1.天津中医药大学第一附属医院,天津市西青区昌凌路88号,300193
2.国家中医针灸临床医学研究中心
fengshiliuwei@163.com
收稿:2024-11-20,
修回:2024-12-28,
纸质出版:2025-03-02
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刘维,卫博文,陆航等.痛风急性期湿热毒蕴证及湿热蕴结证患者血液生物学特征差异的多组学研究[J].中医杂志,2025,66(05):480-491.
LIU Wei,WEI Bowen,LU Hang,et al.A Multi-Omics Study on the Differences in Blood Biological Characteristics between Acute Gout Patients with Damp-Heat Toxin Accumulation Syndrome and Damp-Heat Accumulation Syndrome[J].Journal of Traditional Chinese Medicine,2025,66(05):480-491.
刘维,卫博文,陆航等.痛风急性期湿热毒蕴证及湿热蕴结证患者血液生物学特征差异的多组学研究[J].中医杂志,2025,66(05):480-491. DOI: 10.13288/j.11-2166/r.2025.05.009.
LIU Wei,WEI Bowen,LU Hang,et al.A Multi-Omics Study on the Differences in Blood Biological Characteristics between Acute Gout Patients with Damp-Heat Toxin Accumulation Syndrome and Damp-Heat Accumulation Syndrome[J].Journal of Traditional Chinese Medicine,2025,66(05):480-491. DOI: 10.13288/j.11-2166/r.2025.05.009.
目的
2
联合代谢组学、蛋白组学和转录组学分析痛风急性期湿热毒蕴证及湿热蕴结证的生物学特征差异。
方法
2
从临床收集痛风急性期湿热毒蕴证及湿热蕴结证患者血液样本各15例。采用代谢组学技术检测血清代谢物,构建正交偏最小二乘法判别分析模型,筛选组间变化明显的代谢产物,进行富集通路分析和受试者工作特征曲线(ROC)分析。采用Astral数据非依赖型采集(DIA)检测血清蛋白质,进行主成分分析并筛选差异蛋白质,以雷达图展示差异倍数,亚细胞定位分析蛋白质来源,最后应用加权基因共表达网络分析(WGCNA)寻找关键蛋白质。应用转录组测序技术检测全血mRNA,筛选差异基因并进行WGCNA,构建机器学习模型筛选关键基因。
结果
2
代谢组差异分析发现,正离子模式下有62个差异代谢物,负离子模式下有26个差异代谢物。差异代谢物主要富集于mTOR信号通路和FoxO信号通路,以顺-3
5-二甲氧基-4-羟基肉桂醛、瓜那苯唑、4-氨基苯基-1-硫代-β-D-半乳糖苷的诊断效能较高。蛋白组差异分析发现,湿热毒蕴证样本中有55个蛋白质上调和20个蛋白质下调,髓鞘碱性蛋白(MBP)、转铁蛋白(TF)、DKFZp686N02209、载脂蛋白B(APOB)的差异表达倍数较高,差异蛋白主要富集于脂肪消化和吸收、脂质与动脉粥样硬化、胆固醇代谢等。WGCNA发现湿热毒蕴证与棕褐色模块相关性最高,且模块蛋白质主要富集于低氧诱导因子1(HIF-1)信号通路、脂质与动脉粥样硬化等。转录组差异分析发现252个差异表达基因,WGCNA中湿热毒蕴证与午夜蓝模块相关性最高,随机森林模型(RF)被识别为最佳机器学习模型,其预测的载脂蛋白B受体(
APOBR
)
、
远上游元件结合蛋白2(
KHSRP
)
、
POU结构域2类转录因子2(
POU2F2
)、EH结构域蛋白1(
EHD1
)、家族序列相似性110A(
FAM110A
)将作为关键基因。联合多组学分析发现,痛风急性期湿热毒蕴证与脂质代谢,尤其是APOB密切相关。
结论
2
与痛风急性期湿热蕴结证比较,湿热毒蕴证与脂质代谢尤其是APOB密切相关,脂质代谢紊乱促进痛风急性期患者形成湿热毒蕴证。
Objective
2
To combine metabolomics, proteomics, and transcriptomics to analyze the biological characteristics of damp-heat toxin accumulation syndrome and damp-heat accumulation syndrome in acute gout.
Methods
2
Blood samples were collected from 15 patients with damp-heat toxin accumulation syndrome and 15 patients with damp-heat accumulation syndrome in acute gout in clinical practice. Metabolomics technology was applied to detect serum metabolites, and an orthogonal partial sample least squares discriminant analysis model was constructed to screen for metabolites with significant intergroup changes, and enrichment pathway analysis and receiver operating characteristic (ROC) curve analysis were performed. Astral data independence acquisition (DIA) was used to detect serum proteins, perform principal component analysis and screen differential proteins, demonstrate differential ploidy by radargram, apply subcellular localisation to analyse protein sources, and finally apply weighted gene co-expression network analysis (WGCNA) to find key proteins. Transcriptome sequencing technology was also applied to detect whole blood mRNA, screen differential genes and perform WGCNA, and construct machine learning models to screen key genes.
Results
2
Metabolome differential analysis revealed 62 differential metabolites in positive ion mode and 26 in negative ion mode. These differential metabolites were mainly enriched in the mTOR signaling pathway and FoxO signaling pathway, with trans-3
5-dimethoxy-4-hydroxycinnamaldehyde, guanabenz, 4-aminophenyl-1-thio-beta-d-galactopyranoside showing the highest diagnostic efficacy. The proteome differential analysis found that 55 proteins up-regulated and 20 proteins down-regulated in the samples of damp-heat toxin accumulation syndrome. Notably, myelin basic protein (MBP), transferrin (TF), DKFZp686N02209, and apolipoprotein B (APOB) showed the most significant differences in expression. Differential proteins were mainly enriched in pathways related to fat digestion and absorption, lipid and atherosclerosis, and cholesterol metabolism. WGCNA showed the highest correlation between damp-heat toxin accumulation syndrome and the brown module, with proteins in this module primarily enriched in the hypoxia-inducible factor 1 (HIF-1) signaling pathway and lipid and atherosclerosis. Transcriptomic differential analysis identified 252 differentially expressed genes, with WGCNA indicating the highest correlation between damp-heat toxin accumulation syndrome and the midnight blue module. The random forest (RF) model was identified as the optimal machine learning model, predicting apolipoprotein B receptor (
APOBR
), far upstream element-binding protein 2 (
KHSRP
), POU domain class 2 transcription factor 2 (
POU2F2
), EH domain-containing protein 1 (
EHD1
), and family with sequence similarity 110A (
FAM110A
) as key genes. Integrated multi-omics analysis suggested that damp-heat toxin accumulation syndrome in the acute phase of gout is closely associated with lipid meta
bolism, particularly APOB.
Conclusion
2
Compared to damp-heat accumulation syndrome in the acute phase of gout, damp-heat toxin accumulation syndrome is more closely associated with lipid metabolism, particularly APOB, and lipid metabolism disorders contribute to the development of damp-heat toxin accumulation syndrome in patients with acute gout.
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