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. 2016 Dec 20;5:e21616. doi: 10.7554/eLife.21616

Early dysfunction and progressive degeneration of the subthalamic nucleus in mouse models of Huntington's disease

Jeremy F Atherton 1, Eileen L McIver 1, Matthew RM Mullen 1, David L Wokosin 1, D James Surmeier 1, Mark D Bevan 1,*
Editor: Harry T Orr2
PMCID: PMC5199195  PMID: 27995895

Abstract

The subthalamic nucleus (STN) is an element of cortico-basal ganglia-thalamo-cortical circuitry critical for action suppression. In Huntington's disease (HD) action suppression is impaired, resembling the effects of STN lesioning or inactivation. To explore this potential linkage, the STN was studied in BAC transgenic and Q175 knock-in mouse models of HD. At <2 and 6 months of age autonomous STN activity was impaired due to activation of KATP channels. STN neurons exhibited prolonged NMDA receptor-mediated synaptic currents, caused by a deficit in glutamate uptake, and elevated mitochondrial oxidant stress, which was ameliorated by NMDA receptor antagonism. STN activity was rescued by NMDA receptor antagonism or the break down of hydrogen peroxide. At 12 months of age approximately 30% of STN neurons had been lost, as in HD. Together, these data argue that dysfunction within the STN is an early feature of HD that may contribute to its expression and course.

DOI: http://dx.doi.org/10.7554/eLife.21616.001

Research Organism: Mouse

Introduction

The basal ganglia are a network of subcortical brain nuclei that are critical for action selection and central to the expression of several psychomotor disorders (Albin et al., 1989; Wichmann and DeLong, 1996). Information flow from the cortex to the output nuclei of the basal ganglia occurs via three major pathways. The so-called direct pathway through the striatum promotes movement and ‘rewarding’ behavior through inhibition of GABAergic basal ganglia output (Chevalier and Deniau, 1990; Kravitz et al., 2010; Kravitz and Kreitzer, 2012). In contrast, the indirect pathway via the striatum, external globus pallidus and subthalamic nucleus (STN) and the hyperdirect pathway through the STN suppress the same processes through elevation of basal ganglia output (Maurice et al., 1999; Tachibana et al., 2008; Kravitz et al., 2010; Kravitz and Kreitzer, 2012). Indeed, interruption of the indirect and hyperdirect pathways through lesion or inactivation of the STN is associated with elevated/involuntary movement, impulsivity and psychiatric disturbances such as hypomania and hyper-sexuality (Crossman et al., 1988; Hamada and DeLong, 1992; Baunez and Robbins, 1997; Bickel et al., 2010; Jahanshahi et al., 2015).

Huntington's disease (HD) is an autosomal dominant, neurodegenerative disorder caused by an expansion of CAG repeats in the gene (HTT) encoding huntingtin (HTT), a protein involved in vesicle dynamics and intracellular transport (Huntington’s Disease Collaborative Research Group, 1993; Saudou and Humbert, 2016). Early symptoms of HD include involuntary movement, compulsive behavior, paranoia, irritability and aggression (Anderson and Marder, 2001; Kirkwood et al., 2001). These symptoms have traditionally been linked to cortico-striatal degeneration, however a role for the STN is suggested by their similarity to those caused by STN inactivation or lesion. The hypoactivity of the STN in HD models in vivo (Callahan and Abercrombie, 2015a, 2015b) and the susceptibility of the STN to degeneration in HD (Lange et al., 1976; Guo et al., 2012) are also consistent with STN dysfunction.

Several mouse models of HD have been generated, which vary by length and species origin of HTT/Htt, CAG repeat length, and method of genome insertion. For example, one line expresses full-length human HTT with 97 mixed CAA-CAG repeats in a bacterial artificial chromosome (BAC; Gray et al., 2008), whereas Q175 knock-in (KI) mice have an inserted chimeric human/mouse exon one with a human polyproline region and ~188 CAG repeats in the native Htt (Menalled et al., 2012).

Increased mitochondrial oxidant stress exacerbated by abnormal NMDAR-mediated transmission and signaling has been reported in HD and its models (Fan and Raymond, 2007; Song et al., 2011; Johri et al., 2013; Parsons and Raymond, 2014; Martin et al., 2015). Several reports suggest that glutamate uptake is impaired due to reduced expression of the glutamate transporter EAAT2 (GLT-1) and/or GLT-1 dysfunction (Arzberger et al., 1997; Liévens et al., 2001; Behrens et al., 2002; Miller et al., 2008; Bradford et al., 2009; Faideau et al., 2010; Huang et al., 2010; Menalled et al., 2012; Dvorzhak et al., 2016; Jiang et al., 2016). However, others have found no evidence for deficient glutamate uptake (Parsons et al., 2016), suggesting that abnormal NMDAR-mediated transmission is caused by increased expression of extrasynaptic receptors and/or aberrant coupling to signaling pathways (e.g., Parsons and Raymond, 2014). The sensitivity of mitochondria to anomalous NMDAR signaling is likely to be further compounded by their intrinsically compromised status in HD (Song et al., 2011; Johri et al., 2013; Martin et al., 2015).

Although HD models exhibit pathogenic processes seen in HD, they do not exhibit similar levels and spatiotemporal patterns of cortico-striatal neurodegeneration. Striatal neuronal loss in aggressive Htt fragment models such as R6/2 mice does occur but only close to death (Stack et al., 2005), whereas full-length models exhibit minimal loss (Gray et al., 2008; Smith et al., 2014). Despite the loss and hypoactivity of STN neurons in HD and the similarity of HD symptoms to those arising from STN lesion or inactivation, the role of the STN in HD remains poorly understood. We hypothesized that the abnormal activity and progressive loss of STN neurons in HD may reflect abnormalities within the STN itself. This hypothesis was addressed in the BAC and Q175 KI HD models using a combination of cellular and synaptic electrophysiology, optogenetic interrogation, two-photon imaging and stereological cell counting.

Results

Data are reported as median [interquartile range]. Unpaired and paired statistical comparisons were made with non-parametric Mann-Whitney U and Wilcoxon Signed-Rank tests, respectively. Fisher’s exact test was used for categorical data. p < 0.05 was considered statistically significant; where multiple comparisons were performed this p-value was adjusted using the Holm-Bonferroni method (adjusted p-values are denoted ph; Holm, 1979). Box plots show median (central line), interquartile range (box) and 10–90% range (whiskers).

The autonomous activity of STN neurons is disrupted in the BACHD model

STN neurons exhibit intrinsic, autonomous firing, which contributes to their role as a driving force of neuronal activity in the basal ganglia (Bevan and Wilson, 1999; Beurrier et al., 2000; Do and Bean, 2003). To determine whether this property is compromised in HD mice, the autonomous activity of STN neurons in ex vivo brain slices prepared from BACHD and wild type littermate (WT) mice were compared using non-invasive, loose-seal, cell-attached patch clamp recordings. 5–7 months old, symptomatic and 1–2 months old, presymptomatic mice were studied (Gray et al., 2008). Recordings focused on the lateral two-thirds of the STN, which receives input from the motor cortex (Kita and Kita, 2012; Chu et al., 2015). At 5–7 months, 124/128 (97%) WT neurons exhibited autonomous activity compared to 110/126 (87%) BACHD neurons (p = 0.0049; Figure 1A,B). The frequency (WT: 7.9 [5.2–12.6] Hz; n = 128; BACHD: 6.3 [1.4–10.2] Hz; n = 126; p = 0.0001) and regularity (WT CV: 0.27 [0.14–0.47]; n = 124; BACHD CV: 0.36 [0.20–0.80]; n = 110; p = 0.0012) of firing were reduced in BACHD neurons (Figure 1A,B). The distribution of firing frequency of WT neurons appears unimodal with a mode at ~6–8 Hz (Figure 1C), whereas the distribution of BACHD neurons is relatively bimodal with modes at ~0–2 Hz and ~8–10 Hz (Kolmogorov–Smirnov test, p = 0.0002; Figure 1C). This distribution suggests that BACHD neurons consist of a phenotypic population with compromised autonomous firing, and a non-phenotypic population with relatively normal autonomous firing. At 1–2 months 136/145 (94%) WT STN neurons were autonomously active versus 120/143 (84%) BACHD STN neurons (p = 0.0086). The frequency (WT: 9.8 [6.3–14.8] Hz; n = 145; BACHD: 7.1 [1.8–11.3] Hz; n = 143; p < 0.0001) and regularity (WT CV: 0.17 [0.13–0.26]; n = 136; BACHD CV: 0.23 [0.14–0.76]; n = 120; p = 0.0016) of firing were also reduced in BACHD neurons. Together, these data demonstrate that the autonomous activity of STN neurons in BACHD mice is impaired at both early presymptomatic and later symptomatic ages.

Figure 1. Abnormal intrinsic and synaptic properties of STN neurons in BACHD mice.

Figure 1.

(A) Representative examples of autonomous STN activity recorded in the loose-seal, cell-attached configuration. The firing of the neuron from a WT mouse was of a higher frequency and regularity than the phenotypic neuron from a BACHD mouse. (B) Population data showing (left to right) that the frequency and regularity of firing, and the proportion of active neurons in BACHD mice were reduced relative to WT mice. (C) Histogram showing the distribution of autonomous firing frequencies of neurons in WT (gray) and BACHD (green) mice. (D) Confocal micrographs showing NeuN expressing STN neurons (red) and hChR2(H134R)-eYFP expressing cortico-STN axon terminals (green) in the STN. (E) Examples of optogenetically stimulated NMDAR EPSCs from a WT STN neuron before (black) and after (gray) inhibition of astrocytic glutamate uptake with 100 nM TFB-TBOA. Inset, the same EPSCs scaled to the same amplitude. (F) Examples of optogenetically stimulated NMDAR EPSCs from a BACHD STN neuron before (green) and after (gray) inhibition of astrocytic glutamate uptake with 100 nM TFB-TBOA. (G) WT (black, same as in E) and BACHD (green, same as in F) optogenetically stimulated NMDAR EPSCs overlaid and scaled to the same amplitude. (H) Boxplots of amplitude weighted decay show slowed decay kinetics of NMDAR EPSCs in BACHD STN neurons compared to WT, and that TFB-TBOA increased weighted decay in WT but not BACHD mice. *p < 0.05. ns, not significant. Data for panels BC provided in Figure 1—source data 1; data for panel H provided in Figure 1—source data 2.

DOI: http://dx.doi.org/10.7554/eLife.21616.002

Figure 1—source data 1. Autonomous firing frequency and CV for BACHD and WT STN neurons in Figure 1B–C.
DOI: 10.7554/eLife.21616.003
Figure 1—source data 2. Amplitude weighted decay of NMDAR-mediated EPSCs in Figure 1H.
DOI: 10.7554/eLife.21616.004

NMDAR-mediated EPSCs are prolonged in BACHD STN neurons

As described above, the majority of studies report that astrocytic glutamate uptake is diminished in the striatum in HD and its models. To test whether the STN of BACHD mice exhibits a similar deficit, EPSCs arising from the optogenetic stimulation of motor cortical inputs to the STN (as described by Chu et al., 2015) were compared in WT and BACHD mice before and after inhibition of GLT-1 and GLAST with 100 nM TFB-TBOA. STN neurons were recorded in ex vivo brain slices in the whole-cell voltage-clamp configuration using a cesium-based, QX-314-containing internal solution to maximize voltage control. Neurons were held at −40 mV and recorded in the presence of low (0.1 mM) external Mg2+ and the AMPAR antagonist DNQX (20 µM) to maximize and pharmacologically isolate the evoked NMDAR-mediated excitatory postsynaptic current (EPSC); analysis was performed on average EPSCs from 5 trials with 30 s recovery between trials (Figure 1D–H). The amplitude weighted decay time constant of the NMDAR EPSC was moderately but significantly prolonged in BACHD compared to WT neurons (WT: 38.1 [30.0–44.8] ms; n = 12; BACHD: 47.6 [38.7–55.9] ms; n = 11; p = 0.0455; Figure 1E–H). Subsequent application of TFB-TBOA increased the decay time constant of the NMDAR EPSC in STN neurons derived from WT (WT control: 39.0 [35.2–44.0] ms; WT TFB-TBOA: 50.2 [41.7–68.4] ms; n = 9; p = 0.0039; Figure 1E,H) but had no effect in BACHD neurons (BACHD control: 47.9 [38.9–59.4] ms; BACHD TFB-TBOA: 44.9 [34.7–52.2] ms; n = 10; p = 0.3223; Figure 1F,H). In control conditions, the amplitudes of EPSCs recorded from WT and BACHD neurons were similar (WT: 50.1 [34.7–61.0] pA; n = 12; BACHD: 45.6 [22.1–78.3] pA; n = 11; ph = 0.7399; Figure 2A) and there was no correlation between EPSC amplitude and the decay time constant in either group (WT: r2 = 0.16; n = 12; ph = 0.5871; BACHD: r2 = 0.10; n = 12; ph = 0.6686; Figure 2B). In order to increase spillover of glutamate from synaptic release sites, cortico-STN inputs were optogenetically stimulated 5 times at 50 Hz and the resulting compound NMDAR-mediated EPSC was compared in WT and BACHD STN neurons. Interestingly, the decay of compound NMDAR EPSCs under control conditions or following inhibition of glutamate uptake were not different in WT and BACHD mice (WT control: 79.0 [62.6–102.0] ms; n = 6; BACHD control: 65.2 [44.7–111.5] ms; n = 6; p = 0.4848; WT TFB-TBOA: 125.4 [106.8–146.6] ms; n = 6; BACHD TFB-TBOA: 108.3 [94.5–143.1] ms; n = 6; p = 0.6991; Figure 2C–E). Together, these data demonstrate that individual, but not compound, NMDAR-mediated cortico-STN synaptic EPSCs are prolonged in the BACHD model.

Figure 2. Cortico-STN EPSCs in WT and BACHD mice.

Figure 2.

(A) Boxplot showing the distribution of single optogenetically stimulated cortico-STN NMDAR EPSC amplitudes in WT and BACHD mice. (B) Scatterplot showing NMDAR EPSC amplitude vs amplitude weighted decay time. There was no correlation between NMDAR EPSC amplitude and decay time in WT or BACHD mice. (CD) Example of NMDAR EPSCs generated by 5 × 50 Hz optogenetic stimulation from a WT STN neuron (C) before (black) and after (gray) inhibition of astrocytic glutamate uptake with 100 nM TFB-TBOA and a BACHD STN neuron (D) before (green) and after (gray) TFB-TBOA application. (E) Line segment plots of amplitude weighted decay of compound NMDAR EPSCs before and following TFB-TBOA. The decays of compound NMDAR ESPCs were similar in WT and BACHD before TFB-TBOA application. In addition, inhibition of astrocytic glutamate uptake prolonged the decay of compound NMDAR ESPCs in all neurons tested. ns, not significant. Data for panels AB provided in Figure 2—source data 1; data for panel E provided in Figure 2—source data 2.

DOI: http://dx.doi.org/10.7554/eLife.21616.005

Figure 2—source data 1. Amplitude and amplitude weighted decay of NMDAR-mediated EPSCs in Figure 2A–B.
DOI: 10.7554/eLife.21616.006
Figure 2—source data 2. Amplitude weighted decay of compound NMDAR-mediated EPSCs in Figure 2E.
DOI: 10.7554/eLife.21616.007

Blockade of NMDARs rescues the autonomous activity of BACHD STN neurons

To test whether disrupted autonomous firing in BACHD is linked to NMDAR activation, brain slices from BACHD mice were incubated in control media or media containing the NMDAR antagonist D-AP5 (50 µM) for 3–5 hr prior to loose-seal, cell-attached recordings from STN neurons (Figure 3). D-AP5 treatment rescued autonomous firing in slices derived from 5–7 month old BACHD mice compared to untreated control slices (Figure 3A,B). The proportion of autonomously active neurons was greater in D-AP5 pre-treated slices (untreated: 18/30 (60%); D-AP5 treated: 29/30 (97%); p = 0.0011). The frequency (untreated: 1.0 [0.0–7.6] Hz; n = 30; D-AP5 treated: 13.2 [7.9–17.4] Hz; n = 30; p < 0.0001) and regularity (untreated CV: 0.43 [0.24–1.21]; n = 18; D-AP5 treated: CV: 0.13 [0.09–0.20]; n = 29; p < 0.0001) of autonomous firing were also greater in D-AP5 treated slices. In slices derived from 1–2 month old BACHD mice autonomous firing was also more prevalent in D-AP5 treated slices than in untreated slices (untreated: 10/30 (33%); D-AP5-treated: 27/30 neurons (90%); p < 0.0001) and the frequency of firing overall was greater (untreated: 0.0 [0.0–1.3] Hz; n = 30; D-AP5 treated: 8.7 [4.4–14.5] Hz; n = 30; p < 0.0001; Figure 3B). The regularity of autonomous firing was however not rescued (untreated CV: 0.61 [0.27–0.81]; n = 10; D-AP5-treated CV: 0.25 [0.14–0.69]; n = 27; p = 0.1368; Figure 3B). In slices from WT mice the rate (untreated: 10.4 [6.1–14.2] Hz; n = 27; D-AP5 treated: 5.6 [1.7–15.4] Hz; n = 27; p = 0.1683; Figure 3B), regularity (untreated: 0.18 [0.12–0.27]; n = 24; D-AP5 treated: 0.22 [0.12–0.46]; n = 22; p = 0.4785; Figure 3C) and incidence of firing (untreated: 24/27 (89%); D-AP5 treated 22/27 (81%); p = 0.7040; Figure 3D) were unaltered by D-AP5 treatment. Thus, prolonged blockade of NMDARs rescued autonomous firing in BACHD STN neurons but had no effect on autonomous activity in WT STN neurons. Together, these data demonstrate that NMDAR activation contributes to the disruption of autonomous activity in BACHD STN neurons.

Figure 3. The impaired autonomous activity of STN neurons in BACHD mice is rescued by antagonism of NMDARs.

Figure 3.

(A) Examples of loose-seal cell-attached recordings of 6-month-old BACHD STN neurons from an untreated slice (left) and a slice that was treated with 50 µM D-AP5 for 3–5 hr prior to recording. (B) Population data showing elevated autonomous firing in STN neurons from D-AP5 pre-treated slices from 2-month-old and 6-month-old BACHD mice but not in WT mice. (C) Population data showing increased firing regularity in STN neurons from D-AP5 pre-treated slices from 6-month-old BACHD mice but not 2-month-old BACHD mice or WT mice. (D) Population data showing a higher proportion of active neurons in STN neurons from D-AP5 pre-treated slices from 2-month-old and 6-month-old BACHD mice but not in WT mice. *p < 0.05. ns, not significant. Data for panels BC provided in Figure 3—source data 1.

DOI: http://dx.doi.org/10.7554/eLife.21616.008

Figure 3—source data 1. Autonomous firing frequency and CV for BACHD control and D-AP5 pretreated STN neurons in Figure 3B–C.
DOI: 10.7554/eLife.21616.009

The mitochondria of BACHD STN neurons are subject to elevated oxidant stress

NMDAR activation can elevate mitochondrial oxidant stress (Dugan et al., 1995; Moncada and Bolaños, 2006; Brennan et al., 2009; Nakamura and Lipton, 2011). To test whether STN neurons from BACHD mice exhibit increased mitochondrial oxidant stress, a mitochondria-targeted redox probe MTS-roGFP (Hanson et al., 2004) was virally expressed in 5–7-month-old BACHD mice and WT littermates (Figure 4A). 1–2 weeks later MTS-roGFP was imaged in brain slices under two-photon excitation with 890 nm light. Oxidant stress was estimated from the fluorescence of MTS-roGFP in individual neurons under baseline conditions relative to the fluorescence of MTS-roGFP under conditions of full reduction and oxidation in the presence of 2 mM dithiothreitol and 200 µM aldrithiol-4, respectively (Sanchez-Padilla et al., 2014). STN neurons were selected for analysis based on their appearance under two-photon, Dodt contrast imaging and were distinguishable from STN glial cells by their relatively large diameter (Figure 4A). STN neurons are reliably recorded when this selection strategy is employed to guide patch clamp recording (Atherton et al., 2008, 2010). MTS-roGFP imaging revealed that relative oxidant stress in BACHD STN neurons was elevated compared to WT (WT: 0.35 [0.18–0.42]; n = 23; BACHD: 0.40 [0.37–0.63]; n = 24; p = 0.0332; Figure 4B). In a separate experiment (performed 15 months later) to test whether NMDAR activation ex vivo contributed to elevated mitochondrial oxidant stress, brain slices from a different cohort of BACHD mice were treated for >3 hr in 50 µM D-AP5 prior to imaging. MTS-roGFP imaging confirmed that D-AP5-treated slices exhibited lower oxidant stress compared to untreated slices from the same mice (untreated: 0.39 [0.35–0.48]; n = 13; D-AP5-treated: 0.32 [0.24–0.42]; n = 17; p = 0.0445; Figure 4C). Thus, STN neurons from BACHD mice exhibit elevated mitochondrial oxidant stress, which can be reduced by antagonism of NMDARs.

Figure 4. Mitochondrial oxidant stress is elevated in STN neurons from BACHD mice.

Figure 4.

(A) Two-photon (left), Dodt contrast (center) and combined (right) images showing expression of MTS-roGFP (green) in STN neurons of a BACHD mouse. (B) Population data illustrating greater relative oxidation of mitochondria in BACHD STN neurons compared to WT STN neurons. (C) Population data showing that >3 hr pre-treatment with the NMDA receptor antagonist D-AP5 (50 µM) lowered the relative oxidation of mitochondria in BACHD STN neurons. Arrows indicate MTS-roGFP expressing STN neurons; *p < 0.05. Data for panel B provided in Figure 4—source data 1; data for panel C provided in Figure 4—source data 2.

DOI: http://dx.doi.org/10.7554/eLife.21616.010

Figure 4—source data 1. The relative mitochondrial oxidant stress of WT and BACHD STN neurons in Figure 4B.
DOI: 10.7554/eLife.21616.011
Figure 4—source data 2. The relative mitochondrial oxidant stress of control and D-AP5 pre-treated BACHD STN neurons in Figure 4C.
DOI: 10.7554/eLife.21616.012

Impaired autonomous activity of STN neurons in BACHD mice is due to increased activation of KATP channels

NMDAR receptor-generated mitochondrial oxidant stress in BACHD may lead to the activation of KATP channels, which act as metabolic sensors and homeostatic regulators of excitability in multiple cell types (Nichols, 2006). STN neurons abundantly express all the molecular components of KATP channels including the Kir6.2 pore-forming subunit of the KATP channel (Thomzig et al., 2005) and the SUR1, SUR2A and SUR2B regulatory subunits (Karschin et al., 1997; Zhou et al., 2012). To determine whether KATP channels are responsible for impaired firing in BACHD mice, the effects of the KATP channel inhibitor glibenclamide (100 nM) on WT and phenotypic BACHD autonomous firing ex vivo were compared. Glibenclamide application increased both the rate (BACHD control: 5.7 [1.4–9.1] Hz; BACHD glibenclamide: 11.3 [9.2–13.3] Hz; n = 15; p = 0.0003) and regularity (BACHD control CV: 0.41 [0.16–1.43]; BACHD glibenclamide CV: 0.16 [0.12–0.32]; n = 14; p = 0.0001) of autonomous firing in 5–7-month-old BACHD STN neurons (Figure 5A,B). In contrast KATP channel inhibition had no effect on the firing of WT neurons (WT control frequency: 13.2 [8.4–19.6] Hz; WT glibenclamide frequency: 15.7 [9.7–18.4] Hz; n = 8; p = 0.4609; WT control CV: 0.18 [0.11–0.21]; WT glibenclamide CV: 0.14 [0.11–0.19]; n = 8; p = 0.1094).

Figure 5. The abnormal autonomous activity of STN neurons in BACHD mice is rescued by inhibition of KATP channels.

Figure 5.

(A) Examples of loose-seal cell-attached recordings from STN neurons before (left) and after (right) inhibition of KATP channels with 100 nM glibenclamide. Upper traces show data from a WT mouse; lower traces show data from a BACHD mouse. (B) Population data (from 5–7-month-old mice). In WT neurons, inhibition of KATP channels had mixed effects on firing, whereas in BACHD neurons inhibition of KATP channels increased both the frequency and regularity of firing. (C) Examples of whole-cell recordings from WT and BACHD STN neurons before (left) and after (right) inhibition of KATP channels with 100 nM glibenclamide. (D) Population data (from 5–7-month-old mice). The interspike voltage trajectory was lower in BACHD neurons compared to WT. KATP channel inhibition increased the interspike voltage trajectory in BACHD neurons but had no effect in WT. As with cell-attached recordings, inhibition of KATP channels had mixed effects on firing in WT neurons, whereas in BACHD mice inhibition of KATP channels increased the frequency and regularity of firing. *p < 0.05. ns, not significant. Data for panel B provided in Figure 5—source data 1; data for panel D provided in Figure 5—source data 2.

DOI: http://dx.doi.org/10.7554/eLife.21616.013

Figure 5—source data 1. Autonomous firing frequency and CV for WT and BACHD STN neurons under control conditions and following glibenclamide application in Figure 5B.
DOI: 10.7554/eLife.21616.014
Figure 5—source data 2. Autonomous interspike voltage trajectory, firing frequency and CV for whole-cell recordings from WT and BACHD STN neurons in Figure 5D.
DOI: 10.7554/eLife.21616.015

To further examine the effects of KATP channels on autonomous firing, whole-cell current clamp recordings were obtained from 5–7-month-old BACHD mice and WT littermates (Figure 5C,D). Consistent with the hyperpolarizing and shunting effects of KATP channels, the interspike voltage trajectory was shallower in BACHD neurons compared to WT (WT: 413.8 [317.1–705.0] mV/s; n = 7; BACHD: 125.3 [59.2–298.0] mV/s; n = 7; ph = 0.0210). In addition, the rate, regularity and interspike voltage trajectory of autonomous firing of BACHD STN neurons recorded in this configuration increased following application of glibenclamide (BACHD control frequency: 7.7 [4.5–13.4] Hz; BACHD glibenclamide frequency: 15.1 [8.5–20.9] Hz; n = 7; p = 0.0156; BACHD control CV: 0.24 [0.13–0.35]; BACHD glibenclamide CV: 0.13 [0.10–0.15]; n = 7; p = 0.0313; BACHD control interspike voltage trajectory: 125.3 [59.2–298.0] mV/s; BACHD glibenclamide interspike voltage trajectory: 369.9 [156.7–474.0] mV/s; n = 7; ph = 0.0313; Figure 5C,D). In contrast inhibition of KATP channels did not alter the firing of WT neurons (WT control frequency: 14.7 [12.5–27.4] Hz; WT glibenclamide frequency: 16.8 [8.3–20.3] Hz; n = 7; p = 0.3750; WT control CV: 0.10 [0.07–0.18]; WT glibenclamide CV: 0.08 [0.06–0.16]; n = 7; p = 0.8125; WT control interspike voltage trajectory: 413.8 [317.1–705.0] mV/s; n = 7; WT glibenclamide interspike voltage trajectory: 361.9 [216.7–522.9] mV/s; n = 7; ph = 0.3750; Figure 5C,D).

Glibenclamide can also activate Epac2 and Rap1 (Zhang et al., 2009), which could rescue firing through a pathway that is independent of KATP channel inhibition. Therefore, gliclazide, a sulfonylurea that has no effect on Epac2/Rap1 signaling (Zhang et al., 2009; Takahashi et al., 2015), was applied to STN neurons of 5–7-month-old BACHD mice (Figure 6). In loose-seal, cell-attached recordings gliclazide (10 µM) increased both firing rate (control: 5.1 [2.0–8.0] Hz; gliclazide: 9.9 [4.6–13.8] Hz; n = 6; p = 0.0313; Figure 6) and regularity (control CV: 0.54 [0.31–0.88]; gliclazide CV: 0.29 [0.10–0.47]; n = 6; p = 0.0313; Figure 6) in phenotypic BACHD STN neurons. Together, these data argue that KATP channels are responsible for the impaired autonomous activity of STN neurons in the BACHD model.

Figure 6. The abnormal autonomous activity of STN neurons in BACHD mice is rescued by inhibition of KATP channels with gliclazide.

Figure 6.

(A) Examples of loose-seal cell-attached recordings of a STN neuron from a 6-month-old BACHD mouse before (upper) and after (lower) inhibition of KATP channels with 10 µM gliclazide. (B) Population data (5–7-month-old). In BACHD STN neurons inhibition of KATP channels with gliclazide increased the frequency and regularity of firing. *p < 0.05. Data for panel B provided in Figure 6—source data 1.

DOI: http://dx.doi.org/10.7554/eLife.21616.016

Figure 6—source data 1. Autonomous firing frequency and CV for WT and BACHD STN neurons under control conditions and following gliclazide application in Figure 6B.
DOI: 10.7554/eLife.21616.017

As described above, 3–5 hr NMDAR antagonism with D-AP5 partially rescued autonomous activity in BACHD STN neurons. To determine whether this rescue was mediated through effects on KATP channels, glibenclamide was applied following this treatment. D-AP5 pre-treatment partially occluded the increases in the autonomous firing rate (BACHD glibenclamide Δ frequency: 4.3 [2.2–8.7] Hz, n = 15; D-AP5 pre-treated BACHD glibenclamide Δ frequency: 1.9 [0.7–3.2] Hz, n = 6; p = 0.0365) and regularity (BACHD glibenclamide Δ CV: −0.25 [−0.85– −0.13], n = 14; D-AP5 pre-treated BACHD glibenclamide Δ CV: −0.09 [−0.10– −0.03], n = 6; p = 0.0154) that accompany KATP channel inhibition. Thus, these observations are consistent with the conclusion that prolonged NMDAR antagonism partially rescued autonomous activity in BACHD STN neurons through a reduction in KATP channel-mediated firing disruption.

NMDAR activation produces a persistent KATP channel-mediated disruption of autonomous activity in WT STN neurons

To further examine whether elevated NMDAR activation can trigger a homeostatic KATP channel-mediated reduction in autonomous firing in WT STN, brain slices from 2-month-old C57BL/6 mice were incubated in control media or media containing 25 µM NMDA for 1 hr prior to recording (Figure 7). NMDA pre-treatment reduced the proportion of autonomously firing neurons (untreated: 66/75 (88%); NMDA: 65/87 (75%); p = 0.0444) and the frequency (untreated: 14.9 [7.8–24.8] Hz; n = 75; NMDA: 5.2 [0.0–14.0] Hz; n = 87; ph < 0.0001) and regularity (untreated CV: 0.13 [0.08–0.25]; n = 66; NMDA CV: 0.24 [0.10–0.72]; n = 65; ph = 0.0150; Figure 7A–C) of autonomous activity relative to control slices. In a subset of neurons glibenclamide was applied to inhibit KATP channels. In neurons from untreated slices glibenclamide had no effect on firing rate (control: 16.6 [10.9–31.3] Hz; glibenclamide: 25.0 [16.3–32.8] Hz; n = 6; ph = 0.2188; Figure 7A–D) or regularity (control CV: 0.08 [0.07–0.37]; glibenclamide CV: 0.08 [0.06–0.09]; n = 6; ph = 0.3125; Figure 7A–D). However, in neurons from NMDA pre-treated slices glibenclamide application elevated firing rate (control: 3.3 [2.3–5.1] Hz; glibenclamide: 11.4 [10.8–24.4] Hz; n = 10; ph = 0.0078; Figure 7A–D) and regularity (control CV: 0.83 [0.25–1.03]; glibenclamide CV: 0.12 [0.07–0.16]; n = 8, ph = 0.0208; Figure 7A–D) to levels similar to that seen in untreated slices. Together, these data demonstrate that increased activation of STN NMDARs can lead to a persistent KATP channel-mediated homeostatic reduction in autonomous activity in STN neurons.

Figure 7. NMDA exposure persistently disrupts autonomous activity.

Figure 7.

(A) Example of a loose-seal cell-attached recording from an STN neuron from an untreated slice from a C57/BL6 mouse before (left) and after (right) inhibition of KATP channels with 100 nM glibenclamide. (B) Example of a loose-seal cell-attached recording from an STN neuron from a slice treated with 25 µM NMDA for 1 hr prior to recording, before (left) and after (right) inhibition of KATP channels with 100 nM glibenclamide. (C) Population data. Following NMDA pre-treatment STN neurons had a lower frequency and regularity of firing. (D) Inhibition of KATP channels with 100 nM glibenclamide restored firing in STN cells from NMDA pre-treated slices but had no effect on firing from naïve slices. *p < 0.05. ns, not significant. Data for panel C provided in Figure 7—source data 1; data for panel D provided in Figure 7—source data 2.

DOI: http://dx.doi.org/10.7554/eLife.21616.018

Figure 7—source data 1. Autonomous firing frequency and CV for control and NMDA pre-treated C57BL/6 STN neurons in Figure 7C.
DOI: 10.7554/eLife.21616.019
Figure 7—source data 2. Autonomous firing frequency and CV for control and NMDA pre-treated C57BL/6 STN neurons in control conditions and following glibenclamide application in Figure 7D.
DOI: 10.7554/eLife.21616.020

Break down of H2O2 rescues the firing of BACHD STN neurons to WT levels

Mitochondrial oxidative phosphorylation generates superoxide, which may be dismuted by superoxide dismutase to produce H2O2 (Adam-Vizi, 2005). Superoxide and hydrogen peroxide can also be produced by NADPH oxidase (Brennan et al., 2009). Because KATP channels are activated by H2O2 (Ichinari et al., 1996; Avshalumov et al., 2005), we tested whether H2O2 contributes to KATP channel-mediated disruption of ex vivo autonomous activity in the BACHD model. The effect of a membrane permeable form of the enzyme catalase (polyethylene glycol-catalase), which breaks down H2O2, on the autonomous firing of STN neurons from 4–6-month-old BACHD mice was examined (Figure 8). Application of 250 U/ml catalase increased the rate (BACHD control: 3.4 [0.7–5.5] Hz; BACHD catalase: 10.8 [7.6–13.8] Hz; n = 11; ph = 0.0080; Figure 8A–C) and regularity (BACHD control CV: 1.0 [0.3–2.1]; BACHD catalase CV: 0.17 [0.12–0.21]; n = 11; ph = 0.0060; Figure 8A–C) of autonomous firing. Subsequent application of glibenclamide (100 nM) had no additional effect on firing rate (11.8 [8.2–14.4] Hz; n = 11; ph = 0.9658) or regularity (CV: 0.15 [0.12–0.23]; n = 11; ph = 0.4922; Figure 8A–C). Thus, these results suggest that H2O2 underlies KATP channel activation in BACHD STN neurons.

Figure 8. Break down of H2O2 by catalase rescues autonomous firing in BACHD STN neurons.

Figure 8.

(A) Example showing the instantaneous firing rate of a BACHD STN neuron in control conditions, during the application of catalase (250 U/ml), and during co-application of catalase and glibenclamide (100 nM). (B1) Example of BACHD STN neuron firing in control conditions (marked 1 in A). (B2) Example of elevated firing during break down of H2O2 by catalase (marked 2 in A). (B3) Example showing no further elevation of firing rate during additional inhibition of KATP channels with glibenclamide (marked 3 in A). (C) Population data from 4–6-month old BACHD mice showing an increase in the frequency and regularity of firing following break down of H2O2, with no further changes upon KATP channel inhibition. (D) Population data showing an increase in the frequency and regularity of firing following KATP channel inhibition with no further change in firing rate and a slight increase in firing regularity upon H2O2 break down. *p < 0.05. ns, not significant. Data for panels CD provided in Figure 8—source data 1.

DOI: http://dx.doi.org/10.7554/eLife.21616.021

Figure 8—source data 1. Autonomous firing frequency and CV for WT and BACHD STN neurons under control conditions and following catalase and/or glibenclamide application in Figure 8C–D.
DOI: 10.7554/eLife.21616.022

To test if the actions of H2O2 on autonomous firing are confined to its effects on KATP channels, these experiments were repeated in the presence of glibenclamide. As seen previously, application of glibenclamide increased firing rate (BACHD control: 5.2 [1.0–6.7] Hz; BACHD glibenclamide: 8.5 [7.2–11.6] Hz; n = 8, ph = 0.0156; Figure 8D) and regularity (BACHD control CV: 0.83 [0.27–1.30]; BACHD glibenclamide CV: 0.23 [0.15–0.58]; n = 8; ph = 0.0156; Figure 8D). Subsequent application of catalase had no additional effect on firing rate (8.7 [7.2–14.1] Hz; n = 8; ph = 0.6406; Figure 8D) but did produce a small but statistically significant increase in regularity (CV: 0.14 [0.11–0.21]; n = 8; ph = 0.0156; Figure 8D). In WT mice, catalase application did not change firing rate (WT control: 11.0 [10.5–14.2] Hz; WT catalase: 14.3 [11.3–18.3] Hz; n = 7; p = 0.0781; Figure 9) but lead to a small but statistically significant increase in regularity (WT control CV: 0.12 [0.10–0.23]; WT catalase CV: 0.11 [0.07–0.13]; n = 7; p = 0.0469; Figure 9). The effects of catalase on the frequency and regularity of firing in BACHD mice were greater than those observed in WT mice (frequency: p = 0.0154; CV: p = 0.0007; Figure 9). Together, these data suggest that suppression of autonomous activity of STN neurons in BACHD mice is largely mediated by the modulatory effect of H2O2 on KATP channels.

Figure 9. Break down of H2O2 by catalase has a relatively minimal effect on autonomous firing in WT STN neurons compared to BACHD neurons.

Figure 9.

(A) Line plots showing of the effect of catalase (250 U/ml) on the frequency of autonomous action potential generation in STN neurons from WT (black) and BACHD mice (green; BACHD data same as in Figure 8C). Break down of H2O2 elevated autonomous firing in BACHD STN neurons only. The boxplot confirms that the elevation of firing due to catalase application was greater in BACHD mice. (B) Line plots illustrating a small but statistically significant effect of catalase on the regularity of autonomous action potential generation in STN neurons from WT mice (black) compared to a larger increase in regularity following catalase application in BACHD neurons (green; BACHD data same as in Figure 8C). The boxplot confirms that the increase in regularity due to catalase was greater in BACHD mice. *p < 0.05. ns, not significant. Data provided in Figure 9—source data 1.

DOI: http://dx.doi.org/10.7554/eLife.21616.023

Figure 9—source data 1. Autonomous firing frequency and CV for WT and BACHD STN neurons under control conditions and following catalase application in Figure 9.
DOI: 10.7554/eLife.21616.024

To test if the elevation of oxidant stress can result in KATP channel activation in WT STN neurons, glutathione peroxidase was inhibited with mercaptosuccinic acid (MCS) (Avshalumov et al., 2005). Following the application of 1 mM MCS both the rate (control: 12.0 [7.8–13.5] Hz; MCS: 9.0 [4.8–10.5] Hz; n = 11; ph = 0.0068; Figure 10) and regularity (control CV: 0.21 [0.15–0.22]; MCS CV: 0.30 [0.22–0.34]; n = 11; ph = 0.0137) of firing decreased. Subsequent application of glibenclamide rescued both firing rate (14.6 [10.3–19.2] Hz; n = 11; ph = 0.0020) and regularity (CV: 0.12 [0.12–0.17]; n = 11; ph = 0.0098; Figure 10). These data are also consistent with an action of H2O2 on STN KATP channels.

Figure 10. Increasing H2O2 levels by inhibition of glutathione peroxidase with mercaptosuccinic acid in WT mice leads to disruption of autonomous action potential generation through activation of KATP channels.

Figure 10.

(A) Example of autonomous activity of a STN neuron from a C57BL/6 mouse in control conditions (upper), during application of 1 mM mercaptosuccinic acid (MCS; middle), and during subsequent application of 100 nM glibenclamide (lower). These recordings were made in the presence of 20 µM flufenamic acid to block transient receptor potential (TRP) channels (Lee et al., 2011). (B) Population data showing a decrease in the frequency and regularity of firing following MCS application, which was reversed by subsequent KATP channel inhibition. *p < 0.05. Data for panel B provided in Figure 10—source data 1.

DOI: http://dx.doi.org/10.7554/eLife.21616.025

Figure 10—source data 1. Autonomous firing frequency and CV for WT and BACHD STN neurons under control conditions and following MCS and glibenclamide application in Figure 10B.
DOI: 10.7554/eLife.21616.026

The STN degenerates progressively in BACHD mice

HD patients exhibit 20–30% STN neuron loss (Lange et al., 1976; Guo et al., 2012). Because mitochondrial oxidant stress and reactive oxygen species can trigger apoptotic pathways leading to cell death (Green and Reed, 1998; Bossy-Wetzel et al., 2008), the number of STN neurons in 12-month-old BACHD and WT mice were compared (Figure 11). The brains of BACHD mice and WT littermates were first fixed by transcardial perfusion of formaldehyde, sectioned into 70 µm coronal slices and immunohistochemically labeled for neuronal nuclear protein (NeuN). The total number of NeuN-immunoreactive STN neurons and the volume of the STN were then estimated using unbiased stereological techniques. Both the total number of STN neurons (WT: 10,793 [9,070–11,545]; n = 7; BACHD: 7,307 [7,047–9,285]; n = 7; p = 0.0262) and the volume of the STN (WT: 0.087 [0.084–0.095] mm3; n = 7; BACHD: 0.078 [0.059–0.081] mm3; n = 7; p = 0.0111; Figure 11A,B) were reduced in 12-month-old BACHD versus WT mice. The density of STN neurons was not different in BACHD and WT mice (WT: 121,248 [107,180–126,139] neurons/mm3; n = 7; BACHD: 115,273 [90,377–135,765] neurons/mm3; n = 7; p = 0.8048; Figure 11A,B). To determine whether the difference in cell number represents an early developmental abnormality or a progressive loss of adult neurons, the numbers of neurons in 2-month-old BACHD and WT mice were also compared. At 2-months-old, the total number of STN neurons (WT: 10,373 [9,341–14,414]; n = 7; BACHD: 10,638 [10,513–13,877]; n = 7; p = 0.7104; Figure 11C), the volume of the STN (WT: 0.098 [0.090–0.125] mm3; n = 7; BACHD: 0.085 [0.080–0.111] mm3; n = 7; p = 0.1649; Figure 11C) and STN neuronal density (106,880 [98,100–115,985] neurons/mm3; n = 7; BACHD: 124,844 [115,479–145,711] neurons/mm3; n = 7; p = 0.1282; Figure 11C) were not different in WT and BACHD mice. Together, these data demonstrate that between the ages of 2 months and 12 months BACHD mice lose approximately one third of their STN neurons compared to WT littermates.

Figure 11. Degeneration of STN neurons in BACHD mice.

Figure 11.

(A) Expression of NeuN in STN neurons in a BACHD mouse (ZI, zone incerta; ic, internal capsule). (B) Population data showing a 32.3% reduction in the median STN neuron number and a 10.3% reduction in STN volume at 12 months old. *p < 0.05. ns, not significant. (C) Population data showing no difference in STN neuron number, STN volume or density between WT and BACHD mice at 2 months old. Data for panels BC provided in Figure 11—source data 1.

DOI: http://dx.doi.org/10.7554/eLife.21616.027

Figure 11—source data 1. BACHD STN neuron counts, density and STN volume in Figure 11B–C.
DOI: 10.7554/eLife.21616.028

The STN of Q175 KI mice exhibits similar abnormalities to those observed in the BACHD model

STN neurons from BACHD mice exhibit perturbed autonomous firing that is caused by NMDAR activation/signaling leading to mitochondrial oxidant stress, H2O2 generation and KATP channel activation. Furthermore, STN neurons are progressively lost in BACHD mice. To determine whether these features are specific to the BACHD model or a more general feature of HD models, a subset of experiments were repeated in heterozygous Q175 KI mice (Figure 12). STN neurons from 6-month-old Q175 mice exhibited a severely reduced rate of autonomous activity (WT: 7.8 [1.9–14.7] Hz; n = 90; Q175: 0.0 [0.0–6.3] Hz; n = 90; p < 0.0001; Figure 12A,B), though the regularity of active neurons was unchanged (WT CV: 0.2 [0.1–0.6]; n = 77; Q175 CV: 0.4 [0.1–1.0]; n = 42; p = 0.1506; Figure 12A,B). Additionally, there was a large decrease in the proportion of active neurons in the Q175 STN (WT: 77/90 (86%); Q175: 42/90 (47%); p < 0.0001).

Figure 12. STN neurons exhibit similar dysfunction and degeneration in Q175 mice.

Figure 12.

(A) Examples of impaired (middle, lower) autonomous firing of neurons from Q175 mice relative to WT (upper). (B) Population data showing STN neuron firing rate, CV, and proportion of active neurons from 6-month-old WT and Q175 mice. (C) Example showing the autonomous firing of a STN neuron from a Q175 mouse before (upper) and during (lower) inhibition of KATP channels with glibenclamide (100 nM). (D) Paired data confirming that inhibition of KATP channels in Q175 STN neurons consistently increased the rate (left) and regularity (right) of autonomous firing. (E) Examples of autonomous action potential generation from Q175 STN neurons from an untreated slice (upper) and a slice treated with 50 µM D-AP5 for 3–5 hr prior to recording (lower). (F) Population data showing STN neuron firing rate, CV, and the proportion of active cells in untreated and D-AP5 treated slices. The frequency of autonomous firing increased in D-AP5 treated slices. (G) Expression of NeuN in STN neurons in a Q175 mouse. (H) Population data showing STN neuron number (left), STN volume (center), and neuron density (right) in 12-month-old Q175 mice. The number of STN neurons was lower in Q175 mice compared to WT. *p < 0.05. ns, not significant. Data for panel B provided in Figure 12—source data 1; data for panel D provided in Figure 12—source data 2; data for panel F provided in Figure 12—source data 3; data for panel H provided in Figure 12—source data 4.

DOI: http://dx.doi.org/10.7554/eLife.21616.029

Figure 12—source data 1. Autonomous firing frequency and CV for Q175 and WT STN neurons in Figure 12B.
DOI: 10.7554/eLife.21616.030
Figure 12—source data 2. Autonomous firing frequency and CV for Q175 in control conditions and following glibenclamide application Figure 12D.
DOI: 10.7554/eLife.21616.031
Figure 12—source data 3. Autonomous firing frequency and CV for control and D-AP5 pre-treated Q175 STN neurons in Figure 12F.
DOI: 10.7554/eLife.21616.032
Figure 12—source data 4. Q175 STN neuron counts, density and STN volume in Figure 12H.
DOI: 10.7554/eLife.21616.033

Inhibition of KATP channels with glibenclamide rescued both STN firing rate and regularity in Q175 and increased regularity only in WT (WT control frequency: 9.7 [5.4–13.5] Hz; WT glibenclamide frequency: 10.3 [7.4–15.4] Hz; n = 8; p = 0.1094; Q175 control frequency: 4.8 [3.5–6.2] Hz; Q175 glibenclamide frequency: 11.0 [9.3–13.6] Hz; n = 6; p = 0.0313; WT control CV: 0.19 [0.13–0.47]; WT glibenclamide CV: 0.11 [0.10–0.21]; n = 8; p = 0.0078; Q175 control CV: 0.45 [0.35–0.71]; Q175 glibenclamide CV: 0.15 [0.10–0.17]; n = 6; p = 0.03125; Figure 12C,D). Similar to BACHD, Q175 STN neurons recovered to WT-like firing rate following >3 hr pretreatment with D-AP5 (Q175 control: 4.6 [0.0–11.4] Hz; n = 45; Q175 D-AP5 treated: 11.6 [0.0–18.7] Hz; n = 45; p = 0.0144; Figure 12E,F), although the regularity (Q175 control CV: 0.16 [0.10–0.66]; n = 15; Q175 D-AP5 treated CV: 0.14 [0.09–0.32]; n = 12; p = 0.2884; Figure 12E,F) and proportion of active neurons (Q175 control: 30/45 (67%); Q175 D-AP5 treated: 33/45 (73%); p = 0.6460; Figure 12E,F) were unaltered. The 12-month-old Q175 STN (n = 7) exhibited a median 26% reduction in the total number of STN neurons with no effect on other parameters (WT: 8,661 [7,120–9,376] neurons; Q175: 6,420 [5,792–7,024] neurons; p = 0.0111; WT volume: 0.081 [0.074–0.087] mm3; Q175 volume: 0.079 [0.070–0.091] mm3; p = 0.6200; WT density: 109,477 [82,180–115,301] neurons/mm3; Q175 density: 88,968 [63,624–103,020] neurons/mm3; p = 0.2086; Figure 12G,H). Taken together, these data show that the STN exhibits similar dysfunction and neuronal loss in both the transgenic BACHD and Q175 KI mouse models of HD.

Discussion

Dysfunction of the striatum and cortex has been extensively characterized in HD models, but relatively few studies have examined the extra-striatal basal ganglia. Here, we report early NMDAR, mitochondrial and firing abnormalities together with progressive loss of STN neurons in two HD mouse models. Furthermore, dysfunction was present in HD mice prior to the onset of major symptoms, implying that it occurs early in the disease process (Gray et al., 2008; Menalled et al., 2012). Cell death in the STN also preceded that in the striatum, as STN neuronal loss was observed at 12 months of age in both BACHD and Q175 mice, a time point at which striatal neuronal loss is absent but psychomotor dysfunction is manifest (Gray et al., 2008; Heikkinen et al., 2012; Smith et al., 2014; Mantovani et al., 2016). Together these findings argue that dysfunction within the STN contributes to the pathogenesis of HD.

Astrocytes appear to play a pivotal role in HD. Expression of mutant huntingtin in astrocytes alone is sufficient to recapitulate neuronal and neurological abnormalities observed in HD and its models (Bradford et al., 2009; Faideau et al., 2010). Furthermore, astrocyte-specific rescue approaches ameliorate some of the abnormalities observed in HD models (Tong et al., 2014; Oliveira et al., 2016). In the STN, inhibition of GLT-1 (and GLAST) slowed individual NMDAR EPSCs in WT but not BACHD mice and eliminated the differences in their decay kinetics, arguing that impaired uptake of glutamate by astrocytes contributed to the relative prolongation of NMDAR-mediated EPSCs in BACHD STN neurons. Interestingly, and in contrast to the striatum (Milnerwood et al., 2010), when spillover of glutamate onto extrasynaptic receptors was increased by train stimulation and inhibition of astrocytic glutamate uptake, the resulting compound NMDAR EPSC and its prolongation by uptake inhibition were similar in BACHD and WT mice, arguing against an increase in extrasynaptic STN NMDAR expression/function in BACHD mice. Slowing of astrocytic glutamate uptake has recently been shown to occur during increased presynaptic activity (Armbruster et al., 2016). Thus, train stimulation may have slowed glutamate uptake sufficiently to occlude/eliminate the differences in uptake that were observed in BACHD and WT STN neurons during single stimulation. Whether the modest differences in glutamate uptake that were observed here are sufficient to promote NMDAR-mediated dysfunction in HD STN neurons remains to be determined.

NMDARs play a key role in the abnormal activity of STN neurons in HD models. Antagonism of STN NMDARs in BACHD and Q175 brain slices rescued autonomous STN firing. Conversely, acute activation of STN NMDARs persistently disrupted STN firing in WT brain slices. If the relatively low level of glutamatergic transmission present ex vivo is sufficient to impair firing then this impairment is likely to be more severe in vivo where STN neurons are powerfully patterned by glutamatergic transmission arising from the cortex, thalamus, pedunculopontine nucleus and superior colliculus (reviewed by Bevan, 2017). Non-synaptic sources of extracellular glutamate, such as diffusion/release from astrocytes (Cavelier and Attwell, 2005; Lee et al., 2013) may also contribute to excessive NMDAR activation in HD mice.

Extended antagonism of NMDARs in BACHD slices also reduced mitochondrial oxidant stress in STN neurons. NMDAR activation can elevate ROS through a variety of Ca2+- and nitric oxide-associated signaling pathways and their actions on mitochondria, NADPH oxidase and antioxidant expression (Dugan et al., 1995; Moncada and Bolaños, 2006; Brennan et al., 2009; Nakamura and Lipton, 2011; Valencia et al., 2013). Although we saw no evidence of basal mitochondrial dysfunction that was not attributable to increased NMDAR function, there is considerable evidence that mutant huntingtin causes transcriptional dysregulation, which leads to defective mitochondrial quality control, an increase in the proportion of defective, ROS generating mitochondria and an increase in opening of the permeability transition pore (Milakovic and Johnson, 2005; Panov et al., 2002; Fernandes et al., 2007; Song et al., 2011; Chaturvedi et al., 2013; Johri et al., 2013; Martin et al., 2015). Thus, basal mitochondrial dysfunction could render HD STN neurons especially sensitive to NMDAR-mediated transmission and signaling.

Catalase rapidly restored autonomous firing in the BACHD model, an effect occluded by inhibition of KATP channels, arguing that H2O2, through its action on KATP channels is the major cause of firing disruption. H2O2 can act on KATP channels by decreasing their sensitivity to ATP (Ichinari et al., 1996), reducing the ratio of ATP to ADP (Krippeit-Drews et al., 1999), and/or modulating channel gating through a sGC-cGMP-PKG-ROS(H2O2)-ERK1/2-calmodulin-CaMKII signaling pathway (Zhang et al., 2014). H2O2 is likely to directly modulate STN KATP channels in HD mice because disrupted firing was also observed when STN neurons were recorded in the whole-cell configuration with patch pipettes containing exogenous ATP. Furthermore, H2O2 break down rapidly rescued activity, consistent with a direct action on KATP channels. H2O2-dependent modulation of KATP channels has been extensively characterized in midbrain dopamine neurons where it powerfully suppresses cellular excitability and synaptic transmission (Avshalumov et al., 2005; Bao et al., 2009). The activation of KATP channels in STN neurons may represent a form of homeostasis that suppresses firing when mitochondrial oxidant stress is high, limiting the possibility of oxidant damage and bioenergetic failure (Ray et al., 2012; Sena and Chandel, 2012).

In HD, chronic oxidant stress can lead to damage, such as lipid and protein peroxidation and nuclear/mitochondrial DNA damage, which profoundly impair cellular function and promote cell death (Perluigi et al., 2005; Browne and Beal, 2006; Acevedo-Torres et al., 2009). Consistent with the negative effects of such processes on neuronal viability, we observed progressive loss of STN neurons in both the BACHD and Q175 models. Furthermore, the level of neuronal loss at 12 months in the BACHD and Q175 models was similar to that observed in HD patients (Lange et al., 1976; Guo et al., 2012). The absence of neuronal loss in the cortex and striatum in the same models at an equivalent time point suggests that STN dysfunction and degeneration may be particularly influential in the early disease process. Although the STN is known to degenerate in HD, it is not clear why neuronal loss is ultimately less than that observed in the striatum at the end stage of the disease, despite the fact that dysfunction and degeneration occur earlier (at least in HD models). Future research will be required to determine whether subtypes of STN neurons exhibit selective vulnerability and/or whether the processes promoting their degeneration, e.g. cortical activation of STN NMDARs, ultimately wane.

As a key component of the hyperdirect and indirect pathways, the STN is critical for constraining cortico-striatal activity underlying action selection (Albin et al., 1989; Oldenburg and Sabatini, 2015). In the ‘classical’ model of basal ganglia function, degeneration of indirect pathway striatal projection neurons is proposed to underlie the symptoms of early stage HD (Albin et al., 1989). Here we show for the first time that STN dysfunction and neuronal loss precede cortico-striatal abnormalities in HD models. Thus, dysfunction and degeneration of cortical and striatal neurons occurs in concert with profound changes in other elements of the basal ganglia. Therapeutic strategies that target the STN may therefore be useful not only for treating the psychomotor symptoms of early- to mid-stage HD but also for influencing dysfunction and degeneration throughout the cortico-basal ganglia-thalamo-cortical circuit.

Materials and methods

Animals

All animal procedures were performed in accordance with the policies of the Society for Neuroscience and the National Institutes of Health, and approved by the Institutional Animal Care and Use Committee of Northwestern University. Adult male hemizygous BACHD mice (RRID:IMSR_JAX:008197) and heterozygous Q175 mice (RRID:IMSR_JAX:027410), their WT litter mates, and C57BL/6 mice (Charles River Laboratories International, Inc., Wilmington, MA, USA) were used in this study.

Stereotaxic injection of viral vectors

Mice were anesthetized with 1–2% isoflurane (Smiths Medical ASD, Inc., Dublin, OH, USA). AAV vectors (serotype 9; ~1012–13 GC/ml) engineered to express hChR2(H134R)-eYFP under the hSyn promoter (University of Pennsylvania Vector Core, Philadelphia, PA, USA) or MTS-roGFP under the CMV promoter (Sanchez-Padilla et al., 2014) were injected under stereotaxic guidance (Neurostar, Tubingen, Germany). In order to express hChR2(H134R)-eYFP, AAV was injected bilaterally into the primary motor cortex (three injections per hemisphere; coordinates relative to bregma: AP, +0.6 mm, + 1.2 mm, and +1.8 mm; ML, + 1.5 mm, and −1.5 mm; DV, −1.0 mm; 0.3 μl per injection). In order to express MTS-roGFP, AAV was injected bilaterally into the STN (coordinates: AP, −2.06 mm; ML, +1.4 mm, and −1.4 mm; DV, −4.5 mm; 0.4 µl per injection). Brain slices were prepared from AAV-injected mice 10–14 days after injection.

Slice preparation

Mice were lightly anesthetized with isoflurane, deeply anesthetized with ketamine/xylazine (87/13 mg/kg i.p.) and then perfused transcardially with ~10 ml of ice-cold sucrose-based artificial cerebrospinal fluid (sACSF) that contained 230 mM sucrose, 2.5 mM KCl, 1.25 mM NaH2PO4, 0.5 mM CaCl2, 10 mM MgSO4, 10 mM glucose, and 26 mM NaHCO3 equilibrated with 95% O2 and 5% CO2. The brain was removed, immersed in sASCF and 250 µm sagittal slices were cut with a vibratome (VT1200S; Leica Microsystems Inc., IL, USA). Slices were then transferred to a holding chamber, immersed in ACSF that contained 125 mM NaCl, 2.5 mM KCl, 1.25 mM NaH2PO4, 2 mM CaCl2, 2 mM MgSO4, 10 mM glucose, 26 mM NaHCO3, 1 mM sodium pyruvate, and 5 µM L-glutathione equilibrated with 95% O2 and 5% CO2, held at 35°C for 30–45 min, then maintained at room temperature.

Recording

Individual brain slices were placed in a recording chamber where they were perfused at 4–5 ml/min with synthetic interstitial fluid (SIF) at 35°C that contained 126 mM NaCl, 3 mM KCl, 1.25 mM NaH2PO4, 1.6 mM CaCl2, 1.5 mM MgSO4, 10 mM glucose and 26 mM NaHCO3 equilibrated with 95% O2 and 5% CO2. Somatic recordings were obtained under visual guidance (Axioskop FS2, Carl Zeiss, Oberkochen, Germany) using computer-controlled manipulators (Luigs and Neumann, Ratingen, Germany). Loose-seal cell-attached recordings were made using 3–5 MΩ impedance borosilicate glass pipettes (Warner Instruments, Hamden, CT, USA) that were filled with 140 mM NaCl, 23 mM glucose, 15 mM HEPES, 3 mM KCl, 1.5 mM MgCl2, 1.6 mM CaCl2 (pH 7.2 with NaOH, 300–310 mOsm/l). Whole-cell voltage clamp recordings were made using 3–5 MΩ pipettes filled with 135 mM CsCH3O3S, 10 mM QX-314, 10 mM HEPES, 5 mM phosphocreatine, 3.8 mM NaCl, 2 mM Mg1.5ATP, 1 mM MgCl2, 0.4 mM Na3GTP, 0.1 mM Na4EGTA (pH 7.2 with CsOH, 290 mOsm/l). Whole-cell current clamp recordings were made using 10–15 MΩ pipettes filled with 130 mM KCH3SO4, 3.8 mM NaCl, 1 mM MgCl2, 10 mM HEPES, 5 mM phosphocreatine di(tris) salt, 0.1 mM Na4EGTA, 0.4 mM Na3GTP, and 2 mM Mg1.5ATP (pH 7.3 with KOH; 290 mOsm/l). Electrophysiological records were acquired using a computer running Clampex 10 software (Molecular Devices, Palo Alto, CA, USA; RRID:SCR_011323) connected to a Multiclamp 700B amplifier (Molecular Devices) via a Digidata 1322 A digitizer (Molecular Devices). Data were low-pass filtered at 10 kHz and sampled at 50 kHz. Liquid junction potentials of 10 and 9 mV were accounted for in whole-cell voltage clamp and current clamp recordings respectively, and in voltage clamp recordings series resistance and membrane capacitance were corrected online. All recordings of autonomous action potential generation were made in the acute presence of 50 µM D-AP5, 20 µM DNQX, 10 µM GABAzine, and 2 µM CGP 55845 to block synaptic transmission.

Two-photon imaging

MTS-roGFP-expressing neurons were imaged at 890 nm with 76 MHz pulse repetition and ~250 fs pulse duration at the sample plane. Two-photon excitation was provided by a G8 OPSL pumped Mira 900 F laser (Coherent, Santa Clara, CA, USA) and sample power was regulated by a Pockels cell electro-optic modulator (model M350-50-02-BK, Con Optics, Danbury, CT, USA). Images were acquired using an Ultima 2 P system running PrairieView 5 (Bruker Nano Fluorescence Microscopy, Middleton, WI, USA) and a BX51WI microscope (Olympus, Tokyo, Japan) with a 60 × 0.9 NA objective (UIS1 LUMPFL; Olympus). After baseline fluorescence had been measured, the maximum and minimum fluorescence were determined by the application of 2 mM dithiothreitol and then 200 µM aldrithiol-4 to fully reduce and oxidize the tissue, respectively. The relative oxidation at baseline, a measure of oxidative stress, was then calculated (Sanchez-Padilla et al., 2014).

Immunohistochemistry and stereology

Mice were lightly anesthetized with isoflurane, deeply anesthetized with ketamine/xylazine (87/13 mg/kg i.p.) and then perfused transcardially with ~5 ml of phosphate buffered saline (PBS) followed by 30 ml of 4% formaldehyde in 0.1 M phosphate buffer (pH 7.4). Brains were removed and post-fixed for 2 hr in 4% formaldehyde, then washed in PBS. Brains were blocked and 70 µm thick coronal sections containing the STN were cut using a vibratome (VT1000S; Leica). Sections were washed in PBS and incubated for 48 hr at 4°C in anti-NeuN (clone A60; MilliporeSigma, Darmstadt, Germany; RRID:AB_2298772) at 1:200 in PBS with 0.2% Triton X-100 (MilliporeSigma) and 2% normal donkey serum. Sections were then washed in PBS and incubated for 90 min at room temperature in Alexa Fluor 488 donkey anti-mouse IgG (1:250; Jackson Immunoresearch, West Grove, PA, USA; RRID:AB_2340846) in 0.2% Triton X-100 and 2% normal donkey serum. Then the sections were washed in PBS and mounted on glass slides in Prolong Gold anti-fade medium (Thermo Fisher Scientific, Waltham, MA, USA).

NeuN labeled sections were imaged using an Axioskop two microscope (Carl Zeiss) with a 100 × 1.3 NA oil immersion objective (Plan-Neofluar 1018–595; Carl Zeiss). Unbiased stereological counting of STN neurons in a single hemisphere was performed using the optical fractionator technique (West et al., 1991) as implemented in Stereo Investigator (MBF Bioscience, Williston, VT, USA; RRID:SCR_002526), using a counting frame of 50 µm × 50 µm × 8 µm and a grid size of 150 µm × 150 µm; all sections containing the STN were used for counting (~8 sections). STN volume was calculated from the sum of the areal extent of the STN on each section multiplied by the section thickness (70 µm). For all individual counts the Gundersen Coefficient of Error (CE) (Gundersen et al., 1999) was less than 0.1 (0.080 [0.075–0.090]), and the investigator performing the counting was blinded to the genotype of the mouse.

Drugs

All drugs used in electrophysiology and imaging experiments were diluted to working concentration in SIF and bath applied. D-AP5, CGP 55845, DNQX, GABAzine (SR 95531), NMDA and gliclazide were purchased from Abcam (Cambridge, MA, USA). Glibenclamide, TFB-TBOA and DL-Dithiothreitol were purchased from Tocris Bioscience (Bristol, UK). Catalase (polyethylene glycol-catalase), aldrithiol-4 and MCS were purchased from Sigma-Aldrich (St. Louis, MO, USA).

Data analysis and statistics

Electrophysiological data were analyzed using routines running in Igor Pro 6 and 7 (Wavemetrics, Portland, OR, USA; RRID:SCR_000325) or matplotlib (Hunter, 2007; RRID:SCR_008624). The firing rate of STN neurons was calculated from 1 min of recording or 100 action potentials (whichever covered the longer time period). Imaging data were analyzed using FIJI (Schindelin et al., 2012; RRID:SCR_002285). Statistical analyses were performed in Prism 5 (GraphPad Software, San Diego, CA, USA; RRID:SCR_002798) or R (http://www.r-project.org/; RRID:SCR_001905, RRID:SCR_000432). In order to make no assumptions about the distribution of the data, non-parametric statistical tests were used, and data are reported as median [interquartile range]; outliers were not excluded from the analysis. An α-level of 0.05 was used for two-way statistical comparisons performed with the Mann-Whitney U test for unpaired data (represented with box plots), the Wilcoxon signed rank test for paired data (tilted line segment plots), Fisher's exact test for categorical data (bar plots) or the F-test for linear regression. Where datasets were used in multiple comparisons the p-value was adjusted to maintain the family-wise error rate at 0.05 using the Holm-Bonferroni method (Holm, 1979); adjusted p-values are denoted ph. Box plots show median (central line), interquartile range (box) and 10–90% range (whiskers). For the primary findings reported in the manuscript, sample sizes for Mann-Whitney and Wilcoxon tests were estimated to achieve a minimum of 80% power using formulae described by Noether (1987). The effect sizes used in these power calculations were estimated using data randomly drawn from uniform distributions (runif() function in R stats package). For Mann-Whitney tests, with a 50 percentile change in median between groups X and Y (the interquartile ranges of the groups don’t overlap) P(Y > X) ≈ 0.88 giving an estimation that at least 10 observations per group would be needed to achieve 80% power; for a 25 percentile change (the median of Y falls outside the interquartile range of X) P(Y > X) ≈ 0.72 and the estimated requirement is at least 27 observations per group. For Wilcoxon tests, if all pairs of observations show the same direction of change, P(X + X’ > 0)= 1 giving an estimation that at least 10 observations would be needed to achieve 80% power (note though that it is possible to show empirically that 6 observations gives 100% power in this case); if 90% of observations show the same direction of change, P(X + X’ > 0) ≈ 0.98 and the estimated requirement is at least 12 pairs of observations.

Acknowledgements

This study was funded by CHDI Foundation and by NIH NINDS Grants 2 R37 NS041280 and 2 P50 NS047085. We thank Drs. Vahri Beaumont (CHDI) and Ignacio Munoz-Sanjuan (CHDI) for their comments on the work and Sasha Ulrich, Danielle Schowalter, and Marisha Alicea for management of mouse colonies.

Funding Statement

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

Funding Information

This paper was supported by the following grants:

  • CHDI Foundation to Jeremy F Atherton, Mark D Bevan.

  • National Institutes of Health 2R37 NS041280 to Eileen L McIver, David L Wokosin, Mark D Bevan.

  • National Institutes of Health 2P50 NS047085 to Eileen L McIver, David L Wokosin, D James Surmeier, Mark D Bevan.

Additional information

Competing interests

The authors declare that no competing interests exist.

Author contributions

JFA, Designed the experiments, Conducted electrophysiology, imaging, immunohistochemistry and stereology experiments, Analyzed the data, Wrote the manuscript.

ELM, Conducted electrophysiology, immunohistochemistry and stereology experiments.

MRMM, Conducted immunohistochemistry and stereology experiments.

DLW, Conducted imaging experiments.

DJS, Designed experiments.

MDB, Designed experiments, Conducted electrophysiology experiments, Wrote the manuscript, Directed the project.

Ethics

Animal experimentation: This study was performed in accordance with the policies of the Society for Neuroscience and the National Institutes of Health. All animals were handled according to approved Institutional Animal Care and Use Committee protocols (IS00001185) of Northwestern University. All procedures were performed under isoflurane or ketamine/xylazine anesthesia, and every effort was made to minimize suffering.

References

  1. Acevedo-Torres K, Berríos L, Rosario N, Dufault V, Skatchkov S, Eaton MJ, Torres-Ramos CA, Ayala-Torres S. Mitochondrial DNA damage is a hallmark of chemically induced and the R6/2 transgenic model of Huntington's disease. DNA Repair. 2009;8:126–136. doi: 10.1016/j.dnarep.2008.09.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Adam-Vizi V. Production of reactive oxygen species in brain mitochondria: contribution by electron transport chain and non-electron transport chain sources. Antioxidants & Redox Signaling. 2005;7:1140–1149. doi: 10.1089/ars.2005.7.1140. [DOI] [PubMed] [Google Scholar]
  3. Albin RL, Young AB, Penney JB. The functional anatomy of basal ganglia disorders. Trends in Neurosciences. 1989;12:366–375. doi: 10.1016/0166-2236(89)90074-X. [DOI] [PubMed] [Google Scholar]
  4. Anderson KE, Marder KS. An overview of psychiatric symptoms in Huntington's disease. Current Psychiatry Reports. 2001;3:379–388. doi: 10.1007/s11920-996-0030-2. [DOI] [PubMed] [Google Scholar]
  5. Armbruster M, Hanson E, Dulla CG. Glutamate clearance is locally modulated by presynaptic neuronal activity in the cerebral cortex. Journal of Neuroscience. 2016;36:10404–10415. doi: 10.1523/JNEUROSCI.2066-16.2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Arzberger T, Krampfl K, Leimgruber S, Weindl A. Changes of NMDA receptor subunit (NR1, NR2B) and glutamate transporter (GLT1) mRNA expression in Huntington's disease--an in situ hybridization study. Journal of Neuropathology and Experimental Neurology. 1997;56:440–454. doi: 10.1097/00005072-199704000-00013. [DOI] [PubMed] [Google Scholar]
  7. Atherton JF, Kitano K, Baufreton J, Fan K, Wokosin D, Tkatch T, Shigemoto R, Surmeier DJ, Bevan MD. Selective participation of somatodendritic HCN channels in inhibitory but not excitatory synaptic integration in neurons of the subthalamic nucleus. Journal of Neuroscience. 2010;30:16025–16040. doi: 10.1523/JNEUROSCI.3898-10.2010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Atherton JF, Wokosin DL, Ramanathan S, Bevan MD. Autonomous initiation and propagation of action potentials in neurons of the subthalamic nucleus. The Journal of Physiology. 2008;586:5679–5700. doi: 10.1113/jphysiol.2008.155861. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Avshalumov MV, Chen BT, Koós T, Tepper JM, Rice ME. Endogenous hydrogen peroxide regulates the excitability of midbrain dopamine neurons via ATP-sensitive potassium channels. Journal of Neuroscience. 2005;25:4222–4231. doi: 10.1523/JNEUROSCI.4701-04.2005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Bao L, Avshalumov MV, Patel JC, Lee CR, Miller EW, Chang CJ, Rice ME. Mitochondria are the source of hydrogen peroxide for dynamic brain-cell signaling. Journal of Neuroscience. 2009;29:9002–9010. doi: 10.1523/JNEUROSCI.1706-09.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Baunez C, Robbins TW. Bilateral lesions of the subthalamic nucleus induce multiple deficits in an attentional task in rats. European Journal of Neuroscience. 1997;9:2086–2099. doi: 10.1111/j.1460-9568.1997.tb01376.x. [DOI] [PubMed] [Google Scholar]
  12. Behrens PF, Franz P, Woodman B, Lindenberg KS, Landwehrmeyer GB. Impaired glutamate transport and glutamate-glutamine cycling: downstream effects of the Huntington mutation. Brain. 2002;125:1908–1922. doi: 10.1093/brain/awf180. [DOI] [PubMed] [Google Scholar]
  13. Beurrier C, Bioulac B, Hammond C. Slowly inactivating sodium current (I(NaP)) underlies single-spike activity in rat subthalamic neurons. Journal of Neurophysiology. 2000;83:1951–1957. doi: 10.1152/jn.2000.83.4.1951. [DOI] [PubMed] [Google Scholar]
  14. Bevan MD. The subthalamic nucleus. In: Steiner H, Tseng K. Y, editors. Handbook of Basal Ganglia Structure and Function. Amsterdam, Netherlands: Academic Press, Elsevier; 2017. pp. 277–291. [Google Scholar]
  15. Bevan MD, Wilson CJ. Mechanisms underlying spontaneous oscillation and rhythmic firing in rat subthalamic neurons. Journal of Neuroscience. 1999;19:7617–7628. doi: 10.1523/JNEUROSCI.19-17-07617.1999. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Bickel S, Alvarez L, Macias R, Pavon N, Leon M, Fernandez C, Houghton DJ, Salazar S, Rodríguez-Oroz MC, Juncos J, Guridi J, Delong M, Obeso JA, Litvan I. Cognitive and neuropsychiatric effects of subthalamotomy for Parkinson's disease. Parkinsonism & Related Disorders. 2010;16:535–539. doi: 10.1016/j.parkreldis.2010.06.008. [DOI] [PubMed] [Google Scholar]
  17. Bossy-Wetzel E, Petrilli A, Knott AB. Mutant huntingtin and mitochondrial dysfunction. Trends in Neurosciences. 2008;31:609–616. doi: 10.1016/j.tins.2008.09.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Bradford J, Shin JY, Roberts M, Wang CE, Li XJ, Li S. Expression of mutant huntingtin in mouse brain astrocytes causes age-dependent neurological symptoms. PNAS. 2009;106:22480–22485. doi: 10.1073/pnas.0911503106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Brennan AM, Suh SW, Won SJ, Narasimhan P, Kauppinen TM, Lee H, Edling Y, Chan PH, Swanson RA. NADPH oxidase is the primary source of superoxide induced by NMDA receptor activation. Nature Neuroscience. 2009;12:857–863. doi: 10.1038/nn.2334. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Browne SE, Beal MF. Oxidative damage in Huntington's disease pathogenesis. Antioxidants & Redox Signaling. 2006;8:2061–2073. doi: 10.1089/ars.2006.8.2061. [DOI] [PubMed] [Google Scholar]
  21. Callahan JW, Abercrombie ED. Age-dependent alterations in the cortical entrainment of subthalamic nucleus neurons in the YAC128 mouse model of Huntington's disease. Neurobiology of Disease. 2015a;78:88–99. doi: 10.1016/j.nbd.2015.03.006. [DOI] [PubMed] [Google Scholar]
  22. Callahan JW, Abercrombie ED. Relationship between subthalamic nucleus neuronal activity and electrocorticogram is altered in the R6/2 mouse model of Huntington's disease. The Journal of Physiology. 2015b;593:3727–3738. doi: 10.1113/JP270268. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Cavelier P, Attwell D. Tonic release of glutamate by a DIDS-sensitive mechanism in rat hippocampal slices. The Journal of Physiology. 2005;564:397–410. doi: 10.1113/jphysiol.2004.082131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Chaturvedi RK, Flint Beal M. Mitochondrial diseases of the brain. Free Radical Biology and Medicine. 2013;63:1–29. doi: 10.1016/j.freeradbiomed.2013.03.018. [DOI] [PubMed] [Google Scholar]
  25. Chevalier G, Deniau JM. Disinhibition as a basic process in the expression of striatal functions. Trends in Neurosciences. 1990;13:277–280. doi: 10.1016/0166-2236(90)90109-N. [DOI] [PubMed] [Google Scholar]
  26. Chu HY, Atherton JF, Wokosin D, Surmeier DJ, Bevan MD. Heterosynaptic regulation of external globus pallidus inputs to the subthalamic nucleus by the motor cortex. Neuron. 2015;85:364–376. doi: 10.1016/j.neuron.2014.12.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Crossman AR, Mitchell IJ, Sambrook  MA, Jackson A. Chorea and myoclonus in the monkey inclued by Gamma-aminobutyric acid Antagonism in the lentiform complex. Brain. 1988;111:1211–1233. doi: 10.1093/brain/111.5.1211. [DOI] [PubMed] [Google Scholar]
  28. Do MT, Bean BP. Subthreshold sodium currents and pacemaking of subthalamic neurons: modulation by slow inactivation. Neuron. 2003;39:109–120. doi: 10.1016/s0896-6273(03)00360-x. [DOI] [PubMed] [Google Scholar]
  29. Dugan LL, Sensi SL, Canzoniero LM, Handran SD, Rothman SM, Lin TS, Goldberg MP, Choi DW. Mitochondrial production of reactive oxygen species in cortical neurons following exposure to N-methyl-D-aspartate. Journal of Neuroscience. 1995;15:6377–6388. doi: 10.1523/JNEUROSCI.15-10-06377.1995. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Dvorzhak A, Vagner T, Kirmse K, Grantyn R. Functional indicators of Glutamate transport in single striatal astrocytes and the influence of Kir4.1 in normal and Huntington mice. Journal of Neuroscience. 2016;36:4959–4975. doi: 10.1523/JNEUROSCI.0316-16.2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Faideau M, Kim J, Cormier K, Gilmore R, Welch M, Auregan G, Dufour N, Guillermier M, Brouillet E, Hantraye P, Déglon N, Ferrante RJ, Bonvento G. In vivo expression of polyglutamine-expanded huntingtin by mouse striatal astrocytes impairs glutamate transport: a correlation with Huntington's disease subjects. Human Molecular Genetics. 2010;19:3053–3067. doi: 10.1093/hmg/ddq212. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Fan MM, Raymond LA. N-methyl-D-aspartate (NMDA) receptor function and excitotoxicity in Huntington's disease. Progress in Neurobiology. 2007;81:272–293. doi: 10.1016/j.pneurobio.2006.11.003. [DOI] [PubMed] [Google Scholar]
  33. Fernandes HB, Baimbridge KG, Church J, Hayden MR, Raymond LA. Mitochondrial sensitivity and altered calcium handling underlie enhanced NMDA-induced apoptosis in YAC128 model of Huntington's disease. Journal of Neuroscience. 2007;27:13614–13623. doi: 10.1523/JNEUROSCI.3455-07.2007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Gray M, Shirasaki DI, Cepeda C, André VM, Wilburn B, Lu XH, Tao J, Yamazaki I, Li SH, Sun YE, Li XJ, Levine MS, Yang XW. Full-length human mutant huntingtin with a stable polyglutamine repeat can elicit progressive and selective neuropathogenesis in BACHD mice. Journal of Neuroscience. 2008;28:6182–6195. doi: 10.1523/JNEUROSCI.0857-08.2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Green DR, Reed JC. Mitochondria and apoptosis. Science. 1998;281:1309–1312. doi: 10.1126/science.281.5381.1309. [DOI] [PubMed] [Google Scholar]
  36. Gundersen HJ, Jensen EB, Kiêu K, Nielsen J. The efficiency of systematic sampling in stereology--reconsidered. Journal of Microscopy. 1999;193:199–211. doi: 10.1046/j.1365-2818.1999.00457.x. [DOI] [PubMed] [Google Scholar]
  37. Guo Z, Rudow G, Pletnikova O, Codispoti KE, Orr BA, Crain BJ, Duan W, Margolis RL, Rosenblatt A, Ross CA, Troncoso JC. Striatal neuronal loss correlates with clinical motor impairment in Huntington's disease. Movement Disorders. 2012;27:1379–1386. doi: 10.1002/mds.25159. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Hamada I, DeLong MR. Excitotoxic acid lesions of the primate subthalamic nucleus result in transient dyskinesias of the contralateral limbs. Journal of Neurophysiology. 1992;68:1850–1858. doi: 10.1152/jn.1992.68.5.1850. [DOI] [PubMed] [Google Scholar]
  39. Hanson GT, Aggeler R, Oglesbee D, Cannon M, Capaldi RA, Tsien RY, Remington SJ. Investigating mitochondrial redox potential with redox-sensitive green fluorescent protein indicators. Journal of Biological Chemistry. 2004;279:13044–13053. doi: 10.1074/jbc.M312846200. [DOI] [PubMed] [Google Scholar]
  40. Heikkinen T, Lehtimäki K, Vartiainen N, Puoliväli J, Hendricks SJ, Glaser JR, Bradaia A, Wadel K, Touller C, Kontkanen O, Yrjänheikki JM, Buisson B, Howland D, Beaumont V, Munoz-Sanjuan I, Park LC. Characterization of neurophysiological and behavioral changes, MRI brain volumetry and 1H MRS in zQ175 knock-in mouse model of Huntington's disease. PLoS One. 2012;7:e50717. doi: 10.1371/journal.pone.0050717. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Holm S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics. 1979;6:65–70. doi: 10.2307/4615733. [DOI] [Google Scholar]
  42. Huang K, Kang MH, Askew C, Kang R, Sanders SS, Wan J, Davis NG, Hayden MR. Palmitoylation and function of glial glutamate transporter-1 is reduced in the YAC128 mouse model of Huntington disease. Neurobiology of Disease. 2010;40:207–215. doi: 10.1016/j.nbd.2010.05.027. [DOI] [PubMed] [Google Scholar]
  43. Hunter JD. Matplotlib: A 2D Graphics environment. Computing in Science & Engineering. 2007;9:90–95. doi: 10.1109/MCSE.2007.55. [DOI] [Google Scholar]
  44. Huntington’s Disease Collaborative Research Group A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington’s disease chromosomes. Cell. 1993;72:971–983. doi: 10.1016/0092-8674(93)90585-e. [DOI] [PubMed] [Google Scholar]
  45. Ichinari K, Kakei M, Matsuoka T, Nakashima H, Tanaka H. Direct activation of the ATP-sensitive potassium channel by oxygen free radicals in guinea-pig ventricular cells: its potentiation by MgADP. Journal of Molecular and Cellular Cardiology. 1996;28:1867–1877. doi: 10.1006/jmcc.1996.0179. [DOI] [PubMed] [Google Scholar]
  46. Jahanshahi M, Obeso I, Baunez C, Alegre M, Krack P. Parkinson's disease, the subthalamic nucleus, inhibition, and impulsivity. Movement Disorders. 2015;30:128–140. doi: 10.1002/mds.26049. [DOI] [PubMed] [Google Scholar]
  47. Jiang R, Diaz-Castro B, Looger LL, Khakh BS. Dysfunctional calcium and glutamate signaling in striatal astrocytes from Huntington's disease model mice. Journal of Neuroscience. 2016;36:3453–3470. doi: 10.1523/JNEUROSCI.3693-15.2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Johri A, Chandra A, Beal MF. PGC-1α, mitochondrial dysfunction, and Huntington's disease. Free Radical Biology and Medicine. 2013;62:37–46. doi: 10.1016/j.freeradbiomed.2013.04.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Karschin C, Ecke C, Ashcroft FM, Karschin A. Overlapping distribution of K(ATP) channel-forming Kir6.2 subunit and the sulfonylurea receptor SUR1 in rodent brain. FEBS Letters. 1997;401:59–64. doi: 10.1016/S0014-5793(96)01438-X. [DOI] [PubMed] [Google Scholar]
  50. Kirkwood SC, Su JL, Conneally P, Foroud T. Progression of symptoms in the early and middle stages of Huntington disease. Archives of Neurology. 2001;58:273–278. doi: 10.1001/archneur.58.2.273. [DOI] [PubMed] [Google Scholar]
  51. Kita T, Kita H. The subthalamic nucleus is one of multiple innervation sites for long-range corticofugal axons: a single-axon tracing study in the rat. Journal of Neuroscience. 2012;32:5990–5999. doi: 10.1523/JNEUROSCI.5717-11.2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Kravitz AV, Freeze BS, Parker PR, Kay K, Thwin MT, Deisseroth K, Kreitzer AC. Regulation of parkinsonian motor behaviours by optogenetic control of basal ganglia circuitry. Nature. 2010;466:622–626. doi: 10.1038/nature09159. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Kravitz AV, Kreitzer AC. Striatal mechanisms underlying movement, reinforcement, and punishment. Physiology. 2012;27:167–177. doi: 10.1152/physiol.00004.2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Krippeit-Drews P, Kramer C, Welker S, Lang F, Ammon HP, Drews G. Interference of H2O2 with stimulus -secretion coupling in mouse pancreatic beta-cells. The Journal of Physiology. 1999;514:471–481.. doi: 10.1111/j.1469-7793.1999.471ae.x.. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Lange H, Thörner G, Hopf A, Schröder KF. Morphometric studies of the neuropathological changes in choreatic diseases. Journal of the Neurological Sciences. 1976;28:401–425. doi: 10.1016/0022-510X(76)90114-3. [DOI] [PubMed] [Google Scholar]
  56. Lee CR, Witkovsky P, Rice ME. Regulation of substantia nigra pars reticulata GABAergic neuron activity by H₂O₂ via flufenamic Acid- Sensitive Channels and K(ATP) Channels. Frontiers in Systems Neuroscience. 2011;5:41–52. doi: 10.3389/fnsys.2011.00014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Lee W, Reyes RC, Gottipati MK, Lewis K, Lesort M, Parpura V, Gray M. Enhanced Ca(2+)-dependent glutamate release from astrocytes of the BACHD Huntington's disease mouse model. Neurobiology of Disease. 2013;58:192–199. doi: 10.1016/j.nbd.2013.06.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Liévens JC, Woodman B, Mahal A, Spasic-Boscovic O, Samuel D, Kerkerian-Le Goff L, Bates GP. Impaired glutamate uptake in the R6 Huntington's disease transgenic mice. Neurobiology of Disease. 2001;8:807–821. doi: 10.1006/nbdi.2001.0430. [DOI] [PubMed] [Google Scholar]
  59. Mantovani S, Gordon R, Li R, Christie DC, Kumar V, Woodruff TM. Motor deficits associated with Huntington's disease occur in the absence of striatal degeneration in BACHD transgenic mice. Human Molecular Genetics. 2016;25:1780–1791. doi: 10.1093/hmg/ddw050. [DOI] [PubMed] [Google Scholar]
  60. Martin DD, Ladha S, Ehrnhoefer DE, Hayden MR. Autophagy in Huntington disease and huntingtin in autophagy. Trends in Neurosciences. 2015;38:26–35. doi: 10.1016/j.tins.2014.09.003. [DOI] [PubMed] [Google Scholar]
  61. Maurice N, Deniau JM, Glowinski J, Thierry AM. Relationships between the prefrontal cortex and the basal ganglia in the rat: physiology of the cortico-nigral circuits. Journal of Neuroscience. 1999;19:4674–4681. doi: 10.1523/JNEUROSCI.19-11-04674.1999. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Menalled LB, Kudwa AE, Miller S, Fitzpatrick J, Watson-Johnson J, Keating N, Ruiz M, Mushlin R, Alosio W, McConnell K, Connor D, Murphy C, Oakeshott S, Kwan M, Beltran J, Ghavami A, Brunner D, Park LC, Ramboz S, Howland D. Comprehensive behavioral and molecular characterization of a new knock-in mouse model of Huntington's disease: zQ175. PLoS One. 2012;7:e49838. doi: 10.1371/journal.pone.0049838. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Milakovic T, Johnson GV. Mitochondrial respiration and ATP production are significantly impaired in striatal cells expressing mutant huntingtin. Journal of Biological Chemistry. 2005;280:30773–30782. doi: 10.1074/jbc.M504749200. [DOI] [PubMed] [Google Scholar]
  64. Miller BR, Dorner JL, Shou M, Sari Y, Barton SJ, Sengelaub DR, Kennedy RT, Rebec GV. Up-regulation of GLT1 expression increases glutamate uptake and attenuates the Huntington's disease phenotype in the R6/2 mouse. Neuroscience. 2008;153:329–337. doi: 10.1016/j.neuroscience.2008.02.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Milnerwood AJ, Gladding CM, Pouladi MA, Kaufman AM, Hines RM, Boyd JD, Ko RW, Vasuta OC, Graham RK, Hayden MR, Murphy TH, Raymond LA. Early increase in extrasynaptic NMDA receptor signaling and expression contributes to phenotype onset in Huntington's disease mice. Neuron. 2010;65:178–190. doi: 10.1016/j.neuron.2010.01.008. [DOI] [PubMed] [Google Scholar]
  66. Moncada S, Bolaños JP. Nitric oxide, cell bioenergetics and neurodegeneration. Journal of Neurochemistry. 2006;97:1676–1689. doi: 10.1111/j.1471-4159.2006.03988.x. [DOI] [PubMed] [Google Scholar]
  67. Nakamura T, Lipton SA. Redox modulation by S-nitrosylation contributes to protein misfolding, mitochondrial dynamics, and neuronal synaptic damage in neurodegenerative diseases. Cell Death and Differentiation. 2011;18:1478–1486. doi: 10.1038/cdd.2011.65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Nichols CG. KATP channels as molecular sensors of cellular metabolism. Nature. 2006;440:470–476. doi: 10.1038/nature04711. [DOI] [PubMed] [Google Scholar]
  69. Noether GE. Sample size determination for some common nonparametric tests. Journal of the American Statistical Association. 1987;82:645–647. doi: 10.1080/01621459.1987.10478478. [DOI] [Google Scholar]
  70. Oldenburg IA, Sabatini BL. Antagonistic but not symmetric regulation of primary motor cortex by basal Ganglia direct and indirect pathways. Neuron. 2015;86:1174–1181. doi: 10.1016/j.neuron.2015.05.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Oliveira AO, Osmand A, Outeiro TF, Muchowski PJ, Finkbeiner S. αB-Crystallin overexpression in astrocytes modulates the phenotype of the BACHD mouse model of Huntington's disease. Human Molecular Genetics. 2016;25:1677–1689. doi: 10.1093/hmg/ddw028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Panov AV, Gutekunst CA, Leavitt BR, Hayden MR, Burke JR, Strittmatter WJ, Greenamyre JT. Early mitochondrial calcium defects in Huntington's disease are a direct effect of polyglutamines. Nature Neuroscience. 2002;5:731–736. doi: 10.1038/nn884. [DOI] [PubMed] [Google Scholar]
  73. Parsons MP, Raymond LA. Extrasynaptic NMDA receptor involvement in central nervous system disorders. Neuron. 2014;82:279–293. doi: 10.1016/j.neuron.2014.03.030. [DOI] [PubMed] [Google Scholar]
  74. Parsons MP, Vanni MP, Woodard CL, Kang R, Murphy TH, Raymond LA. Real-time imaging of glutamate clearance reveals normal striatal uptake in Huntington disease mouse models. Nature Communications. 2016;7:11251. doi: 10.1038/ncomms11251. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Perluigi M, Poon HF, Maragos W, Pierce WM, Klein JB, Calabrese V, Cini C, De Marco C, Butterfield DA. Proteomic analysis of protein expression and oxidative modification in r6/2 transgenic mice: a model of Huntington disease. Molecular & Cellular Proteomics. 2005;4:1849–1861. doi: 10.1074/mcp.M500090-MCP200. [DOI] [PubMed] [Google Scholar]
  76. Ray PD, Huang BW, Tsuji Y. Reactive oxygen species (ROS) homeostasis and redox regulation in cellular signaling. Cellular Signalling. 2012;24:981–990. doi: 10.1016/j.cellsig.2012.01.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Sanchez-Padilla J, Guzman JN, Ilijic E, Kondapalli J, Galtieri DJ, Yang B, Schieber S, Oertel W, Wokosin D, Schumacker PT, Surmeier DJ. Mitochondrial oxidant stress in locus coeruleus is regulated by activity and nitric oxide synthase. Nature Neuroscience. 2014;17:832–840. doi: 10.1038/nn.3717. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Saudou F, Humbert S. The biology of Huntingtin. Neuron. 2016;89:910–926. doi: 10.1016/j.neuron.2016.02.003. [DOI] [PubMed] [Google Scholar]
  79. Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B, Tinevez JY, White DJ, Hartenstein V, Eliceiri K, Tomancak P, Cardona A. Fiji: an open-source platform for biological-image analysis. Nature Methods. 2012;9:676–682. doi: 10.1038/nmeth.2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Sena LA, Chandel NS. Physiological roles of mitochondrial reactive oxygen species. Molecular Cell. 2012;48:158–167. doi: 10.1016/j.molcel.2012.09.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Smith GA, Rocha EM, McLean JR, Hayes MA, Izen SC, Isacson O, Hallett PJ. Progressive axonal transport and synaptic protein changes correlate with behavioral and neuropathological abnormalities in the heterozygous Q175 KI mouse model of Huntington's disease. Human Molecular Genetics. 2014;23:4510–4527. doi: 10.1093/hmg/ddu166. [DOI] [PubMed] [Google Scholar]
  82. Song W, Chen J, Petrilli A, Liot G, Klinglmayr E, Zhou Y, Poquiz P, Tjong J, Pouladi MA, Hayden MR, Masliah E, Ellisman M, Rouiller I, Schwarzenbacher R, Bossy B, Perkins G, Bossy-Wetzel E. Mutant huntingtin binds the mitochondrial fission GTPase dynamin-related protein-1 and increases its enzymatic activity. Nature Medicine. 2011;17:377–382. doi: 10.1038/nm.2313. [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Stack EC, Kubilus JK, Smith K, Cormier K, Del Signore SJ, Guelin E, Ryu H, Hersch SM, Ferrante RJ. Chronology of behavioral symptoms and neuropathological sequela in R6/2 Huntington's disease transgenic mice. The Journal of Comparative Neurology. 2005;490:354–370. doi: 10.1002/cne.20680. [DOI] [PubMed] [Google Scholar]
  84. Tachibana Y, Kita H, Chiken S, Takada M, Nambu A. Motor cortical control of internal pallidal activity through glutamatergic and GABAergic inputs in awake monkeys. European Journal of Neuroscience. 2008;27:238–253. doi: 10.1111/j.1460-9568.2007.05990.x. [DOI] [PubMed] [Google Scholar]
  85. Takahashi H, Shibasaki T, Park JH, Hidaka S, Takahashi T, Ono A, Song DK, Seino S. Role of Epac2A/Rap1 signaling in interplay between incretin and sulfonylurea in insulin secretion. Diabetes. 2015;64:1262–1272. doi: 10.2337/db14-0576. [DOI] [PubMed] [Google Scholar]
  86. Thomzig A, Laube G, Prüss H, Veh RW. Pore-forming subunits of K-ATP channels, Kir6.1 and Kir6.2, display prominent differences in regional and cellular distribution in the rat brain. Journal of Comparative Neurology. 2005;484:313–330. doi: 10.1002/cne.20469. [DOI] [PubMed] [Google Scholar]
  87. Tong X, Ao Y, Faas GC, Nwaobi SE, Xu J, Haustein MD, Anderson MA, Mody I, Olsen ML, Sofroniew MV, Khakh BS. Astrocyte Kir4.1 ion channel deficits contribute to neuronal dysfunction in Huntington's disease model mice. Nature Neuroscience. 2014;17:694–703. doi: 10.1038/nn.3691. [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Valencia A, Sapp E, Kimm JS, McClory H, Reeves PB, Alexander J, Ansong KA, Masso N, Frosch MP, Kegel KB, Li X, DiFiglia M. Elevated NADPH oxidase activity contributes to oxidative stress and cell death in Huntington's disease. Human Molecular Genetics. 2013;22:1112–1131. doi: 10.1093/hmg/dds516. [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]
  89. West MJ, Slomianka L, Gundersen HJ. Unbiased stereological estimation of the total number of neurons in thesubdivisions of the rat hippocampus using the optical fractionator. The Anatomical Record. 1991;231:482–497. doi: 10.1002/ar.1092310411. [DOI] [PubMed] [Google Scholar]
  90. Wichmann T, DeLong MR. Functional and pathophysiological models of the basal ganglia. Current Opinion in Neurobiology. 1996;6:751–758. doi: 10.1016/S0959-4388(96)80024-9. [DOI] [PubMed] [Google Scholar]
  91. Zhang CL, Katoh M, Shibasaki T, Minami K, Sunaga Y, Takahashi H, Yokoi N, Iwasaki M, Miki T, Seino S. The cAMP sensor Epac2 is a direct target of antidiabetic sulfonylurea drugs. Science. 2009;325:607–610. doi: 10.1126/science.1172256. [DOI] [PubMed] [Google Scholar]
  92. Zhang DM, Chai Y, Erickson JR, Brown JH, Bers DM, Lin YF. Intracellular signalling mechanism responsible for modulation of sarcolemmal ATP-sensitive potassium channels by nitric oxide in ventricular cardiomyocytes. The Journal of Physiology. 2014;592:971–990. doi: 10.1113/jphysiol.2013.264697. [DOI] [PMC free article] [PubMed] [Google Scholar]
  93. Zhou M, He HJ, Tanaka O, Sekiguchi M, Kawahara K, Abe H. Localization of the ATP-sensitive K(+) channel regulatory subunits SUR2A and SUR2B in the rat brain. Neuroscience Research. 2012;74:91–105. doi: 10.1016/j.neures.2012.08.005. [DOI] [PubMed] [Google Scholar]
eLife. 2016 Dec 20;5:e21616. doi: 10.7554/eLife.21616.034

Decision letter

Editor: Harry T Orr1

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Early dysfunction and progressive degeneration of the subthalamic nucleus in mouse models of Huntington's disease" for consideration by eLife. Your article has been favorably evaluated by Huda Zoghbi (Senior Editor) and three reviewers, one of whom is a member of our Board of Reviewing Editors. The following individual involved in review of your submission has agreed to reveal their identity: Margaret Rice (Reviewer #2).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

This is a very interesting study that implicates enhanced NMDAR signaling, leading to increased mitochondrial oxidant production, including that of hydrogen peroxide which triggers increased KATP activity, and results in slower and more variable autonomous STN neuronal firing in two HD mouse models (BACHD and Q175). This is one of the first studies to explore pathophysiological changes occurring early in the STN of HD mouse models. Overall the reviews are very positive about this work and recognize its important contribution to understanding the early pathogenesis in the STN using tow HD mouse models.

Essential revisions:

Revise the Abstract, Discussion and text of Results by removing conclusions on the role of glutamate uptake in the enhanced NMDA receptor-mediated oxidative stress.

Reviewer #1:

This body of work from Atherton et al. is a rather extensive study on the impact mutant Htt has on the STN. Involvement of the STN in HD has been debated over the years and this work, at least in mouse models of HD, provides the strongest evidence to data that the STN is a central aspect of HD pathology. A strength is that their observation/results were obtained using two full length Htt mouse models of HD. Starting at early stages of disease in these models they found that STN neurons had decreased intrinsic firing rate and related this to activation of KATP channels. This is followed by an elevated extra-synaptic NMDA activation by increased glutamate levels (results of decreased astrocyte uptake). Lastly, they show that at later stages, 12 months, there is a significant loss of STN neurons. Overall the data are strong making a convincible demonstration that the STN is involved in these models of HD. This is important as therapies for HD move to the clinic. This work provides a strong basis for the importance of assessing STN efficacy of treatments

Reviewer #2:

This report describes a thoughtful and thorough series of studies that not only illuminate a critical role for subthalamic nucleus (STN) neurons in mouse models of Huntington's disease, but also point to underlying mechanisms, including impaired glutamate uptake, NMDA receptor activation, H2O2 generation, and consequent K-ATP channel opening that slows STN neuron firing. This discussion carefully considers these factors in terms of cause versus effect. For all of these reasons, the studies should advance the field. There are a few generally minor concerns; most concern how the data are presented and discussed.

1) The notion that the direct pathway promotes "thoughts" is not obviously supported by the references cited (Introduction, first paragraph); cognitive function and thinking are not interchangeable terms.

2) The difference in frequency between WT and BACHD mice (is mean or median reported in the text?) was relatively small (7.9 vs 6.3 Hz, or a decrease of 20%); however, the examples in Figure 1A imply a four-fold difference. More accurately representative records should be used. The data shown in Figure 3A better represent the means reported; however, the much lower range for the untreated cells from BACHD mice (1.0 Hz) than those from BACHD reported in Figure 1 (6.3 Hz) needs some explanation. Were the untreated cells also recorded after the 3-5 h period as for AP5 pre-incubation?

3) The control data for MTS-roGFP imaging in the STN of BACHD mice appear to differ between Figure 3B and C, with a lack of obvious difference between control BACHD in 3C and WT in 3B. Do statistical analyses indicate a difference between control BACHD data in 3C and WT, or between BACHD data in 3B and 3C? The meaning of "relative oxidation" as the y-axis title also needs clarification. How this was determined is explained in the Methods, but should also be noted briefly in the Results. As written, the text makes this murkier rather than clearer: "MTS-roGFP imaging revealed that the relative oxidant stress in BACHD STN neurons was elevated relative to WT."

4) Figure 6A shows data from the cell that had the greatest change with glicazide, not one representative of the mean (or median) response (6B). Similarly, 7B show a cell in NMDA with 3 spikes per 2 s, less than 2 Hz, whereas the lowest frequency in the range reported was 2.3 (subsection “NMDAR activation produces a persistent KATP channel-mediated disruption of autonomous activity in WT STN neurons”).

5) The results with exogenous NMDA do implicate NMDA-induced reactive oxygen species, but the more convincing experiment would have been to block NMDA receptors in BACHD mice (as in the MTS-roGFP imaging studies; Figure 4). They had an opportunity to test this in studies with Q175 mice (Figure 12), but although they show that glibenclamide and AP5 each lead to an increase in STN neurons firing rate in slices from Q175 mice, the key experiment of whether the effect of glibenclamide is absent when NMDA receptors are blocked by APV is not reported. This would strengthen the authors' conclusions, but is not essential.

6) It is not clear whether the decreased firing rate of STN neurons in slices from HD mice reflected inclusion of neurons that did not show autonomous activity (0 Hz). The frequency range for BACHD mice does not include 0 (subsection “The autonomous activity of STN neurons is disrupted in the BACHD model”), but the range for Q175 mice does (subsection “The STN of Q175 KI mice exhibits similar abnormalities to those observed in the BACHD model”).

H2O2Figuresthat Reviewer #3:

This is a very interesting study that implicates enhanced NMDAR signaling, leading to increased mitochondrial oxidative stress that triggers increased KATP activity, resulting in slowed, more variable autonomous STN neuronal firing in two HD mouse models (BACHD and Q175). This study includes a series of elegant experiments with results that convincingly demonstrate a mechanism for altered STN autonomous firing, implicating a pathological process independent of aberrant iSPN input to STN in HD.

One limitation, however, is that although the authors suggest this STN firing alteration underlies a variety of HD-related behaviors, this hypothesis is not tested. Moreover, they don't explore any in vivo recording to confirm altered STN firing or responsiveness to cortical and/or striatal iSPN input. As well, the link between these molecular alterations, aberrant autonomous firing, and STN neuronal loss is not established. The latter is complicated by the fact that the STN is part of the indirect pathway, which is impaired early at the level of striatal iSPNs (early loss of enkephalin and DARPP32, enhanced excitability, loss of LTP, increased extra-synaptic NMDAR signaling, etc.).

In addition, there are some specific concerns:

1) It seems counter-intuitive that by increasing the glutamate load (e.g., with pulse trains), a basal impairment in glutamate transport would no longer be observed; one would think it might instead be augmented. However, this could make sense in the context of a recent study by Armbruster et al. (2016), showing that train stimulation itself slows glutamate uptake, which might occlude a small difference in the NMDAR EPSC decay time constant, as the authors have postulated for the TFB-TBOA. The phenomenon reported by Armbruster et al. seems a more likely explanation for the difference in results, rather than what the authors suggest, that impaired glutamate uptake is restricted to the synapse. The latter seems unlikely since astrocytes ensheath more than one synapse.

2) Related to #1, the NMDAR component of the EPSC decay time constant can be quite variable (as shown in Figure 1G; also see Vicini et al., 1998), and other factors (e.g. differences in access resistance, space clamp, etc.) could potentially impact the accuracy of measuring decay time in the context of such small currents. Moreover, the difference between the mean decay time constant in wild-type vs. BAC HD STN neurons is barely significant (P=0.0455). To further test the idea that impaired glutamate uptake is responsible for slowed decay for single NMDAR EPSCs, the authors could try using the glutamate scavenger GPT to see if this can accelerate decay time and show a greater effect in HD than wild-type, and/or add dextran to the extracellular solution to slow diffusion – this should magnify any differences in transporter function between the genotypes (see Mahfooz et al. NBD 2016 for approach).

3) In general, the focus on impaired glutamate uptake as a determinant of enhanced NMDAR-mediated oxidative stress seems unsupported. First, the authors rely heavily on previous studies showing reduced mRNA and/or protein expression levels of GLT1, but this does not prove impaired glutamate uptake under physiological conditions. In fact, few studies have measured clearance of synaptically released glutamate in brain slice: of these, one study in striatum demonstrated no impairment of glutamate uptake/clearance in R6/2 or YAC28 mice (Parsons et al., 2016), while two others show either minimal impairment with prolonged (200 ms), high concentration (3 mM) glutamate pulses (Dvorzhak et al., 2016) or slowing only of the late component of the iGluSnFr response (> 2 sec after synaptic release; Jiang et al., 2016).

Second, the difference in NMDAR EPSC decay shown in the current study is very small and is eliminated in the setting of trains of 5 stimuli at 50Hz (see points 1 and 2); since the authors mention that cortical input to STN frequently occurs in bursts, it seems unlikely that the Glu uptake impairment they see for single EPSCs could be an important contributor to increased oxidative stress in HD vs. wild-type STN. These points should be addressed in the Discussion, and less emphasis placed on the role of impaired glutamate uptake, which is not directly measured in this study.

4) The authors show increased mitochondrial oxidation and altered firing rate in BAC HD STN that is rescued by APV – this implicates NMDAR activity in these downstream events. However, it could be because of altered signaling via the NMDAR complex, i.e. due to NMDAR association with different effector proteins, rather than a result of increased calcium influx via NMDARs. This could be tested by comparing intracellular calcium transients in STN neurons between the genotypes using GCaMP6.

eLife. 2016 Dec 20;5:e21616. doi: 10.7554/eLife.21616.035

Author response


Essential revisions:

Revise the Abstract, Discussion and text of Results by removing conclusions on the role of glutamate uptake in the enhanced NMDA receptor-mediated oxidative stress.

As requested, we have revised the Abstract, Discussion and text of Results – removing the conclusions on the role of glutamate uptake in NMDA receptor-mediated oxidative stress and autonomous firing dysfunction.

Reviewer #2:

[…] 1) The notion that the direct pathway promotes "thoughts" is not obviously supported by the references cited (Introduction, first paragraph); cognitive function and thinking are not interchangeable terms.

The two sentences referring to the roles of the direct and indirect pathways have been changed to ‘The so-called direct pathway through the striatum promotes movement and ‘rewarding’ behavior through inhibition of GABAergic basal ganglia output (Chevalier and Deniau, 1990; Kravitz et al., 2010; Kravitz and Kreitzer, 2012). In contrast, the indirect pathway via the striatum, external globus pallidus and subthalamic nucleus (STN) and the hyperdirect pathway through the STN suppress the same processes through elevation of basal ganglia output (Maurice et al., 1999; Tachibana et al., 2008; Kravitz et al., 2010; Kravitz and Kreitzer, 2012).’

The reference Kravitz and Kreitzer, 2012, which deals with non-motor aspects of direct and indirect pathway encoding has been added.

2) The difference in frequency between WT and BACHD mice (is mean or median reported in the text?) was relatively small (7.9 vs 6.3 Hz, or a decrease of 20%); however, the examples in Figure 1A imply a four-fold difference. More accurately representative records should be used. The data shown in Figure 3A better represent the means reported; however, the much lower range for the untreated cells from BACHD mice (1.0 Hz) than those from BACHD reported in Figure 1 (6.3 Hz) needs some explanation. Were the untreated cells also recorded after the 3-5 h period as for AP5 pre-incubation?

All data in the manuscript are reported in the form median [interquartile range]. We agree that the BACHD example cells shown in Figure 1A do not exemplify the difference in median frequency between WT and BACHD mice. The neurons from BACHD mice comprise a phenotypic population that displays compromised autonomous firing, and neurons that have relatively normal firing. We have therefore amended Figure 1 with the addition of a histogram illustrating the distribution of autonomous firing for WT and BACHD STN neurons. As can be seen from this histogram, a BACHD neuron that illustrated the median firing rate, would not be representative of either firing mode. We therefore chose to illustrate a neuron that is representative of phenotypic neurons. In addition to amending Figure 1, we have amended the text in the Results section and the legend for Figure 1to clarify this point. The factors that could underlie the disrupted activity of a subset of neurons in the BACHD model are discussed.

The strength of the BACHD firing phenotype varies across animals and the difference between the data in Figure 3 and that in Figure 1 is explained by the smaller sample in Figure 3 from this variable population. In these D-AP5 pre-treatment experiments, recordings from treated and untreated slices were interleaved. Because an untreated slice was usually examined first, observations were on average 1–2 hours earlier than for treated slices. This is not expected to influence the result because in the full BACHD dataset there was no correlation between time ex vivo and firing frequency (r2 = 0.0054, p = 0.4151, n = 126).

3) The control data for MTS-roGFP imaging in the STN of BACHD mice appear to differ between Figure 3B and C, with a lack of obvious difference between control BACHD in 3C and WT in 3B. Do statistical analyses indicate a difference between control BACHD data in 3C and WT, or between BACHD data in 3B and 3C? The meaning of "relative oxidation" as the y-axis title also needs clarification. How this was determined is explained in the Methods, but should also be noted briefly in the Results. As written, the text makes this murkier rather than clearer: "MTS-roGFP imaging revealed that the relative oxidant stress in BACHD STN neurons was elevated relative to WT."

(Note, this comment appears to be in reference to the data in Figure 4 not Figure 3)

The WT data shown in Figure 4B and the control BACHD data shown in Figure 4C are not significantly different (p = 0.1666, Mann-Whitney U test). There is also no significant difference between the BACHD data in Figure 4B and the untreated BACHD data in Figure 4C (p = 0.2089, Mann-Whitney U test). However, these comparisons are not valid because the data in 4Bwas acquired 15 months prior to the data in 4C. Although we strive to maintain identical experimental conditions, experiments separated by 15 months likely utilized different batches of virus and less than identical imaging conditions. In hindsight, it would have been optimal to collect all the data in Figure 4 at the same time, particularly if our intention was to compare the absolute levels of relative oxidation between all the experimental groups. However, the experiment in 4Bwas designed to test whether the relative oxidant stress of mitochondria was greater in BACHD vs. WT STN neurons and the experiment in 4C (15 months later) was designed to test whether NMDAR antagonism could lower the relative oxidant stress of mitochondria in BACHD STN neurons. These comparisons were confirmed statistically and the conclusions from these experiments are also supported by the experiments in the rest of the paper.

Precisely what the relative oxidation measurement represents has been clarified further in the Results section.

4) Figure 6A shows data from the cell that had the greatest change with glicazide, not one representative of the mean (or median) response (6B). Similarly, 7B show a cell in NMDA with 3 spikes per 2 s, less than 2 Hz, whereas the lowest frequency in the range reported was 2.3 (subsection “NMDAR activation produces a persistent KATP channel-mediated disruption of autonomous activity in WT STN neurons”).

We have changed the example cell used in Figure 6A, and selected a more representative segment of firing for the control trace in Figure 7B.

5) The results with exogenous NMDA do implicate NMDA-induced reactive oxygen species, but the more convincing experiment would have been to block NMDA receptors in BACHD mice (as in the MTS-roGFP imaging studies; Figure 4). They had an opportunity to test this in studies with Q175 mice (Figure 12), but although they show that glibenclamide and AP5 each lead to an increase in STN neurons firing rate in slices from Q175 mice, the key experiment of whether the effect of glibenclamide is absent when NMDA receptors are blocked by APV is not reported. This would strengthen the authors' conclusions, but is not essential.

We have now performed this experiment and added the following text to the Results section: ‘As described above, 3–5-hour NMDAR antagonism with D-AP5 partially rescued autonomous activity in BACHD STN neurons. […] Thus, these observations are consistent with the conclusion that prolonged NMDAR antagonism partially rescued autonomous activity in BACHD STN neurons through a reduction in KATP channel-mediated firing disruption.’

6) It is not clear whether the decreased firing rate of STN neurons in slices from HD mice reflected inclusion of neurons that did not show autonomous activity (0 Hz). The frequency range for BACHD mice does not include 0 (subsection “The autonomous activity of STN neurons is disrupted in the BACHD model”), but the range for Q175 mice does (subsection “The STN of Q175 KI mice exhibits similar abnormalities to those observed in the BACHD model”).

Neurons that did not show autonomous activity were included in the firing rate analyses but could not be included in the CV analyses for obvious reasons, as reflected in the lower n for CV data versus frequency data. The ranges described here represent the interquartile range and not the minimum to maximum range. The fact that the interquartile range in BACHD mice does not include silent neurons, whereas the range in Q175 mice does, is a reflection of the relatively severe disruption of autonomous firing in Q175 mice.

Reviewer #3:

[…] One limitation, however, is that although the authors suggest this STN firing alteration underlies a variety of HD-related behaviors, this hypothesis is not tested. Moreover, they don't explore any in vivo recording to confirm altered STN firing or responsiveness to cortical and/or striatal iSPN input. As well, the link between these molecular alterations, aberrant autonomous firing, and STN neuronal loss is not established. The latter is complicated by the fact that the STN is part of the indirect pathway, which is impaired early at the level of striatal iSPNs (early loss of enkephalin and DARPP32, enhanced excitability, loss of LTP, increased extra-synaptic NMDAR signaling, etc.).

The similarity of HD symptoms to those arising from STN lesion or inactivation (Crossman et al., 1988; Hamada and DeLong, 1992; Baunez and Robbins, 1997; Bickel et al., 2010; Jahanshahi et al., 2015), combined with evidence of abnormal STN activity in HD models in vivo (Callahan and Abercrombie, 2015a, b) and STN neuronal degeneration in HD (Lange et al., 1976; Guo et al., 2012), led to our hypothesis that STN neurons are dysfunctional in HD. Now that we have confirmed that STN neurons are indeed dysfunctional in two HD models, we can next pursue whether STN dysfunction contributes to abnormal behavior in these HD models. We are currently evaluating several approaches based on the novel findings presented here that might ameliorate STN dysfunction in HD mice in vivo. However, these experiments will likely generate another full manuscript and are therefore beyond the scope of the current report. We concur that we have not yet established whether the STN dysfunction and neurodegeneration that we have described for the first time contribute to abnormal behavior in these models and have tempered the manuscript accordingly. We also agree that we have not yet established that the neuronal loss that we observed is directly related to the increased NMDAR-dependent, oxidative stress of STN neurons, although this is a well-recognized cell death pathway. We are therefore currently testing whether correcting or compensating for STN dysfunction, using strategies suggested by the abnormalities reported here, will reduce or eliminate cell death.

In addition, there are some specific concerns:

1) It seems counter-intuitive that by increasing the glutamate load (e.g., with pulse trains), a basal impairment in glutamate transport would no longer be observed; one would think it might instead be augmented. However, this could make sense in the context of a recent study by Armbruster et al. (2016), showing that train stimulation itself slows glutamate uptake, which might occlude a small difference in the NMDAR EPSC decay time constant, as the authors have postulated for the TFB-TBOA. The phenomenon reported by Armbruster et al. seems a more likely explanation for the difference in results, rather than what the authors suggest, that impaired glutamate uptake is restricted to the synapse. The latter seems unlikely since astrocytes ensheath more than one synapse.

We thank the reviewer for bringing to our attention this potentially relevant finding. As this paper was published after our initial submission, we were unable discuss it in the original manuscript, but have now added discussion of this paper to the revised manuscript.

2) Related to #1, the NMDAR component of the EPSC decay time constant can be quite variable (as shown in Figure 1G; also see Vicini et al., 1998), and other factors (e.g. differences in access resistance, space clamp, etc.) could potentially impact the accuracy of measuring decay time in the context of such small currents. Moreover, the difference between the mean decay time constant in wild-type vs. BAC HD STN neurons is barely significant (P=0.0455). To further test the idea that impaired glutamate uptake is responsible for slowed decay for single NMDAR EPSCs, the authors could try using the glutamate scavenger GPT to see if this can accelerate decay time and show a greater effect in HD than wild-type, and/or add dextran to the extracellular solution to slow diffusion – this should magnify any differences in transporter function between the genotypes (see Mahfooz et al. NBD 2016 for approach).

We have compared the access resistances associated with the NMDAR current measurements in this study. They were not significantly different across each experimental group (p = 0.2297), and the decay time constant was not correlated with access resistance (r2 = 0.0838, p = 0.18). Technical reasons are therefore unlikely to have produced a consistent measurement error.

The suggestions for further experiments and the iGluSNr approaches described below will be certainly be applied in subsequent studies as we further dissect the mechanisms underlying the significantly extended NMDAR currents in STN neurons in HD models.

3) In general, the focus on impaired glutamate uptake as a determinant of enhanced NMDAR-mediated oxidative stress seems unsupported. First, the authors rely heavily on previous studies showing reduced mRNA and/or protein expression levels of GLT1, but this does not prove impaired glutamate uptake under physiological conditions. In fact, few studies have measured clearance of synaptically released glutamate in brain slice: of these, one study in striatum demonstrated no impairment of glutamate uptake/clearance in R6/2 or YAC28 mice (Parsons et al., 2016), while two others show either minimal impairment with prolonged (200 ms), high concentration (3 mM) glutamate pulses (Dvorzhak et al., 2016) or slowing only of the late component of the iGluSnFr response (> 2 sec after synaptic release; Jiang et al., 2016).

Second, the difference in NMDAR EPSC decay shown in the current study is very small and is eliminated in the setting of trains of 5 stimuli at 50Hz (see points 1 and 2); since the authors mention that cortical input to STN frequently occurs in bursts, it seems unlikely that the Glu uptake impairment they see for single EPSCs could be an important contributor to increased oxidative stress in HD vs. wild-type STN. These points should be addressed in the Discussion, and less emphasis placed on the role of impaired glutamate uptake, which is not directly measured in this study.

We accept these critiques of our assumption that glutamate uptake impairment accounts for NMDAR-mediated oxidative stress and firing disruption made in both points 2 & 3 above. We have therefore removed this conclusion from the manuscript and will make a more in-depth investigation of this potential linkage in future studies.

4) The authors show increased mitochondrial oxidation and altered firing rate in BAC HD STN that is rescued by APV – this implicates NMDAR activity in these downstream events. However, it could be because of altered signaling via the NMDAR complex, i.e. due to NMDAR association with different effector proteins, rather than a result of increased calcium influx via NMDARs. This could be tested by comparing intracellular calcium transients in STN neurons between the genotypes using GCaMP6.

Again, we agree with the reviewer that altered NMDAR and associated signaling pathway properties could contribute to NMDAR-mediated oxidative stress and firing disruption and have therefore modified our interpretation and discussion of the data accordingly throughout the manuscript.

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    Figure 1—source data 1. Autonomous firing frequency and CV for BACHD and WT STN neurons in Figure 1B–C.

    DOI: http://dx.doi.org/10.7554/eLife.21616.003

    DOI: 10.7554/eLife.21616.003
    Figure 1—source data 2. Amplitude weighted decay of NMDAR-mediated EPSCs in Figure 1H.

    DOI: http://dx.doi.org/10.7554/eLife.21616.004

    DOI: 10.7554/eLife.21616.004
    Figure 2—source data 1. Amplitude and amplitude weighted decay of NMDAR-mediated EPSCs in Figure 2A–B.

    DOI: http://dx.doi.org/10.7554/eLife.21616.006

    DOI: 10.7554/eLife.21616.006
    Figure 2—source data 2. Amplitude weighted decay of compound NMDAR-mediated EPSCs in Figure 2E.

    DOI: http://dx.doi.org/10.7554/eLife.21616.007

    DOI: 10.7554/eLife.21616.007
    Figure 3—source data 1. Autonomous firing frequency and CV for BACHD control and D-AP5 pretreated STN neurons in Figure 3B–C.

    DOI: http://dx.doi.org/10.7554/eLife.21616.009

    DOI: 10.7554/eLife.21616.009
    Figure 4—source data 1. The relative mitochondrial oxidant stress of WT and BACHD STN neurons in Figure 4B.

    DOI: http://dx.doi.org/10.7554/eLife.21616.011

    DOI: 10.7554/eLife.21616.011
    Figure 4—source data 2. The relative mitochondrial oxidant stress of control and D-AP5 pre-treated BACHD STN neurons in Figure 4C.

    DOI: http://dx.doi.org/10.7554/eLife.21616.012

    DOI: 10.7554/eLife.21616.012
    Figure 5—source data 1. Autonomous firing frequency and CV for WT and BACHD STN neurons under control conditions and following glibenclamide application in Figure 5B.

    DOI: http://dx.doi.org/10.7554/eLife.21616.014

    DOI: 10.7554/eLife.21616.014
    Figure 5—source data 2. Autonomous interspike voltage trajectory, firing frequency and CV for whole-cell recordings from WT and BACHD STN neurons in Figure 5D.

    DOI: http://dx.doi.org/10.7554/eLife.21616.015

    DOI: 10.7554/eLife.21616.015
    Figure 6—source data 1. Autonomous firing frequency and CV for WT and BACHD STN neurons under control conditions and following gliclazide application in Figure 6B.

    DOI: http://dx.doi.org/10.7554/eLife.21616.017

    DOI: 10.7554/eLife.21616.017
    Figure 7—source data 1. Autonomous firing frequency and CV for control and NMDA pre-treated C57BL/6 STN neurons in Figure 7C.

    DOI: http://dx.doi.org/10.7554/eLife.21616.019

    DOI: 10.7554/eLife.21616.019
    Figure 7—source data 2. Autonomous firing frequency and CV for control and NMDA pre-treated C57BL/6 STN neurons in control conditions and following glibenclamide application in Figure 7D.

    DOI: http://dx.doi.org/10.7554/eLife.21616.020

    DOI: 10.7554/eLife.21616.020
    Figure 8—source data 1. Autonomous firing frequency and CV for WT and BACHD STN neurons under control conditions and following catalase and/or glibenclamide application in Figure 8C–D.

    DOI: http://dx.doi.org/10.7554/eLife.21616.022

    DOI: 10.7554/eLife.21616.022
    Figure 9—source data 1. Autonomous firing frequency and CV for WT and BACHD STN neurons under control conditions and following catalase application in Figure 9.

    DOI: http://dx.doi.org/10.7554/eLife.21616.024

    DOI: 10.7554/eLife.21616.024
    Figure 10—source data 1. Autonomous firing frequency and CV for WT and BACHD STN neurons under control conditions and following MCS and glibenclamide application in Figure 10B.

    DOI: http://dx.doi.org/10.7554/eLife.21616.026

    DOI: 10.7554/eLife.21616.026
    Figure 11—source data 1. BACHD STN neuron counts, density and STN volume in Figure 11B–C.

    DOI: http://dx.doi.org/10.7554/eLife.21616.028

    DOI: 10.7554/eLife.21616.028
    Figure 12—source data 1. Autonomous firing frequency and CV for Q175 and WT STN neurons in Figure 12B.

    DOI: http://dx.doi.org/10.7554/eLife.21616.030

    DOI: 10.7554/eLife.21616.030
    Figure 12—source data 2. Autonomous firing frequency and CV for Q175 in control conditions and following glibenclamide application Figure 12D.

    DOI: http://dx.doi.org/10.7554/eLife.21616.031

    DOI: 10.7554/eLife.21616.031
    Figure 12—source data 3. Autonomous firing frequency and CV for control and D-AP5 pre-treated Q175 STN neurons in Figure 12F.

    DOI: http://dx.doi.org/10.7554/eLife.21616.032

    DOI: 10.7554/eLife.21616.032
    Figure 12—source data 4. Q175 STN neuron counts, density and STN volume in Figure 12H.

    DOI: http://dx.doi.org/10.7554/eLife.21616.033

    DOI: 10.7554/eLife.21616.033

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