Molecular Diagnostics in Sepsis: From Bedside to Bench
Received 11 May 2006; received in revised form 12 June 2006; accepted 28 June 2006. published online 08 September 2006.
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.
Study design
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.
Correspondences address: J Perren Cobb, MD, FACS, Department of Surgery, School of Medicine, Washington University, Campus Box 8109, 660 South Euclid Ave, St Louis, MO 63110.
Competing Interests Declared: Design and conduct of the study and collection, management, analysis, and interpretation of data were supported, in part, by GM59960 (JPC), GM59960-02S1 (JPC), GM44118 (RSH), and the American College of Surgeons George HA Clowes Jr Memorial Research Career Development Award (JPC). The funding agencies did not conduct any portion of the study and did not contribute to manuscript preparation.
1 TD, DJM, and HD are employees of Partek Incorporated, a software company with expertise in microarray analyses, for example, nested cross-validation. GeneChip analyses were performed using Partek software as described herein; there were no monetary contributions.
2 Mr. Laramie’s current affiliation is Bioinformatics and Systems Biology Program, Boston University, Boston, MA.
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