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Enregistrement W4411433611 · doi:10.1016/j.ard.2025.06.731

POS1383 AUTOANTIBODIES IDENTIFIED IN MYOSITIS-SPECIFIC AUTOANTIBODY NEGATIVE JUVENILE MYOSITIS PATIENTS USING IMMUNOPRECIPITATION-MASS SPECTROMETRY

2025· article· en· W4411433611 sur OpenAlexaff
Fionnuala McMorrow, X. Bossuyt, Tom Dehaemers, Birthe Michiels, Lucy R. Wedderburn, Dario Cancemi, Lisa G. Rider, Neil McHugh, Sarah Tansley

Notice bibliographique

RevueAnnals of the Rheumatic Diseases · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueInflammatory Myopathies and Dermatomyositis
Établissements canadiensInstitute of Infection and Immunity
Organismes subventionnairesnon disponible
Mots-clésAutoantibodyMedicineMyositisImmunoprecipitationJuvenileImmunologyAntibodyPathologyGeneticsBiology

Résumé

récupéré en direct d'OpenAlex

Background: Myositis autoantibodies are important clinical biomarkers however, around 30-35% of patients with juvenile myositis (JM) tested by radio-immunoprecipitation (IP) are classified as myositis specific autoantibody (MSA)/myositis associated autoantibody (MAA) negative and therefore cannot benefit from autoantibody associated prognostic information and a more personalised treatment approach. Radio-IP is currently considered the gold-standard method for autoantibody detection; however radio-IP results give only the approximate mass of an autoantigen. By coupling IP with mass spectrometry (MS), the protein target of previously unknown autoantibodies can be identified (Figure 1). Objectives: This study aims to address the seronegative gap and help those currently classified as autoantibody negative by identifying unknown JM autoantibodies, as well as highlighting the benefits of using MS to identify novel and known myositis autoantibodies. Methods: 53 JM patients recruited to NIH myositis natural history studies previously classified as MSA negative by IP-Blot were analysed by radio-IP. Clear unknown bands identified by radio-IP in the NIH samples were excised from an SDS-PAGE and analysed by liquid chromatography (LC)-MS. MS results for NIH samples were confirmed using immunoblotting. Immunoprecipitates of 16 JDM patients recruited to the UK Juvenile Dermatomyositis Cohort and Biomarker Study (JDCBS) previously classified as having unknown bands on radio-IP, were analysed directly by LC-MS without SDS-PAGE step as per the method developed by Vulsteke et al (1) ANA pattern was determined by HEp-2 indirect immunofluorescence. Results: Using the traditional IP methodology, 35/53 NIH samples had unknown bands on IP, autoantigens were detected in 6/35 NIH samples with unknown bands by LC-MS (Table 1). Two samples contained unknown bands identified as adenosine deaminase acting on RNA-1 (ADAR1), and a third contained bands corresponding to two interacting proteins GTP-binding nuclear protein Ran (RAN) and regulator of chromosome condensation 1 (RCC1), detected by LC-MS. Bands in the remaining three samples were identified as Nor90, Scl-70 and Hexokinase 1 (HK1) by LC-MS, the Scl-70 positive patient had JDM overlap with systemic sclerosis (SSc). Results were confirmed by commercial line blot (Nor90, Scl-70) and western blot (ADAR1, RCC1, HK1). ANA patterns correlated with previously described pattern or antigen subcellular localisation. Using IP directly coupled with mass spectrometry, novel and known putative autoantigens were identified in the immunoprecipitate of 13/16 JDCBS samples shown in Table 1. Notably two proteins were detected at very high levels in immunoprecipitates, EEF1A lysine methyltransferase 1 (EEF1AKMT1) in three samples and Reticulon-4 receptor-like 2 (RTN4RL2) in two samples all with JDM. Similarly to NIH samples, autoantibodies targeting known autoantigens TIF1γ, NXP2, and PmScl were also identified. LC-MS identified autoantibodies in 19 JM patients previously classified as MSA/MAA negative, with a median (IQR) age of onset of 7 (3.3-10.1), 79% White and 68% Female. Conclusion: Serum from ‘seronegative' JM patients contains both novel autoantibodies and previously missed known autoantibodies, including MAAs which are not included in routine myositis autoantibody testing panels. We identified anti-ADAR1, anti-RCC1, anti- EEF1AKMT1 and anti-RTN4RL2 for the first time in JM. Anti-ADAR1 has previously been identified in two adults with DM and SLE (2), Anti-RCC1 has been identified in one adult with SSc (1). Understanding the prevalence and clinical associations of these autoantibodies will improve our understanding of JM as a disease and facilitate a personalised treatment approach. IP directly coupled with MS is a promising technique to facilitate identification of rare and novel autoantibodies in JM and other connective tissue diseases. REFERENCES: [1] Vulsteke J-B, Smith V, Bonroy C, Derua R, Blockmans D, De Haes P, et al. Identification of new telomere- and telomerase-associated autoantigens in systemic sclerosis. Journal of Autoimmunity. 2023;135:102988. [2] Muro Y, Ogawa-Momohara M, Takeichi T, Fukaya S, Yasuoka H, Kono M, et al. Clinical and serological features of dermatomyositis and systemic lupus erythematosus patients with autoantibodies to ADAR1. Journal of Dermatological Science. 2020;100(1):82-4. Download: Download high-res image (353KB) Download: Download full-size image Figure 1 . Radio-IP is performed by immunoprecipitating a radiolabelled cell extract with patient sera and separating interacting antigens by SDS-PAGE. Radioactive proteins are visualised by autoradiography and antigens are identified by comparison to known controls and novel autoantigens are seen as unknown bands. In contrast, IP-MS is performed by immunoprecipitating non-labelled cell extract with patient sera and analysing the antigens by HPLC-MS, reporting protein names as output. IP, immunoprecipitation; HPLC, high performance liquid chromatography; MS, mass spectrometry. Table 1 : Putative autoantigens detected by IP-MS in juvenile myositis samples previously classified as autoantibody negative. Known autoantibodies are highlighted in bold, proteins detected at very high levels are in italics. Download: Download high-res image (224KB) Download: Download full-size image * In one EEF1AKMT1 positive patient TIF1ƴ and CIDEC was also identified. † In one RTN4RL2 positive patient DYNLL1, FOXP1, NKRF and XRN2 were also identified. ‡ In one SQSTM1 positive patient NOLC1 and DYNLL1 were also identified, in another EFTUD2 was identified. ICAP, International Consensus on Antinuclear Antibody Patterns, JDM, juvenile dermatomyositis; JPM, juvenile polymyositis; SSc, systemic sclerosis; Autoantigen protein name abbreviations as listed on Uniprot. Acknowledgements: The Juvenile Dermatomyositis Cohort Biomarker Study & Repository (JDCBS) would like to thank all of the patients and their families who contributed to the JDCBS research study. We thank all local research coordinators and principal investigators who have made this research possible. Clinical, research and administrative contributors to JDCBS members were as follows: Dr Kate Armon, Ms Louise Coke, Ms Julie Cook and Ms Amy Nichols (Norfolk and Norwich University Hospitals); Dr Liza McCann, Mr Ian Roberts, Dr Eileen Baildam, Ms Louise Hanna, Ms Olivia Lloyd, Susan Wadeson, Ms Michelle Andrews, Ms Olivia Lloyd and Mrs Jane Roach (The Royal Liverpool Children's Hospital, Alder Hey, Liverpool); Dr Phil Riley, Ms Ann McGovern and Ms Verna Cuthbert (Royal Manchester Children's Hospital, Manchester); Dr Clive Ryder, Ms Janis Scott, Ms Beverley Thomas, Professor Taunton Southwood, Dr Eslam Al-Abadi and Ms Ruth Howman (Birmingham Children's Hospital, Birmingham); Dr Sue Wyatt, Mrs Gillian Jackson, Dr Mark Wood, Dr Tania Amin, Dr Vanessa VanRooyen, Ms Deborah Burton, Ms Louise Turner, Ms Heather Rostron and Ms Sarah Hanson (Leeds General Infirmary, Leeds); Dr Joyce Davidson, Dr Janet Gardner-Medwin, Dr Neil Martin, Ms Sue Ferguson, Ms Liz Waxman, Mr Michael Browne, Ms Roisin Boyle, Ms Emily Blyth and Ms Susanne Cathcart (The Royal Hospital for Sick Children, Yorkhill, Glasgow); Dr Mark Friswell, Professor Helen Foster, Ms Alison Swift, Dr Sharmila Jandial, Ms Vicky Stevenson, Ms Debbie Wade, Dr Ethan Sen, Dr Eve Smith, Ms Lisa Qiao, Mr Stuart Watson, Ms Claire Duong, Dr Stephen Crulley, Mr Andrew Davies, Miss Caroline Miller, Ms Lynne Bell, Dr Flora McErlane, Dr Sunil Sampath, Dr Josh Bennet and Mrs Sharon King (Great North Children's Hospital, Newcastle); Dr Helen Venning, Dr Rangaraj Satyapal, Mrs Elizabeth Stretton, Ms Mary Jordan, Dr Ellen Mosley, Ms Anna Frost, Ms Lindsay Crate, Dr Kishore Warrier, Ms Stefanie Stafford, Mrs Brogan Wrest, Ms Chia-Ping Chou and Mr Paul Pryce (Queens Medical Centre, Nottingham); Professor Lucy Wedderburn, Dr Clarissa Pilkington, Dr Nathan Hasson, Dr Muthana Al-Obadi, Dr Giulia Varnier, Dr Sandrine Lacassagne, Ms Sue Maillard, Mrs Lauren Stone, Ms Elizabeth Halkon, Ms Virginia Brown, Ms Audrey Juggins, Dr Sally Smith, Ms Sian Lunt, Ms Elli Enayat, Ms Hemlata Varsani, Ms Laura Kassoumeri, Miss Laura Beard, Ms Katie Arnold, Mrs Yvonne Glackin, Ms Stephanie Simou, Dr Beverley Almeida, Dr Kiran Nistala, Dr Raquel Marques, Dr Claire Deakin, Dr Parichat Khaosut, Ms Stefanie Dowle, Dr Charalampia Papadopoulou, Dr Shireena Yasin, Dr Christina Boros, Dr Meredyth Wilkinson, Dr Chris Piper, Ms Cerise Johnson-Moore, Ms Lucy Marshall, Ms Kathryn O'Brien, Ms Emily Robinson, Mr Dominic Igbelina, Dr Polly Livermore, Dr Socrates Varakliotis, Ms Rosie Hamilton, Ms Lucy Nguyen and Mr Dario Cancemi (Great Ormond Street Hospital, London); Dr Kevin Murray (Princess Margaret Hospital, Perth, Western Australia); Dr Coziana Ciurtin, Dr John Ioannou, Mrs Caitlin Clifford, Ms Linda Suffield and Ms Laura Hennelly (University College London Hospital, London); Ms Helen Lee, Ms Sam Leach, Ms Helen Smith, Dr Anne-Marie McMahon, Ms Heather Chisem, Ms Jeanette Hall and Ms Amy Huffenberger (Sheffield's Children's Hospital, Sheffield); Dr Nick Wilkinson, Ms Emma Inness, Ms Eunice Kendall, Mr David Mayers, Ms Ruth Etherton, Ms Danielle Miller and Dr Kathryn Bailey (Oxford University Hospitals, Oxford); Dr Jacqui Clinch, Ms Natalie Fineman, Ms Helen Pluess-Hall, Ms Suzanne Sketchley, Ms Melanie Marsh, Ms Anna Fry, Ms Maisy Dawkins-Lloyd and Ms Mashal Asif (Bristol Royal Hospital for Children, Bristol); Dr Joyce Davidson, Margaret Connon and Ms Lindsay Vallance (Royal Aberdeen Children's Hospital); Dr Kirsty Haslam, Ms Charlene Bass-Woodcock, Ms Trudy Booth and Ms Louise Akeroyd (Bradford Teaching Hospitals); Dr Alice Leahy, Amy Collier, Rebecca Cutts, Emma Macleod, Dr Hans De Graaf, Dr Brian Davidson, Sarah Hartfree, Ms Elizabeth Fofana and Ms Lorena Caruana (University Hospital Southampton); and all the Children, young people and their families who have contributed to this research. Disclosure of Interests: None declared . © 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)0,000
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 enseignants0,002
Score d'incertitude au seuil0,008

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

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 Opus0,018
Tête enseignante GPT0,303
Écart entre enseignants0,285 · 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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