School of Mathematics and Statistics - Research Publications
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ItemGene Network Disruptions and Neurogenesis Defects in the Adult Ts1Cje Mouse Model of Down Syndrome(PUBLIC LIBRARY SCIENCE, 2010-07-16)BACKGROUND: Down syndrome (DS) individuals suffer mental retardation with further cognitive decline and early onset Alzheimer's disease. METHODOLOGY/PRINCIPAL FINDINGS: To understand how trisomy 21 causes these neurological abnormalities we investigated changes in gene expression networks combined with a systematic cell lineage analysis of adult neurogenesis using the Ts1Cje mouse model of DS. We demonstrated down regulation of a number of key genes involved in proliferation and cell cycle progression including Mcm7, Brca2, Prim1, Cenpo and Aurka in trisomic neurospheres. We found that trisomy did not affect the number of adult neural stem cells but resulted in reduced numbers of neural progenitors and neuroblasts. Analysis of differentiating adult Ts1Cje neural progenitors showed a severe reduction in numbers of neurons produced with a tendency for less elaborate neurites, whilst the numbers of astrocytes was increased. CONCLUSIONS/SIGNIFICANCE: We have shown that trisomy affects a number of elements of adult neurogenesis likely to result in a progressive pathogenesis and consequently providing the potential for the development of therapies to slow progression of, or even ameliorate the neuronal deficits suffered by DS individuals.
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ItemCopy Number Analysis Identifies Novel Interactions Between Genomic Loci in Ovarian Cancer(PUBLIC LIBRARY SCIENCE, 2010-09-10)Ovarian cancer is a heterogeneous disease displaying complex genomic alterations, and consequently, it has been difficult to determine the most relevant copy number alterations with the scale of studies to date. We obtained genome-wide copy number alteration (CNA) data from four different SNP array platforms, with a final data set of 398 ovarian tumours, mostly of the serous histological subtype. Frequent CNA aberrations targeted many thousands of genes. However, high-level amplicons and homozygous deletions enabled filtering of this list to the most relevant. The large data set enabled refinement of minimal regions and identification of rare amplicons such as at 1p34 and 20q11. We performed a novel co-occurrence analysis to assess cooperation and exclusivity of CNAs and analysed their relationship to patient outcome. Positive associations were identified between gains on 19 and 20q, gain of 20q and loss of X, and between several regions of loss, particularly 17q. We found weak correlations of CNA at genomic loci such as 19q12 with clinical outcome. We also assessed genomic instability measures and found a correlation of the number of higher amplitude gains with poorer overall survival. By assembling the largest collection of ovarian copy number data to date, we have been able to identify the most frequent aberrations and their interactions.
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ItemAmplicon-Dependent CCNE1 Expression Is Critical for Clonogenic Survival after Cisplatin Treatment and Is Correlated with 20q11 Gain in Ovarian Cancer(PUBLIC LIBRARY SCIENCE, 2010-11-12)Genomic amplification of 19q12 occurs in several cancer types including ovarian cancer where it is associated with primary treatment failure. We systematically attenuated expression of genes within the minimally defined 19q12 region in ovarian cell lines using short-interfering RNAs (siRNA) to identify driver oncogene(s) within the amplicon. Knockdown of CCNE1 resulted in G1/S phase arrest, reduced cell viability and apoptosis only in amplification-carrying cells. Although CCNE1 knockdown increased cisplatin resistance in short-term assays, clonogenic survival was inhibited after treatment. Gain of 20q11 was highly correlated with 19q12 amplification and spanned a 2.5 Mb region including TPX2, a centromeric protein required for mitotic spindle function. Expression of TPX2 was highly correlated with gene amplification and with CCNE1 expression in primary tumors. siRNA inhibition of TPX2 reduced cell viability but this effect was not amplicon-dependent. These findings demonstrate that CCNE1 is a key driver in the 19q12 amplicon required for survival and clonogenicity in cells with locus amplification. Co-amplification at 19q12 and 20q11 implies the presence of a cooperative mutational network. These observations have implications for the application of targeted therapies in CCNE1 dependent ovarian cancers.
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ItemEstimating the proportion of microarray probes expressed in an RNA sample(OXFORD UNIV PRESS, 2010-04)A fundamental question in microarray analysis is the estimation of the number of expressed probes in different RNA samples. Negative control probes available in the latest microarray platforms, such as Illumina whole genome expression BeadChips, provide a unique opportunity to estimate the number of expressed probes without setting a threshold. A novel algorithm was proposed in this study to estimate the number of expressed probes in an RNA sample by utilizing these negative controls to measure background noise. The performance of the algorithm was demonstrated by comparing different generations of Illumina BeadChips, comparing the set of probes targeting well-characterized RefSeq NM transcripts with other probes on the array and comparing pure samples with heterogenous samples. Furthermore, hematopoietic stem cells were found to have a larger transcriptome than progenitor cells. Aire knockout medullary thymic epithelial cells were shown to have significantly less expressed probes than matched wild-type cells.
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ItemTranscriptome analyses of mouse and human mammary cell subpopulations reveal multiple conserved genes and pathways(BMC, 2010)INTRODUCTION: Molecular characterization of the normal epithelial cell types that reside in the mammary gland is an important step toward understanding pathways that regulate self-renewal, lineage commitment, and differentiation along the hierarchy. Here we determined the gene expression signatures of four distinct subpopulations isolated from the mouse mammary gland. The epithelial cell signatures were used to interrogate mouse models of mammary tumorigenesis and to compare with their normal human counterpart subsets to identify conserved genes and networks. METHODS: RNA was prepared from freshly sorted mouse mammary cell subpopulations (mammary stem cell (MaSC)-enriched, committed luminal progenitor, mature luminal and stromal cell) and used for gene expression profiling analysis on the Illumina platform. Gene signatures were derived and compared with those previously reported for the analogous normal human mammary cell subpopulations. The mouse and human epithelial subset signatures were then subjected to Ingenuity Pathway Analysis (IPA) to identify conserved pathways. RESULTS: The four mouse mammary cell subpopulations exhibited distinct gene signatures. Comparison of these signatures with the molecular profiles of different mouse models of mammary tumorigenesis revealed that tumors arising in MMTV-Wnt-1 and p53-/- mice were enriched for MaSC-subset genes, whereas the gene profiles of MMTV-Neu and MMTV-PyMT tumors were most concordant with the luminal progenitor cell signature. Comparison of the mouse mammary epithelial cell signatures with their human counterparts revealed substantial conservation of genes, whereas IPA highlighted a number of conserved pathways in the three epithelial subsets. CONCLUSIONS: The conservation of genes and pathways across species further validates the use of the mouse as a model to study mammary gland development and highlights pathways that are likely to govern cell-fate decisions and differentiation. It is noteworthy that many of the conserved genes in the MaSC population have been considered as epithelial-mesenchymal transition (EMT) signature genes. Therefore, the expression of these genes in tumor cells may reflect basal epithelial cell characteristics and not necessarily cells that have undergone an EMT. Comparative analyses of normal mouse epithelial subsets with murine tumor models have implicated distinct cell types in contributing to tumorigenesis in the different models.
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ItemPro-Inflammatory CD11c+CD206+ Adipose Tissue Macrophages Are Associated With Insulin Resistance in Human Obesity(AMER DIABETES ASSOC, 2010-07)OBJECTIVE: Insulin resistance and other features of the metabolic syndrome have been causally linked to adipose tissue macrophages (ATMs) in mice with diet-induced obesity. We aimed to characterize macrophage phenotype and function in human subcutaneous and omental adipose tissue in relation to insulin resistance in obesity. RESEARCH DESIGN AND METHODS: Adipose tissue was obtained from lean and obese women undergoing bariatric surgery. Metabolic markers were measured in fasting serum and ATMs characterized by immunohistology, flow cytometry, and tissue culture studies. RESULTS ATMs comprised CD11c(+)CD206(+) cells in "crown" aggregates and solitary CD11c(-)CD206(+) cells at adipocyte junctions. In obese women, CD11c(+) ATM density was greater in subcutaneous than omental adipose tissue and correlated with markers of insulin resistance. CD11c(+) ATMs were distinguished by high expression of integrins and antigen presentation molecules; interleukin (IL)-1beta, -6, -8, and -10; tumor necrosis factor-alpha; and CC chemokine ligand-3, indicative of an activated, proinflammatory state. In addition, CD11c(+) ATMs were enriched for mitochondria and for RNA transcripts encoding mitochondrial, proteasomal, and lysosomal proteins, fatty acid metabolism enzymes, and T-cell chemoattractants, whereas CD11c(-) ATMs were enriched for transcripts involved in tissue maintenance and repair. Tissue culture medium conditioned by CD11c(+) ATMs, but not CD11c(-) ATMs or other stromovascular cells, impaired insulin-stimulated glucose uptake by human adipocytes. CONCLUSIONS: These findings identify proinflammatory CD11c(+) ATMs as markers of insulin resistance in human obesity. In addition, the machinery of CD11c(+) ATMs indicates they metabolize lipid and may initiate adaptive immune responses.
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ItemedgeR: a Bioconductor package for differential expression analysis of digital gene expression data(OXFORD UNIV PRESS, 2010-01-01)SUMMARY: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. AVAILABILITY: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org).
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ItemOptimizing the noise versus bias trade-off for Illumina whole genome expression BeadChips(OXFORD UNIV PRESS, 2010-12)Five strategies for pre-processing intensities from Illumina expression BeadChips are assessed from the point of view of precision and bias. The strategies include a popular variance stabilizing transformation and model-based background corrections that either use or ignore the control probes. Four calibration data sets are used to evaluate precision, bias and false discovery rate (FDR). The original algorithms are shown to have operating characteristics that are not easily comparable. Some tend to minimize noise while others minimize bias. Each original algorithm is shown to have an innate intensity offset, by which unlogged intensities are bounded away from zero, and the size of this offset determines its position on the noise-bias spectrum. By adding extra offsets, a continuum of related algorithms with different noise-bias trade-offs is generated, allowing direct comparison of the performance of the strategies on equivalent terms. Adding a positive offset is shown to decrease the FDR of each original algorithm. The potential of each strategy to generate an algorithm with an optimal noise-bias trade-off is explored by finding the offset that minimizes its FDR. The use of control probes as part of the background correction and normalization strategy is shown to achieve the lowest FDR for a given bias.