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SCA7 Mouse Cerebellar Pathology Reveals Preferential Downregulation of Key Purkinje Cell-Identity Genes and Shared Disease Signature with SCA1 and SCA2

2021· article· en· W3154919931 on OpenAlexfundno aff
Anna Niewiadomska-Cimicka, Frédéric Doussau, J. Pérot, Michel J. Roux, Céline Keime, Antoine Hache, Françoise Piguet, Ariana Novati, C. R. Weber, Binnaz Yalcin, Hamid Méziane, Marie‐France Champy, Erwan Grandgirard, Alice Karam, Nadia Messaddeq, Aurélie Eisenmann, Emmanuel Brouillet, Julien Flament, Philippe Isope, Yvon Trottier

Bibliographic record

VenueJournal of Neuroscience · 2021
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsnot available
FundersInstitute of GeneticsFondation pour la Recherche MédicaleBundesministerium für Bildung und ForschungAgence Nationale de la RechercheE-RareEuropean CommissionChina Scholarship CouncilEU Joint Programme – Neurodegenerative Disease Research
KeywordsBiologySpinocerebellar ataxiaCerebellumNeurodegenerationNeurosciencePathologyDiseaseAtaxiaMedicine

Abstract

fetched live from OpenAlex

Spinocerebellar ataxia type 7 (SCA7) is an inherited neurodegenerative disease mainly characterized by motor incoordination because of progressive cerebellar degeneration. SCA7 is caused by polyglutamine expansion in ATXN7, a subunit of the transcriptional coactivator SAGA, which harbors histone modification activities. Polyglutamine expansions in specific proteins are also responsible for SCA1-SCA3, SCA6, and SCA17; however, the converging and diverging pathomechanisms remain poorly understood. Using a new SCA7 knock-in mouse, SCA7 140Q/5Q , we analyzed gene expression in the cerebellum and assigned gene deregulation to specific cell types using published datasets. Gene deregulation affects all cerebellar cell types, although at variable degree, and correlates with alterations of SAGA-dependent epigenetic marks. Purkinje cells (PCs) are by far the most affected neurons and show reduced expression of 83 cell-type identity genes, including these critical for their spontaneous firing activity and synaptic functions. PC gene downregulation precedes morphologic alterations, pacemaker dysfunction, and motor incoordination. Strikingly, most PC genes downregulated in SCA7 have also decreased expression in SCA1 and SCA2 mice, revealing converging pathomechanisms and a common disease signature involving cGMP-PKG and phosphatidylinositol signaling pathways and LTD. Our study thus points out molecular targets for therapeutic development, which may prove beneficial for several SCAs. Furthermore, we show that SCA7 140Q/5Q males and females exhibit the major disease features observed in patients, including cerebellar damage, cerebral atrophy, peripheral nerves pathology, and photoreceptor dystrophy, which account for progressive impairment of behavior, motor, and visual functions. SCA7 140Q/5Q mice represent an accurate model for the investigation of different aspects of SCA7 pathogenesis. SIGNIFICANCE STATEMENT Spinocerebellar ataxia 7 (SCA7) is one of the several forms of inherited SCAs characterized by cerebellar degeneration because of polyglutamine expansion in specific proteins. The ATXN7 involved in SCA7 is a subunit of SAGA transcriptional coactivator complex. To understand the pathomechanisms of SCA7, we determined the cell type-specific gene deregulation in SCA7 mouse cerebellum. We found that the Purkinje cells are the most affected cerebellar cell type and show downregulation of a large subset of neuronal identity genes, critical for their spontaneous firing and synaptic functions. Strikingly, the same Purkinje cell genes are downregulated in mouse models of two other SCAs. Thus, our work reveals a disease signature shared among several SCAs and uncovers potential molecular targets for their treatment.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.023
GPT teacher head0.254
Teacher spread0.231 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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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Citations35
Published2021
Admission routes1
Has abstractyes

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