Pathway-pathway network-based study of the therapeutic mechanisms by which salvianolic acid B regulates cardiovascular diseases

Investigation of the potential therapeutic mechanisms of drug candidates is an essential step in the process of new drug discovery.With the rapid development of systems biology,recent network analyses of proteins,drugs,and diseases have enabled great progress in delineating the molecule mechanisms of drug candidates.However,most analyses perform a direct association between gene/protein and disease levels without considering the intermediate biological pathways regulated by the drugs.Given that a protein performs its biological roles through pathways,we propose using a novel pathway-pathway network analysis to investigate the potential therapeutic functions of the drug candidates.Many studies have demonstrated that salvianolic acid B(SalB) of Salvia miltiorrhiza is an effective therapy for cardiovascular diseases(CVD).Using molecular docking methods to identify direct interacting targets of Sal B,we collected all Sal B-regulated proteins with supporting experimental evidence in PubMed abstracts.FDA-approved CVD drugs and their corresponding targets were also collected.From a traditional drug-protein network analysis,we found that Sal B could affect ACE and REN of the renin-angiotensin-aldosterone system to relax vessels and alleviate hypertension.Subsequent pathway-pathway network analysis was attempted to study the mechanisms of Sal B in treating CVD,and demonstrated that Sal B regulates immunity/inflammation,apoptosis,ion transport and basic metabolism processes in the treatment of CVD.Regulating the immune/inflammation process may be the major mechanism of Sal B.We believe that pathwaypathway network analysis is a novel method for studying the therapeutic mechanisms of herbal ingredients.     

基于网络药理学探究黄酮对2型糖尿病的作用机制

应用网络药理学的方法探究了已分离得到的6个黄酮与2型糖尿病相互作用的机制.通过相关数据库的筛选与预测,获得了化合物与疾病的靶基因,进一步得到了两者的交集基因;通过Cytoscape 3.8.0软件构建了这些化合物与2型糖尿病相关的PPI网络与成分-疾病-靶点网络图;通过R语言半编程程序对交集基因进行生物功能富集和通路富...     

Biological networks to the analysis of microarray data

Microarray technology, which permits rapid and large-scale screening for patterns of gene expressions, usually generates a large amount of data. How to mine the biological meanings under these data is one of the main challenges in bioinformatics. Compared to the pure mathematical techniques, those methods incorporated with some prior biological knowledge generally bring better interpretations. Recently, a new analysis, in which the knowledge of biological networks such as metabolic network and protein interaction network is introduced, is widely applied to microarray data analysis. The microarray data analysis based on biological networks contains two main research aspects: identification of active components in biological networks and assessment of gene sets significance. In this paper, we briefly review the progress of these two categories of analyses, especially some representative methods.     

自建 Tg. C57-ras V1. 0 小鼠模型与日本 Tg. rasH2小鼠遗传学及其杂交 1 代生物学特性比较

摘要:目的 比较自主建立的 Tg. C57-ras 小鼠与日本 Tg. rasH2 小鼠模型的遗传学及 Tg. C57-ras 转基因小鼠与BALB / c 小鼠的杂交 1 代小鼠(简称 CB6F1-NIFDC) 与日本 Tg. rasH2 转基因小鼠模型杂交 1 代小鼠(简称 CB6F1-CIEA)的生物学特性,为国内药物安全评价机构提供致癌性模型的多元化选择。 方法 对两种模型的遗传学及生物学特性进行比较,包括模型构建方法、转录水平、蛋白水平、生长曲线、血液生理数据等。 结果 遗传学特性:两种模型均采用转基因方法构建,转入 c-Ha-ras 基因序列无实质差异,拷贝数仅相差一个;两种模型的不同脏器在mRNA 水平基因表达具有相似性;日本模型肺组织的蛋白水平稍高于自主建立模型,但无统计学差异。 生物学特性:根据文献分析两种模型杂交 1 代的生长曲线有差异,但在致癌实验周期内两模型体质量差别不大;血液生理指标有部分差异。 结论 通过遗传学及生物学特性比较,自建模型可作为临床前药物致癌性评价的候选模型。     

人体肿瘤的异种移植与抗癌药物研究

免疫缺陷动物的肿瘤异种移植为研究抗癌药物提供了各种人体癌瘤的实验模型。研究证明,药物对异种移植的人体肿瘤的作用与药物的临床疗效有良好的相关性。因此,人体肿瘤异种移植在药物研究中得到广泛应用。在针对某些类型癌瘤的新药筛选中,在评价药物对人体癌瘤的实验疗效以及在肿瘤的导向治疗研究中,此实验模型具有特别重要的作用。     

采用偏最小二乘法的基因-药物共模块识别

首先,将药物二维化学结构转化为数值序列,计算药物之间的皮尔逊相关系数,进而构建药物关联网络;然后,在带有基因网络约束的稀疏偏最小二乘算法的基础上,加入药物关联网络信息,提出伴有基因和药物关联网络正则约束的稀疏偏最小二乘(SGDPLS)算法;最后,将SGDPLS算法应用于基因-药物共模块识别.结果表明:药物关联网络信息的加入能够有效提高所识别的共模块中基因模块与药物模块的相关性,增加共模块的生物可解释性.     

基于网络药理与分子对接研究百合行治疗认知障碍的机制

为了研究百合治疗认知障碍的作用机制,采用网络药理学方法从TCMSP数据库中筛选出百合的7种主要活性成分及其潜在的465个作用靶点.在GeneCards,OMIM,PharmGkb,CTD,DrugBank数据库中获取认知障碍疾病靶点,然后取百合化学成分与认知障碍靶点两者交集,得到“药物成分—疾病”交集靶点44个.采用Metascape利用STRING数据库,构建交集靶点蛋白相互作用（PPI）网络图,筛选后得到核心靶点与核心成分,其中MAOB（单胺氧化酶B）、CASP3（半胱天冬氨酸蛋白酶3）、ESR1（雌激素受体）、JUN（1号染色体的基因）、CASP8（天冬氨酸蛋白水解酶8）、CASP9（天冬氨酸蛋白水解酶9）、PGR（类固醇受体基因）、AR（肾上腺素能受体）、SLC6A3（多巴胺转运蛋白基因）、PTGS2（环氧合酶-2）为百合药效成分抗认知障碍作用的关键靶点蛋白.利用DAVID和Metascape平台进行GO功能富集分析和KEGG通路富集分析,采用AutodockVina平台实现分子对接.结果表明,百合的4个活性化合物与10个认知障碍关键核心靶标相关,参与调节的关键信号通路有30条.其中,度值较大的化学成分有豆甾醇、3-去甲秋水仙碱、β-谷甾醇、松香酸;度值较大的靶点蛋白有MAOB,PTGS2,CASP3,CASP9等;主要涉及的通路有神经活动配体—受体相互作用通路、钙离子信号通路、小细胞肺癌通路等.百合治疗认知障碍的作用机制可能是通过抑制单胺氧化酶B的活性,改善认知功能.     

基于网络药理学与分子对接的新冠肺炎三焦疫毒寒湿症用药机理研究

针对新冠肺炎（COVID-19）中三焦疫毒寒湿症的一个临床用药药队,运用网络药理学和分子对接方法分析其药物作用机制。结果显示,筛选到中药活性成分 77 种,作用靶标 4760 个;使用DAVID（the database for annotation,visualization and intergated discovery)数据库对核心靶点进行GO（gene ontology)和KEGG(kyoto encyclopedia of genes and genomes)富集分析,主要涉及蛋白结合、ATP结合等生物过程和PI3K-Akt、FOXO等信号通路;Maestro对接结果表明COVID-19涉及靶点与药物有效入血分子成分的对接分数均小于-5。研究显示药队中的多种活性成分可通过与新型冠状病毒（SARS-CoV-2）的相关靶标结合发挥直接抗病毒作用,并通过多种途径与通路发挥对COVID-19的系统调节作用。     

基于网络药理学预测丹参-柴胡药对治疗酒精性肝病的作用机制

基于网络药理学方法预测丹参-柴胡药对治疗酒精性肝病的作用机制。通过数据库筛选丹参、柴胡的有效活性成分及相应的靶蛋白,并预测和筛选丹参-柴胡药对治疗酒精性肝病的作用靶点。运用Cytoscape 3.7.2软件构建药物-成分-靶点-疾病网络图。绘制关键靶点蛋白质-蛋白质相互作用(protein protein interaction, PPI)网络。采用Metascape数据库对有效作用靶点进行基因本体（gene ontology, GO）注释分析、京都基因和基因组百科全书(Kyoto encyclopedia of genes and genomes, KEGG)通路富集分析。筛选出丹参化合物65个,有效靶点162个,柴胡化合物17个,有效靶点251个。通过筛选得到丹参-柴胡药对与酒精性肝病的交集靶点161个。PPI网络发现AKT1、IL6、TP53、VEGFA、TNF、CASP3、EGF等可能是丹参-柴胡药对治疗酒精性肝病的关键靶点。GO注释分析涉及对无机物的反应、对有毒物质的反应、膜筏、枝晶、转录因子结合、蛋白质结构域特异性结合等信号通路。KEGG通路分析涉及肿瘤信号通路、流体剪切应力与动脉粥样硬化、癌症中的蛋白多糖、糖尿病并发症中的AGE-RAGE信号通路等通路。初步探究了丹参-柴胡药对治疗酒精性肝病的关键靶点和涉及的生物学过程及信号通路,发现其作用是多靶点、多通路的。     

基于深度残差网络与迁移学习的地形识别方法

针对现有机器人基于深度网络的地形识别方法准确率低、网络训练时间长且需要大量训练数据的问题,提出一种基于深度残差网络与迁移学习的地形识别方法。首先,基于Resnet网络构建一种深度残差网络;其次,利用现有Imagenet大型数据集对构建的深度残差网络进行预训练,作为预训练网络,保留预训练网络除全连接层的训练权重,实现预训练网络大规模的参数迁移;最后,利用自建地形图像数据集对深度残差网络的全连接层进行训练,实现深度残差网络微调。实验结果表明,通过迁移学习的方法,利用深度残差网络对石子路、水泥路、砖地、沥青、草地、泥地6种自建地形图像进行分类,平均准确率达到了99.3%,同时网络训练时间也显著降低。     

High-throughput decoding of antitrypanosomal drug efficacy and resistance

The concept of disease-specific chemotherapy was developed a century ago. Dyes and arsenical compounds that displayed selectivity against trypanosomes were central to this work, and the drugs that emerged remain in use for treating human African trypanosomiasis (HAT). The importance of understanding the mechanisms underlying selective drug action and resistance for the development of improved HAT therapies has been recognized, but these mechanisms have remained largely unknown. Here we use all five current HAT drugs for genome-scale RNA interference target sequencing (RIT-seq) screens in Trypanosoma brucei, revealing the transporters, organelles, enzymes and metabolic pathways that function to facilitate antitrypanosomal drug action. RIT-seq profiling identifies both known drug importers and the only known pro-drug activator, and links more than fifty additional genes to drug action. A bloodstream stage-specific invariant surface glycoprotein (ISG75) family mediates suramin uptake, and the AP1 adaptin complex, lysosomal proteases and major lysosomal transmembrane protein, as well as spermidine and N-acetylglucosamine biosynthesis, all contribute to suramin action. Further screens link ubiquinone availability to nitro-drug action, plasma membrane P-type H(+)-ATPases to pentamidine action, and trypanothione and several putative kinases to melarsoprol action. We also demonstrate a major role for aquaglyceroporins in pentamidine and melarsoprol cross-resistance. These advances in our understanding of mechanisms of antitrypanosomal drug efficacy and resistance will aid the rational design of new therapies and help to combat drug resistance, and provide unprecedented molecular insight into the mode of action of antitrypanosomal drugs.     

骨髓间充质干细胞向肝样细胞分化的研究进展

摘要: 由各种因素导致的重症肝病的终末治疗的最好手段一直是原位肝移植,但长期以来肝供体的缺乏和免疫排斥引起的一系列问题极大地限制了该手术的运用,同时,在肝脏相关药物的筛选中,原代肝细胞难于培养且易在培养过程中变异,而随着骨髓间充质干细胞研究的深入,越来越多的证据表明骨髓间充质干细胞具有向肝细胞分化的潜能。因此,骨髓间充质干细胞诱导分化而成的肝样细胞在再生医疗和药物筛选领域具有较好的运用前景,本文就间充质干细胞的分离培养及其生物学特性,肝样细胞的诱导培养条件,生物学特性及其运用前景加以综述。     

Loosening of plant cell walls by expansins

Plant cell walls are the starting materials for many commercial products, from lumber, paper and textiles to thickeners, films and explosives. The cell wall is secreted by each cell in the plant body, forming a thin fibreglass-like network with remarkable strength and flexibility. During growth, plant cells secrete a protein called expansin, which unlocks the network of wall polysaccharides, permitting turgor-driven cell enlargement. Germinating grass pollen also secretes an unusual expansin that loosens maternal cell walls to aid penetration of the stigma by the pollen tube. Expansin's action has puzzling implications for plant cell-wall structure. The recent explosion of gene sequences and expression data has given new hints of additional biological functions for expansins.     

hERG potassium channels and cardiac arrhythmia

hERG potassium channels are essential for normal electrical activity in the heart. Inherited mutations in the HERG gene cause long QT syndrome, a disorder that predisposes individuals to life-threatening arrhythmias. Arrhythmia can also be induced by a blockage of hERG channels by a surprisingly diverse group of drugs. This side effect is a common reason for drug failure in preclinical safety trials. Insights gained from the crystal structures of other potassium channels have helped our understanding of the block of hERG channels and the mechanisms of gating.     

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of 'personalized' therapeutic regimens.     

神经网络的CO2吞吐候选井模糊优选方法

CO₂吞吐候选井的优选和综合评价是二氧化碳吞吐项目成功的基础。在综合评价权重的确定中不管是层次分析法还是模糊层次分析法都带很大的主观性,这里将模糊综合评判方法与神经网络技术相结合,通过神经网络的自学习方法来获得评价参数权重,使权重能反应客观事实。实践证明该方法在CO₂吞吐候选井的优选和综合评价中切实可行。     

一种多手性中心氨基醇化合物的合成

手性氨基醇结构广泛存在于天然产物和药物分子中,普遍具有良好的生物活性;同时该类化合物在不对称催化研究中也是一类重要的手性配体,因此手性氨基醇的合成研究具有重要的意义.文章合成了一种多手性中心氨基醇化合物.该化合物不仅具有刚性较大的环丙烷结构,并且具有多个手性中心,可应用于药理学筛选和不对称催化研究.     

基于双流多关系GCNs的骨架动作识别方法

基于图卷积网络(graph convolutional networks,GCNs)的骨架动作识别方法只能对关节点间的单一关系进行建模,缺少描述多种关系的能力.借鉴知识图谱描述实体之间不同关系的思想,提出一种基于关节点流和肢体流的双流多关系GCNs人体骨架动作识别方法,对图结点间的自然连接关系、对称关系和全局关系进行建...