Kinetic analysis and design of experiments to identify the catalytic mechanism of the monocarboxylate transporter isoforms 4 and 1

Abstract

Transport of lactate, pyruvate, and other monocarboxylates across the sarcolemma of skeletal and cardiac myocytes occurs via passive diffusion and by monocarboxylate transporter (MCT) mediated transport. The flux of lactate and protons through the MCT plays an important role in muscle energy metabolism during rest and exercise and in pH regulation during exercise. The MCT isoforms 1 and 4 are the major isoforms of this transporter in skeletal and cardiac muscle. The current consensus on the mechanism of these transporters, based on experimental measurements of labeled lactate fluxes, is that monocarboxylate-proton symport occurs via a rapid-equilibrium ordered mechanism with proton binding followed by monocarboxylate binding. This study tests ordered and random mechanisms by fitting experimental measurements of tracer exchange fluxes from MCT1 and MCT4 isoforms to theoretical predictions derived using relationships between one-way fluxes and thermodynamic forces. Analysis shows that: 1), the available kinetic data are insufficient to distinguish between a rapid-equilibrium ordered and a rapid-equilibrium random-binding model for MCT4; 2), MCT1 has a higher affinity to lactate than does MCT4; 3), the theoretical conditions for the so-called trans-acceleration phenomenon (e.g., increased tracer efflux from a vesicle caused by increased substrate concentration outside the vesicle) do not necessarily require the rate constant for the lactate and proton bound transporter to reorient across the membrane to be higher than that for the unbound transporter; and finally, 4), based on model analysis, additional experiments are proposed to be able to distinguish between ordered and random-binding mechanisms.

Figure 1

Kinetic schemes for MCT. (A) Ordered-binding mechanism. (B) Random-binding mechanism.

Figure 2

Lactate and pH effects on tracer fluxes. (A and B) Lactate influx data for equilibrium exchange (●), zero-trans (■), and infinite-cis (▴) experiments from Juel (3) and respective model fits (solid , dashed, and dotted lines) corresponding to the ordered-binding model (A) and the random-binding model (B). (C and D) Lactate influx data from Juel (3) as functions of cis (♦) and trans (■) pH, and lactate efflux data as functions cis (▴) and trans (●) pH and model fits for cis-pH dependence (dashed line) and trans-pH dependence (solid line), corresponding to the ordered-binding model (C) and to the random-binding model (D). Model equations and fixed parameter settings associated with fits are listed in Table 1.

Figure 3

Apparent Michaelis constants. (A and B) Reported K_m values for zero-trans and infinite-cis experiments at pH 7.4 from Juel (3) and model predictions over a pH range of 6–8 for the ordered-binding mechanism (A) and the random-binding mechanism (B). (C and D) Reported K_m values for equilibrium exchange experiments from Juel (3) at pH 7.4 and model predictions over a lactate range of 0–60 mM for the ordered-binding mechanism (C) and the random-binding mechanism (D). Model equations and fixed parameter settings associated with fits are listed in Table 1.

Figure 4

Lactate and pH effects on MCT4 fluxes in Xenopus oocytes. (A and B) Lactate influx data from Dimmer et al. (8) as a function of external lactate concentration at pH 5 (■), pH 6 (▴), and pH 7 (●) and respective model fits (solid , dashed, and dotted lines), corresponding to the ordered-binding model (A) and the random-binding model (B). (C and D) Lactate influx dependence data (■) as a function of external pH at 1 mM external lactate concentration and model fits (solid lines) corresponding to the ordered-binding model (C) and to the random-binding model (D). Model equations and fixed parameter settings associated with fits are listed in Table 1.

Figure 5

Lactate and pH effects on MCT1 fluxes in Xenopus oocytes. (A and B) Normalized lactate influx data (■) measured using initial rates of cytosolic pH changes from Bröer et al. (7) as a function of external lactate at external pH 7, and model fits using the ordered-binding model (A) and the random-binding model (B). (C and D) Normalized lactate influx data as a function of external pH using radio-isotope-labeled lactate at external lactate concentrations 1 mM (●), 5 mM (■), and 10 mM (▴), and influx data using initial rates of cytosolic pH change at 1 mM external lactate (○). Model fits using the ordered-binding model (C) and fits using the random-binding model (D). Model equations and fixed parameter settings associated with fits are listed in Table 1.

Figure 6

Reported and model-predicted apparent pH₅₀ for MCT1 fluxes in Xenopus oocytes. (A and B) pH₅₀ values corresponding to the data shown in Fig. 5, C and D, reported by Bröer et al. (7) and model predictions of pH₅₀ values for ordered (solid lines) and random (dashed lines) binding models, using the parameter estimates based on the ordered-binding model (A) and using the parameter estimates based on the random-binding model (B). The reported pH₅₀ values correspond to lactate influx flux data obtained using radio-isotope-labeled lactate at external lactate concentrations 1 mM (●), 5 mM (■), and 10 mM (▴) and influx data using initial rates of cytosolic pH change at 1 mM external lactate (○).

Figure 7

Model predictions of pH₅₀ for zero-trans experiments using ordered (solid lines) and random (dashed lines) binding mechanisms as a function of external [LAC]_total/K_LAC, computed using parameters estimated from all three studies analyzed in this article.