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Record W2017296218 · doi:10.1042/bst0350704

Phagocytosis of collagen by fibroblasts and invasive cancer cells is mediated by MT1-MMP

2007· review· en· W2017296218 on OpenAlexaff
H. Lee, Katharine L. Sodek, Queena Hwang, Theodore J. Brown, Maurice Ringuette, Jaro Sodek

Bibliographic record

VenueBiochemical Society Transactions · 2007
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtease and Inhibitor Mechanisms
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMatrix metalloproteinaseCell biologyPhagocytosisChemistryHT1080Cancer cellType I collagenCollagen receptorGelatinaseCellBiologyPathologyCancerIntegrinBiochemistryMedicine

Abstract

fetched live from OpenAlex

Degradation of collagen is required for the physiological remodelling of connective tissues during growth and development, as well as in wound healing, inflammatory diseases, and cancer cell invasion. In remodelling adult tissues, degradation of collagen occurs primarily through a phagocytic pathway. While various steps in this pathway have been characterized, the enzyme required to fragment collagen fibrils for phagocytosis has not been identified. Laser confocal microscopy, transmission electron microscopy and biochemical assays were used to show that degradation of collagen substrates by fibroblasts correlated with the expression of the membrane-bound metalloproteinase MT1-MMP (membrane-type 1 matrix metalloproteinase). The MT1-MMP was localized to sites of collagen cleavage on the cell surface and also within the cells. In contrast with MT1-MMP, the gelatinase MMP-2 was not required for collagen phagocytosis. Similar analyses of several ovarian cancer, breast cancer and fibrosarcoma cells indicated that highly metastatic cells also degrade collagen through a phagocytic pathway that is mediated by MT1-MMP. Collectively, these studies demonstrate a pivotal role for catalytically active MT1-MMP in preparing collagen fibrils for phagocytic degradation by normal and transformed cells.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.289
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.019
GPT teacher head0.291
Teacher spread0.272 · 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.

Study designBench or experimental
Domainnot available
GenreReview

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

Citations40
Published2007
Admission routes1
Has abstractyes

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