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. 2018 Aug 17;7:e36691. doi: 10.7554/eLife.36691

A cell autonomous torsinA requirement for cholinergic neuron survival and motor control

Samuel S Pappas 1, Jay Li 1,2, Tessa M LeWitt 1, Jeong-Ki Kim 3,4,5, Umrao R Monani 3,4,5, William T Dauer 1,2,6,✉
Editors: Louis J Ptáček7, Huda Y Zoghbi8
PMCID: PMC6115190  PMID: 30117805

Abstract

Cholinergic dysfunction is strongly implicated in dystonia pathophysiology. Previously (Pappas et al., 2015;4:e08352), we reported that Dlx5/6-Cre mediated forebrain deletion of the DYT1 dystonia protein torsinA (Dlx-CKO) causes abnormal twisting and selective degeneration of dorsal striatal cholinergic interneurons (ChI) (Pappas et al., 2015). A central question raised by that work is whether the ChI loss is cell autonomous or requires torsinA loss from neurons synaptically connected to ChIs. Here, we addressed this question by using ChAT-Cre mice to conditionally delete torsinA from cholinergic neurons (‘ChAT-CKO’). ChAT-CKO mice phenocopy the Dlx-CKO phenotype of selective dorsal striatal ChI loss and identify an essential requirement for torsinA in brainstem and spinal cholinergic neurons. ChAT-CKO mice are tremulous, weak, and exhibit trunk twisting and postural abnormalities. These findings are the first to demonstrate a cell autonomous requirement for torsinA in specific populations of cholinergic neurons, strengthening the connection between torsinA, cholinergic dysfunction and dystonia pathophysiology.

Research organism: Mouse

Introduction

Multiple lines of evidence implicate striatal cholinergic dysfunction in dystonia pathophysiology (Pappas et al., 2015; Albin et al., 2003; Eskow Jaunarajs et al., 2015; Pappas et al., 2014). The symptoms of DYT1 dystonia, caused by a loss of function mutation in the gene encoding torsinA (Ozelius et al., 1997), are reduced by antimuscarinic treatments (e.g., trihexyphenidyl)(Burke et al., 1986). Antimuscarinic agents also reduce motor (Pappas et al., 2015) and electrophysiological (Maltese et al., 2014) abnormalities in DYT1 mouse models. Striatal cholinergic dysfunction is a common feature of multiple DYT1 animal models (Pappas et al., 2015; Martella et al., 2009; Pisani et al., 2006; Sciamanna et al., 2012a; Sciamanna et al., 2012b), and experimental ablation of striatal cholinergic interneurons (ChI) can lead to abnormal postures (Kaneko et al., 2000).

We demonstrated previously that deletion of torsinA from forebrain GABAergic and cholinergic neurons (using Dlx5/6-cre; ‘Dlx-CKO’) causes highly selective degeneration of dorsal striatal ChI roughly coincident with the juvenile onset of abnormal limb clasping and twisting movements(Pappas et al., 2015). Selective ChI abnormalities are also present in postmortem tissue from DYT1 subjects (Pappas et al., 2015). Abnormal movements in Dlx-CKO mice are reduced by clinically relevant antimuscarinic treatments, strengthening model therapeutic validity and suggesting shared pathophysiology with human dystonia. This work highlights the importance of elucidating the mechanism of selective ChI loss. A critical first step toward this goal is to determine whether the ChI loss observed in Dlx-CKO mice results from a cell autonomous role of torsinA in these cells or, alternatively, whether loss of torsinA from synaptically connected cells is also required. The major aim of these studies was to address this fundamental question.

To determine whether torsinA-related ChI loss is cell autonomous, we generated and characterized cholinergic neuron selective conditional torsinA knockout mice (ChAT-CKO). We find that ChAT-CKO mice phenocopy the selective degeneration of dorsal striatal ChI observed in Dlx-CKO mice (basal forebrain neuron numbers are normal in both models). Assessment of non-forebrain cholinergic populations demonstrates that pedunculopontine and laterodorsal tegmental brainstem cholinergic neurons, and spinal motor neurons also require torsinA for survival or normal function. ChAT-CKO mice exhibit severe motor and postural abnormalities that are distinct from Dlx-CKO mice. These findings are the first to establish a cell autonomous requirement for torsinA in ChI, as well as identifying additional vulnerable cholinergic neuron populations. This in vivo study fundamentally advances and expands understanding of the requirement of torsinA for normal cholinergic system function, opening new directions for the study of mechanisms contributing to selective neuronal dysfunction in dystonia.

Results and discussion

To determine if ChI neurodegeneration is a cell autonomous effect of torsinA loss, we conditionally deleted torsinA from cholinergic neurons (Chat-IRES-Cre+, Tor1aFlx/-; ‘ChAT-CKO’ mice; Cre-recombinase expression occurs before birth and is completely selective for cholinergic neurons; Figure 1—figure supplement 1 [Madisen et al., 2010]). Unilateral unbiased stereology of ChAT-immunoreactive neurons in the dorsal striatum from 1 year old mice demonstrates a ~ 34% reduction in the number of dorsal striatal ChI in ChAT-CKO mice compared to control mice (Figure 1A,B). This finding was confirmed in an independent cohort using bilateral unbiased stereology (48% reduction; Figure 1—figure supplement 2A). The number of striatal non-cholinergic neurons was not different from controls (Figure 1—figure supplement 2B,C), demonstrating that there are no secondary cell loss effects of ChI degeneration, and that torsinA loss of function-mediated neurodegeneration is highly specific. These findings establish a cell autonomous torsinA requirement for ChI survival.

Figure 1. Conditional cholinergic neuron deletion of torsinA causes cell autonomous loss of striatal cholinergic neurons.

(A) Unilateral stereological quantification of the number of ChAT-positive neurons in the striatum of ChAT-CKO and control mice (One-way ANOVA F(3,28) = 3.589, p=0.02, Dunnett’s multiple comparisons test: adjusted p value = 0.049; ‘WT’=Tor1aFlx/+; ‘Cre Control’=ChAT-Cre+, Tor1a Flx/+; ‘Het Control’=Tor1 aFlx/-; ‘ChAT-CKO’=ChAT-Cre+, Tor1aFlx/-). (B) ChAT immunohistochemistry of coronal sections containing dorsal striatum from WT and ChAT-CKO mice (cc = corpus callosum). (C) Percent reduction in cell density by striatal quadrant (DL = dorsolateral; DM = dorsomedial, VL = ventrolateral, VM = ventromedial). (D) Significant ChI loss is selective for dorsal striatal quadrants. Cell density quantification in control and ChAT-CKO striatal quadrants (Two-way ANOVA main effect of genotype F(3,112) = 24.02, p<0.0001; main effect of quadrant F(3,112)=8.398, p<0.0001; interaction F(9,112)=8.11, p<0.0001. Post-hoc Tukey’s multiple comparisons test). (E) Basal forebrain neurons are spared in ChAT-CKO mice. Stereological quantification of P75-immunoreactive basal forebrain cholinergic neurons in the nucleus basalis of meynert (NBM), medial septum/nucleus of the vertical limb of the diagonal band (MS/VDB), and globus pallidus (GP). No differences in the number of cholinergic neurons was observed (NBM, t(13)=1.684, p=0.11; MS/VDB, t(13)=1.537, p=0.148; GP, t(13)=0.5, p=0.625). (F) P75 immunohistochemistry of sagittal sections containing basal forebrain cholinergic neuron populations. i.c. = internal capsule, ST = striatum.

Figure 1.

Figure 1—figure supplement 1. ChAT-Cre is expressed prenatally.

Figure 1—figure supplement 1.

(Upper panels) ChAT-Cre mice were crossed with Ai14 Cre reporter mice. Offspring were collected immediately after birth, brain sections were generated and observed under epifluorescence microscopy. (Lower panels) adjacent sections costained for torsinA. Scale bar = 50 μm.

Figure 1—figure supplement 2. Independent cohort confirmation of selective striatal cholinergic neuron loss in ChAT-CKO mice.

Figure 1—figure supplement 2.

(A) Bilateral unbiased stereology of ChAT-immunoreactive neurons in the dorsal striatum (t(12)=4.42, p=0.0008). (B) Unbiased stereology of parvalbumin (PV) immunoreactive neurons in the dorsal striatum (t9 = 0.699, p=0.50). (C) Unbiased stereology of Nissl positive cells in the dorsal striatum (Welch’s t-test; t7.803=0.655, p=0.53).

Figure 1—figure supplement 3. ChAT-positive neurons are reduced in a topographic pattern throughout the rostrocaudal extent of the dorsal striatum.

Figure 1—figure supplement 3.

Significant decreases in ChAT-positive cells were observed in the dorsolateral and dorsomedial segments of the dorsal striatum (dorsolateral striatum, two-way ANOVA main effect of genotype F(3,156)=74.77, p<0.0001, main effect of rostrocaudal section, F(5,156)=10.07, p<0.0001, no interaction F(15,156)=1.204, p=0.273; Dorsomedial striatum main effect of genotype F(3,156)=50.01, p<0.0001, main effect of rostrocaudal section, F(5,156)=41.81, p<0.0001, no interaction F(15,156)=1.646, p=0.067. Post-hoc Tukey’s test was performed for all significant main effects. * represents significant difference between ChAT-CKO mice and all control groups). No significant reductions were observed in the ventral segments of the dorsal striatum (ventrolateral; genotype F(3,156)=2.84, p=0.039 [post-hoc Tukey’s test adjusted p=0.129 or higher for all comparisons], rostrocaudal section F(5,156)=0.479, p=0.79, interaction F(15,156)=0.249, p=0.99. ventromedial; genotype F(3,156)= 2.706, p=0.047 [post-hoc Tukey’s test adjusted p=0.107 or higher for all comparisons], rostrocaudal section F(5,156)=46.28 p<0.0001, interaction F(15,156)=0.672, p=0.80).

Figure 1—figure supplement 4. Time course of torsinA protein loss in dorsal and ventral striatum.

Figure 1—figure supplement 4.

(A,B) TorsinA and ChAT staining in dorsal and ventral striatum brain sections from P0 ChAT-CKO and control mice. (C) TorsinA mean fluorescence intensity analysis in dorsal or ventral striatal ChI (Two-way ANOVA main effect of genotype F1,195= 85.67, p<0.0001; Region, n.s., F1,195=3.301, p=0.07; interaction F1,195=21.34, p<0.0001. Sidak’s multiple comparisons test, Dorsal striatum control vs ChAT-CKO, p=0.003, Ventral striatum control vs ChAT-CKO, p<0.0001, ChAT-CKO dorsal striatum vs ventral striatum, p=0.0002; Control dorsal striatum vs ventral striatum, n.s., p=0.199). (D,E) Frequency histograms of torsinA mean fluorescence intensity in dorsal (n = 57 control, n = 54 ChAT-CKO neurons) and ventral striatal ChI (n = 52 control, n = 36 ChAT-CKO neurons). Scale bar = 10 µm.

Figure 1—figure supplement 5. Time course of torsinA protein loss in basal forebrain.

Figure 1—figure supplement 5.

(A) TorsinA and ChAT staining in basal forebrain brain sections from P0 ChAT-CKO and control mice. (B) TorsinA mean fluorescence intensity analysis of control and ChAT-CKO (Welch’s t-test t138.2=17.35, p<0.0001) and frequency histograms of torsinA mean fluorescence intensity in basal forebrain cholinergic neurons ChI (n = 91 control, n = 79 ChAT-CKO neurons). Scale bar = 10 µm.
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ChI cell loss is strikingly selective in Dlx-CKO mice, occurring primarily in the dorsal aspects of the striatum, with approximately six times greater cell loss in the dorsolateral compared to ventromedial striatum (57% vs 9% cell density reduction in Dlx-CKO mice; [Pappas et al., 2015]). To examine if the cell autonomous ChI degeneration in ChAT-CKO mice follows a similar subregion-selective pattern, we determined the density of ChAT-immunoreactive neurons in each quadrant of the dorsal striatum (as previously [Pappas et al., 2015]). Significant reductions in ChI number were limited to the dorsolateral and dorsomedial segments of the dorsal striatum (72% and 54% cell density reductions in dorsolateral and dorsomedial, vs 12% and −4% in ventrolateral and ventromedial segments; Figure 1C). This topographic pattern of cell loss was present throughout the entire rostro-caudal extent of the striatum (Figure 1C,D, Figure 1—figure supplement 3). The dorsolateral selectivity of ChI neuron loss is highly relevant, as the dorsolateral striatum is a key motor circuit node functionally integrated according to topographic inputs, whereas ventromedial striatal neurons are connected in associative and limbic circuits (Alexander et al., 1986; Haber, 2016; Parent and Hazrati, 1995). In contrast, the basal forebrain contains cholinergic projection neurons subserving cognitive and attentional control (Hasselmo and Sarter, 2011; Ballinger et al., 2016), which do not degenerate in either model (Figure 1E,F). Conditional deletion of torsinA from forebrain cholinergic neurons therefore mimics the region-selective vulnerability observed in Dlx-CKO mice, demonstrating a cell autonomous requirement for torsinA in select cholinergic populations. To determine if differing time courses of torsinA loss (via differing torsinA half lives) contributes to selective vulnerability, we assessed torsinA levels in dorsal vs ventral striatal ChI at P0. Surprisingly, despite uniform prenatal Cre recombinase expression and preferential loss of dorsal ChI, torsinA levels were reduced to a greater extent in ventral ChI (dorsal ChI contained 82% of control torsinA levels, while ventral ChI had ~52% remaining; Figure 1—figure supplement 4). Non-vulnerable basal forebrain cholinergic neurons exhibited 49% of control torsinA immunoreactivity (Figure 1—figure supplement 5). These findings demonstrate that a more rapid loss of torsinA during development does not contribute to the unique vulnerability of dorsal ChI.

TorsinA deletion is restricted to forebrain structures in Dlx-CKO mice. In contrast, ChAT-CKO mice lack torsinA in all cholinergic neurons throughout the neuraxis, enabling us to assess the impact of torsinA loss in additional cholinergic populations. Unbiased stereology of ChAT-immunoreactive neurons in the brainstem demonstrates significantly fewer cholinergic neurons in the pedunculopontine (PPN) and laterodorsal tegmental (LDT) nuclei in 1 year old Chat-CKO mice (Figure 2A–D). The PPN and LDT also contain GABAergic, and glutamatergic neurons (Mena-Segovia, 2016), which significantly outnumber cholinergic neurons (Mena-Segovia et al., 2009; Wang and Morales, 2009). Unbiased stereology of NeuN +neurons in PPN and LDT showed no significant change in the overall number of neurons (Figure 2A,C). Because cholinergic neurons are a minority of cells in the PPN and LDT, it is possible that a significant reduction of this small sub-population cannot be detected when assessed by counting overall NeuN +neuron number. It is also possible that PPN and LDT cholinergic neurons exhibit reduced ChAT expression rather than actual cell loss. Regardless, either possibility demonstrates a cell autonomous role for torsinA for normal function of these cells. These findings also indicate that the loss or dysfunction of brainstem cholinergic neurons does not have deleterious effects on the viability of surrounding neurons. Consistent with this finding, there was no evidence of reactive microgliosis or astrogliosis in the brainstem (Figure 2—figure supplement 1). Quantification of the number of spinal motor neurons (C3-C5; [Kim et al., 2017]) demonstrated significantly fewer motor neurons in ChAT-CKO mice (Figure 2E,F).

Figure 2. ChAT-CKO mice have significantly fewer brainstem and spinal cord cholinergic neurons.

(A,B) Stereological quantification of ChAT-positive or NeuN-positive neurons in the pedunculopontine nucleus (PPN) of control and ChAT-CKO mice (ChAT; t(14)=4.531, p=0.0005. NeuN; t(14)=0.095, p=0.92). (C,D) Stereological quantification of ChAT-positive or NeuN-positive neurons in the laterdorsal tegmental nucleus (LDT) of control and ChAT-CKO mice (ChAT; t(14)=3.571, p=0.003. NeuN; t(14)=1.934, p=0.073). (E,F) Quantification of the number of ChAT-positive neurons in the cervical spinal cord of control and ChAT-CKO mice (t(6)=3.654, p=0.0107). Scale bars = 100 μm.

Figure 2.

Figure 2—figure supplement 1. Absence of gliosis in the brainstem of ChAT-CKO mice.

Figure 2—figure supplement 1.

Immunohistochemistry of GFAP (glial fibrillary acidic protein; specific to astrocytes) and Iba-1 (ionized calcium-binding adapter molecule 1; specific to microglia) in sagittal sections of control and ChAT-CKO hindbrain demonstrate normal distribution of glia.
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The identification of cholinergic dysfunction or loss in PPN and LDT is notable, as considerable data implicate these cells in motor and postural control. PPN and LDT cholinergic neurons are distributed in a rostrocaudal continuum in the brainstem, forming a coordinated functional unit (Mena-Segovia, 2016; Mena-Segovia and Bolam, 2017). PPN and LDT cholinergic neurons topographically innervate the striatum and striatal-projecting thalamic and midbrain dopamine neurons (Dautan et al., 2014), such that rostral PPN modulates motor-related circuits, LDT innervates limbic circuits, and caudal PPN targets both regions (Mena-Segovia, 2016; Xiao et al., 2016) via both direct and indirect inputs. Consistent with a central role in modulating locomotor activity, optogenetic stimulation of PPN cholinergic neurons alters locomotion speed, while stimulation of adjacent glutamatergic neurons induces locomotion (Xiao et al., 2016; Roseberry et al., 2016; Capelli et al., 2017). Cholinergic PPN lesion alone or in combination with dopaminergic denervation impairs gait and causes postural abnormalities in primates (Grabli et al., 2013; Karachi et al., 2010). In rodents, cholinergic-selective PPN lesion impairs performance on the accelerating rotarod and alters sensorimotor gating (MacLaren et al., 2014a; MacLaren et al., 2014b), while non-specific PPN ablation alters gait (Blanco-Lezcano et al., 2017) and impairs reversal learning (Syed et al., 2016). Human neuroimaging and postmortem studies also provide support for a connection between PPN cholinergic integrity and motor function. PPN cholinergic loss is linked to gait abnormalities in Parkinson disease (Karachi et al., 2010; Bohnen et al., 2009), and brainstem lesions (including PPN loss) can result in complex dystonia (Jankovic and Patel, 1983; LeDoux and Brady, 2003; Loher and Krauss, 2009; Zweig et al., 1988; Mente et al., 2018). Systematic cholinergic brainstem cell counts have not been performed in DYT1 dystonia postmortem samples; most studies have failed to demonstrate neuronal inclusions or overt cell loss in this region (Paudel et al., 2014; Pratt et al., 2016; McNaught et al., 2004).

Motor behavior is severely disrupted in ChAT-CKO mice, but is distinct from the Dlx-CKO phenotype (Figure 3; Table 1). ChAT-CKO pups are initially indistinguishable from littermates, but at approximately 4 weeks of age develop a hunched posture, have unkempt fur, and exhibit reduced responsiveness to handling (Figure 3A, Figure 3—figure supplement 1). Whereas normal mice exhibit a slight dorsal spinal curvature at rest, ChAT-CKO mice exhibit severe kyphosis, including during locomotion (assessed by two observers blind to experimental conditions; Figure 3B; Figure 3—figure supplement 1; Figure 3—video 1) (Guyenet et al., 2010). ChAT-CKO mice also exhibit signs of weakness, including a significantly reduced ability to hang by the forelimbs (Figure 3C), tremulous movements, labored breathing (Figure 3—video 1), and significantly reduced horizontal and vertical movement in the open field (Figure 3D,E, Figure 3—figure supplement 2). Remarkably, performance on the accelerating rotarod during two days of training appears normal (Figure 3F). The normal rotarod behavior differs from models of motor neuron and neuromuscular disease, suggesting that neuromuscular weakness is modest in ChAT-CKO, and less likely to contribute to other behavioral phenotypes (e.g., postural abnormality). The gait of ChAT-CKO mice is also significantly altered (Figure 3G–I). This constellation of behavioral phenotypes is distinct from Dlx-CKO mice (Table 1), in which loss of dorsal striatal ChI is associated with a set of persistent abnormal action-induced motor behaviors, including limb clasping and trunk twisting during tail suspension and open field hyperactivity (Pappas et al., 2015). ChAT-CKO mice did not exhibit fore- or hindlimb clasping during tail suspension, but did exhibit tremulousness and trunk twisting (15 CKO, 19 heterozygous, 22 Cre control, and 19 wild type mice observed; Figure 3—video 2). These results suggest that dorsal striatal ChI neurodegeneration may not, by itself, be sufficient to cause limb clasping during tail suspension. However, the co-occurrence of brainstem and spinal cord neurodegeneration and tremulousness in ChAT-CKO mice could modify a clasping phenotype and therefore limit this strength of this conclusion.

Figure 3. Motor behavior is severely disrupted in ChAT-CKO mice.

(A) Representative image of a control and ChAT-CKO mouse demonstrates severe kyphosis and unkempt coat. (B) ChAT-CKO mice exhibit significantly increased kyphotic curvature during locomotion (Mann-Whitney U = 35, p<0.0001). (C) ChAT-CKO mice exhibit a significantly reduced latency to fall during forelimb suspension (Mann-Whitney U = 71.5, p<0.0001). (D, E) ChAT-CKO mice are hypoactive in the open field (horizontal movement, t(23)=2.345, p=0.028; vertical rears, welch-corrected t(15.1) = 2.345, p=0.033). (F) Performance on the accelerated rotarod does not significantly differ from controls (two-way repeated measures ANOVA, genotype, F(1,43)=0.75, p=0.389; trial, F(9,387)=55.63, p<0.0001; interaction, F(9,387)=1.194, p=0.297). (G - I) ChAT-CKO mouse gait is abnormal during locomotion (paw angle, two-way ANOVA main effect of genotype, F(1,56)=30.54, p<0.0001, main effect of limb F(3,56)=51.02, p<0.0001, interaction F(3,56)=13.51, p<0.0001, post-hoc Sidak’s multiple comparisons test. Stance width, t(14)=3.329, p=0.005. Stride length, two-way ANOVA genotype F(1,28)=3.164, p=0.086, limb F(1,28)=0.02, p=0.887, interaction F(1,28)=0.0001, p=0.989).

Figure 3.

Figure 3—figure supplement 1. Representative examples of control and ChAT-CKO spinal cords demonstrate significant kyphotic curvature.

Figure 3—figure supplement 1.

Figure 3—figure supplement 2. ChAT-CKO mice are significantly hypoactive.

Figure 3—figure supplement 2.

(Upper panel) Open field analysis of horizontal movements (two-way repeated measures ANOVA main effect of genotype F(1,23)=5.498, p=0.02, time F(11,253)=9.222, p<0.0001, interaction F(11,253)=0.899, p=0.541, post-hoc Sidak’s multiple comparisons test). (Lower panel) Open field analysis of vertical rearing movements (two-way repeated measures ANOVA main effect of genotype F(1,23)=4.413, p=0.046, time F(11,253)=2.452, p=0.0063, interaction F(11,253)=0.987, p=0.458).

Figure 3—video 1. Representative video demonstrating tremulousness, kyphosis, and hyperactivity in ChAT-CKO mice, as compared to controls.

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DOI: 10.7554/eLife.36691.013

Figure 3—video 2. ChAT-CKO exhibit twisting and tremulousness, but not limb clasping during tail suspension.

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DOI: 10.7554/eLife.36691.014
Representative video demonstrating tail suspension test in control and ChAT-CKO mice.
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Table 1. Behavioral properties of Dlx-CKO and ChAT-CKO mice.

Motor function Dlx-CKO ChAT-CKO
Pappas et al., 2015 eLife 4:e08352 present manuscript
Tail suspension Trunk twisting Trunk twisting
Forelimb clasping -
Hindlimb clasping -
- Tremulousness
Open field Hyperactivity Hypoactivity
Rotarod Normal Normal
Response to handling Exaggerated Reduced
Weakness, latency to fall Grid hang reduction Wire hang reduction
Gait Normal by eye Abnormal by eye
Slightly reduced stance width Increased stance width
- Increased paw angle
Overt postural abnormalities - Severe kyphosis
Tremulous movement - Severe
Labored breathing - Severe
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While no single system or experimental approach can fully model a disease, the extreme postural abnormalities (kyphosis and twisting) in ChAT-CKO mice are reminiscent of Oppenheim’s original description of dystonia (Klein and Fahn, 2013), suggesting that a constellation of cholinergic abnormalities may contribute to such a phenotype. The abnormal gait, tremulous movement, weakness, labored breathing, and appearance of reduced muscle mass in ChAT-CKO mice are consistent with brainstem and spinal cord pathology, yet the time course of ChAT-CKO abnormalities (beginning during development) differ from motor neuron and neuromuscular disease models, in which behavioral phenotypes typically emerge in adulthood (9–11 months of age; (Dickinson and Meikle, 1973; Bridges et al., 1992; Deconinck et al., 1997; Grady et al., 1997; Laws and Hoey, 2004; Liu et al., 2016; Sopher et al., 2004; Monks et al., 2007). Early motor behavioral manifestations also occur in Dlx-CKO and other DYT1 models, emphasizing the importance of torsinA function during development and maturation at behavioral (Pappas et al., 2015; Liang et al., 2014), cellular (Pappas et al., 2018), and molecular levels (Tanabe et al., 2016).

These findings establish a cell autonomous requirement of torsinA for the normal function and survival of distinct populations of cholinergic neurons. Comparison of basic cellular properties between susceptible and invulnerable cholinergic neuron populations does not identify obvious patterns driving selective vulnerability (Tables 2 and 3). Within the striatum, dorsal ChI are highly vulnerable to cell death, while ventral ChI are spared. It is unclear whether molecular differences within different ChI populations drive vulnerability, or if differences in connectivity or response to inputs contributes to their loss; these possibilities are not mutually exclusive. While often considered a single neuronal class, an existing and enlarging literature demonstrates that dorsal and ventral striatal ChI exhibit significant differences in morphology, regulation, and receptor expression (reviewed in [Gonzales and Smith, 2015]), as well as differing firing patterns during behavioral tasks (Yarom and Cohen, 2011) and responses to serotonergic input (Virk et al., 2016). These differences implicate the presence of multiple ChI subclasses, though it is important to note that the spared ‘ventral’ population here represents the ventral part of the dorsal striatum, not the nucleus accumbens. Thalamostriatal and corticostriatal input is highly topographic (Alexander et al., 1986; Smith et al., 2004), raising the possibility that aberrant input from different thalamic nuclei or cortical regions (or aberrant response to that input) could alter the susceptibility of dorsal vs ventral ChI. It is likely that a combination of these and other factors plays a role in the differential susceptibility of cholinergic neuronal populations, including their molecular profiles (e.g., protective factors in some neurons, susceptibility factors in others), the response to afferent inputs, and their inherent physiological properties.

Table 2. Vulnerability of cholinergic populations.

(*)=Unconfirmed by independent marker.

Cre expression Cell death vulnerability
Cholinergic population Dlx-Cre ChAT-Cre Dlx-Cre ChAT-Cre
Dorsolateral striatum
(including dorsal caudate
putamen)		 Confirmed Confirmed Severe Severe
Dorsomedial striatum
(including ventral caudate
putamen)		 Confirmed Confirmed Mild Spared
Nucleus accumbens Confirmed Confirmed - -
Basal forebrain Confirmed Confirmed Spared Spared
Cholinergic Brainstem Absent Confirmed n/a Severe (*)
Primary Motor Neurons Absent Confirmed n/a Moderate
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Table 3. Properties of cholinergic neuronal populations.

‘Nucleus Basalis Complex’=Nucleus Basalis of Meynert, Horizontal limb of the diagonal band of Broca, Ventral Pallidum, Magnocellular Preoptic Area, Substantia Inominata, Nucleus of the Ansa Lenticularis. ‘Septa”l = Medial Septum, Vertical Limb of the Diagonal Band of Broca. ‘Cholinergic Brainstem’=Pedunculopontine Nucleus, Laterodorsal Tegmental Nucleus (Pappas et al., 2015; Mena-Segovia and Bolam, 2017; Gonzales and Smith, 2015; Manns et al., 2000; Unal et al., 2012; Petzold et al., 2015; Kanning et al., 2010; Kreitzer, 2009; Zaborszky et al., 2012; Garcia-Rill, 1991; Semba et al., 1988; Semba and Fibiger, 1992; Phelps et al., 1990a; Phelps et al., 1988; Phelps et al., 1990b; Phelps et al., 1989; Aroca and Puelles, 2005; Schambra et al., 1989).

Cholinergic population Neuronal class Firing properties Efferent projections Afferent inputs Birth date/final mitosis Embryonic origin ChAT expression
Dorsolateral striatum (including dorsal caudate putamen) Interneuron tonically active, 2–10 Hz baseline firing rate Local - striatal spiny projection neurons and fast spiking interneurons Thalamus, sensorimotor cortex, striatal spiny projection neurons, striatal interneurons E12-E15 MGE ~E16
Dorsomedial striatum (including ventral caudate putamen) Interneuron tonically active, 2–10 Hz baseline firing rate Local - striatal spiny projection neurons and fast spiking interneurons Thalamus, association cortices, striatal spiny projection neurons, striatal interneurons E12-E15 MGE ~E16
Nucleus accumbens Interneuron tonically active, 0.6–12 Hz baseline firing rate Local - striatal spiny projection neurons and fast spiking interneurons Thalamus, frontal cortex, striatal spiny projection neurons, striatal interneurons E12-E15 MGE ~E16
Basal forebrain Projection neuron Tonic/burst, subtype dependent Cortex (Nucleus Basalis Complex), Hippocampus (Septal) Medulla, locus ceruleus, substantia nigra, ventral tegmental area, hypothalamic nuclei, nucleus accumbens, amygdala, local intrinsic GABAergic and glutamatergic collaterals E11-E15 POA/MGE ~E15-16
Cholinergic Brainstem Projection neuron episodic Midbrain, superior colliculus, thalamus, globus pallidus, hypothalamus, septum, striatum, cortex Brainstem reticular formation, midbrain central gray, lateral hypothalamus-zona incerta, cortex, amygdala, basal forebrain, basal ganglia output nuclei, brainstem and spinal cord sensory nuclei E12-E13 Ventral rhombomere 1 (r1)
Primary Motor Neurons Projection neuron subtype dependent Muscle Motor Cortex, local spinal cord interneurons and sensory neurons E11-E12 Ventral spinal cord progenitor domains E13
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These studies greatly strengthen the connection between torsinA and cholinergic dysfunction, demonstrating that specific cholinergic populations exhibit a cell autonomous selective vulnerability to torsinA deficiency, while others – basal forebrain and ventral striatum – are spared. These findings open novel avenues of study aimed at defining the molecular mechanisms responsible for this cell autonomous selective vulnerability, and circuit-level analyses to ameliorate the effects of cholinergic neurotransmission abnormalities.

Materials and methods

Key resources table.

Reagent type (species)
or resource		 Designation Source or reference Identifiers Additional information
Gene
(Mus musculus)		 Tor1a NA NCBI Gene: 30931;
MGI:1353568		 Encodes TorsinA
Strain, strain
background(M. musculus)		 ChAT-Cre Jackson Laboratories Stock ID 006410 Chattm2(cre)Lowl; (Chat-IRES-Cre)
Strain, strain
background(M. musculus)		 Tor1aFlx/Flx Jackson Laboratories Stock ID 025832 Tor1atm3.1Wtd
Strain, strain
background(M. musculus)		 Tor1a-/- Jackson Laboratories Stock ID 006251 Tor1atm1Wtd
Antibody Choline
Acetyltransferase		 Millipore AB144P RRID: AB_2079751 1:100
Antibody P75 Neurotrophin
Receptor		 Santa Cruz sc6188 RRID: AB_2267254 1:100
Antibody NeuN Cell Signaling #12943 RRID: AB_2630395 1:500
Antibody GFAP Cell Signaling #3670P RRID: AB_561049 1:1000
Antibody Iba-1 Wako 019–19741 RRID: AB_839504 1:500
Antibody TorsinA Abcam ab34540 RRID: AB_2240792 1:100
Antibody anti-mouse ThermoFisher
A-31571		 RRID: AB_162542 1:800
Antibody anti-rabbit ThermoFisher
A-21206		 RRID: AB_2535792 1:800
Antibody anti-rabbit ThermoFisher
A-31572		 RRID: AB_162543 1:800
Antibody anti-goat ThermoFisher
A-21432		 RRID: AB_2535853 1:800
Antibody anti-goat Jackson
Immunoresearch
705-065-003		 RRID: AB_2340396 1:800
Commercial assay
or kit		 ABC HRP Kit
(Standard)		 Vector Laboratories Pk-6100 Vectastain elite
ABC kit
Open in a new tab

Animals

ChAT-CKO mice were generated by crossing Chattm2(cre)Lowl mice (Rossi et al., 2011) with Tor1aFlx/Flx mice (Liang et al., 2014), using the breeding strategy described in (Pappas et al., 2015), and maintained as previously described (Pappas et al., 2015).

Sample size estimation

Sample sizes for histological and behavioral studies were determined by performing a power analysis of the open field or striatal cholinergic stereological data (mean and std. dev.) from (Pappas et al., 2015), an alpha of 0.01, and beta of 0.1. (Kane SP. Sample Size Calculator. ClinCalc: http://clincalc.com/stats/samplesize.aspx). Experimental cohorts were generated accordingly.

Imaging and stereology

Brain sections were generated and stained with immunohistochemistry using the methods described in (Pappas et al., 2015; Pappas et al., 2018). Antibodies and reagents are listed in Table 4. Sections were observed with epifluorescence or brightfield microscopy (Pappas et al., 2018), and unbiased stereological cell counting was performed with StereoInvestigator software using the Optical Fractionator probe (specific parameters in Table 5). Striatal cell density was quantified as done previously (Pappas et al., 2015). Spinal cord neurons were quantified as described in (Kim et al., 2017).

Table 4. Antibodies used for immunohistochemistry.

Level Antigen Host Conjugated Dilution Source
Primary Choline Acetyltransferase Goat - 1:100 Millipore AB144P
Primary P75 Neurotrophin Receptor Goat - 1:100 Santa Cruz sc6188
Primary NeuN Rabbit - 1:500 Cell Signaling #12943
Primary GFAP Mouse - 1:1000 Cell Signaling #3670P
Primary Iba-1 Rabbit - 1:500 Wako 019–19741
Primary TorsinA Rabbit - 1:100 Abcam ab34540
Secondary anti-mouse Donkey Alexafluor-647 1:800 ThermoFisher A-31571
Secondary anti-rabbit Donkey Alexafluor-488 1:800 ThermoFisher A-21206
Secondary anti-rabbit Donkey Alexafluor-555 1:800 ThermoFisher A-31572
Secondary anti-goat Donkey Alexafluor-555 1:800 ThermoFisher A-21432
Secondary anti-goat Donkey biotin 1:800 Jackson Immunoresearch 705-065-003
Open in a new tab

Table 5. Stereology parameters.

Region Marker Counting frame (μm) Grid size (μm) Guard zone (μm) Dissector (μm) Section cut thickness (μm)
Striatum ChAT 100 × 100 250 × 250 1 10 40
NBM P75 90 × 90 200 × 200 5 30 50
MS/VDB P75 90 × 90 200 × 200 5 30 50
GP P75 100 × 100 140 × 140 5 30 50
PPN and LDT ChAT 75 × 75 150 × 150 5 30 50
PPN and LDT NeuN 40 × 40 250 × 250 5 30 50
Open in a new tab

Behavioral analysis

Tail suspension, forelimb wire suspension, open field, accelerating rotarod, and gait analysis were performed as described in (Pappas et al., 2015). Kyphosis severity was scored as described in (Guyenet et al., 2010).

Statistical analysis

t-tests, one-way, or two-way ANOVA with posthoc corrections for multiple comparisons were performed to compare experimental groups (details in each figure legend). If variances were significantly different between groups, non-parametric tests were performed.

Acknowledgements

We thank Stephanie Mrowczynski for expert technical assistance and the Dauer lab for helpful comments and suggestions. This research was supported by generous support from Tyler’s Hope for a Dystonia Cure and the following grants: RO1NS077730 (William T Dauer), RO1NS057482, R21NS099921, and R56NS104218 (Umrao R Monani).

Funding Statement

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

Contributor Information

William T Dauer, Email: dauer@med.umich.edu.

Louis J Ptáček, University of California, San Francisco, United States.

Huda Y Zoghbi, Texas Children's Hospital, United States.

Funding Information

This paper was supported by the following grants:

  • National Institute of Neurological Disorders and Stroke RO1NS077730 to William T Dauer.

  • Tyler's Hope for a Dystonia Cure to William T Dauer.

  • National Institutes of Health RO1NS057482 to Umrao R Monani.

  • National Institutes of Health R21NS099921 to Umrao R Monani.

  • National Institutes of Health R56NS104218 to Umrao R Monani.

Additional information

Competing interests

No competing interests declared.

Author contributions

Conceptualization, Data curation, Formal analysis, Investigation, Writing—original draft, Writing—review and editing.

Formal analysis, Investigation, Writing—review and editing.

Formal analysis, Investigation, Writing—review and editing.

Investigation, Writing—review and editing.

Resources, Supervision, Funding acquisition, Writing—review and editing.

Conceptualization, Resources, Supervision, Funding acquisition, Writing—review and editing.

Ethics

Animal experimentation: All experiments were performed according to the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All procedures involving animals were approved by the University of Michigan Institutional Animal Care and Use Committee (animal use protocol PRO00006600). All effort was taken to minimize the number of animals used and to prevent discomfort or distress.

Additional files

Transparent reporting form
elife-36691-transrepform.docx (245.8KB, docx)
DOI: 10.7554/eLife.36691.020

Data availability

All data generated during this study are included in the manuscript and supporting files

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Decision letter

Editor: Louis J Ptáček1
Reviewed by: Louis J Ptáček2

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 "A cell autonomous torsinA requirement for cholinergic neuron survival and motor control" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Louis J Ptáček as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by a Senior Editor.

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:

In a previous study (Pappas et al., 2015), Pappas et al. made the important findings that deletion of torsinA in embryonic progenitors of forebrain cholinergic and GABAergic neurons caused selective degeneration of dorsal striatal cholinergic interneurons, and that the loss of dorsal striatal cholinergic interneurons was sufficient to cause dystonic-like twisting movements that emerged during juvenile CNS maturation.

The original paper already described selective loss of ChI. Dlx5/6 is far broader in its expression than ChAT Cre – it encompasses all forebrain inhibitory neurons and cholinergic neurons. So it would have been hard to know if the ChI degeneration was due to loss of all other interneurons or if was cell-intrinsic, so here they show it is specific to cholinergic. It also associated motor dysfunction with the ChI loss – and a central point of that paper was that cholinergic cells were responsible since motor defects were rescued by cholinergic pharmacology. Given this knowledge, the new data showing torsinA deletion from cholinergic neurons causes motor dysfunction is less interesting. In other work, the authors defined a postnatal developmental window where neurons were most susceptible to torsinA loss. At least, this was the take home message of their study (Tanabe, 2016). Given this, there is some worry about whether the difference in neuronal vulnerability relates to the speed that torsinA is lost after Cre-mediated deletion. We don't know that Cre expression is the same across all ChI, in terms of levels or timing. We also don't know if torsinA half-life is the same in all ChI – and likely is not and may depend on cellular metabolic activity, etc. It doesn't appear that we have a good handle on torsinA half-life in general, and it's potentially quite long lived. Also, what role might torsinB have in reducing vulnerability in some cells? The most important conclusion, that the phenotype is indeed from cholinergic cells and not all inhibitory neurons in striatum, is the most notable part of this manuscript. It is amazing that a very small percentage of neurons in the dorsal striatum can have such dramatic and quite specific effects on behavior in a disease model.

While the data here (and their other papers) are convincing, we feel it is important to completely exclude the possibility that selective neuronal vulnerability (that they focus on here) derives from technical vs. disease-relevant biology. The reviewers wanted to be sure that ChAT-Cre is not expressed earlier or more broadly than expected. The story is more exciting if associated with a satisfactory explanation of differential vulnerability. Also, it is essential to ensure that differential vulnerability is not due to differences in completeness of torsinA deletion in different cell types. Are reagents available to do this in a timely manner? It is important to compare efficiency of deletion in both KOs, although the key finding is that ChAT alone reproduces the broad Dlx5/6 KO. They can do in situ to evaluate effectiveness of KO.

Essential revisions:

1) It is not clear why some populations of cholinergic neurons are more vulnerable to torsinA deficiency. Although this is a hard question to address, inclusion of other prominent cholinergic neurons and contrasting their cellular properties may provide some clues. The manuscript gives the impression that cholinergic neurons related to motor functions are more vulnerable. This seems to be a circular argument (not a mechanistic one).

2) In PPN and LDT, both cholinergic neurons and NeuN+ cells were counted. It is surprising that there was loss of cholinergic neurons but not NeuN+ cells. It is likely that noise in the data diluted the signal in counting NeuN+ cells. However, it is also possible that loss of cholinergic neurons is due to loss of cholinergic marker expression. This needs more data and/or explanation.

3) The motor symptom differences between ChAT-CKO and Dlx-CKO were not explained clearly. It will be helpful to list the main differences between the two manipulations at both the circuit level and behavioral level to give more insights into the phenotypic differences and functional implications.

4) The new information is limited to showing that ChI loss occurs because of torsinA loss in the ChI themselves. This does not answer whether different ChI populations have different molecular profiles that render them more or less sensitive to torsinA loss (so that dorsal vs. ventral ChI might be considered as different cell types). Alternatively, it might still be that the nature of ChI connectivity differs between striatal regions so that only one set of cells requires torsinA (for example if degeneration depends on excitotoxicity, and ChI in dorsal striatum receive more direct glutamatergic inputs).

5) A technical issue is whether it is 100% clear that the genetic technology causes the exact same loss of torsinA in the two populations. The manuscript shows Cre expression across the striatum, but this is not synonymous with a time course of how torsinA protein is lost from the two populations. Is it possible that dorsal neurons are more sensitive because they more rapidly lose torsinA protein? This needs very careful controls given it is central to their conclusion that the regional specificity has physiological relevance.

6) I am also left wondering how ChI degeneration relates to other mechanisms shown by the group like abnormal nuclear pore complexes, or torsinB expression. Do these have any role in the selective vulnerability? Further, is there a link between ChI vulnerability and that of the deep cerebellar nuclei and sensorimotor cortical neurons that they showed are selectively lost when torsinA is deleted across the brain (Liang, 2014).

eLife. 2018 Aug 17;7:e36691. doi: 10.7554/eLife.36691.024

Author response

Essential revisions:

1) It is not clear why some populations of cholinergic neurons are more vulnerable to torsinA deficiency. Although this is a hard question to address, inclusion of other prominent cholinergic neurons and contrasting their cellular properties may provide some clues. The manuscript gives the impression that cholinergic neurons related to motor functions are more vulnerable. This seems to be a circular argument (not a mechanistic one).

We appreciate this comment and have removed all statements implying that connections to motor function render neurons more vulnerable. We also include a new table comparing the cellular properties (neuronal class, firing properties, efferent projections, afferent input, birth dates, embryonic origins, time of first ChAT expression, and vulnerability to cell death) between dorsal striatum, ventral striatum, basal forebrain, PPN/LDT, and primary motor neurons (Table 2). Although obvious patterns between vulnerable populations do not immediately emerge, we believe that this information will be valuable for future studies stimulated by our new findings, aimed at further advancing understanding of the mechanisms of selective vulnerability.

2) In PPN and LDT, both cholinergic neurons and NeuN+ cells were counted. It is surprising that there was loss of cholinergic neurons but not NeuN+ cells. It is likely that noise in the data diluted the signal in counting NeuN+ cells. However, it is also possible that loss of cholinergic neurons is due to loss of cholinergic marker expression. This needs more data and/or explanation.

We are also surprised that NeuN+ cell numbers were not different in the LDT and PPN and agree that there are other potential explanations for the ChAT+ cell numbers, including reduction of ChAT expression without frank cell loss.

Several anatomical studies demonstrate that non-cholinergic cell types in the PPN and LDT greatly outnumber cholinergic neurons. There are at least twice as many GABAergic as cholinergic neurons (Mena-Segovia et al., 2009), and glutamatergic neurons are at least as abundant as GABAergic, or present at even higher numbers (Martinez-Gonzalez et al., 2012; Wang and Morales, 2009). Indeed, in the rostral PPN, the density of GABAergic neurons is more than 5 times higher than cholinergic neurons (Martinez-Gonzalez et al., 2011). Because cholinergic neurons represent a small minority of cells in the PPN and LDT, we believe it most likely that reduction of this small population of cells was ‘lost in the noise’ of the overall NeuN+ cell counts, as suggested.

To specifically address the issue of cell loss versus ChAT downregulation, we attempted to define alternative markers of brainstem cholinergic neuron populations. We performed a series of studies examining P75 and VAChT as additional markers. Unfortunately, these molecules did not reliably or reproducibly mark PPN and LDT cell bodies. P75 was present in synaptic inputs to cholinergic brainstem neurons but not in PPN or LDT neurons themselves. VAChT appeared punctate throughout the region, but cell soma expression was not clear or sufficiently defined for stereological assessment. Unlike striatal ChI, cholinergic brainstem neurons are not uniformly larger than other intermingled cell populations, preventing us from using cell size as a proxy (as previously done for forebrain populations (Pappas et al., 2015)). We therefore cannot provide additional evidence to support the presence of cholinergic brainstem neurodegeneration at this time.

In our revised manuscript, we highlight these valuable new points. We make clear the relative numbers of cholinergic and non-cholinergic cells in these brainstem nuclei and point out that our findings in the brainstem may reflect downregulation of ChAT as opposed to cell loss (as clearly occurs in striatum). Importantly, whether these cells “only” lose ChAT expression, or degenerate, our findings are the first to demonstrate a cell autonomous torsinA requirement for brainstem cholinergic neuron function.

3) The motor symptom differences between ChAT-CKO and Dlx-CKO were not explained clearly. It will be helpful to list the main differences between the two manipulations at both the circuit level and behavioral level to give more insights into the phenotypic differences and functional implications.

We appreciate this feedback. We have now generated a table outlining all known motor features of Dlx-CKO and ChAT-CKO mice (Table 1), which will greatly facilitate for the reader a direct comparison between the models.

4) The new information is limited to showing that ChI loss occurs because of torsinA loss in the ChI themselves. This does not answer whether different ChI populations have different molecular profiles that render them more or less sensitive to torsinA loss (so that dorsal vs. ventral ChI might be considered as different cell types). Alternatively, it might still be that the nature of ChI connectivity differs between striatal regions so that only one set of cells requires torsinA (for example if degeneration depends on excitotoxicity, and ChI in dorsal striatum receive more direct glutamatergic inputs).

We agree that the major mechanistic finding of this paper is that torsinA loss in ChI themselves causes cell death, rather than via non-cell autonomous mechanisms of surrounding torsinA deficient neurons. This result is striking and represents an important advance considering the subregion specificity of the loss (to the most dorsal “motor” aspects of dorsal striatum) is driven by a cell autonomous effect of torsinA in a population of cells typically considered “uniform.” The intriguing and important question raised by the reviewers speaks to the interesting issues raised by our novel finding: do multiple unique ChI cell types exist (one vulnerable, others spared), or do connectivity differences drive differential susceptibility to torsinA loss of function.

There is precedent for either possibility (or both). While often considered a single neuronal class, dorsal and ventral striatal ChI exhibit significant differences in morphology, regulation, and receptor expression (reviewed in (Gonzales and Smith, 2015)), as well as different responses to serotonin input (Virk et al., 2016) and differential firing patterns during some motor behavioral tasks (Yarom and Cohen, 2011). These differences are consistent with the existence of multiple ChI subclasses, though it is important to note that the “ventral” population in our manuscript is the ventral part of the dorsal striatum, not the nucleus accumbens. In our revised manuscript we have better highlighted this literature, which we believe – together with our new findings – will stimulate additional work in this interesting area.

It is also possible that the unique pattern of afferent inputs could contribute to the striking pattern of subregion vulnerability we observe, as thalamostriatal and corticostriatal input is highly topographic (Alexander et al., 1986; Smith et al., 2004).

We think it most likely that a combination of factors plays a role in the differential susceptibility of different cholinergic neuronal populations, including their molecular profiles (e.g., protective factors in some neurons, susceptibility factors in others), the response to differential afferent inputs, and their inherent physiological properties. Our new findings point to future experiments comparing vulnerable and invulnerable populations at multiple mechanistic levels that will be required to elucidate the mechanism(s) responsible differential vulnerability. These important questions will require significant experimental effort – likely years worth of work – which we believe is beyond the scope of this manuscript. However, to help stimulate future work, we have added a short discussion of different possible explanations for differential susceptibility to the Discussion section.

5) A technical issue is whether it is 100% clear that the genetic technology causes the exact same loss of torsinA in the two populations. The manuscript shows Cre expression across the striatum, but this is not synonymous with a time course of how torsinA protein is lost from the two populations. Is it possible that dorsal neurons are more sensitive because they more rapidly lose torsinA protein? This needs very careful controls given it is central to their conclusion that the regional specificity has physiological relevance.

We appreciate the reviewer raising this very important point, which we had not previously systematically examined. To further explore this issue, we assessed torsinA levels in cholinergic neurons at P0 in ChAT-CKO and Cre negative control mice (the same time for which we document uniform Cre expression). Despite unambiguous prenatal Cre expression (Figure 1—figure supplement 1), our new data demonstrate that at least 50% of torsinA remained in cholinergic neurons at P0 using the semi-quantitative measurement of fluorescence intensity. To determine whether, following Cre-recombination, the levels of torsinA loss correspond to cell loss, we compared the levels of torsinA immunofluorescence in dorsal striatal (vulnerable) and ventral striatal (invulnerable) ChI at P0. Surprisingly, the invulnerable ventral striatal ChI exhibited a significantly greater decrease of torsinA (~48% decrement) compared to vulnerable dorsal striatal ChI population (~18% decrement) (Two-way ANOVA with post-hoc Sidak’s multiple comparisons test. ChAT-CKO dorsal vs ventral p<0.0002. Full details in legend to Figure 1—figure supplement 4). We also assessed torsinA levels in basal forebrain cholinergic projection neurons, which are spared in all DYT1 models assessed. Basal forebrain cholinergic neurons from ChAT-CKO mice exhibited an ~51% of loss of torsinA (compared to control; p<0.0001, Welch’s t-test; Figure 1—figure supplement 5), similar to the levels in ventral ChI. Considered together, these data argue against the possibility that more rapid loss of torsinA from dorsal striatal neurons contributes to their selective degeneration. These findings also eliminate the possibility of a technical artifact whereby Cre recombination occurs selectively in dorsal ChI.

6) I am also left wondering how ChI degeneration relates to other mechanisms shown by the group like abnormal nuclear pore complexes, or torsinB expression. Do these have any role in the selective vulnerability? Further, is there a link between ChI vulnerability and that of the deep cerebellar nuclei and sensorimotor cortical neurons that they showed are selectively lost when torsinA is deleted across the brain (Liang, 2014).

We agree that linking previously described torsinA loss-of-function mediated phenotypes (nuclear pore complex abnormalities or ubiquitin accumulation (Liang et al., 2014; Pappas et al., 2018) is of interest and could help to advance a theme underlying selective cell vulnerability. Interestingly, the events occurring in striatal ChI may be distinct from those previously defined (including in DCN and sensorimotor cortex). In contrast to these non-cholinergic neuronal populations, we do not observe nuclear pore complex or ubiquitin abnormalities in striatal ChIs (Pappas et al., 2018). As noted, our prior work a links torsinB levels to the developmental nuclear envelope phenotypes (Kim et al., 2010; Tanabe et al., 2016), but that work did not directly address the relationship between torsinB and cell death. We have used laser capture microdissection to examine the levels of torsinB mRNA in wild type and torsinA null striatal ChI; we find no difference in torsinB levels between these conditions so think it is unlikely to be playing a role in this specific context (we would be happy to include these data if the reviewers deem it essential). As noted in our response to the prior question, these new findings are the first to identify a cell autonomous role for torsinA in a subpopulation of striatal ChI critical for motor function (and which as strongly implicated in the disease). This new finding represents a significant advance in understanding the mechanism of selective ChI loss reported in our original eLife publication, setting the stage for future studies that we believe to be beyond the scope of the current manuscript.

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    DOI: 10.7554/eLife.36691.020

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