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Inflammatory Chemokine Receptors Support Inflammatory Macrophage and Dendritic Cell Maturation

2022· article· en· W4310464576 on OpenAlexaff
Robin Bartolini, Laura Medina‐Ruiz, Alan J. Hayes, Christopher J. Kelly, Heba A. Halawa, Gerard J. Graham

Bibliographic record

VenueImmunoHorizons · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsInstitute of Infection and Immunity
FundersMedical Research CouncilUniversity of GlasgowWellcome Trust
KeywordsChemokineChemokine receptorMacrophage inflammatory proteinCC chemokine receptorsMacrophageDendritic cellImmunologyCCL21ReceptorCCR2Cell biologyInflammationMedicineChemistryBiologyImmune systemInternal medicineIn vitro

Abstract

fetched live from OpenAlex

Dendritic cells form clusters in vivo, but the mechanism behind this has not been determined. In this article, we demonstrate that monocytes from mice deficient in the chemokine receptors CCR1, CCR2, CCR3, and CCR5 display reduced clustering in vitro, which is associated with impaired dendritic cell and macrophage differentiation. We further show that the differentiating cells themselves produce ligands for these receptors that function, in a redundant manner, to regulate cell clustering. Deletion of, or pharmacological blockade of, more than one of these receptors is required to impair clustering and differentiation. Our data show that chemokines and their receptors support clustering by increasing expression of, and activating, cell-surface integrins, which are associated with cell-cell interactions and, in the context of monocyte differentiation, with reduced expression of Foxp1, a known transcriptional suppressor of monocyte differentiation. Our data therefore provide a mechanism whereby chemokines and their receptors typically found in inflammatory environments can interact to promote murine monocyte differentiation to macrophages and dendritic 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.005
GPT teacher head0.206
Teacher spread0.201 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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