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Animal Science Department, University of California at Davis, Davis, CA 95616 and * The University of Reading, Department of Agriculture, Earley Gate, Reading RG6 6AT, UK
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONTHE MODELMODEL EVALUATIONDISCUSSIONREFERENCES |
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KEY WORDS: • rodents • protein synthesis • protein degradation • mathematical model • tracer kinetics
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONTHE MODELMODEL EVALUATIONDISCUSSIONREFERENCES |
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THE MODEL |
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TOPABSTRACTINTRODUCTIONTHE MODELMODEL EVALUATIONDISCUSSIONREFERENCES |
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The model is a dynamic representation of leucine kinetics in a
nongrowing rodent. The model traces the turnover of leucine from the
free amino acid pools to oxidation or the protein pools (Fig. 1
A).Leucine can remain in the protein pools, be released through protein
degradation to be converted back into protein or be oxidized.
Similarly, if radiolabeled leucine is flooded, pulsed or continuously
infused into the extracellular pool, it can pass to the leucyl-tRNA
pool and be used for protein synthesis or enter the intracellular pool.
In the intracellular pool, leucine can be oxidized, remain as a reserve
or be transported to another pool (Fig. 1
B). Thus amino
acids for protein synthesis can arise from several sources, i.e., the
intracellular pool, the extracellular pool (channeling) or protein
degradation (recycling). Because the rodent is nongrowing, protein is
always in steady state; protein synthesis is always equal to protein
degradation (Waterlow et al. 1978
). Definitions and
units of the symbols used in the model are listed in Table 1
.Conservation of mass principles give the differential equations for the
model for unlabeled and radiolabeled amino acid, respectively
(Table 2
).
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The whole-body free leucine pool is divided among an extracellular pool (QE), an intracellular pool (QI), and an aminoacylated tRNA pool (QT). All three free leucine pools interchange fully. Because whole-blood data are not available for the rodent, the extracellular pool is assumed to be homogenous and representative of the free amino acid in the extracellular space and plasma. The influx of amino acid into the extracellular pool (F0E) is absorption; the efflux from the intracellular pool (FIO) is oxidation. The aminoacylated tRNA pool represents amino acids covalently bound to tRNA and can be derived from the extracellular pool (channeling, FET), the intracellular pool (FIT) or protein degradation (FFT, FMT, FST). Flux from the extracellular to the intracellular pool (FEI) represents the transport of leucine into the intracellular compartment. Intracellular leucine can be oxidized (FIO), transported back to the extracellular pool (FIE) or undergo acylation (FIT).
Estimation of initial size of free amino acid pools.
Initial pool sizes provide a starting value for differential equations.
The differential equations describe the inflow and outflow of leucine.
Calculation of initial leucine in the extracellular, intracellular and
aminoacyl tRNA pools was based on body weight. Therefore the model can
be used easily to represent protein turnover in rodents of different
sizes. Equations and values for the initial amino acid pools are listed
in Table 3
.To calculate the extracellular leucine content, it was assumed that the
concentration of leucine in plasma was the same as that in the
extracellular space and that there was rapid equilibration between the
two spaces. Therefore the amount of leucine in the extracellular pool
was the amount in the extracellular space (first term) plus the amount
in the plasma (second term). To calculate the leucyl-tRNA pool size,
total tRNA from liver was used to estimate the amount of tRNA in fast
protein turnover tissue and total tRNA from muscle for slow and medium
protein turnover tissues. The number of leucine codons (6) was divided
by the number of total codons (60) to estimate that 10% of the total
tRNA was leucyl tRNA. Therefore the total charged leucyl tRNA was 0.88
(Palmiter 1975
) times 0.1 (Table 3)
.
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Whole-body protein is divided into pools with fast (QF), medium (QM) or slow (QS) turnover rates. Each pool is assumed to be homogenous. The rates of turnover of the three pools are used to account for the nonhomogeneity of total protein. Amino acids from degradation can go to the intracellular or aminoacyl-tRNA pools. Fluxes from the protein pools to aminoacyl tRNA (FFT, FMT, FST) represent recycling of amino acids from degradation to synthesis without mixing with the intracellular pool. Protein synthesis can occur only via the aminoacyl-tRNA pool.
Estimation of initial size of protein pools.
All pool size calculations were based on a 26-g reference mouse. A 26-g
reference mouse was used because 26 g was the average weight of
mice in which protein content was determined. The ratio of organ
protein to whole-body protein in the mouse was used to calculate organ
protein content in the rat (Table 4
).To determine QF, QM and QS, organs
were classified as fast, medium or slow protein turnover organs (Table 4)
. Organ protein was assumed to contain 6% leucine on a dry basis
(Waterlow et al. 1978
). Therefore the percentage of
leucine in protein was 2% when corrected for water content. Initial
leucine in protein pool sizes are also scaled to body weight to
represent protein turnover in rodents of different sizes. The estimates
of leucine bound to protein are listed in Table 4
.
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Some literature values were available to estimate leucine fluxes among
the free amino acid pools. If literature values were not available,
assumptions about relationships among fluxes were used. Estimates with
the flooding dose method were based on the assumption that the rodent
is essentially not growing over the 15–30 min experimental time
period. Therefore there is no accretion of protein, and oxidation
(FIO) was set equal to intake
(FOE) (Waterlow et al. 1978
).
Equations for FIE and FTI
(Table 2
, Eq. 1.2a and 2.5) are balance equations that are based on the
assumptions that the intracellular pool and leucyl-tRNA pools,
respectively, are in steady state. Table 2
lists the equations and
assumptions and Table 5
lists the values for the free leucine fluxes (µmol/min).
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by the percentage channeling (PC) and the
percentage recycling (PR). Channeling is the amount of leucine from the
extracellular pool that is incorporated directly into protein
(FET, Eq. 1.6). Because the amount of leucine
incorporated into protein is constant, leucine from the intracellular
pool (FIT, Eq. 2.7a) is the amount not supplied
through channeling or recycling. Recycling is the amount of leucine
from protein degradation that is reincorporated into protein
(FFT, FMT,
FST; Eqs. 3.0, 3.1 and 3.2, respectively)
without mixing with the intracellular pool. The amount of leucine from
protein degradation that goes to the intracellular pool
(FFI, FMI,
FSI; Eqs. 2.2, 2.3 and 2.4) is the amount that
is not recycled. Because protein synthesis is equal to protein
degradation in a nongrowing rodent, the protein synthesis rates
KSF, KSM and
KSS, equal protein degradation to the
intracellular pool (Eqs. 2.2, 2.3 and 2.4) and protein recycling (Eqs.
3.0, 3.1 and 3.2).
Because literature values were unavailable for FITand FTI, fluxes were calculated on the
basis of two scenarios (Table 5)
. In the first, leucyl tRNA was charged
solely from the extracellular pool (QE) of leucine directly
(channeling). Therefore, the net flow of leucine was from
QT to QI and FIT was
assumed to equal zero. FTI (Eq. 2.5) could be
calculated by a balance equation for QI on the basis of the
assumption that QI was in steady state. If the assumption
is made that all amino acids in the cell arise from extracellular
channeling, intracellular amino acids are the result of "spillover"
of the protein synthetic machinery, and the difference between
FIT and FTI should be
very close to 0.221 mol/min (i.e., FIT = 0). The
second scenario was that leucyl tRNA was charged primarily from the
intracellular pool (QI). The net flow of leucine was from
QI to QT; therefore FTI
(Eq. 2.5) was assumed to equal zero. FIT (Eq.
2.7b) could be calculated by a balance equation for QI on
the basis of the assumption that QI was in steady state. If
the assumption is made that all amino acids in the cell used for
protein synthesis arise from the intracellular pool, the difference
between FIT and FTI
should be very close, 0.233 mol/min (i.e., FTI =
0). Therefore, the estimated values for FIT and
FTI are the net fluxes of leucine for the
different scenarios and do not represent the total flow of leucine
between QI and QE. All of the estimates of
fluxes are based on the assumptions that the model is balanced (pool
sizes do not change) and that no recycling is occurring. In reality,
charging from both the extracellular and intracellular pools probably
occurs and pool sizes (extracellular and intracellular) do change.
Estimation of protein synthesis and degradation rates in steady state (nongrowing).
In a nongrowing rodent, protein synthesis and degradation are equal;
therefore ,FTF + FTM
+ FTS = (FFT +
FMT + FST) +
(FFI + FMI +
FSI) (Eqs. 2.2, 2.3, & 2.4 and 3.0, 3.1 and
3.2). Values for FTF, FTM
and FTS for the reference mouse were calculated
from estimates of average FSR (Ks) for different
tissues of mice and rats from flooding dose experiments and continuous
infusion experiments separately (Tables 6
and7).Because FSR was relative to the total leucine in protein, it was
expected that it would be approximately the same for rats and mice;
thus no adjustment was made for different body weights. There were not
enough estimates in the literature of whole-organ or mouse FSR using a
pulse dose to calculate average FSR. Organs were classified as fast,
medium or slow protein turnover organs and assigned to an appropriate
protein pool. The estimates of protein synthesis rates were used to
simulate two data sets from flooding dose experiments.
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Flooding dose. Calculations of FSR from flooding dose
experiments are based on the average specific activity of the precursor
pool and the specific activity of the protein pool at the end of the
experiment (15 or 30 min). The method assumes one amino acid and one
protein pool that are homogeneous and well mixed (Garlick et al. 1980
). Terms used the in equations are defined in Table 1
.
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Pulse dose. FSR estimates from pulse dose experiments
can be calculated using the equation of Peters and Peters (1972)
.
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where sp and si are calculated based on data collected at the end of the experiment (60 min). In the original equation, sp was the disintegrations per minute of leucine in protein, multiplied by the time length of the measurement (60 min) and divided by the leucine concentration in protein to obtain the final protein specific radioactivity. The denominator (si) was the disposal rate of the amino acid injected or the dose of amino acid given divided by the area under the curve for disappearance of the radiolabeled amino acid over time.
Continuous infusion.
FSR estimated by the continuous infusion experiments are
calculated using the following equations (Waterlow et al. 1978
):
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where Ks is found by plotting
Ks vs.
sp/si at a fixed tand B. Definition of terms in equations are listed in
Table 1
.
Fractional synthesis and degradation rates estimated by different experimental methods.
KS values estimated by the continuous
infusion method were lower than estimates by the flooding dose method.
This was expected because continuous infusion experiments obtain
measurements over a longer period of time, i.e., 6 h for
continuous infusion compared with 15–30 min for a flooding dose. The
longer experimental period allows for a greater recycling of
radiolabel; therefore the specific radioactivity of the precursor pool
(intracellular, extracellular or aminoacyl tRNA) will decrease over
time. Data compiled by Waterlow et al. (1978)
indicated
that FSR estimates obtained by continuous infusion were between 11 and
24%/d for the slow turnover protein pool, between 48 and 55%/d for
the medium turnover protein pool and between 50 and 68%/d for the fast
turnover protein pool.
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MODEL EVALUATION |
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TOPABSTRACTINTRODUCTIONTHE MODELMODEL EVALUATIONDISCUSSIONREFERENCES |
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, columns
1 and 2). Protein synthesis rate was assumed to equal protein
degradation rate and the protein pools were in steady state. In this
section, these assumptions were tested. Two data sets were fit to the
model using the maximum log likelihood method according to Simusolv
(Dow Chemical 1990
) to find the most likely combination
of fluxes that would cause the model to reproduce the experimental data
sets. The combination of fluxes predicted by the model to reproduce the
specific radioactivity data of Bernier and Calvert (1987)
and Obled et al. (1991)
is listed in
columns 3 and 4 of Table 5
. Nongrowing rodent model.
Protein synthesis was assumed to equal degradation; therefore Equations
2.2, 2.3 and 2.4 were used to represent protein synthesis and Equations
3.0, 3.1 and 3.2 (Table 2)
were used to represent recycling from
protein degradation. The only pools that remained constant in size were
the aminoacyl-tRNA pool (QT) and the protein pools
(QS, QM, QF). Therefore Equation
2.5 was used to represent FTI..
FIT reflects the relative amounts of channeling
and recycling (Eq. 2.7a). Equations for FFI,
FMI, FSI,
FET, FTE and
FEI are those listed in Table 2
.
Data sets 1 and 2.
Two data sets with whole-body protein and amino acid–specific
radioactivities were simulated to determine whether the model could
predict specific radioactivity changes over time and whole-body FSR.
The first data set was a flooding dose experiment in 30-g mice given
370 MBq 14C leucine and 100 µmol leucine per 100 g
body weight (Bernier and Calvert 1987
). Whole-body
specific radioactivity measurements were taken at 2, 5, 10, 15, 20 and
30 min in the free amino acid pool (acid supernatant) and protein pool
(acid precipitate). The second data set was a flooding dose experiment
in 70-g rats given 877 MBq 14C leucine and 140 µmol
leucine per 70 g body weight (Obled et al. 1991
).
The 70-g rat was assumed to be nongrowing because the amount of protein
the rat would gain in 15 min would be within 10–15% of the
experimental error in measuring specific radioactivity changes of the
leucine in protein and free amino acid pools. Whole-body specific
radioactivity measurements were taken at 5, 7, 9, 11, 13 and 15 min in
blood plasma, free amino acid pool (acid supernatant) and protein pool
(acid precipitate). Free leucine in the extracellular space was also
estimated. The plasma specific radioactivity was assumed to equal the
extracellular specific radioactivity. The intracellular specific
radioactivity was obtained by correcting the free amino acid specific
radioactivity for the extracellular contribution. Both C. Obled
(Institut National de la Recherche Agronomique) and C. C. Calvert
(University of California, Davis) graciously supplied raw specific
radioactivity data from the papers by Bernier and Calvert (1987)
and Obled et al. (1991)
to test the
model.
Fluxes (FIO, FOE,
FIE), the percentages of protein synthesized
(KSF, KSM,
KSS), PC and PR were fitted using Simusolv
(Dow Chemical 1990
) to have the model produce specific
radioactivities as close to the data as possible (Tables 8
and
9).Because changes in data on specific radioactivity over time were not
available in the continuous infusion experiments, only fits using the
flooding dose method could be obtained. FSR represented the total
fractional synthesis rate predicted by the model based on
KSS, KSM and
KSF using the flooding dose method. The
resulting fluxes were compared with steady-state model fluxes for
scenarios 1 and 2 in Table 5
.
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Protein synthesis rates from the fits of the data given by Obled
(Obled et al. 1991
) and Bernier (Bernier and Calvert, 1987
) for individual protein pools were lower than
synthesis rates calculated from the literature (Table 6)
. In the Obled
and Bernier data sets, the protein pools were not divided into fast,
medium and slow turnover pools. To fit the protein specific
radioactivity data, therefore, only the slow pool turnover rate was
allowed to vary (KSS). Thus all of the error
associated with fitting the data is in the turnover rate for the slow
protein pool. But, the estimated rates (KSF,
KSM, KSF) from fitting to
each data set were close. FSR calculated from the model simulations of
the data were close to the FSR published by Obled and Bernier, i.e., 31
and 33 %/d, respectively. Because true FSR was relative to the total
leucine in protein, it was expected that it would be approximately the
same for rats and mice. FSR was calculated on the basis of a 2.5-min
measurement of the specific radioactivity of the free leucine pools for
the Bernier data and was based on a 5-min measurement for the Obled
data.
Fits of free amino acid fluxes.
The fluxes determined from the Bernier and Obled data sets were fairly
close to each other. If the Fij were adjusted by
the body weight of the rodent, FEI,
FIE, and FET were fairly
close. However, intake was higher and oxidation was lower in the
Bernier data compared with the Obled data set. To fit the data from
Bernier, dietary intake (5 g/d) had to be increased to 1202 µmol
leucine (or 11g/d) to decrease the specific radioactivity of the free
leucine pool. As a result, oxidation was lowered because a greater (and
quicker) dilution of the free leucine pools could be achieved by
increasing intake instead of increasing oxidation. The estimate of
intake would be expected to be the most accurate because it was the
easiest to measure. The percentage of error was very low for the
Bernier data (<5%); thus much of the error associated with the free
amino acid pools was probably taken up by the intake value. The
predictions of oxidation rates from both data sets were at least four
times greater than if intake was truly equal to oxidation as assumed
for the literature values (scenarios 1 and 2 in Table 5
). In the Obled
solution, however, intake was fixed at that expected for a 70-g rat (14
g/d). Therefore, in both data sets, a dilution of the specific
radioactivity of the free amino acid pools was necessary to fit data.
The dilution could arise from increased oxidation, a recent meal,
experimental error, a differential use of radiolabeled leucine over
unlabeled leucine or another source that was not accounted for in the
model.
Fits of the Bernier and Obled data predicted that
FTI was zero. A small FTI
(as with the flux estimates from the Bernier and Obled data) makes
sense energetically and structurally because once an amino acid was
bound to the leucyl-tRNA-elongation factor 1 
-GTP complex it would
be difficult to escape protein synthesis and a waste of the energy used
to charge the leucyl tRNA. However, tRNA may have other uses in the
cell such as for protein degradation through the ubiquitin system
(Deshpande et al. 1996
). Therefore
FTI might have been occurring, but it was
probably so small that it was essentially zero.
Comparison of fits to a two-pool model of protein turnover.
To check the solution from the fitting of the Bernier data, a two-pool
model of protein synthesis created by Waterlow et al. (1978)
was used to fit the data (Table 5)
. The model was
created for continuous infusion experiments with one free amino acid
pool with an intake flux, an oxidation flux and one protein pool. The
two pools exchanged amino acids; the flux from the amino acid pool to
the protein pool represented protein synthesis and the flux from the
protein pool to the amino acid pool represented protein degradation.
Synthesis was assumed to equal degradation and intake equaled
oxidation. Although the model was created for continuous infusion
experiments in which data for specific radioactivity changed over time
were not available, the model was adapted for a flooding dose
experiment by assuming that even if the animal was not growing, intake
and oxidation were not equal. The percentage of error for specific
radioactivities of the amino acid pool were higher (26%) but about the
same for the protein pool (6%). KS was very
close to the FSR from the six-pool model (32 vs. 33 %/d), but intake
was higher than the previous estimate (1.69 vs. 0.84 µmol Leu/min)
and oxidation was closer to the value for intake (0.27 vs. 0.34 µmol
Leu/min). Therefore the solutions from the Bernier data implied that
there was probably another source of dilution for the amino acid pools.
Comparison of changes in pool sizes.
Specific radioactivity in a pool could change only if there was an
influx of leucine from a pool with a different specific radioactivity.
The greater the change in specific radioactivity over time, the greater
the difference in specific radioactivity must be between the two pools
that were exchanging amino acids. Because specific radioactivity is the
ratio of radiolabeled leucine to unlabeled leucine, only information
about dilutions of radioactivity and not actual pool size changes can
be derived. For instance, there was no way to tell if the change in
specific radioactivity was due to an influx of unlabeled amino acid
such as from intake or protein degradation or from a source with a very
low specific radioactivity such as the intracellular pool. Therefore
more estimates of changes in pool size for QE and
QI are required to determine how much the pools change in
size and would aid in determining if the intake and oxidation fluxes
from the Bernier and Obled data were accurate. Figure 2
shows the estimates for pool sizes for QE and
QI from the Bernier (A) and Obled (B)
solutions from Table 5
.
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Comparison of predicted specific radioactivities to the Bernier data.
The Bernier data were difficult to fit because the model is overparameterized relative to the data set. Overall specific radioactivity changes in the protein and free amino acid pools did not yield enough information to give "good" estimates of individual fluxes between pools. Several possible combinations of FIT, FTI, FEI and FIE could yield the same overall specific radioactivity change for the amino acid pool and KSF, KRF, KSM, KRM, KSS and KRS for the overall specific radioactivity of the protein pool. In general, as the number of pools and therefore fluxes to fit increase, more combinations of estimates will yield the same results. However, because more solutions are possible, the algorithms used to fit the data have more difficulty converging, i.e., have more difficulty solving. Therefore only general conclusions about overall rates could be deduced from the Bernier data. More data on changes in specific radioactivities and pool sizes for individual amino acid and protein pools over the experimental period would lead to better predictions of fluxes among pools.
In Table 8
, the observed specific radioactivities of the free amino
acid pools showed linear incremental decreases of 66.6
Bq/(µmol · min), whereas the observed specific radioactivities of
the protein pools increased at a decreasing rate. Therefore the
decrease in specific radioactivity of 14C leucine in the
free amino acid pools could be estimated by constant outflows and
inflows of leucine between the amino acid pools, i.e., linear flux
equations between amino acid pools (first-order equations). However,
the nonlinearity of the increase of specific radioactivity in the
protein pool implies that equations to predict protein synthesis fluxes
should be dependent on the concentration of substrates (second-order
equations). The curvilinear rise in the specific radioactivity of the
protein pool was probably due to decreasing amounts of radiolabel
passing from the amino acid pools to the protein pool because the
specific radioactivities of the amino acid pools were decreasing and a
constant amount of amino acid was being incorporated into protein.
Comparison of predicted specific radioactivities to the Obled data.
The Obled data (Table 9)
provided much more information about
individual pool specific radioactivity changes. The predictions of the
specific radioactivities of the protein pools were <4% in error and
the predicted specific radioactivities of the free leucine pools were
within 10% of the observed values. Because the data were collected
over only 15 min, the changes in specific radioactivities for all of
the pools were constant except for the intracellular pool, which
decreased at a decreasing rate. Because the model was created using
linear equations to estimate fluxes, better fits for the linear data
would be expected. This suggests that the model can make fairly
accurate predictions of fluxes (FSR) when changes in specific
radioactivity are constant (data are collected over a short period of
time); more long term data about changes in specific radioactivities of
amino acid and protein pools are required before better estimates of
the fluxes can be made.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONTHE MODELMODEL EVALUATIONDISCUSSIONREFERENCES |
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list FSR using the flooding dose and continuous
infusion methods, respectively. Even though each method has
limitations, the organ FSR are in the same protein turnover
classifications between methods. The only exception is skin, which
under the continuous infusion method should be a slow turnover protein
instead of a medium turnover protein with the flooding dose method.
Whole-body estimates of FSR are also within 5% between methods. The
low SD (Tables 6 and 7)
from averaging FSR from a wide
variety of studies suggests that estimated rates of synthesis on an
organ basis are relatively constant, especially for the slow and medium
turnover organs. As the turnover rate increases, however, so do the
SD of the FSR estimates (liver and intestine). Therefore
the time of measurement of FSR may be too long to estimate protein
synthesis in these tissues accurately.
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The lack of data available to define the free amino acid pool sizes and
kinetics made it difficult to compare the fitted fluxes to the values
derived from the literature. Because the amount of extracellular space
seems to be relatively constant (Hider et al. 1969, 1971a and 1971b
, Khairallah et al. 1977
, Roberts and Morelos 1965
) and
QT should not change over a short period of time, the
weakest estimate of free leucine is the value for QI. In
the flooding dose method, QI (5.72 µmol) is relatively
small compared with the influx of unlabeled leucine; because the
initial QI is much larger than QE and
QT, however, an inaccurate estimate of their size could
alter the specific radioactivity predicted by the model. In addition,
QI is likely to be affected by the previous metabolic state
of the animal and is the most difficult pool size to determine. In cell
culture, QI is distinctly different and fairly simple to
separate from QE. However, on whole-tissue or whole-body
bases, QI is usually assumed to be the tissue homogenate
that also includes QE and thus must be corrected for
QE. The difference in changes in QI from
predictions based on the Obled and the Bernier data (Fig. 2)
could be
due to problems associated with estimating QI, differences
between the Bernier and Obled experimental protocols or differences
between the metabolisms of mice and rats. Waterlow et al. (1978)
stated that the interperitoneal dosing technique that
was used by Bernier caused the amino acid infused to be absorbed more
slowly than an intravenous dose as used by Obled. The Obled data may
not represent the spike in leucine concentration in QI if
it occurred before the 5-min measurement. Therefore QI
decreases. The increase in QI observed in the Bernier data
may be the slower absorption of leucine resulting from the use of an
interperitoneal dose. For the purposes of this model, it has been
assumed that the metabolism of the rat and mouse are essentially the
same when comparisons are based on body size. If the fluxes are changed
to micromole per gram, FEI,
FET, FIT,
FTI and the micromoles of protein synthesized
per minute are very close for the Bernier and Obled data fits. However,
FIE from the Bernier data is closer to the
literature value (Table 5)
for charging from QE, and the
Obled value is closer to the literature value (Table 5)
for charging
from QI. The literature value for
FIE is based on the assumption that
QI does not change over time, which is untrue for the fits
from both data sets (Fig . 2). Because there are no data for
determining FIE, more information on pool size
changes between QI and QE is required before
FIE can be determined.
FIO and FOE were
dissimilar among the literature, and the Bernier and Obled predictions.
The assumptions that, for a nongrowing rodent, synthesis should equal
degradation and intake should equal oxidation (Waterlow et al. 1978
) may be true for a continuous infusion or pulse experiment
in which the kinetics of fluxes between pools are close to physiologic
levels. But the large bolus of amino acid given with the flooding dose
method probably perturbs the system (Toffolo et al. 1993
). Therefore synthesis probably still does equal
degradation in a nongrowing rodent, but intake may not necessarily
equal oxidation. The effect of a large dose of amino acid on the system
is unknown. QE may expand, QI may increase or
oxidation may increase. It is unlikely that protein synthesis is
increased by excess leucine (Tovar et al. 1992
).
However, excess leucine may increase the competition for transport of
other amino acids that use the L system, and the oxidation enzymes may
be able to adjust to the large influx of amino acid in a short period
of time (Calvert et al. 1982
).
Protein synthesis rates predicted from the data for each individual
pool are in fairly close agreement among data sets (Table 5)
. Both the
Obled and Bernier specific radioactivity data for the protein pool
(Tables 8 and 9)
show a linear increase in protein synthesis expected
with a first-order process (Waterlow et al. 1978
).
Synthesis rates predicted from the Bernier and Obled data fitting are
lower for the slow turnover pool than the rates predicted by the organ
data (Table 5)
. However, because the specific radioactivities of the
individual protein pools were not available in the Bernier or Obled
data, the predicted FSR for each pool may not be accurate. For example,
a slight increase in KSS could be balanced by a
larger decrease in KSF and still predict the
same number of micromoles of protein synthesized, FSR and true FSR. The
same overall FSR could be predicted from several possible combinations
of KSS, KSM and
KSF. Increasing recycling (in the fitting
solutions in Table 5
, no recycling was allowed) could also increase
KSS, KSM and
KSF without increasing FSR or true FSR and
increasing the micromoles of leucine into protein per minute only
slightly. In addition, the KSS,
KSM and KSF predicted by
the continuous infusion method are proportionally lower than those
predicted by the flooding dose method. The decrease in specific
radioactivity of the protein pool is too great to be caused by the
heterogeneity of proteins at a 15-min measurement vs. a 3-h
measurement. However, recycling could account for the lower FSR values
because increasing recycling decreases the specific radioactivity of
the protein pools. In addition, with a longer experimental time period,
more recycling is probably taking place. To quantitate recycling or
predict if recycling is more likely in a faster turnover tissue, the
individual specific radioactivities in the fast, medium and slow
turnover protein pools must be known.
The six-pool model of protein turnover in a rodent was able to
duplicate data (Bernier and Calvert 1987
, Obled et al. 1991
) of changes in specific radioactivities in free
amino acid and protein pools over time. The model was also able to
predict FSR, channeling and recycling rates. Therefore, if specific
radioactivity data, including changes in protein and free amino acids
over time are available, the model can predict FSR in nongrowing
rodents of different sizes. The model also has the potential to
represent protein turnover in individual tissues; however, it must be
evaluated with tissue-specific radioactivity data.
|
FOOTNOTES |
|---|
1 The costs of publication of this article were
defrayed in part by the payment of page charges. This article must
therefore be hereby marked "advertisement" in accordance with 18
USC section 1734 solely to indicate this fact.
3 To whom reprint requests should be addressed.
4 Abbreviations used: FSR, fractional synthesis rate; PC,
percentage channeling; PR, percentage recycling. For the definitions of
other abbreviations, see Table 1
.
Manuscript received March 26, 1998. Initial review completed August 7, 1998. Revision accepted December 1, 1998.
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