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EXPLORING THE ROLE OF CELLULAR SENESCENCE IN LUPUS NEPHRITIS

2025· article· en· W4410715626 on OpenAlexvenueno aff
Laura Watteyne, Gaëlle Tilman, Émilie Dupré, Antoine Enfrein, Farah Tamirou, Frédéric Houssiau, Sophie Lucas, Nisha Limaye

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLupus nephritisSenescenceCellular senescenceImmunologyNephritisPathologyInternal medicineGeneticsDiseasePhenotype

Abstract

fetched live from OpenAlex

PV017 / #362 Poster Topic: AS02 - Animal Models Background/Purpose Cellular senescence is a physiological process triggered by various stresses, causing cells to enter irreversible cell cycle arrest. These cells nevertheless remain metabolically active, and undergo morphological and functional changes, such as acquisition of a pro-fibrotic and proinflammatory secretome. Cellular senescence has been reported in renal aging and in kidney diseases such as hypertensive nephropathy, IgA nephropathy, and diabetic nephropathy.[1] We previously reported that lupus nephritis (LN) patients with more severe baseline disease and poor long-term outcome exhibit higher levels of cellular senescence (measured using the hallmark marker p16INK4a), in baseline biopsy.[2] We assessed for the presence and time of onset of renal cell senescence in lupus-prone B6.NZMSle1/Sle2/Sle3 (B6. Sle1.2.3) mice, with a view to testing for the effects of senolytic drugs on kidney disease progression in this model. Methods A time-course was performed, with necropsy of 2 C57Bl/6 (B6) and 10 B6.Sle1.2.3 mice, every 2 months from 2 to 12 months. Systemic autoimmunity was assessed by ELISA to measure total IgG and anti-dsDNA IgG in plasma. IgG deposition in kidneys was measured by immunofluorescence. Renal disease was assessed using urine albumin/creatinine ratio and kidney histology (activity and chronicity scores). Cellular senescence in the kidney was assessed by immunohistochemistry for p16Ink4a and senescence-associated β-galactosidase assay. We are now testing the senolytic drug combination dasatinib (5 mg/kg) plus quercetin (50 mg/kg) (DQ) vs vehicle control, in 2 distinct settings: (i) after onset of systemic disease but before onset of any signs of renal disease or renal cell senescence (ie, from 5 months of age until necropsy at 8 months of age); (ii) after onset of renal cell senescence and overt kidney disease (ie, from 8 months of age to necropsy at 10 months of age). Treatment is administered by oral gavage bi-weekly. Systemic and renal disease, as well as cellular senescence, will be assessed as described above. Results We demonstrated, in aged (12-15 month old) B6.Sle1.2.3 lupus-prone mice, that high kidney p16Ink4a positivity is significantly associated with increased proteinuria, histopathological scores, CD8+ T cell infiltration and renal fibrosis. As in patients, p16Ink4a-positivity was not associated with systemic disease parameters or with Ig deposition in the kidney. A time-course showed that systemic disease parameters as well as glomerular IgG deposits increase from 4 months of age in B6.Sle1.2.3 as compared to B6 control mice; kidney disease shows later onset (from 6-8 months of age) and greater heterogeneity. The appearance of p16Ink4a positive cells above B6 levels is observed at 8 months of age in B6.Sle1.2.3 mice.[3] Treatment of a first cohort with DQ is currently ongoing, with preliminary results expected in January 2025. Conclusions Based on the time of onset of renal cell senescence and systemic vs. end-organ disease observed in the B6.Sle1.2.3 mouse model, we are now testing the effect of senolytic therapy (drugs selectively targeting senescent cell anti-apoptotic pathways) on renal disease penetrance and severity. Should renal cell senescence (and the clearance of these cells) prove to have an impact on disease, it could provide a non-immune cell targeting strategy in LN. References: [1.] Valentijn FA. J Cell Commun Signal 2017; 12(1):69-82. [2.] Tilman G. RMD Open 2021;7(3):e001844. [3.] Tilman G. Lupus Sci Med 2023;10(2):e001010.

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)0.001
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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.031
GPT teacher head0.280
Teacher spread0.249 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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