类风湿性关节炎寒热证候分类的系统生物学基础
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摘要
辨证论治——是运用中医理论诊疗疾病的原则和方法,是具有中医学独特理论风格和诊疗经验的体系。证候作为辨证论治的核心内容一直是中医基础研究领域的重要内容。由于证候本质具有复杂的相互关系和多样性以及开放式多维网络特征,应该从一系列信息的系统集成来解释其复杂性。因此以整体性研究为特征的系统生物学,可用来研究证候的发展及变化过程。
本研究首先以类风湿性关节炎(Rheumatoid Arthritis,RA)为例,利用多元统计分析方法,分析了临床随机对照试验的结果,我们发现：借助因子分析可确定RA的寒热证候分类,同时寒热证候分类与中西药干预方法的疗效相关,且证候的变化也会影响疗效的改变,说明中医寒热证候分类在RA治疗方法的选择上具有指导价值。虽然RA寒热证候分类的依据主要是中医相关症状,但其对疗效的影响说明RA寒热证候具有特定的生物学基础。
进而,以类风湿性关节炎为范例,选取临床典型的RA寒、热证患者病例,采用系统生物学思路与方法(基因组学及代谢组学),探索RA寒热证候的内在生物学特征,期望能丰富中医证候理论的科学内涵,同时为类风湿性关节炎临床诊断和有效治疗提供理论工具。
1目的
分析RA寒热证候分类对中西药治疗方法疗效的影响,并通过检测类风湿性关节炎典型寒、热证CD4+T淋巴细胞基因表达谱及血浆代谢谱,研究寒热证候的生物学差异,探讨类风湿性关节炎寒热证候分类的科学基础。
2研究对象和方法
研究思路：利用多元统计分析方法,分析中西药治疗RA随机对照临床数据,探索中医寒热证候分类与疗效的关系；在此基础上,随机选取符合纳入标准的RA女性患者,参照相应标准进行寒、热证候辨别。取患者CD4+T淋巴细胞及血浆,进行基因表达谱及代谢组学测定,寻找类风湿性关节炎寒热证候之间差异表达的基因及生物标记物,并对结果进行生物信息学分析,探寻类风湿性关节炎寒热证候差异的生物学基础。
3研究内容
3.1临床数据分析
数据来源：国家“十五”攻关项目全国9个临床中心RA临床随机对照试验数据。
分析方法：采用回归分析、主成分分析、因子分析、辨别分析、聚类分析等多元统计方法,探索RA证候分类及各证候类型与临床疗效的相关关系。
3.2实验研究
基因表达谱研究：随机选取北京中日友好医院女性患者20例,年龄12-68岁。纳入病例中寒证患者为10例,热证患者为10例。
代谢组学研究：随机选取北京中日友好医院女性患者21例,纳入病例中寒证患者为15例,热证患者为6例；另选取16例女性为正常对照组(依据体检结果排除了感染、炎症等影响因素)。
◇病例纳入标准：疾病诊断、病情进展分类标准：参照1987年美国风湿病学会(ARA)修订的诊断标准。
◇证候诊断标准：参照1993年卫生部《中药新药治疗痹病的临床研究指导原则》、1994年国家中医药管理局发布的《中医病证诊断疗效标准·(?)痹的诊断依据、证候分类、疗效评定》、1997年中华人民共和国国家标准《中医临床诊疗术语证候部分》和1988年全国中西医结合风湿类疾病学术会议拟订的诊断标准。
◇排除标准：合并严重关节外表现,如高热不退、多发类风湿结节、肺间质纤维化、肾脏淀粉样变、缩窄性心包炎、血管炎等需要使用糖皮质激素的患者；长期服用有关治疗RA的慢作用药物,且在本研究前至少1周内未停用甲氨喋呤、氯喹、柳氮磺胺吡啶、环磷酰胺、青霉胺和金制剂等免疫抑制类药物的患者；合并有循环、呼吸、消化、泌尿生殖、造血系统以及中枢神经系统等严重疾病的患者。
4研究方法
4.1基因表达谱研究
◇CD4+T淋巴细胞分离纯化：采集空腹静脉血,EDTA抗凝,加入CD4+T细胞富集抗体混匀,Ficoll淋巴细胞分离液分离,PBS溶液洗两遍,离心即可得到纯度90%以上的CD4+T淋巴细胞。
◇芯片杂交前RNA预处理：按TRIzol说明书,提取细胞总RNA,并进行RNA质检；依据QIAGEN RNeasy(?) Kit操作手册纯化总RNA；一步法合成cDNA链；aaUTP标记合成cRNA; QIAGEN RNeasy Mini kit纯化cRNA并对其质控测定；cRNA荧光标记并纯化
◇芯片杂交及检测：芯片为Agilent人类全基因组芯片(4*44k),所有芯片均以相同对照标记为Cy3,样本标记为Cy5。cRNA样品片段化后与芯片杂交,洗涤后Agilent扫描仪扫描得到基因表达谱原始数据。
◇芯片数据分析：分析Agilent软件导出数据,取各芯片中位数比值进行对数转换,然后用SAS的标准化程序进行均值为0标准差为1的标准化操作；采用SAS的MI Procedure过程对标准化后的数据进行缺失值填补；以alpha=0.05为置信度,对缺失值填补后的数据进行t检验,选择有显著性差异的基因；采用SAS的Power Procedure对有显著性差异的基因过程进行把握度计算,保留Power> 0.85的基因；以Ratio> 2或Ratio<0.5为显著性差异界限,计算寒热证差异基因的比值,筛选寒热差异表达基因。借助BIND、DIP、HPRD、IntAct及MINT等数据库对差异表达基因进行蛋白质相互作用网络分析；IPCA运算法则寻找网络高联接区并借助BiNGO工具对高联接区进行GO功能分析。
4.2代谢组学研究
◇血浆处理：EDTA抗凝管收集外周血,离心取血浆冻存备用。分析前超低温冰箱取出,充分溶解震荡,加入乙腈高速离心取上清,加入内标液充分溶解,离心取上清自动取样器取样分析。
◇样品检测：自动加样器(Agilent7683系列)加到Agilent 6890气相色谱；色谱柱规格：30 m×0.25 mm i.d.×0.25μm DB5-MS熔融石英毛细管柱；喷头温度设定为290℃；载气：氦气,35cm／秒；柱温：起始温度100℃,以2.5℃／mmin速率增加至285℃,持续6 mmin；传输器温度设定为285℃；传输器温度设定为285℃；离子源温度设定为200℃；数据采集范围mm/z40-500；GC/MS(岛津)定性分析。
◇数据分析：数据主要采用Par scaling的方法对所有数据集的变量进行数据尺度同一化处理,采用软件包(SIMCA-Pversion11.0)对原始信息进行主成分分析；偏最小二乘法显著性分析用来找寻能区分RA寒热证候贡献度最大变量。运用质谱确定最大贡献度变量的代谢物结构,交叉验证测试用来确认PLS-DA模型的可靠性。
5结果
5.1临床数据分析
◇西药方案在治疗RA寒热患者过程中,3个月及6个月的疗效均以寒证组为高,具有统计学意义。
◇在患者证候类型变化的情况下,无论中医组及西医组方案其临床疗效均发生显著性改变。
5.2基因表达谱
◇类风湿性关节炎寒热证候之间有29个差异基因表达,其中7个基因在寒证高表达：LPAAT-THETA、COL6A1、ENST00000256367、THC2318696、SPECCl、TLR4、及THC2312748;22个基因在热证高表达：LOC400509、BF210146、CABLES1、APOA1、CD614215、IGHD、SPP1、WWOX等
◇利用ICPA运算法则查找差异基因的蛋白质-蛋白质相互作用网络,发现4个高联接区,GO功能分析发现寒证与toll样受体信号通路有关,而钙离子信号通路、细胞粘附分子、PPAR信号通路以及脂肪酸代谢途径与热证相关联。
◇生物信息学分析结果表明：类风湿性关节炎寒热证患者表现为凋亡及炎症的不同调控机制。5.3代谢组学
◇类风湿性关节炎患者与正常对照组、类风湿性关节炎寒热证候之间均存在代谢物谱差异,PLS-DA得分图显示RA与正常对照,RA寒证与热证之间根据代谢谱数据能够清晰分开。
(?) PCA、PLS-DA结合质谱方法,找到类风湿性关节炎寒热证候差异的7个潜在生物标记物：3-氧-丙酸(3-Oxy propanoic acid)、L-脯氨酸(L-Proline)、尿素(Urea)、5-氧-脯氨酸(5-Oxo-proline)、核糖醇(Ribitol)、纤维醇(Inositol)、L-亮氨酸(L-Leucine)。其中前6个标记物为热证上调,L-亮氨酸为寒证上调。
◇对这些生物标记物进行功能检索分析提示：与寒证相比,热证患者机体存在过多的胶原分解；而寒证患者机体蛋白质合成过程大于蛋白质分解。
6结论
6.1 RA寒热证候分类与中西药临床干预方案疗效相关,且证候的变化也会影响临床疗效的改变,RA中医证候分类在临床治疗方案选择上具有指导意义。
6.2类风湿性关节炎寒热证候存在生物学差异,应用基因表达谱方法及代谢组学技术能够检测出RA寒热证候差异相关的基因表达及生物标记物。
6.3生物信息学分析发现RA寒热证候存在对凋亡、蛋白质代谢等不同的调控机制,对这些机制的深入了解可帮助理解不同证候临床疗效的差异,可为指导临床合理治疗方案提供理论依据。
6.4基因表达谱及代谢组结果均提示RA寒热证表现炎症的不同调控,说明系统生物学技术和方法用于中医证候研究稳定可靠,能够为深入理解复杂证候理论提供技术平台。
1 Objective
The research is aimed to explore the distinct molecular signatures in the rheumatoid arthritis patients with traditional Chinese medicine cold pattern and heat pattern.
2 Methods
For data analysis:This study uses data from a previous multicenter randomized controlled clinical trial. Patients were from 9 clinical centers. They were randomly divided into Westren medicine treated group and Traditional Chinese Medicine treated group. ACR20 response at 12 weeks and 24 weeks was used for evaluation of efficacy. Multivariate statistical analysis was used to investigate the relationship between patern differentiation and clinical efficacy.
For transcriptomics:Twenty patients with typical TCM cold pattern and heat pattern were included in the study. Microarray technology was used to reveal gene expression profiles in CD4+T cells from the patients. The signal intensity of each expressed gene was globally normalized using the R statistics program. The ratio of cold pattern to heat pattern in RA patients at more or less than 1:2 was taken as the differential gene expression criteria. Protein-protein interaction information for these genes from databases was searched, and the highly-connected regions were detected by IPCA algorithm. The significant pathways were extracted from these subnetworks by Biological Network Gene Ontology tool.
For metabolomics:The blood samples of 21 RA patients and 16 healthy volunteers were used. Blood plasma was prepared by centrifuging, then every sample was transferred to an autosampler for analysis. Partial least squares discriminant analysis (PLS-DA) was employed to find potential biomarkers differentiating the RA patients from the control participants. The processed data list was UV-scaled, mean centered and exported for chemometrics analysis using SIMCA-P. To test the statistical significance the resulting PLS-DA models were cross-validated and permutation testing was performed. Error rates were calculated using MatLab. All the data collected have been statistically analyzed with the SPSS software, the count data with Chi-square test and the measuring data with T-test.
3 Results
Transcriptomics:Twenty nine genes differentially regulated between cold pattern and heat pattern were found. Among them,7 genes were expressed significantly more in cold pattern. Biological network of protein-protein interaction information for these significant genes were searched and visualized using Cytoscape, and four highly-connected regions were detected by IPCA algorithm to infer significant complexes or pathways in the biological network. Particularly the cold pattern was related to Toll-like receptor signaling pathway. The following related pathways in heat pattern were included:Calcium signaling pathway; Cell adhesion molecules; PPAR signaling pathway, Fatty acid metabolism.
Metabolomics:A multivariate data-analysis procedure resulted in the discovery of several potential biomarkers related to RA patients with cold pattern and heat pattern. The PLS-DA score plot shows that the RA patients with cold pattern and heat pattern could be divided into two groups based on their metabolite profiles. T-tests were performed on both sets of variables (P<0.05). Differences between the metabolite profiles of cold pattern and heat pattern revealed 7 potential biomarkers, which 3-Oxy propanoic acid, L-Proline, Urea,5-Oxo-proline, Ribitol, Inositol and L-Leucine were included.
4 Conclusions
RA patients with cold pattern and heat pattern have distinct molecular signatures with different biological processes participating. Molecular differences between the RA Cold and RA Heat groups were found which suggest differences in apoptotic activity and protein metabolism. Both the gene and metabolite profiles have elucidated relationships between several of the markers, revealed insight in the mechanism of RA Pattern differentiation. These results suggest that better knowledge of the main biological processes involved at a given pattern in TCM might help to choose the most appropriate treatment.
本研究首先以类风湿性关节炎(Rheumatoid Arthritis,RA)为例,利用多元统计分析方法,分析了临床随机对照试验的结果,我们发现：借助因子分析可确定RA的寒热证候分类,同时寒热证候分类与中西药干预方法的疗效相关,且证候的变化也会影响疗效的改变,说明中医寒热证候分类在RA治疗方法的选择上具有指导价值。虽然RA寒热证候分类的依据主要是中医相关症状,但其对疗效的影响说明RA寒热证候具有特定的生物学基础。
进而,以类风湿性关节炎为范例,选取临床典型的RA寒、热证患者病例,采用系统生物学思路与方法(基因组学及代谢组学),探索RA寒热证候的内在生物学特征,期望能丰富中医证候理论的科学内涵,同时为类风湿性关节炎临床诊断和有效治疗提供理论工具。
1目的
分析RA寒热证候分类对中西药治疗方法疗效的影响,并通过检测类风湿性关节炎典型寒、热证CD4+T淋巴细胞基因表达谱及血浆代谢谱,研究寒热证候的生物学差异,探讨类风湿性关节炎寒热证候分类的科学基础。
2研究对象和方法
研究思路：利用多元统计分析方法,分析中西药治疗RA随机对照临床数据,探索中医寒热证候分类与疗效的关系；在此基础上,随机选取符合纳入标准的RA女性患者,参照相应标准进行寒、热证候辨别。取患者CD4+T淋巴细胞及血浆,进行基因表达谱及代谢组学测定,寻找类风湿性关节炎寒热证候之间差异表达的基因及生物标记物,并对结果进行生物信息学分析,探寻类风湿性关节炎寒热证候差异的生物学基础。
3研究内容
3.1临床数据分析
数据来源：国家“十五”攻关项目全国9个临床中心RA临床随机对照试验数据。
分析方法：采用回归分析、主成分分析、因子分析、辨别分析、聚类分析等多元统计方法,探索RA证候分类及各证候类型与临床疗效的相关关系。
3.2实验研究
基因表达谱研究：随机选取北京中日友好医院女性患者20例,年龄12-68岁。纳入病例中寒证患者为10例,热证患者为10例。
代谢组学研究：随机选取北京中日友好医院女性患者21例,纳入病例中寒证患者为15例,热证患者为6例；另选取16例女性为正常对照组(依据体检结果排除了感染、炎症等影响因素)。
◇病例纳入标准：疾病诊断、病情进展分类标准：参照1987年美国风湿病学会(ARA)修订的诊断标准。
◇证候诊断标准：参照1993年卫生部《中药新药治疗痹病的临床研究指导原则》、1994年国家中医药管理局发布的《中医病证诊断疗效标准·(?)痹的诊断依据、证候分类、疗效评定》、1997年中华人民共和国国家标准《中医临床诊疗术语证候部分》和1988年全国中西医结合风湿类疾病学术会议拟订的诊断标准。
◇排除标准：合并严重关节外表现,如高热不退、多发类风湿结节、肺间质纤维化、肾脏淀粉样变、缩窄性心包炎、血管炎等需要使用糖皮质激素的患者；长期服用有关治疗RA的慢作用药物,且在本研究前至少1周内未停用甲氨喋呤、氯喹、柳氮磺胺吡啶、环磷酰胺、青霉胺和金制剂等免疫抑制类药物的患者；合并有循环、呼吸、消化、泌尿生殖、造血系统以及中枢神经系统等严重疾病的患者。
4研究方法
4.1基因表达谱研究
◇CD4+T淋巴细胞分离纯化：采集空腹静脉血,EDTA抗凝,加入CD4+T细胞富集抗体混匀,Ficoll淋巴细胞分离液分离,PBS溶液洗两遍,离心即可得到纯度90%以上的CD4+T淋巴细胞。
◇芯片杂交前RNA预处理：按TRIzol说明书,提取细胞总RNA,并进行RNA质检；依据QIAGEN RNeasy(?) Kit操作手册纯化总RNA；一步法合成cDNA链；aaUTP标记合成cRNA; QIAGEN RNeasy Mini kit纯化cRNA并对其质控测定；cRNA荧光标记并纯化
◇芯片杂交及检测：芯片为Agilent人类全基因组芯片(4*44k),所有芯片均以相同对照标记为Cy3,样本标记为Cy5。cRNA样品片段化后与芯片杂交,洗涤后Agilent扫描仪扫描得到基因表达谱原始数据。
◇芯片数据分析：分析Agilent软件导出数据,取各芯片中位数比值进行对数转换,然后用SAS的标准化程序进行均值为0标准差为1的标准化操作；采用SAS的MI Procedure过程对标准化后的数据进行缺失值填补；以alpha=0.05为置信度,对缺失值填补后的数据进行t检验,选择有显著性差异的基因；采用SAS的Power Procedure对有显著性差异的基因过程进行把握度计算,保留Power> 0.85的基因；以Ratio> 2或Ratio<0.5为显著性差异界限,计算寒热证差异基因的比值,筛选寒热差异表达基因。借助BIND、DIP、HPRD、IntAct及MINT等数据库对差异表达基因进行蛋白质相互作用网络分析；IPCA运算法则寻找网络高联接区并借助BiNGO工具对高联接区进行GO功能分析。
4.2代谢组学研究
◇血浆处理：EDTA抗凝管收集外周血,离心取血浆冻存备用。分析前超低温冰箱取出,充分溶解震荡,加入乙腈高速离心取上清,加入内标液充分溶解,离心取上清自动取样器取样分析。
◇样品检测：自动加样器(Agilent7683系列)加到Agilent 6890气相色谱；色谱柱规格：30 m×0.25 mm i.d.×0.25μm DB5-MS熔融石英毛细管柱；喷头温度设定为290℃；载气：氦气,35cm／秒；柱温：起始温度100℃,以2.5℃／mmin速率增加至285℃,持续6 mmin；传输器温度设定为285℃；传输器温度设定为285℃；离子源温度设定为200℃；数据采集范围mm/z40-500；GC/MS(岛津)定性分析。
◇数据分析：数据主要采用Par scaling的方法对所有数据集的变量进行数据尺度同一化处理,采用软件包(SIMCA-Pversion11.0)对原始信息进行主成分分析；偏最小二乘法显著性分析用来找寻能区分RA寒热证候贡献度最大变量。运用质谱确定最大贡献度变量的代谢物结构,交叉验证测试用来确认PLS-DA模型的可靠性。
5结果
5.1临床数据分析
◇西药方案在治疗RA寒热患者过程中,3个月及6个月的疗效均以寒证组为高,具有统计学意义。
◇在患者证候类型变化的情况下,无论中医组及西医组方案其临床疗效均发生显著性改变。
5.2基因表达谱
◇类风湿性关节炎寒热证候之间有29个差异基因表达,其中7个基因在寒证高表达：LPAAT-THETA、COL6A1、ENST00000256367、THC2318696、SPECCl、TLR4、及THC2312748;22个基因在热证高表达：LOC400509、BF210146、CABLES1、APOA1、CD614215、IGHD、SPP1、WWOX等
◇利用ICPA运算法则查找差异基因的蛋白质-蛋白质相互作用网络,发现4个高联接区,GO功能分析发现寒证与toll样受体信号通路有关,而钙离子信号通路、细胞粘附分子、PPAR信号通路以及脂肪酸代谢途径与热证相关联。
◇生物信息学分析结果表明：类风湿性关节炎寒热证患者表现为凋亡及炎症的不同调控机制。5.3代谢组学
◇类风湿性关节炎患者与正常对照组、类风湿性关节炎寒热证候之间均存在代谢物谱差异,PLS-DA得分图显示RA与正常对照,RA寒证与热证之间根据代谢谱数据能够清晰分开。
(?) PCA、PLS-DA结合质谱方法,找到类风湿性关节炎寒热证候差异的7个潜在生物标记物：3-氧-丙酸(3-Oxy propanoic acid)、L-脯氨酸(L-Proline)、尿素(Urea)、5-氧-脯氨酸(5-Oxo-proline)、核糖醇(Ribitol)、纤维醇(Inositol)、L-亮氨酸(L-Leucine)。其中前6个标记物为热证上调,L-亮氨酸为寒证上调。
◇对这些生物标记物进行功能检索分析提示：与寒证相比,热证患者机体存在过多的胶原分解；而寒证患者机体蛋白质合成过程大于蛋白质分解。
6结论
6.1 RA寒热证候分类与中西药临床干预方案疗效相关,且证候的变化也会影响临床疗效的改变,RA中医证候分类在临床治疗方案选择上具有指导意义。
6.2类风湿性关节炎寒热证候存在生物学差异,应用基因表达谱方法及代谢组学技术能够检测出RA寒热证候差异相关的基因表达及生物标记物。
6.3生物信息学分析发现RA寒热证候存在对凋亡、蛋白质代谢等不同的调控机制,对这些机制的深入了解可帮助理解不同证候临床疗效的差异,可为指导临床合理治疗方案提供理论依据。
6.4基因表达谱及代谢组结果均提示RA寒热证表现炎症的不同调控,说明系统生物学技术和方法用于中医证候研究稳定可靠,能够为深入理解复杂证候理论提供技术平台。
1 Objective
The research is aimed to explore the distinct molecular signatures in the rheumatoid arthritis patients with traditional Chinese medicine cold pattern and heat pattern.
2 Methods
For data analysis:This study uses data from a previous multicenter randomized controlled clinical trial. Patients were from 9 clinical centers. They were randomly divided into Westren medicine treated group and Traditional Chinese Medicine treated group. ACR20 response at 12 weeks and 24 weeks was used for evaluation of efficacy. Multivariate statistical analysis was used to investigate the relationship between patern differentiation and clinical efficacy.
For transcriptomics:Twenty patients with typical TCM cold pattern and heat pattern were included in the study. Microarray technology was used to reveal gene expression profiles in CD4+T cells from the patients. The signal intensity of each expressed gene was globally normalized using the R statistics program. The ratio of cold pattern to heat pattern in RA patients at more or less than 1:2 was taken as the differential gene expression criteria. Protein-protein interaction information for these genes from databases was searched, and the highly-connected regions were detected by IPCA algorithm. The significant pathways were extracted from these subnetworks by Biological Network Gene Ontology tool.
For metabolomics:The blood samples of 21 RA patients and 16 healthy volunteers were used. Blood plasma was prepared by centrifuging, then every sample was transferred to an autosampler for analysis. Partial least squares discriminant analysis (PLS-DA) was employed to find potential biomarkers differentiating the RA patients from the control participants. The processed data list was UV-scaled, mean centered and exported for chemometrics analysis using SIMCA-P. To test the statistical significance the resulting PLS-DA models were cross-validated and permutation testing was performed. Error rates were calculated using MatLab. All the data collected have been statistically analyzed with the SPSS software, the count data with Chi-square test and the measuring data with T-test.
3 Results
Transcriptomics:Twenty nine genes differentially regulated between cold pattern and heat pattern were found. Among them,7 genes were expressed significantly more in cold pattern. Biological network of protein-protein interaction information for these significant genes were searched and visualized using Cytoscape, and four highly-connected regions were detected by IPCA algorithm to infer significant complexes or pathways in the biological network. Particularly the cold pattern was related to Toll-like receptor signaling pathway. The following related pathways in heat pattern were included:Calcium signaling pathway; Cell adhesion molecules; PPAR signaling pathway, Fatty acid metabolism.
Metabolomics:A multivariate data-analysis procedure resulted in the discovery of several potential biomarkers related to RA patients with cold pattern and heat pattern. The PLS-DA score plot shows that the RA patients with cold pattern and heat pattern could be divided into two groups based on their metabolite profiles. T-tests were performed on both sets of variables (P<0.05). Differences between the metabolite profiles of cold pattern and heat pattern revealed 7 potential biomarkers, which 3-Oxy propanoic acid, L-Proline, Urea,5-Oxo-proline, Ribitol, Inositol and L-Leucine were included.
4 Conclusions
RA patients with cold pattern and heat pattern have distinct molecular signatures with different biological processes participating. Molecular differences between the RA Cold and RA Heat groups were found which suggest differences in apoptotic activity and protein metabolism. Both the gene and metabolite profiles have elucidated relationships between several of the markers, revealed insight in the mechanism of RA Pattern differentiation. These results suggest that better knowledge of the main biological processes involved at a given pattern in TCM might help to choose the most appropriate treatment.
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