Correspondence regarding "Effect of active smoking on the human bronchial epithelium transcriptome"

<p>Abstract</p> <p>Background</p> <p>In the work of Chari <it>et al. </it>entitled "Effect of active smoking on the human bronchial epithelium transcriptome" the authors use SAGE to identify candidate gene expression changes in bronchial brushings from never, former, and current smokers. These gene expression changes are categorized into those that are reversible or irreversible upon smoking cessation. A subset of these identified genes is validated on an independent cohort using RT-PCR. The authors conclude that their results support the notion of gene expression changes in the lungs of smokers which persist even after an individual has quit.</p> <p>Results</p> <p>This correspondence raises questions about the validity of the approach used by the authors to analyze their data. The majority of the reported results suffer deficiencies due to the methods used. The most fundamental of these are explained in detail: biases introduced during data processing, lack of correction for multiple testing, and an incorrect use of clustering for gene discovery. A randomly generated "null" dataset is used to show the consequences of these shortcomings.</p> <p>Conclusion</p> <p>Most of Chari <it>et al.</it>'s findings are consistent with what would be expected by chance alone. Although there is clear evidence of reversible changes in gene expression, the majority of those identified appear to be false positives. However, contrary to the authors' claims, no irreversible changes were identified. There is a broad consensus that genetic change due to smoking persists once an individual has quit smoking; unfortunately, this study lacks sufficient scientific rigour to support or refute this hypothesis or identify any specific candidate genes. The pitfalls of large-scale analysis, as exemplified here, may not be unique to Chari <it>et al</it>.</p

A knowledge discovery object model API for Java

BACKGROUND: Biological data resources have become heterogeneous and derive from multiple sources. This introduces challenges in the management and utilization of this data in software development. Although efforts are underway to create a standard format for the transmission and storage of biological data, this objective has yet to be fully realized. RESULTS: This work describes an application programming interface (API) that provides a framework for developing an effective biological knowledge ontology for Java-based software projects. The API provides a robust framework for the data acquisition and management needs of an ontology implementation. In addition, the API contains classes to assist in creating GUIs to represent this data visually. CONCLUSIONS: The Knowledge Discovery Object Model (KDOM) API is particularly useful for medium to large applications, or for a number of smaller software projects with common characteristics or objectives. KDOM can be coupled effectively with other biologically relevant APIs and classes. Source code, libraries, documentation and examples are available at

DiscoverySpace: an interactive data analysis application

DiscoverySpace is a graphical application for bioinformatics data analysis. Users can seamlessly traverse references between biological databases and draw together annotations in an intuitive tabular interface. Datasets can be compared using a suite of novel tools to aid in the identification of significant patterns. DiscoverySpace is of broad utility and its particular strength is in the analysis of serial analysis of gene expression (SAGE) data. The application is freely available online

Statistical analysis and significance testing of serial analysis of gene expression data using a Poisson mixture model

<p>Abstract</p> <p>Background</p> <p>Serial analysis of gene expression (SAGE) is used to obtain quantitative snapshots of the transcriptome. These profiles are count-based and are assumed to follow a Binomial or Poisson distribution. However, tag counts observed across multiple libraries (for example, one or more groups of biological replicates) have additional variance that cannot be accommodated by this assumption alone. Several models have been proposed to account for this effect, all of which utilize a continuous prior distribution to explain the excess variance. Here, a Poisson mixture model, which assumes excess variability arises from sampling a mixture of distinct components, is proposed and the merits of this model are discussed and evaluated.</p> <p>Results</p> <p>The goodness of fit of the Poisson mixture model on 15 sets of biological SAGE replicates is compared to the previously proposed hierarchical gamma-Poisson (negative binomial) model, and a substantial improvement is seen. In further support of the mixture model, there is observed: 1) an increase in the number of mixture components needed to fit the expression of tags representing more than one transcript; and 2) a tendency for components to cluster libraries into the same groups. A confidence score is presented that can identify tags that are differentially expressed between groups of SAGE libraries. Several examples where this test outperforms those previously proposed are highlighted.</p> <p>Conclusion</p> <p>The Poisson mixture model performs well as a) a method to represent SAGE data from biological replicates, and b) a basis to assign significance when testing for differential expression between multiple groups of replicates. Code for the R statistical software package is included to assist investigators in applying this model to their own data.</p

A SAGE Approach to Discovery of Genes Involved in Autophagic Cell Death

AbstractProgrammed cell death (PCD), important in normal animal physiology and disease, can be divided into at least two morphological subtypes, including type I, or apoptosis, and type II, or autophagic cell death [1]. While many molecules involved in apoptosis have been discovered and studied intensively during the past decade, autophagic cell death is not well characterized molecularly. Here we report the first comprehensive identification of molecules associated with autophagic cell death during normal metazoan development in vivo. During Drosophila metamorphosis, the larval salivary glands undergo autophagic cell death regulated by a hormonally induced transcriptional cascade [2–6]. To identify and analyze the genes expressed, we examined wild-type patterns of gene expression in three predeath stages of Drosophila salivary glands using serial analysis of gene expression (SAGE) [7]. 1244 transcripts, including genes involved in autophagy, defense response, cytoskeleton remodeling, noncaspase proteolysis, and apoptosis, were expressed differentially prior to salivary gland death. Mutant expression analysis indicated that several of these genes were regulated by E93, a gene required for salivary gland cell death [6]. Our analyses strongly support both the emerging notion that there is overlap with respect to the molecules involved in autophagic cell death and apoptosis, and that there are important differences

Whole transcriptome analysis reveals differential gene expression profile reflecting macrophage polarization in response to influenza A H5N1 virus infection

Abstract Background Avian influenza A H5N1 virus can cause lethal disease in humans. The virus can trigger severe pneumonia and lead to acute respiratory distress syndrome. Data from clinical, in vitro and in vivo suggest that virus-induced cytokine dysregulation could be a contributory factor to the pathogenesis of human H5N1 disease. However, the precise mechanism of H5N1 infection eliciting the unique host response are still not well understood. Methods To obtain a better understanding of the molecular events at the earliest time points, we used RNA-Seq to quantify and compare the host mRNA and miRNA transcriptomes induced by the highly pathogenic influenza A H5N1 (A/Vietnam/3212/04) or low virulent H1N1 (A/Hong Kong/54/98) viruses in human monocyte-derived macrophages at 1-, 3-, and 6-h post infection. Results Our data reveals that two macrophage populations corresponding to M1 (classically activated) and M2 (alternatively activated) macrophage subtypes respond distinctly to H5N1 virus infection when compared to H1N1 virus or mock infection, a distinction that could not be made from previous microarray studies. When this confounding variable is considered in our statistical model, a clear set of dysregulated genes and pathways emerges specifically in H5N1 virus-infected macrophages at 6-h post infection, whilst was not found with H1N1 virus infection. Furthermore, altered expression of genes in these pathways, which have been previously implicated in viral host response, occurs specifically in the M1 subtype. We observe a significant up-regulation of genes in the RIG-I-like receptor signaling pathway. In particular, interferons, and interferon-stimulated genes are broadly affected. The negative regulators of interferon signaling, the suppressors of cytokine signaling, SOCS-1 and SOCS-3, were found to be markedly up-regulated in the initial round of H5N1 virus replication. Elevated levels of these suppressors could lead to the eventual suppression of cellular antiviral genes, contributing to pathophysiology of H5N1 virus infection. Conclusions Our study provides important mechanistic insights into the understanding of H5N1 viral pathogenesis and the multi-faceted host immune responses. The dysregulated genes could be potential candidates as therapeutic targets for treating H5N1 disease

Large-scale production of SAGE libraries from microdissected tissues, flow-sorted cells, and cell lines

We describe the details of a serial analysis of gene expression (SAGE) library construction and analysis platform that has enabled the generation of >298 high-quality SAGE libraries and >30 million SAGE tags primarily from sub-microgram amounts of total RNA purified from samples acquired by microdissection. Several RNA isolation methods were used to handle the diversity of samples processed, and various measures were applied to minimize ditag PCR carryover contamination. Modifications in the SAGE protocol resulted in improved cloning and DNA sequencing efficiencies. Bioinformatic measures to automatically assess DNA sequencing results were implemented to analyze the integrity of ditag structure, linker or cross-species ditag contamination, and yield of high-quality tags per sequence read. Our analysis of singleton tag errors resulted in a method for correcting such errors to statistically determine tag accuracy. From the libraries generated, we produced an essentially complete mapping of reliable 21-base-pair tags to the mouse reference genome sequence for a meta-library of ∼5 million tags. Our analyses led us to reject the commonly held notion that duplicate ditags are artifacts. Rather than the usual practice of discarding such tags, we conclude that they should be retained to avoid introducing bias into the results and thereby maintain the quantitative nature of the data, which is a major theoretical advantage of SAGE as a tool for global transcriptional profiling

Abstract 1428: A CpG island methylator phenotype defines a clinically aggressive subgroup of posterior fossa ependymoma

Abstract Ependymoma is the third most common pediatric brain tumor and remains incurable in 45% of patients. It arises in the spinal cord, supratentorial brain, and most commonly in children, the posterior fossa (PF). We recently reported the identification of two molecularly and clinically distinct subgroups of PF ependymoma, which we named Group A and B. While patients with Group B tumors harbor a large number of gross chromosomal gains and losses (approx. 17 arm events per tumor) and have favorable prognoses (5 year PFS = 92%), patients with Group A tumors have balanced genomic profiles (approx. 1 arm event per tumor) with poor clinical outcomes (5 year PFS = 24%). We hypothesized that aberrant DNA methylation could be a mechanism driving the tumorigenesis of Group A PF ependymoma. To this end, we isolated methylated DNA in 92 ependymomas by Methyl Binding Domain 2 protein assisted recovery, and hybridized enriched DNA to promoter tiling arrays (Nimblegen). Using unsupervised hierarchical clustering we determined that the DNA methylation profiles of ependymoma were regionally specified, dividing tumors into subgroups according to their anatomical origin. Using both gene expression and DNA methylation platforms, we identified a subset of PF ependymoma, which clustered with spinal tumors, supporting the vast molecular differences between Group A and B PF ependymoma. We next compared the number of methylated genes identified in Group A versus B, and observed that Group A tumors exhibited a greater number of methylated genes at specific CpG islands, a feature described as a CpG island methylator phenotype (CIMP) in glioma, colon cancer, and breast cancer. We validated these findings in a non-overlapping cohort of 48 PF ependymomas, analyzed using a different array technology (Illumina Infinium 450K). Using various unsupervised clustering methods (HCL, K-MEANS, NMF, and SOM), we verified that Group A and B exhibited highly distinct DNA methylation profiles. Further, we confirmed that Group A tumours were defined by a greater overall number of methylated genes (A: 855, B: 233; Wilcoxon-Rank Sum Test), and a greater number of methylated genes per tumour (A: 511, B: 425; Wilcoxon-Rank Sum Test). We performed Gene Set Enrichment analysis and observed that many genes methylated in Group A exhibited a significant overlap with genes marked by the polycomb repressor (PRC2) complex in embryonic stem cells (p&amp;lt;0.0001, FDR&amp;lt;0.1%), a phenomenon seen in other cancer CIMPs. We propose two diverse mechanisms leading to tumourigenesis in Group A and B ependymoma. The greater number of chromosomal alterations in Group B suggests a Chromosomal Instability (CIN) phenotype, while the greater number of methylated CpG islands in Group A suggests a CpG island Methylator (CIMP) phenotype. Understanding these underlying mechanisms driving Group A and B pathogenesis may yield new leads for subgroup-specific treatments of PF ependymoma. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1428. doi:1538-7445.AM2012-1428</jats:p