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Record W1972631549 · doi:10.1083/jcb.1773rr2

Glycan growth switch

2007· article· en· W1972631549 on OpenAlexaboutno aff
Nicole LeBrasseur

Bibliographic record

VenueThe Journal of Cell Biology · 2007
Typearticle
Languageen
FieldImmunology and Microbiology
TopicGalectins and Cancer Biology
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyGlycanCell biologyBiochemistryGlycoprotein

Abstract

fetched live from OpenAlex

Those sugary glycan moieties that adorn cell surface receptors are more than decoration. Their ability to prevent receptor endocytosis is well established. Now, Ken Lau, James Dennis (University of Toronto, Canada), and colleagues show that differences in receptors' glycan decorations time a cell's transition from growth to arrest. Receptors that promote growth generally have more sites for glycan addition than do receptors that halt growth and start differentiation. The authors found that these receptor groups responded differently to changes in metabolite status, which determines the complexity of the added glycans (more sugar-nucleotides means more intricately branched glycans are created in the Golgi). With their many glycans, growth receptors were cross-linked by sugar-binding galectins and retained on the surface even in stringent growth conditions. Receptors that promote differentiation required higher sugar-nucleotide levels before their fewer glycans gained enough galectin-binding branches to counter their loss by endocytosis. The upshot, says Dennis, is “a principle of how cells regulate the ratio of growth and arrest receptors in a cell-autonomous manner downstream of nutrients. First, an increase in proliferation is accompanied by glucose uptake and increased metabolism.” Then when metabolite flux sufficiently increases sugar-nucleotides and the branched glycans, differentiation receptors can accumulate, turning off proliferation. Reference: Lau, K.S., et al. 2007. Cell. 129:123–134. [PubMed]

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)0.002
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 score0.133
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.008
GPT teacher head0.234
Teacher spread0.226 · 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
Published2007
Admission routes1
Has abstractyes

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