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Transcription and redox enzyme activities:
comparison of equilibrium and
disequilibrium levels in the three-spined
stickleback
M. Nikinmaa1, R. J. S. McCairns2, M. W. Nikinmaa1, K. A. Vuori1, M. Kanerva1,
T. Leinonen2, C. R. Primmer1, J. Merilä2 and E. H. Leder1
Research
Cite this article: Nikinmaa M, McCairns RJS,
Nikinmaa MW, Vuori KA, Kanerva M, Leinonen
T, Primmer CR, Merilä J, Leder EH. 2013
Transcription and redox enzyme activities:
comparison of equilibrium and disequilibrium
levels in the three-spined stickleback. Proc R
Soc B 280: 20122974.
http://dx.doi.org/10.1098/rspb.2012.2974
Received: 13 December 2012
Accepted: 10 January 2013
Subject Areas:
physiology, evolution, systems biology
Keywords:
mRNA – protein correlation, temperature,
population differentiation
Author for correspondence:
M. Nikinmaa
e-mail: [email protected]
1
2
Department of Biology, University of Turku, 20014, Finland
Ecological Genetics Research Unit, Department of Biosciences, University of Helsinki, 00014, Finland
Evolutionary and acclimatory responses require functional variability, but in
contrast with mRNA and protein abundance data, most physiological measurements cannot be obtained in a high-throughput manner. Consequently, one
must either rely on high-throughput transcriptomic or proteomic data with
only predicted functional information, or accept the limitation that most
physiological measurements can give fewer data than those provided by transcriptomics or proteomics. We evaluated how transcriptional and redox
enzyme activity data agreed with regard to population differentiation (i.e. a
system in steady state in which any time lag between transcription, translation
and post-translational effects would be irrelevant) and in response to an acute
68C increase in temperature (i.e. a disequilibrium state wherein translation
could not have caught up with transcription) in the three-spined stickleback
(Gasterosteus aculeatus). Transcriptional and enzyme activity data corresponded
well with regard to population differentiation, but less so with regard to acute
temperature increase. The data thus suggest that transcriptional and functional
measurements can lead to similar conclusions when a biological system is in a
steady state. The responses to acute changes must, as has been demonstrated
earlier, be based on changes in cellular conditions or properties of existing
proteins without significant de novo synthesis of new gene products.
1. Introduction
A drawback of most genomic studies is that functional information is not combined with genomic information [1]. Yet, studies often discuss functional
implications (e.g. enzyme activity changes) on the basis of transcript data (as
given by quantitative PCR or cDNA microarrays) without information about
how the protein levels and their activities change. In part, genomic data translate
to information of gene product activity via protein abundance. In the past 10
years, there have been numerous studies examining the relationship between
protein abundance and mRNA abundance (reviewed, e.g. in Abreu et al. [2]).
About 40 per cent of the variance in protein expression is explained by mRNA
expression [2,3]. A complicating factor is that not only does the relationship
vary among organisms and among genes within an organism, but also with a
given gene in different conditions. The relationship between genomic data and
overall metabolic activity becomes even more complex, as the same physiological
response can be a result of different cellular pathways [4]. Because of this,
network-based analyses are often used. The idea behind network-based analysis
is that by grouping differentially expressed transcripts into networks of related
predicted functions, the biological function related to the observed transcriptional
changes can be inferred. This, however, is not the full story as regulation can also
occur translationally and post-translationally. Both of these regulatory steps influence the final outcome of any metabolic network. Also, information on the
genome-to-gene product activity relationship is confined mostly to a small
& 2013 The Author(s) Published by the Royal Society. All rights reserved.
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redox enzyme activities to estimate some physiological
features, as the redox balance is a major evolutionarily
adjusted parameter [14] integrating both temperature and
oxygen-level changes [15–17].
2. Material and methods
(a) Fish
(b) Sample preparation
For transcriptome work, total RNA was isolated from liver tissue
by means of Tri Reagent (Sigma; St Louis, MO, USA), following
the manufacturer’s protocol. RNA was treated with DNase (Promega; Madison, WI, USA) and re-isolated using Tri Reagent.
RNA concentration was quantified using a Nanodrop ND-1000
(Thermo Scientific; Waltham, MA, USA), and RNA quality was
assessed using an Experion automated electrophoresis system
(Bio-Rad; Hercules, CA, USA).
For glutathione (GSH) concentration and enzyme activity
measurements, the frozen liver samples were homogenized
using TissueLyser II Bead mill (Qiagen, Hamburg, Germany)
in 0.1 M K2HPO4 þ 0.15 M KCl-buffer ( pH 7.4). The homogenate
was centrifuged for 15 min at 10 000g at þ48C. The supernatant
was divided into several aliquots of which one was used for
the preparation of the GSH determination sample and the rest
were frozen in liquid nitrogen and stored at 2808C until further
measurements.
(c) Microarray analysis
A short oligo microarray was custom-designed as earlier
described [9]. Each sample (1.65 mg) was hybridized to the
custom-designed stickleback array at 658C overnight (17 h)
using Agilent’s GE Hybridization Kit. Washes were carried out
as recommended by the manufacturer using Agilent’s Gene
Expression Wash Pack. Arrays were scanned with Agilent
Technologies Scanner, model G2505B. Spot intensities and
other quality control features were extracted with Agilent’s
Proc R Soc B 280: 20122974
Mature sticklebacks from Lake Pulmankijärvi in Finnish Lapland
(698580 500 N, 278590 3500 E), the Baltic Sea in the vicinity of Helsinki, Finland (598490 1700 N, 228580 1400 E) and Lake Vättern
(588380 0000 N, 148510 0000 E) from south-central Sweden were
spawned, and fertilized eggs were transported to the laboratory
at the University of Helsinki where the fish hatched. After hatching, the fish were maintained at 17 + 18C, with a photoperiod of
18 L : 6 D cycle for six months, whereafter their environmental
conditions were gradually changed to simulate wintering conditions (24 h darkness; temperature 9 + 18C). After five
months, fish were stimulated to breed by gradually reverting
the photoperiod back to an 18 L : 6 D cycle and the water temperature to 17 + 18C. The generated F2 offspring from each
population was used in the experiments at approximately 20
months of age. Although sexually mature, all experimental fish
were reproductively inactive. Ten F2 fish from each population
were housed in one of two identical tanks (20 fish per population
altogether). Fish were acclimated overnight. One tank was maintained as a control at 178C, and water in the second tank was
heated approximately 18C per hour for 6 h to a final temperature
of 238C. After 1 h at the final temperature, every fish was euthanized with a lethal amount of MS-222 in water, and its liver
removed and immediately frozen in liquid nitrogen and stored
at 2808C. In total, sufficient quantities of RNA and protein
could be extracted from 50 fish and used to measure enzyme
activities; a random subset of these same individuals (n ¼ 35)
were also used for transcriptome-wide (microarray) analysis of
transcript expression.
2
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number of model organisms. This biases functional predictions
based on gene sequences—they can give accurate results as
long as the function of the gene product is similar to that in
model organisms, but in the case of divergent pathways or
neofunctionalization, such predictions may be erroneous. Consequently, there is a need for investigations simultaneously
assessing transcriptional changes and protein activities for
the same animals with the ultimate goal of evaluating conditions under which conclusions of functional or structural
changes can be drawn from transcript data and those in
which it cannot.
Self-evidently, the ecological and evolutionary success of
organisms depends both on their functions and the adaptability of these functions. However, a full understanding of
the evolutionary potential and constraints of such functions
remains a challenge since inference based strictly on gene
sequences or protein abundances rather than actual protein
activities gives us an incomplete picture. For example,
Oleksiak et al. [5] have estimated the relationship between
transcriptional differences of genes coding for enzymes
involved in cardiac energy metabolism and protein activity
changes. They found that there was marked inter-individual
variation in the relationship between mRNA expression and
activity of enzymes coded for by the genes. The individuals
could be divided into different groups with regard to major
metabolic substrate type used. These results strengthen the
conclusion that information regarding gene product activity
is required to get a complete picture about the ecologically
and evolutionarily important organismal activities and their
relationship to genomic data.
It is now possible to use high-throughput methods to
obtain transcriptomic [6] and protein abundance [7] data. Similar high-throughput methodology is not available for most
protein activity measurements. As an example, while it is possible to determine how a treatment affects the transcription of
thousands of genes with microarrays, one cannot measure
the biological activities of the thousands of gene products
even if rapid measurements of one or a few gene product
activities are possible. It has become common to carry out
microarray and quantitative reverse transcription PCR
measurements also in evolutionary studies, and use thus
obtained transcript data to infer functions, or use variations
in protein abundance to infer the overall activity of the protein.
To increase their usefulness in evolutionary studies, the
relationship of such genomic and proteomic data to gene
product activity data should be evaluated.
In the present study, we evaluated the correspondence of
genomic and enzyme activity data in both steady-state and
disequilibrium conditions using the three-spined stickleback
as model. This species was selected for the following reasons:
first, its genome has been sequenced [8] which has facilitated
development of genomic resources, such as a high density
custom microarray [9]; second, it is a major model in evolutionary studies [10]; and third, physiological and genomic
measurements in this species have been integrated [11].
Two different types of data were examined: the relationship
between transcription and redox enzyme activities at the
steady state was studied by evaluating the differences
between populations, and the relationship in disequilibrium
conditions by looking at the responses to acute temperature
change. The acute temperature increase was of such a short
duration that transcriptional and translational responses
could be uncoupled [12,13]. We used redox balance and
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The database for annotation, visualization and integrated discovery (DAVID) [21] was used to determine if differentially
expressed probes were significantly over-represented by genes
of particular functional categories [22]. Although DAVID will
accept several probe identifiers, Entrez Gene identifiers generally
produce the most comprehensive annotation data [23,24]. Stickleback genes were initially matched to their human orthologues
using BIOMART [25,26], and further supplemented via BLAST
search to increase inferred annotations. Of the 23 946 probes, 75
per cent (18 060) were assigned an Entrez GeneID number.
Approximately the same proportions (1874) of the 2509 probes
differentially expressed among populations, and (2698) of the
3516 probes differentially expressed between temperature treatments were also assigned ENTREZ identifiers. Input for
analysis consisted of the list of differentially expressed probes
with Entrez GeneID, contrasted with a customized ‘background’
set, including only those genes represented on the custom array.
Though DAVID can integrate non-redundant functional annotations across multiple databases, we focused on gene ontology
(GO) annotations at the ‘biological process’ level [27]. We further
restricted output by grouping enriched terms into functional
annotation clusters, using default settings [28].
(e) Glutathione concentration determinations
Samples for GSH determination were deproteinized with 5 per
cent sulfosalicylic acid. Reduced and oxidized GSH species
(GSH/GSSG) were measured with ThioStar GSH detection
reagent (Arbor Assays, MI, USA) using reduced GSH as the standard (Sigma Chemicals, St. Louis, MO, USA) as described by
Lilley et al. [29].
(f ) Determinations of enzyme activities
The enzyme activity determinations were carried out mainly as
described by Vuori et al. [30] at 258C. Briefly, glutathione
(g) Analysis of trait divergence in enzyme and
glutathione concentration data
To infer if between population differences in enzyme data were
best explained by neutral or putatively selective processes, we contrasted an index of quantitative genetic differentiation (QST) with
that of neutral genetic divergence [35]. Among and withingroup phenotypic variance were estimated as random effects via
mixed effects modelling, after first controlling for temperature
effects as a fixed model term. Since family groups were
unavailable within each population—the standard source of
within-group variance—we first created five pseudo-replicate
samples within each population-by-treatment group by random
sampling with replacement. Within-group variance was estimated
as variation among replicate samples, nested within each population. Confidence intervals (95%) were obtained by parametric
bootstrapping (10 000 simulations). The range of neutral expectation was defined based on a previously published dataset of
17 microsatellite markers [36], using data pertaining only to the
three populations in the current study. We evaluated the dataset
via outlier analysis to ensure that all markers fell within a simulated range of neutral expectation [37], and used the observed
range of FST values for comparison against QST estimates.
(h) Multivariate association between mRNA expression
and enzyme activity
Associations between multivariate descriptors of enzymatic and
transcriptional variation were inferred via co-inertia analysis
(CoIA). In brief, CoIA finds common sets of principal axes for
two datasets pertaining to the same cases—in this instance cases
were individual fish, and datasets were an individual’s transcriptional profile at all 23 946 probes detected above background and
activity data for four enzymes and total GSH concentration—such
that principal components have maximum covariance, while also
accounting for partitioning between and co-variation within
discrete classes/groups of individual cases [38,39]. Only individuals for which both transcriptomic and enzyme activity data
were available were used for CoIA. Between-class (i.e. among
treatment-specific population groups) PCA and CoIA of both
data projections were performed using the ‘ade4’ package [40]
implemented in R. Significant contributions of transcriptional
probes to among-group discrimination was ascertained by evaluating their respective loadings on the dominant principal axes:
probes whose squared score exceeded the 90th quantile of a distribution describing all squared loading scores for a focal axis were
deemed to be significant.
3
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(d) Functional annotation analysis
reductase (GSR) activity was measured according to Smith
et al. [31]. Glutathione S-transferase (GST) activity was measured
according to Habig et al. [32] with the exception of using 2 mM
GSH instead of 1 mM. Glutathione peroxidase (GPX) activity
was measured using a Sigma kit (Sigma Chemicals) with
2 mM H2O2 as a substrate. The inhibition rate of superoxide dismutase (SOD) was measured using a Fluka kit (Fluka, Buchs,
Germany). The measurement of catalase (CAT) activity was
modified to microplate from the Catalase Assay kit (Sigma
Chemicals), where a reaction of CAT and H2O2 is stopped with
NaN3 [33], an aliquot is pipetted to a microplate and the leftover
H2O2 is detected with colorimetric reaction [34]. The protein contents of the samples were determined using Pierce BCA Reagents
(Thermo Fisher Scientific, Rockford, IL, USA) with bovine serum
albumin (Sigma Chemicals) as the standard.
Both GSH and enzyme activity analyses were conducted
with EnSpire plate reader (Wallac, PerkinElmer Life Sciences,
Turku, Finland) using either 96-well ( protein and CAT assays)
or 384-well plates (GSH, GSR, GST, GPX and SOD).
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Feature Extraction Software v. 9.5.3.1. Array quality was assessed
through the use of Agilent control features, as well as spike-in
controls (Agilent One-Color Spike-in Kit for RNA experiment).
Feature Extraction Software was used to flag features above background at the 99%; however, to further filter the data within each
population, only probes with a background-corrected median
intensity value of greater than 50 across their respective groups
were retained. Because probes with very low expression levels
may not be reliable, this is similar to keeping only the probes
that are more than two times as intense as background levels.
Post-processed signals were further normalized within and
across arrays using the quantile method, implemented in the
R/BIOCONDUCTOR package ‘limma’ [18]. Probes with missing
values were removed from further analyses, which were thus
based on data for 23 946 unique probes, representing 11 203
predicted/projected genes (Ensembl Stickleback Genome
v. 65.1, updated May 2010). The microarray data are available
at http://www.ebi.ac.uk/microarray-as/ae/ with accession no.
A-MEXP-1443.
Differentially expressed transcripts between populations
were identified using the empirical Bayes procedure [19], as
implemented in the R/BIOCONDUCTOR package ‘siggenes’ [20].
This was performed within each temperature treatment separately,
and by iteratively selecting a Z-score correction factor ‘a0’ parameter to achieve an acceptable posterior false discovery rate
(less than or equal to 0.10). Transcript lists were then pooled
prior to enrichment analyses. Similarly, to identify probes differentially expressed between temperature treatments, data were
analysed via the empirical Bayes procedure (populations
pooled) contrasting control and temperature treatment groups.
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glutathione peroxidase
***
2.0
0.20
1.8
mM
0.25
0.15
1.6
0.10
1.4
0.05
1.2
1.0
0
glutathione reductase
55
nmol mg–1 min–1
*
Proc R Soc B 280: 20122974
inhibition (%)
SOD
78
76
74
72
70
68
66
64
4
total glutathione
2.2
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µmol mg–1 min–1
0.30
50
45
40
35
30
HEL
VAT
PUL
VAT
PUL
catalase
250
µmol mg–1 min–1
HEL
200
150
100
50
0
HEL
VAT
PUL
Figure 1. GPX, GSR, SOD and CAT activities and total GSH concentration in Helsinki (HEL, n ¼ 18), Vättern (VAT, n ¼ 13) and Pulmanki (PUL, n ¼ 18) populations.
The statistical significance of the difference between the means was tested using two-way ANOVA with population and sex as explanatory factors. An ad hoc test to
indicate differences between individual groups was carried out using the Holm – Sidak method. Asterisks indicate that the population with asterisks differs
significantly from the other populations. *p , 0.05; ***p , 0.001.
3. Results and discussion
With regard to population differentiation, transcriptional and
enzyme activity data lead to the same conclusion. There is a
marked difference in the activities of GPX and SOD enzymes
between the lake Vättern population on one the hand, and
lake Pulmanki and Helsinki populations on the other hand
(figure 1). In the case of GPX, the difference also exceeds neutral
expectation (i.e. QST . FST; figure 2). This divergence cannot be
explained either by genetic salinity adaptation, as the two lake
populations are different, or by latitude-related adaptation, as
the populations from similar latitudes are also different. Thus,
the result shows that there are some physiological differences
between populations which may reflect divergent selection
pressures. The populations are different, although northern
European populations of three-spined stickleback share a relatively recent and common evolutionary origin [41]. However,
only a small number of amino acid changes (i.e. only few
nucleotide changes in a gene) are required to change the function of a protein markedly [42]. On the other hand, although
CAT activity showed a tendency for population differentiation,
and SOD activity differed significantly among populations, the
tail of their bootstrapped QST distributions overlapped with the
range of FST estimated from neutral microsatellite markers
(figure 2). Neither GSR activity nor total GSH concentration
(the level of which depends on the function of several gene
products) differed significantly from neutrality (figure 2).
GSH
CAT
GPX
GSR
SOD
0
0.2
0.4
0.6
0.8
1.0
Figure 2. Box-percentile plots showing the bootstrap distribution (10 000
iterations) for estimates of the index of quantitative trait differentiation
(QST) for enzyme data. Vertical lines denote the putative range of neutral
differentiation (FST), as inferred from variation in 17 assumedly neutral
microsatellite loci. (Online version in colour.)
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enrich. score
no. BP
no. genes
fold enrich.
regulation of protein localization transport and secretion
detection of external stimuli
2.34
1.66
14
4
63
13
2.50 (1.50 – 4.20)
2.95 (2.05 – 4.28)
response to steroidal stimuli
regulation of cellular growth
1.36
1.11
4
10
12
24
2.83 (2.02 – 3.60)
1.76 (1.19 – 2.40)
regulation of GTPase activity
1.11
12
40
2.23 (1.10 – 3.90)
regulation of cell adhesion
glucose and carbohydrate homeostasis
1.08
1.08
3
3
12
9
2.17 (1.83 – 2.55)
2.37 (2.20 – 2.65)
regulation of ion transport
nuclear organization
1.08
1.03
6
6
12
11
2.40 (1.83 – 3.70)
2.18 (1.55 – 2.68)
regulation of protein signalling
SMAD protein localization
0.99
0.91
3
3
9
11
2.70 (2.70 – 2.70)
2.50 (1.61 – 3.95)
glucose and carbohydrate metabolism
0.87
27
26
2.37 (1.63 – 3.87)
membrane protein proteolysis
regulation of immune response
0.87
0.86
4
13
8
28
3.23 (1.44 – 5.82)
2.31 (1.04 – 4.80)
response to intra-cellular pathogens
mitochondrial organization
0.85
0.83
7
3
10
7
2.49 (1.33 – 3.80)
3.70 (2.00 – 4.70)
regulation of intra-cellular protein transport
0.81
8
19
1.68 (1.24 – 2.03)
water homeostasis
response to oxidative stress
0.81
0.75
6
9
10
25
3.22 (1.65 – 4.70)
1.92 (1.22 – 2.62)
regulation of lipid metabolism
regulation of macromolecular secretion
0.74
0.64
14
7
17
7
2.33 (1.23 – 3.80)
2.29 (1.76 – 2.70)
exocytosis
GSH, peptide and sulphur metabolism
0.63
0.60
6
3
26
17
1.53 (1.13 – 2.03)
1.57 (1.24 – 1.87)
regulation of cellular development
0.59
7
25
1.90 (1.22 – 2.70)
transport of organic acids
regulation of muscle development
0.55
0.53
5
10
16
10
1.60 (1.14 – 2.52)
1.86 (1.19 – 3.33)
DNA catabolism
0.51
7
14
1.71 (1.36 – 1.97)
The differentiation between populations was also seen in
the transcriptome data. In the microarray analysis, we identified 1698 genes (1834 splice variants in total) which were
differentially transcribed among populations. This reduced
probe-set was largely divided into transcripts that were
differentially expressed in the lake Vättern population when
compared with the other two populations. Enrichment
analysis grouped the differentially transcribed genes into 164
functional clusters. We excluded those clusters with an
enrichment score less than 0.5, and for which the mean fold
enrichment of constituent GO biological processes was significantly less than 1. Ultimately, we retained 27 functional
annotation clusters which met these criteria (table 1). Of
these, most inferred functions relate to basic cellular function
and maintenance. However, of the retained groups, ‘responses
to oxidative stress’ and closely related ‘GSH, peptide and
sulphur metabolism’ were in line with trends in independent
enzyme activity data, suggesting inferential correspondence
between datasets.
Even though the number of parameters and the number
of data points in the enzyme activity measurements were
small, they revealed the same clusters as microarray determinations based on several thousand genes with regard to the
different populations. Common ordination in multivariate
space revealed a significant co-variation (35.7%; p ¼ 0.002)
between transcriptional and enzymatic data. A total of
65.7% co-inertia was captured by a single axis which separated Vättern samples from the other populations (figure 3).
A second co-inertia axis captured 22 per cent of total (co)variation in the data, and described common differences
attributable to thermal treatments. This finding is very reassuring for environmentally oriented physiologists, as it
suggests that evolutionarily relevant findings can be made
with datasets that are manageable in terms of measuring.
Proc R Soc B 280: 20122974
functional cluster
5
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Table 1. Functional enrichment analysis of differentially transcribed genes. (Functional clusters are based on inter-relationships and redundancies among GO
biological processes, implemented in DAVID. Ad hoc descriptions of retained clusters are based on common themes/functions of constituent GO annotations.
Clusters are ranked by overall enrichment score (enrich. score). Data pertaining to the number of GO terms (no. BP), the number of differentially expressed gene
transcripts (no. genes) and the mean (95% quantiles) fold enrichment score (fold enrich.) of constituent GO terms are also presented. Functional clusters
relevant to enzymatic data are italicized.)
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6
PUL (23°C)
GPX
VAT (23°C)
CAT
PUL (17°C)
VAT (17°C)
GSR
Proc R Soc B 280: 20122974
HEL (23°C)
HEL (17°C)
0.8
0.6
0.4
0.2
0
ColA1 ColA2
ColA3
ColA4 ColA5
Figure 3. CoIA of between-group ordinations of log2 transcript abundance (triangles) and GSH enzymatics (circles). Global similarity (co-inertia) between transcription and enzyme datasets is 35.7% ( p ¼ 0.002). A total of 65.7% of co-inertia is described by the first axis (screeplot; inset lower right) which appears to describe
differentiation between Lake Vättern samples from the other populations. Loadings on this axis (correlation circle; inset upper right) suggest that these differences
are largely driven by SOD and GPX activity. The second co-inertia axis capture 22% of total (co)variation in the data, and describes common differences attributable
to thermal treatment—this ‘effect’ seems more pronounced in Helsinki and Lake Pulmankijärvi populations. Loadings on axis two suggest that thermally induced
patterns of (co)variation are mostly observed with respect to GSR and CAT activity. (Online version in colour.)
The observation is also in line with the recent result that there
was more divergence in markers linked to physiologically
important genes than to neutral genes, indicating that physiological processes are important targets of selection
underlying adaptive divergence in this species [43]. The present study goes further than the previous one, as we have
looked into both transcriptional and physiological (i.e.
enzyme activity) data. The observation also lends support
for the use of using transcriptional findings for suggesting
functional pathways in cases where a priori function is not
evident when population divergence is studied.
While transcriptional and enzyme activity data led to
similar conclusions with regard to population differentiation,
the responses to acute temperature increase were somewhat different. With regard to enzyme activity data, no
significant differences between populations in their temperature response were observed. Also, the acute temperature
increase did not affect any of the measured parameters significantly (figure 4). Only 199 genes (215 splice variants) were
identified as potentially differentially expressed in both population and temperature contrasts. By contrast, 1925 genes (2141
unique transcripts) responded transcriptionally to the acute
temperature increase in the three populations. Among the
transcripts differentially regulated, only 11 were in the genes
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SOD
GSH
coding for redox-relevant proteins. Although two of them
were transcripts of a gene coding for GPX (GPX4), post
hoc analysis of probe-specific false discovery rate-corrected
significance levels (q-values) indicated that the fold change in
response to the acute temperature treatment (0.85 in treatment
relative to control) was not statistically significant. It should be
noted that one of the main clusters in GO, based on
transcriptional responses to acute temperature increase, is
regulation of metabolism suggesting that acute temperature
changes may be more associated with metabolism than
redox changes.
There are a couple of possible explanations as to why
there can be significant transcriptional changes but enzyme
activities remain unchanged after an acute temperature
increase. The first is that transcription and translation have
different time courses. Even if transcription of a gene
would later lead to a change in protein activity, the different
time courses [12] would result in transcriptional change
immediately upon an increase in temperature without a measurable change in protein level. The second possibility is that
cellular and tissue conditions are such that increased transcription and further production of a protein is needed to
maintain a constant activity [4]. This can be caused by the
facts that temperature increase speeds up the breakdown of
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glutathione
2.5
0.30
0.25
0.20
0.15
0.10
2.0
mM
0.35
1.5
1.0
0.5
0.05
0
0
glutathione reductase
60
nmol mg–1 min–1
(%) inhibition
80
60
17 23
HEL
17 23
VAT
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SOD
100
40
7
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µmol mg–1 min–1
glutathione peroxidase
50
40
30
20
10
0
17 23
PUL
17 23
HEL
17 23
VAT
17 23
PUL
catalase
µmol mg–1 min–1
300
250
200
150
100
50
0
17 23
HEL
17 23
VAT
17 23
PUL
Figure 4. The effects of temperature on GPX, GSR, SOD and CAT activities, and total GSH concentration in the different populations. n ¼ 9 for the Helsinki population, six for the Lake Vättern population and nine for the Lake Pulmankijärvi population at 238C, and nine for the Helsinki population, seven for the Lake Vättern
population and nine for the Lake Pulmankijärvi population at 178C . The statistical significance of the differences between means was determined using two-way
ANOVA with population and temperature as explanatory factors. An ad hoc test to indicate differences between individual groups was carried out using the
Holm – Sidak method. No statistically significant effects were observed.
the proteins, and that the activity of unit amount of protein is
reduced by increased temperature [44,45]. Notably, while the
former could be investigated using a proteomics approach,
the latter requires protein activity measurements. Overall,
as a response to acute temperature change, any physiological
change must occur either with pre-existing proteins or as a
result of changes in protein –lipid interactions (for reviews
see [46–48]). On the other hand, the population differentiation has occurred over many generations, whereby the
system is in the steady state regarding transcription and
translation which is also reflected in the concordance of
transcriptional and enzyme activity data.
In conclusion, the results show a very good agreement
between transcriptional and enzyme activity data for a biological system in steady state as is the case for population
differentiation, but less so for a system in disequilibrium as
when an acute environmental stress, temperature increase, is
applied. Because of this, and as any fitness-related effect has
to be functional, conclusions of environmental effects on populations should be based on functional data whenever possible.
Predictions of functional effects can be made with highthroughput transcriptional or proteomics observations when
population differentiation is studied, but, if possible, they
should be supported with functional data to ascertain that the
predicted function is actually affected. The use of high-throughput transcriptional data requires no pre-conceived notions of
which ecological and/or selective factors may have been important in driving functional differences. Another possibility is to
make an a priori guess of possibly important environmental
differences, and measure traits that are likely to be associated
with physiological responses to such environmental variations.
The work with stickleback was approved by the National Authority
on Animal Experimentation (Finland).
The authors’ work is supported by grants from the Academy of Finland (grant nos. 129662, 250435, 258078, 133875, 136464 and 134728).
We thank the following people for help: Jose Cano Arias (animal
breeding), Wolfgang Waser and Anti Vasemagi (sampling) and
Heidi Viitaniemi (sample processing). In addition, several other persons helped us during different stages of the study, for which we
extend our gratitude.
Downloaded from http://rspb.royalsocietypublishing.org/ on June 14, 2017
References
2.
3.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15. Heise K, Puntarulo S, Nikinmaa M, Abele D, Portner
HO. 2006 Oxidative stress during stressful heat
exposure and recovery in the North Sea eelpout
Zoarces viviparus L. J. Exp. Biol. 209, 353 –363.
(doi:10.1242/jeb.01977)
16. Heise K, Puntarulo S, Nikinmaa M, Lucassen M,
Portner HO, Abele D. 2006 Oxidative stress and HIF-1
DNA binding during stressful cold exposure and
recovery in the North Sea eelpout (Zoarces viviparus).
Comp. Biochem. Physiol. A-Mol. Integr. Physiol. 143,
494–503. (doi:10.1016/j.cbpa.2006.01.014)
17. Lesser MP. 2006 Oxidative stress in marine
environments: biochemistry and physiological
ecology. Annu. Rev. Physiol. 68, 253 –278. (doi:10.
1146/annurev.physiol.68.040104.110001)
18. Smyth GK, Speed T. 2003 Normalization of cDNA
microarray data. Methods 31, 265–273. (doi:10.
1016/S1046-2023(03)00155-5)
19. Efron B, Tibshirani R, Storey JD, Tusher V. 2001
Empirical Bayes analysis of a microarray experiment.
J. Am. Stat. Assoc. 96, 1151 –1160. (doi:10.1198/
016214501753382129)
20. Schwender H, Krause A, Ickstadt K. 2006 Identifying
interesting genes with siggenes. RNews 6, 45 –50.
21. Debat V, David P. 2001 Mapping phenotypes:
canalization, plasticity and developmental stability.
Trends Ecol. Evol. 16, 555 –561. (doi:10.1016/
S0169-5347(01)02266-2)
22. Huang DW et al. 2007 The DAVID gene functional
classification tool: a novel biological module-centric
algorithm to functionally analyze large gene lists.
Genome Biol. 8, R183. (doi:10.1186/gb-2007-8-9r183)
23. Maglott D, Ostell J, Pruitt KD, Tatusova T. 2005
Entrez Gene: gene-centered information at NCBI.
Nucleic Acids Res. 33, D54–D58. (doi:10.1093/nar/
gki031)
24. Sherman BT et al. 2007 DAVID Knowledgebase: a
gene-centered database integrating heterogeneous
gene annotation resources to facilitate highthroughput gene functional analysis. BMC
Bioinform. 8, 426. (doi:10.1186/1471-2105-8-426)
25. Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor
B, Brazma A, Huber W. 2005 BIOMART and
BIOCONDUCTOR: a powerful link between biological
databases and microarray data analysis.
Bioinformatics 21, 3439 –3440. (doi:10.1093/
bioinformatics/bti525)
26. Smedley D, Haider S, Ballester B, Holland R, London
D, Thorisson G, Kasprzyk A. 2009 BIOMART—
biological queries made easy. BMC Genomics 10, 22.
(doi:10.1186/1471-2164-10-22)
27. Harris MA et al. 2004 The gene ontology (GO)
database and informatics resource. Nucleic Acids Res.
32, D258 –D261. (doi:10.1093/nar/gkh036)
28. Huang DW, Sherman BT, Lempicki RA. 2009
Systematic and integrative analysis of large
gene lists using DAVID bioinformatics resources.
Nat. Protoc. 4, 44 –57. (doi:10.1038/nprot.2008.
211)
29. Lilley TM, Ruokolainen L, Meierjohann A, Kanerva
M, Stauffer J, Laine VN, Nikinmaa M. In press.
Impacts of organic tin compounds on redox
regulation and complement reaction in natural
populations of Daubenton’s bats (Myotis
daubentonii). Comp. Biochem. Physiol. C Toxicol.
Pharmacol.
30. Vuori KA, Kanerva M, Ikonen E, Nikinmaa M. 2008
Oxidative stress during Baltic salmon feeding
migration may be associated with yolk-sac fry
mortality. Environ. Sci. Technol. 42, 2668–2673.
(doi:10.1021/es702632c)
31. Smith IK, Vierheller TL, Thorne CA. 1988 Assay of
glutathione reductase in crude tissue homogenates
using 5,50 -dithiobis(2-nitrobenzoic acid). Anal.
Biochem. 175, 408–413. (doi:10.1016/00032697(88)90564-7)
32. Habig WH, Pabst MJ, Jakoby WB. 1974 Glutathione
S-transferases. The first enzymatic step in
mercapturic acid formation. J. Biol. Chem. 249,
7130– 7139.
33. Deisseroth A, Dounce AL. 1970 Catalase: physical
and chemical properties, mechanism of
catalysis, and physiological role. Physiol. Rev. 50,
319–375.
34. Fossati P, Prencipe L, Berti G. 1980 Use of
3,5-dichloro-2-hydroxybenzenesulfonic acid/4aminophenazone chromogenic system in direct
enzymic assay of uric acid in serum and urine. Clin.
Chem. 26, 227–231.
35. Spitze K. 1993 Population—structure in Daphniaobtusa—quantitative genetic and allozymic
variation. Genetics 135, 367–374.
36. Cano JM, Matsuba C, Makinen H, Merila J. 2006 The
utility of QTL-Linked markers to detect selective
sweeps in natural populations: a case study of the
EDA gene and a linked marker in threespine
stickleback. Mol. Ecol. 15, 4613–4621. (doi:10.
1111/j.1365-294X.2006.03099.x)
37. Antao T, Lopes A, Lopes RJ, Beja-Pereira A, Luikart G.
2008 LOSITAN: a workbench to detect molecular
adaptation based on a Fst-outlier method.
BMC Bioinform. 9, 323. (doi:10.1186/1471-2105-9-323)
38. Dray S, Chessel D, Thioulouse J. 2003 Co-inertia
analysis and the linking of ecological data
tables. Ecology 84, 3078 –3089. (doi:10.1890/030178)
39. Jombart T, Pontier D, Dufour AB. 2009 Genetic
markers in the playground of multivariate analysis.
Heredity 102, 330– 341. (doi:10.1038/hdy.2008.
130)
40. Dray S, Dufour AB. 2007 The ade4 package:
implementing the duality diagram for ecologists.
J. Stat. Softw. 22, 1– 20.
41. Makinen HS, Cano M, Merila J. 2008 Identifying
footprints of directional and balancing selection in
marine and freshwater three-spined stickleback
(Gasterosteus aculeatus) populations. Mol. Ecol.
17, 3565–3582. (doi:10.1111/j.1365-294X.2008.
03714.x)
Proc R Soc B 280: 20122974
4.
Furlong EEM. 2011 Molecular biology: a fly in the
face of genomics. Nature 471, 458–459. (doi:10.
1038/471458a)
Abreu RD, Penalva LO, Marcotte EM, Vogel C. 2009
Global signatures of protein and mRNA expression
levels. Mol. Biosyst. 5, 1512 –1526.
Schwanhausser B, Busse D, Li N, Dittmar G,
Schuchhardt J, Wolf J, Chen W, Selbach M. 2011
Global quantification of mammalian gene
expression control. Nature 473, 337– 342. (doi:10.
1038/nature10098)
Nikinmaa M, Waser W. 2007 Molecular and cellular
studies in evolutionary physiology of natural
vertebrate populations: influences of individual
variation and genetic components on sampling and
measurements. J. Exp. Biol. 210, 1847 –1857.
(doi:10.1242/jeb.002717)
Oleksiak MF, Roach JL, Crawford DL. 2005 Natural
variation in cardiac metabolism and gene expression
in Fundulus heteroclitus. Nat. Genet. 37, 67 –72.
Fu N, Drinnenberg I, Kelso J, Wu JR, Paabo S, Zeng
R, Khaitovich P. 2007 Comparison of protein and
mRNA expression evolution in humans and
chimpanzees. PLoS ONE 2, e216. (doi:10.1371/
journal.pone.0000216)
Shim YH, Paik YK. 2010 Caenorhabditis elegans
proteomics comes of age. Proteomics 10, 846–857.
(doi:10.1002/pmic.200900542)
Jones FC et al. 2012 The genomic basis of adaptive
evolution in threespine sticklebacks. Nature 484,
55 –61. (doi:10.1038/nature10944)
Leder EH, Merila J, Primmer CR. 2009 A flexible
whole-genome microarray for transcriptomics in
three-spine stickleback (Gasterosteus aculeatus).
BMC Genomics 10, 426. (doi:10.1186/14712164-10-426)
Östlund-Nilsson S, Mayer I, Huntingford F. 2007 The
biology of three-spined stickleback. Boca Raton, FL:
CRC Press.
Leveelahti L, Leskinen P, Leder EH, Waser W,
Nikinmaa M. 2011 Responses of threespine
stickleback (Gasterosteus aculeatus, L) transcriptome
to hypoxia. Comp. Biochem. Physiol. D Genomics
Proteomics 6, 370 –381. (doi:10.1016/j.cbd.
2011.08.001)
Buckley BA, Gracey AY, Somero GN. 2006 The
cellular response to heat stress in the goby
Gillichthys mirabilis: a cDNA microarray and proteinlevel analysis. J. Exp. Biol. 209, 2660–2677.
(doi:10.1242/jeb.02292)
Nikinmaa M, Rytkonen KT. 2011 Functional
genomics in aquatic toxicology: do not forget the
function. Aquat. Toxicol. 105, 16 –24. (doi:10.1016/
j.aquatox.2011.05.019)
Rytkonen KT, Vuori KAM, Primmer CR, Nikinmaa M.
2007 Comparison of hypoxia-inducible factor-1
alpha in hypoxia-sensitive and hypoxia-tolerant fish
species. Comp. Biochem. Physiol. D-Genomics
Proteomics 2, 177–186. (doi:10.1016/j.cbd.2007.03.
001)
rspb.royalsocietypublishing.org
1.
8
Downloaded from http://rspb.royalsocietypublishing.org/ on June 14, 2017
44. Coquelle N, Fioravanti E, Weik M, Vellieux F, Madern
D. 2007 Activity, stability and structural studies of
lactate dehydrogenases adapted to extreme thermal
environments. J. Mol. Biol. 374, 547 –562. (doi:10.
1016/j.jmb.2007.09.049)
45. Gatt S. 1969 Thermal lability of beta galactosidase
from pink salmon liver. Science 164, 1422– 1423.
(doi:10.1126/science.164.3886.1422)
46. Cossins AR, Raynard RS. 1987 Adaptive responses of
animal cell membranes to temperature. In
Temperature and animal cells (eds K Bowler,
BJ Fuller), pp. 95– 111. Cambridge, UK: Company of
Biologists.
47. Hazel JR. 1984 Effects of temperature on the
structure and metabolism of cell membranes in fish.
Am. J. Physiol. 246, R460 –R470.
48. Hochachka PW, Somero GN. 2002 Biochemical
adaptation: mechanism and process in
physiological evolution. Oxford, UK: Oxford University
Press.
9
rspb.royalsocietypublishing.org
42. Weber RE, Hiebl I, Braunitzer G. 1988 High altitude
and hemoglobin function in the vultures Gyps
rueppellii and Aegypius monachus. Biol. Chem.
Hoppe-Seyler 369, 233 –240. (doi:10.1515/bchm3.
1988.369.1.233)
43. Shimada Y, Shikano T, Merila J. 2011 A high
incidence of selection on physiologically important
genes in the three-spined stickleback, Gasterosteus
aculeatus. Mol. Biol. Evol. 28, 181– 193. (doi:10.
1093/molbev/msq181)
Proc R Soc B 280: 20122974