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Record W4288068795 · doi:10.1186/s13041-022-00955-2

Copper ions, prion protein and Aβ modulate Ca levels in central nervous system myelin in an NMDA receptor-dependent manner

2022· article· en· W4288068795 on OpenAlexafffund
Shigeki Tsutsui, Megan L. Morgan, Hugo W. Tedford, Haitao You, Gerald W. Zamponi, Peter K. Stys

Bibliographic record

VenueMolecular Brain · 2022
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsAlberta Children's HospitalHotchkiss Brain InstituteUniversity of Calgary
FundersAlberta Prion Research InstituteCanadian Institutes of Health ResearchNational Institutes of HealthCanada Research Chairs
KeywordsNMDA receptorChemistryReceptorCell biologyNeuroscienceMyelinBiophysicsCentral nervous systemBiochemistryBiology

Abstract

fetched live from OpenAlex

Abstract As in neurons, CNS myelin expresses N-Methyl-D-Aspartate Receptors (NMDARs) that subserve physiological roles, but have the potential to induce injury to this vital element. Using 2-photon imaging of myelinic Ca in live ex vivo mouse optic nerves, we show that Cu ions potently modulate Ca levels in an NMDAR-dependent manner. Chelating Cu in the perfusate induced a substantial increase in Ca levels, and also caused significant axo-myelinic injury. Myelinic NMDARs are shown to be regulated by cellular prion protein; only in prion protein KO optic nerves does application of NMDA + D-serine induce a large Ca increase, consistent with strong desensitization of these receptors in the presence of prion protein limiting Ca overload. Aβ 1-42 peptide induced a large Ca increase that was also Cu-dependent, and was blocked by NMDAR antagonism. Our results indicate that like in neurons, myelinic NMDARs permeate potentially injurious amounts of Ca, and are also potently regulated by micromolar Cu and activated by Aβ 1-42 peptides. These findings shed mechanistic light on the important primary white matter injury frequently observed in Alzheimer's brain.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.018
GPT teacher head0.282
Teacher spread0.264 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2022
Admission routes2
Has abstractyes

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