Direct observation of proton pumping by a eukaryotic P-type ATPase

Abstract

In eukaryotes, P-type adenosine triphosphatases (ATPases) generate the plasma membrane potential and drive secondary transport systems; however, despite their importance, their regulation remains poorly understood. We monitored at the single-molecule level the activity of the prototypic proton-pumping P-type ATPase Arabidopsis thaliana isoform 2 (AHA2). Our measurements, combined with a physical nonequilibrium model of vesicle acidification, revealed that pumping is stochastically interrupted by long-lived (~100 seconds) inactive or leaky states. Allosteric regulation by pH gradients modulated the switch between these states but not the pumping or leakage rates. The autoinhibitory regulatory domain of AHA2 reduced the intrinsic pumping rates but increased the dwell time in the active pumping state. We anticipate that similar functional dynamics underlie the operation and regulation of many other active transporters.

Fig. 1. Imaging proton pumping into the lumen of single surface-tethered vesicles using TIRF microscopy

A) Illustration of AHA2^R reconstituted vesicles tethered to a passivated glass surface and imaged on individual basis with TIRF microscopy. Zoom: extra-vesicular addition of both ATP and Mg²⁺ activated exclusively outward facing AHA2^R molecules triggering H⁺ pumping in the vesicle lumen. We quantified changes in the vesicular H⁺ concentration by calibrating the response of the lipid-conjugated pH sensitive fluorophore pHrodo^™. Valinomycin was always present to mediate K⁺/H⁺ exchange and prevent the build up of a transmembrane electrical potential. B) TIRF image of single vesicles tethered on a passivated glass slide. C) Acidification kinetics of single vesicles upon addition of ATP and Mg²⁺. Red traces highlight three representative signals from single vesicles showcasing: absence of transport activity, continuous pumping of protons and fluctuations in proton-transport activity. The black trace is the average of 600 single vesicle traces. As expected, addition of the protonophore CCCP collapsed the proton gradient established by AHA2^R.

Fig. 2. Single-molecule observation of proton pumping reveals active and inactive states

A) Typical examples of pH changes inside individual AHA2^R reconstituted vesicles. ATP and Mg²⁺ (2 mM) were added to initiate proton pumping and CCCP (5 μM) was added to collapse the pH gradients. Traces show −ΔpH defined as a difference between the initial and final pH. Images of each respective liposome at different time points are shown below each trace. At the right-hand side of the traces we plotted histograms of pH plateaus numbered to indicate the number of active pumps per vesicle. The pH inside the majority of vesicles showed no changes indicating the absence of functional transporter molecules (top panel). For a small fraction of vesicles we observed intermittent H⁺ pumping indicating the presence of single-molecules (middle panels). The observation of two discrete steady-state pH plateaus in single vesicle traces indicated the occasional presence of two active pumps per single vesicle (bottom panel). B) Population histogram of pH plateaus for AHA2^R-reconstituted vesicles (n = 3, where hereafter n is the number of independent experiments). C, D) Same as in A, B but for full length AHA2. For D n = 2. Labeling of AHA2 with Alexa Fluor^® 647 enabled counting on the same vesicles of both the number of labeled AHA2 proteins (E), and of the respective activity dynamics (C). F) The histogram of active proteins per vesicle was calculated from step-bleaching analysis of the data in E that was corrected for labeling efficiency and the probability that a proton pump is active (12). The two independent methods for estimating the number of active molecules agreed that ~70% of vesicles containing a protein have one active proton pump.

Fig. 3. Modeling active, inactive and leaky states, and their role in autoinhibiting proton pumps

A) Main parameters of the physical model we used to fit changes in the vesicular pH were pumping rate (I_p), protein associated leak (P_AHA2), membrane leak (P_leak), valinomycin induced K⁺ permeability (P_K+), buffering capacity in the interior of the vesicle (β), electrical potential across the membrane (Ψ) (12). B) Example of a typical proton pumping trace and respective fits without (blue) and with (red) a transprotein proton leak. A threshold in the first derivative of the pH kinetics (12)was used to define the lifetime of the active state t_on and the time between pumping events t_off. C) Temporal evolution of the proton pumping rate (grey) and the proton efflux rates due to passive membrane (red) leakage and transprotein back-flow (blue) for the pH trace shown in B. D) Histogram of proton permeability associated with the membrane, AHA2 and AHA2^R. Respective counts were 95, 37 and 45. E) Histogram of pumping rates for AHA2^R and AHA2. Respective counts were 126 and 95. F) Histogram of t_on for AHA2^R and AHA2. Respective counts were 241 and 134. The bar at >1200 s shows the number of traces that did not switch in the duration of the experiment. G) Histogram of t_off for AHA2^R and AHA2. Respective counts were 69 and 39. For AHA2^R and AHA2 respectively: the number of independent experiments was 3 and 2, while the number of individual proteoliposomes analyzed was 126 and 95.

Fig. 4. Regulation of proton pumping by pH gradients

A, B) Relation between t_on and the maximal pH gradient for AHA2^R and AHA2, respectively. Each data point represents the average of 3 consecutive values. Error bars represent corresponding standard deviations. The decay constants from the error weighted exponential fits to the data are 6.9 ± 2.0 s and 0.4 ± 0.1 s for (A) and (B), respectively, where uncertainties represent 95% confidence intervals from the fits. C) Probability to observe transprotein leak as a function of pH gradient proton pumping. For AHA2^R and AHA2 respectively: data were binned with 0.25 and 0.5 pH units; number of independent experiments and individual proteoliposomes analyzed was the same as in Fig. 3. Spearman’s rank order correlation coefficients ρ (126) = 0.40 P =10⁻⁴ and ρ (95) = 0.30 P = 0.03 indicated a strong positive correlation between leakage probability and ΔpH_max for both AHA2^R and AHA2.