, 20122974, published online 30 January 2013280 2013 Proc. R. Soc. B Primmer, J. Merilä and E. H. LederM. Nikinmaa, R. J. S. McCairns, M. W. Nikinmaa, K. A. Vuori, M. Kanerva, T. Leinonen, C. R. sticklebackequilibrium and disequilibrium levels in the three-spined Transcription and redox enzyme activities: comparison of  

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ResearchCite this article: Nikinmaa M, McCairns RJS,

Nikinmaa MW, Vuori KA, Kanerva M, Leinonen

T, Primmer CR, Merila 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: miknik@utu.fi

& 2013 The Author(s) Published by the Royal Society. All rights reserved.

Transcription and redox enzyme activities:comparison of equilibrium anddisequilibrium levels in the three-spinedstickleback

M. Nikinmaa1, R. J. S. McCairns2, M. W. Nikinmaa1, K. A. Vuori1, M. Kanerva1,T. Leinonen2, C. R. Primmer1, J. Merila2 and E. H. Leder1

1Department of Biology, University of Turku, 20014, Finland2Ecological 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 measure-

ments 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 tran-

scriptomics 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. IntroductionA drawback of most genomic studies is that functional information is not com-

bined 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 influ-

ence the final outcome of any metabolic network. Also, information on the

genome-to-gene product activity relationship is confined mostly to a small

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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. Con-

sequently, there is a need for investigations simultaneously

assessing transcriptional changes and protein activities for

the same animals with the ultimate goal of evaluating con-

ditions 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 adapta-

bility 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. Simi-

lar high-throughput methodology is not available for most

protein activity measurements. As an example, while it is poss-

ible 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 evol-

utionary 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

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) FishMature sticklebacks from Lake Pulmankijarvi in Finnish Lapland

(698580500 N, 2785903500 E), the Baltic Sea in the vicinity of Hel-

sinki, Finland (5984901700 N, 2285801400 E) and Lake Vattern

(5883800000 N, 1485100000 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 hatch-

ing, 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 con-

ditions (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 temp-

erature 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 main-

tained 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 eutha-

nized 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.

(b) Sample preparationFor 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 (Pro-

mega; 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 K2HPO4þ 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 analysisA 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

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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 back-

ground 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’ par-

ameter 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 differen-

tially expressed between temperature treatments, data were

analysed via the empirical Bayes procedure (populations

pooled) contrasting control and temperature treatment groups.

(d) Functional annotation analysisThe database for annotation, visualization and integrated discov-

ery (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]. Stickle-

back 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 treat-

ments 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 annota-

tions 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 determinationsSamples 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 stan-

dard (Sigma Chemicals, St. Louis, MO, USA) as described by

Lilley et al. [29].

( f ) Determinations of enzyme activitiesThe enzyme activity determinations were carried out mainly as

described by Vuori et al. [30] at 258C. Briefly, glutathione

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 dis-

mutase (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 con-

tents 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).

(g) Analysis of trait divergence in enzyme andglutathione concentration data

To infer if between population differences in enzyme data were

best explained by neutral or putatively selective processes, we con-

trasted an index of quantitative genetic differentiation (QST) with

that of neutral genetic divergence [35]. Among and within-

group 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 popu-

lation. Confidence intervals (95%) were obtained by parametric

bootstrapping (10 000 simulations). The range of neutral expec-

tation 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 simu-

lated range of neutral expectation [37], and used the observed

range of FST values for comparison against QST estimates.

(h) Multivariate association between mRNA expressionand 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 transcrip-

tional 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 individ-

uals 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 evalu-

ating their respective loadings on the dominant principal axes:

probes whose squared score exceeded the 90th quantile of a distri-

bution describing all squared loading scores for a focal axis were

deemed to be significant.

0.30 ***

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catalase

SOD glutathione reductase

glutathione peroxidase

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Figure 1. GPX, GSR, SOD and CAT activities and total GSH concentration in Helsinki (HEL, n ¼ 18), Vattern (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 toindicate differences between individual groups was carried out using the Holm – Sidak method. Asterisks indicate that the population with asterisks differssignificantly from the other populations. *p , 0.05; ***p , 0.001.

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 000iterations) for estimates of the index of quantitative trait differentiation(QST) for enzyme data. Vertical lines denote the putative range of neutraldifferentiation (FST), as inferred from variation in 17 assumedly neutralmicrosatellite loci. (Online version in colour.)

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3. Results and discussionWith 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 Vattern 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 rela-

tively 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 func-

tion 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).

Table 1. Functional enrichment analysis of differentially transcribed genes. (Functional clusters are based on inter-relationships and redundancies among GObiological 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 genetranscripts (no. genes) and the mean (95% quantiles) fold enrichment score (fold enrich.) of constituent GO terms are also presented. Functional clustersrelevant to enzymatic data are italicized.)

functional cluster enrich. score no. BP no. genes fold enrich.

regulation of protein localization transport and secretion 2.34 14 63 2.50 (1.50 – 4.20)

detection of external stimuli 1.66 4 13 2.95 (2.05 – 4.28)

response to steroidal stimuli 1.36 4 12 2.83 (2.02 – 3.60)

regulation of cellular growth 1.11 10 24 1.76 (1.19 – 2.40)

regulation of GTPase activity 1.11 12 40 2.23 (1.10 – 3.90)

regulation of cell adhesion 1.08 3 12 2.17 (1.83 – 2.55)

glucose and carbohydrate homeostasis 1.08 3 9 2.37 (2.20 – 2.65)

regulation of ion transport 1.08 6 12 2.40 (1.83 – 3.70)

nuclear organization 1.03 6 11 2.18 (1.55 – 2.68)

regulation of protein signalling 0.99 3 9 2.70 (2.70 – 2.70)

SMAD protein localization 0.91 3 11 2.50 (1.61 – 3.95)

glucose and carbohydrate metabolism 0.87 27 26 2.37 (1.63 – 3.87)

membrane protein proteolysis 0.87 4 8 3.23 (1.44 – 5.82)

regulation of immune response 0.86 13 28 2.31 (1.04 – 4.80)

response to intra-cellular pathogens 0.85 7 10 2.49 (1.33 – 3.80)

mitochondrial organization 0.83 3 7 3.70 (2.00 – 4.70)

regulation of intra-cellular protein transport 0.81 8 19 1.68 (1.24 – 2.03)

water homeostasis 0.81 6 10 3.22 (1.65 – 4.70)

response to oxidative stress 0.75 9 25 1.92 (1.22 – 2.62)

regulation of lipid metabolism 0.74 14 17 2.33 (1.23 – 3.80)

regulation of macromolecular secretion 0.64 7 7 2.29 (1.76 – 2.70)

exocytosis 0.63 6 26 1.53 (1.13 – 2.03)

GSH, peptide and sulphur metabolism 0.60 3 17 1.57 (1.24 – 1.87)

regulation of cellular development 0.59 7 25 1.90 (1.22 – 2.70)

transport of organic acids 0.55 5 16 1.60 (1.14 – 2.52)

regulation of muscle development 0.53 10 10 1.86 (1.19 – 3.33)

DNA catabolism 0.51 7 14 1.71 (1.36 – 1.97)

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The differentiation between populations was also seen in

the transcriptome data. In the microarray analysis, we ident-

ified 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 Vattern 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 signifi-

cantly 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 determi-

nations 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 separ-

ated Vattern samples from the other populations (figure 3).

A second co-inertia axis captured 22 per cent of total (co)var-

iation in the data, and described common differences

attributable to thermal treatments. This finding is very reas-

suring for environmentally oriented physiologists, as it

suggests that evolutionarily relevant findings can be made

with datasets that are manageable in terms of measuring.

PUL (23°C)

HEL (23°C)

PUL (17°C)

HEL (17°C)

VAT (17°C)

0.8

0.6

0.4

0.2

ColA1 ColA2 ColA3 ColA4 ColA50

VAT (23°C)

GSH

CAT GSR

GPX

SOD

Figure 3. CoIA of between-group ordinations of log2 transcript abundance (triangles) and GSH enzymatics (circles). Global similarity (co-inertia) between transcrip-tion 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 describedifferentiation between Lake Vattern samples from the other populations. Loadings on this axis (correlation circle; inset upper right) suggest that these differencesare 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 attributableto thermal treatment—this ‘effect’ seems more pronounced in Helsinki and Lake Pulmankijarvi populations. Loadings on axis two suggest that thermally inducedpatterns of (co)variation are mostly observed with respect to GSR and CAT activity. (Online version in colour.)

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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 phy-

siological processes are important targets of selection

underlying adaptive divergence in this species [43]. The pre-

sent 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 some-

what different. With regard to enzyme activity data, no

significant differences between populations in their tempera-

ture response were observed. Also, the acute temperature

increase did not affect any of the measured parameters signifi-

cantly (figure 4). Only 199 genes (215 splice variants) were

identified as potentially differentially expressed in both popu-

lation 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

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 mea-

surable change in protein level. The second possibility is that

cellular and tissue conditions are such that increased tran-

scription 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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40

30

20

10

0

80

60

40

SOD glutathione reductase

17 23 17 23 17 23PULVATHEL

glutathione peroxidase glutathione

catalase

17 23 17 23 17 23PULVATHEL

17 23 17 23 17 23PULVATHEL

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 popu-lation, six for the Lake Vattern population and nine for the Lake Pulmankijarvi population at 238C, and nine for the Helsinki population, seven for the Lake Vatternpopulation and nine for the Lake Pulmankijarvi population at 178C . The statistical significance of the differences between means was determined using two-wayANOVA with population and temperature as explanatory factors. An ad hoc test to indicate differences between individual groups was carried out using theHolm – Sidak method. No statistically significant effects were observed.

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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 differen-

tiation 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 biologi-

cal 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 popu-

lations should be based on functional data whenever possible.

Predictions of functional effects can be made with high-

throughput 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-through-

put transcriptional data requires no pre-conceived notions of

which ecological and/or selective factors may have been impor-

tant 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 Authorityon Animal Experimentation (Finland).

The authors’ work is supported by grants from the Academy of Fin-land (grant nos. 129662, 250435, 258078, 133875, 136464 and 134728).We thank the following people for help: Jose Cano Arias (animalbreeding), Wolfgang Waser and Anti Vasemagi (sampling) andHeidi Viitaniemi (sample processing). In addition, several other per-sons helped us during different stages of the study, for which weextend our gratitude.

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