Clinical Investigations

Gene-expression profiling of peripheral blood mononuclear cells in sepsis*

Tang, Benjamin M. P. PhD; McLean, Anthony S. MD; Dawes, Ian W. DPhil; Huang, Stephen J. PhD; Lin, Ruby C. Y. PhD

From the Department of Intensive Care Medicine (BMPT, ASM, SJH), Nepean Hospital and Western Clinical School, University of Sydney, Penrith, NSW 2750, Australia; and Ramaciotti Centre for Gene Function Analysis and School of Biotechnology and Biomolecular Sciences (ASM, IWD, RCYL), University of New South Wales, Sydney, NSW 2052, Australia.

The authors have not disclosed any potential conflicts of interest.

Dr. Tang has full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Dr. Tang organized the study concept and design. Dr. Tang acquired the data. Dr. Tang analyzed and interpreted data. Drs. Tang, Lin, Dawes, Huang, and McLean drafted the manuscript. Statistical analysis was done by Dr. Tang.

For information regarding this article, E-mail: [email protected]

Critical Care Medicine 37(3):p 882-888, March 2009. | DOI: 10.1097/CCM.0b013e31819b52fd

Abstract

Objectives:

It has been shown that gene-expression profiling of circulating neutrophils could identify signature genes of sepsis. However, whether similar transcriptional changes occurred in peripheral blood mononuclear cells (PBMC) was not known. Using microarray technology, we performed gene-expression profiling of PBMC to identify signature genes that distinguish sepsis from noninfectious causes of systemic inflammatory response syndrome (SIRS), between Gram-positive and Gram-negative sepsis.

Design:

A cross-sectional, observational study.

Setting:

A 20-bed general intensive care unit of a tertiary referral hospital.

Patients:

Seventy critically ill patients (46 sepsis and 24 SIRS).

Interventions:

Intravenous blood was collected for leukocyte separation and RNA extraction. Gene-expression profiling was performed on PBMC using Affymetrix GeneChip microarrays with 54,675 transcripts. Data were divided into a training set (n = 35) and a validation set (n = 35). A molecular signature was developed in the training set using support vector machine and was then validated in the validation set.

Measurements and Main Results:

We identified a molecular signature of 138 genes that could differentiate between sepsis and SIRS patients with 91% and 80% accuracy in the training and validation sets, respectively. There were no signature genes that could differentiate between Gram-positive and Gram-negative sepsis. The expression of genes involved in inflammatory response and immune function was significantly reduced in septic patients when compared with those with SIRS. Genes involved in apoptosis, on the other hand, were more highly expressed in septic patients.

Conclusion:

There was evidence of sepsis-related immunosuppression and reduced inflammatory response in mononuclear cells on a transcriptome level. These characteristic transcriptional changes can be used to aid the diagnosis of sepsis.