Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

# 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.

This is a preview of subscription content, access via your institution

## Access options

\$32.00

All prices are NET prices.

## 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.

## References

1. Vasanthakumar, T. & Rubinstein, J. L. Structure and roles of V-type ATPases. Trends Biochem. Sci. 45, 295–307 (2020).

2. Ueno, H., Suzuki, K. & Murata, T. Structure and dynamics of rotary V1 motor. Cell. Mol. Life Sci. 75, 1789–1802 (2018).

3. Forgac, M. Vacuolar ATPases: rotary proton pumps in physiology and pathophysiology. Nat. Rev. Mol. Cell Biol. 8, 917–929 (2007).

4. Spikes, T. E., Montgomery, M. G. & Walker, J. E. Structure of the dimeric ATP synthase from bovine mitochondria. Proc. Natl Acad. Sci. USA 117, 23519–23526 (2020).

5. Okuno, D., Iino, R. & Noji, H. Rotation and structure of FoF1-ATP synthase. J. Biochem. 149, 655–664 (2011).

6. Takamori, S. et al. Molecular anatomy of a trafficking organelle. Cell 127, 831–846 (2006).

7. Mutch, S. A. et al. Protein quantification at the single vesicle level reveals that a subset of synaptic vesicle proteins are trafficked with high precision. J. Neurosci. 31, 1461–1470 (2011).

8. Furuike, S. et al. Resolving stepping rotation in Thermus thermophilus H+-ATPase/synthase with an essentially drag-free probe. Nat. Commun. 2, 233 (2011).

9. Abbas, Y. M., Wu, D., Bueler, S. A., Robinson, C. V. & Rubinstein, J. L. Structure of V-ATPase from the mammalian brain. Science 367, 1240–1246 (2020).

10. Rost, B. R. et al. Optogenetic acidification of synaptic vesicles and lysosomes. Nat. Neurosci. 18, 1845–1852 (2015).

11. Farsi, Z. et al. Single-vesicle imaging reveals different transport mechanisms between glutamatergic and GABAergic vesicles. Science 351, 981–984 (2016).

12. Grabe, M., Wang, H. & Oster, G. The Mechanochemistry of V-ATPase proton pumps. Biophys. J. 78, 2798–2813 (2000).

13. Gowrisankaran, S. & Milosevic, I. Regulation of synaptic vesicle acidification at the neuronal synapse. IUBMB Life 72, 568–576 (2020).

14. Dilworth, M. V., Findlay, H. E. & Booth, P. J. Detergent-free purification and reconstitution of functional human serotonin transporter (SERT) using diisobutylene maleic acid (DIBMA) copolymer. Biochim. Biophys. Acta Biomembr. 1863, 183602 (2021).

15. Ahmed, S., Holt, M., Riedel, D. & Jahn, R. Small-scale isolation of synaptic vesicles from mammalian brain. Nat. Protoc. 8, 998–1009 (2013).

16. Budzinski, K. L., Zeigler, M., Fujimoto, B. S., Bajjalieh, S. M. & Chiu, D. T. Measurements of the acidification kinetics of single SynaptopHluorin vesicles. Biophys. J. 101, 1580–1589 (2011).

17. Hernandez, J. M. et al. Membrane fusion intermediates via directional and full assembly of the SNARE complex. Science 336, 1581–1584 (2012).

18. Preobraschenski, J., Zander, J.-F., Suzuki, T., Ahnert-Hilger, G. & Jahn, R. Vesicular glutamate transporters use flexible anion and cation binding sites for efficient accumulation of neurotransmitter. Neuron 84, 1287–1301 (2014).

19. Castorph, S. et al. Synaptic vesicles studied by dynamic light scattering. Eur. Phys. J. E 34, 63 (2011).

20. Veshaguri, S. et al. Direct observation of proton pumping by a eukaryotic P-type ATPase. Science 351, 1469–1473 (2016).

21. Stamou, D., Duschl, C., Delamarche, E. & Vogel, H. Self-assembled microarrays of attoliter molecular vessels. Angew. Chem. Int. Ed. 42, 5580–5583 (2003).

22. Bendix, P. M., Pedersen, M. S. & Stamou, D. Quantification of nano-scale intermembrane contact areas by using fluorescence resonance energy transfer. Proc. Natl Acad. Sci. USA 106, 12341–12346 (2009).

23. Mathiasen, S. et al. Nanoscale high-content analysis using compositional heterogeneities of single proteoliposomes. Nat. Methods 11, 931–934 (2014).

24. Fitzgerald, G. A. et al. Quantifying secondary transport at single-molecule resolution. Nature 575, 528–534 (2019).

25. Singh, A. et al. Protons in small spaces: discrete simulations of vesicle acidification. PLoS Comput. Biol. 15, e1007539 (2019).

26. Taoufiq, Z. et al. Hidden proteome of synaptic vesicles in the mammalian brain. Proc. Natl Acad. Sci. USA 117, 33586–33596 (2020).

27. Zhao, J., Benlekbir, S. & Rubinstein, J. L. Electron cryomicroscopy observation of rotational states in a eukaryotic V-ATPase. Nature 521, 241–245 (2015).

28. Drory, O. & Nelson, N. The emerging structure of vacuolar ATPases. Physiology 21, 317–325 (2006).

29. Kettner, C., Bertl, A., Obermeyer, G., Slayman, C. & Bihler, H. Electrophysiological analysis of the yeast V-type proton pump: variable coupling ratio and proton shunt. Biophys. J. 85, 3730–3738 (2003).

30. Kishikawa, J., Nakanishi, A., Furuike, S., Tamakoshi, M. & Yokoyama, K. Molecular basis of ADP inhibition of vacuolar (V)-type ATPase/synthase. J. Biol. Chem. 289, 403–412 (2014).

31. Uner, N. E. et al. Single-molecule analysis of inhibitory pausing states of V1-ATPase. J. Biol. Chem. 287, 28327–28335 (2012).

32. Davies, J. M., Hunt, I. & Sanders, D. Vacuolar H+-pumping ATPase variable transport coupling ratio controlled by pH. Proc. Natl Acad. Sci. USA 91, 8547–8551 (1994).

33. Accardi, A. Structure and gating of CLC channels and exchangers: structure and gating of CLC channels and exchangers. J. Physiol. 593, 4129–4138 (2015).

34. Schenck, S., Wojcik, S. M., Brose, N. & Takamori, S. A chloride conductance in VGLUT1 underlies maximal glutamate loading into synaptic vesicles. Nat. Neurosci. 12, 156–162 (2009).

35. Maycox, P. R., Deckwerth, T., Hell, J. W. & Jahn, R. Glutamate uptake by brain synaptic vesicles. Energy dependence of transport and functional reconstitution in proteoliposomes. J. Biol. Chem. 263, 15423–15428 (1988).

36. Minagawa, Y. et al. Basic properties of rotary dynamics of the molecular motor Enterococcus hirae V1-ATPase. J. Biol. Chem. 288, 32700–32707 (2013).

37. Imamura, H. et al. Evidence for rotation of V1-ATPase. Proc. Natl Acad. Sci. USA 100, 2312–2315 (2003).

38. Grabe, M. & Oster, G. Regulation of organelle acidity. J. Gen. Physiol. 117, 329–344 (2001).

39. Nakanishi, A., Kishikawa, J., Tamakoshi, M., Mitsuoka, K. & Yokoyama, K. Cryo EM structure of intact rotary H+-ATPase/synthase from Thermus thermophilus. Nat. Commun. 9, 89 (2018).

40. Yasuda, R., Noji, H., Yoshida, M., Kinosita, K. & Itoh, H. Resolution of distinct rotational substeps by submillisecond kinetic analysis of F1-ATPase. Nature 410, 898–904 (2001).

41. Noji, H., Yoshida, M. & Kinosita, K. Direct observation of the rotation of F1-ATPase. Nature 386, 299–302 (1997).

42. Adachi, K. et al. Coupling of rotation and catalysis in F1-ATPase revealed by single-molecule imaging and manipulation. Cell 130, 309–321 (2007).

43. Watanabe, R. et al. Arrayed lipid bilayer chambers allow single-molecule analysis of membrane transporter activity. Nat. Commun. 5, 4519 (2014).

44. Soga, N. et al. Monodisperse liposomes with femtoliter volume enable quantitative digital bioassays of membrane transporters and cell-free gene expression. ACS Nano 14, 11700–11711 (2020).

45. Phan, N. T. N., Li, X. & Ewing, A. G. Measuring synaptic vesicles using cellular electrochemistry and nanoscale molecular imaging. Nat. Rev. Chem. 1, 0048 (2017).

46. Maxson, M. E. et al. Detection and quantification of the vacuolar H+ATPase using the Legionella effector protein SidK. J. Cell Biol. 221, e202107174 (2022).

47. Lu, H. P., Xun, L. & Xie, X. S. Single-molecule enzymatic dynamics. Science 282, 1877–1882 (1998).

48. Ciftci, D. et al. Single-molecule transport kinetics of a glutamate transporter homolog shows static disorder. Sci. Adv. 6, eaaz1949 (2020).

49. Akyuz, N., Altman, R. B., Blanchard, S. C. & Boudker, O. Transport dynamics in a glutamate transporter homologue. Nature 502, 114–118 (2013).

50. Erkens, G. B., Hänelt, I., Goudsmits, J. M. H., Slotboom, D. J. & van Oijen, A. M. Unsynchronised subunit motion in single trimeric sodium-coupled aspartate transporters. Nature 502, 119–123 (2013).

51. Akyuz, N. et al. Transport domain unlocking sets the uptake rate of an aspartate transporter. Nature 518, 68–73 (2015).

52. Dyla, M. et al. Dynamics of P-type ATPase transport revealed by single-molecule FRET. Nature 551, 346–351 (2017).

53. Rundlet, E. J. et al. Structural basis of early translocation events on the ribosome. Nature 595, 741–745 (2021).

54. Chung, S. H. & Kennedy, R. A. Forward-backward non-linear filtering technique for extracting small biological signals from noise. J. Neurosci. Methods 40, 71–86 (1991).

55. Kemmer, G. C. et al. Lipid-conjugated fluorescent pH sensors for monitoring pH changes in reconstituted membrane systems. Analyst 140, 6313–6320 (2015).

56. Pobbati, A. V. N- to C-terminal SNARE complex assembly promotes rapid membrane fusion. Science 313, 673–676 (2006).

57. Stein, A., Radhakrishnan, A., Riedel, D., Fasshauer, D. & Jahn, R. Synaptotagmin activates membrane fusion through a Ca2+-dependent trans interaction with phospholipids. Nat. Struct. Mol. Biol. 14, 904–911 (2007).

58. Rigaud, J.-L., Lévy, D. & Düzgünes, N. (ed.) in Methods in Enzymology Vol. 372, 65–86 (Elsevier, 2003).

59. Rigaud, J.-L., Pitard, B. & Levy, D. Reconstitution of membrane proteins into liposomes: application to energy-transducing membrane proteins. Biochim. Biophys. Acta Bioenerg. 1231, 223–246 (1995).

60. van den Bogaart, G. et al. One SNARE complex is sufficient for membrane fusion. Nat. Struct. Mol. Biol. 17, 358–364 (2010).

61. Huttner, W. B., Schiebler, W., Greengard, P. & De Camilli, P. Synapsin I (protein I), a nerve terminal-specific phosphoprotein. III. Its association with synaptic vesicles studied in a highly purified synaptic vesicle preparation. J. Cell Biol. 96, 1374–1388 (1983).

62. Nagy, A., Baker, R. R., Morris, S. J. & Whittaker, V. P. The preparation and characterization of synaptic vesicles of high purity. Brain Res. 109, 285–309 (1976).

63. Upmanyu, N. et al. Colocalization of different neurotransmitter transporters on synaptic vesicles is sparse except for VGLUT1 and ZnT3. Neuron 110, 1483–1497 (2022).

64. Takamori, S., Riedel, D. & Jahn, R. Immunoisolation of GABA-specific synaptic vesicles defines a functionally distinct subset of synaptic vesicles. J. Neurosci. 20, 4904–4911 (2000).

65. Preobraschenski, J. et al. Dual and direction-selective mechanisms of phosphate transport by the vesicular glutamate transporter. Cell Rep. 23, 535–545 (2018).

## 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.

## Author information

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.

## Ethics declarations

### Competing interests

The authors declare no competing interests.

## Peer review

### Peer review information

Nature thanks Ken Yokoyama and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

## 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

### 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.

### 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

### 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))

### 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

### 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

### 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

### 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

## Supplementary information

### Supplementary Information

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

### Supplementary Table 1

Steps involved in the sliding method.

### Supplementary Table 2

Model parameters.

## Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

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

• Accepted:

• Published:

• Issue Date:

• DOI: https://doi.org/10.1038/s41586-022-05472-9