Gene expression profiling in sepsis: timing, tissue, and translational considerations

Abstract

Sepsis is a complex inflammatory response to infection. Microarray-based gene expression studies of sepsis have illuminated the complex pathogen recognition and inflammatory signaling pathways that characterize sepsis. More recently, gene expression profiling has been used to identify diagnostic and prognostic gene signatures, as well as novel therapeutic targets. Studies in pediatric cohorts suggest that transcriptionally distinct subclasses might account for some of the heterogeneity seen in sepsis. Time series analyses have pointed to rapid and dynamic shifts in transcription patterns associated with various phases of sepsis. These findings highlight current challenges in sepsis knowledge translation, including the need to adapt complex and time-consuming whole-genome methods for use in the intensive care unit environment, where rapid diagnosis and treatment are essential.

Keywords: bioinformatics; gene expression; genomics; microarrays; sepsis; septic shock.

Figure 1

Trends in gene expression profiling of sepsis. Bars represent the number of Pubmed citations per year for the search term “gene expression AND sepsis”. Trend line shows the number of individual microarray assays added each year to a publicly available repository of gene expression data (ArrayExpress). The size of the points at the bottom of the plot reflect the number of clinical trials initiated in each year, as identified by a trials registry (clinicaltrials.gov, search terms "gene expression AND sepsis OR septic shock OR severe sepsis").

Figure 2

Overview of gene expression profiling in sepsis. (A) As sepsis syndromes are characterized by rapid shifts in gene expression over hours and days, blood samples can be collected for analysis at a variety of time points. Multiple samples taken over the course of resuscitation, stabilization, and convalescence, can be used to generate time series of gene expression. (B) Once samples are collected, RNA can be extracted either directly from whole blood, or from different leukocyte fractions. RNA transcript levels are derived from gene expression microarrays. (C) Bioinformatics pathways can be used to compare gene expression profiles between two or more groups of patients (e.g. sepsis and non-infectious SIRS), resulting in a list of differentially expressed genes, and their associated pathways. Unsupervised machine learning methods including partitional clustering algorithms can be used to identify previously unrecognized sepsis subclasses. Expression data from multiple time points can be analyzed together to generate expression trajectories, which may differ between patients. Interpretation of differences in gene expression is facilitated through comparison with clinical phenotypes derived from patient data collected from electronic medical records or patient registries, or in the context of a clinical trial. (D) Unlike with diseases managed in the outpatient setting, the treatment of sepsis relies on diagnostic testing that can rapidly returns easily interpreted results. High-dimensional gene expression data must therefore be “downsized” to more easily derived and understood signals. Strategies include using serum biomarker assays develop patient classifiers, generating gene expression mosaics that visually represent complex expression signals, and deploying sophisticated multiplex assays that measure a limited number of transcripts using molecular barcoding technology. Abbreviations: whole blood, WB; polymorphonuclear neutrophils, PMN; peripheral blood mononuclear cells, PBMC.