Alcohol excites Cerebellar Golgi Cells by inhibiting the Na+/K+ ATPase (Botta et al.2010)

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Accession:127021
Patch-clamp in cerebellar slices and computer modeling show that ethanol excites Golgi cells by inhibiting the Na+/K+ ATPase. In particular, voltage-clamp recordings of Na+/K+ ATPase currents indicated that ethanol partially inhibits this pump and this effect could be mimicked by low concentrations of the Na+/K+ ATPase blocker ouabain. The partial inhibition of Na+/K+ ATPase in a computer model of the Golgi cell reproduced these experimental findings that established a novel mechanism of action of ethanol on neural excitability.
Reference:
1 . Botta P, de Souza FM, Sangrey T, De Schutter E, Valenzuela CF (2010) Alcohol excites cerebellar Golgi cells by inhibiting the Na(+)-K(+) ATPase. Neuropsychopharmacology 35:1984-96 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Electrogenic pump;
Brain Region(s)/Organism: Cerebellum;
Cell Type(s): Cerebellum golgi cell;
Channel(s): Na/K pump;
Gap Junctions:
Receptor(s):
Gene(s): HCN1;
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Detailed Neuronal Models; Markov-type model; Alcohol Use Disorder;
Implementer(s): Simoes-de-Souza, Fabio [fabio.souza at ufabc.edu.br];
Search NeuronDB for information about:  Na/K pump;
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Golgi_cell_NaKATPAse
sessions
README.html
Golgi_BK.mod *
Golgi_Ca_HVA.mod *
Golgi_Ca_LVA.mod *
Golgi_CALC.mod *
Golgi_CALC_ca2.mod *
Golgi_hcn1.mod *
Golgi_hcn2.mod *
Golgi_KA.mod *
Golgi_KM.mod *
Golgi_KV.mod *
Golgi_lkg.mod *
Golgi_Na.mod *
Golgi_NaP.mod *
Golgi_NaR.mod *
Golgi_SK2.mod *
K_conc.mod *
Na_conc.mod *
Pregen.mod *
pump.mod
Synapse.mod *
Channel_dynamics.hoc *
Golgi_ComPanel.hoc *
Golgi_count.txt
Golgi_Pump_Ouabain.ses
Golgi_template.hoc
mosinit.hoc
nerst
Save_data.hoc *
Screenshot20100716_4.02.55PM.png
Start_Golgi_Ouabain.hoc
Synapses.hoc *
utils.hoc *
                            
: $Id: pregen.mod,v 1.3 2000/05/16 11:16:56 hines Exp $
: comments at end

NEURON	{ 
  POINT_PROCESS Golgi_SpikeGenerator
  RANGE y
  RANGE fast_invl, slow_invl, burst_len, start, end,delay
  RANGE noise
}

PARAMETER {
	fast_invl	= 10 (ms)	: time between spikes in a burst (msec)
	slow_invl	= 0 (ms)	: burst period (msec)
: actually, above is interburst period in conformity with original version
: see
	burst_len	= 10		: burst length (# spikes)
	start		= 50 (ms)	: start of first interburst interval
	end		= 1e10 (ms)	: time to stop bursting
	noise		= 0		: amount of randomeaness (0.0 - 1.0)
	delay		= 4
}

ASSIGNED {
	y
	burst
	event (ms)
	burst_off (ms)
	burst_on (ms)
	toff (ms)
	on
}

PROCEDURE seed(x) {
	set_seed(x)
}

INITIAL {
	on = 1
	toff = 1e9
	y = -90
	burst = 0
	event = start - slow_invl
	:
	event_time()
	while (on == 1 && event < 0) {
		event_time()
	}
	if (on == 1) {
		net_send(event, 1)
	}
}	

FUNCTION interval(mean (ms)) (ms) {
	if (mean <= 0.) {
		mean = .01 (ms) : I would worry if it were 0.
		: since mean is a local variable, if the number it is set
		: to is dimensionless, mean will be dimensionless.
	}
	if (noise == 0) {
		interval = mean
	}else{
		interval = (1. - noise)*mean + noise*(mean*exprand(1)+delay) : (delay+noise*mean*exprand(1))
	}
}

PROCEDURE event_time() {
	if (slow_invl == 0 || (burst != 0. && burst_len > 1)) {
		event = event + interval(fast_invl)
		if (event > burst_on + burst_off) {
			burst = 0.
		}
	}else{
		burst = 1.
: if slow_invl from beginning of burst to beginning of burst
:		event = event + interval(slow_invl - (burst_len-1)*fast_invl)
: use following if slow_invl is interburst interval
		event = event + interval(slow_invl)
		burst_on = event
		burst_off = interval((burst_len - 1)*fast_invl)-1e-6
	}
	if (event > end) {
		on = 0
	}
}

NET_RECEIVE (w) {
:printf("Pregen receive t=%g flag=%g\n", t, flag) 
	if (flag == 1 && on == 1) {
		y = 20
		net_event(t)
		event_time()
		net_send(event - t, 1)
		net_send(.1, 2)
	}
	if (flag == 2) {
		y = -90
	}
}

COMMENT
Presynaptic spike generator
---------------------------

This mechanism has been written to be able to use synapses in a single
neuron receiving various types of presynaptic trains.  This is a "fake"
presynaptic compartment containing a fast spike generator.  The trains
of spikes can be either periodic or noisy (Poisson-distributed), and 
either tonic or bursting.

Parameters;
   noise: 	between 0 (no noise-periodic) and 1 (fully noisy)
   fast_invl: 	fast interval, mean time between spikes (ms)
   slow_invl:	slow interval, mean burst silent period (ms), 0=tonic train
   burst_len: 	mean burst length (nb. spikes)

Written by Z. Mainen, modified by A. Destexhe, The Salk Institute

Modified by Michael Hines for use with CVode

Modified by Michael Hines to use logical event style with NET_RECEIVE
ENDCOMMENT


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