Statistical significance analysis of nuclear magnetic resonance-based metabonomics data

Use of nuclear magnetic resonance (NMR)-based metabonomics to search for human disease biomarkers is becoming increasingly common. For many researchers, the ultimate goal is translation from biomarker discovery to clinical application. Studies typically involve investigators from diverse educational and training backgrounds, including physicians, academic researchers, and clinical staff. In evaluating potential biomarkers, clinicians routinely use statistical significance testing language, whereas academicians typically use multivariate statistical analysis techniques that do not perform statistical significance evaluation. In this article, we outline an approach to integrate statistical significance testing with conventional principal components analysis data representation. A decision tree algorithm is introduced to select and apply appropriate statistical tests to loadings plot data, which are then heat map color-coded according to P score, enabling direct visual assessment of statistical significance. A multiple comparisons correction must be applied to determine P scores from which reliable inferences can be made. Knowledge of means and standard deviations of statistically significant buckets enabled computation of effect sizes and study sizes for a given statistical power. Methods were demonstrated using data from a previous study. Integrated metabonomics data assessment methodology should facilitate translation of NMR-based metabonomics discovery of human disease biomarkers to clinical use.     

Human body fluid proteome analysis

The focus of this article is to review the recent advances in proteome analysis of human body fluids, including plasma/serum, urine, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, synovial fluid, nipple aspirate fluid, tear fluid, and amniotic fluid, as well as its applications to human disease biomarker discovery. We aim to summarize the proteomics technologies currently used for global identification and quantification of body fluid proteins, and elaborate the putative biomarkers discovered for a variety of human diseases through human body fluid proteome (HBFP) analysis. Some critical concerns and perspectives in this emerging field are also discussed. With the advances made in proteomics technologies, the impact of HBFP analysis in the search for clinically relevant disease biomarkers would be realized in the future.     

现代生物学对药物发现的影响

随着后基因组时代的到来,药物发现研究领域不断涌现出一系列新思路、新技术、新方法,从而迅速推进药物发现的多元化发展。一方面,基因组学、蛋白质组学、转录组学、代谢组学、生物信息学、系统生物学等新兴学科的崛起与发展,为药物发现提供更为广泛而深刻的理论基础;另一方面,计算机辅助药物设计、高通量筛选、高内涵筛选、生物芯片、转基因和RNA干扰等高新技术的发展和完善,为药物发现提供了新的技术手段和有力工具,极大地拓宽了药物发现的途径。本文结合近年来现代生物学的研究进展,综述现代生物学对药物发现过程的影响。     

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.     

Proteomics technologies and challenges

Proteomics is the study of proteins and their interactions in a cell. With the completion of the Human Genome Project, the emphasis is shifting to the protein compliment of the human organism. Because proteome reflects more accurately on the dynamic state of a cell, tissue, or organism, much is expected from proteomics to yield better disease markers for diagnosis and therapy monitoring. The advent of proteomics technologies for global detection and quantitation of proteins creates new opportunities and challenges for those seeking to gain greater understanding of diseases. High-throughput proteomics technologies combining with advanced bioinformatics are extensively used to identify molecular signatures of diseases based on protein pathways and signaling cascades. Mass spectrometry plays a vital role in proteomics and has become an indispensable tool for molecular and cellular biology. While the potential is great, many challenges and issues remain to be solved, such as mining low abundant proteins and integration of proteomics with genomics and metabolomics data. Nevertheless, proteomics is the foundation for constructing and extracting useful knowledge to biomedical research. In this review, a snapshot of contemporary issues in proteomics technologies is discussed.