Schiz.-linked gene effects on intrinsic single-neuron excitability (Maki-Marttunen et al. 2016)

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Accession:169457
Python scripts for running NEURON simulations that model a layer V pyramidal cell with certain genetic variants implemented. The genes included are obtained from genome-wide association studies of schizophrenia.
Reference:
1 . Mäki-Marttunen T, Halnes G, Devor A, Witoelar A, Bettella F, Djurovic S, Wang Y, Einevoll GT, Andreassen OA, Dale AM (2016) Functional Effects of Schizophrenia-Linked Genetic Variants on Intrinsic Single-Neuron Excitability: A Modeling Study. Biol Psychiatry Cogn Neurosci Neuroimaging 1:49-59 [PubMed]
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:
Cell Type(s): Neocortex L5/6 pyramidal GLU cell;
Channel(s): I Na,p; I Na,t; I L high threshold; I T low threshold; I K; I K,leak; I M; I h; I K,Ca; I A, slow; Ca pump;
Gap Junctions:
Receptor(s):
Gene(s): Nav1.1 SCN1A; Nav1.7 SCN9A; Cav3.3 CACNA1I; Cav1.3 CACNA1D; Cav1.2 CACNA1C; Kv2.1 KCNB1; HCN1;
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Coincidence Detection; Active Dendrites; Detailed Neuronal Models; Schizophrenia;
Implementer(s): Maki-Marttunen, Tuomo [tuomo.maki-marttunen at tut.fi];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; I Na,p; I Na,t; I L high threshold; I T low threshold; I K; I K,leak; I M; I h; I K,Ca; I A, slow; Ca pump;
/
Maki-MarttunenEtAl2015
models
morphologies
readme.txt
Ca_HVA.mod *
Ca_LVAst.mod *
CaDynamics_E2.mod *
epsp.mod *
Ih.mod *
Im.mod *
K_Pst.mod *
K_Tst.mod *
Nap_Et2.mod
NaTa_t.mod
NaTs2_t.mod
SK_E2.mod *
SKv3_1.mod *
collectscalings.py
collectthresholddistalamps.py
drawfig1.py
drawfig2.py
drawfig3.py
drawfig4.py
drawfig5.py
findthresholddistalamps.py
mutation_stuff.py
mytools.py
runcontrols.py
savesynapselocations.py
scalemutations.py
scalings.sav
                            
# mutation_stuff.py
# Tools for implementing the effects of certain genetic variants in the neuron model by Hay et al., 2011.
# 
# Tuomo Maki-Marttunen, Jan 2015
# (CC BY)


#Function getMT: returns the table of variants. Each entry of the list MT is a list corresponding to a specific gene, and each
#entry of MT[i] is a list corresponding to a type of variant in gene i. Each entry of MT[i][j] is a list corresponding to a
#variant j in gene i, and each entry MT[i][j][k] is a pair [A,B], where A is a model parameter, and B tells how much the model
#parameter is changed in the considered variant. If an identical change is applied to many model parameters at once, then A
#is a list of model parameters, and if the change may obtain a range of values, then B is a pair [s1,s2] showing the range
#of possible values for this parameter change.
def getMT():
 #       List of genes
 #         List of mutations
 #           List of groups of variables
 #             Pair (variable list + range)
 #               List of variables
 MT = [] 
 #CACNA1C:
 MT.append([ [ [ ['offma_Ca_HVA', 'offmb_Ca_HVA'], -25.9 ],                       #http://www.ncbi.nlm.nih.gov/pubmed/19265197
               [ ['offha_Ca_HVA', 'offhb_Ca_HVA'], -27.0 ] ],
             [ [ ['offma_Ca_HVA', 'offmb_Ca_HVA'], -37.3 ],                       #http://www.ncbi.nlm.nih.gov/pubmed/19265197
               [ ['offha_Ca_HVA', 'offhb_Ca_HVA'], -30.0 ] ] ])
 #CACNB2:
 MT.append([ [ [ ['offha_Ca_HVA', 'offhb_Ca_HVA'], -5.2 ],                        #http://www.ncbi.nlm.nih.gov/pubmed/19358333
               [ ['sloha_Ca_HVA', 'slohb_Ca_HVA'], 0.69 ] ], 
             [ [ ['tauha_Ca_HVA', 'tauhb_Ca_HVA'], 1.7] ],                        #http://www.ncbi.nlm.nih.gov/pubmed/7723731
             [ [ ['offma_Ca_HVA', 'offmb_Ca_HVA'], [-4.9, 4.9] ],                 #http://www.ncbi.nlm.nih.gov/pubmed/19723630
               [ ['offha_Ca_HVA', 'offhb_Ca_HVA'], [-5.1, 5.1] ],
               [ ['tauma_Ca_HVA', 'taumb_Ca_HVA'], [0.6, 1.68] ],
               [ ['tauha_Ca_HVA', 'tauhb_Ca_HVA'], [0.6, 1.66] ] ] ])
 #CACNA1D:
 MT.append([ [ [ ['offma_Ca_HVA', 'offmb_Ca_HVA'], -10.9 ],                       #http://www.ncbi.nlm.nih.gov/pubmed/21998309 and
               [ ['sloma_Ca_HVA', 'slomb_Ca_HVA'], 0.73 ],                        #http://www.ncbi.nlm.nih.gov/pubmed/21998310
               [ ['offha_Ca_HVA', 'offhb_Ca_HVA'], [-3.0, 3.5] ],                 #(42A)
               [ ['sloha_Ca_HVA', 'slohb_Ca_HVA'], 0.81 ],
               [ ['tauha_Ca_HVA', 'tauhb_Ca_HVA'], 1.25 ] ],
             [ [ ['offma_Ca_HVA', 'offmb_Ca_HVA'], [-10.6, 3.4] ],                #http://www.ncbi.nlm.nih.gov/pubmed/21998309 and
               [ ['sloma_Ca_HVA', 'slomb_Ca_HVA'], [0.8, 1.12] ],                 #http://www.ncbi.nlm.nih.gov/pubmed/21998310
               [ ['offha_Ca_HVA', 'offhb_Ca_HVA'], [-5.3, 1.2] ],                 #(43S)
               [ ['sloha_Ca_HVA', 'slohb_Ca_HVA'], 0.66 ],
               [ ['tauha_Ca_HVA', 'tauhb_Ca_HVA'], 0.72 ] ],
             [ [ ['offma_Ca_HVA', 'offmb_Ca_HVA'], 6.6 ],                         #http://www.ncbi.nlm.nih.gov/pubmed/20951705 and
               [ ['sloma_Ca_HVA', 'slomb_Ca_HVA'], [0.75, 1.19] ],                #http://www.ncbi.nlm.nih.gov/pubmed/21054386
               [ ['tauha_Ca_HVA', 'tauhb_Ca_HVA'], [0.5, 1.12] ] ] ])             #(CaV1.3 KO)
 #CACNA1I:
 MT.append([ [ [ 'offma_Ca_LVAst', 1.3 ],                                         #http://www.ncbi.nlm.nih.gov/pubmed/15254077
               [ 'offha_Ca_LVAst', 1.6 ],
               [ ['taummin_Ca_LVAst', 'taumdiff_Ca_LVAst'], [0.87, 1.45] ],
               [ ['tauhmin_Ca_LVAst', 'tauhdiff_Ca_LVAst'], 0.8 ] ] ])
 #CACNA1S:
 MT.append([ [ [ ['tauma_Ca_HVA', 'taumb_Ca_HVA'], 0.67 ] ],                      #http://www.ncbi.nlm.nih.gov/pubmed/20861472
             [ [ ['offma_Ca_HVA', 'offmb_Ca_HVA'], -30.02 ],                      #http://www.ncbi.nlm.nih.gov/pubmed/19134469
               [ ['sloma_Ca_HVA', 'slomb_Ca_HVA'], 0.62 ],
               [ ['tauma_Ca_HVA', 'taumb_Ca_HVA'], 0.49] ] ])
 #ATP2A:
 MT.append([ [ [ 'gamma_CaDynamics_E2', 0.6 ] ] ])                                #http://www.ncbi.nlm.nih.gov/pubmed/10970890
             
 #ATP2B:
 MT.append([ [ [ 'decay_CaDynamics_E2', 1.97 ] ],                                 #http://www.ncbi.nlm.nih.gov/pubmed/22789621 
             [ [ 'decay_CaDynamics_E2', 1.5 ],                                    #http://www.ncbi.nlm.nih.gov/pubmed/21232211
               [ 'minCai_CaDynamics_E2' , 1.4 ] ],
             [ [ 'decay_CaDynamics_E2', 4.45 ] ] ])                               #http://www.ncbi.nlm.nih.gov/pubmed/17234811
 #NRGN:
 MT.append([ [ [ 'gamma_CaDynamics_E2', 0.4 ] ] ])                                #http://www.ncbi.nlm.nih.gov/pubmed/15564582 (Not relevant in this study)
 #SCN1A:
 MT.append([ [ [ 'offm_NaTa_t', -0.3 ],                                           #http://www.ncbi.nlm.nih.gov/pubmed/18632931
               [ 'offh_NaTa_t', 5 ],
               [ 'slom_NaTa_t', 1.15 ],
               [ 'sloh_NaTa_t', 1.23 ] ],
             [ [ 'offm_NaTa_t', 2.8 ],                                            #http://www.ncbi.nlm.nih.gov/pubmed/18632931
               [ 'offh_NaTa_t', 9.6 ],
               [ 'slom_NaTa_t', 0.984 ],
               [ 'sloh_NaTa_t', 1.042 ] ] ])
 #SCN9A:
 MT.append([ [ [ ['offh_Nap_Et2', 'offha_Nap_Et2', 'offhb_Nap_Et2'], 6.8 ] ],     #http://www.ncbi.nlm.nih.gov/pubmed/22136189
             [ [ ['offh_Nap_Et2', 'offha_Nap_Et2', 'offhb_Nap_Et2'], 3.5 ],       #http://www.ncbi.nlm.nih.gov/pubmed/18945915
               [ 'sloh_Nap_Et2', 0.55 ],
               [ 'offm_NaTa_t', -7.1 ],
               [ 'offh_NaTa_t', 17.0 ],
               [ 'sloh_NaTa_t', 0.69 ] ],
             [ [ 'offm_NaTa_t', -9.1 ],                                           #http://www.ncbi.nlm.nih.gov/pubmed/16392115
               [ 'offh_NaTa_t', 3.1 ] ],
             [ [ 'offm_NaTa_t', -7.6 ],                                           #http://www.ncbi.nlm.nih.gov/pubmed/15958509
               [ 'offh_NaTa_t', 4.3 ] ] ])
 #KCNS3:
 MT.append([ [ [ ['taummin_K_Pst', 'taumdiff1_K_Pst', 'taumdiff2_K_Pst'], 2.0 ],  #http://www.ncbi.nlm.nih.gov/pubmed/10484328
               [ ['tauhmean_K_Pst', 'tauhdiff1_K_Pst', 'tauhdiff2_K_Pst'], 2.5 ],
               [ 'sloh_K_Pst', 0.5 ] ] ])
 #KCNN3:
 MT.append([ [ [ 'offc_SK_E2', 0.86 ],                                            #http://www.ncbi.nlm.nih.gov/pubmed/14978258
               [ 'sloc_SK_E2', 1.24 ] ] ])
 #HCN1:
 MT.append([ [ [ ['offma_Ih', 'offmb_Ih'], -26.5 ],                               #http://www.ncbi.nlm.nih.gov/pubmed/17185333
               [ ['sloma_Ih', 'slomb_Ih'], 0.64 ] ] ])
 #KCNB1:
 MT.append([ [ [ 'offm_K_Pst', 5 ],                                               #http://www.ncbi.nlm.nih.gov/pubmed/21455829 (T203K)
               [ 'offh_K_Pst', 3 ],
               [ 'slom_K_Pst', 1.11 ],
               [ 'sloh_K_Pst', 0.86 ],
               [ ['taummin_K_Pst', 'taumdiff1_K_Pst', 'taumdiff2_K_Pst'], 0.5 ],
               [ ['tauhmean_K_Pst', 'tauhdiff1_K_Pst', 'tauhdiff2_K_Pst'], 0.53 ] ],
             [ [ 'offm_K_Pst', 1 ],                                               #http://www.ncbi.nlm.nih.gov/pubmed/21455829 (T203D)
               [ 'offh_K_Pst', -6 ],
               [ 'slom_K_Pst', 1.22 ],
               [ 'sloh_K_Pst', 1.0 ],
               [ ['taummin_K_Pst', 'taumdiff1_K_Pst', 'taumdiff2_K_Pst'], 0.89 ],
               [ ['tauhmean_K_Pst', 'tauhdiff1_K_Pst', 'tauhdiff2_K_Pst'], 1.13 ] ],
             [ [ 'offm_K_Pst', 6 ],                                               #http://www.ncbi.nlm.nih.gov/pubmed/21455829 (S347K)
               [ 'offh_K_Pst', -8 ],
               [ 'slom_K_Pst', 1.33 ],
               [ 'sloh_K_Pst', 1.0 ],
               [ ['taummin_K_Pst', 'taumdiff1_K_Pst', 'taumdiff2_K_Pst'], 0.5 ],
               [ ['tauhmean_K_Pst', 'tauhdiff1_K_Pst', 'tauhdiff2_K_Pst'], 0.87 ] ],
             [ [ 'offm_K_Pst', -28 ],                                             #http://www.ncbi.nlm.nih.gov/pubmed/21455829 (S347D)
               [ 'offh_K_Pst', -27 ],
               [ 'slom_K_Pst', 1.11 ],
               [ 'sloh_K_Pst', 0.71 ],
               [ ['taummin_K_Pst', 'taumdiff1_K_Pst', 'taumdiff2_K_Pst'], 1.13 ],
               [ ['tauhmean_K_Pst', 'tauhdiff1_K_Pst', 'tauhdiff2_K_Pst'], 2.27 ] ],
             [ [ 'offm_K_Pst', 14 ],                                              #http://www.ncbi.nlm.nih.gov/pubmed/21455829 (T203W)
               [ 'offh_K_Pst', -21 ],
               [ 'slom_K_Pst', 2.0 ],
               [ 'sloh_K_Pst', 1.0 ],
               [ ['taummin_K_Pst', 'taumdiff1_K_Pst', 'taumdiff2_K_Pst'], 0.39 ],
               [ ['tauhmean_K_Pst', 'tauhdiff1_K_Pst', 'tauhdiff2_K_Pst'], 1.2 ] ],
             [ [ 'offm_K_Pst', -13 ],                                             #http://www.ncbi.nlm.nih.gov/pubmed/21455829 (S347W)
               [ 'offh_K_Pst', -13 ],
               [ 'slom_K_Pst', 1.33 ],
               [ 'sloh_K_Pst', 0.71 ],
               [ ['taummin_K_Pst', 'taumdiff1_K_Pst', 'taumdiff2_K_Pst'], 0.95 ],
               [ ['tauhmean_K_Pst', 'tauhdiff1_K_Pst', 'tauhdiff2_K_Pst'], 5.13 ] ] ])
 print "MT loaded successfully:"
 print MT
 return MT

#Function getdefvals: Returns a dictionary containing the default values for each of the model parameters. Maximal conductances
#omitted. Values based on L5PCbiophys3.hoc. Ca dynamics parameters may have different parameters in different parts of the cell.
def getdefvals():
 defVals = {'gamma_CaDynamics_E2': [0.000501, 0.000509], #if two values, the first is for somatic and second for apical segments
           'decay_CaDynamics_E2': [460.0, 122.0],
           'depth_CaDynamics_E2': 0.1,
           'minCai_CaDynamics_E2': 1e-4,
           'offma_Ca_HVA': -27.0,
           'offmb_Ca_HVA': -75.0,
           'offha_Ca_HVA': -13.0,
           'offhb_Ca_HVA': -15.0,
           'sloma_Ca_HVA': 3.8,
           'slomb_Ca_HVA': 17.0,
           'sloha_Ca_HVA': 50.0,
           'slohb_Ca_HVA': 28.0,
           'tauma_Ca_HVA': 1.0/0.055,
           'taumb_Ca_HVA': 1.0/0.94,
           'tauha_Ca_HVA': 1.0/0.000457,
           'tauhb_Ca_HVA': 1.0/0.0065,
           'offma_Ca_LVAst': -40.0,
           'offmt_Ca_LVAst': -35.0,
           'offha_Ca_LVAst': -90.0,
           'offht_Ca_LVAst': -50.0,
           'sloma_Ca_LVAst': 6.0,
           'slomt_Ca_LVAst': 5.0,
           'sloha_Ca_LVAst': 6.4,
           'sloht_Ca_LVAst': 7.0,
           'taummin_Ca_LVAst': 5.0,
           'taumdiff_Ca_LVAst': 20.0,
           'tauhmin_Ca_LVAst': 20.0,
           'tauhdiff_Ca_LVAst': 50.0,
           'ehcn_Ih': -45.0,
           'offma_Ih': -154.9,
           'sloma_Ih': 11.9,
           'tauma_Ih': 1.0/0.00643,
           'offmb_Ih': 0.0,
           'slomb_Ih': 33.1,
           'taumb_Ih': 1.0/0.193,
           'offma_Im': -35.0,
           'sloma_Im': 10.0,
           'tauma_Im': 1.0/3.3e-3,
           'offmb_Im': -35.0,
           'slomb_Im': 10.0,
           'taumb_Im': 1.0/3.3e-3,
           'offm_K_Pst': -11.0,
           'slom_K_Pst': 12.0,
           'offmt_K_Pst': -10.0,
           'slomt_K_Pst': 1.0/0.026,
           'taummin_K_Pst': 1.25,
           'taumdiff1_K_Pst': 175.03,
           'taumdiff2_K_Pst': 13.0,
           'offh_K_Pst': -64.0,
           'sloh_K_Pst': 11.0,
           'offht1_K_Pst': -65.0,
           'offht2_K_Pst': -85.0,
           'sloht_K_Pst': 48.0,
           'tauhmean_K_Pst': 360.0,
           'tauhdiff1_K_Pst': 1010.0,
           'tauhdiff2_K_Pst': 24.0,
           'offm_K_Tst': -10.0,
           'slom_K_Tst': 19.0,
           'offh_K_Tst': -76.0,
           'sloh_K_Tst': 10.0,
           'offmt_K_Tst': -81.0,
           'slomt_K_Tst': 59.0,
           'taummin_K_Tst': 0.34,
           'taumdiff_K_Tst': 0.92,
           'offht_K_Tst': -83.0,
           'sloht_K_Tst': 23.0,
           'tauhmin_K_Tst': 8.0,
           'tauhdiff_K_Tst': 49.0,
           'offm_NaTa_t': -38.0,
           'offh_NaTa_t': -66.0,
           'slom_NaTa_t': 6.0,
           'sloh_NaTa_t': 6.0,
           'tauma_NaTa_t': 1.0/0.182,
           'taumb_NaTa_t': 1.0/0.124,
           'tauha_NaTa_t': 1.0/0.015,
           'tauhb_NaTa_t': 1.0/0.015,
           'offm_Nap_Et2': -52.6,
           'slom_Nap_Et2': 4.6,
           'offma_Nap_Et2': -38.0,
           'offmb_Nap_Et2': -38.0,
           'sloma_Nap_Et2': 6.0,
           'slomb_Nap_Et2': 6.0,
           'tauma_Nap_Et2': 1.0/0.182,
           'taumb_Nap_Et2': 1.0/0.124,
           'taummax_Nap_Et2': 6.0,
           'offh_Nap_Et2': -48.8,
           'sloh_Nap_Et2': 10.0,
           'offha_Nap_Et2': -17.0,
           'offhb_Nap_Et2': -64.4,
           'sloha_Nap_Et2': 4.63,
           'slohb_Nap_Et2': 2.63,
           'tauha_Nap_Et2': 1.0/2.88e-6,
           'tauhb_Nap_Et2': 1.0/6.94e-6,
           'tauhmax_Nap_Et2': 1.0,
           'zTau_SK_E2': 1.0,
           'offc_SK_E2': 0.00043,
           'sloc_SK_E2': 4.8,
           'offma_SKv3_1': 18.7,
           'offmt_SKv3_1': -46.56,
           'sloma_SKv3_1': 9.7,
           'slomt_SKv3_1': 44.14,
           'taummax_SKv3_1': 4.0}          
 print "defVals loaded successfully:"
 print defVals
 return defVals

#Function getgenenames: The names of the genes corresponding to the output of the function getMT.
def getgenenames():
 return ['CACNA1C', 'CACNB2', 'CACNA1D', 'CACNA1I', 'CACNA1S', 'ATP2A2', 'ATP2B2', 'NRGN', 'SCN1A', 'SCN9A', 'KCNS3', 'KCNN3', 'HCN1', 'KCNB1'] 

           

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