Physiological and morphological correlates of among-individual variation in standard metabolic rate in the leopard frog Rana pipiens
Department of Biology, University of St Thomas, St Paul, MN 55105, USA
* Author for correspondence (e-mail: acsteyermark{at}stthomas.edu)
Accepted 10 January 2005
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Summary |
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Key words: standard metabolic rate, thyroxine, mitochondria, leopard frog, Rana pipiens
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Introduction |
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Rates of minimal metabolism have been referred to as a fundamental
energetic trait (Else and Hulbert,
1985; Konarzewski and Diamond,
1995
; Labocha et al.,
2004
; McNab, 2002
;
Wikelski et al., 2003
), in
large part because they represent a fixed cost that all organisms must incur.
This fundamental trait, however, appears to be highly variable in vertebrates,
both between populations (Wikelski et al.,
2003
) and among individuals within a population
(O'Steen and Janzen, 1999
;
Steyermark and Spotila,
2000
).
Considering the proportion of daily energy expenditure (DEE) accounted for
by BMR or SMR among-individual variation in SMR may affect other energetic
processes (Steyermark, 2002),
and potentially fitness. On the one hand, assuming a fixed energy intake, an
individual with a low SMR, as compared to another individual, may have more
energy to dedicate to other processes, such as growth
(Gadgil and Bossert, 1970
).
Assuming changing energy intakes, an individual with a low SMR, as compared to
another, may have a lower DEE, resulting in less foraging time, and thus less
risk of predation. Both scenarios posit that selection should act to keep SMR
low because a low SMR results in benefits - energy allocation or a moderated
risk - to the individual. On the other hand, selection may act to keep SMR
high because a high SMR may be functionally related to increased total daily
energy expenditure budgets or a high sustainable metabolic rate
(Hammond and Diamond, 1997
;
Meerlo et al., 1997
;
Speakman et al., 2003
;
Tinbergen and Verhulst, 2000
;
Weiner, 1992
), allowing for
benefits such as increased lactation, thermoregulatory capacity or aerobic
scope. A diversity of environmental conditions may maintain variation in SMR
in the population, such that individuals with low SMRs do well in times of low
resource availability, while individuals with high SMRs do well in times of
high resources availability (Bateson et
al., 2004
).
But what is the mechanism underlying variation in BMR and SMR? The
mechanism given the most attention has been the positive relationship between
body-mass adjusted organ size and SMR (or BMR) in several groups of
vertebrates, including mammals
(Konarzewski and Diamond,
1995; Selman et al.,
2001
), birds (Chappell et al.,
1999
; Daan et al.,
1990
; Hammond et al.,
2000
) and lizards (Garland,
1984
; Garland and Else,
1987
). However, it is not clear whether large, energetically
expensive organs cause a high SMR or BMR, or whether in order to support a
high SMR or BMR one needs large central organs. Thus, this relationship
remains only correlative: which is the cause and which the effect remains
unresolved.
Other variables in addition to body-mass-adjusted organ mass may affect
SMR, however. Here, we seek to understand potential mechanisms underlying
among-individual variation in SMR. In a novel approach, we examine two
potential proximate correlates of variation in SMR, in addition to organ mass,
in leopard frogs Rana pipiens. We measured SMR of the frogs, then
measured the masses of energetically expensive organs, serum-free T4
thyroxine, and relative mitochondrial content using flow cytometry
(Johnson et al., 1980;
Shapiro, 1981
). We asked three
questions: first, how much among-individual variation exists in the metabolic
rate of an amphibian? Second, does organ mass correlate with metabolic rate in
frogs, as it does in other vertebrate groups? Third, do sources of
among-individual variation in metabolic rate exist other than that correlated
to variation in organ mass?
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Materials and methods |
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Determination of standard metabolic rate
As an index of metabolic rate, we measured carbon dioxide production of
postabsorptive leopard frogs at 22°C for 24 h under constant light
conditions using an open-flow, push-through respirometry system (Withers,
1977; Sable Systems, Las Vegas, NV, USA). We placed six frogs into individual
237 ml containers at approximately 0800 h. Air was first purged of
H2O and CO2 with an external purge gas generator (Parker
Hannifin Corp., Haberhill, MA, USA), and was then pumped into a
temperature-controlled chamber (Hotpack, Philadelphia, PA, USA), where the air
was further filtered of CO2 and H2O through a column
containing drierite/ascarite/drierite to remove residual CO2. The
air then passed through copper tubing to bring the air to 22°C, and then
into a respirometer manifold (MF8; Sable Systems) fitted with a bleed valve.
The manifold split the airstream into seven paths, six of which were directed
into animal chambers, and one was used for baseline measurements. The air was
humidified before entering the animal chambers to prevent desiccation, was
dried (using drierite) upon exiting the animal chamber, and then entered an
air multiplexer (TR-RM8; Sable Systems), which allowed for sampling one
individual airstream while venting the rest to the room. The sampled airstream
flow was regulated to 100 ml min-1 by a thermal mass flow
controller (Model 840, Sierra Instruments, Amsterdam, The Netherlands and
TR-FC1, Sable Systems). We then subsampled the airstream using a subsampler
flow controller (TR-SS1, Sable Systems) at a rate of 75 ml min-1,
and drew the air through a Licor carbon dioxide analyzer (LI6252, Lincoln, NE,
USA).
We recorded baseline measurements with the CO2- and H2O-free air before and after each animal recording. We sampled CO2 and flow rate for each animal chamber at two samples per second for a 10 min duration (one file), after which another set of baselines and the next animal chamber were measured. Chambers were measured in sequence from the first frog to the last, and the sequence was then repeated eight times, for a total of eight 10 min files per frog.
We calculated the minimal CO2 production from the lowest continuous 120 samples (1 min) for each 10 min file, and averaged these values across the final six files of each animal. We disregarded the first two files of each animal because they were significantly higher than the remaining six (most likely caused by animal activity). Standard metabolic rate for each frog was calculated according to the method of Withers (1977). All 29 frogs were measured for CO2 production within 7 days of each other. Body mass measurements taken immediately before and after CO2 measurements indicate that frogs lost less than 5% body mass, suggesting that they remained adequately hydrated throughout the trial.
Measurements of organ mass and thyroxine
Less than 6 h after CO2 measurements ended, we anaesthetized the
frogs in a neutral 1:1000 Tricaine methanesulfonate solution (MS-222, Argent
Chemical Laboratories, Redmond WA, USA). All frogs were sacrificed between
1200 h and 1700 h, reducing potential effects of diel fluctuations in serum T4
concentrations (Gancedo et al.,
1996). We exposed the frog's thoracic cavity, drew
1-2 ml
blood by cardiac puncture, centrifuged the blood sample (12 min at 894
g), and froze the serum at -20°C for later analysis of T4.
Next we removed the liver, blotted it and weighed it whole (±0.00005
g). We then removed approximately one third of its left lobe and weighed it,
and then immediately froze the sample for later analysis of mitochondria.
Next, we removed and blotted the heart, kidneys, small intestine and right
gastrocnemius, trimmed them of fat and connective tissue, and weighed them. We
dried the organs at 60°C until constant mass (
72 h), and then weighed
them again to obtain organ dry mass.
We measured total T4 serum thyroxine using a neonatal high sensitivity microplate enzyme immunoassay (Monobind Inc., Los Angeles, CA, USA). Briefly, enzyme-T4 conjugate and serum sample T4 competed for immobilized T4 antibody sites on the microplate. After a 60 min incubation, unbound T4 was removed by aspiration, and then the bound T4 was measured colorimetrically. Results were compared to a standard curve. We measured each serum and reference sample in triplicate, and read the absorbance of the microplates at 630 nm (ELX800 Microplate Reader, Winooski, VT, USA). We used the mean of the three replicate absorbance readings in the data analyses described below.
Measurements of mitochondrial content
After thawing the liver sample, we washed it several times with Hank's
balanced salt solution (HBSS; Sigma-Aldrich Chemical Co., St Louis, MO, USA)
until no visible blood remained on the liver. We then transferred the liver
into dissociation solution containing 200 U ml-1 collagenase IA
(Sigma-Aldrich Chemical Co.) and 200 U ml-1 hyaluronidase II
(Sigma-Aldrich Chemical Co.) together in 10 ml of HBSS, at 37°C, minced
it, mixed it by pipetting up and down several times, and then placed it in a
37°C water bath, shaking at 200 r.p.m. for 30 min. Next, we pushed the
suspension through a 100 µm sieve (BD Biosciences, Bedford, MA, USA)
several times until there was no resistance, and placed it back into a
37°C water bath shaking at 200 r.p.m. for 15 min. Finally, we pushed the
suspension through a 40 µm sieve (BD Biosciences) several times.
We collected the disaggregated cells from the suspension by centrifuging the sample at 250 g for 5 min at room temperature. To lyse red blood cells (RBC), we resuspended the pellet in 5 ml of ice-cold RBC lysis buffer (0.15 mol l-1 NH4Cl, 0.1 mmol l-1 KHCO3, 0.1 mmol l-1 Na2EDTA, pH to 7.2-7.4), and centrifuged as above for a total of three washes. We then resuspended the final hepatocyte pellet in 5 ml of ice-cold HBSS.
We divided the liver cell suspension into two 1 ml samples, and centrifuged
them at 250 g, at 4°C for 5 min. We resuspended each
cellular pellet in either 1 ml of 100 nmol l-1 MitoTracker Deep Red
(MDR; Molecular Probes, San Jose, CA, USA) in HBSS (stained cells), or in 1 ml
of HBSS alone (unstained cells), incubated the suspensions in a 37°C water
bath shaking at 200 r.p.m. for 30 min, and then collected the cells by
centrifugation at 250 g, at 4°C for 5 min. Finally, we
re-suspended the cells in 1 ml HBSS for fluorescence analysis. We acquired
fluorescence emission intensity of 20 000 total counts (a count is any
particle, such as a cell, that the flow cytometer detects) at 663 nm with a
FACSCaliber flow cytometer (BD Bioscience, San Jose, CA;
Johnson et al., 1980;
Shapiro, 1981
).
Before analyzing frog liver samples, we conducted a series of control
experiments. First, we examined the effects of using frozen liver samples by
comparing fresh versus frozen samples from the same individual, and found no
difference in fluorescence intensity. To determine whether the mitochondrial
stain was fully permeable through cell membranes, we permeabilized
disaggregated cell membranes with 0.1% Triton X-100 (Sigma-Aldrich Chemical
Co.), stained them as above, and then compared their fluorescence intensity
with non-permeabilized cells. Fluorescence intensities of the two treatments
were similar. As a negative control, we lysed disaggregated cells with 10%
sodium dodecyl sulfate (SDS; Sigma-Aldrich Chemical Co.) and then stained them
as above. As anticipated, at that concentration the SDS lysed both cell and
mitochondrial membranes, resulting in no fluorescence. Lastly, we estimated
the repeatability of the method by dividing the solution of disaggregated
hepatocytes into five equal samples before the staining procedure. We then
calculated a coefficient of variation (3.5%), and repeatability (0.98;
Lessells and Boag, 1987).
Data analysis
We used body mass of frogs before CO2 measurements began for all
data analysis. We first removed three outliers from the total data set
(including metabolic rate, organ mass, serum T4 thyroxine concentration and
mitochondrial number) following a Jackknife Distance Outlier Analysis. Thus,
all following data analyses are performed on 26 frogs. Using ratios as a
size-specific index for either metabolic rate or organ mass can introduce bias
into statistical analyses (Packard and
Boardman, 1999). Therefore, to investigate relationships between
organ mass and metabolic rate, we first computed residuals of SMR on body
mass, and residuals of organ mass on body mass, then regressed residual SMR
against residual organ mass (Konarzewski
and Diamond, 1995
). To investigate relationships between serum T4
thyroxine and metabolic rate, we regressed whole body SMR against serum T4
thyroxine concentration.
To investigate relationships between mitochondrial number and SMR, we first
used Cellquest Pro 4.0 (BD Bioscience) to plot a histogram of fluorescence
intensity on a logarithmic scale, from which we calculated the geometric mean
fluorescence for each sample. Geometric mean better represents the typical
signal intensity than the arithmetic mean because it is less influenced by
high outliers for logarithmic data distributions
(Sokal and Rohlf, 1995). The
calculated geometric mean of the fluorescence intensity is proportional to the
amount of mitochondria in 20 000 counts. To obtain a final mitochondrial
fluorescence intensity, we subtracted autofluorescence from the fluorescence
of the respective stained sample. To normalize number of liver mitochondria to
a relative measure of mitochondrial content, we computed residuals of
fluorescence intensity on liver mass, and then regressed residual fluorescence
intensity against residual SMR.
All data analyses were performed using JMP 5.0 (SAS 2002; SAS Institute, Inc., Cary, NC, USA).
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Results |
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Organ mass, T4 concentration and mitochondrial content
There was considerable variation in organ dry masses between individual
frogs, ranging from 18 to 34% CV (Table
1). Dry mass of all organs increased significantly with body mass
(Table 1), which accounted for
between 17 and 75% of variation in organ mass
(Table 1).
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Mean T4 thyroxine concentration was 0.810 µg dl-1 (S.E.M.=0.006, min=0.753, max=0.873), and did not change with body mass (P=0.69). Mean relative mitochondrial content was 2367 (CV=73%), and did not change with body mass (P=0.61).
Relationship between SMR and organ mass, T4 and mitochondrial number
There was a significant relationship between residuals of SMR and residuals
of dry kidney mass (P=0.01, r2=0.25;
Fig. 2), suggesting that frogs
with bigger kidneys had higher SMR values. However, if the circled data point
(in Fig. 2) is removed from the
analysis, the relationship is no longer significant (P=0.4). There
were no significant relationships between residuals of SMR and residuals of
dry liver, heart, small intestine and gastrocnemius mass
(Fig. 2).
|
There was a significant relationship between serum T4 thyroxine concentration and SMR (P=0.04; r2=0.16; SMR=-4.84+8.023[T4]; Fig. 3), suggesting that individuals with higher serum thyroxine T4 levels had higher resting metabolic rates. Residual relative mitochondrial content increased with residual SMR (P=0.05; r2=0.15; residual SMR=3.292e-16+0.0001238 residual relative mitochondria content; Fig. 4), suggesting that individuals with more mitochondria in the liver have a higher metabolic rate.
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Lastly, we examined the relationship between serum T4 concentration and relative mitochondria amount. However, both serum T4 concentration and relative mitochondria amount correlate with SMR. To remove the effects of SMR on both variables of interest, we calculated residuals of serum T4 concentration against SMR, and relative mitochondria amount against SMR. With the effects of SMR on each variable removed, and thereby avoiding colinearity, we plotted residual relative mitochondrial content against residual T4 (Fig. 5). The results indicate a significant relationship (P=0.03; r2=0.17; residual mitochondrial content = -6.512x10-13+23861 x residual T4), suggesting that individuals with high levels of serum T4 also have a high relative mitochondrial content independent of SMR. Alternatively, we performed a multiple regression with relative mitochondrial content as the dependent variable, and SMR and serum T4 concentration as independent variables. Results indicate a marginally significant relationship between relative mitochondrial content and serum T4 concentration (P=0.059). Both approaches give qualitatively similar results, both suggesting that high levels of serum T4 correlate with relative mitochondrial content independent of SMR.
|
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Discussion |
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Physiological mechanisms underlying among-individual variation in SMR
Standard metabolic rate is a sum total of many different body processes,
each with several layers of regulatory mechanisms. Therefore, isolating an
individual cause of among-individual variation in SMR is illogical. However,
previous studies suggest three possible proximate causes of among-individual
variation in SMR. First, a recent body of literature suggests that organ mass,
when adjusted for body size, may correlate with SMR in both ectotherms
(Garland, 1984;
Garland and Else, 1987
) and
endotherms (Burness et al.,
1998
; Konarzewski and Diamond,
1994
; Konarzewski and Diamond,
1995
; Meerlo et al.,
1997
; Speakman and McQueenie,
1996
). Though each study found relationships between SMR and
different organs, they all focused on a common set of energetically expensive
organs, including heart, kidney, small intestine and liver.
Our results indicate a positive correlation between mass-corrected kidney
mass and mass-corrected metabolic rate. The frog kidney aids in the regulation
of both water and ion re-absorption. Under normal body hydration, water
re-absorption in the kidney is typically low, because of the need to excrete
water gained through the permeable skin
(Vondersaar and Stiffler,
1989). However, absorption of electrolytes is high, and probably
energetically expensive (Boutilier et al.,
1992
). Thus, although kidney mass accounts for a small proportion
of total body mass (ca. 0.5%), the kidney's energetic demands may be a partial
determinant of whole body SMR. It should be noted that the relationship
between SMR and kidney mass is significantly affected by a single data point
(Fig. 2). However, this data
point was not identified as an outlier in the Jackknife Distance Outlier
Analysis, and thus should not be considered as an outlier, but rather as a
valid data point.
However, any relationships between SMR (or BMR) and organ mass should
appropriately be thought of as correlations rather than as cause and effect,
because it is not at all clear whether organ size or SMR is the causative
factor. One possibility is that a large organ size drives a high metabolic
rate, as large organs - whatever their value - have a cost associated with
their upkeep and function. In this scenario, large organs may provide benefits
such as extra machinery for a high aerobic capacity
(Bacigalupe and Bozinovic,
2002; Speakman,
2000
). In another scenario, large organs are necessary to provide
fuel for a high metabolic rate, which may be driven by some unknown factor
(such as high levels of thyroxine, or a high mitochondrial content). This
scenario is supported by several studies that have examined the change in
organ mass due to increased metabolic demands, such as lactation or thermal
stress (Hammond and Diamond,
1992
; Hammond et al.,
1994
; Hammond and Kristan,
2000
; Hammond et al.,
1996
). Because of this organ size plasticity in response to
metabolic needs, it may be reasonable to posit that individuals that have high
SMR - for whatever reason - may need large organs to provide for the high
maintenance energy expenditure.
The second possible cause of among-individual variation in SMR we
investigated was T4 thyroxine levels. Thyroxine is well-known as a potential
effector on metabolic rate in vertebrates
(Hulbert, 2000), including
ectotherms (John-Alder, 1983
).
It is thought to act on metabolic rates through a variety of ways, including
changes in mitochondrial membrane composition
(Hulbert, 2000
),
Na+,K+-ATPase pump activity
(Ismail-Beigi, 1993
) and
maintenance of mitochondrial H+ gradients
(Gong et al., 1997
). Thyroxine
supplementation (both T3 and T4) certainly stimulates ectotherm standard
metabolic rates (Gupta and Thapliyal,
1991
; Joos and John-Alder,
1990
). More importantly, however, metabolic rate appears to
correlate with endogenous variation of thyroxine levels in both endotherms
(Astrup et al., 1996
;
Stenlof et al., 1993
;
Toubro et al., 1996
) and
ectotherms (O'Steen and Janzen,
1999
). In the latter study, thyroxine levels of neonatal snapping
turtles from different egg incubation temperatures correlated with mean SMR.
Their results suggest an ultimate causation of among-individual variation in
SMR: egg incubation temperature, as mediated through T4 thyroxine. Results
from the present study also show a positive relationship between T4 and SMR,
confirming at least a part of the results of O'Steen and Janzen
(1999
).
In addition to correlations between SMR and organ mass and thyroxine, we
also report a correlation between SMR and mitochondrial content. Several
mechanisms might explain this correlation. The first is a simple
among-individual variation in mitochondrial content, which drives variation in
SMR. Though we have found no studies that have detailed among-individual
variation in mitochondrial content, variation in correlates to mitochondrial
content, and their relationship to performance, have been well studied (e.g.
citrate synthase activity; Zimmitti,
1999). Therefore, considering the variation seen in other anatomic
traits, such as organ mass (see above), it seems probable that similar
variation may exist in mitochondrial content in other organisms.
Two subsequent mechanisms that may explain the mitochondrial content and
SMR correlation involve potential effects of thyroxine on mitochondria. First,
thyroxine may stimulate the production of mitochondria. We present here a
previously unreported relationship between T4 thyroxine and mitochondrial
content, suggesting that endogenous high levels of thyroxine may promote an
increase in mitochondrial number. Second, thyroxine affects mitochondrial
membrane composition (Hulbert,
2000). Thyroxine may affect the amount of inner mitochondrial
membrane (Brand et al., 1992
),
thus resulting in more mitochondrial stain binding. In this scenario, the
increase in fluorescence intensity would be due to an increase in inner
mitochondria membrane surface area, rather than in mitochondrial content.
Our results suggest that a considerable amount of variation in standard
metabolic rate exists in an amphibian, which is comparable with reported
variation in SMR in other ectotherms
(Steyermark and Spotila,
2000). The finding that variation in SMR correlated only with
variation in kidney mass, but not with the mass of other energetically
expensive organs, is therefore partially consistent with organ mass-metabolic
rate relationships reported for other vertebrate species. Finally, both
thyroxine and mitochondrial content correlated with SMR. The relationship
between SMR and endogenous thyroxine was not surprising, given the well-known
endocrine controls of metabolic rate. However, it is not known what causes the
variation in thyroxine levels in the first place. The correlation between
mitochondrial content and SMR is a novel result, which again begs the question
of the source of that variation. Finally, it is not at all clear how selection
may act on metabolic rate and related life-history traits, though it has been
suggested that selection may act on metabolic rate to create diverse life
histories that can be adapted for changes in local environment
(Wikelski et al., 2003
).
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Acknowledgments |
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