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Effect of Reproduction on the Consistency of the Between–Line Type Divergence in Laboratory Mice Selected on Basal Metabolic Rate Julita Sadowska* Andrzej K. Gębczyński Marek Konarzewski Institute of Biology, University of Białystok, Świerkowa 20B, 15-950 Białystok, Poland Accepted 12/12/2014; Electronically Published 1/29/2015

ABSTRACT Artificial selection experiments are an effective tool for testing evolutionary hypotheses, because they allow one to separate genetic and environmental variances of the phenotype. However, it is unclear whether trait divergence typically selected early in life persists over an animal’s life and altered physiological states, such as reproduction. Here we analyzed the longterm consistency of the between–line type divergence in basal metabolic rate (BMR) selected at 12 wk of age in laboratory mice. We measured BMR in nonreproducing and reproducing females at the age of 22 wk and then at 27 wk of age. Our results show that within both the reproducing group and the control group, the between–line type separation in BMR is consistently retained over time and reproductive status. Metabolically active internal organs (heart, liver, kidneys, and small intestine) also consistently differed in size between the two line types with no significant long-term effect of reproduction. The observed consistency of the between–line type divergence in BMR suggests the existence of the persistent effect of the selection on metabolic traits applied early in life. Moreover, BMR variation achieved by means of artificial selection is considerably higher than that found in natural/unmanipulated populations. The latter may therefore be characterized by insufficient variance to statistically resolve correlations involving BMR.

Introduction Selection experiments are an effective method of testing evolutionary hypotheses in the comfort of controlled laboratory conditions (Garland and Carter 1994; Rose et al. 1996; Gibbs 1999; Feder et al. 2000; Bennett 2003; Garland 2003; Konarzewski et al. 2005; Garland and Rose 2009; Konarzewski and *Corresponding author; e-mail: [email protected]. Physiological and Biochemical Zoology 88(3):328–335. 2015. q 2015 by The University of Chicago. All rights reserved. 1522-2152/2015/8803-4071$15.00. DOI: 10.1086/680167

Książek 2013; Sadowska et al. 2013). Such experiments enable one to separate the environmental and genetic variance components of the phenotype and therefore provide information on direct and correlated responses to well-defined selection criteria (Konarzewski et al. 2005; Garland and Rose 2009). However, despite undeniable efficacy of the artificial selection approach, some aspects still remain understudied. For instance, selection protocols are typically applied early in life (Nielsen et al. 1997; Koch and Britton 2001, 2005; Selman et al. 2001; Garland et al. 2002; Książek et al. 2004; Holt et al. 2005; Konarzewski et al. 2005; Rosochacki et al. 2005; Sadowska et al. 2005, 2008; Bronikowski et al. 2006; Rezende et al. 2006; Brzęk et al. 2007; Fedorowicz et al. 2007; McDonald and Nielsen 2007; Kane et al. 2008; Gębczyński and Konarzewski 2009a, 2009b), so it is unclear whether a desired response in the selected trait persists at a relatively unchanged level later in life, or, in other words, how strongly the trait is correlated with itself across subsequent life stages (Morgan et al. 2003; Bronikowski et al. 2006; Kane et al. 2008). Indeed, even measurements of the same trait at different life stages or physiological conditions may represent the expression of different genes and therefore may not be directly comparable (Dohm 2002; Morgan et al. 2003). Long-term consistency of the selected traits therefore cannot be automatically assumed, as the expression of selected and correlated traits across different ages later in life may change (Morgan et al. 2003; Kane et al. 2008). For example, expression of genes regulating energy metabolism (e.g., fatty acid breakdown, oxidative phosphorylation, mitochondrial electron transport) is known to decrease with age (Bronikowski et al. 2003). Król et al. (2011) demonstrated downregulation of the genes directly related to respiration and uncoupling in brown adipose tissue of lactating mice. On the other hand, in the mouse lines divergently selected for basal metabolic rate (BMR) at 12 wk of age, the between-line separation is preserved even after exposure to calorie restriction for 24 wk (Brzęk et al. 2012). Also Morgan et al. (2003) demonstrated that selection for high early-age wheel-running activity in mice is strongly correlated with itself later in midlife but not at older age (84 wk). Another relevant and still debatable idea is the postulated positive long-term link between basal metabolic rate and resting metabolic rate (BMR/RMR) and reproductive performance. The idea behind the link has emerged with the work of Daan et al. (1990), who proposed that BMR and energy expenditures incurred by parental effort should be functionally coupled through the size/capacity and metabolic costs of maintenance of energy-acquiring organs (i.e., alimentary tract; see also Konarzewski and Diamond 1995). Since then a number of studies tackled this problem with mixed results (Glazier

Basal Metabolic Rate Divergence Consistency 1985; Derting 1989; Hayes et al. 1992; Kam et al. 2003; Sadowska et al. 2013). The inconsistency of results has been recently attributed to the lack of repeatability of BMR/RMR in reproducing individuals, stemming from a poor correspondence between BMR measured before and after a reproductive bout (Duarte et al. 2010; see also Glazier 1985; Johnson et al. 2001; Kam et al. 2003; Król and Speakman 2003; Speakman et al. 2004; Johnston et al. 2007; Lovegrove 2009). Low reproducibility of BMR is attributed to reproduction-elicited hypertrophy of key viscera (mainly intestines and liver, whose upkeep contributes heavily to the total BMR)—a well-documented phenomenon essential for coping with energetic requirement of reproduction (Hammond 1997; Casirola and Ferraris 2003). In laboratory mice the enlargement of viscera mass/size may persist for up to 300 d after weaning and elevates metabolic costs of maintenance (BMR/RMR) in comparison to the prereproductive state (Hammond 1997; Casirola and Ferraris 2003; Duarte et al. 2010). The lack of repeatability of pre- and postreproductive BMR/ RMR separated by long time periods may simply stem from low levels of phenotypic variation in studied populations, which may simply be insufficient to statistically resolve the postulated correlations. For this reason, it is meaningful to test them on animals characterized by a considerable variation in studied traits, achieved through, for example, artificial selection. In our mice selected divergently for high (H-BMR) and low (L-BMR) level of basal metabolism, the between–line type divergence in BMR measured at the age of 12 wk is currently maintained at a relatively stable level of a 45%–50% difference. Furthermore, animals from the H-BMR line type have a considerably higher food intake and larger guts than their L-BMR counterparts (Książek et al. 2004; Gębczyński and Konarzewski 2009a). Mother mice with genetically determined H-BMR are also characterized by higher parental investment capacity, measured as the offspring growth rate (Sadowska et al. 2013). Our mouse line types are therefore a relevant model for testing (1) whether the selection protocol applied early in life results in long-term effects and (2) whether the between–line type BMR divergence is consistently preserved over time and changing physiological state incurred by reproduction. To tackle this problem we measured BMR in reproducing and nonreproducing animals of both line types at three remote time points. We expected that in the nonreproducing individuals the between–line type separation in BMR would be similar to that observed at the age of first measurements, when the selection protocol had been applied. In the reproducing group, we expected to find a slight decrease in the between–line type difference due to the changes elicited by reproduction, however, with the line type divergence still preserved at a highly significant level. Material and Methods Animals and Experimental Design We used female mice from the thirty-eighth generation of a selection experiment conducted at the Institute of Biology,

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University of Białystok. The imposed selective regimen is designed to produce two line types of animals differing with respect to body mass–corrected BMR: the H-BMR line type and the L-BMR line type (for details see Książek et al. 2004; Gębczyński and Konarzewski 2009a; Konarzewski and Książek 2013). In each generation we maintain 30–35 families depending on the current reproductive success in each of the selection line types. BMR measurements are carried out when the animals are 12 wk old. After BMR measurements no more than two individuals per family with the highest (for the H-BMR line type) and lowest (for the L-BMR line type) BMR scores are chosen as progenitors. In this study we used 99 (47 H-BMR and 52 L-BMR) females and divided them into two groups. One group (thereafter called the experimental group) of 23 H-BMR and 27 LBMR females was mated at the approximate age of 14 wk and allowed to fully raise their litters. The remaining females (24 H-BMR and 25 L-BMR individuals) were ascribed to the control group, which was not mated. All animals were housed at the temperature of 237C and 12L∶12D with unlimited access to murine chow (Labofeed, Kcynia, Poland) and water. In both groups we measured BMR at three different time points: at the age of 12 wk (as a part of the selection protocol), at the age of 22 wk (measured 2 wk after weaning in the experimental/reproducing group), and at 27 wk. After the last BMR measurement animals were killed and metabolically active organs (liver, kidneys, heart, intestines, spleen, and also interscapular brown adipose tissue [IBAT]) dissected, cleaned of blood and food remains, and weighted to the nearest 0.01 g. BMR Measurements BMR was measured as oxygen consumption with a flowthrough respirometry system during 2 h of a 3-h trial. Before the measurement all animals were fasted for approximately 3 h to eliminate the costs of food digestion. Mice were placed individually in three metabolic chambers (each 350 cm3 volume), which were submerged in a 327C water bath (a temperature that is within our animals’ thermoneutral zone) and monitored sequentially (approximately 20 min per chamber) during the measurement period. Air was pumped from outside the building, dried (Drierite), and pushed through a copper coil submerged in the water bath for temperature equilibration. The air stream was then divided into three separate streams (plus a control stream), with flow rates of 400 mL min21 individually controlled by mass flow meters (Sierra Instruments, Monterey, CA, or Beta Erg, Warsaw, Poland). Air was then forced through the metabolic chambers with the animals and further through the computer-controlled channel multiplexer (Sable Systems, Las Vegas, NV). Air was thereafter scrubbed of CO2 (Carboabsorb AS, BDH Laboratory Supplies), dried one more time (Drierite), subsampled at the rate of 75 mL min21, and fed to an oxygen analyzer (S-3A/I Applied Electrochemistry). Signal from the analyzer was converted from analog to digital format and averaged by the com-

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puter every 1 s. BMR was defined as the lowest stable reading that did not vary by more than 0.01% of oxygen concentration for at least 4 min.

et al. (2005). To estimate the 95% confidence intervals (thereafter called ddrift) for the dX we modified equation (16) from Henderson (1997):

Statistics

ddrift

We analyzed BMR variation with repeated-measures ANCOVA with the line type and experimental treatment (reproducing vs. nonreproducing) as the fixed factors, family affiliation nested within the line type as a random factor, and body mass as a covariate. Since we found the reproductive state # measurement order interaction statistically significant, we further analyzed BMR variation separately at each of the three time points by means of ANCOVA with the line type and experimental treatment as fixed factors, family affiliation nested within the line type as a random factor, and body mass as a covariate. In these analyses we applied the Bonferroni correction with a p 0.016. For comparative purposes we also calculated coefficients of variation (CVs) for each of the three subsequent BMR measurements, within the nonreproducing and reproducing groups of animals, as well as the within each of the line types. Also, within the nonreproducing and reproducing groups of animals of each of the line types, we calculated the intraclass correlations of body mass–corrected and whole–body mass BMR using the results of all three measurements (t; Sokal and Rholf 1981; Lessells and Boag 1987). Organ masses were analyzed by ANCOVA with the line type and reproductive history status (control vs. postreproductive mice) as fixed factors, family affiliation nested within the line type as a random factor, and body mass as covariate. All statistical analyses were carried out with SAS 9.3 (SAS Institute, Cary NC). Since our selection line types are not replicated, we took into account a possibility that differences in BMR level may be due to genetic drift (or founder effect), rather than a genuine effect of artificial selection. Therefore, we additionally analyzed the examined traits (BMR and internal organ masses) according to Henderson’s guidelines (Henderson 1997; Konarzewski et al. 2005), in both groups (experimental and control) and for all three measurements obtained through their life. We expressed the magnitude of separation of the high and low line type of a given trait as multiples of intraline-type phenotypic standard deviations (dX), following methods of Konarzewski

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 ≅ 4 h2X F 1 ; n

where h2 is trait heritability (for BMR h2 p 0.4; Konarzewski et al. 2005; for organ masses h2 p 0.4; Schlager 1968) and F the inbreeding coefficient (F p 0.26; calculated from eq. [3.5] from Falconer and Mackay 1996 for the effective population size for generation 38). We assumed the same heritability estimated at 12 wk of age (h2 p 0.4) for BMR in all three analyzed time points. Results BMR Divergence Repeated-measures ANCOVA of BMR showed significant effects of the line type (F1, 55 p 390.52, P ! 0.001), the experimental treatment (F1, 220 p 33.02, P ! 0.001), body mass (F1, 220 p 51.70, P ! 0.001), and the measurement order (F2, 220 p 21.68, P ! 0.001). The line type # experimental treatment interaction was not significant (F1, 220 p 0.18, P p 0.673); however, the experimental treatment # measurement order (F2, 220 p 16.56, P ! 0.001) and line type # measurement order (F2, 220 p 4.84, P p 0.008) interactions were significant. We therefore analyzed our data further, separately for each of the three measurements. ANCOVA of BMR at 12 wk of age showed a highly significant line type effect and no differences in the experimental treatment (since the initial physiological state was the same for all animals) but a significant effect of body mass (table 1; fig. 1). There was no line type # experimental treatment interaction (table 1; fig. 1). In the second measurement, with 22-wk-old mice, the line type effect was also highly statistically significant, along with the experimental treatment (the postreproductive animals of both line types had elevated BMR; table 1; fig. 1). Body mass also significantly affected BMR. There was no line type # physiological state interaction (table 1; fig. 1). At the third measurement point (27-wk-old animals) we also found a highly significant between–line type difference in BMR level, as well as a significant effect of body mass on BMR (table 1; fig. 1). The effect of physiological state was not sig-

Table 1: ANCOVA results of basal metabolic rate (BMR) measured at 12, 22, and 27 wk of age in the high BMR– and low BMR– type mice from the control (nonreproducing) and experimental (reproducing) groups Line type Age (wk) 12 22 27

Reproduction effect

Line type # reproduction effect

Body mass

F (df )

P

F (df )

P

F (df )

P

F (df )

P

477.21 (1, 55) 108.84 (1, 54) 184.79 (1, 52)

.0001 .0001 .0001

1.80 (1, 39) 38.05 (1, 35) 2.60 (1, 38)

.30 .001 .12

39.29 (1, 39) 15.68 (1, 35) 16.88 (1, 38)

.001 .001 .001

.59 (1, 39) 1.50 (1, 35) .01 (1, 39)

.45 .23 .91

Basal Metabolic Rate Divergence Consistency

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0.95) and in the nonreproducing groups of animals significant only for the H-BMR line type (H-BMR: t p 0.33, P p 0.006; L-BMR: t p 0.14, P p 0.13). Likewise, the whole-body BMR repeatability was not significant in the reproducing group (HBMR: t p 20.01, P p 0.52; L-BMR: t p 20.19, P p 0.96) and in nonreproducing animals significant only in the H-BMR line type (H-BMR: t p 0.37, P p 0.002; L-BMR: t p 0.17, P p 0.1). The standardized between–line type differences (d) in BMR for the reproducing group of animals in all the three consecutive measurements was higher than that expected due to a possible effect of genetic drift alone (d p 5.42 vs. ddrifit p 1.41, d p 1.82 vs. ddrift p 1.41, and d p 2.79 vs. ddrift p 1.41 for the first, second, and third measurements, respectively). In the group of nonreproducing animals the standardized between– line type differences were also higher than ddrift (d p 5.15 vs. ddrift p 1.40, d p 2.91 vs. ddrift p 1.40, d p 3.13 vs. ddrift p 1.41 for the first, second, and third measurements, respectively). Please note that ddrift depends strongly on h2. However, even assuming an unlikely high heritability of h2 p 1, our results for dX in the three time points still fell outside ddrift. Morphometrics

Figure 1. Body mass–corrected basal metabolic rate (BMR) in reproducing (A) and control (B) mice from the high-BMR (filled circles) and low-BMR (open circles) line types measured at 12, 22, and 27 wk of age.

nificant, along with the line type # physiological state interaction (table 1; fig. 1). The CV of BMR of the combined control and experimental (reproducing) groups was approximately 20% (table 2). The within–line type CV of the first BMR measurement was ca. 6%–8% and increased consistently within both line types after reproduction, as well as in the control group (table 2). Repeatability of body mass–corrected BMR, calculated as the intraclass correlation coefficient for the H-BMR– and LBMR–line type animals was not significant in the reproducing groups (H-BMR: t p 0.04, P p 0.35; L-BMR: t p 20.17, P p

Masses of all examined organs (intestines, kidneys, heart, liver, spleen, IBAT) differed significantly between line types, with the H-BMR–line type animals having heavier viscera in both the control and experimental (postreproductive) groups (tables 3, 4; with the exception of IBAT, which was consistently heavier in the L-BMR animals). Reproductive history had no effect on the masses of organs (tables 3, 4; fig. 2). Body mass affected only intestine, kidney, and heart mass but not liver, spleen, or IBAT (tables 3, 4). There were also no line type # reproductive history interactions (table 3). In case of the intestine and kidneys in both groups, the magnitude of the between–line type divergence in organ mass was high enough to ascribe the observed differences to the applied selection protocol, rather than to genetic drift (fig. 2). Discussion We demonstrated here that the between–line type divergence in the primary selected trait (BMR) in nonreproducing animals at approximately 6 mo of age is sustained at the magnitude comparable to that found in mice of 12 wk of age

Table 2: Coefficient of variation (CV) of basal metabolic rate (BMR) estimated within the combined group of nonreproducing and reproducing females (CVselection) and within each line type, separately in the control and reproducing groups of mice CVselection (%) Age (wk) 12 22 27

CVL (%)

CVH (%)

Reproducing

Control

Reproducing

Control

Reproducing

Control

20.2 16.6 22.0

20.4 21.0 21.8

8.0 11.8 12.5

6.6 9.2 18.9

6.1 10.0 12.6

7.5 12.8 12.1

Note. CVH p CV for high BMR; CVL p CV for low BMR.

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Table 3: ANCOVA results for organ mass differences between high BMR– and low BMR–type mice Line type

Intestine Liver Kidneys Heart Spleen IBAT

Reproduction effect

Line type # reproduction effect

Body mass

F

P

F

P

F

P

F

P

80.54 17.56 92.81 20.57 5.43 4.88

!.0001 !.0001 !.0001 !.0001 .022 .029

.42 .14 .07 .08 1.07 1.65

.518 .714 .79 .773 .307 .206

6.80 3.41 35.26 10.04 .26 1.09

.013 .072 !.0001 .003 .61 .302

.07 .24 .75 2.53 .01 1.54

.794 .629 .392 .120 .927 .222

Note. All F values tested with 1 and 52 degrees of freedom. BMR p basal metabolic rate; IBAT p interscapular brown adipose tissue.

subjected to the divergent selection on BMR. More importantly, the group of females that underwent reproduction also maintained highly significant between–line type divergence in BMR. Differences in BMR were paralleled by significant differences in organ masses between the two line types in both the control group and the experimental group. Our results therefore strongly indicate that the selection on H-BMR/L-BMR applied early in life has long-lasting phenotypic effects. Furthermore, since the magnitude of divergence of BMR at all three time points fell outside the confidence limits of the divergence expected under genetic drift, we suggest that the observed between–line type differences are the genuine effect of the applied selection. Therefore, we feel confident in using those selection animals as a model for studies on physiological correlations, especially in light of recent work by Duarte et al. (2010). Duarte et al. (2010) showed that high repeatability of RMR in nonbreeding laboratory mice is lost when mice are bred between subsequent metabolic measurements. This result is important because the majority of published studies on the associations between BMR/RMR and reproductive performance are based on prereproductive BMR/RMR levels (Johnson et al. 2001; Kam et al. 2003; Johnston et al. 2007; Duarte et al. 2010; Sadowska et al. 2013). The loss of repeatability can be therefore attributed to the changing physiological state of reproducing females, chiefly to an increment of viscera size/mass essential for powering milk production (Hammond 1997; Sadowska et al. 2013). This effect has been shown to be only partially reversible, which suggests that the costs of metabolic maintenance remain elevated not only during reproduction but

also afterward (Casirola and Ferraris 2003; Duarte et al. 2010; Careau et al. 2013). In our view, however, the main problem hampering the analyses of the associations between RMR/BMR and reproductive performance is the low pre- and postreproductive variation of RMR/BMR. Such analyses may therefore be insufficiently powered to detect significant effects. For example, the total phenotypic variation of BMR expressed as the CV within the nonreproducing and reproducing groups of Duarte et al.’s study was 6.4% and 5.6%, respectively (Duarte et al. 2010, data gleaned from their fig. 4). These values are much lower than those found in our study (table 2), which provide us with higher BMR variation to begin with. We found similar patterns of BMR variation in the subsequent measurements carried out at 22 and 27 wk of age, which shows that the effects of selection are long-lasting and not appreciably influenced by physiological changes incurred by reproduction (table 2). Phenotypic variation in Duarte et al.’s study is therefore at the level of our within–line type variation of 6%–8% (table 2), which in our case mainly reflects environmental effects, as genetic variance within our selection line types is small. The latter assertion is supported by the lack of within–line type repeatability of BMR of our mice, which was significant only in nonreproducing H-BMR individuals. To compare the within- and among-individual components of BMR variance Duarte et al. (2010) calculated the intraclass correlation coefficients. In the case of reproducing females this yielded statistically insignificant values of repeatability of RMR. Our approach is totally different: we compared the between–line type differentiation mostly of genetic origin,

Table 4: Organ masses (g) of H-BMR- and L-BMR-type mice in the control and reproducing groups H-BMR control Intestine Liver Kidneys Heart Spleen IBAT

3.731 2.327 .554 .169 .558 .100

5 5 5 5 5 5

.156 .141 .010 .004 .104 .020

H-BMR reproducing 3.576 2.333 .566 .161 .426 .094

5 5 5 5 5 5

.157 .143 .010 .004 .105 .020

L-BMR control 2.324 1.883 .438 .144 .319 .153

5 5 5 5 5 5

.145 .132 .009 .004 .097 .019

Note. H-BMR p high basal metabolic rate; L-BMR p low basal metabolic rate; IBAT p interscapular brown adipose tissue.

L-BMR reproducing 2.248 1.757 .432 .149 .205 .099

5 5 5 5 5 5

.151 .137 .001 .004 .101 .020

Basal Metabolic Rate Divergence Consistency

Figure 2. Magnitude of the between–line type separation (dX) of organ masses in reproducing (gray bars) and nonreproducing (white bars) groups. Vertical bars represent confidence intervals expected under genetic drift alone (ddrift). IBAT p interscapular brown adipose tissue

rather than the between-individual variation, which was mostly not significant, as indicated by low values of the intraclass correlation coefficients calculated within our line types. Our approach allowed us to demonstrate the consistency in preand postreproductive BMR at the level of the line types. It must be borne in mind, however, that our comparisons are essentially based on the sample size of 2, as our selection experiment is not replicated. For this reason we applied Henderson’s (1997) approach, to demonstrate that the observed between–line type divergence is greater than that expected under genetic drift (i.e., assuming that the differences arose purely by genetic drift, rather than founder effects and/or selection). Interestingly, BMR measured at the twenty-seventh week of age showed no effect of experimental treatment, which indicates that BMR of postreproducing females was comparable to that of their same-aged, nonreproducing counterparts (fig. 1; table 1). This result is also reflected in viscera mass, as it was not affected by reproductive history (fig. 2; tables 3, 4), even though viscera in both line types tend to get heavier at peak lactation (Sadowska et al. 2015). Such hypertrophic increases are reported to persist even up to 300 d (Casirola and Ferraris 2003), which evidently was not the case in our study. The reversal of the viscera mass in our selected line types is in good agreement with the lack of persistent effect of reproduction on RMR. Furthermore, at the time of the third measurement, the magnitude of the between–line type separation (calculated as phenotypic standard deviations) of the primary selected trait, BMR, is sustained at the level suggesting the persistent effect of the applied selection regimen. This long-term sustainability

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of the between–line type divergence in BMR is in agreement with recent study of our selected line types by Brzęk et al. (2012), who demonstrated that males subjected to restricted feeding regimen (calorie restriction) for 6 mo as well as fed ad lib. control animals were also characterized by the significant between–line type separation in BMR measured even later in life, at 12 mo of age. To conclude, artificial selection on metabolic traits applied early in life generates a long-term consistent genetic response (Bronikowski et al. 2006; Sadowska et al. 2013; Brzęk et al. 2014), which suggests that BMR at a young age (12 wk old) has a relatively large genetic covariance with BMR at later ages (22 and 27 wk old). Such response is also apparent even at very early stages of life of an individual (e.g., as higher growth rate of H-BMR-type pups in their first week of life; Sadowska et al. 2013) and remains consistent with age and changes in physiological state, for example, reproduction (this study) or dietary restriction (Brzęk et al. 2012). In contrast to Casirola and Ferraris (2003) we also demonstrated that phenotypic changes in organ size and BMR incurred by reproduction are reversible within a relatively short time period. We also demonstrated that BMR variation incurred by artificial selection is considerably higher than that found in unmanipulated/ outbred populations and may therefore provide a useful resolving model of correlations involving BMR. Acknowledgments This study was supported by the Polish National Science Centre (grant 2011/01/B/NZ8/01721). This study was approved by the Local Ethical Committee on Testing Animals at the Medical University of Białystok (permit 61/2009). Literature Cited Bennett A.F. 2003. Experimental evolution and the Krogh principle: generating biological novelty for functional and genetic analyses. Physiol Biochem Zool 76:1–11. Bozinovic F. 2007. Long-term repeatability of body mass and body temperature (but not basal metabolism) in the freeranging leaf-eared mouse. Evol Ecol Res 9:547–554. Bronikowski A.M., P.A. Carter, T.J. Morgan, T. Garland Jr., N. Ung, T.D. Pugh, R. Weindruch, and T.A. Prolla. 2003. Lifelong voluntary exercise in the mouse prevents agerelated alternations in gene expression in the heart. Physiol Genomics 12:129–138. Bronikowski A.M., T.J. Morgan, T. Garland Jr., and P.A. Carter. 2006. The evolution for ageing and age-related physical decline in mice selectively bred for high voluntary exercise. Evolution 60:1494–1508. Brzęk P., K. Bielawska, A. Książek, and M. Konarzewski. 2007. Anatomic and molecular correlates of divergent selection for basal metabolic rate in laboratory mice. Physiol Biochem Zool 80:401–409. Brzęk P., A. Książek, A. Dobrzyń, and M. Konarzewski. 2012. Effect of dietary restriction on metabolic, anatomic and

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molecular traits in mice depends on the initial level of basal metabolic rate. J Exp Biol 215:3191–3199. Brzęk P., A. Książek, Ł. Ołdakowski, and M. Konarzewski. 2014. High basal metabolic rate does not elevate oxidative stress during reproduction in laboratory mice. J Exp Biol 217:1504–1509. doi:10.1242/jeb.100073. Careau V., D. Reale, D. Garant, F. Pelletier, J.R. Speakman, and M.M. Humphries. 2013. Context-dependent correlation between resting metabolic rate and daily energy expenditure in wild chipmunks. J Exp Biol 216:418–426. Casirola D.M. and R.P. Ferraris. 2003. Role of the small intestine in postpartum weight retention in mice. Am J Clin Nutr 78:1178–1187. Daan S., D. Masman, and A. Groenewold. 1990. Avian basal metabolic rates: their association with body composition and energy expenditure in nature. Am J Physiol 259:R333– R340. Derting T.L. 1989. Metabolism and food availability as regulators of production in juvenile cotton rats. Ecology 7:587– 595. Dohm M.R. 2002. Repeatability estimates do not always set an upper limit to heritability. Funct Ecol 16:273–280. Duarte L.C., L.M. Vaanholt, R.E. Sinclair, Y. Gamo, and J.R. Speakmand. 2010. Limits to sustained energy intake. XII. Is the poor relation between resting metabolic rate and reproductive performance because resting metabolism is not repeatable trait? J Exp Biol 213:278–287. Falconer D.S. and T.F.C. Mackay. 1996. Introduction to quantitative genetics. 4th ed. Longman, Harlow. Feder M.E., A.F. Bennett, and R. Huey. 2000. Evolutionary physiology. Annu Rev Ecol Syst 31:315–341. Fedorowicz K., M. Sacharczuk, M. Konarzewski, I. Łapo, M. Kawka, B. Sadowski, A. Świergiel, and K. Jaszczak. 2007. Divergent selection of mice for high and low swim stressinduced analgesia alters polymorphism at microsatellite loci. Anim Sci Pap Rep 25:259–274. Garland T., Jr. 2003. Selection experiments: an underutilized tool in biomechanics and organismal biology. Pp. 23–56 in V.L. Bels, J.-P. Gasc, and A. Casinos, eds. Vertebrate biomechanics and evolution. BIOS Scientific, Oxford. Garland T., Jr., and P.A. Carter. 1994. Evolutionary physiology. Annu Rev Physiol 56:579–621. Garland T., Jr., M.T. Morgan, J.G. Swallow, J.S. Rhodes, I. Girard, J.G. Belter, and P.A. Carter. 2002. Evolution of a small-muscle polymorphism in lines of house mice selected for high activity levels. Evolution 56:1267–1275. Garland T., Jr., and M.R. Rose, eds. 2009. Experimental evolution: concepts, methods and applications of selection experiments. University of California Press, Berkeley. Gębczyński A.K. and M. Konarzewski. 2009a. Locomotor activity of mice divergently selected for basal metabolic rate: a test of hypotheses on the evolution of endothermy. J Evol Biol 22:1212–1220. ———. 2009b. Metabolic correlates of selection on aerobic capacity in laboratory mice: a test of the model for the evolution of endothermy. J Exp Biol 212:2872–2878.

Gibbs A. 1999. Laboratory selection for the comparative physiologist. J Exp Biol 202:2709–2718. Glazier D.S. 1985. Relationship between metabolic rate and energy expenditure for lactation in Peromyscus. Comp Biochem Physiol A 80:587–590. Hammond K.A. 1997. Adaptation of the maternal intestine during lactation. J Mammary Gland Biol Neoplasia 2:243– 252. Hayes J.P., T. Garland Jr., and M.R. Dohm. 1992. Individual variation in metabolism and reproduction of Mus: are energetics and life history linked? Funct Ecol 6:5–14. Henderson N.D. 1997. Spurious associations in unreplicated selected lines. Behav Genet 27:145–154. Holt M., T. Meuwissen, and O. Vangen. 2005. Long-term responses, changes in genetic variances and inbreeding depression from 122 generations of selection on increased litter size in mice. J Anim Breed Genet 122:199–209. Johnson M.S., S.C. Thomson, and J.R. Speakman. 2001. Limits to sustained energy intake. II. Inter-relationships between resting metabolic rate, life-history traits and morphology in Mus musculus. J Exp Biol 204:1937–1946. Johnston S.L., D.M. Souter, S.S. Erwin, B.J. Tolkamp, J.M. Yearsley, I.J. Gordon, A.W. Illius, I. Kyriazakis, and J.R. Speakman. 2007. Associations between basal metabolic rate and reproductive performance in C57BL/6J mice. J Exp Biol 210:65–74. Kam M., S. Sohen-Gross, I.S. Khokhlova, A.A. Degen, and E. Geffen. 2003. Average daily metabolic rate, reproduction and energy allocation during lactation in the Sundevall jird Meriones crassus. Funct Ecol 17:496–503. Kane S.L., T. Garland Jr., and P.A. Carter. 2008. Basal metabolic rate of aged mice is affected by random genetic drift but not by selective breeding for high early-age locomotor activity or chronic wheel access. Physiol Biochem Zool 81: 288–300. Koch L.G. and S.L. Britton. 2001. Artificial selection for intrinsic aerobic endurance running capacity in rats. Physiol Genomics 5:45–52. ———. 2005. Divergent selection for aerobic capacity in rats as a model for complex disease. Integr Comp Biol 45:405–415. Konarzewski M. and J. Diamond. 1995. Evolution of basal metabolic rate and organ masses in laboratory mice. Evolution 49: 1239–1248. Konarzewski M. and A. Książek. 2013. Determinants of intraspecific variation in basal metabolic rate. J Comp Physiol B 183:27–41. Konarzewski M., A. Książek, and I.B. Łapo. 2005. Artificial selection on metabolic rates and related traits in rodents. Integr Comp Biol 45:416–425. Król E., S.M. Martin, I.P. Huhtaniemi, A. Douglas, and J.R. Speakman. 2011. Negative correlation between milk production and brown adipose tissue gene expression in lactating mice. J Exp Biol 214:4160–4170. Król E. and J.R. Speakman. 2003. Limits to sustained energy intake. VI. Energetics of lactation in laboratory mice at thermoneutrality. J Exp Biol 206:4255–4266.

Basal Metabolic Rate Divergence Consistency Książek A., M. Konarzewski, and I.B. Łapo. 2004. Anatomic and energetic correlates of divergent selection for basal metabolic rate in laboratory mice. Physiol Biochem Zool 77: 890–899. Labocha M.K., E.T. Sadowska, K. Baliga, A.K. Semer, and P. Koteja. 2004. Individual variation and repeatability of basal metabolism in the bank vole, Clethrionomys glareolus. Proc R Soc B 271:367–372. Lessells C.M. and P.T. Boag. 1987. Unrepeatable repeatabilities: a common mistake. Auk 104:116–121. Lovegrove B.G. 2009. Age at first reproduction and growth rate are independent of basal metabolic rate in mammals. J Comp Physiol B 179:391–401. McDonald J.M. and M.K. Nielsen. 2007. Renewed selection for heat loss in mice: direct responses and correlated responses in feed intake, body weight, litter size, and conception rate. J Anim Sci 85:658–666. Morgan T.J., T. Garland Jr., and P.A. Carter. 2003. Ontogenesis in mice selected for high voluntary wheel-running activity. I. Mean ontogenesis. Evolution 57:646–657. Nespolo R.F. and M. Franco. 2007. Whole-animal metabolic rate is a repeatable trait: a meta-analysis. J Exp Biol 210: 2000–2005. Nielsen M.K., L.D. Jones, B.A. Freking, and J.A. DeShazer. 1997. Divergent selection for heat loss in mice. I. Selection applied and direct response through fifteen generations. J Anim Sci 75:1461–1468. Rezende E.L., T. Garland Jr., M.A. Chappell, J.L. Malisch, and F.R. Gomes. 2006. Maximum aerobic performance in lines of Mus selected for high wheel-running activity: effects of selection, oxygen availability, and the mini-muscle phenotype. J Exp Biol 209:115–127. Rose M.R., T.J. Nusbaum, and A.L. Chippindale. 1996. Laboratory evolution: the experimental wonderland and the Cheshire cat syndrome. Pp. 221–241 in M.R. Rose and G.V. Lauder, eds. Adaptation. Academic Press, New York. Rosochacki S.J., E. Wirth-Dzięciołowska, M. Zimowska, T. Sakowski, J. Poloszynowicz, E. Juszczuk-Kubiak, and M. Gajewska. 2005. Skeletal muscle and liver protein degradation in mice divergently selected for low and high body weight over 108 generations. Arch Anim Breed 48:505–517.

335

Russell G.A. and M.A. Chappell. 2007. Is BMR repeatable in deer mice? organ mass correlates and the effects of cold acclimation and natal altitude. J Comp Physiol B 177:75– 87. Sadowska E.T., K. Baliga-Klimczyk, K.M. Chrząścik, and P. Koteja. 2008. Laboratory model of adaptive radiation: a selection experiment in the bank vole. Physiol Biochem Zool 81:627–640. Sadowska E.T., M.K. Labocha, K. Baliga, A. Stanisz, A.K. Wróblewska, W. Jagusiak, and P. Koteja. 2005. Genetic correlations between basal and maximum metabolic rates in a wild rodent: consequences for evolution of endothermy. Evolution 59:672–681. Sadowska J., A.K. Gębczyński, and M. Konarzewski. 2013. Basal metabolic rate is positively correlated with parental investment. Proc R Soc B 280:20122576. doi:10.1098/rspb .2012.2576. ———. 2015. Milk output and composition in mice divergently selected for basal metabolic rate. J Exp Biol 218:249– 254. Schlager G. 1968. Kidney weight in mice: strain differences and genetic determination. J Hered 59:171–174. Selman C., S. Lumsden, L. Bunger, W.G. Hill, and J.R. Speakman. 2001. Resting metabolic rate and morphology in mice (Mus musculus) selected for high and low food intake. J Exp Biol 204:777–784. Sokal R.R. and F.J. Rholf. 1981. Biometry: the principles and practice of statistics in biological research. W.H. Freeman, New York. Speakman J.R., D.S. Talbot, C. Selman, S. Snart, J.S. McLaren, P. Redman, E. Król, D.M. Jackson, M.S. Johnson, and M.D. Brand. 2004. Uncoupled and surviving: individual mice with high metabolism have greater mitochondrial uncoupling and live longer. Aging Cell 3:87–95. Szafrańska P.A., K. Zub, and M. Konarzewski. 2007. Longterm repeatability of body mass and resting metabolic rate in free-living weasels, Mustela nivalis. Funct Ecol 21:731– 737. ———. 2013. Seasonal variation of resting metabolic rate and body mass in free-living weasels Mustela nivalis. Physiol Biochem Zool 86:791–798.

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