Age-independent co-expression of antimicrobial gene clusters in the blood of septic patients

International Journal of Antimicrobial Agents

Volumn 42, Issue SUPPL.1, 2013, Pages

  Lindig, Sandro ^a   Quickert, Stefanie ^a   Vodovotz, Yoram ^b   Wanner, Guido A ^c   Bauer, Michael ^a  

^a JENA UNIVERSITY HOSPITAL   (Germany)

^b MCGOWAN INSTITUTE FOR REGENERATIVE MEDICINE   (United States)

^c UNIVERSITY HOSPITAL ZURICH   (Switzerland)

Author keywords

Blood; Sepsis; Transcriptomic markers

Indexed keywords

ADAPTIVE IMMUNITY; AGE; ANTIMICROBIAL GENE CLUSTER; ARTICLE; BLOOD SAMPLING; CONTROLLED STUDY; DOWN REGULATION; GENE CLUSTER; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENE MAPPING; GENETIC SELECTION; HUMAN; INNATE IMMUNITY; MICROARRAY ANALYSIS; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SEPSIS; SUPPORT VECTOR MACHINE; UPREGULATION; VALIDATION PROCESS;

BACTEREMIA; BIOLOGICAL MARKERS; BLOOD; DRUG RESISTANCE, BACTERIAL; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENES, BACTERIAL; HUMANS; MULTIGENE FAMILY;

EID: 84879086212     PISSN: 09248579     EISSN: 18727913     Source Type: Journal    
DOI: 10.1016/j.ijantimicag.2013.04.012     Document Type: Article

References (29)

1. J.P. Cobb, J.M. Laramie, G.D. Stormo, J.J. Morrissey, W.D. Shannon, and Y. Qui Sepsis gene expression profiling: murine splenic compared with hepatic responses determined by using complementary DNA microarrays Crit Care Med 30 2002 2711 2721
2. D. Chaussabel, V. Pascual, and J. Banchereau Assessing the human immune system through blood transcriptomics BMC Biol 8 2010 84
3. C. Pierrakos, and J.L. Vincent Sepsis biomarkers: a review Crit Care 14 2010 R15
4. B.M. Tang, S.J. Huang, and A.S. McLean Genome-wide transcription profiling of human sepsis: a systematic review Crit Care 14 2010 R237
5. L. Diacovich, and J.P. Gorvel Bacterial manipulation of innate immunity to promote infection Nat Rev Microbiol 8 2010 117 128
6. A. Blum, and P. Langley Selection of relevant features and examples in machine learning Artif Intell 97 1997 245 271
7. I. Guyon, and A. Elisseeff An introduction to variable and feature selection J Mach Learn Res 3 2003 1157 1182
8. T. Barrett, D.B. Troup, S.E. Wilhite, P. Ledoux, C. Evangelista, and I.F. Kim NCBI GEO: archive for functional genomics data sets - 10 years on Nucleic Acids Res 39 2011 D1005 D1010
9. A. Sutherland, M. Thomas, R.A. Brandon, R.B. Brandon, J. Lipman, and B. Tang Development and validation of a novel molecular biomarker diagnostic test for the early detection of sepsis Crit Care 15 2011 R149
10. B.M. Tang, A.S. McLean, I.W. Dawes, S.J. Huang, and R.C. Lin Gene-expression profiling of peripheral blood mononuclear cells in sepsis Crit Care Med 37 2009 882 888
11. H.R. Wong, N.Z. Cvijanovich, G.L. Allen, R. Lin, N. Anas, and K. Meyer Genomic expression profiling across the pediatric systemic inflammatory response syndrome, sepsis and septic shock spectrum Crit Care Med 37 2009 1558 1566
12. T. Dolinay, Y.S. Kim, J. Howrylak, G.M. Hunninghake, C.H. An, and L. Fredenburgh Inflammasome-regulated cytokines are critical mediators of acute lung injury Am J Respir Crit Care Med 185 2012 1225 1234
13. R.C. Gentleman, V.J. Carey, D.M. Bates, B. Bolstad, M. Dettling, and S. Dudoit Bioconductor: open software development for computational biology and bioinformatics Genome Biol 5 2004 R80
14. R. Sandberg, and O. Larsson Improved precision and accuracy for microarrays using updated probe set definitions BMC Bioinformatics 8 2007 48
15. L. Shi, W.D. Jones, R.V. Jensen, S.C. Harris, R.G. Perkins, and F.M. Goodsaid The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies BMC Bioinformatics 9 Suppl. 9 2008 S10
16. D.W. Huang, B.T. Sherman, and R.A. Lempicki Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources Nat Protoc 4 2009 44 57
17. E. Boutet, D. Lieberherr, M. Tognolli, M. Schneider, and A. Bairoch UniProtKB/Swiss-Prot Methods Mol Biol 406 2007 89 112
18. X. Robin, N. Turck, A. Hainard, N. Tiberti, F. Lisacek, and J.C. Sanchez pROC: an open-source package for R and S+ to analyze and compare ROC curves BMC Bioinformatics 12 2011 77
19. V.N. Vapnik The nature of statistical learning theory 1995 Springer-Verlag New York, NY
20. M. Slawski, M. Daumer, and A.L. Boulesteix CMA: a comprehensive Bioconductor package for supervised classification with high dimensional data BMC Bioinformatics 9 2008 439
21. E.P. Skaar The battle for iron between bacterial pathogens and their vertebrate hosts PLoS Pathog 6 2010 e1000949
22. K. Reinhart, M. Bauer, N.C. Riedemann, and C.S. Hartog New approaches to sepsis: molecular diagnostics and biomarkers Clin Microbiol Rev 25 2012 609 634
23. J.C. Boldrick, A.A. Alizadeh, M. Diehn, S. Dudoit, C.L. Liu, and C.E. Belcher Stereotyped and specific gene expression programs in human innate immune responses to bacteria Proc Natl Acad Sci USA 99 2002 972 977
24. O. Ramilo, W. Allman, W. Chung, A. Mejias, M. Ardura, and C. Glaser Gene expression patterns in blood leukocytes discriminate patients with acute infections Blood 109 2007 2066 2077
25. H.R. Wong, S. Salisbury, Q. Xiao, N.Z. Cvijanovich, M. Hall, and G.L. Aleen The pediatric sepsis biomarker risk model Crit Care 16 2012 R174
26. W. Xiao, M.N. Mindrinos, J. Seok, J. Cuschieri, A.G. Cuenca, and H. Gao A genomic storm in critically injured humans J Exp Med 208 2011 2581 2590
27. M. Weber, S. Lambeck, N. Ding, S. Henken, M. Kohl, and H.P. Deigner Hepatic induction of cholesterol biosynthesis reflects a remote adaptive response to pneumococcal pneumonia FASEB J 26 2012 2424 2436
28. F.A. Gonnert, P. Recknagel, M. Seidel, N. Jbeily, K. Dahlke, and C.L. Bockmeyer Characteristics of clinical sepsis reflected in a reliable and reproducible rodent sepsis model J Surg Res 170 2011 e123 e134
29. S. Lambeck, M. Weber, F.A. Gonnert, R. Mrowka, and M. Bauer Comparison of sepsis-induced transcriptomic changes in a murine model to clinical blood samples identifies common response patterns Front Microbiol 3 2012 284