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Regulation of the mammalian-brain V-ATPase through ultraslow mode-switching

Abstract

Vacuolar-type adenosine triphosphatases (V-ATPases)1,2,3 are electrogenic rotary mechanoenzymes structurally related to F-type ATP synthases4,5. They hydrolyse ATP to establish electrochemical proton gradients for a plethora of cellular processes1,3. In neurons, the loading of all neurotransmitters into synaptic vesicles is energized by about one V-ATPase molecule per synaptic vesicle6,7. To shed light on this bona fide single-molecule biological process, we investigated electrogenic proton-pumping by single mammalian-brain V-ATPases in single synaptic vesicles. Here we show that V-ATPases do not pump continuously in time, as suggested by observing the rotation of bacterial homologues8 and assuming strict ATP–proton coupling. Instead, they stochastically switch between three ultralong-lived modes: proton-pumping, inactive and proton-leaky. Notably, direct observation of pumping revealed that physiologically relevant concentrations of ATP do not regulate the intrinsic pumping rate. ATP regulates V-ATPase activity through the switching probability of the proton-pumping mode. By contrast, electrochemical proton gradients regulate the pumping rate and the switching of the pumping and inactive modes. A direct consequence of mode-switching is all-or-none stochastic fluctuations in the electrochemical gradient of synaptic vesicles that would be expected to introduce stochasticity in proton-driven secondary active loading of neurotransmitters and may thus have important implications for neurotransmission. This work reveals and emphasizes the mechanistic and biological importance of ultraslow mode-switching.

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Fig. 1: Ultralong measurements of proton-pumping into single SVh reveal mode-switching of individual mammalian-brain V-ATPases.
Fig. 2: Dynamics of switching between the proton-pumping, inactive and proton-leaky modes.
Fig. 3: Mode-switching enables the regulation of the V-ATPase by electrochemical gradients.
Fig. 4: Regulation of functional modes by ATP and ADP.
Fig. 5: Mode-switching of the V-ATPase in intact SVs.

Data availability

The data that support the findings of this study are available from the corresponding author upon request. Source data are provided with this paper.

Code availability

Source code and a demo for the Bayesian filtering algorithm that was used for the detection of events can be found at https://github.com/pete906/Bayesian_Filtering_Proton_Pump.git. Support on the use of the algorithm can be provided by the corresponding author upon reasonable request.

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Acknowledgements

We thank S. C. Blanchard for conversations, H. Grubmüller, C. Kutzner and P. E. Gourdon for providing and helping with visualizations of the SV. This work was supported by the Novo Nordisk Foundation (grant NNF17OC0028176), the Villum Foundation (grants 17617 and 17646) and the Lundbeck Foundation (grant R249-2017-1406 to E.K. and R250-2017-1175 to S.V.), and a grant from the European Research Council to R.J. (SVNeuroTrans). M. Grabe and F.M. were supported by NIH R01-AG057342. J.P. was financially supported by the Deutsche Forschungsgemeinschaft (German Research Foundation) under Germany’s Excellence Strategy EXC 2067/1-390729940.

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Authors and Affiliations

Authors

Contributions

D.S. conceived the strategy and was responsible for project management and supervision. E.K., J.P. and D.S. designed research with initial help from S.V. E.K. developed the single-molecule assay and collected and analysed all data. C.G.S. was the principal software developer. J.P. prepared all biochemical samples, with help from M. Ganzella, under the supervision of R.J. P.J.J and J.L.P. developed the stochastic event-detection model and estimations. J.H. collected the data for Fig. 5h. M.P.M. established the pH calibration. M. Grabe and F.M. developed the non-equilibrium physical model. O.M. simulated the spatial distribution of vesicles. D.S. and E.K. wrote the main text. E.K. prepared all figures and Supplementary Information. All authors discussed the results and commented on the manuscript.

Corresponding author

Correspondence to Dimitrios Stamou.

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Extended data figures and tables

Extended Data Fig. 1 Ultralong-term activity recordings of the V-ATPase in single SVh.

Activity recordings that spanned the duration of three hours revealed mode-switching of single V-ATPases. Each liposome can contain up to hundreds of fluorophores and the system has been optimized for minimal photon budget usage, therefore minimizing photobleaching. a–f, Left panels: Representative single molecule acidification kinetics displaying long-lived proton pumping and inactive modes. Activity was initiated upon addition of ATP. Red traces correspond to active vesicles that have been normalized to the baseline. Black traces correspond to inactive vesicles. The data as not been corrected for photobleaching. A mild linear filter has been used in the visualization of these traces. Right panels: Histograms of acidification plateaus. Red histograms correspond to active vesicles while black histograms correspond inactive vesicles

Source data

Extended Data Fig. 2 Single-vesicle acidification kinetics are fast (~ 30 s), the slow apparent ensemble average acidification kinetics are governed by slow mode-switching (~ 1000 s). Under our imaging conditions we never observe single-molecule photo-blinking or photo-bleaching.

a, Ensemble average acidification kinetics of single SVh. Solid black line corresponds to the mean acidification. Red shaded area corresponds to one s.d. of all single vesicle traces. Note: this figure is also shown in Fig. 1b. b, Solid lines correspond to the mean while shaded areas correspond to one s.d. The majority of inactive single vesicle traces (~ 95% of the population) showed photostability with no indication of photobleaching or self-quenching. ~ 5% of the vesicles showed signs of minor photobleaching on the order of 30% after 25 min of recording. We never observed an increase in fluorescent intensity while imaging which would be indicative of self-quenching. c, Collections of active and inactive vesicles. Inactive vesicles retained stable baseline signals throughout the course of experiments. No photoblinking was observed for the totality of inactive vesicles d, Ensemble average acidification kinetics immediately after ATP injection. Black line corresponds to the mean, n = 4 . Error bars correspond to one s.d, n=4. Acidification until the dynamic equilibrium of pumping and leakage is reached takes place in the order of tens of seconds, consistent with previous ensemble measurements of acidification. e, Cumulative histogram of the time elapsed until the onset of the first proton-pumping event. Mode-switching dynamics are slow (~ 1000 seconds) and are responsible for the slow kinetics seen in the ensemble average traces in a. f, Zoom in regions of representative single-vesicle traces showcase that acidification occurs in the order of tens of seconds. g, Histogram of single-vesicle acidification kinetics shows that most vesicles acidify in 10-100 s.

Source data

Extended Data Fig. 3 pH calibration methodology for single molecule activity data.

a, Schematic illustration of pH calibration experiments. LUVs are incubated in a K-gluconate-MOPS buffer at pH 7.23 in the presence of valinomycin and CCCP to equilibrate chemical gradients. Subsequent injection of the same buffer (including valinomycin and CCCP) at decreasing pH concentrations leads to equilibrated acidification of both the lumen and the extraluminal space around the vesicles. The pH changes are reported as an increase in fluorescence. b, Representative examples of single vesicle calibration data. Data has been normalized to the activity baseline value (see supplementary text). Each vesicle reports a change in fluorescence as a function of pH which is then fitted with a sigmoidal function. Values correspond to the mean fluorescence for a stack of 20 images. Error bars represent one s.d. of fluorescence intensity for a stack of 20 images. c, Distributions of the sigmoidal fit parameters. The dissociation constant and growth rate of the vesicles show a normally distributed population with very narrow standard deviations. The fluorescence, Imax, at saturating pH, however showed a larger spread. Thus, the dissociation constant and the growth rate are globally determined from these experiments while the Imax saturation value is locally determined for SVh post activity. d, Ensemble average of sigmoidal fits for the entire population of vesicles. Black line corresponds to the mean while red-shaded area corresponds to one s.d. of the population. e, Illustration of the typical process during activity measurements. After recording a baseline, ATP is injected into the system and V-ATPase activity is triggered. After the end of the activity recording, the SVh are treated with activity buffer at signal saturating pH 2.85 including CCCP and chloride to allow for influx of protons and chloride counterions to determine Imax locally for each vesicle. f, Example activity traces and their corresponding calibration curve determined by the global dissociation constant, the global growth rate (determined in panel (c)), the local baseline and saturation values, Ibase and Imax respectively. Henceforth, normalized intensities of activity measurements can be mapped onto corresponding pH values, as shown in the right-hand side traces. At saturation the data points correspond to the mean fluorescence for a stack of 20 images. Error bars represent one s.d. of fluorescence intensity for a stack of 20 images

Source data

Extended Data Fig. 4 Single vesicle acidification kinetics in the presence and absence of chloride.

Data analysed in these experiments was used to determine dwell times for both proton-pumping and inactive modes and their relation to electrochemical gradients (Fig. 2c,d, Fig. 3d–f). a, Ensemble average acidification kinetics of SVh. In the absence of chloride, a membrane potential clamps the activity of the V-ATPase (black line). When 30 mM choline chloride is introduced, the potential is released and the V-ATPase can establish larger gradients (red line) (also see Fig. 3a, b). Shaded areas correspond to one s.d. b,c, Typical single molecule traces for the different conditions investigated in these experiments. d, Histograms of maximum intensities of single molecule data. Data shows different distribution of signals. The number of independent experiments were n = 8 for data in the presence of chloride (Panel (a), (d): Red data and Panel (b)) and n = 5 for data in the absence of chloride (Panel (a), (d): Black data and Panel (b))

Source data

Extended Data Fig. 5 ~30% of mode-switching events are too fast to reach a pH plateau or decay to zero ∆pH.

In approximately 30% of single-molecule data, dynamics of switching are faster than the dynamics of pumping or leakage. a, Schematic representation of different cases of mode-switching events, pH build-up and leakage lifetimes. Top left: the pumping dwell time (ton) is longer than the pH build-up time (τbuild-up), allowing for the acidification to reach a constant plateau (ΔpHmax). Bottom left: ton is shorter than τbuild-up, and therefore ΔpHmax cannot be reached. Top right: the inactive dwell time (toff) is longer than the leakage lifetime (τleak) allowing for the ΔpH to reach zero. Bottom right: toff is shorter than τleak and therefore the pH will not be able to reach the baseline value. b, Populations of pH build-up lifetimes and proton-pumping mode dwell durations. pH build-up data (red) was produced by fitting single exponentials on kinetic traces during periods of rise of activity. Data for ton (yellow) is taken from Fig. S6b, second row and shown in a logarithmic scale. c, Populations of leakage lifetimes of periods of activity of SVh and resting dwell times. Dataset for leakage is the same as that in Fig. 2e while dataset for resting dwells is taken from Fig. S6c, second row

Source data

Extended Data Fig. 6 Electrochemical regulation of mode-switching dynamics.

a, Proton-pumping mode probabilities in the presence or absence of chloride. No statistical significance was detected between the two populations. Error bars correspond to one s.d. between experiments, n = 8 (red) and n = 5 (grey) for ΔΨ+Cl and ΔΨ–Cl respectively. A two-tailed Mann-Whitney U test gave a P-value = 0.62 (n.s.). b, Representation of the free energy landscape for the two different electrochemical conditions. The forward activation barriers, \({\varDelta G}_{{on}}^{+{Cl}}=86\,.\,2{\rm{kJ}}\,\bullet \,{{\rm{mol}}}^{-1}\pm 0\,.\,3{\rm{kJ}}\,\bullet \,{{\rm{mol}}}^{-1}\) and \({\varDelta G}_{{on}}^{-{Cl}}=86.7{\rm{kJ}}\,\bullet \,{{\rm{mol}}}^{-1}\pm 0.4{\rm{kJ}}\,\bullet \,{{\rm{mol}}}^{-1}\), are higher than those for backwards activation, \({\varDelta G}_{{off}}^{+{Cl}}=85\,.\,3{\rm{kJ}}\,\bullet \,{{\rm{mol}}}^{-1}{\rm{kJ}}\,\bullet \,{{\rm{mol}}}^{-1}\,\pm \)\(0\,.\,6{\rm{kJ}}\,\bullet \,{{\rm{mol}}}^{-1}\) and \({\varDelta G}_{{off}}^{-{Cl}}=86.3{\rm{kJ}}\,\bullet \,{{\rm{mol}}}^{-1}\pm 1.3{\rm{kJ}}\,\bullet \,{{\rm{mol}}}^{-1}\), highlighting the fact that the probability the V-ATPase is found in a proton-pumping mode is higher. Additionally, both forward and backwards activation barriers were lower when the membrane potential was released which indicates that the frequency of transitions between modes is higher

Source data

Extended Data Fig. 7 Acidification kinetics under different catalytic substrate concentrations.

Data analyzed from these experiments was used for calculating pumping-mode probabilities (Fig. 4d) and dwell times (Extended Data Fig. 6). a, Ensemble average acidification kinetics of SVh at different concentrations of ATP. Activity was initiated upon addition of ATP and 20-30 mM chloride. b–e, Typical single molecule traces at different concentrations of ATP (as stated in corresponding grey boxes). f, Histograms of maximum intensity of single molecule data. Total number of experiments was n = 4–8

Source data

Extended Data Fig. 8 Non-equilibrium physical modelling of pumping, resting and leaky modes provides the single-molecule pumping and leaking rate.

a, Schematic illustration of the main parameters used in the model. Note: this is illustration is also shown in Fig. 2a. b,c, Top: Representative single-molecule traces displaying mode-switching dynamics. Proton pumping was stochastically interrupted by inactive and proton-leaky modes. During active periods of the V-ATPase, a dynamic equilibrium between proton-pumping and passive leakage is established, therefore reaching a single acidification plateau. Leakage currents are a convolution of transprotein and passive membrane proton efflux. Transprotein leakage currents are temporally distinct from proton pumping and may activate directly after the enzyme switched off. Data was fitted with a non-equilibrium model as described in the supplementary information. Pumping rates and permeabilities (both membrane and transprotein) are calculated as free parameters by the model. Bottom: Pumping dynamics for the proton-pumping rate (red), the passive membrane efflux rate (grey) and the transprotein efflux rate (yellow). In b the transprotein leak was the primary efflux pathway while in c proton efflux manifested only passively through the membrane. d, Proton pumping rates and permeability estimates of the model for the data shown in this figure (b, c, and e). Pumping rates were found to be 7 ± 5 H+/s. Membrane and V-ATPase permeability were 3 ± 2 × 10−5 cm/s and 18 ± 9 × 10−5 cm/s. Transprotein permeability of the V-ATPase is nearly an order of magnitude larger than passive membrane permeability indicating the regulatory importance of the proton-leaky mode. Error bars correspond to one s.d. Number of independent model outputs are N = 8, 11 and 8 for membrane permeability, V-ATPase permeability and proton-pumping rates respectively. e, Additional single-molecule traces fitted the model. Arrows point out the mode-switching events during which dynamic (ΔpH = ΔpHmax) or static (baseline, ΔpH = 0) equilibrium was not reached

Source data

Supplementary information

Supplementary Information

This file contains Supplementary Discussion, Methods, References and Figs 1–9 with legends.

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Supplementary Table 1

Steps involved in the sliding method.

Supplementary Table 2

Model parameters.

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Kosmidis, E., Shuttle, C.G., Preobraschenski, J. et al. Regulation of the mammalian-brain V-ATPase through ultraslow mode-switching. Nature 611, 827–834 (2022). https://doi.org/10.1038/s41586-022-05472-9

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