Distinct roles of trauma and transfusion in induction of immune modulation after injury

Abstract

Background: Trauma and transfusion can both alter immunity, and while transfusions are common among traumatically injured patients, few studies have examined their combined effects on immunity.

Study design and methods: We tracked the plasma levels of 41 immunomodulatory proteins in 56 trauma patients from time of injury up to 1 year later. In addition, a murine model was developed to distinguish between the effects of transfusion and underlying injury and blood loss.

Results: Thirty-one of the proteins had a significant change over time after traumatic injury, with a mixed early response that was predominantly anti-inflammatory followed by a later increase in proteins involved in wound healing and homeostasis. Results from the murine model revealed similar cytokine responses to humans. In mice, trauma and hemorrhage caused early perturbations in a number of the pro- and anti-inflammatory mediators measured, and transfusion blunted early elevations in interleukin (IL)-6, IL-10, matrix metalloproteinase-9, and interferon-γ. Transfusion caused or exacerbated changes in monocyte chemotactic protein-1, IL-1α, IL-5, IL-15, and soluble E-selectin. Finally, trauma and hemorrhage alone increased CXCL1 and IL-13.

Conclusions: This work provides a detailed characterization of the major shift in the immunologic environment in response to trauma and transfusion and clarifies which immune mediators are affected by trauma and hemorrhage and which by transfusion.

Figure 1. Proteins with early elevation following trauma

Blood samples were collected from trauma patients beginning with arrival to the ER and up to 1 year after injury. Multiplexing techniques were used to measure the levels of 41 immunomodulatory proteins in the plasma. Multivariable GEE models were generated using the natural log of the concentration of each protein as the dependent variable and time since trauma, ISS, injury type, size of transfusion, age, sex, and microchimerism as the independent variables. Concentration of proteins with a statistically significant change in concentration over time since trauma (p<0.05) are plotted in black (raw data) with the model’s prediction of the influence of time since trauma on concentration (controlling for all other independent variables) overlaid in red. **p<0.01, ***p<0.001.

Figure 2. Proteins with early depression following trauma

See Figure 1 for experimental details. **p<0.01, ***p<0.001.

Figure 3. Proteins with late elevation following trauma

See Figure 1 for experimental details. *p<0.05, **p<0.01, ***p<0.001.

Figure 4. Proteins associated with injury type or severity, gender, and age

(A) Proteins with a statistically significant difference (p<0.05) between blunt and penetrating injury are plotted in black (raw data) with the model’s prediction of the influence of injury type on concentration (controlling for all other independent variables) overlaid in red. (B) IL-10, the only protein measured with a statistically significant association (p<0.05) with injury severity score is plotted in black (raw data) with the model’s prediction of the influence of injury severity on concentration (controlling for all other independent variables) overlaid in red. (C) Proteins with a statistically significant difference (p<0.05) between male and female patients are plotted in black (raw data) with the model’s prediction of the influence of gender on concentration (controlling for all other independent variables) overlaid in red. (D) Proteins with a statistically significant association (p<0.05) with age are plotted in black (raw data) with the model’s prediction of the influence of age on concentration (controlling for all other independent variables) overlaid in red. *p<0.05, **p<0.01. ***p<0.001.

Figure 5. Proteins associated with transfusion

(A) Proteins with a statistically significant difference (p<0.05) between no transfusion and a modest transfusion (≤4 units in the first 48 hours after trauma) are plotted in black (raw data) with the model’s prediction of the influence of no versus modest transfusion on concentration (controlling for all other independent variables) overlaid in red. (B) Proteins with a statistically significant difference (p<0.05) between modest transfusion and a large transfusion (≥5 units in the first 48 hours after trauma) are plotted in black (raw data) with the model’s prediction of the influence of modest versus large transfusion on concentration (controlling for all other independent variables) overlaid in red. *p<0.05, **p<0.01, ***p<0.001.

Figure 6. Traumatic blood loss and transfusion in mice

(A) Time-line of traumatic blood loss, transfusion, and blood sample collection. BALB/cJ mice were bled 25–30% of their total blood volume or not at t=0, then given no transfusion (squares), 100 μL allogeneic C57Bl/6J packed red cells + 400 μL 0.9% NaCl (triangles), or 500 μL 0.9% NaCl (circles) at t=1hr. At t=4hr, peripheral blood was harvested. (B) Serum was screened for cytokines using multiplexing techniques. Pooled data from 2 representative experiments with 5 mice per group each are shown. Experiments were repeated 6 times. Concentrations were compared between treatment groups with one-way ANOVA, and Dunnett’s multiple comparisons post-test was used to compare each treatment group to the untreated controls. *p<0.05, **p<0.01, ***p<0.001.

Figure 7. Kinetics of immune response to trauma

Overlays of the models’ prediction of the influence of time since trauma controlling for the other covariates are plotted by protein type. Predicted values at 1 year after trauma are set as the baseline (0) for each cytokine to show elevation or depression relative to this value. The inflammation plot includes the pro-inflammatory cytokines IL-1α, IL-5, IL-9, IL-17, TNFα, TNFβ, and MIF, the anti-inflammatory cytokines IL-1Ra and IL-10, and IL-6, which has both pro- and anti-inflammatory properties. The healing plot includes the wound healing proteins EGF, FGF-2, VEGF, MMP-9, and tPAI-1, the activated endothelial markers sE-Selectin, sICAM-1, and sVCAM-1, and the homeostasis cytokines IL-7 and IL-15. The apoptosis plot includes the pro-apoptotic sFasL and the anti-apoptotic sFas. The chemokine plot includes IP-10, IL-8, MIP-1α, MCP-1, eotaxin, fractalkine, and MDC.