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.

To determine if ChI neurodegeneration is a cell autonomous effect of torsinA loss, we conditionally deleted torsinA from cholinergic neurons (Chat-IRES-Cre⁺, Tor1a^Flx/-; ‘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 with 5 supplements see all

Download asset Open asset

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 with 1 supplement see all

Download asset Open asset

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 with 4 supplements see all

Download asset Open asset

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.

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.

1. 1
   Diminished striatal [123I]iodobenzovesamicol binding in idiopathic cervical dystonia

   1. RL Albin
   2. D Cross
   3. WT Cornblath
   4. JA Wald
   5. K Wernette
   6. KA Frey
   7. S Minoshima

   (2003)

   Annals of Neurology 53:528–532.

   https://doi.org/10.1002/ana.10527

   • PubMed
   • Google Scholar
2. 2
   Parallel organization of functionally segregated circuits linking basal ganglia and cortex

   1. GE Alexander
   2. MR DeLong
   3. PL Strick

   (1986)

   Annual Review of Neuroscience 9:357–381.

   https://doi.org/10.1146/annurev.ne.09.030186.002041

   • PubMed
   • Google Scholar
3. 3
   Postulated boundaries and differential fate in the developing rostral hindbrain

   1. P Aroca
   2. L Puelles

   (2005)

   Brain Research Reviews 49:179–190.

   https://doi.org/10.1016/j.brainresrev.2004.12.031

   • PubMed
   • Google Scholar
4. 4
   Basal Forebrain Cholinergic Circuits and Signaling in Cognition and Cognitive Decline

   1. EC Ballinger
   2. M Ananth
   3. DA Talmage
   4. LW Role

   (2016)

   Neuron 91:1199–1218.

   https://doi.org/10.1016/j.neuron.2016.09.006

   • PubMed
   • Google Scholar
5. 5
   Motor dysfunction and alterations in glutathione concentration, cholinesterase activity, and BDNF expression in substantia nigra pars compacta in rats with pedunculopontine lesion

   1. L Blanco-Lezcano
   2. J Jimenez-Martin
   3. ML Díaz-Hung
   4. E Alberti-Amador
   5. M Wong-Guerra
   6. ME González-Fraguela
   7. B Estupiñán-Díaz
   8. T Serrano-Sánchez
   9. L Francis-Turner
   10. S Delgado-Ocaña
   11. Y Núñez-Figueredo
   12. Y Vega-Hurtado
   13. I Fernández-Jiménez

   (2017)

   Neuroscience 348:83–97.

   https://doi.org/10.1016/j.neuroscience.2017.02.008

   • PubMed
   • Google Scholar
6. 6
   History of falls in Parkinson disease is associated with reduced cholinergic activity

   1. NI Bohnen
   2. ML Müller
   3. RA Koeppe
   4. SA Studenski
   5. MA Kilbourn
   6. KA Frey
   7. RL Albin

   (2009)

   Neurology 73:1670–1676.

   https://doi.org/10.1212/WNL.0b013e3181c1ded6

   • PubMed
   • Google Scholar
7. 7
   The neuromuscular basis of hereditary kyphoscoliosis in the mouse

   1. LR Bridges
   2. GR Coulton
   3. G Howard
   4. J Moss
   5. RM Mason

   (1992)

   Muscle & Nerve 15:172–179.

   https://doi.org/10.1002/mus.880150208

   • PubMed
   • Google Scholar
8. 8
   Torsion dystonia: a double-blind, prospective trial of high-dosage trihexyphenidyl

   1. RE Burke
   2. S Fahn
   3. CD Marsden

   (1986)

   Neurology 36:160–164.

   https://doi.org/10.1212/WNL.36.2.160

   • PubMed
   • Google Scholar
9. 9
   Locomotor speed control circuits in the caudal brainstem

   1. P Capelli
   2. C Pivetta
   3. M Soledad Esposito
   4. S Arber

   (2017)

   Nature 551:373–377.

   https://doi.org/10.1038/nature24064

   • PubMed
   • Google Scholar
10. 10
    A major external source of cholinergic innervation of the striatum and nucleus accumbens originates in the brainstem

    1. D Dautan
    2. I Huerta-Ocampo
    3. IB Witten
    4. K Deisseroth
    5. JP Bolam
    6. T Gerdjikov
    7. J Mena-Segovia

    (2014)

    Journal of Neuroscience 34:4509–4518.

    https://doi.org/10.1523/JNEUROSCI.5071-13.2014

    • PubMed
    • Google Scholar
11. 11
    Utrophin-dystrophin-deficient mice as a model for Duchenne muscular dystrophy

    1. AE Deconinck
    2. JA Rafael
    3. JA Skinner
    4. SC Brown
    5. AC Potter
    6. L Metzinger
    7. DJ Watt
    8. JG Dickson
    9. JM Tinsley
    10. KE Davies

    (1997)

    Cell 90:717–727.

    https://doi.org/10.1016/S0092-8674(00)80532-2

    • PubMed
    • Google Scholar
12. 12
    Genetic kyphoscoliosis in mice

    1. AG Dickinson
    2. VM Meikle

    (1973)

    The Lancet 1:1186.

    https://doi.org/10.1016/S0140-6736(73)91186-0

    • PubMed
    • Google Scholar
13. 13
    Striatal cholinergic dysfunction as a unifying theme in the pathophysiology of dystonia

    1. KL Eskow Jaunarajs
    2. P Bonsi
    3. MF Chesselet
    4. DG Standaert
    5. A Pisani

    (2015)

    Progress in Neurobiology 127-128:91–107.

    https://doi.org/10.1016/j.pneurobio.2015.02.002

    • PubMed
    • Google Scholar
14. 14
    The pedunculopontine nucleus

    1. E Garcia-Rill

    (1991)

    Progress in Neurobiology 36:363–389.

    https://doi.org/10.1016/0301-0082(91)90016-T

    • PubMed
    • Google Scholar
15. 15
    Cholinergic interneurons in the dorsal and ventral striatum: anatomical and functional considerations in normal and diseased conditions

    1. KK Gonzales
    2. Y Smith

    (2015)

    Annals of the New York Academy of Sciences 1349:1–45.

    https://doi.org/10.1111/nyas.12762

    • PubMed
    • Google Scholar
16. 16
    Gait disorders in parkinsonian monkeys with pedunculopontine nucleus lesions: a tale of two systems

    1. D Grabli
    2. C Karachi
    3. E Folgoas
    4. M Monfort
    5. D Tande
    6. S Clark
    7. O Civelli
    8. EC Hirsch
    9. C François

    (2013)

    Journal of Neuroscience 33:11986–11993.

    https://doi.org/10.1523/JNEUROSCI.1568-13.2013

    • PubMed
    • Google Scholar
17. 17
    Skeletal and cardiac myopathies in mice lacking utrophin and dystrophin: a model for Duchenne muscular dystrophy

    1. RM Grady
    2. H Teng
    3. MC Nichol
    4. JC Cunningham
    5. RS Wilkinson
    6. JR Sanes

    (1997)

    Cell 90:729–738.

    https://doi.org/10.1016/S0092-8674(00)80533-4

    • PubMed
    • Google Scholar
18. 18
    A simple composite phenotype scoring system for evaluating mouse models of cerebellar ataxia

    1. SJ Guyenet
    2. SA Furrer
    3. VM Damian
    4. TD Baughan
    5. AR La Spada
    6. GA Garden

    (2010)

    Journal of Visualized Experiments, 10.3791/1787, 20495529.

    • Google Scholar
19. 19
    Corticostriatal circuitry

    1. SN Haber

    (2016)

    Dialogues in Clinical Neuroscience 18:7–21.

    • PubMed
    • Google Scholar
20. 20
    Modes and models of forebrain cholinergic neuromodulation of cognition

    1. ME Hasselmo
    2. M Sarter

    (2011)

    Neuropsychopharmacology 36:52–73.

    https://doi.org/10.1038/npp.2010.104

    • PubMed
    • Google Scholar
21. 21
    Blepharospasm associated with brainstem lesions

    1. J Jankovic
    2. SC Patel

    (1983)

    Neurology 33:1237–1240.

    https://doi.org/10.1212/WNL.33.9.1237

    • PubMed
    • Google Scholar
22. 22
    Synaptic integration mediated by striatal cholinergic interneurons in basal ganglia function

    1. S Kaneko
    2. T Hikida
    3. D Watanabe
    4. H Ichinose
    5. T Nagatsu
    6. RJ Kreitman
    7. I Pastan
    8. S Nakanishi

    (2000)

    Science 289:633–637.

    https://doi.org/10.1126/science.289.5479.633

    • PubMed
    • Google Scholar
23. 23
    Motor neuron diversity in development and disease

    1. KC Kanning
    2. A Kaplan
    3. CE Henderson

    (2010)

    Annual Review of Neuroscience 33:409–440.

    https://doi.org/10.1146/annurev.neuro.051508.135722

    • PubMed
    • Google Scholar
24. 24
    Cholinergic mesencephalic neurons are involved in gait and postural disorders in Parkinson disease

    1. C Karachi
    2. D Grabli
    3. FA Bernard
    4. D Tandé
    5. N Wattiez
    6. H Belaid
    7. E Bardinet
    8. A Prigent
    9. HP Nothacker
    10. S Hunot
    11. A Hartmann
    12. S Lehéricy
    13. EC Hirsch
    14. C François

    (2010)

    Journal of Clinical Investigation 120:2745–2754.

    https://doi.org/10.1172/JCI42642

    • PubMed
    • Google Scholar
25. 25
    Motor neuronal repletion of the NMJ organizer, Agrin, modulates the severity of the spinal muscular atrophy disease phenotype in model mice

    1. JK Kim
    2. C Caine
    3. T Awano
    4. R Herbst
    5. UR Monani

    (2017)

    Human Molecular Genetics 26:2377–2385.

    https://doi.org/10.1093/hmg/ddx124

    • PubMed
    • Google Scholar
26. 26
    Translation of Oppenheim's 1911 paper on dystonia

    1. C Klein
    2. S Fahn

    (2013)

    Movement Disorders 28:851–862.

    https://doi.org/10.1002/mds.25546

    • PubMed
    • Google Scholar
27. 27
    Physiology and pharmacology of striatal neurons

    1. AC Kreitzer

    (2009)

    Annual Review of Neuroscience 32:127–147.

    https://doi.org/10.1146/annurev.neuro.051508.135422

    • PubMed
    • Google Scholar
28. 28
    Progression of kyphosis in mdx mice

    1. N Laws
    2. A Hoey

    (2004)

    Journal of Applied Physiology 97:1970–1977.

    https://doi.org/10.1152/japplphysiol.01357.2003

    • PubMed
    • Google Scholar
29. 29
    Secondary cervical dystonia associated with structural lesions of the central nervous system

    1. MS LeDoux
    2. KA Brady

    (2003)

    Movement Disorders 18:60–69.

    https://doi.org/10.1002/mds.10301

    • PubMed
    • Google Scholar
30. 30
    TorsinA hypofunction causes abnormal twisting movements and sensorimotor circuit neurodegeneration

    1. CC Liang
    2. LM Tanabe
    3. S Jou
    4. F Chi
    5. WT Dauer

    (2014)

    Journal of Clinical Investigation 124:3080–3092.

    https://doi.org/10.1172/JCI72830

    • PubMed
    • Google Scholar
31. 31
    C9orf72 BAC Mouse Model with Motor Deficits and Neurodegenerative Features of ALS/FTD

    1. Y Liu
    2. A Pattamatta
    3. T Zu
    4. T Reid
    5. O Bardhi
    6. DR Borchelt
    7. AT Yachnis
    8. LP Ranum

    (2016)

    Neuron 90:521–534.

    https://doi.org/10.1016/j.neuron.2016.04.005

    • PubMed
    • Google Scholar
32. 32
    Dystonia associated with pontomesencephalic lesions

    1. TJ Loher
    2. JK Krauss

    (2009)

    Movement Disorders 24:157–167.

    https://doi.org/10.1002/mds.22196

    • PubMed
    • Google Scholar
33. 33
    Assessment of sensorimotor gating following selective lesions of cholinergic pedunculopontine neurons

    1. DA MacLaren
    2. T Markovic
    3. SD Clark

    (2014a)

    European Journal of Neuroscience 40:3526–3537.

    https://doi.org/10.1111/ejn.12716

    • PubMed
    • Google Scholar
34. 34
    Deficits in motor performance after pedunculopontine lesions in rats--impairment depends on demands of task

    1. DA MacLaren
    2. JA Santini
    3. AL Russell
    4. T Markovic
    5. SD Clark

    (2014b)

    European Journal of Neuroscience 40:3224–3236.

    https://doi.org/10.1111/ejn.12666

    • PubMed
    • Google Scholar
35. 35
    A robust and high-throughput Cre reporting and characterization system for the whole mouse brain

    1. L Madisen
    2. TA Zwingman
    3. SM Sunkin
    4. SW Oh
    5. HA Zariwala
    6. H Gu
    7. LL Ng
    8. RD Palmiter
    9. MJ Hawrylycz
    10. AR Jones
    11. ES Lein
    12. H Zeng

    (2010)

    Nature Neuroscience 13:133–140.

    https://doi.org/10.1038/nn.2467

    • PubMed
    • Google Scholar
36. 36
    Anticholinergic drugs rescue synaptic plasticity in DYT1 dystonia: role of M1 muscarinic receptors

    1. M Maltese
    2. G Martella
    3. G Madeo
    4. I Fagiolo
    5. A Tassone
    6. G Ponterio
    7. G Sciamanna
    8. P Burbaud
    9. PJ Conn
    10. P Bonsi
    11. A Pisani

    (2014)

    Movement Disorders 29:1655–1665.

    https://doi.org/10.1002/mds.26009

    • PubMed
    • Google Scholar
37. 37
    Discharge properties of juxtacellularly labeled and immunohistochemically identified cholinergic basal forebrain neurons recorded in association with the electroencephalogram in anesthetized rats

    1. ID Manns
    2. A Alonso
    3. BE Jones

    (2000)

    The Journal of Neuroscience 20:1505–1518.

    https://doi.org/10.1523/JNEUROSCI.20-04-01505.2000

    • PubMed
    • Google Scholar
38. 38
    Impairment of bidirectional synaptic plasticity in the striatum of a mouse model of DYT1 dystonia: role of endogenous acetylcholine

    1. G Martella
    2. A Tassone
    3. G Sciamanna
    4. P Platania
    5. D Cuomo
    6. MT Viscomi
    7. P Bonsi
    8. E Cacci
    9. S Biagioni
    10. A Usiello
    11. G Bernardi
    12. N Sharma
    13. DG Standaert
    14. A Pisani

    (2009)

    Brain 132:2336–2349.

    https://doi.org/10.1093/brain/awp194

    • PubMed
    • Google Scholar
39. 39
    Brainstem pathology in DYT1 primary torsion dystonia

    1. KS McNaught
    2. A Kapustin
    3. T Jackson
    4. TA Jengelley
    5. R Jnobaptiste
    6. P Shashidharan
    7. DP Perl
    8. P Pasik
    9. CW Olanow

    (2004)

    Annals of Neurology 56:540–547.

    https://doi.org/10.1002/ana.20225

    • PubMed
    • Google Scholar
40. 40
    Rethinking the Pedunculopontine Nucleus: From Cellular Organization to Function

    1. J Mena-Segovia
    2. JP Bolam

    (2017)

    Neuron 94:7–18.

    https://doi.org/10.1016/j.neuron.2017.02.027

    • PubMed
    • Google Scholar
41. 41
    GABAergic neuron distribution in the pedunculopontine nucleus defines functional subterritories

    1. J Mena-Segovia
    2. BR Micklem
    3. RG Nair-Roberts
    4. MA Ungless
    5. JP Bolam

    (2009)

    The Journal of Comparative Neurology 515:397–408.

    https://doi.org/10.1002/cne.22065

    • PubMed
    • Google Scholar
42. 42
    Structural and functional considerations of the cholinergic brainstem

    1. J Mena-Segovia

    (2016)

    Journal of Neural Transmission 123:731–736.

    https://doi.org/10.1007/s00702-016-1530-9

    • PubMed
    • Google Scholar
43. 43
    Pedunculopontine Nucleus Cholinergic Deficiency in Cervical Dystonia

    1. K Mente
    2. NA Edwards
    3. D Urbano
    4. A Ray-Chaudhury
    5. D Iacono
    6. A Alho
    7. EJL Alho
    8. E Amaro
    9. SG Horovitz
    10. M Hallett

    (2018)

    Movement Disorders 33:827–834.

    https://doi.org/10.1002/mds.27358

    • PubMed
    • Google Scholar
44. 44
    Overexpression of wild-type androgen receptor in muscle recapitulates polyglutamine disease

    1. DA Monks
    2. JA Johansen
    3. K Mo
    4. P Rao
    5. B Eagleson
    6. Z Yu
    7. AP Lieberman
    8. SM Breedlove
    9. CL Jordan

    (2007)

    PNAS 104:18259–18264.

    https://doi.org/10.1073/pnas.0705501104

    • PubMed
    • Google Scholar
45. 45
    The early-onset torsion dystonia gene (DYT1) encodes an ATP-binding protein

    1. LJ Ozelius
    2. JW Hewett
    3. CE Page
    4. SB Bressman
    5. PL Kramer
    6. C Shalish
    7. D de Leon
    8. MF Brin
    9. D Raymond
    10. DP Corey
    11. S Fahn
    12. NJ Risch
    13. AJ Buckler
    14. JF Gusella
    15. XO Breakefield

    (1997)

    Nature Genetics 17:40–48.

    https://doi.org/10.1038/ng0997-40

    • PubMed
    • Google Scholar
46. 46
    Forebrain deletion of the dystonia protein torsinA causes dystonic-like movements and loss of striatal cholinergic neurons

    1. SS Pappas
    2. K Darr
    3. SM Holley
    4. C Cepeda
    5. OS Mabrouk
    6. JM Wong
    7. TM LeWitt
    8. R Paudel
    9. H Houlden
    10. RT Kennedy
    11. MS Levine
    12. WT Dauer

    (2015)

    eLife 4:e08352.

    https://doi.org/10.7554/eLife.08352

    • PubMed
    • Google Scholar
47. 47
    Mouse models of neurodevelopmental disease of the basal ganglia and associated circuits

    1. SS Pappas
    2. DK Leventhal
    3. RL Albin
    4. WT Dauer

    (2014)

    Current Topics in Developmental Biology 109:97–169.

    https://doi.org/10.1016/B978-0-12-397920-9.00001-9

    • PubMed
    • Google Scholar
48. 48
    TorsinA dysfunction causes persistent neuronal nuclear pore defects

    1. SS Pappas
    2. CC Liang
    3. S Kim
    4. CO Rivera
    5. WT Dauer

    (2018)

    Human Molecular Genetics 27:407–420.

    https://doi.org/10.1093/hmg/ddx405

    • PubMed
    • Google Scholar
49. 49
    Functional anatomy of the basal ganglia. I. The cortico-basal ganglia-thalamo-cortical loop

    1. A Parent
    2. LN Hazrati

    (1995)

    Brain Research Reviews 20:91–127.

    https://doi.org/10.1016/0165-0173(94)00007-C

    • PubMed
    • Google Scholar
50. 50
    Neuropathological features of genetically confirmed DYT1 dystonia: investigating disease-specific inclusions

    1. R Paudel
    2. A Kiely
    3. A Li
    4. T Lashley
    5. R Bandopadhyay
    6. J Hardy
    7. HA Jinnah
    8. K Bhatia
    9. H Houlden
    10. JL Holton

    (2014)

    Acta Neuropathologica Communications 2:159.

    https://doi.org/10.1186/s40478-014-0159-x

    • PubMed
    • Google Scholar
51. 51
    Decoding brain state transitions in the pedunculopontine nucleus: cooperative phasic and tonic mechanisms

    1. A Petzold
    2. M Valencia
    3. B Pál
    4. J Mena-Segovia

    (2015)

    Frontiers in Neural Circuits 9:68.

    https://doi.org/10.3389/fncir.2015.00068

    • PubMed
    • Google Scholar
52. 52
    Embryonic development of four different subsets of cholinergic neurons in rat cervical spinal cord

    1. PE Phelps
    2. RP Barber
    3. LA Brennan
    4. VM Maines
    5. PM Salvaterra
    6. JE Vaughn

    (1990a)

    The Journal of Comparative Neurology 291:9–26.

    https://doi.org/10.1002/cne.902910103

    • PubMed
    • Google Scholar
53. 53
    Generation patterns of four groups of cholinergic neurons in rat cervical spinal cord: a combined tritiated thymidine autoradiographic and choline acetyltransferase immunocytochemical study

    1. PE Phelps
    2. RP Barber
    3. JE Vaughn

    (1988)

    The Journal of Comparative Neurology 273:459–472.

    https://doi.org/10.1002/cne.902730403

    • PubMed
    • Google Scholar
54. 54
    The generation and differentiation of cholinergic neurons in rat caudate-putamen

    1. PE Phelps
    2. DR Brady
    3. JE Vaughn

    (1989)

    Developmental Brain Research 46:47–60.

    https://doi.org/10.1016/0165-3806(89)90142-9

    • PubMed
    • Google Scholar
55. 55
    Generation patterns of immunocytochemically identified cholinergic neurons in rat brainstem

    1. PE Phelps
    2. LA Brennan
    3. JE Vaughn

    (1990b)

    Developmental Brain Research 56:63–74.

    https://doi.org/10.1016/0165-3806(90)90165-U

    • PubMed
    • Google Scholar
56. 56
    Altered responses to dopaminergic D2 receptor activation and N-type calcium currents in striatal cholinergic interneurons in a mouse model of DYT1 dystonia

    1. A Pisani
    2. G Martella
    3. A Tscherter
    4. P Bonsi
    5. N Sharma
    6. G Bernardi
    7. DG Standaert

    (2006)

    Neurobiology of Disease 24:318–325.

    https://doi.org/10.1016/j.nbd.2006.07.006

    • PubMed
    • Google Scholar
57. 57
    Diminishing evidence for torsinA-positive neuronal inclusions in DYT1 dystonia

    1. D Pratt
    2. K Mente
    3. S Rahimpour
    4. NA Edwards
    5. S Tinaz
    6. BD Berman
    7. M Hallett
    8. A Ray-Chaudhury

    (2016)

    Acta Neuropathologica Communications 4:85.

    https://doi.org/10.1186/s40478-016-0362-z

    • PubMed
    • Google Scholar
58. 58
    Cell-Type-Specific Control of Brainstem Locomotor Circuits by Basal Ganglia

    1. TK Roseberry
    2. AM Lee
    3. AL Lalive
    4. L Wilbrecht
    5. A Bonci
    6. AC Kreitzer

    (2016)

    Cell 164:526–537.

    https://doi.org/10.1016/j.cell.2015.12.037

    • PubMed
    • Google Scholar
59. 59
    Melanocortin-4 receptors expressed by cholinergic neurons regulate energy balance and glucose homeostasis

    1. J Rossi
    2. N Balthasar
    3. D Olson
    4. M Scott
    5. E Berglund
    6. CE Lee
    7. MJ Choi
    8. D Lauzon
    9. BB Lowell
    10. JK Elmquist

    (2011)

    Cell Metabolism 13:195–204.

    https://doi.org/10.1016/j.cmet.2011.01.010

    • PubMed
    • Google Scholar
60. 60
    Ontogeny of cholinergic neurons in the mouse forebrain

    1. UB Schambra
    2. KK Sulik
    3. P Petrusz
    4. JM Lauder

    (1989)

    The Journal of Comparative Neurology 288:101–122.

    https://doi.org/10.1002/cne.902880109

    • PubMed
    • Google Scholar
61. 61
    Cholinergic dysregulation produced by selective inactivation of the dystonia-associated protein torsinA

    1. G Sciamanna
    2. R Hollis
    3. C Ball
    4. G Martella
    5. A Tassone
    6. A Marshall
    7. D Parsons
    8. X Li
    9. F Yokoi
    10. L Zhang
    11. Y Li
    12. A Pisani
    13. DG Standaert

    (2012b)

    Neurobiology of Disease 47:416–427.

    https://doi.org/10.1016/j.nbd.2012.04.015

    • PubMed
    • Google Scholar
62. 62
    Cholinergic dysfunction alters synaptic integration between thalamostriatal and corticostriatal inputs in DYT1 dystonia

    1. G Sciamanna
    2. A Tassone
    3. G Mandolesi
    4. F Puglisi
    5. G Ponterio
    6. G Martella
    7. G Madeo
    8. G Bernardi
    9. DG Standaert
    10. P Bonsi
    11. A Pisani

    (2012a)

    Journal of Neuroscience 32:11991–12004.

    https://doi.org/10.1523/JNEUROSCI.0041-12.2012

    • PubMed
    • Google Scholar
63. 63
    Afferent connections of the laterodorsal and the pedunculopontine tegmental nuclei in the rat: a retro- and antero-grade transport and immunohistochemical study

    1. K Semba
    2. HC Fibiger

    (1992)

    The Journal of Comparative Neurology 323:387–410.

    https://doi.org/10.1002/cne.903230307

    • PubMed
    • Google Scholar
64. 64
    Different times of origin of choline acetyltransferase- and somatostatin-immunoreactive neurons in the rat striatum

    1. K Semba
    2. SR Vincent
    3. HC Fibiger

    (1988)

    The Journal of Neuroscience 8:3937–3944.

    https://doi.org/10.1523/JNEUROSCI.08-10-03937.1988

    • PubMed
    • Google Scholar
65. 65
    The thalamostriatal system: a highly specific network of the basal ganglia circuitry

    1. Y Smith
    2. DV Raju
    3. JF Pare
    4. M Sidibe

    (2004)

    Trends in Neurosciences 27:520–527.

    https://doi.org/10.1016/j.tins.2004.07.004

    • PubMed
    • Google Scholar
66. 66
    Androgen receptor YAC transgenic mice recapitulate SBMA motor neuronopathy and implicate VEGF164 in the motor neuron degeneration

    1. BL Sopher
    2. PS Thomas
    3. MA LaFevre-Bernt
    4. IE Holm
    5. SA Wilke
    6. CB Ware
    7. LW Jin
    8. RT Libby
    9. LM Ellerby
    10. AR La Spada

    (2004)

    Neuron 41:687–699.

    https://doi.org/10.1016/S0896-6273(04)00082-0

    • PubMed
    • Google Scholar
67. 67
    Pedunculopontine tegmental nucleus lesions impair probabilistic reversal learning by reducing sensitivity to positive reward feedback

    1. A Syed
    2. PM Baker
    3. ME Ragozzino

    (2016)

    Neurobiology of Learning and Memory 131:1–8.

    https://doi.org/10.1016/j.nlm.2016.03.010

    • PubMed
    • Google Scholar
68. 68
    Neuronal Nuclear Membrane Budding Occurs during a Developmental Window Modulated by Torsin Paralogs

    1. LM Tanabe
    2. CC Liang
    3. WT Dauer

    (2016)

    Cell Reports 16:3322–3333.

    https://doi.org/10.1016/j.celrep.2016.08.044

    • PubMed
    • Google Scholar
69. 69
    Adult mouse basal forebrain harbors two distinct cholinergic populations defined by their electrophysiology

    1. CT Unal
    2. JP Golowasch
    3. L Zaborszky

    (2012)

    Frontiers in Behavioral Neuroscience 6:21.

    https://doi.org/10.3389/fnbeh.2012.00021

    • PubMed
    • Google Scholar
70. 70
    Opposing roles for serotonin in cholinergic neurons of the ventral and dorsal striatum

    1. MS Virk
    2. Y Sagi
    3. L Medrihan
    4. J Leung
    5. MG Kaplitt
    6. P Greengard

    (2016)

    Proceedings of the National Academy of Sciences 113:734–739.

    https://doi.org/10.1073/pnas.1524183113

    • PubMed
    • Google Scholar
71. 71
    Pedunculopontine and laterodorsal tegmental nuclei contain distinct populations of cholinergic, glutamatergic and GABAergic neurons in the rat

    1. HL Wang
    2. M Morales

    (2009)

    European Journal of Neuroscience 29:340–358.

    https://doi.org/10.1111/j.1460-9568.2008.06576.x

    • PubMed
    • Google Scholar
72. 72
    Cholinergic Mesopontine Signals Govern Locomotion and Reward through Dissociable Midbrain Pathways

    1. C Xiao
    2. JR Cho
    3. C Zhou
    4. JB Treweek
    5. K Chan
    6. SL McKinney
    7. B Yang
    8. V Gradinaru

    (2016)

    Neuron 90:333–347.

    https://doi.org/10.1016/j.neuron.2016.03.028

    • PubMed
    • Google Scholar
73. 73
    Putative cholinergic interneurons in the ventral and dorsal regions of the striatum have distinct roles in a two choice alternative association task

    1. O Yarom
    2. D Cohen

    (2011)

    Frontiers in Systems Neuroscience 5:36.

    https://doi.org/10.3389/fnsys.2011.00036

    • PubMed
    • Google Scholar
74. 74
    The Mouse Nervous System

    1. L Zaborszky
    2. A van den Pol
    3. E Gyengesi

    (2012)

    684–714, The basal forebrain cholinergic projection system in mice, The Mouse Nervous System, 10.1016/B978-0-12-369497-3.10028-7.

    • Google Scholar
75. 75
    Pathology in brainstem regions of individuals with primary dystonia

    1. RM Zweig
    2. JC Hedreen
    3. WR Jankel
    4. MF Casanova
    5. PJ Whitehouse
    6. DL Price

    (1988)

    Neurology 38:702–706.

    https://doi.org/10.1212/WNL.38.5.702

    • PubMed
    • Google Scholar

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).

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.

Author details

1. Samuel S Pappas

   Department of Neurology, University of Michigan, Ann Arbor, United States

   Contribution

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

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6980-2058

2. Jay Li

   1. Department of Neurology, University of Michigan, Ann Arbor, United States
   2. Cell and Molecular Biology Program, University of Michigan, Ann Arbor, United States

   Contribution

   Formal analysis, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8146-4450

3. Tessa M LeWitt

   Department of Neurology, University of Michigan, Ann Arbor, United States

   Contribution

   Formal analysis, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

4. Jeong-Ki Kim

   1. Department of Cell Biology, Columbia University Medical Center, New York, United States
   2. Center for Motor Neuron Biology and Disease, Columbia University Medical Center, New York, United States
   3. Department of Pathology, Columbia University Medical Center, New York, United States

   Contribution

   Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0218-1215

5. Umrao R Monani

   1. Department of Cell Biology, Columbia University Medical Center, New York, United States
   2. Center for Motor Neuron Biology and Disease, Columbia University Medical Center, New York, United States
   3. Department of Pathology, Columbia University Medical Center, New York, United States

   Contribution

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

   Competing interests

   No competing interests declared

6. William T Dauer

   1. Department of Neurology, University of Michigan, Ann Arbor, United States
   2. Cell and Molecular Biology Program, University of Michigan, Ann Arbor, United States
   3. Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, United States

   Contribution

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

   For correspondence

   dauer@med.umich.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1775-7504

• 967

  Page views

• 167

  Downloads

• 6

  Citations

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