Identifying biomarkers deciphering sepsis from trauma-induced sterile inflammation and trauma-induced sepsis

Abstract

Objective: The purpose of this study was to identify a panel of biomarkers for distinguishing early stage sepsis patients from non-infected trauma patients.

Background: Accurate differentiation between trauma-induced sterile inflammation and real infective sepsis poses a complex life-threatening medical challenge because of their common symptoms albeit diverging clinical implications, namely different therapies. The timely and accurate identification of sepsis in trauma patients is therefore vital to ensure prompt and tailored medical interventions (provision of adequate antimicrobial agents and if possible eradication of infective foci) that can ultimately lead to improved therapeutic management and patient outcome. The adequate withholding of antimicrobials in trauma patients without sepsis is also important in aspects of both patient and environmental perspective.

Methods: In this proof-of-concept study, we employed advanced technologies, including Matrix-Assisted Laser Desorption/Ionization (MALDI) and multiplex antibody arrays (MAA) to identify a panel of biomarkers distinguishing actual sepsis from trauma-induced sterile inflammation.

Results: By comparing patient groups (controls, infected and non-infected trauma and septic shock patients under mechanical ventilation) at different time points, we uncovered distinct protein patterns associated with early trauma-induced sterile inflammation on the one hand and sepsis on the other hand. SYT13 and IL1F10 emerged as potential early sepsis biomarkers, while reduced levels of A2M were indicative of both trauma-induced inflammation and sepsis conditions. Additionally, higher levels of TREM1 were associated at a later stage in trauma patients. Furthermore, enrichment analyses revealed differences in the inflammatory response between trauma-induced inflammation and sepsis, with proteins related to complement and coagulation cascades being elevated whereas proteins relevant to focal adhesion were diminished in sepsis.

Conclusions: Our findings, therefore, suggest that a combination of biomarkers is needed for the development of novel diagnostic approaches deciphering trauma-induced sterile inflammation from actual infective sepsis.

Keywords: A2M; IL1F10; SYT13; bacteremia; biomarkers; sepsis; systemic inflammatory response syndrome; trauma.

Figure 1

Protein abundance after MALDI screening. (A) Top 100 differentially regulated proteins at day 1, 3, 5 and 7 contrasting trauma and sepsis groups. The relative abundance of protein levels is shown as log2-fold change from sepsis to trauma (S/PT) group. Protein names that occurred only on a single day were printed in bold. (B) Venn diagram showing the common and unique proteins among all time points. (C) Heatmap highlighting the 21 recurring proteins on all days.

Figure 2

Protein abundance after MAA screening. (A) Top 100 differentially regulated proteins at day 1, 3, 5 and 7 contrasting trauma and sepsis groups. The relative abundance of protein levels is shown as log2-fold change of sepsis to trauma (S/PT) group. Protein names that occurred only on a single day were printed in bold. (B) Venn diagram showing the common and unique proteins among all time points. (C) Heatmap highlighting the 15 recurring proteins on all days.

Figure 3

Enrichment analysis of KEGG pathway terms for differentially regulated proteins contrasting trauma and sepsis group at various days. The top 15 KEGG pathway terms are listed for the days 1, 3, 5 and 7. Additionally, Gene-Concept Networks illustrate the relevant proteins associated with the top 5 KEGG pathway terms per time point. The relative abundance of protein levels is shown as log2-fold change of sepsis to trauma (S/PT) group.

Figure 4

Identification of sepsis biomarkers. We assessed plasma protein levels using quantitative ELISA at various stages of disease development. Data are displayed as mean +/- SEM. Healthy individuals (n=6), fever (n=6), bacteremia (n=5), trauma (n=4), and sepsis (n=6).

Figure 5

Potential biomarker panel to distinguish trauma and sepsis patients. Quantitative ELISA was used to determine the levels of A2M, IL1F10, SYT13, and TREM1 in trauma and sepsis patients at various time periods. The comparison of data was performed by one-way ANOVA, Tukey’s multiple comparison test and area under the curve (AUC) analysis were performed with 95% confidence interval, using Wilson/Brown method. Healthy (n=7); trauma ans sepsis (n=23).

Figure 6

SYT13 and IL1F10 differentiate trauma and sepsis patients. (A) The levels of the proteins A2M, IL1F10, SYT13, and TREM1 at the early (day 0) and later stage (day 5) of the infection. (B) A comparison of the protein levels of SYT13 at high and low concentrations [+Ve; positive; from (B)] with those of A2M, IL1F10, and TREM1. (C) Comparison of the A2M, SYT13, and TREM1 and protein levels with the high and low protein levels of IL1F10 [+Ve; positive; from (B)]. Data are displayed as mean +/- SEM. Healthy (n=7); trauma and sepsis (n=23).