肥皂草素对斑玉蕈菌丝体抗氧化酶活性及相关基因表达量的影响

【目的】研究肥皂草素对斑玉蕈菌丝体的活性氧代谢、超氧化物歧化酶(SOD)和过氧化氢酶(CAT)活性及其基因表达的影响。【方法】在摇瓶培养基中添加不同浓度的肥皂草素研究菌丝体在不同培养时间内活性氧代谢、SOD和CAT的活性及其基因表达的变化。【结果】添加肥皂草素后,在7?11 d内超氧阴离子及丙二醛(MDA)的含量整体较对照组有升高趋势,但是在13?15 d含量较对照又有所下降;肥皂草素处理的菌丝体SOD活性随着浓度的增加而逐渐增强(第9天除外),特别是在培养13?15 d更加明显;CAT活性同样随着浓度的增加而逐渐增强,特别是0.05 g/L实验组酶活性始终保持在较高的水平;不同浓度的肥皂草素都能使Mn-SOD基因、CAT基因的表达出现上调趋势,在0.05 g/L时,肥皂草素诱导Mn-SOD基因的表达与其活性的变化趋势基本一致,而CAT基因表达与其活性的变化并不一致,其活性可能受基因翻译后的修饰调控等因素的影响。【结论】肥皂草素的添加能够诱导SOD和CAT酶活性的增强及上调Mn-SOD、CAT基因的表达量,减缓菌丝培养后期活性氧和MDA的积累。     

The gene of golden plumage colour linked with lower fertility in Mangurian Golden quail

Summary Relation between golden plumage colour and lowered fertility has been found in quails (Coturnix coturnix japonica). Golden colour of plumage is due to the presence of a single gene G, while its recessive alleles gg determine partridge-like colour. Homozygotic birds of GG pattern die during incubation.Project No. 09.3.1.3.1.6.     

基因表达差异显示分析技术在创伤研究中的应用

基因是细胞增殖,分化,成熟等各项生命活动的调控中心,也是许多痢疾发生,发展和转归的决定性因素。基因表达的变化必然导致细胞,组织,器官乃至整个机体的各种异常。包括创伤在内的各种内外刺激,都可不同程度地引起基因表达的变化,最终妨碍机体健康。随着生物信息学的逐渐兴起和分子生物学的不断发展并向其他学科的逐渐渗透,业已建立起一系列研究基因表达变化的切实可行的技术手段（即“基因表达差异分析技术”,如DNA微阵列）,对捕获基因表达的种种变化具有重要价值。这些技术已经在肿瘤及其他疾病的研究中得到广泛应用,近几年也逐渐进入创伤研究领域,在一定程度上推动了创伤研究的发展。     

Visualization of Differential Gene Expression – Using Fluorescence‐Based cDNA‐AFLP

cDNA‐AFLP is one of the techniques developed to study differentially expressed genes. This recent technique is advantageous because it does not need prior sequence knowledge and is reliable due to highly stringent PCR conditions. The traditional cDNA‐AFLP method uses radioactively labelled products and is characterised by high sensitivity and resolution. Here, the use of Cy5‐labelled primers to detect products on polyacrylamide gels is reported. This non‐radioactive method, based on fluorescence, is shown to be faster and the recovery of interesting bands is easier. The study of the differential gene expression of the interaction between potato and Phytophthora infestans was used for the valuation of this method. Different gene expression profiles – such as up‐regulation, down‐regulation or point expression – were obtained. Moreover, this technique was shown to be highly reproducible.     

The correlation of gene expression and co-regulated gene patterns in characteristic KEGG pathways

There is great interest in chromosome- and pathway-based techniques for genomics data analysis in the current work in order to understand the mechanism of disease. However, there are few studies addressing the abilities of machine learning methods in incorporating pathway information for analyzing microarray data. In this paper, we identified the characteristic pathways by combining the classification error rates of out-of-bag (OOB) in random forests with pathways information. At each characteristic pathway, the correlation of gene expression was studied and the co-regulated gene patterns in different biological conditions were mined by Mining Attribute Profile (MAP) algorithm. The discovered co-regulated gene patterns were clustered by the average-linkage hierarchical clustering technique. The results showed that the expression of genes at the same characteristic pathway were approximate. Furthermore, two characteristic pathways were discovered to present co-regulated gene patterns in which one contained 108 patterns and the other contained one pattern. The results of cluster analysis showed that the smallest similarity coefficient of clusters was more than 0.623, which indicated that the co-regulated patterns in different biological conditions were more approximate at the same characteristic pathway. The methods discussed in this paper can provide additional insight into the study of microarray data.     

合成基因线路规模化设计面临的挑战

随着合成基因线路规模的增加,传统的合成基因线路设计思路的瓶颈逐渐凸显,许多之前被忽略的因素对大规模基因线路的性能可能造成显著影响,这对合成基因线路的设计带来了新的挑战。本文重点梳理了基因表达噪声和竞争效应两方面对基因线路性能的影响,阐释了二者间的紧密联系,并基于理性设计的思路,从模拟-数字运算设计、网络拓扑设计、基因线路中的信息传递理论和动态信号等方面,归纳总结了解决这些问题的潜在方案,并展望了规模化合成基因线路理性设计的未来发展方向。     

Assessing reliability of gene clusters from gene expression data

The rapid development of microarray technologies has raised many challenging problems in experiment design and data analysis. Although many numerical algorithms have been successfully applied to analyze gene expression data, the effects of variations and uncertainties in measured gene expression levels across samples and experiments have been largely ignored in the literature. In this article, in the context of hierarchical clustering algorithms, we introduce a statistical resampling method to assess the reliability of gene clusters identified from any hierarchical clustering method. Using the clustering trees constructed from the resampled data, we can evaluate the confidence value for each node in the observed clustering tree. A majority-rule consensus tree can be obtained, showing clusters that only occur in a majority of the resampled trees. We illustrate our proposed methods with applications to two published data sets. Although the methods are discussed in the context of hierarchical clustering methods, they can be applied with other cluster-identification methods for gene expression data to assess the reliability of any gene cluster of interest. Electronic Publication     

Dynamic models of gene expression and classification

Powerful new methods, like expression profiles using cDNA arrays, have been used to monitor changes in gene expression levels as a result of a variety of metabolic, xenobiotic or pathogenic challenges. This potentially vast quantity of data enables, in principle, the dissection of the complex genetic networks that control the patterns and rhythms of gene expression in the cell. Here we present a general approach to developing dynamic models for analyzing time series of whole genome expression. In this approach, a self-consistent calculation is performed that involves both linear and non-linear response terms for interrelating gene expression levels. This calculation uses singular value decomposition (SVD) not as a statistical tool but as a means of inverting noisy and near-singular matrices. The linear transition matrix that is determined from this calculation can be used to calculate the underlying network reflected in the data. This suggests a direct method of classifying genes according to their place in the resulting network. In addition to providing a means to model such a large multivariate system this approach can be used to reduce the dimensionality of the problem in a rational and consistent way, and suppress the strong noise amplification effects often encountered with expression profile data. Non-linear and higher-order Markov behavior of the network are also determined in this self-consistent method. In data sets from yeast, we calculate the Markov matrix and the gene classes based on the linear-Markov network. These results compare favorably with previously used methods like cluster analysis. Our dynamic method appears to give a broad and general framework for data analysis and modeling of gene expression arrays. Electronic Publication     

Intrinsic noise, gene regulation and steady-state statistics in a two-gene network

The intrinsic noise in a two-gene network model is analysed. The technique of the Fokker-Planck approximation is used to investigate the statistics of noise when the system state is near a stable equilibrium. This is called also the steady-state statistics. The relative size of noise is measured by the Fano factor that is defined as the ratio of the variance to the mean. Our main result shows that in general, the noise control in a two-gene network might be a very complicated process, but for the repressor-repressor system that is a very important case in investigating the genetic switch, the relative size of noise, i.e. the Fano factor, must be bigger than one for both the repressor proteins.     

Adeno-associated virus-based vectors in gene therapy

Adeno-associated virus (AAV) vectors were shown capable of high efficiency transduction of both dividing and nondividing cells and tissues. AAV-mediated transduction leads to stable, long-term transgene expression in the absence of apparent immune response. These properties and the broad host range of AAV vectors indicate that they constitute a powerful tool for gene therapy purposes. An additional potential benefit of AAV vectors is their ability to integrate site-specifically in the presence of Rep proteins which can be expressed transiently, thus limiting their suspected adverse effects. The major restrictions of AAV as vectors are their limited genetic capacity and strict packaging size constraint of less than 5 kb. Another difficulty is the labor-intensive and expensive procedure for the production and packaging of recombinant AAV vectors. The major benefits and drawbacks of AAV vectors and advances made in the past 3 years are discussed.     

High-throughput analysis of gene expression on tissue sections by in situ hybridization

Genome-scale sequencing projects, high-throughput RNAi screens, systematic gene targeting, and system-biology-based network predictions all depend on a validation of biological significance in order to understand the relevance of a particular finding. Such validation, for the most part, rests on low-throughput technologies. This article provides protocols that, in combination with suitable instrumentation, make possible a semi-automated analysis of gene expression on tissue sections by means of in situ hybridization. Knowledge of gene expression localization has the potential to aid, and thereby accelerate, the validation of gene functions.     

Stochastic gene expression in switching environments

Organisms are known to adapt to regularly varying environments. However, in most cases, the fluctuations of the environment are irregular and stochastic, alternating between favorable and unfavorable regimes, so that cells must cope with an uncertain future. A possible response is population diversification. We assume here that the cell population is divided into two groups, corresponding to two phenotypes, having distinct growth rates, and that cells can switch randomly their phenotypes. In static environments, the net growth rate is maximized when the population is homogeneously composed of cells having the largest growth rate. In random environments, growth rates fluctuate and observations reveal that sometimes heterogeneous populations have a larger net growth rate than homogeneous ones, a fact illustrated recently through Monte-Carlo simulations based on a birth and migration process in a random environment. We study this process mathematically by focusing on the proportion f(t) of cells having the largest growth rate at time t, and give explicitly the related steady state distribution π. We also prove the convergence of empirical averages along trajectories to the first moment , and provide efficient numerical methods for computing .      

Class-specific correlations of gene expressions: identification and their effects on clustering analyses

Current microarray studies primarily focus on identifying individual genes with differential expression levels across different conditions or classes. A potential problem is that they may disregard multidimensional information hidden in gene interactions. In this study, we propose an approach to detect gene interactions related to study phenotypes through identifying gene pairs with correlations that appear to be class or condition specific. In addition, we explore the effects of ignoring class-specific correlations (CSC) on correlation-based gene-clustering analyses. Our simulation studies show that ignoring CSC can significantly decrease the accuracy of gene clustering and increase the dissimilarity within clusters. Our results from a DLBCL (distinct types of diffuse large B cell lymphoma) data set illustrate that CSC are clearly present and have great adverse effects on gene-clustering results if ignored. Meanwhile, interesting biological interpretations may be derived from studying gene pairs with CSC. This study demonstrates that our algorithm is simple and computationally efficient and has the ability to detect gene pairs with CSC that are informative for uncovering interesting regulation patterns.