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Record W2104001632 · doi:10.1091/mbc.e07-06-0596

Caveolin-1 and -2 Interact with Connexin43 and Regulate Gap Junctional Intercellular Communication in Keratinocytes

2007· article· en· W2104001632 on OpenAlexafffund
Stéphanie Langlois, Kyle N. Cowan, Qing Shao, Bryce J. Cowan, Dale W. Laird

Bibliographic record

VenueMolecular Biology of the Cell · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnexins and lens biology
Canadian institutionsUniversity of British ColumbiaWestern University
FundersCanadian Institutes of Health ResearchUniversité du Québec à Montréal
KeywordsBiologyCell biologyLipid raftGap junctionColocalizationIntracellularCaveolin 1Small interfering RNARaftTransfectionConnexinESCRTEndosomeCell cultureSignal transductionGeneticsChemistry

Abstract

fetched live from OpenAlex

Connexin43 (Cx43) has been reported to interact with caveolin (Cav)-1, but the role of this association and whether other members of the caveolin family bind Cx43 had yet to be established. In this study, we show that Cx43 coimmunoprecipitates and colocalizes with Cav-1 and Cav-2 in rat epidermal keratinocytes. The colocalization of Cx43 with Cav-1 was confirmed in keratinocytes from human epidermis in vivo. Our mutation and Far Western analyses revealed that the C-terminal tail of Cx43 is required for its association with Cavs and that the Cx43/Cav-1 interaction is direct. Our results indicate that newly synthesized Cx43 interacts with Cavs in the Golgi apparatus and that the Cx43/Cavs complex also exists at the plasma membrane in lipid rafts. Using overexpression and small interfering RNA approaches, we demonstrated that caveolins regulate gap junctional intercellular communication (GJIC) and that the presence of Cx43 in lipid raft domains may contribute to the mechanism modulating GJIC. Our results suggest that the Cx43/Cavs association occurs during exocytic transport, and they clearly indicate that caveolin regulates GJIC.

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 score0.005
Threshold uncertainty score0.377

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 head0.006
GPT teacher head0.232
Teacher spread0.227 · 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

Citations120
Published2007
Admission routes2
Has abstractyes

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