Analysis of host responses to microbial infection using gene expression profiling

Gene expression profiling offers new opportunities for understanding host-cell responses to microbial pathogens and their products. Current strategies involve either first identifying mRNAs that differ in their expression status under different experimental conditions and later defining the identity of the respective genes (for example, differential display or serial analysis of gene expression), or alternatively assessing changes in the expression of already defined genes (for example, cDNA or oligonucleotide microarrays). Early studies indicate the power of gene expression profiling for providing new insights into groups of genes whose expression is altered during the course of host-microbe interactions, and for the discovery of cellular genes that were not previously recognized to be regulated by infection.     

Sample size for gene expression microarray experiments

MOTIVATION: Microarray experiments often involve hundreds or thousands of genes. In a typical experiment, only a fraction of genes are expected to be differentially expressed; in addition, the measured intensities among different genes may be correlated. Depending on the experimental objectives, sample size calculations can be based on one of the three specified measures: sensitivity, true discovery and accuracy rates. The sample size problem is formulated as: the number of arrays needed in order to achieve the desired fraction of the specified measure at the desired family-wise power at the given type I error and (standardized) effect size. RESULTS: We present a general approach for estimating sample size under independent and equally correlated models using binomial and beta-binomial models, respectively. The sample sizes needed for a two-sample z-test are computed; the computed theoretical numbers agree well with the Monte Carlo simulation results. But, under more general correlation structures, the beta-binomial model can underestimate the needed samples by about 1-5 arrays. CONTACT: jchen@nctr.fda.gov.     

New insights about host response to smallpox using microarray data

Background  

Smallpox is a lethal disease that was endemic in many parts of the world until eradicated by massive immunization. Due to its lethality, there are serious concerns about its use as a bioweapon. Here we analyze publicly available microarray data to further understand survival of smallpox infected macaques, using systems biology approaches. Our goal is to improve the knowledge about the progression of this disease.     

Bayesian hierarchical model for identifying changes in gene expression from microarray experiments.

Recent developments in microarrays technology enable researchers to study simultaneously the expression of thousands of genes from one cell line or tissue sample. This new technology is often used to assess changes in mRNA expression upon a specified transfection for a cell line in order to identify target genes. For such experiments, the range of differential expression is moderate, and teasing out the modified genes is challenging and calls for detailed modeling. The aim of this paper is to propose a methodological framework for studies that investigate differential gene expression through microarrays technology that is based on a fully Bayesian mixture approach (Richardson and Green, 1997). A case study that investigated those genes that were differentially expressed in two cell lines (normal and modified by a gene transfection) is provided to illustrate the performance and usefulness of this approach.     

Nuclear lncRNA stabilization in the host response to bacterial infection

Long non‐coding RNAs (lncRNAs) play important roles in many cellular pathways, but their contribution to the defense of eukaryotic cells against pathogens remains poorly understood. A new study from Imamura et al in The EMBO Journal reports that Salmonella infection in human cells impacts nuclear RNA decay, which in turn drives the accumulation of otherwise unstable nuclear lncRNAs, some of which may have protective effects against this common bacterial pathogen. These unexpected findings demand more efforts to fully decrypt the molecular functions of lncRNAs in innate and adaptive immunity.     

The effect of replication on gene expression microarray experiments

MOTIVATION: We examine the effect of replication on the detection of apparently differentially expressed genes in gene expression microarray experiments. Our analysis is based on a random sampling approach using real data sets from 16 published studies. We consider both the ability to find genes that meet particular statistical criteria as well as the stability of the results in the face of changing levels of replication. RESULTS: While dependent on the data source, our findings suggest that stable results are typically not obtained until at least five biological replicates have been used. Conversely, for most studies, 10-15 replicates yield results that are quite stable, and there is less improvement in stability as the number of replicates is further increased. Our methods will be of use in evaluating existing data sets and in helping to design new studies.     

Mixture modelling of gene expression data from microarray experiments

MOTIVATION: Hierarchical clustering is one of the major analytical tools for gene expression data from microarray experiments. A major problem in the interpretation of the output from these procedures is assessing the reliability of the clustering results. We address this issue by developing a mixture model-based approach for the analysis of microarray data. Within this framework, we present novel algorithms for clustering genes and samples. One of the byproducts of our method is a probabilistic measure for the number of true clusters in the data. RESULTS: The proposed methods are illustrated by application to microarray datasets from two cancer studies; one in which malignant melanoma is profiled (Bittner et al., Nature, 406, 536-540, 2000), and the other in which prostate cancer is profiled (Dhanasekaran et al., 2001, submitted).     

A random variance model for detection of differential gene expression in small microarray experiments

MOTIVATION: Microarray techniques provide a valuable way of characterizing the molecular nature of disease. Unfortunately expense and limited specimen availability often lead to studies with small sample sizes. This makes accurate estimation of variability difficult, since variance estimates made on a gene by gene basis will have few degrees of freedom, and the assumption that all genes share equal variance is unlikely to be true. RESULTS: We propose a model by which the within gene variances are drawn from an inverse gamma distribution, whose parameters are estimated across all genes. This results in a test statistic that is a minor variation of those used in standard linear models. We demonstrate that the model assumptions are valid on experimental data, and that the model has more power than standard tests to pick up large changes in expression, while not increasing the rate of false positives. AVAILABILITY: This method is incorporated into BRB-ArrayTools version 3.0 (http://linus.nci.nih.gov/BRB-ArrayTools.html). SUPPLEMENTARY MATERIAL: ftp://linus.nci.nih.gov/pub/techreport/RVM_supplement.pdf     

Helminth-primed dendritic cells alter the host response to enteric bacterial infection

To examine whether intestinal helminth infection may be a risk factor for enteric bacterial infection, a murine model was established using the intestinal helminth Heligomosomoides polygyrus and a murine pathogen Citrobacter rodentium, which causes infectious colitis. Using this model we recently have shown that coinfection with the Th2-inducing H. polygyrus and C. rodentium promotes bacterial-associated disease and colitis. In this study, we expand our previous observations and examine the hypothesis that dendritic cells (DC) stimulated by helminth infection may play an important role in the regulation of the intestinal immune response to concurrent C. rodentium infection as well as in the modulation of the bacterial pathogenesis. We show that H. polygyrus infection induces DC activation and IL-10 expression, and that adoptive transfer of parasite-primed DC significantly impairs host protection to C. rodentium infection, resulting in an enhanced bacterial infection and in the development of a more severe colonic injury. Furthermore, we demonstrate that adoptive transfer of parasite-primed IL-10-deficient DCs fails to result in the development of a significantly enhanced C. rodentium-mediated colitis. Similarly, when the DC IL-10 response was neutralized by anti-IL-10 mAb treatment in mice that received parasite-primed DC, no deleterious effect of the parasite-primed DC on the host intestinal response to C. rodentium was detected. Thus, our results provide evidence to indicate that the H. polygyrus-dependent modulation of the host response to concurrent C. rodentium infection involves IL-10-producing DCs.     

Lipid A diversity and the innate host response to bacterial infection

Lipopolysaccharide, a component of the outer membrane of Gram-negative bacteria, is a potent immunostimulatory molecule which activates the innate host defense system. Over the past few years progress has been made in identifying the molecular mechanisms of host recognition of lipid A (a component of lipopolysaccharide), the identification of the genes required for Escherichia coli lipid A biosynthesis, and the role of lipid A acylation when viable bacteria are presented to host cells. Recent data indicate that bacteria can regulate this molecule in response to different host microenvironments. Host factors that induce lipid A modifications and the resultant changes in host response remain to be determined.     

Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection

MOTIVATION: The immune response to bacterial infection represents a complex network of dynamic gene and protein interactions. We present an optimized reverse engineering strategy aimed at a reconstruction of this kind of interaction networks. The proposed approach is based on both microarray data and available biological knowledge. RESULTS: The main kinetics of the immune response were identified by fuzzy clustering of gene expression profiles (time series). The number of clusters was optimized using various evaluation criteria. For each cluster a representative gene with a high fuzzy-membership was chosen in accordance with available physiological knowledge. Then hypothetical network structures were identified by seeking systems of ordinary differential equations, whose simulated kinetics could fit the gene expression profiles of the cluster-representative genes. For the construction of hypothetical network structures singular value decomposition (SVD) based methods and a newly introduced heuristic Network Generation Method here were compared. It turned out that the proposed novel method could find sparser networks and gave better fits to the experimental data. CONTACT: Reinhard.Guthke@hki-jena.de.     

Computational strategies for analyzing data in gene expression microarray experiments

Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.     

AtCAST, a tool for exploring gene expression similarities among DNA microarray experiments using networks

The comparison of gene expression profiles among DNA microarray experiments enables the identification of unknown relationships among experiments to uncover the underlying biological relationships. Despite the ongoing accumulation of data in public databases, detecting biological correlations among gene expression profiles from multiple laboratories on a large scale remains difficult. Here, we applied a module (sets of genes working in the same biological action)-based correlation analysis in combination with a network analysis to Arabidopsis data and developed a 'module-based correlation network' (MCN) which represents relationships among DNA microarray experiments on a large scale. We developed a Web-based data analysis tool, 'AtCAST' (Arabidopsis thaliana: DNA Microarray Correlation Analysis Tool), which enables browsing of an MCN or mining of users' microarray data by mapping the data into an MCN. AtCAST can help researchers to find novel connections among DNA microarray experiments, which in turn will help to build new hypotheses to uncover physiological mechanisms or gene functions in Arabidopsis.