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Record W3094421202 · doi:10.1038/s41467-020-18949-w

Selective neuronal degeneration in MATR3 S85C knock-in mouse model of early-stage ALS

2020· article· en· W3094421202 on OpenAlexafffund
Ching Serena Kao, Rebekah van Bruggen, Jihye Rachel Kim, Xiao Xiao Chen, Cadia Chan, Jooyun Lee, Woo In Cho, Melody Zhao, Claudia Arndt, Katarina Maksimovic, Mashiat Khan, Qiumin Tan, Michael D. Wilson, Jeehye Park

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsSickKids FoundationUniversity of AlbertaHospital for Sick ChildrenUniversity of Toronto
FundersHospital for Sick ChildrenCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaCanada Research ChairsALS Society of CanadaScottish Rite Charitable Foundation of CanadaGovernment of CanadaNatural Sciences and Engineering Research Council of CanadaFondation Brain Canada
KeywordsGene knockinBiologyDegeneration (medical)Knockout mouseNeuroscienceCell biologyGeneticsMedicineGenePathology

Abstract

fetched live from OpenAlex

A missense mutation, S85C, in the MATR3 gene is a genetic cause for amyotrophic lateral sclerosis (ALS). It is unclear how the S85C mutation affects MATR3 function and contributes to disease. Here, we develop a mouse model that harbors the S85C mutation in the endogenous Matr3 locus using the CRISPR/Cas9 system. MATR3 S85C knock-in mice recapitulate behavioral and neuropathological features of early-stage ALS including motor impairment, muscle atrophy, neuromuscular junction defects, Purkinje cell degeneration and neuroinflammation in the cerebellum and spinal cord. Our neuropathology data reveals a loss of MATR3 S85C protein in the cell bodies of Purkinje cells and motor neurons, suggesting that a decrease in functional MATR3 levels or loss of MATR3 function contributes to neuronal defects. Our findings demonstrate that the MATR3 S85C mouse model mimics aspects of early-stage ALS and would be a promising tool for future basic and preclinical research.

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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.081
GPT teacher head0.357
Teacher spread0.276 · 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

Citations42
Published2020
Admission routes2
Has abstractyes

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