2. Academic Validation
  3. Low-input deep learning platform for citrullinated peptide identification, autoantigen discovery and rheumatoid arthritis treatment stratification

Low-input deep learning platform for citrullinated peptide identification, autoantigen discovery and rheumatoid arthritis treatment stratification

  • Nat Biomed Eng. 2026 Mar 3. doi: 10.1038/s41551-026-01628-4.
Meng Hu # 1,  Chenxi Zhu # 1 2 3,  Rui Sun # 1,  Zhiqiang Xu # 1 4,  Yanqiu Gong # 1,  Yi Liu # 1,  Yan Liu 1,  Jiayi Xu 1,  Huifang Hu 1,  Tao Chen 1,  Mengyue Zhang 5,  Qinghua Zou 6,  Pingting Yang 7,  Jinmei Zou 8,  Linchong Su 9,  Wenfeng Tan 10,  Liesu Meng 11,  Martin Herrmann 2 12,  Luis E Muñoz 12,  Shijian Feng 13,  Tao Lin 13,  Heng Xu 1,  Binwu Ying 1,  Yong Peng 1,  Inger Gjertsson 14,  Rikard Holmdahl 15,  Yi Zhao 16 17,  Lunzhi Dai 18
Affiliations

Affiliations

  • 1 Department of Rheumatology and Immunology and Department of Laboratory Medicine, National Clinical Research Center for Geriatrics, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
  • 2 Department of Rheumatology and Immunology, Clinical Institute of Inflammation and Immunology, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
  • 3 Department of Dermatology and Venereology, West China Hospital, Sichuan University, Chengdu, China.
  • 4 Tianfu Jincheng Laboratory, Chengdu, China.
  • 5 College of Optical Science and Engineering, Zhejiang University, Hangzhou, China.
  • 6 Department of Rheumatology and Immunology, First Affiliated Hospital of Army Military Medical University, Chongqing, China.
  • 7 Department of Rheumatology and Immunology, The First Hospital of China Medical University, Shenyang, China.
  • 8 Department of Rheumatology and Immunology, Mianyang Central Hospital, Mianyang, China.
  • 9 Department of Rheumatology and Immunology, Minda Hospital of Hubei, Minzu University, Enshi, China.
  • 10 Department of Rheumatology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
  • 11 Department of Rheumatology, and National Joint Engineering Research Center of Biodiagnostics and Biotherapy, Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China.
  • 12 Department of Pediatric Surgery, University Medical Center Mannheim, University of Heidelberg, Mannheim, Germany.
  • 13 Department of Urology and Institute of Urology (Laboratory of Reconstructive Urology), State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
  • 14 Department of Rheumatology and Inflammation Research, Institute for Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
  • 15 Section of Medical Inflammation Research, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden.
  • 16 Department of Rheumatology and Immunology and Department of Laboratory Medicine, National Clinical Research Center for Geriatrics, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China. [email protected].
  • 17 Department of Rheumatology and Immunology, Clinical Institute of Inflammation and Immunology, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China. [email protected].
  • 18 Department of Rheumatology and Immunology and Department of Laboratory Medicine, National Clinical Research Center for Geriatrics, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China. [email protected].
  • # Contributed equally.
PMID: 41776034 DOI: 10.1038/s41551-026-01628-4
Abstract

Post-translationally modified proteins are crucial autoantigens in autoimmune diseases, with citrullinated proteins being key targets of autoantibodies in rheumatoid arthritis (RA). However, accurate citrullinome profiling and autoantigen identification remain limited by insufficient detection methods and computational tools. Here we develop Iseq-Cit (internal standard-assisted enrichment-free approach for high-throughput quantitative analysis of citrullinome), for global citrullinome profiling in individuals at RA risk and in patients with RA across a longitudinal cohort, requiring less than 1% of the sample input needed for conventional methods. We find that plasma citrullinome profiles closely correlate with RA development and severity. Moreover, we develop models integrating clinical indicators and citrullination data, achieving high accuracy in predicting treatment response. To evaluate the RA-sera reactivity of identified citrullinated peptides, we train a bidirectional gated recurrent unit model using 67,399 RA-sera negative and 8,816 RA-sera positive peptides. External validation through enzyme-linked immunosorbent assays confirms 84.2% accuracy in predicting RA-sera reactivity of citrullinated peptides, yielding 19 promising candidates for RA diagnosis. This work provides strategies for citrullinated peptide identification, autoantigen discovery and RA treatment stratification.

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