Evolution of basal metabolic rate and organ masses in laboratory mice.
Received September 14, 1993. Accepted August 16, 1994.
A key issue in evolutionary physiology concerns the observed wide range of variation among organisms in their basal metabolic rates (BMR). What is the functional significance of a high or low BMR, and what is the metabolic machinery that varies among taxa to create the observed variation in BMR? These questions have received particular attention in connection with the evolution of the high BMRs necessary for sustaining endothermy (e.g., Bennett and Ruben 1979; Else and Hulbert 1985), but they also arise in comparisons among ectotherms or among endotherms.
To date, most studies on BMR variation have focused on interspecific differences in BMRs allometric relation to body mass. (e.g. Hayssen and Lacy 1985), its relation to peak or sustained metabolic rates (Koteja 1987, 1991), and its possible relation to life-history variables (e.g., offspring mass, litter mass, and developmental rate: McNab 1988; Harvey et al. 1991; Hayes et al. 1992). These studies have uncovered large differences in BMR between homeotherms of similar size. For instance, 21% of the mammal species examined by Hayssen and Lacy (1985) had BMRs more than 50% above or below the values predicted from the allometric relation between BMR and body mass calculated for all mammal species. As one example, the Brazilian mouse opossum Marmosa microtarsus and the water shrew Neomys anomalus have approximately the same body mass of 13 g, but the BMR of the latter is more than three times that of the former species (5.1 vs. 1.4 ml [O.sub.2]/g,h: Gebczynski and Gebczynska 1965 vs. Morrison and McNab 1962). Insectivores tend to have high values of BMR for their body mass, while marsupials tend to have low values.
Recently, Daan et al. (1990) suggested that BMR variation between species of similar body size reflects evolution of the metabolic machinery necessary to generate high nonbasal metabolic rates during energetically demanding periods such as reproduction, cold exposure, or sustained physical activity (see also Calder 1984; Schmidt-Nielsen 1984). According to this hypothesis, BMR represents the maintenance costs especially of those tissues and organs involved in supporting high metabolic rates--organs that make metabolic energy available to the animal (e.g., liver), organs that transport energy (e.g., heart), and organs that excrete the resulting waste products (kidneys). Daan et al. pointed out that even though these organs constitute only a small fraction of an animal's lean body mass, they have very high mass-specific metabolic activities and may thus contribute disproportionately to the observed interspecific variation in BMR. To test this hypothesis, Daan et al. examined the relationship between BMR and vital organ masses of 22 bird species, and found that the combined masses of the heart and kidneys alone explained almost 50% of BMR variation.
In contrast to these interspecific comparisons of BMR, intraspecific variation in BMR has received little attention except in lizards (standard metabolic rate: Garland 1984; Garland and else 1987) and in humans. If it could be detected, its proximate origins might prove easier to identify than the origins of interspecific variation, because many fewer genes differ between conspecific individuals than between individuals of different species. For this purpose of studying intraspecific variation, domesticated animal species potentially offer two advantages over wild species. First, since metabolism must be closely related to fitness, stabilizing selection is likely to minimize genetic variation within wild populations in BMR and in the machinery underlying it (Fisher 1930). Hence statistical analysis of intraspecific variation in wild animals may lack sufficient power to detect postulated small variations in organ masses contributing to BMR variation. In domesticated species, however, humans may have selected and may maintain strains that (as a by-product of artificial selection by humans for other traits) differ widely in BMR and in organ sizes but that would be unable to survive in the wild. Second, both BMR and organ sizes vary phenotypically with age, diet, reproductive activity, environmental temperature, and other factors (Silcock and Parsons 1973; Horowitz and Soskolne 1978; McNab 1988; Kenagy et al. 1990; Hammond and Wunder 1991; Konarzewski and Diamond 1994). Individual animals sampled from a wild population are likely to differ to an unknown extent in these influences, hence to exhibit large confounding phenotypic variability that would obscure the search for genetically determined variation.
The laboratory house mouse (Mus musculus) is an ideal organism for such an intraspecific analysis of a domesticated species. Humans have developed a large variety of laboratory mouse inbred strains by selecting them for diverse traits. Strain differences in BMR and in organ masses have been well documented (Pennycuik 1967; Storer 1967; Schlager 1968; Mount 1971; Roderick et al. 1973). Strain differences in BMR span almost a two-fold range (Storer 1967), as do strain differences in mass of the kidneys (Schlager 1968), one of the most active organs metabolically (Krebs 1950). Laboratory conditions permit one to minimize or eliminate differences in confounding factors such as sex, age, diet, environmental temperature, and reproductive status. Thus, inbred strains of mice potentially offer greater genetically determined intraspecific variation in BMR and organ masses, and fewer confounding variables, than do conspecifics from a wild population.
In the present paper we measure differences in BMR and in masses of four metabolically active organs (heart, kidney, liver, and small intestine) in six inbred strains of mice. We use regression and correlation analysis to examine possible associations between BMR and organ masses, both between strains and within strains. In effect, we ask: do those individual mice with the highest BMR values have the largest masses of energetically expensive organs?
MATERIALS AND METHODS
Animals and Their Maintenance
We used only female mice 9-10 wk in age. Mice were of the following six inbred strains: A/J (n = 12 individuals studied), AKR/J (n = 10), C57BL/6J (n = 10), DBA/2A (n = 10), HRS/J (n = 20), and SWR/J (n = 10). These strains represent much of the range of genetic variation present among laboratory mice (Atchley and Fitch 1991). Average genetic differences between these strains (except for HRS/J, for which information is unavailable) range from 35 to 45 allele fixations (Atchley and Fitch 1991). In choosing these strains, we guided ourselves by published studies of strain differences in BMR by some authors (Pennycuik 1967; Storer 1967; Mount 1971) and in organ masses by other authors (Schlager 1968; Roderick et al. 1973). These previous studies did not carry out our goal of measuring BMR and organ masses simultaneously. We thereby tried to cover as much as possible of the available between-strain range of genetic variation and range of variation in BMR and in organ sizes.
None of these six strains was purposely selected for high or low BMR. Instead, their differences in BMR are a byproduct of other traits: they were developed for use in medical research as animal models of different pathological processes (Staats 1981). Briefly, H/J mice have a high sensitivity to X-irradiation. AKR/J and HRS/J have a high incidence of leukemia and short life span, while HRS/J mice in addition have very low blood pressure. In contrast, C57BL/6J and SWR/J mice show low incidences of various tumor types, and C57BL/6J mice in addition have an unusually long life span and high preference for morphine. DBA/2J mice show low preference for morphine and alcohol but often develop mammary tumors.
We obtained mice from the Jackson Laboratory at an age of 5 wk, housed 5 mice of the same strain per pen, and fed them Purina Lab Chow ad libitum until they reached 9-10 wk of age, when we measured BMR and then killed the mice to determine organ masses. Room temperature was maintained at 23[degrees]C (except as noted in the next section), and a 12:12 h light:dark cycle was maintained. Thus, mice and their environments were as similar to each other as possible except for the genetic differences between strains.
Temperature Acclimation of Nude (HRS/J) Mice
Nude mice have higher BMRs than haired mice, stemming from their hairlessness, resulting higher thermal conductance, and greater heat loss (Mount 1971). Even at the relatively high environmental temperature of 23[degrees]C recommended for haired mouse strains, nude mice may be cold-stressed, since their food intake at this temperature is 30% higher than for haired mice (Weihe 1984). But studies of cold-exposed mice of other strains show that increased heat loss at low environmental temperatures results in elevated BMR and enlarged organs (Toloza et al. 1991; Konarzewski and Diamond 1994). Hence the differences observed in earlier studies between BMRs of nude and haired mice may have been partly due to phenotypic effects of cold acclimation on nude mice, rather than to genetic differences between nude and haired mice.
In order to test for this possible phenotypic effect of cold acclimation on nude mice, we maintained 10 nude mice at 23[degrees]C and transferred 10 others to an environmental cabinet at 30 [+ or -] 1[degrees]C. In the cabinet they were housed 5 per pen and maintained for 8 d before measurement of BMR and organ masses. We chose 30[degrees]C because that temperature avoids cold stress for nude mice; because food intake by nude mice at and above 30[degrees]C declines to a value equal to that of haired mice at 23[degrees]C; and because comparisons of lower critical temperatures and thermal conductances indicate that nude mice acclimated to 30[degrees]C should be physiologically comparable to haired mice acclimated to 23[degrees]C (Mount 1971; Lynch et al. 1976; Lacy and Lynch 1979; Weihe 1984).
Measurements of Resting Metabolic Rate
We measured resting metabolic rate (RMR) as an approximation of BMR, by fasting mice for 6 h before a metabolic trial. We elected not to fast mice for longer times, since longer fasts may elevate rather than depress metabolic rate (Hayes et al. 1992). Fasted mice were then transferred to two simultaneously monitored metabolic chambers (780 cc each) in an open-circuit respirometry system. Each chamber received dried air at 300 cc/min from mass flow controllers (Tylan General, Torrance, Calif.), which kept the flow rate through the chambers within 10 cc/min of the preset value. Air leaving the chambers was passed through soda lime and Drierite to remove C[O.sub.2] and water and was monitored every 5 s for at least 20 min of each hour by an Applied Electrochemistry [O.sub.2] analyzer (Amtek, Pittsburgh, Pa.) interfaced to a microcomputer. Measurements were taken for 3 h at 32[degrees]C-33[degrees]C, a temperature within the thermoneutral zone of both haired mice and nude mice (Mount 1971; Lacy and Lynch 1979). Air temperature was held constant within [+ or -] 0.2[degrees]C by submerging the chambers in a water bath. We calculated the RMR value by equation (4b) of Withers (1977), taking the lowest 3-min value that did not change by more than 0.01% in [O.sub.2] concentration. Sample sizes for these BMR (RMR) measurements were the same as those specified above for the number of mice of each strain, except that one HRS/J mouse had to be excluded from BMR calculations because it behaved restlessly in the metabolic chamber. All metabolic trials were completed between noon and 6:00 P.M.,that is, during the inactive phase of the circadian cycle.
Upon completing BMR measurements, we killed mice with an intraperitoneal injection of N[a.sup.+] pentobarbital (Nembutal), removed the gut, and measured the wet masses of the small intestine, heart, kidneys, and liver after carefully dissecting away adherent fat. To remove possible residues of digesta, we washed out the small intestine with Ringer's solution before weighing it. We also measured dry masses of the same organs and of the remaining carcass by drying to constant mass in an oven at 70[degrees]C.
Differences between strains were tested statistically by one-way ANCOVA, with strain affiliation as the main effect and body mass as the covariate. We also used ANCOVA to test differences between nude mice acclimated to 23[degrees] and 30[degrees]C. Since we were a priori interested in identifying differences between strains, we tested these differences by using probabilities associated with pairwise planned comparisons (SAS Institute 1985). Unlike multiple-range tests, these comparisons use the error mean square for the entire model and are specifically designed for planned comparisons (Sokal and Rohlf 1981).
Measured values are reported as least square means from ANCOVA analysis [+ or -] 1 SEM (standard error of the mean). In effect, these least-square mean values correct for differences in body mass. We quote F values, with model and error degrees of freedom as two consecutive subscripts (e.g., [F.sub.1,56] = 12.5).
To study possible associations between BMR and organ masses, we used regression and correlation analyses, taking the level of significance as P < 0.05. To avoid repetition, we discuss further details below. All statistical analyses were carried out with the SAS statistical package for personal computers (SAS Institute 1985).
Effects of Acclimation Temperature on Nude Mice
We refer to nude mice acclimated to 23[degrees]C as NC (nude cold) mice and nude mice acclimated to 30[degrees]C as NW (nude warm) mice. As expected, the higher acclimation temperature resulted in NW mice having significantly lower BMR than NC mice (30.5 [+ or -] 0.9 vs. 34.6 [+ or -] 1.1 ml [O.sub.2]/h; P = 0.003 by ANCOVA, [F.sub.2,18] = 8.2, body mass used as a covariate), as well as significantly lower masses of all four organs measured (Ps < 0.001 by ANCOVA). Wet masses of the small intestine, liver, heart, and kidneys of NW mice averaged 84%, 92%, 90%, and 86% of corresponding masses of NC mice, respectively (table 1). All those differences remained similar in magnitude and statistically significant at the P = 0.001 level when dry instead of wet body mass and organ masses were used in the analysis. For the reasons explained in the preceding section on Methods, NW mice are the nude mice most comparable physiologically to the other (haired) mouse strains that we studied at acclimation temperatures of 23[degrees]C. Hence, all between-strain comparisons discussed below use NW rather than NC mice.
[TABULAR DATA 1 OMITTED]
Between-Strain Differences in Body Mass and BMR
The six strains differed significantly in body mass (ANOVA, [F.sub.5,56] = 10.83, P = 0.0001). The lightest strain (SWR/J) averaged 20% lighter than the heaviest strain (AKR/J) (fig. 1A). Corrected for this variation in wet body mass, BMR also differed significantly between strains (ANCOVA, [F.sub.6,54] = 16.14, P = 0.0001). BMR of the strain most inactive metabolically (A/J) was almost 30% lower than BMR of the strain most active metabolically (HRS/J) (fig. 1B). These differences remained highly significant when we used dry rather than wet body mass as a covariate (ANCOVA, P = 0.0001). Table 1 summarizes all our raw data: that is, wet and dry body mass, organ masses, and BMR of each strain.
Between-Strain Differences in Organ Water Contents and Masses
Between-strain comparisons of organ water contents revealed significant differences for each of the four organs examined (ANCOVA, Ps = 0.0001, dry organ mass as a covariate). Hence between-strain comparisons of fresh (wet) organ masses would be confounded by the differing water contents of the organs compared. In the remainder of this paper we therefore compare organ dry masses rather than wet masses.
Dry masses of all four organs examined differed significantly between strains (ANCOVA, Ps = 0.0001, body dry mass as a covariate). However, in this case ANCOVA also revealed statistically significant interactions between strain affiliation and body dry mass (P < 0.05). Those interactions arose from slopes of the relations between organ dry masses and body dry mass being lower for strain DBA/2J than for other strains (Tukey test, P < 0.05). That is, organ dry masses change less with changing body dry mass in this strain than in other strains. When we repeated between-strain comparisons of organ masses after removing the data for strain DBA/2J, ANCOVA for the five remaining strains revealed no interaction between strain affiliation and body dry mass for any of the organs examined (ANCOVA, Ps variously 0.05-0.61). Hence we confined the following analyses to the five strains SWR/J, C57BL/6J, HRS/J, A/J, and AKR/J.
As illustrated in figure 2, liver dry mass was higher in strains HRS/J and AKR/J than in other strains (ANCOVA followed by paired t-test for planned comparisons). This was also true for heart and intestine masses of AKR/J mice and for kidney mass of HRS/J mice. Conversely, A/J and SWR/J mice tended to have the lowest masses of small intestine, liver, and heart. Organ masses of strain C57BL/6J were intermediate.
Relationship between BMR and Organ Masses
To test for a possible relationship between BMR and organ masses, we used two levels of analysis. First, we asked whether such a relationship exists across the pooled sample of mice of all strains. In this first analysis we purposely did not remove the large between-strain differences in BMR and in organ masses. This test enabled us to take advantage of the whole range of between-strain variation and within-strain variation in BMR and organ masses, created by artificial selection. We then reanalyzed the relationship between BMR and organ masses after taking into account the large between-strain differences revealed by the first analysis. Thus, the second analysis could focus on the smaller differences between individual mice within the same strain. This two-step analysis enabled us to separate the effect of within-strain variation from the effect of between-strain variation on the relation between BMR and organ masses.
Pooled Data for All Strains.--Figures 1B and 2 demonstrate the existence of significant between-strain differences in BMR and in organ masses after both have been corrected for differences in body mass. Comparison of figures 1B and 2 shows that the strains characterized by the highest BMRs (HRS/J and AKR/J) tended to have larger-than-average organs, while the strain with the lowest BMR (A/J mice) had small organs. In order to verify this observation, we regressed BMR and dry mass of each organ (ODM) on body dry mass (BDM). To avoid the possibility of spurious autocorrelation, we subtracted the mass of the organ under consideration from BDM before each computation. We then plotted the residuals from these regressions of ODM versus BDM against the residuals from the regression of BMR versus BDM. These residuals represent the deviations of values for individual mice from the grand mean values for all mice of all strains. Hence these residuals represent the sum of both between-strain and within-strain variation of BMR and organ masses.
Examination of the resulting scatterplots of residuals shows that the residuals for HRS/J and AKR/J mice tended to fall within the upper right quadrants of the scatterplots, whereas residuals for A/J mice fell within the lower left quadrants, for all four organs (figs. 3, 4). Points for SWR/J and C57BL/6J mice were located closer to the origins of the scatterplots. Thus, this residual analysis confirms that mouse strains with high BMR tend to have organs of above-average mass.
Although these relationships are visually apparent from figures 3 and 4, we also wanted to express the strength of these relationships as correlation coefficients. A technical problem arises in doing so: since the individual residuals for each strain are clustered, there is an autocorrelation between residuals that violates the basic assumptions of correlation analysis (Sokal and Rohlf 1981). We overcame this problem by using the jackknife technique to estimate the unbiased distribution of each correlation coefficient (Mosteller and Tukey 1977). Briefly, we repeatedly calculated a given correlation coefficient on all possible subsamples of the residuals, where each subsample omitted one of the original residuals. We then calculated the average ([+ or -] 1 SEM) of the resulting pseudovalues of the correlation coefficient and used the resulting values for statistical testing.
This jackknife analysis revealed that the positive correlations between BMR and ODMs visible in figures 3 and 4 were highly significant (t-test) for all four organs: r = 0.47 for small intestine, 0.73 for liver, 0.65 for heart, and 0.52 for kidney; P [less than or equal to] 0.0001 for all four organs. Hence, high BMR values and high organ masses (corrected for body mass differences) are indeed associated with each other in the pooled sample of all mice of all strains.
Principal-Component Analysis.--We next asked whether mice with an above-average mass of one organ tended also to have above-average masses of other organs. To answer this question, we computed correlation coefficients between the residuals from regressions of ODM versus BDM (figs. 3, 4). These residuals serve as measures of organ dry masses corrected for differences in body dry mass. Masses of all organs proved to be highly correlated positively with each other (table 2).
TABLE 2. Pearson correlation coefficients between organ dry masses, corrected for the differences in body dry mass. The significance level is shown in parentheses. Intestine Liver Heart Kidneys Intestine 1.0 0.74 0.62 0.32 (0.0) (0.0001) (0.0001) (0.02) Liver 1.0 0.84 0.57 (0.0) (0.0001) (0.0001) Heart 1.0 0.39 (0.0) (0.004) Kidneys 1.0 (0.00)
We then carried out a principal-component analysis on the same residuals. The first two principal components (PCA1 and PCA2) accounted for 99.5% of the total variation in the residuals, of which 94% was explained by PCA1 alone. Hence, the value of PCA1 for a given mouse may be interpreted as an overall score of its organ dry masses compared to other mice. Figure 5 shows that A/J mice had the lowest value of PCA1, C57BL/6J and SWR/J mice had intermediate values, and AKR/J and HRS/J mice had the highest values.
The values of loadings of the four organs on PCA 1 indicate that liver (0.96) and intestine (0.29) dry masses account for most of the variation in organ masses. Loadings for heart (0.03) and kidney (0.05) dry masses are much smaller, reflecting both their much lower absolute masses and their lower relative variabilities (mean coefficients of variation of the four organs' masses are 10.6, 11.5, 7.7, and 7.1%, respectively; see table 1).
The principal-component analysis also let us estimate the proportion of the variance in BMR explained by between-strain and within-strain variation in all four organ masses. To do so, we regressed BMR (corrected for body mass) on PCA1. The resulting regression was highly significant (P = 0.0001 by F test) and explained 52% of the variation in BMR.
Between-Strain versus Within-Strain Differences.--The preceding analyses did not separate effects of between-strain variation from effects of within-strain variation. The clustering of points by strain in figures 3 and 4 indicates that there is a large effect of between-strain variation. Do the strong correlations between organ masses (corrected for body mass) and BMR arise solely from those large differences between strains? Or, is there also a contribution from differences between individual mice within a strain?
To test this question, we coded the strain affiliation of each mouse as a "dummy variable" by assigning a unique combination of 0 and 1 to each strain (Draper and Smith 1981). Dummy variables provide a means to remove the effect of strain affiliation in view of the fact that strain is a categorical variable, so that its effect cannot be removed in the same way as we did for the effect of body mass. We computed multiple regression equations in which BMR and ODMs were the dependent variables, while BDM and the black of dummy variables were the independent variables.
For all four organs, BDM and strain affiliation proved to be significant independent variables (t-test, P < 0.05). The residuals from those multiple regressions can be interpreted as measures of BMR and ODM after removing the effects of differences in body mass and strain affiliation. Hence, a positive correlation between those residuals of BMR and residuals of ODM would imply an association between BMR and organ masses independent of the association arising from the differences between strains. In fact, we found significant positive correlations between the residuals for BMR and for liver dry mass (r = (0.30, P = 0.03, fig. 6A), as well as for BMR and kidney dry mass (r = 0.35, P = 0.01, fig. 6B). The corresponding correlations for BMR and intestine and heart dry mass were not significant (r = 0.17, P = 0.23, and r = 0.03, P = 0.87, respectively).
These results tell us that the positive correlation, observed for the pooled sample of mice of all strains, between BMR (corrected for body mass) and intestine or heart dry mass (figs. 3A, 4A), arises mainly from differences between strains. However, the strain-independent correlations between BMR and liver or kidney mass (fig. 6) mean that the positive correlation observed in the pooled sample between BMR and liver or kidney mass (figs. 3B, 4B) also receives a contribution from a corresponding correlation within samples of individual mice of the same strain. In short, strains with high values of organ masses tend to have high values of BMR, but in addition individual mice of the same strain with large livers or kidneys also tend to have high BMR.
To identify which particular strains exhibited such within-strain associations, we calculated separately for each strain the partial correlation coefficients between BMR and liver or kidney dry mass (correcting for effects of BDM). The only correlation that reached statistical significance was between BMR and kidney dry mass in A/J mice (r = 0 67, P = 0.02). The corresponding correlations between BMR and liver dry mass in A/J and C57BL/6J mice exhibited a weaker trend that failed to reach significance (P = 0.13 and 0.14, r = 0.48 and 0.53, respectively). Hence, the positive correlations that we found between BMR and liver or kidney dry mass after controlling for strain differences (fig. 6) must have been due to a cumulative effect of trends presumably present within most of the strains but (except in A/J mice) too weak to be detected in each strain individually.
Finally, we proceeded as follows to partition quantitatively the total variation in BMR. We carried out a principal-component analysis on the just-described residuals corrected for strain affiliation. The PCA1 accounted for 86% of the total variation in the residuals. The regression of BMR corrected for strain affiliation on PCA1 explained 10% of the variation in BMR. But we had already found that regression of BMR not corrected for strain affiliation on the PCA1 in that analysis accounted for 52% of variation in BMR. Hence total BMR variation can be partitioned as follows:
approximately 42% is due to organ mass differences between-strains;
ca. 10% is due to organ mass differences within strains; and
ca. 48% is unrelated to differences in organ mass.
Between-Strain Differences in BMR and Organ Masses
Our results confirm previous findings that inbred mouse strains differ markedly in BMR, and also in organ masses. Storer (1967) measured a 1.76-fold range in BMR among 18 inbred strains, whereas Schlager (1968) measured a 1.94-fold range in body-mass-adjusted fresh kidney mass among 21 strains. In particular, Schlager found that the strain with the largest kidneys was HRS/J, in agreement with our results (fig. 2). Schlager was able to estimate that the coefficient of genetic determination of kidney mass corrected for body mass is 0.60-0.85.
In our study, we chose mice of the same sex and age, provided them with the same diet, and maintained them in the same room under the same environmental conditions (except that we chose a higher environmental temperature for nude mice, for the reasons already explained). We nevertheless observed differences in BMR and organ masses between inbred, genetically relatively uniform strains, and the observed between-strain variation exceeded the within-strain variation. These facts indicate that the observed differences in BMR and organ masses are heritable (Falconer 1960). It also suggests that our observed positive association between BMR and organ masses is heritable.
In apparent contrast with our results, Lacy and Lynch (1979) and Lynch (1986) reported insignificant heritabilities of BMR, body temperature, and brown adipose tissue in mice. The probable reason is that those authors used only a single stock of mice (HS/Ibg), whereas we compared five strains selected intentionally to maximize the range of between-strain variation. Although we did succeed in detecting some significant examples of within-strain variation, it was only with difficulty and as a second-order effect after removing the larger effect of between-strain variation. The coefficient of variation of BMR that Lacy and Lynch (1979) observed in HS/Ibg females was only 17.9 and 14.4% in two consecutive generations, within the range that we observed for each of our six inbred strains (range from 6% for SWR/J mice to 19.1% for AKR/J mice). This modest within-strain variation may make it difficult or impossible to estimate heritabilities of these physiological traits in these mouse strains, because standard genetic techniques such as parent/offspring regression (Falconer 1960) may lack sufficient power to resolve the relationship.
HOW DO LARGE ORGAN MASSES RESULT IN HIGH BMR?
The positive correlation that we observed intraspecifically between BMR and masses of four metabolically active organs (heart, kidney, liver, small intestine) agrees with the positive correlation that Daan et al. (1990) observed interspecifically in birds between BMR and heart or kidney masses. Garland (1984) and Garland and Else (1987) similarly observed a positive interspecific correlation between standard metabolic rate and liver and heart masses in lizards. The same interspecific correlation is implicit in the observations that, compared to temperate-zone birds, tropical birds tend to have low BMRs (Weathers 1979) and low masses of internal organs (Rensch and Rensch 1956). In both our study and that of Daan et al. (1990), the organ masses examined explained about 50% of variation in BMR. Yet these organs constituted a much smaller percentage of total body mass. For example, the summed wet masses of heart, kidney, liver, and small intestine accounted for only 17% of total body wet mass even in AKR/J and HRS/J mice, the strains with the highest BMR (fig. 2) and largest organs (figs. 4-6). Why are such small organs such good predictors of BMR?
There are at least two explanations for this paradox. First, these organs have disproportionately high mass-specific metabolic rates, so that they do in fact contribute disproportionately to BMR (Krebs 1950). Second, large sizes of these four organs are part of a wider syndrome of characteristics contributing to high BMR. We already noted that masses of these four organs correlate with each other (table 2). Their masses are also likely to correlate with still other metabolically important masses or activities. One such correlated factor is brain mass: mice exhibit a positive correlation between BMR and brain mass (Sacher and Duffy 1979). Interestingly, among 25 mouse strains examined, one of the highest values of brain mass was found for AKR/J mice (Roderick et al. 1973), which we also found to have the largest heart, liver, and small intestine (fig. 2) and the second-highest value of BMR (fig. 1B). Coefficients of genetic determination for mass of brain and spinal cord in mouse strains are 0.62-0.67 (Roderick et al. 1973). Another correlated factor is cell proliferation rate: its value is notably low in A/J mice (Heiniger et al. 1971), which we found to have the smallest hearts and livers (fig. 2) and lowest BMR values (fig. 1B) among our six strains.
We estimate as follows the relative contributions of metabolism by the four organs themselves, and of correlated factors, to variation in BMR related to those organ masses. Martin and Fuhrman (1955) measured organ masses and mass-specific metabolic rates of tissue slices for all major organs of mice and dogs. They also measured BMR of whole living mice and dogs. The summed tissue respiration (i.e., the sum, over all organs, of organ mass times mass-specific tissue slice respiration) proved to be in reasonable agreement with the measured BMR of the whole animal: 72% of the mouse's BMR, 106% of the dog's BMR.
We therefore multiplied our measured organ masses times Martin's and Fuhrman's mass-specific tissue slice respiration rates (column 3 of their table 4), summed the products over our four studied organs for each individual mouse of our five analyzed mouse strains, and plotted our measured BMRs against that "four-organ BMR" for all five strains. The resulting "four-organ BMRs" and the measured BMRs fell in approximately the same sequence. Linear regression yielded the relationship
measured BMR = 1.95 (four-organ BMR) + 8.1
in units of ml [O.sub.2]/h, with an explained variation of 48%. This value of 48% is in good agreement with our previous conclusion, derived from principal-component analysis, that 52% of BMR variation is explained by between-strain and within-strain variation in organ masses. The slope of 1.95 suggests that about (1/1.95)(100) = half of our observed variation in BMR related to variation in those four organ masses represents variation in metabolism by those four organs themselves, whereas the remaining half represents variation in correlated factors.
Adaptive Significance of BMR and Organ Sizes
There is a growing literature on the association between BMR and life-history traits in wild animals (e.g., McNab 1988; Harvey et al. 1991; Hayes et al. 1992; Koteja and Weiner 1993). For example, Koteja and Weiner (1993) found correlations between BMR and food habits, climate, and biotope among closely related rodent species. BMR values are lower in tropical birds than in temperate zone birds (Rensch and Rensch 1956). BMR correlates intraspecifically with energy assimilation rate in kestrels in the wild (Dean et al. 1989) and in mice kept in the laboratory (Konarzewski and Diamond 1994). These associations suggest selection acting on BMR, whether directly or indirectly.
Some selective trade-offs involved in setting metabolic rates are clear. On the one hand, given an adequate food supply, animals with higher metabolic rates can be physically more active, obtain more food, sustain higher reproductive outputs, grow faster, and become more independent of environmental temperature through metabolic heat production. From the point of view of our study, large and metabolically expensive masses of metabolically active organs are the price that such active animals must pay to reap these advantages of high metabolic rates. On the other hand, animals with low metabolic rates require less food, are less likely to starve if food supply declines, and may be able to sustain themselves in unproductive environments (or with unproductive lifestyles) that would be uneconomic for a metabolically more active animal (McNab 1986). Small masses of metabolically active organs are a means by which such animals could succeed in reducing their metabolic costs.
This reasoning applies to an animal's whole energy budget: its sustained metabolic rate (SusMR), time-averaged over the course of its daily activity cycle (Peterson et al. 1990). BMR constitutes only a portion of this SusMR. What, if anything, is the adaptive significance of BMR itself?
One hypothesis is that SusMR rather than BMR is the target of natural selection. BMR might be viewed merely as the costs involved in generating SusMR (Kersten and Piersma 1987). This hypothesis postulates a close positive association between SusMR and BMR, as has been found interspecifically in mammals (Koteja 1991). An alternative hypothesis argues that natural selection acts to maximize the difference between SusMR and BMR, since this difference represents the amount of energy that can be utilized for reproduction (Ricklefs et al. 1996). By this hypothesis, BMR itself could also be a target of natural selection, since it typically accounts for at least 25% of SusMR even at times of peak SusMR (Drent and Daan 1980; Peterson et al. 1990). The former hypothesis emphasizes the benefits, the latter hypothesis emphasizes the costs, of the basal metabolic rates and organ masses that we studied.
We thank M. Chappell for logistical support, J. Gornbein for statistical advice, and K. Hammond for review of the manuscript. This work was supported by National Institutes of Health Grants GM14772 and DK42973.
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Marek Konarzewski, Present address: Institute of Biology, University of Warsaw, Branch in Bialystok, P.O. Box 109, Swierkowa 20B Str., 15-950 Bialystok, Poland.
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|Author:||Konarzewski, Marek; Diamond, Jared|
|Date:||Dec 1, 1995|
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