A Two-Channel Patch-Clamp System on a Chip

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Extracting Dwell Time Sequences from Processive Molecular Motor Data

Processive molecular motors, such as kinesin, myosin, or dynein, convert chemical energy into mechanical energy by hydrolyzing ATP. The mechanical energy is used for moving in discrete steps along the cytoskeleton and carrying a molecular load. Single-molecule recordings of motor position along a substrate polymer appear as a stochastic staircase. Recordings of other single molecules, such as F1-ATPase, RNA polymerase, or topoisomerase, have the same appearance. We present a maximum likelihood algorithm that extracts the dwell time sequence from noisy data, and estimates state transition probabilities and the distribution of the motor step size. The algorithm can handle models with uniform or alternating step sizes, and reversible or irreversible kinetics. A periodic Markov model describes the repetitive chemistry of the motor, and a Kalman filter allows one to include models with variable step size and to correct for baseline drift. The data are optimized recursively and globally over single or multiple data sets, making the results objective over the full scale of the data. Local binary algorithms, such as the t-test, do not represent the behavior of the whole data set. Our method is model-based, and allows rapid testing of different models by comparing the likelihood scores. From data obtained with current technology, steps as small as 8 nm can be resolved and analyzed with our method. The kinetic consequences of the extracted dwell sequence can be further analyzed in detail. We show results from analyzing simulated and experimental kinesin and myosin motor data. The algorithm is implemented in the free QuB software

Cloning and identification of tissue-specific expression of KCNN4 splice variants in rat colon

KCNN4 channels that provide the driving force for cAMP- and Ca2+-induced anion secretion are present in both apical and basolateral membranes of the mammalian colon. However, only a single KCNN4 has been cloned. This study was initiated to identify whether both apical and basolateral KCNN4 channels are encoded by the same or different isoforms. Reverse transcriptase-PCR (RT-PCR), real-time quantitative-PCR (RT-QPCR), and immunofluorescence studies were used to clone and identify tissue-specific expression of KCNN4 isoforms. Three distinct KCNN4 cDNAs that are designated as KCNN4a, KCNN4b, and KCNN4c encoding 425, 424, and 395 amino acid proteins, respectively, were isolated from the rat colon. KCNN4a differs from KCNN4b at both the nucleotide and the amino acid level with distinct 628 bp at the 3′-untranslated region and an additional glutamine at position 415, respectively. KCNN4c differs from KCNN4b by lacking the second exon that encodes a 29 amino acid motif. KCNN4a and KCNN4b/c are identified as smooth muscle- and epithelial cell-specific transcripts, respectively. KCNN4b and KCNN4c transcripts likely encode basolateral (40 kDa) and apical (37 kDa) membrane proteins in the distal colon, respectively. KCNN4c, which lacks the S2 transmembrane segment, requires coexpression of a large conductance K+ channel β-subunit for plasma membrane expression. The KCNN4 channel blocker TRAM-34 inhibits KCNN4b- and KCNN4c-mediated 86Rb (K+ surrogate) efflux with an apparent inhibitory constant of 0.6 ± 0.1 and 7.8 ± 0.4 μM, respectively. We conclude that apical and basolateral KCNN4 K+ channels that regulate K+ and anion secretion are encoded by distinct isoforms in colonic epithelial cells

Maximum Likelihood Estimation of Molecular Motor Kinetics from Staircase Dwell-Time Sequences

Molecular motors, such as kinesin, myosin, or dynein, convert chemical energy into mechanical energy by hydrolyzing ATP. The mechanical energy is used for moving in discrete steps along the cytoskeleton and carrying a molecular load. High resolution single molecule recordings of motor steps appear as a stochastic sequence of dwells, resembling a staircase. Staircase data can also be obtained from other molecular machines such as F(1)-ATPase, RNA polymerase, or topoisomerase. We developed a maximum likelihood algorithm that estimates the rate constants between different conformational states of the protein, including motor steps. We model the motor with a periodic Markov model that reflects the repetitive chemistry of the motor step. We estimated the kinetics from the idealized dwell-sequence by numerical maximization of the likelihood function for discrete-time Markov models. This approach eliminates the need for missed event correction. The algorithm can fit kinetic models of arbitrary complexity, such as uniform or alternating step chemistry, reversible or irreversible kinetics, ATP concentration and mechanical force-dependent rates, etc. The method allows global fitting across stationary and nonstationary experimental conditions, and user-defined a priori constraints on rate constants. The algorithm was tested with simulated data, and implemented in the free QuB software

Maximum Likelihood Estimation of Ion Channel Kinetics from Macroscopic Currents

We describe a maximum likelihood method for direct estimation of rate constants from macroscopic ion channel data for kinetic models of arbitrary size and topology. The number of channels in the preparation, and the mean and standard deviation of the unitary current can be estimated, and a priori constraints can be imposed on rate constants. The method allows for arbitrary stimulation protocols, including stimuli with finite rise time, trains of ligand or voltage steps, and global fitting across different experimental conditions. The initial state occupancies can be optimized from the fit kinetics. Utilizing arbitrary stimulation protocols and using the mean and the variance of the current reduce or eliminate problems of model identifiability (Kienker, 1989). The algorithm is faster than a recent method that uses the full autocovariance matrix (Celentano and Hawkes, 2004), in part due to the analytical calculation of the likelihood gradients. We tested the method with simulated data and with real macroscopic currents from acetylcholine receptors, elicited in response to brief pulses of carbachol. Given appropriate stimulation protocols, our method chose a reasonable model size and topology

A phenomenological approach to the quality index of Al-Si-Mg casting alloys

A simple continuum mechanics model relating the quality index,* Q, to parameters of the deformation curve of alloys A356/357 is presented. The model incorporates the limiting effect of particle cracking on the ductility on a phenomenological base and it is used to predict the behaviour of the quality index as the material is strengthened by either ageing or increased content of Mg. The analysis shows that for every temper there is a matrix strength that maximises the Q-value. For strong alloys the model suggests that peak ageing can reduce Q. This effect is stronger when the ductility of the material is low. The possibility of using the quality index to optimise the combination of the alloy chemical composition and temper is discussed