Abstract
Compelling genetic evidence links the amyloid precursor protein (APP) to Alzheimer’s disease (AD), and several theories have been advanced to explain the involvement of APP in AD. A leading hypothesis proposes that a small amphipathic fragment of APP, the amyloid β-protein (Aβ), self-associates to form soluble aggregates which impair synaptic and network activity. Here, we report on the plasticity-disrupting effects of Aβ isolated from AD brain and the requirement of APP for these effects. We show that Aβ-containing AD brain extracts block hippocampal long-term potentiation (LTP), augment glutamate release probability and disrupt the excitation/inhibition balance. Notably, these effects are associated with Aβ localizing to synapses, and genetic ablation of APP prevents both Aβ binding and Aβ-mediated synaptic dysfunctions. These findings indicate a role for APP in AD pathogenesis beyond the generation of Aβ and suggest modulation of APP expression as a therapy for AD.
Acknowledgments We thank Dr. Tiernan T. O’Malley for useful discussions and technical advice. This work was supported by grants to DMW from the National Institutes of Health (AG046275), Bright Focus, and the United States-Israel Binational Science Foundation (2013244, DMW and IS); grants to TSJ from Alzheimer’s Research UK and the Scottish Government (ARUK-SPG2013-1), Wellcome Trust-University of Edinburgh Institutional Strategic Support funds, and the H2020 European Research Council (ALZSYN); and to the Massachusetts Alzheimer’s Disease Research Center (AG05134).
Introduction
Mutation, over-expression or altered-processing of the amyloid precursor protein (APP) underlie all known monogenic cases of familial Alzheimer’s disease (fAD) (Tanzi, 2012; Guerreiro and Hardy, 2014). Although the physiological roles of APP are not fully understood, myriad studies indicate that APP plays a role in synaptic plasticity, dendritic morphogenesis, and neuroprotection (Muller and Zheng, 2012). Membrane-tethered APP can act as a cell-adhesion molecule linking the pre-and post-synapse (Soba et al., 2005) and APP has been shown to regulate synaptic vesicle proteins, synaptic transmission and plasticity (Seabrook et al., 1999; Lassek et al., 2013; Fanutza et al., 2015; Lassek et al., 2016). In the dentate gyrus (DG) of rat, APP expression is known to change during memory consolidation (Conboy et al., 2005) and intraventricular administration of anti-APP antibodies or antisense oligonucleotides results in profound amnesia (Doyle et al., 1990; Huber et al., 1993; Mileusnic et al., 2000). Notably, APP is a component of the presynaptic GABA-B1a receptor (GABAB1a-R) complex (Bai et al., 2008; Schwenk et al., 2016) and neuron-type specific knock-out of APP indicates an important role for APP in GABAergic transmission and maintenance of the excitatory–inhibitory balance (Wang et al., 2014).
APP is a complex molecule that undergoes substantial post-translational modification and processing. More than 10 different proteolytic fragments of APP have been identified (Weidemann et al., 1989; Esch et al., 1990; Golde et al., 1992; Haass et al., 1992; Sisodia, 1992; Portelius et al., 2013; Welzel et al., 2014; Willem et al., 2015). Several of these are suggested to be pathogenic (Neve and McPhie, 2007; Yankner and Lu, 2009; Tamayev et al., 2012; Willem et al., 2015), whereas others are neuroprotective (Mockett et al., 2017). The fragment from which the precursor protein derives its name, the amyloid β-protein (Aβ), is found in the tell-tale amyloid plaques which litter the brains of individuals who die with AD. Aβ comprises a family of APP-derived peptides that share a common core of ∼30 amino acids (Walsh and Teplow, 2012) which are produced by the concerted action of two aspartyl proteases, β–secretase and γ-secretase (De Strooper, 2010). Aβ peptides are prone to self-associate and multiple studies indicate that certain forms of Aβ adversely affect synaptic form and function (Shankar and Walsh, 2009).
The synaptotoxic activity of Aβ and the involvement of APP in synapse formation and activity are particularly relevant to AD since in vivo and postmortem studies indicate that synapse dysfunction and loss are prominent early features of AD (Scheff et al., 2006; Scheff et al., 2007; Johnson et al., 2012). Transgenic (tg) mice over-expressing APP either alone, or in combination with PS1, produce high levels of Aβ, deposit amyloid plaques, and exhibit deficits in learning and memory (Ashe and Zahs, 2010). Certain APP tgs also manifest aberrant changes in synapses, neuronal microcircuits and complex networks (Palop and Mucke, 2016) and some authors have sought to link the network hyperactivity observed in APP tgs with epileptiform changes detected in a segment of individuals with early stage AD (Palop and Mucke, 2009; Busche and Konnerth, 2015). However, there is no human parallel for the high level of APP over-expression seen in APP tg mice. This supraphysiological production of APP induces artifacts such as increased mortality and behavioral hyperactivity (Ashe and Zahs, 2010; Nilsson et al., 2014), and it is difficult to differentiate between effects mediated by Aβ, APP, or non-Aβ APP derivatives (Seabrook et al., 1999). Indeed, there is now evidence that the epileptiform changes seen in APP tgs, that had formerly been attributed to Aβ, are in fact mediated by non-Aβ APP derivatives (Born et al., 2014). Surprisingly, mice which produce and deposit human Aβ, but do not over-express human APP (i.e. APP knock-in mice or BRI2-Aβ mice) show no changes in electroencephalogram (EEG) activity or deficits in synaptic plasticity (Kim et al., 2013; Born et al., 2014).
Acute studies in wild type rodents show that non-fibrillar, water-soluble Aβ from a variety of sources are potent synaptotoxins (Lambert et al., 1998; Walsh et al., 2002; Cleary et al., 2005; Klyubin et al., 2008; Minkeviciene et al., 2009; Kurudenkandy et al., 2014). Furthermore, in vitro and in vivo studies demonstrate that the most disease-relevant form of non-fibrillar Aβ, Aβ extracted from the water-soluble phase of AD brain, inhibits long-term potentiation (LTP), facilitates long-term depression (LTD), reduces synaptic remodeling, and impairs memory consolidation (Shankar et al., 2008; Barry et al., 2011; Freir et al., 2011; Borlikova et al., 2013; Hu et al., 2014; Yang et al., 2017). Here, we show that the block of LTP mediated by Aβ-containing AD brain extracts is accompanied by opposing changes in excitatory and inhibitory pre-synaptic release probabilities and consequent disruption of the excitation/inhibition (E/I) balance. The net increase in the E/I ratio and inhibition of LTP require expression of APP and are associated with Aβ localizing to synapses. These findings suggest a link between Aβ toxicity and perturbation of the normal regulatory role of APP, and are consistent with prior studies which have imputed a role for APP in Aβ toxicity (White et al., 1998; Lorenzo et al., 2000; Shaked et al., 2006; Sola Vigo et al., 2009; Fogel et al., 2014; Kirouac et al., 2017). In light of these results we suggest that down-regulation of APP expression or modulation of its interaction with synaptotoxic Aβ species should be investigated as an approach to treat AD.
Results
We previously reported that aqueous extracts of certain end-stage AD brains block hippocampal LTP in vivo and in vitro (Shankar et al., 2008; Li et al., 2009; Barry et al., 2011; Freir et al., 2011; Hu et al., 2014). Here we further investigated the mechanism of this effect and the requirement of endogenous APP.
The water-soluble extract from AD brain contains both Aβ monomers and oligomers and blocks LTP in a manner dependent on Aβ
Brain extracts were prepared as described and a portion was immunodepleted (ID) of Aβ or mock-ID with pre-immune rabbit serum. Here, the mock-ID extract is referred to as the AD sample, and the material depleted of Aβ as ID-AD. ID-AD and AD samples were analyzed using IP/WB, and MSD immunoassays that preferentially recognize either Aβ oligomers (oAssay) or Aβ42 monomers (Mc Donald et al., 2015). IP/WB analysis allows the capture of Aβ structures under native conditions and their detection following denaturing SDS-PAGE. The Aβ present in the AD sample migrated on SDS-PAGE with molecular weights of ∼4 and ∼7-8 KDa (Figure 1A) — a pattern we have seen in aqueous extracts of more than 100 AD brains analyzed in our laboratory (Mc Donald et al., 2015). Since SDS-PAGE is highly denaturing, the ∼4 and ∼7 kDa species do not necessarily reflect native Aβ species. Rather, these simply indicate that at least two different Aβ species are present. The same samples were treated plus or minus 5 M GuHCl and then analyzed using MSD assays. In prior studies we found that GuHCl effectively disaggregates high molecular weight Aβ species such that the signal detected by our oAssay is greatly decreased, whereas the signal detected by the monomer-preferring Aβx-42 immunoassay is proportionately increased (Mc Donald et al., 2015). A similar outcome was evident when the extract of AD1 was treated with GuHCl (Figure 1B). Specifically, GuHCl treatment caused a ∼70% decrease in the oligomer signal and a more than 8-fold increase in the monomer signal. Together these immunoassay and IP/WB results indicate that the majority of Aβ in the AD1 extract exist as labile aggregates made up of ∼4 kDa Aβ and ∼7 kDa Aβ. Importantly, AW7 ID effectively removed the large majority of the various Aβ species detected (Figure 1A and B). For instance, AW7 ID reduced the oligomer signal from 5.1 ± 0.03 ng/ml to 0.32 ± 0.12 ng/ml (Figure 1B, left panel) and monomer from 3.42 ± 0.03 ng/ml to 0.12 ± 0.04 ng/ml (Figure 1B, right panel).
For slices that received vehicle aCSF-B (Control), TBS induced strong potentiation which lasted the whole recording period (Figure 1C, black dots, 181.1 ± 10.7 %, n = 17), and ID-AD allowed a similar response (green downward triangles, 173.6 ± 8.7 %, n = 11, p= 0.12, One Way ANOVA test) (Figure 1C and D). Consistent with prior reports (Shankar et al., 2008; Freir et al., 2011), application of the AD1 extract significantly decreased LTP compared to both the Control and ID-AD treatment (red diamonds, 136 ± 4.2 %, n = 18, F=4.26,p=6.98E-9 AD vs. Control; F=4.14, p=3.56E-12 AD vs. ID-AD, One Way ANOVA test). The fact that the ID-AD and AD samples are identical except that the latter contains more Aβ than the former, is evidence that some form of Aβ is responsible for the block of LTP induced by the AD1 extract.
Aβ-containing AD brain extract affects presynaptic release probabilities
Accumulating evidence indicates that soluble Aβ species may interact with excitatory and inhibitory presynaptic terminals, modulate neurotransmitter release and cause synaptic dysfunction in the very early stages of AD (Nimmrich et al., 2008; Abramov et al., 2009; Kabogo et al., 2010; Parodi et al., 2010; Russell et al., 2012; Sokolow et al., 2012; Huang et al., 2013; Ripoli et al., 2013; Kurudenkandy et al., 2014). Although the effects of Aβ on LTP are well established (Klyubin et al., 2012), little is known about whether and how Aβ-containing AD extracts affect pre- and post-synaptic elements. To investigate affects on presynaptic release, we measured short-term synaptic facilitation (Zucker and Regehr, 2002) in slices before and 30 min after treatment with AD extract. As synapse release probability is inversely correlated to synaptic facilitation (Zucker and Regehr, 2002), we employed high-frequency burst stimulation (5 pulses with 20 ms intra-burst stimulus interval). Application of AD extract induced a reduction in the short-term facilitation during burst stimulation (Figure 1E-F). When responses were normalized based on the ratio of each fEPSP to the first response, we found that treatment with AD extract had no effect on the 2nd response, but significantly decreased the 3rd, 4th, and 5th response (red dots, p= 0.02 at 3rd stimulation, p= 0.004 at 4th stimulation and p= 0.004 at 5th stimulation, n = 6, student t-test, and also by group and time with Two way ANOVA, F(4,7)=6.39, p=0.006) (Figure 1F). In contrast, the slices treated with ID-AD yielded a pattern highly similar to that obtained with aCSF-B control (data not show). Thus, Aβ in the AD extract caused a reduction in short-term synaptic plasticity due to an initial increase in pre-synaptic glutamate release.
Aβ-containing AD brain extract disrupts the excitation-to-inhibition balance
To estimate the effect of Aβ on the total synaptic input at the single-neuron level, we used whole-cell voltage clamp recordings to measure spontaneous excitatory postsynaptic currents (sEPSCs) on CA1 pyramidal neurons before and 30 min after addition of AD extract. The holding potential was kept constant at -70 mV and sEPSCs measured before and 30 min after addition of AD extract – this 30 min interval was chosen to match the pre-incubation time used in our LTP and short-term facilitation experiments. Application of the AD extract significantly decreased the inter-event interval (p=1.65E-6, K-S test) and increased the mean frequency of sEPSCs (from 1.8 ± 0.2 Hz to 2.7 ± 0.3 Hz, p=0.02, n = 7, students t-test) (Figure 2A and B), but did not alter the sEPSCs amplitude (mean amplitude from 11.7 ± 1.8 pA to 10.1 ± 1.6 pA, p= 0.65, n= 7, student t-test) (Fig 2A and C). In contrast, the ID-AD sample had no effect on the frequency or the amplitude of sEPSCs (mean frequency: from 2.2 ± 0.5 Hz to 2.3 ± 0.7 Hz, mean amplitude: from 9.7 ± 1.7 pA to 10.2 ± 1.4 pA, p=0.45, n = 6, student t-test) (Figure 2D–F). These results indicate that the AD brain-derived Aβ significantly augments excitatory synaptic input on CA1 pyramidal neurons.
Pyramidal neurons receive both excitatory (sEPSCs) and inhibitory (sIPSCs) inputs and GABAergic axon terminals more easily form synapses with perisomtatic regions of pyramidal cells and strongly influence the output of neurons (DeFelipe, 2002; Garcia-Marin et al., 2009). To record sIPSCs on the same neurons, we adjusted the holding potential to 5 mV, a voltage close to the calculated sEPSCs reverse potential. As shown in Figure 2G–I, the AD sample significantly increased inter-event intervals(p=6.19E-6, K-S test) and decreased the frequency of sIPSCs (from 4.7 ± 0.7 Hz to 3.1 ± 0.7 Hz, p=0.008, n = 7, student t-test), without altering sIPSCs amplitude (from 14.8 ± 1.4 pA to 14.2 ± 0.9 pA, p=0.75, n = 7, student t-test). In contrast, the ID-AD sample had no effect on sIPSCs (frequency: from 5.3 ± 0.4 Hz to 4.8 ± 0.7 Hz, amplitude: from 13.6 ± 1.6 pA to 13.2 ± 2.1 pA, p=0.21, n = 6, student t-test) (Figure 2J-L). These results revealed that brain-derived Aβ significantly reduces GABAergic input on CA1 pyramidal cells.
To assess whether the changes of excitatory input (E) and inhibitory input (I) to the same neuron affect the E/I balance of that neuron, we calculated the integrated conductance of sEPSCs and sIPSCs over a 5 min period (Figure 2M). Comparison of the charge transfer before and 30 min after AD sample application revealed E was increased ∼3 fold and I was decreased ∼50%, consequently, the E/I balance was increased ∼6 fold (n=7) (Figure 2N). These results show that AD brain-derived Aβ oppositely affects excitatory and inhibitory synaptic transmission, causing an increase in the E/I ratio. These changes, especially the reduction of GABAergic tone on individual neurons, may contribute to neuronal hyperactivity and disturb network homeostasis, and thus perturb LTP (Wang et al., 2014; Gillespie et al., 2016).
Genetic ablation of APP occludes the effects of Aβ on LTP and pre-synaptic activity and normalizes the E/I balance
Multiple lines of evidence suggest that the APP may play a role in both GABAergic and glutamatergic neurotransmission (Bai et al., 2008; Kabogo et al., 2010; Pliassova et al., 2016; Schwenk et al., 2016) and separate studies impute a link between Aβ and APP (Lorenzo et al., 2000; Fogel et al., 2014; Kirouac et al., 2017). Thus, having found that brain-derived Aβ acts on pre-synapses and modulates both GABA and glutamate transmission, we investigated if APP was required for these effects. For this, we employed mice null for APP (Figure 3A). In agreement with prior reports, brain slices from APP KO and WT littermate mice exhibited similar levels of basal activity (P=0.19, One Way ANOVA test) and LTP (Figure 3B-F) (Dawson et al., 2000; Jedlicka et al., 2012). In both WT and APP KO slices treated with the aCSF-B control, TBS induced strong potentiation which lasted the whole recording period (158.1 ± 6.3 % in WT, n = 11, black dots; 151.2 ± 8.5 % in APP KO, n = 9, gray hexagons; F=4.4, p=0.79, comparison of the last 10 min recording using One Way ANOVA test) (Fig 3C and D). In agreement with experiments shown in Figure 1, addition of AD extract to WT slices significantly decreased LTP compared to addition of aCSF-B (121.8 ± 5.4 % in WT + AD, red dots, n = 7, F=4.5, p=0.0005, WT Ctr vs. WT + AD, One Way ANOVA test). However, application of the same extract to slices from APP KO mice had no effect on LTP, with the level of LTP in APP KOs indistinguishable from that of WT or APP KO treated with aCSF-B control (145.4 ± 4.2 % in APP KO + AD, pink upward triangles, F=4.5, p=0.41, APP KO Ctr vs. APP KO + AD; One Way ANOVA test). Similarly, AD extract had no effect on short-term facilitation (Figure 3 - figure supplement 1).
To assess the generalizability of the rescue of LTP by APP ablation, we tested the effect of an extract from a second AD brain (AD2) (Figure 3 - figure supplement 2). As with the AD1 extract (Figure 1), the AD2 extract blocked LTP in slices from WT mice in an Aβ-dependent fashion (161.9 ± 5.2 % in WT Ctr, black dots, n = 8; 123 ± 4.3 % in WT + AD2, red diamonds, n = 6; F=4.8, p=0.0001, One Way ANOVA test), but had no effect on LTP elicited from APP KO mice (165.4 ± 5.2 % in APP KO Ctr, gray hexagons, n = 11; 170.5 ± 8 % in APP KO + AD2, pink upward triangles, n = 3; F=4.8, p=0.29, One Way ANOVA test) (Figure 3E and F). These results were confirmed using another APP KO line (Zheng et al., 1995) and an extract from a third AD brain (AD3) (Figure 3 - figure supplement 3). Thus, it appears that the well-documented plasticity-disrupting activity of Aβ extracted from AD brains (Klyubin et al., 2008; Shankar et al., 2008; Barry et al., 2011; Freir et al., 2011; Klyubin et al., 2012) requires expression of APP.
To investigate whether APP is necessary for the effect of Aβ on the E/I balance (Figure 2), we studied the effects of Aβ on sEPSCs and sIPSCs in brains of APP KO and WT littermate mice (Figure 4). When applied to WT slices, AD extract again increased mean sEPSC frequency (from 2.2 ± 0.1 Hz to 3.4 ± 0.2 Hz, p=0.003, n = 5, student t-test) and decreased inter-event intervals (p=6.34E-15, K-S test), without altering the amplitude of sEPSCs (mean amplitude: 17.8 ± 0.4 pA vs. 18 ± 1.5 pA, p=0.32, n = 5, student t-test) (Figure 4A-C); and on the same neuron decreased mean sIPSCs frequency (from 4.2 ± 0.8 Hz to 2.7 ± 0.4 Hz, p=0.006, n = 5, student t-test) and increased inter-event intervals (p=9.44E-20, K-S test), but not amplitude (mean amplitude from 20 ± 3 pA to 19.3 ± 1.3 pA, p=0.34, n = 5, student t-test) (Figure 4D-F). These results, which were obtained with WT mice from an entirely different colony as those used in Figure 2, nicely demonstrate the robustness of the Aβ effect (Compare Figure 2 vs. Figure 4). Most importantly, when the AD extract was applied to APP KO slices there was no change in the frequency or amplitude of sEPSCs (mean frequency: from 2.6 ± 0.1 Hz to 2.7 ± 0.4 Hz, mean amplitude: from 15 ± 1.4 pA to 14.6 ± 0.5 pA, p=0.14, K-S test; p=0.26, n = 6, student t-test) (Figure 4G-I). Similarly, sIPSCs were also unchanged (mean frequency: from 3.5 ± 0.5 Hz to 3.5 ± 0.3 Hz, mean amplitude: from 16.7 ± 1 pA to 16.4 ± 1.6 pA, p=0.58, K-S test; p=0.25, n = 6, student t-test) (Figure J-L). Thus, as with our LTP experiments (Figure 3), ablation of APP completely rescued the effects of Aβ on excitatory and inhibitory input on CA1 pyramidal neurons. Further, since APP KO occluded Aβ alterations on the E and I input at individual neurons, it also prevented Aβ-mediated changes in the integrated conductance of sEPSCs and sIPSCs (Figure 4M). When AD extract was applied to WT slices, E increased ∼3-fold and I decreased ∼44%, resulting in ∼5.8-fold increase in the E/I ratio. However APP KO significantly reversed those E/I ratio changes (p=0.001, E/I in WT vs. E/I in APP KO, One Way ANOVA test) (Figure 4M). These results indicate that APP plays an important role in regulating the acute effects of Aβ on excitatory and inhibitory pre-synaptic release, and consequent maintenance of network homeostasis.
Aβ binding to synapses requires APP
To further investigate the targeting of synaptic elements by Aβ and how this might be influenced by APP we used a powerful high resolution microscopic technique, array tomography (AT), to search for evidence of Aβ binding to synapses in the same brain slices used in our electrophysiology experiments. Upon completion of LTP recording, certain slices from the treatment groups used in Figs. 3C and E were immediately fixed, processed and used for AT. Brain slices were stained with 1C22, an antibody that preferentially recognizes aggregated forms of Aβ (Mably et al., 2015; Pickett et al., 2016), synapsin-1 (for pre-synapses) and PSD95 (for post synapses). Approximately 7,000 synapses (∼3,500 pre-synapses and ∼3,500 post-synapses) per slice were analyzed. AT revealed significant anti-Aβ staining at synapses of slices incubated with AD1 extract with only background staining in samples incubated with aCSF and ID controls (Figure 5A-C; Kruskal Wallis test for synapsin-1 (x2(4)= 10.844, p=0.028), Kruskal Wallis test for PSD95 (x2(4)= 11.583, p=0.021)). In slices incubated with AD1 extract 1.27 ± 0.47% of pre-synapses and 0.58 ± 0.19%of post-synapses stained with 1C22, whereas in slices that had been incubated with aCSF, only 0.0076% ± 0.013% of pre-synapses and 0.0184% ± 0.087% of post-synapses were 1C22 positive (Dunns post-hoc between AD and control for pre-synapses p=0.024 and for post-synapses p=0.010). Slices incubated with extracts immunodepeleted of Aβ exhibited similar background staining with 1C22 as the aCSF control (Figure 5A-C). Thus, the same treatment with AD1 extract that disrupts synaptic plasticity in an Aβ-dependent fashion (Figs 1 and 3) also leads to Aβ binding to synapses (Figure 5A-C). Moreover, the finding that Aβ is present at more pre-synapses than post-synapses (Mann-Whitney U between AD pre-synapses and AD post-synapses U=0, p=0.004) is consistent with our results that suggest a pre-synaptic effect of Aβ (Figure 1E and F).
Importantly, when brain slices from APP KO mice were incubated with AD extract, little or no synaptic 1C22 staining was detected (Figure 5A, B and C). These results are notable since expression of APP was found to be required for Aβ-mediated disruption of both long-term plasticity (Figure 3) and neurotransmitter release (Figure 4). In sum, our AT data are completely congruent with the results of our electrophysiological experiments and indicate that expression of APP is required for the binding and subsequent plasticity-disrupting effects of Aβ, and that these effects are largely mediated on the pre-synapse.
Discussion
To better understand how Aβ disrupts synaptic plasticity we combined the use of the most disease relevant form of Aβ, Aβ extracted from human AD brain, with electrophysiological approaches and high-resolution microscopy. Consistent with prior studies, we show that extracts from the brains of individuals who died with AD block LTP (Shankar et al., 2008; Barry et al., 2011; Freir et al., 2011; Yang et al., 2017). We further show that concomitant with the block of LTP there is an increase in presynaptic release and disruption of E/I balance. In accord with these synaptic effects of Aβ, we demonstrate that exogenously applied AD brain-derived Aβ binds to synapses, with more Aβ oligomers detected on pre-synapses than on the post-synapses. Our finding that treatment with brain-derived Aβ enhances excitatory drive agrees well with studies which show that aggregated forms of synthetic Aβ increase EPSPs, action potentials, and membrane depolarizations (Hartley et al., 1999; Minkeviciene et al., 2009; Kurudenkandy et al., 2014). Our study is unique in that we employed brain-derived Aβ, and that the concentration of this material was much lower than the synthetic Aβ used in prior studies.
The apparent paradox that ectopic application of Aβ causes a net increase in excitation, yet impairs LTP may result because of glutamate spillover and activation of extra- or perisynaptic NR2B-enriched NMDARs, which play a major role in LTD induction (Li et al., 2011; Zhang et al., 2016). In such a scenario, synaptic depression may result from an initial increase in synaptic activation of NMDARs by glutamate, followed by synaptic NMDAR desensitization, NMDAR/AMPAR internalization, and activation of extrasynaptic NMDARs and mGluRs (Hu et al., 2014). However, it is not clear why ablation of APP could recover such effects.
An alternative explanation that accounts for a role for APP in the impairment of post-synaptic efficacy is that exogenous AD-derived soluble aggregates and endogenously produced monomer have differential effects. Aβ is known to be released in an activity-dependent manner (Kamenetz et al., 2003; Cirrito et al., 2005), whereas elevated Aβ levels result in depressed glutamatergic synaptic transmission and glutamate receptor endocytosis (Kamenetz et al., 2003; Hsieh et al., 2006). Thus, it is plausible that the increase in glutamate release induced by soluble Aβ aggregates may also lead to an increase in de novo produced Aβ monomer and this in turn may depress post-synaptic activity. Such a scenario would necessarily require expression of endogenous APP and explain why ablation of APP can obviate the block of LTP caused by brain-derived soluble Aβ aggregates. With regard to the protection of LTP upon ablation of APP, it is important to emphasize the robust nature and generalizability of this phenomenon. We observed the same protection using two different APP KO mouse lines (Zheng et al., 1995; Callahan et al., 2017) and extracts from 3 different AD brains. In all cases AD extracts blocked LTP in an Aβ-dependent manner when applied to wild type mouse brain slices, but the same AD extracts had no effect on LTP elicited from APP KO slices. Moreover, the extent of Aβ binding to synapses was similar in two different sources of wild type mice (Figure 5B and C), and the pattern observed was reminiscent of that seen in AD brain (Pickett et al., 2016).
There is evidence that APP can act as a receptor for Aβ (Melchor and Van Nostrand, 2000; Van Nostrand et al., 2002; Yankner and Lu, 2009; Fogel et al., 2014; Kirouac et al., 2017) and that APP may mediate increased - excitatory drive (Fogel et al., 2014). Specifically, Aβ was unable to promote aberrant neurotransmitter release in the absence of APP (Fogel et al., 2014). Our finding that binding of soluble Aβ aggregates to synapses requires expression of APP is consistent, but not proof, that APP may act as a receptor for Aβ. In this regard, it is worth noting that APP is known to both regulate L-type calcium channels in GABAergic neurons, interact with the pore-forming subunit Cav1.2 (Yang et al., 2009), and is a member of the GABAB-R receptor complex (Schwenk et al., 2016). In addition, there is evidence from proteomic studies which indicates that APP interacts with more than 30 different proteins including proteins key to synaptic vesicle turnover (Kohli et al., 2012; Del Prete et al., 2014; Lassek et al., 2014; Wilhelm et al., 2014), and proteins (such as the prion protein) which are implicated in binding Aβ (Bai et al., 2008; Lauren et al., 2009). Thus, Aβ could exert an APP-dependent effect either by directly binding to APP or binding to protein complexes of which APP is a component and stabilizing member.
So far we have considered the effects of Aβ on synapses and a single hippocampal pathway (the Schaffer Collateral), but Aβ is also thought to have network-wide effects (Palop and Mucke, 2010). For instance, Aβ-induced increases in excitatory network activity could lead to synaptic depression through homeostatic mechanisms. It is well established that acute treatment of primary neurons with bicuculline (a GABAA antagonist) increases overall neuronal activity and firing rates (Vertkin et al., 2015). However, after a couple of days, neuronal activity returns to control levels. By analogy, it is reasonable that the disruption of E/I balance seen with our acute Aβ treatment may also cause both short-term local and long-lasting network effects. Given the fact that Aβ treatment increases excitatory drive and decreases inhibitory drive, and that GABA-ergic interneurons express high levels of APP in DG (Wang et al., 2014; Del Turco et al., 2016) it is tempting to speculate that Aβ-mediated disruption of GABA-ergic interneurons may play a special role in the cognitive impairment that occurs early in AD (Gillespie et al., 2016).
Considerable data from the study of APP tgs implicate impairment of GABAergic interneurons as central to the network disturbances evident in these models (Busche and Konnerth, 2015; Palop and Mucke, 2016). However, the unphysiological expression of high levels of APP and the concomitant release of Aβ from the expressed transgene make it difficult to differentiate between effects mediated by Aβ versus APP, or non-Aβ APP metabolites (Seabrook et al., 1999; Melnikova et al., 2013; Born et al., 2014; Fowler et al., 2014). Nonetheless, growing evidence suggests that GABAergic interneurons play a prominent role in homeostatic regulation of hippocampal networks and there is compelling proteomic and physiological data that link APP and GABAB1a-R (Wang et al., 2014; Gillespie et al., 2016; Schwenk et al., 2016). Consequently further investigations on how Aβ effects GABAB-R expression, GABAB-R-APP interactions and whether GABAB-R KOs are resistant to Aβ are merited and may lead to a pharmacological means to attenuate Aβ synaptotoxicity. Similarly, modulation of APP expression may also offer therapeutic potential. However, while our results demonstrate that ablation of APP in brain slices from young (2-3 month) mice protects against the acute synaptotoxicity of Aβ, widespread knock-out of APP is not recommended. APP appears to be involved in many physiological processes (Yang et al., 2009; Muller and Zheng, 2012; Del Prete et al., 2014; Lassek et al., 2014; Wang et al., 2014) and aged APP null mice exhibit hypersensitivity to kainate-induced seizures (Steinbach et al., 1998), altered exploratory behavior, deficits in spatial memory, and impairment of LTP (Dawson et al., 1999; Phinney et al., 1999; Seabrook et al., 1999; Ring et al., 2007). No such deficits have been reported in APP hemizygous mice, thus it maybe possible to down regulate APP expression so as to maintain normal function, yet attenuate Aβ synaptotoxicity.
Materials and Methods
Reagents
All chemicals and reagents were purchased from Sigma-Aldrich unless otherwise noted. Synthetic Aβ1–42 was synthesized and purified using reversed-phase HPLC by Dr. James I. Elliott at the ERI Amyloid laboratory Oxford, CT, USA. Peptide mass and purity (>99%) were confirmed by reversed-phase HPLC and electrospray/ion trap mass spectrometry.
Preparation of human brain extracts
All human specimens were obtained and used in accordance with the Partner’s Institutional Review Board (Protocol: Walsh BWH 2011). Tissue was from the brains of individuals (referred to as AD1, AD2 and AD3) who died with AD. AD1 was an 87 years old man who 9 months prior to death had scored 23 on the MMSE and at postmortem had pathological changes consistent with mild AD. AD2 was a 65 years old female who 3 years prior to death scored 24 on the MMSE and at postmortem was diagnosed as having AD. AD3 was a 68 years old female with end-stage AD and fulminant amyloid and neurofibrillary tangles pathology. Aqueous extracts of brain were prepared by homogenizing cortical tissue in a buffer which we refer to as artificial cerebrospinal fluid base buffer (aCSF-B) (124 mM NaCl, 2.8 mM KCl, 1.25 mM NaH2PO4, 26 mM NaHCO3, pH 7.4). aCSF-B is the core buffer used in subsequent electrophysiology experiments. Whole frozen temporal cortex was left at 4°C until the tissue was sufficiently soft to cut. Meninges and large blood vessels were removed and gray matter dissected from white matter. The total amount of gray matter obtained was between 12-14 g. Two gram lots of tissue were diced using a razor blade and then homogenized in 10 ml of ice-cold aCSF-B (containing 5 mM Ethylenediaminetetraacetic acid, 1 mM Ethyleneglycoltetraacetic acid, 5 μg/ml Leupeptin, 5 μg/ml Aprotinin, 2 μg/ml Pepstatin, 120 μg/ml Pefabloc and 5 mM NaF) with 25 strokes of a Dounce homogenizer (Fisher, Ottawa, Canada). Homogenates from 6, 2 g lots were pooled and centrifuged at 198,000 g and 4°C for 110 min in a SW 41 Ti rotor (Beckman Coulter, Fullerton, CA). The upper 90% of supernatant was dialyzed (using Slide-A-Lyzer™ G2 Dialysis Cassettes, 2K MWCO, Fisher Scientific) against fresh aCSF-B to remove bioactive small molecules and drugs. Dialysis was performed at 4°C against a 100-fold excess of buffer with buffer changed 3 times over a 36 h period. Thereafter extracts were divided into 2 parts: 1 portion was immunodepleted (ID) of Aβ by 3 rounds of 12 hour incubations with the anti-Aβ antibody, AW7, plus Protein A sepharose (PAS) beads at 4°C (Freir et al., 2011). The second portion was treated in an identical manner, but this time incubated with pre-immune serum plus PAS beads. Samples were cleared of beads and 0.5 ml aliquots stored at -80°C until used for biochemical or electrophysiological experiments. Samples were thawed once and used.
Immunoprecipitation/Western blotting (IP/WB) of Aβ in brain extracts
Extracts were first pre-cleared with PAS beads to minimize non-specific interactions in the subsequent IP. One ml aliquots of extracts were incubated with 15 μl PAS beads for 1 hour at 4°C with gentle shaking. Aβ-antibody-PAS beads were removed by centrifugation (4000 g for 5 minutes) and the supernatant divided into 0.5 ml aliquots. Each aliquot was incubated with 10 μl of AW7 and 15 1/4l PAS beads overnight at 4°C with gentle shaking. PAS complexes were collected by centrifugation and washed as previously described (Shankar et al., 2011). The immunoprecipitated (IP’d) Aβ was eluted by boiling in 18 1/4l of 1 × sample buffer (50 mM Tris, 2% w/v SDS, 12% v/v glycerol with 0.01% phenol red) and electrophoresed on hand poured, 15 well 16% polyacrylamide tris-tricine gels. Synthetic Aβ1-42 was run as a loading control and protein transferred onto 0.2 μM nitrocellulose at 400 mA and 4°C for 2 h. Blots were microwaved in PBS and Aβ detected using the anti-Aβ40 and anti-Aβ42 antibodies, 2G3 and 21F12, and bands visualized using a Li-COR Odyssey infrared imaging system (Li-COR, Lincoln, NE).
MSD Aβ immunoassays
Samples were analyzed for Aβ content using 2 distinct assay formats: the Aβx-42 assay that preferentially detects Aβ42 monomers and the oAssay that preferentially detects Aβ oligomers and aggregates (Mably et al., 2015; Mc Donald et al., 2015; Yang et al., 2015). Immunoassays were performed using the Meso Scale Discovery (MSD) platform and reagents from Meso Scale (Rockville, MD). The Aβx-42 assay uses mAb m266 (3 μg/ml) for capture and biotinylated 21F12 (1 μg/ml) for detection, and the oAssay uses mAb 1C22 (3 μg/ml) for capture and biotinylated 3D6 (1 μg/ml) for detection. Samples, standards and blanks were loaded in triplicate and analyzed as described previously (Mc Donald et al., 2015).
Since GuHCl effectively disaggregates high molecular weight Aβ species (Mc Donald et al., 2015), samples were analyzed both with and without incubation in 5 M GuHCl. Analysis of samples in the absence of GuHCl allows the measurement of native Aβ42 monomer using the Aβx-42 assay, and native Aβ aggregates using the oAssay. Analysis of samples treated with GuHCl allows detection of disassembled aggregates with Aβx-42 assay. To dissociate aggregates 20 μl of extract was incubated overnight with 50 μl of 7 M GuHCl at 4°C. Thereafter samples were diluted 1:10 with assay diluent, so that the final GuHCl concentration was 0.5 M. Aβ standards were prepared in tris-buffered saline (TBS), pH 7.4 containing 0.5 M GuHCl, 0.05% Tween 20 and 1% Blocker A so that both standards and samples contained the same final concentration of GuHCl.
Mice
All animal procedures were performed in accordance with the National Institutes of Health Policy on the Use of Animals in Research and were approved by the Harvard Medical School Standing Committee on Animals. Wild type (WT) C57BL/6 mice were purchased from Jackson Labs (Bar Harbor, ME). APP KO mice on a C57BL/6 background and littermate WT controls were obtained from the Young-Pearse lab (Callahan et al., 2017). A second line of APP KO mice were purchased from the Jackson Laboratory (APPtm1Dbo/J,The Jackson Laboratory, Bar Harbor, ME) (Zheng et al., 1995). Animals were housed in a room with a 12 h light/dark circadian cycle with ad libitum access to food and water. Mice were genotyped by PCR prior to use, and after use certain brain slices were used for Western blotting (Figure 3A).
Brain slices preparation
Two to three months old male and female animals were anaesthetized with isoflurane and decapitated. Brains were rapidly removed and immediately immersed in ice-cold (0-4°C) artificial cerebrospinal fluid (aCSF). The aCSF contained (in mM): 124 NaCl, 3 KCl, 2.4 CaCl2, 2 MgSO4·7H2O, 1.25 NaH2PO4, 26 NaHCO3 and 10 D-glucose, and was equilibrated with 95% O2 and 5% CO2, pH 7.4, 310 mOsm. Coronal brain slices (350μm) including hippocampus (Wang et al., 2008) were prepared using a Leica VT1000 S vibratome (Leica Biosystems Inc, Buffalo Grove, IL) and transferred to an interface chamber and incubated at 34 ± 5°C for 20 min and then kept at room temperature for 1 h before recording.
Long-term potentiation (LTP) recording
Brian slices were transferred to a submerged recording chamber and perfused (10 ml/min) with oxygenated (95% O2 and 5% CO2) aCSF 10 min before electrophysiological recording. Brian slices were visualized using an infrared and differential interference contrast camera (IR-DIC camera, Hitachi, Japan) mounted on an upright Olympus microscope (Olympus, Tokyo, Japan). Recording electrodes were pulled from borosilicate glass capillaries (Sutter Instruments, Novato, CA) using a micropipette puller (Model P-97; Sutter Instruments, Novato, CA) with resistance ∼2 MΩ when filled with ACSF. To induce field excitatory post-synaptic potential (fEPSP) in the hippocampal CA1, a tungsten wire stimulating electrode (FHC, Inc., Bowdoin, ME) was placed on the Schaffer collaterals of the CA3 and a recording electrode was placed at least 300 μM away on the striatum radiatum of the CA1. Test stimuli were delivered once every 20 s (0.05 Hz) and the stimulus intensity was adjusted to produce a baseline fEPSP of 30–40% of the maximal response. A stable baseline was recorded for at least 10 min prior to addition of sample. Thirty minutes following application of sample LTP was induced by theta burst stimulation (TBS, involved 3 trains, each of 4 pulses delivered at 100 Hz, 10 times, with an interburst interval of 200 ms with a 20 sec interval between each train). Field potentials were recorded using a Multiclamp amplifier (Multiclamp 700B; Molecular Devices, Sunnyvale, CA) coupled to a Digidata 1440A digitizer. Signal was sampled at 10 kHz and filtered at 2 kHz and data were analyzed using Clampex 10 software (Molecular Devices, Sunnyvale, CA).
Whole-cell patch clamp recording
Brain slices were prepared from male and female WT and APP KO mice (1-2 months old) as described above for LTP experiments but using a different cutting solution contained sucrose (in mM: 72 sucrose, 83 NaCl, 2.5 KCl, 1 NaH2PO4, 3.3 MgSO4·7H2O, 26.2 NaHCO3, 22 dextrose, and 0.5 CaCl2) saturated with 95% O2 and 5% CO2, pH 7.4, 310 mOsm (Wang et al., 2015). Slices were incubated in oxygenated slicing solution for 20 min, and held at room temperature for a further 40 min prior to recording. Slices were transferred to a submerged recording chamber and perfused (10 ml/min) with oxygenated (95% O2 and 5% CO2) aCSF for 30 min at room temperature. Whole-cell recordings were made from the somata of CA1 pyramidal neurons visualized using an IR-DIC camera mounted on an upright Olympus microscope (Olympus, Tokyo, Japan). Patch pipettes (4–7MΩ) were filled with an internal solution containing (in mM): 120 CsGluconate, 5 MgCl2, 0.6 EGTA, 30 HEPES, 4 MgATP, 0.4 Na2GTP, 10 phosphocreatine-Tris, 5 QX-314; 290 mOsm; pH was adjusted at 7.2 with CsOH. Signal was acquired using a Multiclamp amplifier (Multiclamp 700B; Molecular Devices, Sunnyvale, CA) with Clampex 10 software (Molecular Devices, Sunnyvale, CA) and sampled at 10 kHz and filtered at 2 kHz. Data were stored on a PC after digitization by an A/D converter (Digidata 1440A, Molecular Devices, Sunnyvale, CA) for offline analysis. Membrane potential was corrected for the liquid junction potential of 13.7 mV. Neurons with negative resting membrane potential less than -60 mV were not analyzed. Input resistance and patching access resistance were continuously monitored during the experiment and cells which exhibited more than 15–20% changes in these parameters were excluded from analysis.
In order to preserve a relatively intact neuronal circuit no receptor antagonists were used. Spontaneous excitatory post-synaptic currents (sEPSCs) were collected at a membrane holding potential of -70 mV, which is close to the calculated reverse potential of GABA. In order to measure the excitatory and inhibitory input on the same neuron, the spontaneous inhibitory post-synaptic currents (sIPSCs) were also measured on the same neuron but this time the holding potential was increased to 5-10 mV, a potential close to the reverse potential of excitatory input, without visual negative deflection. Recorded neuronal activities were detected as described previously (Lillis et al., 2015) by custom software (DClamp: available at www.ieeg.org/?q=node/34). Integrated excitatory conductance (sEPSCs, GE) and intergreated inhibitory conductance (sIPSCs,GI) were calculated as previously reported and (Slomowitz et al., 2015).
Preparation of mouse brain homogenates and detection of APP
Certain brain slices from wild-type and APP knock-out mice were frozen immediately after completion of electrophysiological recording (Figs 3 and 4) and stored at -80°C until analyzed. Tissue (∼0.1 mg) was homogenized in 5 volumes (w/v) of ice-cold 20 mM TBS-TX, pH 7.4 containing protease inhibitors and centrifuged at 100,000 g and 4°C for 78 minutes in a TLA-55 rotor (Beckman Coulter, Fullerton, CA). The upper 90% of the supernatant was removed, aliquoted and stored at -80°C pending analysis. Ten μg of total protein was boiled in 1 × sample buffer (62.5 mM Tris, 1% w/v SDS, 10% v/v glycerol, 0.01% phenol red and 2% β-mercaptoethanol) for 5 min and then electrophoresed on hand poured, 15 well 10% polyacrylamide tris-glycine gels. Gels were rinsed in transfer buffer (10% methanol, 192 mM Glycine and 25 mM Tris) and proteins electroblotted onto 0.2 μM nitrocellulose membranes at 400 mA and 4°C for 2.5 h. Membranes were developed using the anti-APP antibody, 22C11, and bands visualized using a LI-COR Odyssey infrared imaging system (LI-COR, Lincoln, NE).
Array tomography (AT) imaging of mouse brain slices
Upon completion of electrophysiology recordings certain brain slices from wild-type and APP knock mice (Figs 3 and 4) were processed for array tomography (Koffie et al., 2009; Pickett et al., 2016). Slices were fixed in PBS containing 4% paraformaldehyde and 2.5% sucrose at 4°C overnight. Samples were then washed three times (10 min each) in cold wash buffer (PBS containing 3.5% sucrose and 50 mM glycine), and the hippocampus dissected out under a Leica Wild M3Z Stereozoom Microscope (Heerbrugg, Swizerland). Thereafter hippocampi were dehydrated using an ethanol series of: 50%, 70%, 95% and 100%. Tissue was then placed into a solution of 1:1 ethanol: LR White resin (Electron Microscopy Sciences) for 5 min and then washed 3 times with LR white. Tissue was incubated overnight at 4°C in LR white and then embedded in a gelatin capsule and polymerized overnight at 53°C. Three embedded blocks per condition were cut into ribbons of 70 nm sections on an ultracut microtome (Leica) using a Jumbo Histo Diamond Knife (Diatome). Ribbons were collected on gelatin-coated glass coverslips, stained with antibodies and imaged along the ribbon. Two ribbons per slice were collected and one was stained for PSD95 and 1C22 and the other for synapsin-1 and 1C22. Primary antibodies were 1C22 (1:50), rabbit anti-PSD95 (3450P, Cell Signaling, at 1:5), and rabbit anti-synapsin-1 (AB1543P, Millipore, at 1:100). Secondary antibodies donkey anti-mouse 488 (A21202) and donkey anti-rabbit 594 (A21207) were from Invitrogen and used at 1:50.
Two image stacks per ribbon were collected from the stratum radiatum using a Zeiss axio Imager Z2 epifluorescent microscope with a 63X 1.4NA Plan Apochromat objective. Images were acquired with a CoolSnap digital camera and AxioImager software with array tomography macros (Carl Zeiss, Ltd, Cambridge UK). Images from each set of serial sections were complied to create a 3D stack and aligned using ImageJ multistackreg macros (Kay et al., 2013). Regions of interest (10 μm x 10 μm) were selected, cropped and thresholded in Image J (Schindelin et al., 2012; Ollion et al., 2013). Custom MatLab macros were used to remove single slice punctuate, count synaptic punctuate and assess co-localization with 1C22 (a minimum of 50% overlap between 1C22 and synaptic punctuate was required to be designated as co-localization). All custom analysis macros will be freely available on http://datashare.is.ed.ac.uk after publication.
Data analysis and Statistics test
IP/WB and MSD Aβ immunoassay is representative of at least 2 experiments. For electrophysiologcial experiments, the AD, ID-AD and aCSF samples were coded and tested in an interleaved manner to avoid variances in animals or slice quality influencing on results. There was no outliners were excluded from the analysis. Electrophysioloical data were analyzed offline by pclamp 10.2 (Molecular Devices, Sunnyvale, CA) and tested with One-way or Two-way analysis of variance (ANOVA) with Bonferroni post-hoc tests or student t-tests (# P<0.05, ## P<0.01, and ### P<0.001). A Kolmogorov–Smirnov (K–S) test was used to compute differences in distributions of sEPSCs and sIPSCs. Array tomography was analyzed using SPSS Version 22. A single percent co-localization for each parameter was calculated for each slice from approximately 41 regions of interest and ≈7500 synapses (∼3,500 pre-synapses and ∼3,500 post-synapses) were analyzed per slice and tested with a Kruskal-Wallis with Dunns post-hoc test and Mann-Whitney U test. Electrophysiology data are shown as means ± SEM. Array tomography data is shown as medians ± the interquartile range, each point representing all synapses measured within 1 slice. Analyses of the same sample using different slices are considered technical replicates and analysis of extracts from different AD brains are considered biological replicates.