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

Advancing the serial analysis of gene expression technique and its application to the study of the development of squamous cell lung cancer

Lung cancer is one of the most common and deadliest forms of cancer. Squamous cell lung carcinomas (SCC), a common lung cancer subtype, feature a series of identifiable premalignant and early malignant forms that progress sequentially into full-blown tumours. This thesis describes a sophisticated and statistically rigorous analysis of global gene expression profiles taken from samples of several key stages of progression. This dataset was generated using serial analysis of gene expression (SAGE), a powerful transcriptome profiling technique that captures small sequence tags from each transcript in an mRNA population. These tags can then be counted and mapped back to a matching transcript sequence to quantitatively determine the expression of a given gene. The analysis identified several genes which show changes in expression that are highly correlated with the progressive steps of SCC. In addition, gene expression changes were identified in samples of bronchial epithelium that correspond to an acute response to tobacco smoke exposure, a major contributor to SCC development. The use of multiple sample types, the presence of extensive cellular heterogeneity, and the rarity of biological material for the purpose of validation introduced an additional layer of complexity that are not well-suited to conventional methods of SAGE analysis. To address these challenges, this thesis describes the development of two methodological improvements to SAGE data analysis. The first describes a computational strategy to identify additional sequence information that effectively increases the length of SAGE tag sequences, greatly enhancing the fidelity of tag to gene mapping. The second describes a new statistical method that shows improved performance in modelling SAGE data. The Poisson mixture model used in this work provides better estimates of statistical significance, is highly effective when using multiple sample types, and is a flexible framework for more complex meta-analyses.Medicine, Faculty ofBiochemistry and Molecular Biology, Department ofGraduat

Temperature-Regulated Transcription in the Pathogenic Fungus Cryptococcus neoformans

The basidiomycete fungus Cryptococcus neoformans is an opportunistic pathogen of worldwide importance that causes meningitis, leading to death in immunocompromised individuals. Unlike many basidiomycete fungi, C. neoformans is thermotolerant, and its ability to grow at 37°C is considered to be a virulence factor. We used serial analysis of gene expression (SAGE) to characterize the transcriptomes of C. neoformans strains that represent two varieties with different polysaccharide capsule serotypes. These include a serotype D strain of the C. neoformans variety neoformans and a serotype A strain of variety grubii. In this report, we describe the construction and characterization of SAGE libraries from each strain grown at 25°C and 37°C. The SAGE data reveal transcriptome differences between the two strains, even at this early stage of analysis, and identify sets of genes with higher transcript levels at 25°C or 37°C. Notably, growth at the lower temperature increased transcript levels for histone genes, indicating a general influence of temperature on chromatin structure. At 37°C, we noted elevated transcript levels for several genes encoding heat shock proteins and translation machinery. Some of these genes may play a role in temperature-regulated phenotypes in C. neoformans, such as the adaptation of the fungus to growth in the host and the dimorphic transition between budding and filamentous growth. Overall, this work provides the most comprehensive gene expression data available for C. neoformans; this information will be a critical resource both for gene discovery and genome annotation in this pathogen. [This paper is dedicated to the memory of Michael Smith, founding director of the Biotechnology Laboratory and the British Columbia Genome Sciences Centre. The following individuals kindly provided reagents, samples, or unpublished information as indicated in the paper: Brendan Loftus, Claire Fraser, Richard Hyman, Eula Fung, Don Rowley, Ron Davis , Bruce A. Roe, Doris Kupfer, Jennifer Lewis, Sola Yu, Kent Buchanan, Dave Dyer, and Juneann Murphy.

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

Additional file 1: of Whole transcriptome analysis reveals differential gene expression profile reflecting macrophage polarization in response to influenza A H5N1 virus infection

Table S1. Sequencing statistics of mRNA transcriptomes. Table S2. Significantly enriched Gene Ontology terms in response to H5N1 virus infection. Table S3. Sequencing statistics of miRNA transcriptomes. Table S4. MiRNAs and their target mRNAs in RIG-I like receptor signaling pathway. (DOCX 41 kb

Additional file 5: of Whole transcriptome analysis reveals differential gene expression profile reflecting macrophage polarization in response to influenza A H5N1 virus infection

Pathway enrichment of the mRNA genes inversely regulated with the targeting miRNAs for H1N1 and H5N1 virus-infected macrophage cells at 1-, 3-, and 6-h post-infection. (XLS 154 kb