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Enregistrement W4411427508 · doi:10.1016/j.ard.2025.06.1723

ABS0273 NEW PATHOGENIC PATHWAYS IN ANKYLOSING SPONDYLITIS IDENTIFIED THROUGH MULTI-ANCESTRY GENOMEWIDE ASSOCIATION STUDY

2025· article· en· W4411427508 sur OpenAlexaff
Z. Li, Nicholas C. Harvey, Xinyu Wu, José Garrido‐Mesa, Helena Marzo‐Ortega, Dennis McGonagle, Ashley E. Morgan, Nurullah Akkoç, Bora Nam, Handan Yarkan Tuğsal, GÖKÇE KENAR ARTIN, Gus R. McFarlane, Gareth T. Jones, Paul Leo, Kelly S Zimmerman, Erica Duncan, Jason L. Brown, Mahdi Mahmoudi, Ahmadreza Jamshidi, Elham Farhadi, Nigil Haroon, Robert D. Inman, M. Breban, Michael M. Ward, Michael H. Weisman, L.S. Gensler, David M. Evans, Tony Kenna, T.H. Kim, P Wordsworth, J. D. Reveille, Hui Xu, Martin M. Brown

Notice bibliographique

RevueAnnals of the Rheumatic Diseases · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueSpondyloarthritis Studies and Treatments
Établissements canadiensUniversity Health NetworkUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésMedicineAnkylosing spondylitisSpondylitisAssociation (psychology)Genetic associationGenome-wide association studyBioinformaticsGeneticsImmunologySingle-nucleotide polymorphismGenotypeBiologyGene

Résumé

récupéré en direct d'OpenAlex

Background: Genetic factors are major determinants of ankylosing spondylitis (AS) risk. To date, 116 loci have been shown to be definitively associated with AS, collectively accounting for <30% of the total liability to disease. At many loci, the key associated genes and genetic variants remain unclear. Objectives: To identify further genetic variants influencing AS-risk, and to pinpoint causative genes involved, using a large multi-ancestry genome-wide association study (GWAS). Methods: GWAS was performed using SNP microarray data from 25,645 cases and 71,224 controls after quality control. This included cohorts of European ancestry (15,913 cases, 59,769 controls), East Asian ancestry (8,372 cases, 9,754 controls), and Iranian/Turkish ancestry (1,360 cases, 1,701 controls). Likely causative genes were identified using 2-sample Mendelian randomization (in UK Biobank genotype, whole blood mRNA and serum protein data) and Transcriptome-Wide Association Study (TWAS) and Proteome-Wide Association Study (PWAS) (OmicsPred) analyses in the overall dataset. Findings for 250 proteins were confirmed using Nulisa proteomic assays (Alamar Biosciences) in 298 European AS cases and 142 healthy controls. Results: Excluding the MHC, the genomic inflation factors (λ 1000 ) were 1.007 (European), 1.019 (East Asian), and 1.004 (combined). A total of 71 genomewide significant (GWS; P<5x10 -8 ) loci were identified, including 32 novel loci. Whilst all novel loci were identified in the European-ancestry cohort, strong correlation was observed between European and east Asian ancestry findings (Pearson correlation of log (OR) for SNPs with P<5x10 -5 = 0.9), consistent with strong genetic correlation across ancestries. Novel GWS loci included ATG16L1 (an autophagy gene associated with Crohn's disease) , SELE (E-selectin, expressed by activated enthothelial cells and involved in leukocyte migration into inflammed tissues), IL12A ( IL12B being a known AS-associated gene), GNLY (granulysin, produced by cytotoxic T-lymphocytes and NK cells with proinflammatory and antimicrobial functions), GPR55 (interacts with AHR , a known AS-gene, and involved in gut inflammation), TNFSF15 (TL1A, a marker of proinflammatory mononuclear phagocytes found in AS gut samples [1]), ABO (encoding ABO, the blood group antigens), and the telomerase genes, TERT and TERC . Imputation of blood group antigens revealed increased AS risk with blood type A amongst European cases (OR=1.064, P=0.0059). Association was also observed with FUT2 (fucosyltransferase 2), which determines secretor status (blood group H), the ability to secrete blood groups across mucosal surfaces, a form of mucosal innate immunity. Increased risk was observed in all ancestries with imputed non-secretor status (OR=1.13, P=1.11x10 -7 in Europeans; OR=1.21, P=3.59x10 -7 in east Asians). No interaction was observed between FUT2 and ABO on risk of AS. These findings provide further evidence supporting AS being driven by interactions between the gut microbiome and the host immune system. PWAS analysis identified association with 111 unique serum proteins and TWAS with 434 gene transcripts (false discovery rate <0.05). Considering cytokines and related gene products, increased levels of IL-10, IL-17F, IL-23R, IL-11RA, IL-34, IL-37 and reduced levels of IL-1R1 IL-6, IL-6R, IL-12A/B, IL-21, and IL-27RA protein were associated with increased disease risk. Amongst imputed mRNA levels, increased transcript levels of IL2RA, IL2RB, IL6ST, IRF5 and JAK2 were identified in AS cases, whereas mRNA levels of cytokine receptor genes IL7R and IL1RL1 were reduced. We found evidence to support B3GNT2 (β1,3-N-acetylglucosaminyltransferase 2) to be an AS-causative gene encoded at the previously reported chr 2p15 intergenic region. Using 2-sample MR, reduced levels of B3GNT2 mRNA were associated with increased risk of AS (β=-0.250, SE=0.034, P=3.20x10 -13 ). B3GNT2 mRNA levels were imputed to be lower in cases than controls (P=1.90x10 -32 ). B3GNT2 levels were also found to be lower in cases than controls in direct measurements (P=0.0076). The risk allele of the lead SNP at chr 2p15, rs4672505, is an eQTL for B3GNT2 (GTEX, whole blood normalised effect size 0.19, P=1.07x10 -29 ), the risk A allele being associated with reduced B3GNT2 transcription. B3GNT3 elongates cell surface long-chain poly-N-acetyl-lactosamine, leading to suppression of immune responses; B3GNT2-/- mice develop T- and B-cell hyperactivity [2]. Conclusion: This study identifies multiple novel genetic loci and pathways involved in AS pathogenesis, highlighting potential therapeutic targets. These findings underscore the role of gut immunity in the disease, and identify widespread genetically encoded dysregulation of cytokine expression as being involved in AS development. REFERENCES: [1] Ciccia F, et al. Arthritis & rheumatology . 2018 Dec; 70(12):2003-2013. [2] Togayachi A, et al. Methods Enzymol . 2010; 479:185-204. Acknowledgements: NIL . Disclosure of Interests: Zhixiu Li: None declared, Nicholas Harvey: None declared, Xin Wu: None declared, Jose Garrido-Mesa: None declared, Helena Marzo-Ortega: None declared, Dennis McGonagle: None declared, Ann Morgan: None declared, Nurullah Akkoc: None declared, Bora Nam: None declared, Handan Yarkan Tugsal: None declared, Gökçe Kenar Artin: None declared, Gary McFarlane: None declared, Gareth T. Jones: None declared, Paul Leo: None declared, Kate Zimmerman: None declared, Emma Duncan: None declared, Julia Brown: None declared, Mahdi Mahmoudi: None declared, Ahmadreza Jamshidi: None declared, Elham Farhadi: None declared, Nigil Haroon: None declared, Robert Inman: None declared, Maxime Breban: None declared, Michael M Ward: None declared, Michael H Weisman: None declared, Lianne S Gensler: None declared, David Evans: None declared, Tony Kenna: None declared, Tae-Hwan Kim: None declared, Paul Bryan Wordsworth: None declared, John D Reveille: None declared, Huji Xu: None declared, Matthew Brown No, Clementia, Grey Wolf, Incyte, Ipsen, Altis Medicines, UCB. © 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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,019

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,003
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

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.

Tête enseignante Opus0,089
Tête enseignante GPT0,359
Écart entre enseignants0,270 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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