乙型病毒性肝炎及相关疾病蛋白质组学及临床诊断的研究
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摘要
乙型病毒性肝炎世界范围内流行,是已知各型肝炎中危害最严重的一种。目前全世界范围内约有3.5亿慢性乙肝感染者,其中15%-25%将发展并最终死于相关的肝损伤、肝硬化和肝细胞癌,严重威胁人们的健康。而我国是世界上的肝炎大国,情况更加严重,其总的感染率为57%,约有7亿人感染过乙肝,1.3亿乙肝病毒携带者,2300万慢性肝炎患者;每年死于肝炎约23万人;每年防治经费逾500亿人民币,更为严重的是迄今为止尚无彻底根治乙型肝炎的办法,因此对于国民经济及全民健康来讲都是巨大的灾难。鉴于此,应该最大限度的利用现有的有关乙肝及相关疾病的临床检测结果,深度挖掘数据背后隐藏的信息,提高诊断的准确度。同时积极研究发现新的、灵敏、特异的诊断方法和有价值的生物标志物,力争做到早诊断、早治疗,改善病人预后。遗憾的是,临床医生在面对大量检测结果时往往孤立看待,很少综合分析,因而造成大量有价值信息的丢失和浪费。而即使是大量检测项目的本身,如鉴别诊断急性乙肝和慢性乙肝急性发作、慢性乙肝轻、中、重的判断以及筛检肝细胞肝癌等方法,往往由于灵敏度、特异度不高、依从性差及适应症受限等原因,严重影响了乙肝及相关疾病的临床诊断和治疗。本课题研究目的即在于尝试采用高效数理统计方法,提高检测数据的利用率。同时利用飞行时间质谱技术积极探索寻找新的生物标志物,为临床诊断和治疗乙肝及相关疾病提供帮助。
第一部分决策树模型在乙型病毒性肝炎及相关疾病诊断中的应用
目的:用决策树模型对临床常用检测数据进行二次发掘利用,是一种有效的降维数理统计方法。由于乙型肝炎及相关疾病如肝硬化、肝癌等病情复杂多变、病程长,因此在临床上将会产生大量、多维的检测数据。尽管在这些数据中蕴含了丰富的疾病信息,但临床医生往往孤立地看待每一个检测结果,很难把它们综合利用及分析,更谈不上对这些数据进行深加工利用,其结果就是疾病信息的丢失和浪费,并最终导致临床诊断的不准确、治疗的不及时和没有针对性、直至延误病情。本部分将以慢性乙肝与肝硬化及肝癌鉴别比较、慢性乙肝轻、中、重不同型别的鉴别比较为例,用决策树模型的方法将临床常用数据进行再挖掘,为临床决策提供参考。
方法:目前构建决策树模型主要有四种算法,即CRT(Classification and Regression Tree,分类与回归树)、QUEST(Quick, Unbiased, Efficient, Statistical Tree,快速无偏高效统计树)、CHAID(Chi-Squared Automatic Interaction Detection ,卡方自动交互探测)和EXHAUSTIVE CHAID(穷举CHILD)。前两种算法为一类,构建的树模型称二叉树;后两种算法为一类,构建的树模型称多叉树。研究中,在两类算法中各选择一种算法作为代表,即CHAID和CRT,对慢性乙肝与肝硬化及肝癌,慢性乙肝轻、中、重不同型别分类构建决策树模型并进行风险和准确度评估。同时以慢性乙肝分型为例用主成分分析方法加以验证,考察模型的实用性。
结果:利用CHAID和CRT两种算法用于慢性乙肝轻型和中重型分类诊断时,所构建的两个模型的正确分类率分别达到77.5%和78.4%,模型的拟合效果良好,风险评估结果均为0.353,在可接受范围内。ROC(Receiver Operating Characteristic,受试者工作特征)曲线下面积分别为0.787(CI 95%:0.695–0.879)和0.802(CI 95%:0.714–0.890),准确度评估良好;而利用CHAID和CRT两种算法用于慢性乙型肝炎与肝硬化及肝癌分类诊断时,所构建的两个模型的正确分类率分别达到71.4%和74.2%,模型的拟合效果良好,风险评估结果分别为0.451和0.352,虽在可接受范围内,但CHAID模型风险稍高。ROC曲线下面积分别为0.785(CI 95%:0.692–0.878)和0.807(CI95%:0.720–0.894),准确度评估良好。
为了验证模型的实用性,以慢性乙肝轻、中、重判别为例采用主成分分析方法进行考察分析。最终确定五个主要成分,第一主成分:ALT、AST、总胆红素三个指标;第二主成分:抗-HBe指标;第三主成分:PT、HBsAg指标;第四主成分:HBV定量、HBe-Ag、抗-HBs、A/G比值四个指标;第五主成分:抗-HBc指标。在进行慢性乙肝病变程度判定时,决策数模型的第一预测变量和主成分分析的第一主成分都是反映肝功能的ALT和AST指标,结果高度一致。
结论:决策树模型在对临床常见数据的挖掘再利用方面具有一定价值,可以大大提高诊断的准确度,为临床医生决策提供帮助。而且此结论已经得到主成分分析的验证,两者有极好的吻合,较为可信。在进行慢性乙肝中重型诊断时,应优先考虑ALT、AST和乙肝病毒载量等指标;而试图从慢性乙肝患者中筛检肝硬化或肝癌时,年龄、职业和胆红素指标更为重要。肝纤四项(HA、LN、PCⅢ和Ⅳ-C)未进入决策树模型,表明该四项指标对判定肝纤维化程度价值不大。
第二部分基于磁珠样品制备和基质辅助激光解析离子化飞行时间质谱实验条件的优化
目的:利用基质辅助激光解吸离子化飞行时间质谱(matrix-assisted laser desorption/ionization time-of flight mass spectrometry,MALDI-TOF MS)技术检测样本血清蛋白或多肽,考察实验的重复性及血清不同的冻融次数和基质与样品搭配比例对结果的影响。以便摸索一套有效的血清样品收集、处理、存储和实验方法。同时比较分析两种磁珠的优劣,为后续大规模检测研究提供科学的选择依据。
方法:首先采用IMAC-Cu磁珠进行样本蛋白分离提纯,经MALDI-TOF MS检测后,选择不同分子量范围内9个蛋白重复测定,比较组间和组内变异系数以考察重复性及实验室条件的影响;比较不同条件下蛋白质谱图来判断冻融次数和基质与样品搭配比例对实验结果的影响。然后比较WCX和IMAC-Cu两种磁珠的平均出峰量、平均峰面积和平均峰强度等参数,择优为后续研究做准备。研究过程中仪器操作、数据分析和图像采集分别用flexControlMS3.0、ClinProToolsTM2.1和flexAnalysis 3.0软件。
结果:研究发现,样品与基质的适宜比例为1:5(ul),并且实验室温度控制在20℃～25℃,湿度在10%～30%时点样结晶效果最佳。样品的冻融最好不要超过3次,否则对质谱出峰有影响。冻融次数越多,对小分子量蛋白或多肽的影响比大分子量蛋白或多肽影响大。反映实验结果重复性的变异系数为1.26%～30.79%,在可接受范围内。相同样本利用WCX磁珠处理后的平均出峰量和平均峰面积均明显优于IMAC-Cu磁珠。
结论:MALDI-TOF MS作为一种高灵敏度的研究平台,严格操作程序可以减少变异,提高结果的重复性和可信性。WCX磁珠的蛋白分离效果优于IMAC-Cu磁珠,选择用于后续大规模检测。
第三部分乙型肝炎及相关疾病血清蛋白质组的比较分析
目的:检测急性乙肝(acute hepatitis B, AHB)、慢性乙肝(chronic hepatitis B, CHB)、肝硬化(liver cirrhosis, LC)和原发性肝细胞癌(hepaticcell carcinoma, HCC)患者血清蛋白质谱图,比较组间差异,寻找差异蛋白并评估其分类价值,为临床诊断应用提供参考。
方法:首先按照实验设计要求,收集14例AHB、76例CHB、41例LC和14例HCC患者的血清样本,用WCX磁珠进行样本前处理,然后用MALDI-TOF MS检测不同组患者蛋白质谱图并加以对比分析。研究过程中仪器操作、数据分析和图像采集分别用flexControlMS3.0、ClinProToolsTM2.1和flexAnalysis 3.0软件。对所发现的差异蛋白分别计算分类的灵敏度、特异度和验证正确率,或差异蛋白组合后诊断的效果,以指导临床应用。此外追踪观察4例AHB患者血清蛋白质谱图变化,寻找判断急性乙肝转慢的生物标志物。
结果:AHB和CHB患者血清蛋白质组比较,在所发现的49个差异蛋白中,仅4154Da和4210Da分类效果相对较优,联合作为生物标志物应用于诊断AHB的灵敏度、特异度、验证正确率分别达到100%(14/14)、84.21%(64/76)和86.67%(78/90)。对4例AHB患者追踪观察发现,血清2952Da、4210Da、5337Da和5904Da蛋白表达量因不同转归而表现迥异。CHB轻、中、重型血清蛋白质组研究发现,轻型和中型无差异而合并为一组,然后与CHB重型比较。差异蛋白中1866Da蛋白表现最优,单独用来区分慢性重型乙肝的灵敏度和特异度达到100%和88.89%,验证正确率为93.42%,是一个良好的生物标志物,值得进一步研究。CHB患者和HCC患者血清蛋白质组的对比,11243Da和4210Da两个蛋白分类效果相对较好,联合应用诊断HCC的灵敏度、特异度和验证正确率分别达到92.86%(13/14)、77.63%(59/76)和80%(72/90)。HCC和LC患者血清蛋白质组差异不大,仅一个差异蛋白。
结论:AHB和CHB患者血清蛋白质组存在明显差异,其中4154Da和4210Da两个蛋白组合使用可以有效鉴别AHB,应当成为今后分类诊断的候选蛋白。AHB患者因不同转归而表现迥异的蛋白2952Da、4210Da、5337Da和5904Da是今后进一步研究的重点。CHB轻、中、重型血清蛋白质组比较发现,1866Da蛋白表现最优,完全可以作为独立的生物标志物用来鉴别诊断CHB重型。血清11243Da和4210Da蛋白组合应用从CHB患者中间筛选HCC具有实际价值,而从LC患者中间筛选HCC患者意义不大。在所发现的差异蛋白中,1866Da和4210Da蛋白最为重要,需要下一步鉴定。
第四部分差异蛋白质的鉴定
目的:对所发现的1866Da和4210Da蛋白进行鉴定,并初步推测其在乙肝病毒感染过程中的作用机制。
方法:采用液相色谱-串联质谱法(liquid chromatography tandem mass spectrometry, LC-MS/MS)进行氨基酸序列测定,然后进行数据库检索比对从而定性。其中4210Da蛋白鉴定使用数据分析软件BioworksBrowser3.3.1 SP1进行Sequest?检索,检索数据库为IPI Human(3.45);1866Da蛋白使用数据分析软件DataAnalysis3.4. (Bruker Daltonics)进行MS/MS检索,检索数据库为Mascot database。
结果:采用LC-MS/MS方法检测1866Da和4210Da蛋白,并通过网络查询和同源性比较发现,1866Da蛋白被鉴定为DRC3f,为补体C3f脱精氨酸而成,而补体C3是补体系统中的主要成分,其血清含量可以反映人体的免疫状况;4210Da蛋白被鉴定为“GSPT2,真核肽链释放因子GTP结合亚单位(eRF)”,即eRF3b裂解后的一部分—36肽。GSPT2编码eRF3b,具有参与蛋白合成中止的功能。
结论: 1866Da蛋白鉴定为DRC3f,是补体C3的衍生物。4210Da蛋白鉴定为eRF3b裂解后的一部分—36肽。由于补体系统参与人体免疫调节、eRF3b参与调控蛋白合成。因此,C3f和/或DRC3f以及eRF3b的裂解物的出现可能反映了肝脏感染乙肝病毒后炎症反应的某种机制,其表达量的变化很可能与肝脏的炎症改变程度有关,应当是今后进一步研究的重点。
Hepatitis B virus (HBV) infection and its sequelae are now recognized as serious global problems. Worldwide, over 350 million individuals are chronically infected with HBV and 15-25% of them are at risk of developing and dying from HBV-related chronic liver disease, including cirrhosis and hepatocellular carcinoma.
In China, HBV infection is even more serious. The infection rate is almost 57% throughout the country; more than 700 million individuals have been infected, and there are 130 million HBV carriers. At present, there are 23 million chronic hepatitis B patients in China and 230 thousand deaths every year. Each year, the Chinese government had to spend over 50 billion RMB for prevention and treatment. Unfortunately, there have been no effective means of eliminating this problem. As a result, it has been both a health and a national economic disaster.
In view of the situation, it is critically important to find a new method to diagnose and classify hepatitis B accurately, and a new biomarker which can be used in detecting hepatitis B and associated-disease. At present, although there are many testing methods to diagnose acute hepatitis B, chronic hepatitis B and its associated-diseases, they have poor diagnostic efficiency. In this study, our purpose is try to find a new thread, a new method, and new biomarkers which can be used to diagnose hepatitis B and associated-diseases in clinical practice.
Part One: Application of the Tree Structure Model in diagnosing chronic hepatitis B and associated-diseases
Objective: Data mining based on the tree structure model is a procedure to extract concealed, unknown, and potentially useful information and knowledge from massive, incomplete, noisy and fuzzy data. Because hepatitis B and its associated-diseases, for example liver cirrhosis (LC) and hepatocellular carcinoma (HCC), were complicated, changeable and long pathogenesis, so there must be a mass of clinical testing results and multi-dimensional data on every one patient. Although there was lots of valuable information about the disease in the result, but it was seldom used in diagnosing in clinical by doctors. The consequence is that much valuable information about the disease were lost, incorrect diagnosis were made, and patients were not treated properly. In this study, we constructed a tree structure model and try to mine potential information about the disease from patient’s clinical data.
Materials: Totally there were four algorithms in tree structure model construction, include CRT (Classification and Regression Tree), QUEST (Quick, Unbiased, Efficient, Statistical Tree), CHAID (Chi-Squared Automatic Interaction Detection) and EXHAUSTIVE CHAID. In this study, we selected two of these algorithms (CHAID and CRT), as representives to construct tree models which can be used in classifying CHB, LC and HCC. Meanwhile, the tree structure model must be evaluated by risk and accuracy analysis. Its practicability was validated by Principal Component Analysis (PCA).
Result: The two tree structure models made in CHAID and CRT algorithms which be used to classify severe type from mild and moderate type CHB, yielded correct classification rates of 77.5% and 78.4% respectively; risk evaluation were both 0.353. Model’s accuracy was evaluated by ROC (Receiver Operating Characteristic), the area of ROC curve were 0.787 (CI95%: 0.695-0.879) and 0.802 (CI95%: 0.714-0.890) respectively.
The two tree structure models made in CHAID and CRT algorithms which be used to classify LC and HCC from CHB patients, their correct classification rate, risk evaluation, the area of ROC curve were 71.4% and 74.2%,0.451 and 0.352,0.785 (CI 95%: 0.692-0.878) and 0.807 (CI 95%: 0.720-0.894) respectively. The model’s practicability validation was studied with Principal Component Analysis (PCA). Taking chronic hepatitis B as an example, after analysis by PCA, five main components were defined. The first main component (AST, ALT) was as same as the first predictive variable in tree structure model.
Conclusion:Tree structure model is a promising method in data mining and re-utilizing.
Part Two: Standardized approach to proteome profiling of human serum based on magnetic bead separation and MALDI-TOF MS
Objective: Magnetic bead purification for the analysis of proteins in body blood serum facilitates the identification of potential new biomarkers with matrix-assisted laser desorption/ionization time-offlight mass spectrometry (MALDI-TOF MS). The aim of our study was to establish a proteome fractionation technique and to validate a standardized blood sampling, processing, and storage procedure for proteomic pattern analysis.
Materials: Serum sample’s protein spectrum was detected by MALDI-TOF MS after serum samples were purified by IMAC-Cu magnetic bead. In order to evaluate the reproducibility of MALDI-TOF MS at different times, nine proteins in different mass ranges were selected and their CV% was calculated. The influence of freeze-thaw cycles and the ratio of sample to matrix were evaluated by comparison of their mass spectrum. Of course other factors that may be disturbing the test results be optimized. Before large scale patient’s serum testing, two kind of magnetic bead (WCX and IMAC-Cu) were compared about their peak number, peak area and peak intensity of mass spectrum. The best one was selected and used in large scale testing later. During the process, flexControlMS3.0, ClinProToolsTM2.1 and flexAnalysis3.0 software were used respectively, in instrumentation control, data analyzing and mass spectrum collection.
Results: when the ratio of sample to matrix keep at 1:5(ul), laboratory temperature and humidity keep at 20℃-25℃, 10%-30%, the sample crystallize was the best. More freeze-thaw cycles had more influence on mass spectrum, especially in small range proteins, so the samples should be operated within 3 cycles in order to get good results. Reproducibility of MALDI-TOF test in this study was quite satisfactory; its CV% was within 1.26%-30.79%. After comparison, WCX was better than IMAC-Cu magnetic bead.
Conclusion: MALDI-TOF MS as a high-tech method in proteomics analysis, quality control of operating sequence in whole procedure is very important. WCX was better than IMAC-Cu magnetic bead in protein purification and should be selected in large scale testing later.
Part Three: The comparison of serum proteomics of chronic hepatitis B and associated-diseases
Objective: To search biomarkers and evaluate their diagnostic value by comparing different patient’s serum protein with the aim to provide information for clinical use.
Materials: According to the experimental design, serum was collected from patients with 14 acute hepatitis B (AHB), 76 chronic hepatitis B (CHB), 41 liver cirrhosis (LC) and 14 hepatocellular carcinoma (HCC). After separation and purification by magnetic bead WCX, their mass spectrums were detected by MALDI-TOF MS. During the process, flexControlMS3.0, ClinProToolsTM2.1 and flexAnalysis3.0 software were used in instrumentation control, data analysis and mass spectrum collection respectively. The serum protein profiles of different patients were compared and different proteins were searched. In this study, we only selected the top ten significant different proteins in each comparsion and calculated their efficiency for classifying or diagnosing.
Result: 49 distinguished proteins were found by comparison between AHB and CHB, 4154Da and 4210Da were better in diagnosing AHB among them. Combined them together and used in diagnosing AHB, its sensitivity, specificity and correct rate validation were 100% (14/14), 84.21% (64/76) and 86.67% (78/90) respectively. In follow-up study of four AHB patients, 2952Da, 4210Da, 5337Da and 5904Da proteins were quite different in serum expression according to patient’s outcome. Because there was no difference between mild and moderate types of CHB patient’s serum protein, combined them together as one group and compared it with severe type. 1866Da protein was found to be the best one in diagnosing severe type CHB; its sensitivity, specificity and correct rate validation were 100%, 88.89% and 93.42% respectively. After comparing CHB and HCC group, 9 distinguished proteins were found. Among them, 11243Da and 4210Da proteins were better in diagnosing HCC. Combined them together and used in diagnosing HCC, its sensitivity, specificity and correct rate validation were 92.86% (13/14), 77.63% (59/76) and 80% (72/90) respectively. There was only one protein different in comparison between HCC and LC group.
Conclusion: There was significant difference between AHB and CHB patient’s serum protein profile. 4154Da and 4210Da protein can be combined as a biomarker used in diagnosing AHB and should be studied further. In addition, 2952Da, 4210DDa, 5337Da and 5904Da also need study further. 1866Da can be used as a biomarker in classifying severe type among CHB patients. 11243Da and 4210Da protein are valuable in diagnosing HCC from CHB, exhibiting significantly more diagnostic value than LC.
Part Four: Identification of the distinguished proteins
Objective: To identify 4210Da and 1866Da protein, analyze their mechanism of action in disease development after virus B infected.
Materials: Liquid chromatography tandem mass spectrometry (LC-MS/MS) was used in protein identification. The data were analyzed by BioworksBrowser3.3.1SP1 and DataAnalysis3.4, searched from IPI Human (3.45) and Mascot database.
Result: Considering the result in part three, 4154Da, 4210Da, 2952Da 5337Da, 5904Da and 1866Da proteins were valuable in diagnosing virus B infection and associated disease; among them, 4210Da and 1866Da were most important. 1866Da and 4210Da protein were identified as“DRC3f”and“Eukaryotic peptide chain release factor GTP-binding subunit ERF”, respectively.
Conclusion: 1866Da was identified as“DRC3f”which was a derivant from complement C3. The expression of C3f or DRC3f in serum maybe reflected some kind of mechanism of liver inflammatory reaction. 4210Da protein was identified as“Eukaryotic peptide chain release factor GTP-binding subunit ERF”, a portion of eRF3b (36 peptides). The expression of 4210Da in serum may also reflect some kind of mechanism of liver infection. Complement system is a very important part in human immunity and ERF plays an important roal in participate protein synthesis. Although the mechanism of the two proteins work in liver inflammatory reaction still unknow, but showed a significant potential value in clinical diagnosing as biomarkers.
第一部分决策树模型在乙型病毒性肝炎及相关疾病诊断中的应用
目的:用决策树模型对临床常用检测数据进行二次发掘利用,是一种有效的降维数理统计方法。由于乙型肝炎及相关疾病如肝硬化、肝癌等病情复杂多变、病程长,因此在临床上将会产生大量、多维的检测数据。尽管在这些数据中蕴含了丰富的疾病信息,但临床医生往往孤立地看待每一个检测结果,很难把它们综合利用及分析,更谈不上对这些数据进行深加工利用,其结果就是疾病信息的丢失和浪费,并最终导致临床诊断的不准确、治疗的不及时和没有针对性、直至延误病情。本部分将以慢性乙肝与肝硬化及肝癌鉴别比较、慢性乙肝轻、中、重不同型别的鉴别比较为例,用决策树模型的方法将临床常用数据进行再挖掘,为临床决策提供参考。
方法:目前构建决策树模型主要有四种算法,即CRT(Classification and Regression Tree,分类与回归树)、QUEST(Quick, Unbiased, Efficient, Statistical Tree,快速无偏高效统计树)、CHAID(Chi-Squared Automatic Interaction Detection ,卡方自动交互探测)和EXHAUSTIVE CHAID(穷举CHILD)。前两种算法为一类,构建的树模型称二叉树;后两种算法为一类,构建的树模型称多叉树。研究中,在两类算法中各选择一种算法作为代表,即CHAID和CRT,对慢性乙肝与肝硬化及肝癌,慢性乙肝轻、中、重不同型别分类构建决策树模型并进行风险和准确度评估。同时以慢性乙肝分型为例用主成分分析方法加以验证,考察模型的实用性。
结果:利用CHAID和CRT两种算法用于慢性乙肝轻型和中重型分类诊断时,所构建的两个模型的正确分类率分别达到77.5%和78.4%,模型的拟合效果良好,风险评估结果均为0.353,在可接受范围内。ROC(Receiver Operating Characteristic,受试者工作特征)曲线下面积分别为0.787(CI 95%:0.695–0.879)和0.802(CI 95%:0.714–0.890),准确度评估良好;而利用CHAID和CRT两种算法用于慢性乙型肝炎与肝硬化及肝癌分类诊断时,所构建的两个模型的正确分类率分别达到71.4%和74.2%,模型的拟合效果良好,风险评估结果分别为0.451和0.352,虽在可接受范围内,但CHAID模型风险稍高。ROC曲线下面积分别为0.785(CI 95%:0.692–0.878)和0.807(CI95%:0.720–0.894),准确度评估良好。
为了验证模型的实用性,以慢性乙肝轻、中、重判别为例采用主成分分析方法进行考察分析。最终确定五个主要成分,第一主成分:ALT、AST、总胆红素三个指标;第二主成分:抗-HBe指标;第三主成分:PT、HBsAg指标;第四主成分:HBV定量、HBe-Ag、抗-HBs、A/G比值四个指标;第五主成分:抗-HBc指标。在进行慢性乙肝病变程度判定时,决策数模型的第一预测变量和主成分分析的第一主成分都是反映肝功能的ALT和AST指标,结果高度一致。
结论:决策树模型在对临床常见数据的挖掘再利用方面具有一定价值,可以大大提高诊断的准确度,为临床医生决策提供帮助。而且此结论已经得到主成分分析的验证,两者有极好的吻合,较为可信。在进行慢性乙肝中重型诊断时,应优先考虑ALT、AST和乙肝病毒载量等指标;而试图从慢性乙肝患者中筛检肝硬化或肝癌时,年龄、职业和胆红素指标更为重要。肝纤四项(HA、LN、PCⅢ和Ⅳ-C)未进入决策树模型,表明该四项指标对判定肝纤维化程度价值不大。
第二部分基于磁珠样品制备和基质辅助激光解析离子化飞行时间质谱实验条件的优化
目的:利用基质辅助激光解吸离子化飞行时间质谱(matrix-assisted laser desorption/ionization time-of flight mass spectrometry,MALDI-TOF MS)技术检测样本血清蛋白或多肽,考察实验的重复性及血清不同的冻融次数和基质与样品搭配比例对结果的影响。以便摸索一套有效的血清样品收集、处理、存储和实验方法。同时比较分析两种磁珠的优劣,为后续大规模检测研究提供科学的选择依据。
方法:首先采用IMAC-Cu磁珠进行样本蛋白分离提纯,经MALDI-TOF MS检测后,选择不同分子量范围内9个蛋白重复测定,比较组间和组内变异系数以考察重复性及实验室条件的影响;比较不同条件下蛋白质谱图来判断冻融次数和基质与样品搭配比例对实验结果的影响。然后比较WCX和IMAC-Cu两种磁珠的平均出峰量、平均峰面积和平均峰强度等参数,择优为后续研究做准备。研究过程中仪器操作、数据分析和图像采集分别用flexControlMS3.0、ClinProToolsTM2.1和flexAnalysis 3.0软件。
结果:研究发现,样品与基质的适宜比例为1:5(ul),并且实验室温度控制在20℃～25℃,湿度在10%～30%时点样结晶效果最佳。样品的冻融最好不要超过3次,否则对质谱出峰有影响。冻融次数越多,对小分子量蛋白或多肽的影响比大分子量蛋白或多肽影响大。反映实验结果重复性的变异系数为1.26%～30.79%,在可接受范围内。相同样本利用WCX磁珠处理后的平均出峰量和平均峰面积均明显优于IMAC-Cu磁珠。
结论:MALDI-TOF MS作为一种高灵敏度的研究平台,严格操作程序可以减少变异,提高结果的重复性和可信性。WCX磁珠的蛋白分离效果优于IMAC-Cu磁珠,选择用于后续大规模检测。
第三部分乙型肝炎及相关疾病血清蛋白质组的比较分析
目的:检测急性乙肝(acute hepatitis B, AHB)、慢性乙肝(chronic hepatitis B, CHB)、肝硬化(liver cirrhosis, LC)和原发性肝细胞癌(hepaticcell carcinoma, HCC)患者血清蛋白质谱图,比较组间差异,寻找差异蛋白并评估其分类价值,为临床诊断应用提供参考。
方法:首先按照实验设计要求,收集14例AHB、76例CHB、41例LC和14例HCC患者的血清样本,用WCX磁珠进行样本前处理,然后用MALDI-TOF MS检测不同组患者蛋白质谱图并加以对比分析。研究过程中仪器操作、数据分析和图像采集分别用flexControlMS3.0、ClinProToolsTM2.1和flexAnalysis 3.0软件。对所发现的差异蛋白分别计算分类的灵敏度、特异度和验证正确率,或差异蛋白组合后诊断的效果,以指导临床应用。此外追踪观察4例AHB患者血清蛋白质谱图变化,寻找判断急性乙肝转慢的生物标志物。
结果:AHB和CHB患者血清蛋白质组比较,在所发现的49个差异蛋白中,仅4154Da和4210Da分类效果相对较优,联合作为生物标志物应用于诊断AHB的灵敏度、特异度、验证正确率分别达到100%(14/14)、84.21%(64/76)和86.67%(78/90)。对4例AHB患者追踪观察发现,血清2952Da、4210Da、5337Da和5904Da蛋白表达量因不同转归而表现迥异。CHB轻、中、重型血清蛋白质组研究发现,轻型和中型无差异而合并为一组,然后与CHB重型比较。差异蛋白中1866Da蛋白表现最优,单独用来区分慢性重型乙肝的灵敏度和特异度达到100%和88.89%,验证正确率为93.42%,是一个良好的生物标志物,值得进一步研究。CHB患者和HCC患者血清蛋白质组的对比,11243Da和4210Da两个蛋白分类效果相对较好,联合应用诊断HCC的灵敏度、特异度和验证正确率分别达到92.86%(13/14)、77.63%(59/76)和80%(72/90)。HCC和LC患者血清蛋白质组差异不大,仅一个差异蛋白。
结论:AHB和CHB患者血清蛋白质组存在明显差异,其中4154Da和4210Da两个蛋白组合使用可以有效鉴别AHB,应当成为今后分类诊断的候选蛋白。AHB患者因不同转归而表现迥异的蛋白2952Da、4210Da、5337Da和5904Da是今后进一步研究的重点。CHB轻、中、重型血清蛋白质组比较发现,1866Da蛋白表现最优,完全可以作为独立的生物标志物用来鉴别诊断CHB重型。血清11243Da和4210Da蛋白组合应用从CHB患者中间筛选HCC具有实际价值,而从LC患者中间筛选HCC患者意义不大。在所发现的差异蛋白中,1866Da和4210Da蛋白最为重要,需要下一步鉴定。
第四部分差异蛋白质的鉴定
目的:对所发现的1866Da和4210Da蛋白进行鉴定,并初步推测其在乙肝病毒感染过程中的作用机制。
方法:采用液相色谱-串联质谱法(liquid chromatography tandem mass spectrometry, LC-MS/MS)进行氨基酸序列测定,然后进行数据库检索比对从而定性。其中4210Da蛋白鉴定使用数据分析软件BioworksBrowser3.3.1 SP1进行Sequest?检索,检索数据库为IPI Human(3.45);1866Da蛋白使用数据分析软件DataAnalysis3.4. (Bruker Daltonics)进行MS/MS检索,检索数据库为Mascot database。
结果:采用LC-MS/MS方法检测1866Da和4210Da蛋白,并通过网络查询和同源性比较发现,1866Da蛋白被鉴定为DRC3f,为补体C3f脱精氨酸而成,而补体C3是补体系统中的主要成分,其血清含量可以反映人体的免疫状况;4210Da蛋白被鉴定为“GSPT2,真核肽链释放因子GTP结合亚单位(eRF)”,即eRF3b裂解后的一部分—36肽。GSPT2编码eRF3b,具有参与蛋白合成中止的功能。
结论: 1866Da蛋白鉴定为DRC3f,是补体C3的衍生物。4210Da蛋白鉴定为eRF3b裂解后的一部分—36肽。由于补体系统参与人体免疫调节、eRF3b参与调控蛋白合成。因此,C3f和/或DRC3f以及eRF3b的裂解物的出现可能反映了肝脏感染乙肝病毒后炎症反应的某种机制,其表达量的变化很可能与肝脏的炎症改变程度有关,应当是今后进一步研究的重点。
Hepatitis B virus (HBV) infection and its sequelae are now recognized as serious global problems. Worldwide, over 350 million individuals are chronically infected with HBV and 15-25% of them are at risk of developing and dying from HBV-related chronic liver disease, including cirrhosis and hepatocellular carcinoma.
In China, HBV infection is even more serious. The infection rate is almost 57% throughout the country; more than 700 million individuals have been infected, and there are 130 million HBV carriers. At present, there are 23 million chronic hepatitis B patients in China and 230 thousand deaths every year. Each year, the Chinese government had to spend over 50 billion RMB for prevention and treatment. Unfortunately, there have been no effective means of eliminating this problem. As a result, it has been both a health and a national economic disaster.
In view of the situation, it is critically important to find a new method to diagnose and classify hepatitis B accurately, and a new biomarker which can be used in detecting hepatitis B and associated-disease. At present, although there are many testing methods to diagnose acute hepatitis B, chronic hepatitis B and its associated-diseases, they have poor diagnostic efficiency. In this study, our purpose is try to find a new thread, a new method, and new biomarkers which can be used to diagnose hepatitis B and associated-diseases in clinical practice.
Part One: Application of the Tree Structure Model in diagnosing chronic hepatitis B and associated-diseases
Objective: Data mining based on the tree structure model is a procedure to extract concealed, unknown, and potentially useful information and knowledge from massive, incomplete, noisy and fuzzy data. Because hepatitis B and its associated-diseases, for example liver cirrhosis (LC) and hepatocellular carcinoma (HCC), were complicated, changeable and long pathogenesis, so there must be a mass of clinical testing results and multi-dimensional data on every one patient. Although there was lots of valuable information about the disease in the result, but it was seldom used in diagnosing in clinical by doctors. The consequence is that much valuable information about the disease were lost, incorrect diagnosis were made, and patients were not treated properly. In this study, we constructed a tree structure model and try to mine potential information about the disease from patient’s clinical data.
Materials: Totally there were four algorithms in tree structure model construction, include CRT (Classification and Regression Tree), QUEST (Quick, Unbiased, Efficient, Statistical Tree), CHAID (Chi-Squared Automatic Interaction Detection) and EXHAUSTIVE CHAID. In this study, we selected two of these algorithms (CHAID and CRT), as representives to construct tree models which can be used in classifying CHB, LC and HCC. Meanwhile, the tree structure model must be evaluated by risk and accuracy analysis. Its practicability was validated by Principal Component Analysis (PCA).
Result: The two tree structure models made in CHAID and CRT algorithms which be used to classify severe type from mild and moderate type CHB, yielded correct classification rates of 77.5% and 78.4% respectively; risk evaluation were both 0.353. Model’s accuracy was evaluated by ROC (Receiver Operating Characteristic), the area of ROC curve were 0.787 (CI95%: 0.695-0.879) and 0.802 (CI95%: 0.714-0.890) respectively.
The two tree structure models made in CHAID and CRT algorithms which be used to classify LC and HCC from CHB patients, their correct classification rate, risk evaluation, the area of ROC curve were 71.4% and 74.2%,0.451 and 0.352,0.785 (CI 95%: 0.692-0.878) and 0.807 (CI 95%: 0.720-0.894) respectively. The model’s practicability validation was studied with Principal Component Analysis (PCA). Taking chronic hepatitis B as an example, after analysis by PCA, five main components were defined. The first main component (AST, ALT) was as same as the first predictive variable in tree structure model.
Conclusion:Tree structure model is a promising method in data mining and re-utilizing.
Part Two: Standardized approach to proteome profiling of human serum based on magnetic bead separation and MALDI-TOF MS
Objective: Magnetic bead purification for the analysis of proteins in body blood serum facilitates the identification of potential new biomarkers with matrix-assisted laser desorption/ionization time-offlight mass spectrometry (MALDI-TOF MS). The aim of our study was to establish a proteome fractionation technique and to validate a standardized blood sampling, processing, and storage procedure for proteomic pattern analysis.
Materials: Serum sample’s protein spectrum was detected by MALDI-TOF MS after serum samples were purified by IMAC-Cu magnetic bead. In order to evaluate the reproducibility of MALDI-TOF MS at different times, nine proteins in different mass ranges were selected and their CV% was calculated. The influence of freeze-thaw cycles and the ratio of sample to matrix were evaluated by comparison of their mass spectrum. Of course other factors that may be disturbing the test results be optimized. Before large scale patient’s serum testing, two kind of magnetic bead (WCX and IMAC-Cu) were compared about their peak number, peak area and peak intensity of mass spectrum. The best one was selected and used in large scale testing later. During the process, flexControlMS3.0, ClinProToolsTM2.1 and flexAnalysis3.0 software were used respectively, in instrumentation control, data analyzing and mass spectrum collection.
Results: when the ratio of sample to matrix keep at 1:5(ul), laboratory temperature and humidity keep at 20℃-25℃, 10%-30%, the sample crystallize was the best. More freeze-thaw cycles had more influence on mass spectrum, especially in small range proteins, so the samples should be operated within 3 cycles in order to get good results. Reproducibility of MALDI-TOF test in this study was quite satisfactory; its CV% was within 1.26%-30.79%. After comparison, WCX was better than IMAC-Cu magnetic bead.
Conclusion: MALDI-TOF MS as a high-tech method in proteomics analysis, quality control of operating sequence in whole procedure is very important. WCX was better than IMAC-Cu magnetic bead in protein purification and should be selected in large scale testing later.
Part Three: The comparison of serum proteomics of chronic hepatitis B and associated-diseases
Objective: To search biomarkers and evaluate their diagnostic value by comparing different patient’s serum protein with the aim to provide information for clinical use.
Materials: According to the experimental design, serum was collected from patients with 14 acute hepatitis B (AHB), 76 chronic hepatitis B (CHB), 41 liver cirrhosis (LC) and 14 hepatocellular carcinoma (HCC). After separation and purification by magnetic bead WCX, their mass spectrums were detected by MALDI-TOF MS. During the process, flexControlMS3.0, ClinProToolsTM2.1 and flexAnalysis3.0 software were used in instrumentation control, data analysis and mass spectrum collection respectively. The serum protein profiles of different patients were compared and different proteins were searched. In this study, we only selected the top ten significant different proteins in each comparsion and calculated their efficiency for classifying or diagnosing.
Result: 49 distinguished proteins were found by comparison between AHB and CHB, 4154Da and 4210Da were better in diagnosing AHB among them. Combined them together and used in diagnosing AHB, its sensitivity, specificity and correct rate validation were 100% (14/14), 84.21% (64/76) and 86.67% (78/90) respectively. In follow-up study of four AHB patients, 2952Da, 4210Da, 5337Da and 5904Da proteins were quite different in serum expression according to patient’s outcome. Because there was no difference between mild and moderate types of CHB patient’s serum protein, combined them together as one group and compared it with severe type. 1866Da protein was found to be the best one in diagnosing severe type CHB; its sensitivity, specificity and correct rate validation were 100%, 88.89% and 93.42% respectively. After comparing CHB and HCC group, 9 distinguished proteins were found. Among them, 11243Da and 4210Da proteins were better in diagnosing HCC. Combined them together and used in diagnosing HCC, its sensitivity, specificity and correct rate validation were 92.86% (13/14), 77.63% (59/76) and 80% (72/90) respectively. There was only one protein different in comparison between HCC and LC group.
Conclusion: There was significant difference between AHB and CHB patient’s serum protein profile. 4154Da and 4210Da protein can be combined as a biomarker used in diagnosing AHB and should be studied further. In addition, 2952Da, 4210DDa, 5337Da and 5904Da also need study further. 1866Da can be used as a biomarker in classifying severe type among CHB patients. 11243Da and 4210Da protein are valuable in diagnosing HCC from CHB, exhibiting significantly more diagnostic value than LC.
Part Four: Identification of the distinguished proteins
Objective: To identify 4210Da and 1866Da protein, analyze their mechanism of action in disease development after virus B infected.
Materials: Liquid chromatography tandem mass spectrometry (LC-MS/MS) was used in protein identification. The data were analyzed by BioworksBrowser3.3.1SP1 and DataAnalysis3.4, searched from IPI Human (3.45) and Mascot database.
Result: Considering the result in part three, 4154Da, 4210Da, 2952Da 5337Da, 5904Da and 1866Da proteins were valuable in diagnosing virus B infection and associated disease; among them, 4210Da and 1866Da were most important. 1866Da and 4210Da protein were identified as“DRC3f”and“Eukaryotic peptide chain release factor GTP-binding subunit ERF”, respectively.
Conclusion: 1866Da was identified as“DRC3f”which was a derivant from complement C3. The expression of C3f or DRC3f in serum maybe reflected some kind of mechanism of liver inflammatory reaction. 4210Da protein was identified as“Eukaryotic peptide chain release factor GTP-binding subunit ERF”, a portion of eRF3b (36 peptides). The expression of 4210Da in serum may also reflect some kind of mechanism of liver infection. Complement system is a very important part in human immunity and ERF plays an important roal in participate protein synthesis. Although the mechanism of the two proteins work in liver inflammatory reaction still unknow, but showed a significant potential value in clinical diagnosing as biomarkers.
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