title: Molecular diagnostics in sepsis: from bedside to bench.
abstract: BACKGROUND
  Based on recent in vitro data, we tested the hypothesis that microarray expression profiles can be used to diagnose sepsis, distinguishing in vivo between sterile and infectious causes of systemic inflammation.
  
  
  METHODS
  Exploratory studies were conducted using spleens from septic patients and from mice with abdominal sepsis. Seven patients with sepsis after injury were identified retrospectively and compared with six injured patients. C57BL/6 male mice were subjected to cecal ligation and puncture, or to IP lipopolysaccharide. Control mice had sham laparotomy or injection of IP saline, respectively. A sepsis classification model was created and tested on blood samples from septic mice.
  
  
  RESULTS
  Accuracy of sepsis prediction was obtained using cross-validation of gene expression data from 12 human spleen samples and from 16 mouse spleen samples. For blood studies, classifiers were constructed using data from a training data set of 26 microarrays. The error rate of the classifiers was estimated on seven de-identified microarrays, and then on a subsequent cross-validation for all 33 blood microarrays. Estimates of classification accuracy of sepsis in human spleen were 67.1%; in mouse spleen, 96%; and in mouse blood, 94.4% (all estimates were based on nested cross-validation). Lists of genes with substantial changes in expression between study and control groups were used to identify nine mouse common inflammatory response genes, six of which were mapped into a single pathway using contemporary pathway analysis tools.
  
  
  CONCLUSIONS
  Sepsis induces changes in mouse leukocyte gene expression that can be used to diagnose sepsis apart from systemic inflammation.
authors.affiliation: Department of Surgery, School of Medicine, Washington University, St Louis, MO 63110, USA.
authors.forename: T Philip
authors.initials: TP
authors.lastname: Chung
authors.forename: Jason M
authors.initials: JM
authors.lastname: Laramie
authors.forename: Donald J
authors.initials: DJ
authors.lastname: Meyer
authors.forename: Thomas
authors.initials: T
authors.lastname: Downey
authors.forename: Laurence H Y
authors.initials: LH
authors.lastname: Tam
authors.forename: Huashi
authors.initials: H
authors.lastname: Ding
authors.forename: Timothy G
authors.initials: TG
authors.lastname: Buchman
authors.forename: Irene
authors.initials: I
authors.lastname: Karl
authors.forename: Gary D
authors.initials: GD
authors.lastname: Stormo
authors.forename: Richard S
authors.initials: RS
authors.lastname: Hotchkiss
authors.forename: J Perren
authors.initials: JP
authors.lastname: Cobb
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000818:Animals; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D007249:Inflammation / Q000175:diagnosis / Q000235:genetics; D008297:Male; D051379:Mice; D008810:Mice, Inbred C57BL; D008875:Middle Aged; D025341:Principal Component Analysis; D040081:Protein Array Analysis; D018805:Sepsis / Q000175:diagnosis* / Q000235:genetics; D013154:Spleen / Q000293:injuries