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。

IF 4.2 2区 医学 Q2 IMMUNOLOGY
Journal of Inflammation Research Pub Date : 2025-07-07 eCollection Date: 2025-01-01 DOI:10.2147/JIR.S505306
Kai Wang, Rujie Zhu, Min Xu, Kexin Zhu, Ju Li, Chang Li, Deqian Meng, Hongwei Chen, Lingyun Sun
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引用次数: 0

摘要

目的:本研究旨在利用代谢组学方法和机器学习算法识别系统性红斑狼疮(SLE)的潜在诊断生物标志物,并评估SLE治疗的治疗靶点。方法:采用超高效液相色谱-高分辨率质谱(UPLC-HRMS)分析44例SLE合并狼疮肾炎患者、40例类风湿关节炎患者、39例原发性Sjögren综合征患者及匹配的健康对照者的血清样本。采用8种机器学习算法建立诊断模型。采用偏最小二乘判别分析(PLS-DA)和正交PLS-DA (OPLS-DA)鉴定差异代谢物。通过组织学检查、流式细胞术和生化分析,在MRL-Fas lpr小鼠中验证了鉴定的代谢物的治疗潜力。结果:共检测到129种代谢物,机器学习模型的曲线下面积(AUC)值为>0.8。主成分回归模型在训练集和测试集上的AUC值分别为0.99和0.96,表现最佳。两种关键代谢物色氨酸和β -丙氨酸在SLE患者中的水平明显低于健康对照组。结论:本研究证明了机器学习算法成功应用于SLE分类的代谢组学数据,并确定色氨酸和β -丙氨酸是潜在的SLE特异性生物标志物。色氨酸补充剂通过对T细胞亚群和肾保护的免疫调节作用在狼疮小鼠模型中显示出治疗前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
Journal of Inflammation Research
Journal of Inflammation Research Immunology and Microbiology-Immunology
CiteScore
6.10
自引率
2.20%
发文量
658
审稿时长
16 weeks
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.
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