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Fas expression promotes proteasomal activity in toxin-induced parkinsonism

2011· article· en· W2150840878 on OpenAlexaff
Anne M. Landau, Rosmarie Siegrist‐Johnstone, Julie Desbarats

Bibliographic record

VenueActa Neuropsychiatrica · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell death mechanisms and regulation
Canadian institutionsMcGill University
Fundersnot available
KeywordsParkinsonismToxinChemistryCell biologyExpression (computer science)NeuroscienceCancer researchMedicineBiologyInternal medicineBiochemistryComputer scienceDisease

Abstract

fetched live from OpenAlex

OBJECTIVE: Fas (CD95), commonly categorised as a death receptor due to its well-defined role in apoptosis, can paradoxically also promote neuroprotection. We have previously found that defects in Fas signalling render mice highly susceptible to neural degeneration in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine mouse model of Parkinson's disease (PD). Decreased activity of the ubiquitin proteasome system and accumulation of protein aggregates are implicated in PD pathogenesis. Here, we investigate the relationship between Fas and ubiquitin proteasomal activity in neuronal cells. METHODS: We performed proteasome assays in neuroblastoma cells and in midbrain cultures of wild-type and Fas-deficient mice. RESULTS: Neuroblastoma cells upregulated proteasomal activity in response to an activating Fas antibody in vitro. Furthermore, neural tissue from Fas-deficient mice showed decreased proteasomal activity compared with the tissue from wild-type mice when exposed to a PD-inducing toxin in vivo. CONCLUSION: These findings suggest that mechanisms for Fas-mediated neuroprotection may include Fas-induced upregulation of proteasomal activity, and consequently less accumulation of toxic protein aggregates.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.625

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 head0.021
GPT teacher head0.229
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 teacher head, 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

Citations0
Published2011
Admission routes1
Has abstractyes

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