生物信息学及生物信息查询与应用

本文介绍了基因组学与蛋白质组学和生物信息学的基本内容,列举了主要核酸、蛋白质数据库及其查询软件并说明如何利用查询工具对生物信息资源进行查询和对核酸蛋白质进行分析.     

无标记定量蛋白质组学分析AMACR过表达对肝癌细胞生物学行为的影响

研究α-甲酰基辅酶A消旋酶(AMACR)过表达对肝癌细胞生物学行为的影响及其分子机制.首先建立AMACR稳定过表达细胞株,然后提取AMACR过表达细胞株的全蛋白,进行无标记定量蛋白质组学研究,最后对鉴定结果进行生物信息学分析和结果验证,共筛选出138种差异表达蛋白.这些差异表达蛋白主要参与代谢加工、细胞加工等,证明AMACR的过表达对于肝癌细胞的生物学行为影响巨大.IPA分析发现,这些差异表达蛋白主要参与了ERK1/2信号通路和NF-κB信号通路.以上结果说明,AMACR的过表达通过调节ERK1/2和NF-κB信号通路等手段改变肝癌细胞的生物学行为.     

Sequential proteome alterations during genesis and progression of colon cancer

Changes in the proteome of colon mucosal cells accompany the transition from normal mucosa via adenoma and invasive cancer to metastatic disease. Samples from 15 patients with sporadic sigmoid cancers were analyzed. Proteins were separated by two-dimensional gel electrophoresis. Relative differences in expression levels between normal tissue, adenoma, carcinoma and metastasis were evaluated in both intra- and inter-patient comparisons. Up- and down-regulated proteins (<twofold) during development to cancer or metastasis were excised and submitted to peptide mass fingerprinting and MS/MS sequence analysis, facilitated by the use of a compact disc workstation. In total, 112 protein spots were found to be differentially regulated, of which 72 were determined as to protein identity, 46 being up-regulated toward the progression of cancer, and 26 down-regulated. Several of the identifications correlate with proteins of the cell cycle, cytoskeleton or metabolic pathways. The pattern changes now identified have the potential for design of marker panels for assistance in diagnostics and therapeutic strategies in colorectal cancer.Received 2 February 2004; received after revision 16 March 2004; accepted 18 March 2004     

蛋白质组学用于疾病特殊蛋白质的鉴定

蛋白质组学是一门新的启动技术.它将便利越过一切生物学系统或疾病的蛋白质系统分析.在疾病特殊蛋白质的鉴别过程蛋白质组学高度补充基因组方法,且第一次提供科学家使蛋白质组信息,表达mRNA,它们各自的蛋白质和亚细胞定位一体化的能力.期望会导致对疾病机制新的重大认识.     

蛋白质组学及其在布鲁氏菌中的研究进展

蛋白质组学研究蛋白质组成及其活动规律,是对不同时间和空间发挥功能的特定蛋白质群体的研究.本文介绍了蛋白质组学基本概念、研究技术及其在布鲁氏菌致病机理、毒力因子、免疫靶点上的研究与应用.     

蛋白质组学的研究方法

蛋白质组学是后基因组时代出现的一个新兴研究领域,具有巨大的商业应用前景,将会推动整个生命科学的发展．本文对蛋白质组学的定义和主要使用的研究方法作一简要介绍．     

Proteomic comparison of four maize inbred lines with different levels of resistance to Curvularia lunata （Wakker） Boed infection

Protein profiles of leaves in four maize inbred lines with different disease resistance to pathogen Curvularia lunata(Wakker)Boed were studied by two-dimensional electrophoresis(2-DE)and mass spectrometry.Proteins were extracted from the forth leaf of maize seedlings 24 h after fungal inoculation,and fractionated by polyethylene glycol to precipitate the most abundant leaf protein,Rubisco,before gel separation.Protein profiles from 2-DE showed that total numbers of protein spots were increased in all four inbred lines inoculated with C.lunata CX-3 strain compared with the control.The numbers of changed protein spots in abundance were higher in resistant inbred lines than in susceptible ones,which implied that resistant inbred lines were more sensitive than susceptible ones to pathogen infection.Among proteins identified by MALDI-TOF MS,germin-like protein GLP and translation initiation factor eIF-5A were supposed to play important roles in maize resistance against C.lunata infection.     

Proteomics study of the effects of high pigment-1 on plastid differentiation during the ripening of tomato fruits

The chloroplast-to-chromoplast difference is a highly regulated process. Tomato （Solanum lycopersicum） high-pigment mutant hp-1 shows an enhanced accumula- tion of carotenoids coupled with an increase in the volume and number of plastids. However, how chromoplasts of hp-] acquire their specific metabolic characteristics is still unclear. A comparison of proteome profiles from plastids at the mature green, breaker, and red stages of tomato fruits showed 45 differentially expressed proteins. These identi- fied proteins fell into six different functional categories. Our results showed that most of the proteins related to the Calvin cycle increased transitorily only at the early breaker stage, and remained unchanged at the early red stage. We found 18 proteins that were differentially expressed between the wild type and hp-1. The abundance of GCPE in chromoplasts of hp-1 was higher than that in the wild type. Meanwhile, we found that heat shock proteins were only present specifically in the mature green stage chromoplasts of hp-1 but not in the wild type. This suggested that GCPE and heat shock proteins might play important roles in the accumulation of high carotenoids in hp-1. Overall, our results could be helpful for understanding chloroplast-to-chromoplast differentiation and the function of hp-1.     

小麦灌浆期籽粒粒重差异的代谢蛋白质组学初步研究

高粒重（48号）和低粒重（154号）小麦株系是01-35（大粒）和6044（小粒、多穗）的稳定杂交后代,具有相似的遗传背景.取灌浆期（开花后17天）的小麦籽粒,提取代谢蛋白,双向电泳后,进行图谱分析.结果显示：两品系中分别鉴定了380和367个蛋白点,其中52个蛋白点为两品系差异显著性表达.将其中13个差异点采用基质辅助激光解析电离飞行时间质谱（matrix assisted laser desorption/ionizaton time of flight mass spectrometry,MALDI-TOF-MS）进行了肽质指纹图分析,通过采用Mascot软件对MSDB数据库查询,其中9个蛋白质点得到了鉴定,包括葡聚糖合成酶、葡萄糖-1-磷酸-腺苷转移酶小亚基前体、一种胚胎晚期富集蛋白等.本研究初步建立了小麦籽粒灌浆其代谢蛋白蛋白质组分析的双向电泳（2-DE）方法,通过质谱分析、数据库搜索的结果对粒重形成的机制进行了探讨,并对以后的研究工作提出了改进方案.     

A data-mining approach to biomarker identification from protein profiles using discrete stationary wavelet transform

Objective： To develop a new bioinformatic tool based on a data-mining approach for extraction of the most informative proteins that could be used to find the potential biomarkers for the detection of cancer. Methods： Two independent datasets from serum samples of 253 ovarian cancer and 167 breast cancer patients were used. The samples were examined by surfaceenhanced laser desorption/ionization time-of-flight mass spectrometry （SELDI-TOF MS）. The datasets were used to extract the informative proteins using a data-mining method in the discrete stationary wavelet transform domain. As a dimensionality reduction procedure, the hard thresholding method was applied to reduce the number of wavelet coefficients. Also, a distance measure was used to select the most discriminative coefficients. To find the potential biomarkers using the selected wavelet coefficients, we applied the inverse discrete stationary wavelet transform combined with a two-sided t-test. Results： From the ovarian cancer dataset, a set of five proteins were detected as potential biomarkers that could be used to identify the cancer patients from the healthy cases with accuracy, sensitivity, and specificity of 100%. Also, from the breast cancer dataset, a set of eight proteins were found as the potential biomarkers that could separate the healthy cases from the cancer patients with accuracy of 98.26%, sensitivity of 100%, and specificity of 95.6%. Conclusion： The results have shown that the new bioinformatic tool can be used in combination with the high-throughput proteomic data such as SELDI-TOF MS to find the potential biomarkers with high discriminative power.