北京大学学报(医学版) ›› 2025, Vol. 57 ›› Issue (6): 1042-1050. doi: 10.19723/j.issn.1671-167X.2025.06.005

• 论著 • 上一篇    下一篇

非靶向代谢组学揭示原发性干燥综合征血小板减少患者血清差异代谢物及代谢通路

向钊1, 杨莉2, 杨静2,*()   

  1. 1. 达州市中心医院风湿免疫科,四川达州 625000
    2. 绵阳市中心医院风湿免疫科,四川绵阳 621000
  • 收稿日期:2025-08-13 出版日期:2025-12-18 发布日期:2025-11-25
  • 通讯作者: 杨静
  • 基金资助:
    四川省科技厅重点研发项目(2017SZ0148)

Untargeted metabolomics reveals differential serum metabolites and metabolic pathways in patients with primary Sjögren syndrome and thrombocytopenia

Zhao XIANG1, Li YANG2, Jing YANG2,*()   

  1. 1. Department of Rheumatology and Immunology, Dazhou Central Hospital, Dazhou 625000, Sichuan, China
    2. Department of Rheumatology and Immunology, Mianyang Central Hospital, Mianyang 621000, Sichuan, China
  • Received:2025-08-13 Online:2025-12-18 Published:2025-11-25
  • Contact: Jing YANG
  • Supported by:
    Key Research and Development Project of Science and Technology Department of Sichuan Provincial(2017SZ0148)

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摘要:

目的: 通过非靶向代谢组学技术,系统比较原发性干燥综合征(primary Sjögren syndrome,pSS)血小板减少与血小板正常患者的血清代谢谱差异,鉴定出差异代谢物,分析差异代谢物的相对定量与血小板数量之间的关系,筛选出与pSS血小板减少患者的血小板数量相关的代谢途径。方法: 将pSS患者根据有无血小板减少分组,收集患者血清样本,采用液相色谱-质谱联用(liquid chromatography-mass spectrometry,LC-MS)技术对样品进行分析。通过人类代谢组学数据库(human metabolome database,HMDB)、脂质代谢途径研究计划(lipid metabolites and pathways strategy,LIPID MAPS)等数据库进行分类注释;采用主成分分析(principal component analysis,PCA)和偏最小二乘判别分析(partial least squares discriminant analysis,PLS-DA)进行多元统计分析,筛选出组间差异代谢物。基于京都基因和基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)进行富集分析,研究代谢物的功能和代谢途径。对pSS伴血小板减少患者的血清差异代谢物的表达量与血小板计数进行相关性分析。结果: 共纳入62例pSS患者,其中32例伴有血小板减少,30例血小板正常。与pSS不伴血小板减少患者相比,pSS伴有血小板减少患者血清中一共有137种差异表达的代谢物,分别富集于54条代谢途径。其中,去氧皮质酮、氢化可的松、牛磺酸的表达量与血小板计数呈正相关,新蝶呤的表达量与血小板计数呈负相关。富集分析显示,去氧皮质酮和氢化可的松富集于类固醇激素生物合成途径,牛磺酸富集于牛磺酸和亚牛磺酸的代谢途径,新蝶呤富集于叶酸生物合成途径。结论: pSS患者血小板减少可能与类固醇激素生物合成途径、牛磺酸和亚牛磺酸代谢途径活性减弱以及叶酸生物合成途径活性增强有关。

关键词: 干燥综合征, 血小板减少, 代谢组学, 血清, 生物标记

Abstract:

Objective: To systematically compare serum metabolome differences between patients with thrombocytopenia in primary Sjögren syndrome (pSS) and those with normal platelet count using non- targeted metabolomics technology, so as to identify differential metabolites, analyze the relationship between the relative quantification of these metabolites and platelet counts, and screen metabolic pathways associated with platelet counts in pSS patients with thrombocytopenia. Methods: The patients with pSS were selected and grouped according to the presence or absence of thrombocytopenia. Serum samples were collected from the study subjects and analyzed by liquid chromatography-mass spectrometry (LC-MS). The samples were analysed by human metabolome database (HMDB), lipid metabolites and pathways strategy (LIPID MAPS) and other databases for classification and annotation. The samples were analyzed by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) for multi-variate statistical analysis to screen the differential metabolites between the groups, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was made to study the functions and metabolic pathways of the metabolites. Correlation analysis was performed between the abundance of serum differential metabolites and platelet counts of pSS patients with thrombocytopenia. Results: This study included 62 patients with pSS, of whom 32 had thrombocytopenia and 30 had normal platelet counts. A total of 137 differentially expressed metabolites, enriched in 54 metabolic pathways, were found in the serum of patients with thrombocytopenia compared with those without thrombocytopenia. Among them, the expression of desoxycorticosterone, hydrocortisone, and taurine was positively correlated with platelet count, and the expression of neopterin was negatively correlated with platelet count. Enrichment analysis showed that desoxycorticosterone and hydrocortisone were enriched in the steroid hormone biosynthesis pathway, taurine was enriched in the metabolic pathway of taurine and taurine, and neopterin was enriched in the folate metabolic pathway. Conclusion: Thrombocytopenia in pSS patients may be related to the reduced activity of steroid hormone biosynthesis pathway and the metabolic pathway of taurine and taurine, and the increased activity of the pathway of folate metabolism.

Key words: Sjögren syndrome, Thrombocytopenia, Metabolomics, Serum, Biomarkers

中图分类号: 

  • R593.2

表1

研究对象的临床和人口统计学特征"

Clinical and demographic characteristics pSS with thrombocytopenia (n=32) pSS without thrombocytopenia (n=30) P
Age/years, $\bar x \pm s$ 52.84±13.33 47.9±11.96 0.140 0
Female, n(%) 31 (96.88) 29 (93.33) 0.999 9
Course of disease/years, $\bar x \pm s$ 5.73±5.15 1.65±2.77 0.000 2
ESSDAI, $\bar x \pm s$ 2.59±0.49 1.13±0.67 <0.000 1
Platelet count/(×109/L), $\bar x \pm s$ 39.72±28.31 181±55.23 <0.000 1

图1

PCA得分图(A、B)和PLS-DA得分图(C、D)"

图2

差异代谢物的火山图"

图3

KEGG富集通路气泡图"

图4

差异代谢物表达量和血小板计数之间的关系"

表2

11种差异代谢物的表达量与血小板计数的关系"

Differential metabolite r P q Up/Down KEGG pathway
Taurine 0.376 7 0.036 7 0.041 9 Down Aurine and hypotaurine metabolism
Neopterin -0.578 1 0.000 7 0.007 7 Down Folate biosynthesis
Imidazolelactic acid -0.391 5 0.026 7 0.041 9 Up
P-toluenesulfonic acid -0.404 1 0.021 8 0.041 9 Down
Tetradecanedioic acid 0.365 4 0.039 7 0.041 9 Up
Guggulsterone 0.361 8 0.041 9 0.041 9 Down
Phosphatidylcholine (7 ∶0/8 ∶0) 0.391 5 0.026 7 0.041 9 Down
Desoxycortone 0.425 0 0.015 3 0.041 9 Down Steroid hormone biosynthesis
Cortisol 0.380 6 0.031 6 0.041 9 Down Steroid hormone biosynthesis
10-nitrolinoleate 0.535 8 0.001 6 0.008 8 Up
(11E, 15Z)-9, 10, 13-trihydroxyoctadeca-11, 15-dienoic acid 0.403 7 0.021 9 0.041 9 Up
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