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Biomedical subjects

Gordon K Smyth

Publications and source records attributed to Gordon K Smyth.

17 recordsLinked to original sources
PubMed2024

Increasing histone acetylation improves sociability and restores learning and memory in KAT6B-haploinsufficient mice.

Mutations in genes encoding chromatin modifiers are enriched among mutations causing intellectual disability. The continuing development of the brain postnatally, coupled with the inherent reversibility of chromatin modifications, may afford an opportunity for therapeutic intervention following a genetic diagnosis. Development of treatments requires an understanding of protein function and models of the disease. Here, we provide a mouse model of Say-Barber-Biesecker-Young-Simpson syndrome (SBBYSS) (OMIM 603736) and demonstrate proof-of-principle efficacy of postnatal treatment. SBBYSS results from heterozygous mutations in the KAT6B (MYST4/MORF/QFK) gene and is characterized by intellectual disability and autism-like behaviors. Using human cells carrying SBBYSS-specific KAT6B mutations and Kat6b heterozygous mice (Kat6b+/-), we showed that KAT6B deficiency caused a reduction in histone H3 lysine 9 acetylation. Kat6b+/- mice displayed learning, memory, and social deficits, mirroring SBBYSS individuals. Treatment with a histone deacetylase inhibitor, valproic acid, or an acetyl donor, acetyl-carnitine (ALCAR), elevated histone acetylation levels in the human cells with SBBYSS mutations and in brain and blood cells of Kat6b+/- mice and partially reversed gene expression changes in Kat6b+/- cortical neurons. Both compounds improved sociability in Kat6b+/- mice, and ALCAR treatment restored learning and memory. These data suggest that a subset of SBBYSS individuals may benefit from postnatal therapeutic interventions.

Animals↗
PubMed2007

Normalization of boutique two-color microarrays with a high proportion of differentially expressed probes.

Normalization is critical for removing systematic variation from microarray data. For two-color microarray platforms, intensity-dependent lowess normalization is commonly used to correct relative gene expression values for biases. Here we outline a normalization method for use when the assumptions of lowess normalization fail. Specifically, this can occur when specialized boutique arrays are constructed that contain a subset of genes selected to test particular biological functions.

Animals↗
PubMed2006

Statistical analysis of an RNA titration series evaluates microarray precision and sensitivity on a whole-array basis.

BACKGROUND: Concerns are often raised about the accuracy of microarray technologies and the degree of cross-platform agreement, but there are yet no methods which can unambiguously evaluate precision and sensitivity for these technologies on a whole-array basis. RESULTS: A methodology is described for evaluating the precision and sensitivity of whole-genome gene expression technologies such as microarrays. The method consists of an easy-to-construct titration series of RNA samples and an associated statistical analysis using non-linear regression. The method evaluates the precision and responsiveness of each microarray platform on a whole-array basis, i.e., using all the probes, without the need to match probes across platforms. An experiment is conducted to assess and compare four widely used microarray platforms. All four platforms are shown to have satisfactory precision but the commercial platforms are superior for resolving differential expression for genes at lower expression levels. The effective precision of the two-color platforms is improved by allowing for probe-specific dye-effects in the statistical model. The methodology is used to compare three data extraction algorithms for the Affymetrix platforms, demonstrating poor performance for the commonly used proprietary algorithm relative to the other algorithms. For probes which can be matched across platforms, the cross-platform variability is decomposed into within-platform and between-platform components, showing that platform disagreement is almost entirely systematic rather than due to measurement variability. CONCLUSION: The results demonstrate good precision and sensitivity for all the platforms, but highlight the need for improved probe annotation. They quantify the extent to which cross-platform measures can be expected to be less accurate than within-platform comparisons for predicting disease progression or outcome.

Algorithms↗
PubMed2006

Proximal genomic localization of STAT1 binding and regulated transcriptional activity.

BACKGROUND: Signal transducer and activator of transcription (STAT) proteins are key regulators of gene expression in response to the interferon (IFN) family of anti-viral and anti-microbial cytokines. We have examined the genomic relationship between STAT1 binding and regulated transcription using multiple tiling microarray and chromatin immunoprecipitation microarray (ChIP-chip) experiments from public repositories. RESULTS: In response to IFN-gamma, STAT1 bound proximally to regions of the genome that exhibit regulated transcriptional activity. This finding was consistent between different tiling microarray platforms, and between different measures of transcriptional activity, including differential binding of RNA polymerase II, and differential mRNA transcription. Re-analysis of tiling microarray data from a recent study of IFN-gamma-induced STAT1 ChIP-chip and mRNA expression revealed that STAT1 binding is tightly associated with localized mRNA transcription in response to IFN-gamma. Close relationships were also apparent between STAT1 binding, STAT2 binding, and mRNA transcription in response to IFN-alpha. Furthermore, we found that sites of STAT1 binding within the Encyclopedia of DNA Elements (ENCODE) region are precisely correlated with sites of either enhanced or diminished binding by the RNA polymerase II complex. CONCLUSION: Together, our results indicate that STAT1 binds proximally to regions of the genome that exhibit regulated transcriptional activity. This finding establishes a generalized basis for the positioning of STAT1 binding sites within the genome, and supports a role for STAT1 in the direct recruitment of the RNA polymerase II complex to the promoters of IFN-gamma-responsive genes.

Binding Sites↗
PubMed2006

Empirical array quality weights in the analysis of microarray data.

BACKGROUND: Assessment of array quality is an essential step in the analysis of data from microarray experiments. Once detected, less reliable arrays are typically excluded or "filtered" from further analysis to avoid misleading results. RESULTS: In this article, a graduated approach to array quality is considered based on empirical reproducibility of the gene expression measures from replicate arrays. Weights are assigned to each microarray by fitting a heteroscedastic linear model with shared array variance terms. A novel gene-by-gene update algorithm is used to efficiently estimate the array variances. The inverse variances are used as weights in the linear model analysis to identify differentially expressed genes. The method successfully assigns lower weights to less reproducible arrays from different experiments. Down-weighting the observations from suspect arrays increases the power to detect differential expression. In smaller experiments, this approach outperforms the usual method of filtering the data. The method is available in the limma software package which is implemented in the R software environment. CONCLUSION: This method complements existing normalisation and spot quality procedures, and allows poorer quality arrays, which would otherwise be discarded, to be included in an analysis. It is applicable to microarray data from experiments with some level of replication.

Algorithms↗
PubMed2006

Expression profiling in primates reveals a rapid evolution of human transcription factors.

Although it has been hypothesized for thirty years that many human adaptations are likely to be due to changes in gene regulation, almost nothing is known about the modes of natural selection acting on regulation in primates. Here we identify a set of genes for which expression is evolving under natural selection. We use a new multi-species complementary DNA array to compare steady-state messenger RNA levels in liver tissues within and between humans, chimpanzees, orangutans and rhesus macaques. Using estimates from a linear mixed model, we identify a set of genes for which expression levels have remained constant across the entire phylogeny (approximately 70 million years), and are therefore likely to be under stabilizing selection. Among the top candidates are five genes with expression levels that have previously been shown to be altered in liver carcinoma. We also find a number of genes with similar expression levels among non-human primates but significantly elevated or reduced expression in the human lineage, features that point to the action of directional selection. Among the gene set with a human-specific increase in expression, there is an excess of transcription factors; the same is not true for genes with increased expression in chimpanzee.

Animals↗
PubMed2006

affylmGUI: a graphical user interface for linear modeling of single channel microarray data.

SUMMARY: affylmGUI is a graphical user interface (GUI) to an integrated workflow for Affymetrix microarray data. The user is able to proceed from raw data (CEL files) to QC and pre-processing, and eventually to analysis of differential expression using linear models with empirical Bayes smoothing. Output of the analysis (tables and figures) can be exported to an HTML report. The GUI provides user-friendly access to state-of-the-art methods embodied in the Bioconductor software repository. AVAILABILITY: affylmGUI is an R package freely available from http://www.bioconductor.org. It requires R version 1.9.0 or later and tcl/tk 8.3 or later and has been successfully tested on Windows 2000, Windows XP, Linux (RedHat and Fedora distributions) and Mac OS/X with X11. Further documentation is available at http://bioinf.wehi.edu.au/affylmGUI CONTACT: keith@wehi.edu.au.

Bayes Theorem↗
PubMed2006

Generation of a functional mammary gland from a single stem cell.

The existence of mammary stem cells (MaSCs) has been postulated from evidence that the mammary gland can be regenerated by transplantation of epithelial fragments in mice. Interest in MaSCs has been further stimulated by their potential role in breast tumorigenesis. However, the identity and purification of MaSCs has proved elusive owing to the lack of defined markers. We isolated discrete populations of mouse mammary cells on the basis of cell-surface markers and identified a subpopulation (Lin-CD29hiCD24+) that is highly enriched for MaSCs by transplantation. Here we show that a single cell, marked with a LacZ transgene, can reconstitute a complete mammary gland in vivo. The transplanted cell contributed to both the luminal and myoepithelial lineages and generated functional lobuloalveolar units during pregnancy. The self-renewing capacity of these cells was demonstrated by serial transplantation of clonal outgrowths. In support of a potential role for MaSCs in breast cancer, the stem-cell-enriched subpopulation was expanded in premalignant mammary tissue from MMTV-wnt-1 mice and contained a higher number of MaSCs. Our data establish that single cells within the Lin-CD29hiCD24+ population are multipotent and self-renewing, properties that define them as MaSCs.

Animals↗
PubMed2006

Gene expression changes during step-wise differentiation of embryonic stem cells along the inner ear hair cell pathway.

CONCLUSION: Our study outlines an alternative approach for the selection and investigation of genes involved in inner ear function. OBJECTIVE: To gain understanding of the gene pathways involved in the development of the normal cochlea. MATERIALS AND METHODS: Microarray technology currently offers the most efficient approach to investigate gene expression and identify pathways involved in cell differentiation. Epidermal growth factor (EGF) induces cultures derived from the organ of Corti to proliferate and produce new hair cells. Since pluripotent embryonic stem (ES) cells have the capacity to generate all tissues, we induced murine ES cells to differentiate towards ectodermal and neuroectodermal cell types and from there investigated their commitment towards the hair cell lineage in the presence of EGF. Cells were collected at three points along the differentiation pathway and their expression profiles were determined using the Soares NMIE mouse inner ear cDNA library printed in microarray format. RESULTS: Three genes up-regulated after addition of EGF (serine (or cysteine) proteinase inhibitor, clade H, member 1 (Serpinh1), solute carrier family 2 (facilitated glucose transporter), member 10 (Slc2a10) and secreted acidic cysteine-rich glycoprotein (Sparc)) were selected for further analysis and characterization. Of the three genes, Serpinh1 and Slc2a10 have never been implicated in the hearing process.

Animals↗
PubMed2005

Identification and functional significance of genes regulated by structurally different histone deacetylase inhibitors.

Histone deacetylase inhibitors (HDACis) inhibit tumor cell growth and survival, possibly through their ability to regulate the expression of specific proliferative and/or apoptotic genes. However, the HDACi-regulated genes necessary and/or sufficient for their biological effects remain undefined. We demonstrate that the HDACis suberoylanilide hydroxamic acid (SAHA) and depsipeptide regulate a highly overlapping gene set with at least 22% of genes showing altered expression over a 16-h culture period. SAHA and depsipeptide coordinately regulated the expression of several genes within distinct apoptosis and cell cycle pathways. Multiple genes within the Myc, type beta TGF, cyclin/cyclin-dependent kinase, TNF, Bcl-2, and caspase pathways were regulated in a manner that favored induction of apoptosis and decreased cellular proliferation. APAF-1, a gene central to the intrinsic apoptotic pathway, was induced by SAHA and depsipeptide and shown to be important, but not essential, for HDACi-induced cell death. Overexpression of p16(INK4A) and arrest of cells in G(1) can suppress HDACi-mediated apoptosis. Although p16(INK4A) did not affect the genome-wide transcription changes mediated by SAHA, a small number of apoptotic genes, including BCLXL and B-MYB, were differentially regulated in a manner consistent with attenuated HDACi-mediated apoptosis in arrested cells. We demonstrate that different HDACi alter transcription of a large and common set of genes that control diverse molecular pathways important for cell survival and proliferation. The ability of HDACi to target multiple apoptotic and cell proliferation pathways may provide a competitive advantage over other chemotherapeutic agents because suppression/loss of a single pathway may not confer resistance to these agents.

Apoptosis↗
PubMed2005

Use of within-array replicate spots for assessing differential expression in microarray experiments.

MOTIVATION: Spotted arrays are often printed with probes in duplicate or triplicate, but current methods for assessing differential expression are not able to make full use of the resulting information. The usual practice is to average the duplicate or triplicate results for each probe before assessing differential expression. This results in the loss of valuable information about genewise variability. RESULTS: A method is proposed for extracting more information from within-array replicate spots in microarray experiments by estimating the strength of the correlation between them. The method involves fitting separate linear models to the expression data for each gene but with a common value for the between-replicate correlation. The method greatly improves the precision with which the genewise variances are estimated and thereby improves inference methods designed to identify differentially expressed genes. The method may be combined with empirical Bayes methods for moderating the genewise variances between genes. The method is validated using data from a microarray experiment involving calibration and ratio control spots in conjunction with spiked-in RNA. Comparing results for calibration and ratio control spots shows that the common correlation method results in substantially better discrimination of differentially expressed genes from those which are not. The spike-in experiment also confirms that the results may be further improved by empirical Bayes smoothing of the variances when the sample size is small. AVAILABILITY: The methodology is implemented in the limma software package for R, available from the CRAN repository http://www.r-project.org

Algorithms↗
PubMed2005

A systematic approach for comprehensive T-cell epitope discovery using peptide libraries.

MOTIVATION: T-cell response to peptides bound on MHC Class I or Class II molecules is essential for immune recognition of pathogens. T-cells are activated by specific peptide epitopes that are determined within the antigen processing pathways and presented on the surface of other cells bound to MHC molecules. To determine which part of allergenic or pathogenic proteins can stimulate T-cells is important for the treatment of diseases. We sought to take advantage of the falling cost of synthetic, screening grade peptides, and devise a comprehensive, non-hypothesis-driven screen for T-cell epitopes. We were interested in the study of celiac disease (CD) and used the ELISPOT technique to perform a large number of T-cell assays. We therefore needed to compensate for the lack of statistical data analysis methods for ELISPOT assays. RESULTS: We describe a method to comprehensively screen for T-cell epitopes within a family or a group of proteins. We have implemented an algorithm to generate a set of unique short peptide sequences that incorporate all possible epitopes within a group of proteins. T-cell assays were performed in 96-well plates using the ELISPOT assay to screen for responses in CD patients against any epitopes in glutens. We describe a statistical model to fit the data and an Expectation Maximization algorithm to estimate the parameters of interest and analyze the resulting data. AVAILABILITY: Implementations of our algorithms in R or Perl are available at http://bioinf.wehi.edu.au/folders/immunol.

Algorithms↗
PubMed2004

limmaGUI: a graphical user interface for linear modeling of microarray data.

SUMMARY: limmaGUI is a graphical user interface (GUI) based on R-Tcl/Tk for the exploration and linear modeling of data from two-color spotted microarray experiments, especially the assessment of differential expression in complex experiments. limmaGUI provides an interface to the statistical methods of the limma package for R, and is itself implemented as an R package. The software provides point and click access to a range of methods for background correction, graphical display, normalization, and analysis of microarray data. Arbitrarily complex microarray experiments involving multiple RNA sources can be accomodated using linear models and contrasts. Empirical Bayes shrinkage of the gene-wise residual variances is provided to ensure stable results even when the number of arrays is small. Integrated support is provided for quantitative spot quality weights, control spots, within-array replicate spots and multiple testing. limmaGUI is available for most platforms on the which R runs including Windows, Mac and most flavors of Unix. AVAILABILITY: http://bioinf.wehi.edu.au/limmaGUI.

Computer Graphics↗
PubMed2004

Statistical modeling of sequencing errors in SAGE libraries.

MOTIVATION: Sequencing errors may bias the gene expression measurements made by Serial Analysis of Gene Expression (SAGE). They may introduce non-existent tags at low abundance and decrease the real abundance of other tags. These effects are increased in the longer tags generated in LongSAGE libraries. Current sequencing technology generates quite accurate estimates of sequencing error rates. Here we make use of the sequence neighborhood of SAGE tags and error estimates from the base-calling software to correct for such errors. RESULTS: We introduce a statistical model for the propagation of sequencing errors in SAGE and suggest an Expectation-Maximization (EM) algorithm to correct for them given observed sequences in a library and base-calling error estimates. We tested our method using simulated and experimental SAGE libraries. When comparing SAGE libraries, we found that sequencing errors can introduce considerable bias. High abundance tags may be falsely called as significantly differentially expressed, especially when comparing libraries with different levels of sequencing errors and/or of different size. Truly, differentially expressed tags have decreased significance as 'true'-tag counts are generally underestimated. This may alter if tags near the threshold of differential expression are called significant. Moreover, the number of different transcripts present in a library is overestimated as false tags are introduced at low abundance. Our correction method adjusts the tag counts to be closer to the true counts and is able to partly correct for biases introduced by sequencing errors. AVAILABILITY: An implementation using R is distributed as an R package. An online version is available at http://tagcalling.mbgproject.org

Algorithms↗
PubMed2004

Linear models and empirical bayes methods for assessing differential expression in microarray experiments.

The problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated two-color experiment using a simple hierarchical parametric model. The purpose of this paper is to develop the hierarchical model of Lonnstedt and Speed (2002) into a practical approach for general microarray experiments with arbitrary numbers of treatments and RNA samples. The model is reset in the context of general linear models with arbitrary coefficients and contrasts of interest. The approach applies equally well to both single channel and two color microarray experiments. Consistent, closed form estimators are derived for the hyperparameters in the model. The estimators proposed have robust behavior even for small numbers of arrays and allow for incomplete data arising from spot filtering or spot quality weights. The posterior odds statistic is reformulated in terms of a moderated t-statistic in which posterior residual standard deviations are used in place of ordinary standard deviations. The empirical Bayes approach is equivalent to shrinkage of the estimated sample variances towards a pooled estimate, resulting in far more stable inference when the number of arrays is small. The use of moderated t-statistics has the advantage over the posterior odds that the number of hyperparameters which need to estimated is reduced; in particular, knowledge of the non-null prior for the fold changes are not required. The moderated t-statistic is shown to follow a t-distribution with augmented degrees of freedom. The moderated t inferential approach extends to accommodate tests of composite null hypotheses through the use of moderated F-statistics. The performance of the methods is demonstrated in a simulation study. Results are presented for two publicly available data sets.

Journal Article↗
PubMed2003

Normalization of cDNA microarray data.

Normalization means to adjust microarray data for effects which arise from variation in the technology rather than from biological differences between the RNA samples or between the printed probes. This paper describes normalization methods based on the fact that dye balance typically varies with spot intensity and with spatial position on the array. Print-tip loess normalization provides a well-tested general purpose normalization method which has given good results on a wide range of arrays. The method may be refined by using quality weights for individual spots. The method is best combined with diagnostic plots of the data which display the spatial and intensity trends. When diagnostic plots show that biases still remain in the data after normalization, further normalization steps such as plate-order normalization or scale-normalization between the arrays may be undertaken. Composite normalization may be used when control spots are available which are known to be not differentially expressed. Variations on loess normalization include global loess normalization and two-dimensional normalization. Detailed commands are given to implement the normalization techniques using freely available software.

DNA, Complementary↗