慢性阻塞性肺病急性加重期的生物标志物组群研究

发布时间：2018-04-20 07:18

本文选题：COPD + 蛋白质组学　；参考：《复旦大学》2012年博士论文

【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(p0.05)。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(p0.05)。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(p0.05),16个在AECOPD和健康正常人间存在统计学差异(p0.05)。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(p0.05)。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。
[Abstract]:Literature Review on the Proteomics of Chronic Obstructive Pulmonary Disease in Part I

Objective : Proteomics method is used to study the protein components of cells , biopsy tissues and body fluids . It is widely used to probe into the mechanism of human diseases , to find novel biomarkers for diagnosis , treatment and prognosis , as well as to research and develop drug targets . The first part of this paper is to summarize the existing candidate biomarkers by reviewing the proteomic report of chronic obstructive pulmonary disease ( COPD ) .

Methods : Literature search was used to review the previous COPD reports based on proteomic methods . The subjects , methods and candidate biomarkers of these reports were summarized . At the same time , the changes of some candidate biomarkers in other chronic lung diseases were compared .

Results : A total of 8 articles conforming to the standard were obtained by literature search . In addition to COPD , there were normal non - smokers , healthy smokers , 伪 1 trypsin deficiency , bronchial asthma , pulmonary cystic fibrosis , idiopathic pulmonary fibrosis , bronchiectasis , etc . The selected study samples were derived from lung tissue biopsy specimens , bronchoalveolar lavage fluid , sputum or blood . The methods used in the study included two - dimensional electrophoresis , reverse high performance liquid chromatography , tandem mass spectrometry , matrix assisted laser desorption mass spectrometry , surface enhanced laser desorption ionization mass spectrometry , etc . The large number of candidate biomarkers found in different specimens could be attributed to oxidation - oxidation resistant systems , protease - antiprotease systems , inflammatory mediators , and lung - specific proteins .

Conclusion : Single protein is difficult to be used as biomarker , and biomarker group composed of multiple proteins can better reflect the state of complex disease . Proteomics has unique advantages in the study of COPD , especially in mining biomarkers , but it is still necessary to establish a standardized system , standardize the study of group and maximize the clinical data ;
A bioinformatic tool is used to link clinical data and proteomic results with a view to establishing a digital pattern to monitor the progression and prognosis of COPD .

Qualitative study of plasma inflammatory biomarkers in patients with COPD and acute exacerbation

Objective : To investigate the frequency and severity of chronic obstructive pulmonary disease in COPD / AECOPD . Systemic inflammation plays an important role in COPD / AECOPD .

Methods : Blood samples from healthy individuals , COPD and AECOPD patients were collected . 507 plasma inflammatory factors were qualitatively detected by using antibody chip . A set of clinical scoring system ( DESS ) was designed to evaluate the condition of COPD and AECOPD .

Results : There was a significant difference among the 20 inflammatory factors among the 3 groups ( p < 0.05 ) . Among them , Cerberus 1 , Growth Hormone R , IL - 1F6 , IL - 17B R , IL - 17 , IL - 19 , lymphotoxin 尾 , MMP - 10 , Thrombopoietin and TLR were correlated with the DESS score of the patients ( p < 0.05 ) . There was a down - regulation reaction of systemic inflammation in patients with AECOPD .

Conclusion : 20 inflammatory factors may constitute a candidate biomarker group for COPD / AECOPD . The clinical information is quantified and combined with the results of group learning to provide a new research mode for screening biomarkers specific to disease specificity and disease stage .

Study on the Dynamic Changes of Plasma Chemokine Cluster in the Third Part of COPD and Acute Aggravated Period

Objective : The third part is to quantitatively detect the dynamic changes of chemokine in COPD / AECOPD by proteomic method , and continue to use the scoring system in the second part to dynamically score AECOPD patients with a view to finding the candidate biomarker group associated with the progression of AECOPD .

Methods : Blood samples from healthy subjects , COPD and AECOPD patients were collected . Blood samples were collected from the patients with AECOPD .

Results : There were statistically significant differences ( p . 05 ) between 30 COPD patients and healthy controls ( p . 05 ) . There were 13 chemokine ( p . 05 ) in AECOPD and normal controls . There was a difference between the three groups . There was a correlation between the expression of partial chemokine and DESS score ( p < 0 . 05 ) .

Conclusion : 13 chemokine may constitute a candidate biomarker group for monitoring the development and progression of AECOPD . In the second part , a new research mode is provided for screening biomarkers specific to disease - specific and disease - stage .

【学位授予单位】：复旦大学
【学位级别】：博士
【学位授予年份】：2012
【分类号】：R563.9

【参考文献】