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Chen B, Zhang C, Zhou M, Deng H, Xu J, Yin J, Chen C, Zhang D, Pu Y, Zheng L, Wang B, Fu J. CD4+ T-cell metabolism in the pathogenesis of Sjogren's syndrome. Int Immunopharmacol 2025; 150:114320. [PMID: 39970711 DOI: 10.1016/j.intimp.2025.114320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2024] [Revised: 02/11/2025] [Accepted: 02/13/2025] [Indexed: 02/21/2025]
Abstract
The abnormal effector function of CD4+ T cells plays a key role in the pathogenesis of Sjogren's syndrome (SS) and its associated systematic autoimmune response. Cellular metabolism, including glucose metabolism, lipid metabolism and amino acid metabolism, supports proliferation, migration, survival and differentiation into distinct CD4+ T-cell subsets. Different subtypes of T cells have significantly different demands for related metabolic processes, which enables us to finely regulate CD4+ T cells through different metabolic processes in autoimmune diseases such as SS. In this review, we summarize the effects of disturbances in distinct metabolic processes, such as glycolysis, fatty acid metabolism, glutamine decomposition, mitochondrial dynamics, and ferroptosis, on how to support the effector functions of CD4+ T cells in the SS. We also discuss potential drugs with high value in the treatment of SS through metabolic normalization in CD4+ T cells. Finally, we propose possible directions for future targeted therapy for immunometabolism in SS.
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Affiliation(s)
- Baixi Chen
- Department of Oral Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National Center for Stomatology & National Clinical Research Center of Oral Disease, Shanghai Key Laboratory of Stomatology, Shanghai 200001, China; Department of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai 200011, China
| | - Chenji Zhang
- Department of Oral Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National Center for Stomatology & National Clinical Research Center of Oral Disease, Shanghai Key Laboratory of Stomatology, Shanghai 200001, China
| | - Mengyuan Zhou
- Department of Oral Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National Center for Stomatology & National Clinical Research Center of Oral Disease, Shanghai Key Laboratory of Stomatology, Shanghai 200001, China
| | - Hongyu Deng
- Department of Oral Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National Center for Stomatology & National Clinical Research Center of Oral Disease, Shanghai Key Laboratory of Stomatology, Shanghai 200001, China
| | - Jiabao Xu
- Würzburg Institute of Systems Immunology, Max Planck Research Group, Julius-Maximilians University of Würzburg, Würzburg 97255, Germany
| | - Junhao Yin
- Shanghai Engineering Research Center of Tooth Restoration and Regeneration & Tongji Research Institute of Stomatology & Department of Prothodontics, Shanghai Tongji Stomatological Hospital and Dental School, Tongji University, Shanghai 200072, China
| | - Changyu Chen
- Shanghai Key Laboratory of Craniomaxillofacial Development and Diseases, Shanghai Stomatological Hospital and School of Stomatology, Fudan University, Shanghai 200001, China
| | - Dahe Zhang
- Department of Oral Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National Center for Stomatology & National Clinical Research Center of Oral Disease, Shanghai Key Laboratory of Stomatology, Shanghai 200001, China
| | - Yiping Pu
- Department of Oral Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National Center for Stomatology & National Clinical Research Center of Oral Disease, Shanghai Key Laboratory of Stomatology, Shanghai 200001, China
| | - Lingyan Zheng
- Department of Oral Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National Center for Stomatology & National Clinical Research Center of Oral Disease, Shanghai Key Laboratory of Stomatology, Shanghai 200001, China
| | - Baoli Wang
- Department of Oral Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National Center for Stomatology & National Clinical Research Center of Oral Disease, Shanghai Key Laboratory of Stomatology, Shanghai 200001, China.
| | - Jiayao Fu
- Shanghai Engineering Research Center of Tooth Restoration and Regeneration & Tongji Research Institute of Stomatology & Department of Prothodontics, Shanghai Tongji Stomatological Hospital and Dental School, Tongji University, Shanghai 200072, China.
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Zhang R, Wang J, Zhai X, Guo Y, Zhou L, Hao X, Yang L, Xing R, Hu J, Gao J, Wang F, Yang J, Liu J. Targeted Detection of 76 Carnitine Indicators Combined with a Machine Learning Algorithm Based on HPLC-MS/MS in the Diagnosis of Rheumatoid Arthritis. Metabolites 2025; 15:205. [PMID: 40137169 PMCID: PMC11944147 DOI: 10.3390/metabo15030205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2025] [Revised: 03/11/2025] [Accepted: 03/12/2025] [Indexed: 03/27/2025] Open
Abstract
BACKGROUND/OBJECTIVES Early diagnosis and treatment of rheumatoid arthritis (RA) are essential to reducing disability. However, the diagnostic criteria remain unclear, relying on clinical symptoms and blood markers. METHODS Using high-performance liquid chromatography-mass spectrometry (HPLC-MS/MS) targeted detection, we evaluated 76 carnitine indicators (55 carnitines and 21 corresponding ratios) in the serum of patients with RA to investigate the role of carnitine in RA. A total of 359 patients (207 patients with RA and 152 healthy controls) were included in the study. Screening involved three methods and integrated 76 carnitine indicators and 128 clinical indicators to identify candidate markers to establish a theoretical basis for RA diagnosis and new therapeutic targets. The diagnostic model derived from the screened markers was validated using three machine learning algorithms. RESULTS The model was refined using eight candidate indicators (C0, C10:1, LYMPH, platelet distribution width, anti-keratin antibody, glucose, urobilinogen, and erythrocyte sedimentation rate (ESR)). The receiver operating characteristic curve, sensitivity, specificity, and accuracy of the V8 model obtained from the training set were >0.948, 79.46%, 92.99%, and 89.18%, whereas those of the test set were >0.925, 78.89%, 89.22%, and 85.87%, respectively. Twenty-four carnitines were identified as risk factors of RA, with three significantly correlating with ESR, four with anti-cyclic citrullinated peptide antibody activity, two with C-reactive protein, five with immunoglobulin-G, eight with immunoglobulin-A levels, and eleven with immunoglobulin-M levels. CONCLUSIONS Carnitine is integral in the progression of RA. The diagnostic model developed shows excellent diagnostic capacity, improving early detection and enabling timely intervention to minimize disability associated with RA.
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Affiliation(s)
- Rui Zhang
- Department of Clinical Laboratory Medicine, Xijing Hospital, Fourth Military Medical University, 127 Changle West Road, Xi’an 710032, China; (R.Z.); (J.W.); (X.Z.); (Y.G.); (L.Z.); (X.H.); (L.Y.); (R.X.); (J.H.); (J.G.); (F.W.)
| | - Juan Wang
- Department of Clinical Laboratory Medicine, Xijing Hospital, Fourth Military Medical University, 127 Changle West Road, Xi’an 710032, China; (R.Z.); (J.W.); (X.Z.); (Y.G.); (L.Z.); (X.H.); (L.Y.); (R.X.); (J.H.); (J.G.); (F.W.)
| | - Xiaonan Zhai
- Department of Clinical Laboratory Medicine, Xijing Hospital, Fourth Military Medical University, 127 Changle West Road, Xi’an 710032, China; (R.Z.); (J.W.); (X.Z.); (Y.G.); (L.Z.); (X.H.); (L.Y.); (R.X.); (J.H.); (J.G.); (F.W.)
| | - Yuanbing Guo
- Department of Clinical Laboratory Medicine, Xijing Hospital, Fourth Military Medical University, 127 Changle West Road, Xi’an 710032, China; (R.Z.); (J.W.); (X.Z.); (Y.G.); (L.Z.); (X.H.); (L.Y.); (R.X.); (J.H.); (J.G.); (F.W.)
| | - Lei Zhou
- Department of Clinical Laboratory Medicine, Xijing Hospital, Fourth Military Medical University, 127 Changle West Road, Xi’an 710032, China; (R.Z.); (J.W.); (X.Z.); (Y.G.); (L.Z.); (X.H.); (L.Y.); (R.X.); (J.H.); (J.G.); (F.W.)
| | - Xiaoyan Hao
- Department of Clinical Laboratory Medicine, Xijing Hospital, Fourth Military Medical University, 127 Changle West Road, Xi’an 710032, China; (R.Z.); (J.W.); (X.Z.); (Y.G.); (L.Z.); (X.H.); (L.Y.); (R.X.); (J.H.); (J.G.); (F.W.)
| | - Liu Yang
- Department of Clinical Laboratory Medicine, Xijing Hospital, Fourth Military Medical University, 127 Changle West Road, Xi’an 710032, China; (R.Z.); (J.W.); (X.Z.); (Y.G.); (L.Z.); (X.H.); (L.Y.); (R.X.); (J.H.); (J.G.); (F.W.)
| | - Ruiqing Xing
- Department of Clinical Laboratory Medicine, Xijing Hospital, Fourth Military Medical University, 127 Changle West Road, Xi’an 710032, China; (R.Z.); (J.W.); (X.Z.); (Y.G.); (L.Z.); (X.H.); (L.Y.); (R.X.); (J.H.); (J.G.); (F.W.)
| | - Juanjuan Hu
- Department of Clinical Laboratory Medicine, Xijing Hospital, Fourth Military Medical University, 127 Changle West Road, Xi’an 710032, China; (R.Z.); (J.W.); (X.Z.); (Y.G.); (L.Z.); (X.H.); (L.Y.); (R.X.); (J.H.); (J.G.); (F.W.)
| | - Jiawei Gao
- Department of Clinical Laboratory Medicine, Xijing Hospital, Fourth Military Medical University, 127 Changle West Road, Xi’an 710032, China; (R.Z.); (J.W.); (X.Z.); (Y.G.); (L.Z.); (X.H.); (L.Y.); (R.X.); (J.H.); (J.G.); (F.W.)
| | - Fengjuan Wang
- Department of Clinical Laboratory Medicine, Xijing Hospital, Fourth Military Medical University, 127 Changle West Road, Xi’an 710032, China; (R.Z.); (J.W.); (X.Z.); (Y.G.); (L.Z.); (X.H.); (L.Y.); (R.X.); (J.H.); (J.G.); (F.W.)
| | - Jun Yang
- Comprehensive Cancer Center, Department of Entomology and Nematology, University of California, Davis, CA 95616, USA
| | - Jiayun Liu
- Department of Clinical Laboratory Medicine, Xijing Hospital, Fourth Military Medical University, 127 Changle West Road, Xi’an 710032, China; (R.Z.); (J.W.); (X.Z.); (Y.G.); (L.Z.); (X.H.); (L.Y.); (R.X.); (J.H.); (J.G.); (F.W.)
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Chang K, Luo P, Guo Z, Yang L, Pu J, Han F, Cai F, Tang J, Wang X. Lipid Metabolism: An Emerging Player in Sjögren's Syndrome. Clin Rev Allergy Immunol 2025; 68:15. [PMID: 39934534 PMCID: PMC11813826 DOI: 10.1007/s12016-025-09023-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/07/2025] [Indexed: 02/13/2025]
Abstract
Sjögren's syndrome (SS) is a chronic autoimmune disorder that primarily affects the exocrine glands. Due to the intricate nature of the disease progression, the exact mechanisms underlying SS are not completely understood. Recent research has highlighted the complex interplay between immune dysregulation and metabolic abnormalities in inflammatory diseases. Notably, lipid metabolism has emerged as a crucial factor in the modulation of immune function and the progression of autoimmune diseases, including SS. This review explores the prevalence of dyslipidemia in SS, emphasizing its role in the onset, progression, and prognosis of the disease. We specifically described the impact of altered lipid metabolism in exocrine glands and its association with disease-specific features, including inflammation and glandular dysfunction. Additionally, we discussed the potential clinical implications of lipid metabolism regulation, including the role of polyunsaturated fatty acids (PUFAs) and their deficits in SS pathogenesis. By identifying lipid metabolism as a promising therapeutic target, this review highlights the need for further research into lipid-based interventions for the management of SS.
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Affiliation(s)
- Keni Chang
- Department of Rheumatology and Immunology, School of Medicine, Tongji Hospital, Tongji University, No. 389 Xincun Road, Shanghai, 200065, China
| | - Peiming Luo
- Department of Rheumatology and Immunology, School of Medicine, Tongji Hospital, Tongji University, No. 389 Xincun Road, Shanghai, 200065, China
| | - Zizhen Guo
- Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lufei Yang
- Department of Rheumatology and Immunology, School of Medicine, Tongji Hospital, Tongji University, No. 389 Xincun Road, Shanghai, 200065, China
| | - Jincheng Pu
- Department of Rheumatology and Immunology, School of Medicine, Tongji Hospital, Tongji University, No. 389 Xincun Road, Shanghai, 200065, China
| | - Fang Han
- Department of Rheumatology and Immunology, School of Medicine, Tongji Hospital, Tongji University, No. 389 Xincun Road, Shanghai, 200065, China
| | - Feiyang Cai
- Department of Experimental Medicine, Faculty of Medicine, McGill University, Montréal, Québec, Canada
- Gerald Bronfman Department of Oncology, Segal Cancer Centre, Lady Davis Institute and Jewish General Hospital, McGill University, Montreal, QC, Canada
| | - Jianping Tang
- Department of Rheumatology and Immunology, School of Medicine, Tongji Hospital, Tongji University, No. 389 Xincun Road, Shanghai, 200065, China.
| | - Xuan Wang
- Department of Rheumatology and Immunology, School of Medicine, Tongji Hospital, Tongji University, No. 389 Xincun Road, Shanghai, 200065, China.
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Yin J, Fu J, Shao Y, Xu J, Li H, Chen C, Zhao Y, Zheng Z, Yu C, Zheng L, Wang B. CYP51-mediated cholesterol biosynthesis is required for the proliferation of CD4 + T cells in Sjogren's syndrome. Clin Exp Med 2023; 23:1691-1711. [PMID: 36413274 DOI: 10.1007/s10238-022-00939-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 11/02/2022] [Indexed: 11/23/2022]
Abstract
CYtochrome P450, family 51 (CYP51) is an important enzyme for de novo cholesterol synthesis in mammalian cells. In the present study, we found that the expression of CYP51 positively correlated with CD4+ T cell activation both in vivo and in vitro. The addition of ketoconazole, a pharmacological inhibitor of CYP51, prevented the proliferation and activation of anti-CD3/CD28-expanded mouse CD4+ T cells in a dose-dependent fashion. Liquid chromatography-tandem mass spectrometry indicated an increase in levels of lanosterol in T cells treated with ketoconazole during activation. Ketoconazole-induced blockade of the cholesterol synthesis pathway also caused Sterol regulatory element binding protein 2 (SREBP2) activation in CD4+ T cells. Additionally, ketoconazole treatment elicited an integrated stress response in T cells that up-regulated activating transcription factor 4 (ATF4) and DNA-damage inducible transcript 3 (DDIT3/CHOP) at the translational level. Furthermore, treatment with ketoconazole significantly decreased the amount of CD4+ T cells infiltrating lesions in the submandibular glands of NOD/Ltj mice. In summary, our results suggest that CYP51 plays an essential role in the proliferation and survival of CD4+ T cells, which makes ketoconazole an inhibitor of CD4+ T cell proliferation and of the SS-like autoimmune response through regulating the biosynthesis of cholesterol and inducing the integrated stress response.
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Affiliation(s)
- Junhao Yin
- Department of Oral Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology & National Clinical Research Center for Oral Disease, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Jiayao Fu
- Department of Oral Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology & National Clinical Research Center for Oral Disease, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Yanxiong Shao
- Department of Oral Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology & National Clinical Research Center for Oral Disease, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Jiabao Xu
- Department of Oral Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology & National Clinical Research Center for Oral Disease, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Hui Li
- Department of Oral Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology & National Clinical Research Center for Oral Disease, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Changyu Chen
- Department of Oral Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology & National Clinical Research Center for Oral Disease, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Yijie Zhao
- Department of Oral and Maxillofacial Surgery, Shanghai Stomatological Hospital, Fudan University, 1258 Fuxin Zhong Road, Shanghai, China
| | - Zhanglong Zheng
- Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, Tongji University, Shanghai Engineering Research Center of Tooth Restoration and Regeneration, Shanghai, China
| | - Chuangqi Yu
- Department of Oral Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology & National Clinical Research Center for Oral Disease, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Lingyan Zheng
- Department of Oral Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, Shanghai, China.
- National Center for Stomatology & National Clinical Research Center for Oral Disease, Shanghai, China.
- Shanghai Key Laboratory of Stomatology, Shanghai, China.
| | - Baoli Wang
- Department of Oral Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, Shanghai, China.
- National Center for Stomatology & National Clinical Research Center for Oral Disease, Shanghai, China.
- Shanghai Key Laboratory of Stomatology, Shanghai, China.
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Li H, Zhan H, Cheng L, Huang Y, Li X, Yan S, Liu Y, Wang L, Li Y. Plasma lipidomics of primary biliary cholangitis and its comparison with Sjögren's syndrome. Front Immunol 2023; 14:1124443. [PMID: 37215104 PMCID: PMC10196160 DOI: 10.3389/fimmu.2023.1124443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 04/18/2023] [Indexed: 05/24/2023] Open
Abstract
Background Abnormal lipid metabolism is common in patients with primary biliary cholangitis (PBC). PBC and Sjögren's syndrome (SS) frequently coexist in clinical practice; however, the lipid characteristics of both diseases are unknown. Therefore, we aimed to analyze the plasma lipid profiles of both diseases. Methods Plasma samples from 60 PBC patients, 30 SS patients, and 30 healthy controls (HC) were collected, and untargeted lipidomics was performed using ultrahigh-performance liquid chromatography high-resolution mass spectrometry. Potential lipid biomarkers were screened through an orthogonal projection to latent structure discriminant analysis and further evaluated using receiver operating characteristic (ROC) analysis. Results A total of 115 lipids were differentially upregulated in PBC patients compared with HC. Seventeen lipids were positively associated with the disease activity of PBC, and ROC analysis showed that all of these lipids could differentiate between ursodeoxycholic acid (UDCA) responders and UDCA non-responders. The top six lipids based on the area under the curve (AUC) values were glycerophosphocholine (PC) (16:0/16:0), PC (18:1/18:1), PC (42:2), PC (16:0/18:1), PC (17:1/14:0), and PC (15:0/18:1). In comparison with SS, 44 lipids were found to be differentially upregulated in PBC. Additionally, eight lipids were found to have a good diagnostic performance of PBC because of the AUC values of more than 0.9 when identified from SS and HC groups, which were lysophosphatidylcholines (LysoPC) (16:1), PC (16:0/16:0), PC (16:0/16:1), PC (16:1/20:4), PC (18:0/20:3), PC (18:1/20:2), PC (20:0/22:5), and PC (20:1/22:5). Conclusion Our study revealed differentially expressed lipid signatures in PBC compared with HC and SS. PC is the main lipid species associated with disease activity and the UDCA response in patients with PBC.
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Affiliation(s)
- Haolong Li
- Department of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Haoting Zhan
- Department of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Linlin Cheng
- Department of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Yuan Huang
- Department of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Xiaomeng Li
- Department of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Songxin Yan
- Department of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Yongmei Liu
- Department of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Li Wang
- Department of Rheumatology, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Yongzhe Li
- Department of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
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Untargeted Lipidomics Reveals Characteristic Biomarkers in Patients with Ankylosing Spondylitis Disease. Biomedicines 2022; 11:biomedicines11010047. [PMID: 36672555 PMCID: PMC9855684 DOI: 10.3390/biomedicines11010047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 12/10/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Objective. Ankylosing spondylitis (AS) is a chronic inflammatory disease of the axial skeleton. Early and accurate diagnosis is necessary for the timely and effective treatment of this disease and its common complications. Lipid metabolites form various kinds of bioactive molecules that regulate the initiation and progression of inflammation. However, there are currently few studies that investigate the alteration of serum lipid in AS patients. Methods. Blood samples were collected from 115 AS patients and 108 healthy controls (HCs). Serum-untargeted lipidomics were performed using ultrahigh-performance liquid chromatography coupled with Q-Exactive spectrometry, and the data were determined by multivariate statistical methods to explore potential lipid biomarkers. Results. Lipid phenotypes associated with disease activity were detected in the serum of patients with AS. Of all 586 identified lipids, there are 297 differential lipid metabolites between the AS and HC groups, of which 15 lipid metabolites are significant. In the AS groups, the levels of triacylglycerol (TAG) (18:0/18:1/20:0) were increased, and the levels of phosphatidylcholine (PC) (16:0e/26:4) and PC (18:1/22:6) were decreased. The areas under the receiver operating characteristic curve (AUC) of TAG (18:0/18:1/20:0), PC (16:0e/26:4), and PC (18:1/22:6) were 0.919, 0.843, and 0.907, respectively. Conclusion. Our findings uncovered that lipid deregulation is a crucial hallmark of AS, thereby providing new insights into the early diagnosis of AS.
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Dakterzada F, Benítez ID, Targa A, Carnes A, Pujol M, Jové M, Mínguez O, Vaca R, Sánchez-de-la-Torre M, Barbé F, Pamplona R, Piñol-Ripoll G. Blood-based lipidomic signature of severe obstructive sleep apnoea in Alzheimer's disease. Alzheimers Res Ther 2022; 14:163. [PMID: 36329512 PMCID: PMC9632042 DOI: 10.1186/s13195-022-01102-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022]
Abstract
Background Obstructive sleep apnoea (OSA) is the most frequent form of sleep-disordered breathing in patients with Alzheimer’s disease (AD). Available evidence demonstrates that both conditions are independently associated with alterations in lipid metabolism. However, it is unknown whether the expression of lipids is different between AD patients with and without severe OSA. In this context, we examined the plasma lipidome of patients with suspected OSA, aiming to identify potential diagnostic biomarkers and to provide insights into the pathophysiological mechanisms underlying the disease. Methods The study included 103 consecutive patients from the memory unit of our institution with a diagnosis of AD. The individuals were subjected to overnight polysomnography (PSG) to diagnose severe OSA (apnoea-hypopnea index ≥30/h), and blood was collected the following morning. Untargeted plasma lipidomic profiling was performed using liquid chromatography coupled with mass spectrometry. Results We identified a subset of 44 lipids (mainly phospholipids and glycerolipids) that were expressed differently between patients with AD and severe and nonsevere OSA. Among the lipids in this profile, 30 were significantly correlated with specific PSG measures of OSA severity related to sleep fragmentation and hypoxemia. Machine learning analyses revealed a 4-lipid signature (phosphatidylcholine PC(35:4), cis-8,11,14,17-eicosatetraenoic acid and two oxidized triglycerides (OxTG(58:5) and OxTG(62:12)) that provided an accuracy (95% CI) of 0.78 (0.69–0.86) in the detection of OSA. These same lipids improved the predictive power of the STOP-Bang questionnaire in terms of the area under the curve (AUC) from 0.61 (0.50–0.74) to 0.80 (0.70–0.90). Conclusion Our results show a plasma lipidomic fingerprint that allows the identification of patients with AD and severe OSA, allowing the personalized management of these individuals. The findings suggest that oxidative stress and inflammation are potential prominent mechanisms underlying the association between OSA and AD. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-022-01102-8.
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Affiliation(s)
- Farida Dakterzada
- Unitat Trastorns Cognitius, Clinical Neuroscience Research, IRBLleida-Santa Maria Lleida University Hospital, Rovira Roure n° 44, 25198, Lleida, Spain
| | - Iván D Benítez
- Group of Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain.,Center for Biomedical Research in Respiratory Diseases Network (CIBERES), Madrid, Spain
| | - Adriano Targa
- Group of Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain.,Center for Biomedical Research in Respiratory Diseases Network (CIBERES), Madrid, Spain
| | - Anna Carnes
- Unitat Trastorns Cognitius, Clinical Neuroscience Research, IRBLleida-Santa Maria Lleida University Hospital, Rovira Roure n° 44, 25198, Lleida, Spain
| | - Montse Pujol
- Group of Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain
| | - Mariona Jové
- Department of Experimental Medicine, University of Lleida-Biomedical Research Institute of Lleida (UdL-IRBLleida), E25198, Lleida, Spain
| | - Olga Mínguez
- Group of Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain
| | - Rafi Vaca
- Group of Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain
| | - Manuel Sánchez-de-la-Torre
- Department of Nursing and Physiotherapy, Group of Precision Medicine in Chronic Diseases, University Hospital Arnau de Vilanova and Santa María, IRBLleida, Faculty of Nursing and Physiotherapy, University of Lleida, Lleida, Spain
| | - Ferran Barbé
- Group of Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain.,Center for Biomedical Research in Respiratory Diseases Network (CIBERES), Madrid, Spain
| | - Reinald Pamplona
- Department of Experimental Medicine, University of Lleida-Biomedical Research Institute of Lleida (UdL-IRBLleida), E25198, Lleida, Spain
| | - Gerard Piñol-Ripoll
- Unitat Trastorns Cognitius, Clinical Neuroscience Research, IRBLleida-Santa Maria Lleida University Hospital, Rovira Roure n° 44, 25198, Lleida, Spain.
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Wang K, Li J, Meng D, Zhang Z, Liu S. Machine learning based on metabolomics reveals potential targets and biomarkers for primary Sjogren’s syndrome. Front Mol Biosci 2022; 9:913325. [PMID: 36133908 PMCID: PMC9483105 DOI: 10.3389/fmolb.2022.913325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 08/10/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Using machine learning based on metabolomics, this study aimed to construct an effective primary Sjogren’s syndrome (pSS) diagnostics model and reveal the potential targets and biomarkers of pSS.Methods: From a total of 39 patients with pSS and 38 healthy controls (HCs), serum specimens were collected. The samples were analyzed by ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry. Three machine learning algorithms, including the least absolute shrinkage and selection operator (LASSO), random forest (RF), and extreme gradient boosting (XGBoost), were used to build the pSS diagnosis models. Afterward, four machine learning methods were used to reduce the dimensionality of the metabolomics data. Finally, metabolites with significant differences were screened and pathway analysis was conducted.Results: The area under the curve (AUC), sensitivity, and specificity of LASSO, RF and XGBoost test set all reached 1.00. Orthogonal partial least squares discriminant analysis was used to classify the metabolomics data. By combining the results of the univariate false discovery rate and the importance of the variable in projection, we identified 21 significantly different metabolites. Using these 21 metabolites for diagnostic modeling, the AUC, sensitivity, and specificity of LASSO, RF, and XGBoost all reached 1.00. Metabolic pathway analysis revealed that these 21 metabolites are highly correlated with amino acid and lipid metabolisms. On the basis of 21 metabolites, we screened the important variables in the models. Further, five common variables were obtained by intersecting the important variables of three models. Based on these five common variables, the AUC, sensitivity, and specificity of LASSO, RF, and XGBoost all reached 1.00.2-Hydroxypalmitic acid, L-carnitine and cyclic AMP were found to be potential targets and specific biomarkers for pSS.Conclusion: The combination of machine learning and metabolomics can accurately distinguish between patients with pSS and HCs. 2-Hydroxypalmitic acid, L-carnitine and cyclic AMP were potential targets and biomarkers for pSS.
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Shields SWJ, Sanders JD, Brodbelt JS. Enhancing the Signal-to-Noise of Diagnostic Fragment Ions of Unsaturated Glycerophospholipids via Precursor Exclusion Ultraviolet Photodissociation Mass Spectrometry (PEx-UVPD-MS). Anal Chem 2022; 94:11352-11359. [PMID: 35917227 PMCID: PMC9484799 DOI: 10.1021/acs.analchem.2c02128] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Understanding and elucidating the diverse structures and functions of lipids has motivated the development of many innovative tandem mass spectrometry (MS/MS) strategies. Higher-energy activation methods, such as ultraviolet photodissociation (UVPD), generate unique fragment ions from glycerophospholipids that can be used to perform in-depth structural analysis and facilitate the deconvolution of isomeric lipid structures in complex samples. Although detailed characterization is central to the correlation of lipid structure to biological function, it is often impeded by the lack of sufficient instrument sensitivity for highly bioactive but low-abundance phospholipids. Here, we present precursor exclusion (PEx) UVPD, a simple yet powerful technique to enhance the signal-to-noise (S/N) of informative low-abundance fragment ions produced from UVPD of glycerophospholipids. Through the exclusion of the large population of undissociated precursor ions with an MS3 strategy, the S/N of diagnostic fragment ions from PC 18:0/18:2(9Z, 12Z) increased up to an average of 13x for PEx-UVPD compared to UVPD alone. These enhancements were extended to complex mixtures of lipids from bovine liver extract to confidently identify 35 unique structures using liquid chromatography PEx-UVPD. This methodology has the potential to advance lipidomics research by offering deeper structure elucidation and confident identification of biologically active lipids.
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Affiliation(s)
- Samuel W J Shields
- Department of Chemistry, University of Texas at Austin, Austin, Texas 78712, United States
| | - James D Sanders
- Department of Chemistry, University of Texas at Austin, Austin, Texas 78712, United States
| | - Jennifer S Brodbelt
- Department of Chemistry, University of Texas at Austin, Austin, Texas 78712, United States
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Zhang W, Zhao H, Du P, Cui H, Lu S, Xiang Z, Lu Q, Jia S, Zhao M. Integration of metabolomics and lipidomics reveals serum biomarkers for systemic lupus erythematosus with different organs involvement. Clin Immunol 2022; 241:109057. [PMID: 35667550 DOI: 10.1016/j.clim.2022.109057] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/22/2022] [Accepted: 05/31/2022] [Indexed: 11/24/2022]
Abstract
Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that affects various organs or systems. We performed metabolomic and lipidomic profiles analyses of 133 SLE patients and 30 HCs. Differential metabolites and lipids were integrated, and then the biomarker panel was identified using binary logistic regression. We found that a combination of four metabolites or lipids could distinguish SLE from HC with an AUC of 0.998. Three lipids were combined to differentiate inactive SLE and active SLE. The AUC was 0.767. In addition, we also identified the biomarkers for different organ phenotypes of SLE. The AUCs for diagnosing SLE patients with only kidney involvement, skin involvement, blood system involvement, and multisystem involvement were 0.766, 0.718, 0.951, and 0.909, respectively. Our study succeeded in identifying biomarkers associated with different clinical phenotypes in SLE patients, which could facilitate a more precise diagnosis and assessment of disease progression in SLE.
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Affiliation(s)
- Wenqian Zhang
- Department of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha 410013, China
| | - Hongjun Zhao
- Department of Rheumatology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Pei Du
- Department of Dermatology, Hunan Key Laboratory of Medical Epigenomics, The Second Xiangya Hospital, Central South University, Changsha 410011, China; Research Unit of Key Technologies of Diagnosis and Treatment for Immune-related Skin Diseases, Chinese Academy of Medical Sciences, Changsha 410011, China
| | - Haobo Cui
- Department of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha 410013, China
| | - Shuang Lu
- Department of Dermatology, Hunan Key Laboratory of Medical Epigenomics, The Second Xiangya Hospital, Central South University, Changsha 410011, China; Research Unit of Key Technologies of Diagnosis and Treatment for Immune-related Skin Diseases, Chinese Academy of Medical Sciences, Changsha 410011, China
| | - Zhongyuan Xiang
- Department of Clinical Laboratory, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Qianjin Lu
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China; Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Chinese Academy of Medical Sciences, Nanjing, China
| | - Sujie Jia
- Department of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha 410013, China.
| | - Ming Zhao
- Department of Dermatology, Hunan Key Laboratory of Medical Epigenomics, The Second Xiangya Hospital, Central South University, Changsha 410011, China; Research Unit of Key Technologies of Diagnosis and Treatment for Immune-related Skin Diseases, Chinese Academy of Medical Sciences, Changsha 410011, China.
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