A Large Scale Analysis of Protein-Protein Interactions in the Nitrogen-fixing Bacterium Mesorhizobium loti

Global viewing of protein–protein interactions (PPIs)is a useful way to assign biological roles to large numbersof proteins predicted by complete genome sequence. Here, wesystematically analyzed PPIs in the nitrogen-fixing soil bacteriumMesorhizobium loti using a modified high-throughput yeast two-hybridsystem. The aims of this study are primarily on the providingfunctional clues to M. loti proteins that are relevant to symbioticnitrogen fixation and conserved in other rhizobium species,especially proteins with regulatory functions and unannotatedproteins. By the screening of 1542 genes as bait, 3121 independentinteractions involving 1804 proteins (24% of the total proteincoding genes) were identified and each interaction was evaluatedusing an interaction generality (IG) measure and the generalfeatures of the interacting partners. Most PPIs detected inthis study are novel interactions revealing potential functionalrelationships between genes for symbiotic nitrogen fixationand signal transduction. Furthermore, we have predicted theputative functions of unannotated proteins through their interactionswith known proteins. The results described here represent newinsight into protein network of M. loti and provide useful experimentalclues to elucidate the biological function of rhizobial genesthat can not be assigned directly from their genomic sequence.     

蛋白质－DNA相互作用与真核基因转录

结合最近几年对真核转录调节因子和ＤＮＡ的结构与机能研究,概述了蛋白质－蛋白质及蛋白质－ＤＮＡ相互作用方式以及介导相互作用的分子结构基础,论述了转录因子之间,转录因子与ＤＮＡ之间相互作用过程中的同与拮抗作用,发生机制及其在真核基因转录调节中的遍性和重要意义。     

蛋白质－DNA相互作用与真核基因转录

结合最近几年对真核转录调节因子和DNA的结构与机能研究,概述了蛋白质-蛋白质及蛋白质-DNA相互作用方式以及介导相互作用的分子结构基础,论述了转录因于之间、转录因子与DNA之间相互作用过程中的协同与拮抗作用、发生机制及其在真核基因转录调节中的普遍性和重要意义.     

Role of interferons in the regulation of cell proliferation,differentiation, and development

There now appears to be evidence to support the view that the type I IFNs are naturally produced negative regulators of growth that also modify cell differentiation. Consistent with this, it appears that the ability to produce and respond to IFN is suppressed in early embryonic development when cell proliferation and differentiation are essential. In the later stages of fetal development, IFN production is de-repressed, and cells show increased sensitivity to IFN, which may be important in regulating cell proliferation and/or differentiation processes or the interaction between fetal and maternal tissues. Interestingly, the IFN system can also be suppressed in disease states such as the development of tumours or in the establishment of a (chronic) viral infection. Therefore, understanding the developmental regulation of the IFN system may be important to understanding and controlling the IFN system in disease. More extensive studies of the developmental stage and tissue-specific expression of type I IFNs and their receptors are necessary, as well as more direct in vivo experiments to further elucidate the role of the IFN system in reproduction and development. © 1994 Wiley-Liss, Inc.     

Macromolecular Drug Delivery: Basic Principles and Therapeutic Applications

Macromolecular drugs hold great promise as novel therapeutics of several major disorders, such as cancer and cardiovascular disease. However, their use is limited by lack of efficient, safe, and specific delivery strategies. Successful development of such strategies requires interdisciplinary collaborations involving researchers with expertise on e.g., polymer chemistry, cell biology, nano technology, systems biology, advanced imaging methods, and clinical medicine. This poses obvious challenges to the scientific community, but also provides opportunities for the unexpected at the interface between different disciplines. This review summarizes recent studies of macromolecular delivery that should be of interest to researchers involved in macromolecular drug synthesis as well as in vitro and in vivo drug delivery studies.     

基因转录调控相关数据库集成系统及其应用

通过互联网访问的有关基因转录调控的数据库集成系统及其应用 ,包括调控区 (3’和 5’调控区、内显子和外显子调控区等 )、调控单元 (启动子 ,增强子 ,沉默子等 )和转录因子结合位点相关数据库及其数据库系统的性质、组成和功能。也介绍了这些数据库和系统的查询和搜索方法以及相关开发的程序工具。这些生物信息学资源对于从事生物信息学、分子生物学、遗传工程、基因功能、生物技术、代谢工程、药物设计、病理学和药理学研究的机构及人员在教学研究方面具一定的参考价值和帮助。     

利用RNA-seq数据分析肝细胞癌差异表达基因及蛋白相互作用

RNA-Seq已成为当前转录组学研究的强有力工具,尤其在肿瘤差异表达基因的筛选方面有重要的应用价值。为进一步阐明肝细胞癌(HCC)的分子机制,本研究对GEO中1个包括12对HCC组织标本的RNA-Seq数据集(GSE63863)进行了生物信息学分析。采用edgeR、DESeq2、voom等3种不同算法的软件进行统计分析,共获得976个差异表达基因(adj. p-value<0.01或FDR<0.01,|logFC|≥2),其中上调表达422个(43.2%),下调554个(56.8%)。GO富集分析显示这些差异表达基因主要涉及离子结合、氧化还原酶活性等分子功能以及氧化还原、细胞分裂等生物学过程;KEGG通路分析显示,这些差异表达基因主要涉及细胞周期、视黄醇等代谢通路。STRING分析显示,共有654个基因编码的蛋白质存在相互作用,进一步利用MCODE分析显示,169个基因编码蛋白构成4个子网络,相应的中心节点基因分别为UBE2C、GNG4、TTR、FOS,这些基因的异常表达可能在HCC的发生发展过程中具有重要作用。上述研究结果将为进一步阐明HCC分子发病机制、寻找新型生物标志物提供初步的依据。     

DeepCPI:A Deep Learning-based Framework for Large-scale in silico Drug Screening

Accurate identification of compound–protein interactions(CPIs) in silico may deepen our understanding of the underlying mechanisms of drug action and thus remarkably facilitate drug discovery and development.Conventional similarity-or docking-based computational methods for predicting CPIs rarely exploit latent features from currently available large-scale unlabeled compound and protein data and often limit their usage to relatively small-scale datasets.In the present study,we propose Deep CPI,a novel general and scalable computational framework that combines effective feature embedding(a technique of representation learning) with powerful deep learning methods to accurately predict CPIs at a large scale.Deep CPI automatically learns the implicit yet expressive low-dimensional features of compounds and proteins from a massive amount of unlabeled data.Evaluations of the measured CPIs in large-scale databases,such as Ch EMBL and Binding DB,as well as of the known drug–target interactions from Drug Bank,demonstrated the superior predictive performance of Deep CPI.Furthermore,several interactions among smallmolecule compounds and three G protein-coupled receptor targets(glucagon-like peptide-1 receptor,glucagon receptor,and vasoactive intestinal peptide receptor) predicted using Deep CPI were experimentally validated.The present study suggests that Deep CPI is a useful and powerful tool for drug discovery and repositioning.The source code of Deep CPI can be downloaded from https://github.com/Fangping Wan/Deep CPI.     

光学分子影像技术及其在药物研发领域的应用

光学分子影像技术是一种发展迅速的生物医学影像技术,能够利用生物发光技术或荧光蛋白等,对生物体内特定的生物过程进行无创的定性或定量研究。应用该技术可以对药物进行筛选,选取具有潜在治疗效果的药物进行后续研究,而终止对可能无效药物的研究,同时可以评价药物对肿瘤的代谢、增殖、血管形成、凋亡和组织乏氧等方面的影响。本文主要介绍光学分子影像技术及其在药物研发,尤其是抗肿瘤药物研发领域的应用。     

心血管病患者住院治疗期间潜在的药物相互作用研究

目的:明确住院治疗的心脏病患者中潜在的药物-药物相互作用(drug-drug interaction,DDI),并对DDI相关危险因素进行评估。方法:选择2013年5月至2013年12月入院治疗的至少服用2种药物且入院不少于24小时的心脏病患者为研究对象,应用"标准药物相互作用数据库Micromedex-2(Thomson Reuters)×2.0"对这些患者可能发生的药物相互作用进行分析。结果:共入选150例患者,其中32例患者被确认出现了至少一种药物组合的DDI,发生率为21.3%;明确了48个有潜在危险因素的药物相互作用。阿托伐他汀/阿奇霉素(10.4%)、依那普利/二甲双胍(10.4%)、依那普利/氯化钾(10.4%)、阿托伐他汀/克拉霉素(8.3%)、呋塞米/庆大霉素(6.3%)是最常见的存在相互作用的药物组合,阿托伐他汀、依那普利、地高辛、呋塞米、氯吡格雷、华法林等药物经常参与药物相互作用。大多数药物的相互作用比较温和,但"用药种类增多、住院时间延长、存在合并症"等危险因素与潜在药物-药物相互作用(potential drug-drug interaction,p DDI)密切相关。结论:住院治疗的心脏病患者潜在DDI发生率较高,用药种类增多、住院时间延长、存在合并症均为其发生的危险因素。对那些存在明确危险因素的住院治疗的心脏病患者密切监测是十分必要的,医务人员要尽早发现和预防这些潜在的药物相互作用对人体健康造成的伤害。