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Record W2153825808 · doi:10.1074/mcp.m900190-mcp200

Proteomic Analysis of Microtubule-associated Proteins during Macrophage Activation

2009· article· en· W2153825808 on OpenAlexafffund
Prerna C. Patel, Katherine H. Fisher, Eric Yang, Charlotte M. Deane, Rene E. Harrison

Bibliographic record

VenueMolecular & Cellular Proteomics · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsSunnybrook Health Science CentreHealth Sciences CentreUniversity of Toronto
FundersCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsCell biologyBiologyProteomicsMicrotubuleCytoskeletonLipopolysaccharideCytokineQuantitative proteomicsMacrophageSignal transductionImmunologyIn vitroBiochemistryCellGene

Abstract

fetched live from OpenAlex

Classical activation of macrophages induces a wide range of signaling and vesicle trafficking events to produce a more aggressive cellular phenotype. The microtubule (MT) cytoskeleton is crucial for the regulation of immune responses. In the current study, we used a large scale proteomics approach to analyze the change in protein composition of the MT-associated protein (MAP) network by macrophage stimulation with the inflammatory cytokine interferon-gamma and the endotoxin lipopolysaccharide. Overall the analysis identified 409 proteins that bound directly or indirectly to MTs. Of these, 52 were up-regulated 2-fold or greater and 42 were down-regulated 2-fold or greater after interferon-gamma/lipopolysaccharide stimulation. Bioinformatics analysis based on publicly available binary protein interaction data produced a putative interaction network of MAPs in activated macrophages. We confirmed the up-regulation of several MAPs by immunoblotting and immunofluorescence analysis. More detailed analysis of one up-regulated protein revealed a role for HSP90beta in stabilization of the MT cytoskeleton during macrophage activation.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.214
Teacher spread0.208 · 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
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

Citations50
Published2009
Admission routes2
Has abstractyes

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