乙醇胁迫抑制毕赤酵母表达外源蛋白的转录组学分析

在5 L发酵罐中进行毕赤酵母发酵表达猪?干扰素的实验,发现甘油培养末期乙醇的积累会抑制外源蛋白的表达。从转录组学角度系统分析不同浓度乙醇胁迫条件下,毕赤酵母甘油培养期和甲醇诱导期细胞的生理状态变化。研究结果表明,在甘油培养期,乙醇胁迫使得毕赤酵母细胞中的545个基因发生了显著差异表达(265个基因表达上调,280个基因表达下调),这些差异表达基因的功能主要涉及蛋白质合成、能量代谢、细胞周期和过氧化物酶代谢。乙醇胁迫增加了蛋白质错误折叠的情况,降低了核糖体和线粒体的结构完整性,使得甘油培养末期无法得到大量具有健全功能的酵母细胞。在甲醇诱导期,与甲醇代谢、蛋白质加工合成、氨基酸代谢等途径相关的294个基因发生了显著差异表达(171个基因表达上调,123个基因表达下调),导致内质网胁迫不能被及时解除,破坏了细胞内的氨基酸正常代谢。     

TOR信号调控真菌细胞的生长与代谢

TOR(target of rapamycin)是一类进化上保守的丝氨酸/苏氨酸(Ser/Thr)蛋白激酶,是真核细胞响应环境信号调控生长和代谢的关键因子。真菌TOR信号途径在营养、压力环境等刺激下,通过核糖体生物合成、营养物质摄入及代谢等过程调节维持胞内稳态。本文主要综述了酵母细胞TOR及TOR复合物的结构,以及近年来真菌TORC1蛋白在不同营养环境、压力等条件下对细胞生长与自噬、代谢以及胁迫生理响应等生命活动的调控机制进展及未来发展方向,为真菌TOR调控生长和代谢产物提供新思路。     

鲁氏接合酵母海藻糖耦合发酵过程的转录组差异分析

[目的]为探究鲁氏接合酵母(Zygosaccharomyces rouxii)在海藻糖耦合发酵过程中适应高温胁迫的基因表达差异及内在代谢调节机制。[方法]采用GC色谱仪和Illumina HiSeq^TM高通量测序技术对海藻糖耦合发酵过程(无调控阶段、补料响应阶段、补料与中温刺激耦合响应阶段、补料与高温刺激耦合响应阶段)的生物量、胞内海藻糖含量、基因转录序列等指标进行检测。[结果]转录组测序结果显示，与无调控阶段对比，三个调控响应阶段分别筛选得到1717、522、2038个差异表达基因。海藻糖耦合发酵过程中的鲁氏接合酵母细胞差异表达基因主要富集到次级代谢产物的生物合成、核糖体、氨基酸的生物合成等代谢途径上，高温胁迫条件下(海藻糖高速合成过程)分别涉及158、97、58个差异表达基因，次级代谢主要涉及赖氨酸等氨基酸和嘌呤/嘧啶核苷酸，氨基酸的生物合成主要涉及赖氨酸代谢、半胱氨酸和谷氨酸代谢。[结论]通过转录组分析揭示了鲁氏接合酵母主要通过调控次级代谢产物的生物合成与氨基酸代谢来适应高温热激胁迫。     

TERT对小鼠EL-4淋巴瘤细胞增殖调控的初步研究

为了研究端粒酶催化亚基TERT与细胞周期相关基因的调控关系及其在肿瘤细胞增殖中的调控作用,应用基因芯片及RT-PCR等技术,对靶向mTERT的RNA干涉后的小鼠EL-4淋巴瘤细胞进行细胞周期相关基因的表达谱分析,筛选到43个基因在TERT受抑制前后存在表达差异,并且全部为下调基因.表明在小鼠EL-4淋巴瘤细胞中内源性TERT的表达抑制,导致了调控G1期和S期进程的相关细胞周期相关基因的表达变化,并可能通过此途径影响肿瘤细胞的生长和增殖.     

拟南芥在盐胁迫环境下SOS转录调控网络的构建及分析

研究拟南芥在高浓度盐处理环境下的基因调控网络, 有助于了解其在盐胁迫环境下保持正常生长的防御机制。针对目前广泛研究的SOS (Salt Overly Sensitive)耐盐机制, 文章整合公共数据库中盐胁迫相关的拟南芥基因组表达谱芯片, 通过反向工程方法构建了拟南芥在盐胁迫状态下的SOS转录调控网络。所获得的调控网络包含70个盐胁迫相关且高度互作的互作基因, 其中27个转录因子为主要调控节点。进而根据SOS核心基因的表达特性, 所得调控网络内的不同表达模式得到了鉴别。     

P13K—Akt—mTORC1信号途径在细胞生长增殖中的调控作用

P13K-AKT—mTORCl信号途径在细胞生长增殖中起重要调控作用,P13K-Akt—mTORl信号途径能够调节细胞周期相关蛋白基因的表达来调控细胞的增殖;同时,P13K—Akt-mTORl信号途径也能够调控细胞的生长和大小;P13K-Akt-mTORCl信号途径的异常活化与肿瘤发生紧密相关。就P13K—AKT-mTORCl信号途径在细胞生长增殖中的作用作一综述。     

基于整合方法分析茶树响应病原真菌胁迫的共有模式

为探讨茶树(Camellia sinensis)对病菌胁迫的共有响应模式和抗病机制,运用生物信息学方法对多组RNA-seq数据进行提取、整合及功能富集,结合多种工具和数据库资源对主要调控分子及蛋白互作模块加以分析。结果表明,病原真菌胁迫下,茶树有较多细胞色素P450家族成员表达显著上调;类固醇和激素的代谢过程、苯丙烷合成途径被激活,有丝分裂细胞周期调控、DNA甲基化等生物过程及光合作用途径受到抑制;主要调控分子如转录因子WRKY和NAC、激酶RLK-Pelle和CAMK等以上调为主。差异表达的蛋白互作模块分析表明,有丝分裂周期调控、基于微管运动、淀粉和蔗糖代谢、细胞壁多糖合成、光合作用、类黄酮代谢模块明显下调,木质素合成和萜类生物合成模块上调;且模块之间可能存在互作。病菌胁迫激活的木质素和萜类合成途径的关键基因包括阿魏酸-5-羟基化酶基因F5H、过氧化物酶基因POD和萜类合成酶基因HMGR等。细胞色素P450基因可能在病菌胁迫中起关键作用,增强木质素和萜类物质的合成、削弱光合作用可能是茶树响应真菌胁迫的核心模式。     

植物应答低温胁迫机制的研究进展

低温是植物生长过程中遇到的主要环境胁迫因子之一,而植物响应低温胁迫是一个多因素协同作用的过程,涉及到复杂的基因表达调控网络。尤其是低温下植物体内生理生化、细胞骨架结构及基因表达调控等方面的改变及相关机制,一直受到研究者的普遍关注。该文主要从细胞学及分子生物学等角度入手,将低温胁迫下植物对低温的响应及可能机制进行综述,着重对植物通过细胞内部细胞器结构与功能的改变来抵御或适应低温,尤其对细胞骨架,以及低温信号转导受体及中间体、下游胁迫相关基因的表达及其在细胞内部的调控及应答机制等方面的作用进行探讨,为耐低温植物新品种的培育及农业生产实践提供理论指导。     

果生刺盘孢CfHAC1调控应答二硫苏糖醇胁迫的转录组分析

果生刺盘孢Colletotrichum fructicola是油茶炭疽病优势病原菌。前期研究发现bZIP转录因子CfHac1参与调控该菌的生长发育和致病性。为了揭示转录因子CfHac1调控果生刺盘孢响应内质网压力和致病机理,本研究测定了ΔCfhac1突变体对内质网压力胁迫剂的敏感性,发现突变体对二硫苏糖醇（dithiothreitol,DTT）的耐受性下降,说明CfHAC1基因可能参与调控果生刺盘孢响应内质网压力胁迫过程。进一步利用高通量RNA-seq技术对该病菌野生型菌株和CfHAC1敲除突变体菌株在DTT胁迫下的转录组进行了比较分析,结果表明差异表达基因共有2 680个,其中上调表达基因有1 181个,下调表达基因有1 499个。Gene Ontology 功能分析结果显示,差异表达基因主要参与催化活性、结合、代谢过程、细胞过程、细胞成分合成、生物过程调控和应激反应等生物学过程。KEGG功能富集分析表明,上调表达基因主要被富集到核糖体、真核细胞的核糖体生物合成、RNA转运和氰基氨基酸代谢通路中;下调表达基因显著富集在内质网蛋白质加工、N-聚糖生物合成、类固醇合成和蛋白质分泌等通路中。分析发现转录因子CfHac1调控内质网胁迫应答和致病相关基因的表达。本研究提供了在全基因组水平上对CfHAC1基因与内质网压力胁迫应答之间关联的新认识,为阐明果生刺盘孢响应内质网压力胁迫和致病机制奠定了基础。     

植物miRNA的分子特征及其在逆境中的响应机制

逆境胁迫是影响植物生长发育、生物产量与品质形成的主要因素之一。通过诱导表达抗逆有关的编码基因与部分非编码基因是植物响应逆境的主要方式。miRNA作为一种非编码基因在植物生长、发育以及抗逆等过程中起重要的调控作用。研究表明:逆境胁迫下miRNA可以形成miRNA诱导沉默复合物(miRNA-induced silencing complex,miRISC),并与靶mRNA互补配对结合,进而引起靶mRNA的降解或者抑制其翻译,从而实现对下游抗逆相关基因表达的调控,最终引起代谢与信号转导途径的变化实现对逆境的响应。本文从植物逆境胁迫下诱导miRNA的产生、靶基因的识别以及作用机制等方面进行了综述。     

Gene Networks in Plant Ozone Stress Response and Tolerance

For many plant species ozone stress has become much more severe in the last decade. The accumulating evidence for the significant effects of ozone pollutant on crop and forest yield situate ozone as one of the most important environmental stress factors that limits plant productivity woddwide. Today, transcdptomic approaches seem to give the best coverage of genome level responses. Therefore, microarray serves as an invaluable tool for global gene expression analyses, unravelling new information about gene pathways, in-species and crose-species gene expression comparison, and for the characterization of unknown relationships between genes. In this review we summadze the recent progress in the transcdptomics of ozone to demonstrate the benefits that can be harvested from the application of integrative and systematic analytical approaches to study ozone stress response. We focused our consideration on microarray analyses identifying gene networks responsible for response and tolerance to elevated ozone concentration. From these analyses it is now possible to notice how plant ozone defense responses depend on the interplay between many complex signaling pathways and metabolite signals.     

Gene Networks in Plant Ozone Stress Response and Tolerance

For many plant species ozone stress has become much more severe in the last decade. The accumulating evidence for the significant effects of ozone pollutant on crop and forest yield situate ozone as one of the most important environmental stress factors that limits plant productivity woddwide. Today, transcdptomic approaches seem to give the best coverage of genome level responses. Therefore, microarray serves as an invaluable tool for global gene expression analyses, unravelling new information about gene pathways, in-species and crose-species gene expression comparison, and for the characterization of unknown relationships between genes. In this review we summadze the recent progress in the transcdptomics of ozone to demonstrate the benefits that can be harvested from the application of integrative and systematic analytical approaches to study ozone stress response. We focused our consideration on microarray analyses identifying gene networks responsible for response and tolerance to elevated ozone concentration. From these analyses it is now possible to notice how plant ozone defense responses depend on the interplay between many complex signaling pathways and metabolite signals.     

Retrograde signaling and plant stress: plastid signals initiate cellular stress responses

Retrograde signaling coordinates the expression of nuclear genes encoding organellar proteins with the metabolic and developmental state of the organelle. These plastid signals are essential not only for coordinating photosynthetic gene expression in both the nucleus and in the chloroplasts but also for mediating plant stress responses. The chloroplasts therefore act as sensors of environmental changes and complex networks of plastid signals coordinate cellular activities and assist the cell during plant stress responses. Recent work suggests that information from both cytosolic-signaling and plastid-signaling networks must be integrated for the plant cell to respond optimally to environmental stress.     

Identification of CHO Endogenous Gene Regulatory Elements

The objective of this approach was to identify new CHO endogenous gene regulatory elements that are capable of regulating foreign gene expression in recombinant CHO host cells. The standard technology for the production of many biopharmaceutical products is frequently based on expression vectors that utilize strong mammalian viral promoters like SV40 or CMV which allow for very high expression rates but this may lead to constitutive over-expression resulting in a permanent stress for the cell. In addition, some heterologous promoters are cell-cycle dependent and can be subject to gene silencing generating heterogeneity within the cell population. Here, we describe the construction of a genomic CHO library and the subsequent identification and isolation of selected target sequences that are believed to be responsible for high level expression of the associated genes. The method that was used to isolate these regions of interest relies on gene specific amplification with primer pairs binding on different genes and the vector sequence. Flanking regions of these fragments were identified through Inverse PCR from fragmented and self-ligated genomic DNA. Expression levels of both the initially derived and the mapped fragments were determined through a luciferase reporter assay.     

Learning Gene Networks under SNP Perturbations Using eQTL Datasets

The standard approach for identifying gene networks is based on experimental perturbations of gene regulatory systems such as gene knock-out experiments, followed by a genome-wide profiling of differential gene expressions. However, this approach is significantly limited in that it is not possible to perturb more than one or two genes simultaneously to discover complex gene interactions or to distinguish between direct and indirect downstream regulations of the differentially-expressed genes. As an alternative, genetical genomics study has been proposed to treat naturally-occurring genetic variants as potential perturbants of gene regulatory system and to recover gene networks via analysis of population gene-expression and genotype data. Despite many advantages of genetical genomics data analysis, the computational challenge that the effects of multifactorial genetic perturbations should be decoded simultaneously from data has prevented a widespread application of genetical genomics analysis. In this article, we propose a statistical framework for learning gene networks that overcomes the limitations of experimental perturbation methods and addresses the challenges of genetical genomics analysis. We introduce a new statistical model, called a sparse conditional Gaussian graphical model, and describe an efficient learning algorithm that simultaneously decodes the perturbations of gene regulatory system by a large number of SNPs to identify a gene network along with expression quantitative trait loci (eQTLs) that perturb this network. While our statistical model captures direct genetic perturbations of gene network, by performing inference on the probabilistic graphical model, we obtain detailed characterizations of how the direct SNP perturbation effects propagate through the gene network to perturb other genes indirectly. We demonstrate our statistical method using HapMap-simulated and yeast eQTL datasets. In particular, the yeast gene network identified computationally by our method under SNP perturbations is well supported by the results from experimental perturbation studies related to DNA replication stress response.     

Modeling of Hysteresis in Gene Regulatory Networks

Hysteresis, observed in many gene regulatory networks, has a pivotal impact on biological systems, which enhances the robustness of cell functions. In this paper, a general model is proposed to describe the hysteretic gene regulatory network by combining the hysteresis component and the transient dynamics. The Bouc-Wen hysteresis model is modified to describe the hysteresis component in the mammalian gene regulatory networks. Rigorous mathematical analysis on the dynamical properties of the model is presented to ensure the bounded-input-bounded-output (BIBO) stability and demonstrates that the original Bouc-Wen model can only generate a clockwise hysteresis loop while the modified model can describe both clockwise and counter clockwise hysteresis loops. Simulation studies have shown that the hysteresis loops from our model are consistent with the experimental observations in three mammalian gene regulatory networks and two E.coli gene regulatory networks, which demonstrate the ability and accuracy of the mathematical model to emulate natural gene expression behavior with hysteresis. A comparison study has also been conducted to show that this model fits the experiment data significantly better than previous ones in the literature. The successful modeling of the hysteresis in all the five hysteretic gene regulatory networks suggests that the new model has the potential to be a unified framework for modeling hysteresis in gene regulatory networks and provide better understanding of the general mechanism that drives the hysteretic function.