Properties of <i>Xenopus</i> Kv1.10 channels expressed in HEK293 cells

Fry, Mark; Maue, Robert A.; Moody-Corbett, F

doi:10.1002/neu.20024

Cited by 3 publications

(3 citation statements)

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“…When expressed heterologously, Kv2.2 mRNA alone generates currents typical of type I neurons (Burger and Ribera, 1996), a result that does not support Kv2.2 as a molecular determinant of Kv current in type II neurons. Similar results have been obtained for the other Kv genes expressed in the embryonic Xenopus spinal cord (Ribera and Nguyen, 1993;Lazaroff et al, 1999;Gurantz et al, 2000;Fry et al, 2004 (Fantozzi et al, 2006). Changes in neuronal activity have been shown to induce homeostatic changes allowing neurons to respond appropriately (Turrigiano et al, 1994;Golowasch et al, 1999;Turrigiano, 1999;Davis and Bezprozvanny, 2001).…”

Section: Discussionsupporting

confidence: 76%

Dorsal–Ventral Gradient for Neuronal Plasticity in the Embryonic Spinal Cord

Pineda

Ribera²

2008

J. Neurosci.

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Within the developing Xenopus spinal cord, voltage-gated potassium (Kv) channel genes display different expression patterns, many of which occur in opposing dorsal-ventral gradients. Regional differences in Kv gene expression would predict different patterns of potassium current (I Kv ) regulation. However, during the first 24 h of postmitotic differentiation, all primary spinal neurons undergo a temporally coordinated upregulation of I Kv density that shortens the duration of the action potential. Here, we tested whether spinal neurons demonstrate regional differences in I Kv regulation subsequent to action potential maturation. We show that two types of neurons, I and II, can be identified in culture on the basis of biophysical and pharmacological properties of I Kv and different firing patterns. Chronic increases in extracellular potassium, a signature of high neuronal activity, do not alter excitability properties of either neuron type. However, elevating extracellular potassium acutely after the period of action potential maturation leads to different changes in membrane properties of the two types of neurons. I Kv of type I neurons gains sensitivity to the blocker XE991, whereas type II neurons increase I Kv density and fire fewer action potentials. Moreover, by recording from neurons in vivo, we found that primary spinal neurons can be identified as either type I or type II. Type I neurons predominate in dorsal regions, whereas type II neurons localize to ventral regions. The findings reveal a dorsal-ventral gradient for I Kv regulation and a novel form of neuronal plasticity in spinal cord neurons.

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Section: Discussionsupporting

confidence: 76%

Dorsal–Ventral Gradient for Neuronal Plasticity in the Embryonic Spinal Cord

Pineda

Ribera²

2008

J. Neurosci.

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“…The V 50 values of these "new" VKCs had been determined independently in electrophysiological experiments [34,35] (Salvador-Recala V, Gallin WJ, Abbruzzese J, Ruben PC, Spencer AN: A Kv4 channel cloned from the heart of the tunicate Ciona intestinalis and its modulation by a KChIP subunit. Manuscript submitted).…”

Section: Resultsmentioning

confidence: 99%

“…In the test set, however, we also included VKCs that have V 50 values determined in other cells, such as HEK293 and CHO cells [35,57]. Although it is known that the experimental V 50 values of VKCs can vary if they are measured in different cells, the difference is often not significant, as shown by experimental data of several VKCs that have been characterized in both Xenopus oocytes and other cells.…”

Section: Discussionmentioning

confidence: 99%

Computational identification of residues that modulate voltage sensitivity of voltage-gated potassium channels

Gallin

2005

BMC Struct Biol

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BackgroundStudies of the structure-function relationship in proteins for which no 3D structure is available are often based on inspection of multiple sequence alignments. Many functionally important residues of proteins can be identified because they are conserved during evolution. However, residues that vary can also be critically important if their variation is responsible for diversity of protein function and improved phenotypes. If too few sequences are studied, the support for hypotheses on the role of a given residue will be weak, but analysis of large multiple alignments is too complex for simple inspection. When a large body of sequence and functional data are available for a protein family, mature data mining tools, such as machine learning, can be applied to extract information more easily, sensitively and reliably. We have undertaken such an analysis of voltage-gated potassium channels, a transmembrane protein family whose members play indispensable roles in electrically excitable cells.ResultsWe applied different learning algorithms, combined in various implementations, to obtain a model that predicts the half activation voltage of a voltage-gated potassium channel based on its amino acid sequence. The best result was obtained with a k-nearest neighbor classifier combined with a wrapper algorithm for feature selection, producing a mean absolute error of prediction of 7.0 mV. The predictor was validated by permutation test and evaluation of independent experimental data. Feature selection identified a number of residues that are predicted to be involved in the voltage sensitive conformation changes; these residues are good target candidates for mutagenesis analysis.ConclusionMachine learning analysis can identify new testable hypotheses about the structure/function relationship in the voltage-gated potassium channel family. This approach should be applicable to any protein family if the number of training examples and the sequence diversity of the training set that are necessary for robust prediction are empirically validated. The predictor and datasets can be found at the VKCDB web site [1].

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