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INTRODUCTION |
Detailed information exists about metabolism in a wide variety of
organisms, but there has been little success in designing molecules
ab initio to realize specific metabolic objectives. For
drugs intended to correct metabolic deficiencies, this is perhaps not
surprising, but even drugs intended to eliminate particular pathogenic
organisms (in principle, a much easier objective, as there must be many
different ways of killing a healthy organism, even if there are
extremely few ways of restoring an unhealthy one to health) mostly have
rather broad specificity or they have been found more by chance than
design.
In this paper, we examine why the level of success has been so low,
taking the African trypanosome Trypanosoma brucei as an example. It is attractive for study because it is the causative agent
of a major disease, African sleeping sickness, because its metabolism
has been well studied, and because the kinetic properties of its
glycolytic enzymes are known in as much detail as is available for any
organism, thanks mainly to the efforts of Opperdoes and colleagues (for
reviews, see Refs. 1-4). The glycolytic pathway is an attractive
target because the predominant bloodstream form of T. brucei
has no energy resources, and relies entirely on rapid glycolysis for
its energy supply. Indeed, the ability to kill trypanosomes by halting
glycolysis was demonstrated 20 years ago (5, 6). Glycolysis in T. brucei has recently been the subject of a detailed kinetic model
(7) that allows numerical testing of the likely metabolic consequences
of inhibiting particular enzymes. Such testing is apparently not
usually done, partly because suitable models are not available for many
other organisms, but more particularly because it is commonly regarded
as unnecessary, as it is assumed that delivering a specific inhibitor
of a selected enzyme to the appropriate location will produce metabolic
effects.
This attitude is well illustrated by a recent supplement to
Nature entitled "Intelligent Drug Design"; this contains
a general introduction (8), as well as articles on combinatorial
chemistry (9, 10), improvements in screening procedures (11), antisense oligodeoxynucleotides (12), and methods of determining
three-dimensional structures (13). These are important aspects of drug
design, but the entire supplement neither mentions the words
"metabolic" and "metabolism" nor shows any recognition that
intelligent drug design must involve some consideration of the
metabolic effects of the drugs once they have been designed.
The easiest inhibitors to design are substrate analogs, because they
are likely to bind specifically to the active sites of the same enzymes
as the substrates they resemble, and commonly act as competitive
inhibitors. Unfortunately, as a strategy for designing
pharmacologically active molecules, this approach is doomed to almost
certain failure, because most such molecules do not bind much tighter
than the substrates, and so it is difficult or impossible to deliver
them to the target enzyme at concentrations that greatly exceed their
inhibition constants. Concentrations similar to or lower than the
inhibition constants are almost totally ineffective, because an
organism can easily overcome competitive inhibition by increasing the
substrate concentration enough to restore the rate to that in the
absence of the inhibitor (14); the increase in substrate concentration
necessary for this is typically around 2-fold.
There are two basic metabolic methods of killing an organism. Either
the flux through an essential metabolic pathway can be decreased to the
point where life is no longer possible, or a metabolite concentration
can be increased to toxic levels. Although both of these are likely to
involve enzyme inhibition, they are not the same, because depressing
fluxes need not be accompanied by large changes in metabolite
concentration, and enormous increases in metabolite concentration can
occur with almost imperceptible changes in flux. The former will
normally require a tight-binding inhibitor of an enzyme with a
significant flux control coefficient, whereas the latter is most
effectively achieved with an uncompetitive inhibitor of an enzyme with
a small flux control coefficient. Many successful pesticides and drugs
are tight-binding inhibitors, but these are difficult to design because
of the need to deliver and maintain concentrations at least 1000-fold
higher than the inhibition constants. A few pesticides are
uncompetitive inhibitors, the best-known example being the herbicide
N-phosphonomethylglycine, commonly known as glyphosate
or Roundup, an uncompetitive inhibitor of 3-phosphoshikimate
1-carboxyvinyltransferase (15).
The model of trypanosomal glycolysis developed by Bakker et
al. (7) provides an excellent starting point for assessing effects
of inhibition in whole pathways, as not only is a large amount of
detailed kinetic information embedded in it, but also because the model
gives an excellent account of the known properties of intact
trypanosomes. One apparent exception to this generalization, the
prediction that there is significant glycerol production under aerobic
conditions despite some experimental suggestions to the contrary, may,
as discussed below, reflect problems with the experiments rather than
with the model.
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EXPERIMENTAL MODEL |
Stoichiometry--
Glycolysis occurs in trypanosomes, as
illustrated in Fig. 1. Most of the reactions, together with those that
transform dihydroxyacetone phosphate into glycerol, occur in a special
organelle known as the glycosome. The stoichiometry implies the
existence of four distinct pools of conserved metabolites, of which
three are obvious from inspection; the sums [ATP] + [ADP] + [AMP]
are constant both in the cytosol and in the glycosome, and the sum
[NAD] + [NADH] in the glycosome is likewise constant. The fourth
relationship, however, is not easy to deduce without mathematical
analysis of the kind pioneered by Reder (16).
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(Eq. 1)
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Here and elsewhere in this paper, the subscript c
refers to concentrations of species in the cytosol, and concentrations
without subscripts refer to species in the glycosome. The volume ratio
is the ratio of the cytosolic and glycosomal volumes, estimated to
be 22.3 (7). It enters the relationship because, although in
single-compartment systems one may loosely regard the total concentration as being conserved, it is really mass that is conserved. The simpler expression in single-compartment systems is allowed only
because then there is an exact proportionality between concentration and mass.
As the conservation relationships are automatically recognized and
taken into account by the modeling program (see below), one might
regard them as a complication that did not require discussion. However,
the fact that all of the metabolites in the glycosome, apart from
glucose, glycerol, inorganic phosphate, and 3-phosphoglycerate, are
subject to conservation constraints places severe limits on the enzymes
that may be profitable targets for drug design, as we discuss below. It
is important therefore to recognize the nature of the relationship
shown in Equation 1. Although most of the phosphorylated metabolites in
the glycosome form part of it, 3-phosphoglycerate does not, and
1,3-bisphosphoglycerate has a coefficient of 1 in the equation even
though it contains two phosphate groups. In fact, the conserved moiety
is that part of the internal phosphate pool that is not accounted for
by entry of inorganic phosphate and export of 3-phosphoglycerate.
Kinetics--
All of the processes in the model apart from the
aldolase reaction and the consumption of ATP for growth (see below) are
either equilibria or can be expressed by introducing the kinetic
constants listed in Table I into the following generic equation (in a
few cases with exponents or additional terms, as noted in the footnotes to the table).
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(Eq. 2)
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Both in the table and throughout the paper, all rates (including
values of limiting rates V) are expressed as dimensionless numbers by writing them relative to a standard value of 1 nmol min
1/mg of cell protein.
The rate of the aldolase reaction was given by Equation 3, with
V+ = 184.5, V
= 220, KFruP2 as given by Equation 4,
KGla3P = 0.067 mM,
KDHAP = 0.015 mM,
Ki,Gla3P = 0.098 mM.
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(Eq. 3)
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(Eq. 4)
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Utilization of ATP by the parasite for growth, etc., is assumed
to depend on the ratio of ATP and ADP concentrations in the cytosol,
with a rate of 50[ATP]c/[ADP]c.
With three exceptions, these definitions result in kinetic equations
identical to those given by Bakker et al. (7). For the
mitochondrial oxidation of glycerol 3-phosphate, however, we have used
the results of Eisenthal and Panes (17) to include an explicit
dependence of rate on oxygen concentration. Our equation gives exactly
the behavior assumed by Bakker et al. (7) at the extremes of
aerobiosis and anaerobiosis, but it also takes account of the observed
effects of intermediate oxygen concentrations (17). In view of the
considerations discussed below under "The Transition from Aerobic to
Anaerobic Conditions," we also prefer not to assume zero activity for
glycerol kinase in aerobic conditions, and thus use the kinetic
constants given in Table I at all oxygen concentrations.
The third exception is pyruvate kinase, which Bakker et al.
(7) treated as irreversible, with the kinetic constants shown in Table
I, quite reasonably so in the conditions they studied, in view of the
large equilibrium constant and the low steady-state concentration of
pyruvate. However, as we shall be studying effects of inhibiting
pyruvate export, and it is impossible for pyruvate to accumulate to
very high levels without producing any mass action effects on the
earlier steps, we need to allow for reversibility, however slight, of
the pyruvate kinase reaction. We have therefore set the equilibrium
constant K to 3 × 105, calculated from the
value of
G°' =
7.5 kcal mol
1 (18), and
in the absence of experimental information have approximated Kpyruvate and KATP by
KPEP and KADP,
respectively.
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(Eq. 5)
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This equation gives exactly the kinetics defined in Table I when
the two product concentrations are zero, and it gives a rate of zero
under any equilibrium conditions. The need to satisfy both of these
requirements simultaneously accounts for the apparently excessive
complexity of the equation, which is written in accordance with the
principles discussed elsewhere (19).
Computation--
Steady states for the model as defined in the
previous section were calculated by means of the program Gepasi 3.01, described (in an earlier version) by Mendes
(20).1 Bakker et
al. (7) used two different programs: MLAB (Civilized Software,
Bethesda, MD) and SCAMP (21, 22). We have checked that Gepasi gives the
same numerical results as those reported (7) when given the same input.
Initially, we attempted to use MetaModel (23), but this proved
incapable of handling a model of the required complexity.
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RESULTS |
Control Distribution--
Bakker et al. (24) reported
that the greatest share of flux control in their trypanosome model (7)
resides in glucose transport when the transporter has the level of
activity measured in the bloodstream form of T. brucei, and
we have confirmed that this is correct. We have also examined the
changes in control distribution that result from allowing for the
reverse reaction catalyzed by pyruvate kinase. Although the fact that
this reaction is far from equilibrium under all ordinary conditions
would suggest that taking account of its reversibility would have a
trivial effect, the change is rather more than trivial; the flux
control coefficient for pyruvate transport is exactly zero in the
conditions considered by Bakker et al. (7, 24), but it
assumes the second largest value when the reverse pyruvate kinase
reaction is taken into account. Nonetheless, it remains true that
glucose transport has the largest share of flux control and that no
other process has a major share.
The Transition from Aerobic to Anaerobic Conditions--
Bakker
et al. (7) assumed that glycerol kinase had no activity in
aerobic conditions. They introduced this discontinuity into their model
to obtain a zero efflux of glycerol under those conditions. The
question of glycerol production from glucose is one that has been
addressed by several groups of workers. Simple consideration of the
metabolic scheme shown in Fig. 1 might be taken as predicting the production of 2 mol of pyruvate/mol of glucose
consumed under aerobic conditions, and 1 mol each of pyruvate and
glycerol/mol of glucose used under anaerobiosis (1). However, glycerol/pyruvate ratios in the range 0.1-0.4 have been observed under
aerobic conditions (17, 25-27). Similar results were found by Fairlamb
and Bowman (28), who ascribed the presence of glycerol in aerobic
incubations of trypanosomes with glucose to experimental artifacts. In
all these investigations, measurements were performed under fully
aerobic or fully anaerobic conditions, the latter including
anaerobiosis simulated by inhibition of glycerophosphate oxidase by
salicylhydroxamic acid. However, a systematic study (17) of the ratio
of glycerol and pyruvate effluxes over a range of oxygen concentrations
from zero to 0.25 mM showed that the efflux of glycerol
does not fall to zero in aerobic conditions, but stabilizes at about
10% of the efflux of pyruvate, exactly as the model of Bakker et
al. (7) would predict if glycerol kinase had the same activity in
aerobic as in anaerobic conditions.

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Fig. 1.
Compartmentation of trypanosomal
glycolysis. The enzymes and transporters are numbered as follows:
1, glucose transport; 2, hexokinase;
3, hexose-phosphate isomerase; 4, phosphofructokinase; 5, aldolase; 6, triose-phosphate isomerase;
7, glyceraldehyde-3-phosphate dehydrogenase; 8,
3-phosphoglycerate kinase; 9, phosphoglycerate mutase,
enolase, and phosphoenolpyruvate transport (treated as a
single process); 10, pyruvate kinase; 11,
pyruvate transport; 12, glycerol-3-phosphate dehydrogenase;
13, glycerol-3-phosphate transport; 14,
mitochondrial oxidation of glycerol 3-phosphate; 15,
dihydroxyacetone phosphate transport; 16, glycerol kinase; 17, glycerol transport; 18, "growth" (all
processes that convert ATP to ADP apart from those shown explicitly);
19, glycosomal myokinase; 20, cytosolic
myokinase. This is essentially the model used by Bakker et
al. (7), apart from the fact that we show the participation of
inorganic phosphate in step 7, as well as its (presumed) equilibration
across the glycosomal membrane. It is essential to make this
participation explicit if one is to understand the nature of the most
complicated of the four conservation relationships implied by the
stoichiometry, which are as follows: (i) conservation of the AMP moiety
(represented by sans-serif type) of ATP, ADP and AMP in the
glycosome; (ii) conservation of the AMP moiety (represented
by italic sans-serif type) in the cytosol; (iii)
conservation of NAD + NADH (represented by bold type); and
(iv) conservation of those phosphate groups (represented by
P in italics) that are not accounted for by entry
of inorganic phosphate and export of 3-phosphoglycerate. ATP and ADP
are represented as AMP-PP and AMP-P,
respectively, so as to allow the separate conservation of AMP and of
phospho moieties in the glycosome to be recognized easily.
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We have therefore made a detailed comparison between the experimental
data and the model with glycerol kinase active, and find a high degree
of agreement (Fig. 2). Even if the curve
were a best-fit curve, one could consider the fit quite adequate, but given that it is calculated independently from the data (apart from the
fact that the same experimental data were the source of the
KO2 value defined in Table
I), the fit is essentially perfect. With
glycerol kinase active in all conditions, therefore, the model accounts
not only for the behavior at the extremes of oxygen concentration but
also for the transition between the two. It is a striking illustration
of the robustness of the model that it proves able to predict behavior
that was not taken into account during its construction.

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Fig. 2.
Transition from anaerobic to aerobic
conditions. Three transport fluxes, and the ratio of the fluxes
for glycerol and pyruvate, are shown as functions of the concentration
of oxygen. All of the curves were calculated from the model as
described in the text, but the experimental points are from Ref. 17; in other words, the curve in the lower part of the figure is not a
best-fit curve but is calculated independently of the data. The
inset shows the same comparison extended into the range of oxygen concentrations where the fluxes are virtually independent of
oxygen concentration.
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Table I
Kinetic constants
All constants refer to the generic equation shown in the text as
Equation 2, the substrates and products being numbered in the order
they appear in the equation that defines the stiochiometry of each
process, e.g. for hexokinase, Ks1 refers
to ATP, Ks2 to glucose, etc.
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Bakker et al. (7) partially justified their assumption that
glycerol kinase is inactive under aerobic conditions by pointing out
that a very low external glycerol concentration would be sufficient to
inhibit glycerol efflux completely. We have confirmed that this is
true, but point out that unless this external concentration happened to
have precisely the right value it would bring the efflux not
necessarily to zero but to some other value, which could be either
positive or negative. At higher external glycerol concentrations, it
can act as an alternative substrate to glucose for aerobic metabolism,
not only in the model but also experimentally (27).
Inhibition of Glucose Transport--
Bakker et al. (24)
found the step with the highest degree of control over trypanosome
growth to be glucose transport, and we have confirmed that this remains
true if the reversibility of pyruvate kinase is taken into account,
although its flux control coefficient in this case (0.34) is too small
for it or any other reaction to be regarded as rate-limiting. It
follows that, if the objective of designing a drug is to prevent
trypanosomes from achieving a viable metabolic flux, then the only
realistic candidate for the step to be inhibited is glucose transport,
although it has so little control that one cannot be optimistic that a
large depression of growth can be produced by inhibiting it. Inhibition of any other step can be expected to have even less effect on the
metabolic flux unless the degree of inhibition is very high.
Use of the model to simulate the effects of inhibiting glucose
transport confirmed these expectations; for example, the presence of a
competitive inhibitor at a concentration equal to its inhibition constant, simulated by adding a term equal to unity to the denominator of the rate equation, produced a flux through glucose transport of 79%
of the uninhibited flux, but the growth flux, i.e. the net
production of ATP, decreased only to about 85% of the uninhibited value.
This result has depressing implications for the prospects of lowering a
flux to a non-viable level as a strategy for pest control. First, it is
clear that for decreasing the flux only glucose transport will do;
inhibiting any other step is likely to be futile unless the level of
inhibition that can be achieved is high enough to eliminate the enzyme
activity virtually completely and permanently. Second, the effects of
inhibiting glucose transport are not impressive. Decreasing the growth
flux by 15% is unlikely to eliminate the trypanosomes unless the
inhibition can be maintained for a substantial period.
A mitigating feature may be that flux control coefficients do not
remain constant when the conditions are altered, and, as Bakker
et al. (24) pointed out, a step that appears to have insignificant control in normal conditions may acquire sufficient control in special circumstances to be a worthwhile target for inhibitors. However, it remains to be demonstrated whether this approach can work in practice, or even in the computer model.
Inhibition of Pyruvate Transport--
An alternative strategy to
decreasing the carbohydrate flux would be to cause a metabolite
concentration to rise to catastrophic levels. At first sight, the fact
that nearly all of the steps in the model have very little control over
flux suggests that there are many candidates for enzymes that could be
inhibited to produce an uncontrolled accumulation of their substrates.
In fact, the choice is far more restricted than it may appear because the numerous conservation relationships mean that very few metabolite concentrations can rise in an uncontrolled manner.
Pyruvate transport is one of the few processes that is not involved in
conservation relationships. Although it has a flux control coefficient
of zero (24) in the model as originally described (7), this rises to
about 0.2 when pyruvate kinase is made reversible. This is still low
enough to suggest that it responds approximately to inhibition like a
constant-flux enzyme. Earlier studies (14, 29) of such enzymes showed
that competitive inhibition causes the substrate concentration to rise
linearly, being doubled by an inhibitor concentration equal to the
inhibition constant; by contrast, uncompetitive inhibition produces an
infinite substrate concentration (i.e. no steady state
exists) at a finite inhibitor concentration.
To put this more concretely, consider the inclusion in the kinetics of
pyruvate transport of a term allowing for inhibition by a hypothetical
uncompetitive inhibitor, Iu, with inhibition constant
Kiu.
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(Eq. 6)
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This may be transformed into the following form (cf.
Equation 10.11 of Ref. 29) to give the pyruvate concentration as a function of inhibitor concentration when the flux is constant.
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(Eq. 7)
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Because the computation indicated that this process is well over
half-saturated, only a small value of
[Iu]/Kiu is needed to drive the
system into instability.
Now, it may be argued that impressive as this effect of uncompetitive
inhibition may be it has little practical usefulness, as uncompetitive
inhibitors are not common and are difficult to design. However, there
is no objection to the presence of a competitive component,
i.e. mixed inhibition will work just as well as
uncompetitive, the only requirement being that the inhibitor
concentration must exceed some small factor of the inhibition constant
for the uncompetitive component; whether or not a competitive component
is also present, and its magnitude if it is present, is almost
irrelevant.
As illustrated in Fig. 3, this simplified
analysis based on the kinetics of one irreversible enzyme gives results
that are approximately but not exactly correct. In the case of
competitive inhibition of pyruvate transport, there is virtually no
decrease in flux for [I]/Kic values up to 1, and consequently the curve showing the dependence of the pyruvate
concentration on the inhibitor concentration is indistinguishable by
eye from a straight line. For other types of inhibition, the flux
decreases appreciably as the degree of inhibition increases;
consequently, although the pyruvate concentration does rise extremely
steeply, the slope does not continue increasing indefinitely (see
inset to Fig. 3). Indeed, the pyruvate concentration itself
must eventually level out when the entire system approaches
equilibrium. However, the equilibrium concentration of pyruvate is far
higher than could ever be reached in practice, and so it remains true
that even in a complete system with reversible kinetics inhibition by
an uncompetitive or mixed inhibitor can produce toxic levels of
substrate at low inhibitor concentrations. The model upon which this
analysis is based is a metabolic one, and does not take into account
transcription and translation. However, the high glycolytic flux in the
predominant long slender (bloodstream) form of T. brucei
means that metabolic effects will be manifested far more rapidly than
events depending on protein synthesis. A similar conclusion is likely
to apply also to possible host-parasite interactions, e.g.
at the immunological level, but this will have to be confirmed by
studies in vivo.

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Fig. 3.
Effects of inhibiting pyruvate
transport. The pyruvate transport flux and the pyruvate
concentration are shown as functions of inhibitor concentration, for
three types of inhibition of pyruvate transport, as labeled, the case
of mixed inhibition being one with Kiu = 10Kic, i.e. the competitive component
was 10-fold stronger than the uncompetitive component. For labeling the
abscissa axis, Ki is taken to be the
competitive inhibition constant Kic for the case
of competitive inhibition, but otherwise it is the uncompetitive
inhibition constant Kiu.
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Inhibition of Other Steps--
Inhibition of other steps produced
much less dramatic results than those illustrated in Fig. 3, regardless
of the type of inhibition. At first sight this was surprising, as the
elementary theory (14) suggested that uncompetitive inhibition of any
enzyme with little control over flux would produce very large increases in the substrate concentration. The results will be illustrated by some
data for hexokinase, although similar ones were obtained with other
apparently promising candidates, such as aldolase.
To allow for various possible modes of inhibition of hexokinase, its
kinetic equation was rewritten as follows.
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(Eq. 8)
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In this equation
c,ATP,
u,ATP,
c,Glc, and
u,Glc are dimensionless
inhibition terms allowing for competitive or uncompetitive inhibition
with respect to ATP and with respect to glucose (each can be regarded
as a concentration divided by the appropriate inhibition constant).
When all are set to zero, Equation 8 gives identical kinetics to Table
I. Effects of assigning non-zero values are shown in Table
II.
Although some non-trivial effects on both concentrations and the flux
are visible in the table, they are far smaller than those seen in Fig.
3, and occur at much higher levels of inhibition, because none of the
relevant concentrations can increase in an uncontrolled manner. The
concentration of glucose 6-phosphate is constrained by Equation 1, and
those of ATP and ADP are constrained both by Equation 1 and by the
general conservation of adenine nucleotides. Their concentrations
cannot therefore increase by more than small factors. Although
glycosomal glucose does not appear in Equation 1 or the other
conservation relationships, its concentration is also constrained by
the fact that it cannot rise above the concentration of glucose in the
host bloodstream, which means that it cannot increase by more than a
factor of about 90. Analogous studies were also made of some other
enzymes, such as aldolase, that appeared to have non-negligible flux
control, with similar results.
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DISCUSSION |
Our analysis of effects of inhibiting particular steps in
trypanosomal glycolysis has several implications for antimetabolite drugs in general, because little of it depends on specific
characteristics of the model considered. First of all, destroying a
parasite by decreasing its metabolic flux to a level that cannot
sustain life is a less attractive option than it may appear at first
sight. Unless a specific irreversible inhibitor is available that can decrease an enzyme activity to zero, the effects of inhibition on flux
are usually much smaller than studies on the isolated enzyme may
suggest. This is because few enzymes have flux control coefficients
close to unity, and the effect of a low inhibitor concentration on the
flux in an integrated system is therefore always smaller than the
effect on the isolated enzyme. Although the flux control coefficient of
almost any enzyme increases as it is inhibited, the increase is
gradual, and, as illustrated for glucose transport in the trypanosome,
inhibiting even the enzyme with the highest flux control coefficient is
likely to produce disappointing results.
Increasing the concentration of a metabolite to toxic levels by
uncompetitive inhibition (or mixed inhibition with a perceptible uncompetitive component) is also not straightforward, for more than the
obvious reason that uncompetitive inhibitors are not easy to find or
design. Although this strategy works very effectively if applied to an
appropriate enzyme or transporter (illustrated with pyruvate transport
in Fig. 3), the choice of appropriate enzymes is far more limited than
it may appear at first sight, because the inhibited step must have a
substrate whose concentration is not limited by the stoichiometry of
the network.
For the model illustrated in Fig. 1, such stoichiometric considerations
exclude nearly all of the steps, because nearly all of the
intermediates are involved in one or more conservation relationships.
Even glucose, although not part of one of these relationships, is
limited by the fact that its concentration in the glycosome cannot rise
above the concentration in the host bloodstream. This last limitation
will be even more severe in other organisms, because few have glucose
concentrations as low as that found in the glycosome of T. brucei, which has one of the highest levels of glycolytic activity
known in eukaryotic cells.
It follows that analyzing the stoichiometric structure of a network is
not just an abstract topic of mainly theoretical interest, but has
major practical implications. Without carrying out the analysis, few
would realize that the concentrations of nearly all of the metabolites
in Fig. 1 are constrained by stoichiometric considerations. When the
stoichiometric constraints are taken into account, the only realistic
candidates for inhibition are pyruvate transport, glycerol transport,
and the three reactions between 3-phosphoglycerate and
phosphoenolpyruvate lumped together as step 9. The last two
are ruled out by the fact that they are assumed to be at equilibrium,
and glycerol transport is additionally ruled out by the fact that,
under aerobic conditions, it has such a low rate of efflux that some
authors have doubted whether it occurs at all. Fortunately, the one
remaining candidate, pyruvate transport, proved to respond to
inhibition as expected. Thus, there is good reason to believe that, if
an inhibitor of pyruvate transport with a sufficient uncompetitive
component could be found, it would be expected to have powerful
anti-trypanosomal activity.
We thank Dr. Barbara Bakker for sending us a
copy of her recent paper several months before it was published, and we
also thank Dr. Pedro Mendes for making his program Gepasi available and
for considerable help in resolving problems with its use.