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Record W2099728310 · doi:10.1186/1476-4598-9-233

Coordination of glioblastoma cell motility by PKCι

2010· article· en· W2099728310 on OpenAlexafffund
R. Mitchell Baldwin, Gordon M. Barrett, D A E Parolin, Jana Gillies, J. Paget, Sylvie J. Lavictoire, Douglas A. Gray, Ian A.J. Lorimer

Bibliographic record

VenueMolecular Cancer · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPI3K/AKT/mTOR signaling in cancer
Canadian institutionsUniversity of OttawaOttawa Hospital
FundersNational Cancer InstituteCanadian Institutes of Health ResearchTerry Fox FoundationOttawa Hospital Research Institute
KeywordsBiologyMotilityGlioblastomaCancer researchProtein kinase CCell biologySignal transductionComputational biology

Abstract

fetched live from OpenAlex

BACKGROUND: Glioblastoma is one of the deadliest forms of cancer, in part because of its highly invasive nature. The tumor suppressor PTEN is frequently mutated in glioblastoma and is known to contribute to the invasive phenotype. However the downstream events that promote invasion are not fully understood. PTEN loss leads to activation of the atypical protein kinase C, PKCι. We have previously shown that PKCι is required for glioblastoma cell invasion, primarily by enhancing cell motility. Here we have used time-lapse videomicroscopy to more precisely define the role of PKCι in glioblastoma. RESULTS: Glioblastoma cells in which PKCι was either depleted by shRNA or inhibited pharmacologically were unable to coordinate the formation of a single leading edge lamellipod. Instead, some cells generated multiple small, short-lived protrusions while others generated a diffuse leading edge that formed around the entire circumference of the cell. Confocal microscopy showed that this behavior was associated with altered behavior of the cytoskeletal protein Lgl, which is known to be inactivated by PKCι phosphorylation. Lgl in control cells localized to the lamellipod leading edge and did not associate with its binding partner non-muscle myosin II, consistent with it being in an inactive state. In PKCι-depleted cells, Lgl was concentrated at multiple sites at the periphery of the cell and remained in association with non-muscle myosin II. Videomicroscopy also identified a novel role for PKCι in the cell cycle. Cells in which PKCι was either depleted by shRNA or inhibited pharmacologically entered mitosis normally, but showed marked delays in completing mitosis. CONCLUSIONS: PKCι promotes glioblastoma motility by coordinating the formation of a single leading edge lamellipod and has a role in remodeling the cytoskeleton at the lamellipod leading edge, promoting the dissociation of Lgl from non-muscle myosin II. In addition PKCι is required for the transition of glioblastoma cells through mitosis. PKCι therefore has a role in both glioblastoma invasion and proliferation, two key aspects in the malignant nature of this disease.

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 score0.033
Threshold uncertainty score0.710

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 head0.003
GPT teacher head0.240
Teacher spread0.237 · 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

Citations25
Published2010
Admission routes2
Has abstractyes

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