ABS0240 MECHANO-ACTIVATED CHONDROPROGENITORS (MACS) DERIVED FROM ARTICULAR CHONDROCYTES, WHICH DRIVE INTRINSIC CARTILAGE REPAIR
Notice bibliographique
Résumé
Background: Osteoarthritis (OA) is a highly prevalent, age-associated disease, for which no disease-modifying treatments currently exist. The degradation of articular cartilage, a low-cellularity, avascular tissue with limited regenerative capacity, is the hallmark of OA. Cartilage is highly mechanosensitive, and one mechanism of mechanosensing is by release of growth factors which are sequestered in the pericellular matrix of the tissue. Chronic mechanical stress is a feature of OA. Mechanical off-loading through surgical joint distraction (SJD), has been shown to provide sustained clinical improvement and cartilage regrowth after device removal. Proteins associated with epithelial to mesenchymal transition (EMT) have been identified in the synovial fluid (SF) proteome of individuals with OA (STEpUP OA) [1]. Objectives: This study aimed to identify the intrinsic repair cells of cartilage and to test the hypothesis that they are derived from chondrocytes in response to injury-released growth factors. We also sought to understand how this relates to the biology that changes in the OA joint upon SJD; specifically, how this relates to the EMT signature observed within the SF proteome of individuals with established OA. Methods: Focal cartilage defects were created in 10-week-old mice in which Col2 lineage chondrocytes had been labelled at 2 weeks of age using a Cre-inducible TdTomato transgene. Porcine cartilage explants were injured either by cutting Injury or by high impact loads (from a drop-tower). Tissue was cultured after injury for early (4, 48h) or late (7, 14 day) periods. Single nuclei RNAseq was performed on injured cartilage samples. Chondrocyte clusters were identified and analysed for marker genes using Scanpy and SCVI. Differential expression (using DESeq2) and pathway enrichment (using FGSEA) were performed on pseudobulk counts. Medium conditioned by mechanically injured porcine cartilage (injury CM), containing released growth factors from the pericellular matrix, was collected and used to stimulate primary porcine chondrocytes with/without FGFR inhibitor or appropriate controls. RNAseq was performed at 4h. Cell phenotype and confluency over time were measured using gap assays and InCucyte live imaging over 96h. Paired synovial fluid samples from individuals with knee OA (n=16) before and at end of 6 weeks of SJD were analysed by SomaScan V4.1 (N = 5,471 proteins quantified per sample). Results: (1) Lineage-tracing showed that Col2-lineage chondrocytes contribute to repair tissue in vivo, indicating their role as intrinsic repair cells. (2) A transient chondrocyte population was identified following mechanical injury, characterized by upregulation of ITGA6, LAMC2, YAP1 , and DOT1L and downregulation of mature chondrocyte markers. (3) Injury CM, largely driven by fibroblast growth factor 2 (FGF2), promoted up-regulation of ITGA6, LAMC2, YAP1 , and DOT1L and turned on the EMT pathway with EMT-like behaviour, including cell proliferation and migration evident in vitro. (4) Functional assays demonstrated that reprogrammed chondrocytes could re-differentiate into homeostatic chondrocytes when exposed to chondrogenic cues, thus defining these cells as "mechano-activated chondroprogenitors (MACs)". An anabolic tissue response was observed in the cartilage14 days following an acute single impact injury and in the OA synovial fluid following SJD. The latter was associated with a marked drop in a number of TGF superfamily members, including activin A, and increased expression of chondrogenic markers, including SOX9, COL2A1 and HSPG2 . Recombinant activin A suppressed MAC re-differentiation into homeostatic chondrocytes. Conclusion: This study establishes a new paradigm for how cartilage repairs, reprogramming the chondrocyte into its own repair cell (intrinsic cartilage repair) and involving two critical phases: (1) growth factor driven reprogramming of chondrocytes into MACs and (2) subsequent re-differentiation of MACs into homeostatic chondrocytes. We propose that in OA, chronic injury signals sustain MAC activation and inhibit repair. These findings highlight new therapeutic opportunities to promote cartilage regeneration in joint disease. REFERENCES: [1] Perry, T. A. et al. Deconvoluting synovial fluid molecular endotypes in knee osteoarthritis: primary results from the STEpUP OA Consortium. medRxiv , 2024.06.05.24308485 (2024). Acknowledgements: The STEpUP OA Consortium author block includes: University of Nottingham: Ana M. Valdes, David A. Walsh, Michael Doherty, Vasileios Georgopoulos; Lund University: Staffan Larsson, L. Stefan Lohmander, André Struglics; University of Cambridge: Brian D.M. Tom, Laura Bondi; University of Toronto: Mohit Kapoor, Rajiv Gandhi, Anthony Perruccio, Y. Raja Rampersaud, Kim Perry; University of Manchester: Tim Hardingham, David Felson; University of Oxford: Tonia L. Vincent, Thomas A. Perry, Luke Jostins-Dean, Yun Deng, Vicky Batchelor, Jennifer Mackay-Alderson, Gretchen Brewer, Rose M. Maciewicz, Brian Marsden, Nigel K. Arden, Philippa Hulley, Andrew Price, Stefan Kluzek, Megan Goff, Vinod Kumar, James Tey; Imperial College London: Fiona E. Watt, Andrew Williams, Artemis Papadaki; University College Maastricht: Tim J. Welting, Pieter Emans, Tim Boymans, Liesbeth Jutten, Marjolein Caron, Guus van den Akker; University of Western Ontario: C. Thomas Appleton, Trevor B. Birmingham, J. Daniel Klapak; Biosplice: Sarah Kennedy, Jeymi Tambiah; Fidia: Devis Galesso, Nicola NK; SomaLogic: Joe Gogain, Darryl Perry, Anna Mitchel, Ela Zepko; Novartis: Sophie Brachat, Joanna Mitchelmore, Juerg Gasser, Lori Jennings; UCB: Waqar Ali. We thank the orthopedic surgeon Dr R Custers, Department of orthopaedics, UMC Utrecht, Netherlands, for synovial fluid collection and SJD surgeries. Disclosure of Interests: Linyi Zhu: None declared. Hayat Muhammad: None declared. Thomas A Perry: None declared. Anastasia Ardasheva: None declared. Rebecca A Symons: None declared. Jessica J Mcclure: None declared. Karolina Kania: None declared. Susan M Clark: None declared. Fabio Colella: None declared. Lada A Koneva: None declared. Sumayya Khan: None declared. Suzanne Eldridge: None declared. Francesco Dell'Accio: None declared. Simon C. Mastbergen: None declared. Mylène Jansen: None declared. Fiona E. Watt: None declared. Yoshi Itoh: None declared. Jane Bennett: None declared. Jadwiga Miotla-Zarebska: None declared. Moustafa Attar: None declared. Chris Chan: None declared. Anke Roelofs has received research grant funding through the institution from Biosplice Therapeutics (formerly Samumed LLC), Cosimo De Bari has received consultancy fees from UCB and Galapagos, and research grant funding through the institution from Biosplice Therapeutics (formerly Samumed LLC), Stephen Sansom: None declared. Jana Riegger: None declared. Tonia Vincent has received ad hoc personal consultancy fees from Zoetis, and grant support for STEpUP OA from Pfizer, Novartis, UCB, Fidia, Biosplice, Galapagos. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».