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, Goutagny R, Bethus I, Lopes LV, Migliore M, Marie H (2019) The Amyloid Precursor Protein C-Terminal Domain Alters CA1 Neuron Firing, Modifying Hippocampus Oscillations and Impairing Spatial Memory Encoding. Cell Rep 29:317-331.e5 [PubMed]
Citations  Citation Browser
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
                            
: $Id: netstim.mod 1887 2007-11-19 12:34:00Z hines $
: comments at end
: Modified from NetStim so that spikes are Gaussian distributed around
: regular spike times (BPG 14-1-09)
: Spikes outside regular interval are moved to just inside the interval
: (this will distort the distribution, so noise level should be chosen
: so that this does not happen very often!!)


NEURON	{ 
  ARTIFICIAL_CELL RegnStim
  RANGE interval, number, start
  RANGE noise
  POINTER donotuse
}

PARAMETER {
	interval	= 10 (ms) <1e-9,1e9>: time between spikes (msec)
	number	= 10 <0,1e9>	: number of spikes (independent of noise)
	start		= 50 (ms)	: start of first spike
	noise		= 0 <0,1>	: amount of randomness (0.0 - 1.0)
}

ASSIGNED {
	event (ms)
	on
      er
	ispike
	tspike	: regular spike time
	donotuse
}

PROCEDURE seed(x) {
	set_seed(x)
}

INITIAL {
	on = 0 : off
	tspike = start
	ispike = 0
	if (noise < 0) {
		noise = 0
	}
	if (noise > 1) {
		noise = 1
	}
	if (start >= 0 && number > 0) {
		on = 1
		: randomize the first spike 
            er=erand()
            while (er<0){
                  er=erand()}

		event = start + noise*interval*er
             :printf("event %f\n",event)
            : but not earlier than 0
		if (event < 0) {
			event = 0
		}
		net_send(event, 3)
       
	}
}	

PROCEDURE init_sequence(t(ms)) {
	if (number > 0) {
		on = 1
		event = 0
		ispike = 0
	}
}

FUNCTION invl(mean (ms)) (ms) {
	if (mean <= 0.) {
		mean = .01 (ms) : I would worry if it were 0.
	}
	if (noise == 0) {
		invl = mean
	}else{
:		invl = (1. - noise)*mean + noise*mean*erand()
		invl = tspike + mean + noise*mean*erand() - t
		if (invl <= 0) {
			invl = .01 (ms)	: reset to small interval
		}
:		if (t+invl >= tspike+mean) {
:			invl = tspike + mean - t - .01
:		}
	}
	tspike = tspike + mean
    :  printf(" tspike %f\t invl %f\t t %f  \n",tspike, invl,t)
}
VERBATIM
#ifndef NRN_VERSION_GTEQ_8_2_0
double nrn_random_pick(void* r);
void* nrn_random_arg(int argpos);
#define RANDCAST
#else
#define RANDCAST (Rand*)
#endif
ENDVERBATIM

FUNCTION erand() {
VERBATIM
	if (_p_donotuse) {
		/*
		:Supports separate independent but reproducible streams for
		: each instance. However, the corresponding hoc Random
		: distribution MUST be set to Random.normal(0, 1) (BPG)
		*/
		_lerand = nrn_random_pick(RANDCAST _p_donotuse);
	}else{
ENDVERBATIM
		: the old standby. Cannot use if reproducible parallel sim
		: independent of nhost or which host this instance is on
		: is desired, since each instance on this cpu draws from
		: the same stream
		erand = normrand(0, 1)
VERBATIM
	}
ENDVERBATIM
}

PROCEDURE noiseFromRandom() {
VERBATIM
 {
	void** pv = (void**)(&_p_donotuse);
	if (ifarg(1)) {
		*pv = nrn_random_arg(1);
	}else{
		*pv = (void*)0;
	}
 }
ENDVERBATIM
}

PROCEDURE next_invl() {
	if (number > 0) {
		event = invl(interval)
	}
	if (ispike >= number) {
		on = 0
	}
}

NET_RECEIVE (w) {
	if (flag == 0) { : external event
		if (w > 0 && on == 0) { : turn on spike sequence
			: but not if a netsend is on the queue
			init_sequence(t)
			: randomize the first spike so on average it occurs at
			: noise*interval (most likely interval is always 0)
			next_invl()
			event = event - interval*(1. - noise)
			net_send(event, 1)
		}else if (w < 0) { : turn off spiking definitively
			on = 0
		}
	}
	if (flag == 3) { : from INITIAL
		if (on == 1) { : but ignore if turned off by external event
			init_sequence(t)
			net_send(0, 1)
		}
	}
	if (flag == 1 && on == 1) {
		ispike = ispike + 1
		net_event(t)
		next_invl()
  		if (on == 1) {
			net_send(event, 1)
		}
	}

}

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 spike generator.  The trains
of spikes can be either periodic or noisy (Poisson-distributed)

Parameters;
   noise: 	between 0 (no noise-periodic) and 1 (fully noisy)
   interval: 	mean time between spikes (ms)
   number: 	number of spikes (independent of noise)

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

Modified by Michael Hines for use with CVode
The intrinsic bursting parameters have been removed since
generators can stimulate other generators to create complicated bursting
patterns with independent statistics (see below)

Modified by Michael Hines to use logical event style with NET_RECEIVE
This stimulator can also be triggered by an input event.
If the stimulator is in the on==0 state (no net_send events on queue)
 and receives a positive weight
event, then the stimulator changes to the on=1 state and goes through
its entire spike sequence before changing to the on=0 state. During
that time it ignores any positive weight events. If, in an on!=0 state,
the stimulator receives a negative weight event, the stimulator will
change to the on==0 state. In the on==0 state, it will ignore any ariving
net_send events. A change to the on==1 state immediately fires the first spike of
its sequence.

ENDCOMMENT