Literature DB >> 15870398

Signature patterns of gene expression in mouse atherosclerosis and their correlation to human coronary disease.

Raymond Tabibiazar1, Roger A Wagner, Euan A Ashley, Jennifer Y King, Rossella Ferrara, Joshua M Spin, David A Sanan, Balasubramanian Narasimhan, Robert Tibshirani, Philip S Tsao, Bradley Efron, Thomas Quertermous.   

Abstract

The propensity for developing atherosclerosis is dependent on underlying genetic risk and varies as a function of age and exposure to environmental risk factors. Employing three mouse models with different disease susceptibility, two diets, and a longitudinal experimental design, it was possible to manipulate each of these factors to focus analysis on genes most likely to have a specific disease-related function. To identify differences in longitudinal gene expression patterns of atherosclerosis, we have developed and employed a statistical algorithm that relies on generalized regression and permutation analysis. Comprehensive annotation of the array with ontology and pathway terms has allowed rigorous identification of molecular and biological processes that underlie disease pathophysiology. The repertoire of atherosclerosis-related immunomodulatory genes has been extended, and additional fundamental pathways have been identified. This highly disease-specific group of mouse genes was combined with an extensive human coronary artery data set to identify a shared group of genes differentially regulated among atherosclerotic tissues from different species and different vascular beds. A small core subset of these differentially regulated genes was sufficient to accurately classify various stages of the disease in mouse. The same gene subset was also found to accurately classify human coronary lesion severity. In addition, this classifier gene set was able to distinguish with high accuracy atherectomy specimens from native coronary artery disease vs. those collected from in-stent restenosis lesions, thus identifying molecular differences between these two processes. These studies significantly focus efforts aimed at identifying central gene regulatory pathways that mediate atherosclerotic disease, and the identification of classification gene sets offers unique insights into potential diagnostic and therapeutic strategies in atherosclerotic disease.

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Year:  2005        PMID: 15870398     DOI: 10.1152/physiolgenomics.00001.2005

Source DB:  PubMed          Journal:  Physiol Genomics        ISSN: 1094-8341            Impact factor:   3.107


  30 in total

1.  Upregulation of the apelin-APJ pathway promotes neointima formation in the carotid ligation model in mouse.

Authors:  Yoko Kojima; Ramendra K Kundu; Christopher M Cox; Nicholas J Leeper; Joshua A Anderson; Hyung J Chun; Ziad A Ali; Euan A Ashley; Paul A Krieg; Thomas Quertermous
Journal:  Cardiovasc Res       Date:  2010-02-22       Impact factor: 10.787

Review 2.  Microarray-based analysis of ventilator-induced lung injury.

Authors:  Mark M Wurfel
Journal:  Proc Am Thorac Soc       Date:  2007-01

3.  Evaluation of resequencing on number of tag SNPs of 13 atherosclerosis-related genes in Thai population.

Authors:  Chintana Tocharoentanaphol; Somying Promso; Dianna Zelenika; Tassanee Lowhnoo; Sissades Tongsima; Thanyachai Sura; Wasun Chantratita; Fumihiko Matsuda; Sean Mooney; Anavaj Sakuntabhai
Journal:  J Hum Genet       Date:  2007-11-28       Impact factor: 3.172

Review 4.  Cardiovascular genomics: a biomarker identification pipeline.

Authors:  John H Phan; Chang F Quo; May Dongmei Wang
Journal:  IEEE Trans Inf Technol Biomed       Date:  2012-05-16

Review 5.  A systems biology approach to understanding atherosclerosis.

Authors:  Stephen A Ramsey; Elizabeth S Gold; Alan Aderem
Journal:  EMBO Mol Med       Date:  2010-03       Impact factor: 12.137

6.  Identification and validation of genes affecting aortic lesions in mice.

Authors:  Xia Yang; Larry Peterson; Rolf Thieringer; Joshua L Deignan; Xuping Wang; Jun Zhu; Susanna Wang; Hua Zhong; Serguei Stepaniants; John Beaulaurier; I-Ming Wang; Ray Rosa; Anne-Marie Cumiskey; Jane Ming-Juan Luo; Qi Luo; Kashmira Shah; Jianying Xiao; David Nickle; Andrew Plump; Eric E Schadt; Aldons J Lusis; Pek Yee Lum
Journal:  J Clin Invest       Date:  2010-06-23       Impact factor: 14.808

7.  Coronary risk assessment among intermediate risk patients using a clinical and biomarker based algorithm developed and validated in two population cohorts.

Authors:  D S Cross; C A McCarty; E Hytopoulos; M Beggs; N Nolan; D S Harrington; T Hastie; R Tibshirani; R P Tracy; B M Psaty; R McClelland; P S Tsao; T Quertermous
Journal:  Curr Med Res Opin       Date:  2012-11       Impact factor: 2.580

8.  Dominant negative PPARγ promotes atherosclerosis, vascular dysfunction, and hypertension through distinct effects in endothelium and vascular muscle.

Authors:  Christopher J Pelham; Henry L Keen; Steven R Lentz; Curt D Sigmund
Journal:  Am J Physiol Regul Integr Comp Physiol       Date:  2013-02-27       Impact factor: 3.619

Review 9.  Omics-based approaches to understand mechanosensitive endothelial biology and atherosclerosis.

Authors:  Rachel D Simmons; Sandeep Kumar; Salim Raid Thabet; Sanjoli Sur; Hanjoong Jo
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2016-06-24

10.  Reconstruction and functional analysis of altered molecular pathways in human atherosclerotic arteries.

Authors:  Stefano Cagnin; Michele Biscuola; Cristina Patuzzo; Elisabetta Trabetti; Alessandra Pasquali; Paolo Laveder; Giuseppe Faggian; Mauro Iafrancesco; Alessandro Mazzucco; Pier Franco Pignatti; Gerolamo Lanfranchi
Journal:  BMC Genomics       Date:  2009-01-09       Impact factor: 3.969

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