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

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 metho...

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Main Authors: Hu, Meng (Author) , Zhu, Chenxi (Author) , Sun, Rui (Author) , Xu, Zhiqiang (Author) , Gong, Yanqiu (Author) , Liu, Yi (Author) , Liu, Yan (Author) , Xu, Jiayi (Author) , Hu, Huifang (Author) , Chen, Tao (Author) , Zhang, Mengyue (Author) , Zou, Qinghua (Author) , Yang, Pingting (Author) , Zou, Jinmei (Author) , Su, Linchong (Author) , Tan, Wenfeng (Author) , Meng, Liesu (Author) , Herrmann, Martin (Author) , Muñoz Becerra, Luis Enrique (Author) , Feng, Shijian (Author) , Lin, Tao (Author) , Xu, Heng (Author) , Ying, Binwu (Author) , Peng, Yong (Author) , Gjertsson, Inger (Author) , Holmdahl, Rikard (Author) , Zhao, Yi (Author) , Dai, Lunzhi (Author)
Format: Article (Journal)
Language:English
Published: 03 March 2026
In: Nature biomedical engineering

ISSN:2157-846X
DOI:10.1038/s41551-026-01628-4
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1038/s41551-026-01628-4
Verlag, lizenzpflichtig, Volltext: https://www.nature.com/articles/s41551-026-01628-4
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Author Notes:Meng Hu, Chenxi Zhu, Rui Sun, Zhiqiang Xu, Yanqiu Gong, Yi Liu, Yan Liu, Jiayi Xu, Huifang Hu, Tao Chen, Mengyue Zhang, Qinghua Zou, Pingting Yang, Jinmei Zou, Linchong Su, Wenfeng Tan, Liesu Meng, Martin Herrmann, Luis E. Muñoz, Shijian Feng, Tao Lin, Heng Xu, Binwu Ying, Yong Peng, Inger Gjertsson, Rikard Holmdahl, Yi Zhao, Lunzhi Dai
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Summary: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.
Item Description:Gesehen am 12.03.2026
Physical Description:Online Resource
ISSN:2157-846X
DOI:10.1038/s41551-026-01628-4

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