基因重组型枯草芽孢杆菌芽孢表面展示技术与应用

枯草芽孢杆菌是一种好氧型革兰氏阳性益生菌,在营养匮乏的环境下,可形成具有强抗逆性的芽孢。其芽孢独特的结构和生理功能使得芽孢表面展示技术相较于其他表面展示技术具有多种优势,构建的重组芽孢不但易纯化、回收率高,而且安全性好。近年来,枯草芽孢杆菌芽孢表面展示技术得到迅猛发展,已成功利用CotB、CotC、CotG、CotZ、CotA、OxdD、CotE、CotZ、CgeA等多种锚定蛋白将外源蛋白或多肽展示在芽孢表面,并应用于工业化酶、口服疫苗和药物、大分子量多聚体蛋白的生产以及环境污染的生物治理等领域。本文首先介绍了芽孢展示系统整合策略,并重点阐述了基因重组型枯草芽孢杆菌芽孢展示技术中涉及的锚定蛋白以及该技术在各领域的应用与展望。     

类泛素化修饰Neddylation在心脏生物学中的作用

Neural precursor cell-expressed developmentally downregulated 8(NEDD8)是类泛素蛋白家族的一员,其结构与泛素相似,通过E1激活酶、E2结合酶和E3连接酶等酶促级联反应对蛋白进行翻译后修饰,这一过程即为neddylation。Neddylation异常已被证实与癌症、神经退行性疾病和先天性心脏病等多种疾病密切相关。近年来,neddylation和deneddylation(底物上的NEDD8在deneddylation酶的作用下被去除,称为deneddylation)在心血管系统中的作用备受关注,本文将主要阐述neddylation的生物学过程及其在心脏生物学中的作用。     

酸碱改性粉煤灰对SBR反应器处理氨氮废水的影响

利用1mol/L 盐酸和1mol/L NaOH溶液对粉煤灰进行酸碱改性,将改性前后的粉煤灰加入处理模拟氨氮废水的SBR反应器中,反应器菌源来自于污水处理厂生化池活性污泥,逐渐提高进水NH⁺₄-N质量浓度,检测反应器NH⁺₄-N质量浓度及反应器中污泥质量浓度、优势菌群丰度.实验结果表明,当反应器运行40d,进水NH⁺₄-N质量浓度为861.1mg/L时,R3(添加经酸碱改性粉煤灰)反应器、R2(添加未改性粉煤灰)反应器、R1(未添加粉煤灰)反应器NH⁺₄-N去除率分别为100%,98.76%,91.87%;利用蛋白质质量浓度间接表征三个反应器中的污泥质量浓度,R3反应器中蛋白质质量浓度最高为829.08mg/L,R2次之为789.37mg/L,R1最小为154.2mg/L.此外,高通量测序结果显示R3反应器中硝化菌和亚硝化菌群丰度均高于R1,R2反应器中菌群丰度.     

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.     

An Overview of Kernelization Algorithms for Graph Modification Problems

Kernelization algorithms for graph modification problems are important ingredients in parameterized computation theory. In this paper, we survey the kernelization algorithms for four types of graph modification problems, which include vertex deletion problems, edge editing problems, edge deletion problems, and edge completion problems. For each type of problem, we outline typical examples together with recent results, analyze the main techniques, and provide some suggestions for future research in this field.     

钴、锰改性方法对酚醛炭泡沫除SO_2/NO的影响

研究金属Mn,Co的不同改性方法对酚醛活性炭泡沫表面物理结构、化学性质,以及脱硫脱硝效率的影响.以MnCl2,CoCl2为改性剂,采用内、外两种改性法对酚醛炭泡沫进行金属负载改性,并进行模拟烟气脱硫脱硝的实验.实验结果表明:经CoCl2内、外改性样品的脱硫效率较未改性样品(CF0)分别提高了22.9%,8.2%,脱硝效率提高了58.6%,134%;经MnCl2内、外改性样品的脱硫效率分别提高了4.5%,3.1%,脱硝效率提高了79.3%,10.3%.因此,内改性有利于酚醛活性炭泡沫脱硫脱硝,其中CFCo-n脱硫脱硝效果最佳.     

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.