非模式植物蛋白质组学研究进展

蛋白质组学研究是对基因组学研究的重要补充,它是在蛋白质水平定量、动态、整体性研究生物体。该文简要介绍了蛋白质组学的含义,蛋白质组学及植物蛋白质组学产生的科学背景,蛋白质组学的研究内容。概述了非模式植物蛋白质组学的研究进展,主要包括非模式植物个体及群体蛋白质组学,组织和器官蛋白质组学,亚细胞蛋白质组学,响应环境变化的蛋白质组学以及非模式植物生物环境因子的蛋白质组学的研究情况,同时对植物蛋白质组学的发展前景进行了展望。     

蛋白质组学分析技术在微生物研究中的应用

随着后基因组时代的到来,蛋白质组学分析为研究微生物的生命活动和细胞功能提供了一个广阔的视角。综述了大规模分析微生物蛋白质组的策略和方法,包括自上而下的蛋白质组学分析、自下而上的蛋白质组学分析、蛋白质组定量分析技术、蛋白质修饰研究方法和蛋白质芯片技术。最后,对沙门氏菌蛋白质组学的研究进展进行了简要介绍。     

葡萄蛋白质组学研究进展

人类基因组计划的完成标志着生命科学已进入后基因组时代,蛋白质组学的研究被提升到了前所未有的高度,蛋白质组学旨在阐明基因组所表达的真正执行生命活动的全部蛋白质的表达规律和生物功能。伴随葡萄基因组测序工作的完成,有关葡萄蛋白质组学的研究迅速发展。对近年来蛋白质组学在葡萄上的研究进行了综述,内容主要包括:葡萄蛋白质样品的提取制备,葡萄果实发育和品质形成过程中蛋白质组的变化,葡萄果皮、细胞壁、质膜等特定组织材料的蛋白质组研究,及蛋白质组学在葡萄逆境胁迫、体细胞胚的发生等方面的研究,并对葡萄蛋白质组学的发展趋势进行了展望。     

试论蛋白质组学的专利保护

蛋白质组学是后基因组时代的新兴学科,是当今生命科学领域新的增长点.蛋白质组学的研究为药物筛选、新药开发、临床诊断及新陈代谢途径研究等提供理论依据和研究基础.对蛋白质组学领域中涉及的蛋白质药物、数据库及检测分离设备等的专利保护现状和策略进行了初步探讨,并强调了加强我国蛋白质组学知识产权保护特别是专利保护的重要性.     

脂蛋白质组学研究进展

脂蛋白质组学(lipoproteomics)是一门应用蛋白质组学技术对脂蛋白(lipoprotein)进行全面、系统地分析和鉴定,进而了解脂蛋白的组成和功能以及与相关疾病发生、发展之间关系的新兴学科.近些年来,脂蛋白质组学研究促进了脂蛋白中蛋白质组分的急性期响应、补体激活、免疫响应、炎症响应、蛋白酶抑制等新功能的发现,显示了广阔的应用前景.本文对脂蛋白的功能和分类,以及目前应用于脂蛋白质组学研究的脂蛋白分离方法和蛋白质鉴定方法进行了简述,并综述了高密度脂蛋白、低密度脂蛋白和极低密度脂蛋白的脂蛋白质组学研究,及其在冠状动脉疾病、糖尿病、类风湿性关节炎等疾病研究的最新进展.     

蛋白质组学在水稻与微生物互作研究中的应用

应用蛋白质组学方法揭示植物与微生物的互作机制是当前植物病理学研究的热点之一。结合水稻感染水稻条纹病毒后的蛋白质组学分析经验,综述了蛋白质组学在水稻与微生物互作研究中的应用,包括水稻与真菌、细菌、病毒互作的蛋白组学和突变体蛋白质组学。在总结研究现状的基础上,提出了水稻与微生物互作的蛋白质组学研究中存在的问题,并对该领域的发展前景进行展望。     

蛋白质组学技术在植物类囊体膜蛋白研究中的应用

类囊体作为植物光合作用光反应的重要场所,在植物亚细胞蛋白质组学研究中倍受关注.介绍了植物蛋白质组学相关技术,包括双向凝胶电泳(2DE)、高效液相色谱(HPLC)、高效毛细管电泳(HPCE)、质谱(MS)和蛋白质组学数据库在植物类囊体膜蛋白研究中的应用.同时对类囊体膜蛋白质组学的研究趋势进行了探讨.     

植物蛋白质组学研究进展Ⅰ. 蛋白质组关键技术

随着模式植物拟南芥和水稻基因组测序相继完成, 使植物基因组学研究成功迈入到功能基因组学研究的时代。这为蛋白质组学产生及其发展奠定了坚实的基础。文章重点介绍了蛋白质组学的概念、产生背景和蛋白质组学的关键技术。蛋白质组学的关键技术包括双向电泳、高效液相色谱、蛋白芯片、质谱技术、蛋白质组学的相关数据库、定量蛋白组技术、蛋白复合体标签亲和纯化技术和酵母双杂交系统。同时对当前蛋白质组技术面临的挑战和发展前景进行了讨论。     

蛋白质组学的研究方法

近年来,蛋白质组学的研究在生物医学领域正逐步深入,研究方法也正逐步完善。本文综述了蛋白质组学的几种研究方法,指出了各种方法的优点及局限性,并且对蛋白质组学的研究方法和前景进行了展望。     

蛋白质组学技术及其在心血管疾病研究中的应用

蛋白质组学是旨在研究蛋白质表达谱和蛋白质与蛋白质之间相互作用的新的领域。蛋白质组学的研究必须依赖高通量、自动化程度很高的技术。双向电泳、液相色谱和生物质谱技术的发展推动了蛋白质组学的研究。蛋白质组学为疾病发病机制的研究提供了新的思路和方法 ,本文重点介绍了蛋白质组学技术在心血管疾病研究中的应用     

Quantitative plant proteomics

Quantitation is an inherent requirement in comparative proteomics and there is no exception to this for plant proteomics. Quantitative proteomics has high demands on the experimental workflow, requiring a thorough design and often a complex multi-step structure. It has to include sufficient numbers of biological and technical replicates and methods that are able to facilitate a quantitative signal read-out. Quantitative plant proteomics in particular poses many additional challenges but because of the nature of plants it also offers some potential advantages. In general, analysis of plants has been less prominent in proteomics. Low protein concentration, difficulties in protein extraction, genome multiploidy, high Rubisco abundance in green tissue, and an absence of well-annotated and completed genome sequences are some of the main challenges in plant proteomics. However, the latter is now changing with several genomes emerging for model plants and crops such as potato, tomato, soybean, rice, maize and barley. This review discusses the current status in quantitative plant proteomics (MS-based and non-MS-based) and its challenges and potentials. Both relative and absolute quantitation methods in plant proteomics from DIGE to MS-based analysis after isotope labeling and label-free quantitation are described and illustrated by published studies. In particular, we describe plant-specific quantitative methods such as metabolic labeling methods that can take full advantage of plant metabolism and culture practices, and discuss other potential advantages and challenges that may arise from the unique properties of plants.     

基于质谱的定量蛋白质组学策略和方法研究进展

定量蛋白质组学已经成为组学领域研究的热点之一.相关实验技术和计算方法的不断创新极大地促进了定量蛋白质组学的飞速发展.常用的定量蛋白质组学策略按照是否需要稳定同位素标记可以分为无标定量和有标定量两大类.每类策略又产生了众多定量方法和工具,它们一方面推动了定量蛋白质组学的深入发展;另一方面,也在实验策略与技术的发展过程中不断更新.因此对这些定量实验策略和方法进行系统总结和归纳将有助于定量蛋白质组学的研究.本文主要从方法学角度全面归纳了目前定量蛋白质组学研究的相关策略和算法,详述了无标定量和有标定量的具体算法流程并比较了各自特点,还对以研究蛋白质绝对丰度为目标的绝对定量算法进行了总结,列举了常用的定量软件和工具,最后概述了定量结果的质量控制方法,对定量蛋白质组学方法发展的前景进行了展望.     

生物质谱技术在蛋白质组学研究中的应用

随着技术的进步,蛋白质组学的研究重心由最初旨在鉴定细胞或组织内基因组所表达的全部蛋白质转移到从整个蛋白质组水平上阐述包括蛋白翻译后修饰、生物大分子相互作用等反映蛋白质功能的层次。多种质谱离子化技术的突破使质谱技术成为蛋白质组学研究必不可少的手段。质谱技术联合蛋白质组学多角度、深层次探索生命系统分子本质成为现阶段生命科学研究领域的主旋律之一。本文简要综述了肽和蛋白质等生物大分子质谱分析的原理、方式和应用,并对其发展前景做出展望。     

非酒精性脂肪性肝病蛋白质组学研究进展

非酒精性脂肪性肝病(nonalcoholic fatty liver disease,NAFLD)是一种常见慢性肝脏疾病,其发病率呈逐年上升趋势,但发病机制尚未明确,诊疗手段仍不完善.蛋白质组学(proteomics)的出现使NAFLD研究有了进一步的发展,相关研究已达21个.目前,蛋白质组学技术可以研究疾病相关的分子改变,从而寻找新的生物标志物和治疗靶标.在此,对蛋白质组学在NAFLD诊断及分期、发病机制和其他相关领域研究进展作一个较为全面的综述.首先,对研究中遇到的研究对象、样本种类、实验方法和标志物特征选择进行经验性总结.其次,除了介绍如何运用蛋白质组学研究病因、危险因素和重要分子在NAFLD发病机制中的作用,还介绍NAFLD发病机制的亚细胞蛋白质组学、修饰蛋白质组学以及蛋白质组学与转录组学相结合的研究实例.此外,对差异蛋白质的分析策略和价值作了重点阐述,收集到一些有望成为NAFLD治疗靶标的候选分子.最后,结合新技术展望研究新空间,以期能够有助于推动蛋白质组学在寻找新的疾病标志物、探索疾病分子机制和治疗靶标中开辟新的途径.     

Organellar proteomics: turning inventories into insights

Subcellular organization is yielding to large-scale analysis. Researchers are now applying robust mass-spectrometry-based proteomics methods to obtain an inventory of biochemically isolated organelles that contain hundreds of proteins. High-resolution methods allow accurate protein identification, and novel algorithms can distinguish genuine from co-purifying components. Organellar proteomes have been analysed by bioinformatic methods and integrated with other large-scale data sets. The dynamics of organelles can also be studied by quantitative proteomics, which offers powerful methods that are complementary to fluorescence-based microscopy. Here, we review the emerging trends in this rapidly expanding area and discuss the role of organellar proteomics in the context of functional genomics and systems biology.     

What is targeted proteomics? A concise revision of targeted acquisition and targeted data analysis in mass spectrometry

Targeted proteomics has gained significant popularity in mass spectrometry‐based protein quantification as a method to detect proteins of interest with high sensitivity, quantitative accuracy and reproducibility. However, with the emergence of a wide variety of targeted proteomics methods, some of them with high‐throughput capabilities, it is easy to overlook the essence of each method and to determine what makes each of them a targeted proteomics method. In this viewpoint, we revisit the main targeted proteomics methods and classify them in four categories differentiating those methods that perform targeted data acquisition from targeted data analysis, and those methods that are based on peptide ion data (MS1 targeted methods) from those that rely on the peptide fragments (MS2 targeted methods).     

植物膜蛋白质组学研究技术现状

植物膜蛋白质组学是当前植物科学研究的热点领域。本文概论了蛋白质组学在植物膜蛋白研究中的应用,包括双向电泳前膜蛋白样品的制备以及植物质膜、液泡膜和其他膜蛋白组分的蛋白质组学研究进展,并介绍了植物膜蛋白质组学相关的数据库,最后对其发展作了展望。     

The methods of quantitative proteomics

In modern science proteomic analysis is inseparable from other fields of systemic biology. Possessing huge resources quantitative proteomics operates colossal information on molecular mechanisms of life. Advances in proteomics help researchers to solve complex problems of cell signaling, posttranslational modification, structure and funciotnal homology of proteins, molecular diagnostics etc. More than 40 various methods have been developed in proteomics for quantitative analysis of proteins. Although each method is unique and has certain advantages and disadvantages all these use various isotope labels (tags). In this review we will consider the most popular and effective methods employing both chemical modifications of proteins and also metabolic and enzymatic methods of isotope labeling.     

How to distinguish healthy from diseased? Classification strategy for mass spectrometry‐based clinical proteomics

SELDI-TOF-MS is rapidly gaining popularity as a screening tool for clinical applications of proteomics. Application of adequate statistical techniques in all the stages from measurement to information is obligatory. One of the statistical methods often used in proteomics is classification: the assignment of subjects to discrete categories, for example healthy or diseased. Lately, many new classification methods have been developed, often specifically for the analysis of X-omics data. For proteomics studies a good strategy for evaluating classification results is of prime importance, because usually the number of objects will be small and it would be wasteful to set aside part of these as a 'mere' test set. The present paper offers such a strategy in the form of a protocol which can be used for choosing among different statistical classification methods and obtaining figures of merit of their performance. This paper also illustrates the usefulness of proteomics in a clinical setting, serum samples from Gaucher disease patients, when used in combination with an appropriate classification method.