A receptors (GABAARs). The impact of changes in steady state Cl− gradient is relatively straightforward to understand, but how dynamic interplay between Cl− influx, diffusion, extrusion and interaction with other ion species affects synaptic signaling remains uncertain. Here we used electrodiffusion modeling to investigate the nonlinear interactions between these processes. Results demonstrate that diffusion is crucial for redistributing intracellular Cl− load on a fast time scale, whereas Cl−extrusion controls steady state levels. Interaction between diffusion and extrusion can result in a somato-dendritic Cl− gradient even when KCC2 is distributed uniformly across the cell. Reducing KCC2 activity led to decreased efficacy of GABAAR-mediated inhibition, but increasing GABAAR input failed to fully compensate for this form of disinhibition because of activity-dependent accumulation of Cl−. Furthermore, if spiking persisted despite the presence of GABAAR input, Cl− accumulation became accelerated because of the large Cl− driving force that occurs during spikes. The resulting positive feedback loop caused catastrophic failure of inhibition. Simulations also revealed other feedback loops, such as competition between Cl− and pH regulation. Several model predictions were tested and confirmed by [Cl−]i imaging experiments. Our study has thus uncovered how Cl− regulation depends on a multiplicity of dynamically interacting mechanisms. Furthermore, the model revealed that enhancing KCC2 activity beyond normal levels did not negatively impact firing frequency or cause overt extracellular K− accumulation, demonstrating that enhancing KCC2 activity is a valid strategy for therapeutic intervention.Chloride homeostasis is a critical determinant of the strength and robustness of inhibition mediated by GABA Fast synaptic inhibition relies on chloride current to hyperpolarize the neuron or to prevent depolarization caused by concurrent excitatory input. Both scenarios necessarily involve chloride flux into the cell and, thus, a change in intracellular chloride concentration. The vast majority of models neglect changes in ion concentration despite experimental evidence that such changes occur and are not inconsequential. The importance of considering chloride homeostasis mechanisms is heightened by evidence that several neurological diseases are associated with deficient chloride extrusion capacity. Steady state chloride levels are altered in those disease states. Fast chloride dynamics are also likely affected, but those changes have yet to be explored. To this end, we built an electrodiffusion model that tracks changes in the concentration of chloride plus multiple other ion species. Simulations in this model revealed a multitude of complex, nonlinear interactions that have important consequences for the efficacy of synaptic inhibition. Several predictions from the model were tested and confirmed with chloride imaging experiments. A and glycine receptor-gated Cl− channels (GABAAR and GlyR). Influx of Cl− through these channels produces outward currents that cause hyperpolarization or prevent depolarization caused by concurrent excitatory input (i.e. shunting) − influx through GABAAR necessarily increases [Cl−]i, which in turn causes depolarizing shifts in the Cl− reversal potential (ECl) − gradient is depleted and ECl rises, the efficacy of GABAAR-mediated control of spiking is compromised − gradient are crucial for maintaining the efficacy of GABAAR-mediated inhibition.In the central nervous system, fast inhibition is mediated by GABA− gradient across the membrane +-K+-2Cl− cotransporter (NKCC1), which normally mediates uptake of Cl−+-Cl− cotransporter, isoform 2, (KCC2), which normally extrudes Cl−. Interestingly, a reduction in KCC2 expression and/or function is involved in the pathogenesis of several neurological disorders, including epilepsy and neuropathic pain ECl influence inhibitory control of neuronal spiking Cation-chloride cotransporters (CCCs) play a key role in maintaining the ClECl can change as a result of altered KCC2 expression or activity ECl can also change dynamically, on a fast time scale, as a result of Cl− flux through GABAA receptors, particularly in small structures like distal dendrites ECl changed only slowly, it could be reasonably approximated as static relative to other neuronal processes occurring on a faster time scale; however, since ECl changes rapidly, it may interact in potentially complex ways with important neuronal processes like synaptic integration. To investigate those interactions, one must treat [Cl−] as a dynamical quantity evolving in space and time.−]i depend on several factors, including GABAAR-mediated Cl− flux, longitudinal diffusion within dendrites and the soma, and CCC activity. Furthermore, Cl− dynamics involve complex non-linear interactions with other ion species, which have been overlooked by previous models −, Na+, K+, Ca2+, HCO3−, H+, HPO42−, H2PO4−) across neuronal compartments .The spatio-temporal dynamics of i using FLIM. FLIM measurements have the advantage of being unbiased by the amount of indicator from cell to cell  significant KCC2 co-transport in > 21 DIV hippocampal neurons in culture, maintaining [Cl−]i at a low level, and b) that both furosemide and VU 0240551 could be used under these conditions to selectively block KCC2-mediated transport.To test experimentally the impact of the level of KCC2 activity on intracellular Cl to cell , minimizmulation , in agremulation . At highion  between cellular compartments has been observed in several experiments −]i gradient could be due to differentially distributed cotransporter activity, it could also arise from intense focal GABAAR-mediated input. To test the latter scenario, we simulated high frequency GABAAR-mediated input to a single dendritic synapse and measured [Cl−]i at different distances from the synapse at different times after the onset of input  at varying distances from the original GABAA synapse (activated at 50 Hz). Both synapses were activated simultaneously. As predicted, EGABA at the test synapse was affected by other GABAAR-mediated input on the same dendrite as far away as 50 µm (top), or even farther when KCC2 activity was reduced. However, interactions also depended on synapse position relative to the neuron topology; for instance, synapses in relatively close proximity but located on different primary dendrites exhibited little if any interaction (bottom), consistent with the soma acting as a sink that clamps [Cl−]i.Spatial variation in of input . Under tn 200 ms . There was 50 µm top, or eraction bottom, in vivo conditions, neurons are known to be constantly bombarded by synaptic input −]i differently depending on the cellular compartment. We performed simulations in the presence or absence of KCC2 activity and in the presence or absence of synaptic noise. Simulations of distributed ongoing synaptic input with KCC2 distributed uniformly across the cell compartments yielded a clear somato-dendritic [Cl−]i gradient (black). In contrast, in the absence of simulated synaptic noise, there was no significant somato-dendritic [Cl−]i gradient despite the presence of KCC2 (green). Lack of a significant somato-dendritic [Cl−]i gradient was also observed in the reverse scenario, i.e. in the presence of synaptic noise but without KCC2 (red). Thus, a significant somato-dendritic [Cl−]i gradient can exist when there is ongoing Cl− influx, redistribution of that Cl− load via diffusion, and Cl− extrusion by KCC2. This clearly demonstrates that differential extrusion, i.e. inhomogeneous KCC2 density (see below), is not necessary for inhomogeneous transmembrane Cl− gradients.Under gradient black. I of KCC2 green. Lout KCC2 red. Thu−]i in MQAE-loaded neurons in culture  which was either reduced by bicuculline (middle and 4C) or blocked by the addition of furosemide or the recently developed more specific KCC2 inhibitor VU 0240551 bottom and 4C), consistent with predictions from simulations  intensity units or 3.9±0.2 Miu, and was constant along the dendrite of 5-DIV neurons (52 regions from 11 neurons). Using automated intensity binary masks Our simulations were based on the assumption of even distribution of KCC2 along the dendrites and this configuration appears to be sufficient to explain the somato-dendritic gradient observed. However, this does not rule out the possibility of a gradient of KCC2 along the dendrites. To test for the presence or absence of such gradient, we sought to perform quantitative fluorescence immunocytochemical analysis of the distribution of KCC2 along dendrites. Measuring KCC2 immunolabeling may not be sufficient, however, to obtain an estimate of the distribution of functional KCC2 because it has recently been suggested that the oligomeric form of KCC2 is the functional one − gradients could also arise from inhomogenous CCC activity at small length scales. For instance, non-uniform distribution of KCC2 at the subcompartent-level might produce local gradients comparable to those observed with synaptic inputs  has little impact on the perisynaptic value of ECl.While our experimental results indicate homogeneous distribution along the dendrite length, this does not necessarily apply to all conditions and, in particular, our analysis did not focus on local inhomogeneities, e.g. microdomains. We therefore also sought to determine if longitudinal intracellular Clputs see . Indeed,synapses . In all − transporter NKCC1 in the AIS ECl to be less negative in the AIS. To test ECl in the AIS and how it impacts neighboring compartment, we simulated different levels of NKCC1 in the AIS in combination with different levels of KCC2 in the soma and dendrites with or without background synaptic input  ic input . NKCC1 eendrites . As expeendrites  resultedAR input and CCC activity can produce spatial inhomogeneities in ECl, which should translate into inhibitory input having differing efficacy depending on the location of the synapse. This is true even if KCC2 activity is uniformly distributed in the presence of background GABAAR input. Moreover, focal Cl− influx through one synapse (or a cluster of synapses) can affect the efficacy of neighbouring synapses, although this depends on subcellular localization of those interacting synapses, e.g. proximity to the soma. In contrast, subcompartmental inhomogeneity in KCC2 activity is not sufficient to cause local [Cl−]i gradients.Thus, simulations in our electrodiffusion model demonstrated that subcellular distribution of GABA−]i can arise from ongoing GABAAR input. To extend these results to include temporal changes in ECl, we considered how [Cl−]i evolves during stimulus transients. This was motivated by experimental observations that EGABA can rapidly collapse during bursts of GABAAR synaptic events EGABA depend on the location of the input: somatic input has less impact on EGABA than dendritic input EGABA, which translated into a small reduction in GABAAR-mediated current. The depolarizing shift in EGABA was greater and occurred increasingly faster for input to progressively more distal dendrites. This was despite the presence of KCC2 (red). Removing KCC2 (black) increased the amplitude and speed of the collapse in Cl− gradient during high frequency input to distal dendrites, but had virtually no impact for input to the soma. The finding that amplitude of the initial synaptic event in each of the compartments was unaffected by removing KCC2 appears to contradict the observation that the standing [Cl−]i gradient depends on KCC2 activity , whereas the extent of accumulation depends on KCC2 activity (which reduces intracellular Cl− load via extrusion).vity see . We hypoAR synapses switched from outward to inward whereas the same rate of input to the soma continued to produce strong outward currents , while Cl− accumulates faster in distal dendrites simply because the effective volume is smaller and diffusion is restricted. In summary, under dynamic conditions, restricted diffusion in distal dendrites causes a rapid collapse of EGABA, but the extent of this collapse is limited by KCC2, consistent with experimental measurements To investigate these processes more thoroughly, we systematically varied the intraburst frequency, location of the “test” synapse and KCC2 activity, and we measured the mean IPSC amplitude at the “test” synapse throughout the burst. During high frequency input to distal dendrites, the net mean current through GABAcurrents . Thus, we levels  with dife levels  dependinAR synapses activated at low frequency would produce greater hyperpolarization than a few clustered synapses (or just one synapse) activated at higher frequency, especially for synapses located on distal dendrites. We tested this by comparing the outward current produced by one synapse activated at an intraburst frequency of 50 Hz with the total hyperpolarizing current produced by ten distributed synapses activated at 5 Hz; this was repeated for dendritically and somatically positioned synapses (middle). This is due to the fact that the total synaptic conductance does not scale linearly with frequency because of saturation. Even more important is the fact that distributed dendritic input is capable of producing a strong outward current despite Cl− accumulation, whereas clustered dendritic input was totally inefficient in producing an outward current. These results suggest that dendritic inhibition is most effective when spatially distributed, consistent with data in A synapses in dendrites is also important because the rapid dynamic collapse of distal hyperpolarizing GABAAR currents will limit their effectiveness at controlling somatic signals because membrane potential changes extend farther than changes in conductance ECl collapses, we submitted the neuron to distributed excitatory input and measured the mean firing frequency of the model neuron to verify that loss of hyperpolarizing current translates into effective disinhibition (right). We found that firing rate reduction mirrored the change in charge carried (cf. right and middle panels).The above results led us to predict that, for equivalent total synaptic input, many broadly distributed GABAsynapses . In the synapses middle. hibition right. WEGABA to alter the efficacy of inhibition. Although increasing the rate or duration of GABAAR inputs may initially increase IPSC amplitude, such changes would also accelerate depletion of the Cl− gradient and thereby eventually reduce IPSC amplitude, at least when Cl− influx overwhelms local diffusion mechanisms and Cl− extrusion capacity. Using our model, we studied the influence of KCC2 activity level, synaptic frequency and time constant of GABAAR-mediated events (τIPSC) on the mean current through a dendritic GABAAR synapse. Simulations indicated that increasing KCC2 activity always led to larger mean outward current. In contrast, increasing synaptic input frequency (left) or τIPSC (right) did not necessarily increase the mean current; in both cases, the mean current was largest at intermediate values of those parameters. Similarly, mean firing rate was reduced most at intermediate values of those parameters  channels were concentrated at branch points. Although these modifications to the model changed the overall firing rate, our qualitative finding remained unchanged; that is, firing rate increased if GABAAR input was augmented beyond a certain level (right).In addition to synapse location, the rate and duration of synaptic inputs would be expected to interact with dynamic changes in requency left or or τIPSC right dirameters . To estain level right.AR inputs may not always produce more inhibition, i.e. stronger outward current. We therefore asked what GABAAR input conditions produce the strongest inhibition? This question was addressed by measuring which parameter combinations produced the largest outward current. We found that the GABAAR input frequency yielding the largest outward current increased with KCC2 activity and decreased with τIPSC , more GABAAR input does not necessarily produce more inhibition. Increasing the frequency of GABAAR input showed a similar inverted bell-shaped curve when estimating effective inhibition with either total charge carried or firing rate reduction  masked by the larger outward current produced by Cl− influx, since the permeability ratio between Cl− and HCO3− anions is approximately 4∶1 −-mediated outward current becomes smaller, the HCO3−-mediated inward current becomes relatively larger, eventually causing the net current through GABAAR to become inward. Unlike the Cl− gradient, the HCO3− gradient tends not to collapse  are not depleted, the forward reaction produces H+ in addition to HCO3−. By removing HCO3−, GABAAR activity would be expected to reduce the intracellular pH, which has been observed experimentally + shifts the equilibrium point of the reaction, intracellular HCO3− slowly decreases, with a time constant in the order of several seconds, which explains the small hyperpolarizing shift in EHCO3 seen in ECl and EHCO3 shifting in opposite directions, EGABA tends toward the membrane potential. We therefore predicted that reducing changes in EHCO3 would lead to greater changes in ECl and, vice versa, that reducing changes in ECl would lead to greater changes in EHCO3. To test the first prediction, [HCO3−]i was held constant (thus maintaining HCO3− efflux), which enhanced the depolarizing shift in ECl; on the other hand, increasing intracellular HCO3− depletion by reducing proton extrusion via the Na−-H+ exchanger (thus reducing HCO3− efflux) mitigated the depolarizing shift in ECl  in a model neuron receiving a fixed level of background excitatory and inhibitory synaptic input. Simulations showed that under conditions of distributed GABAAR input at in vivo-like background frequencies, KCC2 activity actually had little impact on EK unlike its large impact on ECl (left and right panels). We investigated this further by monitoring intra- and extracellular concentrations of K+ , changes in EK caused by other factors  reduce KCC2 activity. In other words, there is no closed negative feedback loop directly linking KCC2 and EK, but extrinsic factors can modulate Cl− extrusion by affecting extracellular K+ accumulation. Indeed, it is significant that Cl− extrusion could be reduced (and inhibition thereby rendered ineffective) under conditions where excessive spiking (perhaps the result of disinhibition) causes extracellular K+ accumulation – this would constitute a multi-step positive feedback loop .It is important to understand that changes in i gradient along the somato-dendritic axis resulting from the interplay between background GABAAR input and cotransporter activity may lead, under certain conditions, to differential impact of distal dendritic vs. somatic GABAergic synaptic input such as, for example, concurrent dendritic GABAA-mediated excitation and somatic inhibition The functional impact of the standing i competes with regulation of intracellular pH on long time scales (tens of seconds to minutes) consistent with experimental observations − accumulation  may act as a protective mechanism to prevent an excessive drop in intracellular pH during sustained GABAAR input.In addition to Cl− homeostasis may also be relevant to recent controversies regarding the necessity of ketone bodies for maintenance of EGABA in the developing nervous system 3− dependence of the beta-hydroxybutyrate effect on EGABA in these experiments, it has been proposed that the explanation may reside in the fact that beta-hydroxybutyrate, lactate or pyruvate act as weak organic acids, thus acidifying the neuronal cytoplasm and reversing Cl−/HCO3− exchange; this counteracts the drop in [HCO3−]i due to acidification but, by the same token, it lowers [Cl−]i and drives EGABA to a more negative value The relationship between pH and Cl− and K+ through KCC2, Cl− extrusion happens at the expense of extracellular K+ accumulation. This may appear counter-productive as extracellular K+ accumulation counteracts inhibition and plays a role in the onset of epilepsy + efflux through KCC2 is offset by the fact that KCC2 activity enhances inhibition, thus decreasing firing rate and reducing K+ efflux via transmembrane channels. The net effect is a reduction of excitability because K+ efflux via transmembrane channels is larger than via KCC2. We found that this negative feedback stabilizes [K+]o over a wide range of KCC2 activity. Disrupting this homeostasis requires sustained input from extrinsic factors. For example, intense GABAergic activity, which can maintain a continuous Cl− load leading to a large and sustained K+ efflux through KCC2, has been observed during giant depolarizing potentials + accumulation, which renders KCC2 inefficient, causing a collapse of inhibition due to intracellular Cl− accumulation.Given the coupled efflux of Cl− accumulation because Cl− influx through GABAAR depends on Cl− driving force, which is increased by depolarization. The consequences are profound: If sustained GABAAR input fails to prevent depolarization caused by concurrent excitatory input, the resulting depolarization will accelerate Cl− influx, which in turn further reduces the GABAAR-mediated outward current, thus supporting a positive-feedback cycle of failing inhibition. If the membrane potential reaches the spike threshold under these conditions, spike generation compounds the positive feedback process leading to an absolute failure of inhibition having potentially catastrophic consequences with respect to the neuron's response to stimulation. The only way for a neuron to avoid entering this vicious cycle is to regulate [Cl−]i, through Cl− extrusion via KCC2.Another interesting observation was that membrane depolarization tends to encourage intracellular ClAR. Many of those processes cooperate or compete with one another, thus producing nonlinearities. The most dramatic of those is arguably the catastrophic failure of inhibition that can develop when depolarization and spiking conspire with Cl− accumulation to form a positive feedback loop. As demonstrated in this study, such details may be critical for understanding important aspects of synaptic inhibition, in particular, for understanding why and how inhibition fails under certain pathological conditions.In summary, we built a neuron model that incorporates multiple processes controlling the flux of different ion species in order to investigate how interactions between those processes influence inhibition mediated by GABAx in each compartment at each time step according to the differential equation F is the Faraday constant, z is the ion valence, Vol is the compartment volume, xReac is a term accounting for chemical reactions involving ion species x and xDiff is a term modeling the electrodiffusion of ion xA mediated input We built a conductance-based model of a whole neuron using the NEURON simulation environment eactions  were allxE denotes the reversal potential for ion x and xg is the channel conductance with respect to ion x. Reversal potentials were continuously updated during the simulation using the Nernst equation R is the perfect gas constant and T is absolute temperature, which was taken to be 310°K (37°C). Because GABAA receptors pass both Cl− and HCO3− anions in a 4∶1 ratio EGABA was calculated using the Goldman-Hodgkin-Katz equation Ion currents obey the equation Each of these ionic currents was taken into account for computing change in concentration of their respective ion species and their sum yielded the net current used to update the membrane potential.IPSC of 30 ms AR synaptic density was 60 synapses per 100 µm2 in the AIS, 40 synapses per 100 µm2 in the soma and 12 synapses per 100 µm2 in the dendrites. Density of excitatory synapses was 60 synapses per 100 µm2 in dendrites and no excitatory synapses were present elsewhere EPSC of 10 ms.Synaptic input was modeled as a Poisson process. Each inhibitory synapse was activated at a mean frequency of 0–10 Hz and each excitatory synapse was activated at a mean frequency of 0–2 Hz. Unless otherwise stated, the maximal conductance of inhibitory synapses was 1±0.3 nS (mean ± standard deviation) and kinetics were modeled as instantaneous rise and exponential decay with τ+ current was given byHodgkin-Huxley (HH) channels were modeled using parameter values reported by TV = −58 mV and SV = −10 mV. The voltage gated K+ channels were described byWhere 2 in the AIS and 1.2 mS/cm2 in soma and dendrites + and Na+ leak channels with respective densities of 0.02 mS/cm2 and 0.004 mS/cm2 in soma, 0.03 mS/cm2 and 0.006 mS/cm2 in proximal dendrites, 0.1 mS/cm2 and 0.02 mS/cm2 in distal dendrites, 0.02 µS/cm2 and 0.004 µS/cm2 in axon internodes, and 15 mS/cm2 and 3 mS/cm2 in axon nodes as described in The density of HH channels was 12 mS/cm2+-activated K+ channels and persistent Na+ channels to test spike reducing and spike enhancing mechanisms, respectively. The Ca2+-activated K+ channels obey the following sets of equationsFor some simulations, we added other types of conductances to account for the many possible types of spike generating mechanisms. Namely, we added non-inactivating CaWhere the auxiliary functions are defined byvm = 28.9 mV, ckm = 6.2 mV, ctm = 0.000505 s, cvtm1 = 86.4 mV, cktm1 = -10.1 mV, cvtm2 = -33.3 mV, cktm2 = 10 mV. τz = 1 s, ch = 0.085, cvh = 32 mV, ckh = -5.8 mV, cth = 0.0019 s, cvth1 = 48.5 mV, ckth1 = -54.2 mV, cvth2 = -54.2 mV, ckth2 = 12.9 mV.With the constants defined as c+ channels were described by the following set of equations:The persistent NaWhere the auxiliary functions are defined bysm = −2 mV and vsh = −5 mV.Where the constants are given by v− conductance, in some simulations we added GABAA leak channels with the same ratio of Cl−:HCO3− permittivity as the synaptic channels, the density of such channels was 0.003 mS/cm2 in soma, 0.0045 mS/cm2 in proximal dendrites, 0.015 mS/cm2, 0.003 µS/cm2 in axon internodes and 2.3 mS/cm2 in axon nodes.Finally, to account for non-synaptic, tonically activated Cl+-K+-ATPase pump uses the energy from hydrolysis of one ATP molecule to pump three Na+ ions out of the neuron and two K+ ions inside. The activity of this pump is dependent on [K+]o and [Na+]i as well as on the membrane potential as observed in + current through the pump is given by the following equationsm,Ko = 1.5 mM, Km,Nai = 10 mM and NaHalf = 20 mM. The outgoing Cl− flux though KCC2 was modeled according to The Na+ current through KCC2 was assumed to be equal in absolute value but opposite in polarity to the Cl− current so that net current through KCC2 was equal to zero. The maximal Cl− current going through KCC2 was taken to be Cl,maxI = 0.3 mA/cm2 for the normal activity level. This value was chosen to give ECl  =  −80 mV when mean synaptic input frequencies were 3.2 Hz and 1.6 Hz for inhibition and excitation, respectively. This value also turned out to yield maximal Cl− clearance rates near 10 mM/s, consistent with experimental data ECl-EK) at which the Cl− current through KCC2 reaches its half maximal value  was set to 40 mV. This corresponds to [Cl−]i  =  15 mM under the assumption that [Cl−]o  =  120 mM and EK  =  -95 mV.The K− influx through NKCC1 was modeled byIn some simulations, we also modeled NKCC1 activity in the AIS. The ClENKCC1 is the value of ECl at which Cl− flow through NKCC1 reverses direction and is given by ENKCC1  =  (EK+ENa)/2. The maximal Cl− current going through NKCC1 was taken to be Cl,maxI  =  0.3 mA/cm2 for the normal activity level which was taken to equal the value obtained for maximal current through KCC2. Na+ and K+ currents through NKCC1 were each half of Cl,NKCC1I so that net current through NKCC1 was equal to zero.Where −/HCO3− exchanger which was assumed to be ubiquitous in the neuron and uniformly distributed on the membrane. Kinetics of the exchanger were described by the following simplified equation.ICl,max =  0.1 mA/cm2 and Vhalf was set to 50 mV. The HCO3- and Cl- currents through the exchanger were taken to be equal in amplitude but opposite in direction so that the exchange process is electroneutral.Finally, in some simulations we also modeled the Cl3− ions also flow through GABAAR channels (see above), it was important to model the ionic fluxes and reactions regulating [HCO3−]. Intracellular HCO3− loss due to outgoing flux via GABAAR is compensated by the carbonic anhydrase-catalyzed reaction 2 gas through the membrane is faster than ionic fluxes through channels, we treated pCO2 as constant. The equilibrium constant of the reaction was 10−6.35 M and the rate constant for CO2 hydration was 106 sec−1+ accumulation, we also modeled the reaction between H+ and the main buffering ion H2PO4− such that H+ buffering occurred through the reaction +-H+ exchanger such that the proton flux was given byH,maxI  =  0.03 mA/cm2 and Vhalf  =  10 mV. These values were chosen to make the model consistent with the global proton extrusion rate in healthy neurons that has been measured to be 0.04 pHU/s Since HCO+, Na+, Ca2+, Cl−, HCO3− as well as intracellular concentrations of H+, HPO42− and H2PO4− were treated as dynamical variables updated in each compartment at each time step. Each compartment was divided in four concentric annulus-shaped subcompartments to account for radial diffusion. Diffusion coefficients were assumed to be the same as in water (in 10−5 cm2/s): 2.03 for Cl−, 1.33 for Na+, 1.96 for K+, and 9.33 for H+electrodiffusion is described by the equation y stands for the longitudinal axis, Dx for the diffusion coefficient with respect to ion specie x, Vol for section volume and Surf for the surface of the cross section y representing the radial axis.An important and novel feature of the model is that the intra- and extracellular concentrations of K+]o due only to our cell, and thus does not address changes in [K+]o due to network activity. The study of such network related effects is beyond the scope of the current study. The equation used to update extracellular ion concentration is given by z is the ion valence, bathk is the concentration of ion x in the infinite reservoir and τFH is the time constant taken to be 100 ms Extracellular space was represented as a thin shell (i.e. Frankenhaeuser-Hodgkin or FH space) with equivalent volume equal to one fourth the intracellular volume of the corresponding cell compartment. The inner surface of the FH space communicated with the adjacent intracellular compartment while the outer surface was linked to an infinite reservoir where ion concentrations were assumed to be constant. This modeling takes into account changes in [K−]i = 6, [K+]i = 140, [Na+]i = 10, [HCO3−]i = 15 [H2PO4−]i = 30 and [HPO42−]i = 30. Initial extracellular concentrations were in (mM) [Cl−]o = 120, [K+]o = 3, [Na+]o = 45 and [HCO3−]o = 25 − current through KCC2  was different than the normal one stated above , different initial values of [Cl−]i were used in order to start the simulation near steady state, as determined by preliminary testing. Unless stated otherwise, simulations were run for 200 s of simulated time, short enough to allow manageable simulations and long enough to allow collection of sufficiently large data sample to insure relevance of mean values.The differential equations were integrated using a forward Euler method with a time step of 0.05 ms. Several preliminary simulations showed this time step to be both sufficiently small for accurate equation solving, while sufficiently large for reasonably fast computing. Initial intracellular concentrations were (in mM): i), where τ0 is the fluorescence lifetime in 0 mM Cl−, and Ksv, the Stern-Volmer constant, is a measure of the Cl− sensitivity of MQAE , through a 40X water-immersion objective . Fluorescence lifetime data was collected through the non-descanned port of the microscope using a band-pass filter  coupled to a laser block . Photon emission was detected using a PMC-100-1 photosensor (Hamamatsu). Lifetime in each cell compartment was calculated and extracted using SPCImage software (Becker & Hickl). Lifetime in the cell body was averaged over the total cell body area excluding the nucleus region, whereas in the dendrites it was averaged in segments of 4 µm over 120 µm of dendrite length. Fluorescence lifetime measurements were used because they are not sensitive to dye concentration (peak intensity) in the range we are using  of MQAE .− concentrations, the fluorescence lifetime of MQAE-loaded cells was measured in the presence of different known extracellular [Cl−]  in the bath. To dissipate the Cl− gradient across the membranes, 20 µM tributyltin (Cl−-OH exchanger) was used and 20 µM nigericin (K+-H+ exchanger) was added to clamp the intracellular pH using high K+ driving force while Cl− changes. Calibration solutions contained (in mM) KCl and KNO3 , 10 D-glucose, 10 HEPES, 1.2 CaCl2, 1 MgCl2, pH adjusted to 7.2 using KOH.For calibration of absolute ClPrimary hippocampal cultures (5 and 28 DIV) were fixed for 10 min with 4% paraformaldehyde and then permeabilized for 45 min with 0.2% triton in 10% normal goat serum (NGS). Primary antibody incubations were performed overnight at 4 °C using a polyclonal marker of KCC2  in the presence of 5% NGS. Alexa 546 conjugated secondary antibodies  were applied for 2 hrs at room temperature. Images were obtained on the Zeiss LSM 510 microscope using a 63X/1.4NA oil objective (Zeiss).of N particles is:SpIDA is a recently developed analysis method that can resolve concentration of mixtures of different monomeric and oligomeric labels in single fluorescence images by fitting its intensity histogram. Precise details of the technique and detector calibration are presented in .I(r) is the illumination intensity profile of the excitation laser, ε represents the quantal brightness of a single fluorescent particle, and k is the probability of observing an intensity of light . H is normalized over all intensity values so the integral over k gives one. A constant factor, A, is introduced, which is the number of pixels in an analyzed region of the image where the fluorescent particles are distributed. This allows for the fit of an image intensity histogram to be performed. Three parameters are fit: the number of pixels (A), the fluorescent particle density  and the quantal brightness . In confocal laser scanning microscopes, the fluorescence intensity is measured using photon multiplier tubes (PMTs), and the number of collected photoelectrons is a function of the polarization voltage.WhereIf dimers are present in the sample, they will yield quantal brightness of 2ε. When the monomer and dimer populations are mixed within the same region in space, the total histogram becomes the convolution of the two individual distributions: To obtain accurate results, noise characteristics of the detector also has to be studied and taken into account in the analysis, See z step of 0.5 µm per image.For each sample, an optimal setting of the laser power and PMT voltage was chosen to minimize pixel saturation and photobleaching. The CLSM settings were kept constant for all samples and controls . Acquisition parameters were always set within the linear range of the detector which was determined by calibration All experiments were performed in accordance with regulations of the Canadian Council on Animal Care.


STX17, is associated with a high incidence of melanoma and vitiligo-like skin depigmentation. However, the progressive greying and the incidence of melanoma, vitiligo-like depigmentation, and amount of speckling in these horses do not follow a simple inheritance pattern. To understand their inheritance, we analysed the melanoma grade, grey level, vitiligo grade, and speckling grade of 1,119 Grey horses  measured in six countries over a 9-year period. We estimated narrow sense heritability (h2), and we decomposed this parameter into polygenic heritability (h2POLY), heritability due to the Grey (STX17) mutation (h2STX17), and heritability due to agouti (ASIP) locus (h2ASIP). A high heritability was found for greying (h2 = 0.79), vitiligo (h2 = 0.63), and speckling (h2 = 0.66), while a moderate heritability was estimated for melanoma (h2 = 0.37). The additive component of ASIP was significantly different from zero only for melanoma (h2ASIP = 0.02). STX17 controlled large proportions of phenotypic variance (h2STX17 = 0.18–0.55) and overall heritability (h2STX17/h2 = 0.28–0.83) for all traits. Genetic correlations among traits were estimated as moderate to high, primarily due to the effects of the STX17 locus. Nevertheless, the correlation between progressive greying and vitiligo-like depigmentation remained large even after taking into account the effects of STX17. We presented a model where four traits with complex inheritance patterns are strongly influenced by a single mutation. This is in line with evidence of recent studies in domestic animals indicating that some complex traits are, in addition to the large number of genes with small additive effects, influenced by genes of moderate-to-large effect. Furthermore, we demonstrated that the STX17 mutation explains to a large extent the moderate to high genetic correlations among traits, providing an example of strong pleiotropic effects caused by a single gene.The dominant phenotype of greying with age in horses, caused by a 4.6-kb duplication in intron 6 of  STX17) that explains 18%–55% of phenotypic variation in four complex traits, while polygenic background additive effects also explain 11%–57% of phenotypic variation. This study provides a prime example of complex traits being influenced by genes of moderate-to-large effect and supports further the evidence of recent studies in domestic animals that some complex traits are, in addition to the large number of genes with small additive effects, influenced by genes of moderate-to-large effect. We further show that the STX17 mutation accounts for a large proportion of the estimated genetic correlations between the traits. This case of strong pleiotropic effects of a single mutation on complex traits makes this work of significant general interest for biology and medicine.Clarifying the genetic architecture of complex traits is a problem with profound implications for agriculture, biology, and medicine. Using data from Lipizzan horses with the grey coat phenotype, we present an example of a single mutation (intronic duplication in  Recent developments in molecular genetics have enabled molecular dissection of quantitative traits in humans GG or Gg, non-grey horses carry the gg genotype. Previously we reported that greying with age is caused by a 4.6-kb duplication in intron 6 of STX17, which encodes syntaxin 17 ASIP) locus on melanoma grade. Recently, we have shown that the duplicated region contains a melanocyte-specific enhancer that becomes much stronger when duplicated The genetic variation of several complex traits in Grey horses is considerably affected by at least one gene of moderate-to-large effect. Grey horses are born with their base colour , which then greys early in life due to loss of melanocytes, a process similar to the greying of hair in humans, which typically occurs much later in life. The mode of inheritance of the greying phenotype is autosomal dominant. A grey horse will be either Studies of coat colour in horses led to the definition of discrete phenotypes  controlled by a few genes showing epistatic interaction Melanoma occurs frequently in grey horses, in three histopathologically defined clinical patterns. In the first pattern, which describes the majority of cases, the melanoma grows slowly over many years without evidence of regional or distant metastases. In the second pattern, the melanoma results from malignant transformation of a benign melanoma , we decomposed estimated repeatability into a component due to permanent environmental effects and a component describing narrow sense heritability. We further decomposed estimated narrow sense heritability into a component describing background polygenic heritability and a components describing heritability caused by STX17 and ASIP. We also estimated genetic correlations among all four traits using various models designed to evaluate the contribution from STX17 and ASIP genes and the contribution from pleiotropy due to additional polygenic background versus that of STX17 and ASIP genes. Finally, we graphically illustrated the genetic relationship among melanoma grade, grey level, vitiligo grade and speckling grade with respect to STX17 genotype.When this study was conceived, we hypothesized that while the effects of STX17 were highly significant (P<0.001) for all traits studied, while the corresponding coefficients involving ASIP were significant only for melanoma grade (P<0.001). In quantitative genetics, estimated linear regression coefficients can be interpreted as gene substitution effects of STX17 (αSTX17) and ASIP (αASIP). Thus, the regression coefficient of −13.78 for the Grey mutation in relation to the trait Greying indicates that heterozygotes Gg on average will be 13.78 L* units darker than homozygous GG horses. ASIP has three genotypes, so the melanoma grade is expected to change by 0.38 units (2αASIP) when moving from an AA to an aa genotype.Estimated fixed effects and variance components for melanoma grade, grey level, vitiligo grade and speckling grade are shown in 2) and for repeatability (R), which here corresponds to the upper bound of broad sense heritability, together with estimates of phenotypic variance (VP) and variance due to permanent environmental effects (c2), here variance introduced by repeated measurement  (2POLY = 0.57), followed by the estimated components for vitiligo grade (h2POLY = 0.41), melanoma grade (h2POLY = 0.18) and speckling grade (h2POLY = 0.11). The heritability caused by the STX17 mutation was highest for speckling grade (h2STX17 = 0.55) and moderate for vitiligo grade (h2STX17 = 0.23), grey level (h2STX17 = 0.22) and melanoma grade (h2STX17 = 0.18). The additive component of ASIP was significantly different from zero only for melanoma grade (h2ASIP = 0.02). When polygenic heritability was assessed only for homozygous GG horses, obtained values were still high for all four traits: grey level, 0.76; melanoma, 0.30; vitiligo, 0.57; and speckling, 0.36. An analysis of this data set for melanoma including ASIP revealed no substantial increase of heritability (h2ASIP = 0.03) while the effect of ASIP on other traits remained non-significant.The frequency of the Grey allele was between 0.85 and 0.89 for the various data sets. Univariate estimates were obtained for heritability (hsurement . Proporta grade) . The polP) and genetic correlations (rPOLY) according to three scenarios . When the genetic effects were included (second scenario) or when only homozygous grey horses were considered (third scenario), correlations dropped substantially. This is a strong indication of the pleiotropic effects of the Grey mutation. Only the genetic relationship between grey level and vitiligo grade remained significant and considerable across all three scenarios . Visualization of the genetic correlations due to polygenic effects is presented by scatter plots of estimated breeding values (GG) genotype of STX17 to a heterozygous one (Gg) have a diagonal shift in the contour plot centres. In addition to these pleiotropic effects of the STX17 mutation, the fact that the contour plots for breeding values were elliptical specifically for the GG and Gg genotypes indicates that genetic correlations due to polygenic background additive effects remained even after accounting for the STX17 mutation. This was particularly obvious for the genetic correlation between grey level and vitiligo grade, for which the estimated genetic correlation after accounting for major genes remained significant (rPOLY = 0.48±0.09).Multivariate models were used to estimate phenotypic  and yield  while increases milk protein yield  and milk volume h2DGAT1 = 0.18; h2POLY = 0.49). A strong effect of PLAG1 mutation (h2PLAG1 = 0.07) on the postpartum calf weight was recently estimated in the outbreed dairy cattle population KIT, MITF, and a locus on chromosome 8 together explained 24% of the variation in the proportion of black coat in cattle. Recently, Liu et al. The results obtained here, further, support the evidence of recent studies in domestic animals that some complex traits are, in addition to the large number of genes with small additive effects, influenced by genes of moderate-to-large effect at intermediate to high frequencies. For example, a single nucleotide substitution in intron 3 of Data were recorded from grey Lipizzan horses situated at six state-owned studs from Austria (Piber), Bosnia and Herzegovina (Vucjak), Croatia (Djakovo), Hungary (Szilvasvarad), Slovakia (Topol'cianky) and Slovenia (Lipica). Studs were visited repeatedly over nine years (1999–2007) and a total of 7146 measurements were collected from 1119 horses. All horses analysed were connected by a complete pedigree that includes 4961 members and extends back to the 18th century. Most data came from the offspring of 189 sires  and 496 dams .Melanoma was detected by adspection and palpation. Melanoma grade was defined according to a modified classification system on a scale from 0.0 to 5.0 that allows for intermediate grades , described in Progressive greying was quantified with a Minolta Chromameter CR210 using the CIE L*a*b* colour space. In this system, colour is quantified by its reflection along three axes: white-black (L*), red-green (a*) and yellow-blue (b*). We referred only to the L* parameter defined on a scale from 0  to 100 . Various grey levels in seven Lipizzan horses are illustrated in Vitiligo was graded on a scale from 0 (no vitiligo) to 3 (severe vitiligo) based on adspection of typical sites, such as the peri-anal and anal region, the perineal region, udder, praeputium and the face, especially around the nostrils and eyes. Vitiligo grade was evaluated simultaneously with melanoma. The overall vitiligo grade was the average of included vitiligo grades from patches in the perianal and the facial regions. Variation of vitiligo grade across four grey Lipizzan horses is illustrated in Adult grey horses may present coloured specks or spots on a grey background. The amount of speckling was graded on a scale from 0 (not speckled) to 3 (heavily speckled). Variation in speckling grade across four grey Lipizzan horses is illustrated in STX17 mutation was carried out on 760 horses and for the ASIP gene on 667 horses; the agouti locus controls the inheritance of black and bay base colour STX17 mutation and 873 for the ASIP. However, deducing genotypes may introduce bias in the estimation of major gene effects. To avoid potential bias, we performed analyses on data sets with and without deduced genotypes. The results were very similar, those presented here are therefore based on data sets containing deduced genotypes.Genotyping for the All traits analysed  strongly depend on the age of the horse. Grey level can vary widely in animals during the first 6–10 years; from 10 years onwards, this variation drops considerably and all horses reach their final coat colour. Both melanoma and vitiligo show an age of onset of 6–8 years, though they do appear earlier in rare cases. Speckling has been defined only for adult horses, because the density of spots or speckles is difficult to assess while horses are still dark. Thus, we analysed grey level data only for horses seven years old and younger. Conversely, data on melanoma, vitiligo and speckling grade were used only for horses seven years old and older. The dynamics of melanoma, grey level, vitiligo and speckling with respect to age has been shown in Pielberg et al. STX17 mutation effects , ASIP additive effects  and ASIP dominance effects  and all two-factor interactions. Two factor interactions and the ASIP dominance effects were non-significant for any trait and were dropped from further analyses. Initial and all subsequent models also included additive genetic animal effects (random) and permanent environmental effects (random), since measurements were taken repeatedly.Statistical analyses were performed with univariate and multivariate general mixed linear models using the ASReml package , permanent environmental (Vpe) and residual variances (Ve), respectively, were defined asThe following final model was used to analyse the data:STX17 mutation (VSTX17) and ASIP (VASIP) were calculated as VSTX17 or VASIP = 2pqα2, where estimated linear regression coefficients are equal to gene substitution effects α (α = a for STX17 and α = a+(p−q)d for ASIP), p and q are allele frequencies of STX17 or/and ASIP, a is an additive value and d is a dominance value. Calculation of single gene (mutation) variances enabled us to calculate additive genetic variance as a sum of VPOLY, VSTX17 and VASIP, and phenotypic variance as a sum of VPOLY, VSTX17, VASIP, Vpe, and Ve. In further analysis, we calculated proportions of genetically explained variations from the phenotypic variance as follows: repeatability, defined as correlation among observations within individuals that provides an upper limit to broad sense heritability, R = /VP; permanent environmental effects, c2 = Vpe/VP; narrow sense heritability, h2 = (VPOLY+VA-STX17+VA-ASIP)/VP; background polygenic heritability, h2POLY = VPOLY/VP; STX17 mutation heritability, h2STX17 = VSTX17/VP; and ASIP heritability, h2ASIP = VASIP/VP. For grey level, vitiligo grade and speckling grade, αASIP was not significant, so we assumed VASIP was zero. The analyses were performed for three scenarios: the first and third ignored STX17 and ASIP effects, using the full data set and a data set containing GG animals only; the second applied the full model as described above for melanoma and excluded ASIP (because not significant) for the other traits.Based on variance components and effects estimated from statistical analyses, together with allele frequencies obtained by counting, we derived quantitative genetic parameters to explain the complex inheritance of the traits analysed. Additive genetic variances of rPOLY) and environmental (re) correlations between traits 1x and 2x were calculated as rPOLY = CovPOLY_/[(VPOLY_1x)(VPOLY_2x)]0.5 and re = Cove/[(Ve1x)(Ve2x)]0.5, while phenotypic correlations (rP) were calculated as rP = rPOLY(h2x1)0.5(h2x2)0.5+re[(1−h2x1)]0.5[(1−h2x2)]0.5. Contour plots with 95% confidence intervals, which present breeding values standardized to a mean of zero and a standard deviation of one, were created for two traits obtained from bivariate animal models with respect to STX17 genotype. These plots illustrate the decomposition of genetic correlations into additive polygenic background effects and effects of a single mutation. More detailed derivations of quantitative genetic parameters are provided in Bivariate models were run for all pairs of traits applying the single trait model specifications from above. Genetic (


Sexual imprinting is important for kin recognition and for promoting outbreeding, and has been a driving force for evolution; however, little is known about sexual imprinting by auditory cues in mammals. Male mice emit song-like ultrasonic vocalizations that possess strain-specific characteristics.In this study, we asked whether female mice imprint and prefer specific characteristics in male songs.We used the two-choice test to determine the song preference of female C57BL/6 and BALB/c mice. By assessing the time engaged in searching behavior towards songs played back to females, we found that female mice displayed an innate preference for the songs of males from different strains. Moreover, this song preference was regulated by female reproductive status and by male sexual cues such as the pheromone ESP1. Finally, we revealed that this preference was reversed by cross-fostering and disappeared under fatherless conditions, indicating that the behavior was learned by exposure to the father's song.Our results suggest that female mice can discriminate among male song characteristics and prefer songs of mice from strains that are different from their parents, and that these preferences are based on their early social experiences. This is the first study in mammals to demonstrate that male songs contribute to kin recognition and mate choice by females, thus helping to avoid inbreeding and to facilitate offspring heterozygosity. Sexual imprinting is important for kin recognition and for promoting outbreeding, and has been a driving force for evolution. During sexual imprinting, females develop species-specific strategies for selecting mating partners. When given a choice, female mice in mating or preference assays prefer males whose major histocompatibility complexes (MHC) are different from their own Male and female animals exchange multiple sensory signals, not just olfactory cues, during interaction. Male mice emit ultrasonic vocalizations, the features of which are similar to the calls of songbirds Here, by simultaneously presenting male songs from 2 discrete ultrasonic speakers to female mice, we examined whether female song recognition also has a genetic component. We found that females can recognize and prefer strain-specific song characteristics that differ from those of their parents. Moreover, multisensory information is important for inducing adaptive behavior; for example, maternal retrieving behavior is regulated by multisensory information from pup olfactory and auditory cues Finally, we investigated whether female preference for male songs is innately regulated by genetic control, or shaped by social experience during the developmental period as has been shown in songbirds F = 28.611, p<0.0001, p<0.001, p<0.001, respectively), whereas the BALB song contained more “Harmonics” (p<0.001) compared to the other strains. In addition, the KJR song contained more “Flat” (p<0.001), and the ICR song had more “Downward” syllables (p<0.01). The songs produced by males of these 4 strains were different from one another in song profile: peak frequency , as we previously reported, has been shown to induce female sexual receptivity 2 = 9.64, p = 0.002; Wilcoxon signed-ranks tests, B6, p = 0.028; BALB, p = 0.043). We also performed tests with songs produced by males of the other strains, ICR and KJR. The two-choice test revealed that B6 females preferred ICR songs over B6 songs  and BALB/c (BALB) mice were originally obtained from Japan Clea Co. Ltd., and bred in our laboratory. Food and water were provided ad libitum, and animals were housed under a standard 12 L∶12 D cycle (lights on at 0600 h). An adult male and female mice of the same strain were pair-housed in a cage (17.5×24.5×12.5 cm). After weaning at 21 d after birth, the litters were housed in same-sex groups of 2–5 mice in a cage of the same dimensions as the breeding cage. All female mice were used in the experiment after they reached sexual maturity (7–35 wk old). To avoid litter effects, each experimental group was obtained from 1–2 females from the same litter, such that at least 4 to 5 litters were used in the same groups. Males used as singers were sexually mature (20–30 wk old) and experienced in mating and parenting behavior so that male social and sexual status was controlled. All experiments were conducted in accordance with the guidelines of “Policies Governing The Use of Live Vertebrate Animals” of Azabu University, and approved by The Ethical Committee for Vertebrate Experiments of Azabu University (ID#070418).Ultrasound recording was performed using a condenser microphone  as previously reported The test cage was constructed using clear Plexiglas (42.5×27×25 cm), modified for assessing females' preference for 1 of 2 male songs reproduced through a nanocrystalline silicon thermo-acoustic emitter positioned at the end of each compartment . In our A female mouse was introduced into the Neutral zone, which was temporarily separated from the other compartments by 2 mobile partitions. After an acclimatization period, the mobile partitions were removed and the male songs of B6 and BALB mice were simultaneously reproduced for a 5-min period. The subject's behavior was analyzed during this period. Each experiment was recorded using a CCD camera connected to a video monitor, and the following factors were recorded: the number of entries into the sound compartment, the duration of stay in each speaker zone and middle zone, and the duration of searching time on the mesh. To present mouse chemical signal cues, male-soiled bedding or ESP1 was placed into the Neutral zone to increase the female's sexual arousal before starting the 5-min test. Female-soiled bedding was used as a control stimulus. The male- and female-soiled bedding was a mixture of 2 g each from adult B6 and BALB/c males and females respectively (3 mice per strain). Recombinant ESP1 (20 µg) in Tris buffer was transfused onto a piece of cotton (30 mg) and dried for 1 h before exposure, as previously described Vaginal smears were obtained to determine the phase of the estrous cycle 5–7 h before the trial. The smears were stained with Giemsa and examined under a light microscope at ×20 magnification. The stage of the estrus cycle was determined according to the ratio of cell types found in the smears, and was classified as pro-estrus, estrus, or metestrus and diestrus.A male and female mouse of the same strain were pair-housed in a cage for breeding. Pregnant females were examined every 6–8 h for delivery. When pups were born at the same examined 6 to 8-h time period in both parental strains, a part of the litter was reciprocally cross-fostered to parents of the other mouse strain (B6-fosterer and BALB-fosterer). Control mice were handled in the same manner as fostered pups but were returned to their own parents (B6-sister and BALB-sister). When female mice were raised in fatherless conditions, a pregnant female was singly housed and monitored daily for delivery. All litters were left undisturbed until weaning postnatal day 21 (PD21). After PD21, female pups were housed with other females of the non-cross-fostered control (B6-sister or BALB-sister) of the different strain until the test day.Each behavioral factor was statistically compared between the 2 played-back sounds using a generalized linear model (GLM) with main factors, followed by Wilcoxon signed-ranks tests comparing the 2 sound compartments . Some parameters were not normally distributed; thus only nonparametric tests were used. Female .(MP4)Click here for additional data file.Movie S2Song preference. A representative example of the preference test in a B6 and BALB female for B6 and BALB male songs.(MP4)Click here for additional data file.Figure S1Parametric analysis of song profiles. (a) Mean percentage composition for each syllable category in the 4 strains of mice. Percentages were the average probability calculated in each subject. (b) Mean peak frequency, (c) inter-syllable interval, and (d) duration of syllables in songs of each mouse strain. Different letters above bars represent significant differences at α = 0.05.(TIFF)Click here for additional data file.Figure S2In the absence of male odor stimuli, or when in pro-estrous or estrus, females did not show a preference for songs of males of a different strain. (a) Duration of time searching for songs during pro-estrus in B6 (n = 7) and BALB (n = 6) females exposed to male odor before testing. (b) Duration of time searching during estrus in B6 (n = 6) and BALB (n = 6) females exposed to male odor before testing. (c) Duration of time searching during pro-estrus in B6 (n = 5) and BALB (n = 5) females in the absence of male odor before testing. (d) Duration of time searching during estrus in B6 (n = 6) and BALB (n = 6) females in the absence of male odor before testing. There were no significant differences among these conditions.(TIFF)Click here for additional data file.Figure S3In the presence of female odor stimuli, female mice did not show a preference for songs of males of a different strain. Duration of time searching during diestrus in B6 (n = 5) and BALB (n = 7) females exposed to female odor for 15 min before testing. Female-soiled bedding contained a mixture of 2 g each from adult B6 and BALB/c females. No significant differences were observed.(TIFF)Click here for additional data file.Figure S4Females showed preference for songs produced by different individuals of B6 and BALB males. When songs were recorded from individual B6 and BALB males and played back to a female subject, B6 females (n = 6) showed longer search times for BALB song, and BALB females (n = 5) showed longer search times for B6 song. Asterisks indicate significant differences p<0.05.(TIFF)Click here for additional data file.Figure S5Information about male songs used for each playback test. (a) Principal components in 6 songs used for each playback test. (b) Mean percentage composition of each waveform category in the 6 playback songs.(TIFF)Click here for additional data file.


Predicting how species interactions evolve requires that we understand the mechanistic basis of coevolution, and thus the functional genotype-by-genotype interactions (G × G) that drive reciprocal natural selection. Theory on host-parasite coevolution provides testable hypotheses for empiricists, but depends upon models of functional G × G that remain loosely tethered to the molecular details of any particular system. In practice, reciprocal cross-infection studies are often used to partition the variation in infection or fitness in a population that is attributable to G × G . Here we use simulations to demonstrate that within-population statistical G × G likely tells us little about the existence of coevolution, its strength, or the genetic basis of functional G × G. Combined with studies of multiple populations or points in time, mapping and molecular techniques can bridge the gap between natural variation and mechanistic models of coevolution, while model-based statistics can formally confront coevolutionary models with cross-infection data. Together these approaches provide a robust framework for inferring the infection genetics underlying statistical G × G, helping unravel the genetic basis of coevolution. Coevolution, or reciprocal evolutionary change between two or more species and has shaped key early branches in the tree of life, including the origin of the eukaryotic cell from endosymbiosis Sagan, . Coevolufunctional G × G” following the recent analysis of epistasis by Hansen , parasite genotype (Gp), and their interaction (Gh × Gp). Variation among populations can also be estimated in studies of multiple locations  from both partners are tested against each other in a factorial design, using genotypes sampled from either a single location or from various locations  Is statistical G × G always present in coevolving populations? (2) Is significant statistical G × G an indicator of coevolution, or the strength of coevolution? and (3) Do estimates of statistical G × G provide information about the form of functional G × G underlying coevolution? Here we answer these questions using coevolutionary models that allow us to formally connect different types of functional G × G to the patterns of statistical G × G they produce within a single population. Our brief modeling exercise demonstrates that, because of both sampling error and the coevolutionary process itself, these two definitions of G × G are likely to be loosely related at best. We finish by suggesting some future approaches for merging empirical cross-infection data with theoretical models to better understand the mechanistic basis of coevolution.One way to evaluate the link between G × G at the individual level and statistical G × G measured at the level of a population is to develop and analyze mathematical models of the coevolutionary process. Our basic approach is to first simulate coevolution using simple, well-studied models of host-parasite interactions. These models assume coevolution is mediated by a single locus in each species, with the outcome of encounters between individuals described by one of several well-studied genetic models  into components corresponding to error, host genotype, parasite genotype, and the interaction between host and parasite genotype . A much more detailed description of coevolutionary simulations and simulated cross-infection experiments can be found in Data Sheet 2error is always much larger than σ2host × parasite in Supplementary Figure Absolutely not. We found that low/negligible statistical G × G is quite common even when coevolution is a very strong force shaping allele frequencies in a population Figure . This isUnfortunately, no. As discussed above, strong coevolutionary cycles themselves contribute to a disconnect between the coevolutionary selection acting on host and parasite alleles and the population-level statistical G × G. This generates a seemingly-paradoxical result: the stronger coevolutionary selection, the greater the amplitude of genotype frequency cycles, and the more frequent are periods of time in which statistical G × G is negligible . When large amplitude cycles are produced by coevolution  between the functional models of G × G that drive coevolution, the strength of coevolution, and the population-level  G × G often estimated by empiricists. While statistical G × G is indicative of functional G × G, our results suggest an absence of statistical G × G should rarely, if ever, be used to suggest coevolution is not occurring. Thus, while previous studies of statistical G × G (see references above) are not inherently flawed, our results would suggest a strong false-negative bias, since Although we have not performed the relevant simulations, we suggest that these conclusions are also likely apply to the selection mosaic, defined as G × G × E  of cross-infection experiments between such genotypes is necessary for mapping studies that seek to find candidate genes or genomic regions responsible for G × G (Wilfert and Schmid-Hempel, The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Danio rerio, Hamilton 1822), kin recognition is based on olfactory and visual imprinting processes. If larvae are exposed to visual and chemical cues of kin at day 5 and 6 post fertilization they will recognize kin throughout life, while exposure to non-kin fails to trigger any recognition. Chemical imprinting signals are transcribed by polymorphic genes of the major histocompatibility complex (MHC) code; however, the underlying mechanism for visual imprinting remains unclear. Here we provide evidence for the existence of family-specific differences in morphometry and pigmentation pattern of six day old zebrafish larvae. While rump, tail and body pigmentation were dependent on relatedness, iris pigmentation and morphometry were also influenced by MHC class II genotype. Our study revealed that the MHC not only influences the chemical signature of individuals, but also their visual appearance.Kin recognition can drive kin selection and the evolution of social behaviour. In zebrafish ( Kin recognition is a fundamental process for the evolution of cooperative behaviour, driving assortative allocation of resources, mate choice and inbreeding avoidance We could show that for later kin recognition zebrafish larvae have to experience an imprinting process that consists of two closely linked steps: a visual imprinting phase on day 5 post fertilization (pf) (unpublished data) followed by the olfactory imprinting phase on day 6 pf The highly variable MHC genes are essential for the recognition of extracellular pathogens by the immune system of vertebrates Despite these findings, the mechanism for the zebrafish’s predisposition to the visual cues of kin remained unclear. We hypothesise that sharing MHC class II alleles not only leads to similarity of olfactory but also to similar visual cues. To prove this hypothesis, we looked for family-and MHC-specific differences in the pigmentation of the body and eyes and in the morphometric appearance parameters of zebrafish larvae.Artemia salina). For breeding, each female was kept with one male in a 3 L tank. In the afternoon, egg dishes were placed into the tanks and collected the following morning. All eggs and larvae were maintained in glass dishes in an incubator (SANYO MIR 553) at 25°C±0.5°C. After hatching, which occurred between the 3rd and 4th day pf, larvae were fed with live Paramecium caudatum. Altogether, larvae of 5 breeding pairs were tested.Adult fish were maintained at 25°C±1°C under a 13/11 light: dark cycle and were fed twice daily with commercial flake food and live brine shrimp .Using a binocular  zebrafish larvae were photographed (Canon Eos 5D Mark II) during the imprinting phase and after final establishment of larval pigment pattern (at day 6 pf). Therefore, all larvae were sacrificed using an overdose of MS222 (1 g/100 ml) and immediately positioned on their left side on top of a plexiglas slide in a special larval sized and shaped cavity. All photos were taken within 60 sec after first exposure to MS222 and at the same room temperature to ensure that all larvae experienced the same treatment. After taking the pictures, all larvae were preserved in ethanol for MHC genotyping (see below). The pigment in dead larvae was more intense in colour due to muscle relaxation, but the pattern itself did not differ from that of a live larva when pictures were taken immediately after the death of the animal.k(i), lk(j) and a weighted difference (Dm) were calculated via Dm = ∑k αk|lk(i)−lk(j)| with weights αEL = 0.4, αRAL = 0.1, αRL = 0.3, and αTW = 0.2.Pictures were analysed in ImageJ 1.44p. We measured eye length, rump length, rump-anus length and tail width . All mear-values) by conducting a 2D correlation in ImageJ 1.44p. The r-values spanned from 0–1, with 0 signifying 0% compliance of pixels of two images and 1 signifying 100% compliance. We generated similarity matrices containing the r-values for body and iris pigmentation separately for all pairs of individuals. Additionally, we divided the body into three different sections  and analysed the sections separately in the same way to test whether potential differences in body pigmentation were distributed evenly over the whole body or were limited to specific parts.Using Adobe Photoshop CS2 (Version 9.0.2), pictures of the larvae were aligned so that all were in exactly the same position. The pigmentation of iris and body of each larva was drawn manually in black on a white background using a Wacom Intuos 4 M graphic tablet . The simDNA was extracted from ethanol-preserved fin clips of the parents and from whole larvae. For extraction we used 200 µl of a digestion solution, containing 10% Chelex (Biorad) and 0.07 µg/µl proteinase K. Samples were incubated at 55°C for 1 h, followed by a 10 min heat inactivation step at 95°C. Chelex sedimentation was induced by centrifuging the samples at 15000 rpm for 15 sec and the clear supernatant was transferred into sterile Eppendorf tubes and stored at −18°C until usage.TGCATCTACAGCACCAGTGA; reverse primer: CTGCTTTATCACGTACAGCTGA) were designed based on sequences derived from Genbank (accession numbers NM_131476 and AL928944).In zebrafish, two genes (DAA and DAB) represent functional MHC class II loci The PCR reaction volume was 10 µl, containing 2 µl template DNA, 50 µM of each dNTP, 5 mMMgCl, Q-Solution (Qiagen), 1X PCR buffer (Qiagen), 1 µM of each primer, and 0.25 Units of Hot Star Taq DNA polymerase (Qiagen). PCR conditions were as follows: 15 min at 95°C, 28 cycles  and final extension at 72°C for 10 min.To detect allelic differences between MHC class II genes, we used single strand conformation polymorphism (SSCP) analysis, which allows for the separation of DNA fragments that differ by as little as one base pair. 10 µl PCR product was denaturated in 10 µl formamide buffer (95%) for 10 minutes at 96°C und then cooled down immediately to 0°C in ice-water for another 10 minutes. Probes were loaded onto a polyacrylamide gel (9%) containing 1X TBE-buffer. The same buffer was used as running-buffer in the electrophoresis (Biometra Maxi Gel). Electrophoresis was performed at 10°C for 6 h (8 Watt). Gels were stained for 30 min in a staining solution containing 50 ml distilled water and 5 µl GelRed and photographed on a UV transilluminator. By sequencing cut-out DNA bands of the parental PCR products, we verified that similar band patterns on the SSCP gel represent similar alleles.We genotyped larvae of 5 different mating pairs (n = 20–26 per pair). Two of these pairs shared the same MHC class II while the other pairs differed in all of their MHC alleles. We determined the MHC class II similarity between all photographed individuals according to the number of shared DAB alleles and categorized by 0%, 50% or 100% MHC class II similarity.The basic idea of the analysis for similarity of morphometric and pigmentation patterns was to establish relationships via hierarchical clustering. This is a standard approach to represent kin relationships, resulting in ‘dendrogram’ trees with the individuals as leaves of the trees. By clustering different similarity matrices and comparing the resulting dendrograms, we evaluated whether different traits resulted in similar relationships.r) into distance values (d) via d = 1– r so they could be used as input for the clustering. For the hierarchical clustering, we applied a standard approach, Wards Algorithm ++ is the number of times where the two individuals of a pair had the same relative order in the arrangement of the input and in the leaves. Correspondingly, N+− is the number of times the two individuals appeared in a different relative order in the two arrangements. Hence, the dendrograms were arranged such that the original (family) order was as well preserved as possible. Next, we compared all resulting orders of the leaves of the different dendrograms both pair wise and as well as with the input order, by calculating Goodman-Kruskal’s value for each comparison. Finally, to obtain statistical significance, i.e., p-values<0.05 rD, “null ensemble”). These rD matrices were obtained by generating 112  random points in the square unit and obtaining all Euclidean distances for all pairs of random points. The resulting distribution of values could be well approximated by a Gaussian distribution with mean γσ = 0.063. Hence, if two orders resulted in a γ value larger than p-value<0.05).First, we changed all similarity (matrix) values (γ (GK): γ = 0.298; p = 0.000). The same is true for the correlation of the family relationship with different pigmentation patterns, using the r-values from the image correlations, for the iris , body , rump  and tail , respectively. Furthermore, we found a significant influence of MHC class II similarity on morphometry  and on pigmentation of iris , body , rump  and tail   .γ = 0.761; p = 0.000, r-values) and morphometric similarity separately, we analysed only pair wise comparisons between 100% MHC class II identical individuals and conducted independent two-sample t-tests of similarities within and between families. Similarities in iris, body, rump and tail pigmentation and similarities in EL/SL and RL/BL were significantly higher within families than between families   using only data from pair wise comparisons within (but not among) families. We tested if families . In contfamilies . TherefoIn summary, zebrafish larvae that shared the same MHC class II alleles looked more similar than those that did not share the alleles. Zebrafish larvae showed family-specific differences in all investigated parameters. While body pigmentation was only dependent on genetic relatedness, iris pigmentation and parts of the morphometry (EL/SL and RAL/SL) were additionally influenced by MHC class II genotype.We showed that zebrafish larvae differ in their visual appearance according to their relatedness. Since zebrafish larvae can visually differentiate between kin and non-kin during the visual imprinting phase , we assume that 5 days old larvae must be able to recognize those fine differences. Furthermore, we discovered that MHC genes do not only influence the chemical signature but also the visual appearance of zebrafish larvae, offering a possible basis for predisposition for the visual cues of kin.Gasterosteus aculeatus), one MHC class I allele correlates with male redness Madrillus sphinx), specific MHC genotypes correlate with red facial coloration Because of the relatively large eye size of zebrafish larvae, iris pigmentation is a prominent trait and a good candidate for a key visual imprinting cue. MHC class II genes are expressed in retinal pigment epithelial cells The requirement of combined visual and chemical stimuli for imprinting might increase the specificity ensuring that zebrafish larvae do not imprint on the wrong cues. Since we showed that zebrafish larvae can olfactory imprint on MHC peptides that also influence social behaviour in mice We elucidated an essential step in the imprinting process; however, while our findings help to understand the mechanism for imprinting, some questions remain. Similarity in pigmentation patterns can explain how an individual can differentiate between members of different families, but it cannot explain how naïve zebrafish already ‘know’ the visual appearance of their siblings when they encounter kin for the first time. Two different explanations are conceivable. First, a zebrafish larva could know relatives by recognizing their similarity with its own appearance, or secondly, there could be a genetic predisposition to imprint on conspecifics of a certain appearance. Based on our results, we conclude that it is highly unlikely that a visual self-matching process is involved. A larva might be able to see the caudal part of its body, but definitely not the whole body or its own iris pigmentation. Additionally, it is highly unlikely that zebrafish larvae possess any self-consciousness. Therefore, we regard an innate genetic predisposition for the visual traits of kin as more likely. While olfactory imprinting on MHC-similar kin is easier to understand by assuming that some olfactory receptors might be tuned to MHC signals similar to its own, we assume that the visual template that needs to be activated for visual imprinting on day 5 is encoded in higher brain areas, e.g. in the areas responsible for face recognition Gallus gallus domesticus) imprint on their mother, they have an innate predisposition for visual stimuli that resemble the shape of a head and neck Genetic predispositions for visual traits have been found in other species. When domestic chicks . Larvae might have an innate sensitivity towards the visual traits of MHC class II identical larvae, which are very likely siblings. By imprinting on those individuals, larvae might then learn the visual traits which are independent of MHC class II genotype but family specific, i.e. visual cues that allow the recognition of even MHC class II dissimilar kin. This means that during the imprinting process, the individual learns additional visual and olfactory cues of kin with whom it does not share all MHC class II alleles.In summary, our results demonstrate the existence of highly family specific traits in 6 day old zebrafish larvae and show that some of those traits depend on MHC class II genotype. Our findings support the idea that MHC genes could be involved in visual imprinting on kin and thus may go far beyond their previously-known functions in the immune system and in olfactory imprinting.


We demonstrated 6 months after exposure to 10 and 100 cGy 56Fe radiation at 1 GeV/µ, that APP/PS1 mice show decreased cognitive abilities measured by contextual fear conditioning and novel object recognition tests. Furthermore, in male mice we saw acceleration of Aβ plaque pathology using Congo red and 6E10 staining, which was further confirmed by ELISA measures of Aβ isoforms. Increases were not due to higher levels of amyloid precursor protein (APP) or increased cleavage as measured by levels of the β C-terminal fragment of APP. Additionally, we saw no change in microglial activation levels judging by CD68 and Iba-1 immunoreactivities in and around Aβ plaques or insulin degrading enzyme, which has been shown to degrade Aβ. However, immunohistochemical analysis of ICAM-1 showed evidence of endothelial activation after 100 cGy irradiation in male mice, suggesting possible alterations in Aβ trafficking through the blood brain barrier as a possible cause of plaque increase. Overall, our results show for the first time that HZE particle radiation can increase Aβ plaque pathology in an APP/PS1 mouse model of AD.Galactic Cosmic Radiation consisting of high-energy, high-charged (HZE) particles poses a significant threat to future astronauts in deep space. Aside from cancer, concerns have been raised about late degenerative risks, including effects on the brain. In this study we examined the effects of  This does not come without significant risk. In particular, a major risk factor for human health in deep space is radiation. The galactic environment is dominated by high levels of protons arising from solar flares, and low, but continuous levels of Galactic Cosmic Radiation (GCR) AD is characterized by a progressive cognitive decline over several years 56Fe particle radiation resulted in cognitive impairment and increased Aβ plaque pathology at cumulative doses similar to those that astronauts might be exposed to on exploratory missions to deep space and Mars The potential disease-altering effects of GCR prompted us to examine if HZE radiation influences AD pathological progression using an APP/PS1 mouse model that shows age-associated accumulation of Aβ plaques and cognitive dysfunction This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. Animal protocols were reviewed and approved by the University of Rochester (Protocol Number: 2008–38) and Brookhaven National Laboratory’s (BNL) (Protocol Number: 442) Institutional Animal Care and Use Committees.Twenty-nine male and twenty female APPswe/PSEN1dE9 (APP/PS1) mice (stock no. 004462) on a mixed C3H/HeJ and C57BL/6 background were purchased from The Jackson Laboratory at approximately 3 months of age. Animals were shipped to BNL and allowed to acclimate. Mice were housed five per cage in temperature (23 ± 3°C) and light (12∶12 light:dark) controlled rooms with free access to chow and water. After radiation exposure at 3.5 months of age, animals were shipped back to the University of Rochester until euthanasia. Mice were routinely monitored for health issues and had no observable problems at the time of euthanasia. Male mice were euthanized at 9.5 months of age while female mice were euthanized at 7 months due to concerns raised regarding early death.Radiation exposures were performed at NASA’s Space Radiation Laboratory at BNL. Animals were loaded into ventilated 50 mL polystyrene conical tubes and irradiated, 8 at a time, using a foam tube holder positioned at the center of a 20×20 cm beam of iron ions accelerated to 1 GeV/µ at a dose rate ranging from 0.1–1 Gy/min. Male mice received total doses of either 10 cGy or 100 cGy. Female mice received only a 100 cGy dose. Control mice were similarly placed in tubes and sham irradiated.nd test session was scored. A recognition index (RI) for time spent with the novel object was calculated based on the proportion of total time spent with the novel object.Memory was tested using two different paradigms. The first was contextual fear conditioning, which tests the ability of the subject to recognize an environment associated with an adverse stimulus (foot shock). Fear conditioning was set up, performed, and analyzed as previously described Animals were anesthetized and perfused with saline as previously described Brains were sectioned at 30 µm on a sliding knife microtome with a −25°C freezing stage. Sections were stored in cryoprotectant at −20°C until processing. Antibody staining was visualized using either biotinylated secondary antibodies, avidin-biotin complex (Elite), and a 3,3-diaminobenxadine (DAB) substrate kit (Vector Laboratories) or, immunofluorescent secondary antibodies bound to Alexa fluorophores (Invitrogen) at a dilution of 1∶500. Primary antibodies used were mouse anti-6E10 , rabbit anti-GFAP (DAKO 1∶1000), rabbit anti-Iba-1 , rabbit anti-CD68 , and Armenian hamster anti-ICAM . Biotinylated secondary antibodies against their proper species (Jackson Laboratory) were used at 1∶1000. For Congo red staining, a kit from Sigma-Aldrich was used.http://rsbweb.nih.gov/ij/). The percent area occupied by 6E10 or Congo red of the cortex and hippocampus was calculated and analyzed. In addition to the percent area of 6E10, the total number and average size of 6E10 positive plaques was obtained using this threshold algorithm. The percent area occupied by GFAP was calculated for cortex only. Values obtained for male mice were analyzed with a one-way ANOVA followed by Bonferroni post test comparing the different doses. Values for female mice were analyzed with a Student’s t-test.Brains sections were viewed with an Axioplan 2i light microscope (Zeiss). For plaque area, a 5x lens was used. Multiple images were taken of a single section to obtain pictures of the whole cortex and hippocampus. Images were merged in Photoshop and subjected to threshold analysis using the max entropy threshold algorithm in NIH ImageJ  was used for all statistical analyses. A value of p < 0.05 was considered significant.Western blot and ELISA protein samples were prepared as previously described To assess the effect of iron galactic cosmic radiation on memory and cognition two separate tests were employed prior to tissue harvest at 9.5 mo for males and 7 mo for females. To assess hippocampal-dependent memory, the first test used was contextual fear conditioning A. We fo56Fe particle radiation also showed a significant increase of 300 ± 56 to 447 ± 147    E in the = .052) F in theTo strengthen our histology data and determine whether different forms of Aβ were altered after radiation, we prepared soluble and insoluble fractions of homogenized hemibrains and used ELISAs specific for Aβ peptides with C-terminals of 40 or 42 . For the56Fe particles. As shown in The increases found in the insoluble fraction D confirThe increase in Aβ observed by IHC and ELISA, but lack of evidence for alteration of amyloid processing, directed us to investigate other mechanisms. Due to lack of change in the female mice we elected to focus on samples from males irradiated at 100 cGy for these analyses. Microglia are principle players in CNS inflammation, which has been proposed to be an important driver of amyloid deposition. In addition, they are implicated in phagocytosis and control of Aβ 56Fe particle irradiation .Due to the importance of Aβ clearance out of the brain through the BBB adiation C.56Fe particles 56Fe particle irradiation in the APP23 transgenic mouse model of AD Here we report that GCR caused enhanced AD plaque pathology. To our knowledge, this is the first report of radiation being associated with enhanced plaque pathology in an AD mouse model. In addition to disease acceleration, we observed that low HZE doses are able to cause cognitive impairment as measured by contextual fear conditioning and novel object recognition in APP/PS1 tg mice . While c56Fe particle radiation.In addition to behavioral deficits, we saw enhanced Aβ plaque accumulation as judged by two different markers. 6E10 showed an increase in total deposited Aβ levels and Congo red showed an increase in aggregation of plaques into dense fibrils. These results were further confirmed by ELISA data . Aβ plaq56Fe radiation exposure. This effect was not seen 24 h or 3 weeks after irradiation The doses used in this study are comparable to those astronauts will see on a mission to Mars Microglia have been implicated in plaque maintenance in a number of models 56Fe particles (An additional hypothesis is that radiation causes vascular defects, which impair proper clearance of Aβ. Clearance through the vasculature has been shown to be crucial articles . It is t56Fe particle radiation can cause cognitive impairment as well as increased Aβ plaque pathology in APP/PS1 mice, without clear changes in glial activation. Additionally, the elevation of ICAM-1 expression in irradiated mice raises the possibility that vascular changes might underlie radiation-induced amyloid accumulation. These pathological increases are particularly concerning for astronauts who will be exposed to GCR in upcoming deep space missions. In this regard, one major caveat of our model is that mice were subjected to acute exposures with a single HZE species. It is not known how the CNS will respond to the complex and chronic low-dose GCR environment of space. Moreover, astronauts will not likely be familial AD carriers. Therefore, while many of the pathological processes are believed to be similar, this model does not reflect the complete human condition. However, for the one aspect we can replicate, the accumulation of Aβ, our findings demonstrate that whole body exposure to 56Fe particle HZE radiation enhances pathological processes associated with progression of AD.In conclusion we have demonstrated that 100 cGy of