Descriptors of Secondary Active Transporter Function and How They Relate to Partial Reactions in the Transport Cycle

Abstract

Plasmalemmal solute carriers (SLCs) gauge and control solute abundance across cellular membranes. By virtue of this action, they play an important role in numerous physiological processes. Mutations in genes encoding the SLCs alter amino acid sequence that often leads to impaired protein function and onset of monogenic disorders. To understand how these altered proteins cause disease, it is necessary to undertake relevant functional assays. These experiments reveal descriptors of SLC function such as the maximal transport velocity (Vmax), the Michaelis constant for solute uptake (KM), potencies for inhibition of transporter function (IC50/EC50), and many more. In several instances, the mutated versions of different SLC transporters differ from their wild-type counterparts in the value of these descriptors. While determination of these experimental parameters can provide conjecture as to how the mutation gives rise to disease, they seldom provide any definitive insights on how a variant differ from the wild-type transporter in its operation. This is because the experimental determination of association between values of the descriptors and several partial reactions a transporter undergoes is casual, but not causal, at best. In the present study, we employ kinetic models that allow us to derive explicit mathematical terms and provide experimental descriptors as a function of the rate constants used to parameterize the kinetic model of the transport cycle. We show that it is possible to utilize these mathematical expressions to deduce, from experimental outcomes, how the mutation has impinged on partial reactions in the transport cycle.

Cooperative Binding of Substrate and Ions Drives Forward Cycling of the Human Creatine Transporter-1

“…If currents through a transporter are strictly coupled to substrate translocation, the K M for substrate uptake and the EC 50 for current induction by the substrate must be equivalent readouts of the transport cycle ( Burtscher et al, 2019 ; Schicker et al, 2021 ). We verified that CRT-1 substrate-induced currents report faithfully on the transport cycle by comparing the potency of three different synthetic substrates in inhibiting uptake of [ 3 H]creatine ( Figure 2A ) and in promoting a current through CRT-1 ( Figures 2B,C ): The concentration-dependent inhibition of [ 3 H]creatine uptake by β-guanidinopropionic acid (β-GPA, green upward triangles in Figure 2A ), creatine (black circles in Figure 2A ), 2-amino-1,4,5,6-tetrahydropyrimidine-5-carboxylic acid (ATPCA, red downward triangles in Figure 2A ) and cyclocreatine (blue diamonds in Figure 2A ) was adequately described by a monophasic inhibition curve; the affinity estimates (IC 50 ) extracted from the fit were 18.8 ± 1.1 µM, 48.4 ± 7.0 µM, 105.0 ± 6.7 µM and 167.3 ± 9.9 µM for β-GPA, creatine, ATPCA and cyclocreatine, respectively.…”

“…If currents through a transporter are strictly coupled to substrate translocation, the K M for substrate uptake and the EC 50 for current induction by the substrate must be equivalent readouts of the transport cycle (Burtscher et al, 2019;Schicker et al, 2021). We verified that CRT-1 substrate-induced currents report faithfully on the transport cycle by comparing the potency of three different synthetic substrates in inhibiting uptake of [ 3 H] creatine (Figure 2A) and in promoting a current through CRT-1 β-guaninidinopropionate (β-GPA, green upward triangles), 2-amino-1,4,5,6tetrahydropyrimidine-5-carboxylic acid (ATPCA, red downward triangles) and cyclocreatine (cyclocr, blue diamonds).…”

Optimizing the Substrate Uptake Rate of Solute Carriers

“…Explicit expressions for the substrate uptake rate were derived as described previously (Burtscher et al, 2019;Schicker et al, 2021). Numerical sets of values for the microscopic rate constants maximizing these expressions were generated by a simulated annealing algorithm (Metropolis et al, 1953;Tsallis and Stariolo, 1996).…”

“…We have recently described an approach to kinetic modeling of a solute carrier, which allows for deriving analytical expressions for its functional descriptors (Schicker et al, 2021). These include the K M and the V max for substrate uptake, the rate of basal substrate release from the interior of the cell, etc.…”

Thermodynamic role of main reaction pathway and multi-body information flow in membrane transport

“…The different modes of transportation (symport, antiport, and uniport) would reflect the difference in the main reaction pathways of conformational changes. Intermediate state structures for various SLC proteins have been solved so far, and many molecular dynamics simulation studies have been performed [10][11][12][13][14]. Many mechanical or kinetic models have been proposed for each specific transport mode or transporter protein.…”