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Record W4387270153 · doi:10.1161/atvb.43.suppl_1.453

Abstract 453: Adiponectin Increases AdipoR1/R2 Cell Surface Expression And M1-M2 Macrophage Subpopulations In AdipoR Knockdown Conditions

2023· article· en· W4387270153 on OpenAlexaff
Ioanna Gianopoulos, Styliani S Daskalopoulou

Bibliographic record

VenueArteriosclerosis Thrombosis and Vascular Biology · 2023
Typearticle
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsMcGill University
Fundersnot available
KeywordsAdiponectinAdiponectin receptor 1ChemokineGene knockdownCD80CytokineFlow cytometryReceptorCD86EndocrinologyChemistryInternal medicineBiologyImmunologyMedicineT cellCD40Immune systemCytotoxic T cellApoptosisInsulinBiochemistryIn vitro

Abstract

fetched live from OpenAlex

Introduction: Adiponectin is an adipose-tissue secreted hormone that exerts its anti-inflammatory and anti-atherogenic functions through its two main receptors, adiponectin receptor 1 (AdipoR1) and AdipoR2, which are highly expressed in monocyte-derived macrophages (Mϕs). In response to various stimuli, Mϕs can acquire a more pro-inflammatory M1, or anti-inflammatory M2 phenotype. Herein, we investigated the effect of single AdipoR1, single AdipoR2 and double AdipoR1/R2 knockdown conditions in response to adiponectin on M1 and M2 Mϕ subpopulations. Methods: siRNA-mediated knockdown of AdipoR1 and/or AdipoR2 was performed in THP-1 monocyte-derived Mϕs, and treated in the presence or absence of adiponectin. Flow cytometry was performed to assess AdipoR1/R2, M1 Mϕ subpopulation (CD86/CD80), and M2 Mϕ subpopulation (CD206/CD163). Magnetic bead-based immunoassays were performed on cell culture supernatants to assess 12 pro-inflammatory (TNF-α, MCP-1, IL-1β, IL-6, IL-8, IL-12p40, IL-12p70) and anti-inflammatory (IL-1Rα, IL-4, IL-10, TGF-β1 and CCL17) cytokines and chemokines. Results: In response to adiponectin, flow cytometry analyses for single AdipoR1, single AdipoR2, and double AdipoR1/R2 siRNA resulted in a significant increase in double-positive AdipoR1/R2 Mϕs. Although adiponectin treatment of single AdipoR1, single AdipoR2 and double AdipoR1/R2 siRNA significantly increased both Mϕ subpopulations, this increase was more prominent in M2 Mϕ subpopulation. Immunoassay analyses in knockdown conditions demonstrated a mixed pro- and anti-inflammatory cytokine and chemokine profile in response to adiponectin; levels of TNF-α, MCP-1 were increased and IL-1β decreased, while TGF-β1 and CCL17 were decreased. Conclusions: Adiponectin treatment increases the amount of AdipoR1/R2 cell surface expression on Mϕs without being limited by intracellular AdipoR knockdown conditions. Furthermore, although adiponectin promoted both Mϕ subpopulations, a greater shift towards M2 Mϕs was noted, while producing a heterogeneous pro- and anti-inflammatory cytokine and chemokine profile. Our current work will unravel the underlying mechanisms of the role of the adiponectin-AdipoR pathway in regulating M1-M2 Mϕ phenotypes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.030
GPT teacher head0.296
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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