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Enhanced polyamine catabolism alters homeostatic control of white adipose tissue mass, energy expenditure, and glucose metabolism

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MOLECULAR AND CELLULAR BIOLOGY 2007年第13期27卷 4953-4967页

作者： Pirinen, Eija Kuulasmaa, Teemu Pietila, Marko Heikkinen, Sami Tusa, Maija Itkonen, Paula Boman, Susanna Skommer, Joanna Virkamaki, Antti Hohtola, Esa Kettunen, Mikko Fatrai, Szabolcs Kansanen, Emilia Koota, Suvi Niiranen, Kirsi Parkkinen, Jyrki Levonen, Anna-Liisa Yla-Herttuala, Seppso Hiltunen, J. Kalervo Alhonen, Leena Smith, Ulf Janne, Juhani Laakso, Markku Univ Kuopio Dept Med FI-70211 Kuopio Finland Univ Kuopio AI Virtanen Inst Mol Sci FI-70211 Kuopio Finland Univ Helsinki Minerva Fdn Inst Med Res FI-00290 Helsinki Finland Univ Oulu Dept Biol FI-90014 Oulu Finland Univ Kuopio Natl Bio NMR Facil AI Virtanen Inst Mol Sci FI-70211 Kuopio Finland Tampere Univ Hosp Dept Pathol FI-33521 Tampere Finland Univ Oulu Bioctr Oulu FI-90014 Oulu Finland Univ Oulu Dept Biochem FI-90014 Oulu Finland Sahlgrens Univ Hosp Lundberg Lab Diabet Res Dept Internal Med SE-41345 Gothenburg Sweden

Peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PGC-1 alpha) is an attractive candidate gene for type 2 diabetes, as genes of the oxidative phosphorylation (OXPHOS) pathway are coordinatively downregulated by reduced expression of PGC-1 alpha in skeletal muscle and adipose tissue of patients with type 2 diabetes. Here we demonstrate that transgenic mice with activated polyamine catabolism due to overexpression of spermidine/spermine N-1-acetyltransferase (SSAT) had reduced white adipose tissue (WAT) mass, high basal metabolic rate, improved glucose tolerance, high insulin sensitivity, and enhanced expression of the OXPHOS genes, coordinated by increased levels of PGC-1 alpha and 5'-AMP-activated protein kinase (AMPK) in WAT. As accelerated polyamine flux caused by SSAT overexpression depleted the ATP pool in adipocytes of SSAT mice and N-1,N-11-diethylnorspermine-treated wild-type fetal fibroblasts, we propose that low ATP levels lead to the induction of AMPK, which in turn activates PGC-1 alpha in WAT of SSAT mice. Our hypothesis is supported by the finding that the phenotype of SSAT mice was reversed when the accelerated polyamine flux was reduced by the inhibition of polyamine biosynthesis in WAT. The involvement of polyamine catabolism in the regulation of energy and glucose metabolism may offer a novel target for drug development for obesity and type 2 diabetes.

关键词： ACTIVATED PROTEIN-KINASE OVEREXPRESSING SPERMIDINE/SPERMINEN-1-ACETYLTRANSFERASE TRANSCRIPTIONAL COACTIVATOR PGC-1 TRANSGENIC MICE INSULIN-RESISTANCE OXIDATIVE STRESS GENE-EXPRESSION microarray data MUSCLE-CELLS ACETYL-COA

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A comparative analysis of perturbations caused by a gene knock-out, a dominant negative allele, and a set of peptide aptamers

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MOLECULAR & CELLULAR PROTEOMICS 2007年第12期6卷 2110-2121页

作者： Abed, Nadia Bickle, Marc Mari, Bernard Schapira, Matthieu Sanjuan-Espana, Raquel Sermesant, Karine Robbe Moncorge, Olivier Mouradian-Garcia, Sandrine Barbry, Pascal Rudkin, Brian B. Fauvarque, Marie-Odile Michaud-Soret, Isabelle Colas, Pierre Univ Lyon 1 Ecole Normale Super LyonLab Biol Mol Cellule CNRSUMR 5239 Differentiat & Cell Cycle GrpIFR 128 Biosci Lyon F-69364 Lyon 07 France Univ Grenoble 1 iRTSV DSV CEAINSERM U873Lab Transduct Signal F-38054 Grenoble 9 France Aptanom F-69007 Lyon France Univ Nice Sophia Antipolis Inst Pharmacol Mol & Cellulaire CNRS UMR 6097 F-06560 Sophia Antipolis France CNRS UMR 5249 Lab Chim Biol Metaux F-75700 Paris France

The study of protein function mostly relies on perturbing regulatory networks by acting upon protein expression levels or using transdominant negative agents. Here we used the Escherichia coli global transcription regulator Fur ( ferric uptake regulator) as a case study to compare the perturbations exerted by a gene knock-out, the expression of a dominant negative allele of a gene, and the expression of peptide aptamers that bind a gene product. These three perturbations caused phenotypes that differed quantitatively and qualitatively from one another. The Fur peptide aptamers inhibited the activity of their target to various extents and reduced the virulence of a pathogenic E. coli strain in Drosophila. A genome-wide transcriptome analysis revealed that the "penetrance" of a peptide aptamer was comparable to that of a dominant negative allele but lower than the penetrance of the gene knock-out. Our work shows that comparative analysis of phenotypic and transcriptome responses to different types of perturbation can help decipher complex regulatory networks that control various biological processes.

关键词： FERRIC UPTAKE REGULATOR UPTAKE REGULATION PROTEIN ESCHERICHIA-COLI HELICOBACTER-PYLORI PSEUDOMONAS-AERUGINOSA SUPEROXIDE-DISMUTASE microarray data FUR PROTEIN SACCHAROMYCES-CEREVISIAE DEPENDENT DIMERIZATION

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Multivariate correlation estimator for inferring functional relationships from replicated genome-wide data

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BIOINFORMATICS 2007年第17期23卷 2298-2305页

作者： Zhu, Dongxiao Li, Youjuan Li, Hua Stowers Inst Med Res Kansas City MO 64110 USA Univ Michigan Dept Stat Ann Arbor MI 48105 USA

Estimating pairwise correlation from replicated genomescale (a.k.a. OMICS) data is fundamental to cluster functionally relevant biomolecules to a cellular pathway. The popular Pearson correlation coefficient estimates bivariate correlation by averaging over replicates. It is not completely satisfactory since it introduces strong bias while reducing variance. We propose a new multivariate correlation estimator that models all replicates as independent and identically distributed (i.i.d.)samples from the multivariate normal distribution. We derive the estimator by maximizing the likelihood function. For small sample data, we provide a resampling-based statistical inference procedure, and for moderate to large sample data, we provide an asymptotic statistical inference procedure based on the Likelihood Ratio Test (LRT). We demonstrate advantages of the new multivariate correlation estimator over Pearson bivariate correlation estimator using simulations and real-world data analysis examples. Availability: The estimator and statistical inference procedures have been implemented in an R package 'CORREP' that is available from CRAN [http://***] and Bioconductor [http://***. org/]. Contact: doz@***@*** Supplementary information: Supplementary data are available at Bioinformatics online.

关键词： DIFFERENTIALLY-EXPRESSED GENES PARTIAL LEAST-SQUARES microarray data INTRACLASS CORRELATIONS OLIGONUCLEOTIDE ARRAYS FAMILIAL data MODEL CLASSIFICATION INTERCLASS PROFILES

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Construction of a reference gene association network from multiple profiling data: application to data analysis

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BIOINFORMATICS 2007年第20期23卷 2716-2724页

作者： Ucar, Duygu Neuhaus, Isaac Ross-MacDonald, Petra Tilford, Charles Parthasarathy, Srinivasan Siemers, Nathan Ji, Rui-Ru Bristol Myers Squibb Res & Dev Appl Genom Pennington NJ 08534 USA Ohio State Univ Columbus OH 43210 USA

Motivation: Gene expression profiling is an important tool for gaining insight into biology. Novel strategies are required to analyze the growing archives of microarray data and extract useful information from them. One area of interest is in the construction of gene association networks from collections of profiling data. Various approaches have been proposed to construct gene networks using profiling data, and these networks have been used in functional inference as well as in data visualization. Here, we investigated a non-parametric approach to translate profiling data into a gene network. We explored the characteristics and utility of the resulting network and investigated the use of network information in analysis of variance models and hypothesis testing. Results: Our work is composed of two parts: gene network construction and partitioning and hypothesis testing using sub-networks as groups. In the first part, multiple independently collected microarray datasets from the Gene Expression Omnibus data repository were analyzed to identify probe pairs that are positively co-regulated across the samples. A co-expression network was constructed based on a reciprocal ranking criteria and a false discovery rate analysis. We named this network Reference Gene Association (RGA) network. Then, the network was partitioned into densely connected sub-networks of probes using a multilevel graph partitioning algorithm. In the second part, we proposed a new, MANOVA-based approach that can take individual probe expression values as input and perform hypothesis testing at the sub-network level. We applied this MANOVA methodology to two published studies and our analysis indicated that the methodology is both effective and sensitive for identifying transcriptional sub-networks or pathways that are perturbed across treatments.

关键词： COEXPRESSION NETWORK REGULATORY NETWORKS CIGARETTE-SMOKE microarray data SET ENRICHMENT EXPRESSION PREDICTION CLASSIFICATION DYSLIPIDEMIA DISCOVERY

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Extending the pathway analysis framework with a test for transcriptional variance implicates novel pathway modulation during myogenic differentiation

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BIOINFORMATICS 2007年第11期23卷 1356-1362页

作者： Kemp, Daniel M. Nirmala, N. R. Szustakowski, Joseph D. Novartis Inst Biomed Res Diabet & Metab Dis Area Cambridge MA 02139 USA Novartis Inst Biomed Res Cambridge MA 02139 USA

Motivation: We describe an extension of the pathway-based enrichment approach for analyzing microarray data via a robust test for transcriptional variance. The use of a variance test is intended to identify additional patterns of transcriptional regulation in which many genes in a pathway are up- and down-regulated. Such patterns may be indicative of the reciprocal regulation of pathway activators and inhibitors or of the differential regulation of separate biological sub-processes and should extend the number of detectable patterns of transcriptional modulation. Results: We validated this new statistical approach on a microarray experiment that captures the temporal transcriptional profile of muscle differentiation in mouse C2C12 cells. Comparisons of the transcriptional state of myoblasts and differentiated myotubes via a robust variance test implicated several novel pathways in muscle cell differentiation previously overlooked by a standard enrichment analysis. Specifically, pathways involved in cell structure, calcium-mediated signaling and muscle-specific signaling were identified as differentially modulated based on their increased transcriptional variance. These biologically relevant results validate this approach and demonstrate the flexible nature of pathway-based methods of data analysis.

关键词： .Differentially robust down-regulated microarray data reciprocal myogenic myoblasts myotubes pathway-based transcriptional variance pathway analysis framework variance test genes in pathway pathway activators standard enrichment

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Improved breast cancer prognosis through the combination of clinical and genetic markers

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BIOINFORMATICS 2007年第1期23卷 30-37页

作者： Sun, Yijun Goodison, Steve Li, Jian Liu, Li Farmerie, William Univ Florida Interdisciplinary Ctr Biotechnol Res Gainesville FL 32611 USA Univ Florida Dept Surg Gainesville FL 32611 USA Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

Motivation: Accurate prognosis of breast cancer can spare a significant number of breast cancer patients from receiving unnecessary adjuvant systemic treatment and its related expensive medical costs. Recent studies have demonstrated the potential value of gene expression signatures in assessing the risk of post-surgical disease recurrence. However, these studies all attempt to develop genetic marker-based prognostic systems to replace the existing clinical criteria, while ignoring the rich information contained in established clinical markers. Given the complexity of breast cancer prognosis, a more practical strategy would be to utilize both clinical and genetic marker information that may be complementary. Methods: A computational study is performed on publicly available microarray data, which has spawned a 70-gene prognostic signature. The recently proposed I-RELIEF algorithm is used to identify a hybrid signature through the combination of both genetic and clinical markers. A rigorous experimental protocol is used to estimate the prognostic performance of the hybrid signature and other prognostic approaches. Survival data analyses is performed to compare different prognostic approaches. Results: The hybrid signature performs significantly better than other methods, including the 70-gene signature, clinical makers alone and the St. Gallen consensus criterion. At the 90% sensitivity level, the hybrid signature achieves 67% specificity, as compared to 47% for the 70-gene signature and 48% for the clinical makers. The odds ratio of the hybrid signature for developing distant metastases within five years between the patients with a good prognosis signature and the patients with a bad prognosis is 21.0 (95% CI:6.5-68.3), far higher than either genetic or clinical markers alone.

关键词： TERM FOLLOW-UP MOLECULAR CLASSIFICATION microarray data EXPRESSION METASTASIS PROTEIN CONSENSUS SELECTION SURVIVAL THERAPY

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Detecting differential expressions in GeneChip microarray studies: A quantile approach

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2007年第477期102卷 104-112页

作者： Wang, Huixia He, Xuming N Carolina State Univ Dept Stat Raleigh NC 27695 USA Univ Illinois Dept Stat Champaign IL 61820 USA

In this article we consider testing for differentially expressed genes in GeneChip studies by modeling and analyzing the quantiles of gene expression through probe level measurements. By developing a robust rank score test for linear quantile models with a random effect, we propose a reliable test for detecting differences in certain quantiles of the intensity distributions. By using a genomewide adjustment to the test statistic to account for within-array correlation, we demonstrate that the proposed rank score test is highly effective even when the number of arrays is small. Our empirical studies with real experimental data show that detecting differences in the quartiles for the probe level data is a valuable complement to the usual mixed model analysis based on Gaussian likelihood. The methodology proposed in this article is a first attempt to develop inferential tools for quantile regression in mixed models.

关键词： gene expression microarray data quantile regression random effect rank score test

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Enrichment or depletion of a GO category within a class of genes: which test?

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BIOINFORMATICS 2007年第4期23卷 401-407页

作者： Rivals, Isabelle Personnaz, Leon Taing, Lieng Potier, Marie-Claude Ecole Super Phys & Chim Ind Ville Paris Equipe Stat Appl F-75005 Paris France Ecole Super Phys & Chim Ind Ville Paris Lab Neurobiol & Divers Cellulaire F-75005 Paris France

Motivation: A number of available program packages determine the significant enrichments and/or depletions of GO categories among a class of genes of interest. Whereas a correct formulation of the problem leads to a single exact null distribution, these GO tools use a large variety of statistical tests whose denominations often do not clarify the underlying P-value computations. Summary: We review the different formulations of the problem and the tests they lead to: the binomial, chi(2), equality of two probabilities, Fisher's exact and hypergeometric tests. We clarify the relationships existing between these tests, in particular the equivalence between the hypergeometric test and Fisher's exact test. We recall that the other tests are valid only for large samples, the test of equality of two probabilities and the chi(2)-test being equivalent. We discuss the appropriateness of one- and two-sided P-values, as well as some discreteness and conservatism issues. Contact:***@*** Supplementary information: Supplementary data are available at Bioinformatics online.

关键词： microarray data EXPRESSION data ONTOLOGY TERMS TOOL

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Gene expression network analysis and applications to immunology

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BIOINFORMATICS 2007年第7期23卷 850-858页

作者： Nacu, Serban Critchley-Thorne, Rebecca Lee, Peter Holmes, Susan Stanford Univ Dept Stat Stanford CA 94305 USA Stanford Univ Sch Med Stanford CA 94305 USA Ecole Normale Super F-75231 Paris France

We address the problem of using expression data and prior biological knowledge to identify differentially expressed pathways or groups of genes. Following an idea of Ideker et al. (2002), we construct a gene interaction network and search for high-scoring subnetworks. We make several improvements in terms of scoring functions and algorithms, resulting in higher speed and accuracy and easier biological interpretation. We also assign significance levels to our results, adjusted for multiple testing. Our methods are succesfully applied to three human microarray data sets, related to cancer and the immune system, retrieving several known and potential pathways. The method, denoted by the acronym GXNA (Gene eXpression Network Analysis) is implemented in software that is publicly available and can be used on virtually any microarray data set.

关键词： microarray data PATHWAYS PATTERNS

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Hierarchical tree snipping: clustering guided by prior knowledge

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BIOINFORMATICS 2007年第24期23卷 3335-3342页

作者： Dotan-Cohen, Dikla Melkman, Avraham A. Kasif, Simon Ben Gurion Univ Negev Dept Comp Sci IL-84105 Beer Sheva Israel Childrens Hosp Dept Biomed Engn Boston MA 02115 USA Childrens Hosp Ctr Adv Genom Technol Boston MA 02115 USA Childrens Hosp Bioinformat Program Boston MA 02115 USA Childrens Hosp Harvard MIT Program Hlth Sci & Technol Boston MA 02115 USA

Motivation: Hierarchical clustering is widely used to cluster genes into groups based on their expression similarity. This method first constructs a tree. Next this tree is partitioned into subtrees by cutting all edges at some level, thereby inducing a clustering. Unfortunately, the resulting clusters often do not exhibit significant functional coherence. Results: To improve the biological significance of the clustering, we develop a new framework of partitioning by snipping-cutting selected edges at variable levels. The snipped edges are selected to induce clusters that are maximally consistent with partially available background knowledge such as functional classifications. Algorithms for two key applications are presented: functional prediction of genes, and discovery of functionally enriched clusters of co-expressed genes. Simulation results and cross-validation tests indicate that the algorithms perform well even when the actual number of clusters differs considerably from the requested number. Performance is improved compared with a previously proposed algorithm.

关键词： GENE-EXPRESSION data microarray data SACCHAROMYCES-CEREVISIAE DROSOPHILA-MELANOGASTER ANNOTATION NUMBER CYCLE

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