Analyzing the effect of ion binding to the membrane-surface on regulating the light-induced transthylakoid electric potential (ΔΨm)

Abstract

The transthylakoid membrane potential (ΔΨm) is essential because it can drive the ATP synthesis through the CF0–CF1 type of ATP-synthase in chloroplasts as an energetic equivalent similar to ΔpH. In addition, a high fraction of proton motive force (PMF) stored as the ΔΨm component is physiologically important in the acclimation of photosynthesis to environmental stresses. It has been shown that ΔΨm is the sum of the Donnan potential difference (ΔΨdn) and the diffusion potential difference (ΔΨd). Specifically, ΔΨdn, ΔΨd, and ΔΨm are strongly associated with the ionic activities near the membrane surface, particularly, the extent of ion binding to the charged/neutral sites adjacent to the membrane surface. However, an in-depth analysis of the effect of altered cationic binding to the membrane surface on adjusting the transthylakoid electric potentials (ΔΨdn, ΔΨd, and ΔΨm) is still missing. This lack of a mechanistic understanding is due to the experimental difficulty of closely observing cations binding to the membrane surface in vivo. In this work, a computer model was proposed to investigate the transthylakoid electric phenomena in the chloroplast focusing on the interaction between cations and the negative charges close to the membrane surface. By employing the model, we simulated the membrane potential and consequently, the measured ECS traces, proxing the ΔΨm, were well described by the computing results on continuous illumination followed by a dark-adapted period. Moreover, the computing data clarified the components of transthylakoid membrane potential, unraveled the functional consequences of altered cationic attachment to the membrane surface on adjusting the transthylakoid electric potential, and further revealed the key role played by Donnan potential in regulating the energization of the thylakoid membrane. The current model for calculating electric potentials can function as a preliminary network for the further development into a more detailed theoretical model by which multiple important variables involved in photosynthesis can be explored.