The APP in C-terminal domain alters CA1 neuron firing (Pousinha et al 2019)

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Accession:256388
"The amyloid precursor protein (APP) is central to AD pathogenesis and we recently showed that its intracellular domain (AICD) could modify synaptic signal integration. We now hypothezise that AICD modifies neuron firing activity, thus contributing to the disruption of memory processes. Using cellular, electrophysiological and behavioural techniques, we showed that pathological AICD levels weakens CA1 neuron firing activity through a gene transcription-dependent mechanism. Furthermore, increased AICD production in hippocampal neurons modifies oscillatory activity, specifically in the gamma frequency range, and disrupts spatial memory task. Collectively, our data suggest that AICD pathological levels, observed in AD mouse models and in human patients, might contribute to progressive neuron homeostatic failure, driving the shift from normal ageing to AD."
Reference:
1 . Pousinha PA, Mouska X, Bianchi D, Temido-Ferreira M, Rajão-Saraiva J, Gomes R, Fernandez SP, Salgueiro-Pereira AR, Gandin C, Raymond EF, Barik J, Lopes LV, Migliore M, Goutagny R, Bethus I, Marie H (2019) The Amyloid Precursor Protein C-terminal domain alters CA1 neuron firing, modifying hippocampus oscillations and impairing spatial memory encoding Cell Reports, in press
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell;
Channel(s): I Na,t; I A; I K; I M; I h; I L high threshold; I_AHP;
Gap Junctions:
Receptor(s): NMDA;
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Aging/Alzheimer`s; Oscillations; Action Potentials; Memory;
Implementer(s): Bianchi, Daniela [danielabianchi12 -at- gmail.com];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; NMDA; I Na,t; I L high threshold; I A; I K; I M; I h; I_AHP; Glutamate;
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PousinhaMouskaBianchiEtAl2019
readme.txt
ANsyn.mod *
bgka.mod *
burststim2.mod *
cad.mod *
cagk.mod
cal.mod *
calH.mod *
car.mod *
cat.mod *
ccanl.mod *
d3.mod *
gskch.mod *
h.mod *
IA.mod
ichan2.mod *
Ih.mod *
kadist.mod *
kaprox.mod *
Kaxon.mod *
kca.mod *
Kdend.mod *
kdr.mod *
kdrax.mod *
km.mod *
Ksoma.mod *
LcaMig.mod *
my_exp2syn.mod *
na3.mod *
na3dend.mod *
na3notrunk.mod *
Naaxon.mod *
Nadend.mod *
nap.mod *
Nasoma.mod *
nax.mod *
nca.mod *
nmdanet.mod *
regn_stim.mod *
somacar.mod *
STDPE2Syn2.mod *
mosinit.hoc
pyramidal_cell4b.hoc
ranstream.hoc *
ses.ses
stim_cell.hoc *
testcell.hoc
                            
COMMENT
Two state kinetic scheme synapse described by rise time tau1,
and decay time constant tau2. The normalized peak condunductance is 1.
Decay time MUST be greater than rise time.

The solution of A->G->bath with rate constants 1/tau1 and 1/tau2 is
 A = a*exp(-t/tau1) and
 G = a*tau2/(tau2-tau1)*(-exp(-t/tau1) + exp(-t/tau2))
	where tau1 < tau2

If tau2-tau1 -> 0 then we have a alphasynapse.
and if tau1 -> 0 then we have just single exponential decay.

The factor is evaluated in the
initial block such that an event of weight 1 generates a
peak conductance of 1.

Because the solution is a sum of exponentials, the
coupled equations can be solved as a pair of independent equations
by the more efficient cnexp method.

ENDCOMMENT

NEURON {
	POINT_PROCESS MyExp2Syn
	RANGE tau1, tau2, e, i
	NONSPECIFIC_CURRENT i

	RANGE g
	GLOBAL total
}

UNITS {
	(nA) = (nanoamp)
	(mV) = (millivolt)
	(uS) = (microsiemens)
}

PARAMETER {
	tau1=.1 (ms) <1e-9,1e9>
	tau2 = 10 (ms) <1e-9,1e9>
	e=0	(mV)
}

ASSIGNED {
	v (mV)
	i (nA)
	g (uS)
	factor
	total (uS)
}

STATE {
	A (uS)
	B (uS)
}

INITIAL {
	LOCAL tp
	total = 0
	if (tau1/tau2 > .9999) {
		tau1 = .9999*tau2
	}
	A = 0
	B = 0
	tp = (tau1*tau2)/(tau2 - tau1) * log(tau2/tau1)
	factor = -exp(-tp/tau1) + exp(-tp/tau2)
	factor = 1/factor
}

BREAKPOINT {
	SOLVE state METHOD cnexp
	g = B - A
	i = g*(v - e)
}

DERIVATIVE state {
	A' = -A/tau1
	B' = -B/tau2
}

NET_RECEIVE(weight (uS)) {
	state_discontinuity(A, A + weight*factor)
	state_discontinuity(B, B + weight*factor)
	total = total+weight
}

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