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Record W2915251823 · doi:10.1038/s41388-019-0754-1

TRiC/CCT chaperonins are essential for organ growth by interacting with insulin/TOR signaling in Drosophila

2019· article· en· W2915251823 on OpenAlexfundno aff
Ah-Ram Kim, Kwang‐Wook Choi

Bibliographic record

VenueOncogene · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsnot available
FundersInstitute of GeneticsNational Research Foundation of KoreaNational Research Foundation
KeywordsBiologyTOR signalingCell biologySignal transductionCell growthNotch signaling pathwayRHEBPhosphorylationP70-S6 Kinase 1RNA interferenceCell signalingProto-Oncogene Proteins c-aktProtein kinase BInsulin receptorGeneticsInsulinmTORC1Endocrinology

Abstract

fetched live from OpenAlex

Organ size is regulated by intercellular signaling for cell growth and proliferation. The TOR pathway mediates a key signaling mechanism for controlling cell size and number in organ growth. Chaperonin containing TCP-1 (CCT) is a complex that assists protein folding and function, but its role in animal development is largely unknown. Here we show that the CCT complex is required for organ growth by interacting with the TOR pathway in Drosophila. Reduction of CCT4 results in growth defects by affecting both cell size and proliferation. Loss of CCT4 causes preferential cell death anterior to the morphogenetic furrow in the eye disc and within wing pouch in the wing disc. Depletion of any CCT subunit in the eye disc results in headless phenotype. Overgrowth by active TOR signaling is suppressed by CCT RNAi. The CCT complex physically interacts with TOR signaling components including TOR, Rheb, and S6K. Loss of CCT leads to decreased phosphorylation of S6K and S6 while increasing phosphorylation of Akt. Insulin/TOR signaling is also necessary and sufficient for promoting CCT complex transcription. Our data provide evidence that the CCT complex regulates organ growth by directly interacting with the TOR signaling pathway.

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)0.001
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 score0.004
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.020
GPT teacher head0.282
Teacher spread0.262 · 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

Citations43
Published2019
Admission routes1
Has abstractyes

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