Pan Du

lumi: a pipeline for processing Illumina microarray

UNLABELLED Illumina microarray is becoming a popular microarray platform. The BeadArray technology from Illumina makes its preprocessing and quality control different from other microarray technologies. Unfortunately, most other analyses have not taken advantage of the unique properties of the BeadArray system, and have just incorporated preprocessing methods originally designed for Affymetrix microarrays. lumi is a Bioconductor package especially designed to process the Illumina microarray data. It includes data input, quality control, variance stabilization, normalization and gene annotation portions. In specific, the lumi package includes a variance-stabilizing transformation (VST) algorithm that takes advantage of the technical replicates available on every Illumina microarray. Different normalization method options and multiple quality control plots are provided in the package. To better annotate the Illumina data, a vendor independent nucleotide universal identifier (nuID) was devised to identify the probes of Illumina microarray. The nuID annotation packages and output of lumi processed results can be easily integrated with other Bioconductor packages to construct a statistical data analysis pipeline for Illumina data. AVAILABILITY The lumi Bioconductor package, www.bioconductor.org

Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis

BackgroundHigh-throughput profiling of DNA methylation status of CpG islands is crucial to understand the epigenetic regulation of genes. The microarray-based Infinium methylation assay by Illumina is one platform for low-cost high-throughput methylation profiling. Both Beta-value and M-value statistics have been used as metrics to measure methylation levels. However, there are no detailed studies of their relations and their strengths and limitations.ResultsWe demonstrate that the relationship between the Beta-value and M-value methods is a Logit transformation, and show that the Beta-value method has severe heteroscedasticity for highly methylated or unmethylated CpG sites. In order to evaluate the performance of the Beta-value and M-value methods for identifying differentially methylated CpG sites, we designed a methylation titration experiment. The evaluation results show that the M-value method provides much better performance in terms of Detection Rate (DR) and True Positive Rate (TPR) for both highly methylated and unmethylated CpG sites. Imposing a minimum threshold of difference can improve the performance of the M-value method but not the Beta-value method. We also provide guidance for how to select the threshold of methylation differences.ConclusionsThe Beta-value has a more intuitive biological interpretation, but the M-value is more statistically valid for the differential analysis of methylation levels. Therefore, we recommend using the M-value method for conducting differential methylation analysis and including the Beta-value statistics when reporting the results to investigators.

Model-based variance-stabilizing transformation for Illumina microarray data

Variance stabilization is a step in the preprocessing of microarray data that can greatly benefit the performance of subsequent statistical modeling and inference. Due to the often limited number of technical replicates for Affymetrix and cDNA arrays, achieving variance stabilization can be difficult. Although the Illumina microarray platform provides a larger number of technical replicates on each array (usually over 30 randomly distributed beads per probe), these replicates have not been leveraged in the current log2 data transformation process. We devised a variance-stabilizing transformation (VST) method that takes advantage of the technical replicates available on an Illumina microarray. We have compared VST with log2 and Variance-stabilizing normalization (VSN) by using the Kruglyak bead-level data (2006) and Barnes titration data (2005). The results of the Kruglyak data suggest that VST stabilizes variances of bead-replicates within an array. The results of the Barnes data show that VST can improve the detection of differentially expressed genes and reduce false-positive identifications. We conclude that although both VST and VSN are built upon the same model of measurement noise, VST stabilizes the variance better and more efficiently for the Illumina platform by leveraging the availability of a larger number of within-array replicates. The algorithms and Supplementary Data are included in the lumi package of Bioconductor, available at: www.bioconductor.org.

From disease ontology to disease-ontology lite

Subjective methods have been reported to adapt a general-purpose ontology for a specific application. For example, Gene Ontology (GO) Slim was created from GO to generate a highly aggregated report of the human-genome annotation. We propose statistical methods to adapt the general purpose, OBO Foundry Disease Ontology (DO) for the identification of gene-disease associations. Thus, we need a simplified definition of disease categories derived from implicated genes. On the basis of the assumption that the DO terms having similar associated genes are closely related, we group the DO terms based on the similarity of gene-to-DO mapping profiles. Two types of binary distance metrics are defined to measure the overall and subset similarity between DO terms. A compactness-scalable fuzzy clustering method is then applied to group similar DO terms. To reduce false clustering, the semantic similarities between DO terms are also used to constrain clustering results. As such, the DO terms are aggregated and the redundant DO terms are largely removed. Using these methods, we constructed a simplified vocabulary list from the DO called Disease Ontology Lite (DOLite). We demonstrated that DOLite results in more interpretable results than DO for gene-disease association tests. The resultant DOLite has been used in the Functional Disease Ontology (FunDO) Web application at http://www.projects.bioinformatics.northwestern.edu/fundo. Contact: s-lin2@northwestern.edu

Genomics of Human Intracranial Aneurysm Wall

Background and Purpose— The pathogenesis of intracranial aneurysms (IAs) remains elusive. Most studies have focused on individual genes, or a few interrelated genes or products, at a time in human IA. However, a broad view of pathologic mechanisms has not been investigated by identifying pathogenic genes and their interaction in networks. Our study aimed to analyze global gene expression patterns in the IA wall. Methods— To our knowledge, our group was the first to perform Illumina microarray analysis on human IA via comparison of aneurysm wall and superficial temporal artery tissues from 6 consecutive patients. We adopted stringent statistical criteria to the individual genes; genes with a false discovery rate <0.01 and >2-fold change were selected as differentially expressed. To identify the overrepresented biologic pathways with the differentially expressed genes, we performed hypergeometric testing of the genes selected by relaxed criteria of P<0.01 and fold change >1.5. Results— There are 326 distinct differentially expressed genes between IA and superficial temporal artery tissues (>2-fold change) with a false discovery rate <0.01. Analysis of the Kyoto Encyclopedia of Genes and Genomes pathways revealed the most impacted functional pathways: focal adhesion, extracellular matrix receptor interaction, and cell communication. Analysis of the Gene Ontology also supported the involvement of another 2 potentially important pathways: inflammatory response and apoptosis. Conclusions— The differentially expressed genes in the aneurysm wall may shed light on aneurysm pathobiology and provide novel targets for therapeutic intervention. These data will help generate hypotheses for future studies.

Genome-Wide DNA Methylation Indicates Silencing of Tumor Suppressor Genes in Uterine Leiomyoma

Background Uterine leiomyomas, or fibroids, represent the most common benign tumor of the female reproductive tract. Fibroids become symptomatic in 30% of all women and up to 70% of African American women of reproductive age. Epigenetic dysregulation of individual genes has been demonstrated in leiomyoma cells; however, the in vivo genome-wide distribution of such epigenetic abnormalities remains unknown. Principal Findings We characterized and compared genome-wide DNA methylation and mRNA expression profiles in uterine leiomyoma and matched adjacent normal myometrial tissues from 18 African American women. We found 55 genes with differential promoter methylation and concominant differences in mRNA expression in uterine leiomyoma versus normal myometrium. Eighty percent of the identified genes showed an inverse relationship between DNA methylation status and mRNA expression in uterine leiomyoma tissues, and the majority of genes (62%) displayed hypermethylation associated with gene silencing. We selected three genes, the known tumor suppressors KLF11, DLEC1, and KRT19 and verified promoter hypermethylation, mRNA repression and protein expression using bisulfite sequencing, real-time PCR and western blot. Incubation of primary leiomyoma smooth muscle cells with a DNA methyltransferase inhibitor restored KLF11, DLEC1 and KRT19 mRNA levels. Conclusions These results suggest a possible functional role of promoter DNA methylation-mediated gene silencing in the pathogenesis of uterine leiomyoma in African American women.

Modeling gene expression networks using fuzzy logic

Gene regulatory networks model regulation in living organisms. Fuzzy logic can effectively model gene regulation and interaction to accurately reflect the underlying biology. A new multiscale fuzzy clustering method allows genes to interact between regulatory pathways and across different conditions at different levels of detail. Fuzzy cluster centers can be used to quickly discover causal relationships between groups of coregulated genes. Fuzzy measures weight expert knowledge and help quantify uncertainty about the functions of genes using annotations and the gene ontology database to confirm some of the interactions. The method is illustrated using gene expression data from an experiment on carbohydrate metabolism in the model plant Arabidopsis thaliana. Key gene regulatory relationships were evaluated using information from the gene ontology database. A new regulatory relationship concerning trehalose regulation of carbohydrate metabolism was also discovered in the extracted network.

Expression of Cyclophilin B is Associated with Malignant Progression and Regulation of Genes Implicated in the Pathogenesis of Breast Cancer

Cyclophilin B (CypB) is a 21-kDa protein with peptidyl-prolyl cis-trans isomerase activity that functions as a transcriptional inducer for Stat5 and as a ligand for CD147. To better understand the global function of CypB in breast cancer, T47D cells with a small interfering RNA-mediated knockdown of CypB were generated. Subsequent expression profiling analysis showed that 663 transcripts were regulated by CypB knockdown, and that many of these gene products contributed to cell proliferation, cell motility, and tumorigenesis. Real-time PCR confirmed that STMN3, S100A4, S100A6, c-Myb, estrogen receptor alpha, growth hormone receptor, and progesterone receptor were all down-regulated in si-CypB cells. A linkage analysis of these array data to protein networks resulted in the identification of 27 different protein networks that were impacted by CypB knockdown. Functional assays demonstrated that CypB knockdown also decreased cell growth, proliferation, and motility. Immunohistochemical and immunofluorescent analyses of a matched breast cancer progression tissue microarray that was labeled with an anti-CypB antibody demonstrated a highly significant increase in CypB protein levels as a function of breast cancer progression. Taken together, these results suggest that the enhanced expression of CypB in malignant breast epithelium may contribute to the pathogenesis of this disease through its regulation of the expression of hormone receptors and gene products that are involved in cell proliferation and motility.

The Early Growth Response Gene Egr2 (Alias Krox20) Is a Novel Transcriptional Target of Transforming Growth Factor-β that Is Up-Regulated in Systemic Sclerosis and Mediates Profibrotic Responses

Although the early growth response-2 (Egr-2, alias Krox20) protein shows structural and functional similarities to Egr-1, these two related early-immediate transcription factors are nonredundant. Egr-2 plays essential roles in peripheral nerve myelination, adipogenesis, and immune tolerance; however, its regulation and role in tissue repair and fibrosis remain poorly understood. We show herein that transforming growth factor (TGF)-β induced a Smad3-dependent sustained stimulation of Egr2 gene expression in normal fibroblasts. Overexpression of Egr-2 was sufficient to stimulate collagen gene expression and myofibroblast differentiation, whereas these profibrotic TGF-β responses were attenuated in Egr-2-depleted fibroblasts. Genomewide transcriptional profiling revealed that multiple genes associated with tissue remodeling and wound healing were up-regulated by Egr-2, but the Egr-2-regulated gene expression profile overlapped only partially with the Egr-1-regulated gene profile. Levels of Egr-2 were elevated in lesional tissue from mice with bleomycin-induced scleroderma. Moreover, elevated Egr-2 was noted in biopsy specimens of skin and lung from patients with systemic sclerosis. These results provide the first evidence that Egr-2 is a functionally distinct transcription factor that is both necessary and sufficient for TGF-β-induced profibrotic responses and is aberrantly expressed in lesional tissue in systemic sclerosis and in a murine model of scleroderma. Together, these findings suggest that Egr-2 plays an important nonredundant role in the pathogenesis of fibrosis. Targeting Egr-2 might represent a novel therapeutic strategy to control fibrosis.

Susceptibility to glaucoma: differential comparison of the astrocyte transcriptome from glaucomatous African American and Caucasian American donors.

BackgroundEpidemiological and genetic studies indicate that ethnic/genetic background plays an important role in susceptibility to primary open angle glaucoma (POAG). POAG is more prevalent among the African-descent population compared to the Caucasian population. Damage in POAG occurs at the level of the optic nerve head (ONH) and is mediated by astrocytes. Here we investigated differences in gene expression in primary cultures of ONH astrocytes obtained from age-matched normal and glaucomatous donors of Caucasian American (CA) and African American (AA) populations using oligonucleotide microarrays.ResultsGene expression data were obtained from cultured astrocytes representing 12 normal CA and 12 normal AA eyes, 6 AA eyes with POAG and 8 CA eyes with POAG. Data were normalized and significant differential gene expression levels detected by using empirical Bayesian shrinkage moderated t-statistics. Gene Ontology analysis and networks of interacting proteins were constructed using the BioGRID database. Network maps included regulation of myosin, actin, and protein trafficking. Real-time RT-PCR, western blots, ELISA, and functional assays validated genes in the networks.ConclusionCultured AA and CA glaucomatous astrocytes retain differential expression of genes that promote cell motility and migration, regulate cell adhesion, and are associated with structural tissue changes that collectively contribute to neural degeneration. Key upregulated genes include those encoding myosin light chain kinase (MYLK), transforming growth factor-β receptor 2 (TGFBR2), rho-family GTPase-2 (RAC2), and versican (VCAN). These genes along with other differentially expressed components of integrated networks may reflect functional susceptibility to chronic elevated intraocular pressure that is enhanced in the optic nerve head of African Americans.