Abstract
The expression cloning some 25 years ago of the first member of SLC34 solute carrier family, the renal sodium-coupled inorganic phosphate cotransporter (NaPi-IIa) from rat and human tissue, heralded a new era of research into renal phosphate handling by focussing on the carrier proteins that mediate phosphate transport. The cloning of NaPi-IIa was followed by that of the intestinal NaPi-IIb and renal NaPi-IIc isoforms. These three proteins constitute the main secondary-active Na+-driven pathways for apical entry of inorganic phosphate (Pi) across renal and intestinal epithelial, as well as other epithelial-like organs. The key role these proteins play in mammalian Pi homeostasis was revealed in the intervening decades by numerous in vitro and animal studies, including the development of knockout animals for each gene and the detection of naturally occurring mutations that can lead to Pi-handling dysfunction in humans. In addition to characterising their physiological regulation, research has also focused on understanding the underlying transport mechanism and identifying structure-function relationships. Over the past two decades, this research effort has used real-time electrophysiological and fluorometric assays together with novel computational biology strategies to develop a detailed, but still incomplete, understanding of the transport mechanism of SLC34 proteins at the molecular level. This review will focus on how our present understanding of their molecular mechanism has evolved in this period by highlighting the key experimental findings.
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Notes
Recently, a naturally occurring mutation in human NaPi-IIa has been reported, involving an arginine to glutamine substitution at site 215 (see Fig 7b) [22]. Preliminary kinetic characterisation of this mutation in Xenopus oocytes revealed a significant hyperpolarizing shift in the steady-state voltage dependence. Qualitatively similar changes in IPi vs V have been reported for substitutions at sites where the native residue is uncharged (e.g. [29, 44, 110]), which indicated that the site of substitution as well as the charge can influence the voltage dependence.
The participation of Li+ ions in the NaPi-IIa cotransport cycle may explain the in vivo finding that Li+ ions are indeed reabsorbed in the rat kidney, most likely via NaPi-IIa. This could have clinical relevance for bipolar disorder treatment [102].
This is also underscored by the different voltage dependences displayed by SLC34 isoforms. For example, for the range of hyperpolarizing voltages amenable to oocyte assays, the mouse NaPi-IIb does not show obvious rate limiting behaviour unlike the flounder NaPi-IIb and NaPi-IIa isoforms so far examined (Fig 3b) and the postulated interaction of protons with the final Na+ interaction may therefore not play a significant role in affecting the overall cotransport rate.
Ideally, one should substitute novel cysteines in a so-called Cys-less background; however, removal of native cysteines often compromises functional activity as in the case of NaPi-IIa [60]. For all isoforms examined, it was confirmed experimentally that external application of the MTS reagents had insignificant effect on the wild-type (WT) activity and for NaPi-IIb internal exposure to MTS reagents by means of internal perfusion of the oocyte cytosol also resulted in unaltered transport function (unpublished experiment, K Köhler, I Gautschi, IC Forster). Thus, despite there being 12 or more native cysteines in the SLC34 family (two of which form a disulphide bond), it was most likely that these were not located in functionally important sites or were simply not accessible.
Uptake assays of bacterial members of the DASS family (e.g. [50]) indicated that transport was mediated by 2 Na+ ions but only one of these was resolved [77]. Recently, electrogenic behaviour has been demonstrated for VcINDY and its true stoichiometry found to be 3:1 [31, 81], the same as that of the eukaryotic protein NaDC1 [86, 40] and NaPi-IIa/b.
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Acknowledgements
The author wishes to acknowledge the numerous contributions made by those working in the phosphate transport field past and present. The support and encouragement given by Heini Murer and Jürg Biber (University of Zurich), Ernest Wright and his past and present colleagues Don Loo, Bruce Hirayama and Sepehr Eskandari (UCLA) and more recently the collaborations with Cristina Fenollar-Ferrer and Lucy Forrest (NIH) are particularly appreciated. Special thanks are due to colleagues, postdocs and doctoral candidates at the Murer laboratory, without whom the insights gained over the years would have been impossible, and whose names and contributions appear in the original references. Most of the studies reported here were supported by grants from the Swiss National Science Foundation and Hartmann Müller-Stiftung (University of Zurich) to the author and Heini Murer, as well as other funding sources cited in the original publications. Finally, the author acknowledges the outstanding support from Steven Petrou and colleagues at the Ion Channels in Human Diseases Laboratory (Florey Institute).
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This article is part of the special issue on Phosphate transport in Pflügers Archiv—European Journal of Physiology
Appendix: Tools and protocols used to characterise electrogenic SLC34 proteins expressed in Xenopus oocytes
Appendix: Tools and protocols used to characterise electrogenic SLC34 proteins expressed in Xenopus oocytes
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1.
Steady-state and presteady-state assays-characterising electrogenic behaviour at the macroscopic level
Voltage steps applied to a voltage clamped whole Xenopus oocyte expressing electrogenic cotransporters like NaPi-IIa/b reveal two components of membrane current: a presteady state, transient relaxing component and a steady-state component (Fig. 9, left data set). Analysis of both components yields important kinetic information about the transport mechanism. The currents recorded from a whole oocyte are macroscopic and represent the mean electrogenic behaviour of a large (typically ≥ 1010) population of transporters. Unlike ion channels, whose activity can be resolved at the single molecule level, the slow rate of charge translocation (~ fA or less) of membrane transporters is several orders of magnitude below that which can be presently resolved. However, like ion channels, we assume individual transporters behave independently and the population behaviour can be described in terms of the probability of occupying a particular conformational state. This depends on the membrane potential, substrate availability and the partial reaction rates associated with entering and leaving that state, according to the kinetic scheme (Fig. 1b).
The steady-state current component comprises endogenous oocyte currents and, in the absence of external Pi, the constitutive leak of the transporter. In the presence of Pi (Fig. 9, right data set) the downward deflection (arrowed) of the steady-state current indicates inward movement of charge accompanying active cotransport. Subtraction of these data sets from one another eliminates the endogenous component and yields the Pi-dependent current (IPi) with a small error due to the constitutive leak that we assume is suppressed by Pi. This component can be independently assayed using the inhibitor PFA (e.g. Fig. 6a). Ignoring the leak can potentially lead to an underestimate of the cotransport activity as well as charge translocation in stoichiometry assays depending on its magnitude (e.g. [2] and see Fig. 6 in [25]). By repeating these measurements with different substrate concentrations, standard phenomenological parameters such as apparent substrate affinity and maximum transport rate are derived as a function of membrane voltage. An important caveat for all oocyte-derived kinetic data is that these parameters are generally obtained at temperatures in the range 18-20 °C and extrapolation to mammalian physiology conditions necessitates taking account of temperature.
The presteady-state component comprises the endogenous charging transient of the oocyte lipid bilayer capacitance upon which the transporter-associated presteady-state current relaxations are superimposed (Fig. 9). The former is a linear function of the test voltage and can be eliminated by procedures such as curve fitting, subtracting matching records in the presence of a blocker or P/n protocols used to resolve ion channel gating currents (e.g. [4]). Analysis of the transporter-associated relaxations focuses on the charge displaced (Q) obtained by numerical integration, and the relaxation time constant (τ) obtained by exponential fitting. Both are important for characterising transporter kinetics. The fidelity and validity of presteady-state data can be tested by confirming charge balance for voltage steps to and from the holding potential, verifying independence of total charge movement from the holding potential and voltage independence of τ for the relaxations in response to the return step to the holding potential and establishing a direct correlation between steady-state transport and the total charge displaced.
The Q-V data show a characteristic sigmoidal relationship with saturation at either or both potential extremes due to the fixed number of charges involved. This is usually fit with a single Boltzmann-type function (Eq. 1) to obtain three parameters: the effective charge (z) per transporter (from the slope), the total charge displaced (Qmax) and the midpoint potential (V0.5), an equilibrium potential at which half the charge has been displaced. This procedure is valid for a two state model, and with more than one partial reaction contributing to the charge movement, the single Boltzmann function fit is clearly an approximation (e.g. [62]). Despite this limitation, these parameters can be used to characterise and interpret changes in substrate interactions and voltage dependence. The Q-V data are sensitive to external [Na+] (Fig. 10) and this relationship is a key biophysical signature for understanding cation interactions. As external [Na+] increases, V0.5 shifts towards depolarizing potentials (arrow), consistent with Na+ ions binding more easily according to their availability. The apparent charge associated with the empty carrier and the Na+ ion interactions can be estimated from z for each data set. The invariance of Qmax with changing [Na+] is expected for a given number of transporters, each of which binds a fixed number of Na+ ions. At low [Na+] this is less obvious because of limitations of the fitting procedure and at 0 mM Na+, charge movement is due to the empty carrier alone. The growth in z with [Na+] reflects the increased contribution of Na+ interactions to the effective charge movement contributed by each transporter. The limiting slope of V0.5 vs log10 [Na+] (~ 120 mV/decade) is also consistent with a sequential interaction of 2 Na+ ions per transporter. These findings are also predicted by deriving an analytical expression for Q-V, and numerically solving for V0.5 as a function of [Na+] (see Eq. 2–4) (e.g. [3, 71, 78]. Estimates of the dissociation constants for each cation interaction can be made and compared with the effect of mutations at the predicted cation binding sites (e.g. [29, 71].
In addition, the ratio Qmax/ze (where e is the electronic charge) can also be used to predict (i) the number of transporters (Nt) contributing to the charge movement (and by implication the cotransport activity) (Eq. 5) and, (ii) the turnover rate (Rt) when combined with the steady state cotransport current (e.g. [41] (Eq. 6). However, these should be taken as estimates only, given the inherent assumptions in the method (see [47, 114]).
The dependence of the relaxation time constant on voltage (τ-V) shows a “bell-shaped” form typical for non-linear charge movements associated with membrane proteins (Fig. 10). For a two state system (e.g. the empty carrier transition 0↔1, Fig. 1b), the forward and backward rate constants can be predicted from fits to the τ-V data. For a 3 state model (empty carrier and a Na+ binding step) theory predicts two time constants and analytical expressions become more complex. In practice, it may be difficult to resolve more than one relaxations component due to limitations of the curve fitting procedure and bandwidth of the voltage clamp. A single exponential fit to experimental data shows that like the shift of V0.5, the peak time constant shifts to more depolarizing potentials as [Na+] increases (arrow), consistent with having the empty carrier and at least one Na-dependent partial reaction contributing to the charge movement.
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2.
Voltage clamp fluorometry
This technique is readily applied to whole oocytes by combining the two electrode voltage clamp with a basic fluorescence microscope (e.g. [17, 91]) (Fig. 11). The instrumentation used in Zurich to study SLC34 proteins was modified from a design by the Wright group (UCLA) [69]. It has allowed real-time measurement of electrogenic activity (steady-state and presteady-state) and simultaneous fluorescence emissions from oocytes previously labelled with a fluorophore and covalently linked to an engineered cysteine in the transporter. The same voltage step protocols used for steady-state and presteady-state analysis can be used for VCF studies although more signal averaging may be required to obtain an acceptable signal to noise ratio. As all cysteines exposed to the labelling medium will be potentially labelled, an endogenous component will contribute to the background fluorescence. Generally, this is found not vary with membrane potential, but can still potentially compromise the signal resolution if ΔF from the transporter is small, by limiting the useful dynamic range. To overcome this, electronic offset can be applied before data acquisition. Pre-labelling endogenous cysteines and other procedures can also help reduce the background signal [91]. Precautions must also be taken to take account of loss of fluorescence signal during the course of an experiment due to bleaching, washout of fluorophore or internalisation of proteins (e.g. [45, 108]). Like the presteady-state assays, the fluorescence signal is a mean of emissions arising from a population of labelled transporters that at any instant can reside in different conformational states, depending on the probability of state occupancy. For example, if the fluorophore experiences a more polar or charged environment, the fluorescence is quenched and this can then be related to state occupancy. Collisional quenching is commonly assumed to be the underlying mechanism leading to ΔF and has been observed experimentally (e.g. [88]) but other mechanisms should be considered (e.g. [16]). The ΔF-V data are usually fit with a Boltzmann function although a direct correspondence with the Q-V parameters is not always obtained because there may not necessarily be a correlation between charge movement and the change in microenvironment of the fluorophore, depending on the labelling site. Unlike the electrophysiological assays, changes in fluorescence can also be reported by electroneutral processes in response to changing substrate [45].
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3.
Equations used in presteady-state analysis:
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a)
Boltzmann Q-V relationship
$$ Q={Q}_{hyp}+\frac{Q_{max}}{1+{\mathit{\exp}}^{\frac{ze\left({V}_{0.5}-V\right)}{kT}}} $$(1)where V0.5 is the midpoint voltage, z is the apparent valency/protein, Qmax is the total charge available to move, Qhyp is the charge at the hyperpolarizing limit and is a function of the holding potential and e, k and T have their usual meanings.
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b)
Derivation of Q-V relationship and V0.5 vs [Na] derived from the four state kinetic model (states 0,1–3, Fig. 1b).
The total charge displaced for a voltage step from ∞ (i.e. all transporters in state 0) to V and neglecting charge movement due to transition 0↔7, is given by [3]:
$$ {Q}_{\infty}^V=-{N}_te\left[\frac{\Big(\left({z}_{01}+{z}_{12}+{z}_{23}\right)+\left({z}_{12}+{z}_{23}\right)\alpha +{Z}_{23}\alpha \beta}{1+\alpha +\alpha \beta +\alpha \beta \gamma}\right] $$(2)where Nt is the number of transporters, α = k01/k10, β = k12/k21 and γ = k23/k32, (i.e. the ratio of forward to backward rate constants for each partial reaction), and zij is the apparent valence for the partial reaction that couples states i and j. Using rate theory, the forward and backward rates (kij, kji), assuming symmetrical barriers, are expressed as \( {k}_{ij}={k}_{ij}^0{\mathit{\exp}}^{-e{z}_{ij}V/2 kT} \); \( {k}_{ji}={k}_{ji}^0{\mathit{\exp}}^{e{z}_{ij}V/2 kT} \). For partial reactions involving cation interactions, the forward rates are scaled by the cation concentration.
To link Eq. 2 to the single Boltzmann function formulation of the Q-V data, the voltage at which 50% of the total charge has been displaced (V0.5) is found by setting \( {Q}_{\infty}^V=0.5{N}_te\left({z}_{12}+{z}_{23}+{z}_{23}\right) \), to give:
$$ \left({z}_{01}+{z}_{12}+{z}_{23}\right)\left(1-0.5\left(1+\alpha +\alpha \beta +\alpha \beta \gamma \right)\right)+\alpha \left({z}_{12}+{z}_{23}\right)+\alpha \beta {z}_{23}=0 $$(3)Substituting for α, β, γ, for [Na] large this reduces to:
$$ {\alpha}^0{\beta}^0{\upgamma}^0\ {\left[ Na\right]}^2\ \left({\exp}^{-\mathrm{e}{V}_{0.5}\left({z}_{01}+{z}_{12}+{z}_{23}\right)/ kT}\right)=1 $$(4)where α0 = k001/k010, β0 = k012/k021 and γ0 = k023/k032, and the superscript 0 refers to values at V = 0. A plot of V0.5 vs loge[Na] yields a limiting slope of 2kT/[e(z01 + z12 + z23)] mV/e-fold change in [Na] (or ≈ 116/(z01 + z12 + z23) mV/10-fold change in [Na]) at 20 °C; i.e. for one net charge translocated across the transmembrane field, the predicted slope is then 116 mV/10-fold change in [Na] [3] as observed experimentally (see Fig. 5e).
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c)
Estimation of number of transporters (Nt) and turnover rate (Rt)
$$ {N}_{\mathrm{t}}={Q}_{\mathrm{max}}/\mathrm{ze} $$(5)$$ {R}_{\mathrm{t}}={I_{\mathrm{Pi}}}^{\mathrm{max}}/\left({N}_{\mathrm{t}}{\mathrm{z}}_{\mathrm{t}}\mathrm{e}\right) $$(6)where IPimax is the maximum transport current and zt is the charge translocated/cycle. The estimates of Rt require specification of the experimental conditions ([Na+], Vm etc.) and are dependent on the assumptions related to estimating Nt from a single Boltzmann fit [114].
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a)
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Forster, I.C. The molecular mechanism of SLC34 proteins: insights from two decades of transport assays and structure-function studies. Pflugers Arch - Eur J Physiol 471, 15–42 (2019). https://doi.org/10.1007/s00424-018-2207-z
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DOI: https://doi.org/10.1007/s00424-018-2207-z