1.建立基于富集指数的蛋白质组学数据分析方法研究分泌蛋白质组—小鼠树突状肉瘤细胞分泌蛋白质组 2.正常人尿蛋白质组动态变化过程中稳定表达蛋白质组作为潜在的生物标志物资源

【作者】 陈勇；

【导师】 高友鹤；

【作者基本信息】 中国协和医科大学 ， 病理生理学， 2007， 博士

【摘要】 细胞产生的分泌蛋白是一类功能上非常重要的蛋白。真正的分泌蛋白样品是很难得到,它经常被细胞内蛋白质或环境中的蛋白质所污染。我们建立了一种依靠蛋白质组学数据分析判断分泌蛋白质样品是否合格及分辨真正的分泌蛋白质的方法。阳性鉴定的谱图数用于蛋白质半定量。分别对分泌蛋白质样品和胞内可溶性蛋白质样品进行质谱分析,选择高丰度的分泌蛋白质作为标志蛋白,求它们在两个样品中谱图数的百分比,计算样品富集指数(theenrichment index of sample,EIS),以此评价分泌蛋白质样品的质量。用合格谱图数的百分比计算每个蛋白质在两个样品中的权重,定义分泌蛋白富集指数(theenrichment index for protein,EIP)为:在两种样品中相应蛋白的权重比值。用EIP值鉴定分泌蛋白质,同时参考信号肽预测和swiss-prot解释。新方法能够鉴定“非经典分泌途径”产生的分泌蛋白,也可以排除来源于死细胞的带有信号肽的污染蛋白。谱图数计量方法的优点在于对设备测试要求不高、没有同位素污染和使用方便。对于无血清条件培养基中体外培养的小鼠树突状肉瘤细胞(dendritic cellsarcoma,DCS),采用SCX-RP-MS/MS策略分析了培养基中的DCS产生的分泌蛋白质组。两个分泌蛋白质样品的EIS分别为75.4和84.65。共得到141个阳性鉴定的蛋白,73个认为是分泌蛋白质。33个蛋白质是信号肽预测和swiss-prot解释均为阳性的,10个蛋白质是信号肽预测阳性或swiss-prot解释阳性的,30个蛋白质是信号肽预测和swiss-prot解释均为阴性的,还有待于进一步验证。对于新发现的30个潜在的、swiss-prot解释和信号肽预测都是阴性的蛋白质,我们用“非经典途径”分泌蛋白质的预测服务器(SecretomeP server和ProP server)进行了分析,其中分别有12个蛋白质(占40%)和15个蛋白质(占50%)的蛋白质是阳性鉴定,其中有5个蛋白质是两种方法都被鉴定的。我们还和其他细胞产生的exosome的结果进行了比较,有11个蛋白质在exosome中存在。相比小鼠树突状细胞DC1系产生的exosome的结果,有6个蛋白质是相同的。这些数据证明,这30个蛋白质也很有可能是DCS细胞产生的分泌蛋白质。尿液作为人体的一种正常体液,其中包含的蛋白质成分反映了肾脏及泌尿生殖道的生理和病理状态。尿蛋白质组研究已经取得了很多成果。但是,尿蛋白质组的动态研究还未很好地开展,尚不能得到不同尿样和不同时刻尿的蛋白质组数据。因此在不同尿样的比较分析中,尤其是正常尿液和病理尿液的比较中,很难断定差异蛋白质源于尿样本身,还是尿蛋白质的正常变化。本文用1DLC策略和谱图数半定量的方法分析了正常人汇集尿样和个体尿样中蛋白质组的动态变化。五种汇集尿样是由六名健康的志愿者(三男三女)提供,分别收集了志愿者即日内的五种尿样,即:晨尿、清晨第二次尿、水利尿、随机尿和24小时尿,汇集后进行分析。另外,收集志愿者第0天和第7天的晨尿,用于研究跨天、个体间、性别间的尿差异。分析了汇集尿样分析的利弊。研究表明,尿蛋白质组在不同种类尿样中,50%以上的蛋白质可以被共同鉴定,其中40%以上的蛋白质的谱图数变化小于2倍。基于六名志愿者的小样本数据,同一个体跨天的晨尿之间差异小于同性别不同个体间尿蛋白质组差异,异性个体之间的稳定性最差。其中部分在尿中稳定表达的蛋白质被报道与疾病相关。我们提供了不同条件下,尿样中稳定存在的蛋白质成分,在这些蛋白质中,任何定性和定量的显著性改变,都很有可能成为潜在的疾病诊断的生物标志物。更多还原

【Abstract】 Secretory proteins are important for organisms to maintain the normal biological processes. They are important sources for discovering the candidate tumour biomarkers with clinical significance. However, secretory protein samples are usually polluted by cytosolic proteins from dead cells or other proteins from the environment.We propose that a good secretory protein sample should be enriched with known secretory proteins, and a secretory protein should be enriched in the secretory protein sample compare to soluble cell lysate. A method based on enrichment index for both quality control of the sample and identification of secretory proteins was developed. Positive identifications of the proteins were subjected to quantification by spectral count. Enrichment index of the sample (EIS) and the enrichment index for protein (EIP) were set up by comparing proteins identified from secretory protein sample and soluble cell lysate sample. The quality of secretory protein sample can be evaluated with EIS. EIP was used to identify the secretory proteins.This method can identify novel secretory proteins from unconventional pathways. It can also exclude the contaminating proteins with signal peptide from dead cells. The method is simple to perform. No complicated procedures were used other than common LC-MS/MS. There was no isotopes pollution in whole operation.We analyzed the secretory proteins of mouse dendritic cell sarcoma (DCS) cell line cultured in protein-free media with insulin. The sample’s EISs were 75.4 and 84.65. 141 proteins were detected, of which 73 proteins were significantly enriched and identified as secretory proteins in this study. In these 73 proteins, 33 proteins were both swiss-prot annotated and signal peptide predicted as secretory proteins. 10 proteins were identified by either signal peptide prediction or swiss-prot annotation, 30 proteins were previously unidentified as secretory proteins.The 30 novel potential secretory proteins were analyzed by SecretomeP server and ProP server which can predict the mammalian secretory protein targeted to the non-classical secretory pathway. 11 (36.7%) and 15 (50%) were predicted as secretory proteins by SecretomeP and ProP, respectively. 5 proteins were positive by both methods. Compared with the protein component of exosome in other studies, 11 proteins were in exosome in other studies. Six proteins were found in exosome from mice dendritic cells line DC1. These references provided supporting evidences for these 30 proteins as secretory proteins. Human urinary proteome analysis was a convenient approach to clarify the disease processes affecting the kidney and the urogenital tract. Many potential biomarkers have been identified by differential urinary proteome analyses. However, the urinary proteome variation has not been very well studied; it is hard to conclude whether those potential biomarkers come from differences of biological states studied or just from normal proteome variation.In this paper 1DLC/MS and spectral count as the semi-quantitative analysis were used to study human urinary proteome variation by pooled and individual urine samples. Five types of pooled urinary samples (first morning void, second morning void, excessive water drinking void, random void and 24 hour void) collected in one day from six volunteers (three males and three females) were used to analyze the urinary proteome overall intra-day variations. Six pair first morning urine collected on day 0 and day 7 from above six volunteers were utilized to study inter-day, inter-individual and inter-gender variations. We also discussed the advantages and disadvantages of pooling the samples.The intra-day, inter-day, inter-individual, inter-gender variation results showed 50% of proteins were found in various samples and more than 40% of these proteins whose spectral count variation was less than two times. Based on the proteomics data of urine from six volunteers, the intra-day variation is less than the inter-individual variation. Inter-gender stability is the worst. Some of them have been reported as potential disease biomarkers. The data presented herein should provide a pool of stable urinary proteins at different conditions. Any significant qualitative and quantitative changes of those stable proteins may have greater chances to serve as potential urinary biomarkers.更多还原