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Record W2564859908 · doi:10.1113/jp273149

New old drug(s) for spinocerebellar ataxias

2016· letter· en· W2564859908 on OpenAlexaff
Visou Ady, Alanna J. Watt

Bibliographic record

VenueThe Journal of Physiology · 2016
Typeletter
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsMcGill University
Fundersnot available
KeywordsNeurosciencePurkinje cellCerebellumMetabotropic glutamate receptor 1Spinocerebellar ataxiaBiologyMetabotropic glutamate receptorAtaxiaGlutamate receptorReceptorGenetics

Abstract

fetched live from OpenAlex

Spinocerebellar ataxias (SCAs) are a group of more than 40 neurodegenerative diseases, characterized by progressive impairment of balance, motor coordination and gait. Ataxin-1 was the first gene identified in an SCA (SCA1). Purkinje cell degeneration is a hallmark post mortem feature of most SCAs, including SCA1, and Purkinje cell dysfunction is often observed during early disease stages (Meera et al. 2016). At present, there is no treatment available for SCAs, although a paper by Shuvaev and colleagues in this issue of The Journal of Physiology identifies a promising new therapeutic approach for SCA1 using a drug already approved by the FDA, baclofen (Shuvaev et al. 2017). Shuvaev and colleagues elegantly and thoroughly address the role of metabotropic glutamate receptor type 1 (mGluR1) signalling in cerebellar Purkinje cells during early stages of SCA1, examining time points both before and after disease onset in two models: a well-studied transgenic mouse model of SCA1, and viral expression of mutant ataxin-1 gene product in the cerebellum (Shuvaev et al. 2017). Both approaches yielded largely congruous results: mutant Ataxin-1 led to the progressive reduction of mGluR1 signalling in Purkinje cells as motor coordination deficits emerged. The reduction of mGluR1 signalling produced changes in both short- and long-term plasticity at parallel fibre synapses, which have been associated with impaired motor learning and cerebellar function (Meera et al. 2016; Power et al. 2016a). In cerebellar Purkinje cells, mGluR1 signalling can be enhanced by γ-aminobutyric acid type B (GABAB) receptors (Hirono et al. 2001; Tabata et al. 2004). The authors ingeniously take advantage of this crosstalk, and utilize the GABAB receptor activator baclofen to augment mGluR1 signalling (Hirono et al. 2001; Tabata et al. 2004). Using a single low-dose intracranial administration of baclofen, they produced a long-lasting recovery of mGluR1 signalling and synaptic plasticity in Purkinje cells, and importantly, a long-lasting improvement of ataxic symptoms as well. Changes in mGluR1 signalling have been reported in several ataxias (Meera et al. 2016; Power et al. 2016a), and Shuvaev and colleagues’ findings are in agreement with most SCA1 studies where decreases in mGluR1 have typically been observed (Serra et al. 2004; Zu et al. 2004; Notartomaso et al. 2013; Meera et al. 2016; Power et al. 2016a). Another recent study, however, suggests that mGluR1 regulation in SCA1 may be more complex, as the opposite was found – increased mGluR1 signalling – in a different SCA1 mouse (Power et al. 2016b). It is likely that multiple and even opposing changes – some pathophysiological and some adaptive – occur at different stages during disease progression. Indeed, one recent study of Purkinje cell excitability in SCA1 found both increases and decreases at different stages of SCA1 disease progression (Dell'Orco et al. 2015). Further studies will be needed to elucidate whether mGluR1 signalling is differentially regulated at different disease stages in SCA1. Ten years ago, there were few if any promising therapeutic approaches for SCAs. Shuvaev and colleagues’ finding that baclofen improves ataxia in an SCA1 mouse model (Shuvaev et al. 2017) is exciting, because baclofen is FDA approved and has already been used in patients for decades as a muscle relaxant for spasticity. Its therapeutic potential for treating SCA1 should be investigated further. Baclofen joins a growing pharmacopoeia of potential drug treatments for SCA1 (Fig. 1), including FDA-approved drugs such as the aminopyridines (APs) Di-AP and 4-AP, which restore Purkinje cell firing properties in SCA1 (Hourez et al. 2011), and lithium (Watase et al. 2007), as well as other drugs like flufenamic acid, which increases Purkinje cell excitability (Dell'Orco et al. 2015), Ro0711401, a positive mGluR1 modulator (Notartomaso et al. 2013), and JNJ16259685, a negative mGluR1 modulator which has been reported to both improve SCA1 symptoms (Power et al. 2016b) and worsen them (Notartomaso et al. 2013). While each drug may have limits to its therapeutic potential, the emergence of several new avenues for SCA1 treatment is particularly encouraging. SCA1 is the most studied and common of the rare SCAs; sadly, there is currently almost no research undertaken for several other SCAs. We recently found that 4-AP improves motor function and firing deficits in a mouse model of SCA6 (Jayabal et al. 2016); taken together with its effects on SCA1 (Hourez et al. 2011), this suggests that common treatments for multiple SCAs may be possible. Given that mGluR1 changes have been observed in several ataxias (Meera et al. 2016; Power et al. 2016a), the findings of Shuvaev and colleagues may have an even broader impact in the future (Shuvaev et al. 2017), as baclofen may have therapeutic benefits for other SCAs as well. None declared. Both authors have approved the final version of the manuscript and agree to be accountable for all aspects of the work. All persons designated as authors qualify for authorship, and all those who qualify for authorship are listed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0240.008

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.277
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2016
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