1. Neuroscience

Alzheimer’s disease risk gene BIN1 induces Tau-dependent network hyperexcitability

  1. Yuliya Voskobiynyk
  2. Jonathan R Roth
  3. J Nicholas Cochran
  4. Travis Rush
  5. Nancy VN Carullo
  6. Jacob S Mesina
  7. Mohammad Waqas
  8. Rachael M Vollmer
  9. Jeremy J Day
  10. Lori L McMahon
  11. Erik D Roberson  Is a corresponding author
  1. Center for Neurodegeneration and Experimental Therapeutics, Alzheimer’s Disease Center, and Evelyn F. McKnight Brain Institute, Departments of Neurology and Neurobiology, University of Alabama at Birmingham, United States
  2. Department of Neurobiology, University of Alabama at Birmingham, United States
  3. Department of Cell, Developmental and Integrative Biology, University of Alabama at Birmingham, United States
https://doi.org/10.7554/eLife.57354
Share this article
Cite this article
  1. Yuliya Voskobiynyk
  2. Jonathan R Roth
  3. J Nicholas Cochran
  4. Travis Rush
  5. Nancy VN Carullo
  6. Jacob S Mesina
  7. Mohammad Waqas
  8. Rachael M Vollmer
  9. Jeremy J Day
  10. Lori L McMahon
  11. Erik D Roberson
(2020)
Alzheimer’s disease risk gene BIN1 induces Tau-dependent network hyperexcitability
eLife 9:e57354.
https://doi.org/10.7554/eLife.57354
  2. Download BibTeX
  3. Download .RIS

Abstract

Genome-wide association studies identified the BIN1 locus as a leading modulator of genetic risk in Alzheimer’s disease (AD). One limitation in understanding BIN1’s contribution to AD is its unknown function in the brain. AD-associated BIN1 variants are generally noncoding and likely change expression. Here, we determined the effects of increasing expression of the major neuronal isoform of human BIN1 in cultured rat hippocampal neurons. Higher BIN1 induced network hyperexcitability on multielectrode arrays, increased frequency of synaptic transmission, and elevated calcium transients, indicating that increasing BIN1 drives greater neuronal activity. In exploring the mechanism of these effects on neuronal physiology, we found that BIN1 interacted with L-type voltage-gated calcium channels (LVGCCs) and that BIN1–LVGCC interactions were modulated by Tau in rat hippocampal neurons and mouse brain. Finally, Tau reduction prevented BIN1-induced network hyperexcitability. These data shed light on BIN1’s neuronal function and suggest that it may contribute to Tau-dependent hyperexcitability in AD.

Introduction

Genetic discoveries have provided critical insights into potential mechanisms of Alzheimer’s disease (AD), the most common neurodegenerative disease. Mutations in APP, PSEN1, or PSEN2 cause early-onset, autosomal dominantly inherited AD, but are quite rare. Several more common genetic variants that increase AD risk to differing degrees have been identified. Among these, variants near BIN1 have particularly high population attributable risk, because the risk allele is highly prevalent (~40% allele frequency for the index SNP, rs6733839) and has a relatively large effect size (odds ratio: 1.20; 95% confidence interval: 1.17–1.23) (Kunkle et al., 2019).

BIN1 was first linked to AD in early genome-wide associated studies (GWAS) (Harold et al., 2009; Seshadri et al., 2010) and remains second only to APOE in genome-wide significance in the recent meta-analysis of 94,437 individuals by the International Genomics of Alzheimer’s Disease Project (Kunkle et al., 2019). This association has been replicated in datasets with subjects from diverse genetic backgrounds (Carrasquillo et al., 2011; Hollingworth et al., 2011; Hu et al., 2011; Lambert et al., 2011; Lee et al., 2011; Logue, 2011; Naj et al., 2011; Wijsman et al., 2011; Kamboh et al., 2012; Chapuis et al., 2013; Lambert et al., 2013; Liu et al., 2013; Miyashita et al., 2013; Reitz et al., 2013; Li et al., 2015; Dong et al., 2016; Rezazadeh et al., 2016; Wang et al., 2016). Further, unbiased epigenetic analyses have provided independent evidence linking BIN1 to AD pathogenesis in several epigenome-wide association studies examining DNA methylation patterns in brain tissue from AD patients, in which BIN1 again emerged as a top hit (De Jager et al., 2014; Chibnik et al., 2015; Yu et al., 2015). This association was also observed in tissue from preclinical AD patients, indicating that changes in BIN1 methylation occur early in disease (De Jager et al., 2014; Chibnik et al., 2015). Also, associations between BIN1 methylation and AD are independent of genetic variants identified in GWAS, providing an orthogonal line of evidence for BIN1’s involvement in AD. Importantly, BIN1 variants have been linked to earlier age of onset (Naj et al., 2014). In addition to GWAS reports examining polymorphisms associated with AD diagnosis by clinical criteria, other studies have examined genetic risk factors for AD neuropathology. BIN1 was significantly associated with both amyloid plaque and neurofibrillary tangle pathologies, strengthening the association with AD (Beecham et al., 2014). While these unbiased screens have convincingly implicated BIN1 in AD pathogenesis, the mechanisms underlying the association are not yet known, and many important questions about how BIN1 contributes to AD remain.

One of the main limitations is an incomplete understanding of BIN1’s normal function in the brain. Its structure suggests that a key role may involve protein trafficking at the membrane, since all BIN1 isoforms contain an N-terminal BAR (BIN1/Amphiphysin/RVS167) domain that mediates membrane binding and curvature, plus a C-terminal SH3 domain that mediates protein–protein interactions, including with Tau (Chapuis et al., 2013; Sottejeau et al., 2015). The larger, neuron-specific isoforms also contain a clathrin-AP2 binding (CLAP) domain likely involved in endocytosis (De Rossi et al., 2016).

The BIN1 variants associated with AD do not alter the coding sequence of BIN1 but are rather concentrated in a presumed regulatory region upstream of the promoter. Although BIN1 is ubiquitously expressed throughout the body, levels are highest in the brain and muscle (Butler et al., 1997), and its most critical role is in the heart, as homozygous deletion of murine Bin1 causes early lethality due to severe ventricular cardiomyopathy (Muller et al., 2003). Canonically, BIN1 plays a role in protein trafficking and endocytosis, specifically trafficking L-type voltage gated calcium channels (LVGCCs) in cardiac myocytes to the membrane to strengthen calcium signaling (Hong et al., 2010). However, the function BIN1 plays in neurons remains much less clear.

In this study, we addressed BIN1’s role in neurons by expressing the predominant neuronal BIN1 isoform (isoform 1) in primary hippocampal neuron cultures. Our studies revealed a role for BIN1 in regulating neuronal activity and a potential molecular mechanism involving its interactions with calcium channel subunits.

Results

Higher BIN1 induces network hyperexcitability

To begin studying the effects of altered BIN1 levels in neurons, we first used AAV to express the predominant neuronal isoform of human BIN in primary rat hippocampal neuronal cultures. We verified expression of BIN1 using an mKate2 fluorophore fused to the C-terminus. A construct encoding mKate2 alone was used as a control. We determined that BIN1 expression increased ~8–9-fold by immunocytochemistry and remained stable up to 3.5 weeks post transduction (Figure 1A–C). Higher BIN1 did not change neuronal morphology (Figure 1B), the total number of neurons per well (Figure 1D–E), nor the resting membrane potential (RMP) or input resistance (Rin) of cultured neurons (Table 1), indicating no significant toxic or trophic effect of overexpressing BIN1 under these conditions.

Table 1
Resting membrane potential (RMP) and input resistance (Rin) in patched hippocampal neurons did not differ across untransduced, AAV-mKate2, and AAV-BIN1-mKate2 groups.
RMP, mVRin, MΩN, Cells
Untransduced–60.43 ± 5.36843.04 ± 55.114
AAV-mKate2–59.90 ± 3.70834.83 ± 41.096
AAV-BIN1-mKate2–62.40 ± 2.24790.18 ± 15.747
One-way ANOVA, p0.850.36
Figure 1
Download asset Open asset
BIN1 increases action potential and burst frequency in primary hippocampal neurons cultured on microelectrode arrays (MEAs).

(A) ICC experimental timeline: neurons were plated on day in vitro (DIV) 0, virally transduced on DIV 2, and immunostained at DIV 15, DIV 22, or DIV 27. (B) Representative images of primary hippocampal cultures: untransduced (left), AAV-mKate2 (center), or AAV-BIN1-mKate2 (right), showing Tau and BIN1 immunostaining at DIV 15 (top), DIV 22 (middle), or DIV 27 (bottom). Scale bar = 20 µm. (C) AAV-BIN1-mKate2 increased BIN1 levels ~ 8–9-fold in BIN1 group compared to BIN1 levels in untransduced or mKate2 groups (n = 2–6 fields of view per coverslip, 60x magnification, two-way ANOVA, BIN-DIV interaction p=0.1123, main effect of AAV-BIN1-mKate2 ****p<0.0001, main effect of DIV p=0.6373, Tukey’s multiple comparisons test: DIV 15:UnTD vs. DIV 15:AAV-BIN1-mKate2, ****p<0.0001, DIV 22:UnTD vs. DIV 22:AAV-BIN1-mKate2, ****p<0.0001, DIV 27:UnTD vs. DIV 27:AAV-BIN1-mKate2, ****p<0.0001. (D) Representative images of primary hippocampal cultures at DIV 12: untransduced (left), AAV-mKate2 (center), or AAV-BIN1-mKate2 (right), showing NeuN immunostaining (top), mKate2 fluorescence (middle), or merge of both (bottom). Scale bar = 25 µm. (E) The total number of neurons per well did not change between untransduced, mKate2, or BIN1 groups (n = 6–7 coverslips, 10 × 10 fields of view per coverslip, 20x magnification, from two different primary neuron harvests, one-way ANOVA, p=0.5157). (F) MEA experimental timeline: neurons were plated on day in vitro (DIV) 0, virally transduced on DIV 2, and recorded on DIV 12. (G) Primary neuronal hippocampal cultures grown on an MEA plate. Scale bar = 50 µm. (H) Representative local field potential (LFP) traces. (I) Representative raster plots of firing activity from five different neurons per group. (J) BIN1 increased action potential frequency (n = 27–36 neurons per group from three different primary neuron harvests, normalized to the controls from each harvest, median frequency in controls = 388 mHz; unpaired Mann-Whitney U test; p=0.0233). (K) BIN1 increased burst firing frequency (n = 27–36 neurons per group from three different primary neuron harvests, normalized to the controls from each harvest, median frequency in controls = 11.7 mHz; unpaired Mann-Whitney U test; p=0.0020). (L) The total number of active neurons per well did not differ between mKate2 and BIN1 expressing groups (n = 9 MEA plates for each group from three different primary neuron harvests, unpaired Student’s t test; p=0.346). All data are expressed as mean ± SEM, *p<0.05, **p<0.01, and ****p<0.0001. All data are expressed as mean ± SEM.

We then recorded action potentials and burst firing in these neurons on multielectrode arrays (MEAs) after 10 days (Figure 1F–G). Local field potential (LFP) traces representing neuronal action potential and burst firing were recorded for 20 min then analyzed (Figure 1H–I). We found that higher BIN1 levels were associated with increased frequency of action potentials (2.3-fold, Figure 1J) and action potential bursts (2.1-fold, Figure 1K). There was no change in the total number of active neurons on MEAs (Figure 1L).

Higher BIN1 increases frequency of spontaneous excitatory and inhibitory synaptic transmission

Since higher BIN1 levels increased action potential and burst frequencies in the MEA recordings, we hypothesized that this would be associated with an increased frequency of spontaneous excitatory postsynaptic currents (sEPSCs). To test this, we used whole-cell voltage-clamp recordings from BIN1-transduced neurons at DIV 19–21 (Figure 2A), pharmacologically isolating sEPSCs using picrotoxin to block inhibitory GABAAR currents. (Figure 2B). Consistent with the increased action potential frequency observed in MEA recordings (Figure 1E), higher BIN1 levels were associated with dramatically increased sEPSC frequency (interevent interval decreased >50%) (Figure 2C). sEPSC amplitudes differed by <10% (Figure 2D).

Figure 2
Download asset Open asset
BIN1 increases both excitatory and inhibitory synaptic transmission.

(A) Synaptic transmission recordings experimental timeline: neurons were plated on DIV 0, virally transduced on DIV 2, electrophysiologically recorded on DIV 19–21. (B) Representative traces of sEPSCs recorded from neurons transduced with mKate2 or BIN1. (C) BIN1 decreased mean sEPSC interevent interval (Kolmogorov-Smirnov test on cumulative distribution, ****p<0.0001, KS D score: 0.1657; unpaired two-tailed Student’s t-test on mean IEI, ****p<0.0001). (D) BIN1 slightly decreased mean sEPSC amplitude unpaired (Kolmogorov-Smirnov test on cumulative distribution, ****p<0.0001, KS D score: 0.1803; unpaired two-tailed Student’s t-test on mean amplitude, ***p=0.0004) (n = 12–21 neurons per group from three different primary neuron harvests). (E) Representative images and quantification of NeuN and GAD67+ neurons in primary hippocampal cultures at DIV 12 (n = 255 GAD67+ neurons, n = 2342 NeuN+ neurons, from 10 randomly taken images per coverslip, 10 coverslips from two different primary neuron harvests.) Scale bar = 100 µm. (F) Representative traces of sIPSCs recorded from neurons transduced with mKate2 or BIN1. (G) BIN1 decreased mean sIPSC interevent interval (Kolmogorov-Smirnov test on cumulative distribution, ****p<0.0001, KS D score: 0.06862; unpaired two-tailed Student’s t-test on mean IEI, **p=0.0035) (H) BIN1 decreased mean sIPSC amplitude (Kolmogorov-Smirnov test on cumulative distribution, ****p<0.0001, KS D score: 0.1297; unpaired two-tailed Student’s t-test on mean amplitude, ****p<0.0001) (n = 11–16 neurons per group from three different primary neuron harvests). All data are expressed as mean ± SEM.

To investigate if this effect of higher BIN1 levels was selective for excitatory transmission, we next examined whether higher BIN1 expression had similar effects on spontaneous inhibitory postsynaptic currents (sIPSCs). We determined the proportion of GABAergic interneurons in our primary hippocampal cultures and found that 10% of the neurons were GAD67 positive (Figure 2E), consistent with prior work (Benson et al., 1994). To determine the effect of higher BIN1 levels on inhibitory synaptic transmission from these neurons, we recorded pharmacologically isolated GABAAR-mediated sIPSCs using DNQX, APV, and nifedipine to block AMPARs, NMDARs, and L-type voltage-gated calcium channels (LVGCCs), respectively (Figure 2F). Similar to the effect on sEPSC frequency, higher BIN1 increased sIPSC frequency (decrease in interevent interval, Figure 2G). There was a coincident decrease in sIPSC amplitude (Figure 2H).

Overall, these findings suggest that higher BIN1 levels increase the frequency of both sEPSCs and sIPSCs in primary hippocampal cultures, agreeing with the increased action potential firing observed using MEAs (Figure 1E–F).

Higher BIN1 in mature neurons increases calcium influx

Using AAVs requires transduction soon after plating (DIV 2) because of the time required for transgene expression, so some effects could be due to increasing BIN1 levels during early neuronal development. To dissociate the effect of higher BIN1 on network hyperexcitability from neuronal development, we transiently transfected primary hippocampal cultures at DIV 14, when neurons are more fully developed (Figure 3A). We co-transfected BIN1 constructs with the genetically encoded calcium indicator, GCaMP6f, which allows for single neuron calcium imaging in primary hippocampal cultures. We used two BIN1 constructs, both based on human isoform one tagged with the mKate2 fluorophore. In addition to the full-length BIN1 construct used in Figures 1–2, we also used a construct engineered to remove the BAR domain (ΔBAR), which is predicted to abolish BIN1 membrane localization, and thus likely its activity. As before, mKate2 alone was used as a control construct. Interestingly, the pattern of mKate2 distribution within the neurons was strikingly different across groups, as mKate2 and BIN1-ΔBAR exhibited diffuse localization throughout the soma, neurites, and nucleus, while wild-type BIN1 had a punctate distribution throughout the cytosol but was excluded from the nucleus (Figure 3B). These observations agree with BAR-domain dependent membrane localization of BIN1 found in other cell types (Hong et al., 2010; Picas et al., 2014).

Figure 3
Download asset Open asset
Higher BIN1 levels in mature neurons increase calcium influx in primary hippocampal neuronal cultures.

(A) Calcium imaging experimental timeline: neurons were plated on DIV 0, co-transfected on DIV 14 with GCaMP6f calcium indicator and either BIN1-mKate2, BIN1-ΔBAR-mKate2, or mKate2 control construct, and recorded on DIV 21. n = 14–20 neurons per condition. (B) mKate2 fluorescence in primary transfected primary hippocampal neurons. BIN1-mKate2 fluorescence was punctate in transfected neurons. mKate2 and BIN-BAR-mKate2 fluorescence was diffuse and filled the neuron. (C) GCaMP fluorescence intensity, F, relative to the quiescent period between transients, F0. Neurons were classified as either active (with a range of activity levels indicated by the top and middle traces) or inactive (no calcium transients during the 8 min recording, bottom trace). (D) BIN1, but not BIN1-ΔBAR, increased the proportion of active neurons (Binomial test, **p=0.0071). (E) BIN1, but not BIN1-ΔBAR, increased neuronal calcium influx measured as area under the curve (AUC; one-way ANOVA, p=0.0134; Dunnett’s posthoc, *p=0.0122). (F) BIN1, but not BIN1-ΔBAR, increased the number of calcium transients (one-way ANOVA, p=0.0144; Dunnett’s posthoc, mKate2 vs BIN1-mKate2 adjusted *p=0.0437). All data are expressed as mean ± SEM.

We monitored basal calcium activity of individual transfected neurons by imaging GCaMP fluorescence using laser scanning microscopy. We measured the change in somatic GCaMP fluorescence intensity relative to the quiescent period between transients (defined as F0) and classified neurons as either active (≥1 calcium transient) or inactive (no calcium transients) (Figure 3C).

About a third of neurons were inactive under control conditions (Figure 3D), consistent with prior studies (Kuijlaars et al., 2016; Lerdkrai et al., 2018). However, neurons expressing the full-length human BIN1 construct were almost never inactive (Figure 3D). As expected, the BIN1-ΔBAR construct was similar to controls (Figure 3D), indicating the importance of BIN1 membrane localization for the effect. Increasing BIN1 levels roughly doubled calcium influx as measured by both area under the curve (Figure 3E) and the number of calcium transients (Figure 3F).

BIN1 interacts with LVGCCs in neurons

We were interested to find that the ability of BIN1 to increase neuronal activity was dependent on the presence of the BAR domain, which is critical for its membrane localization. One of BIN1’s known functions outside of the brain is localizing LVGCCs to the membrane of cardiomyocyte T-tubules (Hong et al., 2010; Hong et al., 2014). Therefore, we asked if BIN1 interacts with LVGCCs in neurons and if increased interaction between BIN1 and LVGCCs could be a potential mechanism by which BIN1 increases neuronal activity.

To begin addressing this question, we first examined BIN1 interactions with LVGCC beta-1 subunits (LVGCC-β1), which reside on the inner face of the membrane and target LVGCCs to the membrane (Buraei and Yang, 2010). To determine whether BIN1 and LVGCC-β1 interact directly, we used proximity ligation assay (PLA), which allows quantification and visualization of protein-protein interactions in situ, producing a fluorescent punctum whenever the two antibody epitopes are within 40 nm (i.e., directly interacting or nearby in a complex). We detected endogenous BIN1–LVGCC-β1 interaction in neuronal soma and neurites of untransduced neurons (Figure 4A). If a BIN1-mediated effect on LVGCC’s underlies the observed effects on neuronal activity, then AAV-BIN1 constructs should increase the interaction (Figure 4B). Transduction with BIN1-mKate2 substantially increased BIN1–LVGCC-β1 interaction, while transduction with the mKate2 control vector did not change endogenous interaction levels (Figure 4C,D).

Figure 4
Download asset Open asset
BIN1 interacts with LVGCC-β1 subunits in neurons.

(A) Localization of endogenous BIN1, LVGCC-β1, and endogenous BIN1-LVGCC-β1 interaction detected by PLA. Scale bar = 10 µm. (B) Experimental timeline: neurons were plated on DIV 0, transduced with AAV-BIN1-mKate2 or AAV-mKate2 on DIV 2, and fixed and stained on DIV 19–21. (C) Representative images of mKate2 fluorescence, BIN1–LVGCC-β1 PLA puncta, and BIN1 ICC in primary hippocampal neurons. Scale bar = 10 µm. (D) BIN1–LVGCC-β1 interaction was increased by BIN1 (n = 3–4 coverslips per group, each with 5 fields of view averaged, from three different primary neurons harvests; one-way ANOVA, p<0.0001; Endogenous vs. AAV-BIN1-mKate2 ****p=0.0001 by Dunnett’s post-hoc). All data are expressed as mean ± SEM.

BIN1-LVGCC interaction is Tau-dependent

Multiple studies have demonstrated that BIN1 directly interacts with Tau, both in vitro and in vivo (Chapuis et al., 2013; Lasorsa et al., 2018; Sartori et al., 2019). This interaction between BIN1 and Tau is mediated by the SH3 domain of BIN1 and PxxP motifs in Tau’s central proline-rich region. Interestingly, LVGCC-β1 also harbors an SH3 domain that could also interact with Tau. Thus, we hypothesized that the BIN1 interaction with LVGCC-β1 might be at least in part scaffolded by Tau (Figure 5A).

Figure 5
Download asset Open asset
Tau-dependent BIN1–LVGCC interaction.

(A) Model of Tau-dependent BIN1–LVGCC interaction. BIN1’s BAR domain localizes BIN1 to the plasma membrane, and PxxP motifs in Tau’s central proline-rich domain interact with the SH3 domains of BIN1 and LVGCC- β1. (B) Tau interacts with both BIN1 and LVGCC-β1 SH3 domains detected by bioluminescence resonance energy transfer (BRET) (n = 28–164 wells, one-way ANOVA, ****p<0.0001). (C) Experimental timeline of BIN1–LVGCC-β1 PLA: neurons were plated on DIV 0, transduced with AAV-BIN1-mKate2 or AAV-mKate2 on DIV 2, treated with Tau or scrambled ASO on DIV 7, and stained on DIV 14. (D) AAV-BIN1-mKate2 increased BIN1-LVGCC-β1 interaction, while Tau reduction with Tau ASO decreased BIN1–LVGCC-β1 interactions (n = 4–6 coverslips per group representing an average of 4–5 fields of view (FOV) per coverslip from three different neuronal harvests; Two-way ANOVA, main effect of Tau ASO **p=0.0018, main effect of AAV-BIN1-mKate2 ****p<0.0001). (E) We immunoprecipitated LVGCC-β1 followed by western blotting for LVGCC-β1, Tau, and BIN1 from cortical homogenates of wild-type and Tau KO mice. The amount of LVGCC-β1 immunoprecipitated did not differ between wild-type and Tau KO brains (n = 5–6 mice, 3.56 ± 0.04 months old, unpaired Student’s t test; p=0.5105). However, the amount of BIN1 co-immunoprecipitated with LVGCC-β1 was decreased in Tau KO brains compared to wild-type litter mate controls (n = 5–6 mice, unpaired Student’s t test; *p=0.0157). All data are expressed as mean ± SEM.

We first used a live-cell bioluminescence resonance energy transfer (BRET) assay (Cochran et al., 2014) to determine if Tau interacts with the SH3 domains of both BIN1 and LVGCC-β1. We transfected CHO cells with Tau-mKate2 (acceptor) and either the BIN1 SH3 domain or LVGCC-β1 SH3 domain tagged with click beetle green (donor) (Figure 5B). Both Tau–BIN1 and Tau–LVGCC-β1 demonstrated BRET, indicating that Tau interacts with each of these SH3 domains (Figure 5C).

We then tested the hypothesis that Tau affects the BIN1–LVGCCβ1 interaction, using the BIN1–LVGCC-β1 PLA assay with and without pretreatment with Tau antisense oligonucleotide (ASO) (Figure 5D). We recently demonstrated that this ASO reduces Tau protein by about 50% under these conditions (Rush et al., 2020). Tau reduction decreased BIN1–LVGCC-β1 interaction in primary hippocampal neurons, compared to neurons treated with a scrambled control ASO (Figure 5E), indicating that in cultured neurons the BIN1–LVGCC-β1 interaction is partially Tau-dependent.

Next, we determined if the BIN1–LVGCC-β1 interaction is also Tau-dependent in vivo. Using cortical brain lysates from wild-type and Tau knockout (Tau KO) mice, we immunoprecipitated LVGCC-β1 and blotted for BIN1. BIN1 co-immunoprecipitated with LVGCC-β1 from these brain lysates, and the BIN1–LVGCC-β1 complex was reduced in Tau KO brains, without any difference in LVGCC-β1 immunoprecipitation (Figure 5F). Taken together, these data indicate that the BIN1–LVGCC interaction is partially Tau-dependent both in vitro and in vivo.

Tau reduction prevents network hyperexcitability induced by higher BIN1

Tau reduction is protective in many models of AD and it reduces network hyperexcitability in many disease models, including AD and epilepsy models (Chin et al., 2007; Roberson et al., 2007; Holth et al., 2011; Roberson et al., 2011; DeVos et al., 2013; Gheyara et al., 2014; Liu et al., 2017). Thus, since Tau reduction decreases BIN1–LVGCC-β1 interaction in primary hippocampal neurons and brain homogenates, we asked whether Tau reduction attenuates network hyperexcitability induced by increased BIN1.

To do this, we utilized a 48-well MEA system to permit recordings from many neurons with in-plate controls for each experiment. We grew neurons on the MEA, transduced them with AAV-BIN1 or AAV-mKate2 control, applied Tau ASO or a scrambled ASO control, then recorded neuronal activity (Figure 6A). As in our initial experiments, these manipulations did not affect the number of active neurons (Figure 6B), and higher BIN1 levels increased neuronal firing in this system as well (Figure 6C–D). Tau reduction completely blocked the BIN1-induced increases in action potential frequency (Figure 6E–F) and bursting (Figure 6G–H). These results demonstrate that BIN1-induced network hyperexcitability is Tau-dependent and add to the body of work demonstrating beneficial effects of Tau reduction on limiting network hyperexcitability and AD-related dysfunction.

Figure 6
Download asset Open asset
Tau reduction prevents network hyperexcitability induced by BIN1.

(A) MEA experimental timeline: neurons were plated on DIV 0, virally transduced on DIV 2, treated with Tau or scrambled ASO on DIV 5, and electrophysiologically recorded on DIV 12. (B) The number of active neurons was not different between groups (n = 6–8 coverslips per group from three different neuronal harvests; two-way ANOVA, main effect of Tau ASO p=0.9140, main effect of AAV-BIN1-mKate2 p=0.9026, interaction p=0.1101). (C) Representative LFP traces of MEA recordings. (D) Representative raster plots of MEA recordings. (E) Tau reduction prevented BIN1-induced network hyperexcitability as measured by mean action potential frequency (n = 159–230 neurons from 6 to 8 wells per group from three different neuronal harvests; two-way ANOVA, BIN-Tau interaction **p=0.0073, main effect of Tau ASO p=0.0761, main effect of AAV-BIN1-mKate2 *p=0.0130, Sidak’s multiple comparisons test: UnTd:Scr ASO vs. BIN1-mKate2:Scr ASO ***p=0.0010). (F) Cumulative distribution of the mean action potential frequency (Kolmogorov-Smirnov test on cumulative distribution, UnTD-Scr ASO vs. BIN1-mKate2-Scr ASO **p=0.0028). (G) Tau reduction prevented BIN1-induced network hyperexcitability as measured by mean burst frequency (n = 159–230 neurons from 6 to 8 wells per group from three different neuronal harvests; two-way ANOVA, BIN-Tau interaction ***p=0.0005, main effect of Tau ASO **p=0.0066, main effect of AAV-BIN1-mKate2 p=0.2286, Sidak’s multiple comparisons test: UnTd:Scr ASO vs. BIN1-mKate2:Scr ASO *p=0.0227). (H) Cumulative distribution of the burst frequency (Kolmogorov-Smirnov test on cumulative distribution, UnTD-Scr ASO vs. BIN1-mKate2-Scr ASO, p=0.1107). All data are expressed as mean ± SEM.

Discussion

Genetic data indicate that BIN1 can play an important role in AD pathogenesis, but a major limitation is the relatively poor understanding of BIN1’s function in the central nervous system. We found that expressing the predominant human BIN1 isoform in primary hippocampal cultures led to a Tau-dependent increase in neuronal activity leading to network hyperexcitability. Higher BIN1 levels increased the frequency of both spikes and bursts recorded with multielectrode arrays (Figure 1). Using patch-clamp recordings of neurons overexpressing BIN1, we observed increased frequency of both excitatory and inhibitory synaptic transmission (Figure 2). Similarly, elevating BIN1 levels also increased calcium spikes in neurons co-transfected with the calcium indicator GCaMP6f (Figure 3). To understand the potential mechanism of increased calcium influx, we explored potential interactions with LVGCCs, which contribute to BIN1 effects on cardiac excitability. BIN1 interacted with LVGCCs in neurons in a Tau-dependent manner, assessed by both proximity ligation assay in cultured neurons and co-immunoprecipitation from brain (Figures 4–5). Finally, using a high-content multielectrode array system, we showed that Tau reduction prevented network hyperexcitability induced by BIN1 (Figure 6). Together, these data show Tau-dependent regulation of neuronal activity by the Alzheimer’s disease risk gene BIN1 and generate new insights about the mechanistic role BIN1 may play in AD.

Increasing evidence supports the idea that changes in neuronal excitability may contribute to AD pathogenesis. Functional imaging studies reveal hyperactivation of many brain regions in AD patients (Dickerson et al., 2005; Hämäläinen et al., 2007). This is an early event in AD pathogenesis, seen in asymptomatic individuals at genetic risk for AD (Bookheimer et al., 2000; Reiman et al., 2012). In addition, childhood epilepsy can drive subsequent amyloid accumulation (Joutsa et al., 2017). Furthermore, seizures are more frequent in AD patients than in age-matched controls (Amatniek et al., 2006; Palop and Mucke, 2009; Scarmeas et al., 2009), and in the early stages of disease, even patients without overt seizures often have epileptiform activity on neurophysiological recordings (Vossel et al., 2013; Vossel et al., 2016). Even more importantly, late-onset unprovoked seizures in older veterans are associated with a 2-fold risk of developing dementia, likely a first sign of neurodegenerative disease (Keret et al., 2020). Hyperexcitability is also seen in mouse models of AD, many of which have seizures (often nonconvulsive) and epileptiform spikes (Palop et al., 2007; Minkeviciene et al., 2009), excitation-inhibition imbalance in synaptic recordings (Roberson et al., 2011), and increased intrinsic neuronal excitability (Brown et al., 2011).

Existing data are consistent with a potential role for BIN1 in controlling neuronal excitability. As a membrane scaffolding protein, BIN1 promotes T-tubule formation in skeletal muscles (Tjondrokoesoemo et al., 2011). In cardiomyocytes, Bin1 traffics LVGCCs to T-tubules, allowing for proper T-tubule formation and excitation-contraction coupling (Hong et al., 2010; Hong et al., 2014). Genetic loss of Bin1 in cardiomyocytes decreases surface localization of LVGCCs to T-tubules and decreases LVGCC calcium transients (Hong et al., 2010). In addition, acute knockdown of Bin1 in primary cortical neurons reduced calcium spikes in response to NMDA (McAvoy et al., 2019). Similarly, a chronic genetic deletion of Bin1 in mice decreased mEPSCs frequency, suggesting an effect of Bin1 on synaptic transmission (De Rossi et al., 2020). Complementary to these studies on effects of decreased Bin1 expression in mice, we found that increased human BIN1 expression increases synaptic transmission, neuronal activity, and calcium transients, and that BIN1 interacts with LVGCCs in neurons. Moreover, we found that higher BIN1 increases not only excitatory, but also inhibitory synaptic transmission. Human genetics also support a link between BIN1 and network hyperexcitability, as the risk allele of the rs744373 variant upstream of BIN1, which is linked to AD, is also associated with impaired memory in temporal lobe epilepsy patients (Bungenberg et al., 2016).

Further studies will be needed to elucidate the precise mechanisms by which BIN1 regulates neuronal firing, but our studies suggest an effect on surface trafficking of LVGCCs. While biophysical and pharmacological properties of LVGCCs are tightly controlled by the principal α1 subunit, the cytosolic auxiliary β subunit plays an essential role in trafficking of LVGCCs to the plasma membrane (Buraei and Yang, 2010). Our study revealed BIN1 interaction with these LVGCC β subunits in neurons (Figures 4–5), likely contributing to LVGCC neuronal surface localization. LVGCCs modulate neuronal firing (Liu et al., 2014) and control both basal and bursting neuronal activity through somatic and dendritic Ca2+ transients (Morton et al., 2013; Liu et al., 2014). LVGCCs are also linked to neurodegeneration by carrying toxic amounts of Ca2+ through an increase of LVGCC activity, density, or exposure to β-amyloid peptides (Cataldi, 2013).

Our findings suggest that the effects of BIN1 on neuronal excitability likely involve Tau. A variety of evidence has linked BIN1 to Tau in studies of AD. In AD patients, the BIN1 risk variant, rs744373, is associated with increased Tau-PET levels, as well as reduced functional connectivity and impaired memory (Zhang et al., 2015; Franzmeier et al., 2019). The fact that BIN1 localizes in a complex with Tau (Figures 4–5; Chapuis et al., 2013; Zhou et al., 2014; Sottejeau et al., 2015; Bretteville et al., 2017; Malki et al., 2017; Lasorsa et al., 2018) supports the hypothesis that BIN1–Tau interaction regulates neuronal excitability, as there is now abundant data that a key function of Tau is regulating neuronal excitability, particularly susceptibility to hyperexcitability. This includes the fact that Tau+/– and Tau–/– mice are resistant to epileptiform activity and seizures induced by excitotoxic agents (Roberson et al., 2007; Ittner et al., 2010; Roberson et al., 2011). Tau knockdown using ASOs also has excitoprotective effects against hyperexcitability in mice (DeVos et al., 2013), complemented by our finding of excitoprotective effects against BIN1-induced hyperexcitability (Figure 6).

The precise effects of AD-associated BIN1 variants remains to be fully understood, but their effects are likely to be mediated through changes in expression levels since they do not affect the coding sequence. For example, the risk allele of the AD-associated rs744373 variant drives increased expression of BIN1 (Bungenberg et al., 2016). While another early report suggested increased BIN1 in AD (Chapuis et al., 2013), subsequent reports suggest that variants may reduce BIN1 expression (Glennon et al., 2013; Holler et al., 2014), and the effects may also differ between the neuronal and ubiquitous isoforms (De Rossi et al., 2016). Ongoing studies will provide additional evidence about the directionality of BIN1 variant effects on expression, but our findings are consistent with either increases or decreases in BIN1 contributing to network hyperexcitability in AD, since we found that higher BIN1 was associated with higher activity as a general effect in both excitatory and inhibitory neurons. That is, either increased BIN1 in excitatory neurons with corresponding increased excitatory activity, or reduced BIN1 in inhibitory neurons with corresponding reduced inhibition, could lead to network hyperexcitability. Further study will be required to better understand both the effects of AD-associated BIN1 variants and the relative balance between excitatory and inhibitory neuron effects of BIN1. In addition, BIN1 expression changes in oligodendrocytes or microglia also warrant study for their potential roles in AD (De Rossi et al., 2016; Nott et al., 2019).

Our findings highlight the potential importance of Tau interactions with SH3 domain–containing proteins. We recently demonstrated that inhibiting Tau-SH3 interactions can reduce Aβ toxicity (Rush et al., 2020), and it is notable that BIN1 joins a growing list of SH3 domain–containing proteins that interact with Tau and are implicated in AD. Tau may act as a scaffolding protein through BIN1 interactions mediating membrane localization (Figure 5A), promoting network hyperexcitability through its SH3-domain containing binding partners. This could be a critical role for Tau and explain how mislocalization of Tau in AD contributes to the increased network excitability seen in AD pathogenesis. This would be consistent with the finding that reducing endogenous Tau prevents network hyperexcitability and Aβ-induced dysfunction in AD models.

In summary, we have shown that BIN1 promotes neuronal firing in a Tau-dependent manner. These data contribute new insights into the neuronal functions of BIN1, with implications for our understanding of AD.

Materials and methods

Key resources table
Reagent type
(species) or
resource		DesignationSource or
reference		IdentifiersAdditional
information
Gene (Homo sapiens)BIN1NCBIGene ID 274
AntibodyAnti-NeuN
Rabbit polyclonal		abcamCat# ab104225; RRID:AB_10711153ICC (1:500), Lot #GR3321966-1
AntibodyAnti-GAD67
Mouse monoclonal		Millipore SigmaCat# MAB5406; RRID:AB_2278725ICC (1:500), Lot #3015328
AntibodyAnti-BIN1
Rabbit polyclonal		Santa CruzCat# sc-30099; RRID:AB_2243399ICC/PLA/IP (1:500), Lot #K1605; H-100
AntibodyAnti-LVGCC-β1
Mouse monoclonal		abcamCat# ab85020; RRID:AB_1861569ICC/PLA/IP (1:1000), Lot #413-8RR-52
AntibodyAnti-Tau
Rabbit polyclonal		DAKOCat# A0024; RRID:AB_10013724ICC/IP (1:1000), Lot #20031827
AntibodyAnti-BIN1
Mouse monoclonal		Santa CruzCat# sc-13575; RRID:AB_626753ICC/IP (1:1000), Lot #L3014; 99D
Cell line
(Rattus norvegicus)		Primary neuronCharles RiverFresh from E19 albino Sprague Dawley rats
Genetic reagentAAV-BIN1-mKate2UPenn Vector CoreAAV2
Genetic reagentAAV-mKate2UPenn Vector CoreAAV2
Sequence-based reagentTau ASOPMID:23904623
IDT		5-ATCACTGATTTTGAAGTCCC-3
Sequence-based reagentScrambled ASOPMID:23904623
IDT		5-CCTTCCCTGAAGGTTCCTCC-3
Commercial assay, kitDuolink PLA kitMillipore SigmaCat#s DUO92014; DUO92002; DUO92004
Transfected constructGCaMP6fAddgeneRRID:Addgene_40755
Transfected constructmKate2PMID:25156556
Evrogen		Cat# FP184Actin was removed from the construct obtained
Transfected construct (Homo sapiens)BIN1Horizon Discovery ORFeome Collaboration ClonesOHS5894-202501160Isoform 1
Cell line
(Cricetulus griseus)		CHO-K1Millipore SigmaCat# 85051005-1VLChinese Hamster Ovary cell line
Transfected constructmKate2-Tau-mKate2PMID:25156556
Transfected constructFyn-SH3-CBGPMID:25156556BIN1-SH3 or LVGCC-β1-SH3 was cloned in replacing Fyn-SH3
Transfected construct (Homo sapiens)BIN1-SH3IDTAAC28646.1Codon optimized
Transfected construct (Homo sapiens)LVGCC-β1-SH3IDTAAA35632.1Codon optimized

Primary neuron cultures

Primary hippocampal culture protocols were adapted from Rush et al., 2020. Briefly, hippocampal tissue from E19 Sprague Dawley albino rat (Rattus norvegicus) embryos was harvested on ice in 4°C Hibernate E (Life Technologies, A1247601) and digested with 20 units/mL papain (Worthington Biochemical Corporation, LK003178) for 10 min at 37°C. Neurons were then dissociated by manual trituration to a single-cell suspension in Neurobasal medium (Life Technologies, 21103049) supplemented with 1x B-27 (Gibco, 17504044), 2 mM L-Glutamine (Life Technologies, 25030081) and 10% premium select fetal bovine serum (Atlanta Biologicals, S11550). Neuronal plating conditions depended on the experiment, as follows.

Multi electrode array cultures

Request a detailed protocol

For 6-well multielectrode array recordings, neurons were plated at 100,000 per well in six-well MEA plates (ALA Scientific, ALAMEA-MEMMR5). For 48-well plate multielectrode array recordings, neurons were plated at 30,000 per well in 48-well MEA plates (Axion Biosystems, M768-tMEA-48B-5).

Calcium imaging, electrophysiology, and immunocytochemistry cultures

Request a detailed protocol

Neurons were plated at 50,000 neurons per well on 12 mm coverslips (Carolina Biological, 633029) in 24-well plates coated overnight at 4°C with 0.1 mg/mL Poly-D-Lysine (Sigma, P6407−10 × 5 MG) and 0.2 mg/mL laminin (Sigma, L2020-1MG) 24–48 hr prior to the neuron harvest, with the outer wells containing autoclaved ultrapure water (MilliQ filtered) to prevent evaporation.

Immunoblotting

Request a detailed protocol

Neurons were plated at 200,000 per well in six-well plates (Corning, 08-772-1B) and maintained in a 37°C humidified incubator with 5% CO2. 24 hr after plating, 75% of the medium was exchanged for serum-free Neurobasal supplemented with B-27 and L-Glutamine, with 5 μM cytosine β-D-arabinofuranoside (AraC, Sigma Aldrich, C6645) to inhibit glial proliferation. 50% medium changes were performed weekly with Neurobasal supplemented with B-27 and L-Glutamine until experiments were started at DIV 19–21.

BIN1 constructs and vectors

A BIN1-mKate2 (GE Dharmacon, OHS5894-202501160) construct was developed to encode human BIN1 isoform 1 (593 AA, the major neuronal isoform) tagged with mKate2 (Evrogen, FP184, to allow for fluorescent visualization) at the C-terminus to allow for proper function of the N-terminal membrane-interacting BAR domain. A similar construct lacking the BAR domain (amino acids 32–273, BIN1-ΔBAR-mKate2) was produced as a BIN1 BAR domain deletion mutant. A construct encoding mKate2 only was used as a control. These constructs were then cloned into the CIGW vector (rAAV9-CBA-IRES-GFP-WPRE-rBG) (St Martin et al., 2007). Due to size limitations for efficient gene expression, the IRES-GFP was removed from the CIGW vector.

Neuronal transduction

Request a detailed protocol

BIN1-mKate2 and mKate2 vectors were packaged into rAAV2 at the University of Pennsylvania Vector Core (stock titers: AAV-BIN1-mKate2: 3.2e12 genomes/ml, AAV-mKate2: 9.69e12 genomes/ml; used titers: AAV-BIN1-mKate2 and AAV-mKate2: 1e10 genomes/ml, used MOI: AAV-BIN1-mKate2 and AAV-mKate2: 200,000). AAV vectors were used in MEA and electrophysiology experiments. Neuronal cultures were transduced on DIV 2.

Neuronal transfection

Request a detailed protocol

BIN1-mKate2, BIN1-ΔBAR-mKate2, and mKate2 vectors were used in transient transfections in calcium imaging experiments. Transfections were performed at DIV 14 using a calcium phosphate precipitation protocol adapted from Frandemiche et al., 2014. Briefly, Neurobasal medium was removed and kept until the last step of transfection used as conditioned Neurobasal medium. Neurons were washed for 1–1.5 hr in DMKY buffer containing 1 mM kynurenic acid, 0.9 mM NaOH, 0.5 mM HEPES, 10 MgCl2, plus phenol red 0.05%, pH 7.4. Then, 3.5 μg of the vectors were mixed with 120 mM CaCl2 in HBSS (Life Technologies, 14175095) containing 25 mM HEPES, 140 mM NaCl, and 0.750 mM Na2HPO4, pH 7.06, left for 20 min to precipitate the DNA, and applied to the primary hippocampal cultures for 30 min. The medium was then replaced with conditioned Neurobasal medium (Life Technologies, 21103049) and cultures were returned to the incubator until use.

Multielectrode array recordings

Multi Channel Systems MEA

Request a detailed protocol

MEA recording protocols were adapted from Savell et al., 2019b. Briefly, E19 rat hippocampal neurons were seeded to six-well MEAs containing nine extracellular recording electrodes and a ground electrode. Neurons were transduced with AAV expressing BIN1 or control constructs on DIV2. Transduced neurons had 50% medium changes with BrainPhys (StemcellTech, 05793) medium supplemented with N2A and SM1 at DIV 5 and 9 to promote maturation, then with supplemented Neurobasal (Life Technologies, 21103049) at DIV 12. 20 min MEA recordings were performed at DIV 12–13 in the temperature-controlled headstage at 37°C. Neuronal firing was amplified and acquired at 30 kHz, digitized, and further analyzed in MC_Rack (Multi Channel Systems). Data were filtered at 10 Hz and 10,000 Hz filters and thresholded to detect action potentials at each electrode. Detected action potentials were transferred to Offline Sorter (v. 4.0 Plexon) to differentiate multiple neurons detected with a single electrode using principal component analysis (PCA) of waveform properties. Offline Sorter automatically completes and plots PCA on waveforms for each electrode. Manual inspection of PCA, shape, inter-spike intervals, auto-correlograms, and cross-correlograms allowed us to distinguish between multiple units on a single electrode and to do per-neuron analyses. After waveforms were split into units, analysis of each unit’s action potential frequency and burst firing was completed in NeuroExplorer (v. 5.0, Plexon) using the built-in Burst Analysis function, with Poisson burst surprise = 5. Next, firing rates and bursting analysis were performed in NeuroExplorer (v. 5.0 Plexon). Researchers were blinded to experimental conditions performed in all MEA analyses.

Axion Biosciences MEA

Request a detailed protocol

Single neuron electrophysiological activity was recorded using an Axion Maestero recording system as in Savell et al., 2019a. Briefly, neurons were plated on the 48-well MEA (Axion Biosystems, M768-tMEA-48B-5) with 16 extracellular recording electrodes and a ground electrode per well at a density of 30,000 neurons per well in Neurobasal medium (5 μL) with 10% FBS (Atlanta Biologicals, S11550) and placed in a 37°C incubator with 5% CO2. After allowing neurons to attach to the plate for 2 hr, 300 μL serum-free Neurobasal (Life Technologies, 21103049) was added. The next day, AraC was added as with other experiments and a 50% medium change with BrainPhys (Stemcell Technologies Inc, 05790) supplemented with SM1 and L-glutamine was done at DIV 5. At DIV 6, neurons were treated with ASO to reduce Tau protein levels. At DIV 9, a 50% medium change was completed with supplemented BrainPhys, followed by a 50% medium change with supplemented Neurobasal at DIV 12. At DIV 13, neurons were recorded using Axion AxIS software for 15 min. Electrical activity was measured by an interface board at 12.5 kHz, digitized, and transmitted to an external computer for data acquisition and analysis in Axion AxIS Navigator software (Axion Biosystems). All data were filtered using dual 0.01 Hz (high pass) and 5,000 Hz (low-pass) Butterworth filters. Action potential thresholds were set automatically using an adaptive threshold for each electrode (>6 standard deviations from the electrode’s mean signal). Neuronal waveforms collected in Axion AxIS Navigator were exported to Offline Sorter (v. 4.0 Plexon). Offline Sorter automatically completes and plots PCA on waveforms for each electrode. Manual inspection of PCA, shape, inter-spike intervals, auto-correlograms, and cross-correlograms allowed us to distinguish between multiple units on a single electrode and to do per-neuron analyses. After waveforms were split into units, analysis of each unit’s action potential frequency and burst firing was completed in NeuroExplorer (v. 5.0, Plexon) using the built-in Burst Analysis function, with Poisson burst surprise = 5. Next, firing rates and bursting analysis were performed in NeuroExplorer (v. 5.0 Plexon). Researchers were blinded to experimental conditions performed in all MEA analyses.

Antisense oligonucleotide application

View detailed protocol

Tau anti-sense oligonucleotide (ASO) sequences were adapted from DeVos et al., 2013 and produced by Integrated DNA Technology (Tau ASO: 5-ATCACTGATTTTGAAGTCCC-3, Nontargeting control ASO: 5-CCTTCCCTGAAGGTTCCTCC-3). ASOs were dissolved to 100 μM in 10 mM Tris with 0.1 mM EDTA and stored at −20°C until use. At DIV 6, one week before testing for both MEA experiments and PLA, neurons were treated with ASO to a final concentration of 1 μM.

Calcium imaging

Request a detailed protocol

Calcium imaging was adapted from Léveillé et al., 2008. Briefly, rat primary hippocampal neurons (DIV 14) were transfected (see Neuronal transfection section) with the genetically engineered calcium sensor GCaMP6f (gift from Dr. Alain Buisson, originally developed by Douglas Kim and GENIE Project, Addgene plasmid #40755, Chen et al., 2013). At DIV 21, the neurons were incubated for 15 min at room temperature in HEPES and bicarbonate buffered saline solution (HBBSS) containing 116 mM NaCl, 5.4 mM KCl, 1.8 mM CaCl2, 0.8 mM MgSO4, 1.3 mM NaH2PO4, 12 mM HEPES, 5.5 mM glucose, 25 mM bicarbonate and 10 μM glycine at pH 7.45. Neurons that were transfected with mKate2 or BIN1-mKate2 vectors (see BIN1 constructs and vectors section) were recorded for 8 min. Experiments were performed at room temperature with continuous perfusion at 2 ml/min with a peristaltic pump, on the stage of a Nikon A1R Confocal (Nikon, TE2000) inverted microscope equipped with a 100 W mercury lamp and oil-immersion Nikon 40x objective with 1.3 numerical aperture (Nikon, Tokyo, Japan). GCaMP6f (excitation: 340/380 nm, emission: 510 nm) ratio images were acquired at 8 Hz with a digital camera (Princeton Instruments, Trenton, NJ) using Metafluor 6.3 software (Universal Imaging Corporation, West Chester, PA, USA). Fluorescence ratios (340/380 nm) were converted to intracellular Ca2+ concentration using the following formula:

[Ca2+]i=Kd(R−RminRmax−R)(F0Fs)

where R is the measured ratio of 340/380 fluorescence, Rmin is the ratio measured in a Ca2+-free solution, Rmax is the ratio measured in a saturated Ca2+ solution, Kd = 135 nM (the dissociation constant for GCaMP6f), and F0 and Fs are the fluorescence intensities measured at 380 nm, respectively, in a Ca2+-free solution or in a saturated Ca2+ solution.

Electrophysiology

Request a detailed protocol

All electrophysiological recordings were performed in primary hippocampal neuronal cultures after 19–21 DIV. Whole-cell patch-clamp recordings were made from visually identified pyramidal neurons. Recorded signals were amplified with a MultiClamp 700B amplifier (Molecular Devices), filtered at 5 kHz, and sampled at 10 kHz with Digidata 1550A (Molecular Devices). Recordings were acquired using pClamp (v.10) and analyzed using Clampfit (Molecular Devices). Patch pipettes had a resistance of 2.5–5 MΩ when filled with the internal solution required for the experiments described below. All recordings were performed at room temperature (21–23°C). Internal solution included 120 mM Cs-gluconate, 0.6 mM EGTA, 2.8 mM NaCl, 5 mM MgCl2, 2 mM ATP, 0.3 mM GTP, 20 mM HEPES, and 5.0 mM QX-314. External solution included 119 mM NaCl, 2.5 mM KCl, 1.3 mM MgSO4, 2.5 mM CaCl2, 1 mM NaH2PO4, 26 mM NaHCO3, 11 mM glucose (pH 7.3). Voltage-clamp recordings to measure sEPSCs were performed from cultured neurons by whole-cell patch-clamp holding the neurons at –70 mV with 100 µM picrotoxin (GABAAR antagonist, Tocris, 11–281 G) in the bath solution. Voltage-clamp recordings to measure sIPSCs were performed from cultured neurons by whole-cell patch-clamp holding the neurons at 0 mV in 10 µM DNQX (AMPAR antagonist, Sigma, D0540-25MG), 100 µM APV (NMDAR antagonist, Tocris, 01-055-0), and 10 µM nifedipine (L-type VGCC antagonist, Sigma, N7634-25G) to enrich for sIPSCs from spontaneously active interneurons rather than from interneuron-driven by excitatory transmission.

Immunocytochemistry (ICC) and NeuN quantification

Request a detailed protocol

ICC was adapted from Rush et al., 2020. Briefly, primary neurons on coverslips were fixed with 4% PFA and 4% sucrose in 1x PBS. Coverslips were permeabilized with 0.25% Triton X-100 in 1x PBS for 10 min at room temperature then blocked for one hour in 5% FBS in 1x PBS. Primary antibody for NeuN (abcam, ab104225, 1:500), GAD67 (Millipore Sigma, MAB5406, 1:500), BIN1 (Santa Cruz, sc-30099, 1:500), or LVGCC-β1 (Abcam, S7-18, 1:1,000) in 1% FBS in 1x PBS was applied overnight at 4°C. Coverslips were then washed 3 × 5 min in 1x PBS, then incubated in Alexa Fluor fluorescent antibodies (1:1,000) in 1% FBS in 1x PBS for 1 hr at room temperature. Coverslips were washed 3 × 5 min in 1x PBS, then mounted in Prolong Diamond. For neuron quantification, 10 × 10 images at 20x were taken with an epiflourescent microscope and automatically stitched together using Nikon NIS-Elements. NeuN images were thresholded in ImageJ, then quantified using ImageJ (v. 2.0.0-rc-69/1.52 p) particle analyzer.

Proximity Ligation Assay (PLA)

Request a detailed protocol

PLA was adapted from Rush et al., 2020. Briefly, neurons on coverslips were fixed and permeabilized as with ICC, then were incubated overnight with primary antibodies for BIN1 (Santa Cruz, sc-30099, 1:500) and LVGCC-β1 (Abcam, S7-18, 1:1,000) overnight at 4°C, then PLA was performed using the Duolink In Situ Fluorescence kit (Sigma, DUO92004-100RXN). After PLA, coverslips were incubated with secondary antibody to view BIN1 and mounted with Duolink In Situ Mounting Medium with DAPI. Fluorescent images were taken using an epifluorescence microscope at 60x with four channels: DAPI (nuclei), FITC (PLA), and TRITC (mKate2). 7–9 images per slide were obtained and analyzed using ImageJ (v. 2.0.0-rc-69/1.52 p). PLA puncta were quantified using ImageJ particle analyzer, and the average number of puncta per field of view (FOV) or each coverslip was used for analysis.

Bioluminescence Resonance Energy Transfer (BRET)

Request a detailed protocol

BRET was conducted as described in Cochran et al., 2014. Codon-optimized human BIN1-SH3 or LVGCC-β1-SH3 domains were fused on the C-terminus to click beetle green (CBG) luciferase (Promega, E1461) replacing the Fyn-SH3 in the previously described donor construct. Tau tagged at each terminus with mKate2 (Evrogen, FP184) served as the acceptor. Chinese hamster ovary (CHO) cells (Sigma, 85051005-1VL) obtained from ECACC (Lot number 12G006) were authenticated using DNA Fingerprinting and DNA bar-coding sequencing and tested negative for mycoplasma contamination using PCR, a Vero indicator cell line, and Hoechst 33258 fluorescent detection system (Certificate of Analysis test number 47856). CHO cells were plated in 24-well opaque white plates (Promega, 6005168) using the manufacturer’s instructions and transfected with donor and acceptor constructs using Fugene. Forty-eight hours later, fluorescence was read by excitation with a 530/25 nm filter and emission with a 645/40 nm filter on a Synergy2 (BioTek) to control for the concentration of the donor. Immediately after fluorescence measurement, D-luciferin (Promega, E1605) was added to a final concentration of 200 µM to each well. Two to 4 hr later, after the signal had stabilized, plates were read with 645/40 nm filter. Measured BRET fluorescence was normalized to mKate2 fluorescence.

Co-immunoprecipitation

Request a detailed protocol

Mouse hemibrains were finely chopped while frozen, then thawed on ice in PBS plus protease inhibitors (Fisher PI-78439), phosphatase inhibitors (Sigma-Aldrich, P5726), and 1 mM of the cell-permeable cross-linker DSP (Fisher, PI-22585). Hemibrains were then homogenized for 15 s using a hand-held Kontes Pellet Pestle homogenizer, then pipetted up and down 20x to obtain a smooth lysate. Lysates were spun 2 × 10 min at 800 x g, then cleared lysates were incubated for 15 min at 4°C on an end-over-end rotator. Next, lysates were brought to 100 mM Tris to inactivate DSP and incubated for another 15 min at 4°C on an end-over-end rotator. Samples were then diluted 1:1 with a mild co-IP buffer: 10 mM Tris (pH 7.5), 10 mM NaCl, 3 mM MgCl2, 1 mM EGTA, and 0.05% Nonidet P-40, a mild lysis buffer previously shown to be amenable to co-IP experiment (Filiano et al., 2008). At this point, an input fraction was set aside before adding IP antibody to the lysate, with 5 µg of antibody used in each case. Lysate/antibody mixtures were incubated overnight on an end-over-end rotator. Next, 50 uL of Protein G–coated magnetic beads (Life Technologies, 10004D) were added and incubated for 8 hr at 4°C on an end-over-end rotator. Next, non-interacting lysate was removed, the bead/antibody/antigen complex was washed, then protein was eluted with 50 mM Glycine (pH 2.8) and neutralized with 1 M Tris, reduced with β-Mercaptoethanol and an 80°C incubation for 10 min, then cooled and probed by immunoblotting.

Immunoblots

Request a detailed protocol

5 µg of immunoprecipitated samples were loaded and separated on 4–12% NuPage acrylamide gels (Invitrogen) with NuPage MOPS running buffer for 2 hr at 110 V. Next, proteins were transferred to Immobilon-FL PVDF membranes (Millipore) using the NuPage transfer buffer transfer system (Invitrogen) overnight. The membrane was blocked in LI-COR Odyssey blocking buffer (LI-COR, 927–40000) for 1 hr at room temperature and incubated with the appropriate primary antibody. After primary antibody treatment, membranes were washed three times in tris-buffered saline with 0.1% Tween (TBS-T), followed by incubation for 1 hr with Alexa Fluor 700– or 800–conjugated goat antibodies specific for mouse immunoglobulin G (1:20,000, LI-COR). Membranes were then washed three times in TBST-T, followed by a single wash in TBS, imaged on the LI-COR Odyssey fluorescence imaging system, and quantified using LI-COR Image Studio (v. 5.2.5).

Animals

All breeding and experimental procedures were approved by the University of Alabama at Birmingham Institutional Animal Care and Use Committee and follow the guidelines by the National Institutes of Health. Male and female Tau+/– mice lacking exon 1 of MAPT gene were bred to obtain Tau–/– mice with littermate Tau+/+ controls. Mice were maintained under standard laboratory conditions (12 hr light/dark cycle, 50% humidity, Harlan 2916 diet, and water ad libitum). Genotype was verified by standard PCR protocol.

Statistics

Statistical distribution of data varied widely between data sets in this study, so we analyzed each data set for normality and analyzed using either parametric or non-parametric tests accordingly. The specific test used is indicated in the figure legend in each case. All statistical tests were performed with Prism 8 (GraphPad, v. 8.4.0).

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures 6; high throughput raw electrophysiologic recordings of neuronal activity using Axion Biosciences Maesto are deposited on Dryad at https://doi.org/10.5061/dryad.rbnzs7h8z. Brief Analysis used is described in the methods section; in-depth analysis description is publicly available at https://www.axionbiosystems.com/products/software/neural-module.

The following data sets were generated
    1. Voskobiynyk Y
    2. Roth JR
    3. Cochran JN
    4. Rush T
    5. Carullo NVN
    6. Mesina JS
    7. Waqas M
    8. Vollmer RM
    9. Day JJ
    10. McMahon LL
    11. Roberson ED
    (2020) Dryad Digital Repository
    Data from: Alzheimer’s disease risk gene BIN1 induces Tau-dependent network hyperexcitability.
    https://doi.org/10.5061/dryad.rbnzs7h8z

References

  15. Book
    1. Chin J
    2. Roberson ED
    3. Mucke L
    (2007) Molecular aspects of memory dysfunction in Alzheimer's disease
    In: Byrne J, editors. Learning and Memory: A Comprehensive Reference. Academic Press. pp. 245–293.
    https://doi.org/10.1016/B978-012370509-9.00015-2
    1. Hollingworth P
    2. Harold D
    3. Sims R
    4. Gerrish A
    5. Lambert JC
    6. Carrasquillo MM
    7. Abraham R
    8. Hamshere ML
    9. Pahwa JS
    10. Moskvina V
    11. Dowzell K
    12. Jones N
    13. Stretton A
    14. Thomas C
    15. Richards A
    16. Ivanov D
    17. Widdowson C
    18. Chapman J
    19. Lovestone S
    20. Powell J
    21. Proitsi P
    22. Lupton MK
    23. Brayne C
    24. Rubinsztein DC
    25. Gill M
    26. Lawlor B
    27. Lynch A
    28. Brown KS
    29. Passmore PA
    30. Craig D
    31. McGuinness B
    32. Todd S
    33. Holmes C
    34. Mann D
    35. Smith AD
    36. Beaumont H
    37. Warden D
    38. Wilcock G
    39. Love S
    40. Kehoe PG
    41. Hooper NM
    42. Vardy ER
    43. Hardy J
    44. Mead S
    45. Fox NC
    46. Rossor M
    47. Collinge J
    48. Maier W
    49. Jessen F
    50. Rüther E
    51. Schürmann B
    52. Heun R
    53. Kölsch H
    54. van den Bussche H
    55. Heuser I
    56. Kornhuber J
    57. Wiltfang J
    58. Dichgans M
    59. Frölich L
    60. Hampel H
    61. Gallacher J
    62. Hüll M
    63. Rujescu D
    64. Giegling I
    65. Goate AM
    66. Kauwe JS
    67. Cruchaga C
    68. Nowotny P
    69. Morris JC
    70. Mayo K
    71. Sleegers K
    72. Bettens K
    73. Engelborghs S
    74. De Deyn PP
    75. Van Broeckhoven C
    76. Livingston G
    77. Bass NJ
    78. Gurling H
    79. McQuillin A
    80. Gwilliam R
    81. Deloukas P
    82. Al-Chalabi A
    83. Shaw CE
    84. Tsolaki M
    85. Singleton AB
    86. Guerreiro R
    87. Mühleisen TW
    88. Nöthen MM
    89. Moebus S
    90. Jöckel KH
    91. Klopp N
    92. Wichmann HE
    93. Pankratz VS
    94. Sando SB
    95. Aasly JO
    96. Barcikowska M
    97. Wszolek ZK
    98. Dickson DW
    99. Graff-Radford NR
    100. Petersen RC
    101. van Duijn CM
    102. Breteler MM
    103. Ikram MA
    104. DeStefano AL
    105. Fitzpatrick AL
    106. Lopez O
    107. Launer LJ
    108. Seshadri S
    109. Berr C
    110. Campion D
    111. Epelbaum J
    112. Dartigues JF
    113. Tzourio C
    114. Alpérovitch A
    115. Lathrop M
    116. Feulner TM
    117. Friedrich P
    118. Riehle C
    119. Krawczak M
    120. Schreiber S
    121. Mayhaus M
    122. Nicolhaus S
    123. Wagenpfeil S
    124. Steinberg S
    125. Stefansson H
    126. Stefansson K
    127. Snaedal J
    128. Björnsson S
    129. Jonsson PV
    130. Chouraki V
    131. Genier-Boley B
    132. Hiltunen M
    133. Soininen H
    134. Combarros O
    135. Zelenika D
    136. Delepine M
    137. Bullido MJ
    138. Pasquier F
    139. Mateo I
    140. Frank-Garcia A
    141. Porcellini E
    142. Hanon O
    143. Coto E
    144. Alvarez V
    145. Bosco P
    146. Siciliano G
    147. Mancuso M
    148. Panza F
    149. Solfrizzi V
    150. Nacmias B
    151. Sorbi S
    152. Bossù P
    153. Piccardi P
    154. Arosio B
    155. Annoni G
    156. Seripa D
    157. Pilotto A
    158. Scarpini E
    159. Galimberti D
    160. Brice A
    161. Hannequin D
    162. Licastro F
    163. Jones L
    164. Holmans PA
    165. Jonsson T
    166. Riemenschneider M
    167. Morgan K
    168. Younkin SG
    169. Owen MJ
    170. O'Donovan M
    171. Amouyel P
    172. Williams J
    173. Alzheimer's Disease Neuroimaging Initiative
    174. CHARGE consortium
    175. EADI1 consortium
    (2011) Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer's disease
    Nature Genetics 43:429–435.
    https://doi.org/10.1038/ng.803
  32. Conference
    1. Holth J
    2. Reed JG
    3. Inoue T
    4. Pautler R
    5. Botas J
    6. Noebels J
    (2011)
    Tau loss regulates excitability in mouse and Drosophila genetic models of epilepsy
    Society for Neuroscience Abstracts. 671.08.
    1. Kunkle BW
    2. Grenier-Boley B
    3. Sims R
    4. Bis JC
    5. Damotte V
    6. Naj AC
    7. Boland A
    8. Vronskaya M
    9. van der Lee SJ
    10. Amlie-Wolf A
    11. Bellenguez C
    12. Frizatti A
    13. Chouraki V
    14. Martin ER
    15. Sleegers K
    16. Badarinarayan N
    17. Jakobsdottir J
    18. Hamilton-Nelson KL
    19. Moreno-Grau S
    20. Olaso R
    21. Raybould R
    22. Chen Y
    23. Kuzma AB
    24. Hiltunen M
    25. Morgan T
    26. Ahmad S
    27. Vardarajan BN
    28. Epelbaum J
    29. Hoffmann P
    30. Boada M
    31. Beecham GW
    32. Garnier JG
    33. Harold D
    34. Fitzpatrick AL
    35. Valladares O
    36. Moutet ML
    37. Gerrish A
    38. Smith AV
    39. Qu L
    40. Bacq D
    41. Denning N
    42. Jian X
    43. Zhao Y
    44. Del Zompo M
    45. Fox NC
    46. Choi SH
    47. Mateo I
    48. Hughes JT
    49. Adams HH
    50. Malamon J
    51. Sanchez-Garcia F
    52. Patel Y
    53. Brody JA
    54. Dombroski BA
    55. Naranjo MCD
    56. Daniilidou M
    57. Eiriksdottir G
    58. Mukherjee S
    59. Wallon D
    60. Uphill J
    61. Aspelund T
    62. Cantwell LB
    63. Garzia F
    64. Galimberti D
    65. Hofer E
    66. Butkiewicz M
    67. Fin B
    68. Scarpini E
    69. Sarnowski C
    70. Bush WS
    71. Meslage S
    72. Kornhuber J
    73. White CC
    74. Song Y
    75. Barber RC
    76. Engelborghs S
    77. Sordon S
    78. Voijnovic D
    79. Adams PM
    80. Vandenberghe R
    81. Mayhaus M
    82. Cupples LA
    83. Albert MS
    84. De Deyn PP
    85. Gu W
    86. Himali JJ
    87. Beekly D
    88. Squassina A
    89. Hartmann AM
    90. Orellana A
    91. Blacker D
    92. Rodriguez-Rodriguez E
    93. Lovestone S
    94. Garcia ME
    95. Doody RS
    96. Munoz-Fernadez C
    97. Sussams R
    98. Lin H
    99. Fairchild TJ
    100. Benito YA
    101. Holmes C
    102. Karamujić-Čomić H
    103. Frosch MP
    104. Thonberg H
    105. Maier W
    106. Roshchupkin G
    107. Ghetti B
    108. Giedraitis V
    109. Kawalia A
    110. Li S
    111. Huebinger RM
    112. Kilander L
    113. Moebus S
    114. Hernández I
    115. Kamboh MI
    116. Brundin R
    117. Turton J
    118. Yang Q
    119. Katz MJ
    120. Concari L
    121. Lord J
    122. Beiser AS
    123. Keene CD
    124. Helisalmi S
    125. Kloszewska I
    126. Kukull WA
    127. Koivisto AM
    128. Lynch A
    129. Tarraga L
    130. Larson EB
    131. Haapasalo A
    132. Lawlor B
    133. Mosley TH
    134. Lipton RB
    135. Solfrizzi V
    136. Gill M
    137. Longstreth WT
    138. Montine TJ
    139. Frisardi V
    140. Diez-Fairen M
    141. Rivadeneira F
    142. Petersen RC
    143. Deramecourt V
    144. Alvarez I
    145. Salani F
    146. Ciaramella A
    147. Boerwinkle E
    148. Reiman EM
    149. Fievet N
    150. Rotter JI
    151. Reisch JS
    152. Hanon O
    153. Cupidi C
    154. Andre Uitterlinden AG
    155. Royall DR
    156. Dufouil C
    157. Maletta RG
    158. de Rojas I
    159. Sano M
    160. Brice A
    161. Cecchetti R
    162. George-Hyslop PS
    163. Ritchie K
    164. Tsolaki M
    165. Tsuang DW
    166. Dubois B
    167. Craig D
    168. Wu CK
    169. Soininen H
    170. Avramidou D
    171. Albin RL
    172. Fratiglioni L
    173. Germanou A
    174. Apostolova LG
    175. Keller L
    176. Koutroumani M
    177. Arnold SE
    178. Panza F
    179. Gkatzima O
    180. Asthana S
    181. Hannequin D
    182. Whitehead P
    183. Atwood CS
    184. Caffarra P
    185. Hampel H
    186. Quintela I
    187. Carracedo Á
    188. Lannfelt L
    189. Rubinsztein DC
    190. Barnes LL
    191. Pasquier F
    192. Frölich L
    193. Barral S
    194. McGuinness B
    195. Beach TG
    196. Johnston JA
    197. Becker JT
    198. Passmore P
    199. Bigio EH
    200. Schott JM
    201. Bird TD
    202. Warren JD
    203. Boeve BF
    204. Lupton MK
    205. Bowen JD
    206. Proitsi P
    207. Boxer A
    208. Powell JF
    209. Burke JR
    210. Kauwe JSK
    211. Burns JM
    212. Mancuso M
    213. Buxbaum JD
    214. Bonuccelli U
    215. Cairns NJ
    216. McQuillin A
    217. Cao C
    218. Livingston G
    219. Carlson CS
    220. Bass NJ
    221. Carlsson CM
    222. Hardy J
    223. Carney RM
    224. Bras J
    225. Carrasquillo MM
    226. Guerreiro R
    227. Allen M
    228. Chui HC
    229. Fisher E
    230. Masullo C
    231. Crocco EA
    232. DeCarli C
    233. Bisceglio G
    234. Dick M
    235. Ma L
    236. Duara R
    237. Graff-Radford NR
    238. Evans DA
    239. Hodges A
    240. Faber KM
    241. Scherer M
    242. Fallon KB
    243. Riemenschneider M
    244. Fardo DW
    245. Heun R
    246. Farlow MR
    247. Kölsch H
    248. Ferris S
    249. Leber M
    250. Foroud TM
    251. Heuser I
    252. Galasko DR
    253. Giegling I
    254. Gearing M
    255. Hüll M
    256. Geschwind DH
    257. Gilbert JR
    258. Morris J
    259. Green RC
    260. Mayo K
    261. Growdon JH
    262. Feulner T
    263. Hamilton RL
    264. Harrell LE
    265. Drichel D
    266. Honig LS
    267. Cushion TD
    268. Huentelman MJ
    269. Hollingworth P
    270. Hulette CM
    271. Hyman BT
    272. Marshall R
    273. Jarvik GP
    274. Meggy A
    275. Abner E
    276. Menzies GE
    277. Jin LW
    278. Leonenko G
    279. Real LM
    280. Jun GR
    281. Baldwin CT
    282. Grozeva D
    283. Karydas A
    284. Russo G
    285. Kaye JA
    286. Kim R
    287. Jessen F
    288. Kowall NW
    289. Vellas B
    290. Kramer JH
    291. Vardy E
    292. LaFerla FM
    293. Jöckel KH
    294. Lah JJ
    295. Dichgans M
    296. Leverenz JB
    297. Mann D
    298. Levey AI
    299. Pickering-Brown S
    300. Lieberman AP
    301. Klopp N
    302. Lunetta KL
    303. Wichmann HE
    304. Lyketsos CG
    305. Morgan K
    306. Marson DC
    307. Brown K
    308. Martiniuk F
    309. Medway C
    310. Mash DC
    311. Nöthen MM
    312. Masliah E
    313. Hooper NM
    314. McCormick WC
    315. Daniele A
    316. McCurry SM
    317. Bayer A
    318. McDavid AN
    319. Gallacher J
    320. McKee AC
    321. van den Bussche H
    322. Mesulam M
    323. Brayne C
    324. Miller BL
    325. Riedel-Heller S
    326. Miller CA
    327. Miller JW
    328. Al-Chalabi A
    329. Morris JC
    330. Shaw CE
    331. Myers AJ
    332. Wiltfang J
    333. O'Bryant S
    334. Olichney JM
    335. Alvarez V
    336. Parisi JE
    337. Singleton AB
    338. Paulson HL
    339. Collinge J
    340. Perry WR
    341. Mead S
    342. Peskind E
    343. Cribbs DH
    344. Rossor M
    345. Pierce A
    346. Ryan NS
    347. Poon WW
    348. Nacmias B
    349. Potter H
    350. Sorbi S
    351. Quinn JF
    352. Sacchinelli E
    353. Raj A
    354. Spalletta G
    355. Raskind M
    356. Caltagirone C
    357. Bossù P
    358. Orfei MD
    359. Reisberg B
    360. Clarke R
    361. Reitz C
    362. Smith AD
    363. Ringman JM
    364. Warden D
    365. Roberson ED
    366. Wilcock G
    367. Rogaeva E
    368. Bruni AC
    369. Rosen HJ
    370. Gallo M
    371. Rosenberg RN
    372. Ben-Shlomo Y
    373. Sager MA
    374. Mecocci P
    375. Saykin AJ
    376. Pastor P
    377. Cuccaro ML
    378. Vance JM
    379. Schneider JA
    380. Schneider LS
    381. Slifer S
    382. Seeley WW
    383. Smith AG
    384. Sonnen JA
    385. Spina S
    386. Stern RA
    387. Swerdlow RH
    388. Tang M
    389. Tanzi RE
    390. Trojanowski JQ
    391. Troncoso JC
    392. Van Deerlin VM
    393. Van Eldik LJ
    394. Vinters HV
    395. Vonsattel JP
    396. Weintraub S
    397. Welsh-Bohmer KA
    398. Wilhelmsen KC
    399. Williamson J
    400. Wingo TS
    401. Woltjer RL
    402. Wright CB
    403. Yu CE
    404. Yu L
    405. Saba Y
    406. Pilotto A
    407. Bullido MJ
    408. Peters O
    409. Crane PK
    410. Bennett D
    411. Bosco P
    412. Coto E
    413. Boccardi V
    414. De Jager PL
    415. Lleo A
    416. Warner N
    417. Lopez OL
    418. Ingelsson M
    419. Deloukas P
    420. Cruchaga C
    421. Graff C
    422. Gwilliam R
    423. Fornage M
    424. Goate AM
    425. Sanchez-Juan P
    426. Kehoe PG
    427. Amin N
    428. Ertekin-Taner N
    429. Berr C
    430. Debette S
    431. Love S
    432. Launer LJ
    433. Younkin SG
    434. Dartigues JF
    435. Corcoran C
    436. Ikram MA
    437. Dickson DW
    438. Nicolas G
    439. Campion D
    440. Tschanz J
    441. Schmidt H
    442. Hakonarson H
    443. Clarimon J
    444. Munger R
    445. Schmidt R
    446. Farrer LA
    447. Van Broeckhoven C
    448. C O'Donovan M
    449. DeStefano AL
    450. Jones L
    451. Haines JL
    452. Deleuze JF
    453. Owen MJ
    454. Gudnason V
    455. Mayeux R
    456. Escott-Price V
    457. Psaty BM
    458. Ramirez A
    459. Wang LS
    460. Ruiz A
    461. van Duijn CM
    462. Holmans PA
    463. Seshadri S
    464. Williams J
    465. Amouyel P
    466. Schellenberg GD
    467. Lambert JC
    468. Pericak-Vance MA
    469. Alzheimer Disease Genetics Consortium (ADGC)
    470. European Alzheimer’s Disease Initiative (EADI)
    471. Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE)
    472. Genetic and Environmental Risk in AD/Defining Genetic, Polygenic and Environmental Risk for Alzheimer’s Disease Consortium (GERAD/PERADES)
    (2019) Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing
    Nature Genetics 51:414–430.
    https://doi.org/10.1038/s41588-019-0358-2
    1. Lambert JC
    2. Ibrahim-Verbaas CA
    3. Harold D
    4. Naj AC
    5. Sims R
    6. Bellenguez C
    7. DeStafano AL
    8. Bis JC
    9. Beecham GW
    10. Grenier-Boley B
    11. Russo G
    12. Thorton-Wells TA
    13. Jones N
    14. Smith AV
    15. Chouraki V
    16. Thomas C
    17. Ikram MA
    18. Zelenika D
    19. Vardarajan BN
    20. Kamatani Y
    21. Lin CF
    22. Gerrish A
    23. Schmidt H
    24. Kunkle B
    25. Dunstan ML
    26. Ruiz A
    27. Bihoreau MT
    28. Choi SH
    29. Reitz C
    30. Pasquier F
    31. Cruchaga C
    32. Craig D
    33. Amin N
    34. Berr C
    35. Lopez OL
    36. De Jager PL
    37. Deramecourt V
    38. Johnston JA
    39. Evans D
    40. Lovestone S
    41. Letenneur L
    42. Morón FJ
    43. Rubinsztein DC
    44. Eiriksdottir G
    45. Sleegers K
    46. Goate AM
    47. Fiévet N
    48. Huentelman MW
    49. Gill M
    50. Brown K
    51. Kamboh MI
    52. Keller L
    53. Barberger-Gateau P
    54. McGuiness B
    55. Larson EB
    56. Green R
    57. Myers AJ
    58. Dufouil C
    59. Todd S
    60. Wallon D
    61. Love S
    62. Rogaeva E
    63. Gallacher J
    64. St George-Hyslop P
    65. Clarimon J
    66. Lleo A
    67. Bayer A
    68. Tsuang DW
    69. Yu L
    70. Tsolaki M
    71. Bossù P
    72. Spalletta G
    73. Proitsi P
    74. Collinge J
    75. Sorbi S
    76. Sanchez-Garcia F
    77. Fox NC
    78. Hardy J
    79. Deniz Naranjo MC
    80. Bosco P
    81. Clarke R
    82. Brayne C
    83. Galimberti D
    84. Mancuso M
    85. Matthews F
    86. Moebus S
    87. Mecocci P
    88. Del Zompo M
    89. Maier W
    90. Hampel H
    91. Pilotto A
    92. Bullido M
    93. Panza F
    94. Caffarra P
    95. Nacmias B
    96. Gilbert JR
    97. Mayhaus M
    98. Lannefelt L
    99. Hakonarson H
    100. Pichler S
    101. Carrasquillo MM
    102. Ingelsson M
    103. Beekly D
    104. Alvarez V
    105. Zou F
    106. Valladares O
    107. Younkin SG
    108. Coto E
    109. Hamilton-Nelson KL
    110. Gu W
    111. Razquin C
    112. Pastor P
    113. Mateo I
    114. Owen MJ
    115. Faber KM
    116. Jonsson PV
    117. Combarros O
    118. O'Donovan MC
    119. Cantwell LB
    120. Soininen H
    121. Blacker D
    122. Mead S
    123. Mosley TH
    124. Bennett DA
    125. Harris TB
    126. Fratiglioni L
    127. Holmes C
    128. de Bruijn RF
    129. Passmore P
    130. Montine TJ
    131. Bettens K
    132. Rotter JI
    133. Brice A
    134. Morgan K
    135. Foroud TM
    136. Kukull WA
    137. Hannequin D
    138. Powell JF
    139. Nalls MA
    140. Ritchie K
    141. Lunetta KL
    142. Kauwe JS
    143. Boerwinkle E
    144. Riemenschneider M
    145. Boada M
    146. Hiltuenen M
    147. Martin ER
    148. Schmidt R
    149. Rujescu D
    150. Wang LS
    151. Dartigues JF
    152. Mayeux R
    153. Tzourio C
    154. Hofman A
    155. Nöthen MM
    156. Graff C
    157. Psaty BM
    158. Jones L
    159. Haines JL
    160. Holmans PA
    161. Lathrop M
    162. Pericak-Vance MA
    163. Launer LJ
    164. Farrer LA
    165. van Duijn CM
    166. Van Broeckhoven C
    167. Moskvina V
    168. Seshadri S
    169. Williams J
    170. Schellenberg GD
    171. Amouyel P
    172. European Alzheimer's Disease Initiative (EADI)
    173. Genetic and Environmental Risk in Alzheimer's Disease
    174. Alzheimer's Disease Genetic Consortium
    175. Cohorts for Heart and Aging Research in Genomic Epidemiology
    (2013) Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for alzheimer's disease
    Nature Genetics 45:1452–1458.
    https://doi.org/10.1038/ng.2802
    1. Naj AC
    2. Jun G
    3. Beecham GW
    4. Wang LS
    5. Vardarajan BN
    6. Buros J
    7. Gallins PJ
    8. Buxbaum JD
    9. Jarvik GP
    10. Crane PK
    11. Larson EB
    12. Bird TD
    13. Boeve BF
    14. Graff-Radford NR
    15. De Jager PL
    16. Evans D
    17. Schneider JA
    18. Carrasquillo MM
    19. Ertekin-Taner N
    20. Younkin SG
    21. Cruchaga C
    22. Kauwe JS
    23. Nowotny P
    24. Kramer P
    25. Hardy J
    26. Huentelman MJ
    27. Myers AJ
    28. Barmada MM
    29. Demirci FY
    30. Baldwin CT
    31. Green RC
    32. Rogaeva E
    33. St George-Hyslop P
    34. Arnold SE
    35. Barber R
    36. Beach T
    37. Bigio EH
    38. Bowen JD
    39. Boxer A
    40. Burke JR
    41. Cairns NJ
    42. Carlson CS
    43. Carney RM
    44. Carroll SL
    45. Chui HC
    46. Clark DG
    47. Corneveaux J
    48. Cotman CW
    49. Cummings JL
    50. DeCarli C
    51. DeKosky ST
    52. Diaz-Arrastia R
    53. Dick M
    54. Dickson DW
    55. Ellis WG
    56. Faber KM
    57. Fallon KB
    58. Farlow MR
    59. Ferris S
    60. Frosch MP
    61. Galasko DR
    62. Ganguli M
    63. Gearing M
    64. Geschwind DH
    65. Ghetti B
    66. Gilbert JR
    67. Gilman S
    68. Giordani B
    69. Glass JD
    70. Growdon JH
    71. Hamilton RL
    72. Harrell LE
    73. Head E
    74. Honig LS
    75. Hulette CM
    76. Hyman BT
    77. Jicha GA
    78. Jin LW
    79. Johnson N
    80. Karlawish J
    81. Karydas A
    82. Kaye JA
    83. Kim R
    84. Koo EH
    85. Kowall NW
    86. Lah JJ
    87. Levey AI
    88. Lieberman AP
    89. Lopez OL
    90. Mack WJ
    91. Marson DC
    92. Martiniuk F
    93. Mash DC
    94. Masliah E
    95. McCormick WC
    96. McCurry SM
    97. McDavid AN
    98. McKee AC
    99. Mesulam M
    100. Miller BL
    101. Miller CA
    102. Miller JW
    103. Parisi JE
    104. Perl DP
    105. Peskind E
    106. Petersen RC
    107. Poon WW
    108. Quinn JF
    109. Rajbhandary RA
    110. Raskind M
    111. Reisberg B
    112. Ringman JM
    113. Roberson ED
    114. Rosenberg RN
    115. Sano M
    116. Schneider LS
    117. Seeley W
    118. Shelanski ML
    119. Slifer MA
    120. Smith CD
    121. Sonnen JA
    122. Spina S
    123. Stern RA
    124. Tanzi RE
    125. Trojanowski JQ
    126. Troncoso JC
    127. Van Deerlin VM
    128. Vinters HV
    129. Vonsattel JP
    130. Weintraub S
    131. Welsh-Bohmer KA
    132. Williamson J
    133. Woltjer RL
    134. Cantwell LB
    135. Dombroski BA
    136. Beekly D
    137. Lunetta KL
    138. Martin ER
    139. Kamboh MI
    140. Saykin AJ
    141. Reiman EM
    142. Bennett DA
    143. Morris JC
    144. Montine TJ
    145. Goate AM
    146. Blacker D
    147. Tsuang DW
    148. Hakonarson H
    149. Kukull WA
    150. Foroud TM
    151. Haines JL
    152. Mayeux R
    153. Pericak-Vance MA
    154. Farrer LA
    155. Schellenberg GD
    (2011) Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer's disease
    Nature Genetics 43:436–441.
    https://doi.org/10.1038/ng.801
    1. Naj AC
    2. Jun G
    3. Reitz C
    4. Kunkle BW
    5. Perry W
    6. Park YS
    7. Beecham GW
    8. Rajbhandary RA
    9. Hamilton-Nelson KL
    10. Wang LS
    11. Kauwe JS
    12. Huentelman MJ
    13. Myers AJ
    14. Bird TD
    15. Boeve BF
    16. Baldwin CT
    17. Jarvik GP
    18. Crane PK
    19. Rogaeva E
    20. Barmada MM
    21. Demirci FY
    22. Cruchaga C
    23. Kramer PL
    24. Ertekin-Taner N
    25. Hardy J
    26. Graff-Radford NR
    27. Green RC
    28. Larson EB
    29. St George-Hyslop PH
    30. Buxbaum JD
    31. Evans DA
    32. Schneider JA
    33. Lunetta KL
    34. Kamboh MI
    35. Saykin AJ
    36. Reiman EM
    37. De Jager PL
    38. Bennett DA
    39. Morris JC
    40. Montine TJ
    41. Goate AM
    42. Blacker D
    43. Tsuang DW
    44. Hakonarson H
    45. Kukull WA
    46. Foroud TM
    47. Martin ER
    48. Haines JL
    49. Mayeux RP
    50. Farrer LA
    51. Schellenberg GD
    52. Pericak-Vance MA
    53. Albert MS
    54. Albin RL
    55. Apostolova LG
    56. Arnold SE
    57. Barber R
    58. Barnes LL
    59. Beach TG
    60. Becker JT
    61. Beekly D
    62. Bigio EH
    63. Bowen JD
    64. Boxer A
    65. Burke JR
    66. Cairns NJ
    67. Cantwell LB
    68. Cao C
    69. Carlson CS
    70. Carney RM
    71. Carrasquillo MM
    72. Carroll SL
    73. Chui HC
    74. Clark DG
    75. Corneveaux J
    76. Cribbs DH
    77. Crocco EA
    78. DeCarli C
    79. DeKosky ST
    80. Dick M
    81. Dickson DW
    82. Duara R
    83. Faber KM
    84. Fallon KB
    85. Farlow MR
    86. Ferris S
    87. Frosch MP
    88. Galasko DR
    89. Ganguli M
    90. Gearing M
    91. Geschwind DH
    92. Ghetti B
    93. Gilbert JR
    94. Glass JD
    95. Growdon JH
    96. Hamilton RL
    97. Harrell LE
    98. Head E
    99. Honig LS
    100. Hulette CM
    101. Hyman BT
    102. Jicha GA
    103. Jin LW
    104. Karydas A
    105. Kaye JA
    106. Kim R
    107. Koo EH
    108. Kowall NW
    109. Kramer JH
    110. LaFerla FM
    111. Lah JJ
    112. Leverenz JB
    113. Levey AI
    114. Li G
    115. Lieberman AP
    116. Lin CF
    117. Lopez OL
    118. Lyketsos CG
    119. Mack WJ
    120. Martiniuk F
    121. Mash DC
    122. Masliah E
    123. McCormick WC
    124. McCurry SM
    125. McDavid AN
    126. McKee AC
    127. Mesulam M
    128. Miller BL
    129. Miller CA
    130. Miller JW
    131. Murrell JR
    132. Olichney JM
    133. Pankratz VS
    134. Parisi JE
    135. Paulson HL
    136. Peskind E
    137. Petersen RC
    138. Pierce A
    139. Poon WW
    140. Potter H
    141. Quinn JF
    142. Raj A
    143. Raskind M
    144. Reisberg B
    145. Ringman JM
    146. Roberson ED
    147. Rosen HJ
    148. Rosenberg RN
    149. Sano M
    150. Schneider LS
    151. Seeley WW
    152. Smith AG
    153. Sonnen JA
    154. Spina S
    155. Stern RA
    156. Tanzi RE
    157. Thornton-Wells TA
    158. Trojanowski JQ
    159. Troncoso JC
    160. Valladares O
    161. Van Deerlin VM
    162. Van Eldik LJ
    163. Vardarajan BN
    164. Vinters HV
    165. Vonsattel JP
    166. Weintraub S
    167. Welsh-Bohmer KA
    168. Williamson J
    169. Wishnek S
    170. Woltjer RL
    171. Wright CB
    172. Younkin SG
    173. Yu CE
    174. Yu L
    175. Alzheimer Disease Genetics Consortium
    (2014) Effects of multiple genetic loci on age at onset in late-onset alzheimer disease: a genome-wide association study
    JAMA Neurology 71:1394–1404.
    https://doi.org/10.1001/jamaneurol.2014.1491

Article and author information

Author details

  1. Yuliya Voskobiynyk

    Center for Neurodegeneration and Experimental Therapeutics, Alzheimer’s Disease Center, and Evelyn F. McKnight Brain Institute, Departments of Neurology and Neurobiology, University of Alabama at Birmingham, Birmingham, United States
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing
    Contributed equally with
    Jonathan R Roth
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4169-3002

  2. Jonathan R Roth

    Center for Neurodegeneration and Experimental Therapeutics, Alzheimer’s Disease Center, and Evelyn F. McKnight Brain Institute, Departments of Neurology and Neurobiology, University of Alabama at Birmingham, Birmingham, United States
    Contribution
    Data curation, Software, Funding acquisition, Validation, Investigation, Visualization, Methodology, Project administration, Writing - review and editing
    Contributed equally with
    Yuliya Voskobiynyk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8978-4507

  3. J Nicholas Cochran

    Center for Neurodegeneration and Experimental Therapeutics, Alzheimer’s Disease Center, and Evelyn F. McKnight Brain Institute, Departments of Neurology and Neurobiology, University of Alabama at Birmingham, Birmingham, United States
    Contribution
    Data curation, Software, Formal analysis, Validation, Visualization, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared

  4. Travis Rush

    Center for Neurodegeneration and Experimental Therapeutics, Alzheimer’s Disease Center, and Evelyn F. McKnight Brain Institute, Departments of Neurology and Neurobiology, University of Alabama at Birmingham, Birmingham, United States
    Contribution
    Data curation, Software, Formal analysis, Validation, Visualization, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared

  5. Nancy VN Carullo

    Department of Neurobiology, University of Alabama at Birmingham, Birmingham, United States
    Contribution
    Data curation, Software, Formal analysis, Supervision, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9197-5046

  6. Jacob S Mesina

    Center for Neurodegeneration and Experimental Therapeutics, Alzheimer’s Disease Center, and Evelyn F. McKnight Brain Institute, Departments of Neurology and Neurobiology, University of Alabama at Birmingham, Birmingham, United States
    Contribution
    Data curation, Formal analysis, Writing - review and editing
    Competing interests
    No competing interests declared

  7. Mohammad Waqas

    Center for Neurodegeneration and Experimental Therapeutics, Alzheimer’s Disease Center, and Evelyn F. McKnight Brain Institute, Departments of Neurology and Neurobiology, University of Alabama at Birmingham, Birmingham, United States
    Contribution
    Data curation, Formal analysis, Writing - review and editing
    Competing interests
    No competing interests declared

  8. Rachael M Vollmer

    Center for Neurodegeneration and Experimental Therapeutics, Alzheimer’s Disease Center, and Evelyn F. McKnight Brain Institute, Departments of Neurology and Neurobiology, University of Alabama at Birmingham, Birmingham, United States
    Contribution
    Data curation, Formal analysis, Writing - review and editing
    Competing interests
    No competing interests declared

  9. Jeremy J Day

    Department of Neurobiology, University of Alabama at Birmingham, Birmingham, United States
    Contribution
    Resources, Software, Supervision, Funding acquisition, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7361-3399

  10. Lori L McMahon

    Department of Cell, Developmental and Integrative Biology, University of Alabama at Birmingham, Birmingham, United States
    Contribution
    Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1104-6584

  11. Erik D Roberson

    Center for Neurodegeneration and Experimental Therapeutics, Alzheimer’s Disease Center, and Evelyn F. McKnight Brain Institute, Departments of Neurology and Neurobiology, University of Alabama at Birmingham, Birmingham, United States
    Contribution
    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Project administration, Writing - review and editing
    For correspondence
    eroberson@uabmc.edu
    Competing interests
    EDR is an owner of intellectual property relating to Tau
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1810-9763

Funding

National Institutes of Health (RF1AG059405)

  • Erik D Roberson

National Institutes of Health (R01NS075487)

  • Erik D Roberson

National Institutes of Health (R01MH114990)

  • Jeremy J Day

National Institutes of Health (T32NS095775)

  • Yuliya Voskobiynyk

National Institutes of Health (T32NS061788)

  • Jonathan R Roth

Alzheimer's Association

  • Erik D Roberson

Weston Brain Institute

  • Jonathan R Roth
  • Travis Rush
  • Erik D Roberson

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank all members of the Roberson lab for helpful discussions and critiques, and Andy West, David Standaert, and Alain Buisson for plasmids. This work was supported by the National Institutes of Health grants RF1AG059405, R01NS075487, R01MH114990, T32NS095775, and T32NS061788, the Alzheimer’s Association, and the Weston Brain Institute. The authors declare no financial interests. EDR is an owner of intellectual property relating to Tau.

Ethics

Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols (#20450) of the University of Alabama at Birmingham. The protocol was approved by the Committee on the Ethics of Animal Experiments of the University of Alabama at Birmingham.

Version history

  1. Received: March 29, 2020
  2. Accepted: July 12, 2020
  3. Accepted Manuscript published: July 13, 2020 (version 1)
  4. Version of Record published: July 30, 2020 (version 2)

Copyright

© 2020, Voskobiynyk et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 5,156
    Page views
  • 579
    Downloads
  • 26
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Yuliya Voskobiynyk
  2. Jonathan R Roth
  3. J Nicholas Cochran
  4. Travis Rush
  5. Nancy VN Carullo
  6. Jacob S Mesina
  7. Mohammad Waqas
  8. Rachael M Vollmer
  9. Jeremy J Day
  10. Lori L McMahon
  11. Erik D Roberson
(2020)
Alzheimer’s disease risk gene BIN1 induces Tau-dependent network hyperexcitability
eLife 9:e57354.
https://doi.org/10.7554/eLife.57354

Share this article

https://doi.org/10.7554/eLife.57354

Categories and tags

Research organisms

Further reading

    1. Neuroscience

    Plastic vasomotion entrainment

    Daichi Sasaki, Ken Imai ... Ko Matsui
    Research Article

    The presence of global synchronization of vasomotion induced by oscillating visual stimuli was identified in the mouse brain. Endogenous autofluorescence was used and the vessel ‘shadow’ was quantified to evaluate the magnitude of the frequency-locked vasomotion. This method allows vasomotion to be easily quantified in non-transgenic wild-type mice using either the wide-field macro-zoom microscopy or the deep-brain fiber photometry methods. Vertical stripes horizontally oscillating at a low temporal frequency (0.25 Hz) were presented to the awake mouse, and oscillatory vasomotion locked to the temporal frequency of the visual stimulation was induced not only in the primary visual cortex but across a wide surface area of the cortex and the cerebellum. The visually induced vasomotion adapted to a wide range of stimulation parameters. Repeated trials of the visual stimulus presentations resulted in the plastic entrainment of vasomotion. Horizontally oscillating visual stimulus is known to induce horizontal optokinetic response (HOKR). The amplitude of the eye movement is known to increase with repeated training sessions, and the flocculus region of the cerebellum is known to be essential for this learning to occur. Here, we show a strong correlation between the average HOKR performance gain and the vasomotion entrainment magnitude in the cerebellar flocculus. Therefore, the plasticity of vasomotion and neuronal circuits appeared to occur in parallel. Efficient energy delivery by the entrained vasomotion may contribute to meeting the energy demand for increased coordinated neuronal activity and the subsequent neuronal circuit reorganization.

    1. Medicine
    2. Neuroscience

    Ketamine induces multiple individually distinct whole-brain functional connectivity signatures

    Flora Moujaes, Jie Lisa Ji ... Alan Anticevic
    Research Article

    Background:

    Ketamine has emerged as one of the most promising therapies for treatment-resistant depression. However, inter-individual variability in response to ketamine is still not well understood and it is unclear how ketamine’s molecular mechanisms connect to its neural and behavioral effects.

    Methods:

    We conducted a single-blind placebo-controlled study, with participants blinded to their treatment condition. 40 healthy participants received acute ketamine (initial bolus 0.23 mg/kg, continuous infusion 0.58 mg/kg/hr). We quantified resting-state functional connectivity via data-driven global brain connectivity and related it to individual ketamine-induced symptom variation and cortical gene expression targets.

    Results:

    We found that: (i) both the neural and behavioral effects of acute ketamine are multi-dimensional, reflecting robust inter-individual variability; (ii) ketamine’s data-driven principal neural gradient effect matched somatostatin (SST) and parvalbumin (PVALB) cortical gene expression patterns in humans, while the mean effect did not; and (iii) behavioral data-driven individual symptom variation mapped onto distinct neural gradients of ketamine, which were resolvable at the single-subject level.

    Conclusions:

    These results highlight the importance of considering individual behavioral and neural variation in response to ketamine. They also have implications for the development of individually precise pharmacological biomarkers for treatment selection in psychiatry.

    Funding:

    This study was supported by NIH grants DP5OD012109-01 (A.A.), 1U01MH121766 (A.A.), R01MH112746 (J.D.M.), 5R01MH112189 (A.A.), 5R01MH108590 (A.A.), NIAAA grant 2P50AA012870-11 (A.A.); NSF NeuroNex grant 2015276 (J.D.M.); Brain and Behavior Research Foundation Young Investigator Award (A.A.); SFARI Pilot Award (J.D.M., A.A.); Heffter Research Institute (Grant No. 1–190420) (FXV, KHP); Swiss Neuromatrix Foundation (Grant No. 2016–0111) (FXV, KHP); Swiss National Science Foundation under the framework of Neuron Cofund (Grant No. 01EW1908) (KHP); Usona Institute (2015 – 2056) (FXV).

    Clinical trial number:

    NCT03842800

    1. Neuroscience

    Disruption of awake sharp-wave ripples does not affect memorization of locations in repeated-acquisition spatial memory tasks

    Lies Deceuninck, Fabian Kloosterman
    Research Article Updated

    Storing and accessing memories is required to successfully perform day-to-day tasks, for example for engaging in a meaningful conversation. Previous studies in both rodents and primates have correlated hippocampal cellular activity with behavioral expression of memory. A key role has been attributed to awake hippocampal replay – a sequential reactivation of neurons representing a trajectory through space. However, it is unclear if awake replay impacts immediate future behavior, gradually creates and stabilizes long-term memories over a long period of time (hours and longer), or enables the temporary memorization of relevant events at an intermediate time scale (seconds to minutes). In this study, we aimed to address the uncertainty around the timeframe of impact of awake replay by collecting causal evidence from behaving rats. We detected and disrupted sharp wave ripples (SWRs) - signatures of putative replay events - using electrical stimulation of the ventral hippocampal commissure in rats that were trained on three different spatial memory tasks. In each task, rats were required to memorize a new set of locations in each trial or each daily session. Interestingly, the rats performed equally well with or without SWR disruptions. These data suggest that awake SWRs - and potentially replay - does not affect the immediate behavior nor the temporary memorization of relevant events at a short timescale that are required to successfully perform the spatial tasks. Based on these results, we hypothesize that the impact of awake replay on memory and behavior is long-term and cumulative over time.