Correspondence to: Steven M. Sine, Receptor Biology Laboratory, Department of Physiology and Biophysics, Mayo Foundation, 200 First Street, S.W., Rochester, Minnesota 55905. Fax:507-284-9420, E-mail: sine.steven@mayo.edu
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Abstract |
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We describe the genetic and kinetic defects in a congenital myasthenic syndrome due to the mutation A411P in the amphipathic helix of the acetylcholine receptor (AChR)
subunit. Myasthenic patients from three unrelated families are either homozygous for
A411P or are heterozygous and harbor a null mutation in the second
allele, indicating that
A411P is recessive. We expressed human AChRs containing wild-type or A411P
subunits in 293HEK cells, recorded single channel currents at high bandwidth, and determined microscopic rate constants for individual channels using hidden Markov modeling. For individual wild-type and mutant channels, each rate constant distributes as a Gaussian function, but the spread in the distributions for channel opening and closing rate constants is greatly expanded by
A411P. Prolines engineered into positions flanking residue 411 of the
subunit greatly increase the range of activation kinetics similar to
A411P, whereas prolines engineered into positions equivalent to
A411 in ß and
subunits are without effect. Thus, the amphipathic helix of the
subunit stabilizes the channel, minimizing the number and range of kinetic modes accessible to individual AChRs. The findings suggest that analogous stabilizing structures are present in other ion channels, and possibly allosteric proteins in general, and that they evolved to maintain uniformity of activation episodes. The findings further suggest that the fundamental gating mechanism of the AChR channel can be explained by a corrugated energy landscape superimposed on a steeply sloped energy well.
Key Words: congenital myasthenic syndrome, single channel kinetics, hidden Markov modeling, channel gating, energy landscape
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INTRODUCTION |
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Communication throughout the nervous system relies on submillisecond electrical discharges within and between neurons and their effector organs. These electrical discharges are mediated by ion channels, which have evolved hydrophobic domains to anchor them in the cell membrane, and putative linkage structures that couple voltage sensors or neurotransmitter binding sites to the hydrophobic domains. The onset, duration, and fidelity of the electrical discharges are likely to be under considerable evolutionary pressure, and can be fine tuned by changes in amino acid sequences of the ion channels. Here, we show that a local region of the acetylcholine receptor subunit governs the fidelity of channel gating.
Ion channels are thought to open and close by switching among a small number of discrete states with rate constants invariant in time. Such a discrete state Markov description of ion channel function has received considerable support by kinetic studies over the past two decades (
Episode-to-episode changes in channel kinetics have been described for a wide variety of ion channels. Nonuniform kinetics were evident when comparing sequences of closely spaced events from one channel with temporally distinct sequences from another (
Previous studies of congenital myasthenic syndromes (CMS) identified structural and mechanistic defects of the AChR that underlie the disease. In many such cases, consequences of the mutation could be assigned to changes in a single microscopic rate constant or class of rate constants in a mechanistic description of AChR activation, providing new insights into structurefunction relationships (A411P causes a congenital myasthenic syndrome, but that the mutation affects AChR activation kinetics in a fundamentally new way. Unlike previous CMS cases, where individual activation episodes appeared kinetically uniform (
A411P AChRs exhibit wide-ranging kinetics. Applying hidden Markov modeling analysis to activation episodes from individual mutant AChRs, we show that the wide-ranging kinetics owes to greatly increased variability of channel opening and closing rate constants of individual channels.
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MATERIALS AND METHODS |
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Mutation Analysis
All 12 exons of the AChR subunit gene were sequenced using genomic DNA isolated from blood (
A411P mutation results in gain of an AvaI restriction site; therefore,
A411P was tracked in family members by AvaI restriction analysis of PCR products.
1293insG and
T159P were tracked in family members by allele-specific PCR. Allele-specific PCR was used to screen for the three mutations in 200 normal alleles.
Construction and Expression of Mutant AChRs
Human AChR subunit cDNAs were subcloned into the CMV-based expression vector pRBG4 (
Surface expression of pentameric AChR in transfected HEK cells was determined with 125I-labeled -bungarotoxin (
-bgt) (
-bgt binding (
Patch-Clamp Recordings
Recordings were obtained in the cell-attached configuration at a membrane potential of -70 mV and a temperature of 22°C (
Hidden Markov Modeling Analysis
We used hidden Markov modeling (HMM) to analyze the kinetics of clusters of closely spaced openings due to individual AChR channels. Recordings were initially viewed at 10 kHz bandwidth to identify clusters of events from individual channels, and the corresponding unfiltered segments were selected for analysis. Individual clusters were identified using a critical closed duration that averaged 5 ms. This closed duration was determined from closed-time histograms as the point of intersection of the predominant closed dwell-time component with the succeeding closed time component (
HMM analysis was carried out using the method developed by
Inverse filtering was achieved by passing a triangle wave into the speed test input of the patch clamp amplifier (Axopatch 200B; Axon Instruments, Inc.), recording the resulting step response at the amplifier output, and averaging several thousand step responses (Fig 1 A). The inverse filter then used the averaged system step response and the theoretical step response to produce a digital moving average filter with defined coefficients that reconstructed the original unfiltered signal (60 kHz.
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The HMM analysis of
where two agonists (A) associate with the receptor (R) with the effective rate constant k+ and dissociate with the effective rate constant k-. Doubly occupied receptors (A2R) open with rate constant ß, and open receptors (A2R*) close with rate constant . Our analysis assumed zero conductance for all closed states and a single conductance for the open state. Because Scheme 1 is a simplified version of the standard description of AChR activation, we simulated data according to the standard kinetic description (Scheme 2) and applied HMM analysis assuming Scheme 1 (Appendix). Scheme 2 specifies two agonist binding steps, opening and closing of singly liganded receptors (ß' and
'), and channel block by agonist (k+B and k-B).
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RESULTS |
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Molecular Genetics and Expression of Mutant AChR
We detected the A411P (
1231G
C) mutation in four myasthenic patients in three unrelated families. The mutation is located in the cytoplasmic domain known as the amphipathic helix (
A411P (Fig 2 A). In families 2 and 3, the affected patients are heterozygous for
A411P plus a second mutation, either
1293insG or
T159P (
475A
C) (Fig 2B and Fig C).
1293insG is a previously characterized null mutation (
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After transfection of 293HEK fibroblasts with the A411P mutant and wild-type
, ß, and
subunit cDNAs, surface expression was reduced to 31.0 ± 10.9% (mean ± SD, n = 3) of wild-type. ACh binding to
A411P-AChR, determined by competition against 125I-labeled
-bgt binding, was indistinguishable from that of wild-type AChR. Parallel experiments using the
T159P mutant revealed reduced surface expression of 29.3 ± 11.1% (n = 4) of wild-type, but ACh binding was like that observed with
, ß, and
subunits alone, indicating that all of the
-bgt binding was due to
2ß
2 pentamers, which are predominantly desensitized (
A411P, although recessive, determines the phenotype.
Consequences of A411P for AChR Activation
To delineate mechanistic consequences of A411P, we incorporated the mutant subunit into receptors containing normal
, ß, and
subunits in 293HEK fibroblasts, and recorded currents through single AChR channels (
A411P AChRs exhibit a striking range of current kinetics (Fig 3). Histograms of single channel open probability, computed for individual clusters, show a narrow distribution for wild-type AChRs, but show a much broader distribution for receptors containing
A411P, extending over nearly the entire range of open probability (Fig 3).
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Kinetic Steps Altered by A411P
We next asked which steps in receptor activation, agonist binding or channel gating, are responsible for the wide range of open probabilities produced by A411P. Because each episode of channel activity has its own kinetic signature, we sought an analysis method that could delineate activation rate constants from short segments of data. The standard interval likelihood analysis requires pooling of multiple clusters of activation episodes, and produces rate constants averaged over all the data (
We applied HMM analysis to segments of data originating from individual activation episodes for both wild-type and mutant receptors. ACh was applied at a concentration of 30 µM, a concentration that minimizes channel block by ACh and singly liganded openings, and single-channel currents were acquired at a sampling frequency of 1 MHz. We identified data segments containing activity of only one channel, inverse-filtered them to produce an effective bandwidth of 100 kHz, and analyzed each segment by HMM, assuming Scheme 1 as the Markov model (see MATERIALS AND METHODS).
Applied to a single AChR activation episode, HMM analysis produces both an idealized sequence of channel opening and closing events and a set of rate constants in Scheme 1. Comparison of the idealized event sequence with the same recording filtered at our standard bandwidth of 10 kHz reveals close correspondence between idealized and recorded current pulses (compare Fig 4, middle with bottom). Moreover, HMM resolves additional brief current pulses owing to the increased bandwidth of 100 kHz.
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We then constructed histograms of the fitted rate constants, depositing one entry from each activation episode into the appropriate bin of the corresponding rate-constant histogram. When plotted on a logarithmic abscissa, each rate constant distributes as a Gaussian function for both wild-type and A411P AChRs (Fig 5). Remarkably,
A411P greatly expands the distributions for channel opening and closing rate constants (ß and
), but does not affect the distributions for agonist binding rate constants (k+ and k-) (Fig 5; Table 1).
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Mean values for channel closing and agonist association and dissociation rate constants are slightly affected by A411P, but the mean of the channel opening rate constant is not affected (Fig 5; Table 1). Changes in mean values of the rate constants are relatively small compared with the greatly expanded distributions of the channel gating rate constants.
Analysis of Simulated Data by HMM
The observed Gaussian distributions of the rate constants could potentially result from the stochastic nature of single-channel data. We therefore simulated idealized channel events using a single value for each rate constant in Scheme 1 (A411P AChRs (Fig 5; Table 1), indicating a greatly expanded range for each gating rate constant. We also simulated data according to Scheme 2, which includes two sequential agonist binding steps, singly liganded openings and channel block by ACh. Assuming the simplified Scheme 1 in the HMM analysis, the distributions of the gating rate constants were again much narrower than observed for individual
A411P AChRs (Table 1), indicating that omission of kinetic steps in Scheme 1 does not contribute to variability in gating rate constants. Thus, individual
A411P AChRs open and close at rates that are not fixed, but vary over a greatly expanded range.
Effect of Cluster Duration on Rate Constant Distributions
The number of openings in a cluster of channel events varies because each cluster terminates by stochastic entry into a desensitized state. Because the precision of the rate constants estimated by HMM increases with the number of events analyzed, variation in cluster duration could potentially contribute to the observed Gaussian distribution of the gating rate constants. We therefore compared cluster durations for wild-type and mutant AChRs. Approximately exponential distributions of cluster durations are observed for both wild type and A411P AChRs, and the mean cluster durations differ only slightly for the two types of AChRs (Fig 6). Cluster durations for our simulated data also distribute exponentially with a mean approximating that of the mutant AChR. Thus, the stochastic variation of cluster duration does not contribute to the expanded distributions of gating rate constants produced by
A411P.
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Individual AChRs Activate in More than One Kinetic Mode
Although our recordings monitor current flow through one channel at any given time, a typical cell-attached patch of membrane contains multiple channels. We therefore asked whether each of the multiple receptors in a patch of membrane activates with intrinsically different kinetics, or whether an individual receptor can switch among different kinetic modes. Applying our previously described mode-switching detection method (A411P AChRs (Fig 7). Additionally, open probability plotted for consecutive 10-event windows changes near the time of the mode switch (Fig 7). Mode switches were infrequent, with 17 mode switches detected in 1,294 clusters for
A411P AChR (1.3%) and 40 mode switches in 2,077 clusters for wild-type AChR (1.9%). Although mode switches occurred at similar frequencies in wild-type and
A411P AChRs, detection of mode switches is contrast dependent, so the observed frequencies represent lower-limit values. Thus, switching among kinetic modes is a normal process within individual AChRs, the range of which is minimized by structures built into the wild-type AChR.
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Structural Specificity of A411P
We next asked whether the increased kinetic variability is unique to mutations in the subunit, and further whether it is unique to mutation of alanine 411. We engineered proline into positions equivalent to
A411 in the homologous
, ß, and
subunits, as well as into positions 409413 of the
subunit, and recorded single channel currents through individual mutant AChRs. Corresponding mutations in either ß or
subunits do not affect the distribution of open probabilities (Fig 8 A), while the mutated
subunit does not support expression of AChR, as indicated by lack of ACh-induced single-channel currents. On the other hand, proline mutations scanning this region of the
subunit,
F409P,
V410P,
E412P, and
S413P, all broaden the distribution of open probabilities similar to the original
A411P mutation (Fig 8 B). Thus, increased kinetic variability of the AChR is specific to mutations of the
subunit, where the local region flanking alanine 411 maintains uniformity of AChR activation kinetics.
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DISCUSSION |
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This work illustrates how clinical medicine can lead to advances in basic science and, further, how the emerging insights can extend far beyond the original disorder. The present myasthenic syndrome stems from widely ranging kinetics of channel gating and reduced surface expression of the A411P-AChR, together with failure of the second mutant allele in each patient to rescue a normal phenotype. The molecular defect caused by
A411P, an alanine-to-proline mutation, structurally disturbs the amphipathic helix of which it is part, specifically affecting rates of opening and closing of the channel. Although the M2 domain forming the ion permeation pathway is far away from
A411 in the linear sequence, it may be physically close to the amphipathic helix in the three-dimensional structure (
A411P AChR.
The A411P mutation is the most likely cause of myasthenia in these patients for several reasons. Myasthenia is present in patients homozygous for
A411P, or in heterozygotes with a second null mutation, but heterozygotes with a second normal
allele are without symptoms; the normal
allele therefore compensates for the mutant allele, indicating that
A411P is recessive. The reduced expression of
A411P-AChR may be sufficient by itself to cause myasthenia (
A411P establishes an important locus for regulating kinetic uniformity of AChR channel gating.
The amphipathic helix, positioned on the cytoplasmic side of the membrane preceding the M4 transmembrane domain, was first highlighted by Fourier analysis of residue hydrophobicity as a function of sequence of Torpedo AChR subunits, combined with secondary structure analysis ( subunit, and therefore is predicted to encompass the
A411P mutation. A more recent secondary structure prediction divides the original amphipathic helix into two
-helices, G and H, placing
A411 within helix G (
Previous mutagenesis studies have implicated the amphipathic helix in contributing to AChR gating kinetics. Using chimeras of and
subunits,
subunit) that caused mode-switching kinetics, which was readily detectable within clusters of events from individual channels (
The present results show that structurally perturbing the amphipathic helix causes AChR channel opening and closing kinetics to vary over a greatly increased range. Proline mutations alter gating kinetics only when they are placed in the subunit, which is uniquely present in the adult AChR. Altered kinetics also result when proline is placed one at a time in positions flanking
A411. As proline is expected to disrupt an
helix, our results strongly suggest that uniform channel gating kinetics depend upon a helical structure encompassing at least residues 409413 of the
subunit. Within the three-dimensional structure of the AChR,
A411 is most likely located in one of the five rod-like structures protruding from each subunit to form an inverted pentagonal cone extending into the cytoplasm (
subunit remains unknown. Our findings suggest that the pentagonal cone stabilizes the global structure of the AChR, thus minimizing the kinetic range for channel gating.
Our findings suggest the following physical picture to account for episode-to-episode variation in channel gating kinetics. Opening and closing of the AChR channel are all or nothing global phenomena in which all five AChR subunits rotate back and forth in a concerted manner. For short periods of time, on the order of hundreds of milliseconds, the free energy barrier separating open and closed states does not change, producing kinetically uniform gating events within clusters of activation episodes from individual channels. However on an atomic scale, the protein is in constant motion, causing particular side chains or entire secondary structures to flip between stable configurations. These structural transitions, while not directly involving the gating machinery, change the global energetics of the AChR, incrementing activation energy for gating transitions up or down. To illustrate how this mechanism could broaden the distribution of gating rate constants, imagine that the AChR has two gating-control elements that flip at low frequency between two positions, called + and -, and that each flipping event changes activation energy for gating by the same amount. The three possible energy states of these elements are: ++ (enhancing gating), -- (impeding gating), and +- or -+ (neutral to gating). Probabilities of these states are predicted to follow a binomial distribution, akin to the Gaussian distribution of rate constants observed experimentally. More than two gating-control elements per receptor, or more configurations per element, would broaden the distribution. The wild-type AChR minimizes transitions of these gating-control elements, perhaps through evolution-driven changes in stabilizing structures such as the amphipathic helix.
Functional consequences of A411P can also be viewed in terms of the energy landscape of the AChR. A protein's energy landscape is defined as potential energy as a function of the coordinates of all the atoms. Different stable conformations of the protein correspond to low points in the energy landscape, and reflect the number of structural degrees of freedom, which even for small proteins is expected to be very large. Energy landscapes have been classified into broad topological categories such as funnel-shaped with high barriers, funnel-shaped with low barriers, and flat with many similar-sized barriers (
Our kinetic results suggest that the energy landscape of the AChR is shaped like a funnel, with corrugations running perpendicular to the long axis of the funnel. Each doubly liganded open or closed state corresponds to one such funnel, and transitions between open and closed states correspond to hops from one funnel to another. Evidence for an overall funnel-shaped landscape is the single mean value obtained for each gating rate constant for wild-type and A411P AChRs, while evidence for corrugations in the funnel is the slow switching among a range of kinetic modes. For wild-type AChR, the kinetic range departs only slightly from that of a single kinetic mode, but our detection of infrequent mode switches shows that it can access multiple modes; these observations indicate a corrugated energy landscape superimposed upon a very steeply sloped funnel. For the
A411P AChR, on the other hand, the range and number of kinetic modes is greatly increased compared with the predominant single mode of wild-type AChR, and infrequent mode switches are detected. These observations indicate a corrugated energy landscape superimposed upon a much shallower funnel, accounting for the wide range of kinetic modes accessible to the
A411P AChR. The corrugations of the funnel correspond to the large energy barriers separating kinetic modes; these barriers are on the order of 30 kcal/mol, given an approximate mode-switching rate of 1 s-1 for wild-type and
A411P AChRs, and are much larger than the energy barriers governing channel gating. The overall results suggest that
A411P primarily diminishes the incline of the overall funnel-shaped energy landscape, while retaining the large energy barriers between stable states.
Other types of ion channels may potentially access multiple kinetic modes, as observed for the muscle AChR, and this may explain the nonuniform kinetics observed for many ion channels (
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Footnotes |
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Dr. Middleton's present address is Glaxo Wellcome Co., London, England UB6 0HE.
1 Abbreviations used in this paper: -bgt,
-bungarotoxin; AChR, acetylcholine receptor; HMM, hidden Markov modeling.
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Acknowledgements |
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We thank Dan and Lin Ci Brown and Lalitha Venkataramanan for help implementing HMM analysis.
This work was supported by National Institutes of Health grants to S.M. Sine (NS31744) and A.G. Engel (NS6277), and a research grant from the Muscular Dystrophy Association to A.G. Engel.
Submitted: 16 May 2000
Revised: 5 July 2000
Accepted: 27 July 2000
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Appendix |
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Kinetic Simulations
We tested the HMM analysis method of A411P AChRs (Fig 6). After constructing idealized current pulses and sampling them at 1-µs intervals, we applied the rise time of our recording system to each opening and closing transition (Fig 1), and then added baseline noise recorded from a quiescent patch of membrane (Fig 9). Simulated data were inverse filtered and decimated to produce an effective bandwidth of 100 kHz before HMM analysis.
We determined the optimal number of autoregressive coefficients (AR coefficients) used in HMM analysis by simulating data according to Scheme 1, together with rate constants that mimicked the kinetics of wild-type AChRs, and then varying the number of AR coefficients in the HMM analysis. Increasing the number of AR coefficients gave progressively better agreement between simulated and fitted rate constants (Table 2). However, for practical analysis of real data, the number of AR coefficients could not be increased beyond four; with four AR coefficients, analysis of data from one membrane patch required 4 d of computer time and, with each increment in AR coefficients, the computer time doubled.
We compared individual dwell times from the simulated data with those obtained by HMM analysis of the same data and found accurate detection of intervals longer than 58 µs. When a simulated event was briefer than this dead time, the brief event fused with adjacent events, and the detected event closely agreed with the composite-simulated event (Table 3).
Although the present study was designed to document variability in rate constants underlying AChR activation, we also asked how accurately the rate constants were estimated. We simulated data according to three different kinetic schemes and analyzed the data using HMM, with Scheme 1 assumed as the Markov model. We first analyzed data simulated according to Scheme 1, using rate constants that yielded fitted values close to those obtained in our analysis of wild-type AChR data (Table 1). The results show that the simulation requires somewhat greater values of the gating rate constants to match those obtained for the wild-type AChR, suggesting that the channel might open as quickly as 100,000 s-1, and close as quickly as 4,500 s-1 (Table 1). Second, although we used ACh at a concentration that minimizes singly liganded openings and channel block by ACh, these processes might not be negligible. We therefore simulated data using Scheme 2, using our previously published activation rate constants for the human AChR determined by interval likelihood methods (
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