基于多元统计分析和网络药理学的五味子醋制前后质量标志物预测分析

目的基于多元统计分析和网络药理学分析预测五味子醋制前后潜在的质量标志物。方法采用UPLC-Q/TOF-MS解析生、醋五味子饮片中主要的木脂素类成分,并运用多元统计方法筛选出炮制前后潜在的差异化学成分,即化学标记物。进一步通过网络药理学以及生物信息学分析显著差异成分相关的主要作用靶点和通路,构建"成分-靶点-通路"网络关系,预测生、醋五味子潜在的质量标志物。结果筛选了五味子醋制前后差异性成分40个,其中8个为生、醋五味子之间显著性差异成分(即化学标记物)。鉴定并确认了其中的5个化学成分,分别是5-羟甲基糠醛、五味子甲素及其同分异构体、五味子乙素和五味子酯丁。而其余3个化学标记物通过解析一级、二级质谱信息,推测它们很可能也属于木脂素类成分。网络药理学分析结果表明,鉴定并确认的5个潜在质量标志物与五味子的主要药理作用密切相关。最终五味子乙素和5-羟甲基糠醛被确定为最具代表性的潜在质量标志物。结论五味子醋制前后其化学成分发生了一系列复杂的变化,经研究分析确定五味子乙素和5-羟甲基糠醛可作为五味子醋制前后代表性的潜在质量标志物,并推测木脂素类成分可能为五味子醋制保肝的重要效应物质基础。     

PRPS2表达特点及其与乳腺癌预后关系的生物信息学分析

研究磷酸核糖焦磷酸合成酶2（PRPS2）在不同亚型乳腺癌中的表达及其与乳腺癌预后关系。     

基于TCGA数据库挖掘甲状腺乳头状癌淋巴结转移相关的突变基因及其机制探讨北大核心

目的:挖掘并筛选与甲状腺乳头状癌(papillary thyroid carcinoma,PTC)淋巴结转移相关的突变基因及其潜在的机制。方法:从TCGA数据库获得377例PTC患者的测序数据及完整的临床资料,并分为淋巴结转移组(LM,n=212)与无淋巴结转移组(NLM,n=165)。利用R语言(v3.6.2)对转移组特有的突变基因进行富集分析。采用String在线软件绘制蛋白互作网络,Cytoscape软件筛选网络中的核心基因。在UALCAN网站上验证基因表达量与淋巴结转移之间的关系。利用荧光实时定量PCR测定候选基因在细胞系中mRNA的表达量。结果:一共筛选出1197个仅在LM组发生突变的基因,它们主要富集在黏着连接、细胞黏附分子(cell adhesion molecules,CAMs)通路。CAMs通路的核心基因ITGB1与VCAN的表达量与淋巴结转移相关。与正常甲状腺细胞系相比,VCAN基因在PTC细胞系TPC-1和B-CPAP中mRNA的表达量较高,尤其是B-CPAP细胞系。结论:CAMs通路可能是PTC淋巴结转移相关机制之一,其中VCAN基因可能是潜在的分子标志物。     

Contemporary Approaches for Identifying Rare Bone Disease Causing Genes

Recent improvements in the speed and accuracy of DNA sequencing, together with increasingly sophisticated mathematical approaches for annotating gene networks, have revolutionized the field of human genetics and made these once time consuming approaches assessable to most investigators. In the field of bone research, a particularly active area of gene discovery has occurred in patients with rare bone disorders such as osteogenesis imperfecta (OI) that are caused by mutations in single genes. In this perspective, we highlight some of these technological advances and describe how they have been used to identify the genetic determinants underlying two previously unexplained cases of OI. The widespread availability of advanced methods for DNA sequencing and bioinformatics analysis can be expected to greatly facilitate identification of novel gene networks that normally function to control bone formation and maintenance.     

DISC1: Structure, Function, and Therapeutic Potential for Major Mental Illness

Disrupted in schizophrenia 1 (DISC1) is well established as a genetic risk factor across a spectrum of psychiatric disorders, a role supported by a growing body of biological studies, making the DISC1 protein interaction network an attractive therapeutic target. By contrast, there is a relative deficit of structural information to relate to the myriad biological functions of DISC1. Here, we critically appraise the available bioinformatics and biochemical analyses on DISC1 and key interacting proteins, and integrate this with the genetic and biological data. We review, analyze, and make predictions regarding the secondary structure and propensity for disordered regions within DISC1, its protein-interaction domains, subcellular localization motifs, and the structural and functional implications of common and ultrarare DISC1 variants associated with major mental illness. We discuss signaling pathways of high pharmacological potential wherein DISC1 participates, including those involving phosphodiesterase 4 (PDE4) and glycogen synthase kinase 3 (GSK3). These predictions and priority areas can inform future research in the translational and potentially guide the therapeutic processes.     

化脓链球菌金属结合蛋白Dpr的相互作用蛋白筛选

目的 Dpr蛋白是与细菌毒力密切相关的铁离子结合蛋白,从Dpr蛋白潜在的相互作用蛋白网络出发,通过生物信息学手段分析这些蛋白发挥的功能以及参与的生物过程,揭示Dpr 蛋白在化脓链球菌(Streptococcus pyogenes)中发挥作用的新机制。方法 表达和纯化融合蛋白(GST-Dpr);通过 GST-Pull down 和质谱联用的方式,筛选化脓链球菌中与 Dpr 蛋白相互作用的蛋白。结果 三次独立重复实验共同鉴定到 26 个相互作用蛋白;基于String分析构建各蛋白间的相互作用网络发现,这些相互作用蛋白中有 15 个蛋白与 Dpr 存在直接或间接的联系;利用生物信息学手段分析这些相互作用蛋白的分子功能以及可能参与的生物学途径,发现它们大多能够结合金属离子,主要参与碳代谢、丙酮酸代谢、糖代谢以及糖异生等生物过程。结论 Dpr蛋白可能通过与本研究中鉴定的GAPDH、AccD、Eno、RpsB 和 Fus等蛋白相互作用来共同完成相关的生物学功能。通过建立的Dpr蛋白相互作用蛋白网络,为全面阐述 Dpr 蛋白在细菌定植和毒力方面的功能提供了重要的理论基础和新思路。     

Maximizing computational tools for successful drug discovery

Drug discovery is an iterative cycle of identifying promising hits followed by lead optimization via bioisosteric replacements. In the search for compounds affording good bioactivity, equal importance should also be placed on achieving those with favorable pharmacokinetic properties. Thus, the balance and realization of both key properties is an intricate problem that requires great caution. In this editorial, the authors explore the available computational tools in the context of the extant of big data that has borne out via advents of the Omics revolution. As such, the selection of appropriate computational tools for analyzing the vast number of chemical libraries, target proteins and interactomes is the first step toward maximizing the chance for success. However, in order to realize this, it is also necessary to have a solid foundation on the big concepts of drug discovery as well as knowing which tools are available in order to give drug discovery scientists the best opportunity.