimate to their new environment for one
week. In addition, another two tanks with the same
water conditions were set up, and these tanks were adjacent to the tanks where the fish were stocked. During
the one week acclimation period, the cod were fed 1.5%
of their average body mass once daily. After the acclimation period, one of the two tanks that was set aside had
the water flow interrupted and was heated to 18°C using
a bayonet style immersion heater (8.4 A, 1000 W - Process Technologies, Tampa, FL), while the other tank was
left as a 10°C flow-through tank. Fish from the CT
group were quickly netted and transferred to the 10°C
tank while fish belonging to the HS group were quickly
netted and transferred to the 18°C tank. During this period, oxygen levels were constantly monitored in both
tanks using a dissolved oxygen (DO) meter and probe
(Oxyguard, HandiPolaris, Denmark), and pure oxygen
was gently bubbled into the 18°C tank to maintain the
DO levels above 90% of air saturation. The heat-shock
lasted for 3 hours; after this period the immersion heater was turned off and water in the HS tank was quickly
(within 10 minutes) brought back to 10°C by re-establishing the flow of 10°C water. Eight fish from each tank
were sampled before the heat-shock (BHS; i.e. while still
in their acclimation tank), at the cessation of the 3 hour
heat-shock (i.e. when cold water flow was being reestablished) (CS), and at 3, 12, and 24 hours after the
cessation of heat-shock (ACS). For lethal sampling, fish
were quickly netted from their tanks and placed in a
bath containing an overdose of anaesthetic [400 mg of
tricaine-methane-sulphonate (TMS) × L-1]. Blood, gills,
head kidney, liver, and skeletal muscle samples were
rapidly removed by team dissection, flash-frozen in
liquid nitrogen and stored at -80°C until RNA extractions were performed. All sampling instruments were
cleaned with RNase Away (Molecular BioProducts, San
Diego, CA) between individuals. An aliquot of the blood
was centrifuged at 5000 × g for 10 minutes at 10°C to
separate plasma for cortisol determination.
Plasma cortisol
Plasma cortisol levels were determined in duplicate
using an enzyme-linked immunosorbent assay kit that
has been previously validated for fish (Neogen Corp.
Lexington, KY), and parallelism to the standard curve
was confirmed using serially-diluted plasma samples
[61]. Intra- and inter-assay variation was determined
and never exceeded 10%. The cortisol data were analyzed statistically using MiniTab (Version 14). Data were
tested for normality using the Anderson-Darling normality test. Treatment and time point were used in a
general linear model with 2 crossed factors, considering
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Page 17 of 22
Table 4 Primers used in quantitative reverse transcription - polymerase chain reaction (QPCR)
Primer name
Sequence
Gene name of the best BLASTx hit
Amplification
efficiency
Amplicon size
(bp)
HSP90a
Forward
5’- GAA CAA GAC CAA GCC CCT TT Heat shock protein 90 alpha
-3’
107%
133
HSP90a
Reverse
5’- CTG ACC CTC CAC CGA GAA GT
-3’
91%
97
97%
123
90%
132
93%
100
95%
127
92%
91
5’- ACC AAG CCA GAG AGA GTG
Translationally-controlled tumor protein
GA -3’
5’- ATC CTC ACG GAA GTC AAG CA
-3’
101%
147
Enolase
Forward
5’- GGA CGG CAC TGA AAA CAA
AT -3’
Enolase 3
93%
115
Enolase
Reverse
5’- ACA GAG GAA CCC CCT TCT CC
-3’
PFK Forward1
5’- TGT TTG CCA ACT CCC CAG
AGA -3’
Phosphofructokinase, muscle a
96%
121
PFK Reverse1
5’- TCC GGT GCT TGA AGT CTG
TCA -3’
Aldolase
Forward1
5’- TGA CAT TGC TCA GAG GAT GG Aldolase B
-3’
91%
143
Aldolase
Reverse1
5’- TAG CGA CGG TTC TCC TCA CT
-3’
GRP94 Forward 5’- AGT GTT TCT CTC GAC ACG TTC Heat shock protein 90 kDa beta, member 1
A -3’
(synonym: GRP94)
GRP94 Reverse
5’- CAG ACG ACT TCC ATG ACA
TGA T -3’
HSP70-1
Forward
5’- GAG AAC AAG ATC ACC ATC
ACG A -3’
HSP70-1
Reverse
5’- GGC TGT TAC TTT CTC TCC CTG
A -3’
HSP70-1 (synonym: HSPA1A)
GRP78 Forward 5’- CTC CTT CAT TTT GGT CAG AAC 78 kDa glucose-regulated protein (synonym:
C -3’
GRP78)
GRP78 Reverse
5’- CTC AAG TTC CTC CCA TTC AAA
G -3’
HSP47 Forward 5’- ATG GAA GTC AGC CAC AAC CT Heat Shock Protein 47
-3’
HSP47 Reverse
5’- TCT TGC CCG TGA TGT TAG AC
-3’
CCT1 Forward
5’- GCA GGC GTT TGG GAT AAC TA T-complex-1
-3’
CCT1 Reverse
5’- GCG CTT AAC CCT TCA GAG AA
-3’
CCT5 Forward
5’- CCA GGC GAG GTT GAA GAA
TA -3’
5’- TAG AAC AGG GGA GTG GTG
GT -3’
CCT5 Reverse
TCTP Forward
TCTP Reverse
Chaperonin containing TCP1, subunit
5 (epsilon)
Hori et al. BMC Genomics 2010, 11:72
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Page 18 of 22
Table 4: Primers used in quantitative reverse transcription - polymerase chain reaction (QPCR) (Continued)
Bikunin
Forward
5’- GCC ACT GAG TTC ACA GAC G
-3’
Alpha-1-microglobulin/bikunin precursor
90%
107
Immunoglobulin heavy chain, secretory form
93%
97
Nuclear protein 1
88%
101
100%
133
85%
93
109%
180
Bikunin Reverse 5’- CAG CTC ATG GAG GAG GAG T
-3’
IgM Forward
5’- GAG CAT CCA CTG GCT CTT TA
-3’
IgM Reverse
5’- GCA GCA AGC TAT ATC CAG GT
-3’
Nupr1 Forward
5’- CTT TCT TCT CGC TGT TCT GC
-3’
Nupr1 Reverse
5’- GGA AGG ACC AAG AAG GAG
TC -3’
IL-8 Forward
5’- CTT CAG CAT CCA GAC AGA CC Interleukin-8
-3’
IL-8 Reverse
5’- CAG ACA GAG AGC CGT CAG
AT -3’
TLR22 Forward
5’- TGC AGG TAA TCA CGA CTG AC TLR22
-3’
TLR22 Reverse
5’- GAG ACT TCC AGC CAG ACC TA
-3’
18S Forward
5’- ATG GCC GTT CTT AGT TGG TG
-3’
18S Reverse
5’- GGA CAT TTA AGG GCG TCT CA
-3’
18S ribosomal RNA (normalizer gene)
1
The primers for aldolase and PFK were designed to amplify a region that is conserved between the muscle and liver forms of these genes.
all possible interactions between factors (A B A*B
model). When the effects of each factor on a given variable were found to be significant (p < 0.05), two separate analyses were performed: 1) a one-way ANOVA
within each group (e.g. CT) across sampling points was
used to determine if values were different from their
respective before heat-shock (BHS) values (p < 0.05);
and 2) a one-way ANOVA within each sampling point
was used to determine if the CT and HS groups were
different from the undisturbed control (C) (p < 0.05).
When groups were identified as significantly different
through ANOVA, Tukey’s multi-comparison pair-wise
post-hoc test was used to test the hypothesis that means
were significantly different between groups.
RNA extractions
RNA was extracted from flash-frozen tissues using
TRIzol reagent (Invitrogen, Carlsbad, CA) according to
the manufacturer’s instructions with modifications.
Samples (~50 mg of tissue) were disrupted with disposable pestles and further homogenized using QIAshredder spin columns (QIAGEN, Mississauga, ON). The
remainder of the protocol was carried out following
the manufacturer’s instructions. RNA samples were
treated with DNase-I (QIAGEN) to degrade any
residual genomic DNA and then purified from salts,
proteins and nucleotides using RNeasy MinElute (QIAGEN) spin columns following the manufacturer’s
instructions. RNA quantity and quality were assessed
using spectrophotometry and 1% agarose gel electrophoresis, respectively. Only high quality total RNA
samples (260/280 ratio >1.8, with tight 18S/28S ribosomal RNA bands) were used for library construction
and QPCR.
mRNA isolation
Poly (A)+ RNA (mRNA) was isolated from total RNA
pools using the MicroPurist mRNA isolation kit
(Ambion, Austin, TX) following the manufacturer’s
instructions. For each one of the tissues used for library
construction [head kidney (HK), liver (L) and skeletal
muscle (M)] DNase-I treated, column-cleaned total
RNA samples from the CT and HS groups taken at
sampling times CS, 3ACS, 12ACS, and 24ACS were
pooled for mRNA isolation (n = 32 for each group
within each tissue). Each one of the 32 individuals contributed an equal quantity of cleaned total RNA (HK =
10 μg; L = 8 μg; M = 10 μg) to each tissue/treatment
group-specific pool. Purified mRNA quantity and quality
were assessed by spectrophotometry and 1.5% agarose
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gel electrophoresis, respectively. Poly (A)+ RNA yield
ranged from 1.5 - 4% of total RNA.
SSH library construction
Suppressive subtractive hybridization (SSH) was performed using the PCR-Select cDNA Subtraction Kit
(Clontech, Mountain View, CA) and the manufacturer’s instructions. Pooled HS mRNA samples were
used as testers in the forward subtractions and as drivers in the reverse subtractions. Pooled CT samples
were used as drivers in the forward subtractions and as
testers in the reverse subtractions. “Forward subtracted” libraries were designed to be enriched for
genes that were up-regulated by heat-shock, and
“reverse subtracted” libraries were designed to be
enriched for genes that were down-regulated by heatshock. Two μg of mRNA were used for each firststrand cDNA synthesis. After second strand synthesis,
cDNA samples were RsaI digested for 1.5 hours at 37°
C. For SSH enrichment, two rounds of hybridization (6
h for the first hybridization and 16 h for the second
hybridization) were performed in a hybridization oven
at 68°C. All other procedures were performed according to the manufacturer’s instructions.
The resulting SSH cDNA libraries were cloned into
pGEM-T Easy (Promega, Madison, WI) vectors following the manufacturer’s instructions. The ligation reactions were then transformed into chemically competent
Max Efficiency DH5a cells (Invitrogen) using standard
molecular biology techniques. Prior to sequencing,
library insert size and complexity were evaluated as
described in Rise et al. [20].
DNA sequencing, sequence assembly and annotation
DNA sequencing, sequence assembly and annotation
were done as described previously by Rise et al. [20].
Briefly, individual bacterial clones were inoculated into
LB/glycerol/ampicillin in 384-well format and incubated overnight at 37°C. Sequencing reactions were
carried out using ET terminator chemistry (GE Healthcare, Piscataway, NJ) and after removal of excess fluorescent terminators, samples were loaded onto
MegaBACE (GE Healthcare) capillary sequencers. The
resulting ESTs were analyzed for quality, trimmed and
assembled as described by Rise et al. [20] using Phred
[62,63] and Paracel Transcript Assembler (PTA). Each
EST set resulting from different libraries (e.g. liver forward) was assembled separately. All ESTs were annotated by an automated pipeline using AutoFACT [26]
and have been deposited in the GenBank dbEST under
the accession numbers presented in Additional files 1
and 2 (Tables S1 and S2). One hundred seventy-nine
ESTs were not submitted to GenBank due to low quality. Gene ontology (GO) annotation was obtained
using AutoFACT [26] and GOblet [28]. Annotations
presented within the text were obtained using BLASTx
Page 19 of 22
manually, and reflect a more updated state of the
NCBI’s non-redundant (nr) protein database. AutoFACT summary results are stored in the CGP EST
database http://www.codgene.ca.
cDNA synthesis and quantitative reverse transcription polymerase chain reaction (QPCR)
Complementary DNA (cDNA) was synthesized from 1
μg of high quality, DNase-I treated, column-purified
total RNA (the same individual samples that were
pooled for SSH library construction) using the High
Capacity Reverse Transcriptase Kit (Applied Biosystems,
Foster City, CA) following the manufacturer’s
instructions.
Candidate stress responsive transcript levels were
quantified by QPCR using Power SYBR Green I dye
chemistry and the 7500 Fast Real-Time PCR System
(Applied Biosystems). For QPCR studies, we used 6
individuals per treatment per time-point out of the 8
individuals that were used for SSH library construction.
The sequences of the primers used in mRNA expression analysis are presented in Table 4. Each primer set
was tested for quality before use. QPCR primer quality
control included running serial 1:5 dilutions (with the
exception of TLR22 for which a 1:2 dilution was used)
for both CT and HS samples using cDNA (a pool of 6
individuals sampled at CS) at a starting concentration of
10 ng of input total RNA in order to calculate amplification efficiency (E = 10[-1/slope]). We also performed meltcurves (+1% increases every 30 seconds from 60°C to
95°C) to verify that the primers amplified a single product and that there were no primer dimers or amplification in the no-template controls. Furthermore, random
samples of each QPCR amplicon were subjected to 1.5%
agarose gel electrophoresis with ethidium bromide staining and compared with a DNA size marker (100 bp ladder, Invitrogen) to confirm that the amplicons were of
the expected sizes.
PCR amplification was performed with the 7500 Fast
Real-Time PCR System (Applied Biosystems) in 13 μl
reactions using 2 μl of cDNA (10 ng of input total
RNA), 50 nM each of forward and reverse primer and
1× Power SYBR Green PCR Master Mix (Applied Biosystems). Expression levels of the genes of interest
were normalized to 18S ribosomal RNA. The suitability
of 18S as a normalizer was confirmed by calculating
the standard deviation (SD) of all 18S fluorescence
threshold cycle (C T ) values for a given tissue. The
highest SD found was 0.36 in the muscle, with values
of 0.28 and 0.30 in the head kidney and liver, respectively. The average 18S C T values were 25.66, 25.90,
and 27.99 for muscle, head kidney and liver, respectively. Moreover, the average 18S CT for each group (e.
g. HS) was calculated and shown not to differ by more
than 0.3 cycles from the average of any other group.
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The QPCR cycling parameters consisted of 1 cycle of
50°C for 5 minutes to activate AmpErase Uracil N-glycosylase (UNG), 1 cycle of 95°C for 10 minutes, and
40 cycles of (95°C for 15 sec and 60°C for 1 minute).
On a given 96- well plate, target and normalizer genes
were run in duplicate [64]. The CT values were determined using the 7500 Software Relative Quantification
Study Application (Version 2.0) (Applied Biosystems)
with automated threshold determination and walking
baseline. Each data set from a tissue was analyzed as a
multi-plate study. The relative starting quantity (RQ)
of each transcript was determined using the comparative C T method for relative quantification [65], using
the individual with the lowest gene of interest expression (i.e. lowest normalized expression) within a given
tissue as calibrator.
Calculated amplification efficiencies (Table 4) were
used to calculate RQs. Overall fold up-regulation for
each group (e.g. HS at CS) was calculated as (average
RQ)/(average RQ for the appropriate group at BHS).
Overall fold down-regulation (if applicable) was calculated as the inverse of overall fold up-regulation.
The RQs obtained from the software were statistically
analyzed using MiniTab (Version 14). Data were tested
for normality using the Anderson-Darling normality
test. Treatment and time point were then used in a general linear model with 2 crossed factors, considering all
possible interactions between factors (A B A*B model).
When the effects of each factor on a given variable were
found to be significant (p < 0.05) two separate analyses
were performed. An one-way ANOVA in each group (e.
g. CT) was used across sampling points to determine if
groups (e.g. CT) were different from their respective
BHS values (p < 0.05); when values were significantly
different, Tukey’s multi-comparison pairwise post-hoc
test was used. A t-test within each sampling point was
used to determine if the HS group was different from
the CT group (p < 0.05).
Additional file 1: Supplemental Table S1, assembled ESTs (contigs
and singletons) in the forward heat-shock SSH libraries. Contains 3
tables (S1 A-C) with information such as supporting annotations,
statistics, and contributing EST accession numbers of contigs and
singletons found in all 3 forward libraries.
Click here for file
[ http://www.biomedcentral.com/content/supplementary/1471-2164-1172-S1.PDF ]
Additional file 2: Supplemental Table S2, assembled ESTs (contigs
and singletons) in the reverse heat-shock SSH libraries. Contains 2
tables (S2A and S2B) with information such as supporting annotations,
statistics, and contributing EST accession numbers of contigs and
singletons found in the 2 reverse libraries that were sequenced (head
kidney and liver).
Click here for file
[ http://www.biomedcentral.com/content/supplementary/1471-2164-1172-S2.PDF ]
Page 20 of 22
Additional file 3: Supplemental Table S3, summary of GO
annotation. Contains 4 tables (S3 A-D) with summaries of percentages
of total ESTs with GO (biological process) terms for each of 4 SSH
libraries.
Click here for file
[ http://www.biomedcentral.com/content/supplementary/1471-2164-1172-S3.PDF ]
Acknowledgements
This research was supported in part by Genome Canada, Genome Atlantic,
and the Atlantic Canada Opportunities Agency through the Atlantic Cod
Genomics and Broodstock Development Project. A complete list of
supporting partners can be found at http://www.codgene.ca/partners.php.
Funding was also provided by the National Research Council (NRC), a
Natural Sciences and Engineering Research Council of Canada (NSERC)
Discovery Grant and a Canada Research Chair awarded to MLR, through a
NSERC Major Facilities Access Grant to the Ocean Sciences Centre (Memorial
University of Newfoundland, MUN) and a Memorial University of
Newfoundland SGS fellowship to TSH. We would also like to thank all of the
Dr. Joe Brown Aquatic Research Building (JBARB) staff and Laurie Murphy for
fish husbandry; Danny Boyce for facilitating the experiments, Dr. Laura L.
Brown for reviewing this manuscript and all of the staff at The Atlantic
Genome Center (TAGC) for assistance with sequencing.
Author details
1
Ocean Sciences Centre, Memorial University of Newfoundland, St. John’s,
NL, A1C 5S7, Canada. 2British Columbia Centre for Aquatic Health Sciences,
Campbell River, BC, V9W 2C2, Canada. 3Pacific Biological Station, Department
for Fisheries and Oceans, Nanaimo, BC, V9T 6N7, Canada. 4The Atlantic
Genome Centre, Halifax, NS, B3H 3Z1, Canada. 5Institute for Marine
Biosciences, National Research Council of Canada, Halifax, NS, B3H 3Z1,
Canada.
Authors’ contributions
TSH was involved in the conceptualization, design, and implementation of
all experiments, and took the lead role in data analysis, interpretation of
results, and writing of this manuscript. AKG is the first author’s (TSH) Ph.D.
co-supervisor, was involved in the conceptualization, design and
implementation of experiments, and took an active part in data
interpretation and the writing of this manuscript. LOBA was involved in the
conceptualization, design, and implementation of experiments, and in the
interpretation of data. SCJ was involved in the conceptualization and design
of experiments, and in the interpretation of data. SH was involved in the
characterization of SSH libraries (e.g. sequencing, sequence trimming and
assembly, and BLAST identification and functional annotation of assembled
sequences). JK was involved in the characterization of SSH libraries (e.g.
sequencing, sequence trimming and assembly, and BLAST identification and
functional annotation of assembled sequences). SB was involved in
conceptualizing experiments and characterizing SSH libraries. MLR, the first
author’s (TSH) Ph.D. co-supervisor, was involved in the conceptualization,
design, and implementation of SSH and QPCR-based experiments, and took
an active part in the interpretation of data and the writing of this
manuscript. All authors read and approved the final manuscript.
Received: 23 March 2009
Accepted: 28 January 2010 Published: 28 January 2010
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doi:10.1186/1471-2164-11-72
Cite this article as: Hori et al.: Heat-shock responsive genes identified
and validated in Atlantic cod (Gadus morhua) liver, head kidney and
skeletal muscle using genomic techniques. BMC Genomics 2010 11:72.
Page 22 of 22
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