[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127894-en":3,"doc-seo-127894-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127894,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",8,"Research & Report","The role of lactylation in plasma cells and its impact on rheumatoid arthritis pathogenesis - insights from single-cell RNA sequencing and machine learning","Rheumatoid arthritis is a chronic autoimmune disorder driven by persistent synovitis, systemic inflammation, and autoantibody production. This study investigates how lactylation in plasma cells influences RA pathogenesis using single-cell RNA sequencing with bioinformatics and machine learning. From 10,163 retained cells, clustering reveals plasma cells with the highest lactylation scores and distinct metabolic and immune features. A model based on key lactylation-promoting genes achieves an AUC of 0.918, supporting diagnostic relevance.","TYPE Original Research PUBLISHED 03 October 2024  \nDOI 10.3389/fimmu.2024.1453587  \nOPEN ACCESS  \nEDITED BY  \nWenyi Jin,  \nCity University of Hong Kong, Hong Kong SAR, China  \nREVIEWED BY  \nXiaoxiang Chen,  \nShanghai Jiao Tong University, China Saboor Ahmad,  \nChinese Academy of Agricultural Sciences, China  \n*CORRESPONDENCE  \nQining Yang  \n[jhyangqn@163.com](jhyangqn@163.com)[ ](jhyangqn@163.com)Tiejun Shi  \n [zjshitiejun163.com](zjshitiejun163.com)  \nRECEIVED 23 June 2024  \nACCEPTED 19 September 2024  \nPUBLISHED 03 October 2024  \nCITATION  \nFu W, Wang T, Lu Y, Shi T and Yang Q (2024) The role of lactylation in plasma cells and its impact on rheumatoid arthritis pathogenesis: insights from single-cell RNA sequencing and machine learning.  \nFront. Immunol. 15:1453587 .  \ndoi: 10.3389/fimmu.2024.1453587  \nCOPYRIGHT  \n© 2024 Fu, Wang, Lu, Shi and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nThe role of lactylation in plasma cells and its impact on rheumatoid arthritis pathogenesis: insights from single-cell RNA sequencing and machine learning  \nWeicong Fu, Tianbao Wang, Yehong Lu, Tiejun Shi* and Qining Yang*  \nDepartment of Orthopedics, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua Municipal Central Hospital, Jinhua, Zhejiang, China  \nIntroduction: Rheumatoid arthritis (RA) is a chronic autoimmune disorder characterized by persistent synovitis, systemic inflammation, and autoantibody production. This study aims to explore the role of lactylation in plasma cells and its impact on RA pathogenesis.  \nMethods: We utilized single-cell RNA sequencing (scRNA-seq) data and applied bioinformatics and machine learning techniques. A total of 10,163 cells were retained for analysis after quality control. Clustering analysis identified 13 cell clusters, with plasma cells displaying the highest lactylation scores . We performed pathway enrichment analysis to examine metabolic activity, such as oxidative phosphorylation and glycolysis, in highly lactylated plasma cells. Additionally, we employed 134 machine learning algorithms to identify seven core lactylation-promoting genes and constructed a diagnostic model with an average AUC of 0 .918.  \nResults: The RA lactylation score (RAlac_score) was significantly elevated in RA patients and positively correlated with immune cell infiltration and immune checkpoint molecule expression. Differential expression analysis between two plasma cell clusters revealed distinct metabolic and immunological profiles, with cluster 2 demonstrating increased immune activity and extracellular matrix interactions . qRT-PCR validation confir med that NDUFB3, NGLY1, and SLC25A4 are highly expressed in RA.  \nConclusion: This study highlights the critical role of lactylation in plasma cells for RA pathogenesis and identifies potential biomarkers and therapeutic targets, which may offer insights for future therapeutic strategies.  \nKEYWORDS  \nrheumatoid arthritis, lactylation, plasma cells, single-cell RNA sequencing, machine learning  \nFrontiers in Immunology 01 [frontiersin.org](frontiersin.org)  \nIntroduction  \nRheumatoid arthritis (RA) is a chronic autoimmune disorder characterized by inflammation and progressive joint destruction (1, 2). Affecting approximately 0.5-1% of the global population, RA poses asignificant burden due to its debilitating nature, reduced quality of life, and increased mortality rates (3–5). Despite extensive research, the precise mechanisms underlying the pathogenesis of RA remain incompletely understood, necessitating ongoing investigations to uncover novel therapeuti","cbCaicr3Ld2H2dbT","https://ap.wps.com/l/cbCaicr3Ld2H2dbT","pdf",14273162,3,1,14,"English","en",105,"# Introduction\n## Rheumatoid arthritis overview and disease burden\n## Immune mechanisms in RA\n## Metabolic reprogramming and lactylation\n# Methods\n## Single-cell RNA sequencing and quality control\n## Clustering and lactylation scoring\n## Pathway enrichment and machine learning\n## Biomarker identification and validation\n# Results\n## Lactylation score elevation in RA\n## Correlation with immune infiltration and checkpoints\n## Plasma-cell cluster differences and gene expression\n# Conclusion\n## Lactylation as a driver of RA pathogenesis and targets","[{\"question\":\"What question does the study address about rheumatoid arthritis?\",\"answer\":\"It explores how lactylation in plasma cells contributes to rheumatoid arthritis pathogenesis and whether it can reveal diagnostic biomarkers or therapeutic targets.\"},{\"question\":\"How were the lactylation-related findings derived?\",\"answer\":\"The study used single-cell RNA sequencing data, performed clustering to quantify lactylation scores, and applied pathway enrichment plus machine learning to identify key lactylation-promoting genes.\"},{\"question\":\"What performance did the diagnostic model achieve?\",\"answer\":\"The constructed diagnostic model showed an average AUC of 0.918, indicating strong discriminative ability based on the identified genes.\"}]","The role of lactylation in plasma cells and its impact on rheumatoid arthritis pathogenesis - insights from single-cell RNA sequencing and machine learning | PDF",1785942775,35,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"the-role-of-lactylation-in-plasma-cells-and-its-impact-on-rheumatoid-arthritis-pathogenesis-insights-from-single-cell-rna-sequencing-and-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/the-role-of-lactylation-in-plasma-cells-and-its-impact-on-rheumatoid-arthritis-pathogenesis-insights-from-single-cell-rna-sequencing-and-machine-learning/127894/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What question does the study address about rheumatoid arthritis?","Question",{"text":76,"@type":77},"It explores how lactylation in plasma cells contributes to rheumatoid arthritis pathogenesis and whether it can reveal diagnostic biomarkers or therapeutic targets.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the lactylation-related findings derived?",{"text":81,"@type":77},"The study used single-cell RNA sequencing data, performed clustering to quantify lactylation scores, and applied pathway enrichment plus machine learning to identify key lactylation-promoting genes.",{"name":83,"@type":74,"acceptedAnswer":84},"What performance did the diagnostic model achieve?",{"text":85,"@type":77},"The constructed diagnostic model showed an average AUC of 0.918, indicating strong discriminative ability based on the identified genes.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]