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Karen A Baskerville,
Brandy Edenfield,
David Personett,
David M Menke,
Abba C Zubair,
Brian P O'Neill,
Weil R Lai,
Kurt A Jaeckle,
Michael McKinney, Pamela Kreinest,
Han W Tun,
Peter J Park
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ABSTRACT: MOTIVATION: Type 2 diabetes is a chronic metabolic disease that involves both environmental and genetic factors. To understand the genetics of type 2 diabetes and insulin resistance, the DIabetes Genome Anatomy Project (DGAP) was launched to profile gene expression in a variety of related animal models and human subjects. We asked whether these heterogeneous models can be integrated to provide consistent and robust biological insights into the biology of insulin resistance. RESULTS: We perform integrative analysis of the 16 DGAP data sets that span multiple tissues, conditions, array types, laboratories, species, genetic backgrounds and study designs. For each data set, we identify differentially expressed genes compared with control. Then, for the combined data, we rank genes according to the frequency with which they were found to be statistically significant across data sets. This analysis reveals RetSat as a widely shared component of mechanisms involved in insulin resistance and sensitivity and adds to the growing importance of the retinol pathway in diabetes, adipogenesis and insulin resistance. Top candidates obtained from our analysis have been confirmed in recent laboratory studies.
Bioinformatics 09/2009; 25(23):3121-7. · 5.47 Impact Factor
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Bioinformatics. 01/2009; 25:3121-3127.
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Han W Tun,
David Personett,
Karen A Baskerville,
David M Menke,
Kurt A Jaeckle,
Pamela Kreinest,
Brandy Edenfield,
Abba C Zubair,
Brian P O'Neill, Weil R Lai,
Peter J Park,
Michael McKinney
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ABSTRACT: Primary central nervous system (CNS) lymphoma (PCNSL) is a diffuse large B-cell lymphoma (DLBCL) confined to the CNS. A genome-wide gene expression comparison between PCNSL and non-CNS DLBCL was performed, the latter consisting of both nodal and extranodal DLBCL (nDLBCL and enDLBCL), to identify a "CNS signature." Pathway analysis with the program SigPathway revealed that PCNSL is characterized notably by significant differential expression of multiple extracellular matrix (ECM) and adhesion-related pathways. The most significantly up-regulated gene is the ECM-related osteopontin (SPP1). Expression at the protein level of ECM-related SPP1 and CHI3L1 in PCNSL cells was demonstrated by immunohistochemistry. The alterations in gene expression can be interpreted within several biologic contexts with implications for PCNSL, including CNS tropism (ECM and adhesion-related pathways, SPP1, DDR1), B-cell migration (CXCL13, SPP1), activated B-cell subtype (MUM1), lymphoproliferation (SPP1, TCL1A, CHI3L1), aggressive clinical behavior (SPP1, CHI3L1, MUM1), and aggressive metastatic cancer phenotype (SPP1, CHI3L1). The gene expression signature discovered in our study may represent a true "CNS signature" because we contrasted PCNSL with wide-spectrum non-CNS DLBCL on a genomic scale and performed an in-depth bioinformatic analysis.
Blood 04/2008; 111(6):3200-10. · 9.90 Impact Factor
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ABSTRACT: The basal forebrain (BF) cholinergic system is selectively vulnerable in human brain diseases, while the cholinergic groups in the upper pons of the brainstem (BS) resist neurodegeneration. Cholinergic neurons (200 per region per animal) were laser-microdissected from five young (8 months) and five aged (24 months) F344 rats from the BF and the BS pontine lateral dorsal tegmental/pedunculopontine nuclei (LDTN/PPN) and their expression profiles were obtained. The bioinformatics program SigPathway was used to identify gene groups and pathways that were selectively affected by aging. In the BF cholinergic system, aging most significantly altered genes involved with a variety of metabolic functions. In contrast, BS cholinergic neuronal age effects included gene groupings related to neuronal plasticity and a broad range of normal cellular functions. Transcription factor GA-binding protein alpha (GABPalpha), which controls expression of nuclear genes encoding mitochondrial proteins, was more strongly upregulated in the BF cholinergic neurons (+107%) than in the BS cholinergic population (+40%). The results suggest that aging elicits elevates metabolic activity in cholinergic populations and that this occurs to a much greater degree in the BF group than in the BS group.
Neurobiology of aging 07/2007; 29(12):1874-93. · 5.94 Impact Factor
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ABSTRACT: Type 2 diabetes mellitus is a complex disorder associated with multiple genetic, epigenetic, developmental, and environmental factors. Animal models of type 2 diabetes differ based on diet, drug treatment, and gene knockouts, and yet all display the clinical hallmarks of hyperglycemia and insulin resistance in peripheral tissue. The recent advances in gene-expression microarray technologies present an unprecedented opportunity to study type 2 diabetes mellitus at a genome-wide scale and across different models. To date, a key challenge has been to identify the biological processes or signaling pathways that play significant roles in the disorder. Here, using a network-based analysis methodology, we identified two sets of genes, associated with insulin signaling and a network of nuclear receptors, which are recurrent in a statistically significant number of diabetes and insulin resistance models and transcriptionally altered across diverse tissue types. We additionally identified a network of protein-protein interactions between members from the two gene sets that may facilitate signaling between them. Taken together, the results illustrate the benefits of integrating high-throughput microarray studies, together with protein-protein interaction networks, in elucidating the underlying biological processes associated with a complex disorder.
PLoS Genetics 07/2007; 3(6):e96. · 8.69 Impact Factor
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ABSTRACT: X-chromosome dosage compensation in Drosophila requires the male-specific lethal (MSL) complex, which up-regulates gene expression from the single male X chromosome. Here, we define X-chromosome-specific MSL binding at high resolution in two male cell lines and in late-stage embryos. We find that the MSL complex is highly enriched over most expressed genes, with binding biased toward the 3' end of transcription units. The binding patterns are largely similar in the distinct cell types, with approximately 600 genes clearly bound in all three cases. Genes identified as clearly bound in one cell type and not in another indicate that attraction of MSL complex correlates with expression state. Thus, sequence alone is not sufficient to explain MSL targeting. We propose that the MSL complex recognizes most X-linked genes, but only in the context of chromatin factors or modifications indicative of active transcription. Distinguishing expressed genes from the bulk of the genome is likely to be an important function common to many chromatin organizing and modifying activities.
Genes & Development 05/2006; 20(7):848-57. · 11.66 Impact Factor
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ABSTRACT: MOTIVATION: Array Comparative Genomic Hybridization (CGH) can reveal chromosomal aberrations in the genomic DNA. These amplifications and deletions at the DNA level are important in the pathogenesis of cancer and other diseases. While a large number of approaches have been proposed for analyzing the large array CGH datasets, the relative merits of these methods in practice are not clear. RESULTS: We compare 11 different algorithms for analyzing array CGH data. These include both segment detection methods and smoothing methods, based on diverse techniques such as mixture models, Hidden Markov Models, maximum likelihood, regression, wavelets and genetic algorithms. We compute the Receiver Operating Characteristic (ROC) curves using simulated data to quantify sensitivity and specificity for various levels of signal-to-noise ratio and different sizes of abnormalities. We also characterize their performance on chromosomal regions of interest in a real dataset obtained from patients with Glioblastoma Multiforme. While comparisons of this type are difficult due to possibly sub-optimal choice of parameters in the methods, they nevertheless reveal general characteristics that are helpful to the biological investigator.
Bioinformatics 11/2005; 21(19):3763-70. · 5.47 Impact Factor
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[hide abstract]
ABSTRACT: Type 2 diabetes mellitus is a complex disorder associated with multiple genetic, epigenetic, developmental, and environmental factors. Animal models of type 2 diabetes differ based on diet, drug treatment, and gene knockouts, and yet all display the clinical hallmarks of hyperglycemia and insulin resistance in peripheral tissue. The recent advances in gene-expression microarray technologies present an unprecedented opportunity to study type 2 diabetes mellitus at a genome-wide scale and across different models. To date, a key challenge has been to identify the biological processes or signaling pathways that play significant roles in the disorder. Here, using a network-based analysis methodology, we identified two sets of genes, associated with insulin signaling and a network of nuclear receptors, which are recurrent in a statistically significant number of diabetes and insulin resistance models and transcriptionally altered across diverse tissue types. We additionally identified a network of protein–protein interactions between members from the two gene sets that may facilitate signaling between them. Taken together, the results illustrate the benefits of integrating high-throughput microarray studies, together with protein–protein interaction networks, in elucidating the underlying biological processes associated with a complex disorder.
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[show abstract]
[hide abstract]
ABSTRACT: Motivation: Type 2 diabetes is a chronic metabolic disease that involves both environmental and genetic factors. To understand the genetics of type 2 diabetes and insulin resistance, the DIabetes Genome Anatomy Project (DGAP) was launched to profile gene expression in a variety of related animal models and human subjects. We asked whether these heterogeneous models can be integrated to provide consistent and robust biological insights into the biology of insulin resistance. Results: We perform integrative analysis of the 16 DGAP data sets that span multiple tissues, conditions, array types, laboratories, species, genetic backgrounds and study designs. For each data set, we identify differentially expressed genes compared with control. Then, for the combined data, we rank genes according to the frequency with which they were found to be statistically significant across data sets. This analysis reveals RetSat as a widely shared component of mechanisms involved in insulin resistance and sensitivity and adds to the growing importance of the retinol pathway in diabetes, adipogenesis and insulin resistance. Top candidates obtained from our analysis have been confirmed in recent laboratory studies. Contact: Isaac_kohane@harvard.edu
