Coincident glutamatergic depolarization effects on Cl- dynamics (Lombardi et al, 2021)

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Accession:266823
"... we used compartmental biophysical models of Cl- dynamics simulating either a simple ball-and-stick topology or a reconstructed CA3 neuron. These computational experiments demonstrated that glutamatergic co-stimulation enhances GABA receptor-mediated Cl- influx at low and attenuates or reverses the Cl- efflux at high initial [Cl-]i. The size of glutamatergic influence on GABAergic Cl--fluxes depends on the conductance, decay kinetics, and localization of glutamatergic inputs. Surprisingly, the glutamatergic shift in GABAergic Cl--fluxes is invariant to latencies between GABAergic and glutamatergic inputs over a substantial interval..."
Reference:
1 . Lombardi A, Jedlicka P, Luhmann HJ, Kilb W (2021) Coincident glutamatergic depolarizations enhance GABAA receptor-dependent Cl- influx in mature and suppress Cl- efflux in immature neurons PLOS Comp Bio
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Synapse; Dendrite;
Brain Region(s)/Organism:
Cell Type(s): Hippocampus CA3 pyramidal GLU cell;
Channel(s):
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Short-term Synaptic Plasticity; Synaptic Plasticity; Chloride regulation;
Implementer(s): Jedlicka, Peter [jedlicka at em.uni-frankfurt.de]; Kilb, Werner [wkilb at uni-mainz.de];
Search NeuronDB for information about:  Hippocampus CA3 pyramidal GLU cell; GabaA; AMPA; NMDA; Gaba; Glutamate;
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_For Zip -Neuron-Models_AMPA-GABA
Fig9c-f_Real_Cell_Cl_1GDP_Var-Cl-var-TempCorr
cldif_CA3_NKCC1_HCO3.mod *
gabaA_Cl_HCO3.mod *
vecevent.mod *
Cell1_Cl_HCO3_Pas.hoc
GDP_Cl_All_long.ses
GDP_Cl_All_short.ses *
GDP_Cl_HCO3_All_short.ses *
Random_PSCs_gGABA-0.789-nGABA-100_gAMPA-0.305_Div_Cl_defCorrel.hoc
Random_PSCs_gGABA-0.789-nGABA-534_gAMPA-0.305_Div_Cl_100percCorrel.hoc
Single_GDP_gGABA-0.789-nGABA-100_gAMPA-0.305_Div_Cl_100percCorrel.hoc
Single_GDP_gGABA-0.789-nGABA-100_gAMPA-0.305_Div_Cl_defCorrel.hoc
Single_GDP_gGABA-0.789-nGABA-534_gAMPA-0.305_Div_Cl_100percCorrel.hoc
Single_GDP_gGABA-0.789-nGABA-534_gAMPA-0.305_Div_Cl_normCorrel.hoc
start_Random_PSCs_gGABA-0.789-nGABA-100_gAMPA-0.305_Div_Cl_100percCorrel.hoc
start_Random_PSCs_gGABA-0.789-nGABA-100_gAMPA-0.305_Div_Cl_defCorrel.hoc
start_Single_GDP_gGABA-0.789-nGABA-100_gAMPA-0.305_Div_Cl_100percCorrel.hoc
start_Single_GDP_gGABA-0.789-nGABA-100_gAMPA-0.305_Div_Cl_defCorrel.hoc
start_Single_GDP_gGABA-0.789-nGABA-534_gAMPA-0.305_Div_Cl_100percCorrel.hoc
start_Single_GDP_gGABA-0.789-nGABA-534_gAMPA-0.305_Div_Cl_normCorrel.hoc
                            
//--------------------------------------------------------------------
// Simulation of  a single GDP
//---------------------------------------------------------------------

// ------------Definition of Parameters -------------------------------
// --------------------------------------------------------------------

seed_AMPA = 8  // seed for random function
seed_GABA = seed_AMPA +2 

// Define Cl--Concentration
   Cl_Steps = 6  // Number of Different [Cl-]i  
   objref Cl_List
   Cl_List = new Vector(Cl_Steps)
   Cl_List.x[0] = 5 //mM
   Cl_List.x[1] = 10
   Cl_List.x[2] = 15
   Cl_List.x[3] = 25
   Cl_List.x[4] = 35
   Cl_List.x[5] = 50

// Determination Parameters AMPA ------------------------------------
G_AMPA =  0.000305 * 1
DECAY_AMPA = 11
nampasyn = 100 //Number of AMPA-Synapses  // calculated was 107
aninputs = 1 // number of inputs per synapse
AmpaCtrl_Steps = 2   // 0 = without AMPA, 1 = With AMPA


// Determining Parameters GABA allone ---------------------------
G_GABA = 0.000789   // synaptic weight according to miniature events 
DECAY_GABA = 37
P_GABA = 0.18
ngabasyn = 100
gninputs = 1 // number of inputs per synapse


// Definition of various runtime parameters --------------------------
lenghtoutputvec = 6000  // Number of Lines for output (< 32000 for Excel-Figures)
ndend=56  // Number of dendrites
  
tstop = 5000   // Duration
v_init = -60   // Initial voltage
dt = 0.05       // Step Interval in ms



// ------------Procedures and Functions -------------------------------
// --------------------------------------------------------------------

// Function MakeShort ---------------------------------------//
// Inputs: $1 Objref to Inputvector                          //
//         $2 Objref to Outoutvector                         //
//         lenoutvec  desired lendth of Outputvector         //
//                                                           //
// Reduce Inputvec to Outputvev by averaging n elements      //
// n (reducing factor) = floor(Inputvec.size() / lenoutvec)  //
// ----------------------------------------------------------//

obfunc MakeShort() {local i, n

  n = int($o1.size()/$3)
  $o2.resize($3)
  for i=0, $3-1 {
    $o2.x[i] = $o1.mean(i*n, (i+1)*n-1)
  }
  return $o2
}   //  End of function



// ---------Definition of objects -------------------------------------
// --------------------------------------------------------------------


// Objects for Synapses ---------------------------------------------------------
objref gabasyn[ngabasyn]                            // Definition of synapse objects
objref ampasyn[nampasyn]


// random function for localization of synapses
objref rand_ampa_loc, rand_gaba_loc

// random function for localization of synapses in which dendrite
objref rand_ampa_dend, rand_gaba_dend

// random function for synapses parameters
objref rand_gaba_t, rand_gaba_g
objref rand_ampa_t, rand_ampa_g

// definition of Vectors for Gaba-Stimulation (t_vec = timestamps t_vecr = sorted timestamps, g_vec = rel conductance)
objref gabastim[ngabasyn], gaba_t_vec[ngabasyn], gaba_t_vecr[ngabasyn], synpulsegaba[ngabasyn], gaba_g_vec[ngabasyn]
// definition of Vectors for AMPA-Stimulation
objref ampastim[nampasyn], ampa_t_vec[nampasyn], ampa_t_vecr[nampasyn], synpulseampa[nampasyn], ampa_g_vec[nampasyn]


// Define vectors to link modelled parameter output ---------------------------------
objref timevec, voltvec, clivec[ndend]                                    // vectors linked to parameter-pointers
objref clivec_aver                                                        // vectors for Averagees over all Dendrites
objref shorttimevec, shortvoltvec, shortclivec, spacevec                  // shorter Vectors for output
objref gaba_timestamp, ampa_timestamp

// Matrix for output 0 = time, 1 = Voltage, 2 = average Cli , 3 to 3+ndend, Cli n Dend[i]
objref Outmatrix, TimeStampMatrix

// Define Name of Output-File
strdef OutFileName, OutTimestampFileName

// Define Output File
objref OutFile, OutTimestampFile


// Generate vectors and matrices -------------------------------------
voltvec = new Vector()
timevec = new Vector()
clivec_aver = new Vector()
shortvoltvec = new Vector()
shorttimevec = new Vector()
shortclivec = new Vector()
spacevec = new Vector()
for i=0, ndend-1 {
  clivec[i] = new Vector()
}
Outmatrix = new Matrix()
TimeStampMatrix = new Matrix()
TimeStampMatrix = new Matrix(ngabasyn, Cl_Steps*3)

gaba_timestamp = new Vector(ngabasyn)
ampa_timestamp = new Vector(nampasyn)



// Start of Input generation -------------------------------------------

// Initialize Random Functions -----------
rand_ampa_loc = new Random(seed_AMPA)
rand_gaba_loc = new Random(seed_GABA)
rand_ampa_dend = new Random(seed_AMPA)
rand_gaba_dend = new Random(seed_GABA)
rand_ampa_t = new Random(seed_AMPA)
rand_ampa_g = new Random(seed_AMPA)
rand_gaba_t = new Random(seed_GABA)
rand_gaba_g = new Random(seed_GABA)

//Define properties of random Function
rand_gaba_t.uniform(100, 5000)
rand_gaba_g.normal(1, 0.28) //rel variance of GABA according to results
rand_ampa_t.uniform(100, 5000)
rand_ampa_g.normal(1, 0.26) //rel variance of AMPA according to results

// generate Vectors --- (gniputs, aninputs defines number of inputs per synapse) ------
for i = 0, ngabasyn- 1 {
  gaba_t_vec[i] = new Vector(gninputs)
  gaba_t_vecr[i] = new Vector(gninputs)
  gaba_g_vec[i] = new Vector(gninputs)
}

for i = 0, nampasyn-1 {
   ampa_t_vec[i] = new Vector(aninputs)
   ampa_t_vecr[i] = new Vector(aninputs)
   ampa_g_vec[i] = new Vector(aninputs)
}

  // Distribute GABA synapses -----------------------------------------------------------
  // here only GABA correlated synapses are generated
  for k=0, ngabasyn - 1 {
      pos = rand_gaba_loc.uniform(0,ndend-1)
      pos2 = rand_gaba_dend.uniform (0.001, 0.999)
      dend_0[pos]{
        gabasyn[k] = new gaba(pos2)
        gabasyn[k].tau1 = 0.1
        gabasyn[k].tau2 = DECAY_GABA
        gabasyn[k].P = P_GABA
      }
   }

  // distribute AMPA synapses ------------------------------------------------------------------------
  for k=0, nampasyn-1 {
    pos = rand_ampa_loc.uniform(0,ndend-1)
    pos3 = rand_ampa_dend.uniform (0.001, 0.999)
    dend_0[pos]{
      ampasyn[k] = new Exp2Syn(pos3)
      ampasyn[k].tau1 = 0.1
      ampasyn[k].tau2 = DECAY_AMPA
    }                                              
  }

//-- Simulation starts here -----------------------------------------------------------------
//-------------------------------------------------------------------------------------------

//-- Outer Loop Variation of Cl- --------------------------------------------------

  Cl_Step = 0

  while (Cl_Step < Cl_Steps){

    // 1. define Cl- concentration ----------------------------------------------
  
    forsec all {
      cli0_cldif_CA3_NKCC1_HCO3 = Cl_List.x[Cl_Step]
      cli_Start_cldif_CA3_NKCC1_HCO3 = Cl_List.x[Cl_Step]
      cli_cldif_CA3_NKCC1_HCO3 = Cl_List.x[Cl_Step]
    }


   // 2a. Generate timestamps/conductances for AMPA and correlated GABA synapses --------------------------------------
    for f=0, nampasyn-1 {
      for i=0, aninputs-1 {
          t = rand_ampa_t.repick()
          g = rand_ampa_g.repick()
          ampa_t_vec[f].x[i]=t
          ampa_g_vec[f].x[i]=abs(G_AMPA*g)
          ampa_timestamp.x[f]=t 
          t = rand_gaba_t.repick()              // pick t from GABA random process anyway, so the remaining GABA events are identical
          g = rand_gaba_g.repick()
          gaba_t_vec[f].x[i]=ampa_t_vec[f].x[i] // assign the f-th GABA event to exactly the time of an AMPA event
          gaba_g_vec[f].x[i]=abs(G_GABA*g)
          gaba_timestamp.x[f]=ampa_timestamp.x[f]         
        }
      }

    // 2b. Generate timestamps/conductances for remaining GABA synapses --------------------------------------
    for f=nampasyn, ngabasyn-1 {
      for i=0, gninputs-1 {
          t = rand_gaba_t.repick()
          g = rand_gaba_g.repick()
          gaba_t_vec[f].x[i]=t
          gaba_g_vec[f].x[i]= abs(G_GABA*g)
          gaba_timestamp.x[f] = t
      }
    }


   // 2c. sort the vectors ------------------------------------
    for f=0, ngabasyn - 1 {
      gaba_t_vecr[f] = gaba_t_vec[f].sort()
    }

    for f=0, nampasyn-1 {                     
      ampa_t_vecr[f] = ampa_t_vec[f].sort()
    }   
  
  // Save the vectors to analyze the temporal correlation
     TimeStampMatrix.x[0][Cl_Step*3] = Cl_List.x[Cl_Step]
     TimeStampMatrix.setcol(Cl_Step*3+1,  gaba_timestamp)
     TimeStampMatrix.setcol(Cl_Step*3+2,  ampa_timestamp)


  // 3. generate Vecstim-vectors from the sorted timestamp-vectors -------------------------------
      for i=0, ngabasyn -1 {
        gabastim[i] = new VecStim()
        gabastim[i].play(gaba_t_vecr[i])    // GABA stimulator
      }                                               

      for i=0, nampasyn-1{
        ampastim[i] = new VecStim()
       ampastim[i].play(ampa_t_vecr[i])    // AMPA stimulator
      }    

    // Run the simulation for each stimulation pattern once with and once without AMPA co-stimulation
    for AmpaCtrl_Step = 0, AmpaCtrl_Steps-1 {

      // 4. Play the Vecstim objects to the synapses ---------------------------------------------
          for i=0, ngabasyn-1 {
            synpulsegaba[i] = new NetCon(gabastim[i], gabasyn[i], 0, 0, gaba_g_vec[i].x[0])
          }                                                  // GABA NetCon

        for i=0, nampasyn-1 {
          synpulseampa[i] = new NetCon(ampastim[i], ampasyn[i], 0, 0, ampa_g_vec[i].x[0]*AmpaCtrl_Step)
        }    
                                             // Ampa NetCon
 
        // 5. Link Objects to Output-Vectors -----------------------------------
        timevec.record(&t)   // Time vector
        voltvec.record(&v(.5)) // Volt vector in soma
        for i=0, ndend-1 {
            clivec[i].record(&dend_0[i].cli(0.5))
        }

        printf("100-Correl: Sequence %g of %g; seed-%g; [Cl-]i = %g; gAMPA = %g; ",(AmpaCtrl_Steps*Cl_Step+AmpaCtrl_Step+1),(Cl_Steps*AmpaCtrl_Steps),seed_AMPA,Cl_List.x[Cl_Step],G_AMPA*1000*AmpaCtrl_Step)
      
        // 6. Run Simulation --------------------------------------------------------
        run()
   
       // 8. Put Data in Output Vector ------------------------------------------------------  
   
 
       MakeShort(timevec, shorttimevec, lenghtoutputvec)
       Outmatrix.resize(shorttimevec.size()+1, Cl_Steps*AmpaCtrl_Steps*3+1)
       Outmatrix.setcol(0, shorttimevec)
        
       spacevec.resize(shorttimevec.size()+1) //spavevec caries information about the properties during a loop)
       spacevec.fill(0) 
       spacevec.x[0] = Cl_List.x[Cl_Step]
       spacevec.x[1] = G_AMPA*1000*AmpaCtrl_Step
       spacevec.x[2] = 777
       ActCol = (Cl_Step*AmpaCtrl_Steps+AmpaCtrl_Step)*3
       Outmatrix.setcol(ActCol+1, spacevec)

       MakeShort(voltvec, shortvoltvec, lenghtoutputvec)
       Outmatrix.setcol(ActCol+2, shortvoltvec)

       // Calculate average [Cli] in dendrites -------
       clivec_aver.mul(0) // empty vector
       for i=0, ndend-1 {
          clivec_aver.resize(clivec[i].size())
          clivec_aver.add(clivec[i])
       } 
       clivec_aver.div(ndend)
       printf(", max [Cl-]i %g, min [Cl-]i %g \n", clivec_aver.max, clivec_aver.min)
       MakeShort(clivec_aver, shortclivec, lenghtoutputvec)
       Outmatrix.setcol(ActCol+3, shortclivec)
       
     } // End of with/without AMPA loop

     // Goto next Cl- Concentration
     Cl_Step+=1
  }  
  // End  outer loop


  // Save the Data --------------------------------------------------------------------

   OutTimestampFile = new File()
   sprint(OutTimestampFileName, "TimeStamps_RandomPsc_gGABA-0.789-nGABA-534_gAMPA-0.305_Div_Cl_100pCorrel_seed-%g.asc", seed_AMPA)
   OutTimestampFile.wopen(OutTimestampFileName)
   TimeStampMatrix.fprint(OutTimestampFile, "\t%.12g") 
   OutTimestampFile.close
   // first close the file for the timestamp data
   OutFile = new File()
   sprint(OutFileName, "Result_RandomPsc_gGABA-0.789-nGABA-534_gAMPA-0.305_Div_Cl_100pCorrel_seed-%g.asc", seed_AMPA)
   OutFile.wopen(OutFileName) 
   Outmatrix.fprint(OutFile, "\t%.12g")     
   OutFile.close