Selection and validation of microarray candidate genes from subregions and subnuclei of the brain

DNA array technology now allows an enormous amount of expression data to be obtained. For large-scale gene profiling enterprises, this is of course welcome. However, the scientist interested in follow-up studies of a handful of differentially expressed genes may find it hard to sift through the vast datasets to pinpoint genes with the most desirable and reliable behaviors. Here, we present the methodology we have employed to discover genes differentially expressed in the adult mouse brain. We first used Affymetrix microarrays to compare gene expression from five different brain regions: the amygdala, cerebellum, hippocampus, olfactory bulb, and periaqueductal gray. Second, we identified genes differentially expressed within three distinct amygdala subnuclei. In this case, the tissue was microdissected by laser-capture to minimize contamination from adjacent subnuclei, and extracted RNA was subjected to three rounds of linear amplification prior to hybridization to the microarrays. To select candidate genes, we developed a custom algorithm to identify those genes with the most robust changes in expression across different replicate samples. Confirmation of expression patterns with in situ hybridization uncovered further criteria to consider in the selection process.     

Modeling Genome-Wide Dynamic Regulatory Network in Mouse Lungs with Influenza Infection Using High-Dimensional Ordinary Differential Equations

The immune response to viral infection is regulated by an intricate network of many genes and their products. The reverse engineering of gene regulatory networks (GRNs) using mathematical models from time course gene expression data collected after influenza infection is key to our understanding of the mechanisms involved in controlling influenza infection within a host. A five-step pipeline: detection of temporally differentially expressed genes, clustering genes into co-expressed modules, identification of network structure, parameter estimate refinement, and functional enrichment analysis, is developed for reconstructing high-dimensional dynamic GRNs from genome-wide time course gene expression data. Applying the pipeline to the time course gene expression data from influenza-infected mouse lungs, we have identified 20 distinct temporal expression patterns in the differentially expressed genes and constructed a module-based dynamic network using a linear ODE model. Both intra-module and inter-module annotations and regulatory relationships of our inferred network show some interesting findings and are highly consistent with existing knowledge about the immune response in mice after influenza infection. The proposed method is a computationally efficient, data-driven pipeline bridging experimental data, mathematical modeling, and statistical analysis. The application to the influenza infection data elucidates the potentials of our pipeline in providing valuable insights into systematic modeling of complicated biological processes.     

Global gene expression analysis of bovine somatic cell nuclear transfer blastocysts and cotyledons

Low developmental competence of bovine somatic cell nuclear transfer (SCNT) embryos is a universal problem. Abnormal placentation has been commonly reported in SCNT pregnancies from a number of species. The present study employed Affymetrix bovine expression microarrays to examine global gene expression patterns of SCNT and in vivo produced (AI) blastocysts as well as cotyledons from day‐70 SCNT and AI pregnancies. SCNT and AI embryos and cotyledons were analyzed for differential expression. Also in an attempt to establish a link between abnormal gene expression patterns in early embryos and cotyledons, differentially expressed genes were compared between the two studies. Microarray analysis yielded a list of 28 genes differentially expressed between SCNT and AI blastocysts and 19 differentially expressed cotyledon genes. None of the differentially expressed genes were common to both groups, although major histocompatibility complex I (MHCI) was significant in the embryo data and approached significance in the cotyledon data. This is the first study to report global gene expression patterns in bovine AI and SCNT cotyledons. The embryonic gene expression data reported here adds to a growing body of data that indicates the common occurrence of aberrant gene expression in early SCNT embryos. Mol. Reprod. Dev. 76: 471–482, 2009. © 2008 Wiley‐Liss, Inc.     

Signature patterns revealed by microarray analyses of mice infected with influenza virus A and Streptococcus pneumoniae

We used cDNA microarrays to identify differentially expressed genes in mice in response to infections with influenza virus A/PR/8/34 (H1N1) and Streptococcus pneumoniae. Expression microarray analysis showed up-regulation and down-regulation of many genes involved in the defense, inflammatory response and intracellular signaling pathways including chemokine, apoptosis, MAPK, Notch, Jak-STAT, T-cell receptor and complement and coagulation cascades. We have revealed signature patterns of gene expression in mice infected with two different classes of pathogens: influenza virus A and S. pneumoniae. Quantitative real-time RT-PCR results confirmed microarray results for most of the genes tested. These studies document clear differences in gene expression profiles between mice infected with influenza virus A and S. pneumoniae. Identification of genes that are differentially expressed after respiratory infections can provide insights into the mechanisms by which the host interacts with different pathogens, useful information about stage of diseases and selection of suitable targets for early diagnosis and treatments. The advantage of this novel approach is that the detection of pathogens is based on the differences in host gene expression profiles in response to different pathogens instead of detecting pathogens directly.     

Surrogate variable analysis using partial least squares (SVA-PLS) in gene expression studies

MOTIVATION: In a typical gene expression profiling study, our prime objective is to identify the genes that are differentially expressed between the samples from two different tissue types. Commonly, standard analysis of variance (ANOVA)/regression is implemented to identify the relative effects of these genes over the two types of samples from their respective arrays of expression levels. But, this technique becomes fundamentally flawed when there are unaccounted sources of variability in these arrays (latent variables attributable to different biological, environmental or other factors relevant in the context). These factors distort the true picture of differential gene expression between the two tissue types and introduce spurious signals of expression heterogeneity. As a result, many genes which are actually differentially expressed are not detected, whereas many others are falsely identified as positives. Moreover, these distortions can be different for different genes. Thus, it is also not possible to get rid of these variations by simple array normalizations. This both-way error can lead to a serious loss in sensitivity and specificity, thereby causing a severe inefficiency in the underlying multiple testing problem. In this work, we attempt to identify the hidden effects of the underlying latent factors in a gene expression profiling study by partial least squares (PLS) and apply ANCOVA technique with the PLS-identified signatures of these hidden effects as covariates, in order to identify the genes that are truly differentially expressed between the two concerned tissue types. RESULTS: We compare the performance of our method SVA-PLS with standard ANOVA and a relatively recent technique of surrogate variable analysis (SVA), on a wide variety of simulation settings (incorporating different effects of the hidden variable, under situations with varying signal intensities and gene groupings). In all settings, our method yields the highest sensitivity while maintaining relatively reasonable values for the specificity, false discovery rate and false non-discovery rate. Application of our method to gene expression profiling for acute megakaryoblastic leukemia shows that our method detects an additional six genes, that are missed by both the standard ANOVA method as well as SVA, but may be relevant to this disease, as can be seen from mining the existing literature.     

Gene expression profiles of vitrified in vivo derived 8-cell stage mouse embryos detected by high density oligonucleotide microarrays

Very little is known about the effect of vitrification on gene functions after warming. The goals of our study were to compare the gene expression patterns, and identify those most affected. For this, 8-cell stage embryos were collected from ICR mice and vitrified with solid surface vitrification technique, while maintaining equal numbers of embryos as control. Total RNAs were extracted and two rounds of amplification were employed. Finally three micrograms of contrasting RNA samples were hybridized on the Agilent Mouse 22 K oligonucleotide slides and the results were analyzed with subsequent verification by independent real-time PCR analyses. The two rounds of amplification with 5 ng tRNA input have yielded 15-16 microg of cRNA. The analyses of repeated hybridizations showed 20,183 genes/ESTs as common signatures, and unsupervised analysis identified 628 differentially expressed (P < 0.01) genes. However, with at least 1.5-fold change considerations, 183 genes were differentially expressed (P < 0.01) out of which 107 were upregulated. The independent analysis with real-time PCR and unamplified samples fully confirmed the results of microarray, indicating the linearity of amplification. Furthermore, this novel gene expression study for vitrified embryos identified many new candidate genes with overrepresentation in some important biological processes. Thus, it is possible to conclude that the expression pattern reflected a broad spectrum of consequences of vitrification on embryos, with most effects on metabolism, regulatory role and stress response genes and allowed the identification of new candidate marker genes for cryosurvival.     

Tests for finding complex patterns of differential expression in cancers: towards individualized medicine

Background  

Microarray studies in cancer compare expression levels between two or more sample groups on thousands of genes. Data analysis follows a population-level approach (e.g., comparison of sample means) to identify differentially expressed genes. This leads to the discovery of 'population-level' markers, i.e., genes with the expression patterns A > B and B > A. We introduce the PPST test that identifies genes where a significantly large subset of cases exhibit expression values beyond upper and lower thresholds observed in the control samples.     

PADGE: analysis of heterogeneous patterns of differential gene expression.

We have devised a novel analysis approach, percentile analysis for differential gene expression (PADGE), for identifying genes differentially expressed between two groups of heterogeneous samples. PADGE was designed to compare expression profiles of sample subgroups at a series of percentile cutoffs and to examine the trend of relative expression between sample groups as expression level increases. Simulation studies showed that PADGE has more statistical power than t-statistics, cancer outlier profile analysis (COPA) (Tomlins SA, Rhodes DR, Perner S, Dhanasekaran SM, Mehra R, Sun XW, Varambally S, Cao X, Tchinda J, Kuefer R, Lee C, Montie JE, Shah RB, Pienta KJ, Rubin MA, Chinnaiyan AM. Science 310: 644-648, 2005), and kurtosis (Teschendorff AE, Naderi A, Barbosa-Morais NL, Caldas C. Bioinformatics 22: 2269-2275, 2006). Application of PADGE to microarray data sets in tumor tissues demonstrated its utility in prioritizing cancer genes encoding potential therapeutic targets or diagnostic markers. A web application was developed for researchers to analyze a large gene expression data set from heterogeneous biological samples and identify differentially expressed genes between subsets of sample classes using PADGE and other available approaches. Availability: http://www.cgl.ucsf.edu/Research/genentech/padge/.