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

BACKGROUND: In the work of Chari et al. 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. RESULTS: 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. CONCLUSION: Most of Chari et al.'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 et al.

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

BACKGROUND: 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. RESULTS: 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. CONCLUSION: 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.

A SAGE approach to discovery of genes involved in autophagic cell death.

Programmed 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. 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. 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. 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.

An efficient strategy for large-scale high-throughput transposon-mediated sequencing of cDNA clones.

We describe an efficient high-throughput method for accurate DNA sequencing of entire cDNA clones. Developed as part of our involvement in the Mammalian Gene Collection full-length cDNA sequencing initiative, the method has been used and refined in our laboratory since September 2000. Amenable to large scale projects, we have used the method to generate >7 Mb of accurate sequence from 3695 candidate full-length cDNAs. Sequencing is accomplished through the insertion of Mu transposon into cDNAs, followed by sequencing reactions primed with Mu-specific sequencing primers. Transposon insertion reactions are not performed with individual cDNAs but rather on pools of up to 96 clones. This pooling strategy reduces the number of transposon insertion sequencing libraries that would otherwise be required, reducing the costs and enhancing the efficiency of the transposon library construction procedure. Sequences generated using transposon-specific sequencing primers are assembled to yield the full-length cDNA sequence, with sequence editing and other sequence finishing activities performed as required to resolve sequence ambiguities. Although analysis of the many thousands (22 785) of sequenced Mu transposon insertion events revealed a weak sequence preference for Mu insertion, we observed insertion of the Mu transposon into 1015 of the possible 1024 5mer candidate insertion sites.

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 http://www.bcgsc.ca/bioinfo/software.

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.

Iron-regulated transcription and capsule formation in the fungal pathogen Cryptococcus neoformans.

Cryptococcus neoformans is the leading cause of fungal meningitis in humans. Production of a polysaccharide capsule is a key virulence property for the fungus and capsule synthesis is regulated by iron levels. Given that iron acquisition is an important aspect of virulence for many pathogens, we employed serial analysis of gene expression (SAGE) to examine the transcriptome under iron-limiting and iron-replete conditions. Initially, we demonstrated by SAGE and Northern analysis that iron limitation results in an elevated transcript level for the CAP60 gene that is required for capsule production. We also identified genes encoding putative components for iron transport and homeostasis, including the FTR1 (iron permease) gene, with higher transcript levels in the low-iron condition. An FTR1 disruption mutant grows more slowly than wild-type cells in low-iron medium, and shows delayed growth and altered capsule regulation in iron-replete medium. Iron deprivation also resulted in elevated SAGE tags for putative extracellular mannoproteins and the GPI8 gene encoding a glycosylphosphatidylinositol (GPI) transamidase. The GPI8 gene appears to be essential while disruption of the CIG1 gene encoding a mannoprotein resulted in impaired growth in low-iron medium and altered capsule response to the iron-replete condition. Additionally, we found that iron-replete conditions led to elevated transcripts for genes for iron storage, nitrogen metabolism, glycolysis, mitochondrial function, lipid metabolism and calmodulin-calcineurin signalling. Overall, these studies provide the first view of the C. neoformans transcriptional response to different iron levels.

Effect of TERT and ATM on gene expression profiles in human fibroblasts.

Telomeres protect chromosomes from degradation, end-to-end fusion, and illegitimate recombination. Loss of telomeres may lead to cell death or senescence or may cause genomic instability, leading to tumor formation. Expression of human telomerase reverse transcriptase (TERT) in human fibroblast cells elongates their telomeres and extends their lifespan. Ataxia telangiectasia mutated (ATM) deficiency in A-T human fibroblasts results in accelerated telomere shortening, abnormal cell-cycle response to DNA damage, and early senescence. Gene expression profiling was performed by serial analysis of gene expression (SAGE) on BJ normal human skin fibroblasts, A-T cells, and BJ and A-T cells transduced with TERT cDNA and expressing telomerase activity. In the four SAGE libraries, 36,921 unique SAGE tags were detected. Pairwise comparisons between the libraries showed differential expression levels of 1%-8% of the tags. Transcripts affected by both TERT and ATM were identified according to expression patterns, making them good candidates for further studies of pathways affected by both TERT and ATM. These include MT2A, P4HB, LGALS1, CFL1, LDHA, S100A10, EIF3S8, RANBP9, and SEC63. These genes are involved in apoptosis or processes related to cell growth, and most have been found to be deregulated in cancer. Our results have provided further insight into the roles of TERT and ATM by identifying genes likely to be involved in their function. Supplementary material for this article can be found on the Genes, Chromosomes and Cancer website at http://www.interscience.wiley.com/jpages/1045-2257/suppmat/index.html.