慢性阻塞性肺病急性加重期的生物标志物组群研究
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摘要
第一部分慢性阻塞性肺病蛋白质组学相关文献回顾
目的：蛋白质组学方法用于研究细胞、活检组织、体液中的蛋白组分,广泛用于深入探索人类疾病机制,寻找用于诊断、治疗、预后的特异性的新型生物标志物,以及研发药物靶位等方面。本文第一部分研究意在通过回顾以往慢性阻塞性肺病(chronic obstructive pulmonary disease, COPD)的蛋白质组学报道,从中总结已有的候选生物标志物。
方法：利用文献检索,回顾以往基于蛋白质组学方法的COPD报道。归纳总结这些报道涉及的研究对象、研究方法、候选生物标志物等内容。同时比较部分候选生物标志物在其他慢性肺部疾病的变化。
结果：通过文献检索,得到符合纳入标准的文献共8篇。所涉及的研究对象,除COPD外,还包括正常非吸烟人群,健康吸烟者,α1胰蛋白酶缺陷者,支气管哮喘,肺囊性纤维化,特发性肺间质纤维化,支气管扩张等。所选用的研究标本来源于肺组织活检标本,支气管肺泡灌洗液,痰液或血液。使用的蛋白质组学实验手段包括双向电泳,反向高效液相色谱,串联质谱法,基质辅助激光解离质谱法,表面增强激光解吸电离质谱法等。在不同标本中发现的大量候选生物标志物可归属于氧化-抗氧化系统,蛋白酶-抗蛋白酶系统,炎症介质以及肺部特异性蛋白等。
结论：单一蛋白难以作为生物标志物,而由多个蛋白组成的生物标志物组群则可更好的反映复杂疾病的状态。蛋白质组学在COPD研究中,特别是挖掘生物标志物方面独具优势,但仍需建立标准化体系,规范组学研究,并最大限度的联系临床资料；利用生物信息学工具将临床资料和蛋白质组学结果相联系,以期建立数字化模式监测COPD的进程和预后。
第二部分COPD及急性加重期病例血浆炎性生物标志物组群的定性研究
目的：COPD不断增长的死亡率与症状急性加重(chronic obstructive pulmonary disease in acute exacerbatioins, AECOPD)的频率和严重程度有关。系统性炎症在COPD/AECOPD中发挥重要作用。在第一部分研究中发现,目前蛋白质组学中存在的问题之一是如何将组学的结果与临床信息相结合。因而本文第二部分研究意在通过蛋白质组学方法检测多种炎性介质,并利用新方法将组学结果和临床信息结合,以期从新角度发现候选生物标志物组群。
方法：收集健康正常人、COPD以及AECOPD患者的血液标本,利用抗体芯片,定性检测507个血浆炎症因子。设计一套用于评价COPD及AECOPD病例病情的临床评分系统(DESS),包括主要症状、体征、实验室检查等临床信息,用于评估患者疾病的严重程度。将芯片检测的差异蛋白与DESS评分结合进行相关性分析。
结果：20个炎症因子在3组人群间存在显著差异(p<0.05)。其中Cerberus1, Growth Hormone R, IL-1F6, IL-17B R, IL-17D, IL-19, Lymphotoxin β,MMP-10, Thrombopoietin和TLR4与患者DESS评分相关(p<0.05)。在AECOPD患者中存在系统性炎症的下调反应。
结论：20个炎症因子可能组成检测COPD/AECOPD的候选生物标志物组群。将临床信息量化并与组学结果结合的方法,为筛选疾病特异性和疾病阶段特异性的生物标志物提供了新的研究模式。
第三部分COPD及急性加重期病例血浆趋化因子组群动态变化的研究
目的：第三部分意在利用蛋白质组学方法定量检测COPD/AECOPD循环中趋化因子的动态变化。并继续使用第二部分中的评分系统,对AECOPD患者进行动态评分,以期发现与AECOPD病程进展相关的候选生物标志物组群。
方法：收集健康正常人、COPD以及AECOPD患者的血液标本。AECOPD患者分别在确诊时、治疗中及症状好转后采集血液标本。利用多重趋化因子抗体芯片,定量检测血浆中40个趋化因子。将芯片检测的差异蛋白与DESS评分结合进行相关性分析。
结果：在40个趋化因子中,30个在COPD和健康正常人间存在统计学差异(p<0.05),16个在AECOPD和健康正常人间存在统计学差异(p<0.05)。13个趋化因子,包括BTC,IL-9, IL-18Bpa, CCL22, CCL23, CCL25, CCL28, CTACK, LIGHT, MSPa, MCP-3, MCP-4和OPN,在三组间均存在差异。部分趋化因子的表达量与DESS评分间存在关联(p<0.05)。
结论：13个趋化因子可能组成监测AECOPD发生和治疗进展的候选生物标志物组群。第二部分中的研究方法,为筛选疾病特异性和疾病阶段特异性的生物标志物提供了新的研究模式。
Part Ⅰ Proteomics-based biomarkers in chronic obstructive pulmonary disease
Objective:The proteomic analysis highlights ways to identify novel biomarkers for diagnosis, therapy and prognosis in COPD. Human samples have been used for COPD proteomic research, each with its own merits and demerits. In first part of this thesis, we aimed at discussing the feasibility of clinical studies on COPD proteomics and the potential panels of candidate biomarkers detected in human samples that are sensitive to the progress of COPD, disease-specific to COPD and associated with the status of the patients.
Methods:Human COPD studies based on proteomics were searched and reviewed. Study subjects, methods, potential biomarkers in all these reports were summarized and analyzed. Some biomarkers indicated in COPD were also compared to those in other chronic pulmonary diseases.
Results:Altogether8studies were included. Among these studies, healthy controls, healthy smokers, and patients with al-antitrypsin deficiency, asthma, cystic fibrosis, IPF and bronchiectasis were also recruited for comparison purpose. Samples were collected from biopsy, BALF, induced sputum and blood. Several proteomics methods were employed, such as2-DE, RP-HPLC, tandom-MS/MS, MALDI, SELDI. Potential biomarkers played critical roles in oxidization, inflammation, and remodeling.
Conclusions:Biomarker patterns were better to indicate status of disease than single protein. It is important to clarify the source of the samples, the efficiency and quality when dealing with large amount of candidates and the specificity of biomarkers according to the severity, therapeutic effects, progress and prognosis of the disease. There is also an urgent need to establish a method to combine clinical information with omics data.
Part Ⅱ Alterations of plasma inflammatory biomarkers in the healthy and chronic obstructive pulmonary disease patients with or without acute exacerbation
Objective:Systemic inflammation has been considered as one of major pathophysiologic alterations in AECOPD. This part aimed at developing disease-specific biomarker evaluation by integrating proteomic profiles of inflammatory mediators in AECOPD with clinical and biological informatics.
Methods:Plasma samples from18subjects including healthy people or patients with stable COPD or AECOPD were collected to measure507inflammatory mediators using antibody microarray. Clinical informatics was achieved by a Digital Evaluation Score System (DESS) for assessing severity of patients.
Results:20mediators were significantly different between3groups (p<0.05), of which, Cerberus1, Growth Hormone R, IL-1F6, IL-17B R, IL-17D, IL-19, Lymphotoxin-β, MMP-10, Thrombopoietin and TLR4were correlated with DESS scores (p<0.05). There was a down-regulation of systemic inflammatory responses in AECOPD.
Conclusion:The integration of proteomic profile with clinical informatics as part of clinical bioinformatics is important to screen disease-specific and disease-staged biomarkers.
Part Ⅲ Selection of disease-specific dynamic biomarkers by integrating inflammatory mediators with clinical informatics in patients with AECOPD
Objective:The present part aimed at developing a new protocol of specific biomarker evaluation by integrating proteomic profiles of inflammatory mediators with clinical informatics in patients with AECOPD, in order to understand the biological function and signal networks.
Methods:Plasma of healthy non-smokers or patients with stable COPD was collected, as well as AECOPD patients on days1and3of the admission and discharging day (day7-10). Chemokine concentration was measured using a chemokine multiplex antibody array. Clinical informatics was achieved by DESS for assessing severity of patients. Chemokine data was compared among different groups and its correlation with DESS variables was performed by SPSS software.
Results:Of40inflammatory mediators,30showed statistically significant difference between COPD patients and healthy controls,16between AECOPD patients and controls.13chemokines, including BTC, IL-9, IL-18Bpa, CCL22, CCL23, CCL25, CCL28, CTACK, LIGHT, MSPa, MCP-3, MCP-4and OPN, showed statistically significance between AECOPD patients and both COPD and controls. Correlation was indicated among most DESS variables and most chemokines (p<0.05).
Conclusion:There is a disease-specific profile of inflammatory mediators in COPD and AECOPD patients which may have a potential diagnostics together with clinical informatics of patients. Our preliminary study suggested that integration of proteomics with clinical informatics can be a new way to validate and optimize disease-special biomarkers.
目的：蛋白质组学方法用于研究细胞、活检组织、体液中的蛋白组分,广泛用于深入探索人类疾病机制,寻找用于诊断、治疗、预后的特异性的新型生物标志物,以及研发药物靶位等方面。本文第一部分研究意在通过回顾以往慢性阻塞性肺病(chronic obstructive pulmonary disease, COPD)的蛋白质组学报道,从中总结已有的候选生物标志物。
方法：利用文献检索,回顾以往基于蛋白质组学方法的COPD报道。归纳总结这些报道涉及的研究对象、研究方法、候选生物标志物等内容。同时比较部分候选生物标志物在其他慢性肺部疾病的变化。
结果：通过文献检索,得到符合纳入标准的文献共8篇。所涉及的研究对象,除COPD外,还包括正常非吸烟人群,健康吸烟者,α1胰蛋白酶缺陷者,支气管哮喘,肺囊性纤维化,特发性肺间质纤维化,支气管扩张等。所选用的研究标本来源于肺组织活检标本,支气管肺泡灌洗液,痰液或血液。使用的蛋白质组学实验手段包括双向电泳,反向高效液相色谱,串联质谱法,基质辅助激光解离质谱法,表面增强激光解吸电离质谱法等。在不同标本中发现的大量候选生物标志物可归属于氧化-抗氧化系统,蛋白酶-抗蛋白酶系统,炎症介质以及肺部特异性蛋白等。
结论：单一蛋白难以作为生物标志物,而由多个蛋白组成的生物标志物组群则可更好的反映复杂疾病的状态。蛋白质组学在COPD研究中,特别是挖掘生物标志物方面独具优势,但仍需建立标准化体系,规范组学研究,并最大限度的联系临床资料；利用生物信息学工具将临床资料和蛋白质组学结果相联系,以期建立数字化模式监测COPD的进程和预后。
第二部分COPD及急性加重期病例血浆炎性生物标志物组群的定性研究
目的：COPD不断增长的死亡率与症状急性加重(chronic obstructive pulmonary disease in acute exacerbatioins, AECOPD)的频率和严重程度有关。系统性炎症在COPD/AECOPD中发挥重要作用。在第一部分研究中发现,目前蛋白质组学中存在的问题之一是如何将组学的结果与临床信息相结合。因而本文第二部分研究意在通过蛋白质组学方法检测多种炎性介质,并利用新方法将组学结果和临床信息结合,以期从新角度发现候选生物标志物组群。
方法：收集健康正常人、COPD以及AECOPD患者的血液标本,利用抗体芯片,定性检测507个血浆炎症因子。设计一套用于评价COPD及AECOPD病例病情的临床评分系统(DESS),包括主要症状、体征、实验室检查等临床信息,用于评估患者疾病的严重程度。将芯片检测的差异蛋白与DESS评分结合进行相关性分析。
结果：20个炎症因子在3组人群间存在显著差异(p<0.05)。其中Cerberus1, Growth Hormone R, IL-1F6, IL-17B R, IL-17D, IL-19, Lymphotoxin β,MMP-10, Thrombopoietin和TLR4与患者DESS评分相关(p<0.05)。在AECOPD患者中存在系统性炎症的下调反应。
结论：20个炎症因子可能组成检测COPD/AECOPD的候选生物标志物组群。将临床信息量化并与组学结果结合的方法,为筛选疾病特异性和疾病阶段特异性的生物标志物提供了新的研究模式。
第三部分COPD及急性加重期病例血浆趋化因子组群动态变化的研究
目的：第三部分意在利用蛋白质组学方法定量检测COPD/AECOPD循环中趋化因子的动态变化。并继续使用第二部分中的评分系统,对AECOPD患者进行动态评分,以期发现与AECOPD病程进展相关的候选生物标志物组群。
方法：收集健康正常人、COPD以及AECOPD患者的血液标本。AECOPD患者分别在确诊时、治疗中及症状好转后采集血液标本。利用多重趋化因子抗体芯片,定量检测血浆中40个趋化因子。将芯片检测的差异蛋白与DESS评分结合进行相关性分析。
结果：在40个趋化因子中,30个在COPD和健康正常人间存在统计学差异(p<0.05),16个在AECOPD和健康正常人间存在统计学差异(p<0.05)。13个趋化因子,包括BTC,IL-9, IL-18Bpa, CCL22, CCL23, CCL25, CCL28, CTACK, LIGHT, MSPa, MCP-3, MCP-4和OPN,在三组间均存在差异。部分趋化因子的表达量与DESS评分间存在关联(p<0.05)。
结论：13个趋化因子可能组成监测AECOPD发生和治疗进展的候选生物标志物组群。第二部分中的研究方法,为筛选疾病特异性和疾病阶段特异性的生物标志物提供了新的研究模式。
Part Ⅰ Proteomics-based biomarkers in chronic obstructive pulmonary disease
Objective:The proteomic analysis highlights ways to identify novel biomarkers for diagnosis, therapy and prognosis in COPD. Human samples have been used for COPD proteomic research, each with its own merits and demerits. In first part of this thesis, we aimed at discussing the feasibility of clinical studies on COPD proteomics and the potential panels of candidate biomarkers detected in human samples that are sensitive to the progress of COPD, disease-specific to COPD and associated with the status of the patients.
Methods:Human COPD studies based on proteomics were searched and reviewed. Study subjects, methods, potential biomarkers in all these reports were summarized and analyzed. Some biomarkers indicated in COPD were also compared to those in other chronic pulmonary diseases.
Results:Altogether8studies were included. Among these studies, healthy controls, healthy smokers, and patients with al-antitrypsin deficiency, asthma, cystic fibrosis, IPF and bronchiectasis were also recruited for comparison purpose. Samples were collected from biopsy, BALF, induced sputum and blood. Several proteomics methods were employed, such as2-DE, RP-HPLC, tandom-MS/MS, MALDI, SELDI. Potential biomarkers played critical roles in oxidization, inflammation, and remodeling.
Conclusions:Biomarker patterns were better to indicate status of disease than single protein. It is important to clarify the source of the samples, the efficiency and quality when dealing with large amount of candidates and the specificity of biomarkers according to the severity, therapeutic effects, progress and prognosis of the disease. There is also an urgent need to establish a method to combine clinical information with omics data.
Part Ⅱ Alterations of plasma inflammatory biomarkers in the healthy and chronic obstructive pulmonary disease patients with or without acute exacerbation
Objective:Systemic inflammation has been considered as one of major pathophysiologic alterations in AECOPD. This part aimed at developing disease-specific biomarker evaluation by integrating proteomic profiles of inflammatory mediators in AECOPD with clinical and biological informatics.
Methods:Plasma samples from18subjects including healthy people or patients with stable COPD or AECOPD were collected to measure507inflammatory mediators using antibody microarray. Clinical informatics was achieved by a Digital Evaluation Score System (DESS) for assessing severity of patients.
Results:20mediators were significantly different between3groups (p<0.05), of which, Cerberus1, Growth Hormone R, IL-1F6, IL-17B R, IL-17D, IL-19, Lymphotoxin-β, MMP-10, Thrombopoietin and TLR4were correlated with DESS scores (p<0.05). There was a down-regulation of systemic inflammatory responses in AECOPD.
Conclusion:The integration of proteomic profile with clinical informatics as part of clinical bioinformatics is important to screen disease-specific and disease-staged biomarkers.
Part Ⅲ Selection of disease-specific dynamic biomarkers by integrating inflammatory mediators with clinical informatics in patients with AECOPD
Objective:The present part aimed at developing a new protocol of specific biomarker evaluation by integrating proteomic profiles of inflammatory mediators with clinical informatics in patients with AECOPD, in order to understand the biological function and signal networks.
Methods:Plasma of healthy non-smokers or patients with stable COPD was collected, as well as AECOPD patients on days1and3of the admission and discharging day (day7-10). Chemokine concentration was measured using a chemokine multiplex antibody array. Clinical informatics was achieved by DESS for assessing severity of patients. Chemokine data was compared among different groups and its correlation with DESS variables was performed by SPSS software.
Results:Of40inflammatory mediators,30showed statistically significant difference between COPD patients and healthy controls,16between AECOPD patients and controls.13chemokines, including BTC, IL-9, IL-18Bpa, CCL22, CCL23, CCL25, CCL28, CTACK, LIGHT, MSPa, MCP-3, MCP-4and OPN, showed statistically significance between AECOPD patients and both COPD and controls. Correlation was indicated among most DESS variables and most chemokines (p<0.05).
Conclusion:There is a disease-specific profile of inflammatory mediators in COPD and AECOPD patients which may have a potential diagnostics together with clinical informatics of patients. Our preliminary study suggested that integration of proteomics with clinical informatics can be a new way to validate and optimize disease-special biomarkers.
引文
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