Abstract
The epithelial Na(+) channel (ENaC) mediates the rate-limiting step in transepithelial Na(+) transport in the distal segments of the nephron and in the lung. ENaC subunits are cleaved by proteases, resulting in channel activation due to the release of inhibitory tracts. Peptides derived from these tracts inhibit channel activity. The mechanism by which these intrinsic inhibitory tracts reduce channel activity is unknown, as are the sites where these tracts interact with other residues within the channel. We performed site-directed mutagenesis in large portions of the predicted periphery of the extracellular region of the α subunit and measured the effect of mutations on an 8-residue inhibitory tract-derived peptide. Our data show that the inhibitory peptide likely binds to specific residues within the finger and thumb domains of ENaC. Pairwise interactions between the peptide and the channel were identified by double mutant cycle experiments. Our data suggest that the inhibitory peptide has a specific peptide orientation within its binding site. Extended to the intrinsic inhibitory tract, our data suggest that proteases activate ENaC by removing residues that bind at the finger-thumb domain interface.
Highlights
In an effort to elucidate the mechanism of proteolytic activation of epithelial Na؉ channel (ENaC), we functionally characterized the binding site for an ␣ subunit-derived 8-residue inhibitory peptide
We recently suggested that mutations that shift the equilibrium to favor an open channel state will alter the apparent affinity of the channel for the inhibitory peptide, regardless of whether the mutation directly alters the peptidebinding site [12]
Where KЈP is the apparent P8 affinity, and KCP is the dissociation constant for P8 bound to the ENaC closed state
Summary
Site-directed Mutagenesis and Oocyte Expression—Mouse ␣, , and ␥ ENaC subunit cDNAs in pBluescript SKϪ vector (Stratagene, La Jolla, CA) were used as templates to generate mutations using QuikChange II XL (Stratagene) following the manufacturer’s instructions. To evaluate the uncertainty in these values and calculate the statistical significance of the former being different from the wild type, we utilized Markov Chain Monte Carlo methods. The parameter estimates obtained by nlme were used to initialize the Gibbs sampler (a Markov Chain Monte Carlo algorithm), which was applied to the nonlinear regression problem of Equation 3. P values corresponding to the indicated hypotheses were calculated by Monte Carlo integration from the random samples obtained by the Gibbs sampler. These p values were adjusted by applying the Bonferonni correction, which in this case amounts to dividing the observed p value by the number of tests (number of different mutant types) examined. One-way analysis of variance followed by a Student Newman-Keuls test and curve fitting to the Hill equation were performed using Igor Pro (Wavemetrics, Oswego, OR)
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