International Journal o f Molecular Sciences Article Interactions between Membrane Resistance, GABA-A Receptor Properties, Bicarbonate Dynamics and Cl−-Transport Shape Activity-Dependent Changes of Intracellular Cl− Concentration Aniello Lombardi 1, Peter Jedlicka 2,3,4, Heiko J. Luhmann 1 and Werner Kilb 1,* 1 Institute of Physiology, University Medical Center Mainz, Johannes Gutenberg University, Duesbergweg 6, 55128 Mainz, Germany; alombard@uni-mainz.de (A.L.); luhmann@uni-mainz.de (H.J.L.) 2 ICAR3R - Interdisciplinary Centre for 3Rs in Animal Research, Faculty of Medicine, Justus-Liebig-University, Rudolf-Buchheim-Str. 6, 35392 Giessen, Germany; Peter.Jedlicka@informatik.med.uni-giessen.de 3 Institute of Clinical Neuroanatomy, Neuroscience Center, Goethe University, 60590 Frankfurt am Main, Germany 4 Frankfurt Institute for Advanced Studies, 60438 Frankfurt am Main, Germany * Correspondence: wkilb@uni-mainz.de; Tel.: +49-211-3926-101  Received: 13 February 2019; Accepted: 18 March 2019; Published: 20 March 2019  Abstract: The effects of ionotropic γ-aminobutyric acid receptor (GABA-A, GABAA) activation depends critically on the Cl−-gradient across neuronal membranes. Previous studies demonstrated that the intracellular Cl−-concentration ([Cl−]i) is not stable but shows a considerable amount of activity-dependent plasticity. To characterize how membrane properties and different molecules that are directly or indirectly involved in GABAergic synaptic transmission affect GABA-induced [Cl−]i changes, we performed compartmental modeling in the NEURON environment. These simulations demonstrate that GABA-induced [Cl−]i changes decrease at higher membrane resistance, revealing a sigmoidal dependency between both parameters. Increase in GABAergic conductivity enhances [Cl−]i with a logarithmic dependency, while increasing the decay time of GABAA receptors leads to a nearly linear enhancement of the [Cl−]i changes. Implementing physiological levels of HCO −3 -conductivity to GABAA receptors enhances the [Cl−]i changes over a wide range of [Cl−]i, but this effect depends on the stability of the HCO −3 gradient and the intracellular pH. Finally, these simulations show that pure diffusional Cl−-elimination from dendrites is slow and that a high activity of Cl−-transport is required to improve the spatiotemporal restriction of GABA-induced [Cl−]i changes. In summary, these simulations revealed a complex interplay between several key factors that influence GABA-induced [Cl]i changes. The results suggest that some of these factors, including high resting [Cl−]i, high input resistance, slow decay time of GABAA receptors and dynamic HCO −3 gradient, are specifically adapted in early postnatal neurons to facilitate limited activity-dependent [Cl−]i decreases. Keywords: development; hippocampus; CA3; Cl−-homeostasis; giant depolarizing potentials; ionic plasticity; computational neuroscience; Na+-K+-Cl−-Cotransporter, Isoform 1 (NKCC1); mouse 1. Introduction GABA (γ-aminobutyric acid) is the main inhibitory neurotransmitter in the mature brain and acts via ionotropic GABAA/GABAC receptors and via metabotropic GABAB receptors [1]. In the adult brain, GABA mediates its inhibitory effect by hyperpolarizing the membrane and by shunting excitatory inputs. GABAA receptors are ligand-gated anion-channels with a high permeability for Cl− Int. J. Mol. Sci. 2019, 20, 1416; doi:10.3390/ijms20061416 www.mdpi.com/journal/ijms Int. J. Mol. Sci. 2019, 20, 1416 2 of 22 ions and a considerable additional permeability for HCO −3 ions [1]. In the mature brain the activity of a K+-Cl−-Cotransporter (KCC, mainly in its isoform KCC2) establishes a low intracellular Cl− concentration ([Cl−]i) [2,3], which accounts for a Cl− influx and thus a membrane hyperpolarization upon activation of GABAA receptors [1]. Due to this Cl−-flux, activation of GABAA receptors can influence [Cl−]i on a time scale of seconds to minutes [4–9], a process termed “ionic plasticity” [3,10,11]. The magnitude of activity-dependent [Cl−]i-transients depends on the Cl− influx, dendritic volume and morphology, as well as on the capacity of Cl− extrusion systems [12–17]. In addition, the membrane potential and the HCO −3 permeability of GABAA receptors (PHCO3 ) contribute to the size of [Cl −]i changes [6,18–20]. Therefore recent concepts of inhibition considered neuronal [Cl−]i as a state- and compartment-dependent parameter of individual cells [14,20]. Detectable activity-dependent [Cl−]i changes can occur in the adult nervous system under massive GABAergic stimulation [12,21]. However, already small alterations in [Cl−]i or in the dynamics of the [Cl−]i homeostasis critically influence information processing in neurons [20,22]. As the proper function in the adult nervous system relies on adequate inhibition [1,23,24], these activity-dependent [Cl−]i changes play important roles in physiological and pathophysiological processes [10,11,17]. In the immature nervous system GABA typically induces depolarizing membrane responses [25–30]. These depolarizing GABAergic responses are caused by an elevated intracellular Cl− concentration ([Cl−]i), which is maintained by a Cl− accumulation via the isoform 1 of the Na+-dependent K+-Cl−-cotransporter (NKCC1) [3,29,31,32]. Recent studies suggest that at least in the postnatal neocortex, these depolarizing GABAergic responses mainly mediate inhibition [30,33], likely by increasing membrane shunting [1,34]. Several results indicate that depolarizing GABAergic neurotransmission is of specific relevance for immature spontaneous activity and for the maturation of the central nervous system [35–37]. Giant depolarizing potentials (GDPs) are a well-described network phenomenon in the immature hippocampus and the neocortex that represent spontaneous GABA-dependent activity [25,38,39]. In line with the high [Cl−]i and the depolarizing responses, activation of GABAA receptors causes a decline in [Cl−]i of immature neurons [29,40–42]. This attenuation of the [Cl−] gradient reduces possible excitatory effects of GABA [29,42,43] and may serve to limit GABAergic excitation and/or to stabilize recurrent network events [10,13,40]. A recent study demonstrated that GDPs, which are associated with a high amount of GABAergic activity [25,44], induce long-lasting [Cl−]i transients and influence the steady-state [Cl−]i of CA3 pyramidal neurons in hippocampal slices from early postnatal mice neurons [45], making it a suitable model for ionic plasticity in the immature brain. However, while the existence of ionic plasticity is well accepted and several factors influencing activity-dependent [Cl−]i transients have been described, the role of biophysical membrane characteristics, molecular properties of GABAA-receptors or Cl−-transporters, and the stability of HCO −3 homeostasis on ionic plasticity has not yet been systematically investigated. Here we used a detailed biophysical compartmental model in the NEURON environment to demonstrate how cellular and molecular properties such as input resistance, pH, HCO −3 -selectivity, kinetics of GABAA receptors, the kinetics of NKCC1 mediated Cl− transmembrane transport, and the activity of carbonic anhydrases influence activity-dependent [Cl−]i transients. While most modeling is performed in isolated dendritic compartments, here we also replicate the well-described GDP-induced [Cl−]i transients of immature hippocampal CA3 neurons [45]. 2. Results In order to study the question how various membrane parameters and the properties of different molecules involved in GABAergic transmission influence activity-dependent [Cl−]i transients, we first computed the GABA-induced [Cl−]i changes in an isolated dendrite, which allows a better mechanistic understanding of the underlying processes. Subsequently we also used a model of a reconstructed CA3 pyramidal neuron [45] to compare the results of our computational models with the GDP-dependent [Cl−]i transients recorded in immature hippocampal CA3 neurons [45]. For the latter Int. J. Mol. Sci. 2019, 20, 1416 3 of 22 Int. J. Mol. Sci. 2018, 19, x 3 of 22 model, we implemented experimentally derived parameters of GABAergic synapses and GDP-activity epxrpoevriimdeedntbayllyL odmerbivaerd ipeatralm. [e4t5e]r.s of GABAergic synapses and GDP-activity provided by Lombardi et al. [45]. 2.1. Influence of Membrane Conductance 2.1. Influence of Membrane Conductance First, we analyzed the influence of the membrane conductance on the [Cl−]i changes induced by a sinFgirlset, GwAeB aAnearlygzicedin tphuet ininfluaennicseo loaft ethded menedmrbitrea.neIn cothnidsumctoandceel, otnh etheex p[Cerl⁻i]mi cehnatnalgleys dinetdeurmceidn ebdy a scinognlde uGcAtaBnAcerguinc dineprluyti ning ans iinsogllaetesdp doenntadnrietoe.u Isn tGhAis BmAoedregli, cthpe oesxtpseyrnimapetnictalrleys dpeotnersmesin(egdG cAoBnAd)uwctansce uinmdpelrelmyinengt seidnginlea snpiosnotlanteedoudse nGdArBitAe eqrguiicp poesdtswynitahpptiacs rseivspeocnosnedsu (cgtGaAnBcAe) sw(agsp aims) pvlaermyienngtebde tiwn eaenn is1o0l−at6ed dSe/ncdmri2tea enqdu0ip.1pSe/dc wmi2th. Tphaesssievpe acsosnivdeucctoanndcuesc t(agnpacse) svcaoryrrinesgp boentdwteoenin 1p0u⁻6t Sr/ecsmis2t aandce 0s.1(R SI/ncpm2ut). Tbhetewsee penascsaiv. e c6o7n0duMcΩtanacneds c0o.6r7rekspΩo,nrdes tpoe icntpivuetl yre, swishtaennctehse (yRIwnpeutr)e biemtwpeleemn cean.t e6d70i nMaΩr eacnodn 0s.t6r7u cktΩed, rCesAp3ecptiyvrealmy, iwdahlen thneeyu rwone.reT hime rpelseumltesntoefdt hiins eax preecriomnsetnrut cdteemd oCnAst3r apteydratmhaidt aulp noenusrtoimn. uTlahteio nreosuf latss ionfg ltehiGs AeBxpAeerrigmicent dienmpuotn(sgtrated =th0a.t7 u89ponnS ,sτti=m3u7lamtiso,nP of a si=ng0,le[ CGl−A]BA=e3r0gmic Min)pnuot t(ognGAlyBAt h=e 0d.7e8p9o lnaSr,i zτa t=i o3n7, mbust, aPlHsoCOt3h =e 0, GABA HCO3 i [CGlA⁻]Bi =A 3-i0n dmuMce)d n[oCt lo−n]lyt rtahnes ideenptodleaprieznadtieodn,o bnutth aelspoa stshiev eGcAoBnAdu-icntdanucceesd. [ACld⁻]eit tarialendsiaennat ldyespisernedveeda loedn athe i psatsrsoinvge ,csoingdmuocitdaanlcedse. pAe nddeteanilceydb aentawlyeseins rRevealeadn ad sptreoankg,[ Csilg−m] ocihdaanl dgeepse(nFdigency between RInput and peak Input i ure 1a) or depolarization [C(Fli⁻g]iu crhea1nbg)esu p(Foinguares i1nag)l eorG dAepBoAlasrtiizmatuioluns (.FTighuisree f1fbe)c tuopfogn a soinngtlhe eGGAABABA st-iimnduuluces.d T[hCisl− e]ffecpas i trat nosfi genpats son thwea sGcAauBsAe-dinbdyutcheed fa[Cctl⁻t]hi attraantslioewntesr wg as tchaeuGseAd BbAye tpas rghiec fcaucrtr ethnatst iantd luocwedera gspuasb sthtaen tGiaAl BdAepeorglaicri zcautriroenn,ts inwdhuiccehda att esunbusattaendtitahl edeelpeocltarroimzaotitoivne, wfohrciceho anttCenl−uaitoends t(hDe Fele)ctdruomrinogtivGeA foBrAces otinm Cull⁻a itoionns ((FDiFgCul)r edu1cri)n. g GAAtBaAlo swtimgulaotifon10 (−F7igSu/rcem 12c)(.c oArtr eas plo Cl own dgipas of 10 ⁻7 S/cm2ng to a R of (ccao.rr4eGspΩoninditnhge troe cao nRsintpruut cotfe dcan. e4u GroΩn )itnh ethe rGecAonBsAtr pas eurcgtiecdd neepuorloanri)z tahteio GnAreBaAcheregdicE depolarization input (Figure 1c, s oreliadchliende sE)C. lT (hFiegruefroe r1ec, ,D sFolida lpinperso)x. iTmhaetreedfo0rea, nDdFCl approximated 0 a−nd no persistent Cl⁻ flu Cxl es occurred. In contrast, at a gpas of 0.018C lS/cm2 (corresponding to no persistent Cl fluxes occurred. In contrast, at a g of 0.018 S/cm2 (corresponding to R of ca. Rinput of ca. 41 MΩ) Em remained negative to ECl, thusp aesnabling permanent Cl⁻ fluxes (Figurein 1pcu,t dashed 41 MΩ) Em remained negative to ECl, thus enabling permanent Cl− fluxes (Figure 1c, dashed lines).lines). Figure 1. Passive membrane conductance (g ) influences GABA-induced [Cl−] transients. At low Figure 1. Passive membrane conductance (g paspas) influences GABA-induced [Cl⁻]i tri ansients. At low gpas vgalpuaes sv, aGluAeBs,AGeArgBicA ceurrgriecnctus rirnednutscein sdtruocnegs tdreopnogladreizpaotliarization, attenuating the driving force for Cl − ions and thereby − − on, attenuating the driving force for Cl⁻ ions and thereby decreasing Cdle⁻c frleuaxseisn. g(aC) Tl hefl [uCxle⁻]s .i tr(aan)sTiehnets[ iC−n ldu]ci etdra bnys iae nsitnsgilned GuAceBdAberygiac ssitnimgluelaGtiAonB A(ge =r g0i.c789 nsSt,i mτ =u 3la7t miosn, (Pg = 0 =.7 08,9 [CnSl⁻,]τ == 3037 ms, PHCO3 i mM) shHoCwO a3 s=tr0o,n[gC lde]pie=nd30enmcyM o)ns ghowpas. Tahsretreo tnygpidcaelp ternacdeesn acrye odnisgpplaays.ed aTs hinrseeet.t y(bp)i cTahlet rGaAceBsAa-riendduiscpelda ymeedmabsrainnsee dt.ep(bo)laTrhizeatGioAnB aAls-oi nsdhuowcesd am siegmmboriadnale ddeeppeonldaerinzcayt ioonn galsopas. (c) Esfhfeocwt osfa gsigmoidal − pas on Em (bdleapcken lidneensc),y Eon gpas. (c) Effect of gpas on Em (black lineCl (red lines) and [Cl⁻]i (blue lines) in an isos)l,aEteCdl (dreenddlrinitees u) sainndg [cColns]tai nt G(AblBuAe elirngeics )ciunrraenntiss o(lgatedGABA d=e 0n.1d rµitSe).u Nsiontge cthoants atat nlot wG AgBA vearlguiecsc (u0r.r1e nnSts/c(mgpas G2,A sBoAlid= l0in.1eµs)S E). Nm aoptpertohxaimt aattes 2 Elo,w wghpilaes vata lhuiegsh( g0.1 n(S18/ cmmS/,csmo2l,i ddalisnheesd) Elimneasp) pEro xstiamyast es E , while at high g 2 Cl pas m beloCwl ECl. Accordinglyp a[sC(l1⁻8] mS/ci showsm on, dlya sah semdall trlainnessie)nEtm chsatanygse baetl loow − w EgCl., Awchciolerd ai nstgelayd[yC dl e]cilisnheowpas in s[Conl⁻l]y a smai occurs lal tt rhaingshi egnt .c hange at low gpas pas, while a steady decline in [Cl−]i occurs at high gpas. Next we simulated how gpas influences [Cl⁻]i in a r−econstructed neuron (Figure 2a,b), which receives complNexe xGt AwBeAesrimgicu laintepdutsh otwhat gtpyaps icinalfllyu eonccceusr [dCul ri]ni gi nGDa Pr eaccotnivsittryu c[t4e5d] (nFeiugurorne 2(Fc,idg)u. reFo2r at,bh)e,se ewxpheircihmreencetsi vwese cinoimtiapllleyx eGquAiBpApeedrg tihcei ndpeuntdsrtihtea twtiythp i1c0a1ll yGoAcBcuAredrguirci nsygnGapDsPesa (cgti v= i0ty.7[8495 n] S(F, iτg =u r3e72 mc,sd; )a. ll vFaoluretsh efsroemex pLeormimbaerndtsi wet eailn. i[t4ia5l]l)y, seeqtu Pipped toth eHCO3 0 daenndd uristeedw ainth i1n0it1iaGl A[CBlA⁻]e rtog ic30s ymni Map.s eEsac(gh =of0 .t7h89 nS,−ese 101 GτA=B3A7emrgsic; saylnl avpasleuse ws afsr oramndLoommlyb darisdtiribeut taedl. w[4i5th])in, tsheet dPeHnCdOr3itetso of0 thaen rdecuosnesdtruacntedin niteiualro[nC. lTh]ei ttiome p3o0inmtsM fo.r Ethaech stoimf uthlaetsieon1 0o1f eGvAerBy Asyenrgaipcses yfnolalpowsess aw naosrmraanl ddoismtrliybudtiiostnr i(bµu t=e 6d00wmitsh, iσn =t h9e00d mensd).r Tithesese voaflutehse wreerceo dnesrtirvuecdte fdromne uinr-ovnit.ro Texhpeertiimmeenptso ainntds rfeosermthbele sthtiem duislatrtiibountioofn eovf eGrAy BsAyneragpics einfpoulltos wdusraing an GorDmPa [l4d5]i s(tFriigbuurteio 2nc)(.µ In= o6r0d0emr st,oσ re=d9u0c0e mthse) .coTmhepsleexvitayl uoefs thwee arenadlyersiivs eadndfr otom minim-vicit rthoee pxproecreimduernetss of and resemble the distribution of GABAergic inputs during a GDP [45] (Figure 2c). In order to Inrte. dJ. uMcoel. Stchi.e 20c1o8m, 19p, lxe; xdiotiy: of the analysis and to mimic the procedures of [wCwl − w.]mi depsit.icmoma/tjoiuornnaul/isjmeds by Lombardi et al. [45] (which estimated [Cl−]i change3s from changes in ERev determined by focal GABA Int. J. Mol. Sci. 2019, 20, 1416 4 of 22 Int. J. Mol. Sci. 2018, 19, x 4 of 22 app[lCicla⁻]ti ioesntimwaittihoinn uthseedd beyn dLroimticbacrodmi peta ratl.m [e45n]t )(wwheicuhs eesftoimr aatleldf u[rCtlh⁻]ei rcahnanaglyess efsrotmhe cahvaenrgaegs ein[ CElR−ev] i of all ddeentedrrmitiense.d by focal GABA application within the dendritic compartment) we use for all further analyses the average [Cl⁻]i of all dendrites. FiguFirgeu2re. 2.P Paassssiivvee meemmbrbarnaen ceoncdouncdtauncctea (ngpcaes) i(ngfpluase)nciensfl GuAeBnAce-isndGuAceBdA [C-iln⁻]di turacnesdien[Cts li−n ]ai rtercaonsstireuncttsedi n a recoCnAs3tr upcytreadmiCdAal 3npeyurroanm. iSdiamlilnaer utroo nth. eS ismimilualrattioontsh eins imtheu liastoiloantesdi ndethnderiisteo la(Ftiegdurde en1)d, rGitAeB(FAiegrugirce 1), GAdBeApeorlagriiczadtieopno dlaurriiznagt iao GnDdPu aripnpgroaacGhDesP EaCpl patr oloawch gepsas EvCalluaest, ltohweregby mvinailmueizsi,ntgh ethree bdyrivmining ifmoriczei nfor pas g the Cl⁻ fluxes. (a) Immu−nofluorescence image of a biocytin labeled CA3 pyramidal neuron. (b) Reconstruction driving force for Cl fluxes. (a) Immunofluorescence image of a biocytin labeled CA3 pyramidal of this CA3 neuron as instrumented for NEURON simulation, with the colors representing the [Cl⁻]i during neuarno nex.e(mbp) lRareyc GonDsPt.r (uc)c tTiyopnicoafl Eth itsraCceA o3f an GeuDrPo rnecaosrdinedst irnu am reeanl tCeAd3f opryrNamEiUdaRl OneNursoinm (bulalactki otrnac, ew) aitnhm d the coloar ssimreupresen − lated Emti tnrgacteh oef t[hCel re]ciodnsutrruinctgeda nneeuxreomn upploanry stGimDuPla.t(ioc)n Twyipthi cGaDl PE-mdetrrivaecde poafraamGeDtePrs r(erecdo rtrdaecde).i n a real(dC)A R3eppreysreanmtaitdivael [nCel⁻u]i rtoranns(ibelnatcsk dutrraincge )a aGnDdPa dsisipmlauyleadt efodr E4 marbtritarcaeryo dfetnhderirtecs.o Nnoster uthcete adsynechuronnousp on stimounslaet iofn iwndiitvhidGuDal P[C-dl⁻e]ir itvraendsipenatrsa manedt etrhsat( r[eCdl⁻]tir atrcaen)s.ie(ndt)s Raerep rceosmenpotasetidv eof[ Csyl−na]pi tirca nCsl⁻i einftlsuxd uarnidn g a GDdPifdfuisspiolna yfreodmf oadr j4acaernbt ietlreamryendtse.n (ed)r Tithees .avNeoratgeet hdeenadsryitnicc h[Crlo⁻]ni oduepseonndsse otno tfhien tdoitvali dcounadlu[cCtal−nc]ei t(rgatont) soife nts andththe astim[Cull−at]edt racenlls.i ePnletsasaer encootem tphaots ead coeflls ythnaat prteicseCmlb−leisn flthuex paansdsivdei fcfuonsidounctfarnocme oafd jaanc einmtmelaetumrei e nts. (e) Thhipepoacvaemrapgael ndeeunrodnrsit (irce[dC sly−m] bdole: pReInnpudt s= o9n01t hMeΩt)o stahlowcosn odnulyc taa nmcaerg(ignal )[Colf⁻]tih deecsriemasuel,a wtehdilec einll .cePllles ase equipped with a mature gpas (cyain symbol: RInput = 189 MΩ, green symbol: R totInput = 41 MΩ) larger GPD-induced note[Ctlh⁻]a ttraancseielnl ttsh oactcruers. e(fm) Ebflfeesctti hofe gpatot sosni vtheec poenadk udcetpaonlcareizoaftiaonn dimurminga tau rGeDhPi.p Spyomcba− o mlsp aarle nmeaurrkoend sas( red syminbdoilc:atRedIn ipnu (te)=. (9g)0 R1eMlatΩio)nsshhipo wbestwoenelny GaAmBAaerrggiinc adlri[vCinlg ]foi rdcee c(DreFas)e a,nwd GhiDleCl P-iinndcueclelds [eCqlu⁻]i ptrpanesdiewntsit. h ai matTuhree cgropsases(c myaanrk svyamlubeso ld:eRteIrnmpuinte=d 1ex8p9erMimΩen, tgarlleye inn sryeaml bCoAl3: pRyInrapmutid=al4 n1eMuroΩn)s. laTrhgee croGlorPeDd -ciyncdleusc ed [Cl−d]isi ptrlaynss tiheen t[sCol⁻c]ic cuhra.n(gf)esE cffoemctpuotfegdt ofot ro tnheth tehrpee agkivdenep RoInlpaurt ivzaltuioesn ads uinrdinicgataedG iDn P(.eS).y Nmobteo tlhsaatr feomr tahrek ed as iinmdmicaatuterde RinInpu(te o)n. ly( gn)egRliegliabtlieo GnsPhDi-pinbduetcwede e[Cnl⁻G]i cAhBanAgersg airce dgreinveirnatgedf o(arc, eb (aDndF Cc lm) aondidfieGd DanPd-i nusdeudc ed [Cl−w]iithtr paenrsmieisnstiso.nT frhoemc r[o45s]s)e. s mark values determined experimentally in real CA3 pyramidal neurons. The colored cycles displays the [Cl−] changes computed for the three given R values as indicated Using this model, we investigatedi how different gpas between 10-6 S/cm2 a−nd I n0p.1u tS/cm2 affect the GDP- in (e). Note that for the immature R only negligible GPD-induced [Cl ] changes are generated induced [Cl⁻]i transients. This simulatiIonnp udtemonstrated that also in a complex dei ndritic compartment gpas cr(iat,icbalalny dincflmueondciefide dthaen admuosuendt wofi t[hCpl⁻e] r cmhiasnsgioens (fFroigmur[e4 52]i e)).. Also, under these conditions the GABAergic dUepsoilnagriztahtiisonm doudreinl,gw ae GinDvPe satpigparoteadchhedo wECdl iaftf elroewn tggpas (Fbigeutrwe e2efn), 1w0h−i6chS /mcimni2maiznedd 0D.1FCSl /acnmd 2thaef fect remaining Cl⁻ fluxes−. One particular result of this comput paatsional study was that the GDP-induced [Cl⁻]i the tGraDnsPie-innt damucoeudnt[sC tol le]si st rthaanns i1e nmtMs. iTn hai rsescoimnsutrluacttieodn CdAe3m pon−yra smtriadtaeld netuhraotna elqsouipinpeadc woimthp tlheex pdasesnivder itic commpeamrtbmraennet cgopnadsuccrtaitniccea ldleyteirnmfliuneedn ceexdpetrhime eanmtaolluy nint othfe[sCe lneu]ircohnsa n(rgede ssy(Fmibgoulsr ein2 Fei)g. uArels 2oe,–ug)n, dwehricthh ese condisi ltoiownesr tthhaen tGheA eBxApeerrimgiecntdaellpy odleaterirzmaitnieodn [Cdlu⁻]ri icnhganageGs oDf P10.a3p ±p 3r.3o amcMhe (dn =E 4C) lina at rleoawl CgAp3a spy(rFaimguidrael 2f), whincehumronin aimt ciozmedpaDraFbClel caonnditihoensr e[4m5]a. iTnoin fgurCthle−r sflpuecxiefys .thOe nineflpuaenrtciec uofl agrpars eosnu tlht eo Gf tDhPi-sincdoumcepdu [tCalt⁻i]oi nal study was that the GDP-induced [Cl−]i transient amounts to less than 1 mM in a reconstructed CA3 pyraInmt. Ji. dMaoll. Sncei. u20r1o8,n 19e, xq; udoiip: ped with the passive membrane conductancewwdwe.tmedrpmi.cionme/djouernxapl/iejmrism entally in th ese neurons (red symbols in Figure 2e–g), w 4h ich is lower than the experimentally determined [Cl−]i changes of 10.3 ± 3.3 mM (n = 4) in a real CA3 pyramidal neuron at comparable conditions [45]. To further specify the influence of gpas on the GDP-induced [Cl−]i transients, we simulated the peak dendritic [Cl−]i change for different initial [Cl−]i at three different gpas. For this purpose we used values of 0.049 mS/cm2 (corresponding to a RInput of 901 MΩ, typical for immature hippocampal neurons [45]), 0.28 mS/cm2 (189 MΩ, adult neuron in whole-cell patch-clamp configuration [46]), and 1.8 mS/cm2 (41 MΩ, adult neuron with sharp electrode [47]). These simulations demonstrated Int. J. Mol. Sci. 2019, 20, 1416 5 of 22 that, if mature properties of gpas were implemented in the simulated neuron, the GDP-induced [Cl−]i changes were roughly comparable to the values observed in real CA3 pyramidal neurons (Figure 2g), while at gpas typical for immature CA3 pyramidal neurons only marginal GDP-induced [Cl−]i changes occurred. In order to adapt the simulation of GDP-induced responses to the physiological properties of CA3 pyramidal neurons we incorporated an inward rectification in the background conductance (Supplementary Figure S1a,b). In addition, we had to increase the number of GABAergic synaptic inputs (nGABA) to compensate the influence of massive space clamp problems on the experimental determination of this parameter (Supplementary Figure S1c–f). For all further simulations in the reconstructed neurons we used the inward rectifying background conductance and implemented 302, 395, and 523 GABAergic synapses for PHCO3 values of 0.0, 0.18, and 0.44, respectively. However, even with the inward rectifying conductance and 302 synaptic inputs the GDP-induced [Cl−]i changes were smaller than observed under in-vitro conditions (Supplementary Figure S1e). 2.2. Influence of GABA Receptor Conductivity and Kinetics Next we analyzed the influence of the GABAergic conductance (g −GABA) on [Cl ]i transients. Initial experiments in an isolated dendrite showed that initially the [Cl−]i transient was localized underneath the synapse, and within 3 s a diffusional equilibration throughout the dendrite occurred (Supplementary Figure S2a,b). Therefore, we estimated the total amount of GABA-evoked [Cl−]i changes by averaging the [Cl−]i over all nodes of the dendrite 3 s after the GABAergic stimulus. To analyze the relation between total gGABA and the [Cl−]i changes, we first systematically increase gGABA from 0.789 nS to 78.9 nS (Figure 3a). These simulations demonstrated that the GABA-evoked [Cl−]i changes rose with increasing gGABA, but did not depend linearly on gGABA (Figure 3b, black line). This nonlinear effect was due to the larger membrane depolarization upon stronger GABAergic stimulation, which reduced DFCl under this condition (data not shown). In an additional set of simulations, we enhanced the level of GABAergic stimulation by increasing the number of GABAergic synapses (nGABA) from 1 to 100, with gGABA of 0.789 nS for each synapse. The synapses were for each nGABA evenly distributed across the isolated dendrite. These simulations revealed that this distributed stimulation led to a reduced relative [Cl−]i decrease at higher nGABA (Figure 3b, red line), as compared to the previous simulation paradigm (Figure 3b, black line). This observation is most probably due to the fact that with distributed synapses Em reaches more depolarized values close to ECl (−56.9 mV at 1 × 78.9 nS vs. −40.7 mV at 100 × 0.789 nS, data not shown). To investigate whether a similar dependency between the amount of GABAergic inputs and [Cl−]i could also be observed during a simulated GDP in a CA3 pyramidal neuron we increased gGABA from 0.789 nS (Figure 3c, blue line) to 7.89 nS (red line) at each of the 302 synapses used to simulate a GDP. This 10× increase in gGABA augmented the maximal GDP-induced [Cl−]i decrease from 4.3 mM to 6.8 mM (Figure 3d). This surprisingly small effect was due to the fact that the increased gGABA also reduced the average DFCl from −8.6 mV to −5 mV (Figure 3d). When a similar increase in the amount of GABAergic stimulation was implemented by a 10× increase in nGABA (from 301 to 3010) a slightly larger maximal [Cl−]i decrease by 7.1 mM was observed (Figure 3c,d, green line/symbols). This result indicates that the GDP-induced [Cl−]i changes were close to saturation values when realistic values for nGABA, gGABA and RInput were implemented in a simulated CA3 pyramidal neuron. Int. J. Mol. Sci. 2019, 20, 1416 6 of 22 Int. J. Mol. Sci. 2018, 19, x 6 of 22 Int. J. Mol. Sci. 2018, 19, x 6 of 22 FFigiguurree 33.. IInnfflluueenncec eofo fthteh eGAGBAABeArgeircg ciconcdouncdtauncctea n(gcGeAB(Ag)G oAnB AG)AoBnAG-inAdBuAce-din [dCul⁻c]ei dtra[nCsli−en]itst. r(aan) sTieimntes . (ac)oTuirmsee ocf oauvresreagoef [aCvle⁻]ria igne a[nC ils−o]lai tienda dneinsdorlaittee dupdoenn sdinrigteleu spyonnapstiinc gslteimsyunlaatipotnic usstiinmgu glGaAtiBoA nbeutswinegeng 3G.A94B5A bneStw aenedn 339.9445 5nSn.S (ba)n Adv3e9r.a4g5en [SC.l⁻(]bi )inA avne risaogleat[eCdl −de]inidnriaten sitsiomlautleadted eant dar siitnegsltei msyunlaptesed watitah sgiGnAgBlAe bseytnwaepesne Fwig0iu.t7hr8e9g 3n.S Influence of the GABAergic conductance (gGABA) on GABA-induceGA B(rAedb etrtawce)e nan0d. 77889.9n nSS ((rbeldactkr atcraec)ea).n Ndo7t8e .9thne Sno(bnl-alicnkeatr adceep)e. nNdoentecyth bedet nw[oCenle⁻-n]lii n[tCreala⁻n]ris dciehenaptnesg.n e(dsa e)a nTcdiym e cobugeGrtwAsBeAe o.e Ifnn a [avCnel ra−adg]deci t[hiCoanl⁻a]gil eisnse taa onnfd issigmoluatlaetdio .dendrite upon single synaptic stimulation using gGABA between 3.9i GABA nIns, athne atodtdail tGioAnBaAl seergt iocf csuirmreunlta wtioasn vs,arthieed tboyta ilnGcrAeaBsiAnge rtghiec ncumrrbeenrt 45 nwS( naanGsAdvB Aa3)r9 oi.e4f d5e vnbeSyn.l i(ynb d)c irAsetvarisebirunatggeedt h [sCeinln⁻g]uli eim ns ybannear ips(sonelsa (twed dGABAith) eognGfdAeBrvAit e=en 0sl.ty7i8md9ui nsltSar)ti efbrduo matet d1a tssoii nn1gg00llee ( rsseyydnn atarpapscseee )ws. N(itwoht iegt hGthAgaBAt ubetween GAnBdAer= 0.07t.8h798e s9neS n cS(or)nefdroi ttmiroanc1se,) t osamn1d0a l0l7e8(rr. 9e[ dCnlSt⁻r ](ai bccleha)ca.knN gterosat ceoe)tc.hc uNarto. ute(nc )dt heGer DnthPoen-is-neldincueocaenrdd diateivpoeenrnsad,geesm n[cCayll l⁻be]ier taw[nCedle− nE] m[Cc hcl⁻ha]ani ncghgeeassn ogicnecs u aar n. d i g(GcrA)eBcAGo. DnInsPt ar-uninc atdeduddc CietiAdo3na apvlye srreaatmg oeifd [saCilm ln−ue]ulartaoinno dnusnE, dtherec cthoantnatrlg oGels AcoiBnnAdaeitrrigoencicos cn(uτs rt=rr eu3n7ct tm ewdsa, CsP vHACa3Or3i pe=di m y 0 rb,a gymG iAniBdcAar =el a0ns.e7inu8g9r onthnSe, u nnGuAdmBeArb er (ncGo=A n3Bt0Ar2)o ,o lbfcl oeuvne edtnriatlicyoe dn) iassn(tdri b=uup3to7endm e ssni,nhPagnlec esdy ns=taipm0s,ueglsa (twioint hb= yg0 Ge.Ai7tB8hA9e =rn 0iSn.,7cn8re9a nsiSn)g f =rtoh3me0 c2 o1, ntbodl uu1ec0t0tar n(arcceeed )( gtarGnaAdcBAeu )=.p N7o.8no9te enn tShh, aantG uABnAd er τ HCO3 GABA GABA ncedthset=is m3e0 u2cl,o arnteidoi nttirobancyse,)e iostrmh etahrlelie nnrc ur[meCabls⁻e]irin ogcfh tsahynengaceposs neodsc u(cgucGtraA. BnA(c c=e) 0(Gg.7D89P -niSn,d= nuG7cA.e8BdA9 =na Sv30,e2rn0a,g ge re[eCn=l ⁻t]3ri a0ac2en,)d.r e(dEd)m D cehGABA GABA trac peae)ngdoersnt chiyne a recboentwstereunc tDedFC Cl aAn3d pthyer aGmDiPd-ainl dnueucerdo n[C uln⁻]di terra ncosinetnrtos obtained with different stimulation conditions. number of synapses (gGABA = 0.789 nS, nGABA = 3 l0 c2o0n, dgriteieonnst r(aτc =e )3. 7( dm) sD, ePpHeCnO3d =e n0c, ygGbAeBtAw =e e0n.78D9F nS,a nnGdABA = t3h0e2G, bDluPe- itnradcuec) eadnd[C ulp−o]nt ernanhsain Cl ecnetds ostbimtauinlaetdiown ibthy deiitfhfeerre inntcrsetiamsiunlga tthioen coconndduicttiaonncse. (gGABA = 7.89 nS, nGABA = 3I0n2 a, dreddi ttiroanc,e w) oer stihme unliuamtedbe hr oowf s cyhnaanpgseess (ignG AthBAe =d 0e.c7a8y9 kniSn, entiGcAsB Ao f= G30A2B0,A gAr ereenc etpratocer)-.m (ded) iDateepde ncduernrecyn ts (τGbAIeBnAtw) aiendefndlu DietiFnoCcnle a ,tnhwde et[hCesl i⁻Gm]iD turPal-anintsedieduncthesdo ( wS[Culpc⁻]phi laternamngesenisetnaitrnsy o tFbhitgeauindreeed c2 acw,ydit)hk. iSdnyifesftteeircmesnatot sifcti GmvaAurliBaattAiioonn c oroefn cτdeGipAtiBtoAon brs-e.m tweedeina t1e0d cumrsr eanntds (1000 ms) fionrfl au esinncgelet hseyn[Caplse (g A −] trGaAnBAs i=e n0.t7s8(9S nuSp)p ilne mane instoalarytedF idgeunrderSit2ec r,dev).eaSlyesdt ethaτGABA i ma t ttihcev aavreiaratigoen [ClI⁻n]i sahdodwiteiodn a, nweea rslyim liunleaatre dde hpoenwd ecnhcayn gones τ GinAB tAh (eF idgeucraey 4 ak,i bnleatcikcs l ionfe )G. IAf gGABA was increased by a factor ofofτ G20A B(gA bet w= e15e.n781 0nSms and 1000 ms for a single synapse (gGABA = 0.7 B89AnA Sr)ecinepatnori-smoleadteiadtedde ncudrrrietents (τreGABA) inf GluABeAvealed thantcteh tehaev [eCrl⁻ )] it htrea navs−ieernagtse ([SCulp⁻]pi cleomnceennttarrayti oFnig sutirlel 2shc,odw).e Sdy as tnemeaarly lineaage [Cl ] showed a ne tic varia r tdioenp eonfd τeGnAcByA boent wτGeAeBnA 10 m (Figure 4a, red line). Since these iresponses suggaersltyedli na esatrrodngep inefnludeennccey oof nτ τGAGBAA oBnA t(hFei gGuAreBA4a-i,nbdluaccekdl iCnle⁻) . Ifs gand 100w0a ms sin fcorre aas seidngbley saynfaacptsoer (ogfGA2B0A (=g 0.789 =nS1) 5i.n7 8ann Sis)otlhateedav deernadgreit[eC rle−v]eacloendc tehnattr athtieo navsetrilalge [Cfshl l⁻u]G xsAehsBo,A wweed a las on evaarrliyed l iτnGeAaBrA of all GABAergic sGyAnBaApses that were implemented on thei reconstructed CA3 po iwed a nearly linear dep ednedpeenndcyenocny on τGAB(AF i(gFuigruer4ea 4,ar,e bdlalicnke )li.nSei)n. cIef gthGABA wyramidal neurons. These simulations revτeGaAleBdA that an increase in τGABA indeed iensce re assp ionncsreesasseudg bgyes ate fdacator osft r2o0n (ggGiAnBflAu = n15c.e78o fnS) the aovnetrhaegeG [ACBl⁻A]i -cionndcuecnetrdaCtiol−n flstuilxl essh,owweeadl sao nveaarrileyd lin reeaasre dd ethpee nGdDePn-ciyn dounc eτd G [Cl⁻]i changes (FiguτreG A4bB)A. The maximal GDP-induced decline in [Cl⁻]i increases τfGroAmBA 1.o3f malMl G toA B4.A3 emrgMic ABA (Figure 4a, red line). Since these responses suggested a strong influence of τGABA on the GABA-induced Cl⁻ syandap 1s0e.4s mthMat fwore τrGeAiBmA opfl 3e.m7 menst,e 3d7 omns tahned r3e7c0o mn st, rruecspteedctiCvAely3 (pFyigruamre i4dca).l neurons. These simulations fluxes, we also varied τGABA of all GABAergic synapses that were implemented on the reconstructed CA3 revealed that an increase in τGABA indeed increased the GDP-induced [Cl−] changes (Figure 4b).pyramidal neurons. These simulations reve−aled that an increase in τGABA indeed inc ireased the GDP-induced [CThl⁻e] mi chaaxnimgeasl (GFDigPu-rien d4bu)c.e Tdhdee mclianxeiminal[ CGlD]Pi -iinncdrueaceseds dfreocmlin1e. 3inm [MCl⁻t]o i4n.i c3rmeaMsesa nfrdom10 .14.3m mMMfo troτ 4G.3A BmAM aonfd3 1.70.m4 ms,M37 fmors τand 370 ms, respectively (Figure 4c).GABA of 3.7 ms, 37 ms and 370 ms, respectively (Figure 4c). Figure 4. Influence of the decay time constant of GABA receptors (τGABA) on GABA-induced [Cl⁻]i transients. (a) Relationship between average [Cl⁻]i and τGABA at gGABA of 0.789 nS (black trace) or 15.78 nS (red trace) upon a single synaptic stimulation (PHCO3 = 0, [Cl⁻]i = 30 mM) in an isolated dendrite. (b) GDP-induced average [Cl⁻]i and Em changes (nGABA = 302, gGABA = 0.789 nS, PHCO3 = 0) using τGABA of 37 ms (red trace) and Fig3u70r em 4s. I(nblfuluee tnraccee o) fi nth ae rdeecocanyst triumcteed CA3 pyramidal neuron. (c) Relationship between DFCl and the GDP-Figure 4. Influence of the decay tim coencsotannstt aonf tGoAf BGAA rBeAcepretcoerps t(oτGrAsB(Aτ) on G)AoBnAG-inAdBuAce-idn d[Culc⁻]ei dtra[Cnsl−ie]nts. (a)i nRdeulacetido n[Cshl⁻i]pi t rbaentswieenetns oabvteained w GABA i transients. (a) Relatio shiprbaegtew [C itlh⁻] d aifnferent τGABA een iavedr aτgGeAB[AC al−t ]g of 3.7 mofs 0(g.7r8e9e nn)S, 3 (7b mlasc k(b tlrue) and 370 i GaAnBAd τGABA at gGABA of 0a.c7e8) 9onr S15(.b7 m l8 s a n (Sre (dr)e. ck tracde )troarce) 2.3u. 1pC5oo.nn78 tarin bsSuint(igroelned ostfyr tnahca − eep H)tiucC pOsot3inm⁻ Caulosanitndiogunlce t(asPnyHcnCeOa o3pf =tGi c0A,s Bt[iACml ⁻Ru]iel ac=et ip3ot0no rm(sP MH)C Oin =an0 i,s[oClalte]di =de3n0dmritMe. )(bin) aGnDiPso-ilnduced 3 ated avdeernadgrei t[eC. l(⁻b]i) aGnDd PE-min cdhuacnegdesa v(nerGaAgBAe =[C 3l0−2],i gaGnAdBAE =m 0c.7h8a9n gneSs, (PnHGCAO3B =A 0=) 3u0s2in, ggG τAGBAABA =of0 .3778 9mnsS (,rPedH CtrOace=) 0a)nd 3 37uI0sn imn gas l(τlb lprevious experiments, we simulated GABAA GAueB Atroacfe3)7 inm as r(erceodntsrtarucec)teadn CdA3730 pmyrsam(bilduael tnreaucero) nin. m(ace)r dReiceaoltanetdsiot rnursechstiepdo bnCestAews3 eepunyn rDdaeFmrC li dathnaled n teshiuemr GopnDli.fPi-ed conins(cidd)ueRcreeadltai t[oiConln ⁻t]shih tairpta nGbseAitewBnAtese Aon rbDetacFienpetdaon rwds iaCl tthhree d GliifgDfeaPrne-dnin-tdg τauGtAceeBdAd oC[fCl 3⁻l −.c7h] matnrsa n(ngesrlisee.en nHt)s,o o3wb7e tmaviens re(,bd GluwAei)Bt haAndAdi f rf3ee7cr0ee npmttsoτ ri (Grse aAdreBA). oafnion 3.7 ms (green), 37 ms (blue) and 370 ms (red). 2.3I.n tC. Jo. nMtorli. bSucit. i2o0n1 8o, f1 9t,h xe; dHoCi: O3⁻ Conductance of GABA Receptors www.mdpi.com/journal/ijms 2.3. Contribution of the HCO −3 Conductance of GABA6 Receptors In all previous experiments, we simulated GABAA mediated responses under the simplified considIenraatlilopnr tehvaito GusAeBxApAe rriemceepnttosr,sw aree sliimgaunlda-tgedateGdA CBl⁻A cAhamnnedelisa.t Hedowreesvpeorn, sGeAs BuAnAd ererctehpetosrism aprlei fiaendion consideration that GABAA receptors are ligand-gated Cl− channels. However, GABAA receptors are Inatn. Ji. oMnolc. Shcai.n 2n01e8l,s 19w, xi;t dhoai: considerable HCO −3 permeability [1]. The relativwewHwC.mOd3p−i.c-opme/rjomurenabl/ijlmitsy of 6 Int. J. Mol. Sci. 2019, 20, 1416 7 of 22 GIAnt.B J.A Mol. rSecic. e20p1t8o, 1r9s, xA ( PHCO3 ) ranges between 0.18 (determined in spinal cord neurons [48 7] )ofa 2n2 d 0.44 (determined in adult hippocampal neurons [49]), although also higher values have been suggested [1]. channels with a considerable HCO3⁻ permeability [1]. The relative H−CO3⁻-permeability of GABAA Trheecreepftoorres, w(Pe next simHCO3) rangeus lbaetetwdeheonw 0.P1H8 C(dOe3 taefrfmecintsedG AinB sApeinrgali ccEormd annedur[oCnls ][i4r8e])s paonnds 0e.s4u4 p(doentesrtmiminueladt iionn oafdaulsti hnigplpeoscay − mnappasl eneiunroanns [i4s9o]l)a, taeldthoduegnhd arlistoe .higAhderd vitaioluneso hfaaveH beCeOn 3sugcgoesntdedu c[1ta].n TcheerteofoGreA, wBAe neregxti c cusirmreunlatsteidn dhouwce PaHCdOe3 pafofelacrtsiz GinAgBsAheirfgt iicn Etmh eanpde [aCkl⁻d]ie rpeosplaorniszeast iuopnosni nstdimucueladtiboyn GofA a BsAinegrleg iscynstaipmseu liant aionn (Sisuoplaptleedm deenntdarriyteF. iAgdudreitiSo3na o,bf )a. HSiCncOe3t⁻h ciosnaddudctiatinocnea tlod GeApoBlAareirzgaicti counrraefnfetsc tiendduthcee aD dFeCpl,otlahreizGinAg BsAhieftr ginic [Cthl−e ]pi ecahka ndgeepsolwareizraetiaolnsos iindfluceendc ebdy bGyAPBHACeOrg3 i.c Ustnimdeurlaptiaornti c(uSulaprpcleomnednittaioryn sF, iig.eu.rew 3hae,bn).E Smincreo sthsiesd EaCdl diutiroinagl sdyenpaoplatriiczaretisopno anfsfecst,etdh ethGe ADBFACl,e trhgei cGaActBivAaetrigoinc [lCeal⁻d]i tcohabnipghesa swicer[eC al−ls]oi cinhfalunegnecsed(F bigyu PreHC5O3a. ). FUorndfuert phaerrtiacunlaalry csoisndwiteiopnlso, tit.e.d wfhoerns Eumc hcrboispsehda sEiCcl dreusrpinogn ssyensatphteic mreaspxiomnsaelsa, tnhde GmAinBiAmeargl i[cC alc−ti]vi autipoonn GlAeaBdA toe rbgipichsatsiimc [uClal⁻t]ii ocnha(neg.ge.s F(Figiguurree 55ba,).b Fluore fluinrtehse)r. Aanasylysstiesm waet ipcloatnteadly fsoirs soufcthh beipefhfaescitc orfesGpAonBsAese trhgeic inmpauxtismoanl atnhde m[Cinl−im]acl h[Caln⁻]gi eusporenv GeAalBeAdetrhgaict stthime u[Clalt−io]n c(he.agn. Fgiegsuwre e5rbe, bshluieft leidnetso).w Aa srydsstemmoartiec oaunatwlysaii i rsd floufx tehse aetffheicgt hofe rGPABAerg(Fici ginupruet5s bo)n, itnhed i[cCalt⁻i]ni cghathnagtesw rietvheianlecdre tahsaitn tgheP [Cl⁻]i changes were shift−ed towards HCO HCO a substantial [Cl ]i increase isminodreu ocuedtwbayrdG fAluBxAes 3 e argt ihcigsthiemr uPlHaCtOi3o (nF.igure 5b), indicating that with incre 3asing PHCO3 a substantial [Cl⁻]i increase is induced by GABAergic stimulation. FFigiguurree 5.. InIfnlufleunecne coef tohfe rtehleatirveel aHtiCvOe 3H⁻ cCoOndu−3 ctcivointyd (uPcHtCiOv3i)t yon( GPHACBOA3-)inodnucGedA mBAem-ibnrdanuec eddepmolaermizbartiaonne daenpdo l[aCrli⁻z]ia trioansaienndts[ Cinl −an]i itsroalnasteiedn dtsenindrainte.i sAolcatitveidtyd-deenpdernitdee.nAt cdteivcliitnye- dine p[eHnCdOen3⁻t]id reecdluincesi nG[AHBCAOer3g−ic] i reddepuocelasrGizAatBioAn earngdic adfefepcotsla [rCizl⁻a]ti icohnaanngdesa. f(fae)c Ttsim[Cel −co]iucrhsea nogf eEsm. (aan)dT i[mCle⁻]ci ocuharsnegoesf E(Δm[Canl⁻d]i) [uCpl−on]i ac hsaingles (∆sy[nCalp−t]iic) sutipmounlaatisoin g(gleGAsByA n=a 7p.8ti9c nsSt,i mτ u= l3a7t imons, (PgHGCAO3B A= 0=.178.,8 [9HnCSO,3τ⁻]=i =3 174m.1 sm, MPH) CaOt in=iti0a.l1 [8C,l[⁻H]i oCf O30−3 mM3 ]i = 1(4d.1armk bMlu)ea),t 4i0n imtiMal ([mCild−d]ileo) fa3n0d m50M mM(d (alrigkhbt lbuluee),).4 N0omteM tha(mt aitd idntleer)maenddia5te0 [mClM⁻]i ,(al isgyhntapbtliuce s)t.imNuolutes tchanat aitnidnutceer mbeipdhiaasteic [C[Cl−l⁻]]i ,raesspyonnasepst.i c(bs)t imDeupluensdceanncyi nbdeutwceeebni pΔh[aCsli⁻c]i [Canl−d ] [Crel⁻s]pi ounpsoens .a( bsi)nDgleep seynndaepntic i i cy bsettiwmeuelanti∆on[C (gl−GA]BAa n= d7.8[C9 ln−S], τu =p 3o7n mass,i [nHgCleOs3y⁻n]i a=p 1t4ic.1s mtimMu) lfaotri odniff(egrent PH=CO73.. 8N9onteS ,thτe= bi3p7hmassic, [rHesCpoOns−es i i GABA 3 ]i =fo1r4 .P1HCmO3M of) 0fo.1r8 d(irfefperreesnetnPted by .thNe ottweot hbeluHCO b ei plihneass)i canreds pthoant saets hfiogrhePr PHCO3o tfh0e. 1[8Cl(⁻r]ei pflurexseesn atreed sbhyifttehde HCO twtoowabrlduse inlifnluexs )evaennd fothr ahtigh 3 at ihniitgiahle [rCPl⁻]i. (c) Dthepee[nCdle−n]cyfl buextweseeanr e[HsChO 3 ift3e⁻d]i atnodw [aCrld⁻]si uipnoflnu ax seinvgelne sfyonrahpitgich stimulatio−n using a model with dynam HiCc O[H3 CO3⁻] (g i− i GABA =− 7.89 nS, τ = 37 ms, initial [Cl⁻]i = 30 mM, initial initial [Cl ] . (c) Dependency between [HCO ] and [Cl ] upon a single synaptic stimulation using a [HCO3⁻] ii = 14.1 mM). (d) D−ependency betwe 3en ipeak depoliarization an−d [Cl⁻]i upon a single syna−ptic mstoimdeullawtiiothn d(cyonnadmitiiocn[sH aCs Oin3 c) ]ait( dgiGffAeBreAn=t P7.89 nS, τ =HCO3. Note th3a7t tmhes ,iminpitli−e aml [eCnltat]iio=n o3f0 dmynMa,minici t[iHalC[OH3C⁻]Oi (3pla]iin= 1l4i.n1ems) Mma).ss(div)eDlye rpeednudceesn pcyeabke dtwepeoelnarpizeaatkiodn eaps ocloamripzaartieodn toa ncodn[dCitlio]nisu wpiothn satastiinc g[HleCsOyn3a⁻]p t(ischastdii emdu lilnateiso)n. (c(eo)n Ddietpioennsdeansciyn bc)etawt edeinff e[rCeln⁻]t PchHaCnOg3e.sN aontde [tChla⁻]t tuhpeoinm ap lseimngelne tsaytinoanptoicf dstyimnam − i i ulaitcio[nH (CcOon3dit]iio(npsl aaisn ilnin ce)s. ) mDausasli vlienleys rwedituhc iedsenpteicaakl dcoelpoorsl arreipzraetsioennt absipchoampar − sic reesdpotnosceos.n Ndoittieo nthsew reidthucsetadt i[cC[l⁻H]i CchOa3nge]is (wshitahd deydnlainmeisc) . (e[H) DCOep3e⁻]n −i d aesn ccoymbpeatrwedee tno [sCtaltic] i[cHhCaOng3e⁻]si acnodnd[iCtilo−n]si (usphoown na sinin bg)l eansdyn tahpatt itchset i[mClu⁻]lia atito wnh(iccohn Cdil⁻t iionnflsuaxs inchca)n. gDeus atol lCinl⁻e esffwluixth wiadse snhtiifctaeldc tol olorws erre p[Crel⁻s]ei. nt biphasic responses. Note the reduced [Cl−]i changes with dynamic [HCO −3 ]i as compared to static [HCO −3 ] −i conditions (shown in b) and that the [Cl ]i at wHhoicwheCvle−r,i nthfleusxe cihnaitniagle sastosuCml−pteifoflnusx nwegaslescht itfhteed ftaoctl otwhaetr t[hCel− H]iC. O3⁻ fluxes will also affect [HCO3⁻]i. Rapid regeneration of [HCO3⁻]i levels by carbonic anhydrases, which stabilize [HCO3⁻]i, is absent in immHatouwree nveur,rotnhse s[5e0]i.n Tithiaerleafosrseu, mwpe tfiiorsnts sinmeugllaetcetd tthe GfaAcBt Ath-iantduthcedH EC −m Oan3d [Cfllu⁻]xi ecshawngilels aulnsodear ftfheec t [HasCsuOm−3 pt]iio. nR tahpaitd HrCegOe3n⁻ ewrailtli noont obfe [rHepCleOnis−3 h]ei dle (vbeyl simbpylecmarebnotninigc a nHhCyOdr3a⁻ sresla,xwathioicnh tismtaeb ciolinzseta[HntC (τOHC3O−3⁻)] i, isoaf b1s0e mntini)n ainmdm isa otnulrye rnedeuisrtorinbsut[e5d0 ]b.yT dhieffruesfioorne., Twheesfie rssitmsuimlatuiolantse rdevtheaeleGdA thBaAt -thined auctcievdatEiomn oafn GdA[CBAl−A] i chreacnegpetosrsu innddeurcetdh ea araspsuidm dpectiloinne tinh a[Ht HCOC3O⁻] −3i (Suwpiplllenmoetnbtaeryr eFpigleunreis 3hce).d T(hbey [HimCOpl3e⁻m]i deenctliinnge daepHeCndOed−3 reolna xbaottiho nPHtCimO3 eancdo n[Cstla⁻]ni tan(τd was −m) aoxfim10aml aitn l)oawn d[Cils⁻]oi wnliythr evdaliustersi bouf t1e.8d mbyMd aifnfdu s2i.o3n m. MTh feosre PsHiCmO3u olfa t0i.o1n8 s rea HCO3 vneda l0e.4d4t, hraetspthecetiavcetliyv a(tFiiognuroef 5GcA). BInA linree cweipthto trhsisin [dHuCcOed3⁻a]i rdaepcildinde,e tchlien Ge AinB[AHeCrgOic −d]ep(oSluarpipzaletimone nwtaarsy FdigruasrteicSa3llcy). Tdehcere[HasCedO (−Fi]gudreec l5inde) duen A pdeenrd eddynoanmbico th[HPCO3⁻]i conditi−ons. The atte 3nuaition of activity−- 3 i HCO3 and [Cl ]i and was maximal at low [Cl ]i with values of 1.8 mM and 2.3 mM for PHCO3 of 0.18 and 0.44, respectively (Figure 5c). In line with thInits. J[. HMoCl. O −Sc3i. 20]1i 8d, 1e9c, lxi;n deoi,: the GABAergic depolarization was drastically dwewcrwe.masdepdi.co(mFi/gjourrneal5/ijdm)s under dynamic [HCO −3 ]i conditions. The attenuation of a7c tivity-dependent [HCO −3 ] gradients also reduced th e size of associated [Cl−]i changes (Figure 5e) and at intermediate [Cl−]i even reversed the effect (Supplementary Figure S3d). Int. J. Mol. Sci. 2019, 20, 1416 8 of 22 Int. J. Mol. Sci. 2018, 19, x 8 of 22 depAensdseungt g[eHsCteOd3f⁻r] ogmratdhieenrtess ualltsso inreidsuoclaetde dthdee nsidzrei toefs ,atshseocGiaDtePd- in[Cdlu⁻]ci ecdhadnegpeosl a(rFiizgautrieo n5esi)m aunlda taetd ininttheermreecdoiantset r[Culc⁻t]ei edvnene urerovenrswedas thaeu egfmfecetn (tSeudpipflePmHCenOt3arwya Fsiginucrere 3ads)e. d from 0.0 to 0.18 and 0.44 under the assAusm supgtigoensteodf sfrtaobmle th[He rCesOu −3lts ]ing risaodliaetnedts d(eFnigdurirtes6, ath,ce, GshDaPd-einddsuycmedb odlesp)o. lAarnizdabtieocna suimseuulantdede rinth thesee corencdointisotrnusctEemd nceourlodnb weacso mauegmpoesnitteidv eift PoHECOC3 lw, tahse inCcrl−eaflseudx fersomw e0r.0e teon 0h.1a8n acned 0a.n44d uGnDdePr- tihned auscseudm[pCtilo−n] i troafn stiaebnltes [iHnCcrOea3s⁻]e dgr(aFdiigeunrtse (6Faig,du,rseh 6aad,ce,d shsaydmedbo slysm). bInolcsr)e. aAsnindg bPeHcaCuOse suhnidfterd ththeese[ Ccoln−ditions Em could 3 ]i level at which GbDecPo-mined puocseidtiv[eC tlo− ]EiCtl,r athnes iCeln⁻ tfslucxheas nwgeeref reonmhainncfledu xantod GefDflPu-xin, dreuflceedct i[nCgl⁻]ti htreanimsiepnatcst inocfrEeaHsCedO (Foingutrhee 3 D6Fa, d., Tshheadmeda xsiymmabloinlsfl).u IennccreeaosifnPg PHCO3 osnhitfhteed[ Cthl−e ][Cclh⁻]ai nlegveesl wata ws ohbicshe rGvDedP-aintdlouwced[C [lC−l]⁻]i (tFriagnusrieen6tsCL HCO3 i i d , shchadanegdes fyrommb oinlsfl)u,xb etoc aeuffsluexa, trethfleescetincgo nthdei tiimonpsa,ctt hoef EdHeCpOo3 loanr itzhien gDFeCffLe. cTthoe fmHaCxiOma−l influence of PHCO3 on 3 fluxes opposed the hythpee [rCplo⁻]lia crhizainnggese fwfeacst sobosfeErve.d at low [Cl⁻]i (Figure 6d, shaded symbols), because at these conditions, the depolarizing effect of HCO3⁻C flluxes opposed the hyperpolarizing effects of ECl. FFigiguurree6 6.. IInflfluence off PHHCOC3O o3no GnAGBAAB-iAnd-iuncdeudc [eCdl⁻[]iC tlr −an]isiternatnss iine nat rseicnonasrtreuccotnedst rCuAc3te pdyCraAm3idpayl rnaemuriodna.l n(eau) rToinm.e c(oau) rTseim ofe Ecmo aunrdse avoefraEgme [aCnl⁻d]i dauvreirnagg ae s[iCmlu−la]itedu GrDinPg aat dsiifmferuelnat ePdHCGO3 D(gPGAaBAt =d 0if.f7e8r9e nSt , PinHitCiOal 3 (g[CGAl⁻]Bi A= 3=00 m.7M89, [nHSC, iOn3it⁻i]ai =l [1C4.l1−) ]ui s=in3g0 am mMod, [eHl wCiOth3 a− c]ion=s1ta4n.1t )[HusCiOng3⁻a]i.m (bo) dEeml, waviethraagec o[Cnls⁻t]ai anntd[ H[HCCOO3−3 ⁻]]ii . (bd)uErimng, aav esirmaguela[tCedl− G]DaPn dat [dHifCfeOren−t ]PdHCuOr3 in(ggGAaBAs i=m 0u.7la8t9e dnSG, DinPitiaalt d[Ciflf⁻]ei r=e n3t0P mM,) u(gsing a m=o0d.7e8l 9thnat i 3 i HCO3 GABA S, inimitipallem[Celn−ts] d=yn3a0mmicM [H,)CuOsi3n⁻]gi. aNmoteo dthealtt hmaetmimbrpanleem deenptosladriyznaatimoni can[Hd C[COl⁻]−i t]ra. nNsioentetst haraet diminished i 3 i membrane dueppoonla irmizpaletimonenatantdion[C olf− d]yntarmanics i[eHnCtsOa3⁻r]ei. (dci)m Mianxisimheadl Eum pdounrinimg ap GleDmPe antt adtiifofenreonft idnyitniaal m[Cilc⁻]i[ HanCdO PH−CO3 i 3 ]i. (cu)siMnga xsitmatiacl (Eshaddeudr liinngesa) oGr DdyPnaamt dici f[fHerCeOnt3⁻i]ni (iptilaalin[ Cliln−e]s). a(ndd) GPDP-induusciendg [Cstla⁻]tii cch(asnhgaedse adt dliinffeesr)enotr m initial [Cl⁻]i and P−HCO3 using static (shaded lines) or dynamic [ iHCO3⁻] H (pClOa3i in lines). (e) Dependency between dynamic [HCO3 ]i (plain lines). (d) GDP-induced [Cl−] changes at different initial [Cl−] and PDFCl and the GDP-induced [Cl⁻]i transients o−btained w iith different PHCO3 under dynamiic [HCOHC3⁻O]3i ucsoinngdistitoantisc a(ts Phade dofl i0n (erse)do),r 0d.1y8n mams (ibclu[He)C aOnd3 0.]4i4( p(glareinenl)i.n es). (e) Dependency between DFCl and theHCO3 GDP-induced [Cl−]i transients obtained with different PHCO3 under dynamic [HCO − 3 ]i conditions at PIHmCpOl3eomfe0n(traetdio),n0 .o1f8 am ms (obdluele )thaantd a0ll.4o4w(egdre deny)n.amic [HCO3⁻]i changes (using a τHCO3 of 10 min) in the reconstructed CA3 pyramidal neuron showed that the GDP-indu−ced GABAergic currents induced massive chanImgeps lienm [HenCtOat3io⁻]ni, doefpaemndoidnge lotnh aPt allo (SwueHCO3 pdpdleymneanmtaircy[ HFigCuOre3 3e]i).c Thhains gGeDs P(u-isnidnugcaedτ H[HCOC3Oo3f⁻]1 d0emcriena)sieni thdeurreincgo nas GtruDcPt eddimCiAni3shpeydr athme imdaelmnberuarnoen dsehpoowlaerdizathtiaotnt (hFeigGuDreP 6-bin,cd, upclaeidn GlinAeBs/Asyemrgbicolcsu),r wrehnitcshi nind tuucrend mcaasussievde cah darnagsetisc irned[HucCtiOon3 −i]ni , tdheep GenDdPi-ningdouncePdH [CCOl3⁻](i Sturapnpslieenmtse n(FtaigryurFei g6ubr,de, Sp3lea)i.nT lhinisesG/sDymP-bionlds)u. cIend [HsuCmOm−3ar]yi , daedcdrietiaosne odf uPrHiCnOg3 toa GADBPAedrigmici ncuisrhrendts tahuegmenmtebdr athne DdFeCpl aonladr itzhautsi othne (GFDigPu-rined6ubc,ecd, [pClla⁻i]ni lintreasn/siseynmts b(Foilgsu),rew 6hei)c. hUsiingt uthrensec pauarsaemdetaerds,r athseti scimreudlautcetdi oGnDiPn-inthdeucGedD [PC-li⁻n]id truacnesdien[Cts lr−e]siemtrbalnesdi ethnets (Fsiigzue roef 6[bC,dl⁻],i ptlrainnsileintess /osbysemrvbeodls )in. Irneasul mcemllsa, rhyo, wadedveitri,o onnolyf PinH CthOe qtouaGdAraBnAt ewrgitihc pcuosrriteivnets DaFuCgl 3 mvaelnutesd th(eFiDguFrCel 6aen)d. thus the GDP-induced [Cl−]i transients (Figure 6e). Using these parameters, the simulated GDP-induced [Cl−]i transients resembled the size of [Cl−]i transients observed in real cells, however, on2.l4y. Tinhet hSetaqbiuliatyd roaf nHtCwOi3t⁻h Gproadsiietnivtse IDnfFluenvceasl uAecstiv(Fitiyg-uDreepe6ned)e.nt [Cl⁻]i Transients Cl The previous results clearly demonstrate that GABAA receptor-mediated [HCO3⁻]i transients 2.m4.aTsshieveSltya binilfiltuyeonfcHe tChOe E−3 m aGnrda d[Cieln⁻]tis chInaflnugeens cuensdAecrt tihvietsye- Dcoenpdenitdioennst. [HCol−w]ei vTerra,n tshiee ntwtso conditions used in thesTe heexpperreimvieonutss r(setsaublltes [cHleCaOrl3y⁻]di oerm noengslitgriabtlee t[hHaCtOG3A⁻]Bi rAegenreecraetpiotonA r a-mt τeHdCiOa3 toefd 1[0H mCiOn) a−r]e otrbavniosiuesnlyts mnaosts ipvheylysioinloflguiceanl cien tihmemEatuarne dne[uCrlo−n]s,c whahnicghe slauckn dcaerrbtohneisce acnohnyddirtaisoenss, .bHuto iwn ewvheirc,hth sepo 3ntaitwonceoonudsi tCioOn2s hydration and/or transmemmbrane transiport of HCO3⁻ can occur [50]. Therefore, we next investigated how uτsed i ninthfleuseencexperime − − HCO3 es the stanbtisli(tsyt aobfl e[H[HCCOO3⁻3] g]riaodriennetgs liagnidb leG[AHBCAO i3nd]uicreedg e[nCel⁻r]ationi transaitenτtHs.C OF3oro fth1a0t mwien ) arseysotbemviaotuicsallylyn cohtapnhgeyds itohleo gdieccaalyi-ntimime mofa [tHuCreOn3e⁻]u rreolnaxs,atwiohni c(τh lac)k imcaprlbeomneinctani HCO3 ed hiny dthrea sNeEs,UbRuOt Nin mwohdieclh spontaneous CO2 hydration and/or transmembrane transport of HCO −3 can occur [50]. Therefore, wIent.n J.e Mxtol.i nScvi. e2s01t8ig, 1a9t, exd; dohi:o w τHCO3 influences the stability of [HCO − 3 ] grawdwiwen.mtsdpai.ncodmG/joAurnBaAl/ijminsd uced [Cl−]i transients. For that we systematically chang8e d the decay-time of [HCO −3 ]i relaxation (τHCO3 ) im plemented in the NEURON model (Supplementary Figure S4a,b). A systematic simulation in isolated dendrites revealed that [Cl−]i changes remained rather constant at τHCO3 ≥ 90 ms (Figure 7a). The half-maximal [Cl−]i changes occurred at a τHCO3 around 10 ms, which is substantially shorter than Int. J. Mol. Sci. 2019, 20, 1416 9 of 22 Int. J. Mol. Sci. 2018, 19, x 9 of 22 th(Seuτpplemeonftcaary. 7F0igmursef o4ra,hb)a. lfA-m syasxtiemmaalti[cH sCimOul−at]iocnh iann igseosla(tSedu pdpelnedmrietenst arreyveFailgedu rtehaSt4 b[C).l⁻M]i chaHCO3 3 i ore ntgheasn 85re%moafinthede mraathxeimr caoln[sCtal−nt] acth τaHnCOg3e ≥s t9o0o mk sp (laFcigeuarte 7a). Thbee hloawlf-m10a0xmimsa(lF [iCglu⁻]ri ech7aa)n.gHeso owcecuvrerre, dit amt ua sτtHaClOs3τ o bearcoounnsdid 1e0r emds,t hwahtiachd iesc sru i eabssetadntteiamllyp oshraolrtsetar bth HC ilaitny thoef tτ O3 hHeCO[3H oCf cOa. −70] gmrsa dfoire nhtalwf-imllaaxlismoainl [flHuCenOc3e⁻]tih cehalantgeersa l (Supplementary Figure 4b). More than 85% of the maximal [Cl⁻]3i changes took place at τHCO3 below 100 ms d(iFffiugusi − roen 7ao)f. HHoCwOe3ver. , Iint mdeuesdt ,aalsosy bset ecomnasitdicerseimd tuhlaatt aio dnecorfetahseeds pteamtipaolraaslp steacbtsiliotyf− − tohf ethaec [tHivCitOy3-d⁻]e− g preanddieennt t [HwCilOl a3lso] iintrfalunesniecne ttshree lvaetearlaeld dtihffautstiohne ostfa HbCiliOty3⁻o. fInHdCeeOd3, a smysatsesmivaetlicy siinmfluuleanticoend otfh tehe[H spCaOtia3l a]sgpreacdtsi eonf t alto − hne gactthiveitiyso-dlaepteednddeennt d[HritCeO(F3⁻i]gi utrraen7sibe)n,tas lrtehvoeuaglehd tthheatm thaex ismtaablil[iHty CoOf H3 C]Oi c3h⁻ amnagsesivaetltyh iensflyuneanpcetdic tshiete w[aHsCnOe3a⁻r]l ygrsaadtuiernatt eadlonalgr etahde yisaotlaateτdH dCeOn3d≥rit9e0 (Fmigsu(rFei g7ubr),e a7ltbh)o. uIngha ctchoe rmdaanxicme awl i[tHhCthOe3⁻r]ei scuhlatnsgoeb atat itnheed insyinsoaplattice dsitdee wndasr inteesa,rlayl ssoatiunrattheed raelcreoandsytr autc tae τdHCCOA3 ≥3 9p0y mrasm (Fidigaulrne e7ubr)o. nInτ aHcCcoOr3dhanadce awliathrg tehe frfescutlotsn thoebtGaiDnePd- iinnd iusocleadte[dH dCenOd3r−it]eist, raalnsos iienn tthse( Sreucponpsletrmucetnedta CryAF3 ipgyurraemSi4dca,ld n),eubruotno τnHlCyO3a hmadin ao lraregffee ecftfeocnt othne asthsoe ciGaDtePd-i[nCdlu−c]eidc h[aHnCgOes3⁻(]Fi igtruanresi7ecn)t.s F(oSruτpHpCleOme≥ntary F3 90 msigtuhreeG 4Dc,Pd-)i, nbduutc eodnl[yC la− ]mi cinhoarn gefefsecwt eorne othnely maasrsgociniaatelldy [aCflf⁻e]ic ctheadn(gFeisg (uFrigeu7rce, 7Scu). pFporl eτmHCeOn3 ≥ta 9r0y mFsi gthuer Ge DS4Pc-i)nbdyuccehda [nCgl⁻e]si cihnanτgHeCsO w.erAe tonτlHyC mOar3 3 ogfin1almlys , thaeffemctaexdi m(FaigluGrDe 7Pc-,i nSduuppceledm[eCnlt−ar]y dFeigcurerae s4ec)a mbyo uchnatnegdetso in5 .τ2HmCOM3. A, wt τhHiCleO3 itofw 1a ms 4s,. 6thme Mm,a4x.i5mmal MGDaPn-i d 4.i4ndmuMcedfo [rCτl⁻]i decrveaalsue easmoofu9n0temds t,o5 51.82 mmsMa, nwdh3iles ,itr ewsapse 4c.t6iv melMy., T4h.5i smsMm aalnldHCO e f4f.e4c mt wMa fsoar lτsHoCOre3 flveacluteeds obfy th9e0 mmisn, i5m18a lmchs aann 3 gde s3 isn, trhesepreecltaivtieolyn. bTehtiws esemnaDll Feffeactn dwaGsD aPls-oi nrdefuleccetded[ Cbly− t]hetr amnisniiemnatsl c(Fhiagnugrees 7ind )t.he relation between DF Cl iCl and GDP-induced [Cl⁻]i transients (Figure 7d). FFigiguurree 7.. Inffllueennccee oof tfhteh setasbtailbitiyli otyf HofCHO3C⁻ Ogr −3adigernatsd (iveinat vsa(rviaiatiovnasr iant τioHnCOs3)i nonτ GHCAOB3A) -oindGucAedB Am-eimndburacneed mdempoblraarinzeatdioenp oanladr i[zCalt⁻i]oi tnraannsdien[Ctsl.− (a]i) tDraenpseinednetns.cy(a b)eDtwepeen d[Cenl⁻c]iy chbaentwgeese (nde[Ctelr−m]iincehda n1 gse asft(edre stteirmmuilnuesd) 1asnda fτtGeArBsAt aimt PuHlCuOs3 )ofa 0n.d18τ aGnAdB 0A.4a4 tuPpHonC Oa sionfgl0e. 1s8yna3 anpdtic0 s.4ti4muuplaotinona (sgiGnAgBAle = s7y.8n9a npSt,i cτ s=t 3im7 mulsa, tPioHCnO3( g= G0A.1B8A, =in7i.t8ia9l n[CSl,⁻]τi == 303 7mmMs), inP an iso=lat0e.d1 8d,einnditriiatel. [NCol−te] th=at3 a0t mτHMCO3) oinf caan. 1i sso tlhaete mdadxeimndalr i[tCel.⁻]iN choatengtehsa atrae HCO3 i t τreacheodf. (cba). S1pastitahl eprmofaixlei mofa ml [aCxilm−]al c[HhaCnOg3e⁻s]i achreanrgeeasc huepdo.n (tbhe) Ssipnagtliea slypnraopfitilce sotifmmulaaxtiiomna (lp[aHraCmete−rs HCO3 i O3 ]i cahsa ning eas) uapt odnifftehreensti nτgHlCeO3s.y Nnaoptet icthsatti mτHuClOa3t iionnflu(penacreasm tehtee rsspaastiainl par)oaftilde iofffe r[HenCtO3⁻]i, although the peak τHCO . Note that τHCO in[HfluCeOn3c⁻e]is vthaleuseps aatriea lmparoinfilyle coofm[HpaCrOabl−e.] (,c)a lDtheopuegndhetnhceyp beeatkw[eHenC Oma−xi]mvala lGuDesPa-irnedmu 3caeidn ly[Cl⁻] 3 comi cphaarnagbelse . (gGABA = 0.789 nS, τ = 37 ms, P 3HCO3 = 0.18, n iGABA = 395, ini−tial [Cl⁻]i = 30 mM 3 ) aind initial [Cl⁻]i for different τHCO3 (c) Dependency between maximal GDP-induced [Cl ] changes (g = 0.789 nS, τ = 37 ms, P = in the reconstructed CA3 pyramidal neuron. Note that thie influence oGf ABA− − τHCO3 on [HCO3⁻]i changes is l HaCrgOe3st 0a.1t 8lo, wnG [CABl⁻A] , =bu3t9 t5h,aitn oivtiearlal[lC τl ]i h=a3s 0onmlyM a) mainndiminali tiimalp[aCctl on]i tfhoer [Cdli⁻f]fe crheanntgτeHs.C (Od3) Dinepthenedreenccoyn bsetrtuwceteei HCO3 i nd CDAF3 p aynrda mthied GalDnPe-uinrdounc.eNd o[Ctel⁻t]h tartanthe i − sienntfls uobetnacineeodf wτHitChO d3ifofner[eHntC τO3 ](ishcahdainnggess aiss ilna rcg).e st at low [Cl −] , Cl i HCO3 i but that overall τ −HCO3 has only a minimal impact on the [Cl ]i changes. (d) Dependency between DFCl aGndAtBhAe eGrDgiPc -[in51d]u acendd [gCllu−t]aimtraatnesrigeinct s[5o2b,t5a3i]n esdynwaipthticd itfrfaenresnmtiτsHsiCoOn3 i(ss haacdcoinmgpsaansiiendc )b.y substantial pH changes. These pH changes, however, indirectly affect GABAergic transmission, since they alter the [HCGOA3⁻B]i.A Teorg eicst[i5m1a]taen, dhogwlu tsaumcha tpeHrg icch[a5n2g,5e3s] isnyfnluaepntciec tarcatnivsimtyi-sdseipoennidseanctc o[Cml⁻p]ia tnriaendsibenytss,u bwset afnirtsiat l psHimchulaantegde st.hTe hefefseectp oHf scuhcahn pgHes s,hhioftws beyv ecro,nisntdanirtelyct alyltearfifnegct thGeA pBHA vearlguiec ftrroamns 7m.2i stsoi o7.n0, osri n7c.4e itnh aeny iaslotleartethde [HdeCnOd3ri−te].i .ATcocoersdtiinmga ttoe ,theo wHesnudcehrspoHn-Hchaassneglbeaslicnhfl euqeunactieonac, ttihveistey -pdHe psehnifdtse anltte[rC [lH−C]iOtr3a⁻]ni sfrieonmt s1,4w.1e mfiMrs t sitmo u9 lmatMed otrh 2e2e.7ff mecMt o, fressupcehctpivHelysh. Bifetscabuysec othnist panHt-ldyeapletnerdienngt tdhifefeprHencveasl uine [fHroCmO37⁻.]2i atoffe7c.0t DorFG7A.B4A,i nthaen ismoleamtebdradneen dderpitoel.arAizcactoiornd iunpgotno GthAeBHAAe nredcerpstonr -aHctaivsasteilobna wlchase rqeudautcieodn a, t hpeHs e7.p0 Hansdh eifnthsaanlcterd [aHt aC mOo−3re] i fraolmkal1in4e.1 pmHM oft o7.94 m(FMiguorre 282a.7). mInM li,nree swpiethct itvheisl ya. lBteerceadu GseAtBhAiseprgHic- dmepemenbdraenet diefpfeorleanriczeastioinn,[ HthCe ODF−3 C]l i afdfuecrtinDg FGGAABBAA,etrhgeicm steimublraatinoend weapso alalsroiz aaftfieocnteudp, sohnifGtinAgB tAheA rreescueltpintogr Calc⁻t filvuaxteios.n Twhiass craend buec eexdeamtpplHifie7d.0 anatd inetnehrmanecdeidatea t[Calm⁻]i,o wrehaelrke atlhien ebippHhaosifc 7C.4l⁻ (fFluigxuesr eat8 aa )n.oIrnmlainl epHw iotfh 7t.h2,i swaelrtee rteradnGsfAorBmAeedr tgoi cCml⁻ eemfflburxa ante dae ppoHla orifz 7a.t0i oann,dt htoe aD CFl⁻ idnfulurixn agt Ga ApBHA oefr 7g.i4c (sFtiigmuurela 8taio).n Aw saysstaelmsoataicff eanctaelyds,issh oiff tCinl⁻g ftluhxeerse asut ldtiifnfegreCnlt− fliunxiteias.l [C Cl Thl⁻i]si cdaenmboensetxraetmedp tlhifiaet,d ina ctoinmtpearmriseodni atote p[HC l7−.2], ,thweh Celr⁻e inthfleuxb aipt hloawsi cinCitlia−l fl[Culx⁻]ei swaats i a dencorremasaedl p aHt pH 7.0, while it was enhanced at pH 7.4 (Figure 8b). In contrast, the Cl⁻ efflux at high [Cl⁻]i was enhanced of 7.2, were transformed to Cl− efflux at a pH of 7.0 and to a Cl− influx at a pH of 7.4 (Figure 8a). AInst.y J.s Mteom − − l. Sacti.i 2c01a8n, 1a9l,y xs; idsoio: f Cl fluxes at different initial [Cl ]i demonstrawtwewd.mthdpait.c,oimn/jocuormnalp/iajmrsi son to pH 7.2, the Cl− influx at low initial [Cl−]i was dec9r eased at pH 7.0, while it was enhanced at pH 7.4 (F igure 8b). In contrast, the Cl− efflux at high [Cl−]i was enhanced at pH 7.0 and reduced at pH 7.4 (Figure 8d). In consequence, intracellular acidification shifted the [Cl−]i range at which Cl− efflux occurs to lower initial [Cl−]i, whereas intracellular alkalinization shifted this range to higher initial [Cl−]i (Figure 8b). InItn. tJ. .JM. Mool.l.S Sccii.. 22001198,, 1290,, x1 416 10 of 22 10 of 22 at pH 7.0 and reduced at pH 7.4 (Figure 8d). In consequence, intracellular acidification shifted the [Cl⁻]i rangSei matu wlahtiicohn Csli⁻n eftfhluexr oecccounrsst trou lcotwederC inAit3iapl [yCrla⁻m]i, iwdhaelrneeaus rinotnrarceevlleualaler daltkhaalitnaizlaotiwone rshpiHfteodf th7i.0s rlaendgteo smtoa hlliegrheGr DinPit-iianl d[Culc⁻e]id (Fmigeumreb 8rba)n. e depolarizations, as compared to the standard pH of 7.2 (Figure 8c, SupplSeimeunlattairoynsF iing tuhree rSec4oen, srterducltiende C/Asy3m pybroalms)i.dTalh niseurreodnu rceevdeadleedp tohlaatr aiz laotwioenr prHes uofl t7e.d0 liend ator semdaulcleerd GGDDPP-a-isnsdoucciaedte mdeCml−brainfle udxep(o4l.a6rimzaMtiovns., a7s. 6comMpa)raedt ltow thein sittainadl a[Crdl −p]Hi aonf 7d.2i n(Faignuirne c8rce, aSsuepdplCeml−eneftflaruyx (−Fi6g.u1rme 4Me, vresd. l−in4e./3symMbo)lsa)t. Thhigish reindiutciaedl [dCelp−o]liacrioznactieont reastuioltned(F inig au red8udc,edre GdDsPy-masbsoclsia).teCd oCnl⁻v ienrfsluelxy , at(4a.6h migMh evrs. p7H.6 mofM7). 4at tlhowe mineitmialb [rCaln⁻]ei arnedsp ino nanse isncdruearsiendg Cal⁻G efDflPuxw (−e6r.e1 mMor evsd. −e4p.3o lmarMiz)e adt h(Figihg uinrietia8lc , Su[Cplp⁻]li ecmonecnetnatrryatFioing u(FreigSu4ree g8dre, ernedl isnyems/bsoylsm). bCoolns)v,ewrsheliyc,h arte as uhlitgehderin peHn hofa n7.c4e tdheC ml−eminbflruaxneo fre1s1p.o2nmseMs atdluorwingin ai tGiaDl [PC wl−e]rea mndorae ddeecproelaasriezdedC (lF−igeufflreu 8xco, fSu−p2p.3lemmeMntaartyl oFwigu[Crel −4]e g(Freen lines/symbols), which i i igure 8d, green symbols). Inressuumltemd ainry e,nthhaensceerde Csul⁻l tinsfdluexm oof n11st.2r amteMt haat tlotwhe inaictiiadli [fiCcla⁻]tii aonnda as sdoecciraetaesdedw Cilt⁻h efsfylunxa poft i−c2t.3r amnMsm aits lsoiwon re[dCul⁻c]ie (dFitghuerae c8tdiv, igtyre-dene pseynmdbeonlts)[.C Inl− s]umtramnasriye,n tthseaste lroewsu[lCtsl −de]m, wonhsitlreatteh ethaactt itvhiet ya-cdideipfiecnadtieonnt aCssl−oceiaftfleudx with synaptic transmission reduced thie activity-dependent [Cl⁻]i i transients at low [Cl⁻]i, while the activity- atdhepigehnd[Cl − ent C]il⁻w eaffsluexn hata hnicgehd [Cbyl⁻]s uci wahs aecnihdainccsehdi fbtsy. such acidic shifts. F −Figiguurree8 8.. IInnflfluueennccee ooff ppHH oonn GGAABABA-in-idnudcuedce mdemebmrabnrea dneepdoelaproizlaartiioznat aionnd a[Cnld⁻]i[ tCral ns]ietnratsn. s(iae)n Ttism. (ea c)oTuirmsee cofu Ersme aonfd E[Cml⁻a]i nudpo[nC la− s]iinuglpeo snynaapstiincg slteimsyulnaatipotnic (gsGtAimBA u=l a7t.8io9n nS(g, τG A= B3A7 m=s7, .P8H9COn3S =, 0τ.1=8, 3i7nitmiasl ,[CPlH⁻]Ci O= 330= 0m.18M, )i ninit iaanl [isColl−at]eid= d3e0ndmriMte )aitn daifnferiesnotl apteHd. Ndeonted rthitee eaftfedcti fofef rpeHnt opnH t.hNe odteepothlaerizeaffteiocntso afnpdH thoant tthee dbeipohlaasriicz [aCtli⁻o]in ast apnHd 7t.2h awtatsh terabnispfhoramsiecd[ Cinlt−o] Ci al⁻t epffHlux7 .a2t wpHa s7.t0r ansdf otor mCle⁻ dinifnlutox aCt lp−He 7ffl.4u. (xba) tDpeHpen7d.0enancyd tobeCtwl−eein flGuDxPa-itnpdHuc7ed.4 [.C(bl⁻)]i Dcheapnegneds eancdy inbietitawl [eCeln⁻]iG foDr Pd-iifnfedruencte dpH[C. Fl−or] iecahcha npgHe sthaen tdwion liitniaels [rCepl−re]siefnotr dmiffaexriemnatlp aHnd. Fmoirneimacahl [pCHl⁻]ti hcehatnwgoesl.i nNeostree tphraets peHnt 7m.0a sxhimiftas l[Canl⁻d]i cmhiannigmesa lto[Cwla−rd]isc Chla⁻n egffelsu.xN, wohteertehaast pH 77.0.4s shhiiffttss [[CCll⁻−]i] cihcahnagnegse tsowtoawrdasr dCsl⁻ Cinlf−luexf.fl (cu)x T,iwmhe ecroeuarssep oHf G7.D4Ps-hinifdtsuc[eCdl −d]eipcohlaarnizgaetsiotno wanadr d[Cs lC⁻]li −chiannflguesx . (ci)n Tthime reeccoounrsstreuocfteGdD CPA-3in pdyuracemdiddaelp noeluarroizna (tgioGAnBAa n=d 0.[7C89l− n]S,c τh =a n3g7 emssi,n PtHhCeO3r e= c0o.1n8s,t nruGAcBteA d= C39A5,3 inpiytiraalm [Cidl⁻a]i i l n=e u3r0o mn M(g) at dif=fe0re.7n8t9 pnHS ,(cτo=lo3r 7comdse, aPs in a).= N0o.1te8 ,thnat the =GD39P5-,inindiutciaeld[ C[Cl−l⁻]]i c=ha3nges are diminished at GABA HCO3 GABA i 0 mM) at different pH (cpoHlo r7.c0o daneda seinnhaan).cNedo taet tphHat t7h.4e. G(dD) PD-ienpdeuncdeednc[yC lb−e]twceheann gDeFsCal raenddi mthien iGshDePd-iantdpuHced7 .0[Caln⁻]di ternahnsainecnetsd obtained at different pH. Note that at pH 7.0 the GDPi-induced [Cl⁻]i −increase was diminished, while the at pH 7.4. (d) Dependency between DF and the GDP-induced [Cl ] transients obtained at different [Cl⁻]i decrease was slightly enhanced. Cl i pH. Note that at pH 7.0 the GDP-induced [Cl−]i increase was diminished, while the [Cl−]i decrease 2.5. wInafsluselnigche tolfy Ternahnasnmceemdb.rane Cl⁻ Transport 2.5. InFfliuneanllcye, owf eT raannaslmyzeemdb rhaonwe Cthl−e kTirnaentsicpso rotf the [Cl⁻]i homeostasis influenced the temporal and spatial consFtirnaianlsly o, fw theea ancatilvyiztye-ddehpoewndtehnet [kCinl⁻]ei tcichsanogfetsh. eFo[rC tlh−a]t whoe msyesotesmtaastiiscailnlyfl ucheanncgeeddt thhee tdeemcapyo-triamlea nofd spthaet ia[Cl lc⁻o] i i nrsetlaraxiantisono f(tτhCle) iamctpivleimtye-ndteepde innd tehne tN[CEUl−R]OcNh amnogdees.l. FIonirtitahl astimwuelastyiostnesm ina tiiscoallaltyedc haanndg seodmtah-e attached dendrites rev−ealed that for τCl of ≥ 10 s the d iecay of [Cl⁻]i response was dominated by diffusional deexccahya-ntigme ewoitfht thhee s[Coml a] i(Srueplapxleamtioennta(τryC lF)igimurpel 4efm–he)n. Atetd a iτn tohfe 32N1 Es U99R.2OCl %N ofm Col⁻d fleul.xeIsn witeiarel s− d iemplueltaetdi obnys indiifsfoulsaitoendala enxdchsaonmgea -waitttha cthhee dsodmean, dwrhitieles arte fvaesatelre dτ t ha astCl ufbosrtaτnCtilaol fsm≥a1ll0ers ftrhacetidoenc oafy oonfly[ C83l.5] %i r e(τspCl o= n1s0e wsa),s 3d3o.2m %i n(τaCtle =d 1b sy) adnidff u2.s4i%on (aτlCl e=x 1c0h0a mngse) owf itthhe tChle⁻ fsluoxmesa w(Saus pelpimleimnaetnedta bryy dFiifgfuusrieonS 4frfo–mh) .thAe tdaenτdCrlitoe f 32to1 tshe9 9so.2m%a.o Af nCall−ysflisu oxfe tshew sepraetidale dpilsettreibdubtiyond oifff [uCsli⁻o]in aalolnegx cthhea ndgenedwrititeh retvheeasleodm thaa, tw τhCl ialelsoa taffafescttesr thτeC l assiuzeb sotfa natcitaivl istmy-dalelpeernfdraenctti o[Cnl⁻o]fi ochnalyng8e3s. 5at% d(iτstCaln=t d10ensd)r, i3ti3c. 2si%tes( τ(CSul p=p1lesm)eanntdary2 .4F%igu(rτeC l4=i–k1)0. 0Tmhes ) ofdtohmeinCaln−cefl uofx desiffwusaisonealilm eliinmaitneadtibony odfi fCful⁻s wioans farlosom retfhlecdteedn bdyr itheet obtsheervsaotmiona .thAant alty sslioswo fτCtlh ≤e 1s0psa tthiea l d[iCstlr⁻i]bi wutaiso nsuobfst[aCnlt−ia]li laolwonerg atth tehed epnrodxrimtearl etvheaanl eadt tthhea dt iτstCall aelnsdo aoff tehcet sdtehnedsritzee (oSuf papctlievmiteyn-tdareyp eFnigduerne t [C4li–−k])i. c hanges at distant dendritic sites (Supplementary Figure S4i–k). The dominance of diffusional elimination of Cl− was also reflected by the observation that at slow τCl ≤ 10s the [Cl−]i was substantial loIwnt.e J.r Maotl.t Shcei. 2p0r1o8,x 1i9m, xa; dlotih: an at the distal end of the dendrite (SupplementawrwywF.imgdupri.ecoSm4/ijo–ukrn).al/ijms To analyze the influence of τCl on the spatia 1l0a spects of the [Cl−] i transients we implemented two simultaneous GABAergic inputs that were located equidistant to the [Cl−]i recording site at distances of 10 µm, 30 µm, 100 µm and 300 µm and systematically increased τCl from 1 ms to 220 s (Figure 9a). These simulations revealed not only that the maximal [Cl−]i depended on the distance between GABAergic stimulation sites and the node of [Cl−]i determination, but also that τCl critically Int. J. Mol. Sci. 2018, 19, x 11 of 22 Int. J. MTool . aSncia. l2y0z19e, 2th0,e1 4in16fluence of τCl on the spatial aspects of the [Cl⁻]i transients we implemented1 1twofo2 2 simultaneous GABAergic inputs that were located equidistant to the [Cl⁻]i recording site at distances of 10 inµflmu, e3n0c eµdmt, h1e00[C µlm−] acnhda 3n0g0e µamt a agnidv esnysdteismtaanticcealtloy tihncersetaismedu lτaCtli ofrnomsi t1e sm(Fs itgou 2re209 as )(.FTighuisred 9eap)e. nTdheensec y simulations revealedi not only that the maximal [Cl⁻]i −depended on the distance between GABAergic bsettimwueelantisopna stiitaels raensdtr tihctei onnosdeo fofa [cCtilv⁻]it dye-dteerpmeinndateionnt ,[ bCult a]ilscoh tahnagt eτs acnridticτaClllyw inafsluqeunacnedti fitheed [Cbyl⁻]t hcheaτ at− i Cl Cl i n Cgle waht iac hgihvaelnf- mdiastxainmcea lt[oC tlhe] isctihmaunlgaetisoonc csiuters( τ(Fig50u)r.eI f9tah).e Tdhisist adnecpeeonfdtehnecyG AbeBtwAeeergni cspsaytniaalp rseesstrwicatison10s µofm τCl Clact5i0viatym-doeupnetneddetnot 1[C2l⁻m]i sc,haanndgetsh aisndτ τC5l0 winacsr qeausaendtiftioed6 0b.y5 tmhes ,τ7Cl2 a6t mwshiacnhd h4al.6f-ms aaxt ismyanla [pCtli⁻c]i dcihsatnagnecse s oofc3cu0rµ (mτC,l501).0 I0f µthme dainstdan3c0e0 oµf mth,e rGesApBeActeirvgeicly s.yTnoapasnesa lwyzaes 1th0 eµmte mτCpl5o0 raamloausnpteecdt stoo 1f2[ Cmls−, ]ai nsdu mthmis aτtCil5o0 n winecsreimasuedla ttoe d60fi.5v me sc,o 7n2s6e mcust aivned G4.A6 sB aAt ssytinmaputliac tdioisntasnactesfr oefq 3u0e µnmci,e 1s0(0f GµAmB Aa)ndo f30.03 µHmz, ,r1esHpezc,ti3veHlyz. aTnod 1a0nHalzyzaen tdhed etetmerpmoirnael athspee[cCtsl −of] i[aCtl⁻t]hi esusmtimautliaotnio wnes istiem, uwlahtielde sfiyvset ecmonasteicualtlivyev GarAyBinAg sτtiCml u(Flaigtiuonres 9abt ). Tfhr eqsueesnicmieus l(afGtiAoBnA)s orfe 0v.e3a Hlezd, 1a Hsizg, m3 oHizd alnd e1p0 eHnzd eandcy dbeteetrwmeienne tτhCel [aCnl⁻d]i taht ethte mstipmourlaaltisounm simtea, twiohnileo f [Csyls−te]im. aAticlaarllgye vraarmyinogu nτCtl o(Ffig[Curl−e ]9ibs).u Tmhmesea tsiiomnualantdionasl roewveearleτdC la5 0siwgmasoiodbasl edrevpeedndaetnhciyg bheetrwfereenq uτCel nacnide s. Tthhee tτeCml poarmal osuumntmedattioon1 .o9f s[Cfol⁻r]i.f A largoerf 0am.1oHuzn,t 9o3f 1[Cml⁻s]i fsourmfmationo fan1dH az l,o2w6e8r mτCsl50f owrafs obserovfe3d at 50 GABA GABA GABA Hz, ahnidgh5e3r mfresqfuoernfcies. Thoef τ Cl 1050H azm. oInunstuemd tmo a1r.9y ,st fhoers feGArBeAs uoflt 0s.1d eHmz,o 9n3s1t rmatse fdort hfGaAtBAτ of v1 aHluze, s26o8f mless sfotrh faGnAB1A GABA Cl s aorfe 3r eHqzu, iarnedd 5to3 pmrse fvoern ftGAsBuAb osft a1n0 tHiazl. aIcnt isvuimtym-daeryp,e tnhdeseen rte[sCull−ts] dcehmaonngsetrsaitnedt htheast pτaClt ivaalluanesd o/fo lrestse mthpano r1a l dso marea inreaqtufired to ≥pr1eHveznat nsdublesstasntthiaaln a1c0ti0vitym-ddep i isetnadnecnetb [eCtwl⁻]ei ecnhasnygneasp itnic tshiete ss.patial and/or temporal µ domain at fGAB AGABA ≥ 1Hz and less than 100 µm distance between synaptic sites. FFiigguurree 99.. IInnfflluueennccee oof fCCl⁻l −didffiuffsuiosnio annadn tdhet hkeinkeitnicest iocfs torafntrsamnesmmbermanber aCnl⁻e tCraln−sptroarnt sopno GrtAoBnAG-iAndBuAc-eidn d[Culc⁻e]id [tCraln−s]ientrti as.n (sai)e nDtes.pen(ad)enDceyp beentdweenecny τbCle atwnde e[nCl⁻τ]i deatnedrm[iCnel−d ]ind tehtee rmmiidndelde binetwtheeenm 2i dsdimleulbtaentweoeuesnly Cl i 2 ssitmimuultlaatneedo suysnlaypssteism (pualaratemdetseyrns aaps sine sa() ploacraatmede 1te0r µsMas, 3in0 aµ)Mlo, 1ca00te µdM1 0 aµnMd 1,0300 µµmM f,ro1m00 tµheM noadned o1f0 [0Clµ⁻]mi frreocmordtihneg. nTohde einoseft [rCepl−re]senretsc oa rsdcihnegm.atiTch ileluisntrsaettiorne opfr ethsee nsptsataials acrhreamngaetmiceniltl.u (bst)r Aatniaolnysoisf otfh teemsppoartaial l summation of activity-depiendent [Cl⁻]i transients upon 5 consecutive GABA stimuli (parameters as in a) arrangement. (b) Analysis of temporal summation of activity-dependent [Cl−] transients upon provided at frequencies of 0.3 Hz, 1 Hz, 3 Hz and 10 Hz in the dendrite + soma arrangi ement. The inset 5illcuosntrsaetceus ttiyvpeicGalA [CBlA⁻] sttriamceus loib(tpaainraedm aett e3r Hs za swiintha τ) p orfo 4v1id mesd aant frei Cl d 4.1q su. eTnhcei ersa of−tio i 0n.3 thHe z[C, 1l⁻]Hi bze,tw3 eHenz tahned 1f0irsHt zanind tfhifethd setinmduriltues +(resol. m[Cal⁻a]ri5/r1a) nshgoewmse an ts.igTmheoiidnasle dteiplleuns− d treantceys otynp τical [Cl ] traces obtained aCl . Note thi at with higher stimt−u 3luHs z wfrietqhuτeCnlcioefs 4f1 ms 5/1 aster aτnd 4.1Cl are rse.qTuhireedra ttoio pinretvheent[ Csul m]imbaettiowne eonf t[hCel⁻]first ai transnidenfitsf.t h(cs) tiDmeupleunsde(rnecly. [bCeltw]ei en ) smhoaxwims aal sGiDgmP-oiniddualcedde [pCeln⁻]di cehnacnygeosn (gτGCAlB.A N= o0.t7e89th naSt, wτGiAtBhA =h i3g7h mers,s PtiHmCOu3 l=u s0.1fr8e, qτuHCeOn3 c=i e1s sf, ansGtAeBrA τ=C 3l9a5r) e raenqdu iirneidtiatlo [Cprl⁻e]vi feonrt dsiuffmermenatt iτoCnl ino ft[hCe lr−e]ciotnrsatnrusicetnedts C. (Ac)3 Dneeupreonnd (edn)c Dyebpeetnwdeeenncym baextiwmeaenl GDDFCPl -ainnddu tcheed [GCDl−P]-iincdhuacnegde [sC(l⁻g]iG tAraBnAsi=ent0s. 7o8b9taninSe,dτ wGAithBA di=ffe3r7enmt τsC,l. PHCO3 = 0.18, τHCO3 = 1 s, nGABA = 395) and initial [Cl−]i for different τCl in the reconstructed CA3 neuron (d) Dependency between DFCl and the GInD aPc-icnodrduacendce[ Cwl−it]hi ttrhaenssei ernetssuoltbst aiinn esdinwglieth ddeinffderrietnest,τ aClll. previous simulations of GDP-induced [Cl⁻]i transients in the reconstructed CA3 pyramidal cells revealed substantial [Cl⁻]i changes, because in these simuIlnataiocncso rtdhea necxepewriimthenthtaelslye dreestuerlmtsinineds iτnCgl loef d1e74n ds rfioter sN, aKlCl pCr1e-mvieoduisatseidm aucltaivtieo Cnsl⁻ oref-GacDcuPm-iunldatuiocned [Canl−d ]oi ft r3a2n1s sie fnotrs pianssthiveer Cecl⁻o rnesdtruucctitoend wCeAre3 ipmypralemiednatlecde allnsdr edvuerainlegd as uGbDsPta snttimialu[laCtli−on]i ac haignhg efrse,qbueecnacuys e inof tGheAsBeAseirmgiucl aintpiount swtahse aepxpplierdi.m Ine notradlelyr tdoe gtetr mionred inτsCiglhotsf i1n7t4o hsofwor thNeK cCapCa1c-itmy eodf i[aCtle⁻d]i raecgtuivlaetiConl− rsey-astcecmusm cualna tiinofnlueanncde oacfti3v2it1y-sdefpoernpdaensst i[vCel⁻C]i lt−ranresideuntcst iwonithwine rae reimalipslteicm deenntderditiac ncdomdpuarritnmgenat, GwDe P sftiinmalulyla stiimonulaathedig hhofwre dqiuffeenrecnyt oτCfl GinAfluBeAnecergdi cthien GpuDtPw-inadsuacpedp l[iCedl⁻].i tIrnanosridenetrs tion gtheet rmecoornestirnuscitgehdt sCAin3t o hnoewurothne (Fciagpuarec it9yc,do)f. [TChli−s ]simreugluatliaotnio rnevseyasletedm thsact adnecirnei fl ausienngc τeCla fcrtoivmi ttyh-ed eepxpeenrdimenetnt[aCllly− ]dettrearmnsinieendt s wviatlhuiens >a10r0e asl itsot 1ic0 sd oern 1d rsi htiacdc oonmlyp aa rmtmineimnta,l iwmepaficnt aolfl tyhes iGmDuPl-aitnedducheodw [Cdl⁻i]fi fterarennsitents (Figu ire 9c). The τ influenced the maximal GDP-ind−uced [Cl⁻]i change amounted to 4.41 mM at a τCl of 10 s and to 4.21 mM C alt a τCl of 1 s, but GwDePre- irnedduucceedd [tCo l2.9]9i tmraMns iaetn at sτin thCl of 0e.1r esc aonndst trou c0t.8e8d mCAM3 ant eau τro nof( F1i0g murse. 9Tc,Cl heds)e. Trehsiusltssi mdeumlaotinosntrraetev ethaalet d that decreasing τCl from the experimentally determined values >100 s to 10 s or 1 s had only a minimal imIntp. Ja. Mctolo. Sfcti.h 2e01G8, D19P, x-; idnodi: uced [Cl −]i transients (Figure 9c). The maximal G −wwDwP.m-idnpdi.cuocme/djou[rCnall/ij]mi sc hange amounted to 4.41 mM at a τCl of 10 s and to 4.21 m11M at a τCl of 1 s, but were reduced to 2.99 mM at a τ −Cl of 0.1 s and to 0.88 mM at a τCl of 10 ms. These results demonstrate that fast and efficient [Cl ]i homeostatic processes are required to limit GDP-induced [Cl−]i transients. Accordingly, the ∆[Cl−]i vs. DFCl plot also revealed comparable GDP-induced [Cl−]i changes at τCl of 10 s and 1 s, and smaller [Cl−]i changes at a τCl of 100 ms (Figure 9d). Only a further reduction in τCl to 10 ms substantially suppressed GDP-induced [Cl−]i changes. In summary, these results indicate that τCl influences the temporal and spatial properties of activity-dependent [Cl−]i changes, but that τCl values Int. J. Mol. Sci. 2019, 20, 1416 12 of 22 that are substantially smaller than the experimentally determined values are required to suppress activity-dependent [Cl−]i changes. 3. Discussion In the present study we used a detailed biophysical compartmental modeling in the NEURON environment to systematically investigate how several cellular and molecular neuronal parameters influence the GABAA receptor-mediated [Cl−]i changes. The main observations of this study can be summarized as follows: (i) A high Rinput reduces activity-dependent [Cl−]i transients, while at low Rinput considerable activity-dependent [Cl−]i transients can be observed. (ii) The activity-dependent [Cl−]i transients show a logarithmic impact of gGABA, while τGABA has in a wide gGABA range a nearly linear influence on [Cl−]i. (iii) The PHCO3 of GABAA-receptors enhances activity-dependent [Cl−]i transients, but with instable [HCO −3 ] gradients this effect is largely diminished. (iv) Activity-dependent Cl− fluxes where shifted toward efflux at acidic and towards influx at alkaline pH. (v) τCl has a major impact on the spatiotemporal aspects of activity-dependent [Cl−]i transients, but unrealistically fast τCl values are required to prevent [Cl−]i transients at physiologically-relevant activity levels. By 1990, it was suggested by Qian and Sejnowski [54] that the Cl− fluxes via activated GABAA receptors will dissipate the Cl− gradient in small compartments and thus mediate potentially instable inhibitory responses. This theoretical assumption was proven by experimental studies, which demonstrated that massive GABAergic activation can shift hyperpolarizing responses toward depolarization [12,21] and induce [Cl−]i transients [55]. In the past the physiological and pathophysiological consequences of such activity-dependent [Cl−]i changes have been investigated and discussed [13,17,20,36,56] and the basic principles of activity-dependent [Cl−]i changes and their implications for neuronal information processing have been modeled [7,15,16,22,57,58]. However, the complex interplay and contribution of passive membrane leak, GABAA conductance, Cl− diffusion/ transport and stability of [HCO −3 ] gradients to these activity-dependent [Cl−]i changes have not yet been systematically investigated. Our simulations revealed a strong dependence between RInput and the GABAA receptor induced [Cl−]i transients. While at high RInput GABA-induced [Cl−]i changes were minimal, they increased in a nonlinear relation with decreasing RInput (Figure 1c). This relation between RInput and the [Cl−]i changes is due to the fact that at high RInput even small GABAergic currents bring Em close to ECl, which minimizes DFCl and thus the Cl− fluxes (Figure 1c). At low RInput the passive membrane conductance stabilizes Em and thus DFCl. In consequence, larger Cl− fluxes can be expected. Accordingly, implementation of “adult like” membrane properties [47] in a reconstructed immature neuron massively enhanced activity-dependent [Cl−]i changes (Figure 1c). In contrast, it seems obvious that immature neurons, with their high RInput [59], are less susceptible to activity-dependent [Cl−]i changes. However, in this respect, it is important to consider that in immature neurons [Cl−]i is high and GABAergic responses are depolarizing [30,60,61], therefore activity-dependent Cl− fluxes are directed outward and are leading to [Cl−]i decrease [42,44]. In addition, the HCO −3 -permeability of GABAA receptors also needs to be considered. The high RInput in immature neurons causes Em to approach EGABA, which normally is positive to ECl due to the HCO −3 -permeability of GABAA-receptors [1,4]. Therefore, stable Cl− influx would be expected under this condition and [Cl−]i should ultimately approach the value defined by EHCO3 , which at steady-state HCO − 3 gradients ([HCO −3 ]i = 14.1 mM and [HCO −3 ]e = 26 mM) amounts to 72.4 mM. This estimation suggests that under certain conditions GABAergic activity can even increase [Cl−]i from the already high [Cl−]i-levels in immature neurons (see Supplementary Figure S3d). But further properties of activity-dependent [Cl−]i changes observed in our simulations protect immature neurons from excessive [Cl−]i increases. In particular, we found that the influence of PHCO3 is relatively small at high [Cl −]i (Figure 6d), due to the fact that under this condition the contribution of EHCO3 to EGABA is small (as described by the Int. J. Mol. Sci. 2019, 20, 1416 13 of 22 Goldman-Hodgkin-Katz-Equation [1]). In addition, in immature neurons the HCO −3 gradient is instable because they lack carbonic anhydrases [50], which additionally attenuates the depolarizing effect of HCO −3 on Em and thus reduces DFCl. Under the assumption of a stable HCO −3 gradient, EGABA is in a wide range positive to ECl, thereby permitting Cl− influx during GABAergic stimulation, unless the thermodynamics equilibrium at 72.4 mM is reached. This is supported by the observation that no stable steady-state [Cl−]i is reached with realistic PHCO − values of 0.18 or 0.44 in our simulation (Supplementary Figure S3d,3 shaded lines). Experimental studies indeed demonstrated that massive GABAergic stimulation can shift EGABA from hyperpolarizing towards depolarizing and even excitation [12,62–64]. On the other hand, if we implemented in our model that [HCO −3 ]i can be altered by GABAA receptors, the activity-dependent [Cl−]i changes were massively reduced (Figure 5e). Our simulations also demonstrate that the switch from static [HCO −] to dynamic [HCO −3 3 ] condition shifts the [Cl−]i setpoint at which activity-dependent Cl− influx was replaced by Cl− efflux to considerably lower values (Figure 5e). This observation is due to the fact that the depolarizing HCO −3 fluxes through the GABAA receptor are attenuated by the dissipating HCO −3 gradient, which reduces EGABA and thus DFCl. Similar conclusions were drawn from experiments in which a block of carbonic anhydrases with acetazolamide, which provides a pharmacological destabilization of [HCO −3 ], also reduces EGABA shifts ([18], but see [62]). We conclude from these observations that the lack of carbonic anhydrase VII in immature neurons [50,65] may serve to limit the activity-dependent [Cl−]i changes in these neurons. On the other hand, our simulations in a reconstructed CA3 pyramidal neuron revealed that although massive [HCO −3 ]i changes are induced under this condition, the total GDP-induced [Cl−]i change was only marginally affected by variations in τHCO3 (Figure 7d). This lack of effect was most probably due to the fact that the activity-dependent [HCO −3 ]i change was already maximal at a τHCO3 of 90 ms at the synaptic site. This local saturated [HCO − 3 ]i change at the subsynaptic site is the only determinant for the synaptic effects of the [HCO −3 ]i. Our simulations also suggest that for an effective, physiologically relevant control of [HCO −3 ]i during GABAergic activity τHCO3 should be less than 70 ms (Supplementary Figure S4b). This fast relaxation time requires fast molecular processes that allow effective elimination of HCO −3 . Indeed, carbonic anhydrases, the enzymes that mediate the degradation or regeneration of HCO −3 into/from H2O and CO2, are among the fastest enzymes known. The kcat of murine carbonic anhydrase VII for the hydration oh CO2 is 4.5 × 105 s−1 at physiological pH [66]. From the assumption that the ca. 2mM [HCO −3 ]i change in the dendrite corresponds at a dendritic volume of 16 pL to ca. 0.3 fmol HCO −3 ions, it can be estimated that about 4000 molecules of carbonic anhydrase VII are required to replenish the lost HCO −3 within 100 ms. This estimation suggests that it is reasonable that sufficient carbonic anhydrase activity can be located in the dendritic compartment to reliably stabilize [HCO −3 ]i. However, as the reaction mediated by carbonic anhydrases includes H+ ions, the kinetics and thermodynamics of this process depends on the intracellular pH [67]. Thus dendritic H+-buffering and handling indirectly also affects activity-dependent [Cl−]i changes [15]. The acidification associated with neuronal activity [52,53] will slow down the kinetics of carbonic anhydrases [66]. However, this effect is negligible in comparison to the effect of the intracellular pH on [HCO −3 ]i. The intracellular pH is an essential parameter that determines [HCO −3 ]i [67]. Thus the intracellular acidification observed upon activation of GABAergic and glutamatergic synapses [51–53] will alter [HCO −3 ]i and subsequently influence GABAergic transmission. Our simulation revealed that an intracellular acidification will reduce the activity-dependent [Cl−]i changes at low [Cl−]i. This result, which is in accordance with a previous simulation [15], indicate that in adult neurons a parallel acidification will limit Cl− influx and thus stabilize inhibitory transmission In contrast, at a high [Cl−]i typical for immature neuron the intracellular acidification enhanced the activity-dependent Cl− efflux and may contribute to the loss of depolarizing drive and putative excitatory effects after strong GABAergic stimulation. Another factor that has a stringent effect on activity-dependent [Cl−]i changes in our simulations is τGABA. This confirmed and extended previous computational analyses (c.f. Figure 4d in [7]). It has been Int. J. Mol. Sci. 2019, 20, 1416 14 of 22 found that in general the decay kinetics of GABAergic transmission get faster during development [68]. Therefore the slow decay kinetics of GABAergic transmission in immature neurons [68,69] may be a factor that enables activity-dependent [Cl−]i transients, while the faster GABAergic postsynaptic currents in mature neurons not only improve the temporal precision of GABAergic transmission [68], but also the stability of inhibition. While a stable inhibition is a prerequisite for the proper function of mature neuronal networks, dynamic changes in [Cl−]i can be mandatory for physiological relevant functional features of the immature central nervous system. It has been suggested that activity-dependent changes in [Cl−]i, and the resulting switch from GABAergic inhibition to excitation, can underlie oscillatory activity [13]. In addition, in the immature nervous system the resting [Cl−]i is decreased by GABAergic activity, which will result in a diminished excitatory drive and/or a dominance of shunting inhibition and may thus serve to limit a possible excitatory effect of GABA [40]. Therefore, for immature neurons an unstable [Cl−]i homeostasis may be functionally relevant, as it allows activity-dependent scaling of [Cl−]i-dependent synaptic transmission [42,43]. In consequence, the molecular configuration of immature neurons (high [Cl−]i, long τGABA and missing CA-VII) will generate conditions that allow limited activity-dependent [Cl−]i decreases. This, in addition to the aforementioned effect of the high input resistance, may be an explanation why the [Cl−]i homeostasis of immature neurons is maintained by a relatively ineffective transmembrane Cl− transport [29]. In contrast, in mature neurons the situation is different. In the adult brain, the low [Cl−]i is needed to maintain hyperpolarizing inhibition [1] and an activity-dependent [Cl−]i increase will attenuate membrane hyperpolarization. While it is obvious that massive changes in [Cl−]i will impair GABAergic inhibition and can lead to hyperexcitability [18], recent modeling experiments demonstrate that even minimal changes in the capacity of Cl−-extrusion can have strong effects on information processing and storage in neurons [22]. Although the low RInput and the fast τGABA counteracts activity-dependent [Cl−]i increase in adult neurons, their low [Cl−]i and their effective carbonic anhydrases can lead to substantial [Cl−]i changes in their dendrite. The adverse effect of such local activity-dependent [Cl−]i increases is enhanced by the more elaborated dendritic compartment in mature neurons, which limits diffusional elimination of Cl−-ions [13]. Our simulations reveal that the connection of an isolated dendrite to the soma drastically reduces the equilibrium [Cl−]i after synaptic stimulation (Supplementary Figure S4f–k), demonstrating the important role of diffusional Cl− elimination under this condition. The large volume to surface ratio (and thus volume to conductance ratio) of the soma enables this compartment to serve as Cl− sink in these in-silico experiments. Also in-vitro it has been demonstrated that activation of dendritic GABAA receptors induced massive shifts in EGABA, whereas only minimal changes were observed upon perisomatic stimulation [10,12,26]. The dominance of perisomatic GABAergic terminals [70] may be related to the requirement of stable [Cl−] gradients to maintain stable inhibition over a wide range of activity levels. However, the diffusion of [Cl−]i through dendrite is a relatively slow and inefficient process, due to the small diameter in distal dendrites [7,55]. Addition of spines to dendrites drastically slow down diffusion along dendrites [16], suggesting that the complexity of the dendritic compartment (i.e. the number of arborizations that enhance tortuosity in the dendritic compartment) hinders Cl−-elimination by diffusion to the soma. Therefore, active elimination of Cl− from the cytoplasm is required to prevent or minimize activity-dependent [Cl−]i changes in the elaborated dendritic compartment of adult neurons. The elementary role of transmembrane Cl− transporters for neuronal [Cl−]i homeostasis has been shown by a variety of studies [2,29,32,58,71]. Modeling studies revealed that slightly altered rates of transmembrane Cl−-transport, which does only marginally affect resting [Cl−]i levels, have a strong effect on the spatiotemporal distribution of activity-dependent [Cl−]i-transients in dendrites [15]. Therefore it is not surprising that the simulation of an enhanced capacity of transmembrane Cl− transport by increasing τCl attenuates activity-dependent [Cl−]i transients. However, to minimize these [Cl−]i transients a rather low τCl of < 100 ms is required. These low τCl values are several orders of magnitude below the experimentally determined kinetics of the NKCC1-mediated Cl−-accumulation Int. J. Mol. Sci. 2019, 20, 1416 15 of 22 (τCl = 158 s) in immature neurons [29]. Because of this slow kinetic of transmembrane transport of Cl− in immature neurons, we also consider that a Cl−/HCO −3 exchange mediated by the anion exchanger in immature neurons [72] has only a marginal effect on both activity-dependent [Cl−]i and [HCO −3 ]i transients in these neurons. In the mature situation (low [Cl−]i and effective transmembrane [Cl−]i transport), modeling studies suggest that an interference between [Cl−]i and [HCO −3 ]i by this mechanism can reduce the activity-dependent [Cl−]i changes [15]. While it is generally assumed that the neuron-specific Cl−-extruder KCC2 mediates more efficient Cl− transport than NKCC1, only few experimental studies addressed the kinetics of KCC2-dependent Cl−-extrusion. Experiments in brain stem neurons demonstrated that KCC2 mediated Cl−-extrusion after [Cl−]i increase by ca. 10 mM requires several minutes [73]. In contrast, in-vivo experiments revealed that the activity-dependent [Cl−]i increase after an epileptic seizure recovered within less than 30 s [9] and in hippocampal slices GABA-induced [Cl−]i transients recovered back to low steady-stale levels with a time constant of 3.3 s [12]. However, it is not clear how diffusional processes and/or the kinetics of the used Cl− sensor contribute to these kinetic properties. Simulations suggest that with realistic KCC2 levels τCl in the distal dendrites (≥ 200 µm from the soma) is between 100 ms and 200 ms [15], and thus probably lower than estimated from experimental data. Even this time constant is higher than the τCl required in our simulations to prevent local activity-dependent [Cl−]i changes, suggesting that considerable [Cl−]i changes can occur at GABAergic synaptic sites. While our and other simulations [15] suggest these transients may be restricted to local dendritic domains, it must be emphasized that subtle changes in the efficacy of KCC2 mediated Cl−-transport can already enhance the excitability in single neurons because activity-dependent [Cl−]i transients may superimpose these effects [22]. In consequence, impairments of KCC2 mediated Cl− transport can led to a breakdown of sufficient inhibition in neuronal networks and contribute to hyperexcitability [15,17,20,56,74]. In this respect it is also relevant to consider that the activity of both NKCC1 and KCC2 are regulated by a variety of processes [75–78]. This indicates that the spatiotemporal [Cl−]i dynamics in the dendritic compartment may be adapted to the functional states. The limitation of our model to fully describe GDP-induced [Cl−]i transients in CA3 pyramidal neurons is obvious from the fact, that we massively underestimate the [Cl−]i decrease observed in real CA3 pyramidal neurons at high [Cl−]i (e.g. Figure 6e). Therefore, additional factors must be proposed, which enhance the GABAA-receptor-mediated Cl− efflux. Possible mechanisms that improve Cl− efflux are e.g. an inhomogeneous distribution of voltage-activated K+ channels in the dendritic compartment, an underestimation of nGABA in our in-vitro experiments due to voltage-clamp errors in the elaborated dendrite [79], or the effect of glutamatergic transmission during a GDP [20,80]. In addition, we also found that τGABA has a major impact on the GDP-induced [Cl−]i transients, and it might well be that the decay kinetics of spontaneous GABAergic PSCs of 37 ms [45] reflect the kinetic properties of a subpopulation of GABAergic inputs, that is less involved in the generation of GDPs. Finally, in our simulations the dendrite was implemented as a hollow tube with a diameter determined from the histological reconstruction. Under realistic assumptions the neuron is, however, filled with cytoplasm that contains large proteins, particles of different sizes and vesicles and tubes of intracellular organelles. Thus the free, “unexcluded” volume in the cytoplasm is restricted to an estimated fraction of ca. 60%, a principle termed cytoplasmic crowding [81]. This restricted free cytoplasmic water volume will increase the size of [Cl−]i transients upon identical Cl− fluxes. 4. Materials and Methods Compartmental Modeling The biophysically realistic compartmental modelling was performed using the NEURON environment (neuron.yale.edu). The source code of models and stimulation files used in the present paper can be found in ModelDB [82] at http://modeldb.yale.edu/253369 (access date 14 March 2019) and was included in the supplementary material of this publication. For compartmental modelling we used either Int. J. Mol. Sci. 2019, 20, 1416 16 of 22 a simple ball and stick model (soma with d = 20 µm, linear dendrite with l = 200 µm and 103 nodes) or a reconstructed CA3 pyramidal cell (from Lombardi et al. [45]). Except where noted the dendrite was detached from the soma to analyse dendritic [Cl−]i transients. The reconstructed neuron resembled the somatodendritic morphology of a typical immature CA3 pyramidal neuron (see Figure 2a,b). For this purpose images of a biocytin-filled neuron [83,84] were taken with 60× oil-immersion objectives and the somatodendritic morphology was reconstructed using Fiji (www.fiji.sc). It contained a soma (d = 15 µm), a dendritic trunk (d = 2 µm, l = 32 µm, 9 segments) and 56 dendrites (d = 0.36 µm, 9 segments each). In all of these compartments a specific axial resistance (Ra) of 34.5 Ωcm and a specific membrane capacitance (Cm) of 1 µF cm−2; were implemented. The specific membrane conductance (gpas) varied (see Figures 1a and 2e) and in the majority of the experiments was modeled by a voltage dependent process given by a Boltzmann-like equation: g g maxpas = gmin + ( ( )) (1) 1 + exp (Em−E50)s with gmax = 0.002800 S/cm2 (experimentally determined gInput at depolarized potentials, see Supplementary Figure S1a), gmin = 0.000660 S/cm2 (experimentally determined minimal gInput at hyperpolarized potentials, see Figure 3a), e50 = −31 mV (half-maximal voltage), s = −6 (slope of the voltage-dependency). The reversal potential of this voltage-depended gpas was set to −60 mV. GABAA synapses were simulated as a postsynaptic parallel Cl− and HCO −3 conductance with exponential rise and exponential decay [7]: IGABA = ICl + IHCO3 = 1/(1+P) gGABA (V-ECl) + P/(1+P) gGABA (V-EHCO3 ) (2) where P is a fractional ionic conductance that was used to split the GABAA conductance (gGABA) into Cl− and HCO −3 conductance. ECl and EHCO3 were calculated from Nernst equation. The GABAA conductance was modeled using a two-term exponential function, using separate values of rise time (0.5 ms) and decay time (variable, mostly 37 ms) [45]. Parameters used in our simulations were as follows: [Cl−]o = 133.5 mM, [HCO − − ◦3 ]i = 14.1 mM, [HCO3 ]o = 24 mM, temperature = 31 C, P = 0.44 [49]. For the ball and stick model a single GABAA synapse was placed in the middle of the dendrite, except where noted. For the simulation of a GDP in the reconstructed CA3 neuron 101–3020 GABAergic synapses were randomly distributed within the dendrites of the reconstructed neuron. GABA inputs were activated stochastically using a normal distribution (µ = 600ms, σ = 900 ms) that emulates the distribution of GABAergic PSCs during a GDP observed in immature hippocampal CA3 pyramidal neurons [45]. The properties of these synapses were always given in the results part and/or the corresponding figure legends. From the quotient between the charge transfer of a GDP and of spontaneous GABAergic postsynaptic currents at a holding potential (VHold) of 0 mV it was estimated that 101 GABAergic inputs underlie a GDP [45]. To compensate for the space-clamp problems during a GDP, that were not considered by Lombardi et al. [45], we simulated the charge transfer during a GDP under their experimental conditions ([Cl−]i = 10 mM, VHold = 0 mV) and determined that 302, 395, and 523 (for PHCO3 values of 0.0, 0.18, and 0.44, respectively) GABAergic synapses are required to generate the observed GDP-induced charge transfer (Supplementary Figure S1c–f). For these experiments we implement the single-electrode voltage clamp procedure provided by NEURON, using an access resistance of 5 MΩ. The charge transfer was calculated from the integral of the holding currents (IHold) during the GDP. For the modeling of the GABA receptor-induced [Cl−A ]i and [HCO −3 ]i changes, we calculated ion diffusion and uptake by standard compartmental diffusion modeling [16,85–87]. To simulate intracellular Cl− and HCO −3 dynamics, we adapted our previously published model [7]. Longitudinal Cl− and HCO −3 diffusion along dendrites was modeled as the exchange of anions between adjacent compartments. For radial diffusion, the volume was discretized into a series of 4 concentric shells around a cylindrical core [85] and Cl− or HCO −3 was allowed to flow between adjacent shells [88]. Int. J. Mol. Sci. 2019, 20, 1416 17 of 22 The free diffusion coefficient of Cl− inside neurons was set to 2 µm2/ms [55,89]. Since the cytoplasmatic diffusion constant for HCO −3 is, to our knowledge, unknown, we also used a value of 2 µm2/ms. To simulate transmembrane transport of Cl− and HCO −3 , we implemented an exponential relaxation process for [Cl−]i and [HCO −3 ]i to[restin]g levels [Cl −] rest[ ] i o[r [HCO − rest ] 3 ]i with a time constant τIon. d Ion− i Ion − rest i − e Ion − = i (3) dt τIon Cl− transport was in most experiments (if not otherwise noted) modeled as bimodal process, for [Cl−]i < [Cl−] resti τIon was set to 174 s to emulate an NKCC1-like Cl− transport mechanism. For [Cl−]i > [Cl−] resti τ −Ion was set to 321 s to emulate passive Cl efflux (both values obtained from unpublished experiments on immature rat CA3 hippocampal neurons). The impact of GABAergic Cl− curre[nts on [Cl − ] ]i and [HCO − 3 ]i was calculated as: d Ion− i 1 I= Ion (4) dt F volume To simulate the GABAergic activity during a GDP, a unitary peak conductance of 0.789 nS and a decay of 37 ms were applied to each GABAergic synapse. These values resulted in a unitary currents of pA, which was in accordance with the mean amplitude of spontaneous GABAergic postsynaptic currents in CA3 paramidal neurons [45]. For the isolated neurons the [Cl−] and [HCO −i 3 ]i concentration was averaged over all segments of the dendrite, except where noted. For the simulated neurons we analyzed mean [Cl−] and [HCO −i 3 ]i of all dendrites: [ −] 1 ndend[ ]Cl × ∑ Cl− Dend(j) @ 0.5 o f total lengthi = i (5)ndend j=1 This procedure mimics the experimental procedure of Lombardi et al [45], who determined EGABA by focal application in the dendritic compartment. For the calculation of ∆[Cl−]i the maximal deviation of [Cl−]i upon a GABAergic stimulus ([Cl−] Si ) was subtracted from the resting [Cl−]i before the stimulus ([Cl−] Ri ). For biphasic responses both minima[l and]maxi − R − − [ mal ][Cl ]i w[ere d]etermined an(d[∆[Cl] ]i wa)s calcul(a[ted as: ) ∆ Cl = Cl− S, min − Cl− R if abs Cl− S, min > abs Cl− ]S, max i i i i i (6)[ ] [ ] ( ) ( ) ∆ Cl− Cl− S, max [ ] − Cl− R [ ] [ ] i = i i if abs Cl − S, min i ≤ abs Cl − S, max i (7) The driving-force of Cl− (DFCl) was calculated from the difference between the average Em during a GDP and ECl (DFCl = Em – ECl). To calculate the ratio between transmembrane [Cl−]i transport and diffusional [Cl−]i depletion into the soma, we normalized the diffusional exchange between the last somatic node and the soma (as calculated from Fick’s law) to conditions were transmembrane [Cl−]i loss was absent (τ 9Cl = 10 ms) and diffusional dendrite to soma transport was allowed to equilibrate for 2 min. All electrophysiological data were taken from our previous publication [45]. However, for a comparison of these results with the simulations, we had to take different PHCO3 into account. Therefore the GDP-induced [Cl−]i changes were recalculated using PHCO3 values of 0.0, 0.18 (determined in spinal cord neurons [48]) and 0.44 (determined in adult hippocampal neurons [49]). The [Cl−]i was calculated from EGABA with the Goldman-H(odgk[in-K]atz equation: ) RT PCl [Cl− ]e + PHCO3 [HCO −3 ]E eGGABA = × ln (8)ZF P − −Cl Cl i + PHCO3 [HCO3 ]i Int. J. Mol. Sci. 2019, 20, 1416 18 of 22 For the calculation of [Cl−]i from EGABA we used a [Cl−]e of 133.5 mM, an extracellular HCO −3 concentration ([HCO −3 ]e) of 24 mM and assumed a constant [HCO −3 ]i of 14.1 mM (calculated from an intracellular pH of 7.2 [90], a CO2 pressure (pCO2) of 38 mmHg, a Henry coefficient (α) of 0.0318 mM/mmHg and a pKs of 6.128 [91] with the Henderson-Hasselbalch equation), if not otherwise mentioned. [ HCO − ] 3 i = 10 (pH−pKs+log (α×pCO2)) (9) RInput was calculated from the Em response upon a simulated current injection (IInj) according to Ohms law: E R mInput = (10)IInj Supplementary Materials: Supplementary materials can be found at http://www.mdpi.com/1422-0067/20/6/1416/ s1. Author Contributions: Conceptualization, P.J. and W.K.; Investigation, A.L. and W.K.; Coding, A.L., P.J. and W.K.; Writing—original draft, A.L., P.J., H.J.L. and W.K.; Writing—review and editing, P.J., H.J.L. and W.K. Funding: This research was funded by grants of the Deutsche Forschungsgemeinschaft to WK (KI-835/3) and to HJL (CRC 1080) and by grants of the University Medical Center Giessen and Marburg (UKGM) to P.J. Acknowledgments: The authors thank Beate Krumm for her excellent technical support. W.K. thanks Kristina and Benjamin for their patience. Conflicts of Interest: The authors declare no conflict of interest. Abbreviations DFCl Electromotive driving force on Cl− ions ECl Equilibrium potential for Cl− EGABA Reversal potential of GABAergic currents EHCO3 Equilibrium potential for HCO − 3 GABA γ-Amino butyric acid GDP Giant depolarizing potential gGABA Conductance of GABAergic synapse gpas Passive membrane conductance KCC K+-Cl−-Cotransporter NKCC1 Na+-K+-Cl−-Cotransporter, Isoform 1 nGABA Number of GABAergic synapses PHCO3 Relative HCO − 3 permeability of GABAA receptors VHold Holding potential τCl Time constant of [Cl−] relaxation τGABA Decay time constant of GABAA receptors τHCO3 Time constant of [HCO − 3 ] relaxation References 1. Farrant, M.; Kaila, K. The cellular, molecular and ionic basis of GABA(A) receptor signaling. Prog. Brain Res. 2007, 160, 59–87. [PubMed] 2. Rivera, C.; Voipio, J.; Payne, J.A.; Ruusuvuori, E.; Lahtinen, H.; Lamsa, K.; Pirvola, U.; Saarma, M.; Kaila, K. The K+/Cl− co-transporter KCC2 renders GABA hyperpolarizing during neuronal maturation. Nature 1999, 397, 251–255. [CrossRef] [PubMed] 3. Blaesse, P.; Airaksinen, M.S.; Rivera, C.; Kaila, K. Cation-chloride cotransporters and neuronal function. Neuron 2009, 61, 820–838. [CrossRef] 4. Kaila, K.; Pasternack, M.; Saarikoski, J.; Voipio, J. Influence of GABA-gated bicarbonate conductance on potential, current and intracellular chloride in crayfish muscle fibres. J. Physiol. 1989, 416, 161–181. [CrossRef] [PubMed] Int. J. Mol. Sci. 2019, 20, 1416 19 of 22 5. Bracci, E.; Vreugdenhil, M.; Hack, S.P.; Jefferys, J.G.R. Dynamic modulation of excitation and inhibition during stimulation at gamma and beta frequencies in the CA1 hippocampal region. J. Neurophysiol. 2001, 85, 2412–2422. [CrossRef] 6. Isomura, Y.; Sugimoto, M.; Fujiwara-Tsukamoto, Y.; Yamamoto-Muraki, S.; Yamada, J.; Fukuda, A. Synaptically activated Cl− accumulation responsible for depolarizing GABAergic responses in mature hippocampal neurons. J. Neurophysiol. 2003, 90, 2752–2756. [CrossRef] 7. Jedlicka, P.; Deller, T.; Gutkin, B.S.; Backus, K.H. Activity-Dependent Intracellular Chloride Accumulation and Diffusion Controls GABA(A) Receptor-Mediated Synaptic Transmission. Hippocampus 2011, 21, 885–898. 8. Lillis, K.P.; Kramer, M.A.; Mertz, J.; Staley, K.J.; White, J.A. Pyramidal cells accumulate chloride at seizure onset. Neurobiol. Dis. 2012, 47, 358–366. [CrossRef] 9. Sato, S.S.; Artoni, P.; Landi, S.; Cozzolino, O.; Parra, R.; Pracucci, E.; Trovato, F.; Szczurkowska, J.; Luin, S.; Arosio, D.; et al. Simultaneous two-photon imaging of intracellular chloride concentration and pH in mouse pyramidal neurons in vivo. Proc. Natl. Acad. Sci. USA 2017, 114, E8770–E8779. [CrossRef] 10. Raimondo, J.V.; Markram, H.; Akerman, C.J. Short-term ionic plasticity at GABAergic synapses. Front. Synaptic Neurosci. 2012, 4, 5. [CrossRef] 11. Kaila, K.; Price, T.J.; Payne, J.A.; Puskarjov, M.; Voipio, J. Cation-chloride cotransporters in neuronal development, plasticity and disease. Nat. Rev. Neurosci. 2014, 15, 637–654. [CrossRef] [PubMed] 12. Staley, K.J.; Proctor, W.R. Modulation of mammalian dendritic GABAA receptor function by the kinetics of Cl− and HCO −3 transport. J. Physiol. 1999, 519, 693–712. [CrossRef] [PubMed] 13. Jedlicka, P.; Backus, K.H. Inhibitory transmission, activity-dependent ionic changes and neuronal network oscillations. Physiol. Res. 2006, 55, 139–149. [PubMed] 14. Wright, R.; Raimondo, J.V.; Akerman, C.J. Spatial and Temporal Dynamics in the Ionic Driving Force for GABA(A) Receptors. Neural Plast. 2011, 278395. [CrossRef] 15. Doyon, N.; Prescott, S.A.; Castonguay, A.; Godin, A.G.; Kroger, H.; De Koninck, Y. Efficacy of Synaptic Inhibition Depends on Multiple, Dynamically Interacting Mechanisms Implicated in Chloride Homeostasis. PLoS Comput. Biol. 2011, 7, 9. [CrossRef] 16. Mohapatra, N.; Tonnesen, J.; Vlachos, A.; Kuner, T.; Deller, T.; Nagerl, U.V.; Santamaria, F.; Jedlicka, P. Spines slow down dendritic chloride diffusion and affect short-term ionic plasticity of GABAergic inhibition. Sci. Rep. 2016, 6, 23196. [CrossRef] [PubMed] 17. Buchin, A.; Chizhov, A.; Huberfeld, G.; Miles, R.; Gutkin, B.S. Reduced Efficacy of the KCC2 Cotransporter Promotes Epileptic Oscillations in a Subiculum Network Model. J. Neurosci. 2016, 36, 11619–11633. [CrossRef] [PubMed] 18. Staley, K.J.; Soldo, B.L.; Proctor, W.R. Ionic mechanisms of neuronal excitation by inhibitory GABAA receptors. Science 1995, 269, 977–981. [CrossRef] 19. Sun, M.K.; Zhao, W.Q.; Nelson, T.J.; Alkon, D.L. Theta rhythm of hippocampal CA1 neuron activity: Gating by GABAergic synaptic depolarization. J. Neurophysiol. 2001, 85, 269–279. [CrossRef] 20. Doyon, N.; Vinay, L.; Prescott, S.A.; De Koninck, Y. Chloride Regulation: A Dynamic Equilibrium Crucial for Synaptic Inhibition. Neuron 2016, 89, 1157–1172. [CrossRef] 21. Thompson, S.M.; Gähwiler, B.H. Activity-dependent disinhibition. I. Repetitive stimulation reduces IPSP driving force and conductance in the hippocampus in vitro. J. Neurophysiol. 1989, 61, 501–511. [CrossRef] 22. Doyon, N.; Prescott, S.A.; De Koninck, Y. Mild KCC2 Hypofunction Causes Inconspicuous Chloride Dysregulation that Degrades Neural Coding. Front. Cell. Neurosci. 2016, 9, 516. [CrossRef] 23. Bernard, C.; Cossart, R.; Hirsch, J.C.; Esclapez, M.; Ben Ari, Y. What is GABAergic inhibition? How is it modified in epilepsy? Epilepsia 2000, 41, S90–S95. [CrossRef] 24. Birke, G.; Draguhn, A. No Simple Brake—The Complex Functions of Inhibitory Synapses. Pharmacopsychiatry 2010, 43, S21–S31. [CrossRef] 25. Ben-Ari, Y.; Cherubini, E.; Corradetti, R.; Gaiarsa, J.-L. Giant synaptic potentials in immature rat CA3 hippocampal neurones. J. Physiol. 1989, 416, 303–325. [CrossRef] 26. Luhmann, H.J.; Prince, D.A. Postnatal maturation of the GABAergic system in rat neocortex. J. Neurophysiol. 1991, 65, 247–263. [CrossRef] 27. Owens, D.F.; Boyce, L.H.; Davis, M.B.; Kriegstein, A.R. Excitatory GABA responses in embryonic and neonatal cortical slices demonstrated by gramicidin perforated-patch recordings and calcium imaging. J. Neurosci. 1996, 16, 6414–6423. [CrossRef] Int. J. Mol. Sci. 2019, 20, 1416 20 of 22 28. Hanganu, I.L.; Kilb, W.; Luhmann, H.J. Functional Synaptic Projections onto Subplate Neurons in Neonatal Rat Somatosensory Cortex. J. Neurosci. 2002, 22, 7165–7176. [CrossRef] 29. Achilles, K.; Okabe, A.; Ikeda, M.; Shimizu-Okabe, C.; Yamada, J.; Fukuda, A.; Luhmann, H.J.; Kilb, W. Kinetic properties of Cl uptake mediated by Na+-dependent K+-2Cl− cotransport in immature rat neocortical neurons. J. Neurosci. 2007, 27, 8616–8627. [CrossRef] 30. Kirmse, K.; Kummer, M.; Kovalchuk, Y.; Witte, O.W.; Garaschuk, O.; Holthoff, K. GABA depolarizes immature neurons and inhibits network activity in the neonatal neocortex in vivo. Nat. Commun. 2015, 6, 7750. [CrossRef] 31. Rohrbough, J.; Spitzer, N.C. Regulation of intracellular Cl− levels by Na(+)-dependent Cl− cotransport distinguishes depolarizing from hyperpolarizing GABAA receptor-mediated responses in spinal neurons. J. Neurosci. 1996, 16, 82–91. [CrossRef] [PubMed] 32. Yamada, J.; Okabe, A.; Toyoda, H.; Kilb, W.; Luhmann, H.J.; Fukuda, A. Cl− uptake promoting depolarizing GABA actions in immature rat neocortical neurones is mediated by NKCC1. J. Physiol. 2004, 557, 829–841. [CrossRef] [PubMed] 33. Valeeva, G.; Tressard, T.; Mukhtarov, M.; Baude, A.; Khazipov, R. An Optogenetic Approach for Investigation of Excitatory and Inhibitory Network GABA Actions in Mice Expressing Channelrhodopsin-2 in GABAergic Neurons. J. Neurosci. 2016, 36, 5961–5973. [CrossRef] [PubMed] 34. Kolbaev, S.N.; Achilles, K.; Luhmann, H.J.; Kilb, W. Effect of depolarizing GABA(A)-mediated membrane responses on excitability of Cajal-Retzius cells in the immature rat neocortex. J. Neurophysiol. 2011, 106, 2034–2044. [CrossRef] [PubMed] 35. Owens, D.F.; Kriegstein, A.R. Is there more to GABA than synaptic inhibition? Nat. Rev. Neurosci. 2002, 3, 715–727. [CrossRef] [PubMed] 36. Ben Ari, Y.; Khalilov, I.; Kahle, K.T.; Cherubini, E. The GABA Excitatory/Inhibitory Shift in Brain Maturation and Neurological Disorders. Neuroscientist 2012, 18, 467–486. [CrossRef] [PubMed] 37. Luhmann, H.J.; Kirischuk, S.; Sinning, A.; Kilb, W. Early GABAergic circuitry in the cerebral cortex. Curr. Opin. Neurobiol. 2014, 26, 72–78. [CrossRef] 38. Sipila, S.T.; Huttu, K.; Soltesz, I.; Voipio, J.; Kaila, K. Depolarizing GABA acts on intrinsically bursting pyramidal neurons to drive giant depolarizing potentials in the immature hippocampus. J. Neurosci. 2005, 25, 5280–5289. [CrossRef] [PubMed] 39. Allene, C.; Cattani, A.; Ackman, J.B.; Bonifazi, P.; Aniksztejn, L.; Ben Ari, Y.; Cossart, R. Sequential Generation of Two Distinct Synapse-Driven Network Patterns in Developing Neocortex. J. Neurosci. 2008, 28, 12851–12863. [CrossRef] 40. Chub, N.; O’Donovan, M.J. Post-episode depression of GABAergic transmission in spinal neurons of the chick embryo. J. Neurophysiol. 2001, 85, 2166–2176. [CrossRef] 41. Lindsly, C.; Gonzalez-Islas, C.; Wenner, P. Activity Blockade and GABA(A) Receptor Blockade Produce Synaptic Scaling through Chloride Accumulation in Embryonic Spinal Motoneurons and Interneurons. PLoS ONE 2014, 9, e94559. [CrossRef] [PubMed] 42. Kolbaev, S.N.; Luhmann, H.J.; Kilb, W. Activity-dependent scaling of GABAergic excitation by dynamic Cl− changes in Cajal-Retzius cells. Pflugers Arch. 2011, 461, 557–565. [CrossRef] [PubMed] 43. Gonzalez-Islas, C.; Chub, N.; Garcia-Bereguiain, M.A.; Wenner, P. GABAergic synaptic scaling in embryonic motoneurons is mediated by a shift in the chloride reversal potential. J. Neurosci. 2010, 30, 13016–13020. [CrossRef] 44. Khalilov, I.; Minlebaev, M.; Mukhtarov, M.; Khazipov, R. Dynamic Changes from Depolarizing to Hyperpolarizing GABAergic Actions during Giant Depolarizing Potentials in the Neonatal Rat Hippocampus. J. Neurosci. 2015, 35, 12635–12642. [CrossRef] [PubMed] 45. Lombardi, A.; Jedlicka, P.; Luhmann, H.J.; Kilb, W. Giant Depolarizing Potentials Trigger Transient Changes in the Intracellular Cl− Concentration in CA3 Pyramidal Neurons of the Immature Mouse Hippocampus. Front. Cell. Neurosci. 2018, 12, 420. [CrossRef] 46. Kowalski, J.; Gan, J.; Jonas, P.; Pernia-Andrade, A.J. Intrinsic membrane properties determine hippocampal differential firing pattern in vivo in anesthetized rats. Hippocampus 2016, 26, 668–682. [CrossRef] 47. Behrens, C.J.; Ul Haq, R.; Liotta, A.; Anderson, M.L.; Heinemann, U. Nonspecific effects of the gap junction blocker mefloquine on fast hippocampal network oscillations in the adult rat in vitro. Neuroscience 2011, 192, 11–19. [CrossRef] [PubMed] Int. J. Mol. Sci. 2019, 20, 1416 21 of 22 48. Bormann, J.; Hamill, O.P.; Sakmann, B. Mechanism of anion permeation through channels gated by glycine and gamma-aminobutyric acid in mouse cultured spinal neurones. J. Physiol. 1987, 385, 243–286. [CrossRef] 49. Fatima-Shad, K.; Barry, P.H. Anion Permeation in GABA- and Glycine-Gated Channels of Mammalian Cultured Hippocampal Neurons. Proc. Biol. Sci. 1993, 253, 69–75. 50. Ruusuvuori, E.; Li, H.; Huttu, K.; Palva, J.M.; Smirnov, S.; Rivera, C.; Kaila, K.; Voipio, J. Carbonic Anhydrase Isoform VII Acts as a Molecular Switch in the Development of Synchronous Gamma-Frequency Firing of Hippocampal CA1 Pyramidal Cells. J. Neurosci. 2004, 24, 2699–2707. [CrossRef] 51. Kaila, K.; Voipio, J. Postsynaptic fall in intracellular pH induced by GABA-activated bicarbonate conductance. Nature 1987, 330, 163–165. [CrossRef] 52. Wang, G.J.; Randall, R.D.; Thayer, S.A. Glutamate-Induced Intracellular Acidification of Cultured Hippocampal-Neurons Demonstrates Altered Energy-Metabolism Resulting from Ca2+ Loads. J. Neurophysiol. 1994, 72, 2563–2569. [CrossRef] 53. Kilb, W.; Schlue, W.R. Mechanism of the kainate-induced intracellular acidification in leech Retzius neurons. Brain Res. 1999, 824, 168–182. [CrossRef] 54. Qian, N.; Sejnowski, T.J. When Is an Inhibitory Synapse Effective. Proc. Natl. Acad. Sci. USA 1990, 87, 8145–8149. [CrossRef] 55. Kuner, T.; Augustine, G.J. A genetically encoded ratiometric indicator for chloride: Capturing chloride transients in cultured hippocampal neurons. Neuron 2000, 27, 447–459. [CrossRef] 56. Kaila, K.; Ruusuvuori, E.; Seja, P.; Voipio, J.; Puskarjov, M. GABA actions and ionic plasticity in epilepsy. Curr. Opin. Neurobiol. 2014, 26, 34–41. [CrossRef] [PubMed] 57. Lewin, N.; Aksay, E.; Clancy, C.E. Computational modeling reveals dendritic origins of GABA(a)-mediated excitation in ca1 pyramidal neurons. PLoS ONE 2012, 7, e47250. [CrossRef] 58. Dusterwald, K.M.; Currin, C.B.; Burman, R.J.; Akerman, C.J.; Kay, A.R.; Raimondo, J.V. Biophysical models reveal the relative importance of transporter proteins and impermeant anions in chloride homeostasis. Elife 2018, 7, e39575. [CrossRef] 59. Luhmann, H.J.; Reiprich, R.A.; Hanganu, I.; Kilb, W. Cellular physiology of the neonatal rat cerebral cortex: Intrinsic membrane properties, sodium and calcium currents. J. Neurosci. Res. 2000, 62, 574–584. [CrossRef] 60. Rheims, S.; Minlebaev, M.; Ivanov, A.; Represa, A.; Khazipov, R.; Holmes, G.L.; Ben-Ari, Y.; Zilberter, Y. Excitatory GABA in rodent developing neocortex in vitro. J. Neurophysiol. 2008, 100, 609–619. [CrossRef] 61. Ben-Ari, Y.; Woodin, M.A.; Sernagor, E.; Cancedda, L.; Vinay, L.; Rivera, C.; Legendre, P.; Luhmann, H.J.; Bordey, A.; Wenner, P.; et al. Refuting the challenges of the developmental shift of polarity of GABA actions: GABA more exciting than ever! Front. Cell. Neurosci. 2012, 6, 35. [CrossRef] 62. Kaila, K.; Lamsa, K.; Smirnov, S.; Taira, T.; Voipio, J. Long-lasting GABA-mediated depolarization evoked by high-frequency stimulation in pyramidal neurons of rat hippocampal slice is attributable to a network-driven, bicarbonate-dependent K+ transient. J. Neurosci. 1997, 17, 7662–7672. [CrossRef] [PubMed] 63. Dallwig, R.; Deitmer, J.W.; Backus, K.H. On the mechanism of GABA-induced currents in cultured rat cortical neurons. Pflugers Arch. 1999, 437, 289–297. [CrossRef] 64. Sun, M.; Dahl, D.; Alkon, D.L. Heterosynaptic transformation of GABAergic gating in the hippocampus and effects of carbonic anhydrase inhibition. J. Pharmacol. Exp. Ther. 2001, 296, 811–817. [PubMed] 65. Rivera, C.; Voipio, J.; Kaila, K. Two developmental switches in GABAergic signalling: The K+- Cl− cotransporter KCC2 and carbonic anhydrase CAVII. J. Physiol. 2005, 562, 27–36. [CrossRef] [PubMed] 66. Earnhardt, J.N.; Qian, M.Z.; Tu, C.K.; Lakkis, M.M.; Bergenhem, N.C.H.; Laipis, P.J.; Tashian, R.E.; Silverman, D.N. The catalytic properties of murine carbonic anhydrase VII. Biochemistry 1998, 37, 10837–10845. [CrossRef] [PubMed] 67. Kaila, K. Ionic basis of GABAA receptor channel function in the nervous system. Prog. Neurobiol. 1994, 42, 489–537. [CrossRef] 68. Doischer, D.; Hosp, J.A.; Yanagawa, Y.; Obata, K.; Jonas, P.; Vida, I.; Bartos, M. Postnatal Differentiation of Basket Cells from Slow to Fast Signaling Devices. J. Neurosci. 2008, 28, 12956–12968. [CrossRef] 69. Kilb, W.; Luhmann, H.J. Spontaneous GABAergic postsynaptic currents in Cajal-Retzius cells in neonatal rat cerebral cortex. Eur. J. Neurosci. 2001, 13, 1387–1390. [CrossRef] 70. Kubota, Y. Untangling GABAergic wiring in the cortical microcircuit. Curr. Opin. Neurobiol. 2014, 26, 7–14. [CrossRef] Int. J. Mol. Sci. 2019, 20, 1416 22 of 22 71. Misgeld, U.; Deisz, R.A.; Dodt, H.U.; Lux, H.D. The role of chloride transport in postsynaptic inhibition of hippocampal neurons. Science 1986, 232, 1413–1415. [CrossRef] [PubMed] 72. Gonzalez-Islas, C.; Chub, N.; Wenner, P. NKCC1 and AE3 appear to accumulate chloride in embryonic motoneurons. J. Neurophysiol. 2009, 101, 507–518. [CrossRef] [PubMed] 73. Kakazu, Y.; Uchida, S.; Nakagawa, T.; Akaike, N.; Nabekura, J. Reversibility and cation selectivity of the K+-Cl− cotransport in rat central neurons. J. Neurophysiol. 2000, 84, 281–288. [CrossRef] [PubMed] 74. Kahle, K.T.; Staley, K.J.; Nahed, B.V.; Gamba, G.; Hebert, S.C.; Lifton, R.P.; Mount, D.B. Roles of the cation-chloride cotransporters in neurological disease. Nat. Clin. Pract. Neurol. 2008, 4, 490–503. [CrossRef] [PubMed] 75. Khirug, S.; Ahmad, F.; Puskarjov, M.; Afzalov, R.; Kaila, K.; Blaesse, P. A single seizure episode leads to rapid functional activation of KCC2 in the neonatal rat hippocampus. J. Neurosci. 2010, 30, 12028–12035. [CrossRef] [PubMed] 76. Inoue, K.; Furukawa, T.; Kumada, T.; Yamada, J.; Wang, T.Y.; Inoue, R.; Fukuda, A. Taurine Inhibits K+-Cl− Cotransporter KCC2 to Regulate Embryonic Cl− Homeostasis via With-no-lysine (WNK) Protein Kinase Signaling Pathway. J. Biol. Chem. 2012, 287, 20839–20850. [CrossRef] 77. Russell, J.M. Sodium-potassium-chloride cotransport. Physiol. Rev. 2000, 80, 211–276. [CrossRef] [PubMed] 78. Delpire, E.; Austin, T.M. Kinase regulation of Na+-K+-2Cl− cotransport in primary afferent neurons. J. Physiol. 2010, 588, 3365–3373. [CrossRef] 79. Bar-Yehuda, D.; Korngreen, A. Space-clamp problems when voltage clamping neurons expressing voltage-gated conductances. J. Neurophysiol. 2008, 99, 1127–1136. [CrossRef] 80. Backus, K.H.; Deitmer, J.W.; Friauf, E. Glycine-activated currents are changed by coincident membrane depolarization in developing rat auditory brainstem neurones. J. Physiol. 1998, 507, 783–794. [CrossRef] 81. Ellis, R.J.; Minton, A.P. Cell biology—Join the crowd. Nature 2003, 425, 27–28. [CrossRef] [PubMed] 82. McDougal, R.A.; Morse, T.M.; Carnevale, T.; Marenco, L.; Wang, R.; Migliore, M.; Miller, P.L.; Shepherd, G.M.; Hines, M.L. Twenty years of ModelDB and beyond: Building essential modeling tools for the future of neuroscience. J. Comput. Neurosci. 2017, 42, 1–10. [CrossRef] [PubMed] 83. Horikawa, K.; Armstrong, W.E. A versatile means of intracellular labeling: Injection of biocytin and its detection with avidin conjugates. J. Neurosci. Meth. 1988, 25, 1–11. [CrossRef] 84. Schröder, R.; Luhmann, H.J. Morphology, electrophysiology and pathophysiology of supragranular neurons in rat primary somatosensory cortex. Eur. J. Neurosci. 1997, 9, 163–176. [CrossRef] [PubMed] 85. De Schutter, E.; Smolen, P. Calcium Dynamics in Large Neuronal Models. In Methods in Neuronal Modeling; Koch, C., Segev, I., Eds.; MIT Press: Cambridge, MA, USA, 1998; pp. 211–250. 86. De Schutter, E. Modeling intracellular calcium dynamics. In Computational Modeling Methods for Neuroscientists; MIT Press: Cambridge, MA, USA, 2010; pp. 93–105. 87. Mohapatra, N.; Deans, H.; Santamaria, F.; Jedlicka, P. Modeling Ion Concentrations. In Encyclopedia of Computational Neuroscience; Jaeger, D., Jung, R., Eds.; Springer: New York, NY, USA, 2015. [CrossRef] 88. Hines, M.L.; Carnevale, N.T. Expanding NEURON’s repertoire of mechanisms with NMODL. Neural Comput. 2000, 12, 995–1007. [CrossRef] 89. Santhakumar, V.; Aradi, I.; Soltesz, I. Role of mossy fiber sprouting and mossy cell loss in hyperexcitability: A network model of the dentate gyrus incorporating cell types and axonal topography. J. Neurophysiol. 2005, 93, 437–453. [CrossRef] [PubMed] 90. Ruusuvuori, E.; Kirilkin, I.; Pandya, N.; Kaila, K. Spontaneous network events driven by depolarizing GABA action in neonatal hippocampal slices are not attributable to deficient mitochondrial energy metabolism. J. Neurosci. 2010, 30, 15638–15642. [CrossRef] 91. Mitchell, R.A.; Herbert, D.A.; Carman, C.T. Acid-Base Constants and Temperature Coefficients for Cerebrospinal Fluid. J. Appl. Physiol. 1965, 20, 27–30. [CrossRef] [PubMed] © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).