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Record 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 on OpenAlexaff
Fionnuala McMorrow, X. Bossuyt, Tom Dehaemers, Birthe Michiels, Lucy R. Wedderburn, Dario Cancemi, Lisa G. Rider, Neil McHugh, Sarah Tansley

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicInflammatory Myopathies and Dermatomyositis
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsAutoantibodyMedicineMyositisImmunoprecipitationJuvenileImmunologyAntibodyPathologyGeneticsBiology

Abstract

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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.

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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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.303
Teacher spread0.285 · 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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