Gene expression profiling: Classification of mice with left ventricle systolic dysfunction using microarray analysis

Critical Care Medicine

Volumn 38, Issue 1, 2010, Pages 25-31

  Wong, Jim ^a   Chang, Christine ^a   Agrawal, Rani ^a   Walton, G Brant ^a   Chen, Craig ^b   Murthy, Ashwin ^a   Patterson, Andrew J ^a  

^a STANFORD UNIVERSITY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Cardiac; Classification; Gene; Microarray; Profiling

Indexed keywords

ISOPRENALINE; MESSENGER RNA;

ANIMAL EXPERIMENT; ANIMAL MODEL; ANIMAL TISSUE; ARTICLE; CHEMICAL STRESS; CONTROLLED STUDY; DIASTOLIC DYSFUNCTION; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENETIC ASSOCIATION; GENOTYPE; HEART LEFT VENTRICLE FAILURE; HEART LEFT VENTRICLE PRESSURE; MALE; MICROARRAY ANALYSIS; MOUSE; NONHUMAN; PHENOTYPE; PRIORITY JOURNAL; PROSPECTIVE STUDY; SYSTOLIC DYSFUNCTION; WILD TYPE; ANIMAL; C57BL MOUSE; CLASSIFICATION; COMPARATIVE STUDY; DISEASE MODEL; GENE EXPRESSION REGULATION; GENETICS; HEART LEFT VENTRICLE FUNCTION; MOUSE MUTANT; MOUSE STRAIN; RANDOMIZATION; REFERENCE VALUE; SENSITIVITY AND SPECIFICITY;

ANIMALS; DISEASE MODELS, ANIMAL; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; ISOPROTERENOL; MALE; MICE; MICE, INBRED C57BL; MICE, INBRED STRAINS; MICE, KNOCKOUT; MICROARRAY ANALYSIS; PHENOTYPE; RANDOM ALLOCATION; REFERENCE VALUES; RNA, MESSENGER; SENSITIVITY AND SPECIFICITY; VENTRICULAR DYSFUNCTION, LEFT;

EID: 74049158917     PISSN: 00903493     EISSN: 15300293     Source Type: Journal    
DOI: 10.1097/CCM.0b013e3181b427e8     Document Type: Article

References (32)

1. Alizadeh AA, Eisen MB, Davis RE, et al: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000; 403:503-511
2. Bittner M, Meltzer P, Chen Y, et al: Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature 2000; 406:536-540
3. Sotiriou C, Neo SY, McShane LM, et al: Breast cancer classification and prognosis based upon gene expression profiles from a population-based study. Proc Natl Acad Sci USA 2003; 100:10393-10398
4. Van't Veer LJ, Dai H, Van de Vijver MJ, et al: Gene expression profiling predicts outcome of breast cancer. Nature 2002; 415:530-536
5. Van de Vijver MJ, He YD, Van't Veer LJ, et al: A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002; 347:1999-2009
6. Stoecklein NH, Hosch SB, Belzer M, et al: Direct genetic analysis of single disseminated cancer cells for prediction of outcome and therapy selection in esophageal cancer. Cancer Cell 2008; 13:441-453
7. Tang BM, McLean AS, Dawes IW, et al: Gene-expression profiling of Gram-positive and Gram-negative sepsis in critically ill patients. Crit Care Med 2008; 36:1125-1128
8. Menges T, Konig IR, Hossain H, et al: Sepsis syndrome and death in trauma patients are associated with variation in the gene encoding tumor necrosis factor. Crit Care Med 2008; 36:1456-1462
9. Payen D, Lukaszewicz AC, Belikova I, et al: Gene profiling in human blood leucocytes during recovery from septic shock. Intensive Care Med 2008; 34:1371-1376
10. Ross CJ, Carleton B, Warn DG, et al: Geno-typic approaches to therapy in children: A national active surveillance network (GATC) to study the pharmacogenomics of severe adverse drug reactions in children. Ann NY Acad Sci 2007; 1110:177-192
11. Ross CJ, Katzov H, Carleton B, et al: Pharmacogenomics and its implications for autoimmune disease. J Autoimmun 2007; 28: 122-128
12. Lee MH, Hong I, Kim M, et al: Gene expression profiles of murine fatty liver induced by the administration of methotrexate. Toxicology 2008; 249:75-84
13. Almon RR, DuBois DC, Yao Z, et al: Microar-ray analysis of the temporal response of skeletal muscle to methylprednisolone: Comparative analysis of two dosing regimens. Physiol Genomics 2007; 30:282-299
14. Institute of Laboratory Animal Resources Commission on Life Sciences National Research Council: Guide for the Care and Use of Laboratory Animals. Washington, DC, National Academy Press, 1996
15. Chruscinski AJ, Rohrer DK, Schauble E, et al: Targeted disruption of the |32 adrenergic receptor gene. J Biol Chem 1999; 274: 16694-16700
16. Patterson A, Zhu W, Chow A, et al: Protecting the myocardium: A role for the |32 adrenergic receptor in the heart. Crit Care Med 2004; 32:1041-1048
17. Qiagen: RNeasy Midi/Maxi Handbook. Valencia, CA, Qiagen Worldwide, 2001
18. Ashley EA, Powers J, et al: The endogenous peptide apelin potently improves cardiac contractility and reduces cardiac loading in vivo. Cardiovasc Res 2005; 65:73-82
19. Yang B, Larson DF, Beischel J, et al: Validation of conductance catheter system for quantification of murine pressure-volume loops. J Invest Surg 2001; 14:341-355
20. Nielson JM, Kristiansen SB, Ringgaard S, et al: Left ventricular volume measurements in mice by conductance catheter: Evaluation and optimization of calibration. Am J Physiol Heart Circ Physiol 2007; 293:H534-H540
21. Bolstad BM, Irizarry RA, Astrand M, et al: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003; 19:185-193
22. Irizarry RA, Hobbs B, Collin F, et al: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 2003; 4:249-264
23. Thalamuthu A, Mukhopadhyay I, Zheng X, et al: Evaluation and comparison of gene clustering methods in microarray analysis. Bioinformatics 2006; 22:2405-2412
24. Huttenhower C, Flamholz AI, Landis JN, et al: Nearest neighbor networks: clustering expression data based on gene neighborhoods. BMC Bioinformatics 2007; 8:1-13
25. Do JH, Choi DK: Clustering approaches to identifying gene expression patterns from DNA microarray data. Mol Cells 2008; 25: 279-288
26. Leung YF, Cavalieri D: Fundamentals of cDNA microarray data analysis. Trends Genet 2003; 19:649-659
27. Jeffrey IB, Higgins DG, Culhane AC: Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data. BMC Bioinformatics 2006; 7:1-16
28. Dupuy A, Simon RM: Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Natl Cancer Inst 2007; 99:147-157
29. Chen JJ: Key aspects of analyzing microarray gene-expression data. Pharmacogenomics 2007; 8:473-482
30. Archacki S, Wang Q: Expression profiling of cardiovascular disease. Hum Genomics 2004; 1:355-370
31. Kittleson MM, Hare JM: Molecular signature analysis: using the myocardial transcriptome as a biomarker in cardiovascular disease. Trends Cardiovasc Med 2005; 15:130-138
32. Heidecker B, Hare JM: The use of transcrip-tomic biomarkers for personalized medicine. Heart Fail Rev 2007; 12:1-11