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Record W2070563559 · doi:10.1083/jcb.201202005

ADP ribosylation adapts an ER chaperone response to short-term fluctuations in unfolded protein load

2012· article· en· W2070563559 on OpenAlexfundno aff
Joseph E. Chambers, Kseniya Petrova, Giulia Tomba, Michele Vendruscolo, David Ron

Bibliographic record

VenueThe Journal of Cell Biology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEndoplasmic Reticulum Stress and Disease
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of HealthTechnische Universität MünchenUniversität HeidelbergWellcome TrustYork UniversityUniversität Hamburg
KeywordsUnfolded protein responseEndoplasmic reticulumChaperone (clinical)Cell biologyChemistryProtein foldingBiophysicsAdenosine triphosphateBiochemistryBiology

Abstract

fetched live from OpenAlex

Gene expression programs that regulate the abundance of the chaperone BiP adapt the endoplasmic reticulum (ER) to unfolded protein load. However, such programs are slow compared with physiological fluctuations in secreted protein synthesis. While searching for mechanisms that fill this temporal gap in coping with ER stress, we found elevated levels of adenosine diphosphate (ADP)-ribosylated BiP in the inactive pancreas of fasted mice and a rapid decline in this modification in the active fed state. ADP ribosylation mapped to Arg470 and Arg492 in the substrate-binding domain of hamster BiP. Mutations that mimic the negative charge of ADP-ribose destabilized substrate binding and interfered with interdomain allosteric coupling, marking ADP ribosylation as a rapid posttranslational mechanism for reversible inactivation of BiP. A kinetic model showed that buffering fluctuations in unfolded protein load with a recruitable pool of inactive chaperone is an efficient strategy to minimize both aggregation and costly degradation of unfolded proteins.

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)0.001
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 score0.077
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.014
GPT teacher head0.278
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 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

Citations98
Published2012
Admission routes1
Has abstractyes

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