Patterns of gene expression among murine models of hemorrhagic shock/trauma and sepsis

Abstract

Controversy remains whether the leukocyte genomic response to trauma or sepsis is dependent upon the initiating stimulus. Previous work illustrated poor correlations between historical models of murine trauma and sepsis (i.e., trauma-hemorrhage and lipopolysaccharide injection, respectively). The aim of this study is to examine the early genomic response in improved murine models of sepsis [cecal ligation and puncture (CLP)] and trauma [polytrauma (PT)] with and without pneumonia (PT+Pp). Groups of naïve, CLP, PT, and PT+Pp mice were killed at 2 h, 1 or 3 days. Total leukocytes were isolated for genome-wide expression analysis, and genes that were found to differ from control (false discovery rate adjusted P < 0.001) were assessed for fold-change differences. Spearman correlations were also performed. For all time points combined (CLP, PT, PT+Pp), there were 10,426 total genes that were found to significantly differ from naïve controls. At 2 h, the transcriptomic changes between CLP and PT showed a positive correlation (rs) of 0.446 (P < 0.0001) but were less positive thereafter. Correlations were significantly improved when we limited the analysis to common genes whose expression differed by a 1.5 fold-change. Both pathway and upstream analyses revealed the activation of genes known to be associated with pathogen-associated and damage-associated molecular pattern signaling, and early activation patterns of expression were very similar between polytrauma and sepsis at the earliest time points. This study demonstrates that the early leukocyte genomic response to sepsis and trauma are very similar in mice.

Keywords: cecal ligation and puncture; correlations; mouse; polytrauma; transcriptomics.

Copyright © 2016 the American Physiological Society.

Figures

Fig. 1.

Complete blood count (CBC) analysis 1 day postsepsis [cecal ligation and puncture (CLP)], polytrauma (PT), and polytrauma plus pneumonia (PT+Pp). A: complete white blood count (WBC) shows decreased WBC in CLP compared with naïve mice. PT and PT+Pp do not show a significant difference compared with naïve mice. B: WBC differential. All models show an increase in neutrophils at 1 day. *P < 0.05 Tukey's posttest, **P < 0.01, ***P < 0.001 vs. naïve per 2-way ANOVA, Bonferroni posttests.

Fig. 2.

Heat map illustrating a supervised analysis of gene expression in murine models of sepsis (CLP), PT, and PT+Pp at various time points after insult (2 h, 1 day, and 3 days). The heat map reveals that the closest correlations are at the early time points. Red, upregulation; blue, downregulation. Heat map represents 10,426 unique genes.

Fig. 3.

Spearman rank correlations for all 10,426 genes significantly differ from control at specific points postinsult. Comparisons of all time points combined (A), 2 h post-PT vs. 2 h post-CLP (B), 1 day post-PT vs. 1 day post-CLP (C), and 3 days post-PT vs. 3 days post-CLP (D) are displayed. Although the correlations are poor when all 10,426 genes are compared at all time points (A), the strongest correlations are seen at the earliest time points. By 3 days, there is a negative correlation due to the downregulation of the previously upregulated genes in PT.

Fig. 4.

Comparison of genes with significantly differentiated gene expression at 2 h postinsult with a fold change ≥ 1.5. A: Venn diagram illustrating all genes found to be significantly different from controls. B: Spearman rank correlation graph for all genes found to be significantly different from control 2 h post-PT vs. the corresponding genes 2 h post-CLP. C: Spearman rank correlation graph for all genes found to be significantly different from control with at least a 1.5-fold change that were shared between PT and CLP at 2 h post-insult. D: Spearman rank correlations for all genes found to be significantly different from control with at least a 1.5-fold change for CLP at vs. corresponding genes in PT at 2 h postinsult (whether they met the aforementioned criteria or not).

Fig. 5.

The correlation between the top 100 upregulated genes in CLP compared with the same genes in PT. Correlations between the 2 models are very strong when one considers genes that represent early responders to the inflammatory response. A: illustration showing the top 100 upregulated genes observed for 2 h after CLP and their corresponding changes in various settings (CLP vs. PT vs. PT + Pp) and time points (2 h, 1 day, 3 days). Red, upregulation; blue, downregulation. B: Spearman correlation graph showing the same top 100 upregulated genes in CLP at 2 h vs. the same genes in PT at 2 h.

Fig. 6.

Distance from reference (DFR) analysis for all genes in various injury models and time points. DFR is a simplified method of looking at overall level of change in genomic expression independent of direction, and it can be seen in this graph that all injury models create similar levels of change in expression.

Fig. 7.

Ingenuity Pathway Analysis (IPA) illustration showing upstream regulation of the pathway for NF-κB. The response to injury in both models at 2 h creates a very similar response. Orange, upregulation; blue, downregulation.