A better understanding of why murine models of trauma do not recapitulate the human syndrome

Abstract

Objective: Genomic analyses from blood leukocytes have concluded that mouse injury poorly reflects human trauma at the leukocyte transcriptome. Concerns have focused on the modest severity of murine injury models, differences in murine compared with human age, dissimilar circulating leukocyte populations between species, and whether similar signaling pathways are involved. We sought to examine whether the transcriptomic response to severe trauma in mice could be explained by these extrinsic factors, by utilizing an increasing severity of murine trauma and shock in young and aged mice over time, and by examining the response in isolated neutrophil populations.

Design: Preclinical controlled in vivo laboratory study and retrospective cohort study.

Setting: Laboratory of Inflammation Biology and Surgical Science and multi-institution level 1 trauma centers.

Subjects: Six- to 10-week-old and 20- to 24-month-old C57BL/6 (B6) mice and two cohorts of 167 and 244 severely traumatized (Injury Severity Score > 15) adult (> 18 yr) patients.

Interventions: Mice underwent one of two severity polytrauma models of injury. Total blood leukocyte and neutrophil samples were collected.

Measurements and main results: Fold expression changes in leukocyte and neutrophil genome-wide expression analyses between healthy and injured mice (p < 0.001) were compared with human total and enriched blood leukocyte expression analyses of severe trauma patients at 0.5, 1, 4, 7, 14, and 28 days after injury (Glue Grant trauma-related database). We found that increasing the severity of the murine trauma model only modestly improved the correlation in the transcriptomic response with humans, whereas the age of the mice did not. In addition, the genome-wide response to blood neutrophils (rather than total WBC) was also not well correlated between humans and mice. However, the expression of many individual gene families was much more strongly correlated after injury in mice and humans.

Conclusions: Although overall transcriptomic association remained weak even after adjusting for the severity of injury, age of the animals, timing, and individual leukocyte populations, there were individual signaling pathways and ontogenies that were strongly correlated between mice and humans. These genes are involved in early inflammation and innate/adaptive immunity.

Figure 1. Increasing murine model severity improves gene expression correlations between murine and human trauma

Graphs illustrating genome wide expression correlations of total blood leukocytes between human trauma patients and murine trauma models: TH, a traditional model of murine trauma and shock, PT, a more complex model of trauma and shock with injuries to multiple compartments and an Injury Severity Score >15. Asterisks (*) indicate statistically significant correlations (p<0.05 after Bonferroni’s correction). This was performed at each murine time point (A, B, and C). By increasing model severity, we significantly improve correlations at all human time points.

Figure 2. Murine to human age equivalents and circulating leukocyte populations

A. Diagram representing the mouse age of C57BL/6J mice in months as is equivalent to human age in years (modified from Jackson Laboratories http://research.jax.org/faculty/harrison/ger1vLifespan1.html) and (20). Bars represent WBC differential counts of neutrophils, lymphocytes, and monocytes at each age. (Human differential data obtained from http://www.childrensmn.org/manuals/lab/hematology/018981.asp). B. Murine and human differential percentages of neutrophils, lymphocytes, and monocytes following severe trauma in humans and PT in mice (n=4 per time point).

Figure 3. Analyzing and comparing circulating human leukocyte subsets to murine samples improves gene expression correlations

From left to right, columns one and two show the best correlation between murine PT models and human trauma at each human time point. The murine correlations were chosen as the best correlation of total mouse leukocytes from either the 2 hour, 1 day, or 3 day time points and either young or old mice. Asterisks (*) indicate statistically significant correlations (p<0.05 after Bonferroni’s correction). The best human correlations were chosen with regards to patient age (<55 years old or >55 years old) and leukocyte subset (lymphocytes, monocytes, or PMNs). Column three shows the percentage of up-regulated and down-regulated genes in mice that are in common with the top 100 up and down regulated genes in humans at each time point. Column four shows immune related pathways of interest chosen from the top 30 canonical pathways from an IPA analysis of the top 100 up and top 100 down regulated genes in humans. Column five shows the top up and down regulated genes from the same IPA analysis. (Red=up regulated, Blue=down regulated, Black=both up and down regulated).

Figure 4. Although genome-wide correlations between mice and humans are modest at best, when optimizing the model, age, and leukocyte subset correlations between murine and human gene expression can be significantly improved

Graph shows best murine correlation for each human time point for the analysis of the TH model alone, the PT model alone (increased model severity), and an analysis including all factors (model severity, age, and overall best leukocyte subset) compared to each human time point. All reported correlations are statistically significant correlations (p<0.05 after Bonferroni’s correction). The correlations are improved at each time point as the number of factors considered increases.