Global gene expression profiling in Escherichia coli K12. The effects of oxygen availability and FNR

The work presented here is a first step toward a long term goal of systems biology, the complete elucidation of the gene regulatory networks of a living organism. To this end, we have employed DNA microarray technology to identify genes involved in the regulatory networks that facilitate the transition of Escherichia coli cells from an aerobic to an anaerobic growth state. We also report the identification of a subset of these genes that are regulated by a global regulatory protein for anaerobic metabolism, FNR. Analysis of these data demonstrated that the expression of over one-third of the genes expressed during growth under aerobic conditions are altered when E. coli cells transition to an anaerobic growth state, and that the expression of 712 (49%) of these genes are either directly or indirectly modulated by FNR. The results presented here also suggest interactions between the FNR and the leucine-responsive regulatory protein (Lrp) regulatory networks. Because computational methods to analyze and interpret high dimensional DNA microarray data are still at an early stage, and because basic issues of data analysis are still being sorted out, much of the emphasis of this work is directed toward the development of methods to identify differentially expressed genes with a high level of confidence. In particular, we describe an approach for identifying gene expression patterns (clusters) obtained from multiple perturbation experiments based on a subset of genes that exhibit high probability for differential expression values.     

Using weighted permutation scores to detect differential gene expression with microarray data

A class of nonparametric statistical methods, including a nonparametric empirical Bayes (EB) method, the Significance Analysis of Microarrays (SAM) and the mixture model method (MMM) have been proposed to detect differential gene expression for replicated microarray experiments. They all depend on constructing a test statistic, for example, a t-statistic, and then using permutation to draw inferences. However, due to special features of microarray data, using standard permutation scores may not estimate the null distribution of the test statistic well, leading to possibly too conservative inferences. We propose a new method of constructing weighted permutation scores to overcome the problem: posterior probabilities of having no differential expression from the EB method are used as weights for genes to better estimate the null distribution of the test statistic. We also propose a weighted method to estimate the false discovery rate (FDR) using the posterior probabilities. Using simulated data and real data for time-course microarray experiments, we show the improved performance of the proposed methods when implemented in MMM, EB and SAM.     

Multidimensional local false discovery rate for microarray studies

MOTIVATION: The false discovery rate (fdr) is a key tool for statistical assessment of differential expression (DE) in microarray studies. Overall control of the fdr alone, however, is not sufficient to address the problem of genes with small variance, which generally suffer from a disproportionally high rate of false positives. It is desirable to have an fdr-controlling procedure that automatically accounts for gene variability. METHODS: We generalize the local fdr as a function of multiple statistics, combining a common test statistic for assessing DE with its standard error information. We use a non-parametric mixture model for DE and non-DE genes to describe the observed multi-dimensional statistics, and estimate the distribution for non-DE genes via the permutation method. We demonstrate this fdr2d approach for simulated and real microarray data. RESULTS: The fdr2d allows objective assessment of DE as a function of gene variability. We also show that the fdr2d performs better than commonly used modified test statistics. AVAILABILITY: An R-package OCplus containing functions for computing fdr2d() and other operating characteristics of microarray data is available at http://www.meb.ki.se/~yudpaw.     

Differential analysis of DNA microarray gene expression data

Here, we review briefly the sources of experimental and biological variance that affect the interpretation of high-dimensional DNA microarray experiments. We discuss methods using a regularized t-test based on a Bayesian statistical framework that allow the identification of differentially regulated genes with a higher level of confidence than a simple t-test when only a few experimental replicates are available. We also describe a computational method for calculating the global false-positive and false-negative levels inherent in a DNA microarray data set. This method provides a probability of differential expression for each gene based on experiment-wide false-positive and -negative levels driven by experimental error and biological variance.     

Coupled analysis of gene expression and chromosomal location

Microarray technology can be used to assess simultaneously global changes in expression of mRNA or genomic DNA copy number among thousands of genes in different biological states. In many cases, it is desirable to determine if altered patterns of gene expression correlate with chromosomal abnormalities or assess expression of genes that are contiguous in the genome. We describe a method, differential gene locus mapping (DIGMAP), which aligns the known chromosomal location of a gene to its expression value deduced by microarray analysis. The method partitions microarray data into subsets by chromosomal location for each gene interrogated by an array. Microarray data in an individual subset can then be clustered by physical location of genes at a subchromosomal level based upon ordered alignment in genome sequence. A graphical display is generated by representing each genomic locus with a colored cell that quantitatively reflects its differential expression value. The clustered patterns can be viewed and compared based on their expression signatures as defined by differential values between control and experimental samples. In this study, DIGMAP was tested using previously published studies of breast cancer analyzed by comparative genomic hybridization (CGH) and prostate cancer gene expression profiles assessed by cDNA microarray experiments. Analysis of the breast cancer CGH data demonstrated the ability of DIGMAP to deduce gene amplifications and deletions. Application of the DIGMAP method to the prostate data revealed several carcinoma-related loci, including one at 16q13 with marked differential expression encompassing 19 known genes including 9 encoding metallothionein proteins. We conclude that DIGMAP is a powerful computational tool enabling the coupled analysis of microarray data with genome location.     

哺乳动物转录因子DNA结合谱

基因差异表达是生物发育和对刺激作出应答的分子基础,转录因子在这种基因差异表达中发挥着重要的调控作用。因此,要弄清楚转录因子调控基因差异表达的机理,就必须鉴定出它们全部的靶基因并构建其操纵的转录调控网络。对基因组DNA的序列特异性结合是转录因子调控基因转录的关键环节,因此,要鉴定转录因子的靶基因,就必须从它们与DNA相互作用的分子水平,鉴定它们能够识别并结合的全部DNA序列,即转录因子DNA结合谱。近年来随着DNA微阵列芯片和高通量DNA测序技术的产生和快速发展,出现了建立转录因子体内及体外DNA结合谱的一系列革命性的新技术,对该领域的研究带来重大影响。这些新技术主要包括建立转录因子体内DNA结合谱的染色质免疫沉淀-芯片技术(ChIP-chip)和染色质免疫沉淀-测序技术(ChIP-Seq),以及建立转录因子体外DNA结合谱的双链DNA微阵列芯片技术(dsDNA microarray)、指数富集配体系统进化-系列分析基因表达技术(SELEX-SAGE)、结合-n-测序技术(Bind-n-Seq)、多重大规模并行SELEX技术(MMP-SELEX)、凝胶迁移实验-测序技术(EMSA-Seq)和高通量测序-荧光配体互作图谱分析技术(HiTS-FLIP)。文章将对这些新技术做一综述。     

Lrp, a leucine-responsive protein, regulates branched-chain amino acid transport genes in Escherichia coli.

We investigated the relationship between two regulatory genes, livR and lrp, that map near min 20 on the Escherichia coli chromosome. livR was identified earlier as a regulatory gene affecting high-affinity transport of branched-chain amino acids through the LIV-I and LS transport systems, encoded by the livJ and livKHMGF operons. lrp was characterized more recently as a regulatory gene of a regulon that includes operons involved in isoleucine-valine biosynthesis, oligopeptide transport, and serine and threonine catabolism. The expression of each of these livR- and lrp-regulated operons is altered in cells when leucine is added to their growth medium. The following results demonstrate that livR and lrp are the same gene. The lrp gene from a livR1-containing strain was cloned and shown to contain two single-base-pair substitutions in comparison with the wild-type strain. Mutations in livR affected the regulation of ilvIH, an operon known to be controlled by lrp, and mutations in lrp affected the regulation of the LIV-I and LS transport systems. Lrp from a wild-type strain bound specifically to several sites upstream of the ilvIH operon, whereas binding by Lrp from a livR1-containing strain was barely detectable. In a strain containing a Tn10 insertion in lrp, high-affinity leucine transport occurred at a high, constitutive level, as did expression from the livJ and livK promoters as measured by lacZ reporter gene expression. Taken together, these results suggest that Lrp acts directly or indirectly to repress livJ and livK expression and that leucine is required for this repression. This pattern of regulation is unusual for operons that are controlled by Lrp.     

Histone H1 depletion in mammals alters global chromatin structure but causes specific changes in gene regulation

Linker histone H1 plays an important role in chromatin folding in vitro. To study the role of H1 in vivo, mouse embryonic stem cells null for three H1 genes were derived and were found to have 50% of the normal level of H1. H1 depletion caused dramatic chromatin structure changes, including decreased global nucleosome spacing, reduced local chromatin compaction, and decreases in certain core histone modifications. Surprisingly, however, microarray analysis revealed that expression of only a small number of genes is affected. Many of the affected genes are imprinted or are on the X chromosome and are therefore normally regulated by DNA methylation. Although global DNA methylation is not changed, methylation of specific CpGs within the regulatory regions of some of the H1 regulated genes is reduced. These results indicate that linker histones can participate in epigenetic regulation of gene expression by contributing to the maintenance or establishment of specific DNA methylation patterns.     

Phytochrome regulation of nuclear gene expression in plants

The photoregulation of gene expression in higher plants was extensively studied during the 1980s, in particular the light-responsive cis -acting elements and trans -acting factors of the Lhcb and rbcS genes. However, little has been discovered about: (1) which plant genes are regulated by light, and (2) which photoreceptors control the expression of these genes. In the 1990s, the functional analysis of the various photoreceptors has progressed rapidly using photoreceptor-deficient mutants, including those of the phytochrome gene family. More recently however, advanced techniques for gene expression analysis, such as fluorescent differential display and DNA microarray technology, have become available enabling the global identification of genes that are regulated by particular photoreceptors. In this paper we describe distinct and overlapping effects of individual phytochromes on gene expression in Arabidopsis thaliana.     

Bayesian meta-analysis models for microarray data: a comparative study

Background

With the growing abundance of microarray data, statistical methods are increasingly needed to integrate results across studies. Two common approaches for meta-analysis of microarrays include either combining gene expression measures across studies or combining summaries such as p-values, probabilities or ranks. Here, we compare two Bayesian meta-analysis models that are analogous to these methods.

Results

Two Bayesian meta-analysis models for microarray data have recently been introduced. The first model combines standardized gene expression measures across studies into an overall mean, accounting for inter-study variability, while the second combines probabilities of differential expression without combining expression values. Both models produce the gene-specific posterior probability of differential expression, which is the basis for inference. Since the standardized expression integration model includes inter-study variability, it may improve accuracy of results versus the probability integration model. However, due to the small number of studies typical in microarray meta-analyses, the variability between studies is challenging to estimate. The probability integration model eliminates the need to model variability between studies, and thus its implementation is more straightforward. We found in simulations of two and five studies that combining probabilities outperformed combining standardized gene expression measures for three comparison values: the percent of true discovered genes in meta-analysis versus individual studies; the percent of true genes omitted in meta-analysis versus separate studies, and the number of true discovered genes for fixed levels of Bayesian false discovery. We identified similar results when pooling two independent studies of Bacillus subtilis. We assumed that each study was produced from the same microarray platform with only two conditions: a treatment and control, and that the data sets were pre-scaled.

Conclusion

The Bayesian meta-analysis model that combines probabilities across studies does not aggregate gene expression measures, thus an inter-study variability parameter is not included in the model. This results in a simpler modeling approach than aggregating expression measures, which accounts for variability across studies. The probability integration model identified more true discovered genes and fewer true omitted genes than combining expression measures, for our data sets.     

Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data

MOTIVATION: Biological processes in cells are properly performed by gene regulations, signal transductions and interactions between proteins. To understand such molecular networks, we propose a statistical method to estimate gene regulatory networks and protein-protein interaction networks simultaneously from DNA microarray data, protein-protein interaction data and other genome-wide data. RESULTS: We unify Bayesian networks and Markov networks for estimating gene regulatory networks and protein-protein interaction networks according to the reliability of each biological information source. Through the simultaneous construction of gene regulatory networks and protein-protein interaction networks of Saccharomyces cerevisiae cell cycle, we predict the role of several genes whose functions are currently unknown. By using our probabilistic model, we can detect false positives of high-throughput data, such as yeast two-hybrid data. In a genome-wide experiment, we find possible gene regulatory relationships and protein-protein interactions between large protein complexes that underlie complex regulatory mechanisms of biological processes.     

Parallel multiplicity and error discovery rate (EDR) in microarray experiments

Background  

In microarray gene expression profiling experiments, differentially expressed genes (DEGs) are detected from among tens of thousands of genes on an array using statistical tests. It is important to control the number of false positives or errors that are present in the resultant DEG list. To date, more than 20 different multiple test methods have been reported that compute overall Type I error rates in microarray experiments. However, these methods share the following dilemma: they have low power in cases where only a small number of DEGs exist among a large number of total genes on the array.     

猪链球菌2型强毒株05ZYH33转录调控因子Rgg基因敲除突变体与野生株的基因表达差异分析

目的:为了解猪链球菌2型强毒株05Z33转录调控因子Rgg的调控作用,用基因芯片方法分析野生株与rgg基因敲除突变体之间的差异表达基因。方法:用猪链球菌2型全基因组序列点样制备芯片,将芯片运用于rgg敲除株与野生株的基因表达差异研究,采用定量real-time PCR(qRT-PCR)验证表达谱结果。结果:在突变体中共发现45个基因表达量变化在2倍以上,其中19个基因表达上调,26个基因表达下调。这些基因在细菌毒力、免疫抗原、DNA合成和修复、基础代谢和ABC转运系统等方面起着重要作用。结论:转录调控因子Rgg是一个全局调控因子,但rgg敲除后并不影响猪链球菌的毒力。     

基于基因差异表达的微弱信号识别疾病相关功能

当两组样本间基因表达的差异程度较低或样本量较少时,采用通常的错误发现率(falsediscovery rate,FDR)控制水平(如5%或10%),可能无法识别足够多的差异表达基因以进行后续的功能富集分析。然而,功能富集分析对差异表达基因中的错误发现具有一定的稳健性。所以,采用较低的FDR控制水平(即允许较高的FDR)识别差异表达基因,可能可以可靠地发现疾病相关功能。本文分析了5套研究乳腺癌转移的基因表达谱,通过其中差异表达信号较强的3套数据,论证了即使差异表达基因的FDR达到25%,功能富集分析的结果仍具有较高的稳健性。然后,在另外2套差异表达信号微弱的数据中,采用25%的FDR控制水平筛选差异表达基因来进行功能富集分析,并与前述3套数据的功能富集结果做比较。结果显示,采用较低的FDR控制水平筛选差异表达基因,仍然可以可靠地识别乳腺癌转移相关功能。分析结果也提示,在乳腺癌转移过程中,一些功能较为宽泛的生物学过程(如细胞分裂、细胞周期和DNA复制等)整体受到了扰动,反映出乳腺癌转移是一种涉及广泛基因表达改变的系统性疾病。     

The “Window <Emphasis Type="Italic">t</Emphasis> test”: a simple and powerful approach to detect differentially expressed genes in microarray datasets

This work focuses on differential expression analysis of microarray datasets. One way to improve such statistical analyses is to integrate biological information in the design of these analyses. In this paper, we will use the relationship between the level of gene expression and variability. Using this biological information, we propose to integrate the information from multiple genes to get a better estimate of individual gene variance, when a small number of replicates are available, to increase the power of the statistical analysis. We describe a strategy named the “Window t test” that uses multiple genes which share a similar expression level to compute the variance which is then incorporated a classic t test. The performances of this new method are evaluated by comparison with classic and widely-used methods for differential expression analysis (the classic Student t test, the Regularized t test (reg t test), SAM, Limma, LPE and Shrinkage t). In each case tested, the results obtained were at least equivalent to the best performing method and, in most cases, outperformed it. Moreover, the Window t test relies on a very simple procedure requiring small computing power compared with other methods designed for microarray differential expression analysis. Electronic Supplementary Material  Supplementary material is available for this article at and is accessible for authorized users.     

Regulating the regulator: MicF RNA controls expression of the global regulator Lrp

Studies on the regulatory RNA MicF in Enterobacteriaceae reveal a pivotal role in gene regulation. Multiple target gene mRNAs were identified and, importantly, MicF RNA regulates the expression of the global regulatory gene lrp (Holmqvist et al., 2012; Corcoran et al., 2012). Thus MicF RNA is a central factor in a regulatory network that regulates bacterial cell physiology.