Transcriptomic profiling of the inflammatory response after trauma - GSE70311

The study was aimed to identify mechanisms linked to complicated courses after severe trauma by a systems biology approach. In severe trauma, overwhelming systemic inflammation can result in adverse events and the development of complications, including sepsis. In a prospective study, RNA samples from circulating leukocytes from patients with multiple injury (injury severity score ? 17) were analyzed for dynamic changes in gene expression over a period of 21 days by whole genome screening. Based on their clinical presentation, patients were divided into two subgroups: patients with secondary sepsis after trauma (n=5) and patients with systemic inflammation without infection (n=5). Expression cluster were defined by correlating gene expression data with clinical outcome parameters. Using unsupervised clustering, patients with systemic inflammation only and patients with sepsis showed a distinct expression pattern and the discrimination of clinical presentation was reflected by clustering of the samples. Explorative gene set analysis revealed robust upregulation of genes related to ‘hemoglobin metabolism/oxygen transport’ and ‘pathogenic E.coli infection’.

10 patients with multi-system trauma (ISS ? 17 points) admitted to the Division of Trauma Surgery at the University Hospital Zurich were included. Whole blood from trauma patients was collected within the first 6 h after trauma (day 0) and on days 1, 2, 3, 5, 7, 10, 14, and 21. Total cellular RNA from circulating leukocytes was isolated using PaxGene Blood RNA Kit (PreAnalytix) for transcriptome profiling. RNA from blood of trauma patients was extracted and subjected to microarray analysis for comparison of longitudinal transcriptomic responses of patients. RNA samples of circulating leukocytes covering time points directly after admission (D0) and on the consecutive days (D1-D21) were subject to multifactorial microarray data analysis: Differences in dynamics of transcripts were assessed by contrasting time- and individual-resolved changes for sepsis and systemic inflammation without infection.

Systemic inflammatory response syndrome (2021 ICD-10-CM code* = R65.1)

Sepsis (2021 ICD-10-CM code* = A41)

Raw data was pre-processed using GenomeStudio (v 1.9.0). Robust spline normalization (rsn) and detection filtering were performed via the lumi package within Bioconductor and R statistics software (version 3.13). Detection (p<0.01) was required in at least ten samples (out of 90). Candidates were validated by RT-PCR. Annotation was mapped using HumanHT-12_V4_0_R2_15002873_B from Illumina. Analyses were perfomed with respect to individual resolved fold changes (T0-adjusted) as well as global.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70311