MétaCan
Menu
← Back to cohort
Record W3195387519 · doi:10.1101/2021.08.20.456807

The ataxia protein sacsin is required for integrin trafficking and synaptic organization

2021· preprint· en· W3195387519 on OpenAlexaboutno aff
Lisa E. L. Romano, Wen Yih Aw, Kathryn Hixson, T. V. Novoselova, Tammy M. Havener, Stefanie Howell, Charlotte Hall, Lei Xing, Josh Beri, Suran Nethisinghe, Laura Perna, Abubakar Hatimy, Ginevra Chioccioli Altadonna, Lee M. Graves, Laura E. Herring, Anthony J. Hickey, Konstantinos Thalassinos, J. Paul Chapple, Justin M. Wolter

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsnot available
FundersBiotechnology and Biological Sciences Research Council
KeywordsCell biologyPhenotypeAtaxiaBiologySpinocerebellar ataxiaSynaptic vesicleMicrotubuleCell adhesionCerebellumNeuroscienceCellVesicleGeneticsGene

Abstract

fetched live from OpenAlex

Abstract Autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) is caused by mutations in SACS , which manifest as a childhood-onset cerebellar ataxia. Cellular ARSACS phenotypes include mitochondrial dysfunction, intermediate filament (IF) disorganization, and loss of Purkinje neurons. It is unclear how the loss of SACS causes these deficits, or why they manifest as cerebellar ataxia. We employed a multi-omics approach to characterize molecular and cellular deficiencies in SACS knockout (KO) cells. We identified alterations in microtubule structure and dynamics, protein trafficking, and mislocalization of synaptic and focal adhesion proteins. Targeting PTEN , a negative regulator of focal adhesions, rescued several cellular phenotypes in SACS KO cells. We found sacsin interacts with proteins implicated in vesicle transport, including HSP proteins, and interactions between structural and cell adhesion proteins were diminished in SACS KO cells. In all, this study suggests that trafficking and localization of synaptic adhesion proteins is a causal molecular deficiency in ARSACS.

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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.232
Teacher spread0.209 · 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".

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicGenetic Neurodegenerative Diseases→French-language works237,207→