Time-dependent changes in state occupancies of the model in Figure 1A were evaluated by numerical integration of the resulting system of differential equations using the Systems Biology Toolbox (Schmidt and Jirstrand, 2006) and MATLAB 2018a (MathWorks, Natick, MA, United States).

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). In brief, in an initial step, a set of values for the microscopic rate constants was randomly drawn from independent normal distributions, centered at chosen start values with SDs of the same size. The substrate uptake rate for the drawn set was then calculated and compared with the substrate uptake rate calculated from the original set (i.e., the start values). The probability of accepting the new set was: prob. = exp.[−(−current Value + best Value)/T(iter)], with T being a temperature parameter, which was chosen to decrease exponentially with the number of iterations. If accepted, the new set was used for the next iteration, if not, the old set was retained. This procedure was repeated for 5,000 iterations. We safeguarded against trapping in a local maximum by reinitiating the algorithm with the maximum T value increased by 10% using the best parameter set found in the first round of iterations. If this yielded a better overall value for the substrate uptake rate, the next run was reinitiated with the original T. Otherwise T was increased by additional 10%. This procedure was repeated until reheating was unsuccessful in obtaining a better set of values for 10 times. On completion, the algorithm reported the best parameter set (i.e., the set of values which gave the largest substrate uptake rate).

In the optimization of the sequential binding order schemes, we kept the detailed balance constraint, by allowing the algorithm to vary all microscopic rate constants except one. This rate constant was then calculated from the other rate constants such that detailed balance was maintained. In the case of the random order binding scheme, it was necessary to calculate three rate constants because of the larger number of loops. Each set of microscopic rate constants was also evaluated for adherence to the other imposed constraints (e.g., diffusion limit for the association rates of substrate and co-substrate). Only if a set of values complied with the imposed constraints it was passed on to the annealing algorithm.

Figure 1A shows the reaction scheme of a hypothetical symporter: In each cycle, the transporter translocates one substrate molecule through the membrane together with two Na⁺ ions. We selected this stoichiometry, because it is frequently observed: For instance, sodium-dependent glucose (SGLT1/SLC5A1 and SGLT2/SLC5A2; Wright et al., 2011) and phosphate transporters (PiT-1/SLC20A1 and PiT-2/SLC20A2; Forster et al., 2013) operate with this stoichiometry. For the sake of simplicity, we assumed binding of the two sodium ions to occur in a single reaction. In Figure 1B, we used this model to predict the rate of substrate uptake through the transporter by assuming that two different concentrations of extracellular substrate (S_out) were applied, that is, .1 μM (magenta line in Figure 1B) and 100 μM (blue line in Figure 1B). As seen, on exposure of the cell to the substrate, the substrate uptake rate rose. The rise was large on application of 100 μM S_out and small on application of .1 μM S_out. In the simulation, the substrate was removed after 60 s upon which the substrate uptake rate dropped to zero.

The data in Figure 1B were obtained by numerically solving the system of differential equations underlying the kinetic model. An alternative approach to compute the substrate uptake rate relies on deriving its defining analytical term. For the sake of space, we show the term in the supplement. Figure 1B also displays the substrate uptake rates obtained by this second approach in the presence of .1 μM (open circle in magenta) and 100 μM S_out (open circle in blue). It is evident that the substrate uptake rates predicted by the two methods were identical.

The extracellular concentration of a substrate (S_out) is a given quantity, that is, it is typically not subject to control by a single cell. Accordingly, SLC, which are tasked with transporting a substrate into the interior of a cell, must adjust their operation to the substrate concentration they encounter. The substrate concentration surrounding a cell can therefore, be assumed to exert evolutionary pressure. To emulate optimization of the substrate uptake rate by evolution, we maximized this rate, utilizing its defining function. For this purpose, we employed an optimization algorithm, which can approximate global minima/maxima of a function (i.e., simulated annealing—for details see the method section). The optimization algorithm can find the set of numeric values for the microscopic rate constants, which returns the largest value for the substrate uptake rate at given intra- and extracellular concentrations of Na⁺ and substrate.

We note that microscopic rate constants are a priori not mathematically constrained: They can assume values between zero and infinity. If they are permitted to vary across the entire mathematically possible range, the optimized substrate uptake rate will also adopt values between zero and infinity. It is a futile and meaningless exercise to maximize a function, for which it is known that no maximum exists. Fortunately, however, there are limits to the values of the microscopic rate constants. For instance, the association rates of co-substrates and substrate cannot be larger than the diffusion limit, which therefore imposes an upper limit on these rates. Likewise, the dissociation rates of substrate and co-substrate must also have an upper limit, because raising the dissociation rate constant results in affinity loss, which, when substantial, prevents the co-substrate and the substrate from interacting with the transporter in their physiological concentration ranges. It is also clear that a conformational change cannot occur with infinite velocity. We selected 1,000 s⁻¹ as the upper limit for conformational transition rates. The choice of this value was based on information obtained from the literature (Zhang et al., 2007; Schicker et al., 2011; Hasenhuetl et al., 2018; Erdem et al., 2019). Another necessary constraint was to ensure that every set of optimized values complied with the rule of microscopic reversibility: The product of the rates in the forward direction in a loop must equal the product of the rates in the opposite direction. The combined constraints reshape the parameter space of the function, such that it harbors critical points, which do not exist in the unconstrained parameter space.

We performed ten optimization runs in which we assumed that S_out was .1 μM and 100 μM (Figure 1C). In all runs, we set the extra- and intracellular Na⁺ concentration to 150 mM and 10 mM and the intracellular substrate concentration (S_in) to zero. The data points show the substrate uptake rates, to which the optimization algorithm converged when S_out was set to .1 μM (left column) and 100 μM (right column). The rates were low at .1 μM S_out (7.44 s⁻¹ ± .08 s⁻¹) and high at 100 μM S_out (431.40 s⁻¹ ± .66 s⁻¹). We emphasize that each point in Figure 1C represents a unique set of numeric values for the microscopic rate constants. In Table 1, we show the ten sets, which we obtained from the optimization runs where S_out was set to .1 μM and 100 μM. Although the values of the microscopic rate constants differed between sets, they all gave essentially the same substrate uptake rate when optimized for the same substrate concentration. We therefore conclude that the optimized rate can be realized by different sets of numeric values for the microscopic rate constants. Because of the large differences in these values, each set can be viewed to define a transporter with an individual phenotype. For this reason, we will from here on treat the term “transporter” and “a set of optimized values” as a synonymous description.

The observations summarized in Table 1 warranted further scrutiny. They suggest that not all reactions, which a transporter undergoes, require the same extent of fine-tuning to support a high substrate uptake rate. We analyzed the transporters in Table 1 to understand, which reactions in the transport cycle do and do not require precise adjustment. Accordingly, we examined for each individual transporter, the values for a collection of descriptors of transporter function: (i) These included descriptors, which can be computed with the kinetic model but also obtained experimentally (i.e., V_max and the K_M for substrate/co-substrate) and (ii) descriptors, which can only be extracted from the kinetic model (i.e., K_Ds for substrate and co-substrate to the outward- and inward-facing conformation of the transporter). The rationale was as follows: If the values of a descriptor are all similar for transporters optimized for the same substrate concentration, we can conclude that fine-tuning of the reactions, which affect this descriptor, is essential for the realization of the optimized rate. Conversely, this is not the case, if the values are vastly different.

In Figure 1D, we plotted the substrate uptake rate as a function of S_out for the transporters in Table 1: The V_max values were low and high for the group of transporters, which were optimized for .1 μM and 100 μM S_out, respectively. Within the two groups of transporters, the V_max values varied, but the magnitude of this variation was small: V_max of transporter optimized for .1 μM S_out and 100 μM S_out, were 78.40 ± 6.61 s⁻¹ and 478.38 ± 1.58 s⁻¹, respectively. In Figure 1E, the data were normalized to maximum velocity, because differences in the apparent affinity of the optimized transporters for the substrate can be more readily appreciated in this representation. It is evident that the transporters, which were optimized for .1 μM S_out displayed a higher apparent affinity for the substrate than those optimized for 100 μM S_out. The K_M values within groups fell into narrow ranges, that is, .95 ± .08 μM and 11.1 ± .31 μM for transporters optimized for .1 μM and 100 μM S_out, respectively. Our analysis therefore indicates that transporters, must have low K_M and low V_max values to support a high substrate uptake rate when S_out is low, but a high K_M and a high V_max value when S_out is high.

We computed the K_D values for substrate binding to the outward-facing conformation of the individual transporters (Figure 1F): It is evident that the K_D values for the substrate were low and high for transporters, which were optimized for .1 μM and 100 μM S_out, respectively (.1 μM S_out: .24 μM ± .03 μM; 100 μM S_out: 4.72 μM ± .42 μM). It also evident that the K_D values were smaller and that they covered a larger range than the corresponding K_M values (Figure 1F).

Inspection of Table 1 shows that the rate constants, which govern Na⁺ binding to the outward-facing state, are subject to large variations in transporters 1–10; in contrast, these rate constants differed substantially less in transporters 11–20, which were optimized to cope with 100 μM S_out. We illustrated the resulting difference in apparent affinity for Na⁺ by plotting the absolute (Figure 1G) and normalized substrate uptake rate (Figure 1H) of the optimized transporters as function of the Na⁺ concentration: for those transporters, which were optimized for .1 μM S_out the apparent affinity for Na⁺ varied over five orders of magnitude (blue curves in Figures 1G,H). In contrast, for transporters optimized for 100 μM S_out, the apparent affinity for Na⁺ fell into a narrow range (magenta curve in Figures 1G,H). Thus, if the extracellular substrate concentration is low, the Na⁺ binding reaction does not need to be fine-tuned to obtain optimal rates. However, at a higher substrate concentration, the Na⁺ binding reaction is subject to stringent constraints. This observation can be rationalized by taking into account that, in the optimization, Na⁺ was assumed to be present at a high concentration (i.e., 150 mM). At this concentration Na⁺ binding is unlikely to become rate limiting for substrate transport if S_out is low. At a higher substrate concentration, the apparent association rate of the substrate (k_app) is expected to increase. In this scenario, Na⁺ binding becomes rate limiting, if it occurs at too low a rate.

We compared the K_D values for Na⁺ binding to the outward-facing conformation of the transporter t the corresponding K_M values (Figure 1I): Within each group, the variation in the K_D values was small, that is, K_D = 25.10 ± 3.91 mM and 79.21 ± 3.30 mM for transporters optimized for S_out .1 μM and S_out 100 μM, respectively. This contrasted with the large variation in the corresponding K_M values for Na⁺ seen for transporters optimized for .1 μM S_out. This discrepancy can be explained as follows: The K_D values are determined by the ratio of the dissociation and association rates for Na⁺ but not by the absolute values of these rates. Conversely, given that the K_D values and the K_M values for Na⁺ were found to differ, it is safe to conclude that K_M values are highly dependent on the absolute values of these rates. These observations therefore imply that the affinity for Na⁺—rather than the velocity of Na⁺ binding to the transporter is subject to precise adjustment for supporting optimal uptake rates at low substrate concentrations.

Figures 1J,K summarize the K_D values for binding of substrate and of Na⁺ to the inward-facing conformation of the transporter, respectively. The coefficients of variation in K_D values of the inward-facing state were larger by a factor of 2 to 4 than the corresponding K_D values of the outward-facing state: K_DS_in was 7.46 μM ± 3.4 μM and 701 μM ± 155 μM for transporters optimized for .1 μM S_out and for 100 μM S_out, respectively. Likewise, the K D N a in + was 8.81 mM ± 3.63 mM and 6.46 mM ± .70 mM for transporters optimized for .1 μM and 100 μM S_out, respectively. The high K_DS_in at 100 μM S_out was dictated by the low substrate affinity for the outward-facing state of transporters, which had been optimized for this condition, and the requirement to maintain microscopic reversibility. However, the larger variation in K_DS_in and K D N a in + indicates that the reactions, which define the substrate and co-substrate affinities for the inward-facing conformation, do not require as stringent an adjustment as those, which define the corresponding affinities to the outward-facing conformation. Finally, we surveyed transporter optimization over a large range of extracellular substrate concentration (1 nM to 10 mM; Figure 1L): The resulting optimized substrate uptake rate of the transporters increased as a function of S_out but leveled off at an uptake rate of about 500 s⁻¹. This upper limit reflect the constraint imposed by the boundary conditions of the optimization (i.e., a diffusion-limited k_on and an upper limit of 1,000 s⁻¹ for the rate of conformational transitions, see above). It was 249.4, 499.4, and 997.8 s⁻¹ when the upper limit of the rate of conformational transitions was set to 500, 1,000, and 2,000 s⁻¹, respectively.

The reaction scheme in Figure 1A describes a hypothetical symporter, which binds co-substrate and substrate in a sequential order: The two Na⁺ ions are the first to bind when the transporter adopts the outward-facing conformation and the first to dissociate upon conversion of the transporter to the inward-facing conformation. In the subsequent description, we will refer to this reaction scheme as the NaSNaS scheme. This notation lists from left to right the order of the binding/unbinding events starting at the outward-facing apo-state (To) in clockwise direction. In Figures 2A–C, we show the reaction schemes for the three (sequential) alternatives. According to our notation, we refer to these as NaSSNa, SNaNaS and SNaSNa schemes. In addition, we also examined the reaction scheme of a transporter, in which the two Na⁺ ions and the substrate are allowed to bind in random order (Figure 2D).

We explored the impact of these five reaction schemes (Figures 1A, 2A–D) on the optimized substrate uptake rates by raising S_out from .1 (Figure 2E) to 30 (Figure 2F) and 100 μM (Figure 2G) and by conducting 10 optimization runs for each reaction scheme. As evident from Figure 2E, the substrate uptake rate was roughly the same for all schemes, when S_out was low (i.e., .1 μM). However, when optimized for a higher S_out, the schemes differed in the magnitude of the optimized substrate uptake rates, which they were able to support. The rank order was as follows: NaSSNa < SNaSNa < SNaSNa < SNaNaS = random. The rank order was the same for transporters optimized for 30 μM and 100 μM S_out, (cf. Figures 2F,G). Thus, random order of substrate and co-substrate binding and SNaNaS are best suited to support a large substrate uptake rate.

Due to the way SLC operate, the intracellular concentration of the substrate and the substrate uptake rate are inversely correlated. This can be explained as follows: The substrate must be released into the cytosol to complete a full cycle. As the intracellular substrate concentration (S_in) increases progressively during uptake, rebinding of the substrate to the inward-facing conformation occurs at a more frequent rate. This hampers progression through the transport cycle and thus reduces the substrate uptake rate.

Here we propose that—similar to the extracellular substrate concentration (S_out)—S_in can also exert an evolutionary pressure on the operation of a solute carrier. Depending on the physiological context, transporters may encounter intracellular concentrations of their cognate substrate, which range from low to high levels. This can be illustrated by two examples in the SLC6 family: The cytosolic concentrations of the monoamines dopamine, norepinephrine, and dopamine are expected to be low. This is because of the presence of vesicular monoamine transporters (vMAT1/SLC18A1 & vMAT2/SLC18A2), which shuffle cytosolic monoamines into vesicles (Yaffe et al., 2018). Accordingly, under physiological conditions, monoamine transporters for dopamine (DAT/SLC6A3), norepinephrine (NET/SLC6A2) and (SERT/SLC6A4) are unlikely to encounter high intracellular concentrations of their substrate. Conversely, the creatine transporter-1 (SLC6A8) must maintain substrate influx in the presence of millimolar intracellular creatine (Snow and Murphy, 2001). It is clear that a solute carrier, which must support a high substrate uptake rate against high S_in, must adjust its operation differently than a transporter, which does not need to overcome the hurdle imposed by frequent rebinding of the substrate to the inward-facing conformation.

We first optimized the substrate uptake rate of a transporter operating according to the NaSNaS scheme (illustrated in Figure 1A) by performing 20 optimization runs each, where S_out was 30 μM and S_in was set at 0, .1, 1, and 5 mM. It is evident from Figure 3A that the optimized substrate uptake rate decreased by raising S_in. This is in line with the inverse correlation of S_in and the substrate uptake rate discussed above. Figure 3B illustrates the range of K_D values for substrate binding to the inward-facing conformation of the transporters, which had been optimized to cope with different concentrations of S_in: K_D values of substrate for the inward-facing conformation increased as S_in was raised. This was to be expected, because lowering the intracellular affinity for substrate reduces the extent by which S_in can rebind, and it thus allows for a higher substrate uptake rate in the presence of S_in.

We then compared the uptake rate of transporters optimized for 30 μM S_out and 0 mM or 1 mM S_in over a large range of extracellular substrate concentration. As can be seen from Figure 3C, it was inevitable that transporters optimized in the presence of 1 mM S_in had negative uptake rates at low extracellular substrate concentrations, that is, the transporters cycled in the backward rather than the forward mode and hence mediated substrate efflux from the cell. It is also clear that the presence of 1 mM S_in reduced the maximum achievable uptake rate V_max in the forward transport mode and shifted the K_M. Because K_M and K_D differ (cf. Figure 1F), we examined the range of K_D values for substrate binding to the outward-facing conformation of transporters optimized for 0 and 1 mM S_in; these are illustrated together with the corresponding K_M values in Figure 3D: Both the K_D values and the K_M values for substrate increased, if the transporter had to cope with a high intracellular substrate concentration. The variation in these parameters was low (coefficient of variations = .070 and .025 for K_D and K_M, respectively, of transporters optimized in the presence of 30 μM S_out and 1 mM S_in). We conclude that the decrease in the apparent (K_M) and the true affinity (K_D) for the substrate, is required to allow for rapid cycling of the transporters in the presence of high S_in.

Finally, we examined how the selective pressure exerted by high intracellular substrate affected the affinity of the transporters to the co-substrate ion. As can be seen from Figure 3E, many different solutions emerged: On average, transporters optimized to cope with 1 mM S_in (magenta lines in Figure 3E) required higher extracellular Na⁺ concentrations to support substrate uptake than those optimized in the absence of intracellular substrate (green lines in Figure 3E). However, in both groups, the optimized transporters displayed highly variable responses to N a out + . This is reflected in the large range of the K_M values for Na⁺ (Figure 3F). In contrast, the K_D of Na⁺ for binding the outward-facing conformation did not vary to any substantial extent. This observation is consistent with our conclusion from Figure 1I, namely, that the constraint is imposed by the true affinity for Na⁺ rather than by its association rate (see above). We also computed K_Ds for Na⁺ binding to the inward-facing conformation of the transporters optimized for 0 mM and 1 mM S_in: There was an overlap in the range of K_D values for N a in + (Figure 3G). In addition, their variation was larger than that of K_D values for N a iout + (cf. Figures 3F,G). Hence, we conclude that Na⁺ binding to the inward-facing conformation need not be stringently adjusted to allow for high substrate uptake rates.

We next addressed the question, if the binding order of substrate and co-substrate determined the cycle rate of a transporter challenged with high concentrations of intracellular substrate. Optimization runs were carried out with S_out = 30 μM and S_in = 1 mM for all five schemes (cf. Figures 1A, 2A). It is evident from Figure 4A that these schemes differed in the magnitude of the optimized substrate uptake rates, which they were able to support. The rank order was NaSSNa < NaSNaS < SNaSNa < SNaNaS = random. This rank order differed from that observed in the absence of S_in (cf. Figure 2F). At constant S_out = 30 μM, we also varied the internal substrate concentration by lowering to S_in 100 μM and raising it to the point, where net uptake rat was zero (Figure 4B). For all schemes we found an optimized substrate uptake rate of zero when S_in was 6.75 mM. This was to be expected because at the chosen concentrations of Na⁺ (i.e., 150 mM N a out + and 10 mM N a in + ) the concentrative power (S_in/S_out) of the transporter is 225. This numerical value is identical for all schemes, because they are governed by the same transport stoichiometry. Accordingly, the transporters cannot further cycle productively in a forward direction, when S_in becomes 225 times larger than S_out (30 μM * 225 = 6.75 mM). For all schemes, we extracted the K_D of substrate binding to the inward-facing conformation of the transporters optimized at varying S_in (Figure 4C). In all instances, this K_D increased with increasing S_in. However, the magnitude of the drop in affinity depended on the reaction scheme.

For several secondary active transporters, binding of substrate and co-substrate was shown to occur in a cooperative manner: The apparent substrate affinity for the transporter depended on the concentration of the co-substrate (Meinild and Forster, 2012; Perez et al., 2014; Hasenhuetl et al., 2018; Erdem et al., 2019). It was low and high when the concentration of the co-substrate was low and high, respectively. Thus, the substrate can bind with higher affinity to transporters, when they are bound to the co-substrate (e.g., Na⁺). In this way, the concentration of the co-substrate determines the abundance of high and low affinity states for the substrate. Under physiological conditions, the co-substrate concentration is lower on the intracellular than on the extracellular side. Accordingly, cooperative binding is predicted to promote the forward cycling mode by reducing the substrate affinity to the inward-facing conformation. In fact, the drop in intracellular affinity resulting from cooperative binding is a requirement for maintaining a large substrate uptake rate at high S_in (Erdem et al., 2019). Notably, cooperative binding is contingent on a random binding order for substrate and co-substrate. For this reason cooperative binding can only be assessed in the random binding order scheme. Based on this consideration, a rise in S_in is predicted to increase the extent of cooperativity. We verified this prediction in optimization runs and extracted the cooperativity for transporters optimized at 30 μM S_out and 0 mM S_in or 1 mM S_in by calculating the ratio K_DS_in in the absence of bound Na⁺/K_DSi_n in the presence of bound Na⁺. It is evident from Figure 4D that there is a large range of optimized solutions, but on average cooperativity was more pronounced at higher S_in.

Evolution also optimized SLCs for substrate specificity. Selective transporters presumably arose from unselective ancestors. As a starting point, we posited that an unselective SLC must display low affinity for the various substrates: It is difficult to envisage a substrate binding site, which can provide strong bonding interactions to accommodate many distinct molecular scaffolds. We also assumed that, in the evolutionary trajectory from an unselective to a specific transporter, an increase in substrate specificity ought to translate in higher uptake rates for the substrate. Accordingly, in the optimization, we modeled an unselective solute carrier as a transporter, which had a low (true) affinity (i.e., a high K_D) for substrate by implementing a constraint, which prevented the substrate K_D from dropping below a user-defined arbitrary value (e.g., 10 mM). With this constraint in place, the optimization algorithm only returned sets of values for the microscopic rate constants, which defined transporters with a high K_D for substrate. In the subsequent description, we refer to such sets as unselective transporters. In contrast, sets generated in optimization runs, in which the K_D for substrate was not constrained, are referred to as selective transporters. For the optimizations summarized in Figure 5, we employed the random binding order scheme, we assumed zero-trans conditions and the presence of 1 μM S_out. Figure 5A shows the result for 20 selective and unselective transporters (K_D ≥ 10 mM): It is evident that the optimized substrate uptake rat of the unselective transporters (magenta symbols, Figure 5A) was lower by about three orders of magnitude than that of the selective SLCs (green symbols, Figure 5A). In Figure 5B, we examined the Michaelis–Menten kinetics of the substrate uptake rate of these optimized transporters. Figure 5C shows the same data normalized to V_max to illustrate the distribution of K_M. The V_max values of the unselective transporters were lower than those of the selective SLCs but they varied over about orders of magnitude (Figure 5D). Similarly, the K_M values of the unselective transporters, which were consistently higher than those of the selective SLCs, were again highly variable. In Figure 5E, we show optimized rates as a function of the concentration of substrate, for which the rates were optimized. Displayed in this plot are the data for selective transporters (unconstrained substrate K_D) and unselective transporters (constrained at K_D > .1 mM, 1 mM, and 10 mM). It is evident that at the various [S_out] tested the selective transporters had larger substrate uptake rates than the unselective ones.

These results confirm that SLCs can raise their transport capacity by becoming more specific for their cognate substrates. We, therefore, consider it plausible that specific SLCs arose from ancestors, which were unselective and that this transformation was driven by the need to support high substrate uptake rates. Conversely, there are transporters, which are under evolutionary pressure to remain unselective, because they support the disposition of xenobiotics. This is exemplified by members of the SLC22 family, which recognize diverse substrates to mediate disposition of drugs and xenobiotics: Both organic cation (OCT1-3/SLC22A1-3) and anion transporters (OAT1-3/SLC22A6-8) translocate most of their substrates with K_M values in the high μM range (VanWert et al., 2010; Motohashi and Inui, 2013). This is despite the fact that, in most instances, they are faced with substrate concentrations in the low micromolar range. However, we find this in good agreement with our results, which showed that the unselective transporters that we optimized for 1 μM S_out, displayed K_Ms in the submillimolar range (see Figure 5E). It is worth noting that the SLC22 family also encompasses members, which have a narrow substrate specificity; these have K_M value in the low micromolar range.