bioRxivBiophysics
Spatiotemporal relationships defining the adaptive gating of the bacterial mechanosensitive channel MscS
Abstract
Adaptive desensitization and inactivation are common properties of most ion channels and receptors. The mechanosensitive channel of small conductance MscS, which serves as a low-threshold osmolyte release valve in most bacteria, is unusual because it slowly inactivates not from the open, but from the resting state under moderate tensions. The manifestation of this mechanism is the channels ability to discriminate the rate of tension application, i.e., to ignore slow tension ramps but fully respond to abruptly applied stimuli. In this work, we present a reconstruction of the landscape for tension-dependent MscS transitions based on patch current kinetics recorded under specially designed pressure protocols. The data are analyzed with a three-state continuous time Markov model of gating, where the tension-dependent transition rates are governed by Arrhenius-type relations. The analysis provides assignments to the intrinsic opening, closing, inactivation, and recovery rates as well as their tension dependencies. These parameters, which define the spatial (areal) distances between the energy wells and the positions of barriers, describe the tension-dependent distribution of the channel population between the three states and quantitatively predict the experimentally observed dynamic pulse and ramp responses. Our solution also provides an analytic expression for the area of the inactivated state in terms of two experimentally accessible parameters: the tension at which inactivation probability is maximized, {gamma}*, and the midpoint tension for activation, {gamma}0.5. The analysis initially performed on Escherichia coli MscS shows its applicability to the previously uncharacterized MscS homolog from Pseudomonas aeruginosa. MscS inactivation minimizes metabolic losses during osmotic permeability response and thus contributes to the environmental fitness of bacteria.
- organism: bacteria (other)
- assay: electrophysiology
- research focus: cell signaling & trafficking
- approach: statistical model
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