Kai Wang, Rujie Zhu, Min Xu, Kexin Zhu, Ju Li, Chang Li, Deqian Meng, Hongwei Chen, Lingyun Sun
{"title":"代谢组学分析揭示血清色氨酸是系统性红斑狼疮的潜在治疗靶点。","authors":"Kai Wang, Rujie Zhu, Min Xu, Kexin Zhu, Ju Li, Chang Li, Deqian Meng, Hongwei Chen, Lingyun Sun","doi":"10.2147/JIR.S505306","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to identify potential diagnostic biomarkers for systemic lupus erythematosus (SLE) using metabolomics approaches and machine learning algorithms, and to evaluate therapeutic targets for SLE treatment.</p><p><strong>Methods: </strong>Serum samples from 44 SLE patients with lupus nephritis, 40 rheumatoid arthritis patients, 39 primary Sjögren's syndrome patients, and matched healthy controls were analyzed using ultra-performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS). Eight machine learning algorithms were employed to establish diagnostic models. Partial least squares discriminant analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA) were used to identify differential metabolites. The therapeutic potential of identified metabolites was validated in MRL-Fas <i><sup>lpr</sup></i> mice through histological examination, flow cytometry, and biochemical analysis.</p><p><strong>Results: </strong>A total of 129 metabolites were detected, with machine learning models achieving area under the curve (AUC) values >0.8. The principal component regression model performed best with AUC values of 0.99 and 0.96 for training and test datasets, respectively. Two key metabolites, tryptophan and beta-alanine, showed significantly decreased levels in SLE patients compared to healthy controls (both p<0.05), while exhibiting opposite patterns in other autoimmune diseases. In the mouse model, tryptophan supplementation improved renal histology, reduced proteinuria, increased naïve T cells and central memory T cells, and decreased effector T cell frequencies in both peripheral blood and spleen.</p><p><strong>Conclusion: </strong>This study demonstrates the successful application of machine learning algorithms to metabolomics data for SLE classification and identifies tryptophan and beta-alanine as potential SLE-specific biomarkers. Tryptophan supplementation shows therapeutic promise in lupus mouse models through immunomodulatory effects on T cell subsets and renal protection.</p>","PeriodicalId":16107,"journal":{"name":"Journal of Inflammation Research","volume":"18 ","pages":"8899-8913"},"PeriodicalIF":4.2000,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12255269/pdf/","citationCount":"0","resultStr":"{\"title\":\"Metabolomic Profiling Reveals Serum Tryptophan as a Potential Therapeutic Target for Systemic Lupus Erythematosus.\",\"authors\":\"Kai Wang, Rujie Zhu, Min Xu, Kexin Zhu, Ju Li, Chang Li, Deqian Meng, Hongwei Chen, Lingyun Sun\",\"doi\":\"10.2147/JIR.S505306\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>This study aimed to identify potential diagnostic biomarkers for systemic lupus erythematosus (SLE) using metabolomics approaches and machine learning algorithms, and to evaluate therapeutic targets for SLE treatment.</p><p><strong>Methods: </strong>Serum samples from 44 SLE patients with lupus nephritis, 40 rheumatoid arthritis patients, 39 primary Sjögren's syndrome patients, and matched healthy controls were analyzed using ultra-performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS). Eight machine learning algorithms were employed to establish diagnostic models. Partial least squares discriminant analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA) were used to identify differential metabolites. The therapeutic potential of identified metabolites was validated in MRL-Fas <i><sup>lpr</sup></i> mice through histological examination, flow cytometry, and biochemical analysis.</p><p><strong>Results: </strong>A total of 129 metabolites were detected, with machine learning models achieving area under the curve (AUC) values >0.8. The principal component regression model performed best with AUC values of 0.99 and 0.96 for training and test datasets, respectively. Two key metabolites, tryptophan and beta-alanine, showed significantly decreased levels in SLE patients compared to healthy controls (both p<0.05), while exhibiting opposite patterns in other autoimmune diseases. In the mouse model, tryptophan supplementation improved renal histology, reduced proteinuria, increased naïve T cells and central memory T cells, and decreased effector T cell frequencies in both peripheral blood and spleen.</p><p><strong>Conclusion: </strong>This study demonstrates the successful application of machine learning algorithms to metabolomics data for SLE classification and identifies tryptophan and beta-alanine as potential SLE-specific biomarkers. Tryptophan supplementation shows therapeutic promise in lupus mouse models through immunomodulatory effects on T cell subsets and renal protection.</p>\",\"PeriodicalId\":16107,\"journal\":{\"name\":\"Journal of Inflammation Research\",\"volume\":\"18 \",\"pages\":\"8899-8913\"},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2025-07-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12255269/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Inflammation Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2147/JIR.S505306\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"IMMUNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Inflammation Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/JIR.S505306","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"IMMUNOLOGY","Score":null,"Total":0}
Metabolomic Profiling Reveals Serum Tryptophan as a Potential Therapeutic Target for Systemic Lupus Erythematosus.
Objective: This study aimed to identify potential diagnostic biomarkers for systemic lupus erythematosus (SLE) using metabolomics approaches and machine learning algorithms, and to evaluate therapeutic targets for SLE treatment.
Methods: Serum samples from 44 SLE patients with lupus nephritis, 40 rheumatoid arthritis patients, 39 primary Sjögren's syndrome patients, and matched healthy controls were analyzed using ultra-performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS). Eight machine learning algorithms were employed to establish diagnostic models. Partial least squares discriminant analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA) were used to identify differential metabolites. The therapeutic potential of identified metabolites was validated in MRL-Fas lpr mice through histological examination, flow cytometry, and biochemical analysis.
Results: A total of 129 metabolites were detected, with machine learning models achieving area under the curve (AUC) values >0.8. The principal component regression model performed best with AUC values of 0.99 and 0.96 for training and test datasets, respectively. Two key metabolites, tryptophan and beta-alanine, showed significantly decreased levels in SLE patients compared to healthy controls (both p<0.05), while exhibiting opposite patterns in other autoimmune diseases. In the mouse model, tryptophan supplementation improved renal histology, reduced proteinuria, increased naïve T cells and central memory T cells, and decreased effector T cell frequencies in both peripheral blood and spleen.
Conclusion: This study demonstrates the successful application of machine learning algorithms to metabolomics data for SLE classification and identifies tryptophan and beta-alanine as potential SLE-specific biomarkers. Tryptophan supplementation shows therapeutic promise in lupus mouse models through immunomodulatory effects on T cell subsets and renal protection.
期刊介绍:
An international, peer-reviewed, open access, online journal that welcomes laboratory and clinical findings on the molecular basis, cell biology and pharmacology of inflammation.