相对行常量差异共表达双聚类挖掘算法

在生物信息学上,挖掘差异共表达双聚类有助于研究衰老、癌变类变化的生物过程。以往的差异共表达双聚类定义仅仅从一组基因的角度来衡量差异,导致包含了很多噪声。为了克服上述缺点提出新的差异共表达支持度MiSupport,可以将一组基因的差异细化到基因级别;并由此定义提出MiCluster算法,可以在两个真实的基因芯片数据中挖掘最大的差异共表达双聚类。MiCluster算法首先基于两个基因芯片数据构建差异共表达权值图,然后基于权值图,采用样本扩展和层次扩展,并利用精确的候选产生方法和高效的剪枝策略,挖掘出最大的差异共表达双聚类。实验结果证明,MiCluster算法比现有的算法快速高效,而且通过均方误差(MSE)测试和基因本体(GO)评价,挖掘出来结果具有更大的统计意义和生物学意义。     

乙肝表面抗原在酿酒酵母中的异源表达

乙肝表面抗原HBs Ag是乙肝疫苗的主要组分。从转录水平、翻译水平以及翻译后水平对其进行优化,实现了HBs Ag在重组酿酒酵母中的高效异源表达。与组成型启动子TEF1、TDH3、HXT7、ADH1相比,诱导型启动子GAL1可大幅度提高HBs Ag的表达;与GCCACC和AAAAAA相比,kozak序列TACACA最有利于HBs Ag的翻译表达;而共表达二硫键异构酶pdi对HBs Ag的活性表达具有显著的促进作用。通过3种水平的优化,HBs Ag活性从最初的8.87 IU/g提高至151.08 IU/g;最终,利用Ni柱亲和层析成功获得纯化的HBs Ag,活性为3.32 IU/m L。     

精氨酸合成途径关键酶基因的过量表达及其对L-精氨酸合成的影响

由CarAB、ArgD、ArgJ三个基因分别编码的氨基甲酰磷酸合成酶、乙酰鸟氨酸转氨酶、鸟氨酸乙酰基转移酶是L-精氨酸合成途径的关键酶。以谷氨酸棒杆菌ATCC14067为出发菌株,利用大肠杆菌-谷氨酸棒杆菌的穿梭质粒pEC-XK99E-p’、PEC-T18mob2,分别对精氨酸生物合成途径的关键酶基因CarAB、ArgD、ArgJ进行单个基因表达、对ArgD、ArgJ基因进行串联表达以及对CarAB、ArgD、ArgJ三个基因进行共表达,分别得到过量表达菌株14067-pEC-CarAB、14067-T18-ArgD、14067-T18-ArgJ、14067-T18-ArgDJ、14067-pEC-CarAB-T18-ArgDJ。检测上述三个基因单独表达,串联表达及共表达对L-精氨酸产量的影响。摇瓶发酵实验证明,这五株菌产精氨酸的能力较出发菌株ATCC14067分别提高了1.61、1.14、1.19、1.28、2.91倍。产量最高的共表达菌株14067-pEC-CarAB-T18-ArgDJ,通过实时荧光定量PCR检测,表明CarA、CarB、ArgD、ArgJ基因的表达量分别是ATCC14067的2.50、5.60、3.59、4.70倍。这表明过量表达基因CarAB、ArgD、ArgJ可以有效提高精氨酸的产量。     

黄色短杆菌ilvN基因定点突变和ilvBN、ilvC串联表达对L-缬氨酸产量的影响

由ilvBN、ilvC基因编码的乙酰羟酸合成酶（AHAS）和乙酰羟酸异构还原酶（AHAIR）是L-缬氨酸合成途径的两个关键酶。本实验以黄色短杆菌Brevibacterium flavum MD515为出发菌株,通过PCR技术扩增其ilvBN和ilvC基因,对调节亚基ilvN进行定点突变,获得抗反馈抑制突变型编码基因ilvBN^rC;然后将其插入穿梭表达载体pZ8-1中,构建串联表达质粒pZ8-1-ilvBN^rC并转化出发菌株,筛选获得工程菌株B.flavum MD515/pZ8-1-ilvBN^rC。摇瓶发酵该工程菌株L-缬氨酸产量达29.5 g·L^-1,较出发菌株提高27.7%,同时生长速度和生物量也比出发菌株有所提高,丙氨酸含量降低,L-亮氨酸及L-异亮氨酸含量提高。在30 L发酵罐连续补料发酵60 h后L-缬氨酸产量达61.7 g·L^-1,糖酸转化率为39.2%。菌株MD515/pZ8-1-ilvBN^rC发酵液透光率较出发菌株高且蛋白含量低,这些特性有利于发酵液后期的分离提取。     

An Integrative Transcriptomic Analysis of Systemic Juvenile Idiopathic Arthritis for Identifying Potential Genetic Markers and Drug Candidates

Systemic juvenile idiopathic arthritis (sJIA) is a rare subtype of juvenile idiopathic arthritis, whose clinical features are systemic fever and rash accompanied by painful joints and inflammation. Even though sJIA has been reported to be an autoinflammatory disorder, its exact pathogenesis remains unclear. In this study, we integrated a meta-analysis with a weighted gene co-expression network analysis (WGCNA) using 5 microarray datasets and an RNA sequencing dataset to understand the interconnection of susceptibility genes for sJIA. Using the integrative analysis, we identified a robust sJIA signature that consisted of 2 co-expressed gene sets comprising 103 up-regulated genes and 25 down-regulated genes in sJIA patients compared with healthy controls. Among the 128 sJIA signature genes, we identified an up-regulated cluster of 11 genes and a down-regulated cluster of 4 genes, which may play key roles in the pathogenesis of sJIA. We then detected 10 bioactive molecules targeting the significant gene clusters as potential novel drug candidates for sJIA using an in silico drug repositioning analysis. These findings suggest that the gene clusters may be potential genetic markers of sJIA and 10 drug candidates can contribute to the development of new therapeutic options for sJIA.     

Identification of Novel Potential Genes Involved in Cancer by Integrated Comparative Analyses

The main hallmarks of cancer diseases are the evasion of programmed cell death, uncontrolled cell division, and the ability to invade adjacent tissues. The explosion of omics technologies offers challenging opportunities to identify molecular agents and processes that may play relevant roles in cancer. They can support comparative investigations, in one or multiple experiments, exploiting evidence from one or multiple species. Here, we analyzed gene expression data from induction of programmed cell death and stress response in Homo sapiens and compared the results with Saccharomyces cerevisiae gene expression during the response to cell death. The aim was to identify conserved candidate genes associated with Homo sapiens cell death, favored by crosslinks based on orthology relationships between the two species. We identified differentially-expressed genes, pathways that are significantly dysregulated across treatments, and characterized genes among those involved in induced cell death. We investigated on co-expression patterns and identified novel genes that were not expected to be associated with death pathways, that have a conserved pattern of expression between the two species. Finally, we analyzed the resulting list by HumanNet and identified new genes predicted to be involved in cancer. The data integration and the comparative approach between distantly-related reference species that were here exploited pave the way to novel discoveries in cancer therapy and also contribute to detect conserved genes potentially involved in programmed cell death.     

基于离散时序基因表达数据的双聚类算法

目前应用于基因表达数据上的双聚类算法大多是基于真实数据提出的, 因此易受噪声干扰, 且这些算法很少考虑样本间的时序性。提出了一种有效的时间点连续的双聚类挖掘算法DTCB, 从离散的时序基因表达数据中挖掘出时间点连续的最大共表达双聚类。该算法使用了一种新的数据离散化方法, 同时提出了三种在离散数据集下基因间的共表达关系; 为了提高挖掘效率, DTCB使用了有效的剪枝和输出策略, 可以在不产生候选集的情况下一次性挖掘出所有的最大共表达双聚类。通过实验分析, 证明DTCB具有高效的性能和良好的鲁棒性, 且结果具有较好的统计和生物意义。