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Record W4212804177 · doi:10.1093/jcag/gwab049.040

A41 THE ROLE OF PGC-1α IN MURINE BONE MARROW DENDRITIC CELLS

2022· article· en· W4212804177 on OpenAlexafffund
S Jaleel, Fernando Lopes

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsInflammationImmune systemMediatorImmunologyBiologyCytokineMitochondrial biogenesisBone marrowInflammatory bowel diseaseImmune toleranceColitisMitochondrionCancer researchDiseaseMedicineCell biologyPathology

Abstract

fetched live from OpenAlex

Abstract Background The intestinal immune system tolerates food antigens and the commensal microbiota to prevent chronic inflammation. Inflammatory bowel diseases (IBD) such as ulcerative colitis and Crohn’s disease, are characterized by the loss of tolerance towards the microbiota, leading to inflammation, which is characterized by the high production of cytokines such as TNF and the influx of macrophages and neutrophils to the gut. Tolerance is driven by tolerogenic dendritic cells (tolDCs). Currently, there is no cure for IBD and while available treatments may reduce symptoms, there are no pharmacological therapies that specifically seek to rescue tolerance. Inducing tolDCs in patients with IBD may be a promising therapy since it is a way to directly target the main cause of the disease. tolDCs have been defined by down-regulating TNF (pro-inflammatory cytokine), producing IL-10 (anti-inflammatory cytokine), and active oxidative phosphorylation (OXPHOS) in the mitochondria. Aims Given the tolDC characteristic of active OXPHOS, the objective of our project was to evaluate the role of PGC-1α, a major mediator of mitochondria biogenesis, in generating toDCs using murine bone marrow-derived dendritic cells (BMDC). Our hypothesis is that tolDCs would be (1) generated by pharmacological activation of PGC-1α by ZLN005, and (2) constrained by inhibition of PGC-1α by SR18292. Methods We treated BMDC cultures with ZLN005 (PGC-1α activator) or SR18292 (PGC-1α inhibitor), and LPS. We characterized the effect of PGC-1α manipulation on the metabolism of BMDCs by metabolomics and Seahorse XF Cell Mito Stress Test. In addition, we evaluated features of tolDCs, such as gene expression by qPCR, and cytokine production by ELISA. Results We observed that activation of PGC-1α altered the metabolic profile of BMDC, and upregulates genes known to be expressed in tolDCs. However, this transcriptional upregulation was lost after LPS stimulation and did not alter TNF or IL-10 levels in BMDCs. PGC-1α inhibition, on other hand, decreased features of tolDCs, such as metabolic profile, transcriptional activation, and the levels of LPS-induced IL-10. Our data suggest that PGC-1α activation with ZLN005 does not increase the generation of tolDCs in vitro. However, inhibition of PGC-1α with SR18292 constrains tolDCs generation. Conclusions PGC-1α is important for the development of tolDCs in murine dendritic cells. Funding Agencies NSERC and FRQNT

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.004
GPT teacher head0.187
Teacher spread0.183 · 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 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

Citations0
Published2022
Admission routes2
Has abstractyes

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