The activation process of Arabidopsis thaliana A1 gene encoding the translation elongation factor EF-1α is conserved among angiosperms

In Arabidopsis thaliana, the activation process of the A1 EF-1 gene depends on several elements. Using the GUS reporter gene, transient expression experiments have shown that mutations of upstream cis-acting elements of the A1 promoter, or the deletion of an intron located within the 5 non-coding region, similarly affect expression in dicot or monocot protoplasts. The results reported here strongly suggest that this 5 intron is properly spliced in Zea mays. We show that two trans-acting factors, specifically interacting with an upstream activating sequence (the TEF 1 box), are present in nuclear extracts prepared from A. thaliana, Brassica rapa, Nicotiana tabacum and Z. mays. In addition, a DNA sequence homologous to the TEF 1 box, found at approximately the same location within a Lycopersicon esculentum EF-1 promoter, interacts with the same trans-acting factors. Homologies found between the A. thaliana and L. esculentum TEF 1 box sequences have allowed us to define mutations of this upstream element which affect the interaction with the corresponding trans-acting factors. These results support the notion that the activation processes of A. thaliana EF-1 genes have been conserved among angiosperms and provide interesting data on the functional structure of the TEF 1 box.     

Isolation and characterization of nuclear genes coding for subunits of the yeast ubiquinol-cytochrome c reductase complex

Nuclear genes coding for the M_r 17000, 14000 and 11000 subunits of the ubiquinol-cytochrome c reductase complex (complex III) in yeast have been isolated from a clone bank of yeast nuclear DNA by use of a mRNA hybridization-competition assay. This is based on our observations that levels of mRNAs for these subunits are much reduced during glucose repression and in cytoplasmic petite mutants and the procedure should be of general application for the isolation of other inducible or repressible genes coding for mRNAs present at low levels in the cell. A first characterization of the clones is presented. The genes are not closely linked in the genome and those coding for M_r 14000 and 11000 subunits are present in unique genomic environments, which suggests that there are only single copies of each in the nuclear genome.     

斑马鱼急性无机砷暴露后肝脏差异表达基因的生物信息学分析

砷是一种致癌物,是心血管、外周血管疾病、神经疾病、糖尿病和各种癌症的致病因素。目的:利用GO数据库和KEGG数据库等生物信息学方法对GEO数据库数据中的差异表达基因进行评价。利用生物信息学分析软件对差异基因进行功能富集、功能注释分析和生存分析。利用Cytoscape上的蛋白-蛋白相互作用网络(Protein-protein interaction network, PPI)软件对179个差异基因进行筛选和分析。结果发现126个基因作用于蛋白靶点,其中有10个基因为关键基因分别为:PSMB3、HSP701、HSPE1、STIP1、HSPD1、HSP70、DNAJB1B、HSP90AA1.1、HSPA9H和TCP1。核心基因主要作用于内质网中的蛋白质加工通路。这可能会为砷对肝脏损伤的潜在生物标志物和生物学机制提供新的思路。     

抑郁症易感基因和抗抑郁药物靶点相关[]神经细胞类型及相互作用网络分析

基于抑郁症的全基因组关联分析研究(GWAS),对于获得的单核苷酸多态性位点(SNP)使用Haploreg软件进行基因注释,得到SNP注释的102个易感基因.。使用MAGMA软件对GWAS的汇总统计数据做基因水平的分析,获得了270个校正之后显著的基因,两者合并共得到320个抑郁症易感基因。通过药物数据库Drugbank获取133个抗抑郁药物靶点基因。使用EWCE包对抑郁症易感基因和抗抑郁药物靶点在三套脑组织单细胞测序数据中,分别进行神经细胞类型富集分析。结果发现大脑皮质的GABA神经元(抑制性神经元)和谷氨酸能神经元(兴奋性神经元)是抑郁症易感基因和抗抑郁药物靶点共同的神经元。这两种类型的神经细胞可能是抗抑郁药物与抑郁症易感基因相互作用的神经细胞,另外少突胶质前体细胞可能是抑郁症特有的易感神经细胞。使用Network Calculator软件构建网络并进行进行网络拓扑学参数分析。结果表明抑郁症易感基因与抗抑郁药物靶点组成了一个具有显著的相互连接的网络。本研究从单细胞层面揭示抑郁症的遗传机制,在网络层面为寻找新的抗抑郁药物靶点提供了一定的启示。     

Identification of Coffee Genes Expressed During Systemic Acquired Resistance and Incompatible Interaction with Hemileia vastatrix

Coffee genes associated with systemic acquired resistance (SAR) and incompatible reaction against coffee leaf rust inoculation were identified by suppression subtractive hybridization. Analysis of 384 clones of each of the subtracted cDNA libraries identified genes involved in oxidative burst/apoptosis/hypersensitive response, synthesis of antimicrobial proteins, synthesis and transport of antimicrobial metabolites, signal perception and transduction, metabolism of lipids, regulated protein degradation and cell maintenance and development. Induction of distinct sets of genes in the two resistance responses was observed. A wide range of genes involved in defence responses described in other plant species was also found in coffee plants. Semi-quantitative and quantitative RT-PCR analysis of seven selected genes showed differences in their expression profile within 72 h after treatment. Full-length cDNA sequences of two β-1,3-glucanases, one induced during SAR and the other in the incompatible reaction, were obtained by 5' and 3' RACE and the sequence data suggest different properties and cellular localization of the encoded proteins.     

<Emphasis Type="Italic">Physcomitrella patens</Emphasis> is highly tolerant against drought,salt and osmotic stress

In order to determine the degree of tolerance of the moss Physcomitrella patens to different abiotic stress conditions, we examined its tolerance against salt, osmotic and dehydration stress. Compared to other plants like Arabidopsis thaliana, P. patens exhibits a high degree of abiotic stress tolerance, making it a valuable source for the identification of genes effecting the stress adaptation. Plants that had been treated with NaCl tolerated concentrations up to 350 mM. Treatments with sorbitol revealed that plants are able to survive concentrations up to 500 mM. Furthermore, plants that had lost 92% water on a fresh-weight basis were able to recover successfully. For molecular analyses, a P. patens expressed sequence tag (EST) database was searched for cDNA sequences showing homology to stress-associated genes of seed plants and bacteria. 45 novel P. patens genes were identified and subjected to cDNA macroarray analyses to define their expression pattern in response to water deficit. Among the selected cDNAs, we were able to identify a set of genes that is specifically up-regulated upon dehydration. These genes encode proteins exerting their function in maintaining the integrity of the plant cell as well as proteins that are known to be members of signaling networks. The identified genes will serve as molecular markers and potential targets for future functional analyses.     

脑细胞的基因及其表达与衰老

概述了脑与衰老在神经分子生物学方面的研究进展 ,包括 :细胞衰老分子机制的主要进展 ,衰老脑在基因及其表达水平的研究进展 ;阿尔茨海默病 (AlzheimerDisease ,AD)相关基因研究进展 ,帕金森病 (porkinsondisease ,PD)相关基因研究进展[7] 。这些研究成果对脑与衰老的关系、对脑在基因及其表达水平上衰老机制认识的加深乃至对衰老脑的基因治疗均具有理论意义和应用价值。     

Prediction of potential drivers connecting different dysfunctional levels in lung adenocarcinoma via a protein–protein interaction network

Lung cancer is a serious disease that threatens an affected individual's life. Its pathogenesis has not yet to be fully described, thereby impeding the development of effective treatments and preventive measures. “Cancer driver” theory considers that tumor initiation can be associated with a number of specific mutations in genes called cancer driver genes. Four omics levels, namely, (1) methylation, (2) microRNA, (3) mutation, and (4) mRNA levels, are utilized to cluster cancer driver genes. In this study, the known dysfunctional genes of these four levels were used to identify novel driver genes of lung adenocarcinoma, a subtype of lung cancer. These genes could contribute to the initiation and progression of lung adenocarcinoma in at least two levels. First, random walk with restart algorithm was performed on a protein–protein interaction (PPI) network constructed with PPI information in STRING by using known dysfunctional genes as seed nodes for each level, thereby yielding four groups of possible genes. Second, these genes were further evaluated in a test strategy to exclude false positives and select the most important ones. Finally, after conducting an intersection operation in any two groups of genes, we obtained several inferred driver genes that contributed to the initiation of lung adenocarcinoma in at least two omics levels. Several genes from these groups could be confirmed according to recently published studies. The inferred genes reported in this study were also different from those described in a previous study, suggesting that they can be used as essential supplementary data for investigations on the initiation of lung adenocarcinoma. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.