LC3真核表达载体构建及其在293T细胞中自噬诱导研究

【目的】构建pEGFP-N1-LC3真核表达载体,并将其转染进人胚肾细胞293T,通过Earle's盐平衡液(Balanced salt solution,EBSS)饥饿诱导观察细胞自噬现象。【方法】RT-PCR扩增LC3基因,并将其插入pEGFP-N1真核表达载体构建重组质粒。将重组质粒转染至人胚肾细胞293T,显微镜观察、Western blot检测GFP-LC3融合蛋白表达情况。对转染细胞进行Earle's盐平衡液饥饿诱导,通过Western blot验证自噬指标,激光共聚焦显微镜观察自噬泡形成。【结果】成功构建pEGFP-N1-LC3真核表达载体,并在293T细胞中成功表达目的蛋白,在进行Earle's盐平衡液饥饿诱导后观察到自噬泡的形成,Western blot检测到LC3-Ⅰ向LC3-Ⅱ的转化。【结论】本实验为研究自噬在癌细胞病理进程中的机制提供了实验素材。     

面向同源蛋白质探测的一种新型混合深度学习模型

根据蛋白质氨基酸链探测其同源蛋白质,进而预测蛋白质的功能,是生物信息学研究领域的一个重要挑战,也是众多生物医学研究领域的基础研究内容,有着重要的科研价值和广泛的应用需求。其研究难点在于:(1)如何学习对同源蛋白质预测有效、有用的蛋白质特征信息;(2)如何更好地运用蛋白质特征信息,实现同源蛋白质的探测与识别。为了解决同源蛋白质探测与识别研究中的关键难点,本文提出一种基于混合深度学习架构的同源蛋白质探测与识别模型(HDLM-PHP)。通过采用统一的"管道式"深度学习架构,将蛋白质特征学习和探测识别统一为一个整体,提高同源蛋白质探测与识别的效能。采用多组并行的深度卷积神经网络,学习蛋白质的各种属性信息,以期获得丰富的待检测蛋白质和靶蛋白质的高级相关性特征,并通过全连接方式使用多层RBM结构融合和精炼这些相关性特征为全局相关性特征。通过统一的深度网络连接方式,以探测和识别任务为导向,学习到对于同源蛋白质预测最有效、最全面的蛋白质特征信息。在标准数据集SCOPe上,对所提模型进行性能与效率评测,结果表明:本文提出的模型能有效地学习到符合任务导向的蛋白质特征数据,提升同源蛋白质探测与识别的准确度和召回率,优于现有的模型和算法。     

PBNA： An Improved Probabilistic Biological Network Alignment Method

Biological network alignment is an important research topic in the field of bioinformatics. Nowadays almost every existing alignment method is designed to solve the deterministic biological network alignment problem.However, it is worth noting that interactions in biological networks, like many other processes in the biological realm,are probabilistic events. Therefore, more accurate and better results can be obtained if biological networks are characterized by probabilistic graphs. This probabilistic information, however, increases difficulties in analyzing networks and only few methods can handle the probabilistic information. Therefore, in this paper, an improved Probabilistic Biological Network Alignment（PBNA） is proposed. Based on Iso Rank, PBNA is able to use the probabilistic information. Furthermore, PBNA takes advantages of Contributor and Probability Generating Function（PGF） to improve the accuracy of node similarity value and reduce the computational complexity of random variables in similarity matrix. Experimental results on dataset of the Protein-Protein Interaction（PPI） networks provided by Todor demonstrate that PBNA can produce some alignment results that ignored by the deterministic methods, and produce more biologically meaningful alignment results than Iso Rank does in most of the cases based on the Gene Ontology Consistency（GOC） measure. Compared with Prob method, which is designed exactly to solve the probabilistic alignment problem, PBNA can obtain more biologically meaningful mappings in less time.     

基于全基因组测序的粘绿木霉Gv29-8中分泌蛋白预测

粘绿木霉Gv29-8作为木霉属中重要的生防菌之一,对该菌分泌蛋白进行预测及其特征进行明确具有重要的理论意义。利用SignalP、ProtComp等预测程序对该菌中12427条蛋白质序列进行分泌蛋白找寻,并对上述分泌蛋白的氨基酸分布、信号肽长度大小及切割位点等性质进行分析。粘绿木霉含有分泌蛋白为377个,其氨基酸长度、信号肽长度与植物病原菌不同;信号肽切割位点属于A-X-A类型,与其他已经报道的植物病原真菌、卵菌中分泌蛋白信号肽切割位点一致。     

For whom the bell tolls? DING proteins in health and disease

DING proteins, identified mainly by their eponymous N-terminal sequences, are ubiquitous in living organisms. Amongst bacteria, they are common in pseudomonads, and have been characterised with respect to genetics and structure. They form part of a wider family of phosphate-binding proteins, with emerging roles in phosphate acquisition and pathogenicity. Many DING proteins have been isolated in eukaryotes, in which they have been associated with very diverse biological activities, often in the context of possible signalling roles. Disease states in which DING proteins have been implicated include rheumatoid arthritis, lithiasis, atherosclerosis, some tumours and tumour-associated cachexia, and bacterial and viral adherence. Complete genetic and structural characterisation of eukaryotic DING genes and proteins is still lacking, though the phosphate-binding site seems to be conserved. Whether as bacterial proteins related to bacterial pathogenicity, or as eukaryotic components of biochemical signalling systems, DING proteins require further study. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Improving Students＇ English Reading Skills

This paper aims to help students who study English as a second language improve reading skills. Based on the reading theoretical foundation, some problems which often encountered during the process of reading will be discussed. Finally, a conclusion as to how to solve these problems will be made.     

糖类遗传与生命起源

从遗传学角度探讨了糖类与生命起源，讨论了遗传学上的再认识，提出糖类在遗传学上扮演着重要、核心的角色、糖类是生命起源物质，基因是糖类衍生物。生命诞生过程可能是：先有糖类、然后有氨基酸和碱基；磷酸及其盐类既是生命物质的组成部分、也是催化剂，阳光或地热为能源，于是诞生了基因。生命体是分子相互作用、有序组合、自我调控的耗散体系。     

SCP-2基因的表达调控研究进展

固醇转运蛋白(SCP-2)是广泛存在于脊椎动物和无脊椎动物中的非特异性脂转运蛋白.任何影响固醇转运蛋白基因表达的因素都会影响甾类激素的合成,进而影响生物体的发育.目前对该基因的转录调控研究较少,主要集中在哺乳动物.环境、激素、转录因子等对SCP-2基因的表达调控均有影响,对SCP-2基因作为害虫防治靶标的可能性进行了初步的探讨.C/EBP蛋白作为调控Sl SCPx基因表达的转录因子,可以调控Sl SCPx基因的表达.推测了C/EBP蛋白成为新的害虫防治的靶标的可能.     

蛋白质二级结构预测概率图模型的改进

蛋白质二级结构与蛋白质三级结构及蛋白质功能密切相关,是生物信息学研究的热点,其中概率图模型隐马尔可夫算法(HMM)是该领域研究的重要工具。但是在实际应用中,存在着HMM训练下溢、不同训练集的效果差异较大及参数优化困难等问题。对预测蛋白质二级结构时HMM遇到的训练下溢问题提出了改进方案;首次提出8-状态HMM来预测蛋白质二级结构,并且将参数B改进成为包含状态转移信息的三维参数;为了改进最优HMM模型的确定方法,用每个样本分别对初始HMM模型进行训练,得到一系列新的模型,然后对这些新模型的参数求均值,将求得的均值作为最优模型的参数。这些改进方法提高了HMM预测蛋白质二级结构的准确率,为HMM的进一步优化打下良好的基础。     

多运行水平马尔可夫可修系统可靠性分析

为了更准确地刻画马尔可夫可修系统的运行特征,建立了多运行水平马尔可夫可修系统模型.在该系统中,工作状态被聚合成几个不同的运行水平.应用离子通道理论和Laplace变换方法,给出系统在不同运行水平逗留时间密度函数的Laplace变换及平均逗留时间.分析了系统访问不同运行水平的次数,得到它们的分布律及平均值.通过一个数值示例说明了结论的应用,表明了该模型的有效性.